esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Sensory Evaluation  
A professional sensory assessment was carried out to determine the grades of the selected 18 tea samples, and the results are presented in Table 1. All 18 tea samples were categorized into grade A due to their comprehensive scores of above 90 according to the China national standard [21]. The colors of tea leaves, liquor colors, and appearances of waste leaves were darker from green tea to black tea; meanwhile, the aroma became intensified due to higher leaf oxidation. Green tea tended to have brisk taste, whereas oolong tea and black tea possessed more sweet taste mainly due to glycoside degradation [26].  
According to the aroma profile evaluation results, all 18 kinds of tea could be distinguished based on the eight sensory attributes. The score result was shown in Table S1. Figure 2 was plotted based on the score results of the aroma profile evaluation. Different colors refer to the eight attributes. Eighteen stripes represent 18 samples. The different lengths of the stripes showed the proportion of a certain sensory attribute in each sample. As shown in Figure 2, chestnut-like and fresh aroma was the feature of green tea (No. 1-9); by contrast, the strengths of the two attributes were much lower in oolong tea (No. 10-14) and were very weak in black tea (No. 15-18). However, the scores of fruity, sweet, and fermented aroma attributes in oolong tea and black tea were higher than those in green tea. This is mainly due to the fact that the oolong tea and black tea were semi-fermented and fermented tea, respectively [27]. In addition, a roast-like aroma attribute was described in all kinds of tea, and this aroma is delivered by heterocyclic compounds formed as a result of the Maillard reaction [2]. The aroma was weak in the green tea because the tea was unfermented. Exceptions were observed in green tea samples no. 7 and no. 8, as these two tea samples originated from the types of roasted green tea [28; 29].  
Figure 2: Stacked bar chart for aroma profile evaluation.
###### Abstract  
The purpose of the present study was to characterize the dry-cured loin and dry-cured \"cachago\" of Bisaro pork and evaluate the effect of the inclusion of olive cake in the animals' diet on the cured products. For this purpose, forty loins and forty \"cachacos\" were used, followed by a process of cold curing with controlled ventilation, without adding nitrites or synthetic additives. The dry-cured loin and \"cachaco\" chemical compositions were significantly different in moisture, total fat, protein, chlorides, ash, and _haem_ pigments. The \"cachaco\" showed a much higher value of total fat and a lower protein value. Its chloride content was lower and was related to the lower ash percentage. Neither product differed significantly in the water activity and collagen content. The proportions of saturated (SFA), monounsaturated (MUFA) and polyunsaturated (PUFA) fatty acids were significantly different between these products. The dry-cured \"cachaco\" showed higher values of SFA and PUFA, and the dry-cured loin had higher MUFA content. No significant effect of different olive cakes on diet was observed in the chemical compositions. Both products are of high quality and with good nutritional and physicochemical characteristics, and the introduction of olive cake in the diet did not affect any of the quality parameters analyzed. Bisaro breed; olive industry coproduct; dry-cured meat; chemical composition; fatty acids 20231343990/app13031439 Article  
Effect of the Inclusion of Olive Cake in the Diet on the Physicochemical Characteristics of Dry-Cured Loin and Dry-Cured \"Cachago\" of Bisaro Pig  
[PERSON]  
1 Centro de Investigacao de Montanha (CIMO), Campus de Santa Apolonia, Instituto Politecnico de Braganca, 5300-253 Braganca, Portugal 12 Laboratorio Para a Sustentabilidade e Tecnologia em Regiozes de Montanha, Campus de Santa Apolonia, Instituto Politecnico de Braganca, 5300-253 Braganca, Portugal 12 Laboratorio Para a Sustentabilidade e Tecnologia em Regiozes de Montanha, Campus de Santa Apolonia, Instituto Politecnico de Braganca, 5300-253 Braganca, Portugal 2  
[PERSON]  
1 Centro de Investigacao de Montanha (CIMO), Campus de Santa Apolonia, Instituto Politecnico de Braganca, 5300-253 Braganca, Portugal 12 Laboratorio Para a Sustentabilidade e Tecnologia em Regiozes de Montanha, Campus de Santa Apolonia, Instituto Politecnico de Braganca, 5300-253 Braganca, Portugal 2  
[PERSON]  
1 Centro de Investigacao de Montanha (CIMO), Campus de Santa Apolonia, Instituto Politecnico de Braganca, 5300-253 Braganca, Portugal 12 Laboratorio Para a Sustentabilidade e Tecnologia em Regiozes de Montanha, Campus de Santa Apolonia, Instituto Politecnico de Braganca, 5300-253 Braganca, Portugal 2  
[PERSON]  
4 Centro Tecnologico de la Carre de Galicia, Parque Tecnologico de Galicia, Avd.
## 5 Conclusions and Future Works
### Limitations  
The impact of timestamps and gender is analyzed on a relatively small dataset. The presented model can take only one video at a time to compute students' engagement. We only considered their facial actions and hand gestures for the underlying experiment.
## 2 Existing Solutions and the Combined Approach of AFF_CGE
### Existing Solutions
#### 2.1.3 NLP-Based Feature Learning and Detection  
With the advancement of deep learning and natural language processing (NLP) technologies, researchers have explored the application of these methods to feature extraction and representation learning in encrypted traffic. Traditional feature engineering approaches often struggle to capture the complex patterns in encrypted traffic due to the absence of plaintext information. To address this limitation, recent studies have leveraged NLP techniques, treating traffic as a text sequence and employing methods such as word embeddings and attention mechanisms for feature learning.  
[PERSON] et al. [13] proposed an encrypted malicious traffic detection method based on NLP. In their approach, the TF-IDF model is utilized to extract key information from the data, highlighting the importance of traffic features, which are then used to reconstruct the feature set. This method eliminates the need for field-by-field data analysis, marking a significant improvement over conventional machine learning preprocessing techniques, such as data encoding, in detecting encrypted malicious traffic. Similarly, [PERSON] et al. [14] segmented stream data into flow fragments and applied deep neural networks in conjunction with hierarchical attention mechanisms to integrate three levels of feature information. By training their model on a large-scale dataset, they demonstrated notable improvements in both generalization and detection performance. [PERSON] et al. [15] introduced a method known as TLS2 Vec, which extracts features from TLS handshakes and packet payloads, transforming them into word vectors. Using LSTM and BiLSTM models, TLS2 Vec effectively detects malicious encrypted traffic while preserving data privacy. [PERSON] et al. [16] proposed the Time-Series Feature Network (TSFN), a malicious traffic classification model that combines BERT and LSTM deep learning technologies. BERT is employed to capture global traffic features using a self-attention mechanism, while LSTM [17] extracts time-series characteristics of the traffic. This combination produces a more expressive representation of malicious traffic features. Their results demonstrate that the integration of NLP models with time-series analysis can significantly enhance the detection of encrypted traffic.  
However, these methods predominantly focus on the internal characteristics of encrypted traffic, often overlooking the structural relationships between traffic patterns. As a result, they may face limitations when dealing with more complex network attacks.
## 2 Basic Assumptions and Mechanical Models  
### Basic Assumptions  
1. The horizontal frozen wall and surrounding unfrozen soil are regarded as infinite thick-walled cylinders; in addition, the plane strain model is used to analyze the mechanical characteristics of the horizontal frozen wall.
2. The frozen wall is homogeneous and composed of ideal elastoplastic material, while the unfrozen soil is also homogeneous, but composed of linear elastic material.
3. The influence of the force of the unfrozen soil and frozen wall's own weight is ignored in the model under study.
4. All the frozen soil inside the frozen wall is excavated in one instant, and there is no support on the inner edge of the frozen wall after the excavation; i.e., the radial load on the inner edge is 0.  
There is complete contact between the frozen wall and unfrozen soil; i.e., the radial stress and shear stress on the contact surface are equal; in addition, the radial displacement and circumferential displacement are equal as well.
6. The stress field before and after freezing remains unchanged and is the same as the initial stress field. The initial stress field in the Cartesian coordinate system is expressed as follows: \[\sigma_{x}^{0}=\lambda p_{0},\sigma_{y}^{0}=p_{0},\tau_{xy}^{0}=0\] (1) Equation (1) can be represented in polar coordinates as follows: \[\left\{\begin{array}{c}\sigma_{r}^{0}=\lambda p_{0}\cos^{2}\theta+p_{0}\sin^ {2}\theta=\frac{1+\lambda}{2}p_{0}-\frac{1-\lambda}{2}p_{0}\cos 2\theta\\ \sigma_{\theta}^{0}=\lambda p_{0}\sin^{2}\theta+p_{0}\cos^{2}\theta=\frac{1+ \lambda}{2}p_{0}+\frac{1-\lambda}{2}p_{0}\cos 2\theta\\ \tau_{r\theta}^{0}=(1-\lambda)p_{0}\sin\theta\cos\theta=\frac{1-\lambda}{2}p_ {0}\sin 2\theta\end{array}\right.\] (2) where \(\sigma^{0}\) represents the initial stress, which is equivalent to the initial stress field given by \(\sigma_{r}^{0}\), \(\sigma_{\theta}^{0}\), and \(\tau_{r\theta}^{0}\), with compressive stress being considered positive; \(p_{0}\) is the initial vertical stress value, \(p_{0}=\gamma h\); \(h\) is the buried depth; \(\gamma\) is the soil weight; and \(\lambda\) is the horizontal lateral pressure coefficient.
# Research on an ID-PCA Early Fault Detection Method for Rolling Bearings
## 5 Conclusions  
Aiming at the characteristics of the early faults of rolling bearings in practical engineering, which are weak and difficult to extract in the background of strong noise, a method for early fault detection of rolling bearings based on ID-PCA is proposed. The method decomposes the time-domain features of the vibration signal, uses the depth decomposition theorem to construct a multi-layer subspace, fully excavates the weak early fault features in the vibration signal, and completes the early fault detection of the rolling bearing. Through sensitivity analysis, it is proven that the method can retain more fault feature information and improve the accuracy of early fault detection of rolling bearings. Finally, the accuracy and reliability of the method proposed in this paper are verified by simulation experiments. The results show that the method proposed in this paper has obvious improvements in the missing detection rate, false detection rate, and detection delay.  
Conceptualization, J.G. and Y.L.; software, J.G.; validation, J.G., K.L. and Q.L.; formal analysis, Y.L., K.L. and Q.L.; resources, Y.L., K.L. and Q.L.; data curation, J.G.; writing--original draft preparation, J.G.; writing--review and editing, Y.L; supervision, Y.L. and K.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Science Foundation of China under Grants (62073226, 61603262), Liaoning Province Natural Science Foundation (2020-KF-11-09, 2021-KF-11-05), Shen-Fu Demonstration Zone Science and Technology Plan Project (2020 JH13, 2021 JH07), and Central Government Guides Local Science and Technology Development Funds of Liaoning Province(2021 JH6). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Conflicts of Interest: The authors declare no conflict of interest.
## 5 Conclusions  
This paper proposes an optimal relay selection method among a set of relay nodes using a LLR test. To consider the impact of a common communication scenario, a three state Markov channel based simulation framework is presented to toggle the communication channel among Rayleigh, Rician, and Nakagami-m fading environment. Simulation results on the basis of BER, channel capacity, and outage probability are presented and compared on the basis of the availability of a communication link between the source and destination nodes for a single and multiple relay selection scheme. On the basis of the presented results it is concluded that the proposed scheme provides zero error performance with SNR as low as a 10 dB fading environment for the case of a single relay with source-destination link case for equal probability for Rayleigh, Rician, and Nakagami. While the maximum instantaneous channel capacity offered by the scheme is approximately 6.5 bits/s/Hz, which is obtained for 20 dB SNR. Furthermore, simulation results validate that LLR based relay selection scheme provides robust performance against time-varying channel conditions modelled by a finite state Markov channel.  
Conceptualization, M.S.; Formal analysis, M.S., A.J. and R.G.; Investigation, A.J. and R.G.; Methodology, M.S., A.J. and R.G.; Software, M.S. R.G.; Supervision, A.J. and R.G.; Validation, M.S., A.J. and R.G.; Writing--original draft, M.S.; Writing--review & editing, A.J. and R.G.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} Symbol & Description \\ \(SR_{N}\) & received signal at \(N^{\text{th}}\) relay in first phase \\ \(\sqrt{s}\) & average energy per symbol \\ \(SR_{N}\) & channel coefficient present between source and \(N^{\text{th}}\) relay \\ \(N_{0}\) & Noise variance \\ \(b_{i}\) & best relay pair \\ \(LLR_{N}\) & Log Likelihood ratio magnitude of signal received at \(N^{\text{th}}\) relay in first phase \\ \(\eta_{SRN}\) & transmitted symbol from source \\ \(P_{bi}\) & bit error probability \\ \(\beta_{i}\) & amplification factor \\ \(Z_{R,D}\) & received signal at destination after second phase \\ \(\delta_{1}\) and \(\delta_{2}\) & link weights for MRC \\ \(\tau_{t}\) & Instantaneous SNR \\ \(p_{\gamma_{t}}\) & probability density function (PDF) for fading channel \\ K & ratio between the power in direct path and indirect path in Rican fading \\ \(\Omega\) & total available power in Rician Fading \\ \(I_{0}(.)\) & Zero order Bessel function \\ M & shaping parameter in Nakagami fading \\ \(\omega\) & controlling spread in Nakagami Fading \\ \(P\) & transition probability \\ \(\Pi\) & steady state vector \\ \end{tabular}
# Sustainable Power Consumption for Variance-Based Integration Model in Cellular 6G-IoT System
## 2 Literature Review
### Related Works
[PERSON] et al. [37] suggested a Multi-Agent Deep Q-Network (MADQN) to optimize the EE and throughput resource-allocation framework. Firstly, we introduce a nonlinear EH model for SWIPT to include the underlying harvest circuit. We then formulate EE and total throughput in a mmWave scenario while ensuring the minimum QoS requirements for all users according to the environment. Multiple constraints, such as path-loss model, UAV height, distance and minimum transmission rate are employed to describe our mathematical problem. The proposed MADQN algorithm converges to highly satisfactory results compared to the other approaches. This is because our algorithm handles interference perfectly. Our results also indicated that the EE is affected by the number of D2D pairs to be deployed in the coverage area, as well as the maximum altitude variation. [PERSON] et al. [38] suggested a generalized approach to the performance analysis of relay-aided communication systems for 5G-and-beyond scenarios. We formulate an outage probability expression that is valid for all fading scenarios, irrespective of the nature or complexity of the fading PDF. The relationship between the outage performance and cumulative distribution function (CDF) of the signal-to-noise ratio (SNR) is exploited to derive the expression. We observed that increasing \(\eta\) from 0.5 to 1 while keeping other parameters constant improved the performance of the suggested system because as \(\eta\) approached 1, the fading approached the Rayleigh fading scenario. Additionally, the performance of the suggested system was also affected by changes in the fading parameter. [PERSON] et al. [39] suggested an RF-wireless-power-transfer-enabled dual-hop relay system for generalized fading scenarios which are not limited to only a single family of fading distribution. A closed-form expression is derived for the outage probability of the decode and forward as well as amplify and forward systems valid for all fading scenarios. The current system has a lot of applications in scenarios where the relay and/or destination node has the availability of a proportion of the required energy and only desires some additional energy to be transferred using WPT. The optimum design parameters for the system are also calculated, which act as the upper limit for setting the design parameters while constructing a practical system.
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 1 Introduction
UAVs that function as 5G base stations, referred to as Next Generation Node Bs (gNBs), represent one of the most remarkable examples and are anticipated to perform a critical role in forthcoming generations of wireless networks, as stated by [6]. The benefits provided by UAV-gNBs, like swift deployment, mobility, increased likelihood of unimpeded propagation, and flexibility, have gained extensive interest and are being widely investigated. Figure 1 demonstrates several use cases for 5G, including (i) serving as a backup in areas where a gNB malfunction occurs; (ii) providing coverage in rural areas with limited or no gNB infrastructure; and (iii) enhancing quality of service (QoS) in areas with unexpected surges in demand, such as densely populated sports events, by establishing a network connected to the 5G core network via a backhaul node. Further details on the software-based architecture (SBA) of 5G will be presented in the following sections. At present, communication between UAVs and the ground is restricted to visual line of sight (VLoS) range, which limits reliability, coverage, security, and performance. To overcome these limitations, it is crucial to connect drones to cellular networks to enable BVLoS capabilities. While integrating drones with 5G offers several advantages, it also poses certain challenges [7; 8; 9; 10; 11].
# Neurorotrophic Factors in Glaucoma and Innovative Delivery Systems
## 3 Advances in Drug and Gene Delivery Systems for Neuroprotection in Glaucoma
### Routes of Administration and Devices
As advances in drug sustained delivery to the posterior segment of the eye, various vehicles amenable for intravitreal use have been proposed, including nanoparticles (1-1000 nm), microparticles (1-1000 \(\upmu\)m), and implants (\(>\)1 mm). Carriers are designed to encapsulate the small active fraction and provide a slow, controlled release. Therefore, low levels of drug achieve therapeutical relevance if constantly administered in the target microenvironment. Microparticles and implants provide delivery of the active substances for longer periods of time compared with nanoparticles [104]. In addition, microparticles can be easily administered through conventional 25-34-gauge needles. Two structurally distinct variants are described among microparticles: microcapsules and microspheres (MSs). Microcapsules are composed of a polymer or a mixture of several polymers creating a reservoir for the inclusion of the active substance, while in the MSs the active substance is dispersed into a polymeric matrix [104]. Different implants from non-biodegradable polymers have been approved for intravitreal delivery, with the relevant limitation of another surgical procedure for treatment replacement. These include the static implants Vitrasert(tm) for cytomegalovirus retinitis and Retisert(tm) for chronic non-infectious uveitis, and the suture-free implant Iluvien(tm) for diabetic macular edema (DME), respectively, lasting 6, 30, and 36 months [105; 106]. However, scientific efforts are now focusing on the development of biodegradable intravitreal devices for long-lasting benefit, rather than non-biodegradable implants. The synthetic and biocompatible poly-DL-lactide-co-glycolide (PLGA) polyester is the most commonly used for biodegradable MSs and implants manufacturing, with one currently approved product for ophthalmic use containing corticosteroid dispersed in a PLGA matrix system (Ozurdex(r)) [107]. PLGA is combined with bioactive molecules while modeling, and subsequent gradual degradation of the material which is converted into CO\({}_{2}\) and water in vivo results in slow, sustained release from one to approximately four months. Biodegradable PLGA MSs are relatively inert in the vitreous cavity, with reported minimal inflammatory response, and are able to encapsulate molecules of practically any size with high reproducibility [104]. However, the release of complex large molecules such as NTs from micro-scale devices remains challenging since biological activity is strongly related to three-dimensional structure of these molecules. In accordance, distinct technological strategies have been proposed to increase protein stability after microencapsulation and allow for sustained delivery of neuroprotective agents in neurodegenerative diseases.
# A VPN-Encrypted Traffic Identification Method Based on Ensemble Learning
## 2 Methodology
### VPN-Encrypted Traffic FL-XGB Identification Model
#### 2.2.2 FL-XGB-VPN-Encrypted Traffic Identification Model
The pseudo-code of the method is shown in Algorithm 1. ```
Input: Train Set \(D=\{(x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{m},y_{m})\}\) Output: Index value of Accuracy, Precision, Recall and F1-Score Begin
1. For \(t=1\) to n do
2. If \(t=1\):
3. Then, initialize sample class label identification values \(y_{i}\). 4. Based on the \(FL\), Input actual class label \(Y\) and predicted value \(y_{i}\), calculate \(g_{i}\)_and_\(h_{i}\). 5. according to \(g_{i}\) and \(h_{i}\) training \(f_{t}(x_{i})\)
6. Strong Learner \(\hat{y}_{i}^{(t)}=\hat{y}_{i}^{(t-1)}+f_{t}(x_{i})\)
7. Use \(\hat{y}_{i}^{(t)}\) training sample obtains identification values \(y_{i}\)
8. End for
9. The train set is fed into \(\hat{y}_{i}^{(t)}\), i.e., the FL-XGB model, to obtain the values of each index. End
```  
**Algorithm 1** FL-XGB-VPN-Encrypted Traffic Identification Algorithm  
When identifying VPN-encrypted traffic, there is the problem of uneven distribution of all types of data, i.e., the data class imbalance problem. To address this problem, this paper uses the Focal Loss function to improve the Xgboost traffic identification model. The loss of easily identifiable samples is reduced by modifying parameter a, and the loss of samples received is broadened. This method achieves to retain the loss values of the hard-to-identify samples while reducing the loss values of the easy-to-identify samples. We use the FL-XGB-encrypted traffic identification model to solve the problem of imbalance between the number of hard-to-identify and easy-to-identify samples during model training.