esa-sceva/satcom-chunk-collection huggingface dataset:
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 6 Benefits of CR-LPWAN
### Benefits to IoT Application Areas  
#### 6.2.1 Smart Grids  
Future energy production and supply networks will be based on the concept of smart grid networks (SGN). In addition to being more sophisticated than traditional energy supply methods, SGNs adopt complex communication networks in order to control and manage energy supply. In this use-case, SGNs use smart metering concepts to manage consumer demand and supply. Consequently, there exists constant communication between energy meters at consumer sites and at control facilities managed by the energy utility company. The network architecture for SGNs consists of three layers namely Communication between smart meters and devices in the smart home called the home area network (HAN);  
Communication between HAN and a network gateway called the field area network (FAN);  
Communication between different HANs called the neighborhood area network (NAN)  
CR-LPWAN systems can be deployed in FANs and probably in NANs. Since network gateways are located at great distances from most households, LPWANs can be used for long-range communication. CR in LPWAN can be beneficial under many conditions, for example, it could help to avoid interference between HANs, FANs, and NANs by adopting different frequency bands for each network. In this case, the NAN can be allocated white spaces in the VHF to enable long distance transmission since signals in the low VHF bands propagate much farther in space. CR-LPWANs could also provide increased bandwidths to ensure that utility companies query a large number of energy meters within larger communities at greater transmission speeds.  
In the literature, we found some classic use of CR in smart grids, for example, in [46] a CR system architecture, algorithms and a hardware testbed were developed for SGN. They optimized the computing power and response latency of CR-SGNs. It was demonstrated that CR-SGN was able to recover data from simultaneous smart meter transmissions in the presence of strong wideband interference. Similarly, in [89], an energy harvesting approach was proposed for resource-constrained IoT devices. The node architecture of the energy harvesting CR comprised a power provisioning unit (PPU), an ultra-low power processing unit (ULPP), and an ultra-low power communication unit (ULPC). In the PPU, energy is harvested, rectified, regulated, and used to supply the ULPP and ULPC. The ULPP conducts CR SS, modulation, coding, and smart grid related security processes. The ULPC comprises the communication front-end including a demodulator, decoder, and other CR functions. The architecture in [89] adopts the CREN scheme (recall Section 5.3).
# Short-Term Power Load Forecasting Using an Improved Model Integrating GCN and Transformer
## 4 Analysis of Experimental Results
### Robustness Analysis  
To test the robustness of the model, we adopted the fixed-proportion generation method to generate outliers on the three datasets to simulate real data for testing the model effect.  
Table 6 presents the results of the robust analysis conducted by our model using three datasets. It can be found that when noisy inputs are introduced in the current year, the model can maintain similar predictive performance compared to when non-noisy inputs are used. Figure 19 shows the robust experimental fitting graph of the model based on the ETTh1 dataset. To have a clear observation of the fitting situation, we selected an interval of 1000 to 1500, where the displayed outlier distribution is relatively dense. In the figure, we can see that the model also has a good fit when facing outliers.  
Figure 18: _Cont._Figure 18: Graphs showing the results of the ablation experiment indicators for the three datasets, listed as (**a**) ETTh1; (**b**) ETTm1; and (**c**) Australian electricity load.  
Figure 19: Graphs of robust analysis results for the ETTh1 dataset, listed as (**a**) 1-step, (**b**) 3-step, (**c**) 6-step, and (**d**) 12-step.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Metrics**} & \multicolumn{4}{c}{**Predicted Length**} \\ \cline{3-6}  & & **1** & **3** & **6** & **12** \\ \hline \multirow{3}{*}{ETTh1} & RMSE & 1.800 & 1.963 & 2.301 & 2.567 \\ \cline{2-6}  & MAE & 0.872 & 0.909 & 1.327 & 1.589 \\ \cline{2-6}  & R2 & 0.932 & 0.924 & 0.868 & 0.755 \\ \hline \multirow{3}{*}{ETTh2} & RMSE & 0.533 & 0.617 & 0.728 & 0.981 \\ \cline{2-6}  & MAE & 0.383 & 0.472 & 0.590 & 0.803 \\ \cline{2-6}  & R2 & 0.966 & 0.959 & 0.954 & 0.931 \\ \hline \multirow{3}{*}{Australian Electricity Load} & RMSE & 216.247 & 401.082 & 665.439 & 892.131 \\ \cline{2-6}  & MAE & 193.460 & 314.960 & 582.718 & 719.004 \\ \cline{2-6}  & R2 & 0.973 & 0.948 & 0.836 & 0.682 \\ \hline \end{tabular}
\end{table}
Table 6: Robustness analysis metrics of the ETTh1 dataset.
# FL-LoRaMAC: A Novel Framework for Enabling On-Device Learning for LoRa-Based IoT Applications
## 2 Related Work
The system collects data using the ECG sensors and transmits the collected data to the fog layer using LoRa communication. The fog layer uses deep learning to perform the classification and sends the information over a 4G cellular network to be analyzed by the doctor. Although the article claims that the data rate of LoRa could be acceptable for the transmission of signals such as an ECG, they did not use real-time data transmission in their system. They used an existing dataset [21] for training and testing purposes. At the fog layer, the system uses two models: one to perform the analysis in the time domain and another in the frequency domain. The outputs from both models were merged to obtain more concrete decisions. Their preliminary results show that the proposed system is capable of identifying abnormal heart rhythms. The majority of studies in the field have focused on improving AI models and preparing data for analysis. However, these studies typically assume that the data are stored on a central server and the model is trained there. After training, the model is sent to other devices for making predictions. However, these studies have overlooked important issues related to network challenges during data collection and privacy concerns with centralized training. The network challenges encompass a wide array of issues, ranging from data transmission efficiency and latency to bandwidth limitations and reliability. Overlooking these challenges can negatively affect the overall performance and scalability of AI systems, hindering their real-world applicability. Additionally, the transfer and storage of substantial amounts of potentially sensitive data in a single location create vulnerabilities and increase the risk of privacy breaches.
# Comparative Study for Multi-Speaker Mongolian TTS with a New Corpus
## 3 MnTTS2 Dataset
### MnTTS2
#### 3.2.3 Audio Recording and Audio-Text Alignment  
Different from the MnTTS [16], we invited three native Mongolian-speaking annuncers to record the audio. Each announcer volunteered to participate and signed an informed consent form to agree to the data collection and use protocol. F1, F2, and F3 are three native Mongolian-speaking females, with F2 being a little girl and F1 and F3 being slightly older. All recordings were made in a standard recording studio at Inner Mongolia University. We chose Adobe Audition ([[https://www.adobe.com/cn/products/audition.html](https://www.adobe.com/cn/products/audition.html)]([https://www.adobe.com/cn/products/audition.html](https://www.adobe.com/cn/products/audition.html)), accessed on 16 November 2021) as the recording software.  
The collected and processed texts were divided into three parts without duplicate content. The three annuncers completed the recording according to their respective texts. During the recording process, we asked the announcer to keep a 0.3 s pause at the beginning and end of each audio segment, keep a constant distance between the lips and the microphone, perform a slight pause at the comma position, and perform an appropriate pitch boost at the question mark position.  
To ensure the quality of the recording data, we rechecked the recording data after completing the recording work. Specifically, we invited three volunteers to check each text against its corresponding natural audio. These volunteers were responsible for splitting the recorded audio file into sentences and aligning the split sentences with the text. The Mongolian text is represented by a Latin sequence, where each Latin word in the sequence becomes a word and each letter that makes up the word is called a character. Characters also include punctuation marks, such as commas (','), periods ('.'), question marks ('?'), exclamation marks ('!'), etc. Finally, we obtained about 30 h of speech data, which were sampled at 22.05 kHz with a sampling accuracy of 16 bit.
## 2 Data Preprocessing
### Baseline Removal  
Baseline removal is another important pre-treatment for signal enhancement. A conventional baseline removal process for spectral or time series data consists of three major steps [11]: (1) to determine baseline key points in spectrum, (2) to build a baseline model for the whole spectrum using the detected baseline points, and (3) to correct the signal by subtracting the baseline from original signal. Recently, some new algorithms have been developed, such as an adaptive learning algorithm for electrocardiogram (ECG) baseline removal[12] and a selective filter for ECG baseline correction [13].
# Multiple Object Tracking in Robotic Applications: Trends and Challenges
## 4 Evaluation and Discussion
However, the research utilizing LIDAR and RGB cameras show potential in robotics navigation and autonomous driving applications.
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 4 PADP Algorithm Description
### Network Architecture  
We consider the hierarchical underwater sensor network architecture shown in Figure 2. Lower layer sensor nodes collect sensed information and send them to surface gateway through middle layer relay sensor nodes. The gateways are responsible for transmitting the information back to a data center usually located onshore.. Each sensor node will be equipped with a payload, so that it will stay in a certain depth of the water. The hierarchical structure is formed with different kinds of payload. (Please note that we only consider sensor nodes in layer 1 in this work because we focus on surface gateway deployment.)  
Such a network consists of three types of nodes:  
* _Sinks_: control stations on the shore or aboard a boat, directly connected to all surface gateways through radio communication.
* _Surface gateways_: hybrid nodes associated with buoys, which communicate with underwater sensor nodes using acoustic communication and sinks via radio communication.
* _Underwater sensor nodes_: ordinary sensor nodes organized in layers at various depths to collect environmental information and forward it to surface gateways.
## 3 Results and Discussion
### Liquid Injection  
Results obtained from this extraction method for all substrates studied are shown in Figure 2. The peak areas obtained of the standard volatile 1-BO and volatiles emitted by C4 such as DMNB, cyclohexanone, and 2E1H suggest that volatiles are retained distinctly by each substrate.  
Cardboard showed the largest peak areas for DMNB and 1-BO which suggests that it has an excellent retention capacity for these volatiles. All substrates retained DMNB from C4 except glass. The largest peak area for cyclohexanone was obtained for wood which also showed good retention of DMNB and 1-BO. No peak for cyclohexanone was found for cotton and glass. 2E1H was not found to be extracted from any of the substrates  
Figure 2: Comparison of standard odor compound 1-bromooctane and volatiles emitted from C-4 as extracted from substrates of interest by liquid injection.  
studied using liquid injection. All substrates exhibited large peak areas for the standard odor compound, 1-BO. These results suggest that porous substrates such as cardboard, cotton and wood have higher retaining capacity for volatiles than non-porous substrates such as sheet metal and glass. In contrast to non-porous substrates that have a single-layer structure, porous substrates composed of cellulose such as cotton, cardboard, and wood have a layered structure that increases the available surface area and thereby bring about an increase in sorption of volatiles.
## 4 ME-SSA Receiver Design
### Preamble Searcher
#### 4.2.1 Coherent PS  
In a GEO satellite system scenario and L-Band carrier frequency, the Doppler frequency is negligible, therefore, the maximum frequency uncertainty is mainly due to the local oscillator drift. Assuming a 1 pulse per million (ppm) frequency drift at 1.5 GHz carrier frequency, the maximum frequency uncertainty is \(f_{max}=1.5\) kHz. The number of frequency hy  
Figure 4: ME-SSA receiver block scheme executed on the generic L_th_ GPU (i.e., GPU #L).  
pothesis is set in a way to guarantee a correlation loss (evaluated as \([\sin(\pi\delta fT_{\alpha th})/(\pi\delta fT_{\alpha th})]^{2}\), where \(\delta f\) (Hz) is the frequency offset error and \(T_{\alpha th}\) (s) the coherent integration time [15]) of less than 0.5 dB (typical value), given the coherent integration time. Considering that the chip rate is \(R_{c}=160\) kchips and the preamble length is 96 symbols, the number of frequency hypotheses ranges between 77 and 1239, depending on the spreading factor. Indeed, for a fixed chip rate, the higher the spreading, the longer the preamble duration and hence coherence time, thus, the higher the number of frequency hypotheses to have the same correlation loss. These values do not cause any memory/processing limitation at the receiver. The actual coherent PS implementation is based on the cross-correlation via fast fourier transform (FFT) to efficiently test simultaneously more delay hypotheses for each frequency hypothesis. The block scheme is depicted in Figure 5.
## 4 Case Study Results
### The Core Elements of AeroLogic's Business Model
#### 4.4.1 AeroLogic's Freighter Aircraft Type and Fleet Requirements
\begin{table}
\begin{tabular}{c c} \hline \hline Wingspan & 64.8 m \\ Length & 63.7 m \\ Maximum Payload & 106 tons \\ Maximum Landing Weight & 261 tons \\ Maximum Take-Off Weight & 347 tons \\ Engines &
\begin{tabular}{c} GE90-110B1L \\ \end{tabular} \\ Main Deck ULD Configuration 1  27 AMX Containers \\ Lower Deck ULD Configuration & 32 LD3 Containers \\ \hline \hline \end{tabular} Note. 1: Aircraft unit load devices (ULDs) are pallets and containers which are used to carry air cargo, mail and passengers’ baggage on wide-body passenger and dedicated freighter aircraft [131]. Source: [125].  
\end{table} 
Table 3: Key specifications of the AeroLogic Boe B777-200 LRF aircraft. subsequently sold by Deucallion Capital VII to the KG Fund, Deutsche Capital Management, who will lease the aircraft to AeroLogic for a period of 10 years [136]. AeroLogic has followed a similar aircraft fleet strategy to both the combination airlines operating freighter aircraft and the dedicated all-cargo airlines, such as Cargolux and Nippon Cargo Airline (NCA) in that it operates a homogeneous freighter fleet. Some cargo airlines purchase their aircraft direct from the manufacturers (Airbus or Boeing Commercial Airplanes), lease them from a leasing company, or acquire passenger-to-freighter converted aircraft. As noted, AeroLogic decided to operate new Boeing B777-200 LRF aircraft on a leased basis.
# Coordination of Congestion and Awareness Control in Vehicular Networks
## 4 Evaluation
### Scenario and Simulation Settings
In this study, we used a vehicular network simulator implemented over Matlab to accurately simulate the performance and efficiency of vehicular communications and networking protocols. The simulator implements different propagation models, radio access technologies, and communication and networking protocols. We have extensive validated our implementation under different configurations and scenario conditions.
## 4 Experimentation and Results
### Visualizations  
A feature map is produced as a result of the convolution operation being performed on the input by a convnet filter. To create an output volume, we apply a number of filters and stack the feature maps that are produced. Instead of looking at a single feature map, we have displayed multiple hidden feature maps in Figure 4 to show how the input is modified through subsequent layers. We exhibited eight VGG19 feature maps per layer for viewing purposes. As we move through different levels, there are several observations regarding the feature maps. Simple forms are picked up by the filters in the first layer. The hidden representation shows various cross-sectional anatomies of the brain, such as the hippocampus, cerebral artery, and different lobes. The majority of the information provided in the image is retained in the first layer feature maps, i.e., conv1 of block1. The first layers  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Accuracy** & **Specificity** & **Sensitivity** & **AUC** \\ \hline MobileNet [48] & 0.915 & 0.892 & 0.867 & 0.899 \\ \hline VGG19 [49] & 0.982 & 0.973 & 0.993 & 0.986 \\ \hline DenseNet201 [50] & 0.974 & 0.967 & 0.975 & 0.969 \\ \hline ResNet152 [51] & 0.966 & 0.957 & 0.968 & 0.959 \\ \hline EfficientNetV2S [52] & 0.975 & 0.988 & 0.969 & 0.973 \\ \hline InceptionV3 [53] & 0.952 & 0.947 & 0.962 & 0.942 \\ \hline NASNetLarge [54] & 0.926 & 0.899 & 0.901 & 0.911 \\ \hline Xception [55] & 0.959 & 0.944 & 0.966 & 0.954 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of transfer learning models using cropping technique.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ensemble Method** & **Models** & **Accuracy** & **Specificity** & **Sensitivity** & **AUC** \\ \hline \multirow{4}{*}{Simple Average} & M1 + M2 + M3 & 0.971 & 0.960 & 0.973 & 0.966 \\ \cline{2-6}  & M1 + M2 & 0.969 & 0.958 & 0.983 & 0.979 \\ \cline{2-6}  & M1 + M3 & 0.977 & 0.989 & 0.969 & 0.977 \\ \cline{2-6}  & M2 + M3 & 0.989 & 0.967 & 0.978 & 0.968 \\ \hline \multirow{4}{*}{Weighted Average} & M1 + M2 + M3 & 0.941 & 0.942 & 0.922 & 0.936 \\ \cline{2-6}  & M1 + M2 & 0.976 & 0.961 & 0.984 & 0.978 \\ \cline{1-1} \cline{2-6}  & M1 + M3 & 0.969 & 0.964 & 0.936 & 0.952 \\ \cline{1-1} \cline{2-6}  & M2 + M3 & 0.981 & 0.970 & 0.925 & 0.947 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of ensemble networks using two different methods.  
in CNN architectures typically serve as edge detectors. The feature maps appear to be an abstract depiction of the original when we delve deeper into the network. Despite being less visible to humans, they still encode useful features. As we dig deeper, the feature maps become sparser because the filters pick up fewer features.
## 3 Results and Discussion
### Comparison of Thermal Stabilities of Common PR and B-Containing PR  
Combined with the above analysis, the optimal process parameters (molar ratios of formaldehyde/phenol and potassium borate/phenol were 1.8 and 0.2, and reaction time was 13 h) were carried out to synthesize B-containing PR. The thermal stabilities of common PR and B-containing PR were investigated by TGA, and the TG curves of common PR and B-containing PR are exhibited in Figure 3. When the temperature is below 400 \({}^{\circ}\)C, the two kinds of PR show a similar trend with slight weight loss, in which curing and polycondensation reactions occur to release small solvent molecules. When compared to common PR, there was an additional downtrend at about 200 \({}^{\circ}\)C for B-containing PR, which might be a result of the excess reactant used to react and generate water [19; 25]. Notably, the two kinds of PR display a close temperature range for maximum weight loss within 400-800 \({}^{\circ}\)C, which should be ascribed to the unstable characterization of terminal benzene rings; weight loss still occurs for common PR when the temperature is larger than 800 \({}^{\circ}\)C, while the weight of B-containing PR is relatively stable [19; 26]. Finally, the char yield of B-containing PR is about 71%, which was about 2\(\sim\)3 times that of common PR, demonstrating that the incorporation of B elements could effectively improve char yield. The residual pyrolytic carbon of B-containing PR was observed by SEM; elemental area scanning under SEM was also carried out to confirm successful introduction of the B element, as presented in Figure S1 (See Supplementary Materials for details). It is obvious that the residual pyrolytic carbon of B-containing PR is in the form of nanoparticles with average size of about 200 nm, while the nanoparticles agglomerate easily. From the elemental area scanning images, the composition of residual pyrolytic carbon of B-containing PR is found to contain C, O, and B, thus confirming the successful incorporation of B element in PR.
## 4 Verification and Analysis of Solution
### Consolidation Analyses
Conversely, as consolidation progresses, the rate of soil consolidation becomes progressively influenced by the permeability coefficient \(k_{vi}\), resulting in the gradual decrease in the \(U_{\rm s}\) value with increasing \(N_{\sigma}\) values. Figure 11: Influence of \(N_{\sigma}\) value on consolidation degree curves (\(c_{ci}/c_{ki}=1.5\)). Figure 12 examines the influence of interface parameters on the excess pore water pressure. It is evident that within the internal and boundary regions of the dual-layered foundation, the excess pore water pressure decreases as the interface parameters increase. For the dual-layered foundation, the interface parameters at the top surface of the soil layers have a more significant impact on the excess pore water pressure in the upper layer, while their influence in the lower layer is relatively minor. Furthermore, in contrast to results under traditional boundary conditions, the excess pore pressure at the top surface is no longer consistently zero under continuous drainage boundary conditions. Under the conditions where the top surface interface parameter is set to 7 and the bottom surface interface parameter is set to 5 for the dual-layered foundation, Figure 13 analyzes the impact of proportional variations in the permeability ratio (\(a\)) and the compressibility ratio (\(b\)) on pore water pressure. The curve for excess pore water pressure in the upper layer is steeper, indicating faster dissipation due to superior drainage at the top surface. Additionally, as \(a\) and \(b\) values decrease proportionally, excess pore water pressure decreases, suggesting that foundation with higher upper layer permeability and lower layer compressibility experience faster consolidation. Figure 12: Influence of interface parameter on excess pore water pressure. Figure 2:0 Influence of permeability ratio (\(a\)) and compressibility ratio (\(b\)) on excess pore water pressure. From Figure 15, it is evident that when the interface parameters are set to their maximum values, the pore pressure computed using the finite difference method in this paper closely approximates the results obtained through finite element simulation at various depths, particularly in the later stages, where they nearly coincide. This suggests that the approach presented in this paper holds practical significance and, through further development, can be integrated into ABAQUS to complement existing methodologies. In addition, we also compared the consolidation degree data obtained from experiments, as shown in Figure 2:1 The experimental data are mainly based on the work of [PERSON] [23], as shown in Figure 2:2  
Using the soil parameters from Table 2, this study compares the results with the single-layer soil consolidation test data obtained by [PERSON] et al. [23] to further validate the validity of the findings. It can be observed that the results in this study match very closely with the experimental data under the conditions of \(\alpha=\beta=100\) and \(C_{\mathrm{c}}/C_{\mathrm{k}}=0.5\), thereby confirming the correctness of this study's findings.