esa-sceva/satcom-chunk-collection huggingface dataset:
# Displacement Monitoring of Subway Tracks and Tunnels According to Adjacent Construction
## 3 Displacement Monitoring of Track Structures
### Gaussian Analysis Results  
Figure 9 shows the Gaussian analysis results using track bed settlement sensors (RM-01, RM-11, RM-21, RM-31, RM-41, and RM-52) and monitoring data obtained 1, 10, and 18 months after the adjacent excavation work.  
The Gaussian analysis results of the track bed settlement sensor, shown in Figure 9, revealed that the vertical displacements measured by sensors RM-01, RM-11, RM-21, and RM-31 decreased as the adjacent excavation work progressed. However, for RM-41 and RM-52, the vertical displacement increased owing to the adjacent excavation work. The average vertical displacement measured by sensor RM-41 increased by approximately 0.513 mm 18 months after construction compared with that in the initial stage of the adjacent construction. Table 4 lists the safety evaluation results for the subway track structures. The Seoul Metropolitan Rapid Transit Corporation track maintenance standards were applied to the field maintenance criteria of the track bed settlement sensor [14; 16; 17; 18; 19; 20; 21; 22].  
Figure 8: Track displacement measurements (sensor RM-41).  
Figure 9: Example of gaussian analysis results (1, 10, and 18 months): (**a**) RM-01; (**b**) RM-11; (**c**) RM-21; (**d**) RM-31; (**e**) RM-41; (**f**) RM-52.  
Based on the subway track structure safety evaluation listed in Table 4, the maximum value considering the standard deviation of sensor RM-41 measurements was approximately 0.684 mm. The management standard for subway track structures is 3.6 mm. The vertical displacement at the location of sensor RM-41 was approximately 19% of the management standard. By contrast, the vertical displacements at the locations of sensors RM-01, RM-11, RM-21, and RM-31 were close to 0.
## 4 Path Model Estimation
### IPSO-Based Path Estimation
\[Q_{\rm td}(u+1)=Q_{\rm td}(u)+V_{\rm td}(u+1) \tag{21}\]  
If \(Q_{\rm td}(u+1)\) exceeds the position range [\(Q_{\rm min}\), \(Q_{\rm max}\)], adjust it to the nearest position boundary value. Then, the number of the evolutionary generations is increased by one, i.e., \(u=u+1\). Step 6: Updating the particle swarm  
The fitness value \(f_{t}(u)\) of the \(t^{\rm th}\) particle \(q_{t}(u)\) in the \(u^{\rm th}\) generation particle swarm is calculated by means of Equation (16). If the fitness value of the \(t^{\rm th}\) particle is better than that of its individual extremum, i.e., \(f_{t}(u)\)\(<\)\(f_{t}^{s}\), update the individual extremum and the fitness value of the \(t^{\rm th}\) particle, \(\mathbf{P}_{t}\)\(=\)\(q_{t}(u)\) and \(f_{t}^{s}=f_{t}(u)\). If the fitness value of the \(t^{\rm th}\) particle is better than that of its global extremum, i.e., \(f_{t}(u)\)\(<\)\(f^{s}\), update the global extremum and its fitness value, \(\mathbf{P}_{g}=\mathbf{q}_{t}(u)\) and \(f^{s}=f_{t}(u)\). Step 7: Identifying the noise points  
Let the fitness threshold be \(\Delta f\). If the fitness value \(f_{t}(u)\) of the \(t^{\rm th}\) particle is greater than \(\Delta f\), the particle is identified as a noise point. Step 8: Iterating the particle swarm  
If the number of evolution generations \(u\)\(>\)\(N_{\rm IterMax}\), stop evolving the particle swarm. The current global extremum \(\mathbf{P}_{g}\) is given as the parameter output for the straight path model. Otherwise, go back to Step 5 to continue the evolution process of the particle swarm. The IPSO-based path parameter estimation method is then utilized to further eliminate the influence of under- and over-segmentation errors occurring in the threshold segmentation stage. As shown in Figure 3b,c, the images of a straight path are exposed in the highlight or covered by some wastes. When the Otsu threshold segmentation method is used to extract the possible path pixels from these two original images, the under- and over-segmentation errors result in omitting target pixels or producing fake pixels, as shown in Figure 6b,c. Although the target paths in Figure 6b,c are incomplete and interfered by noise points, the IPSO method is able to distinguish the target points from the noise points, thereby estimating the genuine path correctly, as shown in Figure 10a,b. It is obvious that the IPSO-based path parameters estimation method is not sensitive to the noise points produced by the under- and over-segmentation errors in the threshold segmentation stage. The number of noise points and target points are listed in Table 2, which shows the high recognition accuracy for path pixels in the process of parameter estimation, discriminating between target points and noise ones. Hence, the straight path can be recognized precisely by the vision guidance system for AGVs in a complex workspace.
# The SF12 Well in LoRaWAN: Problem and End-Device-Based Solutions
## 5 End-Device-Based Solutions
### Proposed Solutions  
As explained in Section 2.3, LoRaWAN devices typically increase the SF after two consecutive ACKs not received. This behavior will henceforth be referred to as SF Mode 0 (SFM0). As alternatives, we introduce three new SF parameter management techniques, namely SF Mode 1 (SFM1), SF Mode 2 (SFM2), and SF Mode 3 (SFM3). They are defined as follows:  
1. SFM1: SF7, which corresponds to the greatest DR value, is always used. No SF change is conducted even if the data or the ACK frames are lost.
2. SFM2: this technique adds an extra step to SFM0. When the SF reaches the SF12 value, if the corresponding ACK is not received after two transmission opportunities, the SF is reset to SF7. The rationale for this approach is that after unsuccessful transmission using SF12 it may be better to switch to SF7 as congestion might be the reason for the frame losses. Hence, SFM2 leads to a cyclic use of all the SF values.
3. SFM3: the basis for this technique is also SFM0. However, with this technique if an ACK is received, the ED will decrease its SF value. Hence, this option brings the opportunity to increase the DR, with an expectation to reduce the frame ToA and thus reduce network congestion.  
Note that we consider SFM1 as a benchmark for our simulation scenarios as it minimizes the frame ToA. Obviously, it cannot be considered a general solution (i.e., for any type of LoRaWAN network) because its performance will be severely affected for high numbers of CEDs, due to collisions, or if the radio link quality is not good enough.
# Revising the Observation Satellite Scheduling Problem Based on Deep Reinforcement Learning
## 3 Solving Method
### Task Preprocess: Graph Clustering
#### 3.1.1 Graph Model Establishment  
The establishment of a graph-based clustering model involves two steps, establishing the adjacency matrix and updating the model, as described below:  
(1) Establish the adjacency matrix  
All tasks in \(V(G_{i})\) are traversed and whether two original tasks \(t_{u}\) and \(t_{v}\) satisfy the time window constraint is checked. If the time window constraint is satisfied, \(A_{uv}=1\), otherwise \(A_{uv}=0\). After the iteration, the edge \((v_{v},v_{u})\) is generated, and the initial graph model \(G_{0}\) is gained.  
(2) Update graph model by checking other constraints  
According to the initial graph \(G_{0}\) built by satisfying the time window constraint, the adjacency matrix elements \(A_{uv}(u,v=1,2,\cdots,n)\) are searched. If \(A_{uv}\
eq 0\), constraints of the observation time window and observation slewing angle are checked sequentially.  
Once a constraint condition is not satisfied, \(A_{uv}=0\). Finally, the clustering graph \(G\) and the adjacency matrix \(A_{n\times n}\) are obtained.
# Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images
## 6 Conclusions  
A novel method is proposed for denoising night-light images of cloudy areas. It was used to denoise annual night-light image data from Cambodia that were synthesised by three methods. The \(RE\) value of the annual luminous image synthesized by the month-specific substitution method and processed by this denoising method was 14.83%, which is considered a relatively good denoising effect. The total night-light values were then related to electricity consumption data using several functional models. The results show that the best regression model was the exponential model. The three methods of the annual image composition in Cambodia are ranked in the order of merit of annual average method \(<\) annual cloud-free average method \(<\) month-specific substitution method. The use of the exponential model to forecast electricity consumption and its spatial patterns in Cambodia showed that the model performed well, with an average \(RE\) of 5.47% for consumption forecasts. A simulation of the spatial distribution of electricity consumption showed that from 2012 to 2019, Cambodia's electricity consumption spread outwards along main transport routes from the four major cities to a large number of rural areas. Many unelectrified areas were connected to electricity and the four most economically developed cities in the country.  
This study shows that night-light images in cloudy areas can still be used to simulate electricity consumption and its spatial distribution with high accuracy if denoising is applied. The results of this study will contribute to the planning of electricity infrastructure development in Cambodia and the measurement of the achievement of SDG7.  
Methodology, X.G. and M.W.; formal analysis, X.G.; data curation, J.G. and L.H.; writing--original draft preparation, X.G., J.G. and M.W.; writing--review and editing, X.G., L.H. and M.W.; supervision, Z.N. and F.C.; project administration, M.W. and Z.N.; funding acquisition, Z.N. and F.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Strategic Priority Research Program of the Chinese Academy of Science, grant number XDA19030304.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflict of interest.
# Quantitative Scale Analysis of the Channel Bar in a Braided River and Its Internal Architecture
## 3 Results  
### Braided Flow Zone Sedimentation and Quantitative Scale Analysis  
The coastal beach of the braided flow zone widely extends to the sea. The reservoir sand body in the single braided flow zone is mainly composed of braided river channel and channel bar (Figure 2). Channel bar is the major microfacies type in the sedimentary reservoir of braided river, whose development is controlled by the scale of the river and the waves. In the early stage, the braided river section is narrow, the water is deep and rapid, and the channel bar is more complete and stable in the upper part of the braided river zone. Influenced by water energy, the sediment source supply, and the erosion and trans-formation of waterways, the number of branch channels increases but their scale becomes smaller. As a result, the development of the channel bar is unstable in the lower part of the braid zone, and some of the channel bars are cut by ditches, forming a number of fractured and small sand dams. The single channel bar is mainly in ellipsoidal shape and laterally distributed along the direction of provenance to the downstream. The length of a single channel bar is 38.8-62.2 cm. The width is 9.9-29.9 cm. The lithology is dominated by fine-grained sandstone sediment with pure sand, and has moderate thickness. Affected by the erosion of the braided river, the continuity between two channel bars is poor. However, the continuity between the channel bar and the braided channel is quite good. They cut each other and move continuously. Channel bars on the shoal with different slopes can be independent or connected to each other, constituting a set of sedimentary water system in the braided river. The internal of the channel bar has a complex structure with low slurry content. On the plane, the degree of connectivity is high, resulting in a multi-level and multi-layer structure distribution on the plane [32; 33]. The sand body of the braided waterway has developed numerous inner architecture hierarchical interfaces with various forms. Braided channel sediments are mainly filled with sand. The core of the river is mainly fine sandstone, with few argillaceous sediments. The channel reconstruction is strong. The curvature coefficient of the braided channel is about 1.4. The average length and width of a single channel bar in the braided river are 45 cm and 6.3 cm, respectively.  
Figure 2: Scale measurement of composite braided channel, single braided channel, and single channel bar under the action of tides.
# BICM-ID with Physical Layer Network Coding in TWR Free Space Optical Communication Links +
## 3 ExIT Charts Analysis  
The ExIT charts have become an essential part of iterative error correction code design and have also been used as a complementary design tool for traditional BER simulations. The method of bitwise mutual information was implemented as a tool for studying the convergence behaviour of iterative decoding schemes. The ExIT chart technique relies on the Gaussian approximation [26], but provides some intuition regarding the dynamics and convergence properties of an iteratively decoded code. The formulation of a turbo-cliff after a certain number of iterations enables us to predict a low BER for the ratio between the energy per information bit (\(E_{b}\)) and the variance of noise (\(N_{0}\)), \(E_{b}/N_{0}\), in excess of the turbo-cliff. Therefore, the ExIT characteristics of the constituent decoders describe the processing of mutual information, which measures the dependence between two variables, associated with a priori information inputs to mutual information associated with the extrinsic information outputs. The mutual information \(I\) between the two variables can be expressed from [27] as:  
\[I(X;L)=\frac{1}{2}\sum_{x\in\{0,1\}}\int_{-\infty}^{+\infty}Pr(\xi|x)\log_{2} \biggl{(}\frac{2 Pr(\xi|x)}{Pr(\xi|x=1)+Pr(\xi|x=0)}\biggr{)}d\xi, \tag{16}\]  
where \(X\) is the information of finite size being fed through the input and \(\mathbf{L}\) is the information produced in terms of the LLR with realization \(\xi\). However, the property of symmetry is valid for any linear code decoded by using a posteriori probability (APP)-based decoders [28]. Likewise, the property of consistency is satisfied with the accurate computation of the LLR output [29]. Consequently, the properties of symmetry and consistency integration are closely approximated by using a time average to define the mutual information based on the demapper or the decoder component as:  
\[I(X;L)\approx 1-\frac{1}{N}\sum_{n=1}^{N}\log_{2}\biggl{(}1+e^{-\left(L_{n}[x _{n}]\right)}\biggr{)}, \tag{17}\]where \(N\) is the total number of observed \(L_{n}\) of LLRs. Additionally, the mutual information from the QPSK received decision symbol \(\mathbf{L}\) can be calculated from [30] as:  
\[I(X,L)=\frac{1}{M}\sum_{i=0}^{M-1}\mathbb{E}_{\xi|x_{i}}\bigg{[}\log_{2}\bigg{(} \frac{M}{1+e^{\Lambda_{x_{i}}[L]}}\bigg{)}\bigg{]}, \tag{18}\]  
where \(M\) states the bit per mapped codeword and \(\mathbb{E}\) means expectation over \(\{X,L\}\). Assuming equal transmission probabilities for all modulation symbols, the LLR \(\Lambda\) at \(M\) states of modulation symbols can be determined as:  
\[\Lambda_{x_{i}}[L] =\ln\bigg{(}\frac{Pr(x_{i}|L)}{\sum_{j=1;j\
eq i}^{M}Pr(x_{j}|L) }\bigg{)} \tag{19a}\] \[=\ln\bigg{(}\frac{e^{-\ell_{i}/\sigma^{2}}}{\sum_{j=1;j\
eq i}^{M }e^{-\ell_{j}/\sigma^{2}}}\bigg{)}. \tag{19b}\]  
where \(\ell\) is the distance from receiving variable \(L\) to the symbol transmitted \(X\).
## 3 Price Predictions Using Deep Learning Models
### Time-Series Forecasting Models
#### 3.2.3 LSTM (Long Short-Term Memory) Networks  
LSTM networks augment the standard RNN architecture with specialized memory cells, alongside the traditional hidden state units. These memory cells possess the intriguing ability to selectively remember or forget information over extended periods. This selective control is achieved through three crucial gating mechanisms [41]:  
* Forget gate: This gate, activated by the current input and the previous hidden state, determines which information from the previous cell state becomes irrelevant and should be discarded. It learns a multiplicative factor between 0 and 1 for each cell element, essentially forgetting information deemed unimportant for future predictions.
* Input gate: While the forget gate decides what to discard, the input gate controls the flow of new information from the current input vector into the cell state. It also utilizes the previous hidden state to modulate the importance of new information. This gated mechanism ensures that only relevant new information updates the cell state.
* Output gate: Finally, the output gate determines what information from the current cell state, should be exposed as the output. It applies a sigmoid activation function on both the current cell state and the previous hidden state, generating a vector with values between 0 and 1, effectively controlling the contribution of cell state information to the overall output.
# Enhanced BLIP-2 Optimization Using LoRA for Generating Dashcam Captions
## 4 Experiment
This suggests that a lower dropout rate enhances the model's ability to generate fluent and semantically accurate captions while maintaining training stability. Based on these results, we selected 0.02 as the final dropout setting in our optimization strategy. For the training setup, the parameters were carefully chosen to optimize the model performance. The learning rate was set to \(4e^{-4}\), and the weight decay was set to \(e^{-2}\) to prevent overfitting [25]. Both the training and testing phases were conducted with a batch size of 64. LoRA was configured with specific settings to enhance model responsiveness and efficiency: \(r=8\) and \(a=16\), dropout was maintained at 0.02, and the bias was set to \"none\". These settings were intended to ensure that the model operated efficiently and the model exploited low-rank matrices to enhance its learning capability. In terms of parameter efficiency, the total number of trainable parameters in our model was 3,831,808, while the entire model consisted of 3,748,511,744 parameters. This corresponds to a trainable parameter ratio of 0.1022, demonstrating that only a small fraction of the total parameters were updated during training. By employing LoRA-based optimization, we achieved parameter-efficient fine-tuning, significantly reducing computational overhead while preserving the pre-trained knowledge of the model. This further supports  
Figure 5: Comparison of CIDEr and SPICE scores across different models. Models that were fine-tuned using LoRA showed significant improvements in the semantic quality and contextual richness, with higher values of both CIDEr and SPICE scores. the effectiveness of domain-adaptive fine-tuning, as demonstrated by the improvements in CIDEr and SPICE scores. I conducted the computational experiments using a cloud server equipped with an NVIDIA A100 GPU with 40 GB of VRAM. This setup ensured that the large-scale dataset was processed efficiently, maximizing the throughput and minimizing training durations. The reduced computational demand resulting from LoRA-based fine-tuning further contributed to faster training cycles, making the method highly scalable for real-world autonomous driving applications.
## 4 Proposed Sensing Results Separation
### Sensing Results Separation without Duplication of Sensing Results  
The protocol of data separation is composed of three parts, the estimation of CSI and CFO, the construction of ICI replica and the iterative ICI cancellation, and the data tracking with sensing results, CSI, and CFO.
# Polyisocyanurate Foam Pyrolysis and Flame Spread Modeling
## 2 Materials and Methods
### Modeling
#### 2.4.2 Modeling of Gram-Scale Experiments  
Modeling of the CAPA II tests was performed using a one-dimensional version of ThermaKin2 Ds because the experimental results indicated that both sample thickness and temperature remained radially uniform throughout the experiments. The model was set up to accurately capture convective and radiative boundary conditions of the CAPA II tests, which were carefully characterized in an earlier study [27; 36]. The front sample surface was modeled to have no resistance to gas flow. The back surface, covered with copper foil, was set to be impenetrable to gas flow.  
The densities of the condensed-phase decomposition products of the PIR foam were prescribed to capture the changes in the sample thickness observed in the 70 kW m\({}^{-2}\) tests. The thermal conductivities of the foam and products of its decomposition were assumed to be independent of temperature, to minimize the number of adjustable parameters, and were optimized to capture the experimental back sample surface temperature profile obtained at 70 kW m\({}^{-2}\). As in the case of the TGA data, the optimization was carried out using a hill climbing algorithm. Details of this algorithm implementation can be found in an earlier publication [30]. The results of the 70 kW m\({}^{-2}\) CAPA II tests, rather than the results of the 40 kW m\({}^{-2}\) tests, were used as an optimization target because only in the 70 kW m\({}^{-2}\) tests the PIR foam samples underwent a complete thermal decomposition.  
The cone calorimetry experiments were also simulated using a one-dimensional version of ThermaKin2 Ds. The front sample surface boundary conditions included the incident radiative heat flux from the cone heater (50 kW m\({}^{-2}\)) and convective losses before ignition and a combination of the heater radiative heat flux and convective and radiative heat fluxes from the flame established over the sample surface after ignition. The ignition was assumed to take place once the gaseous fuel mass flux reached the magnitude corresponding to a critical value of HRR of 21 kW m\({}^{-2}\)[37]. The radiative losses from the sample surface were also accounted for throughout the simulation. A complete set of parameters describing this cone-calorimetry-specific flame heat feedback model can be found in an earlier publication [38], where this model was developed and validated. The back surface of the sample, covered with aluminum foil, was set to be impenetrable to gas flow. The heat transfer through Kaowool PM insulation located underneath the foil was modeled explicitly using the material properties provided by the manufacturer [39].  
All simulations were performed with 5 \(\times\) 10\({}^{-5}\) m spatial resolution and 0.01 s time step. Increasing or reducing these integration parameters by a factor of two did not produce any significant changes in the simulation results, indicating convergence.
## 4 Experimental Results and Discussion
### Imperceptibility Test
From the above discussion, it can be inferred that the proposed scheme has high imperceptibility for grayscale as well as color images of different modalities. Further imperceptibility improves by using optimized scaling factor.