esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Fluorescence Characteristics of Animal Feces and Soil  
Figure 2a shows a color photo of representative animal fecal and soil samples as arranged for the imaging study for comparison with each other; Figure 2b shows the mean sample spectra extracted from a nine-pixel (3 \(\times\) 3 pixels) region of interest within the area of each sample type in the hyperspectral images that illustrate the dynamic fluorescence emission characteristics of fecal materials. The most common fluorescence characteristic of the fecal materials was an emission maximum in the red region of the spectrum at about 676 nm, with a secondary peak at about 720 nm, especially for cow and sheep feces. Pig and chicken feces exhibited a relatively strong emission in the green region with an emission maximum at approximately 520 nm. In addition, emission peaks were observed in the pig and chicken spectra near 635 nm, where a more pronounced emission peak can be seen in the chicken spectrum [27]. The emission features of pig, cow, and chicken feces obtained from the HSI system using 405 nm excitation were consistent with previously reported results from a spectrofluorometer. Fluorescence spectral features of sheep feces were nearly the same as those for cow feces with a minimal emission in the green region and dominant emissions in the red region at 676 nm and 720 nm. Figure 2b also shows a representative soil spectrum that exhibits minimal fluorescence emissions through the green and red regions.  
Chlorophyll \(a\) molecules are present in green plants and emit unique fluorescence features in the red and far-red regions of the spectrum, with emission peaks at 685 and 730 nm having been reported [36; 37]. Using a fluorescence excitation maximum at around 410 nm, strong fluorescence emissions were previously observed for the feces of cow, pig, chicken and sheep in the red region of the visible spectrum, with peaks near 676 nm. The red emissions observed in animal fecal matter are suggested to be chlorophyll \(a\) or its metabolites such as pheophorbide \(a\). The spectral change resulted from the green roughages consumed by the animals, and the blue-shifted spectral change was reported to occur with chlorophyll \(a\) having passed through the digestive tract of animals [27]. For chicken feces, the 635 nm emission peak is thought to emanate from Protoporphine IX.  
Figure 2c,d show hyperspectral images of the samples at 520 nm and 676 nm, respectively. The 520 nm green-band image shows that the relative intensities of chicken feces followed by pig feces were significantly higher than those of sheep and cow feces. At 676 nm, the chicken feces exhibited the highest fluorescence emissions compared to other fecal samples. Both cow and sheep feces exhibited some bright spots where the relative fluorescence intensities were similar to chicken feces. The 676 nm image demonstrates that the fecal matter can be potentially differentiated from soil.
## 2 Introduction of Split Column Technology  
### Force Characteristics of Split Columns  
The split column technology utilizes partition plate material to divide a complete reinforced concrete column into several relatively independent unit columns, with its structural diagram shown in Figure 1. Studies (2018, 2019, 2020, 2021, 2022, 2023) have shown that the use of split columns in ground structures can effectively reduce the flexural bearing capacity of the frame structure, causing its failure mode to shift from shear failure to bending failure. The deformation capacity and ductility of frame columns are significantly improved.  
The strain distribution pattern of the split column during loading is given in Figure 2. \(b\) denotes the overall width of the column structure and \(b_{1}\) denotes the width of the split unit column.  
Figure 1: Structure diagram of split column.  
When the horizontal displacement is small, the friction effect between the clapboard and the unit column provides a strong bonding effect. At this stage, the clapboard effectively constrains the adjacent unit columns to produce a synergistic effect, and the stress variation in the concrete section resembles that of an ordinary column. As the horizontal displacement gradually increases, the friction effect between the partition and the unit column decreases, though it still provides some effective bonding. The deformation ability of the concrete on both sides of the friction restraint partition weakens, and the strain of the inner edge of the cross-section of the unit column on both sides is smaller than that of the outer edge. When the horizontal displacement continues to increase, the friction effect between the partition plate and the unit column disappears completely, and the partition plate no longer restrains the deformation of the unit column. At this point, each unit column operates independently, and the strain neutral axis of the unit column section shifts toward the center of each section.
## Materials and Methods
### Groundwater Flow Simulation
#### 2.4.3. Recharge  
Recharge was calculated with Equation (2). Two components (recharge from rainfall and recharge from irrigations flows) were estimated, the latter of which was further divided into two subcomponents: recharge from canal irrigation and recharge from pumping irrigation. Rainfall recharge was estimated from Gridded Climate Products (GCPs) obtained from the Climate Forecast System Reanalysis (CFSR) dataset [40]. The GCP dataset was re-gridded over the groundwater model domain to represent the maximum potential rainfall recharge amount in each cell. Basin and furrow irrigation methods are widely used for supplying water to wheat, cotton, vegetable, fruit, and fodder crop fields during Rabi and Kharif seasons in the study area. The irrigation water allowance of the canal command area was determined in the modeled area on a monthly basis (Figure 5). Since surface irrigation methods, which are prevalent in the area, were 40 to 60 percent efficient, it was estimated that 50 to 60 percent of irrigation water in the modeled area contributed to the aquifer as recharge. The amount was adjusted spatially and temporally during the calibration process. Based on the water allowance, each grid cell was assigned returns from irrigation on the monthly stress period. Initial recharge values were then assigned based on Equation (2), and initial factors \(f_{1},f_{2}\), and \(f_{3}\) of 0.6, 0.5, and 0.5 were used respectively, and were later tuned in the calibration process to estimate net recharge to groundwater.  
\[Rch_{ij}=f_{1}*R_{i,j}+f_{2}*Ic_{i,j}+f_{3}*Ip_{i,j} \tag{2}\]  
where \(f_{1}\), \(f_{2}\), \(f_{3}\) = fitting parameters; \(R_{i,j}\) = rainfall in cell; \(Ic_{i,j}\) = return from canal irrigation; \(Ip_{i,j}\) = return from pumping irrigation.
# On the Accuracy of Turbulence Model Simulations of the Exhaust Manifold
## 3 Exhaust Manifold Analysis
### Results and Discussion  
#### 3.6.1 Temperature Comparison  
Figure 4 reveals the distribution of the temperature at the outlet of the exhaust manifold using the five turbulence models; all models predict a similar maximum temperature at the outlet, showing that the maximum at the outlet of the exhaust manifold is closely clustered between 807.73 K and 808.58 K, with the k-kl-\(\omega\) model predicting the highest maximum temperature at 808.58 K. Minimum temperatures vary more, ranging from 790.23 K to 795.56 K, with the Reynolds stress model showing the lowest minimum temperature at 790.23 K. The k-kl-\(\omega\) model stands out, presenting the broadest temperature range.  
All models show a decreasing temperature trend as the position \(Y\) changes from the inlet 0.20 m to the outlet \(-\)0.11 m, as shown in Figure 5; k-kl-\(\omega\) shows the highest temperatures initially, reflecting sensitivity to transitional flows. The RNG k-epsilon model shows a rapid temperature decrease, reaching the lowest minimum temperature, around 798 K, at \(-\)0.11 m. K-omega STD and k-kl-\(\omega\) models display similar trends, with k-kl-\(\omega\) consistently predicting higher temperatures. The transition SST and Reynolds stress models exhibit closely matched trends, with transition SST initially showing slightly higher temperatures.  
Figure 3: Meshed exhaust manifold, inflation mode around the fluid.  
As depicted in the 2D ZY plane in Figure 6, the temperature distribution among the exhaust manifold shows the contours for different turbulence models--RNG k-epsilon, k-omega STD, k-kl-\(\omega\), transition SST, and Reynolds stress. Each model illustrates a gradient from high to low temperatures, with the highest temperatures generally observed near the inlet and the lowest temperatures towards the outlet. The k-kl-\(\omega\) model predicts the highest maximum temperature of 808.58 K, indicating its sensitivity to transitional flow effects. The RNG k-epsilon model shows a rapid decrease in temperature, with the lowest minimum temperatures around 790 K. K-omega STD and k-kl-\(\omega\) exhibit similar trends, while the transition SST and Reynolds stress models show closely matched distributions. These findings are essential for understanding the thermal behavior of the exhaust manifold, aiding in optimizing design and material selection to enhance performance and durability.
## 4 Security and Privacy in Edge Frameworks
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Privacy and Security** & **Description** & **Proposed Mitigation** \\ \hline Expanded Attack Surface [31] & Edge nodes deployed in unsecured environments become easy targets for attackers. & Use of blockchain, encryption, and secure authentication protocols. \\ Sensor Compromise [32] & Vulnerable medical sensors may be physically tampered with or remotely controlled to leak data. & Tamper-resistant hardware, PUF-based key generation, secure boot mechanisms. \\ Healthcare data security [33] & Secure storage and transmission of medical data & Blockchain integration with cloud, ECDSA, smart contracts \\ Data Leakage from Edge/Cloud Nodes & Sensitive health data can be exposed from edge caches or cloud storage. & Access control, searchable encryption, and federated data sharing. \\ Lack of Standardization [35] & Inconsistent protocols and vendors hinder unified security enforcement. & Development of interoperability standards and unified data governance frameworks. \\ Privacy of User Health Data [36] & Health records must comply with strict data privacy regulations (e.g., HIPAA, GDPR). & Smartcard login, user-controlled access, and privacy-preserving machine learning models. \\ Malicious Node Injection [37] & Rogue devices may be inserted to manipulate or extract data. & Digital signatures, node authentication, and trust management strategies. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Security and privacy challenges and proposed mitigation techniques. nents has become a foundational principle in modern cybersecurity, especially pertinent to embedded systems, IoT devices, and Cyber-physical systems [39; 40; 41]. This approach also incorporates technologies like trusted execution environments (TEEs), trusted platform modules (TPMs), and hardware security modules (HSMs) to provide security solutions across various applications and computing platforms [42]. Ongoing research in edge computing privacy and security is largely driven by unresolved challenges such as establishing user trust, enabling collaboration among heterogeneous systems, developing low-cost fault-tolerant deployment models, and designing lightweight yet secure authentication and verification mechanisms.
## 1 Introduction
Unsupervised and semi-supervised approaches are gaining traction, especially for anomaly detection in real-time vehicle monitoring. Incorporating multi-source data from onboard diagnostics, sensors, and fleet histories poses additional challenges. Concerns surrounding data privacy, computational efficiency, and DL model interpretability must also be addressed for broader implementation [8]. Power converters, which are essential for electric mobility, renewable energy, and smart grids, have also been a major focus of research. Approaches have evolved from physics-of-failure-based models to data-driven techniques like DL and probabilistic models. While model-based methods are often computationally intensive, purely data-driven ones may lack physical interpretability. Physics-informed ML is emerging as a promising compromise, incorporating domain knowledge into AI models to enhance both reliability and generalizability [9]. PdM adoption is also increasing in the energy sector, particularly for medium-voltage switchgear. Here, advanced sensor technologies such as infrared, vibration, and partial discharge sensors are combined with AI analytics. DL models like LSTMs and CNNs have improved fault diagnosis and condition monitoring, although scalability, real-time application, and sensor integration remain key hurdles [10]. A broader trend in recent research is the pursuit of hybrid models that integrate ML with physics-based simulations. In induction motors, for example, CNNs and autoencoders are widely used for fault detection. The next frontier involves combining these with multi-agent systems and digital twins. However, ensuring that AI models trained in controlled environments can generalize to dynamic real-world conditions remains a critical research challenge [11]. Therefore, this review provides a concise application-focused perspective on modern data-driven maintenance methods. By examining core algorithms, datasets, and real-world challenges in multiple industries, it aims to guide the transition from research innovation to practical adoption. In the remainder of this paper, Section 2 details the review methodology, while Section 3 discusses domain-specific applications, key models, datasets, and metrics. Section 4 addresses recurring limitations such as data complexity and scalability, and Section 5 summarizes the main conclusions and suggests future research directions.
## 3 Results and Discussion
### Cloning and Expression of Active spPEP
#### 3.7.3 Gluten Assay  
Both the soluble fraction and the refolded insoluble fraction of the induced lysates exhibited glutenase activity. As shown in Figure 7, very similar glutenase activities were observed for the enzyme in the soluble protein lysate at 0.58 units and the enzyme in the refolded insoluble protein at 0.63 units. Direct analysis of the peptide products from the glutenase assay was not possible due to the fragments being  
Figure 5: GPpNA assay of recombinant spPEP. The buffer was exchanged with 50 mM Tris, pH 8.0, containing 0.1 M NaCl for both the induced and uninduced soluble and inclusion body lysates. The reactions were started by the addition of 100 \(\mu\)L of 3 mM GPpNA to 100 \(\mu\)L of lysate. The A\({}_{410}\) was converted to specific activity (\(\mu\)M\(\cdot\)s\({}^{-1}\cdot\)mg\({}^{-1}\) of total protein) for the respective sample using a \(\varepsilon_{410}=8\),800 L\(\cdot\)mol\({}^{-1}\cdot\)cm\({}^{-1}\).  
too small for gel electrophoresis. Therefore, a separate reaction was carried out to determine which of the glutens, glutenins or gliadins, or both, were being digested. An SDS PAGE of the total gluten after increasing incubation time with the spPEP was performed (Figure 8). With 60 \(\upmu\)g of spPEP and 400 \(\upmu\)g of total gluten, both the glutenins and gliadins had been partially digested. The high molecular weight (HMW) glutenins were digested first, followed by the gliadins, showing that both are substrates for the spPEP. Within this timeframe, not all of the gliadins were susceptible to enzyme digestion. Further studies are underway to identify the specific glutens cleaved by the enzyme and for determination of the enzyme recognition sequence within the respective glutens.  
Figure 6: Michaelis-Menten kinetics of the recombinant spPEP from the soluble protein fraction. PGpNA concentrations were varied from 0 to 300 \(\upmu\)M in 0.1 M ethanolamine buffer, pH 8.5, containing 0.1 M NaCl and 1 mM DTT. The reaction was started by the addition 130 \(\upmu\)g of purified spPEP per well. The A\({}_{410}\) was monitored over time. The Km and Vmax were determined from the double reciprocal plot, 1/v vs 1/[GPpNA]. The inset shows the initial velocities obtained for each substrate concentration plotted _vs._ substrate concentration to generate the Michaelis-Menten curve. A Vmax of 106 \(\upmu\)mole/mL/min was used with the \(\upmu\)M concentration of PEP to determine the turnover number.  
Figure 7: Glutenase assay of recombinant spPEP in both soluble and inclusion body lysates. Buffer was exchanged to 0.1 M ethanolamine, 0.1 M NaCl, pH 8.5. Reaction conditions were at 37 \({}^{\circ}\)C, for 2 h. One unit of enzyme activity is defined as a 1-mm change in gel height [8].
# Kinematic Modeling of a Trepanation Surgical Robot System
## 3 Results
### Workspace  
In order to prove the solutions presented for the inverse (see Section 2.2) and forward kinematics (see Section 2.4) of the trepanning robot, we have performed an experiment in simulation to establish the shape and the volume of the workspace of the system.  
The workspace analysis was performed in Matlab R2022, where a Monte Carlo approach was used to generate the point cloud of the feasible end-effector positions [35]. We have performed this analysis separately for the tripod end-effector as well as the complete system. In both cases, \(N=25,\!000\) random joint space configurations were sampled and the forward kinematics problem was solved numerically.  
The forward kinematics solutions were limited to a set where the \(B\) and \(C\) points of the tripod platform are placed at larger displacements away from the platform base than the end of the effectors \(q_{2}\) and \(q_{3}\).  
Next, a boundary of the obtained point cloud was calculated, and a mesh was generated to represent the approximated workspaces. These workspaces are shown in Figure 6 for the end-effector and Figure 7 for the complete system.  
The dimensions of the robot were obtained from the SolidWorks virtual model file. The dimensions of the system are as follows:  
\[b =0.1924\left[\mathrm{m}\right] c =0.1678\left[\mathrm{m}\right] r =0.1193\left[\mathrm{m}\right]\] \[e =0.1637\left[\mathrm{m}\right] L =0.4020\left[\mathrm{m}\right] h =0.5350\left[\mathrm{m}\right]\] \[d =0.1867\left[\mathrm{m}\right] r_{b} =0.4904\left[\mathrm{m}\right] r_{d} =0.4904\left[\mathrm{m}\right]\]  
Figure 6: Diagram of the tripod effector workspace. The red dot indicates the position of the end effector in the default configuration of the system.  
We have measured the minimum and maximum ranges for the joint variables as follows:  
\[q_{1} \in[0.0994\,\mathrm{m};0.1994\,\mathrm{m}]\] \[q_{2} \in[0.0994\,\mathrm{m};0.1994\,\mathrm{m}]\] \[q_{3} \in[0.0994\,\mathrm{m};0.1994\,\mathrm{m}]\] \[q_{4} \in[0.4216\,\mathrm{m};0.7215\,\mathrm{m}]\]  
The shape of the workspace of the tripod end-effector is shown in Figure 6. The volume of the workspace is \(0.435\,\mathrm{dm}^{3}\).  
The shape of the workspace of the complete trepanation robot setup is shown in Figure 7. The volume of the workspace is \(2.3\,\mathrm{dm}^{3}\).  
The robot is designed to perform exploratory surgery, particularly for brain hernias (herniatio cerebri) [37]. Brain hernias are classified according to the structure through which the tissue has herniated. The points of medical intervention required for these trepenning procedures can also be achieved by changing the patient's position during the procedure: supine, lateral, abdominal. Research into the determination of the surgical trepenning robot's workspace confirms that the workspace achieved is sufficient to perform the indicated procedures.  
Figure 7: Diagram of the trepenation robot’s workspace with a model of the human skull highlighted in red in lateral position during a selected surgical procedure [36].
# An Effective Multi-Label Feature Selection Model Towards Eliminating Noisy Features
## Appendix B Experimental Configuration  
The correlation (or similarity) matrices involved in experiments are all calculated based on the RBF kernel function. Specifically, the label correlation matrix \(\mathbf{S}\) in ELP is defined as \(\mathbf{S}_{ij}=\left\{\begin{array}{l}\exp\left(-\frac{\left\|\mathbf{y}_ {i}-\mathbf{y}_{j}\right\|^{2}}{2\theta^{2}}\right),\ \langle\mathbf{y}_{i},\mathbf{y}_{j}\rangle\
eq 0\\ 0,\ otherwise\end{array}\right.\), where \(\delta^{\prime 2}=\ mean(\left\|\mathbf{y}_{i}-\mathbf{y}_{j}\right\|^{2}),i,j=1, \ldots,l\). The instance similarity matrix in SPFS and CMFS is calculated as \(\mathbf{K}_{ij}=\left\{\begin{array}{l}\exp\left(-\frac{\left\|\mathbf{x}_ {i}-\mathbf{x}_{j}\right\|^{2}}{2\theta^{2}}\right),\ \mathbf{y}_{i}=\mathbf{y}_{j}\\ 0,\ otherwise\end{array}\right.\), where \(\delta^{2}=\ mean(\left\|\mathbf{x}_{i}-\mathbf{x}_{j}\right\|^{2})\). The affinity graph in MIFS is constructed as\[\left\{\begin{array}{l}\exp\left(-\frac{\left\|\mathbf{x}_{i}-\mathbf{x}_{j} \right\|^{2}}{2^{d}}\right),\;\mathbf{x}_{i}\in\mathcal{N}_{p}(\mathbf{x}_{j} )\;\text{or}\;\mathbf{x}_{j}\in\mathcal{N}_{p}(\mathbf{x}_{i})\\ 0;\;\text{otherwise}\end{array}\right.\]  
instance \(\mathbf{x}_{i}\).  
SPFS is implemented via the sequential forward selection (SFS) strategy. For a fair comparison, we tune the regularization parameter for all approaches via a grid search from \(\{10^{-3},10^{-2},10^{-1},1,10\}\). For ELC, the parameter \(\beta\) is fixed to \(\beta=10^{8}\), and \(\tau\) is set to the spectral radius of \(\mathbf{\hat{A}}^{T}\mathbf{\hat{A}}\) in the initial state and updated as \(\tau^{[t]}=\frac{1}{\max\left(\left\|\psi^{t}\right\|\right)}\) in the \(t\)-th iteration, where \(\psi^{t}\) is the \(i\)-th row vector of \(\mathbf{\Psi}\) and \(\mathbf{\Psi}=\mathbf{\hat{A}}^{T}\mathbf{\hat{A}}\mathbf{\hat{A}}^{[t]}\). The convergence state is reached when any of the following two conditions is satisfied: (1) \(t_{max}=10^{3}\); and (2) \(\left\|\mathbf{W}^{[t+1]}-\mathbf{W}^{[t]}\right\|\leq 10^{-4}\).  
Multi-label k-nearest neighbor (ML-kNN) classifier [43] is built on the \(k^{\prime}\) features selected by each compared approach, when \(k^{\prime}\in\{d/10,2d/10,\ldots,9d/10\}\) and \(d\) is the total number of features. All of the numerical features are normalized to zero mean and unit variance, and we employ the excellent features selected by the compared approaches to construct the ML-kNN classifiers and compare their classification performances. The 5-fold cross-validation is conducted, and we report the average performance of the ML-kNN classification under five metrics, i.e., precision, AUC, Hamming loss, ranking loss, and one error [39].