esa-sceva/satcom-chunk-collection huggingface dataset:
# Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks
## 3 PUTPOSH Model
### Framework
#### 3.2.2 Spectrum Mobility Management  
The spectrum mobility management obtains the interarrival information of the PU by spectrum sensing. The dwelling period of a PU is forecasted at the assigned channel with respect to the previous waiting behavior. The stopover stay of the PU at the assigned channel depends upon its communication requirements and can be of any time period. Therefore the dwelling period is assumed and based upon the random distribution method [18; 21]. The decision of either to wait and stay at the ongoing path or to execute the handoff event is based on the current result. On the one hand, the CRU does not make the handoff decision when the PU continued communication for shorter intervals of time. The CRU may carry its communication and can wait for the ongoing spectrum band. On the other hand, the CRU chooses to make a handoff decision when PU continued for a longer period at the previous licensed band (see Spectrum mobility management module in Figure 1). To allocate vacant channels, the waiting time is considered as the overall time of a CRU which can be the reason for delay in the priority queue. It can be resolved by the M/G/1 queuing equation for the network model [43; 44].
## 4 Bibliometric Analysis Results
### Annual Publications  
In a recent period of five years, from 2017 to 2021, more than 75% of all selected papers (67) were published, according to a bibliometric analysis. The number of yearly publications has increased, particularly in 2021, and is expected to reach more than 15 publications in 2022 (Figure 4).  
This development pattern fits the findings of previous UR-related reviews [96; 102; 106; 107], corroborating the notion of UR as an important topic with expanding academic interest. The findings also revealed that there were no prominent publications in terms of UR research in combination with GIS decision support tools.
## 4 Experimental Results
### Forced Vibration Response Under Variable Excitation Frequencies  
The comparative displacement responses of the experimental model under sinusoidal excitations with varying frequencies are presented in Figure 8, with corresponding vibration-reduction rates summarized in Table 1. Implementation of the SPPTMD control system significantly attenuated structural displacement responses while demonstrating robust vibration suppression across a broad frequency range. At the resonant frequency of 1.5 Hz, maximum vibration-reduction rates of 86.13% for peak displacement and 87.88% for RMS displacement were achieved. As excitation frequencies deviated from the structural natural frequency, the control efficacy gradually decreased yet maintained notable reductions of 33.87% (peak) and 31.89% (RMS). These results conclusively demonstrate the SPPTD's effectiveness in vibration mitigation regardless of excitation frequency variations.
## 2 Materials and Methods
### Analyses
#### 2.5.4 High-Performance Liquid Chromatographic (HPLC) Analysis of Organic Acids  
Samples of the RIFs were taken after 24 h and 48 h of incubation and centrifuged at 4000 rpm for 15 min using an SL 16R centrifuge (Thermo Scientific, Waltham, MA, USA). The supernatant was passed through 0.45 \(\upmu\)m filters (Milkev HV, Millipore, Ireland). The filtrated samples were stored at \(-\)40 \({}^{\circ}\)C until the analysis. On the day of the analysis, 1 mL of the filtrated samples was diluted in double-distilled water to a volume of 10 mL. All samples were analyzed in triplicate (intraday). The HPLC analysis was performed on a VWR Hitachi module with a diode-array detector L-2455 Elite La Chrom (VWR International, Radnor, PA, USA). The column was Ultra Aqueous C-18, 4.6 \(\times\) 250 mm, with 5 \(\upmu\)m particle size (Agilent, Santa Clara, CA, USA). The mobile phase consisted of  
\begin{table}
\begin{tabular}{c c c c} \hline  & & **Reconstituted Infant** & **Reconstituted Infant** \\  & & **Formula-1** & **Formula-2** \\  & & **(RIF-1)** & **(RIF-2)** \\ \hline Mc & C. sakazzakii & 10\({}^{2}\) cfu/g & 10\({}^{2}\) cfu/g \\ Cc & C. sakazzakii + BB-12\({}^{\text{\textregistered}}\) & 10\({}^{2}\) cfu/g & 10\({}^{7}\) cfu/g & 10\({}^{2}\) cfu/g & 10\({}^{7}\) cfu/g \\ \hline \end{tabular}
\end{table}
Table 1: Inoculum (cfu/g) of the C. sakazzakii and Bifidobacterium animals subsp. lactis, BB-12\({}^{\text{\textregistered}}\) in monocultures (Mc) and co-cultures (Cc) in two different reconstituted infant formulae (RIFs).  
0.018 N H\({}_{2}\)SO\({}_{4}\), pH 2.2, and a combination of flow rates ranging from 0.2 to 1.0 mL/min was used. The column temperature was set at 25 \({}^{\circ}\)C, and the UV absorbance was measured at 201 nm by the diode-array detector. The concentration of each acid was calculated by their calibration curves. Different concentrations of Formic acid 98% (Merck, Darmstadt, Germany) (0.1-10 mM), Acetic 90% (Merck, Darmstadt, Germany) (5-50 mM), and Lactic acid 95% (Merck, Darmstadt, Germany) (10-100 mM) were used, corrected by the given purity. The HPLC analysis gives the total concentration (mM) of the organic acids in the filtered supernatant, while the ratio ionic/non-ionic form depends on the pH of the culture. For this reason, equation 2, based on Henderson-Hasselbalch equation, was used:  
\[C_{non-ionic}(\text{mM})=\frac{C_{total}(\text{mM})}{1+10^{pH-pKa}}\]  
where \(C_{non-ionic}\) (mM): concentration of the weak organic acid in non-ionic form, \(C_{total}\) (mM): total concentration of the weak organic acid both ionic and non-ionic form, pH: pH of the growth medium, and \(pKa\): the negative logarithm of the acid dissociation constant (\(Ka\)) of the weak organic acid.
# GPR Data Interpretation Approaches in Archaeological Prospection
## 2 GPR Data Cross Interpretation  
A downside discussed in Reference [6] when following the standard process is that the interpretation focuses on the C-scans, often ignoring B-scans entirely, which for some archaeological case studies might lead to losing important information related to the targets' properties (e.g., actual size). The C-scans only show projections of the recorded reflections along the horizontal plane. So, in the case of dipping layers, the target may be either lost or misplaced. Hence, an integrated interpretation using topographically corrected B-scans along with the C-scans is mandatory to understand the true nature of the recorded reflections. Further, B-scans can provide information regarding the polarity changes of the waveforms, which is useful in identifying voids or tombs [6; 7; 8]. Further, B-scans can provide information regarding the conservation state of the buried structures, with wall erosion appearing as non-reflective events in the data [6]. This non-reflective event is caused by depolarization of EM waves on the surface, and when it happens, targets such as walls will more likely be invisible if the standard approach for processing and interpretation is followed [9]. Other examples that show the importance of integrating B-scans, C-scans, and iso-surface amplitudes for data interpretation are presented in Reference [10].  
Figure 1: Overview of the reviewed approaches to improve GPR data interpretation from archaeological prospection.
## 4 Experiments
### Ablation Study
#### 4.3.3 Loss Function  
In order to fully exploit the effect of the multi-level supervision structure, the cross-level loss function was designed. To verify the usefulness of the cross-level loss function, ablation studies were conducted for different combinations of loss functions. As shown in Table 5, four combinations of loss functions were compared. In the first three combinations of loss functions, the same loss function is applied at the output of each BSRC. In the first two combinations of loss functions, the same loss function was applied at the output of each BSRC. The loss function for each BSRC was the L1 loss or perceptual loss. In the third combination of loss functions, both L1 loss and perceptual loss were used. The loss function for the first two BSRCs was perceptual loss, and the loss function for the last two BSRCs was L1 loss. The fourth combination of loss functions was the cross-level loss. From the experimental results in Table 5, it can be seen that the loss functions using all L1 loss and all perceptual loss had better performance on PSNR and SSIM respectively. Using both L1 loss and perceptual loss could result in better performance on both PSNR and SSIM. The cross-level loss function further improved the super-resolution performance of the network and achieved the best results.
# Electric Load Forecasting Based on Deep Ensemble Learning
## 2 Materials and Methods
### DCC-EL Forecasting Framework
#### 2.3.1 Ensemble Learning Layer  
The principle of ensemble learning is to combine multiple weak and less accurate learners through integration techniques to create a single, precise, highly strong learner [29]. These individual models are termed 'weak learners', whereas the combined model is known as the'strong learner' [30]. The training and validation sets are partitioned uniformly to prevent information leakage during the learning phase. Through ensemble learning strategies [31], training three distinct models: XGBoost, CATBoost, and LightGBM, can form a robust learner. The final prediction is a weighted average of the results from these combined models:  
\[y(x)=\frac{1}{M}\sum_{i=1}^{M}w_{i}y_{i}(x) \tag{31}\]  
Figure 3: DCC-EL forecasting framework.  
Among them, \(y(x)\) represents the prediction result of the ensemble model, \(M\) represents the number of models used in the ensemble, \(x\) represents the sample to be predicted, \(y_{i}(x)\) represents the prediction result of the \(i\)-th model, and \(w_{i}\) represents the weight of \(i\)-th model, which should satisfy that all weights must be non-negative. The sum of all weights is one, that is:  
\[w_{i}\geq 0,\sum_{i=1}^{M}w_{i}=1 \tag{32}\]  
Boosting is an ensemble learning technique that begins with a weak learner and enhances it through iterative weighting and training. During this iterative process, base models are trained sequentially. For each iteration, the training set for the base model is adjusted based on a specific strategy, and the base models' predictions are linearly combined to generate the final prediction outcome [32]. The relevant formula is presented below:  
\[f(x)=\sum_{m=1}^{M}\alpha_{m}h_{m}(x) \tag{33}\]  
Among them, \(M\) represents the number of classifiers, \(\alpha_{m}\) denotes the weight of the \(m\)-th weak learner, and \(h_{m}(x)\) means the \(m\)-th weak learner.
# Vertical Handover Algorithm for WBANs in Ubiquitous Healthcare with Quality of Service Guarantees
## 5 Evaluation and Discussion of the Results
### Simulation Scenario  
The proposed model is implemented by using OPNET Modeler 16.0 (OPNET Technologies, Bethesda, MD, USA). In our simulation, we use a network topology that considered in Figure 1, however, the deployed network consists of eight access points (AP), four base stations (BS), and four eNodeBs; this topology is deployed in coverage of 1000 \(\times\) 1000 m\({}^{2}\). A patient can be in the coverage of any AP, BS, and eNodeB with a CCN processing module. Each AP covers a circular area that has a radius of 100 m and employs an IEEE 802.11b standard protocol, whereas each BS and eNodeB covers an area that has a radius of 600 m and 1000 m, respectively.  
We assumed that mobile WBANs have an initial connection to AP and move in the area with 0.5 m/s speed. The ability to make a handover from the current network to another network absolutely  
Figure 3: The handover process flowchart.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{CCN-Packet Types} & Interest Packet (IntPk) & 32 B \\  & Data Packet (DataPk) & 1 KB \\ \hline \multirow{2}{*}{WBAN user} & Buffer size & 1000 Pks \\  & Start time-Stop time & 50 + Uniform(0,10) s-300 s \\ \hline \multirow{2}{*}{Other links} & Gateway-Gateway & OC-24 \\  & Other links & 1000 BaseX \\ \hline Content & Data size & 400 DataPk/s \\ \hline Content Store (CS) & Replacement policy & FIFO \\ \hline \multirow{2}{*}{Max receive-signal threshold} & WiFi (802.11b) & \(-\)89 dBm \\  & UMTS & \(-\)121 dBm \\  & LTE & \(-\)123.4 dBm \\ \hline \multirow{2}{*}{Power transmission} & WiFi (802.11b) & 27 dBm \\  & UMTS & 43 dBm \\ \cline{1-1}  & LTE & 46 dBm \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation parameters.  
depends on the network characteristics, which consist of the RSSI, delay, utilization, packet loss and user preferences such as monetary cost (namely, the cost per byte). Thus, the physician (doctor or nurse) can receive data on the patient anywhere where they are connected to these networks. We also considered two different applications (emergency traffic and normal traffic) in the case of changing priorities and, in addition, increasing the number of WBAN users. Table 5 lists the rest of the simulation parameters.
## 7 Experiments and Results
### Task 1: Priority Classification
The reason for this is that construction sites are more dangerous places than office spaces, warehouses or stores, and, therefore, construction workers face more serious and frequent dangers during their jobs. We can see that, again, all models surpass the random baseline, except for the text-only Greek BERT on the \"Construction\" set. Furthermore, we observe that multimodal architectures surpass their unimodal counterparts by a margin of 3-4%. The best overall performance is achieved by the combination of Greek BERT, ViT with CLSS fusion. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Modality** & **Text Encoder** & **Visual Encoder** & **Fusion Method** & **Accuracy (\%)** & \(\Delta\uparrow\)**Unimodal** \\ \hline \(\mathcal{V}\) & - & Resnet18 & - & 57.5 & - \\ \(\mathcal{V}\) & - & ViT & - & 60.78 & - \\ \(\mathcal{T}\) & Greek BERT & - & - & 59.85 & - \\ \(\mathcal{T}\) & GR-Electra & - & - & 61.18 & - \\ \hline \(\mathcal{T}+\mathcal{V}\) & Greek BERT & Resnet18 & cat & 62.33 & 2.48 \\ \(\mathcal{T}+\mathcal{V}\) & Greek BERT & Resnet18 & CLSS & **62.85** & 3.00 \\ \(\mathcal{T}+\mathcal{V}\) & Greek BERT & ViT & cat & 62.56 & 2.71 \\ \(\mathcal{T}+\mathcal{V}\) & Greek BERT & ViT & CLSS & **64.63** & 4.78 \\ \(\mathcal{T}+\mathcal{V}\) & GR-Electra & Resnet18 & cat & 61.33 & 0.15 \\ \(\mathcal{T}+\mathcal{V}\) & GR-Electra & Resnet18 & CLSS & **62.9** & 1.72 \\ \(\mathcal{T}+\mathcal{V}\) & GR-Electra & ViT & cat & 63.14 & 1.96 \\ \(\mathcal{T}+\mathcal{V}\) & GR-Electra & ViT & CLSS & **63.61** & 2.43 \\ \hline \multicolumn{5}{c}{Random Baseline} & 49.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Multimodal priority classification accuracy on the Safety4 All test set. \(\mathcal{T}\): Textual, \(\mathcal{V}\): Visual.
# Integration and Flight Test of a 7 kW Turboelectric Vertical Take-Off and Landing Unmanned Aircraft
## 6 Conclusions and Recommendations
### Weight Estimation  
The aircraft had a final flight weight of 378 N with a breakdown of 21% for the turboelectric system, 12% for the power and avionics batteries, and 7% for fuel. The weight breakdown of the turboelectric system would likely be applicable to other small-scale UAS turboelectric systems. However, this weight breakdown would depend on vehicle size. The weight of a turbine engine does not scale linearly with power, such that turboelectric systems are attractive for larger UAM aircraft.
# Resource Allocation Strategy for Satellite Edge Computing Based on Task Dependency
###### Abstract  
Satellite edge computing has attracted the attention of many scholars, but the limited resources of satellite networks bring great difficulties to the processing of edge-computing-dependent tasks. Therefore, under the system model of the satellite-terrestrial joint network architecture, this paper proposes an efficient scheduling strategy based on task degrees and a resource allocation strategy based on the improved sparrow search algorithm, aiming at the low success rate of application processing caused by the dependency between tasks, limited resources, and unreasonable resource allocation in the satellite edge network, which leads to the decline in user experience. The scheduling strategy determines the processing order of tasks by selecting subtasks with an in-degree of 0 each time. The improved sparrow search algorithm incorporates opposition-based learning, random search mechanisms, and Cauchy mutation to enhance search capability and improve global convergence. By utilizing the improved sparrow search algorithm, an optimal resource allocation strategy is derived, resulting in reduced processing latency for subtasks. The simulation results show that the performance of the proposed algorithm is better than other baseline schemes and can improve the processing success rate of applications.  
satellite networks; edge computing; resource allocation; sparrow search algorithms Article  
Resource Allocation Strategy for Satellite Edge Computing Based on Task Dependency. _Appl. Sci._**2023**, _13_, 10027. [[https://doi.org/10.3390/app131810027](https://doi.org/10.3390/app131810027)]([https://doi.org/10.3390/app131810027](https://doi.org/10.3390/app131810027))  
Academic Editor: [PERSON]  
Received: 27 July 2023  
Revised: 13 August 2023  
Accepted: 21 August 2023  
Published: 5 September 2023
# Research on the Scope of Spectral Width Parameter of Frequency Domain Methods in Random Fatigue
## 4 Comparative Analysis
### Random Fatigue Life Estimation
AL method: Compared with the NB method, the accuracy of the lifetime calculation result of the broadband spectrum is improved, and the applicable range of the spectrum width parameter is roughly 0.75-1. 4. TB1 method: Since most of the results of the TB1 method have large errors, only a few data can achieve good results, so the TB1 method is not recommended as a frequency-domain method for calculating random fatigue damage and life in engineering, the scope of application of its spectral width parameter is no longer given here. 5. TB2 method: It is suitable for broadband random processes, especially when the spectral width parameter is 0.7-1, the accuracy is high, and it is a commonly used broadband frequency-domain method. 6. DI method: The accuracy of the calculation results is similar with TB2, but the accuracy of some regions is slightly lower than TB2. For the ideal broadband random process, the DI method may underestimate the fatigue life results and become dangerous. The applicable range of the spectrum width parameter is roughly 0.7-1, and the DI method is also one of the most widely used methods at present. 7. ZB method: For the broadband random process, the accuracy of a small part of the cases is high, but most of the errors are large. The applicable range of the spectrum width parameter is roughly 0.8-0.95. 8. OC method: The OC method is also a broadband method, and the applicable range of the spectrum width parameter is roughly 0.85-1. 9. SM method: The SM method is also applicable to the broadband process, but for the curve of the ideal broadband random process, when the k value is small, the life will become too high and unsafe. The applicable range of the spectrum width parameter is roughly 0.7-0.9. 10. NK method: The NK method has a large error in the calculation results of the ideal narrowband and ideal broadband, but for the transition region of narrowband and broadband, the accuracy of the result is significantly higher than other frequency-domain methods, and it is closer to the exact solution. The applicable range of the spectrum width parameter is approximately 0.45-0.7.
## 5 Results and Discussion  
Simulations, described in the previous section, were performed for each sample (polystyrene sample without aluminum, polystyrene sample with aluminum, and silica samples) to derive the probability function \(P(S/N)\) of detecting the periodic signal (see Figure 10) in each case. It is clearly visible from Figure 10 that even the value of the signal-to-noise ratio \(S/N=0.5\) gives 60% probability of detecting the signal. This condition was considered as a criterion for the minimum measurable signal. Using well-known relations between the deviations of the momentum transfer vectors and interparticle distances \(\Delta q(\tau)/q_{0}=-\Delta d(\tau)/d_{0}\), as well as Equation (5), we can determine the relative (and absolute) values of the changes of the interparticle distances \(\Delta d(\tau)/d_{0}\) (\(\Delta d(\tau)\)) for the given values of the experimental noise \(N\). As a result of this analysis, we can obtain the minimum values of these parameters which could be detected in our experimental conditions with the probability of 60%. For all measured samples results of this analysis are summarized in Table 1.  
As it follows from Table 1, the minimum relative changes of the interparticle distance \(\Delta d/d_{0}\) that can be detected with the probability of 60% for all samples were about \(10^{-3}\). Such values correspond to relatively large amplitudes of the deviations in interparticle distances of about 0.2-0.4 nm. Clearly, in our experimental conditions excited vibrations were lower than the noise level of the experiment that prevented us from their experimental determination.  
For the successful measurement of vibrations of colloidal crystals at the FEL sources, some further improvements of the experiment may be suggested. First, the accuracy of the measurement of the incoming X-ray beam intensity from pulse to pulse should be improved, as this would allow measurement of the Bragg peak intensity with higher precision. In our experiment such an accurate measurement of the Bragg peak intensity variation was not possible due to the complicated structure of each XFEL pulse and a relatively low accuracy (about 3%) in the measurement of the incoming X-ray beam intensity from pulse to pulse. Second, colloidal crystals may be covered by a thicker aluminum layer which could lead to a more efficient energy transfer from an IR laser to the colloidal crystal [32]. This would lead to a higher amplitude of vibrations of a PS colloidal crystal, and enable their detection in IR pump and X-ray probe experiments.