esa-sceva/satcom-chunk-collection huggingface dataset:
# Current State of Robotics in Hand Rehabilitation after Stroke: A Systematic Review
## 2 Materials and Methods
### The Development of Hand Rehabilitation Robot
#### 2.1.1 The Exoskeleton Hand Rehabilitation Robot
The wearable hand rehabilitation robot system designed by Harbin Institute of Technology [36] uses a rack and pinion parallel sliding mechanism to achieve flexion/extension movements of the three joints of the finger as well as inward/abduction movements of the MCP joints, and the exoskeleton joint rotation center can coincide well with the human hand joint rotation center, and it can also adapt to different sizes of palms. In 2019, the Department of Mechanical Engineering, UNIST, Ulsan, Korea invented a portable and spring-guided hand exoskeleton for exercising flexion/extension of the fingers [25], as shown in Figure 3. The exoskeleton was designed with a simple structure to help the finger move with one degree of freedom (DOF). The desired joint trajectory of the exoskeleton was determined based on the user joint ROM and general finger motion obtained through hand flexion and extension experiments. The design of the linkage structure was optimized to maximize the desired trajectory. When the finger deviates from the desired position, a spring attached to the structure generates a force to guide the finger to the desired posture. In 2019, [PERSON] et al. invented a HX-\(\beta\) orthosis (exoskeleton) with a SEA structure [37], as shown in Figure 4 introducing Handexos-Beta (HX-\(\beta\)), a novel index finger-thumb exoskeleton for hand rehabilitation. The HX-\(\beta\) system features an innovative kinematic architecture that allows independent actuation of thumb flexion/extension and circumduction (opposition), thus enabling a variety of naturalistic and functional grip configurations. Furthermore, HX-\(\beta\) features a novel series-elastic actuators (SEA) architecture that directly measures externally transferred torque in real-time, and thus enables both position and torque-controlled modes of operation, allowing implementation of both robot-in-charge and user-in-charge exercise paradigms. The rigid exoskeleton type hand rehabilitation robot has been studied by many scholars because of its accurate motion transmission, precise control, and mechanical protection limit, but it has heavy mass, poor self-adaptability, and rigid impact. At present, researchers at home and abroad mainly reduce this deficiency in two ways: one is to reduce the weight of the exoskeleton by selecting lightweight materials, and the other is to use the rigid-flexible coupling method, in which the power drive of the rigid exoskeleton is rearranged and the elastic member is used as the power transmission member to eliminate the rigid impact of the exoskeleton and reduce the weight of the hand mechanism, so as to reduce the weight feeling at the back of the patient's hand. Figure 4: Close-up view of the HX-\(\beta\) orthosis (exoskeleton) [37]. Figure 3: A portable and spring-guided hand exoskeleton [25].
## 6 Analysis of Post-Exploitation Behavior and Defense Mechanisms
While both AppLocker and WDAC offer protection against the default implementation of this post-exploitation technique due to the unsigned nature of the native application executable, circumvention remains possible. If specific file types (e.g., bat,.ps1, dll,.vbs,.src, hta,.wsf,.js,.msi,.msp,.inf,.lnk,.cpl, scf,.xsl,.pptm,.dotm,.xslm,.iso,.wim,.diagcab,.py) are not explicitly controlled, or if an attacker can induce a signed executable to launch the unsigned executable, the protections can be bypassed. Specifically, each value within the path parameter of the _native_app.json_ file is executed via a start command within cmd due to the extension's use of the _chrome.runtime.connectNative_ API. This allows an attacker to execute arbitrary script files (e.g., bat, vbs,.py, assuming the relevant interpreter is available) or even a signed executable (to which they may not have direct access) in conjunction with a DLL sideloading attack. In the latter scenario, the attacker would need to place the DLL within the appropriate directory of the signed executable. Consequently, a wide range of attack vectors exists, requiring only the ability to load the extension, replace the relevant extension files, add a registry entry, and save the _native_app.json_ file to a publicly accessible location. Regarding the relevant registry entry, administrators or SIEM tools can monitor the registry for suspicious entries. However, attackers can circumvent this by avoiding registry entries altogether and instead utilizing WebSockets or WebRTC for communication between the extension and the native application. While such communication can be secured, e.g., through TLS, this approach necessitates prior execution of the native application to establish a listener. This alternative communication pathway, by avoiding reliance on easily monitored registry entries, not only enhances local stealth but also embodies the framework's core design principle of flexible and resilient communication, which underpins its decentralized architecture for remote C2. Another vulnerability stems from the use of Task Scheduler for command execution or persistence. Administrators can mitigate this by restricting Task Scheduler access to authorized domain users or by actively logging and monitoring Task Scheduler activity for suspicious tasks. In summary, the most effective defense against this attack involves using Group Policy Objects (GPOs) to restrict browser extension installation across all managed machines within the network. Implementing AppLocker or WDAC rules can further enhance protection against post-exploitation attacks in general.
###### Abstract  
The leather industries are large producers of industrial wastewater. The present work investigated the performance of calcium-rich biochar derived from cactus cladodes (CCB) as an adsorbent in the treatment of this wastewater. Biochar samples (CCB500, CCB600, and CCB700) were produced by slow pyrolysis at 500, 600, and 700 \({}^{\circ}\)C and tested as adsorbents of Repanil Blue (RB) dye. The tests were carried out under various experimental conditions. The RB dye removal increased with the contact time, adsorbent dose, and concentration. With dye concentrations between 10 and 125 mg/L, removal efficiencies of 80, 80 and 100% were reached using CCB500, CCB600, and CCB700, respectively. The Langmuir model and the pseudo-second-order model described chemisorption with a high correlation factor (R\({}^{2}\)). The highest adsorption capacity of 56 mg/g was obtained with CCB700 within 5 min. The results strongly suggest that calcium-rich biochars are promising adsorbents for the removal of RB dye from aqueous solutions.  
cactus cladodes; biochar; Repanil Blue adsorption; kinetics; leather industry 2025 15 894
## 3 Numerical Example
### Predicted Response Statistics
The six-dimensional selection method resulted in \(n=274\) samples if five bins were used for each intensity. The PDF of the PSDR using structure C is shown in Figure 14e. All artificial neural networks can predict the statistics in the mean region well. Figure 14f shows the CCDF. However, the artificial neural network trained by randomly selected data fails to predict the statistics in the tail region of the distribution. The results of the selected training procedure show that it is beneficial to use two or three intensities during the selection instead of using allsix intensities. Generally, the extreme events are not covered properly if large bin sizes are chosen. Figure 14: Response statistics. Structure A: (**a**) Probability density function and (**b**) complementary cumulative distribution function; Structure B: (**c**) Probability density function and (**d**) complementary cumulative distribution function; Structure C: (**e**) Probability density function and (**f**) complementary cumulative distribution function. Color scheme: Finite element solution (black, dashed), predicted solution from neural network with randomly chosen data (blue), predicted solution from neural network with selected data using selection process based on \(S_{d}(T_{1})\) and \(J_{0.1}^{2.5}\)\(S_{v}\) (orange), predicted solution from neural network with selected data using selection process based on \(S_{d}(T_{1})\), \(J_{0.1}^{2.5}\)\(S_{v}\) and \(S_{d}(T_{1})\) (brown), predicted solution from neural network with selected data using selection process based on all six intensities (dark ruby).
# Task Offloading Based on Lyapunov Optimization for MEC-Assisted Vehicular Platooning Networks
## 3 System Model
### Communication Model  
In this vehicular network, the path loss models between vehicles [34] and between vehicle and BS [35] are computed by  
\[PL(l_{m,k})=\begin{cases}63.3+17.7 lg(l_{m,k})&k\
eq 0,\\ 128.1+37.5 lg(l_{m,k})&k=0,\end{cases} \tag{1}\]  
where \(l_{m,k}\) represents the distance between vehicle \(m\) and computation node \(k\). Then, the corresponding data transmission rate can be calculated by  
\[B_{m,k}(t)=\begin{cases}w_{1}log_{2}(1+\frac{\zeta_{v}PL(l_{m,k})}{N_{0}})&k \
eq 0,\\ w_{2}log_{2}(1+\frac{\zeta_{g}PL(l_{m,k})}{N_{0}})&k=0,\end{cases} \tag{2}\]  
where \(w_{1}\) and \(w_{2}\) are the transmission bandwidths between vehicles and between vehicle and BS, respectively. \(N_{0}\) is the noise power, \(\zeta_{v}\) and \(\zeta_{g}\) are the transmission power of PM and BS, respectively.
## 3 Result  
### Correlation Analysis of Panchromatic Multispectral Sensor (PMS) Inversion Algorithm  
We collected 146 samples of which 78 were used to establish the inversion formula, and the rest were used to verify the retrieval results. The detailed analysis is as follows.  
Sensitive Bands of Chlorophyll-A (Chla)  
The spectral character of water changes with the change of Chla concentrations. With the increase of Chla concentration, the absorption near 450 nm and 660 nm increases, and it decreases near 560 nm, while the reflection value increases relatively near 700 nm [50]. The Chla concentration can be estimated based on the reflectance at different wavelengths [51].  
A linear fit was performed using the measured Chla concentration, based on the reflectance data in 2-5 bands, and the correlation coefficients were analyzed (Figure 2). The correlation coefficient between the single band and Chla is small with the value between 0.04 and 0.71 indicating low correlation (Figure 2). Therefore, the single band cannot be applied to perform Chla inversion. Bohai Sea is a typical type II water body and its spectral characteristics are not entirely generated by Chla, but by the combined action of CDOM, suspended sediment, and Chla [52].
## 4 A Case Study of a Low-Energy House in Bialystok (NE Poland)
Two variants were assumed:  
* variant I: PV system for electrical energy production to power the heat pump,
* variant II: PV system for electrical energy production to meet requirements of total final, annual energy consumption (not only for heating, ventilation, domestic hot water, and auxiliary systems, but also for lighting and other electrical appliances). In variant I, 10 panels with a total active area of 19.226 m\({}^{2}\) were adopted, and in variant II, 21 panels with a total active area of 40.375 m\({}^{2}\) were adopted. The selection of PV systems assumed that 30% would be the current consumption of produced energy, and 70% would be transmission and off-take from the power grid, with a 20% allowance for its storage. The estimation of the required amount of electrical energy production in analyzed variants is presented in Table 6. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multicolumn{2}{c}{**Source of Data**} & \multicolumn{2}{c}{**Usable Energy Demand**} & \multicolumn{4}{c}{**Final Energy Demand**} \\ \hline  & **Heating and** & **DHW** & **Total** & **Heating and** & **DHW** & **Total** & **Units** \\  & **Ventilation** & **Preparation** & **(Q\({}_{\text{U}}\)/EU)** & **Ventilation** & **Preparation** & **(Q\({}_{\text{E}}\)/EF)** & **Ventilation** \\  & **(Q\({}_{\text{U}}\)/EU)** & **(Q\({}_{\text{U}}\)/EU)** & **(Q\({}_{\text{E}}\)/EU)** & **(Q\({}_{\text{E}}\)/EU)** & **(Q\({}_{\text{E}}\)/EW/EW)** & **(Q\({}_{\text{E}}\)/EW)** & **(Q\({}_{\text{E}}\)/EW)** & **(Q\({}_{\text{E}}\)/EW)** \\ \hline  & \multicolumn{6}{c}{Designed energy performance} \\ \hline  & 1462 & 3387 & 4848 & 447 * & 2142 * & 2589 * & kWh/year \\ Computational & & & & 1169 ** & 2152 ** & 3320 ** & kWh/year \\  & & & & 3.18 * & 15.24 * & 18.42 * & kWh/(m\({}^{2}\)year) \\  & & & & 8.32 ** & 15.31 ** & 23.63 ** & kWh/(m\({}^{2}\)year) \\ \hline  & \multicolumn{6}{c}{Actual energy performance} \\ \hline Measured electrical energy consumption & Heat & - & - & - & - & 3059 & kWh/year \\  & pump & - & - & - & & 21.77 & kWh/(m\({}^{2}\)year) \\ \hline  & \multicolumn{6}{c}{*Without auxiliary systems. **Taking into account heating, ventilation, domestic hot water and auxiliary systems.} \\ \end{tabular}
\end{table} 
Table 5: Designed and actual energy performance of analyzed building. The amount of electrical energy production of the PV system in the two considered variants (\(E_{PV}\)) was calculated according to the Equation (1) and results can be seen in Table 7.  
\[E_{PV}=I_{sol}\cdot\eta\cdot A_{PV}, \tag{1}\]  
where \(I_{sol}\) is the unit value of solar radiation energy; \(\eta\) is the module efficiency (in this case equal to 20%); A is total active area of PV panels. Then, the comparison of the required annual final energy demand of the house (Table 6) with the electrical energy production of PV systems (Table 7) confirmed their proper selection. The possibility of reaching the plus energy standard by the presented case study has also been proven. The amount of energy produced on-site is higher (in variant II) than total final energy consumption over the course of a year.
# Optimal Particle Filter Weight for Bayesian Direct Position Estimation in a GNSS Receiver
## 8 Simulations and Real-World Results
### Impact of Constructive and Destructive Multipath  
The influence of constructive and destructive multipath is shown for a realistic case with a multipath amplitude of \(\alpha_{MP}=0.5\) with respect to the LOS signal and a multipath offset of \(\Delta\tau_{MP}=50\) m in Figure 9. As similar to the conventional Delay-Lock-Loop (DLL) in a GNSS receiver and thereof resulting pseudorange measurement, the weighted mean \(\mu\) of the probability function also becomes biased. It can be seen that the resulting bias in \(\mu\) is in the opposite direction for the constructive and destructive case. Furthermore, the destructive multipath reduces the amplitude of the correlation value \(|P|\), which increases the weighted standard deviation \(\sigma_{w}\), while the constructive multipath acts in the opposite direction and may also reduce the variance in distinct cases as visible in Figure 10 for the black line. Furthermore, it can be seen that constructive and destructive multipath changes the resulting probability function in a different way, even if the multipath parameters \(\Delta\tau_{MP}\) and \(\alpha_{MP}\) are exactly the same.
## 5 Experimental Results
### Results by Memory Map  
The results of the memory-map method are as follows. The number of update addresses for each partition is shown underlined in Table 2. In addition, values for IDUR (the range of frames related to the input) are shown inside the bold borders in Table 2; the ranges are 53-74 frames and 253-276 frames, respectively.  
* Total number of partitions = 308
* Number of partitions in which memory update occurred = 67
* FS partitions (\(FSP\)) in Frame #60: objs. #8 and #20 (\(0<UC<AVG=1.84\))
* FS partitions (\(FSP\)) in Frame #261: objs. #8, #19, and #39 (\(0<UC<AVG=1.55\))  
The calculated FSs and rankings are shown in Figure 13. The FSs of the top 18 partitions are the same, so we show these. For reference, the rank 2 FS is 6.52 and the rank 3 FS is 5.95, in comparison to a rank 1 FS of 8.70. Obj files with high FS values in the graph have a common feature. These files are affected by the size of the ratio determined as a fault-suspicious region in IDUR rather than the entire frame.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multirow{2}{*}{**Test Case ID**} & \multirow{2}{*}{**Test Case Name**} & \multirow{2}{*}{**Lab ID**} & \multirow{2}{*}{**Lab Name**} & \multirow{2}{*}{**Frame (N)**} \\ \hline
1 & 1 & WARNING\_TC2 & 74 & 1006\_LAB25 & 400 \\
2 & 1 & WARNING\_TC2 & 75 & 1006\_LAB27 & 400 \\
3 & 1 & WARNING\_TC2 & 95 & 1013\_LAB39 & 400 \\
4 & 2 & TELECMATICS\_TC2 & 96 & 1013\_LAB40 & 400 \\
5 & 11 & TELECMATICS\_TC4 & 127 & 1029\_LAB1 & 650 \\
6 & 12 & TELECMATICS\_TC16 & 131 & TC16\_LAB3 & 800 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of each data set (RAM size = 29.4 KB).  
\begin{table}
\begin{tabular}{c|c|c|c|c|c|c|c|c|c|c|c|c|c} \hline \hline \multirow{2}{*}{**Object File**} & \multicolumn{10}{c}{**Frame**} \\ \cline{2-13}  & \multicolumn{10}{c}{**Normal Range**} & \multicolumn{10}{c|}{**IDUR**} & \multicolumn{1}{c|}{**N.R.**} & \multicolumn{1}{c|}{**IDUR**} & \multicolumn{1}{c|}{**N.R.**} \\ \cline{2-13}  & \#1 & \#2 & \#3 & \#4 & \#5 &... & \#60 & \#61 & \#62 &... & \#261 & \#262 & \#263 &... & \#399 \\ \hline... &... &... &... &... &... &... &... &... &... &... &... &... &... &... &... \\ \hline Obj \#8 & **1** &
###### Abstract
The combined results should aid in the efficient conversion of vibration energy to electrical energy and have a wide range of applications in piezoelectric cantilever harvesters. Typical vibration harvesting systems are built into existing host structures [23; 24] to grasp ambient vibration energy. An intriguing application of vibration energy harvesting exists in unmanned aerial vehicles (UAVs), where weight and aerodynamic considerations necessitate a versatile approach rather than the traditional method. The authors [25; 26; 27] propose a versatile design for energy harvesting in unmanned aerial vehicles (UAVs), in which a piezoelectric harvesting device is integrated into the UAV's wing and provides energy harvesting, energy storage, and load bearing capability. In reference [28] temperature changes had a significant effect on the efficiency of the system designed for energy harvesting from mechanical vibrations based on the use of a macro fiber composite piezoelectric transducer. The modeling process of the studied system, as well as the analytical and experimental determinations of the electric voltage on the resistor connected to the MFC transducer terminals were presented. The decrease in system efficiency with increasing operating temperature is caused by a change in the dielectric constant of the piezoelectric material used to make the transducer's fibers. The results of mathematical modeling were found to be consistent with measurements taken on a real object. However, the temperature effects of laminated MFC were not studied, and the results obtained varied. Based on the above-mentioned background, in this paper, the authors present the results of the work conducted in energy harvesting systems using laminated MFC piezoelectric transducer experimentally as well as theoretically. In the experiment, MFC piezoelectric material is embedded between layers of an epoxy/glass composite. The laboratory stand incorporates a thermal chamber for studying the effects of power generated by temperature differences. The laminated beam was exposed to a temperature below the transducer's Curie temperature to keep the system in the temperature range to which it could be exposed in the ambient environment. A concentric load was applied using an electro-pulse wave form generator with controlled displacement. The frequency chosen was far from the resonant frequency to predict the mechanical vibration energy in normal operating conditions. The theoretical model was validated using actual data, and it accurately predicts the results of the energy harvesting system and mimics the characteristics of the MFC piezoelectric simple supported beam harvester. The study discovered the possibility to equip composite material with MFC piezoelectric as an alternative energy source to supply an associated structure health monitoring (SHM) system.
# Privacy-Preserved Visual Simultaneous Localization and Mapping Based on a Dual-Component Approach
## 4 Results
### Accuracy of Pose Estimation
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sequence** & **Original** & **Proposed Method** & **Black** & **Grey** & **White** & **Gaussian** & **Ran25** & **Ran50** \\ \hline KITTI-SE-00 & 6.093 & **5.905** & 7.130 & 8.993 & 9.150 & 9.337 & 11.30 & 6.308 \\ KITTI-SE-02 & 21.87 & **22.27** & 28.78 & 27.91 & 28.36 & 33.85 & 31.23 & 31.67 \\ KITTI-SE-03 & 0.655 & **0.764** & 1.110 & 0.843 & 1.327 & 0.755 & 1.053 & 1.045 \\ KITTI-SE-04 & 0.358 & **0.347** & 0.702 & 0.437 & 0.819 & 0.997 & 0.879 & 0.859 \\ KITTI-SE-05 & 5.984 & **5.874** & 11.48 & 6.375 & 6.677 & 6.705 & 6.609 & 7.384 \\ KITTI-SE-06 & 14.54 & **15.00** & 15.92 & 16.17 & 19.03 & 16.39 & 17.68 & 18.23 \\ KITTI-SE-07 & 2.195 & **1.894** & 4.428 & 3.039 & 2.631 & 4.851 & 3.871 & 3.612 \\ KITTI-SE-08 & 47.47 & **47.48** & 51.14 & 52.45 & 51.24 & 51.13 & 49.68 & 48.72 \\ KITTI-SE-09 & 6.895 & **7.403** & 9.021 & 7.842 & 8.363 & 7.882 & 9.825 & 9.199 \\ KITTI-SE-10 & 6.495 & **7.096** & 7.202 & 7.473 & 7.830 & 8.752 & 7.945 & 7.639 \\ \hline \hline \end{tabular} Absolute Pose Error can be used to evaluate the overall consistency of SLAM trajectories. RMSE is a common index to measure the deviation between the predicted value and the actual observed value. The larger the RMSE of Absolute Pose Error is, the more the pose estimate deviates from the actual observed value.  
\end{table} 
Table 5: The absolute trajectory error of the trajectory generated by different privacy-preserved ORB-SLAM3. RMSE/m. Figure 8: Comparison of pose trajectories between the original ORB-SLAM3 and privacy-preserved ORB-SLAM3 in the Oxford Robotcar and KITTI dataset. (**a,c**) Comparison of the trajectories of sequences 12 and 001. (**b,d**) The absolute pose errors of sequences 12 and 001, respectively.
# Investigation on Eddy Current Sensor in Tension Measurement at a Resonant Frequency
###### Abstract  
For resolving deficiencies of conventional tension measurement methods, this paper proposes a novel eddy current sensor with a single-coil structure based on the inverse magnetostrictive effect. An inductor-resistor-capacitor (LRC) model of eddy current sensor, which considers more parameters than the traditional inductor-resistor (LR) model, was established. The eddy current sensor was operated by a swept frequency signal that ranged from 0.1 MHz to 1.6 MHz, encompassing the sensor resonant frequency. At the resonant frequency, the data of impedance magnitude and phase were extracted and linear relations between the impedance parameters and the external tension were ascertained. The experimental results show that the resonant frequency and impedance magnitude of eddy current sensor will decrease linearly with the increase of the external tension, which is consistent with the theoretical model. In addition, to improve sensor performance, the sleeve structure was designed to reduce the loss of magnetic field. Both finite element simulations and experimental results demonstrate that the sleeve structure provides a higher permeability path to the magnetic field lines than the non-sleeve structure and effectively improves sensor sensitivity and correlation coefficient.  
ed 29 March 2017; 7, 538; doi:10.3390/app7060538