esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Compressive Sensing Based Preamble Channel Estimation
### Preamble Structures  
In FBMC/OQAM, the preamble pilots exist in all sub-carriers, the preamble sequence is superimposed on the data. Figure 2a,b show the IAM and ICM preamble structures. Assuming that a pilot symbol \(P_{m_{0},n_{0}}\) is transmitted at a prior known position \((m_{0},n_{0})\) to the receiver, the LS estimation is  
\[\hat{H}_{m_{0},n_{0}}=\frac{r_{m_{0},n_{0}}}{P_{m_{0},n_{0}}}=H_{m_{0},n_{0}}+j \sum\limits_{(m_{0},n_{0})\
eq(m,n)}\frac{d_{m,n}}{P_{m_{0},n_{0}}}\langle \underline{s}\rangle_{m,n}^{m_{0},n_{0}} \tag{7}\]  
where \(j\sum\limits_{(m_{0},n_{0})\
eq(m,n)}\frac{d_{m,n}}{P_{m_{0},n_{0}}}\langle \underline{s}\rangle_{m,n}^{m_{0},n_{0}}\) is imaginary interference.  
Figure 1: Implementation diagram of the FBMC/OQAM system [35].  
Figure 2: Preamble structures: (**a**) Interference approximation method (IAM); (**b**) Interference cancellation method (ICM).
## 4 Experimental Setup  
### Dataset  
The ADI17 dataset is provided by MGB-5 [23]. We followed the dataset partitioning of the MGB-5 challenge without any modifications. The ADI17 dataset we used was collected from YouTube, consisting of approximately 3000 h of Arabic dialect speech from 17 Arab countries, with significant variation in data proportions across different languages. As the data were collected via specific country YouTube channels, label errors may exist. Therefore, the official dataset was first adjusted and verified manually for dialect labels from approximately 280 h of speech data, and then 58 h of data was chosen for performance evaluation as the development and test sets. The test set was grouped into three subcategories based on the duration of speech segments: short (<5 s), medium (5-20 s), and long (>20 s). The detailed statistical data of the ADI17 dataset are presented in Table 1.  
Figure 3: MSCA-Res2 Block.  
**Speed Perturbation Data:** To enhance the performance of dialect identification, we applied data augmentation by using the open-source software SoX to generate speed perturbations with coefficients of 0.9, 1.0, and 1.1 for the training set before training. The official speech dataset provided varying durations, so we segmented them into 3 s speech clips and repeated shorter clips to obtain a total of 12,148,293 segments. For the development set and testing set, no manipulation was performed.  
**Balanced Data:** We observed that the speech duration of the IRA dialect in the official raw data was 815.8 h, while the speech duration of JOR and SUD dialects was only 25.9 and 47.7 h, respectively. Therefore, we applied special processing to JOR and SUD. Specifically, we applied speed perturbation to JOR and obtained 98,189 segments of speech, followed by RIR data augmentation, resulting in 239,482 segments of speech. Similarly, we applied speed perturbation to SUD and obtained 186,789 segments of speech, followed by RIR data augmentation, resulting in 447,602 segments of speech. Although special data augmentation was applied to dialects with limited samples, the problem of severe data imbalance remained, greatly affecting training speed and the fairness of the model's identification of dialects with a smaller proportion. To eliminate the issue of unbalanced data, we randomly selected 200,000 3 s speech segments for each dialect to form the balanced training subset for this paper, while maintaining the same development and test sets as the official dataset.
# High-Precision Single-Photon Laser Time Transfer with Temperature Drift Post-Compensation
SLR stations keep all the instruments in a temperature-stable room and use self-calibration to maintain the stability of the measurements. However, the onboard laser time transfer payload temperature cannot be controlled so well. This is why the temperature coefficient of the whole system should be studied and described. This will enable scientists to compensate most of the delay drifts and achieve better time stability. In this paper, a basic laser timing test setup is established and the system delay as a function of temperature is measured. System temperature dependence is analyzed as a whole and a temperature drift post-compensation method is proposed to compensate for the system delay drift. Applying this method, the onboard system delay can be tested and described before the launch. In space operation, it can be modeled and calibrated according to the operating temperature. Sections 2 and 3 present the method and experiments, results and analysis are shown in Section 4, and finally, conclusions are given in Section 5.
# Detecting Fatigue during Exoskeleton-Assisted Trunk Flexion Tasks: A Machine Learning Approach
## 2 Materials and Methods
### Approach
#### 2.2.3 Data Acquisition  
Measures of interest in this study included muscle demands in lower back and leg regions, trunk movement, and whole-body stability. Both lower back and leg regions were selected as participants in our pilot study reported prominent presence of fatigue in legs. Thus, the muscle groups of interest included the left/right erector spinae longissimus (LES/RES) and the left/right biceps femoris muscles (LBF/RBF). Both muscle groups are known to contract the most during trunk flexion as these muscle groups are responsible for pulling the weight of the upper torso and torso to ensure a stable posture. We implemented electromyography (EMG) to record muscle activity during the repetitive bending task using four sensors of the Trigno Wireless EMG system (Delsys, Natick, MA, USA, 1200 Hz). We followed the protocols provided by SENIAM [58] for placing the sensors on respective muscle groups using double-sided tapes. To segment the data into different portions as well as to detect trunk movement, an optoelectronic motion capture system (VICON, Hauppauge, NY, USA, 100 Hz) was used. This included 20 reflective markers placed on the upper body (three on the upper back, two on the middle back, and three on the hip) and lower body (three on each leg and three on each foot) of each participant (Figure 1). Marker placement locations were determined based on guidelines provided by VICON [59]. For determining impacts on balance, two force plates were used (AMTI, Advanced Mechanical Technology, Watertown, MA, USA), where participant stood upon floor-embedded force plates (AMTI OR6-6 platform, MA, USA, 1000 Hz). All three systems were time-synced using the VICON NEXUS system.  
Fatigue level in back and legs was obtained subjectively and was used to drive the duration of tasks. Perceived ratings of exertion (RPE) were obtained using the Borg RPE CR-10 scale [60; 61]. The scale consists of levels denoted by numbers ranging from 0-10, where levels 0, 1, 2, 3, 4, 5, 7, 10 represent 'no exertion','very slight','slight','moderate','somewhat severe','severe','very severe', and'maximal' levels of fatigue, respectively.  
Figure 1: Schematic showing (**a**) sensors for data acquisition systems consisting of surface electromyography, motion capture, and force plates used in this study and (**b**) the experimental setup used in the study.
# Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia
## 4 Results
### Surface Reflectance Adjusted Using HLS Coefficients  
Inspection of the coefficients calculated here (Table 4, Landsat-8 entries) and those calculated for HLS (Table 2) suggests that they appear to be very different. In principle, one might expect the HLS coefficients to be similar to those found here for Landsat-8, but, in most bands, the regression slopes (\(c_{1}\)) do not even adjust in the same direction. In all bands except SWIR1, those \(c_{1}\) values which HLS  
Figure 8: Comparison of Normalized Difference Vegetation Index (NDVI) from Sentinel-2A and Landsat-8, for same-day acquisitions. (**a**) using unadjusted surface reflectance; (**b**) using Sentinel-2A surface reflectance adjusted with Equation (1). Darker regions represent larger numbers of points. Solid line is 1-to-1; dashed line is the ODR regression fit showing proportional bias.  
Figure 7: Comparison of fractional vegetation cover from Sentinel-2A and Landsat-8, for same-day acquisitions, after adjusting Sentinel-2A reflectances to estimate Landsat-8, using Equation (1). Darker regions represent larger numbers of points. Solid line is 1-to-1, dashed line is the ODR regression fit showing proportional bias.  
gives as greater than 1 are found here to be less than 1, and vice versa. This suggests that the two adjustment models are not consistent with each other.  
The adjustment procedure to map Sentinel-2A to match Landsat-8, as shown in Figure 5, was repeated for the same dataset, but using the coefficients fitted for NASA's HLS product (reproduced here in Table 2). In principle, if the two adjustment models are equivalent, then the results should be similar to Figure 5. The comparison of the adjusted Sentinel-2A reflectances with Landsat-8 is shown in Figure 9.  
This shows that, in most bands, the resulting agreement is not noticeably closer to 1-to-1. In the red band, the ODR slope has changed from 1.01 to 1.00, but in most other bands, they remain around the same as for the unadjusted data. In the blue band, the ODR slope has worsened from 0.95 to 0.88.
# Performance and Scalability Analysis of SDN-Based Large-Scale Wi-Fi Networks
## 4 SDN Based Architecture
### Use Case Scenario  
In this section, we took a real-world scenario into consideration by analyzing the smart network facility at Hazara University. The following are the configurations of the smart network at Hazara University.  
There is one router, two layer 2 switches, twenty-four layer 3 switches, forty-six access points, and 470 MB of bandwidth available on this network. The number of registered users of Smart Net exceeds 6000. However, on average, only 1200 users are connected to the network at any given time, with the maximum number of users connected to the network at any given time being up to 4500.  
The use of SDN-based architecture and the evaluation of performance using TCP and UDP protocols are key aspects of the proposed approach for monitoring and evaluating the scalability and resilience of a wireless network. Figure 2 illustrates the suggested architecture, which utilizes SDN to control the network traffic and optimize the routing of packets. This architecture can be used to handle a variety of dynamic scenarios and improve the performance of the network. TCP and UDP are the two most commonly used protocols for data transmission over the internet. TCP is a connection-oriented protocol that provides reliable delivery of data by verifying that all packets have been received in the correct order. On the other hand, UDP is a connectionless protocol that does not provide reliability but is faster and more efficient for applications that do not require reliability. By utilizing these protocols in the performance evaluation of the proposed SDN wireless network, it is possible to measure the efficiency of the network in terms of packet delivery and throughput. This information can then be used to identify potential bottlenecks and optimize the network for better performance. Monitoring the network over SDN allows for greater visibility into the network and the ability to dynamically control the flow of traffic. We are able to monitor Wi-Fi over SDN and notice its scalability in addition to its resilience if we evaluate the output parameters of a variety of different dynamic scenarios [11].  
Figure 2: SDN-based architecture.
## 3 Results and Discussion
### Present Research Gaps and Future Needs  
At this present time, there are still research gaps that need to be narrowed in the observations and analyses of carbon fluxes and their biophysical controls in mangroves. First, the carbon fluxes are significantly different between different mangroves, but the information about annual values are limited, especially for the CH\({}_{4}\) fluxes [24] (Table 2). Many studies have used the chamber method, which can only observe soil or water respiration due to cost of expensive EC techniques [76; 77; 78]. The chamber method has the advantage of separating respiration by canopy, water, and soil. This is also important for understanding the carbon cycling processes in mangroves since mangroves are always surrounded by open water. Future observations should combine both the EC and chamber methods to obtain a more complete understanding of the vertical exchanges in mangroves. We recently began to measure the CO\({}_{2}\) and CH\({}_{4}\) emissions from the water's surface, separating it from vegetation and soil respiration. This will help us to correct the carbon fluxes for the change of water surface proportions and eliminate the effect of different observed heights.  
Second, many EC-based measurements, including those in our study, only focused on vertical exchange (Table 2). However, mangroves are subjected to both tidal and fluvial impacts [79], and lateral carbon exchange should be included to evaluate a complete ecosystem carbon budget [8]. This could also contribute to the parametrization in the present carbon cycling process-based models for mangroves.  
Finally, carbon emissions from human-influenced mangroves are often unreported or underestimated [80; 81]. Most studies have reported carbon stocks rather than soil GHG effluxes in disturbed and restored mangroves. Moreover, mangrove regrowth can cause the biomass carbon stock value to reach that of an undisturbed forest after \(\sim\)40 years, but current observations from restored mangroves are often implemented over short-term periods [20]. Our study was implemented for 4 years but did not show any interannual trend. The observations should be continued for a longer period to capture the interannual trend in mangrove growth.  
Mangroves provide various services to humans and society, including fishery production, nutrient cycling, and natural climate solutions [82; 83; 84]. The extent to which China's mangrove carbon sequestration occurs in the future can help to mitigate energy-related CO\({}_{2}\) emissions [85]. The degree of mangrove carbon stock recovery can vary significantly depending on location, climate, sediment type, coastal geomorphology, and methods of regeneration or reforestation, that is, rehabilitation, restoration, and plantation [86; 87; 80; 88]. Paired studies are needed to assess the whole carbon balance in mangrove ecosystems under different management practices.
## 4 Materials and Methods
### Data Pre-Processing and Statistical Analyses  
Acquired LC-MS data files were pre-processed using Compound Discoverer 3.3 [49], proprietary data processing software developed by Thermo Fisher Scientific (Waltham, USA). With Compound Discoverer 3.3 (Thermo Fisher Scientific [Waltham, MA, USA]), the data were normalized via SERRF, a QC-based normalization method for large-scale untargeted metabolomics data. Peak lists generated by both platforms were filtered to meet the following conditions: (1) Positive mode: features with a pooled-QC: solvent-blank or pooled-QC:fiber-blank peak intensity ratio of <5 were removed from further statistical evaluation. (2) Negative mode: features with a pooled-QC: solvent-blank or pooled-QC:fiber-blank peak intensity ratio of <5 were removed. All multivariate data analysis was performed using MetaboAnalyst 5.0, wherein the data were subjected to a generalized log-transformation and pareto scaling unless otherwise stated.  
Predictive features were identified by first testing each feature individually with five classifiers: linear discriminant analysis (LDA), Naive Bayes (NB), support vector machine (SVM), logistic regression (LR) and random forest (RF). Each feature received an area under the curve (AUC) score from each classifier, and then features were ranked by their best AUC scores. The top k features were then used as an input for each classifier, where k was 1, 2, 5, 10, 20, 40, 80. Model testing was conducted using 10-fold cross-validation (10-fold CV).
## 3 Simulation Implementation
### Implementation of the Communication System
#### 3.2.2 Signal Modulation  
The digital signal is then modulated by using QPSK modulator. Two sinusoidal carrier waves in quadrature to each other are used for implementing QPSK. The carriers have a frequency of 5 kHz and have phase difference of 90 degrees. The bits from the ADC are split into odd and even bits and converted from unipolar to bipolar. Then, even bits are multiplied with the in-phase carrier (cosine), and odd bits are multiplied with the quadrature carrier (sine), thus leading to two independent Phase Shift Key (PSK) modulated signals. The two PSK signals are added to obtain the QPSK modulated signal. The overall process of QPSK modulation is implemented in SIMULINK by the schematics shown in Figure 6.
## 3 Results and Discussion
### Analysis of Color Characteristic Results
The DPPH radical-scavenging activity in the sausages with purslane reached 21.70%, considerably higher than the 13.73% observed in the control, confirming the purslane-enriched sausages' enhanced ability to neutralize free radicals. Additionally, the IC50 value for DPPH radical-scavenging activity was greater in the sausages with purslane (116.55 mg/mL) compared with the control (76.12 mg/mL), indicating that a higher concentration of the purslane-enriched sample was \begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{2}{c}{**Results**} & \(p\)**-Value** \\
**Indicator** & **Control Sausages** & **Sausages with** & \\  & & **Purslane** & \\ \hline Ferric-reducing antioxidant power & & & \\ (FRAP), mg GAE/g & Not detected & 13.5 \(\pm\) 0.05 & \textless{}0.0001 \\ DPPH radical-scavenging activity, \% & 13.73 \(\pm\) 0.005 & 21.70 \(\pm\) 0.01 & \textless{}0.0001 \\ IC50 of DPPH radical-scavenging & & 76.12 \(\pm\) 5.04 & 116.55 \(\pm\) 10.04 & 0.002 \\ activity, mg/mL & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Ferric-reducing antioxidant power (FRAP) and antioxidant activity (DPPH). required to achieve 50% inhibition of DPPH radicals. This further underscores that the addition of purslane significantly elevated the antioxidant profile of the sausages.
# Adaptive Allocation Algorithm for Multi-Radio Multi-Channel Wireless Mesh Networks
## 5 Simulation Module Implementation
### Simulation Instances  
Three distinctive schemes are simulated through this work. The first one is the simulation of static channel allocation, in which we assign static channels to the APs after associating the most extreme number of hosts related to each AP. In other words, the channels are fixed regarding any traffic changes.  
The second schema is the simulation of a fully dynamic change channel assignment; every time the traffic is changed, the channel reassignment is done. The third schema is the proposed adaptive channel allocation one, in which the decision function decides whether to change the channel assignment or maintain the channel assignments as is.  
Through the simulations, we assume that the maximum number of hosts expected to attach to every AP is provided as an input. The hosts are distributed among 24 different times for every AP with random distribution from one of the seven host association patterns, which are illustrated in Figure 2. Each \(AP_{i}\) is loaded with the utmost two or three NICs, \(b_{i}\).  
The simulations are conducted for two network topologies, illustrated in Figure 3. The parameter \(\lambda\) relies on the network topology; therefore, the simulations are performed under different values of \(\lambda\). Different traffic load patterns are used to summarize the simulations, with the result of the channel assignment numbers.
## 3 Results and Discussion  
### Spatial Coherence Control  
Figure 3 shows the simulation results. Figure 3a,b shows the theoretical and simulated SDs (\(S^{\text{out,thy}}\) obtained from Equation (16) and \(S^{\text{out,sim}}\)); Figure 3c,d shows the theoretical and simulated SDoCs (\(\mu^{\text{out,thy}}\) given in Equation (21) and \(\mu^{\text{out,sim}}\)); Figure 3e,f shows the \(y=0\) and \(x=0\) slices through \(S^{\text{out,thy}}\) and \(S^{\text{out,sim}}\); and Figure 3g,h shows the \(y=0\) and \(x=0\) slices through \(\mu^{\text{out,thy}}\) and \(\mu^{\text{out,sim}}\).  
The \(S^{\text{out,thy}}\) and \(S^{\text{out,sim}}\) results noticeably disagree. This occurs because \(\tilde{S}^{\text{src}}\) is not a fast function compared to \(T\), viz., Criterion 3 is violated. Recall that this assumption was made in going from Equation (13) to Equation (14). The width of \(\mu^{\text{out}}\) in the \(y\) direction is larger than in the \(x\) direction (see Equation (21)). The Fourier transform relationship between \(T\) and \(\mu^{\text{out}}\) means \(T\) has complementary widths to \(\mu^{\text{out}}\), i.e., \(T\) is wider in the \(x\) direction than in the \(y\) direction. This \(\mu^{\text{out}}\)-\(T\) relationship explains why \(S^{\text{out,sim}}\) compares better to \(S^{\text{out,thy}}\) in Figure 3e than in Figure 3f.  
Although \(S^{\text{out,thy}}\) and \(S^{\text{out,sim}}\) disagree, the objective here is to control spatial coherence as measured by the SDoC. Figure 3c,d,g,h shows these results. Clearly, the effects of violating Criterion 3 are generally limited to the output SD because the agreement between \(\mu^{\text{out,thy}}\) and \(\mu^{\text{out,sim}}\) is excellent. These results validate the PCS spatial filtering method presented here.  
Figure 2: Spatial filter \(T\) images: (**a**) spatial coherence control simulation (Section 3.1); and (**b**) beam shaping simulation (Section 3.2).
# Fabrication and Analysis of a Ti6 Al4V Implant for Cranial Restoration
## 2 Methodology
### Fabrication
After the preheating and melting of the particular layer, the build platform was descended through one layer thickness (50 \(\upmu\)m) and a fresh powder layer was supplied from hoppers and spread uniformly onto the formerly solidified layer with the help of raking blades. These three stages were reciprocated in a cycle manner until the 3D physical part was obtained. The produced EBM specimens were finally blown in a powder recovery system (PRS) using a high-pressurized air nozzle for taking out the finely trapped powder particles. The support structures (Figure 11b) attached to the cranial implant (Figure 11a) were removed manually using plyers and this procedure did not result in any defect on the fabricated specimen. The fabricated customized cranial implant after support removal (Figure 11c,d) did not show any sign of burr or protrusion (Figure 11e) on its surface. The titanium cranial implant was finally tested on the polymer skull model (Figure 11f) for fitting evaluation. The EBM produced as-built titanium cranial implant does not require any post-processing treatment, as its surface finish is ideal for osseointegration (bone-ingrowth) [41]. Indeed, several prior studies had established that there was no significant improvement in the adhesion or osseointegration with post-processing. An in-vivo study on rabbits using EBM produced Ti6 Al4V implants exhibited similar surface chemical composition and surface roughness in contrast to wrought  
Figure 10: ARCAM A\({}^{2}\) set up and schematics of EBM. T16 Al4V and machined Ti6 Al4V implants [42]. Similarly, [PERSON] et al. observed that the presence of coating or surface treatment of EBM fabricated implant did not result in enhanced bone in-growth or mechanical fixation strength in comparison to the as-built EBM specimen [43]. Based on these earlier investigations, it can be inferred that the as-built EBM specimen does not require any post surface treatment if its surface roughness lies within the specified range of the EBM manufacturer. According to ARCAM, the surface roughness (Ra) of the as-built specimen should fall within the interval of 25-35 \(\upmu\)m [44]. Henceforth, the surface roughness of the fabricated cranial implant in this study was also measured (Ra = 25.3 \(\upmu\)m) and found within the specified range. However, if needed, the EBM built cranial implant can be post-processed for better surface finish using plasma electro polishing [45] and laser ablation techniques [46; 47].