esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Prediction  
#### 2.3.1 Extreme Learning Machine  
Conventional neural network learning algorithms require setting a large number of artificial network training parameters [49]. By contrast, the extreme learning machine (ELM) algorithm can randomly initialize input weights, biases, and the number of hidden layer nodes and obtain the corresponding output weights by least squares. The entire learning is performed by a mathematical change without any iterations, which makes the ELM algorithm a compact yet effective learning algorithm [50]. The ELM algorithm process is as follows:  
\[\sum_{i=1}^{L}\beta_{\mathbf{i}}g\big{(}\mathbf{W_{i}}\cdot\mathbf{X_{j}}+b_{ i}\big{)}=\widetilde{\mathbf{y}}_{\mathbf{j}},j=1,\cdot\cdot\cdot,N \tag{19}\]  
Here, \(g(x)\) is the activation function (_sigmoid_ is chosen as the activation function in this paper), \(\mathbf{W_{i}}\) is the input weight, \(b_{i}\) denotes the bias of the \(i_{th}\) hidden layer cell, and \(\beta_{\mathbf{i}}\) indicates the output weights. In order to obtain \(\beta_{\mathbf{i}}\) with good results using the training sample set, the goal of the ELM algorithm is to minimize the error of the output:  
\[\sum_{j=1}^{N}\left\|\widetilde{\mathbf{y}}_{\mathbf{j}}-\mathbf{t_{j}}\right\|=0 \tag{20}\]  
There exists \(\beta_{\mathbf{i}}\), \(\mathbf{W_{i}}\), and \(b_{i}\), which makes:  
\[\sum_{i=1}^{L}\beta_{\mathbf{i}}g\big{(}\mathbf{W_{i}}\cdot\mathbf{X_{j}}+b_{ i}\big{)}=\mathbf{t_{j}},j=1,\cdot\cdot\cdot,N \tag{21}\]  
Equation (21) can be expressed as:  
\[\mathbf{H}\beta=\mathbf{T} \tag{22}\]  
Here, \(\mathbf{T}\) represents the desired output, \(\beta\) represents the output weight, and \(\mathbf{H}\) represents the output of the hidden node.  
The output weight \(\beta\) can be determined by Equation (23):  
\[\beta=\mathbf{H^{t}}\mathbf{T}=\Big{(}\mathbf{H^{T}}\mathbf{H}\Big{)}^{-1} \mathbf{H^{T}}\mathbf{T} \tag{23}\]  
Here, \(\mathbf{H^{t}}\) is the Moore-Penrose generalized inverse of the matrix \(\mathbf{H}\) and the norm of \(\beta\) is minimal and unique.
## 5 WBAN and the Communications Protocol Stack
### Physical Layer
#### 5.2.1 Characteristics of RF Communications in and along the Human Body
[PERSON] _et al._[31] characterized the physical layer including an estimation of the delay spread, path loss and mean excess delay between two nodes on the body for NB communications. The research reviews the propagation channel between two half wavelength dipoles at 2.45 GHz, placed near a human body. A total of 583 measurements were performed in a multipath environment on real humans considering different parts of the human body: arms, leg, torso and back. Results showed that path loss increases with antenna separation and the loss models of the arm and leg are similar. The researchers found that the path loss along the torso follows the same course as the path loss along the arm but the losses along the torso are the highest of all body parts. It was concluded that this was probably due to the higher absorption in the trunk due to the larger volume and because the surface of the trunk is less flat than the surfaces of the other investigated body parts. The research in [31] did not take body movement into account, which therefore, limits the significance of the research for real life scenarios.
# Fitness Landscape Analysis of Product Unit Neural Networks
## 6 Empirical Procedure
### Network Architecture  
Three different types of network architectures, with different numbers of hidden units, are used, as listed in Table 1:  
* **Optimal architectures:** For fair comparisons between the loss surfaces of PUNNs and SUNNs, optimal architectures were used. For the regression problems, the architectures were taken from [16], and the classification problems were determined by training on an increasing number of hidden units until overfitting was observed. The optimal architectures result in models that do not overfit nor underfit the training data.
* **Oversized architectures:** Oversized architectures were used to investigate the effects of overfitting on the PUNN loss surfaces. Oversized PUNNs used the same number of hidden units as the SUNNs for all problems except for \(f_{3}\), where seven hidden units were used.  
* **Regularized architectures:** Weight decay with the \(L_{2}\) penalty was used on oversized architectures to study the effects of regularization on the loss surfaces. The loss function becomes \(\mathcal{E}=\mathcal{E}_{T}+\lambda\sum_{i=1}^{N}w_{i}^{2}\), where \(\mathcal{E}_{T}\) is the training error, \(\lambda\) is the penalty coefficient, \(N\) is the number of weights, and \(w_{i}\) is the \(i\)-th weight. The effects of regularization were only investigated for the classification problems. The optimal value for the penalty coefficient, \(\lambda\), was obtained using a grid search over values for \(\lambda\) in \(\{10,1,0.1,1e-2,1e-3,1e-4,1e-5\}\). The optimal \(\lambda\) value was 0.0001 for all problems.
## 4 Experiments and Analysis
### Experiments for Measuring Static Object  
In order to verify the effectiveness of the proposed method, many experiments were conducted. One of them is to measuring a crab model as shown in Figure 10a. Figure 10b shows the captured three deformed patterns from the R, G, and B channels of the projected tricolor binary fringe. The corresponding Fourier spectrum is shown in Figure 10c, it can be seen that the higher frequency components modulated by the measured object are hardly overlapped with the higher order harmonic components of the binary fringe. By filtering, the regions of interest (ROI) frequency components as shown in the dotted rectangle region as shown in Figure 10c can be retained. By IFT, the corresponding three nearly unbroken sinusoidal deformed patterns are extracted as shown in Figure 10d; Figure 10e,f shows the corresponding wrapped phase and the reconstructed 3D shape result of the measured crab model with the proposed method. It can be seen that the surface of the measured crab model can be well reconstructed. If the measured object is too complex to extract the effective sinusoidal deformed pattern from the captured deformed pattern due to the over spectrum overlapping between the higher frequency components modulated by the measured object and the higher order harmonic components of the binary fringe, the proposed method may be limited. Although the proposed method may be limited to measuring the overcomplex objects to some extent, it can reconstruct the 3D shapes of most objects with relative complex surfaces.
# Comparison of Automated Extraction Techniques for Volatile Analysis of Whole Milk Powder
## 2 Materials and Methods
### GC-MS Analysis  
The GC-MS system was an Agilent 7890A GC and Agilent 5977B MSD (Agilent Technologies Ireland Ltd., Cork, Ireland). The analysis was undertaken using both a non-polar GC column DB5-MS (60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) and a polar GC column HP-Innowax (60 m \(\times\) 0.25 mm \(\times\) 0.5 \(\upmu\)m) (Agilent Technologies Ireland Ltd., Cork, Ireland). The GC conditions for the non-polar DB5-MS column were as follows: the injector temperature was set at 250 \({}^{\circ}\)C, while the column was initially at 35 \({}^{\circ}\)C, then increased to 230 \({}^{\circ}\)C at 6.5 \({}^{\circ}\)C min\({}^{-1}\), 320 \({}^{\circ}\)C at 15 \({}^{\circ}\)C min\({}^{-1}\), before being held for 5 min, yielding a total run time of 41 min. The carrier gas helium was held at a constant pressure of 23 psi. The GC conditions for the polar HP-Innowax column were as follows: the injector temperature was set at 250 \({}^{\circ}\)C, while the column was initially at 40 \({}^{\circ}\)C for 5 min, then increased to 230 \({}^{\circ}\)C at 5 \({}^{\circ}\)C min\({}^{-1}\), before being held for 10 min, yielding a total run time of 59 min. The carrier gas helium was held at a constant pressure of 23 psi.  
The ion source temperature was 220 \({}^{\circ}\)C and the interface temperature was set at 260 \({}^{\circ}\)C. The mass spectrometer was in electronic ionization (70 v) mode with the mass range scanned between 35 and 250 amu. The analysis was undertaken using MassHunter Qualitative Analysis software (Agilent Technologies, Palo Alto, CA, USA) with target and qualifier ions and linear retention indices for each compound compared to an in-house library based on mass spectra obtained from NIST 2014 mass spectral library MS searching (v.2.3, Gaithersburg, MD, USA) and an in-house library created using authentic compounds with target and qualifier ions and linear retention indices for each compound using the Kovats index. Spectral deconvolution was also performed to confirm identification of compounds using the automated mass spectral deconvolution and identification system (AMDIS). Batch processing of the samples was carried out using metaMS [18], an open-source pipeline for GC-MS based untargeted metabolomics. The results for each identified volatile compound were normalized based on the recovery of the 4-methyl-2-pentanol IS for each sample and expressed as a percent of the total volatiles recovered for each sample. Results in all cases were the averages of triplicate analysis.
# Image Splicing Location Based on Illumination Maps and Cluster Region Proposal Network
## 3 The Proposed Framework  
With the help of the color inconsistency of the illumination maps and image semantic features, the tampered regions can be classified and located. As shown in Figure 1, the  
Figure 1: Overview of the proposed framework, where the image stream and the illumination stream extract semantic features and tampered features, respectively. The two stages are fused by MFPN and C-RPN is to locate tampered regions. \(\otimes\) denotes matrix multiplication.  
framework can be divided into the following parts: the dual-stream framework, the multiple feature pyramid network and the cluster region proposal network. The image stream extracts features of unnatural tampering boundaries, while the illumination stream focuses on the inconsistent feature as a supplement. Then, the multiple feature maps of two streams are fused through MFPN. Finally, the Regions of Interest (ROI) are obtained by C-RPN and the tampered regions are located through data training.
# A New Circuit Design of AC/DC Converter for T8 LED Tube
## 3 Design Parameters
### Determining the Output Ripple Eliminator: \(R_{E}\), \(\text{C}_{E}\), \(Q\)  
The voltage and current relation is almost linear in driving region of LED IV curve [26], so the dynamic resistance of the LED device can be defined as:  
\[R_{LED\_Device}=\frac{V_{LED\_Device}}{I_{LED\_Device}}\approx 3.23\ \Omega \tag{20}\]  
To achieve a percent flicker under 1%, the current variation \(\Delta I_{LED\_Device}\) of the LED device should be within 1.4 mA according to the luminance-current characteristics [26]. Consequently, the allowed LED device voltage \(\Delta V_{LED\_Device}\) is 3.23 \(\times\) 1.4 m = 4.5 mV. Therefore, when the peak-to-peak output voltage \(\Delta V_{O\_ac}(t)\) is 3.33 V, the bias voltage \(V_{B\_ac}(t)\) should be 157.5 mV in a series of 35 LEDs. From Equation (9), \(X_{E}/Z_{E}\) is obtained as:  
\[\frac{X_{E}}{\sqrt{{R_{E}}^{2}+X_{E}^{2}}}=\frac{\Delta V_{B\_ac}}{\Delta V_{ O\_ac}}=\ \frac{0.158}{3.33}=0.047 \tag{21}\]  
A small capacitor can be used to remove the low frequency ripple, and \(C_{E}\) can be implemented with a 100 V/1 \(\mu\)F chip capacitor. Since the capacitor impedance \(X_{E}\) is 1.59 k\(\Omega\) at 60 Hz, \(R_{E}\) should be:  
\[R_{E}=\sqrt{\left(\frac{X_{E}}{0.047}\right)^{2}-X_{E}{}^{2}}\approx 33\ \text{k}\Omega \tag{22}\]The output ripple eliminator greatly reduces the light flicker but at the same time, additional power loss occurs in Darlington transistor Q. The power loss of Q can be estimated as \((V_{LED\_PP}/2+V_{BE})\cdot I_{LED}\), where the \(V_{LED\_PP}\) is peak-to-peak voltage across the LED array and \(I_{LED}\) is the average current flowing to the LED array [24; 27].  
\[P_{Q\_MAX}=\left(\frac{35\times 95\ m}{2}+1.2\right)\times 92.2\ m=264\ mW \tag{23}\]  
Considering Equation (23) and heat dissipation, an MJF6388 Darlington transistor is selected to maintain stable operation.
## 1 Introduction
### Traffic Management Systems
Integrating ultrasonic sensors with Raspberry Pi and IoT allows for smart security and road safety enhancements, gradually improving the overall traffic system [47]. To address frequent traffic congestion and uncontrollable commuting times, a collaborative intelligent traffic planning algorithm uses AI and traffic sensors to optimize signal durations and driving paths dynamically. This method improves on-time arrival ratios and meets commuting delay requirements [48]. To improve the effectiveness of the Internet of Vehicles (IoV), the study [49] proposes a Multi-Criteria Decision-Making (MCDM) approach, utilizing CRITIC and TOPSIS methodologies to select optimal choices based on qualitative and quantitative factors. By analyzing data from sensors, GPS, and communication networks, this research seeks to optimize IoV architecture, enhancing system performance and supporting the development of smart, human-centric transportation solutions. The main contributions of this paper are as follows:  
* Designing a Public Urban Transport Scheduling System (PUTSS) algorithm that has dynamic decision-making capabilities in real-time urban transportation management by adjusting bus schedules, optimizing service for passengers while minimizing operational costs for the transport provider;
* Integrating Google Maps Service data that identify congestion levels with AI techniques that estimate the number of people at transit stations;
* Implementing Microsoft Azure Computer Vision services to monitor and count passengers at each station in real time and address sudden changes in passenger numbers. This paper is structured into five sections. Section 2 presents the materials and methods employed to design the proposed algorithm. Section 3 provides insight into the results of the tests of the proposed algorithm modules. The discussion, limitations, and future research are described in Section 4. Section 5 reveals the conclusions.
## 3 Results
### Temporal Response of Satellite Information to Wet Pulses  
#### 3.2.1 Spectral Indices  
A similar pattern in the EVI and LSWI during wet pulses was observed (Figure 4). Certain delays between the maximum in both indices in relation to peaks in SM are evident. Lags between 12 and 40 days between maximum values of SM and LSWI were observed in ASM. In the Ti Tree station, 7 and 31 days for the 2017-2018 and 2020-2021 seasons, respectively, were observed (Figure 1). This daily analysis complements previous studies that considered coarser temporal information. At a monthly scale, Ref. [11] reported delay times from 0 to 1 month in the relationship between SSM and EVI values. Ref. [55] showed a typical delay of 1 month between SM and NDVI values.
###### Abstract
Data transit attacks are the main security issue in physical, network, and application levels of the proposed LoRa based solution. To solve the issue, Semtech Corporation, the development company of LoRa, has come up with a solution of embedded end-to-end (Advanced Encryption Standard) AES-128 encryption. This AES-128 is also known as an Application Key (AppKey) that encrypts all the data by ensuring optimal privacy and protection. In addition, the AppKey generates two session keys named Network Session Key (NwkSKey), which ensures the message integrity by creating a specific signature for every device and Application Session Key (AppSKey), which is used for both the encryption and decryption of the payload of the application data. Table 9 represents the comparison among the network technologies in terms of data rate, transmission range, power consumption, and battery life. It is noticeable that LoRa has the low power consumption and highest battery life compared to others. LoRa also has the highest transmission range even though it has a lower data rate compared to Wi-Fi or Bluetooth. Table 10 is presenting the comparison of different IoT based health monitoring systems in terms of applied sensor nodes, microcontroller, and power consumption. It is observed that the proposed system has applied the highest number of sensor nodes and it has a low power consumption compared to the systems who have used Wi-Fi. By analyzing the comparison tables, it can be said the proposed system has the highest compatibility compared to other network technology-based health monitoring system. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & \begin{tabular}{c} Transmission \\ Time (ms) for a \\ 100-Byte Packet \\ Sent \\ (Approximate) \\ \end{tabular} & \begin{tabular}{c} Power \\ Consumption \\ 100-Byte Packet \\ Sent \\ (Approximate) \\ \end{tabular} & \begin{tabular}{c} Transmission \\ Time (ms) for a \\ 100-Byte Packet \\ Sent \\ 40 mA in TX \\ (Approximate) \\ \end{tabular} & \begin{tabular}{c} Minimum range, \\ Fast data rate, \\ Minimum battery \\ impact \\ \end{tabular} \\ \hline CH\_01\_900 & \begin{tabular}{c} 905.24 \\ MHz \\ \end{tabular} & \begin{tabular}{c} -134 \\ \end{tabular} & 1 & 700 & 28 & 1500 & \begin{tabular}{c} Minimum range, \\ Fast data rate, \\ Minimum battery \\ impact \\ \end{tabular} \\ \hline CH\_01\_900 & \
\begin{tabular}{c} 905.24 \\ MHz \\ \end{tabular} & 58.33 & 3 & 1000 & 40 & 1800 &
\begin{tabular}{c} Maximum range, \\ Slow data rate, \\ Maximum battery \\ impact \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Range versus transmission time/consumption. ACK: Acknowledgement.
## 4 Simulation Results
### Complexity Evaluation  
Table 2 shows a comparison of the computational complexity of each method. This result shows that the computational complexity of the proposed algorithm, capacitated affinity propagation, and affinity propagation are identical at \(O\left(n^{2}\right)\). The difference in the message passing complexity only appears in constants and can therefore be ignored. In addition, the computational complexity for coalitional game theory is \(O\left(2^{n}\right)\). The updated split and merge algorithm in the coalitional game theory result in a higher computational complexity when compared to the message passing algorithm. The message update process in the proposed algorithm mostly consists of message exchanges among neighbors, which only contains small-sized information. This is the main factor of the low computational complexity in the proposed algorithm.  
Figure 8 provides the results of the comparison of the convergence properties of the proposed algorithm and those of other existing methods. Convergence iteration shows how rapidly an algorithm will reach a stabilized position during iteration. The simulation result shows that the proposed algorithm converges less than the five iterations. It should be noted that the proposed algorithm requires less number of iterations when compared to other iterative algorithms. This implies the overall required complexity for the proposed algorithm is kept minimal, making the proposed algorithm appropriate for practical implementations. The proposed algorithm exchanges linear and scalar quantity messages (36) and (37) at each iteration. These messages only consume a small amount of transmission bandwidth. Therefore, this scheme requires a lower backhaul capacity level during its implementation. The iterative message update have the high potential to incur the latency issue. However, the long-term period of JP-CoMP renders the latency not a critical issue.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & Proposed Algorithm & Coalitional Game Theory [19] & AP [26] & Capacitated AP [27] \\ \hline
**Complexity** & \(O\left(n^{2}\right)\) & \(O\left(2^{n}\right)\) & \(O\left(n^{2}\right)\) & \(O\left(n^{2}\right)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computational Complexity.  
Figure 7: Average edge user throughput versus number of users per cell.
## 2 Related Works
### DSSL Methods for Indoor Positioning
The advantage of this method is its simplicity and strong operability, which is needless to deploy additional models. Whereas in actual positioning scenarios, the target distributions of labeled data and unlabeled data are often inconsistent, and using the same model parameters directly on different distributed data will reduce the positioning accuracy. In [44], the author discussed a semi-supervised indoor positioning scenario based on the Generative Adversarial Network (GAN) by using CSI data, which consists of a generator and a discriminator and aims to generate new CSI that similar to labeled data. Results of using GAN for semi-supervised positioning have confirmed its effectiveness under a few labeled input. However, using explicit data augmentation methods to improve the performance of positioning may increase the computing power burden of the device and consume a large amount of memory when facing the massive data demand. Moreover, when the labeled data are highly resembled, overfitting can be caused by a deep generative method. Moreover, the consistency regularization method as an effective DSSL method achieves a good balance between accuracy and memory occupation. In [46], the ladder network, the first attempt at consistency regularization, was used for indoor positioning using CSI, which is inspired by a deep denoising AutoEncoder. It predicts whether each input has noise, using denoising functions and the unsupervised denoising square to generate consistency loss, and aligns with the supervised learning loss to obtain more accurate user coordinates. The advanced consistency regularization methods after the Ladder Network, such as the \(\Pi\) Model, Temporal Ensembling, and Mean Teacher, have been proposed to improve accuracy without requiring additional data by regulating the consistency loss between noisy inputs to reduce overfitting and enhance the generalization of neural networks. However, these methods have not been really well used yet, and how to efficiently apply them to the positioning field still remains outstanding.
# Edge Convolutional Networks for Style Change Detection in Arabic Multi-Authored Text
## 2 Background
### DNN-Based Methods  
Long Short-Term Memory (LSTM) [53] and Convolutional Neural Network (CNN) [54] models were investigated for the SCD task. [PERSON] and [PERSON] [55] developed a BiLSTM-based solution to tackle the SCD task at the sentence level. [PERSON] [56] and [PERSON] [57] developed CNN-based solutions to extract features. [PERSON] [42; 52] investigated GloVe embeddings [58] and developed a BiLSTM-based Siamese NN solution. A network constructed as a Siamese NN contains two identical neural subnetworks working in parallel and connected at their output [59]. In [PERSON]'s experiments, the BiLSTM-based solution outperformed BiGRU-based and logistic regression-based solutions. However, [PERSON] observed that increasing the number of units or layers consumes additional running time without yielding a better result [42; 52]. [PERSON] and [PERSON] [60] developed an LSTM-based Siamese NN solution involving an attention mechanism. The difference between its subnetworks is in how they receive input. The first subnetwork receives the typical direction of a document while the second subnetwork receives the reverse direction to be similar to the backward pass in a BiLSTM architecture [60]. The similarity functions in Siamese NN-based solutions [42; 52; 60] measured the difference between subnetwork outputs followed by an FC layer to predict style changes. Adding similarity functions instead of other FCs increased the accuracy by 10% [60].  
To further enhance performance, incorporating BERT [61] was investigated with CNN models [62] or with BiLSTM followed by CNN models [63]. [PERSON] and [PERSON] [63] claimed that using the BiLSTM layer addresses the dependency between words in both directions and using the CNN layer reduces features and avoids overfitting.