esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Analysis of Model Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Incarbonsink**} & \multicolumn{2}{c}{**Model (1)**} & \multicolumn{2}{c}{**Model (2)**} & \multicolumn{2}{c}{**Model (3)**} & \multicolumn{2}{c}{**Model (4)**} \\ \cline{2-9}  & **Coef.** & **Std. Err.** & **Coef.** & **Std. Err.** & **Coef.** & **Std. Err.** & **Coef.** & **Std. Err.** \\ \hline linnvest & 0.0025 & 0.0032 & & & & & \\ l1 Innvest & & & & & & & \\ l2 Innvest & & & & & & & 0.0095 *** & 0.0036 \\ (carbononsink \(<\) 5,327,211.87) & & & & & & 0.0218 *** & 0.0036 \\ (carbononsink \(>\) 5,327,211.87) & & & & & & & 0.0218 *** & 0.0036 \\ Inglp & \(-\)0.4726 *** & 0.1355 & \(-\)0.4226 *** & 0.1353 & \(-\)0.5291 *** & 0.1517 & \(-\)0.3747 *** & 0.1178 \\ Inglp2 & 0.0225 *** & 0.0062 & 0.0205 *** & 0.0062 & 0.0251 *** & 0.0069 & 0.0184 *** & 0.0053 \\ Inwage & 0.0053 & 0.0057 & 0.0027 & 0.0058 & 0.0043 & 0.0059 & 0.0048 & 0.0067 \\ Inworkpop & 0.0218 *** & 0.0108 & 0.0342 *** & 0.0125 & 0.0302 ** & 0.0127 & 0.0371 *** & 0.0117 \\ Incarbonsink & 0.1633 *** & 0.0151 & 0.1634 *** & 0.0143 & 0.1343 *** & 0.0173 & 0.1551 *** & 0.0299 \\ Inpre & 0.2460 *** & 0.0309 & 0.2352 *** & 0.0301 & 0.2370 *** & 0.0309 & 0.2244 *** & 0.0183 \\ lntemp & \(-\)0.0147 & 0.0517 & \(-\)0.1008 ** & 0.0442 & 0.0096 & 0.0508 & \(-\)0.0596 & 0.0929 \\ \_cons & 13.5153 *** & 0.9451 & 13.3558 *** & 0.9058 & 14.1423 *** & 1.0501 & 13.1809 *** & 0.9350 \\ \hline \hline \end{tabular} Note: *** and ** indicate significance at the levels of 1% and 5%, respectively; Ilcarchonsink is the Incarbonsink value of lagging phase once; l1 Innvest is the Inmixed value of lagging phase once, and the rest may be deduced by analogy. To reduce the possible heteroscedasticity in the model, we took the logarithm of the variables on both sides of the formula.  
\end{table} 
Table 4: Test results of threshold effect. Figure 7: Division of sample units of threshold model.
# A Design for a Wide-Band Antenna Pair Applied for Mobile Terminals at the Sub-6 GHz Band
###### Abstract  
To meet the requirements of a high transmission rate and strong communication quality for mobile terminals, a planar antenna printed on a 0.8-mm-thick FR-4 substrate is proposed in this paper. The structures are compact, with a size of \(23\times 6\) mm\({}^{2}\). The performance of a single antenna pair in a \(75\times 35\) mm\({}^{2}\) ground plane shows the advantages of both ultra-wide bandwidth and high isolation. The operating frequency of \(-6\) dB is 54% (3.3 GHz-5.9 GHz). To solve the mutual coupling effects exiting in the antenna pair, a hybrid strip defected grounding structure (DGS) is added in the middle of neighboring antenna elements, and good isolation performance of lower than \(-13\) dB is achieved. The evolution and analysis of the proposed antenna pair are given to investigate the operating principle. To verify its practicality, an eight-port antenna system in \(150\times 70\) mm\({}^{2}\) ground plane is constructed along two frames of the smartphone board. The MIMO antenna system performance is simulated and tested. The results show a bandwidth of 3.4-5.2 GHz (\(-6\) dB), an isolation performance lower than \(-10\) dB, an ECC lower than 0.01, and a total efficiency of 46-84%. In addition, the performance of the antenna system under single handheld conditions is also discussed. The correlation results show that the designed antenna pair has good data transmission and interference resistance capability, which proves that this is an excellent antenna model for application in mobile terminals.  
antenna pairs; DGS; MIMO system; mobile terminals +
Footnote †: journal: Applied Physics Letters B
# An Improved Forwarding of Diverse Events with Mobile Sinks in Underwater Wireless Sensor Networks
## 2 Background and Related Work  
As mentioned earlier, underwater network is quite different from the terrestrial networks. The routing protocols for terrestrial networks are broadly divided into two categories: proactive and reactive [4; 5]. In proactive protocols-like OLSR (Optimized Link State Routing) [6] and DSDV (Dynamic Destination Sequenced Distance-Vector) [7]-nodes periodically share their routing table information with each other, routes are established evenly irrespective of their requirement. This would cause extra overhead especially in higher lossy underwater links. Whereas in reactive protocols, like AODV (Ad-hoc On Demand Distance Vector) [8], its variants like MRAODV (Multiple Route AODV) [9] and DSR (Dynamic Source Routing) [10]; routes are established on demand initiated from the source. In underwater networks, this route establishing mechanism would be time consuming and causes larger delay due to low propagation speed of acoustic signals. So these protocols are no longer feasible to work in harsh underwater environment [11; 12]. There have been many proposed protocols for routing in underwater scenarios [13]. These protocols roughly fall into following two categories. Different aspects of these protocols are given in details.
# A Model for Simulating the Upward Flow of a Viscous Fluid in a Fracture Network
## 2 The Upward Flow of a Viscous Liquid in a Single Fracture
### The Validation of Our Model  
To validate our model, we reproduced a thickness profile of a fluid-driven crack, which was measured in the experiment by [PERSON] et al. [24]. As illustrated in Figure 3a, the fluid was injected into the elastic matrix (gelatin) through a syringe pump, then forming a penny-shaped crack. Driven by the pressure, the crack grew, gradually expanding in the radial (\(y\)-) and thick (\(z\)-) directions. The geometrical size in the thick direction, 2 \(W\), represents the thickness of the crack, which reduces from the injection point (where \(r=0\)) to the rim of the crack (where \(r=R\)). Figure 3b is the thickness distribution we reproduced in the article. The light blue line shows the thickness distribution data for the first crack (28 s). The data were then recorded every 8 s until the liquid in the injection pump was fully injected into the gelatin matrix (60 s) and maintained at this state. We assumed that the fracture was fully developed in 60 s. In the experiment, the elastic modulus of the matrix, \(E\), was 202 kPa, and the viscosity of the fluid was 1.02 Pa s.  
We read the thickness distribution for \(r=0\) to 20 mm at the time of 60 s (red line) from Figure 3b, and compare its to the numerical reproduction of our model. In the calculation, we use the experimental size parameters to calculate. Since the thickness of the crack is measured in millimeters, we set the stiffness of the matrix to 2.02 \(\times\) 10\({}^{8}\) Pa/m and the value of \(L\) to 20 mm. Due to the small experimental size, the viscosity of the liquid is assumed to remain unchanged at 1.02 Pa s. The temperature of the rock matrix remains unchanged at 25 \({}^{\circ}\)C. The pressure at the boundary is obtained from the measured crack thickness at \(r=0\) and \(r=20\) mm in Figure 3b, according to Equation (17). The inlet and outlet pressures are set to \(p_{\text{in}}\) = 1\(\times\)10\({}^{6}\) Pa and \(p_{\text{out}}\) = 2\(\times\)10\({}^{5}\) Pa. We apply the above parameters into our model, and solve the governing equations (Equation (9) and Equation (17)) with the grid size of \(\Delta r=0.20\) mm until reaching the steady state, where the velocity satisfies,  
\[\left|\frac{u^{n+1}-u^{n}}{u^{n}}\right|_{\text{max}}\leq 10^{-8}\,, \tag{21}\]  
Figure 3: (**a**) Schematic diagram of a penny-shaped crack, proposed by [PERSON] et al. [24]. (**b**) Reproduced w (thickness)-r (length) distribution map. (**c**) The comparison between our numerical reproduction (NUM) and the experimental measurement (EXP) of the crack thickness, \(h=W\), on \(r=0\) to 20 mm at the time of 60 s.  
where \(u^{n}\) and \(u^{n+1}\) represent the velocity at the time steps n and n + 1, respectively.  
Figure 3b demonstrates that our numerical reproduction reaches a good agreement with the experimental measurement. The crack thickness decreases from the center to the rim, and the decreasing rate increases with the value of \(r\), as a result of the increasing resistance.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 2 Background and Related Research
### Nanogrid Architectural Configurations from a Resilience Perspective
Therefore, centralized control strategies are undesirable for most military applications and decentralized nanogrid control systems that can react to threats to the uninterrupted delivery of power to critical loads is important for energy security and improves resilience. However, decentralized control of nanogrids can inhibit overall microgrid reliability due to there being many more potential independent failures of distributed nanogrid controllers over time [18; 36]. A benefit of a DC nanogrid is the commonality of PV array and ESS output power generally being DC power [18]. This commonality of a DC based nanogrid would allow for a smoother and efficient transition of power amongst renewable energy sources to battery storage. Though an AC based nanogrid will save money on initial cost upfront with no necessary retrofitting, added efficiency benefits of a DC based nanogrid will outweigh initial capital required criteria and benefit the military's mission-focused requirements.
# Aggression Detection in Social Media from Textual Data Using Deep Learning Models
## 4 Results
### Experiments with Word2 vec  
Table 3 depicts the classification performance of a number of conventional machine learning models when fed with Word2 vec features. The NB classifier showed the worst performance in overall classifiers in all classification metrics. In contrast, the GB classification algorithm recorded the highest values in all metrics and outperformed all other classifiers. The second-best performer was the DT classifier. The highest F1 score recorded by the GB  
Figure 2: Framework of the proposed model for DNN-based cyber aggression detection.  
classifier was 77%, which shows that the conventional machine learning models could not be used reliably for aggression detection.  
The classification performance of the deep learning models trained using Word2 vec features is presented in Table 4. The proposed DNN model recorded the highest evaluation metrics over all other deep learning models, followed by the BiLSTM. The F1-score of the introduced DNN is 87%, which is higher than that of the GB machine learning model. This observation highlights the effectiveness of deep learning models over machine learning in the aggression detection problem. Nevertheless, the values of the F1-score reflect the inadequacy of the Word2 vec-driven features for reliable aggression classification using both deep learning and machine learning models. Therefore, manually extracted features were combined with Word2 vec features and introduced to the models.
# MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model
## 5 Conclusions  
To address the issue of accurate classification of multi-band, multi-label, high-resolution remote sensing images, this paper proposes a novel MMDL-Net model. The model introduces a multi-band stacking module and a multi-label classification module and combines a residual network with TensorFlow for feature extraction. It effectively concatenates the information from multiple bands and adjusts the input and output, integrating a multi-label classification strategy to generate more accurate classification outputs. Experimental results demonstrate that the proposed MMDL-Net model exhibits superior classification capability in handling multi-band, multi-label remote sensing image classification. It also performs well in complex remote sensing spatial geographic information. Additionally, the paper explores the impact of changes in network depth and channel numbers on the model.  
**Author Contributions:** Conceptualization, X.C., B.L. and Y.D.; methodology, X.C. and B.L.; software, B.L.; validation, X.C., B.L., J.T. and J.Z.; formal analysis, Y.D.; resources, X.C., J.T., Y.S. and J.Z.; data curation, B.L. and Y.D.; writing--original draft preparation, X.C. and B.L.; writing--review and editing, X.C.; project administration, X.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research work was supported by the National Natural Science Foundation of China (32360374); the central government guides local science and technology development fund projects (GuikeZY22096012) and the Research Project of Guangxi Forestry New Fertilizer Development Center (GXRDCF202307-01).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets presented in this study are accessible via the following link: [[https://bigearth.net/](https://bigearth.net/)]([https://bigearth.net/](https://bigearth.net/)), accessed on 10 August 2023.  
**Acknowledgments:** We are very grateful to the volunteers from Guilin University of Technology for their assistance in the experimental part of this manuscript.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Comparison of Various Factors Affected TID Tolerance in FinFET and Nanowire FET
## 2 Simulation Methodology  
### Design Structure for TID Simulation  
Prior to the simulation, the NW was stacked in five stages to match the current level and device size. Table 1 presents the basic physical device parameters used in this study. With the use of a 3D technology computer-aided design (TCAD) simulation tool, the FinFET and 5 ch-NW were similarly constructed (Table 1). T\({}_{\text{fin}}\) is the FinFET width, D\({}_{\text{hw}}\) is the 5 ch-NW diameter, L\({}_{\text{g}}\) is the channel length, H\({}_{\text{fin}}\) is the FinFET height, and H\({}_{\text{hw}}\) is the 5 ch-NW's total height. In addition, the FinFET was manufactured by referring to the International Technology Roadmap for Semi-conductors (ITRS) [17]. Figure 1 shows the TCAD structure of the FinFET and 5 ch-NW.  
The electrodes of the devices were made of tungsten, the body of silicon, the \(p\)-type doping was boron, and the \(n\)-type doping was phosphorus. The devices placed SiO\({}_{2}\) (0.5 nm, \(k=3.9\)) and HfO\({}_{2}\) (2 nm, \(k=25\)) on the FinFET and 5 ch-NW channel insulator material. The equivalent oxide thickness (EOT) can be calculated using Equation (1).  
\[\text{EOT}=\frac{(\text{SiO}_{2}\ k)}{(\text{High}\ k)}(T_{Hi-k}) \tag{1}\]  
Figure 1: 3D technology computer-aided design (TCAD) structure of the FinFET and 5 ch-nanowire FET (NW). (**Left**) FinFET, (**Right**) 5 ch-NW. The 5 ch-NW was stacked in five stages to match the FinFET’s current level and device size.  
The electrical properties were also calibrated for a fair comparison of the TID effects. Figure 2 shows the I\({}_{\text{D}}\)-V\({}_{\text{G}}\) characteristics with the FinFET and 5 ch-NW and demonstrates that the electrical characteristics of FinFET and 5 ch-NW were calibrated.
## 3 Parameterization Schemes for Fiber Orientation  
The parameterization scheme implements a numerical description of fiber orientation patterns and defines variables for optimization. It should ensure the spatial continuity of fiber angles so that the CF4 technology can produce the structure. It should also provide enough design freedom so that the optimization algorithm can consider more candidate designs.  
For mathematical completeness, the general density-based template for the optimization problem is presented to find the optimal distribution of structural topology, fiber layout, and fiber orientation in functionally graded anisotropic composite structures. In Figure 2, the density distribution accommodates fiber material only; however, the addition of materials is considered through separate density functions in the optimization framework, for example, when optimizing variable fiber fractions or functionally graded anisotropic composites, as depicted in the Equation (10). Thus, as detailed in this section, the template accommodates several parameterization schemes used in the literature to optimize fiber-reinforced composite structures.  
\[\begin{split}\min_{\mathbf{z}}&:\Phi(\mathbf{U},\mathbf{z})\\ &:=\sum_{i}\int_{\Omega_{i}}f(z_{i},\mathbf{U}(z_{i}))d\mathbf{x}\\ \text{s.t.}&:\mathbf{z}:=\left[\rho_{m},\mathbf{\rho}_{f}( \theta)\right]\\ &:\underline{z}_{i}\leq z_{i}\leq\bar{z}_{i},\forall z_{i}\in\mathbf{ z},\quad i=1,\ldots,N\\ &:G_{m0}(\mathbf{\rho}_{m})=\sum_{i}\mathrm{v}_{i}\rho_{m}^{i}- \mathrm{V}_{m}\leq 0,\\ &:G_{f0}(\rho_{f})=\sum_{i}\mathrm{v}_{i}\rho_{f}^{i}-\mathrm{V} _{f}\leq 0,\\ &:G_{j}(\mathbf{z},\mathbf{U}(\mathbf{z}))\leq G_{j}^{*},\quad j=1, \ldots,Q\\ &:\mathbf{K}(\mathbf{z})\mathbf{U}=\mathbf{F}\end{split} \tag{10}\]  
The vector \(\mathbf{z}\) contains all the design variables, i.e., isotropic material (matrix) density, fiber material density, and orientation variables. Each design variable is bound between the values \(\underline{z}_{i}\) and \(\underline{z}_{i}\). The total volume of the matrix material, \(G_{m0}\), is calculated from the density distribution, \(\rho_{m}\), determined by the design variable (or density function) \(\rho_{m}\). Similarly, the fiber material volume is determined through the fiber density function \(\rho(\theta)_{f}\). The system of linear equations is composed of the stiffness matrix, \(\mathbf{K}\), and the force vector, \(\mathbf{F}\), which is derived from the state equation by finite element formulation.
## 6 Experimental Evaluation
### Experimental Results: Performance of Postfiltering Approaches
\begin{table}
\begin{tabular}{l l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Place.**} & \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**PESQ**} & \multicolumn{6}{c}{**STOI (\%)**} \\ \cline{3-14}  & & \multicolumn{3}{c}{**SNR (dB)**} & \multicolumn{6}{c}{**SNR (dB)**} \\  & & **20** & **15** & **10** & **5** & **0** & \(-\)**5** & **20** & **15** & **10** & **5** & **0** & \(-\)**5** \\ \hline \multirow{4}{*}{CT} & PLDwf & 2.81 & 2.38 & 1.98 & 1.64 & 1.36 & 1.20 & 95.94 & 92.21 & 85.70 & 76.11 & 63.53 & 50.42 \\  & WF-Ms-eKF-PLDn & 2.81 & 2.34 & 1.95 & 1.62 & 1.36 & 1.20 & 97.18 & 94.20 & 88.72 & 80.11 & 67.97 & 54.51 \\  & pWF-Ms-eKF-PLDn & 2.86 & 2.40 & 2.00 & 1.64 & 1.37 & 1.20 & 97.24 & 94.35 & 89.00 & 80.53 & 68.41 & 54.54 \\  & OMLSA-Ms-eKF-PLDn & **2.96** & **2.49** & **2.06** & **1.68** & **1.40** & **1.23** & **97.25** & **94.45** & **89.24** & **80.86** & **68.72** & **55.11** \\ \hline \multirow{4}{*}{FT} & SPPCwf & 2.74 & 2.26 & 1.81 & 1.48 & 1.25 & 1.12 & 94.43 & 90.26 & 83.27 & 73.28 & 61.16 & 49.34 \\  & WF-Ms-eKF-P\&C & 2.95 & 2.45 & 1.99 & **1.64** & **1.36** & **1.21** & 96.10 & 92.64 & 86.51 & 76.91 & **64.20** & **50.80** \\ \cline{1-1}  & pWF-Ms-eKF-P\&C & **2.99** & **2.49** & **2.01** & 1.63 & **1.36** & **1.21** & **96.12** & **92.73** & **86.68** & **77.08** & 64.18 & 50.36 \\ \cline{1-1}  & OMLSA-Ms-eKF-P\&C & 2.85 & 2.38 & 1.94 & 1.60 & 1.35 & 1.20 & 95.98 & 92.64 & 86.63 & 77.03 & 64.13 & 50.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: PESQ and STOI results for different postfilters applied to the MVDR beamformer output and for other related state-of-the-art approaches. Results are broken down by SNR and device placement.
## 3 Optical Buffering and TSI with Integrated Delay Lines
### Experimental Setup and Results for Optical Packet Buffering
Both WC1 and WC2 relied on the differentially-biased SOA-MZI configuration [54] that offers inverted logic operation, with WC1 having the role of wavelength-based delay path selection and WC2 being responsible both for restoring the packet's wavelength and logic and for selecting a single-packet for evaluation purposes. Figure 8: (**a**) Close up view of the Si chip (**b**) Experimental setup for the variable optical buffer. The unequal pitch between the 3 input and the 3 output coupling waveguides constrained the use of a standard fiber array for simultaneously incorporating all three integrated delay lines in the Delay Stage. Therefore, the results at all three Delay Stage branches were sequentially obtained by employing every time a single integrated delay line in the Delay Stage and feeding WC1 with a single envelope signal at the appropriate wavelength. Figure 9a illustrates the initial trace of the three packets and the eye diagram of packet (I). Figure 9b-d depict packet (I) and its eye diagram at the WC2 output and after having propagated through on-chip Delay 1, 2 and 3 waveguides, respectively. The extinction ratio of the output eye was measured to be 6.6 dB, 6.4 dB and 6.2 dB in Figure 9b-d, respectively. Figure 9e depicts the Bit error-rate measurements (BER) revealing error-free operation for all three cases with a power penalty of <3 dB for Delays 1 and 2 and <5 dB for Delay 3 at a \(10^{-9}\) BER. The increased power penalty in Delay 3 owes to the higher insertion losses of the longer waveguide that yield to lower input power level at the EDFA and as such degraded OSNR at the amplifier output. The measured OSNR at the EDFA's output was 19.31 dB, 18.06 dB, 13.14 dB for Delay 1, 2 and 3 respectively. Both SOAs of WC1 were driven at 300 mA dc current, while the optical power of the CW biasing beam was 3.3 dBm. The envelope input signal had a peak power of 4.7 dBm, while the two control signals at port D and E had a peak power of 0.6 dBm and 2.4 dBm, respectively. In WC2, SOA1 and SOA2 were driven at 260 mA and 280 mA, respectively, the optical holding beam was 3 dBm, the envelope signal input had a \(-8\) dBm peak power value, and the two control signals at ports H and A had peak powers of \(-3\) dBm and \(-6\) dBm, respectively. The SOAs used in the Delay Stage were operated at a dc current of 300 mA with an CW holding beam of 1.5 dBm. The total power consumption including the two WCs and the EDFA is calculated to be 5.88 W, resulting to a delay versus electrical power efficiency of 2.92 ns/W.