esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation and Data Center Experimental Results  
In this section, we first describe the optimization of the code rate of the LDPC encoder for the PS distribution and show a different ROF factor performance using simulations. Then, we introduce the experimental setup and the improvement we achieved from our PS-NPS-8-PAM scheme.
# A Hybrid DSCNN-GRU Based Surrogate Model for Transient Groundwater Flow Prediction
## 4 Discussion
Future integration of discrete fracture network (DFN) modeling with conditional geostatistical simulation techniques could better capture the hierarchical organization of karst systems and preferential flow dynamics. Second, the current fixed optimization strategy for network architecture may limit performance in complex hydrogeological settings. Subsequent studies could systematically optimize critical parameter combinations in hybrid neural architectures using advanced optimization algorithms while incorporating attention mechanisms to capture the multiscale flow characteristics unique to karst systems. Third, and equally important, future development of surrogate models should explicitly account for conceptual model uncertainty--particularly the impact of geological conceptualizations on flow dynamics [2]. This would involve generating multiple realizations of aquifer heterogeneity to train and test surrogate models under different geological scenarios, thereby enhancing their robustness and applicability in challenging hydrogeological environments.
## 3 Results and Discussion
### Surface Characteristics

Table 2: Flocculation and coalescence stability, average flocculation index (FI), centrifugal stability, HCT (heat coagulation time) stability constant (KE), and zeta potential of emulsions of emulsion prepared with different mixtures of pea, lentil, and fava bean protein. For example, pure lentil protein (T2 = \(-\)23.77 \(\pm\) 2.33) had a lower surface charge when combined with pea or bean protein (T4 = \(-\)26.75 \(\pm\) 2.34 and T6 = \(-\)28.05 \(\pm\) 1.20). The solubility of proteins is important because proteins, when well dispersed and in a solution, generally have good emulsifying properties and can easily migrate to the oil-water interface. The [44] relates \(\zeta\)- potential to solubility and hydrophobicity, i.e., the more negative the charges, the greater the solubility of the protein and the lower the hydrophobicity of the surface. Ref. [45] found that protein solubility is directly related to charge, suggesting that proteins with higher charge are more soluble. The authors also studied proteins from peas, lentils, and broad beans and found a correlation between surface charge and solubility but inversely to hydrophobicity.
# An Energy-Efficient and Robust Multipath Routing Protocol for Cognitive Radio Ad Hoc Networks
## 4 Energy-Efficient and Robust Multipath Routing
### Route Reply  
The selected primary and backup paths are sent back to the source by the destination via the route reply process. The destination node, upon receiving the RREQ copies, forms the reverse paths similar to the process at intermediate nodes. It unicasts separate route reply (RREP) packets to each of the primary and backup paths discovered. The basic structure of the RREP packet is depicted in Figure 5. The main field that should be focused on in the RREP packet are P-id (path identifier), CA (channel assigned), and NH (next hop), which indicate the preference order of the selected paths, the channel identifier, and the next hop node identifier for the forward path, respectively.  
When an intermediate node receives an RREP packet, it sets up the forward path along the channel selected and then forwards a copy of the RREP packet along the reverse path stored in its routing table; at the same time, it updates the hop count, the channel assigned, and the next hop field of the route reply packet. The routing table of intermediate nodes is also updated with the information in the RREP packet to set up the forward path. If an intermediate SU node receives a duplicate of the RREP packet for the same source-destination pair, the packet is dropped except when it is for the new route discovery session.
## 3 Results and Discussion
### Adsorption Isotherms
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Dyes**} \\ \cline{3-4}  & & **AY42** & **AR213** \\ \hline \multirow{3}{*}{_Freundlich_} & \(K_{F}\) (mg/g(mg/L)\({}^{-1/n}\)) & 16.90 & 15.37 \\  & \(n\) & 4.011 & 3.177 \\ \cline{1-1}  & \(\chi^{2}\) & 36.9719 & 18.9157 \\ \cline{1-1}  & \(q_{m}\) (mg/g) & 62.36 & 77.99 \\ \cline{1-1} \cline{2-4} \multirow{3}{*}{_Langmuir_} & \(K_{L}\) (L/mg) & 0.089 & 0.065 \\ \cline{1-1}  & \(\chi^{2}\) & 13.3303 & 17.9031 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Isotherm coefficients for adsorption of AY 24 and AR 213 onto PM4.
## 4 Evaluation and Discussion
### Image Quality Analysis
#### 4.4.2 Cover vs. Stego Image Comparison
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cover Image** & **Secret Image** & **LSB Pattern** & **MSE** & **PSNR** \\ \hline \multirow{8}{*}{pepper} & \multirow{8}{*}{plane} & LSB-1 [11] & 0.3329 & 52.9064 \\ \cline{3-5}  & & LSB-2 [11] & 0.8430 & 48.8721 \\ \cline{3-5}  & & LSB-4 [11] & 7.1273 & 39.6015 \\ \cline{3-5}  & & LSB 3-3-2 & 2.1217 & 44.8639 \\ \cline{3-5}  & & LSB-1 [11] & 0.2243 & 54.6222 \\ \cline{3-5}  & & LSB-2 [11] & 0.5713 & 50.5615 \\ \cline{3-5}  & & LSB-4 [11] & 4.7631 & 41.3518 \\ \cline{3-5}  & & LSB 3-3-2 & 1.2461 & 47.1751 \\ \hline \multirow{8}{*}{kid} & \multirow{8}{*}{plane} & LSB-1 [11] & 0.2553 & 54.0600 \\ \cline{3-5}  & & LSB-2 [11] & 0.6351 & 50.1017 \\ \cline{3-5}  & & LSB-4 [11] & 5.3143 & 40.8763 \\ \cline{3-5}  & & LSB 3-3-2 & 1.5481 & 46.2327 \\ \cline{3-5}  & & LSB-1 [11] & 0.1724 & 55.7646 \\ \cline{3-5}  & & LSB-2 [11] & 0.4260 & 51.8364 \\ \cline{3-5}  & & LSB-4 [11] & 3.5561 & 42.6210 \\ \cline{3-5}  & & LSB 3-3-2 & 0.9273 & 48.4584 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of MSE and PSNR against other embedding techniques. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{4}{c}{**PSNR**} \\ \cline{2-5}  & **R** & **G** & **B** & **Aggr** \\ \hline rainbow to lenna & 49.2779 & 49.4613 & 56.4632 & 39.5926 \\ \hline rainbow to malamute & 65.8861 & 65.8258 & 73.0960 & 47.8428 \\ \hline monarch to malamute & 54.3625 & 71.5566 & 82.8429 & 41.2790 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: PSNR cover image vs. stego image.
## 3 Results and Discussion
### Standard Penetration Test SPT and SPT-Torque  
The groundwater level was determined at the end of the test using a probe inserted into the borehole to locate it. The groundwater level was found to be at 2.0 m in this manner. Dissipation tests were also conducted in the piezocone (Table 1) to verify these results. The number of blows corresponding to 72% efficiency was considered.  
Figure 11 presents the results of SPT-Torque tests on the soil profile. In the first 5 m of soil, fine soils such as silty clays and clayey silts can be found. In the initial meters of the SPT-1 region, clay fills can be found, which were caused by earth movements of anthropogenic origin during the construction of nearby buildings. The presence of the channel causes part of this artificial fill to degrade during flood seasons. N\({}_{\text{SPT}}\) values varied from 2 to 10 during the first 5 m. From 5 to 25 m, N\({}_{\text{SPT}}\) values of 20 blows on average were found, corresponding to more representative values for fine to medium sands. A visual inspection has been carried out to confirm the soil classification. For the torque values, Figure 11 presents, for each test, the measured force to rotate the SPT sampler. Torque values have been measured up to 15 m, ranging from 0 to 8 kgf\(\cdot\)m in the first 7 m, reaching average measurements of 20 kgf\(\cdot\)m, confirming the presence of sandy material up to 25 m deep. Residual torque values ranged from 5 to 15 kgf\(\cdot\)m. The representative curves of inceptisols showed a maximum torque of 60\({}^{\circ}\) before rotation. The residual torque was well-defined before the second complete turn. Additionally, the histosols of Sao Paulo reached the torque maximum almost instantly. The residual torque dropped rapidly, and after the third turn, the value increased slightly, making it difficult to set a correct value.  
Figure 11: Soil profile with SPT-T values: blow counts and torque.
# 2D-Unitary ESPRIT Based Multi-Target Joint Range and Velocity Estimation Algorithm for FMCW Radar
## 4 Simulations and Analysis  
### Performance Metrics and Parameter Settings  
Simulations show the effectiveness of the proposed algorithm and compare it with the existing algorithms including the 2D-FFT algorithm [8], the 2D-CZT algorithm [18], the 2D-Root MUSIC algorithm [21] and the MMEMP algorithm [25]. The parameter settings are indicated in Table 1. In simulations, the additive random Gaussian white noise is considered, and the Signal to Noise Ratio (SNR) varies in the range of [\(-8\) dB, \(16\) dB]. All results are based on \(N\UIM=500\) independent Monte Carlo experiments. Root Mean Square Error (RMSE) is employed as a performance metric to measure the deviation between the estimated and true values. \(RMSE_{R}\) of range estimates is expressed as follows:  
\[RMSE_{R}=\sqrt{\frac{1}{N\UIM}\sum_{i=1}^{N\UIM}\left(R_{i}-R_{0}\right)^{2}} \tag{41}\]  
Similarly, \(RMSE_{v}\) of velocity estimates is obtained.  
In simulations, two scenarios are considered: single-target scenario and multi-target scenario. The single-target scenario is utilized to compare the performance of the proposed algorithm with the existing algorithms in terms of range and velocity estimation accuracy, and the multi-target scenario is considered to compare the performance of the proposed algorithm with the existing algorithms in terms of resolution ability and pairing correctness, respectively.
# Removal of Nitrate Ions Using Thermally and Chemically Modified Bioadsorbents
## 2 Materials and Methods
### Methods
#### 2.2.3 Kinetics and Adsorption Isotherms  
Kinetics was studied to determine the time effect on the adsorption capacity of the evaluated bioadsorbents and the possible mechanisms involved in the adsorption process [36]. For this, 0.5 g of adsorbent was put in contact with 50 mL of synthetic NO\({}_{3}\) solution, at 250 rpm of agitation and room temperature. Aliquots were taken at different time intervals (5, 10, 15, 20, 30, 60, 180, 360 and 540), and the experimental data of adsorption capacity were adjusted to the kinetic models [37]; pseudo-first-order by Equation (3), pseudo-second-order by Equation (4) and [PERSON]'s model by Equation (5), where \(q_{t}\) (mg g\({}^{-1}\)) is the adsorption capacity at a time \(t\), \(q_{e}\) (mg g\({}^{-1}\)) is the adsorption capacity at equilibrium, \(k_{1}\) (min\({}^{-1}\)) is the pseudo-first-order kinetic rate constant, \(t\) (min) is time, \(k_{2}\) (g\({}^{-1}\) min\({}^{-1}\)) is the pseudo-second-order rate constant, \(\alpha\) (mg g\({}^{-1}\) min\({}^{-1}\)) is the constant of the Elovich equation and \(\beta\) (g mg\({}^{-1}\)) is the exponent in the Elovich equation.  
\[q_{t}=q_{e}(1+e^{-k_{1}t}) \tag{3}\]  
\[q_{t}=\frac{t}{\frac{1}{k_{2}q_{e}^{2}}+\frac{t}{q_{e}}} \tag{4}\]  
\[q_{t}=\frac{1}{\beta}\ln(\alpha\beta)+\frac{1}{\beta}\ln(t) \tag{5}\]  
Adsorption isotherms were performed to describe the amount of NO\({}_{3}\) adsorbed at the equilibrium [38]; the tests were performed at different initial contaminant concentrations (50, 80, 134 and 218 mg/L) for 24 h. The experimental data were adjusted to the models of [PERSON] (Equation (6)), [PERSON] (Equation (7)), and [PERSON] (Equation (8)); where \(q_{\rm max}\) (mg g\({}^{-1}\)) is the maximum adsorption capacity, b (L mg\({}^{-1}\)) is [PERSON]'s constant related to the heat of adsorption, \(C_{e}\) (mg L\({}^{-1}\)) is the residual concentration of solute in solution, \(q_{e}\) (mg g\({}^{-1}\)) is the amount of adsorbate retained by the adsorbent in equilibrium, \(K_{\rm F}\) (mg g\({}^{-1}\) (L mg\({}^{-1}\))\({}^{1/n}\)) and n are the Freundlich rate constants referring to adsorption capacity and adsorption intensity, respectively, \(q_{DR}\) (mg g\({}^{-1}\)) is the adsorption capacity of the Dubinin-Radushkevich model, \(K_{DR}\) (mol\({}^{2}\) kJ\({}^{-2}\)) is the Dubinin-Radushkevich rate constant related to adsorption energy, \(E\) (KJ mol\({}^{-1}\)) is the average adsorption energy per molecule of adsorbate required to transfer one mole of the ion from the solution to the adsorbent surface and \(\epsilon^{2}\) is the Polanyi potential which is temperature dependent.  
\[q_{e}=\frac{q_{\rm max}bC_{e}}{1+bC_{e}} \tag{6}\]  
\[q_{e}=K_{\rm F}C_{e}^{1/n} \tag{7}\]\[q_{e}=q_{DR}e^{-K_{DR}\varepsilon^{2}} \tag{8}\]  
\[E=\frac{1}{\sqrt{2K_{DR}}}\ \varepsilon=RT\ln\left(1+\frac{1}{C_{e}}\right) \tag{9}\]  
The non-linear adjustment of the mathematical models was carried out in OriginPro 8 software.
## 3 Methods
### Spatial and Statistical Image Representation
#### 3.1.2 Statistical Image Representation  
The voxelwise probability distributions of \(I_{u}(\mathbf{x})\) and \(b(\mathbf{x})\), \(p_{u}(u(\mathbf{x}))\) and \(p_{b}(b(\mathbf{x});1,\sigma_{b}^{2})\), respectively, are assumed independent. The distortion of the joint intensity statistics is modeled by the non-stationary distribution \(p_{(v|u)}(v|u)=p_{b}(v-u|u)=p_{b}(v-u;0,(\sigma_{b}u)^{2})\). Thus, the intensity statistics of \(v\), \(p_{v}(v)\), result from the convolution, \(\star\), \(p_{v}(v)=p_{(v|u)}(v|u)\star p_{u}(u)=p_{u}(u)\star p_{b}\big{(}v-u;0,(\sigma _{b}u)^{2}\big{)}\). The probability of the distortion is assumed to be bimodal with one mode for \(b(\mathbf{x})>1\) and another mode for \(b(\mathbf{x})<1\) and given by \(p_{b}(b;0,\sigma_{b}^{2})=\frac{G\big{(}b0;\sigma_{b}^{2}\big{)}}{G^{2}\big{(} b0,\sigma_{b}^{2}\big{)}+\epsilon^{2}}\), where \(\epsilon^{2}\) is a regularization constant.  
The statistical representation of images \(v_{i}\) are based on their intensities \(\eta_{i}\) and their counts within neighborhood \(\mathcal{N}_{\rho}\) in image space to offer co-occurrence statistics as [49]  
\[C_{v_{i}v_{j}}(v_{i},v_{j},\eta_{i},\eta_{j}) = C_{v_{i}v_{j}}(\eta_{i},\eta_{j})= \tag{2}\] \[= \int_{\mathbf{x}=I_{v_{i}}^{-1}(\eta_{i})}\left(\int_{\mathbf{x} =I_{v_{j}}^{-1}(\eta_{j})}\big{(}|\mathbf{x}-\mathbf{x}^{\prime}|_{2}\leq \rho\big{)}d\mathbf{x}^{\prime}\right)d\mathbf{x},\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters \(\backslash\)** & **Sequence, Static Field** & **\(\mathbf{T_{1}w}\) TSE** & **STIR** & **\(\mathbf{T_{1}w}\) TSE** & **STIR** \\ \hline Number of & & & & \\ slices & 66 & 66 & 60 & 60 \\ \hline Voxel size & \(1.17\times 1.17\) & \(0.95\times 0.95\) & \(1.02\times 1.02\) & \(1.04\times 1.04\) \\ (mm\({}^{2}\)) & 3 & 3 & 3 & 3 \\ \hline Spacing (slice gap) (mm) & 0.3 & 0.3 & 1 & 1 \\ \hline Matrix size & \(215\times 188\) & \(226\times 152\) & \(233\times 180\) & \(151\times 148\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Acquisition parameters of the imaging sequences for individual coils.  
where \(\rho\) is the radius of the spherical neighborhood considered. The auto-co-occurrences result from \(i=j\) to produce \(C_{00}\) and \(C_{11}\). The joint-co-occurrences are \(C_{01}\). The auto-co-occurrences are dominated by their diagonal, and thus the diagonal is weighted down with the sigmoid, \(1/(1+e^{-(k_{1}|\eta_{0}-\eta_{1}|+k_{2})})\), where \(k_{1}\) and \(k_{2}\) are constants. Examples of the co-occurrences of pairs of \(T_{1}w\) and \(T_{2}w\) images are presented in Figures 3 and 5.
# Digital Image Processing: Advanced Technologies and Applications
###### Abstract  
A few decades ago, conventional image processing methods mostly focused on basic tasks such as image enhancement, registration, or edge detection. Early attempts to achieve these tasks mostly utilized grayscale images. Over time, simple methods to process grayscale images resulted in performance degradation for RGB images [1]. Ultimately, RGB image processing received more attention and subsequent advancements were made, including color preservation and fusion-based processing [2]. Currently, deep learning is used extensively in various fields, such as speech recognition and healthcare domains, with encouraging outcomes in image processing, such as image classification and segmentation [3]. A recent study showed that deep learning-based approaches significantly improve the performance of many image-related tasks, such as object detection, recognition, or segmentation compared, compared to conventional methods.  
With the evolution of convolutional neural networks (CNNs), supervised learning techniques were used to train CNNs, which aimed to extract efficient features to meet their gold label requirements [4]. The performance of these methods strictly relied on the available training data. Subsequently, the limited annotated training data failed to acquire the particulars of the image details. Since the supervised learning approaches learned nonlinear mapping, they tended to primarily focus on the limited training data. As a result, the trained model struggled to yield encouraging results on unseen image data [5].  
The domain of digital image processing has experienced amazing advancements, particularly through the evolution of deep learning-based algorithms, which have enhanced capabilities in many real-life applications, such as image object detection [6], recognition [7], segmentation [8], edge detection [9], and restoration [10]. Despite these advances, critical gaps remain in research and knowledge, especially in the applications and exploration of deep learning models' robustness in several challenging situations. Deep learning models also have great ease and efficiency in processing high-dimensional data [11].  
This Special Issue entitled \"Digital Image Processing: Advanced Technologies and Applications\" addresses these challenges by collecting 15 state-of-the-art research contributions that reinforce current methodologies and offer inventive solutions and novel perspectives. Looking ahead, future research will likely focus on developing more robust and explainable AI models to enhance the feasibility of image processing systems.  
Future research can also focus on exploring the potential of quantum computing to process progressively complex image data, in addition to current deep learning models. These directions will not only satisfy existing knowledge gaps but also open new possibilities for advanced applications and technologies in digital image processing.
## 3 Results
### QRS and R-Wave Identification  
The algorithm identifies, on average, 108,814 beats out of a total of 110,289 labeled. This yields an average PPV value for 51 patients of 97.53 \(\pm\) 0.05%. The sensitivity has an average value of 98.73 \(\pm\) 1.40%. Finally, for the F-score an average value of 0.98 \(\pm\) 0.01 is obtained. An example of QRS and R identification, and of the reconstructed signal are shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Algorithm** & **Description** & **How** & \\ \hline
2. PVCs identification & Apply CWT to obtain a matrix of \(W\) & \([W,f]=CWT(ccg(t),f_{s})\) & (11) \\  & coefficients with associated \(f\) frequencies & where \(f_{s}\) is the ECG sampling rate. & \\ \cline{2-4}  & Extracting energy in the frequency band & \(PVC_{band}=\{f[2.6\leq f\leq 3.2\}]\) & (12) \\  & of PVCs & \(E_{PVC}(t)=\sum_{f\in[2.6\leq 3.2]}|W(f,t)|^{2}\) & (13) \\ \cline{2-4}  & Define adaptive threshold for & \(thr_{PVC}(t)=0.4\textit{max}(E_{PVC}(t))\) & (14) \\  & PCCs identification & \(PVC(t)=find\textit{peak}(E_{PVC}(t),Min\textit{peakProminence}\geq thr_{PVC}(t))\) & (15) \\  & of prominence & & \\ \hline
3. Tachogram & Calculating RR Intervals & \(RR_{i}=t_{i+1}-t_{i}\), \(fori=1,2\), \(\ldots,N-1\) & (16) \\  & \(RR_{intervals}=RR_{2N-1}\) & (17) \\ \cline{2-4}  & Moving average calculation & \(move_{avg_{i}}=\frac{1}{N}\sum k=\hat{t}^{i+n-1}RR_{k}\) & (18) \\ \cline{2-4}  & Calculating the deviation from the & \(d_{i}=|RR_{i}-move_{avg_{i}}|\) & (19) \\  & moving average & & \\ \hline Definition of the variability threshold & \(thr=k\cdot\sigma_{RR}\) & (20) \\ \hline Ectopic beat or artifact detection & \(Ectopic\) beat or \(artifact_{i}=\begin{cases}1,&if\ d_{i}>thr\\ 0,&in\textit{another\ case}\end{cases}\) & (21) \\ \hline Ectopic beat or artifact correction & \(RR_{clamped,i}=\begin{cases}RR_{start,}\\ RR_{previs,}\\ \frac{RR_{previs,}+RR_{final}}{2},&\textit{general\ case}\end{cases}\) & (22) \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 1: Patient 1001, QRS, and R-wave algorithm identification from the ECG signal. **(a)** R-wave database labels (red dots), algorithm identification (black triangles). **(b)** Reconstructed signal after DWT using level 2 of detail coefficients and their match to QRS complexes.
# Geotechnical and Structural Damages Caused by the 2023 Kahramanmaras Earthqu in Golbasi (Adyaman)
## 5 Geotechnical Findings  
Increases in pore water pressure due to earthquakes can cause liquefaction in sand and silt soils. Ground liquefaction is when the layers below the groundwater level temporarily lose their strength and behave like a viscous liquid instead of a solid. Liquefaction is a phenomenon that occurs in loose-grained soils (sandy-silty soils) under dynamic loading such as earthquakes. It occurs when the structure of the water-saturated granular soil deteriorates as a result of sudden loading, the contact force between the separated grains decreases, the pore water pressure increases, and the soil loses its resistance. Liquefaction is shown schematically in Figure 6.  
During the 6 February 2023 Kahramanmaras earthquakes, structural damages caused by liquefaction were widely observed, especially in Hatay's Iskenderun and Adiyaman's Golbasi districts. After an earthquake, the boiling of water and fine sand trapped under pressure in the underlying sand layer on the ground surface is a very common observation that indicates liquefaction. Sand boils observed in Golbasi district are shown in Figure 7.  
Figure 5: Earthquake distributions affecting Golbasi (Mw > 3.0).  
Figure 6: Schematic representation of ground liquefaction (**a**) unliquefied soil (**b**) liquefied soil.  
Figure 7: Sand boils observed in Göblasi district.  
The lateral spreads that occurred on the ground clearly affected the surrounding walls. Examples of damage to surrounding walls are shown in Figure 10.  
As a result of lateral spreading on the ground, small or large ground collapses have occurred in many parts of the district. Some examples of collapse are shown in Figure 11.  
Figure 10: Examples of damage to surrounding walls.  
Figure 9: Examples of damage observed in transportation, infrastructure, and buildings as a result of lateral spread.  
The loss of bearing capacity and settlement damages occurring in the buildings in Golbasi district as a result of ground liquefaction are shown in Figure 12.  
Since the bearing capacity of the ground, whose shear strength decreases as a result of liquefaction, will decrease, the structures on this ground may suffer damage in the form of foundation settlements as well as the structure shifting from the foundation and toppling over. In addition, improper design of the structure-soil interaction or lack of sufficient foundation depth can be considered among the causes of such damages. A building on the main street in Golbasi district collapsed and fell onto a building at the rear. There was no structural damage to the load-bearing system of the building and it remained mostly intact. Images taken from different facades of the building are shown in Figure 13.  
Figure 11: Some examples of ground collapse.  
Figure 12: Examples of damage caused by settlement after soil liquefaction.  
Figure 13: Example of a building with shallow foundations collapsing due to loss of soil-bearing capacity.