esa-sceva/satcom-chunk-collection huggingface dataset:
# Tribological Analysis of Several Coatings under Flood and Cryogenic Cooling Conditions
## 2 Materials and Experimental Set-Up
### Determination of Contact Conditions in Machining of Ti-6 Al-4V Alloy
#### 2.3.1 Determination of the Sliding Speeds between the Pin and the Ti-6 Al-4V Bar  
The sliding speed of the chip against the tool rake face is different from the cutting speed. As shown in Figure 3, a layer of material was separated from the workpiece by the action of the tool to form the chip. The ratio between the chip thickness and the uncut chip thickness, which is equal to the ratio between the cutting speed and the chip velocity, is called chip compression ratio (CCR), \(\zeta\), and it is given by Equation (3) [47]:  
\[\zeta=\frac{h_{1}}{h}=\frac{V_{c}}{V_{1}} \tag{3}\]  
where \(h\) and \(h_{1}\) are uncut chip thickness (mm) and chip thickness, respectively. \(V_{c}\) and \(V_{1}\) are the cutting speed and chip velocity (m/min), respectively. This equation shows that the chip velocity, \(V_{1}\), depends on both cutting speed and CCR. Since the CCR is usually greater that 1 (chips are generally thicker than the uncut chip thickness), the chip velocity is lower than the cutting speed.  
By analogy to the machining tests, in pin-on-bar tribological tests, the sliding velocity, \(V_{s}\), is equal to the chip velocity, \(V_{1}\), in machining. Considering that the average CCR during the orthogonal cutting of Ti-6 Al-4V alloy using uncoated cemented carbide tools is about 1.5 (Figure 4) [49], the sliding speeds can be determined for various cutting speeds using Equation (3), as listed in Table 2. The large range of cutting speeds can be used to determine the influence of this parameter on the tribological behaviour of coatings. It is noteworthy that the upper value of the cutting speed is a bit higher for machining Ti-6 Al-4V alloy using commercial cemented carbide cutting tools. The main reason for using this value is to evaluate the tribological performance of the new coatings under more aggressive cutting conditions than those used today for commercial coated tools.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Cutting speed, \(V_{c}\) (m/min)** & 15 & 30 & 60 & 90 & 120 & 150 \\
**Sliding velocity, \(V_{1}\) (m/min)** & 10 & 20 & 40 & 60 & 80 & 100 \\ \hline \end{tabular}
\end{table}
Table 2: Sliding velocities and correlated cutting speeds determined in tribological tests.  
Figure 3: Single-shear plan model [48].
## 2 Primer Knowledge
### OFDM Signal Preamble  
For IEEE 802.11 WLAN OFDM systems, the physical layer protocol data unit (PPDU) is designed for flexible data transmission. PPDU consists of two main parts: preamble and data fields, which are shown in Figure 3. The preamble field contains the transmission vector format information and the data field mainly contains the payload. Generally, OFDM signals begin with a legacy preamble that is specifically designed for packet detection and synchronization. The legacy preamble contains three parts, which are STF (short training field), LTF (long training field), and SIG (signaling field). STF is used for packet detection and coarse synchronization, LTF is used for fine synchronization and initial channel estimation, and SIG is to determine transmission parameters [11]. STF consists of 10 identical short symbols, which have good auto-correlation properties. This unique structure is specifically designed by engineers for packet detection and synchronization. STF is simplified as short preamble in the later parts.
## 4 Experimental Results and Analysis
### Static Detection Results and Analysis
#### 4.2.2 Error Analysis  
Tables 4 and 5 show the coordinate information of the detection box and relative error of the pixel height of each bamboo shoot in the corresponding image in Figure 9. Column 1 in the table represents the order of the detected moso bamboo shoots (No.); columns 2 to 5 represent the minimum value of the horizontal coordinate (Xmin), maximum value of the horizontal coordinate (Xmax), minimum value of the vertical coordinate (Ymin), and maximum value of the vertical coordinate (Ymax) for each mos bamboo shoot,  
Figure 8: Comparison of detection results of small bamboo shoots that are too close together: (**a**) before; (**b**) after.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Prior Box Size**} & **mAP** \\ \cline{2-5}  & **Small Targets** & **Medium Targets** & **Large Targets** & **(\%)** \\ \hline \multirow{3}{*}{YOLOv4} & \(12\times 16\) & \(36\times 75\) & \(142\times 110\) & \multirow{3}{*}{76.57} \\  & \(19\times 36\) & \(76\times 55\) & \(192\times 243\) & \\ \cline{1-1}  & \(40\times 28\) & \(72\times 146\) & \(459\times 401\) & \\ \cline{1-1}  & \(15\times 49\) & \(47\times 279\) & \(79\times 584\) & \\ \cline{1-1} K-YOLOv4 & \(23\times 117\) & \(62\times 371\) & \(82\times 781\) & \\ \cline{1-1}  & \(36\times 186\) & \(57\times 466\) & \(66\times 987\) & \\ \cline{1-1}  & \(15\times 49\) & \(47\times 279\) & \(79\times 584\) & \\ \cline{1-1} \cline{2-5} Bamboo-YOLOv4 & \(23\times 117\) & \(62\times 371\) & \(82\times 781\) & \multirow{2}{*}{87.42} \\ \cline{1-1}  & \(36\times 186\) & \(57\times 466\) & \(66\times 987\) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of detection results before and after model improvement.  
Figure 9: Sample detection results of moso bamboo shoots in two experiments: (**a**) cloudy, 6–21 \({}^{\circ}\)C; (**b**) heavy rain, 6–23 \({}^{\circ}\)C.  
respectively; columns 6 and 7 represent the height detection value (H\({}^{\prime}\)) and reference value (H), respectively; and the last column represents the relative error (\(\alpha\)). The relative error is calculated as follows.  
\[\text{Relative Error}=100\%\times\text{(Detection value}-\text{ reference value)}/\text{reference value} \tag{6}\]  
In Equation (6), the detection value is the difference between the maximum value (\(Ymax\)) and the minimum value (\(Ymin\)) of the vertical coordinate of each detection frame, and the reference value is the value of each moso bamboo shoot pixel in the image.  
From the analysis of the above results, a total of 25 moso bamboo shoots are shown in Figure 9a, and 23 moso bamboo shoots are shown in Figure 9b. The number of missed moso bamboo shoots was 0 and 1, respectively. The absolute values of the relative errors of height were not more than 4%, with average relative errors of \(-0.13\%\) and \(-0.47\%\) and variances of 0.0617% and 0.0689%, respectively. To avoid subjectivity in the results, the average relative
## 2 Background  
Implementing a mobile robot in a healthcare environment is a crucial task. It is quite important for healthcare directors and policy makers [31]. It is also as much a concern for patients and operators who must operationalize them outside formal clinical situations [32; 33]. It is worth mentioning that mobile robots are often considered the best alternative to explore and implement in aggressive remote areas. Conventional application regions are space examination and treating plants [8]. With the same consideration of exploration, modernized ideas have been explored in the healthcare system and industries [34]. The methodology and the design implementation remain the same to control the mobile robot in a healthcare environment [16; 35]. The recent era of telematics, developed by employing internet service providers, enables the technology over a wide range of commercially available applications. Examples include telemedicine, equipment temaintenance and online learning environments from remote locations [36].  
Another application of telepresence robots is in education. With the rise of online learning, telepresence robots offer a way for students to participate in classroom discussions and interact with their peers and teachers as if they were physically present. This can be particularly helpful for students who cannot attend in-person classes due to illness or disability. Telepresence robots are also used in various other settings, such as museums,trade shows and even as personal assistants for individuals with disabilities. As technology advances, we will likely see even more innovative applications of telepresence robots in the future [37; 38; 39; 40].  
The most common approaches to the simple design of mobile robots can be found in [41; 42]. A robot comprises both software and hardware designs. There are many components which provide much information about the motion and position of the mobile robot. The sensors fulfil the task and send the information to the remote operator to perform specific routine instructions. All this information is controlled using the software module of the robot [43; 44]. The software part of the mobile robot sends the control instructions, which are operator-dependent. These are used to exactly control and locate the robot in the desired location and obtain important information from each actuator. The function of the actuator is to provide complete control over the hardware activities of the mobile robot [8; 10; 44]. The core concern of the design is to control the driving behavior and avoid an obstacle at each instance [6; 38; 45]. The second crucial part is the decision-making device, which controls the orientation robot and self-localizes it to avoid obstacles during the journey and provide a safe track [46; 47]. Another term, \"maneuver,\" is also called, but better and more precise terms are \"behavior\" or localization, which are more frequently used in the article [48; 49; 50; 51; 52]. The control of a robot in an austere and cognitive environment is presented in [52; 53]. More examples of ray robots with a multi-objective design are presented in [54; 55].
## 3 Results and Discussion  
### Microscopic Characterization Analysis  
Figure 8 shows SEM-EDS micrographs of modified mortar (MRWG50) with 50% of sand replacement with RWG. The region indicated in Figure 8a was deeply analyzed by identifying four spots as it was visualized at length scale 100 \(\upmu\)m (Figure 8b), and the found results are depicted in Table 5. The identified Spot 1 indicates chemical components of glass similar to those given in Table 2, Spot 2 shows an important content of calcium to Silicon (Ca/Si) of 5.4 which represents chemical elemental compositions of cement paste products, Spots 3 and 4 in the surrounding glass are characterized by Ca/Si values of 1.51 and 1.87 respectively, representing the chemical constituents of calcium-silica-hydrate C-S-H different from the ones of ASR gel. In this regard, many studies [35; 49; 50] found that ASR gel is characterized by an amount of calcium Ca less than that of silicon Si; which does not match our results found around the glass. This latest finding indicates that the RWG incorporation in the studied samples didn't produce deleterious alkali-silica reaction.  
Figure 8: SEM-EDS micrographs of MRWG50: (**a**) SEM picture at length scale 200 \(\upmu\)m and (**b**) Spots of EDS imaging.  
Figure 7: FP2C device used to measure thermal conductivity and thermal effusivity of the studied samples.  
To further visualize the damage caused by ASR, a microscopic characterization by optical microscope technique was carried out. This technique is among the accurate one to know the degradation state associated to ASR [52]. Figure 9 presents optical microscope characterization of MRGW50. It can be observed that a minority of glass particles were cracked while the majority was not. These cracks were caused by ASR due to penetration of ions inside micro-cracks generated already during the glass bottle crushing operations [35; 50; 51].  
This result matches the one reported by [PERSON] et al. [35], and [PERSON] et al. [51] who both showed that ASR gel occurs in the micro-cracks already existing in the glass particles. It means that the only particles with micro-cracks are attacked by ASR.  
The microscopic view of the bond between glass particles RWG and cement paste in concrete sample CRWG50 after 90 days of curing was also carried out. Figure 10 shows that strong bonds were created between RWG and cement paste and no voids were observed between the two elements. These bounds were created as a result of the pozzolanic CSH due to reaction of dissolved silica with the available portlandite [28; 35].
# Edge and Cloud Computing in Smart Cities
## 4 Application Domains  
The deployment of edge and cloud computing architectures has revolutionized various application domains by enabling real-time data processing, intelligent decision-making, and efficient resource management. These architectures support diverse industries such as transportation, healthcare, industrial automation, and smart-city infrastructure while also transforming energy management, immersive AR/VR experiences, disaster response, and cybersecurity. By optimizing computational efficiency and reducing latency, edge-cloud frameworks ensure that mission-critical applications operate with seamless connectivity and adaptive intelligence. The convergence of distributed intelligence with cloud-based analytics fosters scalable and resilient ecosystems, addressing the growing complexities of modern digital infrastructures.
# Deep-Learning Based Algorithm for Detecting Targets in Infrared Images
## 1 Introduction
However, deep learning in target detection research of infrared images is not yet common. Inspired by the idea of transfer learning, this paper migrates the target detection algorithm on visible images to the infrared image target detection field. Firstly, we propose a target detection model CMF Net to solve the problem of the existence of target multiscale features. The CMF Net model is based on the VGG16 network (a convolutional neural network) and uses two multiscale feature extraction mechanisms for image feature extraction and fusion. This makes the final feature map input from the backbone network to the classification network contain low-level visual features that facilitate target localization and high-level semantic features that enable target recognition. Secondly, to solve the problem of low detection accuracy of the algorithm when the target is occluded, we propose the CMF-3 DLSTM model. The model improves the classification network into a 3D long- and short-term memory network based on the CMF Net model. We use an attention mechanism to assign weights to the contextual features extracted in different dimensions. Finally, target detection features include multiscale features and contextual features to achieve the fusion of spatio-temporal features. The rest of this paper is organized as follows: Section 2 summarizes the infrared image target detection algorithm-related work. Section 3 introduces the details of the CMF Net model. Section 4 introduces the structure and details of the CMF-3 DLSTM model in detail. Section 5 describes the design and results of relevant experiments. Section 6 summarizes the work of this paper.
# Design and SAR Analysis of a Dual Band Wearable Antenna for WLAN Applications
## 3 Results
### Off-Body Analysis
#### 3.1.2 Metamaterial-Based Antenna  
To improve the performance of the designed conventional dual band antenna, two different types of EBG ground planes have been designed in CST software, as shown in Figure 3. Subsequently, these EBG surfaces have been employed as a ground plane for the proposed dual band wearable antenna, as shown in Figure 6, and an analysis on the basis of a performance comparison is presented below.  
Figure 14 shows the comparison of the return loss of the conventional dual band wearable antenna with that of the two EBG-based ground plane antennas.  
Figure 14: Return loss without EBG and with EBG ground planes.  
Figure 12: Gain patterns of conventional dual band antenna at (**a**) 2.5 GHz and (**b**) 5.2 GHz.  
Figure 13: Distribution of surface currents of the conventional dual band antenna at (**a**) 2.5 GHz and (**b**) 5.2 GHz.  
With the help of the mushroom-like EBG ground plane, a 5.2 and 33.6 MHz improvement in bandwidth has been achieved for the lower and upper frequency bands, respectively. By using the EBG with plus-shaped slots as the ground plane for the proposed antenna, a 7.9 and 16.7 MHz improvement has been achieved in the bandwidth for the 2.5 and 5.2 GHz bands, respectively. A small amount of reduction in return loss has been observed for both EBG ground planes. Moreover, for the lower frequency band, a certain amount of shift to the left side for the mushroom-like EBG ground plane and to the right side for the EBG with plus-shaped slots has been observed. This shift in the resonance frequencies has also been observed in [36; 39] when the antenna is employed on a metamaterial-based ground plane.  
The directivity and gain patterns without and with EBG ground planes at 2.5 and 5.2 GHz are shown in Figures 15 and 16. An increase of approximately 1.8% and 28.7% in directivity for the lower and upper frequency bands, respectively, has been achieved by the help of the mushroom-like EBG ground plane. By using the EBG with plus-shaped slots, a 15.13% improvement in directivity for the lower frequency band and a 3.55% improvement in that for the higher frequency band have been observed. Similarly, the mushroom-like EBG and EBG with plus-shaped slots show 4.33% and 16.70% improvements in gain for the lower frequency band, respectively, and 30.43% and 4.57% increases in gain for the upper frequency band, respectively. Furthermore, a reduction in side and back lobes is clearly observed  
Table 4 summarizes the performance comparison of the conventional dual band antenna without and with EBG ground planes.
# Sensors and Communication Simulation for Unmanned Traffic Management
## 6 Validation
### Drone Detection Using Cooperative and Non-Cooperative Sensors  
This second scenario seeks to check the correct behavior of the cooperative and non-cooperative sensors network. For this purpose, a simulation environment was generated that consists of three different types of sensors: a FLARM cooperative sensor, an ADS-B, and a non-cooperative sensor. The positioning of these sensors and their detection range can be seen in Figure 15. In this environment, we simulate two flights whose flight plans can also be seen in the same figure. Flight 1 is operated by a cooperative drone which is equipped with a FLARM emitter. On the other hand, Flight 2 has equipment for the FLARM and ADS-B sensors. However, this second operation is marked as non-cooperative by simulating the pilot's decision not to share his position.  
This scenario was completely simulated without introducing contingencies. The tracks sent to the UTM platform are depicted in Figure 16a. We can first observe how the spatial detection limits of the sensors are respected. Thus, all the tracks sent by the FLARM sensor are limited to the interior of its coverage area. The non-cooperative sensor (NC sensor in the figure) has been able to detect, as expected, both cooperative and non-cooperative drones along their trajectory within the coverage area. Furthermore, we can also observe how this sensor simulated false detections as seen in the randomly distributed plots within the coverage area. Missed detections, resulting in monosensor track reinitialization, were also simulated. This can be seen in Figure 16b, where track numbers (identifiers) are depicted. Longer horizontal lines are related to real drones' identifiers, which change from time to time due to the aforementioned track reinitialization. In this same figure, blue points are related to false alarm tracks, typically with very short life. Apart from this, cooperative sensors only detected cooperative drone with compatible equipment; it is the case of flight 1 and the FLARM sensor. For the same reason, flight 2 was not been detected by any of  
Figure 15: Scenario 6.2 with two different flights and three sensors.  
the cooperative sensors in spite of carrying FLARM and ADS-B equipment, since it is not cooperative.  
Finally, position information obtained from each type of sensor can be compared. Cooperative sensors obtain the position from drones' navigation systems. Thus, we can see in Figure 17b how the information sent to the UTM system by the sensor is the basically the same as the telemetry information sent by the drone (codification errors due to channel quantization may arise). The non-cooperative sensor (NCS) measures the real position of the drone, and therefore there is a divergence with telemetry, due to both navigation error and NCS measurement/tracking error. Hence, the information sent by the non-cooperative sensors to the UTM system is different from the information received from the drones through telemetry, as we can see in the Figure 17a.
# Fungi of the _Trichoderma_ Genus: Future Perspectives of Benefits in Sustainable Agriculture
## 5 Application of _Trichoderma_ Fungi in Promoting Plant Growth
### Temperature  
Most fungi of the genus _Trichoderma_ belong to mesophilic microorganisms for which the optimal temperature of growth and sporulation is between 25 \({}^{\circ}\)C and 35 \({}^{\circ}\)C [189; 190]. However, some species have different temperature requirements and are isolated from the soil in colder or warmer regions of the world. According to [PERSON] et al. [191], _T. harzianum_, _T. koningii_, and _T. viride_ were highly adapted to growth in a wide temperature range from 4 \({}^{\circ}\)C to 42 \({}^{\circ}\)C. [PERSON] et al. [192] demonstrated that _T. polysporum_ developed at temperatures from 0 \({}^{\circ}\)C to 28 \({}^{\circ}\)C. Similarly, [PERSON] et al. [193] reported that the above-mentioned species grew within the range of 6 \({}^{\circ}\)C to 15 \({}^{\circ}\)C. The research of [PERSON] et al. [194] and [PERSON] et al. [195] proved that _T. asperellum_ isolated from desert soil developed and spored properly at temperatures up to 50 \({}^{\circ}\)C and 52 \({}^{\circ}\)C.  
The study by [PERSON] et al. [196] showed that _T. atroviride_, _T. harzianum_, and _T. suppureum_ limited the growth of _R. solani_ at 25 \({}^{\circ}\)C to a greater extent than at 15 \({}^{\circ}\)C. Similarly, [PERSON] et al. [190] reported that the rates of inhibition of _R. solani_ by _T. pseudokoningii_ and _T. viride_ were significantly higher at 30 \({}^{\circ}\)C than 25 \({}^{\circ}\)C. [PERSON] and [PERSON] [197] documented that _Trichoderma_ sp. isolates limited the development of _S. rolfsii_ mycelium at 25 \({}^{\circ}\)C and 30 \({}^{\circ}\)C. In contrast, at higher incubation temperatures (33 \({}^{\circ}\)C-37 \({}^{\circ}\)C), the pathogen inhibited the growth of the tested antagonists. [PERSON] and [PERSON] [198] also provedthat with an increasing temperature (25 \({}^{\circ}\)C-35 \({}^{\circ}\)C), the antagonistic activity of _T. hamatum_, _T. harzianum_, _T. koningii_, _T. longibrachiatum_, and _T. viride_ against _M. phaseloma_ decreases. [PERSON] et al. [199] analyzed the effect of _T. afroharzianum_ and _T. atroviride_ on the induction of expression of genes involved in tomato defense responses. The above-mentioned authors reported that at 20 \({}^{\circ}\)C, _T. atroviride_ significantly increased the activity of nine genes responsible for plant defense against pathogens, while at 25 \({}^{\circ}\)C, only three genes were affected. In contrast, _T. afroharzianum_ significantly induced the expression of two and nine genes at 20 \({}^{\circ}\)C and 25 \({}^{\circ}\)C, respectively.