esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Anomaly Detection with Subspace Periodic Clustering Approach
###### Abstract
To put it another way, a data instance can only be anomalous within a particular temporal context, or more accurately, within a timeframe. Some of these anomalies can be periodic in nature, i.e., they occur after a certain interval of time. These anomalies are difficult to detect. The development of an early warning system is one of the key applications of such abnormalities. In view of the above scenario, it is necessary to design an effective algorithm that locates clusters in the subspace of high-dimensional real-time data and detects anomalies in real-time. [PERSON] proposed rough set theory [35] to address the ambiguity and uncertainty that can be found in any dataset. [PERSON] et al. [36; 37] gave the notion of nano topological space in terms of the two approximations and generated CORE, a subset of conditional attributes used for medical diagnosis. The same notion can be used for generating a subset of high-dimensional real-time data. A comparison of five time-series anomaly detection techniques was conducted by the authors of [38]. Similar efforts were mentioned in [39; 40; 41; 42; 43; 44; 45; 46; 47]. The insider threat, which creates significant issues for the cyber security of industrial control systems, was addressed by the authors of [48]. A random forest-based strategy for online anomaly detection was presented by [PERSON] et al. [49]. In [50; 51; 52], the authors offered fuzzy-based approaches for real-time anomaly detection. In [53], the authors suggested a fuzzy neural network approach with the goal of identifying anomalies in significant cyberattacks. An effective real-time clustering-based anomaly detection system was described by the authors in [54]. As mentioned earlier, while detecting anomalies in most cases, two problems are frequently encountered, namely the high dimensionality of the data and the temporal reference of the detected anomaly. High dimensionality is a serious issue that reduces the efficacy of any anomaly detection method. Also, the temporal reference of the anomaly is important as it gives the time of occurrence of the anomalous data, which can be useful for the design of an early warning system. Though several methods tried to address the aforesaid problems, only a few successfully addressed one or another. So, there is enough scope to work on these problems.
## 3 Method Design
### Network Architecture  
The primary scenarios for data flow based on APIs involve interactions between application servers and between application servers and application databases, as depicted in Figure 3. Each application server is equipped with an LLM for the real-time detection of sensitive data within APIs, enabling early warning and the automatic suspension of risky APIs. The federated learning server conducts protocol analysis and semantic recognition across full traffic flows, incorporating various networks into fine-grained monitoring channels. This approach eliminates potential security risks associated with the LLM itself during the detection of illegal leaks of sensitive API data, effectively addressing data security risks in specific scenarios. Additionally, by displaying the fine-grained flow path of sensitive API data and conducting correlation analysis based on users and paths, the system enables precise tracing and accountability in the event of data breaches.
# AutoFL: Towards AutoML in a Federated Learning Context
## 2 Related Work
### Automated Machine Learning: From Centralized to Federated Learning  
Automated machine learning (AutoML) is the process of automating time-consuming tasks in the development of high-quality ML models for classification or regression purposes. These tasks include data pre-processing, feature selection, model selection and optimization, hyperparameter tuning, etc., and typically multiple variants are tested in parallel. AutoML frameworks such as auto-sklearn ([PERSON] et al., 2020), TPOT ([PERSON] et al., 2020) and H2O AutoML ([PERSON] et al., 2020), can help with ML model selection and optimization. For detailed comparisons of these and other tools, we refer to the benchmarks carried out in other works ([PERSON] et al., 2020; [PERSON] et al., 2020). Even Machine Learning as a Service (MLaaS) providers--such as Azure ([[https://azure.microsoft.com/en-us/products/machine-learning/automatedml/](https://azure.microsoft.com/en-us/products/machine-learning/automatedml/)]([https://azure.microsoft.com/en-us/products/machine-learning/automatedml/](https://azure.microsoft.com/en-us/products/machine-learning/automatedml/)), accessed on 2 July 2023), Amazon ([[https://aws.amazon.com/machine-learning/automl/](https://aws.amazon.com/machine-learning/automl/)]([https://aws.amazon.com/machine-learning/automl/](https://aws.amazon.com/machine-learning/automl/)), accessed on 2 July 2023) and Google ([[https://cloud.google.com/automl](https://cloud.google.com/automl)]([https://cloud.google.com/automl](https://cloud.google.com/automl)), accessed on 2 July 2023)--offer automated machine learning capabilities to data scientists and ML engineers. Many of these frameworks operate on centralized data, and some of them can parallelize and distribute the automation process across a cluster of clients. However, none of them are tailored to construct an effective ML model within the constraints and limitations of a federated learning context.  
[PERSON] et al. (2020) proposed the HANF framework that implements both hyperparameter optimization and neural architecture search (NAS) in a federated learning context. As such, it is an AutoML framework for data distributed across several servers without the need for centralizing the data. Their framework uses a gradient-based approach to optimize both the neural network architecture as well as non-architectural hyperparameters of the learning algorithm. They validate their framework on the FashionMNIST and CIFAR-10 image classification tasks, both in independent and identically distributed (i.i.d.) and non-independent and identically distributed (non-i.i.d.) configurations. Their framework can compete with other NAS methods while optimizing other non-architectural hyperparameters. Their approach is limited to neural network models and does not explore other kinds of ML models or any other optimization trade-offs, including the impact of privacy-enhancing techniques such as differential privacy (DP) or secure multi-party computation (MPC).
# Feasibility of Osseous Landmarks for ACL Reconstruction--A Macroscopic Anatomical Study
## 2 Materials and Methods
### Photographic Setting and Specim Preparation  
First, all extraarticular soft tissue of the 36 specimens was removed and the knee joint was exposed. The ACL was located and divided in its two functional bundles (AM- and PL-bundle) by evaluating their differing tautress during knee movement [27; 28]. Afterwards, the ACL was cautiously removed to expose its bony insertion site. The origin of the two bundles was documented photographically using a standard reflex camera (Nikon Corporation, Tokio, Japan; Type D300s, lens: AF-S DX NIKKOR 18-200 mm 1:3.5-5. G IF-ED VR) (see Figure 2).  
Then, photographs from an arthroscopic view of the bare intercondylar region were taken in a standardized setting (see Figure 3). Photographs were taken using a commonly available reflex camera (Nikon Corporation, Tokio, Japan; Type D300s) and a custom-made adaptor for arthroscopes. The camera was mounted on a Manfrotto (Cassola, Italy) 190 XPROB-tripod with an 804 RC2 three-way head. The camera was aligned horizontally with the help of a bubble level attached to the tripod. To simulate the conditions during knee joint arthroscopy, a standard arthroscope (Arthrex, Wiener Neustadt, Austria; 4 mm, 30\({}^{\circ}\)) was connected via the custom-made adaptor to the camera lens. The specimens were secured horizontally using a bench vice on a table. The arthroscope was exactly positioned in the same level as the intercondylar notch, imitating a 90\({}^{\circ}\) flexion of the knee, as is standard during arthroscopy, and simulating the view into the knee joint via the anterolateral portal [29].  
The position of the bench vice as well as the position of the camera and its tripod were marked on the table and on the ground, respectively. Thus, the same recording conditions were guaranteed for each picture. The picture of each knee was pasted on a DIN A4 page (36 photo series) and then printed on high-quality photo paper (see Figure 4).  
Figure 3: Photographic setting. The camera was mounted on a tripod, via a custom-made adapter, and a 30\({}^{\circ}\) 4 mm arthroscope was connected. An arthroscopic light source provided adequate illumination. The specimen was fixed in a vice.  
Figure 2: Macroscopic view of the lateral femoral condyle. The osseous attachment of the ACL was mapped in the photograph. (**A**) Left femur. (**B**) Right femur. Scale represents 1 cm.
## 2 Materials and Methods
### Statistical Analysis  
The data of GHG flux measurements, temperature, groundwater level, soil chemical parameters were tested for normality using Shapiro-Wilk test, and equality of variances. For normally distributed data two-sample \(t\)-test was performed. For non-normally distributed data, Wilcoxon signed-rank test, Wilcoxon rank sum exact test and Mann-Whitney U test were performed. In order to assess the impact of temperature and groundwater level on CO\({}_{2}\), CH\({}_{4}\) and N\({}_{2}\)O fluxes, Spearman correlation analysis was carried out. Non-linear regression analysis (quadratic model) was performed to assess the impact of soil temperature on GHG fluxes. Linear regression analysis was performed to assess the impact of soil C and N contents on GHG fluxes. In case of CH\({}_{4}\) sites with constantly high groundwater levels, these were excluded. Statistical significance for the performed tests was defined as \(p<0.05\). Data were analyzed using Rstudio (R version 4.2.0).
# A Feature Fusion Model with Data Augmentation for Speech Emotion Recognition
## 3 Proposed Methods
### Structure of MHA-CRNN  
This model is inspired by the CRNN model often used in SER research. The general practice is to use convolutional neural networks to extract high-level features in the spectrogram. The MHA-CRNN model here adds a multi-head attention mechanism to further extract the time domain. The salient emotional information in the frequency domain features then uses Bidirectional LSTM (Bi-LSTM) to process the time-series data and finally use several fully connected layers to complete the classification.  
In our method, the original corpus is divided and filled into 3 s segments as input, Log-Mel spectrogram features are extracted, and then modified VGGish [43] is used to extract deep feature extraction from Log-Mel spectrograms. VGGish is a pre-training model on the large-scale audio classification task set Audioset [8] released by Google in 2018. The audio features obtained as a feature extractor have been proven to perform better than artificially designed features on a variety of audio tasks. To adapt to the SER task, a longer time audio input is used to retain more emotional information. In addition, the dataset samples used for speech emotion are usually small, which makes it easy to produce overfitting problems, Moreover, the output of the VGGish convolution module is maximized, and the extracted features are not compressed. In this paper, a dropout layer is added to the VGGish model to avoid overfitting. The flatten layer and the full connection layer after the last pooling layer of the model is removed.  
After that, a multi-head attention layer is added to project the high-level features extracted by CNN to the attention subspace vectors of different attention heads, learn the salient emotional representation between features from the temporal and spatial dimensions, and jointly output each attention head for further extracted features.  
Finally, the salient emotional features extracted by the attention layer are input into two Bi-LSTM with a length of 128 cells to process the timing information and output the hidden state of the last time step. After that, it is connected to a fully connected layer with 256 nodes, and finally the output is classified using a SoftMax layer. Figure 3 is a schematic diagram of the proposed sentiment classification model MHA-CRNN framework.
## 2 DC-DC Synchronous Buck Converter
### Design Specification  
In the development of a DC-DC synchronous buck converter, a rigorous definition of input and output voltage specifications is essential, as determined by the constraints and capabilities of the underlying technology. For the input voltage, a level close to 20 V is anticipated, due to the high operational voltages required by the transistors employed in the converter. On the output side, the converter is designed to explore various voltage levels, including 12 V, 9 V, 5 V, and 3.3 V, facilitating a wide range of applications, even for scenarios where the delivered voltage needs to be variable within a specific range.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Symbol** & **N-Channel MOS (NMOS)** & **P-Channel MOS (PMOS)** \\ \hline Transistor Width & W [\(\mu\)m] & [3; 20] & [10; 100] \\ Transistor Length & L [\(\mu\)m] & [2; 10] & [6; 10] \\ Gate-Source Voltage & \(V_{gs}\) [V] & [0; 20] & [\(-20\), 0] \\ Drain-Source Voltage & \(V_{ds}\) [V] & [0; 20] & [\(-20\), 0] \\ Temperature & T [\({}^{\circ}\)C] & [25; 300] & [25; 300] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Range of validity for metal–oxide–semiconductor field-effect transistor (MOSFET) devices.  
Regarding current specifications, the converter is constrained by the power capabilities expected from the system, which are limited to a few watts due to the technology specifications. Therefore, an average value of 100 mA is anticipated during the operational phases of each channel, ensuring that the power requirements and technological limitations are effectively balanced.  
An efficiency target of over 80% was established for the converter. This threshold is set with a margin to accommodate the experimental nature of the target technology, as well as the fact that the devices intended for conventional analog/digital functions are being adapted for power applications. Consequently, although the performance may not reach optimal levels due to these adaptations, the defined efficiency goal is deemed achievable under the given conditions.  
The switching frequency for the converter has been chosen conservatively at 75 kHz. This frequency selection is preliminary, as experimental data on the maximum frequency that the technology can reliably support for this application is not yet available. This conservative approach in frequency setting is intended to ensure stability and reliability during the initial testing phases.  
These specifications are summarized in Table 2, which outlines the input and output voltages, current limitations, targeted efficiency, and switching frequency, providing a comprehensive overview of the converter's design parameters. This table serves as a quick reference to the key operational characteristics of the converter, illustrating the careful consideration given to each aspect of its design.
# Electronic Sensing Technologies in Food Quality Assessment: A Comprehensive Literature Review
## 7 Conclusions  
Artificial sense technology imitates the senses such as smell, taste, and vision using sensors and pattern recognition technology. e-Tongue is able to reproduce the human sense of taste using a matrix of different sensors to obtain a \"taste fingerprint\" from a sample. e-Nose can imitate the human sense of smell to determine the smell of a sample through the reaction of the device's sensor system with the sample gas. High-quality e-eye cameras can obtain visual data on colors, shapes, and textures. Since the beginning of sensor research, many studies have focused mainly on individual tools, including e-nose, e-tongue, and e-eye, which did not allow for obtaining complete data on the sensory characteristics of samples, negatively affecting the detection accuracy and generalization capabilities. The bibliography analysis shows that the use of e-nose is the most popular among researchers in food quality assessment. Fewer publications were found for e-tongue, and the smallest number of works focused on e-eye. Food quality testing using electronic sensors has evolved from identifying specific components of a sample to using pattern recognition analyses that provide detailed information about sample properties. Recently, many studies have been undertaken using fusion at the output and hardware level, and the use of various ML algorithms has been analyzed. This is evidenced by the work on the use of e-nose fusion with e-tongue and e-eye characterized by high accuracy, which allows for a comprehensive assessment of food quality. We should also expect the increasingly widespread use of e-sensors in on-line production conditions for real-time quality control and assurance.  
Conceptualization: M.G. and M.R.; methodology: M.G. and M.R.; formal analysis: M.G.; data curation: R.S.; literature research: R.S., M.R. and M.G.; writing--original draft preparation: M.G.; writing--review and editing: M.R. and M.G.; project administration: P.D.-K. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
This study did not involve any testing on humans or live animals.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
###### Abstract  
This study aims to mitigate slope-collapse hazards that threaten life and property at the Lujiawan resettlement site in Wanbi Town, Dayao County, Yunnan Province, within the Guanyinyan hydropower reservoir. It integrates centimeter-level point-cloud data collected by a DJI Matrice 350 RTK equipped with a Zenmuse L2 airborne LiDAR (Light Detection And Ranging) sensor with detailed structural-joint survey data. First, qualitative structural interpretation is conducted with stereographic projection. Next, safety factors are quantified using the limit-equilibrium method, establishing a dual qualitative-quantitative diagnostic framework. This framework delineates six hazardous rock zones (WY1-WY6), dominated by toppling and free-fall failure modes, and evaluates their stability under combined rainfall infiltration, seismic loading, and ambient conditions. Subsequently, six-degree-of-freedom Monte Carlo simulations incorporating realistic three-dimensional terrain and block geometry are performed in RAMMS:ROCKFALL (Rapid Mass Movements Simulation--Rockfall). The resulting spatial patterns of rockfall velocity, kinetic energy, and rebound height elucidate their evolution coupled with slope height, surface morphology, and block shape. Results show peak velocities ranging from 20 to 42 m s\({}^{-1}\) and maximum kinetic energies between 0.16 and 1.4 MJ. Most rockfall trajectories terminate within 0-80 m of the cliff base. All six identified hazardous rock masses pose varying levels of threat to residential structures at the slope foot, highlighting substantial spatial variability in hazard distribution. Drawing on the preceding diagnostic results and dynamic simulations, we recommend a three-tier \"zonal defense with in situ energy dissipation\" scheme: (i) install 500-2000 kJ flexible barriers along the crest and upper slope to rapidly attenuate rockfall energy; (ii) place guiding or deflection structures at mid-slope to steer blocks and dissipate momentum; and (iii) deploy high-capacity flexible nets combined with a catchment basin at the slope foot to intercept residual blocks. This staged arrangement maximizes energy attenuation and overall risk reduction. This study shows that integrating high-resolution 3D point clouds with rigid-body contact dynamics overcomes the spatial discontinuities of conventional surveys. The approach substantially improves the accuracy and efficiency of hazardous rock stability assessments and rockfall trajectory predictions, offering a quantifiable, reproducible mitigation framework for long slopes, large rock volumes, and densely fractured cliff faces.  
airborne LiDAR; stability analysis; geological hazards; numerical simulation; hazardous rock mass
## 4 Simulation Test Analysis
### Simulation Results of High Frequency Part of Power Data  
There are steady-state harmonics and attenuation harmonics in the high frequency portion of the signal. The interpolation DFT and DWT mixed harmonic detection methods based on the Hanning window function can also restore the attenuated harmonic components through layering. If the signal in this frequency band are superimposed, this method cannot subdivide the frequency band. Therefore, the specific parameters of each harmonic cannot be distinguished, and the specific waveform of each harmonic cannot be reconstructed. The method proposed in this paper is based on the original method. It analyzes the harmonics in each frequency band to determine whether there are steady-state harmonics. Using the parameters extracted from the data obtained after windowed interpolation DFT, the harmonics at high frequencies perform the restore. According to the method in Section 3.1, the steady state component and the attenuation component of the high frequency part are separated and extracted. The following three sections will perform harmonic analysis on the three layers in turn.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Number of Harmonic** & Fundamental & 3 & 5 & 7 & Average Value \\ \hline \multirow{4}{*}{**Amplitude Accuracy (\%)**} & **Hanning Window Function** & \multirow{2}{*}{99.4} & \multirow{2}{*}{98.38} & \multirow{2}{*}{97.27} & \multirow{2}{*}{80.23} & \multirow{2}{*}{93.82} \\  & **Interpolation DFT Algorithm** & & & & & \\ \cline{1-1} \cline{5-7}  & **Bartlett–Hann Window Function** & \multirow{2}{*}{99.65} & \multirow{2}{*}{99.71} & \multirow{2}{*}{97.53} & \multirow{2}{*}{84.46} & \multirow{2}{*}{95.3375} \\  & **Interpolation DFT Algorithm** & & & & & \\ \hline \multirow{4}{*}{**Relative Amplitude Error (\%)**} & **Hanning Window Function** & \multirow{2}{*}{\(-\)0.6} & \multirow{2}{*}{\(-\)0.619} & \multirow{2}{*}{\(-\)2.73} & \multirow{2}{*}{\(-\)19.77} & \multirow{2}{*}{\(-\)5.93} \\  & **Interpolation DFT Algorithm** & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison table of analysis results of two methods.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Number of Harmonic** & Fundamental & 3 & 5 & 7 \\ \hline
**Theory Frequency (Hz)** & 50 & 150 & 250 & 350 \\
**Actual Frequency (Hz)** & 50.78 & 148.44 & 250 & 351.56 \\
**Theory Amplitude (V)** & 220 & 21 & 15 & 13 \\
**Hanning Window Function Interpolation** & \multirow{2}{*}{218.68} & \multirow{2}{*}{20.87} & \multirow{2}{*}{14.59} & \multirow{2}{*}{10.43} \\
**DFT Algorithm Amplitude (V)** & & & & \\
**[PERSON]–Hann Window Function Interpolation** & \multirow{2}{*}{219.24} & \multirow{2}{*}{20.94} & \multirow{2}{*}{14.63} & \multirow{2}{*}{10.98} \\
**DFT Algorithm Amplitude (V)** & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Table of analysis results of steady-state harmonic components at low frequencies.