esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential of Advanced Consolidants for the Application on Sandstone
## Materials and Methods
### Methods  
Textural properties of xerogels and rocks dried at 60 \({}^{\circ}\)C were determined by the analysis of adsorption isotherms of nitrogen or krypton at the boiling point of liquid nitrogen (ca 77 K). Prior to the adsorption experiment, the samples were outgassed at 60 \({}^{\circ}\)C for 24 h to ensure the complete cleaning of the surface. The experiments were carried out using a Micrometrics 3 Flex volumetric adsorption unit. Due to the complex character of the samples' porosity, the obtained isotherms were analyzed by a combination of several methods, especially the Broekhoff-de Boer t-plot and several variants of the Non-Local Density Function Theory (NLDFT). The pore width is described using the IUPAC nomenclature, micropores, mesopores and macropores corresponding to a width of less than 2 nm, 2-50 nm and more than 50 nm, respectively.  
The pore size distribution and the total open porosity were determined by mercury intrusion porosimetry using the Quantachrome Poremaster PM-60-13 instrument, working in the pressure range corresponding to pore sizes from several nanometers to about 200 \(\upmu\)m. The mercury parameters were as follows: 480 erg cm\({}^{-2}\) for the surface tension and 140\({}^{\circ}\) for the contact angle. Sample size was about 1 cm\({}^{3}\).  
During the measurement of the water uptake by capillary suction, the stone specimens were partially immersed (3 mm depth) in water for 3 h and the amount of soaked water was determined by weighing [18].  
The mechanical properties during the microscale level were tested using an Hysitron TI 750 L Ubi nanoindentation instrument. A three-sided pyramidal diamond Berkovich indenter and the Oliver-Pharr method were used to get the elastic modulus and hardness. Each sample was scanned by in-situ Scanning Probe Microscopy (SPM) imaging to select a suitable place for experimental testing with minimum roughness. The maximum load was P\({}_{\text{max}}\) = 1.0 mN and the load was increased linearly for 5 s to reach maximum, which was held for 2 s and then full unloading was achieved within 5 s. The samples to be measured were embedded in resin, after 24 h they were ground with SiC paper (up to grit #2000) and polished. Finally, the surfaces were cleaned by sonication. The distance between the indents and the total test area were 1 \(\upmu\)m and 25 \(\upmu\)m\({}^{2}\), respectively. Due to the embedding in resin and porosity of samples, single indents were performed to distinguish the resin from the gel in pores. The location of single indents was selected with the aid of an optical microscope and, for each sandstone sample, at least 30 indents were created.  
The mechanical properties during the macroscale level were determined by a Drilling Resistant Measuring System (DRMS, from SINT Technology, Calenzano, Italy) [19]. The cube blocks of sandstone, 3 cm in size, were drilled with drill bits of 4.8 mm in diameter at a rotation speed of 300 rpm and a penetration rate of 30 mm min\({}^{-1}\).
## 3 Application and Comparison of Various Classification Methods
### Weighted ELM  
The ELM is a competitive machine learning technique, which is simple in theory and fast in implementation. The network types are \"generalized\" single hidden layer feedforward networks, which are quite diversified in terms of the variety of feature mapping functions or kernels. To handle data with imbalanced class distribution, a weighted ELM (W-ELM) that can balance the data was proposed [50]. The proposed W-ELM method maintains the advantages of the standard ELM, such as (\(i\)) simplicity and convenience, (\(ii\)) wide variety of feature mapping functions or kernels, and (\(iii\)) capacity for multiclass classification tasks. Moreover, after applying the weighting scheme,  
1. the W-ELM can deal with data with imbalanced class distribution while maintaining good performance with well-balanced data.
2. By assigning different weights for each example according to users' needs, the W-ELM can be generalized to cost-sensitive learning.
# Temperature Impacts on Endurance and Read Disturbs in Charge-Trap 3D NAND Flash Memories
## 2 Background and Related Work
studied the read disturbs in a 3D CT NAND flash memory, and observed that read disturbs were strongly correlated to retention time and temperatures, and proposed the schemes of precharge-the-storage-layer (PCSL) and thermally-stabilize-the-storage-layer (TSSL) to suppress read disturbs [8]. [PERSON] et al. observed that the temperature effects increase retention loss speed at a super-linear rate and increases program variations and concluded that prior models for planer 2D NAND flash were not suitable for 3D NAND flash [15]. [PERSON] et al. investigated the temperature dependence of cell V\({}_{th}\), string currents, and random telegraph noise (RTN) distributions in 3D NAND, showing that V\({}_{th}\) distributions will be tight at high temperatures but widened at low temperatures [16]. In order to minimize the V\({}_{th}\) distribution widening at low temperature and cross-temperature operations, [PERSON] et al. reviewed the 3D NAND  
Figure 1: (**a**) A schematic of a 2D NAND string and a 3D NAND string, wherein a 3D NAND cell unit contains core oxide, poly-Si channel, tunneling layer, CT layer, blocking layer, a and control gate; (**b**) TLC operations by storing 3 bits in each cell at three pages: MSB, CSB, and LSB; V1–V7 denote read voltages with the definitions of V\({}_{th}\) down-shift and up-shift errors. technologies and pointed out that polysilicon channel engineering was necessary [17]. [PERSON] et al. investigated the dominant failure mechanisms in 3D NAND after cycling [18] and drew their conclusions that failure mechanisms in 3D NAND are complex; it is not reliable to use temperature as an accelerator for burn-in tests on the basis of the Arrhenius model [7]. As a key factor impacting on NAND flash performance and reliability, temperature effects should be well understood. Thus far, in previous reports, temperature effects have been discussed from material-to-device viewpoints or system-level viewpoints, and most of them focused on data retention. It is necessary to have a comprehensive understanding of the temperature effects to conduct device-to-technology co-optimizations (DTCO) and to provide fundamental information for NAND-based applications in a wide temperature range. In this paper, using a high-performance raw NAND chip tester, temperature-dependent characterizations were performed from \(-30\) to \(70\)\({}^{\circ}\)C by focusing on the RBER in P/E cycling, read disturbs, and cross-temperature degradations.
## 3 Results and Discussion
### Sorption Behaviour  
Figure 3 depicts the CO\({}_{2}\) (a) and N\({}_{2}\) (b) sorption isotherms of PIL and PIL-based composite materials. The CO\({}_{2}\) sorption capacities lie within a relatively narrow range for all samples. This behavior is related to the chemical composition of the constituents: identical cationic moieties in P[DADMA][Tf\({}_{2}\)N] and [Pyrr\({}_{14}\)][Tf\({}_{2}\)N], and [Tf\({}_{2}\)N]\({}^{-}\) anion in PIL, IL and salt. The CO\({}_{2}\) sorption isotherms are slightly non-linear in the low pressure region (0-2 bar) and become roughly linear for higher pressures, suggesting a weak dual-mode sorption mechanism (Figure 3a). To describe the sorption behavior of single gases (CO\({}_{2}\), N\({}_{2}\)), experimental sorption isotherms were analyzed and fitted to the dual-mode sorption model (DMM) using non-linear regression (Table 2). However, the estimated values ought to be treated with care, as in the present case, only few points are available in the curved sections of the isotherms. While the CO\({}_{2}\) sorption isotherm for pure **P\({}_{9}\)IL\({}_{6}\)Zn\({}_{0}\)** correlates well with the values calculated according to the traditional DMM sorption [16], the composite materials exhibit hysteresis between sorption and desorption runs with the degree of hysteresis increasing in the following order: **P\({}_{9}\)IL\({}_{6}\)Zn\({}_{0}\)** \(<\)**P\({}_{9}\)IL\({}_{6}\)Zn\({}_{1}\)** \(<\)**P\({}_{9}\)IL\({}_{6}\)Zn\({}_{9}\)**. The presence of hysteresis relates to the micropores that are not connected and are inaccessible to the gas at the low relative pressures [69]. As the CO\({}_{2}\) sorption in **P\({}_{9}\)IL\({}_{6}\)Zn\({}_{0}\)** approximates the linear Henry's sorption model, a significantly lower fractional free volume (FFV) is suggested, probably occupied by IL [70]. The absence of glass transition temperature also supports this hypothesis (ESI Figure S1c). Moreover, the stronger hysteresis revealed by Zn\({}^{2+}\)-containing samples suggests the reversible chemisorption between these composites and CO\({}_{2}\) (Table 2). Although **P\({}_{9}\)IL\({}_{6}\)Zn\({}_{9}\)** showed the broadest hysteresis for CO\({}_{2}\) sorption, we used the average CO\({}_{2}\) sorption values to estimate the DMM parameters based on the fact that hysteresis was closed at higher pressures. Insufficient equilibration time could affect the CO\({}_{2}\) sorption data for the sample with high affinity towards the CO\({}_{2}\) molecules. Our hypothesis rests upon the fact that the large concentrations of [Tf\({}_{2}\)N]\({}^{-}\), known for its strong affinity for interactions with CO\({}_{2}\)[10, 71], and Zn\({}^{2+}\), known for its
## 3 Results  
Both manual and automatic calibration were performed for the correct modelling of the CIRM administrative headquarters building. Both calibration methods included multi-stage process. Three tuning phases, according to the three monitoring period, were in fact involved in the processes:  
* **Calibration A (first floor)**: using five days of summer data (22-26 June), a first tuning was given to the building envelope. Calibration was performed referring to an office room situated in the north part of the first floor of the building. No occupants were included, and lights, equipment and plants were turned off.
* **Calibration B (basement)**: using a further five days of summer data (28 July-1 August), a second building envelope tuning was performed. Calibration referred to a conference room situated in the south part of the basement. Again, no occupants, lights, equipment and plants were considered.
* **Calibration C (heating system)**: using five days of winter data (14-18 December), the heating system was tuned. Calibration was related to a north-west office room situated in the first floor. Internal gains due to occupants, equipment and lights were scheduled according to working time during the day.  
Calibration A and C, due to adjustments of monitoring instruments and room conditions during initial phases of the campaigns, started from the second monitored day. Since different operators carried out the two calibration processes in parallel, apart from the results of each step, the strategies adopted were sometimes different. Regarding the computational time both procedures were executed with a PC equipped with a quad core i7-2600 (3.4 GHz) processor and 8 GB RAM.
## 4 Experiments
### Benchmark Comparisons on Public Datasets
#### 4.6.2 Qualitative Experiments  
We perform visual comparisons of our method with CARN, IMDN, SwinIR-S, ShuffleMixer, and SAFM on the Urban100 dataset under a \(\times\)4 scaling factor, with detailed visualizations for samples img_011, img_024, and img_092 presented in Figure 7.  
In terms of reconstructing image texture details, the advantages of our method are clearly evident across these images. For instance, in the image generated by our algorithm (img_011), the PSNR/SSIM metrics improved by 1.2 dB/0.0281 compared to SwinIR-S. The lines in our result appear notably straighter, whereas those generated by other methods tend to be more distorted. A similar observation can be made for img_024. In the case of img_092, our algorithm achieves PSNR and SSIM improvements of 0.31 and 0.0084 compared to SwinIR-S, respectively. The horizontal and vertical lines by our model are closer to the original image, in contrast to other methods that convert originally vertical lines into slanted ones. These experimental results demonstrate that our model is capable of more comprehensively and accurately restoring high-frequency texture details in complex degradation scenarios, while also effectively suppressing artifacts and distortions.  
To further visualize structural reconstruction accuracy, we provide corresponding error maps in Figure 8. These maps show the pixel-wise absolute differences between the predicted SR images and ground truth. As seen in the error maps, the DRFAN produces minimal red zones, especially along edges and fine structures, indicating superior detail restoration with reduced pixel-wise error compared to other methods.  
Figure 7: Visual comparisons between our method and mainstream SR methods on the Urban100 (\(\times\)4): img_011, img_024, and img_092.
# Prediction of Delayed Surface Subsidence Based on the Improved Knothe-n Model
## 2 Numerical Simulation Programme
### Numerical Simulation of Delayed Surface Subsidence  
The surface subsidence monitoring points WC1-WC9 are located at the edge of the Yutianbao mining area and are close to each other. The average value of the monitoring data of WC1-WC9 is taken as the calculation object to minimize the error. The average surface subsidence values of WC1-WC9 are 17.54 mm from July 2015 to July 2018 and 0.8 mm from February 2018 to July 2018, respectively. The monitored surface subsidence data from February 2018 to July 2018 are listed in Table 2. The numerical simulation of delayed surface subsidence is performed based on the numerical simulation of the Yutianbao mine, where the overall surface subsidence from July 2015 to July 2018 and from February 2018 to July 2018 is simulated. The surface subsidence nephograms are displayed in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Date** & & & & & & \\
**Monitoring Point** & **February** & **March** & **April** & **May** & **June** & **July** \\ \hline WC1 & \(-\)16.4 & \(-\)16.5 & \(-\)16.7 & \(-\)16.8 & \(-\)17 & \(-\)17 \\ WC2 & \(-\)17.5 & \(-\)17.8 & \(-\)18 & \(-\)18 & \(-\)18 & \(-\)18.1 \\ WC3 & \(-\)16.4 & \(-\)16.6 & \(-\)16.7 & \(-\)17 & \(-\)17 & \(-\)17 \\ WC4 & \(-\)16.8 & \(-\)17.2 & \(-\)17.5 & \(-\)17.6 & \(-\)17.7 & \(-\)18 \\ WC5 & \(-\)15.9 & \(-\)16 & \(-\)16.4 & \(-\)16.4 & \(-\)16.7 & \(-\)16.9 \\ WC6 & \(-\)16.5 & \(-\)16.6 & \(-\)17.1 & \(-\)17.2 & \(-\)17.4 & \(-\)17.5 \\ WC7 & \(-\)17.3 & \(-\)17.3 & \(-\)17.4 & \(-\)17.7 & \(-\)18 & \(-\)18.3 \\ WC8 & \(-\)17.1 & \(-\)17.2 & \(-\)17.2 & \(-\)17.3 & \(-\)17.4 & \(-\)17.6 \\ WC9 & \(-\)16.3 & \(-\)16.5 & \(-\)16.6 & \(-\)16.8 & \(-\)16.8 & \(-\)17.1 \\ Average value & \(-\)16.7 & \(-\)16.9 & \(-\)17.1 & \(-\)17.2 & \(-\)17.3 & \(-\)17.54 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Monitored surface subsidence data at WC1-WC9 from February 2018 to July 2018 (mm).  
Figure 4: Stress and displacement nephograms of the monitoring profiles after coal mining.  
Figure 5 confirms large surface subsidence in the middle of both the left and right sides of the mining area. The maximum surface subsidence of 78.2 mm between July 2015 and July 2018 is located in the middle of the left side of the mining area at coordinates of \(x=1830\) m and \(y=970\) m. The farther away from the center of the mining area, the smaller the subsidence. The maximum surface subsidence of 8.8 mm between February 2018 and July 2018 is also located in the middle of the left side of the mining area at coordinates of \(x=1840\) m and \(y=990\) m. For the Yutianbao coal mine, the subsidence on the left side of the mining area is larger than that on the right side during the residual deformation stage.
## 5 Performance Evaluation  
### Experimental Environment  
We simulated the proposed scheme with ns-3 based on ndnSIM 2.7 [35] for experimentation. All experiments were performed on a commodity server installed with four 12-core CPUs (Intel(R) Xeon(R) Gold 5118) and 32 GB RAM. The operating system was Ubuntu 16.04 with kernel 4.15. In order to evaluate the performance of multipath forwarding, we drew the topology as shown in Figure 5 with ndnSIM. We configured the locations of each node in the topology and the connection relationships between different nodes. One content provider, two edge nodes, and six intermediate nodes were simulated in the experiment. In the initial design of a multi-identifier network, two edge nodes need to understand the topology and cooperate by transmitting the same data blocks on the intermediate link to achieve optimal performance. Bottleneck links do not affect encoded block transmission after network coding.  
In order to obtain an intuitive comparison with NetCodCCN, we adopted parameter settings similar to theirs, as shown in Table 6. Content is divided into 100 different segments, each with a size of 5 K bytes, which is sufficient to meet the requirement of multi-identifier routers with packet sizes below 8 K and avoid splitting the data segment again. All encoding and decoding operations are in the finite field \(F_{28}\). At the same time, the content requester requests the required content at an average speed, and the content request satisfies the Zipf distribution. Next, we configure the simulated routing path and conduct performance evaluations. We compared IDEANE with the best path forwarding (NDN-BestRoute) adopted in the original NDN forwarding scheme [36], the flood multicast forwarding  
Figure 5: Simulation topology diagram.  
scheme (NDN-Multicast), and the multipath forwarding method NetCodCCN [13] with the network coding strategy.
# Traffic Light Detection and Recognition Method Based on YOLOv5s and AlexNet
###### Abstract
However, most of the current research in deep learning is on the detection and recognition of circular and arrow signals, while the detection of digital countdown lights is an important part of traffic lights, but few research scholars have conducted recognition studies for this type of signal. This paper, therefore, proposes a method based on YOLOv5s target detection and the AlexNet image recognition network to detect multiple types of traffic lights, both for circular and arrow signals and also for digital countdown lights. The contributions of the proposed approach are as follows: (i) The use of two different algorithms in the target detection and image classification stages avoids the drawback of a single-target detection algorithm with a low recognition rate for small-target detection; (ii) traffic signals can be detected and identified in different installation directions; (iii) detection and identification of various types of traffic lights can be realized. The remainder of the paper is structured as follows: Section 2 outlines the relevant work in the traffic light detection and recognition method proposed in this paper, focusing on the network structure of YOLOv5s and AlexNet. Section 3 outlines the specific experimental procedure of the method in this paper, including the acquisition of the dataset and the training of the network model. Section 4 presents an analysis of the results of traffic light detection and recognition. Finally, Section 5 concludes this study.
## 3 Results and Discussion
### Irradiance  
The amount of energy (radiant power) emitted per unit area from the plasma jet is the irradiance (W/m\({}^{2}\)). The optical emission spectra, the measured dimensions (length and width) of the emerging APPJ (for argon and oxygen/argon admixture discharges) at different flow rates, and the constant applied voltage are important parameters in determining the exposed irradiance, suitable for disinfection processes in medical applications.  
The dose rate of irradiance of the APPJ plume per unit area [42] can be calculated by the plume power (W) measured with a power meter detector divided by the cross-sectional area of the effective window of the detector (70 \(\times\) 10\({}^{-6}\) m\({}^{2}\)).  
Figure 15a,b show that the dose rate of irradiance per unit area (\(\rm{\upmu J/mm^{2}}\)) decreases as the jet length increases:  
* Figure 15a shows the influence of the flow rate on the discharge plasma power of the emerging jet for pure argon discharge; by increasing the flow rate from 1.8 to 3 \(\rm{\upmu J/mm^{2}}\) for a plasma jet distance with 8 mm. With increasing plasma jet length to 18.4 mm, the irradiance decreases to between 15 and 6.09 \(\rm{\upmu J/mm^{2}}\).
* Figure 15b shows the influence of different oxygen ratios; whereas the oxygen ratios increase from 0.25% to 1.5%, the irradiance per unit area of plasma jet discharge decreases from 28.57 to 5.68 \(\rm{\upmu J/mm^{2}}\) for a plasma jet length of 8 mm. By increasing the plasma jet length to 18.4 mm, the irradiance decreases to between 6.9 and 4.02 \(\rm{\upmu J/mm^{2}}\). The dose rate of irradiance per unit area is lower for the oxygen/argon admixture discharges than for the argon discharges. The oxygen percentages increase; inelastic collisions lead to losses in the discharge process and lower irradiance compared with the case for argon discharges.
* According to the measured data and the Guidelines of the International Commission on Non-Ionizing Radiation Protection (ICNIRP), the limits of irradiance exposure of the skin must not exceed 30 uJ/mm\({}^{2}\)[43, 44]. From the results, it is concluded that the measured irradiances under the drawn dashed line in Figure 15a for argon discharges and the irradiances for all oxygen/argon admixture discharges in Figure 15b are compatible with ICNIRP irradiance limits.