esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Refrigerant Charge Reduction  
As mentioned, the conversion of a direct expansion system to an indirect layout allows a large reduction in the refrigerant charge in the plant [7; 9], thus helping reduce the direct impact of the refrigeration since leakages mainly occur in the distribution line fittings or in the evaporator of the cabinets.  
To evaluate the refrigerant charge reduction that allows the conversion in the experimental plant used in this work, the highest heat rejection level (T\({}_{w,in}\) = 43.6 \({}^{\circ}\)C) has been considered as a reference. The experimental refrigerant charges using the direct expansion system configuration for that condition were of 5.41 kg with R-134a and of 6.51 kg with R-507A. These refrigerant charges were the minimum that guaranteed the outlet line of the receiver would be filled with liquid refrigerant.  
The refrigerant charge reduction that allows the system conversion can be evaluated using Equation (6). This equation evaluates the refrigerant charge reduction considering the refrigerant in the liquid and vapour distribution lines, and in the direct expansion evaporator of the cabinet. The average condensing and evaporating pressures (Figure 5) and average outlet temperatures of condenser and evaporators (cabinet evaporator or SF evaporator) have been used to obtain the refrigerant density along the liquid and vapour lines. To compute the refrigerant charge in the evaporators, a mean void fraction of 85% has been considered, as recommended by [PERSON] et al. [26].  
\[\Delta M_{ref}=M_{liquid\ line,\ DX}+M_{vapour\ line,\ DX}+M_{ evaporator,\ DX}-M_{ evaporator,\ SF} \tag{6}\]  
Table 4 summarizes the inner volumes of distribution lines and evaporators, as well as the refrigerant charge contained in those elements when operating with R-134a or R-507A. As can be seen, the major proportion of refrigerant is contained in the direct expansion evaporator and in the liquid distribution line. Finally, Figure 12 illustrates the refrigerant charge reduction obtained in the experimental plant when going from a direct to an indirect system. The main reduction occurs in the evaporator for both refrigerants, due to the large volume of this element in commercial cabinets, followed by the liquid distribution line. The reduction due to the vapour distribution line is insignificant. It needs to be mentioned that the refrigerant charge reduction in the liquid line in a large multiplexed refrigeration system would be larger due to the large distribution system.  
In this case, taking as a reference the initial refrigerant charges of the plant, the conversion of this system to an indirect one allows a 42.9% refrigerant charge reduction when using R-134a as refrigerant  
Figure 11: Increment on system energy consumption of conversion of DX to SF during 24-h test.  
and 32.8% with R-507A. It is important to highlight that the refrigerant charge reduction in a larger system would be higher.
## 2 Numerical Approach
### Fluid Properties and Two-Phase Modeling  
#### 2.3.1 Water Properties  
The physical properties of water are considered to be temperature-dependent while those of the solid nanoparticles are kept constant (see Table 1). The following equations are used to evaluate the properties of pure liquid water (henceforth subscripted by \(bf\) for base fluid) as a function of temperature \(T\):  
* Density [28]: \[\rho_{bf}=2446-20.674T+0.11576T^{2}-3.12895\times 10^{-4}T^{3}+4.0505\times 10^{-7 }T^{4}-2.0546\times 10^{-10}T^{5}\] (1)
* Viscosity [29]: \[\mu_{bf}=A\times 10^{\left(\frac{B}{T-c}\right)}\] (2) where, \(A=2.414\times 10^{-5}\), \(B=247.8\) and \(C=140\).
* Specific heat [30]: \[Cp_{bf}=\exp\left(\frac{8.29041-0.012557T}{1-1.52373\times 10^{-3}T}\right)\] (3)
* Conductivity [31]: \[k_{bf}=-0.76761+7.535211\times 10^{-3}T-0.98249\times 10^{-5}T^{2}\] (4)  
Note that the above equations are similar to those used in the former numerical simulations of [PERSON] et al. [19] to ensure direct comparisons.  
Figure 1: Schematic view of the computational domain with the boundary conditions.
## 2 Handle Recognition and Positioning Method Description  
In this section, the proposed identification and positioning methods are described. With the increasing demand for target detection accuracy and speed in practical applications, the current target detection algorithms mainly use detection methods based on deep learning. Target detection algorithms based on deep learning include Faster R-CNN [20], SSD [21], YOLOv3 [22], YOLOv5, etc. Compared with traditional detection methods, YOLOv5 has the characteristics of good robustness, strong generalization, and high precision.  
The experiment realizes the real-time detection of the door handle by the YOLOv5 method. First, we capture multiple door-handle pictures through the camera. Besides, we annotate images with the Make Sense online tool to create a door handle dataset. Finally, the door-handle model is trained on the server and the trained model is applied to door-handle detection.
## 2 Methods
To evaluate the model in a realistic scenario, the model is trained on data from 2019 and tested on data from 2020 and 2021. The model is also trained on data from both 2019 and 2020 and tested on the 2021 measurements. In this manner, we test whether the calibration done in one year could be applied in the following year, and if the calibration done during regular activities (2019) could be applied when there was a lockdown (2020). The gathered raw pollutant measurements from each of the three years are initially corrected using the Environmental Protection Agency (EPA) co-location method, with linear regression (LR) between the raw and reference values obtained in the two-week period preceding the observed one (LR performed on the data gathered from 2 March to 16 March of the respective year) [18]. After this, each measured variable is grouped into sequences with a length of 24. A window of 24 h (1 day) is moved through the data of length 1248 for a single year (number of hours in the observed main interval), with a stride of 1 hour creating a 1224 \(\times\) 24 matrix of data. The corrected sensor measurements, RH and t data are concatenated, resulting in a 1224 \(\times\) 24 \(\times\) 3 matrix of input data created for each year. The output data for each year are 1224 \(\times\) 24 matrices containing reference measurements. The data instances (1224 per year), each of shape 24 \(\times\) 3, are the input to the developed convolutional ANN, and the reference measurements for the respective 24 h are the output of the network. The architecture of the convolutional ANN used for the correction of the influence of relative humidity and temperature on measurements is given in Table 1.  
Figure 1: Air quality ekoNET device, AQ10x. The whole device is shown on the left, with the bottom view of the device shown in the middle and the interior shown on the right. The sensors are placed at the bottom of the device, with access to open air, while the power supply and the microprocessor with the data transfer module are placed on the back. This convolutional ANN is trained for each pollutant separately, but the architecture itself remains the same for all pollutants. To evaluate the algorithm, the commonly used metrics R2 and root mean square error (RMSE) are used [19].
# A Review of Recent Advancements in Knock Detection in Spark Ignition Engines
## 3 Advancements in Knock Detection
### Other Signals  
A number of studies are evaluating novel techniques for detecting knock in engines. Such techniques generally use sensors and components that are already in the engine, with a goal of being able to replace the knock sensors by simply processing other signals from the engine, as shown in Figure 7, which provides an overview of other signals that are being researched for knock detection. The ultimate goal of many of these studies is to allow for the assessment of knock using sensors that are already in the engine and take advantage of the increased computational power that is available in modern vehicles.  
Figure 7: Summary of advances in signal collecting and processing of different sensor data to detect knock.  
It is important to note that these studies typically base their models on pressure trace data to identify if an engine cycle was knocking or not. Given that these studies use the pressure data to train their models, they are naturally not as accurate as in-cylinder pressure measurements for predicting knock. Regardless, they show promise compared to accelerometer data, perhaps reducing the need for knock sensors in future engines.
## 3 Results and Discussion
### State of the Art Regarding This Implant
#### 3.1.1 Outcome Analyses (Survival Rates and Hard and Soft Tissues Parameters)  
Table 1 reports the nine clinical studies published so far with the longest follow-up available. The follow-up ranged from 1 year to 6 years. Most of the studies reported no implant loss. Only one study reported a 97% survival rate, but it considered multiple abutments performing an immediate load [22]. It should be noted that the clinical data available in the literature on Prama implants were mostly attributable to four European research groups.  
Concerning the surgical techniques, a flap approach was mostly used (eight out of nine studies), while a flapless technique was performed in only one study.  
Interestingly, variations in the implant placement height and neck position were proposed with different clinical approaches. One study placed the neck at the subcrestal level, while it was placed at the crestal and midcrestal levels in three studies and five studies supracrestal level.  
Concerning the prosthetic interventions, screw-retained rehabilitations were performed in 6/9 studies, while the other studies used cemented rehabilitation.  
As regards the outcome measures, marginal bone loss (MBL) was reported in all the studies (Table 2), alongside the pink esthetic score (PES) (3/9) and other parameters related to implant stability and soft tissue changes (bleeding on probing, papilla index, soft tissue thickness changes, and recessions) (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
## 2 Experimental Setup and Experimental Results
Theoretically, as the laser propagated to 5.5 mm in shot #2, the laser intensity decayed. When the plasma length increased sustainably to 7 mm, both the \(e\)-beams did not get energy from the laser wakefield compared to shot #2. However, the low-charge \(e\)-beam still accelerated to 640.7 MeV with a narrow energy spread of 1.1%, and the energy of the high-charge \(e\)-beam decreased rapidly with a large energy spread of 50.3%, which is considerably larger than the theoretical value [25]. Moreover, to investigate the stability of the generation of two-color \(e\)-beams, a series of \(e\)-beam measurements when the plasma length was 7.0 mm were recorded to analyze the fluctuation of the energy and the FWHM energy spread, as shown in Figure 2d. The fluctuation of the \(e\)-beams' energy and the energy spread came from the shot-to-shot fluctuation in laser power and the small jitter in gas density.
## 3 Challenges and Trends in Wireless Sensor Nodes
### Construction of Smart-Transducers  
FPGA technology can be used in the implementation of smart transducers, either sensors or actuators. In so doing, sensors are provided with extra processing capabilities, according to the IEEE 1451 family of standards [31]. Thus, sensors are offered a particular interface, _i.e._, Smart Transducers Interface Module (STIM), which provides ways for performing calibration, self-testing and configuration of the sensor according to different parameters. Therefore, smart transducers can group several sensors at once, sharing the same interface that can be accessed from a sensor node via the IEEE 1451.0 standard [31].  
Likewise, several authors have already proposed the implementation of the IEEE 1451 standard on FPGAs in different measurement systems in order to provide extended capabilities to typical sensors [32, 33].  
In this respect, [PERSON] _et al._ implemented an IEEE 1451 protocol controller that serves as a sensor node and a smart sensor interface in order to provide advanced processing capabilities to a hydraulic demonstration system [34]. Furthermore, in order to support several types of analog sensors in a common acquisition board, [35] proposed the implementation of the IEEE 1451.4 [36] standard via the combination of FPGA and Field-Programmable Analog Array (FPAA).  
However, partial reconfiguration has been rarely used on FPGA implementations of the IEEE 1451 family of standards and has been limited to proposals [37]. In this respect, an FPGA could be used in order to accelerate the complex techniques of self-testing and calibration of an array of different sensors, and it could be executed on demand via wireless communication, like WiFi, Bluetooth or ZigBee [38].
## 4 Proposed Social-Network-Based Crowd Selection Approach
### Expertise Retrieval  
Expertise retrieval is considered as a discipline in information retrieval. It investigates the process of automating expertise. It has two main tasks which are expert finding and expert profiling [74]. In our proposed solution, we focus on expert finding whose goal is to find a person with the appropriate knowledge. It aims to answer the question _\"Who are the experts on topic \(X^{\prime\prime}\)_. For example, for given query (\(q\)) it finds people who are more likely to be candidate experts (\(e\)), then retrieves these experts and sorts them in descending order using their expertise _score_ (\(e_{i},\ q\)) [74]. Candidate experts can be the author of a text, therefore, they can be straightforwardly identified in social networks through their unique social network identifiers [44].  
In our case, we consider the authors of tweets containing information about a mobile app as a source for finding experts and potential experts for a given crowdsourcing RE task for a mobile app. This component concerns retrieving mobile apps users and developers who have used or developed mobile apps in a domain similar to the specific app domain submitted by the requester.  
The collection of stored mobile app users' and developers' IDs along with the app's scraped information is first filtered by the crowd role (user or developer or both) and app category submitted by the requester (e.g., games, health and fitness, education). Furthermore, to retrieve mobile app users or developers that have used or developed apps similar to the one submitted by the requester, semantic similarity techniques are utilized. These techniques are used to compare the scraped descriptions of the apps mentioned in the users' or developers' tweets against the query app description submitted by the crowdsourcing requester. This is performed to find mobile apps that are most similar and related to the query app and retrieve the associated users or developers that have used or developed these apps. Eventually, a crowd of mobile app users and developers that have used or developed apps similar to the app described by the crowdsourcing requester--sorted by similarity--shall be retrieved and returned as an output to the requester via the crowd selection controller. In essence, the selected crowd consists of mobile app users and developers who would most likely possess expertise in an app domain similar to the one the requester was looking for. The selected crowd can then be invited to engage in the RE crowdsourcing tasks.
# Identification of Some Glutamic Acid Derivatives with Biological Potential by Computational Methods
## 1 Introduction
The combination of CB-839 and V-9302 was also successful because of the dual inhibition of glutamine metabolism, resulting in a decrease in GSH levels and a lethal increase in the levels of free radicals. This resulted in severe DNA damage, especially in liver cancer cells [13]. Figure 1: The chemical structures of the glutaminase inhibitor compounds (**a**) CB-839, (**b**) 968, and (**c**) BPTES. Another therapeutic strategy could be inhibiting glutamate carboxypeptidase II (GCPII). This enzyme hydrolys N-acetyl-aspartyl-glutamate (NAAG) to glutamate and N-acetyl aspartate. NAAG is a neurotransmitter in the brain and a glutamate provider to GCPII-positive cancers if other sources do not produce enough glutamate. Therefore, inhibitors of GCPII can lead to cancer cell growth suppression by reducing glutamate concentrations [7]. Antagonists of metabotropic glutamate receptors are also promising anti-cancer alternatives without significant side effects. Metabolropic glutamate receptors (mGluRs) are G-protein coupled receptors (GPCRs) categorised into three groups based on their signal transduction pathways and pharmacological profiles. They seem to be more attractive therapeutic targets since they are not directly involved in excitotoxicity but intervene in modulating glutamate activity [22; 23]. Figure 2: The chemical structures of compounds: (**a**) DON, (**b**) JHU-083, (**c**) azaserine, (**d**) acivicin, (**e**) AOA, (**f**) EGCG, and (**g**) R162. This article aims to identify new structural analogues of glutamic acid as potential candidates for anti-cancer therapy by computational methods. Several stages were followed: (1) analysis of recently published scientific data regarding the role of glutamate and its derivatives in the development of tumour cells; (2) identification of some new molecules with biological potential, starting with the structure of glutamic acid and the creation of a compound library; (3) conjugation of molecules of natural origin with glutamic acid residues to reduce glutamic acid toxicity and/or potentiate the anti-cancer effect; (4) selection of compounds with biological action and minimal toxicity according to the structural, physicochemical, pharmacokinetic, and pharmaco-toxicological properties determined by in silico methods; (5) evaluation of anti-tumour potential of selected molecules and the identification of possible mechanisms of action; (6) molecular dynamics simulation and molecular docking study to identify the binding site of a ligand molecule (with biological potential) on a known target.
## 3 Results and Discussion
### Characterization of the Rods and Rod with ARP
#### 3.3.6 Mechanical Study
In terms of mechanical properties, the Tl 77 rod-ARP is the least promising. However, it should be pointed out that the study of rods with 17-\(\beta\)-estradiol based on poly(l-lactide-\(\alpha\)-glycolide-\(\alpha\)-trimethylene carbonate) with a similar composition (57.9:17.6:24.5) and \(M_{n}\) (45.8 kDa) performed on adult \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Sample**} & \multicolumn{1}{c}{\(\sigma_{y}\)} & \(\epsilon_{y}\) & \(\sigma_{m}\) & \(\epsilon_{m}\) & \(\sigma_{b}\) & \(\epsilon_{b}\) & \(E\) \\  & & **(MPa)** & **(\%)** & **(MPa)** & **(\%)** & **(MPa)** & **(\%)** & **(MPa)** \\ \hline \multirow{2}{*}{Td,l 21} & rod & 4.21 \(\pm\) 1.14 & 20.3 \(\pm\) 10.9 & 4.32 \(\pm\) 1.10 & 108.0 \(\pm\) 138.5 & 3.20 \(\pm\) 0.78 & 488.8 \(\pm\) 118.5 & 62.03 \(\pm\) 28.33 \\  & rod-ARP & 3.62 \(\pm\) 0.58 & 28.0 \(\pm\) 9.8 & 3.77 \(\pm\) 0.50 & 101.0 \(\pm\) 152.0 & 2.62 \(\pm\) 0.29 & 402.0 \(\pm\) 157.7 & 18.90 \(\pm\) 7.96 \\ \multirow{2}{*}{Tl 59} & rod & 7.04 \(\pm\) 1.39 & 28.3 \(\pm\) 16.0 & 10.20 \(\pm\) 1.26 & 533.2 \(\pm\) 57.1 & 9.64 \(\pm\) 1.36 & 539.7 \(\pm\) 53.3 & 194.92 \(\pm\) 100.21 \\  & rod-ARP & 5.15 \(\pm\) 1.01 & 23.0 \(\pm\) 19.1 & 8.48 \(\pm\) 0.58 & 460.3 \(\pm\) 173.7 & 7.58 \(\pm\) 0.74 & 488.5 \(\pm\) 137.2 & 128.67 \(\pm\) 33.52 \\ \multirow{2}{*}{Tl 77} & rod & 10.67 \(\pm\) 1.71 & 28.4 \(\pm\) 7.6 & 14.44 \(\pm\) 1.07 & 436.4 \(\pm\) 100.2 & 13.28 \(\pm\) 1.36 & 456.4 \(\pm\) 131.3 & 85.01 \(\pm\) 22.09 \\  & rod-ARP & 13.24 \(\pm\) 1.61 & 18.7 \(\pm\) 4.1 & 14.30 \(\pm\) 0.97 & 350.3 \(\pm\) 167.1 & 13.12 \(\pm\) 1.02 & 507.0 \(\pm\) 67.1 & 221.94 \(\pm\) 88.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mechanical parameters of blank rods and rods with ARP (\(\pm\)SD). female Wistar rats did not reveal inflammation, necrosis, or exudate after subcutaneous implantation in the back of the neck [22].
## 4 Findings and Discussion
### Discussion on Future Research Avenues
#### 4.6.5. Consideration of Metaverse Evolution Trends  
The design artifacts identified in the review (see Table 8) barely cover any general design requirement except for scalability and accessibility of a client. Consequently, researchers should be curious about the roles of general design requirements on consumer behaviors. For example, in terms of VW design, ref. [38] identified the emerging requirements of sufficiency, reliability, persistency, and credibility. Particularly, persistency and credibility can be highly relevant to consumer purchase in a VW. For persistency, user content persistency is directly related to the permanent existence of a user's digital asset in a VW. Presumably, a low level of content persistency can reduce a user's willingness to buy high-value products in a VW. Similar results can be expected with a low level of credibility, which stresses the trust of users on a VW to securely manage and legally protect their data. These hypotheses can be further argued and refined, and then validated (or rejected) through behavioral research. Again, the behavioral research on these topics need help from application design researchers to build the required VW infrastructure.  
Furthermore, researchers should investigate how to transform virtual commerce along with a broader definition on Metaverse, which has been perceived as the next-generation of Internet and where interconnected, shared, and persistent 3D virtual spaces co-exist [155; 156]. As reflected in the review, immersive technology applied in virtual commerce can sufficiently create the visually immersive virtual spaces for a metaverse. Nevertheless, utilization of other emerging technologies, such as faster communication infrastructures (e.g., 5G), secure distributed ledger (e.g., blockchain), and innovative computing paradigms (e.g., mobile, edge, and cloud computing), is essential to achieve the interconnectivity, persistency, and other features of the metaverse, which is likely to establish a new form of electronic commerce. We tentatively call it meta-commerce.
# Polyacrylonitrile Nanofibers Containing Viroblock as Promising Material for Protective Clothing
## 2 Materials and Methods
### Characterization  
#### 2.3.1 Morphology Analysis  
The morphology of PAN nanofibers and VB/PAN nanofibers was investigated by scanning electron microscopy (SEM, S-3000N, Hitachi Co., Tokyo, Japan) at 10 kV accelerating voltage. The average diameter of the PAN nanofibers and VB/PAN nanofibers was determined by analyzing the SEM images, using the Image J software version 1.49, measuring the diameter of 50 nanofiber strands.