esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 BER Performance Estimation
### Individual Total Average BER Performances  
The next part of this analysis concerns the total ABER performance of an individual link, taking all of the above-mentioned effects into account [48]:  
\[\widetilde{P}_{b,N,TP,TJ,MS}=1-\Big{(}1-\widetilde{P}_{b,N,TP,MS}\Big{)}\left( 1-\widetilde{P}_{b,N,T,J,MS}\right)=\widetilde{P}_{b,N,TP,MS}+\widetilde{P}_ {b,N,T,J,MS}-\widetilde{P}_{b,N,TP,MS}\widetilde{P}_{b,N,T,J,MS} \tag{18}\]  
and the final expression of the ABER is obtained by substituting (15) and (17) into (18) as:  
\[\widetilde{P}_{b,N,TP,TJ,MS}=1-\Big{\{}1-\frac{0.208P_{MS}}{\left(64\pi\right) ^{N}}\Big{[}\prod\limits_{j=1}^{N}\xi_{j}^{2}A_{n}\Xi_{n}(15.536)+0.707\prod \limits_{i=1}^{N}\xi_{i}^{2}A_{n}\Xi_{n}(8.4)\Big{]}\Big{\}}\bigg{\{}1- \Big{[}\frac{1}{2\Gamma\left(\beta^{-1}\right)}G_{1,2}^{2,0}\bigg{(}\left( \alpha^{-1}t_{sl,MS}\right)^{\beta}\bigg{|}\begin{array}{c}1\\ \beta^{-1},\ 0\end{array}\bigg{)}\Big{]}^{N}\bigg{\}} \tag{19}\]  
\[\text{with}\ \Xi_{n}(x)=\sum\limits_{k=1}^{\omega}C_{k,n}\Big{(}\frac{4}{R_{k}} \Big{)}^{\frac{\alpha+k}{2}}G_{6,3}^{1,6}\Bigg{(}\frac{x_{MMS}^{2}p_{n}\left( 1+\xi_{n}^{-2}\right)^{2}}{B^{2}N\left(\alpha+\Omega_{n}\right)^{2}}\bigg{|} \begin{array}{c}1-\xi_{2}^{2}\\ 2\end{array},1-\frac{\xi_{2}^{2}}{2},1-\frac{4n}{2},1-\frac{4n}{2},\frac{1-k} {2}\\ 0,-\frac{\xi_{2}^{2}}{2},\frac{1-\xi_{2}^{2}}{2}\end{array}\Bigg{)}.\]  
According to the simplification previously reported, the equivalent version is finally derived:  
\[\widetilde{P}_{b,N,TP,TJ,MS}=1-\bigg{\{}1-0.208P_{MS}\left(\xi_{ \frac{5}{64\pi}}^{2}A\right)^{N}\left[\Xi^{N}(15.536)+0.707\Xi^{N}(8.4)\right] \bigg{\}}\bigg{\{}1-\bigg{[}\frac{1}{2\Gamma\left(\beta^{-1}\right)}G_{1,2}^{2,0}\bigg{(}\left(\alpha^{-1}t_{sl,MS}\right)^{\beta}\bigg{|}\begin{array}{c} 1\\ \beta^{-1},\ 0\end{array}\bigg{)}\bigg{]}^{N}\bigg{\}} \tag{20}\]  
\[\text{with}\ \Xi(x)=\sum\limits_{k=1}^{\omega}C_{k}\Big{(}\frac{4}{B}\Big{)}^{ \frac{\alpha+k}{2}}G_{6,3}^{1,6}\Bigg{(}\frac{x_{MMS}^{2}p_{n}\left(1+\xi^{-2} \right)^{2}}{B^{2}N\left(\alpha+\Omega\right)^{2}}\bigg{|}\begin{array}{c}1- \xi_{2}^{2}\\ 2\end{array},1-\frac{\xi_{2}^{2}}{2},1-\frac{4}{2},1-\frac{4}{2},\frac{1-k} {2},1-\frac{k}{2}\\ 0,-\frac{\xi_{2}^{2}}{2},\frac{1-\xi_{2}^{2}}{2}\end{array}\Bigg{)}.\]
## 4 Prebiotics, Functional Food, Bioactive Ingredients and Nutraceuticalals
### Cereals and Their Derivatives as Prebiotics  
Cereals constitute a relatively new group of prebiotics. The most common prebiotics are identified as vegetables and fruit fibres, and dairy products act as probiotic carriers [38]. Grain-based prebiotics, which are a part of a natural everyday diet for most of the human population, are very promising. Cereals are rich in insoluble fibre, soluble fibre (\(\beta\)-glucans and arabinoxylan), galactooligosaccharides (GOSs), fructooligosaccharides (FOSs) and resistant starch [39]. Whole grains are also sources of many phytochemicals, including phytoestrogens, phenolic compounds, antioxidants, phytic acid, and sterols [21; 40]. It was shown [41; 42; 43] that dietary fibre prepared from maize starch are well tolerated by the human organism. They activate the growth of _Bacterioidetes_ and _Actinobacteria_ inhibiting simultaneously the growth of _Firmicutes_ responsible for obesity, similarly, they act also as potato dextrins [44; 45].  
Products of the lactic acid fermentation of cereals and their derivatives are safe, they enhance the nutritional value of cereals, either improve or change their taste and help to preserve the food. This kind of processing, very common in Asia and Africa is employed in manufacturing beverages, gruels, pancakes, and porridge from fermented rice, sorghum, maize, millet, non-cereal cassava, and wild legume seeds [46]. That food has relatively long shelf life under ambient temperature, and is widely accepted by consumers including expectant/breastfeeding mothers, and sick and recovering persons [47]. Also, in the Western dietary culture, cereals either fermented in vitro or treated by bacteria in vivo used to be added to a variety of functional foods. Frequently, it is a rediscovery of procedures known for centuries, for instance, kvas (a soft drink) containing living bacteria.  
Below, the novel impact of prebiotics, especially cereals and their modifications, on probiosis and health of the main body systems will be briefly discussed.
# A Practical Method for Controlling the Asymmetric Mode of Atmospheric Dielectric Barrier Discharges
## 3 Examples and Mechanisms of the Discharge Mode Control  
### SP1, AP1P, and AP1N  
Before discussing the specific examples of manipulating the discharge symmetry, it is necessary to first clarify the three period-one discharge modes, i.e., SP1 discharge, asymmetric period-one discharge with a higher positive current pulse (denoted as AP1P hereinafter), and asymmetric period-one discharge with a higher negative current pulse (denoted as AP1N hereinafter), as shown in Figure 3, where the waveforms of total discharge current density under different driving frequencies are given, as well as the corresponding spatiotemporal distributions of electron density.  
Figure 3a shows a typical SP1 discharge where the cycles of the discharge current and the applied voltage are equal, and the shape of the discharge current in positive and negative half-cycles are also identical. Figure 3b presents an AP1P discharge whose current cycle is the same as that of the applied voltage, but the peak value of the positive current pulse is 15 times as large as that of the negative one. Contrary to AP1P, the peak value of the negative current pulse exceeds the positive current amplitude in AP1N mode, as shown in Figure 3c. Moreover, Figure 3d-f illustrate the corresponding spatiotemporal distributions of electron density in SP1, AP1P, and AP1N mode, respectively, in order to explain the reasons for the formation of three discharge modes. From Figure 3d, one can see that, before the initiation of each discharge, the residual electrons generated by the last discharge have decayed to a relatively low level. Therefore, the subsequent discharge can develop to a strong one, whose intensity is the same as that of the last discharge. Under this circumstance, the SP1 discharge mode is maintained. Figure 3e illustrates the influence of the massive residual electrons on the discharge performance in AP1P mode. With a strong positive current pulse, a large number of electrons are generated within the discharge phase. Thus, there are still a lot of electrons left in the gas gap prior to the negative discharge, which serves as the seed electrons of the subsequent negative discharge. Since a high seed electron density leads to a relatively low breakdown voltage [29], the subsequent negative discharge will start earlier, and a weak multi-pulse feature will be observed. As a result,  
Figure 3: Waveforms of the total discharge current density (**a–c**) and the corresponding spatiotemporal distributions of electron density (**d–f**) under different driving frequencies. The voltage amplitude \(V_{\text{am}}\), gap width \(d_{\text{g}}\), and dielectric thickness \(d_{\text{b}}\) are fixed at 2 kV, 4.4 mm, and 1 mm, respectively.  
AP1P discharge ensues. The generation process of AP1N discharge is similar to AP1P, and the only difference lies in the polarity of the strong current pulse, as shown in Figure 3\(f\).
## 6 Discussion
### Scalability  
Scalability was assessed by testing MFG-LEACH across node densities ranging from 0.001 to 0.1 nodes per m\({}^{2}\). MFG-LEACH consistently maintained high performance levels, demonstrating its robustness in both sparse and dense network conditions compared to EZ-SEP, EAMR and BRE. This characteristic of MFG-LEACH's ability to handle large populations indicates its adaptability and flexibility.
## 1 Introduction
This enhances its practicality for polyp segmentation and supports early CRC detection and treatment. The contributions of our study are threefold, which are summarized as follows:  
(1) We introduce an innovative hybrid segmentation network architecture, **HTCSNet**, which synergistically integrates Transformer and CNN architectures. This integration leverages the complementary strengths of both models to effectively capture local and global contextual cues, thereby enhancing polyp segmentation. (2) We propose a novel semi-supervised framework (**SemiPolypSeg**) based on CPS and contrastive learning to address polyp segmentation challenges. By integrating CPS, the training process of SemiPolypSeg synergizes model and data perturbations, reinforcing prediction consistency, and utilizes unlabeled data with pseudo-labels to diversify the training set. This strategy boosts the segmentation network's training performance and the model's adaptability. Further enhanced with contrastive learning, our framework efficiently processes unlabeled samples, diminishing the dependence on extensively labeled datasets. Despite the reduced need for labeled data, our novel algorithms preserve exceptional accuracy and robustness. (3) We thoroughly evaluate our model on challenging polyp image datasets to benchmark our framework against several recent, state-of-the-art (**SOTA**) SSL polyp segmentation methods. The results show that our semi-supervised framework excels in polyp segmentation, requiring minimal labeled data while outperforming existing SOTA approaches.
## 4 Use Cases
During the particle emission process, the particle concentration (\(N_{Tx}\)) is modulated according to the input symbol (\(S_{k}(t)\)), the modulation type, and the signal waveform. The transmitter emits a pilot signal of molecules, which is followed by the encoded symbol. We focus on the MCS system with on-off key modulation (OOK). Let \(b_{k_{n}}=b_{k_{0}},b_{k_{1}}\ldots b_{k_{\infty}}\) be the information sequence. The transmitter emits \(N_{Tx}\) molecules in the form of an impulse to represent \(b=1\) and no molecules are emitted for (\(b=0\)). The transmitted symbol is, therefore, represented as  
\[s_{k_{n}}(t)=\sum_{n=0}^{\infty}b_{k_{n}}N_{tx}\delta(t-kt_{s}) \tag{12}\]  
where \(\delta(t)\) is the Dirac delta function. After releasing the information carriers, they propagate in the channel modeled by Brownian motion. Upon interacting with the receiver, the molecules are received from the environment. It is assumed that the transmitter and receiver are synchronized and the time is divided into equal slots in which a single symbol \(s_{k}\) is sent. The simulation parameters of MCSs are summarized in Table 4.
## 4 Experiments and Discussion
### Realization and Experimental Results of NB-IoT Communication  
In a few areas with a 3G/4G telecommunication signal, as shown in Figure 6, we first set up the NB-IoT-support sensors and continuously sent out their sensing data by using NB-IoT communication technology. Next, the real-time sensing data, including temperature and humidity, were connected to the 3G/4G telecommunication network, and finally, they were transmitted to the web server, as shown in Figure 7.
# Artificial Intelligence-Empowered Doppler Weather Profile for Low-Earth-Orbit Satellites
## 2 Methodology
### AI Model Framework for Doppler Weather Profile Analysis
#### 2.4.1 Globalstar and ISS Satcom Arrangement  
Globalstar and ISS have standardised arrays comprising uniform elements such as A antennas, with \(A_{y}\) antennas on the \(Y\)-axis and \(A_{x}\) antennas on the \(X\)-axis.  
Each antenna is strategically placed equidistantly with half a wavelength for optimal signal reception and transmission. With one antenna, both satellites provide communication services to a user. Further to the models outlined [30]. If it is the time from the satellite, O is the altitude of the orbit, and \(O(t)\)\(\epsilon\)\(w^{A}\) is the channel with obstructions or no obstructions between two transceivers.  
Mathematically,  
\[O(t)=\sqrt{\frac{RF}{RF+1}}\tilde{O}(t)+\sqrt{\frac{RF}{RF+1}}\tilde{O}(t) \tag{2}\]  
\[\tilde{O}(t)=e^{\tilde{\epsilon}2\pi\left(\tilde{f_{ds}}-CF\tilde{\mu}\right) }\
u(\tilde{\epsilon},\tilde{\varphi}) \tag{3}\]  
\[\tilde{O}(t)=\sqrt{\frac{1}{L}}\sum_{l=1}^{L}g_{l}e^{\tilde{\epsilon}2\pi \left(\tilde{f_{ds}}-CF\tilde{\mu}\right)}\
u(\tilde{\epsilon},\tilde{\varphi}) \tag{4}\]  
Below, we mathematically express the equations in the context of our current discussion. Within this framework, we define the following variables for the line-of-sight path:  
\(\tilde{\varphi}\) = azimuth angle,  
\(\tilde{\mu}\) = propagation delay,  
\(\tilde{\epsilon}\) = elevation, and  
\(\tilde{f_{ds}}\) = Doppler shift.  
For the non-line-of-sight path:  
\(\tilde{\sigma}l\) = azimuth angle,  
\(\tilde{\mu}l\) = propagation delay,  
\(\tilde{e}l\) = elevation, and  
\(\tilde{f_{ds}}\) = Doppler shift.  
Moreover, we introduce the symbols CF, L, and RF to signify the frequency offset, NLOS paths, and Rician factor. Lastly, the array response vector \(\
u(\theta,\varphi)\) is characterised as such for our analysis.  
\[\
u(\theta,\varphi)=\
u_{x}(\theta,\varphi)\otimes\
u_{y}(\theta,\varphi) \tag{5}\]  
\[\
u_{x}(\theta,\varphi)=\left[1,e^{-\frac{\frac{12\pi\pi\pi\pi}{c}}}\cos\theta \sin\varphi,\ldots,e^{-\frac{\frac{12\pi\pi\pi}{c}}{c}}(A_{x}-1)\cos\theta \sin\varphi\right]^{T} \tag{6}\]  
\[\
u_{y}(\theta,\varphi)=\left[1,e^{-\frac{12\pi\pi\pi}{c}}\cos\theta\sin\varphi,\ldots,e^{-\frac{12\pi\pi\pi}{c}}(A_{y}-1)\cos\theta\sin\varphi\right]^{T} \tag{7}\]  
\[\
u\in\Delta^{AxSx2} \tag{8}\]  
where \(\otimes\) is the Kronecker product, antenna spacing is 'as', and c is the speed of light.
# Escalator: An Autonomous Scheduling Scheme for Convergecast in TSCH
## 3 Related Work
### Timeslot Scheduling for Convergecast  
The convergecast is a common communication pattern in WSNs and implies the data collection from many or all nodes to a sink. There are many studies to minimize both the delay from nodes to the sink and the total convergecast time in order to improve the convergecast performance of the TDMA network. [PERSON] et al. [25] introduced a distributed scheduling algorithm for the convergecast in a single channel TDMA-based WSN. The algorithm uses a slotframe of size \(N\times 3\) in a general network with \(N\) nodes. [PERSON] et al. [26] proved that the conflict-free scheduling problem using the least number of channels in the multichannel TDMA WSNs is NP-complete. [PERSON] et al. [27] proposed a link scheduling and channel assignment algorithm for the convergecast in a linear topology WirelessHART network. The authors propose a polynomial time scheduling algorithm using a slotframe with the size \(N\times 2-1\) and the number of channels \(\lceil N/2\rceil\) for a linear topology network consisting of \(N\) single buffer nodes. [PERSON] et al. [28] proposed a distributed scheduling algorithm aimed at minimizing the slotframe size required for the convergecast. This algorithm divides the slotframe with a unit called the wave and generates waves repeatedly to form a schedule for all nodes in the network to transmit packets generated at regular intervals to the sink.  
We propose an autonomous scheduling scheme for the convergecast, which is different from the previous studies. Our proposed scheme automatically generates a schedule considering both the node's traffic requirements and the delay of packet transmissions. In the next section, we describe the proposed scheme in detail.
## 4 Results
### Forecasting Performance  
The forecasting capabilities of the GREENIA framework were evaluated over a 30-day period, leveraging the XGBoost-enhanced GRU model to predict energy production. Figure 8 illustrates the comparison between the forecasted energy production values and the actual observed values across these 30 days.  
Figure 7: Main model results.  
The green line in Figure 8 represents the actual energy production values, while the blue line indicates the forecasted values. The model's forecasts demonstrate a significant gap between forecasted and actual values, particularly in the second half of the forecasted period, while in the first five days the model is able to forecast close to the actual values. This divergence suggests challenges in capturing the full complexity of energy production dynamics over a longer forecasting window. However, the model is able to forecast the energy production values for the first five days, indicating the model's ability in the short-term horizon. A key observation is the consistent underestimation of energy production by the model, as shown by the blue line frequently trailing below the green line. This trend indicates that, while the model is capable of capturing the general trend of energy production, it struggles to accurately predict higher peaks. This issue may be attributed to the model's sensitivity to certain meteorological variables. Despite these challenges, GREENIA provides valuable insights into energy production forecasting. The overall trends captured by the model offer a useful foundation for strategic energy management, even as the discrepancies highlight areas for future improvement. Accurate medium-term forecasting remains critical for optimizing resource allocation and maintaining grid stability in renewable energy systems.
# Energy-Saving Breakthrough in the Point-to-Point Control of a Flexible Manipulator
## 4 Trajectory Planning Method Focused on Flexibility Characteristics
As shown in Figure 3c,d, when residual vibration occurs after positioning, torque is required to maintain the joint angle at \(\theta(t)=\theta_{E}\) (\(t\geq T_{E}\)) against this vibration. \(F_{2}\) is then considered to mitigate residual vibration. Hence, \(F\) is the sum of the drive torque from the start of the drive to \(T_{E}+1\) s. By considering the drive torque from the end of the drive to 1 s later, both energy savings and residual vibration suppression are achievable. We employ particle swarm optimization (PSO) [39] to optimize the search parameters for minimizing the evaluation function (Equation (13)). In our previous studies [8; 38], PSO was shown to be an effective method for trajectory planning in flexible manipulators. The PSO algorithm is also described in [7]. The algorithm of the trajectory planning method was implemented in Python. The result of this optimization process generates a trajectory that conserves energy and suppresses residual vibration. By rotating the manipulator along this optimized trajectory, both drive energy and residual vibration can be reduced. Therefore, this method belongs to the category of feedforward vibration control.
## 3 Proposed Schemes  
The propose schemes use a sender based approach for the selection of next forwarder node.  
**Network model:** In proposed schemes, we assume a multi-sink network architecture [30; 31]. The network architecture is housed with numerous sensor nodes (anchored and normal nodes), as shown in Figure 1. As depicted in the network model (Figure 1), many sinks are placed on the water surface which are stationary and equipped with radio and acoustic modems. The radio modem is for communication on the land to deliver data to the base station. While, acoustic modem is to gather data from the nodes deployed inside the water. There are also anchored nodes at the bottom of water only provided with acoustic modem. Moreover, there many free floating nodes which are moving with water currents and we assume that each knows its location at the time of deployment. The assumption made is that data delivered from any acoustic node to any of the sink is considered to be available at all the sinks. Moreover, we have considered mobility of the nodes due to water currents in horizontal direction, while movement in vertical direction is small and thus negligible.
## 4 A Case Study
### Application Details of the Approach  
Modeling of the MEP systems was performed mainly on Revit. In addition, other software such as 3 dx Max was used for design and evaluation of the accuracy of the MEP model. One of the differences between them was that the geometric data of IFC files exported by Revit included axes information for spatial relationship analysis. After extracting the system type and system classification of MEP systems from IFC files, the supply air subsystem was selected to complete its logic chain. First, the approach determined the upstream terminal component in the subsystem by identification rules and users, as shown in Figure 13. The connection table was then automatically generated, and a set of components represented a spatially connected path by the same color. As shown in Figure 13, the fan is the upstream component of fire dampers, ducts, elbows, and transitions. Outlets are the downstream components of transitions. The axial fan is the upstream terminal component, as well as the start node of the directed graph traversal.  
The method was also verified in the fire sprinkler system, where the riser is the upstream component of valves, pipes, transitions, and elbows, while sprinklers are the downstream components of transitions. The pipes include feed mains, cross mains, and branch lines. Most of the logic chain were generated based on prior knowledge while several user-defined rules, as shown in Table 5, were added as a supplement before retrieving the fire sprinkler system.
# Tunneling Current Variations in Small-Sized Devices Based on a Compact Threshold Voltage Model
## 2 Theoretical Model Derivation  
Biaxial strain can significantly increase electron mobility and is widely used in strained Si NMOSFETs [11; 12]. Figure 1 shows the sectional structure of a strained Si NMOSFET. A graded \(SiGe\) layer with a Ge composition gradient is grown on the Si substrate, which can effectively reduce surface scattering and increase electron mobility. A thick relaxed \(SiGe\) layer is grown on the graded \(SiGe\) layer, serving as a virtual substrate. Finally, a very thin Si layer is epitaxially grown on the relaxed \(SiGe\) layer, introducing tensile strain in the Si layer due to different lattice constants between the Si and \(SiGe\) layers. S, G, D, and B represent the source, gate, drain, and substrate contacts, respectively. \(T_{SSi}\) and \(W_{D}\) are the thicknesses of the strained Si layer and substrate depletion layer, respectively. \(N_{SSi}\) and \(N_{SiGe}\) are the doping concentrations of the strained Si layer and relaxed \(SiGe\) layer, respectively.
## 5 Category I: Energy Transaction
### Pre-Trade Communication
#### 5.1.2 Data Hiding  
In pre-trade communications, the data need to be protected from disclosure and damage. The basic idea of data hiding is to hide the data that need to be hidden into a large dataset.  
In [79], a data confusion scheme is proposed. In the bidding mechanism of power trade, only the information regarding the overall customer transactions is released to suppliers, so that they are able to calculate monthly bills but have no access to fine-grained data. Producers need to transfer production assets to an intermediate mixing service, as proposed in [75]. Since the service simultaneously moves assets from multiple producers to multiple anonymous addresses, and the anonymous addresses are randomly generated by the prosumers, the blended assets cannot be traced back to the original prosumer. Similarly, the group signature algorithm adopted in [52] allows members of a group to send transactions in such a way that the receiver can only know that it comes from a group member but nothing more, without using a central authority.  
In [80], a virtual power plant is used as the intermediator for the negotiation between prosumers and the aggregator, without sharing any private information. Additionally, to prevent the information leakage caused the side-channel attacks during data exchange, a bilinear ECDSA (Elliptic Curve Digital Signature Algorithm) signature mechanism is proposed in [81] against adaptively chosen messages attacks under the continual leakage setting.