esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Noise Level to Detect Frame Repetition Forgery in Video Frame Rate Up-Conversion
## 4 Experimental Results and Analyses  
Fifty uncompressed YUV sequences in 30 _fps_ constitute the basic group of the testing video database, in which there are 10, 24, 8 and 8 video sequences with QCIF, CIF, 720P and 1080P formats, respectively. To evaluate the performance of the proposed method, the basic group is used to construct the negative set (NS) and the positive set (PS). All test videos are mixed with Gaussian noise, and the standard deviation of each video frame is evenly distributed in [0, 5]. The NS is composed of original test videos, and PS videos are obtained by down-converting video sequences in NS to 15 _fps_ and then raising to 30 _fps_ by FR forgery. We first evaluate the subjective performance of the proposed method by presenting the amplitude spectrum of the standard-deviation curve, and then evaluate the objective performance by comparing our method with residual detection and similarity detection. The PMR, false positive rate (_FPR_) and false negative rate (_FNR_) are used as objective criteria. _FPR_ and _FNR_ are defined as follows,  
\[FPR=E_{\text{NS}}\ /C_{\text{NS}} \tag{18}\]  
\[FNR=E_{\text{PS}}\ /C_{\text{PS}} \tag{19}\]  
where \(E_{\text{NS}}\) and \(E_{\text{PS}}\) are the counts of mistakes when detecting NS and PS respectively, \(C_{\text{NS}}\) and \(C_{\text{PS}}\) are the capacities of NS and PS respectively. In addition, the detection accuracy (_DA_) is defined as follows,  
\[DA=1-\frac{FNR+FPR}{2} \tag{20}\]
## 4 Illustrative Examples
### Beam Analysis  
A cantilevered thin-walled beam with the illustrative cross-section shown in Figure 2 and a beam length of \(L=6.4\) m was considered for dynamic analyses in order to verify the performance of the proposed higher order models. The beam was meshed with 40 proposed quadratic elements equally distributed along the beam axis. Besides, a 3D shell model considering a mesh of \(44\times 64\) Shell 181 elements over the cross-section and along the beam axis, respectively, was implemented in ANSYS 17.0 in order to compare with the results of the proposed models. Here, the material was assumed to be linear, elastic and isotropic in the calculation, and the beam was under the small displacement hypothesis in the whole vibration process.
## 4 System Validation and Case Study
The developed BIM-based system predicted that the formwork could be removed 4.4 days earlier than the requirement in the BS 8110:1997 (31.4% time-saving), 11.4 days earlier than the standard of IS 456:2000 (54.3% time-saving), and 18.4 days earlier than the PN-B-06251:1963's formwork strike standard time (65.8% time-saving). In addition to the beam, the case study also conducted at the slab with a span of less than 4.5 m. The calculated time for removing the formwork was 6.9 days, which can reduce the time about 50.7% in maximum and minimum 1.4%. In the case of the slab more than 4.5 m, the computed time was indicated as 11.3 days. This reduced the time about 18.6% based on both IS456:2000 and BS8110:1997 standards. The results indicate that the proposed BIM interoperability in this study can aim in monitoring the concrete curing status based on the temperature data received from the sensors and computing the removal strike time based on the maturity method. Also, the BIM model could receive and compute the concrete maturity data through Wi-Fi and the information was exported into the IFC format. Figure 6 shows the schematic of the BIM's interoperability to compute the concrete maturity data from the three sensors. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Concrete Class** & **Type of Cement** & **\(\mathbf{E_{a}}\)** & **k** & \(\mathbf{t_{0}}\) & \(\mathbf{S_{a}}\) & \(\mathbf{t_{50\%}}\) & \(\mathbf{t_{50\%}}\) & \(\mathbf{t_{70\%}}\) & \(\mathbf{t_{70\%}}\) & \(\mathbf{t_{85\%}}\) & \(\mathbf{t_{85\%}}\) \\  & **[k/J]** & **[–I]** & **[–I]** & **[–I]** & **[–I]** & **[–I]** & **[–I]** & **[–I]** & **[–I]** & **[–I]** & **[–I]** & **[–I]** & **[–I]** \\ \hline C30 & OPC & 41 & 0 & 11/30 & 23 & 61 & 2/23 & 130 & 5/92 & 301 & 12/02 \\  & & /018 & 11/30 & 23 & 78 & 2/23 & 7 & 5/92 & 69 & 12/02 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Constant coefficient and activation energy used. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Factors** & **1 st Test (days)** & **2 nd Test (days)** \\ \hline Average minimum temperature & 19.3 & 19.8 \\ \hline Average maximum temperature & 35.8 & 37.9 \\ \hline \(\mathrm{t_{e}}\), 85\% [days] & 9.8 & 9.6 \\ \hline \(\mathrm{T_{85\%}}\) [days] (cured at 20 \({}^{\circ}\)C) & 11.6 & 11.5 \\ \hline \(\mathrm{t_{e}}\), 70\% [days] & 4.65 & 4.08 \\ \hline \(\mathrm{T_{70\%}}\) [days] (cured at 20 \({}^{\circ}\)C) & 5.6 & 5.6 \\ \hline \(\mathrm{t_{e}}\), 50\% [days] & 2.01 & 1.98 \\ \hline \(\mathrm{T_{50\%}}\) [days] & 3.65 & 3.65 \\ \hline \(\mathrm{t_{e}}\), 24 [h] & 28.25 & 29.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Computed concrete formwork removal times and temperature.
# On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks
## 2 Related Work
A brief overview of existing schemes is presented in Table 1. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Technique/Ref** & **Objective(s)** & **Achievements(s)** & **Deficency(ies)** & **Field/Architecture** \\ \hline \multirow{3}{*}{C- ARQ [6]} & i-Provides alternative routing paths & i- Exploits broadcast nature of transmissions & i- High EE delay & i- 2D-UWSN \\  & i- Retransmission of erroneous data & for throughput efficiency & ii- Lower packets acceptance rate & ii- Two terminal cooperative relay model \\ \cline{2-5}  & & i- Best relay node selection & i- Enhanced data integrity & i- Inefficient energy usage & i- 2D-UWSN \\  & i- Data authenticity & ii- Improved packet reception rate & ii- High EE delays & i- Random deployment of nodes \\  & iii- Minimized retransmissions & i- Throughput improvement & i- High EE delay, if packet size & i- 2D-UWSN \\ \cline{2-5}  & & i- Overcome frequency selective fading & i- Reduced delay if relay is selected for & i- Random \\  & ii- Overcome frequency selective fading & minimized OPT time & ii- Increased energy consumption & deployment \\  & ii- Minimized OPT time & ii- Minimized packets collision & ii- Network partitioning & \\ \hline \multirow{3}{*}{[9]} & i- Reble selection based on propagation & i- Improved delay induction & i- Reduced stability period & i- 2D-UWSN \\  & n delay and SNR & i- Improved bit error rate & ii- Reduced network lifetime & i- Random deployment of nodes \\  & i- Data authenticity & i- Improved bit error rate & iii- Efficient energy utilization & \\ \hline \multirow{3}{*}{DEADS [10]} & i- Data reliability & i- PAR & i- High EE delays & i- 3D-UWSN \\  & i- Throughput & ii- Reduced packet drop rate & ii- Control and processing overhead & ii- Randomly deployed \\  & iii- Energy efficiency & ii- Reduction in energy consumption & iii- Stability period compromized & ii- Two mobile sinks \\  & iv- Adaptive routing & iv- Optimized \(d_{i}\) & i- No significant raise in data authenticity & i- 2D-UWSN \\ \hline \multirow{3}{*}{AEDG [37]} & i- Reduced as well as balanced & i- Improved stability period & i- No significant raise in data authenticity & i- 2D-UWSN \\  & i- Energy consumption & ii- Prolonged network lifetime & ii- Message latency & i- Heterogeneous randomly deployed nodes \\  & ii- Efficient data gathering & iii- Comparatively reduced packet drop rate & iii- Control packet overhead & iii-One AUV \\ \hline \multirow{3}{*}{LVRP [36]} & i- Efficient distributed data routing & i- Alleviated energy hole problem & i- Path selection overhead due to random & i- 2D-UWSN \\  & ii- Reduced routing update frequency & ii- Improved packet delivery ratio & ii- AUVs leave field un-attended and penetrate & i- Uniform random circular \\  & iii- Energy conservation & iii- Less forwarding overhead & ii- Less forwarding overhead & ii- Anch Anch another voronoi scope & deployment of nodes \\  & & iii- Long communication paths & iii- Two to six number of AUVs \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the state-of-the-art work.
## 4 Results and Discussion
### Evaluation Metric and Details  
The standard mask AP metric is employed as the evaluation criterion, comprehensively assessing the model's performance through mAP, AP50, AP75, and a range of different IoU thresholds. In addition to the newly designed components, all backbone and method  
Figure 4: Schematic illustration of Dynamic Adaptive Focal Loss.  
hyperparameters remain consistent with the original YOLOv9 approach. In the Dynamic Adaptive Focal Loss, we set the smoothing coefficient \(\epsilon\) to 0.1, initial alpha to 0.25, and initial gamma to 1.5. These parameters are determined through a series of experiments to optimize performance for the current task. The model is trained with a batch size of 12 per GPU using the SGD optimizer, with an initial learning rate of \(1\times 10^{-2}\). AASNet is implemented in PyTorch 1.7.1, and all experiments are conducted on an NVIDIA P40 GPU. Each training cycle consists of 300 epochs.  
To evaluate segmentation performance, we utilize mAP (mean Average Precision) as the primary evaluation metric. mAP represents the average segmentation performance across multiple classes and is defined as  
\[mAP=\frac{\sum_{i=1}^{N}AP_{i}}{N} \tag{25}\]  
where \(N\) is the number of classes. The Average Precision (\(AP\)) for each class is calculated by integrating the precision-recall (PR) curve:  
\[AP=\int_{0}^{1}p(r)dr \tag{26}\]  
Here, \(p(r)\) represents the precision at a given recall value, used to evaluate the prediction accuracy of the model, and is defined as  
\[Precision=\frac{TP}{TP+FP} \tag{27}\]  
where \(TP\) represents true positive samples, and \(FP\) represents false positive samples. Additionally, recall is another important metric that indicates the proportion of actual positive samples correctly identified by the model, and is defined as  
\[Recall=\frac{TP}{FN+TP} \tag{28}\]  
Here, \(FN\) represents false negative samples. In addition, we use AP50 and AP75 to further evaluate the model's performance under different IoU (Intersection over Union) thresholds. AP50 measures the Average Precision at an IoU threshold of 0.5, indicating the model's performance under more lenient criteria, while AP75 represents the average precision at an IoU threshold of 0.75, used for stricter evaluation. Together, these metrics provide a comprehensive assessment of the model's segmentation performance.
# Application of Iron-Modified Activated Carbon for Phosphate Removal in Aqueous Systems
## 2 Materials and Methods
### Soil Phosphate Release  
The soil was sampled at Chudong area, Sediment Monitoring Site 1 in Daecheong Lake. Soil samples were washed to remove debris. To analyze the particle size distribution of the soil, a sieve analysis was conducted using a mechanical shaker under dry conditions. Stainless steel sieves (ChungGye Sieve Co., Ltd., Gyeonggi-do, Republic of Korea) with mesh sizes of #4 (4.75 mm), #2 (2.0 mm), #16 (1.18 mm), #40 (0.425 mm), #60 (0.250 mm), #100 (0.150 mm), and #200 (0.075 mm) were used. The sieves were stacked in descending order from the smallest mesh size at the bottom to the top, and 0.5 kg of washed and-dried soil was poured onto the top sieve. The sample was then shaken for 5 min using a mechanical sieve shaker (CG-211-8, ChungGye Sieve Co., Ltd., Gyeonggi-do, Republic of Korea) at a consistent amplitude, following standard soil testing procedures. After this, 0.5 kg soil was stored with the 0.5 L phosphate solution on a large stainless steel plate for phosphate adsorbed onto the soil. After 48 h, the supernatant was decanted and the wet soil was dried. This will be referred to as \"P-Soil\".  
The control group samples (P-Soil only) were prepared by filling each beaker with 0.6 kg of P-Soil (approximately 300 mm in depth). The first experimental group (P-Soil + AC) and second experimental group (P-Soil + SMAC) were prepared by adding 30 g of SMAC (forming a 30 mm surface layer) on top of each control sample. Each beaker was filled with 0.5 L of distilled water.  
To simulate water flow, a jar tester was used, where the range of rpm for stirring was from 0 to 80. The stirring time was set to 6 h considering the time required to reach concentration equilibrium. After mixing, the supernatant was collected to measure the phosphate concentration.
# A Review on Automated Detection and Identification Algorithms for Highway Pavement Distress
## 4 Evolution of Pavement Image Processing Techniques
### Deep Learning Approaches
#### 4.3.3 Semantic Segmentation
The proposed model outperforms traditional segmentation and machine learning methods by directly segmenting complex concrete microcracks without denoising pre-processing, enabling high-precision pavement crack analysis. However, asphalt crack segmentation presents greater challenges than concrete cases due to (1) more ambiguous crack boundaries and (2) higher prevalence of complex crack patterns. Therefore, optimal difficulty balancing factors must be determined through comparative parameter experiments.
## 3 Design and Analysis
### Inter-Channel Crosstalk  
In regard to parallel optical modules, to suppress influence of inter-channel crosstalk, it is important to optimize the design of signal transmission lines and ground vias structures. Figure 4a shows the simulation model to analyze crosstalk by using high-speed communication analysis tool, Keysight ADS. Victim and aggressor lanes are coupled by capacitors, \(C_{x}\). Crosstalk induced jitter is simulated by changing the capacitance of \(C_{x}\). Calculated relationship between inter-channel crosstalk jitter and insertion-loss-to-crosstalk ratio (ICR) is plotted in Figure 4b. The ICR was defined as the ratio  
Figure 3: Structure of thermal vias in multi-layer substrate.  
Figure 2: Loss budget of 25.78-Gbit/s MMF optical link.  
of the crosstalk and the insertion loss. The result shows that crosstalk jitter can be almost completely suppressed to less than around 0.01 unit interval (UI) when ICR is small of \(<\)\(-\)35 dB.
# Algorithmic Design of an FPGA-Based Calculator for Fast Evaluation of Tsunami Wave Danger
## 2 Materials and Methods  
### Problem Statement  
One of the illustrative examples of using HLS to simplify the development process is the implementation of convolutional (matrix) filters for image processing. Similar operations are also used in convolutional neural networks.  
A two-dimensional array with dimensions (_nx_, _ny_) representing the luminance (\(\gamma\)) values as single precision floating point numbers is given. For each pixel of the image, it is necessary to arrange its convolution with neighboring pixels using the given weight matrix (convolution kernel):  
\[g(x,y)=w*f(x,y)=\sum_{dx=-a}^{a}\sum_{dy=-a}^{a}w(dx,dy)f(x+dx,y+dy)\]  
where \(g(x,y)\) is the resulting pixel with coordinates (\(x\), \(y\)), \(f(x,y)\) is the original image, \(w(dx,dy)\) is the convolution kernel (size \(NxN\)), and \(N=a^{\prime}2+1\) is the size of the convolution kernels. There are no data for convolution at the border of the image, so there are several options for padding the border with pixels. For this article, we implemented convolution with a \(3\times 3\) kernel and padded the border with the edge pixels.  
Listing 1 of the algorithm's realization in C/C++ language is given below.  
```
void filter (const Pixel * input, int width, int height,  Coeff kernel[KERNELSIZE][KERNELSIZE], Pixel * output) { for(int y = 0; y < height; y++) { for(int x = 0; x < width; x++) {  Pixel result = 0.0;  for(int ky = 0; ky < KERNELSIZE; ky++) {  for(int kx = 0; kx < KERNELSIZE; kxx++) {  result += getPixel(input, width, height,  x, y, kx, ky) * kernel[ky * KERNELSIZE + kx]; } }  outputly * width + x] = result; } }
```  
**Listing 1** Reference implementation of the convolutional filter.  
Here, Pixel and Coeff are synonyms for the type of float, the kernel size is set by the constant KERNELSIZE, and getPixel means to obtain the pixel of the original image taking into account the padding of borders. The results are given in Listing 2 below.  
```
inline Pixel getPixel (const Pixel * input, int width, int height,  int x, int y, int kx, int ky) { int ix = x + kx - KERNELSIZE/2;  int iy = y + ky - KERNELSIZE/2;  ix = (ix < 0)? 0: ((ix \(\geq\) width)? (width-1): ix);  iy = (iy < 0)? 0: ((iy \(\geq\) height)? (height-1): iy);  return input[iy * width + ix]; }
```  
**Listing 2** Pixel sampling.  
The described algorithm was treated as the reference algorithm, on the basis of which we developed an implementation on an FPGA.
# Turning Video Resource Management into Cloud Computing
## 3 The Video Resource Management System in HDFS-Based Cloud Computing
### HDFS-Based Architecture of Video Resource Management
#### 3.2.2 The Web Service Clusters  
The web service cluster is a crucial part of the video resource service systems, as it is a direct interface for users to search, upload, and play videos. The web clusters consist of Apache and Tomcat, and load balancing is implemented by Apache, Tomcat and mod_jk. The advantages of the integration of Apache and Tomcat are as follows: (1) It improves the performance of the whole video resource service system. The dynamic web pages are processed by Tomcat, and the static web pages are managed by Apache; (2) The integration of Apache and Tomcat can realize better load balancing of video resource service systems, where the clients get responses from clusters instead of traditional servers. For improving the performance of web servers, Apache as a proxy distributes client requests to every Tomcat, and the web server as a cluster service processes client requests; (3) Applications and fault tolerance are upgraded seamlessly. In the web server clusters environment, if a web server is suspended, another available one will instantly instead and continue its affairs. The web server with the newest version applications will automatically upgrade the one with the out-of-date in background, and client users do not feel the updating process. The functions of the web clusters include releasing, uploading, and playing videos online, etc. (1) The video-playing module provides video searching and online playing services. When users submit a playing request to the video service system, the video information is retrieved from MySQL database servers according to user requests and the results are returned; (2) The video uploading function is improved. Users upload videos to web servers and the system automatically stores the video attributes into MySQL database servers that can respond to manipulation requests for retrieving video information from web servers such as query, update, and delete. The videos are audited and stored in the HDFS clusters.
# Analysis of the Most Relevant Factors for Routing in Internet of Space Things Networks
## 2 Related Work
Then, we use it to provide a comparative analysis of the performance offered by proactive and reactive routing strategies in ad hoc IoST networking. We aim to provide a baseline analysis that can further be used by the research community in the development of novel routing protocols for ad hoc IoST networks.
###### Abstract
In U-OFDM, the values of positive and negative information are transmitted successively; therefore, there is no zero clipping distortion. Underwater optical OFDM has attracted much research attention recently since OFDM is a candidate technique to provide high-speed transmission. Some related work based on underwater optical OFDM has been proposed [11; 23; 24]. The researchers presented an experimental demonstration to show that the underwater OFDM can support up to a 15.36-Mbps data rate for transmitting 4 m when the bandwidth of the LED is 10 MHz [11]. Using a a two-stage injection-locked 405-nm blue light laser diode transmitter, a data rate can be achieved of several gigabits per second [24]. Since the light sources for optical wireless communications are nonlinear and power limited, both the zero and peak power clipping should be considered. In this paper, we compare, analyze, and discuss both the zero and peak power limit distortions for DCO-, ACO-, and U-OFDM in the applications of underwater optical wireless communication systems. The propagation distance, bit error rate (BER), and data throughput are used as evaluation criteria in this paper. The contributions of this paper are listed below:  
* A comparison of the state-of-the-art optical OFDM techniques for underwater optical wireless communication systems is discussed based on the propagation distance, BER, and data throughput. * Considering the turbulence fading underwater channel, the performances of ACO-, DCO-, and U-OFDM using the channels with different bandwidths are tested. * For different transmitted bit rates, the achievable propagation distances of DCO-, ACO-, and U-OFDM with the band-limited channel are discussed. * The underwater channel uncertainty caused by the pointing error is modeled and tested. The notations in this paper are summarized and shown in Table 1, and the rest of the paper is organized as follows. The research problem is described in Section 2. System models and performance analysis are described in Section 3. Section 4 considers the band-limited channel and discusses the system performance. In Section 5, numerical results are shown. Finally, conclusion and future work are discussed in Section 6.