esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment Design and Results
### Numerical Results and Analysis  
The data for selected locations is standardized by subtracting by mean and dividing by standard deviation and used as input in the proposed models. The input variables  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Metrics** & **Definition** \\ \hline MAE & \(\frac{1}{N}\sum_{j=1}^{n}|y_{j}-\hat{y}_{j}|\) \\ MAPE & \(\frac{100}{N}\sum_{j=1}^{n}|\frac{|y_{j}-\hat{y}_{j}|}{|y_{j}|}\) \\ RMSE & \(\sqrt{\frac{1}{N}\sum_{j=1}^{n}{(y_{j}-\hat{y}_{j})^{2}}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance metrics.  
Figure 5: The data sets obtained locations.  
Figure 6: Daily averages of wind speed at selected locations.  
are historical wind speed, surface pressure, wind direction, humidity and temperature values, and the output variable is the forecasted wind speed an hour ahead time horizon. The performance of the global LSTM, the Dirichlet process local LSTM (DP-local LSTM), the dynamic time warping local LSTM (DTW-local LSTM), Dirichlet process ensemble model (DP-ensemble), and dynamic time warping ensemble model (DTW-ensemble) are compared using MAE, MPAE, and RMSE metrics. The performance of each method for each location is given in Table 2. As depicted in Table 2, the ensemble models' performance is the best among all the compared models regardless of evaluation metrics, clustering approach, and locations. For most of the cases (amasra, pinarbasi, and cesme), the DP-local LSTM model is superior to global LSTM and DTW-local LSTM. This result may probably arise due to consistent wind speed. Table 2 also shows that the DP-ensemble model is superior to the DTW-ensemble model, which implies that the clustering approach is also important in wind-speed forecasting.  
Figure 7 shows each method's actual wind speed values and forecast values. While all methods achieve relatively good performance, the ensemble model achieves the best performance in all data sets.
## 4 Deep Reinforcement Learning-Based Resource Allocation Policy
### Markov Decision Process  
Generally, the purpose of reinforcement learning is for the agent to learn how to map each system state to an optimal action through a trial-and-error learning process. In this way, the accumulated sum of rewards can be maximized after a number of training time slots. Figure 4 illustrates the traditional reinforcement learning via agent-environment interaction. In particular, the agent observers the system state and then chooses an action at the beginning of a time slot. After that, the system receives the corresponding reward at the end of the time slot, and transfers to the next state based on the performed action. The system will be updated and will then go into the next interaction between agent and environment.  
We denote the state space and action space of the system in this paper as \(\mathbb{S}\) and \(\mathbb{A}\), respectively; \(s\left(t\right)=\left\{\mu\left(t\right),e^{\mathbf{e}}\left(t\right)\right\}\in \mathbb{S}\) represents the state of the network in time slot \(t\), where \(\mu\left(t\right)\) is the probability (\(belief\)) that the primary channel is free in that time slot, and \(e^{\mathbf{e}}\left(t\right)=\left[\begin{array}{cc}e_{11}^{\pi}\left(t\right),e _{21}^{\pi}\left(t\right),e_{12}^{\pi}\left(t\right),e_{22}^{\pi}\left(t\right),e_{1M}^{\pi}\left(t\right),e_{2M}^{\pi}\left(t\right)\end{array}\right]\) denotes a vector of remaining energy of CUs, where \(0\leq e_{im}^{\mathbf{e}}\leq E_{bat}\) represents the remaining energy value of \(CU_{im}\). The action at the CBS is denoted as \(\mathbf{a}\left(t\right)=\left\{\mathbf{b}\left(t\right),\mathbf{e}\left(t\right)\right\}\in \mathbb{A}\). In this paper, we define the reward as the sum data rate of the system, as presented in Equation (5).  
The decision-making process can be expressed as follows. At the beginning of time slot \(t\), the agent observes the state, \(s\left(t\right)\in\mathbb{S}\), from information about the environment, and then chooses action \(\mathbf{a}\left(t\right)\in\mathbb{A}\) following a stochastic policy, \(\pi\left(a\left|s\right.\right)=\Pr\left(a\left(t\right)=a\left|s\left(t\right) =s\right.\right)\), which is mapped from the environment state to the probability of taking an action. In this work, the network agent (i.e, the CBS) determines the transmission power for each CU and decides whether to allocate the bandwidth portion to the NOMA groups in each time slot. Then, the CUs perform their operations (transmit data or stay silent) according to the assigned action from the CBS. Afterward, the instant reward, \(R\left(t\right)\), which is defined in Equation (5), is fed back to the agent, and the environment transforms to the next state, \(s\left(t+1\right)\). At the end of the time slot, the CUs report information about the current remaining energy level in each CU to the CBS for network management. In the following, we describe the way to update information about the belief and the remaining energy based on the assigned actions at the CBS.
# An L-Slot Frequency Reconfigurable Antenna Based on MEMS Technology
## 3 Simulation Result
### Effects of Structural Parameters on the Antenna  
The size and shape of the slot, as well as the position of the RF MEMS switch, directly affect the resonant frequency and radiation performance of the antenna. Since the electric field direction is parallel to the YOZ plane, the slot along the \(X\)-axis has a larger effect on the current cutoff. Thus, the starting end of the slot is cut in the direction along the \(X\)-axis. The second section of the slot, which turns into the \(Y\)-axis direction, has less cutting effect on the current and belongs to the transition region. Finally, the end fold-back part of the L-slot changes the current path a second time. The on-off state of the RF MEMS switch and its layout are then used to achieve frequency tunability.  
Figure 7: Antenna far-field radiation direction diagram (**a**) state 1; (**b**) state 2; (**c**) state 3; (**d**) state 4.  
In the following, state 4 (both left and right are disconnected) is taken as an example to further illustrate the effects of slot size and switch position on antenna performance, according to the parameter optimization and analysis process. First, Figure 10a shows the effect of the length of the cut-in section of the L-slot along the \(Y\)-axis on antenna S11 parameters. It can be seen that the modal value of the reflection coefficient gradually increases with \(M_{1}\), and the resonant frequency shifts to lower frequency bands. This indicates that the longer the slot along the \(X\)-axis, the longer the electrical length of the current flowing through it, and thus the antenna can operate at lower frequencies. Additionally, Figure 10b shows the effect of the switch position on the antenna reflection coefficient. \(P\) indicates the distance from the RF MEMS switch to the edge of the patch. As the value of \(P\) increases, the modulus of the reflection coefficient increases, and the resonance point shifts to the left. Again, the closer the switch position is to the center, the greater the effect on the path of the current, making the resonance frequency lower. In this paper, we choose \(M_{1}\) = 0.5 mm and \(P\) = 0.3 mm considering the size factor and parameter index of the antenna.  
Figure 8: The current distribution diagram of the antenna in different operating modes is shown in the figure, and the red arrows indicate the direction in which the currents converge. (**a**) state 1; (**b**) state 2; (**c**) state 3; (**d**) state 4.  
Figure 9: Antenna radiation gain.
## 4 Feature Extraction and Selection
### Feature Evaluation and Correlation Analysis
In the _Next File Hotness_ regression setting visualized in Figure 1a, the (current) _File Hotness_ is the most impactful feature, with positive SHAP values for higher feature values, indicating that hotter files typically increase the target variable. Features such as _File Fully Read Frequency_ and _File Size_ also exhibited strong influence, while other features have SHAP values generally concentrated near zero but showing asymmetry for certain cases. In contrast, the classification model shown in Figure 1b revealed a broader spread of SHAP values and highlighted additional influential features such as _Time Since Creation_ and _Directory Access Frequency_, reflecting the model's effort to delineate class boundaries. As shown in Figure 1c, the (current) _Request Offset_ feature dominates the regression model for _Next Offset_, indicating a direct relationship to the predicted next offset, which is to be expected for sequential workloads. _File Length Pattern_ and _File Size_ also reveal a strong influence, while _Time Since Last Access_ has an interesting bi-modal effect, according to which both high and low values can push predictions in either direction, suggesting interaction effects. On the other hand, the classification model shown in Figure 1d identifies features like _File Length Pattern Frequency_ and _Directory Temporal Mild Ratio_ as being more discriminative for distinguishing between _Next Offset_ classes. The broader range and greater dispersion of SHAP values in the classification plot reflect more complex, nonlinear interactions.
# Effects of Resonant Electromagnetic Fields on Biofilm Formation in _Pseudomonas aeruginosa_
###### Abstract  
The global rise of antimicrobial resistance (AMR) constitutes a future health threat and dictates a need to explore alternative and non-chemical approaches. The aim of this study was to explore the use of weak resonant electromagnetic fields as a method to disrupt biofilm formation of a pathogenic bacterium in cystic fibrosis patients. We developed a bioresonance laboratory setup able to distinguish between changes in planktonic growth and changes in biofilm formation and showed that certain resonant frequencies were able to affect biofilm formation without affecting planktonic growth. In addition, we show that the ambient day-to-day magnetic field affects biofilm formation in a non-consistent manner. Overall, we conclude that our assay is suitable for studying the potential of resonant magnetic fields as a treatment and prevention strategy to prevent biofilm infections, and that certain resonant frequencies may be used as future medical applications to combat antimicrobial resistance.  
bioresonance; antimicrobial resistance; microbial biofilm; infection; ion cyclotron resonance
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 3 Cognitive HSTN  
To address the limited spectrum availability due to the increased demand for broadband multimedia applications, cognitive radio (CR) has become an active and well-researched topic in the field of wireless communications [98] and been incorporated into HSTNs [99]. In cognitive radio, secondary users are allowed to share the spectrum with a primary user network, provided secondary spectrum access will not deteriorate the QoS of the primary network [100]. By sharing spectrum resource between the two networks, higher spectrum efficiency can be achieved, alleviating the pressure of scarce spectrum resources. However, existing infrastructure needs to be updated to support the different frequency bands in conventional satellite and terrestrial networks. Additionally, the cooperation of satellite and terrestrial operators is required for interference mitigation and more efficient resource management.  
In the CR underlay model [101], the transmit power of a secondary user is strictly constrained to satisfy the interference temperature at the primary user. In the overlay model [102; 103], the secondary users assist primary transmission through cooperative communication techniques.  
In recent years, several works have been presented on spectrum sharing [100; 104; 105; 106; 107; 108; 109] and cognitive HSTNs, focusing on underlay [101], overlay [102], broadband cognitive radio schemes [111], energy efficiency [112; 113; 114], power allocation and control [99; 115; 116; 117; 118; 119; 120; 121; 122], performance evaluation and optimization [123; 124; 125; 126; 127], ergodic capacity [128], outage analysis [129; 130; 131], the impact of imperfect channel state information (CSI) [132; 133], interference [134], and hardware impairment [133; 135; 136]. Figure 5 shows a cognitive HSTN with cognitive satellite-terrestrial radios (CSTR).
# Mental Workload Classification Method Based on EEG Independent Component Features
## 2 Materials and Methods  
### Experiments and Data Preprocess  
#### 2.1.1 Experiments  
All EEG signals were recorded by a 32-channel Neuroscan Neuamps (Synamps2, Scan4.3, EI Paso, USA) system, and its channel locations are shown in Figure 1. The system uses a common reference, A1 and A2, which are located respectively behind the left and right ears. The data were digitized at 1000 samples per second. Band-pass filter settings were 0.1-200 Hz.  
Figure 1: Channel location.  
The EEG signals were collected from 13 participants with engineering knowledge background in Beihang University (aged between 22 and 25 years, with two females and 11 males). The platform of experiment was multi-attribute task battery (MATB-II, National Aeronautics and Space Administration (NASA), USA) [22]. Its task interface is shown in Figure 2. Some vision and operational flight missions were placed in four areas, respectively. During the experiment, three levels of mental workload were set, and they are low mental workload (LMW), mid mental workload (MMW), and high mental workload (HMW).  
The participants needed to complete the four sub-tasks under different mental loads in MATB-II. The difficulty degree of the experimental tasks was defined by the occurrence frequency of the four sub-tasks. The details of the four sub-tasks are shown in Table 1[23].
# Clutter-Masked Waveform Design for LPI/LPD Radarcom Signal Encoding
## 5 Clutter-Masked Radarcom Waveform Performance Evaluation
### Radar Performance  
To test the potential radar performance of the created radarcom signals, we computed their autocorrelation function (ACF) and contrasted it with ACF of a linear frequency modulated (LFM) signal with the same bandwidth and energy. These characteristics are shown in Figure 3 below. As expected, OFDM-generated signals exhibit worse ACF characteristics than those of LFM. This issue needs to be addressed for acceptable radarcom performance. One approach is to apply weights to the amplitude and phase factors in (12), as was investigated, e.g., in [24; 25]. Also, while the main peak of clutter-masked OFDM is narrower than the OFDMA in [13], the sidelobes are higher than the examples in [11; 12; 13], which is the tradeoff made for enhancing the LPI/LPD capabilities.  
We also tested the two signals in synthetic aperture radar (SAR) simulations, where the method of time-domain backprojection was used for image reconstruction [26]. This method was chosen due to its wide use and robustness, although it is possible that other SAR image reconstruction approaches could provide for better performance and/or computational efficiency. The original simulated scene, shown in Figure 4a, contained an 18-pixel \"target\" model at (47, 50) coordinates in cross-range and range, and a single-point \"target\" at (10, 50). The \"target\" pixels were assigned the value of 1, while the background pixels had value of 0. The backprojection algorithm was implemented under \"stop-and-go\" approximation [26]. The grid contained same number of points in range and cross-range  
Figure 3: Radarcom and linear frequency modulated (LFM) signal illustrations: (**a**) Autocorrelation function (ACF) of LFM signal; (**b**) ACF of clutter-masked orthogonal frequency division multiplexing (OFDM) signal; (**c**) Frequency-domain representation of LFM signal; (**d**) Frequency-domain representation of clutter-masked OFDM signal.  
for all simulations and the SNR was fixed at a very high value of approximately +75 dB so as to eliminate the effects of noise and focus on image reconstruction abilities of the two types of signals only: (a) LFM, and (b) Clutter-masked OFDM.  
Figure 4b,c show the reconstructed SAR images with LFM and clutter-masked OFDM signals, respectively. We observe that while the higher sidelobes of OFDM signals contribute to the unwanted image obscuration, both target positions can still be ascertained very well.
## 6 Static Analysis and Stiffness Matrix Generation
\right]\]  
\[\mathbf{C}_{\text{act}}=\mathbf{\Gamma}\mathbf{C}_{p}\mathbf{\Gamma}^{T}, \mathbf{C}_{\text{con}}=\mathbf{\Phi}\mathbf{C}_{p}\mathbf{\Phi}^{T}.\]  
To eliminate dimensional effect, the characteristic length \(\text{d}_{\text{c}}\) is regarded as a constant, \(\text{d}_{\text{c}}\) is multiplied by the column of stiffness around the z-direction, and divided by the column of stiffness along the z-direction. \[\mathbf{C}_{act}=\left[ \begin{array}{cccc}\text{c}_{11}&\text{c}_{12}& \text{c}_{16}\text{d}_{\text{c}}\\ \text{c}_{21}&\text{c}_{22}&\text{c}_{26}\text{d}_{\text{c}}\\ \text{c}_{61}&\text{c}_{62}&\text{c}_{66}\text{d}_{\text{c}}\end{array}  \right],\mathbf{C}_{\text{con}}=\left[ \begin{array}{cccc}\text{c}_{33}/ \text{d}_{\text{c}}&\text{c}_{34}&\text{c}_{35}\\ \text{c}_{43}/\text{d}_{\text{c}}&\text{c}_{44}&\text{c}_{45}\\ \text{c}_{53}/\text{d}_{\text{c}}&\text{c}_{54}&\text{c}_{55}\end{array} \right]\]  
The active stiffness index (ASI) can be defined in Equation (41), in which a higher value of ASI indicates a lower active stiffness characteristic, and W is a sub-workspace that is determined by the desired trajectory. \[\text{ASI}=\frac{\int_{\text{W}}\sqrt[3]{\det(\mathbf{C}_{act})}\text{d}\text{W }}{\int_{\text{W}}\text{d}\text{W}} \tag{42}\]  
The constraint stiffness index (CSI) can be defined in Equation (42), in which a higher value of CSI indicates a lower constraint stiffness characteristic. \[\text{CSI}=\frac{\int_{\text{W}}\sqrt[3]{\det(\mathbf{C}_{\text{con}})}\text{ d}\text{W}}{\int_{\text{W}}\text{d}\text{W}} \tag{43}\]  
To move a step further to explore the parameter of actuator influence on active and constraint stiffness, we obtain the ASI and CSI varying curve through the range of hydraulic cylinder changes. From the index value of Figure 11, we can find that the active and constraint stiffnesses of typeI is higher than those of typeII. When the range of hydraulic cylinder increases, the active stiffness of both types is decreased. We define the relative variation ratio of ASI and CSI as RVR, Max is the maximum value of indexes, Min is the minimum value of indexes. As for typeI, RVR of ASI is 23.5%, RVR of CSI is 9.4%. As for typeII, RVR of ASI is 263.5%, RVR of CSI is 18.1%. We can find that active stiffness is more affected than constraint stiffness, and typeI is less affected by the range of the hydraulic cylinder, especially on active stiffness. \[\text{RVR}=\frac{\text{Max}-\text{Min}}{\text{Min}}\times 100\% \tag{44}\]  
Figure 11: ASI and CSI variation caused by a range of hydraulic cylinder. (**a**) ASI index variation of two type mechanisms; (**b**) CSI index variation of two type mechanisms; The blue solid line stands for index of typeI mechanism, the red dashed line stands for index of typeII mechanism.
# A Hybrid Hidden Markov Model for Pipeline Leakage Detection
## 2 Hybrid HMM Method
### Architecture of DNN-HMM  
The DNN is built based on the DBN structure. The training process of the DBN is shown in Figure 3.  
Typically, the training process contains two phases: greedy pre-training and fine tuning. First, pre-training is applied to obtain the proper initial weights (e.g., \(W_{1}\), \(W_{2}\) and \(W_{3}\)) of the DBN. In the pre-training phase, the input, i.e., damage indices extracted from PZT sensor, is modeled and trained by a GRBM. After training the RBM using the training set, the inferred states of the hidden units can be used as data for training another RBM that learns to model the significant dependencies between the hidden units of the first RBM. This can be repeated as many times as desired to produce many layers of non-linear feature detectors that represent progressively more complex statistical structures in the PZT sensor signals. The complex statistical structure also represents the complex distributions of the pipeline leakage signals. Stacking RBMs by replacing the connections of lower level RBMs top-down (\(W_{3}\) to \(W_{3}^{T}\), \(W_{2}\) to \(W_{2}^{T}\) and \(W_{1}\) to \(W_{1}^{T}\)), an unsupervised training procedure DBN is made, as shown in the Figure 3. In this way, the DBN is able to learn the  
Figure 3: DBN Training Process.  
complex distribution of the pipeline leakage signals. Then, fine-tuning is used to optimized the initial weights with more pipeline leakage signals. Therefore, the initial weights are updated with small errors (\(\epsilon\)) to get more accurate representation of the distribution.  
After the fine-tuning process of the DBN, the optimized proper weights (\(W_{1}^{\prime}\), \(W_{2}^{\prime}\) and \(W_{3}^{\prime}\)) can be used to construct the DBN-DNN by adding a softmax output layer, as shown in Figure 4. This softmax output layer contains one unit for each possible state of the HMM. In this way, the posterior probability output from DBN-DNN for each state can be converted into the emission probability as the input parameter of the HMM. The initial state distribution \(\pi\), transition probability matrix \(A\) and emission probability matrix \(B\) of the HMM are updated by training with iterations of expectation and maximization steps to obtain the trained model.
# Analyzing the SAR in Human Head Tissues under Different Exposure Scenarios
### Contribution of This Paper  
In this study, different from existing studies, our aim was to provide a more comprehensive understanding of the factors that influence SAR. The effect of age, gender, tissue type, frequency, and exposed RF-EMF levels on SAR values were determined through both laboratory measurements and simulations. Measurements at different electric field values and two different frequencies (900 MHz and 1800 MHz) were conducted in a SAR laboratory in accordance with standards and then simulated in an electromagnetic field solver for four different computational human models. Simulations were carried out with CST to evaluate SAR 10g values in specific head tissues such as the brain, muscles, and fat. The head model in CST includes all tissues and organs present in the head region, such as air inside the body, blood, bone, eye bulb, eye lens, esophagus, salivary glands, skin, teeth, thyroid, tongue, tonsils, and more, in order to offer a realistic representation of a person. The simulations also accounted for the dielectric properties of these tissues and organs. The main contributions of this study can be summarized as follows:  
* The study analyzed the impact of different exposure scenarios, such as varying frequencies, electric field levels, and age/gender, on the SAR distribution within the head tissues.
* The SAR analysis in human head tissues provides valuable insights into the potential effects of electromagnetic radiation and contributes to the development of safety measures for protecting human health.
* The analysis of SAR values in the head helps identify potential vulnerabilities or variations in different populations.
* The study investigated the effect of tissue type on SAR, aiding in the assessment of specific risks associated with different organs and body parts.
* Examining the effect of frequency on SAR values helps identify potential variations in energy absorption across frequencies used in current and emerging wireless technologies.
* SAR analysis facilitates the assessment of potential risks associated with different power levels, allowing for comparison against established safety guidelines.
* The results obtained in this study can help in designing and optimizing wireless communication devices, electromagnetic safety standards, and guidelines to minimize exposure-related health risks.  
The paper is organized as follows. Measurement setup and SAR calculation are given in Section 2, measurement and calculation results are presented and discussed in Section 3. The paper is concluded in Section 4.