esa-sceva/satcom-chunk-collection huggingface dataset:
# Case Study and Risk Assessment of Water Inrush Disaster in Qingdao Metro Line 4
## 3 Analysis on the Causes of the Collapse
### Numerical Simulation for the Collapse
#### 3.2.3 Analysis of Seepage Results  
After tunnel excavation, the groundwater seeped towards the tunnel, as shown in Figure 16. As can be seen from the figure, when groundwater converged toward the bottom of the funnel, most of the groundwater bypassed the powdered clay layer due to the poor permeability of the powdered clay, which plays a certain role in water isolation. In this circumstance, the groundwater is more likely to seep downward from above the tunnel vault, resulting in a greater possibility of seepage failure of the surrounding rock.  
Figure 17 shows the pore water pressure of the ground at the time of the disaster. It can be seen from the figure that a negative pore water pressure zone was formed in a certain area around the tunnel when the disaster occurred. This indicates that the rate of groundwater loss in the region was greater than the rate of recharge. The existence of a negative pressure area increased the groundwater recharge, which indicates that water inrush would cause a larger range of groundwater recharge to the zone and result in an amount of water inflow far beyond the normal amount of water inflow in the area. This, water inrush caused a large amount of groundwater to gush into the tunnel and cause damage to the upper soil layer, causing the ground to collapse. At the site, the volume of water was about 4755.8 m\({}^{3}\). According to the empirical formula of maximum water inflow in the Railway Code, as shown in Equation (1), the maximum water inflow in the area would be 4154.4 m\({}^{3}\), which is less than the actual value.  
\[Q=0.0255+1.9224 KH \tag{1}\]  
where \(K\) is the permeability coefficient of aquifers, and \(H\) is the distance from the resting water level to the center of the equivalent circle of the cave cross-section. Inadequate estimation of the water inflow in the tunnel was an important cause of the accident. The water inrush caused the groundwater level to drop significantly, causing the soil to consolidate and create ground subsidence.  
Figure 16: Groundwater seepage.
# A Study on the Vehicle Routing Planning Method for Fresh Food Distribution
## 3 Algorithm Design  
The location routing problem (LRP) is a complex system problem composed of Facility Location Problem (FLP) and vehicle routing problem (VRP) and there is a dependency relationship between FLP and VRP [22]. The network level of the front warehouse location and path planning studied in this paper belongs to two-level LRP (2E-LRP), where the upper level location is used to solve a single objective multiple constraint problem and the lower level path planning is used to solve a multiple time window path problem. This paper uses k-means-based considerations to introduce temporal and spatial distance factors to cluster customer points and determine the initial set of candidate points for the front warehouse.  
Cluster analysis is an unsupervised learning method that has the advantages of fast computation speed and low computational complexity, making it suitable for processing large-scale data. We divide the samples according to certain rules, gather highly correlated samples in the same cluster and divide irrelevant samples into different clusters to analyze the differences in the properties and correlations between samples.  
This chapter conducts cluster analysis on customer points that require front services, providing a good set of candidate point locations for future front warehouse site selection.
# Enhancing Clay Soil's Geotechnical Properties Utilizing Sintered Gypsum and Glass Powder
## 2 Experiment Program
### Materials
#### 2.1.2 Glass Powder  
The glass material employed in this investigation was obtained from discarded solar panels, known for their challenging disassembly due to their firm adherence to the solar cells. A chopper machine was employed to facilitate the separation process and overcome this challenge. Afterward, the glass pieces were further crushed within a metallic cylinder using a hammer. The crushed glass was sieved through sieve number 230 with a 0.045 mm opening to obtain the required glass powder.
## 5 Discussion
### Practical Implications for Crisis Response
#### 5.3.4 Potential Deployment Scenarios in Real-World Crisis Response  
The CLIP-BCA-Gated framework, optimized for noise resilience, real-time inference, and multimodal alignment, holds promise for operational disaster response. Its technical characteristics suggest feasibility across key scenarios:  
(1) Live Crisis Monitoring: Streamlining Alert Prioritization  
Integrated with social media pipelines, the model could process large-scale disaster-related streams via sub-second inference (0.083s/instance) and \(\geq\)12 tweets/second throughput on mid-tier GPUs, supporting over 1 million posts daily per card--suitable for regional monitoring needs. Its adaptive gating suppresses 20% noisy content, prioritizing high-reliability alerts. Strong recall for infrastructure damage (93.42%) and rescue requests (92.15%) would trigger notifications via dashboards (e.g., Ushahidi), reducing information overload.  
(2) Emergency Response Platform Integration  
Structured outputs could align with UN OCHA/FEMA standards, formatted as JSON/XML for ingestion into platforms like OCHA's HDX. Geotagged classifications, cross-referenced with GIS, would support real-time damage mapping to identify critical failures (e.g., road blockages) for targeted intervention, bridging data analytics with operational action.  
(3) Edge Deployment for On-Site Verification  
Leveraging 50% reduced computational load from mixed-precision training, the model could deploy on field tablets. In low-connectivity zones, rescue teams might upload real-time photos/text for rapid incident classification (e.g., \"trapped individuals\"), bridging grassroots observations with centralized strategies, pending field validation.
## 5 Results and Discussion
### Design of the Proposed System  
This section illustrates the proposed solution's high-level architecture, followed by a general system implementation scenario and detailed low-level scenarios.
# Central Kurdish Text-to-Speech Synthesis with Novel End-to-End Transformer Training
## 6 Results and Discussion
### Inference Speed  
This section compares the inference speed of KTTS with that of both two-stage and one-stage systems. The comparison was conducted on a server with an \"AMD Ryzen threadripper pro 3955 wx\" CPU with 16 cores, 256 GB of memory, and one NVIDIA RTX A5000 GPU with 24 GB of memory. Table 4 shows that the proposed model sped up the inference process by 8.32x compared with that of the one-stage VITS system [13]. Regarding the two-stage systems, the proposed model sped up the inference process by 47.49x with respect to Glow TTS [20] and by 53.73x with respect to Tacotron 2 [1], as the two-stage systems needed more processing time because two parallel models were included in their inference processes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **MOS (CI)** \\ \hline Ground Truth & 4.75 (\(\pm\)0.10) \\ \hline Tacotron 2 (Mel + multi-band MelGAN) & 3.85 (\(\pm\)0.10) \\ \hline Glow TTS (Mel + multi-band MelGAN) & 3.94 (\(\pm\)0.15) \\ \hline VITS & 3.70 (\(\pm\)0.66) \\ \hline
**KTTS** & **3.94 (\(\pm\)0.16)** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the evaluated MOSs with 95% confidence intervals on the Gigant dataset.  
Since our approach relies on VAEs, it is important to acknowledge some inherent limitations of this method. VAEs, though promising in various applications, including TTS, face certain challenges. Being an unsupervised learning system, VAEs lack precise control over the speech features they generate. A key drawback of VAEs is the need to balance regularization and reconstruction accuracy, which can sometimes lead to distorted speech outputs. Additionally, the alignment between synthesized speech and input text is not explicitly defined in VAEs. Moreover, VAEs require meticulous tuning of several hyperparameters, such as the dimensionality of the latent space, the choice of a prior distribution, and the weighting of loss and reconstruction terms. These factors necessitate careful consideration to optimize performance and mitigate potential issues.  
The proposed approach may yield suboptimal results under certain conditions, such as a lack of sufficient training data, complex phonetic variability, and real-time constraints. These conditions can involve complex computations and latent space sampling during inference, which may be computationally expensive.
## 2 A Novel 3D Discrete Element Numerical Analysis Method for Rockburst
### Multi-Parameter Rockburst Proneness Index Based on Energy Theory  
Based on the shortcomings of the above-mentioned rockburst index, considering that rock failure is a coupling process of energy from accumulation to dissipation and release, the analysis from the energy perspective can more truly reflect the rockburst essence. According to the overall element failure criterion proposed by [PERSON] et al. [21], the ratio relationship between the internally accumulated releasable strain energy \(U^{\mathrm{e}}\) and the critical surface energy \(U_{0}\) required for rock failure can be obtained when the rock undergoes overall failure under tensile and compressive stress states. The expression is as follows [22]:  
\[\left\{\begin{array}{ll}\frac{U^{\mathrm{e}}}{U_{0}}=\frac{(\sigma_{1}- \sigma_{2})2E_{0}U^{\mathrm{e}}}{\sigma_{3}^{3}}(\sigma_{3}\geq 0)\\ \frac{U^{\mathrm{e}}}{U_{0}}=\frac{\sigma_{2}E_{0}U^{\mathrm{e}}}{\sigma_{1}^{ 3}}(\sigma_{3}<0)\end{array}\right. \tag{1}\]  
where \(\sigma_{\mathrm{c}}\) and \(\sigma_{\mathrm{t}}\) are the compressive and tensile strength of rock, respectively, and \(\sigma_{1}\) and \(\sigma_{3}\) are the maximum and minimum principal stress, respectively. \(E_{0}\) is the initialelastic modulus, and \(U^{\rm e}\) is the storage elastic strain energy. The expression is shown in Equation (2), and \(U^{0}\) is the ultimate elastic strain energy.  
\[U^{\rm e}=\frac{1}{2E_{0}}\Big{[}\sigma_{1}^{2}+\sigma_{2}^{2}+\sigma_{3}^{2}-2v (\sigma_{1}\sigma_{2}+\sigma_{2}\sigma_{3}+\sigma_{1}\sigma_{3})\Big{]} \tag{2}\]  
where \(\sigma_{2}\) is the intermediate principal stress, and \(v\) is the Poisson's ratio.  
Based on this, [PERSON] et al. considered the importance factor of rock brittleness on rockburst and proposed the multi-parameter rockburst energy index \(C_{rs}\) (Criterion of rockburst) [23] under different stress states. The expression is as follows:  
\[C_{rs}=\frac{\sigma_{\rm c}}{\sigma_{\rm t}}\frac{U^{\rm e}}{U_{0}} \tag{3}\]  
where \(\sigma_{\rm c}/\sigma_{\rm t}\) represents the brittleness coefficient, and \(U^{\rm e}/U_{0}\) is the energy storage factor.  
This index eliminates the one-sidedness using energy or brittleness index alone to judge rockburst (the larger the \(U^{\rm e}/U_{0}\) and \(\sigma_{\rm c}/\sigma_{\rm t}\), the greater the rockburst proneness). Combined with the boundary value division of the rockburst index given by [PERSON] et al. [24], the probability that the boundary index of different factors reaches the maximum value at the same time is small. For the convenience of practical application, the intensity classification of the index \(C_{rs}\) is shown in Table 1.
## 2 Materials and Methods
### Method of Establishing the SSC Prediction Model
#### 2.3.1 Establishment of SSC Prediction Model Sample Set
The Mahalanobis distance method is used to verify the distance between samples through spectral data and does not require SSC data, thus avoiding errors caused by human operation to a certain extent. Therefore, using the Mahalanobis distance method to eliminate outlier samples can help to improve the accuracy of the model [28]. In this research, the Mahalanobis distance method was used to eliminate outlier samples.
## 3 Analysis and Optimization of Friction Flash Temperature of Gears Tooth Surface
### Analysis of Tooth Surface Flash Temperature under Various Load Conditions  
In order to study the relationship between the maximum flash temperature and the working conditions, the working conditions can be divided into two categories:  
1. Constant torque and speed-up: in this condition, the input torque is maintained at 2000 Nm, while the input speed increases from 200 r/min to 1500 r/min.
2. Constant speed and torque-up: here, the input speed is kept at 1500 r/min, and the input torque increases from 200 Nm to 2000 Nm.  
The analysis results of the maximum flash temperature of all gear pairs under each working condition are shown in Figure 11.  
The variation curve of the maximum flash temperature with the rotation speed of each gear pair is shown in Figure 11a. It can be seen that the maximum flash temperature increases with non-linear deceleration as the speed increases. Among all gear pairs, G1735 consistently exhibits the highest flash temperature. Following G1735 are the three fixed-axis gear train sets (G3540, G1835, and G2340). The meshing between the sun gear and the four  
Figure 11: Tooth surface flash temperature under various operation conditions. (**a**) Flash temperature curves with speed variation; (**b**) flash temperature curves with torque variation.  
Figure 10: Tooth surface flash temperature of G1735 under load spectrum. (**a**) Non-modified tooth surface; (**b**) modified tooth surface.  
planetary gears in the planetary gear train have nearly identical flash temperatures. Gear pair G3536 falls next, and finally, the four meshing pairs between the planetary gears and the internal ring gear exhibit the lowest flash temperatures.  
The variation curve of the maximum flash temperature with torque of each gear pair is shown in Figure 11b. It can be seen that the maximum flash temperature increases approximately linearly with the increase of torque, and the order of the maximum flash temperature values of each gear pair is almost the same as that under speed-up conditions.
# Blockchain Enabled Transparent and Anti-Counterfeiting Supply of COVID-19 Vaccine Vials
## 3 Methodology
### System Work Flow  
The architecture of the proposed blockchain-enabled solution for transparent supply of COVID-19 vaccine is presented in Figure 2. The entire track data related to COVID-19 vaccine during supply is stored in distributed ledgers over blockchain networks in order to assure the immutability of data and guarantees that the COVID-19 vaccines vials are transported safely.  
The main participants of the proposed framework considered as the peer nodes of blockchain network are: (i) the recipient who will register for immunization, (ii) the laboratory or pharmaceutical factories that will prepare the vials of vaccine, (iii) the IoT sensors which will monitor the temperature and location at the time of distribution, (iv) the healthcare centers which will provide the vaccine services to the vaccine recipients, (v) the healthcare workers who will verify the details of recipient during the vaccination process also they will control the vaccine conditions during delivery and storage.  
Figure 2: Architecture of the proposed framework for transparent supply of COVID-19 vaccines.  
All the data of all the actions is stored in distributed ledgers that are immutable in nature and is accessible by all the participants of the network. Thus, it enables the feature of transparency at each level and records the data regarding registration for vaccination of COVID-19 vaccines as a digital asset. Figure 3 presents the proof of delivery model.
# Bridging the Gap: A Survey and Classification of Research-Informed Ethical Hacking Tools
## 2 Background
### Ethical Hacking Methodologies  
Penetration testing takes different forms and can cover various areas. [PERSON] et al. [14] describe the process of conducting an attack in five main phases:  
* _Reconnaissance_: The hacker gathers information on systems and users through passive or active techniques. This includes physical methods like social engineering and analysing network packets to identify details such as network configuration, hardware, and security measures.
* _Scanning_: The hacker searches for vulnerabilities in systems through simulated tests, including identifying open ports, active hosts, and weak firewall configurations. Enumeration is then carried out to gather further information while maintaining an active connection.
* _Gaining Access_: The hacker attempts to access the system using penetration testing tools and techniques, aiming to bypass security measures.
* _Maintaining Access_: The hacker establishes backdoors or rootkits to maintain remote access with elevated privileges.
* _Covering Tracks_: The hacker eliminates evidence that could reveal their identity or traces of the attack.  
Each phase is complex and crucial for the success of a cyber attack. Due to the unique nature of each system, there are no strict rules for systematically executing an attack or penetration test. However, various frameworks and methodologies have been developed to guide the penetration testing process in planning and executing cyber attack simulations.  
These frameworks can be categorised into three main areas: open source, maintained by non-profit organisations or security institutes; industrial/governmental, maintained by government entities such as the National Institute of Standards and Technology (NIST); and proprietary, maintained by private companies and accessible through payment of a usage license.
# Magnetic Force Enhancement Using Air-Gap Magnetic Field Manipulation by Optimized Coil Currents
## 6 Conclusions  
This work aimed to propose a magnetic field manipulation using optimized coil current to enhance the tangential direction magnetic force. An ideal field distribution for maximizing the tangential direction force was first provided by investigating [PERSON]'s stress tensor formulation. Subsequently, the optimization for determining the coil current near air-gap was performed. For the optimization, the design domain near air-gap was divided into small areas, and the design variables were assigned at each small area. The design variables determined not only the number of coil turns but also whether the material state was coil or iron. For this, the appropriate material property interpolation functions were proposed in this work. The optimization problem was then formulated to determine the design variables maximizing the averaged magnetic force. By solving the optimization problem using the mathematical programming method, the optimal coil current distribution was successfully obtained. The average force of the design with the magnetic field manipulated by optimized coil current was expected to 11.12% higher than that of the original design without magnetic field manipulation. The future work would include the experimental validation of the magnetic field manipulation using the optimized coil currents for force enhancement. The construction of two current sources for winding and coils near the air-gap is a challenging but interesting topic.  
J.L. ([PERSON]) conceptualization, methodology, software, and writing--original draft preparation; J.L. ([PERSON]) methodology, formal analysis, and writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant (17 TLRP-C135446-01, Development of Hybrid Electric Vehicle Conversion Kit for Diesel Delivery Trucks and its Commercialization for Parcel Services) from Transportation and Logistics Research Program (TLRP) funded by Ministry of Land, Infrastructure, and Transport of the Korean government.  
The authors declare no conflict of interest.  
Figure 9: Magnetic flux density (black-colored arrow) on air-gap at 9 th plunge location (\(m=9\)): (**a**) original design without magnetic field manipulation, (**b**) design with the field manipulation by optimized coil current. The red color in the design domain represents the strength of the current density.
# Design, Deployment and Evolution of Heterogeneous Smart Public Lighting Systems
## 6 Deployment and Configuration of a Smart Lighting Infrastructure  
Getting to the final deployment of a smart lighting infrastructure requires three main steps:  
* Step 1: preliminary design,
* Step 2: in-field verification and light fixtures upgrade,
* Step 3: network partitioning and lighting tasks configuration.  
each of which is thoroughly discussed in the following.
## 2 Materials and Methods
### Ground Pressure Calibration Device  
Due to the fact that there is no mature product for measuring the ground pressure of paddy soil in the market, a calibration device combing ground pressure measurement systems was designed and manufactured in order to detect the error between the ground pressure measured by the measuring device and the actual ground pressure and to calibrate and correct the measurement accuracy of the whole ground pressure measurement system.  
The pressure sensor is connected with the signal amplifier. The signal amplifier converts the resistance signal into a voltage signal of 0-5 V and inputs it to the Arduino controller. In addition, the Arduino controller filters the data and sends it to the computer through the wireless serial port transceiver module. The number of counterweight plates is gradually increased, and the ground pressure is measured in turn. Figure 7 shows the ground pressure combined calibration system.  
Figure 4: Flow chart of measurement system software.  
Figure 5: Pressure sensor installation location.  
Figure 6: Pressure sensor installation location (Side view). 1. Forward resistance sensor. 2. Front pressure sensor. 3. Rear pressure sensor.  
The experimental results show that when the number of counterweight plates is between 0 and 10, the actual ground pressure (\(q_{p}\)) value is linearly related to the total mass (\(m\)).  
\[q_{p}=\frac{m\cdot g}{S_{p}} \tag{1}\]  
where \(S_{p}\) represents the contact area between pressure sensor and soil.  
When the ground pressure is in the range of 0-2000 Pa, the actual ground pressure value measured by the measuring system is compared with the theoretical value. The error between the ground pressure values is within 2%, which meets the actual demand for ground pressure measurement accuracy. The ground pressure calibration data are shown in Table 2.
## 3 Results
### Analysis of the Driving Factors of Farmland Abandonment
#### 3.3.2 Analysis of Irrigation Conditions of Abandoned Farmland  
Irrigation conditions have multiple effects on farmland, such as increasing crop yields, enhancing land productivity, conserving water, promoting the efficient use of water resources, influencing irrigation water demand, improving agricultural irrigation systems, and enhancing agricultural production capacity. In this study, the Euclidean distance to the water system in the study area was calculated, and the results were classified into five buffer zones: 0-100 m, 100-300 m, 300-500 m, 500-700 m, and greater than 700 m. Figure 7a shows the distribution of perennial abandoned farmland across different distances from the water system, While Figure 7b illustrates the distribution of seasonal abandoned farmland. The patches of abandoned farmland were then overlaid with the vector map of the water system's buffer zones. The proportion of farmland in each buffer zone relative to the total farmland area was calculated, along with the abandonment rate for each zone Figure 7c. The results indicate that the irrigation conditions of abandoned farmland are suboptimal. Specifically, more than 50% of perennial abandoned farmland and more than 60% of seasonal abandoned farmland are located beyond the 500 m water system buffer zone. Furthermore, less than 10% of the perennial and seasonal abandoned farmland falls within the 100 m buffer zone. These findings suggest that irrigation conditions play a significant role in the abandonment of farmland in the study area.