esa-sceva/satcom-chunk-collection huggingface dataset:
# Battery Management System Hardware Concepts: An Overview
## 3 Topologies of BMS
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **(a)** & **(b)** & **(c)** & **(d)** & **(e)** \\ \hline Voltage & \(V_{cell}\) & \(V_{cell}\) & \(3\cdot V_{cell}\) & \(3\cdot V_{cell}\) & \(3\cdot V_{cell}\) \\ Capacity & \(Q_{cell}\) & \(2\cdot Q_{cell}\) & \(Q_{cell}\) & \(2\cdot Q_{cell}\) & \(2\cdot Q_{cell}\) \\ Voltage channels & 1 & 1 & 3 & 6 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of the topology variants shown in Figure 2.  
Figure 2: Schematic representations of different battery pack topologies: (**a**) single cell; (**b**) parallel connection of two cells; (**c**) series connection of three cells; (**d**) parallel connection of two strings of three serially connected cells; (**e**) series connection of three modules consisting of two cells connected in parallel. each cell remains way below its maximum allowed voltage and above its minimum allowed voltage, the probability of one cell becoming critical is comparatively small. To implement the basic monitoring functionality needed for a safe operation of the batteries, several off-the-shelf Application-specific integrated circuits (ASICs) are offered by well-known semiconductor manufacturers. As there is a wide variety of available components, only representative examples, which are available or in use at the time of writing, are provided here. For small personal electronic devices (smartphones, e-book readers, audio players, etc.) that often contain only one single battery cell, so-called \"fuel gauges\" ICs exist. These components represent the simplest form of a BMS ASIC. They provide functions like voltage monitoring, current measurement and temperature measurement for one cell. In addition, often some kind of SOC estimation, based on the measured values, is integrated. Some of these devices also contain further functional elements, like charging regulators. Examples for this class of components are TI's (Texas Instruments) (Dallas, TX, USA) \"bq27220 Single-Cell CEDV Fuel Gauge\" [1] or Maxim's \"Micropower 1-Cell/2-Cell Li+ ModelGauge ICs\" [2]. Both are designed to be used in combination with a protection circuit that can disconnect the battery in case of failure. Both ICs measure voltage and temperature and estimate the cell's SOC. The TI component measures the cell's current in order to estimate the SOC via coulomb-counting, while Maxim claims in the datasheet that their IC uses an \"advanced battery model\" to estimate the SOC without needing a current measurement resistor. The measured and calculated values are provided to the surrounding system via serial interfaces in both cases. In the Appendix A and the Supplementary Materials section, a list of all BMS ICs that were mentioned in the text and some similar types can be found (Table A1).
# Effect of Subject-Specific Region of Interest on Motor Imagery Brain-Computer Interface
## 2 Materials and Methods
### Validation of Intrinsic Time-Scale Decomposition Method  
The dataset from BCI Competition IV [34] was used to validate the experimental work. The dataset consisted of two sessions, namely calibration and evaluation sessions. These included seven subjects: four subjects (named \"a\", \"b\", \"f\", \"g\") were healthy, and three artificially generated subjects (named \"c\", \"d\", \"e\") were tested. The calibration session consisted of two runs; a total of 200 trials for motor imagery of left hand, right hand or feet were used. The timeline of each trial is shown in Figure 3. The fixed cross was displayed for 2 seconds to prompt the participant to prepare. Then, an arrow would appear for four seconds to prompt the subject to start the motor imagery task. The directions of the arrows are: left--indicating left imagination task, right--indicating right imagination task, and down--indicating feet imagination task. Finally, the screen was black for two seconds, indicating that the trial was over. EEG signals for 59 channels were recorded at a 1000 Hz sampling rate. These data were then band-pass-filtered within 0.05 Hz to 200 Hz, and down-sampled to 100 Hz.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Ocean Oil Spill (OOS)
#### 2.7.2 Summary and Future Direction  
OOS detection using RS technology has several limitations: (1) there is a lack of open-source annotated datasets that have been compiled from numerous multisensory sources in various places; (2) to discover optimal parameter configurations, fine-tuning of hyperparameters of ML models necessitates substantial trial-and-error testing; (3) the literature lacks a detailed classification of oil spills/slicks, including oil type, thickness, and other chemical parameters; (4) many RS images are accessible; however, the traditional method of image downloading, preprocessing, and core processing should be replaced by web processing algorithms. Given the ongoing advancements in RS technology, cloud computing services, computer vision methods, and the growing availability of publically annotated RS data, the aforementioned issues could be reduced in the future [265].  
There is a clear requirement for server-based algorithms to process images in the cloud. A new type of in situ measurement is also required, such as tiny and low-cost buoys that measure the type of oil and its chemical composition, as well as small UAVs for high-resolution monitoring tasks. Given their strong discriminating capabilities, hyperspectral sensors will have more attention in the future, particularly when combined with UAVs for OOS detection [2]. Furthermore, future approaches will use more background auxiliary information, such as OSC, OSW, and Chl-a distribution, to increase OOS detection accuracy [255; 266].  
Recently, several DL algorithms have been developed for OOS detection from RS data [267; 268; 269; 270] with very high accuracy. These algorithms merit robust feature extraction and autonomous learning capabilities, paving way for the automatic and rapid detection of OOS by clearly distinguishing OOS from lookalikes [271; 272]. Thus, it is expected more DL models along a combination of various RS datasets will be applied for OOS monitoring in the future.
# Integrating Machine Learning Algorithms: A Hybrid Model for Lung Cancer Outcome Improvement
## 5 Conclusions  
The lungs are the primary organs of respiration, providing a constant supply of oxygen to the blood, without which a human could not survive. Lung cancer is the leading cause of cancer-related deaths for both males and females, with most fatalities occurring after diagnosis due to the disease stage at detection. Early diagnosis significantly increases life expectancy. In this study, supervised learning was explored to train a model for diagnosing lung cancer based on feature extraction from symptom-related data. The models were compared using accuracy, precision, recall, F1-score, and AUC values as performance criteria for logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), Gradient Boosting (GB), Gaussian naive Bayes (GNB), multinomial naive Bayes (MNB), decision tree (DT), Random Forest (RF), and multilayer perceptron (MLP). The experimental results obtained through 10-fold cross-validation revealed that Gradient Boosting outperformed other models, achieving 95.61% accuracy, precision, recall, and F1-score, along with an AUC of 98%. After a detailed evaluation of nine machine learning algorithms, both Gradient Boosting (95.61% accuracy) and the hybrid model (96.42% accuracy) were identified as the most suitable predictive models for lung cancer diagnosis.
## 4 Finite-Element Analysis  
### Case Modelling and Summary  
In this study, the raft and pile settlements of piled raft due to tunneling are analyzed, and numerical analysis was performed to derive a predictive model of the change rate of settlement along the length of pile.  
Figure 4 shows the variables considered in this study and each settlement point for measurement. The settlement of raft (\(S_{R}\)) and pile (\(S_{P}\)) was measured in this numerical analysis; then, the change rate of settlement was calculated along pile length (dS). Change rate of settlement dS is defined as \((S_{P}-S_{R})/P_{L}\), where \(P_{L}\) is the length of the pile. As unit \(S_{P}\) and \(S_{R}\) were mm, the measurement unit for \(P_{L}\) was m. It was also assumed that dS acted linearly from raft to pile toe; however, in reality, this could be a nonlinear relationship. This was further studied through additional model tests and numerical analysis.  
Figure 3: Settlement of piled raft due to tunneling from model test.  
The range and meaning of variables discussed in Figure 4 are summarized in Table 2, and mesh generation in FE analysis is shown in Figure 5. Tunneling is modeled using the concept of volume loss recommended by [PERSON]  
Tunneling is modeled using the concept of volume loss that is mentioned by [PERSON] [23]. The volume loss is a widely used method for the modeling of tunnel excavation in soil. In this study, the actual method of tunnel construction such as NATM, NTM or TBM is not applied because the aim of this study is to predict settlement of piled raft due to tunneling, as well as to verify this.  
The allowable load applied to FE analysis was calculated by applying the safety factor after determining the ultimate load through the P-S curve of the unpiled raft and grouped pile. The ultimate load of the unpiled raft was calculated using the P-S curve, and the ultimate load was considered when settlement of about 25-30 mm occurred by referring to [24]. Grouped piles were calculated by classifying them according to pile diameter, using [25].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Ranges** & **Comment** \\
**Name** & & \\ \hline E\({}_{\text{G}}\) & 20e3, 40e3, 50e3 & Relative soil density \\ P\({}_{\text{N}}\) & 4, 9, 25 & Pile number \\ O\({}_{\text{V}}\) & 0.5, 1.5, 5.0 D & Vertical offset pile toe–tunnel crown (D = tunnel diameter) \\ O\({}_{\text{H}}\) & 0.0, 2.0, 5.0 D & Horizontal offset pile toe–tunnel crown (D = tunnel diameter) \\ T\({}_{\text{F}}\) & 10, 4, 3, 2, 1 Br & Horizontal distance of raft–tunnel face \\ \(P_{L}\) & 6.5, 12.5, 22, 10, 20, 35, 12.5, 25, 44 & Pile length (\(P_{L}/\text{B}_{\text{P}}\) = 13, 25, 44) \\ P\({}_{\text{CTC}}\) & 1.25, 2.5, 5.0 BP & Distance to center of piles \\ B\({}_{\text{P}}\) & 0.5, 0.8, 1.0 & Pile diameter \\ Br & 12, 20, 25 & Raft width \\ \hline \hline \end{tabular}
\end{table}
Table 2: Feature conditions for numerical analysis.  
Figure 4: Problem considered for finite-element (FE) analysis.
###### Abstract  
The Internet of Underwater Things (IoUTs) enables various underwater objects be connected to accommodate a wide range of applications, such as oil and mineral exportations, disaster detection, and tracing tracking systems. As about 71% of our earth is covered by water and one-fourth of the population lives around this, the IoUT expects to play a vital role. It is imperative to pursue reliable communication in this vast domain, as human beings' future depends on water activities and resources. Therefore, there is a urgent need for underwater communication to be reliable, end-to-end secure, and collision/void node-free, especially when the routing path is established between sender and sonbouys. The foremost issue discussed in this area is its routing path, which has high security and bandwidth without simultaneous multiple reflections. Short communication range is also a problem (because of an absence of inter-node adjustment); the acoustic signals have short ranges and maximum-scaling factors that cause a delay in communication. Therefore, we proposed Rotational Orbit-Based Inter Node Adjustment (ROBINA) with variant Path-Adjustment (PA-ROBINA) and Path Loss (PL-ROBINA) for IoUTs to achieve reliable communication between the sender and sonbouys. Additionally, the mathematical-based path loss model was discussed to cover the PL-ROBINA strategy. Extensive simulations were conducted with various realistic parameters and the results were compared with state-of-the-art routing protocols. Extensive simulations proved that the proposed routing scheme outperformed different realistic parameters; for example, packet transmission 45% increased with an average end-to-end delay of only 0.3% respectively. Furthermore, the transmission loss and path loss (measured in dB) were 25 and 46 dB, respectively, compared with other algorithms, for example, EBER\({}^{2}\) 54%, WDFAD-BDR 54%, AEDG 49%, ASEGD 55%, AVH-AHH-VBF 54.5%, and TANVEER 39%, respectively. In addition, the individual parameters with ROBINA and TANVEER were also compared, in which ROBINA achieved a 98% packet transmission ratio compared with TANVEER, which was only 82%.  
IoUTs; ROBINA; PA-ROBINA; PL-ROBINA; void node; energy consumption; end-to-end delay; path loss; routing path
## 5 Raspberry Pi-Based Implementation of the Suggested Algorithm
### Parallel Encryption of the QR Code  
The QR code encryption step can also be implemented based on parallel computing, as illustrated in Figure 13.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Length of the** & **Conventional Serial** & **Parallel Computing** & **ETIR (\%)** \\
**Feature Vector** & **Method** & **Method** & **ETIR (\%)** \\ \hline L = 256 & 0.1240 & 0.0325 & 73.7903 \\ \hline L = 1024 & 0.5602 & 0.1520 & 72.8668 \\ \hline L = 4096 & 2.1220 & 0.5810 & 72.6202 \\ \hline L = 8100 & 2.6210 & 0.7024 & 73.2011 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison in terms of execution time between the sequential and parallel methods for the feature vectors extraction and encryption problem.  
The QR code encryption phase can be implemented in parallel using multi-core CPUs by following the next steps:  
1. Divide the QR code into a set of equal-sized blocks where the number of these blocks is proportional to the available CPUs.
2. Use the method described in Section 4.4 to encrypt the QR code blocks on the available CPUs in parallel, as shown in Figure 13.
3. Concatenate the encrypted blocks to generate the encrypted QR code.  
In order to recover the original QR code in the decryption phase, we applied the reverse steps from (iii) to (i). To show the ETIR(%) of parallel QR code encryption in comparison to the conventional encryption method, we used the QR code shown in Figure 8a, which was resized to \(200\times 200\), \(400\times 400\), and \(600\times 600\) pixels, respectively. The codes were then encrypted using the conventional and parallel methods based on a quad-core PC equipped with 4 GB of RAM and 2.4 GHz CPU. The encryption process of each QR code was then run 100 times, and the average running time was determined and is presented in Table 3. From this table, we can clearly note that the encryption time of the QR code improved by \(ETIR>71\%\) when using the parallel encryption method instead of the serial method. This outcome motivated us to implement our scheme on low-cost multi-core hardware such as Raspberry Pi.
## 3 Hybrid GA with Layered Process for MIMO Detection
### Proposed Hybrid Genetic Algorithm
#### 3.1.4 Procedure of the Proposed HGA
If \(MU_{chk}\) is less than the _mutation size_\(M_{u}\), go back to step (ii). v. If \(S_{chk}\) is one, insert the \(\mathbf{s}_{g}\) and vector-neighbor of \(\mathbf{s}_{g}\) into the \(\mathbf{MU}_{pool}\) and insert the \(\mathbf{s}_{g}\) into the \(\mathbf{MU}_{list}\). vi. Insert the \(\mathbf{MU}_{pool}\) to \(\mathbf{P}_{g}\). 4. Evaluate the fitness of the current population \(\mathbf{Fit}_{g}\). 5. If the stop criterion is met, the procedure is stopped. Otherwise, go to Step (d). The above summary is represented in Algorithm 1 where \(\varnothing\) denotes the empty set and \(RWS(\mathbf{A},B)\) is a roulette wheel selection method that select the individual in population \(\mathbf{A}\) based on the fitness value \(B\). \(\mathbf{C}\setminus\mathbf{D}\) is a set difference defined by \(\mathbf{C}\setminus\mathbf{D}=\{x:x\in\mathbf{C}\text{ and }x\
otin\mathbf{D}\}\). ```
0:\(G\), \(C_{r}\), \(M_{u}\) Initialize :  Generate \(\textbf{P}_{0}\) and evaluate \(\textbf{Fit}_{0}\). base on \(\textbf{P}_{0}\)  Generate \(\textbf{MU}_{list}=\varnothing\) for\(g=1\) to \(G\)do  Generate \(\textbf{P}_{g}=\varnothing\), \(S_{\text{chk}}=0\) Selection  Fittest individual \(\textbf{s}_{g}\) in \(\textbf{P}_{g-1}\) is copy and insert to \(\textbf{P}_{g}\) if\(\textbf{s}_{g}\
otin\textbf{MU}_{list}\)then \(S_{\text{chk}}=1\) endif  Crossover  Generate \(\textbf{CR}_{pool}=\varnothing\), \(CR_{\text{chk}}=0\) while\(CR_{\text{chk}}\leq C_{r}\)do \(\textbf{P}_{A}=RWS(\textbf{P}_{g-1},\textbf{Fit}_{g-1})\) \(\textbf{P}_{A}=RWS(\textbf{P}_{g-1}\setminus\textbf{P}_{A},\textbf{Fit}_{g-1})\)  Randomly choose the crossover length \(CR_{len}\) \(\textbf{off}_{\sigma}=SPC(\textbf{p}_{A},\textbf{p}_{B},CR_{len})\) if\(\textbf{off}_{\sigma}\
otin\textbf{CR}_{pool}\)then  Insert \(\textbf{off}_{\sigma}\) into \(\textbf{CR}_{pool}\) \(CR_{\text{chk}}=CR_{\text{chk}}+1\). endif endwhile  Insert \(\textbf{CR}_{pool}\) into \(\textbf{P}_{g}\)  Mutation  Generate \(\textbf{MU}_{pool}=\varnothing\), \(MU_{\text{chk}}=0\) while\(MU_{\text{chk}}=0\leq M_{u}\)do \(\textbf{p}=RWS(\textbf{CR}_{pool},\textbf{Fit}_{g-1})\) \(\textbf{CR}_{pool}=\textbf{CR}_{pool}\setminus\textbf{p}\) if\(\textbf{p}\
otin\textbf{MU}_{list}\)then  Insert \(\textbf{p}\) and the _vector-neighbor_ of \(\textbf{p}\)  into \(\textbf{MU}_{pool}\)  Insert \(\textbf{p}\) into \(\textbf{MU}_{list}\) \(CR_{\text{chk}}=CR_{\text{chk}}+1\). endif endwhile if\(S_{\text{chk}}=1\)then  Insert \(\textbf{s}_{g}\) and the _vector-neighbor_ of into \(\textbf{s}_{g}\)  into \(\textbf{MU}_{pool}\) endif  Insert \(\textbf{MU}_{pool}\) into \(\textbf{P}_{g}\) Fitness  Calculate \(\textbf{Fit}_{g}\) base on \(\textbf{P}_{g}\) endfor
```  
**Algorithm 1** The proposed hybrid genetic algorithm
# FOXS-GSC--Fast Offset Xpath Service with HexagonS Communication
## 4 FOXS-GSC Performance Evaluation
### Network Evaluation
#### 4.1.2 Results  
Figure 7a shows the number of messages re-transmitted. Flooding had the largest number of messages re-transmitted, with more than \(135,000\) messages in the scenario with 500 vehicles/km\({}^{2}\). This occurs because Flooding does not have a broadcast suppression mechanism. The suppression mechanism of DESTINy, which is based on the distance to the destination, reduces the number of messages by 70% in comparison to Flooding. However, many messages continue to be generated until the message reaches the destination, reaching 90% higher than the FOXS-GSC with the same vehicle density. The average number of  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Map & Manhattan downtown \\ \hline Map Size & 1 km\({}^{2}\) \\ \hline Transmission power & 2.2 mW \\ \hline Communication range & 300 m \\ \hline Bit rate & 18 Mbit/s \\ \hline Beacons & 4 s \\ \hline Number of RSUs * & 1, 4, 8 \\ \hline Confidence interval & 95\% \\ \hline Message sending period & 3 s \\ \hline \hline \end{tabular}
* Only one RSU was used in the experiments described in Section 4.2.  
\end{table}
Table 3: Simulation parameters.  
re-transmissions per message sent for FOXS-GSC is equal to the number of sectors on the path to the destination. Thus, the number of messages in the system is reduced.  
Figure 6(b) shows the number of packet collisions relative to the number of vehicles. FOXS-GSC keeps the collision number stable in relation to the number of vehicles, with 120.4 messages for a density of 500 vehicles/km\({}^{2}\). Note that the number of collisions with Flooding (26,000 for the density of 500 vehicles/km\({}^{2}\)) grows much faster than that of DESTINy (8000 for 500 vehicles/km\({}^{2}\)) and FOXS-GSC. This is related to the large number of messages needing to be re-transmitted (see Figure 6(a)).  
Figure 6(c) shows the percentage of vehicles that sent and received at least one message during the simulation. FOXS-GSC reached an average of 98% coverage. Flooding and DESTINy had significantly lower coverage with increasing vehicle density, covering only 33% and 64% of vehicles in the scenario with a density of 500 vehicles/km\({}^{2}\). These values are corroborated by the high number of collisions when using these solutions. On the other hand, that FOXS-GSC is able to reach the entire monitored environment.  
Figure 6(d) shows the percentages of messages sent and properly answered during the simulation. In the low-density scenario (50 vehicles/km\({}^{2}\)), the percentage of messages answered was 79% for FOXS-GSC, 65% for Flooding, and 60% for DESTINy. With increased density, FOXS-GSC maintained an average percentage of messages delivered at 74%, whereas the same value for Flooding and DESTINy was greatly reduced, falling below 20% with a density of 500 vehicles/km\({}^{2}\). This is due to the large number of message collisions generated by these solutions. Thus, the proposed solution proves to be efficient in both the sparse scenario and the dense scenario.
# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 5 Discussion
### Practical Impact of DTs on Water Management  
DT technology is rapidly changing how farmers manage water, helping them make smarter, clearer decisions based on real-time data. By creating digital copies of actual farms, DT allows farmers to see exactly what's happening in their fields without guesswork. Instead of relying on traditional methods, which often involve trial and error, farmers can now use these virtual models to apply water precisely where and when it is needed. This helps conserve water, cuts unnecessary costs, and ultimately improves the health and productivity of crops. One key benefit of DTs is their ability to test different scenarios virtually. For example, farmers can simulate how their crops might react to drought, heavy rainfall, or sudden changes in weather. This means they can explore different strategies safely, without risking their real crops. Such preparation helps reduce risks and makes farms more resilient to unpredictable climate conditions. Additionally, DTs can quickly identify early signs of stress in plants or changes in water quality. Early detection gives farmers a chance to act promptly and avoid losses or damage. Another important advantage is that DT technology is flexible enough to work well for both large-scale farms and smaller family-owned farms. On bigger farms, DTs help manage complex irrigation networks more efficiently, keeping costs down and productivity high. For small-scale farmers, DTs offer affordable tools to use limited resources wisely, helping them stay competitive and sustainable.  
Overall, DT technology offers farmers practical and effective ways to handle modern challenges like water shortages, rising costs, and uncertain weather patterns. By making better-informed decisions, farmers can use their resources efficiently, reduce risks and achieve higher profitability, creating stronger, healthier, and more sustainable farming operations.