esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Robot Stability Analysis  
### Slope Stability Analysis  
In unstructured environments, the theoretical analysis of the robot's stability helps the robot to pass smoothly across different types of terrain and avoid tipping phenomena that can further lead to loss of control of the system and damage to the components [23, 24]. Figure 13 shows the stability model of the robot ramp motion based on the stable cone approach.  
Figure 12: Moving through vertical obstacles stages II and III. (**a**) stage II; (**b**) stage III.  
We establish a right-angle coordinate system with the center of mass of the robot as the coordinate origin. The forward direction of the robot is the positive \(Y\)-axis direction, the right side of the forward direction is the positive \(X\)-axis direction, and the height direction is the positive \(Z\)-axis direction. We define the coordinates of contact point \(N_{i}\) as \(\{x_{i},y_{i},z_{i}\}\), the vector as \(h_{i}=\{x_{0},\,y_{0},\,z_{0}\}\), and the gravity vector as \(f_{g}=(0,\,0,\,-1)\). According to the principle of stable cone analysis, the relationship between stability angle \(\varepsilon_{i}\) and \(\varepsilon_{i}^{\prime}\) under the conditions of sideline tipping and angular tipping can be introduced.  
\[\left\{\begin{array}{l}\varepsilon_{i}=\sigma_{i}\cos^{-1}\left(\hat{f}_{g} \cdot\hat{h}_{i}\right)=\sigma_{i}\arccos\frac{-z_{0}}{\sqrt{x_{0}^{2}+y_{0}^{ 2}+z_{0}^{2}}}\;(i=1,2\ldots 8)\\ \varepsilon_{i}^{\prime}=\xi_{i}\cos^{-1}\left(\hat{f}_{g}\cdot\hat{q}_{i} \right)=\xi_{i}\arccos\frac{-z_{i}}{\sqrt{x_{i}^{2}+y_{i}^{2}+z_{i}^{2}}}\;(i= 1,2\ldots 8)\end{array}\right. \tag{17}\]  
\(\varepsilon_{i}\) and \(\varepsilon_{i}^{\prime}\) take values in the range \([-\pi,\,\pi]\). \(\sigma_{i}\) and \(\xi_{i}\) are used to determine the positive or negative stability angle, and the unit vector defined can be expressed as  
\[\sigma_{i}=\left\{\begin{array}{ll}+1&\left(\hat{f}_{g}\times\hat{h}_{i} \right)\cdot\hat{a}_{i}<0\\ -1&otherwise\end{array}\right. \tag{18}\]  
\[\xi_{i}=\left\{\begin{array}{ll}+1&\left(\hat{f}_{g}\times\hat{h}_{i} \right)\cdot\hat{a}_{i}{<0\;\text{or}\left(\hat{f}_{g}\times\hat{h}_{i+1} \right)\cdot\hat{a}_{i+1}{<0}\\ -1&otherwise\end{array}\right. \tag{19}\]  
Figure 14a shows the stable cone model for the lateral motion of the robot on the slope. When point \(O^{\prime}\) coincides with point \(N_{3}\), the robot is in a critical angle tipping state. Figure 14b shows the stable cone model for the longitudinal motion of the robot on the slope, where \(f_{g}\) gradually points to point \(N_{8}\) as the slope increases. When \(O^{\prime}\) coincides with point \(N_{8}\), the robot is in a critical angle tipping state. When the structural parameters of the robots are certain, the stability of the robot can be improved by increasing the coefficient of friction between the robot and the ground and by reducing the height of the robot's center of gravity.  
Figure 13: Slope stability model.
## 3 Results and Discussion
### Effects of UF, O\({}_{3}\), and Their Combination on EDC Removal  
Effects of O\({}_{3}\), UF, and their combination on EDC removal efficiency were investigated and the results are presented in Figure 8. UF treatment alone resulted in 8%-46% EDC removal. The mechanism of rejection was further investigated. It can be seen from the SEM photos of PVDF clean membrane that the pore size of the membrane varies from dozens to more than 100 nanometers (Figure 9), while the molecular scale of EDCs is several nanometers. The pore size of the PVDF UF membrane is much higher than that of EDCs. Therefore, the PVDF UF membrane cannot reject EDCs in theory. However, the clean membrane has a rejection rate of 8%-46% for EDCs. It can be inferred that the material layer or pore on the surface of PVDF film has physical adsorption on EDCs, which was consistent with previous research [27]. Moreover, the presence of particles in the secondary effluent and the membrane fouling can provide additional adsorption sites for the removal of EDCs. Our previous research indicated that effluent organic matters on the membrane surface enhanced EDC removal [28]. On the other hand, the results of zeta potential measurement show that the membrane surface is negatively charged, while EDCs showed electro-positivity. Therefore, the membrane can also remove EDCs by charge adsorption.  
The removal rates of five EDCs were all over 70% when only O\({}_{3}\) was used. It could be seen that O\({}_{3}\) had obvious effect on EDC removal. Two main oxidants (molecular O\({}_{3}\) and OH radical) may be acting in the ozonation reaction. The electronic structure of O\({}_{3}\) results in its selective oxidation. The oxidation of organic micro-pollutants by O\({}_{3}\) is an efficient process for compounds with functional  
Figure 6: The relationship of DOC and UVA.  
Figure 7: The relationship of DOC and fluorescence spectral.  
groups containing phenols, amines and double bonds, which can be degraded into intermediates and small molecules through redox reaction, dipole addition reaction and electrophilic substitution reaction [29]. O\({}_{3}\) is unstable in water and its active groups are transformed into OH radical, which is a strong oxidizer and ultimately oxidizes the organic compounds to small molecular compounds such as CO\({}_{2}\) and H\({}_{2}\)O [30].  
When EDCs were treated by the combination of O\({}_{3}\) and UF, about 100% EDC removal efficiency was achieved. These data indicated that the combined treatment was much more effective in EDC removal than independent UF or O\({}_{3}\) treatment. In the combined process, O\({}_{3}\) played a more important role in the removal of EDCs. This might be because the UF membrane had the ability to remove EDCs only in the initial stage of adsorption, and once the adsorption was saturated, the removal of EDCs mainly depended on O\({}_{3}\).
# A Matheuristic for Joint Optimal Power and Scheduling Assignment in DVB-T2 Networks +
## 4 A Matheuristic for DVB Design
#### 4.1.1 Representing the Individuals  
In our GA algorithm, a chromosome encodes the power setting of the DVB stations and therefore coincides with a power vector \(p\) of size \(|S|\cdot|D|\) that sets the emission of every station in every direction. A generic position \((s,d)\) in the chromosome specifies the discrete power level \(p_{l}\in\mathcal{P}\) that station \(s\in S\) emits in direction \(d\in D\).  
After having completely characterized a chromosome/power vector, it is still necessary to establish how to choose the serving station of a TP and thus set the values of the variables \(x_{ts}\). To this end, we compute the signal-to-interference ratio \(SIR_{ts}(p)\) for the power vector \(p\) and use the distinction between useful and interfering signals that is originated by selecting \(s\) as serving station of \(t\). We then introduce \(\Sigma(t,p)\subseteq S\) to denote the subset of serving stations that are able to provide service coverage to TP \(t\) for the power vector \(p\) by satisfying the corresponding SIR inequality, i.e.,  
\[\Sigma(t,p)=\{s\in S:SIR_{ts}(p).\geq\delta\}\]  
When \(\Sigma(t,p)\) is not empty, we select as the serving station \(\sigma\) of \(t\), the station \(\sigma\in\Sigma(t,p)\) that grants the highest SIR value \(SIR_{ts}(p)\). In terms of the binary variables representing the server-TP assignment, we therefore impose \(x_{t\sigma}=1\) and \(x_{ts}=0\), \(\forall s\in S\setminus\{\sigma\}\).
# Investigation on Cutting Blasting Efficiency of Hard Rock Tunnels under Different Charge Diameters
## 3 Numerical Simulation
### Simulated Results and Analysis  
#### 3.3.1 Dynamic Development of Stress Wave  
The finite element solution files under the three different charge diameters were delivered into the nonlinear solver LS-DYNA, and the specific dynamic post-processor LS-PREPOST4.5 was employed to export the final simulated results. After the numerical simulation, in order to display the dynamic development of the blasting stress wave clearly, the rock numerical model was divided along the horizontal symmetry plane, and the placement angle of the rock numerical model was also adjusted to some extent. The dynamic developments of the blasting stress wave under the three different charge diameters are shown in Figures 5-7.  
As presented in Figures 5-7, the explosive detonation reactions began at the bottom of each cutting hole, and a huge release of chemical energy induced a blasting stress wave in the rock. With the transmission of the explosive detonation reaction, the blasting stress wave propagated towards the driving face in a conical wave surface. In the middle area of the rock model, the stress waves generated from the cutting holes arranged on the left and right sides were superimposed on each other, which were conducive to the destruction of the rock in the cutting cavity. At 320 us, the explosive detonation reaction in each cutting hole was terminated, and the blasting stress wave could still propagate towards the driving face in an arc wave surface. However, the intensity of the stress wave gradually decreased in the subsequent propagation process, resulting in a low degree of damage to the rock in the non-charge section. At 470 us, the blasting stress wave reached the driving face and was reflected as a tensile wave, which propagated from the driving face to the cutting cavity. Since the tensile strength of rock is usually only about 5-10% of its compressive strength, the rock near the driving face would be prone to tensile destruction, which could make up for the insufficient destruction of the rock in the non-charge section. After that, the backward-propagating tension wave and the forward-propagating compression wave were superimposed on each other to induce a composite blasting stress field, which would be beneficial to the destruction of the rock in the cutting cavity.
## 3 Results and Discussion
### The Response of NEE and LE Fluxes to Extreme Precipitation
#### 3.2.2 Variation in NEE and LE Flux in Cold Season
The opposite effect was detected in most broadleaf forest ecosystems (LE anomalies lower--STD during heavy precipitation) (Figures 11b and 12b), corresponding to the main trends found at these stations during the warm season. Evaporation at these stations can be suppressed by the high water vapor pressure in the air during and after precipitation, as well as by reduced incoming solar radiation due to cloudy weather. The response of LE to anomalous API (API > STD) was more uniform within forest ecosystems: Positive LE anomalies dominated in evergreen needleleaf (green bars in Figure 13c) and mixed forests (green bars in Figure 16a), whereas deciduous and evergreen broadleaf forests exhibited negative LE anomalies (yellow bars in Figure 16a). The exception was the beech forest in Italy (IT-Col), with positive LE anomalies associated with high API. Prevailing positive temperatures during the winter period at the forest site, late leaf fall, and early leaf emergence may have resulted in higher transpiration rates (during the periods when the foliage is present) under sufficient soil moisture conditions (high API). The extremely low API led to the opposite LE anomalies: negative in evergreen needleleaf (yellow bars in Figure 13d) and mixed forests (yellow bars in Figure 16b). In spruce forests in the Czech Republic (CZ-BK1) [62], low API was accompanied by high LE (green bar). Considering the absence of direct effects of precipitation on CO\({}_{2}\) and H\({}_{2}\)O fluxes during the winter period in most of the considered forest ecosystems, some identified relationships between precipitation and flux anomalies, especially in forests located in northern latitudes, may have been due to the combined effects of several atmospheric factors, especially temperature, on atmospheric fluxes. In many cases, such a combined effect of temperature and precipitation can lead to positive NEE (red bars) and LE (green bars) anomalies. Solar radiation may also have had an influence, leading to negative anomalies in NEE and positive anomalies in LE during periods of snow melt, including periods without precipitation.
# Stream Data Load Prediction for Resource Scaling Using Online Support Vector Regression
## 1 Introduction  
With the development of the Internet, Internet of things and big data technology, streaming data, which is the infinite, real-time and dynamic stream of data, has been applied more and more for the fields of financial analysis, social media, sensors, websites, large-scale scientific experiments and so on. Such data is quickly analyzed to get the most value, and traditional data management systems cannot process such large amounts of streaming data in real time. Aiming to achieve the characteristics of infinite, real-time, orderly and large-scale data flow, the industry has designed and developed storm, Spark and other streaming processing systems to process real-time mutation data-flow in an efficient and scalable manner.  
Cloud computing can add on-demand elasticity to these stream processing systems to deal with fluctuating computing demand using autoscaling [1]. To guarantee a service level agreement (SLA) in terms of application performance, we need a prediction model that connects VM configurations and application performance so that the auto-scalers can estimate and provision the right number of VMs. [PERSON]. et al. [2] proposed a maximum sustainable throughput (MST) prediction models for stream processing systems. The models assume a homogeneous VM instance type and predict MST only by the number of VMs, but they do not consider the time window interval performance. The paper strengthens the MST to the maximum sustainable throughput of time window (MSTW). If the incoming data rate exceeds the system's MSTW, unprocessed data accumulates, eventually making the system inoperable. By dynamically allocating and deallocating VMs, service providers can make sure the MSTW remains larger than the incoming data load, to maintain stable service operation. Techniques that have been used to predict data load time series include autoregressive moving averagemodel ARMA [3], neural network [4], and support vector regression SVR [5]. However, these methods are not real online predictions and cannot handle real-time stream data. Our solution responds to the problem by applying a more accurate online SVR algorithm to predict the incoming data-flow load and then measuring the number of virtual machines for the maximum sustainable throughput of time window (MSTW). The main contributions are as follows:  
* Improving online SVR prediction algorithm to predict load time series of stream data to reduce delay of cloud resource distribution.
* According to the maximum throughput calculated by MSTW, and the threshold rule is set up with the prediction value of the stream data load to put forward an elastic resource distribution algorithm to guarantee the system's SLA.
## 5 Simulation Results
### Complexity Analysis of Proposed Inference Latency Prediction Methods  
This section describes the complexity of the proposed methods. The proposed methods predict the inference latency through three steps. First, the inference latency is measured on the client device. Then, the normalized inference latency is computed by reflecting the statistical information on the measured inference latency on the client device. Lastly, the predicted inference latencies of the edge and cloud devices are obtained by considering their statistical information on the normalized inference latency. Since the second and third steps involve simple scaling, most of the complexity is due to the first step of measuring the inference latency on the client. However, the first step can also be executed relatively simply because it uses a simple inference model, such as the SSD-MobileNet model, when measuring inference latency on the client device. In addition, since this process can be performed in parallel on a separate core of the client, inference latency can be predicted with very low time overhead if only the statistical information of each device is updated.
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 3 Architecture of the Network Emulation Platform
### Architecture
#### 3.1.2 Control Plane  
The control plane consists of two parts: the front-end service and the back-end service. The front-end service offers a Web UI to interact with researchers. Researchers can configure various parameters of the network model and visualize the network scenario through the Web UI. The back-end service is backed by the main controller which is responsible for implementing commands and parameters from the logical plane into the data plane. The main controller is a software and operates all elements in the data plane. Researchers can configure and control the environment parameters with different emulation scenarios, as well as monitoring the traffic for each testing through the control plane. The emulation architecture is thus configurable and controllable.
## 4 Experiment Results and Data Analysis  
### Eye-Tracking-Data Analysis  
Before exporting the data from Tobii Studio [31], we divided each scene into three regions of interest (ROIs). In eye-tracking research, there is always disparity between a person's actual gaze location and the location recorded by the eye-tracker [32; 33]. The Tobii T60 eye-tracker has a reported accuracy of a 0.5 visual angle. Therefore, to eliminate the effect of systematic error, we took each texture and its external padding as one ROI. This strategy was adopted to avoid the problem of a participant viewing the edge of a texture and not being inside the ROI as a result of calibration issues.According to viewing distance, we calculated the external padding for different scenes. For Scenes 1 and 3, external padding was around 12 pixels. For Scenes 2 and 4, external padding was around 18 pixels. The size and location of the marked ROIs are shown in Figure 4.  
In the Introduction, we wondered whether global/local features and final similarity selection influence eye-movement patterns in texture recognition. Therefore, we used three metrics to identify the participants' visual behavior on the basis of the outcomes of the eye-tracking experiment: (1) total fixation duration (TFD) in each ROI, (2) fixation-point variance (FPV) in each ROI, and (3) fixation-transfer counts (FTCs) between different ROIs [34; 35]. As the final selection was determined between Textures A and B, we mainly considered the eye-movement data in Textures A and B. (1) TFD in each ROI represents the duration of fixation within an ROI. It was calculated as the average value in seconds. For Scenes 1-4, we calculated the average TFDs and the SD of TFDs in different ROIs (as shown in Table 1). The TFD in Texture B was longer than that in Texture A for the four scenes. This result was in accordance with that of [PERSON] et al. (2016), who demonstrated that horizontal displays were easier to process. Therefore, TFD in Texture A was shorter than that in Texture B. To verify whether global and local texture features influenced the average TFD in Textures A and B, we analyzed the average TFD through ANOVA (Analysis of Variance). ANOVA results (F(1,29) = 0.000, \(p\) = 0.997 for Scene 1, F(1,29) = 1.22, \(p\) = 0.273 for Scene 2, F(1,29) = 0.000, \(p\) = 0.996 for Scene 3, and F(1,29) = 0.090, \(p\) = 0.755 for Scene 4) indicated that no significant difference existed between Textures A and B under the significance level of 0.05. Furthermore, we calculated the effect size (eta-squared) for one-way ANOVA. Eta-squared values also showed that the global and local features of the synthesized textures exerted no significant effects on TFD when participants perceived the textures. Moreover, we hypothesized that the final similarity choices and TFDs in ROIs (especially Textures A or B) were highly related. That is, if the final similarity choice was Texture A (
# Integrated DVB-X2 Receiver Architecture with Common Acceleration Engine
## 3 DVB-X2 Receiver Architecture
### DVB-T2/C2 OFDM Demodulator  
The performance of an OFDM demodulator is mainly dependent on the channel estimation algorithm. A channel estimator does not need real-time processing because it estimates the channel using a number of OFDM symbols. Thus, the channel estimator is easily able to apply various channel estimation algorithms for channel change in operation if implemented with the upgradable and flexible S/W. Functional blocks operating in a sample level are efficient implemented in H/W when high-speed signal processing operations are required. Functional blocks relating to the performance of the demodulator and non-real-time process are effective implemented in S/W.  
Figure 4: DVB-X2 receiver architecture.  
Figure 5 shows the block diagram of the proposed DVB-T2/C2 demodulator. The demodulator can select one of a H/W-based channel estimator or an embedded S/W-based channel estimator. The DVB-T2/C2 demodulator stores pilots passing the FFT block into the pilot memory and stores the residual symbol timing offset (STO) and carrier frequency offset (CFO) recovered from OFDM symbols into the OFDM memory. In addition, the demodulator stores an index of the current OFDM symbol stored in the OFDM memory into the status register. Then, it generates an interrupt signal to a frame processor. The frame processor performs channel estimation using pilots stored in the pilot memory after receiving the interrupt signal, and stores the estimated channel information into the channel memory. The frame processor is aware of the index information of the included OFDM symbol of pilots currently being processed by referencing the index of the current OFDM symbol stored in the status register. Channel equalization is performed by selecting the channel information stored in the channel memory or the channel information generated by the H/W-based channel estimator. OFDM symbols stored in the OFDM memory are used for de-interleaving and frame de-mapping in the frame processor.
## 6 Numerical Examples and Discussion of Results
### Topology Optimization Structure Smoothing for the Support Structure  
In HyperWorks, geometries of two different topology-optimized models were extracted using the OSSmooth tool in the Post panel. Since the output camera bracket model contains elements with multiple-density components, the design not only needs to meet functional and performance requirements but also requires good manufacturing processes. Taking all factors into consideration, the model was re-geometrically modeled. It is worth mentioning that this transformation process was performed using manual modeling. Based on the optimized geometry data, we manually reconstructed the model using the professional CAD software SolidWorks 2024. This manual process ensures the accuracy and completeness of the model, while also allowing us to further fine-tune the model to meet specific design requirements. The result of re-modeling the topology optimization results  
Figure 11: Volume iteration history of the APTS’s L-shaped bracket structure for minimizing compliance considering volume fraction constraints.  
Figure 10: The optimized topology configurations of the APTS’s L-shaped bracket structure for minimizing compliance considering volume fraction constraints.  
considering displacement constraints is shown in Figure 12a. The final model weight is 0.648 kg, compared to the original model weight of 0.680 kg of the same material, resulting in a weight reduction of 4.7%. The result of re-modeling the topology optimization results considering both displacement and stress constraints is shown in Figure 12b. The final model weight with the same material is 0.635 kg, resulting in a weight reduction of 6.6%. The result of re-modeling the topology optimization results for compliance minimization considering volume fraction constraints is shown in Figure 12c. The final model weight with the same material is 0.655 kg, resulting in a weight reduction of 3.6%.