esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Iterative Timing Recovery
The Loop1 is performed at first by our proposed  
\[\Lambda(x_{i}|\mathbf{r})=\log\frac{p(x_{i}=1|\mathbf{r})}{p(x_{i}=-1| \mathbf{r})}\mathbf{=}\frac{2y_{i}}{c_{ch}^{2}}, \tag{24}\]6 Block diagram of the whole timing recovery system by the proposed scheme via steepest descent of LDPC decoding metrics and the M&M timing track. iterative timing recovery via steepest descent of LDPC decoding metrics to obtain large scale timing phase and frequency offset estimations, which are used to compensate the error of the received signal \(y_{i}\) by Equation (31) stored in the buffer in \[\Lambda(x_{i}|\mathbf{r})=\log\frac{p(x_{i}=1|\mathbf{r})}{p(x_{i}=-1| \mathbf{r})}\mathbf{=}\frac{2y_{i}}{c_{ch}^{2}}, \tag{24}\]7 The main steps of the procedure are calculated by Equations (34)-(37) to obtain the final optimized goal denoted in Equation (26). So this loop mainly performs the timing capture, especially at low SNRs. After Loop1, the selector switches the working mode to Loop2. Then, random walk and residual timing errors are corrected by a M&M algorithm with the reference LDPC decoding feedback, which works as the timing track. According to the above iterative timing recovery scheme, the computational complexity can be analyzed as follows. Here, we just calculate the additional computations other than the compulsory LDPC decoding. It is assumed that \(L\) iterations of timing offset estimation and compensation in the length-\(K\) LDPC code are required for the satisfied symbol timing recovery. From Equations (34) and (35), there are \(K\) additions in the top equation and \(K\) additions and \(K\) multiplications in the bottom equation, which sum up as \(2K\) additions and \(K\) multiplications. The coefficients of \(C_{1}\) and \(C_{2}\) in Equations (34) and (35) are empirical parameters and they can be incorporated into the step size of \(\lambda_{1}\) and \(\lambda_{2}\) in Equations (36) and (37). So the calculation of them can be neglected. Then, there are two multiplications and two subtractions (equal to additions in computational complexity) in the update of timing offset estimates in Equations (36) and (37). The step size parameters are decided by the practical occasions, which can be neglected, when compared with the above calculation. Moreover, there are three multiplications and three additions in the update of \(y_{i}\) in Equation (31), plus \(1\) division (equal to multiplication) in the calculation of \(\Lambda_{i}\), i.e., \(L(c_{i})\), in Equation (6). Therefore, the total computational complexity, except from the necessary LDPC decoding, are approximate \((2K+5)L\) additions and \((K+6)L\) multiplications. By later simulations, since the required iterations of the timing recovery are limited, i.e., \(L\leq 6\), the total computational complexity is acceptable in practice.
## 2 Theory
### Line-Scanning Method  
In general, induction thermography is performed in a static mode where relative motion does not occur between the specimen and the IR camera. If the experiment is performed in a static mode, the shape of the coil is measured as it is in the 2D thermal image acquired with an IR camera, which may become an obstacle to defect detection. Therefore, to solve this problem, the device was designed so that movement occurs between the specimen and the IR camera. Figure 2 shows the principle of the line-scanning technique [31]. The specimen moves along the sliding guide rail, and the image is acquired by setting the scanning line in the FLIR software dedicated to the IR camera [32; 33].  
By applying LSM to induction thermography, it is possible to provide a uniform heat source to the specimen. In the case of lock-in infrared thermography (LIT) or pulsed  
Figure 1: The principle of induction thermography.  
Figure 2: The principle of the line-scanning method.  
infrared thermography (PT), which are widely used among active infrared thermography techniques, a lamp is used to provide a heat source. This provides a non-uniform heat source to the surface of the specimen, causing a lot of noise. For this reason, the LSM has the advantage of significantly improving the detectability by providing a uniform heat source and generating relatively little noise [34; 35].
# An Investigation into the Influence of Sample Height on the Consolidation Behaviour of Dredged Silt
## 2 Test Scheme
### Test Method  
A total of 10 bags were methodically assessed, with the mean values of their respective physical properties delineated in Table 1. To discern the effect of drainage distance (denoted by sample height) on test outcomes and the consolidation attributes of the soil samples,  
Figure 1: The sampled location.  
both high-pressure and standard consolidation tests were performed on samples of varying initial heights (as expounded in Table 2). These tests were carried out at room temperature, under conditions of double-sided drainage. Adhering to prevailing test standard specifications, the initial height for soil samples in the high-pressure consolidation test was set at 2 cm. Meanwhile, in the standard consolidation test, the samples had initial heights of 2 cm, 3 cm, and 4 cm, each possessing a cross-sectional area of 30 cm\({}^{2}\). Given the sample's intrinsic low initial strength, the first and second consolidation pressures were calibrated at 5 kPa and 12.5 kPa, respectively. Deformation measurements revealed values falling below the 0.005 mm/h threshold, which was designated as the stability criterion for each loading stage. Any subsequent loading was commenced only upon confirming that the sample exhibited stable deformation under the preceding load. In a bid to achieve comprehensive consolidation, the terminal load stage was maintained consistently, allowing the consolidation process to span the ensuing three days.
## 3 Nonspecific Effects of BCG  
One of the reasons why BCG is still used universally in middle and low income countries is because numerous studies indicate that similar to other live attenuated vaccines in use today, BCG have additional beneficial effects on the initially intended protection against TB [18; 19; 20]. Neonatal BCG is able to induce a strong Th1 cytokine response shown to enhance immune responses to other infant vaccines of the EPI [21]. In countries of low TB endemicity, BCG administration at birth has been related to reduction of childhood hospitalizations due to unrelated respiratory infections and sepsis [22; 23]. In addition, there is emerging evidence that BCG may induce nonspecific resistance (T- and B-cell dependent) to other pathogens [20], which should be taken into consideration for AMR TB.  
The non-specific beneficial effects ascribed to BCG have been attributed to the vaccine's ability to functionally and epigenetically reprogram innate immune cells, such as monocytes, macrophages, and NK (natural killer) cells, a process termed 'trained immunity' [24]. In human monocytes, BCG induced trained immunity has been attributed to the induction of metabolic pathways, which are regulated by epigenetic mechanisms at the level of chromatin organization [25]. In this context, future clinical trials could provide insight on the potential therapeutic role that modulation of these pathways may have during vaccination [26]. A recent large, multinational study conducted in sub-Saharan Africa suggests association of BCG vaccination with a reduced risk of malaria in children under the age of 5 years [27; 28]. If these results are corroborated, they would denote that timely BCG vaccination could aid the global efforts to decrease malaria burden, including resistant forms of the disease.  
To date, the preclinical and clinical down-selection process of new TB vaccine candidates has employed BCG as the reference gold standard comparator, because of its well-established safety profile [29; 30]. Today, WHO also encourages incorporation of the nonspecific beneficial effects ascribed to BCG's ability to induce trained immunity in the design and development of novel TB vaccine candidates, especially those intended for BCG-replacement.
# Proposal for the Evaluation of Eco-Efficient Concrete
## 4 Case Analysis
### Results of Eco-efficiency Assessment for Different Mixing Rates of Admixtures  
As shown in Table 9, eco-efficiency (KRW / year) was shown to increase at all concrete strengths as mixing rates of admixtures (GGBS) increased to 30% during concrete mix design. As representative examples, the eco-efficiency of 24 MPa and 40 MPa concretes was increased by 10%-23% in comparison to OPC when the mixing rates of admixtures increased from 10% to 30% [49].  
As shown in Figure 8, the cost of 24 MPa concrete dropped from 362,000 (KRW/m\({}^{3}\)) to 274,000 (KRW/m\({}^{3}\)) according to increased mixing rates of admixtures due to reduced environmental impacts and manufacturing costs.  
Figure 8: Analysis of eco-efficiency by admixture ratio (30%).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Strength (MPa)**} & \multicolumn{8}{c}{**Admixture Ratio**} \\ \cline{2-9}  & **0\%** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** \\ \hline
21 & 5157 & 5092 & 4636 & 4195 & 4503 & 3973 & 5739 \\
24 & 4066 & 3564 & 3407 & 3101 & 3324 & 2926 & 3450 \\
27 & 3874 & 3860 & 3527 & 3179 & 3398 & 2980 & 4272 \\
30 & 2801 & 2512 & 2401 & 2168 & 2322 & 2043 & 2938 \\
40 & 2014 & 1900 & 1792 & 1614 & 1724 & 1510 & 2162 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Eco-efficiency according to admixture ratio.  
Figure 7: Eco-efficiency according to increasing compressive strength.  
In addition, the service life of concrete based on carbonation was reduced from 89 (year/m\({}^{3}\)) to 79 (year/m\({}^{3}\)) according to increased mixing rates of admixtures. However, the rate of decrease in the service life was lower than the rate of decrease in the costs.  
When mixing rates of admixtures were increased to 40% and 60%, however, the eco-efficiency of concrete samples dropped due to the rapid reduction of service life to 74 (year/m\({}^{3}\)) and 44 (year/m\({}^{3}\)), respectively (Figure 9).  
This tendency was verified in 21 MPa, 27 MPa, 30 MPa, and 40 MPa concretes in addition to 24 MPa concrete.
## 3 The Acquisition Method Based on PIC
### Design of the Coherent Accumulation Module  
In this subsection, we discuss the coherent accumulation algorithm employed in this paper. Our objective is to accumulate the correlation outcomes of various symbols, thereby augmenting the correlation between the target signal and the local PRN code as proposed in [26].  
Figure 5: Single-stage PIC processing flow.  
Figure 6 illustrates the overall process of the coherent accumulation algorithm. Firstly, the received signal \(y_{in}\) is inputted into the parallel code phase acquisition module, which operates in frames. When processing a specific frame, the \(y_{in}\) is shifted after being passed through FFT to simulate sequences under different frequency offsets. The local PRN code is conjugated after being subjected to FFT, and the two sequences are multiplied together to obtain the correlation result. The results of \(m\) frames are stored for future processing. Then, through multiple instances of correlation and coarse Doppler compensation using FFT, the correlation outcomes from the \(m\) sets of acquisition results are accumulated utilizing FFT for processing. This accumulation process can be expressed as  
\[z(n,m,w)=\sum_{v=1}^{V}z^{\prime}(n,m,v)e^{-j\frac{2\pi}{W}zwV}. \tag{4}\]  
For low-complexity considerations, we choose the highest value in the outcomes as the output representing the correlation peak.  
It is worth noting that the quantity of symbols employed for accumulation impacts the ultimate correlation peaks. In low-SNR scenarios, we are inclined to employ the longest feasible sequence for accumulation, but this may result in a too-long processing time. Thus, the selection of the sequence length used for accumulation needs deliberate design.
## 4 Experiments
### Method Analysis Results and Discussion
#### 4.3.2 Scalability Analysis  
To evaluate the scalability of our approach, we conducted experiments comparing the parameter efficiency between the original backbone model and our PCC-enhanced version. We used SegRNN as the backbone model and varied its parameter scale through different hidden dimensions: {64, 128, 256, 512, 1024} for the original design and {16, 32, 64} for the PCC-enhanced design, both based on the same observation and prediction length of 96. According to the fitting curves of the results in Figure 5, the PCC-enhanced SegRNN has better accuracy with learnable parameters of less than about 1% of the original SegRNN's parameters. This indicates that our PCC helps the original forecasting models to more effectively capture crucial meteorological patterns and achieve higher accuracy with fewer parameters. This reduction in model size directly translates to lower computational demands and deployment costs on hardware devices.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Design**} & \multirow{2}{*}{**Operation**} & \multicolumn{5}{c}{**Forecasting Length**} \\ \cline{3-6}  & & **96** & **192** & **336** & **720** \\ \hline \multirow{2}{*}{MCC} & w/o & 0.149 & 0.193 & 0.241 & 0.302 \\  & FC-only & 0.145 & 0.190 & 0.239 & 0.301 \\ \hline \multirow{2}{*}{SC} & w/o & 0.146 & 0.196 & 0.255 & 0.332 \\  & FC-only & 0.148 & 0.192 & 0.245 & 0.312 \\ \hline \multirow{2}{*}{Diff-Rep} & w/o & 0.146 & 0.192 & 0.243 & 0.307 \\ \hline \multirow{2}{*}{PCC} & w/o & 0.162 & 0.208 & 0.264 & 0.340 \\  & Invert & 0.147 & 0.192 & 0.244 & 0.309 \\ \cline{1-1}  & ✓ & **0.144** & **0.189** & **0.238** & **0.299** \\ \hline \hline \end{tabular} Bold values indicate the best results.  
\end{table}
Table 5: Ablation results (MSE) of different PCC designs. The ablation experiments used SegRNN as the backbone model, with a look-back window of 96 and prediction horizon of 96. Here, ✓ denotes that the design is used and unchanged, w/o means that it is removed, and ‘Invert’ means that the order of the two modules is inverted. ‘FC-only’ means that the design was replaced with one fully connected layer, while ‘Diff-Rep’ denotes differential representation.
## 2 Methods
### Image Processing
#### 2.6.1 Noise Reduction  
Noise is defined as unwanted electrical or electromagnetic energy that degrades the quality of signals and data. In the case of a CMOS-based camera, noise complies with all temporal and systematic errors added to a recorded signal during image acquisition. Noise is introduced both by the sensor (e.g., non-uniform pixel responses) and the electronics (e.g., electrical interferences) that amplify the output signal of the sensor for digitization [27]. Due to a random component, it is impossible to calculate the precise proportion of sensor noise to sensor signal within an image. Prominent examples are periodic noise, checkered patterns and horizontal band noise caused by the progressive shutter of CMOS sensors [6].  
The dark offset subtraction technique is a statistical image based approach, which reduces the noise component of an image by subtracting a dark offset image. A dark offset image represents the average per-pixel noise and is generated by multiple repetitions in a completely darkened environment. The dark offset imagery was created for each sensor of the MCA in a darkened room with black painted walls. In addition, the MCA lenses were covered with black cardboard. For each of the 12 sensors, a total of 120 images at exposure level of 4 ms were taken to calculate the per-pixel average dark offset. The examples in Figure 5 show the dark offset images for bands 1 and 11 with strong periodic noise features in b471 (Figure 5a), a global checkered pattern in b899 (Figure 5b) and the overlapping horizontal progressive shutter band noise visible in both images.
## 2 Materials and Methods
### Terrain Classification Methodology  
In robot navigation, terrain recognition can essentially be supposed as surface texture recognition. Terrain recognition based on local features is the most popular because of its robustness to illumination and weather and high recognition rate.  
The terrain classification system proposed in this paper is depicted in Figure 2. The Kinect is installed on top of the robot to collect information on terrain (color, depth, and infrared images) and an obstacle detection module is established to detect obstacles in the front. If there are no obstacles, the information will be directly transmitted to the terrain classifier. Otherwise, the module will locate obstacles and identify their size. Meanwhile, a color image of the terrain is processed by the image infilling method to decrease the influence of obstacles on terrain identification. After classification, the confidence scores of each terrain are summarized into a pie chart. The analysis of the pie chart shows whether the terrain is mixed or not. If the terrain is mixed, the color image would be subjected to image segmentation and infilling. Then, the processed color image will be classified by the terrain classifier again, which provides an accurate identification of multiple areas of complex terrain. Finally, all terrain types can be predicted accurately, thus good performance by the robot is guaranteed. The function modules are described in detail in the following section.
# Data Element Sharing in Convergence Media Ecology Based on Evolutionary Game
## 1 Introduction
Evolutionary game theory is a theoretical framework to describe the interaction between biological populations or social groups in competition or cooperation. Evolutionary game theory combines individual behavior with group dynamics, considers the influence of factors such as natural selection and genetic drift on evolution, and can better explain the evolutionary behavior of biological and social systems, which we usually use to describe the interaction between individuals [34; 35]. Commonly used concepts in evolutionary game theory include evolutionary stable strategy, replicator dynamics, etc. Evolutionary stable strategy (ESS) is a core concept of evolutionary game theory, and it is a strategy that natural selection tends to maintain; even in the face of all other possible evolutionary strategies, this strategy can still maintain its dominant position [36]. Replicator dynamics is a mathematical model that describes how the proportion of different strategies in a population changes over time, where strategies grow at a rate proportional to their advantage over average fitness, and the replication dynamic equation in an evolutionary game refers to the simple imitation of the dominant strategy by the finite rational group players, which is expressed by dynamic differential equation or differential equation systems [35; 37]. In order to solve the problems faced by data element sharing in the convergence media ecology, such as insufficient ecological activity, inadequate implementation of the reward mechanism and imperfect data element circulation mechanism, this paper attempts to study the introduction of evolutionary game into the ecological node to promote data element sharing according to the characteristics of the nodes in the convergence media ecology, and we established a data element sharing model based on the evolutionary game to provide targeted suggestions for promoting data element sharing in convergence media ecology.
## 3 Test Result
### Natural Frequencies of the Model Soil and Model Structure  
The time histories of the flat noise tests at the model soil and deck were obtained from the acceleration sensors of AX6 and AX1, respectively. Then the relevant natural frequencies of the model soil and mode structure were obtained from Fourier transform. The first-order natural frequency and change law are listed in Table 3. It can be seen that frequency variation of model soil was more obvious than that of model structure, and the actual characteristics of the model soil-pile-structure system varied in each experimental stage. After the shaking table tests, the frequency of model soil and model structure decreased by 30.8% and 7.1%, respectively. The frequency variation was consistent with the experimental phenomena.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test Case**} & \multicolumn{2}{c}{**Model Soil**} & \multicolumn{2}{c}{**Model Structure**} \\ \cline{2-5}  & **Fundamental Frequency (Hz)** & **Frequency Variation (\%)** & **Fundamental Frequency (Hz)** & **Frequency Variation (\%)** \\ \hline \(1\) & 11.11 & 0 & 4.68 & 0 \\ \(8\) & 8.33 & 25 & 4.45 & 4.9 \\ \(15\) & 8.33 & 25 & 4.43 & 5.3 \\ \(22\) & 7.69 & 30.8 & 4.35 & 7.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: First-order natural frequency of the model soil and model structure.  
Figure 8: Experimental phenomena: (**a**) before tests; (**b**) after test case No.21.
## 3 Case Study
### Model Application to Multi-Energy Systems  
This section shows the application of the 0D model for the achievement of the best operation in a multi-energy system context, as schematized in Figure 4. The multi-energy system is conceived to supply heat, electricity, and cold to a block of flats with 15 dwellings. The production system is characterized by a group of several technologies. In this context, the adoption of a thermal storage provides significant benefits to improve the system flexibility. In particular, an LHTS can be extremely advantageous in buildings, since it requires small installation volumes. In order to easily model the time-dependent behavior of the LHTS, the 0D model is adopted.  
The most convenient technologies to be operated are selected by a Mixed Integer Non-Linear Programming optimization tool that includes the 0D LHTS model. The optimization algorithm allows achieving the best operation for the multi-energy system. Therefore, its output is the optimal time evolution of (a) the power from the production/conversion technologies; (b) the power purchase from the grid; and (c) the released/absorbed power in the storages.  
The optimization model includes constraints such as (a) the overall produced and purchased energy vectors must be equal to the loads and (b) the energy production of each technology must not exceed its maximum capacity (the same for the conversion technologies and the storages). Moreover, additional constraints are set to model the LHTS. The maximum thermal power that can be absorbed/released by the LHTS is imposed at each time step in relation to the outcome of the LHTS 0D model.  
Concerning the energy production/conversion, both traditional and innovative technologies are considered to be installed. Among the traditional technologies that can be adopted are a heat-only  
Figure 3: LHTS charge (**a**) and discharge (**b**) schedule for District Heating (DH) case study.  
boiler (HOB) for heat production, an electric chiller (EHP) for cold production, and the grid connection for the electricity/heat supply. Among the innovative technologies are a micro-cogeneration system (for combined heat and power production) fed by natural gas, an absorption chiller, fuel cells, photovoltaics (PV), thermal solar, and wind turbine. Furthermore, a series of storages (hot, cold, and electric) provide a significant level of flexibility for the multi-energy system.