esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Study of the Application of Different Commercial Software for Topology Optimization
## 6 Comparison of the Used Commercial Software
### Ansys  
ANSYS SpaceClaim, which is the ANSYS 3D modeling tool, has a user-friendly interface with many capabilities. The created 3D models can be easily exported to different file types or further analyzed in several ANSYS tools. ANSYS Mechanical is the primary ANSYS FEM tool, which also contains the TO module. ANSYS Mechanical offers a complete set of options that can be used in the pre-processing of the models.  
The TO module in ANSYS Mechanical offers level set and lattice optimization in addition to the standard SIMP method. However, the available objective functions are limited to compliance, mass, and volume. On the other hand, multiple manufacturing constraints can be applied, focusing on design for both CPM and AM. Pull direction in mold, extrusion direction, and symmetry are some examples of constraints related to design for CPM, while the overhang angle and 3D building direction support the 3D preparation and, thus, the design for AM. The optimized geometries also consist of facets here. The TO module cannot create any type of plot for the optimized design. Thus, a proper validation study is needed in order to check and validate the design solutions. However, it offers a plot for the topology density (element density distribution plot). The derived density is divided into three density categories, as follows: 0.0\(-\)0.4 (elements that have to be removed), 0.4\(-\)0.6 (elements with intermediate densities), and 0.6\(-\)1.0 (elements that are crucial for the design and should be kept). The threshold of the elements with intermediate densities (isovalues) can be manually changed, creating alternative designs.  
The post-processing of the optimized results is the main benefit of ANSYS. The redesign of the optimized design in SpaceClaim is user-friendly. Furthermore, SpaceClaim contains a plethora of tools that support the 3D preparation by checking, repairing, and smoothing, both manually and automatically, the organic faceted geometries created by TO. The smoothing tools are mainly applied for flattening the sharp areas that could be potential areas for stress concentrations and singularities. However, they can be also used for additional weight reduction. The smoothed designs can be either exported as STL files for 3D printing or further transferred to a validation study. ANSYS creates an automatic validation study in Mechanical where all the inputs of the initial FEA (BCs, load cases, mesh properties) are copied in the validation study.  
ANSYS Mechanical, in general, used the lowest number of design cycles for the optimization of the case studies. The derived optimized designs were close to the lightest designs. Furthermore, its optimization time was reasonable. It can be stated that ANSYS Mechanical offered balanced optimized designs in terms of weight reduction, optimization time, and strength.
## 4 Experiments  
### Experimental Validation  
#### 4.1.1 Sample of Final Users  
The main objective of this work is to perform a technical validation of the robotic platform LOLA and of the application described. For doing so, a fundamental step consists in the selection of a sample of final users so that LOLA and the developed application can be experimentally validated.  
This technical validation we propose includes four different patients. They were selected following the principles described in the protocol detailed in [76]. According to this study, all participants should be classified as having disabilities resulting from diseases or permanent health conditions (with the appropriate certificate, according to Spanish law [77]). Their functional independence level should be moderate-low, their functional skills-mobility level should be moderate-low, and they must be registered at one of the collaboration centers. Each of the patients we selected met these requisites. They ranged in age from 7 to 23 and all had physical limitations and cognitive deficits.  
The first patient is a 7-year-old female diagnosed with idiopathic ataxia. She is level III in the Gross Motor Function Classification System Extended and Revised (GMFCS E&R), level III on Manual Ability Classification System (MACS), and level III in the Communication Function Classification System (CFCS). The nature of her condition required her to use a walker for short distances and a wheelchair for longer distances. She manipulates objects with difficulty and needs assistance in preparing or modifying her activities. She offers a challenging scenario where the AI models will be technically validated when the users need a walker and direct support from the healthcare professionals.  
The second patient is a female 14-year-old who is diagnosed with Cerebral Palsy--Spastic Diparesis. She is level II on GMFCS E&R, level II on MACS, and level III on CFCS. Consequently, she can walk under most conditions and manipulate most objects, but with some reduction in quality or speed.  
The third patient is a 15-year-old female who has Cerebral Palsy--Spastic Triparesis with the predominance on the right side. She is level II on GMFCS E&R, level III on MACS in the right upper limb, and level II on CFCS. She is capable of walking in most conditions, manipulating objects with difficulty, and needs assistance preparing and/or modifying activities.  
A female 23-year-old, the fourth participant in this technical validation study, is diagnosed with metabolic syndrome due to GLUT 1 deficiency. She is level II in GMFCS E&R, level II in MACS, and level II in CFCS. Thus, she is capable of walking under most conditions and manipulating most objects, though with a corresponding reduction in quality or speed.  
In light of the fact that the level of autonomy regarding movement varies from patient 1 to patient 4, our proposed SAR will be validated in all these different situations with a certain granularity.
# Image Deblurring Based on an Improved CNN-Transformer Combination Network
## 4 Experiments and Analysis  
### Dataset and Implementation Details  
We used the GoPro dataset [7] and Realblur [31] dataset for training and testing. The GoPro dataset contains 2103 pairs of blurry and sharp images for training and 1111 pairs for testing. To assess the generalization performance of our model, we directly applied our GoPro-trained model on part of the ReaBlur test dataset, which included 980 pairs of images.  
We trained all the models with the Pytorch framework. For data pretreatment, we randomly cropped the image to 256 \(\times\) 256, and then horizontally flipped it with 0.5 probability. We trained 3000 epochs and the batch size was set to four. The initial learning rate was \(1\times 10^{-4}\), then we adopted the Cosine Annealing strategy [32] to steadily decrease the learning rate to \(1\times 10^{-6}\), with three epochs for warming up. Moreover, our experiments were conducted on a computer with one TITAN RTX GPU.
## 1 Introduction
### Contributions  
To address the challenges of feature generalization in high-dimensional spaces, we introduce expert-trajectory-based features, where the feature space is defined by sampling expert trajectories and the surrounding space. This approach focuses on the regions of the state space that are most relevant to the expert's behavior, capturing essential task characteristics while ignoring irrelevant details.  
To prevent the issue of vanishing goal features, we present an alternative approach to the discount factor by applying it in reverse. This modification prioritizes feature expectations in the final states, which are closer to the task goal, thereby preserving the essential characteristics of the goal state within the feature expectations.  
Furthermore, we propose combining AL-IRL with Proximal Policy Optimization (PPO) [34] for policy optimization. This combination leverages the sample efficiency and stability of PPO while benefiting from IRL's reward inference capabilities. PPO's robustness and stability in continuous action spaces, coupled with its ability to efficiently handle high-dimensional problems, make it an ideal choice for this application. PPO's trust region optimization prevents overly aggressive policy updates, which is crucial when learning from demonstrations of robotic tasks.  
In summary, this paper presents a novel approach that combines AL-IRL with PPO, an expert-trajectory-based feature framework, and a reverse discount application in feature expectation, as illustrated by the scheme in Figure 1. This approach is particularly well suited for large continuous state spaces and robotic applications. The main contributions of this work include the following:  
* Expert-trajectory-based features: The feature space is defined by sampling expert trajectories to capture critical task characteristics while abstracting away from unnecessary details.
* Reverse discounting in feature expectations: We demonstrate an alternative application of the discount factor to emphasize goal-state features and prevent their diminishing impact.
* Integration of AL-IRL and PPO: The proposed approach leverages the strengths of PPO for robust policy optimization alongside IRL's reward inference capabilities.
* Validation in a simple GridWorld environment: We assess the effectiveness of the reverse discounting approach and explore how the parameters of the proposed feature definition affect performance.
* Comparison with other state-of-the-art AL-IRL methods: Experiments in complex robotic manipulation tasks with the TIAGo robot highlight the advantages of our method over others.
## 3 Machine Learning Techniques for Data Preprocessing
### Data Completion and Denoising  
#### 3.1.1 Matrix Completion  
Let \(z_{i}\in\Re^{m\times 1}\) be an irradiation measure vector in the \(i\)-th day, \(i=1,2,\ldots,n\). These \(n\) samples can be expressed as the matrix \(\mathbf{Z}=(z_{1},z_{2},\ldots,z_{n})\). For any real matrix \(\mathbf{Z}\)\(=(z_{ij})_{m\times n}\), its nuclear norm is defined as \(\|\mathbf{Z}\|_{*}=\sum_{j=1}^{\min(m,n)}\sigma_{j}\), where \(\sigma_{j}\) is the \(j\)-th largest singular value of \(\mathbf{Z}\). To indicate all missing entries of \(\mathbf{Z}\), an index set \(\Omega\subset\{1,2,\ \ldots,m\}\times\{1,2,\ \ldots,n\}\) is firstly introduced. Subsequently, a projection operator of \(P_{\Omega}(\cdot)\): \(\Re^{m\times n}\rightarrow\Re^{m\times n}\) is defined as follows. If \((i,j)\in\Omega\), then \(P_{\Omega}(z_{ij})=\)\(z_{ij}\); otherwise, \(P_{\Omega}(z_{ij})=\)\(0\).  
With regard to solar radiation, \(n\) samples can be roughly divided into several groups. Therefore, \(\mathbf{Z}\) is approximately low-rank when \(n\) is relatively large. In the presence of missing elements, the recovery technique by the aid of the low-rank structure is called matrix completion [36; 37]. Matrix completion is initially described as an affine rank minimization problem. However, due to the non-convexity and discontinuity of the rank function, it is intractable to address this problem. To this end, the aforementioned optimization model can be convexly relaxed into the matrix nuclear norm minimization [38; 39]. Thus, the mathematical model of matrix completion is formulated as follows:  
\[\underset{\widetilde{\mathbf{Z}}}{\text{min}}\|\widetilde{\mathbf{Z}}\|_{*}\text{ s.t. }\text{P}_{\Omega}(\widetilde{\mathbf{Z}})= \tag{1}\]  
where \(\widetilde{\mathbf{Z}}\) is the completed matrix. The optimal solution of the above minimization is also denoted as \(\widetilde{\mathbf{Z}}\) to avoid abuse of symbols.
# Design of Three-Dimensional Electrical Impedance Tomography System for Rock Samples
## 3 Overall Design Scheme  
The system consists of two main components: the control system and the measurement system [23].  
The control system comprises an Android application (App) and an embedded system [24]. The Android App serves as the user interface for interacting with the entire instrument. Its primary functions include control, parameter settings, and display of measurement results. The App can also monitor the instrument's operational status by receiving and analyzing log information returned by the instrument during operation. The data stored in the App can be uploaded to the computer for further processing and three-dimensional inversion imaging of the specimens using dedicated software.  
The embedded system, powered by an STM32 microprocessor, plays a crucial role in receiving and parsing commands from the App and transmitting them to the measurement system. It also receives the raw data acquired by the measurement system, saves a backup copy to the SD memory card, and uploads it to the App. Additionally, the embedded system controls the relays through the I2C bus.  
The measurement system primarily consists of a CPLD acquisition circuit board, a signal source circuit based on the AD9956 core, and two relay circuit boards. It is responsible for data acquisition and meets the design requirements of high precision and low power consumption. Figure 2 depicts the physical layout of the instrument's main hardware circuit boards, including the embedded system, the CPLD, the signal source, the current electrode relays, and the potential electrode relays.  
The physical components of this system consist of an instrument host, high-density cables, a portable rechargeable battery (5 V, 2 A), and an Android smartphone, as shown in Figure 3a. The instrument host panel features electrode interfaces, a power supply interface, and a GPS interface, as depicted in Figure 3.  
Figure 2: (**a**) Front view of hardware circuit board. (**b**) Side view of hardware circuit board.
## 3 Results
### Experiment (6): Parameter-Based Absolute Accuracy for Multiple Strip Measurements  
The sixth experiment analyses the orientation of planes within the point cloud according to the parameter-based evaluation as also done in **Experiment (3)**. For the single strip measurements, this approach is capable to detect orientation errors in the trajectory estimation or calibration of the system. The evaluation with the multiple strip measurements in this part will give a general assessment of how well the point cloud is orientated absolutely in space without the ability to trace errors back to their cause due to the complexity of the calculation steps performed. The surfaces of the targets have a size of 0.8 m \(\times\) 0.8 m and are used for the GHM plane estimation with the normal vector as the parameter. With this normal vector the tilt of the planes can be estimated in relation to the plane parameter derived from the reference point cloud. The tilt is further divided into a tilt between the  
Figure 12: Difference of the estimated target coordinates compared to the TLS reference considering the east (blue), north (red) and height (yellow) coordinates. A total of 12 targets are estimated for each of the four repeat flights.  
zenith and north direction and perpendicular between zenith and east direction, which is shown in Figure 13.  
The resulting mean values for the tilt in zenith-north direction is \(-0.017^{\circ}\) with a RMSE of \(0.158^{\circ}\) and in zenith-east direction \(0.015^{\circ}\) with a RMSE of \(0.116^{\circ}\). The mean values for both tilt directions indicate that there is no systematic trend or tilting of the entire point cloud compared to the reference. Since the histogram summarizes the estimations based on four separated flights, the distribution can also be used as a measure for the precision. With both RMSE values the precision is a bit higher compared to the first data set but this is explained due to the 25 m flight height. Furthermore, the histogram includes also larger tilt estimates around \(0.4^{\circ}\), which might come from a misalignment in the UAV point cloud but could also be enhanced by the small uncertainty of the TLS point cloud. The tilt of a magnitude of \(0.4^{\circ}\) would lead to an error of about \(0.6\) cm in the height direction considering the object size, which is within the noise level of the point cloud from the UAV-based laser scanning system. The output of the last experiment is a consistent and well orientated point cloud over larger areas and multiple flights. Furthermore, the additional processing for point cloud optimization performs well without manual interaction in the estimation.
## 2 Background and Related Work
### Analysis Security Testing
#### 2.2.2 Dynamic Analysis Security Testing  
DAST tools are black box tools that allow the analyses of a running application attacking all external source inputs of a web application [37]. In a first phase they try to discover (crawling) the attack surface of the web application, that is, all the possible source inputs of the application. The crawling phase must be manual using the tool as an intercept proxy, while also performing an automatic crawling that includes some information of the web application such as programing languages, application server, database server, authentication or session methods. After a crawling phase, the tools perform a recursive attack with malicious payload to all source inputs of web application discovered. Next each http response is syntactically analyzed to check if a security vulnerability exits. Finally, the vulnerability report must be manually revised to discard false positives and discover possible false negatives. Unlike the white box tools, here the source code of the application is not known. Tests are launched against the interface of the running web application. DAST tools usually discover less true positives and also has less false positives than SAST tools [10; 38].  
DAST tools allow the detection of vulnerabilities in the deployment phase of software development. The behavior of an attacker is simulated to obtaining results for analysis. In addition, these tools that can be executed independently of the language used by the application. DAST tools evolve over time,incorporating new authentication methods (JWT), attacks vectors (XML, JSON, etc.) and techniques to automate the detection of vulnerabilities [1], among which they stand out fuzzing techniques, based on tests on the application trying to make it fail, such as modifying the entries on forms.
## 1 Introduction
Typically, the cross plots are used to visibly recognise or observe anomalies, which could be inferred as the existence of hydrocarbons or other fluids and the type of lithology. Cross-plotting  
Figure 1: HPHT classification by [PERSON] [8]. appropriate rock properties allow straightforward interpretation by clustering the cross-plotted data's common lithologies and fluid types. Pre-stack simultaneous inversion is a seismic exploration method frequently used to identify oil and gas and characterise lithology. Simultaneous seismic inversion involves the invert of seismic reflectivity into elastic rock properties to evaluate the properties of the reservoir away from the well. Many studies and practices have demonstrated that pre-stack simultaneous inversion may accurately forecast gas-bearing reservoirs. The trick is to select a reasonable approximation of the Zoeppritz equation as the basis for the inversion [10]. The original reflectivity data are converted from an interface property (reflection) to an elastic rock property [11]. Using seismic inversion, the effect of wavelet can be reduced as inversion replaces seismic blocks of impedance at a particular time sampling interval. This study utilises pre-stack seismic inversion, whereby seismic angle or offset data are transformed into P-Impedance (Zp), S-Impedance (_Zs_), \(\text{Vp}/\text{Vs}\) ratio, and density volumes through the incorporation of seismic angle/offset gathers, well data, and a basic stratigraphic interpretation. ANNs have been successfully applied in reservoir characterisation [12]. The major objective of ANN application is to integrate acquired data from various geological, petrophysical, geophysical sources in reservoir characterisation by identifying the complex non-linear correlation of this input data [5].
## 1 Introduction  
As mentioned in [1], the proposals of [2] and [3] established operational bases for reasoning from first-order logic formulas. This work favored the advent of logic programming and its most emblematic language; namely, Prolog [4]. Rule-based reasoning systems have remained important in the field of artificial intelligence until today.  
This paper extends the work of [1] by testing several advanced uses of Prolog for developing data warehouses. A data warehouse is a specific type of database used to integrate, accumulate and analyze data [5, 6]. Information from different databases is loaded into a data warehouse for combined analyses. These data are organized in analysis dimensions (time dimension, space dimension, descriptive dimensions, etc.). Indicators are calculated by aggregating a measure according to these dimensions.  
In Prolog, the data used for the reasoning are generally all loaded in the memory [1]. The coupling between Prolog and databases has been carefully studied; the objective was to show how the features offered by Prolog could be used with large volumes of data. Prolog integrates specific functionalities that can be interesting for processing data (recursive queries, functions on graphs, constraint solvers, natural language processing, etc.).  
Today, with the increase in computer RAM and the advent of in-memory databases, Prolog has become a good candidate for reasoning in databases. Based on this observation, this paper shows how to implement in-memory data warehouses in Prolog and focuses on their main function, data aggregation [6]. The objective was to propose a simple method to model this type of query by directly exploiting the existing functionalities of Prolog. This paper is a brief report that opens the way for the use of Prolog for data warehouse queries. This paper targets data managers; it shows them the direct writing of data warehouse queries in Prolog using an easily understandable syntax. The queries are not necessarily in an optimal form from a processing point of view, but a data manager could easily use or write them. More generally, the use and the adaptation of computer-based languages for specific application fields have been a prolific research topic over the years. For example, domain-specific modeling can be related to the use of Prolog for natural processing [7], C++ for design pattern definitions [8], OCL for spatial relation constraints [9], Java for add-on developments [10] and UML for serious games [11, 12].  
The paper is organized as follows. Section 2 presents the main existing contributions related to data warehouses and Prolog. Section 3 provides a case study. Section 4 shows the fundamental concepts for representing and querying data warehouses with Prolog. Section 5 compares the proposed Prolog-based queries with the SQL syntax. Sections 6-9 provide more advanced queries and illustrate the advantages of using Prolog in data warehouse queries. Section 10 is the conclusion, indicating future work.