esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Identification Methods  
#### 2.3.1 Data Preprocessing  
As mentioned, denoising and baseline subtraction operations were conducted to preprocess the raw measurements. For EN-based PI, the noises are mainly high-frequency noise. Thus, the wavelet denoising method [18] was adopted in our experiments. The main steps of wavelet denoising can be expressed as follows.  
1. Compute the wavelet decomposition of the raw measurements at a predefined level.
2. Identify a threshold for the measurement noises from the decomposed measurements by applying soft thresholding to the detailed coefficients.
3. Reconstruct the measurements based on the original approximation and modified detail coefficients.  
Then, a baseline subtraction operation is applied on the denoised measurements. The baseline is defined as the sensing voltage sampled before exposing the sensors in the EN to perfume samples. By subtracting the baselines from the denoised measurements, the influence of background gas residuals on the experiments can be reduced.
## 4 Results
### Optimal Placement Strategy for Air Quality Monitors
#### 4.4.2 Calculation of the Comprehensive Index  
We calculated a comprehensive index (S) for each region to determine the overall priority for air quality monitoring. This index is a weighted combination of the three datasets described above. The weight of each factor was assigned based on its relative importance.  
* W1: Density of Vulnerable Populations (Weight: 0.5) Given the critical importance of protecting vulnerable groups, this factor was assigned the highest weight.
* W2: Accessibility to PM-Related Facilities (Weight: 0.3) Accessibility is a significant factor, as regions with poor access to health care and other facilities are at higher risk.  
Figure 8: Daytime population density in Gangseo-gu: older adult residents (**left**) and children under 14 years old (**right**).  
W3: Environmental Indicator (PM Concentration) (Weight: 0.2)  
While important, air quality data alone were given a lower weight relative to the other factors.  
The comprehensive index for each region was calculated using the following formula:  
\[\text{S}=(\text{W1}\times\text{D1})+(\text{W2}\times\text{D2})+(\text{W3}\times \text{D3}) \tag{1}\]  
This formula produces a value for S that ranges from 0 to 1, with higher values indicating regions where the installation of air quality monitors is urgently required. For example, a region with a comprehensive index S of 0.72. is considered a high priority area for monitor installation.
# Joint SAR Image Time Series and PSInSAR Data Analytics: An LDA Based Approach
## 3 Persistent Scatterers Identification  
A persistent scatterers detection algorithm has been carried in this work to support a comparative study of identified PS candidates position relative to results of classification and change of detection methods. The PS identification is a two-step algorithm, consisting of the analysis of both temporal and spectral behavior of the targets [23]. This approach is synthesized in Figure 2.  
The amplitude's statistics are quantified by computation of the mean-per-sigma ratio in each point of the scene:  
\[MSR=\frac{\mu}{\sigma} \tag{4}\]  
where mean \(\mu\) and standard deviation \(\sigma\) are computed in discrete form, in each pixel of the scene, based on the elements of the vector \(A\) which contains the values of resolution cell's amplitudes along the dataset:  
\[\mu=\frac{1}{N}\sum\limits_{i=1}^{N}A_{i} \tag{5}\] \[\sigma=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}\left(A_{i}-\frac{1 }{N}\sum\limits_{i=1}^{N}A_{i}\right)^{2}}\]  
This mean-per-sigma ratio represents the inverse of the coefficient of variation. A stable target is considered to be present in the analyzed resolution cell if the associated \(MSR\) index of the amplitude is above unity.  
The second analysis is based on the target's spectral coherence \(q_{x}\) which is computed in concordance with Wiener-Hincin theorem. Elements of each dataset's resolution cell form complex data vector \(X\). The number of vector's elements is equal to number of dataset's images. The spectral coherence \(q_{x}\) is therefore computed as the Fourier transform of the complex dataset's values autocorrelation function \(R_{XX}\). The Wiener-Hincin theorem is applied in discrete form:  
\[q_{X}(f)=\sum\limits_{k=1}^{N}R_{XX}(k)e^{-j2\pi fk} \tag{6}\]  
Figure 2: Visual representation of Persistent Scatterers detection algorithm.  
where \(N\) is the number of dataset's images, \(k\) an integer smaller than \(N\) and variable \(f\) denotes the frequency. In every pixel of the scene, each element \(i\) of the data vector, \(X_{i}\) can be modeled as a stochastic process. The autocorrelation function \(R_{XX}\) can be estimated as:  
\[R_{XX}(k)=\frac{1}{(N-k)\sigma^{2}}\sum_{i=1}^{N-k}\left(X_{i}-\mu\right)(X_{i +k}-\mu)* \tag{7}\]  
To avoid the detection of false alarms, especially in regions affected by shadowing effect, a target will be classified as PS candidate only if its amplitude value is situated above the mean of neighboring pixels. Shadowed regions are usually situated behind steep slopes and cannot be illuminated by SAR sensor. The candidate points can form the starting point of the PS-InSAR technique.
# Automatic Detection of Linear Thermal Bridges from Infrared Thermal Images Using Neural Network
## 2 Literature Review  
Thermal imaging cameras were introduced to detect various thermal anomalies in building envelopes via non-destructive methods. In recent years, several studies have been performed to automatically detect thermal anomalies in building envelope by using image processing, in addition to thermal imaging cameras. Asdrubali et al. [8] detected thermal bridges from thermal images of specific areas, such as pillars and beam-pillar joints. This study proposed a method for determining the threshold to distinguish the temperature distribution in areas with normal and abnormal temperatures, to detect thermal bridges. Garrido et al. [12] conducted a study to automatically detect thermal bridges from thermal images by adding thresholds for geometric characteristics in addition to temperature differences caused by thermal bridges. As a result of applying the proposed method to thermal images, the precision, recall, and F-score were 55%, 68%, and 61%, respectively. As presented above, previous studies showed that areas classified as thermal bridges from thermal images can be recognized based on thresholds. However, there are difficulties in selecting a commonly used threshold when using threshold-based methods. There are also limitations in detecting and distinguishing thermal defects other than thermal bridges within thermal images.  
In addition to architecture, a wide variety of studies have been performed to automatically detect problems, using thermal images in objects such as pipelines [16], turbojet engines [17], photovoltaic module [18; 19], and belt conveyors [20]. In particular, some of these studies have proposed an intelligent method, such as machine learning, to increase the accuracy of detection. Tong et al. [16] proposed a method using machine learning, to detect leaks in pipelines. Thermal images of pipelines contain pipes and the background, so the Otsu-Grabcut image segmentation algorithm was applied to segment only the areas corresponding to pipes. Then, the Histogram of Oriented Gradient and Gray-Level Co-Occurrence Matrix were extracted from the segmented thermal images, as features for training. The extracted features were learned via a support vector machine. Through the proposed method, the normal pipeline, leaking pipeline, worn pipeline, and the normal valve were classified with an accuracy of 96.00%. Andoga et al. [17] proposed a method using machine learning to detect defects in a turbojet engine. The Kohonen neural network was applied to segment and recognize the defects. Through the proposed method, the defects were classified with an accuracy of 91.8%. As described above, various studies are being performed to apply image processing and machine learning techniques, to improve the accuracy of recognizing defects from thermal images. Since each application has different characteristics, each application requires a suitable method for segmenting images and extracting features to apply machine learning. However, such methods have not been proposed for thermal bridges in building structures.
# Intelligent Video Surveillance Platform for Wireless Multimedia Sensor Networks
## 4 Design Implementation  
### The Formation and Transmission of Video Packet  
Our aim is to send video streams with high frame rate and good quality. For these reasons, IVSP uses H.264 standard for video compression. The server application generates a video stream from either live or stored source and converts video data in H.264 streams. According to H.264 standard, there are I, P and B frames. We do not use B frames in experiments, because the decoding and encoding of these frames is dependent on the next frame. In our experiments, we use I frames and P frames. I frame priority is always much higher than P frame. The length of a packet should be adjusted in such a way that it can fulfill the requirements of transmission. If a packet has very small amount of data, every node needs to transmit quickly. This will increase the probability of congestion, energy waste, and more collisions in wireless channel. If a packet has large amount of data, which may consist of many frames, every packet has to wait until all the video frames are made. This will cause more delay. Therefore, length of packet must be chosen in appropriate way.  
We assume that maximum packet length is \(l\). If the length goes beyond this threshold, the video data will be divided into many packets. The packet will be I frame packet, if a packet contains the data of I frame. Otherwise, packet will be P frame packet. We know that wireless transmission is unreliable, so packet loss is an obvious feature. An I frame packet loss will result in loss of all P frame packets, because they will not be decoded properly and will be useless. If P frame packets are lost, video decoding will carry on with some disturbances in video data. If I frame packets are retransmitted very  
Figure 3: Network architecture.  
Figure 2: (a) Raspberry Pi model 2B; (b) Edimax Wi-Fi dongle.  
quickly, the consequence of loss packets will not be prominent. Therefore, retransmission of I frame packets is very important for good quality video data.
## 9 WLAN Integration with 6G: Challenges and Opportunities
### High-Bandwidth Backhaul for WLAN  
Most WLANs nowadays experience capacity bottlenecks due to backhaul networks. For instance, copper backhaul technologies such as asymmetric digital subscriber line (ADSL) have quite low capacity compared to WiFi, and do not provide enough bandwidth to support upcoming applications such as AR/VR devices. Hence, the WiFi network cannot operate at its full capacity. Similarly, cellular network-based backhauling also has intermittent capacity issues. The most common solution to this issue is the use of fibre backhaul technologies. For instance, passive optical networks (PONs) provide guaranteed high-capacity backhaul links for WLANs. At the same time, recent developments in 5G fixed wireless access also provides better connectivity for WLANs [137]. However, when we use passive optical networks as the backhaul of VLC/ OWC/ the latest WiFi standards, careful consideration should be given to the QoS requirements and policies of each network. This is mainly because WLANs implement contention-based/contention-free scheduling mechanisms and PON uses queue-oriented QoS mechanisms. Therefore, to guarantee the reliability and low-latency requirements of 6G applications, resource scheduling and QoS mapping between networks need further investigations.
# Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning
## 3 The Proposed Scheme
### Preprocessing Data
#### 3.2.2 Log-Scaling of Monetary Variables  
Utilizing the 'DLNG_AMOUNT', which represents the property sale price, as a direct target variable poses certain challenges. Individuals with substantial financial resources are more likely to reside in larger properties, potentially inflating the 'DLNG_AMOUNT'. Conversely, those with limited resources might correspond to a comparatively lower 'DLNG_AMOUNT'. To circumvent this potential bias and adopt a more objective metric, we introduce the Square Meter Unit Price (SMUT), computed by dividing the 'DLNG_AMOUNT' by the property size, 'XUAR'.  
Building on this, in order to examine regional variations in SMUP we conducted clustering based on the 'ADRES' feature. Figure 2 graphically depicts the SMUP of station proximity areas, with the monetary values benchmarked to 10,000 Korean Won units. We observe that the highest prices are more than double the lowest, and the majority of these regions are concentrated within Seoul.  
As a foundational step in our methodology, it is crucial to log-scale the 'DLNG_AMOUNT'. The need for this transformation emerges from the observed disparities and potential skewness in the distribution of property prices. By implementing log-scaling, we aim to reduce the influence of large or severely skewed values, striving for a more normalized distribution that is favorable for machine learning model performance. The intricacies of this log-scaling process can be visualized in Figure 3. After determining 'SMUT' using the relation \(\text{SMUT}=\frac{\text{DLNG\_AMOUNT}}{\text{XUAR}}\), we subsequently applied a log-scaling transformation to SMUT itself. This transformation is an integral part of our data preprocessing, ensuring that our  
Figure 2: Bar chart illustrating the ranking of real estate prices per square meter in different regions based on actual transaction data. Each bar represents a different region, while the length of the bar denotes the price per square meter. The regions are ranked from highest to lowest based on their respective prices per square meter. This visualization highlights the spatial variations in real estate prices.  
target variable is aptly processed for the subsequent analytical phases in alignment with our foundational methodologies.  
It is worth noting that when the log-transformed 'DLNG_AMOUNT' is divided by 'XUAR', the resultant 'SMUT' inherently undergoes log-scaling. This ensures that the distribution of 'SMUT' is further normalized, setting the stage for potential improvements in our model's performance.
## 5 Results and Discussion
### Deployment Settings  
#### 5.2.1 Datasets  
To test the efficiency and capabilities of EMDI, we employ two types of georeferenced datasets to represent pollution and mobility data. The pollution datasets selected for the demonstration are aimed at showing the capability of the system in accepting the two most widespread formats, GRIB and NetCDF.  
The files received in the two formats are transformed into the standard CSV format that is accepted by big data analytics frameworks.  
Table 1 shows the characteristics of the datasets employed, the type of data, the size in tuples and the main attributes included.
## 3 Improved Stochastic Resonance Model with Output Saturation Limits
### Accelerated Stochastic Resonance Model
This is obtained by substituting \(U_{ASR}(x)\) into Equation (33) and solving for the approximate solution:  
\[R_{ASR}\approx\frac{D}{\sqrt{a/b\,\left(c-\sqrt{a/b\,}\right)}}\exp\left[-\frac{ a^{2}(\exp(c)-1)}{4 Db\left(\exp(c-\sqrt{a/b\,}\right)-1\right)}\right] \tag{43}\]  
Substituting Equation (43) into Equation (39) yields the output signal-to-noise ratio of the accelerated stochastic resonance system:  
\[SNR_{ASR}=\frac{\pi aA^{2}}{2 Db\sqrt{a/b\,}\left(c-\sqrt{a/b\,}\right)}\exp \left[-\frac{a^{2}(\exp(c)-1)}{4 Db\left(\exp(c-\sqrt{a/b\,}\right)-1\right)}\right] \tag{44}\]  
Setting the system parameters \(a=1.5\), \(b=1\), and the input signal amplitude \(A=0.35\), the relationship curves of the signal-to-noise ratios of Equations (40), (41) and (44) plotted as a function of the variation of the input noise intensity \(D\) are shown in Figure 10.  
As can be seen from Figure 10, the output SNR of the unsaturated stochastic resonance algorithm is always higher than that of the classical stochastic resonance algorithm, while the output SNR of the proposed accelerated stochastic resonance algorithm is better than that of the unsaturated stochastic resonance algorithm and the classical stochastic resonance algorithm when the noise intensity \(D\) is larger than 2.8, which shows that the accelerated stochastic resonance algorithm has a significant advantage in larger noise intensities, and this is because of the exponentially shaped inner potential well walls promote the particle. The reason is that the exponential inner potential well wall promotes the jumping process between the potential wells. It is verified through simulation that the amplitude \(A\) of the input signal only affects the height of the output signal-to-noise ratio curve and does not affect the curve trend distribution so that the system with the same parameters has the same response curve to the noise intensity. Figure 10: Output SNR versus noise intensity \(D\).
# Understanding How Image Quality Affects Transformer Neural Networks
## 3 Experimental Setup
### Transformers
These factors contribute to the high latency of ViTs, making them impractical for real-world applications on resource-constrained hardware such as mobile devices and wearables. Additionally, patch embedding with large kernel and stride has been identified as a speed bottleneck on mobile devices, and the choice of token mixer and the implementation of reshape operations also impact the latency of ViTs. In [57], the authors introduce a new model called EfficientFormer, which aims to achieve high performance with low inference latency on mobile devices. The study focuses on revisiting the network architecture and operators used in ViT-based models and identifying inefficient designs. It proposes a dimension-consistent design paradigm for vision transformers and performs latency-driven slimming to obtain a series of final models dubbed EfficientFormer. [PERSON] et al. [58] introduced a novel efficient additive attention mechanism designed to replace the quadratic matrix multiplication operations in self-attention with linear element-wise multiplications. This approach aims to address the computational complexity of self-attention, making it more practical for deployment on resource-constrained mobile devices. The efficient additive attention eliminates the need for expensive matrix multiplication operations and allows for linear complexity with respect to the input image resolution. By incorporating this mechanism, the authors introduce a series of models called SwiftFormer, which achieve state-of-the-art performance in terms of accuracy and mobile inference speed. The proposed efficient additive attention enables the usage of attention at all stages of the network, providing a more effective trade-off between accuracy and latency compared to existing methods. [PERSON] et al. [59] presented a novel linear attention mechanism named large kernel attention (LKA) designed for computer vision tasks. LKA leverages the strengths of both convolution and self-attention mechanisms, addressing their respective limitations. Namely,LKA decomposes a large kernel convolution into three components: depthwise convolution, depthwise dilation convolution, and pointwise convolution. This decomposition allows for capturing long-range dependencies with reduced computational cost and parameters. The visual attention network (VAN) was then introduced, leveraging LKA for feature extraction in a simple hierarchical structure. We present a summary of notable transformer architectures used in our study, including their publication year, number of parameters, and memory footprint. This information, outlined in Table 1, provides insights into the characteristics and scale of the transformer models evaluated in our experiments. The publication year indicates the temporal context of each model's development, while the number of parameters and memory footprint offer insights into their computational complexity and resource requirements.
# Design Procedure of a Mono-Anchor Head for 2360 MPa High-Strength Steel Strand
## 4 Finite Element Modeling of Mono-Anchor Head
### Material Test of Steel for Mono-Anchor Head
#### 4.2.3 Estimation of the Strength of Carburized 42 CrMo4 Considering the Hardened Layer  
The effective hardness (\(HV_{e}\)) values of the outer and inner layer are average values calculated by weighting the area of each layer (\(A_{i}\)) with the hardness at each indent (\(HV_{i}\)). This is expressed by Equation (7).  
\[HV_{e}=\sum_{i=1}^{n}(A_{i}\times HV_{i})/\sum_{i=1}^{n}A_{i} \tag{7}\]  
[PERSON] et al. [23] derived the regression equations, presented as Equations (8) and (9), by defining correlations between hardness, yield, and tensile strength of various steel samples. In this study, the yield strength and tensile strength of the outer and inner layers used in FEM modeling were computed using Equations (8) and (9) for the hardness distribution obtained from the tests above. The values are presented in Table 4.  
\[YS(yield\ strength)=-90.7+2.90 HV \tag{8}\]  
\[TS(tensile\ strength)=-99.8+3.73 HV \tag{9}\]  
Based on the area weighted average strengths of Equations (10) and (11), the yield and tensile strength can be calculated as 937 MPa and 1234 MPa, respectively. The tensile strength by Equation (11) is similar to the test results in Table 2, even though predicted yield strength by Equation (10) shows some discrepancy. Nevertheless, linear correlation between strengths and hardness from test results and [PERSON] et al. [23] can rationalize the new anchor head modeling method considering carburized depth. Therefore, this study proposes a new anchor head modeling that has two layers with different strengths.  
\[YS_{eff}=(A_{outer}\times YS+A_{inner}\times YS)/(A_{outer}+A_{inner}) \tag{10}\]  
\[TS_{eff}=(A_{outer}\times TS+A_{inner}\times TS)/(A_{outer}+A_{inner}) \tag{11}\]