esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Stage II
#### 3.2.6 Hierarchical Cluster Analysis  
Hierarchical cluster analysis (HCA) was presented on the dendrogram indicate similarities between tested variants (Figure 11). Variants spray dried at IAT of 80-120 \({}^{\circ}\)C using every level of IAH and higher FR (0.58-0.83 mL/s) can be distinguished as a separate group, which points to their similarity. Most powders produced using higher FR formed a separate cluster, which confirmed that FR was a crucial factor in effective water evaporation, consequently influencing the tested properties. The variants that were the most similar, as the distance was only 0.5, were the solutions spray dried at 120 \({}^{\circ}\)C using medium level of IAH at FR of 0.38 mL/s and 0.58 mL/s.
# Experimental Study of LoRa Transmission over Seawater
## 3 Materials, Experimental Setup and Methods  
Numerous experiments were carried out in both LOS and obstructed paths for a variety of LoRa parameters and in two different ISM frequency bands. The layout of each experiment was identical; namely, two different ODTs were sending packets simultaneously at 868 MHz and 434 MHz, while three different transceivers were receiving packets at 868 MHz and an additional one at 434 MHz. In the 868 MHz band, ODT [10], Libelium [16], and Uptronics [17] were receiving packets in parallel, while another one ODT was receiving packets at 434 MHz. Commercial transceivers were selected based on their cost and prevalence in different deployments. Libelium is a costly solution, whereas Uptronics is a low-cost solution; both solutions are widely adopted in industry.
# MinerGuard: A Solution to Detect Browser-Based Cryptocurrency Mining through Machine Learning
## 5 Evaluation
### Overhead and Detection Accuracy
However, approaches that adopt blacklists block web pages in a blacklist, even if a web page informs its users of its mining behavior and its users agree to let the web page mine using their devices. In this situation, the detection accuracy of both MinerGuard and MinerBlock for normal web pages is high. Figure 19: Detection accuracy for mining web pages and normal web pages. Figure 18: Resource usage overhead for handling mining web pages. 4. Comparisons among Related Work: We made various comparisons among MinerGuard, three blacklist-based methods, MinerBlock, No Coin, and AntiMiner, two feature-based methods, CMBlock and CMTracker, and two machine learning mechanisms, CapJack and Crypto-Aegis, to show the advantages and disadvantages of these techniques. The comparisons are shown in Table 5. As the mining scripts are constantly updated, the mining methods are continuously improved, and the blacklist-based and feature-based mechanisms must regularly maintain and update their blacklists. MinerGuard adopts behavior-based machine learning techniques to identify mining activities and, therefore, does not require frequent maintenance. As new mining scripts and pages are constantly developed, MinerGuard can detect these 0-day mining pages. However, blacklist-based and feature-based methods cannot see them in time because related information of 0-day mining scripts or pages needs to be added to blacklists, which costs some effort. In addition, MinerGuard will notify a user if a web page contains mining behaviors and let them decide to allow the execution of mining scripts. However, the methods mentioned earlier, other than MinerGuard, will block a mining script no matter whether a user agrees with the mining activity. When using these methods, if a user wants to agree to execute a mining script on a web page, they need additional efforts, such as manually appending hash values or rules. Table 5 contrasts MinerGuard's benefits and drawbacks with those of the three blacklist defense mechanisms: MinerBlock, No Coin, and AntiMiner. The blacklist protection system must periodically maintain and update the blacklist material since mining scripts are updated and techniques are improved. On the other hand, our MinerGuard does not need routine maintenance since it recognizes mining activity based on the features of the mining behavior. Our MinerGuard can identify the new mining scripts constantly being released as long as they fit the traits of regular mining activity. MinerBlock, No Coin, and AntiMiner, on the other hand, need to be manually added to the blacklist regularly and are unable to safeguard consumers promptly. Additionally, if we ask for the user's permission to cease mining operations, we will not do so immediately; instead, MinerGuard will always solicit the user's input before deciding whether to block mining scripts. Users must manually add mining scripts to the whitelist if they wish to provide their consent to mine since MinerBlock, No Coin, and AntiMiner all block mining scripts by default.
## 2 Theoretical Analysis of Cavity-Based WPT Systems
While the impedance boundary conditions approximate the relation between the tangential field component, they consider the conduction losses in the conducting antenna parts with reasonable accuracy. The transmitting antenna's radiator (the dipole) is excited by a feed line consisting of two copper strips located on opposite sides of the substrate and of identical dimensions. The transverse dimensions of the feed line are such that at the operating frequency of 865.5 MHz, only the TEM wave can propagate, while the higher-order modes decay so quickly that their effect can be completely neglected. The widths of the upper and lower  
Figure 4: The HFSS model of the transmitting antenna: top view (**a**) and bottom view (**b**). Figure 3: The HFSS model of the receiving antenna: top view (**a**), bottom view (**b**). strips constituting the feed line are identical and chosen so that the line's characteristic impedance is 50 \(\Omega\).
# Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology
## 4 Real-Time Experiments and Results
### Sdr-Uami-Mbsss Application  
Lastly, the proposed methodology for the implementation of the MBSS technique using low-cost communication devices was consolidated in the development of an application named SDR-UAMI-MBSS. This interface shows the PSD and the occupation of a spectrum interval (see Figure 24), specifically, the radioelectric space that the \(SU_{\text{s}}\) (connected SDR devices) can visualize as a whole; it consists of advanced digital signal processing techniques and was developed in Python language. This application is easy to use, intuitive, and quite descriptive. This allows the user to have an easy interaction. Furthermore, it is an open-source application; it also allows knowing precisely what each module does, and whether it is possible to improve it. The SDR-UAMI-MBSS interface is a branch of the SDR-UAMI application [29].
# Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data
## 2 Literature Review
### Cellular Networks
#### 2.1.1 System Components  
The GSM consists of the MS, base transceiver station (BTS), base station controller (BSC), mobile switching center (MSC), home location register (HLR) and visitor location register (VLR). MSs communicate with the network through the BTS and BSC via the A-bis interface (the interface between the BTS and the BSC). The BSC communicates with the MSC via the A (the interface between the BSC and the circuit switched core network) interface, and MSC can connect the calls from the MSs to the public switched telephone network (PSTN). Moreover, the HLR and VLR can provide mobility management and record the location information of the MS [15].  
GPRS evolved from GSM and added two new components, which include the serving GPRS support node (SGSN) and the gateway GPRS support node (GGSN). GPRS can establish the data connection with the Internet. However, this study focuses on circuit-switched networks, and the signaling of packet-switched networks is not discussed and analyzed.  
The UMTS consists of user equipment (UE), Node B, the radio network controller (RNC) and the components in GSM and GPRS. UEs communicate with the network through Node B and the RNC via the Iub (the interface between the Node B and the RNC) interface. The BSC communicates with the MSC via the IuCS (the interface between the RNC and the circuit switched core network) interface, and the MSC can connect the calls from the UEs to the PSTN [15].
## 2 Data and Methods
### Methodology
#### 2.2.2 A System for Radiative Transfer Calculations--SBDART-NO\({}_{2}\)
The results for clear, liquid and ice cloud-covered skies are then multiplied by the monthly mean daylength [84; 85] and weighted by the corresponding clear-sky/cloud fractions to obtain the monthly mean all-sky daily SSR (e.g., [PERSON] et al. [15]). Prior to weighting, a correction is applied to the MODIS/Aqua cloud fraction data as they characterize only a very specific time range in the afternoon and not the daytime cloud cover. The correction is obtained by multiplying by climatological monthly ratios of daytime cloud cover and cloud cover at 11-12:00 UTC (overpass time of Aqua) from CM SAF CLAAS-2.1. By comparing SSR simulations with and without this correction with ground-based observations we found that the correction reduces the bias by 3-4%. The radiative transfer system is capable of producing gridded solar radiation fields at the top of the atmosphere, within the atmosphere and at the surface similarly to other global and regional systems [15; 17; 19; 21]. Following the study of [PERSON] et al. [86], here we show a demonstration for SSR for the period 2005-2019 for 16 selected stations around Greece, located in the heart of the climatically sensitive region of the Eastern Mediterranean. The locations (see Table 1 for details) are divided into urban and rural taking into account population (GPWv4.11) and air quality criteria (MODIS/Aqua AOD\({}_{500}\) and OMI/Aura tropospheric NO\({}_{2}\); Figure 2), and cover the whole country. Also, the 16 locations are embracing stations where there are or there may be solar radiation measurements in the future, e.g., stations of the Hellenic Network of Solar Energy (HNSE) [87], the PANhellenic GEophysical observatory of Antikythera (PANGEA) [88] station and the Olympus Research Center (EKO) station. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Location** & **Latitude** & **Longitude** & **Urban/Rural** \\ \hline Antikythera & 35.8125 & 23.3125 & R \\ Argos & 37.5625 & 22.8125 & R \\ Athens & 37.9375 & 23.6875 & U \\ HCMR & 35.3125 & 25.3125 & R \\ Ioannina & 39.5625 & 20.8125 & R \\ Kozani & 40.3125 & 21.8125 & U \\ Mytilene & 39.0625 & 26.5625 & R \\ Olympus & 40.0625 & 22.3125 & R \\ Orestiada & 41.5625 & 26.5625 & R \\ Patras & 38.3125 & 21.8125 & U \\ Preveza & 39.0625 & 20.8125 & R \\ Pylos & 36.9375 & 21.6875 & R \\ Rhodes & 36.4375 & 28.1875 & R \\ Thessaloniki & 40.6875 & 22.9375 & U \\ Volos & 39.4375 & 22.9375 & U \\ Xanthi & 41.1875 & 24.9375 & R \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Names of the 16 locations in Greece examined in this work along with the central coordinates of the grid cells they cover and the indication urban or rural. Figure 2: Maps with the (**a**) topography (in meters above sea level—m.a.s.1), (**b**) annual average MODIS/Aqua AOD\({}_{50}\), and (**c**) annual average OMI/AURA tropospheric NO\({}_{2}\) VCD (in \(10^{15}\) molecules/cm\({}^{2}\)) over the area of interest for the period 2005–2019. The grid cells with the 16 locations in Greece examined in this work are also indicated.
## 2 Materials and Methods
### Calculation of OAV of Teas and Analysis of Odor Characteristics  
The threshold values of compounds were referenced in the literature (as shown in Supplementary Table S1) [22; 23]. The OAV of each volatile compound (OAVI) was calculated as OAVi = Ci/Ti, where Ci is the content of compound i (\(\mu\)g kg\({}^{-1}\)), and Ti is the threshold value of compound i (\(\mu\)g kg\({}^{-1}\)). The compounds with OAVi \(\geq\) 1 were identified as the odor-active compounds for further comparative analysis. The total OAV of a tea (OVAt) is the sum of those OAVi of the odor-active compounds, namely OVAt = \(\sum\)OAVi. The OAVt represent the aroma intensity of a tea.  
Based on the reference (Table S1) [17], the OAVi of a compound were split into six odor characteristics, i.e., woody, floral, burnt, green, fruity, and fatty. Next, the OAV values of each odor characteristic in a tea were obtained. The index of each odor characteristic, namely I(o), is the ratio of the OAV value of each odor characteristic to the OAVt of tea. I(o) represents the intensity of an odor. The intensity and characteristic of each odor is OAVt \(\times\) I(o).
## 2 Measurement System Modeling
### Coordinate Transformtions  
Description of body coordinate system is necessary for the transformation. As shown in Figure 2a, the body coordinate system, directly defined on the flying projectile, whose origin is denoted by \(\text{O}_{\text{b}}\), locates at the center of gravity of the flying vehicle; the X-axis, denoted by \(X_{\text{b}}\), points forward to the head of body; the Y-axis, denoted by \(Y_{\text{b}}\), points to the right side of the body and perpendicular to the symmetric plane; and the Z-axis, denoted by \(Z_{\text{b}}\), points downward to comply the right-hand rule. Figure 2b shows the cross-section of projectile.  
Define \(\psi\), \(\theta\), \(\varphi\), as yaw, pitch, and roll angle respectively, and \(C_{\text{n}}^{\text{b}}\) as the transformation matrix from NED to body coordinate system, according to Figures 1 and 2:  
\[C_{\text{n}}^{\text{b}}=\left[\begin{array}{ccc}1&0&0\\ 0&\cos\varphi&\sin\varphi\\ 0&-\sin\varphi&\cos\varphi\end{array}\right]\left[\begin{array}{ccc}\cos \theta&0&-\sin\theta\\ 0&1&0\\ \sin\theta&0&\cos\theta\end{array}\right]\left[\begin{array}{ccc}\cos\psi& \sin\psi&0\\ -\sin\psi&\cos\psi&0\\ 0&0&1\end{array}\right] \tag{2}\]
## 3 Results and Discussion
### Identification of the Compounds Responsible for Group Separation  
The acquired chromatograms were inspected, taking into consideration the information obtained from the SIRR procedure and Table 2. From the table, three specific time intervals can be distinguished, corresponding to different compounds eluting out of the chromatographic column: from 6.7 to 7.7 min (with ions 51, 67, 95, 96 and 97), from 10.3 to 11.0 min (with ions 109 and 110) and from 8.7 to 9.3 min (with ions 51 and 78). The three intervals correspond to variables 5, 7, 8 and 11 (first interval), variables 3, 4, 9 and 10 (second interval), and variables 12 and 13 (third interval), as shown in Table 2. An inspection of the NIST library allows a tentative identification of the compounds for the two first time intervals, namely furfural and 5-methylfurfural (see Figure 4). The identity of the compounds was verified by injecting pure standards. Variables of the third interval could not be identified despite them possibly playing a relevant role in the treatment; these are the only variables associated to compounds that presumably disappear or are removed due to the washing cycles. Taking into account the results shown in Figure 3, we can conclude that the three compounds are responsible for the separation of the 56 cork samples into the treated and non-treated groups, which is achieved by the untargeted analysis of chromatographic data without the possibly subjective contribution of human perception.  
The presence of various furan derivatives in cork samples has already been described [11; 36; 37; 38]. The authors related the presence of various furan derivatives to carbohydrate degradation (Maillard-type and acid-catalyzed sugar degradation). This suggests that the thermal treatment applied to corks intended to reduce the TCA content may have the parallel effect of favoring an increase in the concentration of furans. In fact, furfural and 5-methylfurfural were found to be present in wines aged in barrels. Furfural compounds are formed by thermolysis of the cellulose and hemicelluloses, together with Maillard reactions that take place during cooperative [39]. Moreover, furfural and 5-methylfurfural are described as having an almond-type aroma, which agrees with the sweet notes identified in treated cork by the sensory panel.  
[PERSON] et al. [30] demonstrate a DHS-MS method in conjunction with multivariate analysis as a promising and simple solution to determine the origin of cork. In the present study, the HS-SPME/GC-MS-SSIR method has shown itself to be a useful tool not only in distinguishing between treated and non-treated cork samples but also in identifying the volatile compounds that are responsible for the sensory changes produced as a result of the treatments. The industrial relevance of these findings is evident.
## 5 Performance Analysis
### Experiment Settings  
Prior to feeding the dataset into the model for training, the original data are uniformly standardized using the Z-Score normalization method [13]. Subsequently, the standardized data are split into three sets--a set for training, a set for validation, and a set for testing--following a 6:2:2 ratio. This study seeks to forecast 12 consecutive future time steps for the next hour by using data from 12 consecutive time steps in the preceding hour. During the experiments, the Ranger optimizer [47] is utilized. The batch size is configured as 64, with hidden dimensions set to 32. The model employs 4 attention heads during training, and the training process is conducted over 500 epochs. To prevent overfitting, an early stopping mechanism is used. The starting learning rate is set to 0.001.  
We employ three standard metrics--Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE)--to assess each approach's efficacy [48], which are described as follows:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **PEMS0 4** & **PEMS0 8** \\ \hline Sensors & 307 & 170 \\ Time Steps & 16,992 & 17,856 \\ Time Range & January–February 2018 & July–August 2016 \\ Time Windows & 5 min & 5 min \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed description of the dataset.  
\[MAE =\frac{1}{N}\sum_{i=1}^{N}\lvert Y_{i}-\hat{Y}_{i}\rvert \tag{16}\] \[MAPE =\frac{100\%}{N}\sum_{i=1}^{N}\left\lvert\frac{Y_{i}-\hat{Y}_{i}}{ Y_{i}}\right\rvert\] \[RMSE =\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(Y_{i}-\hat{Y}_{i}\right)^{2}}\]  
where \(N\) is the number of samples, \(\hat{Y}_{i}\) indicates the predicted value, and \(Y_{i}\) indicates the true value.
## 1 Introduction
### Nonlinear Finite Element Model
\begin{table}
\begin{tabular}{c c c} \hline
**Component** & **Element Type** & **Material** \\ \hline \hline Cable & Truss Element & Steel02 Material \\ \hline \hline Pylon & Nonlinear Beam-Column Element & Concrete02 Material \\  & & Steel02 Material \\ \hline Main girder & Elastic Beam-Column Element & Elastic Material \\ \hline \hline Bearing & ZeroLength Element & Elastic-Perfectly Plastic \\  & & Material \\ \hline Pier & Nonlinear Beam-Column Element & Concrete02 Material \\  & & Steel02 Material \\ \hline Pile & Nonlinear Beam-Column Element & Concrete02 Material \\  & & Steel02 Material \\ \hline Soil spring & ZeroLength Element & Elastic Material \\ \hline \hline Connecting rigid arm & Elastic Beam-Column Element & Elastic Material \\ \hline \end{tabular}
\end{table} 
Table 1: Information regarding components, materials and the connecting rigid arm of extradosed cable-stayed bridge. Figure 8: Finite element model of nonlinear dynamic analysis. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**No.** & **Model Parameter** & **Pylon/Pief/File** & **Unit** & **No.** & **Model Parameter** & **Pylon/Pief/File** & **Unit** \\ \hline
1 & Peak compressive strength of & 48.1/30.55/30.55 & MPa & 7 & Peak strain of & 0.002/0.002/0.002 & \\  & confined concrete & & & & uncomified concrete & 0.004/0.004/0.004 & \\
2 & Ultimate compressive strength & 9.62/6.11/6.11 & MPa & 8 & Ultimate strain of & 0.004/0.004/0.004 & \\  & of confined concrete & & & & & \\
3 & Peak strain of confined concrete & 0.0045/0.0045/0.0045 & 9 & Elastic modulus & 36000/33000/33000 & MPa \\  & Ultimate strain of & & & & & \\  & confined concrete & & & & \\  & confined concrete & & & & \\
5 & Peak compressive strength of & 37/23.5/23.5 & MPa & 11 & Elastic modulus of steel & 20000/20000/20000 & MPa \\
6 & Ultimate compressive strength & 7.4/47/47.7 & MPa & 12 & Thickness of cover layer & 0.005/0.04/0.06 & m \\ \hline
13 & Elastic modulus of main girder & 36,000 & MPa & 16 & Friction coefficient & 0.02 & \\
14 & Elastic modulus of cable & 195,000 & MPa & 17 & Damping ratio & 0.05 & \\
15 & Yielding strength of cable & 1860 & MPa & 18 & Concrete bulk density & 26.5 & kN/m3 \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of model parameters.
# Mapping Mountain Landforms and Its Dynamics: Study Cases in Tropical Environments
## 2 Application Test Sites  
### Cerro Chirripo  
The highest summits of Costa Rica are key to understanding glacial landscape dynamics during the Last Glacial Maximum (LGM) in a tropical environment, the best example of the paramo ecosystem in Central America, as well as understanding its hydrological, climatological and soil aspects. The Talamanca Mountain Range is the site of most of the Costa Rican paramos (>90% of the total area covered by the biome), and the range is composed of Tertiary sediments with a thickness of probably several kilometers, intercalated with Tertiary volcanic and plutonic rocks ([38; 39; 40; 41]; among others). According to [PERSON] and [PERSON] [42], the Talamanca Mountain Range is considered an uplifted, inactive segment of the Central American magmatic arc, composed of intrusive batholiths and plutonic stocks of quartz- diorites and monzonites with subordinate granites and gabbros. Cerro de la Muerte (formally known as the Buenavista Massif), with a maximum altitude of 3491 m.a.s.l., is a massif located on the northwestern part of the Talamanca Mountain Range. Moreover, Cerro Chirripo (3820 m.a,s.l.) is located in the central part of the mountain range, and comprises the most preserved paleoglacial landscapes of the LGM in Central America (Figure 1).