esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Background: Shoulder injuries are common among handball players and have been associated with adjustments in shoulder mobility with decreased flexibility. The aim of the study is to analyze the immediate effects of dynamic cupping on shoulder active range of motion (AROM) of senior male handball players. Materials and Methods: A total of 80 senior male handball players completed a socio-demographic and clinical questionnaire. They were then randomly divided into two groups: the Intervention Group (IG; n = 40), which received dynamic cupping therapy, and the Control Group (CG; n = 40), which did not receive any intervention. Shoulder AROM, including flexion, extension, abduction, addiction, horizontal adduction, internal adduction, internal rotation, and external rotation, was assessed in both groups before the intervention (M0) and after the intervention (M1). Results: After the intervention, the IG showed a statistically significant increase in AROM for all shoulder movements. In contrast, the CG only demonstrated significant improvements in internal rotation (\(p\) = 0.042), adduction (\(p\) = 0.011), horizontal abduction (\(p\) = 0.004), and horizontal adduction (\(p\) = 0.005). Additionally, the IG exhibited a statistically significant increase in shoulder AROM across all movements compared to the CG at M1. Conclusions: The findings of this study support that dynamic cupping enhances shoulder AROM in senior male handball players.  
dynamic cupping; shoulder mobility; handball; senior male players +
Footnote †: journal: Applied sciences
## 2 EO Satellite and In Situ Observations for Earth Surface
### SMAP Soil Moisture Active Passive Mission  
The Soil Moisture Active Passive SMAP mission [42], launched on 31 January 2015 carries both an L-band (1.4 GHz) passive microwave radiometer and a radar (although the latter is no longer functioning). Using a 6-m diameter spinning antenna, the observatory provides near-global coverage every two to three days. The primary focus of the SMAP mission is on soil moisture retrieval and freeze/thaw detection, but SMAP observations have also been used for ocean salinity [43] and wind retrievals [44]. To provide estimates of root zone soil moisture and complete spatio-temporal coverage, SMAP radiometer observations are routinely assimilated into the NASA Catchment land surface model to generate the SMAP Level-4 Soil Moisture (L4 SM) data product [45]. The L4 SM product is generated globally at 9 km resolution every three hours and distributed to the public with a latency of 2.5 days from the time of observation.
## 5 Conclusions  
A simulated haptic-aided flight control system of a UAV is developed that uses an aerodynamic model of the UAV to obtain forces acting on the control stick. The developed system is used to study the effects of haptic feedback on longitudinal flight control. Our analysis shows that the proposed haptic aiding system has a significant effect on operator performance and situational awareness. On the basis of six different performance parameters, it is concluded that no force feedback is acceptable for altitude hold tasks, exaggerated force feedback works better for altitude gain, and realistic force feedback is adequate for altitude descent. The findings suggest that it can be used in flight simulators for effective operator training and to increase immersion, which is an important aspect for making a smooth transition from simulator to real flight. The subjective assessment reported by the participants also suggested an increased confidence with haptic feedback. In future, challenges of real-time implementation will be addressed that include the effects of communication time delays, limited control inputs, and designing barrier functions from on-board sensors data.  
Conceptualization, S.R. and A.M.; [PERSON] and S.R. performed the investigation and experiments; [PERSON], S.R., and A.M. analyzed the data; supervision, S.R., A.M. and R.R.; writing--original draft, H.A.M., S.R.; writing--review and editing, S.R., A.M., and R.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
We acknowledge [PERSON] for providing us with the facilities to conduct this study.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
UAVUnmanned Aerial Vehicle  
DOFDegree(s) of Freedom  
GCSGround Control Station  
FODEFirst order differential equation(s)  
SDKSoftware Development Kit  
ANOVAAnalysis of Variance  
HSDHonest Significant Difference
## 3 Proposed System Model  
### System Architecture  
Task offloading comes with a communication cost that needs to be considered for efficient processing. The key challenge is to minimize energy and latency consumption during the communication and computation process. Precise energy, latency, and cost estimation models are important for making informed decisions about task offloading, considering the energy cost of communication according to the time constraints of IoT applications. Similarly, accurate latency estimation models help in determining the appropriate offloading strategy, considering energy, latency, and costs. The goal is to transfer tasks to the edge server if it consumes less energy and the total latency is less than the time constraints of IoT applications. As long as the total latency exceeds the time constraints, our algorithms will choose to execute tasks locally if they can process tasks within the time constraints without considering user device energy consumption. To achieve this, an energy model, latency, and cost model are developed to enable user devices to calculate the energy, latency, and computation cost consumption accurately during task offloading. This approach aims to design an energy-aware and latency-aware offloading strategy. Our proposed network system scenario in Figure 1 represents multi-user devices and multi-edge servers for assisting user devices from overheating task computing. Computational systems have finite resources such as computing power, memory, and bandwidth that need to be shared among multiple tasks or devices. This means that efficient resource allocation and management are crucial to meeting the requirements of the system. In the context of our work, we mentioned that the IoT application is a mission-critical delay-sensitive signaling application. This type of application involves signaling for critical tasks like Mission-Critical Push-to-Talk signaling (MC-PTT) or MC video signaling, where time is a critical factor, and delays need to be minimized. The proposed approach integrates MEC into the 5G network, which can enhance the performance of the network by optimizing resources for each task.
## 3 The Improved 3D Reconstruction Method of Satellite Images
### Stereo Rectification  
After the satellite images are enhanced by the GAN model, the proposed method performs stereo rectification on the enhanced satellite image pair. The purpose of this process is to reduce the epipolar constraint error of the image pair and make it suitable for the subsequent stereo matching. The principle of the epipolar constraint error is shown in Figure 4. As the matching point of \(x_{m}\), ideally, the \(x^{\prime}_{m}\) should coincide with the projection point of the \(X_{m}\) on the epipolar curve \(ep^{x_{m}}_{uv}(\cdot)\). However, the existence of the epipolar constraint error leads to the distance between the \(x^{\prime}_{m}\) and the \(ep^{x_{m}}_{uv}(X_{m})\), and the Euclidean distance between the two points is defined as the epipolar constraint error.  
Suppose that there is a satellite stereo pair consisting of an image \(u\) and an image \(v\), and there are \(M\) pairs of matching points \((\mathbf{x},\mathbf{x}^{\prime})_{m=1,2,\ldots,M}\) on the images. The epipolar constraint error can be expressed as Equation (11).  
\[E_{uv}=\frac{1}{M}\sum_{m=1}^{M}\left(\mathbf{x}^{\prime}_{m},epi^{x_{m}}_{uv}(\mathbf{ X}_{m})\right)^{2}, \tag{11}\]  
where \(\mathbf{x}^{\prime}_{m}=(x^{\prime}_{m},y^{\prime}_{m})\) on satellite image \(v\) is the \(m\)-th matching point of \(\mathbf{x}_{m}=(x_{m},\ y_{m})\) on satellite image \(u\), and the corresponding 3D point is expressed as \(\mathbf{X}_{m}=(X_{m},Y_{m},Z_{m})\). The meaning of \(epi^{x_{m}}_{uv}(\cdot)\) is the epipolar curve of \(x_{m}\), whose form is as below.  
\[epi^{x_{m}}_{uv}(\mathbf{X}_{m})=P_{v}\Big{(}P_{u}^{-1}(\mathbf{X}_{m})\Big{)}, \tag{12}\]  
the \(P_{v}(\cdot)\) represents the satellite camera projection of the image \(v\) and \(P_{u}^{-1}(\cdot)\) denotes the localization model of the image \(u\). The purpose of the stereo rectification is to minimize the epipolar constraint error by image transformation \((R,t)\). Therefore, Equation (11) can be reorganized as  
\[E_{uv}=\frac{1}{M}\sum_{m=1}^{M}\big{(}R\mathbf{x}^{\prime}_{m}+t,epi^{x_{m}}_{uv} (\mathbf{X}_{m})\big{)}^{2}, \tag{13}\]  
where \(R\) and \(t\) denote the rotation and the translation of the point \(\mathbf{x}^{\prime}_{m}\), respectively. Actually, the proposed method adopts a rigid 2D transformation on the satellite image, which is calculated according to the matching point pairs.
# A C-Based Framework for Low-Cost Real-Time Embedded Systems
## 6 System Configuration
### Board Instantiation and PI Variable Mapping  
The I/O configuration file contains a sequence of macro invocations that determine which local or remote I/O devices or boards, are expected to be present in the system. Boards are instantiated from a board database that specifies the capabilities of each class of boards in terms of physical inputs and outputs. It will not be further discussed in this paper for conciseness. For example, an invocation of the macro BOARD_INSTANCE_MODBUS (shown in Figure 6) declares that a Modbus board is being configured. It has four arguments:  
* The name of the class of boards it belongs to. This name must match one of the board classes defined in the database just mentioned.
* The name of the board instance being configured, which must be unique in the whole system and is used to resolve any ambiguity among boards belonging to the same class.
* The list of Modbus addresses the board may appear at, given as an argument to the MODBUS_ADDRESSES macro. It is used during initial bus scan to determine whether all boards mentioned in the system configuration are connected and their actual address on the bus.
* A sequence of macro invocations that map a subset of the physical inputs and outputs available on the board being configured to the corresponding PI variables.  
Each mapping macro invocation (within the fourth argument above) requires five arguments to fully specify a mapping:  
* The name of one of the physical inputs or outputs mentioned in the board database for this class of boards.
* The data type of the PI variable that corresponds to it.
* The name of the PI variable. This is an ordinary C-language identifier that the control function can use to refer to the variable.
* The name of a conversion function, to convert the variable from its device-specific representation into a format suitable for the control function (for inputs) and vice versa (for outputs).
* A pointer to additional arguments specific to the conversion functions.  
Therefore, the I/O configuration file excerpt shown in Figure 6 declares that board b0, belonging to board class test_board must be present on the bus at Modbus address 1 or 3. A field of 16 bits starting from bit 8 of its physical input input_1 is mapped to PI variable int br through the RtFw_bit_range converter. Moreover, its physical output output_2 is mapped to PI variable double ta, without conversion. At configuration time, RtFw checks that in the board database there is a board class called test_board, it has a physical input input_1 and a physical output output_2 of the appropriate size. Then, it generates the appropriate data structures that will be used to implement the mapping at runtime.  
Figure 6: Example of Modbus board instantiation and PI variables mapping.
## 2 Materials and Methods
### Analysis Methods  
For quantitative analysis of TRO, EPA method 330.5 (Spectrophotometric, DPD) was used using spectrophotometer (Qvis 5000, C-mac, Daejeon, Korea) [21]. The quantification range of chlorine was 0.02-4.00 mg/L. TRO was measured at a predetermined time interval after an oxidation was initiated.  
Concentration of ClO\({}_{3}\)\({}^{-}\) and ClO\({}_{4}\)\({}^{-}\) were measured using ion chromatography (883 Basic IC plus, Metrohm, Herisau, Switzerland). The eluent for ClO\({}_{3}\)\({}^{-}\) consisted of 3.6 mM sodium carbonate (Na\({}_{2}\)CO\({}_{3}\)) and was delivered at a flow rate of 0.8 mL/min. The eluent for ClO\({}_{4}\)\({}^{-}\) consisted of a mixture of 10.0 mM Na\({}_{2}\)CO\({}_{3}\) and 45% acetonitrile was delivered at a flow rate of 0.6 mL/min. The separation column was IC 82524A, 4.0 mm \(\times\) 250 mm (Shodex, Tokyo, Japan). The pH was measured using a pH meter (Mettler-Toledo, Greifensee, Switzerland). Conductivity was measured using a multifunction meter (CX-401 waterproof, Elmetron, Zabrze, Poland). TOC was measured using a TOC analyzer (TOC\(-\)VCPH/CPN, Shimazu Co., Kyoto, Japan).  
Figure 1: Schematic diagram for (**a**) measurement of inorganic CBPs and (**b**) of residual TRO measure, and (**c**) electrolysis experimental setup.
# A Systematic Review of Insole Sensor Technology: Recent Studies and Future Directions
## 4 Insole Sensor Technology
### Pressure Sensors
#### 4.1.2 Piezoresistive  
These sensors consist of a conductive polymer that exhibits variations in electric resistance when subjected to external stress. More precisely, when the pressure increases, the resistance reduces [5]. Among the most used sensors in wearable technology is the FSR. There is an increasing fascination with utilizing FSRs that are integrated into shoes for the purpose of analyzing one's walking pattern. This is mainly because FSRs are easy to wear and carry, making it convenient to gather data during regular daily tasks [26]. Nevertheless, several researchers contend that FSRs are not ideal for everyday practical use because of their restricted longevity and susceptibility to how they are affixed to the insole [15].  
A recent study has documented the creation of a flexible textile piezoresistive sensor (TPRS) that has shown exceptional sensing capabilities. TPRSs demonstrate enhanced sensitivity in lower pressure ranges, a wider range of force detection, quicker response times, superior durability, and dependable detection across different pressure levels [14].
# Improved Patch Packing and Refining Segmentation for the V-PCC Standard
## 4 Experimental Results
### Final Coding Performance Overall Performance  
When the two methods proposed in this paper are used in combination, Table 9 shows the comparison results with TMC2-18.0, indicating that the method has performed well. The algorithm proposed in this paper can reduce the time consumption of coding by up to 43.34%, with an average reduction of 31.86%. The average BD-rate coding gain of D1 is \(-\)1.39%, and the soldier test point cloud has the best effect, up to \(-\)2.86%. The average BD-rate coding gain of D2 is \(-\)1.58%, and the longdress test point cloud has the best effect, up to \(-\)3.07%. The BD-rate coding gain of Luma is \(-\)1.35% on average, and the redandblack test point cloud has the best effect, up to \(-\)2.62%. Therefore, the algorithm proposed in this paper can improve the performance of point cloud compression while reducing its complexity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Test Point Cloud**} & \multicolumn{2}{c}{**Occupancy Map Size**} & \multicolumn{2}{c}{**Proportion of Increase in**} \\ \cline{2-4}  & **TMC2** & **Proposed Method** & **Occupancy Area** \\ \hline \hline \multirow{2}{*}{Loot} & 647,072 & 632,472 & 2.26\% \\  & 739,328 & 590,424 & 20.14\% \\  & 866,656 & 730,584 & 15.70\% \\  & 887,680 & 716,568 & 19.28\% \\  & 2,419,712 & 2,380,544 & 1.62\% \\  & 2,389,248 & 2,280,448 & 4.55\% \\ \hline \hline \multicolumn{1}{c}{average} & 1,324,949.33 & 1,221,840 & 10.59\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: The change of the occupancy area of the occupancy map.  
Figure 11: Occupancy map: (**a**) generated by TMC2; (**b**) generated by ours.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test Point Cloud**} & \multicolumn{2}{c}{**Geom.BD-Rate (\%)**} & \multicolumn{2}{c}{**Attr.BD-Rate (\%)**} \\ \cline{2-5}  & **D1** & **D2** & **Luma** & **Cb** & **Cr** \\ \hline \hline \multirow{2}{*}{Loot} & \(-\)1.31 & \(-\)1.49 & \(-\)1.48 & \(-\)1.31 & \(-\)1.67 \\  & \(-\)2.06 & \(-\)2.23 & \(-\)2.23 & \(-\)2.08 & \(-\)2.17 \\  & \(-\)0.78 & \(-\)1.05 & \(-\)0.57 & 0.93 & \(-\)0.14 \\  & \(-\)0.57 & \(-\)0.86 & \(-\)0.80 & \(-\)0.75 & \(-\)0.87 \\  & \(-\)0.64 & \(-\)0.80 & \(-\)0.71 & \(-\)0.42 & \(-\)0.32 \\  & \(-\)0.11 & \(-\)0.52 & \(-\)0.39 & 0.94 & 0.31 \\ \hline \hline \multicolumn{1}{c}{average} & \(-\)0.91 & \(-\)1.16 & \(-\)1.03 & \(-\)0.45 & \(-\)0.81 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Changes in performance after adding the DAPP compared to TMC2.
# Computational Analysis of a 200 GHz Phased Array Using Lens-Coupled Annular-Slot Antennas
## 3 Simulation Results
### A 1 \(\times\) 5 ASA Array Analysis
As compared to that of a 1 \(\times\) 3 array, the SLL of a 1 \(\times\) 5 array is higher since the higher gain generated by off-axis antenna elements lead to a higher SLL even when \(\beta\) is small (e.g., 0\({}^{\circ}\)\(<\)\(\beta\) = 90\({}^{\circ}\)). For the case of \(L\) = 1000 \(\upmu\)m (\(L\)/\(R\) = 0.2), the SSL is increased to about \(-\)4 dB (see the H-plane radiation pattern when \(\beta\) = 130\({}^{\circ}\) in Figure 5c inset), this means that an extension length of smaller than 1000 \(\upmu\)m should be applied to a 1 \(\times\) 5 array design for achieving a satisfactory SLL. As described above, a 1 \(\times\) 5 array can be utilized to obtain a wider beam scanning range. However, this leads to higher SLLs due to more antenna elements located at off-axis positions. In addition, since smaller extension lengths are used (to keep decent SLLs), relatively smaller gain improvement can be achieved. Therefore, to maintain a larger beam scanning range while achieving a high gain and low SLL, the extension lengths of 0 um (no extended part) and 250 \(\upmu\)m are chosen for a prototype demonstration. Figure 6 shows the simulated H-plane radiation patterns of the 1 \(\times\) 5 array at different scanning angles for the above two cases. For \(L\)/\(R\) = 0, the main beam angle can be steered from \(-\)31\({}^{\circ}\) to \(-\)15\({}^{\circ}\), 0\({}^{\circ}\), +15\({}^{\circ}\), and +31\({}^{\circ}\), as \(\beta\) changes from \(-\)120\({}^{\circ}\) to \(-\)60\({}^{\circ}\), 0\({}^{\circ}\), +60\({}^{\circ}\), +120\({}^{\circ}\), respectively, and the 3-dB beamwidth is -20\({}^{\circ}\) (see Figure 6a). Figure 6b shows that for \(L\)/\(R\) = 0.05, the main beam steers from \(-\)26\({}^{\circ}\) to \(-\)14\({}^{\circ}\), 0\({}^{\circ}\), +14\({}^{\circ}\), and +26\({}^{\circ}\) while \(\beta\) increases from \(-\)120\({}^{\circ}\) to \(-\)60\({}^{\circ}\), 0  
Figure 5: (**a**) The feeding phase difference \(\beta\) versus scanning beam angle \(\theta\) (the 1 \(\times\) 5 array configuration is shown as inset), (**b**) the radiation gain versus scanning beam angle \(\theta\) and (**c**) the sidelobe level versus phase difference \(\theta\) at different extension lengthes \(L\) for a 1 \(\times\) 5 array (the H-plane radiation pattern for an \(L\)/\(R\) = 0.2 at \(\beta\) of 130\({}^{\circ}\) is shown as inset). \(+60^{\circ}\), \(+120^{\circ}\), respectively, and the 3-dB beamwidth of the main beam is decreased to \(\sim\)18\({}^{\circ}\), as expected. In summary, for the lens-coupled \(1\times 5\) array, a wider beam scanning range can be achieved as the gain improvement decreases, and SLL raises. By selecting a suitable extension length (e.g., \(L=250\) um), the lens-coupled \(1\times 5\) array demonstrates a remarkable performance for potential near future 5G and beyond communications.
# Indoor Positioning by Double Deep Q-Network in VLC-Based Empty Office Environment
## 4 Proposed Indoor Positioning Method
### Double Deep Q-Network
#### 4.3.2 Action  
The agent performs an action to determine the location of the UE, which means movement of the estimation point in this method. In this study, a total of 5 actions were defined. The 5 actions are as follows:  
* Up = \(P_{E}(t+1)=n_{r}+RP_{x}\);
* Down = \(P_{E}(t+1)=n_{r}-RP_{x}\);
* Left = \(P_{E}(t+1)=n_{r}-1\);
* Right = \(P_{E}(t+1)=n_{r}+1\);
* Stop = \(P_{E}(t+1)=P_{E}(t)\).  
where \(P_{E}\) represents the estimated point location of the agent, \(n_{r}\) represents the current state, and \(RP_{x}\) represents the total number of RPs placed on the x-axis.
###### Abstract  
Ship position prediction plays a key role in the early warning and safety of inland waters and maritime navigation. Ship pilots must have in-depth knowledge of the future position of their ship and target ship in a specific time period when maneuvering the ship to effectively avoid collisions. However, prediction accuracy and computing efficiency are crucial issues that need to be worked out at present. To solve these problems, in this paper, the deep long short-term memory network framework (LSTM) and genetic algorithm (GA) are introduced to predict the ship trajectory of inland water. Firstly, the collected actual automatic identification system (AIS) data are preprocessed and a series of typical trajectories are extracted from them; then, the LSTM network is used to predict the typical trajectories in real time. Considering that the hyperparameters of the LSTM network have difficulty obtaining the optimal solution manually, the GA is used to optimize hyperparameters of LSTM; finally, the GA-LSTM trajectory prediction model is constructed with the optimal network parameters and compared with the traditional support vector machine (SVM) model and LSTM model. The experimental results show that the GA-LSTM model effectively improves the accuracy and speed of trajectory prediction, with outstanding performance and good generalization, which possess certain reference values for the development of collision avoidance of unmanned ships.  
t 2022124073. [[https://doi.org/10.3390/app12084073](https://doi.org/10.3390/app12084073)]([https://doi.org/10.3390/app12084073](https://doi.org/10.3390/app12084073))  
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