esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Power Flow Analysis
### Division of Output Power  
The study starts by defining how much of the average output power undergoes a power conversion process for the TIBuck DC/DC converter alone (without considering the auxiliary buck converter). From Figure 11a, the average output power \(\mathrm{P_{out}}\) is defined as  
\[P_{out}=V_{o}\cdot I_{o}=\frac{V_{o}(V_{o}-nV_{th}(T))}{n\mathrm{R_{\Omega}}}. \tag{17}\]  
with \(\mathrm{R_{\Omega}}\) the dynamic resistance of HB-LED. If \(\mathrm{V_{o}}\) is higher than the HB-LED load threshold voltage, \(\mathrm{nV_{th}}(\mathrm{T})\), then the equivalent of the HB-LED load can be used as in Figure 11b. Hence, the current driven by the HB-LED load is  
\[I_{o}=\frac{V_{o}-nV_{th}(T)}{n\mathrm{R_{\Omega}}}. \tag{18}\]  
To calculate the fraction of the output power that does and does not undergo the power conversion process, the output load is divided in such way that the voltage across the lower side is \(\mathrm{V_{2}}\) and the voltage across the top side is \(\mathrm{V_{o}}-\mathrm{V_{2}}\), as depicted in Figure 11c. For this equivalence to be true, the current driven by both parts of the HB-LED load must be \(\mathrm{I_{o}}\), producing this equation for the top side  
\[\frac{V_{o}-V_{2}-nV_{th1}}{n\mathrm{R_{\Omega}}}=\frac{V_{o}-nV_{th}(T)}{n \mathrm{R_{\Omega}}}, \tag{19}\]  
and this equation for the bottom side  
\[\frac{V_{2}-nV_{th2}}{n\mathrm{R_{\Omega}}}=\frac{V_{o}-nV_{th}(T)}{n\mathrm{R _{\Omega}}}. \tag{20}\]  
The values of the components of both equivalents are calculated from Equations (19) and (20), obtaining  
\[\begin{array}{l}n\mathrm{R_{\Omega}}=n\mathrm{R_{\Omega 1}}=n\mathrm{R_{\Omega 2}},\\ nV_{th1}=nV_{th}(T)-V_{2},\\ nV_{th1}=nV_{th}(T)-V_{2},\end{array} \tag{21}\]  
The expression of \(\mathrm{P_{ti}}\) and \(\mathrm{P_{fr}}\) is obtained from Figure 11 as  
\[P_{ti}=(V_{o}-V_{2})\cdot I_{o}, \tag{22}\]  
\[P_{fr}=V_{2}\cdot I_{o}, \tag{23}\]  
Figure 11: Division process of the output HB-LED load (only one string for the sake of simplicity). (**a**) Initial scenario. (**b**) Modelling the HB-LED load. (**c**) Dividing the HB-LED load in two parts.  
with \(\rm P_{ti}\) being the power of the top side of the equivalent that undergoes power conversion and \(\rm P_{fr}\) the power of the bottom side of the equivalent that does not undergo power conversion.
## 3 Results
### ROC Curves of 2D-Input CNN Models  
Figure 7 presents a one-time experimental ROC curve for the four 2D-input CNN models by using the baseline (red, solid line), the baseline with HV (green, dashed line), and the baseline with SV (blue, dot-dashed line) for identification.  
According to the ROC curves, the baseline ResNet34 achieves the best performance, while both the voting strategies, HV and SV, enable further improvement in the classification results. In addition, the SV strategy can enhance the prediction performance of HiFuse and MedMamba from AUC \(<\) 0.80 to AUC \(>\) 0.92, and the values are dramatically increased.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **Epoch** & **ACC** & **SEN** & **SPE** & **AUC** & **Score** &
\begin{tabular}{c} **Time** \\ **(min)** \\ \end{tabular} \\ \hline ResNet34 [28] & 100 & 0.865 & 0.942 & 0.757 & 0.936 & 0.901 & 63.2 \\ ResNet34 + HV & 100 & 0.907 & 0.990 & 0.864 & 0.907 & 0.907 & \\ ResNet34 + SV & 100 & 0.986 & 0.990 & 0.963 & 0.986 & 0.986 & \\ \hline MedViT [29] & 100 & 0.699 & 0.808 & 0.547 & 0.772 & 0.736 & 88.3 \\ MedViT + HV & 100 & 0.783 & 0.955 & 0.611 & 0.783 & 0.783 & \\ MedViT + SV & 100 & 0.845 & 0.987 & 0.683 & 0.845 & 0.845 & \\ \hline HiFuse [30] & 100 & 0.778 & 0.685 & 0.906 & 0.883 & 0.831 & 71.7 \\ HiFuse + HV & 100 & 0.843 & 0.735 & 0.951 & 0.843 & 0.843 & \\ HiFuse + SV & 100 & 0.928 & 0.864 & 0.980 & 0.928 & 0.928 & \\ \hline MedMamba [31] & 100 & 0.738 & 0.726 & 0.755 & 0.799 & 0.769 & 76.7 \\ MedMamba + HV & 100 & 0.833 & 0.824 & 0.842 & 0.833 & 0.833 & \\ MedMamba + SV & 100 & 0.954 & 0.975 & 0.943 & 0.954 & 0.954 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tumor classification using 2D-input models with and without the strategies.
# Differential Privacy in Federated Learning: An Evolutionary Game Analysis
## 3 System Model and Evolutionary Game Formulation
### Utility Function
#### 3.2.2 Payoff, Cost, and Utility  
The incentive pool is shared among all clients. Since the noise added by the clients affects the performance of the model, and the larger the noise, the worse the performance of the local model, the payoff should be lower for clients with larger noise power. We assume that the client with noise power \(\sigma_{n}^{2}\) should receive a payoff proportional to \(\frac{1}{\sigma_{n}}\). Given that the size of the incentive pool is \(Q_{E}\), using normalization, the payoff for choosing noise power \(\sigma_{n}^{2}\), denoted by \(p_{n}\), is given by  
\[p_{n}=Q_{E}\cdot\frac{\frac{1}{\sigma_{n}}}{I\cdot\sum_{l=1}^{N}\frac{x_{l}}{ \sigma_{l}}}+A, \tag{5}\]  
where \(A\) represents the basic reward granted by the server to participants for their involvement in the training process. We observe that the payoffs, \(p_{1},\ldots,p_{N}\), are determined by the population shares \(x_{1},\ldots,x_{n}\).  
The cost arises from the impact of Gaussian noise on privacy leakage for each client. Most current studies measure privacy leakage through experiments, which, however, requires access to user data, contradicting the original intention of privacy protection. [PERSON] et al. [29] conducted research on the theoretical measurement of privacy leakage, making it possible to theoretically quantify privacy leakage and apply it to various scenarios and datasets. Motivated by their results, we express the cost of strategy, \(S_{n}\), \(c_{n}\), as  
\[c_{n}=\frac{\eta^{2}E_{n}^{2}}{2\sigma_{n}^{2}}. \tag{6}\]  
Here, \(\eta\) is the model learning rate. We emphasize that more local iterations make the model more suitable for the local dataset, increasing the likelihood of data leakage.  
Summarizing the above, the utility for selecting strategy \(S_{n}\) can be expressed as  
\[U_{n}=p_{n}-c_{n}=Q_{E}\cdot\frac{\frac{1}{\sigma_{n}}}{I\cdot\sum_{i=1}^{N} \frac{x_{i}}{\sigma_{i}}}+A-\frac{\eta^{2}E_{n}^{2}}{2\sigma_{n}^{2}}. \tag{7}\]  
Notice that \(U_{n}\) is a function of \(x_{1},\ldots,x_{n}\). The following Algorithm 1 summarizes the calculation of the utility.
## 3 Simulation  
### System Overview  
To validate the effectiveness and feasibility of the proposed algorithm, the Pyoseon D/L in Jeju Island was employed as a test system. As shown in Figure 5, the Pyoseon D/L consists of a total of 117 buses, with a total line length of 22.8 km. In the distribution system, the impact of flexible resources is most pronounced at the midpoints and endpoints of the network. For this reason, VPP resources were primarily applied to these locations for the simulation, and all DERs in the Pyoseon D/L are assumed to be VPP resources.  
Among the VPP resources, the capacity of PV systems was uniformly set to clearly demonstrate the sensitivity differences based on distance. Meanwhile, ESSs, EVs, and FCs were arbitrarily deployed, considering the state of the system. The rated sizes and capacities of the VPP resources used in the simulation are summarized in Table 5.  
For the selection of monitoring buses, the bus located at the endpoint of the network, where voltage violations are most severe, was prioritized. Additionally, another monitoring bus was selected at the feeder head (incoming point), which is the opposite location of the endpoint. This selection strategy was designed to more effectively monitor voltage fluctuations within the system. By simultaneously observing the voltage status at two contrasting locations, namely the endpoint and the feeder head, the algorithm was able to  
Figure 5: Single-line diagram of the Pyoseon D/L.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Resource** & **Rated [kW]** & **Capacity [kWh]** \\ \hline PV1\(\sim\)8 & 2000 & - \\ ESS1\(\sim\)2 & 2000 & 8000 \\ EV Station1\(\sim\)2 & 1500 & 2500 \\ FC1\(\sim\)2 & 1000 & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: VPP Resource configuration.  
provide a more precise analysis of the overall voltage stability of the distribution system. Therefore, control is performed based on the voltage measured at the monitoring bus, and the simulation parameter values are summarized in Table 6.
# A Low-Loss Circuit with High-Pass Low-Pass Broadband Flat Negative Group Delay Characteristics
###### Abstract  
A multifunctional circuit with high-pass and low-pass negative group delays can be achieved by simply changing the values of the components in the circuit, without changing the circuit structure. While achieving negative group delay, the circuit also has flat broadband characteristics and lower losses. Theoretical calculations and equation derivation are provided. The experimental circuit was simulated, and the simulation results were essentially consistent with the theoretical results, indicating the feasibility of the experiment.  
high pass; low pass; negative group delay Article
## 4 WSN-SES/MB Framework
### Entity Structures
#### 4.2.2 Proposed Pruning Algorithm  
In WSN-SES/MB, our pruning algorithm (Figure 2(b)) converts into one PES according to the _Rules_ of the SES tree.  
Algorithm 1 shows our pruning algorithm from an SES tree to a PES for a sensor network. In the pruning algorithm, lines 10 to 13 are newly added for our WSN-SES/MB. Here, the SES tree represent the entity structure of a sensor network. The algorithm inputs the SES tree and searches specialization and multiple-decomposition nodes for the pruning. If a node is a specialization entity, a user can select only one of its child entities. On the other hand, if a node is a multiple-decomposition entity, the user determines a sub-model of DEVS kernel-models and the number of sub-models. If the sub-model is a broadcast-model, a user inputs a sensor field size and a BS location ([10; 49] suggests that the broadcast-model is suitable for WSNs).  
```
1:whilenode of all nodes of SES
2:ifnode == specializationthen
3: Select an entity among all child entities;
4:endif
5:
6:ifnode == multiple-decompositionthen
7: Select a sub-model of DEVS kernel-models;
8: Input number of members
9:
10:ifsub-model == broadcast-modelthen
11: Input sensorFieldSize(X, Y);
12: Input bsLocation(X, Y);
13:endif
14:endif
15:endif
```  
**Algorithm 1** Pruning of WSN-SES/MB
## 2 Materials and Methods
### HPLC-MS/MS Analysis  
The target analytes were separated by a Waters ACQUITY UPLC system at 40 \({}^{\circ}\)C using an XBridge\({}^{\otimes}\) C\({}_{18}\) column XP (3.0 \(\times\) 100 mm, 2.5 \(\upmu\)m) (Waters, Milford, MA, USA). A linear gradient elution program using a mobile phase consisting of (A) methanol and (B) water containing 5 mmol/L ammonium acetate was set as follows: 0-1 min, 10% A; 1-6 min, 90% A; 6-6.5 min, 90% A; 6.5-6.7 min, 10% A; 6.7-8 min, 10% A. The injection volume was 3 \(\upmu\)L and the flow rate was 0.4 mL/min.  
The MS/MS acquisition was realized by a Triple-QuadTM 5500 mass spectrometer equipped with an electrospray ionization (ESI) interface operating in both negative (ESI\({}^{-}\)) and positive (ESI\({}^{+}\)) modes (AB SCIEX, Foster City, CA, USA). The analysis was conducted with the following parameters: ion spray voltage, 4.5-5.5 kV; source temperature, 450 \({}^{\circ}\)C; curtain gas pressure, 35 psi; nebulizer gas, 50 psi; turbo gas pressure, 50 psi. ZEN and ZEN-14G were quantitatively analyzed by multiple reaction monitoring (MRM) (Table 1). Data were processed and analyzed with MultiQuant 3.0.2 software (AB SCIEX, Foster City, CA, USA).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mycotoxin** & **Retention Time (min)** & **Precursor Ion** & **Products Ion (\(m/z\))** & **Collision Energy** (eV)** & **Ionization Mode** \\ \hline ZEN-14G & 5.90 & 481.2 & 319.2 * & 12 & ESI\({}^{+}\) \\ ZEN & 6.78 & 316.9 & 131.0 & \(-\)40 & ESI\({}^{-}\) \\ \hline \hline \end{tabular}  
* ** Primary product ions.  
\end{table}
Table 1: MS/MS parameters of the masked mycotoxin zearalenone-14-glucoside (ZEN-14G) and its metabolite, zearalenone (ZEN).  
Figure 1: MRM chromatograms of zearalenone (ZEN) and zearalenone-14-glucose (ZEN-14G) in blank plasma, small intestine, and urine samples (**A**); plasma, small intestine, and urine samples spiked with ZEN and ZEN-14G at the level of 20 \(\mathrm{\SIUnitSymbolMicro g}\)/L (**B**); and the plasma samples gathered at 10 min, small intestine samples gathered at 24 h, and urine samples gathered at 6 h after the administration of ZEN-14G by gavage (4 mg/kg body weight) in rats (**C**).
# 5G New Radio Open Radio Access Network Implementation in Brazil: Review and Cost Assessment
## 2 Background
### Open RAN Architecture
#### 2.2.4 Energy Efficiency  
A key aspect of RAN architectures is their energy efficiency, which is particularly important for sustainable 5G and 6G deployments.  
The centralized processing in c-RAN and v-RAN can enable more efficient power management and load balancing, leading to potential energy savings.  
On the other hand, the increased reliance on fronthaul networks in these architectures may also introduce additional energy consumption, which needs to be carefully managed.  
The distributed nature of d-RAN can lead to higher overall energy consumption, as each cell site requires dedicated hardware and power sources.  
The Open RAN approach, with its emphasis on open interfaces and interoperability, may provide opportunities for innovative energy-efficient solutions but also introduce coordination challenges that could impact energy efficiency.
## 5 Discussion
Many applications, such as deployable structures and wearable electronics, require repeated shape transformations, and maintaining performance over multiple cycles is essential for commercial viability. Studies on degradation mechanisms, structural integrity under stress, and the development of self-healing SMPNCs are ongoing to improve material longevity. Overall, while SMPNCs have opened new avenues in smart material applications, continued research is necessary to optimize their fabrication, actuation efficiency, biocompatibility, and long-term performance. Addressing these challenges will be key to unlocking their full potential for next-generation adaptive and responsive materials.
## 5 Feasibility Analysis  
Currently, VR and AR glasses on the market usually use liquid crystal displays (LCD) or organic light-emitting diodes (OLED), which are very good for 2D display, but cannot be used as SLM. LCoS is a kind of display module in SLM, and it has a compact structure, which is suitable for 3D HMDs; because its size is about on inch, its resolution is currently up to 4K, and its minimum pixel pitch reaches 3.74 \(\upmu\)m. The viewing angle of holographic HMD can be 16.4\({}^{\circ}\), which is wide enough for near-eye display. Furthermore, with the development of lasers and other light sources, it is easy to get enough high intensity of readout light with a small size and low energy consumption. Therefore, true 3D HMD with a high brightness, high resolution, large viewing angle, and depth cue will be easy to obtain by holography with the development of SLMs. We believe that holographic 3D display for VR and AR can meet the requirements of near-eye display.
# Predicting Decompensation Risk in Intensive Care Unit Patients Using Machine Learning
## 3 Results
### Model Interpretability  
The resulting overall SHAP plot corresponding to the CNN-LSTM model is displayed in Figure 9. From the figure, it was estimated that Oxygen Saturation was the most relevant variable in predicting the decompensation risk, followed by Platelet Count, Heart Rate, Mean Blood Pressure, and Prothrombin Time. Variables such as Capillary Refill, Diastolic Blood Pressure and Weight were found to be the least relevant. It is also noticeable that lower Oxygen Saturation values increase the decompensation risk.  
Figure 8: K-means clusters on the PCA projected risk prediction data.  
Figure 9: Overall SHAP plot of the CNN-LSTM model.  
SHAP values were also calculated for clusters C0 and C5, which seemed to be the most dissimilar. Figure 10 displays their resulting SHAP plots. The figure suggests that although a low oxygen saturation value is a critical factor overall, a decrease in a patient's heart rate could be the most relevant factor in indicating a sudden patient decompensation (first hours).
### Ash Main Elements and Heavy Metal Content
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Bed Ash** & \multicolumn{3}{c}{**Fly Ash**} \\ \hline  & **Overflow** & **Prim. Cyclone** & **Sec. Cyclone** & **Filter** \\ \hline  & & \multicolumn{3}{c}{mass fraction in kg kg\({}^{-1}\)} \\ Al2O3 & 0.105 & 0.105 & 0.107 & 0.108 \\ BaO & 0.001 & 0.001 & 0.001 & 0.001 \\ CaO & 0.235 & 0.252 & 0.302 & 0.335 \\ Fe2O3 & 0.098 & 0.095 & 0.115 & 0.108 \\ K2O & 0.015 & 0.017 & 0.014 & 0.011 \\ MgO & 0.025 & 0.029 & 0.031 & 0.031 \\ MnO2 & 0.003 & 0.002 & 0.003 & 0.002 \\ Na2O & 0.004 & 0.004 & 0.004 & 0.003 \\ P2O5 & 0.141 & 0.128 & 0.137 & 0.135 \\ SO3 & 0.040 & 0.033 & 0.033 & 0.028 \\ SiO2 & 0.307 & 0.267 & 0.228 & 0.228 \\ SrO & 0.001 & 0.001 & 0.001 & 0.001 \\ TiO2 & 0.005 & 0.006 & 0.006 & 0.006 \\ \hline \end{tabular}
\end{table} 
Table 8: Ash main elemental analysis. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **Sewage** & \multirow{2}{*}{**Bed Ash**} & **Prim. Cyclone** & **Sec. Cyclone** & **Filter** & **German Legal Limit** \\  & & & **Cyclone** & **Cyclone** & **Filter** & **German Legal Limit** \\ \hline  & & \multicolumn{3}{c}{mass fraction of trace elements in mg/kg} \\ As & 10 & 38 & 40 & 86 & 38 & 40 \\ Be & 1 & 1 & 1 & 1 & - \\ Cd & 1.7 & 0.2 & 3.3 & 29 & 0 & 4 \({}^{\text{a,c}/6}\) \({}^{\text{a,d}}\) \\ Co & 3 & 29 & 29 & 41 & 29 & - \\ Cr & 377 & 828 & 1018 & 1939 & 828 & 900 \({}^{\text{a}}\)/2 \({}^{\text{c}}\) \\ Cu & 515 & 865 & 946 & 1552 & 865 & 900 \({}^{\text{b}}\) \\ Hg & 0.4 & 0.0 & 0.1 & 0.5 & 0.0 & 1 \({}^{\text{a}}\) \\ Mo & 7 & 25 & 36 & 81 & 25 & - \\ Ni & 25 & 592 & 4639 & 6338 & 592 & 80 \({}^{\text{a}}\) \\ Pb & 60 & 134 & 219 & 710 & 134 & 150 \({}^{\text{a}}\) \\ Sb & 2 & 15 & 12 & 23 & 15 & - \\ Se & 13 & 43 & 103 & 217 & 43 & - \\ Sn & 134 & 75 & 122 & 195 & 75 & - \\ Tl & 3 & \textless{}0.3 & \textless{}0.3 & \textless{}0.3 & \textless{}0.3 & 1 \({}^{\text{a}}\) \\ V & 31 & 52 & 53 & 64 & 52 & - \\ Zn & 1010 & 1607 & 1589 & 2291 & 1607 & 4000 \({}^{\text{b}}\) \\ \hline \end{tabular}
\end{table} 
Table 9: Heavy metal concentration in sewage sludge and ash fractions, for comparison: German legal limit. \({}^{\text{a}}\) German fertilizer law (DüMV), \({}^{\text{b}}\) German sewage sludge treatment law (Abklärv2017), \({}^{\text{c}}\) for dry sewage sludge with 8% P2O5, \({}^{\text{d}}\) for ash with 12% P2O5, \({}^{\text{e}}\) stricter limits for CrVI: 2 mg kg\({}^{-1}\).
## 4 Results  
### Questionnaire Results  
#### 4.1.1 Presence  
The results regarding presence are presented in Figure 4 with means and standard deviations for each statement. The combined mean and standard deviation for the 12 presence statements are 4.21 and 0.76, respectively. [PERSON]'s alpha for the presence statements is 0.77, thus indicating acceptable internal consistency. As the results indicate, most statements related to presence garnered positive responses from the participants. The symptoms of cybersickness were largely avoided among those who finished the experiment (statement 3--\(\mu\): 4.38; \(\sigma\): 0.97); however, four participants left the experiment early due to symptoms of cybersickness and did not answer the questionnaire. Four seasons succeeded particularly well in keeping the interface clean (statement 6--\(\mu\): 4.48; \(\sigma\): 0.75), providing multisensory experience (statement 7--\(\mu\): 4.24; \(\sigma\): 0.62), and making the experience enjoyable (statement 11--\(\mu\): 4.05; \(\sigma\): 0.80). In contrast, the lowest responses to statements 10 (\(\mu\): 3.67; \(\sigma\): 0.86) and 12 (\(\mu\): 3.52; \(\sigma\): 0.98) indicate that \"being there\" with emotional engagement was particularly lacking for some participants. Possible reasons are revealed in the next section, where we present the results of the interview data analysis.
## 3 Results
### Impact of the Selected Processing Conditions on the Characteristics of AP  
#### 3.2.1 Colour  
Colour and the magnitude of its variation (before and immediately after processing) were expressed through the L*a*b* parameters. This was the physical parameter that showed the most differences amongst technologies, with the results presented in Figure 2.  
The results of the L* parameter (Figure 2A) revealed that US (47.46 \(\pm\) 0.66) and CTP (43.52 \(\pm\) 1.17) treatments significantly increased the L* from the untreated AP control (39.91 \(\pm\) 1.81).  
Regarding the a* parameter (Figure 2B), there was a decrease in CTP, US, and HPP-treated AP compared to untreated AP. The decrease was particularly significant for US (1.64 \(\pm\) 0.38) compared to the untreated AP (5.80 \(\pm\) 0.88). The third parameter, b* (Figure 2C), exhibited a reduction in both the BL and treatment groups, in comparison to the control group (21.05 \(\pm\) 0.44). The most significant impact was observed in the AP group treated with CTP (14.34 \(\pm\) 0.70).  
The \(\Delta\)E* values for colour variation immediately after processing (Figure 2D) increased from 3.42 \(\pm\) 0.62 (BL) to 10.10 \(\pm\) 0.20 (US), with US exhibiting the highest variance. This result is in line with the expected variation in individual colour characteristics. The PEF treatment had the lowest \(\Delta\)E* value of 4.67 \(\pm\) 0.31. Figure 2E clearly demonstrate the variance in colour across the samples. In general, the CTP and PEF-processed AP samples have a brighter colour, whereas the US and HPP-processed AP samples tend to have a darker colour.
# Error Sources of Interferometric Synthetic Aperture Radar Satellites
## 2 Intrinsic Height Errors and Location-Induced Errors
### Location-Induced Errors
#### 2.2.3 Shadow  
Shadow typically happens at the back-slope of the terrain. However, due to the oblique observation geometry, not all areas at the back side are indetectable. Here, we define the slopes at the back side of the terrain with a negative sign. The condition of shadow effect occurs when \(\alpha<-(90^{\circ}-\theta)\), meaning radar shadow occurs when the slope angles are smaller than the negative of the depression angle [52]. Also, large ground features or artificial construction which block radar waves from illuminating the surface could result in shadow effect [47].  
The terrain segment \(C\) is located at the back side of the terrain, so when \(C\) is projected to image plane (\(C^{\prime}\)), it appears dark as there was essentially no feature exposed to radar illumination. The existence of shadow hinders image interpretation. The way to limit shadow areas is to reduce the incidence angle. However, smaller incidence angle produces more areas of layover as a drawback.