esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation  
In this section, we conduct simulations in NS2.27 (Network Simulator 2.27) [24; 25]. The simulation scenario is shown in Figure 4. The transmission range of each node is 250 m, and the sensing range is 550 m. The route protocol is DSDV (Destination-Sequenced Distance-Vector routing protocol). We set the channel bandwidth (channel transmission rate) to 1 Mb and use 1000 bytes as the fixed size of each data packet. TCP data flows are sent by nodes (_1_,..., _n_) from _w1_ to _w4_, respectively. Simulations run for 300 s. We initiate UDP/constant bit rate (CBR) traffic flow as the background traffic starting from 0.5 s to 299.5 s at the speed of 500 kbps and initiate TCP/Constant Bit Rate (CBR) traffic flow starting from 1 s to 299 s at the speed of 800 kbps.  
We have made an experiment using UDP (User Datagram Protocol) flows as the background flows and calculated the number of RTS retransmission times, FSD, \(R_{b}\) and \(R_{FTE}\) of a TCP flow, as shown in Table 2.  
Thus, in the following experiments, the threshold of RTS retransmission times is set to two; the threshold of FSD is set to 0.03; the threshold of \(R_{b}\) is set to 0.75; and the threshold of \(R_{FTE}\) is set to 0.6 based on the statistical results in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Setting** \\ \hline prop & TwoRayGround \\ mac & 802.11 \\ ifq & Drop Tail/PriQueue \\ ifqlen & 20 \\ SIFS & 10 \(\mu\)s \\ Phy header & 192 bits \\ Mac header & 224 bits \\ RTS & 160 bits + Phy header \\ CTS, ACK & 112 bits + Phy header \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation scenario parameter setting. prop refers to the propagation model. Mac refers to the MAC protocol. Ifq refers to interface queue. Ifqlen refers to the length of the interface queue. SIFS refers to the Short Interframe Space. The Phy header refers to the length of the Physical layer header. The Mac header refers to the length of the media access layer header.  
Figure 4: Simulation scenario.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**The Number of Background Flows (UDP Flows)** & **1** & **2** & **3** & **4** \\ \hline RTS retransmission times & 1.349 & 1.688 & 2.068 & 2.109 \\ FSD (ms) & 20.234 & 21.289 & 23.560 & 24.395 \\ \(R_{b}\) & 0.66237 & 0.69409 & 0.71038 & 0.72191 \\ \(R_{FTE}\) & 0.58043 & 0.57561 & 0.57334 & 0.57322 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The value of RTS retransmission times, Frame Service Delay (FSD), \(R_{b}\) and \(R_{FTE}\).
# CONCERTS: Coverage Competency-Based Target Search for Heterogeneous Robot Teams
## 4 Methods
### Waypoint Allocation Factor  
We introduce a new metric called Waypoint Allocation Factor (\(WAF\)) to evaluate each agent's contribution of the search task according to the coverage competency. To compute how evenly the area for coverage is allocated, we take a standard deviation of the total swept area and divide by the _coverage competency_ of the robot team. Specifically, we apply the following equation:  
\[WAF=std(\lambda A_{i}\eta_{i}) \tag{10}\]  
where \(A_{i}\) denotes total swept area for agent \(i\), \(\eta_{i}\) is the coverage competency, and \(\lambda\) means a normalizing constant. A value close to zero indicates that the waypoints were evenly distributed among the team after competency considerations. We propose this metric to effectively evaluate the area covered with explicit consideration of team heterogeneity. Because it is a standard deviation, a value of 0 means that the search task was perfectly assigned to account for the team heterogeneity.  
Figure 3: Scenario with six agents. All paths are described in different colors and the effect of coverage competency and H-Clustering is clear. Agent A has the highest competency score and therefore has the largest search region while agent C has the smallest competency score and therefore has the smallest region to cover. (**a**) Agent E (cyan) failure. Letters A–F are the starting position of each of the 6 agents. (**b**) Re-planning step: Because the server has access to all robot plans, the waypoints assigned to Agent E can be reassigned to nearby agents. Ellipses represent the assignment of those waypoints to the agent of the same color. (**c**) CONCERTS guarantees complete coverage by solving a new TSP instance.
# An Analysis of Sound Event Detection under Acoustic Degradation Using Multi-Resolution Systems
## 3 Results and Discussion
### Results under Clipping  
Figure 6 presents the results obtained when applying clipping saturation to the Public evaluation set. The best macro \(F_{1}\) performances are achieved by the multiresolution systems, whereas in some event categories multiresolution is not able to outperform every single-resolution system. This situation is observed for Dishes (\(T_{+}\)), Dog (\(F_{++}\)) and Shaver (\(F_{+}\)).
## 3 Data-Processing Methodology
### Data Quality Control  
Sensor data acquired through the S+ app are packaged in the JSON format. Each data point has an associated timestamp with millisecond precision and value. The number of steps, SpO\({}_{2}\), and respiration rate are reported in 60 s intervals. IBI values with millisecond precision are reported with each detected beat. The incoming data are segmented into 5 min windows for each data type with each point in the window formatted as [timestamp, value] and aligned based on real time so that the windows start at 5 min marks (e.g., 12:00, 12:05).  
To remove artifacts, we specify for each data type a range of acceptable values formatted as [low_accept, high_accept]. If the value is smaller than low_accept or greater than high_accept, the datapoint is flagged. For step count, respiration rate, and SpO\({}_{2}\), the flagged values are removed. For IBI, we flag and remove IBI values where either the value itself or the difference in timestamps are outside of the valid range.  
We define the following data quality control (QC) measures to characterize each of the incoming raw (or minimally processed) data streams:  
1. Raw data fraction, RDF (%), is calculated as the ratio of the number of datapoints collected to the maximum number of datapoints possible in the monitoring period;
2. Artifact data fraction, ADF (%), is calculated as the ratio of the number of the artifacts removed by the QC checks from the data stream to the total number of datapoints collected in the monitoring period.
# Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019
## 3 Results
### Geographical Detection Model for NDVI Drivers  
#### 3.4.1 Driving Factor Impact  
The results of the factor detector reflect the influence of the magnitude of each driver on the NDVI in Inner Mongolia, as expressed by the q value of each driver (Figure 9). Specifically, higher q values indicate a greater influence on the NDVI. As shown in Figure 9, the q value of the total annual precipitation is the largest, reaching 0.81, followed by that of soil type, mean annual temperature, and land use type, all of which have a significant influence greater than 0.5. This indicates that total annual precipitation was the dominant factor of NDVI change in Inner Mongolia during 2000-2019, followed by soil type. Among the factors with an influence below 0.5, the influence of natural factors is higher than that of social factors, in the following order: DEM > slope > population > GDP > livestock density. DEM has a significant influence of 0.34, and the slope has an average influence of 0.13 on the NDVI. The other three drivers had q-values below 0.1, indicating that they did not have a substantial influence on the NDVI.
# Advances in Neural Network-Based Image, Thermal, Infrared, and X-Ray Technologies
## 4 Discussion
### Noticeable New Trends  
Despite the very extensive research in this field, there are still many knowledge gaps. This is due to several factors. One is the development of technology that allows for miniaturisation and use of ANNs in places that were previously inaccessible. Another is demand, such as the COVID-19 pandemic. The next is the lack of greater interest in a given topic. However, it can be expected that in the latter case there will be changes, as they will allow for the improvement of services or reduction of costs. Issues that seem interesting but have not yet been sufficiently researched may include:  
1. Face identification--The main problem here is the reliability of such a solution and the ability to properly detect it despite the lack of access to the full data [30; 46].
2. Expert systems--Interesting results have been obtained using a combination of Bayesian and neural networks [4; 5].
3. Fingerprint identification--Only one article describes this solution, stating that much better results were obtained compared to previous methods [47].
4. Identification of tissues--It is important not only to identify its type, but also to assess its condition and suggest possible treatment [4].
5. Identification of human diseases--Until recently, due to the COVID-19 pandemic this was the main focus of research. However, there are many other diseases which can also be successfully diagnosed, such as joint diseases, breast cancer, or artery diseases [58; 59; 65; 69].
6. Assistance in agriculture--In [100], a method was presented to check the proper level of soil moisture. However, it can be expected that this technology can be used to detect the presence of pests or parasites as well.
7. Hybrid approaches involving ANNs are finding more and more practical applications and are one of the most important fields of deep learning development in image processing.
## Results and Discussion  
### Breakdown Characteristics  
Figure 2a shows the breakdown voltage of W40 and W200 with various \(L_{\text{EX}}\)s. When the drain current is \(3\times 10^{-8}\) A, the breakdown voltage is taken as the drain voltage value at \(V_{\text{GS}}\) = 0 V. For W40, the breakdown voltage increases when \(L_{\text{EX}}\) increases from 0 to 0.6 \(\upmu\)m. However, for W200, the influence of \(L_{\text{EX}}\) on the breakdown voltage is quite small until \(L_{\text{EX}}\) exceeds 0.6 \(\upmu\)m. According to reports in the literature [40; 41], a device with a narrower channel has a higher breakdown voltage due to a lower peak electric field, which is located near the drain; in addition, a device with a wider drift width has a lower electric field and impact ionization in the drift region, which results in a high breakdown voltage [42]. Here, a narrower channel in conjunction with a wider drain--for example, W40 with \(L_{\text{EX}}\) = 0.6 \(\upmu\)m--exhibits a high breakdown voltage (approximately 57 V).  
Figure 3 shows the electric field distribution of W40 and W200 with \(L_{\text{EX}}\) = 0 and 0.6 \(\upmu\)m at \(V_{\text{GS}}\) = 0 V and \(V_{\text{DS}}\) = 20 V (before breakdown). The highest electric field peaks of W40 and W200 are located at the intrinsic drift/\(N^{+}\) junction and cause breakdown. Suppressing the electric field peak at the intrinsic drift/\(N^{+}\) junction is an effective method for preventing breakdowns. In Figure 3a, it can be observed that extending the wide drain length of W40 to \(L_{\text{EX}}\) = 0.6 \(\upmu\)m led to a marked decrease in the electric field peak at the intrinsic/\(N^{+}\) junction and a consequent increase in the breakdown voltage. However, as each channel wire width increased, such as in W200, the difference in the electric field peak between \(L_{\text{EX}}\) = 0 and 0.6 \(\upmu\)m was not pronounced, as shown in Figure 3b. Additionally, the peak electric field values for W200 with \(L_{\text{EX}}\) = 0 and 0.6 \(\upmu\)m were 1.48 and 1.43 MV/m, respectively, compared with 1.34 and 1.11 MV/m, respectively, for W40 with \(L_{\text{EX}}\) = 0 and 0.6 \(\upmu\)m. The narrower each channel wire, the lower was the peak electric field. Therefore, the breakdown voltage of W40 was higher than that of W200. Consequently, extending the wide drain under the FP contributed to a more uniform electric field distribution within the drift region. Therefore, the breakdown voltage improved with an increase in \(L_{\text{EX}}\).  
Figure 2: Breakdown voltage of FinTFTs with various \(L_{\text{EX}}\).
## 3 Results and Discussion
### Simulation of Integrated Control Shift Control Strategy
In summary, when the HMCVT is in control strategy \(H\) = (0,2,1), the speed change of the HMCVT tractor is the most stable, the acceleration shock of the tractor is the smallest, and the transition smoothness is the best. Figure 18 shows the changes in motor output speed and clutch torque when shifting gears. It can be seen that the clutch will have a specific impact on the output speed of the motor when the clutch is switched at the maximum displacement. Figure 16: The jerk in different control strategies (\(H\) = (x,1,1)). Figure 17: Amplitude change of the jerk in different control strategies (\(H\) = (x,y,1)). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Control Strategy**} & **Max-Jerk** & \multirow{2}{*}{**Control Strategy**} & **Max-Jerk** \\  & **(m/s\({}^{3}\))** & & **(m/s\({}^{3}\))** \\ \hline \(H\) (0,1,1) & 7.81 & \(H\) (1,2,1) & 8.21 \\ \(H\) (0,1,5,1) & 8.14 & \(H\) (1,2,5,1) & 9.25 \\ \(H\) (0,2,1) & 6.27 & \(H\) (1,3,1) & 16.33 \\ \(H\) (0,2,5,1) & 8.12 & \(H\) (\(-\)1,1,1) & 10.55 \\ \(H\) (0,3,1) & 14.44 & \(H\) (\(-\)1,1,5,1) & 10.27 \\ \(H\) (1,1,1) & 9.36 & \(H\) (\(-\)1,2,1) & 9.64 \\ \(H\) (1,1,5,1) & 8.61 & \(H\) (\(-\)1,2,5,1) & 12.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Jerk corresponding to different control strategies. Figure 18: Changes in motor speed and clutch torque when shifting. (**a**) Motor output speed; (**b**) Clutch torque change.
## 2 Weather Data Super-Resolution Algorithm Based on CBAM-SRResNet  
SRResNet is a convolutional neural network architecture built upon residual learning principles [35]. Compared with other models, SRResNet has the advantages of a relatively simple structure, a moderate number of parameters, and a more stable training process, which enables it to achieve high-quality reconstruction while maintaining a low computational cost. In addition, SRResNet is based on the residual learning framework, which can effectively alleviate the gradient vanishing problem in deep networks, thereby accelerating convergence and improving model performance. It primarily consists of two components: a deep residual network and a sub-pixel convolutional module. The residual network integrates residual learning into conventional CNNs, effectively addressing issues such as accuracy degradation and vanishing gradients in deep architectures, thereby enabling deeper networks without a loss in performance. The sub-pixel convolutional module is designed to upscale meteorological data, enhancing detail precision through learned up-sampling. SRResNet takes low-resolution weather data as input and generates corresponding high-resolution outputs. The network achieves efficient super-resolution weather  
Figure 1: Modeling process.  
reconstruction by combining these two parts. Figure 2 shows the SRResNet network structure with the CBAM.  
As shown in Figure 2, the model uses a deep residual module for image feature extraction, and the input information is directly transmitted to the network output by introducing Skip Connection multiple times, which effectively improves the stability of the overall structure. Thanks to the design of the deep network structure, compared with the shallow model, the model can more fully mine the complex features in the image to achieve performance transcendence. In addition, the model only adjusts the number of feature channels between layers, and the spatial size of the image remains unchanged. Among them, KS represents the size of the convolution kernel, BN is Batch Normalization, and ES is Elementwise Sum. In addition to the deep residual module and the sub-pixel convolution module, a convolution module is added to the input and output parts of the whole model for data adjustment and enhancement. Next, the overall structure and processing flow of the model will be elaborated in detail.
## 2 Materials and Methods
### Imaging Background  
Traditional X-ray and neutron imaging produce absorption/attenuation images. The addition of grating-based interferometry to an imaging setup produces three image sets: absorption/attenuation, differential phase contrast (DPC), and dark-field (synonymous with small-angle scattering).  
The intensity of the camera recorded fringe pattern, \(I\), can be fitted with a cosine function [35] or its linear expansion [36]:  
\[I(x_{g}) = A+B\cos\left(\frac{2\pi x_{g}}{p_{g}}+\phi\right) \tag{1}\] \[I(x_{g}) = A+\left[\sin\left(\frac{2\pi x_{g}}{p_{g}}\right)\right]B_{cos} \cos\phi+\left[\cos\left(\frac{2\pi x_{g}}{p_{g}}\right)\right]B_{sin}\sin\phi\] (2) \[\text{with }B = \sqrt{B_{cos}^{2}+B_{sin}^{2}} \tag{3}\]  
where \(\phi\) is the phase of the beam, \(x_{g}\) is the stepping motion of the grating, and \(p_{g}\) is the period of the phase grating. The offset, \(A\), and amplitude, \(B\), of Equation (1) are used to define the fringe visibility as visibility = \(B/A\). The image modality called dark-field (DF) is the ratio of fringe visibility with the sample in the beam versus out of the beam:  
\[\text{dark-field}=\frac{\text{visibility}_{sample}}{\text{visibility}_{reference}}= \frac{B_{sample}/A_{sample}}{B_{reference}/A_{reference}} \tag{4}\]  
In the dilute, non-interacting sphere model, the autocorrelation length, \(\xi\), can be thought of as the diameter of the particle size causing scattering in the DF images. In the case of the Helmholtz-Zentrum Berlin (HZB) Talbot-Lau interferometer, the autocorrelation length is set at a fixed autocorrelation length whereas the far-field interferometer is able to probe a wide range of autocorrelation lengths. The autocorrelation length for the HZB Talbot-Lau interferometer was 1.97 \(\upmu\)m for the sample upstream of the G1 grating and is computed by:  
\[\xi(z) = \frac{\lambda z^{\prime}}{p_{g}} \tag{5}\] \[z^{\prime} = (L_{1}+L_{2}-z)\frac{L_{2}}{L_{1}} \tag{6}\]  
where the source to G1 distance, \(L_{1}\) = 4.5 m, the G2 to detector distance, \(L_{2}\) = 22.7 mm, the period of the phase grating, \(p_{g}\) = 4.0 \(\upmu\)m, the wavelength, \(\lambda\) = 0.35 nm, and the sample to detector distance, \(z\) = 66 mm.  
The autocorrelation length for the NIST far-field interferometer ranged from 0.60 \(\upmu\)m to 1.99 \(\upmu\)m and is computed by:  
\[\xi(z)=\frac{\lambda zD}{(L_{1}+D+L_{2})p_{g}} \tag{7}\]  
where the distances are defined in Figure 1 and have values of \(L_{1}\) = 2.3 m, \(L_{2}\) = 2.3 m, \(D\) = 26 mm, \(p_{g1}\) = \(p_{g2}\) = \(p_{g}\) = 2.4 \(\upmu\)m, \(\lambda\) = 0.54 nm, and \(z\) ranged from 0.477 m to 1.537 m.
## 3 Results and Discussion
### Assessment of Antifungal Properties of AuNPs Coated with ITZ
The results demonstrated that AgNPs  
Figure 4: _Candida albicans_ growth kinetics tested for selected hydrogels formulations. and ITZ exhibited the highest degree of synergistic activity against a multidrug-resistant strain of _Candida glabrata_, with a recorded synergy percentage of 74.32%. In contrast, AgNPs demonstrated antifungal activity against _Candida tropicalis_, _Candida albicans_, and _Candida glabrata_ strains, with inhibitory zone diameters of 23.78 \(\pm\) 0.63, 21.38 \(\pm\) 0.58, and 16.53 \(\pm\) 0.21 mm, respectively. However, the results obtained with AuNPs in our study do not corroborate these reports. It is therefore recommended that further optimization of hydrogel formulations containing AuNPs and ITZ be undertaken, as the properties of gold nanoparticles, such as their ability to penetrate biofilms and disrupt fungal cell membranes, may complement the antifungal activity of ITZ, especially against resistant strains [53]. It is, however, noteworthy that our results of ITZ diffusion through a semipermeable membrane demonstrate that hydrogels containing AuNPs may represent a promising form of topical ITZ administration.
# Enhancing the Reliability of Cellular Internet of Things through Agreement
## 5 Verification of The Optimality of CIoTAP
_MEC-tier_: According to the research results of [11] and [27], in an \(n\)-PUs distributed computing system, the condition to achieve the agreement problem is \(f\leq\lfloor(n-1)/3\rfloor\), where \(f\) is the total number of faulty PUs allowed in the distributed computing system, \(n\) is the total number of PUs in the distributed computing system. Since MECIoT is a distributed computing system, the research results of [11] and [27] can be directly applied to _MEC-tier_ to become \(f_{Mj}\leq\lfloor(n_{Mj}-1)/3\rfloor\), where \(f_{Mj}\) is the total number of allowed faulty MEC PUs in MEC cloud \(E_{j}\) and \(n_{Mj}\) is the total number of MEC PUs in MEC cloud \(E_{j}\). Then, \(F_{M}=\sum_{j=1}^{M}f_{Mj}\), where \(M\) is the total number of MEC clouds in the _MEC-tier_ of MECIoT, and \(F_{M}\) is the total number of allowable faulty MEC PUs in the _MEC-tier_. 3. _Cloud-tier_: The research results of [PERSON] and [PERSON] [11] can also be directly applied to _Cloud-tier_. Therefore, \(f_{C}\) is the total number of faulty Cloud PUs allowed in the _Cloud-tier_, \(f_{C}\leq\lfloor(n_{C}-1)/3\rfloor\), where \(n_{C}\) is the number of Cloud PUs. By adding the allowable number of faulty PUs in the three tiers of MECIoT, \(F=F_{A}+F_{M}+f_{C}=\sum_{j=1}^{B}f_{Bj}+\sum_{j=1}^{M}f_{Mj}+\lfloor(n_{C}-1 )/3\rfloor\), then the maximum number of faulty PUs allowed by CIoTAP can be obtained. In other words, \(F\) is the maximum number of faulty PUs allowed by executing CIoTAP in MECIoT to reach an agreement. According to Theorems 1 and 2, it is proved that executing CIoTAP in MECIoT requires the minimum number of data exchanges to reach an agreement, and can tolerate the maximum number of faulty PUs. Therefore, the optimality of CIoTAP has been proven.
## 4 Experiments Analysis  
The proposed model was evaluated based on the three publicly available datasets. It is typically necessary to preprocess an input for optimization purposes. First, 16 facial frames were extracted from the landmarks of the face. A face region was resized into 227 \(\times\) 227 \(\times\) 3 in order for the next processing block, namely feature extraction, to be applied.