esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Accumulation of Sinigrin
\begin{table}
\begin{tabular}{l c c} \hline  & **Sinigrin** & **CV (\%)** \\ \hline DER006 & 6.60\({}^{\text{de}}\) & 1.7 \\ DER008 & 8.07\({}^{\text{ef}}\) & 7.3 \\ DER014 & 9.52\({}^{\text{f}}\) & 10.2 \\ DER021 & 3.31\({}^{\text{c}}\) & 4.9 \\ DER039 & 10.13\({}^{\text{f}}\) & 27.6 \\ DER040 & 5.97\({}^{\text{cd}}\) & 7.3 \\ DER045 & 5.82\({}^{\text{cd}}\) & 6.6 \\ DER052 & 4.65\({}^{\text{c}}\) & 18.0 \\ DER056 & 6.88\({}^{\text{de}}\) & 13.3 \\ DER057 & 9.04\({}^{\text{f}}\) & 10.1 \\ DER060 & 12.128 & 6.5 \\ DER061 & 16.87\({}^{\text{h}}\) & 1.1 \\ DER062 & 5.79\({}^{\text{cd}}\) & 11.4 \\ DER064 & 18.18\({}^{\text{h}}\) & 4.5 \\ DER067 & 2.28\({}^{\text{b}}\) & 24.8 \\ DER068 & 3.06\({}^{\text{c}}\) & 11.5 \\ DER069 & 6.04\({}^{\text{cd}}\) & 14.4 \\ DER071 & 15.79\({}^{\text{h}}\) & 6.4 \\ DER082 & 3.39\({}^{\text{c}}\) & 10.3 \\ DER087 & 15.48\({}^{\text{h}}\) & 15.5 \\ DER089 & 3.60\({}^{\text{c}}\) & 7.6 \\ DER096 & 3.26\({}^{\text{c}}\) & 16.7 \\ DER102 & 1.35\({}^{\text{a}}\) & 8.1 \\ DER103 & 8.98\({}^{\text{f}}\) & 8.3 \\ _Mean_ & 7.76 & 62.5 \\ \hline \end{tabular} Different letters indicate significant differences according to the Student-Newman-Keuls test (\(p=0.05\)).  
\end{table} 
Table 5: Content in sinigrin (mg 100 g\({}^{-1}\) FW) and coefficient of variation (CV, %) in the 24 populations of wall rocket evaluated (\(n=3\)). respectively [13], both detected in the ITCs fraction. By contrast, the breakdown product of glucoiberin could not be identified within the VOCs. Finally, indolic GSLs (glucobrassicin, methoxyglucobrassicin and neoglucobrassicin) derive in unstable ITCs that spontaneously decompose to other products [56], so the corresponding ITCs would not be easily identified. Differences between the GSLs and ITCs fractions represent an interrogative that should be clarified in new studies, for which we suggest the use of concentration steps during the extraction process with an attempt to increase the chromatogram signals of the target compounds.
## 4 The Prediction Model When Signal Approaches the Ground Station (Section B)
### Constructing the Database for the Learner  
In this step, we used the collected data based on (1) the building loss, (2) geometric calculations related to the location of the ground station and the height of the building, and (3) satellite orbits to build a learning database that enabled building loss prediction. The input data came from STK simulation, which links satellite orbit (time and angle) with channel orientation, and geometric calculations relating to a particular scenario. Table 3 depicts an example of the database record layout used for our experiment with a signal frequency of 72 GHz.  
This dataset will train the deep-learning system described in the next step. Above the table header is the source of each table column.
## 1 Introduction
### Related Works and Limitations
Despite these substantial advances, several limitations remain evident when focusing on IoV scenarios:  
* Existing frameworks for vehicular data sharing lack rigorous testing against adversarial attacks (e.g., model poisoning) or inference threats, while integrated blockchain in FL models ignores the computational overhead of consensus protocols in large-scale networks. * Trust mechanisms relying on static and single factors (e.g., IDS or user feedback) fail to adapt to dynamic IoV conditions such as fluctuating network reliability, energy availability, or evolving adversarial tactics. * Privacy-preserving FL architectures depend on centralised cloud aggregation, introducing latency bottlenecks that hinder deployment in mobile vehicular environments. * Publicly available datasets lack realistic IoV-specific scenarios (e.g., adversarial behaviours, heterogeneous network states), and scalability analyses for smart city FL implementations are absent. * Resource management strategies, particularly those using DRL, suffer from untested learning latency and incomplete privacy simulations, undermining real-time decision-making in ITS and V2X applications. * The convergence behaviour of FL models in heterogeneous vehicular networks remains unvalidated, raising concerns about stability under real-world conditions.
## 3 Results and Discussion  
### Life Cycle and Cell Morphology of H. lacustris  
Figure 2 illustrates the life cycle of _H. lacustris_ during a 30 d photosynthetic flask cultivation. The figure highlights the three stages of germination, vegetative growth, and encrystment; the latter is associated with astaxanthin production. Light microscopy revealed several distinct cell types. Initially, bright red cyst cells from a seed flask culture were inoculated into fresh NIES-C medium. The life cycle begins with cyst germination, where each cyst undergoes multiple internal divisions, producing 2, 4, 8, and up to 16 daughter cells. As germination progresses, the cyst wall ruptures, releasing motile daughter cells.  
During vegetative growth, these motile biflagellate cells lose their flagella, enlarge, and transform into spherical green palmella cells rich in chlorophylls referred to as palmella, (I). Under stress conditions, such as nutrient deprivation and aging, _H. lacustris_ palmella cells enter the encrystment stage, triggering astaxanthin biosynthesis as a defense against oxidative stress [16; 44; 45]. These cells then undergo a series of morphological changes, transitioning through green-orange (II), green-brown (III), and reddish (IV) forms, indicating increasing astaxanthin levels. By the end of cultivation, these cells mature into bright red cysts with robust, multilayered cell walls, signifying substantial astaxanthin accumulation [14; 46]. However, it is important to note the heterogeneous morphological distribution of cell types during photosynthetic cultivation, with various cell forms evolving over time [47].  
This study explored the feasibility of using microfluidic collision treatment to promote cyst germination and enhance astaxanthin productivity in _H. lacustris_ cultures. We varied two key microfluidic parameters--flow rate (1.0-3.0 mL/min) and loop number (3-30)--in a microchannel reactor with T-junction loops to optimize these outcomes.
## 4 Characterizing Spectral Efficiency Dynamics
### Accounting for Micro-Mobility  
As we consider UE with two physical antennas located on two opposite sides, the rotations associated with micro-mobility are confined in the range from \(-\pi/2\) to \(\pi/2\). Specifically, once the UE turns to \(\pi/2\) angle from the initial orientation, which is the worst position in terms of the received antenna gain, it either proceeds to rotate in the backward direction or utilizes the other physical antenna. In both cases, the misalignment angle never exceeds the value of \(\pi/2\).  
By following the approach considered in (13), the probability of finding a diffusing point at angle \(\gamma\) at time \(t\) together with the initial conditions \(q(\gamma,t|0,t_{0})=\delta(\gamma-\gamma_{0})\) and boundary conditions  
\[\frac{\partial q(-\pi/2,t|\gamma_{0},t_{0})}{\partial\gamma}=\frac{\partial q (\pi/2,t|0,t_{0})}{\partial\gamma}=0, \tag{20}\]  
can be derived as follows:  
\[q_{H}(\gamma,t|0,t_{0})=q_{V}(\gamma,t|0,t_{0})=\frac{2}{\pi}+\frac{4}{\pi} \sum_{n=1}^{\infty}\exp\biggl{[}-\Bigl{(}\frac{n\pi}{2}\Bigr{)}^{2}\frac{t-t_{ 0}}{\tau_{\gamma}}\biggr{]}\cos(2n\gamma), \tag{21}\]  
with the relaxation time \(\tau_{\gamma}=\pi^{2}/4D_{\gamma}\), and \(D_{\gamma}\) is the diffusion constant for angular shifts. Please note that this expression is applicable to both horizontal and vertical planes.
###### Abstract  
In this paper, we present a new Multiple learning to prediction algorithm model that used three different combinations of machine-learning methods to improve the accuracy of the \(\alpha\)-\(\beta\) filter algorithm. The parameters of \(\alpha\) and \(\beta\) were tuned in dynamic conditions instead of static conditions. The proposed system was designed to use the deep belief network (DBN), the deep extreme learning machine (DELM), and the SVM as three different learning algorithms. Then these learned parameters were trained by the machine-learning algorithms tuned to the \(\alpha\)-\(\beta\) filter algorithm as a prediction module, and they gave the final predicted results. The MAE and RMSE were used to evaluate the performance of the proposed \(\alpha\)-\(\beta\) filter with different learning algorithms. Each algorithm recorded different best-case accuracy results; for the DBN, we achieved 3.60 and 2.61; for the DELM, we obtained the best-case result of 3.90 and 2.81; and finally, for the SVM, 4.0 and 3.21 were attained in terms of the RMSE and MAE, respectively, as compared to 5.21 and 3.95. When assessed in comparison with the typical alpha-beta filter algorithm, the proposed system provided results with better accuracy.  
alpha-beta filter; deep belief network; DELM; SVM; accuracy Article  
A Performance Evaluation of the Alpha-Beta (\(\alpha\)-\(\beta\)) Filter Algorithm with Different Learning Models: DBN, DELM, and SVM.
# Multi-Wavelength Path Loss Model for Indoor VLC with Mobile Human Blockage
## 1 Introduction
In following, we will give a review of the research on path loss which takes into account the wavelength dependence and the human blockage for the visible light channel.
# Performance Research and Optimization of Sound Insulation Hood of Air Compressor Unit
## 1 Introduction
This method uses the damping vibration reduction and noise reduction mechanism to attach the damping material between the outer steel plate and the inner sound-absorbing material of the sound insulation cover to form a structure of a sound insulation layer, damping layer, and sound-absorbing layer, thereby improving the control ability of low-frequency noise. The results showed that the noise level of the testing machine was improved about 5-10 dB(A) in each frequency band below 200 Hz center frequency, and the noise sound pressure level in the whole frequency band was reduced by 4.5 dB(A). Subsequently, [PERSON] et al. studied the correlation between rotor blade vibration and acoustic vibration of a multistage high-pressure compressor and analyzed the generation mechanism of blade vibration [19]. [PERSON] et al. proposed two noise reduction methods for semi-enclosed frequency conversion twin-screw refrigeration compressors: end face attenuation passage and discharge pipe damping [20]. In addition, the vibration and noise characteristics of the two-stage centrifugal compressor were experimentally researched in a refrigeration system [21]. Multilayer micro-orifice plate absorbers fixed on the outlet pipe were introduced to attenuate the compressor noise. The above research has important guiding significance for the causes and control of compressor noise, but the specific noise control means are many. For a certain compressor unit, the layout of noise reduction measures is difficult, the universality is not strong, and it is not suitable for large industrial compressor production units. In this paper, a sound insulation hood of an air compressor unit is proposed, and the influence of different air inlet structures of the sound insulation hood on the noise reduction effect of the unit noise is researched. First, an acoustic finite element numerical simulation analysis is carried out on the sound insulation hood model by acoustic simulation software to improve and optimize the air inlet structure of sound insulation hood according to the simulation results. In addition, the sound insulation effect and sound pressure distribution of the sound insulation hood are analyzed in this paper. Lastly, the different air inlet structures are compared to optimize the performance of the sound insulation hood. A theoretical reference for the noise reduction of the air compressor unit is provided by analyzing the performance of the sound insulation hood.
# Performance Analysis of Two-Way Satellite-Terrestrial Relay Network with SWIPT
###### Abstract
Especially in a network composed of the energy-constrained device, EE will be an indispensable metric to measure system performance. However, the existing theoretical analyses on energy harvesting are focused on terrestrial communication systems. To solve the problem, we introduced the SWIPT technology into account and investigate the performance of the SWIPT aided two-way satellite-terrestrial relay network. Firstly, we propose the framework of the SWIPT aided two-way satellite-terrestrial relay network model and introduce the SPNC protocol to improve the throughput performance of the proposed network. Before deriving exact average end-to-end throughput expressions of PNC and SND protocols, we derive the exact BERs and instantaneous throughputs. Secondly, we derive the probability of single node detection occurrence of SPNC protocol in the SWIPT aided system. Thirdly, to get the energy efficiency performance of the SWIPT aided system, the energy harvesting at the user source node is given. The rest of this paper is organized as follows: In Section 2, the SWIPT aided two-way satellite-terrestrial relay network model is presented, and a brief introduction to SPNC protocol is given. In Section 3, the system performance of the proposed network is investigated. In Section 4, Monte Carlo simulation results are provided to verify the correctness of theoretical results, and the conclusion of this paper is summarized in Section 5.
## 3 Shear Strength Evolution of Structural Plane Infill Specimens
### Freeze-Thaw Induced Stability Analysis of Fractured Rock Slopes with Filled Joints  
#### 4.2.1 Spatial Distribution of Mechanical Parameters Considering Freeze-Thaw Degradation  
A numerical model of a typical section at the 645 m bench of the Wushan open-pit mine was established in COMSOL 6.0. The model height is 40 m, with a bench slope angle of 65\({}^{\circ}\), a bench height of 30 m, and both upper and lower platform widths of 13 m. Vertical boundaries and the horizontal bottom boundary are constrained with displacement perpendicular to the boundary lines, considering only the self-weight of the rock mass.  
In the current numerical model, processes such as moisture migration, capillary pressure, and frost heave in saturated fill materials were not explicitly simulated, primarily due to limitations in parameter availability and multiphysics coupling capabilities within the modeling software. However, the strength degradation behavior observed in laboratory tests conducted under controlled temperature and saturation conditions implicitly reflects the integrated effects of these processes. Therefore, the input strength parameters used in the model can be considered as effective values incorporating the macroscopic influence of freeze-thaw action.  
According to the multi-parameter coupling models for internal friction angle and cohesion established earlier (Equations (4) and (5)), the mechanical parameters corresponding to the freeze-thaw cycle number and burial depth were assigned to each mesh element of the 795 m platform slope model, constructing a spatial distribution field of mechanical parameters considering freeze-thaw effects. Figures 9 and 10 show the spatial distribution trends of internal friction angle and cohesion in the slope rock mass under different freeze-thaw cycle conditions. The results indicate that with increasing freeze-thaw cycles, the mechanical parameters of the slope surface layer (within the freeze-thaw affected depth) continuously deteriorate, exhibiting a significant degradation-layer effect. In particular, the shallow rock mass, being more susceptible to temperature fluctuations and moisture migration, shows a faster rate of strength degradation than the deeper layers, resulting in a stratified mechanical enhancement with depth and an expanding weakened zone over time. After more than 10 freeze-thaw cycles, this stratified weakening feature becomes increasingly evident, demonstrating the reconstructive effect of freeze-thaw and depth coupling on the spatial distribution of rock strength and providing a mechanical basis for subsequent slope stability analysis.  
Figure 9: Spatial distribution of internal friction angle in the slope rock mass under the influence of freeze–thaw cycles: (**a**) 2 freeze–thaw cycles; (**b**) 5 freeze–thaw cycles; (**c**) 10 freeze–thaw cycles; (**d**) 20 freeze–thaw cycles.
## 5 Summary
### FPGA Standalone Sensor Nodes  
Table 6 summarizes the research sensor nodes that were completely based on FPGA, _i.e._, standalone FPGA platforms which are SoC-oriented.  
According to Table 6, there exists a key problem. Most of the authors did not provide detailed information about the power consumption of the proposed platforms. Even worst, the operational frequency was rarely provided. Moreover, there are certain issues that can be pointed out. First, in comparison to Table 4, documents [62; 63; 64; 81] have a very high power consumption (221 mW, 2,850 mW and 700 mW respectively). In those cases, the FPGAs can deplete the battery in very low time without the possibility of switching the board in sleep mode when it is not working. Moreover, their power consumption is up to 32 times higher than the MICA2 node (Table 4). Nevertheless, it would be interesting to estimate the energy reduction and optimization in SoCs based on Nios-II and MicroBlaze using advanced clock gating and other techniques, in order to turn off the system custom logic.  
For obtaining an estimation of the power consumption of the proposed standalone sensor nodes, we have investigated the static power consumption of the platforms via the XPE and EPE tools with no configuration. Hence, the quiescent power consumption of [25; 61; 67; 83; 84] was 60 mW, 29 mW, 193 mW, 29 mW and 78 mW respectively. Consequently, these implementations on FPGA are all ill-suited for sensor node construction.  
To summarize, it is worth noting that using an FPGA to do simple processing of sensors and communication with an external transceiver is an overkill ([61; 62; 83])--even more if the FPGAs are those of general use instead of ultra low-power, e.g., Actel IGLOO. If not, a microcontroller is generally enough to interface sensors and external transceivers, as the commercial sensor nodes depicted in Table 4.  
Finally, authors of [61; 83] utilized Bluetooth whereas 802.15.4/ZigBee is being adopted as networking standard in current WSNs due to its low-power approach.
## 4 Four-Channel TIADC Online RLS Mismatch Error Estimation Compensation Structure  
In this section, the calibration structure for the nonlinear mismatch error of the TIADC is presented. The sinusoidal wave fitting method is adopted in this paper. The proposed calibration method is a digital post-compensation calibration method, in which the error is reconstructed by the estimated error parameters after analyzing the signal from the TIADC output and input. Finally, the error is subtracted from the output of the TIADC to compensate the signal for the corrected signal.  
As shown in Figure 4, for a given input cosine signal \(x(t)\), its discrete signal \(x(n)\) at any given moment \(n\) after passing through the sample and hold amplifier (SHA) is also known. \(y(n)\) is the output of the TIADC system containing the mismatch error.  
The input and output signals are fed to the RLS module simultaneously for recursive operations, which can be calculated as described in Section 3.2. The final RLS module outputs the computed series kernel to the error reconstruction module. Let the RLS error estimation module that performs error estimation for the \(m\)th ADC compute the output series kernel as:  
\[\begin{array}{llllll}\theta_{m}=&[\theta_{m,0}&\theta_{m,1}(0)&\theta_{m,1}( 1)&\theta_{m,1}(2)&\theta_{m,2}(0,0)\\ &\theta_{m,2}(0,1)&\theta_{m,2}(0,2)&\theta_{m,2}(1,1)&\theta_{m,2}(1,2)& \theta_{m,2}(2,2)]\end{array} \tag{30}\]  
The function of the error reconstruction module for a single channel is:  
\[\begin{array}{llll}E_{m}[x(n)]&=h_{m,0}+\sum\limits_{k=0}^{2}h_{m,1}(k)x_{m} (n-k)\\ &+\sum\limits_{k_{1}=0}^{2}\sum\limits_{k_{2}=0}^{2}h_{m,3}(k_{1},k_{2})x_{m} (n-k_{1})x_{m}(n-k_{2})\end{array} \tag{31}\]  
of which:  
\[h_{m,i}=\left\{\begin{array}{ll}\theta_{m,i}-1\,,i=1\\ -\theta_{m,i}\,,\,other\end{array}\right. \tag{32}\]  
Then the complete error function is finally established as:  
\[E(n)=\sum\limits_{m=1}^{4}\left[\begin{array}{ll}h_{m,0}+\sum\limits_{k=0}^{ 2}h_{m,1}(k)x_{m}(n-k)\\ +\sum\limits_{k_{1}=0}^{2}\sum\limits_{k_{2}=0}^{2}h_{m,3}(k_{1},k_{2})x_{m} (n-k_{1})x_{m}(n-k_{2})\end{array}\right] \tag{33}\]  
The overall correction block diagram is shown in Figure 5, and the corrected output signal is:  
\[y_{c}(n)=y(n)-E(n) \tag{34}\]  
Figure 4: Concrete error estimation and reconstruction structure of the TIADC.
# Meta Surface-Based Multiband MIMO Antenna for UAV Communications at mm-Wave and Sub-THz Bands
## 2 Materials and Methods  
This section outlines the antenna configuration and design procedures. The MIMO LWA is initially constructed for specific frequency bands before integrating the MTSs. Incorporating the transversal and longitudinal slots and the MTS arrays enhanced the LWA performances. Moreover, the periodic H-shaped slots cut from a wide strip between two antennas improved the isolation. The radiation characteristics of the one-port configuration are examined and then extended to a two-port setup. Current- and electric-field concentrations were analyzed, and critical parameters were optimized based on actual values obtained from principle equations before detailing the design process.