esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review in Fault Diagnosis and Health Assessment for Railway Traction Drives
## 4 Fault Diagnosis in Railway Traction Drives
### Fault Diagnosis in Power Electronics  
The main power converters in Railway traction drives are the inverter to supply the electric machines and the braking chopper, as it is shown in Figure 14. Usually more than one electric machine is supplied with the same inverter. The goal of the chopper is to dissipate the energy during braking period that it is not possible to return to catenary. IGBT based power converters are mainly used for Railway traction drives [89]. A rectifier needs to be added in case of AC supplied traction units. The most usual faults in the power electronics of Railway traction drive occur in capacitor and power switches [90]. The main faults in IGBTs are given in [91]. DC-link capacitor main faults are summarized in [92].  
In [32] a model-based estimator is proposed to detect faults in the switches of a single-phase PWM rectifier and later for cascaded H-bridge rectifiers [76]. The approach is validated in a HIL platform composed of a Real Time Simulator and a TCU. Only catenary current, DC-link voltage and switching signals from TCU are needed. Wavelet entropy approach is used in [77] to detect open switches in a inverter for High-Speed railways. The detection method is based on the hypothesis that the motor current for faulty case has more frequency components that for the fault-free case. In [78] a discrete wavelet transform is used and then the signal is decomposed into energy vectors. A support vector machine (SVM) is applied for IGBT faults classification. In [79] a signal-based approach is presented to detect open-circuit faults in IGBTs of rectifier. The detection is based of FFT analysis, due to the frequency harmonics change in case of faulty switch. The patterns detected in case of IGBT faults are presented in [80]. This research works affirms that a latch-up process generates an overcurrent whereas a secondary breakdown produces an overtemperature. In [81] a estimation of the capacitance of the DC-link capacitor is done during its discharge, by mean of least mean square algorithm. In this case, a real time fault diagnosis is not implement but it is justified that there are several train stops, so the calculation can be done.
## 2 Related Work and Motivation
The authors reported that for read operations, the maximum number of operations was reduced in case of several concurrent instances, while being increased for write operations in concurrent settings. Compared to the aforementioned works, we primarily aimed at assessment of virtualized Docker performance in a parallel LAN-based environment built out of commodity components such as desktop CPUs and 1 Gbps Ethernet. Such environments are common for: university labs (many are based on several PCs+ Gb Ethernet) and staff/students having access to those, selected companies (same as above using infrastructure for speeding up computations) and even some home environments with 2+ nodes and Gb Ethernet LAN. We shall emphasize that such a cluster built out of commodity nodes (PCs, workstations) is an environment really commonly used in the academic environment and is also feasible for the two other mentioned cases. In line with that, instead of typical aforementioned HPC benchmarks for clusters with top-of-the-line CPUs and fast interconnects like Infiniband, we used parallel codes featuring typical parallel processing paradigms (master-slave, geometric SPMD and divide-and-conquer [9]) that can be used by programmers/users in their applications, not necessarily being typical complex HPC workloads. These could be typical end user workloads that can benefit from parallelization in a multi-node environment with multi-core CPUs that, compared to supercomputing centers, offer some benefits considering that the user base is quite small, e.g., lack of queuing systems that sometimes require a long waiting phase before computations even start and consequently the possibility to rerun codes with other parameters quickly. We focused on best versions obtained with various compilation flags (-O2, -O3, -O3 -march=native) for all 1 to n tested nodes with assessment of best numbers of processes for particular applications. This is important because these processing paradigms follow various compute/communication schemes and the vast majority of parallel applications can be mapped onto these schemes or a combination thereof. Testing scalability with 2, 4, 8 and 16 nodes allows to assess the speed-up potential of an application and can be used as a first evaluation whether the current implementation can be moved to a much larger cluster (i.e., more nodes and possibly even lower latency interconnect). Using Docker in such an environment allows:  
* Easy preparation and testing an application, e.g., in a separate environment and deploying it on such a commodity cluster (e.g., students can prepare at home and deploy at the university). At the same time, Docker allows easy deployment of an application on subsequent computers (with the specific required environment) and scaling with more nodes;
* Easy separation of tasks on such a cluster, e.g., for several research groups to use various parts of the cluster at the same time.
## 5 Results  
Anomaly identification in surveillance videos is a difficult task, especially when using low-quality videos with poor spatial resolution and visual characteristics. Traditional methods, such as spatial interpolation, frequently result in limited improvement and can introduce undesirable artifacts. Alternatively, video super resolution, which improves spatial resolution, can be computationally expensive. This study addresses the challenges of low-quality videos using a video super-resolution approach based on StyleGAN priors. The StyleGAN improves not only the spatial resolution but also image sharpness and contrast. Unlike traditional video super-resolution methods, our approach selectively super-resolves frames that are relevant to anomaly identification, thereby improving computational efficiency. A two-stream architecture is used for classification modeling, which reduces the need for expensive optical flow computation. The RGB stream promotes spatial learning, whereas the SG3I stream emphasizes short-term temporal learning. Both streams use the same pre-trained CNN architecture, which has been fine-tuned for the dataset of interest. The learned features are concatenated and fed into a Gated Recurrent Unit (GRU) for long-term temporal modeling. The proposed approach effectively addresses the challenges posed by low-quality surveillance videos and delivers superior anomaly classification performance while minimizing the computational burden.
## 5 Applications Across Diverse Sectors
### Inventions in 3D Printing Technologies
#### 5.2.4 Automation  
Additive manufacturing has transformed automation, making it possible to produce intricate shapes, tailor-made designs, and quick prototype development. Recent innovations include the following [46]:  
(1) _Mobile 3 DP platform_: Equipped with omnidirectional wheels, this mobile platform can move freely in both the x and y directions, supporting material experimentation and use across various sectors [87].  
(2) _Grippers and robotic hands_: Custom grippers and robotic hands that are strong, light, and able to perform complex grasping and handling tasks may be produced thanks to additive manufacturing, expanding the potential for automation [78].  
(3) _Jigs and fixtures_: In automated manufacturing processes, custom jigs and fixtures help with accurate positioning, alignment, and assembly activities, increasing efficiency and accuracy [46].  
(4) _Electronics and sensors integration_: Additive manufacturing methods facilitate the embedding of electronic elements and sensors within printed components, leading to intelligent robotic systems equipped for immediate data gathering and enhanced automation functionality [46].
# Gene Editing with CRISPR/Cas Methodology and Thyroid Cancer: Where Are We?
## 2 The Principle of CRISPR/Cas
### Why Cas9?  
There are different types of Cas protein in the Class 2 system, in which the same Cas protein, for example Cas9, exerts the function to bind to the sgRNA, unfold the double-strand, and cut the DNA. It is reasonable to infer that this type of Cas protein is better suited for gene editing purposes because the delivery of one single protein with a sgRNA is easier than delivering multiple proteins, as in the Class 1 Cas system.  
Furthermore, Cas9, specifically SpCas9, is a 1368 aa multi-domain protein composed of two lobes, the alpha-helical recognition (REC) lobe and the nuclease (NUC) lobe containing the HNH and the RuvC nuclease domains [14]. The following functions are performed by Cas9: (1) interacts with sgRNA; (2) scans DNA; (3) unfolds DNA double-strand, and (4) cuts both strands of DNA.  
Part of the popularity of SpCas9 is due to its PAM sequence NGG, which is simple and may be present in the majority of targets. However, SpCas9 is considered a large protein, with the coding sequence occupying most of the plasmid cloning space and impairing the cloning of multiple sgRNAs. In this context, researchers have investigated several types of bacteria and archaea genomes in search of new Cas proteins in Type II, V and VI groups, which may be smaller and even more specific (reduced off-targeting) than Cas9 (Table 1). Off-target is an unwanted DNA cleavage at unintended DNA sites due to the partial complementarity of sgRNA 20-nt sequence (usually with few mismatches) [15], that can lead to genomic instability and disable otherwise normal genes. Thus, the biochemical structural studies have focused on understanding the Cas9 protein structure to map and hack its structure to create new versions that are more specific as nucleases with reduced off-targeting.
# Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval
## 4 Experiments
### Further Study  
#### 4.5.1 Effects of Discrete Optimization  
To validate the effects of the proposed discrete optimization strategy, we denote a variant of DSAH, named DSAH-Re. Specifically, we first relax the discrete constraints, then Equation (9) can be solved as  
\[\min_{\mathbf{B}}\alpha\sum_{i=1}^{2}||\mathbf{B}-\mathbf{P}_{i}\phi(\mathbf{X }^{(i)})||_{\mathrm{F}}^{2}+tr(\mathbf{E}^{\top}\mathbf{D}\mathbf{E}). \tag{29}\]  
Setting the derivative Equation (29) w.r.t. \(\mathbf{B}\) to 0, and the value of \(\mathbf{B}\) can be solved with a closed-form solution:  
\[\mathbf{B}=(\mathbf{R}\mathbf{D}\mathbf{R}^{\top})^{-1}\Big{(}\alpha( \mathbf{P}_{1}\phi(\mathbf{X}^{(1)})+\mathbf{P}_{2}\phi(\mathbf{X}^{(2)}))+ \mathbf{R}\mathbf{D}\mathbf{L}\Big{)}. \tag{30}\]  
Then, we obtain the hash codes by mean-thresholding operation. The mAP results of DSAH and DSAH-Re on two datasets are shown in Table 4 and 5. From the table, we can observe that the performance of DSAH is better than that of DSAH-Re on two datasets. These results demonstrate that our proposed discrete optimization algorithm performs well in avoiding quantization errors and improving the performance of cross-modal retrieval tasks.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Task** & **Method** & **8 Bits** & **16 Bits** & **32 Bits** & **64 Bits** & **128 Bits** \\ \hline \multirow{4}{*}{I\(\rightarrow\)T} & DSAH-Re & 0.5982 & 0.6076 & 0.6081 & 0.5911 & 0.5784 \\  & DSAH-Ke & 0.6832 & 0.6872 & 0.6898 & 0.6913 & 0.6906 \\  & DSAH-Nm & 0.6991 & 0.7212 & 0.7245 & 0.7289 & 0.7310 \\  & DSAH & **0.7156** & **0.7236** & **0.7412** & **0.7498** & **0.7556** \\ \hline \multirow{4}{*}{T\(\rightarrow\)I} & DSAH-Re & 0.5543 & 0.5521 & 0.5760 & 0.5773 & 0.5801 \\  & DSAH-Ke & 0.7362 & 0.7690 & 0.7772 & 0.7887 & 0.7921 \\ \cline{1-1}  & DSAH-Nm & 0.7111 & 0.7304 & 0.7297 & 0.7358 & 0.7439 \\ \cline{1-1}  & DSAH & **0.7526** & **0.7782** & **0.8041** & **0.8163** & **0.8180** \\ \hline \hline \end{tabular}
\end{table}
Table 4: mAP scores of different ablated versions of DSAH on MIRFlickr dataset.  
Figure 2: Precision–Recall and P@k curves obtained by different baselines and tested on MIRFlickr and NUS-WIDE datasets.
# Effective IoT Congestion Control Algorithm
## 4 Results and Discussion
### Fifth Experement: Inter-Protocol Approaches  
For this simulation, inter-protocol fairness was tested for both wired and wireless channels. The topologies used were the same as those shown in Figure 2 (for wire channel) and Figure 8 (for wireless channel). In the first step, the topology was set to the wired dumbbell topology, as depicted in Figure 2. In the second step, the parameters were adjusted as outlined in Table 2. In the third step, the TCP variant was set to one of the variants listed in Table 1. The test was then performed and all previous stages for all TCP variations were repeated. The findings were logged, and the previous procedures outlined in Figure 8 were repeated. Figure 11 shows each result's Jain's Fairness Index. The competition here was between TCP BBR and the proposed algorithm. The wired channel gained about 0.6% of link utilization with an expense of 0.3% of fairness. Conversely, when the link is wireless, which is normal for IoT devices, the reduction was 0.4% of link utilization and fairness increased by 0.1%.
## 3 Experimental Results
### QST Results  
Through QSTs, hysteretic curves and skeleton curve for the base shear force versus roof lateral displacement (roof drift ratio) were obtained, as shown in Figure 14a. Figure 14b compares the hysteretic loops for three different drift levels. The hysteretic curves presented significant reversed \(S\) shape pattern with increase of drift. Ultimate load at the last cycle was 189.68 kN, decreasing to approximately 85% of peak load (223.71 kN). Table 5 lists the hysteretic characteristics of the specimen. The yield load and yield drift ratio of the specimen was determined using the general yield bending moment method [22]. The ductility factor is the fraction of ultimate drift to the yield drift. It can be found that the ductility factor could reach a value of 3.6. This ductility comes mostly from the RC pipe columns as a result of yielding in the reinforcing steel.  
Figure 14: Hysteretic behavior of specimen: (**a**) hysteretic curves of base shear force versus roof displacement (drift ratio) and envelope response and (**b**) hysteretic loops at various displacement levels.  
Figure 13: Cumulated hysteresis dissipated energy under PDTs.
## 3 Proposed Method
\begin{cases}\sqrt{2}\frac{\left|\left[U_{p}\right]_{ij}-\left[U_ {n}\right]_{ij}\right|}{\left[U_{p}\right]_{ij}+\left[U_{n}\right]_{ij}} \times 100,\left[U_{p}\right]_{ij}+\left[U_{n}\right]_{ij}>0\\ 0,otherwise\end{cases} \]  
Assuming that \([U_{p}]_{ij}>[U_{n}]_{ij}\) then  
\[Conflict\ value=|[U_{p}]_{ij}-[U_{n}]_{ij}| \tag{23}\]  
Substitute (23) into (22), then  
\[\%CV_{ij}= \begin{cases}\sqrt{2}\frac{\left(Conflict\ value\right)}{\left[U_{p }\right]_{ij}+\left[U_{n}\right]_{ij}}\times 100,\left[U_{p}\right]_{ij}+ \left[U_{n}\right]_{ij}>0\\ 0,otherwise\end{cases}  \tag{24}\]  
From Equation (24), we can observe that as the conflict value increases, the percentage of variance also increases. This implies that the two values are directly proportional to each other, indicating that the percentage variance can be used to measure classification ability. Lemma 3: _Eliminating the element with the lower value could improve classification accuracy._  
Proof of Lemma 3: Assuming that \([U_{p}]_{ij}\gg[U_{n}]_{ij}\) and \(\omega_{p},\omega_{n}=0\) from (10) and (11), then  
\[Conflict\ value=[U_{p}]_{ij}-[U_{n}]_{ij}\]  
\[Conflict\ value\approx[U_{p}]_{ij} \tag{25}\]  
where \([U_{n}]_{ij}>0\). From Equation (25), if we set \([U_{n}]_{ij}\) to \(0\), only the data for \([U_{n}]_{ij}\) will be lost, where \(\lim\limits_{x\rightarrow\infty}[U_{n}]_{ij}=1\). From (24), we can reduce the \(conflict\ value\) and \(\%CV\) by setting \([U_{p}]_{ij}\) to \(0\). This means that the classifier can better classify patterns because a lower \(\%CV\) indicates greater classification ability. Definition 3 (Variance decision table): The variance decision table (\(V\)) is the \(\%CV\) representation in the matrix form. **Definition 4** (Loss of data). : _The loss of data is part of the data converted to zero to improve overall classification accuracy._  
**Lemma 4**. : _Appropriate estimation of the percentage coefficient of variance can produce a suitable rule matrix._  
Proof of Lemma 4.: From Lemma 3\(min([U_{p}]_{ij},[U_{n}]_{ij})\) is eliminated to increase the ability to classify patterns when \([U_{p}]_{ij}\gg[U_{n}]_{ij}\) or \([U_{n}]_{ij}\gg[U_{p}]_{ij}\). However, when \([U_{p}]_{ij}\approx[U_{n}]_{ij}\), if we convert \(min([U_{p}]_{ij},[U_{n}]_{ij})\) to 0, loss of data \(\approx[U_{p}]_{ij}\) and \([U_{n}]_{ij}\), which could result in overfitting of the classifier model. Appropriate approximations were used to prevent the above problems and to obtain results that provided the best classification capability. **Definition 5** (Variation coefficient). : _The variation coefficient (\(\omega_{v}\)) was obtained using \(V\). This was estimated using the m-MBOA algorithm, which can be used to generate the best rule matrices in the synthesis process._  
**Theorem 1**. : _The coefficient of variation and variance decision table can create effective rule matrices for the classification._
## 3 Evolution of the Plastic Zone under Osmotic Pressure  
### Fracture Expansion under Low Osmotic Pressure  
For a positive main fracture surface stress (\(\sigma_{ne}\)) in Equation (1), the fracture surface is in a compressive shear state. Under this circumstance, the low osmotic pressure condition can be expressed in the form below:  
\[P_{p}\leq\frac{\sigma_{xx}}{2}+\frac{\sigma_{xx}}{2}\cos 2\theta \tag{5}\]  
The nonlinear frictional effect on the fracture surface will prevent further expansion of the fracture and the stress field can be expressed as a reduced stress intensity factor \(K_{\rm II}\) when compressive loading closes the fracture surface. The frictional force (\(\sigma_{f}\)) and effective shear stress (\(\tau_{nof}\)) on the fracture surface are as follows:  
\[\sigma_{f}\ =\ \sigma_{ne}f\lambda \tag{6}\]  
\[\tau_{xy}\ =\ \frac{K_{\rm II}}{\sqrt{2\pi r}}\cos\frac{\theta}{2}(1-sin\frac{ \theta}{2}sin\frac{3\theta}{2}) \tag{7}\]  
where \(f\) and \(\lambda\) are the friction coefficient and the closure degree, respectively. When the fracture closes under compressive pressure, the tension-type stress intensity factor \(K_{\rm I}\) is negative and does not have physical significance.  
Zero stress intensity factor in a closed fracture under pressure (\(K_{\rm I}=0\)) reflects pure shear loading. Stresses at the fracture tip can be estimated as follows:  
\[\sigma_{x}\ =\ \frac{K_{\rm II}}{\sqrt{2\pi r}}(2\sin\frac{\theta}{2}+\sin \frac{\theta}{2}\cos\frac{\theta}{2}\cos\frac{3\theta}{2}) \tag{8}\]  
\[\sigma_{y}\ =\ \frac{K_{\rm II}}{\sqrt{2\pi r}}\sin\frac{\theta}{2}\cos \frac{\theta}{2}\cos\frac{3\theta}{2} \tag{9}\]  
\[\sigma_{z}\ =\ \frac{2\
u K_{\rm II}}{\sqrt{2\pi r}}\sin\frac{\theta}{2} \tag{10}\]  
\[\tau_{xy}\ =\ \frac{K_{\rm II}}{\sqrt{2\pi r}}(\cos\frac{\theta}{2}-\cos \frac{\theta}{2}\sin\frac{\theta}{2}\cos\frac{3\theta}{2}) \tag{11}\]\[\tau_{xz}\ =\ \tau_{yz}\ =\ 0 \tag{12}\]  
It should be indicated that stress near the fracture tip affects the plastic region caused by the deformation of the loaded rock. Based on the D-P yielding criterion, the plastic deformation of rock can be determined using the following equations:  
\[f(I_{1},J_{2})\ =\ \alpha I_{1}+\sqrt{J_{2}}-k\ =\ 0 \tag{13}\]  
where \(I_{1}\) and \(J_{2}\) denote the first and second invariants of the stress tensor, respectively; \(\alpha\) and \(k\) are constants related to the angle of internal friction and cohesion, respectively.  
\[\alpha\ =\ \frac{2\sqrt{3}\sin\varphi}{9-3\sin\varphi} \tag{14}\]  
\[k\ =\ \frac{2\sqrt{3}c\sin\varphi}{3-\sin\varphi} \tag{15}\]  
\[\begin{array}{ll}\text{I}_{1}&=\sigma_{x}+\sigma_{y}+\sigma_{z}\\ &=-\frac{2K_{\text{II}}}{\sqrt{2\pi r}}(1+\
u)\sin\frac{\theta}{2}\\ \\ K_{\text{I}(nr)}\ =\ \sigma_{ne}\sqrt{\pi a}\end{array} \tag{16}\]  
Accordingly, the radius (\(r\)) of the fracture plastic zone under low osmotic pressure can be obtained by the following equation:  
\[r\ =\ \frac{K_{\text{II}}^{2}}{2\pi k^{2}}\Bigg{[}\frac{1}{\sqrt{6}}\sqrt{- \frac{9}{2}\sin^{2}\theta+6\cos^{2}\frac{\theta}{2}+8(1-\
u+\
u^{2})sin^{2} \frac{\theta}{2}}-2a(1+\
u)sin\frac{\theta}{2}\Bigg{]}^{2} \tag{18}\]
## 4 Results
### Main Analysis (Mixed-Effects Model)
#### 4.1.1 Mixed-Effects Modeling and Large-Output Tables  
A linear mixed-effects model (LMM) was employed for each task to account for the nested data structure. Every question (question_id) can generate multiple observations (one for each agent, each subgrade, etc.), leading to potentially correlated residuals within the same question. Including \((1\mid\text{question\_id})\) adds a random intercept for each question, thereby allowing each question to have its own baseline level. This approach reduces bias from unobserved question-level factors (such as difficulty, style, or intrinsic complexity) that could otherwise inflate residuals and obscure true agent or design effects.  
The choice of LMM over simpler ANOVA or OLS arises from several considerations:  
* **Repeated Measures on the Same Questions**: Each question is seen by multiple agents, so scores from the same question are not independent.
* **Varying Baseline**: Distinct questions may be inherently \"easier\" or \"harder\" to align, improve, or recreate, making a random intercept beneficial.
* **Complex Interactions**: Agents interact with question design (single-step vs. multi-step), question category (application, knowledge, or creation/evaluation), and course orientation (application vs. theory), potentially at different slopes.  
Hence, the LMM properly captures these within-question correlations and estimates fixed effects for agent, design, category, course orientation, and explanation-quality subdimensions.  
Each fitted model can easily produce 20-30 (or more) fixed-effect coefficients due to main effects, interactions, and explanation-quality dimensions. To maintain clarity, full tables with standard errors, \(z\) scores, \(p\)-values, and confidence intervals are placed in Appendix A.1 (Tables A1-A3). Summaries of critical effects and forest plots appear in the main text to highlight the most relevant findings. This strategy ensures both accessibility (concise narrative) and reproducibility (complete details in the Appendix A).