esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Low-Cost 3D Laser Scanning Technology
## 3 Classification of the Prototypes
### Other Categories
#### 3.3.1 An Irregularly Rotating 2D LiDAR  
In [78], a 2D LiDAR is employed for 3D mapping of a tethered robot in steep terrain. The 2D LiDAR is fixedly mounted on the cable drum of the robot, when the cable drum is rotated by the motor, the 2D LiDAR is rotated too, and the position of the robot on the cliff changes accordingly. In this way, 2D LiDAR can be utilized to build 3D point cloud at different positions of the cliff.  
The above-mentioned solution is somewhat similar to a rotating 2D LiDAR. The main differences are as follows: (1) the rotation of the 2D LiDAR is non-periodic and irregular, while for a rotating 2D LiDAR described in Section 3.1, the rotation of the 2D LiDAR is periodic and regular. (2) The rotation axis of the 2D LiDAR does not coincide with the middle line of the scanning sector, because the 2D LiDAR is installed obliquely on the cable drum. For a rotating 2D LiDAR, the rotation axis of the 2D LiDAR generally coincides with the centerline of the scanning sector.  
In [79], the 2D LiDAR also rotates irregularly, and is carried by a UAV for aerial mapping. The 2D LiDAR is not rotated by a motor; it is rotated by the airflow generated by the four propellers of the UAV. The airflow blows the blades fixed together with the 2D LiDAR to rotate the 2D LiDAR around an axis.  
In the cases of [78;79], because the rotation of the 2D LiDAR is non-periodic and irregular, so this category of a moving 2D LiDAR can be called an irregularly rotating 2D LiDAR. Figure 7 shows the prototypes in [78;79].  
Figure 7: An irregularly rotating 2D LiDAR, the 2D LiDAR rotates irregularly. (**a**) The prototype in [78]; (**b**) the prototype in [79].
## 3 Social Issues  
As mentioned in the introduction section, besides technical aspects, discourse on bandwidth has a strong social dimension. Although technical tools and infrastructure for capturing and accessing geographic information have become cheaper in recent years, we still can observe an inhomogeneous and disparate availability, reliability, and capacity of network bandwidth on a global, regional, and local scale. On a global scale different measures of accessibility reveal a clear divide between the North and the South, the developed and developing counties respectively (Figure 3).  
On a local scale, parts of the territory of a country may have no network access or only low bandwidth access. The reasons are mainly economical constraints faced by the network operators, such as topography, unpopulated areas, etc. An example of heterogeneous bandwidth supply takes place in  
Figure 3: A cartogram showing the number of Internet users in 2002 as published at worldmapper.org [74]. The size of the country is modified to match the number of Internet users. © Copyright SASI Group (University of Sheffield) and [PERSON] (University of Michigan).  
Switzerland [75], that is, mountainous areas in Switzerland are badly covered with high bandwidth offered by technologies such as HSPA.  
There are many implications of the inequality of access to the Internet. In this particular example, we can see that that not all citizens have the same bandwidth at their homes, bandwidth supply varies over space, and ultimately that some types of services (rich data, e.g., maps, landscape visualizations) are not usable in all parts of the country. This example from Switzerland is interesting also from a global perspective as Switzerland is one of the highest \"_bits per capita_\" countries in the world. Bits per capita (BPC) is a measure that expresses the Internet use, taking the international Internet bandwidth as an indicator of Internet activity instead of number of Internet users considering many people, public organizations or commercial services share accounts [76]. A list of countries and their bit per capita measures can be found online in a variety of web pages [77].  
The discrepancy between the developed and developing world (_i.e._, digital divide) has many short, mid, and long term political and possibly ethical implications in terms of \"information poverty\" that is out of scope for this paper (entire books have been written on the subject, e.g., see [78]). However, it is important to be aware of the presence of the problem and further acknowledge the need for designing light-weight, but equally informative geographic services (e.g., level of detail management and filtering) to increase their accessibility.
# Visible Spectrum and Infra-Red Image Matching: A New Method
## 1 Introduction  
Visible Spectrum (VS) and Infra-Red (IR) images are used in a wide variety of computer vision applications, such as image registration [1; 2], face recognition [3], scene category recognition [4], stereo matching [5] and medical image analysis [6]. IR images provide complementary information to visible spectrum images in order to make image analysis more reliable [4].  
In the last two decades, a large number of feature point detectors and descriptors have been proposed [7]. These detectors and descriptors have been designed for grayscale and RGB images to overcome common types of transformations and deformations between the images [8]. In cross-spectral applications, such as VS-IR image matching, the detectors and descriptors under perform due to high textural and intensity changes between VS-IR images [9]. To overcome this problem, several new and modified versions of Scale Invariant Feature Transform (SIFT) [10] have been proposed in order to minimize the effects of intensity and textural changes between the images by using features such as Canny edges [11], local contrast and differential excitations [12], local binary patterns [13; 14], normalized gradients [15] and local self similarity [16] for descriptor construction.  
In contrast, this paper proposes a regression based method to overcome the effects of intensity and textural changes. The proposed method consists of three steps. In the first step it detects SIFT feature points [10]. Then it computes Modified Normalized(MN)-SIFT [17] descriptors for every detected feature point. It identifies correct MN-SIFT descriptor matches between pairs of trainingVS-IR images with projection error [18]. It fits a regression model on correct MN-SIFT descriptors. The MN-SIFT descriptors of test VS images are passed through the regression model to remove the effects of textural and intensity changes prior to their descriptor matching with the MN-SIFT descriptors of test IR images. To the best of our knowledge, no similar method exists in the literature. The main contributions of this paper are as follows:  
* A performance evaluation of different feature point detectors and descriptors on VS-IR images.
* A new regression based method for VS-IR image matching.  
The rest of the paper is organized as follows: Section 2 presents related work. Section 3 presents the proposed method. Section 4 presents the experimental setup and the results. Finally, the paper is concluded in Section 5.
# Development of Communication Protocols between BIM Elements and 3D Concrete Printing
## 3 BIM and 3D Concrete Printing Process
### From the Scanner to BIM  
Next, it is needed to link tools that incorporate the BIM methodology (as is the case of RevitTM), with a central database management system capable of communicating data bidirectionally with computerized maintenance management systems (CMMS). The scan to BIM process consists of three main steps: scanning, registration, and modeling.  
Scanning involves the installation of a total station, a 3D laser scanner, and a set of target artifacts.  
Registration is the process of transforming two or more scans of the same scene, from different positions, into a single and unique points cloud for a common frame of reference. The 3D laser scanner establishes a new coordinate frame for each scan and assigns coordinates to the scanned points; this step is essential to avoid losing the orientation of points.  
Modeling is the process of exporting the points cloud to a CAD environment to model it in BIM. After the points cloud is registered, the modeler exports it to a CAD environment and assign primitive CAD objects to it using adjustment algorithms or manual adjustments. Due to the complexity of tracking points in 3D space, modelers may need to prepare flat sections of the cloud, parallel to the elevation planes. BIM is then created based on measurements made on the CAD model. Figure 3 summarizes the process performed for modeling [44]:
# Statistical Analysis of the Crosswalk Lighting Design Correctness
## 3 Robust Approach in Crosswalk Illumination Planning  
The majority of outdoor lighting projects are made by designers supported by DIALux-like software solutions based on their intuitive simplifications of particular lighting situations (see Subsection 2.2). This approach, despite of its drawbacks (lower energy efficiency and long design preparation time), guarantees compliance with lighting standards in most cases.  
The fundamental question is: can be that approach applied in the case of pedestrian crossing illumination planning?  
To find an answer to the above, let us suppose that a crosswalk lighting project will be created according to the template-based methodology described in Subsection 2.2. In this case, an engineer prepares _template-solutions_ for several typical road and crosswalk widths (\(w_{r}\) and \(w_{c}\) respectively) and pole heights (\(h\)). For values, say: \(w_{r}\in\{4,5,6,7,8,9,10\}\) meters, \(w_{c}\in\{2,3,4,5,6\}\) meters and \(h\in\{4,5,6\}\) meters, we obtain 105 potential reference crosswalks. Then, any particular pedestrian crossing is matched to the closest template and a corresponding template-based solution is proposed.  
To investigate the effectiveness of the approach for crosswalk lighting design, we tested how the changes made to the lighting situation shown in Figure 1 (without changing lamp settings) affect lighting conditions in terms of compliance with the standard. We used customized software, calculating photometry in accordance with the standard [13].  
It was assumed that an installation illuminating the pedestrian crossing of the PC4 class, being in an initial, reference layout (i.e., not modified), fulfills relevant requirements. In the following three scenarios, only one parameter was slightly modified.  
**Scenario 1.** Suppose that a pedestrian crossing and its dedicated lamps are shifted by 3 m relative to roadway lighting luminaires (Figure 4). Note that 3 m is 10% of the actual luminaire spacing.  
**Scenario 2.** In the second scenario we increase the road width by 0.7 m (i.e., by 10%), to 7.7 m (Figure 5). Note that a distance of 0.7 m can be roughly compared to the width of two car mirrors.  
Figure 4: Scenario 1: (**a**) the reference pedestrian crossing, together with dedicated lamps.; (**b**) the crosswalk shifted by 3 m relative to roadway lighting luminaires.  
Figure 5: Scenario 2: (**a**) The reference pedestrian crossing. (**b**) Road width increased by 10% relative to (**a**).  
**Scenario 3.** In the third scenario we increase the crossing width by 0.5 m, from 6 to to 6.5 m (Figure 6).  
The results obtained for the above scenarios are presented in Table 3.  
The results presented in Table 3 suggest that even minor changes of one parameter can lead to violating standard conformity. It should be noted, however, that in the real-life situations, one deals with at least a combination of the above scenarios. Additionally, variations in streetlight spacings, pole heights and so on can exist. As shown, each individual deviation can influence lighting standard conformity.
# Improving IMES Localization Accuracy by Integrating Dead Reckoning Information
## 1 Introduction
In this study, we propose a novel positioning method of IMES with RSS-based trilateration and introduce an integration method of IMES and PDR localization so as to overcome each other's disadvantages; particularly, we combine the PDR's relatively good displacement accuracy in a short time with the IMES's absolute positioning capability by using a Kalman filter. The remainder of this paper is organized as follows. In Section 2, we compare IMES to WiFi, which is also a potential indoor positioning infrastructure. In Section 3, a radio propagation model of IMES is investigated in order to understand the relationship between distance and received IMES radio strength. Based on the obtained radio propagation model, in Section 3, we develop a trilateration algorithm to determine the position using the received IMES radio strength from multiple IMES transmitters. Then, in Section 5, we design a Kalman filter that combines information from the IMES radio and dead reckoning systems (wheel encoders for robots and inertial sensor for humans), and in Section 6, the resulting positioning accuracy is experimentally evaluated. In Section 7, we also investigate the human body influence on the radio propagation. Lastly, in Section 8, we conclude this study and give future work.
# Copernicus Global Land Cover Layers--Collection 2
## 3 The CGLS-LC100 Product and Accuracy Assessment
### Quality Indicators  
The CGLS-LC100 product delivers, next to the discrete map and cover fraction layers, eight additional quality layers, which can be used to evaluate pixel-wise the quality of the input EO data as well as the RF classification and regression quality:  
* _DDI_: The Data Density Indicator indicates the availability of input data from the PROBA-V UTM ARD+ MC5 archive for 100 m and 300 m resolutions. It is a score between 0 = no input data available and 100 = best data availability. Overall, 19 single time-series statistics are calculated and combined via a scoring approach into the DDI. For more details see the ATBD [25].
* _Discrete class probability_: The probability of the discrete classification indicates the quality of the discrete classification and is provided as a number between 0 and 100, in steps of 1%. The class probability is calculated pixel-wise using the RF model and scoring the classification trees within the RF forest. The higher the probability, the more confident we are that the given class is correct.
* _Cover fraction standard deviation_: The standard deviation of the percentage cover regression indicates the quality of the associated cover fraction layer. It is provided as a number between 0 and 100, in steps of 1%. This indicator is given only for the six classes calculated by the CGLS-LC100 workflow (tree, shrubland, herbaceous vegetation, cropland, moss and lichen, and bare/spare vegetation). The standard deviation is calculated pixel-wise over the single regression results of the trees within the RF forest. The lower the standard deviation for a class in a pixel, the higher the confidence that the result is correct.
# A Blockchain-Based Protocol for Fair Delivery for Receips
## 6 Smart Contract Specification
### Main Smart Contract Functions  
The smart contract defines three public functions (see Section 5), which can be executed by the different parties involved in the delivery process:  
* _accept_: Given a delivery identifier, \(B\) provides the evidence necessary for proving that they are the recipient authorized to accept the delivery and confirms acceptance of the document delivery.
* _publish_: Given a delivery identifier, \(A\) provides the key for accessing the document once B has accepted the delivery.
* \(getStatus\): Given a delivery identifier, the sender and the recipient can check the status of the delivery.  
The smart contract performs various verifications when an entity calls any of its public functions. These include checking whether the calling entity is authorized, whether the entity is the sender or the recipient, and whether the deadline for executing the function has expired. To achieve this, the smart contract uses a special type of function, called a modifier, which is defined in Solidity. A modifier [27] is code that can be run before and/or after a function call and can be used to restrict access, validate inputs, etc. We define three modifiers for controlling by whom and when each of the smart contract functions can be executed:  
* _onlySender_: verifies that the caller of the function is the sender of the document;
* _validStatus_: verifies whether the delivery status allows the calling function to be executed;
* _deadline_: checks whether, at the time of calling the function, the deadline has expired.  
In addition, the smart contact provides the following private functions that are used internally by the public functions and modifiers:  
* _validID_: This function is used by the _accept_ function to check whether the given delivery identifier is valid.
* _getCurrentTime_: This function is used by the _deadline_ modifier to provide the current time on the basis of the current timestamp of the block being mined on the blockchain.
# Automated Anomaly Detection and Causal Analysis for Civil Aviation Using QAR Data
## 6 Experimental Evaluations
### Results and Analysis
#### 6.2.2 Ablation Study  
This section outlines ablation experiments we conducted on the MAD-XFP model to investigate the impact of different components in the model on overall performance.  
First, we disabled the feature engineering provided by OpenFE and parameter selection via Optuna to test the impact of temporal features and model hyperparameters on model performance. Then, the entire feature extraction and parameter optimization parts were completely removed, and the entire model was reduced to the most basic XGBoost model, in order to verify the necessity of the feature extraction part of the data in the entire model. The experimental results are shown in Table 9.  
The results indicate that removing different components from the model has a negative impact on the overall performance. Especially after disabling feature engineering via OpenFE, compared to the MAD-XFP model, the values of various evaluation indicators significantly decreased, with the F1 score decreasing by 11.7%. This indicates that data features have a significant impact on the prediction results.
## 4 Architecture of Real-Time AI Agents for Autonomous Ships
### System Layers and Agent Integration  
The architecture of real-time AI agents in autonomous maritime systems is structured as a multilayered framework, integrating sensing, perception, decision-making, control, and supervision. At the heart of this architecture lies the AI agent, which acts as an autonomous entity capable of processing input, generating context-aware decisions, and executing safety-critical actions under time constraints (Figure 2).  
The hardware and sensing layer forms the foundation, comprising components such as LIDAR, radar, sonar, cameras, and AIS receivers. These devices provide a continuous stream of data describing the vessel's environment and internal states.  
Above this sits the data processing and perception layer, which uses machine learning algorithms, computer vision, and sensor fusion methods to transform raw sensor data into actionable situational awareness [32]. The AI agent operates at this level, leveraging edge computing resources (e.g., GPUs, TPUs, FPGAs) to ensure fast and localized decision-making [77].  
The control layer interprets decisions generated by the AI agent and interfaces with navigation, propulsion, and safety systems to execute commands in real time. Redundant and fail-safe pathways are included to guarantee continuity in critical operations [78; 79; 80; 81].  
The supervisory layer oversees the overall system status, supports human-AI collaboration, and facilitates external communication. It also allows for periodic synchronization with cloud systems for model updates, long-term data archiving, and collective learning across fleets [78].  
Computational components are distributed across these layers to balance processing efficiency, energy consumption, and system resilience. Edge computing ensures low-latency decision-making, while cloud integration supports fleet-level intelligence sharing and predictive updates. This hybrid architecture allows AI agents to operate autonomously under normal conditions and escalate decisions to remote operators in exceptional scenarios.  
By embedding AI agents across layered systems, autonomous ships gain the ability to react adaptively and safely to real-time changes, even under uncertain or degraded operational conditions.
# An Image Processing-Based Method to Analyze Driver Visual Behavior Using Eye-Tracker Data
## 3 Methodology
### The Experimental Setup  
A driving simulator was utilized to conduct tests on a driving scenario with manifold aims besides the eye-tracker analysis. The specific eye-tracker testing phase involved four participants, and the data collected during the testing was analyzed using the newly proposed methodology. The driving simulator used in the experiment is made up of a fixed seating buck, a set of vehicle controls (force-feedback steering wheel, gear shifter, and pedals), and three 32\" full-HD monitors with a 175-degree field of view to display the virtual driving scenario (Figure 1). The IPG Automotive CarMaker 7.0.3 (CarMaker, 2024) [50] software was used to simulate the scenarios. CarMaker is a vehicle simulation software with intelligent driver models, detailed vehicle models, and highly adaptable road and traffic models.  
The Politecnico di Milano Campus Bovisa, La-MASA district, located northwest of Milan, is the case study application for evaluating the proposed analysis method. The modeled area is 350 m \(\times\) 350 m wide, and the driving simulation path is 1.12 km long, corresponding to the public one-way ring road that travels along and between the university area. The area consists primarily of isolated structures (university buildings) surrounded by open spaces and fences.  
The 3D models utilized in this study were created using the SketchUp 3D 2019 design software (SketchUp, 2019) (Figure 2).  
A digital camera was used to take images of visible facades from the road. An identical route was proposed for all participants. The route was solely designed to be driven in a counterclockwise orientation. The speedometer display was visible in the top left corner of the screen.  
The survey involved 4 participants aged between 21 and 26 years with driving experience between 2 and 5 years. All the participants hold valid driver's licenses. The drivers were identified as #1, #2, #3, and #4. The ethical committee of Politecnico di Milano approved the survey protocol.  
Figure 1: The driving simulator during the experimental test.