esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed System
### Working Methodology  
To meet the requirements for the current need-of-the-hour initial screening, N-number of FCSN systems could be implemented at N-number of different locations to perform screening. The N-FCSN systems could store the health data of the subject and further transmit the encrypted version to a wireless-connected cloud database server. The detailed flow of the working methodology for the proposed system is presented in Figure 7. The step-by-step procedure is as follows:  
1. Installation of N-FCSN system nodes at N-locations
2. At different locations, health and social data is fetched by each FCSN system node with click buttons provided such as: 1. Fetch social data as a facial image by contactless means through the attached camera with FCSN. 2. Fetch human body temperature by contactless means using an infrared thermopile attached with FCSN. 3. Fetch present location of screening using GPS locating sensor attached with FCSN where facial image and temperature were fetched.
3. Thereceived data is pre-processed and if the proper data cannot be fetched at a particular FCSN node location, then Step-2 is repeated at the respective location.
4. The fetched data is displayed on the local display screen with each FCSN and also stored on the local database server in the prescribed format.
5. The received data is then encrypted to secure transmission and pre-processing is performed to remove any redundancy.  
Figure 5: IoT-fog computing-based multi-node (FCMN) architecture.  
Figure 6: Detailed hardware interfacing of FCMN architecture.  
* Transmission of encrypted data from FCSNs deployed at different locations to cloud databases using a wireless internet connection.
* At the centralized cloud database server, the social facial image, health data and location of capture are stored for run time monitoring and analysis by the authorized health worker and local government officials.
## 6 Results
### Split Tensile Strength
This could be attributed to enhanced microstructure at 90 days, which assists to form a strong bond between cement paste binder and fibers. Therefore, the concrete samples having silica fume gained more strength as compared to concrete samples with no silica fume, it can be seen in Figure 3.  
Relative analysis of the split tensile strength test is displayed in Figure 4a. A relative analysis is performed on concrete samples to show that how much its performance is improved concerning the control sample. It is noted that adding nylon fiber and silica fume, (RCA) level can be exploited to enhanced performance as compared to the reference sample. For example, RCA50-SF20 and RCA50-SF20-NFRC exhibit improved performance as compared to the reference sample at 28 days of curing. Likewise, at 90 days of curing RCA100-SF20 and RCA100-SF20-NFRC also displayed improved performance as compared to the reference sample at 28 days of curing. It can be concluded that the joint utilization of silica fume, nylon fiber can assist to enhance the (RAC) performance. Statistical analysis was performed between compressive strength and split tensile strength, and a strong correlation has been observed between compressive strength and split tensile strength with a value of R\({}^{2}\) greater than 90% as shown in Figure 4b. The tensile strength of concrete is a very significant property that is considered in the design process. It makes a considerable impact on other strength properties of the structural elements such as flexural strength. Concrete is weak in tension (about 10% of concrete compressive strength) and strong in compression which results in brittle failure. This is undesirable for any structural member. Therefore, concrete needs some type of tensile reinforcement to balance its brittle conduct and enhances its tensile strength. The addition of fibers is one of the most widespread methods to improve the tensile strength of concrete. Fiber slows the cracking process by arresting the propagation of cracks and increase the energy absorption capacity of the structure. In the design of reinforced concrete members, the tensile strength of the concrete is generally ignored. However, the tensile strength of concrete still has significance in terms of durability and serviceability. For example, the propagation and control of cracks are highly related to the tensile strength of concrete. Ignorance towards the tensile strength of concrete may lead to problems related to service life and durability of concrete, hence concrete must have the least tensile strength to resist all the applied load effectively. Figure 4: (a) Relative Analysis of Split Tensile Strength, (b) Correlation between Compressive and Split Tensile Strength.
# Application and Evaluation of Energy Conservation Technologies in Wastewater Treatment Plants
## 1 Introduction
### Current Energy Consumption of WWTPs
#### Energy Intensity and Benchmark  
Researchers use benchmarks and energy intensity (EI) to evaluate WWTP energy efficiency, which is an important prerequisite for energy consumption research [24]. Benchmark methods include data envelopment analysis, energy analysis, and pollutant removal efficiency analysis [25]. Compared with the benchmark, EI is simple and easy. A commonly used EI is the energy consumption per unit of pollutant removal, which can be subdivided into energy consumption of population equivalents, chemical oxygen demand (COD) removal, SS removal and total nitrogen (TN) removal, etc. [14; 26].  
Figure 1: Flow chart of common wastewater and sludge (WW&S) treatments.  
However, the single type of pollutant removal is not comprehensive enough for power consumption evaluation [27].  
The energy consumption per unit volume of wastewater treated, widely used in China, which ignores the difference in wastewater quality [28]. Using the EI to analyze the average energy consumption of wastewater treatment in different countries can find some problems. The average energy consumption of urban WWTPs in the United States is 0.20 kWh/m\({}^{3}\), while the consumption of Chinese is 0.27 kWh/m\({}^{3}\), the consumption of Japanese is 0.26 kWh/m\({}^{3}\), the consumption of United Kingdom is 0.62 kWh/m\({}^{3}\)[29; 30; 31]. The difference in energy density in these countries is caused by many influencing factors.
## 4 Stability Analysis of Slopes Under Freeze-Thaw Cycles
### The Influence of Freeze-Thaw Cycles on Slope Displacement  
According to engineering experience, the primary displacement direction during slope landslide disasters is characterized by an inward sliding movement toward the interior of the slope. The displacement nephogram of X-direction toward the interior of the slope plots for the slope under 0, 30, 60, and 90 F-T cycles was generated in Figure 8. With increasing F-T cycles, progressive amplification of X-direction displacements was observed at both the slope toe and crest. This behavior originates from the freeze-thaw damage mechanism in the natural water-bearing slope: repeated water - ice phase transitions within rock masses  
Figure 7: The maximum principal stresses of slopes with different numbers of freezing and thawing. (a) F-T = 0; (b) F-T = 30; (c) F-T = 60; (d) F-T = 90.  
under temperature fluctuations induced strength degradation, volumetric expansion, and subsequent displacement accumulation.  
In the non-F-T scenario (Figure 8a), localized displacements were concentrated near the slope crest under gravitational loading, while the overall slope remained stable. Comparative analysis of Figure 8a (0 cycles) and Figure 8d (90 cycles) revealed a significant displacement intensification, with the maximum X-direction displacement reaching \(-240\) mm (negative values indicating downslope movement) at the mid-lower slope section.  
The curves of X-direction displacement versus time steps for six observation points on the slope under the conditions of F-T = 0, 30, 60, and 90 freeze-thaw cycles are shown in Figure 9.  
Figure 8: X-direction displacement nephogram under different freeze-thaw cycle conditions. (**a**) F-T = 0; (**b**) F-T = 30; (**c**) F-T = 60; (**d**) F-T = 90.  
Figure 9: Curves of X-direction displacement versus time step. (**a**) F-T = 0; (**b**) F-T = 30; (**c**) F-T = 60; (**d**) F-T = 90.  
As shown in Figure 9, the deformation curves of the monitoring points exhibit a tripartite evolutionary pattern. The initial deformation stage was characterized by minor negative displacements in the simulation results. The subsequent quasi-stable stage exhibited negligible displacement increments under sustained loading. The final accelerated stage demonstrated rapid displacement accumulation culminating in failure, collectively defining a triphasic deformation pattern.
## 3 Results and Discussion
### Factors Investigation
#### 3.2.5 Liquid Density  
The investigation result of influence on liquid holdup and wetting efficiency by liquid density is shown in Table 8, and the liquid distribution is shown in Figure 8. It can be found that the liquid holdup and wetting efficiency decreased with the rise of liquid density. In Figure 8, the quantity of liquid in the voids between particles decreased noticeably, for more liquid trapped by surface tension and shear force was drawn out from the voids due to the increased gravity force. The increase of density strengthened the gravity effect on the liquid flow, leading to a faster downward acceleration. Thus, the liquid attained a higher speed and was able to leave the packing and the particle surface faster, resulting in a lower liquid holdup and wetting efficiency. Besides, it was reported that a higher liquid density makes rivulet flow easier than film flow [16], which was another reason for the lower wetting efficiency.  
Figure 8: Liquid distributions with three liquid densities (\(v_{G}=0.245\) m/s, \(v_{L}=0.008\) m/s).  
Figure 7: Liquid distributions with three liquid viscosities (\(v_{G}=0.245\) m/s, \(v_{L}=0.008\) m/s).
## 1 Introduction
Another feature of the proposed setup is its fairly high resolution compared to other wavelength-based setups employing this type of photonic crystal fiber [16; 17; 22]. A disadvantage of the demonstrated setup is the need for careful adjustment of PM-PCF Sagnac interferometer spectrum with the DWDM channel. However, preparation of proper PM-PCF length to correlate the spectrum should not to be a challenging problem. Another issue, which needs to be taken into consideration regarding practical implementation, is the protection of single-mode fibers in order to avoid any bending and elongation of the non-sensing length of the fiber. This paper, for the first time to the best of the authors' knowledge, presents a PM-PCF Sagnac loop sensor setup connected to a DWDM for sensing applications. The paper presents the theoretical background followed by the proposal for a sensor network as well as experimental results.
## 7 Numerical Results
### Ionization Effect of TL  
The RIC phenomenon of a multi-conductor TL is investigated in this section. The TL carrying three HV conductors polarized at \(-500\) kV, \(-334\) kV, and \(-167\) kV, resembles the one conceptualized for the DTT NBI [1, 8]. The three conductors are placed inside an enclosure of radius \(r_{O}=650\) mm, referred to as ground potential (0 V).  
As can be seen in Figure 2, the last part of the TL lying within the Tokamak hall, has a length \(L\) of \(\approx 7.5\) m and is subjected to the irradiation field coming from the machine. The absorbed dose rate \(\phi\), assuming SF\({}_{6}\) as insulating gas pressurized at \(P=0.5\) MPa absolute, is approximately \(1.5\) mGy/s. The relation between \(S\) and \(\phi\) requires the knowledge of gas density \(\rho\) and the energy for the generation of one ion-pair \(W_{ion-pair}\)[1]:  
\[S=\frac{\phi\rho}{W_{ion-pair}}. \tag{20}\]  
Using Equation (20), with \(W_{ion-pair}=34\) eV, the equivalent ion-pair generation rate is \(S\approx 10^{15}\) m\({}^{-3}\) s\({}^{-1}\).  
A 2D model of the cross-section of the TL, as illustrated in Figure 10, is built in COMSOL(r), and the electric current [A/m] at the grounded shell is measured in steady-state conditions as  
\[i_{G}=\int_{\Gamma}\mathbf{J}_{G}\cdot\mathbf{n}d\Gamma, \tag{21}\]  
where \(\mathbf{n}\) is the outward unit normal vector. The results in natural environmental radiation, with the parameters listed in Table 1, and under the RIC phenomenon due to the radiation produced by the Tokamak, are listed in Table 2.  
The estimated power losses in the gas under the RIC phenomenon are approximated as \(P_{loss}=\Delta V\times i_{G}\times L\), with \(\Delta V=500\) kV, resulting in 4.8 kW. For a comparison, \(P_{loss}\) is also estimated based on the experimental law expressed by Equation (4) using \(f_{gas}=2.06\), following [24], and \(v\approx 8.3\) m\({}^{3}\), resulting in \(I_{sat}=12.8\) mA, corresponding to \(P_{loss}=6.4\) kW. This amount of heat can be considered tolerable for the TL operation, even considering the short operational pulse of the NBI in the DTT scenario [8]. Indeed, if the power losses due to the current \(i_{G}\) are too high, severe countermeasures affecting the final design of  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Low Ionization** & **High Ionization** \\ \hline \(|i_{G}|\) [A/m] & 5.2\(\cdot 10^{-10}\) & 1.3\(\cdot 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Current flowing out of the grounded enclosure of TL.  
Figure 10: Cross -section of TL.  
the components (e.g., addition of screens for the irradiation flux), must be conceived. It is worth mentioning that the RIC phenomenon and related power losses in the gas estimated from Equation (4) were sufficient to justify the substitution of SF6 insulation with vacuum insulation for the ITER NBI ion source [40]. Thus, the estimation of power losses under RIC conditions is of fundamental importance.
# Cross-Project Defect Prediction Method Based on Manifold Feature Transformation
## 2 Related Work
Additionally, [PERSON] et al. [PERSON] et al. (2019) extracted and migrated from the source project from the perspective of instance filtering and instance migration, and transformed the training data with high correlation with the target data. Currently, researchers developed multi-source defect prediction methods for the data of multiple source projects. [PERSON] et al. [PERSON] et al. (2019) evaluated 7 composite algorithms on 10 open source projects. When predicting a target project, a collection of labeled instances of other projects is used to iteratively train the composite algorithm. Experimental results show that the use of bagging and boosting algorithms, combined with appropriate classification models, can improve the performance of CPDP. [PERSON] et al. [17] proposed a large-scale combination model HYDRA for cross-project defect prediction. HYDRA considered the weights of instances in the training data and searched for the best weights on the training data for instance selection. [PERSON] et al. [35] proposed a collective transfer learning for defect prediction (CTDP), which includes two stages--the source data expansion stage and the adaptive weighting stage. CTDP expands the source project dataset by using the TCA method, then builds multiple base classifiers for multiple source projects, and finally uses the PSO algorithm to adaptively weight multiple base classifiers to build a collective classifier to obtain better prediction results. From various angles, the above methods ultimately improve the performance of CPDP, especially methods such as feature transformation and feature migration can greatly improve the performance of CPDP. However, few researchers consider the feature distortion phenomenon in the datasets when the feature dimension is large. Therefore, we propose a method of manifold feature transformation from this aspect.
## 4 Experimental Outcomes
### XAI Interpretation
#### 4.3.2 Class Activation Mapping  
The visual explanation of all eight fine-tuned networks is pictorially depicted in Figure 11, considering the Grad-CAM as reference method. In the figure, two sample images for every class are depicted, and the corresponding saliency maps are reported for every network. This figure considers only images for which every network makes the correct prediction, in order to visualize the link between the highlighting of the lesion area and the network performance. The saliency maps of the approximate features are generated considering the ground truth/predicted class view.  
Interestingly, the CNN architectures that find troubles in correctly identifying the lesion areas also appear to have worse performance in the classification task. For instance, SqueezeNet, which is the worst-performing network in terms of AUC, and VGG-16, which also appears to have a trade-off between AUC and the number of parameters, as shown in Figure 8, fail to spotlight the relevant lesion area. Here, the trade-off refers to the fact that the increasing number of parameters seldom yields increased AUC. In contrast, DenseNet-161, DenseNet-121, and ResNet-50 correctly highlight the lesion on the images. Thus, our  
Figure 10: The UMAP embedding plots of the features extracted from the pretrained (**a**,**c**) and fine-tuned (**b**,**d**) DenseNet-161 and SqueezeNet, respectively, on the validation set of 1 st fold. It is distinctly visible that the fully TL paradigm does not allow a clear clustering of the features in low-dimensionality space, whereas the finetuned model is able to discover more discriminative features with respect to its pretrained-only version.
## 4 Trusted Network Secure Routing Algorithm
### Reputation and Ant-Colony Optimization
#### 4.4.1 Pheromone  
An ant-colony optimization algorithm dynamically adjusts the pheromone concentration on each path to achieve the purpose of selecting the current optimal path [26]. Most traditional ant-colony optimization algorithms are based on a single attribute. Although this approach simplifies the algorithm, it tends to cause other important attributes of the nodes to be ignored, which is not conducive to energy balance and has some effect on the experimental results. Therefore, in this paper, we adopt a multi-attribute pheromone that includes the reputation value as the main basis on which to select the optimal path for data transmission [27; 28].  
In this paper, we combine the reputation value \(R_{ij}\), the residual energy \(E_{ij}\) and the communication delay \(T_{ij}\) to formulate a pheromone \(P_{ij}\) that considers trust, energy, time delay and other factors. This approach not only improves the safety of the network but also avoids excessive energy consumption, which would otherwise result in the premature deaths of individual nodes. As shown in Equation (14), the approach proposed in this paper does not simply sum the values of the attributes. Instead, the calculation is based on the particular relationship between each attribute and the pheromone value: if they are directly proportional, then the attribute is directly multiplied by a weighting factor \(\theta\) and included in the sum, whereas if the attribute is inversely proportional to the pheromone, then the inverse of the attribute is multiplied by the corresponding weighting factor and included in the sum. This approach ensures that all attribute terms are ultimately kept in direct proportion to the pheromone:  
\[P_{ij}=\theta_{1}R_{ij}+\theta_{2}E_{ij}+\theta_{3}\left(T_{ij}\right)^{-1} \tag{18}\]  
In the formula above, \(\theta_{1}+\theta_{2}+\theta_{3}=1\). The weights of the three factors must be selected to determine the degrees to which security, energy, and delay are considered in the choice of the next-hop node. The node reputation values, residual energies and time delays are obtained as described in Sections 4.1-4.3.
## 3 Evaluation Methods of the Performance of GSP
### Common Laboratory Testing Methods
#### 3.1.4 Oil Corrosion Resistance  
GSP is made to possess an excellent oil corrosion resistance by its good density. [PERSON] et al., adopted oil corrosion resistance test to assess GSP samples. Marshall samples were soaked in #90 gasoline for 24 h to test its retained Marshall stability (RMS), which was 88.5% twice bigger than AC [6]. [PERSON] et al., also immersed Marshall samples in kerosene for 48 h to attain RMS values, which exceeded 80% [111].
# A Road Defect Detection System Using Smartphones
## 3 A Proposed Road Defect Detection System
### An Automatic Data Collection Mechanism for Road Defect Classification  
Numerous studies have leveraged deep learning techniques for the classification of road defects through acceleration data. A critical factor in developing an effective deep learning model is the acquisition of accurately labeled data. In this section, we introduce a novel system designed for the automated collection of training datasets tailored for road defect classification. This system significantly enhances the capacity for gathering extensive datasets across varied environments, which is instrumental in improving the deep learning model's reliability and generalization capabilities.  
The proposed system offers a pragmatic and cost-effective solution for data collection, relying on dashcams and smartphones--devices already prevalent in most vehicles. This approach not only streamlines the process of dataset accumulation for training and testing deep learning models but also aligns with the practical constraints of research efficiency and budget. By simplifying the data collection process, our system facilitates a more robust and comprehensive exploration of road defect classification through deep learning, setting a new standard for research in this field.  
Figure 1 depicts the architecture of the proposed automatic data collection mechanism designed for road defect classification. Initially, the system gathers raw data through the accelerometers integrated into smartphones, along with footage from vehicle-mounted  
dashcams, which serve as the input for the system. Subsequently, in the second phase, both the collected acceleration and video data are segmented and annotated with relevant labels. In the final stage, the labels generated during the data labeling process are amalgamated with the preprocessed acceleration data to formulate a comprehensive dataset suitable for deep learning analysis. This structured approach ensures the efficient collection and preparation of data, facilitating the development of a robust model for road defect classification.
# Quantitative Assessment and Grading of Hardware Trojan Threat Based on Rough Set Theory
## 2 System Model and Assessment Formulation
### Weight Calculation and Quantitative Assessment
#### 2.2.1 Core Attribute  
In RST, for every set of attributes \(P\subset A\) an indiscernibility relation \(IND(P)\) can be defined in the following way:  
\[IND(P)=\{(x,y)\in U\times U|\forall a\in A,f(x,a)=f(y,a)\} \tag{2}\]  
where \(IND(P)\) is the equivalence class in \(U\) and \(\text{IND}(P)=\bigcap\limits_{a\in P}\text{IND}(\{a\})\). The equivalence class \(IND(P)\) is called the elementary set in \(P\) because it represents the smallest discernible groups of elements. For any element \(x_{i}\in U\), the equivalence class of \(x\) in relation to \(IND(P)\) is represented as \([x_{i}]_{IND(P)}\).  
For an \(a_{i}\in A\), if \(IND(A)=IND(A-\text{a}_{i})\), the attribute \(a_{i}\) is called superfluous. Otherwise, the attribute \(a_{i}\) is indispensable in \(A\).  
A subset \(B\subset A\) is called a reduct of \(IS\) if and only if \(IND(A)=IND(B)\) and \(B\) is independent of \(A\). The set of all reducts in \(IS\) is denoted by \(RED(IS)\) or \(RED(A)\).  
The core of \(A\) is the set of all indispensable attributes of \(A\), denoted by \(Core(A)=\bigcap RED(A)\).  
If the set of attributes is dependent, it may be desirable to find all possible minimal subsets of the attributes. This leads to the same number of elementary sets as the whole set of attribute reducts and finding the set of all indispensable attributes as the core.  
The concepts of core and reduct are two fundamental concepts in RST. The reduct is the essential part of an \(IS\) which can discern all objects discernible by the original \(IS\), while the core is the common part of all reducts.