esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Summary of the Conducted Work  
The main purpose of the work was to use the developed methodology to estimate the value of the principal strain during deformation, thus enabling the control of crack initiation in the material subjected to forging. Numerical simulations and laboratory tests supported by analysis using the DIC system were carried out for that purpose. More specifically, specially designed tools were used for the hot forging tests, which cause the material upsetting and then punching until a crack appears. The shape of the tools prevents a rapid drop in material temperature and enables precise registration of the strain by DIC cameras. Two grades of aluminum alloys were tested and it was assumed that the analysis of the deformation change would be performed with one DIC camera, taking into account two different positions of the samples in relation to the camera.  
It was found that the applied methodology was effective in the entire scope of the research. Precise determination of the failure strain value enabled the accurate selection of the parameters for fracture models. The presented research methodology is used to predict the risk of product fracture in industrial backwards extrusion, where identifying the locations of areas with concentrated unfavorable process parameters is difficult to identify due to the specificity of the process.  
Laboratory tests involving the complex deformation of samples (including upsetting and piercing) and the measurement of the principal strain on their surfaces are aimed at determining the areas of strain concentration accompanying fracture on the external surface and calibrating models for FEM simulation. Based on the achieved correlation of laboratory test results and simulations, it is possible to identify the critical values of the parameters that cause damage, which are concentrated on the internal surface of the sample, which is in direct contact with the tool. The laboratory test closely resembles the backward extrusion process, enabling similar stress and strain states to be achieved. As a result, the test adequately reflects industrial conditions and helps to determine the critical values of the fracture criterion.  
A high level of accuracy in determining the value of the main strain at the point of crack initiation during the test was also observed. The specific shapes of the laboratory tools provide an accumulation of stress on the upper corners of side wall of the sample. DIC analysis enables better understanding of the ductile fracture mechanism during forging. The results of the presented research can be used to analyze industrial forging processes, especially in cases where there is high risk of damage.
# Atmospheric GNSS RO 1D-Var in Use at UCAR: Description and Validation
## 2 Materials and Methods
### The UCAR 1D-Var
#### 2.2.1 Theoretical Framework
The minimization starts with \(\mathbf{v}=0\) and each iteration evaluates \(J\) and \(\
abla_{\mathbf{v}}\ J\) to update \(\mathbf{v}\) in a manner similar to the [PERSON]'s method:  
\[\mathbf{v}^{n+1}=\mathbf{v}^{n}+\gamma\cdot f(\
abla_{\mathbf{v}}\ J). \tag{9}\]  
where superscripts indicate the numbers of iterations, \(f(\
abla_{\mathbf{v}}\ J)\) is the direction of steepest descent, and \(\gamma\) is the step size. The steepest direction and the step size are determined with a quasi-Newtonian limited-memory algorithm for large-scale optimization [62], which takes into account the past few iterations. In most cases, the minimization converges in less than 50 iterations. The convergence is declared when the change of the cost function with iteration is trivial and the norm of the gradient falls below a prescribed threshold. The control vector acquired upon completing the minimization, \(\mathbf{v}_{a}\), gives the analysis increment, \(\delta\mathbf{x}_{a}=\mathbf{B}^{1/2}\ \mathbf{v}_{a}\), which in turn furnishes the analysis vector, \(\mathbf{x}_{a}=\mathbf{x}_{b}+\delta\mathbf{x}_{a}\). The retrieval process does not require the conversion of the state vector into the control vector, \(\mathbf{v}=\mathbf{B}^{-1/2}\delta\mathbf{x}\). What is needed instead is the reverse transform, \(\delta\mathbf{x}=\mathbf{B}^{1/2}\ \mathbf{v}\), to compare observations to their model equivalents. This is important since the inversion of a large matrix is expensive and less stable, and even impossible when the condition number is large [63]. On the other hand, \(\mathbf{B}^{1/2}\), which can be any matrix that satisfies \(\mathbf{B}=\mathbf{B}^{1/2}\ \mathbf{B}^{\mathsf{T}/2}\), is definable with a series of algebraic operations that transform the control vector into the state vector [64; 65].
# Subcritical Water Extraction of Ursolic Acid from _Hedyotis diffusa_
[PERSON]  
1 Beijing Key Laboratory of Lignocellulosic Chemistry, Beijing Forestry University, Beijing 100083, China; Jane2015 extraction (ASE) and microwave-assisted extraction (MAE) [9] have been used to extract triterpenes and their derivatives from plant material. However, conventional organic solvent extractions have an energy-consuming nature, low extraction selectivity, lengthy extraction procedures, and cause environmental problems. Nowadays, the desire to reduce the use of the organic solvents in food and medicine processing has led to new extraction methods including supercritical fluid extraction and subcritical water extraction (SWE). In subcritical water extraction (SWE), water is pressurized to a temperature and pressure under its critical level conditions with dielectric constant changed, which has become an effective means for the isolation of bioactive and nutritional compounds from plants and food materials [17]. The advantages of SWE include the low energy consumption, simplified operation, high efficiency, friendly environment system and potential of scaling up to the industrial level. Recently, SWE has been used to extract phenolic compounds [18; 19], lipids [20], polysaccharides [21], proteins and amino acids [22; 23]. In general, extraction of an active ingredient using SWE from plant sources is influenced by multiple parameters such as extraction temperature, extraction time, extraction pressure and ratio of liquid to material, and their effects may be either independent or interactive. Response surface methodology (RSM) has been described to be an effective statistical technique for solving multivariate problems and optimizing several responses in many types of experiments because it can simultaneously consider several factors at many different levels and corresponding interactions among these factors using a small number of observations [24]. In the present work, we report the optimization of the subcritical water extraction of unsolic acid from _H. diffusa_ using response surface methodology (RSM) experimental design. High performance liquid chromatography (HPLC) is used to quantify UA in _H. diffusa_ extracts, and the purified extract was analyzed by electrospray ionization time-of-flight mass spectrometry (ESI-TOF-MS). Furthermore, the result of the proposed method was compared with other extraction methods, such as maceration extraction, heat reflux extraction, ultrasonic extraction and microwave-assisted extraction.
# Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey
## 5 Analysis of Data-Driven Models for WSNs

Table 2: _Cont._The data processing of each data-driven model is different. Suppose the query-driven model starts the process with query generation for getting data from sensors as per the user requirement. Once the user generates queries, then, it sends the generated query to the WSNs' sensor nodes. If the query matches with the data of the sensor nodes, then it is accepted; otherwise, it is rejected and deleted. For the event-driven model, sensor nodes are mostly in sensing mode. Sensors usually sense data according to an event, and at the moment the event occurs, data are sent to the sink node; otherwise, data are sensed continuously. In a time-driven model, sensors also sensed the data continuously. The sink node sets a timer to all sensor nodes for sensing the data. Sensor nodes divide that specific time into periods and further divide periods into slots. The sensor nodes send the data to the sink within a specific period of time; otherwise, sensor nodes collect the data continuously. Lastly, the hybrid model is made from merging two models. Suppose if the hybrid model is made by merging event-driven and time-driven models. Normally, the hybrid model processes with respect to the time-driven model to sense and send the data periodically. Whenever an event occurs, the hybrid model switches from a time-driven model to an event-driven model for further processing in the WSNs.
## 2 Materials and Methods  
### Experimental Setup and Sample Preparation  
This experiment was conducted in experimental fields in Weifang City, Shandong Province, China. A total of 15 wheat genotypes were randomly planted across 18 randomly selected locations, resulting in 29 randomly composed sample data points, as shown in Table 1. The purpose of this approach was to introduce greater randomness and representativeness into the experiment, which would effectively validate the stability of the model and optimize its performance. Each plot in each site measured three meters and comprised six rows, being about 223 m in length and having a total area of 667 square meters. The collection method was to randomly take three square wheats from the top, middle, and bottom of each plot. The base fertilizer consisted of 50 kg of compound fertilizer with a nitrogen-phosphorus-potassium ratio of 15:15:15. Mechanical sowing was performed with a row spacing of 26 cm. The Fourier transform near-infrared spectrometer (FT-NIR) used in this experiment was an Antaris II, manufactured by Thermo Fisher Scientific Inc. (Waltham, MA, USA). It operated within the near-infrared spectral range of 780 to 2526 nm. The detector employed was DTGS, the beam splitter was a multi-layer coated potassium bromide (KBr) prism, and the light source was EverGlo (Muggensturm, Germany). After receiving the yield, the measurements were repeated three times via FT-NIR spectroscopy and by measuring the reflectance or transmission spectra of the wheat samples in the near-infrared spectral range, and then the spectral data were analyzed using a mathematical model to correlate them with the known data on the quality of protein content, bulk density, dough extensibility, development and stability time, wet gluten, and dough extensibility and ultimately to predict the various quality indices in the wheat samples. The wheat quality indexes were also measured via physicochemical methods for comparative analysis. The physicochemical method of determination of each indicator was as follows:  
1 In the laboratory, wheat protein content was determined using the Kjeldahl method. Initially, wheat samples were subjected to acid hydrolysis in the presence of sulfuric acid and a catalyst. The resulting ammonia was distilled and reacted with boric acid. Subsequently, by titrating the concentration of ammonia, the total nitrogen content in the wheat sample was calculated, allowing for the estimation of wheat protein content determined by the Kjeldahl method. 2 Wheat bulk density measurements were conducted using a standard volunteer. Initially, the mass of the wheat sample was weighed. Then, the sample was gradually added to the volunteer until the wheat inside reached the calibrated volume of 1 L. The
# Threat Analysis for Smart Homes +
## 5 Smart Home Topology Modeling
### Edge Generation  
Next, the algorithm generates the edges between the nodes. Network generators come in two families, namely structure-based that attempt to create a hierarchical structure and degree-based that focus solely on the degree distribution [23]. A degree-based approach was discarded in favor of a structural approach that took into consideration topological information of different communication protocols. All clouds \(Name_{C}\), hubs \(Name_{H}\), smartphones \(Name_{S}\) and PCs \(Name_{PC}\) are connected to exactly one gateway \(Name_{G}\). The scalability of the dependencies for the connections between all IoT appliances \(IoTname_{a}\) and their hubs \(Name_{H}\) has been reduced to ensure clarity and comprehensibility of the final graph. For this reason, we opted to adopt partial mesh topology networks as the standard arrangement for the nodes in a sub-network formed by each hub \(Name_{H}\) and its associated appliances \(IoTname_{a}\). The weight vectors \(IoTW_{i}\)(\(IoTw_{1}\),...,\(IoTw_{m}\)) previously generated are now used to determine whether an IoT device IoTname\({}_{\text{a}}\) is connected directly to a hub \(Name_{H}\) or to a router node \(IoTn_{b}\). If the weight value \(IoTw(c)\) is lower than the maximum range \(R_{H}\) supported by the hub's protocol, then the device is connected directly to \(Name_{H}\). If \(IoTw(c)\) is greater than \(R_{H}\), then the device is connected to a router node \(IoTn_{b}\), which itself is directly connected to \(Name_{H}\).
## 5 Conclusions  
This study characterized the impact of NLTL impedance on RF generation when used in the combined PFL/HPM topology. Such a system provides a compact form factor compared to traditional NLTL systems by eliminating the need for a separate PFN and biasing magnetic field. The reduction in system size and weight provides a promising step toward a man-portable HPM systems. To test our hypothesis that reducing the NLTL impedance increased RF generation when using the NLTL as a PFL, we manufactured NLTLs with a fixed volume loading of BST and NZF and varied impedance by modifying the ratio of the inner and outer diameters of the coaxial structure. Both visual inspections of the output waveforms and power spectrum density analysis suggest that oscillation formation depends on NLTL impedance in this format. The 10 \(\Omega\) NLTL generated 64 kWat 1.08 GHz and 8.5 kW at 1.43 GHz for a 20 kV charging voltage. Lobes had bandwidths of 76 MHz and 102 MHz. The power spectrum for the 25 \(\Omega\) and 50 \(\Omega\) NLTLs show 47 kW and 15 kW peaks at 556 MHz and 513 MHz with bandwidths of 22 MHz and 41 MHz, respectively. We conjecture that this arises because of how the PFL forms the pulse and the mechanism by which ferrimagnetic materials generate the oscillation (coherent gyromagnetic precession). Moreover, while one of our major motivations for this approach was to reduce the system's footprint by eliminating the PFN, it may be possible to further increase the output power by using a biasing magnetic field, albeit at the cost of system size. Furthermore, the flexibility in device design presented by the composite structure and NLTL impedance may permit output frequency tuning in future systems.  
Conceptualization, T.D.C. and A.L.G.; methodology, T.D.C.; investigation, T.D.C.; writing--original draft preparation, T.D.C.; writing--review and editing, T.D.C. and A.L.G.; supervision, A.L.G.; project administration, A.L.G.; funding acquisition, A.L.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Office of Naval Research under Grant No. N00014-18-1-2341 and by a Directed Energy Professionals Graduate Scholarship.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
We thank [PERSON] for his assistance with performing the experiments.  
The authors declare no conflict of interest.
# Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon
## 4 Results and Discussion  
### Susceptibility Maps  
Figure 6 shows the three landslide susceptibility maps, derived from the model proposed in this study. Each map represents the susceptibility of landslide occurrence in the study area, derived through the DNN, kernel-based DNN, and CNN models, as described in the previous step. Due to the small study area, most areas can be classified into the same class when the map is graded. Therefore, the research area is represented by the susceptibility of occurrence from 0 to 1, and continuous susceptibility can be confirmed. The three models generally showed similar spatial distribution.  
The proportions with susceptibility of less than 0.2 were 38.55%, 39.04%, and 38.30% in DNN, kernel-based DNN, and CNN models, respectively. On the other hand, the ratios of 0.8 or higher with a high susceptibility of landslide occurrence were 39.59%, 33.13%, and 34.47%, respectively. Relatedly, the DNN model shows a large number of regions with higher susceptibility, and in the case of kernel-based DNN, the resolution is slightly lowered. This is because the smoothing effect appeared as a result of using the kernel. Areas with high susceptibility of landslides were identified mainly along the north and south sides, where large debris flows occurred.  
Since the slopes of Mt. Umyeon consists of gneiss, the forces remaining in the weathered gneiss layer after a torrential downpour reduce the amount of surface runoff and absorb a large amount of  
Figure 6: Landslide susceptibility maps generated using deep learning models: Using a (**a**) deep neural network (DNN), (**b**) kernel-based DNN, NS (**c**) convolutional neural network (CNN) model.  
moisture into the weathered layer. Moisture in the saturated weathering layer reduces the resistance to bedrock, facilitating the downslope movement by gravity. As the resistance was sharply decreased, the physical erosion power was strengthened by the increase of the surface water flow, which erodes and mobilizes the mud and rocks. In kernel-based DNN and CNN, the effect on not only a single pixel but also neighboring pixels is considered at the same time, thus reflecting this phenomenon more realistically. In Figure 6, the probability of landslide is low in the mountain ridge in all models, and the valley slope forms a steep slope by lateral erosion.
## 4 Experimental Results
### Blade/Non-Blade Classification  
After elaborating the proposed hub object detection, we are going to evaluate the proposed algorithm for binary categorization to identify a blade. For this purpose, as mentioned in Section 2, we use powerful pre-trained models such as _DenseNet_, _SqueezeNet_, _ResNet50_, and _InceptionV3_. In this work, we removed the last layer of mentioned models and added a fully connected layer with two outputs to solve our binary classification problem. Our first experiment is conducted based on the ReNet50 model with 10 epochs fed by 1000 images with dimension of \(32\times 32\) pixels. The achieved results for both accuracy and loss curves for train and test phases are shown in Figure 13. The results indicate that ResNet50 accuracy is more than 90% after the 10 th iteration.  
Then, for a more complete review of the effect of various parameters on our classification accuracy, ten experiments have been performed. In these experiments, the variable parameters are: _database size_ (1000 and 10,000), _image size_ (\(32\times 32\), \(64\times 64\), and \(480\times 480\)), _epochs_ (10, 20, 50, and 100) for four selected models. The implementation results are presented in Table 4. It indicates that after the tenth epoch, the DenseNet fine-tuned model, which is trained on our dataset with 10,000 images can achieve the highest validation accuracy of about 0.9912 among all scenarios.  
Figure 12: Solving false negative detection problem. (**a**,**c**) False negatives with _epoch_=10, _confidence thresh_ = 0.70, _IoU_ = 0.50. (**b**,**d**) True positives with _epoch_ = 10, _confidence thresh_ = 0.3, _IoU_ = 0.50 (with NMS).
# A Heterogeneous Machine Learning Ensemble Framework for Malicious Webpage Detection
## 2 Related Work
### Malicious Code Distribution Pattern Detection  
To cause personal computers to be infected with a malicious code, attackers create a network connecting a landing site and malicious code distribution site. This network is known as a malware distribution network (MDN). JavaScript and iframe tags are used to enable automatic connections without user action, and their links are obfuscated, causing analysis to be difficult [21]. Vulnerable personal computers can easily be infected with a malicious code. To lure potential victims, popular websites with a high number of user connections may be injected with a malicious code that can automatically create a connection between the user and the distribution site. [PERSON] et al. [22] presented a novel approach to identifying landing pages in MDNs that lead to drive-by downloads. To analyze contents that are similar across malicious websites, they extracted commonly used information from known MDNs and then queried the information from a search engine to identify suspicious websites and finally determine whether it is a malicious distribution network. They performed string clustering for similar malicious strings based on the string similarity. [PERSON] et al. [10] proposed an effective system called Nazca to detect infections. Nazca detects web requests used for malicious code binary downloads and is designed to work with large-scale networks such as ISP. It performs network traffic analysis with live packet records and extracts a record for each HTTP connection, focusing on malicious code, distributed malicious code hosting, ad hoc malicious domain, and the use of malicious code droppers. [PERSON] et al. [21] proposed an automated link generation and tracing method called the AutoLink-Tracer. This approach consists of two elements: a link trace and link analysis. The former involves an actual browser and forward proxy, whereas the latter involves the root node of the website that is first visited by a user, the hopping node that is automatically linked to it, and the last node, the final node, which has no further link. All links can be expressed in a graphical form. They determined whether a particular network was responsible for malicious code distribution through the collection of links and automated link analysis.  
Existing studies have analyzed the distribution or propagation of malicious code based on node links. It is important to identify the main malicious nodes. Therefore, in this study, by combining the prediction results of the reputation data and the malicious web detection model, it is possible to evaluate whether the web is related to malicious code and to determine the main malicious node by determining the distribution site. A more precise judgment will be possible if combined with node link analysis in the future.