esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting the Quality of Tangerines Using the GCNN-LSTM-AT Network Based on Vis-NIR Spectroscopy
## 1 Introduction
This mechanism can help the model better handle long sequences, avoid information loss and repetition, and improve its performance. Currently, some researchers suggest that attention mechanisms can be utilized to achieve efficient band selection in hyperspectral imaging [22]. Unlike most spectra-related studies that focus on the 1D-CNN [23] and analyze one-dimensional spectral data for each sample, our model takes advantage of the spatial and sequential characteristics present in the data. The transmittance spectra of 12 locations are collected for each tangerine and the dependencies across sequential spectra are carefully considered. Compared to high-dimensional hyperspectral imaging, which is expensive in both collection and processing, and another type of data that averages the spectra values taken from multiple sampling points, the method of using 12 characteristic locations for each tangerine has the advantage of utilizing both sparse spatial features and rich spectral features. To predict the quality parameters of tangerines based on the two-dimensional Vis-NIR spectra, in this paper, we propose a novel deep-learning network combining graph convolutional network (GCN), two-dimensional CNNs, bidirectional LSTM (Bi-LSTM), and attention mechanism, which shows efficient and accurate performance. The findings of this study have significant implications for the tangerine industry, enabling the rapid and non-destructive assessment of tangerine quality.
# Real-Time Statistical Measurement of Wideband Signals Based on Software Defined Radio Technology
## 4 Results and Discussion  
### Comparison of the SDR Platform with the Spectrum Analyzer  
In this section, the performance of the USRP X310 platform is presented in comparison to the performance of the SA for an 802.11 ax signal with 40 MHz channel bandwidth. Two experimental scenarios were also carried out:  
* Both the SDR platform and the SA were set to run continuously for the same measurement duration;
* Measurements that have the same duration, but with an averaging performed each 0.5 s.  
Figure 10 shows the comparative values of the channel power measured by both the SA and the SDR as a boxplot of the channel power variation. In both continuous and 0.5 s averaging cases there is a very narrow gap between the values measured by the SDR and the SA. Most of the values in the whole band measured by the SDR show a very small deviation from the median (<0.2 dB) compared to the reference analyzer.  
The built-in CCDF function was used to measure the average signal strength and peak values. These values are important for evaluating PAPR. This ratio is an important parameter in wireless communication systems, as signals with a high PAPR require higher power amplification, which can increase power consumption and cause distortion, reducing overall system efficiency.  
Another parameter considered in measurements described by the CCDF curve is the percentage of values above the mean for a given crest value. In wireless communications, the CCDF curve can be used to analyze signal behavior under various channel conditions such as filtering, compression, interference or fading. By calculating the probability that the CCDF curve provides--10%, 1%, 0.1% and 0.01%--we can determine the probability that the signal amplitude will exceed certain thresholds under these conditions.  
Analyzing Figures 11 and 12, it can be seen that the mean values are distributed in a larger interval in the case of the SA. Moreover, the absolute power levels are up to 0.4 dBm higher for the system consisting of SDR and GNU Radio as compared to the SA. The different mean distribution profiles may be due to differences in the samples generated by the ADCs of the two instruments or to differences in the software procedures. However, the differences are not significant enough to change the general outcome of the measurement. In the case of the peak values analysis, the differences were less than 0.8 dB.  
Figure 10: Results from the SDR system and the SA: CP for an 802.11 ax channel, 40 MHz channel bandwidth.  
From analyzing the results, we observed that the measurements obtained from the SDR platform are comparable to those of a high-performance SA, for both CP and CCDF measurements. These results demonstrate that the SDR-based system can be used for measurements outside the laboratory, where signal sources vary according to the service provided.
# Analysis of AgRED Performance in LR-WPAN Dense Ad-Hoc Networks +
## 4 Simulation Parameters  
For the purpose of evaluation of AgRED performance in comparison with RED, a scenario of 500 m\({}^{2}\) is chosen with a dense arrangement of nodes that support IEEE 802.15.4 LR-WPAN. Simulation is run on OMNET++ simulation software with INET framework, where modifications have been carried out in RED filter module. Every simulation is repeated five times with constant seeds for random numbers generation. The routing protocol used to communicate is AODV [1], while any other routing protocol can be chosen, such as OLSR or BATMAN [14]. The nodes are following random motion using Gauss Markov mobility model from INET. The parameters that are analyzed are payload variations and send interval delay. The payload supported by IEEE 802.15.4 is 127 bytes but supports fragmentation; therefore, larger payloads can also be tested. More detailed simulation parameters are listed in Table 1.
# Anomalous Vehicle Recognition in Smart Urban Traffic Monitoring as an Edge Service +
## 4 Anomalous Vehicle Behavior Detection Using mSSA
### Detection of Anomalously Behaved Vehicles  
The objective of this detection procedure can be described as: given a set of \(K\) vehicles \(\mathbb{V}_{K}\), identify the vehicle with different behaviors \(v_{c}\). With \(\mathbb{V}_{K}\), there are the time sequences of vehicle behavior describing information \(\mathcal{X}^{(S)}_{K,N}\). Here, \(N\) is the length of time series, and \(S\) is the number of features describing the movement of the vehicles. The lengths of trajectories for all vehicles in the set are the same. For every feature of an individual vehicle within the time series length  
With the matrix \(X^{(j)}_{n,N}\) is obtained, the second step is to perform SVD and select the first \(l_{j}\) eigenvalues to construct the subspace. The subspace closely represents the original time series but ignores the noise and non-significant factors. Denote \(U_{l_{j}}\) as the matrix of which each column is the corresponding eigenvector to the selected eigenvalues. Similarly, \(V_{l_{j}}\) can be calculated.  
Next, by reconstructing the time series from the \(l\)-dimensional subspace, \(\mathcal{X}^{(j,l)}_{n,N}\) will be obtained. Then, for each feature, calculate the average of the reconstructed time series of all vehicles to obtain the base matrix:  
\[\mathcal{X}^{(j,l)}_{base,N}=\sum_{n=1}^{K}\mathcal{X}^{(j,l)}_{n,N}/K \tag{12}\]  
The SVD operation is conducted again, but, on this base matrix, an \(l\)-dimensional subspace \(U^{(j,l)}_{base,N}\) is obtained. Then, each matrix \(X^{(j)}_{n,N}\) is the test matrix of each feature of each individual vehicle, and calculate the distance of the test matrix to this base matrix:  
\[\begin{split}\mathcal{D}^{(j,l)}_{n,N}=\sum_{i=1}^{L}((X^{(j)}_{ i,n,N})^{T}X^{(j)}_{i,n,N}-\\ (X^{(j)}_{i,n,N})^{T}U^{(j,l)}_{base,N}(U^{(j,l)}_{base,N})^{T}X^{ (j)}_{i,n,N})\.\end{split} \tag{13}\]  
Normalize the distances within each channel, and then the anomaly score is calculated for each vehicle:  
\[P_{n}=\sum_{j=1}^{S}P_{j}\mathcal{D}^{(j,l)}_{n,N} \tag{14}\]  
where \(P_{j}\) is the weight of each characteristic of the motion of vehicles and \(\sum_{j=1}^{S}P_{j}=1\).  
A threshold \(h\) is pre-set by the system operator either based on past experiences or a certain mathematical analysis. If \(P_{n}>h\), then the \(n\)-th vehicle is detected as an anomalous one. It is a complex and challenging process to determine an optimal or suboptimal threshold for SSA algorithms. It is essentially an optimal solution finding problem in high-dimensional space as multiple factors play significant roles, including the window length, number of eigenvalues selected, distance between the base and test matrix, and user expectations. It is worth noting that the trade-offs between the detection delay and the detection accuracy are highly application dependent. A comprehensive discussion is beyond the scope of this paper, and interested readers are referred to [70] for more details.
## 4 Discussion  
### Impact of Vibration Signals  
In the practical application of the HBM, the construction process is brimming with noise, which can affect the acceleration measurements in each direction. This study demonstrates that by combining the three measurements, this noise can be suppressed to some extent, thereby improving the signal-to-noise ratio of the data. During the four key states of the HBM construction process, the focus was more on the magnitude of acceleration rather than its specific direction. In this context, using the modulus \(R_{i}\) can directly provide the magnitude of acceleration, eliminating the need to consider the vibration in each direction separately. This approach only requires handling a comprehensive vibration value, which simplifies the data processing and accelerates the processing speed. Importantly, these methods are insensitive to noise generated by surrounding human activities, underscoring the practical potential of this approach.  
Wind loads have a significant impact on the construction of tall buildings. The HBM is a massive unified steel frame structure, typically weighing over 500 tons. The HBM connects to the core concrete structure through multiple support points, ensuring a high degree of integrity so that its local vibration is less affected by wind loads. In the practical construction management of an HBM, a safe operational wind speed, such as 18 m/s, is typically established. The HBM can function normally within this wind speed, with measurements indicating that it exhibits a cooperative pulsation with the attached building structure. The vibration's fundamental frequency triggered at this moment is significantly different from the fundamental frequency triggered when the HBM is operational. No frequency-domain coupling interval exists between the two, meaning that the wind-induced vibrations minimally impact the identification of the HBM's working state. When the wind speed exceeds 18 m/s, the HBM cannot be climbed, and its ancillary equipment must cease operations. In this state, due to the lack of measured data, the effect of wind load on the vibration of the HBM requires further investigation.
## 3 The Proposed Conversational System: CORES
### The Emotional Detection Mechanism  
In CORES, emotion detection is performed by advanced NLP and emotion classification techniques. The system uses transformer-based models, specifically RoBERTa (a variant of BERT) [41; 42], which are fine-tuned for emotion classification. This approach allows CORES to interpret both the semantic meanings and the emotional states in users' inputs. As shown in Figure 4, the process of the emotion detection mechanism involves the following steps:  
1. **Tokenization**: Users' comments on items are divided into tokens (words or subwords). This step helps manage unfamiliar words by splitting them into smaller interpretable units.
2. **Embedding**: After tokenization, the RoBERTa model converts the tokens into embeddings. These embeddings map words with similar meanings or contexts to nearby locations in a vector space, enabling the model to capture deeper semantic relationships.
3. **Fine-tuning the RoBERTa model**: The pre-trained embeddings are refined during fine-tuning to enhance the model's ability to classify emotions. This optimization is based on patterns observed in the training data.
4. **Emotion classification**: RoBERTa generates context-aware embeddings for each token. These embeddings are processed to produce a classification vector, where each element corresponds to a specific emotion category (e.g., happiness, joy, fear, anger).
5. **Inference and prediction**: When a new user input is received, it performs tokenization, and embeddings are created for each token. These embeddings are passed through the fine-tuned model to predict the emotion, which predicts the emotion by assigning a confidence score to each emotion category. The emotion with the highest score is selected as the predicted label.  
Figure 4: The emotional detection mechanism.  
This process enables CORES to accurately detect and classify emotions during real-time user interactions.
# Hybrid Resonant Converter with Three Half-Bridge Legs for Wide Voltage Operation
## 1 Introduction
If the much wider voltage range is requested, such as \(V_{in,max}\geq 4V_{in,min}\) in solar panel applications or wide voltage variation systems, then these circuit topologies cannot be operated well. A new resonant converter with a three converter legs structure and a variable turns ratio of transformer is presented in this study, having a wide voltage range operation. On the basis of the switching status of active switches, the proposed resonant converter has three equivalent sub-circuits to implement 8:1 (\(V_{in,max}=8V_{in,min}\)) wide voltage range operation such as 50-400 V. If \(V_{in}\) (input voltage) is under low voltage range from 50 to 100 V, the sub-circuit with the full bridge circuit topology and low turns ratio transformer is operated to have high voltage gain. If \(V_{in}\) is under medium voltage range from 100 to 200 V, then the sub-circuit with full bridge circuit topology and high turns ratio transformer is operated to decrease the voltage gain in order to control the load voltage. If \(V_{in}\) is under the high voltage range from 200 to 400 V, then the sub-circuit with the half bridge circuit topology and high turns ratio is operated to further reduce the voltage gain. Three equivalent sub-circuits in the proposed converter were selected by two Schmitt voltage comparators with reference input voltages at 100 and 200 V. Therefore, the half bridge circuit topology or full-bridge circuit topology with two different turns ratios can be selected on the input side to accomplish wide input voltage operation. The circuit topology and control algorithm of the presented converter are easy to implement when compared to the conventional wide voltage range converters with two-stage circuit topologies and hybrid converters in [9; 20; 21; 21]. Because the resonant circuit of the presented DC converter is controlled under the frequency modulation and the input impedance of resonant tank is always at the inductive load operation, active switches are naturally turned on at zero voltage switching operation. Finally, the design procedures and experiments are presented and confirmed with a 480 W laboratory circuit.
# An SSI-Based Solution to Support Lawful Interception
## 4 Proposed Approach
### External Agency Integration  
Consider the employee E of the EA authorized by the LEA to access the data related to the LI procedure on the LEMF. We assume that [PERSON] is preliminarily equipped with a credential \(VC_{E}\), certifying their identity and role within the EA.  
To allow [PERSON] to access the data on the LEMF, the following steps are performed.  
1. Through a secure peer-to-peer channel, [PERSON] sends the credential \(VC_{E}\) to the EA.
2. The EA verifies \(VC_{E}\) (as described in Section 2.2) and sends to [PERSON] a new credential \(VC_{L}\) certifying [PERSON] is authorized by EA to interview in the LI procedure.
3. Through a secure peer-to-peer channel, [PERSON] sends \(VC_{L}\) to the LEA.
4. The LEA verifies \(VC_{L}\) and grants a new credential \(VC_{LM}\) to [PERSON]. This new credential is used by [PERSON] to obtain access to the LEMF.
5. Now, through a secure peer-to-peer channel, [PERSON] sends the credential \(VC_{LM}\) to the LEMF.
6. The LEMF verifies this credential (_Check \(VC_{LM}\)_) to ensure that it is valid (not expired or revoked), issued by the LEA (checking the digital signature of the credential), and its claims satisfy the access control policy. Whether all these checks are successful, the LEMF grants [PERSON] access to the interception data by a procedure they agree on (typically, data are sent via a secure channel or a storage device).  
The above steps are summarized by the sequence diagram in Figure 2.  
Figure 1: Sequence diagram of the Setup in the LI procedure.
## 6 Stochastic Analysis
### Stochastic Analysis
The mean resistances from the EBM and their deviations compared to the IDA results are summarized in Table 4. The maximum deviation is \(-3.17\%\) in Case A and much smaller deviations are found for the other two cases, which shows that the EBM has a good performance to predict the dynamic resistance, in addition to when uncertainties of variables are taken into account. In order to better compare EBM and IDA results, the resistances and corresponding displacements are compared in Figures 19 and 20, respectively. Figure 19a-c show the comparisons of the resistances between the EBM and IDA for Cases A, B, and C, respectively. It can be seen that the data points are located along the line y = x, which means that similar results are obtained between the EBM and IDA. Moreover, coefficients of determination (\(R^{2}\)) are calculated to be 0.67, 0.93, and 0.92 for the three cases, respectively. Apparently, the deviation in Case A is larger (\(R^{2}\) = 0.67) as the dynamic response is not a SDOF oscillation, as discussed before. In general, the IDA results are found to be slightly larger as the assumptions (e.g., damping effect) discussed in Section 1 will result in a higher resistance \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Case A**} & \multicolumn{2}{c}{**Case B**} & \multicolumn{2}{c}{**Case C**} \\ \cline{2-7}  & **Mean** & **St.D.** & **Mean** & **St.D.** & **Mean** & **St.D.** \\ \hline R\({}_{\text{pushdown}}\) [kN/m] & 38.7 & 2.4 & 41.4 & 2.4 & 41.3 & 2.4 \\ R\({}_{\text{IDA}}\) [kN/m] & 34.7 & 2.1 & 37.6 & 2.2 & 37.4 & 2.2 \\ R\({}_{\text{EBM}}\) [kN/m] & 33.6 & 2.1 & 37.2 & 2.1 & 36.9 & 2.1 \\ (R\({}_{\text{EBM}}\)–R\({}_{\text{IDA}}\))/R\({}_{\text{IDA}}\) & \(-3.17\%\) & – & \(-1.06\%\) & – & \(-1.34\%\) & – \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean value, standard deviation, and deviation of the ultimate load–bearing capacity. Figure 17: Histograms and PDFs of resistances for stochastic pushdown and IDA analyses: (**a**) Case A; (**b**) Case B; and (**c**) Case C.  
compared to when using the EBM. This means the EBM provides a conservative result for resistance assessment. Figure 20a-c show the comparisons of the corresponding displacements between the EBM and IDA for Cases A, B, and C, respectively. Additionally, in this case, the data points are located along the line y = x, which indicates a good agreement between the EBM and IDA. However, a larger variation is observed for the displacement responses as the response varies fast in the highly non-linear stage (see the load-displacement curves in Figure 15). This can also be verified from the coefficients of determination (\(R^{2}\)), which are  
Figure 19: Comparison of the resistances between the EBM and IDA: (**a**) Case A; (**b**) Case B; and (**c**) Case C.  
Figure 20: Comparison of the corresponding displacements between the EBM and IDA: (**a**) Case A; (**b**) Case B; and (**c**) Case C.  
calculated to be 0.77, 0.74, and 0.80 for the three cases, respectively.
## 2 Results and Discussion
### Influence of terrain's slope  
With regards to the dependence of the DSM's vertical accuracy and the terrain's slope, for both the test sites we observed an increase of residuals as the terrain's slope increased. Referring to LE90, for Mausanne les Alpilles it was between 4.9 m and 8.7 m for slopes lower than 20%, between 13.7 m and 25.8 m for slopes included in 20% and 40% and between 20.0 m and 30.7 m slopes greater than 40%. For Salon de Provence our investigation assessed a value between 4.7 m and 8.4 m for slopes lower than 20%, between 6.8 m and 25.5 m for slopes included in 20% and 40% and between 9.2 m and 32.3 m slopes greater than 40%. Tests clearly show higher slope corresponding to lower elevation accuracy. The reason for this may be due to the presence of more errors on slopes and, above all, to the use of different interpolation methods for the generation of Cartosat-1 DSM grids and reference DSMs/DTMs grids from the original matched or measured points.
## 2 Materials and Methods
### Analysis
#### 2.4.3 Step 3--Habitat Assessment Approach and Contribution of VHR EO Data  
This step explored the use of VHR EO data, via visual interpretation (manual) and supervised image classification (automated). This VHR-based habitat assessment was performed at the three intensive study sites by combining:  
* prior expert knowledge about the studied species
* either or both visual interpretation and automated classification of features of the VHR imagery
* species occurrence georeferenced records and OS Open Rivers (only for Dragonfly and Damselfly species).  
The advantage of very high spatial resolution (between 0.7-4 m in this study) was to unlock rich information for the species by distinguishing pattern, shape, texture, shadows, and colours (see Figure S1) and to reveal observable detail about the habitat(s) such as field boundary characteristics (e.g., hedgerows and field margins), agricultural management practices, ditches, woodland canopy structure, type of woodlands, continuity of the habitat over time, the interconnectedness of different types of habitats, etc. Patterns, shapes, texture, shadows, and colours were discriminated in \"true colour\" bands and translated into key features (see Table S5 [107] and Figure S1).
## 5 Performance Analysis
### Performance Analysis on Gas Monitoring Application  
The results of the existing early-warning gas leakage monitoring system [41], gas detection method [42], deep learning feature engineering-based approach [43], fast operando monitoring technique [44], optically powered and safe gas monitoring scheme [45], and gas distribution pipeline network model [46] are discussed with the fire, smoke, and gas leakage detection dataset. The URL of the dataset is given as [75]. In total, 500 sensor nodes are deployed to conduct the simulation in gas monitoring.  
Figure 18: Overall performance analysis of error rate.  
Figure 21 presents the Throughput (\(T\)) in Equation (9) of six gas monitoring methods across varying asset data points, ranging from 100 to 1000. Specifically, at 400 gas data points, the \(T\) values for the exiting approaches are 25 kbps, 28 kbps, 38 kbps, 48 kbps, 51 kbps, and 60 kbps, respectively. As the number of data points increases, \(T\) correspondingly rises. Notably, the early-warning gas leakage monitoring system demonstrates a higher \(T\) compared to the other methods. This is attributed to the use of Tunable Diode Laser Absorption Spectroscopy (TDLAS) gas sensors, which enhance detection efficiency due to their compact size and precision. Additionally, the system benefits from real-time data processing and storage on a cloud platform, further contributing to higher \(T\). The early-warning system shows improvements in \(T\) by 13%, 40%, 47%, 51%, and 58% compared to other approaches.  
Figure 19: Measurement analysis of network lifetime.  
Figure 20: Overall performance analysis of network lifetime.  
Figure 22 illustrates the \(T\) performance of six distinct gas monitoring methods. The \(T\) values recorded are 28 kbps, 32 kbps, 46 kbps, 52 kbps, 55 kbps, and 64 kbps for existing approaches. These results show that while the early-warning gas leakage monitoring system achieves competitive performance, the Gas Distribution Pipeline Network Model delivers the highest \(T\) among the methods. Despite this, the early-warning gas leakage monitoring system demonstrates improved efficiency compared to several other approaches, indicating its effectiveness in enhancing gas leakage detection and real-time monitoring capabilities.