esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Attacks and Misbehaviors
### Node ID Attacks  
This type includes node replication attack and Sybil attacks. In node replication attack, the attacker introduces replicas of one compromised node using its same ID at different locations of the network. Upon the introduction of the replica into a new neighborhood--_i.e._, connecting to a set of different nodes than the original one, the system may be encountered with two cases. The first is the case where the replica directly starts sending packets without proper introduction with the hello messages, the neighbors will reject its packets as it is assigned as neither their DL nor as their UL by the BS; and thus the attack is directly isolated in this case.  
The second case occurs if the added replica kicks off with proper introduction of hello messages, then, the neighboring nodes accept it and send their updated activity report with the replica as a neighbor. Here, there are two cases: 1- if the BS receives two different activity reports containing different neighbors from the same node ID, it directly detects the replication and isolates this node ID, both versions, from the network. 2- If the replicas are more advanced and sending a unified activity report with neighbors from both neighborhoods, the BS detects a neighborhood error (to be discussed in the following subsection) and the node ID will be isolated from the network.  
Sybil attacks are where an attacker uses several invented or stolen IDs to sign packets using their IDs and encrypt these signatures by its own ID. This way the attacker injects packets in the name of another node after encrypting its signature by its own ID to appear as a legitimate packet flowing through this path of the network.  
Note that, with the incorporation of the Identity based authentication scheme, the attacker can not affect the network without acquiring the master key, which is saved with the admin away from the network, or having access to one or more nodes. Regarding acquiring the master key, it is considered as highly improbable due to the fact that it is saved offline with the administrator. As for the control over nodes, the attacker is considered as a replica with a dual ID. The analysis is similar as before; the BS detects differences in the forwarded and sent nodes, puts nodes on probation, changes paths, and detects and isolates the bad nodes.
# Optical and SAR Image Registration Based on the Phase Congruency Framework
## 3 Experiments
### The Performance of the Proposed Algorithm  
The proposed algorithm is carried out on more than 30 image pairs, and the performance is evaluated using four optical and SAR image pairs that have significant intensity  
Figure 6: Comparison between the descriptor of OS-SIFT and the proposed descriptor. (**a**) optical image. (**b**) SAR image. (**c**) The descriptor of OS-SIFT. (**d**) The proposed descriptor.  
and geometric differences. Table 2 lists the details of the four test image pairs, and Figure 7 shows the matching results of the test images. The four SAR images are from the 23 rd institute of the second Academy of China Aerospace Science and Industry Corporation, and the four optical images are from Google Earth.  
Four pairs of images describe the suburbs with geometric and intensity differences. Note that the scale and rotation differences of the test image pairs are small. Therefore, the scale factor is set to one. In particular, it is a challenging task since there are roofs and a mountain in Pair B. The side-looking mechanism of SAR sensors makes the roof regions suffer from shadows and strong scattering. The buildings in optical and SAR images have significant geometric and radiometric differences, which leads to a low structural information consistency of the homonymy points. In sum, for the optical and SAR images with small-scale and rotation differences, the matching points of the four image pairs are  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pair** & **Image Source** & **Resolution/m** & **Date** & **Size/Pixel** \\ \hline A & Google Earth & 2 m & 25 July 2020 & 1707 \(\times\) 1321 \\ A & Airborne SAR & 2 m & 5 November 2019 & 1724 \(\times\) 1384 \\ B & Google Earth & 2 m & 25 July 2020 & 1853 \(\times\) 979 \\ B & Airborne SAR & 2 m & 5 November 2019 & 1612 \(\times\) 925 \\ C & Google Earth & 1 m & 9 May 2021 & 789 \(\times\) 696 \\ C & Airborne SAR & 1 m & 1 October 2020 & 785 \(\times\) 679 \\ D & Google Earth & 1 m & 9 May 2021 & 1454 \(\times\) 643 \\ D & Airborne SAR & 1 m & 1 October 2020 & 1460 \(\times\) 608 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of the test images.  
Figure 7: Registration results of the three comparison algorithms. (**a**) Pair A, (**b**) Pair B, (**c**) Pair C, and (**d**) Pair D.  
all located in the correct locations. The proposed algorithm obtains the ideal numbers of matching point pairs, and it achieves the most satisfactory matching results among the three algorithms.
# Detection of Mineralization Zones Using Aeromagnetic Data
## 3 Methodology
### Total Horizontal Gradient (THG) Method  
The THG method is extremely good at detecting structures such as faults from the borders of anomalies [23], and it is generally insensitive to data noise and interference effects between close sources. Many assumptions about the sources were proposed by [24], including (1) the vertical regional magnetic field, (2) the vertical source magnetization, (3) the vertical contacts, (4) the isolated contacts, and (5) the thick sources. This method is widely used in mineral exploration; however, it is lease susceptible to data noise because it only considers the two first-order horizontal derivatives of the magnetic field. If M (x, y) is the magnetic field and the horizontal derivatives of the field are (\(\partial\)M/\(\partial\)x and \(\partial\)M/\(\partial\)y), then the THG (x, y) is given by Equation (1):  
\[\text{THG (x, y)}=\sqrt{\left(\frac{\partial\text{M}}{\partial\text{x}} \right)^{2}+\left(\frac{\partial\text{M}}{\partial\text{y}}\right)^{2}}\dots \tag{1}\]
## 4 Experimental Results for 3 kW Six-Phase PMa-SynRM
### BEMF Characteristic on No Load  
The measured BEMF in the experiment was line-to-line voltage of 21.91 Vrms, which was approximately the same value as the FEM result from Table 4 (21.5 Vrms) with a difference of 0.4 Vrms. The voltage waveform in Figure 12 was the same as the BEMF waveform in Figure 7 determined through FEM. Therefore, the calculated no-load flux linkage had the same value in both FEM analysis and the experiment.
# Research on Optimization of Multi-Objective Regional Public Transportation Scheduling
## 4 Solution Algorithms
### The NSGA-II Algorithm
If \(Z\geq x_{i}-2\), then regenerate the departure time of line \(i\) for one chromosome and exchange the genes of line \(i\) of two chromosomes, and let \(i=i+1,k=k+1\), go to Step2. Step4: If \(k\leq 1\), randomly generate two different lines and exchange the corresponding genes of two chromosomes. In the case of \(Z\geq x_{i}-2\), the crossover process is shown in Figure 2. Assume that the first genes of parents \(P_{1},P_{2}\) are highly similar to the \(N-1\) genes, then randomly select one chromosome from \(P_{1},P_{2}\), regenerate the departure time of the high similarity line, and exchange the corresponding genes of \(P_{1},P_{2}\) (where \(C_{x_{i}}^{P_{i}}\) is the regenerated departure time). The crossover process when \(k\leq 1\) is shown in Figure 3.  
(5) Mutation operator  
Randomly select two different lines from a chromosome and calculate the length of each line gene \(L_{g}\). Randomly select some places form line gene and regenerate these departure time according to the following conditions (\(A_{x_{i}}\), \(A_{x_{i}-1}\) and \(A_{x_{i}+1}\) denote the departure time of bus \(x_{i},x_{i-1}\) and \(x_{i+1}\) respectively, and \(r_{0}\) indicates a random number from uniform distribution \(u(0,1)\)). Figure 3: The crossover process case \(k\leq 1\). Figure 2: The crossover process case \(Z\geq x_{i}-2\). **Case 1**: When \(x_{i}=1\), if \(A_{x_{i}+1}-2I_{\min}>I_{\max}\), then \(A_{x_{i}}=(I_{\max}-I_{\min})r_{0}+I_{\min}\). Otherwise, if \(A_{x_{i}+1}-2I_{\min}\leq I_{\max}-I_{\min}\), then \(A_{x_{i}}=(A_{x_{i}+1}-2I_{\min})r_{0}+I_{\min}\), otherwise, \(A_{x_{i}}=(A_{x_{i}+1}-3I_{\min})r_{0}+I_{\min}\). **Case 2**: When \(x_{i}=L_{g}\), \(A_{x_{i}}=A_{x_{i}-1}+(I_{\max}-I_{\min})r_{0}+I_{\min}\). **Case 3**: When \(1<x_{i}<L_{g}\), if \(A_{x_{i}+1}-A_{x_{i}-1}-2I_{\min}>I_{\max}\), then \(A_{x_{i}}=A_{x_{i}-1}+(I_{\max}-I_{\min})r_{0}+I_{\min}\). Otherwise, if \(A_{x_{i}+1}-A_{x_{i}-1}-2I_{\min}\leq I_{\max}-I_{\min}\), then \(A_{x_{i}}=A_{x_{i}-1}+(A_{x_{i}+1}-A_{x_{i}-1}-2I_{\min})r_{0}+I_{\min}\), otherwise \(A_{x_{i}}=A_{x_{i}-1}+(A_{x_{i}+1}-A_{x_{i}-1}-3I_{\min})r_{0}+I_{\min}\). Figures 4 and 5 are used to illustrate the mutation process for Case 1 and Case 3 respectively, where \(I_{\min}=3\)min, \(I_{\max}=10\)min and the scheduling time window is 30 min. Since Case 2 has only one calculation method, we will give examples of Cases 1 and 3 respectively, the shaded part is the point to be mutated, assuming that the departure interval is [3; 10] min and the scheduling time window is 30 min.
# Enhancing Clay Soil's Geotechnical Properties Utilizing Sintered Gypsum and Glass Powder
## 3 Results and Analysis  
### Impact of Test Variables on Strength and Stiffness  
The study conducted a statistical analysis of the dry densities (1700 and 1500 kg/m\({}^{3}\)), glass-powder contents, gypsum contents, and curing durations. This assessment explains the distinct effects of individual factors on the (UCS), as shown in Figure 2a-c, (\(G_{0}\)) depicted in Figure 2d-f, and (\(E\)) represented in Figure 2g-i. Regardless of other factors' influence, the calculated factors exhibited a noticeable increase when the specimens were compacted to a greater dry density. It is attributed to the porosity reduction and enhanced interaction of soil particles and additive materials in denser specimens. The increase in gypsum content resulted in a consistent improvement across all strength and stiffness features. It is worth noting that the increase was more significant in denser samples when compared to less dense ones.  
Figure 2: Relationship between the studied properties and gypsum amount, glass powder, density, and curing: (**a**) UCS-gypsum, (**b**) UCS-glass powder, (**c**) UCS-curing days, (**d**) \(G_{0}\)-gypsum, (**e**) \(G_{0}\)-glass powder, (**f**) \(G_{0}\)-curing days, (**g**) \(E\)-gypsum, (**h**) \(Es\)-glass powder, (**i**) \(E\)-curing days.  
On the other hand, the behavior of the glass-powder content was inconsistent across all strength and stiffness features. It was observed that, for all mentioned features (UCS, \(G_{0}\), and \(E\)), increments from 0% to 8% led to a decrease, and suddenly, for the 12% content, a shift to an increasing trend was noted. It highlights the significance of a 12% glass-powder content in the samples, implying a critical threshold for this material. The analysis also shows that the test variables exert the most significant impact on the \(G_{0}\) and a comparatively lesser impact on the \(E\).  
The statistical analysis demonstrated a significant correlation between the variables of the tests and the strength and stiffness characteristics of the samples. It was found that the gypsum content had the most pronounced impact on these characteristics.
# A Deep Learning-Based System for Automatic License Plate Recognition Using YOLOv12 and PaddleOCR
## 5 Results and Validation
### Analysis of Trained Model's Performance
At a stricter threshold of 0.7, the precision reached the maximum value, but the recall dropped significantly, leading to a decrease in the F1 score and lower mAP values. This trend highlights the trade-off between detection strictness and coverage rate, indicating that an overly high threshold may negatively impact the overall detection effectiveness.
## 4 Results and Discussion
### Mechanical Response Characteristics  
Extrema of mechanical response characteristics of nodal solutions in previous FEA simulation could be observed in Figures 11 and 12, which illustrate the displacement and stress of reconstructed micron silver sintered joints suffering different thermal shock cycles in the X, Y, and Z directions. It can be seen that the maximum and minimum values of the displacement and stress show a similar fluctuation in the X and Z directions with increasing thermal shocks. This is because the x-z plane represents the parallel interface of this sandwich structure, and the Y axis is the longitudinal constrained sintering direction, so there is a certain degree of anisotropy.  
It can be found that the maximum displacement point appears near the junction of silver phase and larger pores, or the corner of pores, and the value shows an increasing trend, which demonstrates that the rise of porosity and particle dispersion caused by increased thermal shocks leads to the decrease in the size of the load-transmission area between particles, and can further result in a large displacement field across the local bearing surface. This situation also indicates that the elastic performance of micron silver sintered joints declines. On the other hand, the variation range of triaxial stress gradually increases as the thermal shock cycles increases. In addition, stress concentrations exist in all three directions. This situation could be explained by more likely stress concentrations resulting from the growing porosity.  
Table 5 illustrates the triaxial elastic modulus and the effective elastic modulus by FEA, and the apparent density of samples. It can be seen that the \(Y\)-axis elastic modulus is a little greater than those of the X and Z axes. This phenomenon should be derived from the sample structure, and the constrained sintering state leads to a slight anisotropic microstructure [32]. Considering the slight degree of anisotropy, the effective elastic modulus is adopted to evaluate the degradation behavior of the micron sintered silver joints. As the thermal shock cycles increase, the apparent density of micron silver joints decreases, indicating that cyclic thermal stress can drop the material densification.
_applied sciences_  
_Article_  
Synthesis by Sol-Gel Route of Organic-Inorganic Hybrid Material: Chemical Characterization and In Vitro Release Study  
[PERSON] 1\({}^{\copyright}\), [PERSON] 1\({}^{\copyright}\), [PERSON] 1\({}^{\copyright}\), [PERSON] 2\({}^{\copyright}\), [PERSON] 2\({}^{\copyright,\copyright}\) and [PERSON] 2,3  
Footnote  
neurodegenerative diseases [12; 13]. Antioxidants counteract free radicals, preventing cell damage. Several types of antioxidants exist, encompassing vitamins C and E, beta-carotene, selenium, and flavonoids, among others [14].  
Among the class of flavonoids, there is quercetin, whose properties have been widely recognized [15]. Quercetin (Q) is known not only for its potent antioxidant features [16], but also for its role in the prevention of inflammation and its ability to support the immune system thanks to its ability to stimulate the production of white blood cells, which are essential for fighting off infections [17; 18]. Given its characteristics, quercetin is a compound that could offer many advantages if inserted into a material, especially if the purpose of this material is to be used as a biomaterial [19; 20].  
For several years, our research group has been actively engaged in studying the incorporation of bioactive molecules into materials using the sol-gel technique. The traditional sol-gel route involves a series of hydrolysis and condensation reactions of sol-gel-reactive alkoxide precursors with water in the presence of a catalyst usually diluted in an appropriate solvent [21; 22]. By using this method, the hybrid systems made up of silica (S) and polyethylene glycol (P), namely SP hybrids, have been fully characterized and discussed in [23]. Further, a preliminary investigation on silica (S), polyethylene glycol (P), and quercetin (Q) hybrids (SPQ systems) was also reported in [24]. Even though these studies pointed out the thermal properties, the biocompatibility on eucaryotic cells, and the antimicrobial activity of the synthesized hybrids, a deep investigation of the hybrid chemical properties, as well as of the release of the entrapped drug, needs to be carried out. As a consequence, this study aims to shed new light on the interactions among the organic and inorganic components by the combination of FT-IR and solid-state NMR spectroscopies and to investigate the in vitro release of the used drug. The importance of studying this release is fundamental to obtain an entire overview of the applicability of these systems in biomedical fields [25], especially for bone repair, to which glass-based hybrid materials have mainly been applied [26].  
The SPQ hybrid systems, the object of this study, were synthesized with different percentages of P (from 0 to 50 wt%) and Q (from 0 to 20 wt%), without changing the synthesis procedure already reported in [24].
# A Physicist's View on Partial 3D Shape Matching
## 4 Experimental Results
### Partial Shape Similarity  
For nonrigid partial shape similarity, we considered four classes, the cats and horses from TOSCA8, as well as their isolated heads. The heads are taken directly from the meshes of the corresponding full shapes. Those heads contain approximately 700 vertices and 1400 faces. The corresponding dataset, HEAD, includes six instances for both the cats and horses, and all corresponding heads, for a total of 24 shapes. We ran an all-against-all comparison of all the shapes in that dataset, using the four different approaches for shape similarity described above, namely, based on two types of signatures, LD-SIFT and WKS, and two frameworks for computing the correspondences of the vertices, balanced OT and partial OT. We computed the matrix of distances at convergence in \(\beta\), \(D(\infty)\), for all four approaches. We expect the following results: the distance matrix should highlight high similarity within each class (cats, cat heads, horse, horse heads), as well as high similarity between the cat and the cat heads, as well as between the horse and the horse heads, but low similarities between the cats (either full, or head only) and the horses (again, either full or heads only). The actual distance matrices for the four approaches are represented graphically in Figure 7. Only the combination LD-SIFT/partial OT gives us the expected behavior, in striking difference with the results for full shape matching illustrated in Figure 3. For this combination, we even observe subdiagonals in the blocks representing the similarities between the cats and their heads, as well as for the horses and their heads, indicating that the procedure is able to identify the correct head for each cat and for each horse.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **Test-Set 0** & **Test-Set 1** & **Test-Set 2** & **Test-Set 3** & **All** \\ \hline RPTS [68] & 0.920 1  & 0.926 & 0.824 & 0.929 & 0.899 \\ NRP [69] & 0.878 & 0.899 & 0.801 & 0.858 & 0.862 \\ WRAP & 0.853 & 0.920 & 0.772 & 0.870 & 0.856 \\ KM [70] & 0.760 & 0.865 & 0.757 & 0.799 & 0.804 \\ GISC [71] & 0.565 & 0.659 & 0.674 & NA 2  \\ LD-SIFT, balanced OT & 0.3 & 0.24 & 0.18 & 0.28 & 0.25 \\ LD-SIFT, partial OT & 0.08 & 0.04 & 0.05 & 0.06 & 0.05 \\ WKS, balanced OT & 0.6 & 0.64 & 0.55 & 0.32 & 0.54 \\ WKS, partial OT & 0.09 & 0.05 & 0.05 & 0.06 & 0.06 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Computing correspondences between 3D shapes from the SHREC19 benchmark.
## 3 Beacon-Based Hazard Alarm System Design
### Structure of the System
#### 3.2.2 Server Platform  
Two server platforms were used and integrated into this system; one was Estimote Cloud, an Integrated Development Environment (IDE) provided by Estimote Inc. New York, US, and the other was Firebase Cloud Messaging (FCM), provided by Google. Using APIs provided by Estimote Cloud, a web application was developed to process the data from the beacon receiver device. This application analyzed the identifier and the Bluetooth energy power data, defined a threshold for triggering the alarm system, and sent the alarm notification to FCM. Then, the FCM platform worked as a mobile phone notification hub; it received messages from Estimote Cloud and sent them to an Android mobile application.
## 4 Discussion
While this may appear to be a cause for concern for low overall SNR, it is not a problem because summation (on- or off-chip) can be used to improve SNR, if needed [38]. The multiple finer spatial pixels (Figure A3) from airborne HSI in comparison to satellite imagery provide a more uniform contribution of the ground elements to the imagery. This is an important consideration for peatlands with fine (\(\leq\)1 m) spatial structures (e.g., hummocks and hollows). The underutilized dynamic range of the CASI and SASI also suggests that miniaturized versions of the HSI with similar spectral and radiometric properties could be successfully deployed on UAV platforms, where the sensor would be considerably closer to the peatland surface (e.g., \(<\)150 m AGL). The higher spatial resolution from UAV-based imagery would benefit WT position models. Planned hyperspectral satellite missions (e.g., EnMap, WaterSat, HyspIRI) could further improve the ongoing remote monitoring of peatlands globally in light of increased pressures from various drivers of change.
## 6 Experimental Results  
### Experiments' Configurations  
In this experiment, an automotive platform equipped with four fisheye cameras was mounted for parking assistance purposes, as shown in Figure 5. The experimental data were recorded by a car and wheel speedometer equipped with four fisheye cameras. All cameras have a 190-degree viewing angle, take images at a frequency of 25 Hz, and have an image resolution of 1920*1208. Each camera was connected to the in-vehicle computing platform NVIDIA Drive PX2 via a Gigabit multimedia serial interface. Timestamp synchronization is guaranteed by the hardware. The composite ring view has a resolution of 384*384 and covers a range of 15.3 m by 15.3 m around the vehicle circumference. The measured values of the wheel speedometer and IMU are obtained via the CAN bus. The experimental computing platform has a configuration of 3.3 GHz Intel i5-4590 CPU and 16 GB memory. In the indoor scenes with weak GPS signals, the true value of panning is provided by the fusion of wheel odometry and high-precision IMU measurements. In the outdoor scenes, the true value of planning is provided by the high-precision differential GPS.