esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Program
### Test Setup and Loading History  
In this research, the BCJ specimens underwent a 90-degree rotation, with the column ultimately aligning horizontally while the beam transitioned to a vertical orientation. The column ends were pinned to the strong floor of the laboratory using a sandwich-type metal plate mechanism, enabling unrestricted rotation. Furthermore, a constant axial compressive load was applied in the column. The axial load ratio of the column was set to 5% of the maximum bearing compressive capacity. A servo-controlled hydraulic actuator imposed the cyclic loading sequence on the beam's free end. Furthermore, the experimental configuration consisted of two Laser Displacement Sensors (LDS) employed to measure the displacement of the beam, as depicted in Figure 3. In addition, two Wireless impedance/Admittance Monitoring System (WiAMS) devices were utilized to capture the EMI responses of the PZT transducer.  
Figure 2: Retrofitting scheme of BCJ specimens using NSM C-FRP ropes.  
Establishing a loading history that comprehensively addresses the critical issues relating to the capacity of structural components and seismic demands is essential. This is crucial for effectively utilizing the results of quasi-static cyclic loading tests on structural elements to conduct a comprehensive seismic performance evaluation. Due to cumulative damage, the seismic capacity characteristics of structural elements diminish with each excursion in the inelastic range. Thus, this study selected a multi-cyclic loading sequence characterized by increasing displacement steps with significant inelastic excursions. Further, the consideration of cumulative damage resulting from multiple inelastic cycles has been proposed to be incorporated. Given the frequent occurrence of small amplitude cycle loadings in response to natural excitations, the adopted loading program consists of three complete loading cycles per step with identical deformations. It is worth noting that the adopted loading sequence results in significant drift values, which can be viewed as an assessment factor for behavior beyond conventional seismic design targets. The imposed deformations were calculated based on story drift (SD) percentages ranging from 0.5% to 5%. Accounting for the lever arm, the prescribed displacements for each increment were \(\pm\)8.50 mm, \(\pm\)12.75 mm, \(\pm\)17.00 mm, \(\pm\)25.50 mm, \(\pm\)34.00 mm, \(\pm\)51.00 mm, \(\pm\)68.00 mm, and \(\pm\)85.00 mm, as illustrated in Figure 4. Furthermore, at the end of each loading step, the EMI response of the PZT transducers was captured to monitor the performance of the retrofitting technique.  
Figure 4: Loading history and EMI measurements. The loading sequence consists of 8 steps, with each step encompassing three identical full loading cycles.  
Figure 3: Experimental setup of BCJ specimens.
## 2 Methodology
### Methods of Experimental Data Analysis  
Calculations for performance evaluation of PV modules were carried out according to the International Electrotechnical Commission standard IEC 61724-1 [32]. The daily energy (\(E_{DC}^{d}\)) produced by each technology PV array was calculated from the following equation:  
\[E_{DC}^{d}=\sum_{surrise}^{sunset}P_{DC}^{d}(t)\cdot\Delta t, \tag{1}\]  
where: \(\Delta t\)--time step of measurements (5 min), \(P_{DC}^{d}(t)\)--power generated at a particular time of the day measured as a product between the current and the voltage at the inverter inlet.  
Daily irradiation was calculated on the basis of irradiance \(G\) (W/m\({}^{2}\)) according to the formula:  
\[H^{d}\bigg{(}\frac{\text{kWh}}{\text{m}^{2}\cdot\text{day}}\bigg{)}=\sum_{surrise }^{sunset}G(t)\cdot\Delta t, \tag{2}\]  
where \(G(t)\)--irradiance measured at a particular time \(t\) of the day.  
The effect of the differences in energy production caused by different size of installation of each technology array was eliminated by dividing the daily DC energy production (\(E_{DC}^{d}\)) by the nominal power output of the installation at STC (\(P_{M}^{STC}\)). The daily yield is given by the ratio between daily energy produced by each technology PV array and output peak power \(P_{M}^{STC}\) of the modules under STC:  
\[Y^{d}(\text{kWh/kWp})=\frac{E_{DC}^{d}}{P_{M}^{STC}}. \tag{3}\]  
The effect of insolation can be expressed by reference yield \(Y_{R}^{d}\), which is defined as the ratio of daily irradiation and irradiance at STC according to the formula:  
\[Y_{R}^{d}=\frac{H^{d}}{G^{STC}}\, \tag{4}\]  
where: \(H^{d}\)--daily irradiation expressed in kWh/m\({}^{2}\), \(G^{STC}\)--irradiance under standard test conditions (in kW/m\({}^{2}\)).  
For an ideal PV system operating under STC, the calculations based on the Equations (3) and (4) should provide the same results. Nonetheless, the power losses in the PV system (modules and other particular components) lead to lowering of the \(Y^{d}\) value in comparison with \(Y_{R}^{d}\). In order to evaluate real energy production relation to the ideal scenario, performance ratio (PR) of the system was calculated:  
\[PR=\frac{Y^{d}}{Y_{R}^{d}}. \tag{5}\]  
Another parameter used for the assessment of real performance of the modules was efficiency, defined as a quotient between the energy production in reference period and solar irradiation received by the module:  
\[\eta=\frac{E_{DC}^{d}}{H^{d}\cdot Area}\cdot 100\%, \tag{6}\]  
where \(Area\) is the total area of the modules.
## 2 Schematic Description of Unit Cell and Simulation
#### Extraction Method of Effective Medium Parameters  
CST microwave studio based on finite integration technique was used to differentiate the combined symmetrical erection and integration of arbitrary distribution of material and their physical characteristics. The frequency-domain solver with a tetrahedral mesh of CST was used to simulate the expected research.  
As mentioned earlier, the PEC and PEM boundary conditions were applied along the X-axis and Y-axis, while the EM radiation was applied to the Z-axis to simulate the unit cell within the frequency range of 1 GHz to 12 GHz. Three vital material characteristics--permittivity (\(\epsilon_{\mathrm{r}}\)), permeability (\(\mu_{\mathrm{r}}\)) and refractive index (n\({}_{\mathrm{r}}\))--were derived through the Nicolson-Ross-Weir (NRW) technique [29] to realize the EM properties of the MMs. Popularly used MATLAB code is established in the context of Equations (1)-(5). After the execution, the attained results were verified with the CST results:  
\[\mathrm{Permittivity},\ \epsilon_{\mathrm{r}}=\frac{\mathrm{c}}{\mathrm{j }\mathrm{r}\mathrm{f}\mathrm{d}}.\frac{(1-\mathrm{S}_{21})-\mathrm{S}_{11}}{(1 +\mathrm{S}_{21})+\mathrm{S}_{11}} \tag{1}\]  
\[\mathrm{Permeability},\ \ \mu_{\mathrm{r}}=\frac{\mathrm{c}}{\mathrm{j}\mathrm{r} \mathrm{f}\mathrm{d}}.\frac{(1-\mathrm{S}_{21})-\mathrm{S}_{11}}{(1+\mathrm{ S}_{21})-\mathrm{S}_{11}} \tag{2}\]  
\[\mathrm{And\ refractive\ index},\ \mathrm{n}_{\mathrm{r}}=(\epsilon_{\mathrm{r}} \mu_{\mathrm{r}})^{1/2} \tag{3}\]  
\[\mathrm{Or},\ \mathrm{n}_{\mathrm{r}}=\frac{\mathrm{c}[\{1-\left(\mathrm{S}_{21}+ \mathrm{S}_{11}\right)\}\{1-\left(\mathrm{S}_{21}-\mathrm{S}_{11}\right)\}]^{ 1/2}}{\mathrm{j}\mathrm{r}\mathrm{f}\mathrm{d}\left[\{1+\left(\mathrm{S}_{21}+ \mathrm{S}_{11}\right)\}\{1+\left(\mathrm{S}_{21}-\mathrm{S}_{11}\right)\} \right]^{1/2}} \tag{4}\]  
\[\mathrm{Or},\ \ \mathrm{n}_{\mathrm{r}}=\frac{\mathrm{c}[\left(\mathrm{S}_{21}- 1\right)^{2}-\mathrm{S}_{11}]^{1/2}}{\mathrm{j}\mathrm{r}\mathrm{f}\mathrm{d} \left[\left(\mathrm{S}_{21}+1\right)^{2}-\mathrm{S}_{11}\right]^{1/2}} \tag{5}\]
# Systematic Literature Review of Challenges in Blockchain Scalability
## 8 RQ3: How Researchers Address the Scalability Issue in Blockchain?
### Off-Chain Solutions  
The off-chain blockchain scalability solution is designed to increase the transaction throughput by executing the transaction outside of the main blockchain.  
In the lightening network, it is possible that in the Bitcoin network two nodes would be able to create an off-chain trading channel, where those two nodes would be able to process the transaction with low latency [128]. There are three main phases in the lightening network, namely, establishing, trading, and closing the channel. Firstly, it is extremely important to open the payment channel with the nodes sharing the payment channel. After the opening of the channel, some coins need to be placed on the multi-signature address for sharing with other nodes. In this way, nodes are prevented from scamming with the coins arbitrarily. Opening the channel would be accomplished by an on-chain exchange, which would charge a transaction fee to the main chain. Now, once the channel is opened, the transaction will be an off-chain transaction. So, those transactions are not supposed to be stored on the main chain, and it may charge 0 waiting time and transactional fee. The channel would be closed after the process of payment was completed. The final stage would be to notify all nodes and append this new block to the main chain, which would appear on the on-chain. So, in this way, the multiple transactions could take place off the chain and the complete process would create two transaction records on the main chain [8].  
Let us suppose A and B are maintaining a channel and B and C are also maintaining a channel each. This enables A and C to contact each other, which increases the throughput. This technique can reduce transaction costs, wait/standby time, and reduce the load on the main chain. However, as fee for transaction is decreasing or has disappeared, there would be no or less benefit for miners, so their ecosystem may change. Raiden network is another example that has been implemented in Ethereum. It is known as Ethereum version of Lightening Network [18]]. It follows the same process and protocol for operation as Lightening network except for the transaction details. Its state channels transfer smart contract details as well.
## 1 Introduction
Virtual screening or high-throughput virtual screening can be performed by using one or a combination of QSAR, pharmacophore-based virtual screening (PBVS), molecular docking (docking-based virtual screening, DVBS) and modern machine learning techniques [18; 19; 20; 21; 22; 23; 24; 25]. What is crucial and common to all approaches is that extra care should be taken in regard to the input data, e.g., the virtual compound libraries. Pharmaceutical companies have realized the importance of possessing chemical libraries that cover a vast and diverse chemical space. Consequently, They have created large libraries of several million compounds (Pfizer, 4 million [26]; Novartis, 1.7 million [27]; and Astra Zeneca, 4 million, accessible through collaborations [28]). To date, the main method for searching for lead molecules in large compound libraries remains HTS [29]. HTS, the experimental counterpart of vHTS, identifies lead molecules by examining individual biochemical assays for each of the more than one million molecules in the respective library [13]. The main challenge of HTS is to find a balance between the size of the library, its structural diversity and the cost of screening the compounds [30]. From this perspective, the modern HTS has reached the \"plateau of productivity\" according to the Gartner Hype Cycle, leading to the integration of fragment screening and computational methods into the process [31]. Several computational methods can be synergistically integrated into the screening process to mitigate the weaknesses of the remaining methods and to increase the hit rate [26]. One finds several reports of drugs discovered by using computational methods with imatinib, a tyrosine kinase inhibitor, as a prime example [32; 33]. The main difference between computational methods and HTS represents the use of virtual compound libraries in the process. While physical compound libraries reach the number of millions of molecules, virtual compound libraries created by large pharmaceutical companies can range from \(10^{7}\) to \(10^{18}\) molecules. Investigations of these libraries identify specific molecules, synthetic pathways and focus the research toward a specific chemical space [34; 35; 36; 37; 38; 39; 40; 41; 42]. As the CADD methodology is nowadays routinely used in drug discovery, the need for virtual compound libraries increases. Consequently, numerous commercial vendors have emerged, offering such libraries in both physical and virtual forms. To further facilitate the drug discovery, commercial vendors are now offering scientists the so-called targeted or focused compound libraries ready to jumpstart the drug design and discovery in a specific field. These focused libraries often represent a subset of compounds from the manufacturer's full database that may possess specific properties for a selected target or drug design field. This review explores this topic as the first of its kind for the benefit of the medicinal chemistry community and refers the reader to the relevant resources and related reports in the scientific literature.
###### Abstract
We generate MSFI embedding using a 3D-CNN, which is known to capture non-linear correlations of volumetric features extracted by 3D convolutional filters. As the number of 3D filters to tune for a deep 3D-CNN is very large, training randomly initialized filters will be more likely to overfit as the size of training set becomes smaller. For better generalization ability, we use transfer learning [19] with a 3D-CNN pre-trained on a large collection of videos. Finally, we generate multi-parametric MRI embedding by concatenating MSFI embedding and radiomics features and performing dimension reduction for pCR prediction. This enables us to consider both diverse structural features of the tumor volume present in each MRI sequence and novel volumetric features that can only be found by analyzing multiple MRI sequences jointly. We utilize the annotated MRI sequences of 912 rectal cancer patients, a sample size that is significantly larger than those used in previous works. We construct our pCR prediction model and existing models using MRI sequences of 592 patients after enlarging the number of MRI sequences using image augmentation techniques. For the model evaluation, we use the MRI sequences of 320 patients. Our main contributions are as follows. * We propose a method for encapsulating multiple MRI sequences into an MSFI and generating MSFI embedding using 3D-CNN to extract novel volumetric features of tumors. * We introduce multi-parametric MRI embedding that contains diverse discriminative features of tumors by incorporating MSFI embedding and radiomics. * We show the superiority of the proposed method through extensive experiments using the pre-operative MRI sequences of 912 rectal cancer patients.
## 4 Robust Distributed Rendezvous Control
### Handling of Dynamic Scenarios, including Faulty UUVs
#### 4.3.2 Using Static Faulty UUVs as Waypoints  
There may be a case where a static faulty UUV has a communication ability, and thus, can emit a signal from it. We discuss a method which does not discard a static faulty UUV with communication ability. In this method, every UUV utilizes static faulty UUVs as \"waypoints\" along the path to the root UUV. Note that we do not discard faulty UUVs under this method.  
Note that a healthy UUV, which does not have faults, can still measure the signal from a static faulty UUV. Assume that a healthy UUV, e.g., \(u\), has a static faulty UUV, e.g., \(f\), as its ancestor. In this case, the healthy UUV can still visit UUVs along the path, which contains \(f\), to the root. This implies that \(u\) utilizes \(f\) as \"waypoints\" along the path to the root UUV.  
Note that if \(f\) is healthy, then \(f\) waits until it encounters all its descendants. However, since \(f\) is faulty and static, \(f\) cannot move even after all its descendants encounter \(f\). Inthis case, \(p(f)\), the parent of \(f\), cannot begin moving, since \(f\) is faulty and cannot move towards \(p(f)\).  
In order to resolve this problem, \(p(f)\) removes \(f\) from its descendants list. In this way, \(p(f)\) starts moving after all its descendants, other than \(f\), encounter \(p(f)\).
## 2 Influence of Rotor Temperature on Different Excitation Systems of SMs  
This section presents the need to consider the rotor temperature in the model to estimate the exciter field current for detecting ITFs in BSMs.  
In an SM with static excitation, as shown in Figure 1, the excitation current (_I_) is controlled directly by a controlled rectifier which modifies the field winding voltage (_U_). As the temperature increases, the excitation field winding resistance increases, and consequently, the excitation voltage must be raised to maintain a constant excitation current.  
Figure 1: Simplified schema of an SM with static excitation under a self-excited topology.  
However, in a BSM, as can be seen in Figure 2, the main machine field winding current is regulated through the exciter. When the temperature increases, the resistance of the field winding of the main machine increases, following Equation (1) [25]:  
\[R_{\theta}=R_{\theta_{0}}\cdot[1+\alpha\cdot(\theta-\theta_{0})] \tag{1}\]  
where \(R_{\theta}\) is the resistance at the operating temperature \(\theta\), \(R_{\theta_{0}}\) is the resistance at the reference temperature \(\theta_{0}\), and \(\alpha\) is the temperature coefficient of resistance for the conductor material.  
This increase in the field winding resistance leads to an increase in the field winding voltage (\(U_{f}\)), assuming the excitation current (\(I_{f}\)) remains constant. Therefore, to maintain the same operating point (\(I_{f}\) constant), it is necessary to increase the exciter excitation current (\(I_{fe}\)) to raise the induced voltage in the armature winding of the exciter and consequently the field winding voltage (\(U_{f}\)). Additionally, because, as said, the resistance of the exciter field winding increases with the exciter's temperature, this also affects the exciter field voltage (\(U_{fe}\)).
# Motion-Induced Phase Error Compensation Using Three-Stream Neural Networks
## 3 Experiment  
### Data Acquisition  
As shown in Figure 2a, the actual experimental setup of FPP system contained a camera (model: UI-3250 CP-M-GL R2) with a resolution of 1600 \(\times\) 1200 pixels and a digital light processing (DLP) projector with a resolution of 1280 \(\times\) 800 pixels and throw radio of 1.6. The objective lens of the camera had a focal length of 12 mm. The period number of the projected fringe patterns is 64.  
Using the data establishment method mentioned in Section 2.3, we have built a virtual FPP system as a one-to-one mapping from the aforementioned actual system. The parameters of camera and projector in the virtual FPP system were identified by calibration. The mean reprojection error of both the calibrated camera and projector are 0.07 pixels, which are accurate enough for dataset establishment. It is worth noting that the spot light (projector) in the Blender has no concept of focal length, and the projected image is always in focus when the shadow soft size is 0 mm (ideal point light source). For simplicity, the intrinsic matrix of the projector was replaced by its throw radio to adjust the size of projected images. Seven types of motion were selected to generate synthetic data: stationary, uniform translation, accelerated translation, uniform rotation, accelerated rotation, a mixture of uniform translation and rotation, and a mixture of accelerated translation and rotation. One hundred objects and 24 (\(F\)) postures for each have been collected. To reduce memory space and speed up network optimization, the resolution of the rendered images was set to 0.4; that is, the camera resolution was 640 \(\times\) 480 pixels in this virtual FPP system. In total, 100 \(\times\) 7 \(\times\) (24 + 22 \(\times\) 12) = 201,600 fringe patterns were rendered and 100 \(\times\) 7 \(\times\) 22 = 15,400 pairs of input and the ground truth phase maps were obtained to make up the dataset, where 22 represents the number of phase maps calculated from 24 fringe patterns, and 12 represents the 12-step phase shift. All the steps in the dataset generation pipeline were implemented by Python script and performed automatically. It  
Figure 2: Dataset establishment in virtual FPP system. (**a**) Experimental setup of actual FPP system. (**b**) Schematic diagram of virtual FPP system. (**c**) Different motion types used in synthetic data generation.  
takes about one day to generate the dataset by processing different motion type in parallel. The dataset was split into two parts, 90% training set and 10% validation set. The collection of the training set and validation set are shown in Figure 3. Several image preprocessing operations, including normalization, randomly rotating to a degree \(\pm\) 15\({}^{\circ}\), cropping into a size of 480 \(\times\) 480 pixels, were adopted to further increase the diversity of data during training stage.
# Fragility Curves for RC Structure under Blast Load Considering the Influence of Seismic Demand
## 1 Introduction  
In the last years, structural safety under blast load has become a dramatic problem. Extreme events, such as impacts, explosions, etc., can occur in everyday life with unexpectedly high frequency [1; 2]. In fact, the problem of terrorist attacks, important for strategic and military building design [2; 3], can be put side by side with civil building explosion accidents [4; 5].  
Recently, many studies were aimed at assessing the performance of new and advanced materials under blast load: glass [6], fiber reinforced polymer [7; 8], layered composite materials [9], and foam [10]. On the other hand, the structural design itself is evolving to a more general framework in which structural elements are designed and assembled to obtain general properties like robustness. The latter is the ability of a structure to withstand extreme loads without being damaged to an extent disproportionate to the cause. When an extreme load is concerned, structural damages are common and robustness is of paramount relevance. See [11] for a current state of the art review and [12] for a detailed analysis of the problem and of the available quantitative indexes.  
RC structures designed and built in seismic zones should be robust in order to withstand the extreme earthquake load and many studies on this topic have been developed in the last years: [13; 14; 15; 16; 17]. Fewer studies deal with the interaction between earthquake and blast load. [PERSON] and [PERSON] [18] evaluated the robustness of a seismic designed RC structure under blast load with deterministic, probabilistic and risk-based methods but did not investigated the influence of seismic demand. The latter risk-based approach has been developed in [19] considering a multi-hazard analysis for seismic and blast critical events.  
The uncertainties due to blast load imply the need of a probabilistic approach in order to have an accurate estimation of the structural behavior and integrity [20; 21]. Performance based fragilityestimates have been adopted to assess the reliability of structures under impact in [22] showing how it is possible to formulate a Bayesian physical model for these kinds of problems.  
This paper reports on a probabilistic analysis of the effect of a seismic demand on the structural fragility in case of blast load. Starting from the capacity curves of framed structures designed for different seismic loads in the Italian territory, an equivalent single degree of freedom model is developed in order to perform a probabilistic analysis based on Monte Carlo approach. Fragility curves and performance analysis are obtained with a general methodology that can be extended to many other structures. After this introduction, in Section 2 the blast load model is presented, while Section 3 describes the selected structure. Section 4 depicts the structural model while Section 5 presents the probabilistic framework. Results are in Section 6, while some concluding remarks and prospective developments are stated in Section 7.