esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 State of the Art  
Problems of predictive maintenance usage are presented in several publications. They often consider the use of artificial neural networks [4; 5; 6; 7; 8; 9; 10; 11], fuzzy logic [12; 13; 14; 15], genetic algorithms [16; 17; 18], or expert systems [19; 20; 21; 22].  
In the field of Industry 4.0 and IoT, one of the sectors in which research is particularly active is the area of predictive maintenance (PdM), the purpose of which is to improve the industrial production process [23; 24]. Predictive maintenance is fundamental for modern industries, to improve physical assets availability, for decision making, and to rationalize costs [25]. The need to fulfil the requirements of present-day customers has led to the development of IoT for smart manufacturing and smart logistics [26]. This type of Industry 4.0 method can also be used for sustainable management of maintenance resources [27]. The use of artificial intelligence is popular in the area of predictive maintenance. In [28], the authors investigated the health of an engine through experimental observation using an artificial neural network (ANN). An ANN can also be used for early fault detection [29; 30]. Another interesting publication showed how to use deep learning with LPC and wavelet algorithms for driving fault diagnosis [31]. Also, for the dynamic maintenance strategy, genetic algorithms can be used. In [32] the authors presented the use of an ANN and machine learning for predicting ship maintenance and repair labor. In [33], the authors presented this type of strategy. They considered the structure of multicomponent systems; the maintenance strategy was determined according to the importance of the components. In their research, the authors proved that the strategy could minimize the expected depreciation cost of the system and the system could be divided into optimal groups that met the economic requirements. Also, different researchers [33] have used predicting methods based on clustering and autoencoders for fault detection and diagnosis of industrial processes. To ensure a reliable maintenance strategy, establishing a plan for the entire maintenance process for the assets, from preliminary asset confirmation through to entering the master data in the management system, is necessary [34]. ANN-based simulation models integrated into decision support systems (DSSs) significantly improve decision making in complex scenarios requiring quick responses [35].  
Machine learning (ML) is a branch of the science of artificial intelligence (AI). There are two basic types of machine learning (Figure 1). On the one hand, for supervised learning, classification and regression algorithms are used. The developed predictive model, in this case, is built based on input and output data.  
Unsupervised learning, on the other hand, is based on cluster analysis, i.e., clusterization. In this case, the grouping and interpretation of data are based only on the input data.
# Rifampicin-Loaded PLGA/Alginate-Grafted pNVCL-Based Nanoparticles for Wound Healing
###### Abstract
Sodium alginate has a weak mechanical resistance and in order to overcome this problem, alginate has been chemically modified by reactions in the hydroxyl or carboxyl groups. One of the important strategies to improve and to design the characteristics of biopolymers is to modify them according to the desired features. More particularly, modification strategies have been employed to obtain a self-assembly behavior up to temperature or other external stimuli such as pH, glucose level, magnetic field, etc. A good strategy for biopolymer modification consists of chemical modification via graft copolymerization with synthetic monomers [10; 11]. Among the monomers that can be used to induce thermoresponsive properties on the side chains of a biopolymer via grafting, there are the ones that exhibit lower critical solution temperatures (LCST) close to physiological conditions such as poly(N-isopropylacrylamide and poly(N-vinylcaprolactone) [12; 13; 14]. [PERSON] et al. [15] reviewed the use of polymers that present an LCST and their applications in biomedical field. Poly(N-vinylcaprolactone) (pNVCL) is a water-soluble polymer at room temperature that possesses a lower critical solution temperature (LCST) in the physiological range (32-38 \({}^{\circ}\)C), which extended its applications in the pharmaceutical industry. It is biocompatible, non-toxic, and the second most popular thermoresponsive polymer after poly(N-isopropylacrylamide). The biomedical applications of NVCL include improved drug delivery, tissue engineering, and wound healing [16]. Modification of alginate with a biocompatible monomer such as N-vinylcaprolactone can lead to functional biocompatible materials that import both the characteristics of alginate and NVCL. The present research deals with the study of the copolymer potential of alginate with N-vinylcaprolactam synthesized via graft copolymerization and formulated as nanoparticles by coating with a poly(D, L-lactide-co-glycolide (PLGA) shell. The characteristics of the AgA-pNVCL matrix will be explored as sealant materials for surgical sutures. The motivation for formulating the copolymer with a shell of PLGA is to achieve a smaller burst release and prolonged cumulative release of rifampicin, a hydrophobic antibiotic with good antibacterial and anti-inflammatory properties [17; 18]. PLGA offers the advantage of controlling the release of the incorporated drug along with having a very small particle size, and being at the same time a biocompatible material [19].
## 1 Introduction  
The majority of the world's significant oil and gas fields are situated within the piedmont area [1]. The piedmont of the southwestern area in the Tarim Basin has the potential to be a significant location for oil exploration. The development of multiple sets of source rocks in the research area has initiated a debate about oil sources. The early view was that oil and gas originated from multiple source rocks, including the Carboniferous-Permani, Jurassic, and Cretaceous-Paleogene [2]. Moreover, it was found that the oil and gas in the central and southern study area originated from the Carboniferous, Permian, and Jurassic system, rather than a unified period, and the Jurassic system has the smallest contribution [3; 4; 5]. Not only in the central ad southern study area, but also in the southwestern depression of the Tarim Basin, the source of oil is still uncertain. In April 2010, Well Kedong-1 produced high-yield industrial oil and gas flows. This represents a new condensate gas reservoir following the Kekeya field [6; 7]. Consequently, the debate surrounding the origin of oil in the southwest depression shifted from a long-standing dispute between the Carboniferous and Jurassic periods to the significance of the Perman oil source [8; 9; 10; 11]. In 2023, the Well Qiatan-1 exploration demonstrated considerable industrial production capacity for natural gas, with an estimated natural gas resource of 350 \(\times\) 108 m\({}^{3}\)[12]. Organic geochemical studies have concluded that the hydrocarbons in question originated from the marine mud hydrocarbon source rock of the P\({}_{1\text{-}2\text{-}q}\) Formation in the deeper part of the lower plate of the fracture [13].  
The organic matter sedimentary environment and hydrocarbon source rock analysis are crucial topics in petroleum geology. Through geochemical methods such as Rock-Eval pyrolysis and biomarker analysis, the genesis, types, and potential of hydrocarbons in sedimentary basins are studied [14; 15; 16; 17]. Field exploration of outcrop profiles provides data on lithological and sedimentary features. Pyrolysis, TOC, and Ro can evaluate the abundance, type, maturity, and hydrocarbon generation potential of source rocks [18; 19]. Biomarker data and spectral configurations can distinguish the sources and sedimentary environments of hydrocarbons in rock samples [20; 21; 22]. From the perspective of sedimentary background and lithofacies paleogeography, a relatively stable water environment is conducive to the formation of sedimentary environments that are favorable for the development of source rocks [23; 24; 25]. At present, some exploration wells in the mountain front zone have exhibited oil and gas indications, yet the proven oil and gas resources do not align with the predicted ones. This research focuses on the study of source rocks in the Permian Qipan Formation, which depends on the characterization of petroleum by Rock-Eval pyrolysis and gas chromatography/mass spectrometry (GC/MS). Combining field profiles with geochemical analysis helps establish the correlation between geochemical features and biomarkers found in hydrocarbon extracts from source rocks.
## 4 Results and Discussion
### Flow Analysis of Viscosity Conditions in Simulation
#### 4.1.3 High Viscosity and Mass Inflow Conditions  
With the increased mass inflow, similar to the low viscosity case, asymmetrical vortices occur in the front, back, left, and right directions. This phenomenon indicates a correlation between viscosity and extrusion volume, where lower viscosity and higher mass inflow result in a higher likelihood of vortices. For a viscosity of 30,000 cps, stable extrusion is achievable for a mass inflow ranging from 0.078 to 0.247 kg/s.
# High-Performance Flux Tracking Controller for Reluctance Actuator
## 3 DO-DSMC Controller Coupled with Hysteresis Compensator
### Discrete Sliding Mode Control Rate Design Based on Disturbance Observer  
In practice, after the inverse model compensation, the nonlinear system is transformed into a linear system with disturbance, namely \(G(z)\). In [37], in order to attenuate the  
Figure 4: Control scheme.  
Figure 5: Scheme of the inverse compensation of hysteresis in reluctance actuator.  
disturbance, the discrete sliding mode control rate design based on the disturbance observer was used. Specifically, the discrete state equation of the system is expressed as Equation (5):  
\[x(k+1)=Ax(k)+B[u(k)+d(k)] \tag{5}\]  
where \(d(k)\) denotes disturbance.  
Given input flux signal \(x_{d}(k)\), the tracking error is written as \(e(k)=x(k)-x_{d}(k)\), the sliding surface is expressed as:  
\[s(k)=Ce(k) \tag{6}\]  
where \(C=[c\,1]^{T},c>0\).  
According to Expression (5), the sliding mode control rate with disturbance estimation is designed:  
\[u(k)=u_{s}(k)+u_{c}(k) \tag{7}\]  
What should be explained is that  
\[u_{s}(k)=(C^{T}B)^{-1}[C^{T}x_{d}(k+1)-C^{T}Ax(k)+qs(k)-\eta\text{sgn}(s(k))] \tag{8}\]  
\[u_{c}(k)=-\hat{d}(k) \tag{9}\]  
In (9), \(\hat{d}(k)\) is the disturbance observer, which is written as:  
\[\hat{d}(k)=\hat{d}(k-1)+(C^{T}B)^{-1}g[s(k)-qs(k-1)+\eta\text{sgn}(s(k-1))] \tag{10}\]  
Assuming \(\overset{\sim}{d}(k)=d(k)-\hat{d}(k)\), \(\eta\), \(q\), \(g\) is a positive real number.  
Combining (5) and (7), Expression (11) is obtained.  
\[\begin{array}{ll}s(k+1)&=C^{T}e(k+1)=C^{T}x(k+1)-C^{T}x_{d}(k+1)\\ &=C^{T}(Ax(k)+Bu(k)+Bd(k))-C^{T}x_{d}(k+1)\\ &=C^{T}Ax(k)+[C^{T}x_{d}(k+1)-C^{T}Ax(k)+qs(k)-\eta\text{sgn}(s(k))]\\ &-C^{T}B\hat{d}(k)+C^{T}Bd(k)-C^{T}x_{d}(k+1)\\ &=qs(k)-\eta\text{sgn}(s(k))+C^{T}B\overset{\sim}{d}(k)\end{array} \tag{11}\]  
According to Equations (10) and (11), we can find  
\[\begin{array}{ll}\overset{\sim}{d}(k+1)&=d(k+1)-\overset{\sim}{d}(k+1)\\ &=d(k+1)-\hat{d}(k)-(C^{T}B)^{-1}g[s(k+1)-qs(k)+\eta\text{sgn}(s(k))]\\ &=d(k+1)-d(k)+\overset{\sim}{d}(k)-(C^{T}B)^{-1}g(C^{T}B)\overset{\sim}{d}(k) \\ &=d(k+1)-d(k)-(1-g)\overset{\sim}{d}(k)\end{array} \tag{12}\]  
More specifically, in Appendices A and B, the stability and convergence of the controller are also proved. Specifically, a convergence analysis of disturbance observer is shown in Appendix A and stability analysis was provided in Appendix B.
# Intelligent Transportation System Technologies, Challenges and Security
## 2 Intelligent Transportation Systems
### Intelligent Transportation Communication Technologies
#### 2.2.4 Radio Frequency Identification  
Radio Frequency Identification (RFID) systems are used in toll collection systems in places such as bridges and highways during the passage of vehicles. These structures, which have standards, can be used in the field of ITS as both active and passive systems [25].
# Glove Prototype for Feature Extraction Applied to Learning by Demonstration Purposes
## 3 Theoretical Concepts
### Quaternion-Based Complementary Filter  
Complementary filters are the simplest methods for sensor fusion. They are simple to comprehend and are computationally inexpensive. They consist of a low-pass filter and a high-pass filter for each filter with the same cut off frequency. It can also be interpreted as simple scalar gain (\(\alpha\)) that describes the trust in each estimation.  
Let us look at a gyroscope-accelerometer sensor fusion example. We compute a gyroscope estimation and an accelerometer estimation, and we aim to estimate the true orientation of the body.  
\[\mathbf{q}=(1-\alpha)\mathbf{q}_{\omega}+\alpha\mathbf{q}_{a} \tag{9}\]  
Here, quaternions and linear interpolation (LERP) are used because it is a good approximation for small differences between them. If a more accurate interpolation is desirable, a spherical linear interpolation (SLERP) can be used like so:  
\[\widehat{\mathbf{q}}=\frac{\sin([1-\alpha]\Omega)}{\sin\Omega}\mathbf{q}_{ \mathbf{w}}+\frac{\sin(\alpha\Omega)}{\sin\Omega}\mathbf{q}_{\mathbf{a}} \tag{10}\]  
The gain \(\alpha\) describes how close it is from one of the two quaternions, hence the trust in each estimation. For example, a gain of 0 means we fully trust the gyroscope orientation prediction, and a gain of 0.5 means both estimations are equally trusted, and the result of the estimation is a quaternion right in the middle of both gyroscope and accelerometer quaternions.  
This method works very well, for example, for quadcopters that only aim to correct the tilt and ignore the heading or when the magnetometer is used and the heading can be computed (note that quadcopters can still take advantage of heading to fuse magnetometer and GPS data).  
Because an accelerometer lacks the heading information, a 0 degrees angle is assumed. However, the gyroscope can track heading from its original orientation, thus presenting a challenge when fusing both. Gyroscope orientation prediction will track heading and accelerometer orientation prediction will always try to pull the heading back to 0 degrees. This can be fine for some applications, but for the data glove this is not intended.  
To get around this problem, a correction-based method can be used. It works by taking orientation prediction of the gyroscope and then correcting that prediction withdelta quaternions. This method separates the correction of the attitude and correction of the heading from the accelerometer and the magnetometer, respectively.  
\[\mathbf{q}=\,\mathbf{q}_{\omega}\,\Delta\mathbf{q}_{\text{acc}}\,\Delta\mathbf{q }_{\text{mag}} \tag{11}\]  
However, because there are no magnetometer measurements, it is assumed that the magnetometer delta quaternion is a unit quaternion which means no correction.
## 3 Materials and Methods
### Classic Machine Learning Pipeline
#### 3.2.1 Pre-Processing  
To eliminate the differences in voxel size between different datasets and to reduce noise, voxel size resampling and grey-level discretization were used. The images were first resampled using the B-spline interpolation algorithm to \(1\times 1\times 1\) mm\({}^{3}\) isotropic voxels [39]. The Hounsfield Units (HU) values were normalized using the min-max normalization method, considering the interval [\(-1000\), \(400\)]. The values inside the interval were converted linearly to the interval [0,1], and values below -1000 were transformed in 0 and values above 400 HU were converted in 1. Two input forms were evaluated: (1) the use  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Dataset** & **Class** & **Total** & **Train** & **Validation** & **Test** \\ \hline \multirow{3}{*}{Lung-PET-CT-Dx} & ADC & 221 & 146 & 31 & 44 \\  & Non-ADC & 64 & 38 & 13 & 13 \\  & Total & 285 & 184 & 44 & 57 \\ \hline \multirow{3}{*}{NSCLC-Radiomics} & ADC & 47 & - & - & 47 \\  & Non-ADC & 238 & - & - & 238 \\  & Total & 285 & - & - & 285 \\ \hline \multirow{3}{*}{NSCLC-Radiogenomics} & ADC & 102 & - & - & 102 \\  & Non-ADC & 25 & - & - & 25 \\ \cline{1-1}  & Total & 127 & - & - & 127 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characterization of the final training, validation, and testing subsets, grouped by class, for the experiments performed in this work.  
of just one 2D tumour slice, and (2) the use of a 3D tumour volume. For the 2D input, the slice chosen was always the one where the tumour segmentation mask presented the largest area. The pre-processing protocol used for the 2D slices and 3D volumes is shown in Figure 3.
## 5 Stability of System
### Effects of P and G  
The effects on the Q factor when P and G deviate from the theoretical values are analyzed above, respectively. In practical applications, P and G often deviate from the theoretical values at the same time. Figure 19 shows the relationship of the Q factor with the P and G. In Figure 19, the blue marking line indicates a Q factor greater than 6, and the black marking point is the Q factor when G = 6 and P = 33.75\({}^{\circ}\). It can be seen that when G = 6, and P = 33.75\({}^{\circ}\), the Q factor reaches the peak value of 10.82.
## 3 Benchmark Results
### Symmetric Cryptography
\(\bullet\) In [1] the performance of block ciphers in a PDA with a StrongARM SA-1100 processor at 200 MHz was evaluated. This device is slower than the rest ones presented here; however, the result for AES-128 working in ECB cipher mode is very good, 1470.5 KB/s. Figure 2: Throughput (represented with bars) and Energy Performance (represented with dots) of block ciphers. Our AES results (AES-256: 1035.29 KB/s in iPAQ3970 and 4724.55 KB/s in Hx2790) are much better than [2,3], which use similar hardware. We impute the differences with [3] to the way of implementing the algorithms. They wrote the tests in C# using Microsoft.NET programming framework. We programmed them in plain C, using the well-tested OpenSSL libraries. On the other hand, [PERSON] _et al._[2] also coded the tests in C. However, we outperformed their results a little. This is because they tested the cipher with very small input data (128 bits) so we cannot really compare the results; the operational costs of the initialization are not amortized if working with small input files. Finally, we compare the results with [1], that used an optimized self implementation library of AES. Our results are worse bearing in mind we use faster processors, so the implementation of OpenSSL can be clearly optimized. Regarding energy results, they are in accordance with other studies, like [5], in the sense that AES-128 is about a 60% more efficient than DES, and 3 DES, as expected, is three times more consuming than DES. However, values differ substantially depending on the device and implementation. For example, [PERSON] _et al._[3] report that the iPAQ4150 (similar to our iPAQ3970) has an outcome of 0.37 MB/J for AES-256, while we measured 1.53 MB/J. On the other hand, [PERSON], _et al._[1] use a StrongARM SA-1100 processor at 200 MHz with, as we have seen before, has an excellent temporal performance. In this case the expended energy is also high comparing with our results, since it states an outcome for AES-128 of 0.04 MB/J while ours is 2.11 MB/J in iPAQ3970. Finally, [PERSON] _et al._[5], using a similar device to [1] (a StrongARM SA-1110 at 206 MHz) have an outcome of 2.12 MB/J.
## 4 Analysis of Data Sources and Evaluation Factor Selection
### Analysis of Evaluation Factor Selection
#### 4.2.3 Slope Orientation  
Slope orientation refers to the direction of slope orientation, which affects landslides by determining the light time, water/heat ratio, and precipitation distribution on the hillside. It is an important indicator for determining the direction of sliding, and is an important guide for the prediction and avoidance of geological hazards. Slopes facing south are usually considered as quasi-sunny slopes and slopes facing north are considered as standard shady slopes. Due to the different orientations, there are regular differences in the minor environment of the slopes. In general, sunny slopes have longer sunshine hours, greater solar altitude, stronger solar radiation, more abundant heat, higher air and water temperatures, greater daily differences in temperature, and differences in the slope  
Figure 5: Tekes County slope classification and grading map.  
orientation of hydrothermal conditions than shady slopes, leading to regular differences in the natural geographic elements on slopes of different orientations. In general, it appears that shady and semi-shady slopes distribute more geohazards overall than sunny and semi-sunny slopes and that slope orientation affects evapotranspiration, intensity of solar radiation, and changes in vegetation and environmental ecology. The short sunshine hours and weak vegetation cover conditions on shaded slopes lead to reduced slope stability and susceptibility to geohazards. The development of slope flora, water evaporation, and weathering intensity are all impacted by the differing slope orientations' exposure to solar radiation. As shown in Figure 6, there are nine directions that make up the slope direction: plane, northeast, east, southeast, south, southwest, west, northwest, and north. Geological disasters known as landslides are common on the research area's slopes facing the northwest, northeast, west, and north.
## 5 Research Directions
### Blockchain-Based Distributed Security in 6G  
Blockchain is evolving as a prime distributed ledger technology in 6G communication. However, several challenges require in-depth research for optimum blockchain integration in future network infrastructure. The prominent challenge is the blockchain architecture design for secure integration with ultra-low latency and high-throughput in massive 6G-enabled IoTs [171]. Similarly, there are several dominating shortcomings in blockchain technology itself that need critical research to achieve an optimum distributed framework in 6G-enabled networks. The technological obstacles in blockchain include (1) scalability for block validation in massive communication, (2) privacy leakage due to public visibilityof transactions and (3) selfish mining through blockchain reversal in over 51% of processing power nodes [172].
# A New Partitioned Spatial-Temporal Graph Attention Convolution Network for Human Motion Recognition
###### Abstract  
At present, human action recognition can be used in all walks of life, because the skeleton can transmit intuitive information without being affected by environmental factors. However, it only focuses on local information. In order to solve these problems, we introduce a neural network model for human body recognition in this paper. We propose a model named NEW-STGCN-CA. The model is based on a spatial-temporal graph convolution network (ST-GCN), which contains a new partition strategy and coordination attention (CA) mechanism. By integrating the CA attention mechanism model, we enable the network to focus on input-related information, ignore unnecessary information, and prevent information loss. Second, a new partitioning strategy is proposed for the sampled regions, which is used to enhance the connection between local information and global information. We proved that the Top-1 accuracy of the NEW-STGCN-CA model in the NTU-RGB+D 60 dataset reached 84.86%, which was 1.7% higher than the original model; the accuracy of Top-1 on the Kinetics-Skeleton dataset reached 32.40%, which was 3.17% higher than the original model. The experimental results show that NEW-STGCN-CA can effectively improve the algorithm's accuracy while also having high robustness and performance.  
deep learning; human action recognition; ST-GCN; attention mechanism; skeleton recognition Article