esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Mechanical Performance of Porous Asphalt Mixture with High-Viscosity Modified Asphalt
## 2 Preparation and Performance Analysis of High-Viscoelastic Modified Asphalt
### Determination of Optimum Content of High-Viscoelastic Modifier in Asphalt  
This study selected 3%, 6%, 9%, and 12% modifier contents to investigate the compatibility of the modifier-bitumen system. At concentrations below 3%, the modifier is insufficient to form a continuous network structure (evidenced by abrupt changes in penetration and softening point, reflecting weakening of colloidal structure), leading to simultaneous deterioration in high-temperature deformation resistance and low-temperature crack resistance. Within the 3-12% concentration range, the dispersed modifier phase and continuous asphalt matrix achieve thermodynamic dynamic equilibrium, resulting in synergistic enhancement of high- and low-temperature performance to meet engineering mechanical requirements [33; 34]. When the modifier content exceeds 12%, oversaturation induces phase separation, reducing cohesive strength and confirming irreversible performance degradation beyond the thermodynamic stability threshold; such concentrations were excluded from the research scope. Therefore, based on the mechanical performance and economic considerations of modified asphalt, this article prepared high viscoelastic modifiers and added them to the asphalt at proportions of 3%, 6%, 9%, and 12%. Five different contents of high-viscoelastic modified asphalt were tested for dynamic viscosity at 60 \({}^{\circ}\)C [35]. Based on the test results, the optimal content of high-viscoelastic modified asphalt in SBS-modified asphalt was determined.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Raw Material Ratios (\%)** & **SBS** & **Resin** & **Antioxidant** & **Stabilizer** & **Co-Solvent** \\ \hline High viscosity modifier A & 54.9 & 25.1 & 0.9 & 0.7 & 18.4 \\ High viscosity modifier B & 61.4 & 21.2 & 0.4 & 1.2 & 15.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Raw material ratios of high-viscoelastic modifiers.
# Transfer Reinforcement Learning for Combinatorial Optimization Problems
## 2 Background
### Traveling Salesman Problem  
The traveling salesman problem is one of the best-known combinatorial optimization problems [57]. This method aims to determine a Hamiltonian cycle that passes through all the vertices of a graph. In addition, the problem also aims for the Hamiltonian cycle found to be the one with the lowest cost [52; 57; 58].  
The TSP is made up of a set of cities, where the cashier must visit all the cities in the set, and at the end of the journey, the cashier must return to the initial city. As a restriction, each city in the set must be visited only once, except for the final city, which must be the same as the starting city [57; 58].  
The TSP can be classified in various ways, including the symmetrical TSP and asymmetrical TSP. In the symmetrical TSP, the cost of moving from node A to node B is the same as the cost of moving from node B to node A. In the case of the asymmetric TSP, the cost varies according to the direction of travel adopted [57]. Thus, the symmetric TSP can be represented by a graph with bidirectional edges, while the asymmetric TSP can be represented by a graph with directional edges [57].  
The traveling salesman problem has several applications, and the authors of [52; 58] presented some of them:  
* Task sequencing;
* Drilling printed circuit boards;
* Analysis of crystal structures;
* Handling of stock items;
* Optimizing the movement of cutting tools;
* Postal delivery routing.  
Formulation  
There are several possible formulations for the TSP, some of which are presented in [52; 59]. The following formulation was presented in [59] in Equations (2)-(6):  
\[Minimize\sum_{i=1}^{N}\sum_{j=1}^{N}c_{ij}x_{ij} \tag{2}\]  
subject to  
\[\sum_{i=1}^{N}x_{ij}=1\qquad(\forall j=1,\ldots,N) \tag{3}\]  
\[\sum_{j=1}^{N}x_{ij}=1\qquad(\forall i=1,\ldots,N) \tag{4}\]  
\[x_{ij}\in\{0,1\}\qquad(\forall i,j=1,\ldots,N) \tag{5}\]  
\[X=x_{ij}\in S\qquad(\forall i,j=1,\ldots,N) \tag{6}\]  
The objective function of the TSP is represented by Equation (2), in which \(c_{ij}\) represents the cost between cities \(i\) and \(j\), and \(x_{ij}\) represents whether the arc \((i,j)\) is part of the solution to the problem. If \(x_{ij}\) is equal to one, then the arc is part of the solution. If \(x_{ij}\) equals zero, then the arc is not part of the solution. In addition, Equations (3) and (4) ensure that each city will only be visited once. Equation (5), on the other hand, represents the restriction that the value of \(x_{ij}\) will always be binary. Finally, Equation (6) guarantees that no sub-routes will be formed when solving the problem [58; 59].
# RMFNet: Redetection Multimodal Fusion Network for RGBT Tracking
## 5 Performance Evaluation
### Ablation Experiments
#### 5.4.1 Component Analysis  
As shown in Table 2, Baseline is the basic model used in this article. It uses the RT-MDNet network and takes visible light and thermal infrared images as inputs to extract features separately. Then, it performs an element-wise addition fusion operation on the output of the feature by the third convolutional layer of both modalities to obtain the fused features. Finally, the fused feature is fed into three fully connected classification layers for object tracking. LF stands for late-term decision-level fusion, which combines visible light, thermal infrared, and fused features separately through three fully connected classification layers to obtain sample scores. Then, the scores of the three modalities are added and averaged to obtain the final sample score. FB represents the fusion branch, which adds two convolutional layers in parallel between the two specific modality branches to extract shared features of the two modalities. EFM represents the ECA-based multimodal feature fusion module, which adaptively weights the feature maps of two modalities output by the shared branch based on their reliability and fuses them into a single representation. RD stands for our improved re-detection algorithm that incorporates a re-detection process to mitigate the accumulation of failures. AR represents the Alpha-Refine [8] refinement module, which performs fine adjustment of the tracker's coarse localization. As shown in Table 2, all of the proposed components exceed the results of the baseline model, and each component can contribute significantly to the network's performance, proving our network model's superiority.
## 5 Conclusions  
In this contribution, we proposed an adaptive OFDM scheme, in which interference and channel statistics are initially acquired. Subsequently, in place of proceeding with very frequent channel estimation, as considered in most of the literature, we propose a mechanism to predict the channel and also a protocol to rule the re-estimation of the channel when necessary. This mechanism also solves the problem related to potentially outdated channel information due to long propagation delays. Simulation results demonstrated that the proposed approach is able to provide reliable channel tracking, also reflected in high performance in terms of BER and rate. Moreover, the designed communication protocol avoids the exchange of overhead information between transmitting and receiving nodes, thus, allowing the achievement of highly efficient communication, especially when the amount of data to be transmitted is large.  
Figure 7: Efficiency for different lengths of data to be sent for the proposed scheme and the case with frame-by-frame estimation with quantized feedback.  
**Author Contributions:** Conceptualization, M.B.; Methodology, R.C.; Software, M.B.; Investigation, G.S.; Writing--original draft, A.P.; Writing--review & editing, A.P.; Supervision, M.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partially supported by the European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, partnership on \"Telecommunications of the Future\" (PE0000001-program \"RESTART\").  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
(2020, 2019) derived into exploring ultra-high spatial resolution remote sensing techniques and their practical applications in mapping vegetation patterns while assessing the necessary data and resolution criteria [30; 51]. These approaches allow for a comprehensive understanding of peatland dynamics, emphasised in their research, which involved modelling and scaling up ecosystem respiration and using thermal cameras and UAVs. Remote sensing techniques and platforms have many applications in peatland research. However, the choice of platform depends on the specific objectives and constraints of the study, as each platform has advantages and limitations. Therefore, in the following sections of this systematic literature review (SLR), we examine the use of different remote sensing platforms for peatland applications in more detail. The systematic literature review is organised into several sections. The Introduction (Section 1) overviews the study's objectives and summarises previous reviews. Section 2 details the methodology of the review, including the criteria for study inclusion and exclusion. The included studies are highlighted in Section 3, with a categorisation ranging from satellite-based to ground-based measurements and multiple platforms. Section 4 explores utilising remote sensing platforms and their applications in peatlands. Section 5 compares remote sensing platforms and their applications in northern peatlands. Finally, Section 6, titled \"Conclusion\", encapsulates general conclusions and offers insights for future research.
## 6 Conclusions  
Based on our results, it is easy to understand the extensive use of the P&O approach in the industry because it is simple, is lightweight, and can obtain results above 99.9% under constant irradiance. As shown in our results, the INC technique, which is also utilized in industry, is an algorithm that uses slightly more resources than P&O, but produces better results than P&O in most tests.  
On the other hand, FL- and ANN-based MPPT methods produce even better results (simulation) than P&O and INC; however, they are difficult to deploy because they require expert knowledge or a dataset for training, and many more resources.  
The new IC-INC approach appears to be a valuable alternative to P&O and INC, with comparatively better tracking capabilities, a shortened settling time in MPP tracking, and very accurate MPP detection, all while being as light on resources as P&O and INC. Therefore, the proposed algorithm is particularly suitable for use in scenarios where each single PV panel must have its own microconverter to maximize power extraction in difficult conditions, such as partial shading.  
Our evaluation of the hardware resources required by each MPPT algorithm shows that P&O, IC-INC, and INC require the fewest computing resources, such as a very low-power microprocessor, while FL- and ANN-based MPPT require many more resources, such as an FPGA or a fast digital signal processor (DSP).  
Future research could include testing the promising IC-INC algorithm under partial shading to verify its ability to detect the global MPP, or its potential as a low-resource MPPT algorithm for distributed microconverters in every PV string.  
Conceptualization, S.A., F.S., S.P. and P.M.; methodology, S.A., F.S., S.P. and P.M.; investigation, S.A.; writing--original draft preparation, S.A.; writing--review and editing, F.S., S.P. and P.M.; supervision, F.S., S.P. and P.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported, in part, by funds from Portugal's Fundacao para a Ciencia e Tecnologia (FCT) (project reference UIDB/50021/2020) and an FCT PhD grant (reference 2022.12809.BD).  
Not applicable.  
The authors declare no conflict of interest.
# Dual-Frequency Signal Enhancement Method of Moving Target Echoes for GNSS-S Radar
## 3 Simulation and Experimental Results  
In this section, firstly, the processing effect of the dual-frequency echo of a point target was simulated using the B1 and B3 signals of the BDS satellite. Then, detection experiments were carried out to verify the effectiveness of the GNSS-S dual-frequency echo signal processing method for high-speed weak target echo signal processing.
## 2 Case Studies and Operation Modes of the System
### Operation Modes of the HP with Integrated RPW-HEX
Note that contrary to the heating mode, where it would also be possible to use the energy stored in the PCM for heating (of course with a low exergy efficiency), the energy stored in the RPW-HEX during cooling is usually not used in a conventional air source HP. Once the RPW-HEX is fully charged (SoC = 1) by mode (a) or (b), it would be beneficial if the HP switches to DHW generation mode (c) if the DHW storage devices can be charged. Contrary to direct DHW-generation mode (d), the condensing temperature,and therefore the COP, remains at the values for heating mode (c). Therefore, the process water from the return line of the decentralized DHW storages (S) is pre-heated by the condenser at a beneficial COP (heating COP). Subsequently, the process water is boosted by the RPW-HEX (R) and mixed to the DHW set-point temperature (\(\approx\)60 \({}^{\circ}\)C) by the three-way valve (M). The lower the temperature of the water returning from the DHW storage devices is, the higher the contribution of the condenser of the HP is. Hence, a significant amount of energy need not to be taken out of the RPW-HEX for DHW generation and the performance of the system increases. During this operation mode, the hot gas will additionally transfer its sensible energy via the RPW-HEX (R) to the process water. Once the RPW-HEX is discharged, the HP switches back to heating or cooling mode, respectively. Figure 1: Concept of the proposed system for an example scenario with three apartments during: (**a**) heating operation, (**b**) cooling operation, (**c**) energy efficient domestic hot water (DHW) generation by discharging the RPW-HEX and (**d**) direct DHW generation. (S) Decentralized sensible DHW storage, (H) fan-coils suitable for heating and cooling, (C) condenser, (R) RPW-HEX, (W) four-way valve, (B) bypass expansion valve, (X) regular expansion valve, (F) fluid phase heat exchanger, (E) evaporator and fan, (P) compressor, (M) three-way valve. The temperatures indicated in (**a,b,d**) were taken from steady-states at a state of charge (SoC) of 50% for heating and cooling, respectively, whereas they were taken shortly after switching from mode (a) to (c) in (**c**). The ambient temperature (\(\delta\)) was 0 \({}^{\circ}\)C in (**a,c,d**) and 35 \({}^{\circ}\)C in (**b**). In the case that the thermal energy for DHW generation cannot be provided entirely by operating mode (c), e.g., during spring, the HP switches to the direct DHW generation mode (d). In this mode, similar to a conventional system, DHW is generated by increasing the condensing temperature to values suitable for DHW. The RPW-HEX acts in this case solely as HEX and transfers the sensible energy of the hot gas directly to process water. During transitional seasons, it would make sense to bypass the RPW-HEX on the refrigerant side (not depicted in the figure) to avoid unnecessary charging/discharging of the RPW-HEX. In operating mode (e) and (f), the RPW-HEX is fully charged, and the modes are comparable to conventional heating and cooling modes.
## 4 Experiments and Results
### Performance Evaluation
#### 4.2.6 Analysis of Different Camera Postures  
For practical usage, the posture of the wearable device may differ between different subjects and differ from time to time. In Figure 10, we display pictures taken from different postures of the camera to show how the outlook of the hand changes accordingly.  
The parameter \(\theta\) is the angle between the camera axis and the axis of the forearm. As shown in Figure 10b-d, when \(\theta\) gets larger, the hand becomes bigger in the image. Considering the movement of the hand of flipping up and down, a proper value for \(\theta\) is around \(20^{\circ}\). The user can adjust the support of the camera to set this angle during the first usage and keep it unchanged in later times. The parameter \(\varphi\) is the orientation of the camera around the axis of the forearm. It changes when the device rotates around the wrist. Figure 10e,f show the hands when \(\varphi\) equals to \(25^{\circ}\), where the little finger or the thumb is becoming too distant. The best value for \(\varphi\) would be zero.  
The proposed algorithm is featured with a robust processing capability to deal with the variations of the camera posture. As shown in Figure 8, each of the ten hands from the ten subjects has a varied relative position with respect to the camera. For example, the gesture \"Five\" is at a relatively low position in the image while the gesture \"Eight\" is relatively high. Moreover, the hand of gesture \"Two\" is inclined to the left, while the gesture \"Six\" is inclined to the right. In spite of these variations, the recognition accuracy of each subject is still above 96% (see Table 3 Accuracy A). This advantage of the algorithm is ascribed to the additional knowledge provided by the template image, which is taken after the device is worn.
## 3 Performance Analysis
### Capacity Analysis with Adaptive Transmission  
The Shannon capacity is the maximum rate at which the information can be transmitted to the receiver, with a very small error probability, via a communication channel. In designing wireless communication systems, adaptive transmission techniques play a significant role, exploiting the channel conditions for the adaptive rate and/or transmission power. In this section, considering the most widely used adaptive transmission schemes, such as ORA, OPRA, CIFR, and TIFR, we obtain the expressions of the average capacity in analytic form.
# Apparatus and Method of Defect Detection for Resin Films
## 5 Conclusions  
In this paper, an automatic method that could identify nonbackground objects and classify objects was proposed by using a traditional image processing method to detect nonbackground objects, and a CNN network to classify the defects. Based on the proposed method, the inspection was converted to a segmentation and classification problem. Traditional image processing detected objects; a comparison part was used to filter dust on the plastic resin film. The results indicated that the process was successfully able to filter out a sufficient amount of dust. A LeNet-5-adjusted network was proposed for the classification, and the results exhibited excellent accuracy, proving that an average pooling of the data can lead to loss of vital features. The transparent-background data has an 84% recall and a 77% precision, while the white-background data has a 97% recall and a 78% precision. The proposed automatic defect detection with machine vision-based method can improve the disadvantages of the traditional procedure. The proposed method exhibits uniform measurements, unlike the traditional method in which the criteria can vary for each inspector. Whereas human vision for the inspection process is quite time consuming and inefficient, the proposed method is fast. Using the traditional method, the average number of plastic resin films that can be checked by a person in a day is approximately 12 to 15. However, with the proposed method, the amount of time needed to check one plastic resin film can be down to 3 min. In the proposed method, the labor costs of the inspection process can be reduced, the workload of the inspectors can be lightened, and productivity can be increased.  
Methodology, R.-K.S., Y.-H.T. and L.-C.C.; software, Y.-H.T. and C.-H.T.; supervision, R.-K.S. and L.-C.C.; Writing - original draft, R.-K.S., Y.-H.T. and L.-C.C.; Writing - review & editing, R.-K.S., C.-H.T. and L.-C.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by DDS-THU AI Center of Tunghai University, Taiwan.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 17: Confusion matrix of the Lenet-5-adjusted trained on transparent-background plastic. (**a**) CNN trained in three classes, (**b**) CNN trained in two classes.
## 1 Introduction
Two main validation approaches have been defined: (i) The quantitative and qualitative product-to-product validation approach, which is referred to as satellite product intercomparison (i.e., indirect validation); and (ii) the direct point-to-pixel validation, which involves comparisons of satellite products with the albedo measured from in situ tower-based instruments (i.e., direct validation). Indirect validation is helpful because most validation metrics cannot be computed using ground data, due to the limitations of ground measurements in terms of global conditions. Indirect validation approach mainly consists of comparing satellite-derived albedo products, particularly new products, with heritage albedo products [7; 22; 23; 24; 25; 26; 27; 28]. In general, product intercomparison offers a means of assessing the discrepancies (systematic or random) between products. This method is particularlyvaluable for finding spatial disagreements between products over large areas and for a wide range of cover types. However, this approach does not yield absolute validation results, and satellite product intercomparisons alone are insufficient to validate a new product. Then, direct validation enables the assessment of uncertainties, and it may be argued that only such methods can be seen as actual validation in the field of remote sensing [29]. Direct validation consists of comparing satellite retrievals with in-situ albedo measurements [30]. It is, therefore, mandatory to evaluate the spatial representativeness of ground albedo measurements, which depends on the land surface heterogeneity [31; 32; 33; 34]. As the PROBA-V mission is arriving at the end of its lifetime, the aim of this paper is to perform the full quality assessment of PROBA-V based C3S SA v1.0 products. To achieve that, the main objectives are the following:  
1. To evaluate the product completeness and spatio-temporal consistency of C3S PROBA-V SA v1.0 products over global conditions compared to SPOT/VGT SA v1.0 (and MCD43A3 C6 for benchmarking) to verify the continuity of the C3S time series;
2. To assess the precision of C3S PROBA-V SA v1.0 and to intercompare it with reference satellite products (C3S SPOT/VGT SA v1.0 and MCD43A3 C6);
3. To assess the accuracy and the associated uncertainties of the products via direct validation with ground measurements; and
4. To assess the compliance of the product with regards to climate user requirements. The remainder of this paper is structured as follows: Sections 2 and 3 describe the datasets used in the study, which are for satellite-based products and ground data, respectively; Section 4 presents the validation methodology; Sections 5 and 6 gather and discuss the intercomparison and validation results, respectively; and the conclusions are summarized in Section 7.