esa-sceva/satcom-chunk-collection huggingface dataset:
# Coreference Resolution for Improving Performance Measures of Classification Tasks
## 1 Introduction
Doc2 Vec_nocoref--a dataset in its original state without any identified coreference resolution, processed by the Doc2 Vec method; 2. Doc2 Vec_coref--a modified dataset with an identified coreference resolution, processed by the Doc2 Vec method; 3. TfIdf_nocoref--a dataset in its original state without any identified coreference resolution, processed by the TF-IDF method, 4. TfIdf_coref--a modified dataset with an identified coreference resolution, processed by the TF-IDF method. 4. The creation of the text classification models (fake news classification) using pre-processed word vectors as the inputs:  
Figure 1: Example of identification of coreferences in text. Steps 2 and 3 are the important parts of the mentioned methodology. In step 2, a new text data file named \"_coref\" is created, where mentions from the original text data file \"_without\" are identified and replaced (Figure 3). Then, word vectors are created from both the \"_without\" and \"_coref\" data files using both the TF-IDF and Doc2 Vec methods. Figure 2: Workflow of our proposed method. Data: Row 50, Col 2 [_without], Sentence n.15  
... So even if she won in that poll that means the results do not necessarily indicate that people will go to the voting booth to support her....  
Data: Row 50, Col 3 [_coref], Sentence n.15  
... So even if _[PERSON]_ won in _The CNN snap_ poll that means the results do not necessarily indicate that people will go to the voting booth to support _[PERSON]_.... The first phase of the methodology--data preparation--is a traditional stage of processing all NLP tasks. However, some of its parts (e.g., tokenization) are applied when creating word vectors only if the vectors are generated by available libraries. The actual evaluation of CR involves assessing how well we can classify fake news using models created from pre-prepared datasets (with and without any coreference). In other words, the output evaluation will consist of evaluating the created models for classifying fake news, where news articles from the test set will be classified into two categories: fake and real news. The paper is organized as follows. Section 2 provides a summary of the current state in the field of fake news identification studies. Section 3 describes the fake news dataset used in this research along with relevant pre-processing methods and the model creation. Section 4 provides a summary of the most important results. Section 6 of the article contains the discussion and conclusions.
## 4 Results
### Propagated Uncertainty in QoE due to Sub-Optimal Performance of NetEm  
Another source of uncertainty in the evaluated QoE can be due to sub-optimal operation of NetEm to achieve the targeted QoS parameters, especially PLR. NetEm configures PLR by randomly dropping the packets and thus for a certain PLR setting a different number of packets can be dropped by NetEm in different experiments, even with the same PLP see [37]. This results in an uncertainty in the PLR that can also propagate to the evaluated QoE.  
To monitor this variation in PLR using NetEm, we streamed the video 30 times at 5 different PLR configurations and recorded the traffic sent using Wireshark. We used the data collected by Wireshark to evaluate the standard error in PLR due to the sub-optimal operation of NetEm. In addition, we calculated the relative standard error in PLR by taking the ratio of the standard error to the PLR configuration. We then used Equation (5) and the standard error in PLR to evaluate the propagated error in QoE due to NetEm variations in PLR. The evaluated results were used to calculate the confidence intervals and are presented in Table 4.  
The results in Table 4 show that, as the targeted PLR increases, the standard error in PLR due to NetEm increases. This is caused by the random process by which NetEm drop packets. This means that at higher PLR levels, there is a higher quantitative variation in the number of packets lost in comparison to a lower PLR configuration. On the other hand, the relative standard error in PLR decreases for increasing PLR levels. This shows that there is a higher relative variation in PLR from the targeted values at smaller PLR configurations. For instance, the relative standard error in PLR was higher at PLR = 0.05% and PLR = 0.1%, and as discussed earlier, these are the key operating points of PLR because QoE shows great sensitivity to PLR at these levels.  
Moreover, the results in Table 4 show that the propagated statistical errors in QoE due to standard error in PLR because of the sub-optimal performance of NetEm are quantitatively insignificant. These propagated errors are relatively higher at lower PLR levels than at higher PLR levels. The small magnitudes of propagated standard errors and CI width show that the uncertainties imposed by the sub-optimal performance of NetEm do not affect the QoE evaluation significantly. This indicates that the sampling errors in QoS measurements affect QoE evaluation significantly as compared to errors due to the sub-optimal performance of NetEm.  
Furthermore, we model jitter using custom delay distributions, as described earlier in Section 3. After evaluating the results in Section 4.1, we observed that uncertainty in jitter does not affect the propagated statistical errors in QoE of VoD modelled using an exponential function. Therefore, we didn't evaluate the errors in jitter caused by the sub-optimal performance of NetEm.
# OPMSS: Optimal Producer Mobility Support Solution for Named Data Networking
## 4 Formulation Analysis of OPMSS
### Scenario II: Handoff Signaling Cost  
The total handoff signaling cost of the Kite solution can be formulated when the mobile producer disconnects from CR2 and moves to CR3 within D1. Before the resumption of communication, a number of messages are exchanged between the mobile producer, the anchor router or the RS, and the consumer to update the FIB table before transmission takes place. The mobile producer exchanges the \(TI\) and \(TD\) messages or signals with the anchor router or the RS via CR2 to CR1. Then, the consumer sends a message of interest from CR1 to CR3 to the mobile producer. The total number of \(TI\), \(TD\), and interest messages sent between the consumer, the producer, and the router is \(3\times a\), and the one between routers is \(3\times b\) per hops. The signaling cost of the Kite solution is formulated in Equation (7). In addition, if the mobile producer moves to D2, the total signaling that takes place is added with \(5\times a\) and \(7\times b\) per hop, as presented in Equation (9). For the intra-domain, the two equations are generated to analyze the handoff signaling cost for the Kite solution and OPMSS, as presented in Equations (7) and (8), respectively.  
\[HCost_{kite}=\gamma\times(S_{TI/TD}\times(a+b)+S_{int}\times(2a+2b)) \tag{7}\]  
\[HCost_{opmss}=\gamma\times(S_{MI}\times(a)+S_{int}\times(2a+b)) \tag{8}\]  
The total handoff signaling cost of the proposed OPMSS solution can be obtained the same way as in the Kite solution. The number of messages is exchanged as signaling between the mobile producer and the consumer for the FIB table update by broadcasting \(MI\) packets before transmission takes place. For a single domain, the consumer resends the Interest packet after the relocation of the mobile producer from CR1 to CR2, and the signaling cost covers are \(3\times a\) and \(b\) per hops, as shown in Equation (8). For inter-domain mobility, if a mobile producer moves to D2, the total signaling added is \(3\times a\) and \(9\times b\) per hop, due to the \(MI\) broadcasting to both D1 and D2, as presented in Equation (10). Equations (9) and (10) are generated for the signaling cost for the inter-domain mobility of the Kite solution and the OPMSS, respectively.  
\[HCost_{Kite}=\gamma\times((S_{TI/TD}\times 2(a+b)+S_{int}\times(2a+2b)) \tag{9}\] \[+(S_{TI/TD}\times 2(a+2b)+S_{int}\times(2a+4b))\]\[HCost_{opmss}=\gamma\times(S_{MI}\times(a)+S_{int}\times(2a+b)+S_{MI}\times(a+7b)+ \tag{10}\]
# Medical Image Segmentation Network Based on Dual-Encoder Interactive Fusion
## 4 Experiments and Results
### Datasets
#### 4.2.2 Automated Cardiac Diagnosis Challenge (ACDC) Segmentation  
The ACDC [26], initiated by MICCAI in 2017, focuses on the segmentation of the myocardium (Myo), right ventricle (RV), and left ventricle (LV) in cardiac MRI data. The dataset features short-axis slices, extending from the base to the apex of the heart, with slice thicknesses ranging from 5 to 8 mm and spatial resolutions between 0.83 and 1.75 mm\({}^{2}\) per pixel. For experimental purposes, the dataset was segmented as 20 test cases, 10 validation cases, and 70 training cases (comprising 1930 axial slices), following the data setup of TranUnet [20].
## 2 Materials and Methods  
### Samples Collection and Testing  
Water samples were collected from different water sources in the Maasai villages of northern Tanzania, commonly used for drinking. These sources include both wells and surface water bodies. The wells were constructed with donations from foreign donors such as the USA and the United Arab Emirates. By contrast, the surface water sources are natural reservoirs used by humans and animals (including wildlife and livestock), which may deposit excrement into the water bank, and their decomposing bodies add to the water's pollution.  
Water samples were evaluated according to basic parameters, including turbidity (Lovibond THERMO EUTECH TN-100 turbidity meter, produced in Singapore), total hardness, using two commercial kits (Varify (Complete Drinking Water Test Kit--17 parameters, produced in San Diego, CA, USA) and Tsepert (Water Test Strips--16 parameters, produced in China), including free chlorine, bromine, MPS (Monopersulfate), nickel, sulfite, cyanuric acid, carbonate, hardness, hydrogen sulfide, manganese, mercury, sulfate, zinc, fluoride, sodium chloride, copper, iron, lead, nitrate, nitrite, pH, total alkalinity, and total chlorine.
## Materials and Methods  
### Problem Description  
The task of traffic speed prediction is to predict the future traffic speed based on the given historical traffic measurements (such as traffic speed, traffic flow, etc.) of observed sensors in the road network. We first define the road network as a weighted undirected graph \(\mathcal{G}=(\mathcal{V},\mathcal{E},A)\), where \(\mathcal{V}\) is a set of \(|\mathcal{V}|=N\) nodes, representing observed sensors in the road network; \(\mathcal{E}\) is a set of edges, indicating connectivity of nodes; and \(A\in\mathbb{R}^{N\times N}\) is a weighted adjacency matrix, which represents the proximity between nodes and can be computed from the distance in the road network. Then, the traffic data observed at time \(t\) on \(\mathcal{G}\) are denoted as a graph signal \(X_{t}\in\mathbb{R}^{N\times F}\). Here, F represents the number of features observed at each node. The goal of traffic speed prediction is to learn a function \(f\) to predict future \(T\) graph signals based on graph \(\mathcal{G}\) and \(T^{\prime}\) historical graph signals:  
\[X_{t+1},...,X_{t+T}=f_{\theta}(X_{t-T^{\prime}+1},...,X_{t};\mathcal{G}) \tag{1}\]  
where \(\theta\) stands for the learnable parameters.
# Analyzing Markov Boundary Discovery Algorithms in Ideal Conditions Using the d-Separation Criterion
###### Abstract  
This article proposes the usage of the d-separation criterion in Markov Boundary Discovery algorithms, instead of or alongside the statistical tests of conditional independence these algorithms usually rely on. This is a methodological improvement applicable when designing, studying or improving such algorithms, but it is not applicable for productive use, because computing the d-separation criterion requires complete knowledge of a Bayesian network. Yet Bayesian networks can be made available to the algorithms when studied in controlled conditions. This approach has the effect of removing sources of suboptimal behavior, allowing the algorithms to perform at their theoretical best and providing insights about their properties. The article also discusses an extension of this approach, namely to use d-separation as a complement to the usual statistical tests performed on synthetic datasets in order to ascertain the overall accuracy of the tests chosen by the algorithms, for further insights into their behavior. To exemplify these two approaches, two Markov Boundary Discovery algorithms were used, namely the Incremental Association Markov Blanket algorithm and the Iterative Parent-Child-Based Search of Markov Blanket algorithm. Firstly, these algorithms were configured to use d-separation alone as their conditional independence test, computed on known Bayesian networks. Subsequently, the algorithms were configured to use the statistical G-test complemented by d-separation to evaluate their behavior on synthetic data.  
Markov boundary; d-separation; causal inference; feature selection; information theory +
Footnote †: journal: _Algorithms_
# Algorithm for Determining the Position of a Ship Hull-Cleaning Robot
## 5 Data Sets  
In the available literature, one can find many datasets concerning the issue of SLAM (Simultaneous Localization and Mapping), collected in cities, inside buildings, and also in difficult, diverse terrain (e.g., on unpaved ground, in fields, underwater). Unfortunately, most of them did not provide a minimal set of sensors from the point of view of the research problem (i.e., data from GPS, IMU, and odometry), or the recorded routes were not complicated (e.g., the data contained only straight-line travel). Hence, the selection of popular data is defined as Rosario [39]. Experiments carried out with different methods on this dataset resulted in a new algorithm to determine the position of a moving object (Section 5.1).  
Ultimately, the algorithm mentioned above was to be used to determine the position of an object moving along the ship's side. Hence, it was necessary to provide reference data that describe the behavior of the sensors and their readings during sample runs. These experiments are described in Section 5.2.  
Since one of the critical phenomena affecting the accuracy of determining the device's position turned out to be the so-called slippages, further tuning of the model was carried out using a simulator. Its task was to generate signal waveforms for the assumed route of the device, along with the possibility of artificially introducing moments of the slippages mentioned above. The description of these data is included in Section 5.3.
# A Survey on Variable Neighborhood Search for Sustainable Logistics
## 4 Results and Findings
### Uncertainty and Dynamism Analysis  
From the body of studies analyzed, only a limited number address uncertainty or dynamic factors in sustainable logistics problems, despite their critical impact on the effectiveness of the solution. Various sources of uncertainty are considered depending on the specific problem and the field of application. For instance, in [54], the authors tackle a location problem where vehicle travel costs and CO\({}_{2}\) emissions are uncertain, managed through a simulation-based optimization approach. Similarly, refs. [91; 92] examine location/routing issues under uncertain conditions: Ref. [91] focuses on electric vehicle battery swap stations with fluctuating customer demand, applying two extended recourse policies to mitigate route failures. Meanwhile, ref. [92] employs a robust optimization framework to handle stochastic customer locations in a more generalized problem setting.  
In network design, ref. [28] considers the uncertainty introduced by stochastic demand from retailers, applying a scenario-based stochastic model to address these variations. In routing, diverse uncertainties are managed in studies by [93; 94; 95]. Ref. [93] addresses pickup demand uncertainty through a two-stage method: initially estimating the pickup demands to create preliminary routes and then simulating these routes under real-time constraints and dynamically adjusting the unmet demands. In [94], service time uncertainty is tackled with robust optimization, while ref. [96] uses a simheuristic algorithm to manage unpredictable waste levels in a waste collection scenario. Additionally, ref. [95] considers energy consumption uncertainty in a similar context, employing robust optimization to create reliable routes.  
Dynamic elements are even less frequently addressed. In [97], a routing problem for electric vehicles with dynamically arriving customers is managed through periodic re-optimization, transforming the dynamic situation into a sequence of static subproblems. Likewise, ref. [98] applies a dynamic programming algorithm to a scheduling problem influenced by dynamic disruptions, such as new order arrivals, variable raw material release times, and machine breakdowns.  
Although uncertainty and dynamism are recognized as crucial factors in sustainable logistics, they remain underrepresented in optimization research where VNS is used. Studies that consider these elements rely on a range of advanced techniques, including robust and simulation-based optimization, scenario planning, and dynamic programming, each tailored to manage specific types of variability. However, the limited focus on these factors indicates a gap in the comprehensive integration of uncertainty and dynamism in sustainable logistics using VNS, particularly for complex real-world applications.  
Figure 6: Distribution of studies according to the multiobjective approach.
# Optimal Prediction of Individual Vessel Trajectories Based on Sparse Gaussian Processes
## 4 Methodology
### Hyperparameter Optimization  
To develop an SGPR model for the trajectory prediction of a vessel, a hyperparameter optimization process is required. This process is important because the hyperparameters have a significant impact on the predictive performance of the model. The types and candidates of hyperparameters are listed in Table 2.  
In Table 2, \(m\) is the number of inducing points or subset data, \(M_{a}\) is the approximation method, \(\theta_{I}\) is the initial kernel parameters, \(\sigma_{I}\) is the initial value of sigma, \(k(\cdot)\) is the kernel function. The study used a GA to optimize the hyperparameters.
# A Trajectory Generation Method for Multi-Rotor UAV Based on Adaptive Adjustment Strategy
## 3 Method
### Definition of Optimization Function and Adaptive Adjustment Method
#### 3.2.2 Trajectory Optimization Method Based on Adaptive Adjustment  
The safety loss function needs to set the safety distance threshold, which is set as a fixed value in [18; 19]; that is, the trajectory needs to keep a fixed distance from the obstacle. This may cause the multi-rotor UAV to collide with obstacles if the value is set too small, and the optimization algorithm planning may fail if the value is set too large.  
For the safety guarantee conditions proposed in Section 3.1.3, to ensure trajectory safety, the safety distance between the control point and the obstacle is affected by two quantities. The first is the upper bound of the error between the B-spline curve and the control polygon, and the second is the distance between the adjacent control points. The safety distance calculation formula is Formula (24).  
\[d_{thr}=\frac{1}{2}l_{\alpha_{i}}\mathbf{Q}_{i+1}+N_{\infty}(n)\|\Delta_{2}b\|_{\infty} \tag{24}\]  
The upper bound of the error is proportional to the second-order variance of the position control point. After the position control point is optimized according to the optimization function set in Section 3.2.1, the second-order variance of the position control point can be significantly reduced. At the same time, in the optimization process, the distance between control points also changes. Therefore, as the optimization proceeds, the safety distance required by each control point is gradually adjusted. By optimizing the trajectory, the smoothness of the trajectory gradually increases, and its safety threshold gradually decreases. Therefore, this section proposes a trajectory optimization method for the dynamic adjustment of safety distance parameters. During the optimization process, the method dynamically calculates the second-order variance of the trajectory control points and the distance between the adjacent control points, and sets the safety threshold of each control point to obtain a better-optimized trajectory while ensuring safety.  
Compared with the safety assurance method based on the convex hull property described in literature [18], because its safety distance is only determined by the distance between adjacent control points, its safety threshold is a fixed threshold set in advance. Therefore, compared with this method, the proposed method can obtain a better trajectory with better comprehensive quality and adaptability in different environments.
# Selective Extraction of REEs Thanks to One-Pot Silica Hybrid Materials
## 2 Materials and Methods  
### Material Synthesis and Characterizations  
The organosilane sol-gel precursors were synthesized according to the procedure described in the literature through a CuAAC click reaction using a copper(I)-tren catalyst [26; 27].  
The procedure for preparing the silica hybrid materials starting from the organosilane sol-gel precursors was adapted from the procedure described in the literature [28; 29]. Typically, 1.5 mmol of organosilane precursor PX were added to 50 mL of water. Afterwards, the pH was adjusted to basic (NH4 OH, pH 9) conditions for SHM-1 or acidic (HCl, pH 1.5) conditions for SHM-2 to SHM-5, and the solution was stirred for two weeks in a sealed flask. The basic conditions for SHM-1 were chosen because of the low yield under acidic conditions. SHM-3 was synthesized from the corresponding methyl ester organosilane precursor to give the intermediate material SHM-3-ME. The precipitate was dried by sublimation and washed with 60 mL (3 \(\times\) 20 mL) EtOH/water (1/1) followed by 60 mL (3 \(\times\) 20 mL) of EtOH. After analysis, the materials were heated to 130 \({}^{\circ}\)C for 24 h to promote the condensation.  
The carboxylic acid functional group of SHM-3 was obtained in a second step from SHM-3-ME by an hydrolysis reaction inspired by the literature [30]. SHM-3-ME (1 eq) was stirred in an acetonitrile/water (94/6) solution containing LiBr (5 eq), NEt3 (5 eq) for 3 days at 50 \({}^{\circ}\)C.  
Finally, all SHMs were washed using 60 mL (3 \(\times\) 20 mL) of each solution: first a NEt3/EtOH/water (8/60/32) solution, followed by a EtOH/water (1/1) solution, then EtOH, then a EtOH/diethyl ether (1/1) solution, and finally with diethyl ether.  
Solid-state FT-IR spectra were collected on a Perkin-Elmer 100 spectrometer (Perkin Elmer, Villebon S/Yvette, France) using an Attenuated Total Reflectance (ATR) crystal with a resolution of 4 cm\({}^{-1}\).  
Thermogravimetric analysis (TGA) was performed using a Setaram Setsys Evolution 18 instrument (SETARAM Instrumentation, Caluire, France) under airflow (20 mL min\({}^{-1}\)) with a heating rate of 5 \({}^{\circ}\)C min\({}^{-1}\) coupled to a Hiden analytical QGA mass spectrometer (Hiden Analytical, Warrington, UK).  
A Quanta 200 ESEM FEG scanning electron microscope (Thermo Fisher Scientific Electron Microscopy, Lyon France) was used for the morphological characterization of solids. Energy Dispersive X-ray Spectroscopy (X-EDS) analyses were performed thanks to the EDX XFlash(r) 5010 SDD analyzer (BRUKER SYNERGIE4, Evry, France) associated with the microscope.  
Figure 1: Typical structure of organosilane precursor PX used in this study for the synthesis of silica hybrid materials (SHMs) after a sol-gel process.  
Nitrogen adsorption-desorption isotherms were collected at 77 K using a Micromeritics ASAP 2020 apparatus (Micromeritics S.A., Verneuil en Halatte, France). Prior to the measurement, samples were desorbed at 60 \({}^{\circ}\)C for 12 h under vacuum.