esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Generative AI for Detecting Pedophilia Crimes
## 5 Benefits of Generative AI in Detecting Pedophilia
### Automating CSAM Detection
While batch processing techniques are effective for retrospective analysis of large datasets, they often fail to detect CSAM content in real-time, which limits their preventive capabilities. In contrast, real-time AI models, such as those using streaming data architectures (e.g., Apache Kafka integrated with transformer models), enable continuous monitoring and immediate flagging of suspicious activity. Although real-time systems require more computing resources and have higher false positive rates, they offer a critical advantage in preventing live-streamed abuse and enabling faster law enforcement responses. The choice between batch and real-time processing should, therefore, be based on the specific operational requirements and available infrastructure of the implementing agency.
## 2 Development of Direct-Pressure Vulcanization Equipment
### Electromagnetic Induction Heating System  
The induction heating system is another key part of the DPCT equipment. The high-frequency current flowing into the coil winding generates a high-frequency alternating magnetic field, and the magnetic line of force passes through the metal mold to form a closed-loop circuit, causing an eddy current on the mold surface. Due to the thermal effect of the eddy current, the mold can emit heat at high speed, thereby improving energy utilization efficiency and reducing energy consumption in vulcanization. To note that since the polymer matrix is in a solid state, the fillers in the rubber are not likely to orient with the magnetic field.  
The design of the induction heating system of the internal mold is more challenging than that of the external mold. The internal mold is made of metal and composed of irregularly shaped segments, making it difficult to achieve a uniform temperature of a single segment and of all segments. Figure 3 shows the design scheme adopted in this study. Each internal mold segment is independently configured with one induction heating unit. To make the temperature uniform in each segment, each winding contains two series-coils symmetrically arranged on the two sides of the carrier. The magnetizer controls the direction of the magnetic flux so that the temperature at the end of the segment rises rapidly, thereby ensuring that the temperature of each segment in the axial direction of the segment rises synchronously, so that the tire can be uniformly vulcanized. Moreover, the magnetizer can shield parts excluding segments to prevent their being heated through the magnetic flux that escapes from the magnetic field, which not only increases energy consumption but may also hinder the part's normal operation due to lengthy overheating. Table 1 shows the main parameters of the electromagnetic induction heating system. The controller in the electromagnetic induction heating system can use half-bridge or full-bridge modes. The full-bridge mode has greater circuit efficiency than the half-bridge one in general. According to the data listed in Table 1, the half-bridge mode is adequate for the low power and the small inductance, and full-bridge mode is adequate for the high power and the big inductance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multirow{2}{*}{**Internal Mold**} & \multicolumn{4}{c}{**External Mold**} \\ \cline{3-5}  & & **Top Heating Plate** & **Bottom Heating Plate** & **Container** \\ \hline AC Power supply & 380 V/50 Hz & 380 V/50 Hz & 380 V/50 Hz & 380 V/50 Hz \\ Max. Power of & 3 & 15 & 15 & 15 \\ Controller (KW) & 7 & 1 & 1 & 2 \\ Number of Controllers & 7 & 1 & 1 & 2 \\ Resonance Mode & Half-bridge & Full-bridge & Full-bridge & Full-bridge \\ Inductance (\(\mu\)H) & 90 & 150 & 150 & 150 \\ Frequency (KHz) & 18–22 & 18–22 & 18–22 & 18–22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of induction heating system.  
Figure 2: The schematic of the working principle of internal mold: design of the mold in (**a**) contraction and (**b**) expansion states.
## 2 Materials and Methods
### Uncertainty Analysis  
The uncertainty propagation analysis for COP and cooling capacity was calculated by using the Kline and McClintock method [25] expressed in Equation (4):  
\[\omega_{R}=\left[\left(\frac{\partial R}{\partial x_{1}}\omega_{1}\right)^{2} +\left(\frac{\partial R}{\partial x_{2}}\omega_{2}\right)^{2}+\dots+\left( \frac{\partial R}{\partial x_{n}}\omega_{n}\right)^{2}\right]^{\frac{1}{2}} \tag{4}\]  
in which \(\omega_{R}\) is the resulting uncertainty, while \(\omega_{1}\), \(\dots\), \(\omega_{n}\) are the uncertainties of the independent variables \(x_{1}\), \(\dots\), \(x_{n}\). Uncertainties of cooling capacity and COP were obtained by Equations (5) and (6). The maximum uncertainty of the cooling capacity and COP were 3.83 % and 3.83 %, respectively.  
\[\frac{\omega_{Q_{c}}}{Q_{c}}=\left[\left(\frac{\omega_{\rho_{a}}}{\rho_{a}} \right)^{2}+\left(\frac{\omega_{\dot{V}_{a}}}{\dot{V}_{a}}\right)^{2}+\left( \frac{\omega_{C_{p,a}}}{C_{p,a}}\right)^{2}+\frac{{\omega_{T_{a,i}}}^{2}++{ \omega_{T_{a,o}}}^{2}}{\left(T_{a,i}-T_{a,o}\right)^{2}}\right]^{\frac{1}{2}} \tag{5}\]  
\[\frac{\omega_{COP}}{COP}=\left[\left(\frac{\omega_{Q_{c}}}{Q_{c}}\right)^{2}+ \left(\frac{\omega_{W}}{W}\right)^{2}\right]^{\frac{1}{2}} \tag{6}\]  
The thermo-physical properties of CO2 and air were obtained from _NIST REFPROP_[26] during the data reduction process. Finally, it was observed that the maximum discrepancy regarding the heat balance between CO2 and the air was about 10% in the gas cooler.
# Multi-Scale Adaptive Aggregate Graph Convolutional Network for Skeleton-Based Action Recognition
## 3 Multi-Scale Adaptive Aggregate Graph Convolutional Network
### Multi-Scale Adaptive Temporal Convolution Module  
In the defined graph structure, the same joints between consecutive frames are connected by temporal edges (green lines in Figure 1). We choose to decompose the spatio-temporal graph into two spatial and temporal sub-modules instead of directly performing graph convolution operations on the spatio-temporal graph. This is because the strategy of the spatio-temporal decomposition method is easier to learn [14]. For the graph of frame t, we first feed it into the spatial graph convolution and then concatenate its output information with the time axis to obtain a 3D tensor, which is fed into the temporal module for action recognition.  
For temporal convolution, current methods [13] usually use 1D convolution to obtain temporal features. Since the convolution kernel size is fixed to \(9\times 1\), it is often difficult to flexibly capture some temporal correlations of the skeleton data. Moreover, different action classes in the dataset may require different temporal receptive fields. For example, there are actions that can be judged within a short time range, such as falling down, and actions that can be judged within a longer time range, such as carrying a plate. Therefore, we designed a multi-scale temporal convolution module to adaptively extract the feature correlations on the temporal sequence. We use three convolution kernels with kernel sizes of \(T_{1}\times 1\), \(T_{2}\times 1\) and \(T_{3}\times 1\) to model the features in three different temporal scales of short, middle and long terms, respectively, and add the weight coefficients to adaptively adjust the scales of different importance. The proposed multi-scale temporal convolution operation can be formulated as  
\[X_{t}^{l+1}=Concat\left(X_{t}^{l}\left(k\right)|k=T_{1},T_{2},T_{3}\right)W_{t} \tag{4}\]where \(Concat(\cdot)\) denotes the concatenate operation. \(W_{t}\) denotes the weight, which is used to adjust the importance of time features at different scales. Through the dynamic adjustment of the weight, the model can adaptively extract temporal features in different scales to improve the generalization ability of the temporal convolution module.
# Detection of Abnormal Symptoms Using Acoustic-Spectrogram-Based Deep Learning
## 2 Related Research
### Deep-Learning-Based Acoustic Abnormality Detection  
Deep learning techniques have demonstrated significant success in various pattern recognition domains, including object detection and speech recognition. [PERSON] et al. [11] developed a CNN-based model for classifying animal sounds, utilizing Mel-frequency cepstral coefficients (MFCC) as input features. Their model successfully classified ten categories of animal sounds, validating the utility of deep learning in acoustic classification tasks.  
In the domain of respiratory abnormality detection, traditional machine learning models often fall short in terms of classification accuracy. To address this gap, [PERSON] et al. [10] proposed an ensemble approach using CNN models trained on multiple acoustic feature representations, including MFCCs, scalograms, and waveforms. Each model was specialized for a specific feature type, and the models' outputs were aggregated to enhance detection performance. This study highlights the advantage of leveraging deep learning models with diverse input formats to improve the detection of abnormal symptoms in acoustic signals.
# Adsorption of Cadmium and Methylene Blue Using Highly Porous Carbon from Hemp Seeds
## 4 Results and Discussion
### Adsorption Mechanism for Methylene Blue and Cadmium  
The proposed uptake mechanisms for methylene blue and cadmium are shown in Figure 7. The FTIR spectra (Figure 1) show the presence of -OH, -CH, -NH\({}_{2}\) and -C=O groups on the adsorbent's surface for cadmium and methylene blue uptake. Similar observations were reported by [PERSON] et al. (2020) [PERSON] et al. (2020). The mechanism showed different interaction types between cadmium, methylene blue and the surface structures' functional groups. Methylene blue is a cationic dye and positively charged ion in an aqueous solution. Protons are available at lower pHs and on the surfaces of the adsorbents. Therefore, the resulting ionic repulsion with methylene blue's positively charged surface leads to a lower uptake [PERSON] et al. (2020). Cationic dye sorption is ideal at pH > pHpzc because of the occurrence of -OH on the adsorbent surface [PERSON] et al. (2020). This also applies to the adsorption of cadmium. The adsorbents' surface changed from positively to negatively charged with increasing pH--hence the higher sorption ability since there was no repulsion. At above pH 8, the C-OH bond on the surface becomes C-O\({}^{-}\). Therefore, electrostatic attractions between positively charged dye ions, cadmium and negatively charged adsorption sites cause an increase in adsorption [PERSON] et al. (2020). Based on the information above, it can be concluded that different interactions enhanced cadmium and methylene blue uptake by PHSs, CHS-500 and CHS-700.
# A New Method to Determine the Steel Fibre Content of Existing Structures--Evaluation and Validation
## 3 Results and Discussion  
### Tests on Concrete Cubes as Additional Specimens  
#### 3.1.1 General Evaluation of the Cubic Specimens  
In addition to the production of the concrete plates, where the drilling cores were extracted, three cubic specimens with edge lengths of 150 mm were produced out of each concrete batch to analyse the electrical behaviour of the concrete in the early age and the changes with ongoing aging. For this purpose, the cubes were analysed via the basic test setup (Section 2.2.1) in concrete ages of 7, 28, and 135 days. The electrical resistivity was calculated, and the fibre content and fibre orientation were estimated according to Section 3.2. Since the specimens were stored underwater for the duration, a good conductivity of the concrete was reached, and thus no negative effects of the coupling of the electrodes to the concrete surface were observed. Figure 5 shows the results of the measurements. As already explained, the resistivity of the PC, as well as of the SFRC, increases with increasing concrete age and a higher fibre content results in a significantly lower resistivity due to the conductivity of the fibres inside the concrete matrix (see [1,21,33]).
## 3 Methodology
### Discriminative Attention Multi-Network (DAM-Net)
#### 3.1.1 Object Focus Location Module (OFLM)  
The OFLM is designed to precisely delineate the boundary boxes of target pests by focusing on salient features. Feature maps from the final convolutional layer of an input image \(X\) are represented as \(F\in R^{C\times H\times W}\), where \(C\) is the number of channels, and \(H\times W\) is the spatial size. The \(i\)'th feature map of the corresponding channel is denoted as \(f_{i}\). By aggregating these feature maps \(F\), we obtain an activation map \(A\), as defined in Equation (5):  
\[A=\sum_{i=0}^{C-1}f_{i} \tag{5}\]  
where the activation map \(A\) indicates the focus areas of deep neural networks for recognition and accurately locates target regions, as illustrated in Figure 3. The mean value of \(A\), denoted as \(\bar{u}\), is calculated using Equation (6):  
\[\bar{u}=\frac{\sum_{x=0}^{W-1}\sum_{y=0}^{H-1}A(x,y)}{H\times W} \tag{6}\]  
where \(A_{(x,y)}\) represent the pixel intensity value at position \((x,y)\) of the activation map. \(\bar{u}\) is used as the threshold to generate a coarse mask map \(\tilde{M}_{conv\_5c}\) from the final convolutional layer \(Conv\_5c\) according to Equation (7):  
Figure 2: The structure of the DAM-Net.  
\[\widetilde{M}_{(x,y)}=\begin{cases}1&\text{if }A_{(x,y)}>\tilde{a}\\ 0&\text{otherwise}\end{cases} \tag{7}\]  
A bounding box that encloses the largest connected region is used to locate the target. Using the same method, we compute an additional activation map from the preceding convolutional block \(Conv\_5b\) and a corresponding mask \(\widetilde{M}_{conv\_5b}\). We then refine the target mask \(M_{target}\) by intersecting \(\widetilde{M}_{conv\_5c}\) and \(\widetilde{M}_{conv\_5b}\), as indicated in Equation (8):  
\[M_{target}=\widetilde{M}_{conv\_5c}\cap\widetilde{M}_{conv\_5b} \tag{8}\]  
A threshold is applied to the final target mask to highlight the most significant regions of interest within the image. A binary mask \(\widetilde{B}\) is computed using a predefined threshold value \(\tau\), such that  
\[\widetilde{B}_{(x,y)}=\begin{cases}1&\text{if }A_{(x,y)}>\tau\\ 0&\text{otherwise}\end{cases} \tag{9}\]  
where \(\tau\) is established as 0.5 for this analysis. The final target mask \(M_{final}\) is then obtained using  
\[M_{final}=M_{target}\odot\widetilde{B} \tag{10}\]  
This method treats pixels in the activation map with intensity values above a threshold as significant regions. As a result, the final mask effectively distinguishes between the foreground, which includes features of interest like pests, and the background.
## 3 Materials and Methods
### Architecture of U-Net Used for Aircraft Recognition
However, to recover the size of the feature maps in the decoder section, each block includes an up-sampling operation, reducing the number of channels through a 2 \(\times\) 2 filter, effectively implementing deconvolution. This structure enables the decoder to reconstruct detailed information from the reduced feature maps, facilitating accurate semantic segmentation of the aircraft satellite images. Furthermore, to enhance the localization of up-sampled features, the skip-connections technique is employed. This involves combining the features from the deep expansive path with the high-resolution features from the shallow contracting path. As a result, the expansive branch effectively increases the resolution of the output. ReLU activation functions are applied after each convolution operation, with the exception of the last layer. The final layer of the network employs a sigmoid activation function to produce a pixel-wise probability map. To facilitate convergence during training, batch normalization is employed after each convolutional layer, except for the last layer. The last layer of the network is responsible for generating the model's output using a 1 \(\times\) 1 convolution operation to map each pixel to a class, distinguishing between \"Aircraft\" and \"Background\". In summary, the architecture of the network encompasses a total of 23 ReLU activation functions, 24 convolutional layers, 19 batch normalization operations, 2 dropout operations, 4 max-pooling operations, 4 merging operations, and 4 up-sampling operations. To obtain the final predicted aircraft extraction result, the pixel-wise probability map needs to be  
Figure 1: The U-Net-architecture for aircraft recognition. binarized using a specified threshold, typically set to 0.5, yielding the output of the semantic aircraft segmentation network.
## 4 Experimental Design and Results Analysis
### Experimental Results and Analysis
#### 4.4.3 Final Model Performance Analysis
Compared with YOLOv5s and YOLOv8s, the proposed algorithm has fewer parameters, lower computational complexity, and more obvious accuracy improvement. The YOLOv9-s model [32] was adjusted to the same depth and width coefficients according to the YOLOv8n yaml file configuration so that its parameter volume and computational complexity reached a similar scale and was named YOLOv9n. Experiments show that although YOLOv9n has certain advantages over YOLOv8n, its inference speed is significantly lower than that of similar detectors. To sum up, the proposed algorithm has higher accuracy, smaller parameters, smaller calculation amounts, and faster model inference speed. It can provide high-precision detection while meeting the needs of real-time detection. In order to more intuitively verify the effect of the proposed method, Figure 15 shows the detection effects of different algorithms. Although Faster R-CNN and SSD show high detection accuracy, there are also missed detection phenomena. RetinaNet has good detection of small targets, but there are false detections and missed detections. In addition, the above detectors have too much computational complexity and are not convenient to deploy on devices. Although YOLOv3-tiny has a good recall rate, there are false detections. YOLOv4-tiny, YOLOv5s, YOLOv7-tiny, YOLOv8n, YOLOv8s, and YOLOv9n all have missed detections. The proposed algorithm has a high obstacle detection accuracy, good recognition ability for small targets and surrounding occluded targets, and reduces the false detection rate and missed detection rate. Figure 15: _Cont_. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Network** & \
\begin{tabular}{c} **mAP50** \\ **(\%)** \\ \end{tabular} & **P (\%)** & **R (\%)** & **FPS** &
\begin{tabular}{c} **Parameters** \\ **(M)** \\ \end{tabular} & **GFLOPs** \\ \hline Faster-R-CNN-R50 & 94.0 & 93.2 & 89.7 & 20.8 & 41.4 & 172.0 \\ RetinaNet-R50 & 92.5 & 92.6 & 87.1 & 85.2 & 36.5 & 195.6 \\ SSD300 & 94.5 & 93.4 & 90.1 & 115.6 & 34.3 & 34.4 \\ YOLOv3-tiny & 83.3 & 78.5 & 75.5 & 200.0 & 8.7 & 12.9 \\ YOLOv4-tiny & 84.4 & 81.2 & 74.9 & 212.3 & 5.9 & 0.52 \\ YOLOv5s & 92.2 & 91.2 & 88.9 & 147.1 & 7.0 & 15.8 \\ YOLOv7-tiny & 93.6 & 93.3 & 87.7 & 217.4 & 6.0 & 13.1 \\ YOLOv8n & 93.8 & 94.7 & 87.4 & 239.5 & 3.0 & 8.1 \\ YOLOv8s & 94.2 & 94.3 & 89.7 & 177.8 & 11.1 & 28.5 \\ YOLOv9n & 94.6 & 94.2 & 89.6 & 140.8 & 2.3 & 8.2 \\ Ours & 95.3 & 94.1 & 90.7 & 249.3 & 3.2 & 7.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparative experiments on different algorithms. Figure 15: Detection results of different algorithms: (**a**) Faster-R-CNN-R50; (**b**) RetinaNet-R50; (**c**) SSD-300; (**d**) YOLOv3-tiny; (**e**) YOLOv4-tiny; (**f**) YOLOv5s; (**g**) YOLOv7-tiny; (**h**) YOLOv8n; (**i**) YOLOv8s; (**j**) YOLOv9n; (**k**) Ours.
# Characterization and Water Content Estimation Method of Living Plant Leaves Using Terahertz Waves
## 1 Introduction
However, the experimental setup of THz spectroscopy is not portable and requires a complex configuration of lasers [22]. In this paper, we present a novel, non-invasive approach to monitoring the WC of plant leaves using the scattering parameters of a THz pulse. Using a well-known material extraction algorithm, we computed permittivity from the scattering parameters for eight types of leaves, which we observed  
Figure 1: Internal morphology of fresh and water stressed leaf using THz sensing. for four consecutive days. The WC was then gauged from the decrease in the permittivity as the days passed. This paper is an expansion and presents a detailed analysis of our earlier work [8]. The rest of the paper is structured as follows: Section 2 describes the experimental setup followed by the material characterisation methods of plants leaves. Section 3 presents the measurement results and different parameters are discussed such as permittivity, the effect of weight and thickness, followed by a comparison of transmission response of all eight leaves between day 1 and 4. Finally, conclusion is discussed in Section 4.
## 3 Results and Discussion  
### Spray Drying Performance: Solids Recovery  
Solids recovery, also referred to as spray-drying yield, is an important index to assess the spray-drying performance, as it relates to the success of the operation and potential scalability of the process. This parameter is even more relevant for fruit-derived products that contain high concentrations of low-molecular-weight sugars and organic acids, such as cashew juice, since their low glass transition temperature (\(Tg\)) usually leads to excessive stickiness of the dried particles that adhere to the dryer walls and compromise productivity [34; 35; 36; 37]. Drying carriers are often needed to improve spray-drying performance and to produce spray-dried fruit-derived particles with enhanced physicochemical and phytochemical stability [20; 38]. In this study, when no GA carrier was added, no powder could be produced and recovered, which highlights the importance of drying carriers.  
High solids recovery (>70%, Table 1) was obtained for all experimental groups, at above 50%, which is considered as satisfactory performance for lab-scale spray-drying protocols [39]. The highest solids recoveries were observed for groups with 15% \(w/v\) GA (groups C1 and C2; \(p<\) 0.05), independent of the inlet temperature (Table 1). The solids recovery results were higher than those reported for spray-dried cashew particles obtained from cashew apple juice with maltodextrin and chemically modified starch (38.1-69.8%) [24], spray-dried particles obtained from probiotic cashew apple juice with maltodextrin (60%) [40], and spray-dried elderberry particles obtained from elderberry juice with tapioca starch (62.18 \(\pm\) 2.47%) [17].  
Treatments with a higher addition of GA (25% \(w/v\), groups C3 and C4) showed lower solids recovery (\(p<\) 0.05) compared to C1 and C2 (Table 1), independent of the inlet temperature, indicating that the drying carrier addition ratio was the main factor affecting solids recovery. Likewise, when eggplant peel extract was spray-dried with GA and maltodextrin as drying carriers, it was observed that eggplant particles obtained with higher GA concentrations showed lower solids recovery compared to groups obtained with maltodextrin (MD) or an MD-GA 1:1 \(w/w\) blend at the same inlet temperature [41]. Moreover, when date fruit extracts were spray-dried with an MD-GA 1:1 \(w/w\) blend, response surface statistical models showed that the carrier addition ratio had a positive impact on solids recovery up to 45% \(w/v\), above which it compromised powder productivity [20]. This behavior was attributed to the increased viscosity of spray drying feed solutions with higher GA concentrations, which leads to coarse droplets that compromise the drying efficiency due to higher agglomeration [20; 38; 41].