esa-sceva/satcom-chunk-collection huggingface dataset:
# Viability of Cupola Slag as an Alternative Eco-Binder and Filler in Concrete and Mortars
## 2 Materials and Methods
### Design and Testing Methodology
#### 2.2.2 Standardized Tests  
Mortars manufactured with various admixtures were tested to determine consistency in the fresh state, according to the EN 1015-3 standard [32]. Due to the large amount of admixture used, a moderate consistency was obtained for all of the admixtures, so these mortars were subjected to 15 strokes on the shaking table and the diameters of the cones obtained were measured.  
Mortars made with various cement replacements by cupola slag were subjected to a fresh consistency determination, according to EN 1015-3 [32]. In this case, due to the self-compacting nature of the mortar, the runoff of the mortar was determined after filling the mold on the shaking table and without any means of compaction. The mixture was kept at rest for 20 s, at which time the measurement of two conjugated diameters of the cake was carried out.  
The mechanical tests, consisting of the determination of the compressive strength and flexural strength, were carried out in a servohydraulic press with a capacity of 250 kN and in accordance with EN 196-1 [31]. The application rates of the load were 2500 N/s for the compressive strength test and 50 N/s for the flexural strength test. Four test specimens were tested per mix and per age. For mortars with different admixtures, tests were carried out at 7, 28, and 60 days of curing (12 specimens), and for mortars with different levels of cupola slag replacements, tests were carried out at 7, 28, 60, and 90 days of curing (16 specimens).
## 7 RegTech and Industrial Internet of Things
### Data Storage and Archiving  
The daily collected data needs to be stored securely after being processed. Some data may need to go to the blockchain after processing, while others may be stored on a local server. Data treatment is different in terms of importance. Furthermore, centralized storage methods such as cloud storage and decentralized storage is another potential solution for dealing with the massive amounts of data generated daily. Data is more secure to store on blockchain networks. But the limitations of blockchain technology, such as scalability and availability in data processing, retrieval, and storage [106], restrain its application to large files or document storage. The Interplanetary File System (IPFS), which \"identifies, verifies, and transfers files relying on the cryptographic hashes of their contents\" [107], is a content-addressable distributed file storage platform [108] with a decentralized storage protocol. It is a potential solution to address excessive file redundancy when using blockchain technology.  
The IPFS system can allocate a unique hash for each file stored by users who have access to their corresponding files via the hash address. Data owners can encrypt their data before storing it on the IPFS platform. For example, [PERSON] and [PERSON] discussed the implementation of IPFS in medical data storage to control the access to the patient's medical records of various users [109]. They indicate that medical data can be encrypted based on attributes and determine the attributes of users by matching the private key related to their attributes with the access policy set in advance. The users can have secured data access by matching the private key related to the users' attributes with the access policy set in advance and related to the ciphertext. They also advocate that blockchain can record medical data's storage and search processes, providing both tracking of data sources and data retrieval records. In addition, IPFS is a good way to archive data. Centralizing data storage on a cloud server is cost-effective and convenient for data retrieval for companies. However,  
Figure 5: Regulatory Supervision Framework in Non-Financial Sectors.  
the failure of one cloud model may lead to the unavailability of the whole cloud server. In contrast, the IPFS has no single point of failure without a central server, and the data are stored in duplicate copies in different places worldwide [104]. In short, the regulated entities may choose the most suitable data storage mode--cloud storage or distributed storage--as shown in Figure 5.
## 3 Cooperative Perception Information-Sharing
### The Effect of Network Performance on Cooperative Perception
#### 3.3.2 Packet Loss Rate  
The PLR is the ratio of the data packets lost by the target node to the data packets sent by the application layer of the source node. Data integrity is very important for the effectiveness of cooperative perception in the process of data transmission. In fact, under the interference of multiple factors, such as unfavorable weather, illumination, and communication quality, it is difficult to avoid the loss of perception data during transmission. If the data loss is serious, it will reduce the reliability of perception results and even lead to the failure of cooperative perception.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sharing Strategy** & **Authors, Year** & **Purposes** & **Findings** & **Remarks** \\ \hline \multirow{4}{*}{CPM generation rules} & \multirow{4}{*}{[PERSON] et al. 2020 [84,85]} & Optimized the CPM generation strategy formulated by ETSI to reduce redundant information. & Dynamic CPM generation strategy. & Optimizing the CPM generation strategy can effectively reduce redundant information. \\ \hline \multirow{4}{*}{CPM generation rules} & \multirow{4}{*}{[PERSON] et al. 2020 [84,85]} & Designed a & \multirow{4}{*}{Realized the correct reception of information through message response.} & \multirow{4}{*}{Transmitted critical sensory data, reducing communication load.} \\  & & & & \\  & &
# Modeling of Satellite-to-Underwater Integrated FSO-PON System Using NOMA-VLC
## 1 Introduction
### Related Work  
Recently, an FSO-VLC system has been reported to enhance the distance as well as the rate of transmission in satellite to UOWC communication. [PERSON] and [PERSON] presented a cooperative RF-FSO-VLC model utilizing decode-and-forward (DF) relays. The results showed that the performance of the system was badly influenced by the pointing errors, atmospheric turbulence and RF fading. However, the system's performance can be enhanced by employing heterodyne detection at the receiver. Moreover, the system's capacity decreases under the influence of high pointing errors, atmospheric turbulence and with the increase in the number of LEDs [14]. They also presented a cooperative VLC-FSO-VLC system under the impact of double generalized Gamma (DGG) distributed climate turbulence, Rayleigh distributed pointing errors and Lambertian radiation pattern. The results showed that the proposed system employing a coherent binary phase shift keying (CBPSK) modulation scheme is more efficient than other modulation schemes [15]. [PERSON] et al. experimentally demonstrated a hybrid FSO-VLC system using an m-carrier with less amplitude and phase modulation (CAP) to conquer the bandwidth bottleneck of last-mile access networks. The results show that, under the impact of clear channel conditions, the system using 10-CAP provides 40% enhancement compared to 2-CAP over 1 m VLC and 500 m FSO links [16]. [PERSON] et al. demonstrated a three-hop hybrid VLC-FSO-VLC system by following Gamma-Gamma (GG) fading statics and Lambertian emission for FSO and VLC links, respectively. It is observed that system errors increase under atmospheric turbulence ranging from weak to strong [17]. [PERSON] and [PERSON] proposed DF multi-hop-based FSO-UOWC for high-speed island connectivity. The results showed that the proposed design is useful in coastal atmospheres where the climate varied from rainy to clear or hazy days [3]. [PERSON] et al. studied a satellite-aerial-terrestrial network (SATN) employing an amplify-and-forward protocol in a hybrid RF-FSO system. The suggested system provides the same performance as the optimum channel state information (CSI)-based zero-forcing scheme [1]. [PERSON] et al. analyzed the performance of the earth-to-satellite FSO system, considering the merged effect of beam wandering and atmospheric turbulence using M-ary phase shift keying (MPSK). The results showed that adaptive schemes enhance spectral efficiency four times as much as the non-adaptive method at a bit error rate (BER) of \(10^{-2}\). Additionally, a 1 \(\times\) 4 single input multiple output (SIMO) systems attains a threshold BER of 5 dB diminution in signal-to-noise ratio (SNR) compared to a 1 \(\times\) 2 SIMO system and a 10 dB diminution in SNR compared to a SISO system [18]. [PERSON] et al. proposed and investigated multi-user asymmetric FSO/RF links for the satellite to unmanned aerial vehicles using GG distributed turbulence. The numerical results showed that the proposed technique reaches an adequate compromise between service fairness and system capacity [19].
## 5 Fuel-Consumption Assessment Results
### Software Partition  
The processing system of the PSoC (SW partition) is built around a dual-core ARM Cortex-A9 microprocessor. With this architecture in mind, the SW application was developed, enabling the full operation of the hybrid HW/SW system. These functionalities are as follows:  
* I/O management: the system retrieves the driving features from the buses of the vehicle (e.g., CAN-bus) and outputs the natural-language driver recommendations.
* Data logging and windowing: the microprocessor stores 8 s of data (256 samples 32 Hz) to compute the data windows used to extract the driving features. Each window has an overlapping of 4 s with its preceding one; thus, with an 8 s size, a new window is generated every 4 s.
* Feature computation: for each data window, the microprocessor computes the average values of ERPM, GP, and PGP, and the variance of Pos XACC.
* Data exchange: the SW partition sends the features to the HW accelerator through the AXI4 bus and retrieves the identification results (DS clusters) from the HW partition.
* Cluster distribution computation: the cluster distribution of a set of windows, evaluated during a certain time of uninterrupted driving above a certain speed threshold, is computed.
* Driver advice: natural-language advice is provided, depending on the cluster distribution, that is to say, according to the cluster in which the driver spends the longest time.  
Figure 17: Simulation results of the SOM HW accelerator obtained with the Vivado design suite.  
In sum, with the aforementioned data logging, windowing, and feature computation, once the data are fed to the FPGA, the SOM HW accelerator identifies the DS cluster for that window. With those results, the SW partition computes a cluster distribution, decides which cluster registered the highest number of hits, and, according to that maximum, provides eco-driving advice to drivers.
## 2 Materials and Methods
### Evaluation Protocol  
To assess the effectiveness of the models developed for each experiment, cross-validation techniques were implemented. In the case of the first two experiments (classification), twenty folds of stratified random sampling were performed. This involved data segregation into subgroups based on the ground truth labels, data shuffling and splitting into two sets, 75% for training and 25% for testing. For the third experiment (regression), random subsampling was utilized to split the data into two datasets twenty times (train and test), maintaining the same proportions as the classification experiments.  
In addition to cross-validation, specific evaluation metrics were chosen for each experiment to quantify the models' performance. For the classification experiments, a confusion matrix was computed for each model providing a representation of the algorithm's predictions compared to the ground truth. In the 2-class classification, the positive class denotes the Beginning of training, and the negative class represents the End of training. In addition to the confusion matrix, the algorithms' performance was evaluated using the following metrics: accuracy, specificity, and sensitivity.  
For the third experiment, we used the Mean Absolute Error (MAE), which calculates, for each subject, the mean of the absolute differences between the model predictions and the corresponding target values (Equation (2)).  
\[MAE=\frac{1}{n}{\sum_{i}}|y_{i}-y_{i}|, \tag{2}\]  
where \(y_{i}\) denotes the predicted RNT after a specific training session \(i\), \(y_{i}\) denotes the ground truth RNT, and \(n\) is the subject's total number of attempts during training.  
Figure 2 provides a visual overview of the overall experimental design followed in this study.
# Experimental Performance Analysis of Wi-SUN Channel Modelling Applied to Smart Grid Applications
## 3 Channel Modeling Methodology
### Deterministic Modeling  
The deterministic modeling is created from the ray tracing, the electrical properties of the propagation medium, the antenna pattern and the geometry in the environment. The deterministic model (i.e., method of moments (MoM), ray tracing (RT) and finite difference time domain (FDTD)) is of high accuracy, but it requires high computational complexity.
## 4 Experimental Analysis and Results
### Experimental Setup
#### 4.3.2 Error Analysis  
To better understand the limitations of the proposed model, we conducted a detailed error analysis across the evaluation datasets. In this context, errors refer to various types of incorrect predictions made by the model, including entity boundary errors, misclassification errors, omission errors, and over-segmentation errors.  
Entity boundary errors occur when the predicted entity span does not exactly match the correct span. This type of error is particularly frequent in nested entities, where the outer and inner entity boundaries overlap. The ambiguity inherent in Arabic compounding and phrase nesting contributes heavily to these boundary mismatches.  
Misclassification errors arise when the model assigns the wrong entity type to a correctly identified span. For example, a person entity might be misclassified as an organization, particularly in cases where the entity name refers to a company established by a well-known individual. This confusion is further exacerbated by the rich polysemy found in Arabic, where the same phrase can have different meanings depending on the context.  
Omission errors are cases where the model completely fails to detect an entity, often due to its rarity or domain specificity. Such errors were more frequent in the biomedical dataset, where highly specialized terminology is common and less likely to be covered in general-purpose language models.  
Over-segmentation errors refer to cases where a single entity is incorrectly split into two or more separate entities. This typically occurs due to orthographic ambiguity (e.g., inconsistent use of spaces in Arabic compound phrases) or incorrect handling  
These types of errors collectively demonstrate the complexities of Arabic NER and emphasize the importance of architectural components such as the Compound Span Representation Layer and the Hybrid Feature Fusion Layer in the proposed model. By identifying these error patterns, future work can target enhanced morphological analysis, improved external lexicon handling, and dynamic span boundary refinement to further enhance performance.
# Spatial Data Reconstruction via ADMM and Spatial Spline Regression +
## 5 Performance Evaluation  
In this section, we perform an extensive case study of the approach we described above in order to demonstrate its applicability. We picked the cell phone data as an example of how the model can solve empirical problem and compare the model's performance to other approaches.
## 4 Experiments and Analysis
### Objective Evaluation Indices  
There are two ways to evaluate a pan-sharpening methods: subjective visual effects and objective evaluations. Each objective evaluation index considers different dimensions of the problem, and generally has an overall consistency, which can reflect the true image fusion results and also be consistent with the subjective evaluation indices. In order to objectively evaluate the quality of the fused images obtained by each method, five widely used quantitative indices are used in this paper. Some detailed introduction of each index is shown below:  
Figure 8: Four image pairs.  
1. Correlation Coefficient (CC) [22]. It can calculate the correlation between the reference MS image and a fused image. Its optimum value is 1;
2. Erreur Relative Global Adimensionnelle de Synthese (ERGAS) [23]. It can reflect the overall quality of a fused image and its optimum value is 0;
3. Relative Average Spectral Error (RASE) [24]. It can reflect the average performance on spectral errors and its optimum value is 0;
4. Spectral Information Divergence (SID) [25] can evaluate the divergence between spectra and its optimum value is 0. For more specific details about the SID index, please refer to the literature [25];
5. No Reference Quality Evaluation (QNR) [26]. When without a reference MS image, it can reflect the overall quality of a fused image. QNR is composed of two parts: a spectral distortion index \(\rm D_{\lambda}\) and a spatial distortion index \(\rm D_{s}\). Its optimum value is 1. For QNR, a higher value indicates a better fusion effect.  
\[QNR=(1-D_{\lambda})(1-D_{S}) \tag{40}\]
6. \(D_{\lambda}\) can reflect the spectral distortion [26]. For \(\rm D_{\lambda}\), a lower value indicates a better fusion effect and its optimum value is 0;
7. \(\rm D_{s}\) can reflect the spatial distortion [26]. For \(\rm D_{s}\), a lower value indicates a better fusion effect and its optimum value is 0.
## 3 Experimental Results
### Equations Solved
The molecular vapor diffusion is given by Equation (10), where \(T\) is temperature (\({}^{\circ}\)C), \(P_{g}\) is gas pressure (\(P_{g}\) = 100 kPa) and \(D\) is a constant (\(D\) = 5.9 \(\times\) 10\({}^{-6}\) m\({}^{2}\)s\({}^{-1}\)K\({}^{-2.3}\)Pa) [9]. \[j_{g}^{w}=i_{g}=-n\tau\rho_{g}(1-S_{r})D_{g}^{vap}\
abla\omega_{g}^{w} \tag{9}\]  
\[D_{g}^{vap}=D\left[\frac{(273.15+T)^{2.3}}{P_{g}}\right] \tag{10}\]  
It is worth noting that the amount of vapor present or the mass of water in the gas phase, \(\rho_{vap}\) = \(\rho_{g}\omega_{g}^{w}\), is found using the psychrometric law (Equation (11)) to compute vapor pressure \(p_{vap}\), adopting a constant \(\rho_{g}\) = 0.01 g/cm\({}^{3}\) for dry air and water vapor as an ideal gas (ideal gas law, Equation (12), where R is constant, R = 8.315 J/mol\(\cdot\)K and M is the molecular mass of water, M = 0.018 kg/mol). It is temperature-dependent. \[p_{vap}=136075 exp\left(-\frac{5239.7}{273.15+T}\right) \tag{11}\]  
\[\rho_{vap}=\frac{p_{v}M}{R(273.15+T)} \tag{12}\]  
The balance of dry air is computed with an equation similar to Equation (4), replacing by dry air all terms related to water. It is not presented here because its terms are null under the hypothesis that the air pressure is constant, air bubbles are considered perfectly mobile and there is no air dissolution. Energy transfer is computed with Equation (13) and considers the energy from the various phases \(E_{i}\) (where i = s for the solid, i = 1 for the liquid and i = g for the gas phases), heat transport by advection due to liquid and mass movements, \(j_{EI}\) (i = 1 for the liquid and i = g for the gas phases), heat transport by conduction given by \(i_{c}\) and the output or input of energy \(f^{Q}\). \[\frac{\partial}{\partial t}\left[E_{s}\rho_{s}(1-n)+E_{I}\rho_{I}S_{r}n+E_{g} \rho_{g}(1-S_{r})n\right]+\
abla\cdot\left(i_{c}+j_{EI}+j_{Eg}\right)=f^{Q} \tag{13}\]  
The only energy flow considered is the term for heat transfer by conduction, \(i_{c}\), given by [PERSON]'s law (Equation (14)), where T is temperature and \(\lambda\) is thermal conductivity given by Equation (15). This conductivity considers the degree of saturation \(S_{r}\) of the material (Equation (2)) and is computed considering the thermal conductivities of the fully saturated and completely dried material, \(\lambda_{sat}\) and \(\lambda_{dry}\), respectively (values in Table 3) [9]. \[i_{c}=-\lambda\
abla T \tag{14}\]  
\[\lambda=\lambda_{sat}\sqrt{S_{r}}+\lambda_{dry}\sqrt{(1-S_{r})} \tag{15}\]  
Finally, the numerical parameters of the model and those for the constitutive equations to which no experimental data were available were calibrated, adopting default values or following an iterative procedure [15].
# Interspectral Error Tracking and Compensation of DSDT in Satellite Internet of Things
## 3 Adaptive Error Tracking and Equalization  
As mentioned in the previous section, the distortion in the combined signal originates from inconsistent amplitude, frequency, and phase errors among the sub-spectra. Since the combined signal is formed by reconstructing the sub-spectra transmitted across a wide frequency range, the impact of the sub-spectrum amplitude and phase errors is similar to that of frequency-selective fading channels. The carrier errors introduced by multiple RF channels can be considered as time-varying selective fading caused by Doppler frequency offsets. Therefore, this paper attempts to indirectly address the distortion issue in the reconstructed signal caused by sub-spectrum amplitude, frequency, and phase errors from the perspective of reducing Inter-Symbol Interference (ISI).  
Equalization is a common technique used to mitigate ISI. Among them, adaptive equalization methods update tap coefficients using training sequences to track slow time-varying errors and mitigate ISI [16; 17]. However, training sequences incur additional resource overhead. On the other hand, blind equalization algorithms do not require training sequences. Among them, the Decision Feedback Equalizer (DFE) based on the Bussgang technique has good tracking performance for non-linear distortions and is expected to correct the distortion caused by sub-spectrum errors in the combined signal [18].  
Therefore, referring to the DFE algorithm, two error compensation schemes are considered in the specific implementation: a conventional DFE equalization algorithm with sub-spectrum error tracking and an adaptive compensation algorithm.
# Quantum Authentication Evolution: Novel Approaches for Securing Quantum Key Distribution
## 5 QKD Authentication Model Setup and Details
### Mono-Authentication
In summary, our approach leveraged cutting-edge PQC signature algorithms in tandem with a robust PK infrastructure to realize efficient and quantum-resistant authentication for QKD. The utilization of this advanced cryptographic technique not only ensures heightened security against potential quantum threats but also maintains the operational efficiency of QKD systems, such as key generation rates. By integrating PQC authentication, the conventional role of trusted relays within QKD networks can be supplanted by innovative optical switches, ushering in a new era of decentralized and streamlined connectivity. Each user's interaction is streamlined through the acquisition of a singular digital certificate via the PK, enabling seamless direct links between any pair of users without the need for complex symmetric key distribution. Furthermore, the onboarding process for new network participants is simplified, requiring only the acquisition of a digital certificate rather than extensive key exchanges, thus facilitating swift integration into the QKD network fabric. In contrast to traditional pre-shared key methods, the adoption of PQC authentication brings tangible benefits in terms of operational simplicity and resource efficiency. Additionally, by reducing reliance on trusted relays, the security posture of the entire QKD network is fortified, ensuring resilience against potential vulnerabilities in the network infrastructure.