esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Location and Restoration of Microgrids via Particle Swarm Optimization
## 3 Derivation of Fault Location Approach
### Graph Theory-Based Power Flow Algorithm  
A combination of \(Z_{Bus}\), graph theory and backward/forward sweep algorithm are applied in the power flow analysis. A systematic approach, graph theory, is used to build incidence matrices that correspond to network topologies. The proposed algorithm uses the \(A\) incidence matrix, which is the element-bus incidence matrix, and the \(K\) matrix, which is the branch-path incidence matrix. The \(BIBC\) and \(BCBV\) matrices can be established according to the network topology based on these matrices. Furthermore, the \(BIBC\) and \(BCBV\) matrices are applied in the power flow algorithm. The solution procedure of the proposed power flow is described as follows.  
Step 1: Establish \(A\) matrix. Derive \(K\) matrix using Equation (1). Build the \(BIBC\) matrix using Equation (2).  
\[K\;=\;\left[A^{-1}\right]^{t} \tag{1}\]  
\[\left[BIBC\right]\;=\;-K \tag{2}\]  
Step 2: Transpose the \(BIBC\) matrix and add the primitive line impedance, which means total series impedance between two connection buses into the corresponding non-zero element position to derive the \(BCBV\) matrix.  
Step 3: Build the bus impedance matrix using Equation (3) by multiplying the \(BIBC\) and \(BCBV\) matrices.  
\[\left[Z_{Bus}\right]\;=\;\left[BIBC\right]\;\left[BCBV\right] \tag{3}\]  
Step 4: Calculate the equivalent bus injection current at each bus connected to the source or load using Equation (4), where \(P_{i}\) and \(Q_{i}\) are active and the reactive power at each bus, \(V_{i}^{k}\) is the bus voltage magnitude, which is set to 1.0 pu at the initial condition.  
\[I_{i}^{k}\;=\;\left(\frac{P_{i}+jQ_{i}}{V_{i}^{k}}\right)^{*} \tag{4}\]  
Step 5: Compute the voltage derivation of each bus using Equation (5).  
\[\left[\Delta V^{k}\right]\;=\;\left[Z_{Bus}\right]\left[I^{k}\right] \tag{5}\]  
Step 6: Using Equation (6) to update the bus voltage, where \(V_{no\_load}\) is the no-load voltage at each bus, i.e., the bus voltage is assumed as 1.0 pu, that is,  
\[\left[V^{k+1}\right]\;=\;\left[V_{no\_load}\right]-\left[\Delta V^{k}\right] \tag{6}\]  
Step 7: Check the convergence by Equation (7). If Equation (7) is true, then there is no convergence; therefore, proceed to Step 4; otherwise, end the solution procedure. \(\varepsilon\) is the maximum toleration, that is  
\[max_{i}\left(\left|I_{i}^{k+1}\right|-\left|I_{i}^{k}\right|\right)>\varepsilon \tag{7}\]
# Experimental Validation of GNSS Interferometric Radio Occultation
## 4 Data Processing, Analysis and Discussion
### Interferometric RO Processing
#### 4.3.3 SNR Analysis
Although in the PARIS Interferometric  
Figure 10: Relationship between the clean replica SNR and the interferometric SNR. Technique this is not strictly the case (non-Gaussian signal, low SNR), we assume that both situations are comparable. We have to remark that [15] defines the SNR at the output of the correlator as the quotient formed by the mean value of the correlator output and the standard deviation of the correlator output. But other authors as [16] or [14] define the SNR at the output of the correlator as the quotient between the mean squared value of the correlator output and the variance of the correlator output. Thus, the latter definition of SNR is obtained by squaring the former one. In this case we translate the _linear_ SNR loss of \(2/\pi\) to the _square_ SNR loss \((2/\pi)^{2}\), which is about \(-3.9\) dB. For the clean replica case we use [17] where the computation of a matched filter using quantized signals is studied. In this case the SNR definition at the output of the correlator is equivalent to the one in [16], although it is expressed in terms of \(E_{b}/N_{0}\). Our receiver has a bandwidth of approximately B = 40 MHz, the chip \(T_{CA}\) duration is approximately 1 \(\upmu\)s for CA code. The sampling frequency is \(F_{s}=80\) MHz, which corresponds to 40 samples per chip. Using the tables given in that paper with:  
\[BT_{CA} = 40>10\] \[f_{s_{CA}} = 2 BT_{CA}\]  
where \(f_{s_{CA}}\) is the number of samples per chipping symbol, the degradation due to quantization is in the order of \(D_{Q}=-2.1\) dB. This means that the degradation due to quantization in the interferometric technique respect to the clean replica is about \(-3.9\) dB \(-\) (\(-2.1\) dB) = \(-1.8\) dB worse. When the one-bit quantization is taken into account, the SNR comparison is more consistent, and the bias has been reduced to 0.98 dB (see Figure 11). The precision of the iRO measurement depends on the SNR at the auto-correlation output. In Figure 12, the precision of the measured Doppler is presented as a function of the SNR of the cross-correlation output. The Doppler precision is computed from the difference between the time  
Figure 11: Relationship between the clean replica SNR and the interferometric SNR by considering the degradation due to quantization. series of measured Doppler and a simple fitted piecewise linear function every 10 s, and the SNR is also averaged in the same period. It is clearly shown that the precision of the Doppler measurement improves with the increasing of the SNR.
## 6 Simulation Experiments and Performance Analysis  
To validate the effectiveness of the proposed design, this section details the parameters for joint simulation experiments involving the signal and target, along with the corresponding performance analysis.
# Multiclass Segmentation of Concrete Surface Damages Using U-Net and DeepLabV3+
###### Abstract  
Monitoring damage in concrete structures is crucial for maintaining the health of structural systems. The implementation of computer vision has been the key for providing accurate and quantitative monitoring. Recent development uses the robustness of deep-learning-aided computer vision, especially the convolutional neural network model. The convolutional neural network is not only accurate but also flexible in various scenarios. The convolutional neural network has been constructed to classify image in terms of individual pixel, namely pixel-level detection, which is especially useful in detecting and classifying damage in fine-grained detail. Moreover, in the real-world scenario, the scenes are mostly very complex with varying foreign objects other than concrete. Therefore, this study will focus on implementing a pixel-level convolutional neural network for concrete surface damage detection with complicated surrounding image settings. Since there are multiple types of damage on concrete surfaces, the convolutional neural network model will be trained to detect three types of damages, namely cracks, spallings, and voids. The training architecture will adopt U-Net and DeepLabV3+. Both models are compared using the evaluation metrics and the predicted results. The dataset used for the neural network training is self-built and contains multiple concrete damages and complex foregrounds on every image. To deal with overfitting, the dataset is augmented, and the models are regularized using L1 and Spatial dropout. U-Net slightly outperforms DeepLabV3+ with U-Net scores 0.7199 and 0.5993 on F1 and mIoU, respectively, while DeepLabV3+ scores 0.6478 and 0.5174 on F1 and mIoU, respectively. Given the complexity of the dataset and extensive image labeling, the neural network models achieved satisfactory results.  
convolutional neural network; damage detection; semantic segmentation; deep learning; computer vision +
Footnote †: journal: Medical Image Computing
## 3 Results and Discussion  
### Part Classification Results  
#### 3.1.1 Results of Pre-Process for Differentiation of Stems, Leaves, and Backgrounds  
Using NDVI  
Figure 12 shows the transformation of the difference between images in 695, 735, and 800 nm bands and the images in the 470, 500, 505, 525, 635, and 660 nm bands through Equation (1). The quality of the transformed image was evaluated through the PSNR (Peak Signal-to-Noise Ratio) value. The higher the PSNR value, the lower the loss. Table 3 shows the average of PSNR values of images in the 695, 735, and 800 nm bands and the difference images of 10 images in the 470, 500, 505, 525, 635, and 660 nm bands. Among the average PSNR values measured in the 10 images with a total of 18 band differences, the image obtained by transforming the difference between 735 and 660 nm showed the highest value of 871,625. The higher the PSNR value, the lower the loss compared to the original image. Therefore, the difference between 735 and 660 nm confirmed image with the least image loss compared to the visible light region. Stems, leaves, and the background were distinguished using the transformed image difference between 735 and 660 nm.
## 4 Results
### Activation Maps Visualization  
To better understand how the model made its predictions to correctly identify myopia, we constructed activation maps for each image in the test set using Grad-CAM (Gradient-weighted Class Activation Mapping) [42]. In this way, we hoped to gain a better understanding of which areas of the image were most used to make a successful prediction and which ones might have been misleading the model, leading to a false positive or a false negative. Figure 8 shows some examples of activation maps on images: (a) correctly classified as myopia; (b) correctly classified as normal; (c) incorrectly classified as myopic; and (d) incorrectly classified as normal. From our activation maps, it is difficult to definitively determine which region of interest had the highest gradient value and therefore the most significant impact on the model's decision. While the macula and optic disk regions are widely recognized as the most relevant areas for myopia prediction, as demonstrated by [20; 43], we cannot yet fully support this claim. Certainly, analyzing a larger dataset, applying more effective preprocessing techniques, and conducting a more thorough evaluation of the activation maps could help uncover which areas of interest play the most important role in determining the accuracy of the predictions.  
Figure 8: Example of Grad-CAM activation maps created with testing set images: (**a**) correctly classified as myopia; (**b**) correctly classified as normal; (**c**) incorrectly classified as myopic; and (**d**) incorrectly classified as normal. Red colors highlight the regions of an image that significantly influence the model’s prediction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **AP** & **mAP@50** & **mAP@50-95** \\ \hline YOLOv8-nano & 0.680 & 0.848 & 0.744 \\ YOLOv8-small & 0.370 & **0.893** & **0.776** \\ YOLOv8-large & **0.800** & 0.868 & 0.675 \\ \hline \hline \end{tabular}
\end{table}
Table 5: mAP values for models n, s, and l calculated at @50 and @50-95 thresholds. Highest mAP is highlighted in bold.
# Investigation of 2 DOF PID Controller for Physio-Therapeutic Application for Elbow Rehabilitation
## 5 Discussions  
The proposed 2 DOF controller shows that it is fast in response and in reaching stability. In addition, it does not show any undershoot and overshoot. This is a significant feature for this controller, since the prototype is designed to offer therapy to elbow-impaired patients. In addition, its set-point weight feature helps it to overcome the disturbances to the system rapidly, which is also another important feature of the proposed controller.  
The successful development of the controller leads the work towards the hardware implementation. For both the simulation and hardware implementation, different amplitudes of current, such as 0.1 amperes, 0.2 amperes, and 0.3 amperes, have been considered, since the system may deal with patients with different physical conditions. Some patients are frail and feeble in their elbow movements, whereas some patients are capable of moving their elbows, but they need basic mechanical assistance and, hence, they need assistance accordingly of different amplitudes. In general, 0.3 amp is typically reserved for those who are unable to stretch or flex on their own, while 0.1 amp is reserved for those who can move but sometimes need a small amount of assistance from the machine. The proposed controller also performs surprisingly well with negligible tracking errors. However, the experimental work has been carried out only considering the periodic movement of the machine elbow, although this work would benefit from including the responses from the patients. We provide the following limitations of our study, which will be addressed in future works that take our tool and method selection into account:  
1. Our study did not examine the proposed model's compatibility with the existing 2 DOF PID controller. From a translational standpoint, future research should prioritize examining the possibility of bridging that gap.
2. At the time of writing, the global pandemic (SARS-CoV-2) caused a challenge in assessing the clinical experiment designed to evaluate the suggested PID controller's performance.
3. We considered only single-degree elbow movement, although performance can differ when multiple-degree elbow movement is included.
# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 4 Additional Research
### Energy and Capacity Adequacy
#### 4.1.3 Contributions of Emerging Technologies  
The assessment of the energy and capacity adequacy needs to reflect the costs for demand response, energy storage, distributed generation, and sector coupling, in addition to the cost for transmission line expansion. Specifically, most existing adequacy metrics assume fixed loads. Thus, any load reduces system adequacy. This means that consumer (load) types should be accurately assessed. Some load types (EVs and HVAC units) are untapped resources. The energy need is relatively constant, but the times of battery charging and heating- and cooling-energy use are at least somewhat flexible. The trade-off between cost and reliability means seeking a reliability level that considers cost efficiency [2]. It also would include customer disconnection during hours of scarcity, thus rolling blackouts when the load responsiveness to price is inadequate. Moreover, storage devices should be better represented in the metrics, with the incorporation of state-of-charge limitations during hours of scarcity. The operational characteristics of storage devices, if deployed at a large scale, should be better modeled in terms of their contributions to adequacy. Any contribution of distributed generation to adequacy can be limited by the distribution networks and their operational characteristics, which depend on weather-associated parameters, such as temperature, wind speed, and solar irradiance. The contributions of sector coupling across transportation, heating, cooling, gas, and hydrogen are currently difficult to assess, highlighting the need for improvements of current planning tools.
## 4 Relay Based QoS-Aware Routing Protocol for I2O WBAN
### Radio Model  
The analysis of WBAN system energy consumption is given by extending our previous work on flexible QoS WBANs [3]. Assuming the transmission packet length is defined as \(k\), the minimal transmission energy consumption of sensor nodes can be expressed as:  
\[E_{Tx\_min}\left(d,k\right)=kE_{Tx\_dec}+kE_{amp}dn \tag{14}\]  
where \(E_{Tx\_min}\) and \(E_{Tx\_dec}\) mean the minimal required energy for data transmission from a sensor node to a relay and the essential energy consumption to activate the electronic circuit, respectively.  
The distance between the transmitting sensor node and the receiver side is represented by \(d\), and \(n\) is the path loss exponent. Similarly, the minimal energy consumption for the reception process can be regarded as \(E_{Rx\_min}=kE_{dec}\). The minimal total energy consumption for a sensor node \(E_{Total}\) can be expressed as:  
\[E_{Total}\left(k,d\right)=E_{Tx\_min}\left(d,k\right)+kE_{ec} \tag{15}\]  
The PL parameters in Table 3 are utilized when the in-body sensor nodes transfer data to the relay nodes. Two commercially available WBAN transceivers, the Nordic nRF2401A and Chipcon CC2420 are utilized and the related radio parameters have been summarized in Table 5[36].  
Figure 10: Demonstration of the relay-based routing protocol; (a) single relay based scenario; (b)–(d) two-relay based selective routing protocols.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter (Unit)** & **nRF2401A** & **CC2420** \\ \hline Tx current (mA) & 10.5 & 17.4 \\ Rx current (mA) & 18 & 19.7 \\ Voltage (V) & 1.9 & 2.1 \\ \(E_{Tx\_dec}\) (n)/bit) & 16.7 & 96.9 \\ \(E_{Rx\_dec}\) (n)/bit) & 36.1 & 172.8 \\ \(E_{amp}\) (n)/bit/m\({}^{2}\)) & 1.97 & 2.71 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Radio parameters of nRF2401A and CC2420.
# A Regret-Enhanced DEA Approach to Mapping Renewable Energy Efficiency in Asia's Growth Economies
## 2 Methodology
### Regret Theory Extension of Cross-Efficiency DEA Model  
#### 2.4.1 Rejoice Utility Cross-Efficiency DEA Model  
Assume \(DMU^{-}\) is the worst and reference DMU; the rejoice utility of \(DMU_{k}\) corresponding to \(DMU^{-}\) is considered the gain value of \(DMU_{k}\). Based on the regret-rejoice utility structure introduced in Definition 2, the gain values for the rejoice utility DEA model are constructed using Equations (11) and (12). These gain values \(\left(R^{output}_{jk},R^{input}_{jk}\right)\) represent the psychological advantage of \(DMU_{k}\) compared to the least-performing reference DMU across outputs and inputs.  
\[R^{output}_{jk}=R\left(y_{jk}-\min\limits_{k\in K}\left(y_{jk}\right)\right)=1 -e^{-\lambda\left(y_{jk}-\min\limits_{k\in K}\left(y_{jk}\right)\right)} \tag{11}\]  
\[R^{input}_{jk}=R\left(\max\limits_{k\in K}\left(x_{ik}\right)-x_{ik}\right)= 1-e^{-\lambda\left(\max\limits_{k\in K}\left(x_{ik}\right)-x_{ik}\right)} \tag{12}\]  
According to Remark 1, because \(y_{jk}\geq\min\limits_{k\in K}\left(y_{jk}\right)\), \(R^{output}_{jk}=R\left(y_{jk}-\min\limits_{k\in K}\left(y_{jk}\right)\right)\geq 0\) is the gain value of decision maker selecting the \(DMU_{k}\) instead of \(DMU^{-}\) corresponding to \(jth\) output. Hence, the total gain value of the decision maker selecting the \(DMU_{k}\) instead of \(DMU^{-}\) corresponding to all outputs can be considered \(\sum_{j=1}^{l}v_{jk}R^{output}_{jk}\). On the other hand, because \(x_{ik}\leq\max\limits_{k\in K}\left(x_{ik}\right)\), \(R^{input}_{jk}=R\left(\max\limits_{k\in K}\left(x_{ik}\right)-x_{ik}\right)\geq 0\) is the gain value of the decision maker selecting the \(DMU_{k}\) instead of \(DMU^{-}\) corresponding to \(ith\) input. Hence, the total gain value of the decision maker selecting the \(DMU_{k}\) instead of \(DMU^{-}\) corresponding to all inputs being considered \(\sum_{i=1}^{l}u_{ik}R^{input}_{jk}\). Therefore, the total gain value or rejoice utility of the decision maker selecting the \(DMU_{k}\) instead of \(DMU^{-}\) corresponding to all inputs and outputs can be determined as follows:  
\[R_{jk}=\sum\limits_{j=1}^{l}v_{jk}R^{output}_{jk}+\sum\limits_{i=1}^{l}u_{ik} R^{input}_{jk} \tag{13}\]  
In general, decision makers prefer to maximize the total rejoice utility by finding the optimal weights \(v_{jk}\) and \(u_{ik}\). Hence, the rejoice utility cross-efficiency DEA model can be constructed as follows below.  
\[\begin{array}{c}\max\,RJ_{k}=\sum\limits_{j=1}^{l}v_{jk}R^{output}_{jk}+ \sum\limits_{i=1}^{l}u_{ik}R^{input}_{jk}\\ \text{\emph{subject to}}\\ \sum\limits_{i=1}^{l}u_{ik}x_{ik}=1&k=1,\ldots,K\\ \sum\limits_{j=1}^{l}v_{jk}y_{j}-\sum\limits_{i=1}^{l}u_{ik}x_{il}\leq 0&l=1, \ldots,K\\ u_{ik}\geq 0&i=1,\ldots,I;\ k=1,\ldots,K\\ v_{jk}\geq 0&j=1,\ldots,J;\ k=1,\ldots,K\end{array} \tag{14}\]
# Ras Multimers on the Membrane: Many Ways for a Heart-to-Heart Conversation
## 3 Ras Dimerization at Variable G-domain Interfaces
This prompted analysis of existing Ras structures in the PDB, which revealed many trimer arrangements in KRas, HRas, and NRas crystals [88]. [PERSON] et al. suggested that the trimers may use two G-domain interfaces, one partially overlapping with the \(\alpha\)4-\(\alpha\)5 interface and the other a novel interface involving \(\alpha\)1 and switch I [66]. [PERSON] showed that \(\alpha\)3-\(\alpha\)4 as well as \(\alpha\)4-\(\alpha\)5 interfaces play a role in the formation of larger oligomers by combining single molecule FRET and imaging experiments with computational modeling [70]. Formation of dimers and oligomers is a common feature shared by a large number of proteins, with the best known examples including the epidermal growth factor receptors (EGFRs) [123] and the Raf kinases [54; 124; 125]. What differentiates Ras dimers from the others is that Ras-Ras interactions appear to be relatively weak in general and that the transitions among the different dimer conformations may be readily achievable [70; 116; 117]. This unique feature may in part contribute to the functional versatility of Ras despite a much smaller functional domain compared with that of EGFR and Raf. The membrane environment in which Ras resides and functions is known to be highly heterogeneous with drastic, local variations in molecular composition and structure. This could be coupled with the intrinsic variability in Ras dimer conformations to achieve location-specific Ras functions.
## 2 System Model  
### Beam Hopping of Forward Link  
This paper considers the forward link of the multi-beam geostationary satellite systems, where broadband services are transmitted from the gateway station (GW) and transparently forwarded by the satellite to user terminals, referring to the DVB-S2X standard. Regarding the beam hopping architecture, the system resource management unit is within network control center (NCC). It pre-generates the beam hopping time plan (BHTP). BHTP determines dwell time and illumination order of each beam to match the traffic demand. The satellite receives the BHTP signaling message via telemetry, tracking, and command (TT&C) [2,26]. Then, the onboard beam hopping controller resolves BHTP and activates corresponding beams by employing a switching matrix or beamformer [27,28].  
For the sake of spectral efficiency, a beam hopping system usually employs aggressive frequency reuse scheme. In this paper, it is assumed that all beams share the total frequency resource of satellite. However, full frequency multiplexing scheme will inevitably bring co-channel interference [29]. Thus, in order to avoid co-channel inter-beam interference, in a beam hopping system the downlink beams can be divided into several clusters. In each cluster, only one beam can be activated at a time. Meanwhile, it avoids simultaneous illumination of adjacent beams from different clusters. Another optional method is to employ precoding [30, 31, 32] to suppress co-channel interference.  
The simplified beam hopping system architecture is shown in Figure 1.
## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Type** & **1 EGR** & **1 IGR** & **2 EGR** & **2 IGR** & **3 EGR** & **3 IGR** & **4 EGR** & **4 IGR** \\ \hline S1 [\%] & Old roof removal & 0.136 & 0.2478 & 0.313 & 0.49 & 0.065 & 0.095 & 0.19 & 0.28 \\ \hline S2 [\%] & Installation & 7.14 & 6.7 & 16.9 & 13.55 & 3.49 & 2.57 & 10.5 & _7.7_ \\ \hline S3 [\%] & Operation and Maintenance & 4.9 & 6.5 & 11.8 & 12.9 & 2.5 & 2.5 & 7.3 & 7.4 \\ \hline S4 [\%] & Property value & 0 & 0 & 0 & 0 & 50.4 & 59.3 & 39.1 & 44.4 \\ \hline S5 [\%] & Membrane longevity & 0.0833 & 0.042 & 0.24 & 0.113 & 0.053 & 0.015 & 0.14 & 0.063 \\ \hline S6 [\%] & Heating & 39.7 & 34 & 0 & 0 & 19.14 & 12.9 & 0 & 0 \\ \hline S7 [\%] & Cooling & 0.6 & 2.29 & 0 & 0 & 0.3 & 0.88 & 0 & 0 \\ \hline S8 [\%] & Number of RU. & 26.7 & 31.7 & 63 & 63.6 & 12.85 & 12.2 & 39.2 & 36.26 \\ \hline S9 [\%] & Discount rate & 18.2 & 16.7 & 5.7 & 7.46 & 11.6 & 8.7 & 2.33 & 2.85 \\ \hline S10 [\%] & Gr. roof lifespan [years] & 0.5 & 0.43 & 0.0417 & 0.062 & 0.3 & 0.16 & 0.0248 & 0.034 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: First-order sensitivity indices for different parameters in life cycle analysis. Figure 4: The life cycle of green roof—N = 47 years—calculated NPV for proposed models—most probable scenario for different models.