esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Instrument Monitoring Method Based on Few-Shot Learning
## 3 Results and Discussion
Each test box in (**d**) has the test number on the left and the confidence level on the right, from left to right: 0 0.93, 1 0.94, 1 0.94, 0 0.93, 0 0.92, 0 0.92, the last two digits are missed and the other tests are correct. Figure (**e**) shows the number of tests on the left of each box and the confidence level on the right, from left to right: 0 0.96, 1 0.94, 1 0.94, 0 0.93, 0 0.93, 0 0.93, 2 0.92,6 0.94, the last two digits were missed and the other tests were correct. method. At the same time, compared with Pure YOLOv5 and other algorithms that require pure manual annotation, FLIMM can automatically generate annotation files for multiple full simulation data while improving the effectiveness, and for single digit simulation data, only the first source image needs to be annotated manually, and the rest of the augmented images can be automatically added with the annotation information of the simulation target based on the annotation of the source image, which greatly saves the time and cost of manual annotation. The above-mentioned realism, efficiency, fully automated annotation and data collection savings of FLIMM allow for fast migration, as it does not require a lot of time to collect and annotate data when encountering new scenarios. Figure 8 shows an example of a new scene migration, where the detection target is anoven display figures. Using FLIMM, we were able to efficiently amplify only 11 raw data, obtain 1400 training sets, 600 validation sets and, according to the detection results on a test set of 200 real data, an accuracy of over 99%. To generate these 2000 simulated images, FLIMM took only about 120 s, compared to more than 20 h to manually collect and label the 2000 images. This experiment effectively demonstrates the effective migration of the FLIMM method between scenes. In summary, the deep-learning-based approach has greatly improved the error detection rate compared with the traditional algorithm, while FLIMM further reduces the leakage rate based on Improved YOLOv5 by simulating as diverse real data distribution as possible through data augmentation, thus improving the accuracy by 14%. From the perspective of detection time, both traditional methods and deep learning algorithms  
Figure 8: Detection example on migrating new scenarios. (**a**) FLIMM augmentation results. (**b**) Real images examples. (**c**) Improved YOLOv5 detection examples. Figure 7: The confusion matrix of FLIMM. have improved the detection rate compared to manual detection, and since Pure YOLOv5 and FLIMM are based on a lightweight architecture with a small number of parameters, YOLOv5s, the detection speed is greatly improved, taking only about 0.1 s per image. Therefore, FLIMM shows the best performance in terms of high-detection accuracy, ease of labeling, and quick detection time. In addition to dashboard monitoring and automated reading, we believe FLIMM has greater potential for exploration in other similar areas, such as letter recognition and license plate recognition.
## 2 Methods
### Bayesian Neural Networks  
Deep learning is mainly based on neural network models, which have been widely used in fields such as reasoning, decision-making and prediction due to their outstanding advantages and powerful capabilities [48]. Currently, deep learning has attracted widespread attention in academia and industry. This study used a Bayesian neural network model to predict the frequency distribution of poisoning and suffocation accidents in the pre-treatment pool of biogas plants. The main reason for choosing the Bayesian neural network model is that the Bayesian neural network model optimizes the mean and variance of the weights. Both the weights and the outputs are probability distributions, which guarantees the reliability and uncertainty of the outputs [49; 50]. However, the neural network model optimizes weights, and both weights and outputs are certain values, which makes the model not advantageous in terms of uncertainty and confidence. Markov Chain Monte Carlo or variational inference was used to estimate the posterior distribution [51; 52; 53]. This article used Python to implement the prediction of the Bayesian neural network.  
The inference formula, Gaussian prior and likelihood function of the Bayesian neural network are as follows [54]:  
Given a set of data \(x\), we want to predict the corresponding \(y\). A neural network represents this correspondence as a function \(f(x,\theta)\), where \(\theta\) is the weight and bias of the neural network. In a Bayesian neural network, weights and biases are probability distributions, so the function \(f(x,\theta)\) can be expressed as follows:  
\[p(y|x,D)=\int P(y|x,\theta)P(\theta|D)d\theta \tag{1}\]  
where \(p(y|x,\theta)\) is the likelihood function, which represents the probability of observing the value \(y\) given \(x\) and \(\theta\). \(p(\theta|D)\) is the posterior distribution of weights and biases given the observed data \(D\).  
To make accurate predictions on the data, the following posterior predictive distribution is used:  
\[p(y^{*}|x^{*},D)=\int P(y^{*}|x^{*},\theta)P(\theta|D)d\theta \tag{2}\]  
where \(x^{*}\) is the new input and \(y^{*}\) is the corresponding predicted output.  
Our first Bayesian neural network employs a Gaussian prior on the weights and a Gaussian likelihood function for the data.  
To train the network, we define a likelihood function comparing the predicted outputs of the network with the actual data points:  
\[p(y_{i}|x_{i},\theta)=N(NN_{\theta}(x_{i},\sigma^{2}),\text{ with prior }\sigma\ \sim\ \Gamma\ (1,1) \tag{3}\]  
Here, \(y_{i}\) represents the actual output for the \(i\)-th data point, \(x_{i}\) represents the input for that data point, \(\sigma\) is the standard deviation parameter for the normal distribution, and \(NN_{\theta}\) is the shallow neural network parameterized by \(\theta\).  
The model in this article is completed in Python 3.11 software, and the required model is retrieved from the Pyro library.
# Secure Inter-Frame Space Communications for Wireless LANs
## 1 Introduction
A serious problem occurs in the opposite direction, that is, the WLAN's packet signals interfere with radar operation. Although the WLAN's signal is usually low-powered and limited within a small range, the radar detector is designed to detect a very small signal reflected by objects located far away from the radar transmitter. If a WLAN or a corresponding 5-GHz transmitter system works near a radar system, the operation of the radar system will be vulnerable and it can cause a serious information error. Radar systems are used for weather measurement, military information, etc. The false alarm of the radar system may cause much more serious problems than at WLAN communications. To avoid radar malfunction or false alarm, WLAN societies have developed a technique called dynamic frequency selection (DFS) to avoid using the radar-occupied frequency bands. Dynamic frequency selection is meaningful since radar systems usually use narrow frequency bands. Wireless local area network systems use wider frequency bands of 20 MHz or 40 MHz compared to radar systems. If a WLAN system detects a radar pulse signal, it leaves the frequency bands for another clear frequency band, just as it leaves a frequency band when the frequency band is too noisy. At the beginning of the WLAN operation, an access point (AP) can search the best channel or frequency band and can detect and avoid the frequency band that a radar system occupies. The term DFS has been used for avoiding the radar-occupied frequency bands. The DFS operation includes radar signal detection, decision, and frequency band change. The radar signal detection should be made in the physical layer (PHY), while decision and frequency change occur in the medium access control (MAC) layer. The radar signal detection tries to detect aspects of radar signals, such as the pulse width, frequency band, and pulse period, which represent radar signals well. In general, radar signals have many different types and parameters. It can be complicated for the signal detector to match all types of radar signals. The most popular method to detect radar signals is to measure the power of the received signal. Once the radar signal power is measured, the WLAN transceivers find and match the pulse width, pulse period, etc. Since the periods of the radar pulses are relatively long, this match process usually belongs to the MAC layer instead of the PHY. The normal period of radar pulse signals is wider than the WLAN packet lengths. The frequency change is also among the main functions of the MAC layer. This paper comprises the following: Section 2 explains the general background of the periodic interference detection technique. Section 3 explains the inter-frame space communications. Section 4 demonstrates the performance evaluation results, and the conclusion is given in Section 5.
## 4 Experimental Investigation  
### High-Strength Micro Steel Fibre, Test Specim and Test Programme  
The experimental investigation was conducted with an ultra-high performance concrete, which has its origin in the DFG Priority Program 1182 [15], and a high-strength steel fibre. The mixture composition of the UHPCR is listed in Table 1 and [16].  
Figure 2: Flow chart for a deterministic fibre design.  
In [17], the compressive and tensile material behaviour is described in detail. The compressive strength of the UHPC is approximately 149.2 N/mm\({}^{2}\) and the tensile strength is approximately 10.7 N/mm\({}^{2}\), both on average. The [PERSON]'s modulus of the concrete material is approximately 43,300 N/mm\({}^{2}\). All material data come from a concrete age of more than 28 days.  
The high-strength micro steel fibre is 13.0 mm in length and 0.19 mm in diameter. That gives a slenderness ratio according to Equation (1). The tensile strength is more than 2000 N/mm\({}^{2}\) and the [PERSON]'s modulus around 200,000 N/mm\({}^{2}\), according to the manufacturer's data. The fibre surface has a brass coating.  
\[l_{t}/\varnothing^{t}=13.0/0.19\ [\text{mm/mm}]=68. \tag{1}\]  
Most investigations in the SPP1182 were limited to quasi-static loading, so that fundamental experiences on cyclic loaded ultra-high-performance fibre-reinforced concrete (UHPFRC) are missing. However, a comparable type of fibre has been used in fatigue exposed UHPFRC-structures [18].  
The own experimental investigations on the pullout behaviour of high-strength micro steel fibres in ultra-high-performance concrete (UHPC) were carried out on single fibres (abbreviation: VES) as well as on groups of fibres (abbreviation: VMS). Refs. [16; 17; 19; 1] contain detailed information of the test specimen configuration, fabrication, and execution of the pullout tests.  
With the exception of the pullout tests with single fibres, the pullout tests were performed under both quasi-static as well as cyclic loading. For single fibres, cyclic loaded pullout tests were not possible due to a high scattering in the pullout behaviour and an impossible calculation of reliable load amplitude. The test program contained two different embedment depths of the fibres in UHPC, namely 3.25 mm and 6.50 mm. The orientation of the fibres was chosen to be 90\({}^{\circ}\), 75\({}^{\circ}\), 60\({}^{\circ}\), 45\({}^{\circ}\), and 30\({}^{\circ}\) with respect to the crack opening. The amplitude related to the static pullout force varied for the fibre group pullout tests between 0.15 and 0.75.
# Exploring the Impact of AI-Based Cyber Security Financial Sector Management
## 1 Introduction
Financial organizations that deal with large amounts of sensitive data might benefit greatly from the employment of the Enhanced Encryption Standard Technique (EES) and the KNN algorithm for more effective prediction and prevention of cyberattacks [22]. Although this model has been created with banking in mind, it has potential for use in other areas of network safety as well. The CS-FSM model's use of AI techniques such as the KNN algorithm can be generalized to boost cyber security in various sectors. One size does not fit all when it comes to cyber security; different sectors face different threats and have different needs. Therefore, prior to adapting this model or its underlying algorithms for use in other sectors, it would be necessary to conduct extensive research into those sectors' unique requirements and cyber security hurdles [23]. It has been noteworthy to see the amount and diversity of network attacks increase in recent years, despite a lot of research in this field [24]. As a result of AI-powered systems decreasing the number of wrongful convictions, security professionals can allocate resources better. Financial institutions can use innovative surveillance footage to watch numerous places with a small staff of faraway analysts [25]. Cyber threats can bring down entire corporate networks and access confidential data, even if high-tech security protocols and systems protect those networks. Various types of attacks can be carried out on the Internet of Things due to vulnerabilities. Additionally, security issues such as confidentiality, privacy, and accessibility pose a threat to important information [26]. The researcher's main contributions to this research work are as follows:  
* The researcher developed a Cyber Security in Financial Sector Management (CS-FSM) model, with the help of artificial intelligence, to analyze all the intrusions. * The proposed CS-FSM method is formed to find the black holes in the cyber security system. * The proposed CSFM model is compared with other existing approaches to validate the results. * The researcher compared the risk reduction ratio with the existing approach. * The results of the tests, carried out using effective systems based on the proposed methodology, show a significant improvement in data security. The remainder of the paper proceeds in this manner: Section 2 discusses background information on cybersecurity and artificial intelligence. Section 3 presents the artificial intelligence approach to Cyber Security in Financial Sector Management (CS-FSM). A mathematical model and analysis are shown in Section 4, and a conclusion and future scope are illustrated in Section 5.
###### Abstract  
The computational complexity of existing multi-symbol detection (MSD) algorithms grows exponentially as the observation intervals increase, resulting in difficulties in algorithm implementation for detecting pulse code modulation/frequency modulation (PCM/FM) signals, especially for multi-channel signals. To address the challenges, we proposed a low-complexity MSD algorithm based on the averaged matched filtering. The proposed algorithm groups the local reference signals based on the different importance levels of the middle and edge bits in the correlation operations and averages the edge bits, leading to a considerable decrease in matching branches. Furthermore, it leverages the phase symmetry, and the proposed algorithm retains half of the averaged local reference signals for the matching operation, thus further reducing the matching branches. The proposed algorithm reduces the storage of the local signals and correlation operations to one-eighth compared to the traditional MSD algorithm under different observation lengths. Additionally, based on the structure of multiplexed FIR filters, the proposed algorithm optimizes single-channel single-coefficient FIR filters into four-channel double-coefficient FIR filters, further reducing the hardware resource consumption by approximately 25%. The simulation results showed that the proposed algorithm achieved demodulation performance comparable to the traditional MSD algorithms while reducing the computational complexity by 87.5%. Compared to the decision-feedback MSD algorithm, it achieves higher demodulation gain with a 75% complexity reduction. The Field Programmable Gate Array (FPGA) platform implementation results showed that the proposed algorithm reduces hardware resource consumption by nearly 90% compared with the traditional algorithm, and the hardware demodulation performance loss is less than 1 dB compared with the simulation results.  
multi-symbol detection; FIR filter; PCM/FM signal; FPGA; low complexity
# A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube
###### Abstract  
As applications generate massive amounts of data streams, the requirement for ways to analyze and cluster this data has become a critical field of research for knowledge discovery. Data stream clustering's primary objective and goal are to acquire insights into incoming data. Recognizing all possible patterns in data streams that enter at variable rates and structures and evolve over time is critical for acquiring insights. Analyzing the data stream has been one of the vital research areas due to the inevitable evolving aspect of the data stream and its vast application domains. Existing algorithms for handling data stream clustering consider adding various data summarization structures starting from grid projection and ending with buffers of Core-Micro and Macro clusters. However, it is found that the static assumption of the data summarization impacts the quality of clustering. To fill this gap, an online clustering algorithm for handling evolving data streams using a tempo-spatial hyper cube called BOCEDS TSHC has been developed in this research. The role of the tempo-spatial hyper cube (TSHC) is to add more dimensions to the data summarization for more degree of freedom. TSHC when added to Buffer-based Online Clustering for Evolving Data Stream (BOCEDS) results in a superior evolving data stream clustering algorithm. Evaluation based on both the real world and synthetic datasets has proven the superiority of the developed BOCEDS TSHC clustering algorithm over the baseline algorithms with respect to most of the clustering metrics.  
density-based clustering; evolving data stream; temporal spatial hyper cube +
Footnote †: journal: 2022, 12, 6523, [[https://doi.org/10.3390/app1236523](https://doi.org/10.3390/app1236523)]([https://doi.org/10.3390/app1236523](https://doi.org/10.3390/app1236523))
## 4 Discussion
### Future Work  
Future work could explore integrating sensor-based systems in buses, potentially using IoT (Internet of Things) technology to directly track bus movements, passenger counts, and environmental conditions. This would offer a more robust solution for real-time data collection, improving the accuracy of congestion predictions and enhancing the overall efficiency of the PUTSS algorithm.  
We plan to extend the PUTSS algorithm to address scalability by optimizing its computational efficiency and incorporating distributed computing. This will allow it to handle larger, more complex urban environments with numerous variables and stations.  
Regarding privacy concerns, the system complies with data protection regulations like GDPR by anonymizing all collected data and limiting the use of real-time images for traffic analysis without storing or identifying passengers' personal information.  
To improve passenger detection accuracy, we plan to explore alternative computer vision models and complementary techniques, such as advanced deep learning (DL) algorithms that perform better in challenging conditions like low lighting or high congestion. The historical congestion data (\(SDC\)) used in our algorithm is sourced from public transit databases and traffic management systems; however, we acknowledge the need for further validation and are considering integrating additional real-time data sources to enhance prediction accuracy.  
This article considers reducing CO\({}_{2}\) emissions by decreasing the number of vehicles in traffic. However, it does not focus on conducting a complete environmental impact study, which falls outside the scope of this research.  
Moreover, forthcoming work will consider researching alternative technologies for real-time monitoring and congestion management, such as IoT sensors, wearable technology, or mobile app integration, to complement the capabilities of the current algorithm.
## 2 State of the Art
### Convolutional Neural Networks  
Convolutional Neural Networks (CNNs) are a type of deep learning model that have been extensively used in the classification of images. In the context of MS, these models function by extracting features and upsampling those features to obtain lesion predictions in the same space of the input image. This process is often referred to as downsampling/encoding followed by upsampling/decoding. A very popular CNN for the segmentation of biomedical images is the U-Net [10], which uses downsampling and upsampling steps that are accompanied by skip connections. This model was created for the segmentation of 2D images, but recent studies have used a 3D model based on the original U-Net. The 3D U-Net has been shown to perform very well, especially for the automatic segmentation of MS [11]; however, it is important to note that it is relatively more expensive with regards to computational resources and memory [12].  
Another variation of the U-Net uses a mechanism known as attention to increase the robustness of segmentation performance [13]. It has been shown to perform better than the original U-Net in pancreas, spleen and kidney segmentation [13]. Recently, the attention mechanism has also been used by a model known as the Focus U-Net, which has been used for polyp segmentation [14]. Additionally, a recent model known as the E-Net has been proposed as an alternative to the U-Net because of its fast inference and high accuracy despite requiring less data and fewer computational resources [15]. It has outperformed the U-Net in a variety of different biomedical imaging experiments: the segmentation of the aneurysmal ascending aorta in CT [16], the segmentation of lungs in CT [17], the segmentation of prostates in MR [18], and the segmentation of whole gland and zonal prostates in MR [19].
# Application of Artificial Intelligence in Wind Power Systems
## 3 Results
### The Impact of Extreme Weather Conditions on AI Performance  
In extreme weather conditions, such as lower temperatures and higher wind speeds, AI technology in wind turbines faces several external challenges that affect its effectiveness. Extreme cold can affect the accuracy of sensors installed in wind turbines that monitor parameters such as vibration, temperature and blade rotation. Ice formation on sensors or mechanical components can lead to distorted data, reducing the reliability of AI-driven predictive maintenance and real-time adjustments [84]. For example, AI algorithms rely on precise vibration data to detect mechanical wear. However, ice formation can obscure or distort these signals, resulting in delayed maintenance alerts. AI-enabled drones used for automated inspections can struggle in harsh weather (e.g., ice) and delay fault detection.  
Higher wind speeds increase the mechanical stress on turbine components (e.g., blades, gears). While AI dynamically optimizes the pitch and yaw angles of the rotor blades to maximize energy yield, extreme gusts or turbulence can stress turbines beyond their operating limits and cause structural damage. AI models must account for rapid wind changes and adapt control strategies to balance energy production and turbine safety. AI models trained with \"normal\" weather data may perform worse under extreme conditions. Deep learning architecture such as LSTM need to be trained with datasets containing extreme weather scenarios to improve resilience.