esa-sceva/satcom-chunk-collection huggingface dataset:
# A Lightweight Model for DDoS Attack Detection Using Machine Learning Techniques
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **Hyperparameter** & **Parameter** & **Value** \\ \hline \multirow{8}{*}{ExtraTreeClassifier} & criterion & Gini \\ \cline{2-3}  & max\_depth & None \\ \cline{2-3}  & min\_weight\_fraction\_leaf & 0.0 \\ \cline{2-3}  & max\_leaf\_nodes & None \\ \cline{2-3}  & min\_impurity\_decrease & 0.0 \\ \cline{2-3}  & bootstrap & False \\ \cline{2-3}  & oob\_score & False \\ \cline{2-3}  & n\_jobs & None \\ \cline{2-3}  & random\_state & None \\ \cline{2-3}  & verbose & 0 \\ \cline{2-3}  & warm\_start & False \\ \cline{2-3}  & warm\_start & False \\ \cline{2-3}  & class\_weight & None \\ \cline{2-3}  & ccp\_alpha & 0.0 \\ \cline{2-3}  & max\_samples & None \\ \hline \multirow{8}{*}{SMOTE} & \multirow{8}{*}{SMOTE} & sampling\_strategy & ’auto’ \\ \cline{2-3}  & & random\_state & 60 \\ \cline{2-3}  & & n\_jobs & None \\ \cline{2-3}  & & dual & False \\ \cline{2-3}  & & fit\_intercept & False \\ \cline{2-3}  & & intercept\_scaling & 1 \\ \cline{2-3}  & & class\_weight & None \\ \cline{2-3}  & & random\_state & 42 \\ \cline{2-3}  & & max\_iter & 100 \\ \cline{2-3}  & & multi\_class & ’auto’ \\ \cline{2-3}  & & verbose & 0 \\ \cline{2-3}  & & warm\_start & False \\ \cline{2-3}  & n\_jobs & None \\ \cline{2-3}  & & ll\_ratio & None \\ \hline \multirow{8}{*}{Random Forest} & max\_depth & None \\ \cline{2-3}  & min\_weight\_fraction\_leaf & 0.0 \\ \cline{2-3}  & max\_leaf\_nodes & None \\ \cline{2-3}  & min\_impurity\_decrease & 0.0 \\ \cline{2-3}  & bootstrap & False \\ \cline{2-3}  & oob\_score & False \\ \cline{2-3}  & n\_jobs & None \\ \cline{2-3}  & random\_state & 50 \\ \cline{2-3}  & verbose & 0 \\ \cline{2-3}  & warm\_start & False \\ \cline{2-3}  & class\_weight & None \\ \hline \multirow{2}{*}{ccp\_alpha} & \multirow{2}{*}{0.0} & ccp\_alpha & 0.0 \\ \cline{2-3}  & max\_samples & None \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hyperparameter table for all algorithms used in the paper. The precision-, recall-, F1-score-, and accuracy-containing performance matrices are used to assess the performance of all five methods.
## 6 Limitations and Future Research Directions  
This study provides valuable insights for manufacturing companies to improve sustainable competitive advantage as well as theoretical and practical insights into BDA implementation issues in attaining sustainability in processes. However, this study has its own limitations and weaknesses, which can be considered as future research opportunities. For example, the research questions of this study have been developed for different types of manufacturing companies in industrial sectors and it was not a sector-based study; Therefore, analyzing the differentiation between different industrial sectors would be inaccurate. Moreover, the service sector was out of the scope of this study and the findings of this study cannot be generalized to the service sectors. This is because the service sector has its own variables which cannot be measured through the same research questions of this study.  
Although the sample size was enough to make decisions about the relationships between hypotheses using SmartPLS, for more precise results, it needs to be increased. Moreover, the size of the surveyed manufacturing companies was not taken into consideration in the study, which may impact the findings of the model. As BDA is one of the major pillars of Industry 4.0, the study lacks a real-world case study that explains the implementation of BDA within the Industry 4.0 system. These aspects should be considered as future research directions.
## 4 Current Issues and Challenges of Realising the Tactile Internet
### Challenges Related to Haptic Devices
#### 4.11.1.1. _Haptic Sensors_
A trial demonstration is mentioned in [98] where tests have recognised friction, weight, pressure, smoothness, and hardness with the help of gloves with only vibrotactile feedback. Nonetheless, it was quite different making identifications in one place compared to achieving the same with a remote object. Because of these adverse effects, we require sensible recognition to create real kinaesthetic feedback. The design of a real kinaesthetic display will have current challenges, including tactile and kinaesthetic feedback, where displays are not attached to any ground and can move around freely. Another idea is to have a lightweight system at the same time. For some applications such as telesurgery, a haptic display would not be helpful. In such cases, a replica of the remote and the real-life scenario could be developed so that realistic feedback mechanisms will solve the problem.
# Generation of Beam Tilt through Three-Dimensional Printed Surface
###### Abstract  
In this paper, 3D printed surfaces are presented to study this technology's application in generating beam tilt for the electromagnetic waves in the Ku-band. Additionally, the input source is maintained by a feed horn that is additively manufactured and is coated with copper spray paint to add conductivity, which is fed by a WR-75 waveguide. The proposed beam tilt generating surface is also referred to as a Beam Deviating Surface (BDS). There is no relative gap between the BDS and the aperture of the horn, which eventually decreased the overall antenna height. The BDS layer is able to deviate the beam for a fixed elevation angle of 22.5\({}^{\circ}\) and could be consequently rotated along with the rotation of the BDS prototype. The voltage standing wave ratio value is less than two over the operating frequency range, which depicts the wideband behavior. The measured and simulated radiation patterns show that we can tilt the electromagnetic waves in ranges of up to \(+/-22.5^{\circ}\) with a minimum side lobe level of \(-5\,\mathrm{dB}\) at frequencies from 10 to 15 GHz. This signifies the wideband characteristic of the proposed prototype, which is achieved by Vero material from Multijet Printing that is a low-cost and rapid manufacturing 3D printing technology.  
multibeam radiation; antenna design; microwave communication; 3D printing; wideband; beam tilting +
Footnote †: journal: Electronics
# The Cloud-to-Edge-to-IoT Continuum as an Enabler for Search and Rescue Operations
## 2 Related Work
### Sensor Networks and IoT for SAR Operations
The system incorporates inertial sensors, such as a three-axial accelerometer and gyroscope, and a wearable microphone. The computational system collects audio and motion signals, processes the sensor signals, communicates the critical messages using the available network and determines the victim's location based on the dog's location. Incorporating IoT systems into SAR operations increases rescue teams' capabilities, saving time by receiving information in real time. The development of autonomous systems allows the integration of machines precisely and reduces the risks for victims and rescuers.
# Voice Maps as a Tool for Understanding and Dealing with Variability in the Voice
## 3 Voice Maps in Practice
### Hearing-Seeing-Feeling in Real Time  
When one is recording a voice map, a moving cursor indicates the current position in\(f_{\circ}\) and SPL in real time. More than the exact scale values, it is the momentary\(f_{\circ}\)/SPL location on the map that exemplifies a characteristic physiological setting, such as a particular laryngeal posturing, and with it the extent of the region over which a distinct quality manifests itself. Even if the local topography of the mapped metric shows no change, a mere enlargement of the mapped area may still report that an increased control over the voice has been gained. Patients generally appreciate such immediate feedback, which can be included in a therapeutic regime [49].  
The same voice may have a different quality, be it desired or not, at different locations within the voice range. This notion needs no explanation to singers or voice patients. They see, or expect to see, such qualities revealed on the map during an interactive recording session. Many voice users already have a _mental_ map of how their voice works. During recording with real-time feedback, they want to connect the visuals on the screen with their proprioception. To them, a goal of reducing all information to a single representative numerical value, detached from its production settings, is counterintuitive, as it implies a gross reduction of the recorded information. A voice that is considered to be pathological may exhibit an aberration over a part of the voice range, yet still function well over much of the remaining range. Highly trained singers may encounter specific phonatory problems only when forced to go to certain points in their range, and even then may skillfully avoid them.  
It is fascinating and instructive to watch a voice map emerge, while listening at the same time. It extends one's mental model of the voice, not only for the clinician/pedagogue but also for the patient/student. Real-time visual feedback makes the patients/students more aware of their vocal control, while creating a record of their performance and progress [49]. This in turn can provide an incentive for adhering to a training regime, for instance. It is actually fun to \"paint with one's voice\".  
The FonaDyn system [46] allows the user to listen post hoc to the sounds that contributed to the map in a mouse-clicked location, regardless of when in the recording those sounds occurred. This can help to reinforce one's understanding of the map representation, and of the roles of the constituent metrics. It can also be used to conduct auditory-perceptual validations of what the maps are showing.
## 3 Methods
### Study Phases
#### 3.4.2 Prototype Development  
A prototype was then developed based on the information obtained through the co-design survey. Smartwatch data (FitBit(r)) from a publicly available dataset--PMData: A sports logging dataset Simula PMData [37]--was used to train a machine learning algorithm for the detection of anomalies in the data. The PMData dataset [38] contains five months of data collected from 16 smartwatch users, who were encouraged to wear the watch as much as possible, as well as when sleeping. Smartwatches collect enormous amounts of data using different sensors and record data points at daily, per minute, or per second frequencies, thus generating hundreds of megabytes of data per user per day. The Fitbit Versa 2 (Google LLC, Mountain View, CA, USA) smartwatch was used to collect data in the PMData dataset between November 2019 and March 2020. The following data were available in the dataset (i) heart rate (i.e., the number of heartbeats per minute (bpm) at a given time), (ii) calories burned per minute, (iii) steps per minute, and (iv) distance moved per minute (in centimetres). These parameters were used as features in the development of the machine-learning algorithm as they were the most relevant variables from the smartwatch data and had the highest sampling frequency. The data was used to train and build a predictive machine learning model to detect irregular heartbeats based on heart rate, calories, steps, and distance data over time. A web-based dashboard was also developed to visually present the potential health anomalies with the possibility for our participants to filter out false positives using their domain knowledge.
## 3 Conventional BEMS Techniques
### Energy Efficiency
#### 3.3.1 Passive Methods  
The use of energy in many buildings can be greatly decreased by implementing passive techniques. These methods may not necessitate additional financial resources. For example, an integrated building rehabilitation approach that incorporates passive approaches can reduce a building's energy usage while also compensating for the higher cost of new technologies [66]. Passive energy conservation strategies aim to reduce energy demand by maximizing the use of natural heating, cooling, and lighting resources, as well as limiting energy losses through the building envelope [67].  
Traditional buildings used to be built with climatic conditions in mind and incorporated passive cooling and heating methods for both summer and winter, respectively. Most buildings nowadays have mechanical or active heating, cooling, and lighting systems, which consume a lot of energy [66]. Passive methods, such as the use of solar energy or creative architectural design, can greatly reduce total energy usage in a building. Passive solar energy is becoming increasingly popular. In 1974, a large south-facing window was used as the first passive solar heating system in New Mexico [68]. Using direct or indirect passive solar energy to alleviate heating, increase cooling capacities, and reduce a building's energy consumption is a basic approach, taking advantage of free solar energy [69]. In the winter, solar energy is used to warm the indoor atmosphere to a comfortable temperature. Solar radiation is absorbed directly through the transparent parts of the building exterior, particularly those facing south. This radiation is converted to heat, which elevates the temperature within the house. Solar radiation can also be employed as a source of natural illumination. Indirectly, solar radiation can be utilized in the winter. Multi- or double-skin facades, Trombe walls, and linked sunspaces are all popular methods for indirect solar advantages [67]. The effect of different insulating materials, construction systems based on sustainable materials, the incorporation of thermal energy storage (TES), phase-change materials (PCM) in building envelopes, and green infrastructures such as green roofs and walls are among the research fields of passive technologies [70]. By lowering the energy demand in buildings, passive energy techniques could reduce reliance on fossil fuels and capital-intensive renewable energy technologies. However, understanding building science ideas and energy control principles is essential for the effective implementation of these strategies.
## 2 Materials and Methods  
### Requirements  
**Medical Requirements**  
SpO2 and ECG data collection are required in order to evaluate the adherence of patients to a cardiac rehabilitation treatment. Analysis of these biosignals may produce alarms to warn about emergency events. For SpO2, the oxygen saturation level must be higher than a minimum safety threshold (90-95% [18; 19; 20]). If the level decreases under this threshold, a warning must be generated.  
For ECG, lead II was registered as it is the best ECG lead in order to record heart rhythm and heart rate [21]. The lead II has the configuration: II (RA--Right Arm; LA--Left Arm; and RL--Right Leg). Pediatric ECG electrodes, round fabric, 40 mm diameter, wet gel, snap electrode, silver chloride (Ag/AgCl) sensor, repositionable. Our clinicians wanted to track the changes in the heart rate (HR) such as bradycardia (HR < 60 beats per minute (bpm)) and tachycardia (>100 bpm) [22]. In the case of such an event, we must acquire and store at least 10 minutes of signal; this corresponds to 5 min before and 5 min after the warning event.  
**Technical Requirements**  
Real-time and wear specifications have resulted in a system with fixed latency, power supply, and space limitations. Medical requirements have led to the inclusion of storage capacities.  
In this work, we proposed the use of a DSP as the processing element. As the architecture of a DSP is optimized specifically for digital signal processing, it is the best solution regarding the performance and cost in relation to others, such as FPGAs or microprocessors.  
For clinical use, the signals should be digitalized with at least 12 bits of resolution. Moreover, several algorithms should be included to remove noise, extract parameters from physiological data, and identify warning events.
# Addressing the Non-Stationarity and Complexity of Time Series Data for Long-Term Forecasts
## 2 Methodology
### Architecture Overview  
The suggested framework, as illustrated in Figure 1, begins by transforming the non-stationary input into a weakly stationary form using the \"weak-stationarizing\" block. Subsequently, the resulting weak-stationary output undergoes a DFFT operation to obtain its spectral representation. Since the DFFT output comprises imaginary numbers, the real and imaginary parts are concatenated as separate channels for further processing. To acquire suitable features from the input containing spectral information, we employ a ConvMixer architecture. The learned features are then transformed back into the time domain. To address information loss due to the \"weak-stationarizing\" block, we revert the processes via the \"non-stationarity restoring\" block. The resulting combination of learned features with additional non-stationarity information is fed to a projection layer to generate the forecasts.
# Reinforcement Learning-Based Anti-Jamming in Networked UAV Radar Systems
## 4 Algorithm Design
### Information Representation Reward Design
The amount of information \(I_{rd}\) when measuring the \"range-Doppler\" by the radar swarm is:  
\[I_{rd}=\frac{\eta^{2}}{4} \tag{16}\]Next, the information of the radar UAV swarm in angle measurement is calculated. Beam scanning method and equal signal method are two commonly used angle measurement methods in radar. Due to the relatively small number of elements in the UAV swarm, it may be difficult to effectively form the dual beam needed by the equal signal method. Thus, we mainly consider the beam scanning method here. When the radar UAV swarm performs beam scanning, the direction corresponding to the maximum amplitude of the pulses is the direction of the target. According to the measurement accuracy theory of the angle gate-tracking technology, under the optimal integration processing condition, the accuracy of angle measurement is:  
\[\sigma_{\theta}\approx\frac{\theta_{0.5}}{2\sqrt{\eta}} \tag{17}\]  
where \(\theta_{0.5}\) is the half power width of the beam. According to the root mean square value of the angle measurement error, the Fisher information of the radar angle measurement \(I_{a}\) can be obtained:  
\[I_{a}=\frac{1}{\sigma_{\theta}^{2}}\approx\frac{4\eta}{\theta_{0.5}^{2}} \tag{18}\]  
On this basis, we can set the reward for reinforcement learning algorithm according to different working modes of radar networking. Information fusion theory needs to be used here, which is to obtain a total estimate by processing the estimated value of target state of each radar. At this time, the total Fisher information is the sum of the Fisher information of each radar. For two simultaneous ranging radars, if their received SIRs are \(\eta_{1},\eta_{2}\) respectively, its joint reward should be:  
\[r=\frac{{\eta_{1}}^{2}}{4}+\frac{{\eta_{2}}^{2}}{4} \tag{19}\]  
For one ranging radar and the other angle-measuring radar, its joint reward should be:  
\[r=\frac{{\eta_{1}}^{2}}{4}+\frac{4{\eta_{2}}}{\theta_{0.5}^{2}} \tag{20}\]  
It can be seen from (20) that \(\theta_{0.5}^{2}\) makes angle-measuring radar no longer pursue high SIR completely, but seeks a balance between SIR and angular resolution. In practical applications, we take the logarithm of reward and normalize it to avoid the value of the information being too high or too low for observation.
# Prediction of Water Leakage in Pipeline Networks Using Graph Convolutional Network Method
## 2 Materials and Methods
### Graph Convolutional Networks
The algorithm updates the node features by utilizing the neighborhood information from the graph structure and uses the softmax activation function for classification. During the training stage, the weights are updated using an optimization algorithm, and during the testing stage, the trained model makes predictions on new data. These steps describe the implementation stages of the Graph Convolutional Networks (GCN) algorithm, including initializing the node features, determining the number of layers, creating weight matrices, applying the graph convolution operation, defining the output layer, defining the loss function, choosing an optimization algorithm, updating the weights during training, and making predictions on new data during testing. Table 1 below gives the parameters of the GCN model used in this study.
# Mechanisms of Noise Transmission in Velocity Broad-Band Seismometers: Modeling and Analysis
## 5 Experiment  
### Experimental Setup  
To validate the proposed self-noise model, we conducted a self-noise test at a seismic station using three CS60 seismometers. The self-noise test adopted the three-channel correlation method [31], as illustrated in Figure 12, which shows the experimental setup at the seismic station. Three CS60 seismometers were placed side by side on a cement pier, with a STS-2.5 seismometer used as a reference instrument for sensitivity calibration. All seismometers recorded data at a sampling rate of 500 Hz for three days. This experiment focused on estimating the self-noise of the vertical channel, and the self-noise was calculated using the following equation:  
\[N_{ii}=P_{ii}-P_{ji}\times\frac{P_{ik}}{P_{jk}} \tag{47}\]  
Here, \(N_{ii}\) represents the self-noise PSD of seismometer \(i\), \(P_{ii}\) is the auto-PSD of seismometer \(i\), and \(P_{ji}\), \(P_{ik}\), and \(P_{jk}\) are the cross-PSD between seismometers, where \(i\
eq j\
eq k\). The value of PSD was estimated using the Pwelch method [32] with a Hann window for data processing. The window length was set to 60% of the signal length, with an overlap of 2/3. Three datasets were used in total, yielding nine sets of self-noise data for the seismometers.
## 5 Experiments
### AE Signal Noise Reduction  
To reduce the computational load associated with the high sampling frequency and large number of AE signal data, 100 sets of data are selected at equal intervals from the time sequence. Each data set consists of 1024 sampling points. The FCI-VMD algorithm is applied to decompose each set of signals, simultaneously calculating the fuzzy entropy of the resulting IMF components. Figure 10 illustrates the decomposition results and the corresponding fuzzy entropy values. In the figure, \(\sigma\) represents stress; \(\sigma_{c}\) represents peak stress; and \(\sigma/\sigma_{c}\) represents different stress stages. It can be observed that the optimal decomposition level for the AE signal is 5, and there is a significant variation in fuzzy entropy among the IMF components. The fuzzy entropy values are arranged in descending order, with IMF1 exhibiting the highest entropy, and IMF5, the lowest. This suggests that IMF1 contains a greater proportion of noise signal components, and each layer of IMF components contains noise modes. In this study, components with fuzzy entropy exceeding a certain threshold are filtered out, while the remaining components are reconstructed to obtain the effective signals. This approach avoids filtering out only IMF1 or selectively filtering out some effective signals, thereby preventing the presence of residual noise in the reconstructed signals. Based on statistical analysis of the fuzzy entropy values of each IMF component, it is observed that the IMF component displays relative discreteness when fuzzy entropy exceeds 0.015. Hence, a threshold of 0.015 is set in this study.  
Figure 10: Fuzzy entropy of each IMF component.  
Figure 9: Stress, AE count, and AE accumulative counts versus time curve during loading process. OA: initial compaction stage; AB: linear elastic stage; BC: elastic-plastic stage; CD: post-peak failure stage.  
After filtering the noise signal and reconstructing the effective signal, the signal after noise reduction outcomes is shown in Figure 11.  
Figure 11 demonstrates that the AE signal waveform becomes smoother, and the high-frequency noise is effectively eliminated after denoising using FCI-VMD. The improvement is particularly noticeable in the frequency domain, where the elimination of noise is evident. This indicates that the improved VMD approach not only filters out the noise signal but also preserves the integrity of the effective signal without distortion or loss.