esa-sceva/satcom-chunk-collection huggingface dataset:
# 3D Reconstruction of Cultural Heritage Sites as an Educational Approach. The Sanctuary of Delphi
###### Abstract
The current investigation aim is (a) to develop the 3D model and for students to become acquainted with the use of modern 3D imaging technology focused on the Delphi sanctuary, (b) to identify students' desired learning outcomes, (c) to reconstruct for first time a pilot model for the Delphi environment, and (d) to conduct an exploratory study. Its innovation lies mainly in the development of some data of serious gaming in University education of archaeology and history students, on cultural heritage assets, and in the making together of the frame of knowledge that serves their needs. Moreover, the environment is not simulated with false virtual data but verisimilitude with accuracy and real data and accurately positioning objects and monuments, avoiding arbitrary settings making the learning environment closer to the real one. The aim of the application is twofold; first to let students virtually visit an important archaeological site, and second to deliver to them the associated educational material whilst virtually 'being there' in a fun and exploratory way. There are many archaeology students worldwide, and even in Greece, that have not yet visited the site of Delphi, and would like to have this experience. Of course, plenty of media is available about the place and its buildings, but a presentation in a virtual environment has some advantages: users are free to explore the environment at their own pace and observe the constructions from the desired distance or angle. Additionally, it is more effective to learn about a place and its history when the information is delivered through a visual and interactive experience. Instead of learning the bulk of information from a textbook, students access on demand the educational media associated to the place they are currently located in the virtual environment. Along with the aim, research questions posed such as (a) which is the scientific contribution of answering such questions? (b) how will the academic community or professional use the results to further expand research? and (c) what the effectiveness of the proposed virtual environment for learning purposes is, together with questions related to the posed exploration questions during the implementation of the project (see end of Section 3), are important issues which have been satisfied. The following sections unfold as follows: an overview of related work with basic theories, the rationale and design of a virtual educational tour in the sanctuary of Delphi, the virtual construction of the environment, the educational content, the exploratory study, and then, the results and conclusion.
# Performance Analysis of Dual-Hop DF Multi-Relay FSO System with Adaptive Modulation
## 4 The Performance Analysis of Dual-Hop Single-Relay System
### Modulation Level Selection Probability  
Modulation level selection probability (MSP) is an interesting performance metric that shows the behavior of the adaptive system in selecting the modulation level which depends on the received SNR. The modulation level selection probability can be expressed as  
\[P_{ms}(i)=P_{r}\{\gamma_{i}\leq\gamma_{I}\,<\,\gamma_{i+1}\}= \begin{cases}F_{\gamma_{I}}(\gamma_{i+1})-F_{\gamma_{I}}(\gamma_{i}),&i=1, \ldots,N-1\\ \overline{F}_{\gamma_{I}}(\gamma_{i}),&i=N\end{cases}. \tag{14}\]  
Note that \(F_{\gamma_{I}}(\gamma_{N+1})\to 1\).
# A Web of Things-Based Emerging Sensor Network Architecture for Smart Control Systems
## 4 Proposed Framework
### Overview of the Proposed Framework  
The household appliances are fixed at specific places and they are discovered with the help of specific ids assigned to the sensors that are controlling the appliance. Each new appliance is first registered with a sensor in the MS. Therefore, every HTTP request for an appliance always passes through the MS. Appliance registration in the MS reduces the performance degradation caused by the device discovery process. In order to make the proposed framework work as fast as possible, the proposed RESTful API always communicates with the MS. The MS works as a bridge between the web and the household appliances. Apart from the household appliances control, the working of the RESTful API framework is divided into two modules, i.e.: (1) Process Management Module and (2) Presentation Module. Multiple requests for a single household appliance are divided into multiple processes. Similarly, each process is assigned an ID that distinguishes the respective household appliance/s or sensors deployed in the WoT-ESN. A process is alive until the household device is functioning properly. Once the household appliance is removed from the home, the process terminates autonomously. Whenever a client generates a request, the process of the respective household appliance redirects the request to the resource repository available in the MS. This gives the user many options to retrieve information. For example, if a client requests the energy consumption information of a household appliance, the process shows monthly energy consumption and current energy consumption. Thus, the home users can customize the information to be displayed from the process management. Moreover, multiple clients can generate more than one process for a single household appliance. However, a smart home user can only access services of his own home WoT-ESN. The multi-processing functionality supports multi-user environments, while facilitating a smooth and user-friendly environment for the WoT-ESN. Moreover, the presentation module provides accessibility to the RESTful API from the Web. The smart home users can use any web-browser to view the status of their smart home WoT-ESN owing to the integration of RESTful WoT. Each household appliance is represented as a hyperlink with the name of the appliance. The user can explore the desire services by clicking the links of the respective appliance. The entire web architecture of the smart home is shown in Figure 1.
# Connectivity Analysis in VANETs with Dynamic Ranges
## 4 Discussion
### Single-Hop Probability of Connectivity  
#### 4.2.1 Connectivity with Density  
We plot the impact of vehicle density on the inter-vehicle connectivity in Figure 6 using a fixed SNR \(\gamma_{th}=10\) dB which signifies an ideal highway environment. A general increase can be noticed in the inter-vehicle connectivity probability with increasing vehicle densities. The static ranges due to constant transmitter power, \(P_{t}=0.5\) W maintain a more fairly stable connectivity than the connectivity induced by the dynamical ranges from the different fading environments. Moreover, the synchronised traffic flow model has a better maintained connectivity than for exponential traffic due to similarity within group model scenario where the inter-vehicle distances are sustained for a longer period of time. Generally as the channel conditions improve, for the case of communication environments with \(m=3\) and \(C=6\) from urban environments, the connectivity probability is improved regardless of wider inter-vehicle distance (for \(\alpha=3\) in Figure 6b) is worse for Rayleigh dynamic ranges where the LOS path dominates. Therefore, the only significant factor affecting connectivity is the vehicle density.  
Figure 5: Weibull Range distributions for \(\alpha=1,2,3,4,5\). (**a**) Weibull PDF. (**b**) Weibull CDF.
## 2 Materials and Methods
### Method
#### 2.3.1 The Linear Relationship of EC GPP and OCO-2 SIF at Different Bands and Timescales  
Figure 2 shows the workflow of this study. First of all, we tried to evaluate the performances of SIF-GPP models in terms of the timescale. Therefore, SIF-GPP models were established using instantaneous and daily SIF data, respectively. We selected the OCO-2 product within 10 km of the EC tower for comparison with the GPP product of the EC flux tower. The SIF product of OCO-2 is the instantaneous data in two bands, 757 nm (ins757) and 771 nm (ins771). We also converted the instantaneous SIF data to daily data (SIF757 and SIF771) using the daily average correction factor from the OCO-2 L2 file. Since the average of SIF771 is 1.2 to 1.5 times smaller than SIF757, we multiplied the value of SIF771 by 1.5. For the convenience of comparison, the value of SIF771 is multiplied by 1.2-1.5. We multiplied the value of SIF771 by a factor of 1.5. The instantaneous GPP (insGPP) is calculated as the average of hourly/semi-hourly GPP data between 1:00 and 2:00 pm local time. In this study, all EC tower stations provide semi-hourly GPP except US-PFA, which provides hourly GPP data. On that basis, linear correlations between instantaneous SIF data (ins757 and ins771) and instantaneous GPP (insGPP) were established and analyzed. Moreover, we converted the hourly/semi-hourly GPP data of EC towers to daily GPP after filling some missing data using an interpolation process. Then, we have examined the linear correlation between daily SIF (SIF757 and SIF 771) and daily GPP. Furthermore, we compared performances of linear models those utilize SIF data of different bands (757 nm and 771 nm) as independent variables at both timescales to find out which SIF data is more suitable for estimating GPP. In evaluations of linear models, goodness of fit, R\({}^{2}\), is used to describe the degree of interpretation of GPP by SIF. The correlation coefficient (R) indicates how close the two variables are. We used p to represent the significance of the model. It is generally considered that the linear analysis is significant when p value is less than 0.005.  
Figure 2: The flow diagram of this study.
## 4 Results and Discussions
### Evaluation of Rainfall Forecast  
Forecasting precipitation remains very challenging, and the skill and accuracy of quantitative forecasts of precipitation are generally low, even over well-instrumented regions. Figure 8 compares the 6-h (from 03 UTC-09 UTC) accumulated rainfall forecast by ARPS and CALDAS, and estimated by the TRMM satellite on 6 June 2006. As shown in Figure 8a, the location of the rainfall from ARPS was completely different from the location where rain was actually observed by TRMM (Figure 8c). In contrast, the assimilation of the 89-GHz information for cloud data produced rainfall at the same locations as observed by TRMM. Although the rainfall amounts predicted by CALDAS did not correspond completely with the TRMM retrievals, the locations of the heavy rainfall were better predicted by CALDAS. In the case of ARPS, the spatial correlation of rainfall with TRMM was very low (R = 0.01), whereas in the case of CALDAS, the spatial correlation of rainfall with TRMM was improved significantly (R = 0.47).  
The quantification of model accuracy, errors, and uncertainties is crucial in determining the reliability of model forecasts, but it is a very challenging task without accurate observations in this study region. In this research, we utilized satellite retrievals (_i.e._, soil moisture, cloud distribution, and rainfall) because of the unavailability of ground-based observations (except radiosonde data). However, these products are subjected to significant uncertainties due to bias of retrieval algorithms (e.g., soil moisture measurements obtained from JAXA and NASA differed greatly, even though data from the same sensor were used for the retrievals), few calibration and verification practices, low spatial resolutions, and the indirect nature of satellite measurements, which included uncertainties from various factors (e.g., estimation through cloud top reflectance, thermal radiance, and penetration depth of microwave signals). Consequently, our present discussions on validating model outputs with satellite retrievals are based mainly on statistical correlations, because the retrievals capture spatial distribution well, even though the absolute values differ among the similar products used. Conversely, further coordinated efforts are required to organize more intensive and comprehensive field observations to calibrate and assess the uncertainties of the retrievals, as well as to validate model outputs quantitatively.  
Figure 7: Same as Figure 6 but for 2230 UTC on 6 June 2006; (**a**) potential temperature (K) and (**b**) specific humidity (g/kg).
## 3 Research Methodology
### Data Collection  
We utilize nine email datasets commonly used in phishing and spam detection research to answer RQ2 (models' effectiveness on multiple individual datasets and a merged dataset). In addition, we have created a merged dataset of all nine public datasets. The following list summarizes the datasets used in our framework.  
* Ling: Derived from the Linguist List online email communication in the early 2000s [36], it consists of 458 spam and 2401 legitimate emails. This dataset is useful for linguistics-related studies but is limited in scope and size.
* Enron: The dataset was extracted from the Enron email corpus around the year 2005 [37]. It consists of \(13,976\) spam and \(15,791\) ham emails. It includes extensive corporate communications but with limited diversity.
* SpamAssassin: Developed by the Apache Software Foundation from 2002 to 2006 [38], it consists of 1670 spam and 3928 ham emails. It provides diverse public spam emails but might contain outdated tactics.
* TREC: It includes TREC-05 (2005), TREC-06 (2006), and TREC-07 (2007), focusing on diverse email communications [39; 40]. TREC-05 consists of \(20,643\) spam and \(31,842\) ham emails. Its diverse sources enhance utility but may include specific period biases. TREC-06 consists of 3642 spam and \(12,079\) ham emails. It is useful for text classification but may not mirror real-world interactions. Dataset TREC-07 consists of \(29,093\) spam and \(24,338\) ham emails. It is a large and diverse dataset, but its periodic nature may affect consistency.
* CEAS: This dataset is from the CEAS 2008 Challenge Lab Evaluation Corpus [41]. It contains \(21,839\) spam and \(16,853\) ham emails. It captures a broad range of communications but may reflect dated spam characteristics.
* Nazario_5: It is a balanced dataset of different sources [42]: 1469 phishing and 1482 legitimate emails. It is useful for model testing; however, it lacks broad phishing tactic coverage.
* Nigerian_5: This is a publicly available dataset featuring advance-fee scans from 1998 to 2007 [43]. It contains 1586 scam emails and 2967 legitimate emails.
* Our merged dataset: We integrated all nine datasets, covering various features and fields. This merged dataset contains \(206,057\) emails, of which 94,376 (45.81%) are phishing emails, and 111,681 (54.19%) are safe emails. The spam/scam emails from all datasets were labeled as phishing emails. We have used the Pandas library in Python to combine the data into a single DataFrame. To balance this dataset, we used the down-sampling technique to reduce the number of safe emails to match the number of phishing emails.  
Figure 1: Overview of the Proposed Framework.
# Secure and Efficient Authentication Scheme in IoT Environments
## 3 Proposed ECC-Based Lightweight Authentication Scheme
### Registration Phase
Then, every time communication is initiated with \(TS\), the owner's device is authenticated by \(TS\) through an open communication channel, and then, after successful authentication, the device sends an encrypted message to \(TS\) using their session key \((S_{k})\).
# Production of Photovoltaic Electricity at Different Sites in Algeria
###### Abstract  
The necessity for further growth in the demand for electricity, particularly in the remote countryside, has been essential to Algeria's economic and social progress in recent years. The Saharan regions account for around 80% of Algeria's surface and have significant power demands. These areas are distinguished by their scattered population, extremely hot weather with high radiation levels (7 kw h/m\({}^{2}\)/day), and minimal energy use. Photovoltaic technology is one of the finest ways to harness solar power. The conversion of radiant energy (light quanta) into electrical energy can be achieved with the use of semiconductor materials. The effects of a solar cell depend on parameters such as solar cell technology, the position of the photovoltaic array, climate, and geographic data of the site. This paper presented: the role of site choice in the production of photovoltaic electricity. The results obtained for five sites in Algeria: Ouargla, Algers, Bechar, Sidi Bel Abbes, and Batna, confirmed that desert sites (Ouargla, Bechar) have a good photovoltaic efficiency result with large solar power irradiance.  
solar photovoltaic; irradiance; efficiency 20 October 2022020 October 2022020 October 2022020 October 2022020 October 202202020 October 202202  
the world lacks access to electricity. Temperature and insolation both affect how much electricity a photovoltaic (PV) generator can produce. Due to the changing nature of the PV generator's output, a battery is required. An inverter is used to change the direct current (DC) power generated by solar cells into alternating current (AC) [3].  
In Algeria, which has a high insolation level, there are around 3300 h of sunlight every year. Although the environment is generally conducive to the use of solar energy, the dispersion of the solar radiation is not well understood [4]. The major goal of this study is to compare observed data for several sites in Algeria, including Ouargla, Algers, Bechar, Sidi Bel Abbes, and Batna, with findings from the use of certain models that forecast daily and monthly radiation as well as photovoltaic power on a horizontal surface. As a result, the best models are chosen.
# A Comprehensive Review of Face Morph Generation and Detection of Fraudulent Identities
## 3 Results  
In the recent past, the research community has proposed noteworthy methodologies for MAD. Different techniques have been used by adopting various modifications to achieve maximum accuracy using different datasets. Different tools for morph image creation have been used. Different pre-processing methods have been utilized to remove artifacts from the created morphed images. The previous work has been summarized in Table 2 for comparison and analysis. Few of the reported studies have shown very impressive results. But most of the reported results are derived from less challenging data. Experimental data lacks variation in lighting conditions and facial poses. Moreover, very limited data with eyewear and headgear have been added for experimentation. Facial hair can also pose morph detection issues. These issues are totally or partially missing from the reported research to date [1, 2, 20, 21, 41]. Moreover, the morphed images can be created with dynamic weight scores of different subjects [5]. Morphed images used in these studies only contain the merger of not more than two persons. Most of the research in the literature fails to deal with images having headgear or eyewear. Furthermore, very high-quality images are used for experimentation. Low quality will pose a detection problem because of noise residuals and artifacts. Another very important problem is the usage of two-morph images only. It is very possible that the attacker might use n-morph images, with varying weight contributions. N-morph images will be more challenging to detect if n > 2. It is also assumed that simple scripts and applications like FaceMorpher, OpenCV, and FaceFusion are used for morph creations. Resultant images are easy to detect, resulting in high accuracy.
## 6 Seismic Sedimentology Technology
### Stratal Slicing  
Stratal slicing has emerged as a novel technique in basin analysis and sandbody characterization, distinguished by its high resolution and ability to delineate depositional interfaces with enhanced precision. This approach facilitates the generation of numerous  
Figure 5: Seismic wavelet amplitude spectrum in the Dainan Formation of the Gaoyou Sag.  
sequence framework-constrained slices, thereby providing robust support for basin-scale investigations and sequence stratigraphic studies [10; 11].  
As extensively documented in sedimentological research, sedimentary bodies typically exhibit lateral dimensions significantly exceeding their vertical thickness [2]. Consequently, seismic attributes derived from planar distributions prove particularly effective in identifying thin-bedded deposits, which often challenge detection through vertical resolution-limited seismic interpretation. This fundamental characteristic underpins the development of striatal slicing technology. The interpretation of striatal slices constitutes an integrated research process that synthesizes well log data, core analysis, and depositional geomorphological patterns to systematically characterize depositional systems and their domains [10; 11; 15; 16]. The litho-seismic relationship manifests through specific amplitude patterns. (1) Positive amplitudes typically correspond to shale intervals, whereas negative amplitudes indicate sandstone layers or condensed sections. (2) Variable/low-amplitude responses may represent thin-bedded sandstone/shale alternations, while prominent negative amplitudes often correspond to gas-bearing sandstones [17; 18; 19; 20; 21; 22; 23]. These diagnostic relationships demonstrate the critical value of striatal slicing in reconstructing sedimentary facies and evolutionary histories from seismic datasets. The seismic profiles from the Dainan Formation in the Gaoyou Sag's deep depression zone present exceptional characteristics for seismic sedimentological analysis, including superior data quality, a high signal-to-noise ratio, and well-defined seismic waveform features. This investigation employs striatal slicing combined with seismic attribute interpretation to reconstruct the dynamic evolution of depositional systems and associated sandbodies within sequence stratigraphic frameworks. The resultant findings provide substantial geological evidence supporting the exploration of lithologic traps in this hydrocarbon-prone basin.
## 3 Proposed Methodology
### Automated Deep Learning with AutoGluon
#### 3.2.1 Data Preprocessing  
The preprocessing step is fully automated, minimizing the need for manual intervention and ensuring consistency across training folds. AutoGluon first determines the predictive task and categorizes each input feature into one of four types: numerical, categorical, text, or temporal.  
For numeric features, standardization or normalization is applied as appropriate. Categorical variables are encoded using one-hot or ordinal encoding, and missing values are handled using statistical imputation or assigned to an \"unknown\" category. Textual features are processed using natural language processing (NLP) techniques, such as _n_-gram tokenization and embedding generation, while temporal variables are converted into numerical format. Features deemed uninformative (e.g., unique identifiers) are automatically excluded from the pipeline.