esa-sceva/satcom-chunk-collection huggingface dataset:
# Shadow Estimation for Ultrasound Images Using Auto-Encoding Structures and Synthetic Shadows
## 4 Results  
### Setting  
The encoder \(E\) and the decoder \(D\) were constructed just like U-Net [30]. Details of the architecture are described in Appendix B. The model was optimized by [PERSON] [34]. The size of the mini-batch was 32 and the number of training epochs was 10. The parameters of Adam were set to default except for the learning rate \(\alpha\) that was set \(10^{-4}\) initially and decayed to \(10^{-5}\) through 10 epochs of training. The weight for the reconstruction error \(\lambda_{\text{recon}}\) was fixed to 1.0. Other hyperparameters were set by grid search using the validation data. The search spaces of the hyperparameters are following: \(\lambda_{\text{synth}}=\lambda_{\text{label}}\in\{10^{-2},10^{-1},1,10,10^{2},10 ^{3}\}\), \(\lambda_{\text{synthreg}}\in\{10^{-3},10^{-2},10^{-1},1,10\}\), and \(v_{\text{min}}\in\{0.1,0.5\}\) in Algorithm A1. Note that \(\lambda_{\text{synthreg}}\) was set to zero in the semi-supervised situations because we empirically found that the labels play the same role as the regularization term. The selected hyperparameters are shown in Appendix C. To stabilize the training, in semi-supervised situations, the labeled training dataset was oversampled so that the number of the labeled data was almost equal to the size of the unlabeled dataset. Specifically, the labeled data was simply repeated \(\lfloor N_{\text{unlabeled}}/N_{\text{labeled}}\rfloor\) times.  
As a reference DNN based segmentation method, we used vanilla U-Net [30]. It was trained on pixel-wise cross-entropy and optimized by [PERSON] with \(\alpha=10^{-4}\). The size of the mini-batch was 32. Because U-Net only uses the labeled dataset, the number of training epochs was set so that the number of iterations equals that of the proposed method to prevent underfitting. We empirically confirmed that the training of U-Net converged with this setting.  
For both the proposed method and U-Net, the parameters of the models were saved every epoch. Then, the parameters that perform best for the validation dataset were selected. In addition, random cropping is applied to input data and they are resized to \(128\times 128\) for both the methods.  
The geometrical method [19] and the random walk method [20] are also used as references of methods that do not use labels. The parameters were in accordance with the original papers. Because the geometrical method outputs shadow detection results along scattered scanlines, we applied morphological closing filters to the results for adjusting to pixel-level detection.  
We show the experimental results on the testing dataset in the following sections. For all the experiments, the testing images are cropped so that they contain no metadata and resized to \(128\times 128\). Quantitative results for the validation dataset is shown in Appendix D.
## 5 Performance Evaluation
This decline is due to the sparser distribution of user service requests as the task types increase. For individual MEC servers, the proportion of service data stored in the limited space of the local cache pool becomes relatively smaller compared to the overall request volume, leading to a decrease in \(C_{r}\). Comparing the four models, it can be concluded that the Cache Chord mechanism effectively assists in computation offloading. Moreover, with the increase in node scale, higher benefits can be achieved. Figure 8: Average energy consumption under different Zipf parameters: (**a**) Zipf L; (**b**) Zipf R.  
Figure 9: Cache hit rate under different server numbers in Cache Chord. (**a**) User quantity. (**b**) Task quantity.
## 3 Simulation and Experimental Results
Therefore, the antenna may potentially be used in those low observable military applications where it is desired to have communication in an off-broadside direction. Figure 11: Normalized angular performance of monostatic RCS for x-z plane with HP incident wave at 10 GHz. (**a**) Reference antenna. (**b**) Proposed antenna. Figure 10: Normalized angular performance of monostatic RCS for y–z plane with VP incident wave at 10 GHz. (**a**) Reference antenna. (**b**) Proposed antenna. The monostatic RCS reduction for oblique angle incidence is shown in Figure 13. For the VP wave incidence, RCS reduction is shown in Figure 13a, and for the HP wave incidence, the RCS reduction is shown in Figure 13b. From the curves, it can be observed that for the incident angles of 5\({}^{\circ}\) and 10\({}^{\circ}\), the performance is close to that of 0\({}^{\circ}\) case (bore-sight). For the wave incidence of 15\({}^{\circ}\), the RCS reduction for both polarizations is close to 0 dB at the frequency points of 6, 10, and 11 GHz. It is important to mention that at these frequencies, both the proposed as well as the reference antennas realize nulls (or steep slopes just around the nulls) in their monostatic reflectivity patterns. For the incidence angle of 20\({}^{\circ}\), the average RCS reduction becomes somewhat lower, and the RCS reduction value at 12 GHz is a little compromised. Thus, it can be asserted that the overall angular stability of the proposed design is slightly less than \(\pm\)20\({}^{\circ}\). To have further insight into absorption as well as off-broadside radiation mechanism, surface E-field plots are presented in Figure 14. The log magnitude of E-field plotted on the absorber surface is show in Figure 14a. The plot is for a VP (x-polarized) incident wave, and at the frequency of 9 GHz. By inspection of Figure 14a, the spots of higher field intensity are easily identifiable. This field appears across the gaps in the metallic loops where resistors are present. The energy dissipation of the incident field occurs as heat within these resistors, thereby leading to the absorption of incident wave at the surface. Identical field plot is expected for an HP incident wave, except that the higher field intensity spots would now appear across the other two gaps for every loop. Likewise, field overlay plot on the surface containing gradated apertures (phase gradient surface) is shown in Figure 14b. This plot represents the field induced as a result of the wave radiated from the patch antenna (6 GHz frequency). It can be witnessed that a steady variation of surface E-field appears along the y-axis, in that, the E-field concentration increases gradually along -y-axis. This validates the mechanism behind beam deflection operation. Figure 12: Normalized monostatic RCS angular response at various frequencies. (**a**) y–z plane, VP incident wave at 7.6 GHz. (**b**) x–z plane, HP incident wave at 7.6 GHz. (**c**) y–z plane, VP incident wave at 14 GHz. (**d**) x–z plane, HP incident wave at 14 GHz.
## 2 Materials and Methods
### Data Sourcing and Processing
Negative indicators, such as impervious surface rate, were inverted during processing. Entropy weighting (EW) was applied to reflect the informational contribution of each indicator objectively [31; 32]. Higher entropy indicates greater variability and, hence, a higher weight in the composite index. The entropy formula and subsequent weight calculations followed standard multi-criteria decision-making procedures. In parallel, the Analytic Hierarchy Process (AHP) was employed to incorporate expert judgment. Indicators were structured into hierarchical levels, and pairwise comparisons were conducted to generate a judgment matrix. A consistency ratio (CR) test ensured the logical coherence of expert inputs. To balance subjectivity and objectivity, EW and AHP weights were integrated using a linear combination approach [33; 34]. The combined weight was adopted for each of the 16 indicators, as described in Equation (1):  
\[Z_{j}=aH_{j}+(1-a)W_{j} \tag{1}\]  
where \(Z_{j}\), \(H_{j}\), and \(W_{j}\) represent the combined weightings, subjective weightings (AHP), and objective weightings (EW), respectively, and \(j\) is the supply and demand impact factor, where \(1\leq j\leq 16\). The value of the coefficient a ranges from 0 to 1 and is taken as 0.5, since both subjective and objective factors are of equal importance.
## 2 Islanding Detection Methods
### Passive Methods
#### 2.1.5 Phase Jump Detection Method (PJD)  
This method involves keeping an eye on the phase difference between the voltage and current of the DG output during an abrupt phase leap. The inverter current stays the same under islanding conditions, but the voltage will swing its route due to the phase difference, which is detected using modified PLL. Therefore, a phase difference will occur due to the change in voltage path and the inverter will be disconnected when the voltage jump exceeds the acceptable value [12].  
This method which does not affect power quality is easy to implement and its detection speed is fast. The time of detection is around 10 ms to 20 ms [13]. On the other hand, in this method, the threshold is not easy to choose due to the load switching, which may cause some errors in the islanding detection.
# A New Resource Allocation Protocol for the Backhaul of Underwater Cellular Wireless Networks
## Average resource utilization  
Figure 13 shows the average message overhead for this case. As shown in the graph, when \(\beta=0\), the average message overhead fluctuates within some range. However, when \(\beta=2\) and \(\beta=4\), the message overhead becomes higher as the packet arrival rate increases. We can interpret this as follows. When \(\beta=2\) and \(\beta=4\), each UBS can have the \(\mu_{UBSmax}=12\) and \(\mu_{UBSmax}=14\), respectively. In this case, if a UBS has large packet arrival rate compared with the other UBSs, its backhaul capacity becomes \(\mu_{UBSmax}\). Accordingly, the reserved capacity of the UBSC becomes small due to the limited capacity. Since we assume that the packet arrival rate of each UBS is random with Poisson distribution, some UBS with small backhaul capacity in the previous simulation interval will have high probability of packet drop. So, this will trigger the resource reallocation procedure in the UBSC, resulting in more message overhead.  
Figure 12: Average Resource Utilization for different \(\mu_{UBSmax}\) adjustment parameter (\(\beta\)) and the number of mobile UTs (\(N_{MN}\)).  
Figure 13: Average Message Overhead for different \(\mu_{UBSmax}\) adjustment parameter (\(\beta\)) and the number of mobile UTs (\(N_{MN}\)).  
Figure 14 illustrates the average reserved capacity of the UBSC, denoted by \(\mu_{e}\) for this case. For \(5<\lambda<10\), we can observe that \(\mu_{e}\) becomes larger as \(\beta\) becomes smaller. The high value of \(\mu_{e}\) can be interpreted in two different aspects. One is that the UBSC backhaul resource is not efficiently allocated. The other is that the high reserved capacity of the UBSC can be used to provide more capacity to the UBS with sudden burst traffic. Consequently, this will reduce the message overhead due to resource reallocation.
## 2 Study Area and Methods
### Monitoring Scheme
#### 2.2.1 Monitoring Point Arrangement  
Considering the extent of rock fragmentation along the monitoring section, 14 measurement points are arranged, as depicted in Figure 2. The monitoring sensors include vibrating steel anchor bolt dynamometers, vibrating steel strain gauges for steel reinforcement, and vibrating wire earth-pressure cells.  
The frequency receiver is equipped with three batteries inside, and the battery level can keep the frequency receiver working normally for 10 days. All sensors are connected to a frequency receiver through wires to achieve automatic data collection. The frequency receiver continuously collects data every day. Data are downloaded every 2 to 3 days during tunneling. Afterward, the accumulated data are sorted, categorized, and analyzed until displacement achieves a stable state.
## Materials and Methods
### Feature Extraction
#### 2.4.1 Histogram Features  
Histogram features were used by selecting the object with respect to rows and columns [27]. This binary object was used as a mask of the original image for feature extraction. Histogram features were calculated based on the intensity of the individual pixels that were part of the objects. These features were based on the histogram, also called first-order histogram or statistical features. The first-order histogram probability \(\psi(\epsilon)\) is described in Equation (7).  
\[\psi(\epsilon)=\frac{\kappa(\epsilon)}{N} \tag{7}\]  
Here, N represents the total number of pixels in the image, and \(\kappa(\epsilon)\) measures the complete instances of the gray-scale value of \(\epsilon\). Eight first-order histogram features are calculated (mean, standard deviation, skewness, energy, and entropy, etc.). Mean is the average of values describing the bright mean and dark mean in an image. It is defined in Equation (8).  
\[\eta=\sum_{i}\sum_{j}\frac{\kappa\;(i,j)}{\kappa} \tag{8}\]  
The consequent values of \(i\) (rows) and \(j\) (columns) show the pixel. Standard deviation (SD) describes the contrast of the image. It is presented in Equation (9).  
\[\sigma_{\epsilon}=\sqrt{\sum_{\epsilon=0}^{P-1}(\epsilon-\overline{\epsilon}) ^{2}\psi(\epsilon)} \tag{9}\]  
When, around the central value, there is no symmetry (mean, median, mode), it is the degree of asymmetry, and this is called skewness and denoted by \(\gamma\). It is defined in Equation (10).  
\[\gamma=\frac{1}{\sigma_{\epsilon}^{3}}\sum_{\epsilon=0}^{P-1}(\epsilon- \overline{\epsilon})^{3}\;\psi(\epsilon) \tag{10}\]  
The gray-level distribution is called energy, denoted by \(\vartheta\) and defined in Equation (11).  
\[\vartheta=\sum_{\epsilon=0}^{P-1}[\psi(\;\epsilon)]^{2} \tag{11}\]  
Entropy is denoted by \(\
u\) and describes the randomness in the image data. It is defined in Equation (12).  
\[\
u=-\sum_{i}\sum_{j}\kappa\;(i,j)log_{2}\kappa\;(i,j) \tag{12}\]
## Appendix A Existing Control Schemes Used in Experimental Study  
This appendix contains equations relevant to the control schemes to which the newly developed control scheme was compared in our experimental study.  
**TDE-based PID with synchronization errors (Syn-TDE-PID)**  
\[\tau=\underbrace{\tau_{t-L}-\mathbf{\widetilde{M}}\mathbf{\tilde{q}}_{t-L}}_{ \text{TDE}}+\underbrace{\mathbf{\widetilde{M}}(\mathbf{\tilde{q}}_{d}+\mathbf{ K}_{D}\mathbf{\tilde{e}}_{c}+\mathbf{K}_{P}\mathbf{e}_{c}+\mathbf{K}_{I} \mathbf{\int e}_{c}dt)}_{\text{PID control}} \tag{1}\]  
where  
\(\bullet\quad\mathbf{K}_{D}\), \(\mathbf{K}_{p}\), and \(\mathbf{K}_{I}\) are PID gain diagonal design matrices.  
**TDE-based SMC with synchronization errors (Syn-TDE-SMC)**[13]\[\tau=\underbrace{\tau_{l-L}-\mathbf{\widetilde{M}}\mathbf{\widetilde{q}}_{l-L}}_{\text {TDE}}+\underbrace{\mathbf{\widetilde{M}}\big{(}\mathbf{\widetilde{q}}_{d}+ \mathbf{K}\mathbf{\dot{e}}_{c}+\mathbf{K}_{gu}\text{sign}(\mathbf{s})+\mathbf{ K}_{1}\mathbf{s}\big{)}}_{\text{SMC control}} \tag{12}\]  
where  
* The nonsingular terminal sliding surface is defined as follows: \[\mathbf{s}=\mathbf{\dot{e}}_{c}+\mathbf{K}\mathbf{e}_{c}.\] (13)
* \(\mathbf{K}_{1}\), \(\mathbf{K}\), and \(\mathbf{K}_{gu}\) are diagonal design matrices.
## 5 Discussions
### Pulse-Mode USCT versus WCE-Mode USCT
The bandwidth at 500 kHz was broader for the signals in the pulse-mode method than for those in the WCE-mode method. Figure 15: Detailed examples of the signals (envelope) at 1 MHz for the chicken drumstick in pulse-mode (solid line) and in WCE-mode (dotted line) for the angles 5\({}^{\circ}\) (top), 90\({}^{\circ}\) (middle), and 336\({}^{\circ}\) (bottom). AA: increase in resolution; BB: increase in amplitude and resolution; CC: increase in amplitude; DD: decrease in amplitude. At 1 MHz, the bandwidths in pulse-mode and in WCE-mode were identical, and equal to 900 kHz for similar values of (0.5-1.4) MHz. At 2.25 MHz, the bandwidth increased between pulse-mode and WCE-mode, from 1 MHz (pulse) to 1.8 MHz (WCE); that is, about twice as much, which was remarkable. This corresponded well with the observation of the resolution of USCT. The multifrequency mode was strongly favored by the effect of high frequencies for a bandwidth up to almost 3 MHz. However, the bandwidth alone did not explain the differences between the two imaging modes. The study of the area under the curve (AUC) was interesting. The AUC represents a spectral density and a quantity of energy propagated frequency by frequency. The higher the AUC, the more energy is propagated for each frequency in the bandwidth of the spectrum. At 500 kHz, the AUC in pulse-mode was higher than the AUC in WCE-mode, but the difference was not significant (12%). For the two other frequencies, 1 MHz and 2.25 MHz, the AUC was markedly higher in WCE-mode than in pulse-mode, with differences of 43% and 87%, respectively. This strong increase reflected a better energy transport for the WCE-mode method, and thus explained the improvement of the CNR. The differences between the pseudo-wavelets and the theoretical analysis wavelets, which were not very significant (<3%), did not influence this improvement. Despite the difficulty in experimentally \"designing\" a pseudo-wavelet such as a theoretical analyzing wavelet, the WCE-mode USCT remained more interesting.
# Software Defined Wireless Mesh Network Flat Distribution Control Plane
## 4 Topology Discovery
### Aggregated Topology Discovery Mechanism  
Among the FD-SDWMN architecture, the aggregated topology discovery mechanism can have the ability to apply the topology discovery protocol (OFDP) in multi-hop networks such as a wireless mesh network. The mechanism is based on OFDPv2 and benefited from the multi-hop control channel breaking into a single-hop by dividing the process into two phases: local domain topology discovery and global network topology aggregation, the proposed mechanism reduces the heavy load of topology discovery in the controller.
## 2 Materials and Methods  
### Case Study and Data Availability  
The area of interest is geographically focused around the island of Crete, in the south of Greece, an area of high economic interest and human activity as the Heraklion port is the third highest passenger traffic port in Greece, with strategic positioning as it interconnects three continents (Europe, Asia, and Africa) [1]. Thus, perspective users of the marine tools created for better marine planning, accident avoidance, decrease in CO\({}_{2}\) emitted by ships, etc., can include but are not limited to the Hellenic Coast Guard, port authorities, coastal municipalities, ship owners and companies, citizens, etc. The users of those tools require a Digital Twin of the Ocean solution that offers validated, high-resolution information and services; accurate short-term forecasts; decision support; early warnings; and alerts. To that end, we describe improvements to the models that are being made in our case study, as well as their integration with such DTO tools for thematic applications. The initial forcing for the operational Weather Research and Forecasting (WRF) is derived in real time from the Global Forecast System (GFS) forecasting model by the National Oceanic and Atmospheric Administration (NOAA) and National Center for Atmospheric Research (NCAR) (2015) that provides forecasts with a spatial resolution of approximately 27 km \(\times\) 27 km and a temporal resolution of 6 h, being extremely coarse for observing fine-scale wind phenomena [21]. As seen in Figure 1, the domain of interest is dynamically downscaled two times, firstly to a 9 km \(\times\) 9 km grid named D01, and then to a 3 km \(\times\) 3 km finer grid centered around Crete (D02). Firstly, we introduce the available data sources for the area of interest, for both initializing the models with relevant forcing data and observational data that are used for DA and validation of the models. The area of interest is the South Aegean Sea; the Cretan Sea; part of the border region of the Ionian Sea; and the Levantine Sea surrounding the island of Crete, Greece. The two nested subdomains of the WRF program are shown in Figure 1. The available data are as follows:  
**Data Source I:** Cretaweather.gr offers in situ data information in high temporal resolution (5 min) in available stations. Additional station locations from CretaWeather are marked in Figure 1 as orange dots. **Data Source II:** NOAA METAR ISH ground data: NOAA National Centers for Environmental Information (2001): Global Surface Hourly ([[https://www.ncei.noaa.gov/access/search/index](https://www.ncei.noaa.gov/access/search/index)]([https://www.ncei.noaa.gov/access/search/index](https://www.ncei.noaa.gov/access/search/index)), last accessed online 28 October 2024). These data were accessed through the NOAA library, which downloads and updates real-time data from the NOAA ISD-ISH database. This dataset is curated by NOAA, so each observation is accompanied with control flags that indicate its quality, and the stations' uptime is monitored to reduce missing value cases.
## 4 Discussion
### Do Temporal Trends in EVI and NDVI Correlate with Canopy Growth?
Greater areas of observed NDVI greening may be influenced by the sensitivity of the NDVI to changes in vegetation type or differences in spatial resolution between Landsat and lidar-derived products. Other studies have reported an overestimation in NDVI greening trends, which could be unrelated to in situ vegetation structural properties. Over different land covers within a semi-arid to arid region of northwest China, [PERSON] et al. [118] attributed NDVI overestimation or underestimation to spatial resolution (250 m MODIS and 8 km GIMMS). [PERSON] et al. [119] also found an overestimation of greening trends when using NDVI to detect vegetation change at 1 km resolution. Given some potential for overestimation, it is important to consider the magnitude and extent of the observed NDVI greening. NDVI is derived through a non-linear transformation, which can lead to the overestimation of NDVI values in areas with low vegetation coverage and underestimation in areas with high vegetation coverage [20]. It can be assumed that any systematic overestimation in NDVI would persist throughout the entire study period, suggesting that temporal variations in NDVI should be minimally impacted [120]. EVI also demonstrated some areas of negative slope (browning), in some upland forest landcover areas, while the NDVI trends showed greening throughout the survey polygons. The observation of loss or contraction in the EVI slope signal is encouraging because it matches the observation for \(\Delta\)P90 CHM woody ecotone contraction. However, there was limited direct spatial correspondence between the two losses (e.g., Figure 9), indicating that both the EVI and \(\Delta\)P90 CHM observations, while synergistic, are responding to subtly distinct in situ ground-level processes. Several studies have overlooked the classification of EVI browning in northern latitudes, attributing its exclusion to its occurrence over relatively small areas (e.g., Chen, Lantz, Hermosilla and Wulder [22]; [PERSON], Lantz, Hermosilla, Wulder and Wang [23]). Other researchers have documented EVI browning in boreal forests (e.g., Guay, Beck, Berner, Goetz, Baccini and Buermann [24]; Lebre [25]) with a suggestion that increases in surface water presence is a likely cause [121].