esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion  
### Synthesis  
Two synthetic pathways were followed. Path A is classic with the reaction of [Cu(H\({}_{2}\)O)\({}_{6}\)](AsF\({}_{6}\))\({}_{2}\) or a mixture of Cu(CH\({}_{3}\)COO)\({}_{2}\) H\({}_{2}\)O and (NH\({}_{4}\))\({}_{2}\)CuCl\({}_{4}\) 2H\({}_{2}\)O, with NH\({}_{2}\)trz in aqueous MeOH, leading to complexes **I** and **III**, respectively. Path B is more original. It requires first the synthesis of the formamidine **L**-**HCI** which was obtained by reacting NH\({}_{2}\)trz with NaN\({}_{3}\) and HC(OEt)\({}_{3}\) in AcOH in an attempt to get the corresponding triazole-tetrazole molecule. The elemental analysis data suggest the resulting product to be a hydrochloride salt of a novel molecule (Scheme 1). Its \({}^{1}\)H NMR spectrum recorded in DMSO-\(d_{6}\) contains two singlet signals at 8.65 and 8.83 ppm, corresponding to the formamidine and triazole CH protons, respectively.  
**Scheme 1.** Synthesis of _N,N_-bis-(1,2,4-triazole-4-yl)formamidine hydrochloride.  
During our attempts to react Cu(II) salts with **L**-**HCl**, not surprisingly we noticed that this molecule was hydrolyzed in aqueous solution, and proves to be effective to slowly release NH\({}_{2}\)trz and obtain crystals of Cu(II) complexes **II**-**IV**. It should be noted that the formulation of complex **IV** is rather unusual. In addition to the **L**-**HCl** hydrolysis, SiF\({}_{6}^{2^{-}}\) anions were identified. These anions area actually generated _in situ_ by the reaction of BF\({}_{4}^{-}\) anions (provided by the Cu(II) salt) with the glass of the H-tube used to grow crystals of the complex [21]. Furthermore, since **L**-**HCl** was introduced as a hydrochloride salt, complex **IV** contains Cl\({}^{-}\) ions, which were provided by the ligand. This might be also true for **III** although CuCl\({}_{2}\) 2H\({}_{2}\)O was used as a source of Cu(II) ions.
# Seizure Detection in Medical IoT: Hybrid CNN-LSTM-GRU Model with Data Balancing and XAI Integration
## 5 Conclusions  
Being the control center of the body, the brain is fundamental to the execution of cognitive tasks, retention of information, articulation, movements as well as the homeostasis of body systems. This paper presents an effective EEG-based system for the early detection of epileptic seizures from an EEG signal, utilizing the hybrid Deep Learning model of the CNN, LSTM, and GRU models. The proposed framework applies the SMOTE technique to efficiently solve the challenge of imbalanced EEG data for undistorted and correct classification. The model achieved 99.15%, ensuring that the accuracy surpassed the Deep Learning and Machine Learning models. The specialists were able to understand the model better using Explainable AI techniques such as SHAP which made the process of medical decision-making more user-friendly. This framework aligns with the objectives of the Medical Internet of Things (MIoT) scheme as it encourages the development of better medical applications toward timely and accurate epileptic seizure detection.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Paper** & **Accuracy** \\ \hline [PERSON] et al. [39] & 98.98\% \\ [PERSON], [PERSON] [40] & 97.17 \\ [PERSON] et al. [30] & 97.10 \\ [PERSON] et al. [41] & 99.00\% \\ [PERSON] et al. [42] & 98.65 \\
**Proposed model** & **99.13\%** \\ \hline \hline \end{tabular}
\end{table}
Table 5: A comparison between the proposed system with previous studies in the same dataset.  
**Author Contributions:** Conceptualization, H.T., E.E.-D.H. and A.S.; methodology, E.E.-D.H.; project administration, H.T.; software, A.S.; validation and resources, A.S. and S.S.; visualization, S.S. and H.T.; writing--original draft, E.E.-D.H., A.S. and H.T.; writing--review and editing, S.S., S.H., H.T., E.E.-D.H. and A.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number (PSAU/2024/01/29858).  
**Data Availability Statement:** The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Results
### Comparison of Different Methods
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{6}{c}{**IoU**} & \multicolumn{6}{c}{**Indicators**} \\ \cline{2-10}  & **Imp. Surf.** & **Building** & **Low Veg.** & **Tree** & **Car** & **Background** & **mIoU** & **mF1** & **OA** \\ \hline Deeplabv3+ & 80.40 & 88.09 & 70.79 & 72.55 & 76.04 & 33.05 & 70.15 & 80.98 & 86.59 \\ Segformer & 81.79 & 89.64 & 71.72 & 73.72 & 77.98 & 32.97 & 71.30 & 81.69 & 87.35 \\ DNLNet & 82.14 & 89.54 & 71.91 & 73.95 & 81.43 & 33.15 & 72.02 & 82.15 & 87.15 \\ Segmenter & 82.38 & 90.69 & 73.09 & 74.76 & 75.80 & 36.71 & 72.24 & 82.57 & 87.99 \\ SegneXT & 80.73 & 88.11 & 70.90 & 73.39 & 72.53 & 34.05 & 69.95 & 80.92 & 86.79 \\ CMTFNet & 84.70 & 90.28 & 74.31 & 76.13 & 90.28 & 40.77 & 76.08 & 85.19 & 86.92 \\ SAN & 84.80 & 91.36 & 74.23 & 74.73 & 90.5 & 35.13 & 75.13 & 84.17 & 87.68 \\ Proposed & 84.19 & 92.08 & 73.94 & 76.30 & 84.06 & 35.48 & 74.34 & 83.77 & 88.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparisons of the methods on Potsdam (%). Figure 4: Visualization results for Vaihingen. Figure 5: Visualization results for Potsdam. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{8}{c}{**IoU**} & \multicolumn{8}{c}{**Indicators**} \\ \cline{2-13}  & **Background** & **Building** & **Road** & **Water** & **Barren** & **Forest** & **Agricultural** & **mIoU** & **mF1** & **OA** \\ \hline Deeplabv3+ & 54.22 & 57.94 & 49.58 & 65.57 & 20.87 & 38.24 & 51.06 & 48.21 & 63.81 & 68.61 \\ Segformer & 51.52 & 60.37 & 51.26 & 67.87 & 32.70 & 41.52 & 54.09 & 51.33 & 67.16 & 69.31 \\ DNLNet & 53.28 & 57.70 & 49.17 & 64.85 & 31.50 & 40.30 & 54.09 & 50.13 & 66.17 & 69.31 \\ Segmenter & 52.99 & 58.55 & 49.98 & 69.58 & 30.37 & 42.25 & 49.46 & 50.45 & 66.29 & 68.82 \\ SegneXT & 53.51 & 55.95 & 48.46 & 69.76 & 22.07 & 39.78 & 51.58 & 48.73 & 64.30 & 68.62 \\ CMTFNet & 52.61 & 55.05 & 51.15 & 57.95 & 22.02 & 37.31 & 45.87 & 45.99 & 62.05 & 68.48 \\ SAN & 53.51 & 64.00 & 56.90 & 69.73 & 26.23 & 39.53 & 51.50 & 51.63 & 66.95 & 72.01 \\ Proposed & 52.76 & 62.49 & 54.94 & 68.16 & 27.60 & 44.23 & 57.81 & 52.57 & 67.98 & 70.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparisons of the methods on LoveDA (%). Figure 6: Visualization results of LoveDA.
Efficient and sustainable agriculture requires the application of new technologies in all aspects of the production system. These technologies must combine energy efficiency, sustainable use of the resources and care for the environment, while obtaining crops of ever greater quality and quantity. Of particular importance is the application of remote image capture systems, which are increasingly being used as a means to obtain information of interest from the crops, the soil and the environment. They enable the acquisition of frequent and high-resolution information from great extensions of land, helping in agricultural decision-making in all stages of production. The field of remote imaging in agriculture includes different types of capturing devices: from satellites [1, 2] and drones [3, 4, 5, 6], to in-field devices [7, 8, 9, 10, 11, 12] or combinations of them [13, 14]; different types of spectral information, from visible RGB images [4, 6, 8, 9, 11, 12], to thermal and infrared images [7, 13], and multispectral images [1, 2, 3, 5, 14]; different types of applications, including water management [3, 7, 10], plant monitoring [6, 13], yield estimation [9], mapping and detection of plants [1, 2, 4, 5, 14], automatic harvesting [8, 12], and learning applications [15]; and different types of techniques in the areas of image processing, computer vision, pattern recognition and machine learning. This book covers all these aspects, through a series of chapters that describe specific recent applications of these techniques in various problems of interest in agricultural engineering. It is complete with a systematic mapping study [16] that surveys the current state of the art in this exciting and highly active domain.
# A Compact Sub-GHz Wide Tunable Antenna Design for IoT Applications
## 3 Results of Simulation and Measurements
### Radiation Patterns
Both planar and non-planar IoT antennas, [2; 3; 4; 5; 6; 15; 2; 7; 8; 9; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36], are reported in Table 2. None of the antennas as reported in [2; 3; 4; 5; 6; 15; 2; 7; 8; 9; 16; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36] have a continuous frequency sweep in the sub-GHz band. The proposed antenna is the only candidate that can cover sub-GHz bands over a wide frequency range.
## 3 Trackside Measurement through Field Instrumentation
### Trackside Measurement Method  
For measuring the wheel load and the lateral wheel load, as shown in Figure 4, strain gauges are attached at an angle 45\({}^{\circ}\) to the neutral axis of the rail bottom and web of the rail at a position 100 mm from the center between the sleepers. The sampling rate is set to 1200 Hz. For the dynamic response of the track, vertical and lateral rail displacement and vertical sleeper displacement are measured using the LVDTs. The field instrumentation installation overview is shown in Figure 4 below.  
A result example for vertical rail displacement is shown in Figure 4 The elastic modulus of the rail, using a general value of 60 kg/m rail steel (\(E=2.1\times 10^{3}\) MPa) is applied.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Type** & **Model** & **Manufacture Co.** & \begin{tabular}{c} **Measurement Item** \\ **and Purpose** \\ \end{tabular} \\ \hline \multirow{4}{*}{Sensor} & 2 axis-strain gauge & FCA-5-11-1L & Tokyo Sokki & \begin{tabular}{c} Vert/lateral Wheel \\ load \\ \end{tabular} \\  & 1 axis-strain gauge & FLA-5-11-1L & Tokyo Sokki & \begin{tabular}{c} Bending stress in rail \\ Vert./lateral dis. of rail, \\ Vert. Dis. of sleeper \\ \end{tabular} \\  & Dis. transducer & CDP-10 (10 mm) & Tokyo Sokki & \begin{tabular}{c} Vert/lateral dis. of rail, \\ Vert. Dis. of sleeper \\ \end{tabular} \\  & Data acquisition device & MGC plus & HBM & \\ Measurement & for dynamic responses & SDA-810 (8 ch) & Tokyo Sokki & \
\begin{tabular}{c} Data acquisition and \\ collection \\ \end{tabular} \\ instrument & Bridge box & DB-120 (1 ch, 8 ch) & Kyowa &
\begin{tabular}{c} collection \\ \end{tabular} \\  & Laptop & Windows 10 & Samsung & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Field instrument equipment list.  
Figure 4: Strain gauge and LVDT installation image.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Parameter** & **Calculation Method** & **Requirement Criteria** \\ \hline \multirow{3}{*}{Deraliment Coefficient} & \begin{tabular}{c} _Latest Wheel Land_ \\ _Vertical Wheel Land_ \\ - Maximum Value: 1.2 \\ \end{tabular} & \begin{tabular}{c} Allowable Limit \\ - 100\%: \(\leq\) 0.8 \\ - 0.10\%: \(\leq\) 1.1 \\ \end{tabular} \\  & \begin{tabular}{c} \(V_{static}-V_{dynamic}=[\)WLF\(]\) \\ - \(V_{static}\)= 85 KN: Static Wheel Load \\ \end{tabular} & \
\begin{tabular}{c} Allowable Limit \\ - 100\%: \(\leq\) 0.5 \\ - 0.10\%: \(\leq\) 0.8 \\ \end{tabular} \\  & \(\sigma\) & \(\leq\)200 MPa \\ Rail Vertical Displacement & mm &
\begin{tabular}{c} Concrete track: 3 mm \\ Ballast track: 4 mm \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Dynamic stability evaluation criteria.
## 3 Macro-Control and Micro-Autonomy Pathfinding Strategy
### Overall Design of Macro-Control and Micro-Autonomy Pathfinding System
#### 3.1.2 System Robustness Discussion  
The macro-control and micro-autonomy pathfinding system is based both on AGV communication and on each AGV's autonomous obstacle avoidance. We analyzed its behavior under the following special adverse conditions:  
Communication Delays or Interruptions: Packet loss or latency between an AGV and the central system can hinder timely updates on the exact position of the AGV, causing the AGV to receive a stale pre-planned path or to share outdated data with its neighbors. However, such issues do not halt system operation. Once the AGV reaches its next node and rescans the ground code, it can broadcast its true location.  
In rare cases where an AGV completely loses connectivity, its distributed planner will lack a fresh pre-planned path or updated information from the neighbor AGVs, potentially causing it to fail its current task. However, other AGVs will treat a disconnected vehicle as a dynamic unpredictable obstacle and simply avoid it. Thus, the system as a whole remains operational, demonstrating strong resilience to communication interruptions.  
Unexpected Situations: An AGV might be at risk of collision if it receives delayed position updates on its neighbors or fails to predict the trajectory of a dynamic unknown obstacle. To protect against this, each AGV retains an Emergency Stop Mechanism; its LiDAR continuously monitors for unforeseen obstacles within a safety radius. If such an obstacle appears, the AGV immediately halts, overriding any commands to ensure safety.  
Figure 4: The overall framework of macro-control and micro-autonomy pathfinding strategy.  
Furthermore, during docking maneuvers with conveyors or elevators, the AGV temporarily suspends obstacle detection to ensure a smooth connection. In these cases, it proceeds slowly, ensuring that no severe collisions occur.
# Evaluation of a New-Concept Secure File Server Solution
## 4 Results
### Results for 10 Gbit/s Connection
#### 4.2.1 Results of Reading Tests on 10 Gbit/s Connection  
The detailed results of the reading tests conducted on the network with a 10 Gbit/s throughput are illustrated in Table 4 and Figure 4.  
In the reading tests, ARDS TestServer was typically faster. However, when reading files of 4 MiB and 500 kiB using Windows 10 clients, the Windows 2019 server performed better; it outperformed the open-source file server by 27.3 percent in the case of the former size and by 7.2 percent for the 500 kiB file sizes. It is important to note that the fully open-source client-server combination was faster than any other operating system version in all cases. At this network speed, the client is subject to a significant I/O workload, which, especially for the two smaller file sizes, meant a spectacular performance degradation due to the intensive writing activity to the disk [25]. Also, it could be clearly observed that the file transfer time exceeded the theoretical throughput of the SATA III interface (750 MiB/s) in several cases due to the different caching mechanisms (SSD, CPU data cache, internal network cache, NIC data cache, etc.) operating in various manners and at different rates.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Files**} \\  & **1 \(\times\) 1 GiB** & **4 \(\times\) 256 MiB** & **256 \(\times\) 4 MiB** & **2097 \(\times\) 500 kiB** \\ \hline
**Test ID** & \multicolumn{4}{c}{**Average Transfer Speed in MiB/s**} \\ \hline
**Windows 10—ARDS TestServer** & 961.41 & 796.50 & 144.62 & 39.97 \\
**Windows 10—Windows 2019 Server** & 759.98 & 689.00 & 184.00 & 42.87 \\
**Ubuntu 22.04—ARDS TestServer** & 1020.51 & 998.14 & 620.99 & 208.60 \\
**Ubuntu 22.04—Windows 2019 Server** & 630.24 & 634.34 & 304.59 & 154.75 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Reading test results for 10 Gbit/s bandwidth.  
Figure 3: Measurement results of writing on 1 Gbit/s bandwidth.
# Analyzing Markov Boundary Discovery Algorithms in Ideal Conditions Using the d-Separation Criterion
## 4 Algorithms and Implementation
### Ipc-Mb  
The Iterative Parent-Child-Based Search of Markov Blanket (IPC-MB) [11] algorithm takes a more fine-grained approach and breaks the discovery problem into smaller subproblems. It relies on concepts refined by PCMB [7], originally introduced by HITON [5] and MMMB [6]. In short, IPC-MB first discovers the parents and children of the given target variable, then repeats this discovery process for each of the parents and children. The goal is to find the spouses of the target variable, which are the variables that have common children with the target.  
The key improvement of IPC-MB lies in the tactic employed when searching for parents and children: it ensures that the first CI tests performed during the search are those with the smallest conditioning set. The algorithm ensures this by iteratively incrementing the size of the conditioning set used in the CI tests. This way, the algorithm eliminates false positives as early as possible, therefore performing fewer tests. Moreover, the tests that it needs to perform have smaller conditioning sets overall.  
IPC-MB requires no heuristic at all, only a CI test implementation. This makes IPC-MB very appropriate for the experiment described in this article.
## 2 Materials and Methods
### Feedforward Artificial Neural Networks  
Artificial neural networks (ANNs), first proposed in the 1950s, are used in regression and classification tasks [37]. Feedforward neural networks (FNNs) are ANNs that propagate signals from input to output without feedback elements [52]. The neuron output in the output layer is given by Equation (2).  
\[\mathbf{a}^{m+1}=f^{m+1}(\mathbf{W}^{m+1}\mathbf{a}^{m}+\mathbf{b}^{m+1}) \tag{2}\]  
where \(f^{m+1}\) is the activation function, \(\mathbf{W}^{m+1}\) is the weight matrix, and \(\mathbf{b}^{m+1}\) the bias matrix in the layer \(m+1\), \(\mathbf{a}^{m}\) as the previous neuron output in the layer \(m\), and \(m=0\), \(1\ldots,M-1\), where \(M\) represents the number of layers [52].  
The \(f^{m+1}\) activation functions are hyperbolic tangent sigmoids (tanh) represented in Equation (3) for hidden layers. Moreover, the linear function indicated in Equation (4) provides approximations with finite discontinuities [52].  
\[\tanh(\ n^{m}{}_{i})=\frac{2}{1+e^{-2\mathbf{n}^{m}_{i}}}-1 \tag{3}\]  
\[linear(\mathbf{n}^{m}{}_{i})=\mathbf{n}^{m}{}_{i} \tag{4}\]  
With \(\mathbf{n}^{m}{}_{i}=\sum_{j=1}^{\mathbf{a}^{m-1}}w^{m}{}_{i,j}\mathbf{a}^{m-1}{}_{j}+\mathbf{ b}^{m}{}_{i}\), where \(w^{m}{}_{i,j}\) is the \(i,j\) element of the weight's matrix \(\mathbf{W}^{m}\), corresponding with the \(i\) neuron in the \(m\) layer and its \(j\) input; \(\mathbf{a}^{m-1}{}_{i}\) is the output of the neuron \(i\) at layer \(m-1\); and \(\mathbf{b}^{m}{}_{i}\) is the bias of the neuron \(i\) at layer \(m\).  
In this proposal, we train the FNNs with scaled conjugate gradient (SCG) backpropagation, which calculates the gradient in specific conjugate directions with increased convergence speed, as proposed by [PERSON] in 1993.
# Performance and Requirements of GEO SAR Systems in the Presence of Radio Frequency Interferences
## 4 Performance Evaluation and Discussion: Example of GEO SAR Design  
In this section, we evaluate the performance of GEO SAR in the presence of RFI by using System Tool Kit (STK) software to generate the GEO SAR orbit, LEO SAR orbits and the GNSS orbits for the simulation. The High-Precision Orbit Propagator (HPOP) in STK is adopted to obtain more accurate orbit simulation. The input parameters of our inclined orbit GEO SAR system are given in Table 2[19; 21]. We consider the classical \"Figure 8\" inclined GEO SAR orbit with the small eccentricity and the large inclination. The designed GEO SAR system can work in three frequency bands (L, C and X). The large antenna size is utilized to obtain the higher gain of the GEO SAR signal.  
The performance of a GEO SAR defined in Table 2, is shown in Table 3. We assume that both the antenna size and the swath are kept constant with different frequencies (from L band to X band), by using scan SAR mode. From Table 2, when the working frequency of GEO SAR increases from the L band to X band, the number of beams will accordingly change from 1 to 64 to keep the coverage requirement. We design the basic SINR requirement as 10 dB to make the system reach the imaging requirement for interferometric applications. Proper signal bandwidth and integration times make the GEO SAR system have range and azimuth resolutions better than 20m.
## 6 Conclusions and Future Trends  
In this systematic review, we explored the primary methods and algorithms employed in AI-based dental image analysis, a field that has experienced significant growth through the integration of AI techniques. We identified several essential implications from our thorough examination of the current research; AI-based dental image analysis has many uses, including digital dental diagnosis, caries detection, gender and age classification, tooth segmentation, disease classification, and more. This range demonstrates the flexibility of AI in addressing various problems in dental healthcare.  
Researchers use a range of deep learning and machine learning approaches that are customized to their specific research objectives. While unique architectures and specialized algorithms are also used, convolutional neural networks (CNNs) are the main characteristic. This variety shows how flexible AI techniques are in response to the intricacies of dental image processing.  
A variety of imaging types, such as radiographs, X-ray images, panoramic radiographs, 3D point cloud data, and near-infrared transillumination images, are included in dental image analysis. AI-powered systems show how well they can analyze and understand these various kinds of images.  
Deep clinical ramifications follow from the results of AI-based dental image analysis. Increased accuracy and precision in diagnostic and treatment planning can result in better patient outcomes and more productive dental practices.  
Data collection, annotation, privacy, and clinical validation issues still raise concerns. Still, there are chances for creativity and further development of AI-based dental treatments in addition to these difficulties. It is emphasized how critical standardized datasets are to the process of benchmarking diagnostic systems. Standardization promotes the creation of AI solutions that can be used in a variety of clinical situations and allows for fair model comparisons.  
AI-based dental image analysis is a rapidly developing field with huge opportunities to enhance dental care. The main techniques and algorithms discussed here demonstrate the impressive progress made in utilizing AI to improve dental diagnostics and treatment planning accuracy and effectiveness. It is critical that researchers and practitioners work together to address issues, create standards, and take advantage of fresh opportunities as the field of AI-based dental image analysis develops. Ultimately, the attempt to improve dental care will benefit patients, researchers, and clinicians alike.
# The Customized Automatic Processing Framework for HY-2A Satellite Marine Advanced Products
## 4 Case Studies
### Cloud Liquid Water  
Because the cloud liquid water is also derived from the HY-2A scanning microwave radiometer, the customized processing workflow is similar to the previous two workflows. With the seven steps, the workflow can read the cloud liquid water dataset from the level-2 HDF file of the HY-2A scanning microwave radiometer. After extraction, interpolation and gridding, the data can be made as a thematic map, which is shown in Figure 8.