esa-sceva/satcom-chunk-collection huggingface dataset:
# Jam Mitigation for Autonomous Convoys via Behavior-Based Robotics
## 5 Discussion  
Jamming attacks are a simple, yet effective type of denial-of-service attack that have the potential to adversely degrade the performance of autonomous convoys. While many efforts have been undertaken to detect the presence of jams, we focused our efforts on finding ways to mitigate a jammer's effects to allow for continued convoy operation. The prevalence of behavior-based robotics approaches in multi-vehicle teaming made it a prime starting point for developing an approach to allow convoy following when confronted with jamming, and lead to the development of the Behavior Manager.  
By utilizing the Behavior Manager and creating behaviors and assemblages to mitigate the effects of jamming, we were able to show improved convoy performance with up to an 86.61% reduction in average MAE. This demonstrates that a behavior-based robotics approach can be leveraged to dramatically improve the robustness of autonomous convoys when faced with jamming attacks. Furthermore, the modular nature of the Behavior Manager means that the capabilities can be extended to mitigate the effects of other types of attacks, such as LiDAR spoofing, replay attacks, and RADAR absorption [49], through the development of additional behaviors and assemblages.  
A potential future area of research on the usage of behavior-based robotics towards mitigating the effects of attacks is dynamic behavioral weighting. While the usage of Q-learning towards behavior selection has been researched in the past [50], the application of modern learning techniques, such as deep reinforcement learning, towards multi-robot attack mitigation is a novel area that warrants further investigation. By training on datasets that demonstrate human reactions to attacks on manned convoys, the robustness of autonomous convoy systems utilizing behavior-based robotics could be greatly improved on, creating even more stable systems able to handle a broad spectrum of attacks.  
Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing--original draft preparation, visualization, project administration: C.C.; writing--review and editing, supervision, funding acquisition: A.M. and S.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# New Hydraulic High-Pressure Impulse Generator for Long-Duration Impulse Tests
## 2 Structure and Working Principles  
The main architecture of the new hydraulic impulse generator, as shown in Figure 2, can be divided into two parts. The left part, which is considered as the booster part, comprises a left block (3) with an asymmetric booster piston (4) and a connector (2) that is connected to the tested hydraulic component (1). The booster piston partitions the interior into four chambers with different functions. The high-pressure chamber (A) forms the inner space of the tested components and is filled with the hydraulic oil for transmitting the pressure signals. The withdraw chamber (B) is connected to the rated pressure source (\(p_{R}\)) and contributes to increasing the declining slope of pressure signals. The air chamber (C) is connected to the air, and the low-pressure chamber (D) filled with hydraulic oil is directly connected to the output of the impulse generator part, a rotary spool (8), and electro motor (10). The impulse generator part has a right block (6) with a sleeve (7) inside. The rotary spool is supported by two deep groove ball bearings and a thrust bearing for the axial force balance. The electro motor is solidly set on a motor base (9) to drive the rotary spool rotating in the sleeve. The setup of the sleeve and rotary spool is shown in Figure 3. Three pairs of windows are symmetrically set on the sleeve, and from left to right, they are sequentially but independently connected to the back pressure (\(p_{T}\)), peak pressure source (\(p_{P}\)), and rated pressure source (\(p_{R}\)). Since a couple of rectangular slots is also symmetrically located on the rotary spool and connected to the central drilling as shown in Figure 2, each window is scanned by the slot when the spool is rotated using the electro motor. Thus, the pressure signals from three different sources are obtained by the slot and outputted into the low-pressure chamber through the throttle valve (5) twice per revolution. Meanwhile, several small oblique holes on the right side of the drilling connect the two sides of the rotary spool to reduce the axial hydraulic unbalancing force. Thereafter, the pressure impulse signal is amplified by means of the booster piston and transferred to the high-pressure chamber to produce the required pressure waves for the tested hydraulic components.  
Figure 2: Structure of the new hydraulic impulse generator. Here, 1 indicates the tested hydraulic components; 2, the connencer; 3, the left block; 4, the booster piston; 5, the throttle valve; 6, the right block; 7, the sleeve; 8, the rotary spool; 9, the motor base; 10, the electro motor. Further, A indicates the high-pressure chamber; B, the withdraw chamber; C, the air chamber; D, the low-pressure chamber; E, the inner space of the spool.
# Multi-Band Unmanned Aerial Vehicle Antenna for Integrated 5G and GNSS Connectivity
## 2 Dual-Port Inverted T-Shaped MIMO Antenna and Analysis
### VSWR Parameter Analysis  
The voltage standing wave ratio (VSWR) shows how well a transmission line or radiating element stabilizes the impedance of the system or devices. Reduced VSWR values, preferably near 1, indicate reduced signal reflection and improved impedance matching. Poor impedance matching is indicated by high VSWR values, usually exceeding 2, which can cause signal loss and possibly damage to equipment. The VSWR for all the configurations is shown in Figure 12.
# Acute Respiratory Distress Identification via Multi-Modality Using Deep Learning
## 4 Experimental Analysis  
### Datasets  
To evaluate the effectiveness of using multi-modalities for the ARD task, we conduct experiments on an ARD patient dataset. The dataset was collected at the Sainte-Justine Hospital Pediatric Intensive Care Unit (PICU) with approval from the Review Ethics Board (REB) (Ste-Justine REB number 2016-1242, approved on 31 March 2016) and parental consent.  
Videos were recorded for each patient with a respiratory illness for a duration of 30 s. In total, we collected 210 videos in the PICU, with each video representing a unique patient. However, videos where the patient's torso region was covered, of poor quality, or with excessive noise were excluded. The remaining videos were labeled by two professionals using the Silverman scoring system [43], where the presence of at least one retraction indicated ARD. One professional labeled the data in real time during the recording process, and the second analyzed the videos to ensure information is captured effectively. Videos with labeling conflicts were also removed, resulting in a final dataset of 153 patients. Out of the 153 patients, 133 are aged 6 years or younger, with 63.16% exhibiting ARD, while the remaining 20 patients are older than 6 years, with 11.11% exhibiting ARD. The data distribution of ARD and Non-ARD patients categorized by age group, retraction type, and overall totals is presented in Figure 3. The figure on the left presents the ARD patients' statistics with respect to age. The figure in the middle presents the distribution of retraction signs in ARD patients. The figure on the right presents the overall class-wise data distribution. Of the 153 patients, 86 exhibit ARD, with signs of retractions distributed as follows: subcostal (74), intercostal (28), subternal (40), and suprasternal (16). The remaining 67 patients show no signs of chest retraction, indicating the absence of ARD.
# Alginate-Chitosan Microencapsulated Cells for Improving CD34+
## 3 Results and Discussion
### Paracrine Effects of Microencapsulated CD34+ Cells  
To reconstitute the communication condition in HSC niche, co-culture experiments of microencapsulated CD34+ cells and bare CD34+ cells were set up. The experiments were performed in a static bath culture to facilitate paracrine factors excreted from microencapsulated cells. The microcapsules were placed on the top part, while the non-encapsulated/bare CD34+ cells were in the lower part. A polycarbonate insert was used for easy cell harvest (Figure 2A). Initially, we prepared 3 microcapsules/well that contains a total of 6 \(\times\)\(10^{3}\) cells/well (Donor 1; upper well) for co-culture with 2.5, 5, or 10 \(\times\)\(10^{4}\) cells/mL (Donor 2, lower well). There was a significant increase in CD34+ cell population of 2.5 \(\times\)\(10^{4}\) cells (lower well) after 8-day co-culturing, in comparison with those without microencapsulated cells (data not shown). However, the CD34+ ratio did not improve in higher seeding density cultures. With the same treatment, no impact was seen in cell proliferation of all seeding densities (Figure S1). Therefore, we increased the cell loading capacity of each of 3 microcapsules to contain \(10^{4}\) cells/microcapsule (total 3 \(\times\)\(10^{4}\) cells/well) (Donor 3; upper well) for co-culture with 2.5, 5, and 10 \(\times\)\(10^{4}\) cells/mL (Donor 4; lower well).  
As seen in Figure 2B, microcapsules containing 3 \(\times\)\(10^{4}\) cells per well improved the viable cell densities of bare CD34+ cells (lower well) seeded in 2.5 and 5 \(\times\)\(10^{4}\) cells/mL, but made no difference at a higher seeding density. At seeding density of 2.5 \(\times\)\(10^{4}\) cells/mL and 5 \(\times\)\(10^{4}\) cells/mL, cell concentrations of those with microencapsulated cells increased from 2.7 \(\pm\) 0.3 \(\times\)\(10^{4}\) cells/mL to 4.7 \(\pm\) 0.6 \(\times\)\(10^{4}\) cells/mL and from 3.5 \(\pm\) 0.5 \(\times\)\(10^{4}\) cells/mL to 7.1 \(\pm\) 1.6 \(\times\)\(10^{4}\) cells/mL, respectively. We expected that for seeding density 10 \(\times\)\(10^{4}\) cells/mL required a higher dosage of microencapsulated cells.  
Typically, freshly isolated CD34+ cells from UCB have a purity of 93.0 \(\pm\) 0.2% (indicated as a striped line in Figure 2C). After 8-day culture, the CD34 positive cell percentage dropped to 83.2 \(\pm\) 1.7, 82.7 \(\pm\) 2.0 and 88.4 \(\pm\) 0.5% in CD34+ cells seeded at 2.5, 5, 10 \(\times\)\(10^{4}\) cells/mL, respectively. Interestingly, CD34+ ratio increased to 89.3 \(\pm\) 1.2, 96.0 \(\pm\) 0.1 and 94.4 \(\pm\) 0.4% in cultures treated with microencapsulated cells. These results suggested that microencapsulated cells potentially maintained CD34 expression in in vitro culture.
## 3 Underwater Routing Protocols
### Mees
#### Protocols Addressing Void Region  
These protocols avoid the condition when a sender node does not find any neighbor node for data routing. The absence of a neighbor node of a sender node is either due to the placement of the sender node in a region where no neighbor exists or when all of its neighbor nodes die. The void condition severely affects the data delivery during routing, which as a consequence, leads to data loss. Protocols that take into account the void region are below.  
AVN-AHH-VBF  
The avoiding void node with adaptive hop-by-hop vector-based forwarding (AVN-AHH-VBF) protocol improves the VBF protocol in three ways [41]. Firstly, every node forwards a packet only if the next hop is not a void region. A void region is the one where only one neighbor exists for the source node. In this case, the source node drops the packet (rather than forwarding it to the void) to reduce energy consumption. Secondly, the depth of a relay node in the pipeline is also taken into account to receive packets from a source node and forward them to the sink. This ensures that packets reach the sink in less time. Thirdly, the holding time considers the number of hops a packet has traversed and the number of neighbors of the node holding the packet. This reduces end-to-end delay. However, the protocol reduces energy consumption at the expense of packet loss. It also suffers from the major problem with VBF: nodes in the pipe are overloaded by data traffic.
## 10 Results  
### Mesh Throughput Results  
The average TCP and UDP throughput results for the tunnel as well as the slope are shown in Figure 8. Transmissions were automatically recorded at 1 s intervals, and a total of 120 samples were recorded for each test case. Each test was performed three times, after which the average throughput was calculated. The 2.4 GHz experiments in the tunnel proved ineffective in a multi-hop scenario due to the limitation of a 40 MHz channel bandwidth; hence, only the 5 GHz experiments were performed in the stope.  
Figure 9 shows the samples used to calculate the average results that were utilized in Figure 8. Each of the 120 samples from Figure 9 were averages calculated from three separate test cases.  
\begin{table}
\begin{tabular}{c c} \hline \hline Hardware & Asus RT-AX53U, nRF7002 dk, Nordic Power Profiler Kit 2 \\ \hline Software & OpenWrt, Zephyr OS, iperf3, zperf, ping, Nordic Power Profiler software \\ \hline Frequency & 2.4 GHz and 5 GHz \\ \hline \multirow{3}{*}{Node distance} & 5 m (stope) \\  & 10 m (spacious tunnel) \\  & 0.5 m (nRF power evaluations) \\ \hline Routing & HWMP (hybrid of reactive and proactive) \\ protocols & B.A.T.M.A.N.-adv (proactive) \\ \hline \multirow{2}{*}{Packet size} & Default iperf3 OpenWrt values (128 KB TCP, 1460 B UDP) \\  & 1 KB (nRF power evaluations) \\ \hline Sample Rate & 100 KHz (nRF power evaluations) \\ \hline \hline \end{tabular}
\end{table}
Table 1: General experimental parameters.  
Figure 8: Average Wi-Fi6 throughputs for tunnel and stope.  
Figure 9: Wi-Fi6 sample throughput.  
10.1.1. Frequency and Channel Bandwidth  
At 2.4 GHz, the single hop throughput was significantly better than the double hop throughput. However, at 5 GHz, the single hop throughput was marginally better than the double hop throughput with the exception of HWMP UDP throughput in a stope, which was greater with a double hop. Static hops were assigned to ensure that a wireless hop did occur to ensure accurate results. To confirm the root cause behind the vast difference in multi-hop performance between the 2.4 GHz and 5 GHz experiments, an additional experiment was conducted. Multi-hop performance was recorded at different channel bandwidths, and the results are given in Table 2. The 5 GHz multi-hop experiments performed well due to it utilizing an 80 MHz channel bandwidth, whilst the 2.4 GHz experiments performed poorly due to its utilization of a 40 MHz channel bandwidth. For HWMP transmitting at 5 GHz, at a 20 MHz channel bandwidth, there was 50% loss of throughput across a wireless hop; at a 40 MHz channel bandwidth, there was 43% loss of throughput across a wireless hop; and at an 80 MHz channel bandwidth, there was 14% loss of throughput across a wireless hop. The higher channel bandwidth, alongside BSS Colouring, OFDMA, and beamforming, allowed for the efficient use of the spectrum, which enabled Wi-Fi6 to utilize the waveguide effect provided by the stope to enhance the signal quality rather than attenuating the signal.  
Table 2 shows the multi-hop performance which was recorded at different channel bandwidths.
# Progress of Process Monitoring for the Multi-Mode Process: A Review
## 4 Steady-State Mode Process Monitoring Methods
### Supervised Process Monitoring Method
#### 4.1.4 Multiple-Model Method  
Since the mean, variance and correlation of process variables under various modes are different, it is difficult to describe them with a unified model. The multiple-model method establishes sub-models for each mode, then designs a pattern-matching rule and selects the best matching model for monitoring. [PERSON] et al. [33] first divided the sample data into multiple modes, based on prior knowledge of the process, then established PCA models for each mode, and finally selected the model with the minimum squared prediction error (SPE) statistics for monitoring. [PERSON] et al. [34] established PCA models for training data in multi-modes, determined the matching coefficient of the monitoring control limits in each mode by selecting common multiple methods, and formulated a unified control limit for monitoring.  
Another aspect of the multiple-model method is the establishment of sub-models for each mode and the construction of a global statistical index to monitor the multi-mode process. The authors of [35, 36, 37] proposed a multiple-model method based on Bayesian theory and constructed a global probability index for monitoring. [PERSON] et al. [38] proposed an integrated monitoring index \(D_{global}\) integrating the Mahalanobis distance in multi-modes, which can describe the internal statistical information of each mode and include global information for all modes.  
In the actual industrial environment, most of the parameters in the multiple-model method change with the system state during monitoring and monitoring ability will not be lost as a result of failure of one of the methods. The method has great advantages over the overall modeling method and has been widely used in recent years with fruitful research results.
## 3 Research Methods
### Steps of BasicVSR++ Restoration
#### 3.1.2 Alignment  
Existing alignment methods include unaligned, image alignment, and feature alignment. Unaligned methods risk accuracy loss in aggregation modules, degrading performance. Image alignment relies on optical flow for frame warping, often causing blurring and detail loss. Feature alignment employs motion estimation, compensation or deformable convolutional networks to align frames via motion extraction and distortion. The process can be described as follows:  
\[\begin{array}{l}\mathrm{J}=MC(I,F_{i\to t},\theta_{MC})\\ F_{i\to t}=(h_{i\to t},v_{i\to t})=ME(I_{i}^{LR},I_{t}^{LR},\theta_{ME}) \end{array} \tag{5}\]  
where \(MC\) is the motion compensation function, \(I\) is the continuous adjacent frames, \(F_{i\to t}\) is the optical flow vector field, \(\theta_{MC}\) is the parameter of the motion compensation function, \(h_{i\to t}\) and \(v_{i\to t}\) are the horizontal and vertical optical flow vector fields, \(ME\) is the optical flow vector field calculation function, and \(\theta_{ME}\) is the parameter of the optical flow vector field calculation function.  
In addition, since BasicVSR++ introduces DCN, the VSR technology based on DCN calculates the spatial offset by processing the target frames and adjacent frames in parallel and deforms the adjacent frames to achieve accurate alignment of the feature map. This process fully considers the spatial variability of each feature point and its corresponding offset. The mathematical expression of the process is the following:  
\[\begin{array}{l}F_{t+i}^{a}(p_{0})=\sum\limits_{k=1}^{k}\omega_{k}\cdot F_ {t+1}(p_{0}+p_{k}+\Delta p_{k})\cdot\Delta m_{k}\\ p_{k}
## 6 Numerical Study and Comparison
### Trigonometric Relation between the Dimensions of PCCSW  
To determine the relationship between the geometric variables of the PCCSW, a single wavelength PCCSW is taken, as shown in Figure 6. Here, \(s\) is a single wavelength, \(l\) is the projected length of a single wavelength in relation to the longitudinal axis, \(a\) is the length of the longitudinal panel, \(c\) is the length of the inclined panel, \(b\) is the projected length of the inclined panel in relation to the longitudinal axis, \(R\) is the radius of curvature, \(h_{r}\) is the amplitude height of the PCCSW, \(t\) is the panel thickness, \(\theta\) is the angle of the inclined panel in relation to the tangent line of the longitudinal axis, \(\theta_{1}\) is the outer folded angle, \(\theta_{2}\) is the inner folded angle, and \(\alpha_{1}\), \(\alpha_{2}\), \(\alpha_{3}\), \(\alpha_{4}\) and \(\alpha_{5}\) are all auxiliary angles. Because of the difference between the inside and outside folded angles, the curved shape appearance of the PCCSW is formed.  
The following equations (expressions about \(a\), \(R\), \(h_{r}\) and \(c\)) are obtained in accordance with the triangle cosine theorem:  
\[cos\alpha_{1}=\frac{a}{2(R+h_{r}/2)} \tag{39}\]  
\[cos\alpha_{2}=\frac{c^{2}+2 Rh_{r}}{2c(R+h_{r}/2)} \tag{40}\]  
Figure 5: Global elastic shear buckling mode of the PCCSW.  
\[cos\alpha_{3}=\frac{c^{2}-2 Rh_{r}}{2c(R-h_{r}/2)} \tag{41}\]  
\[cos\alpha_{4}=\frac{a}{2(R-h_{r}/2)} \tag{42}\]  
\[cos(\theta+\pi/2)=\frac{\left(c/2\right)^{2}+R^{2}-\left(R+h_{r}/2\right)^{2}}{ cR} \tag{43}\]
## 3 Results and Discussion
### Digital Optical Microscope  
The surface of PLA/alcohol bars printed in a horizontal orientation and a fragment of the fractures (after the impact test) were subjected to observation with an optical microscope. Figure 6 shows the image of the surface of the bars, enlarged 300 times; the filling layers are arranged at an angle of 45\({}^{\circ}\) to the outline line. The samples were printed on a 3D printer with a nozzle with a diameter of 0.4 mm; therefore, the width of the fill line of the printed samples is close to this value. The images in Figure 7 depict fractures in samples following Charpy impact testing, viewed at 100 times magnification. The photos display contour lines and filling due to the fragment of the fractures being observed. These structures are typical of polylactide, resulting in brittle fractures. Figure 7A-F exhibits samples modified with alcohols, while Figure 7G illustrates neat PLA, revealing air gaps between the layers arranged uniformly, which is usual for printed samples. Saturation of the filament with methyl alcohol was observed to decrease air gaps and make printing paths less visible. This is caused by the easy diffusion of small MeOH molecules into the polymer, increasing sample crystallinity (see Sections 3.4 and 3.5). MeOH addition also causes layers to melt better, leading to no visible printing paths, indicating the plasticizing  
Figure 4: SEM and EDS images of surface (**A**,**B**) and fractures (**C**,**D**) of the printed bars of sample PLA/BRB 442. (**A**,**C**)—SEM images, (**B**,**D**)—C/O/Si/S EDS mapping.  
effect of the additive. As the linear alcohol chain length increases, larger air gaps are formed. Aromatic alcohols result in smaller defects for PLA/CyOH samples. The largest structural defects were observed for the BnOH sample, which also presented problems during the material feeding process.  
Figure 5: SEM and EDS images of surface (**A**,**B**) and fractures (**C**,**D**) of the printed bars of sample PLA/BRB 294. **(A,C)**—SEM images, (**B**,**D**)—C/O/Si/S EDS mapping.  
Figure 6: Optical microscope images of surface of the printed bars; **(A)**—PLA/MeOH; **(B)**—PLA/EtOH; **(C)**—PLA/i-POH; **(D)**—PLA/BnOH; **(E)**—PLA/CyOH; **(F)**—PLA/n-BuOH; **(G)**—PLA ref.
# An Energy-Efficient Routing Algorithm
## 5 Cs-Culm
### Data Reconstruction  
In this phase, the sink node will extract node IDs, projection coefficients, and fused data in its received packets to form measurement matrix \(\Phi_{M\cdot N}\). The number of packets sink received determines the number of measurements \(M\) and the non-zero coefficients \(\phi_{i,j}\) in each row of \(\Phi_{M\cdot N}\), which correspond to the projection coefficients, whose positions are based on the node IDs in the packet header. If the node ID is included in the packet header, \(\phi_{i,j}\
eq 0\); else, it is equal to 0.  
Eventually, the sink node uses the OMP algorithm to reconstruct original data \(\mathbf{x}\). If \(\mathbf{\hat{x}}\) is the reconstructed vector, the data reconstruction error can be calculated by  
\[\varepsilon=\frac{\left\|\mathbf{x}-\mathbf{\hat{x}}\right\|_{2}}{\left\| \mathbf{x}\right\|_{2}} \tag{14}\]