esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Detection Techniques
### Detection Technique III  
Detection Technique I prioritizes complexity over bit error rate, while Detection Technique II aims to balance both bit error rate and detection complexity. However, the bit error rate of Detection Technique II does not approach the ideal performance of maximum likelihood detection. Therefore, it is essential to identify a detection technique that enhances the bit error rate compared to both Techniques I and II, with only a slight increase in complexity. Accordingly, the output symbol can be decoded from Equations (26) and (27) as follows:  
\[\hat{x_{3}}=\arg\min_{x_{3}\in\xi}\|\hat{y_{3}}-cx_{3}\|^{2}\quad\quad\hat{x_{4 }}=\arg\min_{x_{4}\in\xi}\|\hat{y_{4}}-cx_{4}\|^{2} \tag{29}\]  
\[\hat{x_{1}}=\arg\min_{x_{1}\in\xi}\|\hat{y_{1}}-b\hat{x_{3}}-ax_{1}\|^{2}\quad \quad\hat{x_{2}}=\arg\min_{x_{2}\in\xi}\|\hat{y_{2}}-b\hat{x_{4}}-ax_{2}\|^{2} \tag{30}\]  
In this method, rather than using Equation (26) to jointly identify two pairs of transmitted symbols in the LC-ML decoder, we first use Equation (27) to calculate the values of \(\hat{x}_{3}\) and \(\hat{x}_{4}\). Then, by replacing \(x_{3}\) and \(x_{4}\) into (29), we can determine \(x_{1}\) and \(x_{2}\). Equation (19) yields the following when the first and second rows are eliminated:  
\[\left[\begin{array}{c}\hat{y}_{1}^{\prime}\\ \hat{y}_{2}^{\prime}\end{array}\right]=\left[\begin{array}{ccc}m&0&n&0\\ 0&m&0&n\end{array}\right]\left[\begin{array}{c}x_{4}\\ x_{3}\\ x_{2}\\ x_{1}\end{array}\right]+\left[\begin{array}{c}\hat{n}_{1}^{\prime}\\ \hat{n}_{2}^{\prime}\end{array}\right] \tag{31}\]  
Then, \(x_{3}\) and \(x_{4}\) can be detected as  
\[\hat{x_{3}}=\arg\min_{x_{3}\in\xi_{6}}\|\hat{y}_{2}^{\prime}-n\hat{x}_{1}-mx_{ 3}\|^{2}\quad\hat{x_{4}}=\arg\min_{x_{4}\in\xi}\|\hat{y}_{1}^{\prime}-n\hat{x _{2}}-mx_{4}\|^{2} \tag{32}\]  
The D-QR-IC can be summed up using the steps below:  
* Find \(x_{3}\) and \(x_{4}\) from (27) first. After substituting \(x_{3}\) and \(x_{4}\) into (26), \(x_{1}\) and \(x_{2}\) should be discovered.
* To get the final values of \(x_{3}\) and \(x_{4}\), substitute \(x_{1}\) and \(x_{2}\) in Equation (32). Utilizing the \(\hat{x_{1}}\), \(\hat{x_{2}}\), \(\hat{x_{3}}\), and \(\hat{x_{4}}\), GQSM symbol detection is accomplished.
## 3 Hybrid Topology Architecture
### Procedure Description
Then, it sends topology change messages to be queued at the LoRaWAN server as downlink messages addressed to the end devices, indicating that their transmission mode (single-hop or multiple-hop, that is, star or mesh topology, respectively) should change. The end devices receive the messages and change their transmission mode accordingly. As a result, the topology of the network is modified and the end devices with worse performance deliver their data to other end devices with better performance for their re-transmission (multi-hop). Therefore, as shown in Table 2, end devices using a single-hop transmission mode would form a star topology. Conversely, end devices in multi-hop transmission mode could be part of a mesh topology, sending messages to other end devices, or a hybrid (star and mesh) topology, re-transmitting messages from other end devices to the gateway. The packet transmission scheme describing this process is exemplified in Figure 2. The packet format of LoRaWAN is maintained; therefore, no extension of packet specifications is required. The deployment of the proposed adaptable hybrid topology framework for LoRaWAN networks is server and application agnostic and, as a result, fully compatible with any LoRaWAN implementation. End devices can be configured for three types of topologies, as shown in Figure 3. Star topology is the standard/conventional LoRaWAN connection, where the end device is activated manually, and the topology of the network is in the shape of a star or star-of-stars. This state can be reached when the device is restarted (event 0) or a message with the request to change to star topology is received (event 3). Mesh topology is configured in the end devices that transmit their data to other end devices for their further re-transmission. \begin{table}
\begin{tabular}{c c} \hline \hline
**Transmission Mode** & **Topology Mode** \\ \hline Single-hop & Star \\ Multi-hop & Hybrid \\ Mesh \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Relationship between transmission modes and topology modes. This state is reached when a message with the petition to change to mesh topology is received (event 1). Lastly, hybrid topology is configured in the intermediate end devices that receive data from other end devices and re-transmit them to the gateway. This state is reached when a request to change to the hybrid topology is received (event 2). Figure 3: Diagram of the end device states. Figure 2: Message exchange among the elements of the architecture.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 3 Threats and Challenges in Underwater Wireless Sensor Networks (UWSNs)
### Active Attacks
The main categories of this kind of attacks are: routing protocol attacks and media access control (MAC) protocol attacks. Routing protocol attacks can cause packets unable to be transferred to the destination node, and even disrupt the operation of the network. These types of attacks are mounted on the routing protocols, such as routingtable overflow, routing table poisoning, packet replication, and rushing attacks. Through these malicious behaviors, attackers can attract packets and analyze or even drop packets at its will. MAC protocol attacks aimed at disrupting the mechanism that control nodes access to channel. Malicious attackers can continuously occupy the channel to deprive legitimate nodes' chances of sending packets. Moreover, for the request to send/clear to send (RTS/CTS) handshake MAC protocol, continuously sending RTS packets would consume nodes' battery which is a cheap and easy way to lunch attacks. To defend against these attacks, the feasible solution is using encryption, authentication and trust management mechanisms suitable for UWSNs. * _DoS attacks_: DoS attacks attempt to make resources and services unavailable to the legitimate nodes. To achieve this goal, the attacker tried to prevent legitimate nodes to access services offered by the network. DoS attacks can be passive or active, and can be carried out in many different ways. Combined with other passive or active attacks, it is more difficult to detect and defend against these attacks. Among these active attacks discussed above, DoS attacks are more destructive, complicated, and hard to detect [15]. To guard UWSNs from DoS attacks, the approaches and features should be comprehended.
# Dual-Channel Feature Enhanced Collaborative Filtering Recommendation Algorithm
###### Abstract  
The dual-channel graph collaborative filtering recommendation algorithm (DCCF) suppresses the over-smoothing problem and overcomes the problem of expansion in local structures only in graph collaborative filtering. However, DCCF has the following problems: the fixed threshold of transfer probability leads to a decrease in filtering effect of neighborhood information; the K-means clustering algorithm is prone to trapping clustering results into local optima, resulting in incomplete global interaction graphs; and the impact of time factors on the predicted results was not considered. To solve these problems, a dual-channel feature enhanced collaborative filtering recommendation algorithm (DCFEC) is proposed. Firstly, the self-attention mechanism and weighted average method are used to calculate the threshold of neighborhood transition probability for each order in local convolutional channels; secondly, the K-means++ clustering algorithm is used to determine the clustering center in the global convolutional channel, and the fuzzy C-means clustering algorithm is used for clustering to solve the local optimal problem; then, time factor is introduced to further improve predicted results, making them more accurate. Comparative experiments using normalized discounted cumulative gain (NDCG) and recall as evaluation metrics on three publicly available datasets showed that DCFEC improved by up to 2.3% and 4.1% on two metrics compared to DCCF.  
recommendation algorithm; collaborative filtering; transition probability threshold; fuzzy c-means clustering; time factor +
Footnote †: journal:  
00 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Experimental and Numerical Simulation Data Analysis
### Material Constitutive Model  
The explosive action belongs to the strong impact action, indicating that the metal materials have a strain rate effect under a strong impact load. The material for the numerical model is selected for a constitutive model that can describe the mechanical behavior of metal under a high strain rate, and mainly considers two factors: (1) material strength, that is, the charge of material strength under a large strain rate, and (2) equation of state, that is, the material will have plastic flow due to its large deformation or even failure. Therefore, there must be a state equation describing the fluid mechanical behavior of the material in the constitutive relation.  
The strength model adopts the Johnson\(-\)Cook model:  
\[\sigma=(A+B\cdot\varepsilon^{n})\cdot(1+C\cdot\ln\ \dot{\varepsilon}^{*})\cdot(1-T^{*m}). \tag{2}\]  
where \(A\) is the yield stress, \(B\) is the strain hardening coefficient, \(\varepsilon\) is the equivalent plastic strain, \(n\) is the strain hardening index, \(C\) is the strain rate correlation index, \(\varepsilon^{*}\) is the reference strain ratio, \(T\) is the relative temperature, and \(m\) is the temperature softening coefficient, as shown in Table 3. The three parentheses reflect the strain (pressure) effect, the strain rate effect, and the thermodynamic effect, respectively.  
Figure 5: Numerical model.  
The temperature term of the Johnson\(-\)Cook constitutive equation obtained from the macroscopic test can describe the thermal softening property of the material itself, but fails to better reflect the softening property of the material under adiabatic shear that is described by introducing the failure model of plastic shear softening.  
\[D=(\epsilon-\epsilon_{\alpha})/(\epsilon_{f}-\epsilon_{cr}). \tag{3}\]  
where \(D\) is the adiabatic shear, \(\epsilon_{cr}\) is the initial strain for the occurrence of adiabatic shear damage, and \(\epsilon_{f}\) is the failure strain when the material produces adiabatic shear. Once damage occurs inside the material, the flow stress in the damage zone is as follows:  
\[\sigma=\sigma_{0}(1-D). \tag{4}\]  
where \(\sigma\) is the flow stress, \(\sigma_{0}\) is the initial flow stress, and \(D\) is the adiabatic shear. This state equation is similar to the Gruneisen state equation in LS-DYNA. The failure criterion is based on the ideal material, which is not limited by defects and other factors. In this study, a single failure threshold (strain 0.25) was selected as the failure criterion.
# Consolidating ICESat-2 Ocean Wave Characteristics with CryoSat-2 during the CRYO2 ICE Campaign
## 5 Discussion
With ocean waves able to reach several hundred meters, larger gaps in the data reduce the number of available waves that can be used to determine the SWH; this could induce a bias toward smaller SWHs for rougher seas, and of course using a small number of waves to determine the SWH will probably result in a bad estimate. While we do find a significant negative difference between ICESat-2 and CryoSat-2 at high SWHs (above 8 m), for the comparison between CryoSat-2 and ICESat-2, we have very few CryoSat-2/ in situ observations in this region to use to accurately determine the behavior of CryoSat-2 and ICESat-2 at these SWHs. While there is no clear difference compared to other satellite altimeters, as seen in Figure 3, the difference at this wave height between ICESat-2 and CryoSat-2 cannot be directly validated with in situ data. A limitation of this study is the use of co-linear orbits containing ocean data that are only located in the Northern Hemisphere. Validating the model for larger SWHs requires enough observations at these wave heights, and although cloud cover is a problem for laser observations because it limits observations of rough seas, observations of the rough seas of the Southern Ocean would be of great importance. However, the co-linear orbits of ICESat-2 and CryoSat-2 drift too far apart at these latitudes under the current orbital configuration and their orbits would need adjustments to allow them to have the same collinearity in the Southern Hemisphere. Further usage of ICESat-2 ocean data will also be of interest when the next radar altimeter satellite, CRISTAL, is launched. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **Reference** & **Mean Diff. [m]** & **RMSE [m]** & **Corr.** & **N** \\ \hline \multicolumn{5}{c}{Validation} \\ CryoSat-2 & In Situ & \(-\)0.01 & 0.31 & 0.98 & 2124 \\ CryoSat-2 & Sentinel-3A & 0.11 & 0.29 & 0.98 & 11,358 \\ CryoSat-2 & Sentinel-3B & 0.12 & 0.29 & 0.98 & 6602 \\ CryoSat-2 & SARAL & 0.00 & 0.25 & 0.96 & 16,140 \\ \hline \multicolumn{5}{c}{Method Comparison} \\ Histogram Model & CryoSat-2 & 0.14 & 0.42 & 0.95 & 37,861 \\ Standard Deviation Model & CryoSat-2 & 0.17 & 0.33 & 0.97 & 37,861 \\ ATL12 & CryoSat-2 & \(-\)0.12 & 0.29 & 0.97 & 36,763 \\ \hline \multicolumn{5}{c}{Analysis by [3]} \\ ATL08-Ccean \({}^{\ddagger}\) & ERA-5 & 0.07 & 0.38 & 0.91 & N/A \\ ATL12 & ERA-5 & \(-\)0.16 & 0.33 & 0.95 & N/A \\ CryoSat-2 & ERA-5 & \(-\)0.01 & 0.36 & 0.95 & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Collection of the comparisons done in this study, including the validation of CryoSat-2, comparisons between the methods derived in this paper and CryoSat-2, and the comparison of similar methods with ERA-5 [3] (\({}^{\ddagger}\) ATL08-Ocean determines the SWH from the individual surface waves, like the histogram model).
## 3 Finite Element Method (FEM)
### Boundary Conditions  
Solar radiation, convection, and ambient temperature were set as boundary conditions for the simulated model. The main heat source was the incident solar radiation over time, which varied according to the geographic location of the sample and the Sun orientation (zenith angle and solar elevation). This parameter was included using the External Radiation Source feature in the software, which was configured based on the latitude (28\({}^{\circ}\)13\({}^{\prime}\)36.28\({}^{\prime\prime}\) S), longitude (52\({}^{\circ}\)23\({}^{\prime}\)10.92\({}^{\prime\prime}\) W), and time zone (\(-\)3) of the test location.  
The next boundary condition was the heat transfer by convection. Since [PERSON] and [PERSON] [42] established an approximate correlation between the wind speed (V\({}_{\rm r}\)) and convective heat transfer coefficient (h), their study was used to determine a daily wind-induced convective heat transfer in the present study, using the following equation:  
\[\rm{h}=6.5+3.3V_{\rm r}\left(W/m^{2}.K\right)V_{\rm r}\leq 6~{}m/s \tag{4}\]  
[PERSON] et al. [23; 24; 35] used a similar approach to calculate the convection coefficient for their numerical thermographic simulation. However, they performed a 1-day test and stipulated a constant heat transfer coefficient based on the maximum wind speed data. In this study, the daily average of wind speed was used to find the daily convective heat transfer coefficients.  
The hourly ambient temperature variation was also used as a boundary condition. The diurnal variation of the ambient temperature (T\({}_{\rm amb}\)) follows a simple sinusoidal periodic distribution of 24 h, depending on the average daily temperature (T\({}_{\rm avg}\)) [43]:  
\[\rm{T}_{\rm amb}=T_{\rm avg}+\Delta T*\cos\left(2\pi\frac{t-14}{24}\right) \tag{5}\]  
where T\({}_{\rm avg}\) and \(\Delta\)T are parameters corresponding to the average daily temperature and half of the daytime temperature variation, respectively. The variable t represents the time and was expressed in hours [43].
## 3 Experiments and Methods  
### NMR Tests  
Using X-ray diffraction (XRD) and total organic carbon (TOC) measurements of the selected samples, this study further employed low-field nuclear magnetic resonance (NMR) analyses to quantitatively study the characteristics of the pore structures. The MesoMR23-060H-I instrument of Niumai Electric Technology limited liability company in Shanghai, China was used for the NMR tests, with experimental conditions of 200 KHz resonance frequency, 0.1 ms echo interval, 1000 ms waiting time (TW), 5000 echo number, and a scan count of 64. The experiments were conducted to obtain information on the \(T_{2}\) distribution of the samples under conditions of oil and water saturation, which can be approximated by the transverse relaxation time:  
\[\frac{1}{T_{2}}=\frac{1}{T_{2S}}=\rho\bigg{(}\frac{S}{V}\bigg{)}=F_{S}\frac{ \rho}{r} \tag{1}\]  
where \(\rho\) is the surface relaxivity, \(\mu\)m/ms; and \(S/V\) is the ratio of pore-specific surface area to volume, \(\mu\)m\({}^{-1}\). \(F_{S}\) is the pore shape factor, related to the pore properties, with cylindrical pore \(F_{S}\) being 2 and spherical pore \(F_{S}\) being 3. \(r\) is the pore radius. Considering \(F_{S}\) and \(\rho\) as constants, with \(F_{S}\) taken to be 2 and \(\rho\) taken to be 50 \(\mu\)m/ms, the relationship between the pore diameter and the \(T_{2}\) relaxation time can be described as:  
\[d=4\times\rho\times T_{2} \tag{2}\]  
According to the correlation between NMR signals and shale pores tested, pore structure characteristics such as pore size distribution, NMR porosity, and permeability can be obtained. Based on Equation (2) and the basic principle of NMR, the relaxation times of the \(T_{2}\) spectra of 0.02-1 ms, 1-10 ms, 10-100 ms, and 100-1000 ms correspond to pores with pore sizes of 1 nm-20 nm, 20 nm-200 nm, 0.2 \(\mu\)m-2 \(\mu\)m, and 2 \(\mu\)m-20 \(\mu\)m, respectively.
# Low Discrepancy Sparse Phased Array Antennas
## 3 Sparse Planar Phased Array Antennas  
In this section, our example is a planar \(32\lambda\times 32\lambda\) aperture with 576 elements. Note that a fully populated array with \(\lambda/2\) element spacing with this aperture size would require 4096 elements, so this sparse array removes 86% of the elements. With a uniform grid, the average element spacing of this sparse array is \(4\lambda/3\).
## 1 Introduction
Currently, most high spatial resolution LAI estimation methods mainly use data fusion or neural network methods. Several data fusion methods have been proposed to combine high spatial resolution and high temporal resolution data to generate synthetic imagery with a high spatiotemporal resolution. For instance, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) [32] estimates high-resolution (HR) pixel values by combining information from all input images by weight functions. STARFM assumes that the change in reflectance is consistent and comparable in coarse and high resolutions, and the transfer function is empirical, so uncertainty is easily introduced during the transfer process. The Nonlinear Auto-Regressive with Exogenous Inputs (NARX) model is a recurrent nonlinear dynamic neural network with a global feedback. In this model, autofeedback effects occur constantly during estimation, and this network has been widely used in timing sequence modeling. NARX has a rapid training and convergence speed and strong representativeness and is characterized by favorable dynamics and resistance to interference. The NARX is well suited for modeling the complicated nonlinear systems because of its long term-dependencies and its computational power [33; 34]. It is demonstrated promising performance in research for time series modelling. By comparing validation period results, NARX showed a significant superiority over static neural networks [35]. For example, this method has been used for change detection in snow caps [36] and LAI time series estimation [37] based on MODIS data. In this paper, a recursive update model using neural networks with a long time series auto-feedback function [38; 39] was used to generate HR LAI time series. The new method has the following strengths: (1) it uses historical low spatial resolution MODIS nadir BRDF-adjusted reflectance (NBAR) and LAI time series as background information to estimate HR LAI; (2) it needs fewer predefined parameters; and (3) the estimation results have the advantage of a high temporal resolution (8 days) and a high spatial resolution (30 m). A recurrent update model was developed in this study and tested in eight pilot sites using HR images.
# FREDC: A Few-Shot Relation Extraction Dataset for Chinese
## 4 Experiments
### Results  
In our experiments with FREDC as the baseline model, we observed that the RE performance of various models aligns well with their performance on the original dataset. Notably, in the context of 5-way-1-shot in-domain RE tasks, FREDC demonstrated an extraction accuracy rate exceeding 50% across multiple models. It is evident that the models exhibit superior performance on in-domain tasks compared to cross-domain, attributable to the inherent characteristics of FREDC.  
However, the BERTMB model, which is optimized for generalization, exhibited a less significant performance disparity between in-domain and cross-domain experiments on our dataset. The performance demonstrated by the models on FREDC in the cross-domain experiments is based on the average of the prediction correctness in both the financial and healthcare domains. Comprehensive experimental results are presented in Table 3.  
Furthermore, to more thoroughly examine the performance disparities of our model across Chinese and English datasets, we have conducted cross-domain experiments using FREDC and FewRel2.0, along with NOTA experiments. In the cross-domain experiment, the test subset of FewRel2.0 was only the biomedical sector, whereas our test subset includes financial and medical fields. The cross-domain experiments using FREDC show that both models perform poorly in the 5-way 1-send and 10-way 1-send configurations, with a performance gap of about 20% compared to FewRel2.0, as illustrated in Table 4.  
In the NOTA experiments, we examined scenarios with thresholds set at 0% and 50%. Models exhibited a performance gap of approximately 35% on the two datasets when the threshold was set at 50%. However, it is noteworthy that at 0% NOTA and 5-way-5-shot settings, the discrepancy in experimental results between the two datasets was approximately 10%, whereas at 0% NOTA and 5-way-1-shot settings, this discrepancy increased to approximately 25%, as detailed in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**5-1**} & \multicolumn{2}{c}{**10-1**} \\ \cline{2-5}  & **FewRel2.0** & **FREDC** & **FewRel2.0** & **FREDC** \\ \hline Proto & 40.12 & 24.20 & 26.45 & 13.16 \\ BERT-Pair & 56.25 & 23.77 & 43.64 & 19.29 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The accuracy (%) of models in cross-domain task on different datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**5-1**} & \multicolumn{2}{c}{**10-1**} \\ \cline{2-5}  & **In-Domain** & **Cross-Domain** & **In-Domain** & **Cross-Domain** \\ \hline Proto & 56.34 & 24.20 & 40.55 & 13.16 \\ BERT-Pair & 52.62 & 23.77 & 36.83 & 12.24 \\ BERTEM & 34.96 & 34.35 & 20.86 & 19.29 \\ HCRP & 67.41 & 28.53 & 55.51 & 16.50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The accuracy (%) of baseline models tested with FREDC.