esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
Figure 1 summarizes the methodology employed in this study. SAR satellite images from Sentinel-1 (S1) that cover the three study sites are downloaded and preprocessed to ensure their suitability for subsequent processing. Subsequently, SAR-SLs are extracted and used to obtain two quality indicators: heatmaps and distances from the reference line (RL). These indicators are employed to filter SL vectors with the aim of improving the ability to detect coastal changes based on them (see Section 2.4.2). Finally, the resultant SAR-SL time series are subjected to a quantitative assessment based on the comparison with ground-truth data and on the assessment of their ability to represent SL changes. To achieve this, a suitable, tailored validation technique is applied to consider the distinct characteristics of each study site.
# Development of an Anthropomorphic Prosthetic Hand with Underactuated Mechanism
## 2 Design of the Hand Mechanism  
### Design Concepts and Desired Specifications  
The grasping motions of an underactuated hand are classified into eight motions, as shown in Table 1[15; 16]. We decided that the desired hand motions are to hold objects used in daily life, such as remote controls, computer mice, paper cups, and spoons, as well as to carry bags. These desired motions are affiliated with the cylinder power, sphere power, tripod, and hook grip of the eight grasping motions. Therefore, the hand mechanism should be designed to make these motions involving natural motion to play the role of a prosthetic hand. To realize the selected grasping motions, the finger and thumb modules were designed with one active and three passive motions, which are realized by one actuator and three torsional springs in each module. In the proposed prosthetic hand, the natural motion (extension and flexion) is produced by the five actuators, and the fifth torsional springs are used for self-adaptive grasping when the surfaces of each phalanx of the finger and thumb are in contact with external objects.  
The target weight of the prosthetic hand was set to be \(<\)500 g, suggested in [2] as a suitable weight for a prosthetic hand. The desired velocity of the fingertip was \(>\)115 deg/s, and the desired receptible force of the hardware in a finger module was 3 N. Moreover, the ranges of flexion/extension and adduction/abduction in the finger and thumb modules are 0-90 deg, 0-60 deg, 0-90 deg, and 0-20 deg, respectively.
## 2 Background Overview
### Internet of Things  
IoT is the network of everyday objects that connect over the Internet with other devices or with other networks, for the purpose of monitoring and controlling these objects, among others. The IoT devices are embedded with sensors and a communication unit, which enable gathering and sharing information with each other in order to achieve a common goal. Vehicles can also connect to the Internet through cellular wireless technologies and interact with other networks of IoT. The integration of connected vehicles with IoT brings many advantages, as vehicular networks can use the information made available from other integrated components. For example, drivers will get real-time information about traffic, weather conditions, or the condition of a remote road. Therefore, better safety and traffic management can be achieved even in terrains where inter-vehicle communication is impossible or the IoV infrastructure is lacking. On the other hand, other networks can exploit the information coming from vehicular networks, too. Despite the attractive features, IoT faces scalability problems as the number of connected IoT devices increases dynamically.
# An Improved Fuzzy Vector Signature with Reusability
## 1 Introduction
### Source Distributions  
A source distribution of reusable biometric cryptosystems can be categorized into four types, according to correlation between two repeating readings, as in Table 1.  
\"\(w_{i}=w+\delta_{i}\)\" implies that the hamming distance between \(w\in W\) and \(w+\delta_{i}\) is less than a threshold value \(t\), where \(W\) is an input source. Since \(\delta_{i}\) is randomly chosen by an adversary, \(w+\delta_{i}\) may not be included in \(W\), which is a little far from reality. Especially in Reference [16; 19], additional assumptions were made that \(W\) should be a small error distribution \(\chi\) of Learning with Errors (LWE) problem and both \(w\) and \(w+\delta_{i}\) should be in \(W\) to tolerate logarithmic errors.  
\"\(\mathbf{H}_{\infty}[w_{i}|w_{i}-w_{j}]>m\)\" implies that, for any two distinct readings \(w_{i}\) and \(w_{j}\) in \(W\), \(\mathbf{H}_{\infty}[w_{i}|w_{i}-w_{j}]>m\) holds where \(m\) is a minimum level of security. In other words, the difference between \(w_{i}\) and \(w_{j}\) (i.e., \(w_{i}-w_{j}\)) should not leak too much information of \(w_{i}\) even if \(w_{i}-w_{j}\) is correlated to \(w_{i}\), which is a strong requirement for the input source.  
\"\((w,w_{i})\)\" implies that any two distinct readings are arbitrarily correlated, which would be the most realistic assumption. However, as a trade-off, schemes based on this distribution require additional assumptions on the input source. For example, in Reference [17; 18], any subset of \(W=(W[1]\),..., \(W[n])\) should have high min-entropy even if indices are exposed, and, in Reference [12], each component \(W[j]\) should have a high min-entropy even if other components are exposed.  
Unlike the above three types, a previous fuzzy vector signature [20] used a \((T,k)\)-block source in Reference [21], although following the subset concept in Reference [17] for construction. \((T,k)\)-block source means that for input sources \(W_{1},\ldots,W_{T}\), \(i\)-th source has a high min-entropy even though \(i-1\) readings are set to \(w_{1},\ldots,w_{i-1}\), i.e., \(\mathbf{H}_{\infty}[W_{i}\mid W_{1}=w_{1},\ldots,W_{i-1}=w_{i-1}]>k\) for all \(i\in[1,T]\) where \(k\) is a minimum level of security. Namely, \(W_{1},\ldots,W_{T}\) are not correlated. However, since fuzzy (biometric) data from the same source must be correlated, it is inappropriate to use the \((T,k)\)-block source when considering reusability.
## 2 Materials and Methods  
This study adhered to the guidelines of the Declaration of Helsinki. Ethical review and approval were not required due to its retrospective design.  
A retrospective analysis was performed on patients who underwent multiparametric MRI (mp-MRI) for prostate cancer staging at our Radiological Department between 2016 and 2022. The inclusion criteria encompassed patients who subsequently underwent prostatectomy and lymphadenectomy, yielding a final sample size of 85 individuals. Patients were excluded if they had received hormonal therapy, radiation therapy, or random biopsy prior to mp-MRI. Additional exclusion criteria included prior radio- or chemotherapy, transurethral resection of the prostate (TURP), partial gland surgical removal, or cases where lymphadenectomy was not performed.  
The clinical and demographic characteristics of the patient cohort are summarized in Table 1. Data collected for each patient included clinical information (age and PSAlevels prior to mp-MRI), tumor MRI features [17] (signal intensity on T2-weighted images, signal intensity on DWI/ADC maps, and PIRADS score [18]), and histopathological tumor characteristics (Gleason score, TNM staging, including extracapsular extension, seminal vesicle invasion, neurovascular bundle involvement, and histological subtype). These characteristics were categorized as \"semantic features\" [19]. The lymphadenectomy was performed at a mean distance of 102 days (min 91 and max 115) by mp-MRI.  
Based on lymph node status at the time of lymphadenectomy, patients were divided into two groups:  
- Lymph node positive status (\(n=35\)): at least one lymph node showed metastatic involvement.  
- Lymph node negative status (\(n=50\)): all examined lymph nodes were free of metastases.
## 2 Materials and Methods
### Visualization and Post-Processingof Predicted Results  
In order to intuitively display the contamination proportion information in a spatial domain, a visualization map of detection results is generated by transferring a multivariate analysis model to each pixel's spectra of the image. Specifically, the hyperspectral image was transformed into a two-dimensional matrix, and then the matrix was multiplied with the regression coefficients from the best PLSR model. The resultant vector of predicted values was refolded to form a two-dimensional color image [28]. In this study, the hyperspectral image data that was used for visualization for each type of samples was a sub-image, which was cut out from the raw calibrated hyperspectral images with the size of 150 \(\times\) 150 pixels at selected wavelengths, and then processed with SNV.  
Additionally, in order to obtain a more precise prediction map, a post-processing method was applied on the prediction values of all of the pixels before forming the visualization map. The post-processing method was shown by the following equations:  
\[Y_{p}=Y_{0.95-},\text{ if }Y\leq Y_{0.95-}\]  
\[Y_{p}=Y,\text{ if }Y_{0.95-}<Y\leq Y_{0.95+}\]  
\[Y_{p}=Y_{0.95+},\text{ if }Y_{0.95+}<Y\]  
\[Y_{0.95-}=\overline{\mu}-1.96\times\sigma\div\sqrt{n}\]  
\[Y_{0.95+}=\overline{\mu}+1.96\times\sigma\div\sqrt{n} \tag{2}\]  
where the \(Y_{p}\) values were the predicted values of the pixels after they were post-processed, \(Y\) were the predicted values of the pixels, \(Y_{0.95-}\) and \(Y_{0.95+}\) were the values of the endpoints of 95% confidence interval of the mean of \(Y\), \(\overline{\mu}\) was the mean of \(Y\), \(\sigma\) was the standard deviation of \(Y\), and \(n\) was the number of \(Y\).
# Fault-Tolerant Visual Secret Sharing Schemes without Pixel Expansion
###### Abstract  
Visual cryptography encrypts a secret image into two meaningless random images, called shares, such that it can be decrypted by human vision without any calculations. However, there would be problems in alignment when these two shares are staked by hand in practice. Therefore, this paper presents the fault-tolerant schemes of stacking two shares that are acquired from secret image encryption without pixel expansion. The main idea of these schemes is to combine several pixels as a unit and then to encrypt each unit into a specific combination of pixels. Both theoretical analysis and simulation results demonstrate the effectiveness and practicality of the proposed schemes.  
Keywords:visual cryptography; visual secret sharing; random grid; tolerance; pixel expansion +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Data Source and Preprocessing  
The data of this study mainly came from the Landsat7 ETM SLC-off satellite digital products (Day 353 and Day 346 in 2008), downloaded from the Geospatial Data Cloud Platform of the Computer Network Information Center of the Chinese Academy of Sci  
Figure 1: Map of study area.  
ences ([[http://www.gscloud.cn](http://www.gscloud.cn)]([http://www.gscloud.cn](http://www.gscloud.cn)), accessed on 1 January 2020), Landsat8 OLI_TIRS (Day 305, 314, 353 in 2017 and Day 13 in 2018) and Meizhou DEM data; the purchased 2013 Landsat8 OLI_TIRS data (L4-level product); 2008, 2013, and 2018 MOD17A2 products and MYD11A2 products, downloaded from NASA's MODIS data-sharing platform([[https://ladsweb.modaps.eosdis.nasa.gov](https://ladsweb.modaps.eosdis.nasa.gov)]([https://ladsweb.modaps.eosdis.nasa.gov](https://ladsweb.modaps.eosdis.nasa.gov)), accessed on 1 January 2020), etc. (Table 1).  
In order to reduce the influence of external factors, the images downloaded from the geospatial data cloud platform were first subjected to image mosaicing, stripping repair, radiometric calibration, and atmospheric correction. Then, the images were cropped using the Meizhou administrative boundary vector data, and finally, we obtained an image of the study area. The MODIS data was formatted and synthesized for the whole year [32], and then cropped with the rectangular vector data to a slightly larger area than the administrative boundary of Meizhou City. After calculating and obtaining the raster data related to the remote sensing ecological index, the vector data of the administrative boundary of Meizhou City are used to obtain the ecological environment data within the study area. (Figure 2). Due to the different resolutions of the Landsat ETM and Landsat8 OLI_TIRS and MODIS data products, the resolutions of the two Landsat images were resampled to 1000 m.
# Disjoint Tree Mergers for Large-Scale Maximum Likelihood Tree Estimation
## 4 Discussion
### Comparing DTM Pipelines to ML Heuristics  
Our study showed that the best DTM pipelines we explored (which use IQ-TREE starting and constraint trees) provide improved accuracy as compared to both FastTree and IQ-TREE, but they are not as reliably accurate as RAxML, a finding that should be contrasted with the trends (and interpretation) reported in the prior study evaluating Constrained-INC pipelines for gene tree estimation [24]. It is worth noting that [24] never found their best pipeline using Constrained-INC to be competitive with RAxML and, indeed, their pipelines were not reliably more accurate than FastTree. This is very different from what we observe, where the best GTM pipeline is reliably more accurate than FastTree and IQ-TREE and, in some cases, as accurate as RAxML (or even more accurate than RAxML).  
The difference in trends can be explained by the choices made in [24] for the Constrained-INC pipeline, which--in many cases--reduced accuracy when compared to the choices that we made. Of the many differences, the three most likely to have negatively impacted the Constrained-INC pipeline in [24] as compared to the better pipelines that we studied are (1) the use of Constrained-INC instead of GTM, (2) the smaller maximum subset size (they used 200) compared to ours (we used 500), and (3) the use of FastTree instead of IQ-TREE as the starting tree in all analyses. Thus, the choice of algorithmic parameters has a substantial impact on pipeline accuracy, and our choices improved accuracy when compared to theirs.  
Thus, unlike [24], we find that the better DTM pipelines can provide competitive accuracy with leading ML heuristics. Moreover, the best DTM pipeline that we developed--GTM using IQ-TREE for starting tree and constraint trees-was competitive with RAxML, and only clearly less accurate than RAxML on the high fragmentary 1000M1-HF condition. Furthermore, for that high fragmentary condition, the problem was that the starting tree (that was produced using IQ-TREE) had high error--30.2%. The GTM pipeline reduced this error by 1.8%, but this was not enough to come close to RAxML, which had error of only 24.9%. In other words, the GTM pipeline that we presented was only able to reduce the error over the starting tree by a small amount, and when the gap in accuracy between the starting tree and RAxML is large; this means that the pipeline will not be competitive with RAxML.  
This examination reveals that DTM pipelines can be competitive with RAxML in accuracy (and much faster) when IQ-TREE or FastTree can produce a tree that is close to the accuracy of the RAxML tree. However, as the high fragmentary condition (1000M1-HF) shows that there are conditions where neither FastTree nor IQ-TREE are nearly as accurate as RAxML. For such cases, DTM pipelines will fail to be competitive in terms of accuracy with RAxML.
## 5 Experiments
### Experimental Results and Analysis
#### 5.2.3 Parameter Analysis  
There are some algorithms whose parameter sizes and ranges are specified, such as \(\varepsilon\)-greedy [20], Boltzmann [18], and UCB1 [3]. We kept the basic parameters (_Parameters_ 1) and exploration coefficient parameters (_Parameters_ 2) of each algorithm consistent to ensure the fairness of the experiment. The basic parameters (_Parameters_ 1) exist in the original algorithm; some _Parameters_ 2 exist and some do not. In DG-MOSS, _Parameters_ 1 are  
Figure 2: Average rewards. (**a–d**) The X axis represents the value of total rewards, and the Y axis represents the training epochs.  
fixed \(\sqrt{3}\) in \(mis=\sqrt{\frac{3\log{(S)}}{s}}\), and _Parameters_ 2 are the exploration probability \(c\). The term \(mis\) in the current round signifies the threshold demarcating the total number of arm pulls, denoted as \(S\), and the number of pulls from the current arm, denoted as \(s\). As the exploration probability \(c\) undergoes alterations, there are corresponding shifts in both the average reward and average regret. Consequently, for fairness in comparison, the selection of identical parameter values was made. This approach ensures consistency across different exploration probabilities, facilitating a more precise evaluation of outcomes under varied conditions. Here, the exploration probability \(c=0.1\) was used. Although random algorithms \(\varepsilon\)-greedy [20], Boltzmann [18] and CNAME [19]; upper bound of confidence algorithms UCB [3] and UCB1 [3]; grouping algorithms FP-UCB [23] and BBANK [26]; and optimal strategy algorithms MOSS [24] and R-MOSS [25] have better performance, DG-MOSS has the maximum average reward and the lowest average regret. This shows the stability of the algorithm.  
In the analysis of these parameters, the average rewards and average regrets of DG-MOSS are shown in Table 1. We used the dynamic grouping to improve reinforcement learning algorithm of MOSS by increasing the exploration coefficient.  
According to step 9 of Algorithm 3, it can be inferred that the value of probability parameter \(P_{1}\) is related to the control of action selection. The quantity of dynamically grouped data also plays a significant role, and the varying impacts of different parameters on the final results further indicate the intentional design of probability parameters in DG-MOSS. Here, the selection of exploration parameters is related to \(K\). The \(K\) used in this Ads-CTR-Optimisation datasets experiment is 85. We can choose according to the actual situation. Through experimental comparison, we use \(\frac{6}{85}\) as the probability parameter \(0<P_{1}<1\). The average rewards of different probability parameters are shown in Table 2.
# A Multi-Mode Recognition Method for Broadband Oscillation Based on Compressed Sensing and EEMD
## 4 Analysis and Comparison of the Results  
To prove the accuracy of the abovementioned method, this paper verifies the compression and reconstruction effect of the original broadband oscillation signal and the accuracy of EEMD decomposition of the reconstructed signal, based on the energy coefficient from two aspects: the theoretical model of the multimode broadband oscillation and the simulation test signal.
## 2 Materials and Methods
Queries #13 and #14 exclude publications from the years 2023 and 2024, respectively. A total of 46 articles were removed, leaving our final database with 180 articles that will be used in the analysis. This methodical process ensures that the dataset used for our bibliometric analysis is both comprehensive and relevant to the intersection of ABM and AI. The sequential queries help filter articles based on titles, abstracts, keywords, language, document type, and publication years, contributing to the robustness and specificity of our bibliometric study. The resulting dataset, comprising 180 articles related to AI utilization in ABM, is close to the recommendations from the field related to the size of the data sample [52]. Furthermore, as the purpose of the dataset collection step was to extract all the papers indexed in WoS pertaining to AI utilization in ABM, no further actions were needed to be taken for expanding the dataset as, in this paper, we are not working with small or fractional parts of research output. As [PERSON] et al. [52] suggested, in cases where the bibliometric analysis uses an approach that relies on small or fractional parts of the researcher's output, one should pay more attention to the size of the dataset, as there might be cases in which the extracted dataset does not accurately represent the average. To ensure understanding and clarity of the methodological steps undertaken, Figure 1 describes each step that we will apply in our study. Considering the research questions outlined in this study's introduction, the initial step in constructing the methodological flow involves the research framework. Subsequently, the queries from Table 1 are employed to extract the database, and the RStudio software is utilized to generate the graphs that will be visualized and interpreted. Figure 1: Methodological flow. In the section related to mixed analysis, the interconnections between various analyzed elements, such as authors and keywords from our bibliometric dataset, have been discussed. For this purpose, we used the VOSviewer software, a specialized tool for analyzing and visualizing networks in bibliometric data [53]. VOSviewer, initially created in 2010 by [PERSON] and [PERSON] [54] at Leiden University's Centre for Science and Technology Studies (CWTS), is a software tool designed for generating maps using network data and for visualizing and exploring these maps. The application allows users to create graphical visualizations of networks, highlighting the connections between the analyzed elements. Additionally, it is useful for identifying and highlighting clusters or groups of elements that are closely related within the dataset. VOSviewer is widely used in the academic community [55; 56; 57; 58] to understand and graphically represent scientific information networks, facilitating the identification of trends, connections, and patterns in the literature.
## 1 Introduction
If this signal processing method is utilized in this situation, the inherent measurement performance with the resolution of \(0.1\) nm will not be achieved in the actual experiment. Furthermore, to avoid the signal drift during the measurement procedure, the DC offset compensation module is also introduced in the polarized Fabry-Perot interferometer. The conversion of analog to digital and digital to analog is performed in real-time to acquire the signal center and then is to eliminate the DC offset. Since signal processing involves more complex feedback control, if the noise occurs during the measurement, the measurement accuracy will be affected severely. Therefore, this kind of interferometer cannot be performed in high precision and high-speed industrial applications. By reviewing the development of the Fabry-Perot interferometer employed for the displacement measurement, the major problems in critical structures are summarized in Table 1. Therefore, how to provide a measurement system based on the Fabry-Perot interferometer with high measurement performance for employing in the large range is an essential issue. From the above-mentioned description, a differential quadrature Fabry-Perot interferometer with variable measurement mirrors employing in different measuring ranges is proposed in this study. By the integration of the differential  
\[{\rm I}_{\rm s}=\frac{\frac{1}{2}{\rm A}_{0}{}^{2}\times{\rm T}^{2}\times{\rm T }}{1+{\rm R}^{2}\times{\rm T}^{\prime^{2}}-2\times{\rm T}\times{\rm R}\times \cos\!\left(\delta-\frac{\pi}{4}\right)} \tag{1}\]0 Nonlinearity error of polarized Fabry–Perot interferometer. optical structure and the Fabry-Perot interferometric technique, the nonlinearity error can be improved effectively, and the DC offset can also be eliminated during the measurement process to realize high precision measurement performance conveniently and flexibly.