esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Systematic Content Review Results for Sensors Monitoring Mine Tailings Storage Facilities
#### 3.3.1 Content-Based Data Perspective Analysis
Although TSF dam monitoring can be undertaken in many ways, only an autonomous near-real-time system can both reduce the risk of failure and enhance productivity [68]. A data logger is an electronic device that is integrated on-site and records data over time from geotechnical instruments and its own or externally connected sensors. The main advantage of geotechnical data loggers is that they can operate independently, making them effective for conducting counts and generating a traceable database over time to record the trends in the physical and chemical parameters monitored. Data loggers are essential for the management of a TSF to guarantee compliance with regulations according to the projected engineering thresholds and with ICMM global standards [34; 37; 68]. For example, vibrating wire piezometers, which are water pressure sensors installed in the dam and foundation of a TSF, are connected to data loggers that can transmit piezometric information wirelessly to a web-based platform that is tailored to the mining operation. This information can be uploaded and automatically entered into a database to enable various functions, including sending automatic alerts via email or mobile platforms and allowing data viewing, recording, and downloading [72]. Other facilities that monitor and evaluate seismic activity and rainfall events can also be integrated into an automated monitoring system. The types of sensors that are used to monitor structural health and safety management aspects of mine TSFs in the selected articles are summarized in Table 6. This table includes (i) sensor typology, (ii) data obtained/variable measured, (iii) monitoring results, (iv) monitoring frequency, and (v) real-time monitoring.
## 5 Issues in Data Anomaly Classification
### Supplementary Issues  
Other issues in the data anomaly detection process are discussed in this section. Although the accuracy of the CNN-based classification was high, false detections were observed, as shown in Figure 16. Because the converted images were used as inputs to the CNN, data with similar shapes can be misclassified, as shown in Figure 21. For example, an image of impulse responses belonging to the _Normal_ class (see Figure 21a) can be classified as _Outlier_ by the CNN-based method.  
Figure 19: Misclassification of the _Normal_ class into the _Minor_ class.  
Figure 20: Misclassification of the _Missing_ class due to low-resolution images.  
One of the most salient drawbacks of the rule-based classification is that an appropriate selection of threshold values is necessary for each SHM dataset. Determining the threshold values defined in this study is a subjective task that cannot be generalized. In addition, as the threshold values are manually selected before the actual application, the automation of data anomaly detection becomes difficult.
# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 2 Technology Overview
### Transmission Parameters
#### 2.3.1 Spreading Factor  
SF is the chip rate divided by the symbol rate. It is the number of raw bits that can be encoded to a symbol. As SF increases the signal-to-noise ratio (SNR) increases, resulting in an increase in sensitivity and range. However, it results in an increase in the packet airtime. The expression \(2^{SF}\) denotes the number of chips each symbol can hold [10]. The SF characterizes the relationship between the chip rate and the baseband data rate. LoRaWAN SF values range from 7 to 12, which implies that an SF value of 12 increases the strength of the communication signal by increasing the sensitivity of the receiver-equipment but the data rate decreases as a result. Conversely, a reduction in the SF causes the data rate to increase, but the message being forwarded requires a higher TP to be properly decoded at the receiver. When the signal is weak, LoRa devices use a higher SF and using a higher SF implies a longer ToA. The distance from the gateway also affects the SF. The further away the end node is from the gateway the weaker the signal and therefore the higher the SF.
## 4 Analysis of Security and Performance
### Security Analysis
#### 4.1.2. Forgery Attacks  
In a broadcast message, \((ID_{Rt}\), \(T_{X}\), \(ID_{Vi})\), \((M\), \(T_{S})\) and \((F(x)\), \(HMAC(M\parallel T_{S})_{K})\), constitute the identity \((ID_{Vi})\), the broadcast message \((M)\), and the polynomial function \((F(x))\) that is embedded the \(HMAC\) key \(K\), and the \(HMAC\) of message. Without the pairing value that is derived with the common secret that is embedded in the private key, the \(HMAC\) key cannot be retrieved, and without the \(HMAC\) key, an attacker cannot forge a message that can pass the \(HMAC\) check. In the \"hello\" message, \(DS_{Vi}\) are broadcast with \(ID_{Vi}\) attached so, without the secret pool \((SP_{TA}\) or \(SP_{Rx})\), an attacker cannot obtain the pairing values with other vehicles.
## 2 System Requirement
### Impedance Matching  
The input impedance is a fundamental parameter that influences the reflection coefficient (S\({}_{11}\)) as well as the impedance bandwidth (BW). This section describes the design of a matching network that will decrease the reflection coefficient (S\({}_{11}\)) level at the desired resonance frequency while increasing the impedance bandwidth. The matching circuit  
Figure 4: FCB Design. (**a**) PCB layout, and (**b**) fabricated prototype.  
Figure 5: T-matching circuit (Z\({}_{\text{IN}}\): 50 \(\Omega\)).  
ensures maximum power transmission from the source (A) to the button antenna (C) and maintains the required performance even when detuning occurs (Figure 5).  
Input impedance (\(Z_{in}\)) was measured using a vector network analyzer (VNA), which was then utilized to create an optimum matching network. A T-shaped matching network was used, as shown in Figure 5. CST MWS was used to optimize the T-shaped matching network. By modifying the values of circuit components C\({}_{1}\), L\({}_{1}\), and L\({}_{2}\), the matching network optimization was performed to reduce the power reflection at the button antenna's input port. Table 2 shows the final realized values. At the button antenna input, a T-shaped matching circuit with two inductors (1 nH) and a capacitor (1 pF) cancels the reactive part of the impedance and gives an excellent match to 50 \(\Omega\) with an impedance bandwidth (\(S_{11}<-10\) dB) in both frequency bands. Furthermore, the antenna produced an omnidirectional 3D radiation pattern at 2.45 GHz.
## 3 Results
In contrast a large proportion of the control cell families had gone through three divisions during 48 h. In other words, salinomycin treatment specifically inhibited the cell proliferation of the CD24\({}^{-}\) cells, resulting in a relative decrease in the CD24\({}^{-}\) population compared to the CD24\({}^{+}\) population. In addition, salinomycin treatment affected the cell proliferation in CD24\({}^{+}\) expressing cells as well. For further analysis, the cells were divided into six groups depending on the expression of CD24 and E-cadherin. The CD24 labeling was used to group cells with positive or negative expression, while the E-cadherin was used to group the cells with low, medium, or high expression. Only cells present in the last frame of the DHM time-lapses could be matched with a fluorescence expression. Thus, when data is presented that includes cells not present in the last frame, those cells are designated as unknown. These are cells that were followed in the time-lapses but were not found in the last frame, most likely because they have moved out of the defined area where the time-lapses were captured. In the control 0.3% of the cells were characterized as unknown, while in salinomycin-treated cultures, 21% of the cells were unknown. First, we evaluated all tracked cells present in the last image of the time-lapse. The proportions of CD44\({}^{+}\)CD24\({}^{-}\) cells in the control and salinomycin-treated cultures after 24 and 48 h of treatment were compared. In the control, this subpopulation increased from 43 to 70%, while in the presence of salinomycin the subpopulation was found to be 29% and 32% for the respective time-points (Figure 2(B1)), showing that salinomycin treatment inhibited the increase of CD24\({}^{-}\) cells in the population. For E-cadherin, the expression was characterized as low for 57%, as medium for 32%, and as high for 10% of the tracked control cells (Figure 2(B2)). For salinomycin-treated cells the respective numbers were found to be 10%, 43%, and 27%, showing an increase in E-cadherin expression after salinomycin treatment (Figure 2(B2)). Then the effect of salinomycin treatment on the co-expression of CD24 and E-cadherin was evaluated (Figure 2(B3,B4). In the control (Figure 2(B3)), the CD24\({}^{+}\)E-cadlow subpopulation contained 15% of the cells, CD24\({}^{+}\)E-cadmid 12.5%, and CD24\({}^{+}\)E-cadhigh 2% of the cells, while the CD24\({}^{-}\)E-cadlow subpopulation contained 42%, CD24\({}^{-}\)E-cadmid 21%, and CD24\({}^{-}\)E-cadhigh 7.5% of the cells. For salinomycin-treated cells (Figure 2(B4)), the CD24\({}^{+}\)E-cadlow subpopulation contained 3.5% of the cells, the CD24\({}^{+}\)E-cadmid 20% of the cells, and CD24\({}^{+}\)E-cadhigh 30% of the cells. For the CD24\({}^{-}\) subpopulations, the distribution of the salinomycin treated cells were 8% CD24\({}^{-}\)E-cadlow, 35% CD24\({}^{-}\)E-cadmid, and 3.5% CD24\({}^{-}\)E-cadhigh. In other words, this shows that the increase in the expression of E-cadherin after treatment with 0.5 uM salinomycin is independent of CD24-expression.
## 4 Examples and Simulation
### Accuracy Comparison in a Single Day  
A comparison of the prediction results of the three models during a certain sunny day in January in 2015 is shown in Figure 4, where 600 on the \(X\)-axis stands for 6:00.  
The \(nRMS\)s of models 1, 2 and 3 are 0.023, 0.035 and 0.053, respectively; the corresponding \(MAP\)s are 9.707, 10.893 and 12.706. It is obvious that the accuracy of Model 1 is the best among the three models.  
A certain cloudy/rainy day in January in 2015 is also used to compare the prediction results of the three models, as shown in Figure 5.  
The \(nRMS\)s of models 1, 2 and 3 are 0.067, 0.074 and 0.082, respectively; the corresponding \(MAP\)s are 13.833, 14.303 and 15.112. It is obvious that the accuracy of Model 1 is still the best among the three models.  
In addition, comparing Figures 4 and 5, we can see that the accuracy is higher when it is sunny than when under cloudy or rainy conditions.  
Figure 4: Comparison of three models on a sunny day. (**a**) FOS-ELM; (**b**) online sequential extreme learning machine (OS-ELM); (**c**) extreme learning machine (ELM).
## 2 Generation of Precision Timing Information
### Steering Algorithm  
As illustrated by the system diagram in Figure 3, the steering process is facilitated by the AOG. This steering device takes in the signal from the H-maser and adjusts it based on the output provided by the time-scale algorithm (\(x_{re}\)). For this paper, frequency steering has been implemented, and the steering value \(s\) for a given timestamp \(k\) is calculated as shown in Equation (5). The process is automatic and is controlled by a script written in Python. The script generates the steering values every two-hours.  
\[s(k)=(x_{re}(k)-x_{re}(k-t))/t \tag{5}\]  
Enhancements to the steering process can be achieved by incorporating information on the difference between AOG and \(UTC(SG)\), as illustrated in the system diagram in Figure 3. This adjustment aims to refine the results obtained from our lab and FP2, and this optimization will be a focus of our upcoming work. In the meantime, the outcomes of AOG after the steering process, utilizing Equation (5), define the physical signal at FP2, denoted as the backup time for Singapore (\(UTC(FP2)\)). Subsequently, when this signal was compared to \(UTC(SG)\) for validation purposes, almost 82.5% of the differences were within \(\pm 10\) ns [23].
# Bio-Sourced and Biodegradable Membranes
## 4 Green Membrane Technology Applications
### Gas Separation  
The separation of pure gases from gas mixtures is another critical application of biodegradable membranes. Gas separation in membrane-based applications mostly relies on the solution-diffusion mechanisms. Thus, the permeability and selectivity of each component in membranes play a crucial role in the separation process [2]. A common gas separation process is removing CO\({}_{2}\) and H\({}_{2}\)S from natural gas or to recover N\({}_{2}\), H\({}_{2}\), and O\({}_{2}\) from gas mixtures [2,200]. Some functional groups in the membrane structure could facilitate gas transportation and improve the separation efficiency. For instance, amine groups in biodegradable membranes like chitosan and PVA could enhance the CO\({}_{2}\) separation from a gas mixture [200,201]. In fact, amine groups act as carriers for Co\({}_{2}\) molecules and improve the gas transport through the reversible reactions presented in Equation (1) [201].  
\[\text{CO}_{2}+\text{NH}_{2}+\text{H}_{2}\text{O}\leftrightarrow\text{HCO}_{3} ^{-}+\text{NH}_{3}{}^{+} \tag{1}\]  
Upon exposure to gas mixture, amino groups and water in the film react with carbon dioxide to form bicarbonate ions. As the bicarbonate ions passed through the film, they were converted back into carbon dioxide at the permeate side [201].  
Furthermore, the morphology and structure of polymers influence gas selectivity and separation efficiency such that glassy polymers pose the biggest challenge to gas separation processes because of non-selective voids in their structure. Therefore, rubbery polymers are more favorable for light gas separation [200]. Biodegradable rubbery polymers like polyurethanes have been widely used in gas separation applications, owing to their film-forming property, excellent selectivity, and permeability [200,202,203]. Considering the effect of polymer structure, the free volume, packing density, and chain mobility would also affect the separation efficiency [204]. To address this challenge, biodegradable fillers with desirable selectivity would be incorporated into the polymeric membranes (mix matrix membranes) to modify the polymer structure and improve the gas permeability. In order to robust the permeability and selectivity or even mechanical and thermal stability of the membrane, a composition of biodegradable polymers (mix matrix membranes) would also be used in the separation process [200,201,204,205,206]. A number of studies have examined the use of biodegradable membranes in gas separation, as shown in Table 1.
# Opportunities and Challenges of Smartglass-Assisted Interactive Telementoring
## 4 Five Scenarios for Smartglass-Assisted Interactive Telementoring
### SAIT for Paramedics Scenario  
* _Mentors and Mentees of Different Levels_. This SAIT scenario involves dispatched paramedics as mentees and more experienced mentors at the medical center. The paramedics are trained to perform various skills in emergency settings (i.e., airway management, breathing, circulation, cardiac arrest, intravenous infusion, patient assessment, wound management) and provide emergency medications. Mentors with more medical expertise can guide and instruct the less-experienced mentees (i.e., newly employed, treatment for exceptional cases).
* _Sharing of First-person and Eye-level View via Smartglass_. The mentees can allow their views captured by HMDs to be shared when they need assistance. Then, the mentors can intervene to assess a patient's condition visually or walk through medical treatment together with the mentee. Figure 6 illustrates dispatched paramedics mentees in this SAIT scenario.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Top 5 XR Applications in Smart Cities** & **Share of Respondents** \\ \hline Navigation & 66\% \\ Urban planning & 57\% \\ Smart traffic signals and camera monitoring & 55\% \\ Smart parking & 49\% \\ Smart building management systems & 40\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Top 5 XR Applications in Smart Cities, source: [4].  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Top 7 XR Applications in Healthcare** & **Share of Respondents** \\ \hline Training simulations & 68\% \\ Assisted surgeries & 56\% \\ Studying diseases like cancer in 3D & 53\% \\ Addressing visual disorders & 46\% \\ Pain management & 43\% \\ Emergency navigation & 42\% \\ Assessing and addressing mental health conditions & 40\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Top 7 XR Applications in Healthcare, source: [4].  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Top 5 XR Applications in Education** & **Share of Respondents** \\ \hline Immersive teaching experiences & 66\% \\ Soft skills development & 57\% \\ Build interactive 3D models for learning & 55\% \\ Exploratory expeditions & 49\% \\ Recreation/simulation of past experiences for new learners & 40\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Top 5 XR Applications in Education, source: [4].  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Top 6 XR Applications in Manufacturing** & **Share of Respondents** \\ \hline Offering real-time remote assistance & 75\% \\ Workforce training & 48\% \\ Reduction of assembly errors & 45\% \\ Improvement of supply chain management & 45\% \\ Remote inspection and maintenance & 43\% \\ Prevention of accidents & 40\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Top 6 XR Applications in Manufacturing, source: [4].  
* _Extended Use Cases with Bi-directional Interaction_. In this scenario, voice-based UIs are used dominantly to communicate between mentors and mentees in real-time.
# Resilience Evaluation of Multi-Path Routing against Network Attacks and Failures
## 4 Evaluation  
Figure 3 shows a part of the world map (Background world map designed by [PERSON]/[PERSON]) with the nodes used to measure the effectiveness of FMP and MMP on SCIONLab, which deploys SCION on research networks. The core ASes are placed at Carnegie Mellon University in the United States, Magdeburg in Germany, SWISSCOM (SCMN) in Switzerland, NUS in Singapore, and KISTI Daejeon in Korea. We evaluate network performance in terms of latency and loss rate [12].  
To evaluate latency and loss rate, we employ the SCION ICMP (SCMP) testing tool and SCION bandwidth testing tool provided by SCIONLab [15]. We generate normal/heavy traffic adopting the routing policies of SP, FMP, and MMP on the SCION protocol. The results of the evaluation show proper operation for each routing policy. Under the attack using the heavy traffic, the background ongoing traffic of the network is very small compared to the heavy traffic, and it is enough to be ignored to see the results. This evaluation does not cover the whole cases of the multi-path but it shows the point of comparison for the main characteristics among SP, FMP, and MMP routing.  
Initially, for comparison with SP, FMP, and MMP, we select six SCION ASes between Korea and Switzerland. Next, to test the effectiveness of MMP at the Internet-scale during  
Figure 3: The nodes used for the evaluation with the 6 participating countries including 18 participating SCION ASes. Some dots have several nodes. The nodes _Ohio_ and _Japan_ are deployed on Amazon Web Services (AWS).  
a transit-link DDoS attack, we test using a larger topology with 6 countries and 18 SCION ASes participating in it. During this test, we select four core ASes (the United States, Germany, Switzerland and Korea) from four different countries and set the transit-link between the core ASes as direct path selected countries as shown in Figure 4. We then use these transit-links and measure the loss rate of the paths between the countries.  
As shown in Figure 5, six ASes are involved between the source \(Src\) and destination \(Dst\) for real Internet evaluation between Switzerland and Korea. \(A\), \(B\), \(C\), \(D\), \(E\), and \(F\) ASes indicate KU, KISTI Seoul, KAIST, KISTI Daejeon (KR core AS), CMU (US core AS), and SCMN (Switzerland core AS), respectively. There are two links between \(F\) and \(Dst\). After then, we use given global networks to show effectiveness of multi-path routing under high latency.
# Grain Boundary--A Route to Enhance Electrocatalytic Activity for Hydrogen Evolution Reaction
## 3 Results and Discussion
### Origin of GB-Enhanced HER
The \(\overline{\text{CN}}^{d}\) of the five active sites mentioned above is calculated according to the following expression [34]:  
\[\overline{\text{CN}}^{d}=\frac{\sum_{i\
eq j}^{r_{ij}<r_{c}}\sqrt{\left(\
u_{ in}^{d,\infty}\right)^{2}}}{\sqrt{\left(\
u_{nn}^{d,\infty}\right)^{2}}} \tag{8}\]  
where \(\
u_{id}^{(2)}\) is the second moment of the local density of states projected on the d orbital of site \(i\), which is calculated by summing the square of the d-electron hopping integrals to the neighboring d orbitals by only counting the first nearest neighboring atom \(j\) within a cutoff distance of r\({}_{\text{c}}\) = 3.2 A. \(V_{dscr}\), \(V_{ddr}\), \(V_{ddr}\) and \(V_{ddg}\) are the d-electron hopping integrals to the s or d orbital of the first nearest-neighbor atom for the formation of \(\sigma\), \(\pi\) and \(\delta\) bonds. \(V_{nn}^{d,\infty}\) is the corresponding d-electron hopping integral in bulk metal, which is a constant for the catalysts of the same metal. In Figure 7b, we can see that \(\overline{\text{CN}}^{d}\) exhibits an approximately linear relationship with U\({}_{\text{L}}\), with \(r\) = 0.88. The sites around the GBs (s1-s5) have a smaller \(\overline{\text{CN}}\) than those on the flat Au(110) surface (=7.00). Of note is that a decrease in \(\overline{\text{CN}}^{d}\) corresponds to a decrease in U\({}_{\text{L}}\). As stated above, U\({}_{\text{L}}\) is strongly correlated with H* binding strength; therefore, a smaller \(\overline{\text{CN}}^{d}\) implies a higher E\({}_{\text{B}}\)[H*] for Au GBs and, thus, better HER performance. Therefore, HER activity (or H* binding) can be directly evaluated by the \(\overline{\text{CN}}^{d}\) of the active sites around the GBs. Compared with the flat extended surface, atoms around GBs have no regular arrangement; that is, they are rich in atomic disorder, leading to the lack of complete coordination (unsaturated coordination). Thus, strong Au-H bonds have to form to compensate for the lack of coordination, following the bond-order conservation theory [35,36], leading to a higher H* binding capacity of the sites around GBs. By making a comparison with the U\({}_{\text{L}}-\overline{\text{CN}}^{d}\) relationship, it is evident that a smaller \(\overline{\text{CN}}^{d}\) corresponds to a larger \(\varepsilon_{\text{d}}\) shift, indicating that the lack of a coordination number alters the \(\varepsilon_{\text{d}}\) of active sites around GBs. Hence, in the present case, the bond-energy-integrated coordination number or the \(d\)-band center theory explains the origin of the improved HER activity; both originated from the disorder of the atomic arrangement, which inherently exists at GBs. Figure 7: (**a**) Au \(\overline{\text{\text{\text{\text{\text{\text{\text