esa-sceva/satcom-chunk-collection huggingface dataset:
# Ground Target Detection and Damage Assessment by Patrol Missiles Based on YOLO-VGGNet
## 4 Target Damaged Components Analysis Based on Convolutional Feature Clustering
### Deconvolution Feature Visualization Analysis
\[L^{c}_{CAM}=ReLU(\overset{512}{\underset{k=1}{\sum}}\alpha^{c}_{k}A^{k}_{l-1} )=ReLU(\overset{512}{\underset{k=1}{\sum}}W^{c}_{l,l+1}|k|A^{k}_{l-1}) \tag{9}\]  
\[Deconv(\delta(W^{c}_{l,l+1}|k|A^{k}_{l-1})) \tag{10}\]  
where \(\alpha^{c}_{k}=W^{c}_{l,l+1}|k|\), \(W^{c}_{l,l+1}|k|\) is the weight of the \(k\)th neuron of layer \(l\) after GAP. \(\delta\) is the filter, which retains the first 36 activation units in the feature map and sets the other to zero. Figure 5: Schematic diagram of the deconvolution. For the radar target, we visualized the feature extracted by the top 10 (when comparing the activation intensity of feature maps, only the sum of the top 36 units are compared) convolution kernels in the 26 th layer. Some of the visualized results are shown in Figure 7, where the numbers are the convolution kernel serial numbers, and the central recognition areas are framed by ellipses. Fifty different radar images were tested, and the frequency distribution of the top 10 in 512 convolution kernels is shown in Figure 8. The test results of the tank, tent, and supply vehicle are shown in Table 1.  
Figure 6: Maximum activation unit deconvolution visualization. Figure 7: Deconvolution visualization of clustered convolution kernels for radar target. Table 1 and Figure 8 reveal that the same type of targets activates almost the same convolution kernels in the high-layer convolution (26 th layer) because they contain the same characteristics(radar bracket and antenna). In addition, different types of targets activate different convolution kernels. Therefore, we can conclude that different convolution kernels are trained to extract different features. Several convolution kernels exist that mainly extract features from the target's specific components.
## 2 Related Work
[39] trained the U-Net segmentation network for cropland extraction through weakly supervised learning, and compared it with traditional logistic regression, support vector machine, random forest, and other pixel-level classification algorithms. The experiment showed that the neural network has better classification performance. [PERSON] et al. [40] and [PERSON] et al. [41] proposed a method to detect the edge of cultivated land based on holistically nested edge detection with richer convolutional features. Compared with traditional image edge detection, suchas the canny algorithm [42], it can extract the boundary more smoothly. However, the extracted edge image will have topology problems in subsequent applications, which makes it difficult to carry out subsequent applications. [PERSON] et al. [43] designed a multiple-dilation, fully convolutional network for field boundary detection from Sentinel-2 images. They merged results with a novel super-resolution semantic contour detection network using a transposed convolutional layer in the CNN architecture to enhance the spatial resolution of the field boundary detection output from 10 to 5 m resolution. The F\({}_{1}\) score they have obtained from their model is 0.6. [PERSON] et al. [44] improved PSPNet and combined depth distance features with shadow local features to provide predictions with a higher level of detail. During the experiment, high-resolution satellite images were used to verify the extraction of cropland in four provincial research areas from northern to southern China. The overall accuracy of the extraction ranged from 89.99% to 92.31%. The above approaches have provided convincing segmentation results, but have not been evaluated in complex terrain and changeable phase scenes. Without considering the scale characteristics of objects, the segmentation accuracy fluctuates greatly in different research areas. As a result, the cropland extraction performance achieved by using these approaches is typically compromised, reducing the practicality of the approaches.
## 3 Gene Discovery and Gene Expression Analysis in the Pre-NGS Era
### Other Plant Metabolites Produced by Endophytic Fungi  
#### 4.6.1 10-Hydroxycamptothecin  
The anticancer terpenoid alkaloids camptothecin (CPT) and 10-hydroxycamptothecin (10-HCPT) (Figure 1), originally described from the bark of the happy tree (_Camptotheca acuminata_), are also well-known products of endophytic fungi, such as _N. solani_ and _Xylaria_ sp. [5]. However, genes involved in the biosynthesis of camptothecin in fungi remained unknown until 2017 [78]. The transcriptome analysis of the endophytic strain _Xylaria_ sp. M71 treated with salicylic acid revealed 13 functional genes mapped to MVA, which might be putatively involved in the production of 10-HCPT, which encode 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR), 10-hydroxygeraniol oxidoreductase (10-HGO), acetyl-CoA C-acetyltransferase (AACT), geranylgeranyl pyrophosphate synthase (GGPS), 3-hydroxymethyl- 3-methylglutaryl-CoA lyase (HMGL), mevalonate kinase (MK), geraniol-10-hydroxylase (G10H), and secologanin synthase (SCS).  
The biosynthesis pathway of terpenoid alkaloids is conserved both in plants and fungi. However, while plants can utilize a number of pathways to produce CPT, i.e., MEP and the 1-deoxy-D-xylulose 5-phosphate pathway, genes associated with MEP were not identified in M71, suggesting that the biosynthesis of 10-HCPT occurs via MVA, similarly to the CPT-producing fungus _Ganoderma lucidum_[78].
## 3 Data Acquisition and Processing  
### Data Acquisition  
This study selects the Beijing-Tianjin-Hebei (BTH) region as the research sample, based on the following considerations: First, as a critical economic core area in China, the BTH region features a dense and diverse distribution of medical resources, reflecting the developmental trajectories and regional characteristics of large hospitals at various stages. Second, the region includes a significant number of hospitals of the same grade, comprising both general and specialized hospitals. This high level of sample homogeneity ensures the comparability and representativeness of the research findings.  
This study conducted data collection based on the proposed model framework and research hypotheses. The data sources primarily include hospital fundamental information and transportation-related data. The data on hospital size and medical level were primarily obtained from institutional health reports. Specifically, hospital size data included indicators such as construction area, number of medical departments, number of beds, and  
Figure 1: Theoretical framework and research hypotheses.  
number of staff. Medical level data encompassed the number of national key specialties and the number of positive comments after diagnosis. Notably, the number of positive comments was retrieved from online platforms, providing a more intuitive measure of the hospital's reputation and service quality. For transportation data, indicators such as the number of bus routes, subway lines, and parking spaces within a 500 m radius of the hospital were selected to examine hospital accessibility and patient travel convenience. The data collection process adhered to standardized procedures, involving the organization of data from hospital official websites and the use of Python 3.10 web-scraping techniques. Ultimately, data for 135 large hospitals in the Beijing-Tianjin-Hebei region were obtained, including 65 general hospitals and 70 specialized hospitals. The geographic distribution of the hospitals is shown in Figure 2, and the descriptive statistics of the sample data are presented in Table 1.  
As shown in Table 1, general hospitals significantly exceed specialty hospitals in terms of size (building area, number of beds, number of departments, and staff size) and medical level (number of national key specialties). Furthermore, the variation in the variables for general hospitals is much larger. In terms of transportation accessibility, both general and specialty hospitals have an average of over 10 bus routes, indicating high coverage and convenience in public transportation connectivity. However, in terms of private car parking planning, specialty hospitals have significantly fewer parking spaces than general hospitals.  
Figure 2: Schematic map of hospital location distribution.
# A Spectral-Aware Convolutional Neural Network for Pansharpening
## 5 Conclusions  
In this paper, we proposed a novel pansharpening neural network, SA-PNN, for MS/HS images. The network comprised of two branches, the detail branch and the approximate branch, has clear interpretability of detail injection. Furthermore, a spectral-aware strategy is used in SA-PNN, which is composed of SFT operations and 3D convolutions to strengthen the spectral processing. Thus, our network offers potential to reduce spectral distortion in the final pansharpening result. The experimental results on real-world MS/HS images validated the remarkable performance of our SA-PNN method.  
All authors made significant contributions to the manuscript. [PERSON], [PERSON], and [PERSON] designed the research framework, conducted the experiments, and wrote the manuscript. [PERSON] and [PERSON] were the supervisors of the work who provided funding. [PERSON] provided many constructive suggestions on the motivation analysis and the methodology design. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under grant 61571195,grant 61771496 and grant 61836003, and in part by the Guangdong Provincial Natural Science Foundation under grant 2017A030313382, and in part by Guangzhou Science and Technology Program under grant 202002030395.  
The authors declare no conflict of interest.  
Figure 8: Spectral difference curves between the ground truth and the pansharpening results for the Pavia dataset at randomly selected spatial coordinates: (**a**) (27, 68); (**b**) (125, 68); (**c**) (11, 12); and (**d**) (131, 8), which are marked in Figure 7a with red crosses.
# Estimation of Optimal Speed Limits for Urban Roads Using Traffic Information Big Data
## 3 Data Collection and Model Equation Development
### Data Collection
#### 3.2.2 Selection of Road Sections  
The road section selection criterion used in this study was based on traffic breaks, that is, road sections with relatively uniform traffic characteristics. The speed limit and number of lanes were also considered. After selecting sections considered to be homogeneous roads in each district in Seoul, detailed data were collected from Internet map sites. As road design factors were not considered in this study, we selected uniform road sections with a minimum length of 800 m and without distinct slopes (FHWA, 2012). The detailed number of road sections and Distances are shown in Table 6.
## 3 Results
### Identification of Phytochemical Compounds of Artichoke By-Products by HPLC-ESI-TOF-MS
#### 3.1.1. Phenolic Acids  
Peak 2, with a precursor ion at _m/z_ 353.0878 was identified as chlorogenic acid [21], whereas peak 4, with _m/z_ 359.0772 and a molecular formula C\({}_{18}\)H\({}_{16}\)O\({}_{8}\), was proposed as rosamarinic acid [22]. Moreover, peaks 5 and 8, which displayed a _m/z_ 515.1195 and retention times of 7.36 and 8.08 min, respectively, were identified as cynarin isomers, also named \"dicaffeoylquinic acid isomers\" [21; 22].
# Lightweight CoAP-Based Bootstrapping Service for the Internet of Things
## 3 The Bootstrapping Service: CoAP-EAP
### Bootstrapping Security Associations
#### 3.4.3 CoAP Message Protection with DTLS  
DTLS is the standard protocol used to protect CoAP messages by default. If the Controller wants to use DTLS for protecting messages it will not send the AUTH option (see Figure 6) (step **16**\"). In this case, it derives a DTLS_PSK from the MSK (the Smart Object will do the same) and starts a DTLS negotiation over port 5684. Thus, the Smart Object and the Controller do not consider the bootstrapping to be complete until the DTLS handshake finishes successfully.  
Figure 5: AUTH-based protection example.  
Now, messages related with the bootstrapping service that may be exchanged from this point (_i.e._, Confirmable DELETE) are protected by DTLS. Any other service can still use DTLS (steps **18'** and **19'**) if the URI scheme for this service contains \"coaps\" (or even decide to use AUTH option deriving the COAP_PSK if the URI scheme contains \"coapa\").  
The DTLS_PSK will also have 16 byte length and will be derived as follows:  
\[DTLS\_PSK=KDF(MSK,\"IETF\_DTLS\_PSK^{\prime\prime}||nonce-c||nonce-s,64,length) \tag{3}\]  
where  
\(MSK\) is exported by the EAP method; \"IETF_DTLS_PSK\" is the ASCII code representation of the non-NULL terminated string (excluding the double quotes around it); 64 is the length of the MSK; _length_ is the length of the label \"IETF_DTLS_PSK\" (13 bytes) plus the two nonces; _nonce-c_ is a random value sent from the Controller to the Smart Object; _nonce-s_ is a random value sent from the Smart Object to the Controller.  
As mentioned in [83], a PSK identity is needed. We consider the use of the Token value chosen during the EAP authentication as PSK identity.
# A New Compound Staged Gelling Acid Fracturing Method for Ultra-Deep Horizontal Wells
## 1 Introduction
A combination of particulate diverter and fiber was often applied in the fields [35; 36]. [PERSON] et al. [37] designed a multi-stage triaxial fracturing system and experimental procedures to satisfy the requirements of diverted fracturing in horizontal wells. Among the various existing chemical diverters, temporary plugging agents have the greatest potential for being used in HPHT reservoirs. Temporary plugging agents are made from degradable polymers, typically polylactic acid (PLA), which can degrade at the reservoir temperature and leave no residue [38]. All reported field applications with this type of diverter are in reservoirs less than 100 \({}^{\circ}\)C, and only used for plugging the perforation holes during the refracturing [39; 40; 41]. [PERSON] et al. [42] investigated the filtration characteristic of temporary plugging and the length of filter cake under the condition of different fracture widths. [PERSON] et al. [43] presented a comprehensive workflow to model hydraulic fracture by accounting for interactions with numerous crosscutting natural fracture or joint sets, as well as the effect of temporary plugging in opened fractures. This model is a fully coupled seepage flow in porous media, fluid flow in fractures, and rock deformation finite element model with adaptive insertion of cohesive elements as a crosscutting natural fracture or joint sets. [PERSON] et al. [44] designed a high-pressure evaluation system for fracture temporary plugging and investigated the key factors and their influencing pattern. [PERSON] et al. [45] conducted a series of experiments to investigate the plug formation time, diverter consumption, and plugging zone characteristics with different fracture widths and diverter concentrations. [PERSON] et al. [46] established the visualization model of a microfluidic chip and CFD-DEM numerical simulation to evaluate the effect of particle plugging in a fracture-vuggy reservoir. According to the previous studies, it can be concluded that there are still many technical problems in the staged acid fracturing of ultra-deep horizontal wells. Therefore, this paper introduces a new compound staged gelling acid fracturing method for ultra-deephorizontal wells. Using productivity simulation technology, we optimize the optimal fracture spacing and fracture length of segmented acid fracturing in horizontal wells. Through dissolution and plugging pressure tests of the temporary plugging agent, the combination of high-strength temporary plugging agent is optimized, and the key parameters of alternating injection acid fracturing process are optimized by the VOF model. This study deepens the understanding of the staged acid fracturing mechanism in an ultra-deep horizontal well, and provides great help for acid fracturing stimulation design. The compound staged gelling acid fracturing technology is widely used in ultra-deep horizontal wells in the Sichuan Basin, which has achieved a good stimulation effect and improved the development effect of gas reservoirs.
# Insider Threat Detection Using Machine Learning Approach
## 2 Literature Review
### Deep Learning  
Traditional shallow ML models are unable to fully utilise user behaviour data despite existing approaches demonstrating excellent performance on insider threat detection because of their high dimensionality, complexity, heterogeneity, and sparsity [45]. Deep learning, on the other hand, can be an effective tool for examining user behaviour in an organisation in order to spot malicious insiders. Deep learning is a representation learning algorithm that can learn multiple levels of hidden representations from complex data based on its deep structure [45].  
Several methods for detecting insider threats have recently been proposed, deep feed-forward neural networks, convolutional neural networks, and recurrent neural networks. This section introduces some contemporary deep-learning methods for the detection of insider threats.
## 2 EDFA's Basic Design and Specifications
The output power can be controlled by one of the SCPI commands via the UART serial communications by adjusting the 2 nd-stage pump laser driving current. The EDFA's control mode is based on an automatic pump-current control (ACC) to maintain a fixed pump laser current. Automatic power control (APC) may seem more appropriate since the important parameter in laser communication is the transmitted power. However, in a CubeSat, there is a very severe restriction in the available power, thus the current is limited, and rather than maintaining constant power, it is preferable to transmit the maximum available power, which can also be controlled indirectly through the current. Additionally, APC would require some added complexity for a tap splitter and a photodiode to monitor the power. When the EDFA is operating in the 2 W (full power) regime with saturated gain, the measured consumption of the device is around 18 W (see Table 1 for more detailed consumption figures), taking 85% of the current from the 5 V line and 15% from the 3.3 V line.
## 4 Conducting the Cyber-Attack Scenario and Evaluating the Results
### Impact Assessment
If a pattern for their simultaneous values in a nominal operation situation could be found, this in principle would allow the detection of elaborate cyber-attacks against one of them; such as replay attacks, where the value shown in the HMI corresponds to the oscillations of that variable values in a previous period. In normal operation with the Main Reactor (RX) power at 100%, the variable CC_PumpSpeed, held at the constant value of 100 by our cyber-attacks, actually oscillates with values close to 102. This can be seen most clearly in the graphs in Figure 31, where the first 1000 or so measurements after the start of normal reactor operation are shown. The graph depicted on the left shows the values for each observation and the graph on the right shows the density or frequency of values associated with the various measurements, for the same data set. Furthermore, small operation transients, even in normal operation, are expected and were observed during the simulations. Therefore, we used MySQL Workbench to extract a sample of 600 observations where one of these transients were present, as shown in Figure 32, and thereafter studied the relationship between the values of CC_PumpSpeed and CD_Press in this range. Figure 31: CC_PumpSpeed nominal oscillations (around 102). Figure 30: TFR comparison—MITM. After normalizing these values between 0 and 1, as shown in Figure 33 (a standard preparatory procedure necessary to employ various ML algorithms), we determined the linear correlation between the variables to be very low (\(-0.1925\)). However, the respective scatter plot clearly displays the formation of an underlying non-linear pattern, as seen in Figure 34. This found locus could presumably be used to detect anomalies. Although mere visual inspection will be insufficient to detect non-linear relationship patterns for more than two chosen variables or features, appropriate mathematical and computational techniques can be employed instead. The same procedures could be expanded in order to train ML algorithms, by including new variables and enlarging the dataset. Besides the possibility of studying anomaly detection techniques, this testbed also allows the application of a graded approach and defense in depth, by implementing computer security levels and computer security zones, and assessing the technical computer security measures needed to protect facility functions. One example of practical implementation of a defensive computer security architecture (not part of this study), would be the use of a decoupling mechanism between computer security zones containing the PLCs and the supervisory. Figure 32: CC_PumpSpeed and CD_Press simultaneous transients.
## 3 Airgap Eccentricity Fault Effect in the Apparent Power  
### Case of a Healthy Induction Motor  
Assuming that a healthy induction motor, powered by a balanced three-phase source of sinusoidal voltage and driving a constant load, the input voltage waveforms can be expressed as follows:  
\[\left\{\begin{array}{l}u_{a}=\sqrt{2}U\cos\omega_{s}t\\ u_{b}=\sqrt{2}U\cos(\omega_{s}t-\frac{2\pi}{3})\\ u_{c}=\sqrt{2}U\cos(\omega_{s}t+\frac{2\pi}{3})\end{array}\right. \tag{7}\]  
Figure 3: Schematic model of a motor rotational under dynamic eccentricity.  
Under the effect of the aforementioned input voltage, the three-phase stator currents can be written by Equation (8):  
\[\left\{\begin{array}{l}i_{a}=\sqrt{2}I\cos(\omega_{s}t-\alpha_{I})\\ i_{b}=\sqrt{2}I\cos(\omega_{s}t-\alpha_{I}-\frac{2\pi}{3})\\ i_{c}=\sqrt{2}I\cos(\omega_{s}t-\alpha_{I}+\frac{2\pi}{3})\end{array}\right. \tag{8}\]  
where \(U\) and \(I\) represent the amplitudes of input voltage and stator current root mean square, respectively; \(\alpha_{I}\) is the phase angle. By considering the Park's transformation in the stationary reference frame and the generalized theory, the active power \(p_{0}(t)\), the reactive power \(q_{0}(t)\) and the apparent power \(s_{a}\) in the healthy case become [17]:  
\[p_{0}(t)=u_{a}i_{a}+u_{b}i_{b}+u_{c}i_{c}=3 UI\cos(\alpha_{I}) \tag{9}\]  
\[q_{0}(t)=\sqrt{3}(u_{a}i_{b}-u_{b}i_{a})=3 UI\sin(\alpha_{I}) \tag{10}\]  
\[s_{a}=\sqrt{p_{0}^{2}(t)+q_{0}^{2}(t)} \tag{11}\]  
Under the case of a healthy induction motor, the spectrum of apparent power modulus only contains the DC component. Therefore, the DC component can be removed and the characteristic signal related to eccentricity fault can be identified easily.
# Prediction of Temperature Distribution for Previous Cement Concrete Pavement with Asphalt Overlay
## 4 Model Verification with Measured Field Data
### The External Environment of Temperature Field
#### 4.1.2 Pavement Surface Convection Coefficient  
The measured average wind speeds at the pavement site are 0.48 m/s for 24 August 2015 and 1.09 m/s for 16 December 2015. Further, the corresponding convection coefficients (\(h_{f}\)) calculated by Equation (8) are 11.2 W/(m\({}^{2}\).\({}^{\circ}\)C) for 24 August 2015 and 13.5 W/(m\({}^{2}\).\({}^{\circ}\)C) for 16 December 2015.