esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of an Adaptive Force Control Strategy for Soft Robotic Gripping
## 2 Materials and Methods  
This research investigated methods for estimating and controlling the force applied by a flexible robotic gripper, specifically, a Festo fin ray effect gripper. The proposed solution uses finite element analysis (FEA) to generate an extensive dataset to train an autoencoder for feature extraction. The autoencoder compressed images into a latent set of features that describe the shape of the gripper fingers. These features were then used to construct a regression model that estimates the normal force applied by the gripper based on the deformed shape. Using feedback generated by the visual force sensor, an adaptive control algorithm, model reference adaptive control (MRAC), was designed to control the uncertain and nonlinear dynamics of the gripping process. Incorporating visual force feedback into an MRAC control scheme was called virtual reference adaptive control (VRAC) for this work. One of the goals of this work was to design the VRAC to be generally applicable to a wide range of objects and operating conditions, which also lends itself to being able to transfer to other soft grippers, creating general-purpose control strategies for force control of compliant mechanisms. To further define the scope of this work, all objects were rigid, and, as such, the methods presented here were not tested on objects that could be deformed significantly by the gripper. It is likely that the methods proposed would still be effective to some extent, with a reduction in accuracy to some degree and the controller would likely need to be re-tuned to better match the dynamics of a flexible structure.
# A Cross-Layer Media Access Control Protocol for WBANs
## 2 Related Works
### MAC Protocol  
The mobile edge computing (MEC) network technology is applied in [9], and a multi-channel MAC protocol is also designed for WBAN based on the Markov decision process (MDP). Both the channel condition and the reward value are considered in the protocol. In order to improve the network throughput while reducing the overhead of the protocol, the FETRO protocol is proposed in [10]. When assigning time slots to sensors, the data rate is an important parameter considered by the coordinator, and different transmission requirements can be satisfied. The MG-HYMAC protocol [11] aims to improve the network energy efficiency and prolong the lifetime of the bio-sensors. A transmission scheduling scheme is proposed to allow bio-sensors with less critical information to upload packets following the duty cycle operation of the coordinator. The data uploading plan can be scheduled among nodes to reduce transmission collisions and prolong the network lifetime. A slot allocation mechanism based on a bargaining game theory approach is proposed in [12], which optimizes the slot allocation among competing devices in a WBAN. A slot allocation mechanism is optimized in [13], which assigns dedicated slots to high-priority data flows while optimizing a dynamic superframe structure to improve the transmission delay and network throughput performance of WBANs. Differentiated priorities can be assigned to different data types in WBANs according to a priority-based MAC protocol proposed in [14], which improves reliability and delay performance. A MAC protocol specifically designed for in-body sensors in WBANs is proposed in [15], which aims to improve energy efficiency and reduce energy consumption. A Hybrid Multi-Class MAC Protocol for IoT-Enabled WBAN Systems is proposed in [16], which presents a hybrid MAC protocol that combines both TDMA and CSMA/CA schemes to improve the efficiency of data transmission in WBANs. Traffic class prioritization is considered in [17], which adopts slotted-CSMA/CA to improve the reliability and delay performance of intra-WBAN communication. AT-MAC [18] is an adaptive MAC protocol that adjusts the payload size of the MAC frame according to the wireless channel quality, which improves the performance of data transmission in WBANs.
## 1 Introduction
Such simplifications neglect the 3-D features of mountainous terrain, leading to less precise estimation of lightning EM wave propagation and reflection over complex terrain. Thus, to more accurately simulate the propagation characteristics of lightning EM fields across mountainous terrain and to evaluate LIVs under various lightning strike locations, the adoption of a 3-D modeling approach becomes essential. To provide a more comprehensive understanding of the influence of mountains on LIVs, in this study, a size-controllable Gaussian-shaped mountain model is implemented using the 3-D FDTD method [39; 40], and overvoltages induced by lightning are calculated by the Agrawal coupling model. The height and width of the mountain are adjustable, and two typical line configurations are incorporated. Existing studies generally assume lightning strikes the mountain top, different lightning strike locations are considered in our 3-D model, which represents an advantage of the 3-D model over the 2-D axisymmetric model. This research contributes to the lightning protection of overhead lines in mountainous terrain. The rest of this paper is organized as follows. In Section 2, a 3-D FDTD method in Cartesian coordinates is developed to calculate the lightning EM fields over a Gaussian-shaped mountain, combining with the Agrawal coupling model for the calculation of LIVs on a single overhead distribution line over lossy ground. The adoption of Gaussian distribution modeling enables the precise characterization of the curve surface of mountainous terrains, which overcomes the limitations of traditional rectangular approximation models. The proposed 3-D model is validated by comparison with the existing results obtained by [PERSON] et al. [31]. In Section 3, LIVs along the distribution line are evaluated over a Gaussian-shaped mountain using the 3-D FDTD method. Three different strike locations and two line configurations were considered, with Gaussian-shaped mountains having different widths and heights. A comprehensive analysis of mountain terrain's impact on LIVs characteristics by incorporating multiple lightning strike locations. A concise discussion is provided in Section 4. The main conclusions are summarized in Section 5.
## 4 Optimal ANN Selection
### Predictability Analysis  
To evaluate the performance of the analysis in this study, different statistical indicators were considered. These indicators were MAE (\(\text{AE}=\frac{1}{\text{n}}\sum_{i=1}^{n}\lvert\text{Mod}_{i}-\text{Exp}_{i}\rvert\)), average absolute error (\(\text{AAE}=\frac{\sum_{i=1}^{n}\lvert\frac{\text{Mod}_{i}-\text{Exp}_{i}}{ \text{N}}\rvert}{\text{N}}\)), mean (\(\text{M}=\frac{\sum_{i=1}^{n}\frac{\text{Mod}_{i}}{\text{Exp}_{i}}}{\text{N}}\)), and root mean square error (\(\text{RSME}=\sqrt{\frac{\sum_{i=1}^{n}(\text{Mod}_{i}-\text{Exp}_{i})^{2}}{ \text{N}}}\) or \(\text{MSE}=\frac{\sum_{i=1}^{n}(\text{Mod}_{i}-\text{Exp}_{i})^{2}}{\text{N}}\)), where Modi and Expi are predicted and experimental values, respectively. MAE and AAE exhibit the accuracy of the model overall, M determines if the model exhibits overestimation or underestimation, and RMSE is an indicator of high errors.
# From Complex Modulus E* to Creep Compliance D(t): Experimental and Modeling Study
## 1 Introduction
\[E^{*}(\omega)=\left(\frac{1}{E_{00}}+\frac{1}{i\omega\eta_{00}}+\sum_{i=1}^{n }\frac{1}{E_{i}+i\omega\eta_{i}}\right)^{-1} \tag{5}\]  
\[D(t)=\frac{1}{E_{00}}+\sum_{i=1}^{n-1}\frac{1}{E_{i}}\left(1-e^{\frac{t}{i}} \right)+\frac{1}{\eta_{00}} \tag{6}\]  
where: \begin{tabular}{l l} E\({}^{*}(\omega)\): & complex modulus (MPa). \\ E\({}_{00}\) and \(\eta_{00}\) & value of the complex modulus and the viscosity when \(\omega\tau\to 0\). \\ \(\omega\): & is the angular frequency, so that \(\omega\)/2\(\pi\) is equal to the frequency. \\ \(\eta_{\text{i}}\) and E\({}_{\text{i}}\): & viscosity and elastic modulus of each Kelvin Voigt branch. \\ D(t): & creep compliance (I/MPa). \\ \(\tau\): & is the relaxation time for single branch. \\ \end{tabular}   
The unidimensional rheological model with continuous spectrum called 2S2P1D (Figure 1b) [18] is widely used for the prediction of the LVE behavior of bituminous materials; it consists of two elastic (spring) elements, two parabolic creep elements and one viscous (dashpot) element. The complex modulus is given by the Equation (7). However, no analytical expression of the creep compliance function of this model is available in the time domain. \[E^{*}(\omega)=E_{00}+\frac{E_{0}-E_{00}}{1+\delta(i\omega t)^{-k}+(i\omega t)^{- h}+(i\omega gt)^{-1}} \tag{7}\]  
where:  
E0 is the value of the complex modulus when \(\omega\tau\rightarrow\infty\). \(\delta\), k and h are dimensionless constants and \(\beta\) is a dimensionless parameter, related to Newtonian viscosity \(\eta\). Several studies present a method to get viscosities (\(\eta_{\rm i}\)) and modulus (E\({}_{\rm i}\)) of the generalized Kelvin-Voigt (GKV) model from 2S2P1D model [19; 20; 21]. After the calibration of the 2S2P1D model in the frequency domain (E*), the method consists of minimizing the difference between the GKV model results and the results given by a reference viscoelastic model (2S2P1D).
###### Abstract  
Flash drought is characterized by rapid onset and short-duration drought conditions caused by a combination of factors, including high evaporation, high temperature, and prolonged periods of little to no precipitation, leading to a sudden and severe decrease in soil moisture levels. In comparison to conventional drought, it is more susceptible to the effects of global warming and has the potential to become a common drought phenomenon in the coming years, necessitating further research. In this paper, we focused on flash drought events, specifically in forest parts of northeastern China that are included within the Greater Khingan Mountains (GKM), Lesser Khingan Mountains (LKM), and Changbai Mountains (CM), using daily soil moisture data as well as SPOT-VEGFATION NDVI satellite data from 2000 to 2020 and determined their impact on the forest NDVI. Our major findings are as follows. (1) The forest within GKM had the maximum area being affected by flash drought events. (2) The frequency ranged from 1 to 2 times, whereas the total duration varied between 20 and 55 days over the study area in a 21-year period. (3) Flash drought was most common in the plant-growing seasons. (4) The flash drought events had a negative influence on the forest NDVI. Our study contributes to a deeper understanding of the flash drought dynamics in forest areas of northeast China for flash drought monitoring, prediction, and management strategies in this region.  
flash drought; northeast China; 2000-2020; forest; NDVI 2020
# Investigation of Energy Cost of Data Compression Algorithms in WSN for IoT Applications
## 3 Data Compression Techniques
### RLE (Run Length Encoding)  
Run Length Encoding (RLE) is a frequently used compression method. This algorithm's basic premise is laid forth in [40]. In the case that a data item \(\mathbf{d}\) appears in the input stream \(n\) times in a row, we replace each occurrence \(\mathbf{n}\) with a single pair of data items \((\mathbf{n},\mathbf{d})\). The RLE method [41] is shown graphically in Figure 3. However, the findings of RLE are dependent on the data source because it is based on the same sequential input stream.
###### Abstract
This method allows for more objective data analysis compared to the previously mentioned speech audiometry, as the test is non-invasive and measures hearing thresholds across various frequencies, providing detailed insights into the patient's hearing condition [8]. This study presents research that uses pure-tone audiometry data to predict speech audiometry scores. The deep learning models used in this study include a multi-layer perceptron (MLP) and a one-dimensional convolutional neural network (1D-CNN). Each deep learning model was applied to analyze pure-tone audiometry data and predict speech audiometry scores through regression analysis. The reason for using regression analysis is that the speech audiometry score is measured as a continuous value ranging from 0 to 100. Since pure-tone audiometry data are measured in dB thresholds, and speech audiometry scores are measured in percentages, a regression model was deemed suitable for predicting these values. Pure-tone audiometry data include hearing threshold data across various frequencies, which may have a non-linear relationship with speech audiometry scores. The MLP model is capable of learning these non-linearities, providing the ability to predict speech audiometry scores [9]. Furthermore, using multiple layers in the neural network makes learning the complex non-linear relationships between the input and output variables advantageous. The 1D-CNN excels in learning local patterns, showing strong learning capabilities [10]. Since pure-tone audiometry data consist of continuously measured hearing threshold values at specific frequency ranges, the 1D-CNN can identify critical frequency bands within the pure-tone audiometry data that significantly impact speech audiometry scores. The 1D-CNN integrates information across various frequency bands using convolutional filters, effectively modeling the non-linear relationships between the data [11]. The main contributions of this paper are as follows:  
* We proposed a model to predict speech audiometry scores by using tone audiometry data and comparing the performance of the MLP and 1D-CNN models. * The MLP and 1D-CNN models effectively learn the non-linear pure-tone audiometry data, with 1D-CNN showing strengths in processing sequential data. * The two models' performances were evaluated using the R\({}^{2}\) score and mean absolute error (MAE), and it was found that the 1D-CNN demonstrated slightly better MAE performance than the MLP. The following sections of this paper are as follows: Section 2 covers the related work on PTA data, speech audiometry data, MLP, 1D-CNN, and performance evaluation metrics. Section 3 provides an in-depth overview of deep learning models for classifying PTA data and predicting speech audiometry scores. Section 4 outlines the benchmark datasets, evaluation metrics, and comparative analysis. Lastly, Section 5 provides the conclusions and suggests directions for future work.
## 4 Analysis of the Basic Unit of GSMLI
### Modulation Scheme  
Different modulation schemes are used for controlling the MLI output voltage. Apart from reducing THD, fundamental frequency switching schemes are also capable of minimizing the switching losses. Fundamental switching frequency schemes such as Selective Harmonic Elimination (SHE), nearest level control, and space vector control are preferred for high power applications. The main disadvantage of SHE is to solve the system of nonlinear trigonometric transcendental equations, which consume more computational time. Hence, the SHE technique is not concerned with real-time (closed-loop) applications. The nearest control techniques can eliminate this drawback of SHE. Nearest Level Control (NLC) can be classified as (1) the nearest space vector control and (2) nearest level control [26]. In this work, an optimized nearest level control is utilized for controlling the output voltage and different carrier signals are compared with a reference signal [26]. The level generation method and block scheme are shown in Figures 6 and 7, respectively, for the NLC.  
The equation for output voltage is shown below:  
\[V_{out}=M*(N_{level}-1)/2*V_{dc}*cos(wt)\]  
where m is the modulation index and is expressed as  
\[M=V_{ref}(max)/nV_{dc}\]  
where \(n=(N_{level}-1)/2\).
## 2 The Duality of Linear Quadratic Regulation (LQR) and Estimation (LQE)
### Duality Theorem  
Consider a dynamic model in the LQR regulation problem  
\[\mathbf{x}_{k+1}=\mathbf{F}_{k+1,k}\mathbf{x}_{k}+\mathbf{B}_{k}\mathbf{u}_{k} \tag{5}\]  
and its dual system model in the LQE estimation problem  
\[\left\{\begin{array}{l}\mathbf{x}_{k}=\mathbf{F}_{k,k-1}^{T}\mathbf{x}_{k-1}+\mathbf{w}_{k} \\ \mathbf{z}_{k}=\mathbf{B}_{k}^{T}\mathbf{x}_{k}+\mathbf{v}_{k}\end{array}\right. \tag{6}\]  
According to the duality theorem [19; 20], if the given initial conditions \(\mathbf{P}_{N}\), \(\mathbf{Q}\), and \(\mathbf{R}\) in these two systems are equal, respectively, the obtained optimal solutions \(\mathbf{P}_{k}\), \(\mathbf{K}_{k}\) in system (5) and (6) will also be the same, not only for the ranks of these matrices, but also for each element in the matrices \(\mathbf{P}_{k}\), \(\mathbf{K}_{k}\).  
Since the value of the optimal solution of \(\mathbf{P}_{k}\) and \(\mathbf{K}_{k}\) in the LQR and LQE of the dual system are the same, then the criteria for qualitative characteristics based on \(\mathbf{P}\), \(\mathbf{Q}\), and \(\mathbf{R}\) of the original system and the dual system will also be the same. That is, the degrees of observability and controllability are dual. We can demonstrate the duality of the degrees of observability and controllability in various forms, for example [4; 7; 10].  
Next, we considered a numerical criterion for the degree of observability, which is more useful in practice. Using the criterion of the degree of observability for the analysis of state variables of the dual system, it is possible to calculate the degree of controllability of the corresponding state variables of the original system.
## 4 Discussion
### Importance of Defining an Operating Range  
Our overall and long-range goal in developing a smart phone-based motion capture and analysis system is to create a means that is accurate, effective over a wide distance range, mobile (not affixed to physical structures), readily deployable in a wide range of settings (e.g., home office, clinic, and field), made of readily available components keeping cost down, as well as user-friendly for both professional and patient. Further, in use, it should be adaptable to a spectrum of movement assessments for a range of applications. As such, as a first step we defined the operating limits of this system examining marker capture, identification, and discrimination over a range of working distances. Our study revealed  
Figure 11: (**a**) Max angle of finger abduction captured at 6 ft distance using 2 mm markers. (**b**) Max angle of lateral bend test captured at 5 ft distance using 10 mm markers.  
that the MO\({}^{2}\)CA system can accurately detect and resolve markers over considerable distances, ranging from 0.5 ft to 18.5 ft. This overall operating range of \(>\)18 ft will allow the detection of markers when affixed to an individual that is useful for measuring the extent or range of motion associated with a wide range of activities and maneuvers.  
A useful way to consider motion quantitation in the context of activities relates to the specific body or body movement and elements that are being measured. For our purpose we have divided these into three groups: 1. whole body movement, 2. large region or component movement, upper or lower extremity, and head, and 3. localized movement, e.g., digits and regions of the face. This categorization affords utility for a wide variety of applications. In the present study we first measured markers while static, i.e., serially, at defined positions in an experimental movement protocol. These were utilized here as facsimiles for positions associated with the extent of motion that may be encountered in real-world use scenarios of human movement. The extent of distances tested and verified to be effective will allow the use for determination of the range of motion associated with generalized health and wellness training and flexibility determination, as well as for specific diagnostic maneuvers utilized in medical practice. A table of representative uses is included herein (Table 2).  
Following our static studies, we then extended our approach to specifically examine the utility of motion capture for clinically useful maneuvers as outlined in Table 2. In studying regional motion-finger abduction/adduction, and whole-body motion-lateral bend, we demonstrated clear discrimination afforded by this approach to detect and quantitate motion differences between normal and abnormal conditions.
## 4 Conclusions  
This study comprehensively explained the DR technique, enabling the readers to comprehend and apply it when necessary. The presented case studies demonstrated that the technique was straightforward in linear systems and more rigorous than the existing methods employed in commercializing and quality systems of petroleum products. This approach effectively reduced the measurement uncertainties, making it suitable for resolving legal disputes with meticulous examination.  
In the petroleum industry, effective DR measures enable organizations to reconcile data discrepancies, identify potential risks, and take proactive steps to mitigate them, ensuring accurate and consistent data across various systems and processes. This streamlines operations and promotes a culture of transparency and accountability. The petroleum industry uses DR in its processes to ensure improved operational efficiency, legal clarity, and compliance in its commercial transactions.  
In future works, DR can be applied in various areas within the applied sciences, such as engineering, materials science, environmental science, agricultural sciences, food science and nutrition, geosciences, biotechnology, and pharmaceutical sciences. By applying DR in these applied science areas, researchers and scientists can ensure accurate and reliable data analysis, improve process optimization, support evidence-based decision-making, and advance scientific knowledge and innovation.  
Conceptualization, R.M.M., A.M.S.R. and E.C.d.O.; methodology, R.M.M., A.M.S.R. and E.C.d.O.; software, R.M.M., A.M.S.R. and E.C.d.O.; validation, R.M.M., A.M.S.R. and E.C.d.O.; formal analysis, R.M.M., A.M.S.R. and E.C.d.O.; investigation, R.M.M., A.M.S.R. and E.C.d.O.; resources, R.M.M., A.M.S.R. and E.C.d.O.; data curation, R.M.M., A.M.S.R. and E.C.d.O.;  
Figure 4: Reconciled data associated with the compatibility study (---, reconciled uncertainty bars) [33; 34; 35].  
writing--original draft preparation, R.M.M., A.M.S.R. and E.C.d.O.; writing--review and editing, R.M.M., A.M.S.R. and E.C.d.O.; visualization, R.M.M., A.M.S.R. and E.C.d.O.; supervision, R.M.M., A.M.S.R. and E.C.d.O.; project administration, R.M.M., A.M.S.R. and E.C.d.O.; funding acquisition, R.M.M., A.M.S.R. and E.C.d.O. All authors have read and agreed to the published version of the manuscript.  
The authors are thankful for the financial support provided by the scholarship from the Brazilian agency CNPq (305479/2021-0). This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brasil (CAPES)--Finance Code 001.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
Author [PERSON] was employed by the company Petrobras S.A. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.