esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
We proposed a learning framework for position-controlled robot manipulators to solve contact-rich manipulation tasks. The proposed method allows for learning low-level high-dimensional control policies in real robotic systems. The effectiveness of the learned policies was shown through an extensive experimental study. We showed that the learned policies had a high success rate at performing the insertion task under the assumption of a perfect estimation of the goal position. The policy correctly learned the nominal trajectory and the appropriate force-control parameters to succeed at the task. The policy also achieved a high success rate under varying environmental conditions in terms of uncertainty of goal position, environmental stiffness, and novel insertion tasks.  
While model free reinforcement-learning algorithm SAC was used in this work, the proposed framework can easily be adapted to other RL algorithms. The choice of SAC was due to its sample efficiency as an off-policy algorithm. The pros and cons of using other learning algorithms would be interesting future work.  
One limitation of our learning framework is the selection of the force-control parameter range (see Section 2.2.3). The choice of a wide range of values may allow for the policy to adapt to very different environments, but it also increases the difficulty of learning a task, as small variations in the action may cause undesired behaviors, as was the case during the first 20,000 to 30,000 steps of training (see Figure 11). On the other hand, a narrow range would make it easier and faster to learn a task, but it may not generalize well to different environments. Defining a range is much easier than manually finding the optimal parameters for each task, but it is still a manual process. Therefore, another interesting future study would be to use demonstrations to learn a rough estimation of the optimal force parameters to further reduce training times.  
Methodology, C.C.B.-H., D.P., I.G.R.-A. and K.H.; Software, C.C.B.-H.; Investigation, C.C.B.-H., D.P. and I.G.R.-A.; Writing--Original Draft Preparation, C.C.B.-H.; Writing--Review & Editing, C.C.B.-H.; Supervision, D.P., I.G.R.-A. and K.H.; Project Administration, K.H.; Funding Acquisition, K.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 12: Comparison of policies with different inputs. Learning curve for cuboid-peg insertion task with random initial positions and random desired insertion force.
## 2 Materials and Methods
### Fixed-Bed Catalytic Test  
The CO\({}_{2}\) methanation reaction was performed in a fixed-bed quartz reactor, with an internal diameter of 6 mm and a length of 300 mm, inserted into a cylindrical oven. The reactive mixture was fed by three independent mass flow controllers with a CO\({}_{2}\):H\({}_{2}\):N\({}_{2}\) ratio of 1:4:5 and a total flow of 100 cm\({}^{3}\) min\({}^{-1}\), and a weight hourly space velocity (WHSV) of 60,000 cm\({}^{3}\)g\({}^{-1}\)h\({}^{-1}\). Before the catalytic test, the sample was reduced in situ with a 50% H\({}_{2}\)/N\({}_{2}\) stream (50 cm\({}^{3}\) min\({}^{-1}\)), raising the temperature from RT to 500 \({}^{\circ}\)C, and held isothermally at this temperature for 60 min. The catalytic test was performed in the temperature range from 500 \({}^{\circ}\)C to 250 \({}^{\circ}\)C, with 25 \({}^{\circ}\)C decrements. A cold trap and a dryer removed H\({}_{2}\)O from the flow before the analysis. The dry gas was analyzed by Agilent 7820 gas chromatograph. The conversion of CO\({}_{2}\) (\(X_{\text{CO}_{2}}\)) the selectivity of CH\({}_{4}\) (\(S_{CH_{4}}\)) and the yields of CH\({}_{4}\) (\(Y_{CH_{4}}\)) and CO (\(Y_{CO}\)) were calculated by Equations (7)-(9), where \(CO_{2,\ out}\), \(CH_{4,\ out}\) and \(CO_{out}\) are the gas volume concentrations in the effluent:  
\[X_{\text{CO}_{2}}(\%)=\frac{CH_{4,\ out}+CO_{out}}{CO_{2,\ out}+CH_{4,\ out}+CO_{out}}\times 1 0^{2} \tag{7}\]  
\[S_{CH_{4}}(\%)=\frac{CH_{4,\ out}}{CH_{4,\ out}+CO_{out}}\times 10^{2} \tag{8}\]  
\[Y_{CH_{4}}\ or\ Y_{CO}(\%)=\frac{CH_{4,\ out}\ or\ CO_{out}}{CO_{2,\ out}+CH_{4,\ out}+CO_{out}}\times 10^{2} \tag{9}\]  
The turn over frequency TOF (s\({}^{-1}\)) was calculated using Equation (10), where: \(F_{CO_{2,\ in}}\) is the CO\({}_{2}\) flux in the reactor, \(X_{\text{CO}_{2}}(\%)\) is the CO\({}_{2}\) conversion, \(M_{Ni}\) is the Ni molar mass, \(m_{cat}\) is the catalyst mass, \(W_{Ni}(\%)\) is the Ni weight percentage, \(D(\%)\) is the Ni dispersion obtained by the H\({}_{2}\)-TPD technique.  
\[TOF_{\text{CO}_{2}}\left(\text{s}^{-1}\right)=\frac{F_{\text{CO}_{2,\ in}}\ X_{\text{CO}_{2}}(\%)M_{Ni}}{m_{cat}W_{Ni}(\%)D(\%)}= \frac{-r_{\text{CO}_{2}}M_{Ni}}{W_{Ni}(\%)D(\%)} \tag{10}\]
## 8 Numerical Results  
### Manipulator Parameters  
Given the manipulator at hand with legs having length of \(L_{1}=486\) mm, \(L_{2}=500\) mm and \(L_{3}=507\) mm. The moving platform has dimensions of \(x_{p2}=x_{q3}=x_{p}=315\) mm. Hence, two upper revolute joints are coincident. The COM of the legs are \(L_{1G}=199\) mm, \(L_{2G}=244.8685\) mm and \(L_{3G}=238.9118\) mm whereas the COM of the moving platform is \(x_{PG}=165.9629\) mm. The masses of all of the sliders, all of the three legs and the moving platform are \(m_{s1}=m_{s2}=m_{s3}=7\) kg, \(m_{L1}=8.5\) kg, \(m_{L2}=9.8470\) kg, \(m_{L3}=10.8420\) kg and \(m_{p}=15.2425\) kg, respectively. The mass moments of inertia of the legs with respect to their COM are given by \(l_{Cl}=0.1673\) kg m\({}^{2}\), \(l_{G2}=0.2051\) kg m\({}^{2}\) and \(l_{G3}=0.2322\) kg m\({}^{2}\), whereas the mass moment of inertia of the moving platform with respect to its COM is given by \(l_{Gp}=0.1260\) kg m\({}^{2}\). All the links have an identical modulus of elasticity \(E=7.0\times 10^{10}\) Pa and an identical rectangular cross section of \(b\times h=150\times 50\) mm. Assuming that the manipulator is on XY plane (and accordingly the gravity applies), the depth \(b\) of 150 mm is measured in \(z\)-direction(perpendicular to the paper) whereas the depth \(h\) of 50 mm is measured on XY plane (on the paper plane). It is assumed that there is no friction at all the joints.
## 1 Introduction
They compared the path planning effectiveness of these methods in submerged urban environments to identify the most suitable approach for urban flood disaster path planning [20]. [PERSON] et al. employed drones for flood disaster monitoring and applied a Firefly Algorithm for path planning, improving rescue efficiency and ensuring real-time information transmission [21]. [PERSON] et al. introduced an optimal evacuation route scheme based on an improved three-dimensional Dijkstra algorithm, designed for various flood disaster risk levels, ensuring that evacuees reach shelters in the shortest possible time [22]. [PERSON] et al. developed a frontier path planning algorithm based on deep reinforcement learning. It incorporates enhanced random reward utilization and heterogeneous reward exploration mechanisms to address simulated rescue path planning under uncertainty and complexity [23]. These methods are typically based on static or simplified road network data. They do not fully account for dynamic flood evolution or road network failure over time. As a result, these methods cannot accurately represent spatio-temporal changes in flood disasters. This leads to evacuation plans that lack timeliness and flexibility. This study hypothesizes that integrating flood simulation data with real-time road network information will enhance the effectiveness of evacuation strategies. Our goal is to develop an optimized flood evacuation model that accounts for flood evolution and dynamic road network failure. Unlike traditional models that rely on static network data or simplified flood scenarios, our model integrates real-time flood dynamics and road network status. This allows it to adapt evacuation strategies on-the-fly, ensuring that paths are not only optimal in theory but also realistic in the face of evolving flood conditions. This study addresses these limitations by considering the uncertainty and time constraints associated with reservoir dam failure floods. In this study, we employ unmanned aerial vehicle (UAV) measurement technology to obtain high-resolution DEM data. Based on the dynamic evolution of dam breach floods, we integrate flood simulation results with downstream road network data to construct a coupled flood-road network coupling model. This model allows for the analysis of road network failure dynamics and provides a more scientifically robust and practical approach to flood evacuation route planning in dam failure scenarios. The proposed method offers valuable insights for future dam breach emergency management, flood risk mitigation, and emergency evacuation planning in similar sudden water disasters.
# Convolutional Neural Network Architecture for Recovering Watermark Synchronization
## 5 Experimental Results
### Robustness Test for RST Attack  
Figure 9 shows the robustness test results for rotation, scaling, and translation. The following four methods were tested for robustness: (1) proposed watermarking method without recovery, (2) proposed watermarking method with proposed template recovery, (3) proposed watermarking method with ground-truth recovery and (4) [PERSON]'s method. The robustness performance was measured based on the bit-error-rate (BER).  
[PERSON]'s method shows good robustness to low-level geometric distortion. This is because the absolute values of curvelet coefficients do not have a large variation in weak geometric distortions. Especially, this method shows low BER for weak scaling and translation. However, it shows a relative weakness in rotation.  
The proposed watermarking method without recovery showed lower performance despite using a similar insertion method in the same domain as [PERSON]'s method. This is because the watermark is inserted with block-units unlike with [PERSON]'s method. Since block synchronization is broken due to geometric distortion, block-based watermarking is not effective without correction for geometric distortion. On the other hand, low BER is obtained for scaling even without recovery because the proposed watermarking method uses resized images in fixed sizes of 512 \(\times\) 512 for the watermark insertion/decoding process to fit the image size to the network size.  
If an image is recovered using the proposed template, the proposed watermark shows a low error even for strong geometric distortion. This result shows that the robustness is almost identical to the results of recovery with ground-truth, which means that the template almost completely recovers the image from the geometric distortion. The error that occurs even when the image is completely recovered is because the watermark information is cropped together with the image information. Except for the cropped part, the watermark is normally decoded.  
Figure 8: (**a–c**) Original image, (**d–f**) Proposed template and watermark-embedded image.
## 3 Impact of Rainfall on Slope Stability
### Numerical Simulation Model and Parameters  
As shown in Figure 3, the slope to is 40\({}^{\circ}\), and the slope height is 30 m. The initial groundwater level is level with the slope toe. This example assumes that pore water pressure (including suction) is linearly distributed with depth. See Figure 4 for the variation process of rainfall amplitude simulated for 72 h. The rainfall infiltration intensity of the slope is set at 0.015 m/s, and the rainfall infiltration intensity of the slope top is set at 0.02 m/s. The elastic modulus of the soil used for the calculation is 10 MPa, Poisson's ratio is 0.3, cohesion \(c^{\prime}\) = 15 kPa, and friction angle \(\varphi^{\prime}\) = 30\({}^{\circ}\).  
The relationship between the material permeability coefficient and the matrix suction is:  
\[K_{\mathrm{w}}=a_{\mathrm{w}}K_{\mathrm{w}s}/\left[a_{\mathrm{w}}+\left(b_{ \mathrm{w}}\times\left(u_{\mathrm{a}}-u_{\mathrm{w}}\right)\right)^{c_{\mathrm{ w}}}\right] \tag{1}\]  
where \(K_{\mathrm{w}s}\) is the permeability coefficient when the soil is saturated, taking 5.0 \(\times\) 10\({}^{-6}\) m/s (0.018 m/h). \(u_{\mathrm{a}}\) and \(u_{\mathrm{w}}\) are air pressure and the water pressure in the soil, respectively.  
Figure 4: Rainfall intensity amplitude.  
Figure 3: Finite element model.  
Since the slope surface is in contact with the atmosphere, \(u_{\rm a}\) is simply taken as 0. \(a_{\rm w}\), \(b_{\rm w}\), and \(c_{\rm w}\) are material coefficients, which are taken as 1000, 0.01, and 1.7, respectively, in this example.  
The relationship between saturation and matrix suction is:  
\[S_{\rm r}=S_{\rm i}+(S_{\rm n}-S_{\rm i})a_{\rm s}/\left[a_{\rm s}+\left(b_{\rm s }\times(u_{\rm a}-u_{\rm w})\right)^{c_{\rm s}}\right] \tag{2}\]  
where \(S_{\rm r}\) is saturation; \(S_{\rm i}\) is residual saturation, which is 0.08 in this case; \(S_{\rm n}\) is the maximum saturation, taken as 1; \(a_{\rm s}\), \(b_{\rm s}\) and \(c_{\rm s}\) are material coefficients, which are taken as 1, 5 \(\times\) 10\({}^{-5}\), 3.5 in this example.  
The relationship between the efficiency of capability reduction and matrix suction and water content obtained from the above two formulas is shown in Figure 5.
## 2 Materials and Methods
### Dynamic and Static Combination SHPB Test Device  
This experiment used a dynamic and static combined SHPB test device for deep coal mine mining response and disaster prevention from a national key laboratory, as shown in Figure 2.  
The device consists of five parts: impact loading system, data acquisition system, load transfer system, velocity measurement system, and preloading axial pressure system. The diameters of the impact rod, transmission rod, and absorbing rod are 50 mm, and their lengths are 2000 mm, 1500 mm, and 1000 mm, respectively. They were all machined from 40 Cr alloy steel with a material elastic modulus of 210 GPa, a longitudinal wave velocity of 5190 m/s, and a Poisson's ratio of 0.28. The impact rod (impactor) was chosen to be a spindle shape to achieve a semisinusoidal wave loading. The power loading device (high pressure gas) provides the impact strength for the impact rod. When the impactor (bullet) in the chamber strikes the impact rod at a given velocity, an incident stress pulse is generated in the impact rod and transmitted through it to the specimen, which deforms under the action of the stress pulse. The transmitted and incident (reflected) wave signals in the rod can be picked up by the strain gauges on the transmitting and incident rods and displayed on the oscilloscope.  
Figure 1: Sandstone specimens before and after temperature–water coupling. (**a**) Before temperature–water coupling; (**b**) after temperature–water coupling.  
Figure 2: Dynamic and static combination SHPB test device.  
The schematic diagram of dynamic-static combined loading is shown in Figure 3, where PS represents the dynamic impact load and P represents the axial preloading.
# Studying the Cable Loss Effect on the Seismic Behavior of Cable-Stayed Bridge
###### Abstract
using the DAF value determined by PTI [7] uniformly. Additionally, it is suggested that additional research is needed for concrete bridges, as this study was conducted on relatively flexible steel bridges rather than concrete bridges. [PERSON] et al. [8] studied the change in stress, displacement, and structural behavior when a single cable is lost by analyzing the static response according to the position of the lost cable, considering the nonlinear characteristics of the cable-stayed bridge. On the other hand, [PERSON] et al. [9] sequentially performed the initial shape analysis, cable loss analysis, and nonlinear analysis under live load to analyze the effect on the structural behavior of the cable-stayed bridge under extreme load conditions. [PERSON] et al. [10] proposed a relative reliability index for sequential cable cutting of a two-sided cable using the limit state response for the Incheon Bridge and presented a structural reliability evaluation method using earthquake vulnerability for cable cutting scenarios. Additionally, KBRC [11; 12] reviewed the deflection of girders, displacement of the tower, and cable stress under fire and explosion loads through numerical analysis for various fire and explosion scenarios and conducted a reliability-based performance evaluation of the cable-supported bridges currently in use. [PERSON] and [PERSON] stated that cable losses cause very high levels of acceleration in bridges, and certain cables can even cause progressive collapse when damaged [13]. Additionally, [PERSON] and [PERSON] recommended dynamic time history analysis for critical cable losses, as cable loss can cause significant dynamic amplification effects [14]. Therefore, the purpose of this study is to evaluate the usability of cable-stayed bridges by investigating their structural behavior when cables are lost. To achieve this, the structural impact caused by the continuous loss of multiple cables, as well as the impact of seismic loads in extreme situations while cables are already lost, are evaluated. The deflection of the bridge, cable tension, and axial forces of the girders were analyzed. The first step is to understand the changes in the initial equilibrium state according to the position and quantity of the lost cable, and the second step is to investigate how the bridge structure behaves when an earthquake load applies after the cable is lost. This study analyzed the structural behavior of a cable-stayed bridge by comparing it to the current cable bridge design guidelines, considering the dynamic amplification effect of sequential loss of multiple cables by giving time intervals between cable losses.
## 3 The Proposed HSTMAC Framework and System Model
### Foundation of the HSTMAC Protocol
#### 3.1.2 System Capacity of TDMA Protocol  
The resources in TDMA system are divided into two parts for contention based random access and the following contention free data transmission. The random access procedure in TDMA can be modeled as a multi-channel S-ALOHA system [43], where each preamble is regarded as an access slot. If the RACH occupies too many RBs, the reserved data channels would be insufficient for the successful devices. On the contrary, if the reserved RBs for data channels are too many, the devices may fail due to lack of random access opportunities, which may also cause underutilization of the reserved data channels. Therefore, a tradeoff must be made for the resource allocation between random access procedure and data transmission procedure.  
In order to model the uplink signaling overhead in the random access procedure, we assume that a resource block can bear \(\gamma\) random access opportunities. Let \(\theta\) denote the proportion of RBs reserved for RACH, the resource utilization of TDMA is:  
\[R=\min\Bigl{\{}\delta\cdot Xe^{-\frac{X}{LD\gamma}},L(1-\theta)\Bigr{\}} \tag{3}\]  
where \(L\theta\gamma\) is the reserved random access opportunities and \(L(1-\theta)\) is the reserved RBs for data channels. According to Equations (2) and (3), the resource utilization is maximized when:  
\[\begin{array}{l}X=L\theta\gamma\\ \delta Xe^{-\frac{X}{LD\gamma}}=L(1-\theta)\end{array} \tag{4}\]  
Then the capacity of TDMA system is obtained by solving Equation (4):  
\[\begin{array}{l}C_{TDMA}=\delta\cdot L\theta^{*}\gamma\cdot e^{-1}=\frac{L \delta\gamma}{e+\delta\gamma}\\ \theta^{*}=\frac{1}{1+\delta\gamma/\epsilon}\\ X^{*}_{TDMA}=L\theta^{*}\gamma\end{array} \tag{5}\]  
where \(X^{*}_{TDMA}\) is the maximum supportable devices of the TDMA system.
# Palm Date Leaf Clipping: A New Method to Reduce PAPR in OFDM Systems
## 1 Introduction
By analogy, Palm Clipping does not trim the signal after a threshold, but reduces it, which is something that simplifies its reconstruction. We analyze its performance in terms of PAPR and BER. The proposed method can effectively enhance the PAPR reduction performance with the best BER for certain Clipping Ratios (CR). Simulation results show that the proposed algorithm significantly outperforms the conventional scheme in terms of PAPR reduction of OFDM signals. A comparative study was carried out between our method, PC, and other advanced methods. The obtained results confirm that the PC method has the same performance compared to the studied methods and sometimes outperforms them in terms of PAPR and BER. In addition, the PC method resists degradation of the signal compared to other conventional clipping methods that use a High-Power Amplifier (HPA) under the same conditions. The remainder of the paper is organized as follows. An introduction to OFDM systems and the PAPR problem is presented in Section 2. Section 3 presents some classic clipping techniques designed for the purpose of PAPR reduction. Analyses and descriptions of the proposed method are presented in Section 4. The results and discussion are presented in Section 5, while our conclusions are summarized in Section 6.
###### Abstract  
One of the key requirements for next generation wireless or cellular communication systems is to efficiently support a large number of connections for Internet of Things (IoT) applications, and uplink non-orthogonal multiple access (NOMA) schemes can be used for this purpose. In uplink NOMA systems, pilot symbols, as well as data symbols can be superimposed onto shared resources. The error rate performance can be severely degraded due to channel estimation errors, especially when the number of superimposed packets is large. In this paper, we discuss uplink NOMA schemes with channel estimation errors, assuming that quadrature phase shift keying (QPSK) modulation is used. When pilot signals are superimposed onto the shared resources and a large number of devices perform random accesses concurrently to a single resource of the base station, the channels might not be accurately estimated even in high SNR environments. In this paper, we propose an uplink NOMA scheme, which can alleviate the performance degradation due to channel estimation errors.  
Keywords:IoT; massive IoT; NOMA; random access; channel estimation; QPSK +
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