esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model and Problem Formulation
### Problem Formulation  
All the signals from other users (i.e., \(s_{1},\dots,s_{k-1},s_{k+1},\dots,s_{K}\)) are treated as interference by the \(k\)-th user. Therefore, the decoding SINR of \(s_{k}\) can be expressed as  
\[\gamma_{k}=\frac{\left|(\mathbf{h}_{d,k}^{H}+\mathbf{h}_{r,k}^{H}\mathbf{ \Theta}^{H}\mathbf{G}^{H})\mathbf{w}_{k}\right|^{2}}{\sum_{i=1,i\
eq k}^{K} \left|(\mathbf{h}_{d,k}^{H}+\mathbf{h}_{r,k}^{H}\mathbf{\[PERSON]}^{H}\mathbf{G} ^{H})\mathbf{w}_{i}\right|^{2}+\sigma_{0}^{2}}. \tag{3}\]  
According to Equation (3), EE and SE of this RIS-aided downlink communication system can be expressed as  
\[\text{EE}=\frac{\sum_{k=1}^{K}\omega_{k}\log(1+\gamma_{k})}{\mu\sum_{k=1}^{K} \left\|\mathbf{w}_{k}\right\|^{2}+P_{\text{s,total}}} \tag{4a}\] \[\text{SE}=\sum_{k=1}^{K}\omega_{k}\log(1+\gamma_{k}) \tag{4b}\]  
where \(\mu\) is the reciprocal of the transmit power amplifier efficiency, and \(\omega_{k}\) refers to the weight in the SE of the system given to \(k\)-th user. In Equation (4a), the term \(P_{\text{s,total}}=P_{\text{BS}}+NP_{n}+KP_{k}\) is the sum of the static hardware power consumed by the BS, the RIS, and the users. It refers to the fixed power consumed by the system [5]. Let \(\mathbf{W}=[\mathbf{w}_{1},\mathbf{w}_{2},\dots,\mathbf{w}_{K}]\in\mathbb{C}^ {M\times K}\). Thus, we can get the joint optimization problem below.  
\[\mathrm{(P1)}\quad\max_{\mathbf{W},\mathbf{\Theta}}\;a\frac{\sum_{k=1}^ {K}\omega_{k}\log\left(1+\gamma_{k}\right)}{\mu\sum_{k=1}^{K}\|\mathbf{w}_{k}\| ^{2}+P_{\mathrm{s,total}}} \tag{5a}\] \[\qquad\quad+(1-a)\sum_{k=1}^{K}\omega_{k}\log\left(1+\gamma_{k}\right)\] s.t. \[\sum_{k=1}^{K}||\mathbf{w}_{k}||^{2}\leq\mathrm{P_{T}}\] (5b) \[\qquad\theta_{n}\in\mathcal{F},\;\forall n=1,\cdots,N \tag{5c}\]  
where \(a\) refers to the weight of EE, \(0<a<1\), and (5b) refers to the constraint on the transmit power at the BS side. In this paper, \(\mathcal{F}\) is chosen as the continuous phase set [4; 27; 28; 29], which means that \(\|\theta_{n}\|^{2}=1\). Therefore, \(\mathcal{F}\) can be expressed as  
\[\mathcal{F}=\{\theta_{n}\;|\;\theta_{n}=e^{\phi_{n}},\;\phi_{n}\in[0,2\pi)\}. \tag{6}\]  
The problem in (5a) is non-convex with the fractional expression of EE, and it is harder than the problem that only chooses SE as the objective. This paper tries to optimize the objective in problem (P1) with low computational complexity.
## Materials and Methods
### Controlled Plant
#### 2.1.3 Linearisation and Eigenvalue Analysis
\right] \tag{9}\]  
and its eigenvalues are calculated with the characteristic Equation (10):  
\[\det(\lambda\mathbf{I}-\mathbf{A}(t))=0, \tag{10}\]  
where \(\lambda\) denotes eigenvalues and \(\mathbf{I}\) represents an identity matrix. The system matrix \(\mathbf{A}(t)\) of the linearised model of the fermentation process is calculated with partial derivation of the functions \(f_{1}(\,\dots\,)\) to\(f_{2}(\,\dots\,)\) of the non-linear model (6)-(8) [32]:  
\[\mathbf{A}(t)=\left[\begin{array}{ccc}\frac{\partial f_{1}(X(t),S(t),P(t))} {\partial X(t)}&\frac{\partial f_{1}(X(t),S(t),P(t))}{\partial S(t)}&\frac{ \partial f_{1}(X(t),S(t),P(t))}{\partial P(t)}\\ \frac{\partial f_{2}(X(t),S(t),P(t))}{\partial X(t)}&\frac{\partial f_{2}(X(t ),S(t),P(t))}{\partial S(t)}&\frac{\partial f_{2}(X(t),S(t),P(t))}{\partial P (t)}\\ \frac{\partial f_{3}(X(t),S(t),P(t))}{\partial X(trajectory. The non-linear model is described with Equations (1)-(4), its parameters are written in Table 1, and, from this, the trajectory's state-space variables \(X^{\star}(t)\), \(S^{\star}(t)\) and \(P^{\star}(t)\) were calculated. They are shown in Figure 1. The eigenvalues were calculated from the determined system matrix \(\mathbf{A}(t)\) (B1)-(B4) with the characteristic Equation (10). The system matrix \(\mathbf{A}(t)\) has three eigenvalues that are time-varying. The variation of eigenvalues during the entire fermentation process was calculated, and is presented in Table 2.  
An eigenvalue analysis of the linearised model showed that the variation of the dynamical characteristics due to the non-linearity of the fermentation process in the batch bioreactor are not extreme. One eigenvalue was constant, and two eigenvalues were changeable in a small range around zero. Moreover, the range of parameters and changeable eigenvalues of the time-dependent linearised model was also relatively small for other analysed non-linear models of batch bioreactors with data obtained in the references.
# Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
## 7 Open Challenges
### Energy Management  
Energy is a scarce resource in WSNs and constant research in this area is necessary to ensure efficient energy use in WSNs. A common technique to minimize the energy use is by control of sleep and wake states of sensor nodes however SDN-based management of WSNs requires a robust link between the control and data planes for effective management and thus should be investigated further to reduce unreliability in communication between the planes as a result. A paradigm shift is required to move away from data aggregation and further develop efficient long range low power radio techniques such as LoRa. Low power single hop communication techniques like this can also saveenergy by shifting in-network processing to the controller leaving the energy deprived sensor nodes with the task of only forwarding data to the controller and going back to sleep. Other improvements of energy efficiency based on reduction of overhead control traffic and redundancy in routing tasks are also a promising area of research. While new methods of extending network lifetime through wireless power transfer [126] have been proposed, the feasibility of implementing such a technique in a large-scale WSN based on SDN has to be addressed in terms of the scalability-cost ratio.
# Boosting Safety Protocol Compliance with Eye-Tracking in Chemical Plants
###### Abstract  
In high-risk industrial environments such as chemical plants, where operators are frequently exposed to time-sensitive hazards, ensuring strict safety protocol compliance is essential. Traditional safety training approaches often fail to effectively engage workers in cognitively demanding tasks, highlighting the need for immersive and feedback-driven alternatives. This study investigates the integration of extended reality (XR) and real-time eye-tracking feedback into safety training programs. Participants were divided into two groups: XR-only training and XR with real-time gaze feedback. The compliance assessment is based on five key metrics: reaction time, operational accuracy, attention focus, error count, and corrective response time. Results showed that eye-tracking-enhanced training reduced reaction time by 25%, improved accuracy by 15%, increased attention focus by 20%, and decreased errors by 30%. Additionally, real-time gaze feedback improved corrective response time by 40%, particularly benefiting inexperienced operators. These findings demonstrate that integrating gaze-tracking into XR-based training enhances cognitive engagement, reduces human error, and improves compliance with safety protocols. This study provides empirical evidence supporting the adoption of XR with eye-tracking for industrial safety training, with potential applications in other high-risk fields.  
safety protocol compliance; training system; eye-tracking; chemical process Article
# Entropy-Regularized Federated Optimization for Non-IID Data
## 5 Results  
### UNSW-NB15 Intrusion Detection Task  
Table 2 presents the federated learning performance on the UNSW-NB15 network intrusion dataset. Our proposed ERFO method achieves the highest classification accuracy (approximately 81.1%) and macro-averaged \(F_{1}\) (macro-\(F_{1}\)) score (around 0.79) among all evaluated methods. It substantially outperforms the baseline algorithms--for example, the next best approach attains roughly 80.2% accuracy and a macro-\(F_{1}\) score of 0.78, falling short of ERFO's results. Notably, ERFO's improvement in macro-\(F_{1}\) indicates that it better recognizes minority attack classes compared to conventional FedAvg and SCAFFOLD, which tend to be biased toward the majority class (benign traffic). In other words, although some baseline models reach reasonably high overall accuracy on UNSW-NB15, their lower macro-\(F_{1}\) scores reveal imbalanced performance across attack categories. By contrast, ERFO maintains both high accuracy and a high macro-\(F_{1}\) score, reflecting more balanced detection of each class.  
Figure 1 illustrates the learning curves of accuracy and macro-F\({}_{1}\) over communication rounds for UNSW-NB15. We observe a rapid initial improvement during the first few rounds, after which both metrics continue to rise gradually and converge to a stable plateau by around 80-100 rounds. The ERFO method not only converges to a higher final macro-\(F_{1}\) score and accuracy than the baselines, but it also exhibits a smooth training trajectory with minimal oscillations. This suggests that the model's training remains stable even under the non-i.i.d. client data distribution of UNSW-NB15. The convergence trends indicate diminishing returns in later rounds--once macro-\(F_{1}\) saturates near 0.79 and accuracy near 81%, further rounds yield only marginal gains. In summary, on the UNSW-NB15 task ERFO reaches superior performance and maintains it without volatility, whereas the baseline methods plateau earlier at lower macro-\(F_{1}\) levels (signifying poorer recall on some intrusion categories).
# Propagation Attenuation Maps Based on Parabolic Equation Method +
## 4 Efficiency of Creating PAM versus Its Dimensions  
This part of the paper is devoted to studying the effectiveness of creating PAMs according to the previously presented algorithms. First, the metrics are defined, and then the research results are presented.  
Figure 5: (**a**) Level of receiving antenna height marked above exemplary lowland terrain profile and (**b**) path losses versus terrain profile radius for analyzed receiving antenna height and calculated field distributions from Figure 4.  
Figure 6: PAMs for two selected Tx locations in (**a**) lowland and (**b**) hilly areas.
# Game Theory-Based Smart Mobile-Data Offloading Scheme in 5G Cellular Networks
## 3 System Model
### Mobile-Data Offloading Model
where \(\lambda^{o}_{nm}\) is the amount of data of the offloaded transmission between UE\({}_{m}\) and UE\({}_{n}\) which is given as follows:  
\[\lambda^{o}_{nm}=\omega_{mn}N_{m}BW\log_{2}\left(1+\gamma_{mn}\right) \tag{7}\]  
where \(\omega_{mn}\) is the offload coefficient from UE\({}_{m}\) to UE\({}_{n}\), and \(\gamma_{mn}\) is signal-to-interference-plus-noise ratio (SINR) for transmission from UE\({}_{m}\) to UE\({}_{n}\), which is shown as:  
\[\gamma_{mn}=\frac{P_{m}g_{mn}}{P_{BS}g_{n}+\sum\limits_{i\in\mathcal{M}\setminus \{m,n\}}P_{i}g_{in}+\sigma^{2}} \tag{8}\]  
where \(P_{m}\) is UE\({}_{m}\)'s power for the offloaded transmission, \(P_{BS}\) is the BS's transmission power, and \(\sigma^{2}\) is the estimated noise level. In a BS-based transmission process, UE\({}_{m}\) uses \(\omega_{mn}\) of the amount of data for offloading transmission to UE\({}_{n}\), and UE\({}_{m}\) will use the remaining \(\left(1-\omega_{m}\right)\) of the amount of data for BS-based transmission to UE\({}_{n}\). When the decode-and-forward (DF) scheme is used, the amount of data for the BS-based transmission is defined as follows:  
\[\lambda^{c}_{mn}=\left(1-\omega_{mn}\right)N_{m}\min\left\{\lambda_{mBS}, \lambda_{BSn}\right\} \tag{9}\]  
where \(\lambda^{c}_{mn}\) is the amount of data of the transmission from UE\({}_{m}\) to UE\({}_{n}\) via the BS; \(\lambda_{mBS}\) is the amount of data between UE\({}_{m}\) and the BS, and \(\lambda_{BSn}\) is the amount of data between the BS and UE\({}_{n}\), which are given as:  
\[\lambda_{mBS}=BW\log_{2}\left(1+\gamma_{mBS}\right) \tag{10}\]  
\[\lambda_{BSn}=BW\log_{2}\left(1+\gamma_{BSn}\right) \tag{11}\]  
where \(\gamma_{mBS}\) is the SINR for transmission from UE\({}_{m}\) to the BS, and \(\gamma_{BSn}\) is the SINR for transmission from the BS to UE\({}_{m}\), represented as follows:  
\[\gamma_{mBS}=\frac{P^{{}^{\prime}}_{m}g_{m}}{\sum\limits_{i\in\mathcal{M} \setminus\{m\}}P_{i}g_{i}+\sigma^{2}} \tag{12}\]  
\[\gamma_{BSn}=\frac{P_{BS}g_{n}}{\sum\limits_{i\in\mathcal{M}\setminus\{n\}}P_ {i}g_{in}+\sigma^{2}} \tag{13}\]  
where \(P^{{}^{\prime}}_{m}\) is UE\({}_{m}\)'s transmission power for BS-based transmission. For a fair comparison in offloading and BS-based transmissions, UE\({}_{m}\) is assumed to use the same power for offloading transmission and BS-based transmission to UE\({}_{n}\). Therefore, we can get:  
\[\lambda_{mn}=\lambda_{mn}^{o}+\lambda_{mn}^{c} \tag{6}\]0
## 5 Results and Discussion
### Comparative Analysis: Meshed Configuration  
For the particular case of the meshed network, the baseline case presented in [10] was employed, as well as the XPRESS, SBB, DICOPT, and LINDO optimization algorithms available in the General Algebraic Modeling System software. The percentage differences for the aforementioned algorithms, in comparison with the proposed solution methodology, are favorable by 11.57, 2.13, and 1.90%, respectively. Table 6 shows the figures associated with the percentages describing the differences between the implemented solutions.  
Finally, it is important to mention that the number of salps in the population was set as 20 in the radial and meshed configuration cases because of the compromise between the total processing time and the quality of the optimal solutions reached. This number of individuals was reached after constructing a meshed grid with sizes of populations between 5 and 30, evaluating the total processing time and the variability of the final solution after 100 consecutive iterations per population size.
# Forecasting Multivariate Time Series
### Data Cleaning
#### 2.4.3 Moving Average  
Moving averages, such as simple moving averages (SMAs), cumulative moving averages (CMAs), and exponential moving averages (EMAs), are commonly used for imputation in time series analysis. SMA calculates the average of a specified number of data points, using a simple summation formula. CMA computes the average for all entries up to the current time, with each value equally weighted. EMA employs exponentially decreasing weighting factors to compute the average, with the value at any time determined by a coefficient and the previous EMA value. These moving averages can aid in data imputation, provided the time series is stationary. Stationarity, indicating consistent behavior over time, can be assessed using statistical tests like the Augmented Dickey-Fuller (ADF) test [37] or by observing rolling statistics, such as rolling mean and standard deviation plots.
## 6 Method Comparison
### Compare with the Zerocash  
Zcash is a cryptocurrency designed to provide enhanced privacy, while the BTPP scheme incorporates the UTXO-based note mechanism in Zerocash. The comparison of the BTPP scheme with the Zerocash system is shown in Table 6.  
The Zerocash system improves transaction privacy using the zk-SNARKs, achieving protection for both transaction relationships and amounts. However, it relies on the zk-SNARKs for proof of note ownership and requires trusted setup involving the third party, resulting in lower transaction efficiency. Essentially, the Zerocash system is a weakly centralized system. In contrast, the BTPP method embeds the transaction's stealth address into the payment note. The mapping between the note and its corresponding stealth address is maintained on the blockchain through the Note Commitments table. This binding between the note and the stealth address enables efficient transfer of note ownership. The receiver can independently compute the confirmation key for the stealth address based on common knowledge. By leveraging the binding relationship between the stealth address and the note, efficient proof of note ownership is achieved. Additionally, the integration of the stealth address and note mechanism enhances transaction privacy. As mentioned earlier, the time consumption for relevant operations is milliseconds, demonstrating high efficiency. Compared to the Zerocash system, the advantage of the BTPP method is that it is completely decentralized, and the whole process does not require the participation of third parties.
# False Alarm Reduction Method for Weakness Static Analysis Using BERT Model
## 3 False Alarm Reduction Method for Reliability Analysis of Static Analysis Results
### BERT-Based Line-Level Weakness Analysis Model
#### 3.1.3 Feature Inference  
The position information of each token and its relationship to the position were obtained from the output of the BERT model. Subsequently, tokens of each line were collected based on the input source code. We classified the tokens of each reconstructed line into 119 classes (118 CWE has defined in [PERSON] test suite C/C++ 1.3 version and a class that is not weak) trained through a single linear layer. The attention score may be output to each line of the entire source code through the attention mechanism of BERT. This not only solves the token input limitation problem of BERT but also allows for comparison and evaluation of the attention and static analyzer results output on each line. Table 1 shows an example file tested and printed on the BERT model.  
The score indicates the relationship with other lines, based on lines that are likely to cause weaknesses. In other words, the higher the score, the higher the probability of weakness in the line. In the example shown in Table 1, the line where weakness occurred was analyzed at line 7. Lines 3 and 5 had a command to declare the variable \"data\"; thus, the score was higher. For annotations or braces, the score was zero and weaknesses could not occur.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Line** & **Source Code** & **Score** \\ \hline
1 & void CWE476\_NULL\_Pointer\_Dereference\_int\_01\_bad() & 0.1 \\
2 & \{ & 0 \\
3 & int *data; & 0.5 \\
4 & /* Set data to NULL */ & 0 \\
5 & data = NULL; & 0.63 \\
6 & /* POTENTIAL FLAW: Attempt to use data, which may be NULL */ & 0 \\ \hline
2 & print(\%\%d\)\(\backslash\)n\({}^{*}\)\_data); & 0.85 \\ \hline
8 & \} & 0 \\
9 & \(\dots\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example analysis result of BERT-based line-level weakness analysis model.
## Appendix A The Flamant-Boussinesq Approximation  
### Mathematical Formulation  
To predict the deformation of the ground in response to a vehicle's weight, [PERSON] et al. [17] modeled it as a series of four point loads moving on an isotropic semi-infinite elastic half-space.  
Since the speed of the vehicle is slow compared to the Rayleigh wave velocity in the ground, the Flamant-Boussinesq approximation theory was used to describe the static deformation caused by a point load on the ground surface. According to this theory [19], the ground displacement \(u(u_{x},u_{y},u_{z})\) in the ith direction is given by the following:  
\[u_{i}=\frac{F}{4\pi^{c}}\bigg{[}\frac{x_{3}x_{i}}{r^{3}}+(3-4v)\frac{\delta_{3}} {r}-\frac{(1-2v)}{r+x_{3}}\Big{(}\delta_{3i}+\frac{x_{i}}{r}\Big{)}\bigg{]} \tag{11}\]  
where  
* \(u_{i}\) is the particle displacement in the ith direction.
* \(F\) is the force of the point load (weight of the vehicle) applied on infinite half-space.
* \(\mu\) is the shear modulus of the half-space.
* \(r\) is the distance from the origin and is given by \(r=\sqrt{x^{2}+y^{2}+z^{2}}\).
* \(\delta\) is the Kronecker sign ([PERSON] notation); assuming \(i\
eq j\), then \(\delta=0\).
* \(v\) is [PERSON]'s ratio.  
If we assume the Kronecker sign to be 0 (when \(i\
eq j\)) and that \(x_{3}=z\), then we arrive at the next equation, as used by [PERSON] [20]:  
\[u_{x}(x,y,z)=\frac{F}{4\pi\mu}\frac{x}{r^{2}}\bigg{(}\frac{z}{r}+\frac{2\
u-1 }{1+\frac{z}{r}}\bigg{)} \tag{12}\]  
where  
* \(u_{x}\) is the particle displacement in the x direction, defined parallel to the road (and the cable), at a point \((x,y,z)\) (\(y\) being the horizontal distance perpendicular to the road and \(z\) the depth beneath the surface) relative to the location of a point load positioned at the origin.
* \(z\) is the depth at which the fiber is buried with respect to the surface.  
Equation (12) describes the quasi-static deformation at a point in the subsurface caused by a series of point loads moving on an isotropic, semi-infinite elastic half-space. In this case, the Flamant-Boussinesq approximation can also be expressed based on [PERSON]'s modulus, since \(E=2\mu(1+v)\). Therefore arriving to Equation (1), where, the main parts are the same as the previous equation, except for the following:  
* \(m\): mass of the vehicle point load (mass per wheel).
* \(g\): gravitational acceleration.
* \(E\): [PERSON]'s elastic modulus of the road.  
However, there are several assumptions made on these approximations that will limit the model's use and application.
## 3 Results
### Statistical Analysis  
#### 3.4.1 Comparison of Ground Data with Image Pixels  
Table 2 shows Pearson correlation coefficients between ground values for 40 ROIs and image pixels from MTMF, NBR, dNBR, and RdNBR. Strong correlation between ground data, MTMF scores, NBR, dNBR, and RdNBR values for vegetation can be seen as indicative of a high susceptibility to fire. The values ranged from 0.70 to 0.76. The RdNBR values for charred/scorched vegetation were 0.76 compared to the MTMF values of 0.72. The soil cover showed a much lower correlation with values ranging from 0.34 for MTNF to 0.53 for RdNBR. Increases in values from dNBR to RdNBR were noted from 0.75 to 0.76 for scorched vegetation and 0.51 to 0.53 for soil cover. This can be attributed to the fact that the region is covered in unexploited dense vegetation, predominantly mixed deciduous forest, and minimal soil cover.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**dNBR**} & \multicolumn{1}{c}{**Burn Severity**} \\ \hline \(-\)0.1 to 0.1 & Unburned or no change \\
0.1 to 0.27 & Low-severity burn \\
0.27 to 0.44 & Moderate-low severity burn \\
0.44 to 0.66 & Moderate-high severity burn \\ \textgreater{}0.66 & High-severity burn \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values derived from dNBR indicating burn severity and classified based on the USGS Fire Effects Monitoring and Inventory Protocol (FireMON) program [17].  
Figure 6: Thematic burn severity layer of Palo Verde National Park (PVNP) derived from RdNBR depicting severity as low, moderate, high; Transition areas between the zones are also noticed.  
The scatterplots derived from correlations of ground data and MTMF scores for vegetation cover resulted in a higher R squared value of 0.72 for vegetation compared to soil data that was 0.34 (Figure 7), which were attributed to the driest and partially charred vegetation components. NBR values were also significantly high for the ground cover classes. The increase in dNBR and RdNBR values for ground classes is attributed to the magnitude of change from pre-fire conditions. The strongest correlation was found in vegetation that was 0.76. The results from the linear regressions show that the groundcover and MTMF scores for the two endmembers were statistically related and that MTMF scores may be used to quantitatively predict ground cover components, especially scorched vegetation.