esa-sceva/satcom-chunk-collection huggingface dataset:
# Dependence of PV Module Temperature on Incident Time-Dependent Solar Spectrum
## 1 Introduction  
The operating temperature of photovoltaic (PV) modules plays an important role in the photovoltaic conversion process and, hence, on the energy assessment and on the economics of PV systems. The operating temperature depends on various environmental conditions, such as: the solar irradiance and its spectrum; the ambient temperature and wind speed; on material-dependent properties of the PV modules (cell technology, encapsulation, back-sheet material); properties needed for the calculation of the radiation heat transfer as albedo, sky temperature; and, on installation factors of the collector deployment in the PV field. Many expressions for the operating temperature have been proposed in the literatures, some are simplified working Equation as NOCT (Nominal Operating Cell Temperature), others are based on energy balance Equations, and many more on combinations of the energy balance Equation and NOCT model [1; 2; 3; 4; 5; 6; 7; 8]. Expressions for the operating temperature of the PV modules are either explicit forms directly giving the temperature or are implicit forms involving variables, which themselves depend on the temperature [3; 7]. The various models may be grouped into three types [4]: (a) constant temperature models (standard test condition (STC), NOCT), (b) physical based-models (such as I-V characteristics parameters, material band-gap, etc., and (c) statistical models (mathematical Equations derived from physical model; fitting coefficients to measured tested data and artificial intelligent methods). Therefore, it is expected to arrive to different operating temperatures of the PV module, also depending on the method used. The simulation of module temperature based on NOCT model is widely used, as it is simple and easy to apply and manufacturers usually include NOCT in the module characteristic data.  
The incident solar energy that is absorbed by the module is converted to electrical and thermal energies of which partly is dissipated to the external surrounding. This leads to the use of an energy balance Equation for formulating the operating temperature of the module and it further leads to a combined energy balance and NOCT model. The energy balance Equation and the combined energy and NOCT models consider the broadband irradiance and not the time-dependent solar spectrum. The proposed novel approach of the present study to determine the operating module temperature is based on a combination of the energy balance Equation and the time-dependent solar spectrum. This study calculates the operating temperature for four module technologies (c-Si, a-Si/\(\mu\)c-Si, CdTe, and CIGS for a site in the Israel desert) based on the proposed method and shows that the operating temperatures of the different cell types are close to the manufacturers' NOCT temperatures.
## 6 Conclusions  
Without a doubt, the visual cryptography scheme is one of the safest ways by which to protect confidential images and transfer them via mobile phones, computers, or over the Internet. In this paper, we propose advanced XOR-based (\(n\), \(n\)) visual cryptographic schemes with meaningful sharing. Unlike other VCS studies that focus on binary images, our study proposes two algorithms that can handle grayscale and color images. Our scheme can encrypt a secret grayscale (or color) image into \(n\) meaningful grayscale (or color) shares that can be imported into \(n\) different camouflage images. The theoretical analysis and experimental results showed that both of our schemes can completely recover the original secret image using the XOR operation and that they are secure. Our method can be applied to more situations than the existing schemes, most of which deal with binary images. We believe our technology can lay the foundation for the future.  
Conceptualization, J.S.[PERSON] and [PERSON]; methodology, [PERSON]-[PERSON]; software, [PERSON]-H.C.; validation, [PERSON] and [PERSON]; formal analysis, Y.-H.C.; investigation, [PERSON]-[PERSON]; data curation, [PERSON]-H.C.; writing--original draft preparation, [PERSON]-H.C.; writing--review and editing, [PERSON]-[PERSON]; visualization, Y.-H.C.; supervision, [PERSON]-[PERSON]; project administration, [PERSON]-[PERSON]; funding acquisition, [PERSON] and [PERSON]-[PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministry of Science and Technology of the Republic of China grant number MOST 110-2221-E-260-003, and 111-2813-C-260-033-E. And The APC was funded by MOST 110-2221-E-260-003, MOST 111-2115-M-260-001 and 11-2813-C-260-033-E.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Schemes**} & **Type of** & **Pixel** & **Number of** & **Number of** & **Meaningful** & **Decryption** & **Contrast** \\  & **Image** & **Expansion** & **Cover Image** & **Share Image** & **Share** & **Operation** & **Contrast** \\ \hline
[1] & Black and white & Yes & 0 & \(n\) & No & OR operation & 1/2 \\
[16] & Black and white & No & 1 & \(n\) & Yes & XOR operation & 1 \\
[18] & Black and white & No & \(n/2\) & \(n\) & Yes & XOR operation & 1 \\
[17] & Black and white & No & \(n\) & \(n\) & Yes & XOR operation & 1 \\ Ours & Grayscale and color & No & \(n\) & \(n\) & Yes & XOR operation & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparisons of capabilities of our scheme, [PERSON] and [PERSON]’s scheme [1], [PERSON] et al.’s scheme [16], [PERSON] et al.’s scheme [18], and [PERSON] and [PERSON]’s scheme [17].
# Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol
## 5 Experimental Results, Analysis, and Discussion
### Probability of Delivery Success  
Energy is one of the best and most efficient properties for FANETS nodes. FANETS usually have a small dry-cell battery and can operate for only a couple of minutes, about 30 to 50 min. The broad application of these FANETS is affected by this limited amount of energy. Optimizing energy in these nodes is hugely beneficial to improving the durability of FANETS.  
The energy used by various sensors, the energy used for communication between FANETS, and the power used for motor control to carry UAVs into the air are the three mechanisms in the Flying Adhoc network that use the most energy. The most crucial factor for optimizing the energy in FANETS is the probability of success in delivering the packet to the Base station. This parameter indicates how successtully the packet is delivered to the intermediate nodes based on an average number of packets received by the BS. As shown in Figures 10 and 11, our proposed protocol can send up to 75% of packets to BS, the LEACH and LEACH-C success rates are 9% and 16%. In our proposed protocol, it can be seen that efficiently selecting CH increases the network density and increases the probability of success for delivering a packet to BS. This is because, with the optimal number of clusters and optimal CH selection in a network, the packet drops ratio decreases.  
Figure 10: Delivery Success.  
Figure 9: Residual energy vs. rounds.
# Rate of Penetration Estimation with Parameter Correction
###### Abstract  
Accurate estimation of the Rate of Penetration (ROP) is essential for optimizing drilling operations, particularly in deep wells where traditional methods based on nominal depth measurements often fall short. These conventional approaches typically overlook dynamic influences such as drill string elongation caused by tension, hydraulic pressure, and thermal effects, leading to significant errors in ROP estimation. This study introduces a comprehensive method that integrates well depth correction and dynamic modeling of drill string elongation to enhance ROP accuracy. Furthermore, model-based filtering techniques are incorporated into a dynamic ROP estimation framework to improve real-time performance. Simulation results indicate that the combination of elongation corrections and filtering significantly reduces estimation errors and enhances reliability under varying drilling conditions. The proposed method provides a more accurate and robust ROP estimation framework, contributing to improved real-time monitoring and operational decision-making in drilling processes.  
rate of penetration; well depth correction; Kalman filter; parameter estimation
# Performance Analysis of Maximal Risk Evaluation Formulas for Spectrum-Based Fault Localization
## 4 Empirical Study
### Subject Programs and Test Suite
As discussed earlier, the eLOC column reports the number of executable statements collected by SLOCCount 2.26 ([PERSON] and [PERSON], 2019). The mutants generated from the UNIX utilities, the selected faulty versions of space and the _Siemens suite_, the number of manually mutated or selected faulty versions, and the total number of faulty versions provided in SIR are listed in the third column. The size of each test suite is listed in the second to last column--obtained from the individual \"universe\" test plan. In summary, our empirical study had 15 mutants for the three UNIX utilities, seven faulty versions for the space program, and 26 faulty versions for the _Siemens suite_. The size of the corresponding test suite ranged from 441 to 13,550 test cases, with an average of 3782.9 test cases per selected faulty version.
## 4 Field Experiments
### Water Spewing Problems  
The water spewing occurred from the 180 th ring, and became serious at the 198 th ring (Figure 9a). The difference was compared between the front pressure of the screw conveyor (\(P_{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{ \it{ \it{ \it{ \it{   }}}}}}}}{}}{}}{}}{}{}{{\it{{\it{ \it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{ \it{ }}}}}}}}}}}}{{{{{\it{ \it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{{\it{ }}}}}}}}}}}}}}}}{{{{{{{\it{ \it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{{\it{\it{{}}}}}}}}}}}}}}}}}}}{{{{{{{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{\it{{\it{\it{{\it{{\it{{\it{{\it{{}}}}}}}}}}}}}}}}}}}}}}{}}{}}}}}}}}}\)) and the outlet pressure of the screw conveyor (\(P_{\it{o}}\)) when water spewing occurred (the position of the pressure sensor  
Figure 8: Sections of excavated strata in the study area.  
is shown in Figure 9b). The \(P_{o}\) and \(P_{f}\) of the 160 th and 206 th rings during the construction process varies with the forward distance, as shown in Figure 10. The results showed that the \(P_{o}\) of the 160 th ring was lower (average 0.18 bar) and relatively stable, while the \(P_{o}\) of the 206 th ring was higher (average value was 0.56 bar), and the fluctuation was very obvious. When significant water spewing from the conveyor occurred, the outlet pressure was usually greater than 0.5 bar. Moreover, water spewing did not occur when the outlet pressure was less than 0.2 bar.  
A foam and bentonite slurry were mainly used to condition the excavated soil during engineering. According to the statistics of the 1-250 th rings, the average amount of foam solution liquid that was consumed was 43 kg per ring. The average construction time of each ring was 40 min, and the FIR was approximately 33% (FER = 10). The foaming agent (\(T_{\rm{hl}}\) = 8.2 min) was used in the 0-220 th rings, and the parameters were a 3% concentration, FER = 10 and FIR = 30-40%. Starting from the 220 th ring, the foaming agent was replaced by ZD30 foam (\(T_{\rm{hl}}\) = 27.4 min), and the foam using parameters remained unchanged. In addition, the amount of sodium bentonite was 10 wt%, the injection ratio was 5%-7%, and the apparent viscosity was 13 mPa.s.  
Figure 10: Changes in \(P_{o}\) and \(P_{f}\) with forward distance during the excavation of the 160 th and 206 th rings.  
Figure 9: Water spewing from the screw conveyor. **(a)** The water spewing at ring 198 th, and **(b)** the pressure sensor position.
# Assessment of Close-Range Remote Sensing Methods for DTM Estimation in a Lowland Deciduous Forest
## 2 Materials and Methods
### Remote Sensing Data Collection  
Four different close-range remote sensing data sources were examined in this study; UAVimage, ULS, TLS and PLShh. The detailed information on the data collection is summarized in Table 1. The PLShh data collection included placing georeferencing spheres on previously surveyed and stabilized points, TLS included in situ planning of multi-scan positions and placing registration/georeferencing spheres, UAVimage included placing the ground control point (GCP) markers (it did not include a GCP survey) and ULS included inertial navigation system (INS) initialization.  
Figure 1: The study area. **(a)** The location of the study area in Croatia; **(b)** leaf-off condition orthophoto from UAVimage dataset, unmanned aerial vehicle photogrammetry (UAVimage) trajectory and terrestrial laser scanning (TLS) stations; **(c)** unmanned aerial vehicle laser scanning (ULS) and handheld personal laser scanning (PLShh) trajectory on the leaf-on orthophoto produced from ULS onboard camera. Plot boundaries are represented by white circles in figure **(b,c)**.
## 2 Evolution of Maritime Communication Networks
### Reference Architecture of Multi-Layered MCNs  
The architecture of modern MCNs is inherently heterogeneous and multi-layered, integrating technologies across space, air, sea surface, underwater, and shore-based domains [10]. The reference architecture of a future MCN is illustrated in Figure 1, reflecting a multi-communication and heterogeneous ecosystem that spans five major domains: space, air, sea surface, underwater, and ground [26]. These domains collaborate to provide ubiquitous, adaptive, and resilient connectivity to ships, unmanned systems, and maritime infrastructure. The architecture enables not only traditional communication but also modern 6G-era services such as autonomous navigation, real-time monitoring, and distributed AI. At the top layer, LEO satellites provide global connectivity, acting as the backbone for data relaying and backhaul connections, especially in deep-sea areas. Flying nodes, such as HAPs, UAVs and Balloons, constitute the aerial tier, offering low-latency, high-throughput links to ships, offshore platforms, and buoys. These platforms can host mobile BSs, relay nodes, or edge servers, supporting real-time communication and computation services [27].  
On the sea surface, ships, buoys, and sea BSs form a dynamic network layer that handles short-to-medium range communication using a combination of terrestrial cellular (4G/5G), maritime VHF/UHF, and mmWave links. Channels for S2S and S2 Sh connections use LTE, Wi-Fi 6, and specialized maritime communication bands to facilitate navigation coordination, data exchange, and crew connectivity [7; 28]. Underwater communication is supported via acoustic links [29], optical communication [30], and magnetic induction [31], enabling interaction with AUVs and underwater sensors.  
This reference architecture also reflects the increasing demand for edge intelligence, with edge computing nodes deployed on ships, buoys, UAVs, or satellite payloads to enable local processing, reduce latency, and support AI-driven services. The entire MCN system requires tight coordination of routing, resource allocation, and link scheduling, given the dynamic topology, diverse QoS requirements, and intermittent connectivity that characterize the maritime environment. Table 2 summarizes the key MCN entities and their roles.  
Figure 1: A reference architecture for multi-layered heterogeneous MCN.
## 3 Results
### The Comparison of the \(\Delta p\) Model Developed by RSM and Ergun Model
#### 3.4.2 The Single Factor Analysis of the \(\Delta p\)
In order to further investigate the fitting performance of the \(\Delta p_{RSM}\) and the \(\Delta p_{Eg}\) in the above three single factor experiment, the statistical indexes mentioned in Section 2.3.3 were also calculated and the results are tabulated in the following Table 7.  
As can be seen from Table 7 that the \(R^{2}\) value of each single factor model based on the \(\Delta p_{RSM}\) is higher than that of the corresponding model based on the \(\Delta p_{Eg}\), while the other indexes (\(MSE\), \(MAE\), and \(RMSE\)) of each single factor model based on the \(\Delta p_{RSM}\) are \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Statistical Indexes**} & \multicolumn{3}{c}{**RSM Model**} & \multicolumn{3}{c}{**Ergun Model**} \\ \cline{2-7}  & \(\Delta p_{Mc}\) & \(\Delta p_{pair}\) & \(\Delta p_{L}\) & \(\Delta p_{Mc}\) & \(\Delta p_{pair}\) & \(\Delta p_{L}\) \\ \hline Determination coefficient (\(R^{2}\)) & 0.9755 & 0.9909 & 0.9918 & 0.8539 & 0.9846 & 0.9737 \\ Mean-square error (\(MSE\)) & 35.9195 & 89.4276 & 64.5378 & 213.7543 & 151.9289 & 206.1363 \\ Mean absolute error (\(MAE\)) & 5.6094 & 8.4528 & 6.3943 & 13.9521 & 10.1731 & 12.6444 \\ Root-mean-square error (\(RMSE\)) & 5.9933 & 9.4566 & 8.0335 & 14.6203 & 12.3259 & 14.3574 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The statistical results for the single factor experiments. Figure 9: The variations of \(\Delta p\) with \(L\) under the constant \(v_{air}\) of 0.35 m/s and \(Mc\) of 18.9 %w.b. lower than the corresponding index of the corresponding model based on the \(\Delta p_{Eng}\). Which indicates that the single factor model \(\Delta p_{Mc}\), \(\Delta p_{pair}\), and \(\Delta p_{L}\) based on the \(\Delta p_{RSM}\) has better fitting performance than that of the corresponding single factor model based on the \(\Delta p_{Eng}\). By combining the results obtained in Section 3.4.1, it can be concluded that the developed ventilation resistance model based on the RSM has a better predicting performance than the traditional Ergun model.
## 2 Related Work
### Separation of e-Government Services  
E-Government can be classified into different forms based on who the recipients of the services are.  
One of the main categories is G2C (Government to Citizen), where the government provides digital services to citizens. This allows access to public information and electronic transactions such as paying taxes or submitting requests for documents, as well as citizen participation in decision-making processes through interactive platforms.  
Figure 1: Unified theory of acceptance and use of technology [22].  
Another important category is G2B (Government to Business), which concerns services to businesses. In this context, government platforms facilitate processes such as licensing, tax management and regulatory compliance.  
In addition, there is G2G (Government to Government), which refers to the exchange of information and cooperation between different government organizations. This form of governance aims to improve efficiency, reduce bureaucracy and create a unified data infrastructure for decision-making.  
Finally, G2E (Government to Employee) concerns the provision of services to public employees, such as payroll management, training and access to internal government systems.
## 4 Case Study
### Virtual Sensor Fault-Detection Performance Metrics
#### 4.3.1 Accuracy  
Accuracy indicates the proportion of data out of the total data in which the virtual sensor correctly identifies the actual normal data as normal and the actual faulty data as faults. The accuracy ranges from 0 to 1. The proportions of TN and TP increase as the accuracy increases. The accuracy is calculated using the following equation:  
\[\text{Accuracy}=\frac{TN+TP}{TN+FP+FN+TP} \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Fault Type**} & **Offset** & **Simulation** & & **Offset** & **Fault Occurrence Scenario** \\  & **Type** & **Case** & & **Scenario** \\ \hline \multirow{3}{*}{Point anomaly} & \multirow{3}{*}{Point} & A-1 & \(-3\)\({}^{\circ}\)C & One hour each time, \\  & & A-2 & \(3\)\({}^{\circ}\)C & once a week \\ \cline{3-5}  & & B-1 & \(-3\)\({}^{\circ}\)C & \\ \cline{3-5}  & \multirow{3}{*}{Fixed} & B-2 & \(-1\)\({}^{\circ}\)C & \\ \cline{3-5}  & & B-3 & \(1\)\({}^{\circ}\)C & \\ \cline{3-5}  & \multirow{3}{*}{Random} & B-4 & \(3\)\({}^{\circ}\)C & From 16 August \\ \cline{3-5}  & & C & \(-3\)\({}^{\circ}\)C to \(3\)\({}^{\circ}\)C & to 31 August \\ \cline{3-5}  & & D-1 & \(-0.1\)\({}^{\circ}\)C/h (max \(-10\)\({}^{\circ}\)C) & \\ \cline{3-5}  & \multirow{3}{*}{Drift} & D-2 & \(0.1\)\({}^{\circ}\)C/h (max: \(10\)\({}^{\circ}\)C) & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation cases with various fault types.  
Figure 7: Confusion matrix according to fault-detection result values.
# An Overview of Shade Selection in Clinical Dentistry
## 2 Literature Review
### Measurement of Colour  
The manufacturing of indirect restoration of a tooth needs strong communication between dental practitioners and laboratory technicians. Moreover, the choice of shade has always been the most challenging phase in the restoration to maintain the natural esthetic of teeth [17]. Some previous studies stated that variances among practitioners about the matching of shade for the same teeth arise after a few days [18; 19]. Indeed, investigators have measured the matching of shade as a subjective technique, which is dependent on some influences, such as source of light, object and viewer [20].  
Shades can be measured using two techniques:  
1. Visual Technique;
2. Instrumental Technique.
# A Collaborative Domain Adversarial Network for Unlabeled Bearing Fault Diagnosis
## 4 Experimental Verification and Analysis
### Dataset Description
#### 4.1.2 PU Dataset  
The Paderborn University (PU) experimental setup is illustrated in Figure 4. The setup consists of an electric motor (1), torque measurement shaft (2), rolling bearing test module (3), flywheel (4), and load motor (5). The bearing data in the PU dataset corresponds to three bearing conditions: outer race fault (OF), inner race fault (IF), and normal (N).  
Some bearing faults were artificially created through discharge machining, while others occurred naturally over extended operational periods. There are four operating conditions (labeled as A, B, C, D) with different speeds and load torques, detailed in Table 5.  
Based on the causes of different bearing faults, the PU bearing dataset is categorized into eight distinct classes, as shown in Table 6. For each class, 500 data samples were randomly extracted from the original vibration signals under various operating conditions, with each sample length set to 2048. Domain adaptation tasks for the PU bearing dataset were conducted under different operating conditions, denoted as A\(\rightarrow\)B, B\(\rightarrow\)C, C\(\rightarrow\)D, and D\(\rightarrow\)A.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Labels** & **Fault Type** & **Fault Source** \\ \hline
0 & Healthy & Non \\
1 & OF & EDM \\
2 & OF & Carved \\
3 & OF & Drilling processing \\
4 & OF & Fatigue and pitting corrosion \\
5 & IF & EDM \\
6 & IF & Carved \\
7 & IF & Fatigue and pitting corrosion \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification of fault types in PU dataset.  
Figure 4: Paderburn University experimental platform [32].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor Position** & **Working Condition** & **Speed (\(\prime\)/min)** \\ \hline  & A (1 hp) & 1772 \\ Driver End & B (2 hp) & 1750 \\  & C (3 hp) & 1730 \\ \hline  & D (1 hp) & 1772 \\ Fan End & E (2 hp) & 1750 \\  & F (3 hp) & 1730 \\ \hline \hline \end{tabular}
\end{table}
Table 4: CWRU dataset working condition settings.