esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions
### Computational Efficiency Analysis of Deep Learning vs. Shallow Models  
The computational requirements of deep learning models compared to shallow models present critical trade-offs in practical deployment scenarios. Our experimental analysis reveals three key dimensions of divergence:  
(1) Training complexity: Shallow models (e.g., linear regression, Ridge, LASSO) exhibit near-instantaneous training on standard CPUs, while random forests show moderate training times (minutes to hours) but benefit from parallelization. In contrast, LSTM-DNN architectures demand substantial resources--each of the five independent training runs requires 5-30 min on NVIDIA GPUs (e.g., T40 or RTX 3060) due to iterative backpropagation and high parameter counts.  
(2) Inference efficiency: Linear models achieve millisecond-level latency (<5 ms), ideal for real-time applications, whereas random forests exhibit 200-500 ms delays, suitable for near-real-time tasks. Although LSTM-DNN requires GPU acceleration, optimized implementations (e.g., tensor core utilization) achieve sub-second inference (~300 ms). Notably, ensemble methods introduce 2.3\(\times\) training and 2.8\(\times\) inference overheads from multi-model cascading.  
(3) Hardware-specific optimization: The LSTM-DNN (64, 1, 2) configuration demonstrates balanced performance, with T40 GPUs completing 100 epochs in 10-20 min (0.96 ms/batch) versus 20-30 min on RTX 3060 (1.73 ms/batch). Memory footprint analysis shows 50-100 MB usage (FP32), dominated by Adam optimizer states (18.58 MB) and gradients (1.03 MB). Techniques like mixed-precision training (FP16) and gradient accumulation can halve memory usage while accelerating computation.  
In sum, the computational efficiency is a multi-faceted consideration, requiring alignment between model selection, hardware capabilities, and deployment constraints. Future work could explore advanced parallelism (e.g., 3D hybrid strategies combining tensor/pipeline/data parallelism) or memory-efficient techniques like ladder-side-tuning.
## 4 Results and Discussion: Unbalanced Magnetic Forces
### Evaluation on a 24-Slot, 22-Pole PMSM and MPMSM  
As an example, in Figure 4 the outcomes of the FEM evaluation of a 24-slot, 22-pole PMSM and a 24-slot, 22-pole MPMSM are presented for both SE and DE. The results were extracted considering the rotor structure completes one full turn. In Figure 4a,b the \(x\)-axis and \(y\)-axis forces acting on the rotor structure are shown when DE is present for the modular and the conventional machine, respectively. The X represents the average radial forces generated in a full rotation, which is zero for the case of DE. It may be noted that a rotating force vector is generated in both cases, with an almost constant magnitude and low ripple (visualized as saw teeth in the force circumference periphery).  
From Figure 4c,d, which show the spatial harmonic content of the radial forces, it can be seen that the spatial frequency of the radial force ripple matches the slot pitch of the machine (\(\text{HO}=24\)). This can be explained since for DE the relative position between  
Figure 4: FE evaluation of radial forces acting on the rotor structure in the presence of DE (left column) and SE (right column) for a 24-slot, 22-pole PMSM and MPMSM: (**a**) \(x\)-axis and \(y\)-axis forces of DE on MPMSM, (**b**) \(x\)-axis and \(y\)-axis forces of DE on PMSM, (**c**) radial force spatial harmonic content of DE on MPMSM, (**d**) radial force spatial harmonic content of DE on PMSM, (**e**) \(x\)-axis and \(y\)-axis forces of SE on MPMSM, (**f**) \(x\)-axis and \(y\)-axis forces of SE on PMSM, (**g**) radial force spatial harmonic content of SE on MPMSM, (**h**) radial force spatial harmonic content of SE on PMSM.  
the magnet symmetry axis and teeth symmetry axis is periodical and repeats each \(360/Q_{\rm s}\) mechanical degrees. Similar findings were described for the cogging torque in conventional PMSMs in [34]. In turn, in Figure 4e,f, the \(x\)-axis and \(y\)-axis forces acting on the rotor structure are shown when SE is acting in the modular and the conventional machine, respectively. The X represents the average radial forces generated in a full rotation, which points from the minimum airgap position towards the center of rotation for the case of SE. It may be noted that the \(x\)-axis forces are not zero and configure the radial force ripple. From Figure 4g,h, which show the spatial harmonic content of the radial forces, it can be seen that the spatial frequency of the radial force ripple matches the pole pitch of the machine (\(\rm HO=22\)). This is because in SE the relative position between the magnet symmetry axis and teeth symmetry axis is periodical and repeats each \(360/2p\) mechanical degrees, as described in [24].  
In summary, relevant features of the radial force results can be summarized by the following indicators, useful for evaluating and comparing the different slot/pole combinations in Table 2.  
* Mean value of the radial force acting on the rotor structure, measured in N.
* Peak-to-peak value of the radial force, representing the radial force ripple and measured in N.
# Distributed Fault Detection and Isolation Approach for Oil Pipelines
## 3 Model-Based Distributed FDI
#### Local Diagnosers Design  
The method proposes the creation of local diagnosers for each subsystem that can operate independently of each other by sharing the minimum possible amount of sensor information. This approach considers predefined subsystems and, in that context, it is designed in a distributed framework. Given a set of faults of interest, known and unknown variables for each subsystem, first, Algorithm 1 calculates FMSO sets for the set of faults of each subsystem starting from the equation (or the set of equations) that contains the fault of interest. This set of equations is called evaluation equations \(\Sigma_{E}\).  
**Definition 13** (Subset \(\mathcal{R}\)).: _Given a subsystem \(\Sigma_{i}\) and a set of unknown variables under evaluation \(x_{E}\subseteq x_{i}\), \(\mathcal{R}(x_{E},\Sigma_{i})\) is the subset of equations that contains at least one of the elements of \(x_{E}\), \(\mathcal{R}(x_{E},\Sigma_{i})\subseteq\Sigma_{i}\)._  
If there is any fault \(f\in F_{j,i}\) not detectable or not isolable with the ARRs computed by Algorithm 1, that is, if FMSO sets are not found with the exclusive information of that subsystem, Algorithm 2 is used, which uses a criterion of adding equations of neighboring subsystems, initially of level 1: one-by-one sub-systems are added and, if it is necessary, two by two until the whole set of subsystems of level 1 is brought together. If the diagnostic objectives cannot yet be achieved, subsystems of level 2 are incorporated in addition to the set of subsystems of level 1.  
Figure 3: An illustrative example of classification of neighboring subsystems.  
```
1:\(\Sigma_{i}(z_{i},x_{i},\mathbf{\xi}_{i})\), \(i=1,\ldots,n\),
2:\(\Sigma_{E}\leftarrow\) Set of equations directly impacted by faults of \(F_{i}\);
3:\(\Sigma_{E}=\emptyset\); \(\Phi_{i}=\emptyset\); L = 0;
4:while\(ARR_{i}=\emptyset\)do\(\triangleright\) level analyzed;
5:L = L + 1;
6:while\(\overline{\Sigma}_{E}\
eq\Sigma^{L,i}\)do
7:\(x_{E}\leftarrow\) Set of unknown variables of \(\Sigma_{E}\cup\overline{\Sigma}_{E}\);
8:\(\overline{\Sigma}_{E}\leftarrow\mathcal{R}(x_{E},\Sigma^{L,i})\);
9:\(\overline{\Sigma}_{E}\) is decomposed in \(m\) parts \(\overline{\Sigma}_{j}^{L,i}\);
10:if\(L\geq 2\)then;
11:\(\Sigma_{E}\leftarrow\) All \(\Sigma^{L,i}(l<L)\cup\Sigma_{i}\);
12:else
13:\(\Sigma_{E}\gets R(x_{E},\Sigma_{i})\);
14:endif
15:for\(p\gets 1\) to \(m\)do
16:\(perm\leftarrow\) all combinations of \(p\) subsystem elements(\(\overline{\Sigma}_{j}^{L,i}\)) from \(\overline{\Sigma}_{E}\);
17:\(k=1\);
18:while\((ARR_{i}=\emptyset)\) and \((k<|perm|)\)do
19:\(\Phi_{i}\leftarrow\) Compute FMSO sets of \(\Sigma_{E}\cup perm(k)\);
20: Execute an optimal selection of computed FMSO sets by solving a BILP problem;
21: Compute ARRs for \(\Sigma\) from selected FMSO sets;
22:\(ARR_{i}\leftarrow\) analytical residual generators of \(\Sigma\);
23:\(k\gets k+1\);
24:endwhile
25:endfor
26:endwhile Result: Local optimal FMSO sets for subsystem \(\Sigma_{i}\), \(i=1,\ldots,n\).
```  
**Algorithm 2** Local diagnoser design with neighbors
# Cybersecurity and Medical Imaging: A Simulation-Based Approach to DICOM Communication
## 4 Implementation  
This section provides detailed insights into the implementation of the DICOM simulator, with a focus on the necessary configurations, setup of the environment, and the steps involved in establishing communication with the PACS server and sending DICOM images.
## 4 Changing Laws of BER Performance
### BER of Long-Code System
It can be seen that the threshold SIR that affects the system output is (in units of decibels) as follows:  
\[SIR_{\text{threshold}}=10\lg\frac{P}{I}=20\lg\left(\bigg{|}\frac{\cos\Delta \varphi}{N}\bigg{|}\right) \tag{28}\]  
The threshold signal-to-interference ratio (SIR) is dependent on the spread spectrum ratio \(N\) and the phase deviation \(\Delta\varphi\). When the spreading ratio \(N\) is fixed, the threshold SIR varies periodically with the phase deviation \(\Delta\varphi\), with a period of \(\pi\). When \(\Delta\varphi\) is an integer multiple of \(\pi\), the maximum SIR threshold of the system is \(-20\lg N\). For a spread spectrum ratio of 15, the SIR needs to be lower than \(-24\) dB, meaning that for effective interference the interference power is 251 times that of the signal. Similarly, for a spread spectrum ratio of 31, to achieve effective interference the SIR should be lower than \(-30\) dB, corresponding to an interference power 1000 times that of the signal. Thus, increasing the spread spectrum ratio enhances the system's ability to resist single-frequency interference. However, due to the limited bandwidth of the underwater acoustic channel, increasing the spreading ratio results in a sacrifice in the information transmission rate. Therefore, in practical communication, the spreading multiple should be selected reasonably based on the environmental conditions. Optionally, the transmission rate can be reduced if necessary to ensure that the system has high immunity to interference. BER of Short-Code System Under Interference with a Frequency Offset of a Non-Zero Integer Multiple of the Bit Bandwidth, That Is, \(\Delta f=kR_{b}\) (\(k\) Is Integer, \(k\
eq 0,\ |k\leq N|\) )  
From Equation (10), it is evident that the value of \(A_{j}(m)\) is influenced by the structure of the spread spectrum sequences, frequency offset, and phase deviation, making direct calculation challenging. Based on Equations (10) and (11), \(A_{j}(m)\) can be  
According to the previous definition, the system SIR threshold can be expressed as  
\[SIR_{\text{threshold}}=20\text{lg}\bigg{(}\bigg{|}\frac{K\cos\beta_{k}}{N}\bigg{|} \bigg{)} \tag{34}\]
# Research Progress on Flexible Oxide-Based Thin Film Transistors
## 4 Summary and Outlook  
Oxide-based TFTs can be fabricated on (1) rigid substrates like glass, foils; (2) flexible substrates like PI, PEN and PET. These excellent characteristics have caused oxide-based TFTs to become actively investigated for one of the most popular TFT technologies in flexible applications. In this review, we have mainly focused on recent advances for the oxide-based TFTs employing room temperature and low-temperature processes. Particularly, we have emphasized representative TFTs fabricated on PI, PEN, PET and other substrates. The detailed performance for oxide-based TFTs is described in Table 1.  
The degradation of mobility and stability of TFTs originates from mechanical stress. Besides, the strain in films could also affect critical parameters such as the energy bandgap, dielectric behavior, and thermal conductivity. To reduce this degradation effect, two optimized methods have been introduced. One is the structure optimization of TFT and the other layout design optimization. With these methods, degradation could be reduced effectively.  
Over the past ten years, the performance of flexible oxide-based TFTs has been enhanced step-by-step. To date, there are still many challenges hindering the development of commercial productions, including repeatability, reliability, uniformity in large areas. Particularly, repeatability  
Figure 16: Three kinds of flexible TFTs for bending tests. Transfer curves and optical images of a-IGZO TFTs with (**a,d**) conventional structure with block electrode shape, (**b,e**) conventional block electrode shape and strip semiconductor layer shape, and (**c,f**) metal mesh electrode shape [181].  
means after how many bending cycles on the flexible device we can still promise the full performance of the device. Reliability means to what extent flexible devices can sustain adverse environmental conditions, such as strong mechanical stress, humidity, as well as high and low-temperature. Finally, uniformity means what the size of the flexible display we could fabricate without any mismatch on TFTs or distortion on the display. After solving the mentioned issues, the prospect for mass production will be bright.  
[PERSON], [PERSON], and [PERSON] conceived the idea; [PERSON], [PERSON] and [PERSON] wrote the paper, [PERSON], [PERSON] and [PERSON] advised the paper. All authors reviewed the paper.  
The authors are grateful to the National Natural Science Foundation of China under Grants 61874046 and 61574062, the Science and Technology Program of Guangdong Province under Grants 2016B090906002 and 2017B090901055, international cooperation program of Guangzhou Economic and Technological Development Zone under Grant 2017 GH29, Fundamental Research Funds for the Central Universities under Grant 2017 ZTD059, Natural Science Foundation of Guangdong Province of China (2015A030310522), Shunde district science and technology program (SDKJ_2017052).  
The authors declare no conflict of interest.
## 2 Results
### Inhibition of Nrf2 Recruitment by the Keep1-Ligand Complex
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Result**} \\ \cline{2-6}
**Keap1-Ligand** & \multirow{2}{*}{**File ID**} & **Affinity** & **Total Energy** & **vdW Energy** & **Electrostatic** \\
**Complex** & & **(k)/mol** & **(k)/mol** & **(kcal/mol)** & **Energy** \\ \hline
4 IQK-apigenin & 1331 eaefe & \(-1.162\) & 993.266 & \(-0.001\) & \(-2.430\) \\
4 IQK-luteolin & 6945f11 fae & \(-1.180\) & 993.573 & \(-0.000\) & \(-2.273\) \\
4 IQK-omaevalcone & 4 fdba4a & \(-1.239\) & 993.991 & \(-0.001\) & \(-2.396\) \\
4 IQK-Cpd16 & 11a1a05c7f & \(-1.417\) & 993.960 & \(-0.001\) & \(-2.572\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Outcomes from docking simulations of Keap1-ligand complexes with nuclear factor erythroid 2-related factor 2 (Nrf2). Figure 2: _Cont._  
The Nrf2-derived peptide binds to a shallow pocket on the top surface of the Keep1 domain, defined by the D-A and B-C loops (Figure 3). This pocket is crucial for the Nrf2\(-\)Keap1 interaction, but under normal conditions, Keep1 should not bind Nrf2 to prevent its premature activation. When Nrf2 binds to this pocket, it affects Keep1's conformation, which is intended to inhibit Nrf2. Typically, Keep1 binds Nrf2 and facilitates its degradation to regulate the antioxidant response. However, apigenin, a flavonoid with antioxidant properties, can bind to Keep1 and alter its structure. This change reduces Keep1's ability to bind and degrade Nrf2, thereby decreasing Nrf2 inhibition and enhancing its activation of the antioxidant signaling pathway [29]. The visualization reveals that non-polar peptides from Nrf2 bind to a hydrophobic area on Keep1-apigenin. This binding site, located within the D-A and B-C loops of Keep1, is crucial for the interaction between Nrf2 and Keep1. When compared to other Keep1 crystal structures, there are noticeable differences in this binding area. These differences may result from changes in the shape of the D-A and B-C loops due to peptide binding, which can affect how Nrf2 attaches to Keep1. This interaction is important because it influences whether Keep1 stays in an open or closed state, which in turn affects the recruitment of Nrf2. Figure 2: Depiction of the initial pose (white) and final pose (colored) from the molecular dynamics simulation in the 4 IQK-ligand complexes: 4 IQK-apigenin, 4 IQK-luteloin, 4 IQK-omaevoloxone, and 4 IQK-Cpd16.
## 3 Results and Discussion
### Autonomy of the Multiple Converter Configuration Versus Typical Configurations  
This section analyzes the autonomy of the multiple converter configuration (C-MC) against typical configurations (C-B/UC, C-UC/B). The multiple converter configuration simulation results against the configurations are shown in Table 5. The following table shows the autonomy of the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**CITY Driving Cycle**} \\  & **C-MC** & **C-B/UC** & **C-UC/B** \\ \hline \(E_{b}(J)\) & 1.4289 \(\times 10^{6}\) & 1.4289 \(\times 10^{6}\) & 1.4289 \(\times 10^{6}\) \\ \(E_{uc}(J)\) & 1.5266 \(\times 10^{6}\) & 1.3740 \(\times 10^{6}\) & 1.3679 \(\times 10^{6}\) \\ \(\Delta SOC_{b}\) & 0.0307 & 0.0307 & 0.0307 \\ \(\Delta SOC_{uc}\) & 0.3165 & 0.2563 & 0.2552 \\ \hline \multicolumn{3}{c}{**ECE Driving Cycle**} \\  & **C-MC** & **C-B/UC** & **C-UC/B** \\ \hline \(E_{b}(J)\) & 6.8254 \(\times 10^{6}\) & 5.9617 \(\times 10^{6}\) & 5.5394 \(\times 10^{6}\) \\ \(E_{uc}(J)\) & 3.0364 \(\times 10^{5}\) & 1.0506 \(\times 10^{6}\) & 9.7406 \(\times 10^{5}\) \\ \(\Delta SOC_{b}\) & 0.1138 & 0.0999 & 0.0927 \\ \(\Delta SOC_{uc}\) & 0.1979 & 0.1947 & 0.1826 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different configurations before different driving cycles.  
different configurations before different driving cycles by discharging the batteries at their minimum charge state.  
The above table shows that C-UC/B has greater autonomy in both scenarios (with CITY II and ECE cycles) compared to C-MC and C-B/UC. The C-MC has a better autonomy of the HEV unlike the C-B/UC before the CITY II management cycle, although the difference is not significant. Despite the above, the C-MC before the ECE cycle has less autonomy than that of the other configurations. In the scenario of vehicle autonomy analysis, it is clear that the C-MC configuration has lower performance compared to the other configurations before the two driving cycles. In this sense between the two remaining configurations (C-MC and C-B/UC) with a single converter, the C-B/UC configuration has better performance compared to C-B/UC in the same scenarios. Despite the results presented in this work directed towards the C-MC, it has the advantage of having a better energy flow due to the two converters, thus improving the safety of the vehicle.  
The power demand P_load is calculated using the methods of [PERSON] and colleagues [5] through vehicle equations [3; 5] with the characteristics shown in Table 1. All equations were programmed in MATLAB. Figure 15 shows the flow chart how the results were calculated in this work.
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 3 Case Studies and Issues for Conventional CVR
### Issues: Implementation Problems and Decision Frameworks  
The benefits and complexities associated with CVR and other voltage optimization solutions are not lost on the technical community.  
Although the utility of coordinating multiple control devices in the distribution grid can be shown to produce a flatter voltage profile, the benefit of CVR is described as being isolated to only a small number of feeders, or 80% of the benefit of CVR is reaped from 20% of the feeders used [21]. Furthermore, even though the benefit of operating feeders in the \"bottom half\" of the allowable ANSI C84.1 voltage range is clear, relatively complicated architecture of real-world feeders can be quite problematic, requiring extensive system modeling and only effectively servicing a minority of available locations [31]. In fact, in testing by Idaho Power, only 264 of 600 available feeders were suitable for CVR evaluation, and only 30 were chosen due to cost of implementation, complexity, and likelihood of success [31].  
As a result of these issues, in [32] the authors propose a \"decision framework\" for assisting in the process of implementing a CVR solution. In addition to the noted issues in numerous previous deployments, including [21; 31], the general consensus is that implementing a CVR solution is extremely difficult due to challenges with identifying load characteristics, complexities of validating model parameters via field measurements, and the problems associated with data gathering and analysis from a nonstationary platform. Regardless, the decision framework proposed in [32] accounts for topology differences and peripheral system impact in order to assist in the selection of \"low hanging fruit\"for CVR deployments or trials. The framework compares candidate feeders in terms of observable, quantifiable characteristics including power transfer distribution factor (PTDF) and voltage sensitivity factor (VSF) [32]. In developing their framework, the authors in [32] provide a comprehensive summary of CVR methods and trials.
## 2 Materials and Methods
### Methodology  
#### 2.2.1 Imbalanced Data  
Many practical classification problems are imbalanced. The class imbalance problem happens if there are several more samples of some classes than others. As with traffic crash data, if there is a numerically extreme difference between the case where the crash occurred and the case where the crash did not occur, the problem might occur in the analysis process. Especially standard machine learning classifiers tend to be overwhelmed by the majority classes and overlook the minority ones. The performance of such classifiers in predicting highly unbalanced data could decrease significantly. The substantial problem arises from the imbalance of the class. In order to improve model performance, it is essential to solve the imbalance problem, and the resampling technique is a considerably effective approach to settle this problem. Resampling methods, such as SMOTE and Tomek Link, have been widely used to address imbalanced datasets, as demonstrated by [PERSON] et al. (2018) [23] and [PERSON] (2021) [5]. These methods improve classification accuracy while maintaining data integrity.  
To address class imbalance, three categories of resampling techniques were evaluated: oversampling (SMOTE and ADASYN), undersampling (Tomek Link and ENN), and hybrid methods (SMOTEENN and SMOTETomek). For example, SMOTE gener  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Name** & **Variable Description** & \begin{tabular}{c} **Statistical Description** \\ **Min.** \\ \end{tabular} & \begin{tabular}{c} **Max.** \\ \end{tabular} \\ \hline \multirow{8}{*}{Exploratory variables} & Existence of speed control devices & The existence of a speed control system/ device & 0 & 1 \\ \cline{2-4}  & Existence of delineator & The existence of lane guidance [yes = 1, no = 2] & 0 & 1 \\ \cline{2-4}  & Median type & \
\begin{tabular}{c} Type of median land [None = 0; concrete = 1; \\ guardrail = 2; greenbelt = 3; others = 4] \\ \end{tabular} & 0 & 4 \\ \cline{2-4}  & Median width & Median lane width [m] & 0 & 2.8 \\ \cline{2-4}  & Posted speed & Speed limit [kph] & 30 & 80 \\ \cline{2-4}  & & Type of land-use for catchment areas & & \\  & Land-use &
\begin{tabular}{c} [residential area = 1, commercial area = 2, \\ farmland area = 3, industrial area = 4, \\ residential-commercial area = 5, farmland- \\ industrial area = 6, residential-farmland area = 8] \\ \end{tabular} & 1 & 8 \\ \cline{2-4}  & The number of crosswalks & The number of crosswalks & 0 & 2 \\ \cline{2-4}  & The number of bus stop & The number of bus stops & 0 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._ates synthetic samples in feature space using linear interpolation, whereas ENN removes majority-class samples near minority-class instances, reducing noise and improving classification accuracy [22; 23]. These approaches have their own advantages and disadvantages, and a combination technique is also widely used to compensate for these disadvantages.