esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of Tools and Technologies for Anxiety and Depression Management Using AI
## 4 Results of Overview
### LLMs in the Treatment of Anxiety and Depression
#### 4.6.1 The Potential of Large Language Models
Given the significant impact of emotional states on anxiety and depression, it is crucial to effectively manage these disorders. [PERSON] et al. [90] introduce the E-Prevention system, a sophisticated support tool developed for the purpose of monitoring and preventing relapse in individuals diagnosed with psychotic disorders. This system uses data from wearables and video captures to offer thorough monitoring and analysis. The incorporation of multiple data sources highlights the significance of improving tools for managing mental health. The study demonstrates the potential for enhancing the precision and dependability of mental health interventions by integrating LLMs with other technologies [90]. [PERSON] et al. [91] investigate how mHealth technologies are used in metropolitan regions to improve public health and well-being. The paper emphasizes how important mHealth tools are for helping people with mental health disorders like anxiety and sadness. Thesedevices provide insightful analysis of patients' emotional and physical well-being as they gather location and health-related information to track vital signs and physical activity. The integration of mobile health (mHealth) and low-cost, low-maintenance (LLMs) devices exemplifies a pragmatic method for improving mental health care using technology [91]. In their study, [PERSON] et al. [92] present a sophisticated cloud-based platform that has been specifically developed to efficiently monitor individuals with psychotic disorders. This platform utilizes cloud computing to store and analyze data from wearable devices and home-based video recordings. Utilizing a cloud-based approach allows for efficient processing of large volumes of data, facilitating real-time monitoring and analysis. Continuous surveillance and sophisticated data analysis may provide significant observations about the patient's state, allowing for timely intervention and customized therapy. The potential advantages of LLMs in the management of anxiety and depression are substantial, however, it is essential to acknowledge and tackle the associated risks and difficulties. LLMs have a notable inclination to generate outcomes that are both unexpected and non-deterministic, which is a significant concern to consider. [PERSON] et al. [88] performed research which found that LLMs have the capacity to provide replies that are factually inaccurate or illogical. This might cause difficulties, especially in therapeutic environments where precision is essential. For example, an LLM might possibly provide counsel that is unsuitable or inaccurate, with substantial consequences for those experiencing anxiety or depression. Ensuring the safety and effectiveness of LLM outcomes in mental health treatment requires prioritizing their reliability and correctness.
# EL-MTSA: Stock Prediction Model Based on Ensemble Learning and Multimodal Time Series Analysis
## 4 Experimental Results and Discussion
### Experimental Results and Analyses
#### 4.4.3 Ablation Experiment Results and Analysis  
To validate the effectiveness of incorporating the cycle layer, we conducted ablation experiments. Table 4 presents the comparative experimental results of the EL-MTSA model and the model without the cycle layer across different frequency data. The symbol \"\" denotes the ablation model.  
The resulting visualization is shown in Figure 7. Through comparative experiments, it is observed that the EL-MTSA model achieves a 7.54% reduction in MAE, a 10.55% reduction in SMAPE, and a 10.54% reduction in RMSE compared to the EL-MTSA* model. These results demonstrate that the inclusion of the cycle layer effectively captures temporal patterns in the data, significantly enhancing the model's prediction accuracy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Model** & **Frequency** & **MAE** & **SMAPE** & **RMSE** \\ \hline \multirow{5}{*}{CSI 300} & EL-MTSAs & 1 day & 66.53035 & 45.86216 & 3.645549 \\  & EL-MTSAs & 30 min & 64.62842 & 44.8067 & 3.569873 \\ \cline{1-1}  & EL-MTSAs & 15 min & 59.40408 & 41.17131 & 3.281625 \\ \cline{1-1}  & EL-MTSAs * & 1 day & 71.38656 & 51.39556 & 4.099139 \\ \cline{1-1}  & EL-MTSAs * & 30 min & 67.95656 & 49.08186 & 3.921806 \\ \cline{1-1}  & EL-MTSAs * & 15 min & 63.81433 & 45.47347 & 3.640053 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiment results.  
Figure 7: Ablation experiment. The symbol \"\" denotes the ablation model.
# Deep Learning-Enhanced Spectrogram Analysis for
## 5 Discussion
### Clinical Relevance and Comparison with Traditional Methods  
Traditional physical examination techniques, such as inspection, ausculation, percussion, and palpation, rely heavily on the clinician's experience and subjective judgment [38]. These methods, particularly ausculation and percussion, have been used for centuries to diagnose respiratory and cardiovascular conditions [39]. However, inter-observer variability and inconsistencies in interpretation limit their reliability [40]. By applying deep learning techniques, this study introduced an objective and reproducible approach to biomedical signal classification, significantly reducing diagnostic subjectivity [41].  
The proposed model outperformed current approaches that rely on manual feature extraction or traditional machine learning classifiers, achieving an overall accuracy of 93.37% [42]. Studies have demonstrated that convolutional neural networks (CNNs) effectively extract spatial and frequency-domain features from spectrograms, leading to superior classification accuracy [43]. This research broadens the focus beyond respiratory sound classification [44] to encompass percussion and palpation signals, thus offering a more thorough evaluation of the biological signals.  
Figure 6: Representative spectrograms from all eight anatomical regions, each sampled from a different human subject. These spectrograms demonstrate both inter-region spectral differences and inter-subject variability, which the model learns to distinguish. The figure highlights the classification challenges posed by overlapping frequency features in adjacent anatomical zones.  
The suggested classification paradigm possesses considerable clinical importance, particularly in contexts where remote diagnosis and telemedicine are critical. For example:  
* Remote Monitoring for Pulmonary Disorders: Individuals afflicted with chronic obstructive pulmonary disease (COPD) or asthma may benefit from ongoing, AI-facilitated categorization of auscultation and percussion data to identify deteriorating situations [42].
* Telemedicine and Primary Care: Physicians in rural or underserved areas can use automated signal classification to support initial diagnoses and improve access to healthcare.
* Early Detection of Respiratory Infections: AI-based spectrogram analysis can assist in the early screening of pneumonia or post-COVID-19 lung complications, reducing hospital admissions by enabling timely intervention.
* Objective Assessment of Disease Progression: Longitudinal monitoring of spectrogram fluctuations across anatomical regions can facilitate the assessment of progressive lung disorders, such as interstitial lung disease or pulmonary fibrosis, offering quantitative insights that surpass subjective clinical evaluations [43].
# Practical Performance Analyses of 5G Sharing Voice Solution
## 4 Voice Services Based on 5G Sharing
### SA Sharing EPS Fallback Process  
At the early stage of SA sharing network deployment, VoNR is not mature, and EPS fallback is needed as a temporary transition solution to fallback the voice service to the LTE network, as shown in Figure 5. gNodeB needs to enable the base station sharing function and configure both eNodeBs as neighbors [36].  
EPS fallback can be implemented based on either handover or redirection. The process is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(\eta_{\mathrm{C}}\) & Time delay between sharer UE and contractor eNodeB \\ \hline \(\eta_{\mathrm{S}}\) & Time delay between sharer UE and sharer eNodeB \\ \hline \(\mu_{\mathrm{C}}\) & Time delay between contractor eNodeB and sharer EPC \\ \hline \(\mu_{\mathrm{S}}\) & Time delay between sharer eNodeB and sharer EPC \\ \hline \(T_{TAU}\) & TAU process delay \\ \hline \(T_{\mathrm{VoLTE}}\) & Delay in setup VoLTE service \\ \hline \(\delta\) & Time delay between UE and gNodeB \\ \hline \(\epsilon\) & Time delay between UE and 5 GC \\ \hline \(\eta\) & Time delay between UE and eNodeB \\ \hline \(\gamma\) & Time delay between gNodeB and 5 GC \\ \hline \(\theta\) & Time delay between 5 GC and EPC \\ \hline \(\mu\) & Time delay between EPC and eNodeB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation Description.  
Figure 5: EPS Fallback.
# Combined kNN Classification and Hierarchical Similarity Hash for Fast Malware Detection
## 3 Preliminaries
### Tlsh  
The purpose of a similarity hash is to find variants of a file. Hundreds of thousands of variant malicious files are created every day, which makes it difficult to detect them using existing pattern-based antivirus solutions. A similarity hash can help to solve this problem. When existing files are known to be normal or malicious, if a new file has similar similarity-hash values, it can be judged to be similar to an existing file. The properties of the similarity hash are as follows. A cryptographic hash, e.g., MD5, produces a completely different hash value if the file is only slightly different. However, for similarity hashes, similar files have similar hash values and different files have different similarity-hash values. TLSH is a typical example of a similarity hash [11]. A TLSH is created as follows. First, a byte string from the file is processed using a five-element sliding window to compute the bucket-count array. Second, quartile points \(q_{1}\), \(q_{2}\), and \(q_{3}\) are calculated. Third, the digest header values are constructed. Fourth, the digest body is constructed by processing the bucket array. First, as shown in Figure 2, a byte string of a file is counted into a 128-dimensional array using a Pearson hash [29]. That is, the value computed by the Pearson hash through a five-element window is matched to one of the 128 buckets and the bucket count is incremented by one. Figure 2: TLSH. Second, the quartile points are calculated. Seventy-five percent of the bucket counts are greater than or equal to \(q_{1}\), 50% of the bucket counts are greater than or equal to \(q_{2}\), and 25% of the bucket counts are greater than or equal to \(q_{3}\). Third, as shown in Figure 2, the first byte of the first three bytes of the similarity hash is the checksum, the second byte is the length of the file, and the third byte is calculated from \(q_{1}\), \(q_{2}\) and \(q_{3}\), as follows. \[q_{1}\_{\text{ratio}} =(q_{1}\times\!100/q_{3})\text{ MOD 16}\] \[q_{2}\_{\text{ratio}} =(q_{2}\times\!100/q_{3})\text{ MOD 16}\]  
Finally, if the value of a bucket is less than or equal to \(q_{1}\), 00 is entered into \(c_{i}\). If the value is less than or equal to \(q_{2}\), 01 is entered. If it is less than or equal to \(q_{3}\), 10 is entered. Otherwise, 11 is entered. The TLSH is created through these four steps. A method for calculating the similarity difference of two files using two similarity hashes is as follows. First, the difference between the similarity-hash header values is calculated. Second, the difference between the similarity-hash body values is calculated. The difference in the header values of the similarity hash is calculated as follows.
## 4 ANN-Based Prediction Model Development and Validation
### Prediction Model Development
#### 4.1.1 Weights and Weight Initialization  
In an ANN, a set of weights is attached to the neurons in the hidden layer(s) and output layer. Initial weights are initialized randomly, and these initial values determine the starting point where the ANN starts the search to find the error minima during its training, where different random initializations mean different starting points. Due to different random initializations, the ANN model follows a different path each time during its training to arrive at different error minima, even though they are trained using the same data and have the same ANN topology. Therefore, to incorporate the effect of the random initialization, we applied the five different random weight initialization techniques given in Table 2. When the model is run, the generated output is used to calculate the difference between predicted values and actual values. The weights are then updated iteratively to minimize the error. The iterations continue until the error is minimized. This is what is called the learning process during ANN-based modeling.
###### Abstract  
In this work, a gas hydrate formation and dissociation study was performed on two multiphase pipeline systems containing gasoline, CO2, water, and crude oil, CO2, water, in the pressure range of 2.5-3.5 MPa with fixed water cut as 15% using gas hydrate rocking cell equipment. The system has 10, 15 and 20 wt.% concentrations of gasoline and crude oil, respectively. From the obtained hydrate-liquid-vapor-equilibrium (HLVE) data, the phase diagrams for the system are constructed and analyzed to represent the phase behavior in the multiphase pipelines. Similarly, induction time and rate of gas hydrate formation studies were performed for gasoline, CO2, and water, and crude oil, CO2, water system. From the evaluation of phase behavior based on the HLVE curve, the multiphase system with gasoline exhibits an inhibition in gas hydrates formation, as the HLVE curve shifts towards the lower temperature and higher-pressure region. The multiphase system containing the crude oil system shows a promotion of gas hydrates formation, as the HLVE curve shifted towards the higher temperature and lower pressure. Similarly, the kinetics of hydrate formation of gas hydrates in the gasoline system is slow. At the same time, crude oil has a rapid gas hydrate formation rate.  
gas hydrates; multiphase flow behavior; HLVE data; rate of hydrate formation +
Footnote †: journal: Applied Sciences
## 2 Nutrition Profile of Hazelnuts, Almonds and Pistachios  
### Hazelnuts  
Worldwide, numerous hazelnut varieties exist due to differences in growth environments, and each geographical region imparts specific nutritional and taste characteristics. European hazelnut (_Corylus avellana_ L.) has the most cultivars. Among them, Italian cultivars such as _Tonda di Giffoni_ and _Tonda Gentile delle Langhe_ have been awarded the European Protected Geographical Indication (PGI) label [11].  
In general, nuts are rich sources of proteins and essential and non-essential amino acids, as indicated by the USDA National Nutrient Database for Standard Reference. Specifically, hazelnuts typically contain between 14 and 15 g of protein per 100 g of product, with glutamic acid (3.7%), arginine (2.2%), aspartic acid (1.7%), and leucine (1.1%) as the main amino acids [12].  
Although amino acid profile can vary significantly depending on the variety and location of cultivation, these compounds can influence several attributes, including taste, aroma, and color [13]. [PERSON] et al. (2006) reported that hazelnuts cultivated in Turkey contain 2.322 g/100 g of arginine, 1.271 g/100 g of leucine, and 1.475 g/100 g of glutamic acid as non-essential amino acids [12].  
Hazelnuts are characterized by a high fat content, exceeding 60-70%. The lipid profile of hazelnuts is dominated by monounsaturated fatty acids (MUFAs), with oleic acid as the primary component, accounting for approximately 75-80% of the total fatty acid content. Polyunsaturated fatty acids (PUFAs), primarily linoleic acid, constitute the second major lipid fraction (7.9-13%). Palmitic, stearic, and palmitoic acids are also present in significant amounts. The ratio of unsaturated to saturated fatty acids is approximately 13:1, while the ratio of polyunsaturated to saturated fatty acids is 1.23:2.87 [12; 14].  
Carbohydrates constitute approximately 6 to 26% of the total composition, comprising dietary fiber and sugars. Sugars, accounting for 17% of the total carbohydrate content, include disaccharides such as sucrose, stachyose, and raffinose; monosaccharides such as glucose and fructose; and polyols such as myo-inositol. Sucrose is the predominant sugar, comprising over 74% of the total sugar content. The quantity, quality, and composition of sugars directly and indirectly contribute to the sensory profile of hazelnuts, influencing sweetness and serving as fundamental precursors for the Maillard reaction during the roasting process [14; 15].  
The levels of niacin, vitamin B1, vitamin B2, vitamin B6, folic acid, and ascorbic acid in hazelnuts are 1.45 mg/100 g, 0.276 mg/100 g, 0.052 mg/100 g, 0.501 mg/100 g, 43 mg/100 g, and 2.45 mg/100 g, respectively. The authors reported that the \"Kan\" hazelnut variety has the highest levels of folic acid and vitamin B6. Regarding fat-soluble vitamins,the concentrations of retinol, \(\alpha\)-, \(\gamma\)-, and \(\delta\)-tocopherol were found to be 3.25 mg/100 g, 24.7 mg/100 g, 1.36 ug/100 g, and 1.78 mg/100 g, respectively [12].  
Table 1 summarizes the main nutritional values of hazelnuts.
###### Abstract  
Since wireless systems allow for easier access to communication paths than wired systems, it is necessary to improve their dependability against cyberterrorists. To make wireless systems more dependable, additional measures at the lower layer are required, in addition to those at the upper layers. Our proposal uses an integrated terminal-like cellular phone which has multiple radio access technologies (RATs) such as cellular, wireless local area network (LAN), Bluetooth, and an ultra-wide band (UWB). We propose to communicate information encoded by shortened code using multiple RATs. Redundancy by RATs and their error correction capability can simultaneously improve wiretap resistance and attack resistance. A codeword of shortened code is obtained by removing a part of a codeword of popular code. A decoder can improve the error correction capability if the removed part is known. By using shortened codes, the dependability can be further enhanced because the error correction capability between the legitimate receiver and the cyberterrorist can make a difference. To do this, it is necessary to securely share the removed part between the sender and receiver. Our proposal is to securely measure the distance between the sender and receiver using UWB and use it as the removed part. It was confirmed that the secrecy capacity is improved.  
dependable wireless system; shortened code; distance information; integrated terminal; secrecy capacity +
Footnote †: journal:  
1
Footnote 1: _Telecom_**2020**, 1, 266–282; doi:10.3390/telecom1030018
###### Abstract  
Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM), have provided hydrologists with important precipitation data sources for hydrological applications in sparsely gauged or ungauged basins. This study proposes a framework for statistical and hydrological assessment of the TRMM- and GPM-era satellite-based precipitation products (SPPs) in both near- and post-real-time versions at sub-daily temporal scales in a poorly gauged watershed in Myanmar. It evaluates six of the latest GPM-era SPPs: Integrated Multi-satellite Retrievals for GPM (IMERG) \"Early\", \"Late\", and \"Final\" run SPPs (IMERG-E, IMERG-L, and IMERG-F, respectively), and Global Satellite Mapping of Precipitation (GSMaP) near-real-time (GSMaP-NRT), standard version (GSMaP-MVK), and standard version with gauge-adjustment (GSMaP-GAUGE) SPPs, and two TRMM Multi-satellite Precipitation Analysis SPPs (3B42 RT and 3B42V7). Statistical assessment at grid and basin scales shows that 3B42 RT generally presents higher quality, followed by IMERG-F and 3B42V7. IMERG-E, IMERG-L, GSMaP-NRT, GSMaP-MVK, and GSMaP-GAUGE largely underestimate total precipitation, and the three GSMaP SPPs have the lowest accuracy. Given that 3B42 RT demonstrates the best quality among the evaluated four near-real-time SPPs, 3B42 RT obtains satisfactory hydrological performance in 3-hourly flood simulation, with a Nash-Sutcliffe model efficiency coefficient (NSE) of 0.868, and it is comparable with the rain-gauge-based precipitation data (NSE = 0.895). In terms of post-real-time SPPs, IMERG-F and 3B42V7 demonstrate acceptable hydrological utility, and IMERG-F (NSE = 0.840) slightly outperforms 3B42V7 (NSE = 0.828). This study found that IMERG-F demonstrates comparable or even slightly better accuracy in statistical and hydrological evaluations in comparison with its predecessor, 3B42V7, indicating that GPM-era IMERG-F is the reliable replacement for TRMM-era 3B42V7 in the study area. The GPM scientific community still needs to further refine precipitation retrieving algorithms and improve the accuracy of SPPs, particularly IMERG-E, IMERG-L, and GSMaP SPPs, because ungauged basins urgently require accurate and timely precipitation data for flood control and disaster mitigation.  
TRMM; GPM; IMPA; IMERG; GSMaP; satellite precipitation; hydrological modeling 2019 11, 140; doi:10.3390/rs1102014
## 3 Methodology
### Simulation and Modeling  
The simulation and modeling phase utilizes sophisticated computational tools to replicate and assess the performance of the integrated BESS and HESS within the building's energy infrastructure. This step is essential for predicting system behavior under various conditions and optimizing their design and operation. It involves selecting suitable software and tools, defining assumptions and parameters for realistic modeling, and creating different scenarios for analysis. The goal of the project is to simulate these scenarios in order to identify the most effective ways to integrate and manage the energy storage systems, improving the Engineering 5 Building at Kangwon National University's sustainability and energy efficiency:  
* Software and Tools Used: MATLAB software was employed for simulation and modeling due to its powerful computational capabilities and versatility in handling complex energy systems. It provides a robust platform for developing, testing, and validating models of BESS and HESS, allowing for detailed performance analysis and integration studies.
* Assumptions and Parameters: The simulation is based on several assumptions and parameters, including typical energy consumption patterns of the Engineering 5 Building, renewable energy generation profiles, and efficiency metrics for both BESS and HESS. These assumptions create realistic analysis scenarios, ensuring the results are applicable to actual operational conditions.
* Scenarios for Analysis: Various scenarios were analyzed to understand the integrated energy storage systems' performance under different conditions. These scenarios include different levels of renewable energy availability, varying load demands, and operational strategies for energy storage and utilization. Simulating these scenarios helps identify optimal configurations and strategies for integrating BESS and HESS.