esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
###### Abstract
An equivalent network is shown in Figure 1.  
There are two main features of 5G. Firstly, _dynamic channel access_, whereby a SU node senses underutilized channels in licensed channels (or white spaces) owned by PUs, and subsequently accesses the white spaces in an opportunistic manner without causing unacceptable interference to PU activities in order to improve the overall spectrum utilization. Secondly, D2D communication, whereby a node can communicate directly with its neighboring nodes without passing through a BS, which helps to (a) offload traffic from highly-utilized BSs, particularly the MC BSs, to improve load balancing and reduce traffic congestion at BSs, and (b) extend coverage. This paper proposes a cognition-inspired hybrid route selection scheme called CenTri for 5G network scenarios to embrace these two main features. CenTri consists of centralized and distributed route selection mechanisms and establishes multi-hop routes across the macrocell and small-cell layers. The centralized route selection mechanism by the CC adopts dynamic channel access to address the dynamicity of channel availability. In contrast, the distributed route selection mechanism adopts D2D to address heterogeneity and ultra-densification. Cognition enables a decision-maker (or an agent) to observe the respective operating environment and learn about the right decisions on the route selection in the operating environment at any time instant. In CenTri, cognition is embedded in the source node, which is the final 'decision maker' to learn about and select the best possible route with a high amount of white spaces to facilitate the traffic offload from the macrocell through traffic distribution to the femtocell layer. The CC sends a list of routes, which are ranked (or prioritized) based on the route length (or the number of hops), to the source node. The source node reranks the routes while considering the traffic offload, using the femtocell layer and the congestion level of the MC BS.
###### Abstract  
This study aims to obtain findings on the internal water behavior, the presence of water channels, and the degree of washout due to rainfall infiltration in Japanese municipal solid waste (MSW) final disposal sites. Electrical resistivity tomography (ERT) monitoring and undistributed waste sampling for X-ray computed tomography (X-ray CT) analysis were conducted in the field. The study sites were targeted at Site A, which is mainly composed of non-combustible residues, and Site B, which is mainly composed of incineration ash. The time-dependent resistivity distributions obtained from real-time ERT monitoring were effective for us to understand the water content distribution after water infiltration during water injection tests. As a result, the global flow behavior and the local water channel flow were determined. In addition, X-ray CT analysis of the undisturbed waste samples obtained from the sites clarified the different pore structures, indicating the possibility of more advanced washing out at Site A than at Site B. Furthermore, the soil cover layer and gas extraction wells had a significant effect on the resistivity structure with respect to water flow behavior. Since soil cover layer and gas extraction wells are significant factors affecting waste stabilization by washout, it is suggested that these factors should be considered in the design and maintenance of landfills.  
electrical resistivity tomography; waste landfill; in situ real-time monitoring; water channel flow; X-ray computed tomography 2020
# Blockchain-Based Multistage Continuous Authentication for Smart Devices
###### Abstract  
With the increasing connectivity between multiple smart devices in the Internet of Vehicles, privacy and security have become stringent threats due to unauthorized access. To overcome this issue, designing continuous authentication systems has become an important research topic because of the advantages of continuous monitoring of users after the initial access to the smart devices. Unfortunately, the existing systems are based on a third-party centralized structure, and most of them suffer storage pressure on equipment, thus resulting in significant security hazards and limited performance. In this paper, we propose a multistage continuous authentication system based on blockchain technology and the IPFS, which achieves decentralization and reduces storage pressure. In the first stage of authentication, we adopt Hyperledger Fabric to implement the underlying technical architecture of the blockchain to enhance the security and reliability of identity parameters. The preoutputs of the first-stage authentication are compared against behavioral biometric characteristics stored in the IPFS that aim to accomplish the final authentication. In particular, we use fuzzy extractors to deal with behavioral biometric feature templates, thus solving the privacy problem caused by user information leakage. To evaluate the security of our system, we prove the correctness of the communication protocol and two-way authentication of the scheme using BAN Logic. Furthermore, we use Hyperledger Caliper to analyze the impact of the sending rate of authentication requests on various performance parameters such as throughput, memory, and CPU utilization of the authentication system. Security and experimental results show that (i) We solve the problem of centralized authentication and can resist replay attacks. (ii) Our scheme can maintain high throughput and effectively reach consensus. Compared to related works, the throughput is improved by 8.6%.  
blockchain; multistage continuous authentication; gait recognition; fuzzy extractor +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### Compatibility Rate  
The so-called 'compatibility' refers to the nature of the combination of two substances and mutual adaptation. Based on the theory of SFE, the influence of aging on the compatibility of asphalt and limestone aggregate is analyzed from the point of view of moisture damage with the compatibility rate as the evaluation index. The results are shown in Figure 8.  
Figure 8: _Cont._  
Figure 8 shows that the compatibility ratio of the three kinds of asphalt and limestone aggregates decreases with the aging time, but the variation range is slightly different. The compatibility ratio of matrix asphalt, Evotherm M1 warm asphalt, and limestone aggregate decreased with the aging time, and stabilized after 150 h of UV aging. With aging time increase, the compatibility ratio of Sasobit WMA decreased and then increased. At 100 h of UV aging, the inflection point appeared, that is, the compatibility decreased first and then increased. Specifically, compared with the neat asphalt, the compatibility ratio of the matrix asphalt and the Evotherm M1 warm asphalt after 150 h of UV aging was reduced to 0.44 and 0.63, respectively, and the rates of decline were 90.52% and 97.24%. Different from Evotherm M1 WMA, Sasobit WMA had the lowest compatibility rate after 100 h of UV aging, only 0.36, a decrease of 83.03%. The compatibility rate was 0.88 at 200 h of UV aging, and 0.52 on the basis of 100 h of UV aging, which was equivalent to the compatibility of RTFOT aging.  
The calculation results of the compatibility ratio after aging indicate that, for the same limestone aggregate, the base asphalt, Sasobit warm asphalt, and Evotherm M1 warm asphalt, Evotherm M1 warm asphalt had the worst compatibility with limestone. The compatibility between Sasobit WMA and limestone is relatively good--that is, the asphalt mixture of Sasobit WMA and limestone aggregate has relatively optimal water damage under UV aging condition.
## 2 Endocannabinoid System (ECS)
### ECS--Cannabinoid Receptors
#### 2.1.3 ECS  
Enzymes Regulating Biosynthesis and Biodegradation of Cannabinoids  
The most prominent and well-studied endocannabinoids acting as transmitters in the ECS are anandamide (AEA) and 2-arachidonovglglycerol (2-AG). They follow distinct pathways of biosynthesis and degradation. Anandamide is biosynthesized from N-arachidonovlphosphatidylylethanolamine (NAPE). In turn, NAPE arises by transfer of arachidonic acid from arachidonic acid containing phospholipids (i.e., lecithin) to phosphatidylethanolamine through an N-acyltransferase enzyme. Subsequently, involving the enzyme N-arachidonovlphosphatidylylethanolamine phospholipase D (NAPE-PLD), from NAPE, phosphatidic acid is removed, which leads to anandamide creation. However, anandamide synthesis from NAPE may also occur via other pathways and includes enzymes such as phospholipase A2, phospholipase C, or ABHD4 enzyme; during transformations mediated by these enzymes, various intermediate compounds are formed from NAPE. The natural occurrence of anandamide is minimal, and it has a brief duration of activity because of the enzymatic action of fatty acid amide hydrolase (FAAH), which breaks it down into free arachidonic acid and ethanolamine [25; 26].  
Regarding 2-AG, it is synthesized from arachidonic acid-containing diacylglycerolcerol (DAG), which is derived from phosphatidiloinositol or phosphatidic acid (membrane phospholipids) involving the activity of phospholypose C\({}_{\beta}\) (PLC\(\beta\)). DAG is subsequently hydrolyzed by the enzyme diacylglycerollose (DAGL), resulting in the formation of 2-AG. Another pathway for its biosynthesis also exists--instead of DAG, with the involvement of the enzyme phospholipase A2 (PLA2), 2-arachidonvl-3-phosphate is produced, which is then transformed into 2-AG through phospholipase C. Similar to anandamide, shortly after its formation, it undergoes biodegradation, facilitated by the enzyme monoacylglycerolloser lipase (MAGL), leading to the creation of arachidonic acid and glycerol [25; 26]. The biological significance of these endocannabinoids is detailed in the subsequent section.  
Figure 2: A. Distribution of CB1 (**A**) and CB2 (**B**) receptors in the body. The figure was created with BioRender.com, accessed on 29 April 2024.
## 4 Presumptions and Network Structuring
### State of the Art
#### 4.3.1 Data Aggregation at Source Level  
The data collected by the sensor nodes in WSNs are highly correlated due to weather monitoring. It gives redundant data. If sensor nodes send all the collected data then energy wastage will lead to depletion of network lifetime. In WSNs, each period is divided into equal time slots. Periodic data collection forms a vector measure of the data. Suppose, in WSN \(S\), period \(p\) is divided into time slots as \([a_{1},a_{2},\ldots\ldots a_{T}]\) having vectors of measurements \(N_{i}=[n_{i1},n_{i2},\ldots\ldots\ldots,n_{ir}]\). Suppose \(S\) takes 4 measurements at the end of each period. It may happen that \(N_{i}\) contains homogeneous measurements. So, the Similar function is assigned to identify whether two data measurements are similar or not. The Similar function for period \(p\) can be defined as:  
\[\text{Similar}\left(n_{ij}n_{ik}\right)=\left\{\begin{array}{c}1\text{ if }\|n_{ij}-n_{ik}\|\leq\Psi\\ 0\text{ elsewise}\end{array}\right.\]  
where, \(n_{ij},n_{ik}\) are two similar measurements of a data vector and \(\Psi\) is the user-defined threshold value.  
The weight of data \(n_{ij}\) is the number of similar measures of a vector. The source level aggregation searches for similar measurement of a data vector. In case it finds any redundant data, it deletes the same and updates the weight to 1.
# Quality Assessment of Photogrammetric Methods--A Workflow for Reproducible UAS Orthomosaics
## 4 Discussion
### Time Series  
The combination of multiple reproducibility layers enables the validation of UAS derived orthomosaic timeseries. The high reproducibility of multi-temporal grassland orthomosaics confirms the valid analysis of vegetation dynamics in grassland and agricultural studies. Forested area time series are also possible, however non-reproducible regions have to be considered.  
The checkpoint error of each orthomosaic in the time series alone gives no insight into the positional relation between the individual time steps. Image acquisition with the UAV, analysis tools (processing software), field experiment designs and environmental conditions have a strong impact on the geometric accuracy of photogrammetric products [6; 45; 46]. Hence, it is essential to quantify the geometric accuracy on aerial imagery when combining UAS data from different flights, dates and sources [47]. We suggest the addition of geometry-based deviations from digitized objects. In case of the grassland time series, a maximum positional shift of 0.07 m between the time steps is tolerable for most use cases such as the modelling of the temporal dynamics of biophysics and biochemical variables of the meadow canopy or even analyze the variability in size and distribution of vegetation patterns ([PERSON] et al. in prep). This error also lies in the range of the individual checkpoint errors (0.04 to 0.08 m). In the forest time series, similar accuracies were achieved in the surrounding meadow areas. However, the canopy showed positional deviations of up to 0.5 m in digitized trees. A proportion of this error comes from the actual movement of the canopy due to wind and changes in the lighting conditions [44]. The non-reproducibility of some parts of the canopy, especially at forest clearings may also contribute to this error. We suggest object-based analysis instead of pixel-based approaches when high-resolution forest time series are regarded.
# On the Entropy of Oscillator-Based True Random Number Generators under Ionizing Radiation
## 4 Experimental Results  
As stated before, the FPGA power supply has been continuously monitored during the experiment. In Figure 4 is depicted the FPGA current during the experiment. As reported in other similar FPGA radiation experiments [24], the current increases almost linearly with the accumulated dose. It is important to note that during all the experiment the current is within the limits of the manufacturer recommendations for this board. At pre-irradiation conditions, the measured current was 77.9 mA, reaching 83.2 mA at the end of the experiment [45 krad(Si)]. Meanwhile, the power supply was fixed to 5 V. The first communication error appeared at 38.1 krad(Si) (83.1 mA) and the final valid bit-stream (correct CRC) was captured at 40.8 krad(Si) (83.2 mA). These results are in line with those reported in [24] where some Igloo FPGAs were irradiated at room temperature using JPL's Co-60 source. In these experiments, functional failures appear from 30 krad(Si) to 40 krad(Si) of accumulated dose.  
Figure 3: Radiation setup. (**a**) DUT setup; (**b**) Irradiator.  
Finally, in Table 1 the p-values obtained with NIST tests suite are summarized for the two TRNGs at pre-irradiation conditions.
## Results and Discussions
### FTWR Desorption Thermodynamic Analysis  
A thermodynamic analysis was performed to further reveal the nature of the desorption behavior. The thermodynamic parameters of the desorption process include Gibbs free energy change (\(\Delta\)G), enthalpy change (\(\Delta\)H), entropy change (\(\Delta\)S), and the related calculation equations are as follows [37]:  
\[\text{K}_{\text{C}}=\frac{C_{e}}{\text{q}_{\text{e}}} \tag{6}\]  
\[\Delta\text{G}= -\text{RT }\ln\text{ K}_{\text{C}} \tag{7}\] \[\ln\text{ K}_{\text{C}} =\frac{\Delta\text{S}}{\text{R}}-\frac{\Delta\text{H}}{\text{RT}} \tag{8}\]  
where K\({}_{\text{C}}\) is the thermodynamic equilibrium constant, and R and T represent the universal gas constant (8.314 J/mol K) and solution temperature (K), respectively. The values of \(C_{e}\) and q\({}_{\text{e}}\) are obtained from Equation (4), the value of \(\Delta\)G is obtained from Equation (7), and the values of \(\Delta\)H and \(\Delta\)S are determined by the slope and intercept of the curve fitting in Figure 6.  
The thermodynamic parameter values of these desorption processes are summarized in Table 9.  
The experimental data of \(\Delta\)G < 0 showed that the desorption of organic matter from the particles was a spontaneous process, and its size mainly depends on the strength of adhesion. The value of \(\Delta\)H was 12.092 kJ/mol, which showed that the reaction was an endothermic process, which also explains why the increase in temperature is beneficial to the desorption of organic matter. A positive value of \(\Delta\)S indicates that the confusion of  
Figure 6: The plot of lnKc vs. 1/T for thermodynamic parameters determination.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**T** (\({}^{\circ}\)**C**)} & \(\Delta\)**G** & \(\Delta\)**H** & \(\Delta\)**S** \\  & **(kJ/mol\({}^{-1}\))** & **(kJ/mol)** & **(J/mol K)** \\ \hline
25 & \(-\)20.702 & & \\
50 & \(-\)23.451 & & \\
60 & \(-\)24.563 & 12.092 & 110.049 \\
70 & \(-\)25.784 & & \\
80 & \(-\)26.603 & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Thermodynamic parameter value.  
the whole reaction has increased. During the desorption process, the vacancies left by the organic molecules will be occupied by solvent molecules. In addition, the positive value of \(\Delta\)S also showed that was not only van der Waals force in the desorption system, but other strong forces such as hydrogen bonds or chemical bonds could exist.
## 3 Results and Discussion
### Micromechanism of Bonding Property Reduction  
In the experiment, a thin layer of ECC was peeled with a steel plate, irrespective of whether shear or tensile stress caused the failure, as shown in Figure 14. The micromechanical behavior of bonding interface is related to the microstructure and properties of bonding interface. Two interfaces exist from the microscopic perspective: the interface between adhesive and ECC and the interface between adhesive and steel bridge deck. Some water from the ECC mix was retained between the ECC and epoxy, so it was difficult to guarantee the interface performance between adhesive and ECC. Based on the microbonding mechanism, the bonding force of ECC and adhesives is composed of chemical bonding force, van der Waals force, and mechanical biting force. In this study, the chemical bonding force is the adsorbing force that is produced by the cement gel on a colloid surface. It originated from the growth and hardening of cement crystal during the penetration and curing of cement paste into the colloid layer, which depended on the properties of cement and the roughness of colloid surface. However, the adhesion force was lost when the interface was deformed and slipped. Van der Waals force is an attractive force that exists between molecules. Chemical forces and van der Waals forces coexist on the surfaces, but their proportion in surface forces depends on the specific situation.  
Vibration and hardening of cast-in-situ ECC along the interface, the colloid thickness between steel plate and ECC is not completely consistent, owing to the influence of pouring (shown in Figure 15). This affects the interface strength. The effect of interface morphology on shear is obvious, because the concave and convex surfaces at the interface caused the two sides of the interface to have a significant mechanical biting effect. When the interface was formed, the side of concave and convex part provided an excess bonding area as compared to the plane area. The increase in contact area was beneficial to the strength of interface along the vertical direction (i.e., pull-off strength), even though the effect was weak.  
Microscopic interface bonding morphology was observed by using an SEM in this study. The results confirm that the presence of many small defects at the interface between ECC and adhesive. Those microholes or microcracks not only reduced the bearing area, but also became the channel through which water entered the interface and caused damage. Figure 16 shows that exposure to freeze-thaw cycles and the hot and humid environment increased the interfacial cracks. The presence of water further reduced the effective bearing area, worsened the interface bonding ability, and reduced the macroperformance.  
Figure 14: Schematic diagram of interfacial failure.
###### Abstract  
The growing size and complexity of industrial datasets have intensified the need for efficient fault diagnostics tools. This study addresses the challenge of handling large-scale data by developing a data-driven architecture for fault classification in industrial systems. To extract meaningful insights, 15 time-domain features were combined with 5 Feature Selection Methods to optimize model performance by eliminating redundant features. The sensor data and selected features were analyzed using the Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) algorithms to enable accurate fault detection and prediction. The proposed framework was validated using publicly available datasets, namely the Case Western Reserve University (CWRU) bearing dataset and the National Aeronautics and Space Administration Ames Prognostics Center of Excellence (NASA PCoE) lithium-ion battery dataset. The results demonstrate the framework's adaptability and high efficacy across diverse scenarios, achieving an average F1-score exceeding 98.40% using only 10 selected features. This finding highlights the effectiveness of embedded Feature Selection Methods in improving classification performance while reducing computational complexity. The study underscores the potential of the proposed framework as a foundational tool in intelligent manufacturing, offering a versatile solution to enhance fault diagnostics in diverse industrial applications.  
fault classification; feature selection; long short-term memory; support vector machines; time-domain feature extraction Article
# Efficient Deep Learning-Based Detection Scheme for MIMO Communication Systems
## 3 Proposed DL-Based Detection Scheme
### Direct Symbol Encoding (DSE)  
In the DSE labeling strategy, we take the bit labels defined in Table 2, where each two-bit label for each Tx antenna is concatenated. An example considering a 4-QAM modulation is presented in Table 3. First, we indicate the original symbol index and then concatenate the corresponding label as referenced in Table 2. The number of different class labels is the number of possible combinations \(Nc=\log_{2}(M)\times N_{t}\). The activation function that provides better performance in this case is a sigmoid since the entries in Table 3 are binary but contain multiclass values, that is, more than one value of the binary label contains a \"1\" bit in all cases. This label encoding is useful when the available hardware and processing capacity are limited.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Transmitted** & **Bit Label** \\  & **Symbol** & **Mapping** \\ \(\mathbf{a_{1}}\) & \(\mathbf{a_{2}}\) & **Mapping** \\ \hline \(s_{1}\) & \(s_{1}\) & 1 0 1 0 \\ \(s_{1}\) & \(s_{2}\) & 1 0 1 1 \\ \(s_{1}\) & \(s_{3}\) & 1 0 0 0 \\ \(s_{1}\) & \(s_{4}\) & 1 0 0 1 \\ \(s_{2}\) & \(s_{1}\) & 1 1 1 0 \\ \(s_{2}\) & \(s_{2}\) & 1 1 1 1 \\ \(s_{2}\) & \(s_{3}\) & 1 1 0 0 \\ \(s_{2}\) & \(s_{4}\) & 1 1 0 1 \\ \(s_{3}\) & \(s_{1}\) & 0 0 1 0 \\ \(s_{3}\) & \(s_{2}\) & 0 0 1 1 \\ \(s_{3}\) & \(s_{3}\) & 0 0 0 0 \\ \(s_{4}\) & \(s_{1}\) & 0 1 1 0 \\ \(s_{4}\) & \(s_{2}\) & 0 1 1 1 \\ \(s_{4}\) & \(s_{3}\) & 0 1 0 0 \\ \(s_{4}\) & \(s_{4}\) & 0 1 0 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: DSE labeling of symbols using as reference Table 1. MIMO system with \(N_{t}=2\) and 4-QAM modulation.