esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Vpm  
The Vegetation Photosynthesis Model (VPM) uses environmental factors such as temperature and moisture, as well as eddy observations of carbon flux data, and takes into account photosynthetically active radiation absorbed by vegetation chlorophyll to estimate _GPP_[39]. It divides the vegetation canopy into a chlorophyll fraction and a non-photosynthetic vegetation fraction. _GPP_ in the model is estimated as follows [39]:  
\[GPP=\varepsilon_{0}\times FAPAR_{PAV}\times PAR \tag{23}\]  
\[\varepsilon_{0}=\varepsilon_{\max}\times ft\times fw\times P \tag{24}\]  
\(\varepsilon_{0}\) is the actual LUE; and \(ft,\;fw\) are the stress functions of temperature on LUE, separately. \(P\) is the effect of leaf age on vegetation photosynthesis. \(PAPAR_{PAV}\) stands for the _FAPAR_ of photosynthetically active vegetation. _PAR_ is the total incident photosynthetically active radiation. Their expressions are as follows:  
\[fw=\frac{1+LSWI}{1+LWSI_{\max}} \tag{25}\]  
\[P=\frac{1+LSWI}{2} \tag{26}\]  
\[PAPAR_{PAV}=a\times EVI \tag{27}\]  
\[EVI=\frac{2.5(\rho_{NIR}-\rho_{red})}{(\rho_{NIR}+(6*\rho_{red}-7.5\rho_{blue} )+1)} \tag{28}\]  
\[LSWI=\frac{\rho_{NIR}-\rho_{red}}{\rho_{NIR}+\rho_{red}} \tag{29}\]  
\(ft\) is the same as TL-LUE, and _fw_ is the water stress function calculated from the land surface water index (_LSWI_). \(LSWI_{\max}\) is the maximum _LSWI_ during the photosynthetically active period. \(\rho_{NIR},\;\rho_{red},\;\rho_{blue}\) present the reflectance of NIR, red, and blue. In this study, we used the MODIS reflect data with NIR (841-875 mm) and blue (459-479 mm).
## 3 Results and Discussion
### Thermal Stress Distribution Caused by the Subsurface Impurity Defects
#### 3.2.1 Comparison of Thermal Stress Distributions with and without Impurity Defect
However, for CeO\({}_{2}\) defect as shown in Figure 8b, since the thermal energy has been not already transferred to the downmost part (\(z\)-position of 8.5 \(\upmu\)m) of impurity defect, the temperature  
Figure 8: Comparison of the von Mises thermal stress distributions with and without impurity defects. (**a**) Thermal stress distribution along the radial direction (\(z=10\)\(\upmu\)m) on fused silica surface without impurity defect. The maximum stress at \(r_{\text{p}}=0\)\(\upmu\)m could be applied in the comparison with those caused by the impurity defects. The von Mises thermal stress distribution along the central axis (\(r_{\text{p}}=0\)\(\upmu\)m) caused by (**b**) CeO\({}_{2}\); (**c**) Al and (**d**) Fe defects for various laser loading times. The \(z\)-positions of 8.5 \(\upmu\)m and 9.5 \(\upmu\)m are the intersection points of fused silica with the downmost and upmost parts of the impurity defect. rise in this region is very small. Besides, the CeO\({}_{2}\) defect and fused silica materials share a similar thermal expansion coefficient. Thus, for CeO\({}_{2}\) defect, the thermal stress curve at the lower intersection point of fused silica and impurity defect (z-position of 8.5 \(\upmu\)m) behaves continuously. While for the upper intersection point (z-position of 9.5 \(\upmu\)m), the temperature of impurity defect is much higher than that of fused silica. Under this situation, an obvious heat load would be probably formed, resulting in evident thermal stress discontinuity as shown in Figure 8b. It can be concluded that the impurity defect would lower the laser damage resistance of fused silica optics by its induced thermal expansion and stress. The thermal stress of impurity defect may incur high stress in the surrounding silica materials, which would exceed the compressive strength of fused silica optics and finally lead to its fracture and breakdown.
###### Abstract  
The accurate evaluation of rock mass quality and competent roadway-support decision-making requires the rapid and accurate acquisition of the distribution of structural planes in rocks. To address this need, a program was developed that uses deep learning to automatically recognize the structural plane in-borehole images. First, borehole images from 30 mines in China were collected during field tests, and the structural planes in the images were categorized into five types. Second, a deep Coral architecture based on a convolutional neural network (CNN) was established to automatically extract features from the borehole images and classify the structural planes therein. The experimental results indicate that the CNN model classifies the structural planes in the borehole images with an overall accuracy of 86%. Validation tests in field applications demonstrated recognition accuracies ranging from 0.76 to 1.0 compared to manual markings, meeting engineering requirements. Finally, based on the proposed method, an intelligent system to recognize surrounding rock fracture was developed. Engineering application cases are presented and discussed to demonstrate the method and confirm the accuracy of this approach. Compared with traditional classification methods, the proposed method rapidly recognizes and classifies structural planes in borehole images at low cost, with precision, and in a non-destructive and automated manner.  
structural planes of rock mass; machine learning; identification method of structural planes; deep learning; program application Article
# LVRT and Stability Enhancement of Grid-Tied Wind Farm Using DFIG-Based Wind Turbine
## 4 Design of the DFIG System
### DFIG Model  
The DFIG system was specified in the synchronous reference frame. The \(d\) and \(q\)-axis stator and rotor voltages are derived below:  
\[\left\{\begin{array}{l}\begin{array}{l}V_{sd}=R_{s}.i_{sd}+\frac{d\varphi_{sd }}{dt}-\omega_{s}.\varphi_{sq}\\ V_{sq}=R_{s}.i_{sq}+\frac{d\varphi_{sq}}{dt}+\omega_{s}.\varphi_{sd}\\ V_{rd}=R_{r}.i_{rd}+\frac{d\varphi_{rd}}{dt}-\omega_{r}.\varphi_{rq}\\ V_{rq}=R_{r}.i_{rq}+\frac{d\varphi_{sq}}{dt}+\omega_{r}.\varphi_{rd}\\ \end{array}\right. \tag{9}\]  
\[\left\{\begin{array}{l}\begin{array}{l}\varphi_{sd}=L_{st}i_{sd}+L_{m}i_{rd }\\ \varphi_{sq}=L_{st}i_{sq}+L_{m}i_{rq}\\ \varphi_{rd}=L_{rt}i_{rd}+L_{m}i_{sd}\\ \varphi_{rq}=L_{rt}i_{rq}+L_{m}i_{sq}\\ \end{array}\right. \tag{10}\]  
Here, the resistance and self-inductance of stator and rotor winding are illustrated as \(R_{s}\), \(R_{r}\), and \(L_{st}\), \(L_{rt}\), sequentially. \(L_{m}\) is denoted as mutual inductance. The currents of the stator and rotor are denoted as \(i_{sd}\), \(i_{sq}\), \(i_{rd}\), and \(i_{rq}\), respectively. Finally, \(\omega_{s}\) is presented as the angular frequency of the grid.  
Figure 7: DFIG system with cascaded control strategies.
## 2 Materials and Methods
### Parallel Algorithms
#### 2.4.1 objective_function Algorithm (Loss Function)
Applying a shift to the error signal: Depending on the sign of the proposed shift shift, the error signal Error is shifted relative to the beginning of the signal y. If shift\(\geq 0\) (forward shift), shift zeros are added to the error signal on the left, and the result is truncated to length length(x). If shift\(<0\) (backward shift), -shift zeros are added to the error signal on the right, and the result is trimmed from the beginning to length(x). Building the feature matrix and approximating the least squares. * A feature matrix features_matrix is created, consisting of two columns: the first column is filled with units (to account for bias) and the second column is filled with a shifted error signal arr_error. * coefficients and the input signal y. * The approximated signal approximated_signal is computed as the product of the feature matrix features_matrix by the found coefficients coefficients. Calculation of MSE: The mean square error (MSE) between the original signal y and the approximated signal approximated_signal is calculated using the function MSE. Output: The algorithm returns a computed MSE value, which is a measure of the \"mismatch\" between the signal \(y\) and its approximation using a given error type and the proposed size and shift parameters. The goal of Optuna (TPESampler) is to minimize this MSE value. Remarks  
The objective_function is a key component for optimizing the parameters of an error classification model using TPESampler. It evaluates the quality of the model for given values of the size and bias of the error signal, allowing TPESampler to iteratively search for parameters that minimize the MSE and hence provide the best fit between the error model and the input signal. The use of the least squares method provides an optimal linear fit of the error signal to the input signal based on the criterion of minimizing the MSE.
# Route Availability as a Communication Quality Metric of a Mobile Ad Hoc Network
## 5 Evaluation of the RA Calculation Method and Verification of Usefulness of RA  
Using the evaluation environment described above, we evaluated the proposed RA calculation method and examined how useful RA is as a metric of network QoS and video streaming QoE.
###### Abstract  
Pattern projection-based 3D measurement systems are widely used for contactless, non-destructive optical 3D shape measurements. In addition, many robot-operated automation tasks require real-time reconstruction of accurate 3D data. In previous works, we have demonstrated 3D scanning based on statistical pattern projection-aided stereo matching between two cameras. One major advantage of this technology is that the actually projected patterns do not have to be known a priori in the reconstruction software. This allows much simpler projector designs and enables high-speed projection. However, to find corresponding pixels between cameras, it is necessary to search the best match amongst all pixels within the geometrically possible image area (that is, within a range on the corresponding epipolar line). The well-established method for this search is to compare each candidate pixel by temporal normalized cross correlation of the brightness value sequences of both pixels. This is computationally expensive and interdicts fast real-time applications on inexpensive computer hardware. We show two variants of our algorithm \"Binary Correspondence Search\" (BICOS), which solve this task in significantly reduced calculation time. In practice, our algorithm is much faster than traditional, purely cross-correlation-based search while maintaining a similar level of accuracy.  
Keywords:real-time; active stereo vision; 3D measurement; GOBO projection; statistical pattern projection; aperiodic sinusoidal fringes; GPGPU; BICOS; correspondence search; binary features +
Footnote †: journal: Applied Optics and Precision Engineering IOF
###### Abstract  
Generative Adversarial Networks (GANs) have surfaced as a transformative approach in the domain of cryptography, introducing a novel paradigm where two neural networks, the generator (akin to Alice) and the discriminator (akin to Bob), are pitted against each other in a cryptographic setting. A third network, representing Eve, attempts to decipher the encrypted information. The efficacy of this encryption-decryption process is deeply intertwined with the choice of activation functions employed within these networks. This study conducted a comparative analysis of four widely used activation functions within a standardized GAN framework. Our recent explorations underscore the superior performance achieved when utilizing the Rectified Linear Unit (ReLU) in the hidden layers combined with the Sigmoid activation function in the output layer. The non-linear nature introduced by the ReLU provides a sophisticated encryption pattern, rendering the deciphering process for Eve intricate. Simultaneously, the Sigmoid function in the output layer guarantees that the encrypted and decrypted messages are confined within a consistent range, facilitating a straightforward comparison with original messages. The amalgamation of these activation functions not only bolsters the encryption strength but also ensures the fidelity of the decrypted messages. These findings not only shed light on the optimal design considerations for GAN-based cryptographic systems but also underscore the potential of investigating hybrid activation functions for enhanced system optimization. In our exploration of cryptographic strength and training efficiency using various activation functions, we discovered that the \"ReLU and Sigmoid\" combination significantly outperforms the others, demonstrating superior security and a markedly efficient mean training time of 16.51 s per 2000 steps. This highlights the enduring effectiveness of established methodologies in cryptographic applications. This paper elucidates the implications of these choices, advocating for their adoption in GAN-based cryptographic models, given the superior results they yield in ensuring security and accuracy.  
neural network; activation function; security; generative adversarial network; cryptography +
Footnote †: journal: Applied Sciences
## 3 Theoretical Model Description
### Geometrical Design  
The model described above is implemented for three specific channel geometries: circular, box-shaped with a linear current collector, and box-shaped with an extended current collector, as shown in Figure 5[54]. Each geometrical design allows the different direct contact rate between the electrode phase and the CC. With the flow channels 5 mm wide and 120 mm long, the contact area for circular flow channel geometry is approximately 1884 mm\({}^{2}\). The contact area is reduced to 600 mm\({}^{2}\) and 1800 mm\({}^{2}\) for the box-shaped geometry with linear and extended CC, respectively [54]. The circular-shaped channel geometry resembles the prototype used for experimental analysis and later used for the model optimization and sensitivity testing of modeling parameters.
## 4 Results and Discussion  
### Effect of Pressure  
As shown in Figure 5, the inlet pressure has little effect on the amount of direct leaked air in either different sealing number case or different pressure difference case. By contrast, the leakage amount gradually increases with the pressure difference. The leakage in the case of \(\Delta p_{\text{tot}}\) = 5 kPa is larger than that of \(\Delta p_{\text{tot}}\) = 4 kPa and \(\Delta p_{\text{tot}}\) = 3 kPa by 10.6% and 28.9%, respectively. In the process of direct air leakage, the pressure difference between air and flue gas is main cause of the leak. According to the Bernoulli equation, a portion of the pressure energy will be converted into kinetic energy during the  
Figure 3: Mesh management: (**a**) mesh generation, (**b**) local mesh refine, and (**c**) mesh-independent verification.  
airflow passing through the gap of the sealing sheets [24]. Thus, in our study, the pressure difference rather than the inlet pressure would be regarded as a factor affecting the direct leakage of the RAPH.
## 4 Proposed Scheduling Algorithm  
The problem we defined is proven to be NP-hard in a general topology [4]. We propose a heuristic algorithm to find the minimum scheduling time for convergecast in a general topology (FMSTC). The algorithm we propose finds several trees first, where each tree is constructed based on the nodes within a one-hop distance from the sink node. We assume that each tree is an independent set after constructing the line topology for each tree. The algorithms of [PERSON] _et al._[3] are applied to find the minimum scheduling time for a general topology. The details are discussed in Section 4.1 and 4.2.
## 5 Analysis of the Hybrid Propagation Channel Model
### Comparison and Analysis of the Proposed 3 DFHPPCM with a Single Deterministic Model  
In this study, the proposed 3 DFHPPCM is compared and analyzed with a single deterministic model (conventional three-dimensional RT and the three-dimensional PE). 'SP_N9_A5' denotes the proposed 3 DFHPPCM. It can be seen in Figure 12 that the proposed 3 DFHPPCM is close to the experimental data, while the three-dimensional PE and three-dimensional RT deviate from the experimental data significantly. As shown in Table 6, the RMSE and MAE of the proposed 3 DFHPPCM are 4.1778 and 3.4927, respectively; in comparison, those of the three-dimensional PE are 12.0166 and 10.7764, respectively, and those of three-dimensional RT are 8.5142 and 7.333, respectively, which are much greater values than the RMSE and MAE of the proposed 3 DFHPPCM. Moreover, its runtime is the shortest among the methods. These results demonstrate that the proposed 3 DFHPPCM has higher accuracy.  
Figure 11: The path loss varies with distance for scattering angles of \(5^{\circ}\) and \(5.1^{\circ}\) when the number of scattered rays is 9. (**a**) The scattering angle is from \(5^{\circ}\) and (**b**) the scattering angle is from \(5.1^{\circ}\).  
Figure 10: The path loss varies with distance for scattering angles of \(4.8^{\circ}\) and \(4.9^{\circ}\) when the number of scattered rays is 9. (**a**) The scattering angle is from \(4.8^{\circ}\) and (**b**) the scattering angle is from \(4.9^{\circ}\).
# Assessment of Transformer Fault Severity from Online Dissolved Gas Analysis Using Positive CUSUM
## 3 Assessment of Fault Severity from NEI  
The approach to classifying severity based on dissolved gases' analysis from transformer oil samples began with the research of [PERSON], who used a scoring system for DGA results to help prioritize the abnormalities that occur. This method provides a more precise representation of developing issues within transformers and helps to consistently and reliably assess the health of transformer groups.  
Research by [PERSON] and [PERSON] presents the Normalized Energy Intensity (NEI) by considering the energy values of gases generated from faults to analyze the severity of the occurring faults. The NEI assigns a score known as the 'NEI score,' categorizing severity into four levels according to IEEE and IEC styles. Transformers are classified into high- and low-oxygen types based on the N\({}_{2}\)/O\({}_{2}\) ratio; if the value is greater than 5.94, it is considered a low-oxygen transformer, while a value less than 5.94 indicates a high-oxygen type. The NEI limits on the 90 th, 95 th, and 98 th percentile are as per Table 1.  
Assuming the calculated NEI value equals \(x\), we can calculate the oil sample's NEI value using hydrocarbon gas concentrations and then determine the NEI score by following these steps.  
* If x \(>L_{3}\), the NEI Score is 4.0.
* If \(L_{2}\leqslant\) x\(<L_{3}\), the NEI Score is \(3+\dfrac{x-L_{2}}{L_{3}-L_{2}}\).
* If \(L_{1}\leqslant\) x\(<L_{2}\), the NEI Score is \(2+\dfrac{x-L_{1}}{L_{2}-L_{1}}\).
* If x \(<L_{1}\), the NEI Score is \(1+\dfrac{x}{L_{1}}\).  
While the NEI score effectively ranks the fault severity, there are still questions about the applicability of its limits to transformers with different applications, such as those used in steel melting plants. Can these limits be applied to environmental conditions in other regions?  
Several subsequent research studies have utilized the NEI to assess the severity of faults occurring within transformers [13; 14; 15; 16]. The IEEE C57.104-2019 standard introduced the NEI in Anne F [10] as an alternative for analyzing the severity of faults occurring within transformers by incorporating the NEI of cellulose insulation that produces carbon dioxide (CO\({}_{2}\)) and carbon monoxide (CO).  
[PERSON] and [PERSON] proposed a method for tracking NEI values using cumulative sums [17]. If the next value increases, the difference is added to the previous value; if the next value is lower than the previous one, it remains unchanged.
# Remote Sensing Analysis of Geologic Hazards
## 3 Landslides
### Detection and Mapping  
[PERSON] et al. [29] focused their work on landslide mapping. They used optical (Sentinel-2) and SAR (Sentinel-1) satellite images from Google Earth Engine to map the landslides triggered by a rainstorm in western Norway. To detect the landslides, they manually analyzed the differential Normalized Difference Vegetation Index and the SAR amplitude difference of VV and VH polarizations (from the Sentinel-2 and Sentinel-1, respectively). Their results showed that, by using a stack of pre-event images to improve the signal-to-noise ratio, the number of detected landslides increased from 14 (registered by the Norwegian Landslides Inventory) to 120. Moreover, they found that optical images performed better than SAR amplitude images with the aim of landslide detection.
## 4 IEC61850 Multicast Communication for Wide-Area Applications
### R-SV/R-GOOSE vs. SV/GOOSE  
Although there are some differences [30] between R-SV and R-GOOSE, their communication stacks are very similar, and so they will be considered together (as R-SV/R-GOOSE) in the rest of this paper. R-SV/R-GOOSE messages use the same communication model as SV/GOOSE, i.e., the publish-subscribe model that is used in multicast communication. In this model, the sender (publisher) sends the same data across a computer network to multiple receivers (subscribers) at the same time. While SV/GOOSE uses Ethernet-multicast over LAN (in other words, multicasting inside a substation), R-SV/R-GOOSE messages can be published via IP-multicast over both LAN and WAN (in other words, acting as a multicasting inter-substation). Ethernet-multicast is based on the Media Access Control (MAC) address, but IP-multicast is based on a specific range of IP addresses reserved for that multicast communication. Figure 1 depicts the communication stack as well as the structure of the messages with respect to the OSI model (ISO/IEC 7498-1) layers. As can be seen, SV/GOOSE communications are directly mapped to the Ethernet frames. However, the R-SV/R-GOOSE messages are longer messages since they additionally use Network, Transport, and Session layers.  
R-SV/R-GOOSE messages use the Session layer, based on the IEC61850-90-5 session protocol, and the Key Distribution Center (KDC), which will be explained in Section 5.2.1. The IEC61850-90-5 Session protocol includes: a Session Identifier (that identifies the message type: R-SV, R-GOOSE, Routable-Tunneled SV/GOOSE (or Management message); a Session Header (cybersecurity-related information provided by KDC); and session user data (payload data based on the Session Identifier). It should be noted that the focus of this paper is on the R-SV and R-GOOSE messages. The Session layer data are encapsulated in the User Datagram Protocol (UDP), which is used for transporting messages at the Transport Layer. In the Network layer, IP is used for routing and multicasting R-SV/R-GOOSE messages over a communication network in which Ethernet is used for device communication at the Data Link Layer.
## 3 LEO-SMC-ICN-IoT Caching Framework
### Caching Process in LEO-SMC-ICN-IoT  
We consider a scenario of accessing the internet for ground terrestrial UT. Service providers are linked to the GS, which is responsible for distributing content via satellite mega-constellation networks. For UT content requests, the satellite nodes examine if the required content is currently cached in the local before forwarding the requested content to the next hop: if so, the satellite nodes send the requested content to the UT. Otherwise, the satellite nodes forward the requested content to the next hop. The caching process is shown in Figure 8. In Figure 8, the solid lines denote the connection of the two nodes to each other, and the dashed lines illustrate the content access route. Due to the consideration of the caching policy of satellite nodes only, we assume that all of the BS, and GS have no cached content at the beginning. The caching process is described as follows. Satellite \(S_{0-1}\) has cached content \(c_{1}\), and UT\({}_{1}\) requests content; \(c_{1}\) can access it via BS\({}_{1}\) and GS\({}_{1}\). In the case of UT\({}_{2}\) requesting content \(c_{1}\), it will access the requested content via ISLs from the satellite \(S_{0-1}\). The other case is when UT\({}_{3}\) accesses content \(c_{7}\) from GS\({}_{n}\) which is connected to the service provider. And \(c_{L}\in C\), \(C\) is the set of contents.  
Caching popular content in the LEO-SMC-ICN-IoT allows users with the same content requirements to be satisfied, without duplicate transmissions [52]. As a result, there is a significant decrease in redundant bandwidth and content access latency [53; 54].