esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Renewable energy generation methods such as wind power and photovoltaic power have problems of randomness, intermittency, and volatility. Gravity energy storage technology can realize the stable and controllable conversion of gravity potential energy and electric energy by lifting and lowering heavy loads. The hoisting system is an important component of a gravity energy storage system, and its lifting capacity and speed seriously restrict its energy storage capacity, energy conversion efficiency, and operational safety and reliability. In this paper, a design method for a multi-rope friction hoisting system of a vertical shaft gravity energy storage system is proposed. The parameter design and calculation of the hoisting rope, balance rope, and friction wheel of the friction hoisting system under typical conditions were carried out. The static and dynamic anti-slip capabilities of the friction hoisting system under the typical condition were explored. The results show that the maximum acceleration and deceleration speed of the compacted strand wire rope scheme is the largest, and the lifting and lowering time is the shortest. The maximum acceleration and deceleration speed of the triangular strand wire rope scheme is the lowest, and the lifting and lowering time is the longest. The dynamic tension of the hoisting rope at the heavy-load end is positively correlated with the acceleration, and the maximum value occurs in the accelerated lifting stage and decelerated lowering stage of the heavy load. The static anti-slip safety factor between the hoisting rope and the friction lining and the specific pressure between the hoisting rope and the friction lining comply with the requirements of China's Safety Regulations for Coal Mines. The dynamic anti-slip safety factor of the hoisting system under different rope selection schemes is greater than the minimum value of 1.25 stipulated in the Safety Regulations for Metal and Nonmetal Mines. The research results are of great significance for the safety, reliability, and stable and efficient energy storage of a gravity energy storage system.  
vertical shaft gravity energy storage; design of friction hoisting system; anti-slip capacity +
Footnote †: journal: Applied Sciences
## 3 Results
### Thermal Expansion  
Figure 5 shows the thermal expansion behavior of La\({}_{0.8}\)Ca\({}_{0.2}\)CrO\({}_{3}\) ceramics synthesized by the two methods. The relative densities are approximately 93%. It can be seen that--under the same density level conditions--the relative expansion (\(\Delta\)L/L) of the ceramic synthesized by the combustion method was evidently higher compared with that of the specimen by the SSR method. It is likely attributed to the different microstructures for the two methods. As inhomogeneous components in the ceramic bulk, perhaps the more grain boundaries and holes would reduce the relative expansion of the materials. Another possible mechanism may be the compositional difference because of volatilization of chromium oxide. In order to achieve the sufficient density of the perovskites, a higher sintering temperature (\(\geq\) 1500 \({}^{\circ}\)C) would be required for the SSR method. The higher sintering temperature  
Figure 4: Plots of \(\ln(\sigma_{e}T)\) versus 1000/\(T\) for La\({}_{0.8}\)Ca\({}_{0.2}\)CrO\({}_{3}\) ceramics synthesized by the different methods.  
results in volatilization of chromium oxide and the formation of more quadrivalent chromium ions in the perovskites structure (as for the ionic radii, Cr\({}^{4+}\) = 0.069 nm, Cr\({}^{3+}\) = 0.0755 nm) [20], and lower the TEC of the sample synthesized by SSR method.  
In addition, there is a kink in the measuring temperature range from 300 \({}^{\circ}\)C to 400 \({}^{\circ}\)C, which may be owing to the orthorhombic to rhombic phase-transition, in agreement with [PERSON] et al. [21] and [PERSON] et al. [22], and confirmed by [PERSON] et al. [23] using the DSC and high temperature XRD results. The thermal expansion coefficients (TECs) of the two samples prepared by different methods were \(10.5\times 10^{-6}\) K\({}^{-1}\) and \(9.6\times 10^{-6}\) K\({}^{-1}\) in high temperature, respectively. Obviously, the former was thermally compatible with that of other high-temperature components for use in SOFC, such as 8 YSZ electrolyte (\(10.5\times 10^{-6}\) K\({}^{-1}\)), so as to minimize thermal stresses. All the above results demonstrated that the discrepancies in the electrical conductivity and TEC corresponded well to the microstructural variations of the ceramics, due to the different sinterability of La\({}_{0.8}\)Ca\({}_{0.2}\)CrO\({}_{3}\) synthesized by the two methods. By contrast, the specimen by the combustion method exhibits lower densified temperature and better physical properties. Present research work provided new clue for the synthesis of new composites and nanomaterials [24; 25; 26; 27; 28].
## 4 Experiment and Analysis
### Experimental Results
#### 4.3.2 Quantitative Analysis of Enhancement Treatment  
In order to analyze the enhancement effect of the self-order enhancement algorithm proposed in this paper from a quantitative point of view, structural similarity (_SSIM_), mean square error (_MSE_), and peak signal-to-noise ratio (_PSNR_) are chosen to evaluate the images of different scenes.In the experimental process, two types of data are used for experimental validation: one type is to use the same camera to collect data for daytime and nighttime hours respectively, and the data of sunny daytime hours are treated as the standard dataset; the other type is to use the simulated nighttime data for validation, i.e., the data of the daytime hours are treated as the nighttime pictures after the blackening process, and then the enhancement correction is carried out by using the correction algorithm in this paper. Finally, these two types of data are used to evaluate the indicators of the data before and after enhancement. The different scene data are shown in Figure 6 below, the first column is the different scene data collected during the daytime hours, the second column is part of the real collected nighttime data and part of the nighttime simulation data, The third column is the resultant graph after image enhancement by the algorithm of this paper.  
Figure 5: Image enhancement results of automatic color scale algorithm.  
As shown in Figure 6, it can be observed that the algorithm proposed in this paper for an adaptive self-scale enhancement had good color-scale correction results for dark night scenes, and the corresponding evaluation indicators are shown in Table 3.  
As shown in Table 4, the values of the mean square error (_MSE_), peak signal-to-noise ratio (_PSNR_), and structural similarity (_SSIM_) of the self-order enhancement algorithm proposed in this paper in foggy days were good, which indicates the algorithm greatly preserved the original information of the image while improving the image quality and achieved the image enhancement target. In the dark scene, because the pixel distribution in the original scene was relatively uniform, the contrast of the image was greatly improved after the image enhancement, the original background of the scene was well preserved, and its _MSE_, _SSIM_, _PSNR_ values performed well to achieve the self-correction of the dark field image.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frames\_Index** & _MSE_ & _PSNR_ & _SSIM_ \\ \hline
1 & 0.1658 & 193.8824 & 0.9591 \\
2 & 0.0063 & 202.5396 & 0.9991 \\
3 & 0.0063 & 203.6259 & 0.9996 \\
4 & 0.6299 & 148.9663 & 0.9921 \\
5 & 2.6259 & 136.1131 & 0.9695 \\
6 & 5.5518 & 129.6057 & 0.9554 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Assessment indicators of foggy scenes.  
Figure 6: Self -order correction results.
The principle of energy conservation, a universally applicable law of nature, enables a comprehensive understanding of the energy states within a slope during the destabilization process by analyzing the transformation, transfer, and dissipation of energy within the slope system [27; 28]. Energy analysis not only illuminates the characteristics of energy changes during slope failure but also provides strong support for the quantitative assessment of slope stability [29]. Dissipative energy, a fundamental concept in physics, refers to the energy lost during irreversible processes, where internal energy is converted into heat or other non-recoverable forms. In slope stability analysis, dissipative energy describes the energy loss and transformation during the deformation and failure processes of a slope [30; 31]. When a slope is subjected to external loads or natural forces, it undergoes complex deformation and failure, accompanied by energy transformation and dissipation. By analyzing the various forms of energy dissipation within the slope system, we can evaluate slope stability and the associated risk of failure [32]. The integration of cusp catastrophe theory with energy analysis offers a powerful approach to evaluating slope stability [33]. Currently, the application of cusp catastrophe theory to slope stability primarily involves the development and analysis of displacement and stress catastrophe models, with limited consideration given to energy catastrophe models [34]. Cusp catastrophe theory, when applied to energy catastrophes, providessignificant advantages, including intuitiveness, broad applicability, strong predictive and explanatory capabilities, and the ability to combine quantitative and qualitative analysis without requiring detailed information about the internal system. This study, using a specific open-pit mine as the research context, defines energy damage variables for both individual elements and the overall system based on the principle of energy conservation. By integrating dissipative energy with cusp catastrophe theory, a dissipative energy catastrophe model is developed. This model, combined with on-site monitoring of anchor cable axial forces and slope displacements, is employed to analyze slope stability under the conditions of downward extension mining. The findings provide theoretical guidance for future mining operations and contribute to the advancement of slope stability evaluation methods.
## Abstract
[22] used banana pseudostems for the removal of lead ions using a batch process, and studied the adsorption kinetics by altering the operating parameters. The authors reported an adsorption capacity of 34.21 mg/g, and the pseudo second-order model showed the best fit. [PERSON] [23] prepared a green, low-cost, solid-phase adsorbent from coffee husk biomass waste in order to uptake Pb(II) ions, and evaluated the effect of analytical parameters. They reported high removal yields, i.e., up 98% and an adsorption capacity of 19.02 mg/g, and sorption kinetics data were fit with a pseudo-second order model. [PERSON] et al. [24] studied the equilibrium and thermodynamics of heavy metals onto activated carbon prepared from olive branches, and reported a maximum adsorption capacity of 41.32 g/g for lead ions. The aim of this work is to prepare bioadsorbents from biomasses in order to address disposal problems of agricultural waste and mitigate the environmental impact of heavy metal contamination. To this end, residues from yam, cassava, banana, and oil palm were selected to develop novel materials whose adsorption capacities were tested using the batch adsorption system. During the agricultural activities of tubers and fruits in the department, huge quantities of waste are generated because of the decomposition of organic products. Such lignocellulosic materials have no commercial value and represent an alarming disposal problem; hence, the preparation of bioadsorbents from agricultural biomass must be more profitable than adsorbents with high demand of raw materials. The novelty of this work lies in a comparison of the adsorption performances of different agroindustrial biomasses which are available in the Department of Bolivar in order to propose remediation alternatives for water pollution based on this removal mechanism.
## 5 Experimental Results
### Object Tracker Performance
The reason for the high precision measure was the strong validation module presented in Section 4.1.2.2. We made the validation module very sensitive, due to which it rejected candidates with less feature matching. Consequently, in the case of object re-identification, it had been rejecting the candidates as soon as it got a candidate object with high matching. This resulted in a slight decrease in the recall measure, as seen in the videos \"Snowboarding\" and \"Airplane Flight.\" Another reason for the least recall value for the three videos is the division of an object into two parts for a long time while it went out of the margin of the 360\({}^{\circ}\) frame and appeared on the other side. If we reduce the sensitivity of the validation module towards matching, there is a possibility of an increase in the recall measure. Figure 10 presents a clear picture about recall and precision for object tracking results in all 360\({}^{\circ}\) panoramic videos. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Video** & **Desired Object** & **Frames** & **Recall** & **Precision** & **F-Measure** \\ \hline Street & Pedestrian & 500 & 0.94 & 1.0 & 0.97 \\ Sonkeling & Sonokeler & 962 & 1.0 & 1.0 & 1.0 \\ WaterSkating1 & Front water skater & 944 & 1.0 & 1.0 & 1.0 \\ WaterSkating2 & Water skater in background & 1092 & 0.92 & 0.94 & 0.93 \\ Cartoon & Doll & 2122 & 0.89 & 0.91 & 0.9 \\ Snowboarding & Snowboarder & 4796 & 0.85 & 0.88 & 0.86 \\ Airplane Flight & Airplane cockpit & 4598 & 0.79 & 0.9 & 0.84 \\ Street2 & Playing child & 389 & 1.0 & 1.0 & 1.0 \\ \hline
**Mean** & - & - & 0.92 & 0.95 & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation results in terms of recall, precision, and F-measure. Figure 9: Visual results of proposed object tracking method on 360\({}^{\circ}\) videos showing correctly detected desired object and successful object tracking. Three frames from each video, (**a–d**) represent different challenges faced during tracking
## 2 Materials and Methods
### Methodology
#### 2.1.4 Procedure for Experimental Tests  
Experimental tests of the system were performed using a known low-amplitude input signal of 1 pV (Vpp) and the gains were obtained with voltage levels within the millivolts to volts. Also, a sinusoidal signal with a peak-to-peak amplitude of 1 mV and a frequency range of 1 Hz to 100 kHz was injected into the system to assess any significant distortion within the frequencies relevant to the biopotentials being measured.  
The system is evaluated by acquiring EEG, ECG, and sEMG biopotentials using the proposed shielding driver, applying the Signal-to-Noise Ratio (SNR), which is defined as the ratio between the amplitude of the signal and the amplitude of the noise, expressed in decibels. This can be represented by the following [56; 57; 58; 59; 60; 61; 62; 63]:  
\[SNR_{dB}=10\times log_{10}\frac{P_{signal}}{P_{noise}}=20\times log_{10} \frac{V_{signal}}{V_{noise}}, \tag{9}\]  
where \(P_{signal}\) is the power of a signal, \(P_{noise}\) is the power of the background noise common gain, \(V_{signal}\) is the voltage amplitude of the signal, and \(V_{noise}\) is the voltage amplitude of the noise.  
To calculate the SNR (Equation (9)) of each type of the acquired biopotential signals, the following measurements were performed [64]: To obtain the \(V_{signal}\) value, the difference between the peak to maximum peak voltage of the signal was measured, as shown in Figure 8. Likewise, to obtain the \(V_{noise}\) value, the peak to minimum peak voltage of the signal was measured.  
Figure 7: Methodology used to perform the measurement and acquisition of EEG, EMG, and ECG biopotentials whose electrode connections are represented by solid line, dotted line, and dashed line, respectively.  
In addition, the CMRR of the system is found using a signal generator to produce a signal with a frequency of 1 Hz and a peak-to-peak voltage of 2 mV, as well as an autotransformer with a frequency of 60 Hz and a peak-to-peak voltage varying from 1 to 200 volts. Figure 9 shows the connections used to make the measurements and obtain the CMRR. The voltage of the autotransformer is varied in steps of one volt and the measurement for the CMRR is performed; the process is continued until 200 Vpp of the autotransformer is reached. Then, the CMRR is computed as follows:  
\[CMRR=20\times log_{10}\frac{|A_{d}|}{A_{c}} \tag{10}\]  
where \(A_{d}\) is the differential gain and \(A_{c}\) is the common gain, both of which are dimensionless [61; 62; 63].  
To perform this analysis of the acquired biopotentials, an interface is developed in MATLAB(r) where signal processing and storage are carried out. The FFT is applied to obtain the frequency spectrum of the acquired signal, with the specific analysis of the spectrum at 60 Hz in both acquired signals.
## 3 Results and Discussion
### Changes in Void Ratio  
Using Equation (2), change curves of the void ratio of waste rock specimens with various particle sizes are calculated, as illustrated in Figure 11.  
Figure 11 shows that, as the compressive stress gradually increases, the void ratios of waste rock specimens decrease at a decreasing rate. This is because, under compression, the skeleton structure of waste rock particles prevents the further closure of voids. The initial void ratio of specimens with continuous particle size distributions is larger than that of specimens with uniform particle size distributions. However, the rate of decrease in the void ratio of specimens with continuous particle size distributions is greater than that of specimens with uniform particle size distributions after compressive deformation. This is because specimens with continuous particle size distributions contain pre-existing fine particles that can fill the voids, which results in an initial void ratio that is smaller than that of specimens with uniform particle size distributions. As the compression continues, pre-existing fine particles in the specimens with continuous particle size distributions do not readily slip and fill the voids due to the presence of the skeleton, while fine particles formed due to breakage of waste rock particles fill the voids more efficiently than pre-existing fine particles in the specimens.  
Changes in the void ratio of waste rock specimens with particle size distributions of 0-10 mm in compressors with different inner diameters are obtained using Equation (2), as shown in Figure 12.  
As shown in Figure 12, the waste rock specimens have the same initial void ratio. The void ratio changes after pre-compression, while it differs slightly in compressors with different inner diameters. With the application of axial pressure, the difference in void ratio gradually increases. The maximum and minimum void ratios are separately 0.25659 and 0.22281 under the compressive stress of 20 MPa, and the void ratio gradually decreases when increasing the inner diameter of the compressors. This is because the larger the inner diameter of compressors, the lower the probability of formation of stable skeleton structures, and the less numerous the voids that are maintained by stable skeleton structures.  
Figure 11: Changes of the void ratio of waste rock specimens with various particle sizes.
# Direct Mobile Coaching as a Paradigm for the Creation of Mobile Feedback Systems
## 2 Background  
### Feedback Systems  
Feedback systems provide computerised feedback to athletes and coaches. They \"acquire, determine and present information on the motor task performed which is not directly observable\" [4]. A wide variety of different types of feedback systems exists, and, consequently, there are also different systems for their categorisation. One method of categorisation is based on the type of data to be used. Consequently, categorisations of feed back systems as video-based [8], audio-based [9] or even more general as sensor-based [10] systems exist. However, the categories have no clear-cut boundaries, e.g., a sensor-based system can also provide auditory feedback. Moreover, sensor-based systems can further be categorised into systems using sensors that are attached to either athlete, equipment, or both. In addition, feedback systems can be categorised/separated according to the information presented to the users. Most often, the systems are either designed to present information about the result of an action (\"knowledge of result\") or about how a result was achieved or how a movement was made (\"knowledge of performance\") [11]. However, similar to the categorisations presented above, the lines between the individual categories can be blurred, and a system often cannot be put into only one category.  
Mobile feedback systems offer the ability to be deployed outside a laboratory. In order to enable this, they often make use of mobile platforms such as smartphones or tablets [12]. Additionally, using mobile and wireless sensor technology not only reduces interferences with the movements of the athletes (which is vital for any feedback system) but again allows mobility and measurements outside of laboratories.  
Some modern sensor-based feedback systems employ--at least in some sense--the so-called \"Mobile Coaching\" (_MC_) paradigm [13] or variations and extensions of it. One example of this is the so-called \"Mobile Motion Advisor\", a feedback system with the aim of motivating high school students during their physical activity by means of exercise instructions that are customised for each student [14]. This paradigm combines data acquisition methods with feedback functionalities in one system. A mobile device equipped with a client app for athletes collects data from various sensors and sends it to a server using a mobile internet connection. On this server, the data is processed and stored in a central database. An additional interface--the Expert-Client--provides coaches and experts with the ability to give feedback to the athletes in real-time or post hoc. While the feedback during the activity is a verbal message either generated by text-to-speech software or recorded and played back to the athlete, the post-hoc feedback can be realised via emails or simple text messages. This interface accesses the central database as well. Additionally, algorithms can be used in order to analyse data on the server in real-time and thus provide immediate feedback to the athletes on their movements or performances [15].