esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation on Reference Frames and Time Systems in Multi-GNSS
## 3 Data Used and Adopted Model of the Pseudorange
\begin{array}{cccc}-\frac{x_{1}c-x_{0}}{\varrho_{1C}}&-\frac{y_{1C} -y_{0}}{\varrho_{1C}}&-\frac{z_{1C}-z_{0}}{\varrho_{1C}}&100000&\frac{1}{ \sin e_{1I_{G}}}\\ -\frac{y_{2C}-x_{0}}{\varrho_{2C}}&-\frac{y_{2C}-y_{0}}{\varrho_{2C}}&-\frac{ z_{2C}-z_{0}}{\varrho_{2C}}&1000000&\frac{1}{\sin e_{2G}}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ -\frac{x_{nC}-x_{0}}{\varrho_{nC}}&-\frac{y_{nC}-y_{0}}{\varrho_{nC}}&-\frac{ z_{nC}-z_{0}}{\varrho_{nC}}&100000&\frac{1}{\sin e_{nG}}\\ -\frac{x_{1}x_{0}}{\varrho_{1C}}&-\frac{y_{1R}-y_{0}}{\varrho_{1R}}&-\frac{z_{ 1R}-z_{0}}{\varrho_{1R}}&010000&\frac{1}{\sin e_{1R}}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ -\frac{x_{nR}-x_{0}}{\varrho_{nR}}&-\frac{y_{nR}-y_{0}}{\varrho_{nR}}&-\frac{ z_{nR}-z_{0}}{\varrho_{nR}}&010000&\frac{1}{\sin e_{nR}}\\ -\frac{x_{1}x_{0}}{\varrho_{1E}}&-\frac{y_{1R}-y_{0}}{\varrho_{1E}}&-\frac{ z_{1R}-z_{0}}{\varrho_{1E}}&001000&\frac{1}{\sin e_{1I_{E}}}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ -\frac{x_{nR}-x_{0}}{\varrho_{nC}}&-\frac{y_{nC}-y_{0}}{\varrho_{nC}}&-\frac{ z_{nC}-z_{0}}{\varrho_{nC}}&0001000&\frac{1}{\sin e_{nC}}\\ -\frac{x_{1}y_{1}-x_{0}}{\varrho_{1I}}&-\frac{y_{1I}-y_{0}}{\varrho_{1I}}&- \frac{z_{1I}-z_{0}}{\varrho_{1I}}&000010&\frac{1}{\sin e_{1I_{I}}}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ -\frac{x_{nI}-x_{0}}{\varrho_{nI}}&-\frac{y_{nI}-y_{0}}{\varrho_{nI}}&-\frac{ z_{nI}-z_{0}}{\varrho_{nI}}&0000010&\frac{1}{\sin e_{nI}}\\ -\frac{x_{1N}-x_{0}}{\varrho_{1N}}&-\frac{y_{1N}-y_{0}}{\varrho_{1N}}&-\frac{ z_{1N}-z_{0}}{\varrho_{1N}}&000001&\frac{1}{\sin e_{1N}}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ -\frac{x_{nN}-x_{0}}{\varrho_{nN}}&-\frac{y_{nN}-y_{0}}{\varrho_{nN}}&-\frac{ z_{nN}-z_{0}}{\varrho_{nN}}&0000001&\frac{1}{\sin e_{nN}}\\ \end{array} \right] \tag{8}\]  
\[\Big{(}H^{T}H\Big{)}x=H^{T}y \tag{9}\]\[x=\left[ \begin{array}{c}\Delta x\\ \Delta y\\ \Delta z\\ TSC_{\mathrm{G}}+dT_{Rcc}\\ TSC_{R}+dT_{Rcc}\\ TSC_{E}+dT_{Rcc}\\ TSC_{C}+dT_{Rcc}\\ TSC_{I}+dT_{Rcc}\\ TSC_{N}+dT_{Rcc}\\ TZD\end{array} \right] \tag{9}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Constellation** & **Carrier/Frequency [MHz]** & **Pseudorange Codes** \\ \hline GPS & L1 (1575.42) & L2 (1227.60) & C1C & C2W \\ GLONASS & G1 (1602 + k \(\times\) 9/16) & G2 (1246 + k \(\times\) 7/16) & C1C & C2P \\ Galileo & E1 (1575.42) & E5b \({}^{1}\) (1207.14) & C1C & C7I/C7Q/C7X \\ BeiDou & B1 (1561.098) & B2 (1207.14) & C1I & C7I \\ QZSS & L1 (1575.42) & L2 (1227.60) & C1C & C2S/C2L/C2X \\ NAVIC & L5 (1176.45) & & C5A & \\ SBAS & L1 (1575.42) & L5 (1176.45) & C1C & C5I \\ \hline \end{tabular} \({}^{1}\)I/NAV.  
\end{table} 
Table 6: Frequencies and observation codes used.
## 3 Results
The proportion of individuals requiring HFNCO, in-hospital mortality, and mean LOS were similar in the cases and controls (Table 2). Pharmacological treatment for COVID-19, which could influence the outcomes, was administered in a similar proportion of cases and controls (Table 2). Even in the multivariate analysis, there was no significant association between asthma and hospitalization (OR 1.93; 95% CI 0.83-4.53; \(p\) = 0.52) or critical COVID-19 (OR 0.74; 95% CI 0.31-1.78; \(p\) = 0.50). Interestingly, even with the limitation of the small sample size, a higher eosinophil count was associated with a better outcome in the group of asthma patients. Indeed, AEC was significantly lower in asthmatics classified as critical COVID-19 patients (23.5 \(\pm\) 26.4 vs. 49.2 \(\pm\) 89.9 cells/\(\mu\)L; \(p\) < 0.05; Figure 2). Furthermore, AEC was lower in the hospitalized patients than in the patients discharged from the ED (40.3 \(\pm\) 79.4 vs. 51.5 \(\pm\) 74.9 cells/\(\mu\)L), without reaching a statistical significance. Furthermore, the AEC was lower in the hospitalized patients than in the patients discharged from the ED (40.3 \(\pm\) 79.4 vs. 51.5 \(\pm\) 74.9 cells/\(\mu\)L), but without reaching statistical significance. **AEC (mean, cells/\(\mu\)L)**  
Figure 2: Mean absolute eosinophil count (AEC) in patients with asthma admitted to the ED at our institution, classified as critical or non-critical COVID-19. * = \(p\) < 0.05. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Asthma** & **Controls** & \(p\)**Value** \\ \hline Hospitalization (\%) & 70 (84.3\%) & 66 (79.5\%) & ns \\ Critical COVID-19 (\%) & 21/70 (27.7\%) & 23/66 (34.8\%) & ns \\ ICU admission (\%) & 17/70 (24.3\%) & 21/66 (31.8\%) & ns \\ High flow nasal cannula (\%) & 10/70 (14.3\%) & 13/66 (19.7\%) & ns \\ Non-invasive ventilation (\%) & 8/70 (11.4\%) & 11/66 (16.7\%) & ns \\ Mechanical ventilation (\%) & 8/70 (11.4\%) & 15/66 (22.7\%) & ns \\ In-hospital mortality (\%) & 8/70 (11.4\%) & 6/66 (9.1\%) & ns \\ LOS (mean \(\pm\) SD, days) & 13.1 \(\pm\) 7.8 & 14.6 \(\pm\) 9.7 & ns \\ In-hospital treatments: & & & \\ Corticosteroids (\%) & 45/70 (64.3\%) & 41/66 (62.1\%) & ns \\ Anticoagulants (\%) & 48/70 (68.6\%) & 53/66 (80.3\%) & ns \\ Antivirals (\%) & 15/70 (21.4\%) & 19/66 (28.8\%) & ns \\ Immunomodulators (\%) & 8/70 (11.4\%) & 5/66 (7.6\%) & ns \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Clinical outcomes in COVID-19 patients, with and without asthma, and the most common pharmacologic treatments for COVID-19 administered to the hospitalized patients. The \(p\)-value describes the level of statistical significance in the univariate analysis. Abbreviations: ICU, intensive care unit; LOS, length of stay. Finally, regarding the small group of patients discharged from the ED after enrollment, a total of 2 out of 13 patients with asthma (15.4%), and 1 out of 17 controls (5.8%) returned to the ED within 1 month (\(p\) = ns). All required medical attention for worsening symptoms related to COVID-19 and were hospitalized. None of them were classified as critical COVID-19 patients.
## 4 Experimental Results and Analysis  
### Dataset Description  
The dataset used in this study originates from a real-world FMCG company, encompassing monthly sales data from January 2018 to December 2023, covering a total of 60 months and over 120,000 records. It provides a detailed perspective on FMCG market dynamics.  
The core features of the dataset include sales volume (target variable), price, promotion, seasonal factors, regional attributes, and macroeconomic indicators. Specifically, sales volume represents the number of units sold for each product in a given time period, while price refers to the average selling price of each product category. Promotion captures the number of promotional activities conducted during the period. Seasonal factors include time-related features such as months and holidays, regional attributes cover demographic and economic information such as population density and average income, and macroeconomic indicators include metrics such as the Consumer Confidence Index. These features collectively constitute the multi-dimensional information in the dataset, supporting the forecasting tasks. Figure 6 highlights the performance of various product categories, while Figure 7 illustrates sales performance across different regions.  
Figure 6: Monthly sales trend chart of different types.  
The dataset exhibits significant seasonality and high variability across regions and product categories, making it an ideal benchmark for evaluating forecasting models.
### Experimental Results
#### 3.2.1 Blast-Loaded Plates with Single FSP Impact  
Figure 15 represents the incident pressures for a 10 g C4 spherical explosive charge mass of three repeated tests as a function of time. No target plate is present at the tube's exit. The pressure histories were compared for three tests, and small variations in both the overall pressure history and the initial peak pressure were found. An average incident peak pressure of 778 kPa was recorded. The figure shows the setup's ability to generate a reproducible blast loading on the target plate. Further investigation of the setup's reproducibility can be found in [3].  
To acquire valid displacement fields from the analysis of the 3D-DIC results, the spray-painted speckle pattern must remain intact throughout the dynamic response of the target plates. Fortunately, only some of the plates experienced a slight detachment around the holes induced by the FSP impact. Moreover, during the deformation process, most images acquired from the high-speed cameras had good visibility and no saturation was recorded due to the fireball resulting from the detonation. As a first observation, none of the three repetitions of the blast-loaded plates in configurations 1 and 2 show any evidence of fracture around the edge of the hole formed by the FSP impact. Figure 16a displays the 3D full-field out-of-plane displacement of the permanent deformation of an aluminum plate under blast loading in configuration 2, which was previously perforated by a single 12.7 mm FSP. The maximum displacement results in a deformation profile resembling a global dome with a superimposed local dome around the center of the plate. The point displacement analysis as a function of time illustrated in Figure 16b reveals that the plate center experiences higher displacement compared to the rest of the plate. The entire plate is deformed, with less marked differences in the out-of-plane displacement of the considered points than in impacts without blast, where the effects are more localized.  
In addition to the analysis of the displacements, the strains are also analyzed and represented in Figure 17. It is found that blast loading creates an elongation of the entire plate with a deformation concentration around the impact hole. As far as the principal direction strains are concerned, they show elongation around the entire hole with the amplitude decreasing as the distance from the center of the hole increases.  
Figure 15: Pressure profile recorded by the high-frequency pressure sensor for the explosion of a 10 g C4 charge mass at the entrance of the EDST.  
In conclusion, our findings indicate that although the influence of an FSP impact was localized around the impact hole, the deformations due to the blast loading are twenty times bigger. After blast loading is applied, a \"hole enlargement phenomenon\" occurs in the deformed plates and the maximum damage is found when the holes are located in the center of the target. Even when the holes are slightly offset, the blast wave effects and plate displacements are concentrated.
# Unsaturated Hydraulic Conductivity in Composite Porous Media
###### Abstract  
Determining the constitutive properties that describe the incipient hydraulic behavior of the materials, including the matrix domains and the distribution of macro and micropores, is crucial to analyzing the preferential water flow in saturated soils, \(k_{s}\), and unsaturated, \(k_{u}\). This study focused on determining the hydraulic conductivity in porous media under total and partial saturation conditions. The infiltration characteristics of three reconstituted soils were evaluated using five suction ranges employing conventional permeameters, an automated dual system, and mini-disk infiltrometers. The experimental cycles were carried out in granular soils with mixtures of diatomaceous soils, iron oxide (Fe\({}_{2}\)O\({}_{3}\)), and calcium carbonate (CaCO\({}_{3}\)) in 5-40% proportions. The differences between the granular microstructures of each material and the different hydraulic interaction mechanisms (suctione levels) significantly affected the values of \(k_{s}\) and \(k_{u}\) and the coupling between the pore domains and the defined water regime. Additionally, a lower impact was observed in the data set exposed to higher percentages of Fe\({}_{2}\)O\({}_{3}\) and CaCO\({}_{3}\) in different suction ranges, mainly due to a tension effect (meniscus) generated by suction in the granular skeleton. Since both parameters are mutually correlated and have a similar impact between methods and soil cores, \(k_{s}\) and \(k_{u}\) must be optimized simultaneously in each mechanism analyzed. The main findings of this work result in the confirmation that the unsaturated permeability decreases as suction is imposed on the sample. As well as the addition of different materials with Particle Size Distribution finer than the base sample, it also reveals a reduction in hydraulic conductivity, both saturated and unsaturated.  
unsaturated hydraulic conductivity; permeability; suction; diatomaceous soils +
Footnote †: journal: Applied Sciences
## 2 Related Work
### Single Edge Server  
[PERSON] et al. [20] proposed an energy-efficient framework for wirelessly powered mobile cloud computing that integrates microwave power transfer (MPT) with mobile cloud computing. This framework considers only a single-user, single-edge system in which the mobile user can harvest energy from the edge or offload computation to it. Additionally, an optimization problem is formulated as a non-convex formula that aims to minimize the mobile user's energy consumption for local computation harvesting while maximizing the energy savings from mobile users for computation offloading. Meanwhile, an efficient and online deep reinforcement learning-based offloading framework is proposed in [21].  
Specifically, a deep neural network is used to learn and determine the offloading decision for mobile users such that they can either execute the computation tasks locally using mobile device resources or offload them to the edge for remote execution. In addition, an adaptive procedure is designed to reduce the complexity by automatically adjusting the hyper-parameters of the deep model. Finally, the numerical and experimental results show that this framework can provide a near-optimal solution.  
Recently, an efficient algorithm based on deep meta reinforcement learning was proposed in [25] for IoT-edge-cloud computing systems with the goal of reducing the burden of computing and improving the processing of tasks. Specifically, a different number of DNNs are combined with Q-learning to derive an efficient offloading decision and increase the learning ability of the system. Furthermore, the results proved that the proposed algorithm outperforms binary and partial offloading schemes. Meanwhile, in [26], quality of experience (QoE) is optimized in an edge-enabled Internet of Things environment in which transmission and computing delay, the success rate of each task, and energy consumption are considered. In addition, deep reinforcement learning is utilized to solve the proposed model, where double Q-learning and dueling networks-based algorithms are designed to accelerate the convergence and enhance the stability. Similarly, the computation capability limitations of IoT devices and energy consumption are addressed in [27], where an integrated framework is proposed for task offloading and allocating resources with the goal of maximizing energy efficiency while reducing the quality of service (QoS) constraints. However, one of the common drawbacks of [27] is that the transferred data is susceptible to different attacks.
# On the Use of Learnheuristics in Vehicle Routing Optimization Problems with Dynamic Inputs
## 4 Proposed Methodology
### Statistical Learning Model  
Statistical learning tools have been gaining popularity over the last decades. There are plenty of methods to build predictive functions, which estimate the relationship between variables. The nature of the problem analyzed here requires making predictions. In particular, we predict the costs of an edge (dependent variable) for a given state of the problem (predictors), which is needed because the real costs are only revealed after the moment of realization.  
It is crucial to select an appropriate method for a specific problem. Each statistical learning method represents a trade-off between performance, interpretability and computing time. Neural networks and support vector machines rank among the best performers due to their high flexibility. However, they also lack interpretability and require long computational times due to all the parameter tuning needed. Moreover, they are prone to overfitting (i.e., the model may capture the noise in the data). Linear regressions, despite being rigid methods, offer great interpretability and are faster to compute. Obviously, they tend to perform better in problems where the relationship between variables-or a transformation of these-is approximately linear.  
We choose to employ a multiple linear regression model. As mentioned before, the solving approach relies on the WB, which aims to predict the behavior of the BB. The model is fed with the variables: load, number of nodes visited in a route, number of routes, and the capacity of the vehicle. Using multiple regression analysis [42], and after analyzing the correlation matrix and the individual \(p\)-values for the different variables, we found that the relevant variables are the load and the capacity of the vehicle. Thus, we build a linear regression model with just the load and the capacity of the vehicle, reducing the usage of memory and the computational time required.
## 4 Risk Analysis for Current Protocols
### TCP/IPv6  
TCP/IPv6 is a networking protocol designed for large scale internetworking [21]. It combines the network-layer IPv6 protocol with the transport-layer TCP protocol. IPv6 increases the IP address size from 32 bits to 128 bits. IPv6 further supports extensions for authentication, data integrity, and (optional) data confidentiality. Currently, there is no official implementation of IPv6 on marine vessels. However, application of TCP/IPv6 on marine vessels also provides the advantage that networks can interoperate with existing land networks. On the other hand, this introduces security drawbacks.
# Moving Towards Body-to-Body Sensor Networks for Ubiquitous Applications: A Survey
## 4 BBN Design Challenges and Open Issues
### Interference and Coexistence
The cross-technology interference mitigation (CTIM) problem is considered in [97], involving node mobility, to characterize and solve the interference problem that results on the utilization of different wireless technologies in the same radio spectrum, namely the ISM band. Three heuristic approaches are proposed to solve the CTIM problem, and have been proved to be efficient for large-scale BBN scenarios. In the same perspective, a game-theoretical approach called the socially-aware interference mitigation (SIM), has been proposed in our previous work to deal with this issue and considers the mutual interference as well as the cross-technology interference scenarios [98; 99]. Besides, a recent channel selection approach for interference mitigation (CSIM) [100] enables the WBAN operation within IoT environment. The CSIM protocol involves a BLE transceiver together with a cognitive radio module within the WBAN coordinator, so as to inform the WBAN through announcements of the frequency channels used in its vicinity, allowing him to switch to another wireless channel providing less interference within his surrounding.
## 1 Introduction
In the present work, two non-intrusive methods, NIPC and the non-intrusive spectral projection method, were applied to the transitional flow around a flat plate and Aerospatiale A-Airfoil. The random inputs for UQ were the closure model coefficients of [PERSON]'s \(\gamma-R_{\theta}\) transition model and the quantities of the interests (QoIs) were the skin friction coefficient and drag/lift coefficients. Even though the UQ methodologies used in the present work are classic ones in the UQ fields, we tried to access the application capability of two UQ methods with somewhat transitional algorithm in the transitional flow simulation; the first one was Non-Intrusive Spectral Projection (NISP) with the sampling of the quadrature rule and the second was point-collocation NIPC with the oversampling 2. The theory of the non-intrusive method for UQ is described briefly in Section 2. In this section, we also explain the differences between the two methods, point-collocation NIPC and NISP. Next, the numerical method and [PERSON]'s \(\gamma-R_{\theta}\) transition model are presented in Section 3. In Section 4, the deterministic solutions for the flat plate and Aerospatiale A-Airfoil are validated with reference data and the stochastic results of two non-intrusive methods are compared to the Monte Carlo simulation with convergence with respect to the polynomial order and distribution of the probability density function of the outputs. Finally, Section 5 summarizes the main findings of the present study.
## 3 AoI Minimization in UAV-Assisted WSN/IoT Application Architectures
### Trajectory and Energy Management (TE)
#### 4.2.2 TE with Node Selection  
The paper in this category is unique because it considers the use of UAVs to disseminate data for IoT devices apart from the data acquisition task. In many cases, it is also essential to select a few devices from a larger set within a flight round to save energy. Selecting these devices from the AoI perspective constitutes another challenge. Since multiple devices would have to be selected in each flight turn and there is a large number of visiting sequences for selected devices, trajectory planning algorithms are required. Thus, ref. [86] considered a framework where a battery-limited UAV flies in multiple turns towards selected IoT devices to collect and disseminate data provided by the data center to the base station (Figure 9b). The authors focus on the optimal selection of the tasks carried out by the UAV. Particularly, the UAV may not be able to visit several nodes in a flight turn due to its energy limitations, which motivates the consideration of efficient node selection and visiting order (one way of maximizing energy).