esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Finite Element Model Features
### Mesh Features  
As shown in Figure 4, various mesh densities are selected to allow for accurate prediction of strain patterns while maintaining cost-efficient computation. They are the result of a preliminary mesh sensitivity analysis. The main aim is to identify the resulting strain patterns from the dual-sinusoidal placement under various loading conditions along and around FOSs, and finer meshes are used in these areas accordingly. Different parts are then meshed using different element types: continuum shell elements SC8R for the two CFRP face panels; 3D stress solid elements C3D8R for all other parts. The total number of elements is 125,376, of which 121,856 elements are for C3D8R and 3520 for SC8R elements. A short description of meshing is given hereafter. The assessment of the finite  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Materials** & **Modulus (MPa)** & **Poisson’s Ratio** \\ \hline CFRP lamina & E\({}_{1}\) = 103,000; E\({}_{2}\) = 10,400; G\({}_{12}\) = 54,000 & \(\
u_{12}\) = 0.3; \(\
u_{21}\) = 0.03 \\ Epoxy resin & 3500 & 0.3 \\ Acrylate & 2700 & 0.35 \\ Silica glass & 72,000 & 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of the materials used in this research work [1].  
Figure 3: Geometric characteristic of the numerical model (upper CFRP face panel and adhesive hidden for clarity, dimensions in mm).  
Figure 2: Numerical model with dual-sinusoidal FOSs embedded (dimensions in mm).  
element discretization was performed by assuming that the adhesive layer with the embedded FOSs was the most relevant zone for strain concentration. The mesh discretization had a twofold purpose: to evaluate the variation of the strain field with the mesh density in the adhesive layer (number of elements) and to reduce the computational time. The mesh of the adhesive layer was built with varying density, with higher density in the zones with higher curvatures of the FOSs. The minimum size of the element was set as the thickness of the adhesive (approximately 0.023 mm) between the FOS surface and the interface between the CFRP and the epoxy adhesive (see Figures 3 and 4). This resulted in a total number of 125,376 elements. Additionally, assuming the minimum size of the finite element as half of the above, the maximum strain in the adhesive layer had a 3% difference. Therefore, considering the computational time, we have adopted the first mesh with a minimum element size of about 0.023 mm for the results presented in Section 3.
# Automated Evaluation of C-ITS Message Content for Enhanced Compliance and Reliability
## 3 Results
### Scenario 3  
In this scenario, the C-ITS SIM system was configured to transmit DENMs with the following setup:  
* DENM 1: causeCode set to \"Roadworks\", subCauseCode set to \"streetCleaning\", relevanceTrafficDirection set to \"allTrafficDirection\", TrafficFlowRule set to \"NoPassingForTrucks\"
* DENM 2: causeCode set to \"slowVehicle\", subCauseCode set to \"maintenanceVehicle\", informationQuality set to 6, speedLimit set to 30  
The goal of this scenario is to verify that the third-party unit correctly captures the DENMs. An analysis was performed using the Karlos tool on all the logged DENMs.  
Table 3 presents the results of the analysis using the Karlos tool. The numbers in the table represent the count of parameters based on the following rules:  
* If the parameter was always OK for all messages, it is added to the OK column (+1).
* If the parameter contained a warning in at least one case, it is always added to the Warning column (+1), and nothing is added to the OK column.
* If the parameter contained an error in at least one case, it is added to the Error column (+1), with no additions to the previous columns.  
For better clarity, Table 3 includes only results categorized by the containers present in the analyzed DENM.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Container** & **Total** & **OK** & **Warning** & **Error** \\ \hline Header & 3 & 3 & 0 & 0 \\ Management & 10 & 10 & 0 & 0 \\ Situation & 3 & 2 & 1 & 0 \\ Location & 5 & 2 & 3 & 0 \\ Alacarte & 1 & 1 & 0 & 0 \\ \hline Total & 22 & 18 & 4 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: DENM analysis summary for Scenario 3 (by container), showing counts of parameters categorized as OK, Warning, or Error. Parameters are classified based on their occurrence in messages: always OK, containing at least one warning, or containing at least one error.  
Table 3 includes only results categorized by individual containers.  
The following parameters were noted to have warnings:  
* informationQuality--see further;
* speedValue, speedConfidence--optional parameters, not included in the broadcast DENM;
* headingConfidence--the same case as CAM, in laboratory conditions set to unavailable.  
It is important to note that these parameters are not evaluated directly because we are not assessing the C-ITS itself. However, for completeness, many of these parameters default to \"unavailable\" if not specifically set by the C-ITS SIM.  
A clear example is the parameter informationQuality. It was set to a valid value only for the \"Slow Vehicle\" use case, where it was OK. For the \"Roadworks\" use case, informationQuality was not set, resulting in a warning from [PERSON].
# Development of a Multi-Radio Device for Dry Container Monitoring and Tracking
## 3 Materials and Methods
### CTD's Requirements and Functions  
The CTD is a device that can be installed on general-purpose maritime dry containers. It is a device compliant with ISO/TS 18625 [22], ISO 18185-3 [23], and ISO 668 [30] standards based on low-cost and low-power consumption microprocessors suitable for IoT. Its function is to monitor the status of containers in real-time, regardless of location (land, near sea, open sea), through sensors (temperature, motion, pressure, humidity, door opening, position) and two types of TX/RX radio interfaces: NB-IoT and BLE Mesh, in addition to GNSS for position and timing. The NB-IoT interface allows the CTD to connect to a base station installed onboard the ship (Open Sea) or to the land cellular networks (near sea and terrestrial scenario). On the other hand, the BLE Mesh interface will allow CTDs to establish mesh networks when the NB-IoT cellular connectivity is available only for some CTDs, located in the area reached by the cellular signal, but not for those nearby. In this situation, through the mesh network, it will be possible to propagate data from a CTD, in provisioned sensor node configuration, to the CTD hooked to the NB-IoT cell in NB-IoT visibility, which assumes the role of the gateway (see Figure 3). Considering the conventional stacking methods of containers on ships and yards, we found that usually, the maximum number of containers stacked is 5. For this reason, in the 5 GT System, the maximum number of nodes for each BLE Mesh has been limited to 5 as per the design. The following sections will present the implementation of the first CTD prototype, and to demonstrate its mesh functionality, 5 nodes will be put into communication.  
Thus, the CTD is a battery-powered device with sensors, NB-IoT radio, BLE Mesh radio, and GNSS, capable of operating in opportunistic ways as needed, such as:  
1. Monitor and trace unit with NB-IoT connectivity;
2. Monitor and trace unit, provisioner of a BLE Mesh network and NB-IoT gateway;
3. Monitor and trace unit, the provisioned device of a BLE Mesh network (source node or relay).
4. Although, from a functional point of view, these three roles are distinct, from the HW point of view, the CTD has exactly the same elements, both when it performs the function of a mesh network node, when it performs the role of GW and when it just transmits sensing data via NB-IoT. It is, therefore, the same device that contains all the elements to perform all the functions, as described in the next subsection.
# MLUTNet: A Neural Network for Memory Based Reconfigurable Logic Device Architecture
## 4 Experimental Results
### Results Summary  
A summary of the optimal results of different accuracy models under each model is shown in Table 1. The run-time of models are shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **NN** & **MLUTNet** & **BNN** & **B-MLUTNet** \\ \hline MNIST & 0.9877 & 0.9883 & 0.9759 & 0.7693 \\ \hline K-MNIST & 0.9347 & 0.9348 & 0.909 & 0.5552 \\ \hline F-MNIST & 0.9053 & 0.9092 & 0.7909 & 0.5382 \\ \hline CIFAR-10 & 0.5813 & 0.5775 & N/A & N/A \\ \hline STL-10 & 0.4305 & 0.4186 & N/A & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimal accuracy performance.  
Figure 10: Models operation time.  
Figure 9: Comparison of fully connection and sparsely connection.  
On all four datasets, using the results of the standard NN as the baseline, Figure 11 shows the correct rate performance ratio of MLUTNet. The results show that the final accuracy performance of MLUTNet in full-precision is comparable to that of the standard NN model, and the size of the former's weight matrix is about half that of the latter; in binary-precision, depending on the dataset, respectively, the accuracy performance of MLUTNet ranges from 61% to 78.8% that of the standard BNN model.  
The experimental results demonstrate the effectiveness of MLUTNet. Under full-precision, MLUTNet achieves comparable performance to standard NN using a smaller scale weight matrix and fewer inter-layer connections; under binary-precision, MLUTNet suffers from the accuracy impact caused by the reduced weight matrix, and the final accuracy is reduced compared to standard BNN.
# Pollen-Food Allergy Syndrome: Allergens, Clinical Insights, Diagnostic and Therapeutic Challenges
## 6 Protein Families Involved in PFAS
### Profilins  
Profilins are a family of small proteins, typically 12-15 kDa in size, with a highly conserved three-dimensional structure [131]. They interact with actin monomers (G-actin) and polyproline sequences, regulating the assembly of actin filaments [132]. This function is critical for maintaining the integrity of the cytoskeleton and facilitating processes such as cell elongation and vesicle trafficking. Therefore, in plants, profilins are essential for cell growth, division, and response to environmental stimuli [44]. The structural conservation of profiling across plant species forms the basis of their allergenic potential, enabling them to act as panallergens in sensitized individuals by triggering immune responses through their shared molecular similarities. Their ubiquity and high degree of conservation make them key contributors to PFAS [133].  
The prevalence of profilin sensitization varies geographically, reflecting differences in pollen exposure and dietary habits. Profilin-related PFAS is particularly common in areas with high exposure to pollen from birch, grass, or ragweed, such as Northern and Central Europe or North America, where it can affect up to 30% of profilin-sensitized patients [55; 134]. However, the prevalence of profilin-related PFAS is lower in Mediterranean regions, where other allergens, such as LTPs, dominate the clinical spectrum of food-pollen cross-reactivity [135].  
Profilins from pollens such as Bet v 2 from birch, Phl p 12 from grass pollen, and Amb a 8 from ragweed pollen play a crucial role as primary sensitizers in individuals who develop PFAS [136]. Their cross-reactivity with profilins in plant-derived foods is responsible for allergic reactions to foods like peaches (Pru p 12), apples (Mal d 4), and cherries (Pru av 4). Other food profilins include Ara h 5 in peanuts, Cor a 2 in hazelnuts, Cuc m 2 in melons, Mus a 1 in bananas, Api g 4 in celery, and Sola 12 in tomatoes [31; 44].  
Profilin-mediated allergic reactions are generally mild and confined to the oropharyngeal region, manifesting as itching, tingling, or mild swelling of the lips, tongue, and throat [137]. This localized response is attributed to the heat- and digestion-labile nature of profilins, which rapidly denature during cooking or enzymatic digestion in the gastrointestinal tract [119; 138]. As a result, cooked or processed forms of profilin-containing foods are often well-tolerated by affected individuals. However, severe reactions, including anaphylaxis, have been reported after consuming large amounts of profilins, which may overwhelm gastric digestion capacity and lead to significant absorption [139]. [PERSON] et al. noted severe reactions even after small quantities of food in patients previously exposed to high levels of grass pollen [140].
## 3 Wake-Up Radio Communication Protocols
### Single Hop and Multi-Hop Wake-Up Network
#### 3.1.1 Wake-Up Transmission  
This is the wake-up radio, which is transmitting the wake-up signal at the given instance. This can be used to initiate communication or send a wake-up ACK/NACK. The wake-up signal transmission for communication initiation can be started by the message sender or receiver depending on the intentions of the initiator; either to send or request for data, respectively. The process is started by the main radio, which becomes active according to schedule and sends an interrupt signal to its wake-up radio to activate it. The wake-up radio generates the wake-up signal and sends it to the neighboring nodes, while the main radio remains active and ready to transmit or accept data as shown in Figure 4. An example of the wake-up transmitter is the RFID reader in the RFID-based passive wake-up radio.
## 2 Materials and Methods
### Analytical Procedures  
The specific surface area (SSA), total pore volume (TPV), and average pore diameter (APD) of the PACs were calculated with the Brunauer-Emmett-Teller (BET) method by recording N\({}_{2}\) adsorption-desorption isotherms at -196 \({}^{\circ}\)C on an automatic adsorption instrument (Micromeritcs apparatus, ASAP 2010 V4.01 G, Norcross, Georgia, USA). The morphology and the elemental composition of the samples were determined with environmental scanning electron microscopy energy dispersive x-ray analysis (ESEM-EDX) on a FEI-QUANTA200 instrument (FEI Italia S.r.l., Milan, Italy). The chosen blow-up was 2000X for elemental composition and 400X and 1600X for morphology. The PACs skeletal density was measured by a helium pycnometer (Multivolume Pycnometer 1305 by Micromeritcs, Norcross, Georgia, GA, USA). Finally, the point of zero charge (pH-PZC) was calculated using the solid addition method reported in [PERSON] et al. (2008) [57]. 50 mL of 0.1 M KNO\({}_{3}\) solution were prepared adding dropwise a 0.1 M HCl or 0.1 M NaOH solution to get different pH values (2, 4, 6, 8, 10, and 12). Then, 1.0 g of PACs was added in each solution and the samples were kept under stirring (800 rpm) in a six places magnetic plate for 48 h at room temperature (\(20\pm 2\)\({}^{\circ}\)C). The final pH of the supernatant was recorded. The difference between the initial and the final pH (\(\Delta\)pH = pH\({}_{\text{i}}\) - pH\({}_{\text{f}}\)) was plotted against the initial pH value (pH\({}_{\text{i}}\)). The pH-PZC is the point in which the resulting curve intersects the x axis \(\Delta\)pH = 0.  
Organic dyes and BPA concentrations were measured using a UV/VIS spectrometer (PerkinElmer instruments, Lambda 25 UV/VIS Spectrometer, Milan, Italy), keeping a constant wavelength at maximum absorbance (664 nm for MB, 526 nm for EB, 433 nm for BB, and 224 nm for BPA). Ph and CP were determined using a UV-VIS spectrophotometer (Shimadzu Italia S.r.l. UV-1601, Milan, Italy) at a constant wavelength of 510 nm, according to the Italian analytical standard IRSA-CNR 29/2003, vol. 2, n. 5070-A2 [58]. The pH of each sample was adjusted to 4 with concentrated phosphoric acid, and 5 mL of copper sulphate were added. The samples were distilled for 10 min and the condensed solutions were collected. Then, 5 mL of ammonium chloride were added to each solution, then ammonium hydroxide until pH = 10. Finally, by mixing the solutions with 2 mL of 4-aminoantipyrene and 2 mL of potassium ferrocyanide for 10 min, the color was developed. The volumes were brought to 200-250 mL with deionized water before UV-VIS analyses. All concentrations have been determined by extrapolation from calibration curves obtained from standard solution of the target compounds, according to the Beer-Lambert law.
## 4 Results
### Validation
#### 4.2.1 Flow Transition Line  
Figure 13d shows the final computed flow transition line for a comparison of the state-of-the-art (red) and the introduced image processing with both gradient evaluation methods A (blue) and B (green) as well as the manually evaluated flow transition line (black). The state-of-the-art approach is not able to locate most of the premature flow transitions, resulting in most turbulence wedges being undetected. Only in the areas of the turbulence wedges no. 1, 3, 7, and 10 some small parts of the premature flow transition line are located correctly. For the remaining image columns, the method only located the natural flow transition line. The introduced image processing methods A and B detect all 10 turbulence wedges above the critical relative contrast of \(c_{\text{wed}}=40\%\). Both gradient evaluation methods were able to reduce the deviation of the located flow transition line to the actual position considerably. Therefore, the introduced image processing methods were able to locate the transition line with reduced uncertainty and maximized sensitivity with respect to the occurrences of premature flow transition, especially for low contrast turbulence wedges. The capability of the introduced image processing methods is based on assumptions on the orientation of premature transition line segments and the existence of temperature gradients at the positions of premature flow transitions. The orientations of premature flow transition line segments are almost constant throughout the image and therefore allows for an easy selection of local gradient maxima based on their direction. The existence of a high enough local gradient maxima magnitude on the other hand can vary greatly with the contrast of the turbulence wedges. Especially for low-contrast turbulence wedges, the choice of the gradient thresholds is of utmost importance. If the gradients are too low, the image processing is not capable of detecting the premature flow transitions, as noted for the turbulence wedges' tips. Therefore, future work has to focus on alternative procedures on closing the wedges' tips with either a model-based fitting of wedge shapes or extrapolation of the successful detected wedges' flanks.
# A Survey of Public Key Infrastructure-Based Security for Mobile Communication Systems
## 5 New Research Directions and Challenges  
In the case of PKI in the field of mobile communications security, we need a flexible network infrastructure, i.e., the service providers are the main candidates to adopt PKI in their systems. Moreover, we need a cellular system with high data rates such as 3 GPP systems to be able to implement PKI cryptosystems. The most important entities in PKI systems are a trusted third party and the certification authority to bind the public keys with the corresponding users, as well as the mobile station (mobile equipment and SIM card) that will be responsible for the process of issuing digital certificates. Regarding the importance of secure transactions and the fact that network operators are better candidates for the implementation of a PKI, PKI is expedient to use for transmitting signals within the PLMN, and to secure the entire network. In addition, SIM-card is a new approach that implements personal mobility in addition to terminal mobility. In light of international roaming and support from various services such as telephony, data, fax, and short message service (SMS), the SIM-card is the most important component for security in mobile systems, along with center authentication in the base system. It is especially important if we can improve its efficiency or allow it to improve system security, for example, with the use of new methods of integrated circuits (ICs) and smart card technologies that would make it compatible with the public encryption key.
## 3 Underwater Routing Protocols
### Mda-Sl  
A message dissemination approach for storage-limited (MDA-SL) UWSNs involving opportunistic routing is proposed in [23] to track underwater objects. The scheme evaluates the messages received by the nodes to forward or discard packets. Data packets are forwarded towards nodes with high mobility or residual energy. When the memory elements of nodes are full, all newer messages are discarded. In this way, the throughput is optimized. However, nodes with high mobility and residual energy die rapidly due to overloading, which creates energy holes in the network. In addition, packets are lost when the storage elements of nodes are full.
Due to limited coverage, energy, and visual computations, single UAVs are replaced by multi-UAV flying networks (swarms) to support edge-based location updates and increased monitoring coverage of infected areas. With 5G-NR, the peak-data rate of 20 Gbps is supported, and services such as enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (uRLLC), and massive machine-type communications (mMTC) are deployed to support the bandwidth of 100\(\times\) over LTE-A, improved round trip time (RTT) latency, and high availability [15]. Moreover, low-powered device communication supports massive data ingestion for delay-sensitive UAV application scenarios. Such UAVs need an ultra-dense umbrella cell that requires new proliferation with sixth-generation (6G) networks, which support the Internet of Everything (IoE) paradigm. 6G-IoE supports massive air-ground-space communications through the extension of 5G services such as eMBB and uRLLC. 6G networks have artificial intelligence (AI) radio, which addresses intelligent cognition and control of UAV systems. Thus, 6G networks are envisioned to orchestrate a real-time connected autonomous flying multi-UAV ecosystem. 6G-based UAVs can be deployed in infected COVID-19 zones to support self-healing and intelligent network fabric, high localization and sensing controls, and a large coverage range [16]. A comparative analysis of 4G, 4.5G, 5G, and 6G networks is given in Table 1 based on key verticals such as communication-based, UAV-assisted, and delay-sensitive UAVs to address 3 FFs to tackle the spread of the COVID-19 pandemic. Although 6G has improved the UAV network performance, the synchronization issues are still there, which need to be addressed in the future. Figure 1 highlights the improvement in latency consideration through a comparative analysis between 5G-uRLLC against enhanced uRLLC (ERLLC), which is the 6G counterpart service. As evident, 5G-uRLLC has an end-to-end (E2E) latency between 1-10 milliseconds (ms), reliability of 99.999%, and low bit error rate (BER) of \(10^{-9}\). 6G ERLLC has an E2E latency of 0.1 ms, 99.9999999% reliability, and BER close to \(10^{-12}\)[17]. Thus, 6G promises new dimensions to support real-time UAV control, which is effective in performing 3 FFs at massive scales. Figure 1: Statistical measures of COVID-19 pandemic and possible solutions through 6G-enabled UAV communication. (**a**) Benefits of UAVs in early reporting COVID-19 cases [11]. (**b**) Impact of 6G communication to leverage UAVs [14]. To perform 3 FFs via 6G-ERLLC channels, a UAV swarm network (groups of UAVs with controller UAV for path formation, route control, and UAV maintenance) is required. For consistent monitoring purposes, high-end computational requirements are required