esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Internet of Things (IoT) technology has facilitated the deployment of autonomous sensors in remote and challenging environments, enabling substantial advancements in environmental monitoring and data collection. IoT sensors continuously gather data, transmitting it to a central Base Station (BS) via designated Cluster Heads (CHs). However, data flow encounters frequent congestion at CH nodes, negatively impacting network performance and Quality of Service (QoS). This paper introduces a novel congestion control strategy tailored for Wireless Sensor Networks (WSNs) to balance energy efficiency and data reliability. The proposed approach follows an eight-step process, integrating Generative Adversarial Networks (GANs) for enhanced clustering and Ant Colony Optimization (ACO) for optimal CH selection and routing. GANs simulate realistic node clustering, achieving better load distribution and energy conservation across the network. ACO then selects CHs based on energy levels, distance, and network centrality, using pheromone-based routing to adaptively manage data flows. A congestion factor (_CF_) threshold is also incorporated to dynamically reroute traffic when congestion risks arise, preserving QoS. Simulation results show that this approach significantly improves QoS metrics, including latency, throughput, and reliability. Comparative evaluations reveal that our method outperforms existing frameworks, such as Fuzzy Structure and Genetic-Fuzzy (FSFG), Deep Reinforcement Learning Cache-Aware Congestion Control (DRL-CaCC), and Adaptive Cuckoo Search Rate Optimization (ACSRO).  
Keywords:wireless sensor networks (WSNs); congestion control; quality of service (QoS); generative adversarial networks (GANs); ant colony optimization (ACO) +
Footnote †: journal: _Mathematics_
## 4 Experiments and Results
### Attention Deficit Hyperactivity Disorder Diagnosis
#### 4.1.3 Ablation Studies  
To investigate the effect of nonlinear activation layers and dense connections inserted between the separated 1D filters, we report the performances of \(B_{3}\) with and without nonlinear layers and dense connections in Table 5. We can see that both of them contributed to the performance improvement, and the best result could be obtained by a combination of them. B3 networks with different orders provided similar performance. The orders of separated 1D filters hardly influenced the performance of 3D-DSC.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **Classifier** & **Accuracy** \\ \hline
[32] & MKL & 61.54\% \\
[33] & SVM & 69.62\% \\
[34] & SVM & 63.57\% \\
[8] & SM 3D CNN & 66.04\% \\
[8] & MM 3D CNN & 69.15\% \\
[35] & 4D CNN & 71.30\% \\
3D-DSC (\(C_{6}\)) & SM 3D CNN & 73.68\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Diagnosis performance comparisons between the proposed method and state-of-the-art methods based on the ADHD-200 dataset. MKL: Multi-Kernel Learning. SVM: Support Vector Machine. SM: Single Modality. MM: Multiple Modalities. The batch-size is 4 in this experiment.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Network** & **DC** & **Activation** & **Orders** & **Accuracy** & **OD** \\ \hline \(B_{3}\) & no & no & H, V, L & 74.26\% & 0.2733 \\ \(B_{3}\) & no & yes & H, V, L & 74.83\% & 0.2638 \\ \(B_{3}\) & yes & yes & H, V, L & 75.22\% & 0.2526 \\ \(B_{3}\) & yes & yes & V, L, H & 75.28\% & 0.2507 \\ \(B_{3}\) & yes & yes & L, H, V & 75.19\% & 0.2514 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation studies for applying Dense Connection (DC) and activation layer (activation for short) in the proposed 3D-DSC. OD: Overfitting Distance. Orders: the orders of separated 1D filters, including H (Horizontal), V (Vertical) and L (Lateral). The batch-size is 4 in this experiment.
## 5 Numerical Results
### Single-Objective Function Optimization  
In this simulation case, we make a linear combination of the components of the objective function, i.e., \(f_{1}\) and \(f_{2}\), as defined in Equation (1). The maximum sizes of the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Hour (h)** & **Ind. (pu)** & **Res. (pu)** & **Com. (pu)** & **Hour (h)** & **Ind. (pu)** & **Res. (pu)** & **Com. (pu)** \\ \hline
1 & 0.56 & 0.69 & 0.20 & 13 & 0.95 & 0.99 & 0.89 \\
2 & 0.54 & 0.65 & 0.19 & 14 & 0.96 & 0.99 & 0.92 \\
3 & 0.52 & 0.62 & 0.18 & 15 & 0.90 & 1.00 & 0.94 \\
4 & 0.50 & 0.56 & 0.18 & 16 & 0.83 & 0.96 & 0.96 \\
5 & 0.55 & 0.58 & 0.20 & 17 & 0.78 & 0.96 & 1.00 \\
6 & 0.58 & 0.61 & 0.22 & 18 & 0.72 & 0.94 & 0.88 \\
7 & 0.68 & 0.64 & 0.25 & 19 & 0.71 & 0.93 & 0.76 \\
8 & 0.80 & 0.76 & 0.40 & 20 & 0.70 & 0.92 & 0.73 \\
9 & 0.90 & 0.90 & 0.65 & 21 & 0.69 & 0.91 & 0.65 \\
10 & 0.98 & 0.95 & 0.86 & 22 & 0.67 & 0.88 & 0.50 \\
11 & 1.00 & 0.98 & 0.90 & 23 & 0.65 & 0.84 & 0.28 \\
12 & 0.94 & 1.00 & 0.92 & 24 & 0.60 & 0.72 & 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Curves of the load profiles.  
Figure 5: IEEE 69-bus system topology.  
STATCOMs are set as 200 MVAr, and the maximum number of STATCOMs for installation is defined as three. Note that the initial cost of the annual energy losses is MUS $24.9905 (million dollars) per year of operation when no STATCOMs are installed on the network. On the other hand, when the optimization model (1)-(7) is solved with the BONMIN optimizer in GAMS, the solution obtained corresponds with the installation of two STATCOMs in nodes 6 and 10, with nominal capacities of 146.6292 and 22.2332 MVAr. The total annual operative costs for this solution is MUS $17.3541, which implies a reduction of 30.56%. The total investment cost in STATCOMs for this solution is MUS $1.5748; this implies that the annual costs regarding energy losses were reduced to about MUS $9.2112 per year of operation, which clearly confirms that, with an inversion lower than 2 million dollars per year, more than 9 million dollars worth of energy losses can be recovered.  
Figure 6 presents the total reactive power absorbed by the STATCOMs from the power system. Note that this power is absorbed due to the negative symbol, which implies that the STATCOMs remove the excess power capacities from the grid (see model (1)-(7)).  
The results, which are presented in Figure 6, show that both STATCOMs work as reactors. The STATCOM located at node 10 absorbs reactive power constantly during all analysis periods, while the STATCOM located at node 6 varies its absorption depending on the grid system load and generator behaviors.
## 3 Experimental Results  
In this study, we train a model to recognize Chinese speakers using the Libri Speech open-source corpus to learn speaker features. Since the corpus is in English, we recorded a Chinese corpus for meeting members, which contained six Chinese speakers, and each speaker had more than 150 segments of speech data. Transfer learning on the YAMNet requires only a small quantity of data for the self-recorded corpus. A Speaker YAMNet can be achieved.  
To evaluate the model's recognition performance on the six speakers, we let multiple speakers speak consecutively in a staggered manner. The Speaker YAMNet identifies the captured audio sequence. The results are then evaluated using precision, recall, and F1 measures. Precision measures the proportion of correctly identified speakers out of all identified speakers, recall measures the proportion of correctly identified speakers out of all actual speakers, and F1 is the harmonic mean of precision and recall. These measures are used to assess the model's speaker recognition effectiveness.  
In generating meeting minutes, we combine three functions: speaker recognition, speech recognition, and keyword prompting. Speech recognition is accomplished through the Google Speech-to-text API, and the recognition result is then used to generate keywords through the Jieba segmentation tool. The recognition results of the two have a high accuracy rate; thus, the AMMGS can be used in a practical environment.
# Anomalous Vehicle Recognition in Smart Urban Traffic Monitoring as an Edge Service +
## 4 Anomalous Vehicle Behavior Detection Using mSSA
### A Basic Introduction to SSA  
The basic SSA algorithm consists of four steps: embedding, singular value decomposition (SVD), grouping, and diagonal averaging. Let us consider a time series with real values \(\mathbb{X}_{N}=(x_{1},x_{2},\ldots,x_{N})\) with the length of \(N\).  
1. **Embedding:** map \(\mathbb{X}_{N}\) to a \(M\times L\) trajectory matrix \(X\). \(M\) is the window length and \(L=N-M+1\). \[X=[X_{1},X_{2},\ldots,X_{L}]=\left[\begin{array}{cccc}x_{1}&x_{2}&\cdots&x_ {L}\\ x_{2}&x_{3}&\cdots&x_{L+1}\\ \vdots&\vdots&\ddots&\vdots\\ x_{M}&x_{M+1}&\cdots&x_{N}\end{array}\right]\] (1) \[X_{i}=[x_{i},x_{i+1},\ldots,x_{i+M-1}]^{T},i=1,\ldots,L\] (2) where \(M\) is the dimensions of characteristics and \(L\) is the number of observations. Trajectory matrix \(X\) is a Hankel matrix of which the elements along the anti-diagonals (\(i+j=const.\)) are the same: \(x_{ij}=x_{i+j-1}\). The embedding procedure is a one-to-one mapping.
2. **SVD:** the second step is to perform the SVD procedure on the trajectory matrix \(X\). Set covariance matrix \(T=XX^{T}\), then its eigenvalues are \(\lambda_{1},\lambda_{2},\ldots,\lambda_{d}\) and the corresponding biorthogonal eigenvectors are \(U_{1},U_{2},\ldots,U_{d}\), where \(d\) is the rank of \(X\). Note that eigenvalues are arranged in a decreasing order and larger than 0. \(V_{g}=X^{T}U_{g}/\sqrt{\lambda_{g}}\) (\(g=1,2,\ldots,d\)); then, the trajectory matrix \(X\) can be written as follows: \[X=\sum_{g=1}^{d}X_{g}=\sum_{g=1}^{d}\sqrt{\lambda_{g}}U_{g}V_{g}^{T}\] (3) where \(X_{g}\) is the elementary matrix with rank 1 of the trajectory matrix \(X\), and \(V_{g}\) is one of the eigenvectors of the matrix \(S=X^{T}X\).
3. **Grouping:** In the third step, elementary matrices are partitioned into disjoint subsets: \(I_{1},I_{2},\ldots,I_{m}\); then, the trajectory matrix \(X\) can be rewritten as below. Each subset represents one component of the time series, such as trend, oscillation, or noise. \[X=X_{I_{1}}+X_{I_{2}}+\cdots+X_{I_{m}}\] (4)
4. **Diagonal Averaging:** in the last step, the reconstruction process maps the matrix with only principal components back to a time series by Hankelizing the matrix \(X_{l}\) with \(l\) principal components (\(X_{l}=X_{I_{1}}+X_{I_{2}}+\cdots+X_{I_{l}}\)).
## 3 Results
### Spatial Prediction  
The plant density spatial distribution maps of the test fields were created based on four methods: the YOLOv3 best model; SR + NDVI-PLS; NDVI + Climate-RF; 10 VarImp-RF model (Figure 10). The YOLOv3 distribution map reflects the actual plant density of the fields, as it was measured by counting the accurate plant numbers inside the UAV imagery. Visually, the YOLOv3 model demonstrated the spatial variability of plant density in the test fields with a relatively wide variation in the within-field patterns, especially in Fields 2 and 8. From this distribution map, it was also possible to highlight field-specific  
Figure 8: Variable importance (_IncNodePurity_ values) list of the full random forest regression model (SR + NDVI + Climate).  
Figure 9: Relationships between actual plant density derived from the YOLOv3 model and estimated plant density (plants m\({}^{-2}\)) derived from 10 VarImp-RF of the test data set. The red line represents a regression line. The black line represents a 1:1 relationship. Different color points represent the field names.  
plant density. Overall, the distribution maps based on the listed regression models could reflect the general pattern of the plant density spatial variability for both within-field and field-specific patterns. The distribution maps derived from NDVI + Climate-RF and 10 VarImp-RF showed almost similar results, with minor differences in a few locations due to the higher sensitivity for small plant density in the 10 VarImp-RF map, as shown in Fields 2 and 4 (Figure 10).  
For further analysis of the comparison between actual and estimated spatial distribution maps, residual maps were created (Figure 11). In general, the residual maps resemble the spatial distribution of the actual plant density derived from the YOLOv3 model (Figure 10a), with some highlighted features on the overestimated and underestimated pattern from the estimated plant density values. According to the residual map (Figure 11), the spatial patterns showed similar tendencies, although there were differences in degrees across the field. In Field 3, all regression models showed underestimated patterns across the fields. On the other hand, in Field 9, almost two-thirds of the area of the field had a tendency of overestimation, while small areas in the southeast of the field had a tendency to underestimate. In Field 8, the most contrasting differences between the PLS model and RF models was that the underestimation pattern was obvious in SR + NDVI-PLS model. The field-specific RMSE values showed that the accuracy of the models was different from field to field, as the RMSE values ranged between 1.38 and 3.06 plant m\({}^{-2}\) (Table 4).  
Figure 10: Spatial distribution of plant density in test fields as the result of the (**a**) YOLOv3 best model; (**b**) SR + NDVI-PLS; (**c**) NDVI + Climate-RF; (**d**) 10 VarImp-RF. The resolution of the distribution maps was 3 \(\times\) 3 m per pixel to match the spatial resolution of the PlanetScope imagery.
# Adaptive Vector Quantization for Lossy Compression of Image Sequences +
## 4 Experimental Results  
The main objective of our experiments is to compare the compression performances, in terms of _Compression Ratio_ (_C.R._), between the AVQ algorithm (in which each frame is independently compressed) and the AVQIS algorithm. In addition, we measure the _Peak-Signal-to-Noise-Ratio_ (_PSNR_) metric, in order to compare the perceptive quality. It is important to outline that the execution times we have achieved in our experiments are comparable between the two algorithms.  
We used two datasets (we referred to as _Dataset 1_ and _Dataset 2_, respectively), each one composed of three image sequences. Dataset 1 is composed of image sequences extracted from a video, while three infrared image sequences compose Dataset 2.
## 3 Operation of the European Transmission System  
### Stability of the Power System  
Figure 1 depicts the main conditions which may lead a power system to an emergency state and finally to a blackout, according to CIGRE definitions [69]. In order to build suitable design and operating techniques, it is essential to categorize the problem to comprehend its underlying causes. All these unstable state phenomena can occur sequentially or simultaneously during significant disruptions in a complicated way. This is valid in cases where systems are operating at their limits.  
Rotor angle stability in an interconnected power system is the ability of synchronous machines to maintain synchronism in the face of a disruption. The ability of each synchronous machine to maintain or restore mechanical and electromagnetic torque equilibrium will determine how well the system works. One type of instability that may happen is when some generators' angular swings increase to the point where they no longer synchronize with other generators. The ability of the power system to retain synchronism under minor disruptions is addressed by rotor angle stability. Both the system's initial operating state and the magnitude of the disruption affect transient stability.  
Frequency stability is the capacity of a power system to retain consistent frequency after a major system upset leading to a considerable imbalance between generation and load. With the least degree of inadvertent load loss, it depends on the capacity to maintain or  
Figure 1: Stability classification of a power system.  
restore equilibrium between system generation and load. Sustained frequency fluctuations can cause unit generators and/or loads to trip, which can lead to instability.  
The capacity of a power system to maintain constant voltages across all buses following a disturbance from a specific starting operating condition is referred to as voltage stability. It depends on the power system's capacity to preserve or restore the balance between load supply and demand. A gradual decrease or increase in some buses' voltages may cause instability. Instability of the voltage can result in cascading outages by causing a loss of load in a particular area or tripping of transmission lines and other elements by their protective mechanisms. Due to these interruptions or operating conditions that exceed the field current limit, some generators may lose synchronism.
# Monitoring of Structures and Infrastructures by Low-Cost GNSS Receivers
## 2 Concepts
### The a Posteriori Motion Model
Figure 3: Hourly estimates of the coordinates of a point on a bridge: red dots are GPS-only solutions; blue dots are GPS and Galileo joint solutions. Outliers are quite evident. * Model \(s(t)\) as a linear response to one (or more) exogenous forces \(\xi(t)\) possibly shifted in time \[s(t)=\sum_{i=0}^{m}\lambda_{i}\xi(t-iT)\] (5) where \(T\) is the basic interval of the solutions (1 day, 1 h,...), \(\lambda_{i}\) are parameters to be adjusted to the data, and \(m\) is the \"memory\" distance of the influence of \(\xi\) on \(x\). * Not model \(a(t)\), verifying a posteriori whether there are jumps in the trajectory of \(\widehat{x}(t)\) that persist after the discontinuity epoch. * Not model \(o(t)\) for the same reason as for the previous point. * Model \[\varepsilon(t)+b(t)=\
u(t)\] (6) as simple white noise because we have no idea of the exact stochastic model of this error. We wrap up by establishing for \(\widehat{x}(t)\) the model (in the case of cubic splines as base functions for \(m(t)\)):  
\[\widehat{x}(t)=\sum_{i=-2}^{2}p_{i}S(t-k_{i}\tau)+\sum_{i=0}^{m}\lambda_{i} \xi(t-iT)+\
u(t) \tag{7}\]  
where parameters \(\{p_{i},\lambda_{i}\}\) have to be determined from \"data\" \(\{\widehat{x}(t)\}\) by means of a simple least-squares algorithm according to the hypothesis that \(\
u(t)\) is white noise. As we know, this last hypothesis does not significantly influence the estimation of the parameters, and on the other hand, the subsequent estimate of \(\sigma_{\
u}^{2}\) becomes much more realistic than the underestimated \(\sigma_{\varepsilon}^{2}\) coming from the first application of least squares to the original data. The model (7) holds under the hypothesis that there is only one exogenous variable \(\xi(t)\) influencing the changes in the coordinate \(x(t)\), but it can be easily generalized to more variables. Moreover, the idea is that the relation \(\xi\to x\) is just linear. Indeed, more advanced models, including non-linearities, could be treated with different approaches, stochastic or deterministic, using various optimization algorithms, for instance, those of machine learning [30; 31; 32] applied to build expert systems. One has to notice that, once calibrated by interpolation on a suitable batch of data up to some time \(E\), the model could also be used to predict the value of the next observation \(\widehat{x}_{pr}(\overline{t}+T)\); this opens the way to the design of an alarm based on the difference between the effectively observed \(\widehat{x}(\overline{t}+T)\) and the one-step-ahead predicted observation, i.e., on  
\[x(t)=m(t)+s(t)+a(t) \tag{2}\]0
# Evaluation of VoIP QoS Performance in Wireless Mesh Networks
## 4 Results and Analysis  
During the experiments, all the required data such as delay, jitter, packet loss, and other statistical information were collected in order to correlate them for analysis purposes. However, to make the results more readable, we only present the MOS values for each scenarios analysis, which is derived considering the delay, jitter and packet loss values using the QualNet simulation software. We also present delay, jitter, and packet loss analyses for all scenarios for better interpretation of the results. As stated in Section 2, in order to simulate a realistic scenario in all simulation cases, we have configured a VoIP total loss probability of 5 percent. For scenarios where the MOS values are lower as compared to other sub-scenarios, we have looked at other factors to identify possible causes of QoS degradation. In the remainder of this section, we present our research findings for each scenario separately in terms of the obtained MOS values for the VoIP initiators and VoIP receivers; in the analysis summary of all scenarios, we also present the delay, jitter, and packet loss analysis for the VoIP initiators and VoIP receivers.
# Evaluation of IEEE802.15.4g for Environmental Observations
## 3 Results
### Urban Canyon  
In this scenario we collect measurements from 12 locations, between 406 m and 942 m and within 3 non-consecutive days with similar conditions (sunny days, at noon). Tables 9 and 10 show the PDR for packets of 127 B and 2047 B on each RX node location.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **RX at 213 m** & **RX at 337 m** & **RX at 439 m** & **RX at 538 m** & **RX at 615 m** & **RX at 715 m** \\
**PHY Alias** & **PDR** & **PDR** & **PDR** & **PDR** & **PDR** & **PDR** \\  & **127 B–2047 B** & **127 B–2047 B** & **127 B–2047 B** & **127 B–2047 B** & **127 B–2047 B** \\ \hline
2 FSK-50 & 100\%–96\% & 100\%–100\% & 31\%–33\% & 0\%–0\% & 5\%–11\% & 0\%–0\% \\
2 PSK-100 & 100\%–100\% & 100\%–100\% & 0\%–2\% & 0\%–0\% & 0\%–0\% & 0\%–0\% \\
4 FSK-200 & 99\%–100\% & 100\%–100\% & 0\%–0\% & 0\%–0\% & 0\%–0\% & 0\%–0\% \\
2 FSK-FEC-50 & 100\%–98\% & 100\%–100\% & 100\%–100\% & 0\%–33\% & 98\%–64\% & 0\%–0\% \\
2 FSK-FEC-100 & 100\%–100\% & 100\%–91\% & 85\%–32\% & 23\%–6\% & 0\%–0\% & 0\%–0\% \\
4 FSK-FEC-200 & 100\%–100\% & 100\%–60\% & 0\%–0\% & 0\%–0\% & 0\%–0\% & 0\%–0\% \\ \hline OFDM1-100 & 100\%–99\% & 100\%–100\% & 0\%–0\% & 0\%–0\% & 0\%–0\% & 0\%–0\% \\ OFDMM1-200 & 100\%–99\% & 100\%–100\% & 0\%–0\% & 0\%–0\% & 5 In this scenario, interference and multi-path fading are expected since this is a high populated area with many buildings along the street, with smart metering devices already implemented and other applications accessing the same frequency band. As shown in Table 9, the PDR for OFDM2 is lower at 406 m than at 512 m. An explanation for this is external interference. These two measurements were not taken at the same moment, and we can also see that the interference was present during the transmission of packets with OFDM2-50, OFDM2-100, OFDM2-200 and OFDM2-400. Before and after that time, PDR rose to values close to 100%. In addition, for the following node locations, these high values are maintained.  
After 540 m, there is a negative slope in the street level and after 685 m there is a viaduct which is perpendicular to the avenue Daumesnil. High data rates with high PDR are achieved at least up to 540 m, and the maximum coverage of the radio link is around 780 m.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **RX at 406 m** & **RX at 512 m** & **RX at 520 m** & **RX at 540 m** & **RX at 570 m** & **RX at 585 m** \\
**PHY Alias** & **PDR** & **PDR** & **PDR** & **PDR** & **PDR** & **PDR** \\
**127 B-2047 B** & **127 B-2047 B** & **127 B-2047 B** & **127 B-2047 B** & **127 B-2047 B** & **127 B-2047 B** \\ \hline
2 FSK-50 & 100\%–68\% & 100\%–100\% & 10