esa-sceva/satcom-chunk-collection huggingface dataset:
# An Algorithm for Generating a Diverse Set of Multi-Modal Journeys
## 4 Computational Study
### Algorithm Performance  
We now investigate the impact of applying all four techniques from Section 3 to the networks. Table 3 reports the number of arcs in \(G\), average run time in milliseconds, standard deviation of run time, and the aforementioned quality metric for each network  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Network**} & \multicolumn{4}{c}{**Operator Information**} & \multicolumn{4}{c}{**Graph Information**} \\ \cline{2-9}  & **Stops** & **Routes** & **Trips** & **Departure Events** & **Vertices** & **Timetabled Arcs** & **Footpath Arcs** \\ \hline London & 379 & 255 & 53,809 & 1,246,770 & 379 & 844 & 5195 \\ New York City & 991 & 210 & 19,842 & 550,495 & 991 & 1142 & 31,627 \\ Belgium & 30,886 & 7208 & 214,447 & 6,942,668 & 30,886 & 37,786 & 558,953 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Networks employed in the experiments.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{\(\mathcal{R}\)} & \multicolumn{2}{c}{**London**} & \multicolumn{2}{c}{**New York City**} & \multicolumn{2}{c}{**Belgium**} \\ \cline{2-9}  & **Vertices** & **Run Time** & **Quality** & **Vertices** & **Run Time** & **Quality** & **Vertices** & **Run Time** & **Quality** \\ \hline
0 m & 379 & 10 \(\pm\) 6 & 100.0 & 991 & 25 \(\pm\) 10 & 100.0 & 30,886 & 833 \(\pm\) 418 & 100.0 \\
100 m & 372 & 11 \(\pm\) 5 & 98.3 & 473 & 29 \(\pm\) 11 & 96.7 & 16,529 & 769 \(\pm\) 367 & 93.0 \\
250 m  
when using the four pre-processing techniques. Note that graph cutting, vertex merging, arc pruning and arc restriction are applied incrementally, with their order of implementation corresponding to the degree to which they alter the graph.  
The results demonstrate how the algorithm's execution time without pre-processing techniques is, on average, less than a second. Each of the pre-processing techniques further decreases the execution time. However, for both the London and New York City networks it is difficult to infer the impact of the pre-processing techniques because the initial execution time values for the base algorithm were already very short. On the other hand, the Belgium network demonstrated a 68.8% speed-up when applying the four pre-processing techniques, with only a 13.3% decrease concerning the quality of dominant solutions. However, if only graph cutting is employed then a 52.9% speed-up can be obtained with an impact of only 4.3% on solution quality. When examining the number of arcs, the pre-processing techniques greatly reduced the size of the graph, thereby positively affecting algorithm run time. The reduction of the number of arcs after applying graph cutting, vertex merging and arc pruning was 86.3% in Belgium, 46.7% in London and 84.8% in New York City.
## 5 Validation Model
\begin{table}
\begin{tabular}{c c c} \hline
**Abbreviation** & **Formula** & **Description** \\ \hline \hline \(R^{2}\) & \(R^{2}=1-\frac{\sum_{i=1}^{n}(A_{i}-B_{i})^{2}}{\sum_{i=1}^{n}(A_{i}-A_{i02})^{2}}\) & R squared \\ \hline _MSE_ & \(MSE=\frac{\sum_{i=1}(A_{i}-B_{i})^{2}}{\sum_{i=1}^{n}(A_{i}-A_{i02})^{2}}\) & Mean squared error \\ \hline _RMSE_ & \(RMSE=\sqrt{\frac{\sum_{i=1}^{n}(A_{i}-B_{i})^{2}}{\pi}}\) & Root mean squared error \\ \hline _MAE_ & \(MAE=\frac{\sum_{i=1}^{n}(A_{i}-B_{i})^{2}}{\pi}\) & Mean absolute error \\ \hline _MSE_ & \(MSRE=\frac{\sum_{i=1}^{n}(A_{i}-B_{i})^{2}}{\pi}\) & Mean absolute relative error \\ \hline _RMSRE_ & \(RMSRE=\sqrt{\frac{\sum_{i=1}^{n}(A_{i}-B_{i})^{2}}{\pi}}\) & Root mean square relative error \\ \hline _RRMSE_ & \(RRMSE=\frac{\sqrt{\frac{1}{\sum_{i=1}^{n}(A_{i}-B_{i})^{2}}{\pi}}}{\frac{1}{\sum_{i=1 }^{n}(A_{i}-B_{i})^{2}}{\pi}\times 100}\) & Relative root mean square error \\ \hline _MBE_ & \(MSE=\frac{\sum_{i=1}^{n}(A_{i}-B_{i})}{\pi}\) & Mean bias error \\ \hline _erMAX_ & \(erMAX=max(\frac{|A_{i}-B_{i}|}{A_{i}})\) & Maximum absolute relative error \\ \hline _SD_ & \(SD=\sqrt{\frac{\sum(A_{i}-B_{i})^{2}}{\pi}}\) & Standard deviation of the difference between actual and predicted values \\ \hline \(t-stat\) & \(t-stat=\sqrt{\frac{(n-1)MBE^{2}}{\pi MBE^{2}}}\) & \(t\)-statistic \\ \hline _U\({}_{95}\)_ & \(U_{95}=\sqrt{1.96(5D^{2}+RMSE^{2})}\) & Uncertainty at 95\% \\ \hline \end{tabular}
\end{table} 
Table 9: Statistical metrics used for evaluating the ML models. From the results in Table 10, the \(R^{2}\) of the GA-BP neural network is higher, which indicates that the algorithm is more accurate. According to the results in Table 11, the GA-BP neural network is the best method, with scores of 17, 15, 18, 13, and 22, respectively. In addition, GBM also has higher accuracy with scores of 23, 46, 30, 31, and 26, making it second only to the GA-BP neural network. The rest of the algorithms have higher accuracy in the prediction of certain metrics, such as the decision tree, which has an \(R^{2}\) of 0.995 in predicting the maximum vertical displacement, but only 0.305 in predicting the safety factor, which makes it a weak prediction model. The best way to better demonstrate the estimation accuracy of each algorithm is to plot the error curves. Figure 6 demonstrates the predicted versus actual values for each algorithm using five sets of samples.
## 2 Structures and Calculation Methods  
All calculations are performed by employing the VASP Package based on the density functional theory (DFT) [36]. Plane-wave basis sets, together with the projector-augmental wave (PAW) [37] method, are chosen to deal with electron-ion interaction. The valence-electron configurations of Ti (\(3d^{2}4s^{2}\)), Fe (\(3d^{6}4s^{2}\)), Co (\(3d^{7}4s^{2}\)), and Si (\(3s^{2}3p^{2}\)) are selected. The \(7\times 7\times 7\) mesh of special k-points in the Brillouin zone is applied. In the self-consistent calculation, we select the refined \(1\times 10^{-6}\) eV/atom as the SCF convergence criterion, and 360 eV as energy cutoff, respectively. When the positions of atoms are relaxed, we set a convergence criterion of 0.02 eV/A. All structures are built with \(2\times 1\times 1\) supercell. For the doped compound calculations, we geometrically optimize all supercells by using the same parameters employed in the bulk calculation. All technical parameters have been tested carefully to ensure the accuracy of the results.  
Figure 1: Schematic representation of (**a**) Cu\({}_{2}\)MnAl-type Heusler compound; (**b**) Hg\({}_{2}\)CuTi-type Heusler compound; (**c**) LiMnPbSn-type Heusler compound.
# Hydrogen vs. Batteries: Comparative Safety Assessments for a High-Speed Passenger Ferry
## 4 HAZID
### Battery Version  
Following a similar approach, a HAZID was conducted for the battery version of the ferry [25]. A total of 55 hazards with frequencies and consequences were evaluated. The following number of hazards was identified for three categories:  
* Design, construction, installation (21 hazards);
* Operation (25 hazards);
* Emergency (9 hazards).  
In Table 5, the most severe hazards associated with the battery-powered systems are presented; thus, in Section 5.2, the quantitative risk assessment is conducted to evaluate the safety of the vessel in detail.  
\begin{table}
\begin{tabular}{c c} \hline
**No.** & **Initial Accidental Event** \\ \hline
1 & Leakage at FC room \\ \hline
2 & Fire/explosion at FC room \\ \hline
3 & Fire/explosion in the control room \\ \hline
4 & Fire/explosion at the upper deck \\ \hline
5 & Collision during operation \\ \hline
6 & Fuel spill during bunkering \\ \hline
7 & Fire propagation indoors (emergency) \\ \hline \end{tabular}
\end{table}
Table 4: The most severe hazards of the hydrogen-fuelled version from HAZID.  
\begin{table}
\begin{tabular}{c c} \hline
**No.** & **Initial Accidental Event** \\ \hline
1 & Battery breach/punctures during construction and installation \\ \hline
2 & Fire and explosion in battery room during construction and installation \\ \hline
3 & Battery breach while in operation \\ \hline
4 & Internal cell failure/thermal runaway while in operation \\ \hline
5 & Battery on fire while in operation \\ \hline
6 & Fire and explosion while in operation \\ \hline
7 & Fire propagation during an emergency \\ \hline
8 & Evacuation failed during an emergency \\ \hline \end{tabular}
\end{table}
Table 5: The most severe hazards of the battery-powered vessel from HAZID.  
The most effective RCOs identified after the HAZID analysis for the battery version of the ferry were the following:  
* Movement of batteries in the main deck to reduce the fire risk;
* Proper alarm/firefighting system;
* Testing of equipment prior to use;
* Regular inspection and maintenance of battery-related equipment.
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 2 Background on Concepts and Requirements
### GDPR in Healthcare: Expanding Privacy Horizons  
The GDPR, effective from May 2018, directs the use of citizens' data both inside and outside of the European Union (EU), applies to international organizations that manage these data (Article 3, paragraph 2), and has significantly impacted the handling of health data alongside the HIPAA. The main rules that the GDPR addresses are listed as follows [12]:  
1. Personal Data Protection: The GDPR broadens the scope of protection for information collected and processed by IoT devices in the healthcare industry, including health-related data.
2. Legal Basis for Processing: To process personal information, including health information, there must be a clear legal basis that complies with the GDPR, which may include consent, contractual requirements, legal duties, vital interests, public interest, or legitimate interests.
3. Data Minimization: Under the data minimization rule, companies can manage and gather only required information that serves a certain purpose related to healthcare.
4. Individual Rights: Individuals have the right to access, update, and delete their data, as well as making the data portable and limiting its processing. Also, the responsibility of institutions is to manage these data and healthcare services to support these individuals' rights.
5. Data Security: Frequent security evaluations, encryption, and many other security measures are strictly enforced by the GDPR to preserve the security and privacy of individuals' data.
6. Data Breach Notification: In the event that a data breach occurs, the responsible authorities and individuals involved in the case must be notified, and it must be announced immediately. Moreover, the GDPR enforces the presentation of the protocols used to identify and handle a data breach event.
7. Data Protection Impact Assessment (DPIA): The GDPR enforces enterprises to undertake a DPIA to specify and reduce processing operations with high risks.
8. Appointment of a Data Protection Officer (DPO): Designating a DPO is a must for enterprises and organizations handling individuals' data, such as health data, which guarantees their adherence to and compliance with the GDPR.
9. GDPR Enforcement and Penalties: The value of the penalties that the data protection authorities can apply upon organizations' failure to adhere to the GDPR is up to EUR 20 million or 4% of an organization's global annual revenue.  
As interconnected systems are growing rapidly in a world full of digitization, individuals must be familiar with the GDPR, which effectively works to guarantee the safety of their data within the EU, alongside the HIPAA in the U.S. (HHS, 2023).
# Energy-Efficient Hybrid Routing Protocol for IoT Communication Systems in 5G and Beyond
## 5 Performance Evaluation and Discussion
### Delay Analyses  
Conventionally, nodes must route their data over all the attached nodes on the chain schema to reach their target. This further causes data propagation delay and redundant transmission. In contrast, by segmenting the whole network into uneven several clusters where clusters with small radius consist of short chains and clusters with large radius consist of single-hop topology, this attains less communication delay. In Figure 9, we can see that MIMO-HC has a good data delivery time.
## 5 Proposed Models
### Architecture Overview  
This section introduces our proposed framework for integrating diverse data categories to enhance traffic flow prediction. Figure 3 outlines the architecture designed to handle input data with spatial and temporal characteristics. We incorporate CNNs and long short-term memory (LSTM) models into our framework to capture these features.  
Successfully handling diverse data types depends on using a model that optimally processes these sources. Simply combining them arbitrarily may not yield the best results. We leverage deep learning techniques to discover underlying patterns and relationships between data types.  
Our previous research [13] focused on INT data in Milan. In this work, we expand our analysis with weather, news, and social media data to improve our model's predictive power. We recognize that INT flow varies from hour to hour but shows similarities within the same day or week. Our fusion model extracts these periodic features using INT data collected at different times. We also include correlated INT data based on a correlation coefficient map. Additionally, we incorporate Milano Today and Social Pulse data to enhance our predictions.  
Our architectural diagram consists of four stages: grid data processing, large-area dataset processing, feature extraction, and fusion and multi-task regression:  
**Grid Data Processing:** Data are consolidated into a uniform grid format for consistency.  
**Large-Area Dataset Processing:** To ease computation, the resolution of large-area datasets is reduced.  
**Feature Extraction:** CNNs and LSTM models are used to extract spatial and temporal features, respectively, from the data.  
**Fusion and Multi-Task Regression:** A weighted fusion model integrates diverse data types. Multi-task regression models are then trained to generate the final traffic flow predictions.  
Figure 3: Multi-Source CNN-RNN (MSCR).
## 2 Mathematical Model of the Problem  
We consider the steady two-dimensional laminar boundary layer flow with heat transfer of an incompressible nanofluid over a semi-infinite porous plate in a porous medium. The surface of the plate is at constant temperature \(T_{w}\) and admits the partial slip condition. The viscosity and the thermal conductivity of the nanofluid are considered to vary linearly with temperature. The \(x\)-axis is along the surface of the plate, and the \(y\)-axis is perpendicular to it. All body forces are neglected, and there is a constant suction/injection velocity \(V_{w}\) at the surface of the plate. The flow far away from the plate is uniform and in the direction parallel to the plate. The velocity and temperature outside the boundary layer are \(u_{\infty}\) and \(T_{\infty}\), respectively. The geometry of the flow model is given in Figure 1.  
In view of the above assumptions, the continuity, momentum and energy equations for the flow along with heat transfer are:  
\[\frac{\partial u}{\partial x}+\frac{\partial v}{\partial y}=0, \tag{1}\]  
\[u\frac{\partial u}{\partial x}+v\frac{\partial u}{\partial y}=\frac{1}{\rho_{ n_{f}}}\frac{\partial}{\partial y}\left[u_{n_{f}}(T)\frac{\partial u}{ \partial y}\right]-\frac{\mu_{n_{f}}(T)}{\rho_{n_{f}}k}(u-u_{\infty}), \tag{2}\]  
\[u\frac{\partial T}{\partial x}+v\frac{\partial T}{\partial y}=\frac{1}{(\rho C _{p})_{n_{f}}}\frac{\partial}{\partial y}\left[\kappa_{n_{f}}(T)\frac{ \partial T}{\partial y}\right]. \tag{3}\]  
Figure 1: Schematic representation of the geometry. In the above system of equations, \(u\) and \(v\) represent velocities in the \(x\) and \(y\) directions, respectively; \(k\) is the permeability of the medium; \(T\) is the nanofluid temperature; \(\mu_{n_{f}}(T)\) the nanofluid temperature-dependent viscosity; \(\rho_{n_{f}}\) the nanofluid density; \((C_{p})_{n_{f}}\) is the specific heat at constant pressure; and \(\kappa_{n_{f}}(T)\) is the thermal conductivity of the nanofluid. The appropriate partial slip boundary conditions for velocity and temperature are:  
\[u =L_{1}\frac{\partial u}{\partial y},\quad v=V_{w}\quad\text{at} \quad y=0;\quad u\to u_{\infty}\quad\text{as}\quad y\to\infty, \tag{4}\] \[T =T_{w}+D_{1}\frac{\partial T}{\partial y}\quad\text{at}\quad y=0; \quad T\to T_{\infty}\quad\text{as}\quad y\to\infty. \tag{5}\]  
Here, \(L_{1}=LR_{\varepsilon_{x}}\) is the velocity slip factor, and \(D_{1}=D\sqrt{LR_{\varepsilon_{x}}}\) is the thermal slip factor with \(L\) and \(D\) the initial values of velocity and thermal slip factors; and \(R_{\varepsilon_{x}}=\frac{\text{i}\alpha\chi}{v_{f}}\) is the local Reynolds's number with \(v_{f}=\frac{n_{f}}{\rho_{f}}\) the kinematic viscosity of the base fluid. \(V_{w}\) shows the mass transfer at the surface with \(V_{w}>0\) for injection and \(V_{w}<0\) for suction. Following [PERSON] [4], [PERSON] et al.
## 2 Soiling-Related Pollen Topics
### Pollination Strategies  
Once pollen grains have matured within the male reproductive organs of the plant, they must reach the conspecific female counterparts in order to manage pollination. Depending on the vegetal species, this may happen either via self-pollination (i.e., the flower fertilizes itself) or, more frequently, via cross-pollination, in which case the pollen grains are transported by at least one external agent (\"pollination vector\") [29]. A pollination vector can be either abiotic (i.e., wind, rarely water) or biotic (i.e., animals, mostly insects), but more interestingly, a distinct soiling potential should be expected from plants with different pollination strategies. As explained in the following paragraph, the pollination strategy is closely related to the pollen production level, the atmospheric pollen concentration, and the pollen grain properties.  
Almost all the Gymnosperm families and some of the Angiosperm families (\(\sim\)18%) are anemophilous (i.e., wind-pollinated plants) [29]. Typically, anemophilous species produce large amounts of pollen, which has been sometimes interpreted as a compensation for having a lower pollination efficiency than entomophilous species (i.e., insect-pollinated plants) [30]. Moreover, while most anemophilous species release non-aggregated pollen (i.e., smaller, smoother, lighter, and non-sticky particles whose aerodynamics favor wind transport), entomophilous species release aggregated pollen (i.e., larger, coarser clumps of sticky pollen grains which favor insect transport) [29; 31]. Therefore, the pollen produced by strictly anemophilous plants and, to a lesser extent, by ambophilous plants (i.e., vegetal species pollinated by both wind and insects) are notably present in the atmosphere [32]. As confirmed by data from aerobiological monitoring stations, they become relevant contributors to the total airborne pollen concentration and thus to the organic soiling issues underwent by PV modules under the area of influence. In comparison, clumped pollen from strictly entomophilous species would modestly contribute to the total airborne pollen spectrum and, consequently, it might only pose a soiling risk in case of abundant dropping from vegetation placed almost over the PV modules.
# Physical Vulnerability and Landslide Risk Assessment in Tegucigalpa City, Honduras
## 2 Materials and Methods
### Vulnerability to Translational and Rotational Slides
Based on the types of housing materials identified in the surveys and the classification of neighborhoods according to their social status, as defined by BID [21], the MDR was estimated for the 8 states of damage for 4 types of one-story housing: (i) popular brick, (ii) precarious brick, (iii) block, and (iv) wood. Based on the survey data, the mean and standard deviation of the MDR for each type of housing were obtained. Vulnerability functions should consider various sources of epistemological and random uncertainty [13; 45]. The epistemological uncertainties include those related to the simplifications made to characterize the intensity/magnitude of the movements or the models used to describe the response of the structures. Random uncertainties are related  
Figure 4: Location of landslides where housing damage surveys were collected. to variability associated with (i) the intensity/magnitude of the movement and (ii) the response of the infrastructures located on landslides. The variability of the landslide's intensity and its impact on overlaying infrastructures is particularly significant for slow-moving rotational and translational landslides. In slow-moving landslides, although the entire landslide might be in motion, the speed and the associated damage can vary significantly on different areas of the landslide body. Furthermore, the degree of damage will depend on whether the landslide shear zone intersects the infrastructure [3; 13]. If the landslide displacement occurs in blocks with practically no deformation, the overlying infrastructure may experience limited damage [3; 13]. To manage these uncertainties, the damage expressed by the MDR for the condition of a landslide occurring was considered as a random variable, assuming that it follows a Beta probability density function [11; 46] and using the values of the mean and standard deviation of the MDR obtained to estimate the parameters of the Beta function (i.e., \(\alpha\) and \(\beta\)). Treating damage as a random variable makes it possible to manage not only the uncertainty of how houses will respond to a specific landslide intensity but also the uncertainty of the intensity of the movement within the landslide itself. The formulas considered were as follows [47]:  
\[B\left(\alpha,\ \beta\right)=\int_{0}^{1}x^{\alpha-1}(1-x)^{\beta-1}dx \tag{11}\]  
\[\alpha=\overline{x}\Big{\{}\Big{[}\overline{x}(1-\overline{x})/s^{2}\Big{]}- 1\Big{\}} \tag{12}\]  
\[\beta=(1-\overline{x})\Big{\{}\Big{[}\overline{x}(1-\overline{x})/s^{2} \Big{]}-1\Big{\}} \tag{13}\]  
where \(B\) is the Beta distribution, which takes values between 0 and 1; \(\alpha\) and \(\beta\) are the parameters of the Beta distribution; \(\overline{x}\) is the mean or expected value; and \(\sigma\) is the standard deviation.
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 4 Congestion Control Schemes for RPL
### Schemes Exploiting Path Diversity  
Since one of the direct ways to eliminate hot spots in a network is to split traffic via multiple paths, it is natural that a lot of RPL schemes split traffic using multiple forwarders. In this section, we summarize the RPL schemes that try to alleviate congestion by forwarding packets through multiple parents on a per-packet basis. Although some of them aim at reducing the delay or extending the network lifetime rather than at directly mitigating congestion, they are expected to be effective in mitigating congestion because they try to prevent hot spot formation. In Table 5 at the end of this section, we compare the schemes in terms of main features, routing metrics, and performance evaluation tools and metrics.