esa-sceva/satcom-chunk-collection huggingface dataset:
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 5 Conclusions  
In this paper we performed a survey around the smart grid projects in Europe, with emphasis on the role of smart metering LV applications. Special focus has been given on the technologies chosen for the realisation of the smart grid projects with the scope of providing with information about the practical solutions chosen. In addition, we referred to the technological solutions adopted by several European countries in terms of their national smart meter roll-out. For reasons of completeness, the main characteristics of the available telecommunication technologies for the LV smart grid are also presented. To sum up, the following conclusions can be deducted:  
* Smart meters are key components for energy management and saving. It is expected that around 200 million smart meters will be installed by the year 2020. There are more than 50 European projects that are directly or indirectly linked to smart metering applications.
* Two popular technologies for data transmission from the smart meter to the data concentrator are the ZigBee and NB-PLC technology. For data transmission to the control center from the concentrator, wireless cellular solutions are proved to be widely used.
* Other popular topics are the energy and grid management, the integration of RES, energy consumption reduction through smart homes/buildings. These topics have been mainly or partly the subject of research for the 40%, 38% and 30% respectively of the projects analysed.
## 2 Materials and Methods
### White-Sky Albedo  
This study calls for the use of the WSA 16-Day L3 Global 0.05\({}^{\circ}\) CMG MCD43C3 Modis product, which has the best quality assurance values. MCD43C3 Collection 6 datasets, which were produced daily with a 16-day accumulation window at 0.05\({}^{\circ}\) spatial resolution combinations, were used to extract MODIS shortwave white-sky albedo products (0.05\({}^{\circ}\) resolution is approximately 5 km \(\times\) 5 km on ground). Following that, MODIS Albedo data were obtained, processed, and analyzed to determine its link with black carbon.  
The United States Geological Survey (USGS) Earth Explorer website ([[https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov)]([https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov)), accessed on 8 July 2020) has been used to retrieve MODIS MCD43C3 albedo data. Data comprised of 365 days, starting from 1 June 2017, to 31 May 2018, in the form of hierarchical data format (HDF) files. Each HDF file is further comprised of 25 Scientific Datasets (SDS) layers among which 10 layers of BSA, 10 layers of WSA, and five layers of BRDF Albedo qualitative parameters. WSA data of only sampling dates have been processed to establish an association with BC concentrations at the sites. All the data was processed in ESRI ArcGIS software. Data processing mainly includes WSA data extraction from HDF files, clipping of data as per study area boundary, determination of WSA values from extracted data and tabular summarization along with some analysis using MS Excel.
## 2 Materials and Methods
### Sample Selection  
Fifty human permanent anterior teeth, which were extracted due to periodontal reasons, were selected. The teeth were inspected under a dental operation microscope (OMS2380, Zumax, Suzhou, China) to eliminate teeth with cracks, fractures, and caries. Initial bucco-lingual and meso-distal periapical radiographs were taken. The teeth with single root, single canal morphology were included. The teeth with decay, restoration, old root canal filling, any anatomical variations, resorption, calcification, or initial tooth discoloration were excluded.
## 4 Part II: The Impact of Head Tracking on the Localization Performance  
Although in the first part head tracking was included, in the second part we wanted to explicitly assess the impact of head tracking by comparing the localization performances of virtual sources in two listening tests, either with head tracking (referred to as **TestDynamic**) or without head tracking (referred to as **TestStatic**). The same VAHs as in Part I (see Figure 1) were used to synthesize individual BRIRs with spectral weights calculated with the parameters listed in Table 1. Since the BRIRs synthesized with V13 in Part I performed worse than the other VAH BRIRs with respect to azimuth accuracy and externalization rates, V13 was not considered in Part II.  
For both listening tests, BRIRs individually synthesized with V11, V21 and V23, each for 185 head orientations, and KEMAR BRIRs acquired for 37 horizontal head orientations (HTK), were considered. Measurements were performed in the same way as for TestVR described in Section 3.1.3, but for another set of 15 target source positions (see Figure 2c). The BRIRs were the same in TestDynamic and TestStatic; the difference between both tests concerned solely the presentation method, i.e., with or without head tracking. In order to distinguish between the BRIRs in both tests, for TestStatic, BRIRs were assigned with subscripts V11s, V21s, V23s and HTKs.
# Discrete Element Analysis of Grouting Reinforcement and Slurry Diffusion in Overburden Strata
## 1 Introduction
The study demonstrated that the discrete element method effectively captures the grouting process and its underlying mechanics. [PERSON] et al. [16] applied the DEM to investigate Bingham fluid diffusion in silty fine sand, identifying relationships among grouting pressure, diffusion radius, and initial splitting pressure. [PERSON] et al. [17], [PERSON] et al. [18], and [PERSON] et al. [19] utilized particle flow DEM software to elucidate the diffusion range and pressure distribution characteristics of cement slurry during grouting. Unlike continuum-based models, the DEM does not rely on complex constitutive equations, giving it distinct advantages in simulating the motion behavior of heterogeneous materials such as cement slurry and enabling mesoscopic analysis of its diffusion and mechanical properties. The model established using the discrete element method (DEM) can better capture the internal particle structure of rock and soil media. It allows for the simulation of particle flow behavior and enables the modeling of irregular contact and mutual interpenetration between soil particles, as well as between slurry and soil particles. This approach offers a new perspective for in-depth investigation of overburden grouting and facilitates a more accurate understanding and characterization of complex grouting phenomena. Currently, there are few theoretical analyses on the interaction mode between slurry-soil in the grouting engineering of cover layer by discrete element method (DEM). In this study, the fundamental diffusion pattern of cement slurry was validated using a two-dimensional discrete element method (DEM) numerical simulation, and the fundamental diffusion pattern of cement slurry is validated through numerical simulation. A dynamic DEM-based simulation of slurry diffusion in overburden strata is implemented. The diffusion mechanism of grouting slurry in overburden is analyzed, and a predictive formula relating slurry diffusion distance to grouting pressure is proposed. The findings provide a theoretical reference for optimizing grouting design in geotechnical engineering.
## 3 Statistical Methodology
### Error Metrics  
Several steps of the proposed strategy to develop multi-fidelity surrogates (see Section 2) require a quantitative assessment of the obtained models, to evaluate and compare theirperformance to select the best combination of metamodeled variables and surrogate types. Generally, these metrics check the error committed between the observed values and the predicted values, but the roles of the observed and predicted data at the corresponding stage will determine which is the error indicator best suited for the task.  
The first indicator employed in this work is the coefficient of determination \(R^{2}\). It quantifies the proportion of the total variability of the data that the model can explain. To do so, this error metric employs the co-variance of the predicted and observed cases as well as the variance of each case, as can be seen in Equation (7):  
\[R^{2}=\frac{\sigma_{z\epsilon}^{2}}{\sigma_{2}^{2}\sigma_{z}^{2}}=\frac{ \left(\frac{\sum_{i=1}^{n}(z_{i}-z)(z_{i}-\frac{z}{2})}{n}\right)^{2}}{\frac{ \sum_{i=1}^{n}(z_{i}-\frac{z}{2})^{2}}{n}} \tag{7}\]  
Notice that \(R^{2}\) in this work is only employed to assess the fitting capability of each model outcome (\(y_{i}\)) to the training data (\(x_{i}\)). Indeed, the coefficient is known to be inadequate when attempting to evaluate the predicting capability of the models in the verification stage [58]. For the latter purpose, the Mean Absolute Error (MAE) is preferred [58; 59]. MAE directly calculates the average magnitude of the error between the values predicted by a surrogate (\(y_{i}\)) and the values observed at the verification stage (\(y_{i}\)), as Equation (8) shows.  
\[MAE=\frac{\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert}{n} \tag{8}\]  
Following the work performed by [PERSON] et al. [60], MAE can be applied as a means of qualitative assessment to determine whether a surrogate model can be considered to provide a good prediction or not. To do so, the 5% of data with the highest residual values is first removed, and then MAE is normalized considering the training dataset range \(R_{tra}\) and the dispersion of the errors. This elimination of a small part (5%) of the values with the highest residuals in the test set is performed to attenuate the influence of possible outliers that increase prediction errors. This procedure aims to evaluate the quality of the prediction using 95% of the data present in the test set, thus increasing the robustness of the estimate. Equations (9) and (10) show the conditions required to grant a good evaluation to the surrogate. When the two conditions are fulfilled at the same time, a good agreement is obtained. If only one of these conditions is satisfied, a moderate agreement is set. If no condition is met, a bad agreement is therefore considered.  
\[\widetilde{MAE}=\frac{MAE(95\%)}{R_{tra}}\leq 0.1 \tag{9}\]  
\[\widetilde{MAE}_{\sigma}=\frac{MAE(95\%)+3\cdot\sigma}{R_{tra}}\leq 0.2 \tag{10}\]
## 3 Results
### Tiling Aggregation Experiment
performance to assess the model's robustness under reduced contextual information and to verify how aggregation improves final accuracy. Therefore, we first evaluate the model at the patch level, then compare it to parcel-level results after aggregation, as summarized in Table 5.  
Under the 2\(\times\) environment, each patch covers less contextual information compared to the 1\(\times\) environment, while the total number of overlapping patches per parcel increases. In the 1\(\times\) setting, parcel-level accuracy improved by an average of +0.0114 over patch-level accuracy (with a maximum of +0.0238), the macro F1 score rose by an average of +0.0197 (up to +0.0293), and the Kappa improved by an average of +0.0179 (up to +0.0296). In contrast, under the 2\(\times\) setting, parcel-level performance improved by an average of +0.0345 in accuracy (up to +0.0518), +0.0417 in macro F1 score (up to +0.0542) and +0.0458 in Kappa (up to +0.0669), highlighting how an increased number of patches per parcel can enhance aggregation effectiveness. Because each patch necessarily covers less context in the 2\(\times\) setting, every method exhibited lower patch-level performance compared to the 1\(\times\) setting. In particular, for the baseline approach, switching from 1\(\times\) to 2\(\times\) caused patch-level accuracy to drop by 0.2015, the macro F1 score to decrease by 0.1835, and the Kappa to fall by 0.2151, underscoring that the loss of contextual information makes the model's prediction task more challenging. A more detailed analysis of these results is provided in Section 4.1.2. Summarizing all experimental findings in the Results section, our combined approach (RPE + CBS) consistently surpassed both the baseline and single-method variants in in-domain experiments (same year/region) and demonstrated robust performance, even in out-of-domain experiments (different year/region). These findings underscore the method's effectiveness for farmland classification, especially for minority classes such as ginseng and tree nursery. In the following Discussion section, we address potential limitations and propose future directions.
# Diagnosis of the Permian Fengcheng Formation in the Mahu Sag, Junggar Basin, China
## 4 Results
### Petrological Characteristics
#### 4.1.2 Carbonate Rocks  
Carbonate rocks are mainly dolostone and argillaceous dolomite developed in the second member of the Fengcheng Formation, while only thin or small carbonate lenses are found in the Fengcheng 1 Member and the Fengcheng 3 Member (Figure 1b,c). The middle striation of dolomite is developed, and most is interbedded with argillaceous rocks. Dolomites are mostly fine-grained with microcrystalline clays, and the content of dolomite is 45-65 wt.% (atomic percent). A small amount of volcanic tuff, including volcanic eruptions and terrigenous, fine-grained tuff debris, is visible in some dolomites [39].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline  & **Na\({}_{2}\)O** & **TiO\({}_{2}\)** & **SO\({}_{3}\)** & **SiO\({}_{2}\)** & **FeO** & **P\({}_{2}\)O\({}_{5}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **MnO** & **K\({}_{2}\)O** & **MgO** & **BaO** & **CaO** & **Total** \\ \hline  & 0.07 & 0.22 & - & - & 9.39 & - & 0.01 & 0.46 & 0.01 & 15.49 & 0.09 & 27.91 & 53.65 \\  & - & - & - & 0.03 & 9.38 & 0.06 & 0.02 & 0.62 & 0.02 & 15.01 & - & 27.85 & 52.99 \\  & - & - & 0.06 & - & 8.01 & - & - & 0.11 & 0.01 & 17.58 & - & 30.84 & 56.61 \\  & - & - & - & - & 6.44 & 0.02 & 0.02 & 0.37 & - & 17.99 & 0.09 & 32.02 & 56.95 \\ Iron & - & 0.04 & 0.04 & 0.01 & 12.99 & - & 0.05 & 0.33 & - & 13.96 & - & 29.45 & 56.87 \\ dolomite & - & - & 0.04 & - & 10.98 & 0.04 & 0.01 & 0.54 & 0.01 & 13.74 & 0.07 & 28.03 & 53.46 \\  & - & - & - & 0.16 & 6.69 & - & 0.08 & 0.18 & 0.03 & 17.85 & 0.04 & 31.10 & 56.13 \\  & - & - & 0.04 & - & 8.41 & 0.01 & - & 1.21 & 0.04 & 18.25 & - & 32.05 & 60.01 \\  & - & 0.05 & - & - & 7.45 & 0.01 & - & 0.86 & - & 17.49 & 0.13 & 30.46 & 56.45 \\  & 0.07 & 0.08 & 0.25 & 8.54 & 5.43 & 0.08 & 1.86 & 0.16 & 0.54 & 15.58 & - & 23.58 & 56.17 \\ \hline Titanagite & 0.024 & 20.276 & 0.056 & 37.513 & 8.687 & 0.051 & 6.745 & 0.122 & 0.116 & 2.243 & 0.073 & 18.156 & 94.06 \\ \hline \end{tabular}
\end{table}
Table 1: EPMA data table of titanaugite and iron dolomite (wt.%).
## 2 Materials and Methods
### Procedures of tACS  
After the anesthetization and hair trimming, an anode and a cathode were installed on the head and back, respectively. The anode installed on the head was a 10 mm diameter, gold-coated, disk-shaped electrode (LXEL-SAF-DK-21, Lavatha, Daejeon, Korea), which was installed to the left of the bregma of the rat brain. To increase the conductivity of the disk-type electrodes and minimize the noise, sufficient conductive paste (Elefix; [PERSON], Tokyo, Japan) was applied to the heads of the rats. The cathode installed on the back of the rat was a circular electrode (Kendall Medi-Trace(r), Medtronic, Minneapolis, MN, USA) that could be attached to the skin. Two electrodes were connected to a programmable electrical stimulator (Cybermedic, Inc., Iksan, Korea), and the type of electrical stimulation, electrical currents, frequency, and treatment time were entered by a control program. tACS was delivered to the brain of rats under anesthesia. Alternating currents of 1.0, 1.5, and 2.0 mA (at 10 Hz) were transmitted to the rat's brain (2.0 mA: 2 rats, 1.5 mA: 2 rats, 1.0 mA, one rat, and control (no alternating current was applied): one rat). The radius of the electrode mounted on the head of rats was 5 mm, and thus, the current densities of each alternating current were 2.55 mA/cm\({}^{2}\) (2 mA), 1.91 mA/cm\({}^{2}\) (1.5 mA), and 1.27 mA/cm\({}^{2}\) (1.0 mA). The current density applied in this study is much higher than that of tDCS used in clinical treatments (0.05 mA/cm\({}^{2}\)) [27], and it is two to four times the current density for high-definition transcranial direct current stimulation [16]. A schematic of the electrode installation and the delivery of tACS is shown in Figure 1a.
## 3 Results
### Secondary Outcomes
#### 3.7.3 Quality of Life  
QoL was evaluated with different questionnaires, such as the KHQ [24; 25], ICIQ-SF [25; 29; 31], European quality of life (EuroQol) [31], incontinence impact questionnaire (IIQ-7) [28], incontinence quality of life questionnaire (I-Qol) [30] and the pelvic floor impact questionnaire--short form (PFIQ-7) [33]. In most studies, QoL improved significantly in both the BF and non-BF groups (\(p<0.05\)) without significant differences between them [24; 25; 26; 28; 30; 29], except for in [PERSON] et al.'s [33] study, where the BF group had significantly better results compared to the non-BF one.
# Automatic Point-Cloud Registration for Quality Control in Building Works
## 2 Materials and Methods
### Method
#### 2.2.1 Path Planning  
The UGV is equipped with an automatic path planner able to calculate the optimal route for the data acquisition. The workflow of the route planner [43] is shown in Figure 3.  
This planner takes the 2D CAD files of each floor extracted from the BIM model as input data. Constructive elements, such as walls, columns or floors are grouped in layers. Then, each layer is discretized into points with the same resolution (configurable parameter), and a binary occupancy map is performed. A classification of elements based on its non-navigable position (e.g., holes and constructive elements) and navigable position is performed. Next step estimates the candidate positions where the scanner device can make a scan by performing a grid distribution. The dimensions of the UGV are taken in consideration in this step leaving a security margin of 1 m near each building element.  
Figure 3: Path-planning workflow.  
Figure 2: Methodology proposed for automatic registration of point clouds.  
The output of this step is a map with candidate positions, construction element points and no candidate positions. Before designing the optimal route, a visibility analysis on each candidate position is performed in order to know the theoretical area of building elements that would be captured. The visibility analysis is made by a ray-tracing algorithm in combination with the maximum scanner range. The scanner range is a criterion to ensure good density and quality of the acquisition data and is used as the limit of each candidate position analysis. For the study case, the maximum scanner range was 10 m. Finally, an optimization of candidate positions is performed by using the back-tracking algorithm [48]. This algorithm minimizes the scan points ensuring the minimum coverage of the elements selected. For the study case, the minimum coverage was 90%. Once the positions have been optimized, the optimal route is created using the travelling salesman problem followed by the ant colony optimization algorithm [49]. The optimal route is the path with the minimum number of scans that can cover the whole scenario with the setup parameters defined.  
During the execution of the route, the path planner is constantly working for ensuring the good performance of the route. In fact, if any scan point is not reachable due to an obstacle (very common in building works) the path planner re-calculates an alternative scan point and thus the resulting route. The calculation of this alternative trajectory is carried out on the fly immediately when the impossibility of reaching a scan point is detected. As a result, the UGV can automatically acquire the data, guaranteeing the setup parameters conditions without user interaction.
## Receiver-Triggered Handshake Protocol for DTN in Disaster Area  
**[PERSON] and [PERSON] ***  
Graduate School of Engineering, Information Systems Science, Soka University,  
1-236 Tangi-cho, Hachioji-shi, Tokyo 192-8577, Japan; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [PERSON]EMAIL_ADDRESS]; Tel.: +81-426-91-4118; Fax: +81-426-91-9312.  
Academic Editor: [PERSON]  
_Received: 13 March 2015 / Accepted: 15 May 2015 / Published: 27 May 2015_  
**Abstract:** When a disaster hits a wide area, communication services for public use will be rendered unavailable. This will make it difficult to confirm the safety of people in the disaster area. A solution to this problem is to form delay/disruption tolerant networks (DTN) using mobile terminals of victims, those of rescuers, who serve as information carriers, and terminals (servers) in shelters. In this paper, we propose using a receiver-triggered handshake protocol for communication between these terminals. We have developed the bundle layer protocol for this handshake method. The proposed method has been implemented on a network simulator to build an evaluation environment. The disaster area has been modeled on an area around Shinjuku Station in Tokyo. Victims are randomly distributed in the area. We have compared the proposed method with _Epidemic Routing_ and _Spray and Wait_ in terms of the delivery rate at which messages reach their destinations, and the length of time taken for messages to reach their destinations. We have found that the delivery rate of the three methods are, more or less, the same, but that the proposed method is superior to the other two methods in terms of storage usage and battery consumption of terminals, and the number of bundles generated in the network.  
**Keywords:** delay/disruption tolerant networks; bundle layer protocol; disaster; safety information; information carrier