esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review
## 4 Results
### UAV Real-Time Object Detection Evaluation
#### 4.7.2 Speed  
As a study on real-time target detection, real time is of prime importance here, and the computation time of target detection can be considered as the most time-consuming item in the whole task. We counted the 63 pieces of literature that reported the detection speed while they used different units. For easy comparison, the units were standardized to frame rate, noted as FPS (frame per second), which is a more common metric in the literature. A higher frame rate indicates a faster speed and shorter time consumption. Some studies recorded the speed in more than one different experiment situation, and the shortest time consumption in the literature was used to represent that study's result. The detection speed was divided into 9 categories, and the distribution is shown in Figure 11.  
Figure 10: The number of studies using different metrics for accuracy.  
Although such a comparison does not account for factors such as input image size, hardware capabilities, algorithm complexity, network conditions, or other potential influences on task detection speed, it does highlight that the majority of applications concentrate detection speeds within the range of 1 to 30 frames per second (FPS). As a result, detection speeds falling within this range can generally be considered as fulfilling the real-time processing demands for most scenarios. Indeed, faster detection must be better in real practice, but the faster speed sacrifices detection accuracy to some extent, for example, by using simple algorithms. Two studies that used traditional methods for real-time detection reported more than 60 FPS. They both selected RGB cameras to detect vegetation indices and obtain the 311 FPS result in the case of low image resolution [69]. Another study using FPGA hardware and software co-design optimized implementation to achieve 107 FPS [68], which has some specificity and cannot be a general approach for every application.
# Confidence Measures for Deep Learning in Domain Adaptation
## 3 Confidence Measures
### Distance from the Classification Boundary  
The _fast gradient sign method_[56], usually employed to generate adversarial examples, has been used to modify the input image until the classifier changes the original classification of the sample:  
\[\eta=\epsilon\;\text{sign}(\bigtriangledown_{\mathbf{x}}(J(\Theta,\mathbf{x}, \mathbf{y}))) \tag{5}\]  
where \(\epsilon\) is a constant, \(\Theta\) collects the model parameters, \(\mathbf{x}\) is the input to the model, \(\mathbf{y}\) is the one-hot original prediction of the network, and \(J(\Theta,\mathbf{x},\mathbf{y})\) is the cost function used to train the neural network. \(\eta\) is added to \(\mathbf{x}\) until a new image \(\mathbf{x}_{ADV}\), whose classification is different from \(\mathbf{y}\), is generated. If a sample is near the classification boundary, it has a high probability to be uncertain. Based on this assumption, two different ways to measure the confidence have been proposed:  
* Euclidean distance between \(\mathbf{x}\) and \(\mathbf{x}_{ADV}\) \[d_{img}=\|\mathbf{x}-\mathbf{x}_{ADV}\|\] (6)
* Magnitude of the gradient computed at the first step of the adversarial example generation procedure \[g=|\bigtriangledown_{\mathbf{x}}(J(\Theta,\mathbf{x},\mathbf{y}))|\] (7)
# Energy-Aware Control of Error Correction Rate for Solar-Powered Wireless Sensor Networks
## 1 Introduction
On the basis of the RS method, we design an advanced energy-aware RS (EA-RS) scheme that adaptively adjusts the parity length of FEC according to the given energy budgets of both the source and target nodes for the next period. This means that the proposed scheme aims to increase the error recovery rate as much as possible while pursuing the three objectives for a solar-powered node described above. As a result, our efforts to enhance the data reliability never increase the blackout time of a node. In summary, EA-RS satisfies the following properties:  
* Energy-adaptive operation: A node running EA-RS adjusts the energy consumption rate according to the energy collection rate by controlling the parity length used in the RS code. Thus, it can satisfy the ENO condition, minimize the waste of harvested energy and reserve energy for non-harvesting times, which leads to the best utilization of harvested energy. * Enhancing the throughput of a WSN: Because the EA-RS utilizes surplus energy to increase data reliability (i.e., the error-correction rate), the amount of data collected at the sink during a time period can be increased. * Fully-localized algorithm: A node running EA-RS only uses information for itself and its one-hop neighbors. This localized approach is crucially important for the scalability of a WSN. The remainder of this paper is organized as follows. In Section 2, we describe the background of this research and review related work. In Section 3, we introduce our scheme for determining the parity length based on the solar-energy model. A performance verification of the proposed scheme through a simulation is presented in Section 4. Finally, we present our conclusions in Section 5.
# Structural Colors Based on Amorphous Arrays Comprised Solely of Silica Particles
## 4 Conclusions  
The process of structural color fabrication is challenging, owing to the complicated specific conditions required. Thus, a simple fabricating method with low angle-dependence was proposed without using additional processes or particles. Atypical silica particles and spherical silica nanoparticles were synthesized simultaneously through a one-step process, and structural colors were produced by inducing an amorphous array. The one-step process used the sol-gel process in which TEOS and a small amount of phenol-formaldehyde were mixed before injecting the silica precursor. Subsequently, spherical silica nanoparticles and atypical silica particles were produced. Moreover, the size of the synthesized spherical silica nanoparticles increased proportionately with the amount of phenol-formaldehyde resin added. Similarly, the size of the atypical silica particles also increased. Atypical silica particles form necks between spherical silica nanoparticles and inhibit the formation of a crystal array. Consequently, silica powder shows a different color depending on the size of the spherical silica nanoparticles. On the contrary, silica powder without atypical silica particle appears ivory white. Synthesized silica powder was dispersed in DI water to obtain a photonic glass. Once the colloidal particles are separated and packed, it is difficult to redistribute the individual particles because the surface energy has already been reduced by aggregation. For this reason, aggregated silica particles were suspended in DI water due to the presence of atypical silica. Therefore, the coated silica solution showed the same color as the silica powder. We confirmed that the coated silica solution induced an amorphous arrangement from the SEM images. The results showed that the simple one-step method for fabricating a photonic glass produced structural colors that were independent of viewing angle.  
[PERSON]-[PERSON] participated in the experiment design, experiment, analysis, and writing. [PERSON]-[PERSON] participated in the experiment. [PERSON]-[PERSON] participated in the supervision, project administration, and review. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (2017R1C1B2007217). This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2019R1I1 A3A01060180). The authors declare no conflict of interest.
# Risk Assessment of Soil Heavy Metals in the Jiahe River Basin of Yantai City, China
## 5 Conclusions  
(1) A comparison of the risk assessment results for five heavy metals, Cr, Hg, As, Pb, and Cd, in soils of the Jiahe River Basin using GB15618-2018 and four pollution indices indicated that the assessment results from GB15618-2018 and the index of geo-accumulation (or I\({}_{\rm geo}\)) were well consistent relative to those from the contamination factor (or C\({}_{\rm f}\)), ecological risk factor (or Er), and enrichment factor (or EF).  
(2) In accordance with the three-level classification scheme established in GB15618-2018 for the pollution risks of heavy metals, the seven-level classification scheme of I\({}_{\rm geo}\) was also merged into three levels. Specifically, I\({}_{\rm geo}\)\(\leq\) 1 corresponded to the background level, 1 \(<\) I\({}_{\rm geo}\)\(\leq\) 4 corresponded to the screening level, and I\({}_{\rm geo}\)\(>\) 4 aligned with the intervention level. This can be viewed as a supplement to GB15618-2018 to evaluate the pollution risk of heavy metals lacking criteria.  
(3) The pollution risk of heavy metals in the Jiahe River Basin of Yantai City was assessed using the method in GB15618-2018 with the newly established I\({}_{\rm geo}\) as a supplement, and the results indicated that the pollution risks of eight heavy metals (i.e., Hg, Pb, Cr, Ni, Zn, Co, V, and Mn) in soils of agricultural land were at the background level, while Cu, As, and Cd were at the screening level.  
Figure 3: Pollution risk of 11 heavy metals in agricultural soils in the Jiahe River Basin of Yantai City.  
**Author Contributions:** X.C. ([PERSON]): conceptualization, data curation, writing--original draft. P.Z.: data curation, writing--review and editing. J.H.: conceptualization, data curation, formal analysis. J.L.: investigation, resources. X.C. ([PERSON]): project administration, funding acquisition. J.C.: data curation. H.L.: data curation. X.Z.: formal analysis. Q.G.: conceptualization, methodology, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by geological survey projects of the China Geological Survey (DD20242837, DD202020990).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** We greatly appreciate the comments from the anonymous reviewers and editors to improve the quality of this manuscript.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 6 Conclusions, Future Works and Limitation
### Future Wireless Communication
An IRS-aided localization for indoor communications could resolve the object within centimetres which is not currently possible with BLE or WiFi standards. * IRS-aided power transfer: Directing the signal power accurately toward a specific location requires perfect CSI. However, perfect CSI is not available at the IRS. Therefore, the IRS has to utilize imperfect or incomplete CSI to calculate the phases for signal reflection. Due to imperfection in the CSI, the reflected signal power toward the desired transceiver is reduced. This spread of power also increases the interference to the other transceivers in the region [46; 47]. Thus, it lowers the overall throughput of the network.
# Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches
## 3 Methods
### Prediction Model Construction
#### 3.4.3. Svm  
An SVM, which is based on statistical learning theory, is a supervised machine learning model that was developed in the 1990s [31]. It is used to solve problems such as binary classification, multi-classification, and regression problems. An SVM constructs a hyperplane as a decision boundary to between two categories and establish a clear distinction.  
Selecting kernel functions and setting hyperparameters are important steps. Common kernels include the linear kernel, Gaussian RBF kernel, RBF kernel, and polynomial kernel. We employed the most basic linear kernel and the popular nonlinear RBF kernel [32] to construct the prediction models. The regularization parameter (C) affects the performance of the linear-SVM; the regularization parameter (C) and gamma value affect the performance of an RBF-SVM. The C value evaluates the trade-off between the classification accuracy of the training sample and the maximization of the decision function boundary. The higher the C value, the better the decision function can classify all training samples with a smaller margin. If the C value is low, then a larger margin is acceptable, which simplifies the decision-making function; however, the training accuracy is decreased. If the gamma value is too high, then overfitting may occur; however, if it is too low, then the model cannot capture the complexity of the data [33].  
We used GridSearchCV in Python to set parameters for the collected data set. Table 4 lists the accuracies obtained with the linear kernel (for various C values of 1, 10, 100, and 1000) and the RBF kernel (for gamma values set to 0.0001, 0.001, 0.1, 1, 10, 100, and 1000 and C values set to 1, 10, 100, and 1000). Fivefold cross-validation was performed. The table indicates the ideal parameter setting for data set 2. The RBF-SVM with a C value of 1000 and gamma value of 0.1 attained the highest accuracy (93.9%).
## 2 Materials and Methods
### Methodology
#### 2.2.4 Region of Interest (ROI) Separability Test  
To determine how separable the six classes were against each other, the ROI Separability Test was employed. The test computes the spectral separability between selected ROI pairs for a given input file, and both the Jeffries-Matusita and Transformed Divergence separability measures are reported [33]. The transformed divergence separability index (TDSI) was used to assess the discriminability of LULC classes and the need to merge any inseparable classes to reduce spectral confusion and increase accuracy.
# A Novel Cryptography-Based Architecture for Secure Data Asset Sharing and Circulation Systems
## 4 Security Technical Framework
### Security Application Layer  
Through the application of security protection technology to the whole process of the production, storage, exchange, processing, return and destruction of data assets, the systematic protection ability of data asset sharing circulation outsourcing storage, multi-party sharing, trusted operation, cross-domain return and other key links is formed. The protection of each link is related to each other, which is more systematic and has higher protection performance than the stacking of a single technology.
## 4 Experiments
### Comparative Experiments in Datasets with Different Domains  
To further verify the performance of our model, we compare the performance of AP-MDSC and DAM on different numbers of domains. Table 3 presents their average sentiment classification accuracies as the number of domains increases from two to five (books, DVDs, kitchen, electronics, and music).  
As shown in Table 3, both DAM and AP-MDSC generally perform better as the number of domains increases. As previously discussed, the expansion of domains can provide  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ModelDomain** & **Books** & **DVDs** & **Kitchne** & **Electronics** & **Overall** \\ \hline LS-single & 77.80 & 77.88 & 84.33 & 81.63 & 80.41 \\ LS-all & 78.40 & 79.76 & 85.73 & 84.67 & 82.14 \\ SVM-single & 78.56 & 78.66 & 84.74 & 83.03 & 81.25 \\ SVM-all & 79.16 & 80.97 & 86.06 & 85.15 & 82.84 \\ LSTM-single & 78.51 & 78.74 & 81.80 & 80.72 & 79.94 \\ LSTM-all & 83.74 & 79.70 & 83.56 & 82.87 & 82.47 \\ MTL-DNN & 79.70 & 80.50 & 82.80 & 82.50 & 81.38 \\ MTL-CNN & 80.20 & 81.00 & 83.00 & 83.40 & 81.90 \\ MTL-SM & 82.80 & 83.00 & 84.30 & 85.50 & 83.90 \\ MDSC-Com & 79.07 & 80.09 & 85.54 & 83.77 & 82.12 \\ RMTL & 81.33 & 82.18 & 87.02 & 85.49 & 84.01 \\ MTL-Graph & 79.66 & 81.84 & 87.06 & 83.69 & 83.06 \\ CMSC & 81.16 & 82.08 & 87.13 & 85.85 & 84.06 \\ ASP-MTL & 84.00 & 85.50 & 86.20 & 86.80 & 85.63 \\ NeuroSent & 79.66 & 80.90 & 86.86 & 86.41 & 83.46 \\ DAM & 87.75 & 86.58 & 88.93 & 87.50 & 87.69 \\ AP-MDSC & 90.04 & 84.01 & 88.07 & 89.90 & 88.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sentiment classification accuracy (%) on each domain of the Amazon dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ModelDomain** & **Books** & **DVDs** & **Kitchen** & **Electronics** & **Music** & **Overall** \\ \hline DAM-2 & 87.57 & 82.57 & - & - & - & 85.07 \\ AP-MDSC-2 & 90.10 & 84.65 & - & - & - & 87.38 \\ \hline DAM-3 & 88.02 & 86.00 & 84.45 & - & - & 86.16 \\ AP-MDSC-3 & 89.21 & 84.65 & 86.14 & - & - & 86.67 \\ \hline DAM-4 & 87.75 & 86.58 & 88.93 & 87.50 & - & 87.69 \\ AP-MDSC-4 & 90.04 & 84.01 & 88.07 & 89.90 & - & 88.01 \\ \hline DAM-5 & 90.79 & 84.95 & 87.72 & 87.72 & 88.76 & 87.99 \\ AP-MDSC-5 & 91.73 & 83.96 & 87.72 & 87.92 & 88.86 & 88.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average sentiment classification accuracies of AP-MDSC and DAM on Amazon datasets with different numbers of domains.  
more shared knowledge in most cases. In addition, the average classification accuracy of AP-MDSC is higher than that of DAM, regardless of the number of domains. These results demonstrate that the attention mechanism used in AP-MDSC can effectively use the ranking of elements to obtain salient sentiment features. However, we also observed that as the number of domains increases, the performance advantage of AP-MDSC decreases. We believe that for multi-task learning problems such as multi-domain sentiment analysis, an increase in the number of domains can lead to more conflicts between tasks, to which AP-MDSC is particularly sensitive. This is a topic worthy of study, and we plan to explore it in future work.
# Rapid Planning of an Assembly Path by Reusing the Prior Path
## 4 Examples and Comparison
### Comparison of Results of PPR Algorithm with Different Numbers of Sampling Points  
The PPR algorithm is used to plan the assembly path from \(x_{start}\) to \(x_{end}\) with different maximum numbers of sampling points \(N\) = 1000 and \(N\) = 2000, where \(\xi_{prior}\) = 0.8, \(\xi_{exploring}\) = 0.2, and \(S\) = 2. The results of Case Studies 1 and 2 are shown in Figures 17 and 18. The start nodes, connection nodes, and the end nodes are marked in the figure and the \(x\), \(y\)-axes denote the horizontal and vertical coordinates in a plane space, respectively.  
Figures 17 and 18 show that path planning using the PPR algorithm is completed even when the number of sampling points is small. Moreover, as the number of sampling points increases, the exploring path segment becomes asymptotically optimal. Since the weight of the prior path is greater than that of the exploring path, the connection node gradually approaches the end node, and the prior path segment of the final path is asymptotically the shortest.  
Figure 16: Case Study 2.  
Figure 17: Assembly paths planned with different \(N\) of Case Study 1.
###### Abstract  
The Vehicle Routing Problem with Time Windows (VRPTW) is an NP-Hard optimization problem which has been intensively studied by researchers due to its applications in real-life cases in the distribution and logistics sector. In this problem, customers define a time slot, within which they must be served by vehicles of a standard capacity. The aim is to define cost-effective routes, minimizing both the number of vehicles and the total traveled distance. When we seek to minimize both attributes at the same time, the problem is considered as multiobjective. Although numerous exact, heuristic and metaheuristic algorithms have been developed to solve the various vehicle routing problems, including the VRPTW, only a few of them face these problems as multiobjective. In the present paper, a Multiobjective Large Neighborhood Search (MOLNS) algorithm is developed to solve the VRPTW. The algorithm is implemented using the Python programming language, and it is evaluated in [PERSON]'s 56 benchmark instances with 100 customers, as well as in [PERSON]'s benchmark instances with 1000 customers. The results obtained from the algorithm are compared to the best-published, in order to validate the algorithm's efficiency and performance. The algorithm is proven to be efficient both in the quality of results, as it offers three new optimal solutions in [PERSON]'s dataset and produces near optimal results in most instances, and in terms of computational time, as, even in cases with up to 1000 customers, good quality results are obtained in less than 15 min. Having the potential to effectively solve real life distribution problems, the present paper also discusses a practical real-life application of this algorithm.  
vehicle routing problem; time windows; VRPTW; metaheuristic; large neighborhood search; multiobjective optimization; freight transportation +
Footnote †: journal:  
0000-0001-13243
## 4 Joint User Scheduling and Hybrid Beamforming Design for Maximizing EE  
In this section, we focus on the robust joint user scheduling and hybrid beamforming design strategy to maximize system EE. To handle the QCQP form problem and nonconvexity in optimization problem \(Q_{1}\), the SDP method is applied to make the optimization problem more tractable. Then, we transform the optimization problem \(Q_{1}\) into a DC programming problem. To address the DC programming problem, we adopt the CCCP algorithm. Finally, a penalty iterative algorithm is adopted to handle the rank-one matrix constraint.
Currently, state-of-the-art technologies, such as photoactivated localization microscopy (PALM) [13], stimulated emission depletion microscopy (STED) [14], 4 pi microscopy [15], confocal microscopy [16], and nonlinear microscopy [17], are being actively developed, and in the modern day, we tend to take nm-level resolution for granted. An alternative to improve the spatial resolution is the use of shorter wavelengths to reduce the diffraction limit of light. Ultraviolet (UV) wavelengths can be applied without a vacuum environment because of the relatively low absorption of UV light at atmospheric pressure, unlike the vacuum conditions required for the use of soft X-rays with a wavelength of a few tens of nm. However, the optical elements available for use at both UV and soft X-ray wavelengths are very limited, contrary to those for visible and infrared wavelengths. In addition, there are limitations on the selection of materials and imaging sample thickness, because recently developed UV microscopes are mainly based on the transmission mode [18; 19; 20]. The spatial resolution of deep ultraviolet (DUV) microscopes cannot reach the extremely high spatial resolution (\(\sim\)nm scale) of a scanning electron microscope (SEM). Also, DUV microscopes may not be appropriate for the inspection of some UV-sensitive optical materials. In this paper, a 193-nm DUV microscope system based on the reflection mode was developed coupled with a long traveling translational stage capable of observing large optical surfaces of the one-meter scale. Using a 193-nm ArF laser, the DUV microscope imaging was directly performed without requiring preprocessing of the sample at room temperature and atmospheric pressure. In addition, implementing the reflective DUV microscope removed the limitations on the selection of materials and optical sample thickness. As a result, reflective DUV microscope imaging was demonstrated with a spatial resolution close to the wavelength diffraction limit.