esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Nanoplasmonic waveguides utilizing surface plasmon polaritons (SPPs) propagation have been investigated for more than 15 years and are now well understood. Many researchers make their efforts to find the best ways of using light and overcoming the speed limit of integrated circuits by means of SPPs. Here, we introduce the simulation results and fabrication technology of dielectric-metal-dielectric long-range nanoplasmonic waveguides, which consists of a multilayer stack based on ultrathin noble metals in between alumina thin films. Various waveguide topologies are simulated to optimize all the geometric and multilayer stack parameters. We demonstrate the calculated propagation length of L\({}_{\text{prop}}\) = 0.27 mm at the 785 nm wavelength for the Al\({}_{2}\)O\({}_{3}\)/Ag/Al\({}_{2}\)O\({}_{3}\) waveguides. In addition, we numerically show the possibility to eliminate signal cross-talks (less than 0.01%) between two crossed waveguides. One of the key technology issues of such waveguides' nanofabrication is a dry, low-damage-etching of a multilayer stack with extremely sensitive ultrathin metals. In this paper, we propose the fabrication process flow, which provides both dry etching of Al\({}_{2}\)O\({}_{3}\)/Au(Ag)/Al\({}_{2}\)O\({}_{3}\) waveguides nanostructures with high aspect ratios and non-damage ultrathin metal films patterning. We believe that the proposed design and fabrication process flow provides new opportunities in next-generation photonic interconnects, plasmonic nanocircuitry, quantum optics and biosensors.  
nanoplasmonic waveguide; plasmonic nanocircuitry; low-damage alumina etch; multilayer waveguide; noble metals etching; multilayer stack etching +
Footnote †: journal: Applied Physics Letters
# MFCNet: Mining Features Context Network for RGB-IR Person Re-Identification
## 5 Discussion
### Comparison of Different Attention Mechanisms  
The attention mechanism plays an important role in the proposed MFCNet. In order to prove the advantages of the proposed SGAs, we compared them with existing attention methods, including CBAM [16], SE [17], and non-local block [19]. For a fair comparison, the different attention mechanism modules that work on channels or spatially are used to replace the modules that work in the same way. As shown in Table 5, compared with SGA module, the SE block and CBAM drop the r = 1 accuracy by 4.66% and 4.29%, respectively. When a non-local block is replaced by SGA, the model can not only maintain the long-range  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Setting**} & \multicolumn{6}{c}{**All-Search**} \\ \hline
**Design** & **stage1** & **stage2** & **stage3** & **stage4** & **r = 1** & **r = 10** & **r = 20** & **mAP** \\ \hline M1 & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 45.88 & 82.96 & 90.38 & 46.36 \\ \hline M2 & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 46.23 & 82.16 & 90.14 & 45.39 \\ \hline M3 & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & 46.70 & 83.64 & 90.66 & 46.47 \\ \hline M4 & \(\times\) & \(\times\) & \(\surd\) & \(\times\) & 47.15 & 84.72 & 92.53 & 46.75 \\ \hline M5 & \(\times\) & \(\times\) & \(\times\) & \(\surd\) & 47.75 & 85.46 & 93.29 & 44.73 \\ \hline M6 & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & 48.28 & 83.91 & 91.87 & 47.12 \\ \hline M7 & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) & 48.46 & 84.30 & 92.06 & 44.95 \\ \hline M8 & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) & 48.09 & 85.35 & 92.35 & 47.50 \\ \hline M9 & \(\times\) & \(\surd\) & \(\surd\) & \(\times\) & **51.64** & **86.80** & **94.66** & **49.99** \\ \hline M10 & \(\times\) & \(\surd\) & \(\times\) & \(\surd\) & 48.83 & 85.43 & 92.01 & 48.13 \\ \hline M11 & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & 50.17 & 85.46 & 92.30 & 49.08 \\ \hline M12 & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) & 49.65 & 86.41 & 93.48 & 49.16 \\ \hline M13 & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) & 49.33 & 85.25 & 92.82 & 48.55 \\ \hline M14 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 49.22 & 84.30 & 91.11 & 48.66 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison on SGA at different convolution stages. Bold numbers are the best.  
dependency of features but mines the relationship in the spatial domain, and the r = 1 accuracy drop 4.14%, respectively. The result indicates that SGA module outperform the other attention methods for visible-infrared person Re-ID.
# Study on Head Stabilization Control Strategy of Non-Wheeled Snake Robot Based on Inertial Sensor
## 1 Introduction
The above strategies for head stabilization of wheeled snake robots are divided into the following two types. The first type is to place the head of the snake robot into a snake curve and compensate for the head direction by calculating the snake curve; the other type is to introduce virtual joints and calculate the virtual joint angles by using the trunk joint angles, thereby controlling the neck joints to keep the head stable. These two types of methods need to obtain the compensation angle of the head stabilization by calculating the joint angle or the snake curve. Compared with directly obtaining the compensation angle, these methods have large errors, and the above methods are not suitable for non-wheel snake robots. In the inchworm locomotion gait [18; 19], since the serpentine curve of the inchworm locomotion gait is affected by its own gravity, the calculation of the serpentine curve and the virtual joint angle produces errors. This paper proposes a non-wheeled snake robot head stabilization compensation strategy based on an inertial sensor (IMU) [20]. The head controller is fed back the direction data by the sensor, and the head controller is stabilized by head joint compensation. In this paper, the scheme is analyzed theoretically, and the method is verified by simulation and experiment. The reminder of this paper is organized as follows: In Section 2, we introduce the previous work. In Section 3, we analyze the head stabilization of a snake robot. In Section 4, we validate the head stabilization strategy for the snake robot through simulations. In Section 5, we further validate the head stabilization algorithm through experiments on the snake robot. In Section 6, we conduct an analysis and discussion of the results from the experiments and simulations, and finally, the conclusion of this paper is discussed in Section 7.
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 6 Evaluation and Results
### Performance with Test Data  
When using the generated models to make predictions against the test datasets, the classifiers using more features gave better results than the ones only relying on position only or position and time data. If the position and accelerometer data were used the best results could be achieved, with an AUC of approximately 97% and an EER of 6.91% using a group of ten gestures, which are all longer than 50 input points. When increasing the number of grouped gestures to 25, the AUC could be pushed up to 100% with a corresponding EER of 1.34%. Additionally, using timestamp data did not improve the performance of the classifier, which may be due to the fact that time information did not get captured perfectly accurately. However the results achieved with all data combined are still betterthan with, for example, only position data. Single input gestures reach an AUC of 93.51%, while maintaining an EER of 16.23%. Those results can again be improved by combining multiple gestures. With 25 inputs combined, the AUC rises to 100% with an EER of 3.34%. However, even though we are reaching a low EER and a high AUC for our test sets, this does not necessarily mean that our classifier is working flawlessly.
## 4 Smart5 Grid NetApp Use Cases
### UC3--Millisecond-Level Precise Distributed Generation Monitoring  
#### 4.3.1 Description  
The scope of this UC is to monitor, in real time and with millisecond-level precision, multiple DERs, mostly renewables, with the aim of providing reliable flexibility services for real-time balancing electricity markets. In order for an energy entity to be allowed to participate in the real-time market, they need to adhere to strict requirements set by power system operators (TSOs or DSOs, depending on the voltage level where the DERs are connected). While the power output of RES units is uncertain by nature (e.g., it cannot be predicted accurately a few hours ahead of the actual generation time), these power generation units do possess a reasonable level of operation flexibility for participating in real-time balancing market services, such as frequency control. However, for offering such services, they require reliable and accurate real-time monitoring.  
Specifically, in the context of this UC, the real-time monitoring of a wind farm located in southeastern Bulgaria is being performed. It is worth mentioning that while this UC targets a wind farm, it could be easily replicated for other types of RES, such as PV farms or micro hydropower plants.  
Figure 10: Site architecture of UC2.  
On the one hand, high-granularity precise monitoring of the real-time power production offers wind farm owners the capability of minimizing their cost and, at the same time, being eligible for the provision of ancillary and innovative flexibility services (such as voltage regulation, congestion management, etc.) through flexible plant management. On the other hand, as wind farms are often located in remote areas, fast and reliable communication solutions, such as optical fiber, would have prohibitive costs. In addition, 4G technology could hardly guarantee the millisecond-level transmission of field measurements with high reliability of service and high availability of communication as required by this UC. Fifth generation technology appears to possess the right balance between cost and the QoS needed for such types of energy applications. Furthermore, scalability towards the monitoring and operation of thousands of such geographically distributed RES generation units (e.g., in the case of an aggregator) could become a reality on the assumption of large deployment and adoption of 5G technology in the energy sector.
## 2 Materials and Methods
### Network Design: Boundary Specific U-Network (BSU-Net)
#### 2.1.2 Residual Block  
The second component of BSU-net is the application of residual learning. Residual learning is applied to improve the efficiency of the convolutional layer. Conventional U-net is a very deep neural network with a large number of convolutional layers. Conventional U-net used in this study has a total of 38 convolutional layers and 62,803,650 learning parameters. Use of such a large number of consecutive convolutional layers can lead to the problem of gradient vanishing, which can degrade learning efficiency. The concept of residual learning was introduced to solve this problem [16]. Suppose we have a simple network \(\mathcal{H}\) which is a part of a certain deep neural network. When \(\mathcal{H}\) consists of two convolutional layers \(\mathcal{F}_{\mathrm{n}}\) and \(\mathcal{F}_{\mathrm{n+1}}\) and activation functions \(\sigma\) as shown in Figure 4a, output for the network with an input vector \(\mathrm{x}\) is defined as \(\mathcal{H}(\mathrm{x})=\sigma_{\mathrm{n+1}}(\mathcal{F}_{\mathrm{n+1}}( \sigma_{\mathrm{n}}(\mathcal{F}_{\mathrm{n}}(\mathrm{x}))))\), \(\mathrm{x}\in\mathbb{R}^{\mathrm{w}\times\mathrm{h}\times\mathrm{c}}\) where \(\mathrm{w}\), \(\mathrm{h}\), and \(\mathrm{c}\), respectively, denote the width, height, and the number of channels. During back propagation, gradient vanishing can occur if the weights of \(\mathcal{F}_{\mathrm{n}}\) or \(\mathcal{F}_{\mathrm{n+1}}\) are close to zero [16]. But if we change the network output \(\mathcal{H}(\mathrm{x})\) to \(\mathcal{H}(\mathrm{x})-\mathrm{x}\), gradient vanishing is avoided. The changed network \(\mathcal{S}\) is defined as \(\mathcal{S}(\mathrm{x})=\mathcal{H}(\mathrm{x})-\mathrm{x}\) and is also expressed as \(\mathcal{H}(\mathrm{x})=\mathcal{S}(\mathrm{x})+\mathrm{x}\). \(\mathcal{H}\) is converted to \(\mathcal{S}\) with \"shortcut connection\" between input and output as shown in Figure 4b. In this case, gradient vanishing rarely occurs because \(1\) is added to \(\frac{\partial\mathcal{S}(\mathrm{x})}{\partial\mathrm{x}}\). This change improves learning efficiency and allows the network to respond appropriately to small changes in input [16]. Residual block embeds this residual learning in BSU-net as displayed in Figure 3b. The first \(1\times 1\) convolutional layer immediately after the input is arranged to match filter size.
## 3 Results and Discussion  
### Selection of the Major Peak Wavelength and Binder-Mixing Ratio  
The LIBS spectra acquired included 10,239 peaks collected from the wavelength channels ranging from 187 to 894 nm. The sediment samples from Juksan of Yeongsan River and Gangcheon of Han River were taken as examples to show the characteristic LIBS lines based on the database of atomic spectra. Figure 2 shows the spectra of the sediment samples obtained from the Juksan and Gangcheon weirs. The major peak wavelengths of the heavy metals--361.051 nm for Cd, 274.898 nm for Cr, and 537.210 nm for Cd--were selected with the optimal signal-to-background ratio.  
The powder-type sediment samples had to be pelletized with a binding material that had an adequate mechanical strength for LIBS analyses. Because the laser beam interacted with the sample material, the sample would disintegrate during laser ablation because of mechanical shocks [23]. To improve the quantitative and qualitative analyses of the sediment samples, the samples were pelletized with an appropriate amount of binder (starch). The starch was generally composed of 20-25% amylose and 75-80% amylopectin by weight. Because the main components (mainly C, H, and O) of starch did not interfere with the target components (heavy metals) used in this study, the relative standard deviations (RSDs) from repeated measurements were compared to select an appropriate starch-mixing ratio for the sediment samples. The amount of starch was increased from 10 to 50%. The RSDs were calculated using the following equation [24]:  
\[\text{RSDs}=100\%\times\left[\sum(x_{i}-M)^{2}/(n-1)\right]^{1/2}/M \tag{3}\]  
where \(n\), \(x_{i}\), and \(M\) are the number of measurement sets, the result of each measurement, and the arithmetic mean value of the set of repeated measurements, respectively.  
Figure 3 shows the variations in the RSD values of the Cd, Cr, and Pb measurements with increases in the starch-mixing ratio. In the case of Cd, the RSD values decreased  
Figure 2: Main emission lines of the target elements (Cd, Cr, Pb) in the LIBS spectra of the sediment samples.  
with the increasing starch-mixing ratio, particularly from 10-20%, as shown in Figure 3. Therefore, the appropriate mixing ratio for the sediment sample was determined to be 20%. Although the sediment sample that was mixed with a starch content of >20% did not break during the LIBS measurements, the RSD values were low.
# Mycotoxin Level in Winter Wheat Grain as Impacted by Nitrogen and Manganese Fertilisation
## 5 Conclusions  
Wheat infestation by _Fusarium_ spp. was determined by the habitat conditions during crop growth. The performed PCA showed that the temperature (IV-VIII) had an effect on the infection of spikes by _Fusarium_ spp. During the growing season in spring and summer, temperature and rainfall had no effect on the development of MON and DON mycotoxins. Neither nitrogen nor manganese fertilisation affected the presence of _Fusarium_ spp. symptoms on wheat ears, but the infestation intensity decreased with increasing nitrogen and manganese content in the grain. Four mycotoxins were found in wheat grain: deoxynivalenol (DON), nivalenol (NIV), zearalenone (ZEA) and moniliformin (MON). Only the deoxynivalenol level was correlated with _Fusarium_ spp. infestation. Nitrogen application at 150 kg ha\({}^{-1}\) (without Mn) caused more intensive grain contamination with toxins than at 200 kg ha\({}^{-1}\) N. Supplementation of nitrogen fertilisation with manganese reduced the number of mycotoxins in wheat grain (with the exception of ZEA content, which was unproved by statistical means). The grain yield was mainly affected by the varied weather conditions during the wheat-growing periods. Neither nitrogen nor manganese fertilisation differentiated the wheat grain yield.  
Conceptualisation, A.S., K.W., B.C.-A. and A.W.; methodology, A.S., K.W., B.C.-A. and A.W.; validation, A.S. and K.W.; formal analysis, A.S. and K.W.; investigation, A.S. and B.C.-A.; resources, A.S., K.W., B.C.-A. and A.W.; data curation, A.S., K.W., B.C.-A. and A.W.; writing--original draft preparation, A.S. and K.W., writing--review and editing, A.S. and K.W.; visualisation, A.S.; supervision, A.S. and K.W., project administration, A.S.; funding acquisition, A.S., K.W. and B.C.-A. All authors have read and agreed to the published version of the manuscript.  
The results presented in this paper were obtained as part of a comprehensive study financed by the University of Warmia and Mazury in Olsztyn, Faculty of Agriculture and Forestry, Department of Agrocosystems and Horticulture (grant N 30.610.015-110).  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# A Cross-Chain Solution to Connect Multiple DNS Blockchains in Consensus Roots System
## 4 Construction of Our System
### Atomic Deal
#### 4.4.3 Submission Phase  
When the transaction status is flagged as \"successful,\" chainA assembles a data packet that includes key transactional metadata and submits it to a secure storage using storage.save \((info)\). This metadata comprises the following:  
* Identifiers for chainA and chainB (\(A.ID\), \(B.ID\)).
* Initial domain name (domain).
* Resolved IP address (res).
* Transaction packet (tx).
* Completion timestamp (time()).
# Multi-Parametric Analysis of Reliability and Energy Consumption in IoT: A Deep Learning Approach
## 3 System Model
### The Data  
We have used a publicly available dataset, collected over a period of six months, in the IEEE 802.15.4 network [33]. In the experiments, more than 48,000 configurations of seven key stack parameters were used. At the physical layer, parameters used are: \(DT\) between nodes and \(TP\)_level_. At the Medium Access Control (MAC) layer, parameters are: \(MT\), \(Retry\)_Delay_ (\(RD\)), and \(maximum\)_QS_ of the packets waiting at MAC layer. At the application layer, parameters are: _packet Inter-Arrival Time_ (\(IAT\)), and \(PS\). In addition to these stack parameters, rich per-packet meta-data was collected including: _buffer OverFlow_ (\(OF\)), _Actual Queue Size_ (\(AQS\)), and _Actual Transmissions_ (\(AT\)). A list of abbreviations and symbols is provided in Tables 2 and 3 summarizes these parameters along with their explanation and the range of values used in the experiments. The values for performance metrics, such as \(PDR\) and \(EC\), were calculated using:  
\[PDR=\frac{Pkts_{ackd}}{Pkts_{tot}}, \tag{1}\]  
\[EC=\frac{power\times time\times(D+H)}{D\times PDR_{T}}, \tag{2}\]  
where,  
\[PDR_{T}=\frac{Pkts_{ackd}}{Pkts_{att}}, \tag{3}\]and _time_ is 0.004 ms for transmission rate of 250 Kbps, _power_ is taken according to the data-sheet of CC2420, and \(D\) and \(H\) represent packet payload size and stack overhead size, respectively. Figure 1: System model.
## 3 Results
### Study Characteristics
#### 3.1.2 Sample Characteristics  
Eight of the nine studies reviewed (88.89%) assessed visuospatial memory in healthy adult samples [32, 33, 34, 35, 36, 37, 38, 39], and only the study by [PERSON] et al. [1] explored visuospatial memory function in epilepsy patients. Sample sizes ranged from six [1] to forty-one [39], with 22.22% of the studies employing only male participants [32, 39].
# Optical Fibres for Condition Monitoring of Railway Infrastructure--Encouraging Data Source or
## 4 Measurements and Results
### Signal Pattern and Properties of Two Trains in the Same Composition at the Same Channels (Location)
The first four peaks arose due to the load of the locomotive, which also had shorter bogie distances compared to that of the following coaches. Additionally, knowing that the total length of the passing train that was 259.7 m (9 coaches each 26.8 m long and a locomotive with a length of 18.5 m) and the passage took slightly more than ten seconds, the travelling speed at this point could be calculated (\(\sim\)94 km/h).
# Energy-Efficient Partial LDPC Decoding for NAND Flash-Based Storage Systems
## 3 Proposed Partial Decoding of LDPC Codes
### Construction of a Partial H-Matrix  
The H-matrix shown in (1) can be decomposed into \(w_{c}\) sub-matrices, \(\mathbf{h}_{1}\) to \(\mathbf{h}_{w_{c}}\),  
\[\mathbf{H}=\left[\begin{array}{cccc}\mathbf{I}&\mathbf{I}&\cdots&\mathbf{I} \\ \mathbf{I}&\mathbf{A}&\cdots&\mathbf{A}^{w_{r}-1}\\ \mathbf{I}&\mathbf{A}^{2}&\cdots&\mathbf{A}^{2(w_{r}-1)}\\ \vdots&\vdots&\ddots&\vdots\\ \mathbf{I}&\mathbf{A}^{w_{c}-1}&\cdots&\mathbf{A}^{(w_{c}-1)(w_{r}-1)}\\ \end{array}\right]=\left[\begin{array}{c}\mathbf{h}_{1}\\ \mathbf{h}_{2}\\ \mathbf{h}_{3}\\ \vdots\\ \mathbf{h}_{w_{c}}\\ \end{array}\right], \tag{4}\]  
where \(\mathbf{h}_{i}\) is a \(p\times w_{r}p\) sub-matrix denoting  
\[\mathbf{h}_{i}=\Big{[}\begin{array}{cccc}\mathbf{I}&\mathbf{A}^{i-1}& \mathbf{A}^{2(i-1)}&\cdots&\mathbf{A}^{(w_{r}-1)(i-1)}\\ \end{array}\Big{]}. \tag{5}\]  
To support various error-correcting capabilities, a partial H-matrix is organized by including some of the above sub-matrices, \(\mathbf{h}_{1}\) to \(\mathbf{h}_{x}\). A set of sub-matrices is denoted as \(\mathbf{H}_{x}\),  
\[\mathbf{H}_{x}=[\mathbf{h}_{1}\quad\mathbf{h}_{2}\quad\cdots\quad\mathbf{h}_{ x}]^{T}, \tag{6}\]  
where \(x\) is an integer ranging from 2 to \(w_{c}\), since the column degree of a partial H-matrix should be at least 2 in order to decode an LDPC code. When \(w_{r}=4\), for example, a partial H-matrix \(\mathbf{H}_{3}\) is constructed as \[\mathbf{H}_{3}=\left[\begin{array}{c}\mathbf{h}_{1}\\ \mathbf{h}_{2}\\ \mathbf{h}_{3}\end{array}\right]=\left[\begin{array}{cccc}\mathbf{I}&\mathbf{ I}&\mathbf{I}&\mathbf{I}\\ \mathbf{I}&\mathbf{A}&\mathbf{A}^{2}&\mathbf{A}^{3}\\ \mathbf{I}&\mathbf{A}^{2}&\mathbf{A}^{4}&\mathbf{A}^{6}\end{array}\right]. \tag{7}\]
# Modeling and Predicting Carbon and Water Fluxes Using Data-Driven Techniques in a Forest Ecosystem
## 3 Results
### NEE Modeling Using the Machine Learning Methods  
Table 5 shows the predicted accuracies of daily NEE by the data-driven models employed in the present study. As clearly seen from the table, the ANN and GMDH models provide the best and worst model efficiency in estimating the NEE, respectively, which are similar to previous GPP predictions. In addition, according to \(R^{2}\), NSE, RMSE and MAE criteria in the testing period, the overall performance ranks of the models are summarized as follows: ANN, SVM, GRNN, ANFIS, ELM and GMDH (Table 3).  
As shown in Figure 6, similar to the aforementioned GPP and \(R\) estimation, the fit line of the GMDH model (slope = 0.50) also deviates from the ideal fit line more than those for the other models. However, the fit lines of ANN, ANFIS and SVM for daily NEE forecasting in the testing period more closely match the ideal fit line (1:1 lines). Additionally, it is clearly seen from Figure 3c that most of the modeled values of daily NEE closely follow the corresponding observed ones among the three periods, while the peaks are underestimated, mainly in the validation and testing periods. Furthermore, according to the annual estimates by the applied models, the total NEE in 2009 is under-estimated by 21% to 33%, except for the estimates from the GRNN model (Figure 4c).  
Figure 5: Comparisons of daily \(R\) measured by eddy covariance technique and predicted by data-driven models for the testing period. (**a**) ANN model; (**b**) GRNN model; (**c**) ELM model; (**d**) ANFIS model; (**e**) SVM model; and (**f**) GMDH model.