esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Aware Control of Error Correction Rate for Solar-Powered Wireless Sensor Networks
## 3 Energy-Aware Reed-Solomon Scheme
### Determining the Parity Length for the Next Period  
In this section, we describe how the EA-RA scheme dynamically determines the parity length for data reliability according to the amount of collected energy. For example, suppose that a node always transmits data using the maximum parity length. The node is likely to consume more energy than the amount of energy harvested during a period. In this case, the node violates ENO conditions, and thus, it is likely to suffer from blackout, especially during the nighttime. However, the problem of overcharging (inevitably causing dissipation) energy owing to the limited battery capacity will not occur. Moreover, a node that transmits data with a minimum rate of recovery (minimum parity length) can save much energy in the rechargeable battery (reducing the blackout time), but it is possible that there will be energy that is still dissipated owing to the limited battery capacity. To address this trade-off problem, the EA-RA scheme dynamically adjusts the data recovery rate in order to minimize the blackout time while also minimizing the energy dissipation owing to battery capacity limitations.
# A Review of Research on Secure Aggregation for Federated Learning
## 2 Federated Learning
### Applications
#### 2.3.2 Agriculture  
Currently, in agriculture, there is a growing demand for data decentralization and privacy protection, such as farm sensor data [27; 28] and yield monitoring data [29; 30]. Federated learning enables smart agriculture to efficiently collect specific environmental variables, optimize planting strategies, and predict the impact of environmental changes on crop yield and quality, thereby outperforming traditional data collection methods [31; 32; 33].  
In the agricultural supply chain, federated learning facilitates the integration of data across various stages, enhancing transparency and efficiency [34]. For instance, an intelligent agricultural machinery collaboration model based on federated learning [35; 36] can coordinate agricultural machinery systems with external supply chains, storage, and transportation networks. This integration significantly improves operational efficiency, task quality, and land utilization.  
In precision agriculture, federated learning enables efficient management of agricultural resources, ensuring sustainable agricultural development. For example, it supports precise irrigation and water-saving systems [37], accurate crop growth predictions [38], and effective crop monitoring [39].  
Using federated learning for smart agriculture enables disease classification and detection. In [40], federated learning combined with convolutional neural networks was utilized to detect watermelon leaf diseases. The scheme was evaluated across five different clients and five disease categories, as follows: healthy, anthracose, downy mildew, powdery mildew, and bacterial blight mosaic. The results demonstrated a detection accuracy of 97% for these specific diseases.
## 4 Experiments and Results  
### Experimental Environment and Data Set  
CIFAR10 [14] was the base dataset used in our experiment. The CIFAR10 dataset contains 60,000 images spanning 10 categories with a resolution of 32 \(\times\) 32 pixels. Among these images, 50,000 images were classified for training and 10,000 images for testing. In this study, 25,000 images were used for group network weight training and 25,000 images for architecture parameter training.
# A New Circuit Design of AC/DC Converter for T8 LED Tube
###### Abstract  
This study is about an improved high-quality light-emitting diode (LED) converter for a T8 LED tube. The converter is separated into the AC driving circuit and DC driving circuit. Also, the LED tube was applied with an output ripple eliminator for the optical performance. The AC driving circuit and DC driving circuit are assembled at the end of the LED tube in a G13 base and a G13 base dummy, respectively, and the output ripple eliminator is located on an LED PCB. The proposed LED converter is founded on a SSBB (single-stage buck-boost) converter topology and was designed for 10 W operation for a 600 mm T8 LED tube. The light waveform of the LED tube was measured by a photosensor. The waveform had almost no ripple and was the same as a straight line. The average calculated percent flicker of the proposed LED converter was an average of 1.9% at 100 and 240 VAC, 50 and 60 Hz. The proposed converter has lower power efficiency than a conventional converter by 2.7% at 100-240 VAC, but it still has high power efficiency (~87%). The measurement results represent that the LED output current regulation is below 0.92% at 100-240 VAC and the converter obtains the power factor more than 0.84 and the total harmonic distortion is less than 14.3%. All of the current harmonics reach the IEC 61000-3-2 Class D standards for high-quality LED converters.  
high circuit efficiency; photosensor; power factor correction; SSBB converter topology; ripple +
Footnote †: journal: Applied Sciences  
1
Footnote 1: email: [EMAIL_ADDRESS]
# Analysis of Uncertainty in Conveyor Belt Condition Assessment Using Time-Based Indicators
## 3 Results
### Correlation Analysis  
The scatterplot matrix (Figure 10) and Pearson correlation table (Figure 11) indicate significant correlations between several variables:  
* Loop Length is significantly correlated with section length, Age, and Failure Density.
* Age shows a significant correlation with Resurs and Failure Density.  
The correlation between Age and Resurs is expected, as Resurs is calculated using Age and expected operating time (Resurs = Age/expected operating time). However, the correlation between Age and Failure Density is less consistent. Variability can be attributed to differences in conveyor systems: belt segments of the same age may experience different wear rates depending on their location and operational conditions.  
number of pairs of data values used to compute each coefficient (274). The third number in each location of the table is a \(p\)-value that tests the statistical significance of the estimated correlations. \(p\)-values below 0.05 indicate statistically significant non-zero correlations at the 95.0% confidence level (in red). The following pairs of variables have \(p\)-values below 0.05: Loop Length and Section Length, Loop Length and Age, Loop Length and Density, Section Length and Age, Age and Resurs, Age and Density.  
The influence of the conveyor length (belt loop) on the belt operating time is confirmed in the literature [31], as is the section length and operating time [22]. Age and Resurs for the same conveyor are linearly related, but when we analyze data from many conveyors, the correlation drops significantly (Figure 11). The correlation between Age and Failure Density should be obvious, but the downside may be surprising; as with the increase in belt Age, we expect the Damage Density to grow. However, it should be remembered that we are analyzing sections of belts installed on conveyors. Some may already be heavily worn, others may have been recently installed. Even sections of the same age, but operating on different conveyors, may differ in densities. Shorter ones will have a higher Damage Density and lower Density on longer ones. Therefore, the next step will be to try to relate these measures to each other, taking into account other factors.
## Materials and Methods
### NEE Flux Partitioning and the Estimation of NPP\({}_{ec}\)  
Complex filtering procedures alongside with measurement system malfunctions can lead to a significant amount of gaps in time series. The typical data coverage within a year at an EC site is between 65% and 75% [47]. Average annual coverage with flux data of acceptable quality at Jastrebarsko site was even smaller (from 38.0% to 48.4%, see Table 1), among others due to frequent stable conditions and fog causing a condensation on windows of the LI-7500 IRGA. To estimate the annual carbon budgets missing or discarded flux values were replaced with estimates using standardized marginal distribution sampling (MDS) technique that was employed by FLUXNET, which is available as an online tool [17; 46]. Gap-filled \(NEE\) fluxes were partitioned into gross primary production (_GPP_) and ecosystem respiration (\(R_{ECO}\)) while using the same online tool [46; 48]. This tool uses the night-time based respiration model of [PERSON] [49] for the assessment of the \(R_{ECO}\):  
\[R_{ECO}(T)=R_{ECOref}\times e^{\frac{E_{0}}{T_{ref}-T_{0}}-\frac{1}{T-T_{0}}} \tag{5}\]  
where \(T_{ref}\) marks reference temperature that is set to 15 \({}^{\circ}\)C, \(R_{ECO,ref}\) denotes respiration at the reference temperature, \(T_{0}\) is set to \(-\)46.02 \({}^{\circ}\)C, \(T\) marks measured air or soil temperature, while the activation energy parameter, \(E_{0}\), is allowed to vary [48]. Air temperature (\(T_{a}\)) varies more than soil temperature (\(T_{s}\)) and more variance in \(R_{ECO}\) can be explained by \(T_{a}\)[50]. Therefore, \(T_{a}\) was selected for partitioning of \(NEE\) into \(GPP\) and \(R_{ECO}\). After \(R_{ECO}\) has been calculated, \(GPP\) is determined as:  
\[GPP=-NEE+R_{ECO} \tag{6}\]  
\(R_{ECO}\) consists of autotrophic (\(R_{a}\)) and heterotrophic (\(R_{h}\)) parts. At the EC site, \(R_{h}\) was estimated in 2008 and 2009 using soil respiration measurements [38] and the assumption that the share of \(R_{h}\) in soil respiration is 50% [19; 38]. Using data for \(R_{h}\) published in [38] and \(R_{ECO}\) obtained from \(NEE\) flux partitioning for 2008 and 2009, the ratio \(R_{h}\):\(R_{ECO}\) was calculated separately for 2008 and 2009. The ratio \(R_{h}\):\(R_{ECO}\) from the data for 2008 and 2009 was then averaged and this ratio (39.19%) was subsequently used for the partitioning of \(R_{ECO}\) into \(R_{a}\) and \(R_{h}\) for all of the remaining years (2010 to 2017). Net primary production from eddy covariance (\(NPP_{EC}\)) was calculated as the sum of measured \(NEP\) (i.e., \(-NEE\)) and calculated heterotrophic respiration:  
\[NPP_{EC}=NEP+R_{h}=-NEE+R_{h}=-NEE+0.3919\times R_{ECO} \tag{7}\]
## 3 Results and Discussions
### Sem  
The surface morphology of the fractured samples was analysed by SEM, as presented in Figure 9 for ELO/HC composites, while ELO/SB composites are displayed in Figure 10. It can be seen that both HC and SB composites present a good inclusion of natural particles in the polymeric matrix. The original fibre skeleton and honeycomb structure of the spruce wood can be also observed (Figure 10d) [56].  
The presence of the epoxy resin inside of the wood cells (Figure 10d,e) confirms a very good compatibility and impregnation of the wood particles with the resin, which confers a strong interfacial bonding between polymers reinforced by SB. As we can observe in Figures 9 and 10, the surfaces of the HC and SB fibres are very well covered with resin, and the total lack of voids at the resin-fibre interface denotes a very good compatibility between the epoxy matrix and the HC and SB particles. Additionally, the EDI resin reveals a smooth and homogeneous surface. The presence of microcracks on the materials' surface may be due to the manufacturing process or the method of sampling of the analysed samples.  
Therefore, the HC and SB fillers present an excellent compatibility with the ELO-based matrix, leaving the resin to penetrate inside the pores, creating physical and mechanical bonding. The uniform and homogeneous distribution of the filler in the polymeric matrix, as well as the good adhesion between the two compounds, led to the generation of green composites.  
Figure 8: Water absorption of the ELO/HC and ELO/SB polymeric materials after 11 days of immersion in deionized water at room temperature.
# Mining Deformation Life Cycle in the Light of InSAR and Deformation Models
## Materials and Methods
### Sentinel-1 Data and DInSAR Methodology
#### 2.1.3 Trend Removal  
Over the stable area that surrounds the field of deformation, displacement maps calculated using DInSAR technique should demonstrate values close to zero. Nevertheless, due to atmospheric artefacts, decoherence, and/or ambiguities in solving 2D special unwrapping, some of the interferograms demonstrate a shift in the LOS direction. To remove the trend surface representing the apparent miscalculated displacements over stable zones, we used the thin plate spline (TPS) [40]. [PERSON] and [PERSON] [41] provide details related to the numerical implementation of TPS. In the current study, using a set of data points, we looked for a functional model that is as smooth as possible and is simultaneously as close to the data points as possible.  
Figure 4: Quality assessment of the ascending (Asc) and descending (Desc) interferograms. The values in the bars show the perpendicular baselines of each interferogram.  
Figure 3: Procedure for evaluation of the quality of DInSAR displacement maps.  
As reference points in the stable zones, we selected two sets for both ascending and descending displacement maps, applying a rule of a coherence greater than 0.9. To detrend the LOS displacements, we used the same reference points for the complete set of displacement maps of ascending and descending orbits, respectively. The trend function was calculated for each displacement map separately. In Figure 5, an example of the trend removal is presented.  
More detailed improvement of the proposed trend-decreasing procedure will be conducted in our future investigation.
## 4 Discussion
### Internal Camera Parameters and Block a Priori Accuracy  
A lower value in the internal calibration parameters of the camera, shown in Table 5, was observed for Scenarios B and D. These scenarios are characterised by combining nadir and oblique images. They showed the lowest difference between focal length, principal point coordinates, and radial and tangential distortion coefficients during the geometric camera calibration process. Thus, we can deduce that combining nadir and oblique images substantially improves the results in calibrating inner camera parameters, which avoids additional adjustments to improve them. The results correspond to what was stated by the authors of [8], in which these optimised calibration values enhance horizontal and vertical accuracy. Adding oblique images substantially improves the relative difference between the camera's initial and optimised internal parameters. The authors of [68] reported that the systematic error or dome effect typical of UAVs can be corrected using oblique images instead of GCPs. These images allow for the correction of radial distortion coefficients, thus boosting accuracy and reliability in cartography and photogrammetry.  
The weight coefficient \(\mu_{\text{xyz}}\) for GCPs and CPs from the equation described in the methodology measures the accuracy of the results with the UAV-DAP proposed by the authors. This parameter was calculated from Equation 5 for the horizontal estimation and from Equation 6 for the vertical estimation. \(\mu_{\text{xyz}}\) is the total coefficient on the three components.  
Scenario C achieved the lowest coefficient in the horizontal estimate, as shown in Table 7, for both GCPs and CPs. Scenario B, containing the oblique images, obtained the lowest coefficient in the vertical estimate in CPs. Additionally, Scenario B presented the best value \(\mu_{\text{xyz}}\) for CPs. Using these values allows for the estimation of the a posteriori accuracy for the beam block adjustment for flat areas in corridor flights.
# Tag-to-Tag Interference Suppression Technique Based on Time Division for RFID
## 2 RFID Collision Problem
### Types of RFID Collision  
The occurrence of interference due to tag signals is a common issue in RFID systems. Simultaneous transmission from multiple tags leads to collision as the readers and the tags normally use the same channel. Collisions are classified as reader-to-reader, reader-to-tag, and tag-to-tag collisions [7; 8].
# Enhanced QoS Routing Protocol for an Unmanned Ground Vehicle, Based on the ACO Approach
## 4 Conclusions  
A novel protocol to enhance the VANETs using the swarm technique for adaptively picking the appropriate routing path for firefighting UGVs, is proposed in this paper. Initially, the route was viewed as the optimization problem, that could be solved by inventing the ACO method. Using the throughput of a wireless node on each firefighting UGV forwards, the firefighting UGVs investigate the potential candidate routing options between the terminal crossings, and then the corresponding backward firefighting UGVs determine the ideal route and update the latest throughput. Furthermore, we developed QoS models in the road segment situations, by taking three metrics into account, the connection chance, packet delivery ratio, and the latency. Finally, by using extensive simulations, we verified our analytical QoS models and proved that the new enhanced VANETs outperform the GSR and CAR reference paradigms. In the next study, We would like to look at the impacts of the VANET weight factors on the road segment relaying quality, as well as comparing the ACO's performance to other optimization methods.  
Methodology, A.M.A.; Software, R.S. and I.I.A.; Supervision, M.A.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Figure 5: Road segment packet delay in the transmission channel. (**a**,**b**) depict the relationship between the road section delay, cell size, CTR, and the spacing density of the UGV.
## 3 Results and Discussion
### Evaluations of Initial and Residual Strengths
The occurrence of explosive spalling in the present study can be attributed to the formation of dense microstructures which in turn could limit the release of the internal pressure build-up during heating. Concomitantly, the occurrence of thermal gradients between the outside and the inside of the concrete could give rise to associated thermal stresses and once these stresses exceed the tensile strength, spalling can be expected to occur. Similar reports on the poor thermal capabilities of RPC have been presented elsewhere [52; 53; 54]. Figure 4 shows the RPC specimens before and after exposure to elevated temperatures. \begin{table}
\begin{tabular}{c c} \hline \hline
**Sample ID** & **Average Initial Compressive Strength (MPa)** \\ \hline RPC 01-a (25 \({}^{\circ}\)C) & 86.0 \\ RPC 01-a (75 \({}^{\circ}\)C) & 141 \\ RPC 01-b (75 \({}^{\circ}\)C) & 95.1 \\ RPC 02 (75 \({}^{\circ}\)C) & 108 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Average compressive strengths of RPC cubes. Figure 3: A plot of average initial compressive strengths of RPC specimens. The RPGC 01 specimens, which was a combination of GP 05 and RPC 01-a, performed the best in terms of initial compressive and residual strengths, with an initial strength of approximately 76 MPa and residual strengths of approximately 61 MPa and 51 MPa at 400 \({}^{\circ}\)C and 800 \({}^{\circ}\)C, respectively (Table 13). It was also noted that at a lower alkaline solution to FA ratio and a high silica flour content, improved strengths, both initial and residual, can be achieved. Thus the low alkaline solution to FA ratio could provide a denser microstructure with reduced porosity and hence can increase the bondage between the paste and the aggregate particles. The high flour content can be advantageous as the silica content can increase the pozzolanic reaction and can also act as a filler which can further increase internal strength. Similar observations were previously reported [55]. The RPGC specimens were observed to behave exceptionally well after exposure to a high temperature; for instance, no cracking was evident from any of the specimens after exposure to 400 \({}^{\circ}\)C. Additionally, apart from RPGC 03 specimens which displayed slight cracking, no other specimens displayed any cracking even after being exposed to 800 \({}^{\circ}\)C. The RPGC specimens produced strengths within the range of approximately 18-61 MPa at 400 \({}^{\circ}\)C and approximately 14-51 MPa at 800 \({}^{\circ}\)C, with maximum strength drops of approximately 20% and 33% at 400 \({}^{\circ}\)C and 800 \({}^{\circ}\)C, respectively. In particular, RPGC 04 proved to be the weakest mixture producing the lowest initial and residual strength readings. Figure 5 shows a graphical representation of the strengths of RPGC specimens.
###### Abstract  
Recently, the growing demand of various emerging applications in the realms of sixth-generation (6G) wireless networks has made the term internet of Things (IoT) very popular. Device-to-device (D2D) communication has emerged as one of the significant enablers for the 6G-based IoT network. Recently, the intelligent reflecting surface (IRS) has been considered as a hardware-efficient innovative scheme for future wireless networks due to its ability to mitigate propagation-induced impairments and to realize a smart radio environment. Such an IRS-assisted D2D underlay cellular network is investigated in this paper. Our aim is to maximize the network's spectrum efficiency (SE) by jointly optimizing the transmit power of both the cellular users (CUs) and the D2D pairs, the resource reuse indicators, and the IRS reflection coefficients. Instead of using traditional optimization solution schemes to solve this mixed integer nonlinear optimization problem, a reinforcement learning (RL) approach is used in this paper. The IRS-assisted D2D communication network is structured by the Markov Decision Process (MDP) in the RL framework. First, a Q-learning-based solution is studied. Then, to make a scalable solution with large dimension state and action spaces, a deep Q-learning-based solution scheme using experience replay is proposed. Lastly, an actor-critic framework based on the deep deterministic policy gradient (DDPG) scheme is proposed to learn the optimal policy of the constructed optimization problem considering continuous-valued state and action spaces. Simulation outcomes reveal that the proposed RL-based solution schemes can provide significant SE enhancements compared to the existing optimization schemes.  
device-to-device communication; overlay communication; intelligent reflecting surface (IRS); reinforcement learning (RL); spectrum efficiency (SE)