esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Section
### Experimental Setup and Procedure  
The chemical looping conversion of toluene was carried out in a fixed bed system, as is shown in Figure 2. Simply, the system contains inlet gas, reaction bed, gas purification device, and gas analyzer. The flow of the inlet gas (N\({}_{2}\) and O\({}_{2}\)) and H\({}_{2}\)O was accurately controlled by mass flow controllers and micro-pump, respectively. By switching the valve V1 and V2, the carrying gas N\({}_{2}\) can be controlled to enter the air-tight toluene tank to bring toluene into the reactor. The toluene was placed in a water bath. The vapor-liquid equilibrium partial pressure of toluene was adjusted by adjusting the water bath temperature. Then, the toluene input flow can be quantitatively controlled by fixing the carrying gas flow. The reaction chamber was a quartz tube (i.d = 26 mm, L = 600 mm) with a porous distributor plate at the middle. The reaction chamber was heated with the temperature sensor installed 3 mm below the porous plate to monitor the reactor temperature. The outlet gas was introduced to a purification device for water and fine powder removal. Finally, the outlet gas compositions (CO, CO\({}_{2}\), CH\({}_{4}\), H\({}_{2}\), and O\({}_{2}\)) were online analyzed with an X-stream analyzer. C\({}_{1}\) mentioned in the investigation refers to CO, CO\({}_{2}\), and CH\({}_{4}\). For each test, the sample successively underwent toluene/N\({}_{2}\)/steam and air-regeneration. N\({}_{2}\) sweeping was performed between each process.  
Several series of the experiment was conducted, and Table 1 shows the main parameters during the toluene step. In test 1, thermal pyrolysis was conducted at typical temperatures without bed materials as the reference condition. In test 2, a batch sample of 10 g catalytic oxygen carrier was employed as bed material to investigate the catalytic partial oxidation of toluene. The duration for the toluene step was set at 10 min except for the case of LaCu in which the tar (due to low toluene conversion) blocked the outlet pipeline and the experiment was interrupted. Then, test 3 was further conducted to optimize the catalyst effect of oxygen carriers to promote H\({}_{2}\) production by adding Ni into ferrite. In test 4, steam was introduced to enhance the toluene conversion and syngas production (H\({}_{2}\) + CO). Finally, S/C and temperature were further optimized in Test 5 with LaFe\({}_{0.6}\)Ni\({}_{0.4}\).
# FEM Analysis of Superplastic-Forming Process to Manufacture a Hemispherical Shell
## 2 Materials and Methods
### Optimised Forming of a Superplastic Hemispherical Shell Using a Conical Blank  
In order to make the thickness distribution of the manufactured hemispherical shell uniform, superplastic forming is carried out on a circular blank with a conical thickness profile. This profile has a maximum thickness, \(s_{max}\), on the circular blank axis of symmetry and decreases linearly along a radius of 50 mm to reach a minimum thickness, \(s_{min}\), at the edge of the disc. Because the volume constancy of the forming processes requires that the volume of the circular blank with the conical thickness profile be equal to the final volume of the manufactured hemispherical shell (supposed ideally with a constant thickness \(s_{avg}\)), the parameters \(s_{max}\) and \(s_{min}\) were linked to the final thickness of the manufactured shell, considered constant, \(s_{avg}\). The volume of the initial blank was calculated as the sum of the volume of the disc and the truncated cone. Therefore, by equating the initial and final volumes, it is possible to obtain the following relationship:  
\[\frac{1}{6}s_{max}+\frac{1}{3}s_{min}=s_{avg}, \tag{2}\]  
From this equation, it is possible to obtain, in the case of a circular blank with a constant thickness, \(s_{0}=s_{max}=s_{min}\).  
\[s_{avg}=\frac{1}{2}s_{0}, \tag{3}\]  
Figure 2: (**a**) Hemispherical shell schematised using 3D shell elements; (**b**) boundary conditions at the nodes of the blank’s edge; (**c**) boundary conditions at the nodes of x-axis; (**d**) boundary conditions at the nodes of the z-axis.  
Therefore, once \(s_{0}\) (for example, \(s_{0}\) = 1 mm) and \(s_{avg}\) (for example, \(s_{avg}\) = 0.5 mm) are fixed, it is easy to choose different couples of values of \(s_{max}\) and \(s_{min}\) through Equation (2) to have the conical thickness profile of the circular blank to form.  
Five simulations were performed by varying the value of \(s_{min}\) between 0.8 and 1 mm. \(s_{min}\) values lower than 0.8 mm produce thinnings in the sheet metal. \(s_{min}\) = 1 means that \(s_{max}\) = 1, and therefore, the blank has a constant thickness.  
Therefore, once the average thickness \(s_{avg}\) = 0.5 mm was set, 5 values of \(s_{min}\) were chosen (0.8, 0.85, 0.9, 0.95 and 1 mm) in the range of existence. Equation (2) allows us to determine \(s_{max}\).
# Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory
## 3 Results
### Image Quality Metrics  
The enhancement effects on the Street image in Figure 12 are evaluated through three quality metrics, the Structural Similarity Index (_SSIM_), the Peak Signal-to-Noise Ratio (_PSNR_), and Information Entropy (IE). The _SSIM_[27] is used to analyze image quality in terms of image contrast, brightness and structure. A higher _SSIM_ value indicates a greater  
Figure 12: Enhancement effects of image Street via different methods: (**a**) dataset raw Street image; (**b**) algorithm in [24]; (**c**) algorithm in [25]; (**d**) algorithm in [26]; (**e**) our algorithm.  
Figure 13: Histogram comparison of image Street before and after enhancement: (**a**) histogram of the original Street image; (**b**) algorithm in [24]; (**c**) algorithm in [25]; (**d**) algorithm in [26]; (**e**) our algorithm.  
degree of image restoration, signifying improved visual fidelity. The _SSIM_ solution is expressed as follows:  
\[\begin{cases}l(x,y)=\frac{2\mu_{x}\mu_{y}+c_{1}}{\mu_{x}^{2}+\mu_{y}^{2}+c_{1}} \\ c(x,y)=\frac{2\mu_{x}\sigma_{y}+c_{2}}{\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2}}\\ s(x,y)=\frac{\sigma_{xy}+c_{3}}{\sigma_{x}\sigma_{y}+c_{3}}\end{cases} \tag{23}\]  
\[SSIM(x,y)=l(x,y)\cdot c(x,y)\cdot s(x,y) \tag{24}\]  
where \(l(x,y)\) is the luminance component, \(c(x,y)\) is the contrast component and \(s(x,y)\) is the chrominance component. \(\mu_{x}\) and \(\mu_{y}\) represent the mean values of the original image \(x\) and the enhanced image \(y\), respectively; \(\sigma_{x}\) and \(\sigma_{y}\) represent the variance of the two images, respectively, and \(\sigma_{xy}\) represents the covariance.  
The _PSNR_ is another vital metric utilized for assessment, which gauges the difference between the original image and the processed image by calculating the Mean Square Error (_MSE_). A higher _PSNR_ value corresponds to a higher quantity of preserved image information and reduced distortion, leading to enhanced image quality.  
\[PSNR=10\times\lg\left(\frac{MAX_{l}{}^{2}}{MSE}\right) \tag{25}\]  
\[MSE=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}\left\|l(i,j)-K(i,j)\right\|^{2} \tag{26}\]  
where \(MSE\) denotes the mean square error between the enhanced image and the original image; \(m\) and \(n\) denote the length and width of the image, respectively; \(l(i,j)\) and \(K(i,j)\) denote the pixel values of the original image, and the pixel values of the enhanced image, respectively.  
Information Entropy (IE) reflects the average amount of information contained within an image and the complexity of its pixel distribution. The larger the information entropy, the clearer the image and the higher the quality.  
The quality indices of the enhanced Street image using different algorithms are shown in Table 1.  
The quality indicators enhanced by different algorithms in Figure 10 are shown in Table 2.
## 3 Results and Discussion
### The Rate of Dissolving Gold  
As shown in Figure 3a, the dissolving rate of gold in TCCA/N\({}_{4441}\)Cl was rapid. An average 375 mg/h dissolving rate was obtained in the first 8 min. The kinetic model based on this data is shown in Figure S1. Additionally, the deceleration of the reaction rate can be explained with the decrease of exposed gold surface (resulting in the decrease of the amount of gold that can participate in the reaction) and the consumption of TCCA in the reaction. In addition, the equilibrium solubility of the ionic liquid system was as shown in the supporting information.  
Figure 3: (**a**) The dissolution rate curve of Au by TCCA/N\({}_{4441}\)Cl at 25 \({}^{\circ}\)C; (**b**) comparison of average dissolution rates at 25 \({}^{\circ}\)C obtained with the literature [34] (the reagents of the methods mentioned are TCCA 0.16 M, N\({}_{4441}\)Cl 1.59 M\({}_{\text{r}}\) (NH\({}_{4}\))\({}_{2}\)S\({}_{2}\)O\({}_{3}\) 0.3 M, NH\({}_{3}\) 0.3 M, CuSO\({}_{4}\) 0.01 M; CH\({}_{4}\)N\({}_{2}\)S 0.263 M, Fe\({}_{2}\)(SO\({}_{4}\))\({}_{3}\) 0.02 M, H\({}_{2}\)SO\({}_{4}\) 0.15 M; I\({}_{2}\) 0.08 M, KI 0.80 M; Br\({}_{2}\) 0.20 M, KBr 0.67 M; Aqua Regia (HCl:HNO\({}_{3}\) = 3:1); KCN 0.14 M, respectively; (**c**) the dissolution rate curve of Au by TCCA/N\({}_{4441}\)Cl at different temperatures; (**d**) correlation diagram between the rate of Au dissolution and temperature.  
Figure 2: UV-Vis result of the ionic liquid system after dissolving gold and the mixture of N\({}_{4441}\)Cl and HAuCl\({}_{4}\).  
Even though the dissolving rate dropped as the reaction proceeded, the average rate was still higher than those of the other systems reported previously. According to the literature [34], the average dissolution rate in 8 h of thiosulfate, thiourea, I\({}_{2}\)/KL, Br\({}_{2}\)/KBr, Aqua Regia and cyanide are 2, 1.75, 260, 196, 286, 3.5 mg\(\cdot\)h\({}^{-1}\), respectively, at 25 \({}^{\circ}\)C. Obviously, TCCA/N\({}_{4441}\)Cl has the fastest gold dissolution rate, which is 107.1 times and 1.3 times higher than the aqua regia and cyanide (Figure 3b).  
In addition, the dissolution rates of gold at different temperatures were measured. At 0, 25, and 40 \({}^{\circ}\)C, the dissolution rates of gold in the first 2 min were 910.2, 1023.6, and 1126.8 mg/h, respectively, thus the reaction temperature was positively correlated to the dissolution rate of gold (Figure 3c,d).
## 3 Results
### Influence of Integral Squeeze Film Bearing Damper on Energy Distribution  
According to the finite element model in Section 2.3, the energy distribution of the unbalanced single-side cantilevered rotor system corresponding to the four working conditions was analyzed as shown in Figure 12. ISFBD can effectively share the vibration energy of the system and dissipate energy through its own squeeze film damping effect, making the system safer to operate.  
Figure 11: Stability of unbalanced single-side cantilevered rotor system: (**a**) working condition 1; (**b**) working condition 2; (**c**) working condition 3; (**d**) working condition 4.  
Figure 12: Energy distribution of unbalanced single-side cantilevered rotor system: (**a**) working condition 1; (**b**) working condition 2; (**c**) working condition 3; (**d**) working condition 4.  
By simulating the four working conditions, it can be known from Figure 12a that when there is an unbalanced amount on the cantilever disk, 99.99% of the vibration energy of the unbalanced single-side cantilevered rotor system is concentrated on the shaft when it is rigidly mounted, which will make the shaft have larger bending deformation.  
The position and quantity of installed integral squeeze film bearing dampers have a large impact on the distribution of system energy. As shown in Figure 12a-c, the rigid support is mounted close to the cantilevered end and the damper is mounted away from the cantilevered end. Subsequently, it does not distribute the vibration energy of the rotor system.  
The integral squeeze film bearing damper installed near the cantilevered end can distribute the vibration energy of the rotor system by 20.49%. It can be seen from Figure 12a-d that the quantity of dampers also affects the energy distribution of the system. When dampers are installed at both bearing housings, they can share 32.81% of the vibration energy of the rotor system. Figure 12c,d shows that the ability to share the energy of the bearing damper near the cantilevered end can be improved by installing two integral squeeze film bearing dampers.  
Therefore, when designing and installing the system, the right position and quantity should be chosen to allow the integral squeeze film bearing damper to distribute the vibration energy of the system.
# Teleseismic Tomography for Imaging the Upper Mantle Beneth Northeast China
## 3 Northeast China
### Inversion Resolution and Reliability Evaluation
#### 3.3.3 Inversion Results
As shown in Figures 11 and 12, we can see that the upper mantle in Northeast China shows the characteristics of low-velocity anomaly related to volcanoes distributed in the large-scale high-velocity anomaly. In the range of 42\({}^{\circ}\) N to 44\({}^{\circ}\) N, there is a common low-velocity anomaly in the mantle transition zone on the west side of the Changbai Mountain volcano group, the black surrounding area in Figure 11. According to the 42\({}^{\circ}\) N and 44\({}^{\circ}\) N profiles, this anomaly has obvious connectivity with the low-velocity anomaly of the upper mantle and crust, and a large range of high-velocity anomalies are distributed in the mantle transition zone beyond this low-velocity anomaly. The 46\({}^{\circ}\) N and the 48\({}^{\circ}\) N profiles show that the low-velocity anomalies in the Arshan volcanic area are widely distributed in the upper mantle, but there is no low-velocity anomaly distribution below the mantle transition zone; FLGPL volcano group is located in the northern boundary of the study area, with low resolution, but the low-velocity anomalies below are relatively shallow and independent. The profile along the north-south direction shows that the high-velocity anomalies widely distributed in the Songliao basin are flooding the upper mantle. On the 122\({}^{\circ}\) E and 124\({}^{\circ}\) E profiles, it can be seen that the low-velocity anomaly under the Arshan is continuously distributed to the mantle transition zone, as shown in the purple surrounding area in Figure 12. On the 126\({}^{\circ}\) E and 128\({}^{\circ}\) E profiles, we can see that there is a large area of low-velocity anomaly along the upper mantle to the shallow part of the mantle transition zone under Changbai Mountain, which is consistent with the results of the East-West profile as shown in the black surrounding area in Figure 12. The 128\({}^{\circ}\) E to 130\({}^{\circ}\) E profiles show that a large range of low-velocity anomalies under the FLGPL volcano group come from the east and are separated from the low-velocity anomalies in the west of Changbai Mountain. Figure 10: Horizontal section of P-wave velocity disturbance in the upper mantle of the study area (420–720 km).
# Consistency Models of NoSQL Databases
## 4 NoSQL Databases Background
### Cassandra  
Cassandra is a column NoSQL database [26]. It was initially developed by Facebook to fulfill the needs of the company's Inbox Search services. In 2009, it became an Apache Project.  
Cassandra is a database system built with distributed systems in mind, like almost every NoSQL systems out there. Following the CAP theorem, Cassandra will be on the AP (Availability and Network Partition Tolerance) side, hence prioritizing high-availability when subject to network partitioning. As we will further see, Cassandra's consistency can be tuned to be a CP (Consistency and Network Partition Tolerance) database system, so it becomes a strong consistency database when subject to network partitioning.  
Cassandra system is a column based NoSQL database [6]. In other words, Cassandra describes data by using columns. A _keyspace_ is the outermost container for the entire dataset, corresponding to the entire database, and it is composed of many _column-families_. A column-family represents the same class of objects, like a Car or a Person, and each column-family has different entries of objects called _rows_. Each row is uniquely identified by a _row key_ or _partition key_ and can hold an arbitrarily large number of columns. A column contains a name-value pair and a timestamp. This timestamp is necessary when solving consistency conflicts.
## 3 Evaluation and Application of a Thermodynamic Database (TDB) in Geochemical Models
### Evaluation of a Thermodynamic Database (TDB)
Therefore, for application to natural environmental systems, users may need to obtain data from other thermodynamic databases; however, it is necessary to ensure that the selected end states are consistent and represent the chemical reactions of the system of interest. The chemical elements and nuclides in the NEA TDB are as follows: H, B, C, N, O, F, Na, Mg, Al, Si, P, S, Cl, K, Ca, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Ge, As, Se, Br, Rb, Sr, Zr, Tc, Ag, Cd, In, Sn, Te, I, Cs, Ba, Hg, Tl, Pb, Th, U, Np, Pu and Am. The NEA TDB has the following characteristics: a comprehensive literature review and detailed discussion of the performed evaluation; the traceability of all used the documents used in the evaluation and selection of the data; strict guidelines and a strengthened selection of high-quality values; and thermodynamic values conform to the CODATA standards [31; 48; 49].
## 6 Findings and Future Research Scope  
### Findings of the Study  
The authors conducted a bibliometric and critical analysis of papers related to image-based road crack detection research. The bibliometric analysis identified trends in the field, influential papers, journals, authors, countries, and keywords. The results of the bibliometric analysis are presented below. 1. From 2013 to 2015, the number of paper publications was very low, with fewer than five papers per year; from 2016, the number of papers per year began to increase rapidly, fluctuating between 10 and 20 from 2016 to 2019; after 2020, the number of papers published increased dramatically, with the number of papers jumping to between 40 and 50 per year; and after 2022, the number of paper publications per year reached between 70 and 80 papers per year. The number of papers published from 2020 to 2023 represents about 58.52% of the total number of papers published at that time. 2. Refs. [14; 26; 27; 28] are the most influential papers in the field of image-based road crack detection. 3. _IEEE Transactions on Intelligent Transportation Systems_, _Computer-Aided Civil and Infrastructure Engineering, Automation in Construction_, _Construction and Building Materials_, and _Journal of Computing in Civil Engineering_ are the most cited journals in the field. 4. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] are influential authors in this field of research.
## 3 Results and Discussions
### Effect of Impact Energy
Small corrugations have a less complex structure and smaller impact-affected area than the large corrugations that have four ribs to reinforce the buckling strength. Therefore, a specimen with a large corrugation exhibits more impact resistance under the same impact load. Moreover, the displacements of the small corrugation specimens linearly increase over the entire impact energy, but the displacement of the specimens from L123 to L282, except L412, linearly increase. Under the impacts, small corrugations exhibit the characteristics of a localized plastic deformation only for the impacted area rather than the globalized plastic deformation. For a large corrugation to be deformed, it is necessary for the broad area to be deformed; thus, the increase in the displacement of a large corrugation slows down.
# Transparent Redirections in Content Delivery Networks
## 1 Introduction  
Nowadays people are almost always online on their mobile devices, which enables them to share to moment (content) via social networks just using a couple of taps of fingers. Data shared on social media must be effectively delivered and distributed to the required users in a matter of seconds. Users no longer keep their data on their portable devices, they are backing up photos, videos and other content to cloud providers which are shared between multiple devices and locations. Portable data storage is no longer used as they are not considered convenient anymore.  
Content Delivery Servers (CDNs) allow content providers to effectively deliver their content to consumers on a massive scale. CDNs are systems for the efficient delivery of digital objects (e.g., videos, photos) and multimedia streams (e.g., live television streams). Typically, a CDN consists of several (hundreds) servers that deliver the digital objects and a management/control system. The management/control system takes care of the content distribution, request routing, reporting, metadata and other aspects that make the system work [1]. It is needless to say that is not a new technology. It has been around for over 20 years, but redirection mechanisms have not received a significant amount of attention in recent years. Software Defined Networking (SDN) is a new approach towards networking, which decouples the data plane from the control plane. This approach opens new possibilities to enhance redirections used in CDNs. This article presents a transparent SDN based redirection mechanism, which is an extended version of our paper [2] published in 2019 at the 42 nd International Conferenceon Telecommunications and Signal Processing (TSP). This article additionally introduces a transparent load balancing use case.
# Improved U-Net with Residual Attention Block for Mixed-Defect Wafer Maps
## 4 Experiments and Results
### Data Pre-Processing  
#### 4.3.1 Defect Masking  
Since the proposed model is based on U-Net, an image and a mask that is the ground truth of the image are required to train the model. In general, humans manually perform labeling using a program for image masking. These existing methods take a long time and require much manpower. To solve this problem, in this study, we used a method of automatically masking defects. Defects appearing on the wafer map are representations of a collection of defects in the die. Therefore, we performed masking in a way that distinguishes pixels that are connected to a certain number or more. For convenient masking, we converted the image to black and white and then masked it. Figures 6 and 7 show examples of black-and-white images and masking results for single and mixed defects.  
Figure 6: Results of single defect masking: (**a**) Donut defect; (**b**) Edge-Ring defect; (**c**) Center defect; (**d**) Edge-Loc defect.  
The image on the left is a black-and-white conversion of the original image, and the image on the right is the result of masking. We could accurately mask only the defect pattern without recognizing the surrounding defective die that was not a defect. For the Donut and Center, there was no big problem in recognizing the pattern of defects, although some parts protruding from the circle are also included. For the Edge-Ring and Edge-Loc, there were few defective dies on the edge, so breakage occurred.  
For the mixed defects, the left image is a black-and-white conversion of the original image, and the right image is the result of masking. For the mixed defects, as shown in Figure 7, when the defects are separated, they are well distinguished. Both the two-mixed and three-mixed defects were mostly correctly distinguished. However, if the defects are closely connected or small, they can be recognized as a single defect. Therefore, for accurate verification, we excluded cases that could not be distinguished well from the test dataset.