esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Performance Evaluation Metrics  
A performance evaluation is the process of measuring the of a classification model after assigning cases to their various predetermined labels. The performance evaluation considers measures including accuracy, recall, precision, F1-score, and confusion matrix [26]. These metrics are described as follows:  
1. Accuracy: The ratio of all correct predictions over the total number of packets in the dataset [24]: \[\mathrm{Accuracy(Ac)}=\frac{\mathrm{T_{p}}+\mathrm{T_{n}}}{\mathrm{T_{p}}+ \mathrm{T_{n}}+\mathrm{F_{p}}+\mathrm{F_{n}}}\] (3) where \(\mathrm{T_{p}}\) is the \"True Positive,\" which describes the rate where the actual instance of a certain label is categorized as that label; \(\mathrm{T_{n}}\) is the \"True Negative,\" which is the attack instance's value that is classified as an attack; \(\mathrm{F_{p}}\) is the \"False Positive,\" which is the number incorrectly classified for a certain class label, i.e., the instance categorizedvalue as an additional class label for a given dataset; F\({}_{\text{n}}\) is the \"False Negative,\" which is the value of normal traffic which is classified as an attack.
2. Recall: Recall is the number of correctly predicted positive records over all the positive records: a metric that can detect DDoS attack traffic compared to normal traffic [24]. \[\text{Recall}=\frac{\text{T}_{\text{P}}}{\text{T}_{\text{P}}+\text{F}_{\text{n}}}\] (4)
3. Precision: Precision is the proportion of actual positive instances that were correctly predicted, i.e., it is a metric that can detect DDoS attack traffic among normal traffic [24]. \[\text{Precision}=\frac{\text{T}_{\text{P}}}{\text{T}_{\text{P}}+\text{F}_{ \text{P}}}\] (5)
4. F1-score: The F1-score is the balance between the recall and the precision [24]. \[\text{F1}-\text{score}\ =2\times\frac{\text{Recall}\times\text{ precision}}{\text{Recall}+\text{precision}}\] (6)
5. Confusion Matrix: The data classification results appear in a table format. The accuracy of a classification model is evaluated by applying it to test data for which the results have already been determined. The study uses it to show the distribution of the expected outcomes, despite its poor suitability for anything beyond binary classification [24].
# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 3 Description of the Intent Recognition Problem in AUV Collaborative Search Mission
### Dataset Construction for Intent Recognition in Multi-AUV Collaborative Search Mission  
In the context of the multi-AUV collaborative search mission, the dataset construction process is critical for training effective intent recognition models. The quality of the data, as well as the selection of relevant features, greatly impacts the model's ability to predict and understand the intentions of AUVs in complex marine environments. The simulation dataset and real-time feature set used in this study were obtained through a collaborative search simulation platform developed in our previous work. The platform comprises multiple modules, including AUV kinematics and dynamics simulation, underwater acoustic channel simulation, underwater acoustic networking communication simulation, and the simulation of relevant control algorithms. During the simulation of the collaborative search mission, the platform can record the AUV state features in real time and construct the corresponding intent recognition dataset from these features. During the simulation, various state information of the AUV swarm is recorded. These include, but are not limited to, AUV motion characteristics, such as speed, posture, and position information, reflecting the dynamic state of the AUV in space, communication quality indicators, such as packet loss rate and latency, representing the stability of the underwater acoustic communication network and the efficiency of information transmission, and networking communication status, including the communication state between AUVs, signal strength, and effective connections. These state feature data are used to construct the AUV behavior model, from which the AUV's intent can be inferred. To achieve cooperative decision-making and intent recognition for the AUV swarm, the collected state features are matched during the simulation process, and the corresponding intent labels are assigned based on the AUV's behavior and mission requirements. Specifically, within each decision cycle, we determine the AUV's behavior intent based on the feature information from the following two main phases: In the positioning phase, the AUV's mission is to reach the designated position quickly. State features, such as the distance to the positioning point, relative direction to the PNCT, and the number of companions, can indicate whether the AUV has deviated from the mission requirements. In the tracking phase, the AUV's goal is to precisely locate the target. At this stage, information such as detection error and distance to the target is critical for determining the AUV's behavioral goal. Through the mapping relationships in the behavior model, we match the collected state features with the corresponding intent labels (e.g., \"communication priority\", \"positioning priority\") to form the intent recognition dataset. To ensure the training effectiveness of the intent recognition model, we have added corresponding intent labels to each data entry in the simulation dataset. These labels are
## 2 Background and Related Work  
### Flash Memory Features  
NAND flash memory forms the basic building blocks of SSDs today, and most SSDs on the market connect a large number of NAND flash memories to a channel and parallelize them with an SSD controller to provide high performance and large capacity [7; 8; 9; 10; 11]. A NAND flash memory chip is made up of multiple dies and planes with multiple blocks, each with several fixed pages [7; 9]. The most fundamental operations in flash memory are read and write operations, which are conducted in page increments. A typical page write takes hundreds of microseconds, whereas a typical page read takes tens of microseconds, which is much faster than writing a page [12].  
A unique feature of flash memory is that new data cannot be overwritten on a page once data has been written [13]. This is different from HDD where data can be overwritten. To write data back to the page where it was written, the block containing the page must first be erased. This erase operation is slower than the page write operation, and the number of erase-after-write operations, also known as the P/E cycle, is limited by the flash memory's manufacturing technology [14]. Four technologies are widely used today: single-level cell (SLC), multi-level cell (MLC), triple-level cell (TLC), and quad-level cell (QLC). Among them, TLC is widely used because it is inexpensive and suitable for large capacity [15].
## 3 Analysis of the Practical Engineering Data and Results  
The practical engineering data used in this study are listed in Table 1 and were sourced from previous literature [3] that focused on carbonation depth forecasting in concrete structures. The data were used to verify the superiority of the proposed PCA-ANFIS model. Carbonation depth is influenced by many factors, including the compressive strength of the concrete, service life, carbonation time, impact factors of carbon dioxide emission in the air, impact factors of the stress state of the concrete structure, temperature, and humidity.  
Due to the highly nonlinear relationship between carbonation depth and other influencing factors, the principal component analysis method was used to reduce the dimensions of the original input data; the reduced variables were used as the input data of the adaptive neural fuzzy inference system; and the results were obtained after iterative calculation. Finally, the prediction model of carbonation depth based on the PCA-ANFIS network was established.
## 10 Shortcomings of Traveling Waves (TW) Based Methods  
As discussed, detection of the wavefront is the key to finding the location of fault. However, if the wave front does not exist at instant of dc fault occurrence, fault detection and identification is not possible. For instance, in a high resistive grounded TL, magnitude of the transient TW is too small to detect the fault location. Similarly, in the event of variations in transition resistance; traveling transients are too small to be utilized for fault detection [104, 105].  
In traveling wave method, arrival time of wavefront at the VSC station and speed of the wave front is used to calculate distance of fault from substation. Therefore, speed and time measurement is directly related to the accuracy of detection of fault location. These measurements are highly dependent on the electrical parameters of the faulted line. A very high sampling frequency is required for accurate finding of fault location as speed of the wavefront is almost equivalent to the speed of light. Interference signal is another parameter that badly effects the traveling wave. Thus, non-traveling-based fault detection, location and estimation methods are referred to overcome above aforementioned shortcomings [104, 86, 105, 106].  
Non-traveling Wave (NTW) Based Fault Location Estimation in Multi-Terminal High-Voltage Direct Current (MT-HVdc) Systems  
In non-travelling wave-based technique, fault location is identified and detected by evaluating voltage and current values after dc fault in any section of the line. This method is based on distributed parameters of the transmission line model. Current and voltage measurements at both ends are calculated and voltage distribution over the TL is obtained. Fault is detected with the help of this voltage distribution but non-traveling-based techniques are slow [39]. Although TW techniques are  
Figure 19: Process flow of opening closing (**OC**) max—closing opening (**CO**) min filter.  
rapid [33; 59; 63; 66; 77; 78] yet offer aforementioned shortcomings. Non-traveling wave-based method can be made rapid if fault location algorithm is based on time domain. Short window of the data is sufficient to achieve satisfactory accuracy and detection [39; 91; 107]. Data window for non-traveling wave-based method can be acquired precisely from distributed parameter model of dc transmission lines.  
Non-traveling wave-based method is applied on bipolar HVdc transmission line systems. Schematic diagram of bipolar HVdc transmission line is shown in Figure 20[39].
# Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models
## 1 Introduction
For example, it requires a lot of time to train these networks to achieve appreciable performance. In addition, the dataset that is being used for training should contain all the variations of samples so that the model could easily generalize unseen data, but this is not always possible. If DL-based models are trained on a smaller dataset, over-fitting may occur, which in turn causes generalization errors. In real-world problems, we rarely have good quality labeled data with a significant number of instances for training a DL-based model. For example, in our present work, data augmentation can not be used since the dataset consists of chest CT-scan images that cannot be flipped, rotated, or sliced as those will not be realistic. To deal with all these problems, a TL-based approach is the most effective solution. In this work, we have utilized the approach of TL [28] instead of building the network architecture from scratch. We have used three pre-existing models, namely VGG-16 [29], ResNet50 [30] and Xception [31] in our current experimentation. These three models have an innately dissimilar architecture that may abstract unrelated information from the images used for the classification purpose. To make the classification process more efficient, we have also used the ensemble-based learning concept. All of the three mentioned models are combined by the strategy of stacking ensemble inspired from stacked generalization [32] to make the final model generalize and also to perform better on the unseen data. The rest of this paper has been divided into different sections as follows: Section 2 discusses the literature survey mentioning a few important COVID-19 works described by different authors, and Section 3 highlights the dataset used in the current experiment. The proposed methodology for the prediction of COVID-19 has been detailed in Section 4, the observed outcomes with in-depth analysis have been mentioned in Section 5, and lastly, Section 6 concludes the overall work with some future directions.
## 1 Introduction
In the field of AC/DC distribution system optimal scheduling, research has primarily focused on enhancing operational efficiency and integrating diverse resources. Decentralized operation strategies employing analytical target cascading [30] or ADMM [6; 7; 8] have been developed to reduce reliance on central controllers. In parallel, efforts have focused on multi-stage energy management, with schemes combining day-ahead network reconfigur \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Ownership** & **Sharing Mechanism** & **Available Capacity** & **System Coordination Performance** & **Relationship** \\ \hline ESS & Single user or entity & N/A & Fixed & Low & Can be a component of SES \\  & Multiple users & & & & \\ SES & or third-party aggregator & Users share idle capacity & Pre-allocate & Moderate to low & Broader concept for shared storage \\ CSES & Central entity & Dynamic sharing of both capacity and energy & Flexible & High & Specific SES type: unified asset for multiple users \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A comparison of energy storage operational architecture. tion and real-time optimal power flow [31], and developing overvoltage-averse dispatch models for renewable-rich networks [32]. To address uncertainties, particularly from RES, studies have used sophisticated methods such as robust optimization [33] and distributionally robust chance-constrained optimization [34]. Despite these advances in AC/DC distribution system operations, significant research gaps persist: existing approaches lack comprehensive dual-time-scale coordination mechanisms for CSES and do not adequately leverage complementary characteristics across subnetworks. Building upon these developments and addressing the identified research gaps, this paper proposes a CSES optimization framework for AC/DC distribution systems with dual-time-scale coordination. The primary contributions of this work are as follows:  
1. A novel CSES optimization framework is established, where a large-scale CSES directly connects to multiple AC subnetworks. By leveraging temporal and spatial differences in RES and load profiles across subnetworks, complementary operation is achieved, thereby effectively enhancing the utilization of RES. 2. A dual-time-scale coordination optimal scheduling with ADMM is proposed, combining day-ahead scheduling with intraday adjustments. Day-ahead SOC trajectory is converted to an intraday SOC trajectory through linear interpolation, maintaining SOC constraint compliance. This approach effectively manages CSES while addressing source-load imbalance between subnetworks. The remainder of this paper is organized as follows. Section 2 formulates the operation optimization models for AC/DC distribution systems. Section 3 presents the CSES optimization framework with dual-time-scale coordination. Section 4 demonstrates the effectiveness of the proposed method through case studies. Finally, Section 5 concludes the paper and outlines directions for future research.
## 3 Position Estimation of the Occluded Parts  
### Freeman's Chain Code  
The vectorized chain code sequence of the contour curve is established by the eight-direction Freeman chain code. The coding scheme is shown in Figure 7. Taking the target pixel \(pi\) as the center, the directivity of the next pixel \(p_{i+1}\) on the contour is determined along the eight-pixel directions \(\{b_{i},i=0,1,2,\ldots,7\}\) of the neighborhood. \(i\) is the direction amplitude, and the whole contour curve is traversed in turn to obtain the chain code sequence with shape information.  
Figure 6: Contour reconstruction with different numbers of Fourier descriptors: (**a**) initial contour fragment, (**b**) 224 Fourier descriptors, (**c**) 112 Fourier descriptors, (**d**) 56 Fourier descriptors, (**e**) 28 Fourier descriptors, and (**f**) 14 Fourier descriptors.
## 4 Real-Life Signal Analysis  
### Measurement Campaign  
The experimental data were gathered during a measurement campaign using a GSM-based passive radar for monitoring ground moving objects. The source of the illuminating signal was a GSM base transceiver station with an antenna mounted at the height of approximately 50 m. During the trials, a cooperating vehicle was used as an observed target moving with the velocity \(v\approx 10\frac{\mathrm{m}}{\mathrm{s}}\)[25]. Measurements were carried out a few times under similar conditions. This was done to confirm the repeatability of results in comparable scenarios, and to verify the possibility of distinguishing characteristic features in the movement.  
The location of the radar's surveillance antenna and trajectory of the target were selected in order to ensure that the forward scattering effect of the vehicle occurred at the observation spot. The surveillance antenna was mounted 1.5 m above the ground and was located approximately 1.5 km from the transmitter. The cooperating car crossed the baseline between the transmitter and the receiver within approximately 1 km of the transmitter.  
The reference signal was acquired at the same location as where the measurement was performed. In order to reduce the influence of the target echo in the reference channel, an antenna with vertical beamwidth of approximately 25 degrees was mounted on a 12 m mast and tilted upwards.  
The measurement equipment consisted of commercial-off-the shelf components, with a two channel receiver based on the National Instruments PXIe-5667 vector signal analyzer, low noise amplifiers and band-pass filters for the GSM900 band (925-960 MHz) connected after the antennas of both the surveillance and reference channels. The measurement scene utilized during the trials is presented in Figure 11, and the general measurement geometry diagram is depicted in Figure 12. More details about the measurement campaign and the signal processing chain are available in Reference [25].  
Figure 11: Measurement scene. In white—Range from the GSM transmitter to the receiver, in red—range from the receiver to the intersection point, in blue—the target trajectory.
## 3 Results
### At the Plot Level: Visible Land Fragmentation and Dispersion
The SPLIT index of cultivated land in Shanghai suburban districts tends to rise on the whole, among which Minhang, Baoshan, and Songjiang districts have the most obvious changes (Table 12). For nearly 30 years, these three districts have witnessed the fastest growth of urban population and the largest increase of housing scale, as well as the fastest industrialization development and the fastest decrease of cultivated land in Shanghai, so the degree of cultivated land fragmentation is the most prominent. Since the former Pudong New Area located in the inner suburbs was merged with the former Nanhui District located in the outer suburbs, the degree of farmland fragmentation was reduced on the whole, resulting in the change of SPLIT index of cultivated land is not as obvious as the above three districts. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Region** & **District** & **Index** & **1990** & **2000** & **2010** & **2018** \\ \hline \multirow{6}{*}{The inner suburbs} & \multirow{2}{*}{Baoshan} & AI/\% & 96.8683 & 96.1689 & 94.6917 & 94.7574 \\  & & SPLIT & 1.1203 & 1.2587 & 7.2992 & 5.9689 \\ \cline{1-1}  & & AI/\% & 97.7889 & 97.0290 & 95.4900 & 95.6742 \\ \cline{1-1}  & & SPLIT & 1.0322 & 1.0510 & 2.0878 & 1.9077 \\ \cline{1-1}  & & AI/\% & 96.7310 & 96.2126 & 94.9751 & 94.2682 \\ \cline{1-1}  & & SPLIT & 3.0010 & 3.4864 & 7.5870 & 9.9575 \\ \cline{1-1}  & & AI/\% & 98.2016 & 97.8465 & 97.3251 & 97.1281 \\ \cline{1-1}  & & SPLIT & 1.6042 & 1.8257 & 2.7632 & 2.9895 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Aggregation index and SPLIT index of cultivated land and their changes in the suburbs of Shanghai. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **NP** & **MPS** & **PD/100 ha** & **LPI/\%** & **LSI/100 ha** \\ \hline
1990 & 212 & 2261.04 & 0.034 & 23.16 & 43.89 \\
1995 & 103 & 4330.01 & 0.020 & 20.50 & 45.93 \\
2000 & 155 & 2828.74 & 0.025 & 19.63 & 46.85 \\
2005 & 219 & 1835.15 & 0.035 & 19.80 & 48.75 \\
2010 & 310 & 1150.94 & 0.050 & 12.49 & 52.91 \\
2015 & 670 & 505.05 & 0.108 & 12.50 & 52.60 \\
2018 & 613 & 501.02 & 0.18 & 21.85 & 52.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Index of landscape pattern of cultivated land in the suburbs of Shanghai (1990–2018). In general, from the perspective of the traditional indexes AI and DIVISION, as well as MNN, CONNECT, and SPLIT, cultivated land in Shanghai gradually became increasingly discrete and fragmented, especially between 1995 and 2018.
## 4 Proposed Method
### Median Noise Removing  
Sometimes, a background subtraction technique fails to yield palatable outcomes for indoor video segmentation. Thus, we adapted background subtraction by enhancing the strategy used, by utilizing median filtering to improve the extracted foreground mask, as in [23]. A median filter is a simple and powerful non-linear digital filter. It is used for reducing the amount of intensity variation between two pixels. Accordingly, we obtain the binary image (kernel) of every conceivably moving object in the overlap region, and the kernel data can be used with a noise removal filter.  
Specifically, the median filter replaces a pixel by the median, rather than the normal, for all pixels in a neighborhood \(w\), using Equation (4):  
\[y[m,n]=median\{x[i,j],(i,j)\in w\} \tag{4}\]  
where \(w\) represents a neighborhood characterized by the client that is centered on a location \([m,n]\) in the image.  
Moreover, regarding the noise-reducing execution of the median filter, because the median filter is a nonlinear impulse filter, its numerical investigation is moderately complex for an image with irregular noise. For a picture with zero mean noise under an ordinary distribution, the noise difference of the filtering is approximately near the value from the below formula:  
\[\sigma_{md}^{2}=\frac{1}{4 nf^{2}(\widetilde{n})}\approx\frac{\sigma_{i}^{2}}{ n+\frac{\overline{n}}{2}-1}\cdot\frac{n}{2} \tag{5}\]  
where \(\sigma_{i}^{2}\) is the input noise power (the variance), \(n\) is the size of the median filtering mask, and \(nf\) is the function of the noise density.