esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Radiocommunication systems between train and trackside (RSTT) carry essential information for train operations between on-board radio equipment and the related radio infrastructure located along the trackside, such as the train control, voice dispatching, command, operational information as well as the monitoring data. In a high-speed railway environment, the electromagnetic interference (EMI) has been a major threat to RSTT, and may result in critical security issues for railway transportation and even passengers. Given the complex scenario of the high-speed railway, it is significant to monitor the impact of disturbances in order to guarantee the quality of RSTT. On one hand, RSTT operate in a complex electromagnetic environment where transient disturbances coexist with permanent ones, and they both vary dramatically while the train is running. On the other hand, various radiocommunication technologies have been used for railway applications, featuring forward error-correction codes to resist EMI. Therefore, this paper puts forward a novel approach to evaluate the impact on radio transmission based on the joint statistical characteristics of time-varying EMI. This approach applies a dynamic effective signal-to-interference-plus-noise ratio mapping model to establish the relation between the block error performance of on-board radio and the joint statistical characteristics of disturbances with a mutual information-based metric. Simulations on radio transmission using Turbo coding and low-density-parity-check coding under various interferences indicate that this approach is effective to evaluate the degradation of transmission signal with forward error-correction coding due to EMI with different characteristics.  
electromagnetic disturbance; amplitude probability distribution; pulse duration distribution; radio transmission between train and trackside; interference effect +
Footnote †: journal: Applied  
Article
# On the Influence of Suspension Geometry on Steering Feedback
## Materials and Methods
### Analysis of the Torque Steer Effects  
In order to better understand the problem, the lumped element, model-based, vehicle dynamics simulation software VI-CarRealTime(r) (VI-CRT) was used. The sportscar model was selected among the original vehicle models provided within the software database and retained its original steering geometry. The powertrain model was converted to full electric vehicle (FEV) in an all-Wheel drive (AWD) configuration, where a significant portion of the torque is transferred to the front.  
To analyse the vehicle reaction in terms of steering response, a sine steer sweep input with 45\({}^{\circ}\) amplitude at the steering wheel was chosen, with a frequency increasing from 0.5 to 4 Hz. Vehicle speed is 60 km/h.  
At the front, the \"passive\" reference vehicle features a free differential (usually referred to as an \"open diff\"), hence the torque distribution left to right is always even, if not for negligible inertia effects in transients. Therefore, there is virtually no torque steer at all. The steering wheel torque (often referred to as steering effort) is basically a function of the steering geometry design, and it is proportional to tyre forces exchanged with the ground due to accelerations, load transfers, and so on. This usually results in a self-aligning steering torque in phase with the driver's hand motion, hence in an intuitive feedback the vehicle is stable because releasing the hand action from the steering wheel will just realign the vehicle back to straight line running.
# Wind Turbine Anomaly Detection Using Mahalanobis Distance and SCADA Alarm Data
## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Artificial Neural Network Type** & \multicolumn{2}{c}{**Feed Forward Neural Network**} \\ \hline Layer & Hidden & Output \\ \hline Neurons & 20 & 1 \\ \hline Activation function & Sigmoid & Threshold \\ \hline \multirow{4}{*}{List of possible inputs} & Air gap temperature, generator voltage, generator current, inverter current, vector voltage, cooling water low pressure, cooling water temperature, control cabinet temperature, impeller angle, motor current, blade angle, rotor temperature, wind speed, wind direction, vibration \\ \hline \multirow{4}{*}{List of possible outputs} & Generator speed, generator stator temperature, alternator bearing temperature, cooling water high pressure, rotor speed, brake hydraulic pressure, active power \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Specification of the models that use neural networks. The procedure for checking the anomaly was to determine the days covered by certain deviations from the threshold. The days covered by this anomaly were then backtracked to trace the month and day of the occurrence. The alarms in the SCADA system were then checked using the dates of occurrence of the deviations. The threshold for the data set was calculated by using the data set for normal behaviors. In this way, the MD calculated represented the threshold of such a data distribution. Paper [26] illustrated this type of method in determining the threshold of such data distribution; the healthy data values are used in both model generation and calculating the MD in this instance. The averaged MD value represents the threshold of the data distribution.
# Synchronizing Many Filesystems in Near Linear Time
## 3 Filesystem Synchronization
### Mergers Support Asynchronous and Offline Synchronization  
As usual, let the filesystem at the \(i\)-th replica be \(\Phi_{i}=A_{i}\Phi\), with the replica sending the canonical command set \(A_{i}\) to the server for synchronization. Section 3.5 discusses that when the server returns the merger \(M\), the replica should execute  
\[(A_{i}\smallsetminus M)^{-1}\circ(M\smallsetminus A_{i})\]  
on the local copy to transform it to the synchronized state \(\Psi=M\Phi\). Optionally, the replica could present the conflicting command set \(A_{i}\smallsetminus M\) to the user for inspection.  
Suppose the replica \(\Phi_{i}\) has not been locked, and by the time the reply \(M\) arrives from the server, it has changed to \(\Phi^{\prime}_{i}=A^{\prime}_{i}\Phi\), where \(A^{\prime}_{i}\) is a new canonical set describing the differences between the current state \(\Phi^{\prime}_{i}\) and the original common state \(\Phi\); see Figure 2. In this case, the local machine should transform the replica to the synchronized state \(\Psi\) and carry over those extra changes that are still executable. To this end, it invokes the synchronization algorithm for the canonical sets \(A^{\prime}_{i}\) and \(M\), making sure that the returned merger \(M^{*}\) contains \(M\) (this can be achieved, for example, by constructing the merger via conflict resolution and making sure that each conflict is resolved in favor of a command in \(M\)). By Claim 3, \(M\) will be an initial segment of \(M^{*}\), and thus, \(M^{*}=M\circ M^{\prime}\), where \(M^{\prime}\) is the canonical set \(M^{\prime}=M^{*}\smallsetminus M\). Apply the commands  
\[C^{\prime}_{i}=(A^{\prime}_{i}\smallsetminus M^{*})^{-1}\circ(M^{*}\smallsetminus A ^{\prime}_{i})\]  
to the filesystem \(\Phi^{\prime}_{i}\) and return \(A^{\prime}_{i}\smallsetminus M^{*}\) as the conflicting command set. At this moment, the local filesystem is  
\[C^{\prime}_{i}\Phi^{\prime}_{i}=C^{\prime}_{i}(A^{\prime}_{i}\Phi)=M^{*}\Phi= M^{\prime}(M\Phi)=M^{\prime}\Psi,\]  
which is exactly the synchronized filesystem \(\Psi\), to which the command set \(M^{\prime}\) has been applied. The commands in \(M^{\prime}\) can be incorporated easily into the next round of synchronization.  
In fact, the same method works, even if the replica does not take part in the process of determining the merger \(M\). Thus, _latecomers_ or _offline_ replicas, who did not participate in determining the merged state, can still upgrade to it without losing their ability to take part in subsequent synchronization rounds.
## 3 Research Methods, Research Platform, and Data Sources
### Research Methods,  
#### 3.1.1 Sentiment Analysis  
This study applied SnowNLP for sentiment analysis. When using SnowNLP, the Chinese text is first input into the module. SnowNLP then processes the words in the text sequentially, checking if they correspond to sentiment labels in its sentiment dictionary. If a match is found, the module determines the sentiment polarity of the word (e.g., positive, negative, or neutral). Based on this foundation, SnowNLP also analyzes the relationships between words and the sentence structure, such as the impact of negation words. By calculating the number of positive and negative words in the text and considering the context, SnowNLP generates a sentiment score that reflects the intensity of the sentiment. Finally, the system outputs the sentiment analysis results of the text based on these scores [51] (see Figure 2 for the detailed process).  
Figure 1: Research framework.  
Figure 2: SnowNLP flow chart.  
In order to test the accuracy of the model's sentiment classification, SnowNLP and SnowNLP supplemented by Jieba conducted sentiment classification tests. The classification samples used the same corpus, namely 500 manually labeled positive and 500 negative comments. The accuracy reflects the proportion of samples with true positive (negative) sentiment tendency among the positive (negative) sentiment tendency samples determined by the classifier; the recall rate reflects the proportion of correctly determined positive (negative) sentiment tendency samples to all positive (negative) sentiment tendency samples, accurately reflecting the ability of the classifier to determine the entire sample. From the final classification results, the optimized SnowNLP classification results reached 87%, which can basically correctly judge the sentiment polarity of the comments, and can perform sentiment classification on the comment corpus of this study (see Table 1).
# Investigation of Earthquake-Induced Pipe Damage in Liquefiable Soils
In this area, the behavior of steel pipes with diameters of 1500 mm and 2000 mm has been modeled, and the effects of expanding the model boundaries on the pipes and the surrounding soil have been investigated. In Study Area 2, using records of station 3115, the damage that occurred in 1500 mm diameter steel pipes due to liquefaction on Ataturk Boulevard in Iskenderun have been investigated through numerical analyses and evaluated based on field observations. This study aims to evaluate the practical applicability of existing liquefaction analysis methods. For this purpose, it was conducted using the PM4 Sand model, which has been developed to model liquefaction behavior and is continuously updated in this regard. The methodology used is designed to assess whether existing methods accurately evaluate liquefaction risk in pipelines. Our study utilizes existing calculation methods to analyze potential liquefaction consequences in pipelines in more detail. These analyses are crucial for determining how suitable existing methodologies are for real-world scenarios. Our results can provide valuable insights into the applicability and accuracy of existing methods. We emphasize the importance of our study for conducting liquefaction analyses, which directly impact the safety and durability of pipelines, in a more reliable manner. We believe this study will also serve as a foundation for future research, comparable to that presented by [PERSON] et al. (2018), on liquefaction-induced pipeline damage in the seismically active Iskenderun region. Thus, this study aims to shed light on predicting potential damage and implementing necessary improvement measures where critical pipeline networks pass through liquefiable areas.
## 4 Discussion
### Fluorescence Retrieval in the O\({}_{2}\)-A and O\({}_{2}\)-B Bands
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Crop Stages** & **All** & **Without Senescence** & **Without Senescence** \\ \hline
**Sky Conditions** & **All** & **All** & **Clear Sky** \\ \hline Data points & 665 & 531 & 253 \\ \hline
687 nm & & & \\ \(r^{2}\) & 0.11 & 0.16 & 0.007 \\ \(p\)-value & \textless{}0.0001 & \textless{}0.0001 & 0.19 \\ \hline
760 nm & & & \\ \(r^{2}\) & 0.10 & 0.006 & 0.05 \\ \(p\)-value & \textless{}0.0001 & 0.06 & 0.0002 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistics of the linear regression between half-hourly light use efficiency (LUE) and spectral fluorescence yield (SFY) for different crop stages and sky conditions. The analysis includes data between DOY 97 and 167 when fAPAR data is available. However, the \(RMSE\) is also greater for retrieval in the O\({}_{2}\)-B (0.06 \(\upmu\mathrm{mol}\cdot\mathrm{m}^{-2}\cdot\mathrm{sr}^{-1}\cdot\mathrm{nm}^{-1}\)). If we consider the \(RMSE\) in the error budget, we have to acknowledge that SIF687 can be underestimated in some cases by almost 20%.
# Analysis of Surface EMG Parameters in the Overhead Deep Squat Performance
## 2 Materials and Methods
### Investigated Parameters Description
#### 2.2.3 Kurtosis  
Kurtosis for an sEMG signal is a measure of distribution, that is, how often extreme (high) values occur relative to the normal distribution. It can provide information about the structure of the muscle signal, including changes related to muscle activation, fatigue, or contraction type. High kurtosis means that a signal has more \"spikes\", and the presence of extreme values, i.e., values that deviate from the average EMG value, may indicate more impulsive muscle activity (e.g., recruitment of large motor units). Some muscles are more or less active than the average, which may indicate various factors, such as specific movement patterns, muscle fatigue, or the presence of pathological processes. Low kurtosis means the signal has the \"flatter\" plot, and it may in turn indicate even more activation or fatigue (decrease in intense impulses). In classic terms, kurtosis is expressed by (8) [34]:  
\[kurt(s)=E\{s^{4}\}-3(E\{s^{2}\})^{2}, \tag{8}\]  
where \(s\) is a signal and \(E\{s\}\) is the expected value of the signal \(s\). In our study, to calculate the kurtosis of a discrete sEMG signal, the following formula (9) is used:  
Figure 9: The RMS (root mean square) of the sEMG signal after processing (solid blue line) and determined MVC value (dashed red horizontal line) of the biceps femoris for the signal in Figure 8; the dashed black vertical lines define the fragment of the signal that was taken into account during MVC determination. MVC is calculated as an average over a 100 ms window. The window is within \(\pm 50\) ms of the maximum signal value in the range delimited by the vertical black lines.  
\[kurt(s)=\frac{\frac{1}{N}\sum_{i=1}^{N}(x_{i}-\mu)^{4}}{(\frac{1}{N}\sum_{i=1}^{N} (x_{i}-\mu)^{2})^{2}}, \tag{9}\]  
where \(\mu\) is the mean of the discrete signal \(s=[x_{1},x_{2},...,x_{N}]\) and \(N\) is the number of signal samples. Kurtosis for the Gaussian random variable (\(s\)) calculated according to formula (9) is equal to 3.
## 3 Results and Discussion
### The Effects of LiOH Addition on the Cathodic Products
The operating temperature has a significant impact on many parameters of the whole reaction process, such as the molecular activity, theoretical electrolytic potential, molten salt viscosity, molten salt conductivity, gas product composition, and energy conversion efficiency. According to the analysis by theoretical calculation, increasing the operating temperature can effectively reduce the electrolytic potential required by the reaction, but too high a temperature will aggravate the corrosion of the electrode and electrolytic cell and lead to the thermal decomposition of the molten salt. At the same temperature, the electrolytic potential needed to generate H\({}_{2}\) is lower than that required to generate CH\({}_{4}\). As the data in Table 2  
Figure 6: Gaseous products analysis results of Li\({}_{1.427}\)Na\({}_{0.359}\)K\({}_{0.214}\)CO\({}_{3}\)+0.15 LiOH under 2.0 V (red line) and 2.5 V (blue line) at 550 \({}^{\circ}\)C with a 20 cm\({}^{2}\) Ni anode and Fe cathode, as well as a digital photo of the Fe cathode before and after electrolysis. show, at 450 \({}^{\circ}\)C, the H\({}_{2}\) content in the gas products exceeds 81%, while the CH\({}_{4}\) content is only 18.2%. With the increasing temperature, the difference in the electric potential needed to generate H\({}_{2}\) and CH\({}_{4}\) gradually decreases. The content of hydrocarbons in the product gradually increases with the growing temperature, and the content of H\({}_{2}\) gradually decreases. At 500 \({}^{\circ}\)C, the peak values of CH\({}_{4}\) and H\({}_{2}\) are 40.1% and 59.5%, respectively. A high temperature leads to the thermal decomposition of molten salt and excessive side reactions, resulting in a sharp drop in methane gas levels from approximately 40% to less than 27%. In the subsequent experiments, the influence of electrolytic voltage on the composition of the product was mainly studied. The theoretical electrolytic potential required to generate H\({}_{2}\) by the electrolysis of hydroxide alone is low, and the H\({}_{2}\) content in the gaseous products is close to 100% at an electrolytic voltage of below 1.5 V. The electrolytic voltage gradually increased to meet the electrolytic potential required by CH\({}_{4}\), and the content of CH\({}_{4}\) in gas products increased significantly. The highest CH\({}_{4}\) content was found at 2.0 V, shown as No. 3 in the below table. Continuously improving the voltage applied to the electrode can cause the CH\({}_{4}\) content to decrease gradually, and the H\({}_{2}\) content will rise again and a certain amount of CO gas will be detected in the gas product. This result is consistent with the theoretical calculation. The highest CO content is up to 33.6% at 550 \({}^{\circ}\)C under 3.5 V.
# The Influence of Radial Stress on Mechanical Properties of Anchorage Structure
## 4 Numerical Simulation
### Ultimate Anchorage Force under Different Confining Pressures  
Concentrated load is applied to the rockbolt. When the grouting material has large shear plastic strain, the anchorage structure cannot continue to bear the load. The ultimate bearing capacity and the maximum displacement of anchorage structure under different confining pressures are calculated respectively. The calculation results are shown in Table 6. Figure 7 shows the scatter diagram of confining pressure and the ultimate bearing capacity of anchorage structure and the fitting curve of the relationship between them.  
Figure 11: Distribution of shear stress under different confining pressures.  
The following conclusions can be drawn from Table 6 and Figure??:  
1. With the increasing of confining pressure in the anchorage section of the anchorage structure, the maximum displacement of anchorage structure is increasing, which indicates that the anchorage structure will not be damaged even if it has a large deformation under the action of high confining pressure;
2. By analyzing the relationship between confining pressure and ultimate bearing capacity, the ultimate bearing capacity increases linearly with the increase of confining pressure. Through the curve fitting method, the linear relationship between the confining pressure of the anchorage section and ultimate bearing capacity of anchorage structure is obtained, as shown in Equation (6). In this paper, when there is no confining pressure, the ultimate bearing capacity of the anchorage structure is 250 kN. When the confining pressure increases to 4 MPa, the ultimate bearing capacity reaches 510 kN, increasing by more than one time. When the confining pressure increased to 16 MPa, the ultimate bearing capacity of the anchorage structure reached 1140 kN, and the increasing range of the ultimate bearing capacity is amazing. This is clearly shows that the influence of confining pressure on the ultimate bearing capacity of anchorage structure is significant. \[F_{ult}=56.571P+287.71,\] (6) where, \(F_{ult}\) is the ultimate bearing capacity of anchorage structure, kN; \(P\) is the confining pressure of the anchorage section, MPa.
# Fermentation Strategies for Production of Pharmaceutical Ternenoids in Engineered Yeast
## 2 Pharmaceutical Terpenoids
### Paclitaxel (Taxol)  
Paclitaxel, also known as its commercial brand name \"Taxol\", is a tricyclic diterpenoid compound (Figure 3) used as chemotherapy drug. It was first isolated from bark and needle of the pacific yev tree (_Taxus brevifolia_) and has effective anticancer activities on several type of tumors, such as ovarian and breast cancers. Its unique mechanism of action as a microtubule stabilizer and inhibitor of mitosis on ovarian and breast cancers was approved by the US Food and Drug Administration (FDA) in 1992 and 1994, respectively. Since then, Paclitaxel has been also widely used in the treatment of colorectal, head and neck, small-cell and non-small-cell lung cancers (NSCLC), and acquired immune deficiency syndrome (AIDS). It contributes the conjunction of tubulin into microtubules and hinders the breakage of microtubules, which inhibits cell cycle progression, stops mitosis, and prevents the generation of tumor cells [3; 8; 14; 33].  
Paclitaxel is very expensive due to its small concentration found in original plant source (year tree). For instance, 10,000 kg bark of yev trees are required to produce 1 kg paclitaxel. This amount can treat 500 patients but requires the harvest of 300 yev trees [14; 46]. Production is dependent on slow growth and threat of species and there is the risk of yev trees extinction. Due to these reasons, alternative sources are necessary to be developed. Until now, several other options, such as microbial production, semi-synthesis and artificial cultivation of _Taxus brevifolia_ were applied to meet world demand  
Figure 2: Chemical structure of artemisinin.  
of Paclitaxel [8; 14]. Moreover, the production and extraction of this compound from genetically engineered endophytic fungi has been recently announced to be an effective way as well [46].
# An EV Charging Scheduling Mechanism Based on Price Negotiation
## 4 EV Charging Scheduling Based on Price Negotiation
### Price Adjustment Strategy of EV User  
It is hard for an EV user to get more global information. Thus, we assume that our price negotiation system sends the average bidding price \(\overline{p_{k}^{u}}\) of all EV users for the last round to EV users, together with the new bidding price of the charging station operator, and EV users will make their own price adjustment according to these data.  
When the last bidding price of an EV user is not accepted, the EV user will be aware that their bidding price may be lower than the price expected by the charging station operator. If the travelling requirement is urgent, then the next bidding price needs to be increased according to \(\overline{p_{k}^{u}}\). However, to save costs, there must be a variable threshold for the price increasing ratio. To simplify the process, we set the threshold as 5% when \(p_{j,k}^{u}<\overline{p_{k}^{u}}\), and 3% when \(p_{j,k}^{u}\geq\overline{p_{k}^{u}}\). We assumed that an EV user that does not update their bidding price will be eliminated.  
The bidding price of an EV user for the new round of price negotiation is formed as follows.  
\[p_{j,k+1}^{u}=\left\{\begin{array}{l}p_{i,k}^{u}\times\varphi\times(1+\min(5 \%,\frac{(\overline{p_{k}^{u}}-p_{j,k}^{u})}{p_{j,k}^{u}}\times 100\%),p_{i,k}^{u }<\overline{p_{k}^{u}}\\ p_{i,k}^{u}\times\varphi\times(1+\min(3\%,\frac{(p_{j,k}^{u}-p_{j,k}^{u})}{2p _{j,k}^{u}}\times 100\%),p_{i,k}^{u}\geq\overline{p_{k}^{u}}\end{array}\right. \tag{12}\]  
where \(\varphi\) is the decision-making coefficient of an EV user, \(\varphi\in(0,1)\). When \(p_{j,k}^{u}<\overline{p_{k}^{u}}\), the new bidding price of an EV user is close to \(\overline{p_{k}^{u}}\), and the price increases by up to 5%. When \(p_{i,k}^{u}\geq\overline{p_{k}^{u}}\), the new bidding price of an EV user is close to the last bidding price \(p_{j,k}^{cs}\) of the charging station operator at half speed, and the price increases by up to 3%.  
Of course, to save the time of the EV users that have an urgent travelling requirement and can bear more cost, the variable threshold for the price increasing ratio can be set at a higher value.