esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Clinical Trial Results
#### 3.5.1 MoCA Test  
The overall score obtained through the MoCA test can be found in Figure 8. Baseline values revealed that the subjects presented mild cognitive impairment, with values below 26 (from a total of 30) [17]. Briefly, all study groups, including placebo, significantly improved compared to their baseline values at weeks 6 and 12. However, the higher dose of sage and rutin revealed a significant improvement comparing weeks 6 and 12, which was not observed in the placebo group nor in the lower dose. This suggests that from baseline to week 6, there was an acquired learning process, but from week 6 to 12, there was no significant improvement, and that the improvement observed in the higher dose is due to the product's effect.  
The MoCA test assesses seven aspects of memory: short-term memory recall, visuospatial memory, executive function, attention/concentration, language, abstract reasoning and orientation in time and place. These aspects were analyzed independently to evaluate the product's main area of effect (Table 3).  
Regarding intragroup analysis, all three groups revealed significant improvements throughout time in the following aspects: visuospatial memory, executive function, and orientation in time and space. However, there were certain aspects where only the experimental groups revealed significant improvements compared to baseline values: short-term memory recall (250 mg at week 6 and 12, 400 mg at week 12 only) and abstract reasoning (400 mg at week 12 only). No significant differences were observed in attention nor language (except for a slight but significant improvement in the placebo and 250 mg groups at week 6).  
As for intergroup analysis, a significant effect was observed only in the 250 mg group regarding short-term memory recall, at week 12.  
Therefore, the results seem to indicate that the overall MoCA score seems to improve significantly after 12 weeks of intake when consuming the 400 mg dose. Regarding the subcategories, the 250 mg dose contributes to improving short-term memory recall only (starting at week 6), while the 400 mg contributes to improving both short-term memory recall and abstract reasoning after 12 weeks of intake.  
Figure 8: MoCA general score of the healthy volunteers. * \(p<0.05\).
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 4 Framework Architecture
### Hyperparameter Optimization  
Since analyzing the multitude of parameters from Section 4.1 manually can be difficult and time-consuming, it may come to mind to automate the parameter selection process. In this paper, we do this using a machine learning concept called _hyperparameter optimization_. While the training parameters of a machine learning model are modified during the training phase, the hyperparameters are specified prior to the learning phase. The goal is to find a set of hyperparameters that generates the best model according to some heuristic [31]. For a more formal definition of hyperparameter optimization, one may refer to [32].  
Parameter optimization in IoT-NDN and hyperparameter optimization are similar because they both search for a set of parameters that optimizes some predefined objective function. In the case of machine learning, the objective function is evaluated using a validation data set. In contrast, here we evaluate the performance--namely the energy consumption--by actually performing the simulation on the networks.  
The _grid search_ algorithm is of particular interest due to its simplicity. As described in [33], it performs a complete search on a given subset of the hyperparameter space of the training algorithm. In our case, we search on a predefined subset of the network parameters. Grid search is subject to the curse of dimensionality since the number of combinations grows exponentially with every added parameter [31]. Hence, using the algorithm for many parameters or with a large search space is impractical. This is why we keep our test scenarios relatively small in this paper regarding the number of variable parameters and the values per parameter.
## 4 Discussion
### Compliance Assessment with SAR Limits  
The numerical simulations evaluated the electromagnetic hazards related to the use of wearables equipped in WIT devices in exposure scenarios with an ARCM operating with a 1.0 duty cycle at 2.45 GHz. The 10g-SAR occupational exposure (in restricted environments) limits in the head and torso (10 W/kg) and the limits in limbs (20 W/kg) were applied. It was shown that in the considered APD exposure scenarios, the effects of EMR on the human body may exceed the 10g-SAR occupational exposure limits at an input power to the antenna exceeding 190 mW (or exceeding 3.7 mW eirp) and 330 mW (or exceeding 28 mW eirp), respectively [17; 28]. However, in the case of CWED exposure scenarios, these limits may be exceeded at an input power to the antenna greater than 255 mW (or exceeding 16 mW eirp) and 420 mW (or exceeding 70 mW eirp), respectively.  
In turn, the 10g-SAR limits for the general public (in unrestricted environments), i.e., one-fifth of the limits for occupational exposure, may be exceeded at an input power to the antenna that is more than one-fifth of what is specified above for occupational exposure [17; 28].  
This is particularly important due to the fact that the use of data transmission equipment operating in the 2.45 GHz ISM band (such as Wi-Fi 2G/Bluetooth) at these levels (not exceeding 100 mW eirp) does not require special administrative permission. Results from our study confirmed that such devices are unlikely to cause exposure exceeding SAR limits. Additional attention is needed in connection with the commercial availability of devices emitting at an eirp reaching up to 300 mW, the use of which may cause exposure exceeding discussed limits in the worst-case exposure scenario.  
Figure 8: The relative 10g-SAR values (**a**) in the APD and CWED 2.45 GHz exposure scenarios (reference value: 10g-SAR at 100 mW (20 dB) input power to the ARCM) and the relative differences (**b**) calculated in the WIT user simplified [S] versus anatomical [A] models of the head, arm, forearm, and chest, at 100 mW (20 dB) eirp (whiskers—standard uncertainty of relative values determination \(\pm\)35%).
## 4 Findings and Results
### Comparative Analysis of Existing and Proposed STS Framework
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Existing Theories/Frameworks** & **Proposed STS Framework** \\ \hline Actor–network theory (ANT) highlights the relationships between human and non-human factors. The focus is on the interaction of entities within a system. [PERSON] et al. [61] emphasize that ANT recognizes collective efforts to change social conditions as complex systems of action shaped by sociotechnical networks. & While it is valuable for understanding the interactions within a system, ANT lacks a structured approach to evaluate systemic interdependencies across broader dimensions like political or economic contexts. Also, the five subsystems in the STS framework extend beyond the relational focus of ANT, enabling deeper insights into how external factors (e.g., regulatory policies, market dynamics, and environmental constraints) impact cybersecurity. \\ \hline Institutional Theory focuses on how norms, rules, and institutional structures shape organizational behavior, offering valuable insights into governance and compliance. However, it often overlooks technical nuances and environmental factors critical to cybersecurity. Research study states that Institutional Theory tends to focus on formal structures and may not adequately account for the dynamic interplay between technological advancements and environmental contexts in cybersecurity [62]. These perspectives suggest that while Institutional Theory provides a framework for understanding organizational behavior, it may not fully capture the multifaceted challenges present in cybersecurity. \\ \hline Risk management frameworks are instrumental in identifying, assessing, and mitigating risks, primarily concentrating on technical and procedural elements. However, they often overlook the broader sociopolitical and economic contexts that influence risk. [PERSON] and [PERSON] [63] argue that current risk management approaches often fail due to their limitations in addressing the dynamic and interconnected nature of today’s business environment. They assert that “doing so requires new ways of conceptualizing the problem and a willingness to redefine what we mean by risk management”, highlighting the necessity for frameworks that encompass sociopolitical and economic dimensions. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparative analysis of existing and proposed STS framework. making it ideal for addressing the complexity of evolving cyber threats. The framework supports the identification of targeted interventions across multiple dimensions, ensuring more robust and sustainable explanations.
# A New Data Processing Approach for the SHPB Test Based on PSO-TWER
###### Abstract
Due to the mathematical modeling of stress equilibrium factors being diverse single problems, the particle swarm optimization (PSO) algorithm exhibits excellent performance in solving both single-objective and multi-objective optimization problems [15; 16; 17; 18]. In the study of [PERSON] [19], the optimal results of particle swarm optimization, the gravitational search algorithm with particle swarm optimization (GSA-PSO), and the eagle strategy with particle swarm optimization (ES-PSO) were compared. The problem of antenna positioning in satellite systems was solved. [PERSON] [20] improved the swarm position update formula of the PSO algorithm, then combined it with the improved DE algorithm to solve multi-UAV cooperative trajectory planning problems. [PERSON] [21] colligated the firefly algorithm (FA), PSO, and tabu search (TS) to solve problems in resource allocation and scheduling. This article introduces the time-window energy ratio method for the arrival picking of a waveform in the three-wave method for Hopkinson test data processing. The robust loess fitter is used to denoise the waveform, and the fractal dimensions of signals decrease. A mathematical wave signal model of a specimen's stress equilibrium state is established, and the PSO algorithm is used to solve the mathematical model. The article discusses the influence of five variable inertia weight factors on the optimization results. A new evaluation index for test results based on the stress balance factor is proposed, which provides a reference for Hopkinson test data processing and result evaluation.
# Research on an Intelligent Test Method for Interconnect Resources in an FPGA
## 5 FPGA Configuration Method and Test Implementation
### CLB Configuration Method  
The CLB is the main logic resource for implementing the sequential and combinational circuits, and each CLB in the Xilinx's Kintex-7 series FPGA contains two SLICEs. Each SLICE contains a look-up table, registers, carry chains, flip-flops, multiplexers, and other components. In interconnection resource testing, we mainly use the look-up table and the flip-flop in the SLICE [14; 15].  
In the Kintex-7 series FPGA, the logic function generator is implemented as a six-input look-up table, with inputs A1-A6 and outputs O5 and O6. During wiring, the LUTs in the CLBs are connected in series between the interconnection lines to detect faults such as open circuits and bridges. By configuring the LUT to perform logic operations between input signals, such as O5 = A1&A2&A3&A4&A5&A6, the output port of the LUT can be used to monitor its output by connecting it to the trigger in the CLB where the LUT is located. The response value of the trigger is obtained through bitstream readback, and then the output response of the LUT can be obtained. In this way, by configuring the logic operation of the LUT and entering the different excitations into it, the faults can be located, and their types can be analyzed. By setting the LUT to six inputs and one output, the port of the switch matrix can be fully used to improve the coverage of the first configuration.
## 5 Case Study
### Example Results
#### 5.2.1 Scenario 1: Economic Efficiency (EE)  
ES in EI is in great demand, but its cost is generally high. Thus, reducing cost becomes key while ES technology remains at the present level. Therefore, based on the goal of improving EE, PSO and MAPSO are adopted to optimize the system under the constraint conditions of Section 3.2.  
The population size of MAPSO and PSO is usually determined according to the complexity of problems. The population size of the two algorithms is set as 16, comprehensively considering the balance between optimization efficiency and optimization results. The size of the global environment is 8*8. The maximum iterations are set as 100. In order to prevent PSO from falling into local optimal solution and improving search ability, this paper applies the linear decreasing inertia weight [60]. The settings of linear weight and acceleration constants are cited from reference [67]. The parameters of PSO and MAPSO are presented in Table 4.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Factors** & **Types of ESs** \\ \hline Low cost & PS, LTTES, CAES, HTTES \\ High conversion efficiency & SMES, FES, SCES \\ Quick real-time processing & LAB, SCES, SMES, FES, RFB \\ High degree of technological maturity & PS, LAB, RFB \\ \hline \hline \end{tabular}
\end{table}
Table 2: The chosen ES types from Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Type** & **Cost/(S/(kW-h))** & \(\rho_{i}\)_/_(\%)_ & \(X_{imax}\)_/_(MW-h)_ & \(e_{i}\)_/_(MW-h)_ \\ \hline
1 & PS & 50 & 85 & 1000 & 700 \\
2 & LTTES & 15 & 60 & 600 & 300 \\
3 & LAB & 300 & 80 & 600 & 300 \\
4 & SCES & 1000 & 95 & 800 & 200 \\
5 & HTTES & 45 & 60 & 200 & 0 \\
6 & CAES & 25 & 70 & 500 & 0 \\
7 & SMES & 5000 & 96 & 400 & 0 \\
8 & FES & 3000 & 90 & 500 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of selected ESs.  
The CPU time required for solving this optimization problem with a Dell Inspiron 7447 series laptop powered by a Inter Core i7-4720 HQ processor and 8 GB of RAM is computed 20 times and the average time is 0.056915 s of MAPSO and 0.026558 s of PSO. Obviously, the solving time of MAPSO, which is acceptable, is longer than that of PSO. On the other hand, the decision makers are inclined to obtain the optimal results of ESC, thus the optimization results of the two algorithms are discussed below.  
The convergence comparison between PSO and MAPSO is shown in Figure 3. At the same time, the comparison of computing results is shown in Table 5 as well.  
From Figure 3, we can see that PSO reaches the optimal solution after running about 60 times, while MAPSO descends quickly in the first few generations and reaches the optimal solution after about 30 iterations, which shows the effectiveness and superiority of the algorithm's optimization mechanism. Moreover, as can be seen from Table 5, the minimum, mean and maximum cost of MAPSO is lower than PSO. In summary, the MAPSO algorithm shows better convergence and computational accuracy. Though the processing time of MAPSO is longer than PSO, MAPSO is a valid algorithm in terms of computing results.
# Satellite-Based Wireless Sensor Development and Deployment Studies for Surface Wave Testing
## 4 Applications  
The functionality of the sensing node was validated in a real-world experiment of surface wave testing of the soil layer in Harbin. Using the GPS-enable, the proposed sensing node was deployed in an outdoor testing ground of Harbin Institute of Technology as shown in Figure 4. A 4 kilogram sledgehammer was employed to trigger the ground motion by impacting on a polyethylene plate placed on the ground to generate the seismic wave. The surface wave was collected by the wireless sensing node with a sampling rate of 800 Hz. As a proof-of-concept test, only five wireless sensing nodes respectively located at 5, 10, 15, 20, and 25 m from the impact position were employed to acquire the seismic waves. In total 600 points were acquired by the accelerometer in 0.75 s. The raw measurements were encrypted based on the proposed random linear measurement matrix to compress the size of the transmitting data. The compressive data was transmitted via six SBDs by satellite communication. Each SBD contained 60 bytes encrypted information to reduce the possibility of losing data although up to 340 bytes could be transmitted in one message. Meanwhile, the raw measurements were saved to the SD memory card as a backup.  
Figure 4: Satellite-based sensing nodes for in-site surface wave testing: (**a**) Schematic of surface wave testing; (**b**) view of testing at the outdoor site.
# The Significance of Hydrophobicity for the Water Retention Properties of Sand and Coal
###### Abstract  
For mine wastes such as coal tailings, management of these materials requires complex geotechnical engineering that uses many soil properties, such as water retention. However, coal itself is chemically heterogeneous and often appears to be partially hydrophobic, which affects its water retention properties. This study aims to outline how hydrophobic soil particles and coal alter water retention curves compared to hydrophilic materials. The study involves four materials: sand, hydrophobized sand, crushed rock and crushed coal. Mixtures of sand with different proportions of hydrophobic particles had their water retention curves measured and compared, with the only variable being the particle surface characteristics. The rock and coal were separated into different particle size fractions and had their water retention curves measured and compared, with the only variable being particle hydrophobicity. A clear trend was observed for the sand mixtures: the degree of saturation at any suction was reduced when increasing the hydrophobicity of the material. This trend indicates the fundamental water retention behavior expected for soils more hydrophobic than is typical, which was not clearly demonstrated in previous studies. However, a similar trend was not seen when comparing the rock and otherwise identical hydrophobic coal samples, which actually appeared hydrophilic in terms of water retention. ESEM imaging shows a dual hydrophilic and hydrophobic behavior for coal which may explain the result. However, further research is required to understand the discrepancy, which appears to be caused by an unknown coal-water phenomena.  
hydrophobicity; soil water retention; contact angle; coal; ESEM +
Footnote †: journal: Applied Sciences  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# A Brief Review on Micro-Implants and Their Use in Orthodontics and Dentofacial Orthopaedics
## 3 Results
### Microbial Aggregation around Micro-Implants, Biocompatibility
Compressive and tensile forces were recorded at 0.01, 0.02, and 0.03 mm displacement of the implants of zirconia and titanium at various angles of 0\({}^{\circ}\), 10\({}^{\circ}\), 20\({}^{\circ}\), 30\({}^{\circ}\), and 40\({}^{\circ}\); there were no statistically significant differences between the two types of implants made of different materials regarding the maximum insertion torque, maximum removal torque or the amount of movement at displacement test.
## 4 V2H and V2G
### Technological Developments
#### 4.3.3 The U.S.  
SolarCity, the subsidiary of Tesla in the solar industry, launched two services in 2016: the solar roof and the Powerwall 2 storage system [72; 73]. The solar roof generates electricity continuously when sunlight is available. The Powerwall 2 storage system (Figure 6b) could store electricity from any source, including solar power devices such as the solar roof and  
Figure 6: Representative V2G and V2H systems in different countries: (**a**) “Amsterdam Vehicle 2 Grid” system [69]; (**b**) Tesla’s Powerwall 2 [70]; (**c**) V2G-ready vehicle launched by Nissan [71]; (**d**) Smart V2H System developed by APAS and HKPC.  
generic electricity grids. Customers are allowed to use it at night or during grid breakdowns and even sell it back to grid operators at more substantial prices. Statistics show that the Powerwall ($6200) provides a one-time storage capacity of 14 kWh hours [74].
# Double Trapezoidal Wave Transmitting System with Controllable Turn-Off Edge
###### Abstract  
For time domain transient electromagnetic measurement, the negative sign often appears in the polarization region, which contains the induced polarization information. It is considered that the polarization effect is caused by the capacitance charge of the earth. Extending the turn-off time of the emission current means increasing the charging time, and reducing the charging voltage, which makes the polarization effect easier to observe. Therefore, a double trapezoidal wave transmitting system with a controllable turn-off edge is designed in this paper. In the process of current transmitting, the turn-off time can be controlled by changing the clamping voltage depending on the passive clamping technology. By cutting into the absorption resistance, the current oscillation can be eliminated under the condition of ensuring linearity. To verify the effectiveness of the system, we designed a polarized wire loop based on the filament model simulating the polarized earth. Comparing the response of the wire loop, the emission current with short and long turn-off times contributes to inducing the induction and polarization fields respectively. The double trapezoidal wave transmitting system with a controllable turn-off edge is suitable for measuring the induced polarization effect.  
induced polarization effect; transmitting system; turn off time; polarized wire loop +
Footnote †: journal: Applied Physics Letters