esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Utilization of Reversible Colour Transforms for Lossless 2-D Data Compression
## 4 Experimental Setup And Results
### Lossless Compression Of Colour Images
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Image**} & \multicolumn{5}{c}{**Existing Compression Schemes**} \\ \cline{2-6}  & **FLIC** & **ZPAQ** & **FP8** & **PAQ8** & **STUFF IT** \\ \hline kodim02 & 422 & 461 & 352 & 341 & 391 \\ kodim05 & 517 & 731 & 575 & 549 & 499 \\ kodim07 & 390 & 501 & 350 & 332 & 370 \\ kodim08 & 540 & 723 & 595 & 564 & 507 \\ kodim11 & 425 & 556 & 424 & 404 & 432 \\ kodim13 & 561 & 711 & 585 & 562 & 552 \\ kodim20 & 335 & 411 & 325 & 313 & 421 \\ kodim21 & 455 & 543 & 432 & 435 & 488 \\ kodim22 & 480 & 688 & 523 & 503 & 482 \\ kodim23 & 401 & 620 & 417 & 394 & 404 \\ \hline TOTAL & 4526 & 5945 & 4578 & 4397 & 4546 \\ \hline \hline \multirow{2}{*}{**Image**} & \multicolumn{5}{c}{**Existing Compression Schemes**} \\ \cline{2-6}  & **BMF** & **GRALIC** & **BBWCA (RDgDb)** & **BBWCA (LDgDb)** & **BBWCA (LDgEb)** \\ \hline kodim02 & 438 & 397 & 700 & 685 & 539 \\ kodim05 & 512 & 480 & 742 & 742 & 847 \\ kodim07 & 395 & 333 & 654 & 653 & 715 \\ kodim08 & 524 & 498 & 681 & 681 & 878 \\ kodim11 & 438 & 401 & 585 & 586 & 703 \\ kodim13 & 565 & 534 & 707 & 708 & 821 \\ kodim20 & 339 & 308 & 506 & 533 & 612 \\ kodim21 & 465 & 474 & 524 & 525 & 690 \\ kodim22 & 485 & 439 & 675 & 681 & 788 \\ kodim23 & 411 & 367 & 635 & 633 & 659 \\ \hline TOTAL & 4572 & 4231 & 6409 & 6427 & 7252 \\ \hline \hline \multicolumn{6}{c}{**Proposed Compression Scheme**} \\ \cline{2-6}  & **BBWCA (YUV)** & **BBWCA (YUV)** & **BPF for Y U U V Channel** & **BBWCA (YA2 UA2 VA2)** & **BBWCA (YA7 UA7 VA7)** & **BBWCA (YA6 UA6 VA6)** \\ \hline kodim02 & 372 & 2.73 \(\mid\) 0.58 \(\mid\) 0.56 & 506 & 691 & 294 \\ kodim05 & 398 & 3.02 \(\mid\) 1.79 \(\mid\) 1.09 & 580 & 785 & 441 \\ kodim07 & 387 & 2.77 \(\mid\) 1.45 \(\mid\) 1.25 & 442 & 709 & 374 \\ kodim08 & 437 & 3.06 \(\mid\) 1.67 \(\mid\) 1.70 & 628 & 700 & 537 \\ kodim11 & 375 & 2.79 \(\mid\) 1.56 \(\mid\) 1.23 & 451 & 598 & 497 \\ kodim13 & 472 & 3.11 \(\mid\) 1.14 \(\mid\) 1.06 & 483 & 806 & 458 \\ kodim20 & 492 & 2.42 \(\mid\) 1.27 \(\mid\) 0.74 & 334 & 709 & 205 \\ kodim21 & 419 & 3.17 \(\mid\) 1.48 \(\mid\) 1.53 & 480 & 560 & 545 \\ kodim22 & 478 & 3.00 \(\mid\) 1.43 \(\mid\) 1.18 & 527 & 726 & 428 \\ kodim23 & 493 & 2.56 \(\mid\) 1.38 \(\mid\) 1.20 & 460 & 710 & 420 \\ \hline TOTAL & 4323 & & 4891 & 6994 & 4199 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative Analysis of Compression sizes achieved in KB with existing benchmark schemes (for Kodak images). All Kodak images have a resolution of 768 \(\times\) 512 \(\times\) 3 totaling 1152 KB. BPPs for separate Y, U, V channels for the BBWCA(YUV) transform are shown for individual channel efficiency.
## 2 Methodology
### Potential Conflict Risk Index
\(v_{x}\) and \(v_{y}\), respectively, represent the relative speeds in the \(x\) and \(y\) directions, \(x_{t}\) and \(y_{t}\) correspond to the relative positions in the \(x\) and \(y\) directions. Figure 6: The variables of time related to speed and direction for two vehicles. In the actual calculation process, the value range of CRF varies significantly with different driving conditions. To enhance its applicability in reflecting conflict risk and facilitating the identification of conflict events, the CRF is normalized. In this study, the sigmoid function is chosen for normalization. The CRF is input into this function, and the output is used as the final measure, which is named the Potential Conflict Risk Index. In normal sigmoid function, the output values are limited to a range between 0 and 1. By multiplying it with an exponential term, this range is expanded, and the rate of change in the sigmoid function near the central point is accelerated, making the function more sensitive around the center. This enhances the distinction of PCRI for evaluating different conflict risks. The form of the sigmoid function and the transformed sigmoid function are shown in Equations (10) and (11). Figure 7 illustrates the plot of the sigmoid function and the transformed sigmoid function. \[y=\frac{1}{1+e^{-x}} \tag{10}\]  
\[y=\frac{1-e^{-x}}{1+e^{-x}} \tag{11}\]  
Finally, the expression for PCRI is shown as follows:  
\[PCRI=\frac{1-e^{-CRF}}{1+e^{-CRF}} \tag{12}\]  
The sign of PCRI reflects the relative motion state of two vehicles. A positive sign indicates that the vehicles are approaching each other, while a negative sign indicates they are moving apart. The numerical value of PCRI represents the magnitude of conflict risk; closer to 0 indicates higher risk, whereas closer to 1 indicates lower risk. A special case is that PCRI equals 0, indicating a genuine collision between two vehicles.
# Sensitivity Estimation for Differentially Private Query Processing
## 3 Preliminaries
### Differential Privacy  
Differential privacy [7] ensures that any individual in or not in the dataset has little effect on the final released query results.  
**Definition 1**.: \((e,\delta)\)_-Differential privacy. A randomized privacy algorithm \(A\) satisfies \(\epsilon\)-DP if for any pairs of input datasets, \(I\) and \(I^{\prime}\) satisfy \(d(I,I^{\prime})=1\) for all sets \(\mathcal{S}\) of possible outputs._  
\[\Pr[A(I)\in S]\leq exp(e)\cdot\Pr[A(I^{\prime})\in S]+\delta \tag{1}\]  
\(\epsilon\) _denotes the privacy budget, indicating the degree of privacy protection._  
**Theorem 1**.: _Laplace mechanism [8]. Given a dataset 1, function \(f\):\(I\to R^{d}\). So, the random algorithm \(A:A(I)=f(I)+Y\) provides \(\epsilon\)-differential privacy protection, and \(Y\sim Lap(\triangle f/\epsilon)\) presents the randomized noise, which follows a Laplace distribution with scale parameter \(\triangle f/\epsilon\). The factor \(\triangle f\) represents sensitivity._
# Dam Deformation Monitoring Model Based on Deep Learning and Split Conformal Quantile Prediction
###### Abstract  
The construction of an interval prediction model capable of explaining deformation uncertainties is crucial for the long-term safe operation of dams. High effective coverage and narrow interval coverage widths are two key benchmarks to ensure that the prediction interval (PI) can accurately quantify deformation uncertainties. The vast majority of existing models neglect to control the interval coverage width, and overly wide PIs can cause decision confusion when operators are developing safety plans for hydraulic structures. To address this problem, this paper proposes a novel interval prediction model combining bidirectional long-short-term memory network (Bi-LSTM) and split conformal quantile prediction (SCQP) for dam deformation prediction. The model uses Bi-LSTM as a benchmark regressor to extract and explain the nonlinear feature of dam deformation in the continuous time domain. SCQP is used to quantify the uncertainties in dam deformation prediction to ensure that the constructed PI can achieve high effective coverage while further improving the accuracy of the quantification of deformation uncertainties. The effectiveness of the proposed model is validated using deformation monitoring data collected from an arch dam in China. The results show that the average prediction interval effective coverage (PICP) of the proposed model is as high as 0.951 while the mean prediction interval width (MPIW) and coverage width-based criterion (CWC) are both only 5.815 mm. Compared with other models, the proposed method can construct higher-quality PIs, thus providing a better service for the safety assessment of dams.  
Keywords:interval prediction; dam deformation prediction; split conformal quantile prediction; Bi-LSTM; uncertainty quantification _Article_
# Aerial Coverage Analysis of Cellular Systems at LTE and mmWave Frequencies Using 3D City Models
## 3 Simulation Results
### Interference Management
These results indicate that, when deploying a mmWave system for AUEs, interference is still the main problem and bandwidth is not a problem. However, when deploying a mmWave system for ground UEs, blockage is still the main problem and more care should be taken when deploying base stations.
# Applying Real-Time Travel Times to Estimate Fire Service Coverage Rate for High-Rise Buildings
###### Abstract  
Frequent high-rise building fires have posed serious threats to urban public safety. In this study, real-time travel times extracted from online maps were applied to estimate fire service coverage rates for high-rise buildings. Firstly, the minimum fire apparatus requirement for high-rise building \"at least 1 water fire truck and 1 aerial fire truck should arrive at the fire scene of high-rise building within 240 s\" was proposed. Then, a dynamic estimation model of fire service coverage rate for high-rise buildings was established. To validate the model, 5146 high-rise buildings, 61 water fire trucks, and 56 aerial fire trucks in Changsha were selected as case studies. The results show that the total effective coverage rate and total average travel time in Changsha are 20.43% and 421.95 s, respectively. There are 10,840 water fire trucks and 6192 aerial fire trucks in China. Based on the average number of water fire trucks and aerial fire trucks per high-rise building, it can be estimated that the total effective coverage rate in China may not exceed 60.00%. Due to limited fire resources and frequent traffic congestion, only partial high-rise buildings can be effectively covered by fire services, whether in Changsha or China.  
high-rise building; fire service; real-time travel times; online maps +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Throwing a Ball  
To demonstrate the system's real-time capability, we collected data during a ball-tossing motion. Figure 6a illustrates the four stages of this action: initially, the hand is empty and in a preparatory phase (Figure 6a(i)); next, the ball is placed in the hand (Figure 6a(ii)); then, the ball is tossed upward, leaving the palm (Figure 6a(iii)); and finally, the ball is caught after being tossed (Figure 6a(iv)). Figure 6b displays the data collected throughout these four stages. In stage i, the signal strength across all channels is low, generally remaining below 0.3 V. In stage ii, with the ball in hand, the signal strength in channels 2, 5, and 7 significantly increases, reaching approximately 0.5 V, 1.3 V, and 0.7 V, respectively. In stage iii, as the ball is tossed, the upward acceleration causes the signal strength in channel 5 to rise again; however, once the ball fully leaves the hand, the signal will return to its initial level. In stage iv, at the moment of catching the ball, the downward acceleration causes the sensor that first comes into contact with the ball to produce a signal amplitude greater than that when normally holding the ball in the hand. At this point, the sensor corresponding to channel 2 in Figure 6b is the first to come into contact with the falling ball, which is exactly reflected in the data. Once the ball stabilizes in the palm, the signal strength will stabilize again. The changes in the signal can clearly distinguish each stage. Due to slight variations in ball-gripping positions each time, minor differences in channel signal strength are to be expected. Moreover, when testing other individuals, similar results can be obtained, which is reflected in Figure S2a,b in the supplementary document. Additionally, in Figure S2c, during consecutive throws, each throw can be distinguished from the changes in the signal. This action sequence demonstrates that the system possesses excellent real-time responsiveness for capturing dynamic signals, making it well suited for measuring signal changes during dynamic processes.
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## 2 System Architecture
### The Multi-Hop Wireless Network  
Communication between sensors and the central monitoring system relies on a multi-hop wireless network built using ESP-NOW, which allows direct, peer-to-peer communications among ESP32 devices forming a mesh network. Each node in the network relays data from other nodes, allowing the system to cover large distances and navigate around physical obstacles, such as machinery or infrastructure [14]. Additionally, by leveraging ESP-NOW's low-power design, the system reduces the operational energy requirements of each node, making it suitable for battery-powered deployments. Strategic node placement, guided by the REM, ensures robust communication pathways that minimize the impact of electromagnetic interference and physical obstruction. This architecture is essential to ensuring robust data collection and transmission without an extensive infrastructure. The configuration and interactions of the source, relay, and gateway nodes are depicted and explained in the later session of the paper, which highlights their placement and roles within the multi-hop network. Key components include the following:  
* The Source Node (SN): The source node initiates the data transmission process by collecting fire detection data from sensor modules equipped with ESP32-CAM devices. These nodes integrate local environmental data, such as temperature or visual fire cues, and transmit the processed information to the first relay node using ESP-NOW. The selection of the ESP32-WROOM-32 UE System-on-Chip (SoC) for the source node, with its external antenna and enhanced communication range, ensures strong initial data transmission, even in interference-prone areas.
* The Relay Nodes (RN): Relay nodes act as intermediaries in the multi-hop network, receiving data from the source node or preceding relay nodes and forwarding them to subsequent nodes or the gateway. The ESP-NOW protocol's low-power, peer-to-peer communication model minimizes energy consumption and ensures efficient data transfer. Each relay node is strategically placed based on the radio environment map (REM) to maintain strong signal strength and reduce the likelihood of packet loss. This design ensures that relay nodes can overcome the physical barriers present in industrial environments, such as heavy machinery or metal infrastructure.
* The Gateway Node (GN): The gateway node serves as the interface between the ESP-NOW-based local network and the MQTT-based central monitoring system. This node gathers data from the final relay and publishes them to the MQTT broker, enabling remote monitoring and real-time alert generation. By synchronizing Wi-Fi channels across all nodes, the gateway node ensures consistent and reliable data flow from source to server, even under adverse network conditions.
# Intersection-Based Routing with Fuzzy Multi-Factor Decision for VANETs
## 3 Protocol Design
### Road Segment Selection
#### 3.2.3 Traffic Flow
Based on the fuzzy performance score and the fuzzy relative weight of each factor obtained according to the above formulas, the total fuzzy performance score of the \(i\)th candidate road segment can be calculated as following formula:  
\[\widetilde{t_{i}}=\sum_{j=1}^{3}\widetilde{x_{ij}}*\widetilde{w_{j}},i=1,\ 2 \ldots n,j=1,\ 2,\ 3. \tag{13}\]  
Then, the data will be forwarded to the road segment with the highest score:  
\[\text{C}=\left\{\widetilde{t_{i}}\ |\ max\widetilde{t_{i}}(x),\ i=1,\ 2 \ldots n\right\}. \tag{14}\]  
The \(\widetilde{\text{t_{i}}}(x)\) is still a fuzzy number; the next step is to defuzzify to compare the candidate road segments. In general, the common available methods include the mean of maximum,\(\alpha\)-cut method, and center-of-area (COA) [29]. Herein, we select the center of area (COA) method to determine the best non-fuzzy performance (BNP) value [30] of the candidates, and the formula is as follows:  
\[\text{BNP}_{i}=\left[\left(ur_{i}-lr_{i}\right)+\left(mr_{i}-lr_{i}\right) \right]/3+lr_{i}. \tag{15}\]  
According to the value of the BNP obtained according to the above formula, the best candidate road segment can be selected. The above content shows the process of the routing protocol we proposed in detail, and its brief flow is as follows:  
Step 1: The RSU periodically sends a link discovery beacon to determine and establish a link between it and its neighbors based on the information of the beacon message. Step 2: When the message arrives at an intersection, the RSU selects the next-hop road segment and forwards the message to the next intersection based on the score obtained by calculating the three factors. Moreover, the pseudocode is described in Figure 3. It shows how the next-hop road segment is selected. When a packet arrives at an intersection, the RSU determines whether there is a link so that the message can be forwarded to the next intersection; if there is not, the message would return to the last intersection and select the sub-optimal road segment. If there is only one link and the direction is correct, the message would be forwarded along this link to the next intersection; if the direction is wrong, the message also returns. Finally, if there is more than one link, the RSU calculates the BNP of the candidate road segments; then, it selects the optimum road segment and forwards the message.
## 4 Signal Processing
### Observed Signals Extraction  
According to Equation (8), radar sensing data can be expressed as a two-dimensional matrix \(R\); the aim of this step is to select the multichannel range bins \(\left[y_{1}(t),y_{2}(t),\cdots,y_{a}(t)\right]^{T}\) in which the human target and the ground are located, where \(a\) is the number of extracted signals. Assuming that there is only one subject in the detection zone and the subject keeps static, the range bin with the maximum energy will be the location of the people. The multiple range bin selection is completed using a range sampler, which chooses the maximum energy range bin along the slow-time as the centre [23]. In this study, the range length of interest for the range sampler was set as 25 cm to keep the selected signals covering the range bins from the entire thorax of the human target to the ground. The observed signals selected using the range sampler are input for ICA.
# Conveyor Belt Damage Detection with the Use of a Two-Layer Neural Network
## 1 Introduction
### Selection of the Size of the Neural Network
In the case of a desire to use a multilayer network (with one or more hidden layers), the problem of appropriate selection of the number of hidden layers arises. This problem is also based on the properties of approximating functions, and the solution to this problem is provided by the Kolmogorov theory, on the basis of which it can be stated that, with \(N\) input neurons, it is enough to use \((2N+1)\) neurons in the hidden layer to solve a given approximation problem [16,20].
## 3 Results
### Geographical Detection of the Spatial Differentiation of CI in the Sanjiang Plain  
#### 3.4.1 Detection of the Significant Driving Factors for the Spatial Differentiation of CI  
As mentioned in Section 2.3.5, eight natural factors and three anthropogenic factors were selected to analyse the driving factors for the spatial differentiation of CI in the Sanjiang Plain. Here, we first identified the driving factors that had a significant influence on the spatial differentiation of CI based on the factor detector. As shown in Table 4, the gross domestic product and population density had little influence on the spatial differentiation of CI and did not pass the significance test (\(p>0.05\)). Therefore, the vegetation type (Veget), soil type (Soilt), geographic type (Geomt), average annual temperature (Temp), annual precipitation (Prec), elevation (Elev), slope (Slop), aspect (Aspe), and land use type (Landt) were used to investigate the driving mechanism of CI.  
Figure 11: Correlation between CI and LAI: (**a**) spatial distribution of the variation trend of growing season mean LAI (LAI\({}_{\text{GS}}\)); (**b**) spatial distribution of the correlation coefficient between CI\({}_{\text{GS}}\) and LAI\({}_{\text{GS}}\); (**c**) point plot of CI\({}_{\text{GS}}\) and LAI\({}_{\text{GS}}\); and (**d**) statistical analysis of multiyear mean CI\({}_{\text{GS}}\) and multiyear mean LAI\({}_{\text{GS}}\).
## 3 Model and Analysis
### Construction of a Four-Way Evolutionary Game Model
#### 3.2.2. Replicator Dynamics Equation  
Based on the payoff matrix in Tables 3 and 4, the expected payoffs of X for choosing the \"abatement\" and \"no-abatement\" strategies are \(E_{x1}\) and \(E_{x2}\), respectively, and the average payoff of the strategy choices is \(E_{x}\). The replicator dynamic equation for X is \(F(x)\). \[ \begin{array}{l}E_{x1}=yzuL_{1x}+yz(1-u)L_{2x}+y(1-z)uL_{3x}+y(1-z)(1-u)L_{4x} +(1-y)zuL_{5x}+(1-y)z(1-u)L_{6x}\\ +(1-y)(1-z)uL_{7x}+(1-y)(1-z)(1-u)L_{8x}\\ \end{array}  \tag{1}\]  
\[ \begin{array}{l}E_{x2}=yzuL_{9x}+yz(1-u)L_{10x}+y(1-z)uL_{11x}+y(1-z)(1-u)L_{ 12x}+(1-y)zuL_{13x}+(1-y)z(1-u)L_{14x}\\ +(1-y)(1-z)uL_{15x}+(1-y)(1-z)(1-u)L_{16x}\\ \end{array}  \tag{2}\]  
\[ \begin{array}{l}E_{x}=xE_{x1}+(1-x)E_{x2}\\ \\ F(x)=\frac{\mathrm{d}x}{\mathrm{d}t}=x(E_{x1}-E_{x})=x(1-x)[(1+y\beta_{XY}+z \beta_{XZ}+u\beta_{XU})E_{X}-C_{X}+\Pi_{X}+(1-y)N_{Y}+(1-z)N_{Z}+(1-u)N_{U}+G_{ X}]\\ \end{array}  \tag{4}\]  
Similarly, the replicator dynamic equations for Y, Z, and U strategy choices are \(F(y)\), \(F(z)\), and \(F(u)\), respectively.
# Precipitable Water Vapor Retrieval Based on DPC Onboard GaoFen-5 (02) Satellite
## 4 Discussion  
Although the PWV data developed based on DPC data in this study have a low absolute error and relative error, the DPC PWV data still have some room for improvement. We analyzed the validation results and found that the DPC PWV data had a distinct overestimation trend (MB = 0.29 cm). To further explain this bias, we analyzed the DPC TOA reflectance in the 865 nm and 910 nm channels. We removed the absorption effect of water vapor on the 910 nm channel to obtain the corrected TOA reflectance, using the 6 SV model with AERONET PWV data as input. Then, we compared the TOA reflectance in the 865 nm band with the corrected TOA reflectance in the 910 nm band. Figure 7 illustrates this comparison. Previous studies have shown that the TOA reflectance at 865 nm and the 910 nm TOA reflectance after water vapor absorption correction should be equal [37]. However, a distinct and systematic deviation is shown in Figure 7, which is inconsistent with the previous research conclusions. Since the DPC data used in this study were obtained during the on-orbit test period, the above inconsistency should be mainly because the calibration coefficients of the DPC 865 nm and 910 nm channels were not updated in time. A more detailed analysis of Figure 7 shows that the TOA reflectance in the DPC 865 nm band was systematically greater than the TOA reflectance in the DPC 910 nm band after water vapor absorption correction, which caused the WVAT in the DPC 910 nm band to be underestimated, resulting in an overestimation of PWV retrieval. Therefore, to further improve the accuracy of DPC PWV data in the future, it is necessary to update the calibration parameters of DPC 865 nm and 910 nm channels in time.  
Figure 6: Validation results of satellite PWV data based on AERONET PWV data: (**a**) validation results of DPC PWV data; (**b**) validation results of MODIS PWV data. The color of each point denotes the number density at its location. The dotted line is the 1:1 line.
## 2 Theoretical Background  
The theoretical background covers the radio propagation mechanism, log-distance-based path loss prediction models and artificial neural network systems.