esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Collection and Georeferencing  
The data were collected from GIS public databases and local surveys, including exclusion layers, i.e., electric networks, roads, built-up areas, water bodies, and land use/land cover. All layers had their criteria based on the technology being mapped. The collected datasets were used to map solar energy potential. A digital elevation model (DEM) was obtained from the Shuttle Radar Topography Mission (SRTM)--Earthdata (NASA) in a raster format. It was then used to determine the slope and aspects essential in the site suitability analysis of solar energy. The slope and aspect were considered a critical topographical factor that significantly influences land suitability for installing PV systems, where it is best when it faces the south direction [105]. The reason is to avoid the shadow effect on the generation of the PV system [106]. Lands with a slope greater than \(5^{\circ}\) were not considered [107], because it affects the reception of solar radiation, and the flatter the surface, the more radiation it received [108].  
The wind speed is also an important parameter that influences land suitability for installing wind turbines. For the three study areas, the average annual wind speed at the height of 50 m is as follows: for Araceli, it is 5.82 m/s, for Balabac, it is 4.85 m/s, and for Cuyo, it its 6.39 m/s [109]. Since Balabac did not meet the minimum criteria of 5.5 m/s of mean wind speed, it was therefore excluded from the assessment.
# A Review on Local Failure Probability Sensitivity Analysis
## 3 Local RSA in the Original Input Space
### Score Function Method with Advanced Sampling Techniques  
Various advanced sampling techniques have been developed, based on the MC method [15], to estimate the failure probability more efficiently. Some of these techniques can be applied to the context of the score function method, to estimate the expected value \(\mathbb{E}_{f_{\mathbf{Z}}}\left[\mathbb{I}_{D_{f_{\mathbf{Z}}}}(\mathbf{Z}) Q(\mathbf{Z})\right]\) in addition to \(P_{f}\). Therefore, these methods make it possible to evaluate more effectively the derivatives of \(P_{f}\) according to the distribution parameters \(\theta\) of \(\mathbf{Z}\). We derive some of these methods here.
## 2 Related Work
The presence of nitrosamine in red meat samples was identified using SVM and deep CNN models. The authors employed the transfer learning technique to investigate the performance of various deep CNN models (such as ResNet-34, ResNet-50, ResNet-101, VGG-16, VGG-19, AlexNet, and MobileNetv2). It has been concluded that the Resnet-101 model outperforms the others with the highest accuracy of 95.45% and F1-score of 96.54%. [PERSON] et al. [24] investigated the efficacy of deep learning models for beef quality classification. Beef images that include healthy and rancid ones, obtained from Kafreshieikh University, were employed. To mitigate the data scarcity issue, a generative adversarial network was utilized. Thus, the amount of images given to the deep CNN model was greatly increased. The authors reported an overall accuracy of 96.03% with Resnet-50 model. [PERSON] et al. [25] suggested a CNN-based approach to evaluate the quality of meat. Their research intended to maintain the highest level of quality in the retail red meat sector's open department sales service. This was accomplished by using a try of meat cubes for an extended period of time and acquiring RGB images every two minutes. Expert data were also gathered and used as ground truth labels in parallel. Appropriate pre-processing was performed for effective extraction of features. The authors reported a better prediction result with the suggested CNN model to classify the red meat images as fresh or spoiled. [PERSON] et al. [26] classified beef, mutton, and pork images using the Resnet152V2 model. Various pre-processing techniques like augmentation, image sharpening, and image resizing were performed. Then, the discriminative features were extracted from the pre-processed images using the layered architecture of the Resnet152V2 model. The authors considered a total of 600 images with equal instances of beef, mutton, and pork images. The authors achieved an overall classification accuracy of 80%. A majority of the prior research works have investigated the effectiveness of pre-trained models for meat classification tasks. The pre-trained models are very deep in nature. Hence, the computational complexity is high in those models. Furthermore, these deep models may take longer training time. Developing a compact model may mitigate these issues. The ensemble method shows the benefit of combining individual models to improve prediction accuracy and the generalization performance. However, limited work has been carried out to develop an ensemble model for evaluating meat quality. Motivated by recent success, a framework based on the ensemble learning of compact CNN models for meat quality assessment is developed.
# Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems
## 5 Results
### Step 2--Defining SQL Query Patterns
#### 5.3.1 Select Main Artifact Instances  
The artifact metaclass will be directly transformed into a SELECT statement. The SELECT clause specifies the identification properties of the data object to be retrieved, and the FROM clause indicates the table to be used. As stated earlier, these properties are mandatory for each artifact type. The condition metaclass attribute represents the condition to be fulfilled to distinguish various artifacts having the same table, e.g., different purchasing documents can be stored in one table. It will be transformed into a search clause. An extra condition can be used to specify the boundary date values for getting all event data in a certain timeframe. The values are mapped from timestamp attributes of artifact type log metaclass. The main artifact instances can be retrieved using the following query (see Listing 6).  
```
1:CREATEVIEWmainArtifactAS
2:SELECTsubjectArtifactType.FieldName_ID AS [mainArtifactID]
3:FROMsubjectArtifactType.TableName
4:WHEREsubjectArtifactType.Condition AND
5:subjectArtifactType.getFieldTimestamp() BETWEENWEEN
6:ArtifactTypeLog.TimestampFromANDArtifactTypeLog.TimestampTo;
```  
**Listing 6** SQL query pattern that selects main artifact instances.
## 2 Description of the System  
The topology of the grid connected PV generation system is proposed in Figure 1. As it can be noted, a centralized approach is used, as a single inverter serves the equivalent PV generator, composed of several PV modules connected in series and in parallel. The PV generator is interfaced with an interleaved inverter via an LC filter and the capacitance voltage is measured. The filter presence is necessary to the proposed MPPT algorithm, which has been further discussed.  
The inverter is obtained by two interleaved _3-phase H-bridge IGBT_. Each of the two bridges is an inverter itself, capable of generating AC active power with low THD and unitary power factor. The interleaved choice allows distribution of the current among the two bridges, decreasing the rated current of each IGBT. Another advantage of the interleaved structure is regarding the fault-tolerance. The inverter is capable of working with reduced power in case of a failure of one bridge, guaranteeing the continuity of the service. On the other hand, interleaved converters need a proper control strategy, along with a specific modulation technique in order to actively balance the distribution of voltages and currents.
_applied_  
_sciences_  
_Article_  
Duration of Rainfall Fades in GeoSurf Satellite Constellations  
[PERSON]\({}^{\star}\)\({}^{\copyright}\) and [PERSON]\({}^{\copyright}\)  
Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, 20133 Milan, Italy; [EMAIL_ADDRESS]  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]  
We have studied the stochastic processes A and B concerning fade durations due to rain, by simulating attenuation time series \(A(t)\) (dB) in the zenith paths of GeoSurf satellite constellations, at sites located in different climatic regions, with the Synthetic Storm Technique. Process B gives the statistics of outages (occurrences) and process A gives the statistics of outage duration (fraction of time), for the same rain attenuation threshold \(A(t)>S\). The two processes are not independent; therefore, we have studied the relationship between their probabilities and defined a uniformity index \(0<U(S)\leq 1\). \(U(S)\) is useful for comparing real cases-fade durations fragmented in many different intervals, with changing \(S\) and site--and the limiting case of all fades lasting the same time. As \(S\) increases, \(U(S)\) increases, approaching 1 at very large thresholds. These results should guide the designers of satellite constellations to consider the impact of \(A(t)\) on diverse communications services. Process B (occurrences) impacts mainly on non-real-time services, such as data delivery, more disturbed by the number of outages rather than by their duration. Process A (fraction of time) impacts mainly on real-time services such as television, video conference etc., more disturbed by the duration of the outage.  
attenuation; fade duration; GeoSurf; millimeter waves; outage; rainfall; satellite constellation; uniformity index
## 2 Methods and Tools
### Data Acquisition
#### 2.2.2 The Survey Parameters  
The survey area depicted in Figure 4's orthophoto map underwent thorough scanning using land-based GPR (red grid) and drone-based GPR (blue grid). Extensive lines were established in the north-south and east-west directions using a grid-type survey strategy. The non-regular blue lines of the drone-based GPR mean the drone is arriving to charge and departing for the next survey step. The maximum depth for the subsurface investigation was consistently set at 5 m below the surface in both methodologies. The survey velocity maintained a uniform rate of approximately 1 m per second for the ground- and drone-based GPR scans. Precise real-time kinematic (RTK) measurements of the surveying process were meticulously recorded, enhancing the accuracy and reliability of the acquired data. The distance between nearby lines was 3 and 2 m for land-based and drone-based GPR, respectively. The overall area studied by each instrument was 8500 m\({}^{2}\). This mapping initiative was critical to our multidimensional approach, augmenting the GPR data to enhance the accuracy and interpretation of the subsurface archaeological features within the construction site.
## 1 Introduction  
Initially, data visualization in scientific research areas focused on visualizing information that was inherently 3D [1]. This comprised a variety of geospatial information, such as directional movement of seawater driven by the Coriolis effect and information obtained from living organisms, fluid dynamics, and Magnetic Resonance Imaging (MRI), which convey aspects of anatomy and physiological processes of the human body. This type of information was represented as-is, naturally, using large format visual displays. Later, during the nineties, researchers eagerly started investigating immersive representation of abstract information [2]--i.e., utterly quantitative data--and, hence, had a certain liberty in the way it could be visualized using immersive spaces [3]. Specifically, researchers investigated how this type of information could be embedded, and its visual mapping became the main focus of a novel research stream--information visualization. The latter incorporates the fields of graphic design, human-computer interactions (HCI), and data analysis, developing a novel multidisciplinary research area that examines new methodsof how computer science could be employed to represent abstract information and its interaction design [4; 5; 6]. In the mid-1990s, the first information visualization symposium was established in order to welcome new research propositions in this novel stream.  
Although abstract information visualization using immersive environments initially gained attention in the mid-1990s [3], early 21 st century researchers showed less interest in the field [2]. Since it became an independent branch of study, researchers have focused on developing methods for traditional desktop settings and WIMP interfaces. This shift in interest resulted from some research outcomes suggesting that 3D visualizations of abstract information on a desktop are not necessarily beneficial [7]. Until now, the question of whether using new immersive technologies for abstract information offers more benefits and how interaction for this type of information can be designed remains to be answered.
# An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter
## 3 The Proposed Approach
### De-Noising Based on Wavelet Energy  
#### 3.2.1 Wavelet Decomposition of ECG  
Multi-scale and multi-level wavelet decomposition is performed on the signal after the averaging process. As is known to all, the determination of the number of layers of wavelet decomposition affects the subsequent de-noising effect to a certain extent.  
If the number of decomposition layers is too small, the de-noising effect will be unsatisfactory. Generally, in order to eliminate high-frequency noise and extract low-frequency components, the number of decomposition layers will be increased to some extent [21], if the number of layers are too high, the error will be considered as large; moreover, it mainly focuses on the reflection of the characteristics of the wavelet basis rather than the signal being analyzed itself, which results in false frequencies and much information loss. For purpose of obtaining more wavelet coefficients, the number of decomposition layers can be appropriately increased, but when the number of decomposition layers is too much, it is easy to cause false frequency.
## 3 The Conceptual Framework Proposed
### Analyzing the Real-Use Environments and Implementing Living Labs
#### 3.2.1 User Context  
A user is an individual using a product and receiving the benefits from it. According to the literature, product experiences of users can be divided into two types: direct and indirect [37]. On one hand, people use products directly. For example, users wear audio sunglasses and VR equipment, ride a car and an electric kickboard, or carry their bags and smartphones. They also operate products that are usually placed in a designated spot, such as refrigerators, washing machines, and TVs. On the other hand, there are also many products that users normally do not use directly, but benefit from them. For example, automatic spray air fresheners, fire alarms, building air conditioning facilities, traffic lights, and manholes are typical examples.  
These different ways products are used by individuals suggest that the physical, physiological, or mental state of a user acts as the usage environment of a product. As seen in Figure 1, if a product is worn on a user's body (e.g., heating jumpers), specific body size and shape of the user (i.e., physical state) constitutes the real-use environment of the product. In other cases, if a product is attached to the body, the physiological state of the user such as skin types (e.g., oily or dry) can be understood as the actual use context of the product. In addition, if a product is carried by a user, or normally placed in a specific spot like laundry room and pantry, the mental state of a user such as preferences, habits, and life patterns is the key elements of the real-use context of the product.  
On the other hand, in the case of the products that people use indirectly, such as automatic spray air fresheners, other situational elements such as activity of the users are particularly important. Those indirectly-used products are physically away from the users and are not usually operated by them. Therefore, a user's situation at the moment when a product is operating is necessary to understand the real-use context of the product, such as what the users are doing and who they are being with. In much of the literature, user activity has been regarded as an important situation of a user [38].  
Lastly, the framework in Figure 1 also explains that a user's demographic characteristics, such as age, gender, education, occupation, and income, can also determine the real-use environment of a product. For example, the actual use environment of MacBook laptops is often composed of female college students who major in design. Indeed, the information about what age, gender, income,and education levels of people are using a product is indispensable to understand the real-use situation of the product, no matter what way the product is used.
# SBNN: A Searched Binary Neural Network for SAR Ship Classification
## 3 SBNN  
### Overall Network Architecture  
Figure 1 and Table 1 show the proposed SBNN. The inputs of SBNN are normalized SAR images. For a lower computational complexity, binary convolution is wildly used in the novel network. In detail, SBNN contains one floating-point Stem convolution layer, five binary convolutional cells and one floating-point classifier. The same as common networks for classification, the forward propagation has 3 stages to ensure a strong feature extraction capability, where the size of the feature maps is reduced twice and the number of channels are doubled twice. The convolutional cells have two types, i.e., Normal Cell and Reduction Cell, and they will be introduced in Section 3.2. In addition, we apply a very small initial channel number in SBNN in order to further guarantee low computational complexity and high memory efficiency.  
Figure 1: The overall network architecture of SBNN.
# A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features
## 3 Materials and Methods
### Support Vector Machine  
SVM constitute a powerful class of supervised machine learning algorithms widely employed in classification and regression tasks. Developed by [PERSON] et al. (2016), SVM has since gained popularity for their effectiveness in handling high-dimensional data and achieving robust generalization performance. For a binary classification problem with two classes, let's denote the classes as \(+1\) and \(-1\). The goal of SVM is to find a hyperplane represented by the Equation (11) :  
\[f(x)=w\cdot x+b \tag{11}\]  
where \(w\) is the weight vector, \(x\) is the input vector and \(b\) is the bias term. The decision rule for classification is based on the sign of \(f(x)\):  
\[\text{Predict class }y=\begin{cases}+1&\text{if }f(x)\geq 0\\ -1&\text{if }f(x)<0\end{cases} \tag{12}\]  
The goal of SVM is to maximise the margin i.e., the distance between the two closest points of different classes, which are the support vectors. The distance from a data point to the decision boundary is given by the equation:  
\[\text{Distance}=\frac{|f(x)|}{\|\mathbf{w}\|} \tag{13}\]where \(w\) represents the Euclidean magnitude of the weight vector. The objective of SVM is to maximise the margin while reducing the classification errors. It is achieved by solving optimization problem as:  
\[\text{Minimise}\ \frac{1}{2}\|\mathbf{w}\|^{2} \tag{14}\]  
subjected to the constraints,  
\[y_{i}(w\cdot x_{i}+b)\geq 1 \tag{15}\]  
for all data points \((x_{i},y_{i})\) where, \(y_{i}\) is the class label of the \(i\)-th data point. Whenever the data is not linearly separable, SVM uses Kernel Trick i.e., it uses a mathematical function to transform the data into higher dimensions where it is separable [64].
## 3 Results and Discussions  
The results obtained for each material under consideration focus on their ion exchange capacities, metal removal efficiencies, selectivity, and adsorption kinetics. These aspects are evaluated to understand the performance and suitability of each material system for specific metal ions, providing insights into their potential applications and limitations. For convenience and readability purposes, all four materials will be referred to as \"resins\" in the remainder of the discussion, although only the two commercial materials are actual resins. The following paragraphs detail the observed adsorption behaviors, highlighting differences in resin efficiency and kinetic properties across various combinations of metal-resin pairs.
## 3 Results and Discussion
### Practical Application  
The analytical applicability of the GCE/ERGO-NiNP sensor is examined for SY determination in drink samples collected from a local market. A small volume of each drink (7 uL of Mirinda, 35 uL of Samurai, 70 uL of Number 1 and 35 uL of Sting) was spiked in acetate buffer at pH 4 (7 mL). DPV measurements were typically displayed for determining the SY concentration in the Mirinda sample (Figure 8). In fact, the DPV of diluted samples in buffer using GCE/ERGO-Ni exhibited a characteristic peak at 0.74 V/SCE of SY compound. After adding SY standard solution, the identified peak current is considerably increased, indicating the detection of SY in the sample. The accuracy of this assay is evaluated by the standard addition method. All analytical results were presented in  
\begin{table}
\begin{tabular}{c c c c} \hline
**Electrode** & **Linear Range (nM)** & **LOD (nM)** & **Reference** \\ \hline GCE/ZnO-cystic acid & 100–3000 & 30 & [61] \\ GCE/GO-MWCNT & 90–8000 & 25 & [62] \\ GCE/poly(L-cysteine)-Ag & 100–10.000 & 50 & [63] \\ GCE/ERGO-AuNPs & 2–110.000 & 2 & [27] \\ GCE/GN-Ni & 7.4–442 & 2.2 & [64] \\ GCE/GN-CTAB-Pt & 80–10.000 & 4.2 & [21] \\ GCE/rGO/Au-Pd & 686–331.000 & 1.5 & [30] \\ GCE/ERGO-NiNPs & 10–10.000 & 3.7 & This work \\ \hline \end{tabular}
\begin{tabular}{c c c} \hline
**GCE: glassy carbon electrode, GN: graphene, MWCNT: multi-wall carbon nanotube, ERGO: electrochemically reduced graphene oxide, GO: graphene oxide, rGO: reduced graphene oxide, CTAB: ccytrimethylammonium bromide.**  
\end{table}
Table 1: Performance of different modified electrodes for SY determination.  
Table 2. The obtained recovery values, varying between 91.8 \(\div\) 108.3%, are satisfactory. Each sample is determined three times, leading to RSD values lower than 6.48%, indicating the good precision of the method. Finally, SY concentrations in the same soft drinks were also controlled comparatively, using HPLC measurements as described elsewhere [55]. The results obtained were compared between HPLC and those from our sensors, and were in extremely good agreement with a very small deviation, below 6.4%, indicating that GCE/ERGO-NiNP-modified surfaces represent a very promising platform with low costs, simple preparation and good accuracy for sensing applications.