esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 MWW-Type Materials by Post-Synthesis Modifications
The use of confined subnanometric platinum species was reported and made use of a swelling procedure [56]. In this approach, a solution containing subnanometric platinum species was added during the swelling procedure of the ITQ-1 precursor. After calcination, a three-dimensional zeolite containing platinum confined in the supercavities and on the external surface of the MWW crystallites was formed, as shown in Figure 5. The authors also studied the growth of these platinum species by high-temperature oxidation-reduction treatments, obtaining small nanoparticles with sizes between 1 and 2 nm. Figure 4: Representation of the postsynaptic procedures to obtain the interlayer expanded zeolite IEZ-MWW, swollen, pillared (MCM-36), and delaminated (ITQ-2) MWW-type materials. Recently, a novel strategy to obtain delaminated MWW-type zeolite employed a treatment using commercially available telechelic liquid polybutadienes at room temperature and a swollen precursor [57]. The resulting swollen precursor/polymer suspension was subject to ultrasound or a chaotic flow treatment in a planetary mixing system, as shown in Figure 6. The authors confirmed delamination using small angle X-ray scattering (SAXS) results, where no interlamellar reflections were observed using hydroxyl-terminated polybutadiene (HTPB) with 36 min of chaotic flow. On the other hand, the sonication procedure is also effective, but required 5 h to obtain a delaminated material. Another interesting result was the increase in the interlamellar space of the swollen precursor from 4.6 nm to 9.4 nm after manual mixing with HTPB for only 1 min. The authors also pointed out that the end groups of the liquid polybutadienes preferentially interact with the zeolite surface through hydrogen bonds and this is the key factor needed to obtain a delaminated material. Figure 5: Scheme of confining platinum species in the MCM-22 structure by swelling the MWW precursor with surfactants and platinum species and a subsequent calcination. Reprinted with permission from Springer Nature, Reference [56]. Figure 6: Scheme of the formation of delaminated MWW-type material using telechelic liquid polybutadienes. Adapted from Reference [57]. Copyright (2017), with permission of The Royal Society of Chemistry. Another post-synthetic approach to obtain more open structures in MWW-type materials is to generate intracrystalline mesopores [41]. This strategy uses MCM-49 zeolite and mixtures of CTA\({}^{+}\) and NaOH under different temperatures and times to obtain desilicated MWW-type materials, as shown in Figure 7. Under the post-synthesis treatment, fragments of the MWW zeolite structure were removed by the attack of the hydroxide ions in the defects, whereas the CTA\({}^{+}\) molecules acted as a defensive barrier to avoid uncontrollable dissolution by NaOH. Thus, intracrystalline mesopores were formed by the regions where CTA\({}^{+}\) presented \"defensive failures.\" The obtained materials showed a distribution of mesopores with sizes between 2 and 4 nm.
# A Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode
## 5 Evaluation Experiment and Analysis
### Evaluation Results for Sun-Pointing Mode
Therefore, to improve the performance of these two control processes, the aspects of relative attitude pointing accuracy and attitude control energy consumption need to be considered.
## 3 Proposed Sensor Fusion Framework Model Using PDR and W-Fi Localization Systems
### Wi-Fi Positioning
#### 3.2.2 Wi-Fi Fingerprint Algorithm  
The basic idea of creating fingerprint maps for Wi-Fi positioning is explained in [40; 41]. In this localization approach, we use a grid-based representation of the indoor environment. The fingerprint algorithm consists of two stages. The creation of fingerprint maps of the localization area from the sampled RSSI values is the first stage of the fingerprint algorithm. To accomplish this, we divide the location area into different zones and collect RSSI samples at all the grid points. In the second stage, we estimate the position of the receiving module from the fingerprint maps using the nearest neighbor method. Other methods used in position estimation are the support vector machine (SVM) and the hidden Markov model. Figure 3 shows the Wi-Fi fingerprint flow chart.  
The first stage of Wi-Fi fingerprint positioning is explained in Figure 3. The localization area is divided into grid points and RSSI samples are collected from each grid point. The fingerprint gathering utility is used for data collection at each access point. The fingerprint parsing utility takes the RSSI samples as input and generates the data necessary to build the RSSI probability distribution for each reference point. The location estimation procedure for the Wi-Fi fingerprint algorithm is shown in Figure 4. The location estimation procedure uses the localization algorithm on the fingerprint maps of the four APs and the received RSSI samples on-the-fly. The nearest neighbor method is used to  
Figure 3: Wi-Fi fingerprint flow chart [41].  
determine the user location based on the fingerprint data. The nearest neighbor approach calculates the Euclidean distance between the live RSSI sample and each reference point fingerprint. The minimum Euclidean distance is the approximated user location.  
Let the RSSI samples from \(N\) Wi-Fi APs in the fingerprint maps be expressed as a vector \(\rho=\left\{\rho_{1},\rho_{2},\rho_{3},.......,\rho^{N}|\rho_{i}\in R^{N}\right\}\) to generate fingerprints database and \(i=1,2,3,...,N\) be the number of grid points. The measured RSSI of a user from \(N\) Wi-Fi access points at a particular location during the experiment time is expressed as the vector \(\delta=\left\{\delta_{1},\delta_{2},\delta_{3},.......,\delta^{N}|\delta_{k} \in R^{k}\right\}\). The Euclidean distance \(D_{i}\) between fingerprint maps and the corresponding measured values can be expressed as in [42]:  
\[D_{i}=|\rho_{i}-\delta|=\sqrt{\sum_{i=1}^{N}\left(\rho_{i}-\delta\right)^{2}} \tag{13}\]  
From the resulting matrix \(D_{i}\), the minimum value represents the approximated user location and is expressed as  
\[[x,y]=\underset{i}{\arg\min}\;D_{i} \tag{14}\]
# Retinal Vessels Segmentation Techniques and Algorithms: A Survey
## 4 Retinal Vessels Segmentation Techniques
### Adaptive Local Thresholding Techniques
In summary, adaptive thresholding was used to extract large- and medium-sized retinal vessels, while MTVF was used to extract the smallest ones. Finally, as a post-processing step, morphological cleaning was used to remove the non-vasculature components remaining after applying adaptive thresholding. The proposed methodology was tested on a recently available Erlangen dataset ([PERSON] et al., 2016), where it achieved average accuracy of 94.79%, and 85.06% and 95.82% for sensitivity and specificity, respectively. [PERSON] et al. [PERSON] et al. (2016) used adaptive thresholding technique to locate and extract retinal vessels automatically. The statistical-based adaptive thresholding was used to create the binary vascular mask by selecting points that isolate vessels from the rest of image. The proposed method has two major phases: pre-processing and adaptive thresholding phases. In the first phase, the monochromatic RGB retinal image was fed into Gabor wavelet filter for vasculature pattern enhancement, specifically for thin and less visible vessels, based on an image analysis technique proposed by ([PERSON] et al., 2016). The yielded  
Figure 14: Adaptive local thresholding taxonomy. enhanced retinal image has maximum grey values occur for background whereas pixels belong to vessels have a slightly greater intensity values rather than that belong to background. The proposed technique has been tested using DRIVE dataset, where it achieved an average accuracy of 0.9469 with area under ROC curve approximate value of 0.963. A knowledge-guided local adaptive thresholding was proposed by [PERSON] and [PERSON] (1999) where a verification-based multi-thresholding probing scheme was used. In its most basic form, given a binary image \(I_{binary}\) results from thresholding process at a threshold level \(T\), a classification procedure is used to decide if any region in \(I_{binary}\) can be defined as an object. The operation is carried out on a series of different thresholds. The final image segmentation is set by combining different results of different thresholds. In summary, the objects hypotheses in an image are generated by binarization via some hypothetic thresholds, and then, according to a particular classification procedure, the object is accepted or rejected. The classification procedure represents the core of this proposed algorithm, where any piece of information related to object under consideration is incorporated, such as shape, color intensity and contrast. This is why it is called _knowledge-guided_ technique, since, during segmentation, thresholding levels varying according to the knowledge available about the target object.
# Using a Cable-Driven Parallel Robot with Applications in 3D Concrete Printing
## 2 Mechanical Structure
### Architecture of the Model
In the forward dynamic model, we have the input forces and torques to map the output motion of the cable robot, where the motion can be described as a position, velocity, or acceleration of joints, points, or bodies. The inverse dynamics characterizes the reverse mapping, where the motion is given and the forces and torques that generate this motion are sought. A cable robot is a mechatronic system consisting of a mechanical part and an electrical part. The mechanical part includes the mobile platform that is connected by m cables guided by pulleys to the winches. The electrical part consists of m servomotors, position controllers, and a central controller to compute the value of the variable control signals from the calculation results of the kinematic and dynamic problems. To derive the equations of motion for the mechanical components of a cable robot, one can use recursive Newton-Euler equations or Lagrange equations of the second kind. Both mechanical principles are equivalent and result in equivalent equations. The proposed dynamic model is formulated in the operational space (Cartesian space) and it employs inverse kinematics and forward dynamics, which are easy to solve and fast to compute. Based on this approach, the equations can be set up in closed-form, such that computation is basically possible in real-time. However, when actually computing the equations of motion, the methods differ in how simple they are to use, taking into account the different kinds of cable models and subsystems.
## 2 Data and Methods
### Additional Accompanying Data  
To compare our new semi-analytical retracker we have sourced data from previous efforts at retrieving SWH from CS2. RADS data were obtained from [[http://rads.tudelft.nl/rads/rads.shtml](http://rads.tudelft.nl/rads/rads.shtml)]([http://rads.tudelft.nl/rads/rads.shtml](http://rads.tudelft.nl/rads/rads.shtml)) (accessed on 20 August 2021) for the entirety of 2014, with co-located WaveWatch 3 data supplied alongside [24]. We have gathered SWH from the SAMOSA+ (SAR Altimetry MOde Studies and Applications) retracker though the ESA GPDD system (Grid Processing On Demand). Due to the computational time taken to process the SAMOSA+ data using the ESA GPDD system a shorter period of 2018-11 to 2019-4 was used. It was not feasible to process the complete CS2 time period using the GPDD system.  
For the locating of open ocean surface within the seasonally ice covered Arctic we used NSIDC ice concentration data [25]. We use daily ice concentration maps. For analysis in this study all areas where sea ice concentration is greater than 15% were omitted, although all ocean locations were processed.  
To contrast the different satellite data sources we have endeavoured to find in situ wave buoy measurements of SWH above 60\({}^{\circ}\)N over the period of data collection. These buoys, documented in Table 1, are the NOAA NDBC buoys numbered 48,213 and 48,214, the Swift wave buoy array of [26] and the UK Met Office buoy K7 ([[https://www.metoffice.gov.uk/weather/specialist-forecasts/coast-and-sea/observations/164046](https://www.metoffice.gov.uk/weather/specialist-forecasts/coast-and-sea/observations/164046)]([https://www.metoffice.gov.uk/weather/specialist-forecasts/coast-and-sea/observations/164046](https://www.metoffice.gov.uk/weather/specialist-forecasts/coast-and-sea/observations/164046)), accessed on 20 August 2021).
# A Low-Power ECG Processor ASIC Based on an Artificial Neural Network for Arrhythmia Detection
## 1 Introduction
It can be observed that previous works have limitations including limited types of arrhythmia detection, a relatively low classification accuracy, a high hardware overhead, and unsatisfactory power performance. However, mobile health monitoring devices generally have strict cost and energy consumption requirements. To overcome existing problems, a low-power ECG processor ASIC is proposed in this paper, which can be used in wearable or portable healthcare devices with a limited battery life. This ASIC can classify heart beats into five types to detect arrhythmia. A low-computation-complexity method based on the first derivative and moving average comparison is adopted to detect R peaks in ECG signals. The classification is performed by a hardware-efficient computing engine, which is based on a simple ANN model with few parameters, achieving an acceptable accuracy and low detection power. All inference computations are based on 16-bit fixed point values and operated by reusing one processing unit (PU), which makes this ASIC have a low chip area overhead. This paper is organized as follows. Section 2 depicts the overall architecture of the proposed ECG processor. Section 3 describes the details of the system modules. Section 4 presents the hardware implementation and results. Finally, the conclusions are given in Section 5.
## 5 Evaluation
### Experiment 2  
In this experiment, we take a random topology to mimic real-life _ad hoc_ networks. The topology is shown in Figure 13. We randomly choose 10 S-Dpairs and generate traffic according to a randomly-generated ratio.  
In Figure 14 we have plotted the average throughput _versus_ the average offered load. In this experiment, we can still observe that DPSA provides higher throughput than SCAR when the network is not yet saturated. However, the difference in the average throughput of a saturated network almost vanishes. This can be explained by the cascading effect: In Experiment 2, most flows have more than two hops. The scheduling decision made at one node can affect the offered load of the next. Without proper planning (as in round-robin scheduling), a great proportion of packets are discarded along the way. This is a great waste of bandwidth, which effectively decreases the maximum throughput achievable by the round-robin scheduling. This can be explained by the cascading loss effect. In a flow with more than one hop, the scheduling decision made at an upstream node can affect the offered load of the immediate downstream node. Without proper planning (as in round-robin scheduling), the mismatch in each hop would waste a certain amount of bandwidth. The more hops one flow has, the more severe becomes bandwidth loss. This is referred to as the cascading loss. A great proportion of packets can be discarded this way, which effectively decreases the maximum throughput achievable by the round-robin scheduling. Also note that DPSA managed to schedule packets more fairly, and the scheduling maintains a good linkage between upstream nodes and downstream nodes, minimizing the cascading effect. In addition, the significant decrease of throughput, as seen in Experiment 1, is not present. This is simply because the MAC-layer contention is not as severe as in Experiment 1's topology.  
Figure 13: The topology of Experiment 2.
## 4 Numerical Simulation of the Formation Effect of Grout Curtain in Ore-Rock Contact Zone
### Slurry Plane Diffusion Law
#### 4.1.3 Slurry Diffusion Law under Different Water Pressures  
The stone body's morphology was simulated under water pressure settings of 0 MPa, 1.5 MPa, and 3 MPa. The corresponding results are displayed in Figure 5, where P\({}_{w}\) represents the grouting rate. It is evident that when the water pressure gradually increases, the spreading distance of the slurry and the shape of the stone body diminish. Using a time node of 60 s for grouting, it can be observed that when the water pressure is 0 MPa, the slurry diffuses to a distance of 1.67 m. At a water pressure of 1.5 MPa, the slurry diffusion distance decreases to 1.34 m. Finally, at a water pressure of 3 MPa, the slurry diffusion distance further decreases to 1.09 m. This indicates that a higher water pressure results in stronger dispersion and transportation of the slurry by groundwater. Within the area surrounding the boundary between the ore and rock, the movement of the slurry is also impeded and redirected, resulting in a much shorter distance of diffusion in magnetite compared to syenite substances.  
The slurry pressure and rate were simulated under different water pressures of 0 MPa, 1.5 MPa, and 3 MPa. The corresponding findings are displayed in Figure 6. Based on the aforementioned findings, it is evident that there exists a negative correlation between the slurry flow rate and the water pressure. Specifically, when the water pressure increases, the slurry flow rate experiences a large decrease. At the same water pressure, the slurry flow rate decreases as the distance from the grouting hole increases. Furthermore, the flow rate  
Figure 4: Changes in slurry pressure and velocity under different grouting rates.  
Figure 3: Morphology of stone body under different grouting rates.  
decreases more significantly after passing through the ore-rock contact zone, and the rate of change in the flow rate is higher.
# Seismic Resilience Evolution of Shield Tunnel with Structure Degradation
## 3 Numerical Simulation
### Establishment of the Finite Element Model
#### 3.1.2 Material Constitutive  
To study the influence of preload loss at joints on the seismic response characteristics of tunnel structures and to ensure calculation accuracy while simplifying the calculations, the model undergoes the following treatments: (1) high-strength bolts are simplified using beam elements, and the material constitutive model adopts a bilinear plastic strengthening model. The elastic modulus in the plastic strengthening section is taken as 1/100 of the elastic modulus in the elastic section. The nominal diameter of the bolt is 30 mm, with a tensile strength of 800 MPa and a yield strength of 640 MPa. (2) The concrete material constitutive model uses the Concrete Damaged Plasticity model with a dilation angle of 38\({}^{\circ}\) to reflect the nonlinear variation of concrete segments. The uniaxial stress-strain curve for concrete follows the specifications in the Code for Design of Concrete Structures [30]. The concrete damage parameters are provided in Table 2. (3) The uniform soil layer adopts the Mohr-Coulomb material constitutive model with a dilation angle of 0.01\({}^{\circ}\) and a friction angle of 40\({}^{\circ}\). Both concrete segments and soil are modeled using three-dimensional  
Figure 5: Soil–shield tunnel structure model. (**a**) Stratum–structure model. (**b**) Layout of segment rings of tunnel. (**c**) Connection bolts at the joint of shield tunnel segment. (**d**) Refined model of lining ring.  
hexahedral eight-node solid elements (C3 DBR). The geological and material parameters are presented in Table 3.
## 2 Related Work
### Unsupervised-Based Techniques  
The authors in [14] proposed a hybrid algorithm which is a combination of information gain (IG) and principle component analysis (PCA) for the irrelevant feature of the dataset. They applied multiple ensemble classifiers, which are instance-based learning algorithms (IBKs), multilayer perceptron (MLP), and support vector machine (SVM). In terms of accuracy, time, and detection rates, this novel technique provides good performance. An efficient and secure certificate-less authentication system for VANETs was proposed by [15]. Their system consists of three stages which give privacy to the communications of the group. The registration stage is the first stage, in which a vehicle is registered using a trusted party in the offline mode. The authentication stage is the second stage, in which the vehicle is verified depending on the previous registration stage. The last stage is the shared key between the groups created by the Chinese remainder theorem (CRT). The experimental analysis demonstrates that their design is efficient and can deliver the desired security properties. The researchers in [16] introduced an unsupervised end-to-end deep learning system based on the robust software modeling tool (RSMT) which is considered as an automated monitor and also describes the runtime behavior of the web applications. Empirical analysis was performed through supervised and unsupervised algorithms. The results show that RSMT has produced overhead issues; moreover, tests conducted by applying a supervised ML procedure was poor (at the best case, around 0.728 for XSS using SVM). Furthermore, the unsupervised deep learning achievement was not effective (around 0.906). Although unsupervised-based techniques have emerged as a practical way for learning anomaly detection systems from unlabeled data, and as there is no need for experienced domain experts to label a large amount of data, they suffer from poor accuracy and low efficiency [5].
One of the challenges in designing and operating LEO missions is estimating the total radiation dose that the spacecraft and its components will receive. This requires understanding the type, energy, and flux of the particles involved in the radiation environment. Tools like SPENVIS [2] are used to estimate the radiation dose a spacecraft will receive in a given orbit using models like CREME (Cosmic Ray Effects on Micro-Electronics) [3] and SHIELDOSE [4]. Accurately estimating the radiation dose is crucial in ensuring the reliability and longevity of electronic components and systems used in LEO missions, as well as estimating the maximum radiation that astronauts will receive aboard the spacecraft. The popularity of LEO orbits for telecommunications satellites is increasing due to the deployment of mega-constellations. These initiatives require a drastic reduction in the cost of avionics, which can be achieved through the adoption of commercial parts and industrial processes, as well as by integrating more functions and a better design for testability. However, the use of COTS components in low Earth orbit satellites poses challenges due to radiation-induced outages. In this work, we analyze different missions over the years to compare the data obtained by radiation sensors versus those calculated by different prediction models used in space engineering. This information provides a strong basis for the use of COTS technology in future LEO missions, as it should improve the reliability and availability of space systems and ensure the success and safety of these missions. Thus, in this article we continue and extend the scope of previous studies wherein the ability of certain components to resist ionizing radiation has already been demonstrated [5; 6].