esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Deep Autoencoder  
The deep autoencoder model is based on deep learning autoencoder feature extraction methodology. A basic autoencoder is a fully connected three-layer feedforward neural network with one hidden layer. Typically, the autoencoder has the same number of neurons in the input and output layer and reproduces its inputs as its output. We are using a five-layer deep autoencoder (see Figure 2) including input, output, encoder, decoder, and representation layers, which is a different approach than in [33; 37]. In our approach, we first analyze the data to find all floor patterns and then feed the segmented raw sensor data windows in up and down directions separately to the algorithm for profile extraction. Extracted profiles from both acceleration and magnetic signals are fed to the deep autoencoder model for extracting new deep features. Lastly, we apply random forest as a classifier for fault detection based on new deep features extracted from the profiles. We have combined healthy and faulty profiles as a vector from all five traction elevators including both acceleration and magnetic signals before feature extraction.  
The encoder transforms the input \(x\) into corrupted input data \(x^{{}^{\prime}}\) using hidden representation \(H\) through nonlinear mapping  
\[H=f(W_{1}x^{{}^{\prime}}+b) \tag{4}\]  
Figure 2: Off-line profile extraction and deep autoencoder feature extraction approach.  
where \(f(.)\) is a nonlinear activation function as the sigmoid function, \(W_{1}\in\mathbb{R}^{k\times m}\) is the weight matrix and \(b\in\mathbb{R}^{k}\) the bias vector to be optimized in encoding with \(k\) nodes in the hidden layer [37]. Then, with parameters \(W_{2}\in\mathbb{R}^{m^{\prime}k}\) and \(c\in\mathbb{R}^{m}\), the decoder uses nonlinear transformation to map hidden representation \(H\) to a reconstructed vector \(x^{\"}\) at the output layer.  
\[x^{\"}=g(W_{2}H+c) \tag{5}\]  
where \(g(.)\) is again nonlinear function (sigmoid function). In this study, the weight matrix is \(W_{2}=W_{1}T\), which is tied weights for better learning performance [38]. Among multiple input variables the use of nonlinear activation functions provides us better opportunity to capture nonlinear relationships. Effective fault detection is a challenge due to nonlinearity of elevator systems and as a result, time-series data will have temporal dependencies. Our proposed approach can capture nonlinear relationships among multiple sensor variables, which has improved the performance in terms of fault detection.
## 2 Materials and Methods
### Methods
#### 2.2.6. Washburn Capillary Rise Method  
Glass tubes with an inner diameter of 0.7 cm, a wall thickness of 0.1 cm, and a length of 20 cm, closed with 100 mesh metallic screens on one side, were used as cells for the Washburn capillary rise method. Three grams of dry powdered rock before or after treatment were placed into the cell and packed with manual shaking, as reproducibly as possible.  
The cell was hung on a special apparatus for moving the cell upward and downward. Using this apparatus, the cell was immersed in the octane or water in the beaker, which stood on the zeroed analytical balance. The immersion depth was 1 cm from the liquid surface. The weight of the beaker immediately started to decrease, which meant that the liquid was imbibed spontaneously into the powder. The weight of the imbibed liquid was recorded every 5 s. The experimental setup is shown in Figure 3. Six experiments were carried out for the one rock sample (three with water, three with octane).  
A plot of the squared weight of imbibed liquid versus time was drawn, and the slope of the linear portion (\(K\)) was calculated. Then, a ratio \(R\), which is a ratio of adhesion tensions of n-octane and water towards rock [49], was calculated according to Equation (2).  
\[R=(\frac{K\cdot\eta}{\rho^{2}})_{octane}\cdot(\frac{\rho^{2}}{K\cdot\eta})_{ water} \tag{2}\]  
\(\eta\) is the dynamic viscosity and \(\rho\) is the density of water and n-octane. NIST Thermophysical properties of fluid systems database was used to calculate these properties at experimental conditions (atmospheric pressure, 25 \(\pm\) 2 \({}^{\circ}\)C). If \(R\) values are higher than unity, that means that the rock is primarily hydrophobic, and lower values mean that the rock is primarily hydrophilic.
# Performance Enhancement of Consumer-Grade MEMS Sensors through Geometrical Redundancy
## 2 Theoretical Background
### Allan Variance  
The dependence of the state estimates quality on parameters defining noise conditions of sensors outputs is known to be one of the most important issues associated with Kalman filtering. To suitably determining their values, different approaches can be pursued; stemming from their past experience [27; 28], the authors exploited the Allan variance to gain the values of interest.  
Allan variance was first proposed to overcome problems associated with standard deviation evaluation on an increasing sequence of acquired data. Even though it was originally used for oscillator frequency applications, Allan variance can be exploited to highlight and discriminate noise terms added to the signal of interest also in inertial sensors [16]. Allan variance operates with success in the assumptions that (i) considered signal remains constant and flat during the measurements and (ii) noise average should be zero for long-term acquisitions.  
From an operating point of view, the Allan variance for a specific cluster time \(\tau\) is defined as one half of the time average of the squares of the differences between sensor outputs separated by \(\tau\)  
\[\sigma^{2}(\tau)=\frac{1}{2}\langle\left(s(t-\tau)-s(t)\right)^{2}\rangle \tag{14}\]  
When inertial sensors are considered, the evolution versus \(\tau\) of the Allan deviation, defined as the positive square root of Allan variance, allows us to estimate the main noise sources if a log-log scale is exploited (Figure 1). In particular, Bias Instability BI and Random Walk RW, i.e., the parameters values needed to set the entries of the noise covariance matrix, can be estimated from the curve portions characterized by slope values equal, respectively to zero and \(-1/2\) according to:  
\[\sigma^{2}(\tau)=\frac{2 BI^{2}\ln 2}{\pi} \tag{15}\]  
\[\sigma^{2}(\tau)=\frac{RW^{2}}{\tau} \tag{16}\]
## 4 Results and Discussion
### The Impact on Human Toxicity  
Figure 9 presents the impact of the different powertrains on human toxicity. As can be seen, the contribution of the well-to-wheel phase (i.e., non-exhaust, WTT, and TTW) is only marginal for the conventional powertrains, while it increases with battery capacity. Also, the contribution by the electricity production is emphasized in this exercise, indicating the reduction margin for changing the feedstock with renewable sources. What must be noted here is that the HT impact of EVs is far more researched than for the ICE-based technologies. For conventional petrol and diesel cars, for instance, an update of the current situation is needed as the substantial increase in sensor applications over the last decade resulted in more copper use, the 90 kWh performs worst of the given options, followed by the battery trailer combination due to the high impact of the battery production process. Locally, the impact of the manufacturing phase is filtered out of scope in Figure 10. Thus, the impact of non-exhaust PM emissions is emphasized, for which EVs have the benefit of relying less on the conventional brakes. Notice the little difference between the presented powertrains based on internal combustion engines, the generator trailer combination, and the 90 kWh EV.  
What can be concluded from these figures is that the manufacturing phase of EVs should not be neglected in the debate on which powertrain causes the least environmental impact. If we use the petrol car's powertrain as a reference, the impact of EVs on human toxicity ranges from double to nearly four times, although this difference would reduce if more recent LCI data on conventional cars were available. Next, we highlight the need for both more sustainable mining techniques, battery chemistries that rely on more locally and abundantly available feedstocks and an increased uptake of recycled materials in the battery manufacturing process. Nonetheless, if local policy is concerned, attention should primarily go to the impact different technologies have locally. In this regard, neither the PHEV nor the 90 kWh EV prove to be the optimal solution. Instead, a reduced impact on human toxicity is reached by addressing a battery trailer on those occasions when the nominal range of a 40 kWh EV is not sufficient.  
Figure 8: Impact per kilometer on particulate matter formation during use of the vehicle (including the refinery-to-tank (RTT) emissions upstream). Tank-to-wheel (TTW) emissions are further disaggregated to exhaust TTW and non-exhaust PMF (NEx).
## 4 Computational Experiments and Analysis
### Analysis of the Composite Scattering Coefficient as a Function of Incident Frequency  
The frequencies are set to \(f_{1}=3\) GHz, \(f_{2}=6\) GHz, and \(f_{3}=30\) GHz, with corresponding wavelengths \(\lambda_{1}=0.1\) m, \(\lambda_{2}=0.05\) m, and \(\lambda_{3}=0.01\) m. Figure 9 shows the variation in \(\sigma\) with respect to wavelength.  
Figure 8: Variation in the composite scattering coefficient with incident angle.  
Figure 7: Comparison of numerical results between MoM and FDTD.  
As shown in Figure 9, the scattering coefficient increases with increasing frequency. This is because as the wave frequency increases, the wavelength decreases, resulting in an increase in the relative roughness of both the vehicle and the snow-covered terrain. This enhances the coherent scattering effect, thereby increasing the scattering coefficient. However, at frequency \(-30^{\circ}<\theta_{s}<30^{\circ}\), the scattering coefficient decreases with increasing frequency. This anomaly is attributed to a resonance phenomenon between the electromagnetic wavelength and the structural features of the target and terrain.
## 5 Challenges, Open Research Problems, and Future of WBANs
### Effective Methods for Data Collection  
The rate of data collection in pervasive BSANs and healthcare systems is high. Developing effective methods of data processing techniques are considered to be important issue. In some cases 3-lead electrocardiograms cannot be sufficient to detect heart diseases or a single 3-axes accelerometer cannot classify all activities of people. In these cases, more sensors will be required, and the data collection needs will increase. The real time of data collection and analysis of physiological data is essential. In addition, time-stamping and ordering of events, synchronization of different sensors are open problems for study and research. Finally, another important related issue is the integration of various types of sensors, like RFID tags, implantable sensors and wireless body sensors that necessitates the development of modular architectures for further development.
# Improving Machine Learning Identification of Unsafe Driver Behavior by Means of Sensor Fusion
## 3 Method
### Motion Sensors  
Vehicle motion can easily be described by instrumenting it with several micro fabricated sensors such as an inertial measurement unit (IMU), which includes accelerometers, gyroscopes, microphones, cameras, a Global Positioning System (GPS), and magnetometers capable of recording several pieces of information about vehicular motion and the driver's actions. Interestingly, all these sensors are available in current smartphones, which can be easily used to capture valuable information about vehicle motion and driving events.  
In this work we used an Android smartphone, oriented with the main axes as in Figure 2 and rigidly anchored to the car console, to sample accelerometer, gyroscope, and magnetometer sensors. We chose not to use the GPS tracking or other sensors such as a webcam or microphone because they could potentially compromise the driver's privacy with the result of altering the trust perception and therefore discouraging the use and the diffusion of the proposed technology.  
**Accelerometer**: According to the orientation of Figure 2, the accelerometer of the smartphone measures on its \(x\) axis the lateral acceleration \(a_{lat}\) (i.e., the acceleration perpendicular to the motion of the vehicle), and on its \(y\) axis the longitudinal acceleration \(a_{lon}\) (i.e., the acceleration parallel to the vehicle motion), expressed in \(m/s^{2}\). Both \(a_{lat}\) and \(a_{lon}\) are very informative in describing the driving behavior, as the former captures, for example, the centripetal force while running a curve and the latter the brakes and the accelerations of the vehicle. Finally, the acceleration on the \(z\) axis is strictly related to the vertical oscillations of the vehicle that are essentially due to the road pavement imperfections.  
**Gyroscope:** The smartphone built-in gyroscope is natively capable of collecting information on the rotational speed, expressed in \(rad/s\), about its reference system. Thanks to the smartphone orientation described in Figure 2, the signal collected on the \(z\) axis directly corresponds to the car yaw entity, the signal on the \(x\) corresponds to the car pitch, and the signal on \(y\) the roll.  
**Magnetometer:** The magnetometer continuously samples the surrounding magnetic field on its \(x\), \(y\), and \(z\) axes, expressed in \(\mu T\). During driving we can assume that the vehicle is subject only to Earth's magnetic field which can be represented by a vector (\(\bar{B}\)) almost parallel to the ground following the north-south direction (see Figure 2). Depending on the relative orientation of the vehicle with respect to Earth's cardinal points, \(\bar{B}\) leads to the \(\bar{B}_{x}\) and \(\bar{B}_{y}\) components. According to this configuration,a variation on the car yaw is recorded by both \(x\) and \(y\) axes. In the same way, a pitch variation can be captured by both the \(y\) and \(z\) axes, and a modification in car roll is captured by \(x\) and \(z\) axes.
# Application of Long-Short-Term-Memory Recurrent Neural Networks to Forecast Wind Speed
## 1 Introduction
Long Term Memory Model (LSTM) was done to short-term spatio-temporal wind speed forecast for five locations using two-year data for historical wind speed and auto-regression. The model aimed to improve forecasting accuracy on a shorter time horizon. For example, using LSTM for two or three hours can forecast horizons extending up to fifteen days using an NWP model that updates itself typically with a frequency of six hours [13]. The training speed of RNNs for predicting multivariate time series is usually relatively slow especially when used with large network depth. Recently a variety of RNN called long-term memory (LSTM) has been favored due to its superior performance during the training phase by better solving vanishing and exploding gradient problems of standard RNN architecture [14,15]. Long short-term memory (LSTM) and temporal convolutional networks (TCN) models were proposed for data-driven lightweight weather forecasting, and their performance was compared with classic machine learning approaches (standard regression (SR), support vector regression (SVR), random forest (RF)), statistical machine learning approaches (Autoregressive integrated moving average (ARIMA), vector auto regression (VAR), and vector error correction model (VECM)), and dynamic ensemble method (Arbitrage of forecasting expert (AFE)). The results of the proposed model demonstrate its ability to predict effective and accurate weather [16]. Despite the continuous development of research on the LSTM algorithm in long-term prediction of wind speed, the traditional RNN algorithm for prediction is still preferred in most research. In this paper, emphasis has been placed on the application of the LSTM algorithm in the field of forecasting wind speed and a comparison between prediction efficiency and accuracy of the algorithm under different wind speed time series were used for training and testing.
# Advanced DSP for Coherent Optical Fiber Communication
## 2 Principle of Coherent Detection and Linear Impairments Equalization
proposed and demonstrated 400 Gbit/s experiments on a 50 GHz grid and successfully achieved 1200 km and 4000 km transmission [10]. [PERSON] et al. reported a 56 Gbaud, 224 Gbit/s PDM-QPSK 2500 km transmission experiment assisted by coherent detection with all linear impairments compensation. The experiment results of BER and transmission performance are shown in Figure 6a,b [24]. The authors of [25] developed 10-channel 28 Gbaud PDM 16-ary quadrature amplitude modulation (PDM-16 QAM) signals transmitted over 1200 km.
# A Set of Tools and Data Management Framework for the IoT-Edge-Cloud Continuum
## 3 Data Management Framework
### The Backbone
* Application data: data exchanged between user-facing software systems and Edge or Cloud infrastructures, incorporating user-authored content, preferences, and configurations. System data streams are related to the functional and maintenance aspects of the network infrastructure; they include the following:  
* Network Metrics: Information about the efficiency, condition, and utilization of the network. This includes data such as bandwidth usage, error statistics, and the status of network nodes. * Control Signals: Instructions for controlling system elements, including firmware upgrades, security updates, and configuration modifications. For user data streams, minimizing latency and maximizing data transfer rates are essential, setting appropriate MQTT QoS levels ensures a balance between reliability and performance, and organizing topics allows for effective data routing. System data streams focus on reliability and security, requiring hardened parameters for critical messages involving persistent sessions, higher QoS levels, and secure connections for sensitive data protection.
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 4 Experiments and Results
### Simulation  
We simulated drone deployment for the Pettimudi region using Matlab and OMNet++ [44]. OMNeT++ is a discrete event simulator mainly used for network simulation. We used the _INET_ Framework in OMNet++ to simulate the wireless networks.  
The terrain data of Pettimudi region (4 km \(\times\) 4 km) were obtained using the Google Elevation API. A simulation of the terrain is shown in Figure 19(a). After construction of the Height Matrix data structure, the region-filling algorithm produces the positions of the sixteen drones deployed to cover the entire area. The drones are deployed using two base stations placed at two diagonally opposite corners of the rectangular region. The trajectories of the drones deployed from the base stations is shown in Figure 19(b). After the drones reach their specified coordinates based on the output from the region-filling algorithm, the communications network is established. Here, we take \(30^{\circ}\) as the coverage angle (\(\alpha\)). The network coverage obtained is shown in Figure 19(b). Figure 19(a) shows the cone-shaped network coverage of the sixteen deployed drones. Figure 19(b) shows a top view of the network coverage. It can be observed that the region is fully covered, with no voids in between. The altitudes at which different drones are placed is shown in Figure A1 of Appendix A.  
The simulation process proceeds as follows. The terrain map of _Pettumudi_ is generated using the _OsgEarth_ package provided in OMNeT++. The entire 4 km x 4 km is divided into sixteen equal-area rectangles. A cell phone is placed at each of these rectangles and each cell phone is constrained to move inside these rectangles randomly to cover different points in the region. During these movements, the cell phone can send a UDP packet to the nearest drone host. Our simulation shows an average network efficiency (percentage ratio of the number of packets received to the number of packets sent) of 98%.  
The simulation videos are available in supplementary materials. The simulation results confirm that the terrain-aware nature of the region-filling algorithm ensures that every cell phone inside the region is covered by the network.  
Figure 19: The effect of placing the second base station at different corners: (**a**) \(H_{max}\) vs. number of drones required and (**b**) \(H_{max}\) vs. average leftover energy.  
Figure 20: Positions of drones for Pettimudi region: (**a**) terrain simulation of Pettimudi and (**b**) trajectories of the sixteen drones deployed from two base stations placed diagonally opposite to one another.
# Analysis of the Impact of Vibrations on the Driver of a Motor Vehicle
## 2 Measurement Process
### Vibration Parameters  
The basic parameter used to assess the exposure to both local and general vibration is that of adjusted root mean square values of vibration accelerations measured for the x, y, and z directions at the work station while performing each distinctive (i-th) activity.  
Figure 1: Arrangement of the coordinate system.  
Once measured, the acceleration values are adjusted (weighted) using an adjustment characteristic adequate to each type of vibration [52].  
For the vibrations acting via upper limbs, as well as for those of general effect, specific designations of the adjusted RMS values of vibration accelerations were envisaged, where x, y, and z are the directions of the vibrations.  
Another parameter that proves to be of major importance to the assessment of both local and general vibrations is the total time of worker exposure to vibrations within a work shift of t [m], being the sum of duration times t\({}_{i}\) of individual activities, calculated as per the following equation:  
\[t=\sum_{i=1}^{n}t_{i} \tag{1}\]  
where n is the number of activities involving vibration exposure performed at a given work station.  
The basic quantities used to assess the local vibration exposure are the vector sum of adjusted vibration accelerations and the 8-h (daily) exposure to vibrations.  
For each distinctive activity involving exposure to local vibrations, the a\({}_{\text{hv,i}}\) value of the vector sum of the adjusted RMS values of vibration acceleration is calculated as per the following equation [53; 54]:  
\[a_{\text{hv,i}}=\sqrt{a_{\text{hv,x,i}}^{2}+a_{\text{hv,y,i}}^{2}+a_{\text{hv,z,i}}^{2}} \tag{2}\]  
where a\({}_{\text{hv,x,i}}\), a\({}_{\text{hv,y,i}}\), and a\({}_{\text{hv,zi}}\) are the RMS values of vibration acceleration measured for the x, y, and z directions during the i-th activity.  
In order to assess short-term vibration exposure (for activities which take 30 min or less), the vector sum of adjusted RMS values of vibration acceleration is used. From among the vector sums determined for each activity with a duration time shorter than or equal to 30 min, the maximum (a\({}_{\text{hv,30 min,max}}\)) value of the vector sum is taken for the exposure assessment, performed according to the following equation:  
\[a_{\text{hv,30 min,max}}=max\{a_{\text{hv,i}}\} \tag{3}\]  
where the total time of worker exposure to vibrations (t) exceeds 30 min, a quantity referred to as an 8-h (otherwise daily) vibration exposure is used to assess the exposure to local vibrations, calculated as per the following equation:  
\[A(8)=\frac{1}{T_{0}}\sum_{i=1}^{n}a_{\text{hv,i}}^{2}\cdot t_{i} \tag{4}\]  
where n is the number of activities performed with vibration exposure, i is the ordinal number of an activity performed with vibration exposure, a\({}_{\text{hv,i}}\) is the vector sums of weighted RMS values of vibration acceleration for the i-th activity, t\({}_{i}\) is the duration of the i-th activity, and T\({}_{0}\) is equal to 480 min (value corresponding to 8 h).