esa-sceva/satcom-chunk-collection huggingface dataset:
# Game Theory Meets Wireless Sensor Networks Security Requirements and Threats Mitigation: A Survey
## 2 Game Theory: A Brief Overview  
Game theory is an advanced branch of intelligent optimization. The model of game theory represents a game between player groups that choose to behave cooperatively or non-cooperatively and try to promote their benefits (payoffs) through the used strategy(ies) executed through the cumulative players actions. [19; 20] survey the fundamental definitions of game parameters, which can be summarized as follows:  
**Definition 1**: _A game is a description of the strategic interaction between opposing, or cooperating, interests where the constraints and payoff for actions are taken into consideration._  
**Definition 2**: _A player is a basic entity in a game, which is involved in the game with a finite set of players denoted by \(N\) that is responsible for taking rational actions denoted by \(A_{i}\), for each player \(i\). A player can represent a person, machine, or group of people within a game._  
**Definition 3**: _The Utility/Payoff is the positive or negative reward to a player for a given action within the game denoted by \(u_{i}:A\rightarrow\mathbb{R}\), which measures the outcome for player \(i\) determined by the actions of all players \(A=\times_{i\in N}A_{i}\), where the symbol \(\times\) denotes Cartesian product._  
**Definition 4**: _A strategy is a plan of action within the game that a given player can adopt during game play denoted by a strategic game \(\left\langle N,\left(A\right),\left(u_{i}\right)\right\rangle\)._In the security field, game theory application is not only limited to counteracting the effect of external intruders; it can be used to detect the malicious nodes and reveal the nodes that behave selfishly and overburden the whole network. Generally, Nash equilibrium (NE) is the intelligent solution for the social problems that has become a promising concept for wireless networks and more specifically for WSN security [19; 20; 21; 29; 40; 41].  
**Definition 5**: _Nash equilibrium is a profile of optimal actions, \(a^{*}\in A\), such that any player \(i\in N\) cannot benefit due to unilaterally deviating from its strategy and choosing another action [21; 41]. This can be translated in terms of the utility function as, \(u_{i}\left(a_{i}^{*},a_{-i}^{*}\right)\geq u_{i}\left(a_{i},a_{-i}^{*}\right)\) for all \(a_{i}\in A\), where \(a_{i}\) denotes the strategy of player \(i\) and \(a_{-i}\) denotes the strategies of all players other than player \(i\)[19]._  
In Section 3, an overview of the different game types is briefly introduced, focusing on the games that can be particularly useful to describe the security situation in WSN.
## 5 Case Studies for Construction Site Layout Planning and Managerial Implication
### Managerial Implication  
In this section, we discuss the managerial implication, as well as the limitation and consideration needed before using the HSOS-LO to solve the CSLP. The computational results have shown that the proposed HSOS-LO algorithm can support a decision-maker or project manager by providing a recommendation for the best site and facility layout plan, especially for a construction project. In practice, a project manager may need to re-check the result suggested by the algorithm with the real situation in the field. However, rather than going with a decision based on intuition, a well-analyzed and assessed solution provided by the algorithm can improve the quality of the decision. Furthermore, based on the results shown in case study 1, 2, and 3, which consist of single and multi-floor facility layout problems, there is some consideration needed. The proposed HSOS-LO includes several extra steps in the local search procedure to avoid being trapped in local optimal. Moreover, several real-life problems may involve more complex constraints and decision variables that make the HSOS-LO need to be further modified to solve the problem.
# Bio-Sourced and Biodegradable Membranes
## 2 Green Membrane Materials
### Bio-Sourced Polymers for Membrane Fabrication
#### 2.1.4 Other Polysaccharides and Biopolymers  
Starch is another example of a polysaccharide. It is one of the most abundant biopolymers in nature and can be found in such common products as potato, corn, and rice. Due to its branched structure, resulting in poor mechanical properties, starch can be hardly used as the main material for membrane production [38]. Thus, starch was reported to be used as an additive to produce membranes for wastewater treatment [39; 40; 41].  
Cyclodextrin (CD) can also be used as an additive for the improvement of hydrophilicity and permeability of membranes used for water treatment [42]. The functionalization of hydroxyl groups to amino, sulphonyl, and other groups resulted in the further improvement of membrane selectivity by enhancing porosity and hydrophilicity [43].  
Alginate is a naturally occurring anionic water-soluble linear polysaccharide composed of \(\alpha\)-L-guluronic acid and \(\beta\)-D-mannuronic acid (see Figure 2D), typically obtained from brown seaweed (Phaeophyceae), including Laminaria hyperborea, Laminaria digitata, Laminaria japonica, Ascophyllum nodosum, and Macrocystis pyrifera [44]. Alginate-containing membranes were reported to be used for dyes and heavy metal removal [45; 46; 47; 48; 49].  
Silk fibroin is another natural biopolymer, extracted from insects' cocoons when treating them with hot alkali solution [50]. This protein is added to membranes to improve their adsorption of heavy metal ions [51; 52].  
Similar to fibroin, collagen can be extracted from animal connective tissues [53]. Despite being sensitive to pH, temperature, and bacteria, collagen can be used for membrane fabrication, though its application is limited. Due to its protein nature, collagen is perfectly suitable to create fiber-type membranes for oil-water separation [54] and pervaporation [55; 56].  
Polyhydroxybutyrate (PHB) is bio-derived polyester (see Figure 3) that is produced by some microorganisms such as Cupriavidus necator, Methylobacterium rhodesianum, or Bacillus megaterium during assimilation of glucose or starch [57].  
This polymer has a tensile strength close to polypropylene, making it suitable to manufacture fibers. As a result, PHB has been used to prepare electrospun fibers-based membranes with improved antibacterial properties for medical applications, dye removal, and microfiltration [58; 59; 60; 61].  
Figure 3: Chemical structure of polyhydroxybutyrate.
# On the Rate-Distortion Theory for Task-Specific Semantic Communication
## 3 System Model and Problem Formulation
### System Model
Different from traditional communication systems that rely on symbol-level distortion metrics such as Hamming distance and MSE distortion, our proposed semantic communication employs a general task-specific divergence measure to quantify semantic distance. This reflects how effectively the recovered information supports the intended task rather than merely assessing signal-level similarity. Our proposed framework extends classical rate-distortion theory by incorporating the constraint on semantic distance, thereby establishing a theoretical foundation for task-specific communication systems. Figure 1 illustrates a general semantic communication architecture that encompasses both lossy compression and joint source-channel coding scenarios. In our theoretical analysis, we primarily focus on the lossy compression case with a noiseless channel to clearly demonstrate the fundamental semantic rate-distortion tradeoff.
# LSOS: An FG Position Method Based on Group Phase Ranging Ambiguity Estimation of BeiDou Pseudolite
## 3 Algorithmic Principle  
The carrier phase generally consists of two parts: the integer part of the carrier and the fractional part of the carrier. The fractional portion of carrier phase can be estimated accurately through real-time frequency. Therefore, carrier group phase estimation focuses on how to use the fractional portion of carrier phase information to estimate the integer portion of the carrier phase. The pseudo-satellite in this paper has two technical advantages in the design process: (1) the homologous array pseudo-satellite is controlled by the same  
Figure 1: Principle of BDAPS signal generation.  
clock source, and the signal has strict consistency in the process of clock deviation and channel delay; (2) in the design process of the BeiDou multi-frequency pseudo-satellite signal, the nominal frequency has strict consistency, which provides a basis for our research on the multi-frequency phase measurement method indoors. Based on this advantage, the theory of group phase ranging is introduced in this paper. The phase cumulative characteristic of pseudolite signal is estimated by using the period characteristic of multi-level phase deviation produced by multi-frequency signal during phase comparison. Thus, the accurate measurement of distance measurement can be completed without depending on the transmission time.
## 5 Experimental Results
### Evaluation Metrics  
\(M_{p}\): \(M_{p}\) stands for precision, defined as the proportion of correctly generated suffixes to the total number of generated suffixes, given a set of prefixes. This metric is primarily used to evaluate the precision of selected samples subsequent to membership inference attacks and is the most critical measure for evaluating the effectiveness of training data extraction.  
\[M_{p}=\frac{n_{cp}}{N}\times 100\% \tag{11}\]where \(n_{cp}\) is the number of correctly generated and selected samples, and \(N\) is the total number of given prefixes.  
\(\mathbf{M_{np}}\): To independently evaluate the efficacy of the suffix generation strategy, we introduce the total precision of generated texts across multiple experiments. In the suffix generation process, a set of suffixes is produced based on a single prefix \(x_{i}\), and a flag, denoted as \(f_{x_{i}}\), is assigned to each prefix. If at least one correct suffix exists within this group, the flag \(f_{x_{i}}\) is set to 1; otherwise, it is set to 0. Thus, the calculation formula for \(M_{np}\) is as follows:  
\[M_{np}=\frac{\sum_{i=0}^{N}f_{x_{i}}}{N}\times 100\% \tag{12}\]  
\(\mathbf{D_{edit}}\): Edit Distance is a metric used to quantify the dissimilarity between two strings by calculating the minimum number of edit operations required to transform one string into another. This measure reflects the quality of the generated text. For two strings \(a\) and \(b\), with lengths \(\left\lvert\text{a}\right\rvert\) and \(\left\lvert\text{b}\right\rvert\), respectively, their Levenshtein Distance is given by  
\[D\left\{\begin{aligned} max(i,j),&&\quad ifmin(i,j)=0 \\ D_{a,b}(i-1,j)+1&&\\ D_{a,b}(i,j-1)+1&&,\quad otherwise\\ D_{a,b}(i-1,j-1)+1_{a_{i}\
eq b_{j}}\end{aligned}\right. \tag{13}\]  
\(\mathbf{E_{mia}}\): To exclusively evaluate the impact of membership inference attacks, we introduce \(E_{mia}\), defined as the precision \(M_{p}\) divided by the total precision \(M_{np}\) across multiple experiments. Additionally, while determining the method of membership inference attacks, \(E_{mia}\) can also serve as a metric for evaluating the quality of the texts generated by suffix generation strategies.  
\[E_{mia}=\frac{M_{p}}{M_{np}}\times 100\% \tag{14}\]
# Error-Function-Based Penalized Quantile Regression in the Linear Mixed Model
## 3 Monte Carlo Simulation
For Cauchy distribution, we obtain lower PE by DQR and DSQR, compared with DERF-QR. In other distributions, the PE of DSQR is similar to that of DERF-QR. The PE of DLQR is slightly greater than the others in all cases. \begin{table}
\begin{tabular}{c c c c c} \hline
**Error Distribution** & **Method** & **PE (SD)** & **TP** & **TN** \\ \hline \multirow{4}{*}{\(\varepsilon_{ij}\sim N(0,1)\)} & QR & 0.165 (0.081) & 8 & 0 \\  & DLQR & 1.02 (0.366) & 8 & 0 \\  & DSQR & 0.173 (0.133) & 8 & 0 \\  & DERF-QR & 0.169 (0.113) & 7.99 & 0 \\ \hline \multirow{4}{*}{\(\varepsilon_{ij}\sim\text{Cauchy}(1,0)\)} & QR & 0.078 (0.046) & 8 & 0 \\  & DLQR & 1.153 (0.488) & 8 & 0 \\  & DSQR & 0.091 (0.108) & 8 & 0 \\  & DERF-QR & 0.186 (0.115) & 7.99 & 0 \\ \hline \multirow{4}{*}{\(\varepsilon_{ij}\sim t(3)\)} & QR & 0.245 (0.116) & 8 & 0 \\  & DLQR & 1.243 (0.505) & 8 & 0 \\ \cline{1-1}  & DSQR & 0.193 (0.113) & 7.98 & 0 \\ \cline{1-1}  & DERF-QR & 0.178 (0.093) & 8 & 0 \\ \hline \multirow{4}{*}{\(\varepsilon_{ij}\sim\text{mixnorm}\)} & QR & 0.263 (0.146) & 8 & 0 \\ \cline{1-1}  & DLQR & 1.34 (0.493) & 7.98 & 0 \\ \cline{1-1}  & DSQR & 0.252 (0.184) & 8 & 0 \\ \cline{1-1}  & DERF-QR & 0.22 (0.143) & 7.96 & 0 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison among different methods in the dense model. \begin{table}
\begin{tabular}{c c c c c} \hline
**Error Distribution** & **Method** & **PE (SD)** & **TP** & **TN** \\ \hline \multirow{4}{*}{\(\varepsilon_{ij}\sim N(0,1)\)} & QR & 0.182 (0.100) & 3 & 0 \\  & DLQR & 0.455 (0.324) & 3 & 3.95 \\  & DSQR & 0.101 (0.094) & 3 & 2.35 \\  & DERF-QR & 0.085 (0.071) & 3 & 4.38 \\ \hline \multirow{4}{*}{\(\varepsilon_{ij}\sim\text{Cauchy}(1,0)\)} & QR & 0.063 (0.061) & 3 & 0 \\  & DLQR & 0.526 (0.386) & 3 & 3.90 \\ \cline{1-1}  & DSQR & 0.088 (0.105) & 3 & 3.57 \\ \cline{1-1}  & DERF-QR & 0.128 (0.138) & 3 & 4.56 \\ \hline \multirow{4}{*}{\(\varepsilon_{ij}\sim t(3)\)} & QR & 0.264 (0.139) & 3 & 0 \\ \cline{1-1}  & DLQR & 0.565 (0.328) & 3 & 3.99 \\ \cline{1-1}  & DSQR & 0.145 (0.113) & 3 & 1.79 \\ \cline{1-1}  & DERF-QR & 0.078 (0.069) & 3 & 4.51 \\ \hline \multirow{4}{*}{\(\varepsilon_{ij}\sim\text{mixnorm}\)} & QR & 0.242 (0.144) & 3 & 0 \\ \cline{1-1}  & DLQR & 0.582 (0.371) & 3 & 3.94 \\ \cline{1-1}  & DSQR & 0.146 (0.102) & 3 & 2.51 \\ \cline{1-1}  & DERF-QR & 0.110 (0.098) & 3 & 4.44 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison among different methods in the sparse model.
# Carbazoles: Role and Functions in Fighting Diabetes
## 2 Carbazole Derivatives in the Pathogenesis of Diabetes
### Sulfonamide Carbazole and Carbazole-Containing Cyclic Urea  
Variants of the cryptochrome (Cry) gene have been demonstrated to be correlated to the onset of type 2 DM and insulin resistance, as it is involved in glucose homeostasis, control of \(\beta\)-cell function liver lipid content, and the circadian system of mammals [22; 64; 65].  
In order to identify compounds capable of modulating the cryptochrome activity, [PERSON] et al. in 2016 [35] carried out a systematic SAR study that led to a series of sulfonamide and sulfamide carbazole-based derivatives. Among these compounds, cyclic sulfonamide, 2-(3-(9\(H\)-carbazol-9-yl)-2-hydroxypropyl)isothiazol-1,1-dioxide (**5**) (Figure 5) was identified as the first small molecule, orally bioavailable, active as a cryptochrome modulator in an in vivo model of type 2 diabetes. The efficacy of this compound was evaluated in mice with diet-induced obesity (DIO), using rosiglitazone as a positive control.  
The results obtained by an oral glucose tolerance test confirmed a significantly improved glucose clearance [35]. In 2018, the same research team discovered a new class of carbazole-containing amides and ureas as cryptochrome modulators [22]. In particular, compound 1-(3-(3,6-difluoro-9\(H\)-carbazol-9-yl)-2-hydroxypropyl)imidazol-2-one (**6**) (Figure 6) resulted as the most promising derivative, implying its potential use in the treatment of metabolic disorders including type 2 DM [66; 67; 68; 22; 69].  
Figure 5: Structure of 1-((5,6-di(furan-2-yl)-1,2,4-triazin-3-yl)thio)-3-(3,6-dibromo-9H-carbazol-9-yl)propan-2-ol (**4**).  
Figure 6: Structures of: 2-(3-(9\(H\)-carbazol-9-yl)-2-hydroxypropyl)isothiazol-1,1-dioxide (**5**) and 1-(3-(3,6-difluoro-9\(H\)-carbazol-9-yl)-2-hydroxypropyl)imidazol-2-one (**6**).
## 6 Phosphorylation of RIPK1  
### Auto-Activating Phosphorylation  
The serine-threonine kinase activity of RIPK1 is crucial for both complex IIb-dependent apoptosis and neeroptosis [79]. Typically, kinases adopt a closed conformation and require phosphorylation in the activation loop, also known as the T-loop, to activate their kinase activity [80]. These activating phosphorylation events can be catalyzed by upstream kinases or achieved through autophosphorylation. Currently, autophosphorylation is the only known mechanism for activating RIPK1 (Figure 3). For instance, autophosphorylation of S161 stabilizes the open conformation of the T-loop and promotes human RIPK1 kinase activation to induce neeroptosis [43]. Furthermore, mitochondrial reactive oxygen species (ROS) modify three essential cysteine residues of RIPK1, leading to cysteine-mediated aggregation of RIPK1 and subsequent autophosphorylation on S161, which is critical for RIPK1 to effectively promote neerosome formation and cell death [81]. Moreover, S166 autophosphorylation of RIPK1 is indispensable for MLKL activation and neerosome formation. Mutation of S166 effectively prevents multiple RIPK1 kinase-dependent inflammatory lesions in vivo, such as intestinal colitis, hepatitis, liver tumorigenesis, skin inflammation, and TNF-induced SIRS. Interestingly, while autophosphorylation of Ser166 is essential, it alone is not adequate to initiate RIPK1-mediated cell death [82]. Multiple autophosphorylation sites, including serine residues 14/15, 20, 161, and 166, cooperate to induce conformational changes in RIPK1 [43; 83]. These changes facilitate its association with cell death effectors, such as FADD and RIPK3, promoting the assembly of cell-death-inducing signaling complexes, such as complex II and the necrosome. It is noteworthy that the recombinant RHIM domain of RIPK1 exhibits a significantly higher affinity toward itself than the RHIM domain of RIPK3 [5]. Autophosphorylation of RIPK1 is thought to change its conformation, favoring the interaction between the RIPK1 and RIPK3 RHIM domains over the interactions between RIPK1 RHIM domains, thereby promoting necrosome formation [47; 84].
## 3 Results
### Spatial Variability
#### 3.2.5 GPCP  
The monthly precipitation estimates by the GPCP had correlation coefficients ranging from 0.81 to 0.95 and Willmott's coefficients ranging from 0.88 to 0.97. The highest coefficients were obtained in DMT (r = 0.95, d = 0.97). The smallest error in the monthly precipitation estimates was in CCR (RMSE = 39.14 mm month\({}^{-1}\)) and the largest was in RDP (RMSE = 75.62 mm month\({}^{-1}\)) (Figure 10A). The lowest correlation and Willmott coefficients and the highest error in annual precipitation was obtained in PRR (r = 0.06; d = 0.41; RMSE = 456.86 mm year\({}^{-1}\)). The annual precipitation estimates had the highest correlation coefficient in CNR (r = 0.88), the highest Willmott coefficient in GBC (d = 0.88) and the lowest error in CCR (RMSE = 157.76 mm year\({}^{-1}\)) (Figure 10B).
# Tomato Leaf Disease Classification by Combining EfficientNetv2 and a Swin Transformer
## 4 Discussion  
Table 4 summarizes recent relevant studies on tomato leaf disease classification, presenting the employed architectures, publication years, number of considered classes and samples, and the best-performing model based on achieved accuracy. Compared with other single-stream models, the proposed model in this paper exhibits a significant advantage of 1.7-9%. In addition to CNN-based and Transformer-based methods, Fuzzy SVM [17], DVT [18], and R-CNN [17] have all been used to carry out the classification task. (In addition to methods based on CNNs or Transformers, Fuzzy support vector machines and R-CNNs have also been utilized for classification tasks.) To the best of our knowledge, few studies have classified nine diseases and health, and none have classified the thirteen diseases and health. Additionally, our study presents a relatively compact and efficient model that not only eliminates restrictions posed by other factors but also outperforms other single-stream models in terms of accuracy, making it a valuable tool for tomato leaf disease classification.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Models** & **Recall** & **F1-score** & **Precision** & **Accuracy/\%** \\ \hline VggNet16 & 96.76 & 96.70 & 96.65 & 97.09 \\ ResNet18 & 95.34 & 95.59 & 95.84 & 96.10 \\ MobileNet-V2 & 92.17 & 91.63 & 91.09 & 92.25 \\ EfficientNetv2 & 97.38 & 98.00 & 98.63 & 98.54 \\ Vision Transformer & 95.54 & 95.38 & 95.22 & 95.19 \\ Swin Transformer & 94.96 & 94.97 & 94.98 & 96.02 \\ Conformer [15] & 98.16 & 98.41 & 98.66 & 98.55 \\ CMT [16] & 98.58 & 98.52 & 98.47 & 98.90 \\ Eff-Swin & 99.58 & 99.55 & 99.52 & 99.70 \\ \hline \end{tabular}
\end{table}
Table 3: Comparative experiment results of different models.  
However, our study also has certain limitations. First, it exclusively focuses on the task of tomato leaf disease recognition and does not consider other types of recognition tasks. Second, the study is based only on two datasets and does not consider the differences and impacts among multiple datasets.  
In summary, the results of this study indicate that the proposed combined algorithm theoretically exhibits significant effectiveness and superiority in the task of tomato leaf disease recognition. These findings provide useful information for future research and practical solutions for application. Future studies could further explore how to apply the proposed improved algorithm to other types of disease recognition tasks and conduct experimental validation, as well as enhance the real-time performance of the model and better handle the differences and impacts among different datasets.
# DFT-Spread Spectrally Overlapped Hybrid OFDM-Digital Filter Multiple Access IMDD PONs
## 1 Introduction
It should also be noted that the proposed PONs are completely different from the multiband OFDM PON reported in [22], since the PONs proposed here have the following unique features: (1) each subwavelength spectral region is shared by two independent orthogonal OFDM sub-band signals [6]; (2) no extra channel spacing is required between adjacent subwavelengths or sub-bands; (3) the side lobes of each OFDM sub-band are considerably reduced by the digital filtering process, which can minimize the interchannel interference (ICI) effect between sub-bands at adjacent subwavelengths. Furthermore, our results also indicate that for subwavelengths that do not suffer the strong channel fading effect, the ICI effects between different orthogonal OFDM sub-bands in these subwavelengths are negligible; (4) as a direct result of using the digital filtering process, each OFDM sub-band can adaptively and flexibly adjust its signal modulation parameters--such as subcarrier count and channel bandwidth--without affecting the orthogonality between different sub-bands; (5) the digital-filtering-processing-induced ICI reductions greatly enhance the PON's performance and its robustness against the channel frequency offset. In comparison with the up-conversion-based OFDM multiband PONs, which require multiple tuneable electrical local oscillators [20], the spectrally overlapped hybrid OFDMDFMA PONs utilize only digital filters to multiplex multiple OFDM sub-bands without requiring extra electrical/optical components compared to conventional transceivers in both the ONUs and the OLT.