esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The development of LEO satellites offers promising potential to improve the accuracy of wide-area GNSS CIT. However, the current limited number of LEO satellites means that only a small number of actual observations can be made globally, and it is not yet feasible to achieve global, higher-resolution tomography inversion using both ground-based and space-based GNSS data. Additionally, existing LEO constellations have not been specifically designed with the aim of improving GNSS CIT accuracy. This paper investigates the potential of LEO satellites to enhance GNSS CIT by reconstructing a global, higher-resolution 3D ionospheric IED model using a voxel-based approach and simulated observation data. The study explores the effects of heterogeneous LEO constellations on CIT, yielding the following key findings.  
* Impact of LEO satellite altitudes: Adding LEO satellites at different altitudes increased the number of rays crossing voxels. In the altitude range of 100-300 km, all four altitude intervals showed an increase in gain with more LEO satellites, highlighting the importance of increasing satellite numbers in the low-altitude range.
* RMS gain with LEO satellite observations: The percentage RMS gain was positive after adding LEO satellite observations at 300 km and 500 km altitudes. However, at altitudes of 800 km and 1000 km, the percentage RMS gain for the 100-300 km interval was negative.
* Heterogeneous LEO constellation plans: Six different plans for heterogeneous LEO constellations were designed. In terms of overall gain, the percentage RMS gain for all six plans exceeded 25%, with planB yielding the smallest RMS gain of 27.31% and planA providing the largest RMS gain of 32.42%.  
Considering the launch and maintenance costs of LEO satellites, planA, with the largest improvement in RMS gain, can significantly enhance the accuracy of GNSS CIT. This plan is expected to provide useful insights for the future design of LEO constellations, particularly in the context of constructing global 3D ionospheric models.  
Conceptualization, writing--original draft, writing--review and editing, formal analysis, Y.L., X.H., Y.Y. and T.Z.; methodology, Y.L., X.H. and T.Z.; validation, data curation, Y.L. and T.Z.; funding acquisition, X.H. and Y.Y. All authors have read and agreed to the published version of the manuscript.  
This work is supported by National Key Research Program of China (No. 2023 YFA1009100), and National Natural Science Foundations of China (No. 42074045, U24A20603).  
Figure 13: Percentage RMS gain of the CIT results from GNSS and different plans, compared with the NeQuick2 model, across altitudes on 19 March 2024.
# Remote Technologies as Common Practice in Industrial Maintenance: What Do Experts Say?
### Aims and Research Questions  
Based on a literature research, we have gained knowledge about the theoretical possibilities of remote technologies in industrial maintenance. In this paper, we aim for finding out the practical point of view on using remote technologies in maintenance. In order to achieve the aimed objective, we try to find out exploratory how remote technologies are used in applied practice and which challenges and potentials companies have experienced with regard to these technologies. In order to get an updated practical impression of the use of remote maintenance technology in the field of industrial maintenance, an exploratory interview study was conducted. The aim of this study is to collect detailed examples from practice in order to be able to set up a model (category system) oriented towards practical focal points.  
For this purpose, ten manufacturing companies and plant manufacturers were contacted. On the basis of a semi-structured interview guide, they provided information about: 1. the remote technologies used, 2. the framework conditions necessary for the use of remote technologies (e.g., ergonomic requirements), 3. the work tasks in maintenance (scope and complexity) and 4. important human factors such as the performance requirements for the worker or the necessary performance prerequisites to be able to meet these requirements. For example, a machine operator must be qualified enough to be able to exchange technical information with the maintenance expert. Information on the characteristics of these four points enables the development of a suitable remote maintenance strategy. The examples collected can now be used to build up a system of categories. With this, important information can be collected to derive a suitable remote strategy. This reference model represents the result of this study. This should enable an assessment of the use of remote technologies for specific tasks and company requirements. The interviews are not designed to gain insight into the economic efficiency of the use of technology.  
We formulated our research questions as following:  
* **RQ1: What are examples of successful remote strategies in industrial maintenance for intelligent manufacturing systems (Industry 4.0)?**
* **RQ2: What are the characteristics of the remote strategies?**
* **RQ3: Which categories can be used to describe remote strategies carefully in order to evaluate their success?**  
In order to answer these questions, we conducted an exploratory study using semi-structured interviews with experts following a guideline Appendix A.1 as proposed by Doring & Bortz [10].
## 3 Results and Discussion
### Ductility  
The outcome of the ductility test, depicted in Figure 8, illustrates the evaluation of asphalt binder flexibility and deformation. The figure indicates that the inclusion of CR resulted in a decrease in asphalt binder ductility. This reduction in ductility was attributed to the absorption of the asphalt binder's oily components by the rubber powder, leading to an increase in the mass of rubber particles. Consequently, the CR asphalt binder exhibited a greater thickness compared to unmodified asphalt binder samples, aligning with the observations made by [PERSON] et al. [49]. Across all asphalt binders, the introduction of WMA additives brought about a reduction in ductility. Sasobit had a minimal effect on asphalt binder ductility, while Rediset produced the highest difference in the ductility values. [PERSON] et al. [50] reported that Sasobit reduced the ductility value, which is consistent with the result of this research. The 109 and 101 cm ductility values of the SECR and CECR asphalt binder fulfilled the ductility test requirement. However, the RECR asphalt binder did not meet the ductility test requirement of 98 cm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **PI** & **PVN** \\ \hline V & \(-0.54\) & \(-0.06\) \\ \hline CR & \(0.04\) & \(0.39\) \\ \hline SA & \(-0.49\) & \(-0.92\) \\ \hline CE & \(-1.17\) & \(-0.69\) \\ \hline RE & \(-0.83\) & \(-0.59\) \\ \hline SECR & \(-0.02\) & \(0.004\) \\ \hline CECR & \(-0.48\) & \(0.23\) \\ \hline RECR & \(-0.62\) & \(0.07\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The PI and PVN for all asphalt binders.
###### Abstract  
Several implant planning software programs are widely use in implant treatments, but there has been no evidence of how different software programs affect the accuracy of static surgical guides used for implant placement. Thus, in this in vitro study, we aimed to compare the accuracy of static surgical guides that were prefabricated from three different software programs, including Implant Studio (Program A) (3 Shape(r), Copenhagen, Denmark), coDiagnostiX(r) (Program B) ([PERSON](r), Basal, Switzerland), and Blue Sky Plan (Program C) (_Blue Sky Bio_(r), LLC, Libertyville, IL, USA). A total of 90 drillable polyurethane models were used as samples in this in vitro study; 30 study models were used to plan the same implant positions and design the surgical guides by each software program (\(n\) = 30) and then 90 implants were placed in the models using the surgical guides. The outcomes of the surgical guide accuracy were autonomically measured by the evaluation tool in the coDiagnostiX(r) (Straumann(r), Basal, Switzerland) software program. The deviations between the planned and placed implants were automatically evaluated as three-dimensional and angular deviations. The mean three-dimensional implant position deviations from the implant platform of Program A, Program B, and Program C were 0.55 \(\pm\) 0.25 mm, 0.52 \(\pm\) 0.31 mm, and 0.56 \(\pm\) 0.22 mm, respectively. The mean three-dimensional implant position deviations from the implant apex of Program A, Program B, and Program C were 0.72 \(\pm\) 0.37 mm, 0.73 \(\pm\) 0.4 mm, and 0.9 \(\pm\) 0.46 mm, respectively. The mean depth deviations of Program A, Program B, and Program C were 0.19 \(\pm\) 0.13 mm, 0.31 \(\pm\) 0.32 mm, and 0.31 \(\pm\) 0.22 mm, respectively. The mean angulation deviations of Program A, Program B, and Program C were 1.72 \(\pm\) 0.88 degrees, 2.05 \(\pm\) 1.24 degrees, and 2.74 \(\pm\) 1.81 degrees, respectively. The results indicated that there were no significant differences among the three-dimensional positions at the implant platform, the three-dimensional positions at the implant apex, and the depth deviations between all three groups. However, it was found that there was a significant difference in the angular deviation of the implant position between the three groups (\(p\) = 0.02). The mean angular deviation of Program C was significantly greater than the Program A group (\(p\) = 0.001). In terms of the deviation directions of the implant platform and implant apex for the three groups, most of the deviations of a larger magnitude were toward the meso-buocal direction. No matter which program was used to plan the implant position, deviations between the placed implant position and the planned position still occurred. Therefore, when planning implant positions with any implant planning software program, one must take into account an implant position deviation.  
Keywords:dental implant; computer-aided implant surgery (CAS); guided surgery; accuracy; computer-aided design/computer-aided manufacturing (CAD/CAM); surgical guide; guided implant surgery +
Footnote †: journal: Applied Sciences
# A Framework for Synthetic Agetech Attack Data Generation
## 1 Introduction  
According to the World Health Organization (WHO), by 2050, the elderly population above 60 years is expected to double [1]. It is projected that, as years go by, the number of elderly people relative to the rest of the population will continually increase. This means that there will be more people who require elderly care. For such population, agetech, which encourages seniors to age in their homes with the support of smart devices, is a great option instead of going to care facilities that are expensive and disconnect the elderly from their family and community.  
The use of agetech comes with some challenges with regard to security and privacy of sensor data for the aged; hence, it is crucial to develop schemes to safeguard their data [2], such as intrusion detection systems (IDS). Smart device datasets can help bring out interesting behavioral patterns about the user, for instance, by building a profile of the user's daily activities from the records collected [3, 4]. In the area of agetech, data are very scarce and particularly attack data for sensor readings are lacking [5].  
In order to build an IDS using machine learning, there is a need for large volumes of data of both normal events and attack incidents [6]. As suggested by [PERSON] et al, when such data are lacking, an alternative is to generate synthetic attack data [7]. In this regard, we have developed a new approach for generating synthetic attack data for agetech. The contribution of this study is to provide methods and frameworks for reference in generating agetech synthetic sensor records attack data, which can be used in reinforcing security and privacy in agetech. Our approach involved exploring the changes in IoT device records when there are cyber attacks. We performed an in-depth data analysis to understand thechanges in data patterns when these attacks occur. For illustration, we focused our study on the temperature data in the TON_IoT weather dataset. We were able to generate synthetic attack data that replicate the attack patterns from this general IoT dataset and there is some great level of trust that the generated attack records reflect real attacks. The remaining sections are structured as follows. Section 2 discusses related work. Section 3 presents a threat model for agetech devices and discusses why the TON_IoT dataset is ideal for replicating attack data for agetech that can be used to automatically detect attacks. Section 4 presents the datasets involved in our study. Section 5 presents the proposed data generation methods and their validation by training and testing the different machine learning models using corresponding datasets. Section 6 presents the concluding remarks.
# A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization
## 3 Methodology
### Maximum Power Point Tracking
The electric qualities at MPP are named maximum power voltage (\(V_{mpp}\)), maximum power current (\(I_{mpp}\)), and maximum power (\(P_{mpp}\)). Output current, output power, and, therefore, MPP are proportional to the photocurrent \(I_{ph}\), which is heavily dependent on radiation and temperature, according to the following equation:  
\[I_{ph}=\frac{G}{G_{ST}}\big{[}I_{ph,ST}+\mu_{I_{sc}}(T-T_{ST})\big{]}, \tag{12}\]  
Figure 3: P–V curve. Figure 2: I–V curve. where \(I_{ph,ST}\) is the photocurrent under standard test conditions. The equations that combine the saturation current \(I_{s}\), series resistance \(R_{s}\), and the parallel resistance \(R_{p}\) with irradiance and temperature can be illustrated as follows [25]:  
\[R_{p}=R_{p}(G)=\frac{G_{STC}}{G}\cdot R_{p,STC}, \tag{13}\]  
\[R_{s}=R_{s}(G,T)=\frac{T}{T_{STC}}\bigg{(}1-0.217\cdot\ln\frac{G}{G_{STC}} \bigg{)}\cdot R_{s,STC}, \tag{14}\]  
\[I_{s}=I_{s}(G,T)=\frac{\bigg{(}1+\frac{R_{s}}{R_{p}}\bigg{)}\cdot\bigg{(}\frac {G_{ST}}{G}\bigg{)}\cdot I_{sc}-\frac{V_{sc}}{R_{p}}}{exp\bigg{(}\frac{V_{sc}} {nV_{t}}\bigg{)}-exp\bigg{(}\frac{\bigg{(}\frac{G_{ST}}{G}\cdot I_{sc}\bigg{)} /R_{s}}{nV_{t}}\bigg{)}}, \tag{15}\]  
where \(R_{s,STC}\) and \(R_{p,STC}\) are the series and parallel resistance at standard conditions, respectively, pointing out why the MPP varies in radiation and temperature. To ensure that the PV module always produces the practicable maximum power, a converter is connected to its output. This converter controls the output voltage of the PV module to maximize the output power, according to (4) and (7). The output current can be given by (4). If the converter is connected to the output of a combination of PV modules, then the voltage, current, and power of the PV modules are given by the following equations:  
\[I_{sc}=I_{sc}(G,T)=\frac{G}{G_{ST}}\big{[}I_{sc,ST}+\mu_{I_{sc}}(T-T_{ST})\big{]} \tag{9}\]0  
\[I_{sc}=I_{sc}(G,T)=\frac{G}{G_{ST}}\big{[}I_{sc,ST}+\mu_{I_{sc}}(T-T_{ST})\big{]} \tag{9}\]1  
\[I_{sc}=I_{sc}(G,T)=\frac{G}{G_{ST}}\big{[}I_{sc,ST}+\mu_{I_{sc}}(T-T_{ST})\big{]} \tag{9}\]2  
where \(V_{modmpp}\), \(I_{modmpp}\), and \(P_{modmpp}\) are the output voltage, current, and power of the PV module. \(V_{mpp}\), \(I_{mpp}\), and \(P_{mpp}\) are the voltage, current, and power of the combination of the PV module. \(n\) and \(m\) are the number of PV modules connected in series and parallel, respectively.
## 3 Findings
### Prognostics and Health Management (PHM) for Unit Systems
#### 3.1.5 Descriptive and Predictive Model Management  
The arrangement of the present smart manufacturing systems is subjected to the capacity for (a) sensibly modelling the production system, (b) predictable plant information, (c) solving issues proficiently with computational attempts, and (d) including feedback to raise the decision-making on top of time. Hence, enabling descriptive and predictive analytics for the estimation of manufacturing systems performance is a greater concern in the current information and digital age.
## 3 Results
### Field Demonstration in 2021 and 2022  
In 2021 and 2022, there were no significant effects of fertilizer type on any of the factors tested in the maize or grass field demonstration experiment: dry matter (DM) and nutrient uptake. Soil type did have a significant effect in most cases (Table A9). These results do not indicate a significant difference in yield or N uptake between CAN and AS treatments at  
Figure 3: NFRV of AS for two crops, two soils, and two dosages, using CAN as a reference. The \(p\) value was determined by a two-sided sign test. Data for the 50 and 75% treatments were combined and the tests were performed per crop/soil combination. A \(p\)-value < 0.05 is considered significant.  
a dosage of 100% in field conditions. It should be noted that when sufficient fertilizer is applied to provide 100% of the recommended available N, no differences in crop yield are expected; in other words, the yield increase as a function of fertilization becoming low [5]. In addition to yield and N uptake by crops, the amount of mineral nitrogen in the soil remaining after harvest also showed no difference between both treatments: AS and CAN (Table A7).  
However, the sulfur content in grass was affected by the high sulfur dosage using AS or CAN + S as the sole N fertilizers: 2.1 g of S kg\({}^{-1}\) DM average in the unfertilized blank and 3.6-5.2 g of S kg\({}^{-1}\) DM in the treatments (note: Table A9 gives the DM yield and S uptake). The soil was sampled for 0-30, 30-60 and 60-90 cm below the surface level for mineral nitrogen (Nmin) and plant available S [20]. The amount of extractable S in the sandy soil after the second grass cut was 290 to 524 kg of S ha\({}^{-1}\) in the soil (0-90 cm), while the unfertilized blank had 61 to 71 kg of S ha\({}^{-1}\) (Table A7), and at least a part of this S (97 to 167 kg of S ha\({}^{-1}\)) could be found in the 30-60 cm soil layer, demonstrating the potential loss of S by leaching into groundwater.  
The N\({}_{2}\)O emissions from the field demonstration were much higher than the greenhouse experiment, which may be due to the wet weather in early April (cumulative precipitation and temperature in the month of April were 61 mm and 9.3 \({}^{\circ}\)C, respectively) that occurred at the time of fertilization and measurement, as wet weather facilitates N\({}_{2}\)O emission [28]. In this trial, a linear model was used to calculated the emissions. The fertilizer was found to have a significant effect on the emissions, namely that AS had significantly lower N\({}_{2}\)O emissions than CAN + S in both soil types (Figure 4).  
Figure 4: Cumulative N\({}_{2}\)O emissions from grass field trial of 2022 at 17 days after fertilization. Emissions were measured from plots fertilized with calcium ammonium nitrate supplemented with sulfate (CAN + S) and AS (AS) at multiple intervals over 17 days after fertilization (linear interpolations of emissions between measured dates). LSD is 2.94 kg of N-N\({}_{2}\)O/ha. The letters indicate differences based on the least squares difference (LSD): two treatments with different letters are significantly different from each other.
# Fast Converging Gauss-Seidel Iterative Algorithm for Massive MIMO Systems
## 1 Introduction
The literature [16] indicates that combining QR with traditional techniques for detection provides excellent manifestations and effectively improves the plant throughput. In the literature [17], a hybrid iterative algorithm such as QR-MLD, has been suggested that further reduces the complexity of the ML detection algorithm by using a sphere decoding algorithm suitable for high signal-to-noise ratio and an easy-to-use DM algorithm. The literature [18] proposes JAGS detection methods, which is initialized via the Jacobi method and then estimated via GS, but the convergence speed is slow. The preprocessing Gauss-Seidel iterative algorithms [19; 20] use the preprocessing matrix to transform the original linear equation into a completely new linear equation, which results in a faster convergence rate in the new framework. However, complex preprocessing processes create additional computational difficulties.
## 4 Proposed System for Patient-Robot Co-Navigation  
This section presents the proposed system for the patient-robot co-navigation and its architecture. The main task of the system is to help ambulate the patient in a crowded environment, e.g., a hospital. The proposed system is considered an RWA as it provides assistance during the patient's walking; the system supports the patient in avoiding static and dynamic obstacles.  
The hardware setup of the RWA, shown in Figure 1, consists of an omnidirectional mobile base (Clearpath Ridgeback) and a 7-DOF robotic arm (Franka Emika Panda). The mobile base is equipped with two LIDARs, i.e., one on the front and one on the rear of the mobile base. An RGB-D camera (Intel Realsense D435i) is mounted on the mobile base and robotic arm can be substituted with any other robot of choice equipped with similar sensors.  
An overview of the proposed architecture for the RWA system is shown in Figure 2. First, the RWA system detects the patient's legs and estimates their position. It is assumed that the patient walks at the rear of the robot. The robotic arm is configured and locked in a specific manner, as depicted in Figure 1, to facilitate patient support. This configuration is designed to ensure that the robot remains stable and secure while providing the necessary assistance to the patient. If the patient is close to the rear of the robot and starts walking, the RWA detects and estimates the leg's position and then estimates the gait velocity. Subsequently, the RWA starts moving with the same velocity as the patient's gait velocity.  
At the same time, the data from the RGB-D camera and front LIDAR are used to perceive the environment by recognizing static (e.g., chairs, beds, tables, walls) and dynamic (e.g., humans, moving objects) obstacles. The data from environmental perception, the patient's gait parameters, and the robot's parameters (current robot pose and robot's goal pose) are the inputs to a reinforcement learning (RL) module. The RL module outputs the robot's velocity, factoring in the shared control properties between the robot and the patient. It is assumed that the final goal of the patient is known to the robot. For example, the patient may need to perform the 3 m and 6 m walk tasks defined by a nurse or a therapist. Since the task is known to the robot and the patient, both will require to work together to reach the goal. In the consequent subsections, the patient's leg detection, gait velocity estimation, and shared control in crowd environments using RL are presented in detail.