esa-sceva/satcom-chunk-collection huggingface dataset:
# Bio-Sourced and Biodegradable Membranes
## 2 Green Membrane Materials
### Bio-Sourced Polymers for Membrane Fabrication
#### 2.1.2 Chitin and Chitosan  
Chitosan is the second (after cellulose) most abundant biopolymer in nature that can be found in insects, shells, mollusks, shrimps, crabs, fungi etc. It also can be derived from chitin (the exoskeleton of many living creatures, insects in particular) by its deacetylation (see Figure 2B).  
Though this biopolymer is widely spread in nature, the research of chitosan began only in the 1980s because of the structure's features and insolubility in water [28].  
Figure 2: Chemical structure of polysaccharides polymers and their derivatives. (**A**) Cellulose and Its Derivatives; (**B**) Chitin and Chitosan; (**C**) Carrageenan; (**D**) Alginate.  
Examples of chitin and chitosan membranes applications are ion exchange membranes [29; 30; 31], wastewater treatment [32; 33], oil-water separation [34], dye removal [35], etc.
## 3 User-side Constraints
### Applications and the Terminal  
Since the operations of IP are so simple, stateless, and application independent, application complexity is split between the server and the terminal. This trend has created a number of issues at the user terminal.  
**Complexity** Over time, traditional desktop computers and their applications have proven to be quite costly to manage and mass public appeal has moved to a new generation of devices with a simpler user experience. These new mobile or nomadic devices, on the other hand, may have too restricted resources to run the complex applications supported by personal computers and require specific development. Deployment of new applications on such devices, as well as their regular upgrades can also be a complicated--or restricted--operation.  
**Security** Installing and upgrading applications on traditional desktop computers is not only a possibly complex operation, but also one that may create security holes on the computer.  
**Heterogeneity** Whereas the Microsoft Windows platform once was almost the norm--be it under several versions--we are witnessing again the emergence of multiple, different, and incompatible platforms for the general public. Offering services across such a heterogeneous base creates issues for service providers. It is also harder to predict which functionalities (e.g., multimedia) will be supported by any user terminal.  
**Mobility and service continuity** New devices are often nomadic or even mobile, with the connection to the Internet and possibly its nature (_i.e._, the access network) changing over time. This raises issues with the continuity of the service currently used while roaming as services remain strongly attached to the terminal and thus a possibly changing IP address.  
Overall, the traditional model of deploying applications on personal computers seems to have run its course. The new model is to deploy applications in the Cloud, in a Software as a Service (SaaS) model, and access them through the Web browser or some dedicated, restricted software application platform (_i.e._, smartphone \"apps\"), which however also presents limits, e.g., in terms of access to storage.
# Wearable Haptic Device for Stiffness Rendering of Virtual Objects in Augmented Reality
###### Abstract
Total weight and cost of the proposed device per finger are only about 30 g and can be decreased to less than 10 $ (when replacing 3D-printed parts) respectably, which are much more comfortable and affordable than commercial kinesthetic devices or other haptic devices, simultaneously adequate for AR finger-based manipulation. Moreover, this paper quantitatively compares the effects of various feedback conditions for stiffness rendering of AR objects in finger-based interaction. The way to select the feedback condition among numerous available options is crucial for the realistic rendering of physical attributes (i.e., weight, friction, stiffness) of objects in VR interaction. Thus, the studies comparing different feedback conditions are also researched [11; 18; 29]. As opposed to the many existing results in VR, a few studies research stiffness rendering in AR environments, yet they are limited on tool-based haptic interaction [12; 30] or should utilize real tangible objects [7]. In addition, the studies about finger-based haptic interaction in AR [22; 23; 31] do not research about rendering physical attributes of AR objects. In this study, we firstly demonstrate the effects of different feedback (rigid/soft kinesthetic, vibrotactile, and pseudo-haptic feedback--See Section 3) in an AR environment through an experiment with a two-finger grasping task, which has been widely used in many VR finger-based experiments [11; 24; 28; 32]. Since the perceived stiffness is measured in comparison with real objects, our results would be more applicable for AR applications. Moreover, quantitative comparison for various feedback conditions is also demonstrated, which can be a design guideline for rendering stiffness of a specific virtual object in AR. In addition, by measuring the sense of realness in the experiment, we manifest the effect of each feedback in aspects of user experience at the same time. Through the experimental results, the efficacy of the proposed device is clearly verified for realistic rendering of stiffness corresponding to the rigidity/elasticity of AR objects. The rest of the paper is organized as follows. Section 2 presents the design of the proposed wearable haptic device with a fabrication process. The experimental study verifies the efficacy of the kinesthetic haptic feedback from the proposed device, compared with vibrotactile and visuo-haptic feedback, for rendering stiffness in Section 3. The experimental results are discussed in Section 4, followed by concluding remarks in Section 5.
# A Police and Insurance Joint Management System Based on High Precision BDS/GPS Positioning
## 1 Introduction
Its positioning accuracy, which has been verified by several test experiments, can reach about 0.5 m. Thus, the terminal can provide lane level navigation and positioning service in cities. 2. Location algorithm and kernel driver The terminal uses a high precision BDS/GPS positioning algorithm and integrated navigation algorithm. It also supports both PPP and RTK technologies, which mainly apply single frequency and real-time PPP under the requirements of IoV products. The terminal involves different insurance companies and applications which need to use big vehicle behavior data and vehicle networking. Moreover, it needs the support of high precision positioning data. However, it very difficult to master high precision satellite navigation and positioning and vehicle integrated navigation. Therefore, in this paper it is necessary to complete calculation of high precision satellite navigation positioning and vehicle integrated navigation in the Android kernel after modifying the underlying Android GPS driver. The calculation aims to realize high precision positioning when the third-party application calls the location service module. It is easier to modify GPS driver in Android rather than to realize high precision positioning on a single App. Third-party Apps can directly obtain high precision positioning results when they call the location-based service without any modification. 3. Service from police and insurance joint management system  
A scheme has designed to handle minor traffic accidents rapidly is described in this article. The following data will be acquired when an accident happens: (1) the high precision trajectory data based on BDS positioning during the accident; (2) videos during a period of 10 s before and after the accident; (3) vehicle's driving information such as driving lane, accurate rapid acceleration and rapid deceleration, sharp turns and sudden lane change; (4) the photos taken by car drivers after the accident. The four sets of data form a complete evidence chain, by which the police can analyze the scene on the remote platform and accurately simulate the accident process on a high precision map. This helps the traffic police judge fault and liability remotely and improves efficiency of handling urban minor traffic accidents. The test experiments have been carried out in Wuhan and Ningbo, which have been recognized by China's Ministry of Public Security. Two typical actual traffic accidents among more than 30 have been selected as cases and will be explained in great detail.
# Carbon Dioxide Adsorption on Carbon Nanofibers with Different Porous Structures
## 2 Materials and Methods
### CO\({}_{2}\) Adsorption Column Experiments  
A fixed-bed reactor was utilized to carry out the adsorption column breakthrough curves of CO\({}_{2}\) on all samples according to the ASTM D5160-95 method [34] at 25, 40, or 55 \({}^{\circ}\)C. A sample of 0.4-0.7 g was loaded into a glass tube with an inner diameter of 1 cm and a length of 20 cm, with a packed height of 10 cm. The glass tube was covered with a stainless steel sleeve on which a heating strip was wound to control the temperature of the fixed bed, and a thermocouple was positioned on the surface of the glass tube to monitor the temperature of the adsorption bed. Typical untreated flue gas compositions from a power plant burning low sulfur coal contain approximately 15-16% of CO\({}_{2}\)[35; 36]. Thus, the CO\({}_{2}\) concentration was set at 15%, and was supplied by CO\({}_{2}\) and N\({}_{2}\) cylinders under appropriate mixing and quantified by gas chromatography with a thermal conductivity detector (GC/TCD). The total flow rate of the gas stream was 20 sccm. The CO\({}_{2}\) concentration at the exit was measured using GC/TCD. When the exit concentration reached the inlet concentration, the adsorbent was heated to 150 \({}^{\circ}\)C to cause CO\({}_{2}\) desorption and to regenerate the adsorbent in the nitrogen atmosphere. The [PERSON] equation (Equation (2)) was used for data fitting, which is a semi-empirical gas adsorption model that could predict the whole breakthrough curve [37]:  
\[\frac{\rm{C}}{C_{0}}=\frac{1}{1+exp[k^{\prime}(\tau-t)]}, \tag{2}\]where \(C\) is the outlet concentration at time \(t\) (mmol/cm\({}^{3}\)), \(C_{0}\) is the inlet concentration of CO\({}_{2}\) (mmol/cm\({}^{3}\)), \(k^{\prime}\) is the rate coefficient (1/min), \(\tau\) is the time at 50% breakthrough (min), and \(t\) is the breakthrough time (min). Adsorption-desorption cyclic tests were performed to evaluate the regeneration of the adsorbents. A total of 10 successive breakthrough experiments were conducted.
# Synchronization of Heterogeneous Multi-Robotic Cell with Emphasis on Low Computing Power
## 3 Design of Multi-Robotic Cell Synchronization Algorithm  
The principle of the synchronization algorithm design is to dynamically adjust the speed of operation performing \(V_{i,j}\) on the slave side. The speed adjustment means the change of manipulator endpoint movement speed, based on the synchronization coefficient \(K_{i,j}\in K=\{K_{1,1},\ldots,K_{n,m}\}\). This coefficient \(K_{i,j}\) is evaluated for each operation \(O_{j}\) for each slave manipulator \(MS_{i}\) based on Equation (1).  
\[K_{i,j}=T_{i,j}/T_{1,j} \tag{1}\]  
The synchronization coefficient \(K_{i,j}\) therefore represents the ratio of the operation duration \(T_{1,j}\) of the master element and the operation duration of the slave element \(T_{i,j}\) for \(i=(2,\ldots,n)\).  
It is necessary to emphasize that although the speed \(V_{i,j}\) is based on coefficient \(K_{i,j}\) recalculated immediately after operation \(O_{j}\) execution, the new speed value is used only in the next cycle.  
The ability of the master manipulator \(M_{1}\) to distribute operation \(O_{i}\) duration time \(T_{1,j}\) after performing for each slave manipulator \(MS\) is the obligatory condition for feasibility of this proposal.
# Disease Severity Index in Parkinson's Disease Based on Self-Organizing Maps  
[PERSON]  
1 Department of Medical Sciences, University of Beira Interior, Rua Marques de D'Avila e Bolama, 6201-001 Covilha, Portugal; [EMAIL_ADDRESS] (S.M.A.); [EMAIL_ADDRESS] (S.B.M.N.)  
[PERSON]  
1 Department of Medical Sciences, University of Beira Interior, Rua Marques de D'Avila e Bolama, 6201-001 Covilha, Portugal; [EMAIL_ADDRESS] (S.M.A.); [EMAIL_ADDRESS] (S.B.M.N.)  
[PERSON]  
2 Department of Electromechanical Engineering, University of Beira Interior, Rua Marques de D'Avila e Bolama, 6201-001 Covilha, Portugal; [EMAIL_ADDRESS]  
[PERSON]  
3 Department of Neurology, Federal University of Biaui, Teresina 64049-550, Brazil; [EMAIL_ADDRESS]  
C-MAST--Center for Mechanical and Aerospace Science and Technologies, Rua Marques de D'Avila e Bolama, 6201-001 Covilha, Portugal
Alzheimer's disease [5], cancer [6], chronic kidney disease [7], tuberculosis [8], and many others. Techniques such as Convolutional Neural Networks (CNNs) [9], Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) [10] enable rapid processing of large datasets, empowering healthcare professionals to identify patterns and relationships among variables, leading to more effective diagnostic and treatment decisions. Within this context, machine learning techniques have emerged as valuable tools for predicting [PERSON]'s disease [11], allowing models to learn from data and identify relevant patterns to distinguish between healthy individuals and those with the disease. Classification algorithms such as Support Vector Machines (SVMs) [12; 13] have been particularly instrumental in discriminating between patients with PD and healthy controls based on clinical, imaging, or specific biomarker characteristics.  
A promising approach for predicting and detecting Parkinson's disease (PD) at an early stage involves the use of a machine learning technique known as Self-Organizing Maps (SOMs). These are unsupervised Artificial Neural Networks (ANNs) comprising uni- or bidimensional neurons capable of adjusting their synaptic weights through competitive learning. This enables the SOM to organize patient data into similar groups or clusters without requiring pre-labeled data [14; 15].  
In this study, we propose using the SOM in conjunction with a clustering algorithm to develop a severity score for PD, considering not only the neurological aspects that are typically addressed in other scales or indices, but also including symptoms from other organs and systems that play a crucial role in disease staging. By incorporating this comprehensive data analysis, clinicians gain access to accurate information, facilitating informed decision-making for appropriate medical interventions and, in turn, improving the overall quality of life for patients affected by this disease.
# Online Streaming Feature Selection via Conditional Independence
## 5 Experiments and Analysis
### Comparison of ConInd with Two Online Algorithms  
The above algorithms were all implemented in LOFS (Library of Online streaming Feature Selection) [39], an open-source library of online feature selection streaming features in MATLAB. To evaluate selected features in the experiments, we used the following 12 classifiers: Decision Tree (Complex Tree, Medium Tree, and Simple Tree), SVM (Linear SVM, Quadratic SVM, and Cubic SVM), KNN (Fine KNN, Medium KNN, and Cubic KNN), and ENSEMBLE Classifiers (Bagged Trees, Subspace discriminant, and RUSBoosted Trees). The classifiers were integrated into the MATLAB app tool.  
To compare the performance of the proposed _ConInd_ with existing streaming feature selection methods, we evaluated _ConInd_ and its rivals based on prediction accuracy, sizes of selected feature sets, and running time. In the remaining sections, we present the following statistical comparisons to further analyze the prediction accuracy of _ConInd_.
# Collusion Attack Resilient 3D Mesh Watermarking Based on Anti-Collusion Fingerprint Code
## 3 Proposed Method  
The main assumption for designing the anti-collusion fingerprint codes is that the averaging attack of the anti-collusion code has the same effect as a bitwise AND operation of every bit position. Therefore, the embedded bit information has to be zero for the averaging attack when each bit from each colluded work is different. However, conventional robust mesh watermarking methods such as [11; 12; 13; 24; 23] did not consider this aspect. Moreover, anti-collusion fingerprint code requires relatively several payloads in many cases, so that the existing mesh watermarkings are not suitable to carry anti-collusion fingerprint codes. (see Table 1). In contrast to the existing robust mesh watermarking, our proposed method has sufficient capacity to carry the anti-collusion fingerprint code. We will evaluate them in the experimental results section later.  
Figure 1: Model for linear and non-linear collusion.  
Unlike the image and video watermarking described in [8; 18], each vertex of a 3D mesh model is non-uniformly sampled, so that the spatial structure of the mesh is not suitable for utilizing fingerprint codes. Alternatively, we can divide the mesh into disjoint bands in the frequency domain and embed a watermark in them. To perform the frequency analysis, we use a technique called _mesh spectral analysis_[25] that can be considered a principal component analysis of the shape. We now describe each step of watermark embedding and detection in detail.
## 4 Results
In the last part of the study, the values of the above three methods were combined. The results showed that the TD and CD, despite their good capabilities, led to poorer results in this evaluation in combination with the ELD (Table 3). Figure 8: The Architectural Institute of Japan scale (AIJ) [15; 33]. Figure 9: Prediction results of RDF and SVM algorithms by considering texture difference (TD) (Mean and Variance), elevation difference (ELD), coherence difference (CD) and Combination of three methods (4 DR). Examining the results of this study and various researches were done with SVM and RDF algorithms; it can be experimentally concluded that the SVM algorithm using LIDAR images leads to higher accuracy than the RDF algorithm. In addition, the performance of the RDF algorithm in evaluating SAR images in compared to the SVM algorithm is better (Figure 10). Although the RDF is known as a strong classifier due to its \"bagged decision tree\" nature, which can split data on a subset of features, as mentioned above, the main reason for the low overall accuracy in RDF were the increase in the number of damage rates (response variables) and lack of enough training data. In general, we can say that, when we use several different datasets its performance and accuracy may reduce [35]. On the other hand, SVM provides better results when multiple datasets and smaller training set is available [35]. Table 4 shows some previous studies in which SVM and RDF algorithms were used for damage/land classification. Figures 12 and 13 also show the estimated results, of the SVM and RDF algorithms. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Combination of 3 Methods**} \\ \hline
**Type of Accuracy** & **SVM (\%)** & **RDF (\%)** \\ \hline Producer Accuracy (D0, D1) & 26.13 & 14.44 \\ Producer Accuracy (D2, D3, D4) & 55.09 & 94.85 \\ Producer Accuracy (D5, D6) & 91.19 & 5.1 \\ \hline User Accuracy (D0, D1) & 35.01 & 79.06 \\ User Accuracy (D2, D3, D4) & 78.79 & 34.52 \\ User Accuracy (D5, D6) & 61.17 & 66.40 \\ \hline Overall Accuracy (3 category) & 74.1 & 39.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Producer accuracy, user accuracy and overall accuracy [34] for RDF and SVM classifiers by considering the combination method (3 DR). Figure 10: The estimation results of the building conditions by RDF algorithm by considering 4 methods. The green spots represent the intact buildings, blue spots show low to moderate damage and the red spots represent the collapsed buildings. In addition, the Receiver’s Operating Characteristic (ROC) [36] curve (3 DR) is shown. Figure 11: The estimation results of the building conditions by SVM algorithm with considering 4 methods. The green spots represent the intact buildings, blue spots show low to moderate damage and the red spots represent the collapsed buildings. In addition, the Receiver’s Operating Characteristic (ROC) [36] curve (3 DR) is shown. Figure 12: Estimated results, of the SVM algorithm by considering 4 methods (3 DR). Figure 13: Estimated results, of the RDF algorithm by considering 4 methods (3 DR).
## 3 System Model
### System Actors and Their Relationships  
The class of logistic networks under consideration encompasses the structures consisting of \(N\) controlled nodes \(n_{i}\), \(i\in\Theta_{N}=\{1,2,\ldots,N\}\), and \(M\) external sources \(m_{j}\), \(j\in\Theta_{M}=\{1,2,\ldots,M\}\). The nodes form a mesh interconnection topology with a predetermined direction of goods flow. The demand, whose future evolution is unknown at the instant of taking the goods distribution decision, can be placed at any controlled node. The nodes have a finite storage capacity, whereas the external sources are assumed to not have such a capacity. The sources do not receive customer demand, rather they are used to replenish the stock of controlled nodes. The node interconnections are unidirectional, characterized by a pair of attributes (\(\alpha_{ij}\), \(\beta_{ij}\)), where:  
* \(\alpha_{ij}\) is the supplier fraction (SF), which designates how much of the current lot requested by node \(n_{j}\) is to be obtained from node \(n_{i}\), \(\alpha_{ij}\in[0,1]\). * \(\beta_{ij}\) is the lead-time delay (LTD), which is the time of order completion and shipment from node \(n_{i}\) to node \(n_{j}\), \(\beta_{ij}\in[1,\,\mathrm{B}]\), where \(\mathrm{B}\) denotes the maximum LTD between any two directly connected nodes. The analyzed class of networks does not permit isolated nodes (with no connection to other nodes or external sources) or nodes that would supply the stock for themselves. The network dynamics are considered within \(T\) equally-spaced periods, given as an index through the independent variable \(t=0\), \(1\), \(2\), \(\ldots\), \(T\). The on-hand stock level of node \(i\) at time \(t\) is denoted by \(l_{i}(t)\), the external demand imposed on this node is denoted by \(d_{i}(t)\), and the satisfied one is denoted by \(s_{i}(t)\). Figure 1 illustrates the sequence of operations occurring at the controlled node in each period, where solid and dashed lines represent the flow of goods and information, respectively. Let \(\Omega_{i}^{S}(t)\) and \(\Omega_{i}^{R}(t)\) denote the quantity of goods sent and received by node \(i\) in period \(t\), respectively. Introducing \(o_{i}(t)\) as the amount of goods to be ordered by node \(i\) at the end of period \(t\) from its suppliers (both from other controlled nodes within the network and the external sources), the quantity of goods sent by node \(i\) can be expressed as:  
\[\Omega_{i}^{S}(t)=\sum_{j\in\Theta}\alpha_{ij}(t)o_{j}(t). \tag{1}\]