esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data
## 2 Materials and Methods
### Data Sources  
#### 2.2.1 FY3D/MERSI-II  
The Medium Resolution Imaging Spectrometer is one of the main optical imaging payloads on the FY-3 series satellites. The second generation of MERSI (MERSI-II) has been improved in its performance compared to that of the first generation, forming a double satellite network with the FY-3C series, which greatly improves China's meteorological satellite observation capability. MERSI-II uses multiple probes (10 or 40) and provides an image of about 2900 km (cross track) \(\times\) 10 km (along track) per scan. Compared with MERSI-I, MERSI-II has 19 solar reflection channels and 1 infrared emission channel, which is equipped with a total of twenty-five channels, including sixteen visible-near infrared channels, three short-wave infrared channels and six medium-long wave infrared channels. Among them, six channels have a sub-satellite point resolution of 250 m, and the remaining nineteen channels have a resolution of 1000 m. The bands used in this study are listed in Table 1. The MERSI-II L1 data used in this study can be downloaded from the website [[http://satellite.nsmc.org.cn/portalsite/default.aspx](http://satellite.nsmc.org.cn/portalsite/default.aspx)]([http://satellite.nsmc.org.cn/portalsite/default.aspx](http://satellite.nsmc.org.cn/portalsite/default.aspx)). (accessed on 10 April 2022).  
The MERSI-II operational cloud detection products were also downloaded from the above website, including a granular version and daily versions. The cloud mask products using the daily scale at a resolution of 1 km \(\times\) 1 km and 5 km \(\times\) 5 km were released at the same time, and this study adopted the data of the finer resolution one for the cross-comparison. The parameter named CLM_DAILY_D was extracted, and the values were  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Band** & **Center Wavelength (\(\upmu\)m)** & **Width (nm)** & **Spatial Resolution (m)** \\ \hline
1 & 0.47 & 50 & 250 \\ \hline
2 & 0.55 & 50 & 250 \\ \hline
3 & 0.65 & 50 & 250 \\ \hline
4 & 0.865 & 50 & 250 \\ \hline
5 & 1.38 & 50 & 250 \\ \hline
6 & 1.64 & 50 & 1000 \\ \hline
7 & 2.13 & 50 & 1000 \\ \hline
12 & 0.67 & 20 & 1000 \\ \hline
15 & 0.865 & 20 & 1000 \\ \hline
19 & 1.03 & 20 & 1000 \\ \hline
20 & 3.8 & 180 & 1000 \\ \hline
24 & 10.8 & 1000 & 250 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The wavelengths of the FY3D/MERSI-II instrument used in the study.  
represented in bits and bytes for certainly the cloudy, probably cloudy, probably clear, certainly clear, coastal, sun glint and snow/ice areas. Considering that only the pixels over land were identified in this study, the pixels labeled as sun glint were treated as invalid filling values.
## 5 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**POS** & **Model** & **Precision** & **Recall** & **F1** \\ \hline \multirow{6}{*}{Verb} & RNN\_HG & 66.4\% & 75.5\% & 70.7\% \\  & RNN\_MHCA & 66.0\% & 76.0\% & 70.7\% \\  & RoB\_BASE & 77.0\% & 72.1\% & 74.5\% \\  & RoB\_SEQ & 74.4\% & 75.1\% & 74.8\% \\  & DeepMet & **78.8**\% & 68.5\% & 73.3\% \\  & MelBERT & 74.2\% & 75.9\% & 75.1\% \\ \cline{2-5}  & MsW\_base & 74.9\% & **78.1**\% & **76.5**\% \\  & MsW\_cos & 74.0\% & 77.9\% & 75.9\% \\  & MsW\_sbert & 74.5\% & 76.1\% & 75.3\% \\ \hline \multirow{6}{*}{Adj} & RNN\_HG & 59.2\% & **65.6**\% & 62.2\% \\  & RNN\_MHCA & 61.4\% & 61.1\% & 61.6\% \\  & RoB\_BASE & 71.7\% & 59.0\% & 64.7\% \\  & RoB\_SEQ & 72.0\% & 57.1\% & 63.7\% \\  & DeepMet & **79.0**\% & 52.9\% & 63.3\% \\  & MelBERT & 69.4\% & 60.1\% & 64.4\% \\ \cline{2-5}  & MsW\_base & 68.5\% & 65.0\% & **66.7**\% \\  & MsW\_cos & 67.5\% & 65.1\% & 66.3\% \\  & MsW\_sbert & 69.3\% & 63.8\% & 66.4\% \\ \hline \multirow{6}{*}{Adv} & RNN\_HG & 61.0\% & 66.8\% & 63.8\% \\  & RNN\_MHCA & 66.1\% & 60.7\% & 63.2\% \\  & RoB\_BASE & 78.2\% & 69.3\% & 73.5\% \\  & RoB\_SEQ & 77.6\% & 63.9\% & 70.1\% \\  & DeepMet & 79.4\% & 66.4\% & 72.3\% \\  & MelBERT & **80.2**\% & 69.7\% & **74.6**\% \\ \cline{2-5}  & MsW\_base & 76.4\% & 69.3\% & 72.7\% \\  & MsW\_cos & 78.9\% & **69.8**\% & 74.0\% \\  & MsW\_sbert & 78.3\% & 67.5\% & 72.4\% \\ \hline \multirow{6}{*}{Noun} & RNN\_HG & 60.3\% & **66.8**\% & 63.4\% \\  & RNN\_MHCA & 69.1\% & 58.2\% & 63.2\% \\  & RoB\_BASE & **77.5**\% & 60.4\% & 67.9\% \\  & RoB\_SEQ & 76.5\% & 59.0\% & 66.6\% \\  & DeepMet & 76.5\% & 57.1\% & 65.4\% \\  & MelBERT & 75.4\% & 66.5\% & **70.7**\% \\ \cline{2-5}  & MsW\_base & 76.2\% & 64.9\% & 70.1\% \\  & MsW\_cos & 75.4\% & 66.0\% & 70.4\% \\  & MsW\_sbert & 73.9\% & 64.7\% & 69.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results for POS (Parts of Speech).
## 4 Methodology
### Model Development  
The current study used the Python Jupyter Notebook to develop all three machine-learning models, namely CatBoost, XGBoost, and LightGBM. The performance of any ML model is directly and significantly impacted by choosing the optimal hyperparameters [75]. The current research utilizes the grid search and five Kfold cross-validation (K = 5) techniques to identify the optimal hyperparameters for all three models. Grid search is a conventional approach for optimizing hyperparameters that involves a thorough search within predefined boundaries [76]. The training and test folds consist of 60% (6797) and 40% (4532) of the total data set, respectively. Models were trained using the training data after the K-fold and tested on the testing data. All the data preprocessing, standardization, K-fold, grid search cross-validation for hyperparameters, and model training were performed using a Python Jupyter notebook. The hyperparameter settings of the models are outlined as follows:  
* XGBoost: subsample = 0.8, reg_lambda = 2.5, reg_alpha = 0.1, n_estimators = 300, max_depth = 9, learning_rate = 0.1, gamma = 0, colsample_bytree = 1.0.
* CatBoost: iterations = 100, learning_rate = 0.1, depth = 6, l2_leaf_reg = 3, border_count = 32, ctr_border_count = 50.
* LightGBM: num_leaves = 31, n_estimators = 200, max_depth = 20, learning_rate = 0.1, lambda_l2 = 0, lambda_l1 = 0.5, feature_fraction = 0.8, boosting_type = gbdt.
## 7 Simulation Results
### Results Obtained for a Fixed Parameter \(e\) and an Unknown Parameter \(\delta\)  
We formulate LMI stability requirements based on a fixed parameter \(\epsilon\) and an unknown parameter \(\delta\) in Theorem 3. Next, we look into various \(\epsilon\) values to determine the values of the gain \(\mathcal{K}\) and the corresponding parameter \(\bar{\delta}\). The results of resolving the LMI minimization issue in Theorem 3 and integrating the two additional LMI conditions (95a) and (95b) are shown in Table 5. In the present case, the LMI minimization problem is found to be unfeasible for all \(\epsilon\leq 2.75\). Moreover, except for the case \(\epsilon\geq 2.8\), the acquired values of the gain \(\mathcal{K}\) are small and acceptable for practical application. In addition, for all values of the parameter \(\epsilon\) (and for the feasible LMI problem), we obtained almost the same highest value, \(\delta_{\max}\), of the parameter \(\bar{\delta}\), and then \(\delta_{\max}\approx 0.81\).  
By employing the matrix feedback gain \(\mathcal{K}\) corresponding to the value of \(\epsilon=3.3\) as provided in Table 5, in the adopted affine state-feedback controller defined by (25), we produced the graphical outcomes that are shown in Figure 5. These figures demonstrate the actuated knee exoskeleton system's successful control and achievement of its target condition. Furthermore, at equilibrium, the control effort \(u\) approaches zero.  
Figure 4: Simulation results obtained according to the second designed method. Temporal variation of: (**a**) the angular position error \(\phi\), (**b**) the angular velocity error \(\phi\), (**c**) the affine state-feedback controller \(u\), and (**d**) the lumped disturbance \(\Delta_{t}\).
## 1 Introduction
The results show that the bit error performance is improved with the increase in \(Q\); that is, less energy is required to transmit one bit of information. Therefore, a PPM system with a larger \(Q\) can be selected in some application scenarios that require higher energy efficiency. In addition, we also compare the performance of the suboptimal receiver and the optimal receiver in a noncoherent PPM system. The results show that the performance of the optimal receiver is about 2 dB better than that of the suboptimal receiver, but the implementation complexity of the suboptimal receiver is low, so it should be considered comprehensively for use. The rest of this paper is organized as follows. Section 2 introduces the PPM signal structure and describes the noncoherent PPM suboptimal receiver and the optimal receiver, respectively. In Section 3, the accurate expressions of the error performance of the noncoherent PPM suboptimal receiver and the optimal receiver in the Gaussian channel are derived, respectively, and the influence of the decision threshold of the noncoherent PPM suboptimal receiver on the error performance is analyzed. In Section 4, the suboptimal error performance and the best error performance of the noncoherent PPM receiver are numerically simulated, and the simulation results are compared with the theoretical expressions derived in this paper. Section 5 summarizes this paper.
# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 3 Methods
### Ensemble Learning  
This study tested more than ten common models for workload classification and adopted an ensemble learning strategy to derive the final classifier. The application of ensemble learning through a soft voting mechanism can mitigate the issues of high variance and overfitting that often plague individual models [92]. While a single model may exhibit overfitting on distinct data subsets, the ensemble approach using soft voting systematically diminishes these effects, thereby enhancing the stability and accuracy of the overall model on unseen data.  
In selecting the base learners for the ensemble, the choice was made by considering both the enhancement of overall model accuracy and the management of complexity, as well as the characteristics of the base learners themselves. Our dataset contains sequential data from physiological signals, which means that capturing temporal dependencies and  
Figure 1: The architecture of the entire model.  
intricate patterns is crucial. Additionally, the data exhibit non-linear relationships and high dimensionality. Based on these characteristics, three models--random forest (RF), gradient boosting trees (GBT), and FCN-Transformer--were selected as base learners.
###### Abstract  
With the advancement of information technology construction, the integration of radar and communication represents a crucial technological evolution. Driven by the research boom of integrated sensing and communications (ISACs), some scholars have proposed utilizing orthogonal frequency-division multiplexing (OFDM) to separately modulate radar and communication signals. However, the OFDM symbols in this paper incorporate a cyclic prefix (CP) and a virtual carrier (VC) instead of zero padding (ZP). This approach mitigates out-of-band power caused by ZP, in addition to reducing adjacent channel interference (ACI). In addition, we introduce low-density parity-check (LDPC) and use an improved normalized min-sum algorithm (NMSA) in decoding. The enhanced decoding efficiency and minimized system errors render the proposed waveform more suitable for complex environments. In terms of signal processing methods, this paper continues to use radar signals as a priori information to participate in channel estimation. Further, we consider the symbol timing offset (STO) and carrier frequency offset (CFO) issues. In order to obtain more reliable data, we use the minimum mean-square error (MMSE) estimation based on the discrete Fourier transform (DFT) to evaluate the channel. Simulation experiments verify that the system we propose not only realizes the transmission and detection functions but also improves the performance index of the integrated signal, such as the bit error rate (BER) of \(7\times 10^{-5}\), the peak side lobe ratio (PSLR) of \(-13.81\) dB, and the integrated side lobe ratio (ISLR) of \(-8.98\) dB at a signal-to-noise ratio (SNR) of \(10\) dB.  
chirp; carrier frequency offset (CFO); channel estimation; low-density parity-check (LDPC); orthogonal frequency-division multiplexing (OFDM); symbol timing offset (STO) +
Footnote †: journal: Electronics
# The Opto-Electronic Functional Devices Based on Three-Dimensional Lead Halide Perovskites
## 3 The LHP Based Functional Devices
### Photodetectors
#### 3.2.1 Photodetectors by Combining LHP with the 2D Materials
It is also noteworthy that under 895 nm near infrared (NIR) light illumination, which is beyond the absorption edge of LHP, a high responsivity of 5.2 \(\times\) 10\({}^{5}\)A/W is achieved, which is attributed to the carriers excited from the valence band to the trap states in the LHP layer. [PERSON] et al. [91] reported a PdSe\({}_{2}\)/FA\({}_{0.85}\)Cs\({}_{0.15}\)PbI\({}_{3}\) Schottky junction broadband photodetector, which has an expressive optical response in a broadband of 200 to 1550 nm, and the rise/decay time was found to be 3.5 \(\upmu\)s and 4 \(\upmu\)s, respectively. In addition to the high responsivity and detectivity exhibited by the photodetector, the device demonstrated a high polarization sensitivity of 6.04, due to the strong anisotropy in the 2D PdSe\({}_{2}\) crystal structure. [PERSON] et al. proposed a PtSe\({}_{2}\)/Cs-doped FAPbl\({}_{3}\) photodetector, which showed a wideband response from 300 to 1200 nm [99]. The ultra-fast response speed is also impressive, with the rise and decay time being 78 and 60 ns, respectively, which is among the best results reported for LHP-detectors [105; 106]. This ultrafast response was attributed to factors including the built-in electric field at the heterostructure interface [107], the electrical properties of both LHP and PtSe\({}_{2}\), and the reduced trap density in Cs-doped FAPbl\({}_{3}\) as compared to the other LHPs [108]. When the species of material is determined, the specific material quality could affect the device performances. Carrier traps, for instance, possibly lead to the reduction in photoresponsivity and detectivity. By reducing the trap density in the 2D materials through laser healing, the performance of a WSe\({}_{2}\)/LHP photodetector could be improved [98].
## 4 Discussion
This could help any model in identifying local information for a problem where the target is significantly smaller than the whole image. When using multimodal images and different time points, it is also recommended to check and perform image registration. Further basic operations involve normalization and resampling to a standard spacing. Additional strategies that can improve model performance rely on data augmentation, involving not only geometric transformations (rotations, scaling, shifting, mirroring, elastic deformations) but also intensity-based transformations, such as histogram matching and other possible variations in the distribution of gray levels. These techniques can help improve the generalizability of the model, allowing it to handle multicentric data more effectively. Currently, the main segmentation approaches rely on supervised DL, however, unsupervised, semi-supervised, and combined techniques should be further investigated. Most cases rely on a single DNN; however, the possible use of multiple streams for detection, segmentation, and refinement can be a potential alternative. Considering the training strategy, it is essential to carefully balance healthy and lesion inputs. In the case of supervised learning, data imbalance can be addressed by incorporating specific loss functions, such as Dice loss, focal loss, Tversky loss, or even combinations of these losses with basic ones, and using attention mechanisms to maintain focus on the lesion. Thereafter, as misleading tissues such as lymph nodes, microvessels, or small enhanced regions can sometimes lead to false positives and produce multiple fragments, thresholding based on size and analysis of connected components can be used as preprocessing. Moreover, morphological operations can further refine the final output. Ultimately, after validating the segmentation model, clinical applications can also be considered, using the produced segmentation as an input mask for radiomic models, lesion classification tasks, prediction of disease progression, and response to treatment, thereby contributing to improved precision and personalised medicine.
# Communication Technologies in Emergency Situations
## 4 Emergency Communications Technologies--A Systematic Literature Review
### Communication Technologies Integration
The objective of the study in [42] was, first, to construct an improvised antenna to be tested on an area with no cellular network and test the router's operating range using it, and second, to design and develop a mobile multimedia messaging application that utilizes WLAN to send data. Results of the testing show that an improvised antenna achieved an operating range of 192 m, the range achieved 215 m in area with no cellular network, and the application could send multimedia messages over WLAN. Ref. [45] proposes to build ad hoc subnetworks of disconnected smartphones using the Wi-Fi tethering technology and connect them to either the emergency communication equipment deployed in the disaster area or to other smartphones that still have the network connectivity. In [55], they tackle two key technological solutions for future emergency communication networks, such as an architecture based on relay nodes and enhanced user equipment by means of multiple-input-multiple-output (MIMO) textile technology. They implement a real large textile antenna array deployed at user jacket backside.
# Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks
## 3 FSO-Based Backhauling for NTNs
### Self-Sustainability
Strategies included optimal hovering for an unconstrained scenario and a combined hover-and-fly approach using convex programming for constraints. Figure 14: Laser power beaming for airborne nodes. [PERSON] et al. [143] optimized UAV routes for energy-efficient data transfer, charging IoT devices and maximizing throughput under mission constraints. The study compared cooperation strategies, and presented optimal solutions for longer missions and practical approaches for shorter ones. [PERSON] et al. [144] addressed joint UAV trajectory planning and time resource allocation for maximum throughput in a multi-UAV wireless powered communication network. UAVs functioned as base stations to broadcast energy for IoT device charging, while devices transmitted data using harvested energy. They employed a multi-agent Deep Q-Learning (DQL) approach to optimize complex parameters like UAV paths, channel assignments, flight speeds, and transmission powers. The simulation results indicated significant performance gains in minimum throughput compared to the traditional strategies. Simultaneous Wireless Information and Power Transfer (SWIPT) was further explored in the context of airborne nodes [145; 146; 147]. In this approach, the UAV transmits both information and energy to ground-based receivers. The optimization process involves jointly optimizing the transmission power, UAV flight trajectory, and the power splitting ratios used by the receivers. The work in [145] focused on maximizing the harvested energy across users, while considering the constraints on their achievable data rates. UAV-enabled SWIPT was further explored in [147], particularly in relation to its applicability in IoT scenarios, specifically for emergency communication purposes.
# Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks
## 2 Related Works
### Video Analysis in MEC Scenarios  
Due to the increasing demand of video surveillance applications for resources [29], offloading them to edge devices for computing has been widely studied by industry scholars. Additionally, these studies focus on optimizing the target through intelligent offloading or adaptive configuration. The authors in [30] observed ROI changes from the perspective of the UE, when they decoupled the rendering and offloading parts with fast object tracking used locally in order to solve this problem. The authors in [31] investigated edge-end cloud collaboration for real-time video analytics and designed an online algorithm to achieve near-optimal utility by adjusting the quality of video frames generated on the UEs. The authors in [32] designed a new video configuration decision-making system, which examined the influence of video content on the frame rate and the resolution of the video stream. The authors in [33] optimized the video configuration and network bandwidth resource allocation using the Lyapunov and the Markov approximation, which solved the problems of resource limitation and network dynamic changes in edge-based video analysis systems. To consider fairness and long-term system cost, as well as optimizing the overall user QoE, the authors in [34] proposed an intelligent edge cache system to solve the bandwidth requirement and delay tolerance of 3600 panoramic video footage. To realize secure video sharing in vehicular edge computing, the authors in [35] designed an attribute-based encryption algorithm with static and dynamic attributes, and utilized a blockchain to record access strategies, which could ensure the data security and privacy of the video footage. In order to improve the QoE of live-streaming video, the authors in [36] first selected the candidate transcoding tasks by their contribution to popularity-weighted video quality and assigned these tasks to MEC in a greedy manner. The authors in [37] proposed a segment prefetching and edge caching algorithm to improve the QoE of Hyper Text Transfer Protocol (HTTP) adaptive video streaming. They first proposed and analyzed different segmentation prefetch strategies to dynamically adapt to the current conditions of the network and the needs of service providers. Moreover, they presented segment prefetching policies based on different approaches and techniques, and they studied their performance and feasibility. However, for real surveillance video, the resolution can only be selected downward. When the network resources are sufficient, we can then consider selecting the higher resolution video using super-resolution techniques to obtain higher video frame resolution and video analysis accuracy.