esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### High Frequency Coherent Acoustic Phonon Combs
#### 3.1.2 Overview of Experimental Results  
Due to the periodic structure of the MQWs, a train of acoustic wave-packets over the whole time window is detected, as depicted in Figure 2a. The detected acoustic signal is proportional to the integral of \(f_{S}\)(z,\(t\)) * \(f_{d}\)(z), i.e., the convolution of two functions, along the acoustic propagation axis z, where \(f_{S}\)(z,\(t\)) represents the generated stress in each triple-QW stack and \(f_{d}\)(z) represents the detection sensitivity function in each triple-QW stack. The main feature of the detected acoustic signal can be explained quite well by the analysis conducted in [25], where we used the same sample for an initial test of the pump-probe system performance.  
We will quickly reiterate some of the previous findings which are important to establish a deeper and more quantitative understanding of the rich acoustics in the sample. Firstly, as shown in Figure 2b the central frequency of the acoustic spectrum (the peak frequency of the main lobe of the wave-packet FFT depicted by the magenta curve) is 366.5 GHz, which is determined by the travelling time of the stress wave between neighboring QWs. Due to the resemblance of our results with optical frequency combs, we also apply the same terminology here, where the central frequency (or wavelength) refers to the frequency (or wavelength) of the carrier (wave-packet) and spectral lines are equally spaced by the inverse of the pulse repetition rate, i.e., the inverse of time delay between neighboring wave-packets. In theory, the central acoustic frequency induced by the periodic QWs can be calculated as \(f_{0}=(d_{QW}/v_{\textit{eff}})^{-1}=353.5\) GHz, where \(v_{\textit{eff}}\) denotes the effective longitudinal acoustic velocities in GaAs/In\({}_{0.27}\)Ga\({}_{0.73}\)As heterostructure which can be taken from Table 2, and \(d_{QW}\) denotes the distance of adjacent QWs (13 nm) within the triple-QW stack. Secondly, the acoustic frequency comb shows a spacing of around 33 GHz determined by the travelling time of the stress wave between neighboring triple-QW stacks, which is also equal to the round trip time between the first triple-QW stack and the air/GaAs interface. The comb spacing induced by the spatial periodicity of the triple-QWstacks can be approximated by \(\Delta f_{0}=(d_{ss}/v_{GaAs})^{-1}\approx 32.6\) GHz, where \(d_{ss}\) represents the spatial period of the triple-QW stacks (145 nm), and \(v_{GaAs}\) represents the longitudinal sound velocity in GaAs, whose contribution to the SESAM structure width largely dominates over that of In\({}_{0.27}\)Ga\({}_{0.73}\)As. In a word, the experimental results fit well with the above estimations. In addition, the five peaks in the detected individual wave-packets can be explained by the convolution of the stress bursts, consisting of three pulses incident on the triple-QW structures, and the detection sensitivity function, containing three peaks in each of the triple-QW structures (see Figure 3).
## 3 Results and Discussion
### Optimization of USM Processing Parameters
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Sample**} & \(\mathbf{M_{n}}\) & \(\mathbf{M_{w}}\) & \multirow{2}{*}{**PI**} & \multirow{2}{*}{**Amplitude**} & \multirow{2}{*}{**Plunger Velocity**} \\  & **(g/mol)** & **(g/mol)** & & & **(\%)** & **(mm/s)** \\ \hline P3 HB (Pellet) & 58,700 & 135,400 & 2.3 & & & \\ P3 HB (USM) & 57,000 & 134,000 & 2.3 & 36 & 2.5 \\ P3 HB (USM) & 43,308 & 90,768 & 2.1 & 40 & 4.0 \\ P3 HB (USM) & 40,811 & 90,605 & 2.2 & 43 & 4.5 \\ P3 HB (USM) & 33,739 & 71,031 & 2.1 & 45 & 5.0 \\ P3 HB (Microinjection) & 41,182 & 85,466 & 2.0 & & \\ \hline \end{tabular}
\end{table} 
Table 1: Average molecular weights for USM-processed P3 HB samples and the pristine pellet as a control for comparison. For completeness, data from a microinjected P3 HB sample are also included. Figure 2: GPC curves corresponding to the initial P3 HB pellets (blue), P3 HB sample processed under optimal conditions (i.e., amplitude of 36%, plunger velocity of 2.5 mm/s) (orange), and sample processed under nonoptimal conditions (i.e., amplitude of 80%, plunger velocity of 4 mm/s) (black).
# A Parallelized Database Damage Assessment Approach after Cyberattack for Healthcare Systems
## 6 Conclusion and Future Work  
Healthcare databases are a prized target for attackers because they enable identity theft and cybercrimes. Any denial of service of healthcare databases affects patients' lives.  
The gravest metric to be considered while securing healthcare databases is pace. Patient history should be recovered as soon as possible, and healthcare transactions should not be affected for a long time by an attack. Hence, database assessment for the healthcare system recovery should be effective to prevent extended stoppages while enacting these measures.  
This paper presented a new algorithm for database damage assessment after a malicious attack on a healthcare system. We designed an algorithm for damage assessment that can be used after a malicious attack while trying to restore the database into its consistent state. The proposed algorithm was inspired by COVID-19 spread behavior. The malicious transaction was viewed as a COVID19 virus. Then, we dealt with the infected transactions as if they were people with COVID-19. Any transaction that interacted with an infected one was isolated. The algorithm uses multiple threads that simultaneously search several areas to locate affected transactions in the least possible time. Experiments proved that this approach meets the performance expectations and outperforms the two existing efficient algorithms. On average, our algorithm is five times faster than other existing ones. In addition, our algorithm is more efficient in terms of memory. Since the attacks on healthcare systems require fast recovery to decrease downtime, and healthcare databases are usually large in size, such an algorithm can be utilized.  
As an extension of this work, transactions can be further studied to enhance the algorithm. Blind writes are transactions that write the data item without reading it. Thus, an infected data item will be refreshed after a blind write. Considering such types of transactions decreases the number of transactions to be tested, which will decrease the damage assessment time. Moreover, the transactions' natures can be studied to decrease unnecessary dependencies and thus decrease the damage assessment time. In healthcare databases, for instance, a patient cannot depend on another patient since they are separate entities. However, a patient will always depend on a doctor; thus, a malicious transaction that includes a doctor should lead to a doctor automatically moving all their patients to the affected data list without checking any constraints. This step will greatly decrease the damage assessment time.  
Conceptualization, S.K.; software, K.A.K.; formal analysis, R.A.H.; supervision, O.A. All authors have read and agreed to the published version of the manuscript
## 6 Evaluation
### Real Execution
#### 6.2.1 Environment and Settings  
We implemented and executed our work in a real-life testbed that consisted of a master and multiple workers. On the master, we deployed two schedulers based on DynDLOn and DELAY, respectively. On the workers, we implemented task runners to emulate the existing MapReduce-like data-parallel frameworks. The testbed ran on a computing cluster with eight servers. Each server had 12 Intel Xeon X5650 cores, 24 GB main memory, and 1 gigabit per second (Gbps) Ethernet (Intel Corporation, Santa Clara, CA, USA). We generated 640, 1280, 2560, and 5120 MB synthetic data files and split each file into 10 data blocks. Each data block had two replicas. Regarding the initial loads, we ran background processes on servers. Each process' running time was \(t\) seconds, where \(t\) was randomly chosen from \([2,12]\). In the experiments, we ran multiple jobs concurrently and evaluated the scheduling algorithms' effectiveness on multijob systems.  
For the algorithms' parameters, we implemented three delay scheduling algorithms, namely, DELAY3, DELAY6, and DELAY10, whose delay thresholds were set to 3, 6, and 10, respectively. The initial delay threshold of DynDLOn, \(W\), was set to 3. In our implementation, if a server was running a data-remote task, DynDLOn did not assign more data-remote tasks to the server.
## 2 Materials and Methods
### Multispectral Image Analysis
#### 2.4.2 EVI (Enhanced Vegetation Index)  
EVI, similar to NDVI, is used to quantify the presence and health of photosynthetic plants. EVI is derived by improving the quality of images by eliminating noise caused by the distortion of reflected light by airborne particles, the effects of water pipes, and areas with high vegetation density. Unlike conventional vegetation indices, it is calculated using an atmospheric correction factor, a soil-adjustment coefficient, and a gain factor [30].  
EVI is calculated using near-infrared, red, and blue wavelength bands. However, a previous study showed that the blue band's atmospheric calibration is difficult. The results vary significantly, leading to inconsistent computations when calculating EVI values from different sensors [31]. Thus, \(EVI_{2}\) can be calculated without a blue band to reduce noise and uncertainty using formulas such as that shown in Equation (2), which are functionally identical to the EVI index and have atmospheric correction effects.  
Figure 2: Flowchart of the study.  
\[EVI_{2}=G\times\frac{(NIR-Red)}{(NIR+((C_{1}-C_{2})\times Red))+L} \tag{2}\]  
\(C\) indicates an atmospheric correction factor, \(L\) indicates a soil-adjustment coefficient, and \(G\) indicates a gain factor, respectively.
# Utilizing Duplicate Announcements for BGP Anomaly Detection
###### Abstract  
The Border Gateway Protocol (BGP) is the backbone of inter-domain routing on the internet, but its susceptibility to both benign and malicious anomalies creates substantial risks to both network reliability and security. In this study, we present a new approach for deep learning-based BGP anomaly detection utilizing duplicate announcements, which are known to be a symptom of routing disruptions. We developed our methodology based on public BGP data from RIPE and Route Views. We used the number of duplicate announcements as a baseline against which we checked for sporadic and time-based anomalies. Here, we propose a deep learning framework based on the Exponential Moving Average (EMA) model in combination with Autoencoder for anomaly identification. We also apply the Temporal-oriented Synthetic Minority Over-Sampling Technique (T-SMOTE) to overcome data imbalance. Comparative evaluations show that the Autoencoder model is significantly better than LSTM and that existing state-of-the-art methods have higher accuracy, precision, recall, and F1 scores. This study proposes a reliable, scalable, and rapid framework for real-time BGP adversary detection, which improves network security and resilience.  
machine learning; border gateway protocol; anomaly detection; duplicate announcements; prefix hijacking; network security 2025  
6
## 5 Experimental Evaluation
### Effect of Jitter on Prediction Accuracy  
Figure 7 shows the effect of jitter on the MiniatureVQNet-Noise-Network and Miniature VQNet-Noise model performance. We examined the effect of jitter at a 200 ms delay. The models showed significant performance differences at a jitter of 10% delay, which is 20 ms. At this jitter, the model trained on the noise-only dataset showed a 0.268 MSE, whereas that of the noise-network-trained model showed a 0.211 MSE. In all other cases, the noise-network-trained model outperforms the noise-only-trained model, and the difference is large when the jitter is 10% of the delay. Training the model on the noise-network speech dataset improves the model's accuracy on different jitter distortions.
## 3 Methods  
In this section, we will provide a detailed introduction to the proposed ESA-GCN model. The main idea of the ESA-GCN is to use a series of sampling and edge generation techniques in the expressive embedding space obtained by the graph neural network(GNN)-based feature extractor to improve node classification performance on imbalanced datasets. The ESA-GCN consists of four parts: a GraphSAGE-based feature extractor, a resampling module, an edge generator, and a GCN-based node classification module. The conceptual framework of the ESA-GCN model is shown in Figure 1.  
The ESA-GCN can fully utilize the feature extraction ability of GNNs and the strategies for enhancing sample balance to improve node classification effectiveness on imbalanced datasets. What makes the ESA-GCN innovative is its comprehensive use of undersampling, oversampling, and edge generation techniques to enhance the ability to solve node classification problems in imbalanced graph data.
## 1 Introduction  
Sodium titanate, as a typical alkali titanate, has attracted much attention in recent years [1; 2]. The corresponding one-dimensional nanostructures, such as nanowires [3], nanorods [3; 4], nanotubes [5; 6; 7], and nanofibers [8; 9], consisting of titanium oxide layer and interlayer Na ions, possess novel photocatalytic properties and can be used as gas potentiometric sensors [10], electrochemical storage [11], and for the adsorptive removal of heavy metals [8]. It is also of interest for bioactive material research [12; 13]. For the preparation of nanoscale sodium titanate, there are many methods, such as hydrothermal treatment on a template [5], a hydrothermal reaction in alkaline solution [11], and the sol-gel method using alkoxide precursors [10]. Based on our previous study, during the hydrothermal synthesis process of bismuth sodium titanate [14], it was found that under super precursor concentrations, one-dimensional sodium titanate structures will be generated, which are usually considered as intermediate products [15]. Some nanofibers of NaTi\({}_{2}\)O\({}_{4}\) or Na\({}_{2}\)Ti\({}_{4}\)O\({}_{9}\) have been found in our previous work [14]. Recently, Na\({}_{2}\)Ti\({}_{7}\)O\({}_{15}\) (NTO) nanotubes with a tunnel structure have been used for the anode materials of sodium ion batteries (SIBs), showing a high reversible capacity and excellent stability [7]. By first-principles calculations, the doping modulation of NTO can also enhance the electronic conductivity as an anode material for SIBs [16]. However, to the best of our knowledge, there are few studies on the mechanical properties of nanostructured NTO, which is critical to the stability of SIBs [17]. During repeated charge-discharge cycles, the anode materials always undergo huge volume changes [18]. Therefore, for the stability of SIBs, the excellent mechanical properties (such as big elastic deformation and high Young's modulus) of the anode materials should be studied.  
As a metastable structure in the Na\({}_{2}\)Ti\({}_{n}\)O\({}_{2n+1}\) series, the microstructure and mechanical properties of Na\({}_{2}\)Ti\({}_{7}\)O\({}_{15}\) have been rarely reported [2; 7; 16; 19]. In this paper, using the one-step hydrothermal method, Na\({}_{2}\)Ti\({}_{7}\)O\({}_{15}\) nanowires (NTO NWs) with good orientation and a large aspect ratio were prepared successfully. This was accompanied by Na\({}_{0.5}\)Bi\({}_{0.5}\)TiO\({}_{3}\) hydrothermal synthesis at a relatively low reaction pressure, which can be extracted by phase separation technology. The microstructure analysis showed that the NTO NWs were growing along the (020) direction, and there were some \"stripe\" super lattices along the growing direction, which may be beneficial for the transport of Na ions in SIBs. Moreover, based on the analysis of the in situ microscopic mechanical characteristics, the as-prepared NTO NWs had excellent mechanical properties, fitting for the stability of SIBs. Our studies may suggest a new candidate anode material for high-performance SIBs.
# Deep Learning Methods for Space Situational Awareness
## 2 Related Works
### DL-Based Methods  
AI automation can be very useful for optimizing the large amount of data collected by scientific missions such as deep space probes, Earth observation spacecraft, and rovers. It also helps in evaluating data and disseminating the results to the end users. By employing AI aboard spacecraft, it will be possible to build datasets and maps, all independently. AI technology is not only capable of processing a large amount of information but can minimize or eliminate redundant data (e.g., compression) so that networks can operate more efficiently. Artificial Intelligence also enables a variety of monitoring tasks thanks to ubiquitous satellite imagery in space. Deep learning (DL), as a subfield of Artificial Intelligence, can enable precise and automated control and facilitate onboard activities, such as docking or navigation [93; 94; 95].  
The rationale behind this meticulous DL hype is the weakness of traditional ML techniques in meeting the growing analytical requirements of IoT systems. DL models (especially CNN) are superior to traditional ML techniques in several ways: first, compared to ML techniques, they require little or no pre-processing steps to alleviate the prerequisite that tagged data must be used for training. Thus, features that may not be identifiable to a human can be efficiently extracted from DL approaches. Furthermore, these approaches outperform traditional techniques in terms of accuracy. Finally, DL architectures are suitable for modeling complex behaviors of conventional multimodal datasets. So, in summary, DL models can work on any data, be it structured, unstructured or semi-structured [96].  
The rest of this section will focus on several DL-based methods, namely CNN and YOLO (You Look Only Once) networks, and on hybrid convolutional prediction models. Accordingly, in the following, we have three sub-sections. Finally, in Table 2, we summarize the methods, their characteristics, and the bibliographic reference.
# Case Study and Risk Assessment of Water Inrush Disaster in Qingdao Metro Line 4
###### Abstract  
Water inrush is one of the most frequent and catastrophic hazards in tunnel engineering, and poses serious threats to the safety of engineering and personnel. This paper presents a case study of a water inrush and ground collapse in the Qingdao Metro Line 4, which caused a cave-in with the diameter and depth of about 30 m and 6 m, respectively. Based on the field data and numerical modelling, the causes of the disaster were analyzed. A numerical model was used to analyze the changes of surface settlement, vault settlement and water pressure during the tunnel excavation. The results of the study indicate that the cause of this disaster was the failure of the tunnel vault surrounding rock caused by the weakening of the tunnel surrounding rock and water pressure, which in turn triggered the water inrush in the tunnel and caused a large volume of surface collapse. As the tunnel was excavated from the slightly vouthered area to the strongly vouthered area, the vault settlement increased, and the influence zone expanded towards the surface due to the continuous decrease in the strength of the surrounding rock. In particular, a negative pore water pressure zone was formed in a certain area around the tunnel during the water inrush. The negative pressure zone caused the surrounding groundwater to converge here, leading to an increase in the amount of water inflow, which also increased the scope and scale of the impact of this disaster. A risk assessment method for water inrush in tunnels is proposed. According to the geological and engineering characteristics of Qingdao area, the evaluation index system of tunnel water inrush risk was established. An RBF neural network was improved by gray correlation analysis and a PAM clustering algorithm to establish the tunnel water inrush risk assessment model. Comparing the evaluation data with the actual data, the prediction data of a traditional RBF neural network and a BP neural network, the accuracy and reliability of the model were verified. This study has value in reducing the occurrence of water inrush in a composite formation tunnel.  
water inrush; collapse; numerical model; risk assessment Article  
Case Study and Risk Assessment of Water Inrush Disaster in Qingdao  
1 School of Civil Engineering, Qingdao University of Technology, Qingdao 266520, China  
2 Department of Civil and Environmental Engineering, Brunel University London, London UB8 3 PH, UK  
3 Engineering Research Center of Concrete Technology under Marine Environment, Ministry of Education, Qingdao 266520, China  
* Correspondence: [EMAIL_ADDRESS]
## 2 Principles and Methods
### Attention Block U-Net Deep Learning Network
#### 2.3.2 Attention Module  
The attention mechanism, originated from the human visual system, helps humans exploit a sequence of partial glimpses and selectively focus on salient parts to capture visual structure better. Recently, inspired by it, various attention modules have been proposed and integrated with baseline networks. In this paper, a lightweight and general attention block named convolutional block attention module (CBAM) is taken into consideration [70]. It was first proposed in ECCV 2018. The structure of the attention block is illustrated in Figure 2. Therefore, in terms of PA image reconstruction, it is desirable to focus on the critical features of complicated bone PA images and suppress irrelevant ones, effectively promoting the information flow in the network.  
As shown in Figure 2, the attention block consists of two parts: channel attention and spatial attention. The channel attention sub-module is designed to help CNNs to look for 'what' is more important, since each channel of intermediate feature maps can be regarded as a feature detector. The channel dimensional refined feature map \(\mathbf{I}^{\prime}\in\mathbb{R}^{\mathbb{C}\times H\times W}\) will be computed by the element-wise multiplication of the input feature map \(\mathbf{I}\in\mathbb{R}^{\mathbb{C}\times H\times W}\) and the channel attention map \(\mathbf{W_{C}}\in\mathbb{R}^{\mathbb{C}\times 1\times 1}\). The spatial attention submodule is sequentially arranged with the channel attention submodule, where CNN can focus on 'where' is the essential information part. It acts as a complementary one to the channel attention map. At the output of the attention block, the attention map \(\mathbf{W_{I}}\in\mathbb{R}^{1\times H\times W}\) and original feature map \(\mathbf{I}\) are merged by element-wise summation and activated by a ReLU to obtain the final modified feature map \(\mathbf{I}^{\prime\prime}\in\mathbb{R}^{\mathbb{C}\times H\times W}\).  
Figure 2: Schematic of the attention block. It contains two sequentially sub-modules: channel attention module and spatial attention module.