esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials
### Microwave Satellite Data  
The German and Canadian satellites, namely, TerraSAR-X and Radarsat-2, respectively, provide microwave data. They carry SAR instruments that operate in the X-band (f = 9.65 GHz, \(\lambda\) = 3.1 cm) and in the C-band (f = 5.405 GHz, \(\lambda\) = 5.5 cm). The satellite acquisitions are planned in order to constitute the maximum number of pairs of images presenting the two frequencies in close intervals of time. Throughout the agricultural season, 27 images in the X-band and 21 in the C-band were collected on plots presenting bare soil conditions (Table 2).  
Figure 5: Scatter plot between roughness parameters (root-mean-square height (hrms) and correlation length (l\({}_{\text{c}}\))) derived from parallel (**a**) and perpendicular (**b**) profiles. Roughness states are grouped into four categories according to soil practices (plowed, stubble-disked, narrowed, prepared cloudy and prepared smooth).  
TerraSAR-X images are acquired in HH polarization using two beam modes (SpotLight (SL) and StripMap (SM)) and are characterized by a pixel spacing ranging from 1.5 to 3 m [53]. They are mainly acquired at incidence angles of 27.3\({}^{\circ}\) and 53.3\({}^{\circ}\) (11 and 10 images, respectively), although a third part of the images is associated with intermediate incidence angles (28.7\({}^{\circ}\), 31.8\({}^{\circ}\), 32.3\({}^{\circ}\), 41.7\({}^{\circ}\) or 45.5\({}^{\circ}\)). Radarsat-2 images are acquired in the full quad-polarization mode (FineQuad-Pol), which provides images in HH, VV, HV and VH polarizations, with a pixel spacing of \(\sim\)5 m [54]. They are acquired with eight different incidence angles, ranging from 24\({}^{\circ}\) to 41\({}^{\circ}\). All radar images were calibrated and geo-referenced using ortho-photos (with a resolution of 50 cm) provided by the French National Geographic Institute [53; 55].
# A Nested UNet Based on Multi-Scale Feature Extraction for Mixed Gaussian-Impulse Removal
## 3 Noise Model  
We assume that \(x\) is a noise-free image, \(y\) is a corresponding noisy image, and \(x_{i,j}\) is the pixel value at location \((i,j)\). For the AWGN model, the relationship between these elements can be written as  
\[y_{i,j}=x_{i,j}+G, \tag{1}\]  
where \(G\) is an independent and identically distributed zero-mean AWGN.  
There are two types of IN: salt-pepper impulse noise (SPIN) and random value impulse noise (RVIN). SPIN uses two fixed extreme values of \(n_{min}=0\) for pepper noise and \(n_{max}=255\) for salt noise to corrupt the image, whereas RVIN uses any value in the range \([n_{min},n_{max}]\) to corrupt the image. The SPIN and RVIN models are as follows.  
SPIN model:  
\[y_{i,j}=\left\{\begin{array}{ll}x_{i,j},&\text{the probability }1-p_{sp}\\ n_{min},&\text{the probability }p_{sp}/2\\ n_{max},&\text{the probability }p_{sp}/2\end{array}\right.. \tag{2}\]  
where \(p_{sp}\) denotes the probability of SPIN.  
RVIN model:  
\[y_{i,j}=\left\{\begin{array}{ll}x_{i,j},&\text{the probability }1-p_{r}\\ n_{i,j},&\text{the probability }p_{r}\end{array}\right.. \tag{3}\]  
where \(n_{i,j}\) is a random pixel value in the range \([n_{min},n_{max}]\) at the location \((i,j)\) and \(p_{r}\) denotes the probability of RVIN.  
In this paper, three types of mixed noise models are considered [20]:  
1. AWGN mixed with SPIN: \[y_{i,j}=\left\{\begin{array}{ll}x_{i,j}+G,&\text{the probability }1-p_{sp}\\ n_{min},&\text{the probability }p_{sp}/2\\ n_{max},&\text{the probability }p_{sp}/2\end{array}\right..\] (4)
2. AWGN mixed with RVIN: \[y_{i,j}=\left\{\begin{array}{ll}x_{i,j}+G,&\text{the probability }1-p_{r}\\ n_{i,j},&\text{the probability }p_{r}\end{array}\right..\] (5)(3) AWGN mixed with RVIN plus SPIN:  
\[y_{i,j}=\left\{\begin{array}{ll}x_{i,j}+G,&\text{the probability }(1-p_{sp})(1-p_{r})\\ n_{min},&\text{the probability }p_{sp}/2\\ n_{max},&\text{the probability }p_{sp}/2\\ n_{i,j},&\text{the probability }p_{r}(1-p_{sp})\end{array}\right.. \tag{6}\]
# On End-to-End Intelligent Automation of 6G Networks
## 4 6G End-to-End Network Automation Challenges
### Advanced Monitoring  
The sixth challenge that 6G network automation and orchestration mechanisms face is the need for continuous advanced monitoring. In this case, the monitoring process needs to collect metrics at different layers of the network architecture (i.e., at the core, edge, and extreme edge). This includes not only the infrastructure-related metrics (e.g., CPU consumption, RAM consumption, network usage, etc.) currently collected by 5G network monitoring modules, but also from the data plane in which the deployed service is running. For example, the capabilities/functionalities of virtual network functions such as the \"Management Data Analytics Service (MDAS)\" need to be augmented further and deployed at multiple layers. Similarly, functions such as the \"Network Slice Selector Function (NSSF)\" and the \"Network function Repository Function (NRF)\" need to be extended to contain \"Slice Descriptor\" and \"Topology Descriptor\" functions that provide additional information about the network state. This would allow for better network slice elasticity decisions. Moreover, this can also contribute to enhanced real-time fault detection, alerting, and general diagnostics. Additionally, this would assist in describing the most optimized changes for blueprints in closed-loop automation. Accordingly, the 6G monitoring system needs to be able to:  
1. Store real-time data generated by the different connected devices across the network continuum (at the core, edge, and extreme edge levels);
2. Offer real-time analysis and visualization of these data;
3. Take the corresponding right decisions in an autonomous and explainable manner;
4. Run in a decentralized and federated manner to support network management and service orchestration needs.
# A Beam Hopping Scheme Based on Adaptive Beam Radius for LEO Satellites
## 3 Model
### Proposed Algorithm Model  
We can see three sizes of beams to provide an equal number of services to different users from Figure 7. It is worth noting that beam should stay far apart to avoid interference in the full-frequency reuse scene.
# An Improved RAIM Availability Assessment Method Based on the Characteristic Slope
## 4 Slope-Based RAIM Availability Assessment
### Comparison of the Ideal Slope Threshold and the Ideal Protection Level  
The functions of \(T_{slope}\) and \(\mathrm{VPL}_{d}\) are identical, forming a tangent \(P(\mathrm{HMI}|1F)\) curve to the \(lgP_{r}(\mathrm{HMI}|1F)\) line. Referring to the equation for \(T_{slope}\), i.e., Equation (18), the equation for \(\mathrm{VPL}_{d}\), can be formulated as follows:  
\[\begin{array}{ll}P_{a}(HMI|1F)&=P(|\mathrm{VPE}|\geq\mathrm{VPL}_{d}|1F)P(T_ {S}<Td|1F)\\ &=\left[1-\int_{\frac{-\mathrm{VPL}_{d}-\mathrm{VPL}_{d}}{\mathrm{a_{v}}}}^{ \frac{\mathrm{VPL}_{d}-\mathrm{VPL}_{d}}{\mathrm{a_{v}}}}g(t;0,1)dt\right]\int_ {0}^{Td}f\left(y_{r}\left(\frac{\mu_{a}}{Slope_{max}}\right)^{2},K-4\right)dy \end{array} \tag{20}\]  
Analyzing this equation, \(\mathrm{VPL}_{d}\) is determined by \(a_{v}\) and \(Slope_{max}\) with the given \(P_{r}(\mathrm{HMI}|1F)\), \(Td\) and \(K\). Therefore, \(T_{slope}\) is only related to \(a_{v}\) while \(\mathrm{VPL}_{d}\) is related to both \(a_{v}\) and \(Slope_{max}\).  
Considering that the PDF of the noncentral \(\chi^{2}\) distribution is too complicated, both \(T_{Slope}\) and \(\mathrm{VPL}_{d}\) should be solved numerically, which will sharply increase the computation burden of a GNSS receiver or an onboard computer. Thanks to the one-to-one correspondence between \(a_{v}\) and \(T_{Slope}\), \(T_{Slope}\) can be calculated offline for discrete \(a_{v}\) values in the range from 1 to \(Ta_{v}\) and saved in a receiver. It can be on-board searched from the presaved data according to the specific \(a_{v}\) value. However, \(\mathrm{VPL}_{d}\) needs to be calculated online after both \(a_{v}\) and \(Slope_{max}\) obtained.
# Collision Avoidance Using Finite Control Set Model Predictive Control for Unmanned Surface Vehicle
## 4 Simulation Study and Results
### Collision Avoidance in a Real Environment  
#### 4.2.1 Environmental Assumptions  
The improved Good Win ship domain model by [PERSON] expands the USV into a circle with a radius \(R_{USV}\) of half ship length [31]. According to Equation (14), the radius \(R_{Obst}\) of the obstacle is further expanded to \(RR_{UO}\).  
\[RR_{UO}=R_{USV}+R_{Obst} \tag{14}\]  
In this way, the USV can be regarded as a particle, as shown in Figure 8. Therefore, the collision avoidance problem of the USV is converted to the expanded obstacle avoidance of the particle.  
In addition, the height information in the environment is removed, considering only the two-dimensional (2D) plane environment. Therefore, the motion of the USV can be regarded as the particle motion in the plane environment.  
Figure 7: Evaluation results in the simulation environment. (**a**) Speed of the USV; (**b**) course of the USV; (**c**) thruster speed; (**d**) command propulsion angle; (**e**) computation time; (**f**) distance between obstacles and the USV.
# Generalized Sketches for Streaming Sets
## 6 Evaluation
### Metric  
We evaluated both the efficiency and effectiveness of our method, SimCar, in comparison with the above two baseline methods. For efficiency, we evaluated the running time of all methods. Specially, we used the online sketching time to measure the time of online processing stream \(\Gamma\). For user cardinality estimation, common-interest count estimation, and Jaccard similarity estimation, we evaluated the error of estimation \(\hat{\mu}\) with respect to its true value \(\mu\) using the normalized root mean square error (NRMSE), which is defined as \(\text{NRMSE}(\hat{\mu})=\sqrt{\text{MSE}(\hat{\mu})}/\mu\), where \(\text{MSE}(\hat{\mu})=\mathbb{E}((\hat{\mu}-\mu)^{2})=\text{Var}(\hat{\mu})+ \left(\mathbb{E}(\hat{\mu})-\mu\right)^{2}\). For the nearest neighbor search, given a query user, we report the average size of the retrieved user set, as well as the recall of the top 10 most similar users among that set. In our experiments, we computed the above metrics over 100 independent runs.
## 4 Experiment
### Discussion  
Our experimental evaluations on MaSTr1325, LaRS, Water Segmentation in the USVIland, and Cityscapes have demonstrated the significant advantages of LDMNet in terms of its sensitivity to object edges and its robustness in utilizing global information, highlighting the practicality of LDMNet in real-world scenarios. Furthermore, we have compared our proposed Reorganized Attention Module (RAM) and Hilbert Curve Scanning (HCS) with advanced similar modules, as shown in Tables 7 and 8, illustrating their superiority. However, despite the encouraging results, our method is not without limitations. For instance, the computational cost associated with HCS is still higher than that of traditional methods. Additionally, although LDMNet has been proven to perform well on various datasets, its performance may vary with different types of data, which warrants further investigation.
## 3 Antibiotic Resistance in Hospital and Municipal Wastewater
### Municipal Wastewater
[17] conducted an analysis for the detection of ARGs in urban wastewater treatment plants (UWWTP) across various European countries including, Portugal, Spain, Ireland, Cyprus, Germany, Finland, and Norway using a highly parallel quantitative polymerase chain reaction. The authors reported the presence of ARGs such as _aadA_ (Aminoglycosoide), _cmxA_ (chloramphenicol), _blaoxa_ (beta-lactam), _qacE\(\Delta\)11_ (MDR), _ermF_ (Marcolide) and _tetQ_ (tetracycline) genes, which were commonly found in higher abundances across the samples. Likewise, [PERSON] et al. [76] detected the presence of methicillin resistance genes in 29 S. aureus strains (out of 79) and vancomycin resistance _VanA_ gene in 11 (out of 101) strains of _Enterococcus_ species isolated from an urban MWW treatment plant in Kozeigowy, Poland. Metagenomic sequencing has been employed for the characterization of the ARG profile of various MWWTPs across China, Singapore and South Korea [52; 53; 54; 55; 77]. Using the metagenomic sequencing method, ARGs were analyzed from 116 wastewater samples collected from 32 WWTPs in 17 cities of China including Beijing, Hong Kong, Hangzhou and Lasa. The results showed that out of the 128 ARGs detected across 80% of samples, the ARGs against aminoglycosoides, MLS, tetracycline and beta-lactam were the most predominant ones [77]. [PERSON] et al. [52] investigated the presence of ARGs in a full-scale MWW treatment plant in Singapore using metagenomic sequencing methods. The researchers found that the MWW effluent was composed of ARGs resistant towards aminoglycosoide [_aadA_, _aph(6)-I_, _aph(3)-I_, _aac(6)-I_, _aac(6)-II_, _ant(2\(\arcsec\))-I_], beta-lactams [class A, class C, class D beta-lactamases (_bla\({}_{\text{OXA}}\)_)], chloramphenicol (acetyltransferase, exporters, _floR_, _cmLA_), fossilomycin (_rosAB_), macrolide-lincosamide-streptomycin (_macAB_, _ereA_, _ermFB_), multidrug resistance (subunits of transporters), polymyxin (_arnA_), quinolone (_qnrS_), rifamycincin (_arr_), sulfonamide (_sulI_, _sul2_) and tetracycline (_tetM_, _tetG_, _tetE_, _tet36_, _tet39_, _tetR_, _tet43_, _tetQ_, _tetX_). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**S. No** & **Samples** & **Concentrations of ARGs** & **Resistance against Antibiotics** & **ARGs** & **References** \\ \hline
1 & MWW & \(3.35\times 10^{7}\) CFU/100 mL of ARB & Gentamicin, chloramphenicol and ceftazine & _AC(3)-1, comA1_, _ctx-m_-32 & [11] \\ \hline  & & & & & \\  & & & & & \\
2 & MWW & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline
3 & MWW & - & & & \\  & & & & & \\
4 & MWW & & & & \\  & & & & & \\
5 & MWW & & & & \\  & & & & & \\
6 & MWW & - & & & \\  & & & & \\  & & & & & \\  & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Consolidated list of ARGs reported in MWW.
## 1 Introduction
[PERSON] et al. [21] suggested using the rocking column structure instead of the ordinary middle column to reduce damage to the middle column. The research demonstrated that the seismic performance of the new underground frame structure with a rocking column is significantly improved, and it possesses good self-centering ability, achieving the seismic design goals for underground structures. The middle column of the underground structure is similar to the middle column of the ground structure, as it must bear a large vertical load. Due to the large shear span ratio of the column, shear failure is likely to occur. In response to this phenomenon, [PERSON] et al. [22; 23] systematically introduced the split column theory, addressing design, structural requirements, split column frames, and the seismic performance of joints. [PERSON] et al. [24] conducted performance experiments on split columns under low cyclic horizontal load and proposed a calculation model for the bearing capacity of split columns. When the split column is subjected to the same vertical load as the ordinary column, it effectively reduces the lateral stiffness of the column and significantly improves the column's ductility, with enhanced deformation capacity and energy dissipation capacity [25; 26]. [PERSON] [27] studied the mechanical properties of split columns, examining hysteresis curves, load capacity, and deformation capacity for different separation ratios. The research showed that split columns with varying separation ratios experience compression-bending failure. As the separation ratio decreases, the peak load capacity of the split column increases, ductility decreases, and energy dissipation capacity weakens. This behavior is also applicable to underground structures, effectively alleviating earthquake damage. [PERSON] et al. (2018) used the finite element analysis software ABAQUS 2023 to preliminarily examine the influence of split columns on the seismic mitigation effect of underground structures. [PERSON] et al. (2018, 2019, 2020) proposed applying split columns to underground two-story three-span subway station structures. The results indicated that split columns can effectively prevent the middle column from bearing excessive shear force and bending moment, fully utilizing both the vertical support capacity and horizontal deformation capacity of the split column. In summary, this paper examines a two-story double-span rectangular frame subway station structure. The finite element analysis software ABAQUS is used to establish an overall model of the soil-underground structure. The equivalent linear soil-underground structure overall dynamic time history analysis method is employed to study the effects of weak interlayer thickness and shear wave velocity of weak interlayer soil. The seismic response of the underground structure under varying weak interlayer conditions is investigated, and the seismic mitigation effects of split middle columns and prototype cast-in-place middle columns in underground structures at weak interlayer sites are systematically compared.
## 5 Conclusions  
This research has developed a rapid active power control technique for fault ride-through support of the PV-battery-connected MMC-HVDC system. The proposed technique has replaced the dynamic braking resistor-based excess energy control in the HVDC link with fast active power curtailment. Besides this, a control strategy has been developed for the battery for both power levelling purpose during solar radiation and temperature fluctuation under normal condition of AC grids and fault ride-through improvement during fault at the point of common coupling of AC grids. An isolated AC grid has been formed that employs feedforward controller. A modified incremental conductance method has been used for optimum solar energy tracking. The complete system with the detailed model has been implemented in RTDS. The results confirmed the efficacy of active power reduction technique for keeping the HVDC link voltage around the threshold limit during the severe AC side disturbances. In addition, the battery-included system has improved the transient performance during normal and faulty conditions. The proposed controller improves low voltage ride through by supporting reactive power while remaining connected to the network without limit violations of converter current and HVDC link voltage under severe faults.  
Conceptualization, M.I.H.; methodology, M.I.H. and M.A.A.; software, M.I.H.; validation, M.I.H. and M.A.A.; formal analysis, M.I.H. and M.A.A.; investigation, M.I.H.; resources, M.A.A.; data curation, M.I.H.; writing--original draft preparation, M.I.H.; writing--review and editing, M.A.A.; visualization, M.I.H.; supervision, M.A.A.; project administration, M.A.A.; funding acquisition, M.A.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to acknowledge the support provided by King Fahd University of Petroleum & Minerals through the Research Group funded project #DF191004. The authors also acknowledge the funding supportby King Abdullah City for Atomic and Renewable Energy (K.A. CARE), Energy Research & Innovation Center (ERIC) at KFUPM.  
The authors declare no conflict of interest.
## Abbreviations  
The following abbreviations are used in this manuscript:  
aEMP Active External Market Participant (\"aktiver Externer Markettelinhehmer\") AMI Advanced Metering Infrastructure BSI Federal Office for Information Security (\"Bundesamt fur Sicherheheit in der Informationstechnik\") BMWi Bundesministerium fur Wirtschaft und Energie (\"Federal Ministry for Economic Affairs and Energy\") CA Certificate Authority CLS Controllable Local System DER Distributed Energy Resource DSM Demand Side Management DSMgr Demand Side Manager EEG Renewable Energy Act (\"Erneuerbare-Energien-Gesetz\") EMS Energy Management System EMP External Market Participant (\"Externer Marktelinhehmer\") ENWG Energy Industry Act (\"Energiewirtschaftgesetz\") EV Electric Vehicle FNN Forum Grid Technology/Grid Operation (\"Forum Netztechnik/Netzbetrieb\") GWA Smart Meter Gateway Administrator HAN Home Area Network HKS HAN Communication Scenario (\"HAN-Kommunikationsszenario\") iMSyus Intelligent Metering System (\"Intelligentes Messsystem\") LMN Local Metrological Network MsbG German Metering Point Operation Law (\"Messstellenbetriebsgesetz\") mME Modern Metering Device (\"Moderne Messeinrichtung\") MQTT Message Queuing Telemetry Transport PK Public Key Infrastructure RFC Request for Comments SMGW Smart Meter Gateway SM-PKI Smart Meter Public Key Infrastructure SSL Secure Socket Layer TAF Tariff Use Case (\"Tarifanwendungsfall\") TLS Transport Layer Security TR Technical Guideline (\"Technische Richtlinie\") WAN Wide Area Network
## 4 Materials and Methods
### Data Engineering and Preparation Framework  
#### 4.1.1 Dataset Acquisition and Cleaning  
The selection of data is a vital step in developing any ML and DL model. It is a highly important stage in the construction of a robust DDoS attack detection model, since every model requires proper training and validation on sufficiently large datasets that are free from noise, outliers, missing data, and so on. With the right dataset selected for training, an ML model would be able to identify unseen traffic patterns more accurately. The Edge-IIoTset dataset is a popular Kaggle dataset specifically created for DDoS attack identification utilizing both ML and DL frameworks [22]. The Edge-IIoTset corpus is made up of 49 data files that are organized into three distinct sub-directories: regular IoT and IoT application traffic, malicious IoT and IoT application traffic, and a specific dataset used for ML and DL.  
Data cleaning is the act of fixing problems like outliers and missing numbers in order to prepare a dataset for analysis. There are several reasons why there could be missing values, including lost packets or insufficient logging. Methods such as mean/median imputation, forward/backward fill, or deletion are used to deal with missing values. The identification and elimination of outliers is also essential since they can impact the model's  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Research** & **Year** & **Dataset** & **Model** & **Accuracy** & **Performance Metrics** & **ML** & **DL** & **IoT** & **Approach** \\ \hline [PERSON] et al. [22] & 2022 & Edge-IIoTset & DT, RF, SVM, KNN, DNN & 94.67–96.00\% & P, R, F1, DT, TT & ✓ & ✓ & ✓ & CL + FL \\ [PERSON] & 2023 & Edge-IIoTset & CNN, RNN & 92.49\% & P, R, F1 & ✗ & ✓ & ✓ & FL \\ \(\;  
perception of legitimate versus malicious traffic. This process also removes unwanted and useless information, providing data columns to make it more suitable for analysis.
# Towards Feasible Capsule Network for Vision Tasks
## 4 Experiments  
### Dataset  
We considered four datasets.  
* CIFAR10: CIFAR10 is an image classification dataset that contains a total of \(60,000\) images. It consists of 10 different classes, with 6000 images per class. Each image is a \(32\times 32\) color image, making it a widely used benchmark for evaluating image classification algorithms.
* CIFAR100: CIFAR100 is an extension of CIFAR10, offering more fine-grained labeling. It comprises a total of \(60,000\) images across 100 classes, with 600 images per class. This dataset provides a challenging task for fine-grained image classification, enabling researchers to evaluate algorithms with increased specificity.
* LungCT-Scan: The LungCT-Scan dataset is designed specifically for lung image analysis in medical imaging research. It consists of computed tomography (CT) scan images of the lungs. The purpose of the dataset is for image segmentation. We used 213 images for training and 54 images for validation.
* VOC-2012: VOC-2012, or the PASCAL VOC dataset, is a benchmark dataset for object detection, segmentation, and classification. It consists of approximately \(11,540\) images in total. The dataset includes annotations such as object bounding boxes and pixel-level segmentation for various object categories. In this study, we use 1464 images for training and 1449 images for validation.