esa-sceva/satcom-chunk-collection huggingface dataset:
# Chlorobenzene Removal Using DBD Coupled with CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) Catalyst
## 2 Experimental Section  
### Preparation of Catalysts  
The CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) catalyst was prepared by the excessive impregnation method. The amount of precursor (Cu(NO\({}_{3}\))\({}_{2}\cdot\)3H\({}_{2}\)O) necessary to obtain the copper nitrate solution with a concentration of 1 mol/L was dissolved in distilled water, and \(\gamma\)-Al\({}_{2}\)O\({}_{3}\) powder was added to the excess volume of copper nitrate solution, followed by ultrasonic oscillation for 20 min, immersion for 24 h and filtration. The slurry obtained by filtration was dried at 60 \({}^{\circ}\)C for 12 h. The samples were then calcined at 500 \({}^{\circ}\)C for 5 h. For the preparation of pure \(\gamma\)-Al\({}_{2}\)O\({}_{3}\) powder, distilled water was used instead of copper nitrate solution. The CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) catalyst and pure \(\gamma\)-Al\({}_{2}\)O\({}_{3}\) powder were ground and sieved to 40-60 meshes for further experiments.  
The Cu loading of catalyst was measured by ICP-OES (Thermo iCAP 7400, Thermo Fisher Scientific, Waltham, MA, USA). The Brunauer-Emmett-Teller (BET) surface area of catalyst was measured by N\({}_{2}\) adsorption and desorption experiments using Micromeritis Tristar II 3020 instrument (Micromeritis, Norcross, GA, USA). The phase structure of the catalyst was characterized by an X-ray diffractometer (XRD, Bruker D8 Advance, Bruker, Billerica, MA, USA). The Cu K\(\alpha\) radiation (\(\lambda\) = 0.1542 nm) is operated at 40 kV and 40 mA with a 2\(\theta\) range of 10\({}^{\circ}\)-80\({}^{\circ}\) to obtain the XRD patterns. The oxidative states of Cu before and after the discharge were detected by an X-ray photoelectron spectroscopic (XPS) spectrometer (Thermo Scientific K-Alpha). The pass energy of the full spectrum scan is 100 eV with a step length of 1 eV. The pass energy of the narrow-spectrum scan is 50 eV, with a step length of 0.05 eV.
# Detection and Treatment Methods for Perfluorinated Compounds in Wastewater Treatment Plants
## 3 Results and Discussion
### Monthly PFCs Concentration Change  
The average PFOA in the influent was 11.7 ng/L, and that in the effluent was 11.0 ng/L. The PFOA removal was very low. The average PFHxA in the influent was 11.9 ng/L and that in the effluent was 11.1 ng/L, showing the minimal removal. The average PFNA in the influent was 4.1 ng/L, but that in the effluent was 2.9 ng/L, showing an average PFNA removal of 27.2%. The average PFPeA in the influent was 3.2 ng/L and the effluent was 2.7 ng/L, which is 7.8% removal. The average PFHxS in the influent was 1.0 ng/L, and that in the effluent was 0.8 ng/L.  
The concentration distribution of these substances tended to be somewhat higher in the dry winter season. PFHxS is frequently used as a fire extinguisher material and so its concentration tended to be higher in the fall and winter when more fires occur. In addition, PFCs are shown to be largely affected by business operational ratios, and thus, for efficient management, a detailed and continuous emissions survey would be required in industry-concentrated regions. Finally, whereas some compounds were removed by the existing treatments, most of the compounds were shown to be poorly treated.
## 3 FDIA Detection Model Based on GAN-GRU and Transformer
### FDIA Detection Model Based on the Transformer Neural Network  
The neural network model known as the Transformer is rooted in an attention mechanism. Its conceptualization originated from Google and was first elucidated in 2017 by [PERSON] et al., as documented in [26]. This model was conceived to address intricate natural language processing undertakings encompassing machine translation, text summarization, and speech recognition. In contrast to conventional recurrent neural network models, such as LSTM and GRU, the Transformer model boasts enhanced parallel computation capabilities and abbreviated training durations, rendering it adept at managing protracted sequence tasks. This attribute has endowed the Transformer model with widespread utility within the realm of natural language processing.  
However, different from its traditional applications in natural language processing and speech recognition, our study applies the Transformer model to the realm of false data classification in CPPS loads. Figure 4 illustrates the architecture of the Transformer network architecture as delineated within this paper.  
The innovative application of the Transformer model in the context of CPPS applications carries considerable significance. This expansion of the Transformer model into the realm of load false data detection presents the opportunity to leverage its intrinsic self-attention mechanism and robust parallel computing prowess to discern and analyze the distinctive attributes of counterfeit data. As a result, a novel and effective avenue emerges  
Figure 4: FDIA detection process based on the Transformer neural network.  
to address the escalating predicament of false data within real-world scenarios. In our methodology, the initial step entails the utilization of balanced normal data in conjunction with three distinct attack vector datasets as inputs. Consequently, categorical labels are assigned to both the normative dataset and the three distinct attack datasets as follows:  
\[y_{j}=\left\{\begin{array}{l}0\text{ normal operation}\\ 1\,30-\text{node attack vectors}\\ 2\,65-\text{node attack vectors}\\ 3\,100-\text{node attack vectors}\end{array}\right., \tag{15}\]  
Considering the Transformer's ability to process sequence data, we consider each set of data as a sequence and classify it using the Transformer network. Specifically, we input the sequence data into the encoder and decoder for processing.
## 5 Grey Wolf Algorithm
### Improved Design of the Grey Wolf Algorithm for Large-Scale Neighborhood Search
#### 5.2.1 Nonlinear Convergence Factor  
The convergence factor in the traditional GWO algorithm decreases linearly from 2 to 0 with each iteration. However, since the search process in the GWO algorithm is non-linear, the linear convergence factor is unable to fully reflect the change process of the grey wolf position. This can result in slow convergence and an increased likelihood of falling into  
Figure 5: Flow chart of grey wolf algorithm for large neighborhood search.  
local optima. Therefore, we propose a nonlinear convergence factor that varies according to the following formula.  
\[\left\{\begin{array}{l}a=1+\left[\frac{\cos(t-1)\pi}{T-1}\right]^{n}t\leq\frac{ 1}{2}T\\ a=1-\left[\frac{\cos(t-1)\pi}{T-1}\right]^{n}\frac{1}{2}T<t\leq T\end{array}\right. \tag{28}\]  
where \(t\) is the current number of iterations, \(T\) is the maximum number of iterations, and n is the decreasing exponent.  
According to [PERSON]'s proposed theory [34], the grey wolf population performs a global search when \(|\mathrm{A}|>1\) and a local search when \(|\mathrm{A}|<1\). The improved nonlinear convergence factor decreases slowly at the beginning of the iteration, and it maintains population diversity and improves search efficiency while preventing the algorithm from falling into local optimal solutions. In the later iterations, the reduction speed is rapid, thus improving the accuracy of the exact local search and accelerating the global convergence of the algorithm.
## 3 Results and Discussion
### Effect of NaSt on the Hydration Products and Pore Structure of AASRSS  
#### 3.3.1 XRD  
The XRD results of the 28-day AASRSS samples are shown in Figure 6. Calcite (CaCO\({}_{3}\), 20 = 29.41\({}^{\circ}\) and 47.5\({}^{\circ}\)), alite (20 = 22.24\({}^{\circ}\), 27.55\({}^{\circ}\), 29.36\({}^{\circ}\) and 32.21\({}^{\circ}\)) and hematite (Fe\({}_{2}\)O\({}_{3}\), 20 = 24.2\({}^{\circ}\), 33.12\({}^{\circ}\), 35.6\({}^{\circ}\) and 49.52\({}^{\circ}\)) could be detected. An important indication in Figure 6 is the peak in the X-ray diffraction pattern at 30\({}^{\circ}\) and 49.5\({}^{\circ}\), which is attributed to C(N)-A-S-H. The difference in the intensity of this peak is somewhat indicative of the amount of C(N)-A-S-H gel produced; specifically, the intensity of C(N)-A-S-H slightly decreased with the addition of NaSt at around 30\({}^{\circ}\) and 49.5\({}^{\circ}\). This indicates that the addition of NaSt slightly reduced the formation of C(N)-A-S-H in the AASRSS, which is consistent with the heat-of-hydration results.  
Figure 6: XRD results of AASRSS samples at the age of 28 days.
# A Novel Feeder Link Handover Strategy for Backhaul in LEO Satellite Networks
## 3 System Model
### Problem Formulation  
In the system, massive global traffic needs to be backhauled to the terrestrial network through the satellite network and a few GSs, arousing network congestion in the feeder links and surrounding the inter-satellite network. Therefore, in this article, we aim to maximize the available backhaul capacity by optimizing the feeder link handover problem, which is defined as follows:  
\[\sum_{t=1}^{T}\sum_{m=1}^{M}\sum_{n=1}^{N}x_{n,m}(t)f_{n,m}^{g}(t) \tag{12}\]  
Meanwhile, maximizing the instantaneous throughput at each time slot may arouse frequent handover, interrupting ongoing communications and producing massive routing overhead. The handover cost is introduced to reduce the handover frequency as follows:  
\[h_{n,m}(t)=\begin{cases}1,&\mathbf{x}_{n,m}(t)=1,\,\mathbf{x}_{n,m}(t-1)=0\\ 0,&\mathbf{x}_{n,m}(t)=\mathbf{x}_{n,m}(t-1)\end{cases} \tag{13}\]  
where the handover cost is zero if the feeder link does not switch in time slot \(t\). Therefore, the ultimate optimization that balances the long-term backhaul performance and the handover frequency is formulated as follows:  
**OP:** \[\max\sum_{t=1}^{T}\sum_{m=1}^{M}\sum_{n=1}^{N}x_{n,m}(t)(f_{i,m}^{g }(t)-\gamma h_{n,m}(t))\] (14) **s.t.** \[x_{n,m}(t)\in\{0,1\}, \forall n\in N,m\in M,t\in T\] (14a) \[\sum_{m=1}^{M}x_{n,m}(t)\leq L_{s}, \forall n\in N,t\in T\] (14b) \[\sum_{n=1}^{N}x_{n,m}(t)\leq L_{g}, \forall m\in M,t\in T\] (14c) \[f_{n}^{m}(t)=f_{n}^{out}(t), \forall n\in N,t\in T\] (14d) \[0\leq f_{n,m}^{g}(t)\leq C_{n,m}^{g}(t), \forall n\in N,m\in M,t\in T\] (14e) \[0\leq f_{i,j}^{\text{isl}}(t)\leq C^{s}, \forall i,j\in N,t\in T\] (14f) \[0\leq f_{n}^{u}(t)\leq C^{u}, \forall n\in N,t\in T\] (14g)  
where \(\gamma\) represents the handover's influence on the backhaul. The constraint (14a) means that \(\mathbf{x}_{n,m}(t)\) is a binary indicator representing whether \(S_{n}\) establishes a link with \(U_{m}\) or not at time slot \(t\). The constraints (14b) and (14c) limit the number of established links for each node. The traffic of a satellite needs to follow the constraint (14d). The constraints (14e)-(14g) indicate that the backhaul traffic in links cannot exceed the available channel capacity.
## 2 Materials and Methods
### Experimental Work, Settings, and Workpiece Material  
Three levels of each cutting parameter were applied, producing 27 unique combinations with respect to the full factorial design for training the ANFIS model. Another 12 experiments were designed for testing by selecting parameter values from within the range of the study, maintaining a reasonable balance across the input range. To acquire the temperature measurements, a custom sensory system was assembled. A K-type thermocouple coupled with the MAX31855 amplifier was connected to an Arduino Nano. The thermocouple can measure up to 800 \({}^{\circ}\)C and has approximately 40 \(\upmu\)V/\({}^{\circ}\)C sensitivity, \(\pm\)2 \({}^{\circ}\)C repeatability, 0.5 s response time and 1 s time constant. Moreover, it was already precalibrated according to Class 1 accuracy. Additionally, a laptop with the PLX DAQ ver. 2.11 software was used for the data acquisition process. The thermocouple was attached to the back of the tool holder, as shown in Figure 1--step 1, so that its probe comes into contact with the back surface of the tool tip, as described in the work of [PERSON] et al. [31]. Three cuts were machined on each workpiece, and the surface roughness was measured with the DIAVITE DH-8 in terms of the arithmetic mean \(Ra\) by measuring each cut at four anti-diametral points. According to [PERSON] and [PERSON] [32], Ra is the most important measurable characteristic of roughness that can describe surface quality adequately. The cutting conditions were selected with respect to previous works of the authors on this material type [13; 33].  
The 13 workpieces were fabricated with the Fused Filament Fabrication (FFF) method by utilizing the CreatBot D600 Pro 3D printer. Sample fabricated workpieces and their dimensions are shown in Figure 2. The selected material is a polymer-based composite, which belongs to the CFRP material group (80%wt PETG and 20%wt carbon fibers), that is commercially available under the brand name NEEMA3D\({}^{\text{TM}}\) CARBON:PLUS. It utilizes short carbon fibers, as is typical for carbon-fiber-reinforced filaments, which are blended into the thermoplastic matrix. In addition, it presents an elastic modulus equal to 3.8 GPa, a yield strength equal to 52.5 MPa, and a heat deflection temperature of 80 \({}^{\circ}\)C. Such materials are preferred in modern manufacturing industries for component fabrication that require a balance between good strength and low weight. According to [PERSON] et al. [34],  
Figure 1: The experimental setup, tools, and methodologies used in this study.  
carbon-fiber-reinforced PETG presents relatively good machinability; however, its geometric behavior is worse compared to standard PETG. The printing settings are shown in Table 1.
## 6 Discussion
### Generalizability of the Framework and Future Validation Needs  
The effectiveness of the suggested framework is demonstrated by the case study involving Carrefour and Monoprix; nevertheless, it is crucial to remember that depending solely on one use case restricts one's capacity to extrapolate the findings to other retail settings. The unique data from the selected establishments and the consumer actions in these specific circumstances have an impact on the conclusions. Extended validation to several use cases including various retailer types (e.g., independent stores, Internet retailers, and local chains) and geographical regions is required to completely verify the suggested strategy. More thorough assessments of the framework's flexibility and efficacy in other contexts are possible, producing results that are more trustworthy and broadly applicable. Furthermore, the validation of the system's long-term effects on customer engagement and suggestion accuracy would be strengthened by extensive research using longitudinal data.
# Shoplifting Detection Using Hybrid Neural Network CNN-BiLSMT and Development of Benchmark Dataset
## 1 Introduction
### Contribution of This Study  
The main contribution of this study is as follows: (1) a large dataset of 900 videos has been developed, (2) baseline methods 2D and 3D CNN have been implemented to evaluate the dataset, (3) proposed shoplifting detection technique using hybrid neural network CNN-BiLSTM (4) make a comparison of deep learning approaches 2D and 3D CNN with the proposed technique.  
Additionally, our proposed technique of shoplifting detection may be utilized to instantly analyze video from security cameras and find instances of theft. This may be used in a variety of places, including retail establishments, marketplaces, and malls. The efficiency of current security measures to deter theft can be increased by integrating the proposed solution with them. It can also be used to generate alerts and notifications to security personnel when a potential shoplifting incident is detected, allowing them to intervene and prevent the theft from occurring. Moreover, it can help store managers, and owners identify high-risk areas and times where shoplifting incidents are more likely to occur, enabling them to allocate resources more effectively to prevent theft.
## 4 Visual-Inertial Odometry  
The SLAM systems that combine vision and IMU have also been widely studied. With the measurements and short-term motion constraints of IMU can assist visual odometry to recover scale information, and effectively solve the problem of localization drift in scenes with a lack of texture and fast motion. Currently, feature-based methods are commonly used in the front-end of visual-inertial SLAM, with adjacent frames detecting features for matching and estimating camera motion, such as the newer ORB-SLAM framework [31; 32] and MonoSLAM [33]. There are also visual-inertial SLAM frameworks based on direct methods, such as LSD-SLAM [34], which directly estimate the motion of the camera using pixel information. Compared with the feature-based method, the direct method does not require feature extraction, data association, and minimization of feature reprojection errors. They are computationally more efficient and applicable to sparse, semi-dense, and dense point clouds.  
The VIO subsystem is a tightly coupled visual-inertial odometry based on the direct method. It estimates the camera pose and establishes the environment's texture information by jointly minimizing the photometric error and IMU measurement error between image frames. The estimation is further optimized by factor graph optimization to obtain the best estimate state. In order to reduce the complexity of the system and to ensure that the non-linear optimization problem is handled in a reasonable time range, the marginal technique [35] is adopted, which continuously removes the old variables from the factor graph to optimize the latest state.  
First, giving a description of the notation used in this section. \(H\) represent matrices, \(\lambda\) and the vector \(x\) represent scalars. \(T_{i-j}\in SE(3)\) represents the transformation matrix from frame \(i\) to frame \(j\), the equation is \(P_{i}=T_{i-j}P_{i^{\prime}}\), \(\xi\in se(3)\) is a Lie algebra element, where \(\xi\in R6\), and use them to apply small increments \(\zeta_{i-j}^{\prime}=\xi_{i-j}\xi:=\log\big{(}e^{\xi_{i-j}}\cdot e^{\xi}\big{)}\) to the 6D pose. A factor graph G is represented as a set of factors, and G1 \(\cup\) G2 contains all the factors in G1 and G2. Gravity is defined as perpendicular to the negative Z-axis in the world frame. This system is based on non-linear optimization, the camera's pose and scene depth are estimated by continuously iteratively minimizing the energy function. The minimum energy function is established as  
\[\text{E}_{all}=\lambda\cdot\text{E}_{picture}+E_{inertial} \tag{16}\]  
where \(E_{picture}\) denotes the photometric error and \(E_{inertial}\) denotes the inertial error. \(\lambda\) denotes the balance coefficient, which is used to weigh the effects of photometric error and inertial error.
## 5 Conclusions and Perspective  
Electrical simulation of CFRP at the microscopic scale stays a challenge due to the high number of degrees of freedom and the large domain size required for accurate representativeness. The combination of DD and POD offers a promising alternative to classical homogenisation approaches and allows for keeping local information. However, several improvements are necessary to fully exploit this method.  
On the application examples, the relative error calculated by DD-POD is acceptable and the convergence of DD-POD is not limited to a specific geometry. This indicates that DD-POD can be effectively applied to maintain local information in simulations at the microscopic scale, providing a detailed and accurate representation of the material's behaviour. Additionally, the simulation time using DD-POD with parallel computing is faster than direct resolution, as demonstrated in our results. This acceleration is achieved without compromising the accuracy of the simulations, making DD-POD a highly efficient method for conducting electrical simulations of CFRP at the microscopic level.  
Future work will focus on incorporating microscopic defects into the simulation cells to further validate and enhance the method's applicability. By introducing these defects, one aims to test the robustness of the DD-POD approach under more complex and realistic conditions. This will not only improve the method's reliability but also extend its usefulness for practical applications in the analysis and design of advanced composite materials.  
In conclusion, the integration of DD and POD represents some contribution in the field of computational electromagnetic for CFRP materials. Our study demonstrates that DD-POD can effectively reduce computation time while maintaining accuracy, paving the way for more efficient and detailed simulations. Continued research and development in this area will further enhance the method's capabilities, making it an invaluable tool for engineers and researchers working with composite materials.  
[PERSON] generated the model of material based on the idea of G.W. and D.T., and performed the computation according to the original idea of A.P., [PERSON] provided guidance on thermal simulation; [PERSON] wrote the manuscript with support from all co-authors. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Pays de la Loire region through WISE program.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the fact that the materials are randomly generated each time, the data regarding errors may exhibit slight variations for each newly generated material. Therefore, if necessary, we can provide the source code for material generation to facilitate the verification of errors.  
The authors declare no conflicts of interest.
## 2 Electrical Resistivity/Impedance Measurement: Is It Really that Simple?
### Compressive Strength and Elastic Modulus
[137] found a non-linear regression during the early age hydration process of concrete [128]. As compressive strength, also elastic modulus increases as hydration proceeds. Using electrical resistivity for the evaluation of the modulus of elasticity is a meaningful, cheap, sensitive, and non-destructive method, as opposed to the analysis of the load-deformation curve of concrete specimens, which turns to be quite complicated and time consuming, requiring the cast of several dedicated specimens. Moreover, hydration products formation determines the electrical resistivity value, as the propagation velocity of ultrasound waves does. Since the ultrasonic pulse velocity measurement method can be used to investigate the elastic modulus, the combination of electrical resistivity and the elastic modulus obtained with ultrasound techniques enables a more accurate measurement with a correlation coefficient of \(0.92\leq\mathrm{R}^{2}\leq 0.98\).