esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Reconstruction Using Autofocus in Single-Lens System
## 3 Simulation and Experiments
### Autofocus for Object Distance and Image Distance  
The other case in the single-lens CDI we consider is two-unknown-distance autofocus, the object distance, and image distance. In Section 3.2, speckle illumination played a positive role in one-unknown-distance autofocus. We expect it to work for autofocus in two-unknown-distance autofocus. Then we test the experimental data set 78'. Based on the distance obtained in Figure 9, we reset the object distance scanning boundary 50 mm\(\sim\)90 mm, the image distance scanning range from 15 mm\(\sim\)55 mm, and the step length is 1 mm. The results are shown in Figure 101-a9,b1-b9. In a large range and long step length autofocus, neither coherent illumination nor speckle illumination can give a rough focus trend. Therefore, in the single-lens CDI, when the object and image distances are auto-focusing scanned simultaneously, the diffuser makes no sense.  
Therefore, for single-lens CDI dual-distance autofocus, a new focus strategy is proposed. Usually, MSE and SSIM are used to detect the effect of image retrieval in simulation [41; 42; 43; 44; 45] and the reference value of the function is the ground truth. In the experiment, the ground truth of the sample is unavailable. Therefore, the pixel average is selected as the reference value here. Their expressions are as follows  
\[\text{MSE}\Big{(}U_{S_{n}},I_{ref}\Big{)}=\frac{1}{M}\sum_{n=1}^{N}\Big{(}U_{S_ {n}}-I_{ref}\Big{)}^{2}, \tag{20}\]  
where \(U_{S_{n}}\) has been defined in Equation (14), \(M\) represents the number of pixels and \(I_{ref}\) is the pixel mean of the \(U_{S_{n}}\), its data set is written as  
\[I_{ref}=\frac{1}{n}\sum_{n=1}^{N}U_{S_{n}}. \tag{21}\]  
Here the structural similarity (SSIM) function is shown as  
\[\text{SSIM}\Big{(}U_{S_{n}},I_{ref}\Big{)}=\frac{2\mu_{U_{S_{n}}}\mu_{I_{ref}} +c_{1}}{\mu_{U_{S_{n}}}^{2}+\mu_{I_{ref}}^{2}+c_{1}}\frac{2\sigma_{U_{S_{n}}I_ {ref}}+c_{2}}{\sigma_{U_{S_{n}}}^{
# Energy-Saving Routing Protocols for Smart Cities
## 2 Materials and Methods
### Routing Protocols for WSN  
With a higher the amount of sensor nodes, the amount of data exchanged over the WSN increases. This emphasizes the importance of an efficient data routing process when considering the mesh topology.  
In short, a data routing process consists of verifying and evaluating available paths from a source node to a destination node, then determining the best path for forwarding data throughout the network based on a given criterion [20]. Based on this process, the data routing protocol specifies the technique by which routing tables are formed and maintained in order to aid in the forwarding of data [21].  
In general, routing protocols fall into four categories:n Centralized vs. Distributed, Static vs. Adaptive, Flat vs. Hierarchical, and Proactive vs. Reactive vs. Hybrid [21].  
In this work, we highlight proactive, reactive, and hybrid protocols, which differ in the way they operate according to the routing strategy [11]:  
* Proactive: Routing tables are shared with neighboring nodes during network startup and at fixed times, meaning that all nodes know the paths to any destination nodes even before this information is needed [22]. Examples of proactive routing protocols include Distance Vector Routing (DVR), Destination-Sequenced Distance Vector Routing (DSDV), Optimized Link State Routing Protocol (OLSR), and Wireless Routing Protocol (WRP);
* Reactive: Routes to destination are established only when needed, that is, when there are data packets to be sent. Therefore, only active routes to destination nodes that are in use are stored. These protocols do not share data at network startup, and have periodic routing table sharing mechanisms [10]. Examples include Ad Hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), and Temporally Ordered Routing Algorithm (TORA);
* Hybrid: These protocols combine resources from proactive and reactive protocols, enabling convenient use of the advantages of both; examples include Zone Routing Protocol (ZRP) and Fisheye State Routing (FSR).  
When considering routing protocols for WSN applications, energy efficiency is an important characteristic. In [23], the authors analysed wireless network energy models based on five reactive and proactive routing protocols for WSNs, including AODV and DVR protocols. A WSN energy model was proposed in [24] using AODV and DVR routing protocols and considering the energy consumption at each node of the network. A genetic algorithm-based routing protocol for sensor networks was presented in [25]; the authors compared their proposed method with different routing protocols, including DVR and AODV.
# An Energy-Efficient and Robust Multipath Routing Protocol for Cognitive Radio Ad Hoc Networks
## 5 Performance Evaluation
### Performance Metrics  
The following performance metrics are evaluated to measure the performance of the proposed routing protocol.  
* Average network throughput: This refers to the total number of successfully received bits per second over the number of source-destination pairs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Simulator & NS-2.31 \\ Network area & \(1000\times 1000\) m\({}^{2}\) \\ Number of primary users & 10 \\ Number of secondary users & 10, 15, 20, 25, 30 (default), 35, 40, 45 \\ Number of licensed channels & 10 \\ PU modeling & ON/OFF model \\ Traffic type & CBR \\ Packet size & 512 bytes \\ Mobility & Random waypoint \\ Speed & 0–5 m/s \\ Pause time & 30 s \\ Number of sessions & 4, 6, 8, 10 (default), 12, 14, 16 \\ Initial energy & Randomly chosed (750–760 J) \\ Transmission power & 1.15 W \\ Reception power & 1.0 W \\ Ideal power & 0.8 W \\ Switching power & 0.8 W \\ Simulation time & 250 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
* Packet delivery ratio (PDR): This is the ratio for the number of successfully delivered packets to the destinations over the number of packets sent from the sources. It can be regarded as a metric for routing path reliability.
* Average energy consumption per bit: This indicates the average amount of energy in joules consumed for a bit of a data packet to reach the destination successfully.
* Average end-to-end delay: This is the average time a packet takes to reach its destination once generated from the source.
## 3 Analysis Method of iNterference on DME
### Calculation for P(I)  
As mentioned above, \(P(I)\) is the probability that the interrogation pulses from interrogator output (\(\lambda_{i}\)) to transponder (\(\lambda_{r}\)) are not interfered. In this process, intra-system interference consists of collision between two or more interrogations and collision between dead time and subsequent interrogations, inter-system interference is JTIDS. Let \(M\) denotes the event that intra-system interference does not occur and \(P(M)\) is the probability of \(M\), \(J\) denotes the event that JTIDS interference does not occur and \(P(J)\) is the probability of \(J\). Thus \(P(I)\) is given as follows:  
\[P\left(I\right)=P\left(M,J\right)=P\left(J\right)\cdot P\left(M\left|J\right.\right) \tag{3}\]  
where \(P(M|J)\) is the conditional probability.
# Collision Risk Assessment and Operation Assistant Strategy for Teleoperation System
## 3 The Establishment of the Virtual Environment
### The Graphical Modeling of the Environment  
In contrast to the teleoperation robot, there are usually no sensors arranged on the manipulated object to directly obtain location information. Therefore, we achieved the reconstruction of the geometry model by visual localization.  
In this research, a camera was fixed on the table for identification and positioning of the object. Owing to the extremely low price and good performance, the Realsense (D455) vision sensor was employed in our system to locate the target objects. In order to realize accurate and stable posture detection, this research designed a marker-based posture detection method by integrating ArUco markers at the objects. The Sergio algorithm [28] was adopted to locate the posture of the markers.  
The ArUco marker is a square marker composed of a wide black border and an inner binary matrix that determines its identifier (ID number), as shown in Figure 4. The main benefit is that their detection is robust, fast, and simple. By combining the ID number of the ArUco marker and the projection state of the black border in the camera image, the posture of the target relative to the camera can be calculated. Moreover, ArUco markers are not only used for component positioning, but also store and extract component assembly information for multi-component assembly task planning.  
Figure 3: Graphical model of the robot.  
As shown in Figure 5, The following processes make up the positional estimation of ArUco markers:  
1. Enhanced grayscale image boundary thresholding that uses a locally adaptive thresholding technique. Contour extraction was performed on the threshold image using the [PERSON] algorithm [29], and, then, polygon approximation was performed using the Douglas-Peucker algorithm to detect possible candidate markers in the scene.
2. To evaluate if the aforementioned potential markers were \"genuine\" markers, the internal encoding of the markers was recovered.
3. To determine if the aforementioned candidate markers were actual markers or simply a byproduct of the environment, marker ID recognition and error correction were carried out.
4. To determine the marker's position in relation to the camera, corner point optimization and pose estimation were performed.
5. Based on the relationship between the camera coordinate and the base coordinate, the posture of the marker relative to the base of the robot arm was calculated.  
In this study, the 3D model of the object was constructed in SolidWorks, and then these models were assembled in OpenSceneGraph to determine the posture of the surrounding assembly parts by the acquired positioning information. Finally, the robot arm model and the reconstructed virtual environment were combined in one scene, as shown in Figure 6.  
Figure 4: Schematic of ArUco marker.  
Figure 5: Marker-based positioning process.
# An Efficient Local Search Heuristic for Earth Observation Satellite Integrated Scheduling
## 4 An Efficient Local Search Heuristic for Earth Observation Satellite Integrated Scheduling
### Evaluation of a Schedule  
Given the above feature values, we can evaluate a schedule \(\sigma\) easily as:  
\[Eva(\sigma,c)=\sum_{v\in\sigma}p_{v}+c\rho_{1}TV(\sigma)+c\rho_{2}MV(\sigma) \tag{17}\]  
where \(\rho_{1},\rho_{2}\) are penalty parameters and \(c\) is a penalty parameter multiplier. The evaluation of a schedule is the sum of the total profit and penalties on constraint violation and resource consumption. In this way, we can evaluate an insertion with O(1) complexity and improve the efficiency of our algorithm.
# Shear Tests of Deep Hollow Core Slabs Strengthened by Core-Filling
###### Abstract  
Prestressed hollow core slabs (PHCSs) have commonly been applied to long-span structures, due to their excellent flexural capacity and deflection control performance. However, in quite a few cases, the web-shear strength at member ends subjected to high shear forces is insufficient, because the web of the PHCS is very thin, making it difficult to place shear reinforcement, and the prestress is not fully effective in transfer length regions. Accordingly, a variety of shear strengthening methods have been proposed to improve the web-shear strength of PHCS ends. In this study, experimental research was conducted to investigate the shear resistance mechanism of PHCS strengthened by core-filling method, which has been most widely used in the construction field. The number of filled cores and the shear reinforcement ratio were set as the main test variables, and the patterns and angles of shear cracks that occurred in the PHCS units and filled cores, respectively, and the strain behavior of the shear reinforcement, were measured and analyzed in detail. This study also analyzed the test results based on the current design codes, and proposed a modified shear strength equation that can be applied to the core-filled PHCS.  
prestressed hollow core slab; shear strengthening method; core-filling method; web-shear strength; composite performance +
Footnote †: journal: Applied Sciences
## 3 Results  
### Digital Terrain Model  
Figure 6 shows scatter plots comparing the digital terrain model (DTM) derived from the Carbon Sinks and Aerosol LiDAR (CASAL) system and airborne laser scanning (ALS) across all footprints in the study area. The plots reveal a strong linear correlation (\(R^{2}=0.999\)) between the two datasets, with error metrics as follows: root mean square error (RMSE) = 3.83 m, Bias = 0.70 m, median error = 0.57 m, and median absolute deviation  
Figure 5: Example of point cloud representations within a footprint. (**a**,**b**) Spatial distribution of point clouds in the forested footprint area (ground points in red; canopy top points in green). (**c**) Point cloud data cropped to match the TECIS footprint dimensions and position.  
(MAD) = 2.96 m. These results demonstrate excellent agreement between the CASAL-DTM and ALS-DTM datasets.The relationship between CASAL-DTM accuracy and various environments is discussed below, and the corresponding box plot is shown in Figure 7.  
As can be seen in Figure 7, the effect of slope on the accuracy of terrain height estimates is very strong, with median errors increasing with slope as the slope exceeds 20\({}^{\circ}\). Figure 7 shows that the accuracy of DTM terrain variance is almost independent of vegetation height, but Figure 7 shows that DTM terrain variance is relatively constant at lower vegetation cover ranges and the accuracy of the measurements only decreases in areas with more than 80% vegetation cover.
# Real-Time Computing Strategies for Automatic Detection of EEG Seizures in ICU
## 2 Materials and Methods
### Evaluation of Quantitative Electroencephalography and Frequency-Domain Transforms  
Once we had finished the signal preprocessing, we performed a numerical quantitative analysis for each signal in the frequency domain, and we assessed visual changes along with a time interval, evaluating the signal power frequency. Once we had finished the signal preprocessing, we performed a numerical quantitative analysis for each signal coming from each electroencephalographical channel in the frequency domain to assess visual changes along with a time interval and the signal power frequency. Thus, we can evaluate abrupt changes in the signal from the main frequency bands analysed from the cranial scalp, both fast frequencies, such as beta band (12-30 Hz) and alpha band (8-11.9 Hz), and slow frequencies, such as theta band (4-7.9 Hz) and delta band (1-3.9 Hz). This consideration is essential in seizure detection because a sudden change in the beta band and, to a lesser extent, in the alpha band suggests an epileptic seizure.  
To evaluate changes in the signal precisely, we have assessed those transforms usually described for electroencephalographical signals: Wavelet transforms (biorthogonal 1.5, 3.3, and 4.4, and reverse biorthogonal 6.8 [22; 23; 24]; Daubechies (Db) 3, 4, 5, and 8 [25; 26; 27; 28; 29]; [PERSON] transform [30; 31]; Symlets 4 and 6 [32; 33; 34]; and Coiflets 3 and 4 [35; 36; 37]; Haar transform with order 3 [38; 39]; Hilbert transform [40; 41]; and Empirical Mode Decomposition (EMD) and the first two Intrinsic Mode Functions (IMFs) [42; 43; 44].  
After quantitatively assessing the signal from each channel and the visual inspection of quantified signals by a neurophysiologist experienced in EEG analysis, the channels that provide the best diagnostic performance are the left anterior frontal channel, the right anterior frontal channel, the left middle temporal channel, and the right middle temporal channel. The other channels located in parietal and occipital sites did not add enough diagnostic capacity to detect seizures. This selection is due to the neocortical regions, and the hippocampus is more prone to seizures, showing anatomical changes in the hippocampus, amygdala, frontal cortex, temporal cortex, and olfactory cortex in patients with epilepsy [45]. Also, we have selected the following transforms: biorthogonal 1.5 and 3.3; Hilbert transform; Db 3, 4, 5, and 8; and Symlets 4 and 6. These transforms could detect abrupt changes in the power of a time signal distributed by frequency according to the onset of a seizure [32; 46; 47; 48].
# Simulation of Track-Soft Soil Interactions Using a Discrete Element Method
## 2 Material and Methods
### Interaction between Soil and Track  
#### 2.2.1 Shear Stress-Displacement Model  
The locomotion of a tracked vehicle on the surface of soil is mainly based on the shear forces that develop between the track links and surface soil in the longitudinal direction. Soil conditions have significant effects on tractive performance.  
According to prior surveys and tests, the characteristics of clay are similar, in that shear stress initially increases sharply and reaches a \"hump\" of maximum shear stress \(\tau_{\text{max}}\) at a particular shear displacement, and then decreases and approaches a relative constant residual value \(\tau_{res}\) with further increases in shear displacement, which relationship may be characterized by the following empirical equation proposed by [PERSON] [32; 33]:  
\[\tau_{Clay}=\tau_{\text{max}}K_{r}\{1+[\frac{1}{K_{r}(1-e^{-1})}]\cdot e^{1-j(x )/K_{w}}\}\cdot(1-e^{-j(x)/K_{w}}) \tag{7}\]  
where \(\tau_{\text{max}}\) is the maximum shear stress, \(K_{w}\) is the shear displacement where the maximum shear stress occurs and \(K_{r}\) is the ratio of residual shear stress to maximum shear stress.  
However, sand has shear stress characteristics which increase rapidly and then approach a relatively constant value, for which the relationship may be characterized by the following empirical equation proposed by [PERSON] [34]:  
\[\tau_{Sand}=\tau_{\text{max}}(1-e^{-j(x)/K}), \tag{8}\]  
where \(K\) is the shear displacement where the maximum shear stress occurs. Variation trends in shear stress of different types of soil are shown in Figure 5.
# Design of a Compact Multiband Monopole Antenna with MIMO Mutual Coupling Reduction
## 3 Antenna Implementation and Measurement Results
### Channel Capacity Loss (CCL)  
The Channel Capacity Loss (CCL) is also an important parameter for evaluating MIMO antenna systems. The CCL value represents the data loss in bits per second per Hertz due to channel conditions. Therefore, the smaller the CCL value, the better. A CCL value below 0.4 bits/s/Hz can be considered excellent channel conditions. Formulas (4)-(7) are used to calculate the CCL values.  
\[CCL=-log_{2}\text{det}\left(\psi^{R}\right) \tag{4}\]  
where \(\psi^{R}\) is the correlation matrix at the receiving antenna.  
\[\psi^{R}=\begin{bmatrix}\rho_{11}&\rho_{12}\\ \rho_{21}&\rho_{22}\end{bmatrix} \tag{5}\]  
\[\rho_{ii}=1-\left|S_{ii}\right|^{2}-\left|S_{ij}\right|^{2},\ for\ i,\ j=1\ or\ 2 \tag{6}\]  
\[\rho_{ij}=-\left(S_{ii}^{s}S_{ij}+S_{ji}^{*}S_{jj}\right),\ for\ i,\ j=1\ or\ 2 \tag{7}\]  
Figure 21 shows the measured and simulated CCL values of the MIMO antennas with and without slits. The red curve represents the measured CCL of the MIMO antenna with slits. The results represent CCL values below 0.4 bits/second/Hz in the desired frequency band. This means that when the MIMO antenna is used in high data rate communication environments, fewer data bits will be lost.
## 2 Modeling Grounding Electrodes with Internal Resistance: Backgrounds
For an \(L\) length rod driven into the soil of parameters \(\{\rho_{1},\rho_{2}\ldots,h_{1},h_{2}\ldots\}\), the calculated grounding resistance will be denoted as \(R_{calc}(L;\{\rho_{1},\rho_{2}\ldots,h_{1},h_{2}\ldots\})\). In practice, a direct measurement in the field of the grounding resistance \(R_{meas}\) of the rod by the fall-of-potential method is performed. The control variable is the rod length \(L\), thus a set of grounding resistance values corresponding to increasing lengths of the rod are available. From these measurements an optimization algorithm that minimizes  
\[\chi^{2}=\sum_{j}[R_{meas}(L_{j})-R_{calc}(L_{j};\{\rho_{1},\rho_{2}\ldots,h_{ 1},h_{2}\ldots\})]^{2} \tag{17}\]  
could be implemented. This problem falls into the category of so-called inverse problems, which are very often ill-conditioned. That means in practice that uncertainties in the measurements of grounding resistance can led to very dramatic changes in the parameter values of the multilayered model. Consequently, there may be no single solution to the resistivity profile from the measured grounding resistance series. To finish this section, some considerations about the model presented here will be discussed. The internal resistance of a grounding electrode plays an important role for several reasons. In the first place, as already mentioned, the electrode is no longer equipotential, and the grounding resistance must be defined as indicated above. The current injection point is an important choice and must be chosen so that its potential is as low as possible. Second, the calculations made with this model allow validating or discarding materials to be used in electrodes on condition of verifying technical requirements on the value of the grounding resistance and the step and touch potentials required in an installation. Note that increasing the size of the electrodes can help to decrease the grounding resistance as long as the internal resistance of the conductors does not result in a final increase in the grounding resistance. Finally, another effect to consider that can only be evaluated with models such as the one presented here is the heating by the Joule effect on the electrodes. The heat generated can cause an unwanted increase in grounding resistance when taking into account the variation in resistivity with temperature. The effect must be studied and only models that take into account the internal resistivity of the conductors are valid for this purpose.