esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 1 Introduction
### The Purpose of This Study  
In future developments, smart cities will emerge as the predominant trend in urban growth. With the continuous advancement and application of technology, smart cities will play an increasingly significant role in urban management, social governance, and economic development. As a critical component of smart cities, the development level of intelligent transportation systems (ITSs) will directly impact urban traffic conditions and residents' quality of life. The primary objective of this research is to promote innovation and development in traffic flow prediction technologies while providing scientific and reliable decision-making support for urban traffic management. By integrating state-of-the-art data processing techniques and machine learning algorithms, this study will analyze the advantages and disadvantages of various technologies, exploring the challenges that they present and their future development trajectories. This research is anticipated to provide more precise data support for key areas such as urban traffic planning, traffic signal control, and emergency response, thereby effectively mitigating traffic congestion, enhancing the public transportation efficiency, and ensuring traffic safety. Furthermore, this study will offer novel research perspectives for related fields such as transportation science, computer science, and data analysis.
# On Combining DeepSnake and Global Saliency for Detection of Orchard Apples
## 3 Target Fruit Detection Based on Fusion Improved Saliency Calculation
### Histogram Back Projection  
Histogram back projection is used to find the most matching point or region of a particular image [28]. For a A \(\times\) B test image, there is a template image with a resolution of X \(\times\) Y. The calculation process of the histogram back projection is as follows:  
1. Cutting a temporary image of (0, 0) to (X, Y) starting from the upper left corner (0, 0) of the input image;
2. Generating a histogram of the temporary image;
3. Comparing the histogram of the temporary image with the histogram of the template image, the comparison result of \(C(H_{1},H_{2})\) is calculated from \(H^{\prime}{}_{k}(j)\) with Equation (9) and \(H^{\prime}{}_{k}(j)\) is calculated using Equation (10): \[C(H_{1},H_{2})=\frac{\sum\limits_{i}^{N}H^{\prime}_{1}(i)H^{\prime}_{2}(i)}{ \sqrt{\sum\limits_{i}^{N}H^{\prime}_{1}{}^{2}(i)H^{\prime}_{2}{}^{2}(i)}}\] (9) \[H^{\prime}_{k}(i)=H_{k}(i)-(1/N)\sum\limits_{j}^{N}H_{k}(j)\] (10)  
where \(k\) = 1, 2, which represents two categories: temporary image and template image. \(i\) = \(j\) = 1, 2, 3,..., \(N\), \(N\) is the number of intervals in the histogram, \(H_{k}(i)\) is the ith interval in the kth histogram value. The larger the \(C(H_{1},H_{2})\) value is, the more it matches the template image.
4. The histogram comparison result c is the pixel value at the result image (0, 0);
5. Cutting the temporary image of the input image from (0, 1) to (10, Y + 1), comparing the histogram, and recording the result image;
6. Repeat steps (1) to (5) until the lower right corner of the input image.  
In this paper, the circumscribed rectangle was made to the largest connected domain of the binary graph, and the rectangle was reduced to realize the rectangular position inside the apple fruit, and the rectangle was intercepted as a template for the histogram back projection. The rectangle was reduced as follows: x = x + w/2, y = y + h/2, w = w/8, h = h/8, where x, y are the values of the image coordinate (x, y) of the upper left corner of the rectangle, h is the high value of the rectangular box, and w is the value of the rectangular box's width. Target detection result \(S_{C}\) is obtained by histogram backprojection in the original RGB input image. The result of the back projection of the RGB histogram \(S_{C}\) was merged with the salient map(\(S(I_{k})\) (from formula (8)) to obtain the final significance graph \(S(I_{k})\):  
\[S(I_{k})=S(I_{k})+S_{C}(I_{k}) \tag{11}\]  
where \(S_{C}\) is the result of back projection.  
The addition of the significant detection results to the histogram back projection results, the results of the histogram significance test can fill some of the fruit parts that are not recognized by the significant detection. After finding all the contours, calculating the area of the inner contour, and filling in data when the inner contour area is smaller than the threshold, that is, the pixel value becomes 255, the threshold value in this article is 7000.
# Communication Infrastructure Design for Reliable UAV Operations in Air Mobility Corridors
## 2 Materials and Methods
### Markov Modeling of Communication Reliability and Estimation of Availability Metrics  
To evaluate and compare the communication reliability of different radio repeater deployment strategies within the UAV corridor, this subsection presents a modeling approach based on continuous-time Markov chains (CTMCs). Each strategy is characterized by a distinct configuration of radio station redundancy and spatial arrangement, which directly affects the probability of communication availability at any given point along the corridor.  
The reliability analysis in this study is formulated using the CTMC model, which is a standard method for evaluating system availability and mean time to failure in fault-tolerant architectures. Each state in the model represents a discrete operational mode of the communication system, characterized by the number of active repeaters remaining functional. Transitions between states occur due to failure or repair events, governed by exponential probability distributions with constant failure, \(\lambda\), and repair, \(\mu\), rates. The derivation of the steady-state probabilities and system availability, \(A\), follows the classical balance equation approach described in standard references such as [28; 29]. Specifically, the Kolmogorov forward equations are solved to determine the long-run state probabilities, which are then used to compute the system's availability as the sum of probabilities of all operational states. This analytical framework is well established in communication system reliability studies and is adapted here to evaluate the performance of various repeater deployment strategies under fault scenarios.  
This section presents the general form of the Markov models, discusses the derivation of stationary state probabilities, and introduces the mathematical formulations for evaluating communication availability and unavailability across the corridor.
# High-Sensitivity Displacement Sensor Using Few-Mode Optical Fibers and the Optical Vernier Effect
## 5 Discussion  
Table 5 provides an overview of various sensors from different applications that have utilized FMF to achieve the OVE. This table is organized chronologically, listing the year, configuration, application, measurement range, sensitivity, and \(M\) factor. The versatility of FMF in developing robust and sensitive sensors is highlighted, specifically with significant improvements in sensitivity achieved over time. Notably, unlike other sensors, our work presents, for the first time, a displacement sensor. Our sensor offers a measurement range of 0 to 15 mm, making it suitable for applications requiring precise control of small displacements. In terms of sensitivity and the \(M\) factor, both parameters increase significantly with greater FMF lengths; however, the operational range decreases. This trade-off allows for the sensor to be adjusted for various levels of sensitivity or range, providing flexibility in design. While some sensors utilize more complex configurations, such as hybrid structures [4], cascaded microcavities [8; 9; 11], or parallel Fabry-Perot structures [7], our design, based on a previously implemented structure [5], offers simplicity, robustness, high sensitivity, and precision. Additionally, it presents significant advantages in terms of production cost and ease of implementation. However, the limitations of this configuration include sensitivity to external factors such as temperature fluctuations or vibrations, which  
Figure 14: Five devices demonstrating Blueshift (red arrows) in panels (**a**,**c**,**e**,**g**,**i**) and Redshift (blue arrows) in panels (**b**,**d**,**f**,**h**,**j**).  
can introduce noise into the system. Additionally, the limited displacement range might restrict its applicability in scenarios that require measurements of larger displacements.  
A potential future development could involve integrating the SFS structure with fiber Bragg gratings [20] or laboratory-on-fiber microstructures [21] to further improve sensitivity and resolution. This approach would expand the potential applications to new fields, such as medical healthcare and advanced diagnostics, offering a promising avenue for the development of next-generation optical fiber sensors.
## 4 Results and Discussion  
In this section, we will first analyze the mean Accuracy of classifiers under varying values of the pretrain_size parameter, which refers to the number of samples to use for pretraining the model before starting the prequential learning process. We will then compare the overall performance of the classifiers according to Accuracy, Recall, Precision, and F1-score metrics. Plots that monitor the performance metrics of both classifiers over time are also given. Finally, the running time (training time and testing time) of both classifiers are assessed and compared.  
The dataset used for evaluation includes eight features and two classes (Normal, BKH) with 4492 data samples as Normal and 4670 as black hole.  
Regarding the choice of the pretrain_size parameter for prequential evaluation, we evaluated the mean Accuracy of both classifiers under varying values of the pretrain_size in order to choose the most appropriate value. Table 3 reports the obtained results.  
The results exhibited in Table 3 show that both classifiers Adaptive Random Forest (ARF) and K-Nearest Neighbors (KNN) record Accuracy above 86% for the different values of Pretraining size. It is also observed that Adaptive Random Forest (ARF) achieves higher Accuracy values than the KNN, which reaches almost 95% for the different values of the pretrain_size parameter. Further, we note that the overall Accuracy increases as the pretrain_size increases (varying from 400, 600, 1000 to 1200) for both classifiers.  
The Accuracy results of Adaptive Random Forest (ARF) varies from 94.73% to 94.90%, presenting a slight increase, while KNN improves its Accuracy from 86.86% to 87.38% when increasing the pretrain_size from 400 to 1200.  
Overall, the results of the carried-out experiments clearly indicate that using ARF achieves good Accuracy even with small pretraining data sizes, while the KNN's classifier Accuracy improves as the pretrain_size values increase.  
This can be explained by the inherent characteristics of the Adaptive Random Forest (ARF) algorithm which is based on ensemble learning. ARF tends to leverage diverse models trained on different subsets of data to make a decision about the class label. This allows ARF to perform well in situations with a small number of pretraining samples. The K-Nearest Neighbors (KNN) classifier, on the other hand, relies on the nearest neighbors for classification.  
This suggest that when increasing the pretraining data size, the KNN algorithm can achieve better performance.  
Therefore, considering the above findings, we adopt {pretrain-size = 1200} for the evaluation of the classifiers in the rest of our study.
# Durability and Safety Performance of Pavements with Added Photocatalysts
## 5 Conclusions  
The impact of three types of TiO\({}_{2}\) photocatalysts (slurries emulsions, and built-in) on the durability and safety of pavements (tiles and asphalts) has been studied. According to the results, the effects do not seem to be related to the photocatalytic properties themselves but derived mainly from the physical effects of the additions. The slurry solutions on the open-grade asphalt increase the mechanical strength by compacting the asphalt substrate. This filling implies a reduction in surface roughness, which could lead to a decrease in slip resistance. Capillary pores in the cementitious paste were introduced, resulting in an increase in water intake by capillary absorption and reduction of the resistance to freeze-thaw. Photocatalytic emulsions sprayed on pavements should not be detrimental to the freeze-thaw resistance of the substrate itself, although most of the photocatalytic layer would be worn away or damaged in the process. A compact photocatalytic coating reduces capillary porosity accessible to water and consequently raises the resistance to water ingress if it converts the large capillary pores in the matrix into small capillary pores,  
Figure 15: Schema of ultrasonic pulse travel in 15 \(\times\) 8 cm core specimens and whole slabs with differently spaced transducers.  
refining porosity. In contrast, if it generates more capillary pores than initially present, water intake increases. The behaviour of the tiles with built-in photocatalysts is dependent on their differences between their physical-chemical characteristics, loading and mode of implementation. These results indicate the importance of carrying out specified studies of the pavements prior to the application of photocatalytic solutions.  
Test development: M.G.; Conceptualization, M.C.; methodology, M.C.; software, F.R.; validation, M.C. and E.J.-R.; formal analysis M.C. and E.J.-R.; data curation, E.J.-R. and M.C.; writing--original draft preparation, M.C. and E.J.-R.; writing--review and editing, M.C. and E.J.-R.; supervision, M.C. and E.J.-R.; project administration, M.C.; funding acquisition, M.C. All authors have read and agreed to the published version of the manuscript.  
This study was co-funded by the LIFE programme (LIFE PHOTOSCALING, LIFE 13/ENV/ES/001221, [[http://www.life-photoscaling.eu/](http://www.life-photoscaling.eu/)]([http://www.life-photoscaling.eu/](http://www.life-photoscaling.eu/))).  
Not applicable.  
The authors wish to thank the manufacturers for supplying the photocatalytic products used.  
The authors declare no conflict of interest.
## 1 Introduction
Recently, the ability of algal biomass to be conditioned under different forms, such as pure algal beads [32] or algal foams, opened a new perspective for their applications in fixed-bed columns and dynamic systems (e.g., reactive sponges with highly percolating properties) [33; 34; 35]. The reactivity of algal biomass for metal binding can be improved by the incorporation of other functional groups. For example, by reaction with polyethyleneimine (PEI) and crosslinking, it is possible to synthesize polyfunctional sorbents [32]: the incorporation of PEI can be carried out heterogeneously (incorporation of microparticles of crosslinked PEI) or homogeneously (post-crosslinking of PEI, after intimate mixing and impregnation) [36]. It may be useful adding alginate in the synthesis to improve the resistance of the composite material. The one-pot synthesis is based on the partial extraction of alginate from algal biomass, the ionic interaction between carboxylic groups of alginate and amine groups of PEI, followed by the ionotropic gelation of residual carboxylic groups with calcium and the crosslinking of residual amine groups with glutaraldehyde. This double crosslinking and the interpenetration of polymers produce very stable and highly reactive beads (APEI, algal/PEI beads) for the sorption of heavy transition metal ions. The current work describes the functionalization of these composite beads (APEI) with specific functional groups in order to improve the reactivity of the sorbent for strontium. The functionalization is based on the nitrification of amine groups on the composite, followed by the proper amidoximation of the intermediary product. The first part of the study includes an extensive characterization of the materials to clarify the functionalization mechanism and identify the reactive groups and their modes of interaction with Sr(II). In a second step, the sorption properties are investigated through the study of pH effect, uptake kinetics, sorption isotherms, effects of competition (multi-component solutions),metal desorption, and sorbent recycling. Finally, the sorbent is tested for the removal of Sr(II) from two seawater samples as an example of complex media.
# An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors
###### Abstract  
Wireless sensor nodes have a limited power budget, though they are often expected to be functional in the field once deployed for extended periods of time. Therefore, minimization of energy consumption and energy harvesting technology in Wireless Sensor Networks (WSN) are key tools for maximizing network lifetime, and achieving self-sustainability. This paper proposes an energy aware Adaptive Sampling Algorithm (ASA) for WSN with power hungry sensors and harvesting capabilities, an energy management technique that can be implemented on any WSN platform with enough processing power to execute the proposed algorithm. An existing state-of-the-art ASA developed for wireless sensor networks with power hungry sensors is optimized and enhanced to adapt the sampling frequency according to the available energy of the node. The proposed algorithm is evaluated using two in-field testbeds that are supplied by two different energy harvesting sources (solar and wind). Simulation and comparison between the state-of-the-art ASA and the proposed energy aware ASA (EASA) in terms of energy durability are carried out using in-field measured harvested energy (using both wind and solar sources) and power hungry sensors (ultrasonic wind sensor and gas sensors). The simulation results demonstrate that using ASA in combination with an energy aware function on the nodes can drastically increase the lifetime of a WSN node and enable self-sustainability. In fact, the proposed EASA in conjunction with energy harvesting capability can lead towards perpetual WSN operation and significantly outperform the state-of-the-art ASA.  
adaptive sampling; energy harvesting; energy management; power hungry sensors; solar energy harvesting; wind energy harvesting; WSN +
Footnote †: journal: Sensors
## 3 Research Results and Discussion
### Fatigue Test
The studies by [32; 33] showed that the fatigue limit was reduced when TiN and CrN single-layer coatings were applied separately. In our study, as a solution to this problem, multilayer Cr/CrN/TiCrN/TiCrCN coating was grown on the AA6063 Al alloy. However, contrary to the expected increase in fatigue, it reduced the fatigue limit. The decrease in the tensile strength of the AA6063 Al alloy with the coating in the current study is also supported by literature information. Although an adhesion investigation was not performed in our research, it is thought that the coating surface adhesion may be low. It is predicted that the crack that starts in the coating progresses rapidly and results in fracture since it is thought that there is a coating/substrate incompatibility in the ductile AA6063 material and the relatively more brittle Cr and high-strength Ti-based coating.
## 2 Materials and Methods  
### Overall System Architecture  
The smart ocean aquaculture monitoring system proposed in this research is a highly integrated platform that utilizes multiple remote control systems and mobile communication technologies as its core components. This system facilitates the refined management and real-time monitoring of aquaculture sites (Figure 1). The overall system architecture comprises an underwater ROV, a 4G mobile communication network module, and several remote control systems. The design focus of the system is to empower aquaculture operators to perform remote control through various devices while leveraging mobile communication technology to ensure stable and reliable data transmission. This design maintains robust connectivity even in aquaculture environments that are far from land. Each component has clearly defined responsibilities and works collaboratively, ensuring that the system operates efficiently and stably across diverse aquaculture environments.  
The hardware implementation employs a modular design approach, comprising two primary modules: the underwater ROV and the buoy system. The buoy system functions as a communication relay station, linking the ROV with land-based control systems. To optimize resource management, the system architecture is designed to minimize the weight of the ROV while enhancing its mobility. It transmits information and control signals through composite cables, significantly simplifying connection complexity. The system works in different aquaculture settings: offshore cage nets, oyster farms, land-based fish ponds, and similar environments. By utilizing an integrated solution, it not only enables real-time water quality monitoring but also facilitates the periodic inspection and maintenance of underwater equipment, significantly enhancing the management efficiency and productivity of the aquaculture environment.  
Figure 1: Overall architecture of the smart ocean aquaculture monitoring system.
# Quality Assessment of Dual-Parallel Edge Deblocking Filter Architecture for HEVC/H.265
## 2 Related Work on Deblocking Filters  
The deblocking filter and the sample adaptive offset (SAO) filter are the two filters used within the codec in the HEVC coding standard. To enhance the visual quality of the reconstructed frames, these filters are applied in two phases, the first stage applying the deblocking filter and the second stage applying the SAO filter. Blocking artifacts are eliminated by the deblocking filter, and the SAO filter enhances the visual quality by adding the offset values to the first-stage filtered pixel samples [7]. Edge offset or band offset are two possible offset values. The two filters for the HEVC coding standard are implemented in hardware adopting several architectural optimization techniques.  
The SAO filter and deblocking filter are combined and implemented in hardware, or these filters are implemented separately as deblocking filter [2; 8; 9; 10; 11; 12; 13] and SAO filter [15]. Parallel and pipeline-based architectures are utilized to implement the area and throughput optimized deblocking filter. Few deblocking filter architectures use novel ordering for the filtering process to improve the performance. Several filter architectures were implemented by [8; 16; 17; 18; 19; 20; 21; 22; 23] to realize the H.265 deblocking filter. The deblocking filter architecture of H.264 is more complex compared to the deblocking filter architecture of H.265/HEVC deblocking filter [24]. SAO and deblocking filters  
Figure 1: Variation of pixel intensities between block boundaries.  
Figure 2: Smoothing of pixel intensities between block boundaries.  
were combined and are implemented by [17; 23; 25; 26; 27]. In [22; 28], a graphics processing unit (GPU) was used to implement the in-loop filter using parallelism. A multicore coprocessor was used to implement the HEVC in-loop filtering in [19]. Convolutional neural network (CNN) was used by [29; 30] to create an in-loop deblocking filter with coding unit categorization.