esa-sceva/satcom-chunk-collection huggingface dataset:
# Control Strategy of Multiple Battery Energy Storage Stations for Power Grid Peak Shaving
## 2 Methodology
### Evaluation  
In order to illustrate the effectiveness of BESS in peak shaving and valley filling and to evaluate the above control strategies, indicators for evaluating the effectiveness of peak shaving and valley filling are constructed below.  
(1) Absolute peak-to-valley difference \(\Delta P\): The absolute peak-to-valley difference refers to the difference between the peak load and the valley load, which is used to characterize the maximum absolute deviation of load output on a daily time scale. The definition is as follows:  
\[\Delta P=P_{\text{max}}-P_{\text{min}} \tag{1}\]  
(2) Peak-shaving depth: The amount of power reduced or added by the BESS during peak shaving. The definition is as follows:  
\[\Delta P_{peak}=P_{peak}-P^{\prime}_{peak} \tag{2}\]  
\[\Delta P_{valley}=P^{\prime}_{\text{valley}}-P_{\text{valley}} \tag{3}\]  
(3) Peak-valley coefficient \(\alpha\): The peak-valley coefficient is the ratio of the valley value to the peak value of the load, which characterizes the flatness of the load curve. The flatter the curve, the larger the coefficient. It is defined as follows:  
\[\alpha=\frac{P_{\text{min}}}{P_{\text{max}}}\times 100\% \tag{4}\]  
(4) Peak-valley difference rate \(\beta\): The peak-valley difference rate is the ratio of the absolute peak-to-valley difference to the peak load, which indicates the range of load fluctuations. The smaller the peak-valley difference rate, the smaller the range of load fluctuations. The definition is as follows:  
\[\beta=\frac{\Delta P}{P_{\text{max}}}\times 100\% \tag{5}\]  
Figure 4: Variable-power control strategy flowchart.  
(5) Standard deviation of load variation _X_: The standard deviation of load variation indicates the dispersion of load data. The smaller the standard deviation, the more concentrated the data, meaning smaller load fluctuations. The definition is as follows:  
\[X=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{\left({{P_{i}}-\overline{P}}\right)^{ 2}}}} \tag{6}\]
# Wideband Printed Wide-Slot Antenna with Fork-Shaped Stub
## Antenna Design and Analysis
### Parameter Analysis  
In order to comprehensively investigate the effect of critical parameters on the impedance BW, a parameter analysis was conducted. Both of the proposed antennas were designed on the same substrate (FR4) as the normal antenna. The width, length and position of the added stubs that determine the impedance BW to a great extent were studied by changing one parameter at a time  
Figure 3: Geometries and configurations of the proposed antennas (adding extra stubs onto the main fork-shaped stub).  
and fixing others. The simulation results were obtained by using the Ansoft simulation software high-frequency structure simulator (HFSS).  
Figure 4 shows the reflection coefficient of antenna A with the varying width of the added stub (\(w_{a}\)). When \(w_{a}\) varied from 2 mm to 8 mm, the reflection coefficient curve showed little change near the frequency of 1 GHz. As \(w_{a}\) reduced, the upper limiting frequency shifted to a higher frequency. This showed that better impedance matching could be obtained at high frequency with a smaller \(w_{a}\). However, the reflection coefficient will be worse if a too small \(w_{a}\) is selected. Therefore, the proper selection of the width dimension is very important.  
Figure 5 shows the effect of different distances (\(d_{s}\)) on the reflection coefficient of antenna B. When the distance (\(d_{s}\)) varied from 8.7 mm to 11.7 mm, an inconspicuous difference could be found at the frequency of 1 GHz. As the \(d_{s}\) increased, the reflection coefficient in the middle of the frequency band became worse, while a too small \(d_{s}\) worsened the reflection coefficient at high frequency. The conclusion was drawn that the effect of \(d_{s}\) at the low frequency can be ignored, but that it contributed a great deal to the impedance matching over the whole bandwidth. To improve the BW characteristics, the distance between the added stub and the main stub should be set properly.  
Figure 4: Simulated reflection coefficient of the proposed antenna A with different widths (\(w_{a}\)) of the added stub.  
Figure 5: Simulated reflection coefficient of the proposed antenna B with different distances (\(d_{s}\)) between the two stubs.
## 6 Future Development Problems and Prospects
### Development Prospects  
Mature products equipped with passive and active open furrow profiling control systems have been put into use in many countries. In China, the passive type still dominates, and the active type is experimental. China mainly focuses on the direct control of sowing depth, and there needs to be more research on the indirect control of sowing depth by utilizing downward pressure. In China, the stability control of furrowing depth is mainly focused on the flat land, and less research has been done on the sloped fields. There is an apparent gap in the seeding machines between China and other countries regarding intelligent monitoring. To bring China's seeding machine operation to high speed and precision of the development requirements, the depth of the furrow profiling control system development has the following directions:1. Given the operating shorts on sloping arable land in China, we should improve the traditional seeder furrow profiling control, solving the operating needs of sloping arable land in terms of up-and-down and left-and-right profile, improving the effect of seeding operation in sloping arable land and improving the universality of the seeding machine.
2. For the differences in soil properties, crop varieties, and planting modes in different regions of China, we need to determine the most suitable sowing depths and downforce of planter operations for sowing crops in different regions according to the interplay mechanism of soil-furrowing-crop components, to provide references for sowing.
3. Strengthen the optimized design of the profiling adjustment device, reduce the vibration of the profiling adjustment device in the operation process, and improve its stability. Under the premise of ensuring that the operating effect remains unchanged, reduce the operating cost of implements and improve the working life of implements.
4. Enhance the development of active profiling adjustment systems and improve the stability of the system by using multi-sensor fusion on top of the existing ones. Promote the development of China's seeding machine in the direction of high speed and precision and improve the operational quality and efficiency of China's seeding machine.
5. Based on the development of an active profiling adjustment system, optimize the algorithm by combining the physicochemical parameters of different soils and develop a furrow depth control system suitable for different soils to improve the detection accuracy and convenience of the system.
6. Increase the investment in the research and development of intelligent agricultural machinery and devices and promote the development of China's seeding machines in the direction of automatic navigation, automatic monitoring, and evaluation of seeding quality, variable seeding, high-speed seeding, and other intelligent precision seeding.
# Two-Dimensional Full-Waveform Joint Inversion of Surface Waves Using Phases and Z/H Ratios
###### Abstract  
Surface-wave dispersion and the Z/H ratio are important parameters used to resolve the Earth's structure, especially for S-wave velocity. Several previous studies have explored using joint inversion of these two datasets. However, all of these studies used a 1-D depth-sensitivity kernel, which lacks precision when the structure is laterally heterogeneous. Adjoint tomography (i.e., full-waveform inversion) is a state-of-the-art imaging method with a high resolution. It can obtain better-resolved lithospheric structures beyond the resolving ability of traditional ray-based travel-time tomography. In this study, we present a systematic investigation of the 2D sensitivities of the surface wave phase and Z/H ratio using the adjoint-state method. The forward-modeling experiments indicated that the 2D phase and Z/H ratio had different sensitivities to the S-wave velocity. Thus, a full-waveform joint-inversion scheme of surface waves with phases and a Z/H ratio was proposed to take advantage of their complementary sensitivities to the Earth's structure. Both applications to synthetic data sets in large- and small-scale inversions demonstrated the advantage of the joint inversion over the individual inversions, allowing for the creation of a more unified S-wave velocity model. The proposed joint-inversion scheme offers a computationally efficient and inexpensive alternative to imaging fine-scale shallow structures beneath a 2D seismic array.  
adjoint tomography; Rayleigh wave phase; Z/H ratio; joint inversion; 2D seismic array
## 3 Results
### Clinical and Radiographic Outcomes Following NSRME and SARME
#### 3.3.11 Root resorption  
Four studies, all including SARME procedures, measured the incidence of external apical root resorption [7; 18; 36; 39]. Two evaluated patients for root resorption among device-anchoring posterior teeth [18; 39], while the other two assessed resorption of the roots of the central incisors [7; 36]. [PERSON] and colleagues found that the magnitude of root resorption ranged from \(-0.3\) to \(-1.1\) mm among all maxillary first premolar and first molar roots, while [PERSON] et al. found similar values ranging from \(-0.1\) to \(-0.6\) mm [18; 39]. On post-operative radiographs, [PERSON] and coworkers found that 19%, 8%, and 2% of central incisor roots displayed slight, moderate, and extreme root resorption, respectively, according to [PERSON]'s classification [36]. On the other hand, using both pre-operative and post-operative CBCT, [PERSON] and colleagues recorded no increase in resorption of any central incisor roots as a result of SARME [39].
## 5 Discussion  
In this paper, we proposed a sub-optimal tracking controller that does not need numerical iterations or backward calculations for state-constrained optimal tracking problems. The main advantage of the proposed controller is that it can be implemented in real time with a lower computational load compared to the implicit MPC. Though there is a delay compared to the results of the numerical method, the simulation results show that the proposed controller has acceptable performance. However, the proposed controller needs to be verified through more application cases including industrial robots, hybrid vehicles, or other control systems. For future work, the proposed controller may be extended to discrete-time systems. Therefore, studies related to the discrete-time optimal tracking controller based on the proposed method are worth researching. In particular, the controller may be applied to path tracking control for autonomous vehicles [37; 38], which have recently been the subject of much research.  
Conceptualization, J.K. and U.J.; methodology, J.K.; validation, J.K. and U.J.; writing--original draft, J.K.; writing--review and editing, U.J. and H.L.; supervision, H.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work was supported by the research fund of Hanyang University (HY-2017) and \"The Technology Innovation Program\" (10052501, development of design technology for a device visualizing the virtual driving environment and synchronizing with the actual vehicle driving conditions to test and evaluate ADAS), funded by the Ministry of Trade, Industry, and Energy (MI, Korea).  
The authors declare no conflict of interest.  
Figure 5: Experimental and simulation results of the proposed controller with 100 \(\upmu\)s sampling time and the first set of \(r\) and \(\mathbf{w}\): (**a**) motor currents from 0 to 1 s, (**b**) angular speeds of the motor rotor from 0 to 1 s, (**c**) angle of the motor rotor from 0 to 1 s.  
Figure 6: Target motor and controller: (**a**) motor, (**b**) controller.
## 3 Results and Discussion  
### Optimization of Single HAP Conditions  
#### 3.1.1 Effect of Precipitation Time  
Figure 3a shows the influence of precipitation time on the MPS of spiramycin, from which we can see that the MPS of spiramycin increased from 90 \(\pm\) 2 nm to 246 \(\pm\) 11 nm when the precipitation time increased from 2 min to 10 min. However, no significant change in MPS of spiramycin was observed with further increase in precipitation time to 120 min. The reason for this result was interpreted as follows: Small particles were formed immediately when adding spiramycin solution into the antisolvent, but the small particles formed would aggregate together to grow into larger ones during the homogenate process, owing to the Ostwald ripening effect [34; 35], whereas, no further obvious increase of the MPS with further increase in precipitation time was attributed to the destruction of larger crystals into smaller particles by homogenization [36]. As for the precipitation time of 2-8 min, although the MPS was smaller, the precipitates dissolved out incompletely, resulting in a lower yield. Considering both the effect of the precipitation time on the yield and MPS, 10 min was selected as the optimum precipitation time for obtaining small particles and applied in the further experiments.
## 3 Design Challenges of MAC Protocol for IBSN
### Types of Access Mechanisms Recommended for IBSN
#### 3.2.2 Carrier Sense Multiple Access (CSMA)  
The random access of the physical medium is carried out by Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA). In contrast to the IEEE 802.3 standard, which uses collision detection, the IEEE 802.15 (wireless medium) standard uses collision avoidance in order to save some power. The collision avoidance is performed by setting a back-off counter to a random integer in the range of the maximum allowed contention window (CW), uniformly distributed over the interval \([1,CW]\) where \(CW\in(CW_{min},CW_{max})\). Performance of CSMA/CA in terms of throughput and latency depends largely on the number of sensor nodes present in a network and the amount of data being communicated. If a node has a large amount of data, it uses the physical channel to send the data, consequently preventing other nodes from gaining access to the medium. This can result in very low reliability for the nodes trying to send high priority medical data. Also, most of the energy is wasted in idle listening, as the nodes have to listen to the medium for the data packets destined for them. A pure CSMA/CA technique has several limitations, including high energy-consumption, high latency, and very low reliability. However, various hybrid CSMA/CA protocols have been developed for BSN applications including health monitoring. Performance of these hybrid CSMA/CA protocols can be improved by incorporating techniques such as scheduling the random access in dedicated time-slots, prioritizing the sensor nodes, and incorporating WuR to wake-up specific sensor nodes before sending data.
# Understanding Balance Control in the Context of Riding a Personal Mobility Device
## 4 Discussion
### Performance of the Balance Control  
#### 4.2.1 Effects of Target Distance and Direction on the Balance Control  
Maintaining balance with control of the COP at a target within the BOS was found to be most affected by the target distance. Previous studies showed similar results that the leaning posture increased postural sway and instability [31, 46, 47]. In this study, the postural stability was deteriorated significantly with an increase in the COP target distance from the neutral position. When the body is leaning, the moments at the ankle joints are higher than when the body is at the neutral standing position. This also increases muscle activation to maintain the leaning posture and causes more force exertion, which leads to higher postural instability.  
However, the effect of leaning posture on the postural sway can be varied according to the leaning direction. There were four distance-based COP measures that have interaction effects with the target distance and direction, and three of them were the measures in the ML direction. There was also no effect of the target distance on the four measures in the W direction. These results imply that the target distance considerably affects the postural sway in the AP direction, and there may also be some effects of footedness on the postural sway in the ML direction as most of the participants were right-footed. In this study, a comparison of the values of postural stability by the target direction showed no significant differences, but this may be due to the short periods of measurement for each trial. The effects of the leaning direction with longer durations need to be further studied in relation to the directional control of the SPMD.
## 3 Results and Discussion  
### Stretching Test and Morphological Characterization of the Au-SEBS Electrodes  
After preparing the Au-SEBS electrodes (Figure 2a), we prepared the SEBS films with variable Au thicknesses to investigate the effect of the Au nanomembrane thickness on the electrical conductivity (Figure 2b). Naturally, the sheet resistance of the Au-SEBS film decreased as the Au nanomembrane thickness increased. The electrode with an Aunanomembrane thickness of 30 nm was conductive, indicating that the gold particles were sufficiently deposited to form an electrical pathway on the surface of the SEBS film.  
However, as seen from the optical microscope images in Figure 3, morphological changes were observed in the pristine, 100% stretched, and released states of the Au nanomembranes. Multiple micro-nanoscale cracks were seen on the surfaces of the electrodes. On stretching, the cracks spread widely, and larger gaps were observed between the Au particles. However, when they were released, the Au particles reconnected with each other, which indicates that the cracks in the Au nanostructure were supported by the elastic SEBS substrate (its elastic modulus was \(\sim\)3.5 MPa); thus, stable electrical properties were retained during stretching and releasing.
## 3 Data and Information
Security mechanisms of a system are designed to prevent unauthorized access to system resources and data, however complete protection is unrealistic in this fast-paced cyber world users are in. In a recent survey, [PERSON], [PERSON] et al., (2020) showed that since the beginning of the COVID-19 pandemic, the World Health Organization (WHO) had detected a dramatic increase in the number of cyber-attacks. An alarming result came from Italy [10] in which the COVID-19 pandemic had heavily affected cybersecurity from January to April 2020. The total of attacks, accidents, and violations of privacy had doubled due to the detriment of companies and individuals shifting from various securities locations within their country. [PERSON], [PERSON] et al., further explored the various devices that were used during the pandemic to affect security. As hackers become increasingly clever and the uses of bots [11] take over, their \"attacking\" methods rely on Machine Learning (ML) techniques which is a subset of Artificial Intelligence (AI) to help. Bots is a short form for \"Virtual Robots\" that are programmed to use malicious code [11]. Bots are highly adaptable worker bees that do their masters' bidding over a broad internet, in this case, robots scattered throughout the global internet. There exist both good bots and bad bots, it all depends on how they are used. Through this study, the observation looks at various methods to help devices identify these bots and how they behave. In order to keep up with these hackers and their bots and monitor their activities, the use of an IDPS system is now even more important than ever [1]. An IDPS system is able to help learn about bad patterns compared to good patterns on the internet. These various approaches are needed to protect and shield our data, big and small. The following section will raise awareness of cybersecurity and its threats towards handling and protecting Data and Big Data. The types of threats and their preventions are discussed.
## 3 Design of Structural Metrics
### Network Density  
As one of the criteria to evaluate the cohesiveness and structural stability of the network, the network density value is calculated by comparing the actual number of relationships between the participants in the network with the total number of relationships in the network. After analyzing the obtained value, if the value is higher, it shows that the network structure has higher stability and cohesion degree, lower looseness degree, and higher coordination in the overall development. The specific formula is as follows:  
\[D=\sum\
olimits_{i=1}^{l}r_{ij}/n(n-1) \tag{5}\]  
where \(D\) is the network density, \(r_{ij}\) is the number of edges between two nodes, and \(n\) is the number of nodes.
## 2 System Design
### Spectral Efficiency  
For a single user, the length of an MU-FTN-DCSK symbol is given as (\(2\beta\rho_{(d)}T\)). Hence, we obtain the percentage of spectral gain \(S_{e}\) expressed as,  
\[S_{e}=\left(\frac{1}{\rho_{(d)}2\beta T}-\frac{1}{2\beta T}\right)\times 100\%. \tag{11}\]  
The spectral gain is then simplified to the following expression  
\[S_{e}=\left(\frac{1-\rho_{(d)}}{\rho_{(d)}}\right)\times 100\%. \tag{12}\]  
For the total spectral gain for \(U\) users, we sum over the total number of users as follows  
\[S_{e}=\sum_{p=1}^{U}\left(\frac{1-\rho_{(p)}}{\rho_{(p)}}\right)\times 100\%. \tag{13}\]  
Given this expression, we know that the system's spectral efficiency depends on the total number of users and their individual sampling parameters. Therefore, to reach a maximal spectral efficiency, one would have to increase the number of users to maximum while trying to minimize their sampling parameters concerning the desired BER performance.