esa-sceva/satcom-chunk-collection huggingface dataset:
# Game-Theoretic Power and Rate Control in IEEE 802.11\(p\) Wireless Networks
## 2 Related Work
They propose a utility maximisation method by exploiting the network congestion control methodologies. Due to the difficulty of the problem, they proceed to a decomposition of the rate and power control in two subproblems. In the first subproblem, they optimise rate with fixed powers and vice versa. They show asymptotic optimality. However, the iterative methods they propose to solve the two subproblems do not take into account the dynamic relationship between rate and power. A decomposition of the problem solving the problems in a more dynamic manner could be more efficient. In [15], [PERSON] et al. proposed a protocol for adaptive beaconing rate and power. In particular, the vehicles compute transition probabilities in every time step. These probabilities rely on the suspected tracking error on vehicles being close by towards its own position in an Euclidean sense. Note that in this case, the beacon transmission rate increases. This work employs a threshold to determine whether transmission will occur or not. In the case that transmission is mandatory, it takes place with a probability proportional to the magnitude of the suspected error. The transmission power adaptation occurs by using the Channel Busy Ratio (CBR). The authors determine maximum and minimum values of CBR to rule the use of minimum or maximum transmission powers. In the case that the CBR is within the two values, the transmission power is selected between the maximum and minimum values using a linear function. The main drawback of this work is that it misses study and analysis of the fairness. In [16], [PERSON] et al. devised a robust optimisation problem to optimise the network-wide sum rates constrained with the predefined outage probability. The maximisation includes the conversion of the known statistical characteristics of the original problem with probability constraints, such as the mean and variance of V2V gains, to the deterministic problem. Moreover, the non-convex problem is formulated as a successive convex approximation, in order to result in a convex optimisation problem. Using the dual decomposition, the optimal solution can be identified. Finally, an iterative algorithm is used to determine the optimal transmission power of each user. In terms of results, the numerical simulation exhibits the effectiveness and the robustness of the algorithm in dynamic vehicular network settings. Practical considerations and a high density of Cluster Members (CM)s in simulations are subjects that need to be further investigated, since the maximum number of CMs should be restricted to four in a subchannel to achieve a higher average rate of each CM. With respect to the related work, the proposed algorithm attempts to maximise the power and rate control of the VANET by using a duty cycling mechanism, whereby the collisions and interference is diminished or kept stable. In this way, the maximum power can be used, and the rate keeps increasing with the increase of the SINR.
# Cooperative Transmission Mechanism Based on Revenue Learning for Vehicular Networks
## 4 Collaborative Transmission Mechanism Based on Q-Learning for Vehicular Networks
### Multi-Point Collaborative Transmission with Revenue Learning
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Actions**Combination** & **User 1** & **User 2** & **User 3** & **User \(n\)** \\ \hline Leave unused & & & & \\ User 1 & - & & & \\ User 2 & & - & & \\ User 3 & & & - & \\... & & & & - \\ User n & & & & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Q matrix example of a base station in a cell cluster. When scheduling the joint interference coordination mechanism, the idle base station updates the \(Q\) matrix in each time slot. The specific Algorithm 5 is as follows. 1. Determine the coordinates of the small cell network and draw the interval interference diagram. 2. According to the obtained interval interference graph, conduct a maximum clique search of the interval interference graph to obtain a scheduling conflict graph. The corresponding learning matrix (\(Q\) matrix) is initialized for all cell clusters. 3. The anti-joint interference algorithm is used to conduct the maximum clique approximate search on the complement graph of the scheduling conflict graph, and all combinations of maximum cliques are solved with priority, and the maximum clique with the highest priority is scheduled to execute the anti-interference scheduling algorithm  
4. Calculate the priority of different scheduling combinations of the maximum clique executing the anti-jamming scheduling algorithm through (11) in the second largest point, and the combination with the highest scheduling priority. Use (12) of the second largest points to calculate the priority of each user to connect to the macro station, and schedule the link with the highest priority. 5. According to the \(Q\) matrix of the scheduled cell cluster and the cooperative multi-point transmission cooperation algorithm, the cooperation decisions to be executed by all idle vehicular network users are determined. 6. Record the actual communication rate of each user in the time slot and update the revenue matrix. 7. Repeat 1-6. The implementation of the above algorithm is based on the joint multi-point cooperative transmission and joint interference coordination mechanism under reinforcement learning. Algorithm 5 can effectively improve the call rate of vehicular network users and then the global communication performance. ```
1:Initialize \(Q\)(action, combination) for all the cluster arbitrary
2:Repeat (for all time_slots):
3:Initiate process below for the cluster scheduled in this time slot:
4:if random \(<\)\(P_{\text{Investigate}}\)
5: Random take action for all the unused base station in the cluster
6:else:
7: Take action with the greatest value in \(Q\) for all the stations
8: Update \(Q\) as below:
9:\(Q(\text{action}_{i},\text{combination}_{i})=(1-\ a)Q(\text{action}_{i},\text{ combination}_{i})\)
```  
**Algorithm 5** Collaborative multipoint transmission collaborative algorithm flow
# Data Fusion Based on Adaptive Interacting Multiple Model for GPS/INS Integrated Navigation System
## 2 SINS/GPS Fusion Model
### SINS/GPS Fusion with EKF  
An extension of the KF (EKF) can be used in this paper in order to unite the output of inertial unit and GPS measurements. An analytical solution of EKF is a recursive process that contains a prediction step that can be written as [25]  
\[\left\{\begin{array}{l}x_{m}^{-}=\Phi_{m,m-1}x_{m-1}^{+}\\ P_{m}^{-}=\Phi_{m,m-1}P_{m-1}^{+}\Phi_{m,m-1}^{\mathrm{T}}+Q_{m-1}\end{array}\right. \tag{6}\]in which \(x_{m-1}^{+}\) and \(x_{m}^{-}\) denote the posteriori state vector at time \(m-1\) and the priori state vector at time \(m\), respectively; \(\Phi_{m,m-1}\) stands for the discrete state transfer matrix from time \(m-1\) to \(m\); \(P_{m-1}^{+}\) and \(P_{m}^{-}\) represent the posteriori covariance matrix of \(x_{m-1}^{+}\) and the priori covariance matrix of \(x_{m}^{-}\); and then \(Q_{m-1}\) denotes the process noise covariance matrix. The correction procedure is determined by  
\[\left\{\begin{array}{l}G_{m}=P_{m}^{-}D_{m}^{\mathrm{T}}\big{(}H_{m}P_{m}^{- }D_{m}^{\mathrm{T}}+C_{m}\big{)}^{-1}\\ x_{m}^{+}=x_{m}^{-}+G_{m}(R_{m}-D_{m}x_{m}^{-})\\ P_{m}^{+}=(I-G_{m}D_{m})P_{m}^{-}\end{array}\right. \tag{7}\]  
in which \(G_{m}\) denotes the Kalman gain; \(x_{m}^{+}\) represents posteriori state vector at time \(m\); \(R_{m}\) and \(D_{m}\) indicate the measurement residual vector and measurement design matrix, respectively; and \(C_{m}\) represents the covariance matrix of measurement noise.  
The estimated navigation parameters may be updated, provided that the GPS measurements are available, and thus, the predicted errors of sensor will be applied to modify the original inertial measurements.  
Generally speaking, Kalman filter can provide a better solution according to the correct statistics of process noise and measurement noise. However, under the conditions that the mathematical models are subject to uncertainty or the off-centered Gaussian noise do not influence the process model and measurement model, both Kalman filtering and KF-based derivation methods cannot provide convincing results.
# A Novel Approach for Acute Mental Stress Mitigation Through Adapted Binaural Beats: A Pilot Study
## 2 Materials and Methods
### Experimental Protocol  
In a preliminary phase lasting approximately 10 min, we let participants acclimatize before the beginning of the experiments, and we asked them to fill out two forms: an informed consent and a form to gather information like hours of sleep, body weight, and height. With particular reference to the latter, the form proposed by ref. [36] was used. Additionally, participants filled out two questionnaires: the Stress-Trait Anxiety Inventory (STAI) and the Perceived Stress Scale (PSS). All the documents were written in Italian.  
To induce acute mental stress in the investigated sample, a modified version of the mental arithmetic task (MAT) was adopted. The task was developed using a Python script and consisted of 60 operations, with 4 s for each question. All the operations were considered, and a maximum of 2-digit numbers were used. Participants used the workstation's keyboard to respond to the questions. We informed the participants that they could perform the tasks once and that their performances would be compared with all the above to elicit a more significant stress response during the execution of the experiment [17].  
The framework of the experimental protocol can be appreciated in Figure 1B. It includes a sequence of relaxing videos (VIDs) and MATs for a total of 3 blocks. The temporal duration of the tasks was 2 and 4 min for the VIDs and MATs, respectively. For this study, we used videos of natural settings, given their efficacy in enhancing physiological restoration and relaxation [37]. Furthermore, we asked the participants to fill out a visual analog scale (VAS) questionnaire at the end of each step. This procedure allowed us to keep track of the participants' stress perception. The questionnaire had 5 levels ranging from 0 (no stress) to 5 (high stress).  
Data collected during steps 1 and 2 were used to train a regressor for stress detection (described in Section 2.4). Step 3 served as a relaxing phase, whereas step 4 investigated the effects of either a constant or adapted BB stimulation. Step 5 was dedicated to the last resting phase, and step 6 served to test the remaining condition. Participants received auditory stimulation only during steps 4 and 6 (STIM ON), whereas BBs were turned off (STIM OFF) during the relaxing video. Half of the participants received constant stimulation followed by the adaptive one, whereas the latter experienced the opposite condition. The order of stimulation was chosen randomly, and the VIDs were different for steps 1, 3, and 5. Regarding the MATs, participants received the same questions during the three tasks, and the individual performance, expressed as the response's accuracy and reaction time (i.e., the required time to respond), was monitored. At the end of the protocol, a NASA-TLX questionnaire was filled out by volunteers to assess their mental workload. Both the VAS and the NASA-TLX are commonly adopted self-assessment reports used in experimental protocols studying stress [17].
# Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT
## 5 Simulations
### Results
RL-based and DL-based provide high coverage but are susceptible to fluctuations due to their reliance on past training data rather than real-time network assessments. Static selection maintains moderate coverage, as its fixed selection process fails to adapt to varying network conditions. Random selection results in the lowest and most inconsistent coverage, making it unreliable in dynamic IoT environments. These results highlight MACS's superiority in balancing mobility, resource constraints, and computational efficiency, ensuring reliable and effective client participation in federated learning. MACS demonstrates a robust and adaptive approach, making it an ideal choice for dynamic IoT environments where consistent and efficient client selection is crucial. Table 3 summarizes key performance metrics, emphasizing MACS's advantages:  
1. Selected Clients: MACS balances high participation without overwhelming the system. 2. Data Rate and Compute Capacity: MACS prioritizes clients with optimal resources, accelerating training. 3. Delay: MACS minimizes delays by accounting for real-time conditions. 4. Coverage: Higher coverage reflects MACS's ability to handle mobility effectively. These results validate MACS's effectiveness in enhancing federated learning performance under dynamic IoT conditions, outperforming traditional methods in scalability, efficiency, and reliability. Figure 2: Client selection across FL methods. Figure 4: Computational capacity across FL methods. Figure 3: Average data rate across FL methods. Figure 5: Delay comparison across FL methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & **MACS** & **Static Clients** & **Random Selection** & **RL-Based FL** & **DL-Based FL** \\ \hline Selected Clients & 20–70 & 10–50 & 15–55 & 25–60 & 30–65 \\ \hline Data Rate (bps) & \((1.0\)–\(4.0)\times 10^{6}\) & \((1.5\)–\(3.0)\times 10^{6}\) & \((2.0\)–\(2.5)\times 10^{6}\) & \((1.8\)–\(3.5)\times 10^{6}\) & \((2.0\)–\(4.2)\times 10^{6}\) \\ \hline Compute Capacity (Hz) & \((0.6\)–\(1.2)\times 10^{9}\) & \((0.5\)–\(1.0)\times 10^{9}\) & \((0.4\)–\(0.9)\times 10^{9}\) & \((0.7\)–\(1.1)\times 10^{9}\) & \((0.8\)–\(1.3)\times 10^{9}\) \\ \hline Delay (s) & 1.0–2.5 & 1.5–4.0 & 1.5–3.5 & 1.2–2.8 & 1.0–3.0 \\ \hline Coverage & 0.7–1.1 & 0.6–0.9 & 0.5–0.8 & 0.65–1.0 & 0.68–1.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of metrics across methods. Figure 6: Coverage indicator across FL methods.
## 3 Methods
### Strong-Weak Contrastive Consistency
#### 3.2.2 Consistent Supervision of Strong and Weak Perturbation  
To further enhance the quality of pseudo-labels, a weak perturbation feature \(q_{u}^{\text{weak}}\) is introduced for the unlabeled data. Pseudo-label \(\hat{y}_{u}\) is then generated to ensure that semantic consistency of the original data is preserved under weak perturbations, thereby making the reliability of the results of the model.  
\[\hat{y}_{u}=\arg\max P(y\mid q_{u}^{\text{weak}}) \tag{8}\]  
The method proposed in UDA [39] employs RandAugment and back-translation to generate two distinct strongly augmented variants of unlabeled data and computes the consistency loss through pseudo-labeling. This approach ensures that the model remains robust to augmented data. Inspired by this, we incorporated pseudo-labels combined with dual-stream strong perturbation for consistency supervision, and we define the loss function as presented in Equation (9):  
\[L_{\text{consist}}^{\text{weak-strong}}\,=\,-\,\frac{1}{2\,N}\sum_{i=1}^{N}\, \Big{(}\,\log\,P\,\big{(}\,\hat{y}_{u}\big{|}\,\,\,\,q_{u,s1}^{\text{strong}} \,\big{)}\,+\,\log\,P\,\big{(}\,\hat{y}_{u}\big{|}\,\,\,\,q_{u,s2}^{\text{strong }}\,\big{)}\Big{)} \tag{9}\]  
where \(q_{u}^{\text{weak}}\) denotes a weakly perturbed feature, and \(\hat{y}_{u}\) represents a pseudo-label generated from the prediction corresponding to the weakly perturbed input. The weak perturbations cause minimal damage distortion to the original image structure, thereby ensuring that the generated pseudo-labels are closer to the true labels. \(q_{u,s1}^{\text{strong}}\) and \(q_{u,s2}^{\text{strong}}\) correspond to strong perturbation features. Consistency loss is computed between the two strong perturbation features and the pseudo-label to ensure model consistency across various perturbation conditions, This approach mitigates bias potentially introduced by a single perturbation and reduces the impact of potential errors in the pseudo-labels. This process further enhances the robustness of feature representations for unlabeled data and improves the stability of pseudo-label generation. Moreover, each unlabeled sample is reused in two strongly perturbed versions, effectively simulating the effects of data augmentation, which is particularly advantageous in scenarios where medical image annotations are limited.
# Thermophysical Property Measurements with the Finite Bar
## 2 Physical Foundations
### Heat Conduction in the One Dimensional Bar  
When the width and height of the bar are significantly smaller than the length of the bar, the mathematical problem becomes one dimensional with respect to the longitudinal direction of the bar. A sketch of the geometry and the expected temperature profile is given in Figure 1. With the diabatic BC, no heat is transferred to the ambient. The absolute temperature of the bar will continuously, for this reason, only the temperature differences \(\Delta T\) along the bar are shown.  
The situation can be further simplified when the bar is considered to be semi-infinite, i.e., that the bar is of infinite length. Then the boundary conditions need to be specified at \(x=0\) and \(x\rightarrow\infty\). Equation (3) can be made non-dimensional. The arising factor and the boundary conditions  
* \(\dot{q}(x,t)|_{x\rightarrow\infty}=0\)
* \(\dot{q}(x=0,t>0)=\dot{q}_{0}=const\).
* \(\dot{q}(x,t=0)=0\)  
suggest a variable substitution \(\eta=x/(2\sqrt{at})\) in the same way as the standard substitution in the semi-infinite body (see Incropera [13]). This yields an analytical solution for the specific heat flux \(q\). Integration of the specific heat flux yields the temperature profile [14] in the semi-infinite bar.  
\[T(x,t)=T_{0}+\frac{2\dot{q}_{0}\sqrt{at}}{\lambda}\int_{\frac{x}{2\sqrt{at}}}^{ \infty}erfc(\mu)d\mu \tag{4}\]  
Here \(erfc(\mu)\) is the error function complement. The temperature rise at the heated end of the bar \(\Delta T(x=0,t)\) follows a square root dependence on time (\(\propto\sqrt{t}\)). The next step is to consider a bar of finite length. This is possible when the time since the beginning of heating is so small, that the heat flux has not caused the temperature to rise at the adiabatic end of the bar. This condition can be described with small Fourier numbers.
# Residential Buildings Complex Boundaries Generation Based on Spatial Grid System
## 4 Algorithm
### Generation of 10 by 10 and 20 by 20 Arrays
The first aspect is the analysis of the floor plans from traced from the real world [18] where examples are shown in area range of 75 to 125 square meters. The maximum value is 20, and the average is 11.46 edges per building. The second aspect is the analysis of the MIQP-based computer-generated layout [10]. This research presented a similar area range as previously mentioned, but the maximum value of edges is 16, and the average is 7. Results are shown in Figure 6.1  
Finally, the Internet databases were searched for the real buildings that have complex border shapes in order to make the comparison with the described algorithm. In this process floor plans of buildings with a simple form that can easily be found in different databases were eliminated. The focus was on the buildings in the area range similar to our research results from better comparison. In Figure 13 examples of real building floor plans are shown. The range of an area is from 50 to 350 square meters. The maximum value is 34 and the average value is 24.73. Figure 6.2 Efficiency of the method by [PERSON] et al. [13], observed based on the number of edges that are necessary to draw the shape of the building boundary. Values in this research range from 4 to 14. Figure 6.3 Efficiency observed based on the number of edges that are necessary to draw the shape of the building boundary. (**a**) Results of real-life buildings in the research paper by [PERSON] et al. [13] show high variety with 11.46 as the average number of edges. (**b**) Results of the method by [PERSON] et al. [13] have a value of 7 for the average number of edges. All the results are compared and presented in Figure 6.4 Differences in the results of different method applications can be observed based on the parameter of the number of edge counts. The overall average of edges per building is 24.73 for selected real buildings, 28.88 for the method described in the paper, and 10.33 for other methods.
## 4 Results and Discussion
### Discussion on Performance of Keyword-Based Vulgarity Extraction and Classification Baselines
#### 4.1.1 Discussion on Ratio of Vulgar Words Extracted with TF-IDF Weighting
Although the choice of extraction and filtering methods could depend on the specific requirements and trade-offs between the accuracy and efficiency of the given application, the usefulness of such a simple yet effective keyword-based method can be of value both for vulgar sentence detection and especially for the extraction of new vulgar words in the future. Further research could explore more advanced techniques, such as machine learning- or deep learning-based approaches for keyword extraction [35] to optimize the filtering process and improve the accuracy of vulgar word extraction. Additionally, the evaluation of these methods on larger and more diverse datasets would provide a better understanding of their generalizability and robustness in real-world scenarios. Consequently, in the following Sections 4.2 and 4.3 we introduce a detailed description of such machine learning and deep learning frameworks for vulgar word detection.