esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Stock Price Prediction Framework for Intelligent Media and Technical Analysis
## 1 Introduction  
The problem of minimising risk and maximising returns bundled with predicting future price movements is what stock market traders have been trying to solve for years. Many have provided tools and solutions to solve this massive problem of predicting the increase and decrease of selected companies' stock prices. These equity traders have depended on news headlines (fundamental analysis) and technical indicators (technical analysis) as tools for prediction. Stock price prediction and stock price movement prediction predict what the future prices will look like from observing past and present price data. Researchers have also realised that stock price prediction depends not only on historical data but also on social media data. In 2018, a social media influencer expressed her unhappiness with Snapchat on Twitter; her tweet caused the Snapchat share price to drop by 6%, wiping out $1.3 billion [1]. Tesla CEO [PERSON] caused Telsa's share price to decrease by 10% after sending a negative tweet concerning Tesla's share price [2]. The age of mobile devices has seen a vast increase in social media and news data. This social and news data is filled with essential facts and opinions that may be harvested to create a stock price movement prediction and closing price prediction tool. In developing this framework, the research aims to answer the following questions: Can a framework on social media and news headlines be used to forecast a company's stock price movement? Additionally, can that same framework use technical indicators as features to increase the accuracy of predicting a company's stock closing price?This paper presents a framework for forecasting stock price movements and closing prices using machine learning and deep learning models. The framework uses a text classifier model for sentiment analysis on social media and news data. Using machine learning, the research also investigates predicting the increase and decrease of a selected company's stock based on sentiment score. Technical indicators are applied together with times series analysis and deep learning to predict the closing price of a telecommunication company listed on the Johannesburg Stock Exchange.  
The presented research design is based on a quantitative research methodology, and the experimental method is used, whereby actual social media and stock price data is collected and analysed. This paper discusses using social media and technical indicators to predict stock price movements and closing prices; the article compares different machine learning models for stock price movement prediction (fundamental analysis) and deep learning models for closing price prediction (technical analysis). The remainder of the paper is arranged as follows. Section 2 looks at a summary of relevant literature. Section 3 discusses data collection and methodology. Results from the experiments are discussed in Section 4. The conclusion is provided in the final quarter of the paper.
# Classification of Hyperspectral Images Based on Supervised Sparse Embedded Preserving Projection
## 3 Supervised Sparse Embedded Preserving Projection
Thus, the dimensionality reduction sample feature is given as follows:  
\[m_{i}(x_{i})=V^{T}x_{i}, \tag{18}\]  
where \(m_{i}(x_{i})\) is a feature value of the \(i^{\text{th}}\) sample data. The specific steps of the supervising sparse embedded preserving projection algorithm are described in detail in Algorithm 1. ```
Input: The set of sample \(X=\{x_{i}|x_{i}\in\mathrm{R}^{\text{B}},1\leq i\leq n\}\), error value \(\xi\), and dimensionality reduction \(d\). Output: The set of feature after dimension reduction \(M\). (1) Standardize the sample set; (2) Use the SPGL1 [17] algorithm to solve the sparse reconstruction coefficients using Equation (5); (3) Calculate the weight matrix of the labeled sample using Equation (11); (4) Use Equation (15) to calculate the objective function, which minimizes the error of samples of the same class. Then, obtain the multi-objective function according to Equation (16); (5) Obtain the projection matrix by solving the eigenvectors of the generalized eigenvalue decomposition using Equation (17); (6) Use Equation (18) to obtain the reduced-dimension sample feature set \(M\). End For
```  
**Algorithm 1**Supervised Sparse Embedded Preserving Projection (SSEPP)
## 7 Potential Inhibitors of Squalene Synthas  
Squalene synthase is an attractive target mainly because it catalyzes the conversion of farnesyl diphosphate to squalene in the first committed step of cholesterol biosynthesis [36]. Inhibitors of squalene synthase include alkoxy-aminobenzhydrol  
Figure 3: Biosynthesis of cholesterol/ergosterol from mexulonic acid (isoprenoid and squalene pathways). derivatives and fungus-derived zaragozic acids, but due to unfavorable toxicity profiles, none are in clinical use [37,38]. Thus, there is still a need to search for new and safe inhibitors of squalene synthase. In the last three decades, numerous studies have been reported on the identification of squalene synthase inhibitors, and a total of forty hit compounds (Figures 4-7) are summarized in this subsection. An example is the study by [PERSON] et al. [39], which described the potential of squalestatin-1 (**1**) as a potent and selective inhibitor of squalene synthase in vitro with an IC\({}_{50}\) value of 12 nM [39,40]. Four years later, [PERSON] et al. [41] demonstrated that a compound [3-(biphenyl-4-yl)-quinuclidine (**2**)] from the quinuclidine group exhibited in vitro inhibition of squalene synthase from human and marmoset liver microsomes with Ki values of 5 nM and 1300 nM, respectively. Moreover, [PERSON] et al. [42] confirmed the inhibitory potential of 3-(biphenyl-4-yl) quinuclidine (BPQ) (**2**) and 3-(biphenyl-4-yl)-3-hydroxyquinuclidine (BPQ-OH) (**3**) by reporting their apparent inhibition constants (K\({}_{i}\)) as 12 nM and 15 nM, respectively [42]. Likewise, zaragozic acid A (**4**) has also been reported as a potent inhibitor (a competitive inhibitor with a Ki value of <1.6 nM) of squalene synthase [43,44,45]. 3-Hydroxy-3-[4-(quinolin-6-yl)phenyl-l]-1-azabicyclo [2-2-2]octane dihydroride (**5**) is a potent inhibitor of rat liver microsomal squalene synthase, with an IC\({}_{50}\) value less than 0.9 nM. Under in vivo conditions, oral administration of this compound (3-hydroxy-3-[4-(quinolin-6-yl)phenyl]-l-azabicyclo [2-2-2]octane dihydrochloride) (**5**) to rats inhibited de novo [14C]cholesterol biosynthesis from [14C]mevalonate in the liver with an ED\({}_{50}\) value of 5 mg/kg [46]. A quinuclidin derivative ((E)-2-[2-uaroo-2-(quinuclidin-3-ylidene) ethoxyl-9H-carbazole monohydrochloride) (**6**) was reported to inhibit squalene synthase from hepatic microsomes of several species, including rats, hamsters, guinea pigs, rhesus monkeys, and HepG2 cells (IC\({}_{50}\): 90, 170, 46, 45, and 79 nM, respectively). Under in vivo conditions, this compound equally inhibited squalene synthase activities in hepatic microsomes and suppressed cholesterol biosynthesis in rats (ED\({}_{50}\): 32 mg/kg) [47]. Figure 4: Chemical structures of the squalene synthase inhibitors (**1**–**6**).
# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 3 Proposed Method
### General Architecture
#### 3.1.3 Step 3: Calculate DCT Coefficients Directly from Spatial Domain  
To avoid the high cost of computing the full DCT of every selected block, we use a closed-form equation to compute the specific DCT coefficient directly in the spatial  
Figure 1: An example of the perceptual map computation for “Akiyo” video sequence: **(a)** Multiscale detail map, **(b)** Saliency map, **(c)** Semantic mask, and **(d)** Perceptual score map.  
domain [39]. For a block \(f(m,n)\) of size \(M\times M\), the DCT coefficient at position \((u,v)\) is as follows:  
\[F(u,v)=\frac{2}{M}\alpha_{u}\alpha_{v}\sum_{m=0}^{M-1}\sum_{n=0}^{M-1}f(m,n)\cos \biggl{(}\frac{\tau u(2m+1)}{2M}\biggr{)}\cos\biggl{(}\frac{\tau v(2n+1)}{2M} \biggr{)}\, \tag{4}\]  
where:  
\[\alpha_{k}=\begin{cases}\frac{1}{\sqrt{2}},\ \ if\ k=0\\ 1,\ \ otherwise\end{cases}, \tag{5}\]  
This formulation makes it possible to extract or modify any single DCT coefficient with no need to compute the full transformation of the block.
# Tempo and Metrical Analysis by Tracking Multiple Metrical Levels Using Autocorrelation
## 3 Proposed Method  
The proposed method introduces improvements in the successive steps forming the traditional procedure for metrical analysis presented in Sections 2.1-2.3. A modification of the localized method for accentuation curve estimation enables better emphasis of note onsets in challenging audio recordings such as polyphony with vibrato and tremolo (Section 3.1). Periodicity detection is performed using a modified version of autocorrelation function (Section 3.2).  
Moreover, we introduce a new methodology for tracking the metrical structure along a large range of periodicity layers in parallel. The tracking of the metrical structure is carried out in two steps:  
1. a tracking of the _metrical grid_ featuring a large range of possible periodicities (Section 3.3). Instead of considering a fix and small number of pre-defined metrical levels, we propose to track a larger range of periodicity layers in parallel.
2. a selection of core metrical levels, leading to a _metrical structure_, which enables the estimation of meter and tempo (Section 3.4).
## 5 Conclusions  
It is important to identify the critical nodes and lines so that the system reliability and efficiency can be improved by monitoring and servicing them, and blackout risk may be better mitigated. This paper proposes a new method to effectively identify the critical nodes in a power network, and the controllability index is defined and used to rank the critical nodes based on the controllability theories of complex networks. This method firstly applies the network controllability theories to research the power system's vulnerability, which fundamentally reveals the important nodes in the power systems and overcomes the limitation of the hypothesis that the weight of each link or transmission line must be known as compared with the present research. The simulation results on the IEEE 118 and 300 bus systems show that the failure of the important nodes can clearly increase the minimum number of driver nodes, meaning there needs to be an increase the number of driver nodes to maintain full control of the network, and leads to a significant driver nodes shift. Thus, its effectiveness is demonstrated through the simulation studies. However, this paper do not consider the time-delays of the networks, which is the future research direction.
## 4 Experimental Results  
This study evaluates the performance of the proposed method for facial age estimation under various occlusion conditions through a series of experiments. The primary focus was on assessing the baseline model's performance under occlusion, investigating the impact of knowledge distillation strategies on the student model, analyzing the enhancement of model performance through layer-wise training, and exploring the contribution of global fine-tuning to further optimize the model. Additionally, the proposed method is compared with other advanced models for occluded facial age estimation. The following sections detail the datasets, experimental setup, results, and corresponding analysis.
## 2 Background and Related Work
### Transformer Model  
The Transformer model [19], introduced by Google in 2017, is a neural network architecture built upon _attention mechanisms_ instead of recurrent neural networks (RNNs) in LSTM. This model allows for better parallelization for training and thus can take advantage of large-scale computing clusters. This model has achieved promising results in machine translation tasks. Figure 1 shows that Transformer has an _encoder_ and a _decoder_.  
**Encoder.** The encoder has multiple identical layers, each of which has two sub-layers: a multi-head self-attention layer (SL1), and a fully connected feed-forward neural network layer (SL2). Both sub-layers use residual connections and layer normalization.  
An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors [19]. The attention mechanism in the multi-head self-attention layer can be represented in Equation (1):  
\[Attention(Q,K,V)=softmax(\frac{Q\cdot K^{T}}{\sqrt{d_{k}}})\cdot V, \tag{1}\]  
where \(Q\), \(K\), and \(V\) are obtained from the multiplication of the input vector with matrices \(W^{q}\), \(W^{k}\), and \(W^{v}\), respectively. Note that \(W^{q}\), \(W^{k}\), and \(W^{v}\) have to be calculated during model training. The multi-head attention mechanism involves training multiple sets of \(W^{q}\), \(W^{k}\), and \(W^{v}\) matrices to project the input vector differently. The outputs of all attention heads are concatenated.  
The feedforward neural network layer (SL2) mainly provides a non-linear transformation function, using two fully connected layers. The first layer uses ReLU as the neuron activation function, while the second layer does not use the activation function.  
**Decoder.** The decoder has a similar structure to the encoder but includes an additional masked multi-head self-attention layer. This layer masks the future positions of the input to prevent the model from seeing the subsequent words during training. The input to the decoder consists of the output from the encoder and the previous outputs of the decoder. The decoder outputs a probability distribution over the vocabulary for each position. During training, the decoder uses the ground truth from the previous positions to predict the current position. During inference, it uses its own previous predictions for the next step.  
Figure 1: The architecture of the Transformer model [19].  
After the decoder, the Transformer model also includes a linear layer and a Softmax layer. These layers project the decoder outputs to the dimension of the corresponding output word vocabulary. Each dimension represents the predicted probability of a word in the vocabulary, and the word with the largest probability is selected as the output.
# Image-Level Anti-Personnel Landmine Detection Using Deep Learning in Long-Wave Infrared Images
## 4 Experimental Results  
### Experimental Setup  
Our experiments are conducted on an NVIDIA GeForce RTX 3090 GPU using the PyTorch framework. Models are trained using the AdamW optimizer with a weight decay of 0.05. The initial learning rate is set to 0.0001 and is adjusted throughout training using a cosine learning rate scheduler, with a linear warm-up over the first 10 epochs. Figure 6 shows the learning rate curve and examples of loss curves. As well as the aforementioned models in Section 3, we additionally implement the binary CNN-based classifier proposed in [20] to compare it with the proposed framework. All models are trained for 100 epochs with a batch size of 8. For ResNet, ConvNeXt, and InfMAE backbones, the spatial dimensions of the input images are preserved. In contrast, for ViT and OpenCLIP backbones, the input images are resized to \(224\times 224\). For transformer-based models such as ViT, OpenCLIP, and InfMAE, the size of patch is set to \(16\times 16\). For the binary CNN-based classifier, we follow the setup described by the authors in [20], where input images are resized to \(256\times 256\). To further mitigate the limitations posed by the small dataset size, we apply data augmentation techniques involving geometric and photometric transforma  
Figure 5: AP landmine images (**top**) and the corresponding synthetic landmine-free images (**bottom**) produced by the inpainting model, LaMa [51].  
tions available in the PyTorch framework. The specific data augmentation methods and associated parameters are detailed in Table 2.  
To evaluate the performance of the inspected models, we employ three widely used classification metrics: precision, recall, and F1-score. Let us denote P and N as positive (images with AP landmine in it) and negative (images with no AP landmine in it), respectively. Let us also denote TP, TN, FP, and FN as correctly predicting a positive sample, correctly predicting a negative sample, incorrectly predicting a negative sample as a positive sample, and incorrectly predicting a positive sample as a negative sample, respectively. Then, precision, measuring the accuracy of positive predictions, is given by  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}. \tag{3}\]  
Recall, reflecting the model's ability to correctly identify all relevant positive samples, is given by  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}. \tag{4}\]  
Finally, F1-score, the harmonic mean of precision and recall, is given by  
\[\text{F1-score}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}}. \tag{5}\]  
These three metrics are derived from the confusion matrix, which summarizes the classification results by recording TP, TN, FP, and FN.
## 1 Introduction
These methods have been used in many studies to predict the shear wave velocity by employing a different range of artificial intelligent methods, such as Artificial Neural Networks (ANNs) [17; 18], Support Vector Regression (SVR) and neural networks [19; 20; 21], soft computing [22], machine learning, and colony-fuzzy inference systems [23; 24]. The results of these studies show that using AI methods to predict shear wave velocity is reliable and favorable. The present study introduces a novel hybrid method based on the Harmony Search algorithm [25] as a metaheuristic algorithm and GMDH-type neural networks to predict shear wave velocity using well-logging data. In contrast to earlier findings, this study calculates V\({}_{\text{S}}\) by utilizing only two parameters, including V\({}_{\text{P}}\) and density (RHOB). In this context, a Full-Self-Adaptive Harmony Search algorithm (FSHS) is designed and utilized to optimize a GMDH-type neural network. Several modifications are made in the FSHS algorithm. First, a two-dimensional Dynamic Harmony Memory Size (DHMS) strategy is proposed to control diversity during the search process. Therefore, the FSHS algorithm quickly explores the problem space and exploits the best regions at the late iterations. Secondly, all of the parameters values are adaptive and calculated automatically in the FSHS algorithm. Thirdly, a novel method is introduced to adjust the value of the BW parameter based on the cosine wave and each decision variable value. The rest of this paper is organized as follows: The experimental methods are explained in Section 2. The materials and preliminaries of the proposed algorithm will be describedin Section 3. In Section 4, the proposed hybrid algorithm is presented. The experimental results and conclusion will be discussed in Sections 5 and 6, respectively.
## 3 Potential Security Implications of IoT in 6G Environment
### Network- or Device-Level Vulnerabilities
#### 3.1.5 Supply Chain Vulnerabilities  
IoT devices are often manufactured by diverse vendors, creating difficulties in ensuring consistent security standards [5]. These devices may also contain backdoors or other vulnerabilities introduced during manufacturing [46]. As 6G advances toward broader global adoption and incorporates sophisticated IoT hardware, supply chain vulnerabilities will remain a significant risk [45].
## 3 Methodology
### Loads and Material Characteristics
Meanwhile, E\({}_{\rm S}\) was based on [PERSON] and [PERSON] [24], where the V\({}_{\rm S}\) values used to determine the value of G\({}_{\rm max}\) in Equation (14), in which the value of \(\overline{\rm N}\) was in the range of 15 to 50 and V\({}_{\rm S}\) was in the range of 175 to 350. The Mohr-Coulomb model was used in finite element modeling (Table 4). Linear elastic perfectly-plastic is a first-order model that includes only the number of features exhibited by the behavior of the soil. In general, the effective stress uses the Mohr-Coulomb failure criteria with the effective strength parameters of \(\upvartheta\)/and \(c^{\prime}\)[29]. The interaction between the structural elements and the soil was modeled through the interface. The interface element was used to reduce the friction between the structural element and the soil (R\({}_{\rm inter}\)), which has a value between 0.01 and 1.0 (Table 4). The lower limit value of 0.01 indicates no friction between the structural elements and the soil. The upper limit value of 1.0 means that the structural element and the soil are in contact and cannot slip over each other (rigid) [30]. R\({}_{\rm inter}\) is taken by dividing the friction angle in the concrete pile by the soil friction angle at each depth. The concrete friction angle was approximated at 27.90\({}^{\circ}\) based on direct shear testing by [PERSON] et al. [31] to investigate the interaction between the soil and concrete in-situ concerning friction. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameters** & **Symbol** & **Unit** & **MH** & **MH** & **SC-SM** & **SC-SM** & **CL** \\ \hline Depth & & & 0.00–5.50 & 5.50–8.50 & 8.50–11.50 & 11.50–14.50 & 14.50–17.50 \\ Material model & & & Mohr– & Mohr– & Mohr– & Mohr– & Mohr– \\  & & & Coulomb & Coulumb & Coulumb & Coulumb & Coulumb \\ Behavior type & & & Undrained & Undrained & Drained & Drained & Undrained \\ Density above & \(\gamma_{\rm unsat}\) & kN/m\({}^{3}\) & 9.901 & 9.980 & 14.092 & 10.526 & 10.424 \\ water table & \(\gamma_{\rm sat}\) & kN/m\({}^{3}\) & 15.532 & 15.417 & 18.159 & 16.092 & 15.918 \\ Effective density & \(\gamma^{\prime}\) & kN/m\({}^{3}\) & 5.725 & 5.61 & 8.352 & 6.285 & 6.111 \\ Poisson ratio & v & & 0.273 & 0.261 & 0.301 & 0.340 & 0.370 \\ Cohesion & c & kN/m\({}^{2}\) & 35.598 & 17.162 & 4.021 & 1.177 & 15.789 \\ Friction angle & \(\varphi\) & \(\circ\) & 30.45 & 25.62 & 44.26 & 44.29 & 31.75 \\ R\({}_{\rm inter}\) & & & 0.901 & 1.00 & 0.543 & 0.543 & 0.856 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Soil characteristics.