esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Improved Detection Limits through Design Optimization  
We recently demonstrated in a previous work [31] an ambient pressure laser desorption system for the sensitive and flexible mass spectrometric detection of explosives. The detection limits were in a suitable range for forensic application as presented in this work. Nevertheless, an improved detection  
Figure 1: **(a)** Laser desorption sampling for mass spectrometric analysis from a stainless steel surface with a self-built X-Y table; **(b)** Deployed ambient pressure laser desorption (APLD) head with front view on desorption volume and polytetrafluoroethylene (PTFE) ring; **(c)** APLD head side view with attached laser fiber, heating cartridge, and thermocouple; **(d)** Schematic drawing of the APLD chemical ionization-mass spectrometer (APLD-CI-MS). The heating cartridge inside of the brass body ensures a complete heating of the sampling head. A laser fiber transfers pulsed laser light, produced by the Nd:YAG laser, to the surface. The ablated analyte is subsequently transferred into the transferline and afterwards into the IT-MS. There, it can be ionized by electron ionization (EI) or chemical ionization (CI) and then analyzed.  
limit can enhance the reliability of the analytical method. In the original setup, a non-heated front end made out of polyether ether ketone (PEEK) was utilized.  
In this study, several modifications to the existing system were performed. These modifications are the exchange of the type of material, establishing a heatable system, shortening the transferline, and increasing the laser fiber diameter. All these changes are discussed in the following section.
# AgriSecure: A Fog Computing-Based Security Framework for Agriculture 4.0 via Blockchain
## 8 Smart Agriculture 5.0  
With 100 times faster upload and download speeds than \"fourth generation of mobile technology (4G)\" and \"fourth-generation long-term evolution (4 GLTE)\" technologies and latencies as low as 1 ms, the \"5 th generation mobile network (5G)\" is a new technology for wireless that can connect a million gadgets per square kilometer. In order to exchange data effectively, the millions of smart gadgets that make up the precision agricultural networks must be connected to one another over a given piece of land.  
Figure 8: Number of transactions published in Blockchain per minute.  
Figure 7: Number of packets received by Blockchain per minute.  
Therefore, the Agriculture 4.0 scenario is particularly well adapted to 5G technology [112; 113; 114]. A framework for the digitization of agriculture networks and function modules for an intelligent irrigation system was provided by [PERSON] and [PERSON] [115]. [PERSON] and [PERSON] [116] also investigated how 5G technology may be used for agricultural planting, livestock farming, and traditional farming growth [117; 118; 119; 120; 121].
## 6 PAT Segmentation Methods
### Deep Learning-Based Methods  
Deep learning methods for segmenting PAT are similar to ones used for direct EAT segmentation. For instance, [PERSON] et al. [54] use a 3D-based deep attention U-Net model as in [PERSON] et al. [37], trained on CT images with labeled PAT and myocardium regions. The authors train and evaluate their model on 422 cardiac CT scans from six different centers, to our knowledge the largest cohort used to evaluate EAT and PAT segmentation methods. The authors experiment with training on patients with known CVDs and evaluating patients without CVDs, and vice-versa. Evaluation is done with leave-one-center-out cross-validation. The authors report a Dice coefficient higher than 0.88 for PAT segmentation, and a correlation of 0.99. The model is compared to a 3D U-Net model. For PAT segmentation, the model outperforms 3D U-Net in terms of the Dice coefficient by 0.02. The experiments show the potential for using the same deep-learning-based methods for both EAT and PAT segmentation, as they achieve similar results using the same model for both tasks.
## 3 Experimental Activities
### Advanced Shielding for Spacecraft
#### 3.2.1 Carbon-Fiber-Reinforced Panels (CFRPs)  
In the framework of a collaboration between CISAS and JAXA (Japan Aerospace Exploration Agency), a campaign of hypervelocity tests was performed on CFRPs (Figure 4). In total, 45 experiments were performed on panels 2.3, 3.5, and 4.3 mm thick, with spherical projectiles with diameter from 0.8 to 2.9 mm impacting at a speed of 2-5 km/s. This campaign allowed the definition of the panels' Ballistic Limit Equation, as well as the determination of the panels' internal delamination during hypervelocity impacts. Further data can be found in [38].
# Developing a Building Fire Information Management System Based on 3D Object Visualization
## 5 Prototype Development and Scenario Analysis
### Limitations of the System Validation and Future Research  
The expert feedback on the proposed system has some limitations in validating the effectiveness of the system. First of all, the number of focus group interviews should be expanded. At the time of writing, the BFIMS has only been pilot-tested in terms of potential benefits of information exchange via the 3D object-based information storage and retrieval process. Second, the two groups (facility manger and firefighters) may require different types of information. As such, the validity should be examined using different usage scenarios. Thirdly, the real case project should be applied to fully verify the impact of the new system. As a pioneering study, the validation results from two scenario-based applications reveal that the current fire disaster management has much to be improved. Rather than scenario-based applications, a real case study project may provide a robust validation in a building fire disaster. In future research, this novel system should be further developed in terms of technological aspects. Mobile devices (table PC or smart devices) are commonly used in the industry. So, the system would be much benefited when it is operable in a web-based environment. Additionally, a cloud-server programming can be useful to ensure transmission of BIM data at different stages and connectivity between the 3D model and the data because simultaneous information sharing is critical in building fire control. Finally, the sensor information (e.g., fire, smoke, and flame) should be linked with the system in an automatic way. The real-time collection and sharing of information is critical under the fire situation of a building.
# An Unambiguous Delay-And-Multiply Acquisition Scheme for GPS L1C Signals
## 3 Results
### New DM Acquisition Schemes for GPS LIC Signal and the Unambiguous Property
#### 3.1.3 Determination of the Doppler Frequency  
In this section we show the procedure to find the Doppler frequency of the received signal \(r_{n}(\mathrm{t})\) after we obtain the code phase through the proposed DM acquisition scheme.  
Consider the received signal \(r_{n}(t)\) and the generated signal \(g_{comp,P}(\mathrm{t})\) in Figure 7 with \(\tau=0\). Then:  
\[\begin{array}{ll}r_{n}(t)\cdot g  
\(X(k)\) is located at \(\frac{M}{2}\) (suppose that \(M\) is even). There are e obtain \(\Delta f\cdot\frac{M}{2}=\frac{f_{s}}{2}=\frac{1}{2}\frac{M}{T}\), which results in \(\Delta f=\frac{1}{T}\)\([\mathrm{Hz}]\). (2) From the new sampling frequency \(\overline{f_{s}}=\frac{f_{s}}{L}\) and the number of samples \(\overline{M}=\frac{M}{L}\), the similar Equation holds as in proof (1) such as \(\Delta f\cdot\frac{\overline{N}}{2}=\frac{\overline{f_{s}}}{2}=\frac{1}{2} \frac{\overline{N}}{T}\), which results in the same Equation, \(\Delta f=\frac{1}{T}\)\([\mathrm{Hz}]\). \(\square\)  
Considering (18), the above method cannot distinguish positive Doppler frequency from negative Doppler frequency since \(\cos(2\pi f_{D}t)=\cos(2\pi(-f_{D})t)\). Thus after finding the Doppler frequency using Lemma 3, the rate of the relative velocity of the vehicle from the satellite should be used to determine the sign of the Doppler frequency.
# Experimental Demonstration of Secure Relay in Quantum Secure Direct Communication Network
###### Abstract  
Quantum secure direct communication (QSDC) offers a practical way to realize a quantum network which can transmit information securely and reliably. Practical quantum networks are hindered by the unavailability of quantum relays. To overcome this limitation, a proposal has been made to transmit the messages encrypted with classical cryptography, such as post-quantum algorithms, between intermediate nodes of the network, where encrypted messages in quantum states are read out in classical bits, and sent to the next node using QSDC. In this paper, we report a real-time demonstration of a computationally secure relay for a quantum secure direct communication network. We have chosen CRYSTALS-KYBER which has been standardized by the National Institute of Standards and Technology to encrypt the messages for transmission of the QSDC system. The quantum bit error rate of the relay system is typically below the security threshold. Our relay can support a QSDC communication rate of 2.5 kb/s within a 4 ms time delay. The experimental demonstration shows the feasibility of constructing a large-scale quantum network in the near future.  
quantum secure direct communication; quantum network; post-quantum cryptography; secure relay +
Footnote †: journal:  
0000-0001-9399-2231][PERSON]  
1 Beijing Academy of Quantum Information Sciences, Beijing 100193, China; [EMAIL_ADDRESS] (M.W.)1 Beijing Academy of Quantum Information Sciences, Beijing 100193, China; [EMAIL_ADDRESS] (M.W.)1 Beijing Academy of Quantum Information Sciences, Beijing 100084, China1
## 2 Overview of the FBN
### Fuzzy Set Theory  
Fuzzy set theory (FST) is a mathematical framework that extends traditional set theory by incorporating degrees of membership to represent uncertainty and imprecision [34]. In conventional set theory, elements are classified as binary, meaning they are either True or False or Yes or No. In contrast, FST allows elements with membership values ranging from 0 to 1, reflecting varying degrees, making it an ideal tool for modeling uncertainty in  
Figure 1: A sample BN structure with a causal relationship among different risk levels.  
systems like power grid risk assessment [35]. Experts express their input using linguistic variables (e.g., \"low\", \"medium\", \"high\"), later converted into fuzzy numbers that represent the degree of membership of various risk factors. This fuzzy representation enables the incorporation of vagueness and uncertainty in expert knowledge, thereby making the model more flexible and robust [36].  
In power grid risk assessment, FST is applied to model uncertainties in key parameters, such as component reliability, environmental conditions, and operational performance. FST, through expert elicitation, enables domain experts to provide subjective judgments about risk levels, which are then converted into fuzzy numbers [27]. The application of FST in power grid risk assessment follows a structured, three-step process to calculate fuzzy probabilities (FPr) based on expert input:  
* The first step involves converting expert-provided linguistic terms (e.g., \"low\", \"medium\", \"high\") into fuzzy numbers. These numbers represent the degree of membership of a particular risk factor within each category, allowing for a more nuanced representation of expert knowledge [37].
* In the second step, the opinions of multiple experts are aggregated into a collective membership function. This ensures that the model accounts for the diversity of expert judgments, effectively reflecting the uncertainty and variability in expert knowledge [37].
* The third step involves converting the aggregated membership function into FPr, representing the likelihood of specific risk events. These FPr consider expert input and the inherent uncertainty in the data.  
This approach integrates expert knowledge by providing a more adaptive, robust, and flexible model for assessing power grid risks. Using fuzzy probabilities in the Bayesian network (BN) framework enables the more comprehensive modeling of complex, uncertain relationships within power grids, thereby enhancing the model's ability to predict and manage risks. By integrating expert elicitation with Bayesian networks, a fuzzy Bayesian network (FBN) framework can be formed that is more adaptable for analyzing complex relationships among variables in power grids [38]. Expert judgments help the model to handle uncertainties related to environmental and operational factors, such as weather events, equipment failures, and third-party interference. The FBN methodology is more effective in modeling and managing the dynamic and uncertain nature of power grid systems, enabling more informed risk management and resilience planning decision-making [39].
# A Creep Model of Steel Slag-Asphalt Mixture Based on Neural Networks
## 2 Experimental Framework  
### Selection of Materials and Fabrication of Specimens  
Grade 70 road petroleum asphalt, limestone, and steel slag sourced from Hebei Shengli Steel were selected as materials for this study. These materials were tested in accordance with standards. In accordance with an asphalt-to-aggregate mass ratio of 5.3%, the standard Marshall cylinders specimens were fabricated by the compaction method, with approximate dimensions of 101.6 mm in diameter and 63.5 mm in height. Considering the specific gravity coefficient of 1.16 between steel slag and fine aggregate (limestone), a direct replacement of coarse aggregate with steel slag is not feasible. Consequently, based on the AC-13 grading scale, the mass proportion for each coarse aggregate size was enlarged in accordance with the specific gravity coefficient, and the mass proportion of the fine aggregate was recalculated. The compositions of aggregates in this paper are illustrated in Table 1. The standard AC-13 gradation curve and the AC-13 with steel slag gradation curve are shown in Figure 1.  
The binder-to-aggregate ratio exerts a considerable influence on the quality of steel slag-asphalt mixture specimens. An insufficient binder-to-aggregate ratio leads to challenges in compacting the mixture, resulting in a dry surface and a tendency for aggregate particles to dislodge. In contrast, an overly abundant binder-to-aggregate ratio causes the asphalt mixture to exhibit oily efflorescence on its surface. This research adheres to a binder-to-aggregate ratio within the threshold that ensures optimal compaction, specifically between 4.2% and 6.6%. For the purposes of this paper, a ratio of 5.3% has been selected for its adherence to the established compaction standards.  
The fabrication of specimens for the asphalt mixture is as follows:  
1. The aggregates and asphalt are kept in a drying oven under 160 \({}^{\circ}\)C for 6 h, then all heated materials are mixed in a blender under 170 \({}^{\circ}\)C for 120 s.
2. According to the Standard Marshall Test, the mixed material is compacted by the Marshall equipment in the oiled cylindrical molds with two cylindrical plungers.
3. After cooling to room temperature, the modeled cylindrical specimens are removed from the mold, and the dimension and tolerance are controlled to (101.5 \(\pm\) 0.5) mm in diameter and (63.5 \(\pm\) 0.5) mm in height.  
\begin{table}
\begin{tabular}{c c c} \hline
**Aggregate Type** & **Grain Size/mm** & **Mass Fraction/\%** \\ \hline Coarse aggregate (steel slag) & 13.2\(\sim\)16.0 & 2.0 \\ Grain size: 2.36\(\sim\)16.0 & 9.5\(\sim\)13.2 & 18.2 \\ Mass fraction: 69.2\% & 4.75\(\sim\)9.5 & 30.0 \\  & 2.36\(\sim\)4.75 & 19.0 \\ \hline  & 1.18\(\sim\)2.36 & 6.0 \\ Fine aggregate (limestone) & 0.6\(\sim\)1.18 & 6.2 \\ Grain size: 0\(\sim\)2.36 & 0.3\(\sim\)0.6 & 5.9 \\ Mass fraction: 30.8\% & 0.15\(\sim\)0.3 & 3.8 \\  & 0.075\(\sim\)0.15 & 2.0 \\  & \(<\)0.075 & 6.9 \\ \hline \end{tabular}
\end{table}
Table 1: Composition of aggregate qualities in steel slag-asphalt mixture.  
Figure 1: AC-13 with steel slag gradation curve.
## 3 Results and Discussion
### Prediction Potential of the ANN
In Figure 7a,b the 3D simulations provide sufficient data and they confirm that under low optical density conditions the quadratic growth of the yield is valid. Data in Figure 7c have been obtained for an intermediate pulse energy \(E\) = 9 mJ and the cell positioned in the diverging beam at \(z_{i}\) = 10 cm. In this parameter combination there are few 3D simulation results available, being a good opportunity to test the accuracy of the ANN against the basic physics of the HHG process. Figure 7c shows that the ANN works correctly in this respect, predicting quadratic growth of the yield in the low density regime. Moreover, the end of the region for the absorption limited HHG is also reproduced in a similar manner by the ANN as by the complete 3D simulations. It is not possible to show all results, but the main conclusion is that the ANN is capable to predict regions in the multi-dimensional parameter space where the absorption limited HHG can take place. Furthermore, ANN also predicts for which parameter combinations there is a transition from the initially absorption limited HHG to a saturation, then to a drop in obtainable harmonic yield. This dynamics is represented for example in Figure 7b,c at pressure values 0.38 mbar (green symbols and lines) and 1.41 mbar (blue symbols and lines).
## 4 Results and Discussion
### Distance Error  
The magnitude of the distance error plays an important role in optimizing the controllers' performance. Larger errors provide more leeway for the controller to smooth out the vehicle's speed and acceleration profiles instead of rigidly tracking the lead vehicle's behavior. Figures 4 and 5 demonstrate that the DP algorithm extensively utilizes its available margin owing to its prior knowledge of the driving cycle. This allows DP to dynamically adjust the distance error, even occasionally resulting in negative values, although it never leads to zero or negative inter-vehicle distances. The MPC controller, operating with less foresight, exhibits a more conservative use of the distance error. Notably, in scenarios with greater dynamic changes, both DP and MPC exploit this parameter more actively, as illustrated in Figures 6 and 7, which also depict the velocity and acceleration profiles for completeness.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Leader** & **MPCFC** & **MPCnoFC** & **DP** \\ \hline RMS acc UDDS (m/s\({}^{2}\)) & 0.6091 & 0.4924 & 0.4963 & 0.5218 \\ \hline RMS acc AUDC (m/s\({}^{2}\)) & 0.7785 & 0.4728 & 0.4764 & 0.4412 \\ \hline \end{tabular}
\end{table}
Table 4: Acceleration RMS comparison between the lead vehicle and the ego vehicle equipped with MPC and DP.  
Figure 5: Comparison of velocity and acceleration profiles of ego equipped with fuel consumption MPC and DP controllers with lead along AUDC driving cycle, [0, 450 s] zoom.
## 3 Results
Next, the training time was adjusted by setting two distinct training times, 600 s and 6000 s, using the ML.NET framework. The training data are daily values for the monitor parameters. Table 2 provides a summary of the results obtained for the evaluated algorithms. Two testing contexts were established for the two types of tests. In the first context, the model was trained for the two-time intervals using the binary output for magnitude. In the second context, the analysis will focus on the same two-time intervals but with the magnitude output retained in its numerical form. Table 2 provides data that compares various regression algorithms based on performance metrics and training duration across two training times (600 s and 6000 s). LightGBM Regression consistently achieves the highest R\({}^{2}\) values (up to 0.9995) for binary output with minimal loss and low training times. In contrast, LbfgsPoissonRegression and SdcARegression perform poorly in both scenarios, with low R\({}^{2}\) values and higher losses. For numerical output, FastTreeRegression and LightGBM also show strong performance with R-squared values above 0.9 and low error metrics, particularly after 6000 s of training. For the EMM model, the FastTreeTweedieRegression is the best-performing algorithm across binary output for a 6000-s training duration. The EMM model's outstanding performance in binary classification prompted a new series of tests focused on data classification scenarios. During this phase, the model was retrained over 6000 min, yielding results that demonstrated the FastTreeOva algorithm achieves a theoretical accuracy of 99.84%. This model was developed after evaluating 3151 alternative models. This model was used for the algorithm that was created. To analyze the impact of removing each parameter on the EMM's performance, we compare the results summarized in Table 3. This comparison highlights the significance of each parameter in achieving accurate predictions. The ablation study results comparison is as follows:  
* All Parameters Included. The complete EMM (with all parameters) achieves near-perfect performance, with an R\({}^{2}\) of 0.9999, AME of 0.00, and RMS of 0.00. This indicates an excellent fit and minimal error, emphasizing that each parameter contributes significantly to precise predictions;
* Without Temperature. Removing the temperature parameter resulted in a notable drop in R\({}^{2}\) to 0.8904, with an MAE of 0.0044 and RMS of 0.0629. This suggests that temperature is an important parameter for the model, substantially influencing prediction accuracy;
* Without Pressure. Eliminating atmospheric pressure had the most severe negative impact, resulting in an R\({}^{2}\) of \(-0.3578\) (indicating a poor relationship between the
# Special Issue \"Internet of Things for Smart Homes\"
## 1 Introduction  
In recent years, the progress of wireless protocols, the growth of cloud services, the refinement of low-energy and high-performance technologies, the practice of Artificial Intelligence, and other methods of convergence solutions based the Internet of Things (IoT) paradigm have launched a new era for smart homes. Technologies for IoT-oriented smart homes include sensors, interfaces, monitors, and several appliances, networked collectively to promote the automation and local/remote control of the domestic environment. The smart home represents a central hallmark in the new ecosystem of the Internet of Things, both for the centrality of the home in the life of every individual and for the enormous potential regarding the dissemination of objects and services. Thanks to the latest Information and Communication Technologies (ICT) and machine learning algorithms, the smart home environment can monitor the welfare and everyday life activities of residents, learning their distinct necessities and habits. The aim is to rearrange itself to them, thus improving their overall quality of life. Moreover, smart homes can skillfully manage the energy consumption of appliances and all other peculiarities related to the domestic environment, thus creating a healthier and energy-effective area for their inhabitants. While IoT-oriented smart homes can modify how inhabitants interact with the domestic environment, each distinct technology demands distinct levels of security based on the sensitivity of the controlled system and the information it handles. Smart Homes can be exposed to security threats and privacy breach that stem from current ICT and protocols.  
This special issue has converged at collecting high-quality papers aimed at solving well-known technical problems and challenges typical of IoT-oriented smart homes. The primary purpose has been to combine innovative proposals efficiently, converging on the performance evaluation and thecomparison with existing approaches. Among the 36 submissions, the guest editors picked 15 high-level contributions for publication after several rounds of reviews carried out by invited experts.