esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
Based on the continuous-discontinuous thermo-mechanical coupling model, heat conduction, the thermoelastic problem, and double-edge crack propagation under the thermal load of FGMs are studied in this paper. The main conclusions are as follows:  
(1) The simulated steady-state and the transient heat conduction processes under different parameter arrangements and gradient coefficients are in good agreement with the analytical solutions, indicating that model can well simulate the heat conduction problem of FGMs.  
(2) Compared to composite materials, the internal stress distribution of the FGMs is relatively uniform, which can overcome complex environments subjected to thermal shock.  
(3) The interaction between cracks affects the crack propagation path under thermal load. The inclination angle and spacing of double-edge cracks greatly influence crack propagation. When the inclination angle \(\beta\) increases from 60 degrees to 90 degrees, the crack propagation angle \(\phi\) decreases by about 60% (\(d=a\)). Furthermore, when d increases from 0.04 to 0.16, the crack propagation angle \(\phi\) decreases by about 27% (\(\beta=60^{\circ}\)). Larger crack inclination and spacing can lead to a smaller crack propagation angle. The simulation results are consistent with the theoretical research.  
Conceptualization, D.H. and H.F.; methodology, C.Y.; software, C.Y.; validation, D.H. and H.F.; formal analysis, T.W. and Y.Y.; investigation, H.F., D.H. and C.Y.; resources, H.F.; data curation, H.F. and D.H.; writing--original draft preparation, H.F. and D.H.; writing--review and editing, C.Y., G.W., T.W. and S.A.; visualization, H.F. and D.H.; supervision, C.Y.; project administration, C.Y.; funding acquisition, C.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under grant number 11872340; the GHfund A (20220201, ghfund202202019662); Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone (HZQB-KCZYB-2020083).  
Not applicable.  
This work was supported the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (CUGGC09).  
The authors declare no conflict of interest.
## 2 Data, Methods and Techniques
### Parameters of the Advection and Diffusion Model  
To determine the actual filtration velocity \(V_{x}^{*}\) (capillary filtration velocity) and the actual (capillary) longitudinal dispersion coefficient \(D_{x}^{*}\), a tracer experiment was performed. During the experiment, a tracer (sodium chloride solution) was introduced in a pulse-mode into the filter column. The electrolyte content in the filter effluent was determined through conductivity measurements. The specific conductivity \(S\) is proportional to the electrolyte concentration \(C_{e}\) within the analyzed electrolyte concentration range.  
For a pulsed tracer mode, there is an analytical solution of the advection-diffusion equation [19]:  
\[\frac{S(x,t)}{\mu_{S}}=C_{e}(x,t)=\frac{M}{Q}\frac{x}{2\sqrt{\pi D_{x}^{*}t^{3} }}\exp\left(-\frac{(x-V_{x}^{*}t)^{2}}{4D_{x}t}\right) \tag{1}\]  
where  
\(C_{e}\)--electrolyte concentration (val/cm\({}^{3}\));  
\(\mu_{S}\)--equivalent conductivity (uS\(\cdot\)cm\({}^{2}\)/val);  
\(S\)--specific conductivity (uS/cm);  
\(M\)--mass of a tracer introduced (val);  
\(Q\)--flow rate (m\({}^{3}\)/s);  
\(V_{x}^{*}\)--actual filtration velocity (capillary velocity) (m/s), \(V_{x}^{*}=V_{x}/\varepsilon\);  
\(V_{x}\)--apparent filtration velocity (empty bed velocity) (m/s);  
\(\varepsilon\)--average porosity of the filter bed;  
\(D_{x}^{*}\)--actual coefficient of longitudinal turbulent diffusion (coefficient of longitudinal capillary turbulent diffusion) (m\({}^{2}\)/s), \(D_{x}^{*}=D_{x}/\varepsilon\);  
\(D_{x}\)--longitudinal turbulent diffusion coefficient in an empty bed (m\({}^{2}\)/s);  
\(x\)--linear coordinate (height of the bed including the supporting layer and the drain tube \(x=L=3.1\) m);  
\(t\)--time (s).  
Formula (1) allows us to determine \(V_{x}^{*}\) and \(D_{x}^{*}\) using the least squares method, based on the specific conductivities in the filter effluent.  
The parameters \(V_{x}^{*}\) and \(D_{x}^{*}\) were used to calculate adsorption in the filter column.
## 2 Models and Methods
### The Flight Path Phase Model
\begin{array}{l}\frac{d\gamma}{dt}=0\\ \frac{dh}{dt}=0\end{array} \right. \tag{4}\]  
where \(I\) is the solar radiation intensity, \(\eta_{battin}\) the charging efficiency of energy storage battery, \(\gamma\) the aircraft flight path angle, \(h\) the aircraft flight altitude. Phase 3: Maximum power ascending for gravity energy storage  
This flight phase starts at time \(t_{2}\) when the energy storage battery is brimming, and ends at time \(t_{3}\) when the solar irradiation is zero. This phase includes maximum power climbing and powered gravity gliding and depends on whether the solar radiation intensity can meet the power consumption required for flight at its altitude. Before the solar radiation disappears in this process, the maximum flying altitude that the SPA can reach relies on the obtained solar radiation energy. As a result, the flight phase can make full use of the excess solar energy for gravity energy storage. The flight power supply mode of this phase is: all the electrical energy converted by solar cells is used to power the aircraft. Therefore, the IPC of the aircraft in this phase are the same as phase 1, as follows:  
\[\left\{ \begin{array}{l}P=I\cdot S_{sc}\eta_{MPPT}\eta_{mot}\eta_{plr}\eta_{SC }=T\cdot v\\ \frac{dQ}{dt}=q=0\end{array} \right. \tag{5}\]  
Phase 4: Longest gliding endurance path  
The phase begins at time \(t_{3}\) when there is no irradiance, and ends at time \(t_{4}\) when the SPA glides to the cruising altitude \(h_{1}\) at night. Throughout the whole stage, the SPA reduces altitude by gliding without thrust. The SPA energy system does not provide any electricity to the aircraft during this flight phase. In the phase, the IPC of SPA satisfy:  
\[\left\{ \begin{array}{l}T=0\\ \frac{dQ}{dt}=q=0\end{array} \right. \tag{6}\]  
Phase 5: Low-power level flight by discharging battery at night  
This flight phase starts at time \(t_{4}\) when the SPA reaches the cruising altitude \(h_{1}\), and ends at time \(t_{5}\) when the solar radiation energy acquired by the SPA at altitude \(h_{1}\) is sufficient to support the climbing flight to the next day phase 1. Basically, \(t_{0}\) is equal to \(t_{5}\). During this stage, the SPA only relies on energy storage batteries for cruising. The power supply mode is: the energy storage battery provides all power consumption during the night. Thence, the IPC of the aircraft are as follows:  
\[\frac{dQ}{dt}=q=-\frac{P}{\eta_{mot}\eta_{plr}\eta_{battout}} \tag{7}\]\[\left\{ \begin{array}{l}\frac{dy}{dt}=0\\ \frac{dt}{dt}=0\end{array} \right. \tag{8}\]  
where \(\eta_{lbutout}\) is the discharging efficiency of energy storage battery.
# Recent Advances in Sample Preparation for Cosmetics and Personal Care Products Analysis
## 1 Introduction
In this case, a derivatization step is included during the sample pre-treatment to modify several chemical functions of the original low-volatile compounds. Derivatization usually employs acetylation with acetic anhydride, or silyl derivatives to improve the chromatographic peak shape of the most polar compounds. This step is usually performed in situ during the extraction step to obtain a good method performance in a short amount of time. Due to the capability of several compounds present in cosmetics, such as UV filters or some plasticizers, to absorb light in the UV-Vis spectrum, diode-array detector (DAD) was the preferred detection method after LC separation. However, due to its low specificity, which complicates quantifying multiple compounds in complex sample matrices, such as cosmetics, the combination of GC or LC with mass spectrometry (MS) or tandem mass spectrometry (MS/MS) detection became the most suitable option. This approach improves the analytical selectivity and sensibility, allowing the detection of the target analytes at trace levels, which is especially important to identify the impurities and banned compounds. In recent years, the use of high-resolution mass spectrometry (HRMS), mainly based on QTOF detection, has started to play a more important role for multitarget analysis. The main objective of this review is to provide an update on the cosmetics and personal care products' sample preparation methodology. The most relevant analytical methods reported (Scopus database) in the last decade (2010-2020) for the extraction and determination of organic compounds, including cosmetics ingredients such as UV filters, fragrances, preservatives, plasticizers or dyes, as well as the strategies to determine impurities, banned or unexpected compounds (N-nitrosamines, polycyclic aromatic hydrocarbons (PAHs), pesticides, antibiotics, etc.) will be presented. The application of both traditional sample preparation based on direct dilution, LLE or SLE as well as advanced techniques such as ultrasound-assisted extraction (UAE), solid-phase extraction (SPE), pressurized liquid extraction (PLE), matrix solid-phase extraction (MSPD) to cosmetics matrices will be addressed. The recent trends in liquid- and sorbent-based microextraction techniques will be also discussed. Figure 1 illustrates the sample preparation strategies included in this review. The novel developments regarding miniaturization, new sorbent materials with high adsorption properties and the most recent technological trends will also be addressed.
# Radar Voxel Fusion for 3D Object Detection
## 2 Related Work
### 3D Sensor Fusion for Object Detection
#### 2.3.1 Camera Radar Fusion  
For 3D object detection, the authors of [18] propose GRIF-Net to fuse radar and camera data. After individual processing, the feature fusion is performed by a gated region of interest fusion (GRIF). In contrast to concatenation or addition as the fusion operation, the weight for each sensor in the fusion is learned in the GRIF module. The camera and radar fusion method outperforms the radar baseline by a great margin on the nuScenes data set.  
The CenterFusion architecture [19] first detects objects in the 3D space via image-based object detection. Radar points inside a frustum around these detections are fused by concatenation to the image features. The radar features are extended to pillars similar to [16] in the 2D case. The object detection head operates on these joint features to refine the detection accuracy. The mean Average Precision (mAP) score of the detection output increases by 4% for the camera radar fusion compared to their baseline on the nuScenes validation data set.  
While the methods above operate with point cloud-based input data, [PERSON] [20] fuses azimuth range images and camera images. The camera data are projected to a bird's-eye view (BEV) with an Inverse Projection Mapping (IPM). The individually processed branches are concatenated to generate the object detection results. The fusion approach achieves a higher detection score than the individual modalities. The IPM limits the detection range to close objects and an assumed flat road surface.  
[PERSON] [21] similarly fuses radar azimuth-range images with camera images. The data are fused after initial individual processing, and the detection output is generated adopting the detection head of [22]. The fusion approach outperforms both their image and radar baselines on their private data set. Their RPN uses a distance threshold in contrast to standard Intersection over Union (IoU) matching for anchor association. The paper argues that the IoU metric prefers to associate distant bounding boxes over closer bounding boxes under certain conditions. Using a distance threshold instead increases the resulting AP by 4-5 points over the IoU threshold matching.  
The overall detection accuracy of camera radar fusion networks is significantly lower than that of lidar-based detection methods.
# A Versatile Shared-Aperture Antenna for Vehicle Communications
## 5 Conclusions  
A compact shared-aperture antenna is proposed, serving the purposes of GPS, S-band satellite communication, and V2V vehicular communication applications. In summary, the microstrip antenna has a bandwidth of 1.565-1.578 GHz, the cross-dipole antenna has a bandwidth of 2.2-3.8 GHz (53.3%), and the pattern reconfigurable antenna has a 5.8-6 GHz bandwidth. Through fabrication and measurement, the results are compared with simulations, aligning well with the requirements of these diverse applications. Therefore, in in-vehicle communication, the frequency bands of GPS communication, satellite communication, and V2V communication can be covered by the proposed antenna.  
Conceptualization, M.L., Y.S. and X.L.; methodology, M.L.; writing--original draft preparation, M.L. and W.Z.; supervision, Y.S.; funding acquisition, Y.S. and X.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by in part by the National Natural Science Foundation of China under Grant 62201131 and Grant 62271122; in part by China Postdoctoral Science Foundation 2022T150093 and 2021M690538; in part by Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University under Grant 22P03; in part by the Natural Science Foundation of Chongqing under Grant 2022 NSCQ-MSX1547, and in part by Key Research and Development Program of Zhejiang Province under Grant No. 2022C01093, and in part by Science and Technology Commissioner Project of Huzhou under Grant No. 2021 KT13. in part by the Fundamental Research Funds for the Central Universities under Grant ZYGX202063010078; and in part by the Scientific Research Foundation for Yangtze Delta Region Institute of University of Electronic Science and Technology of China, Huzhou, under Grant U03220380.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
## 3 Hash-Based Key-Value Data Structures
### Cuckoo Hash Table  
A cuckoo hash table shown in Figure 0(b) uses two hash tables, and each bucket of a cuckoo hash table stores a signature and a return value. Therefore, if the same amount of memory is used as a multi-hash table, the number of buckets (\(B_{c}\)) in a cuckoo hash table is the same as \(B_{m}\).  
Each element uses a single index per table. When a new element is inserted, cuckoo hashing [18] solves hash collision by pushing the preoccupied element to the other table. In Figure 0(b), when inserting \(x_{p}\in S\), if there is a collision in the bucket of the first table (i.e., \(e_{1}[h_{1}(x_{p})]\)), \(x_{p}\) is stored in the bucket and element \(x_{2}\) (of which bucket is taken away) is pushed to the second table (i.e., \(e_{2}[h_{2}(x_{2})]\)). If a collision also occurs in the second table, this procedure is repeated until the pushed element finds an empty bucket (i.e., \(e_{1}[h_{1}(x_{1})]\) in Figure 0(b). Therefore, in inserting an element, even though each element uses only one hash index for each table, more entries can be examined in the cuckoo hashing by involving other elements already stored in the table. As will be shown in Section 4.2.2, if there occur two loops in inserting an element, the element cannot be saved. The unsaved element, due to an overflow in insertion procedure, causes a false negative in search procedure, and it is classified as a search failure.  
In searching, only two buckets pointed by two hash indexes for each given input are checked. Therefore, the number of bucket accesses in searching an input is always two, even though the number of accesses in inserting a key can be large as the load factor increases.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Meaning** \\ \hline \(M_{h}\) & memory size of a hash table \\ \(n\) & number of elements in a given set \\ \(m\) & number of cells in a functional Bloom filter \\ \(k\) & number of hash functions for an FBF \\ \(d\) & number of hash functions for a \(d\)-left table \\ \(\alpha\) & load factor of a multi-hash table \\ \(B_{m}\) & number of buckets in a multi-hash table \\ \(B_{c}\) & number of buckets in a cuckoo hash table \\ \(B_{d}\) & number of buckets in a \(d\)-left hash table \\ \(L\) & number of bits for a return value in hash tables \\  & number of bits of a cell in an FBF \\ \(2 log_{2}n\) & number of bits of the signature of an element \\ \(r\) & number of the available signatures (\(2^{2 log_{2}n}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation and definition.
# Recommender System for University Degree Selection:
## 7 Limitations and Future Research  
The dataset represents diverse student populations that are limited due to its specific population and educational context. Therefore, socioeconomic status and academic performance may not have the same impact across different educational systems. Including demographic variables like ethnicity, parental education, and rural/urban residence would provide a better understanding of how these factors influence educational and career decisions. The current dataset is limited to a specific educational system, which may differ from others in curriculum and assessment methods. Incorporating data from diverse systems would reveal how different structures and policies affect student outcomes and the recommender system. Future research should include datasets from multiple systems and a wider range of demographic variables to address these limitations. Transfer learning techniques could also be applied to adapt the model to different systems, improving its applicability.  
Secondly, although robust, the prediction models employed in this study depend on the quality and accuracy of the input data. Any inconsistencies or inaccuracies in the standardised test results or socio-demographic information can affect the recommender system's performance. Thus, ensuring high-quality data collection and preprocessing is crucial for the system's reliability.  
Thirdly, while the machine learning algorithms (XGBoost, Random Forest, GLMNET, and KNN) have shown solid predictive capabilities, they also require significant computational resources and expertise to implement and maintain. This may pose a challenge for some educational institutions with limited technical resources. Future research should explore more efficient algorithms and methods to reduce the computational burden without compromising accuracy.  
Additionally, the temporal scope of the data, which spans from 2006 to 2019, presents another limitation. To articulate the dataset, it is necessary to trace the students' academic performance from their high school years through to their university education. Thisprocess requires at least five years to obtain a high school student's university-standardised exam results. Consequently, there is an inherent delay in the availability of comprehensive data, which may impact the timeliness and relevance of the study's findings. Future studies should consider strategies to mitigate this delay, such as employing longitudinal data collection methods, to ensure more timely and applicable insights.
## 2 The Proposed CPV System
### Improving Irradiance Uniformity  
In the proposed CPV system, an array of Fresnel lenses is used as the POE. Fresnel lenses are used as solar concentrators since they offer high optical efficiency along with minimal weight and a low cost [31]. Plano-concave lenses are used to expand light beams in optical systems. They are used in CPV systems to improve the irradiance uniformity of the systems. In each designed CPV unit, a plano-concave lens is chosen as the SOE to improve the irradiance uniformity since plano-concave lenses are very common products in the market and have a low cost compared to other sophisticated SOE types, such as Kohler integration-based SOEs [19; 20; 21] and others [23]. The design of the CPV unit is shown in Figure 3.  
The POE (Fresnel lens) is used to concentrate incident solar radiation into the SOE. The SOE (plano-concave lens) is used to redirect the sunlight into the MJ solar cell and to uniformly distribute the irradiation on the solar cell. Therefore, the designed CPV unit significantly improves the irradiance uniformity compared to the conventional CPV.  
The effective focal length (\(EFL\)) of the Fresnel lens is calculated as follows:  
\[EFL=\frac{r}{n-1}, \tag{1}\]  
where \(n\) is the refractive index and \(r\) is the radius of the Fresnel lens.  
Figure 3: Design of the proposed CPV unit with the secondary optical element (SOE).  
Figure 2: Array layout of the CPV system. MJ, multi-junction.  
The focal length of the plano-concave lens is calculated by [32]:  
\[f=\frac{D_{\mathrm{p}}}{2\times NA}, \tag{2}\]  
where \(NA\) is numerical aperture of the Fresnel lens and \(D_{\mathrm{p}}\) is diameter of the plano-concave lens.  
In our proposed system, the material used for the Fresnel lens is polymethyl methacrylate (PMMA) with a refractive index of 1.494. The material used for the plano-concave lens is borosilicate glass N-BK7 with a refractive index of 1.52. Table 1 summarizes the design parameters of the CPV unit.
# Zernike Coefficient Prediction Technique for Interference Based on Generation Adversarial Network
## 2 Methods
### Generative Adversarial Network (GAN)
#### 2.2.3 Training GAN Model  
We use the Colab service provided by Google for training the GAN model. First, the parameters of the Discriminator network are updated with the initial fake image and actual image, but the Generator network is maintained. Second, the parameters of the Generator network are updated with the interference fringe image and the Discriminator's output that is the judgment of the fake photo, but the Discriminator network is maintained. Third, the Discriminator network is renewed again with the Generator's output image, a fake image, and the ground truth image, but the Generator network is maintained. Fourth, the Generator network is renewed with the interference fringe image and the judgment of fake picture, but the Discriminator network is maintained. Then, these two networks are trained interactively, as shown in Figure 4.  
The actual image is the ground truth image generated by the formulas with Zernike coefficients. The fake image is the Generator's output image, and it gradually approximates the actual picture during the training process. The Discriminator's output is the judgment of a fake photo and can help the Generator update parameters. The training process of the  
Figure 4: Training Process of GAN model.  
Figure 3: The architecture of the Discriminator network.  
GAN model is to maximize the discriminator loss function and to minimize the generator loss function.  
The randomly generated datasets include 400 training images and 40 validation images. The total training epochs are 700 epochs for converging during the five iterations of the training processes. The Generator's optimizer is Root Mean Square Prop (RMSProp), and the learning rate is fixed at 0.001. The Discriminator's optimizer is the Stochastic gradient descent (SGD), and the initial learning rate is 0.00002, used on the first two processes and then become one-tenth for each following procedure. Finally, we obtain the pre-trained model when the training process is complete.