esa-sceva/satcom-chunk-collection huggingface dataset:
# Maximal Ratio Combining Detection in OFDM Systems with Virtual Carriers Over V2V Channels
## 2 System Model
#### Virtual Carrier Assignment  
Figure 1 shows the proposed modification of the conventional OFDM transmitter by incorporating a block for allocating the zero vector \(\mathcal{Z}^{VC}\) for the use of VC. It is assumed that the active carriers are placed on the subcarrier index belonging to the set \(\psi\), and the elements of \(\beta^{k}\) can be expressed as  
\[\beta^{k}[n]=\left\{\begin{array}{ll}x^{k}[n],&\text{if}\quad n\in\psi,\\ 0,&\text{if}\quad n\in\overline{\psi},\end{array}\right. \tag{9}\]  
where \(x^{k}[n]\) denotes a sample of the OFDM symbol in the DT of (3), and \(\overline{\psi}\) denotes the complementary set of \(\psi\). Considering the use of VC, Equation (3) can be rewritten as  
\[\mathbf{y}^{k}=\mathbf{H}^{k}\beta^{k}+\mathbf{w}^{k}. \tag{10}\]  
The number of virtual carriers, \(N_{VC}\), is directly related to the degree of diversity, \(\
u\), being used in the proposed system:  
\[\
u\propto N_{VC} \tag{11}\]  
Due to the inclusion of VC, there are only \(N_{d}=N/\
u\) active carriers, where \(\
u=2^{e}\) and \(\varrho=\{0,1,2,...,log_{2}(N)-1\}\). Figure 2 illustrates an example of the 802.11 p frame with virtual carriers for the case of \(\
u=2\).
## 4 Simulation Setup
In the first five blocks, the maximum pooling layer is used to extract the meaningful features, and the average pooling layer is used in the last block to avoid erasing the details of the previous feature map. Finally, an FC layer is connected to the Softmax layer to output the classification decision. The network structure is shown in Figure 8 below. Figure 6: Spectrogram of the example frames. Weak turbulence with SNR = 10 is as an example above. Figure 7: Constellation diagram of (**a**) OOK, (**b**) BPSK, (**c**) QPSK, and (**d**) 16 QAM. The parameters of feature matrix size and stride size of major layers are listed in Table 1 below. After signal preprocessing, the synthesized signal is put into the CNN network whose structure is described in Section 2. Next, we use SGDM solver with the small batch size of 256. We set the maximum number of rounds to six, as more rounds will not provide further training advantage. The initial learning rate is set to 0.3, and the dropout rate is 0.6. After every four rounds, the learning rate will be reduced by a factor of 0.3. Specific parameters are listed in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Name** & **Size** & **Stride Size** \\ \hline Input & \(2\times 1024\times 1\) & \(1\times 8\) \\ Convolution 1 & \(2\times 1024\times 16\) & \(1\times 8\) \\ MaxPooling 1 & \(2\times 512\times 16\) & \(1\times 2\) \\ Convolution 2 & \(2\times 512\times 24\) & \(1\times 8\) \\ MaxPooling 2 & \(2\times 256\times 24\) & \(1\times 2\) \\ Convolution 3 & \(2\times 256\times 32\) & \(1\times 8\) \\ MaxPooling 3 & \(2\times 128\times 32\) & \(1\times 2\) \\ Convolution 4 & \(2\times 128\times 48\) & \(1\times 8\) \\ MaxPooling 4 & \(2\times 64\times 48\) & \(1\times 2\) \\ Convolution 5 & \(2\times 64\times 64\) & \(1\times 8\) \\ MaxPooling 5 & \(2\times 32\times 64\) & \(1\times 2\) \\ Convolution 6 & \(2\times 32\times 96\) & \(1\times 8\) \\ AveragePooling & \(2\times 32\times 96\) & \(1\times 2\) \\ FullConnected & \(1\times 6144\) & \\ Output & \(1\times 4\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Network parameters of major layers. Figure 8: Simplified network structure with feature matrix size and stride size.
## 4 Physical Audio Features Extraction Techniques
### Frequency Domain Physical Features
#### 4.2.6. Spectrum Shape-Related Physical Frequency Features
It has been used for improving the representation and recognition of environmental sounds ([PERSON] _et al._[96]) and music mood classification ([PERSON] _et al._[86]). * **Entropy:** this is another measure that describes spectrum uniformity (or flatness), and it can be computed following different approaches (Shannon entropy, or its generalization named Renyi entropy) and also in different subbands (see [PERSON] and [PERSON] [102]). It has been used for automatic speech recognition, computing the Shannon entropy in different equal size subbands, like in [PERSON] _et al._[107]. * **Octave-based Spectral Contrast:** also referred to as OSC, it is defined as the difference between peaks (that generally corresponds to harmonic content in music) and valleys (where non-harmonic or noise components are more dominant) measured in subbands by octave-scale filters and using a neighborhood criteria in its computation ([PERSON] _et al._[108]). To represent the whole music piece, mean and standard deviation of the spectral contrast and spectral peak of all frames are used as the spectral contrast features. OSC features have been used for music classification ([PERSON] _et al._[77], [PERSON] _et al._[12], [PERSON] _et al._[109]) and music mood classification, as in [PERSON] _et al._[86]. * **Spectral slope:** this is a measure of the spectral slant by means of a simple linear regression ([PERSON] _et al._[42]), and it has been used for classification purposes in speech analysis applications ([PERSON] _et al._[110]) and speaker identification problems ([PERSON] _et al._[111]). * **Spectral skewness and kurtosis:** spectral skewness, which is computed as the 3 rd order moment of the spectral distribution, is a measure that characterizes the asymmetry of this distribution around its mean value. On the other hand, spectral kurtosis describes the flatness of the spectral distribution around its mean, and its computed as the 4 th order moment (see [PERSON] _et al._[34]). Both parameters have been applied for music genre classification ([PERSON] _et al._[112]) and music mood classification ([PERSON] _et al._[86]).
## 3 Data Processing and Parameter Determination  
### Engineering Overview and Data Acquisition  
The Huanglong Commercial City project is located in the central-western part of the Wenzhou Basin, where the geological structure is relatively stable. Based on the drilling data, the site is covered with Quaternary loose sediments with a thickness of 37.60 to 55.00 m. The surface layer consists of miscellaneous fill soil, which is man-made fill. The upper layer, with a thickness of 20.00-28.50 m, is silt-soft soil, which is marine sediment; its era is the Holocene of the Quaternary period (Q4). The middle part consists of fluvial sediments, which are clayey soils; its era corresponds to the Middle Pleistocene stratum (Q3). The lower part consists of slope residual deposits, which are gravelly silty clay  
Figure 2: SSA-LSTM neural network flowchart.  
layers; its era corresponds to the Early Pleistocene stratum (Q1). The underlying rock layer is the Upper Jurassic (J\({}_{3}\)g) Gaowu Formation of extrusive volcanic rock, with the rock type mainly being tuff. The total land area of the project is 175,562 square meters. The site was originally the Huanglong Commercial City, which is the largest commodity trading market in southern Zhejiang and northern Fujian. The rate of land subsidence in the study area lacks a clear pattern, making it more challenging to accurately predict subsidence. Using the ground subsidence data from monitoring points in the research area between 4 January 2023 and 26 March 2023, this study established a land subsidence prediction model. By combining the prediction with existing ground subsidence data, it is possible to more accurately understand the subsidence trend of the monitoring point and its surrounding areas, offering a valuable reference for subsequent subsidence monitoring and mitigation strategies.
## 5 Results and Analysis
### Evaluation Criteria  
The most commonly used evaluation criteria for the target detection task are Precision (P), Recall (R), F1-Score, mean average precision (mAP), and Detection Rate (fps, number of frames recognized in one second) [50]. When performing transverse crack detection, transverse cracks are defined as positive samples, while non-transverse cracks are defined as false samples (class of disturbances). The samples can be classified into four types on the basis of the combination of true and predicted values [50]:  
* True positives (TP): the positive sample is correctly identified as a positive sample (i.e., the transverse crack image is correctly identified);  
Figure 8: Calculation of the IOU.  
* True negatives (TN): negative samples are correctly identified as negative samples (i.e., the non-transverse crack images are correctly identified as non-transverse cracks);
* False positives (FP): negative samples are incorrectly identified as positive samples (i.e., the non-transverse crack images are incorrectly identified as the transverse cracks);
* False negatives (FN): positive samples are incorrectly identified as negative samples (i.e., the transverse crack images are incorrectly identified as the non-transverse cracks). Therefore, the above evaluation indicators can be described as follows. Precision: it is used to measure the accuracy of the model for positive samples and indicates the ability of the classifier to discriminate between positive and negative samples, which can be defined as \[\mathrm{P}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}}\] (3)  
Recall: it is used to measure the completeness of a classifier for positive samples and indicates the sensitivity or coverage of the classifier for positive samples, which can be defined as \[\mathrm{R}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}}\] (4)  
F1-Score: the summed average of the Precision and Recall metrics, defined as  
\[\mathrm{F1}=\frac{2\times\mathrm{P}\times\mathrm{R}}{\mathrm{P}+\mathrm{R}} \tag{5}\]  
AP: the performance of a model is measured by examining both Precision and Recall metrics, representing the performance of the model in identifying a particular category, with the following expression:  
\[\mathrm{AP}=\int_{0}^{1}\mathrm{P(R)}\mathrm{dR} \tag{6}\]  
mAP: the mean value of AP values for each category, reflecting the average classification performance of the model for all categories, defined as  
\[\mathrm{mAP}=\frac{\sum_{i=1}^{n}\mathrm{AP}_{i}}{n} \tag{7}\]
## 4 Network Model
### Single Event Scenario  
Assume that only one event happened at any moment. The ENs send queuing messages (see Table 2 & 3) to SS one by one (see Figure 1). Finally, SS get all the information of (N\({}_{SS}^{j}+N_{MS}^{j}\)). In order to save energy and take a full advantage of MS's existence, the network is pursuing a perfect performance combination of SS and MS. As a result, we set a threshold distance \(d_{SS-threshold}^{j}\) for SS based on its residual energy \(E_{SS}^{
## 5 Experiments and Results
### Complexity Comparison  
To further assess the trade-off between performance and model complexity, we conducted a comprehensive evaluation of the proposed ResNet-TF and its ablation variants. As presented in Table 5, ResNet-TF slightly increases the number of parameters (from 9.4786 M to 9.6527 M) and multiply-adds (from 2.9814 G to 2.9880 G) compared to the base  
Figure 7: Confusion matrix of the proposed ResNet-TF model on the UN15 dataset.  
line ResNet-50, resulting in a negligible change in training time (1:47:51 \(\rightarrow\) 1:47:11). Despite the modest increase in complexity, the ResNet-TF model achieves a significant improvement in classification accuracy, reaching 85.19%, compared to 81.24% with the baseline.  
To validate the effectiveness and novelty of the TF attention mechanism, we designed ablation experiments by separating the TF-attention module into two independent branches: Temporal Attention (ResNet-T) and Frequency Attention (ResNet-F). These variants achieved 84.93% and 82.96% accuracy, respectively, both lower than the full ResNet-TF model. The comparison reveals that applying attention along only one axis (time or frequency) is insufficient to fully model the complex time-frequency dependencies inherent in urban acoustic scenes. Temporal attention improves recognition of transient or rhythmic sound patterns but lacks sensitivity to spectral variations, while frequency attention enhances important frequency bands but fails to capture temporal dynamics. By integrating both, ResNet-TF effectively emphasizes important time-frequency regions while suppressing irrelevant noise, leading to superior discriminative capability.  
Moreover, the complexity of ResNet-TF remains practical: the parameter count increases by only -1.8%, and multiply-adds increase by less than 0.3%. These findings demonstrate that the proposed TF-attention provides a compelling balance between accuracy and efficiency, making it well-suited for deployment in real-world ESC tasks, especially under resource constraints. This enhanced representational capability, with minimal added computational burden, strengthens the practical applicability and novelty of our method.
# Performance Analysis of Two-Way Satellite-Terrestrial Relay Network with SWIPT
## 2 System Model and Selective PNC
### Selective PNC  
In the conventional PNC protocol, the mobile terminal simultaneously broadcasts the signal \(s_{R}\) to both the satellite source node and the user source node during the second time slot. However, in selective PNC protocol, to outperform conventional PNC protocol in the sense of average end-to-end throughput, we introduce the single node detection protocol. The mobile terminal detects messages from the superimposed signal as conventional PNC protocol or detects the message from the better channel by treating the other message as part of the noise. Hence during the second time slot, the mobile terminal can decide according to the instantaneous throughputs for current channel state information as shown in Figure 2.  
By employing the SPNC protocol, during the first time slot, the mobile terminal relay compares the value of instantaneous throughput \(Th_{pmc}\) with \(Th_{snd}\) after receiving the signals \(s_{1}\) and \(s_{2}\). If \(Th_{pmc}>Th_{snd}\), the mobile terminal relay will detect \(\hat{m}\), and broadcast its corresponding BPSK signal \(s_{R}\) to both satellite source node and user source node as PNC protocol. If \(Th_{pmc}<Th_{snd}\), the mobile terminal will detect the message from the better channel and broadcast its corresponding BPSK signal \(s_{i}\) instead of \(s_{R}\) to both the satellite source node and the user source node.  
During the second time slot, the satellite source node and user source node receive the broadcast signal from the mobile terminal relay. If the broadcast signal is \(s_{R}\), the satellite source node and user source node will detect \(\widetilde{m}_{2}\) and \(\widetilde{m}_{1}\) as PNC protocol, respectively. If the broadcast signal is \(s_{1}\), the user source node will split it into two parts with ratio \(\rho\), where the portion \(\sqrt{\rho}y_{2}\) is used for information decoding and the remaining \(\sqrt{1-\rho}y_{2}\) for RF  
Figure 2: Description of selective PNC protocol. During the first time slot, the satellite source node and the user source node transmit messages \(S_{1}\) and \(S_{2}\), respectively. During the second time slot, the mobile terminal relay broadcast and forward \(S_{R}\) in PNC protocol, while broadcast and forward one of \(S_{1}\) and \(S_{2}\), according to the channel coefficient in SND protocol.  
energy harvesting. The received signal at \(S_{2}\) for decoding information and the harvested energy during the second time slot \((1-\beta)T\) can be respectively written as:  
\[y_{2,ID}=\sqrt{\rho}\Big{(}g\sqrt{P_{R}}s_{1}+n_{3}\Big{)}+n_{3,z} \tag{5}\]  
\[E_{S_{2},EH}=\eta(1-\beta)T(1-\rho)P_{R}|g|^{2} \tag{6}\]  
If the broadcast signal is \(s_{2}\), the satellite source node will detect \(\widetilde{m}_{2}\) as PNC protocol, and the user source node will split the received signal into two parts with ratio \(\rho_{0}\simeq 0\). The received signal at the user source node for RF energy harvesting can be written as:  
\[E_{S_{2},EH}\simeq\eta(1-\beta)TP_{R}|g|^{2} \tag{7}\]
## 3 Experiments
### Experimental Setup
#### 3.1.1 Experimental Scenarios  
The experimental scenarios included the following elements:  
1. Fixed Obstacles: Including metal walls, columnar equipment, and narrow passages, simulating the common equipment distribution in substations. These were primarily used to test the accuracy of robot path planning.  
Figure 6: The specific implementation process of the BBCN model.  
Dynamic Obstacles: Including randomly moving vehicles and people, simulating dynamic interference in industrial environments, and testing the model's dynamic obstacle avoidance ability.
3. Electromagnetic Interference Areas: Using virtual signal sources to simulate the electromagnetic interference caused by high-voltage equipment, leading to Beidou signal instability. This tested the model's emergency handling capabilities under weak- or lost-signal conditions.
4. Target Point Layout: Multiple target points were set in this scenario, covering areas of varying complexity to validate the model's robustness in different navigation tasks.  
All experiments were conducted under controlled and consistent environmental conditions to ensure comparability. The ambient lighting was maintained at approximately 500 lux. The electromagnetic interference was simulated using a signal generator, with \"Moderate\" and \"High\" levels corresponding to predefined, repeatable power spectral densities applied to the Beidou signal receiver.
## 4 Performance Evaluation of Improved SOSK-Means
### Datasets  
During the experiment, 42 datasets were considered. The datasets were grouped into two groups: 18 synthetically generated datasets and 24 real-life datasets. The characteristics of the two categories of datasets are presented in Tables 2 and 3, respectively.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multicolumn{1}{c}{**DE**} & \multicolumn{2}{c}{**FA**} & \multicolumn{2}{c}{**IWO**} & \multicolumn{2}{c}{**PSO**} \\ \hline
**Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** \\ \hline _Pop\_size_ & 25, 50, 100, 150 & _Pop\_size_ & 25, 50, 100, 150 & _Pop\_size_ & 25, 50, 100, 150 & _Pop\_size_ & 25, 50, 100, 150 \\ \hline _Kmin_ & 2 & _Kmin_ & 2 & _Kmax_ & 256 & _Kmin_ & 2 \\ \hline _Kmax_ & 256 & _Kmax_ & 256 & _MaxGen_ & 200 & _Kmax_ & 256 \\ \hline _MaxIt_ & 200 & _MaxGen_ & 200 & s & 5 & _MaxIt_ & 200 \\ \hline \(F\) & 0.8 & \(\beta_{0}\) & 2 & \(\epsilon\) & 2 & \(W_{1}\) & 1.00 \\ \hline _CRmax_ & 1 & \(\gamma\) & 1 & \(\frac{\Gamma}{1}\) & 0.5 & \(W_{2}\) & 0.99 \\ \hline _CRmin_ & 0.2 & & & \(\frac{\Gamma}{2}\) & 0.001 & \(\tau_{1}\) & 1.50 \\ \hline  & & & & \(\frac{\Gamma}{2}\) & 2.00 \\ \hline \hline \end{tabular} _Pop\_size:_ Population Size; _Kmin_: minimum number of clusters; _Kmax_: maximum number of clusters; _MaxIt_: number of iterations; _MaxGen_: Maximum number of Generations; _CR_: Crossover rate; _e_: variance reduction exponent; _s_: maximum number of seeds; \(W_{1}\): Inertial weight; \(W_{2}\): Inertial weight damping ratio; \(\beta_{0}\): attractiveness; \(\gamma\): light absorption; \(\tau_{1}\): personal learning coefficient. \(\tau_{2}\): global learning coefficient. \(\sum\limits_{1}\): initial value of standard deviation. \(;\frac{\Gamma}{2}\): final value of standard deviation.  
\end{table}
Table 1: The control parameters for competing metaheuristic algorithms.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multicolumn{1}{c}{**Datasets**} & \multicolumn{1}{c}{**Number of Data Objects**} & \multicolumn{1}{c}{**Dimension of**} & \multicolumn{1}{c}{**Number of**} & \multicolumn{1}{c}{**Dataset Types**} & \multicolumn{1}{c}{**References**} \\ \multicolumn{1}{c}{**Data**} & \multicolumn{1}{c}{**Clusters**} & \multicolumn{1}{c}{**Dataset Types**} & \multicolumn{1}{c}{**References**} \\ \hline  
\hline \end{tabular}
\end{table}
Table 2: The characteristics of the 18 synthetically generated datasets.
## 1 Introduction  
Due to an increasing global population, the use of liquid fuels, especially in the transportation sector, has grown. Reduction of fossil fuel resources, increasing greenhouse gas emissions, and increasing oil prices created the need to find sustainable, renewable, efficient, cost-effective, and low-emission alternatives [1,2]. One of the alternative fuel types is biodiesel, which was introduced as a suitable fuel instead of conventional diesel fuel for using in heating systems and specifically for diesel engines [3,4]. Chemically, biodiesel is a combination of fatty methyl ester acids that are produced by the reaction of diglycerol with alcohols in the presence of a catalyst along a process called transesterification [5,6].  
Biodiesel is made from bio-oils. The number of carbons in the carbon chain of diesel fuel is similar to vegetable oils (containing 14-18 carbons). Because of the structural properties of biodiesel, this resource can play an important role as a viable alternative to conventional (diesel) energy [7]. Generally, the production price of biodiesel is high, being almost twice that of diesel's price. The price of biodiesel production consists of two main components that are the price of the raw material and price of the biodiesel production process [8; 9]. Although, large amounts of oils, such as restaurant waste oil and animal fat, can be used to produce the biodiesel, the main problem with using these resources is that the transesterification reaction of some of these resources is very difficult. As a result, the cost of biodiesel production from these materials increase [10]. One of the main issues that play an important role in the final price of the biodiesel is the price of the oil that is used as the feedstock. The appropriate way to reduce the price of the produced biodiesel is the use of non-edible oils as feedstocks in the biodiesel production process, which reduces the price of finished product. So far, several researches have been done on the application of non-edible oils as the feedstock of biodiesel [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23], such as rapeseed [24], sea mango [25], rubber seed [26], mahua [27], _Hodgsonia macrocarpa_[28], karanja [29], moringa [30], palm [31], jatropha [32], and tree of heaven [33]. So, intensive research on bioenergy resources, including crops with less food importance, or non-edible, can help to decrease the greenhouse gas emission and control climate change. The ideal non-edible resources should be water and nutrient efficient, fast growing, have a high tolerance to environmental stresses, and low invasive potential [34; 35]. One of the non-edible resources that could be a novel resource for biodiesel is _Chrozzophora tinctoria_ seeds.
## 3 Measurement Results  
Figure 10 shows a die photograph of the proposed potentiostat ROIC. The circuit was implemented using a 0.18 \(\upmu\)m CMOS process, and the proposed potentiostat biasing circuit had an area of 330 \(\upmu\)m \(\times\) 374 \(\upmu\)m. Figure 11 shows the differential output voltage according to a glucose concentration of 2 mM to 20 mM. In Figure 11a, the differential voltage is 679 mV and 960 mV at concentrations of 2 mM and 20 mM, respectively, 8 s after sensing starting. The linearity is shown in Figure 11b. The coefficient of determination R\({}^{2}\) with a feedback resistor of 200 k\(\Omega\) was 0.9997.  
The input-referred noise results are shown in Figure 12. The input-referred noise at 1 Hz was 802.2 fA/\(\upmu\)Hz of the 200 k\(\Omega\) feedback resistor. The input-referred RMS noise from 0.1 Hz to 50 Hz was 11.2 pA\({}_{\text{RMS}}\).  
Table 4 summarizes the performance of the proposed potentiostat readout circuit with those of previous studies [11; 15; 16; 17; 18].  
Figure 11: FDDA output voltage with different glucose concentrations: (a) transient result; (b) linearity.  
Figure 10: Die photograph.