esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[12] discovered that the discoloration of an F-P mechanochromic sensor could be detected in real time during crack propagation in fiber-reinforced concrete. Furthermore, the discoloration ability of the mechanochromic sensor exposed to a freeze-thaw environment was restored in 2 h.  
Reinforced concrete is the main composite used in structures in the civil and architectural fields. Steel reinforcements cause critical damages at about 0.02% of strain. As concrete is used as a covering to prevent corrosion of the reinforced steel, cracks in the concrete cover can deteriorate reinforced concrete structures and are required to be strictly managed according to design standards. For instance, the American Concrete Institute [13] and Korean Concrete Institute [14] generally adopt cracks of 0.3 mm as the allowable crack size under humidity conditions to inhibit the corrosion of rebar. Because the crack width of concrete is measured with the naked eye using a crack width ruler, the assessment of crack width may be subject to the worker's individual judgement. Previously reported mechanochromic sensors can be deformed to more than 100%, and their discolorations are effective within 50% strain. Therefore, it is important to perform a sensitivity analysis of the mechanochromic sensor discoloration for its application in SHM. In this study, the discoloration sensitivity of the mechanochromic sensor was analyzed to investigate its feasibility as a mechanical sensor for SHM in the construction field. Therefore, since the crack width of concrete is managed in terms of length rather than strain, displacement was chosen as the measure of deformation. Uniaxial direct tensile tests were conducted on a steel plate with a mechanochromic sensor attached. The discoloration of the mechanochromic sensor was quantified in the red, green, and blue (RGB) as well as hue, saturation, and value (HSV) color spaces using image analysis and compared with the real displacement. Additionally, we propose a method for regressing displacement by simultaneously inputting each color space into the deep learning algorithm, enabling feature extraction from each color space and integrating the entire displacement process of the mechanochromic sensor into one process.
## 5 Discussion  
The proposed method provides reasonable characteristic stresses for siltstone and granite; therefore, it is reasonable to infer that the method is also suitable for rocks with a similar stress-strain evolution. The proposed method is similar to the CSV method in the principle of determining crack initiation and closure stress but provides an accurate numerical calculation method, and the calculation results are more objective. Compared with the AE, axial stiffness, and volumetric stiffness methods, the proposed method provides a numerical calculation method, and the calculated results are more objective. Furthermore, the proposed method is more reasonable than the volumetric strain method under low confining pressure. The LSIR and dissipated energy ratio methods provide a numerical calculation for crack stress thresholds. However, the LSIR method relies on the damage stress and closure stress obtained by other methods, whereas the dissipated energy ratio method requires the dissipated energy ratio to be an inverted S-shape. Thus, the proposed method offers the advantages of independence from other methods, suitability across high and low confining pressures, and capability for quantitative calculation and processing of numerous samples.  
Current studies show that the crack initiation stress represents the in situ spalling strength of the tunnel surrounding rock, while the crack damage stress threshold reflects the  
Figure 16: Crack stress thresholds calculated by the proposed and existing methods: (**a**) siltstone and (**b**) granite.  
Figure 17: Relationship between crack stress thresholds and peak stress: (**a**) siltstone and (**b**) granite.  
long-term strength and fatigue strength of the material [10; 47; 48; 49]. Therefore, the proposed method provides more accurate crack stress thresholds, enhancing precision in assessing the long-term strength, fatigue strength, and in situ spalling strength. Consequently, this study contributes to the long-term stability analysis of engineering projects.  
It should be noted that the proposed method measures the bulk modulus by the unloading tangent modulus of the stress-volumetric strain curve at the maximum volume strain, which requires the experiment to unload at the maximum volume strain and may lead to errors. Certain rocks, such as coal and marble, can exhibit minimal dilation before peak stress, with the maximum volumetric strain occurring close to the peak stress. Therefore, the proposed method cannot be used to calculate crack stress thresholds of such rocks. Hence, further research is required on crack stress thresholds based on stress-strain characteristics for such rocks.
# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
###### Abstract  
Video deepfake detection has emerged as a critical field within the broader domain of digital technologies driven by the rapid proliferation of AI-generated media and the increasing threat of its misuse for deception and misinformation. The integration of Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) has proven to be a promising approach for improving video deepfake detection, achieving near-perfect accuracy. CNNs enable the effective extraction of spatial features from video frames, such as facial textures and lighting, while LSTM analyses temporal patterns, detecting inconsistencies over time. This hybrid model enhances the ability to detect deep-fakes by combining spatial and temporal analysis. However, the existing research lacks systematic evaluations that comprehensively assess their effectiveness and optimal configurations. Therefore, this paper provides a comprehensive review of video deepfake detection techniques utilising hybrid CNN-LSTM models. It systematically investigates state-of-the-art techniques, highlighting common feature extraction approaches and widely used datasets for training and testing. This paper also evaluates model performance across different datasets, identifies key factors influencing detection accuracy, and explores how CNN-LSTM models can be optimised. It also compares CNN-LSTM models with non-LSTM approaches, addresses implementation challenges, and proposes solutions for them. Lastly, open issues and future research directions of video deepfake detection using CNN-LSTM will be discussed. This paper provides valuable insights for researchers and cyber security professionals by reviewing CNN-LSTM models for video deepfake detection contributing to the advancement of robust and effective deepfake detection systems.  
deepfake; convolutional neural network (CNN); long short-term memory (LSTM); video deepfake detection; feature extraction
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 3 The LoRa Physical Layer
### Performance Evaluation  
To verify whether the specified performance of LoRa receivers is reached in practice, a LoRa testbed is built. The Freescale KRDM-KL25Z development board with Semtech SX1276 MBED shield (Figure 4a) is used as the end-device, and a Cisco 910 industrial router is used as the gateway (Figure 4b). The gateway is connected to the network server provided by Thingpark ([[https://acility.thingpark.com](https://acility.thingpark.com)]([https://acility.thingpark.com](https://acility.thingpark.com))) through Ethernet, so that the packet received can be monitored on the server side.
# Photonic Stopband Filters Based on Graphene-Pair Arrays
## 3 Photonic Bandgaps
As a light beam composed of plenty of wavelengths impinges on the periodic structure, the light waves locating at the stopband may be reflected by the system and the other wavelengths of light can be transmitted. Considering the loss in the substrate dielectric SiO\({}_{2}\) in THz band, the refractive index of material can be denoted by \(n_{d}=n_{d_{r}}+in_{di}\), where \(n_{d_{r}}\) and \(n_{di}\) are the real and imaginary parts, respectively. The symbol of \(n_{di}\) is also called the extinction coefficient, representing the optical loss of materials. In the THz band, the optical loss in SiO\({}_{2}\) is considerable and there are two peaks around 3.96 THz and 7.94 THz in the absorption spectrum [36]. Furthermore, the value of the extinction coefficient decreases with the increases in the temperature. In our concerned wave band, the average value of the extinction coefficient of SiO\({}_{2}\) here is approximately equal to \(n_{di}\approx 0.001\). Compared with the lossless case of \(n_{di}=0\), the transmittance in the passbands and the reflectance in the stopband both decrease. Otherwise, with the increase in the light wave, the transmission and reflection of light beam decrease as well. However, the bandgap number and each position of the bandgaps are unchanged in the spectra for \(n_{di}=0\) and 0.001. Therefore, the loss on substrate may affect the transmission and reflection in the passbands or stopbands, but it is not the physical mechanism in the formation of stopbands, which are induced by the periodic graphene-pair  
Figure 7: (**a**) Transmittance in the parameter space composed of the incident angle and the light frequency. (**b**) Transmission spectra for different fixed incident angles. The spatial distance is \(d_{2}=10\)\(\upmu\)m, the graphene chemical potential is \(\mu=0.4\) eV, and the relaxation time is set as \(\tau=0.5\) ps. arrays. Of course, we can also choose a material with a uniform refractive index and low loss coefficient as the substrate.
# Research on High-Performance Fourier Transform Algorithms Based on the NPU
## 2 Related Works
### NPU Research for Edge Devices  
Due to superior computing power and energy efficiency, the utilization of NPUs for accelerating intelligent computing on mobile edge devices has become a hot research area. Current research in this field mainly focuses on (1) enhancing inference accuracy and (2) boosting NPU efficiency.  
Regarding enhancing inference accuracy, [PERSON] et al. [3,4] focused on optimizing the trade-off between inference time and accuracy within specific constraints. They also addressed the distribution of workload between the CPU and NPU. [PERSON] et al. [23,24] concentrated on improving the performance and accuracy of inference by identifying optimal model structures using model search methods. They also developed an optimization framework to generate optimal NPU dataflow for more accurate and faster deep learning models.  
Regarding boosting the inference efficiency of NPUs, [PERSON] et al. [25] explored ways to enhance the inference efficiency of the NPU by examining the dynamic frequency characteristics of the processor. This was achieved by managing the temperature of the processor cluster with task migration and Dynamic Voltage and Frequency Scaling (DVFS) techniques. [PERSON] et al. [6] introduced a novel framework for managing NPU resources, enhancing efficiency and fairness in multi-user scenarios by enabling concurrent execution, and resource sharing. In addition to these works, there are also studies focused on improving NPU inference efficiency by accelerating parameter updating [26] or task scheduling [27,28].
## 4 Results and Discussion
### Image-Sensor-Based Spatial Object Extraction  
To examine the effectiveness of the image-sensor-based spatial object extraction, an indoor setting replicating an underground utility tunnel was established. Leveraging this environment, LiDAR sample data were gathered to imitate an underground utility tunnel scenario. The initial step involved comparing the obtained LiDAR data to identify displaced objects and implement the required updates. Moreover, a data collection test was conducted to identify target objects at their installation height, considering the height of the power lines.  
Figure 8: Method for updating a spatial object in a digital twin.
## 1 Introduction
[PERSON] [17] conducted experiments at the University of Science and Technology of China (USTC) Large Space Experiment Hall to determine the temperature distribution at the top and bottom of a shaft, and then established a model of the shaft temperature and velocity distribution in a stairwell with only one fire location. [PERSON] et al. [18] evaluated the transport characteristics of the thermal smoke flow driven by turbulent mixing in a stairwell with one fire location at the bottom, changing the opening height during experiments. Their results revealed that the opening height has a significant impact on the air entrainment, and further clarified turbulent mixing in stairwells. In previous studies, experiments and simulations were conducted on the smoke flow induced by adjacent fires in shafts with the fire source only located at the bottom. With an increase in the height of the fire location, different amounts of ambient air that flowed into the stairwell had a significant influence on the combustion process and smoke flow. Moreover, most previous studies were conducted in stairwells with openings set at a fixed height. The height of openings and fire locations have an influence on the turbulent mixing movement. However, the effects of the fire locations, opening height, and number of openings on the upward smoke flow in stairwells were not addressed in previous studies. Furthermore, the effect of fire HRRs has not been studied systematically, although they have been studied. Regarding the research, due to limited time and cost in experiments, several complex and comprehensive fire conditions were difficult to evaluate in previous experiments, and minimal studies have been conducted using a series of simulations focused on the fire case. Therefore, phenomena based on fire dynamics and slight differences may be neglected, such as the wall attachment effect, otherwise known as the Coanda effect. When the smoke moves upward in the shaft, it continuously entrains the air; whereas the left wall surface of the shaft restricts the smoke entrainment, thus resulting in smoke attachment onto the left side of the wall [19; 20; 21; 22]. [PERSON] and [PERSON] [23] observed this phenomenon in a study on an adhering spill plume. In this study, to make the research more comprehensive, the numerical method was used to simulate fire-induced smoke flow through a 21-story stairwell in a high-rise building model. The aim of this study was to investigate the transport phenomenon of thermal smoke, and to determine the flow field in the stairwell under the stack effect with changing fire heights, HRRs, and openings height in the stairwell. In addition, the temperature distributions were considered with respect to different simulation configurations, and the simulation results were used to verify the theoretical model.
## 4 Experimental Results
### 1-D Compressibility of GRMs  
Material compressibility (or the capability to increase/decrease in volume when subjected to an applied load) is one of the most important factors required in design considerations. For conventional granular soils with rigid particles (i.e., sand and gravel), any change in volume is due to the rotation, movement, and rearrangement of non  
Figure 4: Density characteristics of tested GRMs: (**a**) minimum and maximum dry unit weight; (**b**) specific gravity; and (**c**) idealized illustration of volume of solids-biased density packing (adopted from [16]).  
compressible grains [41]. The compressibility of GRMs, however, is completely different to that of granular soils, due to significant differences in the elastic modulus of the rigid particles of the host gravel and that of the soft rubber particles. Furthermore, under normal stress conditions, not much volume change is associated with individual particles of rubber (i.e., this is because the Poisson's ratio of pure rubber is \(\
u\approx 0.5\)), and distortion is the key phenomenon that takes place in pure rubber specimens.  
1-D compression test results with creep loading are reported in Figure 5. In the tests, a 2-h creep (which, under the adopted testing conditions, was sufficient to attain negligible settlement under sustained vertical stress) was applied at different incremental loading stages and the measured vertical stress values were corrected for the loss of transferred load (due mainly to soil-wall friction) that was experimentally determined.  
For any given vertical shear stress level, the trends reported in Figure 5 clearly show an increase in 1-D compression with increasing _VRC_. Furthermore, it can be seen that GRMs with \(\textit{VRC}\leq 10\%\) performed better than those with \(\textit{VRC}\geq 25\%\) (i.e., much smaller vertical strains (\(\varepsilon_{\
u}\)) developed under the same applied vertical effective stress (\(\sigma_{\
u}\)') level). Additionally, in the range of \(\textit{VRC}\) tested, it can be observed that the GRMs made of small rubber particles (\(AR=0.33\)) were much more compressible than those consisting of large rubber particles (\(AR=0.67\)).
# Machine Learning Enabled Performance Prediction Model for Massive-MIMO HetNet System
## 3 Performance Prediction Model  
Before deploying a complex HetNet with each tier supporting massive MIMO, network providers are interested in predicting the overall network performance. The analysis provided in the previous section shows that the network performance metrics are functions of various network parameters, such as the number of transmitting antennas at BSs, the number of active users associated per cell, and the transmitted power of each tier. However, the numerical values of the parameters are stochastic variables because of the stochastic network topology and user location. Moreover, for a given topology, the instantaneous performances of the network are also stochastic processes that depend on the stochastic behavior of multipath channel fading coefficients.  
In this work, a two-tier network (K = 2) is considered--microcell and picocell. In the given scenario, the numerical values of the network performance metrics (PMs), either \(CP\) or \(ASE\), are considered to be stochastically influenced by the number of the antennas present in the BS of both tiers, i.e., \(N_{macro}\) and \(N_{pico}\); the number of active users served in same time-frequency grid, i.e., \(R_{macro}\) and \(R_{pico}\), for both tiers; and the difference in the transmitted powers of the tiers in dB, i.e., \(P_{TD}=10\ log\left(\frac{P_{macro}}{P_{pico}}\right)\). Thus, the performance metrics are given by the following  
\[PM=f\big{(}N_{macro},R_{macro},N_{pico},R_{pico},P_{TD}\big{)}+\epsilon. \tag{15}\]  
Here, \(f(.)\) represents an unknown stochastic function and \(\epsilon\) represents random noise terms that capture the contributions of the unknown parameters that influence the performance metrics. The objective of the present work is to infer supervised learning models to approximate the unknown stochastic functions for predicting the numerical values of the network performance metrics (PMs). The process is implemented with the following steps, listed below.
# Automatic Clustering for Improved Radio Environment Maps in Distributed Applications
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline BS & base station \\ \hline GoB & grid of beams \\ \hline KMC & K-means clustering \\ \hline LOS & line-of-sight \\ \hline LTE & long-term evolution \\ \hline MDT & minimization of drive tests \\ \hline M2M & machine-to-machine \\ \hline QoS & quality of service \\ \hline REM & radio environment map \\ \hline RMSE & root mean square error \\ \hline RSRP & reference signal received power \\ \hline SN & sensor node \\ \hline UE & user equipment \\ \hline \end{tabular}
\end{table} 
Table 1: List of abbreviations. To provide clarification in understanding the expressions in this paper, Table 2 defines the notations throughout the paper; lowercase letters in bold represent vectors and uppercase letter in bold indicate the matrices. The rest of this paper is organized as follows: Section 2 describes the work on improving wireless coverage and wireless prediction models. It also describes the importance of wireless communication in multiple applications and domains, such as IoT and edge computing. Section 3 describes the related work and where this work fits in the literature. Section 4 describes the main approach for optimizing REM and RSRP predictions. Section 5 describes the utilized algorithm and how it is used in the clustering process to improve prediction models. Section 6 provides the research outcomes according to the proposed approach. Finally, Section 7 concludes the work with a brief discussion on future work.
## 4 Integration into IoT
### Intelligent Systems and Biological Objects in the IoT  
Different intelligent methods, mostly from the machine learning field, are widely used in biotechnology today [86; 112; 113]. They have applications in genetics [112], protein structure prediction [114], and metabolic pathway dynamics prediction [115]. These approaches have been widely applied in the development of different types of biosensors, mentioned above. Here, it is necessary to mention a deep learning approach for the lab-of-origin prediction of engineered DNA [113]. Such a method can also be applied for cell-based biosensors quality control.  
Here, the place of intelligent systems in the production biotechnologies mentioned above and how such systems can help with their integration with a city's IoT are discussed. The most obvious application is data from video camera analysis together with data from sensors to control the state of higher plants and the efficiency of rainwater treatment. Such systems based on machine learning approaches are already widely used for smart greenhouses, including vertical farming systems [87; 102; 116]. Therefore, the development of machine learning approaches for rain gardens and green streets can be an effective way to control such systems.  
Machine learning approaches have already been applied to process control [117], the prediction of biomass growth [28; 29; 118], and the production of useful biochemicals [119]. Applications in laboratory and industrial photobioreactors have been shown. In the case of city photobioreactors, additional influencing variables should be taken into account. Main locations for the installation of such systems include building facades and roofs [120], the interior of big buildings such as railway stations, and shopping malls in places with natural illumination, similar to other green systems with plants [121]. This leads to the influence of climate conditions inside a building on biomass growth, so additional data from in-house sensors are required. The operation of a photobioreactor in turn influences the conditions in the building. Carbon dioxide consumption should be additional criteria for maximization since this is a main reason for photobioreactor applications in cities.