esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Results
### Ablation Studies
#### 5.2.2 The Design Choices of Embedding Network  
* We explore different structures for the embedding sub-network and assess their impact on performance. We use four different structures, and the results are presented in Table 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nfeat** & \multicolumn{2}{c}{**ResNet-101**} \\ \hline
**Ndet** & \multicolumn{2}{c}{**Cascade R-CNN**} \\ \hline methods & & \\ \hline multi-frame feature aggregation? & \(\surd\)1  & \(\surd\) & \(\surd\) \\ \hline adaptive weighs? & & \(\surd\) & \(\surd\) \\ \hline spatiotemporal sampling? & & & \(\surd\) \\ \hline mAP & 73.1 & 71.4 & 73.0 & 73.5 \\ \hline MOTA & 58.3 & 57.3 & 58.1 & 62.8 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Different design of feature sampling and aggregation module.  
* The findings indicate that using a fully convolutional network to project features into a new high-dimensional embedding space for similarity measurement does not significantly impact performance.
* Based on these results, we select Design (C) as our embedding sub-network, as it offers the best performance with minimal computational requirements.
# Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review
## 2 Theoretical Background of Wavelet Transforms
### Empirical Wavelet Transform (EWT)  
The EWT is an adaptive method combining the WT and EMD which decomposes the signal into amplitude and frequency-modulated components called IMFs. As introduced in [22], the EWT is defined in three steps. First, the FT method is used to obtain the frequency spectrum of the analyzed signal in the frequency range \([0,\pi]\). Then, the frequency spectrum is segmented into N number of contiguous segments using the EWT boundary detection method. Finally, the empirical scaling and wavelet functions are defined in each segment as the set of band-pass filters. The EWT is discussed in more details in [22].
# Multiscale Analysis for Improving Texture Classification
## 2 Multiscale and Multiresolution Analysis
### Multiscale Analysis  
Multiscale analysis is often used in pattern recognition and texture analysis when the patterns or textures being analyzed are expected to vary in size or scale. This can be due to variations in the distance, orientation, or perspective from which the patterns or textures are observed or inherent variations in the patterns or textures themselves. For example, consider the texture classification task, where the goal is to identify the type of material present in an image based on the texture of the surface. In this case, it may be helpful to use a multiscale analysis to extract features that are robust to changes in scale, as the texture of a material may vary depending on the size of the region being analyzed.  
Multiscale analysis can also be helpful in texture analysis when the texture exhibits patterns at multiple scales. For instance, a texture may have a fine-scale pattern of wrinkles or scratches and a coarser-scale pattern of grain or veins. By analyzing the texture at multiple scales, one can more accurately capture the full range of patterns present in the texture, which can be helpful in tasks such as material identification or classification. Several methods exist for performing a multiscale analysis in pattern recognition and texture analysis. Some common approaches include:  
* Scale-space representation: this involves representing the image or pattern as a scale function and then analyzing the resulting scale space to extract features or detect patterns;
* Multiresolution analysis involves decomposing the image or pattern into multiple scales or resolutions using techniques such as wavelet decomposition or pyramid decomposition and then analyzing the different scales separately;
* Multiscale features: this involves extracting features from the image or pattern at multiple scales using multiscale histograms or edge detection techniques. It is essential to choose an appropriate method for the multiscale analysis based on the characteristics of the patterns or textures being analyzed and the specific task at hand.
## 2 Materials and Methods
### Image Processing Algorithm
#### 2.2.2 Image Segmentation  
_Step 1--area of interest:_ as can be seen in the unrolled picture Figure 7b, the lower and upper parts of the discharge look brighter than the central part of it. The coaxial geometry of the reactor and the transparent quartz walls cause this effect. In the central part of the discharge, the inner electrode creates a solid background for the pictures, whereas in the upper and lower parts, the gas gap between the two electrodes and quartz barriers produces an optical effect that intensifies the discharge. To avoid biased results due to this optical effect and according to the assumption (c) that the plasma volume has no depth, the image is cropped again, erasing the parts which do not present a solid background. They correspond to the gas gap and only the central part of the picture, which has the height of the diameter of the inner tube (red frame figure below), is kept (Figure 8b)--one should notice that this figure is different from the original one, Figure 7a.  
_Step 2--image segmentation_: the discharge area is segmented using a thresholding technique, as shown in Figure 9. It means that a gray-level value called \"threshold value\" is used as the delimiter between the background and the discharge area. All pixels with values less than the threshold value are considered as background, and pixels with values greater than the threshold are considered as the discharge area. In order to find the threshold value, [PERSON]'s method is used [24]: this method chooses a threshold value that minimizes the variance of the thresholded background and discharge pixels. The ability of this method to segment the image even with low intensity discharges (operating points at low power) of uniform appearance is remarkable (as will be shown in Section 3).  
_Step 3--image analysis_: the calculation of the \(\beta\) percentage is achieved with the ratio between the number of pixels of the discharge, obtained with the segmentation, and the total number of pixels. Other statistical results quantifying the discharge's uniformity are also presented in Section 3.3.  
Figure 8: Cylindrical coordinates output image (**a**), and its cropped central region with solid background (**b**).  
Figure 9: Segmented Discharge area (violet is background, yellow is discharge area).
## 4 Comparison between Simulation and Measurement
Finally, previously published works on wideband, bow-tie antennas, with the criteria limited to planar, bow-tie antennas, including both single and multilayered ones, are compared with the proposed antenna in Table 4. The criteria for the references included in this comparison table does not limit the directionality (both bi-directional and unidirectional designs are included), since the main goal of the proposed antenna is improving the bandwidth of the bow-tie antenna while maintaining a planar, relatively small size--rather than the directionality of the antenna. The overall size, in terms of \(\lambda\), and the electrical size (\(kr\)) for each antenna are calculated at the lowest frequency in the \(-10\) dB bandwidth. The proposed wideband, planar, bow-tie antenna has the smallest electrical size, aside from the half bow-tie antenna design in [7], which has a narrower bandwidth and lower gain. Figure 11: Simulated and measured \(S_{11}\) and forward realized gain of the proposed wideband, planar, bow-tie antenna.
## 1 Background Study
Pressure Drop Calculation: Solution to Analytical Model. The final analytical solutions used to compute the pressure drop at the wellbore and along the fracture are defined by Equations (12) and (13). The derivation of the solution is shown as well. \[ \begin{array}{c}p_{flowing}(r=r_{w},t)=p_{i}-\frac{70.6B\mu}{kh}\frac{r_{w}}{ x_{f}}q\bigg{[}-Ei\bigg{(}-\frac{948.1\phi\mu c_{t}r_{w}^{2}}{kt}\bigg{)} \bigg{]}\\ -\frac{70.6B\mu}{kh}\frac{q}{x_{f}}\int_{r_{w}}^{x_{f}}\bigg{[}-Ei \bigg{(}-\frac{948.1\phi\mu c_{t}L^{2}}{kt}\bigg{)}\bigg{]}dL\end{array}  \tag{12}\]  
Equation (12): Pressure Drop at Wellbore. \[ \begin{array}{c}p_{flowing}(r,t)=p_{i}-\frac{70.6B\mu}{kh}\frac{r_{w}}{x_{f}} q\int_{\frac{r_{w}}{t}}^{\infty}\bigg{(}\frac{1}{u}\exp\bigg{(}-\frac{948.1 \phi\mu c_{t}}{k}u\bigg{)}\bigg{)}du\ \frac{70.6\phi B\mu}{kh}\frac{q}{x_{f}}\int_{r_{w}}^{x_{f}} \int_{\frac{1}{t}}^{\infty}\bigg{(}\frac{1}{u}\exp\bigg{(}-\frac{948.1\phi\mu c _{t}}{k}u\bigg{)}\bigg{)}du\ dL\end{array}  \tag{13}\]  
Equation (13): Pressure Drop Solution along Fracture.
## 5 Conclusions  
In this study, the anchoring performance of the dissolvable ball seat (DBS) slip structure is investigated through finite element simulations, yielding the following key findings.  
This study investigates the structural design, operational principles, and horizontal well segmentation fracturing process of the DBS slip. An evaluation index system was established to quantify the anchoring performance of the DBS slip, incorporating parameters such as the equivalent stress between the slip and casing, peak contact pressure, average contact pressure, and contact pressure standard deviation. These criteria provide a standardized framework for the subsequent performance assessment and optimization of DBS in downhole applications.  
Finite element simulations were conducted on two existing slip structures to evaluate the casing contact pressure and the equivalent forces exerted on both the slip and casing. The results demonstrate that, under a seating force of less than 1.6 tons, the peak contact pressure between the slip nail and casing wall reaches 2338.5 MPa, whereas the slip block exhibits a significantly lower peak pressure of 1161.3 MPa. This indicates that the block-type slip design ensures more uniform stress distribution, thereby improving anchoring performance.  
Through an analysis of the block-type slip structural parameters, the effects of the slip tooth apex angle and inclination angle on the stress distribution during the anchoring process were investigated. A comparative evaluation of the casing and slip force states under varying parameter combinations revealed that an optimal slip tooth apex angle of 80\({}^{\circ}\) or 85\({}^{\circ}\) and an inclination angle of 70\({}^{\circ}\) enhance anchoring performance. This configuration not only ensures secure anchoring, but also minimizes stress concentrations on both the slip and casing, thereby preventing structural damage. The safety and reliability of hydraulic fracturing in shale oil and gas reservoirs will be improved.  
S.J.: Writing--review & editing, Investigation. A.M.: Writing--original draft, Methodology. Z.C.: Software, Data curation. X.Y.: Formal analysis, Supervision. N.C.: Supervision, Investigation. Q.R.: Data curation, Formal analysis. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of Sichuan Province (2024 NS-FSC0094), Open Fund of Sichuan Key Laboratory of Shale Gas Evaluation and Exploitation (YSK2022013) and Open Fund of Sinopec Key Laboratory of Elastic Wave Theory and Detection Technology (335500-22-ZC0613-0288).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
DBS Dissolvable ball seats  
FEA Finite element analysis
# An Optimized Load Balance Solution for Multi-homed Host in Heterogeneous Wireless Networks
## 3 Proposed System
### Stage 1: Application Classification  
As seen in Figure 3, the application status is defined as either idle or active. In case the application is active, then the process continues, else it will stop. The idle state is checked based on the existence of an open application session.  
Back to the main system model in Figure 2, the distribution of applications data over the available multiple connections in an adaptive weighted round robin scheme based on the application preference requires special treatment. The proposed special treatment is to treat every application in accordance with its preference and needs. In order to do so, it is recommended to provide a division of applications into groups, and each group has an attribute or weight on which the quality of its performance depends. The division is recommended to be performed through a classification stage as intended in this section. The active application session should be classified into one of the following classes with their related favorite attributes [79].  
1. Social media: depends mainly on availability which is related to power consumption (PR).
2. Multimedia and gaming: depends mainly on the network delay (D).
3. Web application: depends mainly on speed which is related to throughput (T).
4. System application: this is sensitive to the cost of using the network (C).  
Then the process continues to validate the available connections. On the data plan level, the system runs to validate the available network connections based on QoS probing. The network connections are checked against the ICMP echo request and the anticipation of accepted ICMP echo reply value based on the defined delay constraint.
# Solar Energy in Urban Planning: Lesson Learned and Recommendations from Six Italian Case Studies
## 4 Results and Discussion
### Agrovoltaico
#### 4.7.2 Challenges, Issues, and Decision Strategies  
The main challenges encountered during the development of the project were related to the need to overcome the national legislation that did not allow for the installation of large PV systems (above 1 MWp) in agricultural areas [65]. This has been possible thanks to an innovative solution, where the PV modules are suspended 5 m above the ground using a metal structure. Only 2% of the land is occupied by the punctual supports holding the metal structure (in compliance with the regional regulation requiring a soil occupation for PV installation < 10%), leaving great freedom of movement to the harvest machines operating below. To balance the low density of the system compared to a standard one mounted on the ground, double-axis sun-tracking devices were used for the panels. The different configurations of the system during the day and its low density allow a minimal impact on the total radiation falling to the ground. Regarding the decision strategy used for the project, REM collaborated with several industrial partners, a university (the Institute of Agronomy, Genetics and Field crops of the Universita Cattolica del Sacro Cuore of Piacenza), and local authorities to assure the design's requirements.
# A Cross-Layer Approach MAC/NET with Updated-GA (MNUG-CLA)-Based Routing Protocol for VANET Network
## 4 MAC/NET with Updated Genetic Algorithm--A Cross-Layer Approach (MNUG-CLA)
### Multichannel MAC for Prioritization Process  
This model suggests that every car has a single half-duplex transceiver that can constantly shift among channels. The vehicles can only perform one of two things: broadcast or listen. They never do both at the same time. For spatial reuse, the road is separated into sectors, with vehicles travelling in a horizontal path. Every node implies that it is in a saturation condition, which means that it has at least one packet to broadcast. Vehicles consider various speeds, and then each vehicle is aware of its current velocity and acceleration. At any one time, every node has a local table with information relating to available channels and neighbors.  
To meet the QoS specifications, the nodes switch amongst control and service channels. The node uses an IEEE 802.11p strategy with an upgraded distributor accessibility mechanism, as per the WAVE MAC. Nevertheless, the influence of mobility is not taken into account in this method. As a result, an upgrade to IEEE 802.11p is being proposed. The control channel is addressed by the nodes based upon their desire in this method. Every node determines its channel access priority based on its speed, position, load, and  
\begin{table}
\begin{tabular}{r} \hline \hline
**Message Broadcast to Discovery Neighbor Vehicles** \\ \hline Address of the neighbor \\ Position of the neighbor \\ Header size of the neighbor \\ dfor \\ Drev \\ E\_Tx \\ Sequence number of last packet \\ Throughput range of last packet \\ Signal to noise ratio of last packet \\ Signal to interference noise ratio of last packet \\ \hline \hline \end{tabular}
\end{table}
Table 3: Neighbor entry for MAC-based Neighbor Discovery Process.  
network capacity. The arbitrary inter-frame space number (AIFSN) of IEEE 802.11p is, therefore, transferred to this quantity. The range of parameters from 1 to 9 is taken into account. This allows the nodes to have a fair shot for accessing the control channel. Both signals (data exchange signals and emergency signals) are addressed here. The protocol's data exchange phase intends to minimize the complexity of obtaining the channel. The operation of the Multichannel MAC model is depicted graphically in Figure 3.
# Damage Location Diagnosis of Frame Structure Based on a Novel Convolutional Neural Network
## 3 Fault Diagnosis Process
### Adaptive Wavelet Denoising  
During the use of the building frame structure, it is often affected by vibration due to the existence of various surrounding traffic tools, construction machinery, natural wind, and other factors. Therefore, there are strong noise interference signals in the original vibration signals collected during the damage diagnosis of the structural frame, and these strong noise signals belong to low-frequency noise signals [34]. Because the wavelet coefficients of noise signals are small, and the signals with large wavelet coefficients contain a lot of feature information, this paper used adaptive wavelet to decompose and reconstruct the original signals. Adaptive wavelet can automatically select an appropriate threshold, retain the signal whose wavelet coefficient is greater than the threshold, and zero the signal whose wavelet coefficient is less than the threshold, so as to obtain a reconstructed signal with the same length as the original signal. Therefore, using adaptive wavelet decomposition and reconstruction can effectively filter the low-frequency noise signals in the original vibration signals [35]. The principle of wavelet decomposition and reorganization is shown in Figure 3.  
Figure 2: Fault diagnosis process of building frame structure.  
The wavelet decomposition and recombination can filter out part of the low frequency characteristic information while filtering out the low frequency noise signal. Therefore, decomposing the vibration signal to an appropriate level will help to remove the low frequency noise signal, while retaining the low frequency characteristic information to the maximum extent, so as to achieve the purpose of not affecting the recognition rate of the convolutional neural network. The results of multi-level decomposition of the original signal by using wavelet decomposition and reconstruction are shown in Figure 4. It can be seen from the figure that the reconstructed signals are quite different from the original signal. The greater the number of decomposition levels, the greater the difference between the reconstructed signals and the original signals, and the more obvious the loss of features.  
Figure 4: Wavelet decomposition and reconstruction of signals. (**a**) Decomposition signals, (**b**) reconstructed signals.  
Figure 3: Principle of adaptive wavelet decomposition and recombination.
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 3 Engineering Background and Database Construction
### Data Processing and Dataset Construction
#### 3.3.1 Data Cleaning  
Missing values within the raw data were addressed using appropriate techniques such as mean-filling or interpolation. Outliers were identified and corrected or filtered based on predefined, physically reasonable thresholds for each variable (e.g., maximum plausible slope angle, rainfall intensity).  
This step is crucial for maintaining data integrity and preventing spurious results caused by incomplete or erroneous entries. Handling missing values ensures all samples can be used, while outlier treatment prevents extreme, potentially unrepresentative values from unduly influencing model training.
## 1 Introduction
The advantage of the use of the artificial potential fields for this maneuver is twofold: (i) an autonomous way for the desired path is designed with low computational effort and (ii) an online update of the path is guaranteed, in particular in the presence of obstacles. The idea of this paper is to provide computationally efficient algorithms for real time applications in space. The novelties are related to the combination of APF and sliding mode controller (SMC) for a complete space maneuver, in which the SMC design is focused on a real application: (1) reduced frequency of the controller for fuel saving, (2) pulse width modulation of thrusters, and (3) actuator models are considered. For APF algorithms, even if the approach is well known, we propose a simple shape of the artificial field to have a reliable method (the same field for all the maneuver) and to avoid obstacles. In our case, external disturbances are also considered, to demonstrate the robustness of the proposed controller. The paper is organized as follows. In Section 2, a detailed spacecraft model is analyzed, including actuator models, position and attitude dynamics, and external disturbances. The proposed guidance algorithms are described in Section 3; instead, the sliding mode control strategies are presented in Section 4. The simulation results are in Section 5. Conclusions are drawn in Section 6.
## 3 Results and Discussions
### Mechanistic Investigation of Hempseed Protein AE-IEP Extraction
#### 3.1.2 The Effect of pH and Temperature on Free Sulfhydryl Content during Extraction  
Based on the high proportion of cysteine in hempseed protein [6] and the hypothesis present in a previous study [15], the possibility of intermolecular disulphide bond formation during the extraction process must be understood in order to optimise the extraction process with respect to HPI functionality. To the best of our knowledge, no systematic research work regarding hempseed free -SH content changes has been performed before. The changes to free thiol content are shown in Figure 3B,D as a function of pH and temperature, respectively. It can be observed that the free thiol content progressively decreased during all experiments, indicative of SS bonding, and the formation of deposit oligomers (Figure 3B,D). Moreover, increases in pH and temperature can be seen to accelerate the decrease in free thiol content, in a similar manner to the previously observed increase in protein extraction kinetics (Figure 3A,C). The influence of pH can be attributed to the greater extraction and exposure of the hempseed protein at a higher pH condition, resulting in more thiolate (S\({}^{-}\)) available in the system to react. The greater chemical reactivity of S\({}^{-}\) and higher temperature can also be expected to cause more rapid disulphide bond formation.