esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Causes and Control Measures of Mg-Al Spinel Inclusions in U75V Heavy Rail Steel
## 3 Results and Discussion
### Precipitation Transition of the Inclusions Themselves  
After VD refining, the main components of inclusions in steel were calcium, silicon, aluminum, magnesia and oxygen, and the distribution was uniform, and no spinel inclusions were found. However, Mg-Al spinel was detected in the subsequent molten steel, so the formation of spinel inclusions should be considered from the angle of precipitation and transformation of the inclusion itself. The precipitation transition of the inclusion itself during solidification and cooling was calculated by FactSage7.3 thermodynamic software, considering that the tundish had the closest contact with the water outlet and that the composition of the non-metallic inclusions in the steel in the tundish was closest to the composition of the final inclusions. Therefore, the average composition of non-metallic inclusions in the steel in the tundish under steady-state pouring conditions was calculated (45.70% CaO--22.13% SiO--24.41% Al\({}_{2}\)O\({}_{3}\)--7.15% MgO), selecting the three databases FactPS, FToxide and FSsteel, and the calculation results are shown in Figure 10. At 1600 \({}^{\circ}\)C, the component inclusions were a single liquid phase, and as the temperature decreased, the liquid phase gradually decreased; at 1500 \({}^{\circ}\)C, a solid phase MgO-Al\({}_{2}\)O\({}_{3}\) spinel precipitated, and then the CaO-SiO\({}_{2}\) phase began to precipitate. The final inclusions were mainly composed of the CaO-SiO\({}_{2}\) phase and spinel phase, additionally containing a small amount of CaO-Al\({}_{2}\)O\({}_{3}\) and CaO-SiO\({}_{2}\)-Al\({}_{2}\)O\({}_{3}\) phase. This calculation is mainly the transformation of the inclusion phase itself, and does not involve the transformation of the composition of the inclusion. The melting point of U75V heavy rail steel is 1470 \({}^{\circ}\)C, and the pouring temperature is about 1500 \({}^{\circ}\)C, indicating that the inclusions of this component began to precipitate inside the spinel phase under pouring temperature conditions; this also indicates that the precipitation transformation of the inclusions themselves is one of the reasons for the occurrence of spinel inclusions.  
Figure 10: Transition of high CaO low Al\({}_{2}\)O\({}_{3}\) inclusions during cooling.  
Figure 9: Changes in oxygen and nitrogen content.
# The Complex Relationship between Virulence and Antibiotic Resistance
## 3 Virulence Mechanisms
### Virulence Factors
#### 3.1.5 Bacterial Toxin Secretion  
Many bacteria secrete toxins based on upregulation of virulence genes. These can be classified as either endotoxins, which are the lipid portion of lipopolysaccharide that is released upon cell death, or exotoxin, which are secreted into the extracellular space. Recently, a study of _P. aeruginosa_ isolated from children with cystic fibrosis had increased expression of the virulence genes _loxA_, _lasB_ and _exoS_. These genes are involved in both chronic and acute forms of the disease and are involved in tissue damage and establishment of the biofilm [112]. _S. aureus_ is also known for its ability to secrete a host of toxins to aid in host tissue infiltration and acquire nutrients, as discussed above. Additionally, these pervasive pathogens also produce cytolytic toxins among a host of others, including hemolysis, leukotoxin, exfoliative toxin and enterotoxin that aid in bacterial growth [135]. This common mechanism of bacterial virulence should be investigated as a source of new druggable targets.
## 3 Review of Previous Work  
This section gives the modulation channel model characterized by the flat fading envelope \(h_{i}\), and the communication quantization is not considered. Several suboptimal fusion statistics based on such a flat fading envelope are reviewed.  
Consider a fading and noisy communication channel model (see Figure 1). Each sensor obtains noisy measurements and derives the local decision \(u_{i},\ i=1,\cdots,N\) according to given local decision rules, such as the energy detector and correlation detector. The local decision \(u_{i}\in\{0,1\}\), for \(i=1,\ldots,N\) is encoded as \(b_{i}=2u_{i}-1\) for binary phase-shift keying (BPSK) modulation. Then, each sensor transmits the signal \(\rho_{i}(t)=b_{i}\ \sqrt{E_{i}}\cdot s(t)\), where \(E_{i}\) is the data symbol transmit power and \(s(t)\) is a predetermined waveform. At the receiver, _i.e._, the fusion center, the received signal is:  
\[z_{i}(t)=h_{i}(t)b_{i}\sqrt{E_{i}}\cdot s(t)+n_{i}(t)\]  
where \(n_{i}(t)\) is the channel AWGN with zero mean and variance \(\sigma^{2}\) and \(h_{i}(t)\) is a real valued envelope modeled as a Rayleigh distribution with unit second moment gains, _i.e._, \(f(h_{i})=2h_{i}e^{-h_{i}^{2}}\) for \(h_{i}\geq 0\). We assume that the data symbol transmission energy is the unit. After matched filtering by the waveform \(s(t)\) at the FC, we have:  
\[z_{i}=h_{i}b_{i}+n_{i} \tag{1}\]  
The goal is to design the fusion rule according to the data from local sensors. As such, we neglect the waveform carrier modulation and the match-filtering process. The received SNR for the Rayleigh fading envelope with unit second moment gains is defined as \(\text{SNR}=E(h_{i}^{2})/\sigma^{2}=1/\sigma^{2}\), where the \(E(\cdot)\) denotes the expectation. For such a distributed detection model, the received data do not consider the communication quantization and take the value between \(-\infty\) and \(+\infty\). Note that the phase of the channel is assumed to be known, which can be accomplished by shifting the channel estimation from the FC to sensors to pre-compensate for the channel phase rotations [26]. The fusion rule is then applied to obtain a final decision for the target detection system. Here, some widely-used suboptimal fusion statistics based on the flat fading envelope \(h_{i}\) in Equation (1) are summarized as follows.
# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 3 Methodology
### Machine Learning Algorithms
#### 3.2.5 Gradient Boosting Machine (GBM)  
The Gradient Boosting Machine (GBM) algorithm is an ensemble learning method for regression problems. It builds a predictive model by combining the predictions from an ensemble of weak learners, typically decision trees. GBM aims to minimize residual errors stepwise, where each new model is trained to fit the residual errors left by the previous models [51; 52].  
The Gradient Boosting Machine (GBM) algorithm was configured to use decision trees as weak learners. A total of 100 trees were trained iteratively, using the least squares loss function to minimize residual errors. This setup, implemented via MATLAB's _fitensemble_ function, leverages default parameters for learning rate and tree depth, ensuring a robust and efficient model for this application.  
For each boosting iteration, \(t=1,2,\ldots,T\), where \(T\) is the total number of boosting iterations:  
* Compute the negative gradient (in terms of the loss function) of the loss function concerning the current model's predictions. This gradient represents the residual errors.
* Train a weak learner, often a decision tree, to fit the negative gradient (residuals). This creates a new model that is added to the ensemble.
* Update the model by adding the new model's predictions to the current one. This is achieved with a learning rate \(\eta\) that controls the step size of the update.
## 3 Results and Discussion
### Conductivity  
#### 3.3.1 Analysis of Impedance Spectra  
In Figure 5, the impedance spectra of the sample La\({}_{1.9}\)Ca\({}_{0.1}\)ScZnO\({}_{5.45}\) are shown in a Nyquist plot at several temperatures (a) and different air humidities (b), as an example. In general, we can distinguish the contributions due to bulk, grain boundary, and electrode effects. The first semicircle, starting from zero of the complex impedance plot, corresponds to the bulk resistance R\({}_{\rm b}\) (the value of capacitance was in the range of 10\({}^{-11}\)-10\({}^{-10}\) F), the second one to the grain-boundary resistance R\({}_{\rm gb}\) (the value of the capacitance was \(\sim\)10\({}^{-9}\) F), and the third small semicircle in the low-frequency regions corresponds to the electrode contribution R\({}_{\rm electrode}\) (the value of capacitance was \(\sim\)10\({}^{-5}\) F). This shape of the spectra was typical for all investigated phases.  
However, these contributions as well as the shape of the spectra changed with the temperature: with an increase in the temperature, the contribution of the grain boundary decreases, and the electrode contribution appears. In the temperature range below 500 \({}^{\circ}\)C, two overlapping semicircles are observed from the bulk and grain boundary responses. The bulk resistance of the samples was determined from the interceptions of the first high-frequency arc with the real axis at low frequencies. However, with the increase in the temperature, the grain boundary contribution decreases simultaneously with the disappearance of the first arc; and at higher temperatures (T \(>\) 700 \({}^{\circ}\)C), the bulk resistance  
Figure 5: The Nyquist plots for the sample La\({}_{1.9}\)Ca\({}_{0.1}\)ScZnO\({}_{5.45}\). (**a**) Evolution of the spectra with temperature in dry air (pH\({}_{2}\)O = 3.5\(\cdot\)10\({}^{-5}\) atm) and corresponding equivalent circuits; (**b**) evolution of the spectra with water vapor partial pressure pH\({}_{2}\)O = 2\(\cdot\)10\({}^{-2}\) atm (open signs) and pH\({}_{2}\)O = 3.5\(\cdot\)10\({}^{-5}\) atm (closed signs) at 400 \({}^{\circ}\)C.  
was found as the left-hand intercept of the third semicircle with the real axis. The possible uncertainty due to different extrapolations was estimated as \(\Delta\)(log\(\sigma\)) = \(\pm\) 0.10.  
The comparison of impedance spectra in wet and dry air at 400 \({}^{\circ}\)C is presented in Figure 5b, and it can be seen that the bulk resistance decreases with the increasing partial water vapor pressure.  
Further in the article, only the bulk conductivity will be discussed.
# Localization of LHD Machines in Underground Conditions Using IMU Sensors and DTW Algorithm
## 3 Materials and Methods
### Dynamic Time Warping  
As explained earlier, Dynamic Time Warping (DTW) is one of the algorithms for measuring the similarity between two signals (time series) that may differ in length. The idea of the algorithm in the form of a two-dimensional matrix is presented in Figure 2. Values in the following columns and rows correspond to two signals respectively (e.g., \(A\) and _B_). Let's consider the following two time series:  
\[A=\{a_{1},\ a_{2},\ldots,\ a_{i},\ \ldots,\ a_{n}\},B=\{b_{1},b_{2},\ldots,\ b_{j},\ \ldots,\ b_{m}\}. \tag{2}\]  
The presented matrix symbolizes all possible matches of the value of signal \(A\) to the value of signal \(B\). The green points symbolize the optimal match of two signals, found by DTW. The optimal solution is chosen by minimizing the Euclidean distance between the points. The different length of the signals implies the necessity to match at least one value from the shorter signal to several values from the longer signal (surjection). This is illustrated by points arranged vertically or horizontally.  
The above example shows only when the algorithm finds the optimal match indicated by the smallest Euclidean distance value. The values of this distance for each pair of readings, normalized to the range \((0,1)\), correspond to the probability of matching this pair,  
Figure 2: The matchings for the compared A and B signals can be presented on a two-dimensional matrix.  
where 0 is a full match and 1 is no match. The pseudo-code of the Dynamic Time Warping algorithm is presented in Algorithm 1.  
```
//\(\gamma_{1}=(a_{1},\ldots,a_{n})\), \(\gamma_{2}=(b_{1},\ldots,b_{n})\)--time series with n and m observations respectively \(\text{DIVW}(v_{1},v_{2})\) |  Let a two dimensional data matrix \(\mathbb{S}\) be the store of similarity measures such that \(\mathbb{S}[0,\ldots,n,0,\ldots,m]\), and i, j, are loop index, cost is an integer. \(\mathbb{S}[0,0]=0\)  FOR \(i=1\) to m DO LOOP \(\mathbb{S}[0,i]=\infty\)  END  FOR \(i=1\) to m DO LOOP \(\mathbb{S}[0,i]=\infty\)  END // incrementally fill in the similarity matrix with the differences of the two time series \(\text{FOR}:=1\) to m DO LOOP  FOR \(j=1\) to m DO LOOP // \(f\) d functions ensures the distance between the two points \(\text{cost}:=a(v_{1},\mathbb{S}[i],\mathbb{S}[i],\mathbb{S}[i],-1]\) \(\mathbb{S}[i,j]=\text{cost}+\text{MIN}(\mathbb{S}[i-1],\mathbb{S}[i,j]-1]\)  END  END  RETURN \(\mathbb{S}[n,m]\) }
```  
**Algorithm 1** Dynamic Time Warping algorithm pseudo-code [55]
## 3 Results
### Peak Flow Velocity and Flow Volume
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Group 1** & & \multicolumn{2}{c}{**Group 2**} & \multicolumn{2}{c}{**Group 3**} & \multirow{2}{*}{_p-Value_} \\  & **(N = 31)** & & **(N = 29)** & & & \\ \hline \multirow{2}{*}{**Average**} & **Standard** & \multirow{2}{*}{**Average**} & **Standard** & \multirow{2}{*}{**Average**} & **Standard** & \multirow{2}{*}{**F**} & \multirow{2}{*}{_p_} \\  & **Deviation** & & & **Deviation** & & & \\ \hline \hline \multicolumn{8}{c}{Peak flow velocity (cm/s) in the operative limb} \\  & **(N = 31)** & & & & CFV & \\ \hline
1 st day & 18.2 & 3.6 & 26.1 \({}^{\dagger}\) & 9.6 & 30.0 \({}^{\ddagger}\), \({}^{\ddagger}\) & 5.9 & 20.153 & \(<\)0.001 \\
3 rd day & 18.3 & 4.0 & 26.2 \({}^{\ddagger}\) & 8.0 & 31.3 \({}^{\ddagger}\), \({}^{\ddagger}\) & 7.0 & 29.290 & \(<\)0.001 \\
14 th day & 20.5 & 4.7 & 22.6 & 4.4 & 34.6 \({}^{\ddagger}\), \({}^{\ddagger}\) & 10.9 & 31.920 & \(<\)0.001 \\  & & & & PV & & & \\
1 st day & 13.4 & 2.9 & 24.7 \({}^{\ddagger}\) & 10.3 & 26.8 \({}^{\ddagger}\) & 9.0 & 22.984 & \(<\)0.001 \\
3 rd day & 13.4 & 3.0 & 26.1 \({}^{\ddagger}\) & 12.1 & 30.0 \({}^{\ddagger}\) & 11.7 & 22.600 & \(<\)0.001 \\
14 th day & 13.7 & 3.4 & 19.4 \({}^{\ddagger}\) & 8.1 & 29.0 \({}^{\ddagger}\), \({}^{\ddagger}\) & 10.3 & 28.640 & \(<\)0.001 \\  & & & & & & & \\ \multicolumn{8}{c}{Peak flow velocity (cm/s) in nonoperative limb} \\  & & & & & CFV & & \\
1 st day & 19.5 & 4.4 & 19.0 & 4.3 & 20.2 & 3.2 & 0.780 & 0.461 \\
3 rd day & 18.3 & 3.9 & 19.7 & 4.7 & 20.2 & 3.4 & 1.783 & 0.174 \\
14 th day & 20.1 & 4.0 & 20.6 & 5.1 & 22.7 & 5.7 & 2.392 & 0.097 \\  & & & & PV & & & \\
1 st day & 14.7 & 4.3 & 15.5 & 2.9 & 15.5 & 2.5 & 0.578 & 0.563 \\
3 rd day & 14.6 & 4.4 & 16.1 & 3.4 & 15.2 & 2.6 & 1.365 & 0.261 \\
14 th day & 13.6 & 3.4 & 15.4 & 3.9 & 16.5 & 3.2 & 4.936 & 0.009 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c} \hline \hline  & **CFV: common femoral vein; PV: poplileal vein. \({}^{\ddagger}\) Significant difference (\(p<0.05\)) compared with group 1 using LSD. \({}^{\lx@sectionsign}\) Significant difference (\(p<0.05\)) compared with group 2 using LSD. \({}^{\lx@sectionsign}\) Significant difference (\(p<0.05\)) compared with group 2 using LSD. \({}^{\lx@sectionsign}\) Significant difference (\(p<0.05\)) compared with group 2 using LSD. \({}^{\lx@sectionsign}\) Significant difference (\(p<0.05\)) compared with group 2 using LSD. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Peak flow velocity in the common femoral vein (CFV) and poplileal vein (PV) after the surgery. (from 87.6 to 167.6 mL/min), and 93.8% (from 98 to 189.9 mL/min) on the 1 st, 3 rd, and 14 th days, respectively. In the nonoperative limb, there was no significant difference in the flow volume of the CFV or PV between the three groups. A subgroup analysis was performed to stratify comparisons by sex (Table 4), and the differences between groups remained highly significant (\(p\leq 0.001\)) in women; however, the results in men showed less significant differences, indicating different effectiveness between men and women.
## 4 VM-Based Intrusion Detection Mechanism
### System Architecture  
The architecture of the proposed intrusion detection system is depicted in Figure 1. A target device, which would be a router in most deployment scenarios, has network connection with respect to its network configuration, and the connection is established via a L2 switch. The intrusion detection system is also connected with the switch via SPAN (Switched Port Analyzer). SPAN enables all forwarded packets through the target device to be duplicated and mirrored to the monitoring port which the monitoring system is attached to.  
The proposed system, which is actually built on top of commodity x86 hosts, consists of the following components:  
* _Emulated device_: It is a VM instance that emulates the target device. We require the VM to run the same binary image as the target's firmware. All mirrored packets from the target device, being passed through a physical network interface (PNIC) of the host system, TCPSync, and a virtualized network interface (VNIC) in order, are then injected into the emulated device. Due to the agreement of the firmware image and the traffic mirroring, internal states of the emulated device will conform to the target device.
* _TCPSync_: It is a software module, running outside the emulated device that forwards all received packets from a PNIC, transforming some packets if necessary, to a VNIC attached to the emulated device. The main work of this module is to conduct transparent conversion of certain packet flows through connection-oriented protocols such as TCP. Details on TCPSync will be presented in the next section.
* _Memory integrity monitor_: It is a software module that aims to monitor the internal state of the VM. It has an interaction with the emulated device through a guest management interface, which is provided by a virtual machine manager (VMM). By using an introspection library (e.g.,  
Figure 1: System architecture.  
Libvmi [20]) or a debugger such as GDB, it periodically obtains snapshots of the VM's memory and the corresponding hash value, from which it checks whether the integrity has been broken.  
Figure 2 shows how a remote code execution attack against a target device can be detected by the proposed system. Suppose that the target device receives \(m\) ingress packets \(p_{1},p_{2},...p_{i},...p_{m}\) sequentially, among which a packet \(p_{i}\) contains maliciously crafted payload that will cause unauthorized modification of memory in the device. Those packets are mirrored to TCPSync, which in turn forwards the packets to the emulated device. Since a firmware image of the emulated device is identical to that of the target device, the crafted packet \(p_{i}\) will also cause memory corruption accordingly. Memory integrity monitor takes memory snapshots \(S_{0},...,S_{j},...\) at every time epoch \(T_{0},...,T_{j},...\) from the emulated device. It will find out that a snapshot \(S_{j}\) taken at time \(T_{j}\) after the crafted packet \(p_{i}\) has been injected has a variant in its hash value compared to previous snapshots, which indicates that memory corruption by the attack has been occurred.
## 2 Method
### Model Validation and Analysis  
A decision tree model [47] was used to classify and train the six variable factors(the point cloud y-normal vector, the point cloud partial height difference, the point cloud x-normal vector, the point cloud curvature, the point cloud z-normal vector, and the point cloud height difference of the filtering reference surface) mentioned above.  
As a classification algorithm, the decision tree algorithm uses an induction algorithm to generate a series of visible rules and decision trees after processing the data, and then uses the decision tree to distinguish new data by rules [54; 55]. Among them, the processed data are called the training set, and the analyzed data are called the test set. In short, the goal of the decision tree algorithm is to use the training set data to automatically build a decision tree model, and then use the decision tree to classify or predict the test data.  
The main task of the decision tree algorithm is to find the most critical node and the most suitable branch decision rule; impurity is the most critical and most suitable indicator used to measure the classification tree [56]. In general, the lower the impurity, the better the training set fit of the decision tree. Impurity can be characterized by information (_Entropy_), and the calculation method is as Equation (4).  
\[Entropy(t)=-\sum_{j=0}^{c-1}P(i|t)\log_{2}P(i|t) \tag{4}\]  
Figure 3: Feature importance scores ranked using the mRMR method (features 1, 2, and 3 are the point cloud 3D coordinates, feature 4 is the point cloud y-normal vector, feature 5 is the point cloud partial height difference, feature 6 is the point cloud x-normal vector, feature 7 is the point cloud curvature, feature 8 is the point cloud z-normal vector, feature 9 is the point cloud height difference of the filtering reference surface).  
Among them, \(t\) represents a given node and \(i\) represents any classification of labels, and represents the proportion of label classification \(i\) on the node. Note that the information gain based on information ancestry is calculated when using the information ancestry, that is, the difference between the information ancestry of the parent node and the information ancestry of the child nodes. The decision tree that uses information inheritance grows more \"meticulous\", especially when the model training effect is not good, or the weight of factors is not obvious.  
The proposed method trains the model, performs cross-validation, and uses the filtering error evaluation standard proposed by ISPRS [32] to verify the filtering accuracy. The error definition is shown in Table 1, where \(a\) represents the number of ground points that are correctly classified after filtering, \(b\) denotes the number of ground points that are misclassified as non-ground points, \(c\) represents the number of ground points that are misclassified by non-ground points, and \(d\) represents the number of correctly classified non-ground points after filtering. The total error reflects the filtering quality and feasibility of the method. A smaller total error indicates a more accurate filtering result.
# Laminar _Ulva_ Species: A Multi-Tool for Humankind?
## 4 _Ulva_ Compounds of Interest
### Proteins  
Plant proteins are frequently called inadequate protein sources because they lack one or more necessary amino acids [157]. _Ulva_ sp., according to several authors, contains substantial quantities of proteins (7-29% dry weight, DW) [70; 132]. However, some study has indicated that it can reach up to roughly 40% in IMTA systems, where large quantities of nitrogen are incorporated into proteins with a N to P conversion factor of 5 [32].  
[PERSON] et. al. in 2019 used five different ways to extract proteins and obtained between 20% and 62% of the DW of protein on a DW basis, whereas the carbohydrate percentage ranged from 5 to 37%. The remaining non-protein and non-carbohydrate fraction in all extracts consisted of phenolic compounds, minerals, and other phytochemicals such as pigments [132].  
_Ulva_ protein contains large levels of essential amino acids, making it a potentially valuable dietary protein source. Indeed, there is remarkable resemblance in the overall amino acid content of the alga and that of egg ovalbumin, such as histidine [96].  
Among with amino acids, [PERSON] et al., 2013 [158] and [PERSON] et al., 1995 [159] noted the most prevalent amino acids are aspartic and glutamic acids, which can account for up to 26 and 32% of the total amino acids in _U. rigida_ and _U. lactuca_ (as _U. rotutunda_), respectively [70]. On the contrary, [PERSON] et al., 2017 showed that glutamic acid and alanine existed as the highest amino acids in wild-type and sterile thalli of _Ulva_ sp. [87].  
Other essential amino acids such as valine, leucine, lysine, and threonine make up 42% of the total amino acid composition in _U. lactuca_, according to [PERSON] et al., 2011 [160].