esa-sceva/satcom-chunk-collection huggingface dataset:
# Classification of Sleep Stage with Biosignal Images Using Convolutional Neural Networks
## 2 Backgrounds  
During sleep, different stages can be distinguished according to brain activation. The manual for classifying sleep stages was published by the American Academy of Sleep Medicine (AASM). The manual defined four different sleep stages: Drowsiness (stage S1), Light Sleep (stage S2), Slow-Wave Sleep (stage SWS), and Rapid-Eye-Movement sleep (stage REM or R) [5]. One sleep cycle takes about 90 min and consists of S2, SWS, and R. S1 is usually found during falling asleep or after waking up. Usually, 3-5 cycles are repeated during the night. Figure 1 shows an example of scoring throughout a night, a so-called hypnogram.  
PSG recordings include combinations of EEG, EOG, respiratory signals, and EMG signals. The sleep stage is determined every 30 s, and the signals generated, during sleep. The unit of sleep scoring is a 30 s epoch because the biosignals are still recorded on paper every 30 s.  
PSG signals are very complex, but they have certain important patterns for the classification of sleep stage by a somologist. For example, wave frequencies are the important patterns used to distinguish the different sleep stages, called sleep spindles (12-14 Hz), slow waves (0.5-4 Hz), alpha waves (8-12 Hz), and theta oscillations (4-8 Hz) [5]. However, there is ambiguity in the sleep scoring manual, so automatic sleep scoring is very difficult. The manual classification of sleep stage has the following problems and limitations:  
* The ambiguity of sleep scoring: There are no clear criteria to determine each stage. The stages are defined empirically by somnologists, and some sleep stages (SWS, REM) have identifiable characteristics, but the distinction between Wake, S1, and S2 is less clear. Therefore, many scoring disagreements exist in S1 (23-74% agreement for S1) [9].
* The cost of sleep scoring: Polysomnography takes a long time to analyze the biosignals recorded during sleep, since experts manually examine the results. Therefore, it is  
Figure 1: An example of a hypnogram with 6.5 h of sleep. Adapted from [[https://commons.wikimedia.org/wiki/File:Sleep_Hypnogram.svg](https://commons.wikimedia.org/wiki/File:Sleep_Hypnogram.svg)]([https://commons.wikimedia.org/wiki/File:Sleep_Hypnogram.svg](https://commons.wikimedia.org/wiki/File:Sleep_Hypnogram.svg)) (accessed on 3 January 2022).  
difficult to freely prescribe polysomnography to patients because it is an expensive medical treatment. Therefore, automatic sleep-stage classification would have the following benefits:
* Sleep-stage scoring can be reliable. The system will produce the same and stable results every time, while humans are error-prone.
* Somnologists can save an amount of time to label the recorded signals in polysomnography.
# Nonlinear Analysis and Reliability Analysis of Multilink Mechanism Considering Mixed Clearance
## 8 Conclusions  
In this paper, the eight-bar structure of a mixed multilink pressing machine is investigated, and a rigid-body dynamics modeling method for mixed clearances mechanism based on Lagrange multiplier method is proposed. The study examined the dynamic answer of eight-bar mechanisms under various clearance types. A study is conducted on the dynamics of an eight-bar mechanism with mixed clearances, focusing on the influence of clearance sizes, drive speeds, and other factors on the dynamic responses and nonlinear characteristics of the mechanisms, as well as dynamic accuracy reliability of the multilink mechanism.  
Research results indicate that the type and size of clearance have a comparatively small effect on displacement and velocity, but a large influence on acceleration. Driving speed significantly affects the central trajectories at various clearance locations. Phase portraits and Poincare maps, which vary with clearance values and driving speeds, reveal a clear trend in the nonlinear characteristics of the mixed clearance mechanism. Due to the coupling effect of revolute joint clearances and prismatic joint clearances, it is found that when clearance size and driving speed are large, mixed clearances significantly affect nonlinear characteristics of the mechanism. Within the parameter range studied in this chapter, the slider is less affected by parameters and remains in a periodic motion state throughout. Simultaneously, it is concluded from the models of reliability of dynamic accuracy of the mechanism that a smaller clearance size and a smaller drive speed result in a higher dynamic accuracy reliability of mechanisms.  
Figure 28: (**a**) Reliability indices of slider acceleration; (**b**) reliability of slider acceleration.  
**Author Contributions:** Conceptualization, Y.L. and S.J.; methodology, Y.L. and S.J.; software, Y.L. and J.Z.; validation, Y.L., J.Z., Q.Z. and M.C.; formal analysis, Y.L. and J.Z.; investigation, J.Z.; resources, Q.Z.; data curation, Y.L. and J.Z.; writing--original draft preparation, Y.L. and M.C.; writing--review and editing, Y.L., J.Z. and S.J.; visualization, Y.L.; supervision, S.J.; project administration, S.J.; funding acquisition, S.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by [Natural Science Foundation of Shandong Province] grant number [ZR2023 QE039] And The APC was funded by [[PERSON]]; [Open Project of Key Laboratory of Special Motors and High Voltage Electrical Appliances, Ministry of Education] grant number [KFKT202402] And The APC was funded by [[PERSON]].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used to support the results of this research are included in this paper.  
**Conflicts of Interest:** The authors declare that they have no conflicts of interest.
# TTH-Net: Two-Stage Transformer-CNN Hybrid Network for Leaf Vein Segmentation
## 1 Introduction
Vision Transformer (ViT) [15] has demonstrated its effectiveness compared to CNN by dividing the image into patches and flattening them into vectors before inputting them into the Transformer. This approach allows ViT to capture global relationships and context, which is key to the transformer outperforming CNN. Based on ViT, Segmenter [16] designed a pure Transformer model, which is specifically designed for semantic segmentation tasks. Compared to convolution-based methods, Segmenter allows the modeling of global context from the first layer. This approach makes Segmenter highly competitive in the scene-understanding datasets. Additionally, SETR [17], a variant of the ViT model, introduces the global characteristics of ViT and conducts extensive experiments to prove the superior performance of the Transformer compared to CNN in semantic segmentation tasks. However, SETR retains the decoding approach of CNN in the decoder to fully leverage the excellent capability of CNN for understanding local details. To achieve high-accuracy segmentation performance, this paper combines the strengths of the Transformer and CNN to design a coarse-to-fine model termed a two-stage transformer-CNN hybrid network (TTH-Net). Specifically, in the first stage, TTH-Net utilizes the backbone of ViT [15] to extract feature representations that contain rich global context. This stage is responsible for generating coarse-grained feature representations. Based on these features, TTH-Net employs CNN in the second stage for fine-grained recovery of the features generated by the ViT. Furthermore, to enhance the accuracy of the decoder, TTH-Net introduces a cross-stage semantic enhancement (CSE) module to connect the features generated by the encoders and decoders of both stages. Different from other two-stage networks, the CSE module fully utilizes the features generated in both stages and effectively integrates the advantages of Transformer and CNN, which results in the best performance of TTH-Net among all the compared methods. In summary, our contributions are as follows:1. We expand the fusion of CNN and transformer and propose a coarse-to-fine approach that combines the advantages of Transformer and CNN, which is termed a two-stage hybrid network (TTH-Net) for leaf vein segmentation. 2. We introduce the CSE module to guide the feature fusion between the CNN and transformer. This approach breaks away from the traditional stage-wise cascade and significantly enhances the segmentation accuracy of the decoder. 3. Extensive experiments conducted on the LVN dataset and our proposed TTH-Net achieve stage-of-the-art performance. Additionally, corresponding ablation experiments validate the necessity of each stage and the CSE module. The rest of the sections in this paper are as follows. In Section 2, the typical algorithms for the leaf vein segmentation task and two-stage design are reviewed. Section 3 introduces the components of the proposed TTH-Net method. Section 4 presents the experimental results and discussions. Finally, conclusions are drawn in Section 5.
## 2 Materials and Methods
### Data Analysis  
For the field experiment involving excised and non-excised leaves, the SAS procedure GLIMMIX (version 9.4; SAS Institute Inc., Cary, NC, USA) was used to perform statistical analyses of gas exchange data. Net carbon assimilation and stomatal conductance data were analyzed separately for each of five grape cultivars by fitting a repeated measures linear mixed model, with time point being the repeated measures factor. The Akaike information criterion corrected (AICc) produced the appropriate covariance structure for the repeated measures ([PERSON], 1986), from several alternative candidate models. In all cases, the AICc selected a compound symmetry covariance structure for the repeated measurements (30 s, 60 s, 90 s,..., 570 s). Terms for the fixed main effects, the interaction of the factors treatment (_in situ_, excised) and time point of measurement were included in the linear predictor for the mixed model. Year was regarded as a fixed covariate effect and replicate within a year was regarded as a random effect. Comparisons between means were performed through the SAS LSMEANS statement using ADJ = TUKEY option to obtain a Tukey-Kramer multiple comparison adjustment of \(p\)-values for differences in LS means. In each case, significance was tested at \(\alpha\) = 0.05. Correlation analysis between \(A_{\mathrm{n}}\) and \(g_{\mathrm{s}}\) was performed using R v 3.5.1 (R Core Team, 2015). The \"ggplot\" package in R v. 3.5.1 (R Core Team, 2015) was used to create regression lines and equations. GraphPad Prism 8 software (GraphPad Software, San Diego, CA, USA) was used to draw figures.  
Data analysis for past field experiments consisted of estimating time transpired between measuring gas exchange on one leaf to the next. Measurement time was calculated from original LI-6400 XT data sheets. Time differences were estimated for each consecutive measurement under each measurement date using Microsoft Excel (version 2013; Microsoft Corporation, WA, USA). Data outliers (times considered too fast or too slow compared to other data) were removed by arranging data in a scaler order and excluding the slowest and fastest 5% of data from the data set. Gas exchange measurement time efficiency of excised and _in situ_ leaves was compared. \"Treatment\" consisted of time between consecutive measurements in the field. To compare mean duration between measurements in excised and _in situ_ treatments an independent unequal variance samples t-test (\(\alpha\) = 0.05) methodology was implemented using the TTEST procedure of SAS (version 9.4; SAS Institute Inc., Cary, NC, USA).
# Congestion Control and Traffic Differentiation for Heterogeneous 6 TiSCH Networks in IIoT
## 3 The Proposed Congestion Control Mechanism
### Detecting and Controlling Congestion
#### 3.1.1 The HL-Criterion  
The first selection method corresponds to satisfying only the HL-criterion. This case represents light traffic scenarios and mainly aims to select \(P_{i}^{*}\) based on link quality and hop-count. The HL-criterion is defined as:  
\[R^{HL}(P_{i})-R^{HL}(P_{i}^{*})>\theta, \tag{3}\]  
where \(R^{HL}(P_{i})\) is the routing metric with respect to \(P_{i}\) and \(\theta\) is the hysteresis value used to avoid excessive parent switching due to small changes in the routing metric. The HL-criterion is based on the routing metric \(R^{HL}(p_{i})\) that reflects the link quality and the hop distance for each parent candidate \(p_{i}\in Parent(n_{i})\). \(R^{HL}(p_{i})\) is given as:  
\[R^{HL}(p_{i})=\underbrace{H(p_{i})+1}_{RANK(p_{i})}+ETX(n_{i},p_{i}). \tag{4}\]  
When the HL-criterion in Equation (3) is satisfied, a new parent \(P_{i}^{*}\) is selected as:  
\[P_{i}^{*}=\min_{p_{i}\in Parent(n_{i})}\left\{R^{HL}(p_{i})\right\}. \tag{5}\]  
Hence, in light traffic conditions, a node selects its new parent mainly based on the link quality and the hop-distance.
## 4 Algorithm Description
### Improved AODV Routing Discovery Algorithm
When a node receives the RREQ message, it will forward RREP via reverse routing by comparing the C(P) routing value, which can be obtained by (1) in the received RREQ message. Thereby, a path with high link quality will be determined, which balances the network traffic load and improves the overall performance of the network [16]. The improved algorithm also reduces the routing discovery overhead by storing existing paths directly in the routing table and setting flag bits in the routing table to indicate whether the node has a path to the destination node. Unlike the conventional AODV principle of looking for the minimum hop path, when a node finds that there is a path to the destination node, it directly uses the existing path without focusing on finding the minimum hop number. As shown in Figure 2, the following steps occur when a node sends data to the destination node:  
1. When node D is used as the source node, it sends the RREQ message with a link cost. After receiving the RREQ packet, each current node first observes whether there is a path to the destination node through the flag bit of the routing table. Assuming that there is no path at this time in the initial stage of network operation, it extracts the link cost value and accumulates it with the current cost value to store it again in the RREQ packet for forwarding. Then, destination node A will extract the carrying information of several RREQ packets from the cache queue, calculate the minimum routing cost value according to Formula (1), and then reply to the RREP message along the corresponding link to establish a reverse path, assuming the D \(\rightarrow\) C \(\rightarrow\) A route is established. 2. When node E is used as the source node, it first sends RREQ messages and finds whether the node in the communication range has a path that can reach destination node A by observing the flag bit. At this time, node E finds that the flag bit in the Routing table of its adjacent node D shows its existing path to the destination node A, and the route of node E to node A is directly recorded as E \(\rightarrow\) D \(\rightarrow\) C \(\rightarrow\) A. 3. If the adjacent nodes of node E do not have a direct path to reach node A, node E normally performs the RREQ broadcasts similar to step 1 until it finds a path to A or an adjacent node within the communication range of a certain hop that has a path to reach node A. The above routing discovery process has greatly reduced the overhead of routing discovery due to the characteristics of a fixed flooding area and the full utilization of the existing paths. The route with better link quality is selected based on the routing cost carried by the RREQ message, which improves the overall performance of the network. Therefore, the urban rail application scenario in this article has certain advantages over the conventional AODV routing.
# Combined Mie Resonance Metasurface for Wideband Terahertz Absorber
## 3 Further Investigation on Different Incident Angles and Polarization States
Figure 8: (**a**) The cross-sectional electric field distributions in the \(x\)-\(z\) plane with y = 40 \(\upmu\)m, 0 \(\upmu\)m, \(-\)40 \(\upmu\)m and (**b**) the cross-sectional magnetic field distribution in the \(y\)-\(z\) plane with x = \(-\)40 \(\upmu\)m, 0 \(\upmu\)m, 40 \(\upmu\)m for our combined metasurface absorber at the frequency of 0.91 THz. The electromagnetic field distributions in the x-y plane at the height of z = 20 \(\upmu\)m are also shown below the color bar. Figure 9: (**a**) The cross-sectional electric field distribution in the \(x\)-\(z\) plane with y = 40 \(\upmu\)m, 0 \(\upmu\)m, \(-\)40 \(\upmu\)m and (**b**) the cross-sectional magnetic field distribution in the \(y\)-\(z\) plane with x = \(-\)40 \(\upmu\)m, 0 \(\upmu\)m, 40 \(\upmu\)m for our combined metasurface absorber at frequency of 0.923 THz. The electromagnetic field distributions in the x-y plane at the height of z = 20 \(\upmu\)m are also shown below the color bar. The fabrication of our combined metasurface absorber is underway. Firstly, a 500 nm thick aluminum with a 10 nm thick chromium adhesion layer will be deposited on a quartz substrate by the electron-beam evaporator. Subsequently, samples will be coated again with a 10 nm of chromium via electron beam evaporation to avoid charging effects during the writing process. Then, referring to the method in Reference [30], a layer of titanium dioxide will be deposited on the sample by the atomic layer deposition (ALD) method. The deposition of the TiO\({}_{2}\) can also be realized by utilizing chemical vapor deposition (CVD) [31]. The deposition rate is about 100 nm/s. Finally, a sequential process of lithography, mask deposition, lift-off, and reactive ion etching (RIE) can be utilized to obtain the desired structure. On the other hand, the material of the structure is not limited to titanium dioxide, other dielectric with high refractive index is also feasible to deposit on the metal film.
# K/Ka-Band Very High Data-Rate Receivers: A Viable Solution for Future Moon Exploration Missions
## 6 Discussion  
An analysis of the state of the art is proposed in Table 7. The developed receiver compares well with two famous designs, the first from Alenia Sapce [34] and the second from Harris [35]. The power consumption of the Ka-band front-end, including the PLL synthesizer, is of about 2.48 W (breadboard measurements). The SDR (second conversion, demodulation, analog to digital conversion (ADC) and high-speed digital interfaces) consumes about 2 W with 56 MHz of IF bandwidth. The FPGA board with modern (digital signal processing, detector) and the CPU (at 20% of full capacity) consumes  
Figure 11: In-band interference. Measured spectrum (**a**) and constellation in the I/Q plane (**b**) at the output of the receiver breadboard (Ka-band front-end, SDR and demodulator) for a QPSK signal at 40 Msym/s. The modulated signal has an available power of \(-60\) dBm and is centered at 28.6495 GHz. An interferer is added at the receiver input. The interferer is a \(-60\) dBm carrier at 28.6505 GHz (i.e., at 1-MHz offset). This justifies the rotation of the received data around the expected \((\pm\sqrt{2},\pm\sqrt{2})\) points in the I/Q plane.  
another 3 W. The power supply unit (with DC-DC converter and regulators) dissipates about 0.5 W. As a result the total power consumption is of about 8 W, if all is switched on (no power saving strategies). Such a requirement is compatible with the capability of presently available electrical power subsystems (EPS) and, specifically, with the energy generation capabilities of deployable solar panels for cubesat platforms. Recent papers, indeed, show that this kind of panels can supply between 20 and 50 W, when correctly oriented to the Sun [36; 37; 38].  
Concerning size and mass, it has been estimated that the proposed front-end can easily be integrated in a single PCB having a size of \(9\times 9\) cm\({}^{2}\). Three other similar boards are needed for the (i) software-defined radio, (ii) for the modem and CPU and (iii) for the power supply unit. Estimating a thickness of 1 cm for each board, a total volume of \(9\times 9\times 4=324\) cm\({}^{3}\) is obtained. This implies an overall mass of about 0.6 kg if an average specific mass of 1.95 g/cm\({}^{3}\) is assumed for all the electronics.  
The achieved miniaturization level is already compatible with cubesat platforms and will be further improved exploiting the single-chip solution that the microelectronic industry is developing for the ground-based 5G telecommunication and imaging systems [39; 40; 41; 42]. This mean that cubesats could be exploited in future Moon exploration missions, as happened in the fall 2018 with the Mars Cube One (MarCO) satellites.
## 3 Experiment Description  
### Test Facility Establishment  
Static and kinematic point cloud data captured over a newly established testing facility were used in this experiment. Pictured in Figure 2, the testing facility is located in the downtown area (central business district, CBD) of Calgary, Canada. It is comprised of two loops with a combined length of approximately 2.4 km long. Importantly, it passes through a variety of different urban environments including both open sky conditions and narrow urban canyons defined by high-rise buildings between 9 Ave SW, 2 St SW, 3 Ave SW and Barclay Parade SW.  
The test facility was scanned by a professional surveying company using a RIEGL VZ400 terrestrial laser scanner (RIEGL Laser Measurement Systems GmbH, Horn, Austria). The static cloud comprised 184 scans that were registered into a common external coordinate system courtesy of a network of 38 control points surveyed by total station. The network was georeferenced by real-time kinematic GNSS observations of six of the control points located in open sky conditions. The georeferenced scanner data were validated at 173 independently determined points and cross sections in terms of the absolute value of coordinate differences, \(\Delta\). As can be seen from the statistical summary in Table 1, the point cloud quality is very high and more than sufficient for the assessment of MEMS-based, lightweight laser scanner MMS accuracy.  
Figure 1: ICP-based accuracy assessment (point cloud color represents height).