esa-sceva/satcom-chunk-collection huggingface dataset:
# Iterative Learning Control for Vibration Suppression of a Robotic Arm
## 2 Control of Robotic Arm with Elastic Joints
### Elastic-Joint Dynamics Modeling
#### 2.1.1 Forward Method Based on the Elastic-Joint Model  
The elastic-joint robot dynamics were proposed by [PERSON] in [4] as follows:  
\[\mathbf{M}(\mathbf{q})\ddot{\mathbf{q}}+\mathbf{C}(\mathbf{q},\dot{\mathbf{q} })\dot{\mathbf{q}}+\mathbf{g}(\mathbf{q})=\mathbf{\tau_{e}} \tag{1}\]  
\[\mathbf{B}\ddot{\mathbf{\theta}}+\mathbf{G}_{r}\mathbf{\tau_{e}}=\mathbf{\tau_ {m}}. \tag{2}\]  
Equations (1) and (2) formulate the link-side and motor-side equations of motion. \(\mathbf{\theta}\) and \(\mathbf{q}\) represent the angular displacement of the motors and links of the robotic arm. \(\mathbf{M}\), \(\mathbf{C}\), and \(\mathbf{g}\) denote the inertia matrix, Coriolis and generalized centrifugal forces, and the generalized gravitation forces, respectively. \(\mathbf{\tau_{e}}\) represents the elastic torque generated by a linear torsional spring. \(\mathbf{B}\) and \(\mathbf{\tau_{m}}\) denote the inertia matrix and torque of the motor, respectively. \(\mathbf{G}_{r}\) is the gear ratio.
## 1 Introduction
### Contributions
#### 1.1.3 Experimental Rigor  
* Comprehensive 5-fold stratified cross-validation is used for robust evaluation.
* Performance is compared against strong baseline models such as XGBoost, voting ensemble, TabNet, and MLPs.
* An in-depth analysis of the trade-offs between precision, recall, computational efficiency, and interpretability is provided.
## 2 Materials and Methods
### Measurement Equipment  
The polishing rate of the Ge\({}_{1}\)Sb\({}_{4}\)Te\({}_{5}\) film surface was estimated by measuring the Ge\({}_{1}\)Sb\({}_{4}\)Te\({}_{5}\) film thickness before and after CMP using ellipsometry (V-VASE, J.A. Woollam Co., Inc., Lincoln, NE, USA). Scanning electron microscopy (SEM, S-4800, Hitachi, Tokyo, Japan) image of the Ge\({}_{1}\)Sb\({}_{4}\)Te\({}_{5}\) film surface after CMP and etching was observed at an acceleration voltage of 15 kV. The surface roughness of the Ge\({}_{1}\)Sb\({}_{4}\)Te\({}_{5}\) film surface after etching was estimated by atomic force microscopy (AFM, Park system, Suwon, Korea), and the corrosion potential and current were measured using a potentiostat (CH750, CH Instruments, Austin, TX, USA). The chemical composition of the Ge\({}_{1}\)Sb\({}_{4}\)Te\({}_{5}\) film surface after etching and CMP were characterized using X-ray photoelectron spectroscopy (K-Alpha+, Thermo Fisher Scientific, Waltham, MA, USA) at 12 keV and 6 mA with a KE source of Al K\(\alpha\) (1486.6 eV). Cross-sectional LRB-x-RRB-images of the confined memory cells before and after the CMP pattern film were observed using high-resolution transmission electron microscopy (HR-TEM, JEM-2010, JEOL, Tokyo, Japan) with an accelerating voltage of 200 kV.
# Research Progress on the Influence of Thermo-Chemical Effects on the Swelling Pressure of Bentonite
## 3 Influence of Thermo-Chemical Effect on the Swelling Pressure  
This review mainly discusses the changes of the swelling pressure from the perspective of crystal layer swelling and double electric layer swelling in the presence of salt solutions and in different temperature conditions, almost ignoring the influence of mineral alteration during this process.  
Generally, the crystalline-layer swelling mechanism involving the internal minerals and the diffusive double-layer swelling mechanism involving the mineral particles are considered to be the main swelling mechanisms of bentonite [27] and are commonly considered to analyze the changes in swelling pressure in complex environments [28]. As shown in Figure 4, the wedging of water molecules into the crystalline layers leads to the crystalline layer swelling. The swelling changes from crystalline-layer swelling to double-electric-layer swelling when the number of water layers in the crystalline layers reaches a certain value [27; 29; 30]. Both swelling mechanisms are referred to as interlayer swelling. Interlayer swelling above 22 A is associated with double-layer swelling, and interlayer swelling between 10 and 22 A is associated with crystalline layer swelling caused by the adsorption of water molecules and is determined by the layer charge of clay minerals, exchange cations, and the attraction between water molecules and polar surface groups [31; 32; 33; 34]. The crystalline layer interval and the thickness of the diffusion double layer are essential factors in the study of the magnitude of the swelling pressure, and changes in temperature and pore solution can lead to changes in both, thus affecting the magnitude of the swelling pressure.  
Figure 3: Schematic view of the experimental setup using the constant-volume cell [9].
## 3 Methodology  
IoT is a heterogeneous network, as it consists of many different devices, and there can be a change in the number of devices in the network. The interoperability between these devices is a challenge due to the heterogeneous nature of the IoT network. Security, QoS, and network performance are all factors that are affected due to heterogeneity in the IoT network. The implementation of SDN in the IoT network is able to cater to many of these problems with the right approach. In the future, the degree of heterogeneity will increase as the size of IoT networks increases. This motivates the research community to focus on heterogeneity issues and propose methods to resolve them using the SDN-IoT control plane. In this study, we also focused on this problem with the QoS of SDN-IoT and its security perspective. Transferring information on time in a secure manner to the end user is important. This makes end-to-end performance parameters significantly important in multi-domain SDN-IoT networks. The controller response time of a flow is one of the key metrics to maintain QoS requirements and improve security at the same time. Let us discuss some examples and see how QoS and security are interconnected and the controller response time has a direct impact on it. Security mechanisms applied on SDN can be seriously hampered through a huge volume of traffic [46]. In the IoT network, the response time of the controller may be increased due to the heterogeneous nature of the network. This impacts the endpoint security solutions, which result in not identifying violations on time and late responses [47]. It is the responsibility of the controller in an SDN-IoT-driven network to maintain the resources timely; otherwise, the load on the controller will increase, and it will affect controller performance and may lead to security failure [48]. The interconnected node in the network and their nature of heterogeneity may affect the flow-specific QoS. Also, if the service time of the controller is too high, then packet loss will be considered in the network, and duplicate packets will flow into the network upon the request of the switch. This will lead to identical packets in the network and cause flow update problems [13; 49]. Our methodology to alleviate heterogeneity in the SDN-IoT network to maintain QoS and enhance security is very much inspired by [13]. The proposed method is simple and feasible. The centralized SDN controller has a universal view of the underlying IoT network. Therefore, it will receive the service rate of all distributed peer controllers. On the
# Pressure Drop and Particle Settlement of Gas-Solid Two-Phase Flow in a Pipe
###### Abstract  
Particle settlement and pressure drop in a gas-solid two-phase flow in a pipe with a circular cross-section are studied at mixture inlet velocities (\(V\)) ranging from 1 m/s to 30 m/s, particle volume concentrations (\(\alpha_{s}\)) ranging from 1% to 20%, particle mass flows (\(m_{s}\)) ranging from 5 t/h to 25 t/h, and particle diameters (\(d_{p}\)) ranging from 50 \(\upmu\)m to 1000 \(\upmu\)m. The momentum equations are based on a two-fluid model and are solved numerically. Some results are validated through comparison with the experimental results. The results showed that the gas and particle velocity distributions are asymmetrical around the center of the pipe and that the maximum velocity point moves up. The distance between the radial position of the maximum velocity and the center line for the gas is larger than that for the particles. The particle motion lags behind that of the gas flow. The particle settlement phenomenon is more serious, and the particle distribution on the cross-section is more inhomogeneous as the \(V\), \(\alpha_{s}\), and \(m_{s}\) decrease and as \(d_{p}\) increases. It can be divided into three areas according to the pressure changes along the flow direction, and the distinction between the three areas is more obvious as the \(\alpha_{s}\) increases. The pressure drop per unit length increases as the \(V\), \(\alpha_{s}\) and \(m_{s}\) increases and as \(d_{p}\) decreases, Finally, the expressions of the settlement index and pressure drop per unit length as functions of \(V\), \(\alpha_{s}\), \(m_{s}\), and \(d_{p}\) are derived based on the numerical data.  
gas-solid two-phase flow; pipe with circular cross-section; settlement; pressure drop; numerical simulation +
Footnote †: journal: Physics Letters B
# Expansion Lemma--Variations and Applications to Polynomial-Time Preprocessing
## 3 Crown Decomposition and Applications
### Maximum Satisfiability  
We formally define the Maximum Satisfiability problem below.  
It is folklore that such a CNF formula has a truth assignment satisfying at least \(m/2\) clauses, which implies that we can assume that \(m\leq 2k\), as otherwise, the instance is a YES instance. It is also easy to see (by a greedy algorithm that appropriately sets an unassigned variable in each clause in the sequence) that the formula \(\phi\) is satisfiable, i.e., a yes-instance for Maximum Satisfiability (defined in Box 3) for if \(n\geq m\).  
Maximum Satisfiability Problem.  
**Input:**_A Conjunctive Normal Form (CNF) formula \(\phi\) with \(n\) variables and \(m\) clauses, and a nonnegative integer \(k\)._  
**Parameter:**_k._  
**Question:**_Does \(\phi\) have a truth assignment satisfying at least \(k\) clauses?_  
Thus, it can be assumed that \(n<m<2k\). Lokshtanov [25] showed (as below) using Lemma 2, where \(n\) can be made to be less than \(k\). The idea is to construct a bipartite graph with bipartition \(A\) and \(B\) where \(A\) corresponds to the variables in the formula, and \(B\) corresponds to the clauses in the formula. There is an edge between \(x\in A\) and a \(c\in B\) if \(x\) appears in clause \(c\).  
If \(n\geq k\), then from Lemma 2, if we have a matching saturating \(A\), then clauses in the matching can be satisfied by setting the variables in the matching appropriately. Thus, we will be able to satisfy at least \(k\) clauses. Otherwise, it can be argued that the crown \((C,H,R)\) can be used to satisfy the clauses represented by \(H\) by appropriately setting the variables represented by \(C\), and removing those clauses and variables. See [11] for more details.
## 4 Experiment and Discussion
### Results of Projection Correction  
In this paper, we propose an improvement to the existing method by using a hot mirror to adjust the optical path, which reduces the effect caused by curved projection. This is followed by an adjustment of the projection using a homography matrix, which reduces the requirement of the optical path coaxial to the experimental environment, and accurately  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Downsampling Network** & **Miou** & **Precision** & **Size (M)** & **Consumption Time (s)** \\ \hline VGG16 & 89.82\% & 94.33\% & 95 & 0.1963 \\ ResNet50 & 88.62\% & 93.02\% & 168 & 0.2147 \\ DenseNet & 90.30\% & 93.66\% & 111 & 0.3092 \\ MobileNetv2 & 90.16\% & 95.76\% & 19 & 0.1513 \\ Proposed method & 90.01\% & 95.25\% & 15 & 0.0910 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Different segmentation results of various networks comparison.  
Figure 10: Loss function curves for each model.  
projects the image onto the dorsal of the hand. We placed the hand on planes located at different distances from the camera to obtain the results. The images captured by the near-infrared camera and the pre-segmentation and post-segmentation images observed by the human eye are displayed in Figure 12.  
The above images show that the projection performs well both in the center and the edge of the back of the hand and manifests no distortion caused by the curved surface. The projected binary vein image is consistent with the actual blood vessel. Although the homography matrix may alter with the plane's position, due to the prior proximal adjustment of the camera and projector's optical paths, the homography matrix will not change significantly.  
As can be seen from Table 5, the offset errors of the projection in the different planes are within 1 mm, with the maximum average error being 0.53 mm. Considering that the width of the venous blood is typically around 3-4 mm, these errors can be considered negligible.  
Figure 11: Binary image after segmentation.  
Figure 12: Imaging results of the dorsal hand on planes located at different distances from the camera. (**a**) The image captured by a near-infrared camera, (**b**) the original image observed with a mobile phone, and (**c**) the segmented image projected onto the dorsal hand.
## 4 Discussion
### Limits of This Study  
The first limit to this work is the use of a single climate model. It was important for the authors to question the reliability of this climate model. The specificity of our approach was to simulate 400 years of steady-state climate under the conditions of the years 2000 and 2050. Was the variability of other CORDEX-Drias models contained in these 2 \(\times\) 400 years event sets? CORDEX-Drias simulations between 1985 and 2005 (current climate) and 2040-2060 (climate in 2050) for six different models were compared with ARPEGE-Climat. It appears that the current climate, future climate, and evolution ratio of the six models at the French scale were included in ARPEGE-Climat 400-year outputs, as shown in Figures 13 and 14 in Appendix A. After this validation was complete, it was obvious that obtaining extreme event values was tougher when mixing a short-scale event set from six different models than with the use of ARPEGE-Climat model. This study highlights the relevance of using large-scale event sets to represent the variability of climate, especially for extreme values.  
Another limitation is the computation of crop yield losses using the Olympic average. This method allowed us to integrate a certain variability of yields over time. However, the crop yield loss computed was annual and was a sum of different factors, and thisexplains, in part, the errors in the model. Moreover, crop yields are not stable over time and many authors have shown that the cereal yield in France increased until 1996 and then stagnated or decreased [68; 69]. However, the results contrast depending on geographical locations. Many studies have been done to eliminate bias introduced by non-climatic factors in the computation of yield losses [70; 71], and it would be interesting to apply this kind of methodology. However, other factors may arise the same year, such as several climatic events. This was the case in 2003. A significant frost occurred in the central region of France, which contains 20% of the cultivated area for soft winter wheat [72]. The effects of frost accumulated with those of drought, which partly explains the significant crop losses and our difficulty in simulating it using a drought index.  
Finally, this study was conducted with other elements being equal by definition. It did not take into account agricultural adaptation to climate change. The cultivated area for each crop modeled was the same in 2050. Our methodology was to project yield losses based on the relation between index values and historical yield losses. Therefore, cultivation of resistant varieties to extreme drought were not included in these results.
# Enhanced Drug Screening Efficacy in Zebrafish Using a Highly Oxygen-Permeable Culture Plate
## 3 Results
### Developmental Assessment Using 384-Well Plates  
For zebrafish-based drug screening, 384-well plates are primarily used for high-throughput compound screening and automated image analysis, depending on the experimental objectives and specific conditions. To determine the applicability of the 384-well InnoCell\({}^{\text{TM}}\) plate, we performed a developmental assessment similar to that performed using 96-well plates. Compared with the standard normal conditions (Petri dish), embryos cultured in the control plates exhibited markedly delayed development, although this phenomenon was not observed in embryos cultured in InnoCell\({}^{\text{TM}}\) plates. For culture volumes of 40, 60, 80 \(\upmu\)L/well/fish under \"No sealing\" conditions (Figure 3A) and 30, 40, 60, 80 \(\upmu\)L/well/fish under \"Sealing\" conditions (Figure 3B), embryos cultured in the InnoCell\({}^{\text{TM}}\) plate exhibited significantly higher heart rates than those cultured in control plates. Under both \"No sealing\" and \"Sealing\" conditions, the body length of embryos at 72 hpf was significantly increased when cultured in volumes exceeding 20 \(\upmu\)L/well/fish (Figure 3C,D). Accordingly, we concluded that the minimum culture volume should be 20 and 15 \(\upmu\)L/well/fish under \"No sealing\" and \"Sealing\" conditions, respectively, when using the 384-well InnoCellTM plate.  
Figure 2: Evaluation of embryo development cultured in the 96-well control and InnoCell\({}^{\text{TM}}\) plates at different volumes. (**A**) Experimental design and schema. (**B,C**) Heart rate measurement at 56 h post-fertilization (hpf) cultured in control and InnoCell\({}^{\text{TM}}\) plates under “No sealing” (**B**) and “Sealing” (**C**) conditions. (**D,E**) Body length at 72 hpf cultured in the control and InnoCell\({}^{\text{TM}}\) plates under “No sealing” (**D**) and “Sealing” (**E**) conditions. * \(p\) < 0.05, *** \(p\) < 0.01, \(n\) = 4–20. Data are presented as the mean \(\pm\) standard deviation (SD).
# State-of-the-Art Internet of Things in Protected Agriculture
## 5 Disscussion  
### Hardware and Software Challenges  
When IoT technology is applying in protected agriculture, it is inevitable to encounter challenges from all aspects. In the perception layer, various devices must face the harsh and complex greenhouse environment. Strong solar radiation, high temperatures, high humidity, strong vibrations and other hazards can easily damage or destroy sensors or end devices. Moreover, the daily activities of livestock can interfere with the work of the sensor or execution nodes, resulting in poor detection and control. In a general way, the information collection nodes rely on the battery with limited power to maintain its work because the behavior of frequent battery replacement consume a lot of energies and money. Therefore, it is an urgent need to make great progress in developing low-power acquisition equipment, energy-saving sink node routing protocols and energy-balanced communication algorithms [135].  
The devices places in various places generate incalculable data and the storage of such data is also a huge challenge for some small servers. Those agricultural IoT data have real-time, dynamic, granular and fragmented characteristics, which brings great challenges to intermediate design, data large-scale screening, screening, mining, processing and decision analysis techniques. Moreover, existing databases cannot store unstructured data such as audio, video and images. As a result, quite a lot real-time sensing data are not fully exploited. Besides, some models and algorithms are not enough to to reflect objective reality, so that they cannot effectively guide agricultural production.
# Plasma-Activated Tap Water Production and Its Application in Atomization Disinfection
## 2 Experimental Setup
### Bactericidal Effect Analysis
#### 2.3.2 Disinfection of Clothing Surface with Atomized PATW  
In order to determine the bactericidal effect of PATW in a particular application, we atomized PATW in a disinfection door (an engineering prototype, the structure diagram is shown in Figure 3) and disinfected the surface of the bacteria-contaminated clothing on a dummy. We first put the stainless steel sampling plate (G01 Bikeman) on the surface of the experimental cloth, used a sterile cotton swab to take an _E. coli_ suspension with an initial concentration of 10\({}^{8}\) CFU/mL, and spread it evenly on the blank area of the stainless steel sampling plate and dried it naturally. Afterward, we sprayed the atomized PATW onto the surface of the contaminated clothes in the disinfection door for 1 min and 2 min, waited for 10 min for disinfection, and then sampled and cultured the collected bacteria.
# Design and Analysis of an Efficient Energy Algorithm in Wireless Social Sensor Networks
## 4 Checkpoint Triggering Occasions
As Figure 6 shows, \(P_{i,j,e}\) in cluster \(C_{i}\) receives messages \(M_{1}\) and \(M_{2}\) from processes \(P_{m,s,c}\) and \(P_{m,a,v}\) in cluster \(C_{m}\) respectively. If \(P_{i,j,e}\) takes \(DC_{1}\) and \(DC_{2}\) respectively, then \(P_{i,j,e}\) will roll back according to the following situations when processes in \(C_{m}\) fail. \(M_{0}\) exists. If \(P_{m,a,v}\) rolls back to \(C_{m,s,c}^{b}\) because of failure, \(P_{m,s,c}\) will roll back to \(SC_{m,s,c}^{b}\) and \(P_{i,j,e}\) should roll back to \(DC_{1}\); if \(P_{m,s,c}\) rolls back to \(SC_{m,s,c}^{b}\) because of failure, \(P_{i,j,e}\) will roll back to \(DC_{1}\) also. \(M_{0}\) does not exist. If \(P_{m,a,v}\) rolls back to \(C_{m,a,v}^{b}\) because of failure, \(P_{i,j,e}\) will roll back to \(DC_{2}\); if \(P_{m,s,c}\) rolls back to \(SC_{m,s,c}^{b}\) because of failure, \(P_{i,j,e}\) will roll back to \(DC_{1}\). It can be seen, \(P_{i,j,e}\) will roll back to \(DC_{1}\) in most instances. Hence, for reducing the number of DC\({}_{\rm s}\), a process just takes one DC for each different cluster during one DC interval.
###### Abstract  
With the development of information technology, unmanned aerial vehicles (UAVs) have become an indispensable and important part of daily life and they have brought great convenience to life. Evaluating the signal-to-noise ratio (SNR) of UAV communication link is vital to improve the communication performance between UAV and the user. The classical SNR evaluation schemes of UAV communication link are limited in terms of performance, while deep learning (DL) based schemes are always at the expense of computation complexity. To solve the issues mentioned above, a two-path convolution neural network (TP-CNN) is proposed therein to evaluate the SNR of UAV communication link. Firstly, a two-dimensional dataset of UAV control signal is built and expanded thereafter. Then the TP-CNN model is designed and modified by feature fusion of input samples. Finally, the simulations are conducted, and the simulation results indicate that the performance of our proposed model is superior to that of the baseline model in terms of mean absolute error (MAE) and mean relative error (MRE).  
U  
eural-to-noise ratio evaluation; deep learning