esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning and Bagging to Predict Midterm Electricity Consumption in Saudi Arabia
## 1 Introduction  
Electricity is considered a significant secondary energy source and the most adaptable type of energy. The power system must maintain a constant balance between electricity generation and consumption [1]. Forecasting and prediction goals for electricity usage vary depending on the time frame. Relative time scales include long-, medium-, and short-term. This study and the majority of today's studies consider mid-term electricity consumption, which is the monthly forecasting time frame. This is because the monthly time frame is crucial to many government and commercial decision-making processes [2, 3].  
Accurate electricity consumption prediction is essential for the efficient operation of today's electric power systems [4]. Many research studies have shown the importance of accurate electricity prediction. Planning for future electricity generation systems to meet the rising demand for electrical energy is made possible through the accurate and reliable estimation of electrical energy consumption [5, 6].  
Saudi Arabia consumes three times the electricity of the world's average; for instance, its consumption is eight times greater than Japan's. If present consumption rates continue at their current levels, by 2038, Saudi Arabia may be forced to import oil to meet the energy needs of its growing population [7; 8]. Therefore, accurate electricity consumption prediction is vital for developing an efficient energy development strategy in Saudi Arabia.  
This research investigated the effects of using machine learning approaches in predicting electricity consumption in Saudi Arabia. A real dataset provided by the Saudi Electrical Company (SEC) was used in this study with different machine learning models for mid-term electricity consumption. Additionally, this research studied the effects of using bagging ensemble on predicting electricity consumption in Saudi Arabia.  
The paper's remaining sections are organized as follows: A literature review related to predicting electricity consumption is presented in Section 2. The materials and methods are described in Section 3. The main results are presented in Section 4, followed by the discussion in Section 5. Section 6 presents the authors' conclusions.
## 9 Discussion  
The diagnosis of a skin lesion by a dermatologist remains subjective. The diagnostic accuracy range is 64% to 80% [68; 69; 70; 71], measured in specialized dermatology centers. Nowadays, most diagnoses are made by physician assistants, whose accuracy is lower than the dermatologist's [72].  
In this work, we proposed a computer-aided diagnosis to identify eight classes of skin lesions in dermoscopy color-images. The classes are actinic keratosis (AK), basal cell carcinoma (BCC), squamous cell carcinoma (SCC), benign keratosis (BKL), melanoma (MEL), melanocytic nevus (NEV), dermatofibroma (DER), and the vascular lesion (VASC). The methodology uses three fractal signatures, one signature per color in the RGB color-space. To handle the difference of the signatures' lengths, we used the energy, variance, and entropy of the fractal signatures. These nine features concatenate with the 100 features obtained from the Densenet-201. We use three classifier spaces to construct an ensemble classifier based on the majority vote: the K-nearest neighbors (KNN) and the support vector machines (SVM) with linear and Gaussian kernels. The computer-aided diagnosis tested using 25,331 dermoscopy color-images from the ISIC archive-2019. Working with a hybrid methodology of three 1D fractal signatures and DenseNet-201 features allows us to strengthen the computer-aided diagnosis. We have more image information working with the fractal signatures since we do not have to reduce the image in size as required to work with a neural network. The computer-aided diagnosis presented excellent results like those obtained by the four ensembles CNNs by [PERSON] et al. [44] and [PERSON] et al. [45]. A difference from the [PERSON] et al. and [PERSON] et al. methodologies, in this proposal, we do not use artificial image generation or manipulation. Because there are various open-source alternatives for CNNs, the proposed methodology does not require computer equipment with high performance. That makes it viable to reproduce the same results with different programming languages on various platforms. However, we need to notice that CNNs are based on the convolution operation of the image with different filters, which require a large number of training images to extract all the possible ways that the image can be presented. That yields a high computational cost. Furthermore, to properly segment images with low-contrast edges and hair artifacts, such as those of skin lesions, specialized neural networks are required on the topic [8]. Due to these aspects, a viable option to reduce computational cost has been developing, using hybrid methodologies of CNNs with handcrafted feature extraction approaches, like this proposal.
## 2 Materials and Methods  
### Sorbent Characterization  
_Chorella vulgaris_ (Cv) and _Scenedesmus armatus_ (Sc) strains were used. Algae were cultivated in 250 mL flasks in a climatic chamber. An irradiance of 80 \(\pm\) 5 \(\upmu\)mol photons m\({}^{2}\)/s was performed, with a light/dark period of 12/12 h and at a temperature of 29 \(\pm\) 0.5 \({}^{\circ}\)C during the light phase and 25 \(\pm\) 0.5 \({}^{\circ}\)C for dark phase. Algae were harvested in the exponential growth phase.  
_C. vulgaris_ and _S. armatus_ were obtained from the Culture Collection of Baltic Algae (CCBA), Poland. Microalgae were used suspended in solution and embedded in hydrogel beads. The hydrogel was made of alginate. The sorbents were mixed with alginate to produce small granulate particles of 3 mm diameter. Hydrogels are three-dimensional colloidal systems. They are composed of associated linear or branched polymer chains [16].  
Sorption tests were carried out using a mineral sorbent--zeolite (Z). The granulation of zeolite was 0-0.2 mm. Due to the fact that 84% of this zeolite was clinoptiolite, this sorbent was commonly referred to as clinoptiolite. Other minerals in zeolite are as follow: cristobalite--7%, plagioclase--3-4%, loam--4%, others--0.1-0.3%, and quartz < 0.1%. Clinoptiolite was dosed into the wastewater in the form of a powder. A specific surface area of zeolite reached 76 m\({}^{2}\)/g.
## 3 Results and Discussion
### The Classification of the Matrix Architecture for the Different Orders
This involved creating a self-trained model to validate the algorithm and to establish a decision support system for classification purposes. It is crucial for this system to adhere to the rules outlined in Table 5 for both healthy and unhealthy candidates. In Table 5, the variable \(Z\) represents the variance value for the test candidate. This variable is tested against the three conditions presented and will classify the individual accordingly for both the third- and the fifth-order PMLD matrices. An indicator variable \(IND\) is also introduced, which will determine where the test individual falls inside the classification interval according to the conditions explained as follows. Condition 1 (\(IND=0\)): If \(Z\) is less than or equal to the lower boundary of the variance values for healthy individuals \(\left(\mu_{\text{h}}^{3}-\sigma_{\text{h}}^{3}\right)\), the probability indicator is set to zero. This condition signifies a low probability of the candidate belonging to the unhealthy group. Condition 2 (\(IND=1\)): If \(Z\) is greater than or equal to the upper boundary of the variance values for unhealthy individuals \(\left(\mu_{\text{u}}^{3}+\sigma_{\text{u}}^{3}\right)\), the probability indicator is set to one. This condition suggests a high probability of the candidate belonging to the unhealthy group. Condition 3 (\(0\leq IND\leq 1\)): If \(Z\) falls within the range between the upper boundary of the variance values for unhealthy individuals and the lower boundary of the variance values for healthy individuals, the probability indicator is calculated using a linear interpolation formula as follows:  
\[IND=\frac{Z-\left(\mu_{\text{u}}^{3}+\sigma_{\text{u}}^{3}\right)}{\left(\mu _{\text{h}}^{3}-\sigma_{\text{h}}^{3}\right)-\left(\mu_{\text{u}}^{3}+\sigma _{\text{u}}^{3}\right)} \tag{11}\]  
Figure 2: The distribution graphs for the healthy and unhealthy cohort for the third and fifth order PMLD. The condition presented in Equation (11) reflects a probability between 0 and 1, indicating the candidate's likelihood of belonging to either the healthy or the unhealthy group based on the degree to which their variance value deviates from the established boundaries.
# Hybrid Local and Global Deep-Learning Architecture for Salient-Object Detection
## 4 Experimental Results
### Evaluation Metrics  
The following evaluation measures were used to measure the efficiency of the proposed model with other models:  
* Precision-recall (PR) curve: Calculated by the conversion of the predicted saliency map into a binarised map and ground truth. Thresholding of 0-255 is applied to produce the binary map.  
Figure 6: Encoder/decoder flow to extract refined saliency map.  
For all saliency maps present in a dataset, every binarising threshold comes in a set of average precision and recall. An order of precision-recall pair is generated when the threshold varies from 0 to 1, which is used to plot the PR curve.
* F-measure curve: To provide comprehensive analysis, \(F_{\beta}\) is calculated on both precision and recall as: \[F_{\beta}=\frac{(1+\beta^{2})Precision\times Recall}{\beta^{2}Precision+Recall}\] (5) The \(\beta\) value is set as 0.3 to highlight precision more because the rate of recall is not as significant as that of precision. The value of the average F-measure is also presented in this research. The F-measure curve is generated with a comparison of the binary map with the ground truth that is obtained by changing threshold to decide if a pixel is owned by a salient object.
* Mean absolute error (MAE) is used to correctly measure false-negative pixels. It calculates the pixelwise error between saliency map and ground truth: \[MAE=mean(|S^{M}-G^{T}|)\] (6) If the MAE value is less, it indicates that ground truth \(G^{T}\) and predicted saliency map \(S^{M}\) are highly similar.
# Adaptive Hybrid Storage Format for Sparse Matrix-Vector Multiplication on Multi-Core SIMD CPUs
## 4 Our Approach
### Compressed CSR  
Before being converted into CSR5 format, the right submatrix needs to be converted to the CSR format. Figure 4 shows that the right sub-matrix outlined has much fewer non-zero elements comparing with the original matrix A, resulting in many _empty rows_ with a large processing overhead. Specifically, array row_ptr, which is used by both CSR and CSR5 format, contains many duplicate elements. Taking the right submatrix as an ex  
Figure 5: Represent the same matrix A with the SELL-4-8 format.  
ample, \(row\_ptr=\{0\ 0\ 3\ 4\ 4\ 17\ 17\ 17\ 17\ 18\ 23\ 23\ 23\ 24\ 24\ 24\ 32\}\). Besides, buffer empty_offset (stores the correct positions of empty rows used when computing the partial sum) of the CSR5 also contains duplicate entries. Therefore, we compress the matrix to only retain the rows with non-zero elements.  
```
1:functionConvertToHYB5(\(coo\_ntx*\), \(\mathbf{k}\))
2:\(\mathbf{p}\gets 0\)
3:\(\mathbf{q}\gets 0\)
4:for\(i=0\) to nnz do
5:if\(row\_counter[coo\_row\_index[i]]>=K\)then
6:\(csr\_data\_stru[p]\gets coo\_data\_stru[i]\)
7:\(row\_counter[coo\_row\_index[i]]++\)
8:\(p++\)
9:else
10:\(sell\_data\_stru[q]\gets coo\_data\_stru[i]\)
11:\(row\_counter[coo\_row\_index[i]]++\)
12:\(q++\)
13:endif
14:endfor
15:\(CCSR\_mtx*\leftarrow\)CompressCSR(\(csr\_data\_stru\))
16:\(CSR5\_mtx*\leftarrow\)ConvertToCSR5(\(CCSR\_mtx\))
17:\(SELL\_mtx*\leftarrow\)ConvertToSELL(\(sell\_data\_stru\))
18:endfunction
```  
**Algorithm 1** Convert COO to HYB5  
During the conversion process, we introduce a new data structure (nnz_row_idx) to record the original index of each row. For the CSR format, we need to change row_ptr to eliminate the entries of indicating empty rows. If two adjacent elements in row_ptr are equal, we regard that there exists an empty row. Algorithm 2 shows that we remove the empty rows, convert row_ptr to com_row_ptr, and record the original index of the remaining rows in nnz_row_idx. Figure 4 shows that the size of the right submatrix is significantly reduced by compressing the raw data. Consequently, the size of array row_ptr is reduced, and it is unnecessary to use empty_offset. When storing the result to \(\mathbf{y}\) which stores the results of SpMV (e.g., y = Ax), we use nnz_row_idx to retrieve the original row indices for the corresponding non-zeros. Note that we have not changed the column index of the non-zero elements. By doing so, we guarantee that the SpMV results are stored to the right locations of vector \(\mathbf{y}\).  
```
1:functionComCSR(\(row\_ptr*\), com_row_ptr*, nnz_row_idx*)
2:\(j=0\)
3:\(p=0\)
4:for\(i=1\) to \(N\)do
5:if\(row\_ptr[i]!=row\_ptr[i-1]\)then
6:\(j++\)
7:\(com\_row\_ptr[j]\gets row\_ptr[i]\)
8:\(nnz\_row\_idx[p]\gets i-1\)
9:\(p++\)
10:endif
11:endfor
12:endfunction
```  
**Algorithm 2** Compress CSR format.
# Adaptive Threshold-Aided K-Best Sphere Decoding for Large MIMO Systems
## 3 K-Best SD Algorithm  
### Conventional KSD Algorithm  
Tree-search algorithms are categorized as depth-first and breadth-first search methods [8]. In the typical KSD algorithm, the tree is traversed in a breadth-first fashion, and the search proceeds in the forward direction by keeping only the \(K\) best nodes at each layer of the tree [8]. Because of the breadth-first search method, the KSD algorithm does not update the sphere radius [9]. The search process starts from the root node (\(i=N\)), which represents the \(N\)th transmitted symbol, and proceeds down to the leaf layer \((i=1)\). At the \(N\)th layer, \(K\) nodes with the smallest path metrics are kept to extend to the next layer, and the other nodes are pruned.  
Each of the \(K\) retained parent nodes is extended to its \(|\Omega|\) children nodes. Therefore, in the \((N-1)\)th layer, the number of contender paths becomes \(K|\Omega|\), and, again, only \(K\) paths associated with the smallest path metrics at the \((N-1)\)th layer are selected as parent nodes to the next layer. Specifically, we sort the candidate paths in ascending order of their path metrics \(T(\mathbf{s}^{(i)})\) in (6). Let \(\{\tau_{i}^{(l)}\}\) denote the ordered path metrics of \(T(\mathbf{s}^{(i)})\), where \(l=1,2,\ldots,K|\Omega|\). For example, \(\tau_{i}^{(K)}\) is the \(K\)th smallest path metric for the surviving paths at the \(i\)th layer. Then, the paths associated with \(\tau_{i}^{(1)}\), \(\tau_{i}^{(2)},...,\tau_{i}^{(K)}\) are retained, whereas the other paths are pruned.  
The search process continues until it reaches the leaf layer. At the leaf layer, each path corresponds to a candidate of the transmitted signal vector, and the one with the minimum path metric among \(K\) contender paths is chosen as the final solution.
# Anomaly Detection Method in Railway Using Signal Processing and Deep Learning
## 2 Modeling of Railway Vehicles and Wheel Flat  
### Modeling of Railway Vehicles  
The railway, which belongs to a public industry sector, is strongly linked to passenger safety and service, and it is not easy to conduct failure tests on actual operating railways. In other words, since an accident may occur while driving a broken-down vehicle on the railway, it was not allowed to conduct such a test using a broken-down train to detect anomalies for this study. Therefore, due to the difficulty of acquiring data through actual tests, simulation studies are applied in many research works and development projects in the field of railway vehicles.  
In this study, SIMPACK, a multi-body dynamics software, was used to obtain wheel flat signals under various conditions. SIMPACK software with rail module has an advantage similar to the actual railway vehicle driving which considers the contact between the wheel and rail. The railway vehicle model in this study was generated with the Seoul Metro's subway train in Korea.  
Figure 2 shows a railway vehicle modeled by SIMPACK, a multi-body dynamics software, and Figure 3 shows the bogie modeling of a railway vehicle. The specifications of car body, bogie, and track information are shown in Tables 2-4 [17].  
Figure 2: Railway vehicles modeling.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Unit** & **Value** \\ \hline Gauge & (mm) & 1435 \\ Rail Type & - & UIC 60 rail \\ Wheel–Rail Interface Type & - & Kalker Contact \\ Friction coefficient & - & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Modeling values of track information.  
Figure 3: Bogie modeling.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Unit** & **Value** \\ \hline Car Body Lex & (kg\(\cdot\)m\({}^{2}\)) & 25,757 \\ Car Body Iyy & (kg\(\cdot\)m\({}^{2}\)) & 320,790 \\ Car Body Izz & (kg\(\cdot\)m\({}^{2}\)) & 317,990 \\ Bogie Ixx & (kg\(\cdot\)m\({}^{2}\)) & 848.1 \\ Bogie Iyy & (kg\(\cdot\)m\({}^{2}\)) & 1376.3 \\ Bogie Izz & (kg\(\cdot\)m\({}^{2}\)) & 2112.2 \\ Wheelset Ixx & (kg\(\cdot\)m\({}^{2}\)) & 844 \\ Wheelset Iyy & (kg\(\cdot\)m\({}^{2}\)) & 107 \\ Wheelset Izz & (kg\(\cdot\)m\({}^{2}\)) & 844 \\ Primary Suspension Stiffness & (N/m) & 836,338 \\ Secondary Suspension Stiffness & (N/m) & 2,657,143 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Modeling values of inertia and stiffness.
# A Fast Method for Calculation of Marine Gravity Anomaly
## 1 Introduction
In this study, we focused on accelerating the calculation of marine gravity anomalies, and we constructed a faster method. In the fast method, we solve the singularity problem in the ISF method without using judgment statements and use GPU parallel computing technology in the implementation of the calculation process. The geoid height undulation data from the EGM2008 (Earth Gravitational Model 2008) model are used to show the accuracy and performance of the proposed fast method. This paper is organized as follows: In Section 2, the theory of the basic ISF method is described briefly and the details for the fast method are presented. In Section 3, the performance of the proposed fast method is displayed through comparison with the conventional string calculation method. In Section 4, some conclusions and notes are summarized.
## 3 Results
### Scaffold's Geometrical Parameters and Main Findings
#### 3.5.4 Scaffold's Macroscopy  
Based on the different scaffold formats and their respective primary outcomes reported by the authors, scaffold morphology plays a crucial role in bone regeneration. For instance, in the study by [PERSON] et al. (2021) [26], 3D-printed rectangular scaffolds showed callus formation and low bone density at the periphery. In contrast, [PERSON] et al. (2021) [27] demonstrated that circular and conical scaffolds support regeneration with trabecular structures similar to those of native bone. [PERSON] et al. (2006) [28] showed that granular scaffolds facilitated complete ossification with a regular spongy structure. [PERSON] et al. (2017) [31] compared TCP and poly(trimethylene carbonate) (PTMC)-CaP disc-shaped scaffolds, with TCP scaffolds demonstrating an increased osteoconductive effect. Additionally, cubic-shaped scaffolds, such as those used by [PERSON] et al. (2007) [35] and [PERSON] et al. (2006) [29], provided a uniform distribution of mechanical stress and structural support, favoring the formation of irregular osteons and the efficient repair of bone continuity in the jaw. These observations highlight the significant impact of scaffold morphology on bone regeneration, as it accommodates the specific needs of the bone defect, thereby facilitating cellular integration and vascularization for optimal long-term outcomes.  
Only [PERSON] et al. (2016) [34] did not mention the macroscopic characteristics of the scaffolds.
## 4 Risk Analysis for Current Protocols
### OneNet
#### 4.5.2 Spoofing  
In a OneNet network, a Human Interface Device is used to provide to newly connected devices information about network configuration. In particular, the HID communicates to the OneNet Application (running on a device) whether it must have Secure Mode enabled to become operational. In addition, the enabling of Secure Mode is initiated and performed by only the HID. However, in our analysis, we have identified a drawback for this system. Due to this exclusive control that HID has, by spoofing an HID, an attacker can trick an OneNet device to communicate with a fake device through Secure Mode and potentially extract information or even take over the target. Further, switching the device to a separate secure channel means that the device would not be able to communicate with devices on the original secure channel [40].
## 3 Data and Methods
### Use of Economic Model to Evaluate Fire Damage in Wili
The aforementioned layers were used in conjunction with the satellite-derived DBH and burned trees raster maps to derive a spatially continuous economic loss map at a spatial resolution of 3m.
# Tolerance-Aided Interference Degradation for Optical OFDM in Power-Constrained Systems
## 3 Principles of Tolerance-Aided Interference Degradation Algorithm
### Computational Complexity Analysis  
In this section, we conduct an analysis of the computational complexity of the proposed algorithm, which is directly related to processing time. To ensure a fair comparison of computational complexity, we evaluate it based on the transmission of an equivalent number of data. In the case of DCO-OFDM, a transmitter employs an \(N\)-point IFFT module, resulting in a computational complexity of \(\mathcal{O}(N\log_{2}N)\). In contrast, ACO-OFDM uses only half of the available subcarriers for data modulation, leading to a computational complexity of \(\mathcal{O}(2N\log_{2}N)\) when modulating the same data volume as DCO-OFDM.  
Considering the proposed TAID algorithm transmits the same signal structure as CEO-OFDM, which involves sending two additional signal frames compared to DCO-OFDM, its computational complexity matches that of CEO-OFDM, which can be expressed as \(\mathcal{O}(N(\log_{2}N+2))\).  
It is worth noting that, for the proposed TAID algorithm, the receiver is responsible for setting the decision tolerance. Therefore, only a decision process is necessary, eliminating the need for the additional complexity associated with CEO-OFDM.