esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Abrasion in Micro-Deval Apparatus  
The micro-Deval test is a test for determining the resistance to abrasion and durability of a tested material. The most commonly used standards for the micro-Deval test are (1) ASTM D6928-17 \"Resistance of Coarse Aggregate to Degradation by Abrasion in the Micro-Deval Apparatus\" [26], (2) ASTM D7428-15 \"Resistance of Fine Aggregate to Degradation by Abrasion in the Micro-Deval Apparatus\" [27], and (3) BS EN 1097-1:2011 \"Tests for mechanical and physical properties of aggregates, Part 1: Determination of resistance to wear (micro-Deval)\" [28]. We applied a modified combination of standards. Instead of material with standard grading, we abraded each of the particles alone with steel charge in the drum (single clast abrasion). In total, nine dolomite particles were subjected to abrasion in the micro-Deval apparatus (MATEST A077) (Figure 2). A particle was submerged in 2.0 L of water at room temperature inside the micro-Deval drum to saturate for at least one hour before the abrasion cycle. After the saturation, 5.0 kg of steel charge was added (standard steel balls with 10 mm diameter--MATEST A078-11N). The drum filling was on average 56.3%, including steel balls, water, and rock particles, throughout the whole test. The mass loss after each cycle was relatively low and did not considerably affect the filling of the drum. The abrasion cycles had different durations, starting from shorter ones. Altogether, 15 consecutive cycles were applied, with a cumulative abrading time of 930 min. The durations of the consecutive cycles were (in order): 10, 10, 10, 30, 30, 60, 60, 60, 90, 90, 90, 120, 120, 120 min. Abrasion was applied at 100 rpm. For each consecutive abrasion cycle, only the principal particle was kept--if any chipping occurred, the small fragments  
Figure 1: Experimental methodology schemes: (**a**) abrasion in micro-Deval apparatus, (**b**) abrasion in Los Angeles machine.  
were not taken into consideration for further observation. Between each abrasion cycle, particles were oven dried to a constant mass at 105 \({}^{\circ}\)C and weighed to determine mass loss. Additionally, in between abrasion cycles, we analysed each particle in the dynamic image analyser Microtrac Camsizer XL to obtain morphological parameter values.
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 3 Proposed Methods
### Region Filling
#### 3.4.1 Determining the Number and Positions of Base Stations  
Drones are deployed from corresponding base stations. It is necessary to determine both the number of base stations required and their positions. In order to fix the base stations in a deterministic way, we adopt a strategy whereby they are always placed at the corners of the rectangular region. Therefore, one, two, three, or four base stations are possible for a rectangular region. Increasing the number of base stations reduces the required number of drones, as the drones close to base stations can be deployed at greater heights, thereby increasing the coverage area. However, our experiments (detailed in Section 4) show that increasing the number of base stations beyond two does not provide any clear advantage; hence, we fix the number of base stations at two, located at opposite corners. One base station is fixed as the origin (0, 0) and the other as (m, n), as shown in Figure 4.
## 3 Results and Discussion
### Construction of Combined Model  
In this section, a basic model is first established using a machine learning algorithm, with the choice of algorithm being dependent on the specific application. In this study, the SVR algorithm is employed to build both the basic model and the modification models, followed by a discussion on the performance of the prediction model. The SVR algorithm has been widely applied in gas turbine modeling and has achieved good prediction results. The SVR model is established using the SVR function from Python's sklearn library, with its computational principles referenced in Equation (4), with the radial basis function serving as the kernel function. The basic model established by SVR achieves a MAPE of  
Figure 4: Correlation coefficient matrix between input and output variables.  
0.3491%, a MaxAPE of 2.393%, and an R2 of 0.9966 on the test set. To further enhance the modeling accuracy, a clustering algorithm is subsequently used to categorize the dataset, and modification models are then built for each operational condition dataset.  
To establish a set of modification models for different operating conditions, the k-means clustering algorithm is utilized to classify the training set. This paper implements clustering using the Kmeans function from sklearn, with the parameter 'k-means++' for generating initial centroids. The algorithm principles can be referenced in the description of the k-means clustering algorithm in Section 2.2.3. Figure 5 illustrates the results obtained by clustering the data into 5 and 10 clusters. Due to the dimensional constraints in graphical representation, it is not feasible to display the relationships between all input variables and labels. Thus, the focus here is on the correlation between clustering labels, inlet temperature, and fuel flowrate. Consequently, there may be some data overlap in different clusters. Overall, the clustering algorithm effectively segments the operational data into distinct load and temperature zones. It is observed that fuel consumption tends to decrease with higher temperatures, which may be attributed to the reduction in air density caused by temperature increases, subsequently weakening the unit's power generation capacity and thus necessitating less fuel. The subsequent section will develop modification models for each cluster, which, together with the basic model, will form the final combined model.  
Based on the clustering results from the previous section, training of modification models for each operational condition dataset is conducted, which targets the residuals of the basic model. Similarly, the SVR algorithm is employed to establish the modification models, with parameters identical to those of the previous basic model.
## 3 Results  
### Vegetation Health  
#### 3.1.1 Forest Productivity  
Based on the year 2023 MODIS MCD12Q1 v6.1 land cover data, 95% of the AT study area is vegetated, with 93% deciduous broadleaf and mixed forest and 1.9% grassland (Figure 2). In contrast, the PCT and CDT include fewer forests. The PCT is 29.2% evergreen needleleaf forest, 34.9% grassland, and 33.1% savanna. The CDT corridor includes only 2.8% evergreen needleleaf forest, 64.3% grassland, and 32% savanna. Over the 23-year period, the MODIS MCD12Q1 land cover data indicate the generalized land cover classes were stable along the AT corridor, with consistent dominance of deciduous broadleaf forest for all years (Figure 1). In contrast, the PCT and CDT land covers transitioned over time. Along the PCT corridor, grasslands increased from 20.4% of the study area in 2001 to 28.1% in 2023. During the same period, evergreen needleleaf forest along the PCT decreased from 37.2% to 29.1%. For the CDT corridor, grasslands increased from 46.3% to 59.3%, while evergreen needleleaf forest declined from 10.3% to 2.8%.  
To quantify the change in vegetation productivity, 16-day GPP and annual NPP time series were generated for each trail over 35 years (Figure 3). The NTSG Landsat-derived GPP and NPP data indicate the highest productivity within the Appalachian Trail, followed by the PCT and CDT corridors. The PCT shows the greatest variability in the GPP values across the trail, with a wider interquartile range as the trail transverses desert to forested environments. There is less variability in the GPP values across the AT corridor as the study area is more consistently forested and receives higher precipitation within the eastern U.S. climate. Despite the forest loss events and the transition from evergreen needleleaf forestto grassland along the western trails, all three trails showed an increase in NPP over the 35 years.
## 1 Introduction
These software packages support protocols such as OSPF (\"OSPF Routing Protocol\", [[https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/iproute_bgp/configuration/xe-16/irg-xe-16-book/configuring-a-basic-bgp-network.html](https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/iproute_bgp/configuration/xe-16/irg-xe-16-book/configuring-a-basic-bgp-network.html)]([https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/iproute_bgp/configuration/xe-16/irg-xe-16-book/configuring-a-basic-bgp-network.html](https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/iproute_bgp/configuration/xe-16/irg-xe-16-book/configuring-a-basic-bgp-network.html)) (accessed on 15 June 2022)) and BGP (\"BGP Routing Protocol\", [[https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/iproute_bgp/configuration/xe-16/irg-xe-16-book/configuring-a-basic-bgp-netw/ork.html](https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/iproute_bgp/configuration/xe-16/irg-xe-16-book/configuring-a-basic-bgp-netw/ork.html)]([https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/iproute_bgp/configuration/xe-16/irg-xe-16-book/configuring-a-basic-bgp-netw/ork.html](https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/iproute_bgp/configuration/xe-16/irg-xe-16-book/configuring-a-basic-bgp-netw/ork.html)) (accessed on 15 June 2022)), thus enabling the system to create mesh networks realized through point-to-point and point-to-multipoint VPN. Once the infrastructure has been built, the Mosquito software (\"Mosquito Service\", [[https://mosquito.org/](https://mosquito.org/)]([https://mosquito.org/](https://mosquito.org/)) (accessed on 15 June 2022)) can be installed to manage the IoT part. Mosquito is a very light service and can be configured both as a client and as a broker, and therefore has a general-purpose functionality concentrator available in a single device. This paper has the following structure: Section 2 shows related works about VPN implementations and analyses present in the literature; Section 3 presents the components of a VPN architecture; Section 4 provides the deployed scenarios used for the testbeds; Section 5 provides the experimental results in the implemented scenarios; lastly, Section 6 summarizes the paper.
## 3 Results  
### Properties of Raw Biomass Samples  
The biomasses were characterized based on the moisture content, fixed carbon content, volatile matter content, ash content, higher heating value (HHV), and elemental analysis. Table 3 summarizes the values obtained during the experimental analysis performed for the characterization of the different biomasses. The obtained results from the proximate analyses are as follows: volatile matter content for the miscanthus, hops, sewage sludge, and mixtures were 82.8 \(\pm\) 3.3, 82.2 \(\pm\) 0.3, 56.0 \(\pm\) 2.2, 67.9 \(\pm\) 2.7, and 62.1 \(\pm\) 2.5 wt.%, respectively. The results of volatile compounds were higher in the lignocellulosic biomass than in non-lignocellulosic biomass, which indicated that lignocellulosic biomasses possessed higher reactivity compared to non-lignocellulosic materials [73; 74]. Consequently, the contents of fixed carbon were significantly lower (<12 wt.%) than those of the volatile matter, especially in the lignocellulosic biomasses. The ash contents were in the range of 2.8 \(\pm\) 0.2, 3.2 \(\pm\) 0.2, 16.3 \(\pm\) 1.0, 11.7 \(\pm\) 0.7, and 13.5.0 \(\pm\) 0.8 wt.% for each biomass, respectively. The ash contents of sewage sludge and mixtures of sewage sludge and miscanthus/hops were largely higher than others. It is believed that this was due to the higher amount of ash present in the original raw sewage sludge biomass sample. Moisture contents varied from 9.2 \(\pm\) 0.3 to 12.4 \(\pm\) 0.4 wt.% for each biomass separately. HHV was the highest in the miscanthus sample (18.9 \(\pm\) 1.0 MJ/kg), whereas the lowest was in the pure SS biomass sample (14.98 \(\pm\) 0.7 MJ/kg). The contents of carbon in the miscanthus, hops, SS, and mixtures were 45.1 \(\pm\) 1.4, 42.1 \(\pm\) 1.3, 34.7 \(\pm\) 1.0, 40.4 \(\pm\) 1.2, 38.9 \(\pm\) 1.1 wt.%, respectively. These results came from the basic structure of the biomasses. Both miscanthus and hops are mainly composed of cellulose, hemicelluloses, and lignin, whilst SS contain more inorganic elements, such as N, P, K, Ca, and Mg, and other heavy metals and other materials that are present in the industrial wastes [75]. It has been stated in many works [76; 77] that the torrefaction process leads to the enrichment of the carbon content whilst the content of hydrogen decreases. The contents of hydrogen were low, as confirmed in the literature [78] and were in the range from 3.7 \(\pm\) 0.1 to 5.2 \(\pm\) 0.1 wt.%. The greatest hydrogen content was found in the pure SS, and the lowest was found in the raw miscanthus sample. The nitrogen contents varied from 0.8 \(\pm\) 0.02 to 4.8 \(\pm\) 0.1 wt.% and oxygen contents from 36.3 \(\pm\) 1.0 to 47.4 \(\pm\) 1.45 wt.%. The contents of sulphur were unusually very low (0.05 \(\pm\) 0.01, 0.03 \(\pm\) 0.01, 0.82 \(\pm\) 0.04, 0.31 \(\pm\) 0.01, 0.47 \(\pm\) 0.01 wt.%) in accordance with literature [42; 79]. These differences may later affect the torrefaction process obtained from biomass materials and the characteristics themselves. All results agree with other works [33; 80; 81; 82; 83; 41].
## 3 Experiments and Results
### Comparison Model  
Feature selection is the basis of remote sensing image segmentation. At present, there are mainly two methods based on artificial feature selection and machine learning. [PERSON] et al. (1973) put forward the method of gray level co-occurrence matrix, which is a classical artificial selection feature method. This method is mainly used to select image texture features. Since the texture is formed by repeated alternating changes of gray distribution in image space, so there is a certain gray-scale relationship between two separate pixels away certain distance, [PERSON] et al. described this correlation through a matrix [70]. Based on the artificial selection feature, only limited, shallow features can generally be selected. The feature selection based on machine learning can fully explore the deep feature and spatial semantic feature of the image. SegNet and DeepLab are classic semantic segmentation models of images, which have achieved very good results in the processing of images. Moreover, the working principles of these two models are very similar to our work, so we choose these two models as the comparison model, which can better reflect the advantages of our model in feature extraction. A comparative experiment was conducted using the methods established in the published literature.
# Flavonoids as Promising Multitarget Agents in Alzheimer's Disease Therapy
## 2 Activities of Flavonoids from Natural Sources
### Inhibition of Acetylcholinesteraseerase (AChE)
#### 2.4.2. Chalcones  
From the twigs of _Dorstenia barteri_, isobavachalcone (\(2^{\prime}\),\(A\),\(A^{\prime}\)-trihydroxy-\(3^{\prime}\)-prenylchalcone) (**125**) and \(2^{\prime}\),\(A^{\prime}\)-dihydroxy-\(3^{\prime}\),\(6^{\prime}\)-dimethoxychalcone (**126**) were isolated. Both the chalcones exhibited AChE inhibition with IC\({}_{50}\) values of 18.34 and 20.15 uM, respectively [62]. From _Humulus luplus_ L six chalcones were isolated, namely: xanthohumol L, the \(4^{\prime}\),\(A\),\(5^{\prime}\)-trihydroxy-\(2^{\prime}\)-methoxy-\(6^{\prime}\),\(6^{\prime}\)-dimethyldihydropyrano [\(2^{\prime}\),\(3^{\prime}\),\(5^{\prime}\),\(6^{\prime}\)]chalcone (**127**); xanthohumol I, the (\(E\))-1-[4-hydroxy-2-(2-hydroxypropan-2-yl)-6-methoxy-2,3-dihydro-1-benzofuran-7-yl]-3-(4-hydroxyphenyl)prop-2-en-1-one) (**128**); xanthohumol B, the \(2^{\prime}\),\(5^{\prime}\)-trihydroxy-\(6^{\prime}\)-methoxy-\(6^{\prime}\)-dimethyldihydropyrano [\(2^{\prime}\),\(3^{\prime}\),\(A^{\prime}\)]chalcone) (**129**); xanthohumol D, the \(2^{\prime}\),\(A^{\prime}\)-trihydroxy-3-(2-hydroxybut-3-en-1-yl)-\(6^{\prime}\)-methoxy-chalcone (**130**); 3-hydroxy Xanthohumol or \(2^{\prime}\),\(3^{\prime}\)-trihydroxy-\(3^{\prime}\)-(4-hydroxy-3-methylbut-2-en-1-yl)-\(6^{\prime}\)-methoxychalcone (**131**), and xanthohumol (**25**). Only 3-hydroxyxanthohumol (**131**) and xanthohumol (**25**) weakly inhibit AChE with IC\({}_{50}\) values of 51.25 and 71.34 uM, respectively. All the other compounds are  
Figure 11: Structure of some of the catechins and flavanones that inhibit AChE and of the inactive flavanones **109**, **120** and **121**.  
Figure 12: Structure of the AChE inhibitors 3-hydroxyflavanones **122–124**.  
inactive [64]. These results point out the importance of the two hydroxy groups at C2\({}^{\prime}\) and C4\({}^{\prime}\), as well as the prenyl group at C3\({}^{\prime}\) and the methoxy group at C6\({}^{\prime}\). Figure 13 shows the chemical structure of chalcones **125-131**.
## 4 Multiple Services Set Identifier (MSSIDs) Technique
### Time and Spatial Correlation Coefficients of MSSIDs' Signals  
The behavior of N SSIDs' signals on the same physical AP is nearly identical to the deployment of N individual APs. In order to investigate the independency of these signals and the similarity over the AOI, Tables 2 and 3 illustrate the time correlation coefficients of SSIDs' signals at a specific location, and the spatial correlation coefficients over the AOI, respectively. In Table 2, the time correlation coefficients are \(\left|Cij\right|<0.05,\) meaning all these signals are independent and its behaviors are similar to the behavior of 5 separated APs.  
Figure 3: The MSSIDs’ signals time behavior.  
Table 3 shows the spatial correlation coefficient over the AOI. We can see that these values over the AOI are close to 1, meaning that all SSIDs' signals which are deployed on the same AP have a similar spatial power distribution. Furthermore, Figure 4 shows the heat map for two APs, each with three SSIDs in different locations on the AOI. From Figure 4 it can be seen that MSSIDs' signals have the same fingerprinting on the map. Therefore, the use of MSSID signals leads to an increase in the redundancy of these signals and a degradation of the IPS's performance.
## 3 Temperature-Aware Routing Protocols
### ALTR (Adaptive Least Temperature Routing)  
ALTR, or Adaptive Least Temperature Routing [22], is the adaptive form of LTR. A new parameter, named MAX HOPS ADAPTIVE, is presented by this protocol. Each time a packet is received by a node, its hop count value is examined. If the value is lower than MAX HOPS ADAPTIVE, then the packet is routed in the same way as LTR. However, as shown in Figure 5, if the value is greater than MAX HOPS ADAPTIVE, instead of being dropped, the packet is routed using the shortest hop algorithm. ALTR also introduced a mechanism called \"proactive delay\" to reduce the temperature rise at the cost of the packet delivery delay. If a node receives a packet and has no more than two outgoing neighbors, such that its coolest neighbor has a relatively high temperature, the node delays the packet by one time unit before forwarding it to the coolest neighbor.  
Figure 4: An example of Least Temperature Routing (LTR).  
Figure 5: An example of Adaptive Least Temperature Routing (ALTR).
## 3 Mathematical Model for Optimal Trajectory Planning
### Constraint Function Design
#### 3.3.5 Coverage Range Constraints (CRC)  
For the whole coverage, UAV needs to surveille all the areas. If the path planning includes the coverage area, no penalty is given. Otherwise, a negative value is given as a penalty. When the trajectory is generated for each particle, its waypoint values are compared with the important area's waypoint value. The important area's waypoints value are divided into 4 sub-areas. If any value falls between those ranges, no penalty is given. Otherwise, a negative penalty is given. Then, that waypoint's value is updated accordingly. Coverage range constraint can be formulated as:  
\[CRC=\,0,CRC=\,\sum_{i=1}^{N_{w}}CRC_{i} \tag{23}\]  
where,  
\[CRC_{i}=\,\,\left\{\begin{array}{c}P,\,if\,\,\text{waypoint}\,\,i\,\text{ in}\,\,Range\big{(}x_{j},y_{j}\big{)}\\ 0,\,\text{otherwise}\end{array}\right.\]  
where,  
\[range\big{(}x_{j},y_{j}\big{)}=\,\,\{l_{x}\leq x_{j}\leq u_{x}\}\,\cap\,\{l_{ y}\leq y_{j}\leq u_{y}\}\]  
where \(N_{w}\) is the number of waypoints, \(l_{x},l_{y}\) are the lower bounds of \(x\) and \(y\), \(u_{x}\), \(u_{y}\) is the upper bounds of \(x\) and \(y\).
## 2 Literature Review
It mainly includes the following aspects:  
* When designing the algorithms, most of the work focuses on the theoretical models and algorithms proposed, and it lacks the consideration of the evolution of network architectures and protocols, as well as the functional constraints of transmission paths;
* This study only considered inter-satellite links, feeder links, and unidirectional traffic, lacking consideration of user links and end-to-end bidirectional traffic flows. Table 1 shows some existing methods for satellite routing. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Work** & **Optimization Objective** & **SFC Constraints** & **Load Balance** & **Applicability Analysis** \\ \hline [PERSON] et al. [27] & Max-flow & \(\times\) & \(\times\) & Ignore SFC constraints and load balance \\ [PERSON] et al. [28] & Path delay & \(\times\) & \(\times\) & Ignore SFC constraints and load balance \\ [PERSON] et al. [29; 30] & Routing QoS & \(\times\) & \(\times\) & Ignore SFC constraints and load balance \\ [PERSON] et al. [31] & Path delay & \(\times\) & \(\times\) & Ignore SFC constraints and load balance \\ [PERSON] et al. [32] & Load balance & \(\times\) & \(\checkmark\) & Ignore SFC constraints and path delay \\ [PERSON] et al. [33] & Satellites node load & \(\times\) & \(\checkmark\) & Ignore SFC constraints and path delay \\ [PERSON] et al. [26] & Path delay and SFC constraints & \(\checkmark\) & \(\times\) & Ignore load balance \\ [PERSON] et al. [39] & Path delay and load balance & \(\times\) & \(\checkmark\) & Ignore SFC constraints \\ [PERSON] et al. [34] & Path delay and SFC constraints & \(\checkmark\) & \(\times\) & Ignore load balance \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of existing methods for satellite routing. \(\checkmark\) means that the corresponding algorithm considered the features of this column, and \(\times\) means that it did not.