esa-sceva/satcom-chunk-collection huggingface dataset:
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 5 Performance Evaluation
### Results and Analysis
#### 5.2.2 Impact of Transmission Range  
Figure 6 demonstrates the impact of transmission range on coverage. It is easy to see that increasing transmission range improves coverage. From this point of view, it is always helpful to have a long transmission range. However, the transmission range is related to the transmission power [41]. In some cases, increasing transmission power will increase the transmission range, but energy conservation is critical for underwater sensor nodes because they are powered by the battery and are difficult to recharge. Thus, a natural tradeoff between coverage and energy consumption exists. In some scenarios more power means more reflections and hence more difficulties for the modem to correctly decode the message. In this case, by reducing the transmission power, the transmission range can be increased.
## 4 Case Study Results
### A Brief Background of the AeroLogic Joint Venture Partners
#### 4.2.2 Lufthansa Cargo AG  
Lufthansa Cargo AG, one of the world's major air cargo-carrying airlines, is regarded as a market leader in the global air cargo industry [101; 102]. In 1926, the newly-formed German airline Deutsche Luft Hansa transported its first air cargo consignment, making it one of the world's earliest air cargo carriers. On 1 January 1995, Lufthansa Cargo Airlines, the airline's air cargo operation, was established as a totally autonomous public limited company, Lufthansa Cargo A.G. [103], with the strategic objective, according to Lufthansa, \"to further reduce the substitutability of its product and to create a clearly identified Lufthansa Cargo product\" [104]. Lufthansa Cargo now serves over 300 destinations in 100 countries using a combination of 12 dedicated Boeing MD11 freighter aircraft, five Boeing B777-200 LRF freighter aircraft, and the lower-deck belly hold capacities on Lufthansa and Austrian Airlines Eurowings passenger aircraft, as well as an extensive road feeder service (RFS) network [105; 106]. The bulk of the airline's cargo business is routed through its hub at Frankfurt Airport. Other hubs include Munich, and Vienna Airports [107]. Following Lufthansa's acquisition of Austrian Airlines, Lufthansa Cargo and Austrian Airlines created a joint subsidiary in 2010 to market the whole air cargo capacity of both airlines in Austria and the expansion of Vienna Airport into a hub for Lufthansa Cargo [108].  
Lufthansa Cargo has operational equity stakes in:  
* handling counts GmbH (100%)
* Jettainer GmbH (100%)
* time:matters GmbH (100%)
* AeroLogic GmbH (50%)
* Lufthansa Cargo Servicios Logisticos de Mexico, S.A. DE C.V. (100%)
* Shenzhen Airport Int'1 Cargo Terminal (ICCS) (50%)
* Airmail Center Frankfurt GmbH (40%)
* Shanghai, China-based Shanghai Pudong International Airport Cargo Terminal Co. Ltd. (PACTL) (29%) [106]  
In 2016, the airline transported around 1.6 million tonnes of freight and mail and sold 8.4 billion freight tonne-kilometres (FTKs) [106]. Lufthansa Cargo total revenues in 2016 were \(\copyright\)2.1 billion [109].
# Radar-Based Target Tracking Using Deep Learning Approaches with Unscented Kalman Filter
## 3 Simulation Analysis and Results
### Model Training  
This study used 300 carefully designed scenarios in which a target was tracked in 3D to create a dataset for training a machine learning model (MLP, CNN, LSTM, and GRU). The dataset contained columns for measured range, measured bearing, and measured elevation as input features and standard deviation in range, standard deviation in bearing, and standard deviation in elevation as output features. The simulation was performed using a simulator designed in Python to create the dataset.
## 3 Asthma Data Sources
### Benchmark Datasets
#### 3.2.5 Lung X-Ray Radiographs  
Asthma is categorized as a lung disease, with various diagnostic tests available for detection. One such method is a chest X-ray, which captures the lung's health; hence, such images are also helpful in predicting asthma. The dataset contains X-ray images of the lungs, available publicly on the Kaggle platform [50] under the title of NIH chest-Xray. It has 112,120 images alongside disease labels from about 30,805 individuals. The dataset owner team reported that the labels are corrected with an accuracy exceeding 90%. The train and test list of the available images for the AI model evaluation is also available in a separate file. Additionally, 'comma-separated value' files are also given along with a dataset containing information about the bounding box list and data entries of the given radiographs. The complete dataset has 14 common categories presenting lung-related diseases from chest examinations. [PERSON] et al. [19] utilized this dataset to perform binary classification between lung disease and normal. When implementing highly efficient DL models such as the CNN, VGG, and ResNet, the dataset is worth considering.
# Dual-Branch CNN-Mamba Method for Image Defocus Deblurring
## 3 Method
### Convolution Branch  
The convolution branch structure is shown in Figure 4a. The convolution branch is the local detail feature capture branch in the CNN-Mamba parallel structure block. In this paper, a simple residual block is used in the convolution branch. The design of this residual block is inspired by ResNet [35], which effectively alleviates the vanishing gradient problem in deep networks and helps improve the training efficiency and performance of the network.  
First, the input feature map \(F_{\text{cnn}}\) is passed through the first \(3\times 3\) convolutional layer to extract local features from the defocused image. The output of the convolutional layer is then processed by Batch Normalization (BN) and a ReLU activation function, which introduces non-linearity and enhances the feature representation ability.  
Next, after processing by the first convolutional layer, the feature map passes through a second \(3\times 3\) convolutional layer, which further extracts features while keeping the number of channels unchanged. After the second convolutional layer, Batch Normalization is applied again. At this point, the output feature map of the network is added element-wise to the input feature map \(F_{\text{cnn}}\), forming a residual connection.  
After the residual connection, the processed result is passed through the ReLU activation function again to generate the final output feature map. This design enables the  
Figure 4: Local and global feature capture module structure of the CNN–Mamba block. (**a**) Local (CNN) branch: extracts fine-grained spatial details. (**b**) Global (Mamba) branch: models long-range dependencies via cross-scan.  
network to effectively learn and optimize deep features while alleviating the vanishing gradient problem through residual connections. The specific operations can be represented as  
\[\text{out}=\text{ReLU}(\text{BN1}(\text{Conv1}(F_{\text{cnn}}))) \tag{2}\]  
\[F^{\prime}_{\text{cnn}}=\text{ReLU}(\text{BN2}(\text{Conv2}(\text{out}))+F_{ \text{cnn}}) \tag{3}\]  
where Conv1 and Conv2 are two \(3\times 3\) convolution operations, ReLU is the Leaky ReLU activation function, and BN1 and BN2 are the two Batch Normalization operations.
# Integration of Polynomials Times Double Step Function in Quadrilateral Domains for XFEM Analysis
## 4 Results  
The algorithm presented in Section 3.4 has been implemented into a Fortran library called _double discontinuity EQP_, _DD_EQP_, which provides the expressions of the equivalent polynomial functions \(\bar{H}_{i}(\mathbf{x})\) for 2D quadrangular integration domains as a function of the position of two discontinuity lines. In this Section, the library is used to validate the proposed algorithm and prove its robustness by means of numerical examples. The equivalent polynomial functions \(\bar{H}_{i}(\mathbf{x})\) are evaluated into a regular square parent element. More details about the mathematical formulation employed to compute the equivalent polynomial \(\hat{H}_{i}(\mathbf{x})\) for each discontinuity can be found in [13; 19]. It has to be noted that the exact same procedure can be applied to a standard triangular parent element and that an extension of the proposed formulation to three-dimensional parent elements such as tetrahedrons and hexahedrons is possible, but it will not be discussed further in this paper. Moreover, in [20], the standard equivalent polynomial formulation (see Section 3.2), which is the base for the proposed algorithm, has been applied to non-polygonal element shapes such as circle or sphere parent elements. An extension of the proposed method to other non-conventional polygonal shapes can be achieved (as long as the conditions to define an equivalent polynomial for the chosen polygonal shape are met). However, since quadrangular bilinear elements are commonly employed in finite element analysis in contrast to generic polygons [9; 49; 76], this paper focuses on this particular element shape.  
In the following subsections, the library architecture is presented and two numerical examples are carried out. The results obtained by means of the proposed algorithm are then compared with other integration methods and validated.
# A Review of Prediction Methods for Ultra-Low Cycle Fatigue Damage of Steel Piers Under Earthquakes
## 2 Research Status and Development Trend at Home and Abroad
### Seismic Performance and Seismic Response Calculation Model of Steel Pier
#### 2.5.1 Seismic Performance Evaluation Indicators for Steel Bridge Piers  
In 2000, [PERSON] and [PERSON] et al. proposed an empirical formula for assessing the bearing capacity and ductility of steel piers subjected to horizontal loads influenced by local buckling of steel plates. This was based on findings from pseudo-static tests, pseudo-dynamic tests, and finite element analyses of steel piers [84; 85]. Japan's \"Specifications for highway bridges-PartII Steel Bridges\" adopted the research results of [PERSON] and [PERSON] et al. and established an evaluation index system for the seismic performance of steel piers, as shown in Figure 9[5]. Figure 9 shows the skeleton curve of a steel bridge pier under horizontal reciprocating load. \(\delta_{u}\) is the ultimate displacement, and \(\delta_{y}\) is the yield displacement. The size of the horizontal displacement determines the damage state of the steel pier. The state wherein the ultimate bearing capacity reduces by 5% is defined as the limit of damage state for the steel pier. After developing the seismic performance evaluation index for steel piers, it is crucial to create an accurate hysteresis model. This model will help determine the boundary for predicting ultra-low cycle fatigue damage across varying degrees of damage.  
Figure 8: Loading form of forced horizontal displacement [81].  
Figure 9: Evaluation indicators for seismic performance of steel bridge piers [5].
## 4 Switching Adaptive Notch Filter Vibration Control Strategy Based on Fuzzy Control
### Stability Analysis of AFC-ANF  
The input/output stability theory is used to analyze the stability of the AFC-ANF designed for the system. The transfer functions for the controlled object and the controller are as follows:  
\[G_{\mathrm{o}}(s)=\frac{k_{\mathrm{i}}}{ms^{2}-k_{x}}, \tag{13}\]  
\[G_{\mathrm{c}}(s)=K_{p}+K_{d}s+\frac{K_{i}}{s}, \tag{14}\]  
The open-loop poles of the system are not located at the left half-plane of \(S\). Using the stability theorem, the controlled object and the controller are not stable; thus, they are multiplied with \((s+n)\) and \((s-n)^{-1}\), respectively. The updated control object and updated transfer function are shown in Equations (13) and (14), respectively.  
\[G^{\prime}{}_{o}(s)=\frac{k_{\mathrm{i}}(s+n)}{ms^{2}-k_{x}}, \tag{15}\]  
\[G^{\prime}{}_{\mathrm{c}}(s)=\frac{1}{s+n}(K_{p}+K_{d}s+\frac{K_{i}}{s}), \tag{16}\]  
If \(\mathrm{Re}(G^{\prime}{}_{o}(j\omega)\geq 0)\) meets the conditions, \(G^{\prime}{}_{o}(s)\) is stable, which leads to the following equation:  
\[\begin{array}{c}\mathrm{Re}(G^{\prime}{}_{o}(j\omega))=\mathrm{Re}\Big{[} \frac{k_{\mathrm{i}}(j\omega+n)((j\omega)^{2}+k_{x})}{(m(j\omega)^{2}-k_{x})( m(j\omega)^{2}+k_{x})}\Big{]}=\\ \Big{[}\frac{-m\omega^{2}k_{x}+k_{d}nk_{x}}{m^{2}\omega^{4}-k_{x}^{2}}\Big{]},\end{array} \tag{17}\]  
When \(n\geq\frac{mk_{i}}{k_{x}}\), \(\mathrm{Re}(G^{\prime}{}_{o}(j\omega)\geq 0)\), \(G^{\prime}{}_{o}(s)\) is stabilized.  
If \(\mathrm{Re}(G^{\prime}{}_{c}(j\omega)\geq\alpha)\) (where \(\alpha>0\)) is workable, we have the following:  
\[\begin{array}{c}\mathrm{Re}(G^{\prime}{}_{\mathrm{c}}(j\omega))=\mathrm{Re} [\frac{(K_{p}+K_{d}s+\frac{K_{i}}{s})(n-s)}{(s+n)(n-s)}]=\\ \mathrm{Re}[\frac{(K_{p}+K_{d}j\omega+\frac{K_{i}}{s})(n-j\omega)}{n^{2}+ \omega^{2}}]=\\ \frac{K_{p}n+K_{d}\omega^{2}-K_{i}}{n^{2}+\omega^{2}},\end{array} \tag{18}\]  
If \(K_{pn}>0\), \(K_{d}\geq 0\), and \(K_{pn}+K_{d}w^{2}>Ki>0\), then, Equation (18) is significantly stable. Evidently, AFC-ANF satisfies the conditions for I/O stabilization, such as \(n\geq\frac{mk_{i}}{k_{x}}\), \(K_{p}n>0\), \(K_{d}\geq 0\), and \(K_{p}n+K_{d}w^{2}>K_{i}>0\).
# Half-Elliptical Resonator Lowpass Filter with a Wide Stopband for Low Band 5G Communication Systems
## 3 Implementation and Measurement Results  
Figure 11 shows a photo of the fabricated filter, along with its frequency response for both measurements and the simulation results. The measurement shows that the cut-off frequency is 1.26 GHz. As seen in this figure, the proposed LPF has a passband insertion loss (IL) of less than 0.3 dB and a return loss (RL) higher than 12 dB.  
The roll-off rate (\(\zeta\)) parameter, which shows the sharpness of S\({}_{21}\) in the transition band, is defined in (8) as follows:  
\[\zeta=\frac{\alpha_{\rm max}-\alpha_{\rm min}}{\rm f_{s}-f_{c}}\ (\rm dB/GHz), \tag{15}\]  
where \(\alpha_{\rm max}\) and \(\alpha_{\rm min}\) are 40 dB and 3 dB attenuation points, while \(\rm f_{s}\) and \(\rm f_{c}\) are the corresponding frequencies of these points. In the proposed LPF, the transition band is from 1.26 GHz to 1.62 GHz with attenuation levels of 3 dB and 40 dB, respectively. As a result, the roll-off rate (\(\zeta\)) parameter of the fabricated filter is 103.9 dB/GHz.  
Figure 11: (**a**) The photo of the created filter. (**b**) Frequency responses of measurement and simulation.  
Another important parameter is relative stopband bandwidth (RSB), which is defined in Equation (16) as follows:  
\[\text{RSB}=\frac{\text{stopband bandwidth}}{\text{stopband center frequency}} \tag{16}\]  
In the proposed LPF, the stopband ranges from 1.48 to 20 GHz with the attenuation level of 24 dB. Therefore, the stopband bandwidth is 18.52 GHz and the center frequency of the stopband is 10.74 GHz. As a result, the RSB is equal to 1.72.  
The suppression factor (SF) parameter is defined in Equation (17) as follows:  
\[\text{SF}=\frac{\text{Rejection level in stopband}}{10} \tag{17}\]  
As mentioned in the proposed LPF, the stopband ranges from 1.48 to 20 GHz with the attenuation level of 24 dB. Therefore, the SF is 2.4.  
The cut-off frequency of the LPF (fc) and its occupied size are inversely proportional. The filter with the higher cut-off frequency has a smaller size and vice versa. Therefore, the normalized circuit size (NCS) parameter is defined in Equation (18) to have a fair comparison. The relative device size is 0.126 \(\lambda_{\text{g}}\)\(\times\) 0.055 \(\lambda_{\text{g}}\).  
\[\text{NCS}=\frac{\text{physical size (length }\times\text{ width)}}{\lambda_{\text{g}} ^{2}}, \tag{18}\]  
where \(\lambda_{\text{g}}\) is the guided wavelength. The figure of merit (FOM) parameter has been widely used in the literature and shown in Equation (19) as follows:  
\[\text{FOM}=\frac{\zeta\ \times\ \text{RSB}\ \times\ \text{SF}}{\text{NCS}} \tag{19}\]  
The calculated figure of merit (FOM) parameter of the proposed filter is 62,520, which is considerably high for this class of filter compared with other published works. The proposed filter is compared with other published filters in Table 2.  
Table 2 shows that the proposed LPF has the smallest NCS and the highest FOM parameters compared with other related works.
## 1 Introduction  
Feces of the house dust mites _Dermatophagoides farinae_ Huges 1961 and _D. pteronyssinus_ ([PERSON], 1897) (Orbatida, Pyroglyphidae) are a major source for house dust allergens [1; 2] and are responsible for 95% of all cases of house dust allergy [3]. Potential methods for mite control consist in airing and cleaning the bedclothes, structural measures to reduce suitable mite habitats, the use of fungicides to reduce fungi that serve as food, and acaricides to kill the mites [4]. One promising, potential control method that has not been studied sufficiently is the use of mite pheromones to manipulate their behavior. A large number of studies on mite pheromones revealed that these arthropods rely strongly on pheromonal communication to form aggregations, escape from enemies, find mates, and stimulate mating behavior in the other sex [5]. Therefore, pheromones could be used in traps to attract mites, either for monitoring, or in the context of a lure and kill strategy [6].  
Pheromone studies have been performed on both _Dermatophagodes_ species. This resulted in the identification of 2-hydroxy-6-methylbenzaldehyde as sexual pheromone for the American house dust mite _D. farinae_[7], nervl formate as aggregation pheromone for both species [8], and citral as putative defensive compound for _D. farinae_[9], which does also occur in _D. pteronyssinus_[10]. However, several compounds were described to be released by both species for which no biological function has been identified so far [5; 10; 11]. In addition, nothing is known on potential sexual pheromones in _D. pteronyssinus_ and it is unclear which substances are released by which sex. Furthermore, studies with house dust mites so far only addressed pheromone release and reaction to pheromones in adult mites while the role of pheromones for nymphs remained unclear. The characteristic opisthosomal glands (\"oil glands\"), which are made responsible for the release of most pheromones in astigmatid mites [5] are present in nymphs as well [12] and they have also been reported to release pheromones [13; 14; 15].  
To address the abovementioned open questions in the European house dust mite _D. pteronyssinus_, we (1) analyzed the headspace of mite cultures and the stage- and sex-specific release of volatile chemicals. Headspace analysis was used in contrast to mite extracts, because our aim was to study actual pheromonal communication i.e. the compounds that are released by the mites under \"normal\", undisturbed conditions. Therefore, care was also taken not to disturb the mites during this analysis. In addition, we tested the response towards identified compounds (2) and studied the feasibility of using attractive pheromonal compounds in the context of control of house dust mites (3).
## 4 Artificial Intelligence (AI)-Enabled 6G Networks
### Traffic Classification  
The categorization of network traffic into different classes, referred to as traffic classification, is important to ensure the QoS, control pricing, resource management, and security of smart city applications. The simplest method for traffic classification involves mapping the applications' traffic to port numbers [47]. However, this technique provides an inaccurate classification since several applications use dynamic port numbers. Payload-based methods are alternatives to the port-based techniques where the traffic is classified by examining the packet payload [47]. However, the traffic payload cannot be accessed in scenarios where the packets are encrypted due to privacy and security concerns. Consequently, ML/DL-based methods can be used to address the issues of the conventional methods (Figure 6). [PERSON], [PERSON], and [PERSON] [48] proposed a Tree-RNN to classify network traffic into 12 different classes. The proposed DL model consists of a tree structure that divides the large classification problem into smaller ones, with each class represented by a tree node. [PERSON] et al. [49] proposed a hybrid RNN- and CNN-based network to classify traffic from IoT devices and services. CNN layers extract complex network traffic features automatically from the input data, eliminating the feature selection process used in the classical ML approaches.  
Figure 5: Convolutional Neural Network (CNN)-based modulation recognition in networking.  
Figure 6: Deep Learning (DL)-based network traffic classification for Internet of Things (IoT) applications.
# Anomaly Detection in 6G Networks Using Machine Learning Methods
## 4 AI/ML Technology in 6G Network
### Security Improvements for 5G  
In comparison to earlier generations of mobile networks, 5G networks feature significant security enhancements, but they also present new security difficulties. The following are a few of the major security upgrades in 5G networks [66; 67; 68; 69]:  
1. Better encryption: 5G networks employ better encryption methods to secure data in transit. The advanced encryption standard (AES) with 256-bit keys, which is regarded as being very secure, is required by the 5G standard.
2. Network slicing: 5G networks divide the network into virtual networks, each with its own set of security guidelines and restrictions. This makes it possible to separate and secure various types of traffic using various levels of protection.
3. User authentication: To defend against identity theft and other sorts of threats, 5G networks use better user authentication protocols, including 5G authentication and key agreement (AKA).
4. Secure boot: Secure boot certifies that the operating system and software of 5G devices are valid and have not been tampered with.
5. SIM-based security: SIM-based security is used by 5G networks to thwart SIM swapping attacks, in which criminals attempt to seize control of a user's SIM card to obtain access to their account.
6. Software-defined networking: Software-defined networking (SDN) enables dynamic network configuration and security policies to be updated in real-time to address new threats and assaults; 5G networks use SDN to enable this functionality.  
Figure 1: Attacks on each 6G AI/ML security architecture tier are distinct.  
Network function virtualization: Network function virtualization (NFV) is used in 5G networks to enable the implementation of network functions in software instead of hardware. This can lessen the danger of hardware-based attacks and make updating and securing network operations easier.
* 8 Improved IoT security: To guard against assaults on IoT devices, 5G networks include enhanced security mechanisms for IoT devices, including mutual authentication and secure communication protocols.
* 9 Improved privacy: 5G networks include improved privacy features such as the capacity to send data in a manner that conceals the user's identity, making it harder for attackers to monitor or identify users.
* 10 Better network management: 5G networks now have greater granular control over network resources and security regulations thanks to network slicing and SDN solutions.  
While 5G networks have significant security advantages over earlier mobile network generations, they also present new security difficulties, such as the increased use of edge computing and the requirement for security measures to be implemented on a variety of devices. To solve these issues and guarantee the security of 5G networks, network operators, device makers, and other players in the 5G ecosystem must continue to develop and adopt new security measures. This will require continued innovation and cooperation, as well as a \"security-first\" mindset in the development of 5G networks and devices [67].