esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Ice Melting of Bridge Tower Crossbeams under Different Conditions
This method transmits heat to the surface of the object through thermal conduction within the structural layer, and then melts snow and ice through the exchange of sensible and latent heat between the surface of the object and the snow and ice [18; 19]. It is theoretically feasible to consider the electric heating method to increase the temperature of the beam structure to achieve a melting effect on snow and ice. Researchers have conducted a series of studies on the application of carbon fiber heating lines in snow and ice melting under different conditions. [PERSON] et al. [20] determined that carbon fiber heating lines can effectively prevent bridge deck icing through temperature rise tests on bridge decks and found that optimized layouts can improve the efficiency and overall uniformity of bridge deck ice melting. [PERSON] and [PERSON] et al. [21; 22] conducted a temperature rise test on a bridge deck. It was concluded that the temperature difference between different locations was within 3 \({}^{\circ}\)C, with good temperature uniformity, when the spacing of carbon fiber heating lines was 10 cm. [PERSON] et al. [23] embedded three different types of carbon fibers into concrete road samples. The carbon fiber electric heating method can effectively solve the snow and ice problem, which not only has the effect of energy saving and environmental protection, but also can be used for a long period of time at low cost and with fast results. [PERSON] et al. [24] embedded carbon fiber heating lines in concrete pavement, which yielded excellent field test results for concrete slabs with appropriate input power and snow accumulation on slabs with thicknesses of 10, 40, and 70 mm at 0.75, 2.5, and 3.5 h, respectively. The burial depth and arrangement density of carbon fiber heating lines [25] affect the heat transfer from the inside, whereas environmental factors such as ambient temperature and wind affect it from the outside [26; 27]. Therefore, the influence of internal and external factors on the heat transfer of the carbon fiber heating line should be fully considered, and it is necessary to systematically analyze the electric heating method for melting snow and ice with respect to the structural characteristics of the bridge tower beams and the external environmental factors. In order to obtain the internal and surface temperature field distribution of the bridge tower crossbeam under the built-in carbon fiber heating condition, the bridge tower crossbeam model test was carried out at a constant temperature in a large test chamber under different ambient temperature and wind speed conditions. The aim was to study the influence of ambient temperature and wind speed on the internal and surface temperature fields of the bridge tower beam model, to verify the feasibility of the electric heating technology applied to the bridge tower beam for melting snow and ice, and to provide a reliable reference for the actual engineering application.
## 2 Materials and Methods  
### RPC Model  
The RPC model creates a linkage using rational polynomial expressions that map the coordinates of points in an image to their geodetic equivalents on the ground.  
\[\begin{array}{l}R_{n}=\frac{N_{1}(B_{n},L_{n},H_{n})}{N_{2}(B_{n},L_{n},H_{ n})}\\ C_{n}=\frac{N_{2}(B_{n},L_{n},H_{n})}{N_{4}(B_{n},L_{n},H_{n})}\end{array} \tag{1}\]  
In the above formulas, \((B_{n},L_{n},H_{n})\) and \((R_{n},C_{n})\) are the normalized ground coordinates and image coordinates, and \(N_{i}(i=1,2,3,4)\) are the polynomials.  
\[N(B_{n},L_{n},H_{n})=\sum_{i=0}^{m_{1}}\sum_{j=0}^{m_{2}}\sum_{k=0}^{m_{3}}a_{ ijk}L_{n}^{i}B_{n}^{j}H_{n}^{k} \tag{2}\]  
In the above formulas, \(a_{ijk}\) is the polynomial coefficient; \(i,j,k\) are the degrees of the polynomial; and \(m_{1},m_{2},m_{3}\) are the highest degrees of \(i,j,k\).  
To improve the stability of parameter estimation, it is a standard approach to scale both ground coordinates and image coordinates to fall within a range from \(-1\) to \(1\). The formula is as follows:  
\[B_{n}=\frac{B-B_{off}}{B_{scale}},L_{n}=\frac{L-L_{off}}{L_{scale}},H_{n}=\frac {H-H_{off}}{H_{scale}} \tag{3}\]  
\[R_{n}=\frac{R-R_{off}}{R_{scale}},C_{n}=\frac{C-C_{off}}{C_{scale}} \tag{4}\]  
where \((B,L,H)\) represent the latitude, longitude, and elevation in the geodetic coordinate system; \((R,C)\) denote the sample and line coordinates in the image coordinate system; \(B_{off},L_{off},H_{off}\) and \(R_{off},C_{off}\) are the translation parameters for the geodetic coordinates and the image coordinates; while \(B_{scale},L_{scale},H_{scale}\) and \(R_{scale},C_{scale}\) represent the scaling parameters for the terrestrial coordinates and image coordinates.
## 2 Design and Experimental Setup
### Optimized, Subject-Wise Cross-Validated CNN Model
Following are the definitions and formulas: Classification accuracy is defined as the ratio of the number of correctly classified cases and is equal to the sum of _TP_ (true positive) and _TN_ (true negative) divided by the total number of cases \(N\), where _N = TP + TN + FP + FN_, as given in the equation as Equation (1). The precision, recall and F1 score equations are given as Equations (2)-(4). \[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]\[\text{Precision}=\frac{TP}{TP+FP} \tag{2}\] \[\text{Recall}=\frac{TP}{TP+FN}\] (3) \[\text{F1 score}=\frac{2}{Recall^{-1}+Precision^{-1}} \tag{4}\]  
Figure 5: The process of building a CNN-based machine learning model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sl. No** & **Hyperparameters** & **Values** \\ \hline
1. & Activation Function & ReLU \\
2. & Learning rate & 0.00001 \\
3. & Batch size & 32 \\
4. & Epochs & 10-20 \\
5. & Optimizer Algorithm & Adam \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Optimized CNN hyperparameter setting. Figure 6: Layers of optimized CNN model. The receiver operating characteristic (ROC) curve also proves useful for evaluating machine learning techniques. A ROC curve is a graph showing the performance of a classification model at all classification thresholds. This curve plots two parameters, true positive rate, and false positive rate. The area under receiver operating characteristics (AUROC) tells how much the model can distinguish between classes. The higher the AUC, the better the model predicts 0 classes as 0 and 1 classes as 1. Thus, ML models built on different datasets can be compared based on the AUROC and F1 scores. The best-performing model is loaded on a platform with a high processing speed and portability. NVIDIA Jetson's graphics processing unit (GPU) with quad-core ARM processor offers high performance and the ability to handle large datasets [25]. Therefore, the optimized CNN model with the best training accuracy is validated using the NVIDIA Jetson's board. Figure 7 displays an NVIDIA Jetson Nano board, which has a portable size of 45 \(\times\) 69 mm\({}^{2}\), a graphical processing unit (GPU), and memory of 4 GB.
_Algorithms_**2015**, \(8\), 3-18; doi:10.3390/a8010003  
_Article_  
Networked Control System Time-Delay Compensation Based on Time-Delay Prediction and Improved Implicit GPC  
**[PERSON] *, [PERSON], [PERSON] and [PERSON]**  
College of Information Science and Engineering, Shenyang University of Technology,  
Shenyang 110870, China; E-Mails: [EMAIL_ADDRESS] (S.-J.L.); [EMAIL_ADDRESS] (Y.-H.W.); [EMAIL_ADDRESS] (H.-X.Y.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +8624-2549-6349; Fax: +8624-2549-6380.  
Academic Editor: [PERSON]  
_Received: 18 November 2014 / Accepted: 31 December 2014 / Published: 19 January 2015_  
**Abstract:** The random time delay in a networked control system can usually deteriorate the control performance and stability of the networked control system. In order to solve this problem, this paper puts forward a networked control system random time-delay compensation method based on time-delay prediction and improved implicit generalized predictive control (GPC). The least squares support vector machine is used to predict the future time delay of network. The parameters of the least squares support vector machine time-delay prediction model are difficult to determine, and the genetic algorithm is used for least squares support vector machine optimal prediction parameter optimization. Then, an improved implicit generalized predictive control method is adopted to compensate for the time delay. The simulation results show that the method in this paper has high prediction accuracy and a good compensation effect for the random time delay of the networked control system, has a small amount of on-line calculation and that the output response and control stability of the system are improved.  
**Keywords:** networked control system; time delay; prediction; least squares support vector machine; improved implicit GPC
# RNN-Based Sequence to Sequence Decoder for Run-Length Limited Codes in Visible Light Communication
## 3 Learning to RLL Decode
### Seq2 seq Decoder
* **Loss and Update:** Here, the mean square error (MSE) loss function is utilized to measure the uncertainty between the actual outputs \(\tilde{x}\) and the desired outputs \(\mathbf{x}\), such that \[\mathcal{L}(\mathbf{x},\tilde{\mathbf{x}})=\frac{1}{K}\sum_{i=1}^{K}(x_{i}-\tilde{x}_ {i})^{2},\] (6) where \(\tilde{x}_{i}\) is the \(i\)-th output of decoder. * **Training procedure:** To obtain the optimal parameter \(\mathbf{\theta}^{*}\) of the seq2 seq decoder, a batch of the training dataset (i.e., a mini-batch) is randomly selected and fed into the decoder. As such, the loss function is computed using the outputs of decoder and the labels corresponding to the inputs. Later, the adaptive moment estimation (Adam) algorithm is executed to optimize the parameters of the NN. After repeated iterations with mini-batches, the parameters of NN converge and approach optimum values. Figure 2: The seq2 seq model for RLL decoding. (2) _Testing Phase_: In this phase, the received signal \(\mathbf{y}\) is first input to the seq2 seq decoder. Then, the decoder outputs the soft estimates \(\tilde{\mathbf{x}}\) of transmitted data \(\mathbf{x}\). Finally, obtain the final RLL decoded sequence based on rule (2). As a final reminder, since the NN can efficiently execute in parallel and be implemented with low-precision data types on a graphical processing unit (GPU), field programmable gate array (FPGA), or application-specific integrated circuit (ASIC) [9], the well-trained decoder can be integrated into UAVs to decode RLL codes. A more detailed framework diagram of the proposed seq2 seq-based decoding scheme is presented in Figure 3.
## 4 Results and Discussion
### Comparison  
By comparing the updated thickness values of the thermal and structural updating (Table 4), one comes to the conclusions that a thermal model update delivers more precise results than a structural model update. However, some remarks should be made:  
* The mode shapes are only measured in one degree of freedom instead of three, which results in missing mode shape data along the two other degrees of freedom. The main reason for this consists in the complexity of 3D non-contact vibration analysis. However, this problem does not occur when thermal data is used as a reference.
* By performing model updating using vibrational data to evaluate local material thickness, it can be seen that the combination of local measurement and global responses biases the local thickness measurement.
* Although estimating the thermal properties as structural properties is more complex, the thermal results appear to be better matched.
* The thermal updating routine is far more time-consuming in contrast to modal updating. However, the thermal measurements on the structure are easier to perform.  
Modal updating versus thermal updating appears to be comparatively complex when local parameters such as material thickness are evaluated. Considering complex-shaped structures with a high variation in thickness, measurement time during thermal inspection can be feasibly reduced by investing in extensive computation time. This is especially true when a series of similar products is inspected, as the thermal properties are less variable than structural properties during production. The main reason for this is the use of local temperature measurements in model updating. This was demonstrated by the use of local thermal material properties to evaluate local material thickness (Table 4). When the structure contains a wide variety of materials containing high-temperature materials with little conduction, different conclusions may be drawn. Further investigation is needed to make conclusions about those type of structures.
## 4 Materials and Methods
### Chronology  
The chronology of core CHA01 was established using AMS radiocarbon dating conducted on plant remains or organic sediments, as outlined in Table 2. Radiocarbon dates obtained were then calibrated using the R package CLAM and the Intcal20.14C radiocarbon calibration curve [44,45]. The radiocarbon ages were calculated with a 95% confidence range (two sigmas) and expressed as calibrated years before present (cal. years B.P.).  
A deterministic age-depth model was generated (Figure 4), covering the period since the early Atlantic. The base of core CHA01 was estimated to be around 7915 cal. years B.P. However, it should be noted that the top section of the core (the last 4 m) lacks radiocarbon dating and alternative dating methods. Therefore, the age-depth model for the uppermost part of the core should be interpreted cautiously due to this limitation.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Depth (cm)** & **Laboratory Code** & **Material** & **14C Age B.P.** & **Age cal. B.P. (2\(\sigma\))** \\ \hline
485 & ULA-8845 & Plant remain & 5080 \(\pm\) 20 & 5749–5829 (64.1) \\  & & & 5847–5904 (30.7) \\
553 & ULA-8846 & Plant remain & 5175 \(\pm\) 20 & 5904–5944 (67.6) \\
617 & ULA-9257 & Plant remain & 5865 \(\pm\) 15 & 6652–5739 \\
734 & ULA-9254 & Plant remain & 6180 \(\pm\) 15 & 7001–7134 (89.1) \\  & & & 7149–7160 (5.7) \\  & & & 7009–7130 (66.5) \\
810 & ULA-8848 & Plant remain & 6220 \(\pm\) 25 & 7151–7170 (11.4) \\  & & & 7206–7247 (16.9) \\
880 & ULA-539073 & Organic material & 6920 \(\pm\) 30 & 7677–7795 (88) \\  & & & 7811–7832 (6.9) \\ \hline \end{tabular}
\end{table}
Table 2: \({}^{14}\)C dating of the core CHA01.  
Figure 4: Age–depth model of the core CHA01 built with CLAM package in R [44] based on six radiocarbon ages calibrated using Intcal20.14C calibration curve [45]. The uppermost part of the age–depth model should be interpreted cautiously due to lack radiocarbon dating.
## 5 Simulation Results
### Cluster Performance for Static Networks
#### 5.2.1 Convergence  
Figure 5 displays the number of iterations of these clustering methods with different scales of the UAV swarm. To make the comparison fair, the comparison algorithms and our algorithm all adopt the parallel decision-making mechanism described in Section 4.3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Meaning** & **Values** \\ \hline \(T_{th}\) & Link lifetime threshold & 5 \\ \(\omega_{\text{max}}\) & Maximum diameter of the cluster & 3 \\ \(n_{\text{max}}\) & Maximum number of UAVs in one cluster & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The simulation parameters.  
Figure 4: Clustering results for 50 UAVs with 15 groups. (**a**) shows the 3D version, while (**b**) represents the XY axis version.  
As can be seen from Figure 5, our clustering algorithm has fewer iterations as our method includes more candidate switch operations than the COG and CMPG. In addition, with the cluster size growth, the number of iterations required to converge for the clustering algorithm gradually increases. Large-size UAV swarm has high density, which brings similar relations among UAVs that make the swarm challenging to partition and need more iterations.
# Topological Navigation and Localization in
## 3 Topological Navigation and Localization for LHD
### Self-Localization  
Self-localization is composed of two modules: a global topological localization estimation, and a local node localization estimation. The topological localization estimates the location of the LID inside the TMM (and also the TM, because each node in the TMM has an equivalent in the TM), as well as the distance between its current location and the closest AP/WP. The intra-node localization estimates the localization inside the current tunnel or intersection node.
# Enhancing 3D-Printed Clay Models for Heritage Restoration Through 3D Scanning
### 3D-Printing Clay
As technology advanced, specialized 3D printers for clay are developed, allowing precise control over material flow and layer-by-layer deposition, significantly expanding the creative and practical possibilities of this technique [51]. The evolution of materials has been another key focus in the literature [22]. The rheological properties of different clay mixtures for 3D printing have been investigated, demonstrating that the addition of certain additives can improve flow and stability during extrusion without compromising the integrity of the pieces after firing [52]. These advances have enabled the creation of more complex and detailed structures while maintaining the traditional properties of ceramics [53]. 3D printing with clay has found an important niche in cultural heritage conservation, where it is used to reproduce architectural and ornamental elements that require restoration [54]. Previous studies applied 3D printing with clay to the restoration of decorative elements in historic buildings, demonstrating that this technique can produce accurate replicas that integrate well with the original environment [55; 56]. Despite the advances, 3D printing with clay faces several technical challenges. One of the main issues is the visibility of print traces, which can affect the aesthetics of the finished pieces. This can be addressed through post-processing techniques to smooth printed surfaces, although this adds an additional step to the process. Another challenge is the variability in the properties of clay, which can affect the quality and consistency of prints. Previous research has worked on optimizing clay mixtures to improve stability and reduce deformations during printing and drying [57]. There are various types of 3D printers designed to work with clay, each with its own characteristics and advantages [58]. The most common printers use an extrusion system that deposits clay layer by layer, following the digital design [59]. Despite its numerous advantages, 3D printing with clay faces several challenges. One of the primary concerns is the handling of the material during the printing process, as the clay must remain at the correct consistency to be extruded and form stable structures. Additionally, the drying and firing of printed pieces can affect their shape and durability, requiring careful control of these processes. Another challenge is the cost of the printers and specialized software [60].
## 3 Experimental Program  
### Materials Used  
In this study, demolished concrete from building demolition site and industry produced scrap tires from Warsaw, Poland were used. Concrete aggregates were an element of concrete floors, especially concrete curbs from demolition of roads. The age of this material is about 20 years. For the purpose of this research, the authors purchased the aggregates of concrete from the crusher company. The strength class of concrete from which the aggregates were formed was estimated between C16/20 and C30/35. In 99% of weight, the aggregates were composed of broken cement concrete and in 1% of glass and brick (\(\Sigma\) (Rb, Rg, X) \(\leq\) 1% m/m), in accordance with Polish Committee for Standardization [40]. They did not contain asphalt or any tar elements. The basic properties of original concrete are shown in Table 1. Grain gradation curve was adopted in respect to WT-4 [41] and placed between upper and lower grain gradation limits. These limits are typical for the upper layer of the improved substrate [41]. Concrete aggregates required preliminary research on saturation in order to prevent the movement of water necessary for hydration [42]. The material did not show any binding properties. Recycled Concrete Aggregates needed for the specimens are shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Description** & **Unit** & **Value** \\ \hline Apparent density & kg/m\({}^{3}\) & 1900–2500 \\ Porosity & \% & 12–15 \\ Impregnability & \% & 2–8 \\ Water absorption & \% & 4–14 \\ Mineral dust content & \% & 7 \\ Contamination content & \% & 2–12 \\ Sulfur compounds content & \% & 0.2–0.6 \\ Crushing index & \% & 2.5–5.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic physical properties of original concrete.  
Granular rubber in the presented research was shredded and processed in the local tires manufacturing plant. Only one size of rubber, without steel belts, was bought and then applied in the experimental program. It was fine granular rubber (i.e., rubber chips) with the maximum particle size less than 1.0 mm. Generally, the employed rubber grains were with the diameter size between 0.06 and 0.4 mm (Figure 2).
## 5 Reconstruction Methods
### Transformer-Based Models  
Due to the complex temporal characteristics (i.e., temporal dependency, inter-variable correlations, and noise) in multivariate time series, many different hybrid schemes have been proposed. TransAnomaly [50] is the first model combining Transformer and VAE for time series data. TransAnomaly enables more parallelization and reduces learning costs. TranAD [52] proposes the use of attention-based sequence encoders for rapid inference, combined with adaptive conditioning and adversarial training to enhance the stability of the model. However, the problem of over-generalization persists. MEMTO [53] introduces a novel memory module that can learn how each memory item should be updated in response to input data, adapting to diverse normal patterns. Due to the rarity of anomalies in time series data and the difficulty of establishing non-trivial associations from anomalies to the entire series, Anomaly Transformer [51] calculates association discrepancy through a new Anomaly-Attention mechanism and enhances the distinguishability between normal and abnormal points with a minimax strategy, demonstrating the potential of Transformers in capturing associations of anomaly points in time series.  
The aforementioned methods are all in the time domain and do not utilize frequency domain information. Recent studies have attempted to improve the accuracy of anomaly detection by combining information from both the time and frequency domains. Dual-TF [54] proposes a Nested-Sliding Windows (NS-window) technique, where anomaly scores in the time domain are calculated within the outer window, and anomaly scores in the frequency domain are calculated within the inner window, achieving alignment of anomaly scores at the granularity of individual data points. CATCH [55] performs patching operations in the frequency domain to capture fine-grained frequency characteristics and uses a Channel Fusion Module (CFM) to perceive correlations between channels. It uses Frequency-Enhanced Point-Granularity Scoring technology [54] to calculate the anomaly score for each time point, which involves patch scanning and frequency reconstruction within the input window.