esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods and Experiments  
### MR Environment Set-Up  
The MR environment is set up by the mixed reality evacuation LVC simulation system we developed, which is based on the MR headset and evacuation simulation modeling tools. The methodology and operation of the system are described in detail in our earlierpublication [34]. Participants can see the dynamic process of computer evacuation simulation in real space through the headset. The specific workflow and system prototype are as follows:  
First, as shown in Figure 1,, using the SLAM component in HoloLens (Microsoft, Redmond, WA, USA), a coarse building information model consistent with the physical space is obtained by walking around and looking around the room while wearing the headset; Figure 1 shows what the experimenter sees from the first perspective--a virtual character scene.  
Second, using the scan information, a building model of the same size as the experimental site is built using spatial markers in Anylogic (version 8.8.4). Then, pedestrian evacuation and exit selection logic modules were built using the module in the pedestrian library to establish a virtual human motion model to simulate the behavior selection and motion behavior of virtual humans in a dangerous scene.  
Third, after completing the pedestrian simulation in Anylogic, the simulation model was exported as an independent program for secondary development into a server. Through this server, the position and status of the virtual human simulated by Anylogic could be sent to the HoloLens in real-time so that the observed situation of the experiment participants could be synchronized with the evacuation movement in the virtual scene. In other words, what the participant was watching was the virtual human evacuated in a preset manner.  
Fourth, the mixed reality evacuation LVC simulation system realizes the spatial matching between the virtual space and the real space based on the built-in components of HoloLens and the Vuforia platform. During the experiments in this study, the calibration image was placed at the midpoint of the two exits as the connection point and coordinate origin between the virtual space and the real physical space. After completing the matching between the virtual space and the real space, the participant would have a virtual avatar in the virtual space whose spatial position was synchronized with the participant. The coordinates and corresponding time series information of both avatar and the virtual pedestrian in each frame would obtained and saved automatically.
# A Comparative Study of Coverage Hole Detection Techniques in Wireless Sensor Networks +
## 2 Methodology
### List of Coverage Holes Found in WSN  
The sensor node and the sink node are both included in WSN. Therefore, the productive positioning of the sensor and sink is critical to extending the network lifetime. If sensors are constantly transmitted in even symmetrical regions, such as circular or rectangular regions, a static sink will be placed at the region's focus point, or a mobile sink may be used. In any event, if the monitoring zone is in a remote location, the static sink should be placed outside the monitoring zone. The WSN may feature static nodes, portable nodes, or a combination of the two. Although static nodes are less expensive, they cannot relocate or migrate to another location without the assistance of another. Portable sensors, on the other hand, can help enhance network analysis, but they are pricey. A combination of static and portable sensors is used to provide better cost-performance tradeoffs. The haphazard placement of static nodes does not provide coverage or connection. It is critical to deliver the sensor node without coverage holes to maximize the organization's longevity. Coverage hole identification and repair are critical for energy-efficient information collection from WSN. Table 1 shows a summary of coverage hole detection in WSN.
## 3 Results  
### Preliminary Screening on Synthetic Grape juice  
The results of the ethanol content of screening trials, carried out under static and agitation conditions, was reported in Figure 1A.  
_S. bombicola/S. cerevisiae_ sequential fermentation in agitation condition significant enhanced the ethanol reduction if compared with static one and _S. cerevisiae_ pure culture both in static and agitation condition. In particular, _S. bombicola/S. cerevisiae_ sequential fermentation 10\({}^{8}\) cells/mL and 5 \(\times\) 10\({}^{7}\) cells/mL achieved an ethanol reduction of 1.25% (\(v\)/\(v\)), and 1.05% (\(v\)/\(v\)), respectively in comparison with _S. cerevisiae_ pure culture (in both conditions). Moreover, the ethanol content in the trials with inoculum level 5 \(\times\) 10\({}^{7}\) cells/mL in agitation condition was comparable with that obtained with 10\({}^{8}\) cells/mL.  
Figure 1: Ethanol content, (**A**) volatile acidity, (**B**) and glycerol content (**C**) of preliminary screening in sequential fermentation in static and agitation condition in _Synthetic Grape juice_. \(\blacksquare\) Agitation condition; \(\blacksquare\) static condition. Data with different superscript letters (\({}^{\Delta b,c,d}\)) are different according to Duncan tests (0.05%).  
In relation to the volatile acidity (Figure 1B), the fermentation trials at different inoculum level of _S. bombicola_ showed in general similar values exhibited by _S. cerevisiae_. A significant increase was detected only with _S. bombicola_/_S. cerevisiae_ sequential fermentation in static condition using different inoculation levels (0.53 g/L acetic acid). The aeration conditions determined a general enhancement of glycerol production in all fermentation trials. In particular, a significant increase was showed in _S. bombicola_/_S. cerevisiae_ 1 \(\times\) 10\({}^{8}\) cells/mL sequential fermentations (8.58 g/L) compared with _S. cerevisiae_ pure culture with the exception of _S. bombicola_/_S. cerevisiae_\(5\times 10^{7}\) cells/mL in static condition (Figure 1C).  
Considering the similar results obtained and the most practice application in vinery condition of the lower inoculum level, _S. bombicola_ at inoculum level 5 \(\times\) 10\({}^{7}\) cell/mL was identified for the further bench-top fermentation trials in NGJ. Using the following fermentation conditions: Semi-anaerobic (gently agitation 60 rpm) and aeration flow of 20 mL/L/min during the first 72 h.
## 1 Introduction
### Algorithmic Approaches to Anomaly Detection  
From the algorithmic point of view, there are many types of anomaly detection method [25].  
* Statistical Methods -- Parametric Methods--Assume that data follows a known distribution (e.g., Gaussian). Parameters are estimated, and data points with low probability under the model are considered anomalies [26]. -- Non-Parametric Methods--Do not assume a specific distribution. Use techniques like histograms or kernel density estimation to model the data distribution [27; 28].
* Proximity-Based Methods -- Distance-Based--Identify anomalies based on their distance from other data points. Anomalies are far from their nearest neighbors. Example: k-Nearest Neighbors (k-NN) [29; 30]. -- Density-Based--Consider the local density of data points. Anomalies reside in low-density regions compared to their neighbors. Example: Local Outlier Factor (LOF) [31].
* Clustering-Based Methods--Use clustering algorithms to group similar data points. Data points that do not belong to any cluster or belong to small clusters are considered anomalies [32].
* Classification-Based Methods -- Supervised Anomaly Detection--Requires labeled data for both normal and anomalous classes to train a classifier [33]. -- Semi-Supervised or One-Class Classification--Trained only on normal data to recognize anomalies as deviations from the learned norm. Example: One-Class SVM [34].
* Reconstruction-Based Methods--Utilize models that can reconstruct input data. High reconstruction error indicates anomalies. Example: Autoencoders, Principal Component Analysis (PCA) [35; 36].
* Ensemble Methods--Combine multiple anomaly detection models to improve performance and robustness. Example: Isolation Forest [37].
# Low Power EEG Data Encoding for Brain Neurostimulation Implants +
## 3 Results
The use of Delta coding reduced the number of transitions by up to 10% (Figure 6), indicating that the specific solution can extend the power life-cycle of neurostimulation devices. Summing up the findings of this study, a comparison was performed on three different configurations of a system sampling EEG signals, storing them and transmitting to an external device. The implementations under comparison include only the components that were considered, namely the Input Interface, the Encoder, the Memory, the Compressor and the Transmission Unit. Specifically, a XILINX PYNQ development board was used (integrating a ZYNQ-7000 series device), embedding additionally a BLE component (Arduino compatible) for the transmission. The three system configurations are the following: (1) A system implementing the Input Interface, a Memory and the Transmission Unit. This is the case of raw data storage and transmission. (2) A system implementing the Input Interface, a Memory, a Compressor, and the Transmission Unit. This is the most common case of raw data storage and compressed data transmission. The compressor that was selected was the Compressed Sensing (CS), which is widely used for energy consumption and data compression. (3) The proposed system implementing the Input Interface, a Delta Encoder, a Memory and the Transmission Unit. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**A0** & **A1** & **A2** & **A3**  
Figure 4: Bar chart of 0-0, 0-1, 1-0, and 1-1 transitions before and after Delta encoding for all the leads examined. Figure 5: Bar chart of % reduction of 0-1 transitions after the encoding. Figure 6: Bar chart of % reduction of 0-0, 0-1, 1-0, and 1-1 transitions after the encoding. Observing the results given in Table 2, we notice that the compression of data benefits the system since it reduces data to be transmitted; however, overall power consumption is significantly increased due to the complexity of the algorithm and the time required to finalise data compression. For this reason, the implanted systems do not transmit data frequently. In contrast, the proposed work depicts that Delta encoding is not increasing power consumption and encoding is performed in real-time. Furthermore, it presents lower power consumption by the other configurations making it suitable for implanted devices requiring extended battery autonomy. Notice that the power dissipation of the BLE module is not given by any tool. It was derived by exhaustive measuring of the overall power dissipation of the system powered by a limited energy source and measuring the data received to the external device. The data that were used for evaluation is the case 21 depicted in the figures. Any comparison to other systems may not be possible, since they do not offer details of power consumption for each component but rather the overall power consumption. Since this is industrial material, there is no access to this information and our study was focused only in the case of light data encoding for power savings during wireless transmission of data.
## 3 Results
### Farmers Perception of Drought-Related Issues in Rural Communities
For the perceptions of \"Reduced quality of pasture\" and \"Increased cost of feed\", the chi-square values are 3.36 and 2.89, respectively. However, the corresponding _p_-values of 0.067 and 0.089 are greater than the significance level of 0.05, suggesting no statistically significant difference between observed and expected frequencies for these perceptions. Similarly, for the perceptions of \"Reduced body weight\" and \"Increased risk of cattle diseases\", the chi-square values are 0.05 and 1.00, respectively. The associated _p_-values of 0.823 and 0.317 are much greater than the significance level, indicating no statistically significant difference between observed and expected frequencies for these perceptions. In summary, the results indicate that farmers in the rural communities of the Eastern Cape, South Africa, perceive \"Lack of water for livestock\" as the most significant drought-related issue in small-holder cattle production systems. This finding underscores the importance of addressing water scarcity and implementing interventions to mitigate its impact on livestock farming. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Perception of Drought-Related Issues** & \begin{tabular}{c} **Observed** \\ **Frequency** \\ \end{tabular} & \
\begin{tabular}{c} **Expected** \\ **Frequency** \\ \end{tabular} & **Residual** &
\begin{tabular}{c} **Chi-Square** \\ **Value** \\ \end{tabular} & _p_**-Value** \\ \hline Lack of water for livestock & 62 & 48.5 & 13.5 & 8.25 & 0.004 \\ Reduced quality of pasture & 48 & 56.5 & \(-8.5\) & 3.36 & 0.067 \\ Increased cost of feed & 35 & 43.5 & \(-8.5\) & 2.89 & 0.089 \\ Reduced body weight & 21 & 21.5 & \(-0.5\) & 0.05 & 0.823 \\ Increased risk of cattle diseases & 15 & 12.5 & 2.5 & 1.00 & 0.317 \\ \hline \end{tabular} Note: A _p_-value of less than 0.05 suggests a significant difference, while a _p_-value of greater than 0.05 suggests no significant difference.  
\end{table} 
Table 2: Chi-square test results for farmers’ perceptions of drought-related issues. Figure 2: Proportion of farmers viewing drought as natural disaster requiring urgent attention to mitigate its effect on communal cattle production system.
# Evaluation of IMERG Precipitation Products in the Southeast Costal Urban Region of China
## 5 Discussion
### Elevation Effects on IMERG Detection Capability  
The detection performance of IMERG is generally better along the southeast coast and weaker in the northern mountains, which shows some resemblances to the distribution of regional elevation. Therefore, the changes in the metrics with elevation was explored at different temporal scales (Figures S12 and S13). Among the metrics, only _POD_ showed a decreasing tendency with increasing elevation on an hourly scale. The further analysis in different seasons showed that the negative effect of elevation on the IMERG _POD_ was only significant in summer (Figure 10) and not in any other season (Figures S14-S16). This suggested that the influence of topography on IMERG's detection capability was mainly reflected by the capture of sub-daily precipitation events in summer. The reason for this may be that the abundant moisture from the south in summer tends to rise and form orographic precipitation at high elevations [89]. This mechanism usually generates precipitation at lower levels and does not significantly increase the ice content aloft or reduce cloud top temperatures [90]. Thus, it is difficult for PMW and IR to detect such small-scale, short-duration, and often less intense types of precipitation [91], leading to a drop in IMERG hit rates.
# Numerical Simulation of Rock-Breaking Mechanism by Spherical Tooth Impact in Granite Formation
## 3 Results and Discussion
### Study on the Influencing Factors of Single-Tooth Broken Rock
#### 3.2.2 Rock Fragmentation Range  
After the tungsten carbide button fractures the rock, the main regions of the rock, based on the influence of stress from the center outward, can be divided into three zones: the rock fragmentation zone, the stress-affected zone, and the stress dissipation zone. The Rock Fragmentation Zone is the area closest to the button, which is directly subjected to impact and fragmentation; The stress-affected zone is outside the rock fracture-affected zone, this area experiences relatively light stress influence, which may result in a certain degree of deformation and cracking. The stress Dissipation Zone is the outermost area where the stress gradually dissipates, and the rock is only minimally affected by the impact. As shown in Figure 7, in the area around the spherical teeth where the grid undergoes significant displacement, it can be observed that rock debris separates from the rock, resulting in significant displacement of the debris--this area is identified as the rock fragmentation zone, where notable rock breakage occurs. The lower region corresponds to the crack propagation area, where the element displacement shown in the red circle is caused by fracturing, which can be regarded as the rock fragmentation range resulting from the button's impact. To determine the rock fragmentation range, the horizontal X-coordinate values of the two points corresponding to the start and end of the area with significant displacement are subtracted. The resulting value represents the range of the rock debris formation, which is used as the rock  
When the drilling pressure reaches 1.8 kN, the stress in the rock approaches its limit, and the energy transmitted to the rock by the two buttons becomes relatively similar, leading to convergence in the fragmentation ranges. From the analysis of drilling pressures at 1.2 kN and 1.4 kN, it can be observed that as the impact energy increases, the fragmentation range initially increases and then tends to level off. This phenomenon may be due to the fact that as impact energy increases, part of the energy received by the rock is used for fragmentation, while another portion may be lost to internal friction, heat generation, and other forms of energy dissipation. As the impact energy further increases, the proportion of dissipated energy may rise, reducing the effective energy available for fragmentation. Therefore, based on the analysis of the rock fragmentation range, when the drilling pressure is between 1.2 kN and 1.4 kN, the 22 mm button achieves a larger fragmentation range and better fragmentation performance when the impact energy is between 35 J and 40 J.
## 4 Verification and Validation
### Validation of the Applicability of the Proposed Model Using Real Project Data
#### 4.3.6 Discussion of the BNE for Real Project Data  
Table 12 presents the BNE corresponding to project 2 (high-rise building) with the project security level of medium. It can be observed from Table 12 that for both  
\begin{table}
\begin{tabular}{l l  
the files--the structural design file and the resource management file--the BNE was \(\text{A}=\left(R_{\text{cyb}}^{M}R_{\text{aut}}^{L}\,,D_{\text{accept}}^{\text{ aut}}\,D_{\text{reject}}^{\text{cyb}}\,\right)\), with the cost of cyberattack \(C_{cyb}=0.05\) and \(0\), respectively. The equilibrium implied that if the insider was malicious, he/she would launch a cyberattack to access the file at a very low cost or no cost at all. The defender would observe this action and respond to it by rejecting the access request. In this situation, the probability of the malicious insider's identity being detected (\(p_{2}\)) would be approximately \(0.5\), characterized by the project security level of medium. Therefore, the best strategy for the defender would be to reject the file access request. Additionally, the equilibrium stated that if the insider was legitimate, then he/she would launch authentication to obtain access to the file, and the best strategy for the defender would be to accept the access request.  
Furthermore, Table 13 presents the BNE calculation for project 2 (theme park). The BNE for the structural design file was \(\text{B}=\left(R_{\text{aut}}^{M}\,R_{\text{aut}}^{L}\,,D_{\text{accept}}^{ \text{aut}}\,D_{\text{reject}}^{\text{cyb}}\,\right)\), whereas the BNE for the resource management file was \(\text{A}=\left(R_{\text{cyb}}^{M}\,R_{\text{aut}}^{L}\,,D_{\text{accept}}^{ \text{aut}}\,D_{\text{reject}}^{\text{cyb}}\,\right)\) (see Table 13). In the case of the structural design file, the cost of cyberattack was \(0.15\), while, for the resource management file, it was \(0\). As the cost of cyberattack was relatively high, the malicious insider would prefer authentication over launching a cyberattack for the structural design file. However, for the resource management file, with zero cost, he/she would opt for a cyberattack. After observing the cyberattack, the defender would respond by rejecting the file access request. This decision would be driven by the defender's unwillingness to risk data security due to the low probability of detecting the malicious insider's identity (approximately \(0.5\) for a medium project security level). Furthermore, the equilibrium for both files dictated that if the insider was legitimate, he/she would perform authentication to request file access. In response, the defender would accept this request and release the file.
## 3 Results and Discussion
### Measurement Results
In this way, and taking into account that the rays were launched in a volumetric way, the impact of everything that surrounded the radiant sources was considered, whether they were under LOS or NLOS conditions. In addition, the differences between the simulation and measurement results represented in Figure 16 at both frequencies are summarized in Table 6, showing good agreement between them. Finally, the total mean difference is also calculated in Table 6, achieving 1.67 dB at 28 GHz and 2.92 dB at 60 GHz, which validated the potential use of the proposed simulation tool for the RF-EMF exposure assessment before the deployment of 5G and 802.11 ay systems at complex indoor environments.
# Enhancing Resilience in Digital Twins: ASCON-Based Security Solutions for Industry 4.0
## 2 Literature Review
In their research [16],the authors proposed using the lightweight SHA-256 hash algorithm to create a blockchain of sensor readings. This method aimed to ensure secure communication between the control center and remote sensors by linking checksums of current and previous readings, establishing trust based on the unbroken chain length. The study emphasized that this approach enhances the security and reliability of sensor data in Digital Twin applications, especially in critical sectors such as the power grid. In their study [17], a blockchain-based IoV Secure Communication Framework was proposed to enhance secure communication within the Internet of Vehicles (IoVs). Blockchain technology was leveraged to securely store essential data such as public keys and communication history. The framework comprised five core modules: a blockchain network, access control, secure transmission protocol, vehicle ad hoc, and a mechanism for detecting Sybil attacks. To address the growing threat of Sybil attacks in IoV scenarios, the framework employed regular location certificates issued by base stations to verify vehicle location accuracy. This approach aimed to provide an effective solution for improving communication security in IoV environments. The SIGNED framework was introduced by [18] to facilitate the secure and verifiable exchange of Digital Twin data within smart cities. Emphasizing principles of data ownership, selective disclosure, and verifiability through Verifiable Credentials, the framework encompassed five functional components: Cyber and Physical Layer, Workflow Designer, Analysis Layer, Traceability Layer, and Digital Wallet. The Traceability Layer, integrated with a blockchain-based Verifiable Data Registry, was responsible for managing public credentials and monitoring registered assets. Through a proof of concept in smart water management, the effectiveness of SIGNED in ensuring trusted and verifiable data exchange was demonstrated. Overall, enhanced security and privacy were achieved in data sharing across different functional units within smart city environments. The work by [19] contributed to enhancing IoT communication security within digital twin networks. An interference source location scheme featuring a mobile tracker was proposed to bolster resistance against attacks and improve Attribute-Based Encryption (ABE). Key exchange security was ensured through access control policies and symmetric encryption. Addressing observation noise, the paper modified the interference source location using an unscented Kalman filter. The authors concluded that integrating Jamming Signal Strength (JSS) information with the unscented Kalman filter algorithm effectively estimated the interference source location and related state information.