esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methodology
#### 2.2.1 Association Metrics  
This part clarifies the distinction of the proposed association metrics: a Market Basket Analysis' derived support metric and a simpler metric already employed in study [17].  
* Metric 1 (\(M1\)): The weight assigned to each edge \(w_{e}\), symbolizing the connection between two features \(x\), \(y\), is directly proportional to the Root Mean Square Error (RMSE) mean values presented in the ranked quantities in Table 2. Computation of this weight involves the aggregation of the average RMSE, which is accomplished by summing the corresponding values across all combinations \(F\in\mathcal{F}\) in which the two features overlap \(x\), \(y\), as detailed by Equation (1). The outcome of employing this association metric \(M1\) is the generation of an undirected weighted graph. \[w_{e}(x,y)=\sum_{F\in\mathcal{F}}\left(1-rnse(F)\right)\cdot\chi_{F}(x,y) \qquad:\qquad\chi_{F}(x,y)=\begin{cases}1&if\quad\{x,y\}\in F\\ 0&if\quad\{x,y\}\
otin F\end{cases}\] (1)
* Support: this metric quantifies the proportion of combinations in the data set containing a specific item. It represents the ratio of collections containing the itemset to the total number of combinations in the data set. \[\text{Support }(x)=\frac{\text{Number of collections containing }x}{\text{ total number of combinations}}\] (2)
- Confidence: it measures the proportion of collections that contain the antecedent \(x\) that also contains the consequent \(y\). It is calculated as the ratio of the number of collections that contain both the antecedent \(x\) and the consequent \(y\) to the number of combinations that contain the antecedent \(x\). \[\text{Confidence }(x\Longrightarrow y)=\frac{\text{Support }(x\cap y)}{\text{Support }(x)}\] (3)
- Lift: it calculates the ratio of the analyzed support \((x\cap y)\) to the expected support under the assumption of independence between the antecedent and the consequent. It indicates how likely the consequent is to occur when the antecedent is present compared to when the antecedent is absent. It is calculated as the confidence ratio of \((x\Longrightarrow y)\) to support the consequent \(y\). \[\text{Lift }(x\Longrightarrow y)=\frac{\text{Confidence }(x\Longrightarrow y)}{\text{Support }(y)}=\frac{\text{Support }(x\cap y)}{\text{Support }(x)\text{ Support }(y)}\] (4)  
In this study, the edges characterizing the relation between features are computed by applying the _lift_ criteria to the list partially detailed in the publication [17], as indicated by Equation (5). In addition, a minimal _support_ quantity of 0.00001 is required to compute the frequent item sets. This task has been completed using the functionalities apriori and _association rules_ of library _Frequent Patterns_[27]. Each edge connects the antecedent \(x\) and consequent \(y\) items weighted by the support quantity, creating an undirected weighted graph through this orientation.  
\[w_{e}(x,y)=Support(x,y) \tag{5}\]
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 9 Research Gaps and Opportunities
### Opportunities for Future Research
#### 9.2.2 Advancing Transformer-Based Underwater SLAM  
Transformer architectures have revolutionized computer vision and robotic perception, offering superior performance in modeling long-range dependencies compared to traditional CNN-based approaches. Their application to underwater SLAM presents significant opportunities to address key challenges in feature association and dynamic environment handling [173]. Future research should focus on the following aspects:  
* **Efficient Transformer Designs:** developing lightweight transformer variants optimized for resource-constrained underwater vehicles;
* **Multi-Modal Fusion:** exploring cross-attention mechanisms to effectively combine visual, acoustic, and inertial sensor data, improving robustness in turbid conditions where single modalities fail [174];* **Self-Supervised Learning:** developing transformer-based self-supervised approaches that can learn robust feature representations from limited labeled underwater data [175].
## 2 Materials and Methods
### Theoretical Analyses  
Both the simulated and measured data were presented as normalized intensity field correlation for theoretical analyses. Since measurements provide normalized intensity correlations, the theoretical unnormalized field correlation from Equation (2) was transformed into the intensity correlation via the Siegert relation (\(g_{2}(\tau)=1+\beta|g_{1}(\tau)|^{2}\)) as mentioned before [15; 16] and thus experimental data were fitted using Equation (2) via a non-linear least-squares optimizing algorithm (Isqcurvefit in MATLAB) to retrieve coefficients describing \(\langle\Delta r^{2}(\tau)\rangle\) and \(\beta\).  
Two functional forms used previously to describe \(\langle\Delta r^{2}(\tau)\rangle\) were used--the Brownian flow model or the random-flow model [16; 17]. When scatterers are assumed as moving diffusively in the medium \(\langle\Delta r^{2}(\tau)\rangle=6D_{B}\tau\). Scatterers moving under actively pumped flow are modeled as random flow with \(\langle\Delta r^{2}(\tau)\rangle=V^{2}\tau^{2}\). These two flow models can also be combined together as a shear-flow model where the mean squared displacement contains both terms, \(\langle\Delta r^{2}(\tau)\rangle=D_{B}\tau+V^{2}\tau^{2}\)[52]. Experimental data were analyzed using three flow models: for Brownian flow model \(D_{B}\) was obtained, for the random flow the speed of the particles \(V\) was obtained, and for the shear-flow model both coefficients \(D_{B}\) and \(V\) were obtained. All other variables needed to evaluate Equation (2)--i.e., the optical properties of the medium, SDS, refractive index of medium and detector--were set matched to experimentally used values.  
Equation (2) was used in two ways to analyze MC simulated data. The first (as forward model) was to employ it to calculate \(G_{1}(\tau)\) from inputs of a given simulation, to match it directly against to MC simulations (at any needed SDS). The second way was as the inverse model as used for the analysis of experiments. However, to keep this procedure identical to processing of experimental data, simulated \(g_{1}(\tau)\) data were first translated into \(g_{2}(\tau)\) (using a fixed value of \(\beta\) = 0.48) before least-squares fitting. As in the experimental analysis, the fit coefficients describing \(\Delta r^{2}(\tau)\) and \(\beta\) were obtained. All other variables needed to evaluate Equation (2)--i.e., the optical properties of the medium, SDS, refractive index of medium and detector--were matched to simulated inputs.
## 3 Suboptimal Subcarrier and Power Allocation
### Power Allocation Algorithm
For \(k\in\{1,2\ldots,K\}\), \(n\in\{1,2\ldots,N_{l}\}\), assume that  
Figure 3:0  
Then  
Figure 3:1  
where \(p^{{}^{\prime}}{}_{k,n}\) is the sum of power \(p_{k,n}\) and the reciprocal of channel-to-noise ratio \(1/H_{k,n}\). The objective function is formulated as  
Figure 3:2  
The total data rate for user \(k\), denoted as \(R_{k}\), is rewritten as  
Figure 3:3  
where \(x=R_{l}/\varphi_{i}\). In order to obtain the high system sum rate, the user with higher effective subchannel SNR \(H_{k,n}\) should be allocated more power \(p_{k,n}\). The proposed power allocation strategy is that the sums of power and the reciprocal of channel-to-noise ratio for each user in different subchannel are equal, which is presented by  
Figure 3:4  
for \(k\in\{1,2\ldots,K\}\), and \(N_{k}\) is the number of subcarriers in \(\Omega_{k}\). Based on the assumption according to Equations (20) and (23), for the user \(k\), \(k\in\{1,\)\(2\ldots,\)\(K\}\), if the effective subchannel SNR \(H_{k,n}\) in subchannel \(n\) is larger, then user \(k\) would be assigned more power in subchannel \(n\). Moreover, the assumption Equation (23) has the controls over power distribution across subcarriers for each single user, which has no impact on the proportional fairness among users. Then Equation (22) is rewritten as  
Figure 3:5  
Adding the total power constraint Figure 3:6  
Substituting Equations (24) into (25), a single equation with the variable \(x\) can be described as  
Figure 3:7  
where  
Figure 3:8  
and  
Figure 3:9  
Numerical algorithms can be applied to solve the Equation (26). According to Equations (20) and (24), the power distributed to user \(k\) in the subcarrier \(n\) can be expressed as  
\[\text{C2:}\ \ \sum_{k=1}^{K}\sum_{n=\Omega_{k}}p_{k,n}\leq P_{tot}\]0  
Then the sum rate of OFDMA system can be obtained by  
\[\text{C2:}\ \ \sum_{k=1}^{K}\sum_{n=\Omega_{k}}p_{k,n}\leq P_{tot}\]1  
As is seen from the above, the proposed suboptimal power allocation strategy has the effects that the user with higher effective subchannel SNR is allocated more power in the corresponding subcarriers, and the proportional fairness is still strictly maintained. In addition, with respect to the optimal power allocation strategy, the power allocation problem is simplified to a single non-linear equation with one variable.
## 3 Solution Procedure  
### Temperature Gradient  
Following the Fourier series expansion satisfies the thermal edge boundary conditions in Equation (2):  
\[\text{T}=\sum_{m=1}^{\infty}\sum_{n=1}^{\infty}\text{T}_{i}(z)\sin(p_{m}x)\sin( p_{n}y),\ \ \ i=a,f,s \tag{14}\]  
where \(p_{m}=\frac{m\pi}{a}\),\(p_{n}=\frac{m\pi}{b}\). Additionally, substituting Equation (14) into Equations (4) and (5) leads to  
\[\frac{\partial^{2}T_{i}(z)}{\partial z^{2}}-\Big{(}\frac{\lambda_{xi}}{ \lambda_{zi}}p_{m}^{2}+\frac{\lambda_{yi}}{\lambda_{zi}}p_{n}^{2}\Big{)}T_{i}(z)=0 \tag{15a}\] \[\frac{\partial^{2}T_{f}(z)}{\partial z^{2}}+\frac{m_{4}}{z}\frac{\partial T_{f}( z)}{\partial z}-\Big{(}p_{m}^{2}+p_{n}^{2}\Big{)}T_{f}(z)=0 \tag{15b}\]  
Substituting Equation (14) into Equation (15a) and solving the obtained equations leads to the following relations for the sensor and actuator layers, respectively:  
\[T_{s}(z)=C_{1}^{s}e^{\alpha_{s}z}+C_{2}^{s}e^{-\alpha_{s}z} \tag{16a}\]\[T_{a}(z)=C_{1}^{4}e^{\alpha_{a}z}+C_{2}^{4}e^{-\alpha_{a}z} \tag{16b}\]  
where \(\alpha_{s}=\sqrt{\frac{\lambda_{xs}p_{m}^{2}+\lambda_{sy}p_{n}^{2}}{\lambda_{xs}}}\) and \(\alpha_{a}=\sqrt{\frac{\lambda_{xs}p_{m}^{2}+\lambda_{sy}p_{n}^{2}}{\lambda_{xs}}}\). In addition, the solution of Equation (15b) using Equation (14) after some manipulation can be written as  
\[T_{f}(z)=C_{1}^{f}\frac{J_{a_{f}}\left(i\beta_{f}z\right)}{z^{\alpha_{f}}}+C_{2 }^{f}\frac{Y_{a_{f}}\left(i\beta_{f}z\right)}{z^{\alpha_{f}}} \tag{17}\]  
where \(\alpha_{f}=\frac{m_{a}}{2}-\frac{1}{2}\), \(\beta_{f}=\sqrt{p_{m}^{2}+p_{n}^{2}}\), and \(i=\sqrt{-1}\). Additionally, the functions \(J_{\alpha_{f}}\) and \(Y_{\alpha_{f}}\) are Bessel functions of the first and second kind of order \(\alpha_{f}\), respectively.  
Here \(C_{1}^{s}\), \(C_{2}^{s}\), \(C_{1}^{a}\), \(C_{2}^{a}\), \(C_{1}^{f}\), and \(C_{2}^{f}\) are integration constants that can be computed from surface boundary conditions (\(z=0,h\)) and the continuity of the temperature and the normal component of the heat flux at the interfaces (see Equation (A4) in the Appendix A).
## 4 Discussion
### Limitations of the Research and Suggestions for Further Research  
This study included two equal groups with a convenient sample. This sample size, although an acceptance number for a pilot study which other studies have also used [13], is, however, very small and limits the generalizability of the findings. Further studies could consider the statistical power of the study by increasing the sample size and also by including a control group. The control group could participate by having no intervention at all, or a type of intervention, i.e., suggestions for leisure exercise like walking or swimming, or a leaflet with the OEP exercises which the participants could perform alone at home, without supervision. The presence of a control group would increase the interval validity of the study and enhance the results about the effectiveness of the OEP and the guidance time delivery. Additionally, although the effectiveness of the Otago program has not been shown to be affected by gender [94], we should note here that most of the participants were women. A more balanced quantity between males and females for the next studies would be preferable. In terms of research design, a control group with no intervention would best determine the effectiveness of the intervention. This study was also designed in a single-blinded way, where the participants did not know which group they belonged to, in accordance with other similar studies [95]. However, in order to further reduce the potential risk of bias, a double-blinded study could be designed where neither the participants nor the leaders knew which group they were in. Finally, if the person who implemented the program in the two intervention groups was different from the assessor this would further assure less bias and provide a more robust effectiveness of the OEP intervention.  
Regarding the intervention, the duration of the program, 12 weeks, is consider marginal, although there are positive results from programs of shorter duration [96; 97]. Also, the program was interrupted for 2 weeks due to the Christmas holidays between the 2 nd and 3 rd rating, and only had a 12-week follow-up period, which may not be sufficient to determine the long-term effects of exercise guidance timing on the assessed variables. Although other studies use this period of the 3 months (12 weeks) as a follow up period [16], it would, however, be of great value for, in future studies, the falls to be monitored for a follow up period of 6 months and even 1 year. A questionnaire, with a detailed record of the adherence of the participants to the exercises as well as to the suggestions and advice given by the physiotherapists, could be included. In our study, compliance was checked by telephone with participants 3 months after the program was completed, but with no assessment of the effectiveness on balance, gait, falls, and cognitive function over time. Thus, a questionnaire electronically distributed, could add more information about the motivation as well as the compliance and satisfaction of the participants.
# Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
## 5 Challenges and Open Research Topics
### Challenges
#### 5.1.3 Resource Allocation in RIS-Assisted 5G Networks  
RIS technology can enhance energy efficiency and spectral efficiency, but it also complicates EMF exposure management. RISs introduce complex radiation patterns, making accurate EMF measurement more challenging. The lack of comprehensive studies on the impact of RISs in 5G networks necessitates further research. Factors such as beamforming, interference from reflections, and topology-specific measurement issues require innovative strategies for EMF-aware resource management.  
Figure 5: Radio resource management considering EMF exposure metrics.
# Intrusion Detection Model Based on Improved Transformer
## 3 Materials and Methods  
### Model Construction Materials  
Figure 1 illustrates the structure of our enhanced intrusion detection model based on a Transformer, which can be divided into three parts as follows:  
The first part is the data processing strategy, which involves the numerical and normalization transformation of input data, dimension reduction of the dataset through the encoding layer of the stacked auto-encoder (SAE), under-sampling of normal samples using KNN, and the use of a hybrid sampling method consisting of Borderline-SMOTE for over-sampling of abnormal samples to obtain a balanced dataset, while mitigating the problem of data class overlap.  
The second part is the first stage of learning, where the balanced dataset is embedded with positional information using an improved position encoding method, features are extracted using the Transformer encoder, and the binary classification model is learned and trained using softmax function.  
The third part is the second stage of learning, where the binary classification model of the first stage is used to make binary predictions on the balanced dataset, the predicted values are merged with the original features to form a new dataset, which is then embedded with positional information using the improved position encoding method, features are extracted using the Transformer encoder, and the multi-class model is learned and trained using softmax.  
Compared to other intrusion detection models, our model has the following advantages:  
1. Given the reconfigurable nature of SAE for feature handling, we propose to utilize its encoding layer for reducing the dimensionality of the input features. This ensures that the same amount of information content is preserved in the features despite the reduction in dimensionality, thereby expediting the model training process and reducing computational overheads.
2. This paper proposes a novel under-sampling method based on the characteristics of KNN, combined with the Borderline-SMOTE over-sampling method to achieve hybrid sampling. This method not only balances the dataset but also alleviates the problem of class overlap, thereby improving the classification performance of the model.
3. This paper proposes an improved method for position encoding in the Transformer model, which enhances the model's ability to capture dependencies between features by incorporating their positional information, thereby improving the model's classification and detection capabilities.
# Embedded Bayesian Network Contribution for a Safe Mission Planning of Autonomous Vehicles
## 3 Methodology for an Automatic BN Generation and Its Embedded Implementation
### Dedicated Optimizations for an Efficient Implementation of BN
#### 3.3.2 Evidence Optimization  
The evidence optimization can be applied to AC structure to reduce its complexity. This transformation propagates constant values for its reduction. These constant values are related to the observability of the BN nodes. In an AC, we can see that the values \((\lambda_{x},\lambda_{\bar{x}})\) of evidence of a variable X are equal to:  
* \((\lambda_{x},\lambda_{\bar{x}})=(1,1)\) when there is no observation on the variable X,
* \((\lambda_{x},\lambda_{\bar{x}})=(1,0)\) when the evidence is on the variable X and the observation is x,
* \((\lambda_{x},\lambda_{\bar{x}})=(0,1)\) when the evidence is on the variable X and the observation is x.  
Furthermore, in our examples in Section 2, two types of nodes (observable and unobservable) are known: depending on the type of the BN structure (from FMEA_HM, FMEA_MIT, FMEA_EMB pattern) \(A\), \(S\), \(C\) and \(Ress\) nodes are observable (evidence), so they take the values \((1,0)\) or \((0,1)\) for evidence. The other nodes are unobservable, so they take the values (1,1) for evidence. These observations and the symmetrical structure of the AC make it possible to reduce the AC as follows:  
* Delete the left (or right) topmost branch containing a C (or an H) node, and in all sub-branches where C (or H) appears, replace the probability parameters by a choice between the right or left value. We can simplify here, because the value of \((\lambda_{x},\lambda_{\bar{x}})\) of these nodes is never equal to 1 at the same time,
* Repeat this procedure for all C and H nodes,
* Fix all the \(\lambda_{x}\) values for the unobservable nodes at \((1,1)\).
# Intelligent Traffic Model for Unmanned Ground Vehicles Based on DSDV-AODV Protocol
## 2 Method
### VANET Proposed Scheme  
The simulation was implemented according to the conducted scenarios. Three scenarios were designed to be examined in this simulation, and the results are shown in the Section 3.2. The study consists of several experiments that differ in protocol and topological conditions. The simulation using a Discrete Event Simulator (DES) is a tool called OMNET++ Modeller. This simulation divides into three scenarios based on network sizes utilizing the AODV and DSDV-AODV routing protocols, with V2X network topologies employing fixed nodes and mobile nodes with different numbers. In terms of network design and the simulation model, there is a software environment to design a MANET simulation network according to the list of proposed scenarios, as shown in Figure 1. The figure consists of 20 nodes as an example scenario for six scenarios of iterations: 20 static nodes, 20 static nodes + 20 mobile nodes, 50 static nodes, 50 static nodes + 50 mobile nodes,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Description** \\ \hline VANET & Vehicular Ad Hoc Network \\ DSDV & Destination Sequenced Distance Vector \\ AODV & Ad Hoc On-demand Distance Vector \\ QoS & Quality of Service \\ DSR & Dynamic Source Routing \\ DVR & Distance Vector Routing \\ UGV & Unmanned Ground Vehicle \\ V2X & Vehicle To Everything \\ WSN & Wireless Sensor Network \\ DES & Discrete Event Simulator \\ UDP & User Datagram Protocol \\ RSU & Road-Side Unit \\ RREQ & Route Request Message \\ RRER & Route Error Message \\ MPR & Multipoint Relay \\ \hline \hline \end{tabular}
\end{table}
Table 1: The abbreviations used in the proposed study.  
500 static nodes, and 500 static nodes + 500 mobile nodes. In this simulated network model, a WLAN-type IEEE 208.11b was used in each scenario configured with the protocol used in the scenario. Table 2 represents the simulation setting parameters and values.
# Open-Pit Map: An HD Map Data Model for Open-Pit Mines
## 6 Discussion  
Different elements in the HD map for open-pit mines were provided in previous literatures [8; 9; 10]. But the application scenarios of open-pit mines were not closely connected with the elements. Compared to these literatures, there is not only traffic information for autonomous vehicles in the OP map, but also descriptions of rock types such as CaCO\({}_{3}\), MgO, CaO, material information to distinguish waste and ore, blasting ranges in open-pit mines, warning zones, bench lines, etc. The OP map is more professional for mining industries. Therefore, the HD map can be used by all of the different departments in the mine. With these detailed elements in the OP map, the efficiency and quality of mining management can be improved.  
However, the OP map has some limitations. For example, the selected location and traffic elements are common to different open-pit mines and can generally complete the construction of HD maps, fulfilling the business requirements of unmanned transportation activities. Nevertheless, open-pit mining employs various extraction methods, and the application of technical equipment also varies. Additionally, the division and naming of roads and zones may differ between mines. These are aspects for the future expansion and enhancement of the data model.
## 5 Analysis of the Solution and Simulation
### Simulation Results and Comparison with ZigBee
* The delay, as a result of the extension of the network lifetime, is in SENSED-SG, on average 1.1 s higher. In light of the above simulation results, the feasibility of our proposal for furnishing a reliable and cost-effective asset-managing system for the Smart Grid has been widely demonstrated. These simulations, despite having taken into account a large amount of potential aspects (such as different propagation environments, various interfering phenomena, node's hardware constraints, diverse topologies, _etc._), inevitably still represent a simplification of the real world. To address this weakness, we complete our work with a series of test-beds experimentation presented in the next section. They are carried out to prove the suitability of the solution here proposed from a higher perspective and, to further validate the results obtained by the analysis and the simulation frameworks (Sections 5.1 and 5.3).