esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of the Synthesis and Applications of Polymer-Nanoclay Composites
## 2 Synthesis
### Solution-Blending Method
This innovative solution blending method produces a solid-state composite at room temperature, allowing easy removal, handling and processing. [PERSON] et al. [61] synthesized magnetic polymer/nanoclay composites using solution blending to remove three pharmaceutical pollutants, namely atenol, ciprofloxacin and gemfibrozil. Their study indicated that the maximum adsorption was obtained when the composite is at the optimum ratio (clay:chitosan:powdered activated carbon:magnetic nanoparticles) of 1:0.5:0.3:0.3. [PERSON] et al. [62] prepared PS/exfoliated graphite nanoplate composites via the solution blending method assisted by ultrasonication. The produced composites exhibited an enhanced and stable dielectric constant, which was attributed to the proper dispersion of graphite nanoplates in the PS matrix. [PERSON] et al. [63] improved the mechanical and electrical properties of polycarbonate-based graphene composites using a simple and scalable solution blending method. A solvent-exchange process was carried out to re-disperse the exfoliated graphene flakes in an environmentally friendly solvent (1,3-dioxolane). The resulting composite is a promising material for 3D-printing-based applications. [PERSON] et al. [64] employed well-defined, polymerically-modified clay nanoparticles for the preparation of PS composite by nitroxide-mediated radical polymerization and solution blending methods. This approach produced exfoliated or mixed intercalated/exfoliated composites. [PERSON] et al. [65] prepared polymer/zeolite composite beads by a simple solution blending method using chitosan, polyvinylpyrrolidone and zeolite particles. The synthesized composite beads had relatively large loading capacities for heavy metals, making them a promising material for water treatment applications. Solution blending produces relatively uniform nanoclay dispersions and a number of polymer/nanoclay composites have been produced to date, see Table 2. This method has several drawbacks: (1) the intercalation process only occurs for certain polymer/clay/solvent combinations; and (2) copious amounts of organic solvents are typically required, which may be environmentally unfriendly and economically prohibitive [66]. At commercial scales, melt blending is preferred over solution mixing for the preparation of thermoplastic/nanoclay composites.
###### Abstract  
The upper limb force-velocity relationship (FVR) is a crucial aspect of athletic performance, particularly in para-sports where upper limb movements play a leading role in activities such as wheelchair propulsion. Athletes' mechanical capacities can be evaluated on the field or in lab conditions. However, no studies have yet indicated a relationship between ergometers and field FVR or performances. Understanding para-athletes' upper limb FVR can provide important insights for developing effective training programs and improving athletic performance in wheelchair basketball players. Twenty-three wheelchair basketball players (12 women and 11 men) from French national teams performed a battery of three tests consisting of a 20 m sprint (SP) with Inertial measurement units (IMU) on wheels, horizontal upper limb push-offs on a frictionless seld with two Kistler force plates (HBP), and a crank ergometer sprint test (CES) derived from arm ergometer sprits using Brachumera, Lode. For the SP test, the FVR was computed with an estimation of force from the acceleration, the rolling resistance, and an estimation of the air resistance. Correlations between each variable measured were computed via Pearson correlations in R, assuming a strong relationship when r = 0.7-1.0, moderate when r = 0.40-0.69, and weak when r < 0.1-0.39. Significant differences were assumed when \(p\) < 0.05. Strong correlations were found between the results of the three tests conducted. The correlation coefficient between maximal theoretical force (F0) data ranged from 0.85 to 0.88, and that between maximal theoretical power (P0) data ranged from 0.87 to 0.94. However, for maximal theoretical velocity (V0) data, the correlations between the three tests were less important. The performance variables showed a strong correlation with power measured in the HBP test without load. There was a significant relationship between the HBP, CES, and SP variables. There was a correlation between performance on the three tests in our population, especially for power. However, the V0 value of HBP was not representative of any performances. This work uses multiple protocols to assess para-athletes' performance and shows that upper limb symmetry depends on the gesture of the task and the personal sports' wheelchair.  
force-velocity-profile; arm cranking; ballistic push-off; wheelchair basketball 2023
# Quality of Service Based NOMA Group D2D Communications
###### Abstract  
Non-orthogonal multiple access (NOMA) provides superior spectral efficiency and is considered as a promising multiple access scheme for fifth generation (5G) wireless systems. The spectrum efficiency can be further enhanced by enabling device-to-device (D2D) communications. In this work, we propose quality of service (QoS) based NOMA (Q-NOMA) group D2D communications in which the D2D receivers (DRs) are ordered according to their QoS requirements. We discuss two possible implementations of proposed Q-NOMA group D2D communications based on the two power allocation coefficient policies. In order to capture the key aspects of D2D communications, which are device clustering and spatial separation, we model the locations of D2D transmitters (DTs) by Gauss-Poisson process (GPP). The DRs are then considered to be clustered around DTs. Multiple DTs can exist in proximity of each other. In order to characterize the performance, we derive the Laplace transform of the interference at the probe D2D receiver and obtain a closed-form expression of its outage probability using stochastic geometry tools. The performance of proposed Q-NOMA group D2D communications is then evaluated and benchmarked against conventional paired D2D communications.  
Keywords:quality of service; device-to-device communication; non-orthogonal multiple access; stochastic geometry +
Footnote †: journal:  
_Future Internet_
# Radioactivity in Soils of Kosovo and Radiological Implications
## 2 Materials and Methods
### Calculation of Radiological Hazards  
Radium equivalent activity (Ra\({}_{eq}\)) is a widely used hazard index and was calculated through the relation given by [PERSON] and [PERSON] [24]. It was assumed that 370 Bq/kg of \({}^{226}\)Ra, 259 Bq/kg of \({}^{232}\)Th, and 4810 Bq/kg of \({}^{40}\)K produced the same gamma-ray dose rate. The calculation is shown in Equation (4):  
\[\mathrm{Ra}_{eq}=A_{\mathrm{Ra}}+1.43A_{\mathrm{Th}}+0.077A_{\mathrm{K}} \tag{4}\]  
where \(A_{\mathrm{Ra}}\), \(A_{\mathrm{Th}}\), and \(A_{\mathrm{K}}\) are the activity concentrations of related radionuclides, respectively.  
Gamma index (I\({}_{\rm Y}\))--Gamma index (I\({}_{\rm Y}\)) was calculated by using the Equation (5):  
\[\rm I_{Y}=\frac{A_{Ra}}{150 Bq/kg}+\frac{A_{Th}}{100 Bq/kg}+\frac{A_{K}}{1500 Bq/kg} \tag{5}\]  
where \(A_{Ra}\), \(A_{Th}\), and \(A_{K}\) were explained in Equation (4) of this paper [25].  
External hazard indices (He\({}_{\rm ex}\)) were calculated for the samples investigated using the model proposed by [PERSON] [26], where the external hazard index is given by:  
\[\rm H_{ex}=\frac{A_{Ra}}{370 Bq/kg}+\frac{A_{Th}}{259 Bq/kg}+\frac{A_{K}}{4810 Bq/kg}<1 \tag{6}\]  
where \(A_{Ra}\), \(A_{Th}\), and \(A_{K}\), were explained in Equation (4) of this paper  
Absorbed dose rate in air (ADR) is the external terrestrial gamma radiation in the air at 1 m above ground level, due to the presence of \({}^{226}\)Ra, \({}^{232}\)Th, \({}^{40}\)K, and \({}^{137}\)Cs in the soil samples at each site. This was calculated using the following Equation (7) [27],  
\[\rm{ADR}=aA_{Ra}+bA_{Th}+cA_{K}+dA_{Cs} \tag{7}\]  
where \(A_{Ra}\), \(A_{Th}\), and \(A_{K}\), were explained in Equation (4) of this paper. The coefficients \(a\), \(b\), \(c\), and \(d\) are the dose conversion factors, and their values are 0.462, 0.604, 0.042, and 0.1243 in nGy/h per Bq/kg, respectively.  
The annual gonadal dose rate (AGDE)--The activity of bone marrow and bone surface cells are considered organs of interest by UNSCEAR [28]. Therefore, the AGDE, due to the activity concentrations of \({}^{226}\)Ra, \({}^{232}\)Th, and \({}^{40}\)K, was calculated using Equation (8) [29]:  
\[\rm{AGDE}(\mu Sv/year)=3.09A_{Ra}+4.18A_{Th}+0.314A_{K} \tag{8}\]  
where \(A_{Ra}\), \(A_{Th}\), and \(A_{K}\), were explained in Equation (4) of this paper. The coefficients 3.09, 4.18, and 0.314 are conversion factors in (Sv/y)/(Bq/kg).  
The annual effective dose rate (AEDE)--The annual effective dose equivalent is given by using Equation (9):  
\[\rm{AEDE}(\mu Sv/year)=D\times DCF\times OF\times 8760h/year\times 10^{-3} \tag{9}\]  
where DCF is the dose conversion factor absorbed in the air (0.7 Sv/Gy), and OF is the outdoor occupancy factor (0.2) [28].  
Excess lifetime cancer risk (ELCR) is given by using Equation (10):  
\[\rm{ELCR}=AEDE\times DL\times RF \tag{10}\]  
where DL is the life span (70 years) and RF is the risk factor (Sv\({}^{-1}\)), that is, mortal cancer risk per sievert. As stochastic effects, the ICRP 60 used values of 0.05 for the community [30].
# Smart Road Traffic Accidents Reduction Strategy Based on Intelligent Transportation Systems (TARS)
## 3 Smart Road Traffic Accidents Reduction Strategy
### Phases of Traffic Accidents Reduction Strategy (TARS)  
The Traffic Accidents Reduction Strategy (TARS) is divided into three phases. The first is the setup phase, which describes the process of establishing the VANET according to our proposed strategy; the authorization phase, the second phase of TARS, demonstrates the registration process of the entities. Finally, the execution of the proposed strategy is presented in the execution phase. We discuss these three phases in detail.  
_Setup Phase_  
In this phase, the entities of VANETs are set up for the TARS scheme. A RSU periodically broadcasts its identity and location in its vicinity so that vehicles moving in its range can communicate with it. A vehicle broadcasts BSM containing VID, location, speed and direction to the RSU when it enters into the range of RSU and listens to RSU's beacons. The RSU keeps the information of the automobiles (vehicles) travelling in its vicinity through the information received in the BSMs. The RSU data include the vehicle-ID, their velocity (V), location, and Tour-Span (TS). Tour-Span is the minimum time a vehicle spends in the range of a RSU. On reception of the BSM, the RSU extracts the vehicle-ID and verifies it by looking it up in its database. After verifying the vehicle-ID, the RSU calculates the tour-span 'TS' of the vehicle in its region as shown in Equation (1).  
\[\text{TS}_{i}=\frac{\textit{Flocation}_{RSU}-\textit{Position}_{i}}{\textit{ Velocity}_{i}} \tag{1}\]  
where \(\textit{Flocation}_{RSU}\) is the end point of the transmission range of the RSU; \(\textit{Position}_{i}\) is the current location of a vehicle received in the BSM; and \(\textit{Velocity}_{i}\) is the velocity of vehicle \(i\).  
_Authorization Phase_  
This phase describes the procedure of registering the RSUs and vehicles with a Trusted Authority (TA). The TA is a trusted administrative authority that is responsible for the accountability of entities of the VANET system. Once the entities, RSUs, and vehicles become registered with the TA, then they remain liable to it. During the registration process, the TA provides a key and a vehicle ID to a vehicle as shown in Figure 2. A vehicle owner must keep this key in a safe place, for example, at home or at the bank, so that the owner can claim ownership of a vehicle if a vehicle becomes physically compromised.  
Then, the vehicle credentials can be revoked and any possible attacks, for example, misleading attack, can be avoided. The TA hands out the identities of registered vehicles to the RSUs installed at that region as people often travel in the region they live in. A question may arise as to what will happen if a vehicle registered in another region arrives/enters into the transmission range of a RSU. For this purpose, we present different scenarios in the next subsection.
# A 245 GHz Real-Time Wideband Wireless Communication Link with 30 Gbps Data Rate
## 2 System Architectures and Configurations  
Figure 1 shows the configuration of the THz wireless communication system, where the transmitter and receiver are separated by 1.2 m in the wireless communication demonstration setup. Both the transmitter and the receiver are composed of three modules: base-band module, IF module, and THz front-end module. The base-band module on the transmitter side has 4 channels of in-phase and quadrature-phase (I/Q) outputs, with each I/Q output generating signals at a speed of 7.5 Gbps within the 2.5 GHz bandwidth. Hence, the total data rate and bandwidth of the base-band modules are 30 Gbps and 10 GHz, respectively. The IF modules are used for frequency up- and down-conversion between the 0-2.5 GHz base-band signals and 15.65 GHz IF frequency. The bandwidth of the IF module is 12.5 GHz, with a 2.5 GHz guard band between different channels. The IF modules are connected to the 245 GHz THz front-end modules for frequency up-conversion, transmission, detection, and demodulation. A pair of horn antennas with 25 dBi gain are connected to the THz transmitter and receiver for wireless communications while the communication distance can be further improved if higher-gain antennas are used.
### Results for Subcase B  
Figure 5 shows the sound source distribution at different frequencies of the subcase B obtained by the present data processing program based on Clean-SC algorithm. It can be seen from Figure 5 that, even for the complex sound sources, the present program can accurately identify the location of the noise source, and get a clean image of the sound source for all the frequencies tested.  
Figure 6 shows the comparison between the results of four sound sources and the benchmark data for subcase B. It can be observed from Figure 6 that, although the four sources differ in intensity by up to 18 dB (which means 63 times the energy difference), the magnitudes of four sound sources are accurately identified using the present array data processing software based on Clean-SC algorithm.  
Figure 5: Sound source distribution for subcase B at different frequencies.  
Figure 6: Comparison between the present results and the benchmark case for subcase B.  
Figure 7 shows the difference between present identification results and benchmark data, along with the results from other researchers. Compared between Figures 4 and 7, it can be seen that the accurate identification of non-equal intensity complex sound sources requires a higher-resolution algorithm, especially for the accurate identification of the weakest sound source (\(-18\) dB). For the weakest Source3, only the array identification methods of PSA3 and the present paper can achieve high accuracy, for which the error above 3000 Hz is less than 0.2 dB. The error of the array identification method of UniA based on Clean-SC algorithm is within \(1\) dB in subcase A. However, in more complex subcase B, the calculation error increases significantly. For the weakest Source3, the error of the array identification method of UniA can reach 3 dB. The large error for the weak sources might be due to the implementation details of the different methods, which indicates that it is still a challenge for the evaluation of the weak sources in the presence of much stronger sources. This also shows that although the Clean-SC algorithm has been proposed for a long time, it still needs special attention to achieve its correct application, which fully verifies the importance of [PERSON]'s work.  
Figure 7: Comparison of the present results with other studies for subcase B.
## 5 Results and Discussion
### Results of Training and Validation
#### 5.1.1 AP50 Level Analysis  
Tables 3 and 4 present the AP50 results for the various backbone architectures in Faster R-CNN--namely ResNet 101, ResNet 152, and MobileNetV3--and in YOLOv10, which utilizes ViT, Swin Transformer, and PVT. Figure 16 further compares each augmentation method against the original baseline. Overall, contrast augmentation consistently delivered the highest average AP50, boosting the average AP50 of the Faster R-CNN from 80.99 to 83.90 (+2.91) and that of YOLOv10 from 82.08 to 86.04 (+3.96). However, shearing surpassed contrast augmentation in certain backbones--for example, MobileNetV3 in the Faster R-CNN climbed to 85.12 (versus 84.66 using contrast augmentation), and PVT in YOLOv10 reached 88.45 (exceeding contrast augmentation at 87.23). Meanwhile, combining all augmentations occasionally benefitted a single backbone (e.g., ResNet 152 rose to 83.12, surpassing its that of contrast augmentation at 81.56), but it typically did not outperform  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Number of** & **Number of Annotated** \\  & **Images** & **Buildings** \\ \hline Original training & 740 & 740 \\ Original + brightness & 1480 & 1480 \\ Original + contrast & 1480 & 1480 \\ Original + perspective & 1480 & 1480 \\ Original + rotation & 1480 & 1480 \\ Original + scale & 1480 & 1480 \\ Original + translation & 1480 & 1480 \\ Original + blurring & 1480 & 1480 \\ Original + sum of techniques & 5920 & 5920 \\ Validation & 246 & 246 \\ Test & 246 & 246 \\ Total & 17,512 & 17,512 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed distribution of datasets for buildings.  
the top individual augmentation method across all architectures and even dipped below the baseline in some cases.  
These trends underscore that each augmentation interacts differently with each architecture. While deeper or more robust models (ResNet 152 and PVT) often benefit from carefully chosen transformations, lighter networks (e.g., MobileNetV3) may be more sensitive to extensive geometric distortions. Consequently, contrast augmentation stood out as the most dependable method for broad improvements, yet shearing augmentation outperformed it in specific backbones--emphasizing the importance of tailoring augmentation strategies to individual network designs.
## 4 Solvents for CO\({}_{2}\) Capture
### Chemical Solvents
#### 4.2.2 Solvent Blends  
The idea behind the use of blended solvents is to exploit the beneficial properties of single solvents in a synergistic way while reducing bottlenecks. Such bottlenecks include the high cost of regeneration expressed usually as increased thermal needs as well as the resistance of the solvent to thermal and oxidative degradation [61]. The combination of two or more amines in a single solution offers the advantage of utilizing the different reaction mechanisms, inherent to each type of amine, toward maximizing CO\({}_{2}\) capture efficiency or minimizing the heat of reaction. Combinations of MEA and MDEA have been used in pilot plants, showing that the addition of MDEA at a 5:1 ratio can achieve large reductions in thermal energy for solvent regeneration [62]. The mixture of AMP and PZ has been tested by [PERSON] and [PERSON] [63], leading to a great reduction in the liquid flowrate as well as the regeneration energy. Numerous studies exist confirming that solvent blends of MDEA-PZ or MDEA-DEA show improved CO\({}_{2}\) capture behavior [64; 65]. In many cases,mixtures of AMP have shown great potential [66; 67]. According to [PERSON] et al. [68] a bi-solvent blend of AMP and PZ in a molar ratio 2:1 showed a 20% increase in the CO\({}_{2}\) cyclic capacity compared to the benchmark solvent MEA. However, the use of AMP should be done with caution due to the tendency of this solvent to form precipitates [55]. Despite the advantages that bi-solvent blends offer for CO\({}_{2}\) capture, one must not forget that the design of a process involving a mixture of two solvents introduces a severe complexity in the control of the solvent concentration as the balance between the mixture components can easily be disturbed. Furthermore, each solvent component may require different conditions for absorption and desorption, leading to process challenges. In turn, this would lead to changes in the process's capture efficiency.  
Going one step further into solvent development, mixtures of more than two amines can be used. [PERSON] et al. [69] investigated the use of an AMP-PZ-DIPA tri-solvent blend concluding that the presence of AMP-PZ improved the ability of DIPA to capture CO\({}_{2}\), thus increasing the absorption capacity. Moreover, [PERSON] et al. [70] showed that the addition of AMP into a bi-solvent blend of MEA-MDEA greatly improves the kinetics of the amine-CO\({}_{2}\) reaction. When AMP is used, tri-solvent blends can be used to moderate its concentration offering a much better control over precipitate formation [71]. The existence of more than two amine solvents usually signifies increased concentration, leading to increased CO\({}_{2}\) absorption, provided that unwanted interactions between the base molecules of the solvent blend are averted. As with bi-solvent blends, the use of three solvents further increases the complexity in maintaining the concentration within a desired range. However, as long as robust control schemes are present, the careful design of multi-solvent blends often leads to better performance.
## 3 Results and Discussion
### Result of Multi-Body Dynamics Analysis  
The coordinate system of six degrees of freedom (DOF) of a structure is defined as movements in three axial directions--movement in a vertical direction (heave), movement in the direction in which the wave progresses (surge), and movement perpendicular to the heave axis and the surge axis (sway). Yaw, roll and pitch are rotations around the heave, surge and sway axes, respectively.  
Among these, those most important for a heave compensator are heave, roll and pitch. In this study, the behavioral characteristics of a heave compensator for heave motion were investigated.  
Figure 10 shows the result of displacements from the initial locations of the crown block and the frame obtained through MBD analysis. Figure 11 shows the speeds of the DSC cylinder and the AHC cylinder according to time band. When the compensation rate for a movement of \(\pm\)3000 mm was 95%, the maximum speeds of the DSC cylinder and the AHC cylinder were 1.750 and 1.989 m/s, respectively. In addition, in Figures 12 and 13 the maximum load of the DSC cylinder and a heave acceleration of 1.480 m/s\({}^{2}\) were obtained. This result can be used for structural analysis to determine the safety of a structure.  
Figure 8: Basic characteristics of the analysis model.  
Figure 9: Comparison of test result and simulation result, (**a**) time data; (**b**) frequency characteristics.  
Figure 11: Velocity of the AHC and DSC cylinder.  
Figure 12: Load of the DSC cylinder.  
Figure 10: Position of the crown block and frame.