esa-sceva/satcom-chunk-collection huggingface dataset:
# NeuChain+: A Sharding Permissioned Blockchain System with Ordering-Free Consensus
## 2 Background and Motivation
### EV Architecture  
In Figure 2d, the ordering-free EV architecture differs from the architectures above. It eliminates the explicit ordering phase through implicit deterministic ordering, thus overcoming the bottleneck of centralized ordering. Its workflow is summarized as follows:  
1. All nodes independently receive user transaction requests and then exchange transactions through multiple parallel consensus instances. When a node collects the complete transaction set from all other nodes, it executes these transactions according to the deterministic rule and generates blocks.
2. After completion of execution, the execution results are returned to the user, and signatures verifying the execution results are exchanged among nodes.  
Instead of ordering transactions with a single consensus leader, all nodes in the EV architecture can propose blocks concurrently, eliminating the single-node performance bottleneck. Although transactions from different nodes arrive out of order, by using the deterministic concurrency control, executing these transactions out of order can still obtain the same result without internode coordination, eliminating the centralized orderingbottleneck. However, as discussed in Section 1, the EV architecture faces scalability issues in network, storage, and transaction execution: (1) Transactions must be replicated to all nodes via consensus. (2) Each node must execute a complete transaction set. (3) The storage overhead of maintaining a complete ledger is also significant.  
**Insight.** The EV architecture employs deterministic concurrency control, which can determine the order of transactions without additional coordination. Therefore, when employing blockchain sharding to the EV architecture, the order of cross-shard transactions can be determined in advance, and no additional coordination is required during transaction execution, eliminating the costly commit protocols. Therefore, we propose the sharding-EV architecture based on the EV architecture, as shown in Figure 2e.
## 3 Results
### Transmit Subsampling  
#### 3.2.1 Impact of the Region of Interest Selection  
In order to optimize our subsampling, we choose a region of interest which overlays the weld. The variation of the visibility of the region of interest over the transducer elements is displayed in Figure 4. When choosing a map that reflects a region near the front wall, as given by (b), only the center transducer elements contribute to the imaging. Vice versa, a region near the inner pipe radius (c) can be imaged using a much wider aperture.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline transducer radius & 35 mm \\ transducer element count & 128 \\ transducer element width & 0.3 mm \\ pitch & 0.05 mm \\ outer pipe radius & 16.5 mm \\ inner pipe radius & 11.7 mm \\ pipe material & Steel \\ coupling material & Water \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation scenario parameters.  
Figure 3: Simulation scenario as configured in CIVA.  
Consequently, since imaging with the widest possible aperture is beneficial, as defects like cracks can show very direction-specific reflection patterns, the width of the subsampled aperture needs to be chosen carefully. For the remainder of the paper we use the region of interest depicted in (c), which overlays the full weld as our best a priori guess to the defect positions to be observed in the data. It spans an area of \(3.56\,\mathrm{mm}\times 1.02\,\mathrm{mm}\). From Figure 4, it is apparent that this region results in an aperture choice that is a mix of (a) and (b).  
The 16, 8, and 4 transmit elements chosen on the basis of the region of interest in our scenario are displayed in Figure 5. The center of the array is being sampled more densely, as it contributes more energy to our region of interest. Still, a wide aperture is used which can help with the imaging of very specular reflecting defects.
## 2 Operating Principle of the Proposed Circuit
### Filtering Components and Their Design  
The detail of the procedure to design or determine the values of the filtering components, including the energy storing inductor and input and output capacitors, is described in [24]. These components help to improve the power quality of the output voltage and input current by lowering their ripples. For example, the output capacitor (\(C_{o}\)) not only helps to minimize the ripples in the output voltage but also supplies some reactive power in case of inductive load. Similarly, the capacitor connected to the input side (\(C_{s}\)) eliminates the harmonics or ripples that are resulted due to high switching operation of the semiconductor devices. This capacitor also helps to improve the power quality concerns of the input current. The role of the series inductor (\(L\)) is to store the input power in the form of a magnetic field in the on-interval of the switching interval and then release it to load in the off interval of the switching period. In variable frequency operation, the developed topology can also be used to lower the load frequency in some selected discrete steps. There may be a capacitive coupling problem between the rotor and stator due to generated high-frequency harmonics. Moreover, the presence of the series filtering inductor blocks the high-frequency harmonics from going toward the load. The ripple contents in the inductor current and output voltage are represented by the current ripple factor (\(k_{ir}\)) and capacitor ripple factor (\(k_{vr}\)). As a rule of thumb, these values are taken as 20% of the inductor current and 10% of the output or input voltage, respectively. The switching frequency, duty cycle, input voltage, average inductor current, and output or input currents for these calculations are taken as 25 kHz, 0.6, 120 V, 5 A, and 1 A, respectively. Based on these values, the value of the energy storing or filtering inductor is computed as  
\[L=\ \frac{V_{s}k(1-k)}{k_{ir}L_{i}f_{s}}\ =1.15\,\mathrm{mH} \tag{9}\]  
The ripples of the output capacitor are linked with its capacitance. Its value with the given condition is calculated as  
\[C_{o}=\ \frac{k_{lo}}{k_{cr}V_{o}f_{s}}=3.47\,\mathrm{\upmu F} \tag{10}\]A similar approach and the same given values are used to find out the value of the input capacitor is as  
\[C_{s}=\ \frac{I_{s}(1-k)}{k_{cr}V_{s}f_{s}}\ =1.3\,\upmu\text{F} \tag{11}\]  
Therefore, these values are approximated to 1 mH, 4.7 \(\upmu\)F, and 2.2 \(\upmu\)F for filtering inductor, output, and input capacitors, respectively.
## 3 Materials and Methods
### Size Exclusion Chromatography  
The SEC system used consisted of a high-pressure isopump (G1310A, Agilent 1100 series, Agilent Technologies, Waldbronn, Germany), a stainless steel in-line filter with a 0.1 \(\upmu\)m poly(tetrafluoroethylene) (PTFE) membrane (Millipore, Burlington, MA, USA), an automatic injector (G1313A, Agilent 1100 series, Agilent Technologies, Waldbronn, Germany), a column oven (G1316A, Agilent 1100 series, Agilent Technologies, Waldbronn, Germany), a SEC column packed with styrene-divinylbenzene (PS-DVB) copolymer gel (Mixed-B KD-806M, Shodex, Munchen, Germany) preceded by a guard column (Mixed-B KD, Shodex) and a refractive index detector (RID-10A, Shimadzu, Kyoto, Japan). Data acquisition and processing were carried out using ASTRA software (Wyatt Technologies, Solvang, CA, USA).  
SEC conditions were as follows: 1 mg/mL as sample concentration, 100 \(\upmu\)L of injection volume, 0.5 mL/min flow rate and the column temperature set to 60 \({}^{\circ}\)C. The detector cell of RID was kept at 40 \({}^{\circ}\)C. The elution solvent was pure DMSO.
## 3 Simulations for the Model Verification
### Two-Dimensional Model for Verification  
The two-dimensional model is built by the finite element software Midas gen [29], in which the beam element is used to simulate frame beams and the wall columns [30], and fixed constraints are applied at the bottom of the wall column. The wall column model with the frame beam is shown in Figure 12a (both ends of the beam and the bottom of the wall column are fixed constraints, and trapezoidal distributed load is exerted). Figure 12b-d shows the wall column models with elastic supports at the top and middle and fixed constraints at the bottom of the wall column. The stiffness of elastic supports is calculated by Equation (27). Moreover, Figure 12b-d represents the trapezoidal distributed load, the uniformly distributed load, and the triangularly distributed load acting alone on the wall column, respectively. Figure 13a-d shows the calculation results of bending moments under the corresponding model, respectively.  
Figure 11: Section of the opening (unit: mm).  
Figure 10: Plan of the metro station with a reserved large-size opening (unit: mm).  
Based on the analytical solution of Equation (38), control bending moments at the bottom of the column and some significant parameters under various working conditions are obtained and shown in Table 1. In addition, soil pressure within the 3 m range in the horizontal direction is taken into account and converted into line load on the wall column, as the distance in the horizontal direction between two adjacent wall columns is 3 m.  
Figure 12: Two-dimensional simplified simulation model of the wall column (unit: kN/m). **(a)** The wall column model with the frame beam and trapezoidal distributed load; **(b)** The wall column model with elastic supports and trapezoidal distributed load; **(c)** The wall column model with elastic supports and uniformly distributed load; **(d)** The wall column model with elastic supports and triangularly distributed load.  
Figure 13: Bending moment diagram of wall column under two-dimensional simulation model (unit: kN/m). **(a)** Bending moment diagram of the model of Figure 12a; **(b)** Bending moment diagram of the model of Figure 12b; **(c)** Bending moment diagram of the model of Figure 12c; **(d)** Bending moment diagram of the model of Figure 12d.  
As displayed in Figure 13a,b, after the beams are simplified as elastic support, the control bending moment at the bottom of the column is almost the same by theoretical equations and two-dimensional simulation, which indicates that regarding beams as elastic supports is rational and feasible. Meanwhile, it also verifies the correctness of the stiffness of the support deduced in this paper. Combined with the results in Table 1 and Figure 13, we can see that the analytical solution of control bending moments at the bottom of the wall column is the same as the finite element software simulation results. Thus, it certifies the validity of the analytical solution of control bending moments at the bottom of the wall column deduced in this paper.
## 1 Introduction  
The remote monitoring of human vital signals based on radars, including respiratory and heartbeat rate, is cost-effective and can provide a large amount of health information [1, 2, 3, 4]. The methods proposed in previous studies were able to provide good respiratory rate estimations, while the heartbeat rate estimation was always variable [5]. This is because radars mainly measure the micro-Doppler effect caused by the motion of the human body resulting from a variety of activities, among which, respiration is the main one when the heartbeat is relatively weak [6, 7].
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 4 Signal Processing from RSL to Rainfall Map
### Wet/dry Classification
#### 4.1.2 Assisted by Other Rainfall Measurement Methods  
Rainfall periods can be identified with the help of using rainfall data measured by RGs, radar, or satellite [55]. For example, path-averaged mean 15 min rainfall intensity along the link from unadjusted radar data can be used to identify wet and dry weather conditions for each link and time step. If the rainfall intensity is greater than 0.1 mm/h, the current and the following steps are classified as wet [56].
## 3 Results
### Treatment
#### 3.3.1 Decision Making  
[PERSON] et al. (2018) used a ML algorithm, trained with clinical and pathological variables such as age, medical history, PSA, Gleason scores, or number of positive cores, to guide patients in their choice of treatment and to help them understand their disease. This application also allowed urologists to guide their patients towards treatment or further examination by calculating the probability of organ-confined disease with a good accuracy (81%).
## 4 Results
### Optimal Parameters  
Figure 4 compares the initial optimization parameters (\(H_{1}\), \(H_{2}\), \(H_{3}\), \(D_{1}\), \(D_{2}\), \(D_{3}\)) to the optimal ones obtained by multi-objective optimization. The optimized values correspond to two key solutions on the Pareto front: the point closest to the origin and the knee point. The Pareto fronts are based on five mentioned Gramian controllability metrics, with the first five indices corresponding to Pareto fronts with \(\zeta\) constraints and the last five corresponding to Pareto fronts without \(\zeta\) constraints. The red horizontal lines represent initial parameter values, the blue bars indicate the optimal values obtained from the point closest to the origin on the Pareto front, representing a solution with a minimal Euclidean distance to the ideal point, and the green bars correspond to knee point solutions, which represent a compromise solution balancing competing objectives effectively. Knee points align closely with closest-to-origin solutions. With \(\zeta\) constraints, the optimized solutions, in most cases, show a slight increase or decrease in \(D\) and \(H\) compared to the initial values. Without \(\zeta\) constraints, the optimization led to reduced damping, both in terms of \(\zeta\) and \(D\), while \(H\) was only slightly changed in most of the cases. However, when using controllability metric \(\rm EV_{\rm max}\), the optimization considerably increased \(H_{2}\) (constrained and unconstrained case) and \(D_{2}\) (only contained case).
## 3 Wake-Up Radio Communication Protocols
### Passive Wake-Up Signal Packet
#### 3.2.2 MPDU (MAC Protocol Data Unit)  
This is the main body of the wake-up signal data link layer frame, which consists of fields for address, packet type, and payload. The destination wake-up radio address is contained in the address field. The field for packet type identifies whether the packet being sent is a beacon, information, ACK/NACK, command, or stream. The payload field contains the actual sampled data.
## 2 Materials and Methods
### Evaluating Sensitivity to Micro- and Macro-Bending of Optical Fibers  
In addition, an advantage of optical fibers applied for FCSs, discussed in Section 2.2, is that they can be processed during the embroidery fabrication process, i.e., any breaks while embroidering on the PVA nonwoven substrate can be avoided as well as the introduced light attenuation inside the optical glass fiber due to micro- and macro-bending can be neglected. Hence, in order to investigate their sensitivity to micro- and macro-bending when embroidered on the substrate, an experimental set-up was designed that consisted of a 3D printed mold and a power meter (dB-meter from FiboTec). The 3D printed mold enables the periodic and reproducible bending of the glass fiber with a bending radius of five millimeters and the power meter is used to monitor the introduced light attenuation inside the optical glass fiber. The minimum bending radius of 5 mm was determined from the fabrication process of the FCS. In Figure 3, a picture of the experimental setup consisting of the 3D printed mold and the power meter is illustrated.  
Figure 2: 2D-FCSs with different optical fiber sensor configurations. Straight (**a**), with an offset (**b**) and meander (**c**) fiber configurations were applied to investigate the bond between optical fiber and the carbon filament.
# A Real Model of a Micro-Grid to Improve Network Stability
## 4 Microgrid Voltage Stability Control
### Algorithm Using the Weather Forecast to Predict the Microgrid-Generated Volume of Energy  
Conceptually, the algorithm exploits the scenario where the Sun is expected to shine, enabling the 24 V grid battery block to become fully charged, while at the same time, another storage spot of the microgrid (such as the hydrogen canister) contains only a modest amount of energy. A similar scheme applies to hydropower plants and rainy weather.  
The weather forecast is acquired from a remote web server, [[https://www.yr.no](https://www.yr.no)]([https://www.yr.no](https://www.yr.no)), providing data related to the temperature, cloud cover, and precipitation, with an interval of one hour. This server was identified as the most effective tool of its kind in satisfying our basic requirements, namely, weather forecasting for the city of Brno, (Moravia, Czech Republic), where the microgrid is located. As indicated in Figure 9, the data from the server are modified using Hypertext Preprocessor (PHP) script, stored in the MySQL database, and then read out to control the microgrid via LabView. The obtained information enables us to direct the energy flows within the individual means of storage, utilizing CompactRIO and CB1.  
In our case, 48-h weather forecasts are utilized. If we establish that some source will generate a large amount of energy while the direct storage battery is full, the control will attempt to move the energy from the battery to another means of storage (such as the hydrogen circuit) in order to increase the capacity of the batteries located close to the power plant.  
Figure 9: Controlling the energy flow in the microgrid, using the data extracted from the applied weather forecast source.  
Another method to regulate available storage space depending on the weather forecast consists in the demand-response principle; this enables us to switch on the appliances in which the start-up time is not a critical parameter. However, not many appliances of the given type are present in the home, and thus the principle finds wider applications in more extensive networks.
## 3 Results and Discussion
### Slump  
Based on the data presented in Table 6, the influence of replacing cement with FA and FGCBA on the slump of fresh concrete was investigated across various water-binder (W/B) ratios.  
At a W/B ratio of 0.4, the control mixture without any replacement exhibited a slump of 9 cm, which indicates a relatively stiff consistency. Upon the introduction of FA, the slump increased progressively with each level of replacement, reaching 18 cm at 60% replacement, suggesting an enhancement in workability with a higher FA content. In contrast, FGCBA demonstrated a peak in workability at 20% replacement with a slump of 13 cm but showed a decrease at the 60% replacement level, achieving a slump of 13 cm, possibly due to the different particle sizes and shapes affecting the mix's consistency.  
At a W/B ratio of 0.45, the control mixture without replacement had a slump of 17 cm. Replacing cement with FA and FGCBA resulted in varied effects on slump. FA replacements at 20%, 40%, and 60% yielded slumps of 18 cm, 20 cm, and 21 cm, respectively, indicating a  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & **Unit: \%** \\ \hline
**Type** & **7 Days** & **28 Days** & **56 Days** & **91 Days** & **180 Days** \\ \hline FA & 91.23 & 100.7 & 126.19 & 130.55 & 166.88 \\ FGCBA & 67.34 & 86.43 & 93.47 & 97.48 & 109.08 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Pozzolanic strength activity index determined for each material.  
consistent increase in workability. Conversely, FGCBA replacements showed a slump of 18 cm at 20%, decreasing slightly at 40% to 15 cm and then to 13 cm at 60% replacement. This decrease suggests that the FGCBA may have a diminishing effect on the workability of the concrete mix at higher replacement percentages.  
For a W/B ratio of 0.5, the control mix without any FA or FGCBA exhibited a slump of 23 cm. FA replacements demonstrated an increasing trend in workability, with the slump reaching 27 cm at a 60% replacement rate. FGCBA, however, presented a different pattern, with the slump starting at 24 cm for a 20% replacement, dropping to 13 cm at 40%, and then slightly increasing to 12.5 cm at 60%. This behavior might be indicative of the varying effects of FGCBA shape on the fresh concrete's consistency.  
In summary, the data suggest that FA generally tends to improve the slump and workability of concrete as the replacement percentage increases across all water-binder ratios. On the other hand, FGCBA shows an initial enhancement of slump at lower replacement levels but a reduction at higher percentages. These findings highlight the potential for FA to be used as a replacement material to enhance workability, whereas FGCBA may require careful consideration in mix design to achieve desired slump properties, especially at higher replacement rates.
# Adaptive Bat Algorithm Optimization Strategy for Observation Matrix
## 4 Experimental Simulation
### Analysis and Selection of Transformation Basis  
The selection of the transform base, as the preliminaries of the compressed sensing process, has a great influence on the signal reconstruction effect. A good transform base can well represent the original signal containing a large amount of information, and obtain a better reconstruction algorithm. Therefore, to get better transform bases, this section performs signal reconstruction experiments by comparing different transform bases. In addition, it proves the robustness of the ABA to optimize the observation matrix relative to different transformation basis.
## 3 Results and Discussion
### Validation of the Proposed Method on the NEU Dataset  
In order to validate that the benefits obtained by introducing synthetically generated images with the GAN is repeated with other datasets, we replicated the process but using an NEU publicly available dataset [28]. The NEU dataset is a defect database composed of six types of typical defects in hot rolled steel surfaces. These defects are: rolled-in scale (RS), patches (Pa), crazing (Cr), pitted surface (PS), inclusion (In), and scratches (Sc). This database has a total of 1800 greyscale images, where each class has 300 different samples.  
As described in Section 3.2, we also trained a StyleGAN2 network to generate 100 new synthetic images but from the NEU dataset. As for the segmentation process, PAN  
Figure 11: Mean of IoU (%) during evaluation with the original photometric-stereo RGB augmented database and the original one plus synthetic images generated by the GAN with a 38% ratio.  
was also trained with both an original NEU dataset and with an augmented dataset with StyleGAN2 generated images.  
In the training with the original NEU dataset augmented with conventional methods, a total of 2445 images were used. In the training with the original images plus the StyleGAN2 synthesized images, as stated in the experiment of image ratio variation, the number of the training set amounts to 3930. The results on the validation set, which has 42 un-augmented images, was 69.11% in IoU with the original dataset and 75.32% in IoU with the synthetic images. A sample image, its corresponding ground truth, and the network segmentation output are shown in Figure 12.  
The results obtained show that data augmentation by generating synthetic images gives good results in terms of segmentation quality and in the IoU metric. This shows that this data augmentation technique is an additional benefit in network training. It is true that it does not match the results obtained in training using more real images, but it is a good tool to apply variability in training, which traditional data augmentation methods cannot provide.