esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Note that this is a challenging issue for space systems, as the satellite can only communicate with ground stations over very precise time windows, such that the systems may not gather enough measurement data in short time intervals. Recently, [PERSON] et al. proposed a clusterization method for suppressing fading noise without the need to discard measurement data [24]. In detail, the clusterization method reduces fading noise by dividing the measurement data into different clusters and performing security analysis for each cluster separately. Since each cluster contains data with relatively similar transmittance, its fading noise is reduced. Then, [PERSON] et al. discussed the optimal cluster number in the horizontal link case based on the uniform binning of the probability distribution, which is not the optimal binning [25]. Motivated by the previous idea, in this work, we extend the clusterization method to satellite-mediated cases for suppressing fading noise, both downlink and uplink scenarios. We consider the Gaussian-modulated coherent state (GMCS) protocol with homodyne detection [26; 27], while the security analysis is performed under Gaussian collective attacks [28]. To balance the trade-off between the fading noise and the composable finite-size effect, the number of clusters is optimized in terms of the PDT. Numerical analysis shows that the clusterization method can increase the secret key rate when the fading noise is large enough. Our solution does not require an increase in hardware equipment, and will not increase the burden of satellite space and energy. This paper is organized as follows. In Section 2, we introduce the GMCS protocol over the satellite-mediated link and show its asymptotic key rate. In Section 3, we analyze the distribution of fading noise in the downlink and uplink scenarios and propose a clusterization scheme to suppress this noise. The security analysis in the composable finite-size regime is shown in Section 4, and Section 5 concludes this paper.
# Design and Test of Real-Time Detection System for Cleaning Loss of Rapeseed Harvester
## 2 Materials and Methods
### Signal Processing Circuit Design
#### 2.3.2 Envelope Detection Circuit  
Envelope detection is amplitude detection; the specific process demodulates the low-frequency signal from the amplitude modulation signal. Generally, envelope detection uses a diode for unidirectional filtering and then low-pass filtering. If low-pass filtering is performed directly without a diode, the positive and negative envelope lines will cancel out so that the low-frequency signal cannot be detected. The input of the envelope detection circuit of the signal processing circuit is the amplified signal of the original signal after two-stage amplification. The design diagram of the envelope detection circuit is shown in Figure 5.  
The detection diode D3, C18 and R12 were designed to form a detection circuit which can detect the envelope of the original amplified signal. When the input signal is positive and exceeds the diode forward conduction voltage (0.24 V), the diode is on, and the signal is charged to the capacitor C18 through the diode. At this time, the TP6 point voltage increases with an increase in the charging voltage. When the TP5 point voltage begins to drop and is less than the diode-on voltage, the diode reverses and the signal stops charging to C18 at this time; the signal discharges through R12, and the TP6 point voltage decreases with the discharge. Since the capacitor C18 is already discharging to R12 while charging through the diode, taking into account the different charging and discharging  
Figure 4: Amplifier circuit.  
times, the parameters of C18 and R12 can be adjusted to obtain a suitable and flat envelope detection signal.
# Unsupervised Image Classification Based on Fully Fuzzy Voronoi Tessellation
## 5 Discussion
### Time Complexity Analysis  
To verify the time efficiency of the proposed FVT-FCM algorithm, the execution times of the remote sensing images are listed in Table 8. It can be seen that the time complexity of multispectral images (Images 3 and 4) with three bands is significantly higher than that of panchromatic images (Images 1 and 2) and SAR images (Images 5 and 6) with one band. In the six pixel-based comparing algorithms, all other algorithms have lower time complexity except for MSHLICM. The main reason for the high time complexity of MSHLICM is that it needs to calculate the second-level neighborhood based on the first-level neighborhood. The time complexity of VT-HMRF-FCM ranks second because it needs to seek the optimal solution for seed points through random sampling. The proposed algorithm is not as fast as the first five algorithms. The main reason is that the proposed FVT-FCM is region-based. Nonetheless, its execution time is superior to both the region-based algorithm VT-HMRF-FCM and pixel-based algorithm MSHLICM. Based on the comprehensive analysis of classification accuracy and execution time, the proposed method can be regarded as the most optimal one.  
To understand its universality, the time complexity will be further analyzed from the perspective of the model mechanism. It is mainly related to the following factors: the number of pixels \(n\), the number of clusters \(k\), the number of gray levels \(N_{i}\), the size of the neighborhood window \(e\), and the number of iterations \(t\). Then, the time complexities of all algorithms in this paper are given in Table 9. It can be seen that FCM_S and GKWFLICM have an additional neighborhood term \(e\) compared to FCM. In FGPCM, \(N_{i}<<n\). Therefore, it is fast. RFLICM needs to calculate the local coefficient of variation based on the neighborhood, thus its time complexity is \(O(ne+nket)\). In MSHLICM, the second layer neighborhood needs to be calculated based on the first layer neighborhood, so the algorithm complexity is extremely high. In the region-based algorithm, polygons have replaced the neighborhood and become a spatial constraint term, thus \(m\) is also an important factor. Although the time complexity of VT-HMRF-FCM and the proposed algorithm is \(O(nmkt)\), VT-HMRF-FCM needs a large \(t\) to find the optimal solution. Therefore, its time complexity is higher than the proposed.
# Symbol-Level Selective Channel Estimation in Packet-Based OFDM Systems
## 7 Conclusions  
In this paper, we proposed a novel channel estimation scheme that selectively uses DD-TT and TRFI to overcome time-varying characteristics in vehicular environments. To adaptively select TRFI and DD-TT, the proposed SLS scheme was based on the correlation coefficient between channel estimates and uses the better channel estimation scheme on a symbol-by-symbol basis instead of the entire packet.  
The SLS scheme was compared with previous channel estimation schemes through simulations. The simulation results demonstrated that the SLS scheme can be selectively used between TRFI and  
Figure 7: Correlation coefficient (SNR: 24 dB).  
DD-TT on a symbol-by-symbol basis in the packet, thereby improving error performance. In QPSK modulation under V2I and V2V channel environments, the SLS scheme can achieve a performance gain of about 2-4 dB at PER compared with other schemes.  
In terms of the computational complexity, the SLS scheme has a linear time complexity with respect to the number of OFDM symbols constituting a packet based on a complexity analysis. In addition, we analyzed the trade-off between the performance and complexity according to the selection period of the SLS scheme. As the selection period increases, the complexity and performance of the SLS scheme decreases. However, a proper selection period depending on the channel environment can reduce the complexity and minimize performance degradation over symbol-level selection.  
We also analyzed the effective communication range of the proposed scheme through the link budget analysis in the live network. In the link budget analysis, it was confirmed that the distance of the SNR region where DD-TT and TRFI are frequently switched is 35-135 m for the V2V channel and 45-135 m for the V2I channel. Within this range, the proposed scheme achieves maximum gain, and using the proposed technique yields better performance than using other techniques alone.  
Conceptualization: J.-[PERSON] and [PERSON]; methodology: [PERSON]; software: [PERSON]-[PERSON]; validation: [PERSON] and [PERSON]; formal Analysis: [PERSON]-Y.C., [PERSON], and [PERSON]; investigation: [PERSON]-Y.C.; resources: [PERSON]-[PERSON]; data curation: [PERSON] and [PERSON]; writing--original draft preparation: [PERSON]-[PERSON] and [PERSON]; writing--review and editing: [PERSON] and [PERSON]; visualization: [PERSON]-Y.C.; supervision: [PERSON] and [PERSON]-[PERSON]; project administration: [PERSON] and [PERSON]; funding acquisition: [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work was supported in part by the Technology Advancement Research Program funded by the Ministry of Land, Infrastructure, and Transport of the Korean government, Grant 20 CTAP-C151968-02, and in part by the Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Grant 2018R1D1A3B07050327.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Fruit Olive Fly Infestation  
On DOY 199, 209, 216, 248, 259, 277, 287, and 301, a random sample of 50 drupes was collected (for a total of 400 drupes per variety) and immediately transported to the lab for checking the presence of olive fruit fly attacks. First, the olives' surfaces were visually examined to identify the drupes that showed oviposition scars. After the detection, the drupes with scars were counted and kept separate. Later, each drupe with an oviposition sign was dissected with a scalpel, and the pulp was examined under a stereomicroscope to confirm the presence of eggs or larvae. The drupes showing a fertile puncture in any developmental stage of _B. oleae_ (egg or hatched larva) were counted and classified as infested. Punctured fruits without any evidence of olive fly eggs or larvae, were categorized as drupes with a sterile puncture. The total percentage of punctured drupes (\(\%\)PD\({}_{\text{tot}}\)), the percentage of drupes with fertile punctures (\(\%\)PD\({}_{\text{fert}}\)), and the percentage of drupes with sterile punctures (\(\%\)PD\({}_{\text{ster}}\)) were calculated as follows:  
\[\%\text{PD}_{\text{tot}}=\frac{\text{NPD}_{\text{tot}}}{\text{ND}_{\text{tot} }}\times 100 \tag{1}\]  
\[\%\text{PD}_{\text{fert}}=\frac{\text{NPD}_{\text{fert}}}{\text{ND}_{\text{ tot}}}\times 100 \tag{2}\]  
\[\%\text{PD}_{\text{ster}}=\frac{\text{NPD}_{\text{ster}}}{\text{ND}_{\text{ tot}}}\times 100 \tag{3}\]  
where ND\({}_{\text{tot}}\) is the total number of sampled drupes, NPD\({}_{\text{tot}}\) is the total number of punctured drupes, NPD\({}_{\text{fert}}\) is the number of drupes with fertile punctures, and NPD\({}_{\text{ster}}\) is the number of drupes with sterile punctures.
## 1 Introduction
The same result is obtained for the sea level anomalies (SLA): SARAL/AltiKa data quality is as good (or even slightly better) as Jason-2. Another key factor for altimetry applications is the spectral content of the data. AltiKa altimeter provides a signal-to-noise ratio which has never been obtained before. Compared to Jason-2 and CryoSat-2 (SAR mode) data, the spectral content of SARAL/AltiKa observations is largely improved for all wavelengths below 70 km. This is explained by the much lower white noise, due to the excellent Ka-band retracking performances and also to the 40 Hz measurement rate (higher than the standard 20 Hz on Jason-class and CryoSat-2 missions). In conclusion, the mission performances widely confirm the nominal expectations in terms of accuracy, data quality and data availability. The mission objectives are the observation of the oceanic mesoscale dynamics, including the coastal zone, but also the global and regional sea level monitoring, data assimilation and operationaloceanography. It also includes the monitoring of the level of the main continental water bodies (lakes, rivers, enclosed seas), of large-scale sea level variations, the observation of polar oceans (as a result of the high inclination of its orbit), the analysis and forecast of wave and wind fields, the study of continental ice and sea ice, the access to low rains climatology and the marine biogeochemistry (mostly through improved mesoscale access). Beyond the excellent quality of SARAL/AltiKa measurements, there are some specificities that emerge from the Ka-band altimetry. Different scientific results illustrate these specificities. The objectives of this paper are to give precise evidence of the main features of the SARAL/AltiKa that are associated with the Ka-band. In a companion paper [4], we looked at the quality assessment and specificities of AltiKa data. In the present paper, we focus more on the scientific aspects. Rather naturally, the paper is organized following the main scientific applications of SARAL/AltiKa: oceanography, hydrology, ice and geodesy. Some examples are given to illustrate some salient features of today's SARAL/AltiKa data with regard to standard altimetry: data accuracy allowing significant advances in the field of ocean mesoscale studies and for operational applications, in coastal areas, over ice, for inland waters and finally for geodesy.
## 5 Experimental Results and Discussion
### Results and Discussion
The lines in Figure 5 represents the convergence curves of corresponding algorithm. The fitness value of EChOA is the minimum in all the 12 data sets, and the convergence time is very short. The convergence speed of Coimbra, Dermatology, Diagnostic, Divorce and Heart data sets is all higher than ChOA, and some fitness values are also lower than ChOA. At the same time, EChOA convergence curve is relatively flat, in the performance of each data set is relatively good, which are almost below to the average fitness value, does not appear similar to GOA (e.g., Liver) in a certain data set fitness value too much higher than the other algorithms. As it can be seen from Figure 5, the convergence curve of EChOA in the Heart data set is smoother than other algorithms, which shows that the local search ability of the algorithm has been greatly improved. EChOA algorithm can achieve the maximum objective function value when the number of iterations is less than 10 on Dermatology and Divorce data sets, which is earlier than other optimization algorithms. This indicates that the convergence rate of EChOA is improved. As you can see, EChOA is better and more stable than most algorithms. Figure 4: Histogram for the number of features of EChOA and other algorithms based on 17 data sets. Figure 5: The line chart for the fitness of EChOA and other algorithms based on 17 data sets.
## 3 Results
### Effect of Stabilizer Contents on Bearing Capacity  
#### 3.2.1 Cbr\({}_{2.5}\)  
Figure 8 provides an overview of the CBR\({}_{2.5}\) (California bearing ratio at 2.5 mm penetration) of the stabilized soil at different stabilizer contents. The CBR of the lime  
Figure 6: Results of compaction tests of stabilized silty clay with lime: (**a**) Compaction curves; (**b**) Influence of stabilizer content.  
Figure 7: Results of compaction tests of stabilized silty clay with lime and cement: (**a**) Compaction curves; (**b**) Influence of stabilizer content.  
stabilized soil demonstrates a non-linear growth pattern as the lime content ranges from 2% to 8%. With an 8% lime addition, the CBR\({}_{2.5}\) of the soil rises from 5.1% to 18%, exhibiting an increase of up to 3.5 times greater than that of the untreated soil. This enhancement can be attributed to the occurrence of ion exchange and gelation reactions between the soil and lime, where individual soil particles are aggregated into small agglomerates, thus forming a more stable structure [8; 12]. Over time, the formation of denser crystals further enhances the strength of the lime-stabilized soil.  
In comparison to using a single stabilizer, the addition of cement leads to a more notable increase in the CBR value at the equivalent stabilizer content. The bearing ratio of the lime-cement soil exceeded the minimum CBR\({}_{2.5}\) requirement for high-speed and first-class highway subgrade filling by a factor of 2.81. This is due to the fact that the hydration reaction of cement is faster than that of lime, generating calcium silicate hydrate and calcium alumina hydrate through the rigidly coagulate reaction to strengthen the soil skeleton, which rapidly increases the strength of the soil. In addition, the CBR value of lime-cement-stabilized soil is closer to the CBR value of lime soils by increasing its content.  
In conclusion, the untreated soil fails to meet the requirement that the CBR is more than 8% for high-speed and first-class highways (Specification for Design of Highway Subgrade JTG D30-2015). To achieve this target, 4% lime or 1% lime + 1% cement is required for the effective utilization of the stabilized waste soil as subgrade fillers.
# Enhancing Heterogeneous Communication for Foggy Highways Using Vehicular Platoons and SDN
## 5 Conclusions  
We proposed a heterogeneous (DSRC and LTE) communication in SDVN to address the issues faced by drivers during foggy weather. The main controller (MC) selects local controllers (LCs) using a decision tree classifier to assist vehicular platoons in sending periodic alert messages to find an optimal routing path. This safe and stable path takes into account vehicular speed and current visibility in a specific location to communicate the message about speed reduction or lane change in the shortest time possible. The LCs help platoons to make timely decisions to reduce their speed when visibility is getting worse and when there is a leading platoon moving at low speed. The head vehicle of a platoon collects data from within the platoon and updates the LCs with the locally collected data, enabling the MC to construct a global network view. The results demonstrated the validity of this stable communication in terms of delivery ratio, end-to-end delay, average number of hops, and routing overhead ratio compared to two reference schemes. Our future work considers the city model and psychological characteristics of drivers to enhance the performance of the network for heavy fog that results in visibility of less than 5 m.  
Conceptualization, H.G.; methodology, H.Z.A.S. and H.G.; software, H.Z.A.S.; validation, H.G. and I.K.; formal analysis, H.Z.A.S.; investigation, H.G.; resources, H.G.; data curation, H.Z.A.S.; writing--original draft preparation, H.Z.A.S. and H.G.; writing--review and editing, H.G.; visualization, H.G.; supervision, H.G.; project administration, H.G.; funding acquisition, H.G. and I.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the NUST Grant for Young Researchers under grant number NUST-22-41-65 and in part by the Regional Innovation Strategy (RIS) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) under Grant 2021 RIS-003.  
Figure 9: Performance comparison for DTC and GD on highway scenario in terms of (**a**) PDR, (**b**) average delay, (**c**) number of hops, and (**d**) RoR.  
Data Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article.
# Impact of Patient Size and Positioning on Radiation Dose in Multiphase Liver CT Examinations
## 3 Results
#### 3.5.1 Group with Abdominal Imaging Only  
The analysis of the 'Abdomen' group encompassed 53 patient observations. The average height, weight, and BMI of the patients were found to be 1.69 m, 80.35 kg, and 27.96, respectively. When exploring the correlation between these physical characteristics and the operating parameters of the medical X-ray tube, it was observed that the weight and BMI had a negligible impact on the kV settings, which remained constant at 120 kV for all patients. However, the mA settings in phases I, II, and III demonstrated slight variations, though not strongly correlated with height, weight, or BMI. This suggests that while patient physical characteristics are considered, they do not significantly alter the basic operating parameters of the X-ray tube in abdomen-focused procedures (Figures 14 and 15).  
Figure 14: Correlation coefficients between patients’ weight, height, and BMI and the various X-ray tube output parameters for cases involving the abdomen region.
# Junction Selection Based on Optimal-Weighted Multiple Attributes for VANETs
## 1 Introduction  
Vehicular ad hoc networks (VANETs) enables vehicles to communicate with one another to improve the road-safety environment. The self-organized VANETs enable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication [1] to obtain traffic data such as emergency road information, vehicle location and speed, and traffic congestion information, as shown in Figure 1. Vehicles exchange their speed and location information in V2V communication, while V2I communication enables vehicles to share their information with radars, sensors, cameras, lane makers, streetlights, and parking meters information with other vehicles. The V2V communication in VANETs is self-organized and supports various safety/non-safety applications to facilitate the drivers.  
High-speed vehicles change topology frequently [1; 2; 3], which results in frequent disconnections and sporadic connectivity. The mobility pattern, road topology, and impediments all need to be taken into account for successful message transmission. Various routing protocols have been proposed to overcome the disruptive communication links limitation in VANETs [3], but there is still room for improvement, especially in routing schemes. The aforementioned issues in VANETs encourage researchers to propose an optimal solution to increase the efficiency of the network. The 3 GPP standard has proposed two categories for VANETs in its release. The first category includes topology-dependent routing, and the second category includes vehicle location-based routing. The location-based routing further includes junction-based protocols for an urban scenario [3].  
The junction selection protocols are divided into two phases. The junction phase includes the packet transmission between junctions, and the second phase includes data transmitted between road segments. The source vehicle searches the destination vehicle junction to transmit the packet successfully as shown in Figure 2.