esa-sceva/satcom-chunk-collection huggingface dataset:
# Manipulation Planning for Large Objects through Pivoting, Tumbling, and Regrasping
###### Abstract  
Robotic manipulation of a bulky object is challenging due to the limited kinematics and payload of the manipulator. In this study, a robot realizes the manipulation of general-shaped bulky objects utilizing the contact with the environment. We propose a hierarchical manipulation planner that effectively combined three manipulation styles, namely, pivoting, tumbling, and regrasping. In our proposed method, we first generate a set of superimposed planar segments on the object surface to obtain an object pose in stable contact with the table, and a set of points on the object surface for the end-effectors (EEFs) of a dual-arm manipulator to stably grasp the object. Object manipulation can be realized by solving a graph, considering the kinematic constraints of pivoting and tumbling. For pivoting, we consider two supporting styles: stable support (SP) and unstable support (USP). Our proposed method manipulates large and heavy objects by selectively using the two different support styles of pivoting and tumbling according to the conditions on the table area. In addition, it can effectively avoid the limitation arising due to the arm kinematics by regrasping the object. We experimentally demonstrate that a dual-arm manipulator can move an object from the initial to goal position within a limited area on the table, avoiding obstacles placed on the table.  
non-prehensile manipulation; manipulation planning; pivoting; robotics +
Footnote †: journal: Applied Sciences  
Article Manipulation Planning for Large Objects through Pivoting, Tumbling, and Regrasping
# Frequency Selective Surfaces: A Review
## 3 Frequency Selective Surfaces (FSSs)
### Principle of Periodic Structures (e.g., FSS)  
Principally, there are two ways to find the impedance and scattering properties of periodic structures [2], first is mutual impedance method which is linked to MoM and second is the plane wave expansion approach, also called the spectral method. Periodic FSS structure can be designed based on Floquet theorem. By definition, a periodic structure must be infinite in extent. A planar array which is infinite in x- and z-direction with identical inter-element spacing (D\({}_{\rm x}\) = D\({}_{\rm z}\)) is termed as a truly periodic configuration (infinite \(\times\) infinite) and is shown in Figure 3b. Assume if the array is incident by an incoming EM wave propagating in certain \(\hat{\rm s}\) direction is given in (4).  
\[\hat{\rm s}=\hat{\rm s}\rm s_{x}+\hat{\rm y}\rm s_{y}+\hat{\rm z}\rm s_{z} \tag{4}\]  
The amplitude of currents on the entire element will be equal, while the phase of the incident EM field will match with the phases of these currents. By [PERSON]'s theorem, the element currents in column m and row n are given as (5) [31].  
\[\rm I_{mn}=I_{0,0}e^{-j\beta mD_{x}s_{x}}e^{-j\beta mD_{z}s_{x}} \tag{5}\]  
Using [PERSON]'s law for reference element 0, 0 gives (6).  
\[\rm V^{0,0}=\left[Z_{L}+\sum_{m=-\infty}^{\infty}\sum_{n=-\infty}^{\infty}Z_{0,mn}e^{-j\beta mD_{x}s_{x}}e^{-j\beta nD_{x}s_{x}}\right]I_{0,0} \tag{6}\]  
and, array scan impedance is given by (7).  
\[\rm Z^{0,0}=\sum_{m=-\infty}^{\infty}\sum_{n=-\infty}^{\infty}Z_{0,mn}e^{-j \beta mD_{x}s_{x}}e^{-j\beta nD_{x}s_{x}} \tag{7}\]  
Figure 3: (**a**) FSS periodic structure consisting of complimentary array elements, their equivalent circuits and the frequency responses (Left to right). The patch array element exhibits a capacitive response (low pass), whereas the grid array element shows an inductive response (high pass) [31] (**b**) A true periodic structure showing inter-element spacing (D\({}_{\rm x}\) and D\({}_{\rm z}\)) and element length of L.
# Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols
## 5 Industrial Application Perspective
### Smart City  
Large-scale uses of IoT technology, such as intelligent parking, automated lighting, and smart garbage collection, are being developed [31; 92] for Smart City applications. Similar to smart garbage collection systems, smart lighting systems respond to a measure with long variable periods by acting or reporting information [93]. Even though there is no significant dependency upon latency and jitter, in certain cases, the triggering factor is concurrent for many UE devices. LoRaWAN and Sigfox are acceptable solutions in this kind of scenario since they can cover large areas and a sizable number of user equipment at the cost of increased latency, collision, and jitter rates [25; 31; 40; 71]. For example, [PERSON] et al. [94] conducted a case study regarding intelligent city applications in Estonia. They investigated the coverage analysis of two LPWAN technologies, NB-IoT and Sigfox, on university campuses in Estonia's two major cities, Tartu and Tallinn. The results showed that Sigfox and NB-IoT give continuous coverage in outdoor areas, whereas NB-IoT performs better indoors.
## 3 Toward the Generation of Isolated Attosecond Hard X-ray Pulse
### Optimization of Current Profile by Manipulation of Energy Profile of Electron Bunch
(**g**)–(**i**): horizontal (black line) and transverse normalized-emittance (red line) on time scale [42]. Figure 10: (Color online). The change of the characteristics of the 6 kA, 12 \(\upmu\)m-long electron bunch. (**a**)–(**c**): the longitudinal energy distribution. (**d**)–(**f**): current profile. (**g**)–(**i**): horizontal (black line) and transverse normalized-emittance (red line)[42]. Figure 9: (Color online). The change of the characteristics of the 4 kA, 16 \(\upmu\)m-long electron bunch. (**a**)–(**c**): the longitudinal energy distribution. (**d**)–(**f**): current profile. (**g**)–(**i**): horizontal (black line) and transverse normalized-emittance (red line)[42]. We display the simulation results for the case of 6 kA, 12 um long (\(\sim\)40 fs)-electron bunch in Figure 10. The longitudinal energy distributions of this electron bunch before wiggler, after wiggler and after chicane, respectively, are shown in Figure 10a-c. Note that the current profile (Figure 10a) differs from those in Figures 8a and 9b. Figure 10d-f shows corresponding current profiles. Note that there is only one major current peak of 17 kA current in the central part and side peaks almost disappear (Figure 10f) compared to the cases of the 3 kA and 4 kA electron beam. The rise in the peak current in the central part is due to the steepening of the energy spread. This demonstrates that by the manipulation of the energy distribution of electron bunch, the side peaks can be suppressed, resulting in a single isolated current peak. The time length of this current-peak is around one fs FWHM, getting even shorter in the undulator.
## 3 Results and Discussion
### Wound Healing Capacity of Au-Ag Alloy NPs  
Au-Ag alloy NPs exhibited prominent antioxidant and antimicrobial activities (Tables 2 and 3). Therefore, they can promote wound healing and cell/tissue repair [51]. Figure 5 depicts the wound healing capacity of Au-Ag alloy NPs. Both HaCaT and CCD9665K cells exhibited considerable wound healing with increasing concentrations of Au-Ag alloy NPs. Specifically, the rate of wound healing in HaCaT cells was 36.5% \(\pm\) 2.4% for control treatment. By contrast, for treatment with 20 mg/L Au-Ag alloy NPs, this rate was 72.5% \(\pm\) 2.3% within 24 h (highest rate). The proliferation density of CCD966 SK cells was 57.4 \(\pm\) 1.2 cells per wound area after control treatment. After treatment with 20 mg/L Au-Ag alloy NPs, the proliferation density increased to 124.1 \(\pm\) 3.2 cells per wound area, representing a 2.16-fold increase compared with the density in the control group. However, no significant difference was observed between the 15 and 20 mg/L concentrations of Au-Ag alloy NPs (\(p\) > 0.05). [PERSON] et al. (2020) demonstrated that bio-capped NPs effectively promoted wound healing without causing cytotoxicity [52]. In HaCaT and CCD966 SK cells, the wound healing capacity of 20 mg/L MQLE (positive control group) was 52.1% \(\pm\) 1.4% and 90.3 \(\pm\) 2.6 cells per wound area, respectively. The wound closure rate of 25 mg/L sericin-mediated Au-Ag alloy NPs was 63.38% for HaCaT cells [41]. Therefore, MQLE-mediated Au-Ag alloy NPs are more effective than sericin-mediated Au-Ag alloy NPs. Notably, considerable wound recovery (in vivo) was observed in an animal model treated with an Ag-Au bimetallic nonwoven mat [53].
# Decimal Multiplication in FPGA with a Novel Decimal Adder/Subtractor
## 2 Related Work
The solution considers a new decimal adder based on a mixed BCD/excess-6 representation and a 5221 recoding of the multiplier digits. Partial products are obtained from the addition of a multiple in the set {0, 2X, 5X, 2X + 5X} and a multiple in the set {X, 2X}. Two novel decimal multipliers on FPGA with different area/performance tradeoffs with both multipliers improving the area and performance of state-of-the-art multipliers are proposed. Both methods use a new adder/subtractor based on the excess-3 representation of multiples. Two different sets of multiples are considered: {0, X, 2X, 5X, 10X} and {2X, 4X, 5X}. Partial products are obtained by the addition or subtraction of two multiples of the sets. The method permits a very efficient generation of multiples, which considerably reduces the required resources. The area of the largest decimal multiplier is smaller than the area of an equivalent binary multiplier.
## 4 Distributed Hotspot Network Architecture
### Distributed and Decentralized Hotspot Network Layer
#### 4.2.1 Hotspot Gateway  
We can use Wi-Fi or LoraWAN as a promising hotspot provider technology because they have their own advantages over other cellular and LPWAN alternatives. All hotspots that are equipped with Wi-Fi or LoraWAN can be configured as gateways for IoT device data. Standard, off-the-shelf hardware platforms such as Raspberry Pi 4 or higher, Beaglebone black or higher, ASUS Tinker board, Libra Computer board AML-5905X-CC, Odroid N2+, UDOO Blot v3, and other related boards can be used as hotspot gateways. Such gateways must be equipped with a high-gain Wi-Fi or LoraWAN antenna. Anyone can use such hotspot gateways to provide IoT devices in the vicinity with the network.
# Trajectory Tracking Outer Loop Regressor-Based Adaptive Controller for a Quadrotor
## 2 Preliminaries
### Control Objective  
Let us assume that the matrices \(K_{u}\) and \(K_{v}\) in (8) and (9) are unknown, and the vectors \(\mathbf{q}\in\mathbb{R}^{4}\) in (7) and \(\dot{\mathbf{q}}\in\mathbb{R}^{4}\) are available for measurement. Defining the trajectory tracking error as  
\[\mathbf{e}(t)=\mathbf{q}_{d}(t)-\mathbf{q}\in\mathbb{R}^{4}, \tag{10}\]  
and its derivative  
\[\dot{\mathbf{e}}(t)=\dot{\mathbf{q}}_{d}(t)-\dot{\mathbf{q}}\in\mathbb{R}^{4}, \tag{11}\]  
where \(\mathbf{q}\) is defined in (7) and \(\mathbf{q}_{d}=[x_{d}(t)\;y_{d}(t)\;z_{d}(t)\;\psi_{d}(t)]^{\top}\in\mathbb{R}^{4}\) is the reference signal, which is assumed to be three times continuously differentiable. The objective is to design an outer loop controller \(\mathbf{u}(t)=[u_{\theta}(t)\;u_{\phi}(t)\;u_{z}(t)\;u_{\dot{\psi}}(t)]^{\top}\in \mathbb{R}^{4}\) so that the limit  
\[\lim_{t\to\infty}\begin{bmatrix}\mathbf{e}(t)\\ \dot{\mathbf{e}}(t)\end{bmatrix}=\mathbf{0} \tag{12}\]  
is guaranteed.
# Autonomous Visual Navigation System Based on a Single Camera for Floor-Sweeping Robot
## 4 Experiments
### Garbage Target Detection Experiment  
We performed optimization work to improve the performance of the garbage detection model as the following:  
1. The garbage was subdivided into three types of labels, including paper, peel, and rubbish, to improve the accuracy of garbage identification.
2. There was a severe imbalance among the data sample classes, which affected the learning of the SSD model. We used horizontal flip and vertical flip to double the sample size of the images of the peel tag category so that we could balance the ratio of the samples with the other two types.
3. Data with a larger size can improve the neural network's performance effectively, and we used random channel offset and random noise addition techniques to augment the data.  
We choose mean Average Precision (mAP) to measure garbage detection accuracy. The mAP is the average value of AP (Average Precision) for each category, as shown in Equation (4):  
\[mAP=\frac{\sum\limits_{i=1}^{K}AP_{i}}{K}, \tag{4}\]  
where \(K\) is the class of detected targets, and \(AP\) denotes the average precision of each class of targets.  
Figure 8: Turtlebot2 experimental platform.  
\begin{table}
\begin{tabular}{c c} \hline
**Software** & **Version** \\ \hline Operation System & Ubuntu 16.04 LTS, ROS Kinetic \\ Programming Language & Python 3.6.5 \\ Framework of Deep Learning & Tensorflow 1.13.0 \\ \hline \end{tabular}
\end{table}
Table 3: Experimental software environment.  
The classification of the experimental dataset and the performance of mAP on the test set are shown in Table 4, and the experimental parameters of each dataset are set as shown in Table 5.  
In Table 4, the mAP value gradually increases on the three datasets, and the performance on each dataset during training is shown in Figure 9. The expansion and optimization of the dataset improves the balance between the samples, which is beneficial to fitting the detection model. The model appears to overfit after 20,000 steps when the dataset sample size is 310 and 484, and as the dataset sample increases to 1452, the model reaches its best-fit point after only 23,000 steps.  
We selected the 1452 dataset and divided it into training, validation, and test sets in the ratio of 6:2:2. As a result, we can obtain the final ground trash detection model. After 23,000 steps, the Loss value stabilized, the model training was stopped, and the model reached the best fit and converged in Figure 10. The experimental result is shown in Figure 11. It can be seen that the garbage detection module has very high accuracy in detecting garbage such as paper, peel, and crumbs, and it is accurate in locating and identifying multiple targets in a single picture.
## Numerical Observation of the Jet Flight Patterns of  
Linear Shaped Charges  
**[PERSON] and [PERSON] ***  
Energetic Systems Research Group, Department of Mechanical Engineering, New Mexico Tech,  
Socorro, NM 87801, USA; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: lim(@nmt.edu;  
Tel.: +1-575-835-6589.  
_Received: 1 June 2012; in revised form: 27 June 2012 / Accepted: 2 July 2012 / Published: 6 July 2012_  
**Abstract:** Linear Shaped Charges (LSCs) have been assumed to function in the same manner as Conical Shaped Charges (CSCs) in the published literature that describes a metallic jet process for penetration. However, based on the facts that the LSC liner is composed of flat surfaces instead of the curved cone shaped liner of a CSC and the LSC detonation propagation is perpendicular to the penetrator projection direction, the approach to investigate the penetrator formation process of LSCs should be examined from a different perspective. Because there are fundamental differences between CSCs and LSCs in the formation of a jet, the cutting performances would be under different regimes. As a preliminary investigation, in this paper, we report a numerical observation of the LSC jetting properties using AUTODYN\({}^{\circledR}\).  
**Keywords:** linear shaped charges; jet profile; penetration; hydrocode
## 3 Results and Discussion
### Nanopore Structural Evolutions during Coal Structure Deformation  
Figure 6 shows that the relationship between the pore volume/pore-specific surface area of coal and the pore diameter presents a three-stage change, with boundaries of pore diameters of 10 nm and 50 nm, respectively. Therefore, nanopores of tectonically deformed coals are divided into the micropore (2-10 nm), mesopore (10-50 nm), and macropore (50-200 nm). The nanopore structures of tectonically deformed coals are obviously affected by the tectonic deformation of coal. An increase of degree of coal deformation, i.e., in the order of undeformed coal, cataclastic coal, granulitic coal, and crumple coal, causes obvious increases in the total pore volume and total specific surface area of tested pore size range (2-200 nm); the total pore volume increases from 4.371 \(\times\) 10\({}^{-3}\) cm\({}^{3}\cdot\)g\({}^{-1}\) to 14.746 \(\times\) 10\({}^{-3}\) cm\({}^{3}\cdot\)g\({}^{-1}\), and the total specific surface area increases from 0.517 m\({}^{2}\)/g to 7.211 m\({}^{2}\cdot\)g\({}^{-1}\) (Figure 7a,e).  
Figure 5: Absolute adsorption capacity of coal (**a**), and its error with excess adsorption capacity (**b**).  
It is clearly seen that an increase of degree of coal deformation causes the relatively consistent change trends of various types of pore volume and specific surface area. Specifically, as the degree of coal deformation increases, the pore volume and specific surface area present an obvious increasing trend in the case of micropores (Figure 7b,f), an increase at first (cataclastic coal and granulitic coal) and then stabilization (crumple coal) in the case of mesopores (Figure 7c,g), and a gradual decrease in the case of macropores (Figure 7d,h).
# Development of a Thermal Analysis Tool for Linear Machines
## 6 Conclusions  
In this article, a generic thermal analysis tool was developed for linear machines in _MATLAB Simulink_. The model is based on simple shapes and a layered winding model to allow an easy calculation of the thermal resistance and capacitance values.  
The article delivered the necessary guidelines for an easy implementation of the model in _Simulink_. Moreover, the calibration procedure and typical values of the calibration parameters were given. Thus, this article should serve as a reference manual for linear machine designers when implementing this kind of tool.  
The model was experimentally validated with a linear induction machine and a linear switched flux machine, and the calibration procedure of the model was also explained. The results show that the model is precise for both machines, and in a handful of different scenarios. Therefore, the generic model of this tool was validated.  
_Simulink_ has been demonstrated to be a very useful tool when developing this kind of tool. The predefined blocks and the automatic solving make it very easy to implement this model. However, the convective heat transfer and the conductive thermal resistance blocks do not allow adaptive parameters. Therefore, these elements must be modeled as variable thermal resistances.  
The influence of the mechanical system is not contemplated by the model. Thus, when designing a new machine, the authors would recommend to build a dummy prototype with an approximated size, and characterize the mechanical system. In this way, an accurate prediction of the thermal performance is ensured from the design stage.  
**Author Contributions:** Funding acquisition, A.U.; Investigation, I.E. and L.E.; Project administration, A.U.; Software, L.E.; Supervision, G.A. and A.E.; Validation, G.A. and A.E.; Writing--original draft, I.E.; Writing--review & editing, G.A. and A.E. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Acknowledgments:** The work of [PERSON] is partially supported by the Non Doctoral Research Staff Training Program of the Department of Education of the Basque Government, expedient number PRE_2020\(2\)0176.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Appendix A. Thermal Properties of Air**
## 4 Analysis of Optimization Calculation and Results
### Further Discussions
#### 4.3.1 Comparative Analysis of Optimization Objectives  
With the two optimization models, the comparison of the target values based on the optimal decision is shown in Figure 7. For parallel comparison, the operational costs here include only the natural gas and power consumption costs and equipment maintenance costs, under two schemes. It can be seen that the bi-level collaborative optimization model proposed in this study, with multiple constraints based on renewable energy penetration and carbon transaction costs, is superior to the results of single-level multi-objective programming optimization with respect to the life cycle cost and carbon emission objectives; in the former, the total life cycle costs, investment costs, operational costs, and maintenance costs are reduced by 3.0%, 4.0%, 2.3%, and 3.2%, respectively. Life cycle total carbon emissions are decreased by 4.9%. It can be seen, considering the low-carbon operation strategy in the planning optimization, that the low carbon emissions of the multi-energy complementary system replace the high carbon emissions of the purchased energy, on the basis of controlling the economic cost. Thus, the cost optimization and carbon emission results of the bi-level model are superior to the results of single-level multi-objective programming optimization.
# A Unified Machine Learning Framework for Li-Ion Battery State Estimation and Prediction +
## 3 The Proposed Framework for Li-Ion Battery State Prediction
### Configuration for Battery State Prediction  
The \"Configuration for Battery State Prediction\" component illustrates the definition of essential variables for each pipeline applied. Notably, this component facilitates the generation and application of multiple unique pipelines through diverse combinations of configurable dimensions. Thus, the configuration is achieved through a set of parameters that establish the specific characteristics of the applied pipeline. These parameters are designed to be versatile and adaptable, making them applicable across various battery states. It is also important to highlight that the configuration process enables the selection of the most suitable procedures for each pipeline in the following step, allowing flexibility in their application without necessitating the use of all available procedures. This generic approach ensures the framework's flexibility and enables its seamless customization to address diverse analytical objectives related to different battery state predictions.  
Figure 2: The overview of the proposed framework for Li-ion battery state prediction.  
The framework includes four configurable dimensions: reference value estimation, definitions of the training and testing sets, feature selection, and ML model selection. The \"Reference value estimation\" dimension enables the identification of the specific state to be predicted, as well as the definition of the reference values for that state. This step is critical for ensuring a robust and accurate evaluation process. The \"Definition of the training and testing set\" dimension focuses on partitioning the dataset to facilitate effective model training and unbiased evaluations through the testing procedure of the ML model. The \"Features selection\" dimension defines a set of features to be applied to the pipeline, which are relevant to the specific battery state. Finally, the \"ML model selection\" dimension defines the appropriate ML model to be applied for the battery state selected.