esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of the Control Plane Scalability Approaches in Software Defined Networking
## 2 Topology Based Scalability
### Distributed Controller Designs  
This section presents the approaches that distribute the workload among several controllers. This design is classified onto three types--flat topology, hierarchical topology and hybrid topology. These designs enable workload distribution and elimination of the single point of failure risk. Although distributed approaches have many benefits, they pose some challenges such as latency due to state synchronization, overhead from controller communication and policy consistency among controllers.
## 3 Materials and Methods
### Acoustic Feature Extraction  
According to the literature, the acoustic features based on the spectrogram contain rich time-frequency information. In recent years, applying neural networks to learn from the time-frequency spectrograms has become the most popular paradigm in automatic music genre classification. In this work, we perform three spectrogram-based acoustic features to verify the generalizability of our proposed strategy. We describe the feature extraction process in detail as follows:  
STFT spectrogram: First, the input signal is windowed (e.g., using the Hanning window function) to reduce the effect of spectral leakage, which can cause the spectral components of the signal to spread beyond their actual frequency range. Next, the windowed signal is partitioned into short segments with overlapping (this operation is referred to as \"framing\"). For each segment, the Fourier transform is computed, resulting in a frequency-domain representation of the segment. Finally, the modulo frames are assembled into the STFT spectrogram, with time on the x-axis and frequency on the y-axis. The length of the Fourier transform determines the frequency resolution of the spectrogram, while the length of the segment determines its temporal resolution.  
Mel spectrogram: The Mel spectrogram is a variation of the traditional spectrogram that emphasizes frequencies according to the perceptual characteristics of the human ear. As mentioned above, we can obtain the FFT spectrum by windowing, framing, and Fourier transform. The next step involves applying Mel filter banks to the spectrum. The Mel filter bank is a set of triangular bandpass filters that are spaced according to the Mel scale, which is a non-linear perceptual scale of frequency (as depicted in Equation (1)). Each filter in the bank is centered at a particular Mel frequency and has a bandwidth that varies according to the Mel scale. Finally, the filtered spectrum is transformed using a logarithmic scale to obtain the Mel spectrogram.  
\[Mel(f)=2595\times log_{10}(1+\frac{f}{700}) \tag{1}\]  
CQT spectrogram: Constant-Q transform (CQT) is a popular tool for analyzing non-stationary signals with varying frequency content over time, such as musical signals. After obtaining the FFT spectrum, the CQT is computed by convolving the FFT spectrum of each windowed frame with a bank of bandpass filters that are logarithmically spaced in frequency (CQT kernel). Denote the maximum or minimum frequency to be processed as \(f_{max},f_{min}\). \(f_{k}\) represents the frequency of the \(k\)-\(th\) component, and \(b\) is the number of spectral lines contained in an octave (e.g., \(b=36\)). \(f_{k}\) can be formalized as  
\[f_{k}=2^{\frac{k}{6}}f_{min}\qquad k=0,1,\ldots[b\cdot log_{2}(\frac{f_{max}}{f _{min}})]-1 \tag{2}\]  
Then, the magnitude of the filtered spectrum is used to represent the CQT spectrogram. CQT obtains different frequency resolutions by using different window widths so that the frequency amplitude of each semitone can be obtained.
## 7 Experimental Evaluation
### System Overheads  
Finally, we investigate the CPU and memory overheads introduced from training and using streaming ML models while the storage system is running. The overheads include both the data and processing needed to maintain the necessary state, generate feature  
Figure 17: (**a**) F1 score for _Next Offset Class_ and (**b**) MAE for _Next File Hotness_ for streaming ML against rule-based and batch approaches for the Thesios dataset.  
Figure 18: F1 score for streaming ML against rule-based and batch approaches for _Next Offset Class_ for a representative trace from Cluster 1 of the Thesios dataset.  
vectors and target variables to create training instances, train the model online, and use the model to make predictions. The training cost for a regression model for _Next Offset_ is, on average, 1.8 ms per training instance, whereas for a classification model it is only 0.025 ms. The prediction cost is much lower, at 0.05 ms for regression and 0.005 ms for classification. The memory overhead is very low and less than 1.3 MB for both training and storing the models.  
The training cost for a regression model for _Next File Hotness_ is, on average, 1.7 ms per training instance, whereas for a classification model it is only 0.3 ms. The prediction cost is much lower, at 0.026 ms for regression and 0.004 ms for classification. The memory overheads for training are higher, with a maximum of up to 116 MB, because multiple feature vectors are created and kept in memory for some time until a target file hotness can be computed. However, the models are very memory efficient and up to 302 KB and 58 KB in size for regression and classification, respectively, for each trace.  
The total computational cost for training a classification and a regression model constitutes only 0.32% and 2.27%, respectively, of the total time the file system spends serving read I/O requests. As the online learning framework proposed in Section 5 operates asynchronously within the data storage system and the CPU overhead is so low, the performance of the user-issued file requests is not affected. In addition, the computation cost for using a classification and a regression model constitutes, respectively, only 0.02% and 0.28% of the time spent serving read I/O requests, which is minuscule compared to the potential benefits these models can provide to the underlying storage system.  
**Key takeaways:** The streaming ML approach is very practical, with low CPU and memory overheads for both training and utilizing the models while the storage system is running.
## 4 TSN Integration with Edge Computing for Enhanced Reliability  
As discussed in Section 2, the URLLC features and TSN support of the 5G networks, as well as the real-time capabilities of the edge cloud infrastructure, makes it possible that even the time-critical industry device (closed-loop) control (e.g., mobile robot control) functionalities can be offloaded to the edge. It means that the controller applications are virtualized and deployed in the cloud, in a container, or VM environment.  
The reliability of such an offloaded application can be improved both in the network and in the edge computing domain. On the network side, the TSN frame replication and elimination (FRER) mechanism, specified by IEEE 802.1 CB, is the key enabler for high reliability. The FRER provides a mechanism where the frames are duplicated and transmitted over independent paths between the endpoints. In clause 5.33, 3 GPP TS 23.501 specifies several features to support redundant user plane path over the 5 GS, which enables the seamless support of TSN FRER when 5 GS acts as a virtual TSN bridge.  
Similar to network failures, numerous events should be considered in the cloud environment, such as infrastructure failures, failure in the container runtime environment, and pod failure. The current cloud management solution (e.g., Kubernetes) has built-in repair capabilities for failure handling (e.g., automatically restart the failed entity, switch to a (hot-)standby entity); however, these repair processes operate in the seconds timescale, which does not meet the requirements of the industry use cases. Hence, in order to provide seamless end-to-end communication for industry-grade applications, multiple, active application instances should be deployed in the edge domain to secure that in the case of any failure event, at least one application instance remains active and can serve the end device.  
However, the deployment of multiple, active application instances in itself is not enough, but the control application handling capabilities of the end devices, as well as the backward compatibility, should also be considered.  
In the rest of this section, different end-to-end integrated TSN FRER-edge cloud reliability options will be discussed by considering the end device capabilities. Furthermore, the required cloud features for providing seamless interworking with the TSN FRER are also investigated.
## 3 Data and Methodology
### The Proposed Methodology
#### Meta-Cost Method  
Meta-Cost [44] method adds cost sensitivity to the base classifier, such that it generates a weighted dataset for training by labeling the estimated minimal cost classes in the original dataset. Afterwards, an error-based learner uses the weighted dataset in model building process. If the base classifier can produce understandable outcomes, then the adapted cost-sensitive classifier produces explanatory results. Combining bagging with cost-sensitive classifier is a common approach to handle imbalanced data issues. However, Meta-Cost algorithm is proven to gain better results [40; 44].  
Meta-Cost relies on partitioning the samples space \(X\) into \(x\) regions, where the class \(j\) in region \(x\) has the least cost. Cost-sensitive classifications aims at finding frontiers between resulted areas according to the cost matrix \(C\). The main idea behind Meta-Cost is to find an estimate of the class probabilities \(P(j|x)\) then to label the training instances with their least cost classes (i.e., finding optimal classes). The pseudocode of Meta-Cost procedure is summarized and illustrated in Algorithm 1. For the sake of this particular research, Multilayer Perceptron (i.e., back-propagation artificial network) is used as base classifier [40].  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Configuration** & \multicolumn{1}{c}{**Description**} \\ \hline FN-Cost Factor (\(\lambda\)) & 1, 5, 10, 25, 50, 75, 100, 150, 200 \\ FP-Cost Factor (\(\mu\)) & 1 always \\ Base Algorithm & MLP \\ Adding Cost Sensitivity & CostSensitiveClassifier, Meta-Cost \\ Validation & 10-Fold Cross Validation \\ Target Class & Yes ( the client will subscribe a term deposit ) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experiment Setup.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Algorithm** & **Theoretical Base** & **Weka Implementation** & **Basis (Re-Sampling vs. Re-Weighting)** \\ \hline Cost-Sensitive Classifier & Meta-learning: Thresholding [43] & CostSensitiveClassifier [40] & Re-weighting \\ Meta-Cost & Meta-learning: Thresholding [43] & Meta-Cost [44] & Re-weighting \\ \hline \hline \end{tabular}
\end{table}
Table 3: Employed Cost-sensitive analysis algorithms
## 3 Results
### Soybean Yield Estimation with ML
#### 3.3.3. Using Spectral Reflectance and Vegetation Index Features as Predictor Variables  
The dimensionality reduction process yielded six VIs and five REF spectral features which were later used together as predictor variables for SY estimation (Table 4). RF (r = 0.97, rRMSE = 8.72%) yielded the best performance at a 95:5 split when validated over the train dataset; reduced performances were obtained for ANN, KNN, SLR, and SVM, while PLSR yielded the least-best performance at that split (r = 0.83, rRMSE = 19.79%). For validation over test dataset, RF outperformed at a 60:40 split (r = 0.84, rRMSE = 20.00%), whose performance was followed by SLR, SVM, ANN, SLR and PLSR, while KNN was the least-best performer at the same 60:40 split (r = 0.78, rRMSE = 22.7%). When validated over the entire dataset, RF at a 95:5 split (r = 0.93, rRMSE = 13.32%) yielded the best performance followed by ANN, KNN, SLR, and PLSR, while SVM (r = 0.82, rRMSE = 21.39%) was the least performer at a 95:5 split.
# A Visual Odometry Pipeline for Real-Time UAS Geopositioning
## 3 Methodology
### Geospatial Quad-Tree  
Considering the potentially extensive coverage of the UAS flight plans and the sheer size of reference imagery required for geolocalization (see Figure 2a), we use a quad-tree look-up module to store geotagged landmarks (see Figure 2b) from the orthophoto for fast landmark retrieval. The quad-tree is populated offline and can store extensive geographic areas due to its compressed size. Figure 2c shows example landmarks in red from the quad-tree. We used the UAS geoposition converted into coordinates of the reference orthophoto to retrieve the look-up region (shown in red rectangle box in Figure 2a) with a pre-defined range in pixels (we used \(1400\times 1400\) in experiments).  
Quad-tree is a tree-based data structure where each internal node has four children partitioning the space into four quadrants [17]. When each node reaches maximum capacity, it splits to extend the tree. The quad-tree recursively decomposes space into adaptive nodes, making it very efficient for geospatial indexing geographic regions, as shown in Figure 3. In Figure 4, we compare data retrieval efficiency between quad-tree and brute force methods. The inference time of a quad-tree exhibits a linear relationship with the retrieval area, while brute force execution maintains nearly constant performance, as it processes all data regardless of the size of the lookup region. Quad-tree takes less than 1% of the brute force's time to find appropriate landmarks demonstrating the retrieval efficiency of quad-tree for real-time UAS geopositioning applications. An added benefit of the quad-tree is its ability to increase geospatial coverage by updating the underlying tree; hence, we suggest using similar data structures.
## 4 Inversion of Fatigue Damage Parameters
### BP Neural Network  
The BP neural network, short for backpropagation neural network, is a widely used model in machine learning and deep learning. It consists of multiple layers of neurons, including input, hidden, and output layers. The BP neural network undergoes training and optimization through two stages: forward propagation and backpropagation. During forward propagation, input data propagate through the network layer by layer, undergoing weighted summation and activation function processing, ultimately producing output. Then, using the backpropagation algorithm, the network parameters are adjusted based on the error between the network output and the true labels, aiming to minimize the error and continuously improve network performance [33; 34; 35]. The BP neural network is a typical nonlinear algorithm, comprising input, output, and one or more hidden layers, with several nodes in each layer. The connections between nodes in different layers are represented by weights. The determination of the number of neurons in the hidden layer directly influences the training performance of the neural network. If the number of neurons in the hidden layer is too small, it may fail to achieve the desired training accuracy, while too many neurons may lead to overfitting. [PERSON] and [PERSON] [35] provided an upper bound on the number of hidden layers:  
\[k=m/a\times(i+j) \tag{9}\]  
where \(i\) and \(j\) are the neuron's numbers in the output and input layers respectively. \(m\) is the number of datasets. \(a\) is a constant in the range [5; 10]. In this study, 10 neurons are used in hidden layer.  
The purpose of using neural network methodology in this study is to infer the fatigue limit \(C_{f}\) and the corresponding initial cohesive force strength \(\delta_{0}\) of the cohesive elements. Therefore, these two parameters correspond to the output layer of the neural network. The focus of this research is to determine the cracking initiation time of concrete components with cold joints under aerodynamic fatigue loading, i.e., the fatigue life of the components. Therefore, the fatigue life is used as the input layer for inferring the fatigue parameters. The initial cohesive force strength is used to distinguish between cohesive elements withdifferent pouring interval cold joints, and thus it is also included as part of the input layer. Based on the above analysis, the BP neural network constructed in this paper is illustrated in Figure 7.  
In the Figure 7, \(W\) represents the weight parameters in the neural network, and \(b\) represents the bias function in the neural network.
# VPNFilter Malware Analysis on Cyber Threat in Smart Home Network
## 2 Taxonomy of Malware in the Smart Home Network
### Attacks Based on the Smart Home Architecture
#### 2.1.1 Application Layer  
This layer is used in both lower and upper models because of its significance, interacting with the user and user applications. The communication system is involved in this application layer. The application layer should be considered one of the social divisions of IoT, realizing extensive intellectualization and combining with industry demand [13; 14]. A set of different applications is implemented by this application layer. The same application layer is used to process and manage data starting in the middleware layer, which can provide quality of service to the last user in the smart home [15]. The main problem of the application layer normally occurs in the operation of sensitive data, i.e., malicious attack to modify data and secure lifetime permission and access to data [16]. Attackers normally exploit vulnerabilities to allow malicious code to attack the systems, gain sensitive data access, and modify the system.  
* Code injection attack: This type of attack depends on the injection of data in web applications wherein it facilitates the interpretation and execution of malicious data in an unexpected way by exploiting program errors [17]. It can be used for various purposes.  
Figure 2: Taxonomy of malware attacks in the smart home.  
* Buffer overflow attack: A buffer whose memory is allocated by a program is an example of temporary storage to deal with a surplus of data. A buffer overflow attack occurs when a program deliberately tries to occupy more storage space than the buffer can handle, causing all extra data to overflow for the exploitation of program vulnerabilities. As an example, a well in tech king view 6.53 history Svr was threatened by a heap buffer overflow vulnerability in an industrial automation software [18].
* Data manipulation attack, known as a manipulation code attack, involves gaining illegal access by violating user privacy. The data manipulation attack usually exploits design flaws in the permission model [19].
* Authentication attack plays an important role in the protection of IoT security and privacy. The process of confirming the identity or truth of an object is known as authentication. This kind of attack is a way of exploiting and discovering security holes in web applications.
# An Efficient Scalable Scheduling MAC Protocol for Underwater Sensor Networks +
## 6 Performance Evaluation
### Qualitative Comparison  
Both ED-MAC and T-Lohi are deterministic reservation-based MAC protocols. The main difference between the two protocols is that T-Lohi schedules its transmission data by using the presence of a contention phase, and it uses tones at the beginning of each round to reserve the channel. In ED-MAC, after exchanging the neighbouring nodes' information, every sensor node reserves its own slot for its data transmission by using a depth-based timer within only a short scheduling phase compared to the operational phase.  
During the operating phase, with the dismantling of the slots and sub-slots, ED-MAC acts like UWAN-MAC, with the main difference being that it has a deterministic behaviour. UWAN-MAC has a stochastic behaviour in which the sender nodes select random times to send their packets, thereby increasing the number of collisions; however, ED-MAC uniformly distributes the sending times over a given round to make better use of the channel.  
T-Lohi and UWAN-MAC are performed in two different manners. The main difference between these two MAC protocols is the presence of the contention phase in T-Lohi (which is absent in UWAN-MAC). Moreover, nodes in UWAN-MAC enter a back-off state whenever activity is overheard on the channel. While in T-Lohi, the back-off is only necessary to drive the contention phase, so that a single transmitter is granted channel access.
# An Open Data Approach for Estimating Vegetation Gross Primary Production at Fine Spatial Resolution
## 4 Discussion  
### Benefits of the One-Step Process of GPP  
In this study, we provided a web APP for generating fine-spatial-resolution (30-m) GPP (Figure 11), which allows users to derive the GPP at any given study area around the world. This tool can be used to obtain and download the GPP on demand, providing one-step GPP processing. This APP has three advantages: high efficiency, workload reduction, and open access. First, we used this APP to generate 15 years of fine-spatial-resolution GPP from 193 sites in less than 3 h. Second, since this APP reduces the workload by eliminating data download and image preprocessing, it easily obtains spatiotemporally continuous GPP simulations. Third, since GEE is an open-access platform, this APP can be accessed by all users. For specific applications, users can use this platform to compare the fine-spatial-resolution GPP to other data. This helps cross-comparisons of GPP models, as well as the comparison of GPP to any given bio-physiological variable (e.g., soil water content, ozone concentration). Specifically, to our knowledge, this is the first APP to provide access to fine-spatial-resolution GPPs for any selected location around the world. Therefore, with this APP, users can easily evaluate GPP changes, especially in heterogeneous regions such as deserts, savanna, and cropland.
# Research on Micro-Vibration Analysis of Segmented Telescope Based on Opto-Mechanical Integration
## 2 Segmented Telescope Imaging Theory
### Theoretical Analysis of the Effect of Micro-Vibration on Image Quality Under Exposure  
Ideally, the width of the PSF is mainly determined by the diffraction limit, and for a circular aperture, the full-width-half-height (FWHM) of the PSF is approximately \(1.22\lambda/D\), where \(\lambda\) is the wavelength of the light wave and \(D\) is the diameter of the aperture. Under micro-vibration conditions, the width and height of a point PSF can be significantly affected. The vibration causes the optical axis to shift, causing the width of the PSF to increase. At the same time, the intensity of the central peak of the PSF decreases due to the dispersion of light energy over a wider area, in addition to increasing the intensity of the secondary peaks.  
Assuming that the point spread function of the optical system for ideal imaging is \(\rm{h(x,y)}\), and In the integral time, the image shift in the x direction generated by micro-vibration is \(\varepsilon_{\rm x}(t)\), and the image shift in the y direction is \(\varepsilon_{\rm y}(t)\), the energy distribution of the diffuse spot of the image point at the moment of \(t\) is:  
\[\rm{g(x,y,t)}=h(x,y)*\delta\left(x-\varepsilon_{x}(t),y-\varepsilon_{y}(t)\right) \tag{10}\]  
where is the \(\delta(x,y)\) Dirac symbol and \(*\) is the convolution symbol. After the introduction of the image shift, the energy of the diffuse spot is normalized to obtain the point spread function at long exposure time as follows:  
\[\rm{LSF(x,y)}=\frac{1}{t_{1}}\int_{t_{0}}^{t_{0}+t_{1}}g(x,y,t)\ dt=h(x,y)* \frac{1}{t_{1}}\int_{t_{0}}^{t_{0}+t_{1}}\delta\left(x-\varepsilon_{x}(t),y- \varepsilon_{y}(t)\right)dt \tag{11}\]  
where \(t_{0}\) is the initial integration moment and \(t_{1}\) is the integration time. During the imaging process, when micro-vibration acts on the optical system, the center position of the dot diffusion function will deviate from the ideal position. As a result, the micro-vibration will cause the diffuse spot diameter of the image point to become larger and the center energy to decrease, making the image blur geometrically larger.