esa-sceva/satcom-chunk-collection huggingface dataset:
# HCTC: Hybrid Convolutional Transformer Classifier for Automatic Modulation Recognition
## 4 Experimental Results and Discussion
### Classification Accuracy and Confusion Matrix
This method allows for us to halt the training when the validation loss does not improve for a specified number of epochs (patience = 50). By doing so, we prevent the model from overfitting the training data, ensuring it maintains its ability to generalize to new, unseen data. Reducing the learning rate on the plateau was employed to adjust the learning rate dynamically. If the validation loss stagnates, the learning rate is reduced by a certain factor (0.5 in our case). This adjustment helps the model to escape from local minima and can lead to better convergence, promoting improved performance without overfitting. L2 regularization is applied with a kernel regularizer, L2 (\(1\times 10^{-4}\)), to the convolutional and dense layers, which helps to reduce overfitting. In addition to this, Gaussian dropout and batch normalization techniques are used, which help to reduce overfitting.
# Effect of Aging Time on the Composition of the Volatile Components of An-tea
## 1 Introduction  
An-tea is a type of post-fermented and pressed tea mainly produced in 15 southwestern townships of Qimen County, such as Luxi and Rongkou in southern Anhui, China. An-tea has a history of about 300 years and is part of the provincial intangible cultural heritage [1; 2; 3; 4]. Owing to its \"mellow and sweet taste, rich and long aroma\", and medicinal value, An-tea has been included in the Chinese national geographical indication product list [5; 6]. Relatively ripe tea leaves before and after \"grain rain\" are picked to make the tea, then the coarse (green) and aerobic characteristics are removed by a unique stock fermentation and aging process. Aging can be conducted indoors [2], which not only solves the problem of the wastage of fresh leaves in summer and autumn, but also avoids the need for cold storage. However, in contrast to the production of Keemun and other famous teas with their strong development momentum in the region, the disorder and confusion regarding An-tea production and management has meant that its production and sales volume have remained at a low level. Relevant research and product development have also lagged behind, with the basic understanding of the medicinal value of An-tea still remaining at the level of ancient writings and legend.  
Information on the composition of the volatile aromas produced during the aging process of post-fermented tea is essential for understanding tea quality [7; 8; 9]. Studies have shown that, with increasing aging time, the contents of the functional and aromatic components in post-fermented teas, such as Pu'er tea [10; 11; 12; 9; 12], Liupao tea [13], and Greenbrick tea [14], can change significantly. Compared with a post-fermented tea, such as Pu'er, there have been few studies on the compositional changes in the volatile substances of An-tea during the aging process, so the identity of the main aromatic components of aged An-tea remain unclear. In this study, the main volatile components, such as ketones, aldehydes, alcohols, esters, phenols, hydrocarbons, and nitrogenous and other compounds in aged An-tea, will be quantified and identified by headspace-solid-phase microextraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS). The objectives of this study were: (1) to determine the chemical composition of An-tea after different aging times; (2) to determine the key compounds responsible for its aroma; and (3) to comprehensively evaluate the relationship between the composition of the volatile components and the aging time, which causes the main quality changes in An-tea.
# Fusion of Computer Vision and AI in Collaborative Robotics: A Review and Future Prospects
## 6 Cobot Considerations in System Architecture, AI, and Algorithms  
### Advanced System Architectures and Integration  
Modern collaborative robots necessitate integrated system architectures that seamlessly combine perception, planning, and control. Departing from traditional \"sense-plan-act\" pipelines, contemporary designs employ middleware solutions like ROS-Industrial to facilitate real-time sensor fusion and decision-making processes [121]. For instance, the implementation of an autonomous industrial cell utilizing eye-in-hand stereo cameras and visual serving demonstrates the efficacy of such integrations in enhancing flexibility and accuracy in production lines [19].  
Specialized cognitive stacks, such as DAC-HRC, and edge computing configurations are optimized for latency-sensitive visual tasks, enabling cobots to process complex data efficiently [122]. These advancements are particularly beneficial in manufacturing environments, where intelligent cobot systems have been shown to improve human-robot collaboration and overall productivity [16].  
The transition towards lightweight, domain-specific frameworks is replacing monolithic solutions, thereby enhancing both scalability and adaptability across various sectors.  
\begin{table}
\begin{tabular}{c c c} \hline
**Topic** & **Description** & **Key Challenges/Notes** \\ \hline \multirow{2}{*}{General Trends [95, 96, 97, 98]} & Advances in CV–AI fusion for & \multirow{2}{*}{Need for seamless, real-time, and trustworthy HRC} \\  & perception, reasoning, and & \\  & multimodal interaction & \\ \hline \multirow{2}{*}{Vision Integration Challenges [99, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102]} & Deploying object detection, & \multirow{2}{*}{Performance drops in unstructured, dynamic settings; real-time latency issues} \\  & segmentation, and pose estimation in & & \\  & real-world cobots & & \\ \hline \multirow{2}{*}{Real-Time Adaptation and Learning [24, 75, 76, 77]} & Reinforcement learning and & \multirow{2}{*}{Stability, transparency, and sample efficiency} \\  & anticipatory AI for dynamic task & & \\  & adjustment & & \\ \hline \multirow{2}{*}{Hardware Capabilities Trends [105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117]} & Advanced sensors, compliant & \multirow{2}{*}{Complexity, cost, and safety compliance} \\  & lightweight structures & & \\ \hline \multirow{2}{*}{Technological Convergence [24, 118, 119, 120]} & Holistic scene understanding and & & \\  & proactive HRC via multi-level visual & & \\ \hline \multirow{2}{*}{[24, 118, 120]} & \multirow{2}{*}{Holistic scene understanding and proactive HRC via multi-level visual cognition} & \multirow{2}{*}{Need for cognitive empathy and context awareness} \\  & & \\ \end{tabular}
\end{table}
Table 4: Summary of technological trends and challenges.  
In the realm of smart manufacturing, incorporating human-in-the-loop robot learning approaches ensures that cobots can adapt to dynamic tasks while maintaining safety and efficiency standards [29; 123].
## 4 Experimental Results
### Malware Families
#### 4.2.3 Portable Executable (PE)  
This is a file format used for executable files. Attackers can employ various techniques to obfuscate a malicious code within the document's structure, such as embedding malicious scripts or exploiting file format vulnerabilities. Figure 7 is an example of a case where PE is included within an HWP file as it is. Upon opening the file with the HWP OLE viewer, the PostScript codes are displayed, and at the bottom, the DOS signature indicating the beginning of the PE file, 4D 5A (MZ), is shown, as depicted in Figure 7. For more details, The highlighted file, BWAV0.EPS, is likely an EPS file containing a malicious code. The image displays HEX values alongside their ASCII representation, extracted from the EPS file. Repetitive patterns such as 41 41 41 suggest encoded or obfuscated data. This section contains an embedded shellcode that is responsible for executing malicious actions: 'dst/limit' defines a specific length. 'copy get 205 xor put st' performs an XOR 205 operation. The XOR 205 operation is used to restore obfuscated data before executing them, which indicates self-decrypting shellcode behavior. Then, it stores the result in an array. 'The for... loop' reads the values, pushes them onto the stack, and executes them.  
Figure 6: Malware document including EPS.
## Materials and Methods
### Analysis of Phenols by UPLC-DAD  
Extraction of phenols from EVOO was performed according to the modified method proposed by [PERSON], [PERSON], [PERSON], and [PERSON] [42]. Ten grams of EVOO were dissolved in 10 mL of n-hexane, and 5 mL of methanol was added. The mixture was vortexed for 2 min, sonicated for 10 min, and then centrifuged at 5000 rpm for 5 min. The extraction was repeated 2 more times, and unified methanol extracts were defatted by 3 portions of 10 mL n-hexane. The methanol extracts (or standard solutions) were evaporated to dryness, the residue was re-dissolved in a 2 mL of a mixture of HPLCuents (A (95:5 water--acetic acid (_v/v_)):B (methanol) = 90:10 (_v/v_)), and filtered through 0.45 \(\upmu\)m PTFE filters.  
Analysis of phenols was performed by ultra-performance liquid chromatography with diode array detection (UPLC-DAD) using an Agilent Infinity 1260 system (Agilent Technologies, Palo Alto, CA, USA) equipped with a G1311B quaternary pump, a G1329B autosampler, a G1316A column oven, and a G4212B DAD detector. A Kinetex PFP column (2.6 \(\upmu\)m, 100 mm \(\times\) 4.6 mm) with a guard was used (Phenomenex, Sydney, Australia) at 27 \({}^{\circ}\)C. Solvents were water with glacial acetic acid (95:5, _v/v_) (A) and methanol (B), with a flow rate of 1 mL/min. Ten microliters of the extract were injected. A 20-step gradient run used was reported previously [42]. Identification was performed by comparing retention times and UV/Vis spectra with those of pure standards when available, and with UV/Vis spectra from the literature [42]. Detection wavelengths were 280 nm (for simple phenols, vanillic acid, lignans, and secoiridoids), 320 nm (vanillin and _p_-coumaric acid), and 365 nm (flavonoids), while spectra were registered from 200 to 600 nm. Standard calibration curves were constructed for tyrosol, hydroxytrosol, vanillic acid, vanillin, _p_-coumaric acid, luteolin, apigenin, pinoresinol, and oleupopein. For other compounds semi-quantitative analysis was carried out: secoiridoids were expressed in mg/kg as oleupopein, and acetoxypinoresinol as pinoresinol equivalents, respectively.
## 3 Theoretical Background
### Deep Learning Models
#### 3.2.3 Recurrent Neural Networks  
Finally, and for completeness, we also discuss RNNs in this part of our study, as these are specialized in treating time sequences. The main difference between a FFNN and a RNN lies in the fact that an RNN has a feedback loop, as seen in Figure 4. Mathematically, RNNs are specialized in processing time series information: for each input element of the input sequence, \(x_{t}\), it produces an output \(y_{t}\) and a hidden state \(h_{t}\) that contains information that is then introduced as an additional input for \(x_{t+1}\). This means that there are two actual inputs: \(x_{t}\) and \(h_{t-1}\), and thanks to the information contained in \(h\), the RNN is able to propagate information through time. They are trained with the Back-Propagation Through Time (BPTT) algorithm [31], which allows obtaining a meaningful value of the hidden state so that it feedbacks past decisions to have relevance for future ones. There are many possible RNN architectures, being Long-Short Term Memory (LSTM) [32] one of the most popular.  
Although RNNs are widely applied in sequential problems, in our case, they do not provide better results than FFNNs. This is due to a subtle fact that is related to how RNNs are used to feedback information through the hidden state \(h\): in general, when RNNs are applied, the output sequence is conditioned on previous information that impacts the next outputs. Let us illustrate this with the text captioning problem, as shown in [33; 34; 35], where an image is processed and the caption text is automatically generated using an LSTM. Given the image features \(x\), we want to generate a sequence of words \(y\) that captions the text. Since the output \(y\) is the probability of choosing a certain word, note that it does matter whether the first word \(y_{1}\) of our text is \"A\" or \"He,\" as in the former case, the next word must be a noun or an adjective, whereas in the latter it should be a verb. So note that \(y_{2}\), the second word in the caption, depends on the sampled word \(y_{1}\): the hidden state \(h_{1}\) contains that information, which is  
does not add any new information. That is why there is no benefit in applying RNNs to address the proposed problem in this paper, so we do not report their results in Section 4, because they are not better than those provided by FFNNs.
## 3 The Proposed Channel Estimation Methods
### Denoising Method 3  
For the traditional 1D adaptive channel equalization system, the adaptive algorithm can be operated both in the forward and backward directions along with the received data, which is called bidirectional adaptive equalization. It exploits the time diversity to improve the performance. The basic principle of this technique is that the parameters to be estimated in the two directions are the same.  
Inspired by the idea of bi-directional adaptive equalization, we also developed a multidirectional adaptive algorithm for OTFS channel estimation. According to the 2D structure of received data in the DD domain, as shown in Figure 2, there are multiple directions to scan the received data \(\mathbf{Y}\). First of all, the data scanning strategy can be preliminarily divided into column scanning or row scanning. Then, for the column-wise strategy, it can scan from the left column to the right column or from the right to the left. For the row-wise strategy, it can scan from the bottom row to the top row or from the top to bottom. Thus, for each symbol in Figure 2, there are 4 directions to complete the scan. If we only consider starting from the corner of the matrix, at least 8 directions can be generated.  
The filter can obtain an output for each direction. And the estimated performance of each filter is similar since they are the same except for the flow of the input symbols. Then, we also adopt an arithmetic average operation to combine the multiple estimated results. Let \(\tilde{\mathbf{H}}^{(l)},j\in[1,\ldots,N_{d}]\) denote the estimated channel in the different directions. Thus, the final estimated channel of the proposed method is given by  
\[\tilde{\mathbf{H}}=\frac{1}{N_{d}}\sum_{j=1}^{N_{d}}\tilde{\mathbf{H}}^{(l)} \tag{27}\]  
The developed multidirectional adaptive algorithm exploits the diversity to improve performance. According to the Equation (27), the multidirectional technology is also a kind of denoising processing.  
According to the three denoising methods proposed in this section, the IPNLMS-MA-MuD algorithm is given in Algorithm 2. The key steps of the proposed multidirectional moving average filtering algorithm based on the moving average filter are to calculate the channel matrix estimation values in four directions according to the steps of IPNLMS. In each direction of adaptive channel estimation, a moving average filter is added. Specifically, the estimation value of the IPNLMS algorithm at that step is calculated first, followed by the average of the results from the previous D1 iterations, and then, this average value instead of the output of the current iteration is to be the input of the next iteration. The remaining steps are similar to the previously mentioned multidirectional adaptive channel estimation scheme.
# Influence of Roll Diameter on Material Deformation and Properties during Wire Flat Rolling
## 5 Conclusions  
The influence of the roll diameter on the strain distribution, shape change, contact pressure, and damage value of the workpiece was comprehensively investigated during wire flat rolling. The conclusions obtained are as follows:  
1. The strain inhomogeneity of the flat-rolled wire increased with the roll diameter; therefore, the MSBs were strengthened with the roll diameter during wire flat rolling.
2. The \(b\) and \(W\) of the wire increased as the roll diameter increased, leading to a decrease in \(R_{A}\) with the roll diameter.
3. The contour of the normal contact pressure on the wire surface exhibited similar patterns regardless of the roll diameter. The contact pressure of the wire was higher at the entry, edge, and exit zones in the contact area.
4. The distribution of the damage value varied with the roll diameter. The free surface region tended to have the peak damage value during wire flat rolling; however, the center region exhibited the maximum damage value as the roll diameter increased. The optimum roll diameter was in existence during wire flat rolling from the perspective of the damage value.
5. The underlying cause of the different strain inhomogeneities, shape changes, and damage values of the flattened wire was the different contact lengths originating from the different roll diameters during wire flat rolling.  
[PERSON]-[PERSON] designed and wrote the paper; [PERSON]-[PERSON] performed data analysis, and [PERSON]-[PERSON] wrote the draft. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT, South Korea) (No. 2021R1A2C1011700).  
Not applicable.  
The authors declare no conflict of interest.
# A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar
## 4 Network Generalization Experiments
### Other Simulation Scene
#### 4.1.2 Different Radar System and Underground Medium  
In the second group of experiments, we consider the change of multiple parameters, including source waveform, background permittivity, target shape, and depth. The layered geometry of the background is the same as that of model 1. The original cubic PEC target is replaced by an infinite air cylinder with radius R. The simulation parameters are listed in Table 1.  
Figure 13 shows the prediction results of models 5-8. The blue line is the wavelet of the original model in Figure 4. The black line is the A-Scan without the target signal, and it is the ground truth of the wavelet for the new model, which is quite different from the original model. The network is directly used to predict the wavelet for the new models, without retraining. The red line is the predicted wavelet with the A-can echo at position  
Figure 11: Wavelet prediction of derived models.  
Figure 12: Predicted wavelet removal results of models 1–4.  
B of the derived model. Although multiple parameters, such as the layered geometry, medium parameters, target information, and excitation waveform, are all different from the original model in Figure 4, the LSTM network still can predict the accurate wavelets.
# Enhancing OLSR Protocol by an Advanced Greedy Forwarding Mechanism for VANET in Smart Cities
## 4 Proposed Work
### Simulation and Results  
#### 4.4.1 Simulation Architecture  
The architecture of the simulation is shown in Figure 11:  
We start the simulation by creating the traffic simulation using SUMO.  
We use SUMO \"Simulation of Urban Mobility\" as a traffic simulator because it is close to the reality conditions.  
We start the simulation by extracting the map of the Gueliz (Figure 12) district in Marrakech city from Morocco.  
Figure 11: Architecture of the simulation.  
Using the SUMO simulator, we create vehicle traffic and then extract the trace file for the map of the chosen area in order to integrate it into the simulation under NS3.  
Table 3 shows the simulation parameters:
# Combining Facial Expressions and Electroencephalography to Enhance Emotion Recognition
## 2 Materials and Methods
### Dataset Description and Data Acquisition  
In this paper, we ran offline experiments in MAHNOB-HCI and DEAP dataset. The MAHNOB-HCI dataset contained EEG, video, audio, gaze, and peripheral physiological recordings of 30 participants [13]. In this dataset, each participant watched 20 clips extracted from Hollywood movies and video websites, such as youtube.com and blip.tv. The stimulus videos ranged in duration from 35 to 117 s. After watching each stimulus, the participants used self-assessment manikins (SAMs) to rate their perceived arousal and valence on a discrete scale of 1 to 9 [15]. We then divided these selections into a high class (ratings 6-9) and a low class (ratings 1-5). For 6 of the 30 participants, various problems (such as a technical failure) occurred during the experiment [13]. Here, only the 24 participants for whom all 20 trials are available were used.  
The DEAP data set includes the EEG and peripheral physiological signals of 32 participants when watching 40 one-minute music videos [14]. This data set also contains the participants' ratings of each video in terms of the levels of arousal and valence. We divided these values into a high class (ratings 6-9) and a low class (ratings 1-5). Note that we used only 13 participants for whom both the face data and EEG data were available for all 40 trials [14].  
Additionally, we performed on online experiment. In the online experiment, we collected data from 20 participants (50% female, 50% male, aged 17-22). Their facial images were captured by a Logitech camera (25 FPS, 800 \(\times\) 600 image size) while the EEG signals were recorded using EMOTIV INSIGHT, a five channel mobile EEG device (San Francisco, CA, USA). Five channels were placed on the scalp at positions AF3, AF4, T7, T8, and Pz to record the EEG signals. The sensors were placed according to the standard 10-20 system. The impedances of all electrodes were maintained below 5 k\(\Omega\).
# Investigation of Aerosol Climatology and Long-Range Transport of Aerosols over Pokhara, Nepal
## 1 Introduction
The IGP is a densely populated region with high aerosol loading from both anthropogenic and natural sources. Past studies have shown that major sources of aerosol loading over IGP include biomass burning, desert dust, and pollution from industrial and urban activities [26; 29; 30]. The AERONET observation site in Pokhara, Nepal, was established in January 2010 and has continuously provided various long-term aerosol products. After a decade of operation, it has now accumulated the longest continuous atmospheric observation data site in Nepal. While previous studies have shown the influence of transboundary air pollution over the Himalayan region of Nepal [4; 5; 6; 7; 8; 9; 24], this is the first detailed analysis of the seasonal variation of aerosol transport over the Nepal Himalaya using a long-term aerosol dataset, combined with computer simulation and satellite data analysis, providing us with a comprehensive picture of aerosols across temporal and spatial scales. Analysis of aerosol climatology over Pokhara in the foothill of Himalayas, along with its transboundary aerosol sources, provides glimpses of regional aerosols that reach more remote locations in the high Himalayas and Tibetan plateau. In this study, we have used monthly averaged aerosol optical properties between 2010 and 2018, such as spectral aerosol optical depth (\(\tau_{\text{AOD}}\)) from ultraviolet to infrared, 0.34 \(\upmu\)m to 1.02 \(\upmu\)m, to examine the seasonal Angstrom exponents and characterize the aerosol types. Level 2 AERONET inversion products single scattering albedo (SSA), and absorption angstrom exponent (AAE) at four different wavelengths--0.44 um, 0.675 um, 0.87 um, and 1.02 um--are investigated to identify absorbing aerosols. HYSPLIT back trajectory analysis, combined with MODIS satellite images of fire spots, have been used to identify the origins and types of aerosols transported to the receptor site, Pokhara.