esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Statistical Analysis of 3D Kinect Data: A Comparison of the Classification Methods
## 2 Materials and Methods
### Parametric Statistical Model
#### 2.4.2 Multivariate Ordinal Logistic Regression  
Given that we face the problem of classification with multiple, ordered, classes (HB grades) on the basis of the vector of health scores, we have chosen the apply the multivariate Ordinal Logistic Regression (OLR). This is a parametric statistical tool that is derived from the classical logistic regression. Whereas the classical logistic regression requires binary explanatory variable, OLR makes it possible to model ordinal explanatory variable (multiple classes with ordering). In particular, OLR applies a series of classical logistic regressions to cumulative probabilities to estimate the ordinal version of cumulative distribution function of the explanatory variable. Probabilities of individual classes (values of explanatory variable) are then predicted by taking the differences of the cumulative probabilities.  
In our framework, the explanatory variable represents \(HB\) evaluation with ordered \(HB\) grades: \(HB1<HB2<HB3<HB6\). Regression models for cumulative probabilities take the form:  
\[P(HB_{k}\leq j)=\frac{1}{1+\exp{(-\alpha_{j}-\sum_{i}\beta_{i}p_{ik})}}. \tag{3}\]  
The output \(P(HB_{k}\leq j)\) is the probability that the \(k\)-th sample has \(HB\) grade \(j\) or better, \(\alpha_{j}\) is the (\(HB\) specific) intercept parameter, \(\beta_{i}\) is the coefficient for the health score of the \(i\)-th indicator (independent of the \(HB\) level) and \(p_{ik}\) is the health score of the \(i\)-th indicator and the \(k\)-th sample. The parameters \(\alpha_{j}\) and \(\beta_{i}\) are estimated from the data by maximum likelihood method.  
To avoid overfitting and to provide stable model predictions, we performed a stepwise variable selection procedure based on minimising the AIC (considering both directions, starting from the empty model).  
To compensate for the imbalance of the input data, we applied weighted version of multivariate OLR, where the weights of samples were set as the inverse value of the frequency of the corresponding class.  
The probability of a sample's having a specific \(HB\) grade is calculated as the difference of cumulative probabilities:  
\[P(HB_{k}=j)=P(HB_{k}\leq j)-P(HB_{k}\leq j-1). \tag{4}\]  
The resulting predicted class for a sample is determined as the class with the highest probability.  
More details of this method can be found in [27] (which are referred to there as a cumulative logit model). We performed our calculations of OLR using the polr function from the MASS package in R (see [28] for further details).
# Robust Model-Free Control for MMC Inverters in Cold Ironing Systems
## 1 Introduction
### DC Distribution
Consequently, an adaptive approach to these control strategies is recommended to improve performance and resilience in the face of modeling uncertainties and load fluctuations [30]. Sliding mode control (SMC), on the other hand, is renowned for its robustness against disturbances and uncertainties thanks to the dynamics imposed on a predefined sliding surface [31; 32]. However, chattering can occur, which can damage the material, and it is sensitive to the choice of surface and input limitations that are not considered in conventional approaches [32; 33]. Finally, model-based predictive control (MPC) offers a powerful framework for anticipating system evolution and optimizing control actions while respecting constraints [34; 35]. Nevertheless, real-time implementation remains challenging due to computational complexity, dependence on an accurate model, and difficulty in tuning cost functions [36; 37; 38]. This study proposes the use of a modular multilevel converter (MMC) to achieve output voltage and power and reduce the number of transformers required in the topology presented in Figure 2b. An ultra-local adaptive control method has also been introduced to enhance the quality of the power supplied by the converter. This approach relies on an intelligent adaptive PI (iPIa) controller that adjusts its gain dynamically to improve disturbance rejection. Unlike traditional methods, this model-free control does not depend on the system model. Key features of this paper:  
* Application of a robust model-free control approach for the MMC converter in shore power electrification systems. * Comparison with classical controllers to highlight the benefits of model-free control in terms of performance and robustness. * Validation through simulations in Simulink, demonstrating improvements in THD, dynamic response, and stability. This paper is structured as follows:
* Section 2 describes and models the MMC converter. * Section 3 presents the proposed control strategies. * Section 4 evaluates and analyzes the effectiveness of the proposed control method using MATLAB/Simulink R2017a simulations. * Section 5 concludes this study.
# A Mathematical Study of COVID-19 Spread by Vaccination Status in Virginia
## 4 Discussion  
Our analysis suggests that, from 23 January through 11 September 2021, in Virginia, partially vaccinated individuals were 56.0%/52.7%/46.0% less likely to become infected/hospitalized/deceased due to COVID-19 than unvaccinated individuals, while fully vaccinated individuals were 89.8%/88.5%/85.7% less likely to become infected/hospitalized/deceased due to COVID-19 than unvaccinated individuals (see Table 2 and Figure 2). This strongly supports the efficacy of vaccination in combating the spread and severe outcomes of COVID-19. The model furthermore suggests COVID-19 was 2.08 times more transmissible during the time frame during which the B.1.617.2 (Delta) variant became the dominant strain in Virginia (see Table 3). We did not detect any changes in the vaccine efficacy levels during the time frame during which Delta became dominant in Virginia. This suggests that it is the raw increase in transmissibility of the Delta variant that was the primary driver behind the increase in COVID-19 cases from June to August 2021, rather than a decrease in vaccine efficacy.  
We forecast the spread under a variety of different vaccination uptakes levels (see Figure 4) and potential booster shot schedules (see Figure 5). This analysis suggests a five times increase in new cases from 18 September through 31 December 2021, if 10% less of the population had opted to become vaccinated. Our analysis furthermore suggests that a booster shot in the 95-100% efficacy range could reduce the number of new cases between 18 September and 31 December 2021 by 19.7-34.3%, and that this reduction is distributed across vaccination statuses (see Figure 5). These results reinforce the continued push for vaccination as an essential and effective means of containing the spread of COVID-19. It also emphasizes that the benefits of vaccination are distributed across the entire population and not merely restricted to those who are vaccinated.  
We note that there are several limitations of our study. Although we did not find evidence that vaccination provides additional protection against severe COVID-19 outcomes such as hospitalization and death, we caution that we did not consider age-stratified differences in vaccination coverage levels, vaccine efficacy, and severe COVID-19 outcomes. In particular, we note that older populations are at higher risk for severe COVID-19 outcomes but also have higher vaccination uptake levels. We also did not incorporate waning vaccine immunity. Although we did not find evidence for decreased vaccine effectiveness over time in our study, we note that our model only considers population-level average immunity and cannot control for when an individual received their first or second vaccination. We also note that, while the vaccination uptake curves \(V_{i}(t)\) (4) fit the data well on the chosen time intervals, they do not account for the age-dependent differences in availability of vaccines. The predicted total number of people who will become vaccinated are consequently likely to be below the true value.
# Practical Grammar Compression Based on Maximal Repeats +
## 2 Preliminaries
### RePair  
RePair is a grammar compression algorithm proposed by [PERSON] and [PERSON] [2]. For input text \(T\), let \(G=\{V,\Sigma,S,R\}\) be the grammar constructed by RePair. Then, the RePair procedure can be described with the following steps:  
**Step 1.** Replace each symbol \(a\in\Sigma\) with a new variable \(v_{a}\) and add \(v_{a}\to a\) to \(R\).  
**Step 2.** Find the most frequent pair \(p\) in \(T\).  
**Step 3.** Replace every occurrence (or, as many occurrences as possible, when \(p\) is a pair consisting of the same symbol) of \(p\) with a new variable \(v\), and then, add \(v\to p\) to \(R\).  
**Step 4.** Re-evaluate the frequencies of pairs for the updated text generated in **Step 3**. If the maximum frequency is 1, add \(S\rightarrow(\text{current text})\) to \(R\), and terminate. Otherwise, return to **Step 2**.  
Figure 1 illustrates an example of the grammar generation process of RePair.  
**Lemma 1** ([2]).: _RePair works in \(\mathcal{O}(n)\) expected time and \(5n+4k^{2}+4k^{\prime}+\lceil\sqrt{n+1}\rceil-1\) words of space, where \(n\) is the length of the source text, \(k\) denotes the cardinality of the source alphabet, and \(k^{\prime}\) denotes the cardinality of the final dictionary._
## 3 Methodology
### Computation of Monthly Land-Water Masks with Data Gaps
#### 3.2.2 Calculation of Water Indexes
The AWEI\({}_{\text{sh}}\) is expressed as:  
\[AWEI_{\text{sh}}=BLUE+2.5\times GREEN-1.5\times(NIR+SWIR_{1})-0.25\times SWIR _{2} \tag{4}\]  
Tasseled Cap for Wetness (TC\({}_{\text{wet}}\))  
The Tasseled Cap index is based on a method to transform spectral information of satellite data into spectral indicators [13; 34]. Depending on the application, three different types of Tasseled Cap are available, optimized for brightness, greenness, and wetness. In this study, we use the Tasseled Cap for wetness TC\({}_{\text{wet}}\) because the interest lies in the detection of open water. The algorithm, shown in Equation (5), consists of a weighted sum of all six bands from Landsat or Sentinel-2. \[TC_{wet}= 0.1509\times BLUE+0.1973\times GREEN+0.3279\times RED+ \tag{5}\] \[0.3406\times NIR-0.7112\times SWIR_{1}-0.4572\times SWIR_{2}\]  
In summary, one can say that all indexes perform similar to all bands are highly correlated with each other. However, selecting the different combination of bands for the index calculation results in different correlations between them. For example, the correlation coefficients between all indexeshave been computed for Lake Ray Roberts in January 2017. Figure 7 shows the resulting correlations matrix with varying correlation coefficients between 0.90 and 1.00. Despite a high correlation as it is between MNDWI and NWI but also between AWEInsh, AWEIsh and TCwet, the resulting histogram separating land and water behaves differently which will be shown in detail in the next processing step. In this study, we use the advantage of combining information of five different water indexes in order to estimate only one uniform threshold for separating land and water pixels.
## 3 Study Area and Data
### Research Data Acquisition and Processing
#### 3.2.1 Road Data Acquisition and Processing
Such treatment can simplify the road network, avoid repeated calculations, and unify the road representation. Using MapGIS 10.6 software to process the road network, verify the road connectivity, and eliminate the disconnected roads, unconnected roads, and branch lines. Secondly, the redundant road network lines are deleted, so that the road network map used in this study can be initially obtained, and a road network processing example is shown in Figures 3-5. Deleting redundant branch roads in the road network, as shown in Figure 3, simplifies the road network and does not interfere with the research experiments. As shown in Figure 4, the complexity of roadway intersection routes in the road-way network is generally simplified to one adjacent route, with the roadway intersection becoming a simpler cross-connection type. As shown in Figure 5, the two-way lanes in the road network are simplified into one-way lanes, while the lines are connected to form road intersections. After processing the above steps, the road network map used in the experiment can be initially obtained, as shown in Figure 6.  
The road network shown in Figure 7 is processed using MapGIS software to interrupt intersectionality from road intersections to form road intersection nodes, and Figure 7 demonstrates the results of localized road intersection node processing in the Kowloon Peninsula area. The road network road intersection nodes in the entire Kowloon Peninsula area were processed to obtain a total of 2519 intersections, and the results are shown in Figure 8.  
After the above steps, the final road network map and road intersection node location map used in this experiment are obtained.
## 4 System Architecture
### Architecture Description
#### 4.2.4 Development View  
This view shows the local development environment where programmers can write code for software services and perform preliminary tests. Figure 7 shows how the local computer of the members of the development team becomes a reduced test environment that includes the installation of the parts of the system as containerized components from the Docker Hub. Additionally, third-party software products can be installed locally and interact and verify the functionality of the software components.  
Figure 5: (**a**) Diet prediction, prescription, and monitoring process. (**b**) Graphical user interface to collect the personal physical data of the elderly person. (**c**) Graphical user interface for calculating the EER. (**d**) Graphical user interface that helps to prepare meals.  
Figure 6: Data structure.  
The developer workflow is complemented by operations performed to maintain version control of software objects created for the system. The workflow of the development team members can be updated and integrated collaboratively. Here, one of GitLab's features is leveraged, allowing project management and modern source code management through issue tracking and continuous integration. For additional details of the development view, see Appendix B.
# Comparative Analysis of LiDAR and Photogrammetry for 3D Crime Scene Reconstruction
###### Abstract  
Accurate and fast 3D mapping of crime scenes is crucial in law enforcement, and first responders often need to document scenes in detail under challenging conditions and within a limited time. Traditional methods often fail to capture the details required to understand these scenes comprehensively. This study investigates the effectiveness of recent mobile phone-based mapping technologies equipped with a LiDAR (Light Detection and Ranging) sensor. The performance of LiDAR and pure photogrammetry is evaluated under different illumination (day and night) and scanning conditions (slow and fast scanning) in a mock-up crime scene. The results reveal that the mapping using an iPhone LIDAR in daylight conditions with 5 min of fast scanning shows the best results, yielding 0.1084 m of error. Also, the cloud-to-cloud distance showed that 90% of the point clouds exhibited under 0.1224 m of error, demonstrating the utility of these tools for rapid and portable scanning in crime scenes.  
3D crime scene mapping; LiDAR technology; photogrammetry; crime scene investigation Article
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**A7**} \\ \hline
**Binding Energy (eV)** & **Counts** & **S/N Ratio** & **Spectral Line** & **ID** & **CAS Registry No.** \\ \hline
779.8 & 50,222 & 13 & Ba 3d5/2 & BaO, BaCO\({}_{3}\) & 1304-28-5, 513-77-9 \\
530.4 & 323,093 & 102 & O 1s & -O, -CO\({}_{3}\) & multiple \\
438.0 & 91,101 & 3 & Ca 2s & Ca- & 1305-78-8, 471-34-1 \\
413.5 & 173,163 & 32 & Mo 3p1/2 & MoS\({}_{2}\) & 1317335 \\
401.3 & 89,087 & 15 & Sc 2p3/2 & Sc\({}_{2}\)O\({}_{3}\) & 12060081 \\
399.5 & 211,275 & 60 & Mo 3p3/2 & MoO\({}_{x}\) & 18868434 \\
344.9 & 48,186 & - & Ca 2p3/2 & Ca- & 7440-70-2, 471-34-1 \\
288.4 & 135,511 & 16 & C 1s & -CO\({}_{3}\) & multiple \\
284.6 & 330,444 & 96 & C 1s & C (Adv.) & multiple \\
251.4 & 130,263 & 14 & Ba 4s & Ba- & 1304-28-5, 513-77-9 \\
177.7 & 10,465 & - & Ba 4p3/2 & Ba- & 1304-28-5, 513-77-9 \\
116.3 & 17,168 & - & Al 2s & Al- & 1304-28-5, 513-77-9 \\
89.8 & 33,990 & 5 & Ba 4d5/2 & Ba- & 1304-28-5, 513-77-9 \\
63.2 & 45,771 & 8 & Mo 4s & Mo- & 7439-98-7 \\
37.9 & 67,575 & 18 & W 4f5/2 & W- & 1314-35-8 \\
24.8 & 22,300 & 2 & Ca 3p & Ca- & 1305-78-8, 471-34-1 \\
13.0 & 8566 & - & Ba 5p3/2 & BaO & 1304-28-5 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: XPS data for point A6. Binding energy and counts taken from the overview spectra. S/N ratios were computed manually. A “-” indicates an S/N ratio below 1.5, implying signal indistinguishable from background. Spectral line and ID were taken from the NIST XPS Database [22]. See text for details. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**A6**} \\ \hline
**Binding Energy (eV)** & **Counts** & **S/N Ratio** & **Spectral Line** & **ID** & **CAS Registry No.** \\ \hline
779.8 & 41,470 & 12 & Ba 3d5/2 & BaO, BaCO\({}_{3}\) & 1304-28-5, 513-77-9 \\
530.4 & 297,219 & 115 & O 1s & -O, -CO\({}_{3}\) & multiple \\
438.0 & 104,198 & 4 & Ca 2s & Ca- & 1305-78-8, 471-34-1 \\
413.5 & 173,984 & 43 & Mo 3p1/2 & MoS\({}_{2}\) & 1317335 \\
401.3 & 91,046 & 21 & Sc 2p3/2 & Sc\({}_{2}\)O\({}_{3}\) & 12060081 \\
399.5 & 215,924 & 77 & Mo 3p3/2 & MoO\({}_{x}\) & 18868434 \\
344.9 & 51,329 & 2 & Ca 2p3/2 & Ca- & 7440-70-2, 471-34-1 \\
288.4 & 134,937 & 16 & C 1s & -CO\({}_{3}\) & multiple \\
284.6 & 329,406 & 117 & C 1s & C (Adv.) & multiple \\
251.4 & 138,365 & 21 & Ba 4s & Ba- & 1304-28-5, 513-77-9 \\
232.0 & 399,815 & 157 & Mo 3d5/2 & Mo- & 1313-27-5 \\
177.7 & 11,172 & - & Ba 4p3/2 & Ba- & 1304-28-5, 513-77-9 \\
116.3 & 17,742 & - & Al 2s & Al- & 1344-28-1 \\
89.8 & 31,468 & 4 & Ba 4d5/2 & Ba- & 1304-28-5, 513-77-9 \\
63.2 & 43,282 & 9 & Mo 4s & Mo- & 7439-98-7 \\
37.9 & 72,769 & 25 & W 4f5/2 & W- & 1314-35-8 \\
24.8 & 21,560 & 3 & Ca 3p & Ca- & 1305-78-8, 471-34-1 \\
13.0 & 8257 & - & Ba 5p3/2 & BaO & 1304-28-5 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: XPS data for point A7. Binding energy and counts taken from the overview spectra. S/N ratios were computed manually. A “-” indicates an S/N ratio below 1.5, implying signal indistinguishable from background. Spectral line and ID were taken from the NIST XPS Database [22]. See text for details.
## 4 Experimental Results and Performance Evaluation
### Packet Delivery Ratio  
PDR refers to the ratio of successfully received packets to the total transmitted averaged over all flows. This plays a key role in evaluating the efficiency of the transmission deployed in the network. Because PDR increases when there is a higher number of packets dropped because of a change of network event such as link failure. If the selected link has poor transmission quality the packets drop increased. Therefore, PDR is measure with respect to a different time of data transmission and the resulting plot is presented compared to RPF and FSL in Figure 4. Increases in PDR indicate the improved performance of the system, and RPSO exhibits an increase with stable behavior thereby achieving 100%. The performance gain is due to accurate link quality estimation and rerouting of packets through a good link quality channel. Therefore, packets transmission may not be affected even when there is a change in the network. While the RPF shows a 90% PDR due to rerouting of the packet without considering link quality. Therefore, RPSO outperforms RPF with better PDR by 12.5%. The comparative study demonstrates that the proposed scheme is more efficient in terms of the analyzed metric.  
Figure 3: Average Throughput.
###### Abstract  
At present, free-to-move node self-deployment algorithms aim at event coverage and cannot improve network coverage under the premise of considering network connectivity, network reliability and network deployment energy consumption. Thus, this study proposes pigeon-based self-deployment algorithm (PSA) for underwater wireless sensor networks to overcome the limitations of these existing algorithms. In PSA, the sink node first finds its one-hop nodes and maximizes the network coverage in its one-hop region. The one-hop nodes subsequently divide the network into layers and cluster in each layer. Each cluster head node constructs a connected path to the sink node to guarantee network connectivity. Finally, the cluster head node regards the ratio of the movement distance of the node to the change in the coverage redundancy ratio as the target function and employs pigeon swarm optimization to determine the positions of the nodes. Simulation results show that PSA improves both network connectivity and network reliability, decreases network deployment energy consumption, and increases network coverage.  
node self-deployment; network layer; network cluster; pigeon swarm optimization +
Footnote †: journal: Sensors
# Quantum Image Steganography Schemes for Data Hiding: A Survey
## 3 Quantum Image Steganography Schemes
### Controlled Alternate Quantum Walks Protocol  
Inspired by random walks, a newly designed quantum protocol by [PERSON] et al. named controlled alternate quantum walks (CAQWs) [41] can outperform classical computers in terms of exponential algorithmic speedup by taking advantage of the quantum state characteristics.  
Based on CAQWs, [PERSON] et al. [10] promoted protected transmission in cloud-based E-healthcare systems through the development of an innovative steganography scheme. The primary task of CAQWs is to select two LSB pixel places in the carrier picture for the hidden bits to be embedded, which makes the outcome capacity of the algorithm two bits per pixel.  
Before the embedding process, the algorithm transforms the carrier image, detection matrix, and private image information into a vector using CAQWs. Additionally, CAQWs will carry out the vectorization process on a series of odd-valued nodes specified by a bit string using the preliminary parameters, yielding a probability distribution matrix.  
The proposed steganography approach removes some requirements such as the before and after encryption alongside the extraction procedures, which means that only stego images and main states of the CAQWs are obligatory to extract hidden images, making the process easier. The plan performs several processes that include extraction and embedding according to the specifications of CAQWs, and those processes are then utilized to decide upon pixels for the embedded hidden bits in the stego picture.  
Multiple researchers have gone through a lot of experiments and trials to establish an efficient scheme capable of processing a collection of medical photos in color and grayscale regarding good visual quality, resistance to data loss hazards, as well as making sure it meets high embedding capacity and meeting all those goals while having strong security. These experiments had several outcomes that prove that the quantum-inspired scheme achieves satisfactory results compared to similar techniques; they also proposedsome possible applications that are qualified for conveying successful medical image steganography on upcoming computing models [10].