esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface Defect Detection Methods for Industrial Products: A Review
## 3 Surface Defect Detection Method of Industrial Products Based on Deep Learning
### Unsupervised Method
#### 3.2.3 Deep Belief Networks  
The deep belief networks are composed of multiple RBMs (restricted Boltzmann machines), and the training of the whole network is completed by training the RBMs individually layer by layer.  
Literature [69] proposed a defect detection algorithm for solar cells based on DBN. This algorithm took reconstructed images and training images as supervision data and established a good mapping relationship between training samples and non-defect images through the fine-tuning network of BP algorithm. Literature [70] proposed a DS-DBN-SVM (Differential Search-Deep belief network-Support vector machine) model to identify the type of bolt defects. In this model, the DS algorithm is used to optimize the weights and thresholds of the DBN network; the DS-DBN model is used to extract the feature of bolt data, and the extracted feature is used as the input of SVM to identify the bolt defect type.
## 5 Experimental Results and Discussion
### Discussion
The proposed ANC system is independent of transfer functions between sources and error sensors. In this work, every solution constitutes a single measurement with a duration of \(\sim\)3 s; thus, the whole optimization procedure may take several minutes to complete. Nevertheless, this is only what it takes to face and suppress an annoying low-frequency tone for a single source configuration. In the case that the noise tone shifts, which is not met often in industrial equipment operating at a steady cycle rate, then the compound setup can suppress it with a new genetic evolution. It is worth mentioning that adaptive mutation proves to be quite helpful in reducing the experimental duration, as it can reach the optimal result in earlier generations. In the experiments, one generation with 40 individuals could last \(\sim\)2 min. From Figure 11, it is observed that 20 generations are enough to reach the optimal attenuation using adaptive mutation.
## 4 Proposed DETD Algorithm and Simulation Results
### Evaluation of the Proposed Method and Results  
In Table 3, we report the average classification performance in terms of detection rate (DR) and false positive rate (FPR). From the simulation results of Table 3, we see that DETD has 97.5% DR and 4% FPR for d-ToU pricing, while having 98% DR and 3% FPR for f-ToU pricing. We confirm that the proposed scheme in Table 3 has an improved performance when compared to existing schemes in Table 2. We see that DETD detects higher theft cases than 87% and 89% of the theft cases detected by \"vanilla\" GBTD [12] and 67% and 72% of theft cases detected by CPBETD for d-ToU and f-ToU cases, respectively.  
The performance increase can be attributed to having trained with the new window-based theft cases, which are based on not only the energy consumption but also the tariff in contrast with earlier models. CPBETD and \"vanilla\" GBTD were primarily trained only on consumption, with theft occurring throughout the day. When faced with irregular and smart (i.e., window-based) theft cases, these existing algorithms failed to detect the majority of them because of lack of training.  
Lack of historical data is also an important issue that can be addressed using DETD. To study the effect of testing and training set ratio on DETD's performance, we run the DETD algorithm multiple times, starting at 10 percent up to 95 percent of a year's data as training data, with a 5 percent increment at every iteration. Due to the intensive nature of this simulation, we present an average of 50 random consumers as a preliminary report in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**DETD** & **d-ToU** & **f-ToU** \\ \hline DR [\%] & 97.5 & 98 \\ FPR [\%] & 4 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average theft detection performance of DETD when using the d-ToU and f-ToU window-based theft cases for an average of 1000 consumers.  
Figure 4: Novel concatenation data processing in the DETD algorithm.  
We observe that with as little as 5 percent of a year's data being used for training, the DR is around 87% and the FPR is around 24%. As expected, the DRs and FPRs gradually improve over the course of increasing training data, with DR being around 99% and FPR being around 3% when 90 percent of the data is used for training.
# Risk Assessment and Source Apportionment of Heavy Metals in Soils from Handan City
###### Abstract  
Soil-heavy metals are potentially harmful to the ecosystem and human health. Quantifying heavy metals sources is conducive to pollution control. In this study, 64 surface-soil samples were collected in Handan city. Cr, Mn, Ni, Cu, Zn, Cd and Pb were determined; then, their spatial distribution in the sampling area was drawn by ArcGIS. The pollution index (_PI_) method, geo-accumulation index (_lgu_) method, Nemenow integrated pollution index (_NIPI) and pollution load index (_PLI_) were used to evaluate the pollution level of heavy metals in surface soil; then, an ecological and health risk assessment of soil-heavy metals was carried out. Combined with the spatial distribution, correlation analysis, cluster analysis, PCA and PMF model, the pollution sources of heavy metals in soil were identified and apportioned. The results showed that the average content of Cd was nearly ten times that of the background limit, which was the most serious among the studied metals. In terms of non-carcinogenic risk, Cr had the highest value, followed by Pb. In terms of carcinogenic risk, Cd, Cr, and Ni had an acceptable or tolerable risk. Three pollution sources were identified by cluster analysis and PCA, including traffic sources with Cu, Pb and Cd as main loads, industrial sources with Mn, Cd and Zn as main loads, and natural sources with Cr and Ni as main loads. The PMF model analyzed three main factors: traffic source (17.61%), natural source (28.62%) and industrial source (53.77%). The source categories and the main load elements obtained from the source apportionment results were consistent with the source identification results.  
heavy metal; spatial distribution; ecological risk; health risk; PCA; PMF +
Footnote †: journal: _Sci._
## 6 Conclusions  
We investigate the energy efficiency optimization of rate splitting multiple access in an integrated satellite terrestrial network, where multiple users are served by a satellite with a QoS constraint. This problem is non-convex and NP-hard; therefore, we adopt a soft actor-critic deep reinforcement learning approach as it can deal with continuous state/action space tasks. To satisfy the QoS constraint, the Lagrangian relaxation technique is incorporated to convert the original problem into a constrained one. Also, an on-off mechanism is introduced to avoid frequent unnecessary learning processes if the state changes little. The simulation results validate that the proposed constrained SAC algorithm achieves a better performance while meeting the QoS requirement comparing with other algorithms in terms of convergence speed and energy efficiency. Time cost is reduced as well when the on-off mechanism is adopted. Future work will focus on CSAC improvement, such as exploration and sample efficiency, and on RSMA in a more complicated system like integrated space air terrestrial networks and satellite-based sensor networks.  
Conceptualization, Q.Z.; methodology, Q.Z. and L.Z.; software, Q.Z., Y.C. and S.J.; validation, Q.Z., Y.C. and S.J.; formal analysis, Q.Z.; investigation, L.Z.; writing--original draft preparation, Q.Z.; writing--review and editing, Q.Z. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 4 The Scheduler Architecture
### Queue Management System  
In this section, we put forward modern scheduling techniques which are modeled for the scheduling of packets with deadlines and rewards. The Deadline Scheduling with Commitment (DSC) and improved and upgraded EDF scheduling techniques are examined here as the reference techniques for the proposed Optimal Usage and Dropping (OUD) scheduling technique, which is also analyzed in this paper.
## 5 Conclusions and Future Work  
Here, we presented the Vector Fitting-Cauchy Method (VCM) and validation of the CNRs calculated with frequency-domain methods using FSV. These preliminary results show an advantageous procedure for calculating more reliable resonances from backscattered signals originating from buried objects. As the SNR and the effects on recovery are critical, VCM provides an alternative to previous CNR extraction methods for object recognition. More experiments in different soils will provide a better insight into its behavior in different soil conditions that emulate the landmine context in Colombia. These GPR operations for identifying IEDs is of high interest, and this method, alongside other SEM approaches, are currently being studied and used.  
The CNR reliability increase, coming from the validation with FSV and the more accurate pole locations, show a better reconstruction of the signals, and the VCM can be improved in the future by refining the general procedure and reducing the numerical noise in all the matrix operations.  
Conceptualization, A.G.; Formal analysis, A.G.; Investigation, A.G. and E.P.; Methodology, A.G.; Writing--original draft, A.G.; Writing--review and editing, A.G. and F.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Minicencias grant number 727 and the EMC-UN research laboratory.  
Not applicable.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SVD & Singular Value Decomposition \\ CNR & Complex Natural Resonances \\ LT & Linear Prediction matrix \\ LTI & Linear Time Invariant system \\ H & Transfer Function in frequency domain \\ VF & Vector Fitting \\ VCM & Vector Fitting-Cauchy Method \\ FSV & Feature Selective Validation \\ GPR & Ground Penetrating Radar \\ SEM & Singularity Expansion Method \\ TLS & Total Least Square \\ SNR & Signal-to-Noise Ratio \\ \end{tabular}
# Multi-View Visual Relationship Detection with Estimated Depth Map
## 4 Experimental Procedure
### Visual Relationship Detection
#### 4.1.2 Setup  
Based on the recall evaluation setups in [4; 7], the performance of the proposed multi-view VRD framework was evaluated in conditions of recall@50 and recall@100 (recall@x is the fraction of times the correct relationships are extracted in the top x confident relationship extractions). There were three visual relationship detection tasks in the recall evaluation:  
1. Predicate detection (Pre. Det.). The input was an original RGB image and a set of ground truth objects annotations. Our task was to extract the possible predicates between the located object pairs. This task condition was to evaluate the visual relationship classifier performance without effects of the object detector.
2. Phrase detection (Phr. Det.). The input was only an original RGB image. Our task was to output a visual relationship triplet (subject-predicate-object) and the entire bounding box which contained the corresponding subject and object simultaneously. This entire bounding box should have at least \(IoU=0.5\) overlap with the ground truth region.
3. Relationship detection (Rel. Det.). The input was only an original RGB image. Our task was to output a visual relationship triplet (subject-predicate-object), and locate the subject and object, respectively, in the image. These two bounding boxes should both have at least \(IoU=0.3\) overlap with the ground truth bounding boxes.  
The training parameters of the all Faster R-CNN in the RGB-D object detector module were as follows: initial learning rate was 0.001, the optimizer was SGD, the bath size was 256, iterations of the RGB Faster R-CNN were 4 \(\times\) 100,000, and iterations of the depth Faster R-CNN were 4 \(\times\) 90,000.
# 2 CA-R2: A Hybrid MAC Protocol for Machine-Type Communications
## 3 The 2C Algorithm and Its Adaptable Version
### The Adaptive-2C Algorithm
By chance, all of them decide to move to the waiting cell; thus, an empty slot is observed. * Seventh slot. According to the rules of the Adaptive-2C protocol, after an empty slot, all stations in the waiting cell switch their state and transmit. Since another collision is observed, the central station returns a C message. * Eighth slot. Note that the collision contained in the seventh slot involved all active stations. However, it came after an empty slot and not after a successful transmission. Therefore, the estimate for the number of contending stations should not be updated  
Figure 3: Example of a CRI using Adaptive-2C with the update-per-phase policy. with respect to the value used at the beginning of the current phase. Therefore, the stations use the last known value of \(p\) (i.e, \(p=p(\hat{N}-2)\)) to determine whether to continue transmitting or not. In the example, station B continues transmitting and A goes to the waiting cell. A successful transmission is observed. * Ninth slot. The process continues according to the rules of the algorithm, and the last contending station successfully transmits its data packet. When the corresponding feedback returns, the CRI ends. Note that this event is signaled with two consecutive NC messages, as was also the case in the standard 2C algorithm. Note that during the evolution of this CRI, there were successful transmissions in slots 2, 4, 8, and 9. Therefore, at the end of the CRI, it is known that the initial collision (in the first slot) had a multiplicity of 4. This information helps the central station to estimate the initial collision multiplicity in the following CRIs. As a second example, Figure 4, illustrates the different events that can be contained in a time slot and how the transmission probability accordingly changes during the evolution of a CRI. Slot 1 contains the first collision. Slot 2 corresponds to a case where all stations abandon the transmission state and an empty slot occurs. Slot 3 contains a collision after an empty slot. Slot 4 contains a collision that is neither the first collision of the CRI nor a collision after an empty slot. Slots 5 and 6 contain the two successful transmissions. Figure 4: Timing diagram of a CRI corresponding to the Adaptive-2C algorithm in a network consisting of two stations (i.e., “Sta A” and “Sta B”) and a central station (CS).
## 4 Evaluation
### Validation of Scientific Workflow  
An experiment was conducted to determine whether CSS was effective in the scientific workflow type application used in the actual research field. In the experiment, an open-source framework designed for the purpose of assembling high-noise single-molecule arrays called CANU was used. The experiment was conducted based on P6-C4 molecule data published by Pacific Biosciences for _Escherichia coli_ K12 Interpretation [16]. In order to reproduce the container environment with a high resource contention level, the MG C-class workload was executed together in a minute unit during CANU execution. In the experiment, the execution time was measured by configuring a non-_cgroups_ environment in which _cgroups_ were not set as a baseline, and the resource management interval options were set to 5 or 3. Figure 9 shows the form of a scientific workflow job running on the CANU framework. In the figure, the circle means the process being created, and the label inside the circle means the number of futex system calls occurring in the process. \(t\) means the time in seconds since CANU was executed.  
Figure 9 shows that the process structure of CANU changes flexibly at runtime and that futex system calls occur overall in processes with multiple layers. These results indicate that CANU is suitable for validating the effectiveness of CSS.  
As shown in Figure 10, a performance improvement of 3.4% was obtained when the CSS interval parameter was 5, and a performance improvement of 4.5% was obtained when the interval parameter was 3. Through these experimental results, it was verified that performance improvement could be obtained without source code modification or manual resource management for scientific workflow applications used in actual scientific fields.  
Figure 8: Performance of CSS for heterogeneity of concurrent tasks.  
Figure 9: Forms of CANU framework work. (**a**) \(t=100\) (**b**) \(t=200\) (**c**) \(t=300\).
# An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks +
In addition, it is challenging to find an optimal parent node in a large-scale WSN because the scale of WSNs is growing and the deployment is more complex. To eliminate the bias of a decision maker and make adaptive decisions in a complex deployment scenario, in this work, we extend our previous work with the specific goal of achieving multiple objectives, such as high reliability, low latency, and energy efficiency. To find the best parent node, we use empirical data about the target network obtained through Q-learning. Specifically, we define a state space, action set, and reward function using multiple cognitive metrics, and then find the best parent node through trial and error. The main contributions of this work are summarized as follows:  
* To jointly achieve multiple objectives in WSNs, in this work, we revisit the classic problem of finding an optimal parent node in the tree-based routing. Specifically, we propose an enhanced tree-based routing protocol based on reinforcement learning (RL);
* To cope with various network scenarios, e.g., link breakage and congestion, we propose multiple cognitive metrics. Specifically, in addition to hop count, three types of cognitive metrics are formulated: weighted average of received signal strength, buffer occupancy ratio, and power consumption ratio. These metrics affect the decision rules in tree routing;* We present the system model for applying RL algorithm in WSNs and specify the basic operations of the proposed tree-based routing protocol. Specifically, we specify concrete algorithms for loop detection and parent update, as well as tree construction using build and hello messages;
* To make adaptive decisions in a complex deployment scenario, we define a state space, action set, and reward function. The agent recognizes the network state with the proposed cognitive metrics and finds the best parent node with the highest reward through trial and error;
* Through a comparative study using diverse simulations, we verify that the proposed parent selection scheme supports a reasonable trade-off between the performance metrics, i.e., end-to-end delay, packet delivery ratio, and energy consumption.
# A Novel Batch Streaming Pipeline for Radar Emitter Classification
## 2 Background Knowledge
### Bayesian Optimization  
In machine learning and deep learning, model performance is often dependent on a set of hyperparameters that need to be carefully tuned, to achieve optimal results. Bayesian optimization [34] is a powerful and efficient method for the hyperparameter tuning process.  
Bayesian optimization is a global optimization technique that aims to find the maximum (or minimum) of an unknown, expensive-to-evaluate, and possibly noisy objective function. In the context of machine learning or deep learning, this objective function is typically the performance score of the model (e.g., accuracy, loss, or F1-score) on a validation dataset. The objective is to find the best set of hyperparameters that maximize the model's performance. The key idea behind Bayesian optimization is to build a probabilistic surrogate model that approximates the true objective function. This surrogate model is typically a Gaussian process or other probabilistic models. The surrogate model helps in modeling the uncertainty of the objective function and guides the search for the optimal hyperparameters.  
The Bayesian optimization process can be summarized as follows:  
1. An acquisition function, often based on the surrogate model, is used to determine the next set of hyperparameters to evaluate. Common acquisition functions include expected improvement, probability of improvement, and upper confidence bound.
2. The acquisition function suggests a set of hyperparameters for evaluation, and the true objective function is queried, to obtain its performance on the validation dataset.
3. The surrogate model is updated with the new data point, which improves its approximation of the objective function.
4. Steps 1 to 3 are repeated for a predefined number of iterations, and the process converges to the optimal set of hyperparameters.  
Bayesian optimization offers several advantages, including its ability to handle noisy objective functions, make efficient use of limited computational resources, and focus on promising regions of the hyperparameter space. By incorporating Bayesian optimization into machine learning or a deep learning workflow, we can streamline the process of hyperparameter tuning and achieve State-of-the-Art results with less manual trial and error.
## 4 Construction of Core-Shell Nanostructures
### Au@Ag Core-Shell Nanostructures
In 2009, their group produced Au@Ag core-shell triangular bifrustums via chemical reduction route by AsA [100]. Compared with the photocatalyst they used, the sizes of Ag triangles were bigger and the triangular bifrustum became thicker with the increase of the addition amount of AgNO\({}_{3}\), significantly affecting the SPR spectrum. Recently, [PERSON]'s group developed a combination of microwave and polyol reduction route to prepare Au@Ag core-shell triangles [94] as Figure 11 shown. In the first step, Au nanocrystals seeds were obtained by reducing HAuCl\({}_{4}\) in EG in the presence of PVP under microwave heating conditions. In the second step, as-prepared Au seeds were added into a DMF solution of AgNO\({}_{3}\) to form Ag shells in an oil bath. The method can prepare not only triangular Au@Ag core-shells, but also nanoplates, octahedrons and decahedrons depending on the shapes of the Au seeds. In addition, these Ag shell shapes are significantly different from those prepared in EG by microwave heating from the same Au seeds. The reason is the favorable facets of Ag nanostructures produced in EG and DMF are different, which has been discussed before. Using the microwave-polyol method, their group also prepared Au@Ag core-shell icosahedrons [105]. In 2012, they explored the growth mechanism of Au@Ag core-shell octahedra and decahedra using a similar method [106]. They argued that nanocrystal growth process mainly depends on the way of heating. Uniform Au@Ag core-shell octahedra and decahedra can be obtained under oil bath heating, while under fast microwave heating the Ag shell is not uniform. Above all, the microwave-polyol method provides a new and facile path for synthesize various Au@Ag core-shell nanostructures. In addition to these Au@Ag core-shell nanostructures above, Ag nanocubes also receive much attention. [PERSON] _et al_. [107] synthesized Au@Ag core-shell nanocubes by using Au octahedra as seeds in high yield for the first time. Later on, [PERSON] _et al_. [108] described a simple method for preparing Au@Ag core-shell nanocubes from Au nanopheres. In this case, the edge lengths of the Ag nanobox can be finely tuned from 13 to 50 nm by controlling the amount of AgNO\({}_{3}\) or Au seeds which were close to the calculated results, and the thickness of Ag shell can be controlled precisely from 1.2 to 20 nm because the seeds are isotropic and have uniform size. Moreover, they found that the effect of CTAC as capping agent on the formation of Au@Ag core-shell nanocubes was better than that of CATB.