esa-sceva/satcom-chunk-collection huggingface dataset:
# Research and Development of a Large-Scale Axial-Flux Generator for Hydrokinetic Power System
## 4 Discussion  
Existing experience, presented in current research studies, and numerical simulations (electromagnetic analyses using a virtual prototype) results and data were collected and studied in this work. The initially proposed design was improved, reaching \(\approx\)96% efficiency. The improved design also decreased the number of magnets and coils and decreased production costs, too. Additionally, a technical risk assessment focuses further design development on the next major directions:  
* Cooling system (high-fluid flow solution will probably be needed);
* Production of magnets--special attention needs to be paid to the magnets' assembly and mounting;  
Figure 10: Coils and placed aluminum alloy plates.  
Figure 9: Magnet design and mounting over rotor disk using special tool.  
Coils' production and mounting on aluminum plates (disks).  
This study was based on the virtual prototyping of the electromagnetic behavior of a large-scale AFPM generator, and it shows the good feasibility of the examined conceptual design. Further research is needed to go into detail about specific parameters and deepen the understanding of the performance of the generator, as well as improve its virtual prototype. Building and testing a physically scaled-down prototype is an important step in further design development.
## 4 Methodology and Data
### Econometric Models
#### 4.2.2 Panel Threshold Model  
This paper posits that the impact of port productivity on CO\({}_{2}\) emissions in port cities varies with the size of the port, necessitating a consideration of the presence of threshold effects. To delve deeper into this heterogeneity, we employed the threshold panel model developed by [PERSON], which can capture jumps or structural changes in the relationships between different variables [57]. The so-called threshold effect refers to a change in the direction or magnitude of another economic variable when a certain economic indicator exceeds a specific value, which is termed the threshold [57]. By endogenously dividing different port size intervals based on the characteristics of port size data, we further explored how port TFP affects CO\({}_{2}\) emissions in port cities across different port size intervals.  
Here, we primarily introduce the setup of the single-threshold model used, detailed as follows:  
\[Y_{i,t}=\delta_{i}+\beta^{\prime}X_{i,t}+\theta_{1}g_{i,t}I(d_{i,t}\leq r)+ \theta_{2}g_{i,t}I(d_{i,t}>r)+\omega_{i,t} \tag{9}\]  
where \(Y_{i,t}\) still represents the dependent variable (CO\({}_{2}\) emissions of port cities). \(X_{i,t}\) is the set of control variables, with \(\beta^{\prime}\) as their corresponding coefficients. \(r\) is a specific threshold (a specific port size). \(d_{i,t}\) is the threshold variable (port size), for which we use the length of the port's production berths (in meters) as the representative variable for port size, and \(I\) is an indicator function. \(\delta_{i}\) reflects the individual impact of each port city. The random error term \(\omega_{i,t}\) is assumed to be independently and identically distributed. The estimation is based on the principle of minimizing the sum of squared residuals (SSR). Furthermore, it is necessary to verify the existence of a threshold effect, i.e., to test whether the model estimates of \(\theta_{1}\) and \(\theta_{2}\) are significantly different for the two groups of samples divided by the threshold value. We use the bootstrap method to obtain the asymptotic distribution and thereby derive the corresponding probability \(p\)-values and confidence intervals [58].
## 4 Algorithm Design
### Resource Adjustment Among Categories
#### 4.3.2 Mutation in Bandwidth Allocation  
As the bandwidth is fully allocated, the bandwidth allocation model is depicted in Figure 4, and the allocation process can be perceived as the determination of breakpoints on line segments. For the scenario including \(T\) types of IoT devices, there exist \(T\)-1 breakpoints. The mutation of bandwidth allocation can be regarded as the movement of breakpoints. Each breakpoint movement is assigned a probability of execution, denoted as \(PB_{hm}\). Specifically, for the \(t\)-th breakpoint, \(t\in\{1,\dots,T\}\), the mutation operation is descried as follows:  
If \(\rho<0.5\), the \(t\)-th breakpoint moves left:  
\[j^{\prime}_{t} =j_{t}-e^{t}_{bd} \tag{17}\] \[j^{\prime}_{t+1} =j_{t+1}+e^{t}_{bd}\] (18) \[e^{t}_{bd} =random(0,c_{2}\times j_{t}) \tag{19}\]  
Otherwise, the \(t\)-th breakpoint moves right:  
\[j^{\prime}_{t+1} =j_{t+1}-e^{t}_{bd} \tag{20}\] \[j^{\prime}_{t} =j_{t}+e^{t}_{bd}\] (21) \[e^{t}_{bd} =random(0,c_{2}\times j_{t+1}) \tag{22}\]where \(K_{t}^{\prime}\) is the new bandwidth allocation ratio for the \(t\)-th category of IoT devices. \(\rho\sim\mathcal{U}(0,1)\) is used to determine the moving direction of the breakpoint. \(e_{hd}^{t}\) is the movement amount for the bandwidth breakpoint. When the breakpoint moves left, it is randomly determined within \((0,c_{2}\times K_{t})\). When it moves right, it is randomly determined within \((0,c_{2}\times K_{t+1})\). Parameter \(c_{2}\) is defined between 0 and 1; here, it is taken as 0.5. It should be noted that the breakpoints need to be moved sequentially starting from \(bp_{1}\). In Formulas (17) and (19), \(j_{t}\) is the bandwidth allocation ratio of the new \(t\)-th-category IoT determined after the movement of \(bp_{t-1}\).
## 4 Results
### Description of Artifact
The data in Table 7, which evaluate the results for hypothesis 4, show that the experimental group maintained higher results than the control group after three weeks of testing acquired knowledge. The knowledge retention results for the experimental group were, on average, 20.1% higher than those of the control group. The \(t\)-test yielded a \(p\)-value of 0.0028 (less than 0.01). The fourth alternative hypothesis, H\({}_{41}\), is supported. The EG's results are statistically higher. Additionally, further analysis was conducted on each group's knowledge and retention test results, and the findings are detailed in Table 8.  
Upon reviewing the data in Table 8, it is evident that the CG's knowledge retention decreased by 4.8% (from 4.6229 to 4.3988). The EG also exhibited a decrease in knowledge retention and acquisition of 11.6% (from 5.9747 to 5.2841) despite maintaining higher knowledge results. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Group** & **N** & **Average** & **95\% CI** & **95\% CI** & **\(t\)** & **Sig. (2** & **Average** & **Difference** & **Standard** \\  & & & **Lower** & **Upper** & & **Ends)** & **ence** & **Error** \\ \hline \multirow{2}{*}{Results} & CG & 34 & 4.6229 & 4.1918 & 5.0540 & \multirow{2}{*}{\(-4.741\)} & \multirow{2}{*}{0.000012} & \multirow{2}{*}{\(-1.35176\)} & \multirow{2}{*}{0.28515} \\  & EG & 34 & 5.9747 & 5.5865 & 6.3629 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Learning similarity between groups. \begin{table}
\begin{tabular}{c c
## 4 Monitoring of the Technical Condition of Existing Buildings
The HLC system is fully integrated with the software system, e.g., the Quickview interface. Data is streamed online within minutes, so movements of the structure can be monitored in real time. Supervision over the measured structure is clearly achieved by means of a simple graphic visualization and automatically generated reports. If the measurement shows that the displacements of the structure have exceeded the previously set alarm thresholds, the system can send automatic notifications. An example of an HLC system application is shown in Figures 6-8. This system was installed along the facades of buildings and around the planned earthworks (excavations) related to the revitalization of a historic hotel building in the center of Wroclaw. Figure 6: Automatic measurement of vertical deformations (settlements) using a hydrostatic leveling cell system (HLC) in installed along facades of buildings around the planned earthworks (developed by authors). An additional system for monitoring a building's geometry is a system of wireless inclinometers installed on the facades of the building (Figures 9 and 10). This system consists of a control and monitoring module, and also sensor modules to which an inclinometer and a thermometer are connected. The inclinometer is a high-precision measuring device that is used in the construction of cranes, wind turbines and ships. Temperature measurement is used to assess its influence on the operation of the inclinometer and the measured structure. The sensor modules record data from the inclinometer and thermometer and save them in random access memory. On the command sent from the monitoring and control module, they perform the required calculations and send their results to the database. Figure 8: Graph of vertical displacements from monitoring and Quickview GETEC software (developed by the authors). Figure 7: HLC system for baseline measurements with data transfer to the GETEC server (photograph by authors).
###### Abstract  
This study investigates the extraction of cellulose from Saudi Arabia-based date palm biomass utilizing a natural deep eutectic solvent (NaDES) integrated with a microwave-assisted process. A comprehensive life cycle assessment (LCA) was conducted in accordance with the ISO 14040 standard, encompassing four key stages: goal and scope definition, life cycle inventory analysis (LCI), life cycle impact assessment (LCIA) and interpretation. The analysis was confined to a gate-to-gate boundary in which two impact assessment methods, namely, ReCiPe Midpoint (H) 2016 and ILCD 2011 Midpoint, were used to assess the environmental impacts. The OpenLCA software (version 2.1.1) with the European Life Cycle Database 3.2 (ELCD 3.2) was used in the study. The ReCiPe method identified impact categories such as _gossil resource scarcity_, _terrestrial exotoxicity_, _freshwater exotoxicity_, _water consumption_, _human carcinogenic toxicity_ and _marine exotoxicity_. Conversely, the ILCD method identified _freshwater exotoxicity_, _water resource depletion_, _minenal, fossil and resource depletion_, _human toxicity and cancer effects_. The results indicate that _freshwater exotoxicity_ presents the most substantial environmental impact across both assessment methods, surpassing other categories. _Fossil resource scarcity_, even though originally appearing impactful, demonstrated a relatively lower normalized score compared to _freshwater exotoxicity_. _Terrestrial exotoxicity_ and _water consumption_ were found to be negligible in their impact. Our findings provide important insights into sustainable material science and waste management, affording potential applications for biomass utilization in the Gulf region.  
life cycle assessment; cellulose extraction; date palm biomass; natural deep eutectic solvent +
Footnote †: journal: Applied Sciences
# Assessing the Security and Privacy of Baby Monitor Apps
## 5 Mobile App Analysis
### Assessing the Confidentiality of Video Streams
#### 5.6.2 Security Issues Threatening the Confidentiality of Video Streams  
Since implementation flaws of network security measures can severely impact the confidentiality of video streams, we conducted a manual security analysis of each baby monitor app, consisting of a code review to identify security issues (Section 3). We uncovered several issues and classified them according to the types of CWE weakness [43]. Where appropriate, we also incorporated our analysis results from previous Sections 5.3, 5.5 and 5.6.1. As Table 11 illustrates, we identified multiple security issues that threaten the confidentiality of video transmissions. Security Issue #1--Cleartext Transmission of Sensitive Information (CWE-319): As the BabyCam app uses unencrypted HTTP messages to transfer video monitoring streams, sensitive information is transmitted in clear text. It is evident that an eavesdropping attacker can capture the video footage. Moreover, by simply connecting to the app's web server, it is possible to gain access to live video footage directly. In order to exchange control messages with the relay server, the Dormi app relies on unencrypted messages using the TCP protocol. Therefore, an attacker can eavesdrop on exchanged control-channel messages, including sensitive information, such as mobile device names and unique identifiers. Security Issue #2--Insufficiently Protected Credentials (CWE-522): The BabyCam app allows configuring password-protected access to the video web server, whereas the password is transferred as a base64-encoded HTTP header value. However, as the utilized HTTP connections transport payloads in clear text, the encoded password is accessible to existing eavesdroppers in the communication channel. This also holds true for an attacker in our threat model, and as the password is base64-encoded, it can be decoded to clear text. Therefore, in our setting, the password feature provides no security benefits at all. Security Issue #3--Channel Accessible by Non-Endpoint (CWE-300): Security issues of this type occur when the identity of parties at both ends of a communication channel is not correctly verified. This opens up for interception and alteration of exchanged communication, which may result in an attacker masquerading as the original entity, successfully breaking otherwise secure communication channels (MitM attacks). Although the BabyCam app offers rudimentary password authentication, we previously showed the password is transmitted in clear text, effectively compromising the authentication mechanism and identity verification in a MitM attack. Figure 7 demonstrates this observation. Similarly, the Dormi app uses an unencrypted control channel to the relay, which is also used for key exchange using a proprietary public key protocol. While the protocol relies on an anti-tamper code for public key verification, the user must manually verify
## 2 Materials and Methods
### Medical facility case study (MFCS) Water System Description  
Referring to Figure 12 and Table 12 in Appendix A, which links to the MFCS non-potable water system configuration diagram in [51] and [6], four pump sets (P1) divert, store, and convey both the condensate water and the desalinated makeup water to the water storage tanks to serve the site. For each of the eight plant rooms serving the WFs, one pump (as part of the P4 pump set) is used to recirculate the water and one or two pumps (as part of the P4 pump set and depending on the ozone plant room configuration) are used to operate the water treatment system and distribute the water to the WFs. An additional one pump set (P5) and two pump sets (P2) are used to distribute the water to the LI as well as an additional pump (P3) to distribute desalinated makeup water to the WFs. The water system uses a total of 26 pumps to operate a combined 3289 m\({}^{2}\) (1587 m\({}^{3}\) capacity average per month) of decorative WFs and to irrigate 33,257 m\({}^{2}\) of landscape. The energy calculations of the treated non-potable water system will be based on the 2017 Energy Monitoring and Control System (EMCS) records (WF and LI water consumption). The peak power demand, the daily energy use, and the seasonal energy demand calculation method for the site has been discussed in [51] as Equations (6)-(9), respectively. All physical and operational characteristics of the outdoor water system were updated and are detailed in Table 12 in Appendix A.  
The main electrical consumption accounted for in the MFCS energy calculations are the pumps and the water treatment systems' power consumption. More than 85% of the total energy used for water systems is drawn by the energy for pumping water [54] and its associated water treatment system [55]. Real energy use for a given water system can vary widely, according to its source configuration [54], however, the largest energy use is by far pumping (ibid.).  
The LI system operates 13 hours (h) per day according to the landscape contractor irrigation schedules (5 a.m.-12 p.m.; 6 p.m.-12 a.m.). The WF system's operating hours are based on the EMCS  
Figure 5: Summary of the energy, environmental and financial impact calculations.  
pump trends, which showed that pump set (P3) runs for nine minutes (mins) and 75 s (s) every 20 min, which is 10.67 h per day (8.89 min \(\times\) 3 times (20 min \(\times\) 3 = 1 h) \(\times\) 24 h/60 (60 min in one hour). The water demand for the landscape irrigation and the water features were calculated by using Equations (1)-(4) in [51].
# Traffic-Aware Optimization of Task Offloading and Content
## 3 System Model and Problem Formulation
### Execution Time and Energy Consumption Model  
For the task offloading problem, we divide the computational task into multiple parts and define the offloading decision variable \(\alpha_{n}^{i}=(a_{i,n}^{l},a_{i,n}^{m},a_{i,n}^{c})\), where \(a_{i,n}^{l},a_{i,n}^{m},a_{i,n}^{c}\in[0,1]\) denotes the percentage of task \(q_{i,n}\) offloaded to the local vehicle, MEC server, and cloud server, respectively. The constraint \(\alpha_{i,n}^{l}+\alpha_{i,n}^{m}+\alpha_{i,n}^{c}=1\) ensures that the entire task is accounted for. For example, if \(\alpha_{i,n}^{l}=1\), the task is executed exclusively within the vehicle, where \(\alpha_{i,n}^{l}=0\) indicates complete offloading to the MEC or cloud server. The overall offloading decision policy is denoted as \(A=[\alpha_{1}^{1},\alpha_{2}^{1},\ldots,\alpha_{n}^{i},\ldots,\alpha_{N}^{l}]\).
# Advanced Technologies in Data and Information Security
## 3 Secure Multi-Party Computation  
With the development of privacy-enhancing technologies, the growing demand for performing secure multi-party computations (MPC) on mobile devices has become a major challenge. In this direction, [PERSON] et al. [4] proposed an efficient two-party computation protocol, called LPCP (Lightning Polynomial Computation Protocol), which is secure against semi-honest adversaries and is based on the Chinese Remainder Theorem (CRT). The proposed privacy-preserving protocol utilises CRT-based encryption and re-encryption techniques to perform additive and multiplicative homomorphic encryption, which can be transformed into a secure two-party polynomial calculation scheme. In addition, the authors presented an extension of the two-party LPCP protocol to a multi-party LPCP protocol, which is faster and less space-intensive than other existing approaches. As a proof-of-concept, based on LPCP, a distance measurement computation protocol was introduced, which shows that it is affordable for mobile devices. The experimental evaluation of the proposed LPCP scheme in ARM and x86 architecture environments showed that it has a great advantage of both execution time and communication overhead, compared to the state-of-the-art two-party and multi-party computation protocols, allowing the implementation of secure calculations on mobile devices.
## 5 Analysis of Vibro-Acoustic Coupling Characteristics
### Mechanism of ABH and Damping Layer  
It was found in Figure 9 that compared with the uniform thickness plate cavity coupling system, the sound pressure of the six monitoring points of the damping ABH coupling system was much lower at 3701 Hz, but slightly larger at 4501 Hz. To analyse the mechanism of the action of the ABHs and the damping layer in the above phenomenon, the acoustic modal participation factor and structural modal participation factor of the damping ABH coupling system and uniform thickness plate-cavity coupling system were calculated at 3701 Hz and 4501 Hz, as shown in Figure 10. The structural modal participation factor was expressed in dB, with a reference value of 10. It can be seen from Figure 10 that, whether it was at 3701 Hz or 4501 Hz, the difference of the structural modal participation factor of the damping ABH coupling system and uniform thickness plate-cavity coupling system was not obvious. For the acoustic modal participation factor, it could be concluded that at 4501 Hz, the difference of the structural modal participation factor of the damping ABH coupling system and uniform thickness plate-cavity coupling system was not obvious, while at 3701 Hz, the acoustic modal participation factor of the damping ABH coupling system in the 0-100 th order acoustic mode was significantly lower than that of the uniform thickness plate-cavity coupling system. Considering that the shape and size of the acoustic cavities of these two coupling systems were exactly the same, thus the frequency and mode shape of each of their acoustic modes were also exactly the same. At this time, the smaller the acoustic modal participation factor, the smaller the sound pressure of the monitoring points in the corresponding acoustic cavity. Therefore, it can be concluded that the damping ABHs could significantly reduce the participation factor of the acoustic modes within the 100 th order of the plate-cavity coupling system, which was the main mechanism for the significant reduction of the sound pressure in the damping ABH coupling system at 3701 Hz.  
Figure 10: _Cont._
# Multi-Objective Robust Optimization Reconstruction Algorithm for Electrical Capacitance Tomography
## 7 Validation and Discussion
### Sensor Details  
The inherent flexibility of capacitance sensors offers a wide range of layout possibilities, enabling their adaptation to various application scenarios. Among the available configurations, sensors with 12 electrodes are commonly considered optimal for achieving reliable performance across diverse use cases. In accordance with this widely accepted approach, our study also simulates a sensor configuration with 12 electrodes. Specifically, the sensing domain in these simulations measures 80 mm \(\times\) 80 mm, aligning with previous work in the field [78]. This configuration is selected to ensure a balance between sensitivity, spatial resolution, and practicality, making it suitable for a broad spectrum of experimental and industrial applications.
# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 4 Machine Learning Algorithms for SON
### ML Algorithms Taxonomy  
ML taxonomy in computer science is usually split into supervised learning (SL), unsupervised learning (UL) and reinforcement learning (RL). According to the approach of the authors in [15], SON systems' learning algorithms can be classified into supervised and unsupervised learning, similarly to humans, who learn something independently or with the help of a teacher. Figure 9 depicts the taxonomy based on SL, UL, and RL.  
\begin{table}
\begin{tabular}{c c} \hline \hline SON Application & Machine Learning Technique & Algorithms That Can Be Used \\ \hline \hline \multirow{4}{*}{\begin{tabular}{c} Mobility Load \\ Balancing (MLB) \\ \end{tabular} } & \multirow{4}{*}{RL or DRL, UL, SL} \\ \cline{3-3}  & & \\ \cline{3-3}  & & \\ \cline{3-3}  & & \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Mobility Robustness \\ Optimization (MRO) \\ \end{tabular} } & \multirow{4}{*}{RL or DRL, UL, SL} \\ \cline{3-3}  & & \\ \cline{3-3}  & & \\ \cline{3-3}  & & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Coverage and Capacity \\ Optimization (CCO) \\ \end{tabular} } & \multirow{4}{*}{RL or DRL, UL} \\ \cline{3-3}  & & \\ \cline{3-3}  & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: ML technique and possible algorithm used per SON application.