esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The majority of individuals with dyskinetic cerebral palsy cannot use powered mobility with a joystick, due to the lack of manual abilities by the severe presence of dystonia and choreoathetosis. Reliable measurements of these movement disorders is indispensable for good evaluation towards evidence-based insights during powered mobility. This study aimed to develop and assess the Dyskinesia Impairment Mobility Scale (DIMS), a video-based tool to measure presence and severity of dystonia and choreoathetosis during powered mobility. DIMS was measured for the neck and arms region during five mobility tasks. Interrater reliability, test-retest reliability, internal consistency and concurrent validity of the DIMS were assessed. Interrater reliability coefficients ranged between 0.68 and 0.87 for the total DIMS, and the dystonia and choreoathetosis subscales. Test-retest reliability was moderate to excellent (range 0.51-0.93) while Cronbach's alpha was good (range 0.69-0.81) for the total scale and subscale scores. Concurrent validity showed during mobility tasks significant correlations with rest postures in the arm region, and with requested but voluntary activity in the neck region. The DIMS reliably measures the presence and severity of the movement disorders during powered mobility, increasing insights into the underlying mechanisms of independent mobility. This scale may therefore be a promising tool to evaluate mobility training.  
powered mobility; dyskinetic cerebral palsy; dystonia; choreoathetosis; mobility scale; movement disorder; children; youth; reliability; validity +
Footnote †: journal: Applied Sciences
# Blockchain-Driven Generalization of Policy Management for Multiproduct Insurance Companies
## 4 Solution Approach
### Application of Blockchain in the Insurance Market: Selection of Blockchain Type and Technology  
Once we understand the benefits of implementing Blockchain as a technological alternative in an integrated policy management system, we need to determine which type of Blockchain network is most suitable.  
As is well known, Blockchain can be implemented as either public or private.  
To make a decision on the type of network to use, two critical aspects have been considered: the efficiency and the privacy and access of the solution.  
* Efficiency: This is a key aspect in business environments and becomes a critical factor during periods of high demand. Transactions on a public network tend to be slower due to the necessity to validate blocks across a larger number of globally dispersed nodes. This process not only consumes more time but also more computational resources. Additionally, the lack of control over participants can result in network congestion, especially during peak demand times, which can significantly slow down transaction processing. Private networks allow precise control over participants and minimize waiting times, ensuring smooth operations even under conditions of high demand. The ability to manage nodes and optimize resources ensures greater efficiency, which is essential for keeping operational costs low and improving the speed and reliability of the service.
* Privacy and Access: In this business model, sensitive, confidential, and private data about clients (such as personal, health, and financial information) are handled. A public network is accessible to anyone, whereas a private network is hidden from non-participants. A private network allows the insurance company to reduce the risks of data exposure and ensure compliance with both internal and regulatory policies.  
Considering the above factors, our proposed solution is based on a private Ethereum network. As is well known, Ethereum is a widely used network and highly trusted among the world's leading companies. It is open-source technology, highly accessible, and continuously evolving, with substantial support from a large community of developers. These characteristics make it suitable for our solution.
## 2 Results and Discussion
### Application of the HS-SPME/GC-MS Method to Strawberry Fruit Samples
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline
**No.** & **Compound Name and Class** & **RI Calc.** & **Fresh** & **Frozen** & \begin{tabular}{c} **Freeze-** \\ **Dried** \\ \end{tabular} & \begin{tabular}{c} **Microwave-** \\ **Dried** \\ \end{tabular} & \begin{tabular}{c} **Oven-Dried** \\ **(60 ° °C)** \\ \end{tabular} & \begin{tabular}{c} **Oven-Dried** \\ **(45 °C)** \\ \end{tabular} & \begin{tabular}{c} **Shade** \\ **Air-Dried** \\ \end{tabular} & \begin{tabular}{c} **Jam 1** \\ \end{tabular} & \begin{tabular}{c} **Jam 2** \\ \end{tabular} & \
\begin{tabular}{c} **Jam 3** \\ \end{tabular} &
\begin{tabular}{c} **Jam 4** \\ \end{tabular} \\ \hline
154 & Myrtray-J2-methylbutyrate & 1556 & 0.00 & 0.08 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\
155 & (E)-Neufold & 1563 & 5.13 & 31.17 & 17.14 & 11.04 & 8.97 & 10.78 & 9.65 & 40.99 & 9.94 & 13.75 & 17.92 \\
156 & Bisaboloid oxide II & 1655 & 0.00 & 0.03 & 0.06 & 0.62 & 0.15 & 0.06 & 0.04 & 5.32 & 1.51 & 2.00 & 3.27 \\
157 & \(\alpha\)-Bisaboloid & 1683 & 0.01 & 3.97 & 0.00 & 0.11 & 0.08 & 0.09 & 0.00 & 2.65 & 0.67 & 0.63 & 1.63 \\
158 & Nerwidely acetate & 1711 & 0.00 & 0.25 & 0.00 & 0.02 & 0.00 & 0.10 & 0.01 & 0.01 & 0.00 & 0.00 & 0.00 & 0.00 \\
159 & Farnseed & 1720 & 0.00 & 0.07 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\
**Others** & **14.56** & **0.33** & **5.14** & **4.53** & **4.17** & **1.39** & **1.72** & **0.00** & **2.99** & **0.22** & **0.00** \\
160 & Sytren & 887 & 14.29 & 0.02 & 0.05 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 2.09 & 0.22 & 0.00 \\
161 & 2-Pernyl Ryan & 990 & 0.24 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\
162 & Pyranone & 1135 & 0.00 & 0.00 & 0.00 & 0.09 & 0.28 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\
163 & \(m\)-D-trtr-butylbenzene & 1227 & 0.00 & 0.00 & 0.00 & 4.43 & 3.75 & 1.00 & 1.56 & 0.00 & 0.00 & 0.00 & 0.00 \\
164 & 1-Metrology-4(1-property)benzene & 1227 & 0.00 & 0.00 & 5.09 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\
165 & Sulfur & 2049 & 0.03 & 0.31 & 0.00 & 0.01 & 0.14 & 0.39 & 0.16 & 0.00 & 0.00 & 0.00 & 0.00 \\
**Total identified** & **92.79** & **94.81** & **96.88** & **96.85** & **98.66** & **95.24** & **95.88** & **85.03** & **86.81** & **95.14** & **91.96** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Figure 1: Schematic diagram representing the variations in the major chemical classes detected in different samples of strawberry fruits and jams: Jam 1 (Coop Vivi verde jam); Jam 2 (Coop jams); Jam 3 (Zuegg jams); Jam 4 (Alce Nero jam).
## 4 Proposed System Evaluation
### Sum Rate Calculation  
The sum rate of NOMA is the maximum rate at which data can be sent over a channel of a definite bandwidth in the presence of noise. For a NOMA system with \(N\) users, the rate for user \(i\) is given by \(R_{i}\), and the sum rate is given by \(R_{sum}\), where:  
\[R_{i}=log_{2}(1+\frac{p_{i}\text{SNR}|H_{i}|^{2}}{SNR_{i}|H_{i}|^{2}\sum_{i+1} ^{N}p_{i}+1}). \tag{14}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Carrier frequency & 2 GHz \\ Base station (BS) power & 46 dBm \\ System bandwidth (BW) & 5–10 MHz \\ Number of users per cell (\(N\)) & 10–20 \\ Bandwidth per user & 5.4 MHz \\ Number of data subcarriers & 1200 \\ Number of pilot subcarriers (\(x_{i}\)) & 4 \\ Number of guard-band subcarriers & 76 \\ Channel matrices (\(H_{i}\)) & Rayleigh or Rician fading \\ Subcarrier spacing & 15 kHz \\ Bose–Chaudhuri–Hocquenghem (BCH) code length & [4,7] up to [26,31] \\ Symbol length & Data: 66.67 msec + cyclic prefix: 4.69 msec \\ Modulation & QPSK and 64 QAM \\ Constellation rotation angles & 0.506 rad for QPSK \\  & 0.150 rad for 64 QAM \\ AWGN (\(w\)) & \(-\)10 to 30 dBm \\  & 2/3 and 1/3 \\ Power allocation coefficients (\(p\)) & 3/4 and 1/4 \\  & 4/5 and 1/5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: NOMA system parameters.  
And:  
\[R_{\text{sum}}=\sum\
olimits_{i=1}^{N-1}\log_{2}{(1+\frac{p_{i}}{\sum_{i+1}^{N}a _{i}+\text{SNR}|H_{i}|^{2}})}+log_{2}(1+p_{\text{N}}|H_{N}|^{2}\text{SNR}). \tag{15}\]
# Energy Harvesting for TDS-OFDM in NOMA-Based Underwater Communication Systems
## 6 Experimental Results and Discussion
### Energy Efficiency Improvement  
In this sub-section, we evaluate the proposed energy harvesting scheme of the proposed TDS-OFDM-NOMA in two terms, energy efficiency and harvested energy. The energy efficiency of the TDS-OFDM can be formulated as:  
\[EE_{TDS-OFDM}=\frac{N}{N+\beta^{2}M}\times 100\% \tag{50}\]  
Figure 10: BER-(near-user) comparison of proposed TDS-OFDM-NOMA and convention CP-OFDM-NOMA; (**a**) Uncoded system and (**b**) Coded system.  
where \(\beta=\sqrt{2}\) is the amplitude factor clamped on the guard interval in the time domain. The proposed energy harvested TDS-OFDM scheme (EH-TDS) can be calculated as:  
\[EE_{EH-TDS}=\frac{N}{N+\beta^{2}(\gamma(L-1)+F)}\times 100\% \tag{51}\]  
where \(\gamma=\left(1-\frac{\eta|h|^{2}}{2 PL}\right)\) is the efficiency factor for using the guard interval transmitted power in energy harvesting, and \(F=M-L+1\) is the IBI free region length used in equalization and the channel estimation. The conventional TDS-OFDM has energy efficiency calculated by [7]:  
\[EE_{TDS}=\frac{N}{N+\beta^{2}M}\times 100\% \tag{52}\]  
The energy efficiency improvement, \(\xi=EE_{EH-TDS}-EE_{TDS}\), as a function of transmitted distance, \(b\) is shown in Figure 11. We observe that the energy efficiency is decreased by increasing the transmission distance due to the UWA channel path loss. In Figure 11, the guard interval length is set to be \(M=255\); the maximum UWA channel length is set to be \(L=180\); and the IBI Free region length is \(F=75\). The battery of the wireless sensor nodes can harvest at least 30 joules at each transmitted frame of the TDS-OFDM.  
The energy harvested improvement for TDS-OFDM can be reached up to 8% in a short communication distance. Figure 12 shows the performance of the TDS-OFDM over the UWA channel at different maximum channel tap delays, as the maximum channel tap delay increases; the improvement of the energy efficiency and the harvested energy both are increased using the proposed energy harvesting scheme where the duration of the symbols used in harvested energy is increased.  
Figure 11: The performance of the TDS-OFDM using TS-SWIPT verses the communication distance, \(b\).
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 3 Materials and Methods
### Radio Propagation Modeling  
The database obtained from the ray-tracing simulation is discrete, whereas the actual target and UAV sensor position information is continuous. Therefore, radio propagation modeling is employed to interpolate continuous data [8; 10]. Figure 4 shows the spatial distribution of candidate UAV positions used as sampling points in this modeling process. The model parameters are recursively estimated based on the data obtained from the ray-tracing simulation. [PERSON] [14] examined the modeling of propagation loss in typical clustered propagation environments by considering LoS probability models defined by ITU-R. Meanwhile, [PERSON] [11; 12] focused on modeling the arrival angles between the base station (BS) and the user equipment (UE). Based on these computational models and ray-tracing simulation data, numerical simulations were conducted to estimate the positioning error.
## 3 Experimental Research and Analysis
### Model Experiments and Analysis  
#### 3.2.1 Comparative Experiments  
Comparative experiments systematically compare the responses of different groups under specific conditions, providing strong support for scientific research and practical decision-making. Using different datasets, the generalization and robustness of HSS-YOLO were validated.  
As shown in Table 4, HSS-YOLO demonstrated improved performance compared to YOLO11 on both datasets. On the custom dataset, precision increased by 4.9%, recall by  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration** & **Releases** \\ \hline CPU & Intel(R) Core(TM) i7-13650 HXCPU @ 4.90 GHz \\ \hline GPU & NVIDIA GeForce RTX4060 8G \\ \hline RAM & 32 G \\ \hline Python & 3.10.15 \\ \hline PyTorch & 2.0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hardware configuration.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**ParaMeter Name** & **Value** \\ \hline Epoch & 50 \\ \hline Batch size & 16 \\ \hline Input image size & 640 \(\times\) 640 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter settings.  
Figure 7: Display of local datasets.  
2.7%, and [EMAIL_ADDRESS] by 4%. On the DoorDetect Dataset, precision increased by 1.3%, recall by 2.9%, and [EMAIL_ADDRESS] by 1.3%. Based on these comparisons, it can be concluded that HSS-YOLO outperforms YOLO11.
## 3 Results and Discussion
### Evaluation of the Influences of Parameter Settings
(**a**) Regularization parameter \(\lambda_{GRAD}\) for enhanced gradient-based matching cost; (**b**) regularization parameter \(\lambda_{CTg}\) for modified census transform-based matching cost; (**c**) color similarity threshold \(\tau_{1}\); (**d**) arm length threshold \(L_{1}\); (**e**) color similarity threshold \(\tau_{2}\); (**f**) arm length threshold \(L_{2}\); (**g**) distance threshold \(d_{Lim}\).
## 2 Materials and Methods
### Exergy Calculations  
The maximum amount of useful work that can be obtained from any process dealing with two thermal reservoirs, the equation for exergy balance for the selected engine control volume, is represented in Equations (10) and (11).  
\[Ex_{in}=Ex_{out}+Ex_{dest} \tag{10}\]  
\[Ex_{air}+Ex_{fuel}=E(m_{out}\times\in_{out})+Ex_{heat}+Ex_{work}+Ex_{dest} \tag{11}\]  
The terms \(Ex_{air}\), \(Ex_{fuel}\) represent the exergy transfer rate for air intake and fuel, respectively, whereas \(Ex_{heat}\), \(Ex_{work}\) and \(Ex_{dest}\) represent the exergy rate heat transfer from the source to the control volume to the environment, the exergy rate developed by the shaft power that is equivalent to the brake power calculated and the exergy rate of destruction, which is a measure of exergy destroyed in the control volume [45], and is the result of several factors such as friction and combustion. These exergy terms were calculated by Equations (12)-(19).  
\[Ex_{fuel}=m_{f}\times\varphi_{fuel}\times CV \tag{12}\]  
\[Ex_{air}=m_{air}\times c_{p*air}\times\left[(T_{air,j}-T_{amb})-T_{amb}\ln \left(\frac{T_{air,j}}{T_{amb}}\right)\right] \tag{13}\]  
\[\varphi_{fuel}=1.0401+0.1728(\frac{H}{C})+0.0432(\frac{O}{C})+0.2169(\frac{S} {C})\times\left[1-2.0628(\frac{H}{C})\right] \tag{14}\]  
\[Ex_{heat}=\sum\left(1-\frac{T_{amb}}{T_{cw}}\right)\times Q_{loss} \tag{15}\]  
\[Ex_{work}=Bp \tag{16}\]  
\[Ex_{out}=\sum m_{i}(\in_{tm}+\in_{chem}) \tag{17}\]  
\[\in_{tm}=(h-h_{o})-T_{amb}(s-s_{o}) \tag{18}\]\[\in_{chem}=RT_{amb}\ln(\frac{y_{i}}{y_{r}}) \tag{19}\]  
\[\eta_{ex}=\frac{Ex_{zwork}}{Ex_{in}} \tag{20}\]  
\(\eta_{ex}\) is called exergetic efficiency and is characterized as the ratio of exergetic work output to the total exergy input to the system [46] as mentioned in Equation (20), which is based on the second law of thermodynamics and gives a more accurate calculation for the performance of the system.
## 2 Materials and Methods
### Technical Equipment  
The power measurement was carried out with a LabMax Top energy and power meter from Coherent (Santa Clara, CA, USA). An associated PM10 power sensor (Coherent, Santa Clara, CA, USA) was used for power measurement. Due to the high pulse repetition rates (up to the kHz range), the output power was recorded over a period of 10 s. The resulting mean value was used as the value for the output power P\({}_{\text{avg}}\) to be displayed.  
The temporal behaviour of the laser pulses (single pulses and pulse trains) was detected using a Thorlabs photodetector (Model PDA36A(-EC), Newton, NJ, USA). The rise time was calculated according to the manufacturer's specifications as t\({}_{\text{rise}}\) = 30 ns and is, therefore, 2 orders of magnitude below the pulse durations of the laser system (\(\upmu\)s).  
Oscilloscopes from Tektronix Inc. (Type TDS 2024, Beaverton, OR, USA) and Teledyne LeCroy (Type T3 DSO1204, Chestnut Ridge, NY, USA) were used to display the temporal pulse profiles.
# Energy-Saving Traffic Scheduling in Hybrid Software Defined Wireless Reschargeable Sensor Networks
## 3 System Model  
As shown in Figure 2, this paper considers a typical kind of hybrid SDWRSN, where four kinds of nodes are included: sensor nodes, relay nodes, a sink node and some wireless chargers. The sensor nodes collect required information according to some predefined period or discipline, and transmit them to the sink node via some relay nodes. The sensor nodes are dispersedly deployed over the target monitoring area. A certain amount of sensor nodes close to each other form a cluster. Each cluster has a cluster head, which can be dynamically selected by currently existing schemes. The cluster heads can relay data from the cluster it belongs to, as well as data from another cluster head. Therefore, in this paper, we also refer to cluster head as relay node for simplicity.  
Basically, there are two kinds of relay nodes: normal relay node (or shorted as normal node) and SDN relay node (or shorted as SDN node). The normal nodes run traditional routing protocols, such as MTE, Ad hoc On-demand Distance Vector Routing (AODV), etc. In this paper, we use MTE as routing protocol among normal nodes, since MTE has good performance in reducing the energy consumption of the WSN nodes. As to the SDN nodes, a common SDN controller can calculate the optimal flow table according to the whole network's topology and flow distrib  
Figure 2: System model.  
talk to the SDN nodes via 3G/4G (when a cellular network is available) or some other long distance communication methods such as LoRa (3 km urban, 30 km rural). This will not add obvious economic burden since only a part of relay nodes or sensor headers are SDN nodes. In addition, this will not introduce obvious additional energy consumption in the pattern discussed in this paper, as follows: (1) When the locations of the network nodes are static, the network topology is relatively stable. At the same time, the flow characters are usually steady since the services in WSN are generally periodical. (2) The update of the flow table may occur in two patterns: (a) SDN controller can update the flow table periodically. The period can be relatively long, for example, 10 min one time, 1 h one time or even several hours one time; (b) triggered by an event, such as node joining/existing, etc. Therefore, the low frequency update and occasionally triggered update will not introduce obvious additional energy consumption.  
At the same time, some wireless chargers are deployed in proper locations to replenish the energy consumption of network devices, i.e., sensor nodes, the relay nodes, and the sink node. Consequently, considering that the sensor nodes, the sink node, and the SDN controller work according to their predefined pattern and will not be influenced by the algorithm proposed in this paper, we need not count the energy consumption of these three parts, and concentrate on the algorithm's effect on the relay nodes.
# Synchronous Generator Stability Characterization for Gas Power Plants Using Load Rejection Tests
## 2 Theoretical Models
### Speed Governor Model: GAST  
The model chosen to represent the behavior of the power plants speed governor is GAST. The model is plotted in Figure 6. Furthermore, Table 3 collects the parameters related to the GAST model.  
As for the previous AVR model, the speed governor parameters are collected from a gas injection set point changing test and from a load rejection test. The damping factor and tune constants are fitted from the load test rejection; meanwhile, the upper and lower operation limits and permanent droop are fitted from the steady-state set point change tests.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Description** & **Parameter** & **Units** \\ \hline Rate of change in the excitation system numerator & \(T_{A}\) & [s] \\ Rate of change in the excitation system denominator & \(T_{B}\) & [s] \\ Exciter gain & \(K\) & [pu] \\ Exciter time constant & \(T_{E}\) & [s] \\ Maximum AVR output & \(E_{MIN}\) & [pu] \\ Minimum AVR output & \(E_{MAX}\) & [pu] \\ \hline \hline \end{tabular}
\end{table}
Table 2: SEXS model parameters.  
Figure 6: Speed governor model (GAST).  
The parameters' fitting from tests and simulations is an iterative process, and there are some standards parameter proposed by [25; 26]. However, those are used just to initialize the simulation iteration; first, the individual controllers must be simulated, because the regulators (SEXS and GAST) are decoupled and it is easier to get nearer to the final solution.  
After obtaining the parameters through the previous simulations, the load rejection is simulated. The solution is then checked, and if it is unsatisfactory, the no-load set point simulation must be rerun to fine-tune the previous adjustments. Subsequently, the load rejection simulation must be performed again. This iterative process should be repeated until all the simulations converge to a satisfactory adjustment.  
Finally, a schematic flowchart that sums up the power plant characterization methodology using load rejection tests is plotted in Figure 7. The iterative process used to fit the parameters of the GAST and SEXS models has been carried out manually in order to adjust the first transient oscillation, which is the most important for stability studies. However, other fitting tools such as evolutionary algorithms could be used to solve the problem [27; 28; 29].
## 4 Description of the Developed Software  
### General Description  
A detailed description of the software application is presented in the current section. The software was developed on the MATLAB platform [22; 23] and is currently provided as an executable file, while the development of a relevant online tool in Python is currently underway. The main advantages of the MATLAB and the Python software development platforms are their tools for the programming of effective and functional GUIs, as well as the inclusion of machine learning (ML) algorithm libraries required for the software proposed. An overview of the software application is presented in Figure 3. A comprehensive and detailed GUI is developed, including all the necessary features for a prompt and efficient operation (described in Section 4.2). The software was developed on the basis of event-driven programming; therefore, the user controls all the procedures through appropriate functions that are activated using the GUI's control components. More details about these control components are presented in Sections 4.2 and 4.3. A detailed description of the procedures is provided in Figure 3.  
The developed software provides (a) prediction of the expected DS (SDC) of RC buildings subjected to seismic ground excitations (considering two horizontal components of the corresponding seismic records) using properly trained ANNs that can be alternatively added by the user and (b) estimation the buildings' seismic damage rating using the first stage rapid visual screening (FSRVS) procedure, which is practically an RVI methodology proposed by Earthquake Planning and Protection Organization of Greece (E.P.P.O.) for pre-earthquake assessment [44]. Furthermore, the software provides (as intermediate results) the parameters that describe the selected seismic excitation (spectra and seismic parameters) and properly selected structural parameters that are critical for the seismic damage assessment of a studied RC building.  
The software is outlined in the sections below. In particular:  
* Description of the GUI and its components used for the insertion of the required data and the presentation of the results.
* Description of the source code, i.e., the functions used and the interaction between them, along with the required data processing and the corresponding flowcharts.  
Figure 3: Overview of the developed software application.