esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
integrated a new frequency extractor layer in the deep neural network for feature extraction [25]. However, most existing methods only treat frequency domain information as a feature or input, without effectively incorporating it into the model design and kernel function design. In recent years, there have been several relevant studies about manifold learning and dimensionality reduction. [PERSON] et al. introduced spectral information divergence into a neighbor manifold graph for hyperspectral image dimensionality reduction [26]. But, the distance of pairwise data points was calculated by Euclidean distance in the original domain. FMDR conducts a novel kernel function for measuring the similarity between samples based on the frequency domain correlation, which is more precise, especially when samples are corrupted by interference. [PERSON] proposed a manifold learning-based contrastive framework, which applies ISOMAP to extract nonlinear structures and neural networks for comparative learning training [27]. Compared to this framework, our FMDR does not require additional neural networks or training processes and can achieve excellent dimensionality reduction results with minimal computational resource requirements.
# Botnet Detection Based On Machine Learning Techniques Using DNS Query Data
## 2 Related Works
Kopis monitors high-level DNS queries in the DNS hierarchy and analyzes the patterns of global DNS query responses for detecting malware-related domain names. The Kopis's test results show that the system achieves more than 98% accuracy and under 0.5% false detection rates. Kopis also has the ability to detect malware-related domain names many days before they are included in the public blacklists. Using a different approach, [PERSON] et al. [11] introduced the EXPOSURE system that allows for the extensive monitoring of DNS traffic to detect domain names that are associated with malicious behaviors. EXPOSURE uses 15 features to distinguish suspicious domain names from legitimate domain names. The test results show that the system is well scalable and can recognize new domain names related to malicious behaviors, as they are used for C & C servers, for spamming and for phishing websites. For the purpose of detecting suspicious and malicious behaviors originating from botnets, [PERSON] et al. [12] proposed a method for identifying suspicious behaviors based on the analysis of failed DNS queries using a tool called DNS Failure Graphs. DNS failure queries are queries with non-existent domain names, expired domain names, or errors in the DNS system. This suggestion comes from the fact that suspicious behaviors triggered by spamming, trojans' activities, especially bot activities include DNS lookup queries for IP addresses of mail servers, or C & C servers. A significant number of these queries are DNS lookup queries that fail because of the non-existence domain names or the expired domain names. Test results on a three-month DNS query dataset of a large network indicate that the proposed method can identify new behavioral abnormalities with a high probability of originating from unknown bots. In the opposite direction, [PERSON] et al. [13] proposed a system called Mentor that allows the removal of legitimate domain names from the blacklist of domain names of C & C servers using a positive reputation-based DNS system. Mentor uses a crawler and statistical characteristics of site content and domain names to categorize legitimate domain names and suspicious domain names based on supervised learning. Experimental results on some botnet domain blacklists show that the system is capable of accurately identifying legitimate domain names with low error rates. In this paper, we extend the proposals in [8; 9; 14] by examining and evaluating the performance of the bonnet detection method using DNS query data based on several supervised machine learning techniques, including kNN, decision trees, random forest and Naive Bayes. Based on the survey results, we propose the botnet detection model based on machine learning using DNS query data.
# A Dual-Channel End-to-End Speech Enhancement Method Using Complex Operations in the Time Domain
## 3 Experimental Settings
### Generation of the Two-Channel Dataset  
In the experiment, we trained and evaluated the proposed speech enhancement model using the deep noise suppression (DNS) challenge dataset [23]. The clean audio signals were derived from LibriVox, a public English audiobook dataset, and the noises were selected from Audioset and Freesound. All audio signals were sampled at 16 kHz, with clean clips and noise clips having lengths of 31 s and 10 s, respectively. We used the script provided in the DNS challenge to generate 75 h of audio clips, with each clip having a size of 6 s. In total, we generated 36,400 clips (approximately 60 h) for the training set and 4550 clips (approximately 8 h) for the validation set, and the remaining 4050 clips (approximately 7 h) were used as the test set.  
To produce dual-channel datasets, we used the image method [24] and studied the effects of single-direction and multiple-direction target sources on the performance of speech enhancement. We tested two different scenarios.  
**Scenario I**: In the first scenario, the target source was located in a single direction. As shown in Figure 4, the target source was close to the reference microphone, and it was on the same horizontal line as the microphones. The target position was marked with the red box, while the incoming noise was uniformly sampled from 0 and 90 degrees at 15-degree intervals.  
**Scenario II**: In the second scenario, shown in Figure 5, the azimuth angle of the target source was indicated by the red dotted lines and ranged between \(-45\) and \(45^{\circ}\). The azimuth angle of the noise was between \(-90\) and \(90^{\circ}\) and evenly distributed at 15-degree intervals.  
This scenario created a so-called acoustic fence, where the speech inside the region of interest (ROI) could be passed with minimal distortion.  
The clean and noisy signals were located 1 m away from the microphones, while the distance between the two microphones was 3 cm. In the simulated reverberation room, which had a size of 10 m \(\times\) 7 m \(\times\) 3 m (width \(\times\) depth \(\times\) height), the sound source signal was convolved with two different room impulse responses (RIRs) before being received by the two microphones. The signal-to-noise ratios (SNRs) were randomly mixed from \(-\)5 to 10 dB at 1 dB intervals. In the test set, the SNRs were set to \(-\)5, 0, 5, 10, and 20 dB, and the clean speech signal captured by the reference microphone was used as the label for the CNABCFCN model.  
Figure 4: Experimental configuration of a single target sound source. The target source is the position marked by the red box, and the noise source is distributed from 0 to 90\({}^{\circ}\) (Scenario I).  
Figure 5: Experimental configuration of regional target sound sources. The position of the target sound source is evenly distributed between \(-\)45 and 45\({}^{\circ}\). Each target sound source may be affected by noise distributed from \(-\)90 to 90\({}^{\circ}\) (Scenario II).
# Real-Time Moving Ship Detection from Low-Resolution Large-Scale Remote Sensing Image Sequence
###### Abstract  
Optical remote sensing ship target detection has become an essential means of ocean supervision, coastal defense, and frontier defense. Accurate, effective, fast, and real-time remote sensing data processing is the critical technology in this field. This paper proposes a real-time detection algorithm for moving targets in low-resolution wide-area remote sensing images, which includes four steps: pre-screening, simplified HOG feature identification, sequence correlation identification, and facilitated Yolo identification. It can effectively detect and track targets in low-resolution sequence data. Firstly, iterative morphological processing was used to improve the contrast of low-resolution ship target profile edge features compared with the sea surface background. Next, the target area after adaptive segmentation was used to eliminate false alarms. As a result, the invalid background information of extensive comprehensive data was quickly eliminated. Then, support vector machine classification of S-HOG feature was carried out for suspected targets, and interference such as islands and reefs, broken clouds, and waves were eliminated according to the shape characteristics of ship targets. The method of multi-frame data association and searching for adjacent target information between frames was adopted to eliminate the interference of static targets and broken clouds with similar contours. Finally, the sequential marks were further trained and learned, and further false alarm elimination was completed based on the clipped Yolo network. Compared with the traditional Yolo Tiny V2/V3 series network, this method had higher computational speed and better detection performance. The F1 number of detection results was increased by 3%, and the calculation time was reduced by 66%.  
optical satellite image; ship detection; convolutional neural networks; deep learning +
Footnote †: journal: Medical Imaging  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 5 Security Analysis of the New Protocol
### Comprehensive Privacy Preservation  
#### 5.3.1 Data Privacy Preservation  
Upon receiving the challenge from the TPA, the cloud server responds with the proof: \(proof=H_{3}(e(\sigma,T_{1})\cdot\mu^{-1})\), in which we hide the information of \(\sigma\) and \(\mu\) using hash function H\({}_{3}\). Furthermore, the TPA just needs to check the following equation: \(proof=H_{3}(\begin{array}{c}\prod\\ j\in J\\ \end{array}e(H_{2}(j||frame)^{v_{j}},Y_{1D_{j}}^{t}))\), without knowing any information about data file \(\text{blocks }\{m_{i}\}\) or their corresponding tags \(\{\sigma_{i}\}\).
# P-Distill: Efficient and Effective Prompt Tuning Using Knowledge Distillation
## 4 Experiments
### Experimental Results of Applying P-Distill to P-Tuning Methodology  
To evaluate the effectiveness of P-Distill when the prompts directly occupied the input sequence space, we applied P-Distill to the P-tuning methodology that attached prompts to the input embeddings. The experimental results are shown in Table 6.  
These results demonstrate the effectiveness of applying P-Distill to the P-tuning methodology. When P-Distill was used, it showed higher performance across all datasets compared to using prompts of the same length without P-Distill. Particularly, on the CB and COPA datasets, P-Distill achieved the same performance as the teacher prompts despite compressing the prompts to one-eighth. These results indicate that P-Distill effectively compressed the prompt length while maintaining performance, even when the prompts were attached to the input embeddings.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **CB** & **COPA** & **RTE** \\ \hline Fine-tuning & 0.946 & 0.71 & 0.762 \\ \hline P-tuning & \(0.821_{(16)}\) & \(0.76_{(16)}\) & \(0.657_{(16)}\) \\  & \(0.786_{(2)}\) & \(0.70_{(2)}\) & \(0.621_{(4)}\) \\ \hline P-Distill & \(0.821_{(2)}\) & \(0.76_{(2)}\) & \(0.646_{(4)}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental results on the SuperGLUE validation dataset for small datasets (CB, COPA, RTE). For P-Distill, training was performed using a teacher model with the prompt length exhibiting the best performance for P-tuning. The numbers in parentheses indicate the lengths of the prompt attached to the model. All metrics are accuracy.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **CoNLL03** & **CoNLL04** & **CoNLL05** & **CoNLL05** \\  & & & **WSJ** & **Brown** \\ \hline P-Distill & **0.919** & **0.888** & **0.885** & **0.817** \\ P-Distill\({}_{mean}\) & 0.915 & 0.875 & 0.878 & 0.809 \\ P-Distill\({}_{max}\) & 0.912 & 0.872 & 0.872 & 0.803 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of additional experiment results across various tasks based on prompt initialization methods. All metrics are reported as micro-F1-scores. (**bold**: the best).
# Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development
## 4 Results: Standardization, Methodology, and Framework for COP and Metaheuristics
### Framework Structure
#### 4.1.3 Problem Representation System  
The Problem Representation System (PRS) aims to organize problem definitions and all required mechanisms to create and manipulate solutions in a generic way. It is based on a state machine that interprets this encoding to manipulate solutions. In this case, the state is defined by an arrangement of elements that compose a solution instance. For instance, by receiving a solution's encoding, a solution factory component (SFC) should be able to create any solution for a determined problem. In the same way, a manipulation engine component (MEC), by receiving this encoding, should be able to recommend transformations that are requested by subordinated heuristics. For example, considering that one position of the solution must be changed, the MEC can return a proper alternative to replace the current element. It is a generic method that knows how to generate valid elements randomly according to the encoding and without code changes.  
Furthermore, the PRS also is supported by a Problem Syntax, a set of rules that defines and evaluates if a problem is well-defined. It should have a component responsible to test and check if the problem defined is working properly, independently of the metaheuristic that will be used. It is also based on defining how problem templates and instances should be coded.  
Moreover, since the solution factory can create any solution for a determined problem is only based on the encoding, the framework needs to take advantage of this and provide a built-in subordinate heuristic for random initialization. In other words, by interpreting the solutions' encoding, they can initialize any metaheuristic randomly. It enables the experiment's author(s) to focus on the problem definition because the framework already provides a standard way to deal with problems' definitions. This system supports all subordinated heuristics by providing knowledge of the problem to enable proper transformations in the solutions.  
However, the framework should provide the possibility to the authors to use a custom \"build solution function\" as well as an alternative \"initialization SH\". In fact, it is a fundamental assumption for the framework design.
## 2 Operation of the Fast-Charging Station
### Characteristics of the PEB Fleet  
The average electricity cost is important for the optimization threshold judgment of PEBs, and is based on the monthly data of the operating mileage and energy consumption, which can be approximately calculated. Through the buses setting off schedule and the lines' distance [23], this paper calculated the energy cost of each line based on the actual operation data of October, in 2016. Using line H55 as an example, the on-board battery electricity was 119.62 kWh, the length of a single loop was 9 km, the total charging electricity was 13,815 kWh, and the whole day running distance was 522 km. Using Equation (1), the PEB energy consumption per kilometer was calculated as 0.854 kWh/km. The energy consumption of each line is shown in Table 3.  
\[E_{\text{cos t\_H55}}=\frac{E_{month\_H55}}{D_{month}\times S_{H55}} \tag{1}\]  
where \(E_{\text{cos t\_H55}}\) is the energy cost of line H55 in kWh/km; \(E_{month\_H55}\) is the whole charging electricity in October; \(D_{month}\) is the days of month; and \(S_{H55}\) is the whole running distance of line H55 in one day.  
Based on the same data from October 2016, the statistical distribution analysis of the returned PEBs of the state of charge (SOC) was analyzed. As shown in Figure 3, the SOCs of the returned PEBs were mostly above 50%. Thus, the maximum rate was 95.98% and the minimum rate was 79.28%, the rates of the buses over 60% SOC were 73.37% and 46.63% at the maximum and minimum, respectively. The safety SOC threshold of the on-board battery for the bus running back to the charging station was 30%. Because of the underutilization of the on-board battery and drivers' random charging, the total charging number and frequency were higher than necessary. This led to huge peak charging power and load fluctuation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Line No.** & **Electricity (kWh)** & **Days of Month (d)** & \
\begin{tabular}{c} **Distance Per Day** \\ **(km)** \\ \end{tabular} &
\begin{tabular}{c} **Energy Cost** \\ **(kWh/km)** \\ \end{tabular} \\ \hline H55 & 13,815.6 & 31 & 522 & 0.854 \\ H56 & 15,985.6 & 31 & 600 & 0.859 \\
863 & 51,936.8 & 31 & 1890 & 0.886 \\
864 & 27,579.2 & 31 & 928 & 0.959 \\
869 & 48,184.6 & 31 & 1628 & 0.955 \\ Total/Ave. & 157,501.8 & 31 & 5568 & 0.912 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The energy consumption of each line.  
According to the analysis of the station operation characteristics, the onboard battery can support the buses running more loops on different lines above the safety battery electricity threshold. At present, the drivers charge very frequently because of the range anxiety, which leads to a high charging frequency and charging load congestion. This paper proposes a strategy of fast-charging during operating hours and normal charging at night, in order to decrease the peak charging power. In order to conveniently shift the DC charging power and adjust the change of charging ports, a new charging topology is needed.
## 1 Introduction
The application of microwave signals in assessing the seasonality of tropical forests has not been thoroughly studied and should be investigated more extensively. The Microwave Temperature-Vegetation Drought Index (MTVDI) is a microwave drought index proposed by [PERSON] et al. [10] in 2017 based on both the theory of Temperature-Vegetation Drought Index (TVDI) [28] and brightness temperature data (Tb) of the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) microwave sensor. It not only utilized the advantage of TVDI, which combines land surface temperature (Ts) with Normalized Difference Vegetation Index (NDVI) when monitoring the vegetation drought condition but also overcame the shortage of optical remote sensing, which is greatly influenced by atmospheric conditions. The value of MTVDI ranges from 0 to 1, indicating the change from humid to drought conditions. [PERSON] et al. [10] firstly derived Ts based on the AMSR-E 18.7 GHz horizontal, 23.8 GHz and 89.0 GHz vertical polarized Tb, and then developed the Microwave Normalized Difference Vegetation Index (MNDVI) from the AMSR-E 23.8 GHz Microwave Polarization Difference Index (MPDI) and, finally, constructed MTVDI based on Ts and MNDVI derived from microwave signals. In this paper, we use the MTVDI to investigate the seasonality of canopy growth across the tropical evergreen forests of the Amazon. And we compared MTVDI with four climatic dryness indicators including Terrestrial Water Storage (TWS) [29], Vapor Pressure Deficit (VPD) [30], Palmer Drought Severity Index (PDSI) [31], and Climatological Water Deficit (CWD) [32] and two canopy photosynthesis proxies including Enhanced Vegetation Index (EVI) [33] and Solar-Induced Chlorophyll Fluorescence (SIF) [34]. The scientific objectives of our study were (1) to assess the capacity of AMSRE-MTVDI in capturing the tropical forests phenology over the Amazon and (2) to investigate the differences in the response of tropical evergreen forests to climatic seasonal variability (light and water) in different regions of the Amazon.
# A New Workflow to Generate Monoclonal Antibodies against Microorganisms
## 2 Materials and Methods
### Fusion and HAT Selection  
The fusion of splenocytes with a transgenic myeloma cell line was conducted according to [PERSON] et al. [16]. The fused cells were cultivated for 14 days in hypoxanthine-aminopterin-thymidine (HAT) supplemented RPMI full growth medium in three T75 culture flasks together with feeder cells under 6% CO\({}_{2}\), at 37 \({}^{\circ}\)C and 95% humidity. Subsequently, the cells were collected and prepared for cell staining and sorting.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Protein Name** & **Linear B-Cell Epitope Prediction** & **Kolaskar and Tongaonkar** & **Emini Surface** \\  & **Epitope Prediction** & **Antigenicity Score** & **Accessibility Score** \\ \hline \multirow{5}{*}{\(\mathrm{FimH}\)} & NVYVNLAPVVNNV & 1.148 & 0.161 \\  & SAYGGVLSNFSG & 1.032 & 0.269 \\ \cline{1-1}  & YLTPVSSAGG & 1.088 & 0.246 \\ \cline{1-1}  & PGSVPIPLTVYC & 1.444 & 0.162 \\ \cline{1-1}  & PANNTVSLGAVG & 1.036 & 0.3 \\ \hline \multirow{5}{*}{OmpG} & NVEGYGEDMDGL & 0.954 & 0.517 \\ \cline{1-1}  & GPVDYSAGKRGT & 0.99 & 0.931 \\ \cline{1-1}  & HYVDEPGKDT & 1.02 & 2.234 \\ \cline{1-1}  & ANMQRWKIA & 0.953 & 0.922 \\ \cline{1-1}  & NTTGYADRRVET & 0.957 & 1.921 \\ \cline{1-1}  & FNMDDSRNNGEF & 1.089 & 1.618 \\ \cline{1-1}  & SNWDWQDDIERE & 0.909 & 3.308 \\ \cline{1-1}  & WQDHDEGDSDK & 0.916 & 3.204 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Predicted linear B-cell epitopes selected based on their antigenicity and surface accessibility score.