esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Phenolic Composition and Concentration  
The effects of berry thinning on the evolution of phenolic compounds in Shine Muscat grapes were determined in terms of total phenolic concentration (TPC), proanthocyanidin concentration, and polymeric tannin concentration (PTC) as shown in Figure 4 due to their structural diversity giving rise to different biological and organoleptic properties. Berry thinning increased the accumulation of phenolic compounds in berry skin. The TPC of BT30 and BT50 were 65.6 and 66.8 mg GAE/g DW, respectively, and higher than BT0 (55.5 mg GAE/g DW). However, there were no differences in TPC between BT30 and BT50. PAC showed a trend similar to that of TPC. PAC of BT0, BT30, and BT50 were 19.9, 25.2, and 25.9 mg CE/g DW, respectively. [PERSON] et al. [21] investigated the influence of berry thinning and cluster thinning in _Vitis vinifera_ Syrah grapes on wine composition and quality. They found that berry thinning increased the concentration of proanthocyanidin in wine. Furthermore, berry thinning was more effective in increasing the proanthocyanidin concentration in wines than cluster thinning. However, the opposite trend was observed in the study by [PERSON] et al. [8]. It was observed that selective berry thinning of R. peduncolo roso (_Vitis vinifera_ L.) grapes decreased the accumulation of proanthocyanidins. PTC also showed a similar trend to that of TPC and PAC. The concentrations of polymeric tannins in BT0, BT30, and BT50 were 13.4, 17.1, and 16.6 mg TAE/g DW, respectively. Higher PTC values in BT30 and BT50 than in BT0 resulted from higher tannin maturation due to the acceleration of berry ripening. This result was in line with a previous study by [PERSON] et al. [21] that berry thinning increased the degree of tannin polymerization in grape berries.  
Figure 3: Effects of berry thinning on (**A**) the berry horizontal and vertical diameters and (**B**) berry and cluster weights of fresh Shine Muscat grapes: Different letters on the bars with the same color indicate statistically significant differences at \(p<0.05\).
## 3 Experimental Results and Analysis
### Laboratory Permeability Tests  
To further analyze the physical and mechanical properties of the MICP-treated soil, we conducted constant-water rate permeability tests on the MICP-treated soil over three different periods. For constant head permeability tests, soil samples cured to a certain age are used, and the tests are conducted with a GDS consolidation-permeability apparatus. The sample preparation procedures are the same as those for the unconfined compressive strength test. In this test, the \(k_{\rm v}\)-back and \(k_{\rm v}\)-base values represent the permeability coefficients at the top and bottom of the sample, respectively. These values are calculated by the back and base pressure volume controllers of the GDS permeability system based on changes in the volume of the fluid that exudes or infiltrates within a certain unit of time. The permeability coefficient is then calculated using Darcy's law and the relationship between the drainage volume and flow rate of the soil. The results are shown in Figure 4. As the cultivation period increases, the permeability coefficient of the soil notably declines, stabilizing after approximately 14 days, with a final reduction rate of approximately 76%.  
Figure 3: The cohesion and internal friction angle of the MICP-treated soil.  
According to several previous studies, for silty and sandy soils, the permeability coefficient of MICP-treated soil declines sharply [11; 30]. MICP technology can be utilized to close fissures and enhance the anti-permeability performance of soil. Similar to sandy soil, microbially induced carbonate cementation in soft clay results in soil pore filling and a decrease in the permeability coefficient, also enhancing the soil strength. This behavior aligns with the development patterns observed in soil strength. The permeability coefficient stabilizes after approximately 14 days.
# Training from Zero: Forecasting of Radio Frequency Machine Learning Data Quantity
## 2 Materials and Methods
### Predicting the Data Quantity Needed
The log-linear regressions for each metric, dataset, and waveform space combinations are shown in Figure 7, while the log-linear regressions for accuracy are shown in Figure 3, and together help to better visualize the GoF results given in Table 4.  
Figure 6: The change in the values of {Top Left: Accuracy (1-Accuracy, log scale); Top Right: NCE (log scale); Bottom Left: LEEP (log scale); Bottom Right: LogME (linear scale)} as a function of the induced error, \(\epsilon\), expected to achieve accuracy from the whitening the truth labels of the evaluation set \(\Omega_{TC}\). The results plotted are the average values over 1000 iterations per data point. Figure 7: Relationship between the quantity of data used from each dataset (Left: \(\Omega_{C}\), Center: \(\Omega_{S}\), Right: \(\Omega_{A}\)) and the metric (Top: NCE, Middle: LEEP, Bottom: LogME) achieved by networks trained on that amount of data. Making use of the whitening procedure above and the log-linear regressions between data quantity and the metric's score, a prediction of data quantity needed to achieve arbitrarily high performance can then be found. For example, applying an error of \(\epsilon=10^{-5}\) to each examined problem space for the metrics and selecting a value averaged over 1000 iterations, the data quantity predictions can be made for each metric, as shown in Table 5, where the predictions are found by inverting the linear fit to estimate the quantity from the predicted metric as  
\[\tilde{q}_{\chi}=\log_{10}(\tilde{q}_{\chi})=\frac{\hat{M}_{\chi}-b_{x}}{s_{ \chi}}, \tag{20}\]  
where the \(\tilde{q}_{\chi}\) value is the logarithm base ten of the quantity estimate for the selected metric \(\chi\in[\text{Accuracy, NCE, LEEP, LogME}]\), \(\hat{M}_{\chi}\) is the metric value found through the whitening procedure, and \(s_{\chi}\), \(b_{\chi}\) are the slope and y-intercept, respectively, of the log-linear regression given a base ten logarithm applied.
# Influence Analysis of Traveling Wave Effect on Rail Interaction of Long-Span Suspension Bridge
###### Abstract  
At present, there are few research results on the seismic response of the track system on railway suspension bridges, and relevant research has not yet formed a certain standard. In order to provide a certain reference for the development of industry standards and the design of seamless lines on railway suspension bridges, based on the large-mass method based on multi-point excitation, taking China's longest high-speed railway suspension bridge-Wufengshan Yangtze River Bridge as the engineering background, a beam-rail integrated dynamic calculation space model was established with ANSYS, and the influence of traveling wave effect on the beam-rail interaction of large span suspension bridges was studied. The study shows that: the traveling wave effect will increase the relative displacement of the beam and rail, and then increase the stress of the rail; the traveling wave effect will cause the stress of the rail far from the source measurement to lag behind the stress of the rail near the source side, and the lag phenomenon gradually disappears with the increase of the apparent wave speed; the traveling wave effect has a greater effect on the displacement of the main bridge end than that of the approach bridge end; the longitudinal displacement of the main bridge end on the north and south side keeps changing synchronously under high apparent wave speed and consistent excitation, and no longer keeps changing synchronously under low apparent wave speed.  
high speed railway suspension bridge; railway normative response spectrum; ANSYS; LMM; travelling wave effect; beam-rail interaction
# Multi-Layer QoE Learning System Implemented by Fiberhost
## 6 Plans for Implementation of Supervised Learning
The overall evaluation of the system included in the last question allows us to assess the overall health and quality of the service from the customer's perspective. The first five questions are more related to picture quality, while the fifth may also relate to impressions of the decoder's software or GUI, the operating speed of additional functions (timeshift, catchup, startover, playback of recordings), messages displayed in the decoder's GUI, or the quality and number of movies and VOD services available alongside linear content. Obtaining ratings from customers and correlating them with the parameters of the STB, the network environment and the logs related to activities on the set-top box should make it possible to create an algorithm for estimating QoE based on supervised learning, provided a sufficient number of ratings are collected. Our system currently uses unsupervised learning methods. However, unsupervised learning is vulnerable to incorrect data, which may be produced as an error in measurements, wrong implementation, etc. Therefore, we plan to extend its learning capabilities to include the implementation of a supervised learning channel through QoE surveys, as shown in Table 3.  
Even though the supervised learning will occur infrequently (as compared to the number of logs and events reported by the network), we are still of the opinion that it will provide a valuable input which will reflect the end-user perspective (expected quality of service, user experience and preferences). In addition to the QoE surveys presented in Table 3, the operator can use traces of observed user behavior as an indicator of user dissatisfaction. The idea for this is presented in [43]. For example, if a customer performs many bandwidth tests, thus may indicate a problem with the service. However, we have observed that users usually perform a great number of speed tests when they have just purchased a service or are having problems with it. Therefore, it is crucial to distinguish these two completely different user behaviors. Additionally, repeated pressing of the cloud button on the TV remote by the customer may mean that there is a problem with the service or the responsiveness of the service is not provided at the level expected by the user. However, when analyzing these events, it should be noted that not all users know which button on the TV remote is responsible for the cloud function, and some users who do know may simply abuse this function. Therefore, we need to implement an appropriate layer that will filter and interpret user behavior. We consider these issues to be a very interesting part of our work related to QoE and the correct use of machine learning methods, and we intend to continue research in this area in the future.
## 3 Simulation and Results
### Area-A
The distribution of the SNs based on the DL throughput is shown in Figure 4. SNs with relatively low DL throughput may either have poor radio conditions, such as a low SINR, or they may share a common cell with many other SNs. Table 8 summarizes the results and shows the improvement achieved when UAVs are used in conjunction with terrestrial APs. The averages of the CQI and network DL throughput per SN are listed in the table. As can be observed from simulation results, the partial replacement of terrestrial APs by UAVs can enhance the overall system performance, despite the fact that UAVs have smaller antenna arrays than terrestrial APs. Figure 4: SNs distribution (ascending) based on downlink throughputs in (Mbps) using round robin scheduling, Area-A. Figure 3: SNs distribution (ascending) based on the CQI index, Area-A.
# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Index** & **Accuracy** & \(F_{1}\) **Score** & \(F_{2}\) **Score** & \(F_{5}\) **Score** & **MCC** & **Recall** & **Precision** \\ \hline
5 & **0.9981** & 0.5855 & 0.5516 & 0.536 & 0.59 & 0.5325 & 0.6502 \\ \hline
32 & **0.9979** & 0.5334 & 0.5028 & 0.4896 & 0.5388 & 0.4869 & 0.5897 \\ \hline
37 & **0.9979** & 0.5489 & 0.5219 & 0.51 & 0.5533 & 0.5074 & 0.5978 \\ \hline
42 & **0.9979** & 0.5436 & 0.5247 & 0.5163 & 0.5457 & 0.5144 & 0.5763 \\ \hline
36 & **0.9978** & 0.5571 & 0.5617 & 0.5657 & 0.5586 & 0.5868 & 0.5477 \\ \hline \hline \end{tabular} Note: The table is ranked based on the bold values.  
\end{table} 
Table 4: Module prediction rankings based on accuracy. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Index** & **Accuracy** & \(F_{1}\) **Score** & \(F_{2}\) **Score** & \(F_{5}\) **Score** & **MCC** & **Recall** & **Precision** \\ \hline
5 & 0.9981 & **0.5855** & 0.5516 & 0.536 & 0.59 & 0.5325 & 0.6502 \\ \hline
14 & 0.9974 & **0.5796** & 0.6634 & 0.7198 & 0.592  
nodules were actual positives. From the perspective of a screening task, this trade-off was justified, as it prioritized the detection of potential nodules, which is crucial in medical diagnostics. Consequently, in this study we chose the \(F_{5}\) score as our performance metric. The \(F_{5}\) score places a stronger emphasis on recall, making it well-suited for evaluating the effectiveness of different augmentation methods in enhancing model sensitivity. Table 8 illustrates that the MCC strives for a balance between recall and precision. This characteristic of the MCC may prove advantageous in scenarios where the cost of FPs is high, making it a potentially valuable metric for diverse tasks that require careful error management.
# Feasibility of Developing Sustainable Concrete Using Environmentally Friendly Coarse Aggregate
## 3 Experimental Procedure  
### Materials and Mix Design  
ASTM Type 1 Portland cement (PC) was used as cementitious material for the production of recycled coarse aggregate (RCA) and basalt quarried aggregate concretes [45]. Table 1 shows chemical composition of which is derived using X-ray fluorescence. The adopted mix proportions based on absolute volume method are reported in Table 2. Two concretes mix designs were investigated with the targeted compressive strength of 25 MPa and 40 MPa at 28 days. The fine aggregates were used in the form of uncrushed river sand with a fineness modulus of 2.7 and specific gravity of 2.5. The aggregates were oven dried for over 24 h prior to using to control the water content within batches. The properties of RCA and Basalt (quarried) coarse aggregate were tested in accordance with relevant standards, and results are summarised in Table 3. The RCA-25 and RCA-40 represent aggregates produced by recycling fresh returned concrete with Grade 25 and Grade 40, respectively. The RCA-25 has higher water absorption (6.85%) and moisture content (4.3%) than Basalt coarse aggregate (water absorption of 1.45% and moisture content of 0.85%). The coarse aggregates were used in saturated surface dry conditions to control the water absorption from cement paste. In both concretes, total aggregates content was fixed 65% by volume, and other mix ingredients were adjusted to keep the same material volume and water/cement ratio. The workability of all concrete mixes was kept at 80-100 mm range by adjusting the superplasticizer content, Table 2. Demineralized water was used throughout.
# Lithium-Ion Battery State of Health Estimation Based on CNN-LSTM-Attention-FVIM Health Features
## 2 Lithium-Ion Battery Data Explanation and Health Factor Selection
### Correlation Between Health Factor and Battery SOH  
In order to verify whether there was a correlation between HFS and SOH, we used Pearson and Spearman correlation coefficients as evaluation criteria. The main difference between the two is that Pearson focuses on linearity, while Spearman focuses on monotony. The former requires continuity and a normal distribution, while the latter has low requirements for data and can be ordered. Pearson may be more sensitive to outliers, while Spearman has less influence because of the use of ranks. The value range of the Pearson correlation and Spearman correlation coefficients is between \(-1\) and \(1\). The closer the absolute value is to \(1\), the higher the correlation between the variables. If the value is close to \(0\), it indicates that the linear correlation is weak. Using two correlation coefficients simultaneously can make the correlation results more convincing. The following is the specific calculation formula.  
\[Spearman=\frac{\sum\limits_{i=1}^{n}{(x_{i}-\overline{x})(y_{i}-\overline{y})}}{ \sqrt{\sum\limits_{i=1}^{n}{(x_{i}-\overline{x})}^{2}}\sqrt{\sum\limits_{i=1}^{ n}{(y_{i}-\overline{y})}^{2}}} \tag{1}\]  
\[Pearson=\frac{E(XY)-E(X)E(Y)}{\sqrt{E(X^{2})-E^{2}(X)}\sqrt{E(Y^{2})-E^{2}(Y)}} \tag{2}\]  
Here, \(X\) and \(Y\) represent the entire sample, and \(x_{i}\) and \(y_{i}\) represent individuals in the sample. It can be seen from Table 2 that, except for the low correlation of the constant voltage charging time of the B0018 battery, the other five HFS used have a high correlation with SOH, which is above \(0.9\) or near \(0.9\), and its HFS has a good representation of the SOH. Therefore, when predicting and estimating the B0018 battery, we exclude the HF3 constant voltage charging time of the B0018 battery and only use the other four health factors with the higher correlation.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**HFS**} & \multicolumn{2}{c}{**B0005**} & \multicolumn{2}{c}{**B0006**} & \multicolumn{2}{c}{**B0007**} & \multicolumn{2}{c}{**B0018**} \\ \cline{2-9}  & **P** & **S** & **P** & **S** & **P** & **S** & **P** & **S** \\ \hline HF1
# A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues
## 5 Discussion--Open Issues
* The full deployment of the IEC computing systems is inextricably connected with an integration of diverse hardware elements and infrastructures, thus leading not only to a highly heterogeneous environment but also to functions and features that cannot be anticipated at the time of design. The distributed, dynamic, and programmable nature of the entire IEC continuum along with the fragmentation of data as well as the need for supporting cross-platform interoperability, makes the application of security and trust fundamentally challenging. Since such systems unavoidably lead to a corresponding increase in the number and types of potential attacks, such attacks need to be predicted and anticipated with the help of AI systems. * The IEC framework should be able to provide autonomous reconfigurability according to network conditions and user and application requirements, as well as according to other design goals such as minimization of the energy footprint, which was previously mentioned. Indeed, the requirement for increased autonomous reconfigurability is emerging due to the ever more demanding services and the impact these services have on daily human activities (e.g., e-health applications). * Although there are many open-source frameworks for various functionalities involved in the IEC continuum, such as Apache Kafka for telemetry and data streaming [113], TensorFlow for machine learning [114], and Flower for federating learning deployment [115], there is not until now a unified open-source framework that can support scalability over large IEC infrastructures. This diversity in programming frameworks and models hinders the efficient development of the continuum solutions.
## 2 Application Example
Once the equations are defined, the model can be implemented using different numeric representations, depending on the objectives. The selected data type determines the minimum time step achievable for executing the model equations in an FPGA, as well as the design effort and hardware resources required. The two main possibilities for numerical formats are floating-point and fixed-point formats. The floating-point format, represented by the IEEE-754 standard [21], offers high precision and a wide dynamic range, simplifying the design process but requiring more computational resources and higher power consumption. The fixed-point format, where the number of bits for each variable are specified and remain fixed, provides lower computational costs and reduced hardware complexity due to the smaller size and faster computation times. This makes the fixed-point format more efficient in terms of resource usage and power consumption, ideal for real-time FPGA applications. However, it demands higher design effort to balance accuracy and resource utilization. Given these considerations, the fixed-point numeric type is the best option for implementing a combination of the HIL system and MPC algorithm in an FPGA, offering the necessary balance of efficiency, speed, and resource utilization for real-time control applications. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Situation I** & **Situation II** \\ \hline Parameters & Q: _on_ & Q: _off_ \\ \(v_{L}\) & \(V_{in}\) & \(V_{in}-v_{c}\) \\ \(i_{c}\) & \(-v_{c}/R\) & \(i_{L}-v_{c}/R\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the state behaviors of a boost converter in different situations. Figure 1: Switching states of a boost topology; (**a**) MOSFET _on_ in CCM; (**b**) MOSFET _off_ in CCM; and (**c**) MOSFET _off_ in DCM.
## 5 Consensus Mechanism
### Security Analysis  
The main aim of the protocol, analyzed in the previous subsection, is to fortify the collaboration of agents against malicious nodes that may try to falsify information. The malicious behavior is as follows, a malicious node \(q_{m}^{i}\) receives a path-message from an honest node \(u_{h}^{i}\). The data in the path-message received can be altered either by subtracting traffic or by adding traffic. Since the path-message consists of signed traffic information of the \(k\) previous nodes, the malicious node cannot tamper with that information. It can, however, update the current state of the path-message without any restrictions.  
Practically, a malicious node can try to remove evidence of an ongoing DDoS attack by reducing the values summed up by previous nodes regarding the traffic rate against the actual target of the attack. Alternatively, it can try to trigger false alerts, by adding an artificially high rate of traffic against a specific host.  
The state transition validation, committed by the agents, and potentially by the contract, hinders a malicious node from submitting false information. The protocol is mainly being carried out between the agents and the smart contract is engaged only when an inconsistency is identified. Agents are required to put in processing resources to this procedure, in order to minimize the interaction with the smart contract. Figure 3 depicts the communication scheme between the nodes and the smart contract.  
Each agent checks the validity of the most recent state changes, thus the collaboration of more than one malicious node may enable them to surpass the validation mechanism and falsify information, while going undetected. If each agent only validates data provided by the previous one, then two collaborating malicious agents that happen to be subsequent nodes for a specific path message, can break the rules defined above and either hide an actual DDoS attack or trigger a false alert.  
Figure 3: Communication protocol between nodes and the smart contract.  
In practice, if the state transition submitted by the first one of the two malicious nodes is invalid and the second malicious node does not report that, then the malicious behavior will go undetected.  
In order to minimize the probability of this happening, the protocol requires from each agent to validate the latest \(k\) state transitions for the path-message. Practically, in order for collaborating nodes to surpass this validation mechanism, it is required that at least \(k\) of them shall be ordered continuously in the path of a path-message. Because the ordering of the nodes is decided randomly, a higher value for \(k\) parameter, minimizes the probability of malicious nodes being able to collaborate as described above.