esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid DNN Multilayered LSTM Model for Energy Consumption Prediction  
[PERSON]  
1 Information Systems Department, Faculty of Computing and Information Technology,  
King Abdulaziz University, Jeddah 21589, Saudi Arabia; [EMAIL_ADDRESS]  
[PERSON] AL-Ghamdi  
1 Information Systems Department, Faculty of Computing and Information Technology,  
King Abdulaziz University, Jeddah 21589, Saudi Arabia; [EMAIL_ADDRESS]  
[PERSON]  
3 Information Technology Department, Faculty of Computing and Information Technology,  
King Abdulaziz University, Jeddah 21589, Saudi Arabia; [EMAIL_ADDRESS]  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/](https://www.mdpi.com/)]([https://www.mdpi.com/](https://www.mdpi.com/))  
some industries are important in securing public resources and supplies in certain areas. Therefore, predicting energy consumption can maintain household energy distribution in different industries.  
To predict energy consumption, a high-performance model is needed, which requires accurate feature selection and a suitable modeling process. Advances in deep learning and artificial intelligence can aid in examining energy consumption phenomena.  
Energy consumption prediction studies utilize traditional machine learning and deep learning models, often employing hybrid models to enhance performance. However, complex data, nonlinear relationships, and computational challenges restrict the scope of the study, prompting the need for further development.  
In this paper, a deep neural network (DNN) was integrated with a multilayered long short-term memory (LSTM) network to predict energy consumption. The IHEPC data were utilized as a dataset that was cleaned and transferred to the DNN layer. It was then passed to the multilayered LSTM model, which consists of two layers. The second module of the model is the multilayer LSTM. The LSTM layers are used for information processing and time series prediction. Finally, the LSTM layers are connected to the dense layer, which modifies the output dimensionality of the LSTM layers. Therefore, to improve energy consumption prediction, this study makes the following contributions:  
* It presents a new integrated deep neural network (DNN) with a multilayered long short-term memory (LSTM) network to predict energy consumption;
* It uses a real-life dataset called individual household electric power consumption (IHEPC) to develop the proposed model, as well as DNN and LSTM models for comparing and checking how much improvement the proposed model could give compared with the DNN and LSTM.  
The rest of this study is organized as follows. Section 2 introduces the related studies. Section 3 elaborates on the proposed model, datasets, and experimental design. Section 4 demonstrates model evaluation. The experimental results and discussion are exhibited in Section 5. In Section 6, the conclusion and future work are shown.
# Nodal Accuracy Improvement Technique for Linear Elements with Application to Adaptivity
## 5 Numerical Examples
### Stress Concentration Problem  
The Poisson equation defined in Equation (1) is considered on a square domain \([0,1]\times[0,1]\) with a homogeneous Dirichlet boundary condition on all boundaries. The load term \(f\) is derived from the following given solution [29]:  
\[u=x(1-x)y(1-y)\text{arctan}(10\sqrt{2}(x+y)-16) \tag{25}\]  
In order to reveal the difficulties of the problem, define \(\overline{\sigma}=\sqrt{u_{x}^{2}+u_{y}^{2}}\) as the effective gradient. The distribution of effective gradient is presented in Figure 15. It is clearly observed, from Figure 15, that the effective gradient is steeper in a diagonal narrow region, which is the tricky part of the problem. The prescribed tolerance is set to be \(T_{l}=10^{-3}\). Using one element as the initial mesh, the first phase of adaptivity generates a mesh \(\pi^{*}\) after six adaptivity steps, which is also the final mesh \(\pi\) after checking in the second phase, as shown in Figure 16.  
Distribution of the estimated displacement error on the final mesh is presented in Figure 17a and the true error distribution is shown in Figure 17b. It is seen that \((\widetilde{u}^{*}-u^{h})/T_{l}\) serves as a highly qualified point-wise error estimator with the final results strictly meeting the ultimate adaptivity criterion set in Equation (18).  
Setting \(T_{l}=10^{-3}\), \(N_{\text{dof}}\) is only 645 from the adaptive refinement while \(N_{\text{dof}}\) is 1856 from the uniform refinement. Giving different \(T_{l}\), the convergence rates of the three refinement schemes are calculated as shown in Figure 18, where, for comparison, the adaptivity results using cubic elements computed by [PERSON] [22] are also plotted. It is supposed that the optimal adaptive convergence rate [22; 26] should be \((m+1)/2\) for elements of degree \(m\), and it is noted that all the schemes shown can achieve optimal convergence rates. However, the advantages of cubic elements are not substantial when the tolerances are not so stringent (e.g., around \(T_{l}=10^{-2}\)). Moreover, although linear elements with the uniform refinement also achieve the optimal convergence rate 1, the adaptive refinement reduces the cost of DOFs tremendously.  
Figure 15: Distribution of effective gradient.
# Visual-Based Localization Using Pictorial Planar Objects in Indoor Environment
## 2 Related Work  
### Marker-Based Localization  
Unlike outdoor devices, indoor devices cannot use satellite-based positioning systems, i.e., GPS, to get precise locations due to poor satellite radio signal reception inside of buildings. To implement indoor localization, many projects have tried to deploy indoor local active or anchoring devices sending various signal media, such as ultrasound, ultra wide band (UWB) radio, Wireless Fidelity (WiFi), etc. for individual devices to receive and compute their locations. One issue with radio and sound signals is that they are subject to signal bouncing, noisy transmission, and interference from random signals generated by other indoor objects like microwave ovens and washers. Such signals can make localization very imprecise. In comparison, line of sight is usually much less susceptible to external inteference. As long as a subject is visible, VBL can use it as an information source for localization.  
Using cameras to produce localization has been shown to be an effective and low-cost approach [1,2]. The location of a camera can be derived by using relative positions to some pre-located visible objects or surfaces with known patterns. This is the foundation for marker-based localization for indoor devices. Several marker-based pose estimation approaches have been proposed to calculate the location of a camera using specific 2D markers [8,12,13]. Two-dimensional markers used for camera localization are extensively designed as monochrome codes with specific patterns for fast detecting and recognizing [14,15,16]. These methods have high efficiency on detecting and recognizing markers from a camera view. Their technologies have been used in 3D indoor model building [17], location estimation and navigation for UAV or robots [9,18,19]. One important goal for the projects is that markers are designed to have specific patterns so that systems can identify the marker patterns and poses very efficiently. However, the markers (e.g., Figure 1a) produced by specially designed algorithms may look odd in most indoor living environments. Moreover, a camera is required to fully capture a marker for correct detection. The detection would fail if a marker is only partially visible in a camera view. Our work in this paper has addressed the issues of unnatural markers and marker occlusion.
# A Novel High Gain Monopole Antenna Array for 60 GHz Millimeter-Wave Communications
## 2 Antenna Design
### The Proposed Single Microstrip Monopole Antenna  
The optimized monopole antenna size was 12 \(\times\) 9.6 mm\({}^{2}\) printed on Rogers RO4003(tm) substrate having a dielectric constant \(\varepsilon_{r}\) of 3.55, loss tangent (tan \(\delta\)) of 000027, and a height of 0.203 mm. The feeding of the monopole antenna was done using a transmission line feed of length 4.88 m and a width of 0.8 mm. Two rectangular strips with two circular slots were added next to the feed line in order to enhance the matching.
###### Abstract
For some image segmentation tasks with a wide range of contextual information, Transformer may not be able to effectively capture correlations at a distance, resulting in inaccuracies or missing details in the segmentation results, and the secondary complexity of the self-attention mechanism can lead to the inability to handle large resolution images [27]. In addition, the self-attention mechanism in Transformer generally only considers the correlation within a single sample. However, the structure of medical images is more fixed, and the semantic information is simpler, so the correlation between different samples is extremely important. All of these issues provide more possibilities for subsequent optimization. To solve the above problem, we redesigned the network structure, and this network model uses our proposed DAT for coding and decoding images. Our DAT module takes the form of a two-layer attention, which can focus on both the similarity between a single sample and different samples. In this module, we use an effective attention mechanism [28] to compute the correlation within a single sample, which is equivalent to the performance of dot-product attention but with a significant reduction in memory computation. To calculate the correlation between samples, we used an external attention mechanism [29], which learns the potential relationships across the dataset by introducing two learnable shared memory units. Meanwhile, to solve the problem of inadequate feature fusion mentioned above, we introduced the CTrans (Channel Transformer) module [30] to enhance the fusion of feature maps of different scales at the codec side to reduce the semantic divide and further enhance the segmentation performance. Our work can be summarized in two points:  
1. We propose the DAT module, which can focus on both internal attention and external attention and helps to enhance the correlation between different samples. 2. Through a multi-scale feature fusion method, the feature information of different scales is effectively combined, and the effective information is fully preserved. A pure Transformer structure, DAT-Unet, for medical image segmentation is designed, and the effectiveness of our method is proved on two different public datasets. Compared to other methods, our method has many advantages.
# Analysis of Slope Stability in Ion-Adsorption Rare Earth Mine Under In Situ Leaching Condition
## 2 Methodology
### Stability Analysis Method
It provides comprehensive and straightforward analysis capabilities for issues ranging from simple saturated steady-state problems to complex transient problems involving both saturated and unsaturated conditions. The SLOPE/W is employed to study slope stability, applying limit equilibrium theory combined with finite element methods to address slope stability concerns [35]. This study simulated the stability of rare earth mine slopes under in situ leaching conditions using the SEEP/W, SIGMA/W, and SLOPE/W modules of GeoStudio 2020 software. Initially, the SEEP/W module was employed to analyze the seepage characteristics of the rare earth slope under in situ leaching conditions, revealing the internal seepage patterns of the slope. The resulting seepage data were then imported into the SLOPE/W and SIGMA/W modules to analyze changes in the safety factor and displacement field.
## 3 Experiments and Analysis
### Experiment 2: The Laboratory Measurement Bearing Dataset
#### 3.2.2 Spectral Characteristic Analysis and IMFs Screening  
The same as Experiment 1, fault characteristic frequencies of each part can be calculated [41]. The characteristic frequencies of IRF, ORF and BF are \(f_{\rm I}=147.63\) Hz, \(f_{\rm O}=85.47\) Hz and \(f_{\rm B}=62.43\) Hz, respectively. The time domain waveform of seven different conditions and their spectrogram are shown in Figure 23. By observing the main characteristic frequencies in the spectrum of various kinds of state signals, we find that there exists a high frequency and high amplitude component of about 3200 Hz in each kind of state signal. At the same time, in ORF, BF, IRF+ORF, IRF+BF and ORF+BF, there is a frequency component with a high frequency and relatively high amplitude of about 4800 Hz. Meanwhile, although the frequency doubling of fault characteristic frequencies can be seen under some components, there are many interference components, and the spectrum peaks of fault characteristic frequencies and their harmonics are not prominent enough. This means that there are many interference frequency components in the measured signal.  
Figure 23: _Cont._Figure 24 shows IMFs of an IRF+BF signal sample. We find it difficult to select IMFs accurately through the time-domain waveform. As shown in Figure 25, (a) is spectrogram of the raw sample and (b) are spectrograms of its eight-order IMFs. It is easy to find out from the spectrum of each IMF component that IMF1 to IMF5 contain main frequency components of the raw signal from high frequency to low frequency, respectively, and IMF6-IMF8 may be low-frequency false components. So, the first five-order IMFs were selected in Experiment 2.  
Figure 24: Eight-order IMFs of an IRF+BF signal decomposed by EEMD.  
Figure 23: Time domain waveform and spectrogram of different health conditions in Experiment 2. (**a**) Normal condition (N); (**b**) Outer race fault (ORF); (**c**) Ball fault (BF); (**d**) Inter race fault (IRF); (**e**) IRF+ORF; (**f**) IRF+BF; (**g**) ORF+BF.
# Machine Learning Detection of Cloud Services Abuse as C&C Infrastructure
## 2 Background
### Command and Control Communication Channels  
A malicious bot is a type of malware that infect computers via various means, such as phishing attacks, drive-by download attacks, and dropper attacks. Once the bot is executed on a victim's computer, it can be controlled remotely by the botmaster and added to the botnet. The C&C communication channels, which are used by the botmaster to communicate with bots on the botnet, are typically concealed and often encrypted to evade detection. Common C&C channels include Internet Relay Chat (IRC), Domain Name System (DNS) Tunneling, Hypertext Transfer Protocol (HTTP) and HTTPS, and peer-to-peer (P2P) networks. Nevertheless, recent advancements in C&C communication channels have witnessed the abuse of CLS, wherein legitimate services such as cloud services are utilized as a means for C&C communication without detection. Despite the advancement in the detection of IRC, DNS, HTTP, HTTPS, and P2P as C&C channels, there is a limited amount of studies, as stated in the following section, that focus on the detection of the abuse of legitimate services as C&C channels and none of them applied ML detection techniques. This is an active area of research and there are ongoing efforts to develop more effective techniques for detecting botnets that use legitimate services as C&C channels. Despite these efforts, malicious actors continue to exploit these legitimate services for their C&C purposes.
## 4 Results and Discussion
### 4.1 \(PV\) Module Mounting Systems Under Study  
Three mounting systems were selected for the analysis of \(PV\) power plants under real operating conditions. Table 1 shows their main technical and geographical characteristics. The criteria on which this choice of editing systems were based are as follows: (i) The fixed tilt angle system and horizontal single-axis tracker (Scenario 1) are the most commonly used mounting systems in \(PV\) power plants [19], and (ii) the horizontal single-axis tracker (Scenario 2) is used in many studies that do not consider shading between \(PV\) fields, and, although it is not a real case, it is interesting to analyse the differences with Scenario 1.
## Materials and Methods
### Physical Performance Measures
#### 2.4.2 Measurement and Surface Electromyography Instrumentation  
To record sEMG activity of the VL muscle during the 5 CST, skin was shaved and cleaned with 70% alcohol and dead skin was removed before the electrode placement according to the Surface Electromyography for the Noninvasive Assessment of Muscles recommendations [37]. The electrodes were put over the mid-belly of the muscle parallel to the fibers on the right and left VL muscles. The reference electrode was attached to the C7 spinous process. sEMG signals were collected in microvolts (EMG832 WF, EMGSystem, Sao Paulo, Brazil) using passive surface self-adhesive bipolar Ag/AgCl electrodes (Miotec _Equipamentos biomedicos_, Porto Alegre, Brazil). A high-pass filter of 20 Hz and a low-pass filter of 500 Hz were used to minimize interference from movement artifacts of cables and electrodes. To determine the excitation of the VL, an electromyograph with sampling frequency set at 2000 Hz was used. The electromyographic profile of the VL was evaluated during the 5 CST (from the initial movement upward to the last upward movement) before and after the protocol (5 weeks), and the root mean square (RMS) (\(\mu\)V) was calculated by the software (Tool Box BR V1.0 by EMG System do Brasil _LTDA_). The analysis was conducted based on the RMS (\(\mu\)V) measure provided by the equipment software. The test was applied at baseline (before the intervention) and immediately after the last intervention.  
Figure 1: **(a)** The position of participants on the vibrating platform. **(b)** The protocol of the whole-body vibration group (WBVG) intervention.
# A TinyML Approach to Real-Time Snoring Detection in Resource-Constrained Wearables Devices +
## 3 Methods
### Model Architecture  
The neural network architecture, as shown in Figure 6, is built using a sequential model. The input to the network consists of a 2D array of 40 \(\times\) 40 \(\times\) 1 representing audio features. The first layer is 2D convolutional layer which has 8 filters, 3 \(\times\) 3 in size. The filters are constrained using max-norm regularization with a maximum norm of 1. The ReLU activation function is used in this layer. After this, a max pooling layer with a pool size of 2 \(\times\) 2 and a stride 2 is applied. Another 2D convolutional layer having 8 filters, 3 \(\times\) 3 in size, is applied. A 'valid' padding, max-norm constraint, and ReLU activation are added to capture more complex features in this layer. Another similar max pooling layer follows the second convolutional layer. A dropout layer with a rate of 0.25 is included after the pooling layers to prevent overfitting. An average pooling layer with a dynamically determined pool size is used to aggregate features spatially before flattening. A reshape layer converts the 2D pooled feature maps into a 1D vector, preparing them for the fully connected layers. A fully connected layer having 16 neurons and ReLU activation is used, with L1 regularization to encourage sparsity in the learned weights. A second fully connected layer with 8 neurons and ReLU activation is added, also with L1 regularization and a dropout layer. The final fully connected layer has 2 neurons, representing the number of classes, with a softmax activation function to provide class probabilities.  
This architecture is designed to meet the specific requirements of the snore detection problem. For training the model, 100 epochs, 0.0005 learning rate, and a batch size of 32 was used. After training, the model achieved a total accuracy of 96.6% with 0.11 loss. Finally, the TinyML model is quantized from float32 to int8 to fit in the requirements of a low-resource device, making it work efficiently in wearable devices. Table 3 shows the confusion matrix for the training dataset.  
Figure 6: Model architecture.
# Effects of Microbiota on the Soiling Process of PV Modules in Arid Zones of the Atacama Desert
## 2 Materials and Methods
### Chemical Characterization of Soiling  
The sampling was conducted on 25 August 2022 on modules that had accumulated soiling for over two years at the UA and one year at the PSDA. To collect the soiling samples, a fine filament brush capable of removing cemented dust was used. The brush was passed from the top to the bottom of the module to collect the maximum amount of dirt. To improve the representativeness of the collected material, a set of six PV modules was sampled to obtain a total of six samples per location. Independent brushes, gloves, and masks (KN 95) were used at each location and module to avoid contamination. The samples collected from each location were mixed and homogenized into a single composite of 35 g.  
To determine the mineralogical composition of the soiling deposited on the PV module surfaces, characterization was performed using X-ray diffraction (XRD). A Bruker AXS D8 Advance diffractometer was used, which measured in the range of 2\({}^{\circ}\) to 60\({}^{\circ}\) with CuKa radiation (\(\lambda\) = 0.15045 nm) at 40 kV and 30 mA at room temperature. This mineralogical characterization was complemented by a semi-quantitative analysis using TOPAS software (version 3.1) [15]. This software applied the Rietveld method to the obtained diffractograms, and thus, analyzed each detected crystalline structure profile and provided semi-quantitative analytical results.  
Additionally, the soiling was chemically characterized by determining the total organic carbon (TOC), total nitrogen (TN), and total phosphorus (TP). The determination of cadmium (Cd), copper (Cu), iron (Fe), lead (Pb), and zinc (Zn) in the soiling samples was carried out using multi-elemental analysis by inductively coupled plasma atomic emission spectroscopy (ICP-OES) with an Agilent model 5900 SVDV and by ICP-MS-TQe with a Thermo Fischer model iCAP TQe (Thermo Fisher Scientific, Bremen, Germany). The total mercury (Hg) was analyzed using a DMA-80 evo mercury analyzer (Milestone s.r.l, Sorisole, Italy). The presence of TOC was determined by UV-visible molecular absorption spectrophotometry with a HACH spectrophotometer (HACH Company, Loveland, CO, USA). The TN was determined using the manufacturer's manual method for the Velp Scientific-UDK 126 equipment (Velp Scientific s.r.l, Usmate, Italy). The TP quantity was determined using the standard method for the examination of water and wastewater: Ed. 22, 4500-P-C [16].