esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Its unique capability to generate three-dimensional topographic fingerprints (retention time \(\times\) drift time \(\times\) signal intensity) enables real-time visual metabolomic profiling, particularly advantageous for high-throughput screening applications involving complex volatile matrices. While substantial progress has been made in food science applications--exemplified by its implementation in soy-based fermentation matrices, extra virgin olive oil authentication, and enological aroma evolution tracking. However, its potential in postharvest biology of fresh produces remains underexplored. Current literature primarily documents its use in plant variety discrimination through volatile signatures [23; 24]. Notably, its implementation in perishable produce preservation, particularly for monitoring dynamic flavor alterations during fruit storage, constitutes a novel research frontier. This study establishes a multimodal analytical framework to elucidate the impacts of IT storage on blue honeysuckle flavor by integrating volatile profiling through orthogonal detection methodologies (GC-IMS and GC-MS) with quality attribute quantification and ultrastructural observations. This methodological framework aims to elucidate temperature-modulated VOC production patterns during postharvest storage, thereby providing empirical evidence to optimize cold chain protocols for flavor preservation in blue honeysuckle berries. Moreover, flavor stability was correlated with ultrastructural adaptations under IT conditions, providing mechanistic insights into cold stress response pathways in perishable berries. Practically, these findings may enable precision cold chain optimization for blue honeysuckle. Furthermore, the established GC-IMS/GC-MS synergy protocol offers a transferable model for postharvest flavoromics research across small fruit taxa.
# Modeling E-Behaviour, Personality and Academic Performance with Machine Learning
## 2 Methodology
### Long Short-Term Memory  
The Long Short-Term Memory algorithm (LSTM) is a deep-learning architecture designed to model sequences for prediction [50]. The LSTM has been used in studies that range from predicting weather-induced background radiation fluctuation by [PERSON] and [PERSON] [50], to human motion classification and recognition by [PERSON] et al. [51]. The backpropagation through time algorithm computes the error, [PERSON]mathbf{E}_{t}\), at every time step, \(t\), and, then, computes the total error. The [PERSON]'s parameters were updated to minimise the total error \(\frac{\partial\mathbf{E}}{\partial\mathbf{W}}\) with respect to a weight parameter \(\mathbf{W}\):  
\[\frac{\partial\mathbf{E}}{\partial\mathbf{W}}=\sum_{t=1}^{T}\frac{\partial \mathbf{E}_{t}}{\partial\mathbf{W}}. \tag{9}\]  
Letting \(\mathbf{y_{t}}\) represent the output at time \(t\), \(\mathbf{h_{t}}\) represents the hidden state at time \(t\) and by applying the chain rule to the Recurrent Neural Network model, the total error in Equation (9) became:  
\[\frac{\partial\mathbf{E}}{\partial\mathbf{W}}=\sum_{t=1}^{T}\frac{\partial \mathbf{E}}{\partial\mathbf{y}_{t}}\frac{\partial\mathbf{y}_{t}}{\partial \mathbf{h}_{t}}\frac{\partial\mathbf{h}_{t}}{\partial\mathbf{h}_{k}}\frac{ \partial\mathbf{h}_{k}}{\partial\mathbf{W}}, \tag{10}\]  
where \(\frac{\partial\mathbf{h}_{t}}{\partial\mathbf{h}_{k}}\) involves a product of Jacobian matrices:  
\[\frac{\partial\mathbf{h}_{t}}{\partial\mathbf{h}_{k}}=\frac{\partial\mathbf{h }_{t}}{\partial\mathbf{h}_{t-1}}\frac{\partial\mathbf{h}_{t-1}}{\partial \mathbf{h}_{t-2}}\cdots\frac{\partial\mathbf{h}_{k+1}}{\partial\mathbf{h}_{k}}. \tag{11}\]Equation (11) illustrates the problem of vanishing gradients in Equation (9): when the gradient became progressively smaller as \(k\) increased, the parameter updates became insignificant. LSTMs are an architecture of Recurrent Neural Networks (RNNs). [PERSON] et al. [52] suggest that RNNs are challenging to train because of the vanishing error gradient problem. The following section stipulates how the LSTM's architecture mitigates the vanishing error gradient issue through LSTM cells that maintain a state \(\mathbf{c}_{t}\) at every iteration \(t\). The cell state \(\mathbf{c}_{t}\) serves to _remember_ and propagate cell outputs between time steps. Each cell state then allows for temporal information to become available in the next time step, adding greater context to the inputs \(\mathbf{x}_{t}\) that follow. The activation \(\mathbf{h}_{t}\) of an LSTM unit was:  
\[\mathbf{h}_{t}=\mathbf{o}_{t}\tanh(\mathbf{c}_{t}), \tag{12}\]  
where  
\[\mathbf{o}_{t}=\sigma\big{(}\mathbf{W}_{xo}\mathbf{X}_{t}+\mathbf{W}_{ho} \mathbf{h}_{t-1}+\mathbf{b}_{o}\big{)}, \tag{13}\]  
is an output gate that mitigates the amount of content in the memory to expose to the following time step and \(\sigma:\mathbb{R}\rightarrow(0,1)\) is the logistic sigmoid function.
## 3 Approach
### Seedling Floating and Qualified State Recognition and Improvement
#### 3.2.3 Backbone Network Optimization  
The backbone is the main feature extraction network of the YOLOv8n model. The input image undergoes convolution and activation functions in the backbone network to extract features, resulting in three feature layers. The YOLOv8n model continues to use the YOLOv5 model's SPPF (Spatial Pyramid Pooling Fast) to concatenate convolution and pooling results, extracting information from different receptive fields [25]. However, the pooling operation in the SPPF structure may lose some local feature information of seedlings, failing to fully represent the detailed root features of seedlings in the floating state. This affects subsequent feature extraction operations in the neck network. Therefore, this paper replaces the original SPPF structure with ASPP (atrous spatial pyramid pooling) [26] in the YOLOv8n model. ASPP introduces multiple atrous convolutions with different sampling rates to control the size of the convolutional kernel's receptive field. The pooling operation allows the network to adaptively determine the receptive field size of the feature map, extracting higher-level feature information. This enhances the network's ability to recognize and detect the same target in different forms while maintaining the model's lightweight and efficiency. The ASPP structure is illustrated in Figure 9.  
The propagation process of the ASPP structure is as follows: The feature map of the third layer with a size of 20 \(\times\) 20 \(\times\) 1024 is input into the ASPP module. The first branch uses a 1 \(\times\) 1 convolution to reduce the dimensionality of the feature map obtained in the previous step while preserving the original receptive field. The second to fourth branches employ dilated convolutions with a kernel size of 3 \(\times\) 3 and dilation rates of 6, 12, and 18, respectively. These branches stack corresponding dilated convolutional layers to extract features at different scales. The fifth branch directly applies global average pooling to the input feature map, followed by a 1 \(\times\) 1 convolution for integration, normalization, and activation functions. Subsequently, upsampling is used to obtain a feature layer of the same size as the branches. Finally, the five branches are integrated and stacked to enhance the network's ability to capture multi-scale context, resulting in a feature map of size 20 \(\times\) 20 \(\times\) 1024.
# DMCNet-Pro: A Model-Driven Multi-Pilot Convolution Neural Network for MIMO-OFDM Receivers
## 4 The Structure of DMCNet-Pro
Since precise estimation of the output channel matrix is required, a convolution is added after Res-Block, and the output dimension of the convolution is [1; 256; 1]. The output \(\hat{H}\) is computed with the actual sample matrix \(H\) for the mean square error value, which is used as input to the model optimizer to adjust the network parameters. The third step is required to train the network of the signal detection module. The structure of channel estimation and signal detection model is shown in Figure 6. First, the signal is initialized using the Zero Forcing (ZF) method. The ZF method can improve signal quality and system performance, particularly in multi-user scenarios. \[\widehat{x}_{ZF}=\frac{y_{D}}{\hat{H}} \tag{6}\]  
where \(y_{D}\) is the data part of the received signal, \(\hat{H}\) is the output result of the channel estimation module, and the output \(\widehat{x}_{ZF}\) is the input of the signal detection network. Comparing to DMCNet, the input of the signal detection network is the result of superposition of data and the pilot processing results. Here, \(\hat{H}\) and \(\widehat{x}_{ZF}\) are also input to the signal detection network at the same time and superimposed. Similar to the signal monitoring network in DMCNet, the signal detection network structure in DMCNET-Pro is:  
Figure 5: Model-driven MIMO-OFDM receiver. As we can see, the structure of the model-driven network is largely the same as that of DMCNet, but in this network, there are fewer output channels for each layer of convolution. The structure of signal detection model is shown in Figure 7. At eight pilots, \(\widehat{H}\) and \(\widehat{x}_{ZF}\) synthesize a feature map of size [1; 256; 5], which is first enhanced by convolution and then further purified by several refine blocks, while with the 32-pilot Res-Block, the number of convolutional output channels is 100, 100, and 50, respectively. Compared with DMCNet, it has fewer channels and fewer parameters, but the experimental results show that its performance is similar. Figure 6: Channel estimation and signal detection model. Figure 7: Signal detection model.
## 4 Designing an Ontology Framework: A Case Study on Asthma
### Data Preprocessing  
This refers to the initial steps necessary for building robust predictive models for asthma diagnosis. The raw dataset, sourced from an Excel file, contained various medical parameters critical for predicting the presence of asthma. To ensure data integrity, all records with missing values were eliminated, reducing the risk of biased outcomes. Subsequently, a pivotal transformation was performed: converting all categorical data into pure numeric formats.  
The dataset used in this study consists of 2394 patient records with 25 relevant features, covering demographic information, clinical symptoms, lifestyle factors, and environmental exposures. The patient population includes 1212 males and 1180 females, with ages ranging from children to older adults. The class distribution is imbalanced, with 2268 records labeled as non-asthmatic and 124 as asthmatic. A small number of missing values were present in several features. For example, BMI, FEV1, and FVC had a few missing entries, which were handled using mean imputation, replacing the missing values with the average value of the respective feature. Records with missing values in the target variable (Diagnosis) were excluded from the analysis.  
Due to the class imbalance, we applied the Synthetic Minority Over-sampling Technique (SMOTE) to oversample the minority class (asthmatic cases) in the training dataset. Additionally, during feature analysis, potential sources of bias, such as environmental  
Figure 5: Part of the temporal, environmental and organizational ontology.  
exposure, were monitored. Addressing these factors in more depth will be part of future work during the clinical validation phase.  
The dataset was then standardized to a consistent format and saved back to an Excel file for subsequent analysis, as shown in Figure 6. This preprocessing ensures that machine learning models operate efficiently and produce accurate results for asthma prediction.  
After the general contextual ontology was implemented in Protege, the next steps focused on selecting relevant parameters to extract rules.
# An Efficient Time-Variant Reliability Analysis Method with Mixed Uncertainties
## 4 A New Analysis Method of Structural Time-Variant Reliability with Random and Interval Variables  
### Establishment of Equivalent Model  
In time-variant reliability analysis, uncertainty is divided into two kinds of uncertain variables, namely the random variable \(\mathbf{X}\) and interval variable \(\mathbf{Y}\). In the present study, a novel method is initially presented. The random variable \(\mathbf{X}\) is kept stable, while the interval variable \(\mathbf{Y}\) obeys the uniform distribution. Therefore, it can be transformed into the following conventional time-variant reliability problem with only random variables:  
\[\left\{\begin{array}{l}\beta^{L}=\underset{\mathbf{U},\mathbf{Y}}{\text{min}} \sqrt{\|\mathbf{U}\|^{2}+\|\mathbf{V}\|^{2}}\\ \text{s.t.}\ \ \ \ \ G(\mathbf{U},\mathbf{V},t_{i})=0\end{array}\right. \tag{29}\]  
where \(\mathbf{U}\) and \(\mathbf{V}\) denote the standard normal space, vectors transformed by \(\mathbf{X}\) and \(\mathbf{Y}\), respectively. Moreover, \(\ G\prime\) is the limit state function of the transformation in the \(\mathbf{U}\)-\(\mathbf{V}\) space. For the equivalent model after transformation, the most probable point (MPP) in the standard normal space random variable (\(\mathbf{U}+\mathbf{V}\)) and its corresponding optimal value (\(\mathbf{X}^{\prime}\),\(\mathbf{Y}^{\prime}\)) can be calculated according to Equation (29).
## 2 Signal Preprocessing  
Due to differences in sensor sensitivity and other hardware circuits, there are differences in the mean and amplitude of the signals collected by different signal acquisition devices at the same event. To ensure that the initial energy of each seismic and sound signal segment is at the same level, preprocessing of the original signal is required. Signal preprocessing has two parts: the normalization of signal units and the removal of DC components.  
DC component removal is the removal of the DC bias of the signals generated by the acquisition instrument. A fast Fourier transform is applied to the signal to remove the zero-frequency signal. Then, a fast inverse Fourier transform is used to obtain the acoustic and seismic signal after removing the DC component.  
The signal unit of the seismic geophone after acquisition by the AD acquisition card is V. The goal is to convert the signal unit to mm/s. The velocity signal conversion formula of ground vibration can be obtained according to its sensitivity as  
\[SN=SR/SU_{seismic}, \tag{1}\]  
where \(SU_{seismic}\) is the sensitivity of the seismic sensor, \(SR\) is the raw signal collected by the seismic sensor, and \(SN\) is the seismic signal after normalization. After the sound sensor is acquired by the AD acquisition card, the signal unit is V. The goal is to convert the sound signal to a voltage signal collected by a standard microphone. The microphone sensitivity is  
\[SU_{sound}=20\log_{10}\frac{SR}{SS}, \tag{2}\]  
where \(SU_{sound}\) is the sensitivity of the sound sensor, \(SR\) is the raw signal collected by the sound sensor, and \(SS\) is the sound signal collected by a standard microphone. According to the amplification of the amplifier circuit, the standard microphone output signal is  
\[SS=(SR/10^{Mag/20})/10^{SU_{sound}/20}, \tag{3}\]  
where \(SU_{sound}\) is the sensitivity of the sound sensor, \(Mag\) is the magnification of the sound sensor, \(SR\) is the raw signal collected by the sound sensor, and \(SS\) is the sound signal after normalization.
# A Survey of Generative AI for Detecting Pedophilia Crimes
## 4 Literature Review: AI Approaches for Detection
### Deep Learning and Large Language Models
Utilizing the PAN12 chat log dataset, the study employed a SVM and Meta's LLaMA 3.2 1B large language model, after sorting conversations into positive and negative sentiments with a DistilBERT classifier. The results conclusively show that the LLaMA model significantly outperforms the SVM, achieving an F\({}_{1}\) score of 0.99 compared to the SVM's 0.94, demonstrating deep learning's superior ability to capture the complex linguistic patterns of grooming. A primary contribution is the discovery that predators employ distinct strategies based on tone, with negative-toned chats containing more nuanced and harder-to-detect patterns. This finding suggests that positive and negative-toned approaches are structurally dissimilar. The LLaMA 3.2 1B model established a new performance benchmark, outperforming previous models with an F\({}_{1}\) score of 0.99 in author detection. The study concluded that LLMs offer a more effective path for identifying grooming behavior and revealed that diverse predator strategies exist. Future work should focus on validating these findings on contemporary datasets and exploring more granular emotional classifiers.
## 2 Materials and Methods
### Experimental Technique
#### 2.3.2 Air Velocity and Temperature Measurement  
T-type thermocouples were used to measure air temperature in the honeycomb (DAG) and intake grill (RAG) in three measuring planes: \(z=-1.1\) m (left plane), \(z=0\) m (middle plane), and \(z=+1.1\) m (right plane). The three measuring planes are needed to compare the results in different planes to confirm the proper functioning of the refrigerated cabinet. Two data loggers (Carel EasyPJEZC0000 (CAREL Industries S.p.A., Padova, Italy) with 3 probes) were used that recorded the temperature of the thermocouples every 300 s. Before measuring, each thermocouple was calibrated in the temperature range of \(-5\)\({}^{\circ}\)C to 20 \({}^{\circ}\)C in a high-precision water bath (Lauda ECO RE1050S (Lauda dr. R. Wobser GMBH & Co. KG, Lauda-Konigshofen, Germany)) filled with Kryo 30 heat transfer liquid), and the precision of \(\pm 0.2\)\({}^{\circ}\)C was determined.  
Figure 4: Multi-deck refrigerated cabinet with test packages.  
The airflow velocity measurement scheme is presented in Figure 5. A hot wire thermal anemometer (Testo 405-V1 (Testo SE & Co. KGaA, Titisee-Neustadt, Germany)) was fixed on an adjustable length device, and the airflow velocity values were collected during 5 min of measurement. The average values were calculated at each measurement point during the quasi-steady state.
# Semantic Segmentation with High-Resolution Sentinel-1 SAR Data
## 5 Experimental Evaluation and Discussion
### Results  
First, we obtained SAR images of certain regions of Turkey, which ESA provides free of charge. Before labeling the SAR images, we performed six preprocesses to eliminate noise and make geometric corrections. In order to label the data, the 44-class CORINE dataset was reconstructed as five classes by combining similar features using the QGIS program. When combining the CORINE dataset and the SAR images, the exact coordinates and the same pixels were superimposed via the QGIS program. As a result, we created a new dataset with the acquisition of SAR images, and after the preprocessing steps, due to the labelling of the SAR images, we combined CORINE and noise-free SAR images. As detailed in Section 4, we evaluated our dataset using three semantic segmentation deep learning models, namely, U-Net, PSPNet and HRNet. Figure 3 shows ground-truth annotations on the left and images resulting from HRNet segmentation on the right column. By this, results obtained are compared with the original annotated images.  
Figure 3: Illustration of the HRNet model performance: segmentation results, ground-truth CORINE annotations (**left column**) and segmented examples (**right column**).
# Null Subcarrier Index Modulation in OFDM Systems for 6G and Beyond
## 2 Conventional OFDM Systems
The frequency domain representation \(Y(k)\) of the received signal is given by,  
\[ \begin{split} Y(k)&=DFT\{y(\ell)\}\quad k=0,1, \ldots,N-1\\ &=\sqrt{N}\sum_{k=0}^{N-1}y(\ell)e^{-j\frac{2\pi k}{N}\ell}\end{split}  \tag{4}\]  
where DFT indicates the discrete Fourier transform operation.
## 2 Textile UWB Band Notch Antenna Design  
Figures 1a-c demonstrate the detail structures of the UWB band notch textile antenna. It shows front and back views of the antenna with detail sizes of each segment and component. This antenna was developed with the help of a Computer Simulation Technology (CST)TM Microwave Studio. The antenna is printed on Jeans' fabric substrate, which has a height of 1 mm, and the relative permittivity of the substrate is used as 1.7. The antenna does not have full ground plane in the rear side, as noticed from Figure 1c. A slit is introduced to the top of the antenna radiating element to obtain the band notch in the X-band frequency range of (7.25 GHz-7.85 GHz) for satellite communication. The antenna is very compact in size as 25 mm in length and 16 mm in width of the full antenna, with substrate and ground plane.  
Figure 1: Ultra-wideband (UWB) band notch textile antenna: (**a**) Front view; (**b**) Front view with details of slit size; and (**c**) Back view with showing ground plane size.
## 3 Simulation System
### LoS Probability  
In order to use the aforementioned propagation model, it is necessary to perform LoS or NLoS classification on a continuous location fingerprint database. Therefore, a probabilistic LoS/NLoS classification method is used based on the LoS/NLoS information of the discrete data obtained in advance [14].  
For a particle \(i\) at position \(\mathbf{u}_{i}\), we are interested in eight discrete particle neighborhoods, i.e., 4 neighborhoods relative to the two-dimensional plane and 2 neighborhood planes relative to the elevation direction. At the \(k\)-th neighborhood, we define a variable \(c_{k}\) that is 1 if the particle \(\mathbf{u}_{k}\) is in an LoS condition and 0 if it is in an NLoS condition, as follows [14]:  
\[c_{k}=\begin{cases}1\text{ if LoS}\\ 0\text{ if NLoS}\end{cases}.\]  
When the distance between a particle \(i\) and each neighbor point \(k\) is \(d_{i,k}\), the LoS probability is calculated using the weight coefficient \(\omega_{i,k}=d_{i,k}^{-1}\) and variable \(c_{k}\) with the following formula [14].  
\[p_{\text{LoS}}(\mathbf{u}_{i})=\sum_{k=1}^{8}\omega_{i,k}c_{k}. \tag{10}\]  
Based on the LoS probability calculated by the above equation, the LoS or NLoS classification is stochastically determined and applied for our propagation model mentioned in Section 3.2.