esa-sceva/satcom-chunk-collection huggingface dataset:
# MMTD: A Multilingual and Multimodal Spam Detection Model Combining Text and Document Images
## 3 The Proposed MMTD  
### The Framework of the Proposed MMTD Model  
With the increasing complexity of email content, relying solely on text- or image-based spam detection models may lead to unreliable results. To address this issue, we propose a new multimodal spam detection model called MMTD, which integrates both text and image modalities for comprehensive spam detection. The MMTD model consists of three main components: a multilingual text encoder, a document image encoder, and a multimodal fusion module. These components work together to extract text and image features from emails and combine them for effective spam detection. The overall framework of the proposed model is illustrated in Figure 1, and the process can be summarized as follows:  
1. Email data preprocessing: We start by preprocessing the email data. The body content of each email is converted into an image representation, which will serve as the input to the document image encoder. Additionally, the text within the email is extracted and saved as a separate text dataset. This allows us to process the text and image components of the email separately during model training.
2. Obtaining an optimal multilingual text encoder and document image encoder: To enhance feature extraction, we pre-train the multilingual text encoder and document image encoder. The pre-training is performed separately on the text dataset and image dataset, respectively, to optimize each encoder for their respective modality.
3. Extracting deep feature representations of an email text and image: The pre-trained multilingual text encoder is used to extract deep feature representations from the email text. Similarly, the document image encoder processes the email document image to obtain image features. These features capture the underlying characteristics of the text and image modalities, respectively.
4. Training the multimodal fusion module: In this module, we incorporate modal type embeddings into the text and image feature representations. These embeddings help distinguish between the text and image components during fusion. The text and image feature representations are then concatenated and fed into the multimodal fusion model. The output of the fusion model is subsequently passed through a fully connected layer to obtain the final spam classification result.  
Next, we provide a detailed description of the multilingual text encoder (MTE), document image encoder (DIE), and multimodal fusion module.
## 2 Materials and Methods
### The BRIDGE DIAL Data Analysis Technique
#### 2.2.4 Evaluation of CO\({}_{2}\) Flux  
The values of CO\({}_{2}\) concentrations reported both in the profiles (see Figure 2b) and the dispersion maps (see Figure 3a, Figure 4a, Figure 5b) were used to compute the carbon dioxide flux (see Figure 3b, Figure 4b, Figure 5c). To this aim, it is necessary to integrate the excess (i.e., background corrected) carbon dioxide concentrations over the whole cross-section of the plume determined by each scan, and multiply this amount by the transport velocity of the plume. Formally, the carbon dioxide flux (\(\Phi_{\text{CO}_{2}}\), in Kg\(\cdot\)s\({}^{-1}\)) is calculated from the following equation [21; 41; 42]:  
\[\Phi_{\text{CO}_{2}}=\text{ v}_{\text{P}}\frac{\text{PM}_{\text{CO}_{2}}}{10^{3} \text{N}_{\text{A}}}\cdot\text{N}_{\text{molCO}_{2}-\text{total}} \tag{6}\]  
where v\({}_{\text{P}}\) is the plume transport velocity (m\(\cdot\)s\({}^{-1}\)); N\({}_{\text{molCO}_{2}-\text{total}}\) is the total plume carbon dioxide concentration (molecules\(\cdot\)m\({}^{-1}\)); and PM\({}_{\text{CO}_{2}}\) and N\({}_{\text{A}}\) are the CO\({}_{2}\) molecular weight and the Avogadro's constant, respectively. N\({}_{\text{molCO}_{2}-\text{total}}\) was calculated by integrating the effective average excess CO\({}_{2}\) concentrations (\(\overline{\text{C}_{\text{exc},i}}\) in ppm) over the whole plume cross section, according to:  
\[\text{N}_{\text{molCO}_{2}-\text{total}}=\text{ N}_{\text{h}}\cdot 10^{-6}\cdot\sum_{\text{i}} \overline{\text{C}_{\text{exc},i}}\cdot\text{A}_{\text{i}} \tag{7}\]  
where N\({}_{\text{h}}\) is the atmospheric number density [molecules\(\cdot\)m\({}^{-3}\)] at the crater; \(10^{-6}\) converts it into parts per million (ppm); and A\({}_{\text{i}}\) represents the i-th effective plume area provided by:  
\[\text{A}_{\text{i}}=\text{ l}_{\text{i}}\cdot\Delta\text{R} \tag{8}\]  
where \(\Delta\text{R}\) is the ADC resolution (1.5 m) and l\({}_{\text{i}}\) is the i-th arc of the circumference:  
\[\text{l}_{\text{i}}=\text{ R}_{\text{i}}\cdot\theta \tag{9}\]  
where R\({}_{\text{i}}\) is the i-th range vector (m) and \(\theta\) is the angle resolution (rad).  
Figure 3: (**a**) An example of a lidar scan in polar coordinates through the plume at an elevation of 12\({}^{\circ}\) (15 October 15 2014). The contour lines show isopleths of background-corrected CO\({}_{2}\) mixing ratios in the plume (expressed in ppm, the legend is the vertical colored bar), shown as a function of heading and range. (**b**) Time-series of lidar-derived CO\({}_{2}\) fluxes from Pisciarelli obtained from 15 October (afternoon) to 16 October (afternoon), rearranged from Figure 4 reported in [21]. The timescale for the morning scans on 15 October is shown in green. Each point refers to a particular scan through the plume (time is the onset time of the scan, with each scan lasting 10–20 min) [21].
## 2 Mathematical Method
### Static and Perturbed Reynolds Equation
\begin{array}{ll}\frac{\partial}{\partial\theta}\Bigg{(}\frac{\overline{h}_{0 }^{3}}{k_{x0}\overline{\mu}_{0}}\frac{\partial\overline{p}_{d}}{\partial \theta}\Bigg{)}&+\left(\frac{d}{t}\right)^{2}\frac{\partial}{\partial\lambda }\Bigg{(}\frac{\overline{h}_{0}^{3}}{k_{x0}\overline{\mu}_{0}}\frac{\partial \overline{p}_{d}}{\partial\lambda}\Bigg{)}\\ &+\left(b_{1}-1\right)\frac{\partial}{\partial\theta}\Bigg{(}\frac{\overline{ p}_{0}^{3}\overline{\mu}(p_{0},T)}{\overline{h}_{0}}\frac{\partial\overline{p}_{0 }}{\partial\lambda}\Bigg{)}\frac{\partial\overline{p}_{0}}{\partial\theta} \Bigg{)}\\ &+\left(b_{2}-1\right)\left(\frac{d}{t}\right)^{2}\frac{\partial}{\partial \lambda}\Bigg{(}\frac{\overline{g}_{0}\overline{\mu}(p_{0},T)}{\overline{h}_{0 }}\frac{\partial\overline{p}_{0}}{\partial\lambda}\Bigg{)}\frac{\partial \overline{p}_{0}}{\partial\lambda}\Bigg{(}\frac{\partial\overline{p}_{0}}{ \partial\lambda}\Bigg{(}\frac{\partial}{\partial\lambda}\Bigg{)}\Bigg{)}\\ &=\left(b_{1}-3\right)\frac{\partial}{\partial\theta}\Bigg{(}\frac{\overline{h} _{0}^{2}}{k_{x0}\overline{\mu}_{0}}\frac{\partial\overline{p}_{0}}{\partial \overline{h}_{d}}\Bigg{)}\\ &+\left(b_{2}-3\right)\left(\frac{d}{t}\right)^{2}\frac{\partial}{\partial \lambda}\Bigg{(}\frac{\overline{h}_{0}^{2}}{k_{x0}\overline{\mu}_{0}}\frac{ \partial\overline{p}_{0}}{\partial\lambda}\Bigg{(}\frac{\partial\overline{p}_{ 0}}{\partial\lambda}\Bigg{)}+\frac{1}{2}\frac{\partial\overline{h}_{d}}{ \partial\theta}+i\Omega\Bigg{(}\frac{\partial}{\partial t}\Bigg{)}\end{array}  \tag{12}\]  
The static Reynolds equation Equation (11) governs the static pressure \(\overline{p}_{0}\). The perturbed equation Equation (12) is for solving the dynamic coefficients by means of a partial derivative method [16; 23]. The boundary conditions of Equations (11) and (12) for oil-lubricated tilting pad bearings are shown in Equations (13)-(15). The oil is not able to bear the tensile stress. For both static and perturbed equations, the ambient pressure at two axial ends and the leading edge, and trailing edge of the pads is set at zero. \[\overline{p}_{0}(\theta,\lambda=\pm 1)=\vec{p}_{d}(\theta,\lambda=\pm 1)=0 \tag{13}\]  
\[ \begin{array}{ll}\overline{p}_{0}(\theta=\theta_{L},\lambda)=\overline{p}_{0 }(\theta=\theta_{T},\lambda)=0\\ \overline{p}_{d}(\theta=\theta_{L},\lambda)=\overline{p}_{d}(\theta=\theta_{T},\lambda)=0\end{array}  \tag{14}\]  
When a trend of negative pressure occurs in the pads of bearing, the oil film breaks and the cavitation occurs. It is governed by the Reynolds boundary condition in this research shown as Equation (15). The static and perturbed pressure become zero at the fracture area of the oil film. \[ \begin{array}{ll}\overline{p}_{0}(\theta,\lambda)=0\\ \overline{p}_{d}(\theta,\lambda)=0\end{array} \quad if\quad\overline{p}_{0\_ \emph{trend}}(\theta,\lambda)<0 \tag{15}\]
## 3 Proposed Coordinated Control Method
### Strategy 2: Dynamic Offset Adjustment Strategy
#### 3.3.4 Updating Phase Signal Timing  
_Step 1:_ The adjustment amount of the offset \(\Delta T_{k+1,\infty}\) is allocated in sequence according to the number of phases that can be adjusted at the downstream intersection. If an increase in the offset is required in cycle \(k\)+1, then in cycle \(k\), 1 s of green time is added sequentially to each of the non-coordinated phases that can be adjusted. If \(\Delta T_{k+1,\infty}=0\), it indicates that the allocation has been completed. If a reduction in offset is required in cycle \(k\)+1, then in cycle \(k\), 1 s of green time is reduced sequentially for each non-coordinated phase that is available for adjustment. If \(\Delta T_{k+1,\infty}=0\) or \(\Delta T^{ad}_{k+1,\infty,n}=g^{ad,max}_{l_{\mu},k}\), the maximum available green signal time for adjustment has been reached, it indicates that the allocation is complete.  
_Step 2:_ After adjusting the green signal times for each phase, update the cycle duration and offset for that cycle by  
\[\left\{\begin{array}{ll}C^{T,ad}_{l_{\mu},k}=\sum\limits_{n}g^{ad}_{l_{\mu}, k,n}+n\times A\\ T_{\infty,k+1}=g^{b}_{l_{\mu},k,s}(t)+C^{T,ad}_{l_{\mu},k}-g^{b}_{l_{\mu},k+1,s }(t)\end{array}\right. \tag{16}\]  
where \(C^{T,ad}_{l_{\mu},k}\) = updated green signal times for the phases at the downstream intersection in cycle \(k\), \(g^{ad}_{l_{\mu},k,n}\) = adjusted green signal time for phase n at the downstream intersection in cycle \(k\), A is yellow signal time (3 s), \(T_{\infty,k+1}\) = offset for cycle \(k+1\), \(g^{b}_{l_{\mu},k,s}(t)\) is the moment when the green for the through phase with cycle \(k+1\), at the downstream intersection is activated, and \(g^{b}_{l_{\mu},k+1,s}(t)\) = initiation moment of the green for the through phase with cycle \(k+1\) at the upstream intersection.
## 3 Results and Discussion
### The Techno-Functional Properties of Proteins
#### 3.2.5 Antioxidant Properties
This leads to the phenomenon known as acoustic cavitation, where microscopic bubbles form and collapse violently. The energy released during the collapse of these bubbles generates localized high temperatures and pressures, which can disrupt the protein structure. This disruption primarily affects the non-covalent interactions, such as hydrogen bonds and hydrophobic interactions, that maintain the protein's tertiary and quaternary structures [4]. This process involves the unfolding of the protein molecules, allowing hydrophobic regions to be exposed, which improves the solubility and functional properties of the modified protein. The energy imparted by HIU also increases the molecular motion of the modified protein, promoting interactions among molecules \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Time (min)**} & \multirow{2}{*}{**Power (W)**} & \multirow{2}{*}{**\%RSA**} & \multirow{2}{*}{**mg TE/g Sample**} & \multicolumn{2}{c}{**IC\({}_{50}\) Values of ABTS**} \\  & & & & **(mg/mL)** \\ \hline
10 & Control & 36.99 \(\pm\) 1.01 h & 4.52 \(\pm\) 0.21 b & ND \\
10 & 600 & 79.25 \(\pm\) 0.56 c & 4.70 \(\pm\) 0.53 ab & 0.032 \\
10 & 840 & 87.60 \(\pm\) 1.42 b & 5.42 \(\pm\) 0.02 a & 0.029 \\
10 & 1080 & 74.98 \(\pm\) 1.05 e & 1.99 \(\pm\) 0.01 f & 0.033 \\
15 & Control & 28.71 \(\pm\) 1.09 i & 0.6 \(\pm\) 0.08 h & ND \\
15 & 600 & 77.28 \(\pm\) 1.03 d & 2.59 \(\pm\) 0.33 e & 0.032 \\
15 & 840 & 96.65 \(\pm\) 0.56 a & 1.04 \(\pm\) 0.07 g & 0.026 \\
15 & 1080 & 64.72 \(\pm\) 1.13 s & 3.59 \(\pm\) 0.28 d & 0.039 \\
20 & Control & 34.04 \(\pm\) 1.28 h & 2.34 \(\pm\) 0.43 e & ND \\
20 & 600 & 74.66 \(\pm\) 0.68 e & 2.35 \(\pm\) 0.12 e & 0.033 \\
20 & 840 & 71.09 \(\pm\) 0.28 f & 4.02 \(\pm\) 0.05 c & 0.035 \\
20 & 1080 & 64.78 \(\pm\) 0.71 s & 4.02 \(\pm\) 0.03 c & 0.039 \\ \hline \hline \end{tabular} The antioxidant activity was expressed as a Percentage of Radical Scavenging Activity (%RSA) and as milligrams of Trolox equivalents per gram of sample (mg TE/LPC). Different lower letters in the same column indicate significant differences among the treatments (\(p<\) 0.05). IC\({}_{50}\) is the sample concentration (mg/mL) required to achieve 50% of antioxidant activity. ND: No determined.  
\end{table} 
Table 3: Antioxidant properties of leaf protein concentrates treated by high-intensity ultrasound under different powers. and potentially leading to new structural arrangements or aggregates that can improve the functional characteristics of the protein hydrolyses [1; 2; 7]. HIU alters the structural integrity of jackfruit leaf proteins through mechanisms such as acoustic cavitation, protein denaturation, fragmentation, and enhanced molecular motion.
# Regression Equations for Estimating Landslide-Triggering Factors Using Soil Characteristics
## 2 Methodology  
### Soil Properties of Landslide Areas  
The study statistically analyzed the soil properties of landslides that occurred in slopes composed of gneiss and granite soils. A correlation analysis was conducted to discover whether there are distinctions in the soil properties of landslide areas based on their geological weathering conditions. In addition, regression equations for permeability coefficient and shear strength, which are the main variables linked to landslide-triggering factors based on soil properties, were suggested.  
The selected sites for this study were areas with different geological features that have experienced multiple landslides caused by localized heavy rainfall within the same period. As such, the study focused on cities in South Korea, namely Yangju, Gyeonggi-do, and Sangju, Gyeongsangbuk-do, whose primary soil types were gneiss and granite, and 76 landslides in 1998 and 99 landslides in 1998 have occurred, respectively.  
Figure 1 shows the field survey of landslides in Yangju, Gyeonggi-do, a study area, and shows that soil samples are collected using a cylindrical stainless ring sampler around the landslides. Soil samples were collected from their landslide and non-landslide areas, and soil characteristics were analyzed for each.  
In the selected sites, soil sampling and analysis were performed as follows. Disturbed and non-disturbed samples were separately collected by removing the topsoil at a depth of 40-60 cm. For non-disturbed samples, a stainless cylindrical ring sampler that was 10 cm in diameter and 6-15 cm in height was produced. Meanwhile, two zipper bags with a volume of 250 g were used to obtain disturbed samples at the same area where the non-disturbed samples were collected. Then, all samples collected were sealed to maintain the field conditions as much as possible and were delivered to the laboratory for indoor testing. The indoor soil tests, which followed the Korean Industrial Standards (KIS), included a grain-size analysis [37], test for the water content of soils [38], specific gravity test [39], density test [40], and permeability test [41]. Finally, the permeability coefficient, micro-granule soil content, water content by weight, dry unit weight, and void ratio were calculated based on the test results.
# BDCOA: Wavefront Aberration Compensation Using Improved Swarm Intelligence for FSO Communication
###### Abstract
The key contributions are summarized as follows:  
* The SLAO system is used as an important module for compensating the losses caused by WA in the FSO:
* In SLAO, the BDCOA is used for searching optimum control signals for the actuators in the DM. The Brownian Motion (BM) and Directional Mutation Scheme (DMS) are integrated into the BDCOA for avoiding the suboptimal solutions and enhancing the search space efficiency while searching for an optimum control signal;
* The wavefront distortions are compensated by selecting the control signals using BDCOA, which further helps enhance the quality of the received signal and coupling efficiency. The enhanced quality of received signals aids in minimizing the BER and RMS. The remainder of this paper is organized as follows: The BDCOA based WA compensation in FSO is detailed in Section 2, while Section 3 details the outcomes of the research. The research is concluded in Section 4.
## 3 SNR Evaluation Based on Proposed TP-CNN
### Feature Fusion  
The idea of integration refers to the rationalization of different approaches to problem solving in order to achieve better results. Fusion includes, but is not limited to, the direct integration of results; it can also occur at various stages of problem solving, with the aim of gaining a stronger affirmation of the findings at that stage. It has been used in many problems, particularly in classification problems. Specific methods include voting mechanisms, weighted averages, etc.  
To optimize the performance of the deep learning algorithm for signal-to-noise evaluation, further improvements to the model are made. In the experience of previous work, many neural network models have been used to influence the classification results by modifying the depth, parameters, and other information based on a single-way neural network similar to the one mentioned. Additionally, the comparative experiments presented show that none of the participating models performed optimally on all SNR, i.e., the same test image with different convolution kernels may yield inconsistent classification results. The difference in the convolution kernels means that the features are extracted differently and, therefore, the features are extracted differently.  
Feature fusion is the combination of different levels or branches of features [23] and is a ubiquitous part of modern network architectures, allowing the proposed model to use as many features as possible for further classification and reducing randomness. The fusion of multiple features is an effective method for visual and multimedia applications. Therefore, if one feature extraction method can be overlaid with another, the effect of numerous feature complementary can be achieved, avoiding the omission of small features. Therefore, we argue that using them together is better than using any one alone and that this complementary fusion of elements is formed at the fully connected level. A literature review shows that even simple fusion schemes can significantly improve results by adding or multiplying features as long as they are complementary [24]. For the networks with deep layers (e.g., ResNet [25]), deep features are often fused with shallow features to weigh the results better globally and locally. The idea of the Inception module in GoogleNet [26] is also to collect multi-scale data information by stacking different networks.  
The first method is to join the features to be fused, and the dimension of the fused vector is the sum of the dimensions of the original vector, which is shown in Figure 2a. The second add method combines the feature vectors into a composite vector, and the size of the fused vector is the maximum number of dimensions of the original vector, which is shown in Figure 2b. The second add method combines the feature vectors into a composite vector, and the size after fusion is the maximum number of dimensions in the initial vector.
# Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake
## 1 Introduction
In this study, our work included: (1) establishing a large-scale water classification method based on the U-Net CNN model, (2) extracting Poyang Lake water body data from 2014 to 2021 and evaluating their accuracy, and (3) analyzing the inundation frequency distribution and the relationship between the water level and the water area.
# Sampling-Based Motion Planning for Free-Floating Space Robot without Inverse Kinematics
## 2 Motion Equations of an FFSR  
The motion model is fundamental for the motion planning and control of a robot, and this part establishes the motion equation of FFSRs. In this paper, RRTs are used for the motion planning of FFSRs, which uses a random configuration and the goal EE pose to guide the growth of the tree. We call them random configuration guiding growth (RC-GG) and goal EE pose guiding growth (GEE-GG), respectively. The Jacobian matrix related to the state transition is needed to perform local planning in RC-GG. For local planning of GEE-GG, the generalized Jacobian matrix and the Jacobian matrix related to base attitude will be used, of which the former is used to reduce the end error and the latter is used to adjust the attitude of the base. In order to detect whether an FFSR is in collision, a method is proposed to calculate the centroid position of each rigid body of the FFSR.  
The derivation of the above equations is based on the geometric relations between links. However, the special characteristic also needs to be considered when establishing the motion equations of FFSRs. Therefore, this section also gives the linear momentum and angular momentum conservation equations in velocity form and the linear momentum conservation equation in position form. The symbol definition is the same as that of general space robotics literature.
## 2 System Arrangement
### Structure of a Wireless Sensor Network-Based Monitoring System  
Multilevel structure composed of hybrid communication has been proposed in several works [12; 13; 14; 15; 16] for monitoring systems. In this regard, our work is based on a practical two-layer monitoring network as shown in Figure 2. The first layer consists of sensor nodes installed at every tower. Data gathered by sensor nodes is transmitted wirelessly to a sink node by many-to-one communication. The sink node is connected to a terminal that has enhanced computation and communication. The terminal compresses monitoring data such as acceleration, tension and video and transmits them to the higher layer. The second layer is burdened with high-speed and large-data transmitting tasks. In such cases, enabling some of these towers with the capability of optical or cellular communication comes as a solution. Such a tower acts as a head in an area. This enhanced tower gathers information from sensor nodes in this area and transmits it directly to the substation. The recommended second layer in the field adopts OPGW optical-electric separation technology and an Ethernet Passive Optical Network (EPON) [23]. This communication channel is vulnerable against various forms of natural disasters and malicious physical events. In this paper, we make further study on the communication channel reconstruction and focus on these WSN modules in the first layer.