esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Clinical Implications  
Our findings support the potential use of the ULSPT as a functional assessment tool in individuals with RCRSP. The test's ability to discriminate between affected and unaffected individuals, coupled with its feasibility and correlations with strength measures, suggests that it could provide valuable information in clinical settings. The ULSPT offers a simple, cost-effective method for assessing upper extremity function that may complement existing assessment techniques.  
The observation of bilateral deficits in individuals with unilateral RCRSP underscores the importance of comprehensive upper body assessment and rehabilitation approaches that address both the affected and unaffected sides. This finding aligns with the emerging understanding of the complex, multifaceted nature of RCRSP and its impacts on overall upper extremity function.  
We believe the applicability of these tests should not vary significantly across different RCRSP presentations. Based on our findings and theoretical considerations, the ULSPT and SMBT may be appropriate for patients with mild to moderate shoulder symptoms who can maintain appropriate positioning and generate sufficient force for the throwing action. These tests might be less suitable for acute and severe cases or for individuals with significant shoulder stiffness, which could limit the throwing position.
# Wideband Channel Characterization for 6G Networks in Industrial Environments
## 5 Post-Processing and Parameters Extractions  
### Power Delay Profile  
The obtained S\({}_{21}\) from the VNA measurements represents the channel transfer function \(H(f)\). The inverse discrete Fourier transform (IDFT) of \(H(f)\) represents the channel impulse response \(|h(\tau)|\), and \(|h(\tau)|^{2}\) represents the power delay profile (PDP), which shows the multipath components (MPCs) with a resolution limited by the total measured frequency span. To suppress the undesired effect of side lobes when performing the IDFT, a Hanning window (\(h_{w}\)) was used in the frequency domain. Other windows like Blackman and Hamming were also tested; however, a Hanning window, which is considered a good compromise between low side lobes and reduced resolution, fit the data well and significantly reduced the interfering effect between the MPCs. The mathematical expression for extracting the PDP from the measured S\({}_{21}\) from the number of frequency samples (\(N\)) is:  
\[\text{PDP}(\tau)=|\frac{1}{N}\sum_{n=1}^{N}[H(f)\times h_{w}]\text{exp}(j2\pi f _{n}\tau)|^{2} \tag{1}\]  
Figure 4: Measurement setup in the Manufacturing laboratory (MANULAB)at the Norwegian University of Science and Technology (NTNU). It contains many pieces of medium size equipment.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Frequency (GHz) & 108 \\ Bandwidth (GHz) & 2 \\ Delay Resolution (ns) & 0.5 \\ Tx Power (dBm) & 0 \\ Tx Antenna Type & Horn \\ Rx Antenna Type & Horn \\ Tx/Rx Polarization & Vertical/vertical \\ Tx/Rx Antenna Gain (dBi) & 21.14/21.14 \\ Tx/Rx Antenna Azimuth HPBW & 15.7\({}^{\circ}\)/15.7\({}^{\circ}\) \\ Tx/Rx Antenna Height (m) & 1.6/1.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measurement setup parameters. HPBW, half power beam-width.  
As mentioned in the measurement setup, horn antennas were used at the Tx and Rx; hence, to capture more environmental effects of the channel, the Tx and Rx antennas were rotated to different angles with a step size of 15\({}^{\circ}\) at each Tx-Rx separation distance. The PDP that represents all the received power from all angles at Tx (\(A_{T}\)) and Rx (\(A_{R}\)) can be defined as [56]:  
\[\mathrm{PDP}(\tau)=\max_{i,j}\lvert h_{A_{T_{i}},A_{R_{j}}}(\tau)\rvert^{2} \tag{2}\]  
where \(i\) and \(j\) are the indices of the rotation angles at Tx and Rx, respectively. Figure 5a-c show the measured PDP among all angles based on (2) at the maximum Tx-Rx separation distance of 5 m in the three testbed industrial environments. The PDP was normalized to 0 dB relative to the maximum power, which was the power of the LoS path in our measurements. Figure 6a-c show the normalized values of the PDP mentioned in Section 5.4.
###### Abstract  
Inverse synthetic aperture ladar (ISAL) can achieve high-resolution images for long-range moving targets, while its performance is affected by atmospheric turbulence. In this paper, the dynamic evolution of atmospheric turbulence is studied by using an infinitely long phase screen (ILPS), and the atmospheric coherent time is defined to describe the variation speed of the phase fluctuation induced by atmospheric turbulence. The simulation results show that the temporal decoherence of the echo induced by turbulence causes phase fluctuation and introduces an extra random phase, which deteriorates the phase stability and makes coherent synthesis impossible. Thus, we evaluated its effects on ISAL imaging and found a method to mitigate the impact of turbulence on ISAL images. The phase compensation algorithm could correct the phase variation in different pulses instead of that within the same pulse. Therefore, the relationship between the atmospheric coherent time and pulse duration time (rather than that between the atmospheric coherent time and ISAL imaging time) ultimately determines the ISAL imaging quality. Furthermore, these adverse effects could be mitigated by increasing the atmospheric coherent time or decreasing the pulse duration time, which results in an improvement in the ISAL imaging quality.  
inverse synthetic aperture ladar; atmospheric turbulence; atmospheric coherent time; image integral time; pulse duration time 2023
# Simulation of the Influence of Wing Angle Blades on the Performance of Counter-Rotating Axial Fan
## 3 Wing Angle Blade Model and Orthogonal Test
### Optimal Wing Angle Blade Fan  
Range analysis was conducted on the efficiency of rated flow points, and the efficiency values of each test were shown in Table 4. According to the principle of orthogonal test, factors with larger range values had greater influence on efficiency. Therefore, the degree of influence of each factor on efficiency was A (s1 of first-stage blade angle), C (s2 of second-stage blade angle), B (a1 of first-stage blade tip offset), and D (a2 of second-stage blade tip offset) in descending order. It could also be concluded that compared with the prototype fan (\(\eta\) = 75.18%), the fan efficiency of nine experimental schemes was improved at the rated flow point. The degree of influence of the corresponding factors of the first stage impeller was greater than that of the second stage impeller, which was mainly because in the cyclone machine, the number of blades of the first-stage impeller was more than that of the second-stage impeller, and the flow velocity into the first-stage impeller was lower  
Figure 5: Blade model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Test Serial** \\ **Number** \\ \end{tabular} } & \begin{tabular}{c} **Position of First-Stage** \\ **Blade Angle** \\ **A/mm** \\ \end{tabular} & \begin{tabular}{c} **Tip Offset Distance of** \\ **First-Stage Blade** \\ **B/mm** \\ \end{tabular} & \
\begin{tabular}{c} **Position of Second-Stage** \\ **Blade Angle** \\ **C/mm** \\ \end{tabular} &
\begin{tabular}{c} **Offset Distance of Blade Tip** \\ **of Second-Stage Impeller** \\ **D/mm** \\ \end{tabular} \\ \hline
1 & 100 & 10 & 100 & 10 \\
2 & 110 & 15 & 110 & 15 \\
3 & 120 & 20 & 120 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Orthogonal experiment factors level table.  
than that into the second-stage impeller, and the rotational speed of the two-stage impeller was the same. Therefore, in a unit time, the effect of the first-stage impeller on the flow was stronger than that of the second stage impeller.  
\(k_{i}\) in the table represents the average efficiency of test parameters at the level of \(i\), and the level corresponding to the maximum \(k_{i}\) is the optimal level of this factor. In conclusion, the optimal level combination of the orthogonal test is A\({}_{1}\)B\({}_{1}\)C\({}_{3}\)D\({}_{3}\).
# Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks
## 2 System Model
### Signal-to-Interference-Plus-Noise Ratio (SINR) Analysis  
In general, for each GMT\({}_{l}\)\((1\leq l\leq N)\) in the considered communication system, the A2G channel gain is calculated as:  
\[\Gamma_{l}^{u}=\frac{G_{l}^{u}G_{r}^{u}}{\mathrm{PL}_{l}^{u}N_{u}}|h_{l}^{u}|^{2}, \tag{9}\]  
including additional gains, losses, and the noise power of the UFBS receiver \(N_{u}\). Hence, using (5), the instantaneous signal-to-noise ratio (SNR) \(\gamma_{l}^{u}\) of the strong GMT\({}_{i}\) to detect its own signal \(s_{i}\), assuming perfect SIC, is given as follows:  
\[\gamma_{i}^{u}=\alpha_{i}P_{u}\Gamma_{i}^{u} \tag{10}\]  
where \(\Gamma_{i}^{u}\) is the A2G channel gain of the strong GMT\({}_{i}\), which involves the noise power of the UFBS receiver \(N_{u}\), as it can be observed in (9). Furthermore, the instantaneous signal-to-interference plus noise ratio (SINR) \(\gamma_{k}^{u}\), for detecting the signal \(s_{j}\) of the weak user GMT\({}_{j}\) on the strong user GMT\({}_{i}\), is expressed as:  
\[\gamma_{k}^{u}=\frac{\alpha_{i}P_{u}\Gamma_{i}^{u}}{\alpha_{i}P_{u}\Gamma_{i} ^{u}+1}. \tag{11}\]  
Moreover, the SINR \(\gamma_{j}^{u}\) at the weak user GMT\({}_{j}\), for detecting its own signal \(s_{j}\) from the UFBS is obtained by:  
\[\gamma_{j}^{u}=\frac{\alpha_{j}P_{u}\Gamma_{j}^{u}}{\alpha_{i}P_{u}\Gamma_{j} ^{u}+1}, \tag{12}\]  
where \(\Gamma_{j}^{u}\) is the A2G channel gain for the weak GMT\({}_{j}\). Furthermore, the SINR \(\gamma_{k}^{d}\) at the weak user GMT\({}_{j}\) for detecting its signal, which is relayed by the strong user GMT\({}_{i}\) in the same pair \(k\), equals:  
\[\gamma_{k}^{d}=P_{d}\Phi_{k}, \tag{13}\]  
where \(\Phi_{k}\) is the channel gain of the D2D link between the strong GMT\({}_{i}\) and the weak GMT\({}_{j}\) belonging to the same NOMA pair \(k\)\((1\leq k\leq K)\) and is expressed as:  
\[\Phi_{k}=\frac{G_{l}^{d}G_{r}^{d}}{\mathrm{PL}_{k}^{d}N_{d}}|h_{k}^{d}|^{2}, \tag{14}\]
## 2 Materials and Methods  
### Experimental Set-Up  
#### 2.1.1 Experimental Site  
The object of this research is a distant-water double-deck trawler, which is mainly used for single bottom trawling in waters of West African countries such as Morocco. The fishing vessel has the following characteristics: made of steel, double bottom (except engine room), double deck, forward steering tower, stern type, atf side hangar, single machine, large-diameter propeller, single rudder, and stern slipway trawler with stern portal and gantry. The stern line of the vessel has a wide, whole-deck surface fitted with a stern slipway, a rounded straight stern sealing plate, and a ball stern in the front of the hub. The bow line of the vessel has a large outward flapping, a forward-leaning stem, and an SV-shaped ball bow. The total length of the fishing vessel is 33.2 m, the design draft is 3.5 m, and the design displacement is 510.81 t. The fishing vessel's engine room is located on the lower deck, with a length of 10.0 m, a height of 3.5 m, and a total area of about 48.0 m\({}^{2}\). In the middle of the fishing vessel's engine room, a gear box, high-elastic coupling, main engine, and hydraulic pump station are arranged successively. The generator sets are arranged on the left and right sides of the engine room. According to the operating environment of the real fishing vessel's engine room, this experimental work is completed in a full-scale ship engine room laboratory, as shown in Figure 1. The layout of the laboratory is constructed in equal proportion to the fishing vessel's engine room. The laboratory wall is made of a marine steel plate and the floor is made of a patterned steel plate, which are consistent with the interior of the real fishing vessel's engine room. The engine room has shutters to exhaust the air. The laboratory is equipped with a top exhaust system and a bottom air supply system, which can be used to simulate the internal airflow organization of the engine room during fishing vessel operation. Temperature and humidity monitors were set up in the laboratory, which were applied to control the environmental parameters in different scenarios. Meanwhile, a humidity regulator was set up, which can be used to simulate the humidity conditions in the engine room under different scenarios. In addition, since the temperature in the engine room is higher than that in a conventional cabin, a temperature adjustment instrument was set in this experiment to adjust the laboratory temperature to be consistent with the real operation condition of the fishing vessel's engine room.  
Figure 1: Experimental site and arrangement of fishing vessel’s engine room.
## 4 Discussion  
The experimental results show that the proposed method can achieve oblique incidence full-matrix imaging of multi-layered structures. It has a great efficiency advantage compared with the TFM and can suppress the background noise. It also shows an improvement in lateral resolution.  
However, the proposed method also has some limitations. Firstly, the imaging results are not satisfactory and readable when the defect is in an area that the acoustic beam cannot easily access, as shown in Figure 12b. This is due to the existence of the refraction angle, resulting in the defect receiving very little acoustic energy. Secondly, although the proposed method can achieve full-matrix imaging at any tilt angle mathematically, the conversion of transverse and longitudinal waves in a solid medium must be considered in practical applications. When the tilt angle is too large, the longitudinal waves in a solid medium disappear. At this time, if the formula was still derived by using the velocity of longitudinal waves, incorrect results would appear. In this case, the effectiveness of  
Figure 11: The standard B-type specimen and wedge used in experiment C. (**a**) The standard B-type specimen for phased array ultrasonic testing. (**b**) The tilted wedge.  
Figure 12: The experimental results of the detection for standard specimen. (**a**) The image reconstructed by TFM. (**b**) The image reconstructed by the proposed method.  
imaging with other types of ultrasound waves, i.e., transverse waves, should be considered and tested in numerical simulations and laboratory experiments.
# Simultaneous Indoor Tracking and Activity Recognition Using Pyroelectric Infrared Sensors
## 5 Two-Layer Random Forest
### Overview of Data Processing  
The overview of data processing of our system is shown in Figure 11. The sensor data stream generated from all of the sensor nodes is segmented into data frames, as shown in Figure 7. In each data frame, we calculate the location estimation of the human target according to Equation (9). Based on the distance between the locations of two successive data frames, \((x_{t},y_{t})\) and \((x_{t-1},y_{t-1})\), the moving speed \(V_{t}\) of the human target in the \(t\)th data frame is calculated as:  
\[v_{t}=\sqrt{(x_{t}-x_{t-1})^{2}+(y_{t}-y_{t-1})^{2}}/T \tag{10}\]  
where \(T\) is the time interval between two data frames. Then, the vector \(\{x_{t},y_{t},v_{t}\}\) is used as the input feature for the first layer RF; the output of the first layer RF is denoted as \(L_{t}^{1}\).  
To acquire the duration of the same successive activity, a two-state finite state machine (FSM) is used to indicate the change of \(L_{t}^{1}\). Whenever \(L_{t}^{1}\) is changed, the duration of the same successive activity is known.  
\[\left\{\begin{array}{ll}C_{t}=C_{t-1}+1,&\text{if }L_{t}^{1}=L_{t-1}^{1}\\ C_{t}=1\quad\text{and}\quad D_{t-1}=C_{t-1},&\text{if }L_{t}^{1}\
eq L_{t-1}^{1} \end{array}\right. \tag{11}\]  
where \(C_{t}\) is the duration counter of data frame \(t\) and \(D_{t}\) is the duration of the consecutive activity.  
For example, data frame \(\{m,m+1,...t\}\) is the same activity classified by the first-layer RF; that is, \(L_{m}^{1}=L_{m+1}^{1}=...=L_{t}^{1}\
eq L_{t+1}^{1}\), then \(D_{m}=D_{m+1}=...=D_{t}=C_{t}\).  
Based on the output of the first-layer RF, we construct the feature vector \(\{x_{t},y_{t},v_{t},D_{t}\}\) for the second-layer RF. The output of the second RF is the final classification of our system.  
Figure 10: Random forest (RF). The RF consists of a number of decision trees. After splitting, each tree contains green leaf nodes and blue split nodes. For a particular input, the red arrows indicate the different paths from the root node to the left nodes along different trees. The final result is based on the maximum voting among all of the trees.
## 2 Materials and Methods
### System Architecture and Implementation  
To acquire comprehensive motion data, we utilized the MinIMU-9 v5 board (Pololu Corporation, Las Vegas, NV, USA) [8], which integrates two sensor chips manufactured by STMicroelectronics: the LIS3 MDL magnetometer [19] and the LSM6 DS33 accelerometer and gyroscope [20]. Together, these sensors provide measurements along three axes each, totaling nine degrees of freedom essential for detailed analysis of human movement. Sensors with more degrees of freedom can capture more detailed and precise data about movements, ensuring greater accuracy and precision. This can be beneficial for detailed analysis, but may require advanced algorithms and computing power to effectively process and interpret the data. The sensor assembly was connected to the Adafruit Feather M0 microcontroller board via the I\({}^{2}\)C communication interface, as shown in Figure 1. For our initial testing phase, we employed the Arduino IDE 2.0 with the library supplied by Pololu to configure the sensors and retrieve raw data outputs.  
We utilized Pololu's Arduino library to maintain compatibility with the Arduino environment, but for effective debugging, a more advanced Integrated Development Environment (such as a Cmake 4.0.2 project in Visual Studio Code 1.100.2 using the Cortex-Debug extension) is necessary.  
In Figure 2, we present three examples of system implementations that we use in various biofeedback applications developed in our laboratory [21]. These are prototypes that will later be built into different enclosures and used as 9 DoF wearable devices with wireless communication. These are some of the devices used in the tests and measurements presented in later sections of this article.  
Figure 1: Schematic representation of the connection between the sensors and the Adafruit Feather M0 microcontroller via the I\({}^{2}\)C interface. The microcontroller (ATSAMD21) communicates directly with the LIS3 MDL and LSM6 DS33 sensor modules.  
Figure 2: Three examples of the system implementation with Adafruit Feather M0 board with added [PERSON]’s MinIMU-9 v5 board and other elements such as battery or LED indicators.
## 2 Baseline Algorithm, SAR-SIFT: A SIFT-like Algorithm for SAR Images
### Principal Orientation Determination  
In the original SIFT algorithm, both principal orientation determination and feature descriptor construction rely on histograms of gradient directions. These histograms are computed in the vicinity of each key point, weighted by both gradient magnitude and a Gaussian window. The calculation of these histograms can be accomplished using the GR method.  
Principal orientation determination involves computing one or more principal orientations for each key point, derived from local orientation histograms calculated in circular neighborhoods (radius is \(6\sigma\)). By selecting the primary modes of the local orientation histograms, up to two orientations can be chosen for each point, ranging from the dominant mode to the opposite mode.
# Design and Evaluation of Vacuum Central Drum Seed Metering Device
## 3 Key Component Parameter Design and Analysis
### Seed-Agitating Mechanism  
Rice seeds are thin, long, pointed, angular, awned, and downy, and have poor mobility. The drum-type metering device has problems, such as short seed suction time and the tendency to form cavity areas in the seed box, which is not conducive to the adsorption of rice seed. An electromagnetic excitation device was installed in the bottom of the seed box in a preliminary laboratory experiment to facilitate seed suction by the drum-metering device, which is not suitable for application in the field as this device requires electricity. A guided seed-agitating device and a linear seed-agitating device on a vertical disc rice vacuum-metering device have been applied [7,8]. The experimental results showed that the guided seed-agitating device had the effect of separating, guiding, rubbing, and supporting indica rice seeds, which could improve the seed rowing accuracy of the seed-metering device.  
The previous experimental study found that if the thickness of the seed-agitating device is too large [25], the rice seed is easily carried by the seed-agitating device, but the seed-agitating effect becomes worse if the thickness of the seed-agitating device is too thin. For this reason, this paper designed a seed-agitating device with a curved slope for rice vacuum drum seed dispersers, as shown in Figure 6. On the basis of the vertical disc-guided and linear seed-agitating devices, three kinds of agitating devices were designed with an arc-shaped inner and outer radian adhering to the outer surface of the drum, a suction hole on the side of the device that was made by a 3D-printing machine using ABS material, of which the thinnest part of the c-type seed-agitating device with a slope that is 1 mm thick, and the maximum thickness that is 2 mm; the structure is shown in Figure 6c. The uniform thickness of 2 mm and 1 mm agitating mechanism are shown in Figure 6a,b, respectively, and the structure of Figure 6c was chosen as the basis for testing according to the authors' previous experiments and parameter optimization of the agitating mechanism.  
Figure 5: The zone map of the ventilation housing.