esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Antioxidant Effects of FBG
#### 3.3.2 ROS Generation of FBG in HaCat Cells  
In the HaCat cells, the FBG and WG showed a lower toxicity compared with BG in the concentration range of 50-200 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL (Figure 5A). Therefore, a 100 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL concentration was selected for further experiments.  
Figure 4: Effect of FBG, BG, and WG on anti-wrinkle mechanism. The HDF cells were treated with 100 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL of FBG, BG, or WG for 24 h. The expression of MMP-1, MMP-9, and COL-1 at the mRNA level was determined using qRT-PCR. Ursolic acid (500 \(\mathrm{\SIUnitSymbolMicro\text{u}}\)M) was used as a positive control. The graph shows mean \(\pm\) SD values of three replicates. ### \(p\) < 0.001, as compared with the control group; ** \(p\) < 0.01, *** \(p\) < 0.001, as compared with the UVB-irradiated group.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multirow{2}{*}{**TPC**} & \multicolumn{3}{c}{**Antioxidant Activities**} \\ \cline{3-5}  & & **TFC** & **TFPH** & **Reducing Power** \\ \cline{3-5}  & **(\(\mathrm{\SIUnitSymbolMicro\text{g}}\) GAE/mg Extract *)** & & **(\(\mathrm{\SIUnitSymbolMicro\text{g}}\) GAE/mg Extract)** & **(\(\mathrm{\SIUnitSymbolMicro\text{g}}\) GAE/mg Extract)** \\ \hline FBG & 48.7 \(\pm\) 0.83 \(\mathrm{\SIUnitSymbolMicro\text{a}}\) & 30.24 \(\pm\) 0.80 \(\mathrm{\SIUnitSymbolMicro\text{a}}\) & 7.25 \(\pm\) 1.10 \(\mathrm{\SIUnitSymbolMicro\text{a}}\) & 22.34 \(\pm\) 0.54 \(\mathrm{\SIUnitSymbolMicro\text{a}}\) \\ BG & 39.8 \(\pm\) 0.65 \(\mathrm{\SIUnitSymbolMicro\text{b}}\) & 24.40 \(\pm\) 0.29 \(\mathrm{\SIUnitSymbolMicro\text{b}}\) & 6.01 \(\pm\) 0.73 \(\mathrm{\SIUnitSymbolMicro\text{b}}\) & 18.14 \(\pm\) 0.76 \(\mathrm{\SIUnitSymbolMicro\text{b}}\) \\ WG & 7.4 \(\pm\) 0.50 \(\mathrm{\SIUnitSymbolMicro\text{c}}\) & 6.20 \(\pm\) 0.15 \(\mathrm{\SIUnitSymbolMicro\text{c}}\) & 1.4 \(\pm\) 1.21 \(\mathrm{\SIUnitSymbolMicro\text{c}}\) & 5.82 \(\pm\) 0.81 \(\mathrm{\SIUnitSymbolMicro\text{c}}\) \\ \hline \hline \end{tabular}  
* The results are shown as means \(\pm\) standard deviation (n = 3). Values in the same column separated by a different letter (a–c) differ significantly. \(p\) < 0.05 by Tukey (HSD) test.
* \(\mathrm{\SIUnitSymbolMicro\text{g}}\) GAE/mg extract: \(\mathrm{\SIUnitSymbolMicro\text{g}}\) GAE/mg extract. (\(\mathrm{\SIUnitSymbolMicro\text{g}}\) GAE/mg extract.
* \(\mathrm{\SIUnitSymbolMicro\text{g}}\) RE/mg extract.  
\end{table}
Table 3: Total phenolic content, total flavonoid content, and antioxidant activities of FBG, BG, and WG.  
DCFH-DA probes were used to assess ROS generation. Figure 5B,C shows that the ROS level of H\({}_{2}\)O\({}_{2}\)-induced oxidative of HaCat cells increased by 2.7-fold compared with untreated cells (270%). We found that FBG at a concentration of 100 \(\upmu\)g/mL could act against the H\({}_{2}\)O\({}_{2}\)-induced oxidative stress of HaCaT cells by reducing the ROS level (130.7%) more effectively than BG (150.7%) and WG (200.3%).
## 4 Visual-Inertial Odometry
### Optimization of Visual-Inertial Systems
#### 4.3.1 Position Optimization  
Based on the least squares problem, common optimization methods include Gaussian-Newton method, L-M method, and global non-linear optimization method. To ensure computational efficiency and good convergence. The Gauss-Newton method is used for positional optimization. Firstly, define the state vector of each effective key frame  
\[s_{i}:=\left[{(\xi_{cam,a}^{D})}^{T},{v_{i}}^{T},b_{i}^{T},a_{i},b_{i},d_{i}^{ 1},\ldots,d_{i}^{m}\right]^{T} \tag{20}\]  
where \(v_{i}\in R^{3}\) denotes the velocity. \(b_{i}\in R^{6}\) denotes the IMU bias. \(a_{i}\), \(b_{i}\) are the affine illumination coefficients. \(d_{i}^{j}\) is the inverse depth of the point in the key frame. The full state vector is established as  
\[s=\left[{c}^{T},\xi_{m,d}^{T},s_{1}^{T},s_{2}^{T},\ldots,s_{n}^{T}\right]^{T} \tag{21}\]  
where \(c\) denotes the geometric parameters of the camera and \(\xi_{m,d}^{T}\) denotes the rotational transformation from visual-inertial frames to metric frames.  
Using stacked residual vector \(r\) to establish equation  
\[J=\frac{dr(s\boxplus\epsilon)}{d\epsilon}|_{\epsilon=0},H=J^{T}WJ,b=-J^{T}Wr \tag{22}\]  
Figure 3: Graph of visual-inertial optimization factor before and after marginalization. (**a**) The factor graph realizes the optimization of the pose of the key frame, the bias of the imu, and the velocity. (**b**) Changes in the factor graph after performing marginalization on older keyframes.  
where \(W\) is the weight matrix. The photometric error term \(E_{picture}\) and the inertial error term \(E_{imu}\) have no common residual. The update equation is established as Equation (23), and the state vector of inertial residual in metric frame is defined as Equation (25):  
\[\delta=H^{-1}b \tag{23}\]  
\[H=H_{picture}+H_{imu},b=b_{picture}+b_{imu} \tag{24}\]  
\[{s^{\prime}}_{i}:=\left[\xi_{m,jmu_{i}}^{\text{M}}\upsilon_{i},\upsilon_{i} \right]^{T},s^{\prime}=\left[{s^{\prime}_{1}}^{T},{s^{\prime}_{2}}^{T},\dots,{ s^{\prime}_{n}}^{T}\right]^{T} \tag{25}\]  
The inertial residuals lead to:  
\[{H^{\prime}}_{imu}={J^{\prime}_{imu}}^{T}W_{imu}{J^{\prime}}_{imu},{b^{\prime} }_{imu}=-{J^{\prime}_{imu}}^{T}W_{imu}\gamma_{imu} \tag{26}\]  
To achieve the joint optimization of the data. Firstly, the values of \(H_{imu}\) and \(b_{imu}\) are calculated from Equation (21), and \(J_{rel}\) is calculated according to the method proposed by [PERSON] et al. [36]:  
\[H_{imu}={J^{T}_{rel}}\cdot{H^{\prime}}_{imu}\cdot{J_{rel}} \tag{27}\]  
and  
\[b_{imu}={J^{T}_{rel}}\cdot{b^{\prime}}_{imu} \tag{28}\]
## 3 Proposed Solution  
Our proposal, depicted in Figure 2, consists of three main solutions:  
1. Lightweight IO Visor-based packet tracing for security-enhanced visibility: our security-enhanced visibility measurement solution employs IO Visor tracing tools. These traced data are fed into a flow-centric visibility solution to analyze network packet flows, including IP prefix-based inspection. This tool is deployed and executed from Linux-based servers, which are provisioned in distributed cloud sites.
2. Flow-centric visibility to assist networking control: our flow-centric visibility solution analyzes the collected flows to generate a specific recommendation policy for a specific type and source of the flow [19]. These policies are subsequently used to apply networking control amongst distributed cloud sites. This solution is executed in a _visibility center_, which is centrally located inside _operations center_.
3. ONOS intent-leveraged networking control: our ONOS intent-leveraged application translates a recommended policy based on the IP prefix into an intent specification compatible with the ONOS intent framework [20]. This application executes from the _provisioning center_ inside the operations center with API-based access to the ONOS controller or inside the _security checkpoint_ (i.e., a place where the first level of policy checking and enforcement can be applied for a specific location) in each site as an ONOS controller application.  
We describe each solution in more detail below.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Article** & **Purpose** & \multicolumn{2}{c}{**Conceptual**} & \multicolumn{2}{c}{
\begin{tabular}{c} **Implementation Scope** \\ **Collection** \\ \end{tabular} } & \multicolumn{2}{c}{**Mode**} & \multicolumn{1}{l}{**Environment**} \\ \hline [3] & Monitoring & Yes & Partial & Yes & No & Online & Testbed \\ [11] & Monitoring & No & Yes & Yes & No & N/A \({}^{\dagger}\) & Simulation \\ [12] & Monitoring & No & Yes & No & No & Online & Simulation \\ [13] & Monitoring & No & Yes & No & No & Online & Simulation \\ [7] & Security & Yes & No & No & No & N/A & N/A \\ [18] & Security & No & No & Yes & No & Offline & Real world \\ [19] & Traffic eng. & Yes & No & No & Yes & N/A & N/A \\ Proposal & General & No & Yes & Yes & Yes & Online & Testbed \\ \hline \hline \end{tabular} \({}^{\dagger}\) N/A indicates information “not available” in this context.  
\end{table}
Table 1: A comparison of the proposed work and related works.  
Figure 2: The overall design of our proposed solution.
# Bouncer: A Resource-Aware Admission Control Scheme for Cloud Services
## 4 System Design
### Design Considerations  
Our proposed design of Bouncer strictly complies with the following design requirements.  
* _Prioritization of services:_ The prioritization of services minimizes or eliminates the need for a detailed and well-rehearsed plan. In order to enable the prioritization of services, Bouncer enforces prioritization levels at the workload-admission control and scheduling phases. This classification does not violate QoS rules and is also in line with the optimization requirements of the system.
* _Capacity Awareness:_ To avoid overloading, Bouncer uses service profiling through SDN-based control (implemented during evaluation). This allows a system to define and extend its process capacity to avoid any service degradation.
* _Coordination of functions:_ Admission-control systems should be based on the controlled-time-sharing principle implemented on VMs [41]. The fundamental reason for this is that time-sharing if controlled properly, allows an elastic response to a wide variety of disturbances affecting the workload performance. In Bouncer, we, therefore, assume that the requested time (by services) is accurate.
# Generalized Spoof Detection and Incremental Algorithm Recognition for Voice Spoofing
## 4 Experimental Methods
### Training Details
#### 4.2.3 Evaluation Metrics  
To evaluate the performance of the voice spoofing detection module, we recorded the output score of the voice spoofing detection module, called the countermeasure (CM) score. This indicates the similarity between a given voice and a real voice. The equal error rate (EER) was calculated by setting a threshold on the CM decision score such that the false alarm rate was equal to the miss rate. The calculations were as follows:  
\[P_{fa}(\theta)=\frac{\#\{\mathit{spoof\ voice\ with\ score>\theta}\}}{\#\{ \mathit{Total\ spoof\ voice}\}} \tag{14}\]  
\[P_{miss}(\theta)=\frac{\#\{\mathit{human\ voice\ with\ score\leq\theta}\}}{\#\{ \mathit{Total\ human\ voice}\}} \tag{15}\]  
where \(P_{fa}(\theta)\) and \(P_{miss}(\theta)\) are monotonically decreasing and increasing functions of \(\theta\), respectively. EER corresponds to the threshold \(\theta_{EER}\) at which the two detection error rates are equal to 5, i.e., EER = \(P_{fa}(\theta_{EER})\) = \(P_{miss}(\theta_{EER})\). The lower the EER, the better the voice spoofing detection module detects spoofing attacks.  
The tandem detection cost function (t-DCF) [30] is a new evaluation metric adopted for the ASVspoof 2019 challenge. Although EER only evaluates the performance of the voice spoofing detection module, it assesses the influence of the voice spoofing detection module on the reliability of an ASV system. The ASV system was fixed to compare the different voice spoofing detection modules. The lower the t-DCF, the better the reliability of the ASV.  
For the voice spoof algorithm recognition module, we recorded the accuracy of each iteration and the recognition accuracy of each type of algorithm for the last iteration. The higher the accuracy rate, the better the recognition performance.
## 2 Problem Statement  
According to the bibliography, there are a limited number of tools for the process of humanless automated recording of dangerous goods traveling over the motorway. An initial attempt using image processing and Optical Character Recognition (OCR) has been proposed by [22]. Their paper uses a two-stage image processing and an OCR engine for this purpose as the authors of this paper. Nevertheless, the evaluation results are limited and of low accuracy. Ref. [23], implemented a deep learning algorithm and application for the automatic detection of accidents inside tunnels using CCTV inputs. Other approaches include using inductive loops or piezoelectric circuits to detect large trucks passing, as mentioned by [24]. However, they fail to catch the trucks' dangerous material being transported. Moreover, several works have been mentioned in the literature regarding automated robotic systems and algorithms for detecting structural integrity issues in tunnels, as noted by [25].  
The main objective of this research effort under the HMRT (Holistic Management of Road Tunnels--ODOS in Greek) project is to create an intelligent communication system for managing fire risks in tunnels. This paper focuses on the proposed algorithm and application implementation as well as the authors' implementation and evaluation in this direction.  
A fire incident inside a tunnel caused by vehicles carrying dangerous and/or non-dangerous goods can lead to high levels of carbon monoxide and other toxic gases and the release of flammable, volatile liquids or gases on the road inside the tunnel, all potential fire hazards. Therefore, the developed application aims to support road tunnel operators in responding appropriately in the event of potential incidents by taking appropriate actions toward the users (e.g., evacuation) and the response teams, ensuring, in this way, a higher level of safety for these critical infrastructures.  
The initial step to all the above is recognizing the vehicle carried substance. The materials carried by each vehicle can be deduced strictly from the ADR sign. The number and type of substances (i.e., toxic, flammable, explosive) can be controlled over time by tracking vehicles before entering a tunnel and visualizing them on a dashboard. During rush hours or when multiple dangerous substances are being transported simultaneously, tunnel operators can decide whether to delay the passing of ADR trucks, reducing the risk of an accident. Furthermore, if an ADR vehicle passes through the tunnel and causes an accident, tunnel operators will know the vehicle's cargo ahead of time. As a result, they will be better prepared to respond appropriately to a potential incidental event (i.e., in case of substance release, to know in advance if this substance is toxic or flammable; therefore, be prepared to evacuate the tunnel and guide the emergency response team adequately). The proposed HMRT-EF (EF stands for dangerous goods in Greek) application for automatically detecting ADR trucks and its three-stage detection algorithm, ADR-Det, are presented in the following section.
# Study on the Effect of Vibrating Process on the Compactness of Slipform Concrete
## 3 Analysis of Experimental Results  
### Effect of Vibrating Parameters on the Compressive Strength of Slipform Concrete  
The average compressive strength of the slipform concrete under different vibrating parameters (Figure 5) showed that the law of change increased first and then decreased with the increase in vibration time. In addition, the higher the vibration frequency, the more significant the change, and the shorter the time for the compressive strength to reach the peak. When the vibration frequency was 6000 r/min, the vibration time was 50-90 s, and the compressive strength of slipform concrete was the largest and had the  
Figure 4: IPP software processing.  
Figure 3: Specim cutting.  
most stable change. When the vibration frequencies were 8000 r/min and 10,000 r/min, the vibration times were 40-80 s and 40-70 s, respectively. This study analyzed the cause of concrete slipping in the early vibration stage. It was found that the concrete underwent vibration liquefaction, sinking the whole slab in the vibration liquefaction area to discharge the excess air between the particles, making the components more compact and gradually increasing the compressive strength. The greater the frequency of vibration, the better the liquefaction effect of slipform concrete and the faster the gas is discharged. However, as the vibration duration continued to increase, the large-size aggregates sank continuously in the internal vibration liquefaction area of the concrete, and the mortar and small-size aggregates increased in the concrete's upper part. This mechanism resulted in a decrease in the compressive strength of the upper part of slipform concrete.  
Therefore, the slipform concrete vibrating process should focus on controlling the length of vibrating time to avoid the over-vibration of the concrete.
## 5 Discussion
### Research Outcomes  
The first evaluation criterium measures the capability model's effectiveness to practitioners who need to plan the adoption of LA at their institution. The participants to our research represented different stakeholders at educational institutions, which is important for successful LA adoption [34]. The model proved effective in supporting practitioners in planning LA adoption. Our analysis showed that many capabilities are overlooked when creating implementation roadmaps. Most notably, there was almost no attention to privacy and ethics. This is remarkable as these are important aspects of learning analytics [58; 59] and one of the main issues limiting LA adoption [8]. The model enables participants to make more complete and more explicit roadmaps. Using the model, the number of capabilities within roadmaps increased by 45%.  
The second evaluation criterium regards the model's perceived usefulness. During the group discussions, both experts and practitioners were positive about the model's scope and depth. The survey outcomes support this conclusion: both the overall perception and perceived usefulness score well. However, the model's ease-of-use scores mediocre. This might result from the large number of operational definitions. Especially when we used the online tool with 138 different questions in our first pilot pluralistic walk-through, the participants might have been overwhelmed. This relates to the model's instantiationvalidity, i.e., \"the extent to which an artefact is a valid instantiation of a theoretical construct or a manifestation of a design principle (p. 430)\" [60]. Due to the complexity of the model's instantiation, the model itself is perceived as hard to use. The lack of practical guidelines is a common problem shared between LA models [12]. We plan to enhance this during future research.  
The third evaluation criterium considers the model's completeness. All necessary capabilities are present in the model. Nonetheless, some improvements can be made. First, the scope of Performance monitoring must be broadened. Its current definition is, \"In what way the performance of analytical processes and applications are measured.\" This definition is too narrow. The pluralistic walk-throughs' outcomes show that processes and systems' performance must be monitored, and the interventions' performance should be measured. This is in line with other scholars' opinions [2; 61; 62]. Second, the position of pedagogical theory and context and the evaluation of interventions' impact should be made more prominent. This is an essential conclusion as LA's benefits to teaching and education are often overlooked [34]. Lastly, some participants suggest the development of supporting materials to enhance the practical use of the model.
## 2 Methods Used for Integrated Monitoring and Management of Diet-Related Diseases
### Methods for the Development of the Tailored RHMS
#### 2.3.2 Data Accuracy and Evaluation
It is important to note that data are processed once in the cloud before being permanently stored and a suite of applications and analysis tools await, including alarms triggering, diagnostic support services, nutritional status evaluation, AI-predictive models, or wellness and activity tracking. The AI-based predictive models are trained and tested using the open data available and the specific data from the patients as well as their measured parameters. The models are based on ML algorithms that are specific to different types of data, such as support vector machine, decision tree, or random forest. The framework also underscores the importance of user interaction, fostering seamless communication and collaboration between patients and healthcare professionals. As data flows through the system, paramount considerations of data accuracy, quality, and security are maintained to ensure the reliability and effectiveness of the RHMS in diet-related disease management. The cornerstones of successful RHMS and smart monitoring environments, especially in the management of diet-related disorders, are upholding data accuracy and putting in place efficient update procedures. Reliable data provide the foundation for healthcare professionals' well-informed decision making, which results in patient treatments that are more efficient and customized. Ensuring the integrity of these systems requires regular validation, calibration, and compliance with best practices in data management. Figure 2: Conceptual framework of the three tailored RHMS.
## 3 Net Impact and Capturing Risks
### Structural Risks  
Before presenting in depth the structural risks related to Envisat, the case of the ASAR antenna is briefly mentioned. In fact, structural failure of the ASAR is not expected. This is due to two main reasons. The first reason is that the ASAR is rigidly attached to the main Evisat body with a number of joints that distribute the experienced loading [28]. The second reason relates to the ASAR's relatively large cross-sectional geometry, with a width and approximate thickness of 1.5 m and 0.156 m [28], respectively. Due to this,  
Figure 11: Solar array angular deflection and velocity.  
Figure 12: Structural hinge moment loading, \(\mathbf{M}_{sn}\), in hinge frame due to the frontal net impact of \(V_{net}=20\) m/s.  
maximum compression and shear loads of 2000 N and 750 N resulted in only a maximum compression and shear stress of 8.55 kPa and 2.78 kPa [18]. Additionally, the maximum bending stress at the joints' location has been found to be low (three orders of magnitude below the allowable stress).  
Lastly, in the case of structural risks, the identified weak locations (3 and 4, see Figure 5) of potential failure for the Ka-band antenna were checked for bending (Figure 13) and shear (Figure 14) failure from the aforementioned structural forces and moments.  
The antenna or DRS structure of the Envisat can be assumed to be a combination of titanium alloy (TiGAI4V) [29] and CFRP (carbon fibre-reinforced polymer) [30], with associated ultimate limits for normal and shear stresses of \((950,3500)\) MPa and \((50,100)\) MPa [31; 32], respectively. Whereas the normal stress limit is not attained, the range of shear stress values at the antenna-dish interface [33] is already reached within 1 s (Figure 14). Thus, secondary space debris is generated in the form of a \(0.9\times 1\) m antenna dish [33].  
Figure 14: Maximum shear stress (torque and linear combined) of the Ka-band antenna at two locations (hinge: 3, dish root: 4) as a function of time.  
Figure 13: Maximum normal stress (bending and axial combined) of the Ka-band antenna at two locations (hinge: 3, dish root: 4) as a function of time.