esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Results and Discussion  
In this section, we apply the closed-form results from the previous section to the statistics of the combined received SNR given in (1) for the d-CDD-based CP-SC system. Note that the PDF of \(\gamma_{k}\) in (4) follows the central Chi-squared distribution with \(2m_{k}\) degrees of freedom; therefore, the average SNR of \(\gamma_{k}\), \(\overline{\gamma}\), can be obtained as \(\eta_{k}m_{k}\).  
Figure 1 shows the PDF of \(S\), \(f_{S}(x)\), for various values of \(K\) when \(M=6\) and \(\overline{\gamma}=1\). All the analytical results derived in this study were compared and verified by using Monte Carlo simulations, e.g., refer to the perfect match when \(K=3\). Figure 1b shows a special case of \(m_{k}=1\) corresponding to the exponential distribution, that is, the Rayleigh distribution. It can be further confirmed that, when \(M=K=6\), the results in Figure 1a,b are consistent with the well-known maximal ratio combined in Nakagami and Rayleigh fading  
environments [48; 49], respectively, and when \(K=1\), selection combined in Nakagami and Rayleigh fading environments [48; 49], respectively. Figure 2 shows the effects of \(m_{k}\) on the statistics of the combined received SNR when \(M=6\), \(K=3\), and \(\overline{\gamma}=1\). Similarly, all the analytical results derived in this study were compared and verified by using Monte Carlo simulations, e.g., refer to the exact match when \(m_{k}=2\).  
Note that common functions or core equations must be obtained to derive the final results by applying the proposed approach. This is a major limitation of this study. However, if it is difficult to obtain the core equations in closed form, similar closed-form results cannot be obtained even if other existing methods are applied. In any circumstance, our approach provides considerably simpler solutions than the conventional method involving multifold integration.  
Figure 1: PDF of \(S\), \(f_{S}(x)\), for various values of \(K\) when \(M=6\) and \(\overline{\gamma}=1\).  
Moreover, the process of obtaining the resulting statistics and core functions in this study can find applications in several research problems in wireless communication systems that deal with the partial sums of ordered RVs. Specifically, based on the results of this study, we can expand the scope of previously limited analysis to more versatile Gamma-fading environments. Therefore, various research topics, such as advanced diversity combining [29; 30], channel adaptive transmission [31], multiuser scheduling [50], and advanced RAKE receiver [51; 52; 53; 54], can be sophisticatedly analyzed over the Gamma distribution.
# Molecular Techniques and Target Selection for the Identification of _Candida_ spp. in Oral Samples
## 3 Molecular Identification of _Candida_ spp.
### Peptide Nucleic Acid Fluorescence In Situ Hybridization (PNA FISH)  
Peptide Nucleic Acid molecules are synthetic DNA fragments in which the negatively charged sugar-phosphate backbone of DNA is replaced with a noncharged polyamide [130]. This grants probes to hybridize to their complementary DNA targets with higher affinity and specificity, which means this technique is perfect for targeting highly secondary structured rDNA molecules [131; 132]. The technique has the capacity to identify _Candida_ spp. within 2.5 h [133]. PNA confers very low background noise, showing it to have a high sensitivity [71].  
The PNA FISH probe test was developed to evaluate multiple _Candida_ spp. from blood cultures [134; 135; 136]. It encompasses three coverage colors: green, red, and yellow for _C. albicans_ or _C. parapsilosis_, _C. glabrata_ or _C. krusei_, and _C. tropicalis_, respectively [137]. An alternative multi-_Candida_ probe was used by [PERSON] and co-workers to identify all _Candida_ species under study [138].  
The PNA-FISH assay major throwback is that visualization implies the use of a fluorescence microscope, adding costs to the laboratory equipment. This method has proven to be expensive [139], reaching high economic costs per patient [140].
## 6 Reduction Rules  
We now present some data reduction rules that are used in our implementation. The first two reduction rules are obvious and can be applied in the general case when we do not search for a \((D,G)\)-supported path between \(u\) and \(v\), but for any shortest or longest \((D,G)\)-supported path.  
**Rule 1**.: _If \(D\) contains an arc \((a,b)\), such that \(a\) and \(b\) are in different connected components of \(G\), then remove \((a,b)\) from \(D\)._  
This rule is correct since the arc \((a,b)\) cannot be part of any \((D,G)\)-supported path.  
**Rule 2**.: _If \(G\) contains an edge \(\{a,b\}\), such that there is no path from \(a\) to \(b\) and no path from \(b\) to \(a\) in \(D\), then remove \(\{a,b\}\) from \(G\)._  
This rule is correct, since \(a\) and \(b\) cannot be part of any \((D,G)\)-supported path. The next rule is trivial; we mention it since it is part of our implementation.  
**Rule 3**.: _Remove all vertices that are isolated in either \(G\) or \(D\)._The next two rules apply to the problem when we search a \((D,G)\)-supported path of length at most \(k\) from \(u\) to \(v\). Their correctness is obvious as they delete vertices that cannot be in any \((D,G)\)-supported path of length at most \(k\) from \(u\) to \(v\).  
**Rule 4**.: _If \(V\) contains a vertex \(w\), such that the sum of the length of the shortest path from \(u\) to \(w\) in \(D\) and the length of the shortest path \(w\) to \(v\) is more than \(k\), then remove \(w\) from \(D\) and \(G\)._  
**Rule 5**.: _If \(V\) contains a vertex \(w\) that is in \(G\) in a different component than \(u\) or \(v\), then remove \(w\) from \(D\) and \(G\). If \(w=u\) or \(w=v\), then return \"no\"._  
Our final rule removes vertices that can be reached from \(u\) and that can reach \(v\), but that need to visit the same vertex on both paths.  
**Rule 6**.: _If \(V\) contains a vertex \(w\) and a vertex \(x\) such that, in \(D[V\setminus\{w\}]\), there is no path from \(u\) to \(x\) and no path from \(x\) to \(v\), then remove \(x\) from \(D\) and \(G\)._  
The correctness of the rule follows from the fact that every path from \(u\) to \(x\) contains \(w\), and every path from \(x\) to \(v\) also contains \(w\). Hence, every path from \(u\) to \(v\) that visits \(x\) is not simple. As we will discuss in Section 7, these reduction rules considerably reduce the size and the maximum degrees of the biological instances.
## 5 Pre-Processing Techniques
### Non-Uniform Frequency Resolution
#### 5.2.3 Kautz Filters and Parallel IIR Filters with Fixed Poles  
Kautz filters are rational orthonormal filter structures. They are orthonormal since they have orthonormal impulse responses. Continuous-time rational transfer functions with orthonormal impulse responses were studied by [PERSON] in [110]. Discrete-time orthonormal transfer functions were later studied by [PERSON] in [111], who named them _\"discrete Kautz functions\"_. Kautz filters can be considered as a generalization of warped FIR filters and Laugerre filters, where the chain of all-pass filters with equal poles is replaced by a chain of all-pass filters with individual poles, possibly complex [54]. Figure 6 shows the results of Kautz modelling a measured RTF.  
By properly choosing the poles, it is possible to realize an arbitrary allocation of the frequency resolution of the designed filter. An approximation of the log-frequency scale resolution with Kautz filters can be found in [55]. The poles can be chosen a priori on the basis of the desired resolution, but they can also be tuned to the specific application by matching the pole frequency with the resonances of the system to be modeled [54]. In practice, fine tuning of the poles is necessary when designing low-order models for highly resonant systems [54]. Once  
Figure 5: Frequency warping of a measured RTF: (**a**) RTF; (**b**) warped RIR with \(\lambda=0.2\); (**c**) warped RIR with \(\lambda=0.5\).  
Figure 6: Kautz filter applied on a measured RTF: (**a**) RTF; (**b**) Kautz model of order 1000; (**c**) Kautz model of order 300.  
system identification using Kautz filters can benefit from the orthogonality of the impulse responses. The reader is referred to [54] for a discussion about pole fitting and identification methods.  
Kautz filters have been used for RRE, exploiting the nonuniform frequency resolution of these filters. They have been applied both for minimum-phase and mixed-phase equalization, using both fixed poles or tuned poles [54; 55]. When a fixed pole approach is used, the Kautz filters can also be designed and implemented in the form of a filterbank of second-order sections [112; 113; 114], with advantages for the computational complexity. In [114; 115], the theoretical equivalence of parallel filters and Kautz filters is shown, and formulas to convert the parameters of the two structures into each other are given. Figure 7 reports a parallel filter design example following the methodology of [114].
## 3 Results and Discussion
### Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA)  
The major goal of using such factor analysis methods is to explain the variance in the data and to discover the essential processes that drive the pumped water's quality. The main components are shown in Table 3 and Figure 8. PC1 contains very substantial positive  
Figure 6: Gibbs diagram for the groundwater samples.  
Figure 7: Water types of the studied water samples.  
loadings for Ca\({}^{2+}\), Mg\({}^{2+}\), Na\({}^{+}\), Cl\({}^{-}\), SO\({}_{4}\)\({}^{2-}\), and TDS, accounting for 64.8 percent of the variance. This implies that the main source of groundwater salinity is the dissolution of carbonate and evaporite minerals. PC2 and PC3 account for 16.7% and 10.4% of the total variation, respectively. PC2 had a lot of HCO\({}_{3}\)\({}^{-}\) positive loadings, whereas PC3 had a lot of pH-positive loadings. This is due to the fact that in the case of groundwater salinity, both HCO\({}_{3}\)\({}^{-}\) and pH are ineffective (Table 3 and Figure 8). The HCA analysis confirmed that Ca\({}^{2+}\), Mg\({}^{2+}\), Na\({}^{+}\), and Cl- are the primary components of TDS in pumped water, with these parameters grouped in one group (cluster A) and the HCO\({}_{3}\)\({}^{-}\) and pH parameters clustered in group B, as indicated by HCA (Figure 9).
## 3 System Model
### Dataset  
For inference latency analysis, the COCO 2017 validation dataset [32], with 5000 images, was used to measure the inference latency of the RFCN and SSD-MobileNet algorithms. We selected ten image samples out of 5000 images as shown in Figure 2 and showed the results of object detection through the RFCN and SSD-MobileNet in Figure 3. The object detection service using the RFCN model shows that most objects on the image are detected with high accuracy (95% or more), but the SSD-MobileNet model shows that only major objects on the image are detected, small objects are not detected, and the detection accuracy of major objects is lower than that in the RFCN model.
## 1 Introduction
It is particularly effective when there is a mix of continuous and categorical data, when the relationships between input features are nonlinear, or when the dataset is noisy or has missing values. Random forests are versatile, interpretable to some extent, and generally offer strong predictive performance with minimal hyperparameter tuning, making them a reliable choice for many prediction problems. This offers a new method for rapidly and precisely estimating the electric field's intensity under various test conditions close to 5G base stations. Hence, the key contributions to this article are summarized as follows:  
1. Using a machine learning model, this work develops a novel method for determining the intensity of electromagnetic radiation on the ground plane in proximity to 5G base stations. 2. The dataset is built using measured data collected in proximity to numerous 5G base stations, and the random forest regression model provides a reliable estimate of the electric field's intensity: specifically, when the transmit power, antenna gain, distance between the 5G base station and 5G terminals, number of 5G terminals, and environmental complexity surrounding the 5G base station are added into the model. Consequently, the electric field intensity at any given radiation point under different combinations of 5G terminals and terminal service modes can be estimated. 3. The proposed approach can quickly and accurately estimate the electric field intensity under various test scenarios, which can significantly increase estimation efficiency and offer recommendations for 5G base station management and radio wave coverage site selection. The remainder of this paper is organized as follows. Section 2 provides a detailed overview of the 5G base station technology and measurement methods (Figure 3), along with the proposed approach utilizing a random forest regression model. Section 3 presents the results and corresponding discussion. Finally, Section 4 concludes the paper.
## 5 Conclusions and Future Work  
Aiming at wind turbine blade cracking recognition under imbalanced data, this paper designs a novel roundtrip auto-encoder method. Two generator networks and two discriminator networks are designed to ensure the generated samples well fit the distribution of historical cracking samples. Auto-encoder method is applied to reduce the dimension of historical samples and thus the complexity of generator and discriminator. From the reconstructed results, it is concluded that auto-encoder is effective for extracting low-dimensional intrinsic features. From the results under different imbalance levels, the detection performance shows significant improvement with the RTAE method. When the imbalance level is above 4:1, all the cracking samples cannot be identified without data augmentation, which shows the importance of considering the imbalance problem in real application. Analysis of the real wind turbine blade cracking data is carried out, the recognition of cracking samples improves by 19.8%, 23.8% and 22.7% on precision, recall and F1-score. The cracking detection under imbalanced data and the comparisons show that: (1) compared to the popular data-level methods, RTAE is superior under the influence of highly imbalanced data; (2) through integrating auto-encoder and roundtrip model, the framework provides a possibility for solving imbalance problem with high dimension data. As is known, a wind turbine blade contains many types of cracks in real industrial applications. Currently, subject to roundtrip, RTAE can only deal with the binary-class imbalanced data problems. Multi-class cracking problems are more complex as they may contain more than one minority classes. In the future work, we will extend the application of RTAE and apply it to multi-class problem. Meanwhile, we will explore more effective roundtrip structures and apply them to high-dimensional time-series data. Conceptualization, [PERSON] ([PERSON]); methodology, [PERSON] ([PERSON]); software, L.Y. ; vali-dation, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); formal analysis, [PERSON] ([PERSON]) and H.W. ; investigation, [PERSON] ([PERSON]) and H.W. ; resources, H.L. ; data curation, H.L. ; wit-ing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]); visualization, X.W. ; supervision, H.L. ; project administration, X.W. ; funding acquisition, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (52105536); the Guangdong Basic and Applied Basic Research Fund (2022A1515140066); the Henan Provincial Key R&D and Promotion Special Project (Science and Technology Research) (232102221009). Data Availability Statement: Data are contained within the article. The data presented in this study are available in Section 4.1. Author [PERSON] was employed by the company CITIC Heavy Industries Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Methodological Background: LMI-Based Control and Observer Design
**Case 2:**: The control signal is defined as  
\[\mathbf{u}=\mathbf{u}_{\text{ff}}-\mathbf{K}_{\text{o}}\cdot\mathbf{C}\cdot \hat{\mathbf{x}}, \tag{12}\]  
with the same observer as in (11). Note that the output matrix \(\mathbf{C}\) does not necessarily coincide with the matrix \(\mathbf{C}_{\text{m}}\) defined in (9). **Case 3:**: The control signal is defined as  
\[\mathbf{u}=\mathbf{u}_{\text{ff}}-\mathbf{K}_{\text{y}}\cdot\mathbf{C}_{\text {y}}\cdot\hat{\mathbf{y}}_{\text{f}}, \tag{13}\]  
where \(\hat{\mathbf{y}}_{\text{f}}\) is a vector consisting of filtered system outputs and estimated output derivatives, cf. [21]. **Remark 2**: _Case 3 can be interpreted as a dynamic output feedback control approach, whereas the Cases 1 and 2 are model-based approaches employing feedback controllers that rely on an estimation of the complete state vector by an appropriate full observer. It should be noted that the different control structure of Case 3 leads to a similar, however not identical, LMI approach. Therefore, Case 3 is treated separately in [21]. Moreover, as already discussed in [21], Case 3 should typically only be used if a maximum of two time derivatives of measured output signals are estimated with the model-free filter. In all other cases, the observer-based concepts of Cases 1 and 2 are advantageous._  
To guarantee solvability of the control design task in all three cases above, it is assumed that the system (8) is stabilizable using either of the system inputs (10), (12), or (13), and that the pair \((\mathbf{A}(\mathbf{p}),\mathbf{C}_{\text{m}})\) is robustly observable (or at least detectable) in Case 1 as well as in Case 2. Here, robust observability is defined as observability of all possible values of the parameters \(\mathbf{p}\) according to the definition given in [8]. Note that the requirements of both stabilizability and detectability will not be proven explicitly. These properties are instead verified constructively by determining a solution that ensures input-to-state stability of the system dynamics as well as of the observer's error dynamics.
# Improved Video Anomaly Detection with Dual Generators and Channel Attention
## 3 Method
### Components
#### 3.2.2 Reconstruction Generator  
The reconstruction generator (Figure 5) is composed of an autoencoder and a second-order channel attention module. The autoencoder consists of an encoder and a decoder, and U-net is used as its network structure.  
During the training phase, the generator is trained using both pseudo-abnormal and real frames. When a frame is input into the generator, it first goes through the encoder module, which includes a series of convolution pooling operations. This results in the extraction of latent features of varying sizes and channels from each layer. Next, the second-order channel attention module processes these latent features to enhance the interdependence and relationships between the information. The resulting features, which have stronger correlations, are then decoded by the decoder module through up-sampling and skip connections to produce a reconstructed frame of the same size as the input frame.  
Finally, constraints are applied to maximize the difference between the reconstructed pseudo-abnormal frame and the real frame, and minimize the difference between the  
Figure 4: Illustration of the noise module. After inputting random noise, the noise module reconstructs it to obtain noise suitable for generating pseudo-frames. The module consists of a three-layer fully connected structure. Among them, noise A is added at the first skip connection, and noise B is added to the latent feature, and the size of both is the same as the corresponding feature size. “Conv 3 \(\times\) 3” refers to using a convolution kernel of size 3 \(\times\) 3. “BatchNorm3D” is a three-dimensional batch normalization used to normalize the input so that the input of each layer has the same data distribution. “ReLU” stands for using the ReLU activation function.  
Figure 5: Illustration of the reconstruction generator. During the training phase, the generator is trained with both pseudo-abnormal frames and normal training frames to enhance its reconstruction capability. During the testing phase, the generator classifies input frames as abnormal or normal based on the distance between the reconstructed test frame and the input test frame.  
reconstructed real frame and the real frame. During the testing phase, the generator uses the distance between the reconstructed test frame and the input test frame to classify the input frame as either normal or abnormal.
## 4 Performance Validation
### Results and Discussion  
In our simulation, we tested our RRAPS algorithm and its performance against traditional scheduling algorithms such as Round Robin with fixed time quantum and Round Robin with dynamic time quantum. In the simulation, we considered a scenario where the ready queue is filled with many processes, all with different burst times and priorities. The RRAPS algorithm uses a priority-based approach, where higher-priority processes are executed first, and lower-priority processes are executed later. In this way, important processes are prioritized and processed efficiently. For processes with the same priority, a decision is made on a first-come-first-served basis to decide which process is executed next. This ensures that processes that have been waiting for longer get executed first. To assess the algorithm's effectiveness, we computed the processes' mean turnaround time (in milliseconds) and mean waiting time (in milliseconds). We also counted the number of context switches that transpired while executing the processes. We then compared the results of the RRAPS algorithm to those of traditional Round Robin algorithms, such as Round Robin with fixed time quantum with no priorities and Round Robin with dynamic time quantum with no priorities.  
Additionally, we conducted a comparative analysis of RRAPS and the AN algorithm, which utilizes dynamic time quantum [3]. The experiment aimed to assess the efficiency of two scheduling algorithms in a scenario with several processes in the ready queue. The findings demonstrated that the RRAPS algorithm outperformed other approaches, as evidenced by its lower average turnaround and waiting times. Note that we have priorities also associated with processes. We have defined the priority scale from 1 to 5, with 5 being the highest priority level and 1 being the lowest. So, the structure of the process will be a tuple containing two values; the first value is the burst time, and the second value is the priority (Process P = [burst time, priority]).