esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review: Thermal Stability of Methylammonium Lead Halide Based Perovskite Solar Cells
## 6 Conclusions  
Organic-inorganic halide perovskites have been extensively used in solar cells as a promising light harvesting material due to their excellent optoelectronic properties. Although perovskite solar cells have achieved photo-conversion efficiencies above 20%, the instability of their materials at elevated temperatures is a major issue that needs to be solved for commercial use. Particularly, perovskite films tend to exhibit intrinsic thermal degradation mechanisms under thermal stress. Typically, the commonly used MAPbI\({}_{3}\) perovskite material is decomposed into MAI and PbI\({}_{2}\) at elevated temperatures. The decomposition of perovskites has been discussed here from the chemical, morphological, and optical points of view, confirming the deterioration of perovskites during thermal stress. Furthermore, since perovskite solar devices are composed of many layers, notably an electron transport layer, a hole transport layer, and electrodes, other decomposition challenges exist. Thus, to facilitate better thermal stability, the selective incorporation of electron and hole transport layers and the replacement of silver or gold electrodes is discussed, and the enhancement of the stability of the perovskite layer by encapsulation or including additives is also reviewed. Currently, perovskite solar cells have been made thermally stable for annealing of up to 500 h at 85 \({}^{\circ}\)C by using quadruple-cation perovskites, but with available solar products on the market (c-Si, CdTe, CIGS, GaAs) guaranteed to remain stable for up to 25 years, perovskites continue to need research attention. Recent advances in perovskite solar cell thermal stability and in determining the causes of degradation indicate that it should be possible to achieve similar longevity, and support the scalable solutions that would allow this promising technology to reach its full potential and compete in the existing solar market.  
Authors equally contributed to the review paper.  
This research was partially supported by the National Science Foundation under projects (1355678 and 1547771) and the Virginia Microelectronic Consortium Program.  
The authors declare no conflict of interest.  
Figure 12: (**a**) Current density–voltage (J-V) curve of the highest performance RbCsMAFA solar cell. The inset shows the scan rate–independent maximum power point (MPP) tracking for 60 s. (**b**) J-V curve of the highest-V\({}_{\text{OC}}\) device. The inset illustrates the V\({}_{\text{OC}}\) for 120 s (**c**) EQE electroluminescence (EL) versus voltage. The left and right inset show the corresponding EL spectrum and two areas of a solar cell, respectively (**d**) Thermal stability as a function of time for the perovskite solar cell. Reprinted with permission from [103].
# I-BIM Applied in Railway Geometric Inspection Activity: Diagnostic and Alert
## 4 Railway Inspection Activity
### Degradation of Railway Track Components  
Safe travel on railways is an important research aspect for transportation engineers, in order to maintain railways in good condition, and is associated with a frequent inspection maintenance practice [29]. The material degradation of rail tracks' components is an important aspect within the railway entities as it marks the railway's comfort. When a train passes over an irregularity verified in the rail surface, the vehicle experiments with excitations that lead to the forces applied over the track components. These forces can impose an important augment of the track material's deterioration, which leads to the increase in maintenance costs [30].  
Figure 5: I-BIM model of the railway track segment.  
The material degradation of the rail components caused by exposure to the natural environment and the action of the train circulation reduces the efficiency, functionality and quality of the railway infrastructure. As such, periodic inspections of the railway infrastructure must be planned with regular frequency, allowing engineers to analyze the collected data and proceed with maintenance or preventive actions if required [31]. The national standard EN 13848:2022 addresses the railway track components' material properties, providing guidance on testing methods for the materials used in rails, sleepers and fastening systems [26]. The track geometry parameters normally inspected include the track gauge, cant, longitudinal level, alignment and twist.  
Ballast and sub-ballast layers placed under the train tread elements may present irregular deformations. Whether in the vertical direction or in the transverse direction, they can naturally impose serious irregularities on the structure immediately above. The ballast layer is the main component for the ballasted track. Moreover, the inspection of the ballast layer must be analyzed considering the differential deformation, under the train's cyclic loadings after a long service time, as the ballast layer gradually loses its elasticity. The present work aims to define a strategy for modeling and analyzing the severity of irregularities, which can be applied in a broader scope in order to also incorporate the lower layers.
# Hybrid Deep Learning Approach for Automated Sleep Cycle Analysis
## 3 Methodology
### Model Training
#### 3.4.7 Convolutional Layers  
Convolutional neural networks are designed to process structured data [55] through convolutional operations, where learnable filters slide across the input to capture local features [56]. The convolution operation in discrete time for a 1D input is defined as [57]  
\[y(n)=(x*w)(n)=\sum_{k=0}^{K-1}w(k)\cdot x(n-k), \tag{20}\]  
where \(x(n)\) is the input sequence at index \(n\), \(w(k)\) is the filter kernel at index \(k\), and \(K\) is the number of coefficients in the filter kernel. When considering stride \(s\) and padding \(P\), the convolution operation is adjusted as follows:  
\[y(n)=\sum_{k=0}^{K-1}w(k)\cdot x(n\cdot s+k-P), \tag{21}\]  
where \(s\) denotes the step size of the moving window filter, and \(P\) represents the number of zeros added around the input to maintain the size [58]. A 1D convolutional layer involves \(\mathcal{O}(N\cdot K\cdot\text{C}_{\text{in}}\cdot\text{C}_{\text{out}})\) operations, where \(\text{C}_{\text{in}}\) and \(\text{C}_{\text{out}}\) denote the channel dimensions beforeand after convolution, respectively. To further reduce the computational load and control overfitting, CNN architectures commonly employ pooling operations. The scheme of Figure 7 shows the layered structure of a CNN.  
The operation of combining layers downsamples the sizes of feature maps by summarizing local regions [59]. Two widely used pooling strategies are maximum pooling, which retains the maximum value found in a size window \(K\), highlighting the dominant features of the signal, and average pooling, which smooths the data by averaging the values across a sequence.  
In CNNs, filters, also known as kernels, are trainable parameters optimized through backpropagation. These filters function similarly to finite impulse response filters (FIRs), modifying the signal by enhancing or attenuating specific patterns [60]. The convolution in the frequency domain can be represented by the multiplication of Fourier transforms [57]:  
\[Y(f)=X(f)\cdot W(f), \tag{22}\]  
where \(X(f)\), \(W(f)\), and \(Y(f)\) are the Fourier transforms of the input, filter, and output, respectively. This relationship highlights how convolutional filters can enhance or suppress specific frequency components of the input signal [61]. By directly extracting these filters from the data, CNNs provide a framework capable of adapting to various input types.
# Design and Performance Research of a Wearable Airbag for the Human Body
## 2 Design of a Wearable Airbag System
### Airbag Sensing System  
The airbag in the vehicle is triggered by the acceleration sensor installed on the vehicle body. When the deceleration in the impact direction reaches the set value, the airbag will be triggered. This sensor is not suitable for wearable airbags. It needs to be accurately determined whether the human body collides or falls. The six-axis gyroscope is selected, which can obtain acceleration and angular acceleration in three directions. From the moment of vehicle-pedestrian contact, the sensor device of the wearable airbag system would finish the signal collection and analysis process within 30 ms. When both acceleration and angular acceleration exceed the set threshold, the signal will be transmitted to the electronic control unit (ECU) to trigger the inflaton, and the airbag inflation will complete within 50 ms.
## 1 Introduction
### Related Works
#### 1.1.2 Dense-Indirect Methods  
Dense-indirect techniques incorporate preprocessing stages and recover dense depth maps. Some important prior works that defined this category were [PERSON] et al. and [PERSON] et al. [PERSON] et al. (2015) proposed a novel two-step method for estimating the fundamental matrix from a dense optical flow. Their key contribution was demonstrating that accurate epipolar geometry robust estimation was possible using dense correspondence fields computed by variational optical flow methods. They introduced a joint variational model that recovered the optical flow and epipolar geometry within a single energy functional, thus improving the results. However, their method was limited by its sensitivity to large displacements and occlusions. [PERSON] et al. (2015) presented an approach to estimate dense depth maps for complex dynamic scenes from monocular video, built on the use of dense optical flow. The key concept is a motion segmentation stage that decomposes the scene into independent rigid motions, each with its epipolar geometry enabling moving objects' reconstruction. Its method was optimized to work with object scales and geometry to assemble a globally consistent 3D model determined up to scale. A key difference from [PERSON] et al. was the explicit handling of multiple independently moving objects and the recovery of dense depth for fully dynamic scenes. However, [PERSON] et al.'s approach still relied on approximate scene rigidity and the connectivity of objects to the environment. [PERSON] et al. introduced a dense optical flow for fundamental matrix estimation, while [PERSON] et al. extended dense the geometric reconstruction to complex dynamic scenes. Both moved from sparse features to dense correspondence fields; in contrast, [PERSON] et al. focused on depth estimation and scene assembly.
# Effects of Time Content on Road Performance of Low Liquid Limit Clay
## 5 Conclusions  
In this research, the CBR test, compaction test, and limit water content test are used to assess and compare the differences between lime-improved soil and an untouched soil sample. The results of this inquiry were as follows:  
1. After lime digestion improves the initial low liquid limit clay, the liquid limit essentially stays the same, the plastic limit increases as the lime content rises, and the plastic index gradually declines. After lime is introduced to the soil, ions from the lime and the soil exchange, causing the clay particles to form a granular structure. The rise in the plastic limit often remains steady once the lime content reaches 6%. It is also shown that a low liquid limit clay mixture with lime has a reasonable ash concentration of about 6%. When the lime content is 6%, the maximum dry densities are 1.61 g/cm\({}^{3}\), 1.65 g/cm\({}^{3}\), and 1.73 g/cm\({}^{3}\), respectively. The optimal water content was 16.7%, 17.2%, and 17.4%, respectively.
2. The CBR value of the soil samples under various compaction durations considerably rose with an increase in ash content, showing that ash mixing had improved the water stability of low liquid limit clay containing sand. The acceptable ash content is 6% at the same time. When the lime content is 6% and the compaction times are 98, the CBR values are 57.4%, 54.2%, and 68.9%, respectively.
3. The ideal water content and CBR value of the clay with low liquid limit are clearly impacted by the amount of dissolved lime added. The ideal water content falls as the incorporation amount increases, while the CBR value rises.
4. Plain soil does not meet the filling requirements of subgrade in areas 93, 94, and 96 of expressways. The performance of the improved soil has been improved to varying degrees after the ash mixing improvement, and it can meet the filling requirements of subgrade in areas 93, 94, and 96. According to the test, the most reasonable ash mixing amount of the improved soil is determined to be 6%.  
Conceptualization, J.Z.; Investigation, Z.Z.; Writing--original draft, H.L.; Project administration, J.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and technology project of Anhui Transportation Holding Group Co., Ltd. (JKKJ-2020-16).  
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 5 Adaptive Dual-Mode Routing-Based Mobile Data Gathering Algorithm  
The number of charging cells is limited, and some nodes may have relatively longer upload distances when communicating with the MCGV directly, and they should select the next hop to relay data. However, the nodes near the MCGV should upload their data directly to avoid energy waste caused by the multi-hop mode. It is important to choose the appropriate upload mode for each node in order to balance energy. Thus, in this section, we propose adaptive dual-mode routing which comprises direct and multi-hop modes.
###### Abstract  
This paper presents a novel forensic watermarking method for digital evidence distribution in non-cloud environments. The approach addresses the critical need for the secure sharing of Joint Photographic Experts Group (JPEG) images in forensic investigations. The method utilises an adaptive Discrete Cosine Transform-Discrete Wavelet Transform (DCT-DWT) domain technique to embed a 64-bit watermark in both standalone JPEGs and those within forensic disk images. This occurs without alterations to disk structure or complications to the chain of custody. The system implements uniform secure randomisation and recipient-specific watermarks to balance security with forensic workflow efficiency. This work presents the first implementation of forensic watermarking at the disk image level that preserves structural integrity and enables precise leak source attribution. It addresses a critical gap in secure evidence distribution methodologies. The evaluation occurred on extensive datasets: 1124 JPEGs in a forensic disk image, 10,000 each of BOSSBase 256 \(\times\) 256 and 512 \(\times\) 512 greyscale images, and 10,000 COCO2017 coloured images. The results demonstrate high imperceptibility with average Peak Signal-to-Noise Ratio (PSNR) values ranging from 46.13 dB to 49.37 dB across datasets. The method exhibits robust performance against geometric attacks with perfect watermark recovery (Bit Error Rate (BER) = 0) for rotations up to 90\({}^{\circ}\) and scaling factors between 0.6 and 1.5. The approach maintains compatibility with forensic tools like Forensic Toolkit FTK and Autopsy. It performs effectively under attacks including JPEG compression (QF \(\geq\) 60), filtering, and noise addition. The technique achieves high feature match ratios between 0.684 and 0.690 for a threshold of 0.70, with efficient processing times (embedding: 0.0347 s to 0.1187 s; extraction: 0.0077 s to 0.0366 s). This watermarking technique improves forensic investigation processes, particularly those that involve sensitive JPEG files. It supports leak source attribution, preserves evidence integrity, and provides traceability throughout forensic procedures.  
forensic watermarking; disk image security; DCT-DWT watermarking; leak source attribution; digital evidence protection; jpeg image security 2025
# Research on Mechanical Performance of Porous Asphalt Mixture with High-Viscosity Modified Asphalt
## 3 Research on Mechanical Performance of Porous Asphalt Mixture
### Research on Mechanical Performance of High-Viscosity Modified Asphalt Mixtures
#### 3.2.3 Water Stability  
According to the Chinese specification JTG E20-2011, asphalt pavements with poor water stability are prone to water damage. This damage often takes the form of potholes and loose pavements. Therefore, improving the water stability of asphalt mixtures is particularly important [48]. In this study, the water stability of porous asphalt mixtures was evaluated using a freeze-thaw splitting test. The front and back sides were compacted 50 times each to form two groups of Marshall specimens. One group of specimens was placed in a refrigerator at -18 \({}^{\circ}\)C for 16 h after vacuum soaking. The other group was kept at room temperature as a control group. After freezing, the specimens from the frozen group were immersed in a 60 \({}^{\circ}\)C constant temperature water tank for 24 h. Subsequently, both groups of specimens were placed together in a 25 \({}^{\circ}\)C constant temperature water tank for 2 h. The tests were conducted using a loading rate of 50 mm/min. The test results are presented in Figure 16.  
From Figure 16, the freeze-thaw splitting strength ratios of both 10-A and 10-B were higher than 85%. However, the freeze-thaw splitting strength ratio of 10-A was higher than that of 10-B, indicating that the water stability performance of 10-A was superior  
Figure 16: Freeze–thaw splitting test results of porous asphalt mixture.  
Porous asphalt mixtures have a skeleton void structure. During freeze-thaw cycles, a larger void ratio can affect the internal mechanical behavior of the mixture. The incorporation of high-viscosity modifier A improves the adhesion between the binder and aggregate. Additionally, the addition of high-viscosity modifier A reduces the spalling effect on asphalt during freeze-thaw cycles, thereby enhancing water stability [49]. A high dosage of SBS and co-solvent in the modifier can adversely affect the high-temperature stability and water stability of asphalt mixtures, a trend observed in similar studies ([PERSON] et al., 2017 [50]). This suggests that increasing the content of co-solvent may promote the exposure of hydrophobic groups within the modifier, particularly after experiencing high and low temperature cycles. The aforementioned phenomenon is notably evident. The underlying mechanism can be further corroborated by the research conducted by [PERSON] et al. [51].
## 4 Discussion
The developed mathematical system leverages these different resistance reduction profiles in order to accurately calculate the acetic acid concentration in a mixed gas system employing the resistance values provided by the two sensors. In this way, it was possible to achieve a LOD for acetic acid of 30 ppb, after an exposition time of the sensors to the vapor mixture of only 10 min. The system proved to be stable even under constant exposition to the released gases: after 1 month of exposition, the results remained constant with less than 3% overall variation. The currently used method to evaluate the acetic acid released by films in conservation institutes are A-D strips. These are small paper strips, imbibed with a pH-sensitive dye which initially is blue and progressively turns first green, then orange-yellow when exposed to increasing concentrations of acetic acid. In order to provide an accurate measurement, A-D strips need to be put in intimate contact with the film for a period of time depending on the storage temperature (at least 24 h at room temperature, 3 weeks or more if stored in a refrigerated room at 4 \({}^{\circ}\)C). For historical archives, where there is a need to monitor thousands to hundreds of thousands of independently stored film rolls, this means having a large staff dedicated solely to periodically checking and changing the strips. Furthermore, being a colorimetric method based on naked eye comparison with a provided reference, it may be affected by subjective interpretation and/or recognition of the color change, which may lead to false alarms or tardy action. A well-trained staff could theoretically recognize variations in the color change of A-D strips corresponding to acetic acid concentrations of 500 ppb but, usually, it is difficult to reliably detect concentrations lower than 1 ppm. On the other hand, the developed MOS sensor duplex has a limit of detection for acetic acid of 30 ppb, which is independent of human subjectivity. Furthermore, the MOS sensor can provide an accurate evaluation of the acetic acid concentration after only 15 min since the introduction of the film in the box. Further measurements, require even shorter time (about 5 min), needed for the warm-up of the MOS sensor. Having both the sensors and the Arduino processing unit integrated in the film box allows for periodic measurements on the cinematographic film, without need for human intervention and a consequent risk of human exposure to the gases accumulating in the film box. Finally, the system can be fully automatized by connecting the Arduino boards using cables to a central unit, from which the state of all cinematographic films subjected to monitoring can be checked. In Figure 5, a
# Rapidly Deployable IoT Architecture with Data Security: Implementation and Experimental Evaluation
## 4 Application Implementation
### Indoor Environmental Monitoring  
Indoor environmental monitoring is essential to ensure a safe office and home environment. In this variant of the LiB IoT system as in Figure 9, the BME 680 sensor (Adafruit Industries, New York, NY, USA) was used to measure temperature, humidity, atmospheric pressure, and gas and a light dependent resistor was used for measuring the ambient light in rooms. The BME 680 is a low power all-in-one sensor for indoor environmental monitoring [64]. As the integrated sensor measures all three parameters, it reduces latency, power consumption, and overall efficiency of the system. The sensor communicates via SPI protocol, and can detect harmful particles in the air and gives a 0-500 point quantified value of the quality of indoor air with 1-point resolution.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Active Current (mA)** & **Sleep Current (mA)** \\ \hline
8-bit MCU & 5 & 1 \\ XBee S1 & 50 & 0.03 \\ PIR & 10 & 10 \\ LDR & 0.4 & 0.4 \\ DHT11 & 1 & 0.1 \\
**Total** & 66.4 & 11.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Current consumption in the occupancy monitoring system.  
Figure 9: System Architecture for Indoor Environmental Monitoring.  
The PIC18F27K40 microcontroller (Microchip Technology, Chandler, AZ, USA) had all the necessary peripherals such as SPI and ADC to collect data from the sensors and consumed low power [58]. The system is powered from a 200 mAh Li-ion rechargeable battery. From Table 2, the device consumed 0.77 mA in sleep mode and at that current, even the small 200 mAh battery can theoretically run up to 260 h. The CC2540F128 BLE system on a chip compliant with Bluetooth 4.0 technology [65] is utilized for low-power, and efficiency in integration with other devices. The internet gateway device received the BLE data and pushed it to MQTT-dashboard, where the data can be view and stored for analysis.