esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliable Augmentation and Precise Identification of EPG Waveforms Based on Multi-Criteria DCGAN
###### Abstract  
The electrical penetration graph (EPG) technique is of great significance in elucidating the mechanisms of virus transmission by piercing-sucking insects and crop resistance to these insects. The traditional method of manually processing EPG signals encounters the drawbacks of inefficiency and subjectivity. This study investigated the data augmentation and automatic identification of various EPG signals, including A, B, C, PD, E1, E2, and G, which correspond to distinct behaviors exhibited by the Asian citrus psyllid. Specifically, a data augmentation method based on an improved deep convolutional generative adversarial network (DCGAN) was proposed to address the challenge of insufficient E1 waveforms. A multi-criteria evaluation framework was constructed, leveraging maximum mean discrepancy (MMD) to evaluate the similarity between the generated and real data, and singular value decomposition (SVD) was incorporated to optimize the training iterations of DCGAN and ensure data diversity. Four models, convolutional neural network (CNN), K-nearest neighbors (KNN), decision tree (DT), and support vector machine (SVM), were established based on DCGAN to classify the EPG waveforms. The results showed that the parameter-optimized DCGAN strategy significantly improved the model accuracies by 5.8%, 6.9%, 7.1%, and 7.9% for DT, SVM, KNN, and CNN, respectively. Notably, DCGAN-CNN effectively addressed the skewed distribution of EPG waveforms, achieving an optimal classification accuracy of 94.13%. The multi-criteria optimized DCGAN-CNN model proposed in this study enables reliable augmentation and precise automatic identification of EPG waveforms, holding significant practical implications for understanding psyllid behavior and controlling citrus huanglongbing.  
electrical penetration graph; citrus psyllid; data augmentation; deep convolutional generative adversarial network; waveform classification
## 2 Materials and Methods
### Biomass and Sugar Kinetics  
Biomass evolution was evaluated by viable cell count (CFU/mL) on lysine agar selective medium and WL nutrient agar (Oxoid, Hampshire, UK). Wild non-_Saccharomyces_ yeasts (WNSs) were easily distinguished by _S. bombicola_ through macro- and microscopic characterization of the colony on WL nutrient agar. To confirm the belonging of the isolates to the species _S. bombicola_, some strains underwent DNA extraction and were then identified through ITS1- 5.8S rRNA-ITS2 region analyses using the primer pairs ITS1 (5\({}^{\prime}\)-TCCGTAGGT GAACCTCGCG-3\({}^{\prime}\))-ITS4 (5\({}^{\prime}\)-TCCCCCGCTTATTG ATATGC-3\({}^{\prime}\)). PCR products were separated by horizontal electrophoresis (Bio-Rad, Hercules, CA, USA) in a 1.5% (\(w/v\)) agarose gel using 0.5\(\times\)TBE buffer and used for identification by sequencing [35]. The genomic sequences obtained were compared with those already present in the data library using the BLAST program and the GenBank database of the ITS. The fermentations were carried out in duplicate. The glucose and fructose (K-FRUGL) concentrations were determined using specific enzyme kits (Megazyme International, Wicklow, Ireland).
# Privacy-Aware Access Protocols for MEC Applications in 5G +
## 5 Final Remarks  
We have considered privacy challenges when using multi-access edge computing (MEC) applications in 5G in the basic scenario, where a user wants to access a MEC application in a MEC host that is deployed in a network of a mobile network operator (MNO). Since the network entities in this scenario may have different owners, the privacy of the MEC user should be protected not only against external attackers (outsiders) but also against entities that serve the user.  
We defined a system model of this situation and then determined an adversary model and privacy requirements for the entities in the system model. We then introduced a privacy-preserving solution for accessing a MEC application and analyzed how this solution meets the privacy requirements.  
The defined system model can be applied to cases other than the one that includes MEC, e.g., three parties provide services to Alice. These possibilities are to be considered in future work.  
Formal verification and formal modeling of our solution are left for future work. The future work also includes the experiments of our solution through simulation or other means of concrete examples of executing APPIFY by Alice and privacy requirements when the network is under attack, as well as the numerical results of such experiments. Another direction for future work is to protect user privacy in more complicated scenarios, such as (i) where the user switches from one MEC host to another while moving and (ii) roaming situations involving more than one MNO. Privacy-preserving protocols could be developed and analyzed for those scenarios.  
Conceptualization, G.A., P.G. and V.N.; Supervision, P.G. and V.N.; writing--original draft, G.A.; writing--review and editing, P.G. and V.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is a result of funded collaboration project between University of Helsinki and Huawei Technologies.  
**Acknowledgments:** The authors would like to thank [PERSON] and [PERSON] for helpful comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Semantic Features Based N-Best Rescoring Methods for Automatic Speech Recognition
## 4 N-Best Rescoring with Coordination Scores
### Sentence Coordination Scores
#### 4.1.2 Topic Similarity Score Using LDA  
To discriminate the mistakenly recognized word that differs in the topic with the sentence \(s\), we use the cosine similarity to measure word-sentence topic bias:  
\[c_{w_{i}}=\frac{<P(z|w_{i}),P(z|s)>}{|P(z|w_{i})||P(z|s)|}=\frac{\sum_{k=1}^{|T |}P(z_{k}|w_{i})P(z_{k}|s)}{\sqrt{\sum_{k=1}^{|T|}P(z_{k}|w_{i})^{2}}\sqrt{\sum_ {k=1}^{|T|}P(z_{k}|s)^{2}}} \tag{9}\]  
where \(P(z|w_{i})\) and \(P(z|s)\) are both \(|T|\)-dimensional vectors, which are the topic distributions of \(w_{i}\) and sentence \(s\) respectively. Provided that in LDA algorithm, all the topics are equally modelled in the training process (\(P(z_{j})=1/|T|\), for all \(j\)), the probability of topic distribution given word can be normalized by the word probability distribution of each topic:  
\[P(z_{j}|w_{i}) =\frac{P(w_{i}|z_{j})P(z_{j})}{\sum_{k=1}^{|T|}P(w_{i}|z_{k})P(z_ {k})} \tag{10}\] \[=\frac{P(w_{i}|z_{j})}{\sum_{k=1}^{|T|}P(w_{i}|z_{k})}, \tag{11}\]  
Thus the topic probability given word can be calculated by column normalization of the \(\beta\) matrix from the LDA pre-training model.  
The range of cosine similarity is \([-1,1]\), where the value close to 1 means that the topic of the word is very similar to the sentence, and in contrast the value near \(-1\) means that the topic of the word is nearly the opposite to the sentence.  
We calculate the sentence topic similarity score by simply adding the word similarities:  
\[Score_{s}=\sum_{i=1}^{|S|}c_{w_{i}} \tag{12}\]
## 3 Results and Discussion
### Color  
The most important factor in judging meat before purchase is the color. The preferred meat color is usually reddish-pink which shows the meat is fresh and wholesome [38]. There were no significant differences in initial L*, a*, and b* values (_p_ > 0.05) between using CO2 and electrical stunning.  
The initial L*\({}_{\text{CO2}}\) 46.96 \(\pm\) 0.82 and L*\({}_{\text{electric}}\) 46.92 \(\pm\) 0.81 were in the normal range (L* < 50) recommended by [27] in both stunning methods (Figure 3A). A slight increase in L* was seen two hours post mortem, and then a decrease occurred until the fourth hour. After death, physicochemical, biochemical, and structural changes begin in the muscles. The major physicochemical change is the development of rigor mortis in which muscle contractions occur and its structure becomes compact. This increasingly compact structure results in less light dispersion on muscle surfaces and a less light appearance. Complete development of rigor mortis occurs at approx. 197 min in pigs [39] which can be traced in the changing of the measured L* values. Post rigor, muscle increasingly relaxes and its lightness increases again. The GM of pigs stunned electrically showed significantly higher L* values compare to those submitted to CO2 stunning (_p_ < 0.05). A similar significant relation was found in pig m. longissimus lumborum [19], \(m\). longissimus thoracis [10], and lamb m. longissimus dorsi [40].  
Figure 3: _Cont._Using electrical stunning resulted in significantly higher a* values compared to using CO2 stunning (\(p\) < 0.05) (Figure 3B). Increasing redness post mortem is likely related to the oxygen consumption of muscle cells. After slaughter, mitochondria in the cells remain capable of consuming oxygen if substrates are available for their operation. However, post mortem, mitochondrial activity decreases due to the decreasing amount of substrates and this allows the formation of oxymyoglobin resulting in higher red color [41]. In the yellowness of GM, no significant difference was shown between electrical and CO2 stunning methods (Figure 3C).  
Changes in color difference over time can be seen in Figure 4. The \(\Delta\)E* values showed an increasing tendency post mortem. Two hours post mortem, the color difference was <1 which means a standard observer does not notice the difference between CO2- and electrically stunned GM. After three hours post mortem, the color difference increased to the range of 1.5-2.0. From three hours post mortem, the color difference is already noticeable to an experienced observer. The decrease in \(\Delta\)E* values can be seen at four hours post mortem which can be explained by a slight reduction in the differences between L* and b* values. This is associated with the development of rigor mortis. The contraction of muscle fibers in the meat results in a more compact structure. The contracted muscle fibers absorb more light and less light is reflected from them, resulting in a reduction in L* and b* differences.
## 5 Bolt Looseness Detection Results and Discussion
### Noise Injection Testing  
Measurement noise significantly impacts bolt looseness detection accuracy, particularly when dealing with time series data or normalized wave energy reflection ratios. Noise injection testing was carried out to test the robustness of the proposed method by adding white noise to the received signals. For the training and testing signals, white noise levels of 10% and 20% of the signal's RMS value were considered, corresponding to signal-to-noise ratios of 10 dB and 7 dB, respectively. Figure 18 shows the received signal at S1-S3 with 10% and 20% noise under the intact case. The bolt looseness condition estimation results for the CNN-LSTM-MHSA method under various noise levels are summarized in Table 5. For the cases with 10% and 20% of white noise in the training dataset, the bolt looseness localization accuracy decreased slightly for testing samples without noise. The bolt looseness localization accuracy remained excellent by increasing noise level in the testing samples, and the local bolt looseness severity estimation accuracy decreased in an acceptable range. In real applications, obtaining numerous training samples with varying bolt looseness scenarios can be challenging. Utilizing the CNN-LSTM-MHSA method trained with FE simulation results to test measurement data may address this issue. Incorporating different levels of noise into the training dataset to account for the noise effect and improve detection accuracy will be explored in future studies.
## 2 Linear and Nonlinear Methods
### _Svr_  
The support vector regression (SVR) is a typical extension of the support vector machine in regression [39]. The basic principle is to map the data into a high dimensional feature space via nonlinear mapping, after which the linear regression is performed in the feature space. It avoids overfittings in the traditional machine learning algorithms. Similar to artificial neural networks, the performance of SVR heavily depends on the input parameters and associated coefficients. The regression formula of SVR can be expressed as  
\[f(\mathbf{x})=w\mathbf{\varphi}(\mathbf{x})+b, \tag{15}\]  
where \(\mathbf{\varphi}(\cdot)\) denotes a nonlinear mapping function and \(W\) and \(b\) represent the weight vector and bias, respectively. To find an optimal SVR model, it needs to minimize the optimization function subject to tolerance and slacks. The governing target function is given as  
\[\min_{w,b}\frac{1}{2}\parallel w\parallel^{2}+C\sum_{i=1}^{N}(\xi_{i}+\xi_{i} ^{*})\text{,s.t.}\left\{\begin{array}{l}y_{i}-f(\mathbf{x})\leq\epsilon+\xi_{i} \\ f(\mathbf{x})-y_{i}\leq\epsilon+\xi_{i}^{*}\\ \xi_{i}\geq 0,\xi_{i}^{(*)}\geq 0\end{array}\right., \tag{16}\]  
Figure 3: Structure of the GRU cell.  
where \(C\) is a factor for tradeoff between over-fitting and underfitting; \(\epsilon\) is the tolerance that determines the width of the decision boundary; \(\xi_{i}\) and \(\xi_{i}^{*}\) are slack variables; and \(N\) stands for the number of samples.  
Using the Lagrange multiplier method with Karush-Kuhn-Tucker conditions, the optimization function can be converted to a quadratic optimization problem. Then, the regression function is simplified as  
\[f(\mathbf{x})=\sum\limits_{i=1}^{N}{{{\left({{\alpha_{i}}-{\alpha_{i}^{*}}} \right)}{k({x_{i}},x)}}}+b, \tag{17}\]  
where \(\alpha_{i}\) and \(\alpha_{i}^{*}\) represent positive and negative Lagrange multipliers, respectively; and \(k({x_{i}},x)\) is the kernel function, including linear, polynomial, Gaussian kernels, and so on. In this study, the Gaussian kernel, also known as the radial basis function (RBF) kernel, is employed because of its popularity [40].
## 5 Results and Discussion
### Field Geological Verification
In addition, the experimental petrological evidence shows that chromite in the Luobusa ophiolite is formed by the reaction between boninite melts and harzburgite. Boninite is a typical product of the forearc basin in island arc systems, and the subduction zone is the most favorable environment for finding podiform chromite ores. Therefore, the discovery of podiform chromite can even be used as a marker for determining the tectonic environment. The formation of podiform chromite is closely related to the formation of  
Figure 15: Field photographs showing the rock units of (**a**,**c**) the view of Luobusa by open pit; (**b**) structural lenticular body; (**d**,**e**) outcrop of chromite; (**f**) a block of gabbro; (**g**) contacts between harzburgite and dunite in Xiangkashan; (**h**) anti-nodular structure; (**i**) tectonic breccia. ophiolites [41; 42]. In recent years, a large number of ultra-high pressure minerals have been found in mantle peridotites and podiform chromites [10; 45; 106], suggesting that the formation depth of podiform chromite may reach the transition zone, and even the lower mantle [10; 38]. Therefore, the formation process of Luobusa podiform chromite involves both deep and shallow processes, and may have undergone multiple stages of evolution, including from the transition zone, mid-ocean ridges, subduction zones, and even late-stage surface evolution [1; 6]. The involvement of fluids in the mineralization process of podiform chromite favors the liquation of chromite and silicates, forming orientated chromite that indicates the direction of fluid movement (Figure 2c). Conversely, Figure 15h shows a reversed pisolitic structure, which also represents the hydrodynamic record [107]. The most direct evidence of the effect of tectonic structures on the spatial distribution of podiform chromite deposits is the east-west distribution of chromite ore belts caused by the convergence in the north-south direction, often accompanied by tectonic lenses and breccias [32; 108; 109] (Figures 15b,i and 16c). The slate is distributed in the southern area of the ophiolite belt and is in a fault contact with ophiolite. It has a large outcrop area and is well exposed (Figure 14d). The WV-2 image clearly shows a large-scale reverse fault (Figure 5a). The quartz magnesite rock is distributed between the serpentinite ultramafic rock and the slate (Figure 14c). It is formed by the periodtites or serpentinite under the condition of carbon dioxide-rich hydrothermal activity. It can be divided into three alteration zones when observed in the field from north to south: peridotite zone, serpentinite zone, and quartz magnesite zone [110]. This has a good correspondence with the WV-2 image (Figure 5c) and shows the interpretation advantages of WV-2 high spatial resolution. The contact boundary between quartz magnesite and slate is clear, and they outcrop intermittently along the southern boundary of the ophiolite massits (Figure 16a). Quartz and carbonate veins of varying widths (3-15 cm) are also developed in the quartz magnesite (Figure 14i).
# Dynamic Path Planning for Vehicles Based on Causal State-Masking Deep Reinforcement Learning
## 1 Introduction
* Improved convergence and generalization capabilities of DRL in dynamic path planning: Introducing encoding configurations optimized through backpropagation learning enables the model to adaptively adjust the causal features of each state dimension. * Algorithm validation: The effectiveness of CSM-TD3 was validated through multiple simulation experiments. The experimental results demonstrate that this method performs excellently in terms of convergence and generalization capabilities in dynamic path planning. The remainder of this paper is structured as follows. The related work is introduced in Section 2. The problem formulation is presented in Section 3. The implementation of the improved CSM-TD3 is elaborated in Section 4. The simulation results are compared in Section 5. The conclusions are summarized in Section 6.
## 2 Materials and Methods
### Kernel Density Estimation Method  
Kernel density estimation (KDE) is a non-parametric test method to estimate the unknown probability density function in the case of given data. Because the analysis needs to select the bandwidth, it is also called the Parzen window [31]. The idea is to define a kernel function, create a kernel function (\(K\)) on each data point \((x_{i},i=1,2,3,\ldots,n)\), and superimpose it to receive the kernel density estimation, which can be expressed as:  
\[\hat{f}(x)=\frac{1}{n}\underset{i=1}{\overset{n}{\sum}}K(||x-x_{i}||_{2}) \tag{3}\]  
where \(\hat{f}\) is the overall probability density function and \(\frac{1}{n}\) is normalized so that the sum of probability density is 1.  
Figure 3: Spatial overlay analysis method and examples.  
Figure 2: U-net network structure.  
In general, the kernel function K needs to follow three conditions: 1. nonnegative: \(K(x)\geq 0\); 2. symmetry: \(K(x)=K(-x)\); 3. decreasing: when \(x>0\), \(K^{\prime}(x)\leq 0\). In the case of many data points, the estimation results are very similar when the kernel functions, such as triangular kernel, biweight kernel, triweight, normal, Epanechnikov, and Gaussian, are selected. In this study, Gaussian function is selected as the kernel function to calculate the kernel density of data points. The formula is:  
\[K(t)=\frac{1}{\sqrt{2\pi}}e^{-\frac{t^{2}}{2}} \tag{4}\]  
Considering bandwidth \(h\), Equation (3) can be expressed as:  
\[\hat{f}(x)=\frac{1}{nh^{2}}\sum_{i=1}^{n}K\left(\frac{\left|\left|x-x_{i} \right|\right|_{2}}{h}\right) \tag{5}\]  
Among them, \(\left|\left|x-x_{i}\right|\right|_{2}\) is a 2-norm, also known as Euclidean distance, which uses a circular search for data points near \(x_{i}\).  
Bring Equation (4) into Equation (5):  
\[\hat{f}_{h}(x)=\frac{1}{nh^{2}}\sum_{i=1}^{n}\left(\frac{1}{\sqrt{2\pi}}e^{- \frac{\left(\left|x-x_{i}\right|\right)^{2}}{2h^{2}}}\right) \tag{6}\]  
The kernel density maps of residential, commercial, industrial, and public service buildings and the four kernel density values corresponding to each building are obtained by kernel density analysis. Set different thresholds for different target buildings and select the best classified attribute value according to the actual situation. The buildings with a kernel density value of function greater than or equal to the set threshold are defined as the corresponding function so that buildings are classified and extracted according to residential, commercial, industrial, and public service functions, and the functional classification of buildings in urban areas is realized. In case of simultaneous exceeding of non-one functional threshold, the determination of functions follows this rule: industrial is preferred over public service, public service is preferred over residential, and residential is preferred over commercial.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
# A Multi-Bandwidth Reconfigurable Patch Antenna for Devices in WLAN and UWB Technology Applications
## 2 Materials and Methods
### Surface Current Distribution  
Figure 8 illustrates the dependence of the surface current distribution on the different switching states of two PIN diodes at a working frequency of 4 GHz. In the case where both PIN diodes are on (Figure 8a), the study observed that most of the surface current spreads from the bottom of the patch toward the middle of the main patch, as well as to the semicircular and rectangular patches. When the left diode is on while the right diode is off (Figure 8b), the surface current mainly extends from the bottom to the middle and the edge of the main patch. Additionally, it propagates toward the left side of the left diode in the rectangular patch. Conversely, when the left diode is off and the right diode is on (Figure 8c), the surface current distribution exhibits an opposite pattern. It primarily extends from the bottom to the middle and the edge of the main patch while propagating toward the right side of the right diode in the semicircular patch. Finally, when both PIN diodes are off (Figure 8d), the surface current spreads from the bottom to the edges of the two PIN diodes in the main patch.
## 4 Results
### Implications for Theory and Practice  
The validation work on challenges for implementing ESG in the construction sector of an emerging economy has important implications for both theory and practice. From a theoretical perspective, this research contributes to the literature by offering empirical validation, based on the experience of industry professionals, of specific challenges faced in the context of construction in emerging economies. The application of the Lawshe method provides a systematic and rigorous approach to validating these challenges, offering valuable insights that can theoretically enrich the field of ESG in construction. Additionally, by validating these challenges, the study may stimulate the development of new conceptual models and theories that explain the complexities of ESG implementation in emerging economies, contributing to the construction of a more robust and applicable body of knowledge.  
In practical terms, the results of this research have significant implications for professionals and stakeholders in the construction sector in emerging economies. By identifying and validating specific challenges related to ESG implementation, the study provides practical guidance for managers, engineers, and other professionals involved in decision-making and strategic planning. These insights can inform the development of public policies, regulatory guidelines, and corporate strategies that promote more effective and sustainable adoption of ESG practices. Additionally, by better understanding the challenges faced, stakeholders can direct resources and efforts to address specific issues such as education, regulation, and waste management, thereby driving the transformation towards a more responsible and sustainable construction sector in emerging economies.