esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a GPS Forest Signal Absorption Coefficient Index
## 4 Discussion
In a triple layer dense canopy, there will be higher CC values compared to a single layer forest canopy. The results shown in Table 2 suggest that for each individual forest, the CCPM performs well. However, when applying all the observations from each forest as a whole, the Beer's Law component was not found to be statistically significant at 90% confidence. This goes against our initial hypothesis. However, based on the results for dummy variable modeling for seasons at West Point, it is not surprising. The West Point seasonal study found that applying dummy variables for the seasons helped adjust the overall model [21]. This adjustment likely had to do with the health of the canopy. For example, during the fall season the CC values derived from HSOPs included foliage that was dry. However, these leaves were counted the same as leaves during the spring or summer that were healthy. Applying the same logic for different forests, each study site has different conditions. Some sites had received recent record rainfall while other sites were in drought-like conditions. Additionally, when comparing many different species of forests there are numerous factors that could influence the absorption component associated with Beer's Law. Most importantly, Beer's Law has both a concentration and an absorption component and the CCPM attempts to capture both using just CC. Therefore, it is justifiable that individual forests, sharing many of the same attributes, are successfully modeled individually using the CCPM, but as a conglomerate, the model does not work as well. As a result, the use of the CCPM is effective but would require a calibration prior to implementing for a specific forest, meaning that when photos are taken in a forest to get the CC values, GPS SNR observations should also be collected. If a GPS calibration is not feasible, a user of this work may benefit from a different approach, such as the SSFIM. When comparing the results of the SFIM to the SSFIM, we identified that the SSFIM provides an equation that removes the need for photography. With both models having the same RMSE and adjusted \(R^{2}\), ultimately, there is no need to go through the added complexity of taking photos. Rather, a user can reference Tables 1 and 3 to identify a forest with similar attributes and gain an insight into how signals may be attenuated in a particular forest site. The challenge with this concept is identifying what the key similarities are between forests. Would species play the largest role, or would tree height and DBH have a larger influence? In a similar vein, there is a seasonal influence on attenuation, as shown in Table 3 at the West Point study site. As such, a larger index is needed with more forest types. Further research is also needed to determine the variability of absorption within a single forest based on rainfall, foliage conditions, and other factors to ensure a good prediction of signal attenuation. All of these are just some of the questions that could be addressed in future work.
## 3 Results
### Influence of Geometric Parameters  
#### 3.3.1 Influence of the Cavern Depth (Parameter H/b)  
The cavern depth with respect to the ground surface has been considered as a determining parameter, with null eccentricity. In the calculations, the depth H has been varied from a minimum value until its influence is negligible.  
The following model parameters are constant in all the cases analysed:  
UCS = 50 MPa, GSI = 50, m\({}_{\text{i}}\) = 17.  
B = 4 m  
E/a = 0 (null eccentricity)  
The following cases are studied:  
IH-0: 2a = 2 m 2b = 1 m b/a = 0.50  
IH-1: 2a = 4 m 2b = 2 m b/a = 0.50  
IH-2: 2a = 6 m 2b = 3 m b/a = 0.50  
Figure 12: Influence of the geomechanical parameters of the rock, UCS and GSI, on the ultimate bearing capacity value when varying the load eccentricity. Detail between E/a = 0 and E/a = 2.  
The influence of the cover on the variation in the ultimate bearing capacity (q\({}_{\rm u}\)/q\({}_{\rm uH\&I}\)) is linear if the width of cavern and footing are equal (case IH-1, 2a = 4 m), and substantially linear when the footing width is greater than the cavern width (case IH-0, 2a = 2 m), although its slope varies.  
When the cavern width is higher than the footing, the variation is also substantially linear but only with covers ranging from H/b = 4.50 to H/b = 7.50, the slope changes abruptly at this point, up to H/b = 9.00, at which point it almost reaches the ultimate bearing capacity value without the cavern.
# Aerial Tele-Manipulation with Passive Tool via Parallel Position/Force Control +
###### Abstract
A human operator using a haptic device (with a limited workspace) commands the aerial manipulator pose (with virtually unlimited workspace) in free flight. When the aerial manipulator's end-effector comes in contact with the environment, the haptic device movement is interpreted as desired force. Position control in free flight and force control in contact are achieved by utilizing a cascaded parallel position/force controller. The reference pose is composed by the operator's pose command (free flight pose command) and the output of a force controller in an outer loop. To avoid losing the contact, the desired force is always kept in a feasible range that satisfies the friction and compliance constraints. On the master side, the human operator is provided with force feedback proportional to the robot's velocity in free flight and the applied force in contact. The aerial tele-manipulation system presented in this paper may contribute to addressing and solving a broad class of relevant use-case applications where a UAV is remotely operated in hardly accessible and life-threatening sites, for example, at high altitudes, by a human safely located in a protected place, thus relieving him/her from potentially dangerous tasks. A conceptual example is depicted in Figure 1. Apart from the aforementioned applications of remote sensor placement, contact holding and remote button pushing, another idea that could be envisioned is to employ such a system to push boxes located on a shelf onto a conveyor belt, in an industrial warehouse scenario. Thanks to the enhanced situational awareness guaranteed by the haptic feedback, the operator could easily regulate the force applied to the load. Furthermore, the designed control law would ensure that the contact is maintained throughout the manipulation. As should be appreciated, many other relevant applications involving the aforementioned conditions can be easily conceived. The rest of the paper is organized as follows. The next subsection reviews the related works and the contribution of the paper. Section 2 explains the platform and its dynamic model in the teleoperation system. Section 3 presents the proposed control approach, the stability analysis of which is presented in Section 4. Experimental results are presented in Section 5, while concluding remarks and hints about intended future works are outlined in Section 6.
## 1 Introduction
proposed a mixing entropy data compression based on interval wavelet transformation to achieve the compression on streaming data in the sensor network [7]. [PERSON] et al. presented a universal algorithm for sequential data compression through which performance is investigated with respect to a non-probabilistic model of constrained sources [8]. How to make use of the correlation between mobile gas data and realize the simultaneous recovery of multiple measurement nodes with a small number of measurement data still needs to be studied. Compressed sensing takes full advantage of the signal's sparsity and can perform non-adaptive measurement encoding on the signal at a far lower sampling rate than the Nyquist sampling rate. Due to the novelty in its theoretical framework, compressed sensing now has provided some new ideas for many practical signal processing problems [9]. Sparse recovery algorithms can be classified into three main categories: Convex and Relaxation, Greedy, and Bayesian [10]. In the application of data monitoring based on Wireless Sensor Network, [PERSON] put forward multiple access and data reconstruction in wireless sensor networks based on compressed sensing [11]. [PERSON] et al. presented a data compression algorithm to maximize network lifetime in wireless sensor networks [12]. Lossy transmission is a common problem for monitoring systems based on wireless sensors; [PERSON] et al. presented a data loss recovery technique based on compressed sensing to enhance communication reliability [13]. In view of the long linear structure of coal mine roadways and the time sparsity of gas sensors, a compression and reconstruction algorithm for a coal mine network transmission structure is studied in this paper. The algorithm takes advantage of the time correlation of the mobile gas monitoring data in multi-hop networks and realizes the sparsity of monitoring data by introducing total variation. By studying the relationship between data transmission and compressed sensing in multi-hop networks, a Total Variation Sparsity based on Multi-Hop (TVS-MH) algorithm is proposed, and the effectiveness of the algorithm is verified. The rest of this paper is organized as follows. Section 2 introduces the model of the mine IoT monitoring system, which is a mixed network of wired and wireless networks. Section 3 introduces the basic theory of compressed sensing, and a compressive acquisition model based on compressed sensing for gas data flow is presented in Section 4. Then, in Section 5, a high-efficiency gas data compression and reconstruction method TVS-MH is proposed. Section 6 presents gas data reconstructed based on TVS-MH. A series of tests show that the moving measurement data flow from an underground distributed mobile network can be acquired and transmitted efficiently by using the proposed method. Finally, conclusions are drawn in Section 7.
# Fuzzy-Based Quality Adaptation Algorithm for Improving QoE from MPEG-DASH Video
## 1 Introduction
between the selected video rate and the available bandwidth. The ABR algorithms should be able to guarantee QoE under different settings. However, the existing algorithms ignore important parameters, such as segment duration and the client's playback buffer size, when selecting video quality. This results in inconsistent performance from algorithms under different settings. In this paper, we present a buffer- and segment-aware fuzzy-based ABR algorithm for a single cell with multiple clients in order to optimize the QoE of competing clients. The objective of the proposed algorithm is to achieve optimal performance across a variety of video client settings and QoE objectives. To this end, the proposed algorithm considers segment duration, client playback buffer size, estimated throughput, and the amount of video in the playback buffer to select rates for the next video segments. We conducted extensive experiments to evaluate the performance of the proposed algorithm with multiple segment durations, playback buffer sizes, and video sequences. The results from our extensive experiments reveal that the proposed long video adaptation algorithm outperforms state-of-the-art algorithms, with average improvements in video rate, QoE, and bandwidth utilization, respectively, ranging from 5% to 18%, by about 13% to 30%, and by up to 45%. Moreover, the proposed algorithm manages the clients' playback buffers to avoid unnecessary rebuffering. The rest of this paper is organized as follows. Section 2 reviews the existing video streaming algorithms. Section 3 describes the QoE maximization problem. Section 4 describes the proposed fuzzy-based logic controller, while Section 5 details the quality adaptation algorithm, and Section 6 presents the simulation results. Finally, Section 7 concludes the paper.
## 3 Results and Discussion
### Influential Variables in the Wingate Anaerobic Test
#### 3.1.1 Duration
[31] conducted research on nine trained cyclists to ascertain which duration between 10, 30, 40 and 60 s would be the most appropriate to evaluate both power and anaerobic capacity, based on the work generated, lactate concentrations and power levels obtained. Their conclusion was that the most appropriate duration would be 60 s. Later, [PERSON] et al. [32] studied the most appropriate duration of the test--30-60 or 90 s--to assess anaerobic capacity in 6 males, based on lactate concentrations and oxygen deficit. They concluded that 60 and 90 s were the most appropriate durations in this case. Along the same lines, [PERSON] et al. [18] researched the most appropriate duration between 30 and 45 s based on the oxygen deficit during the test in order to evaluate the anaerobic capacity in 19 trained males, concluding that 45 s duration was the most appropriate for such cyclists. However, in most subjects, it is still insufficient to allow full utilisation of anaerobic capacity (many of them only used 80-90% of their anaerobic capacity). On the other hand, in elite track cyclists, they observed that full anaerobic capacity could be used in a test lasting between 40 and 50 s [19]. [PERSON] et al. [33] also claim that the duration of the test is insufficient for the purpose of suitably measuring the subjects' anaerobic capacity. It is important to remember that different values are obtained from the WAT-peak power, fatigue index and average power, among others. Thus, the duration of the test deemed suitable for the measurement of one value may be different from the duration recommended for another value. It is also important to take into account that, depending on the subject's level of physical condition, speciality or genetics, etc., the duration may also need to be varied. Therefore, it seems that depending on what our purpose is when performing the WAT, we can adjust its duration. Although it appears that in a young athletic or active population, a 20 s test could predict the result of the 30 s test, a 6 to 10 s test would be sufficient to obtain PP in any case. When assessing the anaerobic capacity of trained subjects, the duration of the test should be increased to 45-60 s.
## 4 Implementation Analysis of a Reasonable Completion State
### Verification of the Proposed Multiple-Control Method  
When the errors of \(L_{td}\), \(L_{sa}\), and \(P\) in each tensioning step were within their respective allowable ranges. The measured tag line positons \(L_{td}\) were compared with the target values. As shown in Figure 18, taking the north hangers as examples. The errors of \(L_{td}\) are within \(\pm 1.5\) cm, which reflects the accuracy of the proposed multiple-control method. In addition, the mutual check between the multiple control parameters was realized during each tensioning step. The phenomenon of repeated installation of tensioning equipment, if checking the tag line positons at the end of a hanger tensioning round according to the traditional control method, was avoided.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Step**} & \multicolumn{4}{c}{**North Side**} & \multicolumn{4}{c}{**South Side**} \\ \cline{2-11}  & **Hanger** & **\(P\) (kN)** & **Hanger** & **\(P\) (kN)** & **Hanger** & **\(P\) (kN)** & **Hanger** & **\(P\) (kN)** & **\(L_{td}\) (m)** \\ \hline DS14-N & 3120 & 0.333 & DS21-N & 3120 & 0.383 & DS14-S & 3120 & 0.353 & DS21-S & 3120 & 0.380 \\ DS16-N & 3120 & 0.334 & DS22-N & 3120 & 0.369 & DS16-S & 3120 & 0.373 & DS22-S & 3120 & 0.382 \\ DS17-N & 3120 & 0.359 & DS24-N & 3120 & 0.359 & DS17-S & 3120 & 0.376 & DS24-S & 3120 & 0.367 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Target values of the control parameters of tensioning round 4.  
Figure 18: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Step**} & \multicolumn{4}{c}{**North Side**} & \multicolumn{4}{c}{**South Side**} \\ \cline{2-11}  & **Hanger** & **\(P\) (kN)** & **Hanger** & **\(P\) (kN)** & **Hanger** & **\(P\) (kN)** & **Hanger** & **\(P\) (kN)** \\ \hline \multirow{2}{*}{5-1} & B2 & 3380 & B30 & 3400 & N4 & 3080 & N32 & 3080 \\  & B7 & 3500 & B31 & 3500 & N7 & 3380 & N33 & 3080 \\ \hline \multirow{2}{*}{5-2} & B4 & 3140 & B32 & 3140 & N2 & 3370 & — & — \\  & B5 & 3140 & B33 & 3140 & N10 & 3030 & — & — \\  & B15 & 3060 & — & — & N15 & 3060 & — & — \\ \hline \hline
5-3 & B34 & 3080 & B36 & 3100 & N1 & 3620 & N37 & 3560 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Specific step and control parameter of tensioning round 5.
## 2 Materials and Methods
### Scanning Electron Microscopy  
Air-dried electrospun scaffolds (before and after various sterilization protocols) were mounted on aluminum stubs using standard double-sided tape, sputter coated with gold and examined at an accelerating voltage of 20 kV using JEOL JSM 5610 LV (Peabody, MA, USA) scanning electron microscope (SEM). Average fiber diameters were calculated from a total of 50 randomly selected fibers from corresponding SEM images using ImageJ (NIH). For imaging cells seeded onto scaffolds, the scaffolds were fixed in 2.5% glutaraldehyde, sequentially dehydrated in ethanol, followed by 5 min incubation in gradations of hexamethyldisilazane (HMDS, Sigma, Saint Louis, MO, USA) prior to imaging. Alvetex scaffolds were also imaged under the same conditions.
## 2 Data and Methods
### Models and Calibration
#### 2.2.3 WRF-Hydro Calibration  
Before analyzing the influence of WRF and the fully coupled WRF/WRF-Hydro, we calibrated the stand-alone WRF-Hydro parameters, and then selected the same parameter group in the coupled system. It should be pointed out that the stand-alone WRF-Hydro forcing input time step must be consistent with the WRF model output time step (1 h) for it was a necessary compilation rule for guaranteeing the data generated by WRF can be used to drive WRF-Hydro.  
Four parameters were used for the calibration and sensitivity analysis, including the runoff infiltration parameter (REFKDT), the surface retention depth scaling parameter (RETDEPRTFAC), the overland flow roughness scaling parameter (OVROUGHRTFAC) and the channel Manning roughness (MannN). REFKDT is a significant parameter for the infiltration excess process. RETEDPRTFAC should be adjusted depends on the surface slope, and in general, in regions where slopes are steeper than 30\({}^{\circ}\)-45\({}^{\circ}\), and there is no accumulation or retention. Increasing REFKDT and RETEDPRTFAC affect the total water volume by encouraging more local infiltration near the river channel to wetter soils and thus, to better riparian conditions. OVROUGHRTFAC depends on the land-use type, and also influences the amount of water that is transferred to the channel grids. It also has an impact on the hydrograph shape, but OVROUGHRTFAC in controlling the speed of the overland transmitters downstream and the amount of water on a column scale is relatively minimal. Instead, MannN has obvious impact on the streamflow to channel grids. Decreasing MannN and OVROUGHRTFAC accelerated the routing of the river channel, resulting in a higher flood peak [45; 46]. This is also supported by the conclusions from other studies (e.g., [PERSON] et al. [6], [PERSON] et al. [25], [PERSON] et al. [45] and [PERSON] et al. [46]). To minimize model runs and cut down computational time, the calibration process used a stepwise approach [47] which denotes that the determination of one parameter will be verified before the next  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Subject** & **Chosen Option** \\ \hline Forcing input interval & 1 h \\ Subgrid size & 100 m \\ Routing model time step & 6 s \\ Aggregation factor & 10 \\ Subsurface routing & On \\ Overland flow routing & On \\ Channel routing & On with the steepest descent \\ Baseflow bucket model & Off \\ \hline \hline \end{tabular}
\end{table}
Table 3: Configurations of the WRF-Hydro model.  
parameter is calibrated. This approach made it possible to adjust the model to best fit the observations of streamflow and timing. We mainly calibrated order is REFKDT, MannN, RETDEPRTFAC and OVROUGHRTFAC. Aside from the four main parameters, the other parameters are set to their defaults, owing to the lack of observational data.
## 5 Empirical Model: A Case Study of Krasnodar Region of Russia
The use of solar power and energy storage by households is largely related to the current tariff menu for electricity from the network in the region. Today, one-rate tariffs are applied in Kranodar region, differentiated by two and three zones of the day, as well as the volume of consumption (Table 2) [59]. According to the current tariff menu and the \"dynamic\" differentiated tariff proposed in the previous part of the paper, the cost of electricity per month was estimated for households consuming electricity only from the grid, households with solar panels and households with solar panels and storage using given electrical load graphs. The calculation results are shown in Table 3.  
Figure 6: Household electrical load graph on a day off with a superimposed graph of electricity generated by solar installations. Figure 7: Household electrical load graph on a working day with a superimposed graph of electricity produced by solar installations with energy storage. As can be seen from the data, the cost of electricity when applying the existing tariff differentiation scheme for two and three times zones of the day is the highest for households that do not use solar power. These results are also true for households with solar panels and storage, which suggests that the existing tariff menu does not encourage consumers to use solar energy efficiently. This is due to the discrepancy between the time zones used in the existing differentiated tariffs and the actual peak and half-peak areas of household electricity consumption, as well as the high values of household electricity consumption in the morning and evening hours. Thus, it is beneficial for the consumer to use the one-rate undifferentiated tariff regardless of the availability of renewable energy sources and the use of energy storage devices. This problem can be solved using the proposed method of dynamic tariff differentiation. For households that do not use solar installations, the simplest one-rate tariff still remains the cheapest. However, for households that use solar installations, and the solar systems with stored energy, there are obvious advantages for a \"dynamic\" differential tariff. Reducing the cost of electricity for households will stimulate the population to more active and, most importantly, rational use of solar installations, will reduce the load on the energy system, as well as reduce the need for building new generating capacity of traditional energy with high capital costs.