esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Results of Scenario 3: Parking with Full Rotation Optimization (0-180 Degrees) on Parking Area 2  
In this scenario, vehicle rotations were fully optimized, allowing rotation between 0 and 180 degrees. Providing full angular flexibility in a perpendicular parking area increased the complexity of the problem and posed additional optimization challenges. According to Tables 1 and 2, the MDE algorithm achieved the best performance, successfully placing a maximum of 10 vehicles, while the CSA, ABC, and PPSO placed up to 9 vehicles. GWO managed to place 8 vehicles, while PSO and HHO exhibited the lowest performance in this scenario.  
According to Table 3, the MDE algorithm provided the fastest solution, placing 10 vehicles in an average of 9.65 s. The ABC (14.91 s), CSA (27.88 s), and PPSO (69.63 s) algorithms required significantly more time, while GWO (29.15 s), HHO (34.22 s), and PSO (12.66 s) exhibited lower efficiency. In terms of cumulative maximization time, MDE achieved the best efficiency with a total computation time of 19.92 s, while CSA and ABC required 50.10 and 23.55 s, respectively.  
The inclusion of full angular optimization significantly increased the number of parameters to be optimized, making the problem more complex. This complexity negatively affected the performance of some algorithms, leading to longer computation times and reduced success rates. However, MDE successfully reached maximum capacity in the shortest time despite these challenges. Notably, ABC performed better in this scenario compared to previous ones, suggesting that it is more effective when angular flexibility is introduced. This improvement may be attributed to ABC's strong exploration capability and ability to efficiently navigate a larger search space. Similarly, PPSO outperformed GWO, PSO, and HHO in terms of vehicle count, suggesting its relative effectiveness under high-complexity conditions. These findings highlight the varying performance of different optimization methods when applied to complex problems.  
As shown in Table 5, MDE achieved a 47% success rate in placing 9 or 10 vehicles, making it the most reliable algorithm. CSA and ABC followed with success rates of 33% and 27%, respectively. However, MDE managed to place 10 vehicles in only 3% of the trials, while CSA and ABC failed to achieve this in any trial. Based on these results, MDE was identified as the best optimization method for Scenario 3.  
Overall, allowing full angular optimization enabled higher vehicle placement capacity, but the added flexibility also made the problem significantly more challenging. CSA, ABC, and PPSO produced relatively successful results under specific conditions, but MDE once again demonstrated the best performance. GWO, HHO, and PSO exhibited lower success rates and longer computation times compared to the other methods.
## 2 Dataset Description and Processing
### Data Processing
#### 2.3.3 Denoising Layer  
The extracted features are passed through another slice of denoising. The denoising process, in this case, is based on Cauchy a priori wavelets. Such algorithm is very useful in denoising, because it takes advantage of the inherent sparsity of wavelet coefficients and the robustness provided by the Cauchy prior [9]. The Cauchy prior assumes a heavy-tailed distribution for noise, which is often a more realistic model for real-world data. This helps to remove outliers or extreme noise values, thereby improving denoising performance. Additionally, by using wavelet coefficients, which encode the signal into various frequency components, the method can effectively separate the noise from the signal in the wavelet domain and adaptively estimate the noise-free coefficients. This allows for better preservation of important signal features while reducing noise, making it a powerful technique for denoising applications [9]. According to recent works on bearing fault diagnosis, such as [22; 23], the wavelet denoising algorithm can help to improve fault signature extraction, increase classification accuracy, improve robustness to noise and outliers, improve multi-resolution signal analysis, improve feature extraction, enable the use of traditional machine learning and deep learning methods, and achieve a more interpretable signal representation.
# Introduction to Photonics: Principles and the Most Recent Applications of Microstructures
## 1 Introduction
The word \"electro-optics\" was utilized in the past to specifically relate to nonlinear interactions between electrical and optical devices. These devices include bulk crystal modulators and later include advanced imaging sensors that are typically employed by both government and private individuals in surveillance activities [5; 6]. Photonics is said to be an \"All-Pervasive\" technology because it allows unlimited light to travel faster than the electrons that are used in electronic computer chips, which means that optical computers will compute thousands of times faster than any electronic computers because of the physical limitations of electronic conduction. More wavelengths can be packed into an optical fiber to allow an increase in the transmission bandwidth that can be in conventional copper wires. There is no electromagnetic interference in light compared to electrons in copper wires [7; 8].
# Cluster-Based Transmission Diversity Optimization in Ultra Reliable Low Latency Communication
## 2 Related Work
The main intention of our proposed C-OFSMA is to increase the transmission diversity that ensures the usages of minimum subcarrier channels to achieve reliability of 99.999% within end-to-end latency of 0.2 ms. The main rationale for choosing 0.2 ms is to use URLLC for intra-vehicular communication. The majority of recent research in this area has focused on 0.2 ms latency in vehicular Communication [44, 45]. The C-OFSMA network scheme for intra-vehicular data transmission has not been used yet. As mentioned earlier, a finite number of sensors are placed in different clusters. The cluster-based topology brings more effectivity, and we estimate the optimal clusters are needed to gain a better result. The top contribution of the paper is as follows:  
* We propose the C-OFSMA protocol that ensures higher transmission reliability and minimizes the latency in the intra-vehicular network. * We compare C-OFSMA scheme with OFSMA:
* Evaluating the minimum subcarrier channels to reach the reliability of 99.999% for different packet duplication within stringent latency bound of 0.2 ms.
* Evaluating reliability responsiveness, using fixed channels condition. * We evaluate the optimal number of clusters for different packet duplication in terms of reliability analysis. * We determine the collision probability for different packet duplication at different arrival conditions.
## 2 Materials and Methods
### Simulation of Supply and Demand Coupling  
Composite indices for supply and demand were calculated using standardized values and integrated weights. The CD was computed to assess the internal interaction between supply and demand subsystems, while the CCD evaluated the degree of synchronization with external urban factors. The CD-CCD model categorizes districts into five coordination levels, ranging from severe imbalance (0.0-0.2) to strong synergy (0.7-1.0), based on established benchmarks [35].  
The supply and demand levels of GSI in the GBA were calculated based on the respective normalized values as well as combined weightings. Whereas the coupling coordination model is composed of CD and CCD. The category of the coupling coordination level was determined based on reported work [35; 36]. As such, the coupled coordination model can be used to elucidate the state of balance of GSI [8; 37]. In addition, the magnitude of CD and CCD values can reflect the state of equilibrium between the two subsystems of GSI supply and demand. The GSI supply and demand levels are computed using Equation (2):\[S(x)=\sum_{j=1}^{n}Z_{j}X_{j} \tag{2}\]  
where \(Z_{j}\) denotes the combined linear weighting of indicator \(j\), \(S(x)\) represents the combined level of supply and demand, and \(X_{j}\) is the normalized value.  
The GSI supply and demand relationship contains internal similarity and turning points, and can be used to classify clusters of different types. In light of the intrinsic characteristics of the data, natural divisions were employed to optimize the determination of classification breakpoints, full information utilization, and data visualization [8; 38]. The GSI supply and demand levels were categorized as high, medium, and low using ArcGIS 10.5.  
\(CD\) is formulated in accordance with Equation (3):  
\[CD=\left\{\begin{array}{c}S_{(x)}D_{(y)}\\ \left[\frac{S_{(x)}+D_{(y)}}{2}\right]^{2}\end{array}\right\}^{2} \tag{3}\]  
where \(S_{(x)}\) denotes the composite coefficient of the supply system, \(D_{(y)}\) represents the composite coefficient of the demand system, \(CD\) is the degree of coupling; the larger the \(CD\) value, the better the state of balance of GSI supply-demand.  
\(CCD\) is calculated using Equations (4) and (5):  
\[T=\alpha S(x)+\beta D(y) \tag{4}\]  
\[CCD=\sqrt{CD\times T} \tag{5}\]  
where \(CCD\) denotes the coordination index; \(T\) is a coefficient indicating the level of integrated supply and demand of GSI; and \(\alpha\) and \(\beta\) represent the weightings of the supply and demand of GSI, and both values are set to 0.5 following the work of [39; 40]. As such, the category of coupled coordination can be classified into five groups according to the range of the index values. The significance of these indices will be illustrated based on the findings of the case study.
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 2 V2X Technology
### Comparison of State-Of-The-Art V2X Technologies  
Table 1 shows a detailed comparison between state-of-the-art C-V2X and DSRC in terms of various components, functions, and mechanisms [18; 19]. According to the 5 GAA [20], C-V2X can be used for basic safety applications across the world, based on device-to-device connectivity operating on the 5.9 GHz bandwidth, which is a primary typical spectrum. This is one of C-V2X's major advantages over DSRC when it comes to direct communication for interoperable service. Table 1 shows the advantages of C-V2X over IEEE 802.11p (or DSRC) [18].  
Figure 3: Transmission modes 3 and 4 of Cellular V2X (C-V2X) communications.
# On-Orbit Experimental Result of a Non-Deployable 430-MHz-Band Antenna Using a 1U CubeSat Structure
## 5 Testing on the Ground
### Transmitter Power  
The transmitter for the Loop Hentenna is not shared with the bus system's transmitter to ensure that communication is not lost in the event of a mission failure. It has a dedicated CW transmitter. Figure 10 shows the transmitter for the HNT mission. The HNT mission transmitter shares the board with the ADCS (Attitude Determination and Control System) mission of BIRDS-4. The transmit power of the CW transmitters for the bus system and the Loop Hentenna were measured by spectrum analyzer with a 40-dB attenuator to compare them by radiating radio waves on orbit. Figure 11 shows the transmitting power of the Tsuru satellite. The CW transmitter for the bus system is part of the UHF communication board made by ADDNICS. Its transmitting power was 17.3 dBm. The CW transmitter for the HNT mission is based on an SX1278 chip from Semtech. Its transmitting power was 13.0 dBm. The difference was 4.3 dB.  
Figure 11: Transmitting power. (**a**) Bus system CW transmitter; (**b**) HNT mission transmitter (Tsuru).  
Figure 10: Transmitter for HNT mission.
# Autonomous and Earth-Independent Orbit Determination for a Lunar Navigation Satellite System
###### Abstract  
In recent years, the number of expected missions to the Moon has increased significantly. With limited terrestrial-based infrastructure to support this number of missions, as well as restricted visibility over intended mission areas, there is a need for space navigation system autonomy. Autonomous on-board navigation systems in the lunar environment have been the subject of study by a number of authors. Suggested systems include optical navigation, high-sensitivity Global Navigation Satellite System (GNSS) receivers, and navigation-linked formation flying. This paper studies the interoperable nature and fusion of proposed autonomous navigation systems that are independent of Earth infrastructure, given challenges in distance and visibility. This capability is critically important for safe and resilient mission architectures. The proposed elliptical frozen orbits of lunar navigation satellite systems will be of special interest, investigating the derivation of orbit determination by non-terrestrial sources utilizing celestial observations and inter-satellite links. Potential orbit determination performances around 100 m are demonstrated, highlighting the potential of the approach for future lunar navigation infrastructure.  
navigation; lunar; PNT; orbit determination; guidance
# Effect of Buildings on the Radiation Characteristics of MF Broadcast Antennas
## 1 Introduction
Evidently, buildings are far more complex bodies and contain a variety of components with different electrical and mechanical characteristics. However, the accurate modeling of buildings is beyond the scope of this paper. The wire-grid model used herein implies a perfectly conductive body; thus, it may be considered as the worst-case scenario regarding the effect of buildings on the radiation characteristics of the antenna. A description of the models used herein for the antenna and the buildings is given in Section 2. Two types of MF antenna are considered, and the buildings are assumed to consist of wire segments that form a rectangular parallelepiped mesh. Both components of the electric field intensity, i.e., the field radiated by the antenna and the scattered field by the building-model, are taken into account. The indicative results concerning the antenna radiation pattern in the presence of buildings, are provided in Section 3, while the discussion in Section 4 focuses on establishing a quantitative relationship between the size of the building and its effect on the antenna radiation. Finally, Section 5 comprises the conclusions of our study.
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 2 The Principles of MALDI-TOF MS
### Reduced Dependence on Reagents and Kits:  
MALDI-TOF MS does not rely on enzymatic reagents, diagnostic kits, or colorimetric substrates, all of which are subject to lot-to-lot variability, expiration, and supply chain interruptions. This independence enhances the robustness and sustainability of laboratory workflows, especially in resource-constrained or high-throughput settings [19; 26; 91].  
Furthermore, since the technique is label-free and non-destructive (in some protocols), remaining culture material can often be used for confirmatory testing, strain preservation, or downstream genomic analysis [17; 25].
# Chaos-Based Underwater Communication With Arbitrary Transducers and Bandwidth
## 3 Simulation Results and Performance Analysis
### The Effect of Spreading Gain on BER  
In order to determine the optimal spreading gain for the proposed method over an underwater acoustic channel, the BER performance versus the spreading gain at various \(E_{b}/N_{0}\) ratios is given in Figure 6. Here, the underwater acoustic channel is given by Chitre [37], which is proved to be consistent with sea measurements. The channel parameters are given in Table 1 in the Appendix. The characteristics of the underwater acoustic channel require the communication system to be more robust as compared to a free space wireless communication system. We find that there is an optimal spreading gain for the lowest BER at a fixed \(E_{b}/N_{0}\), and the optimal spreading gain varies slightly with different \(E_{b}/N_{0}\), as shown in Figure 6. This figure provides the optimal spread gain as 1000, considered in our simulations.  
Figure 5: BER versus \(E_{b}/N_{0}\) for conventional DCSK, NR-DCSK, HE-DCSK, RM-DCSK, PS-DCSK, CDSK and the proposed method over AWGN channel.  
Figure 6: BER performance versus spreading gain \(L\) for the proposed method.
# Comparison of Degassing Efficiency on a Foundry Degassing Unit Using Different Rotor Types
## 3 Data Processing  
Collection and evaluation of measured data is schematically described in Figure 4. Experimental data in the form of degassing curves were obtained by measurements. These data were further normalized according to Relation (2) and processed in Matlab [18], resulting in the determination of parameter \(a\).  
\[conc.stand.=e^{\frac{t+\text{rpm.}}{a}}, \tag{2}\]  
where: \(t\)--degassing time (s), \(conc.stand.\)--standardized concentration (1), _rpm_--rotor speed (rpm) and \(a\)--parameter of the replacement area, which corresponds to the given rotor and gas flow.  
The function according to Relation (2) is a decreasing exponential function of time and rotor revolutions. It provides the advantage that for the time equal to zero (the beginning of degassing), it is guaranteed to be 1, and for the time approaching infinity, it approaches 0. The experimental data used for the results of this study only cover the time to t = 200 s from the beginning of degassing. Although degassing was measured after this time, the data had no essential impact on the summary results, and they are not relevant in terms of operational conditions because normally, no degassing takes place after this time. The parameters \(a\) for individual degassing curve groups are generally negative and achieve the dimension of \(10^{4}\) when the above dimensions of the quantities are observed. A lower absolute value of the parameter \(a\) means a greater degassing capability of the rotor. Other possibilities of reduction were also tested; according to [44], replacement of the group of the curves with Relation (2) has proven to be most effective provided that a small deviation of experimental data from the replacement function is observed; this deviation mostly reaches single-digit percentage values in the processed data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline Working height & mm & 150/160 \\ Rotor speed & rpm & 275, 300, 325, 350, 375, 400, 425 \\ Ar flow rate & \(\text{Nl}\cdot\text{min}^{-1}\) & 13, 15, 17, 19, 21 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of experiments on the physical model.  
Figure 4: Data collection and evaluation scheme.  
Using the corresponding parameter \(a\), the degassing time \(t\) for the required concentration can be back-calculated according to Equation (3):  
\[t=\frac{ln(conc.stand.)}{rpm}a, \tag{3}\]  
where: \(t\)--expected degassing time (s), \(conc.stand.\)--the required resulting standardized concentration (1), \(rpm\)--rotor speed (rpm) and \(a\)--the parameter of the replacement area corresponding to the rotor and gas flow rate. For example, a standard concentration value of 0.3 means degassing of 70% of the original gas content.
# Neuroimaging of Pediatric Cerebellum in Inherited Neurodegenerative Diseases
## 1 Introduction  
Neuroimaging has improved the understanding, diagnosis, and management of several pediatric cerebellar degenerative diseases.  
The accurate observation of structural imaging studies with MRI in search of atrophic changes and subtle signal abnormalities due to neuronal loss, gliosis, changes in the water content of the nervous tissue is the best possible way to reach a neuroradiological diagnosis.  
Advanced MR techniques such as MR spectroscopy [1, 2] and voxel-based morphometry (VBM) have been applied for years in adults and children with sporadic and genetic ataxia, but nowadays play a minor role. Other techniques, such as and diffusion tensor imaging (DTI) [3, 4], have more application in selected cases.  
Functional MRI (fMRI) using the BOLD (blood oxygenation level-dependent) effect has also been applied more recently to the investigation of pediatric patients with neurodegeneration and is still a matter of research.  
In this paper, we shall concentrate on MRI, pointing out the characteristic MRI findings of some paediatric cerebellar degenerative diseases: pontocerebellar hypoplasia, (PCH1-13), infantile neuroaxonal dystrophy (INAD), autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS), inherited defects in glycosylation (GDC), Cockayne Syndrome, GM2-gangliosidosis and neuronal ceroid lipofuscinoses (NCLs), Progressive encephalopathy, edema, hypsarrhythmia, and optic atrophy (PEHO), ataxia-telangiectasia (AT), ataxias with oculomotor apraxia (AOAs, AOA1, AOA2), mitochondrial diseases and spinocerebellar ataxia (SCAs). Furthermore, we only mention two leukodystrophies with hypomyelination and cerebellar atrophy, 4H-syndrome, and H-ABC.
## 3 Results
### Long Short-Term Memory Neural Network Model Results  
#### 3.4.1 LSTM Established by Temperature and Humidity Variables  
The LSTM model is built with the full set of temperature and humidity variables as the independent variables, and some of the model results are shown in Table 8.  
After training under various parameters, Table 8 presents representative parameter nodes along with their corresponding model results. It is observed that the model's prediction accuracy is most favorable when the batch size is set to 30. As the number of iterations increases, the model error initially decreases sharply and then tends to stabilize. Notably, the computing time for the model does not scale significantly with the number of iterations; thus, there is no need to excessively increase the number of iterations. The optimal parameters for the whole network were obtained by setting the batch size to 30,  
Figure 7: Prediction model of moisture content of corn outlet based on UVE-ELM.  
the learning rate to 0.01, and the number of iterations to 500, under which the model runs in about 9 s.  
As shown in Figure 8, a model training set with an R\({}^{2}\) of 0.886 and an RMSE of 0.883% and a validation set with an R\({}^{2}\) of 0.894 and an RMSE of 0.861% are obtained. The prediction accuracy of this method is lower than that of the MLR and ELM models, and the prediction error lies between the two. This may be attributed to the high sensitivity requirement of LSTM to the input data. In addition, from Figure 8 we can intuitively observe the comparison between the predicted and actual values.
# Human Activity Recognition Based on Deep Learning Regardless of Sensor Orientation
## 4 Experimental Setup, Results, and Analysis
### Analysis on Publicly Available Datasets  
To further validate the generalization performance of both the fusion algorithm and the models, we conducted experiments on two major publicly available datasets, namely, WISDM and UCI HAR. The experimental outcomes are illustrated in Table 6.  
As shown in Table 6, ResNet-34 outperformed CNN and MLP. On WISDM, the accuracy of the optimized ResNet-34 model was 96.33%, a 0.99% improvement from ResNet-34. On UCI HAR (12D), the accuracy of the optimized ResNet-34 model reached 92.08%, surpassing ResNet-34 (91.89%) and outperforming the non-fused approach (90.80%). Comparing the results on UCI HAR, UCI HAR (6D), and UCI HAR (12D), it is evident that simply fusing accelerometer and gyroscope data led to a slight decrease in accuracy; however, when employing the fused data as input features and concatenating these data with the original data, the performance surpassed that with the original data. This underscores the fusion algorithm's capability to mitigate the device angle change problem.