esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Governing Equation of RESRAD and HotSpot  
RESRAD-ONSITE, RESRAD-OFFSETITE, and HotSpot computer codes all use a Gaussian plume model derived from a three-dimensional (3-D) equation for atmospheric diffusion to evaluate radionuclide concentration. The 3-D diffusion equation for atmospheric diffusion is given as follows:  
\[\frac{\partial X}{\partial t}=\frac{\partial}{\partial x}\bigg{[}\ P_{x}\left( \frac{\partial X}{\partial x}\right)\bigg{]}+\frac{\partial}{\partial y} \bigg{[}\ P_{y}\left(\frac{\partial X}{\partial y}\right)\bigg{]}+\frac{ \partial}{\partial z}\bigg{[}\ P_{z}\left(\frac{\partial X}{\partial z} \right)\bigg{]} \tag{1}\]  
where  
\(x\), \(y\), and \(z\) = downwind, cross-wind, and vertical directions, and  
\(P_{x}\), \(P_{y}\), \(P_{z}\) = diffusivity in \(x\)-, \(y\)- and \(z\)-directions, respectively.  
The Gaussian plume model is given by the following equation:  
\[C(x,y,\ z)=\frac{Q}{2\pi\sigma_{y}(x)\sigma_{z}u}exp\left\{-\frac{1}{2}\bigg{[} \frac{y}{\sigma_{y}}\bigg{]}^{2}\right\}\times\left(exp\left\{-\frac{1}{2} \bigg{[}\frac{z-H}{\sigma_{z}}\bigg{]}^{2}\right\}+exp\left\{-\frac{1}{2} \bigg{[}\frac{z\mp H}{\sigma_{z}}\bigg{]}^{2}\right\}\right)\times exp\bigg{[} \frac{\lambda x}{u}\bigg{]}\text{DF}\ (x) \tag{2}\]  
However, if the \(\sigma_{z}\) exceeds the inversion height (L), the following relation is used:  
\[T(x,y,\ z)=\frac{R}{2\pi\sigma_{y}Lu}exp\left\{-\frac{1}{2}\bigg{[}\frac{y}{ \sigma_{y}}\bigg{]}^{2}\right\}exp\bigg{[}-\frac{\lambda x}{u}\bigg{]}\text{ PDF}\ (x) \tag{3}\]  
where  
\(T\) = time-integrated atmospheric concentration (Ci-s)/(m\({}^{3}\));  
\(R\) = source term (Ci);  
\(H\) = effective release height (m);  
\(\lambda\) = radioactive decay constant (s-1);  
\(x\) = downwind distance (m);  
\(y\) = crosswind distance (m);\(z=\) vertical axis distance (m);  
\(\sigma_{y}=\) standard deviation of integrated concentration distribution in the crosswind direction (m);  
\(\sigma_{z}=\) standard deviation of the integrated concentration distribution in the vertical direction (m);  
\(u=\) average wind speed at the effective release height;  
\(L=\) inversion layer height (m);  
PDF(\(x\)) = plume depletion factor.
## 5 Proposal Architecture and Its Deployment
### Scalability Considerations  
To ensure our architecture can scale effectively, we have:  
* Implemented a distributed broker architecture to handle increased load.
* Designed the system to allow for easy addition of new IoT devices and MQTT clients.
* Incorporated load balancing mechanisms to distribute traffic across multiple TOR circuits.
* Developed monitoring tools to track system performance and identify bottlenecks.  
The deployment adheres to the same concepts described in the previous work [14], particularly for VLAN usage as detailed in [49]. The extracted data from the campaigns are real, focusing on total ratio, total runtime, and message time metrics, and the installation spans both the clear web and the dark web.  
Figure 3 illustrates our MQTT Benchmarking Architecture deployment, which includes the Raspberry Pi devices in which the encryption routines are implemented. Moreover, to measure the time intervals necessary for the benchmark estimation the synchronization procedure based on network time protocol (NTP) is also included in the architecture.  
The tests are conducted on various channel technologies using a utility for the bandwidth and different loss probability measurements.
## 4 Numerical Examples
### Numerical Example 2  
The governing equation of the numerical example 2 is given as Equation (1). The left boundary data are described as Equation (19). We consider the right boundary to be  
\[\frac{\partial h(x=L_{f},t)}{\partial n}=e^{-(\frac{L+T}{S})t}\cos x. \tag{21}\]  
The initial condition is  
\[h(x,t=0)=\sin x. \tag{22}\]  
The exact solution is found as  
\[h(x,t)=e^{-(\frac{L+T}{S})t}\sin x. \tag{23}\]  
Figure 2a depicts the configuration of the one-dimensional tide-induced groundwater problem. The dimension of the space domain, the transmissivity, the storage coefficient, the leakage coefficient, and the elapsed time are considered to be 5000 m, 1.25 m\({}^{2}\)/hr, \(2\times 10^{-4}\), \(5\times 10^{-3}\)/d and 5 hr, respectively. The boundary and initial data are given on the lateral and bottom of the spacetime domain, as shown in Figure 2b. We place a source point and 153 boundary points uniformly distributed on the boundary. The Dirichlet and Neumann boundary data are assigned on boundary points. We consider the order of the basis function to be 10. To yield the field solution, we collocate 2601 inner points. Figure 6 indicates the computed and the exact solutions. From Figure 6, the computed tide-induced groundwater fluctuations may be completely consistent with the exact solution. Figure 7 shows the correctness for the SCA. Using the SCA, the MAE is of the order of \(10^{-16}\). It is obvious that the SCA can acquire high accuracy performance.  
Figure 5: Absolute error of the results computed with the analytical solution at the final time.
## 4 Evaluation
### Evaluation Criteria
#### 4.1.4 Load Adequacy  
The index for load adequacy is calculated for every point excluding the last point in the path by finding the closest maximum principle direction vector \(\vec{v}\) from the data set and checking for alignment by computing the dot product \(\langle\vec{v},\vec{u}\rangle\) with the vector \(\vec{u}=p_{i}-p_{i+1}\) connecting the current point with the next one. Before calculation \(\vec{u}\) is normalized, as the sign does not matter in this case, the absolute value of the resulting dot product is taken. If several load cases are to be examined at this point, the highest alignment is chosen. Finally, the index for load adequacy is calculated as the mean of the computed dot products for all points. This value will be between 0 and 1 representing the amount of alignment with the load transmission paths.
## 1 Introduction
Once this phase is completed, large-scale deployment or implementation will follow. Observe that this work starts from a TRL6, verified by simulations in other scenarios, to TRL 7, testing the system in the real scenario. Then, it moves to TRL8, certifying the system/product to be used as a commercial solution and is now available for companies as technology transfer from the university. The proposed warning system measurements include several key performance indicators such as the number of people that enter a specific location in a given time, the average time spent in particular areas, or the amount and density of people that interact over a particular period. An EWS sensor data fusion approach was adopted in this work by combining multiple sensor data sources using Internet of Things (IoT) technology, artificial intelligence algorithms, and application programming interfaces, creating a robust real-time early warning system for pandemic control at specific areas, allowing for efficient, cost-effective event prevention policy implementation and community warning. This EWS approach to disease outbreaks in a population can serve as a critical application toward a more extensive adoption of intelligent management systems by health organizations and multiple stakeholders. Such systems, combined with appropriate infrastructure, data hubs, and service applications at the city/regional level, can eventually lead to the implementation of smart cities, significantly improving the overall quality of life for citizens and creating a trusted system for governments and organizations to manage event-led situations. The rest of the paper is organized as follows. Section 2 explains how complex contexts such as those where warning systems are used can become a booster for innovation and how effectuation can be applied. In Section 3, previous works from the scientific literature are presented. Section 4 describes the proposed warning system and presents the results. Finally, the paper ends by summarizing the most important findings in the conclusions.
## 2 Theoretical Analysis Model and Calculation Method
### Analysis of Transmission Characteristics Based on Average Vibration Energy Level  
Figure 7 illustrates the average vibration energy levels of the track, track bed, bridge roof, and floor of the box girder bridge. The power flows of these four components in the track structure are recorded as 275.59 dB, 207.37 dB, 184.32 dB, and 122.86 dB, respectively. The transmission rates of vibration energy from the track, integral ballast bed, box girder bridge roof, and box girder bridge floor are 75.2%, 88.9%, and 66.3%, respectively.  
When considering the law of energy conservation, it becomes evident that 24.8% of the vibration energy from the common integral ballast bridge segment in urban track transit is stored in the fastener system. This portion of energy can significantly impact the wheel-track contact state and the service performance of fasteners.  
Concurrently, 33.7% of the total vibration energy transmitted to the box girder bridge is retained within the box girder bridge. This stored vibration energy is subsequently converted into low-frequency noise within the bridge, influencing the acoustic environment along the urban track transit route.
# Blockchain-Assisted QoS-Aware Routing for Software-Defined Wide Area Network
## 3 System Architecture
### QoS-Aware Route Selection (QRS) Model
#### 3.1.1 Cloud Users' QoS Preference Model (QPM)  
QRS generally gathers Quality of Service (QoS) data from the cloud end-user, encompassing requirements such as bandwidth, throughput, latency, etc., necessary for interactions with the cloud provider to access specific services. These preferences are contingent upon the user's ongoing use of a specific type of application and may fluctuate dynamically. However, if substantial alterations do not occur, these preferences can be reused, and the user's historical preference data can also serve as a valuable source.  
Figure 1: Examplesenario displaying a number of ISPs between the cloud end-user and the cloud provider.  
The preference model for cloud users comprises two key components: preference quality values and the relative weights assigned to preferences. If there are a total of tpreference items of interest, each application A\({}_{i}\)'s preference requirements can be denoted as (v\({}_{i}\), w\({}_{i}\)).  
\[\begin{array}{ccccc}v_{i}=[v_{i1}&v_{i2}&v_{i3}&...&v_{il}],\\ w_{i}=[w_{i1}&w_{i2}&w_{i3}&...&w_{il}].\end{array}\]  
Here, v\({}_{ij}\) signifies the application's preference requirement value for the jth feature of the QoS requirement (for example, delay tolerance is low, etc.). These values in v\({}_{i}\) are standardized, and all quality requirement values must adhere to this normalization criterion by using a suitable method such as Min-Max normalization [28].  
\[0\leq v_{ij}\leq 1,\text{where }1\leq j\leq t\]  
The parameter w\({}_{ij}\) indicates the relative priority or relative importance that the cloud user assigns to A\({}_{i}\) for feature j (for example, satisfying bandwidth requirements is more important than other QoS preferences, etc.). In cases where there are no preference requirement values, these weight values are not utilized. The preference weights of A\({}_{i}\) should satisfy  
\[0\leq w_{ij}\leq 1,\text{where }1\leq j\leq t,\text{ and }\sum_{j=1}^{t}w_{ij}=1\]  
The QoS preferences, along with the associated preference weights for a total of s potential cloud applications, can be depicted using the following pair of matrices--V and W--and these matrices are used by the routers to consider QoS preferences of a number of applications in traffic route selection.  
\[V=\begin{bmatrix}v_{1}\\ v_{2}\\ v_{3}\\ \vdots\\ v_{s}\end{bmatrix}=\begin{bmatrix}v_{11}&v_{12}&v_{13}&\dots&v_{1t}\\ v_{21}&v_{22}&v_{23}&\dots&v_{2t}\\ v_{31}&v_{32}&v_{33}&\dots&v_{3t}\\ \vdots&\vdots&\vdots&&\vdots\\ v_{s1}&v_{s2}&v_{s3}&\dots&v_{st}\end{bmatrix}\]  
\[W=\begin{bmatrix}w_{1}\\ w_{2}\\ w_{3}\\ \vdots\\ w_{s}\end{bmatrix}=\begin{bmatrix}w_{11}&w_{12}&w_{13}&\dots&w_{1t}\\ w_{21}&w_{22}&w_{23}&\dots&w_{2t}\\ w_{31}&w_{32}&w_{33}&\dots&w_{3t}\\ \vdots&\vdots&\vdots&&\vdots\\ w_{s1}&w_{s2}&w_{s3}&\dots&w_{st}\end{bmatrix}\]
## 4 Results
### SDB Severity Classification Performance  
The FMTL framework demonstrated strong generalization ability in the SDB severity classification task across all three datasets. As shown in Table 4 and Figure 5, the model achieved per-class MAUCs of 85.2%, 71.1%, 90.3%, and 82.8% on the APPLES dataset for the normal, mild, moderate, and severe categories, respectively. The overall accuracy, MF1 score, and MAUC reached 79.2%, 74.6%, and 82.4%, respectively, with a macro recall of 73.4% and macro specificity of 91.3%. On the SHHS_rest dataset, the model further improved, achieving per-class MAUCs of 86.7%, 82.2%, 90.6%, and 94.9% for the four categories. The overall accuracy, MF1, and MAUC were 84.5%, 83.8%, and 88.6%, respectively, with macro recall and specificity reaching 82.7% and 94.5%. Similarly, the HMC dataset results confirm the robust generalization of the framework, with per-class MAUCs of 92.1%, 82.9%, 89.5%, and 95.4%, and overall accuracy, MF1, and MAUC of 85.4%, 85.0%, and 89.9%, respectively. Both macro recall (84.8%) and macro specificity (95.0%) indicate high reliability in distinguishing all four SDB severity levels. The confusion matrices in Figure 4 further illustrate that the model maintains good balance in classification across different categories and datasets, though slight variation in performance can be attributed to differences in class distribution and signal quality among the cohorts.  
Figure 4: Sleep stage confusion matrixes of the three clients (APPLES, SHHS_rest, and HMC).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Dataset** & \multicolumn{4}{c}{**Per-Class F1 Score**} & \multicolumn{4}{c}{**Overall Metrics**} \\  & **W (\%)** & **N1 (\%)** & **N2 (\%)** & **N3 (\%)** & **REM (\%)** & **Recall (\%)** & **Specificity (\%)** & **Acc (\%)** & **MF1 (\%)** & **MGm (\%)** & \(\kappa\) \\ \hline APPLES & 88.3 & 53.7 & 93.5 & 70.6 & 77.3 & 74.7 & 96.1 & 85.3 & 76.7 & 83.7 & 0.71 \\ SHHS\_rest & 88.1 & 57.8 & 89.5 & 85.6 & 86.4 & 82.5 & 96.4 & 87.1 & 81.5 & 89.0 & 0.86 \\ HMC & 81.9 & 61.4 & 82.4 & 80.3 & 79.1 & 76.7 & 94.6 & 79.3 & 77.0 & 85.0 & 0.72 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The performance of clients on the sleep stage classification task by using the FMTL framework.
# Experimental Study of a Signal Modulation Method to Improve eLORAN Data Channel Communications
###### Abstract  
There are mainly two types of data modulation methods used for enhanced LOng-RAnge Navigation (eLORAN) systems: pulse position modulation (PPM) and supernumerary interpulse modulation (SIM). The typical application for PPM is tri-state PPM (3S-PPM), also known as Eurofix. The typical application for SIM is ninth pulse modulation. Both of these methods are phase modulation methods. Phase modulation coding, a very mature technology, is used at present. To achieve a better demodulation success rate of eLORAN digital modulation signals at longer distances, a method of using the transmitting station duplex mode to transmit a digital modulation pulse group after LORAN-C transmitting a pulse group is proposed to realize modulation pulse on-off modulation. In this method, a broadcasting experiment was performed on the BPL (The call sign of eLORAN time service system in China) broadcaster station. After monitoring, a good receiving demodulation effect was initially obtained.  
Keywords:signal modulation; eLORAN signal; LORAN data channel; data demodulation +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Data-Collecting Sensors Used in the Project: GNSS, Meteorrones, In Situ Sensors, Sentinel
#### 2.2.1 Gnss-Based Atmospheric and Soil Moisture Observations  
GNSS data are used to retrieve tropospheric delay parameters for atmospheric water vapor estimation, as well as soil moisture content via GNSS-Reflectometry (GNSS-R). The MAGDA project utilizes a combination of permanent GNSS networks--such as the European Permanent Network (EPN)--and newly installed GNSS receivers at agricultural demonstration sites to provide high-resolution data.  
The primary meteorological parameter derived from GNSS is Zenith Total Delay (ZTD), computed using Precise Point Positioning (PPP) algorithms [38], which represent the total atmospheric delay experienced by GNSS signals due to refraction by water vapor and dry air. In the MAGDA project, ZTD is assimilated into the WRF model (WRFDA), where it helps improve short-term weather forecasts by refining atmospheric moisture fields [39]. The assimilation of ZTD allows for a direct constraint on atmospheric humidity, reducing errors in water vapor estimation and improving forecast skill, particularly in convective events.  
For soil moisture estimation, GNSS-R techniques analyze signal-to-noise ratio (SNR) interference patterns from dual-frequency GNSS receivers, enabling the retrieval of surface soil moisture variability [40]. The integration of GNSS-R soil moisture retrievals with Sentinel-derived soil moisture products and in situ soil sensors provides a multi-source validation framework for hydrological modeling.
# Comprehensive LCA of Biobased Sustainable Aviation Fuels and JET A-1 Multiblend
## 2 Materials and Methods
### Selected Options of Biobased SAF
#### 2.1.1 Atj  
Alcohol-to-jet (ATJ), a synthetic parafinic kerosene, is made from sugar or raw materials that can be broken down into sugar molecules. Alcohols such as ethanol and butanol are produced and further dewatered at high temperatures and high pressure. The dehydrated molecules are converted into longer-chain hydrocarbons by oligomerization and separated by thermal fractionation. The kerosene fraction is finally saturated with hydrogen by hydrotreating [10].  
In the present case, ATJ was mainly produced from isobutanol from the fermentation of maize. Besides ATJ, isooctane was produced and separated via distillation after hydrotreatment. For the considered concept, a system capacity of approximately 31 kt a\({}^{-1}\) ATJ-SPK [22] and an annual operating time of 8200 h were assumed. The maize was pressed in the pretreatment process, which resulted in maize oil as a by-product. Then, fermentation took place with the addition of yeast, whereby CO\({}_{2}\) was emitted. During product separation, waste water (including impurities) and dry stillage were separated from the fermentation products. The heating was done with natural gas. Electricity was required for operation of the complete system (especially for pretreatment).
# Usage of V2X Applications in Road Tunnels
###### Abstract  
Many smart city applications work with calculated position and time using the Global Navigation Satellite System (GNSS) signals for enhanced precision. However, there are many places where the availability of GNSS is limited, e.g., road tunnels, which are an essential part of transport infrastructure. Tunnels also require greater attention and greater importance of approaches to ensure the safety and security aspects of traffic. The safety, distribution of information, awareness, and smooth traffic can also be ensured by V2X applications, but the current position is also needed. An experimental analysis of data connection and communication availability was performed in the Blanka tunnel (Prague) and its surroundings. The main objective of the work was to find and clearly describe the tunnel blind spots, with an emphasis on communication between cars and potentially between cars and infrastructure. This article summarizes the evaluation results of the V2X tunnel experimental test, the outputs from the analysis of these blind spots, and it provides a future perspective and suggestions that make tunnels smart by using advanced positioning approaches.  
C2X; C-ITS; connectivity; dead reckoning; GNNS; positioning; safety; smart city; stationary vehicle; tunnel; VANET; V2X 2022110390/app120946244.  
1 2022
# Deadline-Aware Scheduling for Transmitted RSU Packets in Cooperative Vehicle-Infrastructure Systems
## 2 Related Work and Contributions
They proposed a hard-deadline low-computing-requirement method (HLCA) and a soft-deadline high-computing-requirement method (SHCA) aimed at traditional and intelligent data requests, respectively. [PERSON] et al. [16] proposed the random unloading problem of minimizing long-term energy consumption. They used the perturbed Lyapunov optimization technique to construct a virtual queue and transformed the task deadline guarantee problem into a virtual queue stability control problem. They proposed an energy-saving online unloading algorithm for DelayAware, which can adaptively unload more tasks under good network quality conditions. Additionally, the transmission is delayed when the connection is poor, but the deadline is not compromised. [PERSON] et al. [17] considered the delay sensitivity of different vehicles to receive early warning information and studied the release of emergency information using V2X communication. We divided the vehicles covered by the roadside unit (RSU) into primary and secondary priority groups according to the distance between the vehicle and the emergency point. A joint optimization problem of content division, user groupings, and channel allocation was proposed to improve resource utilization and emergency message delivery efficiency. The goal was to ensure that all vehicles in the primary priority group could reliably receive warning messages within a fixed deadline. In addition, RSUs could send countless warning messages to vehicles in the secondary priority group. The main contributions of this paper are as follows:  
We proposed an optimization strategy based on a vehicle carrier relay to minimize end-to-end delays of RSU packets. This strategy decides packet scheduling using the high- and low-priority data cache queue and vehicle speed status of multiple RSUs. According to the queue and speed states of multiple RSUs, this strategy dynamically selects the data cache queue and the number of packets sent to ensure system performance and minimize end-to-end delays. The packet scheduling problem is transformed into a knapsack problem, and simulation results verify the algorithm's effectiveness. The remaining organizational structure of this paper is as follows. Section 3 describes the packet scheduling strategy and system model. In Section 4, Lyapunov optimization theory transforms the model, and the knapsack problem is solved. In Section 5, the performance of the proposed optimization strategy is evaluated and compared to existing algorithms. The superiority of the proposed scheme is renamed here. Section 6 contains the conclusion of this paper.