esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Methodology for Predicting Psychological Disorders Using Facial Analysis and Pulse Data
#### 3.2.2 Integration with Facial Emotion Recognition  
The methodology combines pulse data with face expression recognition outputs to provide a thorough evaluation of the individual's psychological state. The process includes several interconnected components to accomplish this goal.  
During the emotion identification phase, a convolutional neural network (CNN) model analyzes the specific area of the face that is relevant (known as the facial region of interest or ROI) in order to identify and classify emotions. This model underwent extensive training to accurately identify six fundamental emotions including anger, fear, happiness, neutrality, sorrow, and surprise.  
At the same time, the tool performs pulse analysis to calculate the heart rate, providing an extra physiological measurement. This simultaneous approach guarantees the acquisition and analysis of both emotional and physiological data.  
An algorithm based on rules is used to analyze the combined data. This method correlates particular emotions and pulse ranges with suspected psychological disorders. For example, the algorithm might interpret a high pulse rate along with feelings like \"happy\" or \"surprise\" as a sign of a manic condition. In contrast, a decreased heart rate observed in combination with feelings of sadness or fear could indicate a depressive state.  
By incorporating these various data sources, the system offers a comprehensive perspective on the individual's mental condition, enabling more precise and detailed evaluations.
# Synchronization of Acoustic Signals for Steganographic Transmission
## 3 Embedding and Extraction Algorithm  
In order to study synchronization methods, it is necessary to have a mechanism for embedding and extracting steganographic data. Among the many methods presentedin Section 2, one of the algorithms was selected for further analysis. The algorithm is presented in [26; 28], and described in detail in [45]. This method uses a narrowband speech signal as a carrier of steganographic data. Additionally, the authors showed that the method is resistant to a number of factors that degrade the steganographic signal during its transmission over the VoIP link.  
For the purposes of this paper, there have been little changes introduced to the algorithm. The signal frame size was determined to be 192 samples (24 ms). Moreover, when determining the masking curve, a procedure using the psychoacoustic model of the MPEG-1 standard was used [46]. Hiding a single bit of information in a steganographic encoder consists of bipolar modification of the amplitude spectrum of the original signal in two adjacent signal frames. The steganographic data transmission rate was 20.83 bit/s.  
The last modification of the original algorithm consisted in adding a feedback loop and a local decoder in the transmitting part, whose task is to constantly check whether it is possible to correctly extract the information bit embedded in the steganographic signal. In the steganography literature, such solutions are referred to as informed sender algorithms or \"dirty paper codes\" [47]. In case of error detection, a coefficient \(C_{i}\) is determined at the output of the local decoder for the curve \(SMR_{i}(k)\) (Equation (1)). In the feedback loop, we determine such value \(C_{i}\), for which the instantaneous signal value at the output of the local decoder, which is also the average \(R\) value of the previous instantaneous values, exceeds the specified threshold \(K_{min}\).  
It should be additionally emphasized here that the greater the value of the signal at the output of the local decoder, the greater the energy of the watermark signal. Therefore, it will be more resistant to possible disturbances. At the same time, the higher energy value of the watermark signal makes it \"audible\" to the user of the system.
## 3 System Specification
The GPS RTK receiver specifications are given in Table 3. The GPS RTK used for position registration was a state-of-the-art survey grade receiver, embedded in the SPLITBOX-STD-T hydrographic equipment and based on the Trimble GNSS receiver.
## 5 Result and Discussion
### Numerical Simulation of Thermal Shock Resistance
Fiber content significantly influences the transition layer's thermal shock resistance, while coating porosity has the least impact. After the initial thermal shock, the A\({}_{3}\)B\({}_{3}\)C\({}_{2}\) configuration (20% porosity, 100 \(\upmu\)m thickness, and 30% fiber content) exhibited superior resistance. This indicates that under initial thermal shock conditions, moderate porosity levels and high fiber content effectively inhibit initial crack formation [62; 63]. The thicker coating enhances resistance to crack propagation, as the increased thickness provides greater energy absorption capacity, thereby resulting in improved thermal shock stability [64]. The A\({}_{1}\)B\({}_{3}\)C\({}_{3}\) configuration (porosity = 0%, 100 \(\upmu\)m thickness, and 60% fiber content) performed best after subsequent shocks. The zero porosity design made the coating more dense, which reduced the number of crack propagation pathways [63]. The high fiber content (60%) combined with the non-porous structure provided excellent toughness, enabling superior performance under multiple thermal shock cycles [62]. Additionally, the 100 \(\upmu\)m thickness provided sufficient structural strength for this configuration to resist thermal fatigue [64]. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**No.**} & **Factor A** & **Factor B (Coating** & **Factor C (Fiber** & **Mean SDEG** \\  & **(Porosity)** & **Thickness)** & **Content)** & **Value** \\ \hline
1 & 1 (0\%) & 1 (20 \(\upmu\)m) & 1 (0\%) & 0.7600 \\ \hline
2 & 1 (0\%) & 2 (60 \(\upmu\)m) & 3 (60\%) & 0.3502 \\ \hline
3 & 1 (0\%) & 3 (100 \(\upmu\)m) & 2 (30\%) & 0.2482 \\ \hline
4 & 2 (10\%) & 1 (20 \(\upmu\)m) & 3 (60\%) & 0.2990 \\ \hline
5 & 2 (10\%) & 2 (60 \(\upmu\)m) & 2 (30\%) & 0.4032 \\ \hline
6 & 2 (10\%) & 3 (100 \(\upmu\)m) & 1 (0\%) & 0.6566 \\ \hline
7 & 3 (20\%) & 1 (20 \(\upmu\)m) & 2 (30\%) & 0.1317 \\ \hline
8 & 3 (20\%) & 2 (60 \(\upmu\)m) & 1 (0\%) & 0.6906 \\ \hline
9 & 3 (20\%) & 3 (100 \(\upmu\)m) & 3 (60\%) & 0.3048 \\ \hline \multirow{2}{*}{Level} & Factor A & Factor B (Coating & Factor C (Fiber & \\  & (Porosity) & Thickness) & Content) \\ \hline K\({}_{1}\) & 1.36 & 1.19 & 2.11 \\ \hline K\({}_{2}\) & 1.36 & 1.44 & 0.78 \\ \hline K\({}_{3}\) & 1.13 & 1.21 & 0.95 \\ \hline k\({}_{1}\) & 0.45 & 0.39 & 0.70 \\ \hline k\({}_{2}\) & 0.45 & 0.48 & 0.26 \\ \hline k\({}_{3}\) & 0.38 & 0.40 & 0.32 \\ \hline R & 0.08 & 0.08 & 0.44 \\ \hline Priority of & & & & \\ Factors & & & \\ \hline Optimal & A\({}_{3}\)B\({}_{1}\)C\({}_{2}\) (Porosity 20\%, 20 \(\upmu\)m Thickness, and 30\% Fiber & \\ Configuration & & Content) \\ \hline \end{tabular}
\end{table} 
Table 6: Orthogonal simulation results and analysis of interlayer degradation efficiency (mean SDEG) after the first thermal shock.
## 2 Deflecting Cavity Design
### Power Coupling for Operation  
There is a slot to bring RF power in to establish the electromagnetic fields and to deflect the beam. The slot is on the top of the middle cell with a racetrack shape. To guarantee the symmetry of RF field in the cavity, a same size slot is placed at the bottom of the middle cell which can be used for pumping. With mechanical design presented in the next section, the top height of the coupling slot is 149.19 mm. For this normal conducting cavity, the coupling factor of the input port is set to about 1.05, the \(Q_{e}\) should be about \(1.78\times 10^{4}\) with the consideration the actual \(Q_{0}\) is 95% of the simulated value with pure copper. It is concluded from the simulation that the length \(u\) of the coupling slot is 58.9 mm and the width \(w\) is 30.7 mm. The field probe port which is used for RF measurement is placed at the side face of pumping pipe, and the external quality factor of the copper antenna is set to approximately \(2\times 10^{8}\).  
Figure 3 illustrates the working mode's transverse electromagnetic field which plays a major role on deflecting electron. The top left plot is the y component of the electric field \(E_{y}\). The top right plot is the x component of the magnetic field \(H_{x}\). The bottom plots are normalized \(E_{y}\) and \(H_{x}\) distributions along the cavity axis, respectively.
# A Dual-Branch Self-Boosting Network Based on Noise2 Noise for Unsupervised Image Denoising
## 4 Experiments  
### Datasets and Experiment Setup  
In this section, we present extensive experiments conducted on diverse benchmark datasets to validate the advantage of the proposed DBSNet for both synthetic and real-world noise removal scenarios. Our method is compared with several state-of-the-art techniques, including BM3D [14], N2N [21], DnCNN [15], IRCNN [30], FFDNet [27], VDNet [31], SwinIR [32], Restormer [19], and FEUNet [33]. The benchmark datasets utilized in the experiments include Set12 [15], BSD68 [34], SIDD [35], and DND [36]. Set12 is widely acknowledged as a benchmark database in the literature, while BSD68 comprises a randomselection of 68 images converted to grayscale from the BSD500 database [37]. These images are characterized by rich content and texture details, rendering them suitable for evaluating the robustness of competing methods to image content. SIDD is a representative real-world dataset containing well-aligned noise-clean image pairs for training purposes. The DND benchmark consists of 50 noisy images captured with consumer-grade cameras of various sensor sizes. Each image is cropped into 20 patches of size \(512\times 512\), resulting in a total of \(50\times 20=1000\) samples for evaluation. Compared to the SIDD dataset, images in the DND dataset are captured under normal lighting conditions, thus exhibiting weaker noise. Moreover, to quantify the performance of different methods, we adopted PSNR (peak signal-to-noise ratio) and the SSIM (structural similarity index measure) as evaluation metrics. PSNR evaluates the intensity similarity between undistorted and generated images, with higher PSNR values indicating better denoising ability. The SSIM is an image quality evaluation metric closely related to human perception of image quality, where higher SSIM values imply more satisfactory synthesis performance visually. Our experiments were implemented in PyTorch 1.7.1, developed by Facebook's artificial intelligence research team, [38] and conducted on a single NVIDIA RTX 4090 GPU, a powerful graphics card manufactured by NVIDIA Corporation in Santa Clara, CA, United States. The neural network was initialized using the Xavier initialization method. The bias parameters were set to a constant value of zero. The Adam optimizer was employed with a learning rate of 0.0001 and a weight decay parameter of \(1\times 10^{-9}\).
###### Abstract  
(1) _Background_: Free space optics communication (FSO) has improved wireless communication and data transfer thanks to high bandwidth, low power consumption, energy efficiency, a high transfer capacity, and a wide applicability field. The FSO systems also have their limitations, including weather conditions and obstacles in the way of transmission. (2) _Methods_: This research assesses the atmospheric conditions' influence on the intensity of received radiation, both experimentally and theoretically. The construction of a laboratory test stand of the FSO system, which is operating in the third-atmosphere transmission window (8-12 \(\upmu\)m), is proposed. Next, considering different atmospheric conditions, the experimental validation was conducted, both in a laboratory and real conditions. (3) _Results_: The measurements were carried out for two optical links working with wavelengths of 1.5 \(\upmu\)m and 10 \(\upmu\)m. It was found that optical radiation with a wavelength of about 10 \(\upmu\)m is characterized by better transmission properties in the case of limited visibility (e.g., light rain and fogs) than in the case of near-infrared waves. The same conclusion was found in analytical investigations. (4) _Conclusions_: The results obtained show that optical radiation with a wavelength of about 10 \(\upmu\)m in limited visibility is characterized by better transmission properties than near-infrared waves. This demonstrates the validity of designing FSO links operating in the range 8-12 \(\upmu\)m band, e.g., based on quantum cascade lasers and HgCdTe photodiodes.  
free space optical communication; IR photodetector; quantum cascade laser; wireless communication +
Footnote †: journal: Physics Letters B
## 3 Numerical Simulation Analysis of Deformation Mechanism of Tunnel-Landslide Orthogonal System
### Establishment of Model Contact Surfaces and Constraints  
Considering the large differences in soil material properties and physico-mechanical parameters between the computational model slide, slide belt, and slide bed, this will have a large impact on the results of slide displacement [18]. Therefore, in FLAC3D version 6.0, by setting up contact surfaces between different strata, joint surfaces, faults, etc., the effect of errors due to large differences in stiffness is reduced. At the same time, it can also better reflect the shear-slip damage characteristics between the contact surfaces. In this model, contact surfaces are set up on the lower surface of three slide belts, with a total of six to reflect the deformation and damage characteristics of the upper slide body, as shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Stratification** & **Gravity** & **Elastic** & **Poisson Ratio** & **Angle of** & **Force of** \\  & **kN\(\cdot\)m\({}^{3}\)** & **Modulus** & **MPa** & **Friction** & \({}^{\circ}\) & **Cohesion** \\ \hline moderately weathered & & & & & \\ sandstone-mudstone & 21 & 800 & 0.35 & 37 & 200 \\ \hline strongly weathered & & & & & \\ sandstone-mudstone & 20 & 600 & 0.3 & 28 & 52.5 \\ \hline fully weathered sandstone-mudstone & 20 & 200 & 0.32 & 22 & 30 \\ \hline sliding belt & 18 & 80 & 0.35 & 17.13 & 20.1 \\ \hline loess & 19 & 100 & 0.35 & 27 & 15 \\ \hline earth fill & 18 & 100 & 0.35 & 28 & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of physical and mechanical parameters of materials.  
Figure 10: Solid modeling of the tunnel–landslide system. (**a**) Integral model of the tunnel–landslide system, (**b**) grid local encryption, (**c**) tunnel lining structures.  
This calculation model sets the displacement constraints in each direction, and the initial displacement is set to zero on the front, back, left, and right surfaces of the model as well as the bottom surface of the model, respectively. There are five artificial intercept boundaries in this model, in which the bottom of the model fixes the \(z\)-axis displacements of all nodes in the plane, the left and right sides fix the \(x\)-axis displacements of the model, the front and rear surfaces fix the \(y\)-axis displacements of the model, and the top of the model does not set the constraints, as shown in Figure 12.
## 2 Data and Methodology
### Selection of the Study Area  
The study area is located in MATOPIBA, an acronym for the names of four Brazilian states that compose this region (Maranhao, Tocantins, Piaui, and Bahia). Intensive savanna clearing for single cropping (soybean, maize, and cotton plantations) or double cropping (soybean-corn or soybean-cotton rotations) activities in this region is a consequence of several factors. These factors include the spillover effect of the Amazon Soy Moratorium to the _Cerrado_ biome and several local actions for promoting recent agriculture expansion [11; 12; 13]. Savanna clearing rates vary with: market oscillations in commodities and land prices; local policies of credit incentives to agriculture; infrastructure improvement; and technological advances in soil/crop management practices [14; 15].  
We selected a study area of 215 km \(\times\) 215 km located close to the municipalities of Balsas and Urucui in the Brazilian states of Maranhao (MA) and Piaui (PI), respectively (Figure 2). Intensive savanna clearing, especially for soybean expansion, has already removed 22% of the native vegetation cover. Native vegetation is mainly composed of  
Figure 1: Main methodological steps used for detecting disturbance over the savannas.  
savanna grasslands (_Formacoes Campestres_), shrub savannas (_Formacoes Savunicas_), and woodland savannas (_Formacoes Florestais_) [30].  
The study area has well-defined rainy and dry seasons, with an average annual precipitation of 1100 mm [31]. In 2019, the year of our analysis of disturbance, the total precipitation was 1065 mm. From May to September (dry season), the monthly accumulated precipitation was generally lower than 35 mm. During the rainy season, rainfall reached values higher than 170 mm per month between January and March (Figure 3).  
Figure 3: Variations in monthly accumulated precipitation over the study area during the rainy and dry seasons of 2019.  
Figure 2: Location of the study area in the _Cerrado_ biome in Brazil. The MATOPIBA region, indicated by the hatched area in yellow, is the newest agricultural frontier of the country.  
From a geomorphological point of view, there are plateaus and depressions in the study area. The elevation ranges from 160 m to 663 m with plateaus having more than 400 m of elevation. In contrast to depressions, plateaus allow mechanized agriculture because of the more favorable slope conditions for the displacement of tractors [16]. Crops are mostly planted over oxisols that are managed to improve soil fertility.
# Speaker Diarization: A Review of Objectives and Methods
## 8 Summary of Speaker Diarization Methods
### Recent ANNs for SD  
E2E ANN-based SD (EEND) requires labelled data for all the joint speech activities of every speaker at each time frame in a recording [81, 82, 83]. It is usually trained on simulated data and then adapted to the target condition with labelled data. Current state-of-the-art systems include encoder-decoder attractor architecture EDA-EEND [84], x-vector clustering [55], the self-attentive EEND (SA-EEND) model [52], and Region Proposal Network-based SD [48]. Some of these deal with the problem of determining the total number of speakers in a conversation. A limitation of EEND is its fixed number of output heads, i.e., it is difficult to handle varying numbers of speakers. EDA-EEND uses an LSTM encoder-decoder to predict an _attractor_ for each speaker (to group frames). Re-segmentation is an optional step that further refines the diarization prediction [34]. EEND has difficulty with verbal meetings with more than three speakers and long-form audio (e.g., recordings exceeding 10 min). Basic EEND operates in batch processing mode, leading to excessive computer memory with long recordings.  
SD DNNs often have multiple recurrent layers and develop them with the _permutation invariant training_ technique [45]. However, they only show good performance in constrained settings and do not generalize well to real-world conditions.
# Adaptive Method for Packet Loss Types in IoT: An Naive Bayes Distinguisher
## 2 Related Work
The method has not been affected by various factors in data transmission process, but its accuracy was difficult to guarantee under different wireless loss rate in hybrid networks. Based on above considerations, we propose a new improved end-to-end congestion control algorithm. By selecting characteristic parameters and training with Naive Bayesian model, we construct an efficient and accurate identification model to correctly distinguish the types of packet loss, so as to reasonably adjust TCP congestion control mechanism and improve network performance.
# A Survey of Data Representation for Multi-Modality Event Detection and Evolution
## 4 Single-Modality Data Representation and Event Detection
### Text Based Data Representation
#### 4.1.3 Graph Based Methods  
In recent years, some topic detection research based on graph analytical approaches was proposed and achieved excellent performance. In graph analytical approaches, a graph is used to model words co-occurrence frequency in documents and then the graph can be divided into several parts by community detection. Each part represents a topic and the documents in corpus can be assigned to the most related topic. The framework of graph based topic detection is shown in Figure 5. For example, [PERSON] et al. [2] proposed a topic detection method based on word co-occurrence, which converts documents into graph structure by word co-occurrence between documents, and then used community detection to divide graphs into different parts. [PERSON] et al. [40] combined word co-occurrence graph with semantic information graph established by LDA. Their model can emphasize the potential co-occurrence relationship between words in text data, so that important but rare topics can be detected. [PERSON] et al. [41] introduced WordNet as external  
Figure 4: The basic idea of LDA.  
Figure 3: The basic idea of term based event detection methods.  
semantic knowledge in word co-occurrence graph, enriching the semantic information of graph structure. Then they extracted topics from the topic graph by community discovery. They defined a topic pruning process to find the optimal topic with the Markov decision processes. The graph analytical approaches had achieved excellent performance in topic detection task. However, its time complexity is also very high.
## 5 Experiments
### Object Classification  
In this subsection, we evaluate LPLC-HDL on the Caltech-101 dataset [23] for object classification. This dataset contains a total of 9146 images from 101 object categories and a background category. The number of images for per category varies from a minimum of 31 to a maximum of 800 images. The resolution of each image is about 300 \(\times\) 200, as shown in Figure 4. Following the settings [6; 40], we perform the Spatial Pyramid Features (SPFs) on this dataset. Firstly, we partition each image into \(2^{L}\times 2^{L}\) sub-regions with different spatial scales \(L\) = 0, 1, 2, then extract SIFT descriptors over a sub-region with a spacing of 8 pixels and concatenate them as the SPFs. Next, we encode the SPFs with a codebook of size 1024. Finally, the dimension of the features is reduced to 3000 using the the Principal Component Analysis (PCA) algorithm.  
For this dataset, 10 samples of each category are selected as the training data and the remaining are for testing. The parameters of LPLC-HDL on this dataset can be seen in Table 1. The hybrid dictionary size is set to 102 \(\times\) 9 + 100 = 1018, which denotes nine class-specific atoms for each category and 100 shared atoms for all the categories. The proposed LPLC-HDL and the comparison methods are carried out 10 times, the average classification rates are reported in Table 5. As can be seen in Table 5, our LPLC-HDL method achieves the best classification result again, with improvement margins of at least 1.3% compared with the comparison methods.