esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The energy hole creation, high interference, unbalanced data load distribution, and naive node-level energy consumption approaches are considered some of the key reasons for poor UWSN performance. The integration of various compatible approaches often results in improved network performance. This paper incorporates three state-of-the-art UWSN approaches: transmit power control, mobile sink use, and clustering. The results of the assessment validate the fact that the ratio of network life, throughput, and packet distribution can be maximized by integrating the regulation of node-level transmission power, clustering in dense regions, and the use of mobile sinks in sparse network regions. The formation of energy holes is considered one of UWSN's most important problems.  
Figure 11: Received packets at the sink: Transmission range 200 m, nodes from 50 to 250.  
Figure 12: Received packets at the sink: Transmission range 150 m, nodes from 50 to 250.  
The proposed scheme has addressed this problem efficiently; however, it is observed that the smart and adaptive mobility mechanism of mobile sinks has a great impact on the avoidance of energy hole creation.  
The research conceptualization and methodology are done by [PERSON], [PERSON] and [PERSON]. ([PERSON]). The technical and simulations are performed by [PERSON] ([PERSON]), [PERSON] ([PERSON]) and [PERSON] The technical review and improvements have been performed by [PERSON] ([PERSON]), [PERSON], [PERSON], [PERSON] and [PERSON]. The overall technical support, guidance and project administration is done by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the EMMAT E-mobility and sustainable materials and technologies (E-mobilnosc oraz zrownwazone materialy i technologie) Projekt nr PPI/APM/2018/1/00027.  
The authors acknowledge the support from the Najran University, Kingdom of Saudi Arabia.  
There is no conflict of interest.
# Bionic Artificial Lateral Line Underwater Localization Based on the Neural Network Method
## 4 Experiment Setup
### Data Pre-Processing  
We conducted experiments on the localization of single and double dipole source vibrations in the two-dimensional plane. To determine the relative positions of the dipole spheres, we set the coordinate system at the location of the central axis of the sensor array as the coordinate origin. To train the neural network model, the plane 140 \(\times\) 240 mm was divided into a 20 \(\times\) 20 mm grid to form 91 data acquisition points. Five sets of data were acquired at each acquisition point for 455 collections of experimental data. The data sampling frequency was 1000 Hz, and 14 seconds of data were collected each time, totaling 14,000 data points. These data were randomly divided into a training set and a test set according to a ratio of 8:2. The ball was moved at each data collection point, and the obtained pressure signal was used to train the neural network.  
Figure 7 shows the pressure vibration signal acquired by the pressure sensor when the sphere was at the (20 mm, 0 mm) position. Using MATLAB's time-frequency tool, the time domain signal acquired by the pressure transducer could be converted into the frequency domain signal by using the fast Fourier transform (FFT).  
For the MLP network, we converted the signal into a frequency domain signal, extracted the output voltage amplitude at a specific frequency, multiplied it by the individual pressure sensor sensitivity to obtain the pressure amplitude, and then normalized the data.  
\[S_{j}^{\prime}(i)=\frac{S_{j}(i)-S_{j}(M)}{S_{j}(\sigma)} \tag{2}\]  
here, \(S_{j}(i)\) denotes the signal amplitude of sensor \(j\). \(S_{j}(M)\) and \(S_{j}(\sigma)\) are the mean and standard deviations of sensor \(j\), respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pressure Number** & \
\begin{tabular}{c} **Proportional** \\ **Constant (Mpa/V)** \\ \end{tabular} & **Pressure Number** &
\begin{tabular}{c} **Proportional** \\ **Constant (Mpa/V)** \\ \end{tabular} \\ \hline
**P\({}_{1}\)** & 0.103 & **P\({}_{5}\)** & 0.079 \\
**P\({}_{2}\)** & 0.081 & **P\({}_{6}\)** & 0.078 \\
**P\({}_{3}\)** & 0.082 & **P\({}_{7}\)** & 0.092 \\
**P\({}_{4}\)** & 0.084 & **P\({}_{8}\)** & 0.101 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sensitivity of 8 sensors after calibration.  
Figure 6: Output signal amplitude of eight sensors in a laboratory environment.
# MAToC: A Novel Match-Action Table Architecture on Corundum for 8 \(\times\) 25G Networking
## 3 Implementation
### Match Stage
This is applicable in the assumption that the total input rate would not rise to the upper bound of the pipeline's capability, which is 250 Mpps at 250 MHz clock domain. The assumption is always true as long as packet length is larger than 76 bytes. In that case, there are spare clock cycles for update logic to write match rules. Figure 2 shows how the surrounding logic is designed for a single TCAM unit. A large TCAM is combined from many of these units instantiated from Xilinx configurable logic block (CLB) primitive RAM32M. This element is a 32-bit deep by 8-bit wide random-access memory implemented using the LUT memory resources, named SelectRAM. To clarify,match rules are the actual data stored in LUT RAM, which has a mapping relationship with the masked search key in the form of data bits and care bits, e.g., a five bits width search key of 5\({}^{\prime}\)b00001 maps to a 32 bits match rule of 32\({}^{\prime}\)h0000002, and 5\({}^{\prime}\)b0000X maps to 32\({}^{\prime}\)h0000003, where X is a 'don't care' bit. The read and write logic blocks are responsible for this mapping work. The write process contains two parts: first is preparation of the match rules, then storing the rules into the quad of 32-deep LUT RAMs. More specifically, its logic uses a counter, counting from 0 to 31 to calculate the 32-bit match rules from given data and care bits. The calculated match rules are buffered in a 32-bit shift right register, then written into the LUT RAM with its counter counting one more round. The counting cycle is limited to the address space of a single LUT RAM unit, which is 32 in our case. Read logic needs no preparation, so the response latency is the time it takes to traverse the LUT RAM, which is 32 cycles, plus one more cycle to register the output. Logic in the first write counting cycles is isolated from search or read logic, which means that either of them can be processed at this temporal interval. The match process is straightforward. Take a 10-bit wide TCAM with a depth of 32 as an example. It is composed of eight units shown in Figure 2, and it is arranged in two columns and four rows. It is able to store 4 \(\times\) 8 = 32 match rules and match for keys in 2 \(\times\) 5 = 10 bits width. Every time the match process begins, the search key is segmented into two 5-bit wide pieces, and each is sent to the whole column of units. Then the column takes the input 5-bit piece as the address of its RAM32 Ms and output is a 32-bit deep indicator. The indicators from each column are sent to an AND gate, whose outputs report whether all the segmented pieces are matched. In this example, the input of AND gate is 32 \(\times\) 2 matrix, and the output is a 32-bit deep vector. The final output is called the match line, which is an array indicating valid entries in the instruction table. One indicator is selected as priority and used to fetch an entry from the instruction table, then the fetched instruction is sent to the action stage in our scheme.
## 3 Numerical Configuration
### Independence of Mesh  
Once the meshing strategy and configuration were exposed, as well as the determination of the boundary conditions, the next step consisted in ensuring the independence of the spatial discretization. Three possible mesh sizes were calculated in order to check the independence mesh. In Table 1, the main averaged values are included. It is worth mentioning that the inlet referred to the inlet of the whole compressor (total), the outlet of the core (core) and the outlet of the bypass (bypass), whereas the outlet always referred to the outlet of the mixer.  
\[\pi_{t}=\frac{P_{t,outlet}}{P_{t,inlet}} \tag{1}\]  
\[\eta_{t}=\frac{\pi_{t-t}^{\frac{\gamma-1}{\gamma}}}{\frac{T_{t,outlet}}{T_{t, inlet}}-1} \tag{2}\]  
\[BPR=\frac{\dot{m}_{bypass}}{\dot{m}_{core}} \tag{3}\]From the differences obtained between the 3 different meshes, with a general difference of about 3% between the first two meshes and almost no variance with the fine mesh, it can be concluded that the medium mesh already achieved mesh independence and was therefore used.  
An analogous process was performed for the 2D axisymmetric mesh, and the obtained results are summed up in the next table. By looking at Table 2, the chosen mesh was the medium mesh. It could be reasoned that, as the differences between the coarse and the medium mesh were all around 1%, except for the BPR, where the difference was up to 3%, the coarse one already reached independence. Nevertheless, as the number of elements was still low in the next one, and looking for future possible calculations, the second one was chosen, which presented almost negligible differences in comparison with the last mesh tested.
# Instability Characteristics of and Control Techniques for Mudstone-Clay Composite Roof Roadways
## 4 Deformation and Instability Law and Prevention Technology of Double Soft Composite Roof
### Prevention Techniques for Roadway Deformation
#### 4.3.2 Design of Roof Support Parameters  
The composite roof at Xiegou Coal Mine exhibits thick mudstone-clay layer with low strength level, looseness, fragility, poor stability, and easy detachment after roadway excavation. Based on these conditions, the following support criteria and strategies are proposed:  
First, adopt a \"low-medium-high\" multiple support method and address long anchor drilling difficulties through joint support.  
Second, apply initial support using anchor rods, steel mesh, and short anchor cables to prevent delamination of low to medium composite roof, and using long anchor cables to control high-level rock layer stability.  
Third, install long anchor cables at a specified distance behind the excavation face after roof water drainage.  
Fourth, enhance support strength and initial prestress of anchor rods, short anchor cables, and long anchor cables, with long anchor cables supported by truss anchor cables.  
Based on geological conditions in the material roadway of the 18,511 th working face at Xiegou Coal Mine, with the roadway dimensions of 5 m width and 3.6 m height, support parameters were determined using the stress superposition principle. As stability control is needed for natural balance arches, invisible balance arches, and extended invisible balance arches, the following specifications were established: roof anchor rods should be 2.1 m-3.5 m in length with spacing under 1.5 m \(\times\) 1.5 m; short anchor cables should be 3.5 m-4.1 m in length with spacing under 2.5 m \(\times\) 2.5 m; and long anchor cables should exceed 4.1 m with row spacing less than 3 m \(\times\) 3 m.  
Therefore, the designed parameters for anchor rod and cable support are as follows: (1) roof anchor rod: 2.6 m length with row spacing less than 1 m \(\times\) 1 m; (2) short anchor cable: 4 m length with row spacing less than 2 m \(\times\) 2 m; and (3) long anchor cable: 5.5 m length with row spacing less than 2.85 m \(\times\) 2 m. The final roadway support design is shown in Figure 18.  
Figure 17: Control principle diagram of multiple supports for stress superposition reinforcement.
# A Methodological Approach to the Study of Retroreflective Pavements
## 2 Materials and Methods
### Methods
#### 2.2.3 Work Stages  
In the first stage of the work, reflectance was evaluated using two procedures. A spectrophotometer with an integrating sphere was employed for the evaluation of total reflectance. Additionally, a field spectroradiometer was used to assess directional reflectance (see Figure 10). Although the spectrophotometer with an integrating sphere measures reflectance across the entire spectrum (ultraviolet, visible, and infrared), the spectroradiometer works only within the visible spectrum. Therefore, the comparison of results was made exclusively in the visible range (wavelengths between 400 and 700 nm).  
Figure 9: Spectroradiometer, measurement booth, and tested sample.  
In the second stage, the aim was to approximate the retroreflectance measurement. For this, the reflectance was evaluated using the spectroradiometer when both the emitter and receiver were approximately in the same position. This is when the observation angle, \(\theta\), was very small. The observation angle typically used between the direction of the entrance light and the measurement direction of the reflectance is \(45^{\circ}\), with measurements taken perpendicularly to the sample surface. However, for the measurement of the retroreflectance, an observation angle close to \(0^{\circ}\) was considered while maintaining the same entrance angle of incident light on the surface (Figure 11). Nevertheless, all reflectance measurements on the bituminous mixtures could be affected by the texture of the sample itself.  
Therefore, a third and final stage of the work was conducted. The purpose was to evaluate the effect of the entrance angle of light on the retroreflectance. In this stage, the emitter and the receiver were placed very close, so that the observation angle, \(\theta\), between the direction of the incident light and that of the reflectance measurement was nearly zero. The tilt of the sample was varied relative to the illumination axis, such that three different entrance angles, \(\beta\), of \(60^{\circ}\), \(45^{\circ}\), and \(0^{\circ}\) were considered (see Figure 12). These measurements were carried out on the AUTL-PB and AUTL-LW mixtures corresponding to the extreme colours (black and white). To further highlight the effect of the texture of the mixtures, two additional types of mixtures were included, a BBTM type and an AC type (the latter being more compact) with the extreme colours (black and white). These mixtures are BBTM-PB, BBTM-LW, AC-PB, and AC-LW.
## 4 Experiments
### Compared Methods  
To evaluate the effectiveness of our proposed system (Proposed) on the VC task, four recent state-of-the-art systems (S1, S2, S3, S4) designed for VC were chosen for comparison using parallel and non-parallel training data. Note that all systems were tested under the same conditions for a fair and complete evaluation. The details are described below:  
* Baseline system 1 (S1): Refers to parallel VC system [11] based on ASR and text-to-speech (TTS)-oriented pretraining strategy using Transformer models for sequence-to-sequence VC. This method provides a significant improvement in performance in terms of intelligibility and speech quality, even when training data are limited.
* Baseline system 2 (S2): Refers to parallel baseline VC system [10] based on sequence-to-sequence mapping model with attention, which achieved better performance on naturalness and speaker similarity when compared with conventional methods.
* Baseline system 3 (S3): Refers to non-parallel VC system [49] based on a variant of the GAN model called StarGAN. This system can generate converted speech signals at a high speed, allowing for real-time applications and requiring only a few minutes of training to produce realistic speech.
* Baseline system 4 (S4): Refers to non-parallel baseline VC system [41], which aims to jointly train conversion model and WaveNet vocoder using mel-spectrograms and Phonetic Posteriorgrams.
## Abbreviations  
The following abbreviations are used in this manuscript:  
ADCS Attitude Determination and Control System  
ASIC Application-Specific Integrated Circuit  
ConOps Concepts of operations  
DECO Decommissioning  
DRAMA Debris Risk Assessment and Mitigation Analysis  
DoDAF Department of Defense Architecture Framework  
ECSS European Cooperation for Space Standardization  
EPS Electrical Power System  
FAC Colombian Aerospace Force  
FECF Frame Error Control Field  
FOP Full Operations  
GS Ground Station  
IAC IoT Access Code  
IMU Inertial Measurement Unit  
INCOSE International Council on Systems Engineering  
IOP Initial Operations  
ISS International Space Station  
IVVQ Integration, Validation, Verification, and Qualification  
JAXA Japan Aerospace Exploration Agency  
LA Logical Architecture  
LEOP Launch and Early Orbit Phase  
MB Mother Board  
MBSE Model-Based Systems Engineering  
MCU Microcontroller  
MTQs Magnetorquers  
OBC On-Board Computer  
OMG Object Management Group  
OPM Object-Process Methodology  
PCU Power Conditioning Unit  
RAAN Right Ascension of the Ascending Node  
RAC Remote Control Access Code  
SE Systems Engineering  
SH Segment Header  
SpOC Space Operations Center  
SysML Systems Modeling Language  
TFPH Transfer Frame Primary Header  
TT&C Telemetry, tracking, and control  
UNOOSA United Nations Office for Outer Space Affairs \begin{table}
\begin{tabular}{c c} \hline \hline
**Requirements** & **Description** \\ \hline \hline
**Launch** & \\ \hline \hline \multirow{2}{*}{Laun-001} & The deployment platform for small satellites, utilizing J-SSOD [59], \\  & specifies the requirements for launching a 1U satellite from the Japanese Kibo \\  & module of the International Space Station (ISS). \\ \hline \hline \multirow{2}{*}{Laun-002} & Satellite Size: The platform accommodates CubeSats of 1U size, measuring \\  & approximately 10 \(\times\) 10 \(\times\) 10 cm. \\ \hline \hline \multirow{2}{*}{Laun-003} & Satellite Mass: For a 1U CubeSat, the mass is limited to 1.33 kg or less. \\ \hline \multirow{2}{*}{Laun-004} & Orbital Altitude: The satellite is deployed at an altitude approximately \\  & between 380 and 420 km above Earth’s surface. \\ \hline \multirow{2}{*}{Laun-005} & Inclination: The deployment orbit maintains an inclination of 51.6\({}^{\circ}\) \\  & relative to the equator. \\ \hline \multirow{2}{*}{Laun-006} & Deployment Direction: Deployment occurs in the Nadir-aft direction, \\  & which is 45\({}^{\circ}\) from the ISS nadir side. \\ \hline \multirow{2}{*}{Laun-007} & Deployment Velocity: The deployment velocity for a 1U CubeSat \\  & ranges from 1.1 to 1.7 m/s. \\ \hline \multirow{2}{*}{Laun-008} & Ballistic Coefficient: The platform ensures a ballistic coefficient of \\  & 100 kg/m\({}^{2}\) or less to facilitate safe deployment. \\ \hline \hline \end{tabular}
\end{table} 
Table **A1**. _Cont.
# Optimization of Redundant Degrees of Freedom in Robotic Flat-End Milling Based on Dynamic Response
## 3 Milling Experimental Validation
### Modal Parameter Identification  
During robot milling, the dominant mode of the robot tool tip, which contributes the most vibration, is considered dominant in this article. The influence of other modes is neglected. Using the frequency response data from the experiment, the inverse distance weighted method is employed to predict the frequency response for point #1. Figure 5 shows the frequency response function in the \(x\) direction at \(\gamma=-40^{\circ}\). The least-squares complex exponential method is used to fit the dominant modes within a specific frequency range in the milling process. As illustrated in Figure 5, a single dominant mode is observed in this frequency range. It is apparent from the figure that the fitted curve is consistent with the original curve, achieving a good approximation.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Points**} & \multicolumn{3}{c}{**Coordinate**} & \multirow{2}{*}{**Points**} & \multicolumn{3}{c}{**Coordinate**} \\ \cline{2-2} \cline{5-7}  & \(x\) & \(y\) & \(x\) & & \(x\) & \(y\) & \(z\) \\ \hline
1 & 935 & \(-95\) & 121.2 & 9 & 860 & \(-65\) & 121.2 \\
2 & 910 & \(-95\) & 121.2 & 10 & 835 & \(-65\) & 121.2 \\
3 & 885 & \(-95\) & 121.2 & 11 & 935 & \The identification of modal parameters is the most commonly utilized method for analyzing frequency response functions, providing crucial insights for future analysis of chatter stability. Through examination of the fitted frequency response function, key modal parameters such as the natural frequency, damping ratio, and others for the robot processing system can be determined using the modalfit function. The modal mass is calculated by scaling the mode shapes to unity modal A.  
As illustrated in Figure 6, the distribution of the modal stiffness at different redundant angles is presented for point #1. The results indicate that at redundancy angles of \(0^{\circ}\), \(10^{\circ}\), and \(20^{\circ}\), the modal stiffness is relatively small. Conversely, at redundancy angles of \(-40^{\circ}\), \(-30^{\circ}\), and \(-20^{\circ}\), the modal stiffness shows minimal variation. A redundancy angle of \(40^{\circ}\) represents the maximum stiffness. The modal stiffness undergoes significant changes as the redundancy angle increases, while it undergoes relatively little change as the redundancy angle decreases.  
Figure 5: Frequency response curve for point #1 with \(\gamma=-40^{\circ}\).  
Figure 6: Modal stiffness at different redundant angles for point #1.
###### Abstract  
The paper proposes a new method for measuring the azimuth pointing of spaceborne synthetic aperture radar (SAR) antenna beams based on the ground receiver, which can receive and record complex sampling data of the pulse signals transmitted from the spaceborne SAR. The center of the antenna pattern is extracted from the complex sampling data amplitude envelope to obtain the time when the beam main lobe center irradiates the ground receiver, and the range migration information is extracted from the complex sampling data to obtain the time when the satellite is over the top of the ground receiver. The results of Chinese civilian remote sensing GaoFen-3 SAR satellite experiment data processing show that the measurement accuracy of this method is better than 0.002\({}^{\circ}\), which can be applied to the accurate measurement of azimuth pointing of various low Earth orbit (LEO) SAR antenna beams.  
Keywords:Gaofen-3; SAR; calibration; antenna beam pointing; Image radiation correction +
Footnote †: journal: Accepted: 8 August 2018
# Effect of Muscle-Specific Fatigue on the Risk of Anterior Cruciate Ligament Injury in Females
###### Abstract  
The aim of our study was to investigate the effect of muscle-specific fatigue of the quadriceps and hamstring muscles on the biomechanical factors of anterior cruciate ligament (ACL) injury using musculoskeletal modeling techniques during directional diversion maneuver. Fifteen female subjects performed a directional diversion maneuver under three treatment conditions (quadriceps fatigue, hamstring fatigue, and control gait). Data from the 3D motion capture system and force platform were used to extract anterior/posterior ACL forces using the two-bundle ACL musculoskeletal modeling approach. A decrease in maximum extension (51.3%) and flexion (50.7%) torque after fatigue was observed. After quadriceps fatigue, the extension (\(p\) = 0.041) and adduction moments (\(p\) = 0.046) of the knee joint and the mean anterior bundle of ACL force (\(p\) = 0.021) decreased significantly. The knee flexion angle (\(p\) = 0.003), knee valgus angle (\(p\) = 0.013), and shear force (\(p\) = 0.043) decreased significantly after hamstring fatigue. The decrease in ACL force after quadriceps fatigue confirms its significant role in causing an ACL injury. However, no significant differences in ACL load after hamstring fatigue leads us to speculate that the antagonist muscle group, i.e., the hamstring, might not have a preventive mechanism against ACL injury.  
ACL; biomechanics; musculoskeletal modeling; running; knee kinematics Article