esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Distribution Fitting  
The parameters of the pulse shape of the estimated input GAW are used for generating synthetic GAWs. These parameters are fitted to Gaussian distributions independently of each other and random numbers are drawn from the distribution to obtain original synthesis parameters. \(A\) is a parameter matrix containing \(N\) rows and \(P\) columns. \(N\) is the number of rows of a single GAW type, and \(P\) is the number of frames in each GAW, which may be different for each GAW. First, the mean \(\mu_{A}\) and the standard deviation \(\sigma_{A}\) of the matrix is taken over all frames for each GAW, i.e.,  
\[\mu_{A}=\frac{\sum_{i=1}^{P}A_{i}}{P} \tag{3}\]  
\[\sigma_{A}=\sqrt{\frac{\sum_{i=1}^{P}\left(A_{i}-\mu_{A}\right)^{2}}{P}} \tag{4}\]  
These two measures describe the distribution of a parameters observed in a GAW. To reflect inter-GAW variation of the parameter distribution, means and standard deviations of \(\mu_{A}\) and \(\sigma_{A}\) are also obtained.  
\[\mu_{A}=\frac{\sum_{i=1}^{N}\mu_{A,i}}{N} \tag{5}\]  
\[\sigma_{\mu_{A}}=\sqrt{\frac{\sum_{i=1}^{N}\left(\mu_{A,i}-\mu_{\mu_{A}} \right)^{2}}{N}} \tag{6}\]  
\[\mu_{\sigma_{A}}=\frac{\sum_{i=1}^{N}\sigma_{A,i}}{N} \tag{7}\]\[\sigma_{\sigma_{A}}=\sqrt{\frac{\sum_{t=1}^{N}(\sigma_{A,i}-\mu_{\sigma_{A}})^{2}}{N}} \tag{8}\]  
In particular, two Gaussian distributions are estimated indicating the variation of the means and standard deviations of a GAW type's parameter over time. From each of these two Gaussian distributions \(\mathcal{N}(\mu,\sigma)\) with mean \(\mu\) and standard deviation \(\sigma\), \(M\) random numbers are drawn.  
\[\mu_{A_{s},M} \sim \mathcal{N}\big{(}\mu_{A_{s}},\sigma_{\mu_{A}}\big{)} \tag{9}\] \[\sigma_{A_{s},M} \sim \mathcal{N}(\mu_{\sigma_{A}},\sigma_{\sigma_{A}}) \tag{10}\]  
These \(M\) random pairs of \(\mu_{A_{s}}\) and \(\sigma_{A_{s}}\) indicate the means and standard deviations of the parameters for synthesizing \(M\) GAWs. \(\{\backslash\,\)displaystyle \(\{\backslash\,\)matthcal \(\{\)N\(\}\)(\(\backslash\)mu\({}_{\backslash}\)sigma \({}^{\prime}\)(\(\downarrow\))}\(\cdot\)(\(\downarrow\)))Using each pair of \(\mu_{A_{s}}\) and \(\sigma_{A_{s}}\), a Gaussian distribution is obtained. From this distribution, \(R\) numbers are drawn which indicate the values of a parameter for \(R\) frames.  
\[A_{s,M}\ \sim\ \mathcal{N}(\mu_{A_{s}},\sigma_{A_{s}}) \tag{11}\]  
All parameters of the pulse shape are generated using the same process, but different distribution parameters. The parameters are input to the synthesizer for generating synthetic GAWs.
## 3 Results
### Secondary Analysis (Initial Model)  
As we discussed earlier, our initial model utilized KNN imputation and fine-tuning the gridsearch using hyperparameters. Although these techniques were used to provide a more accurate and efficient model, the results showed potential overfitting/underfitting problems, and the final results indicated that the model was not capable of providing correct detections. Thus, this led us to redevelop the model with simple imputation instead of KNN imputation and without the use of hyperparameter fine-tuning. Below, we provide the results of the model during the training and testing phases. Figures S1-S16 are the results and metrics from our initial model.  
The initial model displayed average to low **accuracy 0.68** (almost based on random chance) **with 0.85 precision, 0.45 recall** and 0.58 F1 score**, as shown in Table S3. It has also shown a low percentage of certainty for predicting the correct class and a high percentage of certainty for predicting the wrong class based on the true label. This indicated potential overfitting. We ruled out other factors that could have contributed to this poor performance, such as class or data imbalance, noise, outliers, incorrect labels and improper training [31]. Considering the fact that Gradient Boosting and Random Forest Classifier are both ensemble models and that KNN imputation along with fine-tuning could potentially create overfitting problems, since these models already have mechanisms that inherently help fine-tune the model by design, we decided to drop the initial model and redesign the new one by skipping fine-tuning and replacing KNN imputation with simple imputation.
# A Joint Channel Estimation and Compression Method Based on GAN in 6G Communication Systems
## 2 System Model and Problem Formulation
### Problem Illustration  
Before concentrating on the studied problem, the conventional channel estimation procedure of data elements is summarized. Generally, it consists of the following two steps:  
* Firstly, the channel coefficients of pilot elements are determined via using the received pilot symbols \(y_{p}(i,j)\) and the transmitted pilot symbols \(s_{p}(i,j)=\alpha_{p}(i,j)s(i,j)\). The LS estimator [24] is an efficient method to solve this problem, and its optimal solution can be written as \[\hat{h}_{p}(i,j)=y_{p}(i,j)/s_{p}(i,j).\] (3) Note that if the block fading channel is considered within an OFDM block, the channel coefficients of data elements can be estimated directly by averaging all estimated results of pilot channels since the channel coefficient remains constant during the whole OFDM block transmission. However, in the case of frequency selective fast fading assumption, the channel with fast variation makes the above scheme impractical. To acquire the channel coefficients of data elements more precise, the following step needs to be implemented.
* Secondly, based on the estimated results of pilot channels \(\hat{h}_{p}(i,j)\), the remaining channel coefficients of data elements can be obtained by using two dimensional interpolation methods, which include nearest neighbor interpolation, linear interpolation, bicubic interpolation and other methods [25]. In addition, the MMSE estimator [26] is a competitive method to achieve high accuracy estimation with extra cost of computation complexity. Its optimal solution can be found by multiplying a filtering matrix \(A_{\text{MMSE}}\) with the pilot channel coefficient matrix \(\hat{\mathbf{h}}_{p}\), which is denoted as \(\hat{\mathbf{h}}_{d}=\mathbf{A}_{\text{MMSE}}\hat{\mathbf{h}}_{p}\). By minimizing the gap between \(\hat{\mathbf{h}}_{d}\) and the ideal channel coefficient \(\hat{\mathbf{h}}_{\text{ideal}}\), the optimal \(A_{\text{MMSE}}\) can be derived as [6] \[A_{\text{MMSE}}=\mathbf{R}_{\hat{h}_{d}\hat{h}_{p}}(\mathbf{R}_{\hat{h}_{p}\hat{h}_{ p}}+\mathbf{\sigma}^{2}(\mathbf{s}\mathbf{s}^{H})^{-1})^{-1},\] (4) where \(\mathbf{R}_{\hat{h}_{d}\hat{h}_{p}}=\mathbb{E}\{\hat{\mathbf{h}}_{d}\hat{h}_{p}^{H}\}\) denotes the cross-correlation matrix between \(\mathbf{h}_{d}\) and \(\mathbf{h}_{p}\), \(\mathbf{R}_{\hat{h}_{pwhere \(\mathcal{M}\) denotes the trained GAN model, and \(\mathcal{H}(\cdot)\) denotes the interpolation function that uses \(\hat{\mathbf{h}}_{p}\) with \(\mathcal{M}\) to infer \(\hat{\mathbf{h}}_{d}\).
###### Abstract  
This research presents an easy to fabricate isotropic printed arc antenna element to be used for direction of arrival (DoA) arrays. The proposed antenna exhibits a total gain variation of 0.5 dB over the entire sphere for 40 MHz impedance bandwidth at 1 GHz, which is the best design isotropy reported in literature so far. In addition, the isotropic bandwidth of the antenna for total gain variation of \(\leq\)3 dB is 225 MHz with 86% efficiency. The isotropic wire antenna is first designed and simulated in Numerical Electromagnetic code (NEC). An equivalent printed antenna is then simulated in CST, where single (short circuited) stub is integrated with the antenna for input matching and the results of NEC simulations are verified. The planar antenna is then manufactured using FR4 substrate for measurements. Good agreement between the measured and simulated results is observed, however the total gain variation is increased to 2 dB for the fabricated antenna. This is because of the unavoidable field scattering from the antenna substrate, the feed cables, and the antenna testing platform.  
isotropic pattern; direction of arrival; gain variation +
Footnote †: journal: Electronics Letters
## 3 Mathematical Modeling
### Hovering Within the Triple NEA System (153591) 2001 SN263
#### 4.2.2 Tracking Long-Lasting Orbits Within the System
Figure 13 presents the respective satellite-asteroids distances,  
We made then use of the SDRE control to make a spacecraft track a long-lasting trajectory shown in Figure 14, in which we may consider the spacecraft as a co-orbiting satellite of Beta as it occupies the same orbit \(180\deg\) shifted from the moonlet, similar to the case shown in \[r_{*} =\sqrt{\left(x-x_{\alpha}\right)^{2}+\left(y-y_{\alpha}\right)^{2 }+\left(z-z_{\alpha}\right)^{2}}\]0 In Figure 14, we also show the effect of considering different penalty matrices for tracking reference trajectories. For the first case, we used \(Q=10^{-6}diag(1,1,1,1,1,1)\) and \(R=10^{8}diag(1,1,1,1,1,1)\). For the second case, the adopted coefficients are \(Q=10^{-4}diag(1,1,1,1,1)\) and \(R=10^{6}diag(1,1,1,1,1,1)\), meaning a higher cost/penalty \(PI_{{}_{\rho k}}\) (see Equation (28)) for higher values state errors (higher values of \(\|\mathbf{X}\|\) in Equation (29b)), thus imparting faster convergence \(\|\mathcal{X}\|\to 0\), while allowing lower cost/penalty (higher values for \(\|\mathcal{U}\|\)), thus allowing higher fuel expenditure. Figure 15 shows the control effort for the referred associations of penalty matrices. Here, also, one can observe the large amplitude of choices afforded by SDRE through the selection of the matrix coefficients in Equation (29b), resulting in trajectories converging almost \(10\times\) faster (thicker curve in Figure 14), but with more than \(100\times\) cost. \[r_{*} =\sqrt{\left(x-x_{\alpha}\right)^{2}+\left(y-y_{\alpha}\right)^{2 }+\left(z-z_{\alpha}\right)^{2}}\]1 Long-lasting reference-trajectory tracking using SDRE with different associations of Penalty matrices \(Q\) and \(R\). \[r_{*} =\sqrt{\left(x-x_{\alpha}\right)^{2}+\left(y-y_{\alpha}\right)^{2 }+\left(z-z_{\alpha}\right)^{2}}\]2 Control effort in the trajectory tracking with different associations of Penalty matrices \(Q\) and \(R\). As an intermediate case, we present in Figure 16 a maneuver considering a trajectory starting some kilometers away from Alpha and targeting the stable equilibria \(\sim\)100 m above the asteroid' surface. For this case we used \(Q\)\(\sim\)10\({}^{-2}\) and \(R\)\(\sim\)10\({}^{9}\).
## 1 Introduction  
The global shift toward sustainable and renewable energy sources has acquired tremendous traction in recent years [1]. Universities, as centers of research and learning, increasingly understand the importance of embracing environmentally sustainable energy solutions [2]. This paradigm shift is motivated not only by environmental concerns but also by the possibility for educational institutions to serve as models for sustainable practices [3]. In today's energy consumption situation, the need to shift toward more sustainable methods is becoming increasingly clear [4]. Traditional reliance on nonrenewable energy sources has exacerbated environmental challenges, demanding a rethinking of energy management practices [5, 6]. In this perspective, the integration of solar-hydrogen storage systems appears to be a promising answer to universities' energy concerns. Several strong reasons motivate us to conduct research into the integration of solar-hydrogen storage devices into university energy management (Table 1) [7, 8, 9]. In essence, research into the integration of solar-hydrogen storage systems is consistent with a broader commitment to environmental stewardship, fiscal restraint, and the instructional purpose of institutions [10]. It positions institutions as innovators in the adoption of revolutionary technologies that not only meet their energy requirements but also contribute to a more sustainable and resilient future [11]. The development of solar-hydrogen energy presents a viable and sustainable approach to address the negative environmental effects associated with traditional energy sources. This method encourages the production of carbon-neutral energy by using solar electricity to produce hydrogen via electrolysis of water. When operating, solar-hydrogen systems emit no greenhouse gases, in contrast to fossil fuels, which helps to reduce air pollution and mitigate climate change. Additionally, by providing a flexible and environmentally benign substitute in industries like transportation, manufacturing, and power generation, hydrogen as a clean energy carrier aids in the shift to a low-carbon economy. Adopting solar-hydrogen energy supports a cleaner and more sustainable energy future and is in line with international efforts to stop environmental damage [12]. Solar-hydrogen energy combines solar power with electrolysis to produce hydrogen, thereby emulating a clean and sustainable energy paradigm. Sunlight is captured by solar
# A Hierarchical Fusion SAR Image Change-Detection Method Based on HF-CRF Model
###### Abstract  
The mainstream methods for change detection in synthetic-aperture radar (SAR) images use difference images to define the initial change regions. However, methods can suffer from semantic collapse, which makes it difficult to determine semantic information about the changes. In this paper, we proposed a hierarchical fusion SAR image change-detection model based on hierarchical fusion conditional random field (HF-CRF). This model introduces multimodal difference images and constructs the fusion energy potential function using dynamic convolutional neural networks and sliding window entropy information. By using an iterative convergence process, the proposed method was able to accurately detect the change-detection regions. We designed a dynamic region convolutional semantic segmentation network with a two-branch structure (D-DRUNet) to accomplish feature fusion and the segmentation of multimodal difference images. The proposed network adopts a dual encoder-single decoder structure where the baseline is the UNet network that utilizes dynamic convolution kernels. D-DRUNet extracts multimodal difference features and completes semantic-level fusion. The Sobel operator is introduced to strengthen the multimodal difference-image boundary information and construct the dynamic fusion pairwise potential function, based on local boundary entropy. Finally, the final change result is stabilized by iterative convergence of the CRF energy potential function. Experimental results demonstrate that the proposed method outperforms existing methods in terms of the overall number of detection errors, and reduces the occurrence of false positives.  
Keywords:change detection; hierarchical fusion; CRF; D-DRUNet; local boundary entropy +
Footnote †: journal:  
_remote sensing_
# Aluminum-Based Plasmonic Photodetector for Sensing Applications
## 2 Materials and Methods
### Optical Characterization  
The optical constants of the thin aluminum films deposited on substrates by vacuum thermal evaporation were measured in the spectral range of 250-2100 nm using a SE-2000 (Semilab Ltd., Budapest, Hungary) spectroscopic ellipsometer. Figure 3a,b present the spectral dependencies of the refractive index \(n\) and extinction coefficient \(k\) for the Al films without thermal annealing after sputtering. As can be seen in the figures, the optical constants of the thin films somewhat differ from the literature data, which typically correspond to bulk materials. This difference is primarily due to enhanced electron scattering at the film boundaries and spatial confinement effects. It is crucial to consider this phenomenon when modeling the resonant properties of PPPDs to ensure accurate predictions and optimized device performance.  
The spectral and angular dependences of the short-circuit photocurrent \(\mathrm{I_{Ph}}\) of the manufactured PPPD samples in the wavelength range of 0.4-1.1 \(\upmu\)m were also studied under illumination with p- and s-polarized light at incidence angles of \(\theta=\) 0-70\({}^{\circ}\). The measuring setup included a tungsten lamp light source (Ocean Insight, Rochester, NY, USA), a light chopper (MD Helicopters, Mesa, AZ, USA), a monochromator (Instrument Design Technology Ltd., Vidnes, UK), a polarizer of the Glan prism type (Thorlabs, Inc., Newton, NJ, USA), a photodetector with an amplifier (Thorlabs, Inc., Newton, NJ, USA), a rotating sample holder (ICE Oxford Ltd., Witney, UK), and a controller computer (JHC Technology, Taipei City, Taiwan) [31].
## 1 Introduction
In those publications, the IME process was investigated under predefined conditions (in narrow range of parameters) and were focused on the highest efficiency of COD removal. The correlation between the efficiency of the process and the dose of microelectrodes or the iron/GAC ratio has not been studied in detail. Moreover, the researchers used different methods of microelectrode preparation before the process. Some authors propose soaking GAC in raw leachates before the IME process to saturate the active surface of the activated carbon and eliminate the sorption process [28; 29]. The main aim of the presented study was to investigate in more detail the IME process, which seems to be effective in COD removal but more complex than it is considered in the literature. The authors focused on a more detailed description of the correlations between the wide range of parameters and the efficiency of the IME process. As part of the presented research, the following issues were investigated:  
* A determination of the impact of different parameters on the effectiveness of the IME process: pH; microelectrodes ratios and doses; time; the addition of H\({}_{2}\)O\({}_{2}\) in different doses and the moment of the process. * A determination of the impact of humic substance removal by precipitation in pH 2, 3 and 4 (preliminary coagulation PC) on the IME effectiveness. * An investigation of the validity of the soaking of the GAC in LL before the IME process (in different variants).
# Antimicrobial Peptide Screening from Microbial Genomes in Sludge Based on Deep Learning
## 3 Experiments
### Training of the Combined Deep Network and Screening Results  
The training of the combined screening networks was conducted using the training set provided in Section 2.1. All experiments were performed on a desktop computer equipped with an AMD R7-5800 processor, 4.0 GHz, and 16 GB of memory. In the training experiments, we used the open-source deep learning library Pytorch to build the combined network model, and the Adam algorithm was employed to supervise the learning process of the model. Both LSTM and ALSTM had 2 layers.  
The ROC curve for training is shown in Figure 3. In the ROC curve, the horizontal axis represents the false positive rate (FPR), and the vertical axis represents the recall rate. By comparing the ROC curves, we observed that both models exhibit excellent performance, with the ALSTM model performing particularly well. This indicates that critical information plays a crucial role in DNA fragments, and utilizing ALSTM allows for comprehensive modeling of these features, thereby enhancing the recognition of crucial information.  
Figure 3: ROC curves for LSTM, ALSTM, and fusion models. The blue curve represents LSTM with an AUC of 0.95, the orange curve represents ALSTM with an AUC of 0.99, and the green curve represents the merged model with an AUC of 1.00. The dashed line represents the line y = x. The points on the line represent the results of a classifier using a random guessing strategy, included as a reference to highlight model performance in the figure.  
Based on the confusion matrix, we calculated evaluation metrics such as accuracy, precision, recall, and \(F1\) score (as shown in Table 2 and Figure 4). An accuracy close to 1 indicates excellent overall model performance in sample classification, high precision suggests high accuracy in positive class predictions, and high recall indicates successful capture of true positive samples by the model. The \(F1\) score combines precision and recall, and it tends to be high when both precision and recall are relatively high. Our fusion model demonstrates outstanding performance, especially in terms of precision and recall. Specifically, the LSTM model showed a high recall rate of 0.966, while the ALSTM model performed better in terms of precision with a score of 0.952.  
By combining the predictions of the two models, our fusion strategy ensures that only highly reliable peptide segments are ultimately predicted as AMPs, providing a finely tuned prediction framework. After fusion, the model only has true positives and true negatives, with counts of 2211 and 2543, respectively. The accuracy, precision, recall, and \(F1\) all reach 1, achieving successful accurate predictions.  
Using the filtering model, we selected 17 short peptide sequences with high scores, as shown in Table 3.
# Instantaneous Beam Prediction Scheme against Link Blockage in mmWave Communications
## 2 Related Work
### Beam Recovery Schemes in Indoor mmWave Networks  
Beam blockage for mmWave communications in indoor networks have also been addressed. For example, the authors of [19] investigate a beam cooperation mechanism that configures a beam pair between user equipment (UE) and multiple access points (AP) installed on a ceiling, and is based upon three GPP channel models. However, indoor settings feature increased directivity and rich scattering environment, as compared to sparse outdoor scattering. Additionally, the multi-connectivity and UE association lack power and time models since the UE is always required to possess a beam pair with multiple APs. Furthermore, the work in [20] proposes a MAC layer approach to enable multi-link-connectivity (redundancy). The approach dynamically determines a subset of redundant links that can be used to overcome a certain blockage level, i.e., using several device centric methods. Redundancy levels here are adjusted based on the channel conditions. However, this device-centric approach can aggregate the redundancy at several blockage levels, thus adding more requirements to the multi-link connectivity, along with increased power consumption. Again, the settings are limited to indoor networks and thus lack feasible investigations as to its efficiency in outdoor networks due to their sparse nature, which results in multi-connectivity challenges. Another MAC layer-based approach is introduced in [21] for indoor networks that delegates the control plane from a default AP to other AP member-nodes that have direct LoS link with the UE, in an effort to improve node visibility and alleviate blockage scenarios without requiring additional network infrastructure. Additionally, the discrete-time Markov decision process (MDP) is leveraged to account for load variations in the APs load and predicting indoor channel dynamics. Finally, a recovery scheme against mobility and blockage is presented in [22] that explores the best available LOS or NLOS path by utilizing spatial and temporal correlations of the link state in different directions. The aim is to eliminate exhaustive beam searches and improve the quality-of-service (QoS), sustain seamless communication, and enhance throughput gains. However, the work is limited to indoor settings at 60 GHz frequencies (IEEE 802.11 ad standard) and thus cannot be scaled for outdoor networks.