esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### Communication Model  
Tasks consume bandwidth resources and incur transmission delays only when they are offloaded to the edge server for execution. The offloading of the MDs task at time slot \(t\) is \(x^{t}_{\text{all}}\ =\ \left[x^{t}_{1},x^{t}_{2},x^{t}_{3}\cdots x^{t}_{m}\right]\), for the bandwidth occupied when offloading the task is \(B^{t}_{\text{all}}\ =\ \left[B^{t}_{1},B^{t}_{2},B^{t}_{3}\cdots B^{t}_{m} \right]^{\text{T}}\), and the task is subject to the bandwidth constraint when offloading, i.e., \(x^{t}_{\text{all}}\cdot B^{t}_{\text{all}}\ \leq\ B_{\text{max}}\). According to [PERSON]'s formula, we can find the transmission rate.  
\[r_{m,n}=B^{t}_{m}\log_{2}\left(1+\frac{P^{\text{tran}}_{m,n}g}{\sigma^{2}}\right) \tag{10}\]  
where \(g\) is the channel gain and \(\sigma^{2}\) denotes the power of additive Gaussian white noise. Thus, the transmission delay and transmission energy consumption of the task can be derived from Equations (11) and (12).  
\[\tau^{\text{tran}}_{m,n}\ =\ \frac{D_{m}(t)}{r_{m,n}} \tag{11}\]  
\[E^{\text{tran}}_{m,n}\ =\ {P^{\text{tran}}_{m,n}\tau^{\text{tran}}_{m,n}} \tag{12}\]  
In summary, the overall model of the system is a trade-off between the time delay and energy consumption of the task computation to create a minimization cost problem (13), and the solution goal is to minimize the total cost of the tasks generated in the system over time.  
\[\begin{split}&\mathbb{C}=\sum_{n=1}^{N}\sum_{m=1}^{M}\left( \alpha\left(\tau^{t}_{m,MD}+\tau^{t}_{m,nEC}\right)+\beta\left((1-x_{m}(t))E^{ \text{device}}_{m}\ +x_{m}(t)y_{m,n}(t)E^{edge}_{m,n}\right)\right)\\ &\min\ \lim_{T\rightarrow\infty}\frac{1}{T}\sum_{t=1}^{T} \mathbb{C}\\ & s.t.\ \ \ \ \mathbb{C}1:x^{t}_{m}\in\{0,1\}\\ &\mathbb{C}2:y^{t}_{m,n}\in\{0,1\}\\ &\mathbb{C}3:\sum_{n\in\mathcal{N}}y^{t}_{m,n}=1,\forall m\in \mathcal{M},t\in\mathcal{T},x^{t}_{m}=1\\ &\mathbb{C}4:\tau^{t}_{m,MD},\tau^{t}_{m,nEC}\leq\tau^{t}_{m, \text{max}}\\ &\mathbb{C}5:f^{\text{device}}_{m}\ \leq\ \int^{\text{device}}_{\text{max}}\\ &\mathbb{C}6:\sum_{n\in\mathcal{N}}f^{EC}_{n}\leq f^{EC}_{\text{ max}}\\ &\mathbb{C}7:0\leq p^{\text{tran}}_{m,n}\leq P_{\text{max}}\\ &\mathbb{C}8:\sum_{n\in\mathcal{M}}x^{t}_{\text{all}}\.\ B^{t}_{\text{all}}\ \leq B_{\text{max}},\forall t\in\mathcal{T}\end{split} \tag{13}\]  
The constraints C1-C3 indicate that the generated task can only select one computing node for computational processing; C4 indicates that the total processing delay of the task, whether it is processed locally or offloaded to an edge node for processing, should not exceed the maximum tolerated delay of the task; C5-C6 indicate that the computational resources allocated for the task are not allowed to exceed the maximum processing capacity of the node; C7-C8 indicate that the transmission power of the task cannot exceed the maximum transmission power, and the bandwidth occupied by all offloaded tasks in the same time slot should not exceed the maximum bandwidth resource. Table 1 lists the important notations used in this paper.
# Pilot-Based Adaptive Channel Estimation for Underwater Spatial Modulation Technologies
## 7 Conclusions  
SMTs are playing vital role in underwater sensor nodes because that can enhance battery life. SMTs need a semi-perfect channel known at the receiver side to be capable of estimating the constellation and index of information bits. In this paper, an adaptive pilot-based RLS algorithm is proposed to estimate the UWA channel for UWA-SMTs. The SMT data has been modulated by 16-QAM, and transmitted over 4 \(\times\) 4 UWA-MIMO channel, under different pilot lengths 2%, 5%, 10%, and 15%, at 7 bpcu for FGSM and 6 bpcu for SM and GSM. Using pilot-based RLS channel estimation, both the data and antenna indices are detected by ML optimal detector. The simulation results are conducted under a UWA-MIMO channel with and without Doppler-scaling factor. Based on the simulation results, we demonstrated that the pilot-based RLS channel estimator is capable for the SMTs-UWA-MIMO system and provide BER performance nearly as good as a P-CSI.  
The authors contributed equally to this manuscript. This research was funded by The National Natural Science Foundation of China (61671394). This work is also supported by the National Key research and development Program of China (2018 YFC0809200), Science and Technology Program of Shenzhen, China (JSGG20170414090428464), the Fundamental Research Funds for the Central Universities (20720170044). The National Natural Science Foundation of China (61671394) supports this work. This work is also supported by the National Key research and development Program of China (2018 YFC0809200), Science and Technology Program of Shenzhen, China (JSGG20170414090428464), the Fundamental Research Funds for the Central Universities (20720170044). The authors declare no conflict of interest.
# Evaluation of AEP Predictions for Commercial Wind Farms in Sweden
## 5 Conclusions  
Compared to the wind-index-corrected and availability-corrected production, the predicted AEP from the planning phase of the wind turbines installed in Sweden after 2005 with a rated power of at least 1.8 MW was overestimated, on average, by 7.1%. This is higher than the previously proven overestimated AEP predictions for projects in the British Isles and South Africa. Dividing the results by terrain type, the error is larger for wind turbines in open terrain than in forest areas, indicating that the more complex forest terrain is not the reason behind the error. Dividing the results by installation year, improvement, which had been expected due to continuous refinement of methods and better data availability, was not observed over time. The major source of error is regarding whether the predicted AEP reported from the wind turbine owners to Vindstat comes from the wind energy calculation from the planning phase (i.e., the P50 value). However, this is likely to be the case for most multi-megawatt wind turbines installed after 2005.  
Conceptualization, E.M. and D.L.; methodology, E.M. and D.L.; formal analysis, E.M.; writing--original draft preparation, E.M.; writing--review and editing, E.M. and D.L.; Visualization, E.M.; project administration, E.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Special thanks to [PERSON] from Vindstat and to [PERSON] from Halmstad University. Thanks also to [PERSON] and [PERSON] from Halmstad University.  
The authors declare no conflict of interest.  
Figure 4: Error of P50 evaluations compared to the production-based WCP depending on the installation year plotted with the standard deviation (blue) and number of wind turbines (red).
# Emerging Topics in Joint Radio-Based Positioning, Sensing, and Communications
## 3 Emerging Topics in JPSAC Context
### Near-Field Channel Modeling and Estimation  
Near-field and far-field propagation are concepts in electromagnetics that describe how electromagnetic waves behave at different distances from a source, when distance is measured with respect to the signal wavelength and antenna dimension. Traditional communications, with a low-to-moderate number of antennas and wavelength in cm-Wave and mm-Wave frequencies reach the far-field mode very close to the emitter, and therefore the near-field effects can be ignored. However, with the advent of mMIMO and umMIMO and the interest in higher and higher carrier frequencies, moving towards the THz ranges, the near-field propagation mode can no longer be ignored and it has started to garner significant interest among researchers, especially over the past year [44; 45; 57; 58; 59].  
According to [57], a signal is placed in near-field propagation conditions as long as its range to the emitter is below the near-field threshold \(R_{NF}=\frac{2A^{2}}{\lambda}\) and it propagates according to far-field laws when its distance to the emitter becomes higher than \(R_{NF}\), where \(A\) is the antenna's maximum linear size (in m) and \(\lambda=\frac{\varsigma}{\gamma_{c}}\) is the signal wavelength (in m), with \(c\) being the speed of light (in m/s) and \(f_{c}\) being the carrier frequency (in Hz). For example, a linear antenna with a moderate 8 antenna elements spaced at \(\lambda/2\) apart and operating in the cm-Wave bands at 1 GHz would have a near-field threshold \(R_{NF}=7.3\) m, which means that one could neglect the near-field propagation without a significant impact on the modeling, but an umMIMO antenna with 1024 antenna elements spaced at \(\lambda/2\) apart and operating in the mm-Wave bands at 60 GHz would have \(R_{NF}=2614.5\) m; thus, the near-field propagation mode can no longer be ignored.  
Unlike the far-field situation, when the signal strength depends only on the distance between the transmitter and the receiver, but not on the angle, the signal strength in near-far conditions is dependent on both the angle and the distance.  
The relevant open research questions here are, for example, how to derive unified channel models for far- and near-field propagation, how to address the increased complexity of the channel models in near-field conditions, and how to perform precise beam alignments.
## 2 Geological Setting  
The Central Anticline Belt is located in the middle of the Xihu Sag, bordered by the western slope zone to the west and the Diaoyudao Uplift to the east (Figure 1a), develops a series of anticlines, and is located in the hydrocarbon-generating sub-sag. It was formed in the Longjing Movement at approximately 13 Ma, and is one of the most favorable areas for gas accumulation. The Xihu Sag has undergone several tectonic events: the Paleocene-Eocene syn-rift stage, the Oligocene-Micene early post-rift stage, and the Pliocene-present late post-rift stage (regional subsidence) [23; 24]. Among them, the Longjing Movement since the Miocene has had the most significant impact on the formation and evolution of the Central Anticline Belt, as well as on oil and gas accumulation. The E-W compressional stress during the Longjing Movement led to the formation of this NE-SW-oriented Central Anticline Belt and caused the strata in the Central Anticline Belt to be significantly uplifted and eroded [25; 26; 27].  
The Xihu Sag deposits a series of Cenozoic strata (Figure 1b): from bottom to top, this includes the Eocene Pinghu Formation (T40-T30), the Oligocene Huagang Formation (T30-T20), the Miocene Longjing Formation (T20-T16), Yuquan Formation (T16-T12), Li-Uang Formation (T12-T10), the Pliocene Santan Formation (T10-T0), and the Quaternary Donghai Formation. It is worth noting that the Eocene-Oligocene Pinghu Formation and Huagang Formation are the study intervals of this research. The Pinghu Formation was formed during the syn-rift stage, depositing shallow marine to continental sediments [28,29]. It contains significant source rocks and reservoirs and is a hydrocarbon-bearing formation in the Xihu Sag. The Huagang Formation mainly developed during the early post-rift stage of the basin, depositing a typical set of fluvial-lacustrine strata with a thickness of 600 m to 1000 m. It is also one of the main target layers for oil and gas exploration in the Xihu Sag.
## 1 Introduction
To verify the effectiveness, the proposed SV strategy is compared to the hard voting (HV) strategy, and the latter uses slice-based weighting. Furthermore, the baseline deep networks are compared to those with proposed strategies. Experimental results suggest that the proposed SV strategy improves tumor classification performance due to the utilization of tumor sizes and slice-level predicted probabilities.
## 3 Materials and Methods
### Data Collection, Dynamic Analysis, and Dataset Generation
#### 3.1.2 Cuckoo Sandbox Setup  
The dynamic analysis component of this research is a critical aspect of the methodology, leveraging the capabilities of Cuckoo Sandbox [49] a renowned open-source tool for analysing malicious code. It provides an isolated environment using virtual machines to ensure that the malware does not affect the host system or network. Cuckoo Sandbox employs API hooks, which are instrumental in capturing the malware's behaviour, providing insights into how it interacts with the system. Multiple virtual machines, with the usage of docker containers, were employed for this purpose. Each virtual machine ran a 64-bit Windows 10 system. A virtual machine snapshot was used to maintain the integrity of the analysis environment. After the execution of each PE file, the virtual machine was reverted to a clean state using these snapshots to ensure an uncontaminated environment. The execution of each Windows PE file resulted in the generation of a JSON file. These JSON files were comprehensive, detailing the behaviour of the malware during its execution. The study amassed a significant collection of these files, with 583 malware and 438 goodware JSON files. The selection criteria were based on the presence of imported functions, API calls, or both within these files. This process led to a curated dataset comprising 1021 JSON files to facilitate a comprehensive analysis.
## 2 Materials and Methods
### Data Preprocessing and Feature Extraction  
The signal was initially processed by applying Notch filters at 50 and 100 Hz to eliminate power line interference, and a high-pass filter at 1 Hz was implemented to remove the DC offset and baseline fluctuations on all signals using the MNE package Python version 3.8.5 [31; 32]. Next, a low-pass filter was implemented at 125 Hz to maintain higher frequencies that could characterize abnormal brain activity [33; 34]. After preprocessing, the EEG signal was divided into 5 s non-overlapping time windows, and a set of 53 commonly used features [16] was extracted using the MNE-Features subpackage (see Table A2). These features contained time domain features such as the mean, variance, standard deviation, skewness, and kurtosis, as well as essential frequency domain features such as the power spectral density, spectral entropy, and Hjorth parameters (mobility and complexity).
# Design of an SVM Classifier Assisted Intelligent Receiver for Reliable Optical Camera Communication
## 3 OCC System Overview
### Proposed SVM Classifier Assisted Intelligent Receiver
#### 3.3.2 Accurate Data Transmitting LED Region Separation  
In Figure 5, the detected LED regions with bounding boxes are the output of the CNN. The bounding box LED objects are then processed employing several image processing techniques. At first, each LED image is converted into a grayscale format \(f_{p\times q}\) to specify the distinct pixel intensity values of the stripes generated in the image sensor. Next, a \(2\times 2\) kernel full of ones is used to reduce the shape of the projected stripes by removing small anomalies near the stripe boundaries. To make the receiver an intelligent and robust retrieval system, the system needs to respond by differentiating accurate LEDs as quickly as possible. Therefore, the selection of features is very important. The extraction of appropriate features is very challenging since the objects are almost the same. LED objects of the same type can have a different shape, stripe pattern based on the communication distance, camera frame rate, and mark and space frequency. To analyze the different geometrical shapes, computation of the features from the contour line has been considered significant distinguishable features [31]. Contours are the outline that is designed using the edges of the object to represent the shape. They contain some geometrical attributes that are effective to recognize and segment objects. After drawing a contour on each separated region, a feature set \([M_{00},S_{T},P_{R},N_{L}]\) is formed that includes zero order moments or area of LED region, no. of stripes per LED region, perimeter of combined stripes contour, and no. of line segment of combined stripes contour, respectively. Based on the extracted input features, a trained SVM classifier detects accurate data transmitting LED regions.
## 3 Experimental Investigation
### Time Reversal Focusing of Longitudinal Modes  
Considering the entire symmetry of the testing system, the longitudinal modes are mainly excited and received. Alternatively, the bending modes are hardly captured by transducers due to their anti-symmetric vibrating characteristic with respect to the axis of circular section. To verify the system reliability of realizing TRM for focus and reconstruction of signals with multimode and multipath in a rod, the guided waves consisting of multiple longitudinal modes are generated. Afterwards, a truncated captured signal including multiple paths is inverted in the time domain and then reemitted.  
Figure 2: Experimental setup overview.  
To reduce the complexity of hardware system and experimental operation, the modified TRM [29], which always allows one transducer to work as an actuator while the other as a receiver during the whole process, is used to achieve TRM in all the experiments in this study.  
When a 8-cycle Hanning-windowed tone burst with the central frequency 760 kHz and the amplitude of 8 V shown in Figure 3a is applied to the actuator as an input signal, the response signal at the receiver mainly contains multiple modes of \(L(0,2)\) and \(L(0,3)\), dispersion of which is relatively severe at the frequency. Furthermore, the both modes are of different transmission routes, namely, the direct path and the boundary reflected paths, as shown in Figure 3b. After the signal in Figure 3b is truncated at time \(t=T\), the extracted part is time reversed, normalized to the same amplitude as the original input signal, and then reemitted by the same actuator. Figure 3c shows the received signal after TRM and the reconstructed main band with no dispersion and more than 3 times improved amplitude. Reversing the signal in Figure 3c with respect to an axis of \(t=T/2\) in accordance with Equation (7), the reconstructed input signal focuses on the trigger position exactly, which is obvious to see in Figure 3d, and bears a striking similarity to the original excitation signal particularly exhibited in Figure 3e.  
Figure 3: _Cont._
# Progress in Transparent Nano-Ceramics and Their Potential Applications
## 6 Discussion
### Comparison of Different Sintering Methods  
At present, the main sintering methods for preparing transparent nano-ceramics include HP, vacuum sintering, HIP, SPS, and microwave sintering. HP sintering is a high-pressure, low-strain-rate powder metallurgy process in which the creep process of sintering is controlled by applying heat and pressure. Due to the simultaneous application of force and heat, transparent nano-ceramics can thus be prepared at relatively low temperatures and achieve the desired density. High pressure can inhibit grain growth and induce plastic deformation to eliminate pores in grains, so the sintering mechanism under high pressure is completely different from that under normal pressure. Vacuum sintering belongs to the method of pressureless sintering. In a vacuum environment, a pressure difference is formed between the inside of the ceramic and the outside world, which helps the discharge of pores, reduces the porosity of the ceramic, makes the grains grow, and forms a high-density ceramic material. Vacuum sintering has the advantages of simple operation, low cost, and high production efficiency. It is currently the most widely used transparent ceramic sintering technology. Some oxide-based ceramic materials, such as rare-earth-doped Y\({}_{2}\)O\({}_{3}\) and Al\({}_{2}\)O\({}_{3}\), and other transparent nano-ceramic ceramic materials, can be prepared by vacuum sintering. For the traditional sintering process, HIP is a key step in the preparation of transparent nano-ceramics with high light transmittance, which can reduce the porosity inside the material and thus maximize the material density. To reduce manufacturing costs, HIP is usually used as the last step in the two-step sintering methods (HP + HIP sintering; vacuum sintering + HIP sintering).  
The SPS method is a new technique for heating and sintering by directly passing a pulse current between the nano-powder particles. Compared with the HIP method or the HP method, it has the distinctive features of a fast heating rate, short sintering time, controllable structure, energy savings, and environmental protection. In addition, since the sintering of transparent nano-ceramic materials requires the consideration of various factors, the SPS method can also be easily combined with optimization, such as doping of sintering aids, which makes its application range larger. During the microwave-sintering process, the processing material heats up very quickly, which can be carried out at low sintering temperatures and short sintering times to obtain nano-ceramics with high transparency. In contrast, microwave sintering has the advantages of a short fabrication time and low processing cost.
## 3 Proposed Method
### AE (Autoencoder) for Subject Modeling  
The _S-vector_ extracted by the CNN feature extractor is used as the input of an AE. An AE is a type of deep neural network that has the same dimensions for input and output [14]. In the training phase, the AE is trained by using the same data (sample signatures) as input and output. In the test phase, the trained AE generates an output corresponding to an input (test signature). An AE can generate  
Figure 5: Operation of the one-dimensional convolution layer.  
highly similar output for trained data patterns, whereas it does not for unfamiliar data. Therefore, a test signature is verified by a similarity comparison between the test signature and the output of the AE. Consequently, an AE is used in modeling a subject for authentication.  
The proposed scheme uses a five-layer AE, as illustrated in Figure 6. Note that the entire architecture of the CNN-AE model is depicted in Figure 4. The AE itself consists of two blocks: encoder and decoder. The outputs of the encoder and decoder are denoted as \(z\) and \(S^{\prime}\), respectively, and defined as Equations (10) and (11), respectively. The encoder accepts \(S\) as the input and produces \(z\) that is implemented as hidden nodes in the neural network. The decoder accepts \(z\) and produces \(S^{\prime}\). \(\sigma_{1}\) and \(\sigma_{2}\) are the activation functions.  
\[z=\sigma_{1}(WS+b) \tag{10}\]  
\[S^{\prime}=\sigma_{2}(W^{\prime}z+b^{\prime}) \tag{11}\]  
The loss function that is the objective for training a neural network is defined as in Equation (12). It is the sum of all differences between the input and output.  
\[L(S,S^{\prime})=\sum|S-S^{\prime}| \tag{12}\]  
The training of the AE is a procedure involving the modification of weights \(W\) and \(W^{\prime}\) in Equations (10) and (11), respectively, for minimizing \(L\) with \(S\) as both input and output. In the test phase, which is the verification procedure in this problem, a newly input signature \(S\) to be verified is entered into the AE and compared with the output \(S^{\prime}\) of the AE. The proposed scheme applies mean square error (MSE) for the similarity comparison. MSE refers to the difference between \(S\) and \(S^{\prime}\) and is defined as Equation (13). Using MSE, the verification rule is defined as Equation (14). If the difference is less than a pre-defined threshold \(t\), the signature is accepted as a valid one.  
\[Diff=MSE(S,S^{\prime}) \tag{13}\]  
\[Diff<t,t:threshold \tag{14}\]