esa-sceva/satcom-chunk-collection huggingface dataset:
# Regulation of the _Pseudomonas syringae_ Type III Secretion System by Host Environment Signals
## 4 Additional Global Regulators of the T3 SS in _P. syringae_
### Regulation of the T3 SS by Alginate Master Regulator AlgU  
AlgU, an extra-cytoplasmic function (ECF) alternate sigma factor, also functions in the activation of the T3 SS signaling cascade [161; 162]. In some _P. syringae_ pathways, AlgU was required for full expression of T3 SS-associated genes, though AlgU does not appear to regulate T3 SS expression in other pathways, suggesting the diversification of the AlgU regulon within the _P. syringae_ species complex [103; 161; 162]. In _P. syringae_ pv. tomato DC3000, an _algU_ loss-of-function mutant exhibited reduced expression of _hrpL_ and _hrpRS_, and influenced the expression of a significant portion (38%) of the HrpL regulon [161; 162]. AlgU additionally directed the expression of _algW_, a potential positive regulator of the T3 SS identified in _P. syringae_ pv. _maculicola_ ES4326 [163]. The expression of AlgU was stimulated by a variety of environmental stressors, including osmotic stress and perturbation of the bacterial cell envelope; however, it is unknown whether these environmental stimuli also regulate the T3 SS in _P. syringae_[164; 165; 166; 167]. The expression of AlgU was also indirectly represented by the calcium-induced CvsSR two-component system previously reviewed in Section 3.5[149].  
The functionally diverse regulon of AlgU provides some indication as to how _P. syringae_ may coordinate the regulation of the T3 SS with other phenotypes relevant to host infection. Per its nomenclature, AlgU stimulates the biosynthesis of alginate, an extracellular polysaccharide that plays a role in the virulence of select _P. syringae_ strains on host plants [166; 167; 168; 169; 170]. Alginate may provide protection against cellular osmotic stress caused by plant immune defenses, and _algU_ expression and alginate production by _P. syringae_ are potential targets of the host immune response [171; 172]. Additionally, the AlgU regulatory system is broadly implicated in negative regulation of the bacterial flagella, providing further evidence that the T3 SS and flagellar motility may be subject to inverse regulation in _P. syringae_[163; 161; 173]. The production of coronatine, a phytotoxin involved in the regulation of host stomatal aperture, is also subject to regulation by AlgU [162]. These findings suggest that alginate and coronatine production may be activated concurrently with T3 SS deployment during _P. syringae_ infection, while flagellar motility is downregulated.
## 1 Introduction
As the amount of material removal in the micro-cutting process is significantly lesser than that of traditional machining, which leads to the redistribution of thermal softening and hardening effects in the micro-cutting process, the influence of the J-C constitutive parameters on serrated chips is not entirely similar to traditional cutting. Therefore, the serrated chip formation mechanism using the Johnson-Cook parameters in the micro-cutting of Ti6 Al4V is analyzed. Although several studies regarding the experiment and simulation of the serrated formation mechanism of Ti6 Al4V have been conducted, a thorough analysis of the J-C constitutive parameters concerning the serrated chip formation mechanism in the micro-cutting of Ti6 Al4V is limited. Aiming to reduce the inconsistency between the experimental and cutting simulation results due to varying J-C constitutive parameters, as well as to increase the reliability of the cutting simulation model, the serrated chip formation mechanism with the Johnson-Cook parameters in the micro-cutting of Ti6 Al4V are analyzed. Serrated chip sensitivity, shear band spacing, serrated segmentation frequency, chip serrated intensity, temperature field, and cutting force are obtained and analyzed. Further, this study provides guidance to improve the accuracy of cutting simulation models.
# Defacement Detection with Passive Adversaries
## 4 System Architecture and Implementation
### Keyed Scheduler  
As a first step, we will analyse the concept of a \"keyed scheduler\", i.e., a system component that will spawn a process and determine its inputs in a way that is unpredictable for our adversary. This will be an essential component of our defacement detection architecture, and is described in Figure 4.  
Figure 3: Keyed Learning—the correct key k helps select a hypothesis space \(H_{2}\) that is easier to learn, including a predictive classifier \(C\).  
The keyed scheduler will use as input a list of URLs and corresponding weights:  
\[<URL_{1},w_{1}>,...,<URL_{n},w_{n}> \tag{2}\]  
as well as the secret key \(k\). It has two components: a keyed selector and a keyed delayer.  
The keyed selector will select one URL out of the input list. The weights associated to each URL determine the frequency of their use:  
\[frequency(URL_{i})=w_{i}/\sum_{1\leq j\leq n}w_{j}. \tag{3}\]  
The choice of the URL will depend on the key \(k\) and should respect the above defined frequencies. As a first step, a frequency table \(FT\) is constructed:  
\[FT=[[URL_{1},1],...,[URL_{1},w_{1}],...,[URL_{n},w_{n}]] \tag{4}\]  
where each \(URL_{j}\) is repeated \(w_{j}\) times. The length of \(FT\), that we will call \(m\), is then computed as  
\[m=\sum_{1\leq j\leq n}w_{j} \tag{5}\]  
The keyed selector's output \(URL_{i}\) will be computed as \(URL_{i}=FT[index][0]\), where  
\[index=HMAX_{k}(A_{t})\mod m \tag{6}\]  
and \(A_{t}\) is obtained as part of our pseudo-random function PRF as defined in Equation (1), computed for the current iteration \(t\). The value of \(t\) will be incremented for every new cycle of the keyed scheduler, i.e., every time the keyed selector is activated.  
In summary, \(index\) will be an unpredictably chosen element of \(FT\), and thus \(URL_{i}\) will be chosen unpredictably out of the input list, respecting the given frequencies. The user will then be able to assign different weights to the URLs in the list, depending on their relative importance.  
The keyed delayer will wait for an unpredictable time interval T, measured in milliseconds, where \(T\leq MaxTimeInterval\):  
\[T=HMAX_{k}(A_{t})\mod MaxTimeInterval \tag{7}\]  
The adversary will then be unable to predict this time interval, and hence know when the detector will be running.  
Figure 4: Keyed scheduler.  
After the keyed delay, the keyed scheduler will spawn a process and pass to it the selected URL. The process will run on a new selected client. Again, the client could be selected based on the secret key \(k\). In our implementation we have used a simpler round robin strategy, so that all available clients are equally used. The resulting effect is a monitoring process being spawned at unpredictable times and with an unpredictable input URL.
## 6 Experimental Results and Evaluation
### Effect of Preprocessing on Bug Classification  
The preprocessing stage includes tokenization, lemmatization, and removing stop-words for bilingual bug definitions. Bilingual data poses different challenges for NLP [21]. Although some researchers apply different parsing techniques for multilingual data [22], we use two-step lemmatization and two-step removal of the stop words, as shown in Figure 4. Details of this process are given in our previous study [23]. Since we are dealing with two different languages (Turkish, English), we have two different steps to eliminate stop words and two different steps for lemmatizing. Accordingly, we use two different temmatizers, one for English and one for Turkish, executed for each word. In the automated bug classification process, we first use a Turkish lemmatizer followed by an English lemmatizer. During the experiments, we applied a particular stop-word removal process. Hence, applying this process means we have removed both Turkish and English words.  
Similarly, not applying the stop-word removal process means that we did not remove stop-words in either language. The same is true for lemmatizing. For the bug classification using the various ML algorithms, we could use the alternative with or without removing stop-words and similarly with or without lemmatization. These combinations led us to four different alternatives, each of which had a different impact on the bug classification using the corresponding ML algorithm. The computed F1 measures of the decision for using or not using stop-word elimination and lemmatizing are given in Table 8.  
The experimental results showed that the most optimal ML algorithm appears to be the SVM linear kernel (71.66%). Subsequently, we wanted to examine the effect of stop word removal and lemmatizing in preprocessing process. We took the \"no stop-words and no lemmatizing\" case (first table column) as the baseline. Then we saw that the lemmatizing and stop-word removal processes affected the results of the classifiers positively or negatively slightly in any combination of these. Therefore, it is not possible to determine how these processes affect the classification results. However, this result is also consistent with the work of [PERSON] and [PERSON] [24]. In their work, they applied several preprocessing techniques and investigated the effect on the text classification results. They reported the effect of each technique separately, concluding that only lowercase conversion improves classification success in terms of accuracy and dimension reduction regardless of domain and language. Thus, no particular combination of preprocessing tasks results in better classification performance for any domain and language studied.
## 4 Experiments and Results
### Discussion  
The proposed digital twin in sport is distinguished by four characteristics: user-friendliness, robustness, reliability, and accuracy. The first characteristic refers to the usage of the application. As shown in the analysis of the GUI, the interface is simple and easy to use. The graphical design is minimalist, and consists of only those elements that are necessary for efficient decision making. Both the cyclists involved in the study helped the designers by improving the GUI with their comments and advice. Robustness means that the digital twin works well in mobile circumstances, where the computer is subject to vibration. The results of the predictions by the digital twin are reliable and accurate.  
The results of the mentioned Case Studies 1 and 2 confirmed that the behavior of the professional and amateur cyclists in cycling training are enormously different, and, therefore, easily identified by using the DT for cycling. While the professional cyclist did not have any problem in achieving the border values of the upper-speed interval and the lower-recovery interval heart rates, the amateur cyclist never achieved the lower interval heart rate. This means that the trained cyclist is capable of adapting the heart rate quicker than the non-trained one. Moreover, the transitions from the lower to upper heart rate values and vice versa are more prominent for the professional cyclist.  
The aggregate results of both cyclists are illustrated in Table 7, from which it can be seen that the efficiency of the proposed interval training session was \(\overline{SR}=91.45\%\) for the professional cyclist (confirming RQ-1), and only \(\overline{SR}=72.61\%\) for the amateur one (confirming RQ-2). Indeed, the digital twin informs the athlete what to do, but not how to do it. As an answer to the question, the information of sport trainers still remains valuable.
# Optimizing CNN-LSTM for the Localization of False Data Injection Attacks in Power Systems
## 4 Simulation Experiments
### Comparative Experiments and Analysis
#### 4.4.2 Comparison with Other Detection Methods  
Figures 11 and 12 illustrate the comparison between the proposed GA-CNN-LSTM positioning algorithm and several other detection algorithms, i.e., GA-CNN-MLP [33], GA-CNN [34], CNN [35], deep neural networks (DNN) [36], and multi-layer perception (MLP) [37]. To ensure a fair comparison, all algorithms were evaluated using the same dataset. It can be seen that for the IEEE14-bus test system, the proposed positioning algorithm demonstrated superior performance, with accuracy, F1 score, and recall values of 99.88%, 99.71%, and 99.84%, respectively, outperforming all other algorithms in this evalua  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **0\% Noise** & **5\% Noise** & **10\% Noise** \\ \hline IEEE14-bus & 99.73\% & 99.71\% & 99.70\% \\ IEEE118-bus & 99.15\% & 99.10\% & 99.08\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detection effects under different noises.  
Figure 9: IEEE118-bus localization effect.  
The method proposed in this paper, due to its more complex model and a larger number of hyperparameters, had a longer detection time compared to the other models. However, generally, power systems acquire measurement data every few seconds and perform a state estimation every few minutes. This means that in the case of FDIAs, our method can effectively locate the attacked data.
# Improvement of ID3 Algorithm Based on Simplified Information Entropy and Coordination Degree
## Improved ID3 Algorithm
### Variety Bias Problem and the Solution
#### 3.2.3 Uncontrollable Tree Size and the Solution  
The splitting degree of internal node is hard to control, which will bring a lot of disadvantages, such as large tree size, long classification rules, and so on. Therefore, the concept of coordination degree is introduced in this paper, which plays a great role in determining the split property of the internal  
Figure 4: Decision tree model based on the improved method.  
node in advance, and avoids the subsequent pruning step [36] that used to deal with the over-fitting problem. The basic idea of the proposed algorithm is that the standard of the splitting property of every branch is determined by the size of coordination degree and the condition certainty degree.  
Assume that \(D=\{(x_{1},y_{1}),(x_{2},y_{2}),\cdots,(x_{n},y_{n})\}\) stands for training set, \(A=\{a_{1},a_{2},\cdots,a_{d}\}\) denotes condition attribute set, which is variable, and \(a_{di}\) stands for the subset of a value in the attribute \(a_{i}\). \(D^{l}=\{y_{1},y_{2},\cdots,y_{n}\}\) denotes label attribute set, which is stationary.  
Coordination degree is defined by:  
\[Con(a_{i}\to D^{l})=\left|a_{i}\cup D^{l}\right|/\left|a_{i}\right|. \tag{22}\]  
The condition certainty degree is defined by:  
\[K(Ea_{i})=\left|a_{di}\cup D^{l}\right|/\left|a_{di}\right|. \tag{23}\]  
The splitting property of different branches depends on the size of coordination degree and condition certainty degree in the subset. If the size of the coordination degree is greater than or equal to that of the coordination degree, the node will be a leaf node, on the contrary, the node will be an non-leaf node, which can continue be splitted.  
The decision trees built by the two different methods are shown in Figure 5, and the right figure that has fewer nodes and branch rules is more concise than the left.
# Piezomagnetic Anomalies Associated with the 2021 \(M_{\text{W}}\) 7.3 Maduo (China) Earthquake
## 4 Discussion  
### Validation of Piezomagnetic Model  
We compare the PMs of the Maduo earthquake with that of other major continental strike-slip earthquakes (\(M>6\)), including the 1989 \(M_{\rm L}\) 7.1 Loma Prieta earthquake [19], the 1992 \(M_{\rm W}\) 7.3 Landers earthquake [20], and the 2004 \(M\) 6.0 Parkfield earthquake [4]. It should be noticed that the PMs strongly depend on parameters. Therefore, we focus on relative values of PMs here.  
The PMs are proportional to initial magnetization and stress sensitivity according to Equation (1). We calculate the normalized PMs (NPMs) as follows:  
\[\text{NPMs}=\text{PMs}/\beta J \tag{5}\]  
The PMs exhibit a negative correlation with depth of slip (\(H\)s) [38] and positive correlation with slip (\(S\)). The earthquake magnitude or moment considered by [PERSON] and [PERSON] [29] is substituted by the ratio of \(S\) and \(H\)s in this paper. Because we cannot obtain the value of stress sensitivity for the Loma Prieta earthquake, 1 \(\times\) 10\({}^{-3}\) MPa\({}^{-1}\) and 2 \(\times\) 10\({}^{-3}\) MPa\({}^{-1}\) are assumed in the piezomagnetic model of Loma Prieta. Here, we choose the maximum PMs of these earthquakes to analyze. The correlation between maximum NPMs and \(S/H\)s is shown in Figure 5. All values of these piezomagnetic models are shown in Table 2. Figure 5 shows that NPMs increase with \(S/H\)s. In other words, given the same \(\beta\) and \(J\), the larger \(S/H\)s is, the larger the PMs are. As shown in Figure 5, NPMs of Maduo earthquake are consistent with those of other strike-slip earthquakes and the Loma Prieta earthquake with stress sensitivity of 2 \(\times\) 10\({}^{-3}\) MPa\({}^{-1}\). It indicates that our piezomagnetic model is reasonable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Event**} & **Maximum PMs** & **Magnetization** & **Stress Sensitivity** & \(S\) & \(H\)**s** \\  & **(nT)** & **(A/m)** & **(10\({}^{-3}\) MPa\({}^{-1}\))** & **(m)** & **(km)** \\ \hline Maduo & \(\sim\)10 & 3 & 2 & 8.1 & 6 \\ \hline Landers & \(\sim\)5 & 2 & 2 & 5 & 5 \\ \hline Loma Prieta & \(\sim\)1.5 & 1.5 & 1 or 2 & 2.3 & 6 \\ \hline Parkfield & \(\sim\)0.4 & 2 & 2 & 0.4 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of piezomagnetic models of four earthquakes (\(M>6\)).  
Figure 4: East component of PMs on three profiles across fault trace. The dash lines represent fitted PMs instead of calculated ones as the PMs are neglected artificially in order to avoid singularity.
# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
## 3 Results  
### Land Cover Classification and Change Detection  
The classification model of the before image had an 89% overall accuracy (kappa 0.86), and the after image had a 91% overall accuracy (kappa 0.88). The land cover of the before image was 49% forested, 16% developed, 8% grass, 6% buildings, and 3% barren, while the land cover of the after image classification showed that the buildings class increased from 7.1 km\({}^{2}\) to 9.7 km\({}^{2}\), and the barren class decreased from 3.6 km\({}^{2}\) to 3.0 km\({}^{2}\) (Figure 5). The building class of the before land cover had user accuracy of 0.88 and a producers accuracy of 0.44, while the after classification had a user accuracy of 0.85 and a producers accuracy of 0.68. Grassland cover also contained greater uncertainty with producer accuracies ranging from 0.79 (before) to 0.75 (after). These discrepancies likely account for the larger variations found in these classes.
# Anomaly Detection Method for Unknown Protocols in a Power Plant ICS Network with Decision Tree
## 3 Results
### Test Verification
#### 3.2.3 Analysis of the Verification Results of the Proposed Model  
The proposed model, DMP, was compared and verified with the solutions from previous experiments, DT and i-DT. Verification was performed using traffic data extracted from 100 points in an arbitrary section of the 4-month collection of operational data. The verification was performed five times on a total of 26 TB collected to determine whether abnormal symptoms were detected. Table 14 presents the results of comparing the reading rates of DT, i-DT, and the proposed model. The results show that the proposed model's detection efficiency is superior to that of the other solutions.  
It is worth noting that DT is a solution for traffic detection on an open network, while i-DT is an improvement of the method of detecting only the session count. i-DT could improve the function to detect even the byte unit to optimize it for a closed network environment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Item** & **1** & **2** & **3** & **4** & **5** \\ \hline Source IP & 172.15.100.12 & 172.15.100.12 & 172.15.100.12 & 172.15.100.12 & 172.15.100.12 \\ Source Port & 2916 & 2916 & 2916 & 2916 & 2916 \\ Bytes & 11,541,934 & 11,541,934 & 11,541,934 & 11,541,934 & 11,541,934 \\ Data Count & 100 & 100 & 100 & 100 & 100 \\ Query ID & 1 & 1 & 1 & 1 & 1 \\ Duration & 0.00224869 & 0.00011751 & 0.00014457 & 0.00011805 & 0.00011718 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Test results for the metadata search speed.  
Figure 12: Occurrence frequency of the proposed model.  
The proposed model in this study showed significant improvements compared to the existing method. As indicated in Figure 13, the spy rate, which distinguishes normal and anomalous traffic, was 89.2%, which was 1,064% and 84% better than the existing method, respectively. Additionally, the false-positive rate for problematic abnormal signs was 8.4%, representing an improvement of 961% and 419% compared to the existing model, respectively. In addition, precision, sensitivity, and F1 score were all improved compared to those of the traditional methods. The detection processing time was also faster than that of the existing model.  
It is important to note that DT, which is optimized for detecting anomalies in a general network, detected almost no anomalies in the private control network protocol. However, for a fair comparison, the proposed model's reading rate of anomalies was found to be higher than that of i-DT, which was tuned for a closed network environment. This shows the effectiveness of the proposed model in detecting anomalies in a closed network with a private protocol.  
Figure 13: Proposed model detection rate compared to that of the existing method.
## 6 Discussion
As our results illustrate, interference can render IEEE 802.11 networks useless for such applications, leading us to recommend that network professionals implement a quality-of-service algorithm in which, for example, they prioritize network traffic and/or distribute devices to different frequency bands [22]. Indeed, network professionals should consider running only less-important applications on the 2.4 GHz band, given that this frequency is used by wireless audio/video transmitters, audio headsets, and Bluetooth handsets. At the very least, such devices could change operating frequencies in order to avoid other wireless devices. The less-congested 5 GHz band could be reserved for critical industrial communications [22]. In addition, a possible upgrade to the latest IEEE 802.11 ax standard should be considered in order to mitigate the effects of interference [38]. To facilitate network testing and troubleshooting, we recommend using a spectrum analyzer during network planning and deployment. This will help diagnose and eliminate any potential non-IEEE 802.11 radiofrequency energy on the PHY layer, which is particularly important given that this layer serves as the basis for optimal functioning of upper layers [22]. Identifying interfering devices may be easier because every device transmits a unique signal shape in the frequency spectrum. If the interference is intensive and continuous, as in the present experiments, alternative anti-jamming techniques may need to be considered [39]. Our results support a holistic approach to the design and operation of IEEE 802.11 networks, in which attention is paid not only to signal coverage but also to network performance by focusing on both the PHY and MAC layers [22]. In addition, wireless network solutions should be optimized for specific industrial applications, rather than applying a \"one-size-fits-all\" approach.
## 3 Underwater Routing Protocols
### Ovar  
The opportunistic void avoidance routing (OVAR) protocol uses opportunistic routing to address the void problem [42]. In OVAR, every node is capable of adjusting its number of neighbor nodes in the forwarder set to transmit data to the destination. The forwarder set is selected for a node on the basis of packet delivery probability and packet advancement. The protocol improves system performance in terms of throughput, energy consumption and end-to-end delay. However, it involves unbalanced energy consumption of nodes as nodes close to the sink are frequently selected for data forwarding.