esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Axl Participates in Early Steps of LASV Entry
The upper blot (+TCEP) was exposed for 5 min and the lower blot (\(-\)TCEP) for 30 s. Ratios of +TCEP/\(-\)TCEP signals (densitometry) were calculated using ImageJ software; (**F**) Schematic experimental setting of the post-entry assay; (**G**) Relative entry of rLCMV/LASV\({}_{\rm GP}\) in entry and post-entry assays in the presence of CytoD, BafA1, or R428. Values were normalized with those obtained in the presence of dimethyl sulfoxide (DMSO). Error bars represent standard deviations (\(n=3\)). To further evaluate the possible effects of R428 in viral fusion or early post-fusion steps during rLCMV/LASV\({}_{\text{GP}}\) infection, we synchronized virus escape from late endosomes with the drug treatment, as described in [92]. Briefly, for the post-entry assay (Figure 2F); precooled cells were incubated at 4 \({}^{\circ}\)C with rLCMV-LASV\({}_{\text{GP}}\), for 1 h, allowing virus binding without internalization. Then, cells were washed to remove the unbound virus, and quickly shifted to 37 \({}^{\circ}\)C, for 5 min, to allow virus internalization. Then, cells were incubated for 45 min, at 37 \({}^{\circ}\)C, with a medium containing 20 mM NH\({}_{4}\)Cl, in order to prevent viral pH-dependent endosomal escape. Then, indicated inhibitors were added to the cells for 30 min, and kept for 4 h in the absence of NH\({}_{4}\)Cl, to test their effect on post-entry steps. Finally, NH\({}_{4}\)Cl was added for 16-20 h, to prevent secondary infections, followed by IFA for LCMV NP. A comparison of entry and post-entry assays (Figures 1C and 2F, respectively) revealed that R428 did not inhibit viral fusion and endosomal escape of rLCMV/LASV\({}_{\text{GP}}\) (Figure 2G and Figure S2D) but interfered with early entry steps. This is in contrast to bafilomycin A1 (BafA1), an inhibitor of lysosomal proton pumps used as a positive control here, which suppressed productive viral infection in pre- and post-entry assays. The actin inhibitor cytochalasin D (CytoD), which interfered with actin dependent entry, did not prevent endosomal fusion and early replication of rLCMV/LASV\({}_{\text{GP}}\), as expected (Figure 2G and Figure S2D).
# Real-Time Detection of Overloads on the Plasma-Facing Components of Wendelstein 7-X
## 2 Real-Time Imaging System  
The thermal loads on the PFCs of W7-X are detected with the thermography diagnostic that monitors the 10 divertors and the surrounding baffles. W7-X is a stellarator with five-fold symmetry, but as far as safety is concerned, toroidal symmetry cannot be assumed. Heat loads in one module cannot be used to infer heat loads in other modules. We must provide 100% observation of divertors.  
Figure 3: Infrared image overlaid on the CAD of the different plasma-facing components with their maximum operational temperatures.  
In OP1.2, nine immersion tubes (Figure 4) and 1 endoscope (Figure 5) were used [10]. At the beginning of OP2, with plasma energy still limited to 1 GJ, the diagnostic will comprise 8 water-cooled immersion tubes and 2 steady-state endoscopes. The immersion tubes, however, are not designed for steady-state operation and steady-state endoscopes will replace them when the plasma energy will progressively increase up to 18 GJ.  
The image analysis of the thermographic images is a fundamental part of the machine safety system of W7-X (see Figure 6). The algorithm is based on real-time image processing techniques. The overload detection algorithm detects when a PFC is overheated and interrupts operation immediately through the interlock system to guarantee that the operational limits of the PFCs are not exceeded.  
Figure 4: The immersion tubes provide an optical resolution of the order of 5 to 20 mm/pixel on the divertor, depending on the distance and the viewing angle of the divertor. In the high-iota region, the lower resolution may lead to measuring lower temperatures from hot spots smaller than the resolution limit. The immersion tubes are equipped with microbolometer cameras IRCam Caleo 768k L covering the spectral range from 8 to 10 μm at a frame rate of 100 Hz.  
Figure 5: The steady-state endoscopes consists of an off-axis Cassegrain optical system with a pinhole aperture of 6 mm. A set of mirrors transmits the light and a dichroic splitter divides it into visible and infrared beams, which are detected by the cameras after being corrected by a group of lenses. They have an optical resolution of 8 mm/pixel and they are equipped with an Indium Antimonide (InSb) infrared camera covering the spectral range from 2 to 5.7 μm (filtered at 4.1 μm) at a frame rate of 100 Hz.
# Data Naming Mechanism of LEO Satellite Mega-Constellations for the Internet of Things
## 5 Results and Discussion  
The simulation results in Section 4.2.2 shows that no matter how big the data packet is, the data packet based on the ICN-HMcH naming mechanism performs markedly better than the data packet based on the IP address in terms of transmission performance. Additionally, comparing with data packets based on the IP address, the transmission delay based on the proposed hierarchical structure of the ICN-HMcH naming mechanism decreases for different types of IoT services. In addition, the ICN-HMcH naming mechanism improved bandwidth utilisation and energy efficiency.  
This paper applies ICN in space-based networks to alleviate the traffic load of terrestrial networks and provide global \"anywhere-anytime\" Internet access. In this study, data naming can clearly identify services, content and equipment; meet the requirements of  
Figure 17: Number of interest.  
Figure 18: Multi-source data retrieval results.  
heterogeneity of IoT equipment and applications; and achieve equipment interoperability from different vendors and across domains.
# A Survey of Secure Time Synchronization
## 3 Current Time Synchronization Methods
### Reference Broadcast Synchronization (RBS) Protocol  
The reference broadcast synchronization protocol was proposed by [PERSON], [PERSON], and [PERSON] in 2002 [33]. Nearly all other synchronization protocols use a sender-to-receiver synchronization method [47]. However, the approach of the RBS protocol is different because it uses a receiver-to-receiver synchronization method by using a third party [32]. The concept is that a single broadcast signal will be sent by a third party to two receivers which will not contain any type of time information or timestamp. The receivers will both exchange the receiving time of that signal and the difference between the clocks [34]. For example, as shown in Figure 10, a reference Node R sends a synchronization message to all the slave nodes. Two nodes, Nodes A and B, are in the communication range of Node R, and they will receive the message. The receiving times of the message are recorded as \(T_{a}\) on Node A and \(T_{b}\) on Node B.  
Nodes A and B exchange this timing information with each other, and Node B calculates the timing difference to Node A and records it using \(d\) as follows:  
\[d=T_{a}-T_{b} \tag{13}\]  
After that, Node B can adjust its time to synchronize with Node A using \(d\) as follows:  
\[T_{b}=T_{a}-d \tag{14}\]  
Figure 9: FTSP broadcast message.  
The information gained is enough to continue a local time scale. By doing this, it eliminates the send time and access time from the critical path, as shown in the Figure 11[21; 33].  
Figure 11: Critical path analysis (RBS protocol).  
Figure 10: RBS protocol message exchange.  
In the RBS protocol, a third party sends a broadcast signal which makes it easy to spoof the nodes and stop the actual senders' information [46]. In addition, because of its heavy overheads, it consumes more energy in exchanging packets, which makes it unsecure and unsuitable for low energy consumption networks.  
The information given above shows that security was not the top priority during the designing of the current available protocols. This problem can be solved by reducing the communication overhead which reduces the energy requirement, and the saved energy can be used for the purpose of the security. In further sections, we examine the nature of the problem and explore related work conducted in the past to find a suitable solution.
# A Cognitive Anycast Routing Method for Delay-Tolerant Networks
## 3 Anycast Routing  
The network is identified by a topology that is dynamically changing due to node mobility, scheduling decisions, and signal propagation issues. The changes mainly follow well-defined patterns, e.g., resulting from orbital mechanics, and therefore, the future contact times between nodes can be predicted. The service unit is called _bundle_ and the use of multiple contacts and nodes may be needed to deliver each bundle to the sink. Any node can operate as a gateway routing bundles for other nodes. It is assumed that the one-hop transmissions are provided by a reliable convergence layer protocol and the use of the Licklider Transmission Protocol (LTP) is assumed. The routing process aims to deliver each bundle both efficiently and with a minimum delay to any member node of the anycast group. The former aim prevents the use of bundle duplication, and therefore any form of epidemic routing, whereas the latter requires the optimization of both the member selection and the path to reach that node. Furthermore, the routing decisions must be done autonomously without any central coordination. The gateways are not aware of the global network state. The main concepts behind the cognitive space gateway approach, which was previously introduced for unicast routing, are concisely discussed followed by the proposed anycast extension.
## 2 Background
### Multi-Criteria Decision Analysis (MCDA) Methods
The fuzzy COMET method is well suited for representing subjective preferences and uncertainties in criteria such as performance stability or long-term reliability. * SPOTIS (Stable Preference Ordering Towards Ideal Solution) [48]: This method simplifies the decision process by comparing alternatives to an ideal solution while considering fuzzy criteria. It is advantageous when evaluating hardware which must satisfy certain threshold criteria, such as a minimum acceptable level of power consumption or processing speed, under uncertain conditions. * COCOSO (Combined Compromise Solution) [49]: This method combines different compromise solutions to provide a more robust ranking of alternatives, which is beneficial in multi-criteria hardware selection problems where performance and cost must be balanced. Fuzzy COCOSO helps handle the uncertainty in performance and energy efficiency. * VIKOR [17]: This method focuses on finding a compromise solution, which is particularly relevant when there are multiple conflicting criteria with trade-offs. It considers both the best and worst performance across the criteria and aims to minimize regret. Fuzzy VIKOR is useful in edge hardware selection because it addresses trade-offs between attributes like cost, power efficiency, and processing power under fuzzy conditions.
# Guava Disease Detection Using Deep Convolutional Neural Networks: A Case Study of Guava Plants
## 3 Methodology
### Basics of Convolutional Neural Networks
#### 3.3.2 Batch Normalization  
Batch normalization is a normalization operation, such as the min-max data normalization performed in data cleaning. Batch normalization is a normalization in which a batch of input data is normalized, and it can be written as in Equation (6).  
\[x_{i}^{\prime}=\frac{x_{i}-\mu_{B}}{\sigma_{B}^{2}} \tag{6}\]  
It normalizes data, where the data transformation has taken place, such as a mean output close to 0, and the standard deviation output remains close to 1. In Equation (6), input \(x\) of a particular instance is subtracted from the mean (\(\mu\)) of batch \(b\), where after subtraction, a ratio is calculated over the square of the standard deviation \((\sigma)\) of that particular batch \((B)\) where instance \(x\) belongs, and a normalized value of \(x_{i}^{\prime}\) is returned as output.
## 3 The Proposed Algorithm
### Seed Initialization  
Given the lookup table (LUT), we generate \(2^{\alpha}\) random variants of the data from the LUT. Each variant is created by applying small random perturbations to the states \((x^{\prime},y^{\prime},\theta^{\prime})\) defined in the LUT.  
The perturbation can be expressed as  
\[(x_{\text{variant}},y_{\text{variant}},\theta_{\text{variant}})=(x^{\prime}+ \Delta x,y^{\prime}+\Delta y,\theta^{\prime}+\Delta\theta) \tag{7}\]where \((\Delta x,\Delta y,\Delta\theta)\) are small random perturbations.  
We can represent the generation of these variants in pseudocode as shown in Algorithm 2.  
```
1:Input: Lookup table \(\text{LUT}(M_{j})\), number of variants \(N=2^{\alpha}\)
2:Output: Set of generated variants \(Variants\)
3:Initialize \(Variants\leftarrow\emptyset\)
4:for each movement \(M_{j}\in R\)do
5:for each variant index \(i=1\) to \(N\)do
6:\(\Delta x\leftarrow\text{RandomPerturbation}()\)
7:\(\Delta y\leftarrow\text{RandomPerturbation}()\)
8:\(\Delta\theta\leftarrow\text{RandomPerturbation}()\)
9:\((x_{\text{variant}},y_{\text{variant}},\theta_{\text{variant}})\leftarrow(x ^{\prime}+\Delta x,y^{\prime}+\Delta y,\theta^{\prime}+\Delta\theta)\)
10: Add \((x_{\text{variant}},y_{\text{variant}},\theta_{\text{variant}})\) to \(Variants\)
11:endfor
12:endfor
13:return\(Variants(M_{k})\)
```  
**Algorithm 2**Generate Random Variants from LUT  
Random perturbations can be generated using a CUDA kernel to enable parallel computation (for more details, refer to the cuRAND library documentation provided by NVIDIA (2018)), where each thread can handle the perturbation for different movements simultaneously.
## 1 Introduction
[14] found that PDMS fibre is particularly selective for esters which positively contribute to the quality of wine products, such as ethyl octanoate, ethyl-9-decenoate and ethyl decanoate. In addition to fibre type selection, extraction time and temperature optimization, sample saturation with a salting out agent has also been proven to enhance the extraction efficiency. This occurs due to the increase in the ionic strength of the solution and the decreased solubility of the apolar analytes in the solution [18]. Most of the practical work involving SPME optimization uses strategies based on systematic studies of one variable at a time (OVAT), during which all the variables affecting the SPME efficiency (i.e., fibre coating, extraction time and temperature, ionic strength and sample volume) are maintained at a constant level during test runs, except for the one being studied. However, the use of the classical univariate approach could result in incorrect optimizations since it does not consider the interactions between different factors, assuming instead that all the variables are independent and the effect, at a given set condition, is also the same if the remaining variables are changed [19]. In order to avoid erroneous run tests, an optimization planned carried out according to an experimental design (DoE) is the best option. Indeed, in DoE all variables are studied at the same time, enabling a reduction in the number of experiments with a complete exploration of the experimental domain [20; 21]. Thus, it is a very suitable means with which to investigate most of the variables involved in the HS-SPME process. Anyway, it should be considered that the quality of the results of an experimental design depends on the distribution of experiments in the experimental domain. When DoE does not allow the resolution of the problem, a redefinition of the experimental domain or a change in the postulated model could be needed [22]. Here, a face-centred composite experimental design has been developed, taking advantage of information gained from previously acquired experimental data. This design was used to optimize the headspace solid-phase microextraction of the volatile compounds occurring in experimental wine samples. The samples were obtained by laboratory-scale fermentations performed in Aglianico del Vulture grape must, one of the most diffuse grape varieties in the Basilicata region of Southern Italy [23; 24]. The grape must was inoculated with a commercial _Saccharomyces cerevisiae_ strain, used both as a single starter and as a mixed starter in combination with an indigenous _Hanseniaspora osmophila_ strain, as both free and immobilized cells. The optimized SPME-GC/MS method was used to ascertain the aromatic compounds of the experimental wines obtained by different starter cultures.
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 4 Techniques of VLC-Based Indoor Positioning Systems
### Multiple Access Schemes
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline \multicolumn{1}{c}{**Receiver**} & **Algorithm** & \begin{tabular}{c} **Complexity** \\ **(Evaluation** \\ **Method**) \\ \end{tabular} & \begin{tabular}{c} **Multiple** \\ **Access** \\ **Scheme** \\ \end{tabular} & \
\begin{tabular}{c} **Operating** \\ **Range (m)** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy (cm)** \\ **Assumptions** \\ \end{tabular} \\ \hline
[77] & PD & Fingerprinting & Complex (S) & Not mentioned & \(10\times 9\times 3.1\) & max 80 & The reflectivity of obstructions is taken into account but not other noise \\ \hline
[78] & PD & Fingerprinting & Simple (S) & TDM & \(0.9\times 0.9\times 1.5\) & Mean 1.5787 & The positions in simulation limits in a 60 cm equilateral triangle \\ \hline
[73] & Multiple PDs & Fingerprinting & Complex, need training (P) & FDM & \(1.8\times 1.2\times 1\) & Mean 14.8674 & Experiment tested in dark \\ \hline
[79] & Image sensor & Co-linearity + image processing & Complex (S) & No & Street & \(<\)150 cm (pixels) & The received point and the center of the lens of receiver must be on the same straight line \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._resented by a combination of low and high frequencies. The advantage of DTMF is that the system can easily separate different received signals by analysis in their frequency and time domain. Another solution is framed slotted ALOHA (FSA) in [50]. Due to asynchronous random multi-access, transmission collisions are expected. So, this system would require a large number of slots in a frame to maintain a high probability of successful transmission for a given number of users. For example, in [98], the authors use a frame structure containing 400 slots in each frame to provide transmission reliability.
# Using AI-Based Tools to Quantify the Technical Condition of Bridge Structural Components
###### Abstract  
The main task of the operation of engineering structures is to ensure the stability of structures with aggressive external influences, which have a complex probabilistic nature. The reliable functioning of bridge structures requires the development and application of modern systems for inspection and assessment of the technical condition of the structure to take timely measures to ensure the safe operation of the structure in changing operating conditions. With the rapid development of AI, modern approaches are increasingly adopted, offering distinct advantages compared to classical methods. The article aims to develop an AI-based model for quantifying the technical condition of bridge structural components based on data obtained from the survey. To achieve this goal, the authors analyzed existing approaches to the inspection and assessment of bridges and studied the experience of using AI in bridge assessment. Based on the Polish Principles of Bridge Technical Condition Assessment, three datasets were formed to quantify the condition of the bridge components made from reinforced concrete: bridge deck, span structures, and piers and abutments. This study created and compared the performance of five AI-based models: XGBoost, Decision Trees, Random Forest, Support Vector Regression, and Artificial Neural Networks (ANNs). The initial comparison revealed relatively low performance across all models, with the ANN model showing a slight advantage. Subsequently, nine ANN models were optimized to achieve higher performance levels. The performance of models was conducted based on a comparison of mean absolute percentage error (MAPE) and R\({}^{2}\) metrics. The ANN model with ReLU activation functions for hidden layers and the RMSprop optimizer achieves optimal performance at 100 epochs (MAPE = 3.5%; R\({}^{2}\) = 0.994). The practical implementation of such a model can considerably reduce uncertainties stemming from subjective expert judgments and enhance the accuracy of assessments.  
bridge components; AI tools; artificial neural networks; technical condition; reinforced concrete +
Footnote †: journal: Applied sciences
## 4 Results
### Intuitive Motion Control with Reliable Metadata  
Following a good practice implemented in the previous SPICE software, we grouped the control of multiple devices into pseudo-motors. For example, for collimator motion control with eight different sections of guide motion control, a single \"nguides (NGuides)\" (number of guides) command can coordinate the control of guide motions in and out to keep a specific number of guides in the beam, along with apertures in the beam configuration. Note that the number of guides usually define the apparent source-to-sample distance (the flight path from the end of last guide to sample position). The distance needs to be coordinated with the sample-to-detector distance, various aperture sizes, and detector pixel resolution to provide optimal scattering geometry in SANS [18]. We included additional enhancements such as the newly defined 20 mm aperture in the beam configuration (aka, NGuides = \"-1\" in Figure 4); automated motor homing procedures; and logic embedded within the collimator motion control software to consistently compute and update the source aperture diameter and the metadata for the source-to-sample distance, based on motor positions. Similarly, detailed detector distance calculation logic is also integrated within the motion control to simplify or automate various offsets (e.g., sample-to-silicon window offset and detector motor position, see Figure 5) with a combined pseudo-motor total sample-to-detector distance. The latter matches the typical convention employed by SANS users in detector geometry and is critical in experiment planning and data  
Figure 3: Experimental Physics and Industrial Control System (EPICS) architecture (NestedLoops is a generic graphic user interface tool that was previously developed to build scan jobs with simple nested loops logic. It aims for general use and thus retains a simple design. It can be commissioned if needed.).  
reduction. The pseudo-motor is controllable like any physical motor, and the corresponding actual motor position is calculated based on offset values that can be changed according to different experiment setups. Note that all individual values and combined pseudo-motor values are captured in metadata redundantly in case they need to be cross-checked. The development and testing effort in motion control has been rewarded by a clean, simplified interface with enhanced functionalities, tighter integration with high-level tools, and more reliable metadata.