esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Related Work
#### 1.1.2 Error Communication  
While interacting with a system, the user and the system itself may cause errors. To manage the errors caused _by users_, research on user experience (UX) design emphasizes that designers should offer simple ways to correct errors [49; 50]. Similarly, designers should show easy-to-understand information on the origin of the error and its occurrence, such as highlighting errors in a web form [51]. At the same time, insufficient error communication may cause frustration, and, in the worst case, users may lose their trust in the system [52].  
Looking at _system_ errors, there are even long time risks if errors are communicated in an inappropriate way. If communicated wrongly, users may adopt harmful strategies leading to disruptive behaviors [53; 54]. For example, if a voice-based interface does not recognize the user's command, some users adapt their voice and rely on hyperarticulate speech to \"assist\" the system in recovery [54], which may cause discomfort and further increase the user's algorithm aversion [47]. Even showing an error message can already cause unpleasant emotions in users [55]. However, the tone of voice used to communicate an error can alter how unpleasant the users see these messages as. As prior work shows, using an apologetic tone of voice reduces the negative effects of error messages [56].  
In recent years, more advanced artificial intelligence systems have been developed to support individuals in making optimal decisions in high-risk scenarios [57]. Contrary to UX design strategies, prior work shows that communicating errors in such situations may increase the probability that users stop trusting such systems or begin to rely more on their own intuition. Thus, error communication can lead to decreased performance [58].  
Moreover, it has been shown that users lose their trust in an AI much faster than when interacting with other humans [59]. As these data suggests, designers are challenged with two apparently contradictory recommendations: On one side, the UX design literature suggests being open and directly communicating errors to the user. On the other side, the experiments show that being open about system errors can cause users to distrust the results of the AI's prediction and, in the worst case, stop using the technology.
## 2 The Surface Energy Balance: A Cornerstone in Understanding ET and RS Derived Variables
### The Sensible Heat Flux (H)  
The sensible heat flux refers to the rate at which heat is transferred to the air through convection and conduction, resulting from the difference between the air and the surface temperature. The magnitude of \(H\) relies heavily on both the temperature difference between the surface and the air, and the resistance of the surface to heat transfer. Unlike one-source, two-source surface energy balance models consider the separate contributions of soil and canopy to the overall heat flux. Early studies played an important role in the development of these models. Notable examples of the initial two-source surface energy balance models include the Two-Source Model introduced by [20].  
In the case of the single source approach, the calculation of \(H\) implies considering one source resistance resulting from both the soil and the canopy. The formula for calculating \(H\) under these circumstances is expressed as follows:  
\[H=\rho_{a}C_{p}\frac{T_{aero}-T_{a}}{r_{r}} \tag{3}\]  
where \(\rho_{a}\) is the air density (kg\(\cdot\)m\({}^{-3}\)), \(C_{p}\) is the specific heat of the air (J\(\cdot\)kg\({}^{-1}\)\(\cdot\)K\({}^{-1}\)), \(T_{aero}\) is the aerodynamic surface temperature (K) and \(r_{r}\) is the aerodynamic convective resistance to heat (s/m).  
In two source models, \(H\) is often computed as the sum of canopy sensible heat \(H_{c}\) and soil sensible heat \(H_{s}\) that are estimated as follows [20].  
\[H=H_{canopy}+H_{soil}=\rho_{a}C_{p}\frac{T_{c}-T_{ac}}{r_{c}}+\rho_{a}C_{p} \frac{T_{s}-T_{ac}}{r_{s}} \tag{4}\]  
where \(T_{c}\) and \(T_{s}\) are, respectively, the canopy and non-vegetated soil temperatures, \(T_{ac}\) is the air temperature in the canopy air layer. \(r_{s}\) and \(r_{a}\) are the soil resistance and the boundary layer resistance associated with the canopy, respectively. Both single and two source models' concept is illustrated in Figure 1.  
Figure 1: Surface and aerodynamic resistance for one-source and two-source surface energy balance models.
# Physically-Data Driven Approach for Predicting Formation Leakage Pressure: A Dual-Drive Method
## 3 Selection, Processing, and Correlation Analysis of Training Samples  
### Overview of Data Sources  
The research data for this study are obtained from two vertical wells, Well M-1 and Well M-2, located in Block M. Well M-2 is situated approximately 6.7 km southwest of Well M-1. Figure 4 illustrates a lithost stratigraphic comparison between the two wells. Sequentially, the M block drills through formations A, B, C, and D. Formation C pertains to the principal oil and gas development stratum within this block, primarily characterized by gray and sandy mudstone, intercalated with multiple thin coal seams. The M block exhibits a complex internal structural configuration, featuring well-developed, deep-seated faults. Faulting has led to the fragmentation of the reservoir into isolated blocks. The internal fault structure displays pronounced heterogeneity, characterized by localized stress concentration and varying stress fields. The substantial drilling depth within this block results in a complex vertical lithological profile. The shallow sections comprise readily hydratable mudstone, whereas the middle and deeper sections consist of hard, brittle mud shale. These formations are susceptible to wellbore collapse, leading to intricate challenges such as sticking. The occurrence of faults, coal-rock interlayers, or fractured formations during drilling increases the likelihood of leakage. Consequently, these sections become zones of high drilling risk. Statistical data from prior drilled wells indicate that among the 35 wells within the M block, 13 wells encountered leaks, constituting 37.14% of the total. Additionally, the presence of abnormally high formation pressures in the middle and deeper sections constrains the safety window for drilling operations. Designing drilling fluid density using conventional formulaic approaches poses challenges.
## 6 Recommended Stack Configurations and Their Comparative Performance Evaluation
With the Recommended-Unrestricted configuration, where the amount of allowed MAC layer retries is further increased to 11 and the default RTO algorithm is replaced by the CL algorithm, an additional improvement in the PDR performance is observable for medium and high traffic scales. In terms of PDR, the improvement achieved by the Recommended-Unrestricted configuration in comparison with the ZigBee default one is 4.1%, 15.3% and 33.6% for low, medium and high traffic, respectively. Figure 21: Comparison of average delays between default, Recommended-Compliant and Recommended-Unrestricted stack configurations, including 95% confidence intervals. Figure 20: Comparison of overall PDR between default, Recommended-Compliant and Recommended-Unrestricted stack configurations. Correspondingly, the end-to-end delays decrease by 61.2%, 66.6% and 61.9%, while the energy efficiency improves by 43.2%, 48.7% and 28%. Hence, it has been shown that by relaxing the allowed range of settings for network parameters and by allowing the cross layer feedback from the routing layer to the application layer, a further PDR improvement of up to 2.1%, a delay improvement of up to 6.5% and an energy efficiency improvement of up to 6.6% are possible. The energy efficiency of the Recommended-Unrestricted settings stays very similar to the one of the Recommended-Compliant protocol stack configuration (Figure 22).
# RGB-D Camera for 3D Laser Point Cloud Hole Repair in Mine Access Shaft Roadway
## 3 Data Acquisition
### Data Acquisition of RGB-D Camera  
There are various RGB-D cameras, and the Realsense depth camera [23] is highly integrated with a low cost, and it can be applied in both indoor and outdoor scenarios. Its ideal measurement range is 0.6 m to 6 m, and the depth accuracy reaches 4 m < 2%. Therefore, the Realsense D455 depth camera was used in this study to measure the depth by classical binocular vision and binocular vision ranging assisted by an infrared projector. The data acquisition flow chart is shown in Figure 6.  
Before data acquisition, the depth camera needed to be calibrated to ensure the accuracy of the data collected by the camera. Realsense D455 has laser-fused steel cages designed to help maintain calibration and performance over their lifetime. Intel RealSense Viewer software has an automatic camera calibration function. Unlike traditional camera calibration, it does not require the use of a target reference, grid pattern, or complex reference to perform calibration; we just keep the depth of the camera stationary and stable and select the software self-calibration function to complete self-calibration.  
After the depth cameras were calibrated, the data connection cable was connected to the computer and the handheld depth camera was used for data acquisition. The Rosbag dataset was recorded for the access shaft roadway through the Intel Realsense SDK depth  
Figure 4: Flow chart of 3D laser data acquisition.  
Figure 5: Point cloud data acquisition diagram of manhole roadway. (**a**) Point cloud data acquisition of 3D laser scanner; (**b**) Coordinate data acquisition of target points.  
camera control software, and finally, an offline dataset package was obtained, as shown in Figure 7. Each frame of the 2D color image and synchronous depth image of the recorded access shaft roadway can be obtained in the data package, and the unprocessed color 3D point cloud can also be obtained directly.
## 4 Coordinate System Conversion Algorithm-based Depth Estimation
### Test Results  
To test the accuracy of the 2D \(\rightarrow\) 3D mapping conversion formula, the image coordinates of the corner points of the license plate were projected onto the floor, and the intersection point world coordinates (X, Y, Z) were obtained and compared with the known world coordinates. Figure 11 shows a 3D coordinate conversion result image for the nine corner points of the license plate used in the test.  
Table 5 lists the errors calculated by the Euclidean distance \(\sqrt{(x-x^{\prime})^{2}+(y-y^{\prime})^{2}+(z-z^{\prime})^{2}}\). The average error of the nine points was about 105 cm.  
Figure 11: Result of calculating the world coordinates of the floor plan intersection point.
## 2 Materials and Methods
### Sampling the FLUXNET 2015 Datasets for Training and Evaluation  
In this work, we examine how well global GPP estimated from flux towers can be explained using simple relationships with satellite-based observations. Here, we use flux tower-derived GPP estimates to both calibrate and evaluate a variety of models. We use half of the available observations for calibration (i.e., deriving slopes between various satellite-based products and GPP) and the other half (independent) for evaluation of the derived relationships. We split the data alphabetically, i.e., according to the flux site name (even- or odd-numbered flux tower sites). In this way, half the sites are used for training and the other half for independent evaluation. This sorting selects sites such that if there are two (or more) sites per country, one site will be used for training and the other for evaluation. It also eliminates the possibility of data from a single site being included in both training and evaluation samples. This leads to more independence between the training and evaluation datasets. We find that results are similar, although not identical, depending on which half is used for training and that all general conclusions hold no matter which half of the sites is used for training.  
In order to test GPP models on a wide range of conditions and plant functional types, we use all flux tower sites except for 23 identified by [PERSON] et al. [21] as having incompatibility with MODIS pixels and a few more additional sites at coastlines where FLUXCOM-RS did not produce good values. Each site was checked visually with MODIS 5 km data. Sites were retained if the land cover and vegetation condition within the flux tower footprint (assumed 250-m radius) was similar (\(\geq\)80%) to that within the overlapping MODIS data. All analyses are conducted for years 2003-2014 when FLUXNET 2015 and high quality MODIS data from both the Terra and Aqua satellites are available.
# Considered Factors of Online News Based on Respondents' Eye Activity Using Eye-Tracker Analysis
## 4 Results
### Analyzing Text and Images from Predominant News
#### 4.9.2 Text Characteristics Analysis
In each category, there were data from three respondents, namely respondents who had the highest image ratio in each FA category (News 1), UU ITE (News 2), and EARTHQUAKE (News 3) compared to the other respondents. The ratio was obtained from the formula below, which is the amount of gazing at text in the news divided by the total of text ratios from the whole news. \[TextGazeRatioX=GazePointX/\sum_{x=\text{news 1}}^{\text{news 3}}GazePoint \tag{5}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **News as Source** & & & \\
**Respondent** & **Of Anxiety for** & **Gaze Ratio On** & **Gaze Ratio On** & **Gaze Ratio On** \\
**Anxiety Level** & **Respondent** & **Text FA** & **Text UU ITE** & **Text** \\  & **(Self-Assessment)** & & & \\ \hline \multirow{4}{*}{Low Anxiety} & FA & **59.78\%** * & 26.45\% & 13.77\% \\  & UU ITE & 15.65\% & **55.33\%** * & 29.02\% \\  & Earthquake & 27.83\% & 20.52\% & **51.65\%** * \\  & FA & **53.04\%** * & 35.72\% & 11.24\% \\ \cline{1-1}  & UU ITE & 31.17\% & **41.37\%** * & 27.46\% \\ \cline{1-1}  & Earthquake & 26.97\% & 32.34\% & **40.69\%** * \\ \cline{1-1}  & FA & **47.96\%** * & 32.37\% & 19.68\% \\ \cline{1-1}  & UU ITE & 8.99\% & **54.79\%* & 36.22\% \\ \cline{1-1}  & Earthquake & 24.10\% & 26.21\% & **49.70\%** * \\ \cline{1-1}  & FA & **39.59\%** * & 26.72\% & 33.69\% \\ \cline{1-1} Severe Anxiety & UU ITE & 39.43\% & **39.78\%** * & 20.79\% \\ \cline{1-1}  & Earthquake & 30.44\% & 33.11\% & **36.45\%** * \\ \hline \hline \end{tabular}  
* * Highest gaze ratio for each respondent  
\end{table} 
Table 27: Comparison of text gaze ratio from respondents with highest text gaze ratio on each news grouped by anxiety level. Different from the data about images, the highest text ratios were for one news source in one anxiety level in the respondents themselves. With respondent low anxiety, medium anxiety, high anxiety, and severe anxiety in the earthquake category, it can be seen that they had ratios of 51.65%, 40.69%, 49.70%, and 36.45% respectively. The gaze point ratio on text for the earthquake news was the highest among respondents in the earthquake category, and it was the highest ratio in the respondents themselves compared to the ratio in the other two news sources. Based on the results, in contrast to images, a high gaze point text ratio determines high anxiety.
# Inertial Parameter Identification in Robotics: A Survey
## 3 Inverse Dynamic Identification Model (IDIM) and Least-Squares (LS) Estimation Methods
### Total Least-Squares (IDIM-TLS)  
The issue of noisy observation matrix, can in theory, be tackled using Total Least-Squares (IDIM-TLS) approach. As exposed in [54, 55], the TLS estimate \(\mathbf{\hat{\beta}}_{TLS}\) can be computed using a singular value decomposition of the augmented matrix \(\mathbf{X}=[\mathbf{W}\,\mathbf{y}_{\top}]\in\mathbb{R}^{(n\cdot N)\times(b+1)}\) as  
\[\mathbf{X} = [\mathbf{U}_{W}\,\mathbf{U}_{y}]\begin{bmatrix}\mathbf{S}&0\\ 0&S_{min}\end{bmatrix}\begin{bmatrix}\mathbf{V}_{WW}&\mathbf{V}_{Wy}\\ \mathbf{V}_{yy}&V_{yy}\end{bmatrix}^{\top}, \tag{26a}\] \[\mathbf{\hat{\beta}}_{TLS} = -\mathbf{V}_{Wy}V_{yy}^{-1} \tag{26b}\]  
where \(\mathbf{V}_{Wy}\in\mathbb{R}^{b}\) and \(V_{yy}\in\mathbb{R}^{*}\). Following [4, 56], the covariance \(\mathbf{\Sigma}_{TLS}\) of the TLS estimate \(\mathbf{\hat{\beta}}_{TLS}\) can be approximated as  
\[\mathbf{\Sigma}_{TLS}\!\approx\!\left(1+\left\|\mathbf{\hat{\beta}}_{TLS}\right\|_{2 }^{2}\right)\!\frac{S_{min}^{2}}{n\cdot N}\!\left(\mathbf{W}^{\top}\mathbf{W}-S_{min}^ {2}\mathbf{I}\right)^{-1} \tag{27}\]  
where \(S_{min}\in\mathbb{R}\) is the minimum non-zero singular value of \(\mathbf{X}\). Only a few pieces of research actually explore the potential use of total least-squares in the context of robot dynamic parameters identification. In [6], the authors observed that the IDIM-TLS estimate is biased if the joint data are not suitably filtered. Moreover, they noted that although TLS convergence occurs with adequate data-filtering, it does not outperform ordinary and weighted least-squares. Motivated by [57, 58], in [59] part 4.2.6, the author raised some general comments on the IDIM-TLS method and stressed that its disappointing performance can be explained by the fact that it is based on the hypothesis that the noises have all the same variances whereas this assumption is violated in the case of an improper data-filtering according to [60]. It would in fact be more appropriate to use the Generalized Total Least-Squares (GTLS) method popularized by [PERSON] and [PERSON], [4, 61]. However, to properly use the GTLS method, the covariance matrix of noises involved in \(\mathbf{W}\) must be known. Unfortunately, if such an information is easily accessible for linear systems, it is not for robots. Indeed, besides the noise amplification effect induced by the process of numerical time derivation of the joint angles--used for joint velocity and acceleration computation--the IDM involves nonlinear functions such as the sine, cosine, square or sign operators, making the calculation of this covariance matrix difficult, if not intractable. This comment explains the reason the TLS or GTLS methods are seldom employed in the context of robot identification (see e.g., [54; 62]) whereas they are widely considered in signal processing, see e.g., [63] and the references therein.
# Evaluating a Proposed E-Government Stage Model in Terms of Personal Data Protection
## 2 The Proposed Stage Model
S1 and S2 are the two primary systems that make up the website. S1 collects a set of personal data (PD) from its users. Three processes via three servers, namely, the Local Server (LS), Encryption Server (ES), and Central Server (CS), are involved in processing PD. Users interact with the LS in S1 via a Public Interface (PI). In the LS, the users argued about PD collection and obtaining their consent on processing PD. However, the LS distinguished PD from Public Data, then it sends them to Encryption Server. The PD is stored in a Local Data Store (LDS) in an encrypted manner. The Local Admin Interface (LAI) allows the Local Admin (LA) to interact with the LS in S1. In the third system (S3), the Central Server retrieves the encrypted PD from the LDS, then processes it by the Central Admin (CA), which is manipulated via the Central Admin interface (CAI). In S3, there are two data stores, which are the Central Data Store (CDS) and the External Data Store (EDS). The Central Server replicates the PD in both data stores. The proposed model suggests a mix of legal, social, organizational, and technological control measures because technical security control is generally nonexistent in developing countries. The General Data Protection Regulation (GDPR) and international standards such as the Personal Data Protection and its Critical Security Controls (CIS CSC), ISO 27001, COBIT 5, and NIST 800-53 serve as the foundation for these controls. The measures relate to internal security, perimeter security, risk management awareness, and personal information security in terms of physical and logical assets. These procedures will guarantee that personal data are protected during every stage of its existence. The number of impacted factors, compliance with security measures for each factor, and the anticipated level of security based on the necessary measure for that factor may all be used to determine the security level of each stage. For instance, using Formula (1), according to the above algorithm, we can evaluate the security of personal data at a stage such as the Interaction Stage [8]. \[\%\text{PISL}=\frac{\sum\frac{\text{PLM}}{\text{ELM}}+\sum\frac{\text{POM}}{ \text{EOM}}}{2} \tag{1}\]  
PISL = Personal Information Security Level  
PLM = Practiced Technical Measure  
ELM = Expected Technical Measure  
POM = Practiced Organization Model  
EOM = Expected Organization Model  
Thus, the personal information security level of integration stage determines as the compliance levels of the technical and organizational measures of the model to the expected level of security based on required control measures that come from the collection of international standards.
## 3 Proposed Model
### Issuance of Academic Information  
The process, illustrated in Figure 2, starts when the academic institution (\(E\)) issues the diploma containing some sensitive (name, surname, degree, date, etc.) and nonsensitive information (type of academic information, educational institution, course name, number of credits, etc.) to the holder (\(H\)) with a certain custom structure in the form of a Merkle tree  
Figure 1: Proposed design (overview).  
(A Merkle tree is a structure of data that allows efficient and secure storage and verification of content with a large amount of information) [21] and generates also the corresponding manifesto. Both organization \(E\) and holder \(H\) are represented by their blockchain accounts. It is highly recommended (as described in [5]) for the holder to generate a new account for each academic certificate issued to avoid linkability and to store them all securely in a mobile app or a computer.  
All the information included in the certificate is recorded by the institution in its private and isolated database in the register \(idp\) and encrypted with a secret key only known by \(E\) and \(H\).  
The academic institution is also connected to a private blockchain and sends a transaction containing: a pointer to its internal database register (\(idp\)), the location (hostname and port) of the database server access point (\(se\)), the root of the Merkle Tree (\(mt\)) of the academic information encrypted with its private key (\(l\)), that information is valid and a list of accounts that can access this information (\(aa\)). These data are recorded in a transaction (\(id\)).  
Some nonsensitive information _public data_ (\(pd\)) such as the type of certificate may also be added to the transaction in the blockchain to support global data analytics by the issuer, but it is not mandatory.  
The issuer transmits using a secure channel to the holder the complete certificate (\(c\)), its encrypted Merkle tree (\(h\)), the manifest (\(m\)), and the transaction id (\(id\)). The institution must register every operation in a local log, which must be also hashed and sent to the blockchain to provide accountability of any modifications (smart contract \(SCLog\)).  
The private smart contract \(SCService\) is the only authorized external access point to the records stored in the local database of the institution, which verifies that the requesting account is allowed to access the data.  
Periodically, each private blockchain transmits to the main consortium the registered information, which is divided into the \(SCData\) smart contract that contains the encrypted root of the Merkle tree -\(h\)-, \(idp\), \(se\) and information that the data are valid and \(SCAccess\) with the list of authorized accounts (\(aa\)).