esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Theoretical Background
#### 2.1.4 Bodies Growth Inversion Algorithm
The coefficients to be optimized can be computed as  
\[c_{y}^{[k+1]} = \left(G_{xx}G_{yd}-G_{xy}G_{xd}\right)\,/\,\left(G_{yy}G_{xx}-G_{ xy}G_{xy}\right)\, \tag{15}\] \[c_{x}^{[k+1]} = \left(G_{xd}-c_{y}^{[k+1]}G_{xy}\right)\,/\,G_{xx}\,\] (16) \[c_{0}^{[k+1]} = \left(F_{ud}-c_{x}^{[k+1]}F_{ux}-c_{y}^{[k+1]}F_{uy}\right)\,/\,F _{uu}\,\] (17) \[f^{[k+1]} = \left(s_{nd}-c_{0}^{[k+1]}s_{ru}-c_{x}^{[k+1]}s_{rx}-c_{y}^{[k+1] }s_{yy}\right)\,/\,\left(s_{rr}+s_{pp}\right)\, \tag{18}\]  
where \(s_{pp}=\mathbf{p}^{\mathsf{T}}\Delta_{p}^{-1}\mathbf{p}\), the subindexes \(r\), \(d\), \(u\), \(x\) and \(y\) refer to the vectors \(\mathbf{r}=\mathbf{m}^{[k]}\), \(\mathbf{d}=\mathbf{T}\), \(\mathbf{u}=\mathbf{1}\), \(x=x-x_{M}\) and \(\mathbf{y}=y-y_{M}\), and where the coefficients \(s_{rg},s_{ru}\), \(s_{xx}\), \(s_{ry}\), \(s_{rr}\), \(s_{mm}\), \(F_{ug}\), \(F_{ux}\), \(F_{uy}\), \(G_{xy}\), \(G_{xx}\), \(G_{yg}\) and \(G_{yy}\) are computed as  
\[G_{ab}=F_{uu}F_{ab}-F_{ua}F_{ub}\,\qquad\quad F_{ab}=s_{ab}(s_{rr}+s_{mm})-s_{ ar}s_{br}\,\qquad\quad s_{ab}=a^{\mathsf{T}}Q_{D}^{-1}b\, \tag{19}\]  
where \(a\) and \(b\) can take the values of the subindexes \(r\), \(g\), \(u\),\(x\) and \(y\). In each step, \(f^{[k]}\) is diminished, and the inversion process stops when \(f^{[k]}\approx 1\) and the inverted model is accepted. If the parameters (contrasts) are changed during the inversion process, the election of excessively high contrasts shall fit the observed anomaly, yielding to very simple models, while very small contrasts would fit not only the real anomalous bodies but also the observed noise, growing fictitious bodies in the model. In this way, a criterion for the variation of contrasts along the iterative process is necessary. Taking the maximum possible (positive or negative) contrast \(\mathbf{p}_{\max}\), it will evolve according to  
\[\Delta\mathbf{p}=\mathbf{p}_{\max}\left(1-\frac{1}{\left(f+0.1\right)^{\mathsf{T}}} \right)\, \tag{20}\]  
where \(f\geq 1\) is a scale factor used to describe the growth instant, and \(\mathsf{T}\) is the smoothing parameter that indicates the desired contrast's variations. High values of \(\mathsf{T}\) produce homogeneous models with angulate geometries and contrasts close to \(\mathbf{p}_{\max}\), while low values yield models with very varied contrasts which decrease to the exterior of the grown body, with smoothed geometries.
# Design and Implementation of a Smart Traffic Signal Control System for Smart City Applications
## 4 EVSP Process
### Traffic Signal Switching and Compensation Mode  
After the EV has passed through the intersection, the RSU switches back to original signal phase and starts the traffic flow compensation. The purpose of the compensation mechanism is to improve the vehicle's continuation rate and reduce the unnecessary stagnation. Two compensation modes including positive compensation and negative compensation are designed, as shown in Figure 13, which enables the proposed STSC system to reduce the time of the signal coordination and is backward compatible with legacy traffic signal controllers. The RSU will switch to negative compensation mode if the current signal phase is more than half; otherwise, it switches to the positive compensation mode.
## 4 Results
Overall, the proposed approach provides satisfactory performance when compared against XmasNet, a state-of-the-art CNN architecture specifically designed for dealing with PCa mpMRI data. These results suggest the suitability of integrating CRF-RNN within XmasNet. While experimental results showed very good performance achieved by VGG16 and AlexNet, these results could be related to the reduced size of the PROSTATEx17 dataset [69]. That is, considering a dataset with thousands of MRI studies, XmasNet might outperform the other competitors because it is specifically designed for extracting salient features from MRI data. Therefore, we believe that CRF-XmasNet provides an important contribution for practitioners in the medical imaging field, as it shows how the integration of CRFs into XmasNet outperforms the baseline XmasNet architecture. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **ResNet** & **CRF-XmasNet** & **CRF-AlexNet** & **CRF-VGG16** & **XmasNet-CRFpp** & **XmasNet** & **AlexNet** \\ \hline CRF-XmasNet & \(5.65\times 10^{-1}\) & & & & & \\ CRF-AlexNet & \(\mathbf{1.24\times 10^{-2}}\) & \(9.12\times 10^{-1}\) & & & & \\ CRF-VGG16 & \(\mathbf{9.88\times 10^{-3}}\) & \(4.35\times 10^{-1}\) & \(6.03\times 10^{-1}\) & & & \\ XmasNet-CRFpp & \(3.85\times 10^{-2}\) & \(\mathbf{1.61\times 10^{-2}}\) & \(\mathbf{7.78\times 10^{-3}}\) & \(\mathbf{1.68\times 10^{-3}}\) & & \\ XmasNet & \(9.05\times 10^{-1}\) & \(5.65\times 10^{-1}\) & \(9.12\times 10^{-1}\) & \(4.35\times 10^{-1}\) & \(3.32\times 10^{-2}\) & & \\ AlexNet & \(\mathbf{1.66\times 10^{-2}}\) & \(6.89\times 10^{-1}\) & \(6.82\times 10^{-1}\) & \(2.46\times 10^{-1}\) & \(\mathbf{3.0\times 10^{-4}}\) & \(\mathbf{1.66\times 10^{-2}}\) & \\ VGG16 & \(\mathbf{1.45\times 10^{-8}}\) & \(\mathbf{2.56\times 10^{-3}}\) & \(3.26\times 10^{-2}\) & \(\mathbf{1.10\times 10^{-2}}\) & \(\mathbf{2.6\times 10^{-4}}\) & \(\mathbf{5.55\times 10^{-9}}\) & \(\mathbf{2.01\times 10^{-8}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: \(P\)-values obtained from the statistical validation procedure. The Wilcoxon rank-sum test for pairwise data comparison was used with the alternative hypothesis that the samples do not have equal medians of AUROC (test set values). A significance level of \(\alpha=0.05\) with a correction for multiple comparisons was used. **Boldaface** indicates that the null hypothesis can be rejected. Figure 4: Bar graph with 10 groups that represent the occurrences of the test AUROC regarding XmasNet, CRF-XmasNet, and VGG16 architectures observed over 30 runs. The blue, light gray, and dark gray bars refer to the CRF-XmasNet, XmasNet and VGG16 architectures, respectively. The star markers of the same color lie on the average ROC values for the three architectures.
# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 5 Position Related Aspects
### Detection and Localization
Finally, three deep neural networks (DNN) are trained and tested in Reference [102] using an open source RF database in order to detect the presence of UAVs, to identify their type, and to determine their flight mode. This database includes raw RF signals, which were collected for a variety of flight modes. The performance of these DNNs was verified via a tenfold cross-validation process. According to the classification results, the number of classes dramatically affects the accuracy, since some UAVs were manufactured by the same company. In particular, the mean accuracy decreased from 99.7% for the first DNN (2-classes) to 84.5% for the second DNN (4-classes) and to 46.8% for the third DNN (10-classes). In UAV swarm mission-critical scenarios, each UAV should detect and track the other swarm members in order to acquire information regarding the relative distance and bearing. In this direction, a DL- and visual-based detection/tracking algorithmic framework based on the You Only Look Once (YOLO) object detection system [103] was proposed in Reference [104]. In this framework, the tracker, i.e., UAVs equipped with a low-cost and lightweight visual camera, aims at reliably predicting the location of target UAVs on the image plane of their camera. To experimentally evaluate the performance of this approach and to collect multiple data sets of images to train the learning algorithms, a flight test campaign comprising different UAVs and cameras with varying resolution was accomplished. The results underlined the accuracy of the proposed method, which obtained 90% of correct detection instances, in a timely manner. These results also demonstrated that this method offers robustness against challenging conditions, such as sun illumination, as well as background and target-range variability. Table 5 summarizes papers tackling detection and localization issues through ML-based techniques in UAV networks.
# Address Space Layout Randomization Next Generation
## 7 ASLR-NG: Address Space Layout Randomization Next Generation
### Allocating Object Strategy  
Two methods are available to allocate an object in ASLR-NG: Isolated and in an specific-zone.  
* **Isolated:** the object is independently randomized using the full virtual memory space of the process. Unlike current implementations, ASLR-NG can use the full VM range to allocate an object, and as a result there no order to the objects and it prevents any kind of correlation attack.
* **Specific-zone:** objects of the same class are mapped together and isolated from others. A specific-zone is defined by a base address and a direction flag, both of which are initialized when the specific-zone is created (see function new_zone() in Listing 1). The base address is a random value taken from the full VM space, and new objects are placed by following the direction flag (toward higher or lower addresses) with respect to the base address. The main benefit of using specific-zones is that it reduces both fragmentation and page table footprint, which makes ASLR practical and realistic. Furthermore, specific-zones can be created according to MILs criteria, in that objects of the same criticality level may be grouped together. Criticality depends, among other factors, on the permissions and the kind of data stored on the object. Following this rule, ASLR-NG defines five specific-zones (depending on the configuration, see profile modes below): **Huge pages:**: placing all huge page objects in their own specific-zone removes the correlation weakness between huge pages and normal mmapped objects. This is a specially dangerous form of correlation weakness as described in Section 8.3. **Thread stacks:**: following the same criteria as the main stack, the thread stacks are isolated from the rest of objects on their own specific-zone. **Read-write-exec objects:**: although these types of object are seldom used, for example in JIT mapping, they are very sensitive; in fact, Google implements custom randomization in their Chromium browser for these objects as part of its sand-boxing framework. **Executable objects:**: map requests with executable permission are grouped in a specific-zone. This zone is mainly used to group library codes. **Default zone:**: any other objects that do not match previous categories are allocated to this specific-zone. In addition, applications can create custom specific-zones to isolate sensitive data. For example, the credentials or certificates of a web server can be isolated from the rest of the regular data. This mechanism can prevent a Heartbleed [42] attack by moving sensitive data (certificates) away from the vulnerable buffer.
# Real-Time Detection of Insulator Defects with Channel Pruning and Channel Distillation
## 5 Conclusions  
This study addresses the real-time detection of insulator defects by introducing the OD-YOLOV7-tiny algorithm. This method refines the YOLOv7-tiny framework by incorporating network improvements, channel pruning, and channel distillation. We evaluated our enhanced algorithm alongside the original YOLOv7-tiny using the public PCB Defects dataset to validate its efficacy. The experimental results demonstrate that our algorithm significantly improves performance by reducing model size, increasing detection speed, and enhancing overall detection accuracy. The OD-YOLOV7-tiny model achieved a 53% reduction in parameters, a 37.6% increase in detection speed, and a 3.9% improvement in accuracy. This balance between model compression and minimal accuracy loss makes it highly suitable for deployment on small-scale edge devices, meeting the demands of practical applications. In future work, we will continue to explore insulator defect detection, aiming to further improve detection performance with more efficient methods while reducing computational requirements.  
Conceptualization, D.M.; Methodology, D.M., X.X. and Z.J.; Software, D.M.; Validation, L.X.; Formal analysis, X.X.; Investigation, L.X.; Writing--original draft, D.M.; Writing--review & editing, X.X.; Supervision, L.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China grant number 61803390, 61501525; Major Scientific Instrument Development Project of National Natural Science Foundation of China grant number 61927803.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
## 1 Introduction
In this paper, Adaptive Correlation Space Adjusted Open-Loop Tracking Approach (ACSA-OLTA) is proposed to improve the code phase measurement accuracy of open-loop tracking. In the method, different from the previous open-loop tracking schemes, the space between the batch correlators, namely the code search grid size, is adjusted according to the signal carrier-to-noise density ratio (\(C/N_{0}\)) which is an indicator of the signal power [20]. The code phase calculating methods are also switched according to \(C/N_{0}\). Since the grid size should not be too large or too small so as to gain the balance between the errors induced by the uncertainty within the grid and false peak detection at the same time, the new Equivalent Weighted Pseudo Range Error (EWPRE) is raised based on the false peak detection probability and mean measurement error of the search grids to gain the optimal grid size for different \(C/N_{0}\). The code phase measuring error of EMLD is analyzed and compared with other measurement extracting method of open loop tracking for the first time. The \(C/N_{0}\) to switch the pseudo range calculation methods is developed by accuracy comparison to further improve the performance but reduce the correlators needed. The remainder of the paper is organized as follows. In Section 2, the basic concept of open-loop signal tracking method is described based on the batch architecture of GNSS signal processing. Hereafter in Section 3, the ACSA-OLTA is proposed via the calculation of the designed EWPRE and the measurement deviation. Section 4 presents the simulation and the test with the help of the collected real satellite signal data which confirm the performance improvements of ACSA-OLTA. Section 5 concludes the paper.
## 3 Methodology of the Resampling Strategy and Variable Circular Correlation Time
### Fine Acquisition with Pilot Channel  
For GPS L2C signals, the phase of CL code is aligned with that of the CM code. The reference signal \(S_{fr}(i)\) of length \(L_{x}\) is directly extracted from the main lobe signal \(S_{L2}(t)\) with code phase \(\hat{\theta}_{d}\). Similarly, the CL code is removed from \(S_{fr}(i)\) by multiplying with the local CL code replica to get the baseband signal \(S_{fx}(i)\). Considering that no navigation message is modulated on the signal of pilot channel, FFT acts as the frequency discriminator to find out the accurate carrier frequency.  
\[\mathcal{F}[S_{fx}(i)]=\sum_{i=1}^{N_{L}}S_{fx}(i)e^{\frac{-2\pi uni}{N_{L}}} \tag{23}\]  
where \(\mathcal{F}[S_{fx}(i)]\) is the FFT result of \(S_{fx}(i)\). The amount of FFT samples \(N_{L}\) is set as the power of 2 closest to \(L_{x}\) to speed up FFT calculation, when the signal length \(L_{x}\) is not an exact power of 2. As \(\mathcal{F}[S_{fx}(i)]\) is a periodic complex even signal, the frequency spectrum is symmetrical in the frequency range \([0,\ f_{ds}]\). Hence, the one-sided FFT is adopted.  
Assume \(M_{fx}(\widehat{m})\) as the maximum magnitude of \(\mathcal{F}[S_{fx}(i)]\) in the frequency range \([0,~{}f_{ds}]\), and \(\widehat{m}\) is the corresponding index of \(\mathcal{F}[S_{fx}(i)]\). The high-accuracy carrier frequency of the received signal is discriminated as \(f_{dc}\) in the fine acquisition:  
\[f_{dc}=\left\{\begin{array}{c}\widehat{m}~{}\frac{f_{ds}}{N_{L}},~{}if~{} \widehat{m}\leq\frac{N_{L}}{2}\\ (N_{L}-\widehat{m})~{}\frac{f_{ds}}{N_{L}},~{}if~{}\widehat{m}>\frac{N_{L}}{2} \end{array}\right. \tag{24}\]  
Note that the frequency resolution \(f_{ds}/N_{L}\) is inversely proportional to \(N_{L}\), and \(N_{L}\) is highly dependent on \(L_{x}\). From this perspective, circular correlation time is positively relative to the resolution of acquired carrier frequency. That is, the longer circular correlation time will ultimately result in higher accurate carrier frequency in the fine acquisition. Besides, circular correlation time also affects computation and time cost of the fine acquisition, for that FFT is utilized as the frequency discriminator.
## 3 Numerical Analysis of the Influencing Mechanisms of Prodder-Object System Parameters
### Effects of Buried Object'S Parameters on the Measured Signal Features
#### 3.4.1 Effects of \(k_{2}\), the Stiffness of the Moving Part of Plastic Box  
Figure 10 shows the effects of landmine upper casing stiffness \(k_{2}\) on the measured signal features, the amplitude of the measured signal decrease, while the related resonance frequency increases as the value of \(k_{2}\) increases from 30 kN/m up to 66 kN/m. Since the casing stiffness of the plastic landmines are smaller than the stiffness of metal landmines [26], according to the analysis results above, the prodder may have better performance in the detection of non-metallic landmines that cannot be detected by the common device, for example, metal detector.  
Figure 10: Effects of \(k_{2}\) on the frequency response of the plastic box.  
Figure 9: The frequency response of the one-degree freedom model of the plastic box.  
3.4.2. Effects of \(c_{2}\), the Damping Coefficient of the Moving Part of Plastic Box  
Figure 11 shows the upper casing damping effects on the measured signal characteristics; the amplitude at the main resonance frequency decreases significantly as the value of \(c_{2}\) increases from 4 Ns/m up to 24 Ns/m. This is due to the significant amount of vibrational energy that can be consumed by damping effects. However, the actual value of damping coefficient \(c_{2}\) increases with the burial depth. As a result, the signal strength, namely the amplitude at main resonance frequency of buried landmine, would decrease with burial depth. These results are consistent with the experimental results in Ref. [26]. It highlights the importance of designing an optimized prodder that can obtain larger signal strength to achieve a greater depth of detection.  
According to the analysis above, the effects of each prodder's component on buried landmine's vibrational characteristics can be summarized in Table 2 as the relevant parameter values increase. The notation \"\(\uparrow\)\" represents an increase, \"\(\downarrow\)\" represents a decrease, and \"--\" means no influence.
# DM-SLAM: Monocular SLAM in Dynamic Environments
## 2 Materials and Methods.
### Neighborhood Mutual Exclusion for Selecting Candidate Map Points.  
The newly added map points are more likely to be used for subsequent system tracking in SLAM, which will affect subsequent estimation of the camera pose and map expansion. Therefore, it is important to ensure that the newly selected map points belong to a stable map, or else the system will quickly fail. Conformity to geometric constraints in more key frames must be verified, but this will slow down map expansion and reduce robustness of the system, especially in low-texture area.  
The matches introduced by the new key frame are the main source for generating new map points. When key frame K\({}_{i}\) inserts, matches are searched for between features in K\({}_{i}\) and other unmatched features in connected key frames K\({}_{c}\). If a match passes the following test, it will be taken as a candidate map point.  
1. The new match satisfies the fundamental matrix.  
2. Triangular ORB pairs after checking parallax, projection error, uniform scale, and forward depth.
3. There are no alternate map points in the neighborhood of current tested feature match. Details can be seen Algorithm 1.  
```
For new match (x-x') between optimal co-view key frames with new key frame; Triangulate match(x-x') to 3D point X; If triangulate fails, then Continue; Else check parallax, projection error, uniform scale, and forward depth; If fail, then Continue; Else check whether there has been new candidate map points in the neighborhood [50*50 in our system]; If has exist candidate map points, then Continue; Else check number of candidate map points Cn>\(\alpha^{*}\)Ln; If inequality above is false, then Add X as candidate map point; Else Continue; End if End if End if End for
```  
**Algorithm 1**Candidate map points selection mechanism  
The distribution and number of added map points are constrained by the neighborhood. The advantage of this is that the map points on the objects that become moving can be removed, and the method of neighborhood suppression is used in the process of selecting the map points. There are two benefits to this:  
* Due to the concentrated distribution of moving objects, the number and probability of features sampled onto moving objects are reduced by the neighborhood suppression selection
* Limit the number of selected objects that become dynamic. Even if features on moving objects are selected, the impact on reconstruction is weakened
# Determining Thresholds for Optimal Adaptive Discrete Cosine Transformation
###### Abstract
In Table 1, we present a summary of popular existing compression algorithms and compare the proposed implementation of adaptive discrete cosine transform to them. The research into adaptive discrete cosine transformation aims to prove the usefulness of adaptive elements in existing compression methods, such as JPEG2000, MPEG, etc., comparing it with the conventional, non-adaptive transformation that underlies the operation of most algorithms. Considering the abovementioned statements, the contribution of the paper is as follows:  
* We introduce and analyze adaptive DCT based on two thresholds (named \(pm\) and \(ps\)), explained in detail in the paper further, aiming for an optimal tradeoff between quality and compression. The \(pm\) threshold determines averaging the fragment to a solid color, and the \(ps\) threshold sets the criterion for splitting the fragments. * We find the optimal \(pm\) and \(ps\) values corresponding to the target multi-scale structural similarity index measure (MS-SSIM) and compression ratio. MS-SSIM measures the perceptual difference between two similar images, i.e., input and decompressed images, at different scales [18; 19; 20]. * We compare the proposed ADCT with standard DCT and find numerical estimates of the compression ratio improvement. The rest of the paper is organized as follows. In Section 2, we describe a novel approach for optimizing the adaptive discrete cosine transform based on the two introduced \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Compression** & **Core** & **Fragment** & **Adaptive** & **Fragmentation** & **Video Compression** \\
**Algorithm** & **Transform** & **Size** & & **Technique** & **Precision** \\ \hline Modified & Cosine & \(8\times 8\) by & No & Fixed-size grid & No \\ DCT [16] & & default & & & \\ \hline Adaptive & Cosine & \(4\times 4\)–any & Yes & Comparing tonal & No \\ DCT [17] & & & & distribution to & \\  & & & & thresholds & \\ \hline JPEG 2000 & Wavelet & \(64\times 64\) by & No & Fixed-size grid & No \\  & & default & & & \\ \hline AVC & Cosine & \(4\times 4\)– & Yes & Rate–distortion & Yes \\ (H.264) & & \(16\times 16\) & & optimization & \\ \hline HEVC & Cosine & \(4\times 4\)– & Yes & Rate–distortion & Yes \\ (H.265) & and Sine & \(64\times 64\) & & optimization & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: ADCT compared to other algorithms. thresholds. The improvement in the image compression ratio value is shown in Section 3. A summary of the results obtained is given in Section 4. Appendix A contains additional illustrations, plots and tables obtained during the experiments.