esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The diagnosis of patellofemoral pain (PFP) remains imprecise due to the variable sensitivity and specificity scores of most clinical tests. More functionally relevant assessments and metrics are needed to provide better diagnostic insights. A total of 33 participants (control: n = 17 [M/F = 9/7]; PFP: n = 18 [M/F = 2/14]) volunteered for this study. The control group followed no intervention program, whereas the PFP group completed a joint-specific intervention program at a clinical practice for 8 weeks. At 0- and 8-week isometric knee extensor torques, unilateral drop jumping metrics, and anterior knee pain scale (AKPS) scores were collected and analysed. Statistically significant between-group differences were evident for the reactive strength index (RSI) (M\({}_{\text{diff}}\) = 0.05 m/s, \(p\) = 0.032) and ground contact time (GCT) (M\({}_{\text{diff}}\) = 0.28 s, \(p\) = 0.011) but not for peak force (\(p\) = 0.390), jump height (\(p\) = 0.721), impulse (\(p\) = 0.740), or peak torque (\(p\) = 0.359). While controlling for gender as a confounder, the RSI and GCT showed diagnostic utility for differentiating PFP from healthy controls (AUC = 0.83-0.87, sensitivity = 0.85-0.91, and specificity = 0.59-0.72). The AKPS score demonstrated moderate positive correlations with jump metrics (impulse: r = 0.58, 95% confidence interval (CI) [0.11, 0.83]; RSI: r = 0.50, 95% CI [0.01, 0.80]), accounting for 25-34% of performance variability. The unilateral drop jump provides useful metrics for differentiating those with PFP from their healthy counterparts. The RSI and GCT may serve as diagnostic metrics in evaluating PFP patients and can be coupled with the AKPS to track performance and rehabilitation progress in PFP patients.  
classification; jumping; sensitivity; specificity +
Footnote †: journal: applied sciences
## 3 Numerical Experiments and Discussions
### Simulation Data
#### 3.1.2 Vision System  
Three landmarks were generated with a fixed height at Chinese LinZhi airport. They were evenly distributed on a circle with a center point \(\mathbf{O}_{p}=[90.3359\,\,^{\circ}\), 29.3065 \(\,^{\circ}\), 2950 m]\({}^{T}\) in the Longitude Latitude Height (LLH) frame, and a radius of 100 m. As shown in Figure 5, the azimuths of the landmarks \(\mathbf{p}_{1},\mathbf{p}_{2}\), \(\mathbf{p}_{3}\) relative to the center point \(\mathbf{O}_{p}\) are 0 \(\,^{\circ}\), 120 \(\,^{\circ}\), and 240 \(\,^{\circ}\), respectively. To simulate the obstructions, two mountain chains with unified heights of 1000 m are symmetrically located in the 3000 m east and the 3000 m west of the center point.  
As shown in Equation (3), the error of vision peseudorange is decided by the landmark position error (LPE) and the feature detection error (DE). In our experiment, different value of LPE and DE were set to evaluate the performance of vision system. LPE was assumed to be centimeter-level that varies from 1 cm to 10 cm (zero LPE is an ideal scenario). DE was set as a white Gaussian distribution [30] with a standard deviation of 1 pixel to 4 pixels on an image with resolution of 120 dpi, 300 dpi, 480 dpi and 720 dpi, where dpi is a unit of image resolution, which means the number of pixels per inch.
## 5 Discussion  
### IGWO Algorithm Planning Efficiency  
From Figure 10, it can be observed that the fitness value of the improved IGWO algorithm is 2239.36, while the fitness value of the traditional GWO algorithm is 2371.93, indicating an improvement of approximately 5% in fitness. The IGWO algorithm tends to converge by the 10 th iteration, whereas the traditional GWO algorithm requires around 30 iterations to start converging. The improved IGWO algorithm exhibits an approximately 60% improvement in convergence compared to the traditional GWO algorithm. When compared to other path planning algorithms, the IGWO algorithm shows significant improvements in both optimization efficiency and effectiveness. In comparison, the IGWO algorithm shows significant improvements in both optimization efficiency and effectiveness compared to the GWO algorithm. Moreover, the IGWO algorithm outperforms the PSO algorithm, GA algorithm, and WPA algorithm in terms of optimization effectiveness. Additionally, the convergence efficiency of the IGWO algorithm is remarkable. Compared to the PSO algorithm, although the PSO algorithm tends to converge after the 5 th iteration, the  
Figure 13: Comparison diagram of path planning.  
fitness value is nearly 100 higher than that of the IGWO algorithm. Furthermore, compared to the other two algorithms, GA and WPA, the IGWO algorithm tends to converge after the 10 th iteration, while the GA algorithm takes 25 iterations to converge, and the WPA algorithm starts converging at the 21 st iteration. This indicates that the IGWO algorithm possesses stronger global optimization capabilities. Additionally, while the IGWO algorithm achieves fast convergence, it also maintains superior search accuracy in the later stages. This results in higher path efficiency and lower path cost in the planning conducted by the IGWO algorithm.  
Considering the fitness iteration curve in Figure 10, it can be observed that the IGWO algorithm tends to converge after 10 iterations. In order to enhance the planning efficiency of the fusion algorithm IGWO-IAPF, this study sets the iteration count for the initial path planning generated by the IGWO algorithm, as mentioned in Section 3.3.4 and Steps 2-10. This approach ensures both the high efficiency of the initial path planning by the IGWO algorithm and fast planning speed during the subsequent global path planning process.
## 4 Application
### Non-Clinical Application
#### 4.2.9 War Zone or Natural Calamity  
[PERSON] et al. [117] have demonstrated that a hovering UAV can be used to capture video of subjects to extract HR and RR, considering multiple persons together at significant distance. This method has opened a new area for the potential application of iPPG during war or natural calamities such as earthquakes.
## 4 Experiment and Results  
### Setting Up  
To verify the algorithm, we use static numerical experiments in 2d with a range of 20 m \(\times\) 20 m. Consider the following three connection scenarios to gradually increase the difficulty of locating the target node: two anchor nodes, one anchor node, and no anchor node. NBP [10], SPAWN [9], and PVSPA [16] are compared to SBPVEM.  
The RMSE of the position error is calculated to evaluate the accuracy of different algorithms.  
\[err=\frac{1}{N}\|\mathbf{x}_{gt}-\mathbf{x}_{pre}\| \tag{41}\]  
where \(N\) indicates the number of nodes to be located. \(\mathbf{x}_{gt}\in\mathbb{R}^{N\times 2}\) is a vector composed of the ground truth positions of all nodes to be located. \(\mathbf{x}_{pre}\in\mathbb{R}^{N\times 2}\) is a vector composed of the predicted positions of all nodes to be located.  
\[err=\frac{1}{N}\sum_{i=1}^{N}\|x_{gt}^{i}-x_{pre}^{i}\| \tag{42}\]  
where \(x_{gt}^{i}=[p_{gt}^{i1},p_{gt}^{i2}]\) is the ground truth position of node \(i\). \(x_{pre}^{i}=[p_{pre}^{i1},p_{pre}^{i2}]\) is the predicted position of node \(i\). \(\|x_{gt}^{i}-x_{pre}^{i}\|=\sqrt{(p_{gt}^{i1}-p_{pre}^{i1})^{2}+(p_{gt}^{i2}- p_{pre}^{i2})^{2}}\) is the Euclidean distance between the ground truth position and the predicted position of the node \(i\).
# Research Progress of Dynamic Measurement Technology of Atom Gravimeter
## 3 Main Error Sources and Suppression Methods of Dynamic Measurement
### Dynamic Effect of the Carrier  
#### 3.3.1 Eotvos Effect  
Gravity is the resultant force of universal gravitation and the inertial force corresponding to the centripetal force of the earth's rotation. When the gravity measurement is carried out on the motion carrier, the instrument is moving relative to the earth, which is affected by the Coriolis acceleration. The superposition of the carrier velocity and the earth rotation velocity vector changes the inertia force, which leads to the deviation between the observed gravity and reality, which is called the Eotvos effect. The Eotvos effect correction is made for the gravity measurement, and its value is  
\[\delta a_{\mathrm{c}} =2\omega_{\mathrm{e}}v_{\mathrm{E}}\cdot\cos\varphi+\frac{v_{ \mathrm{E}}^{2}}{R_{\mathrm{N}}+h}+\frac{v_{\mathrm{N}}^{2}}{R_{\mathrm{M}}+ h}, \tag{13}\]  
where \(\omega_{\mathrm{e}}\) is the angular velocity of the earth's rotation, \(R_{\mathrm{N}}\) and \(R_{\mathrm{M}}\) are the radius of curvature in the meridian and prime vertical of the earth, respectively, \(v_{\mathrm{E}}\) and \(v_{\mathrm{N}}\) are the eastward and northward velocities of the carrier, \(\varphi\) is latitude, and \(h\) is height.  
Although the influence of Eotvos effect on gravity measurement can reach the order of mGal, it can be greatly reduced by the high-precision velocity and position information provided by the navigation system of the carrier.
# NIRS-EMG for Clinical Applications: A Systematic Review
## 2 Electromyography
### Force/EMG Signal Relationship  
It is known that, in general, a larger EMG signal (envelope) originates from a higher recruitment (number and strength) of the muscle fibers and, thus, a higher produced force. Several works propose approaches to detect a closed form relationship for EMG and force, in order to estimate force from EMG recordings; however, it is not possible to determine it with general rules. In fact, apart from issues related to investigation of non-isometric contractions (which alter the properties of the muscle during the recording, as well as the force-length relationship), the intrinsic EMG-Force relationship is potentially non-linear, is muscle and subject-dependent, and, because of the many factors that can influence this relation, an EMG amplitude-force relationship has not shown general validity [10; 35; 40]. Furthermore, the recruitment of the motor units is differently weighted, depending on their distance from the electrodes. While filtering methods may attenuate these two effects, their use may alter the instantaneous and intimate link between force and EMG [35]. The measure of the Force/EMG relationship is usually performed by comparing the Root Mean Square (RMS) of the EMG to a reference value, represented by the Maximum Voluntary Contraction (MVC) which defines the maximum reference value for the RMS.
## 2 Cooperative SCARA System
### Kinematic and Dynamic Analyses of the SCARA Robot
#### 2.1.4 Actuators  
An actuator is a mechanical device designed to generate force and movement. In the context of SCARA robots, DC motors play a crucial role as actuators, fundamental for the precise and agile control of movement, a distinctive feature of these robots.  
In this SCARA system, DC motors are used and controlled by servomechanisms. These motors are specially optimized for applications that require high precision and repeatability. DC servomotors offer several advantages, such as precise control of position, speed, and torque, as well as a rapid dynamic response.  
Upon receiving a voltage at its input, the motor converts electrical energy into mechanical movement through an efficient and controlled process, which is essential for controlling the axes of the SCARA robot.  
The specific characteristics of the motor used in the SCARA robot prototype are detailed in Table 3, which includes parameters such as efficiency, torque, and speed. These parameters are essential for understanding the motor's performance and its suitability for the tasks that the robot must perform. The motor's efficiency influences the system's energy consumption, while the torque and speed determine the robot's ability to perform quick movements and handle different loads.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Value** \\ \hline J & \(3.2284\times 10^{-6}\) kg\(\cdot\)m\({}^{2}\) \\ B & \(3.5077\times 10^{-6}\) Ns/m \\ k\({}_{\mathrm{e}}\) & \(0.0274\) V/rads \\ k\({}_{\mathrm{t}}\) & \(0.0274\) Nm/A \\ R & \(4\;\Omega\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Motor parameters.
## 2 Transcriptional Regulation in Integrons
### Transcription of Gene Cassettes Independent of Pc and Cassette-Borne Promoters
#### 2.4.3 ORF-Less Cassettes  
It has been documented the existence of non-coding cassettes in integrons from distinct environmental metagenomes. These atypical cassettes, named ORF-less cassettes, were devoid of open reading frames, but carried classical _attC_ site in their 3' end [94; 95]. Moreover, some of them encode regulatory RNAs, such as ORF-less cassettes identified in the _Xanthomonas campestris_ pv. _campestris_ integron that were involved in the regulation of virulence [96]. A recent study using a dual report plasmid approach identified functional promoters in ORF-less cassettes belonging to _Treponema_ species from the oral metagenome. This study also demonstrated that these promoters resembled _or_?0 E. coli_ consensus sequences, and they, indeed, were functional in that organism. Moreover, these ORF-less cassettes were characterized by providing promoter sequences at the antisense strand. Therefore, once in the first position of the integron array, these ORF-less cassettes could potentially increase expression of the integrase with a consequent increase of recombination events, leading to the acquisition of new gene cassettes at the first position. When found embedded in cassette array, these ORF-less cassettes could act on transcription of distal gene cassettes relative to Pc (Figure 6). Therefore, these ORF-less cassettes might have an important role in gene expression regulation, allowing bacteria to adapt to multiple stresses within complex physicochemical environments, such as that of the human oral cavity [75].  
Figure 5: Schematic representation of a class 1 integrin carrying the Group IIC-_attC_ intron. The _S.ma._I2 insertion that split the _aadB_ gene from its cognate _attC_ site and the presence of P\({}_{\text{OUT}}\) promoter provided by this Group IIC-_attC_ intron involved with the expression of downstream cassettes are indicated.
# Study on the Propeller Rope Cutter Concerning Transient Torsional Vibration Due to Cutting Action
## 2 Permissible Torsional Stresses Determination
### Permissible Torsional Stresses Concerning Fatigue Strength
#### 2.2.3 The Torsional Stress Criterion  
The most important aspect of the calculation is the estimation of the low-cycle and high-cycle criteria corresponding to each specific torque. For transient torsional vibration, DNV AS recommends choosing the number of reversals for low-cycle and high-cycle fatigue as \(10^{4}\) and \(3\times 10^{6}\), respectively [32]. One cycle includes two stress reversals. In this case, (N) denotes the number of cycles and (2N) denotes the number of reversals. As described in Figure 1, the fractional damage can be determined at each torsional stress level, i.e., one reversal at stress level \(\tau_{pHC}\) makes a fractional damage of \(1/\left(10^{4}\right)\), and one reversal at stress level \(\tau_{pLC}\) makes a fractional damage of \(1/\left(3\times 10^{6}\right)\). That is the linear relationship in log-log scale according to the Miner's rule for cyclic/time-variant loading [34]. The failure occurs when the accumulated fractional damage reaches the value of 1 [35; 36].  
The torsional stress amplitude corresponding to failure at (2N) reversals is calculated by:  
\[\log(\tau_{vT})=\log\tau_{vHC}+\frac{\log\left(\frac{3\times 10^{6}}{10^{4}} \right)-\log(2N)}{\log\left(\frac{3\times 10^{6}}{10^{4}}\right)}\times\log \left(\frac{\tau_{vLC}}{\tau_{vHC}}\right) \tag{11}\]  
In case that the cutting action occurs once every week, resulting in 520 times for 10 years, corresponding to \(2N=520\) reversals, then the corresponding transient critical torsional vibration stress is \(\tau_{vT}=64.5\) N/mm\({}^{2}\). The critical alternative torque in the intermediate shaft for the transient fluctuation during cutting action is:  
\[T_{amax}=\frac{\pi d^{3}\tau_{vT}}{16}=\frac{\pi\times 120^{3}\times 64.5}{16}=21.9 \times 10^{6}\quad\left(\text{N}\cdot\text{mm}\right) \tag{12}\]  
or 21.9 kN\(\cdot\)m.  
Assuming the cutting action occurs once every day, then the corresponding transient torsional stress is \(\tau=52.7\) N/mm\({}^{2}\). The permissible alternative torque is \(T_{amax}=17.9\) kN\(\cdot\)m.
###### Abstract  
The problems of gully and soil erosion caused by large-scale urban construction and agricultural development in China have become more and more serious in recent years. In an effort to solve this problem, a series of gully stabilization and highland protection projects have been carried out on the Loess Plateau, and this has resulted in a large number of high-loess-filled-slopes (HLFSs). Although these filled slopes uses several different mitigation measures, the HLFSs have been eroded and destroyed under the action of water. In order to study the influence of different mitigation measures on the stability of HLFSs and their failure process, this paper uses a flume test of the effects of various mitigation measures on this failure process. The results show that: (1) the failure processes of slopes with different mitigation measures are obviously different. Slope deformation u with a declining gradient mitigation mainly occurs on the surface of the slope body, and although slope erosion is quite serious, the slope does not fail as a whole. Slopes with a stepwise drainage channel mitigation show little erosion, but material can easily slide along the horizontal drainage channels. (2) The slope deformation process is correlated with changes in pore-water pressure. When local instability occurs, there is always a pre-process of continuously rising pore-water pressure. When a failure occurs, the pore-water pressure of the soil at each position of the slope body suddenly fluctuates under instantaneous excitation. (3) The response of soil pore pressure and the development characteristics of tension cracks affect the deformation of the slopes, which is also the cause of the differences slope instability caused by different mitigation measures. These research results provide reference for the protection of HLFS engineering projects from heavy rains.  
loess; high filled slope; failure process; mitigation measures; experimental +
Footnote †: journal: Applied Sciences
# COVID-19 Detection Model with Acoustic Features from Cough Sound and Its Application
## 3 Proposed Method  
### Data  
The cough sound databases of Cambridge [20], Coswara [25], and COUGHVID [27] presented in Section 2 were used in the experimental data of this study. From the COUGHVID data, only data with a cough detection score of 0.9 or higher were extracted based on metadata that included score information in relation to the degree of cough sound detection. Because all three databases had more cough sound data from COVID-19-negative participants than from positive individuals, only a portion of the cough sound data from negative participants was used; this was conducted to balance the data. Due to the nature of crowdsourced data, which is collected in various environments, it may contain data that are difficult to use for a study. Therefore, the audio files were directly listened to and inspected. Data were deleted through inspection in the following cases.  
* The cough sound is quieter than the noise.
* The recording quality is too poor.
* Background noise (conversation, road noise, music, TV/radio, etc.) is mixed with the cough sound.
* It is difficult to recognize the cough sound.  
As indicated in Tables 1 and 2, 4200 audio files were inspected, and finally 2049 cough sound audio files were selected. The selected database consisted of 1106 audio files of cough sounds from COVID-19-positive participants, 530 audio files of cough sounds from healthy people, and 413 audio files of cough sounds from people with symptoms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Cambridge** & **Coswara** & **COUGHVID** & **Total** \\ \hline COVID-19 & 299 & 1336 & 441 & 2076 \\ healthy & 499 & 400 & 201 & 1100 \\ symptomatic & 295 & 428 & 301 & 1024 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of audio files before inspection.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Cambridge** & **Coswara** & **COUGHVID** & **Total** \\ \hline COVID-19 & 247 & 656 & 203 & 1106 \\ healthy & 299 & 123 & 108 & 530 \\ symptomatic & 179 & 92 & 142 & 413 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of audio files after inspection.
## 3 Our Advanced Proposed Methodology
### Dataset Description  
The dataset utilized in this study is composed of data collected from 15 distinct physical sensors, each designed to capture various environmental parameters such as temperature, humidity, light intensity, smoke, and sound at regular 10 s intervals. These sensors are strategically deployed to monitor different aspects of the environment in a dynamic, real-time setting, capturing a broad spectrum of data that reflects the evolving conditions. Over multiple time points, the data from each sensor are recorded, creating a series of measurements that are structured into one-dimensional arrays for each sensor.  
The dataset consists of data collected at regular 10 s intervals from 15 distinct physical sensors, resulting in a high-resolution, time-series dataset. The total number of datapoints depends on the duration of the monitoring period, with each sensor contributing continuous measurements over time. This results in a large volume of data, with each sensor generating one datapoint every 10 s. These datapoints are structured into one-dimensional arrays for each sensor, forming a comprehensive dataset that captures the dynamic environmental conditions at fine temporal granularity.  
We define a supervector of the 15 sensors' physical readings, as shown in Figure 1 (without the virtual sensing reading parts), to be shown later in Section 3.4 below. The aforementioned supervector is a consolidated collection of these individual physical sensor readings over a defined time window. Each element in the supervector corresponds to a time-series measurement from 1 of the 15 physical sensors, capturing a snapshot of the environmental state at each specific time interval. By combining these time-series measurements into a single supervector, we create a unified representation of the environmental conditions at each moment in time. This method of aggregation allows us to preserve the temporal relationships and dependencies between the readings of different sensors, enabling the model to better understand the complex interactions and patterns present in the environmental data, which is crucial for effective prediction and classification tasks. Moreover, it facilitates the association of supervectors to the emulated scenarios used for the early prediction of forest fire.  
Figure 1: Effect of varying the number of virtual sensors between two physical sensors on measurement accuracy, highlighting optimal performance with no virtual sensors.
# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 3 Components of AIoT in Aquaculture
### IoT Sensors
#### 3.1.3 Motion Sensors in Aquaculture  
Accelerometers and other motion sensors are crucial for evaluating fish feeding habits, movement patterns, and stress reactions. These devices offer a non-intrusive way to monitor fish behaviour continuously, aiding in effectively managing health and productivity. Typically integrated into tags or attached externally, accelerometers record detailed movement data, including burst swimming, tail-beat frequency, and resting periods. Such information offers insight into feeding efficiency, activity levels, and metabolic rates.  
Research has demonstrated that accelerometer-based monitoring can detect subtle behavioural changes that may not be easily observed, such as heightened activity caused by deteriorating water quality or reduced movement linked to health issues [63]. Moreover, integrating accelerometers with telemetry systems enables remote, real-time behavioural monitoring, allowing for rapid interventions when abnormalities are detected. This advancement enhances fish welfare management by facilitating timely responses to environmental or physiological stressors.