esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The exceptional properties of two-dimensional (2D) materials have focused an enormous amount of research activity on developing next-generation devices in the field of electronics and optoelectronics [1]. Graphene had the merit of being the prototype of these 2D materials and it is extremely interesting as a transparent electrode, but, being a gapless semiconductor, has several drawbacks in its digital logic and optoelectronic applications [2]. In this context, transition metal dichalcogenides (TMDs), such as MoS\({}_{2}\), WS\({}_{2}\), WSe\({}_{2}\), PdSe\({}_{2}\), and many others, were recognized as valid candidates to be exploited in those fields, due to their physical and chemical properties, including a finite band gap tuned by the number of layers, the absence of dangling bonds, chemical stability, etc. [3; 4; 5; 6].  
In this study, we report a detailed investigation of the effects caused by pressure, gas type and electron beam irradiation on the electrical transport properties of back-gated field-effect transistors with PdSe\({}_{2}\) or MoS\({}_{2}\) nanosheets as conducting channels. We demonstrate that the conduction of the devices can be tuned from the \(n\)-type (dominant in a high vacuum) to the \(p\)-type by modifying the gas pressure. In addition, the transfer characteristics are significantly modified by the gas absorption, showing a widening of the observed hysteresis.  
Interestingly, we also show that the transport properties are clearly affected by electron bombardments at fluences as low as those typically involved in electron beam-based imaging and processing techniques. Finally, we report a characterization of the field emission properties of the nanosheets under investigation. Their \(n\)-type conduction as well as their high aspect ratio at the borders allow us to extract a current at the relatively low electric fields applied. These field emission properties open up new opportunities for the exploitation of 2D materials in vacuum electronics.
## 3 Methods
### V-RTI Validation: Ground-Truth Comparisons
#### 3.3.1 SfM vs. SLS  
In order to assess the dimensional accuracy of the V-RTI model of the Ellesiya basrelief,a 3D model was acquired with an SLS Artec Leo to be used as the ground-truth. The Artec Leo is equipped with a Vertical Cavity Surface Emitting Laser (VCSEL) light to detect the morphology and 12 LEDs (Light Emitting Diodes) to generate the visible texture. The data captured with this technology are usually of excellent quality in terms of precision and accuracy as SLSs are originally calibrated by the manufacturer (Table 2).  
To evaluate the achieved outcomes, a comparison was conducted between the two meshes using Cloud Compare [42]. The purpose of this comparison is to measure the dimensional discrepancy between the V-RTI model generated by SfM and the one obtained  
\begin{table}
\begin{tabular}{l l} \hline \hline  & **Artec Leo SLS** \\ \hline
3D model resolution: & up to 0.2 mm \\ Texture resolution: & 2.3 mpx, 24 bpp \\ Data acquisition: & 35 mln points/s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Structured Light Scanner specifications as defined by the manufacturer.  
Figure 10: The exported V-RTI shown in RTI Viewer.  
with an SLS Artec Leo (Figure 11). These were aligned within the same coordinate system, by applying an Iterative Closest Point (ICP) registration process. Following this, a deviation map was calculated to assess any disparities between the two surfaces.
## Appendix A Derivation of the RMS Expression  
Starting from the trigonometric relation \(sin(x\pm y)=sin(x)cos(y)\pm cos(x)sin(y)\), it is easy to show that  
\[sin(a-\omega t)=sin(a)cos(\omega t)-cos(a)sin(\omega t) \tag{10}\]  
and  
\[\begin{array}{c}sin(a-\omega t)sin(b-\omega t)=sin(a)sin(b)|cos(\omega t)|^{2 }\\ +cos(a)cos(b)|sin(\omega t)|^{2}-2[sin(a+b)]cos(\omega t)sin(\omega t)\end{array} \tag{11}\]  
Additionally, if one sets \(a=b\), one also gets  
\[\begin{array}{c}|sin(a-\omega t)|^{2}=|sin(a)|^{2}|cos(\omega t)|^{2}+|cos(a )|^{2}|sin(\omega t)|^{2}\\ -2[sin(2a)]cos(\omega t)sin(\omega t)\end{array} \tag{12}\]  
First, let us evaluate  
\[\int_{0}^{T}\!\!|sin(\omega t)|^{2}dt=\frac{1}{\omega}\int_{0}^{\omega T}\! \!|sin(u)|^{2}du \tag{13}\]  
with \(\omega T=2\pi\), by definition. One knows that  
\[|sin(u)|^{2}=\frac{1-cos(2u)}{2} \tag{14}\]  
Therefore  
\[\int_{0}^{2\pi}\!|sin(u)|^{2}du=\frac{1}{2}\bigg{[}u-\frac{1}{2}sin(2u)\bigg{]} _{0}^{2\pi}=\pi \tag{15}\]  
since periodicity imposes that  
\[\bigg{[}\frac{1}{2}sin(2u)\bigg{]}_{0}^{2\pi}=0 \tag{16}\]  
Therefore  
\[\frac{1}{T}\int_{0}^{T}\!\!|sin(\omega t)|^{2}dt=\frac{1}{T}\frac{\pi}{\omega }=\frac{1}{2} \tag{17}\]  
From the trivial relationship  
\[|cos(\omega t)|^{2}+|sin(\omega t)|^{2}=1 \tag{18}\]  
One immediately gets  
\[\frac{1}{T}\int_{0}^{T}\!\!|cos(\omega t)|^{2}dt=\frac{1}{T}\int_{0}^{T}\!\! dt-\frac{1}{T}\int_{0}^{T}\!\!|sin(\omega t)|^{2}dt=1-\frac{1}{2}=\frac{1}{2} \tag{19}\]  
Through integration by parts, it's easy to show that  
\[\int_{0}^{2\pi}cos(u)sin(u)du=\bigg{[}\frac{1}{2}|sin(u)|^{2}\bigg{]}_{0}^{2 \pi}=0 \tag{20}\]  
Therefore  
\[\frac{1}{T}\int_{0}^{T}cos(\omega t)sin(\omega t)dt=0 \tag{21}\]  
All in all, by Equations (12), (17), (19) and (21), one gets  
\[\frac{1}{T}\int_{0}^{T}\!\!|sin(a-\omega t)|^{2}dt=\frac{1}{2}\Big{(}|sin(a) |^{2}+|cos(a)|^{2}\Big{)}=\frac{1}{2} \tag{22}\]On the other hand, by Equations (A2), (A8), (A10) and (A12), one gets  
\[\frac{1}{T}\int_{0}^{T}sin(a-\omega t)sin(b-\omega t)dt\ =sin(a)sin(b)\frac{1}{2}+ cos(a)cos(b)\frac{1}{2}=\frac{1}{2}cos(a-b)\] (A14)
# A Comprehensive Review of Computer Vision in Sports:
## 8 Research Directions in Sports Vision
### Open Issues and Future Research Areas  
Computer vision plays a vital role in the area of sports video processing. To analyze sports events, there are many issues open for research. Calibration and viewpoints of the camera to capture the sports events such as close-up views, far views, and wide views in a degree of occlusions are still issues that have not been satisfactorily addressed.  
Detecting the ball in various sports helps to detect and classify various ball-based events such as goals, possession of the ball, and many other events. Due to the size, speed, velocity, and unstructured motion of the ball compared to the players and playfield in various sports, it is still an open issue to detect and track the ball. Various AI algorithms have been developed to achieve better performance in various sports such as soccer [62; 84], basketball [33; 38], tennis [148], and badminton [195; 196] in terms of detecting and tracking concerning various aspects of the ball.  
Tracking players and the ball is one of the most open areas for research which includes various issues such as fast and frequent movements of the players, the similar appearance of players due to jersey color in team sports, often partial and full occlusions of players. Various algorithms use linear motions for multi-player tracking, resulting in poor performance but solves data association problems with appearance models. However, this algorithm fails in various conditions such as severe occlusions, the ambiguity of appearance between players, etc. [296; 297] applied context information to track the players in soccer and volleyball sports.
# An Efficient On-Chip Data Storage and Exchange Engine for Spaceborne SAR System
## 3 The Data Storage and Exchange Method Based on Superscalar Pipeline
### The Three-Dimensional Cross-Mapping Method Based on Equal Division of Sub-Matrix with Dual-Channel
#### 3.1.2 Sub-Matrix Equal Division Cross-Mapping  
After the three-dimensional matrix block mapping method mentioned above is complete, the sub-matrix cross-mapping method is used in this section to equally divide the sub-matrices so that they can be mapped to both DDR3A and DDR3B.  
Two pieces of DDR3 can be regarded as a whole with twice the bit width, capacity, and bandwidth of a single piece of DDR3. When the system issues an access command to the matrix transposition module, the two pieces of DDR3 execute the same read or write  
Figure 6: Schematic diagram of the matrix block three-dimensional mapping scheme based on parallel dual channel.  
command together. Since the burst length of a single DDR3 is 8, the burst length could be 16 when two DDR3s are read and written at the same time; that is, 16 pieces of data are continuously read or written in 4 clock cycles. The 16 pieces of data can be mapped to a set of data arranged according to a square structure in the sub-matrix, and these 16 data belong to four consecutive azimuth directions and four consecutive range directions, respectively. They are divided into two groups by an equivalent amount, which are stored in DDR3A and DDR3B, respectively. In SAR imaging processing, the FFT operation is processed with complete data in the range or azimuth line, and the range and azimuth directions of the data transmitted in a single burst are balanced, making the system computing engine utilization 100%. Figure 7 is a schematic diagram of an equal division cross-mapping of a sub-matrix with a dual channel.  
In this mapping method, two consecutive rows of data in the sub-matrix are mapped to the same DDR3, e.g., rows 0 and 1 in the sub-matrix are mapped to DDR3A, rows 2 and 3 are mapped to DDR3B, etc., in that order. The 16 data in the box are the data of the first burst transmission. After the 16 data are transmitted in burst mode, 16 more data are transmitted in range direction if it is a range access, and if it is an azimuth access, 16 data will be transmitted in the azimuth, and so on until the data access is completed.
## 2 Materials and Methods
### Data Pre-Processing and Feature Extraction
#### 2.2.1 Handling of Outliers and Unsynchronized Sensor Data  
During experiments, sensors may intermittently fail to record data, causing interruptions in the dataset. Hardware malfunctions may also lead to irregular values in the dataset. To address the former issue, any unsynchronized sensor data during processing (pointed out by Capture.U) led to the exclusion of measurements from the remaining sensors to ensure reliable and consistent results throughout the analysis. Additionally, any outliers in the dataset were identified and removed in the early stages through a statistical z-score technique (involving how many standard deviations (SDs) the data are from the mean, with values beyond 3 SDs considered outliers [39]). In the present analysis, the most common challenge was the non-synchronization of the sensors, while outliers were very limited in number and most of the time could be removed manually.
## 3 Results and Discussion
### Discussion
#### 3.4.3 Consideration of Rain  
It can be seen in the rainfall data from the closest station (Figure 2) that extreme events exceeding 40 mm/day are quite frequent (some even reach 70 mm/day). Note that we can distinguish two types of flash floods typical of the Seine-Maritime and northwestern France: the flash floods qualified as \"winter event\", which are generally observed from October--associated with long and repeated rains--and the \"spring\" events, which are mainly related to thunderstorms with a strong intensity. The latter generally occur between April and August. Spring floods are generally observed in small watersheds, such as ours (which are located downstream from larger watersheds). The rainfall associated with this type of phenomenon is characterised by a very high intensity but is often very punctual [49; 62; 63]. In this case, rainfall amounts vary from 40 to 70 mm, with strong amplitude of the storm duration. This is the case of the event presented in this article (24 August 2015), which is a spring phenomenon (rainstorm bringing more than 40 mm of rain in two hours). The rainfall simulated in our model fully represents these types of phenomena. (This makes the model and its assumptions even more adapted to our watershed).
## 2 Related Work
#### Discussion on QoE Factors Beyond Latency  
Although the proposed model primarily focuses on reducing latency, other aspects of QoE are also crucial for ensuring an optimal gaming experience. User feedback mechanisms--such as visual, auditory, and tactile responses--play a significant role in providing real-time information to players, enhancing their immersion and control. Furthermore, factors such as bandwidth optimization, jitter minimization, and packet loss reduction are vital for delivering smoother and more engaging gameplay experiences. For instance, bandwidth-saving techniques like those proposed in [36] help ensure stable performance by reducing network strain. At the same time, studies in [37; 38] emphasize the disruptive impact of jitter and packet loss on gameplay, affecting player satisfaction and performance.  
Integrating these additional QoE dimensions into a comprehensive evaluation framework highlights the interconnected nature of these factors and their collective importance in improving the overall player experience in MMO games.
###### Abstract  
This paper tackles the challenge of secure and reliable data transmission in diamond network configurations featuring two untrusted relays with low-security clearance. We propose an innovative approach that employs lossy-decode and -forward relaying at these untrusted relays to boost transmission reliability while safeguarding the source information from potential eavesdroppers. An essential contribution of this work is the introduction of the reliable and secure probability (RSP) metric. This metric assesses the likelihood of the destination successfully retrieving the original information while maintaining its confidentiality from untrusted relays. Our analysis shows that the integration of cooperative jamming signals markedly enhances the RSP, resulting in superior security and reliability. Simulation results confirm that optimal power distribution among the source, relays, and destination further maximizes the RSP. These findings underscore the effectiveness of our proposed scheme in ensuring secure and reliable communication in environments with untrusted relays, suggesting its potential as a robust solution for secure communications in diamond network configurations.  
physical layer security; untrusted relay; diamond network; optimal power allocation; cooperative jamming +
Footnote †: journal:
## 2 Related Work
### Rate Adaptive MAC in Wireless Local Area Networks (802.11)  
A number of rate adaptation algorithms [22; 23; 24; 25] have been proposed in the area of Wireless Local Area Networks (WLANs) in the last few years. The basic idea is to increase the data rate upon consecutive transmission successes and to use probe packets to access new transmission rates. While all these algorithms seem logically correct, little is known about their practical performance.  
Auto Rate Fallback (ARF) [22] was the first published rate adaption algorithm in WLAN, and has been implemented commercially. ARF increases the transmission data rate when a sender receives acknowledgements for a number of consecutive packets, and decreases transmission data rate when a sender fails to receive acknowledgements. Instead of using a fixed number of consecutive successful transmissions to determine the increase (or decrease) in data rates, Adaptive Auto Rate Fallback (AARF) [23] uses an adaptive number of consecutively successful transmissions by a binary exponential back-off algorithm.  
Receiver Based Auto Rate (RBAR) [24] considers the problem of asymmetric wireless links and uses the channel quality information, e.g., SNR, at the receiver to select a data rate. Instead of using a fixed maximum message size, Opportunistic Auto Rate (OAR) [25] attempts to exploit the wireless channel whenever the link quality is good by allowing higher data rate transmissions to use larger message sizes. Therefore, OAR can provide better throughput. [PERSON] et al. [26] propose a hybrid rate adaptive approach based on both SNR and delivery rate statistics for streaming applications in _single-hop_ WLANs. Our work is closest to MiSer [27] because both algorithms try to minimize energy consumption by adapting the transmission rates at the physical layer. However, there are two key differences between RA-MAC and MiSer. First, MiSer is heavily reliant on the IEEE 802.11b MAC protocol (_i.e._, Request To Send (RTS), Clear To Send (CTS), data, and acknowledgement). However, the packets in WSNs are typically considered _too small to justify RTS/CTS exchanges_. Second, MiSer does not take _the cost of acknowledgement packets_ into account because the sizes of the data packets are significantly larger than the acknowledgement packets. However, because the data packets in sensor networks are typically small, previous research [28] shows that it is important to consider the cost of acknowledgement packets. As show in [27], MiSer is most suitable for data transmissions with large data payloads (e.g., 1,500 bytes). On the other hand, RA-MAC is optimized to work with tiny payloads (e.g., 34 bytes) in WSNs.
## 2 Materials and Methods
### Analysis of Milk Composition  
The milk samples were placed into 50 mL tubes at 8:00 a.m., and then two subsamples per animal were formed for each sampling time. One of them was stored at minus 4 \({}^{\circ}\)C  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Diets** & **TR** & **TMR1** & **TMR2** & **TMR3** \\ \hline Chemical components \% & & & & \\ Dry matter \% & 90.97 & 93.16 & 92.94 & 93.26 \\ Crude protein \% & 14.68 & 12.70 & 12.2 & 12.95 \\ Fat \% & 0.71 & 2.43 & 2.36 & 2.31 \\ EE \% & 2.88 & 2.48 & 2.63 & 2.28 \\ NDF \% & 37.23 & 40.77 & 40.25 & 41.81 \\ ADF \% & 20.15 & 26.47 & 26.25 & 26.65 \\ Ash \% & 6.06 & 10.35 & 9.89 & 10.01 \\ Calcium (\%) & 0.70 & 2.24 & 2.16 & 2.12 \\ Phosphorus (\%) & 0.29 & 0.57 & 0.4 & 0.64 \\ Fatty acids composition \% & & & & \\ C6:0; caproic acid & 0.02 & Nd & 0.07 & Nd \\ C8:0; caprylic acid & 0.21 & 0.34 & 0.27 & 0.38 \\ C10:0; capric acid & Nd & 0.50 & 0.35 & 0.60 \\ C12:0; lauric acid & 1.67 & 9.11 & 6.53 & 11.26 \\ C14:0; myristic acid & 2.05 & 3.74 & 2.76 & 4.61 \\ C16:0; palmitic acid & 20.43 & 15.25 & 14.33 & 14.89 \\ C16:1 cis 9; palmitoleic acid & Nd & 0.28 & 0.31 & 0.27 \\ C17:0; margaric acid & 0.43 & 0.31 & Nd & Nd \\ C18:0; stearic acid & 6.12 & 4.13 & 4.02 & 3.425 \\ C18:1 trans 9 & 1.66 & 0.81 & Nd & Nd \\ C18:1 cis 9; oleic acid & 8.65 & 33.96 & 33.68 & 32.12 \\ C18:2 cis 9, 12 (LA) & 34.46 & 23.69 & 14.04 & 9.645 \\ C18:3 cis 9, 12, 15 (ALA) & 21.45 & 1.51 & 1.01 & 0.49 \\ C20:0; arachid acid & 2.43 & 0.59 & 0.62 & 0.515 \\ C22:0; henecisicylic acid & 0.97 & 0.36 & 0.40 & 0.265 \\ C24:0; lignocoric acid & 0.615 & 0.36 & 0.39 & 0.31 \\ SFA; saturated fatty acid & 30.70 & 34.69 & 29.78 & 36.27 \\ UFA; unsaturated fatty acid & 72.02 & 60.84 & 49.06 & 42.53 \\ MUFA; monounsaturated fatty acid & 14.44 & 35.64 & 34.00 & 32.40 \\ PUFA; polyunsaturated fatty acid & 57.59 & 25.20 & 15.06 & 10.14 \\ \hline EE = ether extract; NDF = neutral detergent fiber; ADF = acid detergent fiber; LA = linolenic acid; ALA = \(\alpha\)-linoleic acid; Nd = not detected. & & \\ \end{tabular}
\end{table}
Table 1: An analysis of the chemical composition and FA profile (g/100 g of FA) for traditional feed (TR) (barley and alfalfa hay) and total mixed ration (TMR1, TMR2 and TMR3) on a dry matter basis.  
for the analysis of the fat, protein, total solids, solids not fat (SNF), and freezing point depression (FPD) using Milko_Scan technology and the Fossomatic Minor (Fossomatic 90, FOSS Electric, Hillerod, Denmark) to calculate the number of somatic cells (SCC) as the number/mL. Another sample was stored at minus 20 \({}^{\circ}\)C for the subsequent analysis of FA using gas chromatography-mass spectrometry.
## 3 Results and Discussion
### Effect of Weld Pool Oscillation on the Welding Voltage Signal  
The oscillation phenomenon of the weld pool causes the fluctuation of the distance between the welding wire and the weld pool surface, which, in turn, changes the arc length. The voltage signal of the GTAW process can reflect the dynamic change process of the arc length caused by the oscillation of the weld pool in real time; then, it can realize the sensing of the penetration state of the weld [1]. For the S-GMAW welding process, the droplet transfer process complexes the relationship between arc length and voltage in the welding process. The previous article has proved that the change of the weld pool penetration state could change the oscillation mode and amplitude of the weld pool [8]. Therefore, the internal relationship between the oscillation phenomenon of the weld pool and the arc stage voltage of the welding process is the base of the research on the sensing method of the penetration state of the weld pool in S-GMAW. Figure 11 shows the weld pool surface height change process and the synchronous electrical signal in both full and partial penetration states. Although the oscillation amplitude of the full penetration weld pool is twice that of the partial penetration state, both the synchronous voltage signals were similar, as shown in Figure 11b,c.  
The electric explosion of the S-GMAW welding process increased the density of metal vapor in the arc space and increased the conductivity of the arc column area, weakening the influence of the molten pool oscillation phenomenon on the arc voltage. The large amplitude of current change in the arc phase further concealed the voltage fluctuations caused by the oscillation phenomenon of the weld pool. Therefore, the voltage signal in the arc phase of the S-GMAW welding process cannot respond to the oscillation process of the weld pool.  
Figure 10: Calculation and measured frequency of weld pool oscillating phenomenon in S-GMAW.
# Optimization of Fuzzy Logic Controller Used for a Differential Drive Wheeled Mobile Robot
## 2 Problem Description
### Optimization of the Fuzzy Logic Controller Based on the Genetic Algorithm
#### 3.2.2 Fitness Functions Used for the Genetic Algorithm
For all used controllers here, based on the parameters of the DDWMR and parameters of the given paths, the linear velocity (\(v\)) is limited to the range [0 m/s to 2.0 m/s], and the angular velocity (\(\omega\)) is limited to the range [-0.75 rad/s to 0.75 rad/s] because the movement of a mobile robot will be directly affected by the speed that its actuator can provide. If the provided velocity is too large, it will cause some difficulty in tracking, a high energy loss, and a big tracking error. Conversely, if the provided velocity is too small, the robot will move too slowly, which may affect its performances.