esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Click-Through Rates Prediction Model Based on Deep&Cross Network
## 2 Preliminaries
### Optimization for DNN
#### 2.3.3 Activation Functions  
The DNN model has a strong nonlinear fitting ability, which comes from the activation function. In the DNN-based model, it is very important to select the appropriate activation function to capture the non-linearity inside the data [35; 36].  
The activation functions commonly used are sigmoid function [37] of formula (2), tanh function [38] of Formula (3), Rectified Linear Unit (ReLU) function [39] of Formula (4), PReLU (Parametric Rectified Linear Unit) function [40] of Formula (5), and Exponential Linear Unit (ELU) function [41] of Formula (6).  
\[\sigma(x)=\frac{1}{1+e^{-x}}, \tag{2}\]  
\[tanh(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}}, \tag{3}\]  
\[ReLU=max(0,x), \tag{4}\]  
\[PReLU=max(\alpha x,x), \tag{5}\]  
\[ELU=\begin{cases}x,&x>0\\ \alpha(e^{x}-1),&x\leq 0\end{cases}. \tag{6}\]  
Generally speaking, each activation function has its own advantages and disadvantages. No activation function is suitable for all neural network models. In this paper, when designing the DNN-based CTR prediction model, we have tried each activation function separately, and carried out a lot of comparative experiments. Finally, we find that the sigmoid function and tanh function have better performance for our model and the baselines.
## 4 Results Analysis and Discussion
### Double-Channel Overlapping Deposition
#### 4.3.1 Calculation and Comparison of Double-Channel Overlapping Deposition Shapes
Since the solid-state phase transition of IN718 alloy occurs mostly between 800 K and 1500 K [16], the rewarming phenomenon may influence the creation of solid-state tissue in the initial deposition channel. In order to prevent large changes in the cooling environment between the temperature zones of a deposition channel's solid state phase shift, it is important to accomplish delayed overlap by modifying the scanning route during the overlap forming processes.
# A Review on Internet of Things for Defense and Public Safety
## 5 Building IoT for Tactical and Emergency Environments
### Digital Analytics  
Analytical software manage the excessive volume of data that needs to be transferred, stored and analyzed. It would require flexible acquisition processes by the governments to integrate cutting-edge technologies quickly. Many applications would depend on real-time analysis to enable automated responses. Other system would process data into simple interfaces that allow humans to leverage big data in convenient ways.  
Semantic Web technologies have been acknowledged as important to support for data integration, reasoning and content discovery [165]. Particularly, three established elements have been identified as desirable IoT tactical capabilities:  
* Open Integration standards: they facilitate interoperability among devices with different capabilities and ownership through supporting ontologies. IoT ontologies should be integrated with existing community standards.
* Reasoning support: Ontology-based reasoning has been applied towards military sensor management systems, including those tasked with pairing sensors to mission tasks. [PERSON] et al. [166] present an ontology based on Military Missions and Means Framework that formalizes sensor specifications as well as expressing corresponding task specifications. When there is limited network connectivity, such reasoning capabilities could be applied locontinually assess how available IoT resources can be utilized.
* Data Provenance: the steps taken to generate data have been commonly acknowledged as important towards assessment of data quality and trustworthiness. In a military context, issues of provenance will be a dominant concern because the state, ownership, and reliability of devices will be uncertain. The capability will be critical when automated or semi-automated content assessment becomes desirable. New architectures will need to incorporate provenance and trust management tightly integrated in IoT technologies. The W3 PROV specification [167] is a primary standard for digital provenance representation, which is now being extended for IoT.
# Bloom Filter Approach for Autonomous Data Acquisition in the Edge-Based MCS Scenario +
###### Abstract
At the same time, the actual energy savings depends on a specific MCS application and its setup. The remainder of this article is organized as follows. First, in Section 2, we briefly survey related work addressing hierarchical fog and MEC solutions focusing on energy-efficient sensing, and the usage of Bloom filter across different domains. Then, we explain in detail a distributed edge-based architecture for MCS and present an algorithm that enables mobile devices to control the data acquisition process autonomously in Section 3. Finally, we extensively evaluate the applicability of the proposed approach and overhead introduced by filter control messages on a real-world dataset in Section 4 and conclude the paper in Section 5.
## 5 Results and Discussion
### Mesh Refinement and Discretization Errors  
Accurate FE simulations rely heavily on appropriate mesh density to minimize discretization errors. Table 4 summarizes the maximum discretization errors for different models, measured by the normalized flux density discrepancy (\(BN_{\text{ERR}}\)).  
The 2D models exhibit lower discretization errors (0.19% to 0.30%) compared to the 3D models (1.72% to 2.39%). This disparity is primarily due to the larger model geometry in comparison with the element counts (which is limited by the computational resources) inherent in 3D simulations, which can introduce more numerical inaccuracies if not meticulously refined. Nevertheless, the overall low error percentages affirm the reliability of both modeling approaches for segmented cables with parallel conductors, with 2D models offering superior accuracy at reduced computational costs.
# Analysis of the Dynamic Characteristics of Coaxial Counter-Rotating Planetary Transmission System
## 3 Dynamic Model of the Transmission System
### Calculation of Component Relative Displacements  
The generalized coordinate and mass matrix of each component are as follows:  
\[\left\{\begin{array}{l}X_{j}=[x_{j},y_{j},z_{j},\theta_{zj}]^{T}\\ M_{j}=diag[m_{j},m_{j},m_{j},J_{j}]\end{array}\right., \tag{1}\]  
where \(M_{j}\) represents the mass of the component, and \(J_{j}\) represents the moment of inertia with respect to the axis of rotation of the component.  
For the purpose of modeling and solving the dynamic model, a fixed coordinate system, \(o-xyz\), is chosen for the fixed-axes PGT, while a follower coordinate system, \(o_{h}-x_{h}y_{h}z_{h}\), is selected for the differential PGT, with the carrier serving as the reference. The displacements and deformations of the components are projected onto meshing lines to obtain the relative displacements among the components.
## 3 Dielectric-Based Passivation Layers
### Solar Simulator Results
The quality of the solar cells can be determined by \(FF\). After adding the passivation layer, a good \(FF\) indicates that the contacting area is well-defined. However, a low \(FF\) shows the need for optimization in the passivation layer [9]. \(FF\) was positively affected for nearly all the studies given in Table 1. For few studies, like [15], it was decreased due to the increase in series resistance and shunt conductance. The effect of the passivation layer on the efficiency of both conventional and ultra-thin CIGS solar cells is given in Figure 4. Figure 4 indicates the efficiency of the passivated and reference CIGS solar cells with respect to the thickness of the absorber layer for all the studies given in Table 1.  
After the addition of the passivation layer with point contact openings, the power conversion efficiencies of both conventional and ultrathin CIGS solar cells were increased in nearly all studies given in Table 1. However, considering Figure 4, the difference between passivated and un-passivated cells becomes apparent for the thinner (\(<\)800 nm) cells. It is seen that for some cases the power conversion efficiency of ultrathin CIGS solar cells reached up to 14% after the addition of the passivation layer. This value is nearly equal to the average power conversion efficiency of conventional unpassivated CIGS solar cells (Figure 4). It is a well-known fact that the efficiency values of ultra-thin CIGS solar cells are still way too low from the record efficiency of conventional CIGS solar cell. However, the aforementioned studies have proven that the addition of the passivation layer on the rear contact is a promising technique to achieve higher values, i.e., record efficiencies.
# A Multi-Constrained Optimization Method for THz Backward Wave Oscillators
## 3 Optimization Method
### Successive Iteration for Local Optimum
#### 3.2.2 Evaluation of Local Solutions  
As for the evaluation function, unit step functions are used to describe the constraints. The evaluation function is defined as  
\[f\left(\overset{\rightarrow}{S}_{i}\right)=\max\left\{\begin{array}{l}f\left( \overset{\rightarrow}{S}_{j}\right)\cdot\overset{\cdot}{H}_{1}\left(g_{1} \left(\overset{\rightarrow}{S}_{j}\right)\right)\cdot\ldots\\ \cdot\overset{\cdot}{H}_{m}\left(g_{m}\left(\overset{\rightarrow}{S}_{j} \right)\right)\end{array}\right\},\]  
\[\cdot\cdot\cdot\left(-g_{k}\left(\overset{\rightarrow}{S}_{j} \right)+\left(\begin{array}{l}t_{k}+\frac{1}{4}(r_{k}-l_{k})\\ +\max\left\{l_{k}-t_{k}+\frac{1}{4}(r_{k}-l_{k}),0\right\}\\ -\max\left\{t_{k}+\frac{1}{4}(r_{k}-l_{k})-r_{k},0\right\}\end{array}\right)\right) \tag{6}\] \[l_{k}=\min\left\{g_{k}\left(\overset{\rightarrow}{S}_{j}\right)\right\}\] \[r_{k}=\max\left\{g_{k}\left(\overset{\rightarrow}{S}_{j}\right)\right\}\] \[1\leq i\leq v^{2},j=1,\ldots,v^{2},1\leq k\leq m\]  
and \(H\) is the unit step function. It can be observed from Equation (6) that a solution will be eliminated if the evaluation function value is equal to zero, which means that at least one of the \(H\) functions is equal to zero. Half of the optional solutions, whose working parameters are farther to the expectation values, are eliminated by each constraint, considering that the local samples are obtained by orthogonal design and distribute uniformly. The remaining solutions tend to satisfy the constraints since their working parameters are closer to the reasonable values.  
Figure 4: Normalized working parameters corresponding to the sample of the local solutions around \(\overset{\rightarrow}{S}_{1}\). The sample consists of \(v^{2}\) (\(v=7\)) solutions obtained by orthogonal design. The working parameters are normalized by that of \(\overset{\rightarrow}{S}_{1}\).  
The reasonable value of \(v\) can be given as  
\[v=\left\lceil\sqrt{5.2^{m}}\right\rceil \tag{7}\]  
Equation (7) is given conservatively to guarantee that more than one local solution can pass the evaluation. It is reasonable to choose the local solution that has the largest non-zero evaluated value without worrying about the satisfaction of the constraints.
# Generating Bessel-Gaussian Beams with Controlled Axial Intensity Distribution
###### Abstract  
This paper investigated the diffraction of a Gaussian laser beam on a binary mask and a refractive axicon. The principles of the formation of a zero-order Bessel beam with sharp drops of the axial field intensity edges were discussed. A laser optical system based on an axicon for the formation of a Bessel beam with quasi-uniform distribution of axial field intensity was proposed. In the laser optical system, the influence of the axicon apex did not affect the output beam. The results of theoretical and experimental studies are presented. It is expected that the research results will have practical application in optical tweezers, imaging systems, as well as laser technologies using high-power radiation.  
Gaussian laser beam; Bessel beam; laser optical system; axicon; amplitude binary mask Article
# Low-Cost Solution for Kinematic Mapping Using Spherical Camera and GNSS
## 3 Results
### Relative Accuracy and Deformation from Spherical Images  
The cloud-to-cloud method was used to evaluate the relative accuracy and cloud deformation obtained from georeferenced spherical images from the Insta360 X3 camera. CloudCompare 2.13.1 [34] software was used for this purpose. The point clouds were aligned (congruent transformation) to several identical points, and the compute cloud/cloud distance function was run. Depending on the test, either full 3D (XYZ) distances or horizontal (XY) deviations were computed. The visualized errors correspond to the selected type and are shown in 2D views for clarity. The spatial standard deviation of the first point cloud compared to the Faro Orbis point cloud is 47 mm. In the second case, compared to the Mavic 3E point cloud, the spatial standard deviation reaches 82 mm. The resulting standard deviations are presented in Table 5.  
The following figures provide a top view of the measured test area with histograms showing the magnitude of the deviations produced from the point cloud differences. Figure
# Synthesis, Structure, and In Vitro Biological Evaluation of Semi-Synthetic Derivatives of Betulin
## 2 Materials and Methods
### Mechanistic Studies for Compound 9  
#### 2.5.1 Apoptosis Determination by Annexin V Staining  
The MV4-11 cells were seeded at a density of 2.25 \(\times\) 10\({}^{5}\) (for 24 h) or 0.75 \(\times\) 10\({}^{5}\) (for 72 h) cells/well on 12-well plates (Greiner Bio-One) in culture medium to the final volume of 3 mL. The cells were exposed for 24 or 72 h to compound 9 at the following concentrations: 55 \(\upmu\)M and 75 \(\upmu\)M (about 1.5 \(\times\) IC\({}_{50}\) and 2 \(\times\) IC\({}_{50}\) value). Campthotecin at a concentration of 0.05 \(\upmu\)M was used as a positive control. After incubation, the cells were collected and counted, and 1 \(\times\) 10\({}^{5}\) of cells were washed twice with PBS and then suspended in 100 \(\upmu\)L of binding buffer (Hepes buffer: 10 mM HEPES/NaOH, pH 7.4, 150 mM NaCl, 5 mM KCl, 1 mM MgCl\({}_{2}\), 1.8 mM CaCl\({}_{2}\), (IIET, Wroclaw, Poland)) with 4 \(\upmu\)L of APC-Annexin V (Becton Dickinson, Pharmingen, Franklin Lakes, NJ, USA). After 15 min of incubation in the dark at room temperature, prior to the analysis, propidium iodide (PI) solution was added (final concentration of 4 \(\upmu\)g/mL). Data acquisition was performed by flow cytometry using a BD LSRForefessa cytometer (BD Bioscience, San Jose, CA, USA). Compounds at each concentration and combination were tested at least three times independently. The results were analyzed using Flowing software 2.5.1 (Turku, Finland). The data were displayed as a two-color dot plot with an APC-Annexin V (AnV) vs. PI. Double-negative cells were live cells, PI+/AnV+ cells were late apoptotic, PI\(-\)/AnV+ cells were early apoptotic, and PI+/AnV\(-\) cells were necrotic.
# Improved DeepFM Recommendation Algorithm Incorporating Deep Feature Extraction
## 4 Conclusions  
This paper proposes an improved DeepFM recommendation algorithm incorporating deep feature extraction to address the problem that the DeepFM model does not consider the effect of time on user selection and has low recommendation accuracy under sparse data. The model makes full use of the attributes of items and user features and mines the low-order features and high-order features for training. At the same time, it can also learn the changing pattern of user interest. Through comparison experiments with several algorithms of the same series on MovieLens-20M and Amazon datasets, this algorithm has the best results in four evaluation indexes, _MAE_, _RMSE_, _F1-Score_, and _AUC_. This proves that the proposed methods alleviate the data sparsity problem and also effectively improve the accuracy of the recommendation algorithm. The next step in the research is to study the application of deep learning algorithms in recommendation systems.  
Conceptualization, M.M.; Formal analysis, M.M.; Funding acquisition, G.W.; Methodology, M.M.; Writing--original draft, M.M.; Writing--review and editing, G.W. and T.F. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key R & D Program of China (2019 YFB1802700).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **80\%** & **70\%** & **60\%** & **50\%** & **40\%** \\ \hline AFM & 0.9442 & 0.9573 & 0.9668 & 0.9831 & 1.1892 \\ NFM & 0.9395 & 0.9490 & 0.9592 & 0.9778 & 1.0323 \\ ONN & 0.9321 & 0.9489 & 0.9634 & 0.9790 & 1.0528 \\ DeepFM & 0.9354 & 0.9499 & 0.9684 & 0.9743 & 1.0044 \\ xDeepFM & 0.9240 & 0.9391 & 0.9430 & 0.9577 & 0.9733 \\ fDeepFM & 0.9114 & 0.9273 & 0.9322 & 0.9481 & 0.9694 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison experiments of algorithm effects under a different scale of sparsity.
## 1 Introduction
In [14], an inverted L-shaped microstrip with a via connecting to the ground plane achieved a 58.06% RLBW (4.4-8 GHz) and 51.96% ARBW (4.7-8 GHz). An inverted L-shaped microstrip radiator with two L-shaped branches adjoining the partial ground plane for the L-band spectrum could achieve a 44.9% RLBW (1.14-1.8 GHz) and 36.87% ARBW (1.15-1.67 GHz) [15]. In addition, an inverted L-shaped microstrip radiator with a hook-shaped branch adjoining the partial ground plane realized a 56% RLBW (2.25-4.0 GHz) and 63.61% ARBW (2.38-4.60 GHz) [16]. The findings showed that the concurrent use of an inverted L-shaped microstrip and adjoining branch effectively enhanced the ARBW of SCPA. Specifically, the adjoining branch perturbs the LP field to generate a CP field without an external polarizer. However, the existing inverted L-shaped strip antennas for L-band applications achieved an unsatisfactory ARBW (19.86% and 36.87%) [13; 15]. As a result, this research proposes an inverted L-shaped patch antenna with a corner-truncated partial ground plane diagonally adjoined with a square branch for L-band applications. The inverted L-shaped radiating patch could achieve linear polarization (LP), while the adjoining square branch was used to perturb the LP field to generate a CP field. The corner-truncated partial ground plane was utilized to enhance the ARBW. Simulations were performed using CST Studio Suite [17], an antenna prototype was fabricated, and experiments were carried out. The proposed antenna scheme could achieve an ARBW of 77.87% (1.09-2.48 GHz), rendering the technology suitable for satellite communication and navigation applications.
# Model Tests on Jacked Pile Penetration Characteristics Considering a Static Press-in Piling Machine
## 3 Test Results and Analysis
### Soil Pressure around the Pile  
Figure 13 depicts the variation curve of soil pressure at different positions throughout the entire process of pile sinking. It can be concluded that as the pile sinking depth increases, the soil pressure is at its highest when the pile end reaches the buried depth of the earth pressure gauge. Subsequently, as the pile jacking depth gradually exceeds the buried depth of the earth pressure gauge, the soil pressure gradually decreases. We analyzed the earth pressure at 1D, 2D, and 3D from the pile shaft at the same buried depth. In test PS1, when the soil depth is 35 cm and the distance from the pile shaft is 1D to 2D and 2D to 3D, the soil pressure around the pile decreases by 4.55 kPa and 8.819 kPa, respectively. Under the same conditions, test PS2 shows a decrease of 3.3 kPa and 13.2 kPa, respectively. Through analysis and comparison, it can be concluded that the decrease in soil pressure is proportional to the distance from the pile shaft. In other words, the farther the distance from the pile shaft, the greater the decrease in soil pressure.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Number** & **Pile Penetration Depth** & **Pile Head Axial Force** & **Pile End Axial Force** & **Axial Force Decline** \\  & **(cm)** & **(kN)** & **(kN)** & **Rate (\%)** \\ \hline PS1 & 60 & 1.79 & 1.19 & 34 \\ PS2 & 60 & 2.13 & 1.36 & 36 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The decreasing rate of axial force of the pile shaft when the pile sinking depth is 60 cm.  
Figure 12: Curve of pile axial force with the change in pile jacking depth. (**a**) Test PS1. (**b**) Test PS2.  
Figure 13: Change curve of soil pressure–pile penetration depth around the pile. (**a**) Test PS1. (**b**) Test PS2.  
According to the analysis of Figure 13a,b, it can be observed that when the influence of the static press-in piling machine is considered, the soil pressure around the pile at the same position increases significantly. The reason for this is that the volume of the soil is the same at the same depth of the pile row during the two test pile jacking processes. However, the soil is compacted under the influence of the static press-in piling machine in test PS2, leading to stronger extrusion during the pile sinking process, thus generating greater lateral earth pressure.
# Practical Approach for Smart and Circular Cities: Chatbots Used in Waste Recycling
## 4 An Analysis of Five Representative Chatbots That Support the CE
### GOA Plastic Waste Chatbot  
Starting in June 2023, the first chatbot that allows users to report plastic waste in the public domain was launched and tested in Goa by the World Wildlife Fund (WWF)-India. It will be integrated into the WhatsApp application, allowing users to connect to the chatbot by this means. At first, it will ask some questions to understand the plastic management context: the type and quantity of plastic that is to be throw away. Also, it utilizes the location and users can send photos of the waste. The information collected by the chatbot will subsequently be analyzed by WWF-India to formulate a plan to address the problem areas, particularly focusing on the removal of waste sites in marine ecosystems. Following its trial phase, WWF-India intends to expand the deployment of chatbots to additional regions throughout the country.  
Tourism operations in Goa and southern Maharashtra result in significant plastic waste entering the marine environment. These areas, renowned for their untouched natural landscapes, face the task of protecting endangered marine species and crucial habitats, such as coral reefs, from the threat posed by plastic pollution. WWF-India collaborated with Parmanoo Data Labs to develop the chatbot in response to this issue. [PERSON], senior coordinator of marine biodiversity conservation at WWF-India's Goa office, mentioned that \"The project received funding from a financial services company as part of its CSR initiative.\" Citizens in Goa can report plastic dumps by simply typing 'plastic' and sending it to WWF's WhatsApp number.  
The chatbot leads users through predetermined questions to collect essential information. \"While there are apps designed to address similar issues, the chatbot eliminates the need for citizens to download an app, which could consume storage space on their phones\", explained the source. \"The chatbot utilizes a widely used messaging service,\" [PERSON] explained, and \"Once the information is gathered through the chatbot, we will analyze it and collaborate with panchayats and manufacturers of disposable plastic products to develop a strategy to address the problem areas.\" He also highlighted that the collected data will help map the scope of unmanaged plastic waste in natural environments and facilitate the identification of practical solutions. \"The expansion of nature tourism, such as coral reef scuba diving and dolphin watching, has resulted in increased plastic pollution in these delicate ecosystems,\" [PERSON] stated. \"WWF-India is actively collaborating with dive and dolphin tour operators to enact efficient plastic waste management protocols. Recognizing the urgency of the intervention, we also introduced the chatbot, allowing citizens to report multiple dumping incidents in minutes\", he elaborated [21].