esa-sceva/satcom-chunk-collection huggingface dataset:
# Malware Variants Detection Model Based on MFF-HDBA
## 3 Methodology
### Data Preprocessing
#### 3.1.2 Bilinear Interpolation Algorithm for Size Normalization  
In a classical convolutional neural network, the number of neurons input to the fully connected layer must be fixed, since the size of the weight matrix of the fully connected layer is fixed. This means that the size of the feature map must be consistent after convolution and pooling operations. Thus, malware image sizes which are input to CNN must be the same. However, the input malware image sizes are different from each other because different malicious code binaries files vary in size. Therefore, it is necessary to normalize the visualized malware images to the same size.  
In order to keep the original texture features of the normalized malware images unchanged as much as possible, this paper utilized a bilinear interpolation algorithm to normalize the image size. The algorithm selects four pixel points near the interpolation point of the malware image, and then twice implements linear interpolation operations in the \(X\) direction first. Then, it implements linear interpolation calculations in the \(Y\) direction to obtain the pixel of the interpolation point.  
\[f(x,y_{1})=\frac{x_{2}-x}{x_{2}-x_{1}}f(x_{1},y_{1})+\frac{x-x_{1}}{x_{2}-x_{1 }}f(x_{2},y_{1}) \tag{1}\]  
\[f(x,y_{2})=\frac{x_{2}-x}{x_{2}-x_{1}}f(x_{1},y_{2})+\frac{x-x_{1}}{x_{2}-x_{1 }}f(x_{2},y_{2}) \tag{2}\]  
\[f(x,y)=\frac{y_{2}-y}{y_{2}-y_{1}}f(x,y_{1})+\frac{y-y_{1}}{y_{2}-y_{1}}f(x_{2 },y_{2}) \tag{3}\]  
where \(f(x,y)\) is the pixel value of the interpolation point in the malware image; \((x_{i},y_{j})(i,j=1,2)\) are the four pixels near the interpolation point in the malware image. Figure 3 shows the normalized malware image of a sample in the C2 LOP.P family, which has an original size of 370 \(\times\) 256; its image size changes to 32 \(\times\) 32, 64 \(\times\) 64, 128 \(\times\) 128, and 256 \(\times\) 256 after normalization. Note that the basic texture features in the malware image are preserved after size normalization by bilinear interpolation algorithm.  
Figure 3: Malware samples after size normalization.  
Figure 2: Malware samples after visualization from different families.
## 2 Fundamental Theory  
### The Vibration Equation of Traditional ABH Structure  
Conventional ABH beams are non-uniform structures and their equation of shape is usually a quadratic function. The detailed dimensions of each part of the structure are shown in Figure 1. \(L_{0}\) and \(L\) are the length of the part with an equal cross-sectionand ABH part, respectively. The heights \(h(x)\) of the ABH part satisfied the following quadratic function:  
\[h(x)=\epsilon(a-x)^{m},0\leq x\leq L, \tag{1}\]  
The vibration equation of the whole structure is  
\[EI(x)\frac{\partial^{4}w}{\partial x^{4}}+\rho A(x)\frac{\partial^{2}w}{ \partial t^{2}}=q(x,t), \tag{2}\]  
where \(E\) is the elastic modulus of the structure; \(\rho\) is the density of beam; \(A(x)\) is the cross-sectional area; \(q(x,\,t)\) is the distributed force; \(w=w(x,\,t)\) is the lateral displacement; \(I(x)\) denotes sectional moment of inertia.  
\[I(x)=\frac{1}{12}bh^{3}(x).\]  
where \(b\) is the width of the beam and \(h(x)\) is the height of the ABH structure.
# Converting MST to TSP Path by Branch Elimination
## 4 Experimental Results  
### Datasets and Methods  
We use two open loop datasets: O-Mopsi [2] and Dots [35]; see Figure 14. Both can be found on web: [[http://cs.uet.fi/o-mopsi/datasets/](http://cs.uet.fi/o-mopsi/datasets/)]([http://cs.uet.fi/o-mopsi/datasets/](http://cs.uet.fi/o-mopsi/datasets/)). O-Mopsi are 147 instances  
Figure 12: Example of the _Greedy_ and _All-pairs_ variants. Greedy removal leads to the same sub-optimal result as TSP-Kruskal while allowing to remove shorter link, the optimal solution can be easily reached.  
Figure 13: Parallel chains where only MST-to-TSP (All-pairs variant) finds the optimal path.  
of mobile orienteering game played in real-world. Haversine distance is used with the constant 6356.752 km for the earth radius. The dots are 6449 computer-generated problem instances designed for humans to solve on computer screen. Euclidean distance is used. Optimal ground truth solutions are available for all problem instances.  
Table 1 summarizes the properties of the datasets. On average, the datasets have only a few branches (1.90 in O-Mopsi; 1.48 in Dots). This means that that the MST-to-TSP algorithm needs to run only a few iterations. Even the most difficult D  
Figure 14: Examples of the O-Mopsi (above) and Dots (below) datasets.  
11 branches to be broken. The total number of link swaps need are 280 for the O-Mopsi games and 9548 for the Dots games.  
Table 2 shows the methods used in the experiments. We include all the main MST-based algorithms including nearest neighbor, Prim-TSP (greedy), Kruskal-TSP, and the proposed MST-to-TSP. Randomized variants are included from Kruskal-TSP and MST-to-TSP with 100 repeats. Among the local search heuristics, we include 2-opt [22] and the randomized mixed local search [9]. The performance is evaluated by _gap_, which is relative difference of the algorithm and optimal solutions:  
\[gap=\frac{\left|L-L_{opt}\right|}{L_{opt}} \tag{1}\]
## 4 Discussion
### UAS and Field Work for Validation
The strongest argument for UAS-based training and validation is that, within a fixed budget, it allows much larger training and testing sample sizes. If we had determined it was worthwhile we could have used 2-3 times as many UAS-based training and testing points for this study, or covered much more area in flights, with only marginal increases in cost and time. Increasing the field-based sample sizes by two times would have been prohibitive, as it would have been a proportional increase in cost and time. New classification techniques are being developed, including deep learning methods,and additional satellite constellations are being launched. In the context of these new classifiers and satellite imagery, highly accurate testing and training datasets covering large areas will be critical for monitoring ecosystems and change across time. We suggest UAS as a critical component of this process moving forward.
# Pyrophyllite: An Economic Mineral for Different Industrial Applications
## 3 Pyrophyllite's Industrial Applications
### Pyrophyllite as Filler
#### 3.3.3. Paint Industry  
High-quality finely ground pyrophyllite is used in paints as a pigment extender and suspending agent. Using pyrophyllite as an extender enhances the nature of a product by increasing the volume of paint when mixed with the product and increases resistance to film cracking. Moreover, pyrophyllite helps film dry and promotes good dispersion. The main properties of pyrophyllite exploited in the paint industry as an extender are the mineral's high refractive index, platy structure, softness, smooth surface, white color, chemical inertness, mechanical strength, and yearly luster. Another application of pyrophyllite in the paint industry is as a suspending agent in powder form. The fibrous crystalline type of pyrophyllite is used as a substitute for other, more expensive materials such as China clay. The fine fibers of pyrophyllite trap the primary pigment particles and keep them suspended long enough to facilitate brushing. In this way, the use of pyrophyllite ensures the yearly appearance, luster, and smooth brightness of the painted surface. Recently, finely ground paint-grade pyrophyllite has been used in wallboard joint cement and mastics to increase crack resistance and control rheology [3; 51; 53; 87]. All these applications must consider the effects of iron oxide content, moisture, and volatile substances on the quality of the coating, along with the other specifications shown in Table 5.
## Abbreviations  
The following abbreviations are used in this manuscript:  
MBSTALRU Multi-Branching Spatial-Temporal Attention-based Long-Short term Memory  
LSTM Long-Short term Memory  
CNN Convolutional Neural Networks  
ITS Intelligent Transportation System  
GPS Global Positioning System  
UN United Nation  
SVR Support Vector Regression  
KF Kalman Filters  
ARIMA Auto-Regressive Integrated Moving Average  
K-NN K-Nearest Neighbors  
RF Random Forest  
ANN Artificial Neural Network  
NLP Nature Language Processing  
CV Computer Vision  
RNNs Recurrent Neural Networks  
MLT Multi-Tasking Learning  
ST-ResNet Spatial-Temporal Residual Network  
GRU Gated Recurrent Unit  
ConLSTM Convolutional Long-term Short-term Memory  
HMM Hidden Markov Model  
GMM Gaussian Mixture Model  
FC Fully Connected  
GRU Gated Recurrent Unit  
HA Historical Average  
STDN SPATIAL TEMPORAL DEEP NETWORK  
MGSTC MULTI-GATED SPATIAL TEMPORAL CONVOLUTION  
CB Closeness Branch  
CDB Closeness Daily Branch  
CDWB Closeness Daily Weekly Branch  
AD Artificial Intelligence and Deep learning  
ATMS Advanced Traffic Management System
# Trajectory Clustering and k-NN for Robust Privacy Preserving Spatiotemporal Databases
###### Abstract  
In the context of this research work, we studied the problem of privacy preserving on spatiotemporal databases. In particular, we investigated the \(k\)-anonymity of mobile users based on real trajectory data. The \(k\)-anonymity set consists of the \(k\) nearest neighbors. We constructed a motion vector of the form (\(x\),\(y\),\(g\),\(v\)) where \(x\) and \(y\) are the spatial coordinates, \(g\) is the angle direction, and \(v\) is the velocity of mobile users, and studied the problem in four-dimensional space. We followed two approaches. The former applied only \(k\)-Nearest Neighbor (\(k\)-NN) algorithm on the whole dataset, while the latter combined trajectory clustering, based on K-means, with \(k\)-NN. Actually, it applied \(k\)-NN inside a cluster of mobile users with similar motion pattern (\(g\),\(v\)). We defined a metric, called vulnerability, that measures the rate at which \(k\)-NNs are varying. This metric varies from \(\frac{1}{k}\) (high robustness) to 1 (low robustness) and represents the probability the real identity of a mobile user being discovered from a potential attacker. The aim of this work was to prove that, with high probability, the above rate tends to a number very close to \(\frac{1}{k}\) in clustering method, which means that the \(k\)-anonymity is highly preserved. Through experiments on real spatial datasets, we evaluated the anonymity robustness, the so-called vulnerability, of the proposed method.  
\(k\)-NN; K-means clustering; anonymity; uncertainty; trajectories +
Footnote †: journal:  
_Article_
## 4 Distributed Computation Offloading and Resource Allocation
### Computation Offloading is Modeled as a DRL Problem
#### 4.1.3 Reward Function  
The goal of a reward is to provide feedback to an RL model regarding the performance of a previous action. Therefore, it is essential to define the reward appropriately for the proper learning process. The reward function defined precisely and effectively helps to find the best action policy [35]. Efficient computation offloading can help address the energy optimization problem, which is closely related to the proposed reward function. The immediate reward is considered as the negative sum cost of energy. Precise mode selection, optimal computing resource allocation, and power allocation are crucial factors that significantly influence the immediate reward \(r_{t}\). As a result, the immediate reward can be expressed as:  
\[r_{t}=-\Big{(}E_{k}^{service}\Big{)} \tag{16}\]  
We consider the long-term accumulative reward as energy minimizing at a more extended period of time \(T\). The controller performs the best actions using exploration and exploiting all possibilities to maximize future accumulative rewards. Assuming a discount factor of \(\xi\in[0,1]\), the long-term cumulative reward \(R_{t}\) can be defined as \(R_{t}=\sum_{t=0}^{T}\xi^{t}r_{t}\). The \(\xi\) is the indicator of future reward, which will influence the future reward based on the current mode selection; when the \(\xi\) value is lower, it will provide the priority for accumulating the immediate reward. Since our ultimate object is to minimize the energy, the reward is correlated with the negative value of the energy. Afterward, we endeavor to maximize the reward in order to minimize the energy.
## 3 Methodology
### Dataset  
This study utilizes a publicly available dataset derived from the levodopa response study [4]. This dataset is specifically tailored to explore anomalies related to motor fluctuations observed in individuals afflicted with PD. The dataset encompasses three distinct anomalies: tremors, dyskinesia, and bradykinesia. Notably, the raw data are readily accessible through an open data repository. Data collection procedures involved the participation of 28 subjects diagnosed with PD, with Hoehn and Yahr scores ranging from II to IV, over a span of 4 days. The Hoehn and Yahr scale was employed to gauge the progression of [PERSON]'s symptoms and the degree of disability. It included subjects aged from 30 to 80 years and undergoing L-Dopa treatment, exhibiting at least mild dyskinesia and motor fluctuations. Subjects with significant neurological disorders such as epilepsy, brain tumors, or hydrocephalus were excluded from this study. Data acquisition was conducted using three distinct sensors, Samsung S2 smart phone, GeneActiv IMU, and a Pebble smartwatch, positioned at the waist, most affected upper limb, and least affected upper limb, respectively. Three-dimensional acceleration data, capturing movement along the x, y, and z axes, were collected at a frequency of 50 Hz. Additionally, the dataset includes information on vector magnitude. The dataset also encompasses labeled data, providing details on symptom severity (for tremors) and presence (for dyskinesia and bradykinesia) for each limb and motor task, as annotated by a clinician. Data collection spanned a 4-day period. On the first day, participants performed various motor tasks in an on-medication state within a laboratory setting. These tasks included a range of activities, such as walking, finger movements, and fine motor tasks, with repetitions occurring every 30 min over a 3- to 4-hour period. Subsequently, participants were equipped with sensors to record their daily activities over the second and third days, yielding two days of unlabeled data. On the fourth day, participants returned to the laboratory in a non-medicated state to repeat the motor tasks performed on the first day. Following this initial testing phase, participants ingested their scheduled medication dose and performed an additional set of motor tasks. Annotation of all the data was conducted by a trained clinician.
## Framework
### The Physical Layer  
The physical layer consists of manufacturing resources, such as smart production cells, smart AGVs, materials, pallets, RFID readers and tags, etc. These manufacturing objects are equipped with embedded devices and sensors so that they can perceive real-time status of themselves as well as others. The objective of the physical layer is to realize real-time status information perception and interaction between machine tools, AGVs, buffers, workers, and cloud clients.  
In a smart production cell, cameras and sensors are used to perceive real-time status of machines during machine processing. RFID tags are attached to machines, materials, pallets, and workers. Thus,  
Figure 1: An integrated framework based on cloud service platform for production-logistics systems. AGV: automated guided vehicle.  
RFID readers can obtain the location information and basic information of these manufacturing objects. For a smart AGV, embedded devices and sensors are used to collect real-time status information. For example, global positioning system (GPS) devices can capture real-time location information in a real manufacturing environment. An AGV with RFID tags can be associated with the pallet and materials that are being transported. Hence, by integrating manufacturing objects with IoT technology, real-time status information is more easily collected and accessed. The real-time status information can be divided into five parts, including product information, production cell information, AGV information, storage information, and manpower information.
# Symmetric Keys for Lightweight Encryption Algorithms Using a Pre-Trained VGG16 Model
## 2 Theoretical Background  
### Symmetric Key  
Symmetric key cryptography employs the same key (K) for encryption and decryption, as illustrated in Figure 1. The sender encrypts the plaintext using key 'K' and transmits the resulting ciphertext to the receiver. Subsequently, the receiver decrypts the cipher data using the identical key 'K', restoring it to its original form [7, 8]. Maintaining the secrecy of the key is crucial, ensuring its exclusive exchange through a secure channel between the sender and the receiver [9]. There are many symmetric encryption algorithms, such as:
## 3 System Design and the Proposed HPR-DCC Methodology
### Feasibility Analysis of the Proposed HPR-DCC Algorithm  
The feasibility of using a hybrid control strategy that merges TPC and TRC for congestion management is supported by multiple factors. First, TPC provides precise control over wireless nodes' transmission power, enabling adjustments in communication range and link quality. This capability effectively mitigates congestion by reducing interference and contention within the network. Additionally, TPC ensures efficient power allocation, optimizing energy consumption and extending the network's operational lifetime. Second, TRC allows for regulation of data transmission rate, enabling dynamic channel capacity control and ensuring optimal network resource utilization. By adopting a hybrid control strategy, the limitations of individual control methods, such as exclusive reliance on power or rate control, are overcome. Moreover, TRC offers flexibility in managing traffic, as it can promptly adapt to changing network dynamics. By combining TPC and TRC in a hybrid control strategy, the limitations of individual control methods are effectively addressed. The hybrid approach uses the precision of power control and the adaptability of rate control, resulting in a more versatile and resilient congestion management solution. The HPR-DCC is expected to improve network throughput, decrease packet loss, and minimize delays, making it a promising solution for congestion management in wireless networks.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Carrier frequency & 5.9 GHz \\ Bandwidth & 10 MHz \\ Number of subchannels & 5 subchannels each 10 RBs \\ Subcarrier spacing & 15 kHz \\ Subchannel size & 10 RB \\ MCS & 11 \\ Channel model & Winner + B1 \\ Measurement period & 100 ms \\ Resource allocation period & 100 ms \\ Transmitter antenna gain & 3 dB \\ Receiver antenna gain & 3 dB \\ Noise figure & 9 dB \\ \hline \hline \end{tabular}
\end{table}
Table 2: System specifications for HPR-DCC algorithm.
## 3 Tests Results and Discussion
### Microstructure and Phase Analysis
#### 3.4.3 Results of the XRD Analysis  
As shown in Figure 22, the diffraction spectrum indicated an obvious peak at the main reflection angle (2-theta) of 30.6\({}^{\circ}\). This is in good agreement with the strong peaks of pure calcite. At the same time, the 2-theta values corresponding to other peaks of the mineralized precipitate had a high degree of matching with the peaks of pure calcite crystals. This result shows that the main component of the induced precipitation was calcite crystal-based CaCO\({}_{3}\). Regardless of the target temperature, the X-ray reflected energy intensity of Groups B and C at all angles was higher than that of Group A. Another strong peak appeared at a 2-theta angle of 26.5\({}^{\circ}\), which had a high degree of matching with the peak of quartz crystal. In addition, there were also strong peaks at 2-theta angles of 18.0\({}^{\circ}\) and 34.1\({}^{\circ}\), which had a high degree of matching with the peaks of CH crystals. At different temperatures, the X-ray reflection energy intensity of Groups B and C at all angles was lower than that of Group A. In essence, CH crystals are hexagonal sheets with a loose structure, and their contribution to the strength of concrete is less significant. It can be seen from the test results of the experimental group that adding the _S. pasteurii_ strain could effectively reduce the formation of CH crystals and convert them into cubic crystals and agglomerated substances. This illustrates that the experimental group indeed showed a higher strength in the pull-out test.  
Figure 21: EDS analysis of the near-surface blocks of each group of samples (after being subjected to a temperature of 500 \({}^{\circ}\)C and with a healing period of 28 days): (**a**) Group A; (**b**) Group B; (**c**) Group C.
# SIoT: A New Strategy to Improve the Network Lifetime with an Efficient Search Process
## 4 Our Proposal
### Social Network Creation  
The objective of the proposed social network is to improve the network navigability, support the rapid service search, and initialize a semi-balance of the available resources. The friendship selection strategy uses the obtained groups, AC value, and other criteria to create a social network that can achieve the goals stated above.  
Firstly, each node selects its neighbors that belong to its group as friends. Then, while the maximum number of allowed friends does not exceed \(F_{max}\) (the maximal number of allowed friends), the process of friendship selection is performed using Algorithm 3.
```
1. Notations
2. \(G_{id}^{i}\): The master group of the node i
3. \(c_{s}\): The capability of node x
4. \(L_{x}^{(i)}\): The list of the friends of the node x
5. \(L_{i}^{(n)}\): The list of neighbors of the node i
6. \(L_{i}^{(gf)}\): The list of groups of the friends of node i
7. \(c_{s}^{(s)}\): The sum of capabilities of nodes in \(L_{x}^{(f)}\)
8. \(CR_{j}\): The correspondent criteria are verified by j
9. Output: Social network
10. For each node i
11.  Send \(G_{id}^{i}\), \(c_{i}\), \(\left|L_{i}^{(i)}\right|\), \(c_{i}^{(s)}\), and \(L_{i}^{(gf)}\) to \(L_{i}^{(n)}\)
12. End
13.  For each node j from \(L_{i}^{(ft)}\) do
14.  If \(CR_{j}==\) true
15.  Send confirmation to the node j
16. \(L_{i}^{(f)}=L_{j}^{(f)}+\{i\}\)
17. \(c_{s}^{(s)}=c_{s}^{(s)}+c_{i}\)
18. \(L_{j}^{(f)}=L_{j}^{(f)}+\{j\}\)
19. \(c_{i}^{(s)}=c_{i}^{(s)}+c_{j}\)
20.  Else
21.  Decline the friend request
22. End
```  
**Algorithm 3**Friendship selection  
Secondly, each node i sends friend requests to its neighbors that are accompanied by the group id, the capability, the list of friends, the sum of friends' capabilities, and the list of groups in which its friends belong to. Thus, when a node j receives a friend request, it checks the corresponding criteria with node i, they are verified if each criterion below is true:  
* _Criterion 1:_ Nodes i and j belong to different groups.
* _Criterion 2:_ Non-common friends between groups of i and j.
* _Criterion 3:_ The friends' sum of capabilities of i and j should be less or equal to the AC value, respectively.
* _Criterion 4:_ The number of friends of i and j must not exceed \(F_{max}\). The following set of criteria is taken into consideration when creating a social network:
* The nodes will have the same role, no master node is needed.
* The diversification of friendships in an area, where each node has friends that belong to different groups.
* The enhancement of network navigability.  
* The initialization of a balanced resource between groups (same group capability AC).
* The support of the rapid service search mechanism.
* The degree of friends will be in the shortest range.