esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
The laser-structured areas were illuminated perpendicularly with full-intensity LED illumination. Observations of the colours were made from one direction and an Axiocam 105 colour camera was used to record optical images for analysis. The wetting behaviour of the laser-structured surfaces of the CP Ti was evaluated using contact angle (CA) measurements using the sessile drop method (See System E, Advex Instruments, Brno-Komin, Czech Republic) at 22 degrees Celsius. In the sessile drop method, 3.0 \(\upmu\)L droplets of distilled water were placed on the laser-structured surfaces in an ambient environment. The 3.0 \(\upmu\)L droplet was used because it experienced less gravitational forces [28]. The circle fitting mode was used to calculate the average contact angles. The CAs measurements were determined after two months after laser ablation to ensure the achievement of steady-state values of the CAs measurements [33]. After measuring the CAs, the laser-structured sample was immersed in seawater collected on 19 November 2023 from the Caspian Sea for 14 days. This was to understand the effects of immersion on the wettability of the laser-structured CP Ti sample in salty seawater. The properties of the Caspian Sea water can be found in the literature [34; 35]. The immersed sample was thereafter subjected to wettability tests immediately after immersion. After immersion, the immersed sample was left in ambient conditions before being subjected to wettability tests again after 10 days. The aim of performing CA measurements after 10 days was to try to establish if hydrophobicity could be attained 10 days following immersion. Determination of the surface free energies (SFEs) at the laser-structured surfaces was based on the Li-Neumann model which requires only one liquid with known surface tension to be used [28; 36; 37]. In our study, distilled water with known surface tension of 72.83 mJ/m\({}^{2}\) at 20 \({}^{\circ}\)C was used [38]. A Raman confocal microscope (Alpha300R, Witec, Ulm, Germany) was used to perform the Raman measurements to analyse the surface oxide phases present after laser processing. For comparison purposes, the Raman spectrum of the polished surface of the CP Ti was also measured. The microscope has a motorised X-Y-Z stage and an objective lens of X100 was used. The laser source selected had a 532 nm excitation wavelength and a laser power of 25 mW was used. Prior to measurements, a silicon wafer with a particular Raman peak at 520 cm\({}^{-1}\) was used to check the calibration of the Raman microscope. Within a spectral range of 1146 cm\({}^{-1}\) to 0 cm\({}^{-1}\), 50 accumulations were measured with a 5 s integration time to obtain the Raman spectra. The resolution of the measurements was 1 cm\({}^{-1}\). Raman measurements were acquired using WITec Project FIVE 5.1.8.64 proprietary software and processed using OriginPro 8.5.0 SR1 proprietary software (OriginLab Corporation, Northampton, MA, USA). Raman measurements were made after immersion of the sample in seawater.
## 3 Methods
### Unimodal Neural Networks
#### 3.1.3 Text  
We use BERT to obtain embeddings from texts. We do not use word segmentation tools, due to the characteristics of BERT. We add two unique tokens to indicate the beginning and the end for each text. We define the textual feature extracted by BERT as \(X_{T}\).  
During the training of the unimodal emotional classification models, we find that the performance of the textual modality emotional classification model is superior to the other modalities, so we want to extract more information from the textual modality. Inspired by [54], we design the CNN-attention-based model.  
Specifically, we generate the query and key for the textual modality using two 1D CNNs. We use the \"Scaled Dot-Product Attention\" from [56] as the attention mechanism, too. The matrix of outputs is computed as  
\[\begin{array}{c}Que_{T}=W_{Que}^{T}\!\cdot\!X_{T}+b_{Que}^{T},\\ Key_{T}=W_{Key}^{T}\!\cdot\!X_{T}+b_{Key}^{T},\\ score_{T}=softmax\big{(}W_{Attn}^{T}\!\cdot\!tanh(Que_{T}\!\cdot\!Key_{T}) \big{)},\\ R_{attn}^{T}=score_{T}\!\cdot\!\big{(}W_{Val}^{T}\!\cdot\!Val_{T}\big{)},\end{array} \tag{12}\]  
where \(Que_{T}\), \(Key_{T}\), and \(Val_{T}\) stand for query, key, and value, respectively. \(Val_{T}\) is equal to \(Key_{T}\), and \(score_{T}\) represents the weight on the value. \(Softmax\) is the softmax function and \(tanh(\cdot)\) is the hyperbolic tangent function. \(R_{attn}^{T}\) is the output of the attention module. \(W_{Que}^{T}\), \(W_{Key}^{T}\), \(W_{attn}^{T}\), and \(W_{Val}^{T}\) represent the weight matrices. \(b_{Que}^{T}\) and \(b_{Key}^{T}\) represent the bias vectors.  
Subsequently, we conduct a global average pooling operation on \(Que_{T}\) and \(R_{attn}^{T}\), and concatenate them together to formulate the textual representation, \(H_{T}\). \(H_{T}\) is computed as follows:  
\[H_{T}=\Big{[}GAP(Que_{T}):GAP\Big{(}R_{attn}^{T}\Big{)}\Big{]}, \tag{13}\]  
where \(GAP(\cdot)\) represents the global average pooling operation.  
Lastly, we send \(H_{T}\) to a fully connected layer for emotional classification. We formulate the classification operation as follows:  
\[Y_{T}=W_{T}\!\cdot\!H_{T}+b_{T}, \tag{14}\]  
where \(Y_{T}\) represents the annotation of the textual modality, \(W_{T}\) represents the weight matrix, and \(b_{T}\) represents the bias vector.  
These networks for each modality are trained with IUAs to ensure the difference of representation between the inter-modal information, and are then saved to construct Uni2 Mul framework.
## 6 Algorithms and Frameworks in Federated Learning
### Model Aggregation Methods
#### 6.3.1 Simple Averaging  
The most straightforward method of aggregation is simple averaging, prominently used in the Federated Averaging (FedAvg) algorithm [88]. In this method, updates from each client (e.g., gradient updates or model parameters) are averaged to produce the global update. Mathematically, for \(N\) clients,  
\[\theta_{global}=\frac{1}{N}\sum_{i=1}^{N}\theta_{i}\]  
where \(\theta_{i}\) represents the model parameters from the \(i\)th client.  
Advantages:  
* Computational simplicity.
* Scalability to a large number of clients.
* Challenges:
* Assumes IID data across clients, which is often not the case.
* Susceptibility to adversarial attacks, as malicious clients can skew the average.
## 3 The Proposed Model
### Vectorization and Sentiment Scoring  
At this stage, the CLBEDC-SND model undergoes vectorization and sentiment scoring process using fuzzy approach. Vectorization: the text dataset can be transformed into vector format for the usage of ELM model [25]. For these purposes, a python library Gensim algorithm is used to implement word embedding that can capture context, learn word relations, syntactic and semantic similarity of words in the document. Word2 Vec is used to vectorize the text dataset, and the output can be used as a primary weight for these models. Fuzzy sentiment scoring: the degree of sentiment extracted the features that specify the sentiment degree expressed within the text, the procedure of extracting the degree of sentiment passes by initial recognizing opinionated word, and the linguistic hedge with Part-Of-Speech tagger, then applying WordNet and SentiWordNet dictionaries to relate polarity for this opinionated word as a primary score value \(f(u_{s})\), afterward, it adapts this value according to the type and the existence of linguistic hedges, using the fuzzy logic function developed by [PERSON] for getting the concluding sentimental score in the following: An opinion word has a complement hedge (\"Not\"), the fuzzy score is reformed as:  
\[f(u_{s})=1-(u_{s}) \tag{5}\]  
when the hedge is concentrator (\"extremely\"), then:  
\[f(u_{s})=[u_{s}]^{2} \tag{6}\]  
when the hedge is dilator (\"somewhat\"), then fuzzy score is deduced as:  
\[f(u_{s})=[u_{s}]^{\frac{1}{2}} \tag{7}\]  
The last fuzzy score is normalized and applied as sentiment feature in fake news classification algorithm.
## 7 Datasets
### Internet of Things (IoT)  
In the field of IoT, time series anomaly detection datasets are primarily used for monitoring and analyzing data streams generated by a vast array of interconnected devices and sensors. This allows for the timely detection of equipment failures, network anomalies, or unexpected behaviors [112]. The GECCO IoT [84] dataset, for instance, contains sensor data from smart home or industrial IoT devices, which is utilized for detecting device anomalies and system malfunctions. The CICIDS dataset [85] contains samples of various network attacks and normal traffic. It includes data from normal activities and simulated malicious attacks collected over five days. The data was generated using the CICFlowMeter tool and provides rich labeled data. Deep learning based models on the CICIDS dataset have significantly improved the accuracy of intrusion detection. Researchers have explored deep learning methods, such as multilayer perceptrons and convolutional neural networks, to enhance detection accuracy and efficiency.  
In the IoT, millions of connected devices and smart sensors generate highly dynamic, large-scale, heterogeneous, and timestamped data. This timestamped data is at the core of IoT automation and has the potential to significantly impact industrial processes. Effectively detecting anomalies in time-series data and providing real-time actionable insights to drive improvements in industrial processes presents significant challenges. [PERSON] et al. [113] proposed the AMCNN-LSTM model, which emphasizes communication efficiency and federated learning (FL) methods on devices, enabling distributed edge devices to collaboratively train an anomaly detection model. To achieve real-time anomaly detection, [PERSON] et al. [114] proposed a hybrid end-to-end deep anomaly detection framework that leverages CNN and LSTM networks to develop an advanced anomaly detection system. The system architecture consists of three layers: sensors and machines in the industrial setting, an edge layer for real-time data processing, and a cloud layer for offloading processing tasks. The framework is designed to accurately detect anomalies and extremely rare events in IoT streaming data.
## 5 Results and Discussion
### Channel-Wise Frequency Analysis
Figure 10: Wavelet scalograms for channels 4 (Ch4), 8 (Ch8) and 12 (Ch12) of the wall-to-fluid heat fluxes for the fixed axial coordinate \(z=0.02\) m at different heat loads to the evaporator (horizontal orientation). To clearly provide a quantitative description of the channel-wise oscillatory behavior, all the evaluated dominant oscillation frequencies from the power spectra curves of Figures 9-11 are listed in Tables 3 and 4 for the vertical BHM and horizontal orientations, respectively. In Table 3, at low heat loads applied to the evaporator, the vertical BHM orientation denotes several channels in which the fluid does not present any dominant oscillation within the observation window, thus suggesting that the high intermittency of the overall device operation prevents any identification of the dominant frequencies. At average-high heat loads, a dominant oscillation frequency can be perceived in every channel, confirming the transition from intermittent flow to full activation of the device. In the horizontal orientation (Table 4), a lack of fluid motion hampers a clear identification of a dominant frequency in the overall device at \(Q\) =  
fluid oscillation frequencies are in agreement with those typically found with direct fluid visualizations [1; 7; 15; 44; 45]. In addition, they confirm the working regimes identified in [29] by means of standard techniques, i.e., evaporator temperature fluctuations, and statistical reduction in wall-to-fluid heat fluxes, for which the overview is reported in Table 5.
## 10 Future Directions
### Advanced Byzantine-Resilient Aggregation  
**Robust Aggregation Techniques:** There is a need to develop more robust aggregation methods to withstand sophisticated byzantine attacks, where malicious clients might send incorrect model updates. Research should aim at refining techniques such as robust weighted aggregation and adaptive outlier detection to be applicable in diverse FL environments, ensuring the integrity of the global model.  
**Dynamic Defense Mechanisms:** Implementing dynamic defense mechanisms that can adapt to evolving attack patterns and client behaviors is essential. This includes utilizing machine learning techniques to predict and counteract potential byzantine attacks in real time, enhancing the resilience of FL systems.
## 6 Experiments
### Datasets
#### 6.2.1 NSL-KDD Dataset  
The NSL-KDD dataset was created by curating the KDD'99 dataset [32]. Although the NSL-KDD dataset may not be a perfect representative of existing real networks, it can be applied as an effective benchmark dataset to help researchers compare different intrusion detection methods. The dataset contains eight data files of different formats that are compatible with most experimental platforms. The dataset consists of 41 predictor attributes and 1 target attribute, indicating 1 of 39 different attacks. Out these 39 attacks, 10 of them are DDoS attacks, namely, back, land, neptune, pod, smurf, teardrop, apache2, mailbomb, processable, and udpstorm.
# A Systematic Approach for Energy-Efficient Design of Rolling Bearing Cages
###### Abstract
In [24], a friction torque model for tapered roller bearings concerning the geometric homogeneity of rollers has been proposed, in which the geometric homogeneity of rollers was represented by the diameter deviation value and distribution form of rollers with a diameter deviation. In [25], for the first time, a mechanism was proposed for transferring motion from the rollers to the bearing cage, which utilizes the fact that the side clearance in the cage pocket decreases gradually and that a multiple of the rollers are in the radial loading zone as the cage moves. Models of the cage dynamics are constructed based on studies of the kinematics of a real bearing. Advanced dynamics models allow for calculating the interaction forces of parts for any operating conditions. Analyzing studies [19; 20; 21; 22; 23; 24; 25] shows that the known theoretical, numerical, and experimental examinations of the frictional moment in rolling bearings do not fully take into account the peculiarities of the design and load of cages or are difficult to implement practically. In this paper, a model of heat generation caused by a cage in rolling bearings is developed. At the same time, a simplified approach is proposed to determine the value of the frictional moment of the bearing. Based on the proposed model, by comparing various design solutions of cages according to the criterion of their heat generation during interaction with parts, a systematic approach is proposed for choosing an energy-efficient design of a rolling bearing cage under given operating conditions. The model has been validated with experimental results.
## Materials and Methods
### Assimilation and Validation Datasets  
#### 2.5.1 SMOS Dataset  
We used the reprocessed SMOS L3 (ver. 300) daily soil moisture product (L-band, 1.4 GHz) from CATDS (www.catds.fr, last accessed 6 July 2018) for the assimilation. The SMOS L3 soil moisture product represents soil moisture at a spatial resolution of \(\sim\)25 km (EASE grid) within a thin surface layer (4 cm). The data were filtered considering radio frequency interference (RFI) according to [19]. Similar filtering was applied at grid points with a high probability of snow, frozen soil, water body, strong topography, and urban area.  
We used the extended triple collocation (ETC) [42] analysis to calculate the uncertainty estimate for the SMOS L3 data. The other two datasets entered into the ETC calculation were the surface soil moisture computed by BETHY based on prior parameter values, and the Advanced SCATerometer (ASCAT) soil moisture observations (H109 Metop ASCAT DR2016 SSM time series 12.5 km sampling (see [[http://hsaf.meteoam.it](http://hsaf.meteoam.it)]([http://hsaf.meteoam.it](http://hsaf.meteoam.it))) derived from an active microwave radar, C-band, 5.255 GHz [43]). Then, the calculated uncertainty from ETC (model uncertainty) and uncertainty from data retrieval (data uncertainty) were formulated into the final SMOS soil moisture uncertainty, by:  
\[\sigma_{SMOS}=\sqrt{\sigma_{data}^{2}+\sigma_{model}^{2}} \tag{4}\]  
where \(\sigma_{SMOS}\) is the SMOS soil moisture uncertainty used in the assimilation, and \(\sigma_{data}\) and \(\sigma_{model}\) are data and model uncertainty, respectively.  
Additionally, we corrected the remotely sensed SMOS soil moisture to obviate the need for accurate soil texture data, and to retain as much useful information from the SMOS measurements as possible [19]. This was done by using the mean and standard deviation of the simulated surface soil moisture to correct the SMOS soil moisture:  
\[S=\frac{\sigma_{w}(s-m_{s})}{\sigma_{s}}+m_{w} \tag{5}\]  
where \(s\) denotes the SMOS soil moisture (converted to mm), \(m_{s}\) is the long-term mean and \(\sigma_{s}\) its standard deviation, and \(S\) is the corrected SMOS soil moisture that is matched to the modeled surface soil moisture (in mm). \(m_{w}\) and \(\sigma_{w}\) are the long-term mean and standard deviation of the modeled soil moisture derived from the prior model parameter set.
## 2 Why the Electroencephalogram (EEG)?
### qEEG and Structural Integrity of the Brain  
Indeed, numerous studies have demonstrated that qEEGs reflect the brain's _structural_ characteristics (or _'hardware'_), such as the number of connections between neurons, white matter density, axonal diameter, degree of myelination and white matter integrity, as well as the integrity of corticocortical and thalamocortical circuits, hippocampal volume, the number of active synapses in thalamic nuclei, and the number of potential neural pathways [7; 8; 110; 111; 112; 113; 114; 115; 116]. For example, reduced EEG amplitude is believed to be partially due to a reduced number of synaptic generators and/or reduced integrity of the protein/lipid membranes of neurons [127, 128].
# Improvement of Perception Layer Routing Protocol with Static Nodes in IoT-Based Microgrids
## 3 System Models
### Network Models under Static Nodes  
Based on the distributed network in the perception layer of the Internet of Things, a network model is established. The major characteristics are described as follows:  
1. The nodes in the network are randomly deployed in the specified area, and the coordinate positions of all nodes can be derived through GPS and other methods;
2. The nodes in the network have no mobility and remain stationary;
3. The nodes in the network are consistent in physical and network properties, that is, the nodes are same in terms of energy reservation, communication capabilities etc;
4. After the nodes in the network are initialized, the system will neither continue to supply energy to them, nor provide any other resources. In other words, the energy is fixed.  
Under the constraints of the above conditions, the IoT perception layer network model of static nodes established in the paper is shown in Figure 2: Figure 2 shows a schematic diagram of the network after a round of clustering. CH represents the cluster head, CM represents the member nodes in the cluster, and BS is the base station. At this time, the network has been divided into four clusters. The sensor nodes are randomly distributed within a certain network range during initialization. The cluster head CH receives the data transmitted by the member nodes CM within the cluster and performs data fusion. Then the cluster head transmits the packed data packets to the base station BS beyond the network range.  
Figure 2: Schematic diagram of the network model.
###### Abstract  
Estimation of fault classification and location in a multi-terminal high voltage direct current (MT-HVdc) transmission system is a challenging problem and is considered to be a fundamental maneuver of dc grid protection. This research paper critically reviews traveling and non-travelling wave methods of classification and location of dc faults in multi-terminal HVdc transmission systems. Detailed mathematical analysis of MT-HVdc systems composed of high grounding resistance, cable and overhead line segments, and bipolar coupled transmission network under healthy and faulty conditions, are evaluated. The gravity of this research paper addresses benefits and shortcomings of traveling and non-traveling wave methods and futuristic techniques of fault classification and location.  
DC faults; fault classification and location (FCL); MT-HVdc; non-traveling waves (NTW); traveling waves (TW); transmission system +
Footnote †: journal: Applied Sciences
# Micro-Location Temperature Prediction Leveraging Deep Learning Approaches
## 1 Introduction
The comprehensive experiments revealed that the proposed prediction model can predict the air temperature for the given micro-location more accurately than other general-purpose weather prediction systems. The original novelties of the proposed prediction model Maximus can be summarized as follows:  
* To build the optimal prediction model based on the lower-resolution CERRA dataset including long-term weather data;
* To find the more optimal characteristics of the used LSTM Neural Network (NN) by indicating those features, their number and types that have the greatest impact on the correct prediction of the air temperature on the micro-location;
* To improve the simulation of the optimal prediction model with weather data obtained from the reference, general-purpose ICON-D2 weather prediction model updated with current weather data from the weather station positioned at micro-location;
* To change the location of the weather station, determining the micro-location exactly, easily with its mobility. In line with this, we pose the following research question: How can we train a weather classification model to predict air temperature for a given micro-location? The structure of the remainder of the paper is as follows: In Section 2, the materials and methods used in the study are discussed. Section 3 illustrates the proposed prediction method Maximus. A description of the experiments and obtained results are the subjects of Section 4. The paper concludes with a summarization of the performed work in Section 5, which also outlines potential directions for further work.