esa-sceva/satcom-chunk-collection huggingface dataset:
# A Consensus-Based Likert-LMPP Model for Evaluating the Earthquake Resistance of Existing Buildings
## 3 Methodology
### Building Vulnerability Parameters (Inputs to the Proposed Model)
#### 3.3.6 Number of Stories  
A study by [PERSON] et al. concluded that building height is one of the most significant or perhaps even the most dominant determinants of seismic vulnerability in Turkish multistory concrete structures, and there is a strong correlation between damage and the number of stories [54], since higher buildings can suffer great deformation and damage during an earthquake [18]. In a report prepared for the Metropolitan Municipality of Istanbul following the Kocaeli and Duzce earthquakes of 1999, it was found that building damage correlated strongly with the number of stories [53]. Sen also found that the most vulnerable buildings in Istanbul were medium-rise (4-7 stories) reinforced concrete frame buildings designed based on outdated regulations [24]. It is clearly seen that there is almost a linear increase in damage with the number of stories. The ratio of moderately and severely damaged buildings increases steadily with the number of stories [53]. However, if buildings are designed to comply with modern seismic codes, damage will be distributed uniformly regardless of the number of stories [54].
# Pitch Channel Control of a REMUS AUV with Input Saturation and Coupling Disturbances
## 4 Results and Discussion
### Conditions and Parameters  
Linear system plant with uncertainties and unknown parameters is mostly utilized in adaptive control, but neither the influence of roll angle offset nor the effect of the hydrostatics is considered in most studies. The hypothetical linear model for pitch channel is determined with \(A_{p}=[0,1;-0.7,-2]\) and \(B_{p}=[0;-4]\). To signify the shortcoming of L1 AC controller in the presence of input limitations, performances of L1 AC and our proposed controller are compared in the following with \(A_{m}=[0,1;-9,-4.2]\) and \(b=[0;-9]\). The parameters for compensator are listed: \(\rho=0.01\), \(W=1\), \(\varepsilon=0.1\). In the case of linear plant for pitch channel, the input saturation for elevator angles is restricted from \(-20\) to \(20\) degrees owing to the effectiveness of input values within physical space.  
Note that the variables with subscripts \(L1 AC\) are the relevant variables for \(L_{1}\) adaptive control without compensators, while the ones with subscripts \(L1 AC_{AW}\) are related variables of our proposed approach. All the annotation in Figures 5-8 follows this principle.
# Effect of Methane Adsorption on Mechanical Performance of Coal
## 4 Discussion  
### Quantification of Coal Mechanical Parameters  
Herein, the changes in the mechanical properties of coal caused by methane adsorption are expressed using a mathematical model [27]. After methane adsorption reached equilibrium, changes in the compressive strength of coal \(\Delta\sigma_{c}\) increased with adsorption pressure, which can be expressed using the Langmuir equation [28]:  
\[\Delta\sigma_{c}=\frac{\alpha p}{p+\beta} \tag{1}\]  
where \(\Delta\sigma_{c}\) represents the reduction in the compressive strength of coal caused by methane adsorption (MPa), \(p\) indicates methane adsorption equilibrium pressure (MPa), and \(\alpha\) and \(\beta\) correspond to the curve fitting parameters.  
Coal compressive strength after adsorption equilibrium of methane can be determined by:  
\[\sigma_{c}=\sigma_{0}-\Delta\sigma_{c} \tag{2}\]  
where \(\sigma_{0}\) is the compressive strength of the coal sample before methane adsorption in MPa.  
Figure 5 shows the relationship between equilibrium pressure for methane adsorption and the reduction in coal compressive strength caused by methane adsorption. As shown in Figure 5, least square regression analysis indicates that meager coal and gas coal displayed R\({}^{2}\) values of 0.9983 and 0.9979, respectively. In addition, coal samples presented \(\alpha\) values of 7.3217 and 0.3795, respectively, and \(\beta\) values of 5.9793 and 3.0916, correspondingly.  
Reduction in the elastic modulus of coal caused by methane adsorption can be expressed as shown in Equation (3) and the elastic modulus of coal samples under different equilibrium pressures can be expressed by Equation (4), that is:  
\[\Delta E=\frac{\lambda p}{p+\varphi} \tag{3}\]  
\[E=E_{0}-\Delta E \tag{4}\]  
where \(\Delta E\) represents the reduction in the elastic modulus of coal caused by methane adsorption (MPa), \(p\) is the equilibrium pressure for methane adsorption (MPa), \(\lambda\) and \(\varphi\) are the curve fitting parameters, and \(E_{0}\) corresponds to the elastic modulus of coal before methane adsorption (MPa).  
Figure 6 displays the relationship between equilibrium pressure for methane adsorption and elastic modulus reduction caused by methane adsorption on coal. In this case,  
Figure 5: Reduction in compressive strength of coal sample vs. methane adsorption equilibrium pressure.  
\(\mathrm{R}^{2}\) values for meager and gas coal were 0.9976 and 0.9991, respectively. The data also indicated \(\lambda\) values of 170.9973 and 9.9137, correspondingly, and \(\varphi\) values of 7.1612 and 2.0103, respectively.  
In summary, the results obtained in these experiments were similar to those obtained using the Langmuir adsorption equation. Thus, we determined that the relationship between compressive strength reduction and the elastic modulus reduction for coal after methane adsorption and the adsorption equilibrium pressure depend on the experimental conditions and the metamorphic degree of coal.
# Method for Estimating the Optimal Coefficient of L1C/B1C Signal Correlator Joint Receiving
## 4 Experiment and Analysis
### Carrier-to-Noise Ratio Estimation of Combined Signals  
Figures 12 and 13 show the amplitude of the signal data stream (I_P) and the noise data stream (Q_P), which were used to calculate the navigation message in the data component tracking, the 1:1 combined tracking, the power-ratio combined tracking, and the amplitude-ratio combined tracking mode. Scheme 4 had the highest amplitude value, followed by Scheme 5, Scheme 3, and Scheme 1. The signal and noise data stream amplitudes do not have physical meaning because they are related to the reference level setting of the equipment that collects and receives the signal. However, the two data streams can be used to estimate the C/N0 of the navigation signal.  
Figure 11: Std of PLL and DLL output for B1C and L1C.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**NUM** & **1** & **2** & **3** & **4** & **5** \\ \hline Combination scheme & data component tracking & pilot component tracking & 1:1 combination & power ratio combination & Amplitude ratio combination \\ \hline \end{tabular}
\end{table}
Table 3: L1C/B1C signal combination schemes.  
The variance summation method was used to estimate the carrier noise ratio [36]. The \(\frac{\text{C}}{\text{N0}}\) can be obtained as follows:  
\[\frac{\text{C}}{\text{N0}}=10\log 10[\frac{\left(NA/2\right)^{2}}{2T_{accu} \sigma_{IQ}^{2}}] \tag{39}\]  
where N is the number of samples in the time series Z, and A is the signal amplitude at the front-end output. \(\left(NA/2\right)^{2}\) is the average carrier power, which is given as follows:  
\[\left(NA/2\right)^{2}=\sqrt{\overline{Z}^{2}-\sigma_{Z}^{2}} \tag{40}\]  
where \(Z_{k}=\left(I_{k}P_{k}^{2}+Q_{z}P_{k}^{2}\right)\) is the time series sample output by the loop, which has a mean \(\overline{Z}\) and a variance \(\sigma_{Z}^{2}\). Among them, \(I_{-}P_{k}\) and \(Q_{z}P_{k}\) accumulate signal and noise, respectively, each time in the loop. The variance in the noise accumulation terms is given as follows:  
\[\sigma_{IQ}^{2}=\frac{1}{2}[\overline{Z}-\sqrt{\overline{Z}^{2}-\sigma_{Z}^{2 }}] \tag{41}\]  
The C/N0 of the signal is calculated for different tracking schemes according to the above method, as shown in Figures 14 and 15.  
Figure 12: The amplitude of the signal data stream.  
Figure 13: The amplitude of the noise data stream.  
The result of C/N0 estimation shows that the improvement in C/N0 after combined tracking of the BIC signal is very similar to that of the L1C signal. The C/N0 is approximately 6 dB higher than that of the data-component tracking, and approximately 1.3 dB higher than that of pilot-component tracking, which is consistent with their 1:3 signal design scheme. It is worth mentioning that the C/N0 of the amplitude ratio combination is the highest, and can best exert the signal design performance. Most importantly, the C/N0 in the \"amplitude ratio combination\" mode signal is approximately 0.2 dB higher than that in the traditional 1:1 combination mode.
# Enhancing Robotic-Assisted Lower Limb Rehabilitation Using Augmented Reality and Serious Gaming
## 4 Proposed Methodology for the Lower Limb Rehabilitation Treatment
### The Identification of the Targeted Group  
One of the key factors in identifying treatment beneficiaries is the underlying condition causing neuromotor problems. Based on these conditions, three major groups have been identified.  
* **Vascular group**: diseases affecting the circulatory system. Primary target disease: stroke.
* **Extrapyramidal group**: diseases associated with the brain's cerebral cortex. Primary target disease: Parkinson's Disease (PD), which is the fastest-growing cause of disability and death worldwide.
* **Neuromuscular group**: disorders that impair the function of motor nerves, muscles, or neuromuscular junctions. Target diseases include various forms of sclerosis, cerebral palsy, and spinal cord injury.  
Another crucial factor is related to the patient's psychological state, as their willingness to accept the treatment plays a key role. The proposed strategy for addressing this includes the following steps:  
* Informing the patient about their condition and the treatment they will undergo;
* Educating the patient on the benefits of robotic-assisted rehabilitation and the implications of serious gaming;  
Figure 11: Calibration of the sensors. Both the traditional and the biometric goniometer display the identical values.  
* Demonstrating the procedure through a therapist (the patient can observe both the robot's movements and the therapist playing the game);
* Providing opportunities for the patient to ask questions and express any concerns;
* Incorporating patient feedback to tailor the treatment based on their specific needs and preferences.  
In addition to the patient's psychological readiness, their technological proficiency must also be considered, especially since the entire system is primarily designed for elderly patients. For this reason, HoloLens 2 was selected as the device for the game, as it provides an intuitive working environment.
# A BIM Based Framework for Damage Segmentation, Modeling, and Visualization Using IFC
## 1 Introduction  
Bridges are designed to last for more than 50 years. During this period they consume up to 50% of their life-cycle costs [1; 2]. In conventional bridge inspections, data acquisition and exchange are performed in the form of paper-based reports as well as proprietary and incompatible data formats, so that any data transfer is done manually. Inspections start with an inspector or an engineer performing the visual inspection on-site. All defects and related data are recorded in paper-based reports and the inspector subsequently digitizes the data later in the office. Other stakeholders, such as structural engineers, retrieve these data either as paper-based reports, as exports of databases, or in other proprietary data formats. Then, engineers would have to integrate or import this data again into their digital models. Such repetition of manual data digitization is cumbersome, time consuming, and leads to information loss [3; 4]. The concept of Building Information Modeling (BIM) has been designed to overcome these issues. BIM should cover the entire life-cycle of structures including the operation phase with inspections and maintenance [4]. Defects and related information are vital for the inspection process. However, [PERSON] et al. remarked that \"There is currently no accepted, consistent or thorough way to represent the defects that may occur in bridges\" [5] (p. 144). This leads to the conclusion that BIM requires an extension to support the inspection process.  
Hereinafter, the concept of modeling defects and related information will be called Damage Information Modeling (DIM) and the associated model as Damage Information Model. Throughout the life-cycle of a BIM model, several states, such as as-planned, as-designed, as-built, and as-is, are known. The operation phase includes inspections to ensure the safety of a structure. Inspections deliver additional information of a structure, for instance, measurements, report documents, or photos. Based on this information, defects areidentified. Models, which include damage information, will be called as-damaged models. As-damaged models provide required information to the related domains, for example, inspection, simulation, and material analysis. Figure 1 shows a conceptual overview of the as-damaged model and some related processes. This study aims to provide a framework that covers data acquisition and visualization. The framework is designed with open data formats in mind, enabling integration of further domains in future work.
# Refinement Orders for Quantitative Information Flow and Differential Privacy +
## Appendix C Proofs of the Results on the Refinement Verification  
**Proposition A2** (Projection theorem, [23] (Prop. 1.1.9)).: _Let \(C\subset\mathbb{R}^{n}\) be closed and convex and let \(z\in\mathbb{R}^{n}\). There exists a unique \(z^{*}\in C\) that minimizes \(\|z-x\|_{2}\) over \(x\in C\), called the projection of \(z\) on \(C\). Moreover, a vector \(z^{*}\) is the projection of \(z\) on \(C\) iff_  
\[(z-z^{*})\cdot(x-z^{*})\leq 0\qquad\text{for all }x\in C\] (A12)  
**Theorem A8**.: _Let \(B^{*}\) be the projection of \(B\) on \(A^{\uparrow}\)._  
1. _If_ \(B=B^{*}\)_, then_ \(A\sqsubseteq^{\rm avg}B\)_._
2. _Otherwise, let_ \(G=B-B^{*}\)_. The gain function_ \(g(w,x)=G_{x,w}\) _provides a counter-example to_ \(A\sqsubseteq^{\rm avg}B\)_, which is_ \(V_{g}(\pi^{u},A)<V_{g}(\pi^{u},B)\)_, for uniform_ \(\pi^{u}\)_._  
Proof.: (1) is immediate from the definition of \(\sqsubseteq^{\rm avg}\). For (2), we first show that  
\[B\cdot G>B^{*}\cdot G\geq X\cdot G\qquad\text{for all }X\in A^{\uparrow}\] (A13)  
(in other words that \(X\cdot G=B^{*}\cdot G\) is a hyperplane with normal \(G\), separating \(B\) from \(A^{\uparrow}\)). For the left-hand inequality, we have \(B\cdot G-B^{*}\cdot G=G\cdot G=\|G\|_{2}^{2}>0\). Moreover, since \(A^{\uparrow}\) is closed and convex, from the projection theorem (Proposition A2), we get that \((B-B^{*})\cdot(X-B^{*})\leq 0\) for all \(X\in A^{\uparrow}\), from which \(B^{*}\cdot G\geq X\cdot G\) directly follows.  
The proof continues similarly to the one of [7] (Theorem 9). We write posterior vulnerability (for uniform prior) as a maximization over all remapping strategies \(S_{A},S_{B}\) for \(A,B\) respectively, namely  
\[V_{g}(\pi,A) =\tfrac{1}{|\mathcal{X}|}\max_{AS_{A}\in A^{\uparrow}}AS_{A}\cdot G\] (A14) \[V_{g}(\pi,B) =\tfrac{1}{|\mathcal{X}|}\max_{BS_{B}\in B^{\uparrow}}BS_{B}\cdot G\] (A15)  
Then, \(V_{g}(\pi^{u},A)<V_{g}(\pi^{u},B)\) follows from (A13) and the fact that \(B\in B^{\uparrow}\) (the identity is a remapping strategy).
# Spectrum-Based Logistic Regression Modeling for the Sea Bottom Soil Categorization
## 2 Method
### Experimental Setup  
A Bathy-2010 PC chirp sub-bottom profiler (SBP) was selected as the acoustic device for the data collection campaign. This instrument's properties are as follows: a linear chirp with a frequency sweep ranging from 2.75 kHz to 6.75 kHz, a signal duration of 5.4 ms, and a beam angle of 30\({}^{\circ}\). It is anticipated that in this context, the reflected signal will be influenced by the fine-scale morphology of the reflector, such as boulders and sand ripples. The data were collected at two offshore locations near Ashkelson, \"Site 1\" and \"Site 2\", approximately 1.2 km apart. The bottom at Site 1 and Site 2 was previously determined to be sandy, and located at a depth of 26 m. Similarly, data were collected at two other locations, \"Site 3\" and \"Site 4\", about 0.8 km apart. The bottom was determined to be sandstone at 33 m at these sites. The sea bottom's composition at each site was examined through soil assaying and a drop camera. Each site had an area with similar soil composition that extended several hundreds of meters in radius. The vessel was moored at each site, and its position was slightly adjusted due to wind and wave direction variations during data collection. Each trace was captured at a slightly different location, but over the same soil type. The transducer was installed on the vessel's side and placed 1 m below the waterline. The sound speed in water was measured to be 1530 m per second. The data were recorded in an unprocessed format without any chirp compression applied. Each trace was captured for 100 milliseconds, encompassing the transmitted signal, the water column, the bottom, and approximately 120 m of soil penetration. The transmission parameters used are summarized in Table 1.
# Computational Acceleration of Topology Optimization Using Deep Learning
## 4 Model Design
### Res-U-Net  
Res-U-Net is an encoder-decoder architecture and refers to deep residual networks and U-Net. It takes advantage of both models, such that residual blocks prevent the vanishing or exploding of gradients in building deeper neural networks, while U-net reconstructs images from generated features.  
Similar to U-Net, Res-U-Net has encoding and decoding networks that are connected by a bridge. However, Res-U-Net consists of pre-activated residual blocks instead of simple convolution filters. In the encoder block, neural networks learn the abstract representation of the input image. The bridge is a pre-activated residual block used to stride value 2. The decoder receives features from the bridge and skip connections from encoder blocks to generate better semantic representation.  
In our experiments, the encoder of the model consists of three residual blocks, and the decoder of model has fur residual blocks. A general description of the proposed Res-U-Net is given in Figure 6. For simplicity, we grouped parts of the networks as Stem, Residual_b, Batch_nor_a, UpSamp_b, and Conv_b as shown in Figure 7.  
In the experiments, we use mean squared error as the loss function in the training process:  
\[L(W)=\frac{1}{N}\ \sum_{i=1}^{N}\|Net(I_{i};W)-s_{i}\|^{2} \tag{13}\]  
where \(N\) is the number of the training samples, \(Net(I_{i};W)\) is the generated segmentation, \(s_{i}\) is the ground truth.  
In both U-Net and Res-U-Net models, we use the same input and output data from Dataset #1 and Dataset #2. The outputs of neural networks are taken to be optimal density distribution (i.e., optimal structure). For Dataset #1, the inputs of the neural networks are intermediate density distributions, which are obtained after n-th iteration of the SIMP model and its gradient. For Dataset #2, inputs of the model are physical parameters as shown in Figure 3.
## 1 Introduction
[1] and in [PERSON] et al. [28]. On the other hand, the hydrogeological modeling approach remains an important tool to grasp the objectives of groundwater management in different sectors: irrigation, drinking water supply and industry. Numerical modeling is a very practical tool and is often used by researchers for the study of groundwater flows [29] and contaminant flows in aquifers [30]. It has been used in groundwater vulnerability assessment but is usually confined to a small area since it requires an over-parametrization of the aquifer system. However, it is a powerful tool allowing the simulation of different flows and groundwater contamination scenarios (actual and future states of the model) [31]. However, the implementation of a model requires an understanding of the aquifer's behavior and a prior geometric description of the hydrogeological system [32]. In recent years, Algeria has considered a new policy for the management of water resources. This policy aims to protect water resources in their deposits, before they are contaminated, through legislative tools that respond to socioeconomic and demographic constraints. This research concerns the Tebessa-Morsott alluvial aquifer located in a semi-arid region, a stressed aquifer that, today, supplies several urban centers and cities with a population of around 150,000 inhabitants. Currently, the aquifer is threatened by many sources of pollution, in particular, discharges from domestic water and small industries in the region,which discharge into the aquifer without prior treatment and without any measure to protect the water resource. The agricultural expansion experienced by the region after the application of the National Program for the Development of Agriculture (NPDA) in 2000 and that of the National Fund for the Regulation of Agricultural Development (NFRAD) in 2001 has led to an increase in water demand, exposing this resource to both groundwater depletion and quality degradation. To address these threats, the present study aims to investigate the hydrodynamic behavior of the aquifer by combining hydrogeological modeling and groundwater vulnerability to propose measures and ensure groundwater management of this resource for the near future (2020-2030). To assess aquifer vulnerability in actual and future conditions, we propose a new approach by coupling the groundwater flow model using the MODFLOW model with the vulnerability assessment method (DRASTIC model) using GIS technique. The groundwater flow model will be used to calculate the dynamics of groundwater level under actual and future climate and exploitation conditions in order to delineate the actual and predict the future status of groundwater vulnerability in the aquifer.