esa-sceva/satcom-chunk-collection huggingface dataset:
# Silicon Waveguide Optical Isolator with Directly Bonded Magneto-Optical Garnet
## 6 Conclusions  
We reviewed the progress on MZI optical isolators fabricated by direct bonding. The compact footprint on a silicon platform enabled high isolation at the peak of resonant spectra. The device structure and operation principle were described. The operation bandwidth related to the phase difference in the MZI was discussed. Next, demonstration of temperature-insensitive design and operation was presented. Finally, MZI isolator integrated with a TE-TM mode converter was presented. The remaining problem was insertion loss. The inherent loss of the MZI isolator was related to the Ce:YIG upper cladding region. An absorption loss of Ce:YIG can be minimized by reducing the Ce:YIG length along the light propagation to the minimum for achieving just a \(\pi/2\) phase difference. There were scattering and reflection losses owing to mode mismatch at the boundary between Ce:YIG and air cladding regions. The loss was larger for the TM mode than for the TE mode because of the mode field distributions. Therefore, the scattering and reflection losses were mitigated so that light propagated as the TE mode at the boundaries. New configurations of optical isolator had been proposed using TE-TM mode converter to achieve this [27; 28]. The insertion loss can be \(\sim\)3 dB if other excess losses were resolved.  
Conceptualization, Y.S. and T.M.; methodology, Y.S. and T.M.; software, Y.S.; validation, Y.S.; formal analysis, Y.S.; investigation, Y.S.; resources, T.M.; data curation, Y.S.; writing--original draft preparation, Y.S.; writing--review and editing, T.M.; visualization, Y.S.; supervision, T.M.; project administration, T.M.; funding acquisition, T.M.  
This study was supported by MIC/SCOPE #162103103; JST Core Research for Evolutional Science and Technology (CREST) #PMJCR15N6 and #PMJCR18T4; JSPS KAKENFI #16K06295; and TEPCO Memorial Foundation.  
The authors declare no conflict of interest.
# Generative Adversarial Networks for Zero-Shot Remote Sensing Scene Classification
###### Abstract  
Deep learning-based methods succeed in remote sensing scene classification (RSSC). However, current methods require training on a large dataset, and if a class does not appear in the training set, it does not work well. Zero-shot classification methods are designed to address the classification for unseen category images in which the generative adversarial network (GAN) is a popular method. Thus, our approach aims to achieve the zero-shot RSSC based on GAN. We employed the conditional Wasserstein generative adversarial network (WGAN) to generate image features. Since remote sensing images have inter-class similarity and intra-class diversity, we introduced classification loss, semantic regression module, and class-prototype loss to constrain the generator. The classification loss was used to preserve inter-class discrimination. We used the semantic regression module to ensure that the image features generated by the generator can represent the semantic features. We introduced class-prototype loss to ensure the intra-class diversity of the synthesized image features and avoid generating too homogeneous image features. We studied the effect of different semantic embeddings for zero-shot RSSC. We performed experiments on three datasets, and the experimental results show that our method performs better than the state-of-the-art methods in zero-shot RSSC in most cases.  
remote sensing scene classification; zero-shot learning; generative adversarial network  
2022  
Accepted: 6 April 2022  
Published: 8 April 2022
## 2 Research Methods for Reservoir Fluid PVT High-Pressure Physical Property Analysis
### Prediction and Analysis of Formation Crude Oil Viscosity
#### 2.3.6 Model Training  
In this paper, the ChebNet graph convolutional neural network model was trained as many as 400 times. Meanwhile, the model was improved by constantly modifying its parameters. For the training process, a batch size of 64 was chosen, and the learning rate was fixed at 0.001.With the aim of enhancing the prediction accuracy, this paper made use of the second-order Chebyshev polynomial and the Adam algorithm as means of optimization. The model was directed by the feedback from the loss function to change the parameter direction. This loss function has the ability to gauge the disparity between  
\begin{table}
\begin{tabular}{c c} \hline
**Data Processing Step** & **Description** \\ \hline Data source selection & Select 5 representative oil and gas reservoirs as data sources \\ \hline Experimental measurement & Obtain 7 high-pressure physical property parameters through PVT experiments, with each oil and gas reservoir measured 10 times \\ \hline Data integration & Integrate all data into a unified CSV file, containing 50 records \\ \hline Data cleaning & Remove 2 outliers and handle 3 missing values \\ \hline Data normalization & Use the Min-Max normalization method to scale the data to the range of [0, 1] \\ \hline Dataset division & Training set:Validation set:Test set = 7:2:1 \\ \hline \end{tabular}
\end{table}
Table 2: Description of dataset processing.  
the predicted value and the real value. During the optimization process, the step size and the direction of the iteration are determined depending on the loss function. The goal here was to make the loss as small as possible or to meet the preset threshold. More precisely, this paper employed the mean square loss function to judge the prediction effect of the model. The specific formula is presented in Equation (14). Through all of these operations in sequence, the model parameters were eventually updated, and consequently, the overall performance of the model was enhanced.  
\[1=\frac{\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}}{n} \tag{14}\]  
In the aforesaid equation, \(\hat{y_{\mathrm{i}}}\) indicates the actual value that belongs to the training sample and \(n\) is utilized to signify the predicted value of the training sample. Additionally, it is important to know that \(n\) also refers to the count of the training samples.
# A Study of Cislunar-Based Small Satellite Constellations with Sustainable Autonomy
## 3 Proposed Approach  
Considering the concerns above, a strategic plan is needed to navigate through these concerns and have a sustainable Cislunar space. This is only possible through enhanced technology, but to utilize them effectively for a long time, there needs to be methodologies governing them. For the strategies to develop autonomy in this region, Ref. [16] presents detailed and stepwise emphasis with creative techniques. This paper will focus on the results and the approach that was considered and designed for small satellite constellation operations in the Cislunar domain.  
Utilizing the autonomous strategies mentioned in [16], the way forward is to have a dedicated analysis plan that includes the major elements of the space environment and the congestion factors in the Cislunar space. These two aspects have a disastrous impact if not designed and implemented with the due and optimal utility of autonomy. In fact, these factors influencing the small satellite constellations dynamically, theoretically, and practically need to be analyzed in a long-term sustainable propagation.  
In consideration of these elements, a dedicated and detailed analysis has been proposed to study the impact of these elements on the small satellite constellations for both the Moon and the Earth, respectively. Research was carried out to make simulations in three stages: 1. Parametric numerical study, 2. Monte Carlo analysis of congestion (Debris and missions), and 3. Space environment analysis (impact on small satellites). Each of these three stages is interrelated and are highly impactful on the satellites to operate without halting their functions and daily tasks to attain their mission objectives in continuity.  
Figure 3 below determines the ethos and the motivation of this proposed approach dedicated to both the Moon and the Earth's orbits. The essence of the autonomous operations is taken into consideration for the framework, simulations, and analytical results presented in this paper. Hence, sustainability is the major objective behind this designed method.  
Figure 3: Proposed methodology for the continuity in autonomous operations (Cislunar).
## 1 Introduction
### Technical Challenge  
Contemporary ESC implementations predominantly employ static error detection mechanisms (e.g., Cyclic Redundancy Check (CRC) validation) that lack adaptive capacity to address dynamic attack vectors [7, 8]. Recent studies demonstrate that sophisticated timing-based attacks can bypass these safeguards, potentially causing catastrophic failures [9, 10, 11].  
The emergence of lightweight ML offers unprecedented opportunities to embed intelligent decision making within highly resource-constrained embedded systems [12, 13, 14]. However, deploying Machine Learning (ML) models within the stringent latency bounds of motor control loops (sub 10 \(\upmu\)s) while guaranteeing deterministic execution presents formidable challenges that remain inadequately addressed.  
The critical gap necessitating ML frameworks stems from the fundamental temporal mismatch between security requirements and embedded motor control constraints:  
Temporal Constraints: Motor control loops operate at sub-10 \(\upmu\)s intervals, while traditional security algorithms require significantly longer processing times. This creates a fundamental'security gap' where attacks can occur faster than conventional detection systems can respond.  
Adaptive Intelligence Requirement: Static rule-based systems cannot adapt to evolving attack patterns. The dynamic nature of timing-based attacks requires adaptive algorithms capable of real-time learning and adjustment--a capability only ML can provide within embedded constraints.  
Hardware Integration Necessity: Software-only solutions introduce unacceptable latency through context switching and I/O operations. Our FPGA hardware integration achieves the required sub-10 us response times by eliminating software overhead, as demonstrated in our implementation results.  
The practical viability is demonstrated through our PolarFire SoC implementation, which achieves 2.16% LUT utilization and 16.6 mW per channel power consumption, enabling immediate deployment on existing flight controllers without hardware redesign.
###### Abstract  
In order to achieve high-precision path following of autonomous underwater vehicle (AUV) in the horizontal plane, a three degrees-of-freedom adaptive line-of-sight based proportional (3 DOFAPLOS) guidance law is proposed. Firstly, the path point coordinate system is introduced, which is suitable for the conversion of an arbitrary path. Then, the appropriate look-ahead distance is obtained by an improved adaptive line-of-sight (ALOS) according to three degrees-of-freedom (3 DOF), including the cross-track error, the curvature of reference path, and the forward speed. Moreover, combining three degrees-of-freedom ALOS (3 DOFALOS) with proportional guidance law, the desired heading is calculated considering the drift angle. 3 DOFAPLOS has two functions: in the convergence stage, 3 DOFALOS plays a leading role, making AUV converge to the path more quickly and smoothly. In the guidance stage, proportional guidance law plays a major role in effectively resisting the influence of drift angle and making AUV sail along the reference path. If the path is curved, 3 DOFALOS makes contributions in both stages, adjusting look-ahead distance in real time with respect to curvature. The stability of the designed closed system is proved by _Lyapunov_ theory. Both simulation and experiment results have verified that 3 DOFAPLOS has a satisfactory result, which improves tracking performance more than 50% compared with the traditional line-of-sight (LOS). Specifically, the mean average error (MAE) of path following under 3 DOFAPLOS can be reduced by about 60%, and the root mean square error (RMSE) can be reduced by about 50% compared with LOS.  
AUV; path following; 3 DOFAPLOS; drift angle +
Footnote †: journal: Applied Sciences
# A Transformer-Based Approach for Efficient Geometric Feature Extraction from Vector Shape Data
## 2 Methods
### Serialization of Vector Shape Data  
A building shape outline can be represented in a vector data format as \(S=[P_{1}(Lon_{1},Lat_{1}),P_{2}(Lon_{2},Lat_{2}),\ldots,P_{m}(Lon_{m},Lat_{m})]\), which is two-dimensional data. To extract features from these data using a vector polygon transformer deep neural network (VPT-DNN), the data must be serialized. In this case, a control point-based regional model was utilized to serialize the data sequence, following the specific steps:  
1. Determine the center point \(O\) of the shape outline as \(O=1/m\cdot\sum_{i=1}^{m}P_{i}\).
2. Determine a square area with side length L centered at point \(O\), such that all points in \(S_{i}\) fall within that area, and take the minimum value of the resulting squares. Within this area, determine \(t\times t\) control points, distributing each control point uniformly within the square region according to Formula (1): \[P_{control}^{ij}=\bigg{(}P_{Lon}^{O}+\bigg{(}i-\frac{t-1}{2}\bigg{)}\cdot\frac {L}{t},P_{Lat}^{O}+\bigg{(}j-\frac{t-1}{2}\bigg{)}\cdot\frac{L}{t}\bigg{)},i \in[0,t),j\in[0,t)\] (1)
3. For each point \(P_{i}\) in outline S of the building shape, find the closest control point \(P_{control}^{ij}\) using the Manhattan distance \(D\big{(}P^{i},P^{j}\big{)}=\Big{|}P_{x}^{i}-P_{x}^{j}\Big{|}+\Big{|}P_{y}^{i} -P_{y}^{j}\Big{|}\). The outline S of the building shape can be represented as \(C_{S}=\Big{[}P_{control}^{i_{1},i_{2}}P_{control}^{i_{2},i_{2}}\ldots,P_{control }^{i_{m},i_{m}}\Big{]}\).
4. Encode each control point using a one-hot encoding scheme as in Formulas (2) and (3). \[Serialize\bigg{(}P_{control}^{ij}\bigg{)}=[v_{k}],k=1,\ldots,2t\] (2)  
\[v_{k}=\begin{cases}1,&k=i\;or\;k=t+j\\ 0,&other\end{cases} \tag{3}\]  
Conversion from the vector to sequence data was completed using the method described above.
## 3 Method
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Azimuth** & **Angle Search Step** & **Average Error (*)** & **Standard Deviation (*)** \\ \hline \(5^{\circ}\) & \(1^{\circ}\) & 0.06 & \textless{}0.001 \\  & \(0.1^{\circ}\) & 0.02 & \textless{}0.001 \\ \hline \(15^{\circ}\) & \(0.1^{\circ}\) & 0.02 & \textless{}0.001 \\ \hline \(25^{\circ}\) & \(1^{\circ}\) & 0.28 & \textless{}0.001 \\  
peaks (see Figure 3a,b), which requires fine grid spacing to ensure accurate peak detection. However, this small grid spacing substantially increases both computational and memory requirements. The performance of the conventional MUSIC algorithm is critically sensitive to the selection of search parameters. Suboptimal choices of search range and resolution can lead to significant estimation errors, fundamentally compromising the algorithm's ability to accurately determine signal direction of arrival. The precision of angle estimation is directly tied to the comprehensiveness of the angular search space, introducing inherent limitations in the algorithm's reliability and effectiveness. The computational complexity of generating a two-dimensional spectral model is given by  
\[OM^{2}N^{2}+M^{3}N^{3} \tag{26}\]  
where \(O\) represents the number of snapshots, and \(M\) and \(N\) are the number of antennas in the two respective directions. The complexity involved in searching the two-dimensional spatial spectrum model is  
\[(j_{1}\times j_{2})^{2}[MN(MN-K)+MN-K] \tag{27}\]  
where \(j_{1}\) and \(j_{2}\) correspond to the number of search points for the elevation and azimuth angles, respectively. The computational complexity for the specific execution of the two graphs shown in Figure 3a,b was evaluated using simulated hardware configurations on an Intel(R) Core(TM) i5-12400F CPU operating at 2.50 GHz, equipped with 16 GB of RAM. The results are presented in Table 5.  
The results demonstrate that the overall computational complexity is significantly influenced by the accuracy of the angular search. To reduce angular estimation errors, a substantial computational cost must be incurred. For scenarios requiring both high accuracy and real-time performance, it is essential to optimize the MUSIC algorithm to make it feasible for practical localization applications.
# Using Modeling to Select the Type of Microwave Field Emitter for Dense-Layer Grain Dryers
## 4 Discussion  
Results of simulations show that a proper matching between the semicircular waveguide with the slot radiators makes it possible to attain values of SWR = 1, over the entire frequency range from 2 GHz to 3 GHz. The radiation efficiency of the electromagnetic field was 94.28 dB (see Figure 14b) compared to 94.43 dB, for the 55 \(\times\) 110 rectangular waveguide. With the assumption that the radiation efficiency of semicircular waveguide was due to the absence of type \(E_{11}\) waves, we can conclude that their share is just 0.15 dB, which does not exceed 0.16% of the aggregate power transmitted through the waveguide.  
It follows from the simulation results that the radiation efficiency for rectangular waveguides has a more uniform character in a wide range of waves. The radiation efficiency reduction in the range from 2 GHz to 3 GHz does not exceed 10%. In semicircular waveguides, the radiation efficiency changes stepwise, in this frequency range, and has its maximum value at its standard frequency of 2.45 GHz. Therefore, the operation conditions of magnetrons have to be tightly controlled, avoiding operation modes leading to their overheating, when semicircular waveguides are applied.  
The overall efficiency of microwave energy transmission is 14.28 dB, for the semicircular waveguide (see Figure 15a), while that of the rectangular waveguide is 14.43 dB (see Figure 13a). At the same time, their radiation directionality features better uniformity compared to the 55 \(\times\) 110 rectangular waveguides (see Figure 16).  
In consideration of the issues discussed above, we may conclude that the application of more easily manufactured semicircular waveguides does not affect indicators of transmission efficiency and those of electromagnetic field distribution uniformity.
# A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones
## 4 Neutralization Systems
### Comparison of the Neutralizers  
A comparison of the neutralizers considered in this paper is shown in Table 6. For each neutralizer, both features and limitations are shown together with the pros and cons of using them as integrated in mini-drone-based platforms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Neutralizers** & **Features** & **Limitations** & **Pros and Cons with Drones** \\ \hline \multirow{9}{*}{Spoofing} & \multirow{4}{*}{Can be used to replace the C2 link or the GNSS service, allowing users to control the malicious drone} & \multirow{4}{*}{Information on the systems that are integrated in the malicious drone (sensors, link C2) must be available} & \multirow{4}{*}{No substantial advantages compared to static platforms other than the possibility of dropes to increase the operative range} \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & & \\  & & & \\ \hline \multirow{9}{*}{Protocol-Based and Replay Attacks} & \multirow{4}{*}{Ave often easy to implement} & \multirow{4}{*}{Ave can be mitigated with the help of machine learning} & \multirow{4}{*}{Ave can be implemented with the help of machine learning} & \multirow{4}{*}{Ave can be implemented with the mobility of drones to increase the operative range} \\  & & & & \\  & & & \\  & & & \\  & & & & \\  & & & \\  & & & & \\  & & & & \\ \hline \multirow{9}{*}{High-Power Electromagnetics} & \multirow{4}{*}{Can damage the electronic systems of the malicious drone} & \multirow{4}{*}{A Accurate pointing towards the required required} & \multirow{4}{*}{A Not suitable for mini drones because they require large size and weight and a high power supply} \\  & & & & \\  & & & & \\  & & & & \\  & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \multirow{9}{*}{Projectiles} & \multirow{4}{*}{Traditional neutralizer (ammunition, guided missiles, etc.)} & \multirow{4}{*}{A. Can cause crashes of the malicious drone with possible collateral damage} & \multirow{4}{*}{A. Small projectiles could be installed on mini drones, but they can be assimilated to the nets case} \\  & & & & \\  &
# Reinforcement Learning Based Resource Management for Network Slicing
## 2 Related Work
proposed a dynamic resource pooling and trading mechanism in network virtualization using Stackelberg game to achieve more efficient utilization of network resources. In [13; 14], [PERSON] et al. proposed an algorithm for trading resources among tenants. However, their approach involves a high computational cost that can make it impractical for real-scenarios. In contrast, we consider Q-learning-based approach to determine how much amount of resource a tenant needs to purchase in advance using only the current information while considering the future rewards at upcoming slots. This reinforcement learning based approach can solve the resource trading problem for network slicing with reasonably low complexity, and thus is applicable to real-world applications in practice.
## 4 Discussion
### Response of Climatic Sensitivity of Vegetation Indices to MJP
We also found that the below-ground net primary production (BNPP) of the grasslands on the northern TP is mainly affected by precipitation rather than temperature [70]. A meta-analysis across nine sites suggested warming and drought increase the abundance of grass, which has a deeper root system and a higher tolerance to drought but decreases the abundance of the sedge and forb (mesic species) [3]. For the sub-Mediterranean grasslands, [PERSON] et al. [53] found spring precipitation is important for above-ground net primary productivity (ANPP), and a dry spring results in lower annual ANPP, even as the summer precipitation increases. This is consistent with our results that early soil drought reduces the sensitivity of vegetation to precipitation in the middle growing season; thus, the increase in precipitation in summer may not affect ANPP and the greenness. Thus, a dry or wet early growing season may regulate the community composition and shift the dominance of xeric and mesic species, which possibly results in the inverse trend of temperature and precipitation sensitivity in response to altered MJP over the meadow (Figure 6a,c,e,g). However, the climatic sensitivity of the steppe vegetation indices responds indistinctly to MJP (Figure 6b,d,f,h). On the one hand, the green-up date of the alpine steppe on the TP is later than the alpine meadow. In particular, some areas in the southwest TP green two months later than the eastern region [71; 72]. It may make the steppe insensitive to precipitation in May and June. On the other hand, the alpine steppe had a higher above-ground productivity allocation that was dominated by temperature but not precipitation [70]. Moreover, we found precipitation in May over the steppe had no significant effect on soil moisture, but significantly correlated (\(p<0.1\)) with the 0-28 cm depth soil moisture in June (Figure 7e). This may be due to precipitation in May partly in the form of snowfall and the negative feedback of snow and cloud cover on temperature may delay the greening up of the steppe [73; 74]. However, previous studies also showed the positive effects of snow and preseason precipitation on spring phenology, especially for the arid areas [34; 66; 75]. Therefore, the positive and negative effects may complicate the influence of the precipitation changes in May and June on the steppe \"greenness\". Future studies are necessary to examine the effects of precipitation in May and June on the climatic sensitivity of the alpine steppe.