esa-sceva/satcom-chunk-collection huggingface dataset:
# Evolutionary Algorithm to Optimize Process Parameters of AI/Steel Magnetic Pulse Welding
## 3 Conclusions  
To improve the maximum load and weld length, this study used ICA and GA as evolutionary algorithms to determine optimal process parameters for Al/Steel MPW joints. The conclusions can be summarized as follows:  
1. The maximum load and weld length of Al/Steel joints varied under different welding conditions, as determined by CCD. In all cases, the maximum load was measured, but there were five cases in which no weld zone was observed, indicating a separation between Al tube and the steel rod during cutting. From these results, selected MPW process parameters were found to be closely related to joint quality.
2. Based on the experimental results, prediction models for maximum load and weld length were developed using RSM. The developed prediction models showed high coefficients of determination (R\({}^{2}\)) of 96.02% and 95.81% for maximum load and weld length, respectively. The deviation between actual response values and predicted values was less than 10%, indicating reasonable agreement between the developed prediction models and the experimental results.
3. The peak current was discovered to be a crucial factor in enhancing joint quality, specifically in relation to maximum load and weld length, contributing over 48% to the overall quality. Increasing the peak current and frequency improved the quality because the peak current is closely related to the generation of the Lorentz force needed for the collision of workpieces during MPW.
4. Optimization was performed using ICA and GA as evolutionary algorithms to select the optimal process parameters. Results from the proposed ICA and GA showed that  
Figure 12: Results of the verification test. (**a**) Maximum load on Al/Steel joint. (**b**) Weld length on Al/Steel joint.  
ICA performed at a slightly faster rate and yielded higher-quality results than GA. This indicates that the evolutionary algorithm was useful for optimizing the MPW process parameters.
* The optimal process parameters selected using ICA were verified through a verification test. The maximum load and weld length of the Al/Steel MPW joint were measured and found to be 2.139 kN and 0.8 mm, respectively. These results showed good agreement between the ICA-predicted and experimentally-obtained maximum values of load and weld length, confirming that ICA is an effective and useful method for finding optimal process parameters.  
Conceptualization, I.K. and J.S.; Methodology, J.S.; Software, J.S.; Formal analysis, J.S.; Investigation, J.S.; Resources, J.S.; Writing--original draft, J.S.; Writing--review & editing, I.K.; Supervision, I.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Korea Institute of Industrial Technology (KITECH) under one of its major projects, \"Development of Smart Welding System Modules with Dynamic Variable Control for Full Penetration Weld (KITECH EH-23-0007).  
The data presented in this study are available in this article.  
The authors declare no conflict of interest.
## 7 SMEP Algorithms
### AuxDataQueue Compression Algorithm
In Section 9.2.2, moreover, we analyze characteristics of the SMEP method presenting graphs of _Zones_ value patterns. #### 9.2.1 Average Error Rates in Round Compressions

We experimented with four samples for each of the five compression methods, which are _winavg_, _delta_, _CQP_, _2 MC_, and _SMEP_. We constructed one sample from each of four real-field sensor data sets, which are relative humidity, air temperature, underwater pH, and underwater temperature sensor data sets. Each sample consisted of 129 consecutive ground sensor data extracted from their corresponding sensor data set. Varying the number of round compressions between one and four for each of four samples per method, we estimated individual AERs and we compared them for the five methods (here, the sizes of original sample in round compressions from one to four times are reduced to 50%, 25%, 12.5%, and 6.25% in compression ratios, respectively). In Table 3, we present the results. Compared with the other methods, the SMEP shows for each round compressions better AER than the other methods, in the sensor data samples with more frequent and higher value changes, i.e., in the relative humidity and air temperature samples. In the underwater pH and underwater temperature samples with infrequent and low value changes, though the differences among the AERs of five methods in the same round compressions are small, the SMEP shows mostly better AER than the other methods. Specifically, comparing with the _2 MC_, note that the AER of the SMEP method is better than the _2 MC_. Meanwhile, in Table 2 the SMEP and the _2 MC_ show that their AER differences between these methods and the other methods are much bigger in the relative humidity and air temperature samples than in the underwater pH and underwater temperature samples. As an example, in Table 2, while the AER difference between the SMEP and _delta_ in the 3 rd round compression of the underwater temperature sample is only 0.37%, while their AER difference in the same round compression of the air temperature sample is 21.44%. Therefore, in compressions of ground sensor data with more frequent and higher value changes, the SMEP and _2 MC_ methods are more effective than the other methods. We can show that the number of round compressions until which a method is feasible depends definitely on the sensor data change properties in all the compression methods. As shown in Table 2,
## 3 Control Strategies
### Description of the Closed-Loop System  
The compensation system does not transmit power outward during stable operation. Hence, the compensation power is equal to zero when the impact load action is completed. As a result, to achieve the power tracking control, define two error control signals as follows:  
\[\begin{bmatrix}e_{p}\\ e_{q}\end{bmatrix}=\begin{bmatrix}p_{ref}-p_{1}\\ q_{ref}-q_{1}\end{bmatrix} \tag{16}\]  
where \(p_{ref}\) and \(q_{ref}\) represent the reference values of the output power of the compensation system, respectively. According to the analysis above, the reference value is considered to be equal to zero. These two signals ensure that the power can converge to the reference value in a finite time.  
Meanwhile, another control objective of this thesis is to achieve frequency tracking control, so the third error control signal is designed as follows:  
\[e_{\omega}=\omega-\hat{\omega} \tag{17}\]  
Next, solving the time derivative of (17) and substituting (12) into it, such that  
\[\begin{bmatrix}\dot{e}_{p}\\ \dot{e}_{q}\end{bmatrix}=\begin{bmatrix}-\frac{R}{L}p_{1}-\omega q_{1}-\frac{ 9}{2L}U_{m}^{2}+\frac{3}{2L}\sqrt{\frac{3}{2}}U_{m}u_{1}+\frac{3U_{m0}U_{d0} \cos\delta_{0}}{X}(\omega-\omega_{0})\\ \omega p_{1}-\frac{R}{L}q_{1}+\frac{3}{2L}\sqrt{\frac{3}{2}}U_{m}u_{2}+\frac{3 U_{m0}U_{d0}\sin\delta_{0}}{X}(\omega-\omega_{0})\end{bmatrix} \tag{18}\]  
where \(u_{1}=u_{d}\) and \(u_{2}=u_{q}\) represent the two control inputs of the system, respectively.
# Secure Audio-Visual Data Exchange for Android In-Vehicle Ecosystems
## 3 Protocol and Results
### Test Scenarios and Results
#### 3.3.3. In-Vehicle Experiments with Smartphones and Head Unit  
In our experiments we relied on an Android PX5 head unit which was connected to the car radio using an AUX cable. Thus we were able to use the car speakers and the microphone from the head unit. We carried out the experiments in two hatchback cars, a BMW 120d and an older Volkswagen Golf 3, which do not benefit from exquisite isolation from the outside noise. This type of cars should cover a sufficiently large market segment, while on more expensive cars equipped with superior isolation from the outside noise, the results should be even better. As stated in the introduction, the short range of audio signals is beneficial from a security point of view since such channels are harder to eavesdrop. While indeed the driver and the front passenger are favored by being closer to the head unit, we expect that getting a phone closer to the infotainment unit is practical in most scenarios.  
During the experiments the engine was running and thus the engine noise also influenced our experiments. This kind of typical in-vehicle noise is useful to generate a random seed (the same on both devices) for the key exchange protocol. Due to the poor quality of the microphone from the head unit, we were not able to use high frequencies and we had to lower these to the range of 8.7 kHz to 12 kHz. Also, we reduce the distance between smartphone and head unit to 5 cm during the audio transmission for the head  
Figure 13: The BER (**up**) and the percentage of correct keys (**down**) for each smartphone pair for in-vehicle experiments with smartphones.  
unit to be able to record the frequencies. In Table 8 we present the BER and the key receive rate for each smartphone paired with the head unit. The BER values are between 0% and 9.25%. Also in Table 9 we present the same values but we used the environment noise to generate the seed for the keys. Also in Figures 14 and 15 we created bar charts using the results from the tables for better visualization.  
The experiments performed on this set of distinct devices led us to several observations. Smartphones with distinct hardware characteristics might not work identically when running the same application. We noticed some synchronization problems when playing the tones, e.g., the delay between the frequencies was not the same on the Samsung A3 as on the other three devices. This is likely because of clock issues, so we had to increase the duration of the tones only on the Samsung A3 so that the pairing could be made. Other factors that we expected to impact our experiments were the quality of the speakers, microphones and digital-to-analog converters which are all variable on all distinct smartphone models.
# Noncoherent Channel Combining for GNSS Signal Tracking with an Adaptive Antenna Array
## 2 Mathematical Model
### Array Weighting  
To suppress the strong interference, a weight vector \(\mathbf{w}\) is applied to the received signal vector in Equation (1) as  
\[y(t)=\mathbf{w}^{\mathrm{H}}\mathbf{x}(t) \tag{3}\]  
where \((\cdot)^{\mathrm{H}}\) denotes the conjugate transpose operation.  
The weight vector can be obtained based on different criteria. The MVDR algorithm is well known in various fields with the following cost function  
\[\begin{cases}\underset{\mathbf{w}}{\min}\mathbf{w}^{\mathrm{H}}\mathbf{R}_{ xx}\mathbf{w}\\ \text{s.t.}\;\mathbf{w}^{\mathrm{H}}\mathbf{a}_{0}=1\end{cases} \tag{4}\]  
where \(\mathbf{R}_{xx}=E[\mathbf{x}(t)\mathbf{x}^{\mathrm{H}}(t)]\) denotes the auto-correlation matrix of the received signal vector, and \(E[\cdot]\) denotes the expectation operation. The solution is given by  
\[\mathbf{w}_{\mathrm{MVDR}}=\frac{\mathbf{R}_{xx}^{-1}\mathbf{a}_{0}}{\mathbf{ a}_{0}^{\mathrm{H}}\mathbf{R}_{xx}^{-1}\mathbf{a}_{0}} \tag{5}\]  
where \((\cdot)^{-1}\) denotes the matrix inversion operation.  
The MVDR algorithm can not only steer nulls in the jammer directions but also maximizes the gain in the satellite direction with the unity constraint. However, it needs to know the satellite steering vector, which requires the DOA of the GNSS signal and the array configuration, increasing the complexity of the system. Suppressing the interference without this kind of prior knowledge is more attractive.  
The PI algorithm is one possible choice, using the following cost function  
\[\begin{cases}\underset{\mathbf{w}}{\min}\mathbf{w}^{\mathrm{H}}\mathbf{R}_{ xx}\mathbf{w}\\ \text{s.t.}\;\mathbf{w}^{\mathrm{H}}\mathbf{b}=1\end{cases} \tag{6}\]  
where \(\mathbf{b}=[1,0,\ldots,0]^{\mathrm{T}}\) is a constraint vector, and \([\cdot]^{\mathrm{T}}\) denotes the transpose operation. The solution is expressed as  
\[\mathbf{w}_{\mathrm{PI}}=\frac{\mathbf{R}_{xx}^{-1}\mathbf{b}}{\mathbf{b}^{ \mathrm{H}}\mathbf{R}_{xx}^{-1}\mathbf{b}} \tag{7}\]  
The PI algorithm is widely used in practical engineering since it can suppress the strong interference from any direction without any prior knowledge. The PI algorithm is more cost effective than the MVDR algorithm, but it may attenuate the satellite signal, which affects the signal tracking precision.
# Recent Advances in Efficient Dynamic Graph Processing
## 1 Introduction
* _Challenges._ Specifically, the challenges of dynamic graph processing can be listed as follows:
* _High frequency of updates._ For instance, in online social networks, the graphs are typically large and continually evolving. As of 2023, Facebook has approximately 2.9 billion monthly active users. On average, there are over 50 million posts, comments, and likes per minute. This translates to about 72 billion interactions per day [34]. Twitter has around 461 million monthly active users as of 2023, about 6,000 tweets are posted every second, which amounts to approximately 500 million tweets per day [35]. * _High requirement of efficiency._ Many applications that rely on dynamic graph processing require real-time or near-real-time response. For instance, fraud detection in financial services or targeted advertising in social media platforms demand immediate analysis of new connections or patterns within the graphs [36]. * _High time complexity of update computation._ Graphs are inherently complex structures, and performing graph analysis such as path traversal, community detection, or centrality measures can be computationally intensive. For instance,searching many community models, such as clique and biclique, is proven to be an NP-hard problem [16]. As the graph changes, these operations need to be updated efficiently without recalculating from scratch, especially in large-scale scenarios. In order to cover the above challenges of dynamic graph processing, we survey papers published in recent years, as shown in Table 1. The problems addressed in the existing research on dynamic graphs can be categorized into the following main topics:  
* _Centrality._ Measure the importance of vertices in graphs. Typical definitions of centrality include betweenness centrality, closeness centrality, and eccentricity. They can be utilized in several application domains such as social media, advertising push, and online search engines. * _Graph Coloring._ Assign a color to each vertex such that no two adjacent vertices share the same color. Graph coloring has many practical applications including register allocation, making schedule, and map coloring, and can also be an optimized technique for other graph problems such as clique enumeration and graph partitioning. * _Cohesive Subgraph._ Discover dense groups of vertices in graphs. Typical cohesive subgraph models include clique, biclique, \(k\)-core, \(k\)-truss, their diverse variants, and community models. They are widely applied in community detection, recommendation systems, and anomaly detection. * _Path Traversal._ Path traversal is a foundational graph computation problem, including depth-first search, shortest distance queries, and reachability queries, and is known to have direct applications in various fields, especially in road networks, such as path planning, navigation, and traffic control.
## 3 Research Framework of Internet of Emergency Services
### Components and Devices of Internet of Emergency Services
#### 3.2.3 Wearable Devices
\begin{table}
\begin{tabular}{p{123.8 pt} p{123.8 pt} p{123.8 pt} p{123.8 pt}} \hline \hline
**Reference** & **Device** & **Context of Use** & **Description of Use** \\ \hline [PERSON] et al., (2022) [49] & Bio-signal sensor suite & Search and rescue missions & Monitoring stress, anxiety, and physical fatigue in firefighters \\ \hline [PERSON] et al., (2022) [12] & Smart helmet & Industry, sports, first responding scenarios & Continuous monitoring of health status and environmental conditions using sensors & Continuous monitoring of health status and environmental conditions using sensors \\ \hline [PERSON] et al., (2021) [66] & Gas sensor & Occupational safety and health & Development of wearable gas sensor for NH3 detection based on organic semiconductor PEMO and hydrogel film \\ \hline [PERSON] et al., (2021) [26] & Mobile relay system & Emergency situation detection & Proposed security-enhanced emergency situation detection system using 3 rd party unknown mobile relays \\ \hline [PERSON] et al., (2020) [78] & Smart patch-based sensor system & Continuous monitoring of human body biological signals & Proposed fully integrated wearable smart patch-based sensor system with temperature, humidity, and motion sensors \\ \hline [PERSON] et al., (2021) [79] & Wireless sensor network & Fire rescue & Proposed mobile adaptive routing algorithm for WWSN to monitor and transmit rescuers’ physiological information in fire scenarios \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of papers on wearable devices for emergency management and response.
## 5 Experiments
### Model Compilation
* **CNN & Bi-LSTM**: this model's architecture combines both the CNN and Bi-LSTM layers: A bidirectional LSTM layer with 64 units and a 1D convolutional layer with 64 filters and a kernel size of three extract local patterns from the sequence. Then, the MaxPooling1D layer with pool size two was applied to reduce the dimensionality of the feature maps. The flattening layer converts the 3D tensor output from the previous layer into a 1D tensor. Last, there is the dense layer with two units and the sigmoid activation function, which outputs the probability distribution over the classes. * **BERT** (Bidirectional Encoder Representations from Transformers): BERT employs bidirectional self-attention transformers to capture both short- and long-term contextual dependencies in the input text. We use AraBERT [86], which has a vocabulary capacity of 64,000 words, 12 attention heads, 12 hidden layers, 768 hidden sizes, a total of 110 M parameters, and 512 maximum sequence lengths. It was trained using a dataset of 3B Arabic words. Specifically, we used the available version \"paraphrase-multilingual-mpnet-base-v2 model ([[https://huggingface.co/](https://huggingface.co/)]([https://huggingface.co/](https://huggingface.co/)) (accessed on 1 September 2023))\", which is a pretrained BERT model that is designed to encode multilingual texts into high-quality embeddings. Notably, the Adam optimizer with a learning rate of 1e-4, batch size of 512, and sequence length of 128 was utilized. * **GPT** (Generative preTrained Transformer): ArAGPT2 is the largest publicly accessible collection of filtered Arabic corpora was used to train the model [89]. The complexity metric, which assesses how effectively a probability model predicts a sample, was used to assess the model. The model is trained on 77 GB of Arabic text. ARAGPT2 is available in four training size variants: base, medium, large, and mega; the smallest model, base, has the same dimensions as ARABERT-base. This enables it to be accessible to a greater number of researchers. Larger model variants (medium, large, and xlarge) provide enhanced performance but are more difficult to fine-tune and computationally costly. The ARAGPT2- detector is based on a pretrained ARAELECTRA model that was refined using a synthetically generated dataset. In this study, we used the ARAGPT2 base model; which has a batch size of 1792, a learning rate of 1.27e-3, LAMB optimizer, 12 heads and 12 layers, and a training size of 135 M. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Batch size & 32, 64, and 128 \\ Epochs & range 1 to 15 \\ Dropout Rate & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Unified parameters for deep learning classifiers.
# Advancing Visible Spectroscopy through Integrated Machine Learning and Image Processing Techniques
## 5 Conclusions  
In conclusion, the presented research offers a methodology for visible spectroscopy through integrating polynomial regression with machine learning algorithms. The results demonstrate the effectiveness of this approach in accurately predicting the dominant wavelengths emitted by various vapour sources. The methodology achieves low error percentages for most elements, indicating a high precision in wavelength prediction. Specifically, for sodium, neon, and helium, the error percentages are exceptionally low at 0.04%, 0.01%, and 0.07%, respectively. Despite minor discrepancies observed in elements like copper and mercury, with error percentages of 1% and 3.7%, respectively, the overall performance of the methodology remains good.  
The combination of polynomial regression and machine learning provides a robust framework for spectroscopic analysis, enhancing the accuracy and versatility of the methodology. While some discrepancies exist, particularly for elements with complex spectral characteristics such as copper and mercury, overall, the methodology shows promise for a wide range of applications in visible spectroscopy.  
Further refinement and optimisation may be necessary to improve accuracy, particularly for elements with challenging spectral profiles. Additionally, future research could explore the scalability of the methodology to analyse a broader range of vapour sources and its applicability in real-world scenarios.  
Overall, this research represents a significant advancement in visible spectroscopy, offering a comprehensive and effective approach that holds promise for various scientific and industrial applications. With continued development and validation, the methodology has the potential to contribute to advancements in fields such as colorimetric assay, the analysis of transition metal complexes, and the analysis of natural pigments.  
Conceptualisation, A.P., K.K., A.B. and S.P.; data curation, A.P., A.B. and S.P.; formal analysis, A.P., K.K. and S.P.; investigation, A.P. and A.B.; methodology, A.P. and K.K.; resources, A.P. and K.K.; software, A.P.; supervision, S.P.; validation, A.P. and A.B.; visualisation, A.B.; writing--original draft, A.P., K.K. and A.B.; writing--review and editing, K.K., A.B. and S.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data is contained within the article.  
The authors declare no conflicts of interest.
## 2 Research Methods  
The QC-RL-LDPC encoder is implemented in low-power microcontroller unit from the STM32 family, as will be presented in Sections 5 and 8.1. All the remaining parts of the communication model considered, presented in Figure 1, are implemented as software models developed in the Matlab environment. The research methodology is outlined in Figure 2.  
The parity check matrix construction is a flexible algorithm that enables construction of QC-RL-LDPC code with any code length, base rate, submatrix size and chunk size, as is presented in Section 7. The developed decoding algorithm is presented in Section 6, and in general, it can be used in software or hardware implementations; however, for this research, the software model created in Matlab is used. Similarly, the transmission system with QAM/QPSK modulation and communication channel are modeled with software.  
Figure 2: Outline of research methods.  
The models are then used in Monte-Carlo simulations that provide, as a result, the estimated error correction performance of the system, in the function of channel corruption level represented by signal-to-noise ratio (SNR). The simulation results are provided in Section 8.3. The second part of the experimental design is the encoding timing estimation, which is conducted by injection into the encoder source code, a time measurement unit based on one of the hardware timers in the STM32 microcontroller. The timer is executed for as long as the encoding and the measurement are read by an interface with PC computer and then converted into time units. The results of these experiments are presented in Section 8.1.  
Finally, both the system performance results and the encoding timing results are used to assess energy consumption of the coding and transmission module in an IoT node, and the results are provided in Section 8.4.