esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Integrated Modeling
### The Integrated Model  
Divide the degrees of freedom of the satellite into the degrees of freedom of the optical component \(\mathbf{q}_{\rm o}\) and the rest as \(\mathbf{q}_{r}\), as follows:  
\[\mathbf{q}=\begin{bmatrix}\mathbf{q}_{\rm o}\\ \mathbf{q}_{\rm r}\end{bmatrix}=\begin{bmatrix}\mathbf{\Phi}_{1}&\mathbf{\Phi}_{2}&\cdots& \mathbf{\Phi}_{k}\end{bmatrix}\begin{bmatrix}\eta_{1}\\ \eta_{2}\\ \vdots\\ \eta_{k}\end{bmatrix}=\begin{bmatrix}\mathbf{\Phi}_{\rm o1}&\mathbf{\Phi}_{\rm o2}& \cdots&\mathbf{\Phi}_{\rm ok}\\ \mathbf{\Phi}_{\rm r1}&\mathbf{\Phi}_{\rm r2}&\cdots&\mathbf{\Phi}_{\rm rk}\end{bmatrix} \begin{bmatrix}\eta_{1}\\ \eta_{2}\\ \vdots\\ \eta_{k}\end{bmatrix}, \tag{22}\]  
where \(k\) is the number of modes.  
Equation (23) can be obtained from Equation (22), as follows:  
\[\mathbf{q}_{\rm o}=\begin{bmatrix}\mathbf{\Phi}_{\rm o1}&\mathbf{\Phi}_{\rm o1}&\cdots& \mathbf{\Phi}_{\rm ok}\end{bmatrix}\begin{bmatrix}\eta_{1}\\ \eta_{2}\\ \vdots\\ \eta_{k}\end{bmatrix}=\mathbf{\Phi}_{\rm o}\mathbf{\eta}. \tag{23}\]  
Substitute Equation (23) into Equation (20) and receive:  
\[\mathbf{p}(t)=\mathbf{C}_{\rm o}\mathbf{\Phi}_{\rm o}\mathbf{\eta}=\begin{bmatrix}\mathbf{C}_{ \rm o}\mathbf{\Phi}_{\rm o}&\mathbf{\theta}\end{bmatrix}\begin{bmatrix}\mathbf{\eta}\\ \dot{\mathbf{\eta}}\end{bmatrix}=\mathbf{C}_{\rm P}\begin{bmatrix}\mathbf{\eta}\\ \dot{\mathbf{\eta}}\end{bmatrix}. \tag{24}\]  
Rewrite Equations (12) and (24), and the integrated model of the satellite is provided, as follows:  
\[\left\{\begin{array}{l}\left[\begin{array}{c}\dot{\mathbf{\eta}}\\ \dot{\mathbf{\eta}}\end{array}\right]=\mathbf{A}_{\rm s}\begin{bmatrix}\mathbf{\eta}\\ \dot{\mathbf{\eta}}\end{array}\right]+\mathbf{B}_{\rm s}F\\ \mathbf{p}=\mathbf{C}_{\rm P}\begin{bmatrix}\mathbf{\eta}\\ \dot{\mathbf{\eta}}\end{bmatrix}. \tag{25}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Value**} \\ \cline{2-3}  & **PM** & **SM** \\ \hline \(l_{11}\) & 5.46 & \(-3.79\) \\ \(l_{12}\) & 0 & 0 \\ \(l_{13}\) & 0 & 0 \\ \(l_{14}\) & 0 & 0 \\ \(l_{15}\) & \(-54.46\) & \(15.34\) \\ \(l_{16}\) & 0 & 0 \\ \(l_{21}\) & 0 & 0 \\ \(l_{22}\) & 5.46 & \(-3.79\) \\ \(l_{23}\) & 0 & 0 \\ \(l_{24}\) & 54.46 & \(-15.34\) \\ \(l_{25}\) & 0 & 0 \\ \(l_{26}\) & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The optical sensitivity coefficients.
## 4 Discussion
### Impact of Land Cover on MODIS GPP  
The impact of land cover classification on GPP estimation is non-negligible. In this paper, the land cover at site QYZ was of type ENF, but it was misclassified to type MF by MCD12Q1 (Figure 11). This misclassification directly led to the misestimation of the FPAR by MOD15A2 and the misjudgment of \(\epsilon_{\max}\) by BPLUT and thus affected the MOD17A2 GPP estimate for site QYZ.
## 4 Promising Neuropeptides Inhibitors for the Development of Effective Cancer Therapy
### Neuropeptides Inhibitors: A Promising Approach also for Other Cancers
#### 4.2.1 Breast Cancer  
Breast cancer is a heterogeneous disease resulting mainly from a mutation in BRCA1, BRCA2, and other important regulatory genes. It is one of the most common malignancies affecting women worldwide. Various novel therapeutic approaches, including bioactive lipids, plant-derived secondary metabolites, multi-omics approach, next-generation sequencing (NGS), and precision medicine, have been exploited for its treatment; however, the search for successful clinical therapy is still active [138; 139; 140]. Substance P (SP) has been associated with activation of the neurokinin-1 receptor system (NK1R), which upregulates various intracellular processes responsible for tumor initialization and development in breast cancer [90]. The inhibitory effects of various NK1R antagonists, such as Apreprint, L-732,138, L-733, 060, CP-96345, C-9994, MEN 11467, and SR14033, on different breast cancer cell lines have been identified experimentally and their role in inducing apoptosis and inhibiting cellular growth has been established [141; 142; 143].  
Another therapeutic approach targeting SP took advantage of Spantile III, which effectively caused cell death and reduced cell proliferation [144]. Neurotensin and its receptors are directly correlated to the size of the tumor, recurrence, and survival rate in breast cancer patients [105]. Possible treatments with a previously developed radio-labeled neurotensin analog, Tc-NT-XI, were inefficient and inaccurate [145]. Therefore, new generations with more stable compounds, such as \({}^{99 mTc}\)Tc-NT-XIX (\({}^{99 mTc}\)Tc-(N-His)Ac-(Arg-N-CH\({}_{3}\))-Arg-Pro-(dimethyl-Tyr)-(_tert_-Leu)-Leu, \({}^{188}\)Re-NT-XIX, and \({}^{18}\)F-DEG-VS-NT (\({}^{18}\)F-(2-(2-(2-fluoroethoxy)ethoxy)ethylsulfonyl)ethene-neurotensin), have been developed which mainly target the NTSR receptor and efficiently inhibit tumor development and progression in BC [146]. Bombesin/gastrin-releasing peptide (GRP) and GRPR have been found to be elevated in 62% of breast cancer patients. A study highlighted the role of two BB/GRP antagonists, i.e., RC-3095 and RC-3940-II, in significantly reducing the growth and proliferation of BC cells. These antagonists directly downregulated the secretions of bFGF, IGF-II, and VEGF-A in breast cancer cells, thereby inhibiting angiogenesis [92]. Similarly, there is a well-recognized association between neuropeptide Y and its receptors and human breast cancer, which has been demonstrated to promote cell proliferation, migration, and vascularization. Investigation through the Y5R agonist resulted in an increased proliferation rate and further application of Y5R blockade L-152,804 led to attenuation in a concentration-dependent manner, which resulted in the inhibition of the proliferative impact of the Y5R agonist treatment [147].
# Virtual Pseudonym-Changing and Dynamic Grouping Policy for Privacy Preservation in VANETs
## 6 Experimental Evaluation Setup
### Evaluation Metrics
#### 6.2.2 Entropy of ASS  
The entropy value measures an adversary's confusion level to identify a target vehicle in the group of vehicles. It computes the degree of uncertainty of an adversary to link a target vehicle's pseudonyms at various locations. To calculate the entropy, let us take \(V_{i}\) to be a set of vehicles in a group that takes part in the pseudonym-changing process, and \(V_{j}\) is the number of vehicles that update pseudonyms successfully in a group. Let \(P_{V_{i}}\rightarrow_{V_{j}}\) be the uniform probability of distribution that measures the level of entropy and confusion for an adversary. The adversary tries to find a link between the used pseudonyms of a vehicle at different periods. The entropy of vehicles at time \(t\) can be computed as follows [46].  
\[H_{t}=\sum_{V_{i}V_{j}\in V}P_{V_{i}}\rightarrow_{V_{j}}log_{2}P_{V_{i}} \rightarrow_{V_{j}}. \tag{57}\]  
where \(H_{t}\) is the entropy of vehicles in a group, and \(V\) is the total number of vehicles. The average entropy \(H_{avg}\) is calculated as follows:  
\[H_{avg}=\frac{1}{V}\sum_{i,j\in V}H_{t}(i,j). \tag{58}\]
## 4 Results and Discussion
### Ordinary Cokriging (COK)  
A comparison between the use of secondary information, such as elevation, both in KED and COK interpolators, confirms that the accuracy improvement was not reached considering the error of estimates. Consequently, although adding secondary variables can increase precision, in the case study the produced maps by COK are similar to those obtained by KED and OK. Figure 6 shows the rainfall maps for 12 months obtained by using the COK technique. Therefore, as already evidenced in previous studies [12] and confirmed in this one, when the correlation between rainfall and elevation is too small, the advantages derived by using multivariate techniques is negligible. The values of the correlation coefficients of measured versus predicted monthly rainfall ranged between 0.68 and 0.84 (Figure 2 and Table 2). The COK interpolator can be evaluated as the best approach to analysing rainfall observation in many analysed months (Table 3) as confirmed by the MAE and the RMSE results.
# Sex Estimation from Computed Tomography of Os
## 3 Results  
The descriptive statistics for all four variables are presented separately for males and females in Table 2.  
According to the \(t\)-test, female PUM (M = 72.54, SD = 2.02) is significantly higher than that of males (M = 70.30, SD = 2.70), t = \(-\)4.185, \(p\) < 0.032. The \(t\)-test showed significant differences between females and males for ISSM (female M = 101.12, SD = 2.46, male M= 111.20, SD = 3.92, t = 13.74, \(p\) = 0.021). A \(t\)-test showed no significant differences between males and females for the SCOX (t = 6.055, \(p\) = 0.707) and VEAC (t = 8.103, \(p\) = 0.803).  
All variable measurements obtained from the CT scans were analyzed using DSP software. For sex classification, a posterior probability equal to or superior to 0.95 was used as a threshold. The resulting sex estimation from the output from DSP software was compared to the known sex of each individual. For the male cases, out of a total of 40 males, 22 were correctly identified as male, and only 1 male was misclassified as female. Conversely, for the female cases, out of a total of 40 females, 34 were correctly classified, and none were misclassified. The data are presented in Table 3.  
In this study, the frequency of cases in which sex estimation was possible (sex estimation) was possible in 71.25% of cases overall, with varying rates between males (57.50%) and females (85%). Despite encountering undetermined specimens, which comprised 42.5% males and 15% females, only one error in sex classification was observed, where a male was misclassified as a female.  
In terms of accuracy (the frequency of samples in which sex estimation was performed correctly from the total possible to be estimated), the overall accuracy remained notably high at 98.24%. From the total sample of 40 females that could be estimated, all were classified correctly, with an accuracy of 100%, whereas for males, the accuracy was 95.65%. The data are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{4}{c}{**Female**} & \multicolumn{4}{c}{**Male**} \\ \cline{2-10}  & **Mean** & **SD** & **Min** & **Max** & **Shapiro–Wilk Test** & **Mean** & **SD** & **Min** & **Max** & **Shapiro–Wilk Test** \\  & & & & & \(p\) & & & & \(p\) \\ \hline PUM & 72.5420 & 2.02767 & 67.89 & 75.34 & 0.008 & 70.3070 & 2.70177 & 64.90 & 74.78 & 0.330 \\ ISMM & 101.1232 & 2.46951 & 96.56 & 105.67 & 0.158 & 111.2050 & 39.2618 & 99.69 & 117.43 & 0.232 \\ SCOX & 151.2537 & 3.18184 & 146.21 & 156.56 & 0.029 & 155.7050 & 3.37979 & 145.38 & 160.88 & 0.059 \\ VEAC & 50.9690 & 2.13204 & 46.99 & 54.89 & 0.501 & 54.9145 & 2.2202 & 51.05 & 59.21 & 0.088 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative analysis of four pelvic measurements among males and females.  
\begin{table}
\begin{tabular}{l c} \hline \hline  & **Male** & **Female** \\ \hline Correct & 22/40 & 34/40 \\ Misclassified & 1 & 0 \\ Undetermined & 17/40 & 6/40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sex classification via DSP software for males and females.
## 4 Simulation Experiment and Mathematical Model Validation
### Simulation of Driving Under Pressure Dynamic Model
The supporting force of the top beam of support 1 on the roof is 623 kN within 0 s to 60 s, gradually increases to 1303 kN within 60 s to 70 s, and remains stable. The supporting force of the top beam of support 2 on the roof is 1643 kN within 0 s to 60 s, gradually decreases to 473 kN within 60 s to 70 s, and remains stable. As shown in Figure 17, the pushing force value of the moving cylinder obtained by simulation gradually decreases from 426 kN to 398 kN within 0 s to 16 s, remains stable within 16 s to 44 s, and gradually increases from 399 kN to 426 kN within 44 s to 60 s. The force value decreases to 0 N at 60 s. At 70 s, the pushing force of the moving cylinder changes to pulling force, and the force value is 333 kN, which decreases from 333 kN to 305 kN within 70 s to 86 s, remains stable within 86 s to 114 s, and increases from 305 kN to 330 kN within 114 s to 130 s, and remains constant at 0 N after 130 s. The theoretical values of the pushing and pulling force of the moving cylinder in the dynamic model of driving under pressure considering the Stribeck friction theory gradually decrease from 427 kN to 399 kN within 0 s to 16 s, remain stable within 16 s to 44 s, gradually increase from 399 kN to 427 kN within 44 s to 60 s, and remain constant at 0 N within 60 s to 70 s. At 70 s, the pushing force of the moving cylinder changes to pulling force, and the force value is 331 kN, which decreases from 331 kN to 309 kN within 70 s to 86 s, remains stable within 86 s to 114 s, increases from 309 kN to 331 kN within 114 s to 130 s, and remains constant at 0 N after 130 s. The theoretical value of the pushing and pulling force of the moving cylinder in the driving under pressure dynamic model without considering the Stribeck friction theory is 398 kN within 0 s to 60 s and 0 N within 60 s to 70 s. From 70 s to 130 s, the pushing force of the moving cylinder changes to the pulling force with a force of 309 kN and remains constant at 0 N after 130 s.  
The simulation results show that the theoretical value of the dynamic model of driving under pressure, considering the Stribeck friction theory, has the same trend as the pushing  
Figure 17: Simulation results of the pushing force for the moving cylinder. force value of the moving cylinder obtained by the simulation, and its maximum error is 1.3%, which does not exceed 5%. The maximum error between the theoretical value of the driving under pressure dynamic model without considering the Stribeck friction theory and the pushing force value of the moving cylinder obtained by the simulation is 7.8%. Therefore, considering the Stribeck friction theory can make the driving under pressure dynamic model reflect the real values of pushing and pulling force required by support 1 and support 2 during the driving under pressure process of TSR more accurately.
# Scanning Electron Microscopy Techniques in the Analysis of Gunshot Residues: A Literature Review
## 4 Discussion
### Emerging Technologies and Future Perspectives in SEM for Forensic Science  
The future of SEM in forensic science is promising, with several emerging technologies poised to enhance the accuracy, efficiency, and scope of GSR analysis. One of the most exciting developments is the advent of portable SEM devices, which offer the potential for in-field forensic investigations. Unlike conventional laboratory-based SEM systems, portable SEM instruments are compact and can be deployed directly at crime scenes, enabling rapid preliminary screening for GSRs and other trace evidence. This capability could significantly reduce the time required for forensic analysis, allowing investigators to make more informed decisions in real time. Another major trend is the integration of artificial intelligence (AI) and machine learning algorithms into SEM systems. These algorithms can be trained to recognize and classify GSR particles with high accuracy, reducing human error and improving the overall reliability of the analysis. AI-driven SEM systems can also process large datasets more efficiently, facilitating the detection of patterns that might otherwise be overlooked in manual analyses [57; 58; 59; 60; 61; 62; 63].  
Additionally, research into alternative elemental markers for GSR detection is expanding. With the increasing use of lead-free ammunition, traditional elemental markers such as Pb, Ba, and Sb may no longer be sufficient for comprehensive GSR detection. New analytical strategies, including the use of advanced spectroscopic techniques such as X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (TOF-SIMS), offer a broader chemical perspective that could enhance GSR identification. The combination of these emerging technologies with traditional SEM-EDS analysis promises to revolutionize forensic science, providing investigators with more powerful tools to solve crimes and deliver justice.  
This study advocates for continued innovation in SEM methodologies, particularly in refining sample collection and preparation techniques, to bolster the reliability of forensic conclusions. Future research should also explore the integration of SEM with other analytical techniques, which could provide a more comprehensive framework for GSR analysis and enhance the interpretative power of SEM results.
## 4 Experiments
### Implementation Details  
We adopt the efficient convolution operator (ECO) tracker [3] as our single object tracker. ECO addresses computational complexity and over-fitting problems in state-of-the-art discriminative correlation filters (DCF) trackers by presenting a factorized convolution operator and a compact generative model of the training sample distribution. Due to the significantly increasing computational complexity when applying a single object tracker to a MOT scenario, we choose the faster version of ECO using hand craft features (histogram of oriented gradients (HOG) [31] and colour names (CN) [32]).  
The proposed method is implemented in Python running on a desktop with Intel(r) CoreTM i7-3930K @ 3.20 GHz CPU. With the implementation of the binary-channel verification model and the delay processing model, the average speed of the proposed method on the MOT17 dataset is about 1.72 fps, which is acceptable compared with other state-of-the-art methods including LSST17 (offline) [33] at 1.5 fps, and DMAN (online) [34] at 0.3 fps. The average densities on each frame of the MOT17 training set and test set are 21.1 and 31.8 persons per frame, respectively.  
The widely accepted clear MOT metrics [35] are adopted to evaluate the performance of our method, including multiple object tracking accuracy (_MOTA_) and multiple object tracking precision (MOTP), computed from false positives (_FP_), false negatives (_FN_) and the identity switch (_IDs_) as Equation (7). The calculation of aforementioned metrics are as follows:  
\[MOTA =1-\frac{FN+FP+IDs}{GT}\in(-\infty,1] \tag{7}\] \[MOTP =\frac{\sum_{t,i}d_{t,i}}{\sum_{t}c_{t}}. \tag{8}\]  
Here \(d_{t,i}\) is the bounding box overlapping hypothesis \(i\) with its assigned ground truth object, and \(c_{t}\) denotes the number of matches in frame \(t\). The _MOTA_ score takes into account the number of times a tracker makes an incorrect decision. However, under some circumstances the ability to track the identities of objects is also worth measuring. In [35] it is introduced the mostly tracked (_MT_),mostly lost (_ML_), and ID F1 (_IDF1_) scores to measure the performance of a tracker for those abilities. The computation of the _IDF1_ score is formed as follows:  
\[IDP=\frac{IDTP}{IDTP+IDFP} \tag{9}\] \[IDR=\frac{IDTP}{IDTP+IDFN}\] (10) \[IDF1=\frac{2}{\frac{1}{IDP}+\frac{1}{IDR}}=\frac{2 IDTP}{2 IDTP+ IDFP+IDFN} \tag{11}\]  
Here, _IDTP_ is the number of true positive ID matches. This can be seen as the percentage of detections correctly assigned in the whole sequence. _IDFN_ is the number of false negative ID matches, and _IDFP_ denotes the sum of false positive IDs.
# Performance Analysis of Post-Quantum Cryptography Algorithms for Digital Signature
## 5 Performance Analysis
### Comparative Tests  
This section presents a comparison of measured post-quantum algorithms with the well-known RSA algorithm, the cornerstone of current cryptographic systems but endangered by the vision of quantum computers. This comparison aims to evaluate the performance and usability of post-quantum cryptographic solutions in the context of real-world applications. It investigates how post-quantum algorithms compare to the popular asymmetric cipher in modern applications. One of the main points of this analysis will be an attempt to answer a pivotal question: is the time overhead of post-quantum algorithms justified to achieve quantum resilience? This question is of paramount importance as the transition to post-quantum cryptography might become necessary. To obtain results relevant for comparison, an RSA implementation originating from the Python library 'cryptography' was used. The size of the used key was 2048 bytes, and the exponent was set at 65,537. The measurements were performed in the same environment as the post-quantum algorithm measurements.  
Figures 11-13 present a comparison of the average file signing times of the post-quantum and RSA algorithms. Due to the complexity of post-quantum algorithms, a time overhead is expected compared to RSA. Examples with 100 MB and 1 GB file sizes meet this hypothesis, while an example with a 10 MB file size shows that a post-quantum algorithm can be even a bit faster than RSA. Looking at the full context of these three scenarios, it can be observed that differences are not overwhelming. The question then remains, is the significant security improvement worth the time overhead? Dilithium algorithms, including the best one--labelled Dilithium5, appear to be suitable candidates for replacing RSA. It has the NIST security level 5 declared by OpenQuantumSafe, while the tested version of RSA is estimated to be level 1. In the conducted measurements on the largest file (1 GB), Dilithium5 achieved an average result of 3.31 s, while RSA scored 2.56 s. This represents an overhead of approximately 27.7% in exchange for resistance to quantum cryptanalysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Function** & **Verifying 10 MB File** & **Verifying 100 MB File** & **Verifying 100 MB File** \\ \hline
**Algorithm** & & **[s \(\times\)10\({}^{-5}\)]** & **[s \(\times\)10\({}^{-5}\)]** & **[s \(\times\)10\({}^{-5}\)]** \\ \hline  
-simple** & & 2810 \(\pm\) 2 & 44,699 \(\pm\) 48 & 329,375 \(\pm\) 37 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance measurements of the post-quantum algorithms for verification of signatures.  
Figure 11: Comparison of average file signing time—10 MB file size.  
Figure 12: Comparison of average file signing time—100 MB file size.  
Figure 13: Comparison of average file signing time—1 GB file size.