esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Therefore, the observed associations should be interpreted with caution, and the clinical implications discussed in this study should be considered as hypotheses to be further tested in prospective designs. Furthermore, using self-report to assess fall history may introduce recall bias or under-reporting, especially in cases where no injury occurred. It is also important to consider the potential for selection bias in our sample. As all participants were physically active and voluntarily engaged in structured exercise programs, they may represent a healthier and more motivated segment of the older adult population. This may limit the generalizability of our findings to more sedentary or clinically vulnerable groups. Therefore, caution should be exercised when extrapolating these findings to populations with lower functional capacity, including frail or homebound older adults. Also, while grip strength was selected for its practicality and predictive validity, we acknowledge that including additional performance-based measures such as the Timed Up and Go or other gait and balance assessments could have provided a more comprehensive evaluation of physical ability. Future studies should consider combining multiple functional tests to strengthen the interpretation of fall risk profiles. Additionally, contextual factors such as home environment, lighting, footwear, or social support were not assessed but may interact with individual risk factors in determining fall risk [7]. While falls were dichotomized for analysis, this approach may overlook important differences between individuals with a single fall and those with recurrent falls. Future studies should consider multinomial or ordinal modelling strategies to reflect the complexity of fall events. Additionally, although we discussed the possibility of a ceiling effect in handgrip strength among physically active individuals, we did not perform stratified analyses (e.g., by HGS tertiles) to explore this in more detail. We recognize this as a limitation and suggest that future studies consider such approaches to better understand strength-related variability in fall risk. Future research should aim to develop more comprehensive models that integrate physical, psychological, and environmental variables to better understand and estimate fall risk--potentially using motion sensor-based algorithms for real-time risk assessment. Also, future research could explore whether the associations between psychological factors (such as fall efficacy and balance confidence) and fall risk differ by sex or other key variables, through stratified analyses or interaction models. Another possibility is to explore interventions specifically focused on improving confidence and self-efficacy and assess whether these psychological changes translate into fewer falls in the medium term.
# Electrification in Maritime Vessels: Reviewing Storage Solutions and Long-Term Energy Management
## 2 Electrification of Marine Vessels
### Battery Management Systems (BMSs) for Marine Vessels
Consequently, a BMS presents several critical benefits including extended battery life through management of operating conditions suited to maritime environments, reduced fuel consumption in hybrid propulsion systems by significantly decreasing reliance on diesel engines, improved operational efficiency by dynamically allocating battery power where it is most needed, and aiding maritime vessels in complying with environmental regulations. In maritime hybrid propulsion systems, the battery management system (BMS) plays a vital role in ensuring the safety, reliability, and efficiency of the energy storage system under harsh sea conditions. Unlike stationary or automotive environments, maritime settings present unique challenges such as constant vibrations, significant temperature fluctuations, and high humidity with salt-laden air. To withstand mechanical shocks and vibrations caused by wave activity and engine operations, BMS hardware must be ruggedized, often requiring advanced testing standards like MIL-STD-810G. Temperature variations, ranging from cold ambient conditions to high internal heating, necessitate real-time temperature sensing, thermal management integration, and protective logic to prevent thermal runaway. Humidity and corrosive environments demand sealed enclosures with high IP ratings (typically IP65 or above), conformal coatings on PCBs, and corrosion-resistant connectors. Beyond environmental durability, the BMS ensures operational safety by continuously monitoring cell voltages, currents, temperatures, and states of charge (SoCs), while also enabling emergency shutdowns in case of faults or thermal anomalies. Furthermore, the BMS enhances efficiency by supporting predictive algorithms to estimate state of health (SoH) and remaining useful life (RUL), enabling optimized switching between diesel and electric modes. It must also be interoperable with the ship's energy management and automation systems via protocols like NMEA 2000 or CANOpen to provide real-time data for decision making. In this context, the maritime BMS is not just a supervisory unit but a core component that supports the safe, efficient, and intelligent operation of hybrid propulsion systems at sea.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 3 Fair Editions of Decision Trees
### Fair and Optimal Decision Trees  
The authors of [9] addressed the challenge of constructing Decision Trees that are both optimal and fair. They proposed a novel dynamic programming algorithm that efficiently generates fair and optimal Decision Trees, overcoming the scalability limitations of Mixed Integer Programming (MIP)-based models, which struggle with large datasets. Their approach incorporates upper and lower bounds for global fairness constraints, enabling early pruning and thus significantly reducing the search space. The authors evaluated their method on twelve benchmark datasets commonly used in fairness research, including Adult, Bank, Communities and Crime, COMPAS recidivism, COMPAS violent recidivism, Dutch Census, German Credit, KDD Census Income, OULAD, Ricci, Student-Mathematics, and Student-Portuguese. They compared their approach against FairOCT [14], demonstrating superior computational efficiency and scalability.
## 2 Simulation Method
### Simulation Details  
Argon phase equilibria and evaporation simulations were performed by initially placing the condensed liquid film (consisting of 18,000 argon atoms) in a simulation box prolonged in the \(z\) direction. The box dimensions for the phase equilibria and all thevacuum simulations were 108.6 \(\times\) 108.6 \(\times\) 443.1 A and 108.6 \(\times\) 108.6 \(\times\) 140 A, respectively. The interaction between argon atoms was described using Lennard-Jones (LJ) potential [24]:  
\[U\big{(}r_{ij}\big{)}=4\varepsilon\left[\left(\frac{\sigma}{r_{ij}}\right)^{1 2}-\left(\frac{\sigma}{r_{ij}}\right)^{6}\right] \tag{11}\]  
where \(\varepsilon\) is the depth of the (LJ) potential well, \(\sigma\) is the interaction length parameter, and \(r_{ij}\) is the distance between \(i\) and \(j\) atoms. LJ interaction parameter values used for argon are \(\sigma=3.405\) A and \(\varepsilon/k_{B}=119.8\) K. The cut-off distance used in simulations was \(3\sigma\). The Newtonian equations of motion of system atoms were solved using the Verlet method with an integration timestep value of \(\Delta\)t = 4 fs. The simulation data output was performed every 200 timesteps while gathering the information on evaporated atoms.  
In all simulations, the system was equilibrated at 90 K before the production run started. In the phase equilibria simulation, the system was equilibrated for 2 ns with the thermostat applied to the whole domain. Then, the production run was performed in the NVE ensemble for 50 ns. In the deletion vacuum simulation, the thermostat was applied only to the volume between the thermostat region boundaries, which were set in the same position as the liquid boundaries during the whole simulation length. Meanwhile, the interface atoms were simulated in the NVE ensemble. The system was equilibrated for only 0.2 ns before the production run because of the limited time before the liquid film depletes. The production run lasted until the number of argon atoms in the system decreased to 10,000. In the case of the steady-state vacuum simulations, the system was equilibrated for 2 ns before the 200 ns production run. The investigated temperature control strategies for the steady-state vacuum simulations are described in the following sections. The thermostat algorithm of choice was the Nose-Hoover thermostat, which maintains the targeted temperature in the system (or the specific parts of the system) by adding a force term to the Newtonian equations of motion [34].  
Unlike in the steady-state vacuum and phase equilibria simulations, in which the profiles were averaged over the whole production run, the temporal temperature profiles presented from the deletion vacuum simulation were averaged over every 40 ps periods due to interface movement of the evaporating film. Furthermore, the temperature profiles were calculated by subtracting the flow velocity of each bin from all atoms within the bin. All the simulations in this paper were performed using LAMMPS molecular dynamics code [35].
# Bandwidth-Enhanced Circularly Polarized Crescent-Shaped Slot Antenna via Circular-Patch Loading
## 2 Antenna Design and Parametric Studies  
### Antenna Design  
Figure 1 presents the configuration of the proposed antenna, consisting of a ground plane with a crescent-shaped slot, circular patch loading, a bent feeding line, and a 50-\(\Omega\) microstrip feed line. The ground plane and circular patch loading were printed on the upper layer of a substrate with the square dimensions of \(g_{w}\times g_{w}\). The substrate used in this work was Taconic RF-35 with a thickness of 0.5 mm, relative permittivity (\(\epsilon_{r}\)) of 3.5, and a loss tangent (tan\(\delta\)) of 0.0019. The crescent-shaped slot consists of a circular slot (\(O\)) jointed with an elliptical patch (\(E_{1}\)). \(O\) has an \(R\) radius, and is centered at the center of the substrate. \(E_{1}\), with a semimajor axis of \(r_{2}\) and a semiminor axis of \(r_{3}\), is positioned on the periphery of \(O\) at \(\phi=45^{\circ}\). Circular patch loading \(O_{1}\) has a \(r_{1}\) radius and its center is located at the \(-s_{x}\), \(-s_{y}\) position, related to the center of \(O\). The 50-\(\Omega\) microstrip feed line with a length of \(l_{f}\) and a width of \(w_{f}\), and the bent feeding line were mounted on the bottom layer of the substrate. The bent feeding line was attached to the 50-\(\Omega\) microstrip feed line and is composed of three rectangular strips with dimensions of \(w_{1}\times l_{1}\), \(w_{2}\times l_{2}\), and \(w_{3}\times l_{3}\). The upper-right corner of the bent feeding line was mitered with a width of \(w_{4}\). The geometrical parameters of the proposed antenna were optimized to maximize the overlapping bandwidth, which is determined by the criteria of \(|S_{11}|<-10\) dB and AR \(<\) 3 dB. The optimized geometrical parameters of the proposed antenna are tabulated in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value (mm)** & **Parameter** & **Value (mm)** \\ \hline \(R\) & 21 & \(g_{w}\) & 50.8 \\ \(r_{1}\) & 10.63 & \(w_{1}\) & 6.12 \\ \(r_{2}\) & 11.42 & \(w_{2}\) & 21.45 \\ \(r_{3}\) & 10.37 & \(w_{3}\) & 36.125 \\ \(s_{x}\) & 4 & \(l_{1}\) & 1.5 \\ \(s_{y}\) & 4 & \(l_{2}\) & 2.3 \\
# Recent Progress on Vegetation Remote Sensing Using Spaceborne GNSS-Reflectometry
## 2 Research Status
### Airborne GNSS-R Vegetation Remote Sensing Experiments  
Two airborne experiments carried out by [PERSON] et al. proved that when the biomass is as high as 300 ton/ha, there is still a correlation between the bistatic reflectivity and the biomass [29].  
Under the funding of the SNSB (Swedish National Space Board) and ESA, researchers carried out the BEXUS 17 (Balloon Experiments for University Students) 25 Km stratospheric balloon experiment in the northern forests of Sweden on 10 October 2013 [30]. The experiments showed that the energy of the received reflected signal is basically independent of the height of the platform for a high coherent integration time (20 ms). The article [30] also suggested that the received coherent power comes from different forest elements (canopy, soil, etc).  
The LEiMON (Land Monitoring with Navigation Signals) experiment was a 6-month GNSS-R experiment on ground-based crops conducted in the Florence agricultural area, Italy, from March to September 2009 [31]. The experiment used the SAM receiver developed by Barcelona Starlab Company, which was installed on a 25 m high hydraulic boom. This experiment compared and analyzed the relationship between surface roughness, soil moisture content, vegetation moisture content, and GNSS-R signal. The research shows that the correlation between reflected signals (both LR polarization and RR polarization) and vegetation moisture content is good (correlation coefficient is 0.8). It should be noted that here LR refers to the transmitted signal as right hand circular polarization (RHCP) and the received signal is left hand circular polarization (LHCP), while RR means the received signal is also right hand circular polarization.  
ESA's GRASS experiment was conducted in Tuscany, Italy in July and November 2011 [32]. The first experiment was performed over field crops and the second over a forest with large changes in biomass. Both experiments have confirmed that cross-polarization and polarization ratio are sensitive to vegetation biomass and have a monotonous decreasing relationship. As for the roughness standard deviation below 3 cm, a high correlation coefficient (r\({}^{2}\) = 0.93) and sensitivity (0.2 dB/SMC(%)) have been obtained. They also pointed out that a level of AGB (Average Ground Biomass) values not detectable by other remote sensing systems can be achieved, the copolarized reflection coefficient shows a stable sensitivity of forest AGB with r\({}^{2}\) = 0.9 with a stable sensitivity of 1.5 dB (100 ton/ha). Different from the monostatic radar biomass saturation at 150 ton/ha, the experimental research proves that GNSS-R remote sensing will not be saturated during biomass monitoring.  
The subsequent LesLandesforest experiment found that at high elevation angles (70\({}^{\circ}\)-90\({}^{\circ}\)), the receiver signal still has good sensitivity at 5 \(\times\) 10-2 dB (ton/ha)\({}^{-1}\). The research once again proves that the GNSS-R signal is indeed not as prone to biomass saturation as the L-band monostatic radar (100-150 ton/ha) [33].
## 1 Introduction
### Classification
#### 1.2.2. Tunnel Application Frameworks  
Anonymous Networks  
An anonymous network protects the privacy of users' communication in an open network setting. Due to its enhanced security, deployment, availability, and flexibility, Tor,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**TCP/IP Protocol Stack** & **Types of Tunnels According to Protocol** \\ \hline data link layer & L2F tunnel, PPTP tunnel, L2 TP tunnel \\ network layer & **ICMP tunnel, IPSec tunnel**, GRE tunnel, VTP tunnel \\ transport layer & **TCP tunnel**, UDP tunnel \\ application layer & **HTTP tunnel, HTTPS tunnel, DNS tunnel, SSH tunnel** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tunnels classified by TCP/IP protocol stack.  
Figure 3: Narrow tunnel.  
Figure 2: Broad Tunnel.  
a typical application of a P2P [8; 9] structure, is widely used by anonymous users. In this scheme, data packets are forwarded at the entry node and bundled into nested layers of encrypted packets of similar size at the entry node. After routing and decrypting these data packets, the data are sent to a destination node.  
Proxy  
Proxy is a typical representative of \(|A\circ B|\), transferring data between client and server. Client requests are sent to proxy servers, which access the targeted website instead of the client, load its content, and then transmit the loaded content back to the client. As a result of Chrome browser's data compression proxy technology, web page loading speeds can be increased, which reduces bandwidth consumption.  
VPN  
In VPN, the initial plaintext data (including the data to be transmitted, source/destination, IP) are encrypted by the VPN client and then a signature is attached. To re-package the data packet, a new data header is added (including the new IP address, the VPN device's security information, and some initialization parameters) and it is sent via a \"specific path\" over the public network. When data arrives at VPN server, they are unsealed and decrypted after checking signature.  
Relationships  
Tor and VPN differ in how they guarantee data anonymity. As a result of the fixed IP address between the VPN client and the VPN server during VPN communication, both client and server can tell which real IP address is sending which data packet to what destination address. However, Tor does not rely on a single server. Rather, it chooses a random relay node and it updates more frequently than VPN. Thus, all nodes in the path cannot obtain the information completely. As a result, Tor can be considered a distributed VPN network if only its start and exit nodes are considered.  
Whether to secure data transmission is the difference between a proxy and a VPN. The final effect of using a user VPN or proxy server is the same, i.e., hiding one's real IP from the website or server of interest. Proxy servers are suitable for users who wish to access websites blocked by their region but do not want to conceal their operations on the Internet. When users need to visit a website for a long time or perform sensitive operations, such as sending personal information over open Wi-Fi, VPNs are typically used.
## 2 Hourly Meteorological Data Analysis Based on Cubic Spline Function Method (FFT)
### Hourly Temperature Analysis  
The original data for dry-bulb temperature were measured 4 times per day (02:00, 08:00, 14:00, 20:00, Beijing Time), in order to find the daily maximum and daily minimum temperatures. Normally, the daily maximum temperature occurs between 14:00 and 16:00, and the daily minimum temperature occurs before sunrise. In this paper the daily maximum temperature and daily minimum temperature are determined, based on a special meteorological data set of buildings' thermal-environment in China [19]. Together with this, 2:00, 8:00, 14:00, 20:00 and the times of daily maximum temperature and daily minimum temperature are set as the basic interpolation points, to obtain the hourly temperature. Moreover, the method of fast calculation of the interpolated FFT algorithm using the cubic spline function was employed to obtain the hourly temperature.  
To verify the rationality of the interpolation method offered by this paper, the 24-hourly measured data from the Miyun station in Beijing and the interpolation method presented in this paper were adopted to obtain the correlation curve of theoretical temperature and measured temperature. The result is shown in Figure 1.  
Figure 1 illustrates that the theoretical and measured temperature are the same for 2, 8, 14 and 20 hours, and the maximum temperature and minimum temperature are also consistent. Moreover, the theoretical results conformed well to the measured results, and the fast calculation of the interpolated FFT algorithm using the cubic spline function, performed well.  
With this method, the dry-bulb temperatures of the original meteorological data from 1980 to 2010 were obtained and analyzed, and the variation curve of the daily time-continuous meteorological data was calculated. Furthermore, the FFT method was employed to obtain a smoother and more accurate curve of hourly temperature. By averaging the hourly temperature for each day over 30 years, the average hourly temperature for each day in Beijing was obtained, and the annual average hourly temperature for each day in January in Beijing is shown in Figure 2.
# Voter Authentication Using Enhanced ResNet50 for Facial Recognition
## 3 Facial Recognition-Based Voting System
### Feature Extraction
#### Mathematical formula:  
Let X be the initial image input.  
Let F(x) be the transformation function performed by ResNet50.  
Let Y1 be the output after the flattened layer.  
Let Y2 be the output after the dense layer (1024) with ReLU activation.  
Let Y3 be the output after the dense layer (512) with ReLU activation.  
Let Y4 be the final output after the dense layer (5745) with ArcFace activation.  
The mathematical formula for the new model can be expressed as follows.  
\[Y_{1} =\text{flatten}(F(X)) \tag{1}\] \[Y_{2} =\text{relu}(Y_{1}\cdot W_{2}+b_{2})\] (2) \[Y_{3} =\text{relu}(Y_{2}\cdot W_{3}+b_{3})\] (3) \[Y_{4} =\text{Arcface}(Y_{3}\cdot W_{4}+b_{4}) \tag{4}\]  
where flatten is the function that transforms the spatial dimensions of the input into a one-dimensional vector, relu is the ReLU activation function, Arcface is the softmax activation function, \(W_{2},W_{3},W_{4}\) are the weight matrices corresponding to each dense layer and \(b_{2},b_{3},b_{4}\) are the corresponding bias vectors.  
The formula indicates that the output \(Y_{1}\) is obtained by applying the flattened layer to the output of ResNet50. Then, the outputs \(Y_{1}\), \(Y_{2}\), \(Y_{3}\) and \(Y_{4}\) are calculated using the dense layers with corresponding weights and biases, applying the ReLU activation function to the outputs \(Y_{2}\) and \(Y_{3}\) and the Arcface activation function to output \(Y_{4}\).
## 4 Experimental Results and Analysis
### Zone Competition  
We took the accuracy of the Doppler velocimetry difference between the solar spectrum and the Ceres spectrum as the optimization goal. We compare OCA with a zone competition mechanism to OCA without a zone competition mechanism in Figure 4. We can see that the OCA algorithm with zone competition has a faster convergence speed than the OCA algorithm without a zone competition mechanism. The zone competition mechanism can increase the population density of Oryctolagus cuniculi in safety zones while decreasing the population density of Oryctolagus cuniculi in dangerous zones. This can enhance the efficiency of the algorithm in searching for sub-optimal solutions. Namely, the zone competition mechanism can improve the algorithm's efficiency and make effective and rational use of resources. The zone competition mechanism also sets maximum and minimum values for the population size in each zone, which helps the algorithm mitigate the risk of falling into a local optimum.