esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
It is thus crucial for any automated algorithm to find a threshold that is adjusted to the local conditions, i.e., for each glacier individually, and as a consequence, for smaller glacier samples, snow lines have often been digitized manually (e.g., [13; 31; 32; 33]). To map SC on comparably small glaciers such as in the Alps and for analysis over long time periods, the 30 m resolution Landsat data are the best choice as they span 1984 up to the present and have higher spatial resolution than MODIS data (250 m). On the other hand, Landsat data have the disadvantage of a much coarser temporal coverage (every 16 days), so autumn snowfall and cloud cover can make it difficult to obtain useable images from the end of the ablation period in certain years. As surface elevation of glaciers changes over time, there is a need for multi-temporal DEMs to correctly determine SLA values for long time series. In this study, we present the fully automated snow mapping on glaciers (ASMAG) tool to (a) map SC on glaciers, and (b) derive the SLA from the 30 year time series of multi-temporal Landsat data and a DEM. The method is applied to the glaciers in the southern Otztal Alps as three glaciers with long-termmass balance measurements are located there (Hintereisferner, Kesselwandferner, and Vernagfterner) providing data for validation of the tool. As explained above, the specific challenges for an automated approach are the discrimination of snow from glacier ice, consideration of clouds (or their shadows) affecting parts of a glacier, terrain shadow, the often patchy nature of snow cover, glacier surface debris cover, and methodological constrains such as the impact of a time invariant DEM. Our method has been designed to handle these and automatically finds a glacier-specific reflectance threshold to separate snow from ice. As an introduction to our methodological approach, we present the workflow, discuss the main challenges, and offer a comprehensive assessment of the accuracy of the approach using three independent methods of validation. We also present the results for the study region (SC ratio and SLA time series) in comparison to field data and investigate potential reasons for deviations.
## 4 Application Examples  
Using the landslide simulation experimental device which has been introduced in Section 2, creates a landslide artificially. In this experiment, the new deep displacement monitor sensor collected 1035 sets of displacement data and angular data for each unit.  
Figure 10 is the initial state of the entire deep displacement monitor in the slope. First, we dig a hole in the entire slope and bury the entire sensor, as shown in Figure 10a. Then the original soil layer is covered and compacted, as shown in 10b. The initial state of the whole sensor array in the slope is shown in Figure 10c.  
Figure 9: Sketch of the angle between the horizontal cumulative displacement and the vertical cumulative displacement.  
When the experiment begins, the soil loading box will be lifted so as to keep the whole sensing system erect. Figure 11 shows the curves of deep displacement measured by the depth displacement sensor. Each point in the figure represents a sensing unit, the depth and horizontal displacement of each sensing unit will change with time throughout the experiment. Figure 12 shows the variation of accumulative displacement with time, respectively. It can be seen that at the beginning, the cumulative displacement of each unit is small and increases in an approximately exponential manner at a later stage. Both the accumulative horizontal displacement and the accumulative vertical displacement conform to the three-stage change theory, and the difference between them is the change rate in the accelerating deformation stage.  
Figure 11: Curve of actual depth deformation.  
Figure 10: Initial state diagram of experiment. (**a**) Description of the initial state of the entire deep displacement monitor sensor on the slope. (**b**) Show a 1 m high slope with a 20 cm high plane to simulate the toe of the slope (**c**) A sectional view of the distribution of the integrated deep displacement three-dimensional measuring sensor buried in the slope.
## 1 Introduction
In this article, we implement the incremental transition of a legacy network into a SoDIP6 network by performing experimental tests and simulations to measure the viability of the real production of an SDN-enabled IPv6 network in service provider networks by implementing Open Network Operating System (ONOS) and SDN-IP, the recently emerging robust networking platform for SDN migration. SDN-IP is an application developed and implemented over ONOS to integrate SDN and legacy networks in terms of routing and interoperability so that service providers can have a gradual migration by smoothly integrating SDN with the legacy networks. SDN-IP implements border gateway protocol (BGP) for communications among multiple autonomous systems (ASes), as with BGP implemented in legacy routing. BGP speakers help to establish the peering with external gateway routers by obtaining route information from the ONOS SDN controller. We propose a heuristic for the best ONOS controller location and routers migration in real time considering optimal path routing and optimal control path latency. Following are the major contributions of this work. * A study on the viable implementation of multi-domain SoDIP6 networks is carried out. * The incremental deployment of an SDN-enabled IPv6 network using ONOS/SDN-IP is implemented. * A solution for controller placement based on control path latency is presented using a breadth-first router replacement (BFR) approach for router migration planning during the migration of carrier-grade service provider networks. The rest of this paper is organized as follows. Section 2 presents the related work in hybrid-SDN migration methods and practices. Section 3 briefly presents the concepts of ONOS and SDN-IP. Section 4 proposes methodology for multi-domain SoDIP6 network implementation. We present a network use-case for SoDIP6 network implementation, simulations and experimental tests/analysis, and evaluations in Section 5. Section 6 provides a summative discussion and future work, while the paper is concluded in Section 7.
\begin{table}
\begin{tabular}{c c|c c c c c} \hline \hline \multirow{2}{*}{**Criterion and Critical Value**} & \multicolumn{2}{c}{**0 \% Cement**} & \multicolumn{2}{c}{**6\% Cement**} & \multicolumn{2}{c}{**9\% Cement**} \\ \cline{2-7}  & **703** & **433** & **703** & **433** & **703** & **433** \\ \hline \multirow{2}{*}{\begin{tabular}{c} Resistance to \\ water erosion \\ \end{tabular} } & Linear shrinkage \textless{} 0.5\% & 0.46\% & 0.42\% & 0.36\% & 0.34\% & 0.33\% & 0.24\% \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} West to \\ water erosion \\ \end{tabular} } & Ratio of susceptibility to water erosion \(\leq\) 2 & 3 & 2 & 1 & 1 & 1 & 1 \\  & Surface condition after exposure to water & Deep losses on the surface of & & & No damage \\ \cline{3-6}  & & the whole sample & & & & \\ \hline \multicolumn{7}{c}{Wet to dry compressive strength ratio \textgreater{} 0.33} & Not tested & 0.33 & 0.44 & 0.42 & 0.43 \\ \hline \multicolumn{7}{c}{
\begin{tabular}{c} Frost resistance \\ after 25 cycles \\ \end{tabular} } & Compressive strength decrease \textless{} 20\% & Not tested & 100\% & 100\% & 5.8\% & 6.3\% \\ \cline{3-6}  & Mass loss \textless{} 5\% & Not tested & 7.24\% & 7.24\% & 6.36\% & 2.51\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: CSRE Durability test results. Green color is used to indicate mixtures that meet the criteria for a given durability characteristic. The three-digit numbers 703 and 433 indicate the proportions of fractions: sand, gravel, and silt + clay fractions. Values in the green cells of the table indicate compliance with the requirements, while values in the red cells indicate non-compliance with the standards [4]. this maturation period and subjected to compressive strength tests. Next, an analysis of variance (ANOVA) was conducted on the obtained results to determine if a conversion factor could be established between the laboratory results on cubic samples and those cored from the CSRE structure.
## 8 NS2 Simulation Study
### Comparative Analysis of the Simulation Results
#### 8.2.1 Comparative Analysis of the Network Throughput  
The network throughput can be calculated as \"the number of bits transmitted per unit time, and it can be mathematically expressed as \((\
u_{r}\times|\rho|)/T_{d}\), where \(T_{d}\) is the total time (in seconds), \(|\rho|\) the size of a packet, and \(\
u_{r}\) the total number of received packets\". The simulation time was 1800 s, which was the actual total considered time. In Figure 5a, the throughput values for the schemes of [PERSON] et al. [3], [PERSON] et al. [21], [PERSON] et al. [18], [PERSON] and [PERSON] [39], [PERSON] et al. [41], and the proposed LDAKM-EIoT are 173.49 bps, 164.34 bps, 160.41 bps, 169.85 bps, 217.16 bps, and 148.95 bps, respectively. It was observed that the throughput of LDAKM-EIoT was less than the other schemes [3; 18; 21; 39; 41]. This was primarily due to the reason that LDAKM-EIoT applied the small sized messages, which caused less communication overhead for the authentication procedure as compared to the other schemes (see Table 6).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Exchanged Messages** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **LDAKM-EIoT** \\
**between Network Entities** & **et al. [3]** & **et al. [21]** & **et al. [18]** & **and [PERSON] [39]** & **et al. [41]** & **LDAKM-EIoT** \\ \hline \(U_{i}/D_{i}\to GW/EN\) & 992 bits & 512 bits & 672 bits & 672 bits & 800 bits & 512 bits \\ \(GW/EN\to SD_{i}/S_{j}/CS_{j}\) & 1024 bits & 1024 bits & 1024 bits & 1024 bits & 1600 bits & 512 bits \\ \(SD_{j}/S_{j}/CS_{j}\to GW/EN\) & \(-\) & 672 bits & 576 bits & 672 bits & 960 bits & 512 bits \\ \(GW/EN\to U_{i}/D_{i}\) & \(-\) & 544 bits & 448 bits & 544 bits & 480 bits & 864 bits \\ \(SD_{j}/S_{j}/CS_{j}\to U_{i}/D_{i}\) & 512 bits & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Different messages exchanged among entities used in the simulation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline Platform & Ubuntu 14.04 LTS \\ Tool used & NS2 2.35 \\ Number of gateway nodes/edge nodes & 1 \\ (whenever applicable) & 9 \\ Number of sensors or cloud server & 10 \\ (whenever applicable) & 10 \\ Simulation time & 1800 s \\ Communication range of sensors/IoT devices & 100 m \\ Routing protocol & AODV [58] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Different parameters used in the simulation. AODV, ad hoc on-demand distance vector.
# Textile-Based Flexible Coils for Wireless Inductive Power Transmission
## 8 Safety Considerations  
A WPT system is by nature going to be radiating electromagnetic energy and while the primary intention is that as much of this energy as possible is absorbed by the receive coil, it is inevitable that some may be absorbed by other materials. In the case of an on-body system, it is likely that some of the radiated energy will be absorbed by the wearer's body. The International Commission on Non-Ionizing Radiation Protection (ICNIRP) publish guidelines for acceptable field strengths at different frequencies [28]. These guidelines provide limits for electric field strength, magnetic field strength and magnetic flux density for both occupational (repeated) and general public (occasional) situations at a selected operating frequency. The field strengths and flux densities can be simulated using models of the human body as done by [PERSON] et al. [29], this approach uses ANSYS HFSS simulation software to calculate the magnetic field distribution within the body resulting from an external radiator. A theoretical calculation and experimental verification method has been presented by [PERSON] et al. [30] to calculate the maximum permissible drive power for a WPT system operating at 100 kHz so as to comply with the ICNIRP guidelines. This approach is continued by [PERSON] et al. [31] to define a design space for the design of WPT devices to be used in on-body applications considering safety, power transfer and user comfort.  
For the purpose of this work on-body testing was not performed since the intention is to concentrate upon the underlying fabrication techniques and the verification of these for use in a WPT system. For compliance with the ICNIRP guidelines, the maximum output of the transmitter drive circuitry should be set so that the measured field strengths and flux densities at the closest possible separation distance are within the limits defined for the frequency of operation.  
As a further exposure limiting action, commercial examples such as the Qi standard used as the power driver and receiver in this work, operate a polling-type approach whereby the output  
Figure 13: DC to DC efficiency of the WPT system employing a deformed receiver coil under different curvature against the separation distance from the flat transmitter coil.  
is energized for a short while and the presence of an appropriate receiver system is queried using communication back from the energy sink. This communication can be achieved by pulsed disconnection of the load, causing the output of the transmitter to be modulated, which can in turn be detected by the driver system. In the case where a suitable energy sink is located power transmission continues, with periodic checks that the energy sink is still present. When an energy sink is no longer present the transmitter output is shut down and the transmitter waits a pre-determined time before repeating the polling process.
## 4 Applications of Fe\({}_{3}\)O\({}_{4}\) Nanoparticles
### Biomedical Applications (Therapeutic and Diagnostic Technologies)
#### 4.1.2 Magnetic Hyperthermia
synthesized monodisperse core-shell nanoparticles Fe\({}_{3}\)O\({}_{4}\)@Co\({}_{x}\)Zn\({}_{1-x}\)Fe\({}_{2}\)O\({}_{4}\), and by controlling the thickness and composition of the shell, the water colloid of these NPs exhibited a large heating power up to 2400 W/g under an 80 mT magnetic field with a frequency of 309 kHz [121]. [PERSON] and coworkers studied the phase transformation of 23 nm FeO@Fe\({}_{3}\)O\({}_{4}\) core-shell nanocubes and their magnetic heating performance. This study identified the roles of subdomains of FeO, Fe\({}^{2+}\) deficiencies, and structural defects in the up to 10-fold increase in the magnetic losses of the nanocubes, resulting in excellent heating efficiency compared to pure magnetite phase nanocubes [122]. Therefore, studies have shown that the sizes, geometries, and nanostructures of Fe\({}_{3}\)O\({}_{4}\) NPs are three main factors that determine their magnetic heating capability.
# Frequency-Separated Attention Network for Image Super-Resolution
## 4 Experiments
### Comparison with State-of-the-Art Methods  
In this section, we show the visualization of PSNR and SSIM results and the analysis results of model comparisons.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & \begin{tabular}{c} **LF Path** \\ **PSNR/SSIM** \\ \end{tabular} & \
\begin{tabular}{c} **HF Path** \\ **PSNR/SSIM** \\ \end{tabular} &
\begin{tabular}{c} **LF Path and HF Path** \\ **PSNR/SSIM** \\ \end{tabular} \\ \hline Set5 & 38.09/0.9608 & 38.15/0.9609 & **38.20**/**0.9612** \\ Set14 & 33.78/0.9192 & 33.73/0.9186 & **33.88**/**0.9200** \\ BSD100 & 32.24/0.9004 & 32.26/0.9005 & **32.33**/**0.9016** \\ Urban100 & 32.46/0.9315 & 32.57/0.9324 & **32.83**/**0.9351** \\ Manga109 & 38.91/0.9776 & 38.98/0.9777 & **39.11**/**0.9781** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The test results of three experiments on the standard dataset.  
Figure 10: Visualizations of feature maps for the LF path and the HF path are presented. (**a**) displays the feature map without attention in the LF path, while (**b**) exhibits the feature map with attention in the LF path. (**c**) showcases the feature map without attention in the HF path, and (**d**) illustrates the feature map with attention in the HF path.
## 3 Cooperative Schemes
### Cooperative Schemes for VLC
#### 3.2.3 Pre-Defined Jt-Comp
This work shows the robustness and reliability of pre-defined JT-CoMP schemes by reducing the outage probability and increasing average cell data rate considerably. It is worth recalling that all the techniques that were previously presented here need a backhaul approach that is often assumed to be ideal [123], i.e., lossless, noiseless and instantaneous, and sometimes they are supposed to involve unlimited capacity [124], which is an unrealistic scenario. That is, the backhauling should not be underestimated when referring to cooperative transmission techniques. Figure 15: Example of pre-defined JT-CoMP showing a resource allocation for a multi-cell VLC network with RGB LEDs (\(|\mathcal{C}|=3\)), three sectors per cell (\(|\mathcal{S}|=3\)), and three electrical frequency sub-bands per color (\(|\mathcal{F}|=3\)). Cell clusters and cooperative sets are shown. \(S_{\text{coop}}\) represents the number of sectors within a cooperation area and \(Q_{\text{coop}}\) is the number of cooperation areas where an AP contributes. (**a**) Resource allocation scheme suitable for single-cell transmission. Dashed black line represents cell clusters (\(Q=3\)); (**b**) Cooperation strategy with 3 cooperative APs per cooperating area (solid yellow lines); (**c**) Orthogonal resources.
## 3 The Present: 2010-2015
### Extensions of UPS Method
#### 3.2.1 Harmonic Ultrasonic Polar Scan  
Numerical simulations suggested that the global view of a UPS image is not only a function of material parameters, but also strongly depends on the (temporal) shape of the employed ultrasound wave [2]. This has led us to the experimental and numerical investigation of a harmonic version of the ultrasonic polar scan (H-UPS) [15]. In Figure 6, a comparison is shown between P-UPS (broadband wave with central frequency \(f_{\mathrm{c}}\) = 5 MHz) and H-UPS (harmonic wave with frequency \(f\) = 5 MHz) recordings of a [0\({}^{\circ}\)]\({}_{8}\) C/E laminate. Apart from the similar global view, imposed by the unidirectional nature of the scanned sample, one observes that the H-UPS image has a \"richer\" fingerprint.  
Contrary to the P-UPS image, the patterns in the H-UPS image depend on the exact ultrasonic frequency. This is easily understood considering that the H-UPS image puts on view the stimulation condition of dispersive, _i.e._, frequency dependent, Lamb waves, while the P-UPS image is mainly governed by bulk wave characteristics [2; 15; 23].  
For the case of harmonic signals, the UPS analysis was extended to the evaluation of both the amplitude and the phase signal of the transmitted wave. In this way, knowledge can be obtained about the complex transmission coefficient. Figure 7 displays the H-UPS images for a [0\({}^{\circ}\)]\({}_{8}\) C/E laminate,  
Figure 5: Schematic of inversion procedure to identify composite properties (**a**) and identified properties for [0\({}^{\circ}\)]\({}_{8}\) C/E laminate (**b**) [13; 19]. (**a**) reproduced from [19], Copyright Society for Experimental Mechanics, 2014.  
considering the analysis of both the amplitude and the phase of the transmission signal. Note that the sharp discontinuities in the phase map are a mere consequence of phase wrapping.
## 5 Discussion
Thus, with the reduced costs, the frequency with which forests are planned can be shortened, thereby greatly improving the robustness of the stand against natural disturbances, increasing the quality of the wood, and optimizing the revenue. As stated earlier, the proposed model is specifically trained to detect the need for selective crown thinnings in spruce-dominated forest stands in the study area. Consequently, further research could examine the feasibility of employing DCNNs for other thinning types, additional tree species, and other geographical areas. The training of a comparable model for deciduous forests might be challenging because of the greater diversity of tree species and the more difficult task of segmenting deciduous tree crowns. The performance of assigning the urgency of thinning was unsatisfying in this study. Hence, one could conduct further research on this objective by providing additional valuable data such as age or yield class. Another potential direction of research could be the prediction of the volume of harvested wood for sales planning. Applicable to both small forest owners with limited funds and large forest management companies as a quick means of providing help, this approach provides the ability to infer critical information about the forest promptly. **Final remarks:** Through the fusion of several types of remote sensing data and a suitable deepnet classifier, this paper presented a method to detect the necessity of thinning in spruce forests (c. 85% F1 score for identifying if forest stands need thinning). The approach was less accurate (c. 63% F1 score) when distinguishing between urgent and less urgent thinning needs. Although the resulting model needs further tuning for production use, this paper showed the potential of using remote sensing data to plan thinning cost-effectively. Conceptualization, P.S. ; methodology, P.S. and R.B.F. ; software, P.S. ; experiment and analysis design: P.S. and R.B.F. ; data preparation: P.S. ; experiments: P.S. ; initial draft: P.S. ; review and rewriting: P.S. and R.B.F. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Data Availability Statement:** Not applicable. **Acknowledgments:** The research reported here was undertaken by the author [PERSON] while as an employee of the Austrian Federal Forests Office (OBFAG). We are grateful for their support for the study and for providing the data. **Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Flux Probe Design
### Probe Unit  
The probe unit forms the core of the flux gate sensor. The sensing units for the \(X\), \(Y\), and \(Z\) axes are precisely arranged on the planes of the probe framework to achieve coaxial alignment and maintain a certain distance, minimizing mutual interference among the three axes. As shown in Figure 12, each single-axis sensing unit comprises a magnetic core, excitation coil, and induction coil, arranged sequentially from inside to outside. Each magnetic core is made from cobalt-based amorphous ribbons composed of CoFeMoSiB. This material has been widely used in magnetic recording and magnetic shielding owing to its high magnetic permeability, low coercivity, and good rectangular hysteresis loop characteristics [21; 22]. It is ideal for manufacturing ultra-thin laminated magnetic cores and magnetic switch components and has become a research hotspot in recent years [22; 23]. This material is easily magnetized and demagnetized quickly and features narrow hysteresis loops and extremely low hysteresis loss. Therefore, flux gate sensors designed with this magnetic core can realize high sensitivity and low noise. The excitation coils are divided into two groups, wound on two cobalt-based amorphous magnetic cores, and connected end-to-end. Under the resonance effect of the periodic current signal and parallel capacitors, the energy of the excitation signal is efficiently transferred to the magnetic cores. The resulting high-intensity alternating magnetic flux periodically drives the magnetic cores to saturation. The larger induction coils detect the secondary harmonic components generated by the excitation signal, thus obtaining information on the external magnetic field. These coils are placed at the center of the two groups of excitation coils. The placement maximizes  
Figure 11: A schematic diagram of the RF excitation circuit.  
the signal-to-noise ratio (SNR) of the probe and ensures that the induction coils are tightly attached to the magnetic cores.  
Based on this structure, the sensor design parameters are as shown in Table 1.