esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Datasets
### ThaiNet Dataset
#### 4.2.1 Steps for Generating Sample Transportation Network
```
1:procedureCheckBoxOverlap(\(bound1,bound2\), threshold = 0.01)
2:\(min\_lon1,min\_lat1,max\_lon1,max\_lat1\gets bound1\)
3:\(min\_lon2,min\_lat2,max\_lon2,max\_lat2\gets bound2\)
4:\(lat\_overlap\leftarrow(max\_lat1\geq min\_lat2-\text{threshold})\wedge(max\_lat2 \geq min\_lat1-\text{threshold})\)
5:\(lon\_overlap\leftarrow(max\_lon1\geq min\_lon2-\text{threshold})\wedge(max\_lon2 \geq min\_lon1-\text{threshold})\)
6:return\(lat\_overlap\wedge lon\_overlap\)
7:endprocedure
```  
**Algorithm 2** Check Extremities Boundary Boxes Overlap  
```
1:\(region1,region2\), distance\(\_\text{threshold}=0.05\)
2:\(box\_overlap\leftarrow\textsc{CheckBoxOverlap}(bound1,bound2)\)
3:if\(box\_overlap\)then
4: Perform detailed proximity checks to confirm adjacency
5:return True if adjacent, False otherwise
6:endif
7:return False
```  
**Algorithm 3** Check if Two Regions Are Adjacent Based on Overlapping Boundary Proximity  
```
1:\(region1,region2\), distance\(\_\text{threshold}=0.05\)
2:\(box\_overlap\leftarrow\textsc{CheckBoxOverlap}(bound1,bound2)\)
3:if\(box\_overlap\)then
4: Perform detailed proximity checks to confirm adjacency
5:return True if adjacent, False otherwise
6:endif
7:return False
```  
**Algorithm 4** Construct Adjacency Region Pairs  
Figure 1: Process of generating regional datasets, which includes collecting location data, constructing adjacency pairs based on regional boundaries, and finally generating adjacency matrix. Adjacency Generation  
Following the establishment of adjacency pairs, we generate matrices to analyze spatial relationships. The adjacency matrix records regions that are directly adjacent. Specifically, we measure the distance between each pair of adjacent regions back and forth, and use the average of these measurements as the distance entry in our adjacency matrix (see Algorithms 5). ```
1:Initialize matrix \(A\) as an \(N\times N\) array filled with zeros
2:for each \(region1,region2\) in AdjacencyPair do
3:\(A[\text{region1\_index},\text{region2\_index}]\gets 1\)
4:endfor
```  
**Algorithm 5** Generate Adjacency Matrix
## 2 Doping of Perovskites with Mono- and Tri-Valent Metals
On the right it is indicated how Al\({}^{3+}\) doping selectively enhances growth of these sites, aiding the formation of a smoother perovskite surface with less defects. Figure 1: SEM images of monovalent cation doped PbI\({}_{2}\) (left) and MAPbI\({}_{3}\) (right) for every panel. The dopants used are: (**a**) no doping; (**b**) CuBr; (**c**) CuI; (**d**) NaI; (**e**) AgI. Reproduced with permission from _Adv. Energy Mater._**2016**, 6, 1502472, Reference [20]. These examples (Na\({}^{1+}\), Cu\({}^{1+}\), Ag\({}^{1+}\), Bi\({}^{3+}\), Al\({}^{3+}\)) show that the presence of mono- or tri-valent metal ions strongly influences the crystallization properties of perovskite films. No clear general behavior can be inferred, predicting which metal ions improve properties.
## 4 Discussion
### General Discussion
#### 4.2.3 Are Baseline Acquisitions Important?
Their comparison with operating-phase data remains the easiest way for people to understand expert conclusions. Surface geochemical monitoring at Lacq-Rousse is thus certainly a valid option, but the way it has been done is perfectible, with e.g., more emphasis on specific locations such as near boreholes or faults. The monitoring scheme as devised for Lacq-Rousse helped, proving that, compared to baseline data, no major changes occurred during injection and post-injection. A baseline dataset, whose internal scattering is mostly linked to densely vegetated surface formations and correlative strong biological activity in soil, thus adds value to an MMV program.
# A New Methodology for Estimating Surface Albedo in Heterogeneous Areas from Satellite Imagery
## 4 Methods
### Evaluation and Comparison of Surface Albedo's Methods  
The aim of the evaluation is to determine which of the surface albedo estimations obtained with the above methods is more accurate, through its comparison to the reference values. Two different evaluations are made for different values of reference: one evaluation taking into account the ABOVE surface albedo and another taking into account the punctual value of the surface albedo of the SURFRAD stations. The main difference between these comparisons is that the first covers a large area with different types of land cover, while the second uses the SURFRAD value (measurement in one point position) to assess the value obtained by each methodology in a single pixel of the images. For both comparisons, satellite data from 27 dates between 2013 and 2016 at different seasons have been examined. These data are divided into 4 heterogeneous zones (FPK, BND, SXF, PSU) in which the four SURFRAD stations are located within the footprint of the ABOVE mission (Table 3).
# Transparent Redirections in Content Delivery Networks
## 2 State of the Art  
### Protocols and Caching  
In the early 90s 44% of Internet traffic originated from FTP (File Transfer Protocol) requests, which later faded away and HTTP (Hypertext Transfer Protocol) communication took the leading position [3; 4]. HTTP is encapsulated into the Transmission Control Protocol (TCP), which is a connection-oriented protocol and provides reliable data transfer between the endpoints as the chunks sent are acknowledged by the receiving side. TCP was first introduced in 1981 [5]. The protocol is responsible for breaking the message into TCP segments and reassembling them at the receiving side, capable of data reordering in case of out of order delivery. TCP assigns a sequence number to each byte transmitted and expects a positive acknowledgement (ACK) from the receiving layer. If the ACK is not received within a timeout interval, data is retransmitted. Sequence numbers ensure that the data stream is passed to the application layer in the right order eliminating duplicate TCP segments. A fundamental notion in the design is that every octet of data sent over a TCP connection has a sequence number. This space ranges from 0 to \(2^{32}-1\). Since this space is finite, all arithmetic dealing with sequence numbers must be performed modulo \(2^{32}\). This unsigned arithmetic preserves the relationship of sequence numbers as the cycle from \(2^{32}-1\) to 0 again [5].  
Over the years of evolution of HTTP (versions 0.9, 1.0, 1.1, 2 and 3 currently under development and standardization), the protocol was extended to allow caching at various locations to eliminate the need to reach out to the origin server or eliminate the need to send full responses to requests. This mechanism saves resources on the network and on the origin server too. The list of cache control mechanisms and various headers are found in RFC 7232 and RFC 7234 [6; 7]. With several cache nodes available, the content could be distributed to various geographical locations to lower the time required to deliver the content to the endpoints. Effective content delivery can be achieved by redirecting user requests to the closest cache server based on the user's location. In the following section, the article gives insight into various redirection mechanisms. Due to the popularity of content distribution via HTTP, this article will focus on this application layer protocol in version 1.1 in an unencrypted form.
## 6 Space-Segment Optimization Aspects
### Launch Services and Constraints
This also adds restriction on with propellant volume and pressure of any secondary payload's satellite design, thus limiting the secondary payload to maneuver to preferred orbital planes [121]. For missions with stringent orbital and design requirements, this may not be a feasible solution and a dedicated launch may be too costly [122]. In the case of constellations, such constraint piggyback launches add complexity to the constellation design and optimization [6]. A rough numerical estimate for the overall costs of a micro-satellite (10-100 kg Sat) for a five-year mission lifetime observed by [PERSON] et al. [123] is given in the following equations, where \(C\) refers to cost of the part of the satellite, denoted by its subscript. \[C_{total}=C_{payload}+C_{platform}+C_{launch}+C_{operation} \tag{6}\]  
According to [123], the payload, platform, and launch costs are approximately equal to each other, while the operation cost is approximately half of the payload cost (and also of the launch cost).Thus, with the above-mentioned approximations, one could compute the total cost as:  
\[C_{total}\approx\frac{7}{2}C_{launch} \tag{7}\]  
To acquire the flexibility offered by a dedicated launch with costs comparable to ride-share launches, there is a demand for dedicated micro-launchers. With more satellite clusters and constellations planned to be launched in coming years, many companies are building these launchers to meet these demands [124]. The target of the launch vehicles is to deliver a payload from 10 to 300 kg to LEO with costs matching to the current ride-share costs [121]. Table 8 gives a classification of Launch Vehicle (LV) according to their payload capacity to LEO.
# Hyperparameter Optimization of Ensemble Models for Spam Email Detection
## 3 Methodology
### Methods and Machine Learning Classifiers  
#### 3.3.1 Random Forest  
Random forest is a supervised ensemble classifier that is used for classification and regression. It is an ensemble learning method that creates a set of decision trees and combines them to make a final prediction. To arrive at a prediction, the random forest follows these steps:  
Step 1: Select a random sample of data from the dataset.  
Step 2: Build a decision tree using the sample data.  
Step 3: Repeat the process a certain number of times, creating a new decision tree each time.  
Step 4: Combine the decision trees by taking the average of their predictions.  
Each decision tree in the random forest makes a prediction, and the final prediction is made by taking the average of all the predictions made by the individual decision trees. This helps to reduce overfitting and improve the overall accuracy of the model. The two important hyperparameters that must be defined by the user when generating a random forest are mtry and ntree [32]. In a random forest, mtry is the number of features that are randomly sampled as candidates for splitting at each decision tree node and the ntree is the number of decision trees in the random forest [32]. The mtry parameter determines how much randomness is injected into the model. A smaller mtry value will make the model more deterministic and potentially more accurate but at the cost of a more complex model that is more prone to overfitting. A larger mtry value will make the model more  
Figure 2: The proposed model workflow.  
robust to noise in the data, but at the cost of accuracy. The ntree parameter determines the overall complexity of the model. A larger value of ntree will make the model more accurate but at the cost of increased computational resources and longer training time. The number of trees in a forest (ntree) is not limited by computational resources, but the performance improvement from having a large number of trees is minimal, according to [33]. However, [34] states that computational resources are the limiting factor for the number of trees in a forest.
## 6 Non-Linear Methods
### Energy Optimization and Environmental Sustainability  
Energy-intensive processes such as drying, freezing, and pasteurization are critical in food manufacturing. These processes often exhibit non-linear relationships between energy input and output efficiency. Non-linear methods enable manufacturers to identify optimal energy usage patterns, thereby reducing costs and minimizing environmental impacts.  
A notable application is in optimizing drying processes for agricultural products. Non-linear optimization models have been used to minimize energy consumption in grain drying systems, achieving up to 15% energy savings compared to conventional approaches.
## 3 Results and Discussion
### Wear Resistance of the A-CF/EP/FEP Composites  
#### 3.3.1 Effect of FEP on the Wettability of A-CF/EP/FEP Composites  
The wear rates of A-CF/EP composites containing 2%, 4%, and 6% PU were studied under the applied load of 2.8 MPa and the sliding speed of 0.51 m\(\cdot\)s\({}^{-1}\). In the continuous friction tests, the wear rates of A-CF/EP composites at a certain abrasion cycle decreased with the increase in PU content (Table 1). At the abrasion cycle of 30 kilo times, the wear rates of A-CF/EP composites with 2.0%, 4.0%, and 6.0% PU are 1.42, 1.19, and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample Information** & **Water Contact Angle/\({}^{\circ}\)** \\ \hline
10\% FEP + 4\% PU & 95.8 \(\pm\) 2.0\({}^{\circ}\) \\
20\% FEP + 4\% PU & 109.9 \(\pm\) 2.6\({}^{\circ}\) \\
30\% FEP + 4\% PU & 107.3 \(\pm\) 1.0\({}^{\circ}\) \\
40\% FEP + 4\% PU & 108.9 \(\pm\) 3.2\({}^{\circ}\) \\
50\% FEP + 4\% PU & 103.8 \(\pm\) 1.8\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Water contact angle (WCA) of M-CF/EP composites with 4% PU and different contents of FEP.  
Figure 4: Morphology of the hydrophobic A-CF/EP/PEF surface (**a**), the combination of carbon fiber and EP/FEP resin (**b**), and incorporation between EP and FEP (**c**).  
Figure 5: Morphology of the A-CF/EP/FEP composite with 50% FEP.  
0.91 \(\times\) 10\({}^{-13}\).m\({}^{3}\).N\({}^{-1}\).m\({}^{-1}\), respectively, indicating that the wear rate of the composites decreases with the increase in PU content. When the number of cycles increases to 60, 90, and 120 kilo times, the wear rates of the A-CF/EP composites still conform to the above rule. Compared with the other two composites, the A-CF/EP composite with 6% PU always shows a slower wear rate in the same wear cycle, indicating that PU has an indelible effect on improving the tribological properties of the composites. After 120 kilo cycles of abrasion, the wear rate of the A-CF/EP composite with 6 wt% PU is only 0.58 \(\times\) 10\({}^{-13}\) m\({}^{3}\).N\({}^{-1}\).m\({}^{-1}\), which further proves the wear-resistance stability of PU-containing composites. In addition, the friction coefficient of the A-CF/EP composites was also investigated. The last column of Table 2 shows that the friction coefficient decreases slightly with the increase in PU, which proves the significance of PU to the tribological properties of the A-CF/EP composites.
## 2 Materials and Methods
### Spatial Datasets  
The different types of data used in this study are shown in Table 1. To assess NCP1, land-use data from MODIS MCD12Q1 with a spatial resolution of 500 m and 94.23% accuracy on the QTP, covering 2000-2020, were used. NCP4 evaluation requires NPP and  
Figure 1: Location of the study area. GPP data [14] with a 500 m spatial resolution for 2000-2020. These datasets, aligned with regional observations, serve as reference data for model accuracy evaluation. All datasets are downloaded from the Google Earth Engine platform. Additionally, bird, amphibian, and mammal datasets from BiodiversityMapping.org ([[https://biodiversitymapping.org](https://biodiversitymapping.org)]([https://biodiversitymapping.org](https://biodiversitymapping.org)), accessed on 23 December 2024), with a spatial resolution of 10 km, are used to verify biodiversity changes on the QTP [15]. For NCP6 evaluation, precipitation and evapotranspiration data are derived from the National Tibetan Plateau Data Center with 1 km spatial resolution for 2000-2020. Snow cover products from NASA's National Snow and Ice Data Center have a 500 m spatial resolution for 2000-2020, with 90.76% accuracy [30]. Glacier products are derived from ETH Zurich, and lake datasets are obtained from HydroSHEDS, quantified using principles of ice flow dynamics to infer ice thickness from surface characteristics [31]. Industrial demand data from Figshare have a 0.25\({}^{\circ}\) spatial resolution for 2000-2020. Irrigation demand data from the National Tibetan Plateau Data Center have a 0.25\({}^{\circ}\) spatial resolution for 2000-2018, with 2019 and 2020 values supplemented from 2018. Industrial and domestic datasets are obtained from Zenodo for 2000-2014 and from the statistical yearbook for 2016-2020 [21]. For vulnerability analysis, population data from Figshare have a 1 km spatial resolution for 2030-2060. Water stress data from the World Resources Institute indicate the ratio of water withdrawal to supply. Future precipitation, temperature, NPP, and GPP datasets from the National Tibetan Plateau Data Center cover 2030-2060 [20; 32]. Precipitation and temperature have a 1 km spatial resolution, while NPP and GPP have a 0.25\({}^{\circ}\) spatial resolution [33]. Future MSA is derived from future land-use datasets with a 1 km spatial resolution for 2030-2060, obtained from Zenodo [34]. This study uses SSP2-4.5 scenarios to reflect future socio-climatic changes [35], with precipitation and temperature
## 2 Materials and Methods
### In Vitro Starch Digestibility  
Starch digestibility was studied in vitro using a slightly modified method of Englyst [28], which directly correlates with the glycemic response [29]. The method is based on the measurement of glucose released from a test food during a time-limited incubation with digestive enzymes under controlled conditions and allows the determination of free glucose (FG), rapidly available glucose (RAG), rapidly digestible starch (RDS), slowly digestible starch (SDS), resistant starch (RS), and total starch (TS). Heat-stable amylase (A3306) used to determine total starch, as well as pancreatin (P7547), amyloglucosidase (A7095), and invertase (I4504) used for in vitro hydrolysis of starch, were purchased from Sigma Aldrich Co. (St. Luis, MO, USA). The absorbance of the standards and samples was measured at 490 nm using a microplate reader (VICTOR X3, PerkinElmer, Shelton, CT, USA). The following formulas were used to calculate TS, RDS, SDS, RS, and RAG:  
\[\text{TS}=(\text{TG}-\text{FG})\times 0.9 \tag{1}\]  
\[\text{RDS}=(\text{G}_{20}-\text{FG})\times 0.9 \tag{2}\]  
\[\text{SDS}=(\text{G}_{120}-\text{G}_{20})\times 0.9 \tag{3}\]  
\[\text{RS}=(\text{TG}-\text{G}_{120})\times 0.9 \tag{4}\]  
\[\text{RAG}=\text{At}\times\text{Vt}\times\text{C}\times\text{D}/\text{As} \times\text{Wt}\times 100 \tag{5}\]  
where G\({}_{20}\) represents glucose released after 20 min, G\({}_{120}\) represents glucose released after 120 min, At is the absorbance of the test solution, Vt is the total volume of the test solution, C is the concentration of standard (mg glucose/mL), D is the dilution factor, As is the absorbance of standard and Wt is the weight (in mg) of the sample taken for analysis.
## 5 Pros and Cons of Apta-POF SPR Sensors  
Apta-POF SPR sensors are characterized by several advantages due to both components: Optamers and POFs. For instance, the simple manufacturing process and the high flexibility can be obtained by exploiting the plastic fibers that allow easy modification and integration in small devices. Examples of these POF-based approaches were reported by [PERSON] et al., who embedded a POF in a disposable microfluidic chip in an integrated fluorescence detection system for biomedical devices [87]; by [PERSON] et al., who proposed a respiratory monitoring device suited for application in environments with strong magnetic fields [88]; or by [PERSON] and [PERSON], who integrated a smartphone with a POF for the diagnosis of respiratory diseases [89]. Instead, the aptamer element guarantees the high specificity of this kind of POF SPR sensor. Moreover, the different configurations that can be simply implemented on the polymeric fiber, such as the D-shaped or U-bend or grating, allowed great flexibility in the sensor design depending on the system requirements [2,41]. On the other hand, with respect to silica fibers, POFs can efficiently work only in a limited range (visible), are characterized by a wide width at half height, and can suffer from environmental or external treatments; high-temperature treatments are, for instance, poor compatible with POF, even if thermal resistance can be improved [90]. Pro and cons of Apta-POF SPR sensors are summarized in Table 3.  
The coupling of POFs and aptamers generates new challenges in the bio-detection field and will require major technological breakthroughs in the future.  
Figure 6: (**a**) Scheme of the POF’s gold surface derivatization based on aptamer SAM. (**b**) Transmission spectra wide range and zoom (inset) for different SARS-CoV-2 spike protein concentrations (\(25\div 1000\) nM); (**c**) SPR spectra of the aptasensor at a SARS-CoV-2 positive swab in UTM 1:2 diluted with a physiological solution (0.9% NaCl) [4].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Advantages** & **Disadvantages** \\ \hline Highly flexible (easily integrable in small device) & Moderate cost due to the aptamers \\ \hline Highly specific due to the aptamers & Possible susceptible to environmental modification (humidity, temperature) \\ \hline Different configurations allowed (D-shaped, U-bend, etc.) & Wide value of the SPR spectra width at half maximum (FWHM) \\ \hline Easy to produce and manipulate & Working efficiency in a limited range (visible) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Advantages and disadvantages of Apta-POF-SPR sensors.
## 4 Experiments
### Analysis of the Population Parameters \(N\)  
In this section, we designed an analysis of algorithm performance under different particle counts \(N\) in two scenarios. It can be observed that in the small-scale scenario scene_1 as Figure 8, larger particle counts demonstrate excellent performance with virtually no deviation. In contrast, smaller particle counts exhibit significant deviations. This may be attributed to the influence of particles, where a higher number of particles can more effectively model the surrounding environment. It was noted that the deviation increased progressively in the latter part of the scenario, possibly because, in the absence of loop closure detection, a greater number of particles can more accurately identify optimal positions, thereby enhancing the precision of the algorithm. Similar behavior is observed in scene_2, as depicted in Figure 9, where smaller particle counts result in greater deviations. The long corridor setup in scene_2 also introduces a higher potential for errors with increased particle counts, hence the superior performance of smaller particle counts in the initial phase. This observation has inspired us to consider whether an adaptive adjustment mechanism could be implemented to cater to different scenarios, thereby achieving more accurate localization results.
## 5 Attacks and Countermeasures
### MANET Countermeasures
#### 5.4.2 Countermeasures for Sybil Attacks
They proposed a PKI-based approach to enable a reliable collaboration and to validate exchanged observations between nodes to increase the effectiveness of the countermeasures. The observations are signed before they are transmitted in order to avoid benign nodes getting accused falsely by modified or forged observation messages. Due to the combined effort of multiple nodes, efficiency can be increased. Yet, this happens at the cost of a required PKI and the resource requirements of asymmetric encryption.