esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Collection and Processing
Regarding the nonviolence audio clips, they were four in count and long in duration and were split in order to ensure an identical number of total audio files between aggression and non-aggression cases (anger and nonviolence audio files). Because these audios were sampled from various real-world daily life interactions, the information within the audio files contained unwanted noise and artifacts. Before any further processing, in order to isolate the distortion of the information, **bandpass filtering** was first applied to isolate frequencies within a specific range. Audio signals were filtered in the range from **85 Hz to 255 Hz** to reduce noise and focus on specific vocal ranges. Unwanted noise within this range was removed. In the second phase of the noise filtering, a **Butterworth filter** was applied, which is known for its flat passband. By applying this filter, both forward and backward phase distortions were prevented [36; 37]. For further noise and artifact reduction in the audio files, **spectral gating was** applied to isolate and remove the background noise. The **noise reduction** library successfully estimated and suppressed the noise spectrum. Finally, using **trim_silence,** all the silent portions of the audio were removed during the whole audio length. This was necessary to remove unnecessary data and improve the accuracy of the extraction of the features later.
# Analyses of GLONASS and GPS+GLONASS Precise Positioning Performance in Different Latitude Regions
## 2 Evaluation Methods
### DD Network Processing Strategy  
The DD network processing was conducted using the Bernese GNSS Software, Version 5.2. The software is developed at the Astronomical Institute of the University of Bern (AIUB), Bern, Switzerland. A daily batch processing scheme is used for the data processing. The final precise orbits from CODE were adopted, containing consistent orbits for GPS and GLONASS. The different code biases (DCB) files and the Earth Rotation Parameters (ERP) of CODE were also used for consistency. The PCC model used was jigs14.atx. The ocean tides model used was FES2004 (Finite Element Solutions). The elevation mask for data preprocessing was set to 3\({}^{\circ}\). The baselines were defined with the OBS-MAX strategy. An attempt to fix the GPS and GLONASS integer value ambiguities was attempted with the Quasi Ionospheric Free (QIF) strategy [22]. The VMF1 (Vienna Mapping Function) [23] grid file [24] and NET WET model were used for the tropospheric estimate. Zenith Tropospheric Delay (ZTD) parameter was estimated per hour. The datum definition was realized with the minimum-constraint solution by a set of reference stations of IGS14. The processing scheme is displayed in Figure 1.
# A Deep Evolution Policy-Based Approach for RIS-Enhanced Communication System
## 2 System Model and Problem Formulation  
As shown in Figure 1, we consider an RIS-aided downlink MU-MISO communication system where an RIS is deployed to provide high-quality virtual links from the multi-antenna BS to \(K\) single-antenna users. The number of transmit antennas at the BS and the number of reflecting elements at the RIS are denoted by \(M\) and \(N\), respectively. The base-band equivalent channels from the BS to the RIS and from the RIS to the user \(k\) are denoted by \(\mathbf{H}_{1}\in\mathbb{C}^{N\times M}\) and \(\mathbf{h}_{k,2}\in\mathbb{C}^{N\times 1}\), respectively, and the direct channel is blocked.  
The complex baseband transmitted signal at the BS can be expressed as \(\mathbf{x}=\sum_{k=1}^{K}\mathbf{\omega}_{k}s_{k}\), where \(s_{k}\) denotes the transmission symbol for the user \(k\) with normalized power (zero mean and unit variance) and \(\mathbf{\omega}_{k}\in\mathbb{C}^{M\times 1}\) is the corresponding beamforming vector. The signal received at the user \(k\) can be expressed as  
\[y_{k}=\mathbf{h}_{k,2}^{H}\mathbf{\Phi}\mathbf{H}_{1}\sum_{j=1}^{K}\mathbf{\omega}s_{j}+n_{k},k=1,\ldots,K, \tag{1}\]  
where \(\mathbf{\Phi}=\text{diag}\{e^{i\theta_{1}},e^{i\theta_{2}},...,e^{i\theta_{N}}\}\) with unit reflection amplitude denoting the configuration matrix, and \(\theta_{n}\) representing the phase shift of element \(n\). \(n_{k}\sim\mathcal{CN}\big{(}0,\sigma_{k}^{2}\big{)}\) denotes the additive white Gaussian noise (AWGN) at the receiver of the user \(k\). In a multi-user system, the user \(k\) treats all the signals from other users as interferences. Hence, the signal-to-interference-noise ratio (SINR) \(\gamma_{k}\) at the user \(k\) can be expressed as \[\gamma_{k}=\frac{\left|\mathbf{h}_{k,2}^{H}\mathbf{\Phi}\mathbf{H}_{1}\mathbf{\omega}_{k}\right|^{ 2}}{\sum_{m\
eq k}^{K}\left|\mathbf{h}_{k,2}^{H}\mathbf{\Phi}\mathbf{H}_{1}\mathbf{\omega}_{m} \right|^{2}+\sigma_{k}^{2}}. \tag{2}\]  
In this paper, our objective is to maximize the system sum rate of all users by jointly designing the active and passive beamforming. The optimization problem is formulated as  
\[\begin{array}{ll}(\mathrm{P1}):\underset{\mathbf{W},\mathbf{\Phi}}{\max}&f(\mathbf{W}, \mathbf{\Phi})=\sum_{k=1}^{K}\log_{2}(1+\gamma_{k})\\ \mathrm{s.t.}&\mathrm{C1}:|\theta_{n}|=1,n=1,\ldots,N,\\ &\mathrm{C2}:\mathrm{Tr}\big{(}\mathbf{W}^{H}\mathbf{W}\big{)}\leq P_{t},\end{array} \tag{3}\]  
where \(\mathbf{W}=\{\mathbf{\omega}_{k}\}_{k=1}^{K}\) and \(P_{t}\) represent the active beamforming matrix and transmit power at the BS, respectively.
# Assessing Cyber Challenges of Maritime Navigation
## 3 Findings
### Cyber Trends and Challenges
#### 3.3.4 Ports, Terminals, and Supply Chains  
Ports are an integral node of maritime transportation and the land transport chain. They rely on information from both shipping lines and land-side logistics companies. The lack of clear standards and requirements addressing critical maritime infrastructure demonstrates a compelling need for standardized policies for assessing, containing, and mitigating cyber risks. Legacy IT systems and an expanding Internet of Things (IoT) contribute to making ports vulnerable [105]. Roughly half, only, of the world's shipping ports understand or are aware of their problems and vulnerabilities concerning cybersecurity [59]. As valves in global economic arteries, the port infrastructures' protection against cyber risks is an absolute imperative [97].  
As for this infrastructure, critical systems (ship's cargo handling, container tracking) may be penetrated, as occurred in the port of Antwerp where hackers gained access to the port's terminal operating system and trafficked drugs [77]. Hackers working with drug-smuggling criminals infiltrated the computerized cargo tracking system to identify the shipping containers in which assignments of drugs had been hidden. The gang then drove the containers from the port, retrieved the drugs, and covered their tracks. The criminal activity continued for two years from June 2011, until it was stopped by investigative authorities [46; 75].  
Another example is the hacking into the Port of San Francisco when their Electronic Information System \"moved\" the port in cyberspace twenty miles north, which became problematic in the foggy weather [106].  
The analysis showed that successful cyber penetration into the port infrastructure information system allows attackers movement or the theft of illicit cargo. This system is vulnerable and it is easy to see how it can be exploited by cybercriminals that will continue to do the unexpected [7; 107]. We can be certain that the nature of attacks of this sort will evolve [46; 73].  
Aids to Navigation (AtoN) are an integral and critical part of marine infrastructure. Traditionally AtoNs have been physical objects such as lighthouses, buoys, and beacons. The introduction of virtual AtoNs that appear on AIS INS displays via the AIS communication system is an achievement of the modern digital era. Though still under development, virtual AtoNs play a vital role in enhancingnavigational safety. The AIS system is not as secure as it should be and can, therefore, be disrupted by malignant action.
# Providing Email Privacy by Preventing Webmail from Loading Malicious XSS Payloads
## 6 Conclusions  
This paper proposes an email XSS detection model based on deep learning. This model can be used for email XSS detection in enterprise intranet, or as a built-in email XSS attack payload identification in Webmail. The email XSS detection model establishes a neural network through a two-way RNN and uses the Attention mechanism to optimize performance. First, the paper gave an overview of the main principles and variants of Webmail attacks. Second, the paper elaborated on the email XSS detection framework proposed in this paper, which includes five levels of specific content. The main contribution of the paper is to collect and analyze the email XSS dataset and prove that the model proposed by the paper has excellent performance. In the experiment, the AUC value of the deep learning model reached 0.9799, which is superior to the traditional machine learning algorithm. After adding the Attention mechanism, the upper limit of the accuracy of the deep learning algorithm is improved, and the loss of the training process is effectively reduced.  
Conceptualization, Y.F. and C.H.; Methodology, C.H. and Y.X.; Software, Y.X. and P.J.; Validation, Y.X., P.J. and C.H.; Formal Analysis, P.J.; Investigation, Y.X.; Resources, C.H.; Data Curation, P.J.; Writing-Original Draft Preparation, Y.X.; Writing-Review & Editing, Y.F. and C.H.; Supervision, C.H.; Funding Acquisition, C.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by National Natural Science Foundation of China (61902265), National Key Research and Development Program (2018 YFB0804503, 2019 QY1405), and the Key Research and Development Plan Project of Sichuan Province (No. 2020 YFG0374).  
We thank those anonymous reviewers whose comments/suggestions helped improve and clarify this manuscript.  
The authors declare that there is no conflict of interest.
## 3 Results
### Spatiotemporal Trends of ET and GPP  
Figure 3a,b show the consistent spatial patterns of the annual average ET and GPP, with an increasing trend from northwest to southeast over Mainland China and a range from lower than 100 mm (lower than 300 g C m\({}^{-2}\) yr\({}^{-1}\)) to more than 1200 mm (2400 g C m\({}^{-2}\) yr\({}^{-1}\)) for ET (GPP). As shown in Figure 3c,d, ET and GPP differ significantly by region, ranging from 276.41 mm yr\({}^{-1}\) over TCon to 782.29 mm yr\({}^{-1}\) over SubT (from 314.49 g C m\({}^{-2}\) yr\({}^{-1}\) over TP to 1628.61 g C m\({}^{-2}\) yr\({}^{-1}\) for SubT).  
The annual average ET and GPP over China in this study are, respectively, 510.11 mm and 994.55 g C m\({}^{-2}\) between 1999 and 2018. Previous studies show that the vegetated Mainland China's annual ET value averaged from 2001 to 2020 is 409.05 mm [21], the national average annual ET and GPP are, respectively, about 475 mm and 1120 g C m\({}^{-2}\) between 2000 and 2017 [6], the vegetated annual ET over China from 2002 to 2016 is 534.7 \(\pm\) 232.8 mm yr\({}^{-1}\) based on eddy covariance measurements [54], the total GPP over China's terrestrial ecosystem is 6.80 P g C yr\({}^{-1}\) between 1999 and 2015 using the model tree ensemble algorithm, and the total GPP in this study is 6.88 P g C yr\({}^{-1}\)[19]. The simulated results of ET and GPP in this study are reasonable based on the previous studies.  
Figure 4 shows the trend values and significance levels of ET and GPP over Mainland China, as well as statistical trends for the four sub-regions between 1999 and 2018. Figure 4a,b show that the ET increases over more than 68% of the study area; the area ratio of significant (\(p<0.05\)) increasing trends over China is 24.67%. The most intense trends (>5 mm yr\({}^{-1}\)) are mainly distributed in Central, Northeastern, Western, and Southeastern  
Figure 3: Average annual ET and GPP between 1999 and 2018: **(a,b)** spatial patterns of average annual ET and GPP over Mainland China; **(c,d)** statistics of average annual ET and GPP for China and four sub-regions.  
China. The spatial patterns of the increases in GPP are similar to those of ET. The most intense GPP trends (>15 g C m\({}^{-2}\) yr\({}^{-1}\)) are mainly in Central, Northeastern, and Southwestern China (Figure 4d). More than 43% of the study area shows significant (\(p\) < 0.05) upward trends (Figure 4e). The statistical results in Figure 4c,\(f\) show increasing ET (GPP) trends of 1.32 mm yr\({}^{-1}\) (8.01 g C m\({}^{-2}\) yr\({}^{-1}\)) over vegetated Mainland China. The four sub-regions also show increasing trends for ET (GPP); TCon (TMon) has the strongest increasing trend of 2.46 mm yr\({}^{-1}\) (11.42 g C m\({}^{-2}\) yr\({}^{-1}\)), while SubT (TP) has the weakest increasing trend of 0.42 mm yr\({}^{-1}\) (2.41 g C m\({}^{-2}\) yr\({}^{-1}\)).
# Difficulties and Challenges of Anomaly Detection in Smart Cities: A Laboratory Analysis
## 2 Background
### Isolation Forest  
Isolation forest [34] is an unsupervised outlier detection technique that, unlike OC-SVM, does not construct a model based on the normal behaviour of a dataset. Instead, this technique builds a model that explicitly isolates anomalies. This has the advantage that models are already optimized to find anomalies. Moreover, compared to other popular anomaly detection techniques, such as ORCA, local outlier factor (LOF) and random forest, a key advantage of isolation forest is not only that it performs better, but also that requires a shorter processing time. In addition, unlike higher computationally complex methods, an isolation forest is capable of handling high dimensional spaces and large data sets.  
Isolation forest finds anomalies building an ensemble of iTrees for a data set. Building an iTree consists of recursively building a tree structure, where in each step a feature is randomly chosen and also a splitting value between the maximum and the minimum of that feature is randomly selected. In this way, for each iTree in the ensemble, it is possible to compute the path length from the root to a leaf that is required to isolate each sample of a data set. A short average path length in the ensemble means that it is easy to isolate that sample and, therefore, it points out an anomaly. This technique requires two parameters: the number of trees in the ensemble and sub-sampling size to build each tree. In our work, we have set the parameter values to the values recommended by the authors proposing isolation forest in [34] (i.e., ensemble size of 100 trees and minimum sub-sampling size of 256 samples).
## 3 Main Technical Research
### Research on Obstacle Risk Detection and Localization Based on Security Boundary Model
#### 3.2.2 Study on Obstacle Pose Localization  
This paper applies the Oriented Bounding Box (OBB) algorithm to the clustering subset's point cloud for feature extraction, constructing a 3D Box bounding box to obtain more precise obstacle localization information. The OBB algorithm considers the spatial distribution of object vertices and finds the optimal direction and size to generate a compact bounding box. The main steps include:  
1. Using the PCA method to obtain the point cloud's three principal directions, centroid, covariance matrix, and eigenvectors;
2. Transforming the input point cloud to the origin, and constructing a bounding box for the origin point cloud;
3. Setting the input point cloud's principal direction and bounding box and obtaining the bounding box vertex coordinates of the original input point cloud through inverse transformation.  
As shown in Figure 8, the visual effect of a triangular frame point cloud before and after the OBB algorithm is given, then its more accurate position and attitude are obtained.
# Research on Phase Stabilization Algorithm of Femtosecond Timing System
## 4 System Algorithm Design
### Algorithmic Flow  
First, the initialization process for the optical delay line and fiber stretcher is executed. Next, real-time phase data of the high-frequency reference signal is obtained through the EPICS channel. Once this phase information is acquired, it is analyzed and evaluated. If the detected real-time phase delay is less than 50 femtoseconds (fs), we will use the fiber stretcher for precise phase control. Conversely, if the phase delay exceeds 50 femtoseconds, the optical delay line will be employed for adjustment. It is important to note that the control amount of the optical delay line is cumulative; thus, while adjusting with the optical delay line, the control amount of the fiber stretcher needs to be reset to zero to prevent interference between the two control methods. The algorithm flowchart is shown in Figure 7.
###### Abstract  
In a beamforming circuit for a modern broadband phased-array system, high accuracy and compactness have received sufficient attention as they are directly related to side lobe level and fabrication cost, respectively. In order to meet the low phase error required, this paper proposed an ultra-broadband 6-bit digital step switched-type attenuator (STA) with capacitive/inductive compensation networks. Compared to the conventional methods, the proposed technique employs an improved simplified T-structure with capacitive compensation networks, which simultaneously achieves low insertion loss and high-accuracy amplitude/phase control. In addition, on-chip level shifting circuit is integrated to avoid complex control schemes. The strategy of prioritizing return loss is adopted to alleviate the performance degradation caused by impedance mismatch after cascade. As a proof-of-principle demonstration, a wideband 6-bit STA with core area of only 0.5 mm \(\times\) 1.8 mm was designed via 0.15-micrometer GaAs pHEMT technology. It exhibits ultra-broadband operation with a 31.5 dB amplitude tuning range and a 0.5 dB tuning step. The insertion loss of the reference state is 4-5.3 dB. The return loss is better than 15 dB for all the 64 states. The RMS amplitude and phase errors are less than 0.2 dB and 2\({}^{\circ}\) over the 10 to 20 GHz.  
digital step attenuator; GaAs; low insertion loss; modified simplified T-type; phase error compensation +
Footnote †: journal: _micromachines_
# On the Performance of Underlay Device-to-Device Communications
## 3 System Model
### Signal-to-Interference-Plus-Noise Ratio (SINR) at D2D Receivers and Base Station  
The received signal at the \(k\)-th D2D receiver and the BS under the \(s\in\{equ,ran,pl\}\) power allocation scheme are formulated as [50,51]  
\[y_{D_{k}}= \underbrace{\sqrt{P_{D_{k}}^{s}}h_{dr_{k}dt_{k}}x_{D_{k}}}_{\text{ Useful signal}}+\underbrace{\sum\limits_{i=1,i\
eq k}^{N}\sqrt{P_{D_{i}}^{s}}h_{dr_{k}dt_{i}}x_{D_{i}}}_{ \text{D2D interference}}+\underbrace{\sqrt{P_{C}}h_{dr_{D_{k}}}x_{C}}_{\text{ Cellular interference}}+n_{D_{k}} \tag{6}\] \[y_{b}= \underbrace{\sqrt{P_{C}}h_{b,c}x_{C}}_{\text{Useful signal}}+ \underbrace{\sum\limits_{i=1}^{N}\sqrt{P_{D_{i}}^{s}}h_{b,dt_{i}}x_{D_{i}}}_{ \text{D2D interference}}+n_{b}. \tag{7}\]  
Here \(x_{D_{k}},k\in\{1,\ldots,N\}\) and \(x_{C}\) with \(\mathbb{E}\Big{\{}\big{|}x_{D_{k}}\big{|}^{2}\Big{\}}=\mathbb{E}\Big{\{} \big{|}x_{C}\big{|}^{2}\Big{\}}=1\) are the transmitted signal of the \(k\)-th D2D transmitter and cellular user, respectively; \(\mathbb{E}\{.\}\) is the expectation operator; and \(n_{D_{k}}\) and \(n_{b}\) are the additive white Gaussian noise (AWGN) at the \(k\)-th D2D receiver and at the BS, respectively. Looking at (6) and (7), we ascertain that the D2D receiver is subjected to interference from both the D2D and the cellular networks, while the BS is only affected by the D2D interference. The signal-to-interference-plus-noise-ratio (SINR) of the \(k\)-th D2D receiver and the BS under the \(s\) scheme (denoted by \(\Xi_{k}^{s}\) and \(\Delta^{s}\)) is then evaluated as [52]  
\[\Xi_{k}^{s}= -\frac{P_{D_{k}}^{s}\big{|}h_{dr_{k}dt_{k}}\big{|}^{2}}{\sum \limits_{i=1,i\
eq k}^{N}P_{D_{i}}^{s}\big{|}h_{dr_{k}dt_{i}}\big{|}^{2}+P_{C} \big{|}h_{dr_{k},c}\big{|}^{2}+\sigma_{k}^{2}}, \tag{8}\] \[\Delta^{s}= \frac{P_{C}\big{|}h_{b,c}\big{|}^{2}}{\sum\limits_{i=1}^{N}P_{D_ {i}}^{s}\big{|}h_{b,dt_{i}}\big{|}^{2}+\sigma_{b}^{2}}, \tag{9}\]  
where \(\sigma_{k}^{2},k\in\{1,\ldots,N\}\) and \(\sigma_{b}^{2}\) are noise variance of the \(k\)-th D2D receiver and the base station, respectively, and are calculated as \(\sigma_{D_{k}}^{2}=\sigma_{b}^{2}=-174+\text{NF}+10\text{log}_{10}(\text{Bw}),\forall k\)[53]; NF is the receiver noise figure in dB; and Bw is the transmission bandwidth in Hz.
## 2 Materials and Methods
### Energy Carrier Characteristics  
The energy carrier characteristics are applied throughout the whole evaluation chain. An overview of the key parameters is listed in Table 1. The data for hydrocarbon fuels are taken from DLR SimFuel property database, which consists of several thousand units of fossil kerosene and several hundred Sustainable Aviation Fuels. All fuels were chosen to be representative of a fuel from their respective category. Furthermore, the Jet A-1 (ECLIF Ref 3) and the drop-in blend (ECLIF SAF2) were both used during flight campaigns measuring fuel impact on emissions and contrail formation [1]. The drop-in fuel is a 30% blend of HEEA (Hydroprocessed Esters and Fatty Acids) and Jet A-1 applies to ASTM D7566 fuel specification, whereas the power to liquid (PtL) synthetic fuel contains no aromatics and is thus outside current fuel specifications (starting in March 2021, different options for 100% Sustainable Aviation Fuel (SAF) specification are being discussed [2]).  
It can be seen in Table 1 that the lower heating value (LHV) increases from Jet A-1 to PtL, whereas the density decreases. This is mainly due to reduced (blend) or zero amount of aromatics in the fuel (PtL), which in turn increases hydrogen and decreases carbon content. This is also visualized by the two emission indexes (EI). The density of the LH2 fuel is valid for a pressure of 0.12 MPa and temperature of 20.96 K. However, the open-source library Cantera [3] is applied for the specific fuel characteristics of LH2 at each flight condition. Both the drop-in blend as well as the PtL synthetic kerosene are utilizing direct air capturing (DAC) as the carbon source as well as green hydrogen. Furthermore, the energy carrier production process is assumed to be carbon neutral.
## 3 Experiment and Analysis  
### Experimental Setups  
#### 3.1.1 Implementation Details  
Our two-phase detection approach is implemented using Pytorch framework on a Windows 10 platform, which has an Intel i9-7900X CPU and an NVIDIA GeForce RTX2070 (8 GB) GPU for acceleration. The software environment is CUDA 10.2, CUDNN 7.6, and Python 3.7. Two YOLOv5 models used for ROI extraction and quality assessment are trained separately. We update the network parameters using SGD optimizer with momentum 0.937 and weight decay 0.0005. Batch size and the maximum epoch are set to 8 and 600, respectively. The OneCycleLR scheduler with lr_man = 0.01 and lr_mix = 0.001 is adopted to adjust the learning rate during each epoch. During the training, depending on the respective training dataset, the optimal values of anchor boxes in two models are adaptively calculated using k-means clustering algorithm. Meanwhile, adaptive image scaling is utilized to resize the images to a size where the longest side is 640.