esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Study
### Remote Real-Time Monitoring System
#### 2.2.2 Data Transmission  
Because a large amount of data was collected by sensors in the field and had to be transmitted back to the data monitoring center, which is located more than 120 km from the field, a wireless sensor network was established in this study by adopting GPRS/CDMA (code-division multiple access) 1x mobile data communication technology in combination with a DTU and DVB (digital video broadcasting) antenna. The remote wireless transmission and control of monitoring data were realized with a lapping wireless router, DVB antenna and industrial computer. In addition, different types of data conversion technology were developed. The structure of the remote transmission network is shown in Figure 8.
## 5 Evaluation and Results
### Experimental Setup and Evaluation
#### 5.3.3 Reactivity of SWIFTNET  
Figure 10 depicts the reactivity of SWIFTNET using a real-world data trace from one of our experiments. It can be seen that the sampling interval remains fixed at 300 s during the CS data acquisition phase, and changes immediately there is a fire event signal, sensed at 40 \({}^{\circ}\)C and above. The sampling interval automatically adapts to the signal; in this case, the sampling interval increases and fluctuates between 12 and 30 s. Similar performance on the reactivity of SWIFTNET is observed from the other nodes. We expect that this behavior should be valid for a larger network, since our algorithm design is distributive. Consequently, all nodes can adaptively adjust their sampling interval, according to the respective phenomenon that they monitor. It is worth mentioning here that, other adaptive sampling approaches such as [67] can also be used on top of the prediction algorithm. However, we believe that  
Figure 10: Dynamic sampling interval of SWIFTNET.  
our approach is suitable for the wildfire application use-case, and on our stack architecture framework. An open research direction in this area could be to conduct a comparative study on the different adaptive sampling strategies on top of the prediction method for different application setups. The objective could be to find the optimal performance that can be achieved with respect to the reactiveness and fault-tolerance. This could serve as a guide to WSN developers and researchers in the field.
## 3 Results and Discussion  
### Mechanical Properties of Composite Mortar  
Figure 2 depicts the flexural strengths of the composite mortar at 3 d, 7 d, and 28 d. The figure shows that compared with group A0, the flexural strength of composite mortar of groups A1-A6 at each age demonstrated a trend of first increasing and then decreasing. The flexural strengths at 3 d increased by 10.5%, 23.7%, 15.8%, 10.5%, \(-\)15.8%, and \(-\)42.1%, respectively. The flexural strengths at 28 d increased by 9.8%, 18.0%, 11.5%, 4.9%, \(-\)1.6%, and \(-\)11.48%, respectively. The highest flexural strengths were 4.7 MPa and 7.2 MPa for the composite mortar of group A2 at 3 d and 28 d, respectively.  
Figure 3 shows the compressive strengths of the composite mortar at 3 d, 7 d, and 28 d. The compressive strength activity indices of the composite mortar for each group of A0-A6 at 3 d were 78.9%, 90.9%, 100.4%, 88.8%, 76.0%, 59.9%, and 40.5%, respectively. At 28 d, the compressive strength activity indices of the composite mortar for each group of A0-A6 were 59.9%, 72.1%, 75.1%, 74.0%, 66.4%, 58.3%, and 47.2%, respectively. Compared with group A0, the compressive strengths of composite mortars of groups A1-A6 exhibited a trend of first increasing and then decreasing at all time periods. The compressive strengths at 3 d increased by 15.2%, 27.2%, 12.6%, \(-\)3.7%, \(-\)24.1%, and \(-\)48.7%, respectively. The compressive strengths at 28 d increased by 20.3%, 25.5%, 23.6%, 10.9%, \(-\)2.7%, and \(-\)21.2%, wherein the highest compressive strengths were 24.3 MPa and 41.4 MPa for the composite mortar of group A2 at 3 d and 28 d, respectively.  
Figure 2: The flexural strength of composite mortar.  
The mechanical strengths of the composite mortars are lower than those of the OPC group because the active mineral components in SS, RM, and EMR are much lower than those in OPC. Their admixture dilutes the cementing ability of OPC, thus decreasing the mechanical properties. Compared with the A0 group, the flexural and compressive strengths demonstrated a trend of increasing and then decreasing with increasing EMR admixture (decreasing RM admixture), mainly because (i) compared with OPC and SS, the particle size of RM and EMR is smaller, and their admixture can play the role of filling the pores and improving the densities of the specimens; (ii) when compounded with suitable proportions of RM and EMR, the free alkali in RM and sulfate substances in EMR can stimulate the hydration activity of SS, thus reducing the negative effect of SS on the mechanical properties of mortar. Among them, the best mechanical properties of the composite mortar were obtained when15% of SS, 12% of RM, and 3% of EMR were mixed.
## 1 Introduction
### MODIS Land Surface Temperature Data  
Land Surface Temperature (LST) is one of the products of the MODerate Resolution Imaging Spectroradiometer (MODIS). MODIS sensor, aboard NASA's Terra and Aqua satellites, captures data in 36 spectral bands, including those related to the temperature of the Earth's surface [25].  
Specifically, MODIS LST data are derived from algorithms taking into account different factors like surface temperature, surface reflectivity, atmospheric emission, and solar radiation.  
The Terra MODIS LST (MOD11A2) and Aqua MODIS LST (MYD11A2) version 6.1, provide an average 8-day per-pixel Land Surface Temperature/Emissivity with a 1 km spatial resolution. Each pixel value is an average of all the corresponding MOD11A1 and MYD11A1 LST pixels collected within that 8-day period [26; 27].  
Both satellites cover the entire Earth's surface every one or two days: their orbits are set so that, relative to the study area, MODIS LST Terra data are available during day and nighttime around 9:06-12:18 and 18.30-23:48 local time, and MODIS LST Aqua around 9:42-15:12 and 0:18-5:30. The details are summarized in Table 3.  
The satellites collect daily and 8-day data, but daily data contain many empty pixels, as shown in Figure 5, so we focused on the latter. An example of the MODIS 8-day data is shown in Figure 6.  
Figure 4: Series of the average hourly air temperature measured by the Fortunago Monitoring Station—ID 8007.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Satellite** & **Sensor** & **Version** & **Bands** & **Spatial Resolution** & **Temporal Resolution** & **Pass Time** \\ \hline MODIS & Terra & MOD11A2 6.1 & LST\_Day & 1 km & 8 days & 9:06–12:18 \\  & & LST\_Night & & & 18.30–23:48 \\ MODIS & Aqua & MYD11A2 6.1 & Day\_view\_time & 1 km & 8 days & 9:42–15:12 \\  & & Night\_view\_time & Night\_view\_time & & 0:18–5:30 \\ \hline \end{tabular}
\end{table}
Table 3: Specification of the MODIS data used to obtain LST maps.  
Figure 5: MODIS data in the study area: raster with 1 km\({}^{2}\) tiles depicting the daily average daytime temperature, captured by the Aqua satellite on 30 March 2018.  
Figure 6: MODIS data in the study area: raster with 1 km\({}^{2}\) tiles depicting the 8-day average daytime temperature, captured by the Aqua satellite from 30 March to 6 April 2018.
# New Insights on Volatile Components of _Vanilla planifolia_ Cultivated in Taiwan
## 2 Results
### Analysis of the Volatile Components of Taiwan-Cultivated Vanilla Pods Using HS-SPME
#### 2.2.2 Different Conditions during the Curing Process  
Current studies have determined that volatile components can be released during the killing and sweating stages of the curing process and adjusting the process can promote the generation of volatile components [17]. [PERSON] et al. [18] focused on the killing stage that destroys the plant cell wall through the use of hot-water immersion or freezing at different temperatures. It is important to quickly and effectively destroy the cell structure without damaging the enzymes to allow for the generation of volatile components. Our current study refers to [PERSON] et al. [18] and modified its methods by designing four different killing temperatures and time conditions (vanilla pod immersion in hot water at 65 \({}^{\circ}\)C three times for 1 min each, 65 \({}^{\circ}\)C for 3 min, 70 \({}^{\circ}\)C three times for 30 s each, and 80 \({}^{\circ}\)C three times for 10 s each) and two different fermentation temperatures (40 \({}^{\circ}\)C and 45 \({}^{\circ}\)C) for vanilla cultivated in Taiwan. We compared the volatiles of the vanilla pods, and all of the components and relative percentage of the different curing conditions are shown in Table 3. The main component of all extracts was vanillin. The first step of the vanilla curing process is killing, and this step aims to promote the reaction of enzymes by destroying the cell structure and halting the further aging of fresh pods [19]. According to the results presented in Table 3, the four different killing methods result in similar component quantities. Only a small number of components are present in very small amounts, and these have not yet been identified. A more obvious difference was observed for vanillin. The highest percentage (79%) was obtained by killing using three treatments at 65 \({}^{\circ}\)C for 1 min each, and the lowest percentage (62%) was observed in pods that were killed using three treatments at 80 \({}^{\circ}\)C for 10 s each. When comparing the relative percentage of the main and minor components of vanilla pods obtained using four
## 3 Results and Discussion  
### Effects of Thermal-Bonded Time and Temperature on Knife Stab Resistance  
Figure 6a,b shows the knife stab resistances with different thermal-bonded time and temperature. The hot-baking time is changed as 1, 5, 10, and 15 min when the sandwich composites are thermally treated at 120 \({}^{\circ}\)C. The hot-baking temperature is varied as 100, 120, 140, and 160 \({}^{\circ}\)C when the composites are thermally treated for 5 min. Comparatively, knife-resistance force and energy of thermal treatment for 5 min are larger than other thermal durations, which represents the best knife-resistance performance. Under the action of knife, surficial nonwoven and interlayer BF are both damaged, and the failure surface presents a thin and long cut, which results from the cutting, friction between yams, and friction between knife and yams, as well as tensile plastic deformation [20]. The higher friction coefficient between yams can generate a larger knife-resistance load and energy [21]. With shorter hot-baking time, the effect of plastic deformation to knife resistance is bigger than the yarn slippage and friction between knife and surface of composites. After longer hot-baking time, LPET fibers are thermally bonded with each other. The plastic deformation becomes smaller, and the friction between knife and composites exhibits to be greater. However, the contributions of plastic deformation and friction to the sandwich composites were distinct at different thermally bonded time; therefore, 5 min showed the maximum knife energy as presented in Figure 6a. With higher temperature, the knife force and energy both firstly increase and then decrease as seen in Figure 6b. This is because heat transmission needs a certain duration; at lower temperature the interlayer LPET fibers cannot melt completely, but at much higher temperature, all LPET fibers in the composite reached melting points,  
Figure 5: The testing apparatus for thermal insulation.  
and fiber structure was completely damaged, which produced the smallest plastic deformation and lower knife energy. Comprehensively speaking, thermal bonding at 140 \({}^{\circ}\)C possesses the biggest knife-resistance force and energy.  
Typical knife displacement-load curves with different thermal-bonded time and temperature are reported in Figure 7. Two sets of data with different time and temperature are similar. They are both divided into two steps. The first stage is due to the fibers cutting and friction force between knife head and composites. In this stage, the knife firstly slides apart the fibers and then cuts the original contacting regions of fibers. It can been seen from Figure 6a,b that the fibers separation becomes more difficult at higher temperature and longer time, which results in an obvious first peak in the first stage. The maximum peak occurs when the original contacting fibers are cut. As the knife penetrated deeply, the cutting fibers and friction between cutting fibers continuously increased, which in turn increased the knife-resistance force.
## 3 Navigation
### Sensors
#### 3.1.4. Wi-Fi, UWB, iBeacon, and Radio Frequency Identification
Therefore, the ratio of ranging error to the actual distance follows a normal distribution with a zero-mean and a standard deviation of \(\frac{0.2203\sigma_{p}}{\gamma}^{2}\), where \(\sigma_{p}\) can be fixed at 4 dB, which is the averagereported standard deviation for wireless communications [43]. In addition, the path loss exponent \(\gamma\) is usually between 1.6 and 6.5 based on actual measurements [44]. Because the RSSI depends not only on the distance but also on the environment, for example, the accuracy of RSSI through a wall decades significantly; thus, most RSSI based approaches only room level accuracy. Noted that Wi-Fi-based localization can adopt other two methods to obtain the locations of targets, namely fingerprinting-based approaches [45] and angle of arrival (AoA)-based approaches [46]. Fingerprinting-based approaches first need to collect \"fingerprints\", such as the vectors of RSSIs or channel state information (CSI) to all APs for all the cells on a map, and then they can locate the target by choosing the most similar vector in the map. This approach can realize the better performance than RSSI based approaches [36], but they require expensive and recurring fingerprinting operations when the environment changes (e.g., a door is opened or a chair is moved). The calibration task becomes tedious and time consuming for large scenarios, because the wireless signal must be measured in many different locations [47]. Typical computation of the AoA is done using the multiple signal classification algorithm. Assuming an antenna array of \(M\) antennas is equally spaced by a distance of \(d\) and a signal arriving at the antenna array through \(L\) propagation paths, the signal travels an additional distance of \(d\times sin\theta\) by the signal to reach the second antenna of the array [48]. Unfortunately, all of the above approaches assume that the APs' positions are known in advance to locate the target. The location performance mainly depends on the number of APs. Regarding the navigation system, the other information, such as the environmental information and movement direction, cannot be obtained.
# Analyzing Port State Control Data to Explore Future Improvements to GMDSS Training
## Materials and Methods
### Statistical Tools and Data Analysis
#### 2.4.1 A Chi-Square (\(\chi^{2}\)) Goodness of Fit Test Using SPSS  
This test was used to test whether the observed distribution (subset) of a categorical variable differs from expectations (the total database). The greater the discrepancy, the more it can be questioned that there are no significant differences.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Radio Communications Defective Item** & **Code** & **Label** \\ \hline Distress messages: obligations and procedures & 5101 & Training \\ Functional requirements & 5102 & Equipment \\ Main installation & 5103 & Equipment \\ MF radio installation & 5104 & Equipment \\ MF/HF radio installation & 5105 & Equipment \\ INMARSAT ship earth station & 5106 & Equipment \\ Maintenance/duplication of equipment & 5107 & Equipment \\ Performance standards for radio equipment & 5108 & Equipment \\ VHF radio installation & 5109 & Equipment \\ Facilities for the reception of marine safety information & 5110 & Equipment \\ Satellite EPIRB 406 MHz/1.6 GHz & 5111 & Equipment \\ VHF EPIRB & 5112 & Equipment \\ SART/AIS-SART & 5113 & Equipment \\ Reserve source of energy & 5114 & Equipment \\ Radio log (diary) & 5115 & Training \\ Operation/maintenance & 5116 & Training \\ Operation of GMDSS equipment & 5118 & Training \\ Other (radio communication) & 5199 & Others \\ \hline \hline \end{tabular}
\end{table}
Table 5: Communication defective codes and explained failures. A re-categorization with statistics and analytical purposes is proposed: (1) equipment failure, (2) training failure of OOWs, and (3) other radio communication problems.
## 3 Results and Discussion
### Effect on Post-Treatment Storage  
#### 3.5.1 Changes in Microbial Growth  
To evaluate the effect of ADCP treatment on storage quality of CBCs, the changes in microbial growth, as well as in pH, color, TVBN, TBARS, tenderness, and sensory properties, during the storage of CBCs following ADCP treatment, were analyzed.  
Changes in the growth of indigenous mesophilic bacteria and _Salmonella_ in CBCs during storage following ADCP treatment were evaluated (Figure 1). The initial population of indigenous mesophilic bacteria on CBCs, \(5.78\pm 0.19\) log CFU/cube, decreased to \(5.08\pm 0.16\) log CFU/cube following ADCP treatment (Figure 1A). ADCP-treated CBCs, stored at 4 \({}^{\circ}\)C for 21 d, showed a steady microbial growth inhibition leading to a reduction of 0.7-0.9 log CFU/cube. Post-treatment storage reportedly enhances the microbial inactivation efficacy of ADCP, due to cold plasma reactive species within closed containers diffusing into product tissues during storage [41]. However, microbial growth or inhibition rates in CBCs with and without ADCP treatment appeared to be similar at both 4 and 10 \({}^{\circ}\)C. This may be due to the non-hermetic sealing of the commercial PET containers used in this study, whereby the reactive species generated during ADCP treatment were lost to the exterior of the container. Thus, it is anticipated that future packaging would involve hermetic sealing of in-package ADCP-treated food, in order to ensure more substantial inactivation of microbial growth during storage.  
No significant difference was observed between the populations of ADCP treated indigenous bacteria, stored at 14 and 24 \({}^{\circ}\)C for 5 d and 1 d (\(p>0.05\)). This verified that storage at refrigeration temperatures exerts an effective inhibitory effect on the growth of indigenous mesophilic bacteria on RTE chicken meat.  
_Salmonella_ inoculated into CBCs were reduced by 1.45 \(\pm\) 0.05 log CFU/cube, from 5.04 \(\pm\) 0.13 log CFU/cube to 3.59 \(\pm\) 0.06 log CFU/cube, due to ADCP treatment (Figure 1B). The microbial level of ADCP-treated CBCs stored at 4 \({}^{\circ}\)C for 21 d were reduced by 1.4-1.7 log CFU/cube (which was maintained), compared to untreated CBCs. However, as a similar result was also observed for indigenous mesophilic bacteria, the sublethal injury could not be ascertained. Storage at 10 \({}^{\circ}\)C for 14 d resulted in microbial levels being reduced by 1.3-1.7 log CFU/cube, where a steady increase in which reduction was maintained, and similarly, the sublethal injury could not be ascertained.
## 2 Related Work
### RTS/CTS Scheme for Previous Standards  
The above studies have not considered hidden and exposed node problems that may degrade performance of the IEEE 802.15.6 networks for all user priorities. These problems have been solved for the previous standards including IEEE 802.11 and IEEE 802.15.4 using the RTS/CTS scheme. In [24],the authors studied the use of the RTS/CTS scheme to reduce the exposed node problem in IEEE 802.11 based wireless networks. By analyzing the signal strength of nodes in a 3D simulation environment, they concluded that the proposed scheme has effective throughput performance compared with that of the standard model. The authors of [25] improved the conventional RTS/CTS scheme proposed for IEEE 802.11 in order to classify why some transmission fail. According to the proposed model, the packets are immediately transmitted after failure due to noise. By adapting a four-dimensional Markov model, the authors observed that the proposed model enhances the performance in terms of average throughput and packet delay. As the RTS packets may collide during transmissions, the authors of [26] proposed an RTS collision avoidance method that decreases transmission cost and overhead of control packets compared to that of the IEEE 802.11 RTS/CTS scheme. In [27], the authors extended the conventional RTS/CTS scheme for multi-hop IEEE 802.15.4 networks. By proposing an adaptive RTS/CTS scheme, the authors concluded that adjusting RTS transmission according to the number of collisions achieves better performance. In a similar study, the authors of [28] analyzed the RTS/CTS scheme combined with packet concatenation for the non-beacon mode of IEEE 802.15.4 and concluded that the proposed scheme shows effective improvements in throughput, delay, and bandwidth efficiency. In [29], the authors studied different types of collisions in the presence of hidden and exposed nodes and analyzed undiscovered collisions between RTS and CTS packets for duty-cycled WBANs.  
As discussed above, the RTS/CTS scheme is the most widely adapted scheme to solve most of the hidden node problems. However, this scheme has not been studied extensively for performance improvement of IEEE 802.15.6 CSMA/CA. In our work, we adapt the RTS/CTS scheme for this standard and provide a detailed analysis to validate our conclusions.
# Vulnerability Analysis of a Multilayer Logistics Network against Cascading Failure
## 2 Methodology and Data
### Attack Strategies  
There are two types of attacks in complex networks: random failures and targeted attacks. Random failure means that the node is attacked with a certain probability. In addition, targeted attacks mean that the nodes are attacked according to certain strategies. It is usually necessary to use the topology properties of the network, such as the importance of each node. Then, the most important node is selected as the first attack object. Furthermore, load attacks considering cascading failures assume that when a node fails, all edges connected to the node are deleted. If a city in a multilayer network is attacked, nodes based on shipping, air transportation, corporate organization networks, and trade flows will be removed (Figure 1).  
Figure 1: Schematic description of cascading failure between a single-layer or multilayer network. (**a**) Assume that node A2 has the largest load; therefore, A2 can be seen as the initial attack object. (**b**) When node A2 fails in network A, disconnect A2 from nodes A1, A3 and A4 and then fail between networks B2, C2 and D2. Then, redistribute the load of A2, B2, C2 and D2 within the network; namely, the load of the adjacent node increases. (**c**) Only the load distribution of the A2 node is considered here; that is, A1, A3 and A4 are adjacent to node A2. When there is more than one node of the same distance to A2, A4 is randomly selected to share the load of A2. If load A4 exceeds its capacity after the distribution of the load, then A4 will also fail. (**d**) After A4 fails, A3 is selected to share the load. If the load of A3 does not reach its capacity and is retained, then the cascade fails. The efficiency of the network could be calculated.