esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Factor Identification and Predictive Models
XGBoost is an optimized distributed gradient boosting library, designed to be highly efficient, flexible, and portable. It implements machine learning algorithms under the gradient boosting framework. XGBoost provides a parallel tree boosting, also known as GBDT (Gradient Boosting Decision Tree), GBM (Gradient Boosting Machine), which solves many data science problems in a fast and accurate way. It has recently been dominating applied machine learning for structured or tabular data [28]. Since we had two predictive algorithms, combined with the two coding methods, we built four models for the crash severity perdition: Model 1 (one hot encoder/Random Forest); Model 2 (one hot encoder/XGBoost); Model 3 (label encoder/Random Forest); and Model 4 (label encoder/XGBoost). Step 4: In this step, apply the four models to the West Texas rural teen driver crash dataset. The dataset is randomly split into training and testing datasets (75%/25%) to avoid any bias from the data itself. Finally, obtain the feature importance graph (as shown in Figure 7) and compare the performance measures in both accuracy and speed for the four models (as illustrated in Tables 1 and 2). Figure 6: The coding and the standard scalar examples in this machine learning process. Note that, in Figure 7, the features with positive values mean that these features are the values that may lead to high-level crash severity (injury or even fatal crashes), while the features with negative values indicate that these are the values that may lead to low-level crash severity or even prevent the occurrence of crash injury. The features that rank highest in importance to crash severity prediction for West rural Texas teen driver crashes are: manner of collision, person restraint used, weather condition, school zone, roadway type, light condition, construction zone, left shoulder use, person age, right should type, traffic control type, number of lanes, speed limit, and road class. In Tables 1 and 2, we can find that, for both coding methods, XGBoost has the highest accuracy (Model 2). As for the speed evaluation, Model 4 takes the shortest time to process the same dataset, which is critical for real-time crash prediction applications.
###### Abstract  
The main aim of this paper was to find the correct method of calculating equations of heat and mass transfer for the adsorption process and to calculate it numerically in reasonable time and with proper accuracy. An adsorption heat pump with a silica gel adsorbent and water adsorbate is discussed. We developed a mathematical model of temperature and uptake changes in the adsorber/desorber comprising the set of heat and mass balance partial differential equations (PDEs), together with the initial and boundary conditions and solved it by the numerical method of lines (NMOL). Spatial discretization was performed with equally spaced axial nodes and the PDEs were reduced to a set of ordinary differential equations (ODEs). We focused on the comparison of results obtained when the set of heat and mass balance ODEs for an adsorber was solved using: (1) the Runge-Kutta fixed step size fourth-order method (RKfixed), (2) the Runge-Kutta-Fehlberg 4.5 th-order method with a variable step size (RK45), and (3) the Gear Backward Differentiation Formulae numerical (Gear BDF) methods. In our experience, all three types of ODE numerical methods (RKfixed, RK45, and Gear BDF) can be applied in simple models to model an adsorber with attention on their limitations. The Gear BDF method usually requires much fewer steps than the RK45 method for almost the same calculating time. RK methods require many more steps to obtain results, and the calculating time depends on accuracy or defined time step. Moreover, one should pay attention to the number of nodes or possible oscillations.  
Keywords:adsorption; adsorption heat pump; numerical method; discretization +
Footnote †: journal: Applied Sciences
# Thermospheric Neutral Wind Measurements and Investigations across the African Region--A Review
## 5 Comparisons with HWM-14 Predictions
In general, the HWM-14 is observed to perform slightly more accurately in Morocco than in South Africa. This could be because the northern hemisphere is more represented empirically in the data used to construct the HWM [13]. In Ethiopia, the HWM-14's predictions of the meridional winds were in good agreement between the months of January and April. However, in November and December, there were discrepancies of about 30 to 50 m/s which were most noticeable before midnight [27]. There were conspicuous differences between the model's zonal wind predictions and FPI measurements during the hours between 23:30 and 01:30 LT for the months of January and February. These differences can be attributed to a temporal apparent phase shift as, for example, noted in [PERSON] et al. (2022) [13]. In November and December, the discrepancies were seen between the hours of 19:30 and 21:30 LT.  
For the Nigerian station, good agreements were noted between the HWM-14 predictions and the FPI measurements of the meridional winds usually after 23:00 LT. During pre-midnight hours in most months of March to June 2016, the HWM-14 meridional predictions were observed to be in an opposite direction to the FPI wind measurements, with discrepancies reaching the order of \(\sim\)60 m/s. The HWM-14 therefore did not accurately reproduce the FPI wind measurements, as there was a negative correlation in the early evenings. The meridional predictions from September 2017 to January 2018 were fairly accurate. The model's predictions of the zonal winds were largely correct in terms of capturing the general slowing of the eastward wind as the nights progress, and the occasional westward turning of the winds before the next sunrise. However, the model mostly underestimated the FPI eastward wind magnitudes, except for few hours in the early evenings, when it did overestimate them. These sort of discrepancies in the African region are perhaps not terribly surprising, considering that the general construction of HWM-14 lacked data from the Africa region [13]. Another possibility could be that these differences are due to the magnetic and geographic latitude offsets, which are different in the African and American sectors; in the American sector, the magnetic equator is south of the geographic one, while the reverse is the case in the African sector. The actual lower atmospheric forcing and geomagnetic forcing are also different from the parameterization of them in the model.
## 2 Materials and Methods
### Experimental Method  
The surface of the specimen was coated with platinum and observed using a field emission scanning electron microscope (SNE-4500M Plus, BRUKER, Pleasanton, CA, USA), and the surface roughness was measured using a nanosurface 3D optical profiler (NV-E1000, Nano System, Daejeon, Korea). To measure the contact angle, a drop of 4 \(\upmu\)L of distilled water was dropped onto the specimen and 30 shots were taken at 0.1 s intervals using a contact angle analyzer (video contact angle measuring device: Phoenix 300, SEO Inc., Suwon, Korea). The surface energy value was also measured. The amount of residual monomers remaining on the surface after washing was measured with a Fourier infrared spectrometer (INVENIO-X, BRUKER, USA) in the wavenumber range of 4000-500 cm\({}^{-1}\) and with a resolution of 4 cm\({}^{-1}\). Ten specimens were tested for each group, and the degree of conversion (DOC) of each group was calculated using the standard baseline method by converting the measured transmittance to absorbance. To determine the degree of conversion, the ratio between unpolymerized aliphatic(C=C) bond at 1640 cm\({}^{-1}\) and the aromatic (C=C) at 1610 cm\({}^{-1}\) peaks was used. The uncured aromatic (C=C) bond at 1610 cm\({}^{-1}\) peak was used as an internal standard.  
\[\text{DC }(\%)=1-100\ \times\ \left[\begin{array}{c}\text{Aliphatic }_{\textit{polymerized}}/\text{ Aromatic }_{\textit{polymerized}}\\ \hline\text{Aliphatic }_{\textit{unpolymerized}}/\text{ Aromatic }_{\textit{ unpolymerized}}\end{array}\right]\]  
Flexural strength was measured after using a post-curing device (LC-3D Print Box, 3D Systems, Rock Hill, SC, USA) for 30 min to cure all residual monomers remaining on the surface after washing. In the three-point flexural fracture test based on the ISO-178 standard, starting with a preload of 1 N, the load value was measured when the specimen was broken while applying a load rate of 1 mm/min with a universal testing machine (Hydraulic type universal testing machine 1000 kN, ELE, Leighton Buzzard, UK).
## 2 Results and Discussion  
### Key Aroma Compounds Identified of Three Black Teas  
In order to completely identify the key aroma compounds in black tea, HS-SPME and SAFE are used in combination with GC-O. A total of 70 volatile aroma compounds in three kinds of black tea were identified, which are listed in Table 1. Among them, JJM (58), DH (49), and KM (62) are expressed by the aroma quality and aroma intensity (AI), or flavor dilution factor (FD) that can be smelled respectively, to clearly show its contribution to the overall aroma of black tea. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Aroma Compounds**} & \multicolumn{2}{c}{**LRI a**} & \multirow{2}{*}{**Odor**} & \multicolumn{2}{c}{**JJM**} & \multicolumn{2}{c}{**KM**} & \multicolumn{2}{c}{**DH**} & \multirow{2}{*}{**ID e**} \\  & & & & **HP-** & & **Innowax c** & & & **Quality d** & & **AI** & & **FD** & **AI** & **FD** \\ \hline \multirow{2}{*}{1} & Dimethyl & \multirow{2}{*}{\(<\)600} & \multirow{2}{*}{750} & \multirow{2}{*}{Corn} & \multirow{2}{*}{0.5} & \multirow{2}{*}{-} & \multirow{2}{*}{4.9} & \multirow{2}{*}{-} & \multirow{2}{*}{1.2} & \multirow{2}{*}{-} & \multirow{2}{*}{RI, MS, A} \\  & sulfide & & & & & & & & & & \\  & 2- & & & & & & & & & \\  & Methylpropanal & & & & & & & & & & \\  & 2- & & & & & & & & & \\  & Methylbutanal & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of flavor dilution factor (FD) and aroma intensity (AI) of the aroma compounds identified in Jinjunmei (JJM), Keemun (KM), and Dianhong (DH) black teas.
## 2 Literature Review
### Methods Used for the Location of Charging Infrastructure for Private Passenger Vehicles
Finally, there are studies that combine quantitative and qualitative methods for the deployment of charging infrastructure. For instance, [31] performed an online survey to gather information on the user requirements and preferences related to the charging process, the locations of fast-charging stations, and the weighting for the evaluation of the applied criteria. According to their study, additional services, accessibility, and reliability are the most important aspects to be considered when planning a charging station since they have strong influence on its use. Reference [24] applied a mixed approach that includes (i) a systematic literature review, looking for the spatial criteria that have been previously analyzed in similar studies; (ii) semi-structured interviews with relevant stakeholders (e.g., policymakers, planners, and market experts) to determine a set of criteria to be applied in the case study; and (iii) an Analytical Hierarchy Process (AHP) a multi-criteria analysis method, to define the criteria and related weights to estimate the suitability level of each road link for the CS's location. Focusing briefly on the use of POIs, in [25], a set of points (taken from the urban development plans) was used in the genetic algorithm at the beginning of the process as candidate locations for a charging station. Then, the algorithm performed a clustering analysis to remove the closest points and to define the area of influence of each point. In [26], the selected POIs characterized differences in urban land use (e.g., business, shopping, and recreational areas) and were considered the potential destinations for the daily travel of residents. The method in [28] was based on a set of large-scale urban POI and other data sources; the results show that the density of three POI categories (i.e., transport, retail, and commercial) is statistically associated with the charging demand. The authors of [30] developed a weighted multi-criteria method identifying attributes mainly related to economic, environmental, demographic, and transportation data, as well as the availability of POI influencing the potential use of charging stations. Table 1 provides a classification of the reviewed literature based on the main methodology used for the analysis. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method/Approach Classification** & **References** \\ \hline \multicolumn{2}{c}{Quantitative} \\ \hline Mathematical location models & [28,29] \\ \hline Multi-agent system & [25] \\ \hline Multi-criteria analysis & [24,30] \\ \hline Spatial analysis/statistics & [24,28,29] \\ \hline Statistics & [31] \\ \hline Big data analysis & [26] \\ \hline \multicolumn{2}{c}{Qualitative} \\ \hline Interviews & [24] \\ \hline Surveys & [31] \\ \hline Use of POI & [25,26,28,30] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Methods used for the location of private electric vehicle infrastructure. Source: own elaboration.
## 6 Conclusions  
A single DC-Link Voltage of seven level VSC based STATCOM has been executed with various continuous, cumulative, and disintegration inductive and capacitive loads. The model has tested by dynamic load variations of nominal system voltage at various currents of inductive and capacitive loads. A combination of binary-weighted transformers and VSC power circuits improves the transmission system voltage levels with unique DC-Link capacitance. The single DC-Link voltage eliminates unbalanced voltage strain on switching devices to maintain smooth voltage waveforms. The firing angles of the switches estimation is based on the levels of VSC based STATCOM for generating the system voltages. The dynamic load performance of the STATCOM has been verified for different instants of continuous inductive and capacitive loads with satisfactory results and low harmonic content. The cumulative load performance was also verified at various inductive and capacitive loads with reasonable results with low harmonic content. The STATCOM also verified the dynamic performance of disintegration inductive and capacitive loads with acceptable results and low harmonic content. STATCOM has been verified with system voltage changes at nominal point value for smooth improving operations, as evidenced by the accurate results. The THD is 12.02%, a lower value than that of conventional VSC configurations. The major advantage of STATCOM is minimizing the number of switches and transformers for voltage enhancement of the high voltage transmission systems.  
L.N.G.: Writing-Original Draft, and Resources; L.N.G. & G.S.R.: Conceptualization, Methodology, Investigation; R.D. & F.P.G.M.: Review & Editing. All authors have read and agreed to the published version of the manuscript.  
The work reported herewith has been financially by the Direccion General de Universidades, Investigacion e Innovacion of Castilla-La Mancha, under Research Grant ProSeaWind project (Ref.: SBPLY/19/180501/000102).  
The authors declare
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 3 Methodology
### Ledger Structure  
The smart contract stores all data on-chain using Solidity mappings and structured arrays [51]. These serve as the blockchain-based ledger, ensuring data persistence and immutability. User information is stored in a mapping that associates each Ethereum address with a _UserDetails_ structure. As shown in Figure 3, this structure records the user's name, ID, and company name, forming the basis for participant identification and role verification.  
Coffee batch information is stored in a mapping from batch numbers to _BeanDetails_. This structure captures the batch's initial weight, its unique batch number, and the timestamp of the most recent transaction. These fields enable the system to monitor the flow of coffee from registration to final sale, preserving batch-level traceability.  
Sales transactions are stored in an array of _SaleDetails_ structures. Each entry in this array represents a confirmed transaction, including the seller's and buyer's addresses, the batch number involved, the quantity of beans transferred, and the time of sale. This array forms the transaction history, recording each step of the batch's movement through the supply chain.  
While the ledger structure is sufficient for functional testing and validation of traceability logic, it currently does not include data fields for environmental conditions or product origin. These can be incorporated in future versions of the contract by extending the data structures used in the mappings.
## 3 KA-DDPG-Based Direct Torque Distribution Method
### KA-DDPG-Based Driving Torque Distribution
#### 3.2.1 State Space  
The state space of a vehicle is valuable information that the agent can attain before the decision-making step, which is used to evaluate the vehicle's situation. In this study, the control objective of the torque distribution strategy for the skid-steering vehicle is to minimize the error in desired value tracking, including minimizing the longitudinal speed error and the yaw rate error. Thus, the longitudinal speed error \(v_{delta}\) and the yaw rate error \(\omega_{delta}\) are included in the agent's state space, defined as follows:  
\[v_{delta}=v_{xd}-v_{xu}, \tag{10}\] \[\omega_{delta}=\omega_{\phi d}-\omega_{\phi a},\]  
where \(v_{xd}\) and \(\omega_{\phi d}\) are the desired values of the longitudinal speed and yaw rate of vehicle, respectively, which are illustrated in Figure 1. The sign of the value indicates the direction of motion. In addition, the longitudinal acceleration \(v^{\prime}_{xu}\) and the angular acceleration \(\omega^{\prime}_{\phi a}\) have a significant impact on the convergence performance of the desired value tracking error. Thus, both are included in the state space. In summary, the state space is defined as follows:  
\[\text{state}=\Big{\{}v_{delta},\;\omega_{delta},\;v^{\prime}_{xu},\;\omega^{ \prime}_{\phi a}\Big{\}}. \tag{11}\]
## 2 Cryptographic Systems in Intelligent Transport Systems  
Decades of research in cooperative intelligent transport systems (C-ITSs) still has not fully harmonized technical solutions for certain security-related challenges. One such challenge is how to setup and manage secret keys used within the eco-system of stationary and moving nodes in a C-ITS system. Different pilots and proof-of-concept projects such as Nordic Way [19] and C-ROADS [20] have chosen to implement traditional PKI architectures. This approach is also chosen for the railway side of C-ITS, where the European Rail Traffic Management System (ERTMS) is one of the major initiatives using PKI [21]. Additionally, the European Telecommunications Standards Institute (ETSI) has drafted a set of technical standardization documents detailing PKI-based solutions for vehicle C-ITS systems [22]. Although PKI is a solid architecture from many perspectives, the deployment of such a solution has some drawbacks if the architecture scales rapidly and there are large volumes of dynamically used key-pairs; the scalability and revocation challenges are still prevalent in the C-ITS domain [23]. For an eco-system that needs to issue, manage, and revoke hundreds or even thousands of new key-pairs every hour, e.g., an inner-city cross-road area during rush hour, a C-ITS system must be both computationally efficient and reliable. Also, for cross-border scenarios where a set of secret keys for a vehicle are issued in one domain or country, the approaching domain needs a way to handle those keys securely, both for verification and revocation.  
Security within C-ITSs, often associated with Vehicular Ad-Hoc Networks (VANET) and Vehicle-To-Anything (V2X) communication, is still an emerging area with needs for standardization and harmonization, even enforced in the European Union via EU directives [24]. In the eco-system of vehicles and infrastructure, several technology stacks must be considered. For example, the usage of 5G and cellular technology, but also short-range communication using IEEE 1609.x and IEEE 802.11p standards [25, 26], e.g., the WAVE stack. Moreover, different layers of these protocols must also be considered from a cryptographic perspective. For example, design choices needs to be made if a certain signature functionality should be available on the application layer or incorporated closer to the physical layers for speed, security and performance. Several initiatives and projects towards cybersecurity within these areas are ongoing, e.g., the U.S. Department of Transportation's Intelligent Transportation Systems Joint Program Office has several projects [27], and the European Telecommunications Standards Institute (ETSI) has several ITS security standardizations ongoing, particularly regarding privacy and trust model architecture [22].
# Dynamic Downsampling Algorithm for 3D Point Cloud Map Based on Voxel Filtering
## 6 Discussion
### Limitations and Future Work  
While our algorithm represents a substantial advancement in point cloud downsampling, certain limitations warrant further investigation. For instance, the algorithm's performance in extremely dense or noisy environments remains an area for improvement. Future research could explore enhancements to the edge preservation mechanism or the introduction of noise-resistant features to address these challenges.  
Moreover, the adaptability of the algorithm to various point cloud data types and sources could be further examined. Extensive testing across a broader spectrum of datasets will help refine the algorithm's applicability and efficiency across different scenarios.
## 4 Experiments
### Experiments settings  
The implementation of our network is based on an open-source deep learning toolbox: Pytorch [35]. The experiments were run in Ubuntu 16.04 system with GeForce RTX 2080, which has 8 Gigabyte memory. In our experiments, the parameters in Equation (4) are set as \(\lambda_{1}=5,\lambda_{2}=5\). The Adam optimizer [36] is used with initial learning rate of 0.001 for OCT layer segmentation and 0.0005 for OCT fluid segmentation. We adopt a learning rate decay schedule where the current learning rate is multiplied by 0.8 after every 15 epochs. The maximum epoch is 100. For every epoch, we randomly select current batches to train with a probability of 0.8 for OCT layer segmentation and 0.5 for OCT fluid segmentation. In addition, selected batches are randomly cropped so as to increase the robustness of the network. In order to apply fair comparison, the experiments settings are kept constant for all comparison methods.