esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Initial Analysis of the Selected Articles
[50]; the authors examined the color stability of three resins like Denture 3D+, Crowntec A3, and Crowntec A2 for 3D printing--these materials were used to make dental crowns in A2 and A3 colors through laser directed energy deposition via powder feeding (LDED-PF) technology. The test samples were in the form of incisors: the first group of samples after curing and washing with alcohol was not subjected to any treatment, the second group of samples was covered with a light-curing varnish, while the third group of samples was polished in a standard way; as a result, to minimize color change using these 3D print materials, these materials must undergo complete curing, otherwise the varnish will crack. An in vitro study comparing the misfit of 3D-printed (SLM), milled (CNC manufactured), and lost wax technique (LWT)-fabricated dental crowns, is presented by [PERSON] et al. [51]; second maxillary premolar teeth were prepared for metal-ceramic crowns and equally divided into three groups for testing purposes. The tests show that fabrication technique and finish line design are critical in reducing the marginal misfit of Co-Cr copings. A novel manufacturing method for dental restoration that combines casting and selective laser melting (SLM) for obtaining one hybrid piece is presented by [PERSON] et al. [52]; by casting Co-Cr inserts on top of dental prosthetic pieces previously manufactured by the SLM of Co-Cr powder (CoMo), the authors gained high precision and high fitting accuracy between the hybrid frameworks. In addition, the process is slow due to the combination of technologies, but since no post-processing is required, it reduces costs. Based on the initial analysis of the selected articles, we conclude that there are numerous 3D printing technologies, as well as a wide range of different materials to produce dental applications, so the following is a comparison of the characteristics of the materials and 3D printing technologies that affect the quality as well as the safe usage of dental applications.
# Internet Video Delivery Improved by Super-Resolution with GAN
## 3 Cloud-Based Content Placement Framework
The DNN runs a factor \(r\) procedure to upscale that representation in surrogate servers. This procedure's output is served as mono-bitrate video sessions or is involved in the multi-bitrate publishing workflow. We designed this framework for VoD streaming services, and thus the SR tasks are performed offline and run based on a pre-established schedule. Additionally, we assume that surrogate server GPUs have the computing resources to complete the SR tasks using a parallel-processing model. The Video-Size Optimization Problem  
Problem and Goal. To pursue a high level of engagement, video services must deal with audience fragmentation. These services have to move and store a large amount of data to surrogate servers close to the audience to keep the QoE of video sessions at high levels, i.e., reduced delay and suitable streams. In this scenario, the cost of moving the video content is highly affected by the size of the encoded videos. Hence, the challenge is in solving the trade-off between decreasing the media size to be moved, consequently lowering the operating costs, and keeping the QoE at a high standard. Figure 1: The content-placement framework of cloud-based video-streaming services. In the following, we present the formulation of this challenge, which is a video-size minimization problem involving the QP and video resolution subject to a quality threshold (Equation (1)). \[ \begin{split}&\min_{q,p}\ \sum_{i}^{K}Size_{V_{i}^{LR}}(q,p)\\ & s.t.\ VQ\Big{(}DNN(V^{LR})\Big{)}-VQ\Big{(}V^{HR}\Big{)}\leqslant VQ _{T}\\ &\forall i,q\in\{0,\cdots,51\}\\ &\forall i,p\leqslant V_{T}^{LR}\end{split}  \tag{1}\]  
where \(Size_{V_{i}^{LR}}\) is the size of the low-resolution video \(i\) (in bytes). \(K\) is the number of videos involved in the operation. The quality of DNN-based super-resolved videos is \(VQ\big{(}DNN(V^{LR})\big{)}\), the quality of the ground truth videos is \(VQ\big{(}V^{HR}\big{)}\), and the threshold of the target quality degradation is \(VQ_{T}\). \(q\) and \(p\) are the QP and the resolution of low-quality videos, respectively. The aspect ratio of the low-quality video is 16:9. \(V_{T}^{LR}\) is the threshold of the low-resolution video. Approach. The \(DNN(. )\) model is trained to satisfy a threshold of levels of compression artifacts and defines the quality threshold restriction. Finding the optimal solution is unfeasible because changes in the artifact levels require the DNN model updating. Thus, we address this problem using JND as a threshold for \(VQ_{T}\) based on studies presented in [48] for an extensive video dataset.
## 3 Results and Discussion
### Physicochemical Properties  
#### 3.2.1 pH, Water Activity, and Water-Holding Capacity  
pH, water activity, and water-holding capacity are important indicators for evaluating the flavor and quality of fermented meat products. They also reflect microbial regulation of the substrate's microenvironment through metabolism. As shown in Figure 2A, the pH value of all treatment groups significantly decreased after fermentation (\(p<0.05\)). Among the groups, the carbon source treatments showed a more pronounced decrease in pH, with  
Figure 1: Total colony count of _D. hansenii_ (**A**), protease activity (**B**), and lipase activity (**C**) in the sausage medium after 5 days of fermentation. FCK: control; FCS: sucrose; FCC: corn starch; FNL: leucine; FNP: soy protein isolates. Different letters indicate significant differences (\(p<0.05\)).  
the FCS group reaching the lowest pH of 5.33. This may be due to the rapid metabolism of sucrose by _D. hansenii_, producing acidic byproducts like lactic and acetic acids [27]. In contrast, the pH decrease in FCC group was slower, which can be attributed to the gradual degradation characteristics of corn starch, a complex polysaccharide. The pH in the nitrogen source treatment groups remained relatively stable, especially in the FNP group, where the pH was maintained between 5.90 and 5.81. This stability may be due to the buffering capacity of amino acids such as aspartic acid and glutamic acid, released during the microbial metabolism of soy protein isolates. These amino acids help neutralize some of the acidic metabolic byproducts, thereby maintaining a relatively stable pH [28].  
Figure 2B,C illustrate changes in water activity and water-holding capacity of the sausage matrix before and after fermentation. Both the FCC group (complex carbon source) and the FNP group (complex nitrogen source) significantly reduced water activity and increased water-holding capacity (\(p<0.05\)). Among these, corn starch exhibited a strong water absorption capacity, continuously adsorbing free water and forming a stable protein-water network structure [29]. Soy protein isolates, possessing both water absorption and gel-forming properties, released degradation products, such as small peptides and hydrophilic amino acids, during microbial metabolism. These products further enhance water binding and improve the structural stability of the system [30]. In contrast, the simple carbon and nitrogen source treatment groups (FCS and FNL) showed no significant differences in water activity or water-holding capacity compared to the control group. This may be because neither the carbon nor the nitrogen sources were able to form stable protein-water networks, nor did they provide the spatial structure and hydrophilic groups required for effective water retention [31].
## 2 Results
### AtMYB2 Reduces ROS Accumulation under Salt Stress  
Under salt stress, plants often experience increased production of ROS. To assess whether genetic modifications could alleviate ROS accumulation under salt stress, nitroblue tetrazolium (NBT) and diaminobenzidine (DAB) staining were employed to visualize the accumulation of O\({}^{2-}\) and H\({}_{2}\)O\({}_{2}\), respectively. The results revealed minimal disparity among the wild-type, pCAMBIA1301, and transgenic _S. milliorrhiza_ under normal conditions. However, upon treatment with 250 mM NaCl, transgenic _S. milliorrhiza_ leaves exhibited reduced staining intensities compared to the WT, indicating a significant reduction in salt-induced ROS production due to the overexpression of _AtMYB2_ (Figure 3A,B). Further quantification of O\({}^{2-}\) and H\({}_{2}\)O\({}_{2}\) content corroborated these observations, revealing a notable decrease of 60.4-68.4% in O\({}^{2-}\) and a 32.4-39.5% decrease in H\({}_{2}\)O\({}_{2}\) accumulation in transgenic lines compared to the WT (Figure 3C,D)  
Figure 1: Expression pattern of _AtMYB2_ in _S. milliorrhiza_ transgenic hairy roots (**A**) and _S. milliorrhiza_ transgenic plants (**B**). * \(p\) < 0.05, **_p_ < 0.01.
# Worldwide Connectivity for the Internet of Things Through LoRaWAN
###### Abstract  
The low-power wide-area network (LPWAN) paradigm is gradually gaining market acceptance. In particular, three prominent LPWAN technologies are emerging at the moment: LoRaWAN(tm) and SigFox(tm), which operate on unlicensed frequency bands, and NB-IoT, operating on licensed frequency bands. This paper deals with LoRaWAN(tm), and has the aim of describing a particularly interesting feature provided by the latest LoRaWAN(tm) specification--often neglected in the literature--i.e., the roaming capability between different operators of LoRaWAN(tm) networks, across the same country or even different countries. Recalling that LoRaWAN(tm) devices do not have a subscriber identification module (SIM) like cellular network terminals, at a first glance the implementation of roaming in LoRaWAN(tm) networks could seem intricate. The contribution of this paper consists in explaining the principles behind the implementation of a global LoRaWAN network, with particular focus on how to cope with the lack of the SIM in the architecture and how to realize roaming.  
t 17 January 2019; Accepted: 25 February 2019; Published: 2 March 2019
# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
## 3 Sparrow Search Algorithm Optimizes Variational Mode Decomposition  
Due to the complexity of factors influencing power loads, the original load curve is usually highly unstable, which makes direct forecasting difficult, so that it is difficult to obtain highly accurate forecast values. Figure 1 shows the load dataset from Place 1 used in this paper, and it can be seen that the random fluctuation of the original load data is relatively strong. Therefore, the method proposed in this paper first decomposes the original load using Variational Mode Decomposition into several intrinsic mode components, and then performs forecasting.
# Deep Subspace Clustering with Block Diagonal Constraint
## 3 Experiments
### Experimental Settings  
#### 3.1.1 Baseline Algorithms  
In our experiments, the proposed DSC-BDC is compared with following algorithms consisting of subspace clustering in the raw space and latent space: Sparse Subspace Clustering (SSC) [17], SSC with the pre-trained auto-encoder features (AE+SSC), Low Rank Representation (LRR) [15], Low Rank Subspace Clustering (LRSC) [18], Efficient Dense Subspace Clustering (EDSC) [47], EDSC with the pre-trained auto-encoder features (AE+EDSC), Low-Rank Kernel Subspace Clustering (LR-kernel SC) [48], Subspace Clustering by Block Diagonal Representation (BDR) [20], Deep Subspace Clustering Networks (DSC-Net) [30] and Convolutional Subspace Clustering Network With Block Diagonal Prior (ConvSCN-BD) [49].  
For DSC-Net method, we run the same epochs as DSC-BDC and report the last epoch performance. For ConvSCN-BD, we use the default setting as the authors' suggestion. In the experimental setting, all the methods involve k-means clustering method for the final clustering results. The initialization of solution heavily influences the performance of k-means. Thus, we run it 20 times, and the average performance and standard deviation are reported.
# Green Hydrogen Storage in an Underground Cavern: A Case Study in Salt Diapir of Spain
## 3 Methodology and Results
### Storage Design
#### 3.1.1 Drilling of the Well-Cavern Construction  
In order to devise a drilling design that ensures the stability of the well in the short and long term, the following is a description of the aspects that must be taken into account when drilling through salt masses, in addition to the problems that may occur and the best way to solve them.  
Salt creep behavior can cause problems in the drilling stages of the well if not properly designed. For this, all aspects involved in the salt deformation process must be taken into account: stress difference, temperature, confining pressure, grain size, and the presence of water inclusions or gas bubbles [20]. The first two are the most influential on salt creep and, therefore, on wellbore stability.  
When drilling begins and the natural stress state is altered, salt flows to restore equilibrium at a rate proportional to the stress difference (between that exerted by the formation and the hydrostatic pressure of the drilling mud) and to the temperature, which increases with depth. The main recommendations to avoid this behavior are as follows:  
* The drilling mud must exert a hydrostatic pressure equal to the horizontal stress. Although the earth stress increases with depth, the hydrostatic pressure of the mud will increase accordingly, counteracting the stresses. Water- or oil-based muds are used to pass through salt formations [20]. Since the latter are more expensive and polluting, an aqueous mud saturated in salt will be used in order to avoid dissolution of the borehole walls.
* The casing design must be based on the stresses that the rock will exert, and the chosen material must have sufficient strength to prevent deformation and collapse. Long-term stability must be considered as halite remains relatively stable during drilling but continues to deform over decades of the well's life [21].
* To isolate the wellbore and prevent the application of non-uniform loads on the casing, the annular spaces between the casings should be filled with cement having high salt concentrations to prevent dissolution of the formation and additives to aid mixing and setting [20].
# Food Allergies: Immunosensors and Management
## 5 Shellfish Allergy  
Shellfish allergy influences about 2% of the global population and continues for life in most patients [54]. The detection of shellfish allergies, particularly for shrimp, is still an urgent issue because of the resemblance of allergen proteins from other invertebrates [55]. The prevalence of FAs has increased continuously in recent years and around 10% of children have various types of FAs. For sensitized persons, the generation of specific IgE antibodies from activated B cells of the immune system has been induced by exposure to shellfish allergens via ingestion or inhalation. These IgE antibodies further conjugate with immune effector cells such as mast cells and basophils to result in degranulation and clinical features of allergic symptoms [48]. Reactions to shrimp allergens are induced by different routes including the inhalation of shrimp particles, and so on. Symptoms of allergy to shrimp affect a number of organs, including skin (52-90%), respiratory tract (42%), digestive tract (35%), and cardiovascular system (10% anaphylaxis) [46]. Two allergens of the muscle protein, tropomyosin (TM) and the enzyme arginine kinase (AK), are the most important for shellfish allergies. Furthermore, tropomyosin is a major allergen in shellfish and contains a specific IgE antibody, one that accounts for the responses of about 60-80% of shellfish allergy patients and has also been demonstrated to be a good biomarker for serious clinical reaction to shellfish [56]. With the high prevalence of fish and shellfish allergies, the development of appropriate theranostic protocols is necessary to provide appropriate prevention and management strategies to eliminating unnecessary dietary confinements. Traditional clinical diagnostic approaches include clinical evaluation, oral food challenge (open or closed), skin pick test (SPT), and serum-specific IgE (slgE) detection [57].
# Simple Approach for Evaluation of Abrasive Mixing Efficiency for Abrasive Waterjet Rock Cutting
## 3 Experimental Program
### Abrasive and Rock Specim  
Figure 5a shows the abrasive, pyrorope-type Indian garnet. It is composed of Mg\({}_{3}\)Al\({}_{2}\)(SiO\({}_{4}\))\({}_{3}\), and its specific gravity is 3.79. The abrasive particles remaining on the 80 th sieve were utilized to ensure a uniform abrasive diameter, as the DBP is affected by the abrasive diameter (Equation (8)). The particle size distribution indicates a mean particle size of 0.18 mm, as shown in Figure 5b [38; 39]. Table 3 provides the details of the abrasive.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Orifice Diameter (mm)** & \multicolumn{2}{c}{**0.15**} & \multicolumn{2}{c}{**0.254**} & \multicolumn{2}{c}{**0.33**} \\
**WFR (mL/s)** & \multicolumn{2}{c}{**10.67**} & \multicolumn{2}{c}{**29.50**} & \multicolumn{2}{c}{**50.00**} \\ \hline Focus diameter (mm) & 0.76 & 1.02 & 0.76 & 0.91 & 1.02 & 0.76 & 0.91 & 1.02 \\ \hline  & 3.3 & 3.3 & 4.0 & 4.5 & 4.0 & 4.4 & 4.5 & 5.2 \\  & 4.4 & 4.5 & 7.1 & 7.5 & 5.0 & 7.4 & 10.2 & 10.5 \\ AFR (g/s) & & 5.6 & 11.0 & 12.0 & 8.6 & 12.5 & 13.3 & 15.3 \\  & & 6.6 & 13.0 & 15.5 & 14.0 & 20.0 & 1.3 & 18.8 \\  & & 7.9 & 20.0 & 18.0 & 24.0 & 24.0 & 24.4 & 23.2 \\  & & 10.0 & & & & 29.0 & & 25.0 \\ \hline Pump pressure (MPa) & & & & & & & & \\ \hline \end{tabular}
\end{table}
Table 2: Abrasive waterjet operation conditions and experimental cases.  
Figure 3: Orifices and focuses.  
The rock specimens to be cut were granite with dimensions of 150 \(\times\) 150 \(\times\) 300 mm. As granite is the most common rock in Korea, it was selected considering the practical applications of waterjet rock cutting. The granite specimens were quarried at the same depth to ensure uniform physical properties. As shown in Table 4, the uniaxial compressive strength (UCS) of the granite specimen was 236 MPa, the shear strength was 23 MPa, and the tensile strength was 12 MPa; hence, it was classified as very strong rock [40,41]. The specific gravity was 2.65, the absorption ratio related to voids was 0.27%, and the shore hardness was 114.
## 3 SSDAE-DRN Network
### Improved Method of SSDAE  
#### 3.2.1 Establishment of Regression Model  
SSDAE is an unsupervised deep neural network, which is usually applied to feature extraction and data denoising, it can be used for target recognition combined with the SoftMax [33] classification algorithm. To realize the extraction of ship shaft frequency, a depth regression network (DRN) is connected after SSDAE. The modulation line spectrum of ship radiated noise is enhanced by SSDAE, and the output vector of SSDAE is used as the input vector of DRN to obtain the regression model based on SSDAE, namely SSDAE-DRN, to realize the feature extraction of modulation line spectrum and the estimation of ship shaft frequency. DRN is a supervised deep learning network with sequence sensitivity [34], which can extract position and structural features of the line spectrum. During the training, the labeled shaft frequency corresponding to the SSDAE denoising modulation spectrum is taken as the output. Then, the features are extracted by the network and the mapping relationship between the modulation spectrum and the shaft frequency is constructed. The weight matrix of DRN is updated by the BP algorithm with the following cost function  
\[J^{N}=\frac{1}{N}\sum_{n=1}^{N}{(t^{n}-y^{n})^{2}}, \tag{14}\]  
The mean absolute error (MAE) is used as an index to measure the accuracy of shaft frequency estimation of the model, the average MAE over \(N\) samples is defined as  
\[MAE=\frac{1}{N}\sum_{n=1}^{N}{|t^{n}-y^{n}|}\;(\mathrm{Hz}), \tag{15}\]  
where \(t^{n}\) denotes the expected value of the output layer node, and \(y^{n}\) represents the actual output value of the output layer node. The improved SSDAE model (SSDAE-DRN) of ship shaft frequency extraction is shown in Figure 2.
# Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest
## 2 Materials and Methods
### Materials
#### 2.1.5 Land Cover Type Data  
These data were released by [PERSON]'s team of the Institute of Aerospace Information Innovation, Chinese Academy of Sciences. All Landsat satellite data from 1984 to 2020 are used to produce a global 30 m fine land cover dynamic monitoring product from 1985 to 2020. The product follows the classification system of baseline data in 2020, including 29 land cover types, with an update cycle of 5 years and a spatial resolution of 30 m [30].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Band**} & **Central** & **Spectral** & **Spatial Resolution** & **Main Applications** \\  & **Wavelength (\(\upmu\)m)** & **Bandwidth (\(\upmu\)m)** & **(km)** & **Main Applications** \\ \hline
1 & 0.47 & 0.45–0.49 & 1 & Visibility, Aerosol \\
2 & 0.65 & 0.55–0.75 & 0.5 & Visibility, Vegetation \\
3 & 0.825 & 0.75–0.90 & 1 & Vegetation, Aerosol \\
4 & 1.379 & 1.371–1.386 & 2 & Cirrus cloud \\
5 & 1.61 & 1.58–1.64 & 2 & Cloud/Snow, Water cloud/Ice cloud \\
6 & 2.225 & 2.10–2.35 & 2 & Cirrus cloud, Aerosol \\
7 & 3.75 & 3.50–4.00 (high) & 2 & Cloud, Fire point \\
8 & 3.75 & 3.50–4.00 (low) & 4 & Earth’s surface \\
9 & 6.25 & 5.80–6.70 & 4 & Upper-level water vapour \\
10 & 6.95 & 6.75–7.15 & 4 & Mid-level water vapour \\
11 & 7.42 & 7.24–7.60 & 4 & Lower-level water vapour \\
12 & 8.55 & 8.3–8.8 & 4 & Cloud \\
13 & 10.80 & 10.30–11.30 & 4 & Cloud, Surface temperature \\
14 & 12.00 & 11.50–12.50 & 4 & Cloud, Total water vapor \\
15 & 13.3 & 13.00–13.60 & 4 & Cloud \\ \hline \hline \end{tabular}
\end{table}
Table 1: FY-4B/AGRI specifications.
# Dolphin Sounds-Inspired Covert Underwater Acoustic Communication and Micro-Modem
## 3 Modern Design
### Digital Domain Processing  
#### 3.2.1 Core Digital Signal Processor  
The core signal processor in our design is TMS320C6748 [35]. It is a low-power application processor based on a C674x DSP core. This DSP consumes significantly lower power than other members of the TMS320C6000 TM platform of DSPs; furthermore, the rich user interfaces and high processor performance make it a better choice. Taking into account the requirements of the design and performance of the chip, the TMS320C6748 has many characteristics. Under the highest core frequency, the computing ability of the DSP chip can be up to 3648/2746 MIPS/MFLOPS, which is quite helpful for the modulation and demodulation of the signal. The kernel voltage is as low as 1.0-1.2 V, and the I/O interface voltage is 1.8 V or 3.3 V. According to the manual, the total power consumption is less than 450 mW [35]. Through the system Power Sleep Control (PSC), in idle mode, the system power consumption can be as low as 10 mW, which makes this chip very suitable as the core of the underwater communication node. Low-power design can make the size of the modem battery cabin smaller and thus easier to carry. The number of independent DMA channels is 64. It can operate at EDMA3, which provides a precondition for the high-speed transmission of data. For the real-time communication system, data transmission based on EDMA is the premise of the whole system.