esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Finite Element Model Verification
### Investigation of the Influence of \(P_{1}\) Change  
In Section 2.2, it was shown experimentally that the non-uniformity could be improved by adjusting P\({}_{1}\) (i.e., the applied pressure to zone 1) for model #4. However, if P\({}_{1}\) is too low, wafer separation may occur, so in this analysis, the MRR changes for model #1 (the best model) are observed by lowering it to 6.5 psi with a margin of 0.5 psi, where separation did not occur through the previous test (i.e., 6.0 psi). As shown in Figure 18a, it can be seen that lowering P\({}_{1}\) generally improves the non-uniformity within the wafer, but if P\({}_{1}\) is too low, such as 6.5 psi, as shown in Figure 18a,b, it results in greater non-uniformity at the wafer edge. When P\({}_{1}\) is 7.5 psi, the best MRR distribution can be obtained.  
Figure 16: Cross section of inner corner-rounded model.  
Figure 17: Finite element analysis result of corner-rounded case: (**a**) displacement of wafer, (**b**) displacement of pad.  
In the previous two cases, as shown in Figures 14b and 15b, the MRR distribution of the wafer was improved due to the change in the contact pressure distribution (i.e., changes of pad displacement distribution) from the retaining ring (i.e., \(x\) = 150-160 mm region in Figures 14b and 15b), but in this case, the displacement of the pad influenced by the retaining (i.e., \(x\) = 150-160 mm region) remains the same in Figure 18b and only the MRR of the wafer has changed. As shown in Figure 18a, the MRMRR in the initial state is 0.7078 \(\times\) 10\({}^{-3}\) mm, and when P\({}_{1}\) is 7.5 psi, the MRMRR is \(0.2628\times 10^{-3}\) mm (= \(5.3103\times 10^{-3}\)-\(5.0733\times 10^{-3}\)), which is improved by about 62.9%.
## 5 Materials, Methods, and Experiment Setup
### Materials  
We utilized the Berkeley Segmentation Dataset 500 (BSDS500) [19], which is publicly available. This is a standard benchmark for contour detection aimed at evaluating edge detection in images. This dataset includes images featuring both object interior and background boundaries. It consists of 500 color images with dimensions of either 321 \(\times\) 481 or 481 \(\times\) 321 pixels, which we converted into grayscale images during our test.
## 2 Materials and Methods
### Models and Evaluation  
A number of machine learning models and feature selection techniques were used in this work. First of all, 10 regression algorithms were tested during the initial experiments, both linear and non-linear, in order to pick the overall best performing one among species. The algorithms used are the following: Linear Regression (LR), Least Angle Regression with Lasso (LARS), Support Vector Machine Regression (SVR), Decision Tree (DT), Random Forest (RF), Extremely Randomized Trees (ET), Adaptive Boosting (AdaBoost), Gradient Boosting (GBM), Extremely Gradient Boosting (XGBoost) and Light Gradient Boosting (LightGBM). 5 feature selection techniques and an ensemble one were used to rank the features according to their importance in determining pelagic fish species distribution over the Med. The goal of feature selection was to eliminate irrelevant and noisy features, by keeping the ones that can predict the target best. This technique decreases the computational time, improves the performance of the algorithm, avoids over-fitting, and creates better general models.  
There are three types of feature selection methods: filters, wrappers, and embedders. Filters are used before machine learning, and they rank the features based on their intrinsic properties, like variance and correlation. Wrappers use a machine learning algorithm as a black box evaluator to rank the features. Embedded methods combine filters and wrappers by first computing statistical measurements on the data and then using an algorithm. We used two filters, Mutual Information and Fisher Score, one wrapper namely SHAP and two embedders using Random Forest and XGBoost. The ensemble technique followed is called Reciprocal Ranking and computes the final rank \(r(f)\) of a feature \(f\) as follows:  
\[r(f)=\frac{1}{\sum_{j}\frac{1}{r_{j}(f)}},\]  
where \(j=1,2,\ldots,N\) are the feature selection methods, and \(r_{j}(f)\) is the rank of the feature according to the \(j\)th method.  
We split the datasets in train (80%) and test (20%) set. For training and validation we used 10-fold cross validation. Validating a model with cross validation reduces the danger of over-fitting and results in a better general model that can have high performance on an unknown dataset. Root mean square error (RMSE), representing the mean standard deviation of prediction errors, was chosen as the metric for all experiments, since the target is a continuous variable.
# Shading by Overhang PV Collectors
## 3 Results
### Incident Radiation on Overhang PV Collectors
#### 3.2.2 Multiple Overhang Collectors  
In multiple overhang collectors the top collector casts a shadow on the bottom collector forming a shaded area as shown in the Figure 11.  
The yearly direct beam incident irradiation on the bottom collector, \(q_{b}^{b}\), per unit area, is given in Wh/m\({}^{2}\) by:  
\[q_{b}^{bot.}=\sum_{n=1}^{n=365}\sum_{T_{R}}^{T_{S}}G_{b}\cos\theta(1-S^{th})\Delta T \tag{37}\]  
where \(S^{th}\) is given by Equation (29).  
Figure 14: Angles involved in solar radiation calculations.  
The yearly diffuse incident irradiation on the bottom collector, \(q_{d}^{bot.}\), per unit area, is given in Wh/m\({}^{2}\) by:  
\[q_{d}^{bot.}=F_{d}^{bot.}\sum_{n=1}^{n=365}\sum_{T_{SR}}^{T_{SS}}G_{dh}\Delta T \tag{38}\]  
where the sky view factor \(F_{d}^{bot.}\) of the bottom collector is calculated based on the \"Crossed-String Method\" [18], see Figure 15:  
\[F_{d}^{b}=\frac{AC+BC-AB}{2 AC} \tag{39}\]  
resulting in:  
\[F_{d}^{bot.}=\frac{H+D+H\cos\varepsilon-\left[D^{2}+\left(H\sin\varepsilon \right)^{2}\right]^{1/2}}{2H} \tag{40}\]  
The yearly global incident irradiation on the top and bottom collectors, respectively, in \(Wh/m^{2}\), are,  
\[q_{y}^{top}=q_{b}^{top}+q_{d}^{top} \tag{41}\]  
\[q_{y}^{bot.}=q_{b}^{bot.}+q_{d}^{bot.} \tag{42}\]  
Multiplying Equations (41) and (42) by the collector area \(H\times L\) results in the collectors' yearly incident energies in \(Wh\). Table 1 shows the results of yearly incident energies, in \(kWh\), on the top and bottom PV overhang collectors for three distances \(R=1.0,2.0,3.0\,m\), \(H=0.941m,L=30m,\varepsilon=70^{\circ}(\beta=20^{\circ})\), (see Figure 11 and Equations (35)-(38), (40)-(42)) for Tel Aviv (latitude \(\phi=32^{\circ}N\)). \(E_{b-top},E_{d-top},E_{g-top}\) are the beam, diffuse and global energy on the top collector, respectively and \(E_{b-bot.},E_{d-bot.},E_{g-bot.}\) are the beam, diffuse and global energy on the bottom collector, respectively. The percent difference in global energy between the top and bottom collectors are 48.87, 32.95, 24.48%, for \(R=1.0,2.0,3.0\,m\), respectively. This difference stems from shading on the bottom collector and from its lower sky view factor.
## 1 Introduction  
The Kingdom of Saudi Arabia, as well as rest of the world, contains a wide range of reinforced concrete infrastructure which ranges from small residential houses to multi-story buildings, towers, and pre-stressed concrete bridges. The majority of the infrastructure in Saudi Arabia was designed and constructed on old design codes, standards, and specifications, and as a result of that, it is currently experiencing weathering because of harsh climate, saltwater, acid attack, extreme temperature changes, and due to the inferior quality of building material. There is a necessity to strengthen old existing infrastructure in Saudi Arabia, which lacks in strength and stiffness. There are many different ways to strengthen reinforced concrete structures, and one of the most common methods is the application of CFRP (Carbon Fiber Reinforced Polymer) to the reinforced concrete member.  
CFRP has been widely used for the purpose of strengthening reinforced concrete, masonry, and steel infrastructure. Over the past few decades, many researchers have proved the suitability of CFRP material for structures composed of different materials, stiffness, and ductility [1; 2; 3; 4; 5; 6]. By adopting the correct retrofitting technique, CFRP can significantly increase the shear and flexure strength of concrete structures as compared to the normal concrete structure. CFRP gains its strength via epoxy, which is the glue that makes a bond of CFRP with the concrete surface. Flexure, shear, and compression cracks can be prevented in reinforced concrete beams with the help of CFRP application. The benefit of using CFRP retrofitting comes from its low density and its resistance to higher tensile forces, fatigue, and corrosion.  
However, the debonding failure mechanism, structural ductility, and long-term durability are the main problems, which CFRP is facing due to poor bonding and reduced vapor pressure [7; 8; 9]. Fracture of CFRP concrete beams is mainly attributed due to debonding of CFRP from the concrete surface. Hence, flexural strength, failure behavior, and structural ductility are the most important parameters that are always under consideration in structural design, especially when the structure is located in the higher earthquake zone region. CFRP is a material which is brittle in nature and fails suddenly, and has a linear elastic behavior up until failure, i.e., the tensile failure strain of FRP ranges from 2% to 4% [6]. Once the failure strain is reached, CFRP shows no signs of warning, breaks suddenly, and results in the loss of its strength. This behavior of low ductility in reinforced concrete structures retrofitted with CFRP is not desirable, as it does not provide any kind of early warning before failure, thus resulting in the sudden collapse of the structure.
# Integrated Silicon Photonics for Enabling Next-Generation Space Systems
## 3 Microwave Photonics
The previous examples focus on photo-mixing and take advantage of the spectral purity of the optical sources. However, another demonstrated approach in [44] was focused on the linearization of an RF receiver. In that work, a distributed low noise amplifier was co-designed with an MZM. To compensate for the nonlinearity of the modulator, the distributed amplifier used an intermodulation injection scheme which was shown to boost the SFDR of the receiver. That design leveraged the ability to create wideband, nonlinear sources which can be used to compensate for photonic component nonlinearities. In that work, an SFDR greater than 109 dB/Hz\({}^{2/3}\) was demonstrated over a 19 GHz bandwidth. These benefits can be extended to space-based applications to enhance the detection of received signals. From all of these examples, it can be seen that the emerging design arena of co-designed electronic-photonic systems has great potential for enhancing microwave communications systems. In addition, with electronics and photonics integrated into the same technology monolithically, new topologies, which would have been infeasible in a multi-chip solution, can be developed.
## 2 Problem Formulation  
### Problem Statement  
In this study, we consider a block sparse time-varying UWA channel estimation problem. For an UWA multipath channel, expect the channel time variation introduced by the dynamic ocean environment or the platform motion, the paths of UWA channel often present in a cluster. The sample of experimental UWA channel obtained from [27] is shown in Figure 1, from which we can observe a block sparse time-varying structure.  
The classic CS-based channel estimation algorithm is subject to performance degradation due to the ignorance of the time-varying structure and block sparsity of the UWA channel. Moreover, to exploit the block sparsity of the channel, the block sparse-based algorithms were proposed to improve the performance of channel estimation under the block sparse condition. On the other hand, the DCS-based algorithms were proposed to tackling the time-varying sparsity recovery problem. However, to our knowledge, very few studies have reported channel estimation on the block sparse time-varying channel. Aiming at this gap, we propose the BS-CS algorithm to enhance the estimation performance, which can exploit the block sparsity for DCS estimation in the block sparse time-varying UWA channel.
## 3 Results
### Zero-State Response Control Strategies
#### 3.1.2 Closing Time at Dusk  
As the solar radiation diminishes, the heat collected by the attached sunspace is insufficient to maintain the thermal environment. The closing of the interior active window is conducive to increasing the thermal resistance of the farmhouse, but it also weakens the heat transfer from the attached sunspace to the room. When the interior active window is closed at different times, the indoor temperature also exhibits a different rate of decrease. In order to obtain the optimal closing time, a comparative of the indoor temperature changes at different closing times by simulation, shown in Figure 8.  
As shown in Figure 8, the attached sunspace and indoor temperature reach a maximum at 14:00, but then rapidly decreases as solar radiation intensity and outdoor temperature decrease. With the disappearance of solar radiation after 17:00, the decline rate of the attached sunspace and indoor temperature slowed due to the small temperature difference  
Figure 7: The temperature comparison for different window opening time.  
between indoor and outdoor. When the window was closed between 14:00 and 17:00, the indoor temperature dropped dramatically. This result shows that, the closing of the interior active window prevents the heat of the attached sunspace entering the room, and the earlier closing of the windows is not conducive to maintaining the room thermal environment. Without the heat transfer from the attached sunspace to the room, the indoor temperature will drop dramatically due to the interior air loses its main heat source and the leftover heat is transferred to the outside through the enclosure construction. Furthermore, the earlier the interior active window closes, the lower the average indoor temperature. However, if the later window closing time for the attached sunspace's temperature is lower, there is not enough temperature difference to provide power for natural convection and the heating effect is limited. Therefore, the best time to close the windows in this simulation should be around 17:00, which is more beneficial to maintain the heat in the room.
###### Abstract  
We propose a secure and scalable file-encryption scheme for cloud systems by integrating Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD), and Advanced Encryption Standard (AES) within a distributed architecture. While prior studies have primarily focused on secure key exchange or authentication protocols (e.g., layered PQC-QKD key distribution), our scheme extends beyond key management by implementing a distributed encryption architecture that protects large-scale files through integrated PQC, QKD, and AES. To support high-throughput encryption, our proposed scheme partitions the target file into fixed-size subsets and distributes them across slave nodes, each performing parallel AES encryption using a locally reconstructed key from a PQC ciphertext. Each slave node receives a PQC ciphertext that encapsulates the AES key, along with a PQC secret key masked using QKD based on the BB84 protocol, both of which are centrally generated and managed by the master node for secure coordination. In addition, an encryption and transmission pipeline is designed to overlap I/O, encryption, and communication, thereby reducing idle time and improving resource utilization. The master node performs centralized decryption by collecting encrypted subsets, recovering the AES key, and executing decryption in parallel. Our evaluation using a real-world medical dataset shows that the proposed scheme achieves up to 2.37\(\times\) speedup in end-to-end runtime and up to 8.11\(\times\) speedup in encryption time over AES (Original). In addition to performance gains, our proposed scheme maintains low communication cost, stable CPU utilization across distributed nodes, and negligible overhead from quantum key management.  
Post-Quantum Cryptography; Quantum Key Distribution; Advanced Encryption Standard; cloud system +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Analysis of Adulterated Milk and the Influence of Temperature
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline  & \multicolumn{4}{c}{**0.0\%**} & \multicolumn{4}{c}{**Percentage of Water in Milk**} & \multicolumn{1}{c}{**75\%**} & \multicolumn{1}{c}{**97.9\%**} \\ \hline \(f\)**(Hz)** & \(|\mathbf{Z}|(\Omega)\) & \(\theta\) & \(|\mathbf{Z}|(\Omega)\) & \(\theta\) & \(|\mathbf{Z}|(\Omega)\) & \(\theta\) & \(|\mathbf{Z}|(\Omega)\) & \(\theta\) & \(|\mathbf{Z}|(\Omega)\) & \(\theta\) \\ \hline \(10^{1}\) & \(254.5\pm 4.4\) & \(-18.0\pm 1.0\) & \(295.0\pm 6.6\) & \(-17.0\pm 0.0\) & \(379.0\pm 5.7\) & \(-16.0\pm 0.0\) & \(584.4\pm 29.7\) & \(-14.8\pm 0.8\) & \(3211.9\pm 145.8\) & \(-8.2\pm 0.3\) \\ \(10^{2}\) & \(214.8\pm 12.2\) & \(-14.8\pm 0.3\) & \(261.4\pm 10.8\) & \(-13.9\pm 0.6\) & \(338.9\pm 7.7\) & \(-12.6\pm 0.2\) & \(534.3\pm 21.9\) & \(-10.5\pm 0.5\) & \(2989.7\pm 132.0\) & \(-4.6\pm 0.5\) \\ \(10^{3}\) & \(183.7\pm 5.4\) & \(-6.7\pm 1.0\) & \(221.2\pm 9.4\) & \(-8.0\pm 1.5\) & \(300.8\pm 7.1\) & \(-8.8\pm 0.4\) & \(497.1\pm 16.1\) & \(-7.3\pm 0.6\) & \(2849.1\pm 97.0\) & \(-2.0\pm 1.8\) \\ \(10^{4}\) & \(169.4\pm 3.0\) & \(-6.0\pm 1.3\) & \(199.6\pm 6.5\) & \(-5.7\pm 0.6\) & \(269.2\pm 5.0\) & \(-4.7\pm 0.3\) & \(439.0\pm 19.3\) & \(-4.8\pm 0.3\) & \(2759.8\pm 96.1\) & \(-2.1\pm 0.8\) \\ \(10^{5}\) & \(155.4\pm 1.9\) & \(-2.4\pm 0.6\) & \(181.8\pm 8.3\) & \(-2.7\pm 0.8\) & \(290.7\pm 4.4\) & \(-18.0\pm 0.3\) & \(417.1\pm 18.8\) & \(-1.5\pm 15.5\) & \(2681.0\pm 106.1\) & \(-6.1\pm 1.6\) \\ \(10^{6}\) & \(146.9\pm 1.9\) & \(1.7\pm 0.8\) & \(172.0\pm 8.3\) & \(0.8\pm 0.8\) & \(299.1\pm 2.0\) & \(-0.3\pm 1.3\) & \(398.9\pm 23.9\) & \(-3.0\pm 1.5\) & \(1933.9\pm 262.7\) & \(-39.0\pm 3.7\) \\ \(2.3\times 10^{6}\) & \(145.5\pm 2.0\) & \(6.7\pm 1.1\) & \(171.0\pm 10.0\) & \(5.7\pm 0.8\) & \(235.5\pm 4.5\) & \(2.8\pm 1.9\) & \(380.0\pm 34.3\) & \(-4.2\pm 2.3\) & \(1077.7\pm 209.6\) & \(-51.7\pm 1.8\) \\ \hline \end{tabular}
\end{table} 
Table 1: Impedance magnitude and phase values measured at 16 \({}^{\circ}\)C for the different dilutions with distilled water in milk (% \(V/V\)) and unadulterated milk (0%). Figure 8: Adulterated milk impedance measurements at 16 \({}^{\circ}\)C. Frequency response is shown in (**A**,**B**). The effect of water dilution on the complex impedance is emphasised in (**C**,**D**). in Figure 8B,D. However, the impedance magnitude measurements are affected by the sample temperature. Table 2 summarises the complex impedance measurements in both unadulterated and adulterated milk samples with distilled water at 28 \({}^{\circ}\)C.  
Figure 10 presents the results for the complex impedance measurements in milk samples at 37 \({}^{\circ}\)C. The phase results in Figure 10B,D are quite similar to those presented at 16 \({}^{\circ}\)C and 28 \({}^{\circ}\)C. The impedance magnitude measurements continue to decrease with the sample temperature. Table 3 summarises the complex impedance measurements in both unadulterated and adulterated milk samples with distilled water at 37 \({}^{\circ}\)C.
## 3 Lightweight Cryptography for Smart Objects
### Symmetric-Key LWC Algorithms
#### 3.1.5 Comparison of Symmetric LWC Algorithms  
LWC algorithms are not intended to supersede existing ciphers, e.g., AES, for widespread use. Their application is limited to those scenarios where classical ciphers might be inefficient, such as scenarios where:  
* a moderate security level is required, so that keys need not be too long;
* encryption should not be applied to large amounts of data;
* the hardware area needed for implementation and the power consumption are considered harder requirements than speed.  
For constrained devices, the choice of the cryptographic algorithm is a primary element that can affect performance. When low cost and energy consumption are hard requirements, computational power must inherently be downsized accordingly. Using 8-bit microcontrollers (such as Atmel AVR microcontrollers [31]), which have limited capabilities in terms of computing power, memory, and storage, requires that implemented ciphers have small footprint and are kept simple. This may result in faster execution and thus in lower energy consumption, which may be critical for battery-powered devices.  
Although most symmetric cryptographic algorithms have been developed focusing on efficient software implementations, the deployment of smart objects will lead to an increasing attention to thoseciphers that will perform well in hardware in terms of speed and energy consumption. In Table 1, we report a direct comparison, presented in [23], among the LWC algorithms outlined in Subsection 3.1, with particular reference to the following metrics: key size, block size, rounds, consumed area measured in gate equivalents (GEs), code size (in bytes). Reported values for gate equivalents are related to hardware implementations, while code size refers to software implementations.
## 3 Research Methods and Data Construction
### Data Resource and Sample Selection  
From the \"China City Statistical Yearbook\" of 2021 and the \"China Energy Statistical Yearbook\" of 2021, data such as industrial R & D funds, the number of employees in the IT industry, and raw coal production are selected. In combination with government work reports on the official websites of the national and provincial people's governments, the Bureau of Energy, and other official information on the Internet (news, conferences, etc.), data related to the construction of intelligent coal mines are collected as variable data sources.  
On the condition that sample homogeneity, diversity, and data availability requirements were all met [35; 36], this study excluded 6 provinces, including Chongqing (which withdrew from coal mining in 2021) and Yunnan Province (data missing) from the 23 coal producing provinces in China and selected 17 provinces as the research objects.