esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 5 Experiments
### Multi-Source Data Fusion Experiment
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Video** & **FA** & **IDP** & **IDR** & **IDF\({}_{1}\)** \\ \hline
01 & 94.32 & 98.46 & 95.44 & 94.91 \\
02 & 84.29 & 97.53 & 94.50 & 91.01 \\
03 & 91.56 & 94.13 & 87.77 & 89.08 \\
04 & 85.16 & 95.21 & 94.04 & 92.18 \\
05 & 92.63 & 94.03 & 89.30 & 88.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average fusion performance metrics. Figure 15: AR function display of waterway traffic situation awareness. By integrating hydrometeorological sensing equipment on the beacons, real-time local traffic environment data are provided. Display of the effects for three consecutive days separately: **(a)** 22 October; **(b)** 23 October; and **(c)** 23 October.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Ocean Surface Salinity (OSS)
#### 2.4.1 Optical  
The first group of spaceborne OSS products is based on reflectance measurements from optical satellites. The corresponding algorithms are based on a direct relationship between OSS and another ocean parameter, such as OC [160]. The Colored Dissolved Organic Matter (CDOM) [160], single-band reflectance from MODIS [173], SeaWiFS [163], Geostationary Ocean Color Imager (GOCI) [174], Landsat [175], and Sentinel-2 [176], as well as band ratios and band combinations from these satellites, are different methods to empirically estimate OSS from reflectance data.  
Both statistical and ML models have been so far applied to derive OSS using optical imagery. For example, [PERSON] et al. (2020) developed a regression model to estimate OSS from CDOM values in coastal areas to estimate the extent of the problem of saline waters. Additionally, [PERSON] (2020) [177] used seven years of cloud-free MODIS and in situ data along with an ANN model to fill the gap of lacking nearshore OSS measurements in the Northern Gulf of Mexico coast. Finally, [PERSON] et al. [163] proposed a method to generate NRT OSS maps with a resolution of 1 km from MODIS and SeaWiFS data using an ANN model.
# Electromagnetic Testing of Rod Cluster Control Assemblies in Pressured-Water Reactor Power Plants
###### Abstract  
This paper presents an electromagnetic testing system for rod cluster control assemblies used in pressurized-water reactors. The system uses several encircling-type magnetic cameras equivalent to a number of the control rods; each sensor probe composes of an encircling Hall sensor array (EHaS) and a bobbin coil. The EHaS has 16 Hall sensor elements that measure the electromagnetic field distribution in the radial direction of the control rod induced by the bobbin coil for defects. Experiments are performed on artificial defects on the cladding tube of real control rods to simulate short-circumferential grooves (SCGs), sliding wears (SWs), and circumferential cracks (CCs). The system can inspect the artificial SCGs, SWs, and CCs with depths up to 20%, 30%, and 40% of the cladding tube thickness (0.47 mm), respectively. Furthermore, the shape and depth of the defects could be estimated. The standard deviations of depth estimation are 18%, 5.8%, and 6.0% for CCs, SCGs, and SWs. The SCGs and SWs have a small and similar estimation error, but the CCs have the highest error of estimation, and have a small width of 0.2 mm.  
rod cluster control assembly (RCCA); Eddy Current testing; magnetic sensor array; magnetic camera +
Footnote †: journal: Applied Sciences
## 7 Conclusions and Future Work  
In this paper, we analyzed a new approach to the Smallest Grammar Problem, which consisted in optimizing separately the choice of words that are going to be constituents, and the choice of which occurrences of these constituents will be rewritten by non-terminals. Given a choice of constituents, we resolve the optimal choice of constituents with the polynomial-time algorithm \(mgp\). We improve classical offline algorithms by optimizing at each step the choice of the occurrences. This permits to overcome a restriction of these classical algorithms which does not permit them to find smallest grammars.  
The separation allows to define the search space as a lattice over sets of repeats where each node has an associated score corresponding to the size of the MGP of this node. We propose then a new algorithm that finds a local minimum. It explores this search space by adding, but also removing, repeats to the current set of words. Our experiments show that both approaches outperform state-of-the-art algorithms.  
We then analyzed more closely how this approach can be used for structure discovery, a main application of the smallest grammar problem. While in applications of data compression and Kolmogorov complexity, we look for the size of a smallest grammar, in structure discovery the structure itself given by the grammar is the goal. We used our formalization of the smallest grammar problem to analyze the number of possible smallest grammars (globally and given a fixed set of constituents). We proved that there may be an exponential number of grammars with the same minimal size, and analyzed _how_ different they are. Because finding the smallest grammar is intractable, we contented ourself here to study the differences between smallest grammars given the best set of constituents our algorithms were able to find. While in theory there may exist several incompatible smallest grammars, our experiments seem to confirm that, in practical cases, there is an overall stability of the different parses. We think that these results can give both some warnings and clues on how to use the result of an approximation to the smallest grammar problem for structure discovery.  
The optimization of the choice of occurrences opens new perspectives when searching for the smallest grammars, especially for the inference of the structure of sequences. In future work, we want to study how this scheme actually helps to find better structure on real applications. Our efficiency concerns in this paper were oriented toward not being exponential. In future work we plan to investigate more efficient algorithms and study the compromise between execution time and final grammar size.
###### Abstract  
Vehicular ad hoc networks (VANETs) can bolster road safety through the proactive dissemination of emergency messages (EMs) among vehicles, effectively reducing the occurrence of traffic-related accidents. It is difficult to transmit EMs quickly and reliably due to the high-speed mobility of VANET and the attenuation of the wireless signal. However, poor network design and high vehicle mobility are the two most difficult problems that affect VANET's network performance. The real-time traffic situation and network dependability will also be significantly impacted by route selection and message delivery. Many of the current works have undergone studies focused on forwarder selection and message transmission to address these problems. However, these earlier approaches, while effective in forwarder selection and routing, have overlooked the critical aspects of communication overhead and excessive energy consumption, resulting in transmission delays. To address the prevailing challenges, the proposed solutions use edge computing to process and analyze data locally from surrounding cars and infrastructure. EDGE-RSUs are positioned by the side of the road. In intelligent transportation systems, this lowers latency and enhances real-time decision-making by employing proficient forwarder selection techniques and optimizing the dissemination of EMs. In the context of 5G-enabled VANET, this paper introduces a novel routing protocol, namely, the supercluster-based urban multi-hop broadcast and best forwarder selection protocol (UMB-BFS). The improved twin delay deep deterministic policy gradient (IT3 DPG) method is used to select the target region for emergency message distribution after route selection. Clustering is conducted using modified density peak clustering (MDPC). Improved firefly optimization (IFO) is used for optimal path selection. In this way, all emergency messages are quickly disseminated to multiple directions and also manage the traffic in VANET. Finally, we plotted graphs for the following metrics: throughput (3.9 kbps), end-to-end delay (70), coverage (90%), packet delivery ratio (98%), packet received (12.75 k), and transmission delay (57 ms). Our approach's performance is examined using numerical analysis, demonstrating that it performs better than the current methodologies across all measures.  
VANET; EMs; supercluster; UMBBFS; improved firefly optimization; IT3 DPG; MDPC  
1
## 3 Game Models for Channel Selection  
### Game Formulation  
In this section, we use a repeated-game framework to formulate the interaction between SUs and EVs. There are (\(N+E\)) players who join the game, where \(N\) is the number of SUs and \(E\) is the number of eavesdroppers. All players join the game intending to maximize their achievements as defined in Equations (2) and (4).  
Let us define the state of the CR system as,  
\[\mathcal{S}=\left\{s^{k}|k=\left\{1,2,3,\ldots,K\right\}\right\} \tag{10}\]  
where \(s^{k}=\left\{P_{0}^{k},\mathcal{P}_{i}^{k}|i=\left\{1,2,3,\ldots,N\right\}\right\}\). \(P_{0}^{k}\) is defined as the belief of the system about channel \(k\) that represents the probability that channel \(k\) is in state \(A\) (i.e., the channel is free), and \(\mathcal{P}_{i}^{k}\) is the probability that SU \(i\) uses channel \(k\).  
According to the states of the system, in each time slot, each player (SU or EV) should choose its own suitable action. The action set for the SUs is defined as:  
\[\mathcal{A}^{\mathcal{SU}}=\left\{c_{i}|i=\left\{1,2,3,\ldots,N\right\}\right\} \tag{11}\]  
where \(c_{i}\) is the selected channel of SU \(i\) and \(c_{i}\in\ \mathcal{K}\).  
The payoff of SU \(i\) that chooses action \(c_{i}\) is determined as:  
\[\mathcal{U}_{i}\left(c_{i},c_{-i}\right)=P_{0}^{c_{i}}\left(\frac{R_{i,c_{i}} -\max_{\epsilon}R_{i,c_{i}}^{\epsilon}}{R_{i,c_{i}}}\right)^{+} \tag{12}\]  
where \(c_{-i}\) is the action of other users.  
The action of an eavesdropper is defined as:  
\[\mathcal{A}^{\epsilon}=\left\{ev_{e}|e=\left\{1,2,3,\ldots,E\right\}\right\} \tag{13}\]  
where \(ev_{e}\) is the selected channel of EV \(e\) and \(ev_{e}\in\ \mathcal{K}\).  
The payoff of the EV \(e\), when it chooses action \(ev_{e}\), is given as:  
\[\mathcal{U}_{\epsilon}\left(ev_{e}\right)=P_{0}^{ev_{e}}\left(\sum_{i}R_{i,ev_ {e}}^{\epsilon}\right). \tag{14}\]
## 3 Case Study  
### Preliminaries  
Recently, [PERSON] affirms that in Spain, irrigated agriculture accounts for 20% of the total agricultural area, consumes 75% of total water resources, and generates 60% of the total agricultural production and 80% of agricultural exports [56]. Under these circumstances, Aquifer 23 located in Castilla La Mancha, Spain, is considered for the case study. Figure 4 shows the location of this aquifer and the agricultural area that depends on this water resource. The area is basically a sedimentary basin immersed in a karstic system. This aquifer varies in depth between 10 and 70 m, occupying an area of 5500 km\({}^{2}\). Recently, it has been declared an overex exploited aquifer as a consequence of not only poor management and a lack of environmental and water control, but also a lack of planning of water resources. Indeed, it has reached drops of 2.3 m/year over several years of severe extraction. Over the last decade, it has been considered as a remarkable resource recovery example, increasing the groundwater level of the aquifer, as depicted in Figure 5. Presently, this aquifer is still considered overex exploited, mainly due to high influence of recent periods of low rainfall. The recovery process is a consequence of the awareness of this situation and farmers' economic dependence on the aquifer [57]. Irrigation is one of the main economic drivers and sources of sustenance of the rural society in this area [58]. Regarding the climate in the area, it can be classified as continental Mediterranean with dry and hot summers with high solar irradiance levels, and cold winters with certain frost periods. Spring and autumn are characterized by soft and humid periods. Annual rainfall is a determining factor, which in the study area presents relevant oscillations between wetter periods and drier periods, accounting for 350-400 mm per year. However, with the conditions imposed by climate change in recent decades, average annual temperatures are slightly rising while rainfall is being partially reduced, posing a serious risk of desertification. Solar resource has high average potential during sunshine hours, with more than 4900 sunshine hours per year. Figure 6 depicts solar irradiance levels and aquifer depth for the case study.
###### Abstract  
Independent System Operators (ISOs) worldwide face the ever-increasing challenge of coping with uncertainties, which requires sophisticated algorithms for solving unit-commitment (UC) problems of increasing complexity in less-and-less time. Hence, decomposition methods are appealing options to produce easier-to-handle problems that can hopefully return good solutions at reasonable times. When applied to two-stage stochastic models, decomposition often yields subproblems that are embarrassingly parallel. Synchronous parallel-computing techniques are applied to the decomposable subproblem and frequently result in considerable time savings. However, due to the inherent run-time differences amongst the subproblem's optimization models, unequal equipment, and communication overheads, synchronous approaches may underuse the computing resources. Consequently, asynchronous computing constitutes a natural enhancement to existing methods. In this work, we propose a novel extension of the asynchronous level decomposition to solve stochastic hydrothermal UC problems with mixed-integer variables in the first stage. In addition, we combine this novel method with an efficient task allocation to yield an innovative algorithm that far outperforms the current state-of-the-art. We provide convergence analysis of our proposal and assess its computational performance on a testbed consisting of 54 problems from a 46-bus system. Results show that our asynchronous algorithm outperforms its synchronous counterpart in terms of wall-clock computing time in 40% of the problems, providing time savings averaging about 45%, while also reducing the standard deviation of running times over the testbed in the order of 25%.  
stochastic programming; stochastic hydrothermal UC problem; parallel computing; asynchronous computing; level decomposition +
Footnote †: journal:  
_articles_
## 2 Real Bridge Test-Based Fatigue Performance Analysis
### Stress Test Scheme  
To determine the influence of diaphragm buckling on fatigue performance, the stress test data of a real bridge with diaphragm buckling were measured. The experimental investigation focused on a long-span suspension bridge with steel box-girders showing diaphragm deformation (Figure 1). Comparative stress analyses were conducted on matching U-rib pairs (R10) in neighboring diaphragms--including both a deformed (diaphragm-3) and undeformed (diaphragm-4) specimen--to account for potential variations in vehicle loading positions. In addition, field data from steel bridge inspections reveal that fatigue cracks in diaphragm-to-U-rib (DU) welds predominantly initiate at the diaphragm weld toe. These cracks initially propagate along the weld direction, with stress perpendicular to the weld orientation playing a dominant role in crack initiation. Consequently, particular attention should be paid to stresses normal to the weld direction. Based on the existing literature and observed crack characteristics in real bridges, this study employed the stress component perpendicular to the weld direction (one of the principal stress projections) to  
Figure 1: Diaphragm plate buckling.  
qualitatively assess the influence of diaphragm deformation on the fatigue performance of diaphragm-to-U-rib (DU) welded joints. This simplified analytical approach was justified for the purpose of qualitative fatigue evaluation in the current investigation. Strain gauges were strategically positioned on both the DU weld and arc notch surfaces, aligned with the normal stress direction at consistent 10 mm spacings (Figure 2). The specific numbering scheme for these gauges is illustrated in the corresponding figure below [11]. The measuring points A1 and A5 located on diaphragm-3 were pasted on the raised side and the sunken side, respectively, the positions of which were same. The measuring points B1 and B5 were located in the same position on both sides of diaphragm-4. The stress test in this study was conducted during normal bridge operation, capturing the structural response to actual traffic loading conditions.
## 6 Model Verification  
### Sensitivity Analysis  
For evaluating the impact of different independent input variables on developed prediction models, Equation (10) is used. This equation is used by various studies in data-driven methods (e.g., [43; 44]).  
\[r(Inp_{k},\mu_{k})=\frac{\sum_{i=1}^{n}\bigl{(}Inp_{k,i}-\overline{Inp_{k}} \bigr{)}(\mu_{i}-\overline{\mu})}{\sqrt{\sum_{i=1}^{n}\bigl{(}Inp_{k,i}- \overline{Inp_{k}}\bigr{)}^{2}\sum_{i=1}^{n}(\mu_{i}-\overline{\mu})^{2}}}, \tag{10}\]  
where \(r\) is the relevance factor. \(Inp_{k,i}\) and \(\overline{Inp_{k}}\) are the \(i\)th of the \(k\)th and average of \(k\)th input variable, respectively. \(\mu_{i}\) is \(i\)th of the \(k\)th dependent variable, and \(\overline{\mu}\) is average of \(k\)th dependent variable. The results of sensitivity analysis for assessing the impact of each input on the ultimate condition predictions are observed in Figure 9. Based on Figure 9a, \(K_{l}\) and \(f_{\infty}\) had highest impact on predicting\(f_{cc}\). Moreover, all the input variables exhibited a positive influence on \(f_{cc}\), except\(f_{f}\) and \(\varepsilon_{fu}\). Conversely, \(\varepsilon_{cu}\) prediction was significantly influenced by \(\varepsilon_{fu}\), according to Figure 9b. Additionally, \(K_{l}/f_{\infty}\), \(t_{f}\), and \(k_{2}\) developed a positive influence, the same as \(\varepsilon_{fu}\), but other input variables showed a negative effect on \(\varepsilon_{cu}\) prediction. It should be mentioned that \(D\) had a negative influence on the \(\varepsilon_{cu}\) prediction. According to Figure 9c, the only parameter with a positive influence on \(\varepsilon_{h,rup}\) prediction was \(t_{f}\), which was physically expected. Other input data, including \(K_{l}\), \(E_{f}\), and \(f_{\infty}^{\prime}\), showed a negative influence. Moreover, height and diameter slightly influenced \(\varepsilon_{h,rup}\) prediction, and their influence was negative.  
Figure 9: Obtained relevance factor for ultimate condition: (**a**) \(f_{cc}^{\prime}\), (**b**) \(\varepsilon_{cu}\), and (**c**) \(\varepsilon_{h,rup}\).  
Figure 10 displays the influence of different input variables on the prediction of the transition zone of the stress-strain curve. According to Figure 10a, geometry parameters of concrete columns (i.e., \(D\) and \(H\)) slightly influenced the \(f^{\prime}_{c1}\) prediction. Moreover, \(f^{\prime}_{co}\), \(K_{l}\), and \(t_{f}\) were the three parameters with the highest influence on \(f^{\prime}_{c1}\) prediction, and their influences were positive. Additionally, \(E_{f}\), \(f_{p}\), and \(K_{l}\)/\(f_{co}\) negatively influenced the \(f^{\prime}_{c1}\) prediction. According to Figure 10b, there were two input variables of \(E_{f}\) and \(f_{f}\), which negatively influenced the prediction of \(\varepsilon_{c1}\), and other variables had a positive influence. It should be noted again that \(D\) as a geometry parameter slightly influenced \(\varepsilon_{c1}\) prediction, similar to \(f^{\prime}_{c1}\) and \(\varepsilon_{h,nup}\) and opposite to \(\varepsilon_{cu}\).
## 1 Introduction
Therefore, we first compute \(s1\)_xor_\(s2\). In the result, the _one_ bits indicate the positions at which \(s1\) and \(s2\) differ. Subsequently, the _one_ bits in the result are counted; this procedure is called _population count_, which is essential for many other fields of applications such as error detection or even clustering of biological sequences [16]. This work targets a simple but realistic scenario in which 100,000 signatures of partially sequenced genomes (m) are compared to an existing database of 100 million entries (n). In this work, we are concerned only with performance, and so it is sufficient to use synthetic data of the same dimension as the biologically derived signatures. Finding the matching or nearest neighbor signatures requires\(\mathcal{O}(n*m)\), 100 billion comparisons. On a state-of-the-art workstation CPU, this process takes a few minutes, using a highly optimized application and employing specialized intrinsic functions. Since the calculation of Hamming distances between signatures can be executed in parallel, this work focuses on leveraging the inherent parallelism of Field Programmable Gate Arrays (FPGAs) to enable greater throughput. For the results of the comparisons often only the signature indices and the Hamming distance of the best x-results are of interest. These calculations can also be parallelized and performed very efficiently on FPGAs. This work focuses on the implementation of a scalable architecture for binary string comparisons, optimized for high-throughput with configurable bit widths. In general, the architecture is capable of supporting bit widths ranging from 16-bit to 2048-bit. In this work, only the most significant bit widths of 128-bit and 512-bit have been analyzed in detail. However, acceleration of a variety of applications such as Google simhash [13], computing 64-bit Hamming distances or image duplicate retrieval [17] with 128-bit comparisons are also possible with our design. This is also the case for the above-mentioned LSH for DNA sequence analysis, for which signature lengths of 512-bit and greater may allow the representation of longer sequences. The architecture has been designed to make the calculation units for the string comparisons as easily interchangeable as possible, to support other algorithms besides the Hamming distance.