esa-sceva/satcom-chunk-collection huggingface dataset:
# TSKT-ORAM: A Two-Server \(k\)-ary Tree Oblivious RAM without Homomorphic Encryption +
## 2 Related Work  
### Oblivious RAM  
Based on the data lookup technique adopted, existing ORAMs can be classified into two categories: hash-based and index-based ORAMs. Hash-based ORAMs [12; 13; 14; 15; 16; 17; 18; 19; 20; 21] require some data structures such as buckets or stashes to deal with hash collisions. Among them, the balanced ORAM (B-ORAM) [17] proposed by [PERSON] et al. achieves the lowest asymptotical communication cost, which is \(O(B\cdot\frac{\log^{2}N}{\log\log N})\), where \(B\) is the size of a data block. Index-based ORAMs [22; 23; 24; 25; 26; 27] use index structure for data lookup. They require the client to either store the index or outsource the index to the server recursively in a way similar to storing data, at the expense of increased communication cost. The state-of-the-art index-based ORAMs are binary tree ORAM (T-ORAM) [22], path ORAM [23] and SCORAM [28]. When \(B=N^{\epsilon}\) (constant \(0<\epsilon<1\)) is assumed, the communication cost for T-ORAM is \(O(B\cdot\log^{2}N)\) with failure probability \(O(N^{-c})\) (\(c>1\)), while path ORAM and SCORAM incur \(O(B\cdot\log N)\cdot\omega(1)\) communication cost with \(O(N^{-\omega(1)})\) failure probability and \(O(B\cdot\log N)\cdot\omega(1)\) client-side storage.
## 4 Reconstruction of Flow  
As previously mentioned, the SVD can be very useful for sensitivity analysis as the relative sizes of singular values can be compared with a SNR, such as the peak signal to mean noise ratio. If the SNR is known for the measurement process, then all singular vectors with singular values lower than the SNR are indistinguishable from noise. Thus, the measurement can be projected onto a truncated basis containing all singular vectors distinguishable from noise. This allows for a simple kind of regularization as the singular vectors from measurement-space and state-space map to one another. For a given measurement \(\mathbf{d}\) and the cut-off point of the \(k\)th singular vector, the estimated state of the system \(\mathbf{\tilde{r}}^{k}\) is  
\[\mathbf{\tilde{r}}^{k}=\sum_{i=0}^{k}\frac{<\mathbf{u}_{i},\mathbf{d}>}{ \sigma_{i}}\mathbf{f}_{i}, \tag{21}\]  
Figure 4: Second singular vectors, for flow (**left**) and boundary potential (**right**).  
Figure 3: First singular vectors, for flow (**left**) and boundary potential (**right**).  
where \(\mathbf{u}_{i}\) and \(\mathbf{f}_{i}\) are the \(i\)th measurement- and state-space singular vectors, and \(<\cdot,\cdot>\) denotes an inner product. The singular vectors and values are ordered in monotonically decreasing order of \(\sigma_{l}\), and \(k\) is the cut off point dictated by the SNR. This regularization is called the truncated singular value decomposition (TSVD).
# Automatic Generation of N-Bar Planar Linkages Containing Sliders
## 5 Check for Rejected Chains Using TLC  
During the enumeration process, the assignment of P-joints should take into consideration three rules introduced by [7]:  
\(R1\)-No link of a chain can contain only P-pairs which directions are parallel.  
\(R2\)-Binary links of a chain with only P-pairs cannot be connected directly.  
\(R3\)-No closed circuit of a chain can have less than two R-pairs.  
All generated graphs must be evaluated according to these rules. The proposed TLC is an efficient tool to detect and eliminate graphs that do not agree with the previous rules as follows:  
**Case 1.**_A KC is said to be rejected due to \(R_{2}\) if two consecutive elements for any loop have: \(L_{x}\) or \(L_{y}\) = 2 (i.e., a binary link is connected) and \(J_{t}\) = 1 (i.e., a P-pair is attached)_  
Figure 8: Generation of ITLC, TLC, KCTM for Stephenson 6-chain.  
Figure 9: Generation of isomorphic and rejected KCs of Stephenson chain containing 3P-joints.  
_For example, Figure 10a illustrates Watt chain with two prismatic joints. A binary link B is attached to links A and C by prismatic joints. This arrangement disagrees with rule R\({}_{2}\). Using the generated TLC for loop-2, two consecutive elements have L\({}_{x}\) = 2 and J\({}_{1}\) = 1._  
**Case 2.** _A KC is said to be rejected due to R\({}_{3}\) if there are (n \(-\) 1) elements, for a given loop, with J\({}_{1}\) = 1. (i.e., loop has n-elements with (n \(-\) 1) P-joints). Loop-1 in the Stephen chain, shown in Figure 10b, has four elements and three sliders. However, it should contain two R-joints at least._  
The established VC ++ code is applied effectively to detect all rejected graphs. Figure 9 illustrates some rejected KCs for the Stephenson chain containing 3-sliders such as 16 th and 17 th chains which are rejected due to R\({}_{2}\). However, 14 th and 19 th chains are rejected due to R\({}_{2}\) and R\({}_{3}\) as well.
# Topology of the World Tourism Web
## 3 Results and Discussion
### Does the Tourism Network Behave as a Social or Technological Network?
#### 3.2.3 Assortativity  
Next, if communities are observed, which are highly interconnected, one can notice that each community has at least one high-degree node (Table 2). In addition, communities do not have the same degree sum, showing that in some areas, people travel more and elsewhere less. However, presuming that community interconnections are stronger than those with other high-degree nodes, it is safe to assume that the high-degree nodes in a community relate to lower-degree nodes within the community (rather than to other high-degree nodes outside the community). This leads us to the conclusion that the network does not show positive assortativity. However, this assumption has been additionally tested, and we received the following correlation coefficients for the in-degrees, out-degrees, degrees, weighted in-degrees, weighted out-degrees, and weighted degrees, respectively: \(r_{in}=-0.1409\), \(r_{out}=-0.0855,r=-0.261;r_{win}=-0.0462\), \(r_{wout}=-0.0426\), and \(r_{w}=-0.0467\) (coefficients are statistically significant at the 5% level). The coefficients point to very weak correlations, but from the assortativity perspective, we are focused on the coefficient sign, which indicates the negative direction of correlation. This means that high-degree nodes make connections to low-degree nodes, and vice versa, proving to be disassortative. From the perspective of assortativity, it seems that the tourism network exhibits a behavior more typical of technological and biological networks.  
[PERSON] and [PERSON] [6] reveal disassortative mixing, which they interpret as the underlying economic influence and the flows of technology and knowledge (such correlation is also present in a trade network [3] and a financial network on the global scale [34]). Our findings confirm the disassortative behavior of the tourism network.  
The ecological approach may be applied in the case of SNA analysis of tourists' behavior, as suggested by [65]. For example, compartmentalized and disassortative networks \"tend to confer a significant degree of stability against disturbance\"--a very interesting property from the COVID-19 crisis perspective. To reach such a conclusion, besides the disassortative network property, it is necessary to examine the network's diffusion properties and compartmentalization possibilities, which is done in Section 3.3.
# Predicting Location of Tweets Using Machine Learning Approaches
## 5 Experiments and Results
### Challenges in Improving Accuracy Further  
Despite the promising results obtained in this study, there are several challenges that must be addressed to improve location prediction accuracy further. For example, accurately labeling the training data can be a challenge, especially when dealing with tweets in languages with complex syntax and grammar. Additionally, the limitations of the machine learning algorithms used in this study may have contributed to the limitations in accuracy gains beyond 140 km.  
To address these challenges, future research could explore alternative approaches to feature extraction, such as incorporating sentiment analysis or social network analysis, or experimenting with different machine learning algorithms.  
Our study provides insights into the relevance of geo-tagging features, the impact of data quality and machine learning models, and challenges in improving accuracy in location prediction. Our results suggest that a combination of textual and geo-tagging features and the use of RF and SVM models may be effective approaches to location prediction. However, further research is needed to address the challenges identified and to further improve location prediction accuracy.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Tweet Text** & **Ent1** & **Ent2** & **Ent3** & **Ent4** & **Ent5** & **Ent6** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Tweets and entity joints.  
Figure 2: Distance versus accuracy for location prediction using tweet text, home location, and named entity (RF model).
# Influence of DMSO Non-Toxic Solvent on the Mechanical and Chemical Properties of a PVDF Thin Film
[PERSON] et al. [47] conducted a phase, conductivity and dielectric analysis and a morphology study of the \(\beta\) phase of PVDF. The objective was to use PVDF as a gel polymer electrolyte for magnesium-ion battery applications. The study reported the presence of a pure \(\beta\) phase PVDF membrane and good affinity with a polar organic electrolyte. [PERSON] et al. [48] used a similar approach. They used scanning electron microscope, Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) techniques to study the morphology, structure and piezoelectric response of a composite film (BaTiO\({}_{3}\)/PVDF) for human motion monitoring. As a result, a pressure sensor based on PDA@BTO/PVDF showed a fast response and a good ability to provide an energy supply with an apparent enhancement to output voltages. Despite these successful applications, neither [PERSON] et al. [47] nor [PERSON] et al. [48] considered the inclusion of ILs or used low-toxicity solvents as alternatives to DMF. Regarding the solvent, DMSO evidences a large spectrum of pharmacological effects, including anti-inflammatory effects, local analgesia and weak bacteriostasis, and it is mainly used as a vehicle for other drugs [49]. It shows a high boiling point (189 \({}^{\circ}\)C) and a coefficient of solubility of 16.4 MPa\({}^{1/2}\) (\(\delta_{p}\)), which is close enough to the solute parameter (\(\delta_{p}\) = 12.5 MPa\({}^{1/2}\)). A relative similarity between the Hansen solubility coefficients of a solute and solvent usually indicates ease with dissolving the first into the second. However, other solvents, such as DMF, present a \(\delta_{p}\) closer to that of PVDF, which can represent an advantage in terms of dissolution time. Still, DMSO is a good candidate to dissolve PVDF according to the literature [50; 51]. Furthermore, as a solvent, DMSO is used in situations where low toxicity is required and desirable, since conventional solvents are assumed to be severely toxic and harmful by the European Chemicals Agency [52]. The combination PVDF + DMSO has been used for different purposes: for instance, [PERSON] et al. [53] evaluated the use of DMSO in the process of making lithium ion batteries, while [PERSON] et al. [54] used them to make antifouling membranes. Both authors pointed out the great potential of DMSO as the ideal candidate to replace hazardous solvents. As a result, with the present study, our research team propose a new experimental protocol to produce a PVDF-based actuator using a non-toxic solvent, which is crucial for biomedical applications. Moreover, the research team expect to better understand the mechanical properties, chemical phase content, as well as the morphology through optical analysis of a composite material based on PVDF by the addition of an IL filler. In the future, we hope the material developed and studied will be integrated into upcoming exosuit solutions, which could be used for aided mobility or rehabilitation scenarios (for instance, for muscle touch feedback for re-educational movement purposes).
## 2 Materials and Methods
### The Optimization Method
#### 2.2.1 Initial Network Configuration  
In some cases, starting the optimization from a (near-)zero number of all repetitions reduces the likelihood of finding the optimal solution as it could lead rather to the identification of a local optimum instead of a global one. This is caused by the fact that the algorithm would prefer adding further measurements from the same standpoint from which the first measurement was performed to adding measurements from new standpoints--just because the second (or any subsequent) measurement from the same standpoint always contains one piece of additional information (namely, the horizontal angle between the previous measurement and the new one). In effect, the algorithm could keep adding measurements from the same standpoint only. One of the possible solutions to this issue lies in determining the initial measurement configuration of the network.  
This initial measurement configuration is determined by declaring the measured horizontal directions to be bearings in the first step. This will allow the algorithm to move to another point in the network without the penalization stemming from the necessity to measure the first horizontal direction on the new standpoint. Subsequently, the above-described maximum accuracy increment method is applied to the network formed by the points, zenith angles, distances, and bearings. This will yield a preliminary (virtual) optimization of the network, which will provide us with measurements that need to be taken (i.e., measurements for which this preliminary optimization yielded the number of repetitions \(\geq 1\)). This information is then adopted to determine measurements for the actual network containing horizontal directions instead of bearings, i.e., the initial measurement configuration (with the initial number of repetitions for each of these adopted measurements set to 1).
# A Sparse Multiclass Motor Imagery EEG Classification Using 1D-ConvResNet
## 2 Materials and Methods
### Sparsity-Based Classification  
#### 2.4.1 Residual Network Theory  
Deep CNN layers encounter vanishing or exploding gradient problems, as the backpropagation calculation of the gradient may lead to an exponential increase or decrease in the gradient with an increasing number of layers. Additionally, the training error might increase because of network degradation [25]. Furthermore, the model's learning rate decreases when there are several deep convolutional layers, and consequently, the accuracy is impacted. For CNN models, each layer's input is derived from the previous layer's output; hence, the model may collapse if it possesses many layers due to the calculation of gradients. Therefore, we introduce a residual network with a'shortcut' structure [19]. The output of the middle layer was added to that of the first layer. The residual structure learns the difference between an optimal solution and a congruent mapping instead of just learning input-output mapping, as shown in Equation (8) [20,26].  
\[F(y)=H(x)-x \tag{8}\]  
where \(x\) is congruent mapping and \(H(x)\) is the optimal solution.  
Despite their advantages, traditional 2D-CNNs cannot be used to train and classify 1D sparse data. The signal should be represented in a 2D space to classify with 2D-CNNs. Therefore, we introduced a 1D-CNN to classify sparse features. A 1D residual learning block can efficiently overcome the performance degradation encountered with traditional 1D-CNNs [26]. The 1D-CNN reduces the features from the one-dimensional data sequence and maps its internal features. 1D-CNN helps analyze the sparse features of time series data, as the convolutional layer works on the fixed-length dataset, and the features can be located at any part of the segment. A kernel of a specified length is moved over the data by overlapping the layers to lose no features.  
The architecture of the proposed 1D-ConvResNet model is shown in Figure 6, and each module proposed in the architecture is detailed in this section.
## 3 Ferry Node Ranking Clustering Algorithm (FNRCA)
### Node Ranking Clustering Algorithm  
In most previously proposed clustering algorithms a node is elected as a cluster head either randomly or based on having the highest residual energy in a cluster. This selection might lead to inefficiencies [33]. One of the common drawbacks of these algorithms is taking a longer path, number of hops, to the base station. The additional number of hops data needs to travel to get to the base station will cause more energy consumption. Also, there can be forgotten nodes or disconnected nodes which are not covered by any of the cluster heads chosen due to being far from any reachable one. Moreover, frequent replacement of cluster heads in each sensing round wastes energy. These problems can be avoided in our proposed algorithm where data can be sent through the correct path or direction with respect to a mobile ferry's path consisting of checkpoints and by the BS maintaining a global knowledge of all nodes in the WSN area to ensure that all alive nodes are connected through the proper choice of cluster heads. Using a ferry to collect sensed data and control traffic data from cluster heads results in a decreased traffic forwarding burden on cluster heads in the network as the number of multi hop forwarding attempts is reduced. In addition, we propose the use of an energy threshold technique in making decisions to replace cluster heads which prolongs the lifetime of nodes especially those closer to the ferry's checkpoints. This in effect prolongs the overall network lifetime as nodes closer to the ferry's checkpoints are more critical than those far away nodes in maintaining connectivity in the network.  
In the proposed algorithm several assumptions are needed: first, the Base Station (BS) is placed at a fixed position and has unlimited energy. Thus no constraints are assumed with regards to power consumption due to data processing and communication. Second, all nodes are assumed to have the same energy level at the set up phase, which is known to the BS. Third, the sensing field dimensions are also assumed to be provided to the BS. Fourth, it is assumed that the mobile ferry is dispatched from the base station and returns back to it once its task is completed. In addition, it is assumed that there are no energy constraints with respect to the ferry which is assumed to be moving at a fixed speed. Nodes throughout the sensing field are randomly and uniformly distributed.  
Figure 1: Illustration of phases used by FNRCA.
# Research on Amphibious Multi-Rotor UAV Out-of-Water Control Based on ADRC
## 2 Dynamic and Kinematic Modeling
### Hydrodynamic (Moment) \(\tau^{\prime}\)
#### 3.3.2 Viscous Hydrodynamic Force (Moment) \(\tau_{B}\)  
Due to fluid viscosity, the motion of a rigid body is also affected by viscous hydrodynamic forces \(\tau_{B}\), which are generated only with the velocity and angular velocity. The viscous hydrodynamic \(\tau_{B}\) was obtained mainly by simulation. First, we consider the error between the actual hydrodynamics and the calculated hydrodynamics as an internal disturbance.  
Then, the N-S equation together with the \(k-\varepsilon\) mode is solved using an implicit solver [41]. The flow field used in this paper is a rectangular parallel hexahedron of sufficient size. The final computational domain is obtained by a Boolean operation between the 3D model and the flow field. When the amphibious multi-rotor vehicle is moving slowly underwater, it is assumed to move along the \(Z\)-axis at \(0-1m/s\). Through simulation, the relationship between the velocity and the combined force (moment) along the \(Z\) direction is shown in Table 3. Simulation tests show that when the amphibious multi-rotor vehicle comes out of the water, only viscous hydrodynamic forces exist in the \(Z\) direction, while the presence of forces and moments in other directions is negligible. Therefore, the relationship between the fitted velocity \(w\) and the viscous hydrodynamic force \(\tau_{B}\) is  
\[\tau_{B}=37.6431\cdot w^{2}+0.3672\cdot w-0.0271 \tag{13}\]
# Game Theoretic Solution for Power Management in IoT-Based Wireless Sensor Networks
## 3 The Proposed Mechanism
### Mathematical Moel and Factor Formulization
#### 3.4.1 Energy Factors  
Each node operates on a limited set of energy. Since all the nodes are supposed to sense, process, aggregate, send, receive, and forward data packets, they will consume energies with the passage of time. The initial energy level can be adjusted to 100 for each node.  
The energy of node \(i\) can be denoted by \(E_{i}\). The energy factor is associated with the energy cost of each node, which can be denoted as:  
\[CostE_{i}=Cost_{i}(Sense)+Cost_{i}(Process)+Cost_{i}(agg)+Cost_{i}(Packet-Trans) \tag{1}\]  
where \(Cost_{i}(Packet-Trans)\) can be formed for \(CHs\) and \(CMS\) separately as:  
\[Cost_{CH}(Packet-Trans)=Cost_{tx(CH,BS)}+Cost_{rx(CH,agg)} \tag{2}\]  
where, \(Cost_{tx(CH,BS)}\) is the cost in term of energy loss by sending a packet from \(CH\) to \(BS\). \(Cost_{rx(CH,agg)}\) is the energy cost of receiving a packet and aggregation from the \(CMs\). The term \(Cost_{tx(CH,BS)}\) can be further defined as:  
\[Cost_{tx(CH,BS)}=d_{CH,BS}^{2}\cdot E(amp)+E(elec) \tag{3}\]  
where, \(d^{2}CH,\ BS\) is the physical distance between the \(CH\) and \(BS\), while \(E(amp)\) is the transmission amplifier dissipation to get the needed signal strength, and \(E(elec)\) is the transmission circuitry dissipation. The cost of reception and aggregation can be defined as:  
\[Cost_{rx(CH\,agg)}=\sum_{CM=1}^{k}d_{CM}^{2}\cdot E(elec)+k\cdot E(agg)+E(lis) \tag{4}\]  
In the above equation, we have a set of connected \(CMs\) denoted as \(k\). \(d_{CM}\) is the physical distance between the \(CH\) and the \(CM\). \(E(lis)\) is the listening cost used by the antenna activation. On the other hand, the cost for the \(CM\) can be formalized as:  
\[Cost_{CM}(Packet-Trans)=Cost_{tx(CM,CH)}=d_{CM,BS}^{2}\ +E(elec) \tag{5}\]  
According to the above-defined energy model, we can conclude that the cost in terms of energy for a \(CH\) is higher than \(CM\):  
\[Cost_{CH}>Cost_{CM} \tag{6}\]
# Smart Internet of Things Power Meter for Industrial and Domestic Applications
## 3 Proposed Solution
### Hardware Architecture
#### 3.1.3 Voltage and Current Conditioning Modules  
To adapt the grid voltage and current values to the ADC maximum allowed input range of \(-1.3\) V to 3.6 V, a specialized signal conditioning circuit was implemented. To reduce the voltage value, we used a voltage divider with a ratio of 301:1. The resulting voltage is passed through a buffer implemented using a Texas Instruments OPA388 operational amplifier. We selected this specific model for its characteristics: low noise, no crossover distortions, true rail-to-rail input and output, and 10 MHz gain bandwidth. Considering the ratio of the divider and the maximum range of the Op-Amp output (\(-1.2\) V to 3 V), a maximum grid peak voltage of 361 V (approx. 256 V RMS) can be measured without saturating the amplifier.  
To measure the current, we used a 500 u\(\Omega\), 10 W shunt resistor. The resistor voltage drop is passed through a non-inverting amplifier, implemented with a Texas Instruments OPA388 Op-Amp. Considering the shunt resistor value and power rating, the amplifier gain (10), and the maximum range of the Op-Amp output, a maximum grid peak current of 141 A (approx. 100 A RMS) can be measured.