esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain Interoperability for Future Telecoms
## 3 Literature Review
### Future Telecom Use Cases with Blockchain Interoperability  
Figure 8 shows two telecom providers with three blockchains deployed in their infrastructure. Each of these blockchains interacts with third parties or service providers. Based on the initial use cases, blockchain interoperability can be achieved in the following scenarios:  
* National/International Roaming Access: Under agreed-upon conditions, a telecom service provider can connect to another national/international service/telecom provider, allowing users to use their roaming services seamlessly. Figure 6 illustrates this process.
* Roaming Payment Settlement: Without a roaming clearing house (intermediary), telecom providers can agree to pay to access customers' roaming records and settle payments directly with interoperating telecom providers.
* Mobile Number Portability: This can be introduced in countries where multiple telecom providers offer services, and users can port their services to other operators. However, this process currently utilizes trusted intermediaries managing the end-to-end process, causing delays. Sixth-generation technology is expected to offer broader services than 4G and 5G. Using 6G with new-generation mobile phones possessing eSIM (embedded SIM) capabilities can boost the need for MNP with almost no operational difficulties. Once blockchain interoperability is deployed in each operator, the layer can eliminate the intermediaries and accelerate the end-to-end process. The new process is illustrated in Figure 7.
* Third-Party Service Access and Payment Settlement: Telecom operators use third-party content services. Blockchain interoperability can automate access to such services and content. In this line, third parties can access operators' blockchain layers to offer services, and payments can be made automatically using blockchain interoperability layers.
* Integrated Sensing and Communications for Roaming Devices: A standalone blockchain integrated with decentralized DID components and ZKP ensures a privacy-preserving and tamper-proof architecture that eliminates eavesdroppers and external attacks. For national and international roaming, a side-by-side interoperability architecture enables seamless and secure connections of visitor devices by preventing unauthorized access and man-in-the-middle attacks.
* Networking Slicing and Dynamic Resource Sharing for External Operators: As shown in Figure 10, external parties and interconnected partners can access a mobile operator's resources using an interoperability architecture. A mobile operator may also conduct an auction if multiple parties are interested in utilizing network resources and spectra. Furthermore, a mobile operator can implement a blockchain-based crowd spectrum sensing model [52] in order to identify unused parts of the spectrum and offer the available spectrum to external parties through interoperability.
## 3 Results
### Authors, Population, Game Details, Goal, and Findings of Each Study  
The major characteristics for the papers, as described in Table 2, will be shown in charts and text. Each row in Table 4 corresponds to a different study. The first column shows the authors of the study and what year the paper was published. The second column shows the region the study was conducted in, how many participants there were in total, and any participant categories and how many participants were in each one. The third shows the game's name, what type of game it is, and what mnemonic types it used. The fourth shows a brief description of what the study is trying to achieve. Lastly, the fifth column shows a summary of important findings from the study.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Authors (Year of Publication)** & **Population (Sample Size and Demographics)** & **Game Name, Game Type, Mnemonic Type** & **Aim and Objective of Study** & **Summary of Findings** \\ \hline [PERSON] et al. (2022) [36] & Spain: 48; 21 experimental, 27 control & Roman Palace; puzzle and simulation; loci method & Compare the effectiveness of Roman Palace based on the local method in remembering words with simply receiving a list of said words. & The loci method was more entertainaining and efficient in terms of accuracy and time than merely memorizing a list of words.
# Using Convolutional Neural Networks for Blocking Prediction in Elastic Optical Networks
## 3 Using CNNs to Predict EON Status
Nevertheless, these values are appropriate for the IAT and HT used during the specific simulations run in this work and should be changed when varying the network-offered traffic profiles. To test and analyze the performance of the machine learning model, we compare the output of the model with the actual simulation output. As depicted in Figures 6 and 7, the snapshots at instants t = n-13, t = n-14, and t = n-15 t.u. before the actual blockage (at t = n) are labeled as leading-to-block. On the other hand, the snapshots that are chosen as not-leading-to-blocking to train the CNN are those for which a blocking event has not occurred until at least 100 t.u. have passed. A dataset of 90,000 leading-to-blocking and not-leading-to-blocking labeled data is created to train and test the CNN. The dataset consists of 90,000.csv files. Each one of these files corresponds to the status of all of the network links during one simulation time unit, recorded during different network simulation runs. A Python program is implemented to label the snapshots as leading-to-blocking or not-leading-to-blocking matrices. The  
Figure 6: Example of leading-to-blocking data: network faces blocking, where there is no continuous and contiguous FS available for the newly required connection. database generated is used to feed the machine learning algorithms used in this work and serves to train as well as to test the different models employed. As explained previously, we use a CNN to observe the pattern of the spectrum allocation in EONs to predict blocking situations. The prediction function might be seen as a binary classifier that divides the input matrices into two classes: not-leading-to-blocking and leading-to-blocking.
## 6 Conclusions  
In this paper, we first propose and analyze an efficient and accurate PL model for the 3D human I2O communication system at 2.45 GHz. Due to the limitation of the implanted sensor batteries, we have investigated and compared several established high efficiency modulation schemes. The threshold SNRs of BPSK, QPSK, 16 PSK and 16 QAM are approximately 11 dB, 13 dB and 15.5 dB and 18 dB respectively, when an acceptable predetermined BER of \(10^{-3}\) is adopted. Results demonstrate that the communication system can achieve satisfactory performance at relatively high data rates of 30 Mbps over distances of up to 1.6 m. Alternatively it can provide an highly reliable communication link for longer distances, at lower data rates (0.25 to 5 Mbps) by adopting the BPSK modulation technique. Based on the proposed I2O WBAN system, an energy efficient incremental routing protocol has been implemented and compared with the existing two-relay strategy. Simulation results demonstrate that our proposed data routing technique could significantly improve the performance of network lifetime, throughput and propagation delay. Open issues and standardization within the I2O WBAN area are summarized and explored as an inspiration towards developments in health IoT applications. Our future work involves radio channel measurements in biological phantoms to validate the PL model and communication system performance. Moreover, this can be extended to animal studies by multi-disciplinary collaboration with clinical professionals and biologists in order to overcome the difficulties of performing measurements in humans.  
This paper is completed by [PERSON] and [PERSON] under the guidance of supervisors [PERSON] and [PERSON]. [PERSON] and [PERSON] conceived and designed the research topics and analyzed the data. [PERSON] wrote the paper under the help of [PERSON].  
The authors declare no conflicts of interest.
# Additive Manufacturing of Variable Density Lenses for Radio Frequency Communications in X-Band
## 3 Horn Antenna and Gradient Index Lens Design
### Electromagnetic Impact of Support Structure  
To assess the scattering and diffraction effects of St. Andrew's cross lattice support, we conducted two complementary studies:  
* Full-wave simulation: The CST model was executed twice--once with the support structures included, and once with them omitted. A comparison of the resulting far-field patterns at 10 GHz showed a maximum change in main-lobe gain of 0.3 dB and an increase in sidelobe levels of no more than 0.5 dB. No additional lobes or ripples were observed.
* Experimental measurement: Using an anechoic chamber, we measured the radiation pattern of the lens-horn assembly both with and without the printed support. The resulting patterns aligned within \(\pm\)0.4 dB across the entire angular range, confirming the simulation findings.  
These results demonstrate that the selected lattice geometry and materials (PA11 and UPP) introduce minimal electromagnetic interference. Consequently, the support structure has a negligible impact on the overall antenna performance.  
Figure 10: Manufactured Prototypes: (**a**) feeding horn antenna, material Rigid 10K resin, technology VPP, sandblasted; (**b**) horn antenna after Ni+Cu autocatalytic coating; (**c**) X-Band lens assembly: feeding horn, support, and lens.
## 4 Results and Discussion  
Through the evaluations we sought answers to the following research questions:  
* **RQ1**: How effectively can we identify sensitive data and services based on context?
* **RQ2**: How effectively can we detect component protection levels?
* **RQ3**: How accurately can we identify vulnerabilities belonging to the CWE-653 category?  
The analysis of runtimes and resources was not conducted for the obtained findings.  
The performance of runtimes is significantly impacted by the load on the GPT API and the network throughput observed during the experiment.In terms of resource requirements, the running tools only needed to read the minified text files, CSV files, and JSON files, outputting the responses in the appropriate formats. The runtime of each step (which includes the sum of the response times of all the prompts for that step) is shown in Table 5. These results are included to demonstrate that the analyses are not particularly lengthy, running in less than 10 min even on the largest projects evaluated, and that there was no significant difference in processing times depending on whether the scripts were run on a more powerful desktop PC or an older, less powerful laptop, since the models were not run on them but accessed via the network as APIs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Content** \\ \hline Element & The name of the sensitive object or variable. \\ \hline Type & The type of the sensitive element, following a conviction similar to the list in prompt \\  & Appendix A.1. \\ \hline Level & The sensitivity level of the detected element. \\ \hline Origin & Userinput, service, or localstorage, the three most common sources of sensitive data in a web application. \\ \hline ServiceName & If the origin is service, then the name of the service which is the source of the sensitive element. \\ \hline Function & The role of the sensitive element in the component where it was detected. \\ \hline Goal & The fate of the sensitive element after its usage: stored, read, or service. \\ \hline GoalName & If the value of the goal field was service, then the name of the service to which the sensitive element is passed to. \\ \hline Classname & The name of the class in which the sensitive element was detected. \\ \hline \hline \end{tabular}
\end{table}
Table 4: The output structure of the sensitivity detection operation.
## 3 Methodological Background
### Gaussian Naive Bayes (GNB)  
The Naive Bayes (NB) algorithm is a supervised probabilistic classification method based on [PERSON] theorem. The NB method classifies the observations by calculating the conditional probability that they belong to a target class given the values of model features, as follows:  
\[P(y|X)=\frac{P(X|y)P(y)}{P(X)}=\frac{P(y)\prod_{i=1}^{n}P(x_{i}|y)}{P(X)} \tag{4}\]  
where \(P(y|X)\) denotes the posterior probability of the observation belonging to the class \(y\) given the set of features \(X\), \(P(X|y)\) denotes the likelihood of feature \(X\) given the class \(y\), and the \(P(y)\) and \(P(X)\) represent the prior probabilities of the occurrence of class \(y\) and feature \(X\) respectively, calculated from the training data. An important assumption in this method is the strong (naive) independence among the model features \(X\). Additionally, the denominator \(P(X)\) does not depend on the class \(y\) and the values of features \(X\) are given, so the prediction only depends on the enumerator in the formula above. In the study presented in this paper the Gaussian Naive Based (GNB) method is applied, which uses the Gaussian (normal) distribution for the likelihood functions of feature \(X\) given class \(y\):  
\[P(x_{i}|y)=\frac{1}{\sqrt{2\pi\sigma_{y}{}^{2}}}exp\left(-\frac{\big{(}x_{i}- \mu_{y}\big{)}^{2}}{2\sigma_{y}{}^{2}}\right) \tag{5}\]  
where the \(\mu_{y}\) is the mean and \(\sigma_{y}\) is the standard deviation of the likelihood function calculated for each feature in the training dataset using maximum likelihood. The GNB method has been used in various road safety studies [10; 24].
## 3 Materials and Methods  
This section describes the methodology and data used in the experiments and also includes a brief description of the machine learning algorithms used in the determination of the prediction models.  
There exist several methods for analytical processes among which the most popular methodologies are a Cross-Industry Standard Process for Data Mining (CRISP-DM) [36] and Sample, Explore, Model, and Assess (SEMMA) [37]. CRISP-DM and SEMMA are similar although they differ in the definition of the stages and the number of stages [37]. Our proposal consists of the following phases that result in an acceptable predictive model: (1) training (apply a machine learning algorithm to the training data set so that the model learns), (2) validation (estimate the error of a predictive model with unseen data), (3) hyperparameters (there is no way to know beforehand which parameters of the algorithm throw the best model, and it is therefore necessary to apply validation strategies), and (4) prediction (once the model is obtained, it is used to predict new observations). Figure 5 presents a flowchart of the proposed method.  
\begin{table}
\begin{tabular}{c l c c c} \hline
**ID** & **Location** & **Altitude (m)** & **Latitude** & **Longitude** \\ \hline
**C406G** & La Orotava & 2150 & 28\({}^{\circ}\)13’27” & 16\({}^{\circ}\)37’35” \\
**C419X** & Adeje & 130 & 28\({}^{\circ}\)4’53” & 16\({}^{\circ}\)42’40” \\
**C428T** & Arico & 418 & 28\({}^{\circ}\)10’52” & 16\({}^{\circ}\)29’1” \\
**C429I** & Tenerife Sur Aeropuerto & 64 & 28\({}^{\circ}\)2’49” & 16\({}^{\circ}\)33’40” \\
**C430E** & Izaña & 2371 & 28\({}^{\circ}\)18’32” & 16\({}^{\circ}\)29’58” \\
**C438N** & Candelaria & 463 & 28\({}^{\circ}\)21’32” & 16\({}^{\circ}\)24’5” \\
**C439J** & Gümar & 115 & 28\({}^{\circ}\)19’6” & 16\({}^{\circ}\)22’56” \\
**C446G** & San Cristobal de La Laguna & 868 & 28\({}^{\circ}\)31’36” & 16\({}^{\circ}\)16’50” \\
**C447A** & Tenerife Norte Aeropuerto\({}^{*}\) & 632 & 28\({}^{\circ}\)28’39” & 16\({}^{\circ}\)19’46” \\
**C449C** & SC de Tenerife\({}^{*}\) & 35 & 28\({}^{\circ}\)27’48” & 16\({}^{\circ}\)15’19” \\
**C449F** & Anaga & 19 & 28\({}^{\circ}\)30’29” & 16\({}^{\circ}\)11’44” \\
**C457I** & La Victoria de Acentejo & 567 & 28\({}^{\circ}\)26’5” & 16\({}^{\circ}\)27’17” \\
**C458A** & Tacoronte & 310 & 28\({}^{\circ}\)29’47” & 16\({}^{\circ}\)25’12” \\
**C459Z** & Puerto de la Cruz & 25 & 28\({}^{\circ}\)25’5” & 16\({}^{\circ}\)32’53” \\
**C468B** & San Juan de la Rambla & 370 & 28\({}^{\circ}\)23’23” & 16\({}^{\circ}\)37’47” \\
**C469N** & Los Silos & 28 & 28\({}^{\circ}\)22’43” & 16\({}^{\circ}\)49’3” \\ \hline \end{tabular}
\end{table}
Table 1: Main weather stations on Tenerife Island. The asterisk \({}^{*}\) identifies the selected Tenerife North Airport and Santa Cruz de Tenerife weather stations.  
Figure 5: General flowchart of the proposed method.
# The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather
## 2 F-H Impacts on Photovoltaic Output and Load Forecasts
### The Influence of F-H on Photovoltaic Output  
The discontinuity, instability and uncertainty issues surrounding photovoltaic output are directly affected by meteorological factors, in which different weather conditions lead to different incident light intensity on PV panels. The incident light on PV panels is weakened only by atmosphere and clouds when no F-H is present. Otherwise, some light intensity is reflected and absorbed by F-H. Light intensity and photovoltaic output are further weakened with a high AQI level. Moreover, more dust appears on PV panels with increasing AQI level, which greatly restricts photoelectric conversion efficiency, as well. Thus, timely dust cleaning is needed.  
The AQI values of Baoding, China, from 24-26 February 2015 are shown in Table 1, and the photovoltaic outputs of the polycrystalline silicon photovoltaic array (PSPA, 10 kW) in the State Key Laboratory of New Energy Power System (SKL of NEPS) at the North China Electric Power University (NCEPU, Baoding) during the same period are shown in Figure 2.  
As shown in Table 1 and Figure 2, the daily photovoltaic outputs (kW) of 24 February, 25 February and 26 February are 34.31 (\(L_{\text{AQI}}=2\)), 43.64 (\(L_{\text{AQI}}=4\)) and 58.06 (\(L_{\text{AQI}}=6\)), which indicates that photovoltaic output decreases with increasing AQI level.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Date** & **AQI** & **AQI Level** & **PM2.5** & **PM10** & **CO** & **NO2** & **SO2** \\ \hline
24 February 2015 & 337 & Serious/6 & 265 & 442 & 4.13 & 58 & 111 \\
25 February 2015 & 180 & Moderate/4 & 136 & 247 & 2.2 & 36 & 68 \\
26 February 2015 & 98 & Good/2 & 68 & 108 & 1.89 & 38 & 92 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Air quality of Baoding, China, from 24–26 February 2015. AQI, Air Quality Index.  
Figure 2: Photovoltaic outputs of the polycrystalline silicon photovoltaic array (PSPA) (10 kW) in the State Key Laboratory of New Energy Power System (SKL of NEPS) at the North China Electric Power University (NCEPU) from 24–26 February 2015.  
In addition, the number of effective power generating hours (NEPGH) of PV panels may decrease with increasing F-H, which could also reduce photovoltaic output. Data from an empirical research platform of a photovoltaic system at the Chinese Academy of Sciences (Shanghai) indicated that the NEPGH was 2.79 on 4 December 2013 when F-H did not exist. However, the NEPGH was reduced to only 0.7, which was a reduction of 74.91%, on 6 December 2013, when the air condition included severe pollution. Therefore, the impact of F-H should be considered during the prediction process of photovoltaic output.
# Design and Assessment of an Austenitic Stainless Alloy for Laser Powder Bed Additive Manufacturing
## 2 Materials and Methods
### Optimization of Alloys with a Genetic Algorithm
#### 2.2.2 Genetic Algorithm for the Global Optimization  
The multi-objective optimization was conducted using the NSGA-II algorithm [66], which was implemented using the pymoo Python package [67]. The optimization was performed on a population of 100 alloys, with the same number of offspring, to ensure a constant population size while keeping computation times reasonable. At each optimization step, new individuals were generated through simulated binary crossover with a distribution index set to \(\eta=20\). The algorithm also used polynomial mutation with a distribution index set to \(\eta=20\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Element** & **Fe** & **Cr** & **Ni** & **Co** & **Mn** & **Al** & **Ti** & **Nb** & **Mo** & **W** & **Si** & **C** \\ \hline Min & 30 & 15 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.02 \\ \hline Max & 100 & 35 & 35 & 25 & 25 & 5 & 2 & 2 & 5 & 2 & 5 & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The compositional search space considered for the optimization (in wt.%).  
The objective functions--i.e., quantities that must be either maximized or minimized--and constraints--i.e., binary conditions that must be fulfilled--are summarized in Table 3 and were employed to evaluate and sort the various alloys within the population. The optimization algorithm followed a three-step process. In the first step, the Hull diagram was used to rapidly assess the potential of an alloy for being austenitic (constraint \(a\) in Table 3). If this criterion was not met, the alloy was penalized by setting low fitness scores for all the other criteria and non-feasible constraints, thereby saving computational time. In the second step, the Scheil model was applied using Thermo-Calc, and its outputs were used to estimate constraints \(b\) to \(e\) and objective \(g\) in Table 3. In the third step, additional objectives were calculated using GPR-ML for objective \(h\) and analytical formulas were used for objectives \(f\) and \(i\) to \(l\). The convergence of the algorithm is measured by the replacement rate: beyond 40 generations it drops to a rather low value of 1% of the population per step, indicating a good convergence. The optimization loop was terminated after 50 generations, for a replacement rate of 0.5% per step.