esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Structural and Chemical Characterization
The bare DE showed a Type II isotherm curve with a porous nature according to the International Union of Pure and Applied Chemistry (IUPAC) classification. The surface area of DE was found to be 28 m\({}^{2}\)/g with the sizes of the pore diameters and pore volumes being 2.44 nm and 0.0225 cm\({}^{3}\)/g (Table 1), respectively. After modification with LDH, the isotherm curve was Type II, suggesting the porous nature of DE-LDH. There is an increase in the  
Figure 3: Characterization of DE and DE-LDH (before dye adsorption) and DE-CR and DE-LDH-CR (after dye adsorption) by (**a**) XRD pattern, (**b**) Fourier transform infrared spectroscopy (FTIR) spectra (c) N\({}_{2}\) adsorption–desorption at 77 K and (**d**) pore size distribution plot using the Barrett–Joyner–Halenda (BJH) method. surface area, pore volumes, and pore diameter from 28 to 51 m\({}^{2}\)/g, 0.022 to 0.069 cm\({}^{3}\)/g, and 2.44 to 3.33 nm, respectively. Therefore, the change in the various Brunauer-Emmett-Teller (BET) parameters endorses that LDH was modified on DE. Further, investigations on surface area of adsorbents after dye adsorption were studied. As expected, the surface areas of DE-CR (18 m\({}^{2}\)/g) and DE-LDH-CR (44 m\({}^{2}\)/g) were decreased after the dye adsorption which indicates that the dye molecules were adsorbed on the available pores of the adsorbent surface. From the BJH method, the pore size distribution curve was not affected after the dye adsorption by the adsorbents (Figure 3d). The surface charge of the adsorbents is known to affect the removal process of charged dye molecules from aqueous solution. Thus, the surface charge characteristics of adsorbents were measured by zeta potential. The zeta potential was calculated between pH 2 and 12 for DE and DE-LDH (Figure 4), which showed that the zero-point charge (pH\({}_{\text{2 pc}}\)) of DE-LDH is 10.9. However, DE showed a negative charge on its surface at all the pH levels studied. This measurement showed that the DE-LDH is positively charged below pH 10.9 and, thus, negatively charged CR dye molecules can adsorb with high efficiency below pH 10.9. The DE, on the other hand, being negatively charged, electrostatically repels the negatively charged CR dye at a wide range of pH levels.
## 6 Discussion and Conclusions
### Ice Crushing and Ice Bending  
In this work, ice bending is assumed to be dominant over ice crushing. Nevertheless, the assumption does not work every time. [PERSON] and [PERSON] [14] found irregularities in resistance values due to changes in the nature of icebreaking pattern: sometimes crushing dominates and sometimes bending. Some parts of the ice sheet are continuously crushed by the vertical parts of the ship hull, e.g., shoulder crushing, without bending failure, then the crushing forces becomes more severe. [PERSON] et al. [25] presented a model to calculate the level ice action on wide conical structures. This model is adopted by the ISO Code, ISO/CD19906 [26]. [PERSON]'s model shows that the contribution of the breaking load component accounts for 20%-30% of the total ice force. [PERSON] [27] found that ice crushing only occurred at 1% of the time during the observation of Molikpaq ship trial. Still, it caused some of the highest forces. Continuous brittle crushing takes place when an ice sheet moves against a structure at high speed. However, such crushing is mainly happening for relative thin ice. When ice sheet thickness increases, ice bending becomes dominant again. [PERSON] also found that for low indentation speed and low loading rate, a smooth variation in ice forces based on ice elastic and creep behavior results in ductile deformation [28]. The ejection phase or extrusion (ice sheet drifts away) gives rise to brittle ice failure and the ice force endures an abrupt decrease. Yet, the transition has not been clearly defined as it is ice and scale parameter depended. The energy force used in this model only considers brittle crushing which can cause result discrepancy at low speed. As a rule of thumb, the interaction can be assumed to be bending failure dominant. Another aspect can be the negligence of the different energy components and distributions during the interaction process [29], for instance friction energy.  
To identify whether ice crushing or ice bending is dominant, ice crushing force can be plotted through 0-\(\zeta_{n}\) together with the bending force. If crushing force is larger at a certain indentation displacement, then bending will not occur.  
In numerical simulation, ice is usually regarded as an isotropic material. Still, ice is a non-isotropic, non-homogeneous and orthotropic material. The reasons behind irregularity and discrepancy between model test and simulation should always be kept in mind.
## 5 Results
### Reliable Node Placing
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Network** & **Nodes per Technology** & **Rank Number** & **Number of** & **ATTR** & **ATTR** \\  & **(\(n_{1},n_{2},n_{3}\))** & **Total Number** & **Post Failure** & **Value** & **Values among** \\  & & **Total Number** & **Connected** & & **Post-Failure** \\  & & **of Configurations** & **Components** & & **Functional** \\  & & & & **Nodes** \\ \hline \multirow{2}{*}{Sprint} & (2,5,4) & 7/7 & (1,1,1) & 0.4545 & 1 \\  & (3,4,4) & 1/7 & (1,1,1) & 0.4327 & 1 \\ \hline \multirow{2}{*}{Abi lene} & (3,4,4) & 1/7 & (1,1,1) & 0.4327 & 1 \\  & (3,4,4) & 1/7 & (1,1,1) & 0.4327 & 1 \\ \hline \multirow{2}{*}{Arpanet19706} & (2,2,5) & 5/5 & (1,1,1) & 0.4722 & 1 \\  & (2,3,4) & **1/5** & (1,1,1) & 0.4463 & 1 \\ \hline \multirow{2}{*}{Navigata} & (2,4,5) & 3/7 & (1,1,1) & 0.4618 & 1 \\  & (3,4,4) & 1/7 & (1,1,1) & 0.4327 & 1 \\ \hline \multirow{2}{*}{Kreonet} & (2,4,5) & 3/7 & (1,1,4) & 0.4036 & 0.7867 \\ \hline \multirow{2}{*}{Gridnet} & (2,3,6) & 3/7 & (1,1,3) & 0.4461 & 0.8110 \\  & (2,3,4) & 1/5 & (1,1,1) & 0.4463 & 1 \\ \hline \multirow{2}{*}{Napnet} & (2,3,4) & 1/5 & (1,1,1) & 0.4463 & 1 \\  & (1,2,3) & 2/3 & (1,1,1) & 0.4533 & 1 \\ \hline \multirow{2}{*}{Reuna} & (2,4,5) & 3/7 & (1,1,2) & 0.4230 & 0.8578 \\  & (4,4,3) & 5/7 & (1,2,1) & 0.3382 & 0.8114 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results from the multiculture network design for the second and third optimization problems. Figure 12: Relationship between the average degree of the network topologies and the average number of post-failure connected components from Table 1.  
network resilience in the face of correlated attacks. A more clear picture of the results of the multiculture network design can be observed from the last column of Table 1. Since ATTR metric is frequently used to assess the connectivity when links fail, the low values listed at the fifth column of the Table could be misleading. Calculating the ATTR among the post-failure functioning nodes is a more accurate approach to describe the remaining network connectivity in our scenario. An ATTR value equal to 1 means the functioning nodes after the failure can all be reached from other working nodes. In addition, ATTR values lower than 1 for Reuna and Kreonet are expected due to there is no single connected component after the occurrence of a failure. Lastly, at the third column of Table 1, we provide both, the rank number and the total number of configurations provided by the fair technology distribution problem, to the optimal reliable node placing problem. In the column, we list first the rank number of the configuration that achieved the optimal resilience listed at the right-most column, and next, we list the total number of configurations provided by the fair technology distribution problem. Note that the best configurations supplied by the fair technology distribution problem do not always achieve a network topology with maximal resilience, in terms of the ATTR metric. Thus, the figures in Table 1 justify our decision about providing the top 10% solutions to the reliable node placing problem.
# Fresh and Aromatic Virgin Olive Oil Obtained from Arbequina, Koroneiki, and Arbosana Cultivars
## 3 Materials and Methods
### Analysis of Volatile Compounds  
The procedure for the determination of the volatile compounds was similar to that used by [PERSON] et al. [31]. The volatile compounds were analyzed by headspace solid-phase microextraction (HS-SPME) and gas chromatography-flame ionization detection (GC-FID). Two grams of the sample was placed in a 20 mL amber glass vial tightly capped with a polytetrafluoroethylene (PTFE)/silicone septum and a magnetic cap. The vial was heated to 40 \({}^{\circ}\)C for 10 min to allow the volatile compounds to reach equilibrium in the headspace. Subsequently, the SPME needle was inserted through the septum and the fiber was exposed for 40 min. The SPME fiber (2 cm length and 50/30 \(\upmu\)m film thickness) was purchased from Supelco (Bellefonte, PA, USA) and was composed of Carboxen/DVB/polydimethylsiloxane (PDMS). The fiber had been previously conditioned following the instructions of the manufacturer.  
GC-FID analysis was performed using a gas chromatograph (model 7890B, Agilent Technologies, CA, USA) equipped with a split/splitless injector and a flame ionization detector. The volatile compounds adsorbed on the fiber were desorbed in the injector port for 1 min in splitless mode. A DB-WAXetr polyethylene glycol capillary column (30 m length, 0.25 mm internal diameter, 0.25 \(\upmu\)m coating) (Agilent Technologies, USA) was used for the chromatographic separation. The carrier gas was helium at a flow rate of 1 mL/min. The injector temperature was 260 \({}^{\circ}\)C and that of the detector was 280 \({}^{\circ}\)C. The oven temperature was initially set to 40 \({}^{\circ}\)C and was held at this temperature for 10 min. Subsequently, the temperature was increased to 160 \({}^{\circ}\)C with a temperature ramp of 3 \({}^{\circ}\)C/min, followed by a ramp of 15 \({}^{\circ}\)C/min to 200 \({}^{\circ}\)C; the sample was then held at 200 \({}^{\circ}\)C for 5 min.  
The chromatographic peaks were quantified using the internal standard method. 4-Methyl-2-pentanol was used as the internal standard, and all the compounds were used as external standards. The internal standard solution was prepared in previously deodorized oil. Then, the standard was added to each oil sample and stirred so that the mixture is homogeneous. The results are expressed as mg of standard compound per kg of oil.
# Fiber Monitoring System Applied to Railway Bridge Structures in a Near-Fault Region
## 4 Results  
### Strain Distribution of Box Girder  
For this research, data were collected on the 12 th of every month. For a single fiber, the length between data points was 5 cm and the length of the bridge span was 27.1 m, which equals 27.1 m/0.05 m = 542 data points for each span. There were a total of 33 bridge spans for this research, which amounts to 542 \(\times\) 33 = 17,886 data points. Distance in this research are presented as n + m. The \"n\" stands for the serial number of the box girder, and \"m\" stands for distance in meters. The starting points of the box girder were at a distance of 30 m apart from each other. The period of measurement was from 12 April 2022 to 12 February 2023. Figure 8 shows the horizontal strain changes throughout the year. The horizontal strain change was obtained by subtracting the initial horizontal strain value from the measured horizontal strain of the DFOS data. The strain values were also processed through data smoothing. During the period of observation, the horizontal strain change was between 50 \(\mu\epsilon\) and \(-\)10 \(\mu\epsilon\).  
From this graph of horizontal strain change, for distances 1 + 79\(\sim\)1 + 82, 2 + 00-2 + 06, there is a uniform inclination of bending movement toward the east. At a distance of 1 + 10\(\sim\)1 + 13, there is a uniform bending movement towards the west, but the strain is relatively small. Whether this shift in strain makes a large enough difference to warrant further action will depend on continuous data monitoring and observation made over a long period of time.  
Figure 7: System for data collection.  
The vertical strain change was obtained by subtracting the initial vertical strain value from the measured vertical strain of the DFOS data. During the period of observation, the vertical strain change was between \(-130\)\(\mu\varepsilon\) and \(70\)\(\mu\varepsilon\).  
From this study of vertical strain, it is observed that there is a downward bending tendency throughout the distance of 1 + 40-1 + 43. Throughout the distance of 1 + 49 \(\sim\)1 + 58, there is an upward bending tendency. According to Figures 8 and 9, the vertical strain is greater than the horizontal strain. It is concluded that vertical strain is the larger affecting factor within this area of the box girder bridge.
# V-SOC4 AS: A Vehicle-SOC for Improving Automotive Security
## 3 Controller Area Network (CAN)
#### Threat on CAN Bus  
Since the CAN protocol does not implement a security mechanism, an adversary could conduct various attacks that can damage the confidentiality, integrity, and availability of the data frames that are exchanged between the ECUs. Moreover, it is possible to distinguish two types of attacks: passive and active. An attacker carries out a passive attack by monitoring the bus to obtain information about the data frames. This type of attack involves the confidentiality of the payload [35]. As a way of example, a researcher of Tencent Security Keen Lab [5] found a vulnerability in the head unit of a Mercedes-Benz model that sent the passphrase and SSID (service set identifier) of Wi-Fi on the CAN bus as plaintext. Therefore, the researchers have been able to capture the data from the bus to obtain the passphrase and SSID. On the other side, an active attack occurs when an adversary can inject a data frame on the bus to cause unexpected behavior. [PERSON] et al. [25] presented two types of attacks on CAN packets: frequency and payload. The frequency attack consists of inserting an extra packet or erasing a legitimate packet from the CAN bus as the time interval for sending a CAN message is fixed and periodic. In addition, the author also stated that an attacker can send highest priority ID of the CAN packet in a short cycle. In the CAN packet payload, an attacker can manipulate the data content of some CAN ID. [PERSON] et al. [20] presented a survey regarding threats on vehicles and also on the CAN bus. [PERSON] et al. [15] introduced a different type of attack that an adversary can do when it has partial control of an ECU. Examples of these attacks are fuzzing, replay, masquerading, and information gathering. In addition, most of the attacks that can be conducted on the CAN bus required the injection of messages to change the behavior of the vehicle [36]. [PERSON] et al. [20] reported several challenges to protecting the CAN protocol against the DoS, fuzzing, spoofing, and masquerading attacks that need to be addressed. We selected two types of attacks based on the literature because the first (DoS) can occupy the bus for long periods denying other ECU to send messages; instead in the second (fuzzing), an attacker can send malicious payloads causing anomalous behavior [36]. These two well-analyzed attacks can be summarized as:  
* DoS: It consists of injecting a low identifier packet to have a high priority in the bus. Most of the datasets in the literature that simulate this type of attack set the arbitration field with the value \"0 x 000\" as hexadecimal since it is the lowest possible.
* Fuzzing: It consists of recreating CAN messages that have a completely random value of both arbitration and data field.
## 4 Simulation Results and Discussion
### Case 1: Sensitivity Analysis for Criterion Variation Within \(\pm 5\%\)
#### 4.2.3 Scenario 3: Variation in Upload Speed, Download Speed, and Ping  
In this scenario, in addition to the previous variation in upload speed and download speed, we randomly vary the initial ping values while keeping the values of packet loss unchanged. Table 9 shows the random variation rates applied to the initial ping values of each satellite. The resulting decision matrix is shown in Table 10.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Satellite ID** & **Upload Speed** & **Download Speed** & **Ping [ms]** & **Packet Loss** [\%] \\ \hline
5 & 14.95 & 218.55 & 47.31 & 0.01 \\ \hline
6 & 13.98 & 226.767 & 57.13 & 3.52 \\ \hline
7 & 14.95 & 174.59 & 60.84 & 1.28 \\ \hline
8 & 12.72 & 226.22 & 53.19 & 13.28 \\ \hline
9 & 17.01 & 179.44 & 65.85 & 0.48 \\ \hline
10 & 15.67 & 208.83 & 68.96 & 19.04 \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Satellite ID** & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\ \hline
**Variation rate (\%)** & 4 & \(-3\) & \(-3\) & \(-1\) & 2 & 3 & 5 & \(-4\) & 1 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Random variation rates applied to download speed, for criterion variation within \(\pm\)5%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Satellite ID** & **Upload Speed** & **Download Speed** & **Ping [ms]** & **Packet Loss** [\%] \\ \hline
1 & 10.97 & 198.39 & 49.50 & 0.01 \\ \hline
2 & 12.46 & 215.81 & 62.67 & 0.16 \\ \hline
3 & 13.31 & 206.83 & 62.34 & 2.88 \\ \hline
4 & 15.88 & 219.18 & 64.16 & 19.46 \\ \hline
5 & 14.95 & 222.93 & 47.31 & 0.01 \\ \hline
6 & 13.98 & 233.57 & 57.13 & 3.52 \\ \hline
7 & 14.95 & 183.32 & 60.84 & 1.28 \\ \hline
8 & 12.72 & 214.9 & 53.19 & 13.28 \\ \hline
9 & 17.01 & 181.23 & 65.85 & 0.48 \\ \hline
10 & 15.67 & 213.01 & 68.96 & 19.04 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Decision matrix of Scenario 2, for criterion variation within \(\pm\)5%.
## 3 Results and Discussion
### Effect of the Inlet Gas Flow Rate Hydrodynamic Characteristics of Sieve Tray  
#### 3.2.1 Effect of the Inlet Gas Flow Rate on Tray Pressure Drop  
To study the effect of the inlet gas flow rate on tray pressure drop, the absorber test rig is equipped with a pressure difference device that measures the pressure difference before and after the third tray as shown in Figure 2. The pressure difference device measures the total pressure drop of the tray, which is the sum of the dry and wet pressure drops, is calculated as  
\[\Delta P_{total,\ tray}=\Delta P_{dry,\ tray}+\Delta P_{wet.\ tray} \tag{1}\]  
where \(\Delta P_{total,\ tray}\) is the total tray pressure drop, \(\Delta P_{dry,\ tray}\) is the dry tray pressure drop, and \(\Delta P_{wet.\ tray}\) is the wet tray pressure drop. \(\Delta P_{total,\ tray}\) is measured during performing the experiments when the liquid and the gas are coming into the column, in contrast \(\Delta P_{dry,\ tray}\) is measured when only the gas is coming into the column at operating conditions of gas flow rates between 12 to 24 Nm\({}^{3}\)/h and different pressure with 0.22, 0.24, and 0.26 MPa.  
By measuring both \(\Delta P_{total,\ tray}\), \(\Delta P_{dry,\ tray}\) one can get the \(\Delta P_{wet}\) as  
\[\Delta P_{wet.\ tray}=\Delta P_{total,\ tray}-\Delta P_{dry,\ tray} \tag{2}\]  
Figure 5 shows the impact of the gas flow rate on the total tray pressure drop, dry tray pressure drop, and wet tray pressure drop. It can be observed that both the total tray pressure drop and the wet tray pressure drop increase smoothly between 12 and 20 Nm\({}^{3}\)/h, and these pressure drops are almost constant when the inlet flow rate increases between 20 and 24 Nm\({}^{3}\)/h, while the dry pressure drop increases due to an increase in the gas flow rate between 12 and 24. It is clear that the trend of this effect is similar for all pressure values studied.  
The standard errors of the measurements of total tray pressure drop are shown in Table 2.
# Acute Effects of Static Stretching on Submaximal Force Control of the Ankle
## 2 Materials and Methods
### Data Processing and Analysis  
All data processing and analyses were conducted using a custom script developed in MATLAB R2024a (MathWorks Inc., Natick, MA, USA). The force signals obtained from the Biodex system were low-pass filtered using a zero-lag second-order Butterworth filter with a cutoff frequency of 10 Hz [28]. A torque-angle curve was generated based on the measured passive resistance torque and dorsiflexion angle data and fitted using a second-order polynomial regression. Musculotendinous stiffness is defined as the slope of the curve. The slope values were computed at 4\({}^{\circ}\) intervals across the final 13\({}^{\circ}\) of the range of motion, and the mean of these four points was used to represent overall stiffness [29; 30]. Identical joint angle ranges were used for calculations in both intervention and control conditions (Figure 3).  
For the force-tracking task, data from five cycles, excluding the initial force trajectory, were obtained for each participant. The data were analyzed separately for the force generation phase (increasing force) and the force release phase (decreasing force). The RMSE values were calculated for each of the five individual cycles, and the average of the three lowest RMSE values, indicating the highest accuracy, was used as the representative value for each condition. This \"best-of\" approach was adopted to minimize the influence of sporadic performance variability, such as attentional lapses or transient motor errors, and to better reflect participants' optimal tracking performance. Similar methods have been employed in motor control research to ensure stable and representative outcomes [31]. The force tracking performance was evaluated in terms of both temporal accuracy and magnitude accuracy. The RMSE between the actual force and target force was calculated for all tasks using the following equation [32]:  
\[\text{RMSE}=\sqrt{\left(\frac{1}{n}\sum_{i=1}^{n}(T_{i}-F_{i})^{2}\right)}\]  
where \(n\) is the number of data points, \(T_{i}\) is the target force, and \(F_{i}\) is the actual force.  
Figure 3: Schematic illustration of the definition and calculation of musculotendinous stiffness. A second-order polynomial was fitted to the passive torque–angle curve, and stiffness was calculated as the average slope (first derivative) at four equally spaced points within the final 13 degrees of each participant’s pre-stretching range of motion. Vertical dashed lines indicate the four stiffness evaluation points.
## 5 Experimental Evaluation and Analysis
### Experimental Results
#### 5.2.6 Percent of Filled Node Buffer  
One factor that causes late arrivals of the generated packets into the network is a high network traffic. The number of filled nodes buffer indicates the status of the network traffic. The number of filled nodes buffer is high when more packets are transmitted. Since the number of transmissible packets in the Flooding protocol is more than in the SFRRP protocol, the percentage of the filled nodes buffer in the Flooding protocol is more than that of the SFRRP protocol. This feature is illustrated in Figure 16. As shown in the simulation results, the percentage of the filled buffer is very low in the SFRRP protocol. However, this feature will gradually increase in the Flooding protocol. The reason is that redundant packets introduced to the network increase slowly.  
Figure 13: Total energy consumption of the nodes.  
Figure 14: The residual energy of the nodes in the proposed protocol.