esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network
## 4 Results and Discussion
### Generalizability of the Proposed Model and Ablation Study
#### 4.3.2 Ablation Study  
We conducted an ablation study to evaluate the contribution of each component within the proposed hybrid CNN-GNN-ViT model. The effectiveness of each stage was systematically assessed and measured by its impact on classification performance using metrics such as accuracy, precision, recall, and F1-score.  
Figure 6: Sample images of Tuberculosis (TB) Chest X-ray Dataset.  
Figure 7: Sample images of Pneumonia Chest X-ray Dataset.  
CNN Only  
In this stage, we evaluated a Convolutional Neural Network (CNN) composed of convolutional and pooling layers, batch normalization, dropout regularization, and fully connected layers. This model primarily captures local spatial features within chest X-ray images. In the Tuberculosis dataset, the CNN achieved an accuracy of 91.67%, a precision of 95.45%, a recall of 75.00%, and an F1-score of 80.95%. On the Pneumonia dataset, the same model yielded 86.02% accuracy, 82.14% precision, 80.33% recall, and an F1-score of 81.16%. While the CNN was capable of detecting prominent local features associated with pulmonary diseases, its limited receptive field hindered the ability to model long-range spatial dependencies across image regions.
# On the Quality of Deep Representations for Kepler Light Curves Using Variational Auto-Encoders
## 2 Materials and Methods
### Light Curve Representation Methods  
A _light curve_ is a time series (function of time) containing measurements of the light intensity of a celestial object or region. When one celestial body crosses in front of another astronomical object and blocks any fraction of its light, it is called a transit. In this section, we briefly introduce different representation approaches used to study transit light curves (detection, vetting, classification, characterization) and related fields with model-based and self-generated representations.
## 3 Optimization Modeling for the Uniformity Design of Accuracy Lifetimes
### Modeling of Constraint Conditions  
First, the constraint conditions of the designed values \(\Delta\overline{v}_{ei}^{\prime}\) should be determined. The accuracy loss velocity of every component is closely related to factors such as the structures, materials and working conditions. For some common or typical components, these factors generally vary within a certain range. Therefore, the average loss velocity of each designed variable \(\Delta\overline{v}_{ei}^{\prime}\) can be limited to a certain range:  
\[\Delta\overline{v}_{ei1}\leq\Delta\overline{v}_{ei}^{\prime}\leq\Delta \overline{v}_{ei2}. \tag{11}\]  
Here, \(\Delta\overline{v}_{ei1}\) and \(\Delta\overline{v}_{ei2}\) are the lower bound and the upper bound, respectively, which are determined by the allowable structures, materials and working conditions of every component.  
Then, the relationships between the average loss velocity of each designed variable \(\Delta\overline{v}_{ei}^{\prime}\)  
Here, \(\delta_{Ai}\) should be known. In addition, \(\rho^{\prime}\) should be bigger than the required uniformity; that is,  
\[\rho^{\prime}\geq\varepsilon. \tag{13}\]  
There may be other constraint conditions. For example, the total improvement cost should be less than \(C\); that is, \(C_{\mathrm{s}}=\sum\limits_{i=1}^{n}C_{i}\leq C\). Another example is that the accuracy lifetime of a system should reach the designed lifetime \(T_{0}\); that is, \(T^{\prime}\geq T_{0}\). In brief, the objective functions and constraint conditions should be selected according to the specific conditions of the measurement systems, design requirements and implementation difficulties.
# Energy Recovery via Thermal Gasification from Waste Insulation Electrical Cables (WIEC)
## 4 Results
### Load Gasification Analysis  
Syngas composition depends strongly on gasification temperature, since the mixture for all tests is the same [46]. When working with a load (engine controlling the operation) the suction of the engine is greater than the vacuum pumps (controlled tests by vacuum pumps), leading to an increase in temperature inside the reactor, an increase in the air intake, and higher consumption of biomass.  
The volumetric fractions of the combustible gases CO, H\({}_{2}\), and CH\({}_{4}\) present in syngas in the co-gasification tests at different loads are shown in Figure 4. Similar to the controlled tests, samples were taken at 60, 120, and 160 min.  
The main gases found in the synthesis gas are CO, H\({}_{2}\), and CH\({}_{4}\), which are resulted from the reduction process occurring inside the reactor, and their concentrations may vary according to the temperature [47]. During the tests performed, the largest volume fraction is of CO followed by H\({}_{2}\). The CO concentration reaches optimum when the amount of air in the reduction zone decreases. Under these conditions, the partial oxidation reaction of char and air in the reduction zone results in an increase in CO and temperature. This is due to the water-gas shift reaction producing more CO than H\({}_{2}\) and CH\({}_{4}\). This tends to increase with higher temperatures, although H\({}_{2}\) production also tends to increase, as shown by the sequence of reaction rates gas water > Boudouard >> methan [6]. Thus, it makes sense that the volumetric fraction of CO is always higher than the other gases present in syngas for all tests performed and all equivalence ratios obtained. Another interesting aspect is the decrease in small hydrocarbons (C\({}_{2}\)H\({}_{4}\), C\({}_{2}\)H\({}_{6}\), C\({}_{2}\)H\({}_{2}\), and CH\({}_{4}\)) as the load increases and as the temperature increases, which may be related to a growth in the reform/cracking reactions.  
The water-gas shift reaction contributes more to the formation of CO and H\({}_{2}\) than the Boudouard reaction. With high temperatures in the reduction zone, it is possible to notice that the Boudouard reaction contributes to increase the conversion of CO\({}_{2}\) and CO, as can be seen in Figure 4. There is a direct relationship, i.e., when the temperature in the reduction zone increases, the concentrations of CO and H\({}_{2}\) in the syngas also increase. The maximum volumetric fraction of CO in syngas, during the different loads, was 19.07% for 15 kW, 19.02% for 10 kW, and 18.96% for 5 kW. Meanwhile, the  
Figure 4: Syngas composition as function to engine loads.  
maximum values for H2 were 17.62% for 5 kW, 16.84% for 10 kW, and 18.48% for 15 kW. As the reaction of methane occurs at a lower speed, the volume fraction of CH4 is the lowest as compared with the tested loads, that is, less than 2.5%.
## 2 Methodology
The ratio of the energy generated by the system in partial time \(E_{gp}\) and the energy generated in full-time \(E_{gf}\) determines the capacity factor [27] and is given by Equation (13):  
Figure 1:0  
where \(CF\) is capacity factor, \(E_{gp}\) is the energy generated in part-time (W), and \(E_{gf}\) is the energy generated in full-time (W)  
The ratio of energy to design point (thermal power produced by the field of heliostats \(q_{sf}\) for different DNI values), and the thermal power required by the power block under nominal conditions \(q_{pb}\) determines the solar multiple (\(SM\)). It is expressed by Equation (14), [34]. Figure 1:1  
where \(SM\) is solar multiple factor, \(q_{sf}\) is the energy generated by the field of heliostats (W), and \(q_{pb}\) is the energy required by the power block (W). For a system without a storage mode, \(SM=1\). Table 2 shows component characteristics and design parameters of the solar tower system used in the research. Annual meteorological database that known as the reference year test (TRY) or typical meteorological year (TMY) was used. It consists of measured values, which are statistically selected from the annual individual values measured over a long period. The file formats used are file extensions: TMY2, TMY3, EPW, and CSV. To optimize the performance of the solar tower system of different regions, one needs to optimize the solar fields by the variation of the solar multiple (\(SM\)) in function of thermal energy storage (\(TES\)) hours, in order to optimize the dimensions of the system and maximize the production of electricity and the capacity factor of the solar tower system.
# Wide Area Key Distribution Network Based on a Quantum Key Distribution System
## 2 Preliminaries  
In this section, we review two cryptographic primitives that are necessary for understanding the proposed complex QKD network scheme, including the re-encryption key distribution scheme and the theory of unicity distance for discussing information leakage of practical key distribution scheme.
# Video Captioning Based on Channel Soft Attention and Semantic Reconstruct
## 3 Method
### VLAD and SeqVLAD
\(a_{t}\) can be viewed as \(a_{t}^{k}(i,j)\), denoting the assignment weights of aggregating the descriptor at location \((i,j)\) of \(t\)-th frame to the \(k\)-th cluster center. Because the range of assignment values should be 0 to 1, the output \(a_{t}^{k}(i,j)\) is wrapped by a \(softmax\) function, which can be described as:  
\[softmax(a_{t}^{k}(i,j))=\frac{e^{a_{t}^{k}(i,j)}}{\sum_{n=1}^{K}e^{a_{t}^{k}( i,j)}} \tag{7}\]  
where \(K\) denotes the number of total cluster centers. The SeqVLAD encoding would be:  
\[\
u_{k}=\sum_{t=1}^{T}\sum_{j=1}^{H}\sum_{i=1}^{W}a_{t}^{k}(i,j)(x_{t}(i,j)-c_ {k}) \tag{8}\]  
where \(x_{t}(i,j)\) represents the descriptor at location \((i,j)\). In addition, T represents the input sequence length. Referring to Equation (8), it accumulates the differences between descriptors and cluster centers locally and temporally. The output of SeqVLAD concatenates \(\
u_{k}\) over \(K\) cluster centers and the final representation with dimension \(K\times D\) is the same as VLAD.
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 5 Towards Algorithm Development: Propagation Concepts, Path Loss and Communication Graph
### Path Loss Graph. Communication Graph
Figure 7: Communication graphs for the technical solutions in [57]. * [57] & [58] & [59] \\ \hline \hline \end{tabular}
* & 0.98583 \\ \hline \hline \end{tabular}  
Based on Tables 7 and 8 and Figures 7-9, the following assumptions must be considered:  
* [57] & Ground-to-ground communication between WSNs and GW is not always possible, and UAVs and USVs act as intermediate gateways. * [58] & The communication links between a gateway and WSN must be adjusted so that none of the intermediate links are fostered to the injury of others (e.g., \(w_{min}=0.75781\) versus \(w_{min}=0.99934\) ). Supposing that a WSN must send data to the gateway at a specific moment, the proposed algorithm should be able to find which vehicles must be selected and their positions such that the communication link is possible. Figure 8: Communication graphs for the technical solutions in [58]. Figure 9: Communication graphs for the technical solutions in [59]. Based on LoRa communication, the communication links can be ensured for a significant percentage of the nodes. Nevertheless, considering the limitations of LoRa technology (low throughput, high latencies and packet delivery time) and the restrictions imposed for UAVs (limited battery life, weather conditions, tear), one should only rely on such a network for limited scenarios. Thus, we envision the provisional use of a LoRa-based ad hoc network to send the locations of the nodes to the gateway at the trigger time and the optimum position data determined based on the proposed algorithm from the gateway to the nodes, such that a higher throughput, lower latency on-demand Wi-Fi network is deployed.
## 6 Conclusions  
Developments in laser scanning technology were believed to have rendered the archaeological applications of photogrammetry obsolete; however, in reality, the techniques of digital image matching and image-based modeling are now considered a valid alternative to laser scanning. In this study, a highly effective and inexpensive tool is presented, which can create 3D models and orthoimages through the application of an image-based approach to modeling in the absence of other geotechnical equipment. This tool is flexible and easy to use, and employs proprietary software to generate images and models of complex scenes and objects, such as the archaeological site for the current demonstration at Wat Maha That.  
This study evaluates the accuracy of the UAV-SfM method for pagoda exploration at Wat Mahathat, which helped determine the RMSE value as 0.028 and 0.052 m in horizontal and vertical directions, respectively. In addition, the results could be possibly applied in further analysis and for historical heritage management. The recording of these changes is particularly important for the architectural heritage of the [PERSON], where the relationship between personal architecture and the neighboring geographic environment has been maintained for hundreds of years.  
Further work can investigate the integration of oblique images to enable the recording of historical objects in order to detect and record small details with high accuracy and completeness.  
Conceptualization, S.M. and N.K.T.; reviewed and edited the manuscript, S.M.; data curation, S.M. and S.A.; methodology, S.M.; software, S.M. and S.A.; validation, S.M.; formal analysis, S.M., N.K.T., and S.A.; writing--original draft preparation, S.M.; writing--review and editing, S.M. and N.K.T.; visualization, N.K.T.; supervision, N.K.T.  
This research received no external funding.  
The authors declare no conflict of interest.
# Analysis of the Water Circulation Structure in the Paldang Reservoir, South Korea
## 2 Materials and Methods
### Field Surveys
A total of three locations (PC1, PC2, and PC3 in Figure 1) were selected as survey stations in order to determine the characteristics of the main stream and water temperature variations in the Paldang Reservoir. Additionally, survey sites were selected among key locations within the Paldang Reservoir by taking the inflow zone and mixing zone of the South Han River and the North Han River into account. Located approximately 3 km upstream from the PDD, the PC3 survey station is the point at which the mixing characteristics of tributary water, for example, from a tributary of the South Han River and the North Han River, can be observed. The PC2 and PC1 survey stations were situated 9.5 and 11 km upstream from the PDD, respectively, which are locations that correspond to the lower reaches of the South Han River and the North Han River, respectively. For the weather station (PA1 in Figure 1), a location adjacent to the Paldang Reservoir and midway between the three monitoring sites was selected, and meteorological instruments were installed accordingly. Observations were conducted on hydraulics, water temperature, and weather conditions for a time period of 37 days from May 12 through June 17, 2016, in consideration of the periods of vertical mixing and thermal stratification. This period was selected by considering certain circumstances such as the flood and freezing seasons. In addition, it was chosen as an important time because it was the time when the algal bloom began to occur due to the increase in temperature. The data are discussed without reference to the downstream reservoir level, as it was maintained constant at 25.5 m during the monitoring temporal span.
## 3 Results and Discussion
### Fluorescence Quenching of MPs by Fermentation-Stinky Compounds  
Fluorescence quenching reflects changes near fluorescent groups in proteins. Generally, Tyr and Trp can exhibit autofluorescence with an excitation wavelength of 280 nm [29]. As shown in Figure 3A-D, a characteristic fluorescence peak of Tyr and Trp around 340 nm was observed in the MP solution, the intensity of which decreased as the concentration of fermentation-stinky compounds increased. In particular, the fluorescence quenching from indole was strongest (Figure 3A). This indicated that these four fermentation-stinky compounds interacted with MPs, exposing Tyr or Trp residues, which could result in changes in the secondary and tertiary structures of MPs.  
Figure 3: (**A–D**) Changes in the fluorescence intensity of MPs-fermentation flavor compounds complexes at \(\lambda\) = 280 nm in different concentrations (0, 2, 5, and 8 mg/L).  
In order to explain the quenching mechanism, the Stern Volmer parameters, such as the bimolecular quenching constant (\(K_{q}\)) and the quenching rate constant (\(K_{\text{sv}}\)), are shown in Table 1. Static quenching is indicated by a decrease in \(K_{\text{sv}}\) with increasing temperature [27]. The \(K_{\text{sv}}\) values of dimethyl disulfide and dibutyl phthalate increased from 293 K to 313 K, where dynamic quenching was dominant. At 303 K, the \(K_{\text{sv}}\) values of indole and isovaleric acid were the highest or lowest. This indicated that MPs exhibited both dynamic and static quenching. Furthermore, the \(K_{q}\) values of all these fermentation-stinky compounds were much greater than the maximum of the dynamic quenching rate constant (\(2\times 10^{10}\) M\({}^{-1}\) S\({}^{-1}\)), indicating that static quenching also contributes to the interactions. The equilibrium constants (\(K_{a}\)) were greater than \(10^{3}\) M\({}^{-1}\), which revealed the strong interactions.
# A New Methodology for Estimating Surface Albedo in Heterogeneous Areas from Satellite Imagery
## 3 Materials
### Artic-Boreal Vulnerability Experiment: ABOVE  
ABOVE [46] is a campaign from the Terrestrial Ecology Program at NASA. It takes place in Alaska, West Canada, and the North of the USA.  
The product for daily mean blue-sky albedo (i.e., surface albedo) is available for Northern North America (Figure 1) for the period 2000-2017 [47] on the EarthData platform. The values of surface albedo are derived from the MODIS BRDF product with 500 m resolution (MCD43 V006) [48]. The daily mean surface albedo is computed by averaging surface albedo instant values per hour, weighted by the solar radiation in each time interval. This product is used as a reference value for the evaluation of the performance of the methodologies under study in big extensions, so the dates and cloud cover meet the same conditions as for Landsat.
# LoRaWAN Communication Protocols: A Comprehensive Survey under an Energy Efficiency Perspective
## 3 Communication Protocols
### Energy Efficiency Protocols
#### 3.1.1 Energy Efficiency Protocols Utilizing Resource Allocation Techniques
The authors implemented a simulation, and the results show that the optimal strategies can achieve a high data rate or long range, while keeping the energy consumption at a relatively low level. In star topologies, they claim that a greater SF can increase significantly the energy consumption in compared to TP, so the optimal strategy is to adapt the TP first and then to increment the SF. In mesh topologies, in order to minimize the energy consumption, they exploit different radio configurations, such as the number of hops, the cell coverage, and the network density. In [47], the authors reported an energy harvesting algorithm based on CRAM algorithm, a MAC protocol for LPWAN, which implements cryptographic frequency hopping and time synchronization, (EH-CRAM). EH-CRAM utilizes a centralized Kalman filter aiming to optimize the information data and energy prediction, where the GWs have the responsibility of adopting the end-device's network parameters configuration based on received traffic aiming energy efficiency. Another goal of the algorithm is the balancing of energy supply utilizing, the stored battery energy, and the solar energy availability. Additionally, that algorithm uses a time-synchronized cryptographic frequency hopping scheme to optimize energy efficiency and performance in terms of communication reliability. The results highlight that EH-CRAM significantly lessens contention, while maximizing both the system's reliability and energy efficiency.
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 3 System Model
### Common Proposed Key Recovery System Model
#### 3.1.1 Common Design Goals of Proposed Schemes  
The two key recovery system models presented in this research were designed in different forms. However, the basic goal of both models is encapsulated key recovery. The first model proposed in this study is a key recovery system using signcryption. This process involves recovering the session key used for communication by using the encapsulated key recovery field. The second model proposed in this study is a key recovery system using proxy re-encryption. The basic goal is the same as the first model described above. However, the design and additional goals of the two models differ from each other, The similarities and differences between the two models can be seen in Figure 1, which will be described in detail below.  
Figure 1: Summary and comparison of proposed key recovery system model.
## 2 Problem Formulation and Mathematical Model for PV Parameters Extraction
### DD Model of PV  
The DD model's equivalent circuit design is displayed in Figure 2. Diode (D2) replicates carrier recombination in the space charge region of the junction, while diode (D1) depicts the minority charge carriers' diffusion process. Diffusion and recombination current  
Figure 1: Schematic diagram of the PV circuit regarding the SD model.  
components are represented by the currents \(I_{S1}\) and \(I_{S2}\), respectively. The DD model's I-V characteristic curve can be described as follows by using KCL:  
\[I=I_{ph}-I_{S1}\bigg{[}\exp\bigg{(}\frac{IR_{ss}+V_{t}}{\eta_{1}\,V_{th}}\bigg{)} -1\bigg{]}-I_{S2}\bigg{[}\exp\bigg{(}\frac{IR_{ss}+V_{t}}{\eta_{2}\,V_{th}} \bigg{)}-1\bigg{]}-\frac{IR_{ss}}{R_{sh}}+\frac{V_{t}}{R_{sh}} \tag{3}\]  
where the ideality factor and reverse saturation current pertaining to the diode (D2), respectively, are represented by \(\eta_{2}\) and \(I_{S2}\).  
From the measured I-V data of solar cells, seven unknown parameters (\(X=[I_{ph},R_{ss},\eta_{1},\)\(\eta_{2},\)\(I_{s1},\)\(I_{S2},\)\(R_{sh}]\)) in Equation (3) must be determined. The precise choice of these factors has a significant impact on a solar cell's performance. To illustrate, the DD model can be used to determine the maximum conversion efficiency if the parameters are chosen accurately.
## 3 Maintenance Process Modeling Based on SCPN
### Structure Expression of Maintenance Process
#### 3.4.3 Expression of Competitive Structures  
Among several maintenance tasks belonging to a maintenance process, if only one of them can be carried out, the several maintenance tasks will have a competitive relationship and the maintenance process will have a competitive structure. In a Petri net, different transitions belonging to the same  
Figure 3: The parallel structure. (**a**) several maintenance tasks start simultaneously; (**b**) several maintenance tasks end simultaneously.  
Figure 2: The serial structure.  
post-set of a status place have a competitive relationship. Among all these transitions, only one of them can be fired. A typical competitive structure is given in Figure 4.  
In Figure 4, \(t_{i}\) and \(t_{j}\) are both immediate transitions, and they have a competitive relationship, namely, only one of them can be fired. In maintenance processes, the competitive structure is usually used to express relationships among maintenance tasks after fault diagnosis. Take Figure 4 as an example, the transition \(t\) can express the task of fault diagnosis. When there is a token in the state place \(s_{s}\), it means the task of fault diagnosis has been completed, and which unit should be replaced or restored has been decided. Then, transition \(t_{i}\) and transition \(t_{j}\) will be used to express the replacing or restoring task of unit \(i\) and unit \(j\). For all \(t_{i}\in s_{s}\bullet\ (i=1,2\cdots n)\), the probability that \(t_{i}\) can be fired is expressed as  
\[p_{i}=\frac{\lambda_{i}}{\sum\limits_{i=1}^{n}\lambda_{i}} \tag{11}\]  
where \(\lambda_{i}\) is the failure rate of the \(i\)th unit pertinent to transition \(t_{i}\), \(n\) is the number of transitions in the post-set of \(s_{s}\).