esa-sceva/satcom-chunk-collection huggingface dataset:
# Peri-Implantitis Therapy Using Surgical Methods:
## 4 Discussion
In addition, it was found that compliance with precise daily control of plaque is of great importance, and the aesthetic outcome can restrict the applicability of this therapeutic method in the anterior maxilla [33]. Certain modifications of treatments following the surgical method were reported by [PERSON] et al. [32]. In this study [32], the full thickness of the flap increased, and the granulation tissue was eliminated. The treatment protocol was performed in three groups as follows: Group 1: chemical decontamination using 50% citric acid for 3 min; group 2: chemical decontamination using citric acid and harvesting the SCTG from the palate; and group 3: implantoplasty and chemical decontamination. All therapies were effective for the treatment of peri-implantitis, but SCTG maintained the greatest keratinized mucosa width. Therefore, surgical therapies along with mechanical and chemical decontamination were highly effective [32]. Overall, all therapeutic approaches were found to be effective in treating peri-implantitis; however, chemical contamination followed by SCTG was found to be the best therapeutic approach to preserving the keratinized mucosa width. [PERSON] et al. [31] used DBBMC and EMD combined with doxycycline as an effective regenerative treatment for peri-implantitis. Furthermore, [PERSON] et al. [31] demonstrated that surgical therapy using a bone graft at implants with intraosseous lesions could be a viable option. This makes us think about whether bone grafting is a promising approach for the treatment of peri-implantitis. In the literature, while the results of some studies [35; 36] demonstrated that the addition of a membrane has a direct benefit in reducing BoP and PPD when a barrier membrane is utilized for covering the bone graft alone [37], [PERSON] et al. [25] did not observe this benefit after the addition of a membrane, demonstrating that further studies are needed to determine the effects of a membrane after covering the bone graft. Moreover, it has been recently demonstrated that the application of some compounds, such as probiotics [38], natural compounds [39], and ozonized water [40], have a significant influence on peri-implant periodontal parameters. Nonetheless, future studies are needed to confirm the effects of these compounds. In this study, the risk of bias was assessed using RoB 2 and ROBINS-I tools for the five included studies, and the results demonstrated that all studies had a low risk of bias. Approximately all of the items met low risk as observed in the follow-up periods of clinical assessments. The randomization, allocation, and blinding were appropriately described.
## 3 Materials and Methods
### Optimization of the Back-Short Distance  
Ideally, a window/slot is cut into the broadside/narrow wall of an RWG from the short end, for the insertion of the MSL, placed at a distance of d = \(\lambda_{g}/4\). The guided wavelength, \(\lambda_{g}/4\), for a WR-28 at 30 GHz was calculated as 3.516 mm, with a cut-off frequency of 21.097 GHz. This was first optimized with a 0.50 mm step size and then a 0.10 mm step size, to achieve a better IL and RL over the entire Ka-band. Excellent performance in terms of IL, RL, and \(-\)10 dB absolute BW was obtained with a back-short distance of 1.9 mm and keeping a slot height constant equal to three times the substrate height (H\({}_{\text{Slot}}\) = 3 \(\times\) h), i.e., 0.381 mm, as shown in Figure 3a,b and depicted in Table 2. From the table, it can be observed that the small variations around the quarter-guided wavelength (back-short distance) decreased, resulting in lowering RL below \(-\)10 dB and the shifting of IL upward close to 0 dB, with an ultra-wide bandwidth over the entire Ka-band. With the back-short distance d = 2.0 mm, resonance occurred at a frequency of 30 GHz, with a wider bandwidth at the cost of low RL values. A more flat response was obtained, with a below \(-\)15 dB return loss and IL above \(-\)0.5 dB over a smaller absolute bandwidth from 23.52 to 45.0 GHz.  
Figure 3: _Cont._
## 4 Experiments and Discussion
### The Effect of Front-Surface Roughness on Notch Detection
of a decrease in notch size on mean amplitude of notch signal was obvious. The SARF value of notch 3 was larger than those of notches 2 and 1 for the same \(R_{a}\) and processing method. On the other hand, the SARF critical solid line that represented the critical value 3.16 is given in Figure 13b. The deeper notch corresponded to larger critical \(R_{a}\). When the roughness was larger than approximately 15 \(\upmu\)m, the SARF value was lower than the critical value for these notches. The weaker notch signals were almost immersed in the noise signal and could not be distinguished easily. Figure 13a shows the relationship between mean amplitude of notch signal and \(R_{sm}\). The horizontal direction parameter \(R_{sm}\) of the test samples obtained from different processing methods were not the same, as shown in Table 1. Comparing the \(As_{max}\) of sample numbers 1-5 and sample numbers 6-10, the mean amplitudes of notch signals changed slightly with an increase in \(R_{sm}\) at the same \(R_{a}\) and notch depth. The decreasing trend in SARF value was similar to the mean amplitudes of notch signals for different \(R_{sm}\), as can be seen in Figure 13b.
# Multibeam Wideband Transmit Beamforming Using 2D Sparse FIR Trapezoidal Filters
## 4 Full-Wave Electromagnetic Simulation Results
### Multibeam Beamforming  
Here, beamforming is achieved in \(K\in\mathbb{Z}^{+}\) directions, where \(K\geq 2\). The DODs are \(\theta_{i}\in[-90^{\circ},90^{\circ}]\), \(i=1,2,\ldots,K\). Results for different independent signal transmissions (i.e., MSMB mode) and the same signal transmissions in different desired directions (i.e., SSMB mode) are discussed below. In the following examples, all the trapezoidal filters' half-angles \(\delta_{k}\) are taken as \(3^{\circ}\) and their maximum transition band is \(0.1\pi\) rad/sample. The region \([-\pi,\pi]\times[0,\pi]\) in the 2-D frequency domain \((\omega_{x},\omega_{ct})\) is uniformly discretized into \(201\times 101\) points. The passband and stopband weights are taken as 1.
# Research on Key Technologies of Elastic Satellite Optical Network Based on Optical Service Unit
## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Networking Type** & **Principle** & **Features** \\ \hline IP & Switching/routing based on layer2/layer3 user data & Statistical multiplexing, flexible scheduling, high bandwidth utilization; different services share network resources, mutually competing and affecting each other [12; 13; 14]. \\ \hline OTN & Layer1 networking without IP resource occupation & Physical resource isolation of different layer1 channels, absolute guarantee of bandwidth, delay, and jitter; suitable for industry-level slicing [2; 3; 15]. \\ \hline All-optical & Layer0 (wavelength-level) networking with all-optical switching & Wavelength-level scheduling, all-optical switching, does not occupy electrical layer resources, lowest efficiency/energy consumption; large scheduling granularity, suitable for high-bandwidth service scenarios [16; 17; 18]. \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of satellite optical networking. (FEC) performance in OISL-OSU. A comparative analysis with RS(255,239) used in M-OTN is provided. Specifically, a tailored encoding interleaving method for RS(255,223) is developed and detailed in Section 2 (Section 2.2.2). 2. To bridge the gap between theoretical models and practical implementation, a hardware-in-the-loop simulation platform is established to evaluate the performance optimization of OISL-OSU compared to existing packet-switching methods in Section 3 (Section 3.1). The remainder of this paper is organized as follows. In Section 2, the framework and principles of OISL-OSU are introduced. Simulation setups and results are discussed in Section 3. Finally, concluding remarks are presented in Section 4.
# Short-Term Photovoltaic Power Generation Based on MVMD Feature Extraction and Informer Model
## 3 Informer Forecasting Model  
PV power generation, compared to other new energy sources like wind power, has higher electricity generation and a more stable power pattern. Time-series forecasting methods can predict future power variations based on historical power patterns, giving them a natural advantage in forecasting tasks with strong periodicity.  
In recent years, the Transformer model in deep learning has shown far better capabilities in handling time-series problems than traditional recurrent neural network (RNN) models. The Informer model, designed to address time-series data processing and forecasting issues, improves upon the Transformer by addressing three challenges in long time-series forecasting: quadratic self-attention computation, the memory bottleneck of long input stacking layers, and reduced speed in predicting long power generation. This section will introduce the Informer model and propose an improved version for PV power generation.
## 3 Results
### Peptide Identification and Molecular Docking against ACE  
Twenty-eight peptide sequences were identified within the cationic peptide fraction between 384.52 and 659.84 Da and 4-6 amino acids (Supplementary Materials Table S1). A search against the BIOPEP database revealed that most of the peptides contained known ACE-inhibiting tri-peptides within their sequence. Other bioactive properties were also identified, including antioxidant, DPP-IV inhibition, and immunomododulation. Since all peptides were predicted to possess antihypertensive (AHT) capabilities [24], these were used in molecular docking studies against human ACE and compared to known ACE inhibitor captopril. A total of three peptides YKPRP, PHGAP, and VGPPQ had the lowest binding energy when bound to ACE and were mainly stabilized by hydrogen bonds and van der Waals interactions with ACE residues (Supplementary Materials Table S2). Peptide YKPRP (\(-\)39.75 kJ/mol) had a lower binding energy compared to PHGAP (\(-\)36.82 kJ/mol), VGPPQ (\(-\)36.87.26 kJ/mol), and captopril (\(-\)24.26 kJ/mol). The ACE molecule includes three main active sites; S1 with three residues Ala354, Glu384, and Tyr523; S2 comprising Gln281, His353, Lys511, and His513; and an S1' pocket including residue Glu162 [26]. YKPRP formed multiple H-bond (Figure 4a), some of which were interactions with S1 (Tyr523, and Ala354) and S2 (His513) active site residues. Although there were not as many hydrogen bond and hydrophobic interactions, PHGAP mainly interacted with Tyr523, Ala354 residues of S1 and Tyr520, His353, His513, Gln281 of S2', and Phe457 of the hydrophobic pocket (Figure 4b). Likewise, VGPPQ showed a high degree of interactions with S1 (Ala354, Glu 384, Tyr523) and S2 (His352, His513) residues, most of which were van de Waals in nature (Supplementary Materials Table S2). The docking results also showed interactions between peptides PHGAP and VGPPQ and Zn\({}^{2+}\), at the enzyme active site, with distances less than 3.0 A to the carbonyl oxygen of the peptide. These interactions were similar to that observed of captopril, having all interactions with the ACE S1 and S2 residues of the hydrophobic pocket (Phe 457, Phe527) and Zn\({}^{2+}\) coordination on the sulfhydryl (Figure 4d).  
Figure 3: Percentage of macrophage cell viability after exposure to (**a**) CPHD and (**b**) its cationic peptide fraction. CPHD = cricket protein hydrolysates after simulated digestion with gastrointestinal enzymes. Cells were incubated with CPHD (100–1000\(\upmu\)g/mL), cationic peptide fraction (0.5–10 \(\upmu\)g/mL), or DMSO (0.25%), for 12 h, to evaluate cytotoxicity by MTT assay, with peptides. There was no significant (\(p>0.05\)) reduction in viability, as compared to the DMSO-treated cells. Data represent the mean \(\pm\) SD representative of eight (n = 8) independent experiments.  
Figure 4: 2D model of the predicted binding mode of (a) YKPRF, (b) THGAP, (c) VGPRQ, and (d) captopril to the angiotensin-converting enzyme (ACE). Images were obtained with Discovery Studios Visualizer Software and the best scored docking pose is shown.
# Microencapsulation of Polyphenols and Their Application in Food Technology
## 5 Applications of Microencapsulated Polypphenols in Food
### Functional Beverages  
Functional beverages are one of the primary applications of microencapsulated polyphenols in the food industry. Polyphenols, known for their antioxidant properties, are widely used to enhance the health benefits of beverages such as juices, energy drinks, and isotonic drinks. However, polyphenols are highly sensitive to environmental factors such as light, oxygen, and heat, which can significantly degrade their bioactivity during processing and storage. Microencapsulation offers a solution by providing a protective barrier that preserves polyphenols' stability in these beverages [173; 185]. For instance, the encapsulation of polyphenols in liposomes or nano-emulsions can protect them from oxidation and degradation during the thermal processing of juices [243]. Additionally, encapsulation allows for controlled release, which ensures that the polyphenols remain bioactive until consumed [253]. In addition to improving the stability of bioactive compounds, encapsulation also enhances the sensory attributes of functional beverages. Unencapsulated polyphenols often impart a bitter taste, which can negatively affect consumer acceptance. Encapsulating these compounds in lipid-based carriers or carbohydrate matrices can mask the bitterness, improving the overall flavour profile of the beverage. This is particularly important in isotonic drinks, where maintaining a balanced flavour while delivering health benefits is key [254; 255]. Furthermore, encapsulation improves the bioavailability of polyphenols in beverages. Many polyphenols are hydrophobic and poorly absorbed in the gastrointestinal tract. By encapsulating them in carriers like lecithin or whey protein isolate, their solubility is enhanced, leading to better absorption and increased health benefits. Examples of products on the market utilizing this technology include enhanced water beverages and sports drinks fortified with encapsulated green tea extracts or resveratrol [185].
# A Real Case of Implementation of the Future 5G City
## 3 Future 5G City Use Cases
### The 5G Project Use Cases
#### 3.2.1 e-Health  
The aim is to develop a platform for telemedicine, telemonitoring, and analysis of behavioral habits, capable of enabling processes to guarantee continuity of care and assistance. The collected data and the possibility of remote assistance allow doctors to establish an interactive relationship with patients, providing them with a personalized therapeutic path. This solution rationalizes costs and promotes the creation of high-added-value services able to meet the needs of both individual and corporate customers, and provides a series of health services that can be used remotely. Drones for the rapid transport of medicines and medical equipment will be also implemented.
## 2 Inverse Solution Algorithms  
In this section, an array model is given, along with its assumptions. Assume that the sound sources are at the farfield of the microphone array such that sound waves impinging on the array can be regarded as plane waves. In the following array model, time-harmonic dependence \(e^{i\omega t}\), \(j=\sqrt{-1}\) and \(\omega\) as angular frequency, is assumed so that the model is essentially formulated in the frequency domain. \(M\) microphones and \(N\) sources are considered. The array pressure vector can be expressed as [29].  
\[\mathbf{p}=\mathbf{A}\mathbf{s}+\mathbf{v}, \tag{1}\]  
where \(\mathbf{p}\in\mathbb{C}^{M}\) is the sound pressure vector received by the microphone, \(\mathbf{s}\in\mathbb{C}^{N}\) is the source amplitude vector, \(\mathbf{v}\) is the noise vector, and \(\mathbf{A}\in\mathbb{C}^{M\times N}\) is the steering matrix associated with the sources. Therefore, given the pressure measurement \(\mathbf{p}\) and the steering matrix \(\mathbf{A}\), solving the problem of Equation (1) for the unknown source amplitude vector \(\mathbf{s}\) is a linear inverse problem. Linear inverse problems can be divided into three categories: square systems (\(M=N\)), overdetermined systems (\(M>N\)), and underdetermined systems (\(M<N\)). In the following, solution strategies are presented for these categories.
# Development of a GNSS Buoy for Monitoring Water Surface elevations in Estuaries and Coastal Areas
## 3 Instrumentation, Results and Discussion  
A GNSS buoy was established, and laboratory and field tests were conducted to verify the capability of the device and the accuracy of the monitored water surface elevations. Furthermore, the 1-D wave spectrum and the directional wave spectrum of the water surface elevation obtained from the GNSS buoy were compared with those obtained from the ATC sensor. For the following discussion, Taiwan Time is used as the time zone.
# Embedded AI-Based Digi-Healthcare
## 3 Proposed Methodology  
The proposed solution is based on the Internet of Medical Things (IoMT) and AI. The IoT part is concerned with developing the wireless sensor network of medical devices and sending the data through the internet to the cloud. The standard sensors (MAX30102-Maxim Integrated and MLX90614-Melexis Semiconductor Solutions) will be used to monitor the body vitals, and these sensors will send data to the edge where edge computing will be performed to make decisions locally using ML inference. In addition to this, the vitals' data and the ML prediction will also be uploaded to the cloud for archival purposes. The proposed system would be useful for the patients, caregivers, and the hospitals to manage the non-COVID patients at home. Doctors can send the patients' medical advice remotely except to those who are critical, need real-time monitoring, require special care, and specialized medical equipment. Further, many of the non-COVID patients are reluctant to visit hospitals owing to the chances of catching this virus due to many pandemic cases in the hospitals. The proposed system would be useful for such cohorts as well as for the people who are not patients but are considerate about their well-being and fitness. As compared to conventional healthcare, which is only limited to hospitals, the new paradigm of remotely monitoring the patients by exploiting the latest technologies such as IoT and AI is the future of health care and can help us to leverage these technologies and maximize their benefits in the context of remote health care.  
The proposed system architecture (Figure 1) is described in detail hereafter. There is a network of sensors that are going to measure body vitals. These sensors will be employing Wi-Fi communication technology to transfer the data to the edge node. The edge node will include a local processing unit for analog to digital conversion, pre-processing methods, and data aggregation in a local data storage. It will furthermore run local decision making based on ML and in case it detects any abnormality in the patient's vitals it will notify the patient, the caregivers, and the doctor. Hence, the alert will be triggered from the edge device. The edge node will transfer pre-processed data to the cloud's database, where the heavier tasks would be implemented such as training ML algorithms since it has maximum computation ability. After training, the model is downloaded by the edge device using Amazon Web Services (AWS) where it can start prediction on real-time data. This architecture was designed to decrease the latency in response time if an abnormality in the vitals is detected. The predictions or the results would be transferred over to the website server, where authenticated users (patients & doctors) can login to view the results and trends derived from the sensor's data.
# Multivariate Time Series Deep Spatiotemporal Forecasting with Graph Neural Network
## 3 Methods
### Graph Learning Module  
During the training of the model, the spatial information representation of the input data and the implicit relationships among multiple sequences are obtained by the graph learning module. In existing research, graphs are mostly constructed by the method of node similarity and distance, and these graphs are usually bidirectional or symmetric. For the prediction task in this paper, other nodes may be affected by the change in one node. For example, if the traffic roads in a certain area are congested, other roads in the area will also change. When solar power generation in a region rises, other sites in the region will also have certain changes. Therefore, a one-way relational graph structure is more suitable to be constructed:  
\[A_{1}=tanh(\alpha\times E_{1}\times W_{1}) \tag{1}\]\[A_{2}=tanh(\alpha\times E_{2}\times W_{2}) \tag{2}\]  
\[B=A_{1}\times{A_{2}}^{T} \tag{3}\]  
\[A=Relu(tanh(\alpha\times(B-B^{T}))) \tag{4}\]  
According to the above formula, the initial adjacency matrix \(A\) can be obtained. \(E_{1}\) and \(E_{2}\) are learnable nodes encoding information. \(W_{1}\) and \(W_{2}\) are model-learnable parameters. The hyperparameter \(\alpha\) is used to prevent the vanishing gradient problem caused by the saturation of the activation function. The adjacency matrix is converted to a one-way matrix by matrix subtraction and activation function Relu. When \(A_{ij}\) is a positive number, \(A_{ij}=0\):  
\[A[i,not\ argtopk(A[i,:],k)]=0 \tag{5}\]  
Argtopk() extracts the indices of the top \(k\) maxima of the vector. In order to simplify the calculation amount, the \(k\) nodes with the largest value among the nodes are selected as the nodes with high relevant information during graph convolution. Unselected nodes are assigned the value 0. This method is more flexible in constructing graphs, and the internal information of the graph can be adjusted according to the data during the training process.