esa-sceva/satcom-chunk-collection huggingface dataset:
# Microbes and Parameters Influencing Dark Fermentation for Hydrogen Production
## 3 Factors Influencing Dark Fermentation Reactions  
Dark fermentation is influenced by many factors, including concentration, composition, and type of substrate. Different organic matter, such as carbohydrates, organic waste, and biomass, can be used as substrates at different concentrations. Based on these factors, the fermentation rate and H2 yield will vary. Excessively high substrate concentrations can inhibit the activity of microbes due to substrate inhibition or osmotic stress [28]. Microbial consortia are another parameter to consider, as the choice of microbes for dark fermentation strongly influences the amount of H2 produced, followed by the pH.  
Different microbes have optimal pH ranges at which they have the highest affinity to produce H2. In most cases, a slightly acidic to neutral pH is preferred by the dark fermentation microbes, as the fluctuations in pH can affect microbial activity and fermentation efficiency [29]. Alongside pH, temperature significantly affects the rate of dark fermentation. Certain microbes require optimal temperatures for their growth and activity. Typically, temperatures ranging from 30 to 40 \({}^{\circ}\)C are favorable for dark fermentation, although certain hydrogen-producing microbes can operate at 55 \({}^{\circ}\)C and even at 70 \({}^{\circ}\)C [30].  
HRT, another factor affecting dark fermentation, is the average time that the substrate is in the bioreactor during the fermentation process. It affects the efficiency of substrate utilization and the amount of H2 produced. HRT varies depending on the substrate and the microbial community. In addition, nutrient availability, inhibitors, agitation and mixing, and H2 partial pressure affect H2 production during dark fermentation. Nutrients aid in microbial growth and fermentation activity. A lack of nutrients can limit microbial activity,  
Figure 3: Two main hydrogen-producing pathways in dark fermentation [27].  
while inhibitors such as heavy metals or phenolic compounds can inhibit microbial growth and reduce their H\({}_{2}\) production efficiency [31]. Figure 4 shows various factors that affect H\({}_{2}\) production in dark fermentation.
# Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks
## 2 Methods
### EEG Preprocessing and Generation
#### 2.1.1 Data Sets Description  
The dataset is publicly accessible via the EEG time series repository at the University of Bonn, Germany [32]. All EEG recordings utilized a 128-channel amplifier system with a common average reference. Signals were sampled at 173.61 Hz and digitized at 12-bit resolution. Interference components (e.g., muscle activity) were eliminated from the signal using a filter with a bandpass range of 0.53 to 40 Hz and a slope of 12 dB/oct.  
The EEG data were segmented into epilepsy-specific stages from continuous recordings. The datasets (labeled A\(\sim\)E) represent five distinct EEG categories, each containing 100 single-channel EEG files spanning 23.6 s. Each file comprises 4096 data points of an EEG time series encoded in ASCII format. Scalp EEG datasets (A/B; \(n=5\) healthy subjects) cover eye-open/closed states. Intracranial EEG datasets (C-E; \(n=5\) epilepsy patients) include: (i) Interictal recordings: Dataset D (epileptogenic focus) and Dataset C (contralateral hippocampal formation); (ii) Ictal recordings: Dataset E (seizure onset zone).
## 5 Discussion
### Limitations and Future Prospects  
This paper has achieved some research results on the characteristics of traditional villages and residential cultural landscapes in multicultural intermingling areas and regional differentiation; however, there are still many uncertainties in the research process and results.  
First of all, the village sample data mainly come from the national and provincial traditional village lists, and may not fully represent those villages that are not listed but also have cultural value. In addition, the sample may lack some small, marginalized villages, whose cultural landscapes may be more unique but have not been given sufficient attention. Secondly, the characteristics of these landscapes and their differentiation patterns are often regionally specific, making it challenging to generalize findings to other multicultural areas.  
Despite the above limitations, future research needs to expand the sample, enhance the diversity and reliability of data sources, and select distinctive cultural landscape factors in order to explore more precise zoning methods. This will help to identify the characteristics of traditional villages in multicultural areas and improve the representativeness of the research results. At the same time, we should deepen the research on the formation mechanism of zonal differentiation and promote the application of quantitative methods so as to provide guidance for the conservation and sustainable development of traditional villages and cultural landscapes.
# A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks
## 6 Future Work  
Despite its promising results, the proposed approach has certain limitations that present avenues for future research. One limitation lies in the computational requirements of the machine learning models, which may pose challenges for deployment on resource-constrained IoT devices with limited processing power and memory. Future work should explore lightweight model optimization techniques, such as pruning, quantization, and knowledge distillation, to enable the deployment of effective attack detection systems on such devices.  
Another avenue for future research involves extending the proposed method to handle multi-class attack detection. While this study focuses specifically on XSS attacks, real-world IoT environments are subject to diverse security threats, such as Denial of Service (DoS), Distributed Denial of Service (DDoS), SQL injection, and malware propagation. Developing a multi-class detection framework that can simultaneously identify various attack types would enhance the versatility and applicability of the model in dynamic IoT settings.  
As 5G and IoT landscapes continue to evolve, future studies should evaluate the proposed solution's performance under emerging attack patterns and data traffic complexities. The integration of advanced 5G features, such as network slicing and edge computing, introduces new vulnerabilities and scenarios that must be addressed. Adapting the proposed approach to these advancements will ensure its continued relevance and effectiveness in safeguarding IoT systems within next-generation networks.  
Conceptualization, R.A., M.A. ([PERSON]) and W.Q.A.-J.; Formal analysis, A.A. and M.A. ([PERSON]); Funding acquisition, N.A.; Investigation, R.A. and A.A.; Methodology, M.A. ([PERSON]) and M.A. ([PERSON]); Project administration, A.A.; Resources, R.A. and M.A. ([PERSON]).; Validation, N.A.; Software, R.A. and M.A. ([PERSON]); Supervision, M.A. ([PERSON]); Writing--original draft, R.A. and M.A. ([PERSON]); Writing--review and editing, A.A., M.A. ([PERSON]), N.A., M.A. ([PERSON]) and W.Q.A.-J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Northern Border University, Arar, KSA.  
Research data will be made available upon request from the corresponding author.  
The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through the project number \"NBU-FFR-2024-1260-02\".  
The authors declare no conflicts of interest.
## 2 Numerical Simulation of Flow Forces in Slide Valves
### CFD Simulation of Flow Force
The geometric model of the flow path is discretized using an unstructured mesh, with five layers of boundary layer mesh near the walls and local refinement applied around the throttle valve port area. A grid independence study is performed for the spool valve under operating conditions of a 0.2 mm valve opening and a 1 MPa pressure drop. The internal flow field within the valve chamber is simulated for various mesh densities, with the steady flow force on the spool serving as the evaluation criterion for grid independence. The results, as shown in Figure 3, indicate that when the number of mesh elements exceeds 8 \(\times\) 10\({}^{5}\), the steady flow force remains essentially unchanged, with the deviation within 1%. This confirms that the mesh resolution meets the grid independence requirement. Therefore, subsequent calculations of the slide valve flow field are conducted using a mesh configuration with 8.27635 \(\times\) 10\({}^{5}\) elements.
## 3 Results
### Deformation Mechanism
#### 3.4.1 Compression in the x Direction  
The deformation mechanism of re-entrant structure under compression in the x direction was analysed through measurement of the change in length of the struts and re-entrant strut angle under compression (Figure 19a). Strains measured for re-entrant struts L1 and L2 indicate compression (negative strain), and strain measured for H indicates tension (positive strain). Strains measured for A1, A2, B1 and B2 (Figure 19b) indicate that A1 and A2 undergo compression and B1 and B2 undergo tension (Table 8), resulting in a bending of the strut, as illustrated in Figure 19c. There is also an increase in the re-entrant angle, \(\theta\). The results indicate that the unit cell deformation through compression in the x direction thus occurs as a result of a combination of vertical strut stretching, re-entrant strut compression, bending and hinging.
## 4 Experimental Results
### Comparison with the State-of-the-Art Methods
However, from the perspective of robustness, it can be seen that for the SDM, the highest performance was related to the ICO with the SD value of \(3.09\times 10^{-17}\). After that, the CO and APLO were placed in the following ranks. For the DDM, hARS-PS showed the best performance in terms of standard deviation with a value of \(1.45\times 10^{-7}\), followed by MSSA and the ICO, which were second and third best, respectively. For the PVMM, Rcr-JADE provided the best stability in comparison with the other algorithms. The ICO could also achieve the second-best SD value of \(3.19\times 10^{-17}\), followed by the CO with the SD of \(4.37\times 10^{-17}\). The results indicate that the ICO provided the most accurate and reliable algorithm for identifying solar PV model parameters. Furthermore, the original CO algorithm also exhibited reasonable performance compared with other hybrid and improved algorithms.
# Spatial Autocorrelation Incorporated Machine Learning Model for Geotechnical Subsurface Modeling
## 2 Study Area and Methodology
### Data Pre-Processing  
#### 2.3.1 Euclidean Distance Feature Generation  
To model the spatial autocorrelation, the raw X-Y coordinate features need to be transformed into a distance vector. Two forms of Euclidean distance feature set were used for machine learning modeling. The first form is the EDF model proposed by [PERSON] et al. [20], termed \"EDFB\" feature set in this study, which integrate X and Y coordinates, Euclidean distances to the corners of a rectangle around the sample set, and the distances to the center location of the sample set [20]. The second form is the one proposed in this study which calculates the Euclidean distance between the query point and all sample points (all borehole coordinates). This distance vector termed as Euclidean distance field are illustrated in Figure 4, and explained by Table 2. Figure 4 describes the location information of two known boreholes and three unobserved points. The Euclidean distance between query points and known boreholes is calculated using the following equation:  
\[d(p,q)=\sqrt{\sum_{i=1}^{n}{|q_{i}-p_{i}|}^{2}}, \tag{1}\]  
where \(p\) and \(q\) are two points in the Euclidean \(n\)-space, \(q_{i}\) and \(p_{i}\) are Euclidean vectors starting from the origin of the space (initial point), and \(n\) is the \(n\)-space.  
The new input features generated by this transformation can be seen in Table 2. When the raw X-Y coordinate is transformed into the Euclidean distance vector, the number of features grows proportional to the number of sample boreholes. For example, as illustrated in Figure 4, there are two known boreholes and three unobserved points (location of targets for the prediction). Therefore, two new features will be generated as a result of the transformation, containing the Euclidean distance data between known boreholes (BH1 and BH2) and the unobserved location (L1, L2, and L3) as presented in Table 2. In the same way, as this study analyzes data from 34 known borehole points, 34 new features will be generated. The new generated EDF features will replace the X and Y coordinates as location information features for machine learning modeling.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Location** & **Distance to BH1** & **Distance to BH2** & **Distance to BHx** \\ \hline L1 & Euc. Dist. L1—BH1 & Euc. Dist. L1—BH2 & \\ \hline L2 & Euc. Dist. L2—BH1 & Euc. Dist. L2—BH2 & \\ \hline L3 & Euc. Dist. L3—BH1 & Euc. Dist. L3—BH2 & \\ \hline Lx & \(\dots\) & \(\dots\) & \(\dots\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Illustration of the Euclidean distance data transformation.  
Figure 4: Illustration of Euclidean distance Field.
###### Abstract  
Under coupled compression-shear loading, the failure and instability behavior of inclined pillars is different from that of horizontal pillars. To enhance the reliability and accuracy of pillar strength design, the influence of different inclination angles and loading rates on mechanical property and the failure behavior of inclined pillar should be studied. In this paper, the combined compression and shear test (C-CAST) system was developed, and mechanical properties and macro failure behavior of coal samples under different inclination angles and loading rates were studied, and acoustic emission (AE) technology was used to determine the internal cracking mode of the sample. The results show that with the increase of inclination angle, the peak shear stress of coal sample increases gradually, while the peak axial stress and elastic modulus slightly increase first and then decrease, and reach the maximum value at an inclination angle of 5\({}^{\circ}\). Within the inclination angle range of 0\({}^{\circ}\)-15\({}^{\circ}\), with the increase of loading rate, the peak axial stress and elastic modulus of coal samples first increase and then decrease, while the loading rate corresponding to peak axial stress and elastic modulus decreases. Within the inclination angle range of 20\({}^{\circ}\)-25\({}^{\circ}\), the peak axial stress and elastic modulus of the sample gradually decrease with the increase of loading rate. The failure mode of coal samples changes from tension-splitting failure (0\({}^{\circ}\)-5\({}^{\circ}\)), tension-shear composite failure (10\({}^{\circ}\)) to single shear failure (15\({}^{\circ}\)-25\({}^{\circ}\)). Meanwhile, the loading rate has little effect on the failure mode of coal samples, but has a significant effect on the failure degree. When the loading rate is 1.0 and 10 mm/min and the inclination angle ranges from 0\({}^{\circ}\)-5\({}^{\circ}\), the proportion of tensile crack is significantly greater than that of the shear crack, and tensile failure is the main failure mode; when the inclination angle ranges from 10\({}^{\circ}\)-25\({}^{\circ}\), the proportion of shear crack is more than 50% and increases gradually with the increase of inclination angle, and shear failure is the main failure mode. This law is consistent with the macroscopic failure mode of the sample.  
**Keywords:** inclination angle; loading rate; shear stress component; crack mode; acoustic emission
## 4 Numerical Simulation Analysis and Results
### Contact Compressive Stress, Friction Stress, and Distributions  
Considering that the bellows are often worn through by the metallic braided tube in service of use, in order to understand the effect of contact pressure and frictional stress on the bellows, the distributions of contact stress are displayed in Figure 13.  
The frictional stress, and the contact pressure are mainly distributed at the crest of the bellows, which is the easiest area to make contact with the braided tube. The distribution of the frictional stress is discrete, and the contact position is not the same for each crest, which is mainly determined by the shape of the braided tube.  
On the contact surface of the bellows, the frictional stress is distributed like concentric circles. At the center of the contact area, the maximum frictional stress of 88.46 MPa occurs. The frictional stress decreases from the center to the outside of the surface.  
In addition, the distribution of the contact pressure is similar to the frictional stress, as Figure 13b demonstrates. The maximum contact stress occurs where the maximum frictional stress occurs, and the maximum value is 498.07 MPa. Therefore, these contact  
Figure 11: Stress diagrams of the DN25 bellows: (**a**) Equivalent stress, (**b**) Axial stress.  
Figure 12: Axial stiffness of the DN25 braided corrugated hose.  
Figure 13: Frictional stress and contact pressure of the braided corrugated hose: (**a**) Frictional stress, (**b**) Contact pressure.  
areas of the bellows are more prone to experience wear and fatigue damage during the service life of the braided corrugated hose.
# Towards Digitalization of Water Supply Systems for Sustainable Smart City Development--Water 4.0
## 5 Potential Applications of Water 4.0
### Water Quality Monitoring  
In water quality applications, the deployment of integrated sensing devices is required to provide continuous real-time measurement of data related to pH, temperature, turbidity, dissolved oxygen concentration, and chlorine residual level along the distribution networks. With the help of the CPS features of Water 4.0, which are integrated with control valves and active decision support systems, an optimal decision regarding the state of the water in the distribution piping networks could be achieved. For instance, in a situation where contaminants are detected and quality is compromised, active decision making is required for immediate action to be taken on water contamination before it spreads to the entire system. In most cases, necessary actions include the closing of valves at the particular node where the pipe conveying the contaminated water is attached. The valves installed at the entrance of a DMA of the water distribution network could also be controlled to stop the spread of contaminants.
## 3 Method
### Embedding Component  
The embedding component can embed a watermark into the original image to minimize the perceptual difference between the original and the watermark images and improve the imperceptibility and security of the watermarked image. The component is composed of DCT transform layers, convolutional layers and IDCT transform layers, responsible for embedding the watermark into the transform coefficients of the images. We use a CNN architecture that receives a one-channel input of \(512\times 512\) pixels and outputs a gray watermarked image. The input watermark is represented as a \(32\times 32\) bit binary, and transformed image blocks are spliced with the watermark image. Specific procedures of watermark embedding is described in Algorithm 1.  
```
Input: Image \(I_{o}\) size of \(M\times M\), watermark \(w_{o}\) size of \(h\times g\), strength factor \(\alpha\). Output: Watermarked image \(I_{w}\).
1: Divide input image \(I_{o}\) size of \(M\times M\) into blocks of size \(h\times g\) and each block will be embedding at least one watermark bit. Then we can obtain a tensor of size \(b\times h\times g\).
2: Input the tensor into the DCT transform layer and output DCT coefficients tensor.
3: Concatenate the output of the DCT transform layer with the watermark, shaping the input tensor of size \((b+1)\times h\times w\) as the input of the embedding network.
4: Get the output of embedding layer, that is watermarked DCT coefficient map.
5: Calculate the residual with watermarked DCT coefficient by the inverse transform layer.
6: Add the residual to the input image \(I_{o}\) with a strength factor \(\alpha\) weight, finally we can obtain watermarked image \(I_{w}\).
```  
**Algorithm 1** Embedding algorithm.
# Automatic Optimal Robotic Base Placement for Collaborative Industrial Robotic Car Painting
###### Abstract  
This paper investigates the problem of optimal base placement in collaborative robotic car painting. The objective of this problem is to find the optimal fixed base positions of a collection of given articulated robotic arms on the factory floor/ceiling such that the possibility of vehicle paint coverage is maximized while the possibility of robot collision avoidance is minimized. Leveraging the inherent two-dimensional geometric features of robotic car painting, we construct two types of cost functions that formally capture the notions of paint coverage maximization and collision avoidance minimization. Using these cost functions, we formulate a multi-objective optimization problem, which can be readily solved using any standard multi-objective optimizer. Our resulting optimal base placement algorithm decouples base placement from motion/trajectory planning. In particular, our computationally efficient algorithm does not require any information from motion/trajectory planners a priori or during base placement computations. Rather, it offers a hierarchical solution in the sense that its generated results can be utilized within already available robotic painting motion/trajectory planners. Our proposed solution's effectiveness is demonstrated through simulation results of multiple industrial robotic arms collaboratively painting a Ford F-150 truck.  
Keywords:industrial robots; automatic vehicle painting; robotic base placement; multi-objective optimization algorithms; industrial automation +
Footnote †: journal: Applied
## 5 Graphene/Graphene Oxide-Based RF Gas Sensing Platforms
### Graphene-Based RFID Gas Sensors  
Figure 11a shows an RFID-based wireless smart-sensor system consisting of a Pt-decorated rGO (Pt/rGO)-based RFID sensor tag and an RFID-reader antenna to detect hydrogen gas. The Pt/rGO, the sensing material, produced using a simple chemical reduction process, was coated onto an antenna pattern in the sensor tag. The proposed sensor tag exhibited a high sensitivity to hydrogen gas at unprecedentedly low concentrations (\(\sim\)1 ppm), with wireless communication between the sensor tag and RFID-reader antenna. [PERSON] et al. [85] demonstrated the flexibility and long lifetime of the wireless sensor tag due to the strong immobilization of the Pt/rGO on the substrate and battery-independent operation during hydrogen sensing, respectively.  
Moreover, the Pt/rGO-immobilized RFID sensor tag exhibited mechanical stability with bending and twisting deformations due to the flexibility of the plastic sensor substrate and strong adsorption of the Pt/rGO on the metal substrate (Figure 11b,c).  
Figure 10: Graphene oxide-based humidity sensor. (**a**) Resonator circuits for permittivity measurement of GO and (**b**) behavior of measured (solid lines) and simulated (dashed lines) \(S_{21}-\)resonance of the sensors with/without GO layer as RH concentrations increase [84].