esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Composition and Structure of The Model
#### 3.2.4 Model Structure  
The semantic information of the text content is extracted by the Bert layer, and the output of [cls] is taken for sentiment presence detection and positive and negative sentiment detection. A BiLSTM layer is connected after the Bert module to extract the temporal features of the sentences. After that, the extracted features are added to the attention module, and the attention and temporal features are summed to obtain the feature output layer. Batch normalization is performed on the feature output layer, and the final sentiment features are later input into the fully-connected network.  
As shown in Figure 4, after the above steps, two outputs are obtained for prediction--one is the output of [PERSON]'s [cls] character, and the other is the output of the sentiment feature with attention--and we use multi-stage prediction for sentiment prediction, after which these two features are input into the following three stages.  
Figure 4: Feature extraction stage structure.  
Figure 3: Multi-head attention [31].  
The first stage is the judgment of presence of emotion (JPOE) (Figure 5); the features of [cls] characters will be used as input. This is a linear layer with 768 input layers and 2 hidden layers, and the predicted results are output as a 2-dimensional tensor.  
The second stage is the positive and negative prediction of emotion (JPNE) (Figure 5); the prediction result of the first stage and the features of the [cls] character will be used as input. This is a linear layer consisting of two layers: the first one is input as 768, the hidden layer as 50, and the second layer is input as 52 and output as 3. In the second layer, the output of the first fully-connected layer and the output of the previous stage are stitched together as input to obtain a prediction result for a three-dimensional vector.  
The third stage is the multi-category sentiment prediction stage (MEJ) (Figure 5); the prediction results of the input JPOE, JPNE, and the sentiment features will be given attention. The first layer is the input layer of 500 \(\times\) 768, with a hidden layer of 50, which will focus on the emotional features' reshaping after the input, while the second layer of the input is 55 with an output of 6. The second layer will contain the output of the first layer before the link as well as the first two stages of the output of the stitching to obtain the final emotional prediction results.
# Metasurface Source Antenna Gain Improvement Using Simple Side Metal Structure
## 2 Design and Analysis of Side Metal Structure for Metasurfaces
### Patch Source with Side Plate Configuration  
The SP acts as a parasitic coupling element that functions as a resonator by interacting with the electromagnetic field radiated from the patch antenna. This coupling effect is achieved by placing a metal plate around the patch antenna, which serves as a parasitic element to enhance the gain and bandwidth of the antenna [34]. As depicted in Figure 2, two SPs are applied to a single patch, with the patch designed in a square ring shape measuring 9.2 \(\times\) 11.3 mm, and the metallic side plates sized at 40 \(\times\) 40 mm and 60 \(\times\) 60 mm, respectively. The ground plane of the patch source was designed to be 200 \(\times\) 200 mm to match the width of the TA described in Section 3. The patch antenna was fabricated using a TLX-9 substrate (copper sheet thickness = 0.035 mm, dielectric constant = 2.2, loss tangent = 0.0009), with the side metal plates implemented by printing the copper sheet onto the board like a patch.  
Figure 1: Configuration of proposed TA.  
Figure 3 presents the electromagnetic response results of a single patch applied to a ground plane measuring 200 \(\times\) 200 mm, showing resonance at 9.55 GHz with a gain of 7.8 dB. The simulation results for S11 (dB) and gain (dB) based on the SP structure indicate that applying SP did not affect the resonant frequency. However, when the SW structure was applied, the resonant frequency shifted from 9.55 GHz to 9.61 GHz due to changes in impedance. Despite this shift, the maximum gain was consistently observed at 9 GHz. Figure 4 further illustrates the gain measurements at 9 GHz and 9.61 GHz for a single patch with the SP structure applied but without the SW. The highest gain was recorded when two SP structures surrounded the patch. The simulation results from HFSS show that the optimal configuration for maximum gain was achieved for SP 1 with a side length of 40 mm and SP 2 with a side length of 60 mm.  
Figure 4: Effect on gain at 9 GHz and 9.61 GHz. (**a**) Patch without SP. (**b**) Patch with one SP. (**c**) Patch with two SPs.  
Figure 3: Electromagnetic response of patch source. (**a**) S11. (**b**) Gain (dB).  
Figure 2: Top view of patch source with side metal plate.
## 2 Materials and Methods
### Data Resources
#### 2.2.2 Drought Data  
Drought index data are obtained using the self-corrected Palmer Drought Index sc_PDSI with a dataset from the CRU climate dataset [19] ([[https://crudata.uea.ac.uk/](https://crudata.uea.ac.uk/)]([https://crudata.uea.ac.uk/](https://crudata.uea.ac.uk/)) cru/data/drought/#global accessed on 19 May 2021) the spatial resolution is 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\), and we resampled the data to a 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) size for spatial scale consistency. The droughs are classified into nine grades according to the results of sc_PDSI, with smaller values indicating drier and larger values indicating wetter conditions, as shown in Table 1[15]. The period chosen for this paper is from 2003 to 2018.  
Figure 1: Overview map of Yunnan Province, China.
## 7 Comparison of Characteristics of FaSHM and FaSynRM  
This section discusses the comparison of the characteristics of the considered electrical machines after optimization. Table 9 compares the performance of the optimized FaSHM and FaSynRM. The FaSHM characteristics are taken from Table 6. The FaSynRM characteristics are taken from Table 8. Table 10 shows a comparison of masses, dimensions, and active material costs.  
Comparing the characteristics of the FaSHM and FaSynRM from Tables 9 and 10, we can draw the following conclusions:  
1. In this application, the average loss of the FaSHM is \(100\%(7.4-7.16)/7.4=3.4\%\) less than that of FaSynRM;
2. By adjusting the excitation flux, the maximum armature current for the FaSHM is \(833/539=1.54\) times less than for the FaSynRM, which potentially makes it possible to significantly reduce the cost of power modules of the traction inverter;
3. The active volume of magnetic cores, excluding the stator housing, in the FaSynRM is \(100\%(47.1-45.4)/47.1=3.9\%\) higher than in the FaSHM;
4. The length of the FaSHM stator laminations is less than that of FaSynRM by \(100\%(240-227)/240=5.4\%\); the length of the FaSHM, taking into account the gap for installing the excitation winding, is greater by \(100\%(260-240)/260=7.7\%\);
5. The mass of active materials of the FaSHM is greater than that of the FaSynRM by \(100\%(359.2-98.9-241)/(359.2-98.9)=7.4\%\) excluding the housing and sleeve;
6. The cost of the FaSHM active materials is \(100\%(1152.7-818.2)/818.2=29.0\%\) higher than that of the FaSynRM (excluding the housing and sleeve) due to the greater mass of copper and magnets;
7. The maximum modulus of the demagnetizing force in the case of the FaSHM is greater than in the case of the FaSynRM by \((2.5-2)=0.5\) kOe. In this case, the demagnetization margin for the FaSynRM is \((4-2)=2\) kOe, and for the FaSHM is \((4-2.5)=1.5\) kOe, where 4 kOe is the coercive force of ferrite magnets. Therefore, the FaSHM requires a more careful design of control algorithms to avoid demagnetization during failures and transients.
# Optimal Base Station Density of Dense Network:
## 1 Introduction
### Contributions  
The contributions of this paper are threefold:  
* We have considered the user association and interference problems in Poisson random networks and derived the expression of the expected link rate of the network as a function of SmBS density, which has revealed both the balance between the gain and the interference brought by densification. Based on the analytical results, we find the optimal BS density that maximizes the capacity of the network. Our work indicates that it is not always reasonable to increase the BS density.
* A method is proposed to analyze the throughput of the contention-based channel in random and irregular networks. Unlike the existing literature about analysis of the contention-based channel, which are all based on a given load, the work in this paper deals with the random load caused by the random network. We have found the junction to introduce the Poisson point process into the Markov chain and thus the contention-based load problems can also be solved in random networks. Our analytical results offer the answers on how the network densification can be load-aware in order to avoid performance degradation. Since the unlicensed bands have played an increasingly important role, the work in this paper can apply to many cases.
* Our analytical and simulation results have provided the optimal BS density for deployment of the dense network. After analyzing the effect of the base station power, density and the network load on the performance of network, the optimal deployment density of the base stations are given under different network conditions. The results can provide insights for network densification.  
The rest of paper is organized as follows. The system model is detailed in Section 2. Based on this, we analyze and derive the expected link rate and the throughput of networks in Section 3 and Section 4, respectively. In Section 5, the simulations are conducted to verify and describe our analysis and the strategy for network deployment. Section 6 concludes the paper.
# Enhanced Beacons Dynamic Transmission over TSCH
## 4 Proposed Enhanced Beacons Dynamic Transmission Mechanism
### EBDT-TSCH Configuration Parameters  
The configuration parameters of EBDT-TSCH (i.e., \(\alpha\) and \(\beta\)) and the theoretical expected time for a join-seeker to get associated to a synchronizer (\(E(t)\)) are shown in Figure 4. Greater \(\alpha\) values reduce the differences in the maximum time between successive EB transmissions in the intensive and predetermined phases, reducing the positive impact of using the proposed EBDT-TSCH. When \(\beta\) increases, the number of EB transmissions in the intensive phase also increases, increasing the probability of getting associated in the intensive phase and reducing the average elapsed time to get associated. However, after certain values of \(\beta\), the impact in the elapsed association time decreases.  
Using (2), we derive the probability of getting associated during the intensive phase as the following:  
\[P_{intensive}=\sum_{j=1}^{u}P_{n}(1-P_{n})^{j-1}, \tag{8}\]  
working in (8) and substituting \(u=\lfloor\beta m\rfloor\) and \(P_{n}=m^{-1}\), \(P_{intensive}\) can be rewritten as follows:  
\[P_{intensive}=1-\bigg{(}\frac{m-1}{m}\bigg{)}^{\lfloor\beta m\rfloor}. \tag{9}\]  
Figure 4: Trade-off between \(\alpha\), \(\beta\), and \(E(t)\) with \(m=16\) available channels, \(\textit{Teb}=4s\), and \(\rho=0.75\).  
Figure 5 shows the impact of \(\beta\) on the probability of getting associated at the intensive phase given by (9). Smaller \(\beta\) values reduce the impact of the intensive phase because the probability of joining at this phase becomes low. On the other hand, there is no incentive to make \(\beta>2.8\) because the joining probability at the intensive phase does not increase significantly, causing an increase in the number of EBs transmitted without a relevant reduction in the elapsed time to get associated. The probability of getting associated at the intensive phase in the range of 0.75-0.95 offers a good trade-off between the number of EBs transmitted and the probability of getting associated at the intensive phase; therefore, \(\beta\) must be in the range 1.4-2.8 to fulfill this requirement.
## 4 Simulation Experiment
### Experimental Results
#### 4.2.1 Boeing-727 Imaging Results  
The Boeing-727 data included 256 successive pulses and the number of range cells per pulse was 64. The radar uses a stepped-frequency waveform, where the carrier frequency was 9.0 GHz, the bandwidth was 150 MHz, and the pulse repetition frequency was 25 KHz. Range compression and motion compensation for the data were completed. The range profile and the RD imaging result are shown in Figure 14. The target azimuth was seriously defocused due to the maneuvering motion of the target.  
In order to eliminate the azimuth defocus, we used the MCFT algorithm proposed in this paper to image the Boeing-727 data. Figure 15a depicts a coarse estimate of the rotation ratio of the 33 th range cell obtained using DCFT. The estimated value was \(\widetilde{K}_{\Omega\omega}=24\). Figure 15b highlights the relationship between image entropy and the number of iterations by using the minimum entropy function. After 180 iterations, the minimum image entropy was obtained, and the corresponding exact rotation ratio was \(\widetilde{K}_{\Omega\omega}=25.39\).  
Figure 16 shows the imaging results using the MCFT method proposed in this paper and several contrast methods. The evaluation indexes of each imaging result are shown in Table 5. It can be seen from Figure 16a that the proposed algorithm effectively eliminated azimuth defocusing. Figure 16b,c are the results of range instantaneous Doppler (RID) imaging using SFT and SPWVD, respectively. The results are consistent with the analysis in the previous section. The time-frequency resolution of  
Figure 14: Boeing-727 imaging results using (**a**) range profile and (**b**) RD imaging.  
Figure 15: Rotation ratio estimation for Boeing-727 data: (**a**) Course rotation ratio estimation and (**b**) RD imaging result.  
STFT was relatively poor and SPWVD exhibited an improved resolution. The advantage of the RID imaging method is that it can show the dynamic change in the imaging result with the orientation Doppler. The disadvantage is that the imaging result may lack scattering points at different moments. Figure 16d shows that the RWT algorithm also improved the azimuth focusing effect, but there is still partial defocusing. From the corresponding evaluation index in Table 5, we can see that the minimum entropy and contrast of the proposed MCFT algorithm are optimal. For running time, the RD and STFT can use FFT, so the computation time is shorter. Compared with RWT, which uses the same parameter estimation method, the computation time of the MCFT algorithm is much shorter. Combined with image results and evaluation indicators, the MCFT algorithm proposed in this paper is effective.
## 3 Results
### LVP Performance
#### 3.1.3 Acceleration Inputs  
The \(a_{in}^{peak}\) (Table 1), showed differences between \(f_{set}\) (F = 558.40, \(p\) < 0.001, \(\eta_{p}^{2}\) = 0.967), postures (F = 115.40, \(p\) < 0.001, \(\eta_{p}^{2}\) = 0.859), and its interactions \(f_{set}\) \(\times\) postures (F = 50.09, \(p\) < 0.001, \(\eta_{p}^{2}\) = 0.725). The higher \(a_{in}^{peak}\) were detected at 25 Hz (1.24 \(\pm\) 0.01, 95% CI: 1.219-1.259), followed by 30 Hz (1.11 \(\pm\) 0.01, 95% CI: 1.094-1.127), and 20 Hz (0.85 \(\pm\) 0.01, 95% CI: 0.831-0.859). Although the posture comparisons showed significant effects, no effect was found among TS-LU (\(p\) = 0.121), ER-HS (\(p\) = 0.271), and HS-DS (\(p\) = 0.089). The highest \(a_{in}^{peak}\) was detected at TS (1.14 \(\pm\) 0.01, 95% CI: 1.131-1.152) and LU (1.11 \(\pm\) 0.01, 95% CI: 1.092-1.131), followed by DS (1.04 \(\pm\) 0.00, 95% CI: 1.027-1.045), HS (1.02 \(\pm\) 0.01, 95% CI: 1.012-1.034), and ER (1.01 \(\pm\) 0.01, 95% CI: 0.996-1.026).
# Self-Supervised Learning across the Spectrum
## 3 Related Work
These multimodal reconstruction models tend to provide a more accurate optical reconstruction than prior unimodal methods like image in-painting [41; 42], demonstrating the potential utility of nonoptical multimodal learning. More recently, there have been efforts to try and incorporate multiple modalities for SITS [27; 43; 44]. Such efforts typically focus on designing fusion techniques for modalities along with reliable spatiotemporal encodings to improve performance. S4 distinguishes itself from these methods by providing a training method that requires significantly less labeled data and only a single SITS modality at inference time. **Self-Supervised Learning** Self-supervised learning for visual representations has gained prominence within the last few years [45; 46; 47; 48; 49]. One of the most recent notable self-supervised methods has been contrastive representation learning, which attempts to align similar pairs of images as a pretraining task to help with downstream model performance [50; 51; 52]. Although prior work mainly focuses on instance-level contrastive learning for downstream classification, recent works explore pixel-level contrastive learningtechniques, which provide better transfer to segmentation tasks [53; 54; 55; 56]. For the majority of these contrastive learning approaches, positive pixel pairs are assigned either by using corresponding pixels with the same label or through corresponding pixels from different augmented views of the same image. In contrast, S4 leverages the spatial alignment between different satellite modalities and associates positive pairs through corresponding pixels in different modalities. **Semantic Segmentation of SITS** Many prior methods have found success in using UNet-based architectures [57] for encoding representations helpful for satellite image segmentation [9; 44; 58]. More recent efforts specific to SITS have also designed multitemporal and multimodal fusion schemes using convolutional encoders [27; 43]. The advantage of S4 is that we require only a single modality of SITS during inference time, whereas every prior multimodal method requires both. S4 also incorporates a novel self-supervised approach that significantly reduces the need for labeled data.
## 4 Discussion  
### Enhancement Mechanisms of Shale's Mechanical Characteristics under Confining Pressure  
In microindentation experiments, the application of confining pressure enhances the mechanical performance of the shale primarily due to the following reasons:  
Figure 9: Correlations between fracture toughness and confining pressure derived from microindentation results.  
1. Compression of micropores and fractures: The application of confining pressure on shale leads to the compression of micropores and fractures, resulting in a reduction in their size and volume. This compression effect increases the density of the rock and decreases the number and size of micropores, thereby enhancing the shale's strength and stiffness [39, 40]. Therefore, this compression improves the fracture toughness of the shale while reducing its deformability.
2. Uniform stress distribution: The application of confining pressure in shale samples ensures a more homogeneous distribution of applied stress [41]. Without confining pressure, external loading can result in stress concentration. However, by applying confining pressure, the stress is more evenly distributed across the sample, reducing stress concentration and enhancing overall strength.
3. Particle contact: Through the application of confining pressure, the particles within the shale come into closer contact and undergo compaction [42]. This densification process leads to increased interparticle interaction, resulting in the enhanced strength and hardness of the shale. Furthermore, the denser particle arrangement reduces microscale particle displacement, effectively reducing the deformability of the shale. The promotion of particle contact under confining pressure contributes to improved mechanical properties and reduced deformation in the shale.  
In summary, the application of confining pressure in microindentation experiments effectively improves the mechanical properties of the shale. This enhancement encompasses increased strength, hardness, and stiffness and decreased deformability of the specimens.
# Accounting for Training Data Error in Machine Learning Applied to Earth Observations
## Guidelines and Recommendations
### Step 5. Evaluate and Communicate the Impact of Training Data Error
#### 4.5.2 Communicating Error  
Finally, uncertainty in ML-generated maps associated with both TD and map reference error should be faithfully reported within the maps and accompanying documents. Incomplete error reporting serves to limit the scientific validity and usefulness of these products [57]. Given that ML-generated maps are increasingly used by the public and policy domains, we advise makers of widely used maps to communicate these uncertainties and their consequences in a manner that is clear and understandable for broad audiences, including non-specialists, so that users can understand the map and its limitations. In general, we recommend including the error on or very close to the actual map, whether by means of metrics, the error matrix, and/or by using cartographic techniques for representing uncertainty. Examples of effective cartographic techniques for conveying uncertainty include selection of appropriate, intuitive, and color-blind friendly color schemes for classes and symbols, varying color value and saturation and font/line weight to indicate levels of uncertainty, use of crisp versus blurred boundaries and symbols to indicate the range of uncertainty, or display of consensus maps or side-by-side juxtaposition in cases of multiple, mutually exclusive predictions for the same place and time (e.g., representing differently specified models) [42; 43]. Maps of consensus in training labels can provide valuable uncertainty information to users, such as shown in Figure 12A,B.
# Optical Transmitters without Driver Amplifiers--Optimal Operation Conditions
## 6 Conclusions  
We investigated the prerequisites for cost-efficient driver-amplifier-less optical transmitters. Without driver amplifiers, a signal with maximum electrical eye-opening is advantageous. For this, we investigated the influence of the pulse shape, clipping, and DPreD on the electrical eye-opening. We showed that increasing the ROF of SRRC signals up to an ROF of 0.4 is advantageous, but there is little advantage for ROFs beyond 0.4. We also showed that DPostD results in better BER performances than DPreD.  
Conceptualization, A.J., B.B., and J.L. Data curation, A.J. and W.H. Formal analysis, A.J., B.B., and J.L. Investigation, A.J., B.B., R.B., W.H., and J.L. Methodology, A.J., B.B., R.B., and J.L. Project administration, A.J. and J.L. Resources, J.L. Software, A.J. and B.B. Supervision, J.L. Validation, A.J., B.B., R.B., and J.L. Visualization, A.J. and R.B. Writing--original draft, A.J. Writing--review and editing, A.J., B.B., R.B., W.H., and J.L.  
The EU-project PLASMOfab (688166) and the ERC PLASILOR (670478) are acknowledged for financial support.  
We thank Oclaro for supplying us with the DP IQ modulators. We also thank unknown reviewers that helped improve the discussions of this manuscript with their valuable feedback.  
The authors declare no conflicts of interest.
# Performance Analysis of FSO-UWOC Mixed Dual-Hop Relay System with Decode-and-Forward Protocol
## 3 Statistical Characteristics for End-to-End Instantaneous SNR
### Derivation of the CDF for End-to-End Instantaneous SNR  
For the DF relay protocol, the CDF of \(\gamma^{DF}\) can be expressed as:  
\[\begin{array}{ll}F_{\gamma^{DF}}(\gamma)&=\Pr(\min\{\gamma_{1},\gamma_{2}\}< \gamma)=1-\Pr(\min\{\gamma_{1},\gamma_{2}\}\geq\gamma)\\ &=1-\Pr(\gamma_{1}\geq\gamma,\gamma_{2}\geq\gamma)=1-\Pr(\gamma_{1}\geq\gamma )\Pr(\gamma_{2}\geq\gamma)\\ &=1-(1-F_{\gamma_{1}}(\gamma))(1-F_{\gamma_{2}}(\gamma))\\ &=F_{\gamma_{1}}(\gamma)+F_{\gamma_{2}}(\gamma)-F_{\gamma_{1}}(\gamma)F_{ \gamma_{2}}(\gamma)\end{array} \tag{27}\]  
By substituting Equations (14) and (25) into Equation (27), the closed-form expression for the CDF of \(\gamma^{DF}\) can be obtained.