esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Uplink and Downlink Resource Allocation for D2D Communications System
## 2 System Model and Problem Formulation
### System Model  
In this paper, we consider the single cell scenario in Figure 1, in which the cell comprises of \(M\) CUs, \(K\) D2D pairs and a base station (BS), in here \(m\in\{1,2,\ldots,M\}\) and \(k\in\{1,2,\ldots,K\}\) denote the index sets of CUs and D2D pairs, respectively. It is assumed that the D2D communications system uses frequency division duplex, where the uplink and downlink channels occupy half of the entire spectrum. Moreover, each of the CU has been pre-allocated one orthogonal uplink channel and one orthogonal downlink channel to reduce the inter-CU-interference. Thus, as to facilitate the management interference problem, we assume that each channel (either uplink or downlink) could be reused by at most one D2D and each D2D link is allowed to reuse no more than one channel.  
We consider the slow fading caused by shadowing and the fast fading caused by multi-path propagation. Thus, the channel gain between CU \(m\) and the BS can be expressed as  
\[g_{mB}=\kappa\beta_{mB}\varsigma_{mB}d_{mB}^{-\alpha} \tag{1}\]  
where \(\kappa\) is the path loss constant determined by system parameters, \(\beta_{mB}\) denotes the fast fading gain with exponential distribution, \(\varsigma_{mB}\) denotes the slow fading gain with log-normal distribution, \(d_{mB}\) is the distance between \(m\)th CU and the BS, and \(\alpha\) is the path loss exponent.  
Figure 1: System model for device-to-device (D2D) communications system.
# Security Issues in IoT-Based Wireless Sensor Networks: Classifications and Solutions
## 3 Security Protection Schemes
### Intrusion Detection Methods and Trust Models
#### 3.7.2 Trust Models  
In order to strengthen the security level, some studies proposed security mechanisms based on the trust index. These new mechanisms protect networks from malicious attacks that encryption solutions cannot perform. The trust model in a WSN plays a vital role in ensuring the security and reliability of the system [95]. The trust model can help to detect suspicious activities or attacks in sensor networks. Sensor nodes can have their reliability evaluated based on their behavior and the collected data [96; 97]. The trust model can be used to optimize resource capacity utilization in the network. Exceptionally reliable sensor nodes can be given priority in the process of sending and receiving data [98; 99]. A trust model was proposed in the study of [PERSON] [100], in which each node calculates the trust index of its adjacent neighbors based on the statistical information, data values, and recommendations of other neighboring nodes. In the study of [PERSON] [101], a trust model was proposed to isolate malicious nodes. The cryptographic method is used to calculate the trust index of each node. A node's position is considered unsafe if the trust index is less than a fixed threshold.  
Intrusion detection and remediation models in WSN-IoT are capable of learning normal network behavior and detecting anomalies, including unknown attacks. For example, Sybil, selective forwarding, and wormhole attacks can be detected based on behavioral patterns. At the same time, the monitoring and decision-making process is also automatically executed. However, the deployment of machine learning or deep learning-based models requires heavy resources, so it is not suitable for direct deployment on network nodes. In addition, data quality and data variety play an important role in the training process (machine learning or deep learning-based detection models will be effective when the training data set is complete, labeled, and balanced between normal and abnormal data).  
In trust models, metrics such as the number of valid packets, interaction level, abnormal response, etc., are used to detect malicious behavior and increase the accuracy of detecting attacks. However, each WSN-IoT application has different functions and operations, so there will be no common trust index for all systems.
## 2 Materials and Methods
### Data
#### 2.2.3 ASCAT Soil Water Index  
For analyzing the root-zone soil moisture, the surface soil moisture (SSM) needs to be assimilated, which is derived from an SWI (Soil Water Index) product. SWI is calculated from MetOp. ASCAT observations using a recursive exponential filter [35]. SWI is obtained at 0.1 degree spatial resolution with daily sampling [36]. To get an SSM value from SWI information, the following relation is used: \(SSM=SWI\times(w_{max}-w_{min})+w_{min}\), where \(w_{min}\) and \(w_{max}\) are the minimal and maximal SSM values that the model can produce at a given grid point during the 2008-2015 period. Since the soil moisture quantity observed by remote sensors differs from that defined in models, soil moisture data must be rescaled before assimilation, to be consistent with the model climatology [37]. The ASCAT data are bias-corrected concerning the model climatology by using a seasonal-based CDF (Cumulative Distribution Function) matching technique, as described in [37]. Figure 2 compares theopen-loop model simulation with the raw ASCAT and the CDF rescaled ASCAT time series at Hegyhatssl (located in the western part of Hungary). The CDF matching with seasonal correction improves the temporal correlations between the data and the model.
## 5 Background Noise Elimination Method
### The Principle of ALO-VMD Method
#### 5.1.3 The Proposed ALO-VMD Method  
The basic idea of the proposed ALO-VMD method is to obtain the optimal parameters (i.e., the modal component number \(K\) and penalty factors \(\hat{a}\)) by the ALO algorithm, so as to ensure the decomposition capability of the VMD. As per the above discussion, a reasonable fitness function is an important factor in the ALO algorithm. To further ensure the optimal decomposition effect, the maximum average weighted kurtosis (MAWK) is setting as the fitness function in the ALO, and it is defined by the equation below.  
\[\left\{\begin{array}{rl}f=argmax[WKI_{i}]\\ \text{s.t.}&K_{\min}\leq K\leq K_{\max}\\ &\hat{a}_{\min}\leq\hat{a}\leq\hat{a}_{\max}\end{array}\right., \tag{34}\]  
where \(K\) and \(\hat{a}\) is the optimal parameters. \(WKI_{i}\) (\(i\) = 1, 2,..., \(K\)) represents the weighted kurtosis index value of the components from the VMD decomposition, which can be calculate by the equation below [51].  
\[WK=KI\text{-}C, \tag{35}\]  
\[KI=\frac{(1/N)\sum_{n=0}^{N}x^{A}(n)}{\left[(1/N)\sum_{n=1}^{N-1}x^{2}(n) \right]^{2}}, \tag{36}\]  
\[C=\frac{E[(x-\overline{x})(y-\overline{y})]}{E[(x-\overline{x})^{2}]E[(y- \overline{y})^{2}]} \tag{37}\]  
where \(KI\) is the kurtosis index of signal sequence \(x(n)\), \(N\) is the signal length, \(C\) is the correlation coefficient between signals \(x\) and \(y\), and \(E(\cdot)\) represents the mathematical expectation.  
The flow chart of the proposed ALO-VMD is illustrated in Figure 17. First, the initialization parameters of ALO-VMD are initialized. Second, the original signal is decomposed by the VMD method. Third, the average weighted kurtosis (AWK) value of each components is calculated and the maximum average weighted kurtosis (MAWK) is replaced if the average weighted kurtosis index value is greater than the current optimal fitness value. Finally, if the number of iterations is less than \(Iter_{\max}\), let \(Iter\) = \(Iter\) + 1, and the positions of each ant are updated. Otherwise, the iteration ends and the optimal parameters are saved.
# Multiple Instances QoS Routing in RPL: Application to Smart Grids +
## 7 Conclusions  
In this paper, we have proposed a new objective function to be compliant with \(RPL\) to support the multi-instance approach proposed by the standard. Our approach takes into consideration different features of both nodes and links and is compliant with the standard. We have run the experiment using realistic settings and results show the high performances of \(OFQS\) It achieves significant improvement in terms of End-to-End delay, network lifetime and PDR while insuring a load balancing among the nodes compared to standard solutions. In the future, we intend to investigate open issues discussed in Section 6.  
**Author Contributions:** Conceptualization, J.N.; Methodology, J.N., N.G. and N.M.; Software, J.N., M.B. and J.D.; Supervision, N.G. and N.M.; Writing--original draft, J.N.; Writing--review & editing, N.G., N.M. and B.Q.  
**Funding:** This work was partially funded by a grant from the MEL (Metropole Europeenne de Lille), SoMel SoConnected project (Ademe, PIA2), Yncea Haut-de-France and CPER Data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Six Sigma-Based Frequency Response Analysis for Power Transformer Winding Deformation
## 4 Discussion
The results confirm the following:  
* FRA6\(\sigma\) successfully detects faults at an early stage, particularly in core deformation, clamping pressure relaxation, winding displacement, and insulation degradation--even when these are not yet physically observable. * The approach outperforms conventional FRA by leveraging statistical control limits (UCL, LCL) and process capability indices (\(C_{p}\),\(C_{pk}\)), making fault detection more quantifiable and repeatable. * The methodology is particularly effective in the mid-to-high-frequency ranges, where insulation weaknesses and partial discharges are challenging to identify using standard FRA or physical assessment alone. Once the baseline FRA fingerprint is established, the proposed FRA6\(\sigma\) framework requires approximately 2 to 5 min to analyze new measurement data using MATLAB 2019 Rb, including control chart generation and process capability calculation. In terms of diagnostic performance, a total of 25 diagnostic events--each representing a deviation indicative of a mechanical or electrical fault--were analyzed. Traditional FRA interpretation, which relies on visual comparison of response signatures, correctly identified 16 of these fault events, corresponding to a sensitivity of 64%. In contrast, the proposed FRA6\(\sigma\) framework, which applies Six Sigma control charts and process capability indices (Cp and Cpk), identified 21 of the 25 fault events, yielding a sensitivity of 84%. This corresponds to a relative improvement in diagnostic sensitivity of 31.25%. These results demonstrate that the statistical rigor and objectivity introduced by FRA6\(\sigma\) significantly enhance early fault detection compared to expert-dependent, deterministic FRA methods. Traditional FRA sensitivity can be expressed as follows in Equation (11). \[Sensitivity_{Traditional}=\frac{16}{25}\times 100\%=0.64=64\% \tag{11}\]  
The FRA6\(\sigma\) sensitivity can be expressed as follows in Equation (12). \[Sensitivity_{FRA\sigma}=\frac{21}{25}\times 100\%=0.84=84\% \tag{12}\]  
The relative improvement in sensitivity can be expressed as follows in Equation (13). \[Sensitivity_{FRA\sigma}=\frac{0.84-0.64}{0.64}\times 100\%=31.25\% \tag{13}\]  
The proposed FRA6\(\sigma\) method improves diagnostic sensitivity by 31.25% compared to traditional FRA interpretation.
## 5 Results and Analysis
### Hosting Capacity Evaluation by HHO Algorithm  
In this case, the HHO algorithm is used to find the PV hosting capacity of the test system. Case study parameters are the same as those of the PSO-based HC evaluation. The obtained results are summarized in Table 4. Maximum and minimum hourly voltages as well as energy loss and SOC profiles in a sample day in January are, respectively, depicted in Figures 15-18.  
The results are quite close to that of the PSO algorithm. As can be observed, minimum and maximum hourly voltages are within the permissible range. Moreover, during 12:00-15:00, when the PV power reaches the highest level, the voltage level also increases. Referring to Figure 17, energy losses also increase during this interval. As can be observed in Figure 18, the BESS is charged during this interval to absorb some power of PV unit and repress the overvoltage in MG. That is how the over voltages beyond the allowable range are avoided. Note that for higher PV capacity values, voltage constraints could not be met, and thus, the PV hosting capacity of the MG under the introduced conditions is equal to 2.3483 MW. Referring to Table 4, the optimal connection node of PV and BESS is node 28, similar to the PSO results. Moreover, the total daily energy loss is equal to 0.7542, equivalent to 5.19% of total energy consumption, which is an acceptable value.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**PV Size (MW)** & **Location** & **Total Daily Loss (MWh)** \\ \hline
2.3483 & Node 28 & 0.7542 \\ \hline \hline \end{tabular}
\end{table}
Table 4: HHO HC assessment results.  
Figure 15: Minimum voltage hourly profile using HHO.  
Comparison of the Results of the PSO and HHO Algorithms for the Optimal Size and Placement of the PV  
As reported earlier, both algorithms determined the same node for connection of DC bus (i.e., PV and BESS). Moreover, both algorithms obtained the same energy losses. The only difference was the maximum allowable capacity of PV, for which the obtained value of the HHO algorithm was 2.5% higher than that of the PSO algorithm.  
On these bases, it can be said that although both algorithms showed essentially the same performance in evaluating the hosting capacity of the test system under study, the introduced HHO algorithm performs slightly better than the PSO algorithm.  
Figure 16: Maximum voltage hourly profile using HHO.  
Figure 17: Energy loss hourly profile using HHO.  
Figure 18: SOC hourly profile using HHO.
# BPMNE4 IoT: A Framework for Modeling, Executing and Monitoring IoT-Driven Processes +
## 4 Related Work
### Using BPMN 2.0 Metamodel As-Is
To support the inherent characteristics of IoT devices (_Objective O2_), BPMS such as Camunda are not sufficient [4; 6; 9; 24]. Consequently, the IoT-driven BPs modeled with BPMN 2.0 need to be (1) converted to a IoT neutral-platform programming code (e.g., Callas, Java, or C#), (2) an appropriate ontology to extend BPs with context data [6] is required, (3) or a suitable IoT middleware needs to be introduced for the IoT-driven BPs (cf. _Objective O4_). The latter is particularly challenging, as existing middleware differs in the underlying technologies [45]. For instance, ref. [39] generates Java and C# code for deploying it on the Monte Runner WSN platform; ref. [41] translates the model of an IoT-driven BP into Callas; [PERSON] et al. [6] suggests combining BPMN with ontologies to execute IoT-driven BPs. Finally, [PERSON] et al. [21] proposes the use of a layered architecture for executing IoT-driven BPs. For the monitoring of IoT-driven BPs, specific solutions exist. As standard BPMN elements (e.g., service or script tasks) are used to express the IoT involvement in processes, IoT behavior cannot be explicitly monitored and tracked (e.g., in an event log) (_Objectives O5 and O6_). For example, it needs to be monitored which IoT devices are currently processing, in what state these devices are (e.g., running, waiting or error), and what outputs are produced by them. Ref. [31] developed a Django web framework to monitor IoT devices as well as the progress of the process state. Ref. [42] uses smart objects, which communicate the state changes of their process or IoT devices to the architecture. In turn, these changes are forwarded to the monitoring system.
# RSS Indoor Localization Based on a Single Access Point
## 6 Conclusions  
In this paper, a novel indoor RSS localization approach was presented. The novelty of the approach lies on the generated RSS, resulting from the various antenna polarisation states, and the antenna elements direction of a single AP system. Testing and evaluation of the proposed methodology indicates that high-quality radiomaps can be generated, leading to satisfactory localization accuracies. The work demonstrates the ability to implement RSS-based Real-Time Localisation Systems (RTLS) in areas where only a single multi-antenna AP exists. This research may be further expanded by investigating various MIMO configurations.  
All authors contributed equally to this work.  
Figure 8: Correlation score per Scenario.  
Figure 7: MMSE algorithm performance. (**a**) Mean error per polarization Scenario; (**b**) CEP95 per polarization Scenario.  
**Funding:** This research was funded by the European Union and Cyprus Research Promotion Foundation, grant number ENTERPRISES/0916/0170.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Analysis of Calculation Examples
### Effect of Speed of Loads on Dynamic Response of Double-Beam System
As shown in Figure 5, the speed of the load-peak deflection of the midspan relationship curve of the primary and secondary beams clearly showed \"abrupt increases\", indicating that the simply supported double-beam system had multiple critical speeds under successive moving loads. The peak deflection of the midspan of the primary beam reached its maximum value \(\overline{p}_{an,1}=-3.754\) mm at \(v=122\) m/s, and the peak deflection of the midspan of the secondary beam reached its maximum value \(\overline{p}_{an,2}=-2.208\) mm at \(v=124\) m/s. Assuming the speed corresponding to the maximum peak deflection of the midspan as the dividing speed, the dividing speeds of primary and secondary beams were \(\overline{v}_{1}=122\) m/s and \(\overline{v}_{2}=124\) m/s, respectively. In the speed of load-peak deflection of the midspan relationship curve of primary and secondary beams, the critical speeds corresponding to the \"abrupt increases\" were close to each other and should be avoided in engineering practice. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{v}\) (m/s) & **Layer** & \(\mathbf{p}_{an}\) & \(\mathbf{p}_{fe}\) & \(\mathbf{e_{p}}\) \\ \hline \multirow{3}{*}{40} & Primary beam & \(-3.067\) & \(-3.112\) & \(-1.46\%\) \\ \cline{2-5}  & Secondary beam & \(-1.572\) & \(-1.571\) & \(0.06\%\) \\ \cline{2-5}  & \(\lambda_{p}\) & \(1.950\) & \(1.981\) & \\ \hline \multirow{3}{*}{100} & Primary beam & \(-2.922\) & \(-2.933\) & \(-0.40\%\) \\ \cline{2-5}  & Secondary beam & \(-1.396\) & \(-1.399\) & \(-0.23\%\) \\ \cline{2-5}  & \(\lambda_{p}\) & \(2.093\) & \(2.096\) & \\ \hline \multirow{3}{*}{122} & Primary beam & \(-3.754\) & \(-3.815\) & \(-1.60\%\) \\ \cline{2-5}  & Secondary beam & \(-2.201\) & \(-2.200\) & \(0.04\%\) \\ \cline{2-5}  & \(\lambda_{p}\) & \(1.706\) & \(1.734\) & \\ \hline \multirow{3}{*}{180} & Primary beam & \(-2.769\) & \(-2.754\) & \(0.53\%\) \\ \cline{2-5}  & Secondary beam & \(-1.122\) & \(-1.121\) & \(0.07\%\) \\ \cline{2-5}  & \(\lambda_{p}\) & \(2.467\) & \(2.456\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Time-history curve peak of midspan displacement (unit: mm). Figure 4: _Cont._
## 4 Case Study and Applications
### Performance Evaluation of a Regression Problem Implementation

Table 4: Parameter configuration for the EC prediction of a set of buildings for the DSPSO and DAPSO variants. Figure 7: Performance evaluation with change in the number of (**a**) particles and (**b**) iterations. The performance of the distributed PSO algorithms is enhanced by using multiple executors for the parallel evaluation of particle fitness on a Spark cluster. It is evident that this performance is more visible in complex optimization problems, where the overhead incurred by Spark is negligible compared with that of traditional PSO implementations. Figure 8 illustrate the outcomes in relation to the error (MSE) observed in the assessments of both the DSPSO and the DAPSO implementations. The network was trained on 80% of the data, designated as the training set, while the remaining 20% were reserved for evaluating the error rate, designated as the test set. Figure 7(a) illustrates that the error values obtained by both algorithms with 100 particles are significantly higher when using 12,000 samples. This is attributable to the excessive creation of tasks when there are insufficient samples, which results in a less representative search space. Consequently, the particles may encounter difficulties in converging towards an optimal solution. Conversely, it is evident that the error variability rises in conjunction with the number of iterations in the scan Figure 7(b) when using 12,000 samples This is a consequence of the modifications made to the best position and fitness value computed by the particles. Nevertheless, as the number of particles increases, the errors converge to the same values as the asynchronous algorithm (DAPSO) for 12,000 samples. These findings indicate that the reliability of both implemented variants mainly depends on the quality of the training dataset. The DAPSO variant is generally more accurate, even with fewer samples. The graphs in Figure 8 demonstrate that, for more than 30,240 samples DAPSO produces the lowest error for higher values of the number of particles and iterations. In order to gain a deeper understanding of the underlying mechanisms and to assess the performance of the algorithms in a more comprehensive manner, it is essential to consider the convergence curves for the regression problem solved by both DSPSO and DAPSO variants. These curves provide valuable insights that extend beyond the mere measurement of raw performance in terms of execution time. DSPSO (Figure 8(a)) shows a steady enhancement in accuracy and reaches a plateau at around five iterations, indicating convergence towards a good solution. DAPSO (Figure 8(b)) may exhibit a rapid initial convergence, although this may be subject to fluctuations due to asynchronous updates. Eventually, it appears to stabilise at a similar point as DSPSO. Figure 8: Mean square error obtained with (a) 100 particles and (b) 100 iterations.
## 1 Introduction
[PERSON] and [PERSON] [29] successfully decomposed quadruple SAR data into three components: Single bounce, double bounce, and volume scattering. A fourth, helix, component was added to [PERSON] and [PERSON]'s decomposition by [PERSON] _et al._[30, 31], to resolve anomalous power problem in the decomposition results. Similarly, several mathematical approaches were proposed in recent studies to resolve anomalous behavior in decomposition maps [32, 33, 34]. More recent studies indicate that conventional scattering theories with simple double bounce and volume scattering models are not sufficient for explaining microwave scattering behaviors in wetland environments [35, 36]. Our study was aimed at examining the applicability of four-component decompositions for wetland vegetation classification. We based our study on quad-polarimetric data from TerraSAR-X (TSX) (X-band, 3.1 cm), which were collected during the Dual Receive Antenna (DRA) Campaign in 2010. We chose this dataset because the TSX decomposition is more sensitive to vegetation variation than the C-band Radarsat-2 decomposition [37].