esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The developed world has focused on Web preservation compared to the developing world, especially news preservation for future generations. However, the news published online is volatile because of constant changes in the technologies used to disseminate information and the formats used for publication. News preservation became more complicated and challenging when the archive began to contain articles from low-resourced and morphologically complex languages like Urdu and Arabic, along with English news articles. The digital news story preservation framework is enriched with eighteen sources for Urdu, Arabic, and English news sources. This study presents challenges in low-resource languages (LRLs), research challenges, and details of how the framework is enhanced. In this paper, we introduce a multilingual news archive and discuss the digital news story extractor, which addresses major issues in implementing low-resource languages and facilitates normalized format migration. The extraction results are presented in detail for high-resource languages, i.e., English, and low-resource languages, i.e., Urdu and Arabic. LRLs encountered a high error rate during preservation compared to high-resource languages (HRLs), corresponding to 10% and 03%, respectively. The extraction results show that few news sources are not regularly updated and release few new news stories online. LRLs require more detailed study for accurate news content extraction and archiving for future access. LRLs and HRLs enrich the digital news story preservation (DNSP) framework. The Digital News Stories Archive (DNSA) preserves a huge number of news articles from multiple news sources in LRLs and HRLs. This paper presents research challenges encountered during the preservation of Urdu and Arabic-language news articles to create a multilingual news archive. The second part of the paper compares two bilingual linking mechanisms for Urdu-to-English-language news articles in the DNSA: the common ratio measure for dual language (CRMDL) and the similarity measure based on transliteration words (SMTW) with the cosine similarity measure (CSM) baseline technique. The experimental results show that the SMTW is more effective than the CRMDL and CSM for linking Urdu-to-English news articles. The precision improved from 46% and 50% to 60%, and the recall improved from 64% and 67% to 82% for CSM, CRMDL, and SMTW, respectively, with improved impact of common terms as well.  
low-resource languages; high-resource language; challenges in preservation; multilingual archive; bilingual news; information systems +
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# MViDO: A High Performance Monocular Vision-Based System for Docking A Hovering AUV
## 2 Mvido: A Monocular Vision-Based System for Docking a Hovering Auv
### Target and Markers  
The docking station choice plays an important role in vision-based docking. We believe that choosing a target composed of well-identifiable markers to attach the dock aids in 3D pose estimation. In the context of this work, a target was designed and built. The developed target is formed by three color spherical markers rigidly connected. Our solution for the dock's target based on spherical markers, as shown in Figures 2 and 3, allows the target to be viewed from different points of view and without shadows to make image segmentation easier We chose to use different colors for each of the markers in order to avoid ambiguities in the relative pose estimation. The first solution was based on passive visual markers, occasionally illuminated outside only in certain circumstances of visibility. This solution proved not to be the best since lighting from outside created unwanted shadows. A second solution was developed (Figure 2) based on hybrid visual markers. In this hybrid target, the markers could be illuminated from the inside allowing to increase the visibility of the markers at a distance or in very poor visibility situations. The interior lighting intensity is controllable from the docking station. The fact that the lighting is controllable allows us to adjust the intensity so that the colors continue to be noticeable. In a limit situation, where a stronger interior lighting is required, when the camera is facing the color lightning marker, what we see is very bright ellipses and a colored halo around that ellipses and in this way we continue to have color information. In situations of good visibility or in close proximity to the target, interior lighting is almost unnecessary and the target becomes quasi-passive avoiding energy consumption. In certain situations, the fact that the markers can be illuminated can also serve as a first beacon signaling the presence of the target. The choice of dimensions for the target (see Figure 2a) was related to three factors: the maximum operating distance Camera-Target, the field of view of the lens and the constructive characteristics of the dock (see Figure 3). The target presents different distances on the two axes \(a\) and \(b\) (see Figure 2a) in order to avoid ambiguities in the relative position estimation.  
Figure 2: The hybrid target. (**a**) The Target dimensions: different distances on the two axes. (**b**) The developed hybrid Target: the interior lighting intensity is controllable from the docking station.
## 3 Results  
### Space-Time Distribution Patterns of mTVWI in Europe  
The spatial distribution of the mTVWI values for the 19 plant species over the time period 2000-2010, is shown in Figure 2A-C. A north-south gradient in Europe is highlighted. Minimum mTVWI values (0-0.20) were mainly found in Southern Europe (Italy and Spain) while the maximum (0.80-1) were observed in Northeastern Europe (Belarus and Norway). In Central Europe, we found mTVWI values ranging from 0.40 to 0.80. Mediterranean evergreen oak (\(Q\). _ilex_ L.) showed a low mTVWI value (0.17) whilst \(B\). _pubscens_, a typical Northern European species growing in wet conditions, showed a higher mTVWI value (0.87). In general, we found that broad-leaf deciduous species showed higher mTVWI values than conifers and the broad-leaf evergreen (Table S1). The analysis of variance (ANOVA) corroborated this evidence. In fact, the ANOVA and Tukey post hoc test indicated significant differences (\(p\) < 0.05) among mean mTVWI values referring to conifers, broad-leaf deciduous, and evergreen species for all years (Figure S3, Table S2). Taking into account temporal variations, Figure 3 shows the mean annual mTVWI values (\(\pm\)standard error, SE) over the time period 2000-2010 when considering all tree species together. We found the lowest mTVWI values in 2007 and 2008 (0.49 \(\pm\) 0.01 and 0.33 \(\pm\) 0.01, respectively) whilst in 2000, 2002, and 2009 we found mTVWI values ranging from 0.60 to 0.63.
# Voice Transformation Using Two-Level Dynamic Warping and Neural Networks
## 3 Voice Transformation Process
### Two-Level Dynamic Warping
```
Input: First spectral sequence, \(\mathbb{S}_{1}=\{\mathbf{s}_{1}(1,1:n_{T}),\mathbf{s}_{1}(2,1:n_{T}),\ldots, \mathbf{s}_{1}(n_{F},1:n_{T})\}\)  Second spectral sequence, \(\mathbb{S}_{2}=\{\mathbf{s}_{2}(1,1:m_{T}),\mathbf{s}_{1}(2,1:m_{T}),\ldots, \mathbf{s}_{1}(m_{F},1:m_{T})\}\)  window size \(w_{T}\) for DTW process Output: Distance between \(\mathbb{S}_{1}\) and \(\mathbb{S}_{2}\)  Indices \(\mathbf{a}(N)\) and \(\mathbf{b}(N)\) for temporal alignment  Indices \(\mathbf{b}(M)\) and \(\mathbf{b}(M)\) for spectral alignment Begin  Initialize DTW array, \(DTW=array[0\ldots n_{T},0\ldots m_{T}]\)  Initialize DFW array, \(DFW=array[0\ldots n_{S},0\ldots m_{F}]\)  Adapt window size, \(w_{T}=max(w_{T},abs(n_{T}-m_{T}))\) For\(i_{T}=1\) to \(n_{T}\) For\(j_{T}=1\) to \(m_{T}\) \(DTW[i_{T},j_{T}]=\infty\) End For\(j_{T}\) End For\(i_{T}\)  Set \(DTW[0,0]=0\) For\(i_{F}=1\) to \(n_{F}\) For\(j_{F}=1\) to \(m_{F}\) \(DTW[i_{F},j_{F}]=\infty\) End For\(j_{F}\) End For\(i_{F}\)  Set \(DFW[0,0]=0\) For\(i_{T}=1\) to \(n_{T}\) For\(j_{T}=max(1,i_{T}-w_{T})\) to \(min(n_{T},i_{T}+w_{T})\)  Adapt window size, \(w_{F}=max(w_{F},abs(n_{S}-m_{F}))\) For\(i_{F}=1\) to \(n_{F}\) For\(i_{F}=max(1,i_{F}-w_{F})\) to \(min(m_{F},i_{F}+w_{F})\) \(cost=abs(abs(\mathbb{S}_{1}(i_{T},i_{F})))-(abs(\mathbb{S}_{2}(j_{T},j_{F})))\) \(DFW[i_{F},j_{F}]:cost+minimum(DTW[i_{F}-1,i_{F}],\) \(DFW[i_{F},j_{F}-1],\) \(DFW[i_{F}-1,j_{F}-1])\) End For\(j_{F}\) End For\(i_{F}\)  Searching minimum path through \(DFW[i_{F},j_{F}]\), save \(\mathbf{a}_{F},\ \mathbf{b}_{F}\) \(cost_{new}=DFW[n_{F},m_{F}]\)  Minimum energy \([\mathbf{s}_{1}\ \text{ and }\ \mathbf{s}_{2}]\) \(d=E_{min}\ cost_{new}\) \(DTW[i_{T},j_{T}]:=d+minimum(DTW[i_{T}-1,j_{T}],\) \(DTW[i_{T},j_{T}-1],\) \(DTW[i_{T}-1,j_{T}-1])\) End For\(j_{T}\) End For\(i_{T}\)  Searching minimum path through \(DTW[i_{T},j_{T}]\), save \(\mathbf{a}_{T},\ \mathbf{b}_{T}\)
```  
**Algorithm 1**Dynamic Warping (\(DW\)) for Voice Transformation.
# A Task-Centric Cooperative Sensing Scheme for Mobile Crowdsourcing Systems
## 3 Problem Statements
### Data Integrity Metric  
Different participant selection schemes lead to different choices of active participant index set \(I_{R}\). To determine which choice is better, a criterion is needed. With a _task centric_ approach, we argue that a better choice of \(I_{R}\) should be one that yields smaller approximation error as defined in Equation (5). To account for different size of the active index set \(R\), in this paper, we propose to use a relative approximation error as a performance metric to compare different choices of \(I_{R}\):  
\[C=\frac{\left|\left|\right.Data_{M-R}-Data_{M-R}\left|\right.\right|_{F}}{ \left|\left|\right.Data_{M-R}\left|\right.\right|_{F}} \tag{7}\]  
The purpose of the MCS organizer is to determine the active participant index set \(I_{R}\) to minimize \(C\). Note that \(0\leqslant C\leqslant 1\). Strictly speaking, \(C\) should be called a metric for the _loss of data integrity_ as it measures the loss of sensing data fidelity due to participant selection. Also note that this cost function is useful only during the training period when \(Data_{M-R}\) is available. But it is sufficient since in a task centric approach, the set of active participant are determined during training period using training data measurements.
## 2 Equipment and Methods of Capturing and Preparing Data
### Study Design  
The study involved 86 volunteers; the data were collected anonymously. The dataset contains information about the breathing of 67 men and 19 women. Of all the volunteers, 38 had COVID-19 (4 of them had lung damage), while 48 did not. The volunteers were of different ages, as shown in Figure 1.  
Each participant provided the following questions:  
* date of birth;
* biological sex;
* the last time interval of having COVID-19 (if any);
* percentage of lung damage;
* the presence of respiratory diseases at the time of data collection.  
Three belts with fasteners for markers were attached to the participant. The fasteners were located on the xiphoid process of the sternum, between the 9 th and 11 th thoracic vertebrae, and above the navel. The belts were tightened moderately to allow comfortable breathing of the participant, but not too loosely so that the markers remained in place.  
The scheme of attaching markers to the participant's body is shown in Figure 2[19].  
The special software was developed to record the results from the motion capture system, marked timestamps, and the data about the volunteer.  
Each of the markers tracks its own position in space and transmits the received information to the program for data collection. Thus, the data from one marker represent the  
Figure 1: The age distribution of experiment participants.  
Figure 2: Marker mounting scheme.  
change of its three spatial coordinates with time. The output of the data collection program is two files in the tsv format. The first file contains information about the participant, and the second file contains records of changes in the position of the marker and changes in the electrical activity of the muscles relative to time.  
The participants were asked to breathe for a minute without any special requirements after attaching the sensor and markers. This test is needed to record the type of breathing common to the participant. After that, the participant was asked to breathe at his usual rate three times (a minute each) with different types of breathing--thoracic, abdominal, and mixed [23; 24]. Before each session, a specially trained laboratory assistant explained to the participant how to breathe correctly for each type of breathing and visually verified that the volunteer had exactly the right type of breathing. For convenience and control, the volunteer could control the movement of the body with his palm so that the markers were not blocked.  
Figure 3 shows how data collection happened. In relation to the participant, the axes were arranged as follows: the Y axis was directed upward, the Z axis was directed towards the person, and the X axis was directed to their left. These results were recorded for further processing.
# Modelling and Stability Analysis of Articulated Vehicles
## 2 Modelling of Non-Linear Systems for Articulated Vehicles
### Hydraulic Steering System Model  
The hydraulic steering system model has a significant impact on the stability of articulated vehicles. When articulated vehicles travel in a straight line or at a fixed radius, the inlet and outlet ports of the steering valve are closed, and the hydraulic steering system is equivalent to a torsion spring acting at the steering hinge point and connecting the front and rear bodies [12]. The hydraulic steering model is shown in Figure 3.  
The lengths of the left and right steering cylinders and the lengths of the two cylinders when \(\beta=0\) are as follows:  
\[\begin{cases}L_{1}L_{2}=\sqrt{R^{2}+r^{2}-2 rR\cos{(\theta+\beta)}}\\ R_{1}R_{2}=\sqrt{R^{2}+r^{2}-2 rR\cos{(\theta-\beta)}}\\ L=\sqrt{R^{2}+r^{2}-2 rR\cos{\theta}}\end{cases} \tag{14}\]The force arms corresponding to the thrust of the hydraulic cylinders on both sides are as follows:  
\[\begin{cases}h_{1}=\dfrac{Rr\sin{(\theta+\beta)}}{L_{1}L_{2}}\\ h_{2}=\dfrac{Rr\sin{(\theta-\beta)}}{R_{1}R_{2}}\end{cases} \tag{15}\]  
\[\begin{cases}V_{c1}=V_{0}+A_{1}(L_{1}L_{2}-L)+A_{2}(L-R_{1}R_{2})\\ V_{c2}=V_{0}+A_{2}(L-L_{1}L_{2})+A_{1}(R_{1}R_{2}-L)\end{cases} \tag{16}\]  
where \(V_{c1},V_{c2}\) is the fluid volume of the \(P_{1},P_{2}\) chamber, \(V_{0}\) is the volume of the fluid in the initial position, \(A_{1}\) and \(A_{2}\) are the cross-sectional area of the rod less and rod cavities, respectively, \(Q_{1}\) is the amount of oil supplied to the steering cylinder and \(Q_{2}\) is the amount of oil discharged from the steering cylinder.  
\[\begin{cases}Q_{1}=(A_{1}h_{1}+A_{2}h_{2})\dot{\beta}+\dfrac{V_{c1}}{K_{c}} \dfrac{\text{d}P_{1}}{\text{d}x}\\ Q_{2}=(A_{1}h_{1}+A_{2}h_{2})\dot{\beta}-\dfrac{V_{c2}}{K_{c}}\dfrac{\text{d}P _{2}}{\text{d}x}\end{cases} \tag{17}\]  
When the articulated vehicle is travelling straight or at a fixed radius, \(Q_{1}=Q_{2}=0\):  
\[\begin{cases}\dfrac{\text{d}P_{1}}{\text{d}x}=-\dfrac{K_{c}}{V_{c1}}(A_{1}h_{1 }+A_{2}h_{2})\dot{\beta}\\ \dfrac{\text{d}P_{2}}{\text{d}x}=\dfrac{K_{c}}{V_{c2}}(A_{1}h_{1}+A_{2}h_{2}) \dot{\beta}\end{cases} \tag{18}\]  
The left and right hydraulic cylinder forces are as follows:  
\[\begin{cases}F_{L}=P_{1}A_{1}-P_{2}A_{2}\\ F_{R}=P_{2}A_{1}-P_{1}A_{2}\end{cases} \tag{19}\]  
The hydraulic steering system has a torque on the articulation points of the articulated vehicles:  
\[M_{O1}=-M_{O2}=-(F_{L}h_{1}-F_{R}h_{2}) \tag{20}\]
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 3 CU Split Likelihood Modelling Using SVMs  
This section first introduces the concept of CU split decision in the HEVC quadtree partitioning structure. Next, the usage of SVMs to model the CU split likelihood as a binary classification problem is discussed, followed by an overview of SVMs and Weighted SVMs.
## 4 Empirical Evaluations Utilizing Open Source Systems
### Results And Discussions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Class Name**} & \multicolumn{2}{c}{**Original Class**} & \multirow{2}{*}{**Threshold**} & \multicolumn{4}{c}{**Extracted Classes**} \\ \cline{2-5}  & **\#Methods** & **CBCS** & & **\#Methods** & **CBCS** & **Avg. CBCS** \\ \hline  & & & & 23 & 0.11 & & \\  & & & & 8 & 0.118 & & \\ Project & 77 & 0.046 & 0.167 & 33 & 0.109 & 0.123 & \\  & & & & 11 & 0.111 & & \\  & & & & 2 & 0.169 & & \\ \hline  & & & & & 9 & 0.183 & & \\ Task & 16 & 0.093 & 0.143 & 2 & 0.5 & & \\  & & & & 2 & 0.37 & & \\  & & & & & 3 & 0.207 & & \\ \hline Zip & 19 & 0.076 & 0.333 & 8 & 0.107 & & \\  & & & & 11 & 0.109 & & \\ \hline Ant & 14 & 0.114 & 0.5 & 7 & 0.143 & & \\  & & & & 4 & 0.167 & 0.159 & \\  & & & & 3 & 0.167 & & \\ \hline Execute & 15 & 0.127 & 0.158 & 7 & 0.225 & & \\  & & & & 6 & 0.303 & 0.343 & \\  & & & & 2 & 0.5 & & \\ \hline  & & & & & 5 & 0.425 & & \\ Extension & 15 & 0.266 & 1 & 2 & 1 & \\  & & & & 2 & 1 & \\  & & & & & 2 & 1 & \\ \hline  & & & & & 3 & 0.333 & & \\  & & & & 5 & 0.45 & & \\ VAJBBuildInfo & 19 & 0.227 & 1 & 3 & 0.667 & & \\  & & & & & 4 & 0.5 & \\  & & & & 2 & 1 & \\ \hline CommandlineJava & 25 & 0.18 & 0.25 & 14 & 0.255 & & \\  & & & & 11 & 0.302 & & \\ \hline Commandline & 22 & 0.053 & 0.114 & & 9 & 0.118 & \\  & & & & & 4 & 0.145 & \\ \hline  & & & & & 7 & 0.147 & \\ Path & 20 & 0.069 & 0.105 & 10 & 0.154 & 0.156 \\  & & & & & 3 & 0.167 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Extracted classes suggested by our approach for the considered classes of the Ant system.
# Proposal of Innovative Methods for Computer Vision Techniques in Maritime Sector
## 2 Innovative Methods to Further Improve the Recognition Accuracy of CV Models
### Integrating Shipping Domain Knowledge: A Tugboat Example  
In the realm of detecting and recognizing water surface targets, conventional methods typically focus on static training data and the CV model itself. Nevertheless, the presence of model errors is an inescapable reality, leading to the possibility of recognition inaccuracies. Consequently, rather than exclusively enhancing the CV model itself, the integration of shipping domain knowledge should be considered as a supplementary component in the process of water surface target detection and recognition. [PERSON] et al. (2016) propose a novel perspective of integrating shipping domain knowledge, such as the length and width of ships and the speed of navigation, into CV models for auxiliary ship recognition in maritime surveillance, and they give two specific examples in practice. Based on that, we propose another example of using shipping domain knowledge for recognizing tugboats to further improve the accuracy of ship recognition, as illustrated in Example 2.  
**Example 2**.: _A tugboat refers to a vessel used for towing harges and other ships, characterized by a small hull, good stability, high power, and strong traction. Tugboats are primarily utilized in maritime transport and rescue operations, specifically for towing harges carrying cargoes and various workboats, as well as for towing giant vessels and other large water structures such as offshore platforms and floating docks. It is important to note that tugboats themselves generally lack cargo handling capabilities. In practice, when a tugboat operates without a load, it typically travels at a faster speed, whereas when towing other vessels through ropes, it usually travels at a slower speed. This practical phenomenon is not only due to the increased load but also to avoid potential collisions between vessels resulting from the sudden decelerations. Based on the above discussion, suppose we have two CV models, one for recognizing ship types and the other for detecting ship speeds. Then, if the two CV models identify a slow-moving tugboat without towing other vessels behind, or a fast-moving tugboat towing other large vessels, at least one of the models' output results is likely to be incorrect. That is because such scenarios are rare in practice, and, thus, it is necessary to re-identify or intervene manually._
## 5 Conclusions  
Taking the steel slag-cement-bentonite barrier wall as the research object. The novel idea of using steel slag to replace part of the cement component as a bentonite barrier wall was adopted, and the variable head penetration test and scanning electron microscope test as the research means the effects of steel slag substitution rate and bentonite dosage on the strength characteristics and permeability properties of the barrier material were investigated. The following conclusions were drawn:  
1. The unconfined compressive strength of steel slag-cement-bentonite barrier material decreases gradually with the increase in steel slag substitution rate and has a linear relationship with the steel slag substitution rate; with the increase in bentonite dosage, the strength has an exponential relationship with the bentonite dosage.
2. Steel slag-cement-bentonite barrier material cohesion with the increase in steel slag substitution rate gradually decreased, and with the steel slag substitution rate is a negative exponential relationship; cohesion with the increase in bentonite dosage and increase, and with the bentonite dosage is a linear relationship.  
Figure 10: Relationship between cohesion and unconfined compressive strength of steel slag-cement-bentonite materials after 28d curing.  
3. A higher unconfined compressive strength of the steel slag-cement-bentonite barrier material corresponds to greater cohesion. By establishing the relationship between cohesion and change in unconfined compressive strength, it is found that cohesion and unconfined compressive strength basically satisfy \(c=(0.23\)-\(0.39)u_{\text{z}}\).
4. The mechanical properties of steel slag-cement-bentonite barrier materials can be determined by establishing a correlation between cohesion and unconfined compressive strength. When meeting the engineering specifications, the unconfined compressive strength serves as a predictive parameter for estimating shear strength characteristics, enabling systematic evaluation of both strength parameters.
5. The permeability of steel slag-cement-bentonite barrier materials exhibits an inverse correlation with steel slag substitution rate (0-50%) and bentonite dosage (46-54%). Both parameters collectively dominate the decreasing trend of permeability as their values increase.  
Conceptualization, H.L. and L.G.; methodology, L.G.; software, A.Z.; validation, H.L. and W.Z.; formal analysis, H.L.; investigation, L.G.; resources, H.X.; data curation, L.G.; writing--original draft preparation, H.L.; writing--review and editing, W.Z.; visualization, L.G.; supervision, H.X.; project administration, H.X. and A.Z.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.