esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The Poprad-Tatry airport plays an important role in the local economic dependence on tourism. Weather risks and wind directly affect the safety of air transportation and the operations at the airport. Therefore, it is important to investigate and analyse the influence of the mountain environment on the course of meteorological phenomena. In this article, we examine the wind speed and direction in this location because they are important for the safety of the airport operations. Based on the long-term observations, it is possible to predict how the wind speed and direction will develop in a given location. Accurate wind speed forecasts are necessary for both short- and long-term warning planning and decision-making in these applications. The parameters of the considered probability distributions were estimated using the maximum likelihood method (MLM) and the calculations were performed in the statistical software STATISTICA and software MATLAB R2020b. The computations carried out in the Matlab software were based on functions available in the Statistics and Machine Learning Toolbox, customized by the authors. In order to assess the goodness-of-fit (GOF) of the selected probability distributions, the commonly used GOF tests--the Kolmogorov-Smirnov (\(KS\)) test and the Anderson-Darling (\(AD\)) test--were applied. Moreover, the root mean square error (\(RMSE\)), the coefficient of determination (\(R^{2}\)), and the information criteria--Akaike's information criterion (\(AIC\)) and Bayesian information criterion (\(BIC\))--were used to assess the suitability of the considered probability distributions and to compare their performances. The rest of this article is organized as follows: in \"Description of studied area\", we briefly describe the geographical conditions of the studied area of the Poprad-Tatry airport and characterise the meteorological station situated there. The section \"Wind speed description and analysis\" provides characteristics of the datasets along with their descriptive statistics. The section \"Wind Direction Analysis\" summarises the results of the analysis in the form of wind roses. In \"Methods\", we give a summary of the modelled probability distributions, of the parameter estimation method, and of the GOF and model selection criteria. The section \"Results and discussion\" summarises and discusses the obtained results. The section \"Conclusion\" recapitulates our findings and provides recommendations for the application of the exponentiated Weibull distribution.
# Multi-Scale Audio Spectrogram Transformer for Classroom Teaching Interaction Recognition
## 3 Methods
### Model Overall Architecture  
Model Input: Firstly, the original audio file was resampled, framed, windowed, put through STFT, Mel frequency scaling, logarithmic calculation and other steps to obtain the Log-Mel spectrogram, on which delta and delta-delta features were calculated. The above features were concatenated in the channel dimension to form input data with three-dimensional features, which were then sent to the backbone network.  
Spectrogram Encoder: An audio spectrogram kernel of size (P \(\times\) P) was cut into different spectrogram tokens by a Patch-Embed CNN and used as input to the MAST backbone. The audio spectrogram representation was different from the conventional RGB image. The width and height of the audio spectrogram represented the time and frequency dimensions, respectively, and the channel dimension was a multi-feature representation (static, delta, delta-delta). Since the time dimension of the multi-channel spectrogram was longer than the frequency dimension, to capture the relationship of spectrogram patches between frequency bands at the same time, the Mel spectrogram was partitioned into windows w1, w2, w3, etc. The local regions within each window were then patched and embedded to obtain the final spectrogram tokens feature representation.  
MAST Backbone: The backbone network was used for feature extraction and consisted mainly of multi-scale self-attention blocks. The most critical part of the multiscale self-attention module was the pooling attention process, which reduced the computational complexity of self-attention by pooling attention, and upscaled the feature representation of each token, as described in Section 2.3. As the network deepened, the pooling mechanism removed redundant spectrogram markers through maximum pooling, while progressively reducing the complexity of the computation in a self-care calculation. In the code implementation, a multiscale Transformer block [51] with a multi-scale hierarchy was used, which was a more efficient mechanism for multiscale global attention. It was helpful to deploy pretrained multiscale Transformer vision models in the experimental phase.  
MAST Head: After obtaining the global representation through the Backbone, MAST prediction head projected the class of the speech spectrogram. Time-level Local Enrichment Module and Frequency-level Local Enrichment Module were designed in the prediction head. Using the overall framework of global + local, the importance of local time-frequency features was emphasized in the global feature representation, as described in Section 3.4.
# Quantum Key Distribution for 5G Networks: A Review, State of Art and Future Directions
## 3 The Importance of Quantum Key Distribution (QKD)
### The Challenges of QKD in 5G Implementation
#### 3.1.1 Implementation Cost  
Firstly, the cost of specialist QKD communication hardware is expected to complicate the migration to quantum cryptography. Recently, many researchers were conducted toward using 5G with QKD to enhance the security of 5G with an attempt to limit the vulnerabilities on QKD. Due to the quantum communication over fiber optic has reached optimal performance caused by fixed loss related to fiber and the restriction of the device, it (fiber optic) can be replaced by free space channel that offers several advantages including flexibility of installation, broader geographical coverage, and cost-effectiveness in terms of infrastructure deployment [67].
# Satellite-Based Wireless Sensor Development and Deployment Studies for Surface Wave Testing
## 2 Hardware Architecture
### Accelerometer  
A MEMS (micro-electro-mechanical systems) sensor was chosen to measure the ground motion in the node. The advantages of MEMS sensors include small size, low cost, low power consumption, high accuracy, and high resistance to vibration and shock [19]. The accelerometer was an Adafruit MMA8451Q 3-axis accelerometer sensor. Its voltage range is 3.3-5.0 V with an operating temperature of -40-85 \({}^{\circ}\)C. It is a general-purpose sensor for various applications such as navigation, robotics, fitness and well-being, augmented reality (AR), context awareness, smart devices and tablets, and ultra-books. The sensor is quite small with a size of 17.8 mm \(\times\) 20.3 mm. Its key sensor is the MMA8451Q (made by NXP Semiconductors), blending a low-power accelerometer to offer three degrees-of-freedom sensing for three-dimensional (3D) orientation. The accelerometer provides programmable acceleration ranges  
Figure 1: Hardware architecture: (**a**) Hardware design for sensing interface, computational core, wireless communication, and power supply; (**b**) hardware connection with key components highlighted; and (**c**) assembled satellite-based sensing package for in-site surface wave testing.  
of \(\pm\)2/\(\pm\)4/\(\pm\)8 g with 14-bit data and up to 800 Hz sampling rate, which is highly accurate for sensing the ground motion. This sensor has a digital interface namely I\({}^{2}\)C for communication. Thus, the I\({}^{2}\)C interface was chosen to communicate with the computational computer, as shown in Figure 1a.
## 4 Results and Discussion  
### Implementation Details and Configurations of Baseline Methods  
FHE was implemented using the widely recognized HElib library, which supports arithmetic operations on encrypted data. The encryption parameters were set to ensure a balance between computational efficiency and security strength, with a key size of 2048 bits.  
For operations involving addition and multiplication, the ciphertext modulus was carefully chosen to avoid excessive noise accumulation. During the experiments, encrypted medical and financial data were processed directly without decryption, following the standard FHE workflow. For DP, we employed the Laplace mechanism to ensure \(\varepsilon\)-differential privacy, where \(\varepsilon\) was set to 0.5 for a moderate privacy-utility trade-off. Noise was added to each query result based on the sensitivity of the function being evaluated. For medical image classification, pixel intensity values were perturbed; for financial predictions, noise was applied to key features such as stock prices and trading volumes. The resulting data retained privacy guarantees but at the expense of reduced accuracy due to noise interference.  
SMC was implemented using a custom setup in a distributed environment. Data were partitioned into shares and distributed among three parties, each performing partial computations. The computations were securely aggregated to reconstruct the final results. The communication protocol followed the GMW protocol, ensuring privacy during the intermediate steps. FL was implemented using a federated setup, where models were trained locally on multiple nodes. Each node processed a subset of the data and shared gradient updates with a central server. The Federated Averaging Algorithm was employed to aggregate these updates into a global model. To ensure privacy, we used differential privacy at the gradient level, introducing noise during transmission. The FL setup used five client nodes and a communication round limit of 50. All baseline methods were tested on the same hardware and software environment to ensure consistency. The experiments were conducted using an NVIDIA A100 GPU with 80 GB of memory, leveraging PyTorch 1.12 for implementation.
## 4 Discussion
### Performance Variation between Measures for Selected Models  
EMG and kinematics data were collected from each of the four wearable sensors positioned on the low back and leg regions. Among the four selected models, models A and D necessitate both EMG and kinematics inputs, whereas models B and C only require kinematic inputs (refer to Table 5). However, due to challenges associated with obtaining accurate EMG data in real-world settings [55], alternatives to model A that rely solely on kinematics data may be more practical, albeit with slightly lower accuracy (\(\sim\)88%) (Table 4). Furthermore, our model encompasses features derived from various activities, including standing still (start/end), sustaining a bent posture, bending, and retracting activities. Hence, a pivotal component of our proposed fatigue detection system involves event detection for each activity to calculate features, necessitating trunk motion data like the segmentation approach employed in this study (utilizing acceleration data from the low back sensor). We selected models with this consideration, and each of the four proposed models incorporates kinematics measures from a low back sensor. The fatigue level prediction model introduced in this study can serve as a tool to monitor changes in physical demands when employing a BSIE.  
The BSIE was designed to assist the wearer during the sustained bending portion of the activity. Interestingly, the feature of normalized mean value of the norm of acceleration from the sensor placed on the RES during the sustained bending portion was among the most prominent in the models (Figures 8 and 9). This indicates that as perceived fatigue level changes, there is a change in body movement while sustaining the posture. Model C incorporated kinematic features from a single sensor on the RES, which shows that features denoting movement from sustained bending and standing at the end are more prominent compared to other portions representing dynamic activities of bending and retraction. One possibility is that as subjects becoming fatigued may cause more sway while performing static and sustained activities. A more detailed comparative analysis of specific features between different study conditions will be conducted in our future studies.
## 2 Materials and Methods
### Ground Truth Data for Training and Validation  
In this study, ground truth data for training were collected by nine groups of researchers in vehicles traversing approximately the entirety of Northeast China from June to August 2011. Before each field trip, the geographic distribution of sampling points and proximity routes for each traverse were prepared to ensure successful data collection. The location of each sampling point was documented using a global positioning system (GPS), with errors of less than 10 m. A vector file of ground survey points with their location (longitude and latitude), land cover types, and photos was created using ArcGIS [51]. In total, 2938 points were selected as training samples for the forest classification, including 59 evergreen needleleaf forest points, 210 deciduous needleleaf forest points, 295 deciduous broadleaf forest points, 200 mixed forest points, 102 shrubland points, and 2072 non-forest points.  
The accuracy of the HJ-1-based forest map was assessed using validation data acquired from the field trips in 2011. The field acquisition approach for collecting the validation data was the same as that for the training data; however, the validation data were provided by individual provincial environmental protection departments of Northeast China. To ensure the consistency of the land cover identification undertaken by different participants, a standard protocol of defining each class and the field sampling procedure was created before each field trip. All field trip participants were required to follow a standard protocol in the documentation of their field sites and landscapes using digital cameras and handheld GPS receivers. In total, 2632 points were obtained, including 685 forest points and 1947 non-forest points. The forest validation points included 110 evergreen needleleaf forest points, 133 deciduous needleleaf forest points, 254 deciduous broadleaf forest points, 118 mixed forest points, and 50 shrubland points for the accuracy assessment of the forest classification. The spatial distribution of the ground truth data for training and validation data are shown in Figure 2a,b.
## 1 Introduction
A k-hop routing tree is elaborated within each cluster in a distributed fashion, where CMs use the Received Signal Strength Indicator (RSSI) to join the tree and select their parents by following the shortest routing path leading to the corresponding CH. Therefore, the k-hop clusters topology will further improve the energy efficiency and extend the network lifespan. Figure 1: Connections toward the base station (**a**) using a flat architecture with individual connections, (**b**) using a structured and cluster-based architecture.
# Autonomous Mobile Station for Artificial Intelligence Monitoring of Mining Equipment and Risks
## 3 Materials and Methods
### Definition of the Positioning Scheme  
Adaptable modular systems were implemented to optimize strategic deployment and wireless communication using RF and Wi-Fi technology. This approach ensures continuous connection with the Central Operational Information Center (CIO) and provides efficient coverage in critical areas of operation, maximizing monitoring flexibility and real-time reconfiguration capability.
# Development of Antibiofilm Therapeutics Strategies to Overcome Antimicrobial Drug Resistance
## 5 Emerging Antibiofilm Agents
### Inhibition of Sortase A by 1,2,4-Oxadiazole Topsentin Analogs  
A series of seventeen 1,2,4-oxadiazol topsentin analogs was synthesized by [PERSON] et al. and assessed for their biofilm inhibition, by targeting the membrane enzyme transpeptidase sortase A (StrA), which attaches surface adhesive molecules to the cell wall in Gram-positive organisms. All these compounds inhibited biofilm formation in _S. aureus_ species with BIC50 values less than 10 uM for the most potent derivatives (**5a-c**). The potent analogues displayed BIC50 values for _S. aureus_ in the range of 0.7 to 9.7 uM, and additionally showed a superior enzyme inhibition with IC50 values of 2.2 to 10.4 uM. SAR  
Figure 4: Chemical structure of DMNP.  
analysis revealed the significance of the presence of the -N=C-O- group in the oxadiazoles for dual antibacterial and biofilm inhibitory activity [156]. In a similar work by [PERSON] et al., thiazole analogs of nortopsentin were synthesized of which the potent derivatives **6a-b** showed BIC50 values against _S. aureus_ of 3.9 and 1.0 uM (Figure 5 and Table 2) [157].
# Millimeter-Wave Multi-Channel Backscatter Communication and Ranging with an FMCW Radar
###### Abstract  
A multi-channel backscatter communication and radar sensing system is proposed and demonstrated in this paper. Frequency modulated continuous wave (FMCW) radar ranging is integrated with simultaneous uplink data transmission from a self-packaged active radio frequency (RF) tag. A novel package solution is proposed for the RF tag. With the proposed package, the RF tag can transmit a 32-QAM signal up to 2.5 Gbps and QPSK signal up to 8 Gbps. For a multi-tag scenario, we proposed using spread spectrum code to separate the data from each tag. In this case, tags can be placed at arbitrary locations without adjacent channel interference. Proof-of-concept simulations and measurements are demonstrated. A 625 Mbps data rate is achieved in a dual-tag scenario for two tags.  
active reflector; backscatter communication; FMCW; millimeter-wave; multi-channel; multi-tag; radar; radar communication; RF tag; spread spectrum; uplink +
Footnote †: journal: _Sensors_  
integrated with a simultaneous data transmission with a data rate of 37.5 Mbps in lab measurements. Paper [15] demonstrates the FMCW radar-communication system in a real working scenario of car-to-car communication at 24 GHz.  
However, few papers [16] dig into the performance of the FMCW radar-communication system with multiple tags. The separation between tags is a challenge in this scenario. Paper [16] presented proof-of-principle measurements using a brass-board S-band (2.45 GHz) radar with 40-MHz bandwidth, showing simultaneous ranging and demodulation of two tags at ranges of 15 and 33 m in a cluttered indoor environment. This paper studied the multi-tag scenario, and in order to separate information from two tags, tags should be sufficiently physically separated so that two signals at the receiver side will not overlap in the spectrum. When the symbol rate increases, the minimum spacing needs to increase accordingly to avoid the spectrum overlapping. Otherwise, the FMCW chirp signal needs to have larger bandwidth or a shorter sweep time. Both cases require either a hardware update or infrastructure re-installation, which complicates the implementation. Besides the described space division multiple access methods, there are also other solutions to avoid collisions between multiple tags; for example, frequency/code/time division multiple access (FDMA/CDMA/TDMA) [17; 18]. CDMA is a suitable candidate because it does not require a radar hardware update. The multi-tag backscatter communication with the FMCW radar system can be really useful in certain application scenarios. In the next section, we will introduce a smart traffic scenario that could benefit from this system.
# A Crack Characterization Method for Reinforced Concrete Beams Using an Acoustic Emission Technique
###### Abstract  
This study aims at characterizing crack types for reinforced concrete beams through the use of acoustic emission burst (AEB) features. The study includes developing a solid crack assessment indicator (CAI) accompanied by a crack detection method using the k-nearest neighbor (k-NN) algorithm that can successfully distinguish among the normal condition, micro-cracks, and macro-cracks (fractures) of concrete beam test specimens. Reinforced concrete (RC) beams undergo a three-point bending test, from which acoustic emission (AE) signals are recorded for further processing. From the recorded AE signals, crucial AEB features like the rise time, decay time, peak amplitude, AE energy, AE counts, etc. are extracted. The Boruta-Mahalanobis system (BMS) is utilized to fuse these features to provide us with a comprehensive and reliable CAI. The noise from the CAI is removed using the cumulative sum (CUMSUM) algorithm, and the final CAI plot is used to classify the three different conditions: normal, micro-cracks, and fractures using k-NN. The proposed method not only for the first time uses the entire time history to create a reliable CAI, but it can meticulously distinguish between micro-cracks and fractures, which previous works failed to deal with in a precise manner. Results obtained from the experiments display that the CAI built upon AEB features and BMS can detect cracks occurring in early stages, along with the gradually increasing damage in the beams. It also soundly outperforms the existing method by achieving an accuracy (classification) of 99.61%, which is 17.61% higher than the previously conducted research.  
acoustic emission features; concrete crack assessment indicator and classification; Boruta-Mahalanobis system; k-nearest neighbor algorithm Article
## 6 Limitations  
The Transformer-CNN architecture exemplifies a sophisticated deep learning framework that combines the capabilities of Transformers and CNNs to bolster performance in classification tasks. Although this innovative approach effectively tackles key issues in intrusion detection systems, such as enhancing accuracy and addressing class imbalances, it is essential to acknowledge various limitations and challenges that may arise:  
* Scalability: As the volume of datasets or the complexity of network traffic grows, the computational demands on the model can intensify, which may hinder its efficiency and its capacity to manage larger datasets or adapt to changing network environments.
* Generalization: Although the Transformer-CNN exhibits impressive performance on the NF-UNSW-NB15-v2 and CICIDS2017 datasets, its efficacy across diverse types of network traffic or newly emerging attack vectors is not yet fully established. To assess its robustness and generalization capabilities, it is crucial to evaluate the model against a wider array of datasets, including KDDCup99 [36], NSL KDD [29], and more recent collections like CSE-CIC-IDS2018 [34], and IoT23 [16].  
Figure 9: Confusion matrix of the Transformer-CNN model on the NF-UNSW-NB15-v2 dataset, demonstrating its effectiveness in detecting zero-day attacks.  
Data Preprocessing: The execution of data preprocessing across various datasets is a vital stage that encompasses activities like addressing missing values, encoding categorical variables, normalizing or standardizing numerical features, and eliminating extraneous information. The model's performance is significantly influenced by the quality and thoroughness of these preprocessing procedures.
* Model Adaptation: Adjusting the model for various datasets necessitates a trial-and-error approach to hyperparameter optimization. This iterative process is essential for refining the model to better match the specific characteristics and nuances of new datasets.