esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Reroute Mechanism for Satellite Networks Based on Segment Routing and Dual Timers Switching
## 2 Related Work
### Overview of the SRDT-FRR Scheme
#### 3.1.2 Backup Path Design  
Based on segment routing (SR), the proposed scheme achieves 100% link protection coverage under single-link failure conditions [21]. As shown in the Figure 2, Backup paths are encoded as ordered segment lists, referred to as backup path label stacks, which guide the satellite nodes in processing and forwarding packets. Each segment, or label, represents an instruction executed by a satellite node upon receiving a packet. By combining multiple segments into an ordered list, packets can be directed along any path within the satellite network, independent of routing protocols or shortest-path constraints. The SR backup path packets carry the backup path label stack in their headers, with nodes executing the instructions in the stack using three basic operations: PUSH, CONTINUE, and NEXT.  
According to the Global Backup Path Calculation and Activation outlined in Algorithm 1, upon detecting a failure, the PLR node promptly identifies the fault through hardware detection and immediately switches to the local backup path, ensuring uninterrupted traffic flow. If the fault evolves into a stable, long-term failure, it is subsequently detected by the link failure detection mechanism as the fault duration extends. This triggers a network-wide topology update, leading to the convergence of a new onboard underlay routing configuration, after which the backup path can revert to the underlay routing.  
Figure 2: Backup path diagram.  
```
0: Network Graph \(G=(V,E)\)
```
0: Efficient rerouting using precomputed backup paths // Step 1: Backup Path Precomputation (For each PLR node): for each node \(n\in V\)do for each link \(l\in E\)do  Precompute backup paths for link 1 failure  Store backup path in the backup routing table of p with lower priority than the base route  endfor endfor // Step 2: Table-based Forwarding with SR Backup Path Activation: for each each packet forwarded by PLR node n do if fault detected then  Redirect traffic to SR backup path label stack endif endfor //Step 3: Backup Path Activation and Deactivation: if a failure occurs then  PLR node activates the backup path by increasing its priority in the routing table  Traffic is redirected to the backup path if the fault is resolved then  Deactivate backup path by restoring the base route priority  Redirect traffic back to the primary path endif endif ```
**Algorithm 1** Global backup path calculation and activation
# Experimental Study on the Torsional Behaviour of Prestressed HSC Hollow Beams
## 3 Global Analysis of the Experimental Results
### Force in the Prestress Reinforcement vs. Twists  
Figure 9 presents the graphs of the force in the longitudinal prestress reinforcement (\(F_{ps}\)) _versus_ the average twist (\(\theta_{m}\)). The force \(F_{ps}\) was obtained directly from the load cell placed in the anchorage zone of the prestressed wires. The evolution of the recorded values starts from the initial value of the applied prestress force (after short-term losses).  
The curves of Figure 9 show the existence of a small horizontal zone where the force in the prestress reinforcement is almost constant and equal to the force due to initial prestress. This zone ends as the first crack appears in the beam. After this zone, the force in the prestressed wires increases gradually. In fact, before cracking, the internal reinforcement steel bars are also under very low levels of stress. The strains in the beam during the pre-cracking state are very small, as confirmed by the readings of the strains in the reinforcement bars (recorded from the strain gauges). Decompression of concrete takes place at a certain point of this initial horizontal zone (before this point, concrete is only in compression).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Beam**} & \(T_{\sigma}\) & \(\theta_{\sigma}\)\({}^{\text{I}}\)**/m** & \((GC)^{\text{I}}\)** & \(\tau^{\text{I}}\)=\(a\theta^{\text{I}}\)+\(b\)** & \((GC)^{\text{II}}\)** & \(T_{ty}\) & \(\theta_{ty}\)\({}^{\text{c}}\)/m** & \(T_{r}\)**kNm** & \(\theta_{T_{r}}\)\({}^{\text{c}}\)/m** \\  & **kNm\({}^{\text{I}}\)** & & & & **kNm\({}^{\text{I}}\)** & **kNm\({}^{\text{I}}\)** & **kNm** & \(\theta_{ty}\)\({}^{\text{c}}\)/m** & \(T_{r}\)**kNm** & \(\theta_{T_{r}}\)\({}^{\text{c}}\)/m** \\ \hline D-0 & 130.5 & 0.04 & 172,940 & \(a=170.87;b=119.43\) & 9790 & - & - & 355.9 & 1.45 \\ D-1.79 & 172.9 & 0.07 & 152,188 & \(a=194.73;b=142.95\) & 11,157 & 314.9 & 0.87 & 396.0 & 1.38 \\ D-3.08 & 184.7 & 0.08 & 141,823 & \(a=148.05;b=132.34\) & 14,212 & 430.0 & 1.34 & 447.7 & 1.57 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Properties of \(T\)-\(\theta_{m}\) curves.  
After cracking, the torsional longitudinal reinforcement bars become effective and the tensile stress in the bars increases as the longitudinal deformation of the beam increases. The elongation of the beams also causes the increasing of the initial force in the prestressed reinforcement. This increasing occurs up to the failure of the beams.
# A Spatio-Temporal Building Exposure Database and Information Life-Cycle Management Solution
## 7 Discussion  
This work provides a conceptual and technological framework for spatio-temporal integration, management, and versioning of exposure information throughout different phases of the disaster risk management cycle. Application scenarios have been provided to showcase the capabilities of the database and lifecycle management solution. Yet, a comprehensive testing of the full capabilities of the solution and the presented workflows on large real-world datasets remains an open task. A dynamic integration of large area OSM data with versioning and history support is envisaged and will be a good opportunity to further test the potentials and limitations of the proposed solutions and, in particular, to assess its scalability.  
The database model and life cycle management solution provide a solid backend for data and metadata management in order to integrate data from different sources with varying quality, vintage, and reliability. In its current form, no explicit integration algorithm is implemented at the database level. However, several promising approaches for probabilistic information integration algorithms  
Figure 9: Valid time history of the database, which includes for each object an information lifecycle with the type of real-world change, valid time of the changes, and recovered attributes at any given valid time. The magnified view and table show the lifecycle of a selected object.  
are available [39; 41] that could be combined with the proposed solution. The decision criterion that matches objects between different datasets and that triggers the check whether an object should be considered for an update or not is currently based on a spatial intersection of existing and new object geometries. This works well when object geometries are simple and of generally high accuracy and/or distributed with enough proximity to each other. In case of an integration of complex, large scale, and low accuracy datasets this, however, could introduce object mismatches. In such a case where pure geometrical matching would be error-prone and unique object identifiers are not available, more sophisticated approaches that match objects between different datasets by using several attributes and fuzzy matching criteria may be required.  
Queries about location, spatial properties and relationships, time, temporal properties and relationships, and spatio-temporal behaviors and relationships are supported. Multi-representation in the form of a multi-resolution approach with dynamic linkage between resolutions is also included. However, some functionalities such as the generalization of geometries between different resolutions should be further extended and different generalization functions should be made available. Only a simple generalization function for polygon geometries is currently implemented in the database model. Modifications and extensions can, however, be applied without major changes to the basic design.
## 2 Materials and Methods
### Numerical Analysis of SMA-Based IAC Actuator  
The design process of the actuator requires different verification, evaluation, and validation from one analysis approach to another to obtain a well-operating actuator at the final stage [43]. In this section, we used finite element analysis to validate the analytical simulations in the previous section. Both approaches were performed to open and close the air flowing in the bypass of the throttle body for idle engine speed. The numerical analysis uses COMSOL Multiphysics software (R2023b) to parameterise, geometrise, and simulate the IAC actuator. The analysis was arranged to fulfil the opening and closing operations of the actuator described in Figure 3. Table 4 identifies the numerical simulation workflow from the start to the computation of the outputs but highlights the different settings between the closing and opening state of the actuator. The model builder uses different physics due to the two operating modes of the actuator. Multibody dynamics is used for the mechanical constraints to simulate the actuator in the closing state while the opening state uses joule heating and thermal expansion physics. After confirming the geometry component with full physics settings, the materials are assigned to the concerned actuator components, as identified in Section 2.1. Table 5 lists the properties of materials assigned to the model, excluding the NiTi properties, which are listed in Tables 2 and 3. When inserting the NiTi input parameters/properties, the basic and phase transformation properties were separated from the martensite and austenite properties. In the meshing process of the model, the mesh element size was 0.005 mm, with a free tetrahedral extra fine that uses a geometric shape order of quadratic serendipity, as shown in Figure 7a. The electric current uses a terminal type of current with 2 A. It is assigned on a positive connector to supply the SMA spring for the thermal effect of the actuator \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Model Builder and Setting**} & \multicolumn{2}{c}{**IAC Actuator’s Operating Mode**} \\ \cline{2-3}  & **Closing State** & **Opening State** \\ \hline Physics interface & Structure Mechanics/Multibody dynamics & Structure Mechanics/Joule Heating and Thermal Expansion \\ Study & Stationary & Stationary \\  & Rigid fixed domain (supports), Prescribed & Fixed constraint (supports), Temperature \\ Physics constraints & displacement, (Assigned on the Pintle as the & (\(T_{e}\)), Ground (negative connector), \\  &  Boundary load (\(F_{b}\) used as pressure and assigned & Electric terminal (positive connector \\  & on the bias spring) & linked with the SMA spring). \\ Meshing & Free tetrahedral extra fine & Free tetrahedral extra fine \\ Solver configurations & Stationary nonlinear solver with segregated steps & Stationary nonlinear solver with \\  & & segregated steps \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Numerical simulation workflow in COMSOL Multiphysics.
# Optical accelerometers for Detecting Low-Frequency Micro-Vibrations
## 2 Principle and Requirements
### Requirements of Low Frequency Accelerometers
#### 2.2.4 Fault Detection of Wind Turbine  
Wind energy has grown rapidly in the last decade due to its clean and renewable nature. By the end of 2021, more than 650 GW of installed wind energy capacity has been put into operation worldwide. Mechanical drive components (gears and bearings) of wind turbines are prone to failure due to exposure to harsh environments, such as random winds, temperature differences, and alternating loads [15; 16; 17]. Mechanical failures of wind turbines may lead to their shutdown and have adverse social impacts. Therefore, the study of mechanical failure detection of wind turbine units has received increasing attention develop a reasonable operation and maintenance plan and to avoid catastrophic consequences. The lowest rotational frequency of the internal rotating mechanism of a wind turbine can be as low as 0.33 Hz [18], so the fault detection accelerometer of a wind turbine needs to have good low-frequency response characteristics, as well as high sensitivity.
###### Abstract  
The study aim was to test the applicability of exoskeletons and molts from mealworm (_Tenebrio molitor_) cultures as sorbents for anionic dyes: Reactive Black 5 (RB5) and Reactive Yellow (RY84). Factors investigated included: characteristics of sorbents (FTIR, pH\({}_{\text{PZC}}\)), the influence of pH on sorption efficiency, sorption kinetics (pseudo-first, pseudo-second-order, intraparticle diffusion models), and determination of the maximum sorption capacity (Langmuir 1, Langmuir 2, Freundlich, and Dubinin-Radushkevich models). The sorption efficiency of anionic dyes on the tested sorbents was the highest at pH 2. The time needed to reach the sorption equilibrium for both dyes was 120-150 min. The sorption kinetics of the dyes were best described by the pseudo-second-order model. Maximum sorption capacity data showed the best fit to Langmuir 2 isotherm, suggesting that at least two types of sorption centers played an important role in dye sorption. Presumably, for both of the tested sorbents, the active sites in question were protonated amine (-NH\({}_{3}\)+), acetamide (NH\({}_{2}\)COCH\({}_{3}\)+), and hydroxyl groups (-OH\({}_{2}\)+) of chitin and protein. The maximum RB5 and R784 sorption capacity of the tested sorbents was 78.70 mg/g and 60.49 mg/g, respectively, for mealworm exoskeletons, as well as 55.72 mg/g and 44.25 mg/g, respectively, for mealworm molts.  
mealworm; exoskeletons; molts; sorption; reactive dyes +
Footnote †: journal: _Applied_  
_aciences_
## 4 Geometry Used in the Calculation and Choice of the Shield Material  
In this work, the calculations are performed considering the experimental setup, which is shown schematically in Figure 3. The simulated geometry consists of an extended proton source, with a diameter of 20 cm, energy ranges from 50 MeV to 1000 MeV, in a vacuum. As a target, instead of the aluminum used in [PERSON] et al., 2017 [14], we now use a Nomex slab (\(\rho\sim 1.15\) g/cm\({}^{3}\)), in the shape of a parallelepiped, with a thickness of 20 g/cm\({}^{2}\) (whereby z = 17.4 cm) in the direction of the primary beam and an area of 30 \(\times\) 30 cm\({}^{2}\) facing the beam. Such a thickness is typical of the storm shelters used by the crew during intense Solar Particle Events (SPEs) [4; 14].  
For each energy value, a run of \(1.5\times 10^{6}\) events was simulated. The Nomex chemical composition is shown in Table 1.  
Figure 3: Experimental setup used in the simulation.  
Figure 2: Flowchart concerning the processes implemented in the DOSE application.  
As already discussed, the shield that can be used in space must possess fundamental characteristics, including the capacity to absorb the secondary radiation generated by the interaction of the primary beam with the atomic nuclei of the chemical elements, this secondary radiation being the largest contribution to the dose absorbed by the astronauts. The choice of the Nomex [28, 29] materials for this test is inspired by a previous study [15] concerning the shielding effectiveness of some materials suitable for applications in space. Nomex was the best in terms of dose reduction to astronauts in the case of the interaction with 1 GeV protons [15]. In Nomex, there is a percentage of air due to the honeycomb structure. It is known that hydrogen is effective in shielding against GCR [30] because it breaks up the heavy ions by forming smaller, less harmful fragments [31], which are mostly absorbed inside the shield.
# Steganalysis of Quantization Index Modulation Steganography in G.723.1 Codec
###### Abstract  
Steganalysis is used for preventing the illegal use of steganography to ensure the security of network communication through detecting whether or not secret information is hidden in the carrier. This paper presents an approach to detect the quantization index modulation (QIM) of steganography in G.723.1 based on the analysis of the probability of occurrence of index values before and after steganography and studying the influence of adjacent index values in voice over internet protocol (VoIP). According to the change of index value distribution characteristics, this approach extracts the distribution probability matrix and the transition probability matrix as feature vectors, and uses principal component analysis (PCA) to reduce the dimensionality. Through a large amount of sample training, the support vector machine (SVM) is designed as a classifier to detect the QIM steganography. The speech samples with different embedding rates and different durations were tested to verify their impact on the accuracy of the steganalysis. The experimental results show that the proposed approach improves the accuracy and reliability of the steganalysis.  
steganalysis; wuantization index modulation (QIM); principal component analysis (PCA); support vector machine (SVM); information hiding +
Footnote †: journal: Future Internet
## 4 Results and Discussion
### Dam Failure Scenarios
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & \begin{tabular}{c} **Peak Discharge in** \\ **Gunnam (m\({}^{3}\)/s)** \\ \end{tabular} & \begin{tabular}{c} **Time of Peak** \\ **in Gunnam** \\ \end{tabular} & \begin{tabular}{c} **Peak Discharge in** \\ **Hwanggang (m\({}^{3}\)/s)** \\ \end{tabular} & \
\begin{tabular}{c} **Time of Peak** \\ **in Hwanggang (m\({}^{3}\)/s)** \\ \end{tabular} &
\begin{tabular}{c} **Percentage of** \\ **Change Compared** \\ **to Observation** \\ \end{tabular} \\ \hline Case 100\% & 22,536.5 & 12:00 pm & 36,818.0 & 10:00 p.m. & 713.8 \\ Case 80\% & 8070.0 & 14:00 p.m. & 10,605.1 & 10:00 p.m. & 191.2 \\ Case 50\% & 4173.5 & 15:00 p.m. & 4585.2 & 10:00 p.m. & 50.6 \\ Case 20\% & 2275.3 & 2:00 a.m. & 2:00 a.m. & 3:00 a.m. & -17.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Peak discharge and time of peak in Gunnam station and Hwanggang dam in the 2010 event. 36818.0 m\({}^{3}\)/s being obtained immediately after the failure. The case of 80% and the case of 50% are also hazardous with peak flows of 10605.1 and 4585.2 m\({}^{3}\)/s, more than three and 1.5 times that of the observed peak flow, respectively.
## 4 Results and Discussion
### The Baseline Leveling Experiment
#### 4.1.2 Tilt Modulation  
The previous experiments demonstrated the feasibility of monitoring the tilt of the platform using the inverted pendulum but did not establish the correlation between the  
Figure 12: The inverted pendulum detects the periodic signal of the platform tilt caused by the water cooler.  
inverted pendulum and torsional oscillations. As shown in Figure 13, an installation lift platform is placed below the platform for 2-point fixation, and a piezoelectric actuator (green) is used for 1-point adjustment. The length of the piezoelectric ceramic is adjusted at the micrometer or even nanometer level to finely tune the tilt angle of the control platform in micro- and nano-radians. As shown in Figure 14, during the square wave modulation of the height of the piezoelectric ceramic, the displacement detected at the end of the inverted pendulum and torsional oscillations almost synchronously change. Each 3 \(\upmu\)m displacement of the inverted pendulum corresponds to approximately 0.4 \(\upmu\)m displacement of torsional oscillations.  
Additionally, the torsional oscillations exhibit a low-frequency baseline drift outside of the square wave modulation, indicating that the roll detected by the inverted pendulum does not reflect the full tilt of the platform. There is also a gravity coupling in the pitch direction, in addition to the tilt modulation by the piezoelectric ceramic. Therefore, center of mass calibration should not only be performed on the length of the pendulum frame, but should also consider the imbalance of counterweights in the roll direction, which is often overlooked during the balancing process.
## 4 Discussion
More precisely, the clinician classified 29 cases as negative that were identified as positive by the AI model, while the AI model classified 11 cases as negative that the clinician tagged as positive. These results underline the necessity for additional refinement of the AI model, particularly to enhance specificity in identifying patients without DFO. This distribution of discordant cases is further illustrated in Figure 4, which provides a visual summary of the overlap between both the diagnostic approaches using an annotated Venn diagram. While the level of numerical agreement for positive classifications is high, the graphical representation emphasizes the asymmetry in non-matching positive classifications between the AI model and the clinician. This visualization highlights the need to better understand the factors contributing to diagnostic divergence, particularly in cases with subtle or borderline radiographic features. The findings of this study suggest that AI-based diagnostic models for DFO might be especially beneficial in specialized diabetic foot units, where the disease prevalence exceeds 70%. The subsequent challenge involves optimizing these models to enhance specificity, thus ensuring their application in primary care environments. By bolstering specificity, AI-driven tools could aid non-specialist clinicians in spotting high-risk cases, thereby enabling timely referrals to specialized centers. Importantly, the integration of AI tools into clinical workflows is not intended to replace expert judgment, but rather to provide valuable support in settings where access to specialized expertise is limited. In primary care or rural environments, where imaging interpretation is often suboptimal and access to MRI or nuclear medicine may be restricted, an AI system could serve as a triage mechanism, flagging high-risk radiographs for referral or further assessment. Additionally, in specialized units, such tools may function as second readers to reduce perceptual errors and mitigate the impact of cognitive fatigue in high-volume practices.
# Statistical Analysis of the Weight and Center-of-Gravity Position of an Empty Container Ship
## 1 Introduction
### Weight Light Ship
The center of gravity of a light ship is calculated based on the centers of gravity of each class (Table 1):  
\[x_{CG}=\frac{\sum_{k=1}^{8}x_{CG}\cdot M_{k}}{\sum_{k=1}^{8}M_{k}} \tag{5a}\] \[y_{CG}=\frac{\sum_{k=1}^{8}y_{CG}\cdot M_{k}}{\sum_{k=1}^{8}M_{k}}\] (5b) \[z_{CG}=\frac{\sum_{k=1}^{8}z_{CG}\cdot M_{k}}{\sum_{k=1}^{8}M_{k}} \tag{5c}\]  
Figure 3: An example container ship general plan and the center of gravity coordinates. Figure 2: Average statistical share of each weight class for the examined container ships. where  
\(x_{CG}\), \(y_{(CG)}\), \(z_{CG}\)--coordinates of the center of gravity of a light ship (it is aimed that \(y_{CG}=0\)--the ship must not list during operation), Figure 3, [m],  
\(x_{GK}\), \(y_{(G)}\), \(z_{GK}\)--coordinates of the centers of gravity in each class (Table 1),  
\(M_{k}\)--weight in each class (Table 1), [t]. Ship speed \(V\), not present in the above formulas and in Table 1, can also affect the weight of the ship (or its class/es). During the design of a ship, the weights and coordinates of the center of gravity of the various parts of the ship's structure and its equipment are calculated at the final stage, after the technical and working design is done. A heel test is carried out after the ship's construction, which will experimentally verify the calculations of the weights and center of gravity of a light ship. However, at the preliminary design stage of the ship, the individual weight classes of the ship are not known (Table 1), and the positions of the centers of gravity of the classes that make up the weight light ship are not known.