esa-sceva/satcom-chunk-collection huggingface dataset:
# Endoscopy at Bedside in Isolated Patients with Severe COVID-19: Our Approach during the Pandemic
## 1 Introduction  
The COVID-19 pandemic has increased the fear of viral spread through both patients/healthcare workers and aerosol-generating procedures, as in endoscopy. At the beginning of the outbreak of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), there were no exploitable recommendations regarding the proper policy to apply for limiting the virus spread during endoscopy. That is why, initially, healthcare workers cancelled or postponed endoscopic procedures that were not clinically relevant in order to avoid potentially dangerous viral exposure of both sides, healthcare workers and patients. [1] During the first wave, this approach was reasonable, but during and after the second wave, it was not ethically allowed to postpone procedures due to the risk of missing pre-cancerous lesions or gastrointestinal (GI) cancers on time [2, 3].  
Patients with severe COVID-19 may develop major GI complications, such as GI bleeding or tracheeesophageal fistula (TEF), or require nutritional support (percutaneous endoscopic gastrostomy (PEG/PEG-jejunal) or feeding tube insertion).  
Where and how to perform endoscopy for these patients has been a tricky issue since the beginning of the pandemic for many reasons, from adequate personal protections to appropriate instrument reprocessing. Furthermore, before the outbreak of SARS-CoV 2, a patient in isolation for any multi-resistant microorganism was accurately and safely transported to the endoscopic theater at the endoscopic service while taking proper precautions. This strategy was initially not applicable to SARS-CoV 2-positive patients, so when the pandemic globally spread, many departments changed their management strategies, and most of the hospitals adapted their facilities to the ongoing condition [4; 5].  
A major concern regards appropriate personal protections, which may minimize the risk of viral transmission during endoscopic procedures. A prospective study examined the extent of viral contamination present in the local environment before and after endoscopic procedures on COVID-19 patients, showing a relatively low risk of contamination compared with many other settings in hospital units during the first wave [6; 7].  
The endoscopic theater is an ergonomic room with easy visualization of monitors, protective radiation shielding, and chairs for sitting during long cases. These comforts help to prevent work-related fatigue and injury. When a complex procedure needs to be performed at bedside in an intensive care unit (ICU) or COVID unit, there is an increased risk of viral contamination and a decrease in healthcare comfort, which leads to more challenging endoscopic procedures. The aim of our study was to evaluate the current evidence regarding endoscopic procedures performed at the patient's bedside and to provide our experience in endoscopy for COVID patients.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### Resistance Against Differential Attacks  
Cryptanalysts use differential attacks to find the relationship between a plaintext image and its cipher by making small alterations to the plaintext image and encrypting it with the same key. An encryption algorithm should have excellent diffusion characteristics, where modifying a single pixel in the plaintext image should generate a completely different cipher image. An encryption algorithm's ability to resist differential attacks can be evaluated by analyzing the algorithm's performance using the avalanche effect, NPCR, and UACI tests. The details of each of these tests are further discussed in the following.
# A Graph Convolutional Network-Based Method for Congested Link Identification
## 3 The GCN Approach
### GCN-Based Congested Link Identification Algorithm  
The congested link identification algorithm based on a GCN consists of two phases: the training phase and the diagnosis phase. In the training phase, the required parameters include the network topology \(T\), the training data set \(\Omega\), and the graph convolutional neural network model \(M\).  
In the training phase, the batch size in each epoch is \(\eta\), and the number of training epochs is \(\epsilon\). In the training process of each epoch, an episode is randomly selected from the training set \(\Omega\) and construct graph \(G\) according to the test results using the method  
Figure 8: GCN model structure.  
Figure 9: Schematic depiction of our entity classification model.  
in Section 3.2. Through the graph convolution model, the estimated value \(X_{e}\) is obtained based on \(G\). The loss is then calculated using the loss function, and then the neural network model \(M\) is optimized using the optimization function.  
We saved the trained model \(M\) from the epoch with the maximum accuracy value as a result of the training.  
We detail our algorithm in Algorithm 1.  
```
training phase require:\(T,\Omega,M,e,\eta,acc\) inputs:\(\Omega\) outputs:\(M\) setps:\(Initialize()\) while\(epoch<e\) while\(iter<\eta\) select randomly\((X,Y)\in\mathbf{\Omega}\) \(G=build\_graph(Y,T)\) \(X_{e}=M.forward(G,Y)\) \(loss=get\_loss(X,X_{e})\) \(M.optimizer(loss)\) calculate acc if\(acc>max\_acc\) \(max\_acc=acc\) save(M)
```  
DIAGNOSIS PHASE  
**require:**\(T,M,Y\) inputs:\(Y=(y_{1},y_{2},\dots,y_{n})\) outputs:\(X=(x_{1},x_{2},\dots,x_{m})\) setps:\(\mathbf{load}(M)\) \(G=build\_graph(Y,T)\) \(X_{e}=M.forward(G,Y)\) return\(X_{e}\) ```  
**Algorithm 1** Congested link identification (GIC) algorithm.
## 7 Future Work  
Based on the results obtained, we consider that future research could focus on analyzing the impact of external signal interference from electronic devices, other WiFi networks, and RF signals on signal propagation. Furthermore, evaluating how data traffic in WiFi networks (number of users, congestion, and transmission speed) affects signal quality and path loss prediction would be of interest. Furthermore, it would be relevant to study the interaction with other wireless networks, such as LTE, 5G, and Bluetooth, to understand how these influence signal propagation and to explore the effects of complex urban environments where buildings and metallic structures may significantly impact signal behavior. These approaches would contribute to improving propagation models in diverse real-world scenarios.
# Actuating Performance Analysis of a New Smart Aggregate Using Piezoceramic Stack
## 1 Introduction
In addition, the energy of the stress waves is easily dissipated, and the waves usually travel a relatively short distance in the concrete structure [63]. Therefore, SAs with large output will be beneficial in damage detection in concrete structures, especially in large-scale structures. In our study, a novel type of smart aggregate using piezoceramic stack (SAPS) is proposed to improve the traditional SA's performance. Due to its strong piezoelectric effect, lead zirconate titanate (PZT) material in the form of discs was adopted, and the discs were connected in series to form a stack, which was used to replace the single traditional piezoelectric patch in the traditional SA. SAPS can significantly increase the output of a smart aggregate. To facilitate the theoretical and numerical studies, the proposed SAPS was simplified to a one-dimensional axial model to theoretically investigate the electromechanical and displacement output characteristics of SAPSs, and the influence of the structural parameters and the elastic modulus of the epoxy on the output performance were analyzed. Experiments were also conducted to validate the analytical and numerical results. The rest of the paper is organized as follows: Section 2 proposes a simplified one-dimensional axial model by using the theory of piezoelasticity and compares the performance of SAPS and the traditional SA. Section 3 investigates the electromechanical and output performance of SAPS by using the simplified model and presents the experimental validation. Lastly, Section 4 concludes the paper.
###### Abstract  
With the acceleration of urbanisation and the diversification of residents' travel needs, rail transit plays a critical role in mitigating traffic congestion. However, existing studies predominantly rely on linear models, neglecting the nonlinear effects and spatial heterogeneity of built environment factors on ridership. To address this gap, this study integrates the Multiscale Geographically Weighted Regression (MGWR) model and the Gradient Boosting Decision Tree (GBDT) model to analyse the impact of built environment factors on total, inbound, and outbound ridership in Shenzhen. Utilising Automatic Fare Collection (AFC) data and multiple built environment variables, we identify six key factors (office type, accessibility, road network density, floor area ratio (FAR), public services, and residential type) through SHapley Additive exPlanations (SHAP) value and partial dependency plot (PDP) analysis. Notably, this study constructs a three-dimensional PDP to explore the linkage effects of building volume ratio and accessibility, revealing their joint influence on ridership. The results demonstrate that the GBDT model outperforms MGWR in handling high-dimensional nonlinear data. This paper provides policy recommendations for transport authorities, highlighting the synergies between optimising the planning of the built environment and the development of rail transport to improve the efficiency of short-distance commuting while supporting long-distance cross-city travel.  
Keywords:built environment factor; multiscale geographically weighted regression model; gradient boosting decision tree model; nonlinear effect; spatial heterogeneity; threshold effects +
Footnote †: journal: Applied sciences
# Research on the Trajectory and Operational Performance of Wheel Loader Automatic Shoveling
## 2 Test Platform and Test Process Analysis  
### Analysis of Comprehensive Performance Test Platform for Wheel Loader Operation Process  
In order to obtain the primary data of the loader operation process and to optimize the shoveling performance, this paper conducts repeatability tests on wheel loaders based on the developed comprehensive performance test platform for the loader operation process. The platform is based on test methods of full bucket rate, fuel consumption, practical work, and resistance. Additionally, then built based on the loader power system with test sensors and self-developed measurement devices. It is mainly composed of an operation parameter measurement system, test dolly power system, automatic shoveling control system, operation material preparation device, data processing system, and other parts. As shown in Figure 1, the operational parameter measurement system mainly consists of pressure sensors, flow sensors, displacement sensors, etc. The sensors are used to monitor real-time resistance, fuel consumption, and other performance parameters. The test dolly power system mainly consists of a wheel loader with a shoveling capacity of 5 tons as test dolly because the vehicle has the advantages of fast operation, high efficiency, and easy operation, which is one of the main types of machines for earthwork sites. As shown in Figure 2, it consists of a working device, frame, hydraulic system, transmission system, double-variable system, drive axle, and engine subsystem. The automatic shoveling control system mainly controls the loader to carry out automatic shoveling according to the designed shoveling trajectory, which avoids the error of human operation and improves the repeatability and versatility of the test. It also increases the manual shoveling mode, and the control system has the function of \"manual operation priority\" to protect the safety of people and loaders. The operational material preparation device is to return the shoveled material to its previous state, thus ensuring the same shoveling conditions and improving the consistency of test data. The data processing system mainly collects, analyzes, and stores the shoveling data collected by the sensors of the test platform.  
Based on the above analysis, the loader operation process comprehensive performance test platform for automatic shoveling test has versatility and repeatability, which can also improve the accuracy of test data and maintain a high degree of consistency.  
Figure 1: The composition of the operational parameter measurement system.
# MEC Server Sleep Strategy for Energy Efficient Operation of an MEC System
## 3 System Model
Since \(\lambda_{i}(t)\) depends on \(a_{i}(t)\), \(\hat{\lambda}_{i}(t)\), and \(\sum_{j\in\mathcal{M}_{i}}(1-a_{j}(t))\hat{\lambda}_{j\to i}(t)\), we do not know the characteristics of the task arrival process to each MECS. Therefore, we take a conservative approach to bound the queue length. In other words, we aim to maintain \(Q_{i}(t)\)s under \(Q_{thr}\). The energy consumed by an MECS depends on its load. According to [9], the energy consumed by an MECS in an active state during a time slot \(t\) is given as  
\[e_{i}(t)=\alpha P_{m}+(1-\alpha)P_{m}p_{i}(t), \tag{2}\]where \(P_{m}\) is the maximum power consumed when the load is the highest, \(\alpha\in[0,1]\) determines the fraction of power consumed when an MECS is idle, and \(\rho_{i}(t)\) is the utilization of a MECS during a time slot \(t\). Since the amount of tasks that an MECS can process during a time slot is given as \(\eta_{i}(t)=\min\{Q_{i}(t)+\lambda_{i}(t),\Delta_{t}F_{i}\}\), the utilization of an MECS \(i\) during a time slot \(t\) is given as  
\[\rho_{i}(t)=\frac{\eta_{i}(t)}{\Delta_{t}F_{i}}. \tag{3}\]  
Therefore, the total energy consumed in an MEC system during a time slot is given as  
\[E(t)=\sum_{i\in\mathcal{M}}a_{i}(t)e_{i}(t). \tag{4}\]  
Our goal is to find an optimal sleep control vector \(\Omega(t)=\{a_{1}(t),\ldots,a_{|M|}(t)\}\) at the start of each time slot that minimizes the total energy consumption in an MEC system while providing a reasonable service to the users. Thus, our MECS sleep control problem is formulated as follows. \[P1: \Omega^{*}(t)=\arg\min_{\Omega(t)}E(t)\] \[s.t. Q_{l}(t)<Q_{th},\quad\forall t,\forall i\in\mathcal{M}.\] \[\sum_{j\in\mathcal{M}_{i}}a_{j}(t)\geq 1,\quad\forall t,\forall i \in\mathcal{M}\text{ whose }a_{i}(t)=0. \tag{5}\]  
The first constraint is that each MECS bounds its queue length. Wireless communication is constrained mainly by the distance between a user and a BS. Thus, to offload a task from a user to an MECS, the distance between them should be below a certain value. In this paper, we assume that MECSs are provisioned so that each user is able to communicate with an MECS located at a distance of two hops. Thus, the second constraint is that at least one of the neighboring MECS of the sleeping MECS must be active. The problem (5) is a combinatorial problem and is NP-hard in general. In addition, to solve the problem at the start of each time slot, \(\lambda_{i}(t)(\forall i\in\mathcal{M})\) should be given. However, \(\lambda_{i}(t)\) cannot be known at the time when we need to determine \(\Omega^{*}(t)\). Therefore, to resolve these issues, we adopt a deep learning model STGCN to predict the task arrival rate for each MECS by considering the spatio-temporal relationship among them. Given the set of predicted task arrival rates, we employ a genetic algorithm to solve the problem (5) fast.
# Location Selection Methods for Urban Terminal Co-Distribution Centers with Air-Land Collaboration
## 3 Modeling
### Assumptions  
Based on the problem description, the following assumptions are made:  
1. The information of all customer demand points is known.
2. After the number and location of the co-distribution centers are determined, the UAV distribution flight routes and vehicle travel routes are fixed. All routes start from a co-distribution center and return to the center after completing the customer distribution service.
3. The distribution capacity of the two types of co-distribution centers is fixed.
4. The cost of UAVs is mainly considered in the flight process, not in the hovering state.  
Figure 1: Schematic diagram of the planning layout of air–ground cooperative co-distribution centers.  
5 Sufficient energy (fuel) is maintained during the transportation of both UAVs and vehicles, without considering the time required for UAVs to take off, land, recharge and change the power source, and load and unload cargo.
6 UAVs and vehicles fly and travel at a uniform speed.
Sialic acids constitute a family of nine-carbon acidic monosaccharides present at sugar chain ends in humans and other animals, and their most widespread form is _N_-acetylheuraminic acid (Neu5 Ac, NANA, and Sia) [10; 11]. Neuraminidase (commonly referred to as sialidase, EC 3.2.1.18) hydrolyzes terminal sialic acids in oligosaccharides, glycoproteins, glycolipids, and mucins, among other substrates [12; 13; 14]. Many viruses and pathogenic bacteria colonizing mucous surfaces contain enzymes that contribute to their pathogenicity and virulence. Pathogenic bacteria that secrete neuraminidaseases follow the scavenger pathway to liberate sialic acid from various host sialoglyoconjugates [15]. Additionally, neuraminidaseates participates in bacterial cell metabolism, providing it with free sialic acids as an alternative carbon and energy source [16; 17]. The enzyme enhances bacterial survival outside microorganisms, particularly in environments containing sialic acids [18]. These enzymes have served as candidates for inhibition in the development of antimicrobials [19; 20; 21]. In this study, we aimed to examine and characterize the major components of \(C\). _ussuriensis_ roots. We used bioactivity-guided fractionation to isolate potential BNA inhibitors from the roots of \(C\). _ussuriensis_. By using spectroscopic methods, the compounds were identified and investigated for their capacity to reduce the activity of bacterial neuraminidase. Lineweave-Burk and Dixon plots were used to determine their inhibitory mechanisms. Additionally, the chemical compounds in the root extracts were identified using ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF/MS).
# A Generic Block-Level Error Confinement Technique for Memory Based on Principal Component Analysis
## 3 PCA-Based Error Confinement Technique  
In this section, we propose a PCA-based error confinement technique for data blocks in memory. As shown in Figure 1, upon data storing, the technique first performs modularization to the original data to reduce processing overhead. Afterwards, PCA is applied to individual data blocks to extract the data features. The Error Confinement Vectors (ECVs) are further calculated according to the extracted features to implement low-cost error confinement. During data read, errors in eigenvectors and projection data are detected, followed by ECV assisted error confinement method during data reconstruction. This section introduces the steps of proposed technique.
## 3 Results and Discussion
#### 3.1.4 Colour of the Films  
The colour of a film plays a vital role in its application to food products [51]. This is because colour properties can affect the consumer acceptability [47]. The colour of the films is typically reflected by lightness (L), redness (a), greenness (b), total colour difference (\(\Delta\)E), Chroma and Hue angle [52]. The results (Table 2) showed that GP film had similar colour to the GPP in term of L values (around 95.55) and Hue angle (around 94). These findings were similar to the results from a previous study by [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [33]. However, there was a significant difference in colour between films which incorporated essential oils and plant extracts. Of note, there was no significant difference in the lightness of GP film compared with essential oil added films, such as GGG, GLG, GPM, GLO, and GGO (Table 2). The results contradicted previous studies, where the addition of essential oils, depending on their nature, could either increase [46] or decrease [48; 53] in the lightness of the films.  
The results showed that the GP film and the films incorporated with essential oils had negative values for greenness and were positive for redness, whereas the films incorporated with plant extracts had positive values for both greenness and redness (Table 2). It is interesting to note that the film incorporated with gac oil had a significant difference in colour attributes in comparison with other films. This is due to the high levels of carotenoids found in gac oil, which produce an intense red colour [54]. The results further indicated that films incorporating essential oils had higher Hue angles, ranging from 95 to 98, as compared to that of the gac pulp control film (94.05 \(\pm\) 0.3). Of note, the film incorporated with gac oil had the highest value of Hue angle (97.54 \(\pm\) 0.2). The results also showed that the films incorporating plant extracts generally had lower values of Hue angle, ranging from 82.77 \(\pm\) 0.23 to 84.6 \(\pm\) 0.25 (Table 2). This is due to the higher concentration of polymeric colour in plant extract-based films [55].  
Figure 3: Opacity of studied edible films incorporating essential oils and plant extracts. Data are means \(\pm\) standard deviations. Data of each column not sharing similar letters are significantly different at \(p\) < 0.05.  
Impact of Essential Oils and Plant Extracts on Barrier and Mechanical Properties of the Seaweed Hydrocolloid and Gac Pulp Edible Films
###### Abstract  
Purpose: Recently, a novel approach to apicoectomy has emerged, involving the use of a hollow trephine with a surgical guide. This innovative technique creates a semicircular section, in contrast to the conventional straight cut (with a bur). The semicircular shape of this section raises questions about the method's effectiveness in eliminating supernumerary canals (SNCs), which are auxiliary canals alongside the main root canal. These canals pose a risk of further infection if not thoroughly eliminated. The aim of this exploratory study was to assess the efficacy of the proposed method in the removal of SNCs. Methods: A sample of 200 extracted single-rooted human teeth was rigorously narrowed down by multiple steps to 33 specimens that contained SNCs in the apical third. These specimens underwent apical semicircular sectioning, removing the apical 3 mm of the root. The specimens then underwent repeated CT scanning. For samples with residual SNCs, the section was transformed into a straight section and another CT scan was performed. Results: The semicircular section eliminated all SNCs in 94% of the specimens, and it eliminated 97.3% of the SNCs in all specimens. The straight-line sectioning eliminated 98.6% of all SNCs and eliminated all SNCs in 96.97% of the SNC-containing teeth. Conclusions: The efficacy of semicircular apicoectomy performed with a trephine appears to be comparable (\(\chi^{2}\) = 1.00, df = 1, and \(p\) = 0.317) to that of the conventional straight apicoectomy performed with a bur as reported in the literature.  
apicoectomy; trephine; endodontic surgery; surgical techniques Article  
An Exploratory In Vitro Microcomputed Tomographic Investigation of the Prestigation of the Efficacy of Semicircular Apicoectomy Performed with Trephine Bur. _Appl. Sci._**2023**, _13_, 9431. [[https://doi.org/10.3390/app13169431](https://doi.org/10.3390/app13169431)]([https://doi.org/10.3390/app13169431](https://doi.org/10.3390/app13169431))