esa-sceva/satcom-chunk-collection huggingface dataset:
# Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading
## 4 Experiments
### Experimental Setup  
To validate the superiority of the proposed model, we selected ProbS, HeatS, HybridS, and LightGCN as benchmark algorithms for comparative experiments. These algorithms are closely related to the LGCNHS model proposed in this research.  
Considering the sparsity of the dataset, we utilized two quantiles \((e_{1},e_{2})\) to ensure that the recommendation algorithm could identify sufficient overlap between users and items, thereby enhancing the effectiveness of the model. We conducted multiple adjustments to these parameters during the experiments, retaining only users whose rating counts fell within the specified quantile range and the items they interacted with. Based on the adjustment results, we selected a quantile that was relatively excellent in terms of both performance and efficiency, ultimately choosing \((0.851,0.85)\) for the MovieLens dataset and \((0.991,0.99)\) for the Douban dataset.  
Subsequently, the datasets were randomly split into a training set, validation set and test set according to 8:1:1. The model was trained and generated recommendations on the training set, evaluated on the validation set, and tested for performance on the test set.  
The LGCNHS model proposed in this research was implemented using the PyTorch 1.13.1 framework, and the experimental environment was configured with the Ubuntu 18.04.1 LTS operating system, an Intel(R) Xeon(R) Gold 5318Y CPU, eight RTX 3090 GPUs, and 377 GB of memory (Intel, Santa Clara, CA, USA).  
The experimental parameters were adopted from the literature [14]. The embedding dimension was set to 64, the batch size to 1024, the learning rate set to \(1\times 10^{-3}\), the regularization coefficient of the loss function to \(1\times 10^{-6}\), the number of propagation layers \(K\) was set to 3, and the weighted contribution of \(k\)th layer \(\rho_{k}\) set to \(\frac{1}{k-1}\).
## 1 Introduction  
Marine natural products are considered as rich sources of cytotoxins, many of which could serve as lead compounds for anticancer drug development [1]. Recently, many marine natural products have become accepted as the biosynthetic products of marine microorganisms [2]. Chemical investigations on marine microorganisms have resulted in the discovery of anticancer agents, such as bretuximab vedotin and salinosporamide A [3]. Epithelial-mesenchymal transition (EMT) is an important morphological process that differentiates epithelial cells into mesenchymal phenotype. It is characterized by the loss of polarity, cell-cell contact, and gain of mesenchymal markers. Epithelial cadherin is degraded in the plasma membrane, and desmosomes are inhibited by transcription. EMT facilitates the migration and invasion of cancer cells [4, 5, 6]. Therefore, EMT inhibitors are effective in cancer chemotherapy, particularly against metastasis.  
For many years, plant-derived natural products such as arcigenin [4], betanin [5], camosol [6], curcumin [7], paeoniflorin [8], and tannic acid [9] have been known to regulate EMT-related signaling pathways. However, a few marine derived natural products were also reported to have EMT-modulating activities. Eribulin mesylate, a marine-derived natural product and FDA-approved anticancer drug, is an EMT inhibitor [10]. In addition, marine microbial natural products such as biemamides [11], actinomycin V [12], pentabromposeudilin [13], and and androsamide [14] were shown to target the EMT signaling pathway.  
As part of our efforts to discover bioactive natural products from marine microorganisms, we found that the ethyl acetate extract of marine-derived _Aquimarina_ sp. MC085 inhibited TGF-\(\beta\)-induced EMT in A549 cells. The genus _Aquimarina_ belongs to the family _Flavobacteriaceae_ and is known for its aerobic and halophilic characteristics. Chemical investigations of this genus were rarely reported. The _Aquimarina_ genus was reported to produce algicidal proteins against toxic cyanobacterium _Microcytsis aeruginosa_[15]. Genome mining of _Aquimarina_ sp. Ap349 resulted in the discovery of curiculenes 6A and 6 B derived from polyketide synthase [16]. However, no other natural product with biological activity was reported from the bacterium in this genus. Bioactivity-guided isolation and chemical investigations of _Aquimarina_ sp. MC085 led to the isolation of three natural products, and a spectroscopic data analysis led to the structural assignment of the isolates.
## 2 Methods  
### KLT Optical-Flow Method  
The unchanged pixel value of each point and the small motion of each point between adjacent frames are presumed in the KLT. In addition, spatial consistency is another assumption of this method, that is, the adjacent pixels have similar motion between adjacent frames. The image pyramid technique [32] is used by KLT to meet the assumption of spatial consistency under different conditions.  
The target points selected in the first frame are used to calculate the inter-frame vector to realize target tracking by KLT. Suppose \(W\) is a constant size window, which is centered on the position of a target point on each frame image and \(d_{x}\) and \(d_{y}\) are the motion of the target point along the x-axis and y-axis between 2 frames; the motion, \(\mathbf{d}=\begin{bmatrix}d_{x}&d_{y}\end{bmatrix}^{T}\), can be obtained by minimizing the value of the following formula [6]:  
\[\varepsilon=\iint_{W}[I_{2}(\mathbf{X}+\mathbf{d})-I_{1}(\mathbf{X})]^{2}\omega(\mathbf{X})d\bm {X} \tag{1}\]  
where \(\varepsilon\) is the residual error, \(I_{1}\) is the image of the previous frame, \(I_{2}\) is the image of the next frame, \(\omega(\mathbf{X})\) is the weight function, \(\mathbf{X}=\begin{bmatrix}x&y\end{bmatrix}^{T}\), \((x,\,y)\) are the coordinates of the pixels in the window, \(W\).  
From the video taken by the UAV, the coordinate of the target points on each frame can be obtained by KLT. The target points displacement time-history curves are obtained by connecting the coordinate information of each frame.
## 4 The Techniques Employed for the Reconstruction of High-Resolution Magnetic Resonance (MR) Images
### The Network for Removing Blur in Magnetic Resonance Images (MRIs)  
This study seeks to reconstruct a clear and exact representation \(I_{S}\) from a degraded picture \(I_{B}\) without prior blurring knowledge. The deblurring process uses a convolutional neural network, known as the Generator (\(G_{\rho_{G}}\)). An estimation determines the best \(I_{S}\) image for each \(I_{B}\) value. The training step includes the critic network (\(D_{\rho_{D}}\)) and adversarial training for both networks. Content and adversarial losses produce the composite loss function:  
\[\mathcal{L}=\mathcal{L}_{G-A-N}+\lambda\cdot\mathcal{L}_{X} \tag{17}\]  
The constant value of \(\lambda\) was 100 in all experiments. This study uses different conditioning than [PERSON] et al. [42]. Interestingly, input-output discrepancies are not penalized. Adversarial loss is essential to machine learning. Loss functions encourage the development of outputs that closely resemble actual creatures or a reference model, see Figure 6. This method works in visual representation, auditory input, language comprehension, and other sectors. Scholars and practitioners say adversarial loss improves machine learning model precision and resilience. This improves reliability and efficiency. Loss determination:  
\[\mathcal{L}_{G-A-N}=\sum_{n=1}^{N}-D_{\rho_{D}}\big{(}G_{\rho_{G}}\big{(}I^{ B}\big{)}\big{)} \tag{18}\]  
Commonly used data loss functions include MAE and MSE. Using the above functions as the sole optimization target produces indeterminate photo anomalies. According to [42], the observed variances are due to the average value of possible solutions in pixel space. The perceptual loss function employs the \(\mathbb{L}2\)-loss approach to quantify the dissimilarity of the synthesized MRI scan and the reference MR scan's CNN feature maps through mathematical expressions. The terms are stated as follows:  
\[\mathcal{L}_{X}=\frac{1}{U_{k,n}B_{k,n}}\sum_{x=1}^{U_{k,n}}\sum_{y=1}^{B_{k,n} }\left(\mathcal{O}_{k,n}\big{(}I^{S}\big{)}_{x,y}-\mathcal{O}_{k,n}\bigg{(}G _{\rho_{G}}\big{(}I^{B}\big{)}_{x,y}\bigg{)}\right)^{2} \tag{19}\]  
The feature map \(\mathcal{O}_{k,n}\) is a representation of the output obtained from the \(n\)-th convolution operation within a pre-trained network specifically developed for MRI analysis [43]. The acquisition of the feature map occurs after activation and before the \(k\)-th maxpooling layer. The variables \(U_{k,n}\) and \(B_{k,n}\) represent the sizes of the feature maps.  
Figure 6: Deblurring net.
# Risk Assessment of Landfalling Tropical Cyclones in China Based on Hazard Risk Theory
## 2 Materials and Methods
### Methodology
#### 2.3.4 Data Standardization  
This analysis utilizes various indicators sourced from multiple datasets, which exhibit significant differences in terms of attributes, scales, and magnitudes. Analyzing tropical cyclone hazards with these raw indicators tends to disproportionately amplify the influence of high numerical values while diminishing those of lower ones, thereby compromising the accuracy of the results. To address this, the data were rescaled proportionally and divested of their original units to fit within a specified range, thus converting them into unitless, standardized numbers. This study adopts deviation standardization for the indicators, ensuring that outcomes are confined to the [0, 1] range [31].  
When \(X_{i}^{\prime}\) serves as a positive evaluation indicator, standardization is conducted according to Equation (5):  
\[X_{i}^{\prime}=\frac{X_{i}-X_{min}}{X_{max}-X_{min}} \tag{5}\]  
When \(X_{i}^{\prime}\) serves as a negative evaluation indicator, standardization is conducted according to Equation (6):  
\[X_{i}^{\prime}=\frac{X_{max}-X_{i}}{X_{max}-X_{min}} \tag{6}\]  
where \(X_{i}^{\prime}\) is the standardized evaluation value for the \(i^{\text{th}}\) indicator, and \(X_{max}\) and \(X_{min}\) represent the maximum and minimum standard values, respectively, for this indicator.
## 3 Dataset  
To train and test the model, a manually annotated eye-tracking dataset was used, which had also been used in many previous studies [9; 21; 22; 30]. The dataset was recorded with a Hi-Speed 1250 eye tracker from SensoMotoric Instruments at 500 Hz. The data were labeled by two manual raters, [PERSON] (MN) and [PERSON] (RA), and annotated into six event types, i.e., fixations, saccades, PSOs, smooth pursuits, blinks, and undefined.  
The subjects were presented with static images, video clips, and simple moving dot stimuli. The evaluation focused on the video domain with event classes including fixations, saccades, PSOs, and smooth pursuits, as the study was centered on these four types. Image viewing and moving dot data were not included because image viewing datasets lacked smooth pursuit events, and the moving dot domain did not contain a time-stamp feature, which was essential to calculate features such as velocity, acceleration, and jerk, which are key features in building the model. Six files with video stimuli were selected for the experiment, containing a total of 16,952 gaze samples. Among these, 36.4% of the samples were labeled as fixations, 6.8% as saccades, 3.1% as PSOs, and 53.6% as smooth pursuits. The statistics of the files are shown in Table 1.  
While this dataset has provided a robust foundation for evaluating the model, potential biases are acknowledged due to the focus on video stimuli and the exclusion of other stimulus types, such as static images and text reading. Eye movement patterns can vary significantly across different tasks and stimulus domains. This limitation may affect the generalizability of our approach to tasks outside the video domain. Additionally, although the dataset was annotated by expert raters, potential inter-rater variability could introduce annotation bias. Future work will explore cross-domain evaluations to further assess the robustness of the proposed method.
# Comparative Analysis of the Methods for Fiber Bragg Structures Spectrum Modeling
## 2 Modeling Methods
### Transfer Matrix Method  
The reflectance spectrum of a homogeneous non-apodized FBG is modeled by the formulation of the resulting transmission matrix in the following form [12; 13; 19]:  
\[T(\lambda)=\begin{bmatrix}\cosh(\gamma\cdot H)-j\frac{\Delta\eta}{\gamma}\text {sinh}(\gamma\cdot H)&-j\frac{\kappa}{\gamma}\text{sinh}(\gamma\cdot H)\\ j\frac{\kappa}{\gamma}\text{sinh}(\gamma\cdot H)&\cosh(\gamma\cdot H)+j \frac{\Delta\eta}{\gamma}\text{sinh}(\gamma\cdot H)\end{bmatrix}\,, \tag{20}\]  
where \(H\) is the length of the FBG, and the parameters \(\gamma\), \(\kappa\), and \(\Delta\beta\) are determined by the following formulas:  
\[\begin{array}{ll}\Delta n_{\text{eff}}=\frac{\Delta\eta}{2},\ \ n_{\text{eff}}=n_{0}+\Delta n_{\text{eff}},\ \ \kappa=\pi\frac{\Delta n_{\text{eff}}}{\lambda},\\ \Delta\beta=\frac{2\pi n_{\text{eff}}}{\lambda}-\frac{\pi}{\lambda},\ \ \gamma=\sqrt{|\kappa|^{2}-\Delta\beta^{2}}\,.\end{array} \tag{21}\]The dependence of \(\mathbf{T}(\lambda)\) on the wavelength \(\lambda\) in (20) is implicit and expressed in the terms of \(\kappa\), \(\Delta\beta\), and \(\gamma\), which are the functions of the wavelength.  
The transfer matrix of the FBG with one discrete phase shift (Figure 1e) is formulated as the product of three transfer matrices, two of which (the first and the third ones) are the transfer matrices of homogeneous FBGs with the length of \(H_{1}\) and \(H_{2}\), respectively, and the second matrix describes the section of the optical fiber with the length \(h\) forming the following phase shift:  
\[\mathbf{T}_{\pi-\mathrm{FBG}}(\lambda)=\mathbf{T}(\lambda,H_{1})\times \mathbf{T}_{\varphi}(\lambda,h)\times\mathbf{T}(\lambda,H_{2})\, \tag{22}\]  
where \(\mathbf{T}_{\varphi}\) is the transfer matrix of the \(\pi\)-phase shift:  
\[\mathbf{T}_{\varphi}(\lambda,h)=\begin{bmatrix}\exp\left(-j\frac{2\pi n_{ \mathrm{eff}}}{\lambda}\cdot h\right)&0\\ 0&\exp\left(j\frac{2\pi n_{\mathrm{eff}}}{\lambda}\cdot h\right)\end{bmatrix}. \tag{23}\]  
In (23), the length \(h\) corresponds to the phase shift of the grating: \(h=\Lambda/2\).  
The reflection coefficient at each wavelength is defined as the square of the ratio modulus of the resulting transfer matrix elements:  
\[R(\lambda)=\left|\frac{T_{1,0}}{T_{0,0}}\right|^{2}. \tag{24}\]
## 4 Risk Assessment Study
### Lost Profit Risk for Imperfect Preventive Maintenance  
In this section, we consider the case of machine failures and their subsequent repairs that cause lost production and perhaps lost sales during downtimes. Production reliability depends on the maintenance strategies; in other words, the quality of both corrective and preventive maintenance actions is essential for improving system availability.  
In this section, we deal with lost profit risk in our assessment in the context of imperfect preventive maintenance. We assume that each preventive maintenance (PM) action restores the machine to between the \"as good as new\" state and the \"as bad as old\" state.  
The failure rate \(\lambda(t)\) can be written as:  
\[\lambda(t^{\pm})=(1-\alpha).\lambda(t) \tag{18}\]  
where \(\alpha\) is a random variable that follows a Bernoulli distribution, \(\alpha\in[0,1]\).  
Thus, based on Equation (12), the failure rate in the case of imperfect maintenance is expressed as follows:  
\[\lambda_{k}(t)=\lambda_{k-1}(\Delta t)(1-\left\lfloor\frac{k-1}{T}\right \rfloor+1)T\right])+\frac{U_{k}}{U_{\max}}\lambda_{n}(t)+\left\lfloor\frac{k- 1}{(\left\lfloor\frac{k-2}{T}\right\rfloor+1)T}\right\rfloor.(1-\alpha). \lambda_{T}(\Delta t) \tag{19}\]  
Let \(LRP_{PM}\) be the risk assessment for lost profit due to imperfect preventive maintenance based on the computation method that was presented previously.  
Consider that \(\overline{A}(U,N)\) is the average number of failures during the horizon \(H\), and \(B\) is the total downtime of the machine over the horizon. We moreover note that for each failure, we need \(b\) time units to repair the machine:  
\[B=\overline{A}(U,N).b \tag{20}\]  
With:  
\[\overline{A}(U,N)=\sum_{i=0}^{N-1}\left[\sum_{k=In(i\times\frac{T}{M})+1}^{In ((i+1)\times\frac{T}{M})}\int\limits_{0}^{\Delta t}\lambda_{k}(t)\,dt\right]+ \sum_{k=N\times T}^{H\Delta t}\int\limits_{0}^{\Delta t}\lambda_{k}(t)\,dt \tag{21}\]  
Thus, the production quantity during \(\left(\mathrm{H}-\mathrm{B}\right)\) is equal to:  
\[C=\frac{\sum\limits_{k}U^{*}(k)\times(H-B)}{H} \tag{22}\]  
Consequently, the loss of production is:\[D=\sum_{k=0}^{H-1}U^{*}(k)-C \tag{23}\]  
In this case, we can express the risk by the following equation:  
\[LRP_{PM}=\frac{D}{\sum\limits_{k=1}^{H-1}U^{*}(k)} \tag{24}\]
###### Abstract  
The progress made in Information and Communication Technologies (ICT) has played a crucial role in turning the Internet of Things (IoT) into a reality. IoT is an emerging technology that refers to networks of interconnected and Internet-enabled objects equipped with sensors, processors, and actuators that interact with each other to create significant collaboration and interaction environments. The field of education is one of the areas where IoT can be applied. However, the implementation of IoT poses security and privacy risks, such as unauthorized access, denial-of-service (DoS) attacks, and interference with wireless signals where IoT devices collect a significant amount of data, including user's personal information like identity, location, and daily behavior. Therefore, it is crucial to protect users' privacy in IoT applications. The innovative Ubiquitous Learning Environments (ULEs) have been created by ubiquitous computing technologies (mobile, wireless, network), which provide learners with learning experiences beyond the traditional classroom in both the real and virtual worlds. Ubiquitous learning (U-learning) is an emerging technology as a result of the tremendous technological revolution of ICT. U-learning is a novel learner-centered approach that aims to enhance learning, motivation, and creativity by utilizing innovative technology and IoT. U-learning enables learners to access the appropriate learning content, collaborate with the right learning partners, and engage in self-learning at the right time and place in a ubiquitous computing environment. To support learners in developing their social skills, in this study a framework for implementing the ULE based on the Internet of Things is designed, which consists of three main layers: perception, network, and application. The article explores the effects of IoT on education and how U-learning, which incorporates IoT to enhance learning experiences, has the potential to replace traditional classroom learning. Furthermore, the article addresses privacy preservation measures for different layers within the IoT environment and ULE. A framework for implementing the ULE model is in progress, which is a part of our future work.  
educational technology; internet of things (IoT); U-learning; ubiquitous learning environments (ULEs); E-learning and M-learning; security; privacy +
Footnote †: journal: Applied Sciences
# Systematic Review: Malware Detection and Classification in Cybersecurity
## 2 Methodology
### Advanced Malware Detection Techniques
#### 3.6.6 Machine Learning for Malware Detection  
The article \"Evaluation of Machine Learning Algorithms for Malware Detection\", published in the journal Sensors, examines the use of machine learning algorithms applied to dynamic malware detection, highlighting their effectiveness compared to traditional signature-based methods [66]. Given the increasing complexity and volume of malicious software, conventional antivirus systems are insufficient for detecting emerging threats, which has led to the development of approaches based on behavioral analysis.  
The study proposes a malware detection model based on dynamic analysis, in which malicious programs are executed in controlled environments--such as sandboxes--to record their behavior. This information is transformed into sparse vectors and used as input for various classification algorithms, including k-NN, decision trees (DT), Random Forest (RF), AdaBoost, Stochastic Gradient Descent (SGD), Extra Trees, and Gaussian Naive Bayes (GNB).  
The system architecture incorporates a feature extraction and selection phase, through which the most relevant variables are identified from the reports generated by the sandbox (Cuckoo). This process optimizes model performance, improves accuracy, reduces overfitting, and lowers computational costs.  
Experimental results indicate that the RF, SGD, Extra Trees, and GNB classifiers achieved perfect performance, with 100% precision, accuracy, sensitivity, and F1 score, demonstrating the effectiveness of the proposed approach. The classification strategy based on dynamic behavior, enhanced by machine learning techniques, allows for the accurate and rapid identification and categorization of malware into evolutionary families.The reported results come from the study by [PERSON] and [PERSON] [66], where multiple classifiers were evaluated on data generated with Cuckoo Sandbox. While perfect metrics (100% precision, recall, and F1-score) are reported, the original article does not specify the dataset size or the validation method used. Therefore, we have incorporated a clarifying note in the revised version that contextualizes these results, warning about possible limitations related to overfitting and generalization.
## 2 Materials and Methods
### Cytotoxic Effect  
Caco-2 and HT29 cell lines derived from human adenocarcinoma (American Type Culture Collection, Rockville, MD, USA) were cultured in Eagle's minimal essential medium (EMEMEM, Sigma-Aldrich, Steinheim, Germany) supplemented with 10% fetal bovine serum (Merck, Darmstadt, Germany), 1.0% sodium bicarbonate (Merck, Darmstadt, Germany), 1.0% sodium pyruvate (Sigma-Aldrich, Steinheim, Germany), 5.0 mM glutamine (Sigma-Aldrich Steinheim, Germany), 1.0% MEM nonessential amino acids (Sigma-Aldrich, Steinheim, Germany), and 1.0% penicillin-streptomycin solution (10,000 units/mL penicillin and 10.0 mg/mL streptomycin, Sigma-Aldrich, Steinheim, Germany). Cultures were incubated at 37 \({}^{\circ}\)C with 5% CO\({}_{2}\), and media were replenished every 2-3 d, and passedaged every 7 d. Cell passages 12-16 were used for the assay; viability was measured using the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide, Sigma-Aldrich, Steinheim, Germany) cytotoxicity assay [24] with modifications. Briefly, Caco-2 and HT29 cells (1:9 ratio) were seeded in 96-well plates at a density of 2.5 \(\times\) 10\({}^{3}\). After 24 h, the cells were treated with twofold serially diluted samples (16-512 ug/mL) for 72 h. Then, MTT reagent (1 mg/mL) in EMEM was added to each well and incubated for an additional 2 h at 37 \({}^{\circ}\)C with 5.0% CO\({}_{2}\). The medium with MTT was removed and the intracellular formazan product was dissolved in 100 uL dimethylsulfoxide. The absorbance was measured at 555 nm using a Tecan Infinite M200 spectrometer (Tecan Group, Mannedorf, Switzerland), and the percentage of viability (IC\({}_{50}\) and IC\({}_{80}\) value) was calculated and compared to that of the untreated control. The results are values from three different measurements.