esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Bibliometric Analysis
### Most Cited Articles Within the Research Theme
Research in Brazil [71] and Colombia [72] demonstrates that hydropower systems are vulnerable to megadroughs, reinforcing the importance of diversified renewable portfolios. Hybrid systems reduce fossil fuel reliance, improve economic feasibility, and enhance rural electrification strategies [79; 84]. The role of biomass energy in agriculture is explored in [73], emphasizing land-use trade-offs in energy production. Studies like [93] integrate water management technologies into biomass systems, improving resource efficiency and reducing carbon emissions. Also, hybrid PV-diesel systems demonstrate cost reductions (37% in dry seasons, 64% in wet seasons) [74], highlighting battery storage's role in compensating for wind and solar variability. Floating solar systems [94] optimize land use and minimize water evaporation, enhancing sustainability in semi-arid regions [77]. Risk assessment for renewable energy in Malaysia [75] highlights biomass vulnerability to climate interactions. Studies such as [76] compare solar towers and natural gas plants, reinforcing the importance of balancing water use and environmental impacts. In this sense, the role of agrivoltaic systems is explored in [78], integrating solar energy with agriculture to enhance land efficiency, reduce evaporation, and improve crop resilience under megadrought conditions. Similar analyses underscore the carbon reduction potential of these systems [94; 95]. Expanding into emerging topics, research on renewable energy in data centers investigates hybrid solar-hydro solutions for high-energy-demand sectors. Studies [96; 97] optimize solar-wind hybrid systems, reducing water constraints and energy costs. The floating photovoltaic system in Brazil [77] reduces reservoir evaporation by 16.7%, demonstrating the technical and economic feasibility of integrating solar with water supply projects. Lastly, decentralized rooftop solar solutions mitigate heatwave-driven grid stress, while hybrid microgrids ensure power supply during extreme climatic events [98]. Research on renewable energy's economic feasibility and climate resilience continues evolving, shaping future sustainable energy strategies.
# A Joint Channel Estimation and Compression Method Based on GAN in 6G Communication Systems
## 2 System Model and Problem Formulation  
In this section, the wireless system model in consideration is provided at first, followed by the illustration of the studied channel estimation problem.
## 3 Analysis and Discussion
### Analysis of Time-Frequency Characteristic Parameters
#### 3.2.3 Influence of Temporal First and Second Derivatives  
The extracted MFCCs (static properties) are usually combined with their dynamic properties (temporal first and second derivatives of the MFCCs) to effectively improve speech systems' recognition performance. So, we investigate the effect of temporal first and second derivatives on the hand-extracted features. Again, 12 models (6 network structure models) are selected for the analysis, and the results are shown in Figure 9b. Combining the MFCCs with their temporal first and second derivatives does not significantly improve the network performance. The A GRU (or RNNs) network is a network with \"memory\", which can discover dependencies in the time dimension for a sample and learn some dynamic features from hand-crafted features. Therefore, redundancy will be generated by increasing the temporally first and second derivative features, which limits the model's performance when the network structure is small.  
As a result, features are extracted without considering their temporal first and second derivatives in this study.
## 5 Discussion
### Potential Applications  
The joint model architecture for SER has significant potential across various real-world applications. In customer service, it can enhance automated systems by recognizing and responding to customer emotions, leading to more empathetic interactions. In mental health monitoring, the model can provide insights into patients' emotional states, supporting early intervention strategies. The human-computer interaction can benefit from the model's ability to improve virtual assistants and chatbots, making interactions more natural and engaging. Educational tools can leverage the model to develop adaptive learning systemsthat respond to students' emotional states, fostering a supportive learning environment. Additionally, the entertainment industry can use the model to analyze audience reactions, providing valuable feedback for content creators and enhancing personalization.  
In summary, the joint model architecture for SER represents a robust and versatile solution for emotion recognition. While there are areas for improvement, its strengths and potential applications underscore its relevance and utility in various domains, paving the way for more empathetic and responsive technologies.
# World-Models for Bitrate Streaming
## 1 Introduction  
Determining bitrates for a positive user experience in unreliable or slow mobile networks can be difficult. Hardcoded algorithms may fail to accurately predict future bandwith, which leads to rebuffering, and users may prefer minimal levels of quality fluctuations. An unreliable network may cause algorithms to select the lowest quality by default; selecting the highest level that bandwidth can support may not satisfy user preferences. Existing computer systems tackling this and other related problems typically make decisions that are based on heuristics in the form of configuration parameters or human-engineered algorithms. However, humans may lack the intuition to tune parameters or design decision-making algorithms. Reinforcement learning is a machine learning paradigm, where an agent can learn to make optimal decisions through interactions with an environment. The reinforcement learning paradigm has been successfully applied to bitrate streaming in the Pensieve project, outperforming rule-based approaches [1]. However, many reinforcement learning algorithms have high sample-complexity, requiring tens of millions of interactions with the real environment to learn an optimal policy. Training these algorithms can be severely constrained by the run time and lack of parallelization in real systems environments. Human-engineered simulators, such as a simulator for bitrate streaming, can replace real environments for training. However, building simulators may require significant developer effort in order to represent the complexities of the environment. Typically, the reinforcement learning algorithms that are applied in practice aremodel-free; while these algorithms learn a policy to make optimal decisions, they do not learn the dynamics of the environment [2]. Conversely, model-based algorithms learn a model of how the environment will behave, and then use the learned model to derive a policy [3]. World-models are a type of model-based reinforcement learning, where a learned model can simulate all aspects of the environment [4]. Recent work [5,6] has shown that it is possible to train agents entirely inside learned world-models of complex environments, rather than the real environments themselves. Successfully learning world-models could provide vast benefits: cheap parallel training, faster training times, safe experimentation, and increased performance through planning. This work explores whether it is possible to build world-models for bitrate streaming, and whether world-models can provide any benefits in learning optimal policies. We provide experiments that show the performance of an agent trained entirely inside a learned world-model and provide sample complexity comparisons with current model-free algorithms. It demonstrates that world-models can successfully learn simulators from recorded data and world-models can outperform model-free approaches in low sample regimes.
# Cosmeceutical Potentials of _Equisetum debile_ Roxb. ex Vaucher Extracts
## 2 Materials and Methods
### Determination of Collagen Inhibition (Anti-Wrinkle Activity)  
Excessive and prolonged oxidative stress can promote the skin aging process associated with an occurrence of wrinkles [46]. Additionally, during a skin injury, dermal fibroblasts engage cell-repairing mechanisms to restore skin integrity. Fibroblast cells play a key role in the production of collagens and other proteins that constitute the dermis and preserve skin function [47]. Therefore, overactivated collagenase caused by oxidative stress could degenerate collagens, leading to wrinkle formation and inhibiting the wound-healing process. An inhibition of collagenase activity was proved to delay the loss of collagen fibers. It is also associated with the potential prevention of skin damage prior to injury. Collagen inhibition could slow skin deterioration and aging processes, thereby reducing the formation of wrinkles. Thus, this study examined the anti-wrinkle activities of _E. debile_ extracts via the inhibitions of collagenase activities.  
Inhibition assays of MMP-1 and MMP-2 collagenase were conducted by using the EnzChek Gelatinase/Collagenase assay kit (E-12055). The DQ collagen 1 was used as a substrate for MMP-1, and the DQ gelatin was used as a substrate for MMP-2. The experiments were performed according to the company's instruction with some modifications. Briefly, _E. debile_ extracts were re-suspended prior to being tested. The testing solution was a mixture of 20 uL of DQ substrates (12.5 mg/mL) and 80 uL of plant extract, which was followed by the addition of collagenase (0.2 units/mL). The experiment was carried out in a 96-well plate and incubated in the dark at room temperature for 90 min. Fluorescent intensity of the enzymatic reaction was measured at excitation/emission of 485/538 nm by a microplate reader. A well-known collagenase inhibitor, 1,10-phenanthroline, was used as a standard. EGCG was used as a positive control. The following equation was used to compute the percent collagenase inhibition [48; 49]:  
\[\%\;\text{collagenase inhibition}=[(\text{A}-\text{B})-(\text{C}-\text{D})]/( \text{A}-\text{B})\times 100,\]  
where A is a reaction with the presence of enzyme but without a testing sample, B is a reaction without both a testing sample and enzyme, C is a reaction with the presence of both a testing sample and enzyme, and D is a reaction with the presence of a testing sample but without enzyme. Additionally, an inhibitory capacity of _E. debile_ against collagenase activity was expressed as an IC\({}_{50}\) value.
# Review on Engineering Application Status of Gob-Side Entry Retaining Technology in China
## 3 Parametric Study of GSER Application Status
### Average Dip Angle of Coal Seams  
The change in the dip angle of coal seams affects the fracture structure of the goaf-side roof after the working face is mined, which in turn affects the stability control of the retained entry roof and entry-side structures. When the average dip angle of the coal seam  
Figure 6: Statistical chart of the GSER engineering projects with different coal seam thicknesses.  
is large, anti-toppling measures are usually required for entry-side filling, which increases the complexity of GSER construction to a certain extent. Among the 587 collected cases of GSER engineering projects, 427 cases provide data on the average dip angle of coal seams. The coal seam with the smallest average dip angle in GSER engineering projects is in Xin'an Coal Mine in Shuangyashan City, Heilongjiang Province, with an average dip angle of 1\({}^{\circ}\). The coal seam with the largest dip angle is in the Boyucun Coal Mine in Liupanshui City, Guizhou Province, with an average dip angle of 67\({}^{\circ}\). Figure 7 shows the coal seams with different average dip angles of coal seams at a statistical interval of 5\({}^{\circ}\) in GSER engineering projects.  
As shown in Figure 7, there are 396 cases of GSER engineering projects in nearly horizontal and gently inclined coal seams with an average dip angle of less than 25\({}^{\circ}\), accounting for 92.74%, and there are 33 cases of GSER engineering projects in inclined and steeply inclined coal seams with an average dip angle of greater than 25\({}^{\circ}\), accounting for 7.26%. It indicates that the current GSER technology in China is mainly applied to near horizontal and gently inclined coal seams. As the dip angle of the coal seam increases, the number of GSER application projects gradually decreases. Therefore, the current GSER technology in China is widely used in nearly horizontal and gently inclined coal seams with an average dip angle of less than 25\({}^{\circ}\).
## 2 Materials and Methods
### Data Collection  
The SRT dataset was extracted from various works of literature related to slurry [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34] and sand pack tests [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34]. The sand retention experimental report from PETRONAS Research Sdn. Bhd. (PRSB) was also added into the dataset to identify the standard variable used for the whole SRT process. The standard set of variables is needed to ensure that no essential variable is left behind. As the SRT modelling problem falls under both classification and regression problems, variable identification works were done thoroughly. The variable was classified into input and output, specifying whether it is qualitative or quantitative and labelled as a discrete or continuous variable.  
The Availability of Sand Retention Test Variables  
Once the factors that affect screen plugging and sand retention efficiency were identified, data quality assessment was done to identify and remove missing values in the dataset. In total, 38 inputs and 21 outputs with 516 observations were identified for the slurry test, while there were 42 inputs and 19 outputs with 683 observations for the sand pack test. However, due to the unmatched variable used between the literature and PETRONAS's report, a lot of missing values were detected in the dataset, especially for the sand pack test. The previous works from the literature did not reveal all the exact parameters used for SRT, leading to missing values. In this study, the variables were reduced to 8 inputs and 4 outputs for the slurry test with a different number of observations for each output. As for the sand pack test, the variables were reduced to 5 inputs and only 1 output with  
Figure 2: One-dimensional convolutional neural network (1D-CNN) model development workflow.  
263 observations. The reduced set of variables is shown in Table 1, and the set of data used to train 1D-CNN is shown in Table 2.
# Blockchain Based Delay and Energy Harvest Aware Healthcare Monitoring System in WBAN Environment
## 4 System Model
### Key Distribution  
We assume that patients move from one place to another dynamically. For this case, we design a secure, energy-efficient and delay-aware healthcare monitoring system for WBANs. We registered each patient to the _KMS_ by their _ID_, _Pwd_, location and biometric records. Patient \(P_{i}\) needs to submit \(\{ID,PW\}\) to the _KMS_. The _KMS_ first verifies _ID_ and _Pwd_ for the corresponding patient. When _ID_ and _Pwd_ are correct, then the _KMS_ verifies the patient's current location and biometric records. Finally, _SK_ is generated for registered patients. This step is formulated as follows,  
\[U_{i}\rightarrow\{ID\oplus PW\}\to KMS \tag{1}\]  
\[KMS\rightarrow\{SK(P),if(ID\&\&PW\&\&BR\&L==True)\} \tag{2}\]  
If the patient is successfully registered, the secret key (_SK_) is generated and forwarded to them and is also stored in the PBA. For authentication, we verify these entities. For authorized patients, data transmission is implemented, and data access is allowed for patients who have the secret key. A flow of security provisioning is represented in Figure 2. The encryption and decryption process is explained in the following sections.  
Figure 2: Security evaluation for WBAN.
###### Abstract  
Waste cooking oil, a known environmental pollutant, has been used as a precursor for the synthesis of onion-like nanocarbons (OLNCs) using flame pyrolysis. The OLNCs were added to TiO\({}_{2}\) to form TiO\({}_{2}\)/OLNC nanocomposites through hydrothermal treatment. The TiO\({}_{2}\)/OLNCs ratio was varied by increasing the mass of the OLNCs (10, 20, 30, and 50 mg), while the mass of TiO\({}_{2}\) (100 mg) was kept constant at C to TiO\({}_{2}\) molar ratios of 1:2, 1:4, 1:6, and 1:10, respectively. The surface area of the photocatalysts increased with an increase in the mass of OLNCs. The nanocomposites were applied in the photocatalytic degradation of methyl orange. The photocatalysts showed a degradation efficiency trend of TC-10 > (99.9%) TC-20 > (90%) TC-30 > (81%) TC-50 > (70%) TiO\({}_{2}\) (44%) in 120 min. A similar trend was observed from the first-order kinetic rate data. The degradation efficiency of methyl orange was improved by adding 5% H\({}_{2}\)O\({}_{2}\) (99.9%) in 30 min. The OLNCs were responsible for increased photocatalytic activity due to a high adsorption efficiency compared to pure TiO\({}_{2}\). The OLNCs acted as an electron acceptor, while the TiO\({}_{2}\) acted as an electron donor. The enhanced catalytic behavior was achieved by hindering the recombination of e\({}^{-}\)/h\({}^{+}\) in the composite and increasing the adsorption capability of TiO\({}_{2}\).  
nanocomposites; solar irradiation; photodegradation; anatase; methyl orange; onion-like nanocarbons +
Footnote †: journal: Applied Sciences
# Efficient Small-Object Detection in Underwater Images Using the Enhanced YOLOv8 Network
## 4 Experiments
### Comparisons with Other State-of-the-Art Methods
#### 4.3.2 Results on Pascal VOC  
To validate the generalization of our proposed method, we conducted experiments on the Pascal VOC dataset and compared the performance with two-stage detectors, one-stage detectors, and lightweight detectors. The experimental results are shown in Table 4, in which the performance indicators of the compared detectors were obtained from their original articles. As the results demonstrate, our method achieves high accuracy (mAP 84.4%) while maintaining lightweight parameters (8.5 M). Compared to YOLOv8s, it not only improves mAP by 0.5% (84.4% vs. 83.9%) but also reduces the parameter count by 23.42% (8.5 M vs. 11.1 M). Compared to YOLOv8m, our method significantly reduces these parameters by 67.18% (8.5 M vs. 25.9 M), while only experiencing a slight decrease in mAP, of 1.7% (84.4% vs. 86.1%). The results indicate that our method significantly improves performance in terms of lightweighting and model accuracy compared to previous mature detectors, demonstrating its generalization on the common dataset.
# Analysis of SNR for High-Orbit Target Detected by Ground-Based Photoelectric System
## 5 Experiment  
An infrared-K band photoelectric system was assembled to verify this model, and a few emblematical objects were observed by this system. The scene of the experiment is shown in Figure 6. The main mirror of photoelectric system was too large, no equipment or light source in the laboratory could match it. So, the moon was chosen as an ideal far-distance light source to set up the optical path. On a clear and cloudless night, the infrared detector and optical system were focused with on moon source. The observed picture of the moon shows that the imaging quality of this whole system is quite excellent.  
The motion tracking data of objects was received from the heavens above [16]. When satellites transit overhead the photoelectric system, the main servomotor detects tracking shots according to the satellite information. As the satellites only have a few transit times, the same target's next transit time is irregular. At the same time, the target moves very fast and has a self-rotation, so the observational area is equivocal. Therefore, the SNR of the target in observation images has a dynamic range, so the average SNR of multi-frames is defined as the result of one observation. Three targets were computed in Table 3 as follows:  
Figure 5: Relationship between target surface temperature and SNR.  
Figure 6: Infrared-K band photoelectric system practicality picture.  
As the targets have their own orbital motion and are composed of different surface materials, the observation of the average SNR has some errors with the simulation value. However, the average SNR can present a good judgement of whether the target can be effectively observed. Target 1962-060-A was tracked in the morning, while targets 13114 and 13777 were tracked while the sun set. We can inference from observation data that there is obvious interference to the target signal with the daylight background.
## 3 Using ML for Water Activities
### Water Quality and Streamflow Management
#### 3.2.3 Advanced Predictive Models and Data Analysis  
* **Water demand forecasting in urban areas**[259]: Hybrid models (WBANN and WANN) were developed for weekly and monthly water demand forecasting in urban areas with limited data. These models were more accurate, with improved reliability through wavelet analysis and bootstrap techniques.
* **Surface water electrical conductivity prediction**[260]: This study investigated surface water electrical conductivity (EC) prediction in the Upper Ganga basin. The random forest (RF) model outperformed others, showing improved accuracy and high correlation.
* **Data-centric water demand prediction**[261]: This study analyzed the impact of training data length, temporal resolution, and data uncertainty on water demand prediction. It was found that random forest (RF) and neural network (NN) models outperformed others, offering accurate short-term water demand forecasts.
* **Daily reservoir inflow prediction**[262]: This study explored daily reservoir inflow prediction using deep learning (LSTM) and ML (BRT) algorithms. LSTM demonstrated superior precision across various statistical measurements.