esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Containment, Recovery, and Response  
The affected company must study a response strategy after an attacker and a data breach have circumvented the security controls. From the technical point of view, the company must quickly contain the data leakage to minimize the potential damage and then identify and eradicate the components of the incident. NIST SP 800-61 [89] provides a more in-depth guide for computer security incident handling.  
This NIST publication divides the incident response process into five steps: Preparation, which occurs before an incident and corresponds to preventive security measures; Detection and Analysis, in which the attack vector and TTPs are identified; Containment, Eradication, and Recovery, which comprises an initial restriction of the malicious activity and a subsequent cleanse of malicious artifacts (though keeping them for forensic analysis); followed by the Restoration of the systems' operation. Finally, post-incident activities include discussing and documenting the incident to understand it better and prevent similar future intrusions.  
For a better comprehension of the causes of the incident and of eventual system modifications made by the intruder, forensic tools and techniques may be helpful. NIST SP 800-86 [90] provides guidelines for integrating forensic techniques into incident response, including data collection, examination, and reporting from different sources, such as files, operating systems, networks, and applications. When performing digital forensics, it is important to maintain a chain of custody and preserve the integrity of the evidence, not removing it.  
Specifically, in the data leakage domain, ref. [91] proposed a data breach response methodology based on ISO 27035 [92] and NIST 800-61 [89]. Their study emphasized the importance of automating this process, especially due to the short time required to notify a breach for legislative compliance.  
[PERSON] et al. [93] conducted 12 interviews with customers regarding their expectations regarding a data breach response. They concluded that expectations vary according to several factors, such as breach severity, data leakage type, and company sector. Hence, a company must adapt its response strategy to the specific scenario to maximize the chance of meeting its customers' expectations.  
As a more general guide, the Federal Trade Commission (FTC) presents recommendations for an adequate data breach response (www.ftc.gov/business-guidance/resources/data-breach-response-guide-business accessed on 7 April 2024), such as assembling an incident response team, fixing vulnerabilities, and removing improperly posted information on the web.  
For notification, especially for complying with the legislation mentioned in Section 5.1, the FTC mentions the importance of notifying law enforcement and affected businesses and individuals, specifying what happened, what information was stolen, how the attackers used the information, what remediation measures were taken, and how customers may contact the organization regarding the breach. They also provide a model letter for data breach notification.
# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 3 Problem Description and Formulation
### Constraints
#### 3.3.5 Load Balancing Constraint  
Resource utilization across all satellites must be balanced:  
\[\frac{\sum r^{b}(s_{i})}{R_{s}^{b}}\approx\frac{\sum r^{b}(s_{j})}{R_{s}^{b}}, \quad\forall s_{i},s_{j}\in S. \tag{17}\]
# Techno-Economic Analysis of Waste Heat Recovery in Automotive Manufacturing Plants
## 3 Methodology
### Boundary Conditions for Photovoltaics  
In this study, the PVWatts Calculator [27], which is provided by National Renewable Energy Laboratory USA, was employed to estimate and predict the electricity generation needed to power the heat pump. The primary goal was to ensure that the PV system could meet the energy demands of the heat pump, at the very least. To achieve this, a simulation was conducted to model the annual global energy production of the PV system based on its location and physical characteristics. An article [28] reported a comparative analysis of PVWatts, PVGIS, and RETScreen for PV energy modelling tools. PVGIS and PVWatts are free, user-friendly tools that offer a range of flexibility options. PVGIS and PVWatts also provide accurate simulations that closely match real-world data, making them reliable choices for PV energy modelling [28,29]. In addition, PVWatts includes options for configuring inverter input parameters. Although the PVWatts Calculator uses simplified assumptions regarding detailed PV system components, certain general information about the system is still required to perform accurate simulations. These general parameters include the location's solar irradiance, system size, orientation, and other key factors that influence PV performance. This approach allows for a reliable estimate of the PV system's energy production despite the inherent simplifications in the PVWatts tool, making it suitable for the early-stage planning and feasibility analysis of PV installations.  
Furthermore, specific assumptions made about the PV system's performance and efficiency are outlined and summarised in Table 3. One of the automotive manufacturing plants in Karawang, Indonesia, was chosen for analysis for the PV simulation. This study utilises a standard PV module with a fixed roof-mounted array configuration. Key input parameters include total system losses in the PV setup, calculated at 14.08%, accounting for factors such as soiling, shading, wiring, mismatch, and system availability. The tilt angle of the array was set at 35\({}^{\circ}\), approximating the optimal angle for the site's location, while a slight azimuth adjustment of \(-2^{\circ}\) provides a westward orientation, which can enhance energy capture at specific times of the day. This tilt angle of 35\({}^{\circ}\) is typical for Bandung, Indonesia, [30] and may be suitable for nearby locations like Karawang, Indonesia, allowing for effective adaptation. The direct current-to-alternating current (DC-to-AC) ratio of 1.2 was applied alongside a 96% inverter efficiency, reflecting the use of high-quality inverters common in modern PV systems. This setup helps ensure consistent, reliable energy conversion over time. In addition, it is important to note that the solar radiation was taken from the NREL National Solar Radiation Database, which has been integrated with PVWatts.
## 5 Conclusions  
Tri-axial linear accelerations and angular velocities data from a single IMU sensor mounted on the mid-foot effectively classified Minimum Foot Clearance (MFC) into the following three categories: (i) less than 1.5 cm, (ii) 1.5-2.0 cm, and (iii) more than 2.0 cm. However, as the data were collected from only six healthy young adults, this study's limitations include a need for a broader dataset encompassing more diverse population groups. Future research should target individuals at higher risk of tripping-related falls, such as older adults, stroke survivors, and individuals with neurological disorders, including Parkinson's disease and dementia.  
In conclusion, this study has confirmed the potential of using machine learning, specifically KNN, to predict MFC heights with a high degree of accuracy (84%) and efficiency. While the initial results are promising, the performance of MFC prediction needs further validation using different machine learning algorithms and a wider range of populations. This research supports the application of such predictive models in the control systems of movement assistive devices. Future studies should consider employing advanced machine learning techniques, such as convolutional neural networks (CNNs), transformers, and ensemble models, to improve predictive accuracy. Additionally, expanding the dataset size is crucial. The analysis also revealed that vertical acceleration and the 'x' and 'z' components of angular velocities are primarily associated with Minimum Foot Clearance height.  
Conceptualization, H.N. and R.B.; methodology, H.N., C.O.A. and R.B.; software, M.P. and C.O.A.; validation, M.P. and C.O.A.; formal analysis, C.O.A.; investigation, H.N., M.P., C.O.A. and E.S.; writing--original draft preparation, H.N. and E.S., writing--review and editing, H.N., M.P. and R.B.; supervision, R.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the VESKI--Study Melbourne Research Partnerships (SMRPs) program, grant number VESKI-SMRP #2003, and the Australian Research Council (ARC) DP200103583.  
This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Victoria University (HRE23-017, approved on 30 June 2023).  
Informed Consent Statement: Informed consents were obtained from all subjects involved in this study.  
Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request.  
We extend our deepest gratitude to [PERSON], CYBERDYNE Inc., for his invaluable guidance and support throughout this project.  
The authors declare no conflicts of interest.
# Transfer-Efficient Face Routing Using the Planar Graphs of Neighbors in High Density WSNs
## 4 Transfer-Efficient Face Routing
### Remote Node Selection and Message Forwarding
The arc in (**c**) signifies the distance between the destination (\(t\)) and node \(s\) where the face routing started. Figure 9 shows the internal process of node selection at the message transfer node required to send a message, which corresponds to the case of forwarding a message within the same face as in Figure 8a. All the processes in Figure 9 are performed internally at message transfer node \(u\), and after the process is finished, a message is forwarded to the selected node. Figure 9a shows the beginning of the face routing simulation to establish the node where the message is to be sent to. Face routing is started at message transfer node \(u\) because there is no node closer than itself to the destination (\(t\)) among the neighbors. Node \(u\) selects \(i_{2}\) as the next node to be explored by applying the left-hand rule based on line \(\overline{ut}\) among neighboring nodes \(i_{1}\) and \(i_{2}\) in the GG edge list in Table 3.  
In Figure 9b, node \(i_{2}\) selects \(i_{6}\) as the next node to explore according to the left-hand rule, and checks whether it is within radio range of node \(u\), whether the routing mode should be switched to greedy routing, and whether an intersecting edge occurs. Node \(i_{6}\) is located within radio range of node \(u\), is farther from the destination than node \(u\) where face routing started, and \(edge(i_{2},i_{6})\) does not intersect line \(\overline{ut}\). Thus, \(i_{6}\) is selected as the next node to navigate to. In Figure 9c, \(i_{5}\) is selected as the next node to travel to from node \(i_{6}\) in the face routing procedure according to the same condition as node \(i_{6}\). In Figure 9d, node \(n_{4}\), which is the next node along from node \(i_{5}\), is beyond the radio range of message transfer node \(u\). Therefore, message transfer node \(u\) terminates the node discovery process and selects \(i_{5}\) as the target node to send the message to. Message transfer node \(u\) improves the transfer efficiency by sending a message directly to the most remote node \(i_{5}\) without actually passing through the intermediate nodes \(i_{2}\) and \(i_{6}\). The pseudo-code for selecting the most remote node using the GG full edge list of a message transfer node and sending a message is as Algorithm 2.
# Airbag Protection and Alerting System for Elderly People
## 1 Introduction
The prevention system consists of the IMU Puck.js to monitor the user movements and an ESP32 node which has the pressure sensors connected to three analog inputs. It can only be utilized if the user is lying on the bed. A human airbag technique is used by [16] for hip protection. The system uses microelectromechanical system (MEMS) motion sensors for detecting the elderly person's imbalance or falling. This system is supposed to have small airbags that will inflate when the MEMS motion detection unit detects their unbalance. A jacket is developed by [17] that automatically inflates an airbag to cushion the impact of a fall and lessen the force on the wearer's head and hips when he/she falls. The system consists of a battery, a gas case, sensors to measure acceleration and angular velocity, a prompt device that releases the gas, and an inflator to pump out the gas which causes the airbag to automatically inflate. For the location tracking category, a wearable-sensor-based fall detection system is proposed by [18]. The wearable equipment will identify the individual and promptly alert caretakers if a fall is determined. The device will return to fall detection mode and send an SMS message to the caretakers if the user disables the alarm. An operational model was implemented by [19] for a fall monitoring wristband. A Raspberry Pi, an IMU chip, a GPS module, a 4G connection module, and a reset button make up the device. The model uses a cellular network to transmit information about major falls, including their location and environment's temperature, to a remote care center. It has high power consumption and its size is not convenient. While the reviewed literature provides one or two of the three services, our proposed system can provide all of them. It provides a fall detection mechanism using a gravity sensor connected to an Arduino board to detect falls. It also protects the elderly person when a fall is detected using an air valve to inflate the airbag from an air tank. The system sends a warning notification to the elderly person's mobile application and uses the GPS on the elderly person's mobile device to determine his/her location. It also sends an SMS to the emergency contact of choice. The system has a relatively low cost (JOD 113.25), very quick response time (only 150 ms delay from the electrical grid), low power consumption (11.1 Wh), and very good accuracy (90%).
# On the Performance of Underlay Device-to-Device Communications
## 4 Performance Analysis
### Performance Trends  
In this section, we investigate the impact of the total transmit power of the D2D networks on the performance of the outage probability. In particular, the behavior of OP with respect to \(P_{\text{tot}}\) is given in Proposition 1.  
**Proposition 1**.: _The OP of \(\Xi_{k}^{s}\), i.e., \(\text{OP}_{k}^{s}(\gamma)\) is monotonically decreasing with \(P_{\text{tot}}\)._Proof.: Let us begin the proof by rewriting the OP as follows  
\[\begin{split}\dot{\text{OP}}_{k}^{s}(\gamma;P_{\text{tot}})=& \frac{d\text{OP}_{k}^{s}(\gamma;P_{\text{tot}})}{dP_{\text{tot}}}=-\frac{ \gamma\sigma_{k}^{2}}{(P_{\text{tot}})^{2}\omega_{k}^{s}\Omega_{dr_{k}d_{k}}} \exp\left(-\frac{\gamma\sigma_{k}^{2}}{P_{D_{k}}^{s}\Omega_{dr_{k}d_{k}}} \right)\frac{A_{k,c}^{s}}{A_{k,c}^{s}+\gamma}\\ &\times\left(\prod_{i=1,i\
eq k}^{N}\frac{B_{kj,i}^{s}}{B_{kj,i}^ {s}+\gamma}\right)-\frac{\omega_{k}^{s}\Omega_{dr_{k}d_{k}}}{P_{\text{C}}\Omega _{dr_{k},c}}\exp\left(-\frac{\gamma\sigma_{k}^{2}}{P_{D_{k}}^{s}\Omega_{dr_{k} d_{k}}}\right)\frac{\gamma}{\left(A_{k,c}^{s}+\gamma\right)^{2}}\\ &\times\left(\prod_{i=1,i\
eq k}^{N}\frac{B_{kj,i}^{s}}{B_{kj,i}^ {s}+\gamma}\right)<0,\end{split} \tag{22}\]  
where \(\dot{F}_{X}(x)=\frac{dF_{X}(x)}{dx}\) is the first-order derivative of \(F\) with respect to \(x\). Q.E.D.
# Research on the Jiamusi Area's Shallow Groundwater Recharge Using Remote Sensing and the SWAT Model
## 3 Materials and Methods
### Sources of Data
#### 3.1.3 Type Data of Land Use  
The land use data originate from the Chinese Academy of Sciences' Academy of Aerospace Information Innovation's 2022 global 30 m resolution land cover. Six categories were created from the reclassification of the land use types in the No. 1 and No. 2 watersheds (see Figure 3). These categories included cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential property, and unused land. Table 5 displays the related categories of model inputs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Coefficient** & **Description** & **Coefficient** & **Description** \\ \hline
**SOL\_BD** & weight of dried soil, comprising soil & & \\
**SOL\_BD** & particles and intergranular pores, per unit volume. It stands for the moist & **CLAY** & Clay content, \%wt, refers to soil particles \textless{}0.002 mm in diameter. \\  & bulk density of soil (SOILdensity). & & \\
**SOL\_AWC** & Indicates the effective water content of the soil layer in mm/mm. & **SILT** & (\%wt); that is, the percentage by weight of soil particles between 0.002 and 0.05 mm in diameter. \\
**SOL\_CBN** & Organic carbon content (\%wt) of the soil layer. & **SAND** & Sand content, \%wt, refers to particles with diameters between 0.05 and 2.0 mm. \\
**SOL\_K** & \
\begin{tabular}{c} Scaturated water \\ conductivity/saturated hydraulic \\ conductivity, mm/h. \\ \end{tabular} & **ROCK** &
\begin{tabular}{c} Gravel content, \%wt, refers to particles with a diameter greater than 2 mm; \\ \end{tabular} \\
**SOL\_ZMS** & \
\begin{tabular}{c} Represents the maximum root depth \\ of the soil profile, mm. \\ \end{tabular} & **USLE\_K** &
\begin{tabular}{c} Erodibility factor \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Related descriptions of soil coefficients involved in the calculation of SPAW.  
Figure 3: Schematic diagram of sub-watershed division, soil type reclassification, and land use reclassification in the calculation area (**A–C**) represents No. 1 watershed, (**a–c**) represents No. 2 watershed. (The numbers in (**A**) and (**a**) are the serial numbers of sub-basians divided by SWAT model).
## 6 Experimental Results and Discussion
On the other hand, the underground nodes may not be as close to the upper sink as the surface nodes, which make the average packet delay longer. Figure 6 displays the effects on the average network overhead of the four techniques at various node concentrations. In Figure 6, the X axis represents the number of nodes, and the Y axis represents the Average Network overhead (bps) [47; 48]. When compared to other approaches, it can be seen that ECCAR has a lower average network overhead. The reason for this is that in ECCAR, a large number of Cooperative nodes collaborate on the packet transmission procedure using an efficient duplicate packet attenuation approach. However, for the reason that it can discover a solution, the average overhead is lower than with other  
Figure 3: Overall Energy Utilization. approaches. Therefore, the suggested method has less average network overhead than the other methods. Figure 7 illustrates the Q value of DL for various Cooperative nodes, as well as various routing strategies. In Figure 7, the X axis represents the number of nodes, and the Y axis represents the Q Value (DB). Investigations have been conducted to compare EHOR, DER, and ECCAR with different quantities of Cooperative nodes on a platform with DL-Q value. It was found that a Cooperative communication node with the lowest Q value helps the system to choose the cognitive node with the lowest power level. Figure 4: Packet Delivery Ratio. Figure 5: Average packet Delay. Figure 8 illustrates the data packet arrival rate, which is directly proportional to the throughput. In Figure 8, the X axis represents the data packet arrival rate (packet/s) and  
Figure 6: Average Network overhead. Figure 7: Q Value of DL under different Cooperative node conditions. the Y axis represents the data packet throughput (Mbps). As a result, ECCAR has some superiority over other methods in achieving better throughput optimization. From Figure 9, it is inferred that as the data packet arrival demand rises, the network's average number of packet losses tends to increase as well. In Figure 9, the X axis represents the data packet arrival rate (packet/sec) and the Y axis represents the average number of system packet losses (packet/sec). The number of bytes delivered by the network per unit time grows as the arrival rate of the data packet increases [25; 49]. Figures 3-9 have been compared individually between them and it is inferred that since the network is prone to failure, the higher the quantity of data messages have been delivered with the greater the percentage of packets lost. Hence, it is concluded that the efficiency of ECCAR has the lowest average number of packet network instability. Figure 8: Data Packet Throughput. Figure 9: Data packet loss vs. Average packet loss.
# A New Improved Multi-Sequence Frequency-Hopping Communication Anti-Jamming System
## 4 Jamming Utilization Method Based on IMSFH  
In Section 3.2, we discussed that implementing the utilization of reactive jamming requires learning its jamming patterns through neural network training. Therefore, we designed the IMSFH communication framework to meet this requirement. Similarly, the communication process is divided into a training stage and a communication stage. In this section, we will present a comprehensive overview of the reception process and the specific workings of the frequency-detection network.
# Marine Internet for Internetworking in Oceans: A Tutorial
## 4 Communication Systems in Oceans
### Wireless Ad Hoc Network (WANET)  
All the above-mentioned systems require network infrastructures such as BSs/APs to coordinate communication between terminals that cannot communicate each other directly. A WANET does not require any pre-established network infrastructure and allows terminals to communicate with each other directly [62]. Its ability in terms of self-organizing and self-curing along easy and fast deployment makes it particularly suitable for dynamic networking environments in oceans. Therefore, several schemes have been investigated as discussed below.