esa-sceva/satcom-chunk-collection huggingface dataset:
# A Single RF Emitter-Based Indoor Navigation Method for Autonomous Service Robots
###### Abstract  
Location-aware services are one of the key elements of modern intelligent applications. Numerous real-world applications such as factory automation, indoor delivery, and even search and rescue scenarios require autonomous robots to have the ability to navigate in an unknown environment and reach mobile targets with minimal or no prior infrastructure deployment. This research investigates and proposes a novel approach of dynamic target localisation using a single RF emitter, which will be used as the basis of allowing autonomous robots to navigate towards and reach a target. Through the use of multiple directional antennae, Received Signal Strength (RSS) is compared to determine the most probable direction of the targeted emitter, which is combined with the distance estimates to improve the localisation performance. The accuracy of the position estimate is further improved using a particle filter to mitigate the fluctuating nature of real-time RSS data. Based on the direction information, a motion control algorithm is proposed, using Simultaneous Localisation and Mapping (SLAM) and A* path planning to enable navigation through unknown complex environments. A number of navigation scenarios were developed in the context of factory automation applications to demonstrate and evaluate the functionality and performance of the proposed system.  
automous service robots; industrial automation; indoor localisation; robot localisation; automated mapping +
Footnote †: journal: Sensors  
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### Machine Learning Application in Reservoir Characterisation Related Works  
Implementing machine learning methods and Artificial Intelligence (AI) to solve complicated problems has gained momentum in many industries, including the petroleum industry [15]. Most of these sophisticated complications impeded critical decision-making and enhanced advancement in the industry. Hence, researchers progressively moved from using empirical correlations and linear regression models to applying AI techniques that have been welcomed due to their added value in the industry [34]. Researchers [14; 35; 36; 37] developed AI to predict subsurface geological and petrophysical properties, identify seismic patterns, analyse drill bit problems. The main aim of their research was to improve production, drive down cost and add value to the industry. However, applying a suitable AI modelling technique is also key in achieving the best results; hence many papers have utilised the different AI models suitable to the industry. As a result, AI application has achieved some excellent results compared to actual data [38; 39].  
Being able to quantify water saturation accurately forms the basis of wettability measurement. However, none of these methods are time-efficient, inexpensive, or gives failproof results due to the many limitations hence the advancement of machine learning models. Therefore, in determining the best algorithm for this research, the accuracy and range of acceptable parameters will be analysed systematically based on performance, complexity, accuracy, reliability, and sensitivity to establish a simplified relationship amongst core data, wireline logs, and NMR wettability measurements. This could invariably overcome complex relationships between surface wettability and other petrophysical data recorded by wireline logs and serves as an immense advantage to accurately predict surface wettability in the field.
# LoRa Communication Using TVWS Frequencies: Range and Data Rate
## 2 LoRa, TVWS, and Related Work
The analog-to-digital TV transition has freed up the TVWS for future 5G networks in Portugal [11]. Different software platforms and hardware operating over various frequency ranges for LPWAN is available. LoRa transceivers operating in the 137 MHz to 1020 MHz frequency band are also currently available. Semtech, a leading member of the LoRa Alliance, has the SX126* and SX127* series of LoRa modules that support different frequency bands [13]. Most of the research and applications are focused on using LoRa in the ISM band, hence this attempt to study the use of TVWS frequencies, primarily the licensed bands for LoRa, as shown in Figure 3.  
Figure 3: TVWS-based LoRa model. Figure 2: TV white spaces illustration.
# Wavelength-Dependent Optical Nonlinear Absorption of Au-Ag Nanoparticles
## 4 Conclusions  
In summary, the nonlinear optical absorption of Au-Ag NPs was studied using an OA Z-scan under 450 nm, 510 nm, 550 nm and 600 nm nanosecond pulsed laser bursts. The experimental results demonstrated that the nonlinear absorption properties of the Au-Ag NPs were not only intensity-dependent, but also wavelength-dependent. Under the excitation energy of 260 uJ, the Au-Ag NPs showed SA. Under the excitation energy of 760 uJ, the switch from SA to RSA occurred. Both the SA and RSA were strong near the resonance absorption peak (510 nm) of the Au-Ag NPs, but relatively weak far away from the absorption peak.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Wavelength (nm)** & **E (μ))** & \(\mathbf{I_{0}\) (W/m2)** & \(\mathbf{I_{S}\) (W/m2)}\) & \(\mathbf{\beta}\) (m/W) \\ \hline \multirow{2}{*}{450} & 260 & \(1.04\times 10^{13}\) & \(4.32\times 10^{11}\) & 0 \\  & 760 & \(3.03\times 10^{13}\) & \(2.16\times 10^{12}\) & \(0.79\times 10^{-11}\) \\ \hline \multirow{2}{*}{510} & 260 & \(1.04\times 10^{13}\) & \(4.15\times 10^{11}\) & 0 \\  & 760 & \(3.03\times 10^{13}\) & \(2.02\times 10^{12}\) & \(1.3\times 10^{-11}\) \\ \hline \multirow{2}{*}{550} & 260 & \(1.04\times 10^{13}\) & \(4.51\times 10^{11}\) & 0 \\  & 760 & \(3.03\times 10^{13}\) & \(2.33\times 10^{12}\) & \(1.24\times 10^{-11}\) \\ \hline \multirow{2}{*}{600} & 260 & \(1.04\times 10^{13}\) & \(5.46\times 10^{11}\) & 0 \\  & 760 & \(3.03\times 10^{13}\) & \(3.06\times 10^{12}\) & \(0.94\times 10^{-11}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nonlinear optical parameters of the Au-Ag NPs.  
Figure 4: Dependence of saturation intensity and nonlinear absorption coefficient on wavelength. (**a**) Relationship between wavelength and saturated absorption intensity under 260 μJ energy excitation. (**b**) Relationship between wavelength and saturated absorption intensity under 760 μJ energy excitation. (**c**) Relationship between wavelength and saturated nonlinear absorption coefficient \(\beta\).  
**Author Contributions:** Methodology and writing--original draft preparation, J.W.; investigation, C.C.; formal analysis, Y.S.; data curation, W.W. and D.K.; supervision, Y.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was supported by the natural science fund of Heilongjiang Province (F2018027, LH2020F041).  
**Institutional Review Board Statement:** Not applicable to studies not involving humans or animals.  
**Informed Consent Statement:** Not applicable to studies not involving humans.  
**Data Availability Statement:** Data supporting the results of this work can be obtained from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Discussion
### Socio-Economic Impacts
#### 4.2.2 Enhancing Trust in Global Innovation Ecosystems  
Trust is a critical factor in successful collaborations within innovation ecosystems. SPARK-IT enhances trust by integrating blockchain technology to ensure data transparency, security, and immutability. The use of smart contracts and decentralized storage safeguards intellectual property and ensures that all transactions and interactions are traceable and verifiable. These measures reduce hesitation among participants to share sensitive information, promoting a culture of openness and collaboration. Furthermore, the dual-token incentive system ensures accountability, encouraging meaningful participation and reinforcing trust among all stakeholders.
## 1 Introduction
The experiment results show that not only the amplitude but also the phase information of the object can be recovered.
## 6 Discussion and Implications
### Future Work  
We intend to expand this work through several key directions. First, we aim to conduct a quantitative analysis of questionnaire responses from current and future deployments to evaluate the tool's robustness, identify stakeholder-specific debt patterns, and explore interdependencies between debt types. Second, we plan to develop platform-specific adaptations of the framework, particularly for widely used environments such as Kaggle, RLGame, and GBG Framework. These adaptations will consider domain-specific characteristics, including competition duration, evaluation metrics, and reproducibility mechanisms.  
Third, we intend to extend the stakeholder scope by including additional roles such as reviewers, mentors, and platform maintainers, whose contributions and liabilities may affect platform sustainability. We also anticipate refining the existing typology, particularly in light of emerging AI trends such as generative models and reinforcement learning pipelines. Potential extensions may include Prompt Engineering Debt or Explainability Debt, which are increasingly relevant in complex, high-impact AI applications.  
Our findings offer a structured starting point for promoting stakeholder-aligned, measurable, and ethically grounded technical debt management in AI competition settings.
# Interconnecting Haptic Interfaces with High Update Rates through the Internet +
## 6 Complements, Differences and Relevancies Between Simulation and Real World Experiments  
In Section 3, our previous real world experiment is described, while in Sections 4 and 5 a simulation experiment has taken place. The simulation tests complement real word experiments.  
The real world scenario helped us understand the status of the Internet and define the values of the variables for the simulation experiment. The only variable that is difficult to define is the available bandwidth of the Internet for the whole path. It is a metric that is changing rapidly as it is based on the number of online users and the data that are exchanged.  
The real world scenario revealed that a real teleoperation task through the Internet is feasible, while the simulation experiment helped us conclude which protocols suits better for these teleoperation tasks under specific network conditions.  
In the real world experiment, it is understood that the experiment results are depending on the physical distance between the source and the destination and the ambiguous network conditions of the Internet. In the simulation tests network conditions are fully controlled, so more accurate results can be produced.  
The real world experiment helped the authors define the mean end to end delay, the standard delay deviation (jitter) the packet loss and the number of hops between source and destination. For this experiment the UDP transport protocol was chosen. It is the simplest transport protocol and it is being used for most cases of real-time multimedia applications. Two completely different pairs of source and destination were chosen, in order to examine the dependency between the results on the distance between source and destination.  
Figure 16: Packet Arrival deviation vs bandwidth.  
Despite the ambiguous network condition of the Internet the simulation results of the UDP protocol matches the results of the real world scenario for the connection between Korea and Japan, Table 5. At the specific simulation, the Internet end to end delay was set 32 ms. The Internet bandwidth was set to 20 Mbps. These settings depict a network with no congestion. Both simulation and experiment transport data over the UDP protocol. For the connection between Japan and Greece there were no corresponding simulation tests as the results from the real world experiment were outside the acceptable limits of Table 1.
# Grid Nodes Selection Strategies for Power Quality Monitoring
## 3 Discussion
### Criteria
This criterion excludes households and commercial customers but it is similar to previous one. One reason of this separation is that voltage sags and power interruptions affect industrial customers more seriously than households. * Remote monitoring possibility. The most modern criterion which arises along with the Smart Grid concept. At the moment, only [24] has executed a pilot project which aimed to test 4G LTE communication network connecting 40 PQ analyzers, thus the criterion is in its beginning state. \begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **Criteria** & **Type 1** & **T 2** & **P** & \(\Sigma\) & **Remarks** \\ \hline
1. & Random selection & A & & 2 & 2 \\
2. & Critical substation & A & & 1 & 1 \\
3. & Specific load investigation 3 & A & & 2 & 2 \\
4. & Customers’ complaints & E & & 3 & 3 \\
5. & All selected-type substations, busbars 4 & E & 3 & 2 & 5 \\
6. & PQ issues, short-circuits, residual voltage & E & 6 & 2 & 8 \\
7. & Faults frequency, power supply reliability & E & & 2 & 2 \\
8. & Renewable energy penetration, EV chargers & E & & 1 & 1 \\
9. & Power losses & E & 1 & 1 & 2 \\
10. & Customers’ density, quantity; line length & E & & 2 & 2 \\
11. & Industrial regions and loads & E & & 1 & 1 \\
12. & Remote monitoring possibility & E & & 1 & 1 \\ \hline \end{tabular}  
* Type of criterion: A—abstract, E—exact. 2 Type of research: T—theoretical, P—practical. 3 Includes abstract definitions such as “all sensitive equipment”, “all interference sources”. Additionally, it can include investigation about both emissions and immunity. 4 Includes the idea about maximal grid coverage, observability.  
\end{table} 
Table 3: Criteria selection frequency.
## 3 The Case Study
Once the case study had been modelled, preliminary dynamic energy simulations were carried out, and the building's natural gas consumption was estimated and compared with data from building bills. The collected bills refer to 2017, and the heating period (1 October-31 March) was considered for the comparisons. Thus, monthly simulations were performed, setting natural gas consumption (heating + cooking) as a key output, expressed in kWh/m\({}^{2}\). Following the recorded data, the consumption was determined for a two-month range, except for January and December. The analyses were simulated using measured weather data collected by the Climate Network, a private and professional network of urban meteorological stations that record hourly values for all climatic parameters [51]. Error bars, set at 5%, were inserted as inferior and superior limits. Figure 3 shows the general accordance between estimated and measured values. The model, in fact, can predict within an acceptable range the energy consumption for January and the February/March bimester. More significant differences were calculated for December. Such behaviour can be explained by considering that the available bill refers to December 2017-January 2018, and the expected consumption for a single month is calculated by dividing the total amount by two. For this reason, the December data were not strictly taken into account for this step. The same inaccuracy can also be seen for the October/November bimester, but, in this case, the principal reason is the limitation of predictions for transitory months because these are deeply influenced by the inhabitants' comfort condition and their use of occupied spaces. In fact, as underlined by various works previously carried on in energy simulations, the building requirement/consumption for mild periods could vary more than the rest of the year, according to human behaviour (inhabitants' age, habits, etc.). It is essential to bear in mind that, considering the cumulative value (bimonthly) of measured data, which are related to a specific apartment located inside the building under analysis, a global comparison, rather than a monthly calibration, was performed. Nevertheless, by applying the ASHRAE guideline 14 criteria only to the accurate data, the Normalised Mean Bias Error (NMBE) and the Coefficient of Variation of the Root Mean Square Error (CVRMSE) were determined, respectively, to be 1% and 0% for January and \(-\)3% and 3% for the February/March bimester, which are both inside the acceptable range established by the standard. Based on these comparisons, the model can be considered able to describe the real energy behaviour of the case study. Figure 3: Comparisons among estimated and measured data. Figure 2: Northwest (**left**) and southeast (**right**) views of the energy model generated in DesignBuilder.
# Analysis of the Influencing Factors of Grinding Uniformity under Three-Body Coupling Grinding Mode
## 5 Effects of Mechanism Error on the Rotating Angular Velocity of the Ball
### Effects of Run-Out of Lower Grinding Disc
\begin{array}{l}\tan\theta=\frac{1+\sin\alpha}{\cos\alpha}\cdot\frac {({R_{A}}+\frac{1}{2}\Delta l_{j}+r_{b}\cos\alpha)\omega_{B}-({R_{A}}+\frac{1 }{2}\Delta l_{j}-r_{b}\cos\alpha)\omega_{C}}{({R_{A}}+\frac{1}{2}\Delta l_{j} +r_{b}\cos\alpha)\omega_{B}+({R_{A}}+\frac{1}{2}\Delta l_{j}-r_{b}\cos\alpha) \omega_{C}}\\ \omega=\frac{({R_{A}}+\frac{1}{2}\Delta l_{j}+r_{b}\cos\alpha)\omega_{B}+({R_{ A}}+\frac{1}{2}\Delta l_{j}-r_{b}\cos\alpha)\omega_{C}}{2({R_{A}}+\frac{1}{2}\Delta l_{j})(1+\sin \alpha)}\\ \omega_{b}=\frac{({R_{A}}+\frac{1}{2}\Delta l_{j}+r_{b}\cos\alpha)\omega_{B}+( {R_{A}}+\frac{1}{2}\Delta l_{j}-r_{b}\cos\alpha)\omega_{C}}{2r_{b}(1+\sin \alpha)\cos\theta}\end{array} \right. \tag{21}\]  
As shown in Equation (21), when the lower grinding disc has face run-out, the geometric kinematic equation of the ball is the same as the original one. To study the influences of run-out on the grinding of the ball, MATLAB was applied for simulation. Figure 13: Influences of face run-out on the contact state of the ball–billet: (**a**) ideal conditions; (**b**) axial run-out of the inner disc; (**c**) radial run-out of the inner disc. Table 4 shows the setting conditions of simulation, \(\Delta h_{z}\) and \(\Delta h_{j}\) are distributions of grinding tracks on the spherical surface at \(\pm 2\), \(\pm 1\), and \(\pm 0.1\) mm. Specifically, the radial unidirection was removed because the disc was symmetric. Figure 14 shows the distribution of spherical grinding trajectory under different end-hop conditions. The distribution of grinding tracks on the spherical surface when \(\Delta h_{z}=0\) and \(\Delta h_{j}=0\) is the distribution of grinding tracks under ideal conditions (no face run-out in the grinding disc). \begin{table}
\begin{tabular}{c c} \hline \hline  & **Simulation Parameters** \\ \hline Ball diameter \(r_{b}\) (mm) & 15 \\ \hline Radius of grinding disc \(R_{A}\) (mm) & 300 \\ \hline V-shaped groove angles \(\alpha\), \(\beta\) (rad) & \(\pi/4\) \\ \hline Rotating speeds of Grinding disc & \(\Omega_{C}\) (rpm) & 20 \\ \hline \(\Omega_{B}\) (rpm) & Variation (Reference to Figures 2–4) \\ \hline Axial run-out distance \(\Delta h_{z}\) & \(\pm 0.1\) mm, \(\pm 1\) mm, \(\pm 2\) mm \\ \hline Radial run-out distance \(\Delta h_{j}\) & 0.1 mm, 1 mm, 2 mm \\ \hline Simulation time (s) & 600 \\ \hline Time interval \(\Delta t\) (s) & 0.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Settings of the run-out of the inner disc. Effects of face run-out of inner disc. Table 5 shows that when the lower grinding disc has axial and radial run-outs, STD changed slightly compared with that under ideal conditions. The spherical surface is covered by grinding the tracks uniformly.
# ChatGPT as a Virtual Dietitian: Exploring Its Potential as a Tool for Improving Nutrition Knowledge
## 3 ChatGPT as a Nutrition Tool
### What Are the Features of Nutrition Applications That ChatGPT Can or Cannot Emulate?
#### 3.1.1 Verified Nutrition Database  
Nutrition applications heavily rely on comprehensive databases that contain accurate information regarding the nutritional composition of different foods and ingredients. One illustrative example is the _'Plan-Cook-Ent'_ application that utilized a combination of the Philippine Food Composition Table, My Food Data, FooDb, and USDA Food Composition Databases [47]. These nutrition databases serve as a crucial resource for analyzing dietary intake, providing personalized recommendations, and facilitating nutritional tracking. Consequently, users can search for specific foods and learn about their macro- and micronutrient compositions (e.g., _What is the nutritional value of 100 g of broccoli_?). Unfortunately, it is plausible that ChatGPT lacks a dedicated and verified nutrition database of its own and instead relies on various publicly available databases for nutritional information retrieval. This reliance on external sources may introduce limitations in terms of ensuring the accuracy and reliability of the nutritional data provided by the system.
# Towards a Digital Twin Prototype of Alpine Glaciers: Proposal for a Possible Theoretical Framework
## 3 DT Theoretical Framework
### DT at the Single Glacier Level
#### 3.1.2 Glacier Inventories
GLIMS has indeed implemented a comprehensive database of glacier outlines and related metadata, providing information about glacier behaviour, including changes in surface area, thickness and volume that can be proficiently used to read the effects of climate change on these important natural resources. GLIMS is a valuable tool that can be used in combination with other data layers (e.g., WGI and the Randolph Glacier Inventory, RGI), to provide a more comprehensive view of the state of the glaciers around the world. RGI is a database of glacier outlines that was developed using a combination of satellite data and ground-based measurements. By combining data from all these sources, snapshots of world glaciers at different times can be possible. Trying to summarize the mutual role of local and global inventories, one can say that: (i) local glacier inventories are important for providing detailed information about individual glaciers within a specific region. They typically rely on a combination of ground observations and measurements as well as satellite imagery and aerial photographs. Unfortunately, they are limited in scope and can only cover small geographic areas. This means that they may not provide a comprehensive view of glacier changes across a larger region or over a large time period; and (ii) on the other hand, worldwide glacier inventories can provide information about glaciers with less detailed information, but for a broader coverage of glaciers and more frequent updates. WGI, GLIMS, RGI inventories are filled in by scientists from different countries, thus providing data and information for glaciers all over the world. Consequently, also in applications at regional scale, such as the one proposed in this work, i.e., the Italian Alps, these inventories can provide useful data and become, consequently, fundamental data sources to feed the DT. Finally, we can say that a DT for Italian Alpine glacier monitoring needs to combine data from multiple glacier inventories in order to upscale information about individual glaciers to the regional or even global scale. The combination of data from multiple inventories can, in fact, help to identify discrepancies and inconsistencies in individual datasets, improving completeness and reliability of deductions about glacier dynamics and their response to climate change.