esa-sceva/satcom-chunk-collection huggingface dataset:
# Fusing Ground-Penetrating Radar Images for Improving Image Characteristics Fidelity
## 5 Methodology and Experimental Results
Figure 7:0 The summation image created after applying masking and fusion techniques effectively highlights the most powerful hyperbolic responses. Using LBP analysis on the summation of the images, we take the result as defined in Figure 7:1 In the fusion stage, we chose a specific linear combination between the two images to enhance the most important features. By assigning more weight to the inverse image, as it contains clearer information, the resulting cumulative image provides a balanced and improved representation. As can be observed, the results of the LBP method differ when it is performed on a part of the image compared to that obtained when it is applied to the entire image. By performing an LBP analysis on a part of the cumulative image, we could obtain an indication of the most relevant and salient features. In contrast, in full-image analysis, irrelevant patterns and noise can cause location interference between neighboring pixels, thereby reducing the clarity and accuracy of the detected features. Combining this targeted  
Figure 7:2 LBP with the summation of all images. Red Circles: These patterns could represent buried objects. Green Rectangles: Such areas could be voids, different material types, or potential points of interest that differ from the surrounding substrate. Figure 7:3 The result of using LBP analysis on part of the summed image. This provides a clearer and more detailed view of hyperbolics. approach with an optimized linear combination of forward and inverse reconstruction images significantly improves the detectability and interpretability of subsurface structures.
## 4 Experimental Results and Discussions
### SOStream  
The SOStream algorithm clustered the Horseshoe, T, Moon, and Three Spirals datasets as shown in Figure 14. In the Horseshoe dataset, four microclusters were found with the settings of \(\alpha=0.015\), \(MinPts=4\), and \(\gamma=0.1\). In the T dataset, SOStream identifies six microclusters using the settings of \(\alpha=0.001\), \(MinPts=5\), and \(\gamma=0.1\), while in the Moon dataset, four microclusters were found with the settings of \(\alpha=0.001\), \(MinPts=5\), and \(\gamma=0.1\). Finally, the algorithm found 226 microclusters in the Three Spirals dataset with \(\alpha=1\), \(MinPts=2\), and \(\gamma=0.3\). Macro SOStream performed an exhaustive search for the best parameters. However, in both datasets the ARI metric maximum values were around 0.38, as illustrated in Figure 15.  
Figure 13: Variation of Macro SOStream hyperparameters and ARI metric values. (**a**) Clustering with the Horseshoe dataset. (**b**) Clustering with the T dataset. (**c**) Clustering with the Moon dataset. (**d**) Clustering with the Three Spirals dataset.  
Figure 14: Clustering using SOStream algorithm. (**a**) Clustering with the Horseshoe dataset; (**b**) Clustering with the T dataset; (**c**) Clustering with the Moon dataset; (**d**) Clustering with the Three Spirals dataset.  
Figure 15: Variation of SOStream hyperparameters and ARI metric values. (**a**) Clustering with the Horseshoe dataset; (**b**) Clustering with the T dataset; (**c**) Clustering with the Moon dataset; (**d**) Clustering with the Three Spirals dataset.  
The poor performance of the SOStream occurred because this algorithm works only with spherical microclusters. Thus, because the datasets have arbitrarily-formed clusters, SOStream cannot find clusters correctly. Moreover, to better compare the methods, the hyperparameters' values used are the same as those utilized in Macro SOStream. However, it was noted that there was a significant discrepancy in the ARI metric values.
## 2 Related Works
### The EMD Method and Applications Based on It
Within this framework, two secret codes derived from a 5-ary numbering system are embedded within a pixel pair, typically referred to as a cover image. An EMD matrix facilitates these operations, resulting in two sets of modified pixel pairs, also termed stego-pixel pairs. Consequently, the secret data can be precisely extracted, and the original cover image fully restored during the data extraction and image reconstruction phases. In 2022, [PERSON] et al. [23] introduced a RDH method based on the revised LSB method and the EMD method in dual images. It was found that the LSB matching revisited method could essentially be considered as a type of EMD method. Unlike conventional methods that typically utilize two pixels of the cover image at a time, this approach operates on a single pixel. The method involves duplicating this single pixel, embedding two secret bits into it, and then distributing these across two steganographic images. Essentially, this method represents an adaptation of the LSB matching revisited approach, involving the duplication of a single pixel value instead of utilizing it directly as input. Figure 2 illustrates the embedding phase of the proposed method. The \(f_{EMD}\) extraction function (Equation (4)) for the LSB matching revisited method can be defined as follows:  
\[f_{EMD}(x_{i},x_{i+1})=LSB(x_{i})*2+LSB(\lfloor x_{i}/2\rfloor+x_{i+1}) \tag{4}\]  
Figure 2: Schematic diagram of reversible modified LSB matching revisited method. Suppose there is an instance where a single pixel value from the original cover image is two. In the data embedding process, this results in an initial pixel pair of \((2,2)\). For a secret message \(s=1\) and a corresponding \(f_{EMD}(2,2)=1\), the steganographic pixel pair remains \((2,2)\), indicating no change. If \(s=0\), the method evaluates two candidate pairs, \((2,1)\) and \((2,3)\). The pair \((2,1)\) is deemed not viable as it does not allow for the restoration of the original pixel value of two through the recovery formula \(x_{i}=\lfloor(x_{i}^{\prime}+x_{i}^{\prime\prime})/2\rfloor\). Consequently, the pair \((2,3)\) is selected as the stego-pair.
# Fast Thumbnail Extraction for H.264/AVC, HEVC and VP9
## 1 Introduction
The memory structure for the proposed partial decoding method uses the minimal thumbnail buffer and the reference line buffer rather than the decoded picture buffer (DPB). Memory is not allocated for pixels whose restoration is omitted, and reference pixels that are no longer required are removed by storing the restored reference pixels of the next block, thereby reducing the memory allocation required. In addition, a down-sampling process using a thumbnail buffer to store thumbnail pixels for output is not performed, thereby reducing computational complexity, memory usage and memory access. Video codecs of H.264/AVC, HEVC and VP9 have different coding structures and transforms. However, the proposed algorithm can be applied in the same manner for these codecs. In order to evaluate the performance of the proposed method, we implemented the proposed algorithm for H.264/AVC, HEVC and VP9. We found that the thumbnail extraction time of the proposed method decreased by 66% in H.264/AVC, 52% in HEVC and 48% in VP9 compared to the full decoding method. Figure 1: The restored pixels for the intra prediction or thumbnail output (gray) and unnecessarily restored pixels (yellow) in a 16 \(\times\) 16 block. This paper is organized as follows: Section 2 explains the proposed partial decoding method for fast thumbnail extraction and an efficient memory structure. Section 3 shows the experimental results of the proposed algorithm and the existing implementation of open software in terms of running times and visual quality. Section 4 concludes the paper.
## 3 Theoretical Basis of Thermal Stress Processes
### The Release of Heat from Concrete  
During the setting of concrete, heat that depends on the chemical and mineral composition of cement is released in the hydration process.  
Experiments demonstrated that in the first 7 to 10 days of setting, up to 80.0% of the total heat produced by the cement hydration is released and that this process decelerates as the setting continues [40,41].  
A curve that portrays the release of hydration heat is often approximated by the following relation [40]:  
\[Q_{l}(\tau)=Q_{max}\big{(}1-e^{-m\cdot\tau}\big{)} \tag{8}\]  
where \(Q_{l}(\tau)\) represents the release of hydration heat from concrete at time \(\tau\) in [J], \(Q_{max}\) is the maximum quantity of hydration heat that can be released from the concrete of a given mixture at the full cement hydration in [J], m is an empirical parameter that depends on the type of cement [1/h] and \(\tau\) is time [h].  
Figure 2: Heat transfer processes represented in this paper.  
Temperature resulting from the heat released from the cement hydration process leads to peaks in thermal tensile stresses (TTS) during the dam construction, whereby an increase in temperature can amount up to 50.0 \({}^{\circ}\)C [40] and it is described by the following equation:  
\[T_{h}=\frac{q_{t}\cdot K}{c\cdot\rho} \tag{9}\]  
where \(T_{h}\) represents the temperature resulting from the heat released from the cement hydration process [\({}^{\circ}\)C], \(q_{t}\) is the quantity of released heat [\(|\)/kg], \(K\) is the cement per unit of volume ratio [kg/m\({}^{3}\)], \(c\) is the specific heat capacity of concrete [\(|\)/(kg K)] and \(\rho\) is the density of concrete [kg/m\({}^{3}\)].
# Model-Based Deep Reinforcement Learning with Traffic Inference for Traffic Signal Control
## 1 Introduction
Giving agents more opportunities to directly explore the environment leads to costly enhancements that are not applicable under traffic signal control tasks. Model-based reinforcement learning aims to solve this problem by first learning a model of the environment dynamics and then reducing the interaction between the agent and the environment based on the learned model. Therefore, inspired by many successful cases of model-based DRL algorithms, we propose a model-based deep reinforcement learning method for the problem of data efficiency of model-free DRL algorithms that are currently widely used in ATSC. Previous model-based work [18] is still far from representing the state of the art under the task of traffic signal control. In contrast, the most successful approaches are based on model-free RL [19; 20; 21]; i.e., they estimate the optimal policy and/or state value function directly from the interaction with the environment. In this paper, we present a DRL algorithm based on future traffic flow information originating from upstream intersections. This is a new method of model-based deep reinforcement learning that achieves superior levels under a single-intersection traffic signal control task. Specifically, this approach uses a traffic inference model based on future information to enhance data efficiency. In addition, this deep neural network based traffic inference model is continuously optimized based on the data collected from the environment. In summary, the main contributions of this paper are as follows:  
* We propose a traffic inference model based on future information, which is able to infer the subsequent traffic state based on the current traffic state and the future traffic flow information given by the upstream intersection. * Based on our proposed traffic inference model, we propose a model-based DRL algorithm. The traffic inference model is embedded into this algorithm to enhance data efficiency and accelerate strategy improvement. * Through comprehensive experiments based on real-world data, we demonstrate that our proposed algorithm outperforms existing model-free deep reinforcement learning methods, with enhanced data efficiency and improved final optimal performance. The remainder of this paper is structured as follows. In Section 2, we review previous related work and present the focus of our research. In Section 3, we present the relevant background of the techniques used. In Section 4, we propose the traffic inference model. In Section 5, we propose a model-based reinforcement learning approach based on the traffic inference model. In Section 6, we conduct comprehensive experiments. In Section 7, we discuss the advantages and disadvantages of our proposed approach. Finally, we present our conclusion and the outlook for future work.
## 3 Study Area and Methodologies  
### Study Area  
Puitan City, with its distinct geographical advantages, is located in the central coastal region of Fujian Province, China (24\({}^{\circ}\)59\({}^{\prime}\)-25\({}^{\circ}\)46\({}^{\prime}\) N and 118\({}^{\circ}\)27\({}^{\prime}\)-119\({}^{\circ}\)39\({}^{\prime}\) E). It is connected to the north with the provincial capital, Fuzhou City, and to the south with the historic city of Quanzhou. To the west, it is bordered by the Daiyun Mountains, and to the southeast, it faces the Taiwan Strait, looking across the sea towards Taiwan Province. Puitian City governs one county and four districts (Xianyou County, Licheng District, Chengxiang District, Hanjiang District, and Xiuyu District), with a maritime area of 11,000 square kilometers (Figure 1). It covers a total area of 3923.49 km\({}^{2}\). Throughout the study period from 2000 to 2020, the city's boundaries remained consistent, with no administrative changes that could influence the land use indicators presented in this analysis.  
Puitian City, the focal point of this study, has experienced notable land use changes in the past [70; 71]. Puitan's topography exhibits a distinct contrast between its northwest mountainous region and its southeast coastal plain, with the Xinghua Plain, the third largest plain in Fujian Province, forming the city's major agricultural and aquaculture hub. This plain is traversed by three rivers flowing northwest to southeast, providing crucial water resources for industry, agriculture, and domestic use. And biodiversity within the region is characterized by a rich tapestry of species with notable diversity and abundance, particularly in the Xinghua Plain, which serves as a critical habitat for numerous endemic and migratory species. The species composition varies across different land use types, reflecting the ecological responses to human activities. Climatically, Putian City is situated in the subtropical zone, marked by distinct seasons, moderate temperatures, and a humid climate. The soil primarily consists of red soil and paddy soil, with evergreen broad-leaved forests dominating the vegetation. In 2020, the city's Gross Domestic Product(GDP) stood at 264.34 billion CNY, with the primary sector (agriculture-based) contributing 4.75%, the secondary sector (industry-based) contributing 51.53%, and the tertiary sector (service-oriented) contributing 43.72%. Faced with the limitations of scarce land resources and the necessity to harmonize economic and social progress with ecological protection, maintaining this balance is of paramount importance.
## 3 System Model and Preliminaries
### Transducer Configuration and Assumptions  
Since we are operating in deep-water environment where separation between the seabed and surface is quite large, links facing more than one reflection are envisioned to lose most of their power and cannot be detected by a receiver. Thus, we only consider in our study the refracted rays and rays facing single reflections, i.e., either from the surface or bottom. Moreover, a transducer is assumed to generate ample power that can overcome experienced underwater attenuation by acoustic signals so that received signal that are refracted or faced a single reflection are decodable. A node is further assumed to be equipped with a transducer that leverages electro-acoustic active elements to generate acoustic pressure waves. [PERSON] shows an example of such a transducer, where the radiation pattern is determined by the shape and size of the used antenna segments [19]. We assume that the transducer can produce a directional beam pattern in the far-field region by means of controlling voltage, phase and amplitude as described by [PERSON] et al. in [20]. Our work assumes that all communication takes place in the far-field region of the radiating element used by the transducer where the pressure waves combine to form a uniform wave-front.  
Formally, we define a finite set of beams \((K)\) with cardinality \((n)\) where \(n\) is equivalent to the distinct beams that the transducer can generate. Furthermore, we refer to each beam by the center angle of that beam \((k_{i})\) and formally write that \(k_{i}\in K\), \(i=1,2,\ldots,n\). In addition, \(\forall k_{i}\in K\), \(\psi_{i}\) corresponds to the smallest beam width that the transducer can generate at the direction of \(k_{i}\). Clearly \(\psi_{i}\) is dependent on the capability of the transducer in generating directional beams at various directions. Moreover, for \(k_{i},k_{j}\in K\) where \(i\
eq j\) we impose a further restriction that \(\psi_{i}=\psi_{j}=\psi\) for all possible values of \(i\) and \(j\), i.e., beam \(k_{i}\) is identical to beam \(k_{j}\) for all values of \(i\) and \(j\). We then define a spherical transmission envelope across the node, where each beam \(k_{i}\)\(\forall i\) is said to have transitioned from the transducer's near field region to the far field. We then regard the node to be at the center of such a spherical transmission envelope. Moreover, we treat the spherical transmission surface as a point source since the distance between each pair in the network is much greater than the radius of such a sphere and the node size. In addition, a node is assumed to be capable of transmitting beams at distinct angles in all 3D directions where \(\cup\)\(ki\) produces a non-uniform acoustic pressure along the entire spherical envelope surface that is much greater than zero. Moreover, we utilize the smallest beam width, i.e., \(\psi\), that a transducer can generate, in order to establish communication links and find the vertical axis. Finally, a beam is visualized as infinite number of rays with infinitesimal angular deviation that span the width of the selected beam.
## 2 Experimental Investigation  
### Materials  
The materials employed in the manufacturing of the mortar specimens were Portland cement CEM I/42.5 R [31] (Table 1), silica sand specified as \"siliceous rolled aggregate 0/4\" (Table 2), urban drinking water, retardant SikaTard, calcium chloride (CaCl\({}_{2}\)), and ladle furnace slag (Table 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Chemical (\%)**} & \multicolumn{2}{c}{**Physical**} \\ \hline SO\({}_{3}\) & 3.4 & Specific surface area (Blaine) & 414 m\({}^{2}\)/kg \\ Cl\({}^{-}\) & 0.01 & Density & 3.15 g/cm\({}^{3}\) \\ \multicolumn{1}{c}{Calcination loss} & 1.72 & Le Chatelier expansion & 0 mm \\ \multicolumn{1}{c}{Insoluble residue} & 0.4 & \multicolumn{1}{c}{Initial setting time} & 108 min \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{Final setting time} & 160 min \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main properties of Portland cement.  
Taking into account the results obtained in other research work [25], the following guidelines for dosages were considered:  
- A ratio of cement/sand/water by weight of 1:6:\(w\), with \(w\) relating to the necessary quantity of water to achieve a slump of 175 \(\pm\) 10 mm;  
- A compressive mechanical strength at 28 days of at least 7.5 N/mm\({}^{2}\);  
- 30% of cement and 25% of sand substituted for ladle furnace slag in the LFS specimens;  
- 0.5% by weight of cement of retardant was used.  
Two series of mortar test specimens were manufactured: one used as a standard test specimen (MCC) and another in which part of the cement and sand was replaced by LFS (MCE) (Table 4). During kneading, chloride ions were added in the form of calcium chloride, corresponding to 0%, 0.4%, 0.8%, 1.2%, and 2% by weight of cement.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Label**} & **Cement** & **Sand** & **Water** & **LFS** & **Retardant** & **CaCl\({}_{2}\)** & **Ion Cl\({}^{-}\)** \\  & **(g)** & **(g)** & **(g)** & **(g)** & **(g)** & **(g)** & **(\%)** \\ \hline MCC-0.0 & 99.6 & 597.5 & 102.9 & — & 0.50 & 0.00 & 0.0 \\ MCC-0.4 & 99.6 & 597.5 & 102.9 & — & 0.50 & 0.80 & 0.4 \\ MCC-0.8 & 99.6 & 597.5 & 102.9 & — & 0.50 & 1.60 & 0.8 \\ MCC-1.2 & 99.6 & 597.5 & 102.9 & — & 0.50 & 2.40 & 1.2 \\ MCC-2.0 & 99.6 & 597.5 & 102.9 & — & 0.50 & 3.99 & 2.0 \\ MEE-0.0 & 68.6 & 441.1 & 113.9 & 176.4 & 0.35 & 0.00 & 0.0 \\ MCE-0.4 & 68.6 & 441.1 & 113.9 & 176.4 & 0.35 & 0.55 & 0.4 \\ MCE-0.8 & 68.6 & 441.1 & 113.9 & 176.4 & 0.35 & 1.10 & 0.8 \\ MCE-1.2 & 68.6 & 441.1 & 113.9 & 176.4 & 0.35 & 1.65 & 1.2 \\ MCE-2.0 & 68.6 & 441.1 & 113.9 & 176.4 & 0.35 & 2.75 & 2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dosages of the samples with and without LFS.
# Multivalent Aptamer Approach: Designs, Strategies, and Applications
## 3 Design Strategies
### Spacers  
Spacers refer to short-strand polymers or oligonucleotides between the recognition domains of aptamers and the conjugated surface that they are attached to. Although multivalent aptamer strategies can enhance avidities compared to free aptamer monomers even without added structural elements [76], avidities can be further improved by employing properly designed spacers. This is because these spacers can either offer enhanced abilities for surface-tethered aptamers to properly bind to their targets or prevent aptamer units from forming unexpected secondary structures.  
Figure 2: Schematic illustration of designs of multivalent aptamers. The optimization strategies include adding different lengths or types of spacer motifs, using a mixture of two or more different types of aptamers, and applying spatial arrangements of aptamers tailored for the geometries of target ligands and binding pockets.  
In general, surface-tethered aptamers with spacers show better performance when compared with those without. With properly selected lengths and materials, spacers can increase the avidities of multivalent constructs by overcoming steric hindrances due to adjacent aptamers. The most common spacers are PEG [77; 78]; alkyl [78; 79]; ssDNA, such as oligo-T and oligo-A [78; 80; 81]; and dsDNA [82]. Among these different types of spacers, oligo-T spacers are perhaps the most commonly used [75]; therefore, in this section, we focus on oligo-T spacers.  
Spacer length is an important consideration in spacer design. A spacer with properly designed length should both support the aptamer sequence and allow the aptamers to stand out from the conjugated surface or backfilling molecules [83]. Surface-immobilized aptamers with no or short spacer lengths experience impairments in freedom motions [84], while long spacer lengths can be too long to ensure correct folding of either the aptamers [81] or the spacers themselves [85] for correct secondary structures. For instance, [PERSON] and coworkers [81] demonstrated the importance of spacer length by conjugating sgc8 aptamers onto poly(amidoamine) (PAMAM) dendrimers via oligo-Ts of lengths from 2 to 20 nucleotides for capturing circulating tumor cells (CTCs). The researchers observed that excessively long spacers resulted in unfavorable alterations in aptamer secondary structures, thereby negatively impacting the capturing performance. In addition, [PERSON] and coworkers [86] reported that an increase in oligo-T spacer length resulted in decreasing number of aptamers immobilized onto capturing surfaces. Furthermore, the researchers also reported that increasing oligonucleotide spacer lengths also caused increasing electrostatic repulsion, which, in turn, resulted in stronger electric-charge barriers that eventually inhibited additional aptamers to be immobilized onto the capturing surfaces [86].