esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Analysis and Simulation Results
### Improvement of the Energy Efficiency
#### 4.1.1 ITXP Scheme  
From (11), the reduction of the PAPR is shown to increase the efficiency, \(\eta\) and it can consequently increase \(P_{tx}\). Therefore, the ITXP scheme can increase the achievable rate and the EE by increasing the TX power. To show the improvement in EE, the relative EE with ITXP scheme, \(EE_{r}^{ITXP}\) is defined as the ratio of \(EE^{ITXP}\) and \(EE_{ref}\). Note that \(EE_{ref}\) is the reference EE without PAPR reduction technique. The relative EE of ITXP is expressed as follows:  
\[EE_{r}^{ITXP} = EE^{ITXP}/EE_{ref} \tag{19}\] \[\approx \frac{\frac{\frac{gR}{P_{tx}^{eff}+P_{tx}^{R}}}{\frac{gR}{P_{tx}^{ eff}+P_{tx}^{R}}}\cdot\left[\log_{2}\left(1+\frac{\eta P_{tx}^{eff}N_{tx}}{(1+N_{0} )gR}\right)\right]}{\left[\log_{2}\left(1+\frac{\eta P_{tx}^{eff}N_{tx}}{(1+N_{0 })gR}\right)\right]}\] \[= \frac{\left[\log_{2}\left(1+\frac{\eta P_{tx}^{opt}P_{tx}^{eff}N _{tx}}{(1+N_{0})gR}\right)\right]}{\left[\log_{2}\left(1+\frac{\eta P_{tx}^{ eff}P_{tx}^{R}N_{tx}}{(1+N_{0})gR}\right)\right]},\]  
where \(\eta^{ref}\) is the reference PA efficiency, when no PAPR reduction technique is applied. Only the log terms of numerator and denominator remain, and as the PAPR reduction increases, the \(EE_{r}^{ITXP}\) also  
Figure 2: (a) Normalized EE versus \(P_{tot}\); (b) Normalized R versus \(P_{tot}\), when \(N_{t}=400\), \(K=40\).  
increases. Since the IUI depends on the precoding method, the \(EE_{r}^{ITXP}\) can change with the applied precoding method.
## 6 Conclusions  
In this paper, we proposed a novel data reduction algorithm that adapts to LDL constraints. It simultaneously address the prototype selection and the label-specific feature selection with two objectives: finding an optimal subset of samples to improve the performance of the AA-kNN learner  
Figure 3: Number of selected prototypes and features (%).  
Figure 2: Bayesian Sign test comparing AA-\(k\)NN(L) vs. ProLSFEO-LDL(R). (**a**) Chebyshev Distance. (**b**) Clark Distance. (**c**) Canberra Metric. (**d**) Kullback–Leibler Divergence. (**e**) Cosine Coefficient. (**f**) Intersection Similarity.  
and selecting a subset of characteristics specific for each one of the output label. Both tasks have been addressed as search problems using an evolutionary algorithm, based on CHC, to optimize the solution.  
In order to verify the effectiveness of the solution designed, ProLSFEO-LDL has been applied on several real-world LDL datasets, showing significant improvements compared to the use of the raw training set. The results of the different measures highlights the best ranking of ProLSFEO-LDL, outcomes subsequently corroborated by statistical tests. In addition, the percentaje of data reduction reached leads to a significant improvement of prediction time.  
In future studies we may introduce some further comparisons between already existing approaches and also make improvements to the presented proposal such as:  
* It might be interesting to compare the results obtained by this study with the following techniques: LDLSF [31] and Binary Coding bases LDL [40].
* Complement the experimental analysis by dealing with larger datasets (Big Data). To this end, we will complement the current proposal with some of the big data reduction techniques presented in [71].
* The experimental settings use the AA-KNN learner to measure the quality of the solution applied over the pre-processed dataset. Other more powerful LDL learners could be considered for this task but they must previously be adapted to support the label-specific feature selection. With this we will be able to carry out more adequate comparisons with state-of-the-art LDL methods like LDLFs proposed in [17] or StructRF [20].
* Another interesting study that we will undertake is how the presence of noise at the label side can affect the performance. In real scenarios the data gathering is often an automatic procedure that can lead to incorrect sample labelling. It would be interesting to inject some artificial noise to the training labels in order to check the robustness of the implemented approach.  
[PERSON] and [PERSON] designed the study; [PERSON] performed the investigation, carried out tool implementation and all of the analyses and wrote the manuscript; [PERSON] revised and validated the manuscript; [PERSON] was in charge of funding acquisition, supervision and project administration. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Spanish National Research Project TIN2017-89517-P.  
The authors declare that they have no conflict of interest.
## 5 Conclusion  
We present functional coupling of crop modelling and remote sensing using an updated GRAMI-rice model that uses remote sensing data, and a CIDS formulated to simulate geospatial rice growth and yield by adapting the GRAMI-rice model for use in this study. The simulated values of rice growth obtained with the parameterized GRAMI-rice model showed good agreement with the corresponding field measurements. We also presented that GRAMI-rice was successfully incorporated with optical satellite data with different geospatial resolutions. Simulated geographical variations in yield were in reasonable agreement with the corresponding observed variations in yield. Therefore, the current study demonstrates that the GRAMI-rice model can be applied to reproducing rice growth and development conditions and productivity based on the integration with remote sensing data from various platforms such as a UAS and different optical satellites with different ground resolutions. The CIDS was applied to monitoring and mapping of rice growth and yield. The GRAMI-rice model has relatively simple environmental input requirements that can be provided by remote sensing. We assume that the CIDS can be potentially applied to crop growth monitoring and yield mapping efforts for croplands of various geospatial scales, ranging from farm fields to regions of interest because remote sensing data can be obtained from observations of small-size unmanned airborne platforms (drones) and existing satellite sensors.  
Conceptualization: J.K. and C.N.; methodology: V.N.; software: C.N.; validation: J.K., S.J., and J.Y.; formal analysis: S.J.; investigation: V.N.; resources: J.Y.; data curation: S.J.; writing--original draft preparation: J.K.; writing--review and editing: J.K. and J.Y.; visualization: J.K.; supervision: J.K.; project administration: S.J.; funding acquisition: J.K.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science, and Technology (NRF-2015R1D1A1A01057586). [PERSON]'s research is supported by a research program of Rural Development Administration, Republic of Korea (project no. PJ01283009).  
We thank the Korea Institute of Ocean Science and Technology (KIOST) for providing the GOCI data.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Spatiotemporal Assessment and Correction of Gridded Precipitation Products in North Western Morocco
## 2 Materials and Methods
### Methodology
#### 2.3.1 Classification Metrics
To strike a balance between the two, when evaluating the effectiveness of a classification model in predicting positive instances, the F1 score is commonly used. The F1 score is calculated as the harmonic mean of precision and recall [51; 52] and ranges between 0 and 1. It represents a compromise between precision and recall for assessing a model's performance [53]. The F1 score formula can be simplified and directly expressed using the components of the confusion matrix. It compares the true positive predictions (TP) with the model errors (FN + FP). An F1 score of 50% indicates that precision and recall are equal, which can occur when the model produces an equal number of false positives and false negatives. The AUC-ROC (Area Under the Curve of the Receiver Operating Characteristic) curve is a valuable tool for visually assessing the performance of a classification model [54]. The ROC curve is constructed by plotting the true positive rate (TPR) against the false positive rate (FPR), with TPR on the \(y\)-axis and FPR on the \(x\)-axis. The AUC value represents the area under this curve, and a higher AUC value closer to 1 indicates superior model performance. The AUC-ROC curve is commonly used in binary classification problems [55]. TPR, also known as sensitivity, measures the proportion of positive examples correctly identified by the model, while specificity measures the proportion of negative examples correctly identified. By varying the decision threshold, the ROC curve evaluates the model's ability to distinguish between positive and negative classes [56]. The false discovery rate (FDR) quantifies the proportion of false positive predictions among all positive predictions made by the model. It provides a measure of the fraction of incorrect estimates within all the model's predictions [57] (Table 3).
# Variable Tap-Length Algorithm with Mixed Parameter
## 5 Simulations and Results
### Case 2: SNR = 0 dB  
The tracking abilities of [PERSON] and coworkers' algorithm [12], FT Algorithm 1, and FT Algorithm 2 deteriorated to a certain extent in high noise condition. Under-modeling, large fluctuations and large overestimations of tap-length all appeared in these algorithms. Due to the lack of appropriate error width, the under-modeling problem appeared in [PERSON] and coworkers' algorithm [12]. From the Figures 6 and 7, the effect of error width on algorithm is greater than ST in a high noise environments. The error width acts as a decisive role in a high noise environment. These algorithms performed poorly in the SNR = 0 dB environment.  
On the contrary, the algorithms of [PERSON] et al. [11] and [PERSON] et al. [13], and the proposed algorithm were close to the optimum value. However, the performance of the algorithms of [PERSON] et al. [11] and [PERSON] et al. [13] were also affected by the lack of reasonable choice for ST. The tap-length of the proposed algorithm converged at 1000, 11,000, 2000, and 10,500 iterations in Figures 6 and 7, respectively. All other algorithms converged after these iterations. Moreover, the proposed algorithm did not fluctuate after completing convergence. The other algorithms still fluctuated. Although the environment changed, the proposed algorithm demonstrated good tracking ability and avoided large overestimations of tap-length.  
The experimental results agreed with the theoretical analysis. The proposed algorithm simultaneously improved the convergence and steady-state performance.
# CubeSat Imaging Payload Design for Environmental Monitoring of Greenland
###### Abstract  
Climate change affects the Earth's ecosystem, and understanding human impact on sparsely populated polar regions is crucial, especially in glacial dynamics. Nanosatellites can play an essential role in monitoring remote regions due to their flexibility in adding remote sensors for Earth observation. However, they have hardware constraints such as physical space limitations, low power generation, and low bandwidth, as well as environmental challenges of vacuum, heat, cold and radiation. This paper details the preliminary system design of an imaging payload integrated into a nanosatellite for monitoring field study sites in Greenland. The payload is capable of supporting advanced image processing and Machine Learning (ML) applications. Key design elements, including the selection of imaging sensors, onboard processing units, and data transmission systems, which are optimized for the constraints of a nanosatellite platform, are presented. As a result, we presented a novel imaging payload system design, which shows a significant step towards leveraging space technology for environmental research.  
Keywords:electronic design; ML systems; nanosatellites; cameras +
Footnote †: journal: Electronics Letters
# Design and Analysis of a Hybrid MPPT Method for PV Systems Under Partial Shading Conditions
## 2 Materials and Methods
### Design of the Proposed MPPT Algorithms
#### 2.3.1 Design of Perturb & Observe MPPT Algorithm  
The Perturb & Observe MPPT algorithm is a traditional online MPPT algorithm. It is one of the most studied and implemented MPPT methods due to its simple structure and low cost. In this algorithm, power is calculated using the voltage and current values measured from the PV array. Power and voltage values are compared with the previous power and voltage values. The duty cycle or reference voltage value is adjusted according to the change in output power. The increment or decrement of the reference voltage affects the power output of the PV module. In this manner, the perturbation direction of the duty cycle or reference voltage can be determined in line with the change in power. The flowchart of Perturb & Observe MPPT algorithm is given in Figure 4.  
Figure 4: Flowchart of Perturb & Observe MPPT algorithm.
## 4 The Dynamic Form of the Problem
### Heuristic Scheduling
The mechanism of packet reserve can take advantage of the opportunities to code more packets, without disturbing the existing quota-based system. It also adds more flexibility to the scheduling scheme. All heuristic rules work together toward the same objective. When the network is able to sustain all flows, we try to stick to the static scheduling scheme, because this is the scheduling that maximizes the throughput. When the network cannot satisfy all flow traffic requirements, we aim to maximize the minimum throughput among all flows, which is the most frequently used metric for measuring fairness.
# Estimation of SAR Average in Rats during 5G NR Chronic Exposure
## 4 Discussion
study presents SAR values ranging from 0.13 for H-polarization to 1.4 W/kg in the E-polarization plane. Using the description from [9], we estimated the distributed absorption using expression (3), taking into account the decreasing incident power density within the cage. Assuming an average mouse size of 10 cm (since this information was missing from the description), we obtained a maximum of 1.387 W/kg, a minimum of 0.663 W/kg, and a mean of 0.951 W/kg for E-polarization. An estimation of SAR, taking into account H-polarization in this case, is not possible because the used frequency does not satisfy the condition \(L/\lambda>>0.4\). For E-polarization, the upper bound of the SAR is close to the presented [9] authors, while the lower value was not presented. Thus, it can be concluded from the obtained results that the proposed expression (3) can be used for approximate SAR estimation in the case of distributed absorption, taking into account the decreasing incident power density. The SAR estimates presented in Figure 7 were necessary to establish exposure regimes so as not to exceed the basic ICNIRP limits for whole-body SAR.
###### Abstract  
As Industry 4.0 advances, emerging use cases demand 5G NR networks capable of delivering high uplink throughput and ultra-low downlink latency. This study evaluates a 5G link between a LiDAR-equipped unmanned ground vehicle (UGV) and its control unit using a configurable industrial testbed. Based on 3 GPP standards and related literature, we identified latency and uplink throughput as key factors for real-time control. Experiments were conducted across different gNB configurations and attenuation levels. The results show that tuning parameters such as CSI, TRS, and SSB significantly improves performance. In this study, we provide a practical analysis of how these parameters influence key metrics, supported by real-world measurements. Furthermore, adjusting the Scheduling Request period and PDCCH candidate settings enhanced uplink reliability. Several configurations supported high LiDAR uplink traffic while maintaining low control latency, meeting industrial 3 GPP standards. The configurations also met throughput requirements specified in UAV-related 3 GPP standards. In favorable radio link conditions, selected configurations were sufficient to also enable cooperative driving or even machine control. This work highlights the importance of fine-tuning parameters and performing testbed-based evaluations to bridge the gap between simulation and deployment in Industry 4.0.  
5G NR; low-latency; uplink throughput; testbed evaluation; Industry 4.0; unmanned ground vehicles (UGV) +
Footnote †: journal: Applied  
_Article_
## 3 Test Results and Analysis
### Transverse Connectivity
The damage index \(\mu_{i}\) corresponding to the joint \(H_{i}\) can be expressed by Equation (6):  
\[\mu_{i}=\frac{\gamma_{i}-\gamma_{i}t}{\gamma_{i}+\gamma_{i}t}. \tag{6}\]  
As the theoretical value of the relative displacement is certain, the damage index increases with the increase of the measured relative displacement. When the measured value is much larger than the theoretical value, the index value is close to 1, indicating severe damage of the joint. When the measured value is close to the theoretical value, the joint is in good condition, and the index value is close to 0. When the measured value is  
Figure 8: FE model. Figure 9: Mid-span deflection of each girder. smaller than the theoretical value, the adjacent joint might be damaged, and the load cannot be well transferred. The joint is also in good condition, and the index value is smaller than 0. Therefore, the joint damage can be assessed by the damage index \(\mu\) when the index value is larger than 0. According to the measured and theoretical value in Section 3.1, the hinge joints of the studied hollow slab bridge were evaluated using the proposed damage index. Figure 10a shows the comparison of the measured and theoretical values of the relative displacement of the joints. For the H3, H4, and H6 joints, the measured relative displacement is much larger than the theoretical results, while the relative displacement of other joints is within 0.15 mm. The proposed damage index of each joint was calculated, and the values are shown in Figure 10b. It could be found that the damage index of H3~6 joints is above 0, and the H3 joint has the highest value of the index. The damage index is consistent with the findings in Figure 9 that the nearby joints of G4 and G6 girders were damaged to an extent. The surface damage of the joints is shown in Figure 11. For all the seven joints, there were through cracks only in H3 and H4 joints. The surface damage is slight for H1 and H2 joints, which verifies the joint evaluation using the proposed damage index. Figure 10: Hinge joint: (**a**) relative displacement; (**b**) damage index \(\mu\).
# Contribution of Atmospheric Fallout to the Soil-Root-Leaf Transfer of PAHs in Higher Plants
## 4 Discussion
### Contribution of Wet Deposition to PAH Transfer  
Wet deposition is an important process in the transfer of atmospheric PAHs both to the soil and onto plant surfaces. According to [PERSON] et al., the PAH content of wet deposition depends on regional PAH emitting sources, with Flt and Phen being the most abundant in the majority of the samples [75]. Several studies have identified traffic as the main contributor to the PAH content of wet deposition [76, 77]. The 3-ring Phen was also a dominant compound in the extract used for this study. Its concentration (0.448 ug/L) is comparable with reported quantities in real-world wet depositions (reviewed by [PERSON] et al. [24]).  
Wet deposition was found to show seasonal patterns: in the study of [PERSON] and [PERSON] [78], dry deposition of PAHs was higher in winter, while wet deposition was higher in summer. [PERSON] et al. [25] also detected higher wet deposition in summer. However, the same study also concluded that the total removal of atmospheric PAHs via wet deposition is generally lower than that of dry deposition, considering the relatively shorter durations of precipitations.
## 3 Aim, Materials, and Methods
### Mixed Transport Scheduling Model under Complex Disasters
#### 4.4.2 Vehicle Scheduling  
The integration of modern communication technology, geographic information system (GIS) technology, and traffic interconnection technology enables real-time transmission of vehicle position, transport status, road conditions, and other Upon careful reading data. This provides accurate and comprehensive support to the transportation management system while enhancing the rationality and efficiency of vehicle scheduling. Communication technology facilitates real-time communication between on-site personnel and the transportation management system during emergency rescue operations, enabling prompt feedback on vehicle position and status for scheduling commands. GIS technology processes and analyzes geographic information to provide relevant data such as road conditions and traffic flow to assist in planning and optimizing transportation routes for improved efficiency. Furthermore, through integrating various types of transportation vehicles' information within a global scope, the entire transportation network can be effectively scheduled and controlled. Additionally, interconnection technology can be employed in emergency rescue scenarios to promote collaborative operation among vehicles for enhanced efficiency and accuracy.
## 1 Introduction  
The increasing demand for all-ceramic restorations has led to the development of yttria-stabilized tetragonal zirconia polycrystal (Y-TZP) with enhanced mechanical properties such as high flexural strength and high fracture toughness [1, 2]. Due to their superior mechanical performance, zirconia-based restorations not requiring high retention may be cemented using non-adhesive luting agents [3]. In many clinical situations, however, a strong, durable adhesion between the zirconia framework and the luting cement is a prerequisite for the long-term success of zirconia-based restorations [4, 5, 6]. Therefore, adhesive resin cements containing acidic functional monomers have been reported as the most appropriate materials, the monomers being capable of interacting chemically with the hydroxyl groups of the zirconia surface [6].  
Although the initial bonding of the resin-zirconia interface is of importance, the natural aging of the interface needs to be simulated to evaluate long-term adhesion durability and to identify more effective bonding techniques and adhesive materials [7, 8]. There are various artificial aging techniques for estimating the gradual degradation of the bonding interface. Among them, long-term water storage at a constant temperature or thermal cycling (TC) are most often used to simulate natural aging of resin bonds [9; 10]. However, the TC method remains controversial and there is no definite evidence that failure in practice occurs by thermal stress [10].  
In TC tests, differential thermal changes can induce mechanical stresses and, as a result, cause crack propagation through bonded interfaces [11]. In mechanical load cycling (MLC), such crack formation and propagation seem to take place more directly, particularly when the bonded interface is brittle. Unlike TC, MLC has not been commonly used in dental research that challenges various bonded interfaces for the evaluation of bonding durability [12; 13; 14]. At present, however, there are no standardized MLC procedures and few studies using MLC to evaluate the adhesion durability between zirconia and resin.  
The purpose of this study was to compare the influence of four aging methods (TC or MLC or their combinations) on the shear bond strength (SBS) of resin bonded to zirconia. Two adhesive luting cements with the potential to bond chemically to zirconia ceramic due to their functional monomers were used [3], and their mechanical/physical properties were investigated prior to bonding. The null hypothesis tested was that the aging methods do not differently decrease the SBS of the resins to zirconia.