esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Link Budget Study  
The proposed mm-wave 1 \(\times\) 4 linear MIMO array antenna is versatile and can be employed in a range of applications, including 5G networks, biomedical wearable communication technologies, and other WBANs. We conducted a link budget analysis to determine the effective mm-wave range over which the proposed antenna can reliably connect with another device. The communication link is influenced by various parameters, including S-parameters, path loss, absorption, and mismatch losses. Therefore, it is essential to study the link margin (LM) in order to estimate the actual communication range of the designed antenna. The parameters considered for the link budget are listed in Table 4[19; 33]. Based on Table 4, the LM was calculated and is depicted in Figure 22. From this figure, it is evident that as the distance increases, the LM decreases. This MIMO antenna design demonstrates successful communication over a distance of 70 m at both 28 GHz and 38 GHz frequencies. This is achieved with a very low transmitter input power, meeting the maximum Effective Isotropic Radiated Power (EIRP) regulations for mm-wave and WBANs. The system also claims an LM exceeding 33.69 dB at both frequencies, indicating a strong signal strength and a significant buffer against signal degradation. The proposed design demonstrates superior results in SAR analysis, MEG, CCL, and link budget analysis compared to findings in the existing literature (Table 5). \begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Transmitter**} \\ \hline  & Frequency (GHz) & 28.0/38.0 \\ \hline \(G_{t}\) & Antenna gain (dBi) & 8.17/8.73 \\ \hline \(P_{t}\) & Transmitted power (dBm) & 10 \\ \hline  & EIRP (dBm) & 18.17/18.73 \\ \hline  & **Receiver** & \\ \hline \(G_{r}\) & Receiver antenna gain (dBi) & 4.1 \\ \hline \(T_{o}\) & Ambient temperature (K) & 293 \\ \hline \(k\) & Boltzmann constant & \(1.38\times 10^{-23}\) \\ \hline \(N_{0}\) & Noise power density (dB/Hz) & \(-203.9\) \\ \hline  & **Signal quality** & \\ \hline \(B_{r}\) & Bit rate (Mb/s) & 1, 20, 60, 100 \\ \hline  & Bit error rate & \(1\times 10^{-5}\) \\ \hline \(E_{b}/N_{0}\) & Ideal PSK (dB) & 9.6 \\ \hline \(G_{c}\) & Coding gain (dB) & 0 \\ \hline \(G_{d}\) & Fixing deterioration (dB) & 2.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Link Budget. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Frequency** \\ **(GHz)** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **SAR (W/Kg)** \\ **(Input Power = 2.5 mW)** \\ \end{tabular} } & \multicolumn{2}{c}{\
\begin{tabular}{c} **Maximum Acceptable** \\ **Power (mW)** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Maximum Acceptable** \\ **Power (dBm)** \\ \end{tabular} } \\ \cline{2-7}  & **1 gm** & **10 gm** & **1 gm** & **10 gm** & **1 gm** & **10 gm** \\ \hline
28.0 & 0.0125 & 0.0079 & 320.00 & 632.91 & 25.05 & 28.01 \\ \hline
38.0 & 0.0189 & 0.0094 & 211.64 & 531.91 & 23.26 & 27.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Maximum average SAR of the proposed 1 \(\times\) 4 MIMO array antenna.
# Three-Dimensional Magnetic Inversion Based on an Adaptive Quadtree Data Compression
###### Abstract  
Three-dimensional magnetic inversion allows the distribution of magnetic parameters to be obtained, and it is an important tool for geological exploration and interpretation. However, because of the redundancy of the data obtained from large-scale investigations or high-density sampling, it is very computationally intensive to use these data for iterative inversion calculations. In this paper, we propose a method for compressing magnetic data by using an adaptive quadtree decomposition method, which divides the two-dimensional data region into four quadrants and progressively subdivides them by recursion until the data in each quadrant meets the regional consistency criterion. The method allows for dense sampling at the abnormal boundaries with large amplitude changes and sparse sampling at regions with small amplitude changes, and achieves the best approximation to the original data with the least amount of data, thus retaining more anomalous information while achieving the purpose of data compression. In addition, assigning values to the data in the quadrants using the averaging method is essentially equivalent to average filtering, which reduces the noise of the magnetic data. Testing the synthetic model and applying the method to mineral exploration a prove that it can effectively compress the magnetic data and greatly improve the computational efficiency.  
adaptive quadtree; data compression; 3D magnetic inversion; average filtering; mineral exploration +
Footnote †: journal: Applied Physics Letters
## Materials and Methods
### Study Design  
This study consisted of 120 subjects. The research protocol was defined using the CONSORT 2010 guidelines [11] (Figure 1). The participants were divided into four groups, each group consisting of thirty subjects. The first group in the study was made up of patients wearing conventional fixed orthodontics with a 0.018\" slot (C18 group, Victory(r), 3M, Saint Paul, MN, USA), the second group with conventional fixed-type multibrackets with a 0.022\" slot (C22 group, Victory(r), 3M, USA), the third group with low-friction fixed multibrackets with a 0.018\" slot (LF18 group, Synergy(r), Rocky Mountain Orthodontics, Denver, CO, USA), and the fourth group with low-friction fixed multibrackets with a 0.022\" slot (LF22 group, Synergy(r), Rocky Mountain Orthodontics). Initially, the orthodontic arch was made of 0.014\"superelastic nickel-titanium (Nitinol(r), 3M, USA). Elastic ligatures (0.12\") were used (G&H Orthodontics, Franklin, IN, USA). Participants were randomly separated into four groups usinga randomization program ([[http://www.randomizer.org/form.htm](http://www.randomizer.org/form.htm)]([http://www.randomizer.org/form.htm](http://www.randomizer.org/form.htm))). This technological tool ensured that patients were equally likely to be assigned to a group. The patients were recruited from April to September 2019 and were treated by the same specialist. The dental bone discrepancy was analyzed for crowding.  
The brackets were bonding on all teeth in both arches, and molar tubes were placed on first and second molars. The blinding of patients was possible in this study. The information about the type of braces was hidden from the patients; however, the operator did know. The results were analyzed in a blind manner, assigning a code number to the patients in the groups. The orthodontic treatment was carried out by one non-blinded experienced orthodontist. All patients were given the same oral hygiene guidelines. The patients were given the questionnaires at the first appointment and were told when to fill out each pain and OHRQoL questionnaire. In the following appointments, the patients gave us the questionnaires.  
The following inclusion criteria were considered: permanent dentition, aged between 18 and 40 years, patients without prior orthodontic treatment, no craniofacial anomalies, no missing teeth, with the exception of third molars, dental bone discrepancy between -1.0 and -2.0 mm in both arches, full complement of erupted teeth except for the third molars and skeletal class I or II malocclusion (ANB 0\({}^{\circ}\)-5\({}^{\circ}\)) [12]. The exclusion criteria were patients requiring tooth extractions, patients with cognitive disorders or chronic medical conditions, history of orthodontic-surgical treatment, mild or high dental crowding (between -2.1 and -6.8 mm), chronic use of analgesic, antidepressantsants, and/or anticonvulsant medications, patients with periodontal pathology and/or dental caries, pregnant women, and/or auxiliary orthodontic appliances. The above exclusion criteria were used to ensure that the study sample was homogeneous.  
Figure 1: CONSORT flow diagram.
# Modeling of Historical Marine Casualty on S-100 Electronic Navigational Charts
## 4 Case Study
### Manual Mapping to Model Attributes  
To structure the collected casualty records in accordance with the proposed data model, a manual mapping process was applied. While the classification of incident types was largely handled during the preprocessing stage through a rule-based approach, the remaining attributes--such as vessel name, ship type, and date of occurrence--required careful manual extraction and interpretation.  
Many records, particularly those from GISIS and MAIB, included narrative fields or inconsistently formatted information that did not directly correspond to the model's predefined attribute structure. To address this, domain experts manually reviewed each record to populate the relevant fields in the feature schema. Table 8 provides examples of selected GISIS records that were used in the mapping process. Each entry includes key elements such as reference ID, vessel name, ship type, date of incident, and the incident category that had been preprocessed. These records demonstrate the practical feasibility of transforming semi-structured casualty data into a model-compliant format through combined rule-based and manual efforts.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ships Involved** & **Ship Type** & **Date** & **Incident Classification** \\ \hline HANMIREU NO. 1 & & tanker & 1 December 2023 & HullAndEngine \\ (IMO 9262261) & & & AccessoryDamage \\ SM JEU LNG1 & & liquefied gas & 17 February 2024 & CollisionAndContact \\ (IMO 9575369) & & general cargo & 25 January 2024 & Navigationallssues \\ KEOYOUNG SUN & & chemical tanker & 20 March 2024 & FloodingAndSinking \\ (IMO 9146924) & & fish catching & 21 April 2023 & FireAndExplosion \\ KALTAN & & & \\ (IMO 9047984) & & & \\ KEOYOUNG & & & \\ PIONEER & & & \\ (IMO 9355020) & & & \\ DONGJIN FORTUNE & & & \\ (IMO 9251145) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Selected GISIS casualty records manually mapped to the proposed data model [28].  
Figure 5: Semi-structured marine casualty report from IMO GISIS [28].
## 2 Results and Discussion
### ppGpp Accumulation in Strain US6-1 and Its Derivatives  
We monitored the endogenous ppGpp levels in US6-1, \(\Delta rsh\) and \(\Delta rsh\) (pCM62-rsh) (Figure 3). The presence of ppGpp in US6-1 culture at 72 h of incubation (stationary phase) was determined while no ppGpp was detected in \(\Delta rsh\). When we analyzed ppGpp accumulation in \(\Delta rsh\) (pCM62-rsh), the complementation of ppGpp production was observed, confirming that Rsh\({}_{\text{US6-1}}\) was responsible for ppGpp synthesis. These results showed that the stringent response in US6-1 and the complementary strain was induced under the current culture conditions. Nutrient limitation is one of the conditions that can induce the stringent response. However, the medium used in this study was nutrient-rich PSY3 medium. Several studies has recently found that antibiotics, acid stress and oxidative stress could also induce the stringent response [38]. Therefore, it seems that many factors which can induce the stringent response of bacteria remain unknown. At 36 h of incubation (exponential growth phase), we could not detect the ppGpp accumulation in US6-1 culture. This was probably because the ppGpp level was below the detectable range, as ppGpp is generally thought to be present at basal levels in the exponential growth phase [3]. However, previous studies showed that ppGpp could still regulate the expression of a wide range of genes during this growth period [3,6].
# WPFD: Active User-Side Detection of Evil Twins
## 4 Evaluation
### Time Efficiency  
In this section, we evaluate the system algorithm's time efficiency. We tested our system with various detection durations. Table 3 shows the outcome. The total number of packets captured is proportional to the number of connection flows detected during the detection period. Therefore, we are not concerned with the increment of complete packages but the relationship between the good twin's probe hits and the evil twin's probe hits. By observing the result, we can see that the number of probe hits of the evil twin is always more significant than the number of probe hits of the good twin. Hence, we can determine the evil twin as soon as a few probes hits occur. Each probe takes about one minute. As a result, we set the Repeats to 5, described in Section 3.3. Hence, the WPFD can determine an evil twin in 5 min.  
Figure 4: Experimental environment for evil twin and good twin detection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**RSSI to Good AP** & **Probe-Hits of the Good Twin** & **Probe-Hits of the Evil Twin** \\ \hline
95\% & 19 & 981 \\
80\% & 8 & 902 \\
70\% & 1 & 985 \\
60\% & 0 & 946 \\
45\% & 0 & 507 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effectiveness experiment results of WPFD with different RSSI values.
# Effect of Microwave-Assisted Processing of Acorn Flour on Muffin Quality
## 1 Introduction
[33] reported an 84% reduction in tannin content in potato flour subjected to microwave processing at 1.1 kW for 2 min. Similarly, [PERSON] et al. [34] demonstrated that microwave pre-treatment of soybean seeds (0.9 kW, 1.5 min) prior to flour production led to an 88% reduction in tannin content. In this case, total phenolic content decreased (from 368 to 330 mg GAE (gallic acid equivalent)/100 g), while total flavonoid content remained relatively stable (90 to 88 mg GAE/100 g). In contrast, [PERSON] et al. [35] found that microwave treatment of sorghum seeds (400 and 700 W for 15 and 30 s)had no statistically significant effect on tannin, phenolic, or flavonoid content. However, to date, no studies have specifically investigated the impact of microwave treatment on tannin content in acoms or acorn flour. Although the degradation of tannins is a desirable outcome of microwave treatment of acorn flour, its impact on other bioactive phenolic compounds must also be considered. Microwave processing can both enhance and degrade phenolic compounds, depending on the processing parameters and the physicochemical properties of the raw material. Alterations in the phenolic profile have been documented; selected phenolic acids, flavonoids, and flavanols may be either released or degraded depending on the power and duration of heating. Compounds such as quercetin and myricetin, which are sensitive to high temperatures, may degrade under microwave exposure, whereas others such as gallic acid may be liberated from hydrolyzable tannins [28; 29; 36]. Therefore, optimizing microwave treatment conditions is critical to achieving the desired reduction in antinutritional factors while preserving or enhancing beneficial bioactive compounds. The objective of this study was to evaluate the effect of microwave treatment on the physical properties and bioactive compound content of acorn flour. Acorn flour was subjected to microwave heating at varying power levels and durations. The treated flour was then incorporated into wheat-acorn muffins, which were analyzed for their organoleptic (color, aroma, taste, and texture) and physical properties (volume, porosity, crumb firmness, and color). A key aspect of this study involved analyzing how microwave treatment influenced the chemical composition of the muffins, specifically focusing on the concentrations of free phenolic compounds, flavonoids, and tannins, along with their associated antioxidant activity.
## 4 Conclusions  
In this work, a new antenna structure with CPW-fed, incorporated with an organic solar cell, was designed and verified experimentally in the frequency range of 5-16 GHz, which is useful for Ku band satellite communication. This new design provided 100% insolation of the OSC and improved the performance of the antenna. The proposed antenna covered the frequency band ranging from \(11.7\) to \(12.22\) GHz downlink, with a \(3\) GHz bandwidth, which is in compliance with that assigned by the ITU. The simulated results demonstrated that, when the OSC was mounted on top of the proposed CPW-fed monopole antenna, the return loss was significantly enhanced and the realized gain became \(6.30\) dB. Furthermore, the antenna produced a directional radiation pattern in both planes (E-plane and H-plane). In this study, the effect of patch width variation on S-Parameters was also investigated. The designed antenna was successfully fabricated and it was found that the experimental results were in a good agreement with the simulated ones. The proposed structure was specifically useful in directional telecommunication applications such as satellite applications.  
Figure 10: Simulated and measured results of return loss for CPW-fed antenna design.  
**Author Contributions:** Conceptualization, Y.I.A. and H.N.A.; methodology, Y.I.A. and H.N.A.; software, Y.I.A. and H.N.A.; validation, M.K., F.F.M. and O.A.; formal analysis, H.N.A.; investigation, Y.I.A.; resources, M.K., F.F.M.; data curation, Y.I.A. and H.N.A.; writing--original draft preparation, Y.I.A.; writing--review and editing, F.F.M., O.A. and H.L.; visualization, Y.I.A. and H.N.A.; supervision, M.K. and L.D.; project administration, H.L., M.K and L.D.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Key Research and Development Program of China (Grant no.2017 YFA0204600), the National Natural Science Foundation of China (Grant no. 51802352) and the Fundamental Research Funds for the Central Universities of Central South University (Grant no.2018 zzts355).  
**Acknowledgments:** The author would like to thank Central South University, Iskenderun Technical University, [PERSON] and [PERSON] at the college of Electrical and Information Engineering at Hunan University for the technical supports.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks
## 5 Numerical Experiment
### Results Analysis  
In the experiment, the RQRCS algorithm simultaneously considers two types of resources: storage and observation. The resource scheduling scheme with minimum time consumption is found by optimizing the network transmission path of multiple resources. As shown in Tables 5 and 6, RQRCS finds the target satellites participating in the collaboration and the least time-consuming path to the target satellite. In addition, we can observe that satellite 7 has received five resource requests, including 4 GB of storage resources and one observation of the use of resources; Satellite 8 received resource requests from seven units, But satellite 8 requests two units of resource quantity from satellite 9. Therefore, satellite 8 participates in allocating five units of resources, respectively; the use of 1 GB storage resources and the use of four observation resources. Satellite 9 needs to allocate 1 GB of storage resources and the use of one observation resource.  
Figures 9 and 10 shows the multiple optimization solution processes using PSO and GA algorithms based on different parameter settings for multi-satellite resource allocation. The minimum ordinal value in the figure is the minimum resource scheduling time  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scheduling Route** & **Resource Transfers** \\ \hline \(1\to 4\) & 1 \\ \(1\to 3\) & 1 \\ \(2\to 4\) & 3 \\ \(2\to 3\) & 1 \\ \(3\to 6\) & 2 \\ \(4\to 7\) & 4 \\ \(6\to 8\) & 2 \\ \(8\to 9\) & 1 \\ Amount of time consumed per unit & 71 \\ \hline \hline \end{tabular}
\end{table}
Table 6: RQRCS scheduling storage resource calculation results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multicolumn{2}{c}{**PSO**} & \multicolumn{2}{c}{**GA**} \\ \cline{2-5}  & _size\_pop_ & _max\_iter_ & _size\_pop_ & _max\_iter_ \\ \hline P1 & 200 & 400 & 200 & 400 \\ P2 & 300 & 600 & 300 & 600 \\ P3 & 400 & 800 & 400 & 800 \\ P4 & 500 & 1000 & 500 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter settings for PSO and GA algorithms.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scheduling Route** & **Resource Transfers** \\ \hline \(1\to 4\) & 3 \\ \(2\to 4\) & 3 \\ \(4\to 5\) & 5 \\ \(4\to 7\) & 1 \\ \(5\to 8\) & 5 \\ \(8\to 9\) & 1 \\ Amount of time consumed per unit & 62 \\ \hline \hline \end{tabular}
\end{table}
Table 5: RQRCS scheduling observation resource calculation results.  
consumed by the algorithms. The minimum time consumption value for PSO and GA algorithms are 230 and 283 units of time, respectively. Table 7 shows that the algorithm RQRCS exhibits less time consumption in the multi-satellite resource coordination process compared to the baseline algorithms PSO and GA. Therefore, the experimental verification and analysis indicate that the RQRCS algorithm has certain advantages.
###### Abstract  
Recognizing 3D objects from point clouds is a crucial technology for autonomous vehicles. Nevertheless, LiDAR (Light Detection and Ranging) point clouds are generally sparse, and they provide limited contextual information, resulting in unsatisfactory recognition performance for distant or small objects. Consequently, this article proposes an object recognition algorithm named Adaptive Scale and Correlative Attention PointPillars (ASCA-PointPillars) to address this problem. Firstly, an innovative adaptive scale pillars (ASP) encoding method is proposed, which encodes point clouds using pillars of varying sizes. Secondly, ASCA-PointPillars introduces a feature enhancement mechanism called correlative point attention (CPA) to enhance the feature associations within each pillar. Additionally, a data augmentation algorithm called random sampling data augmentation (RS-Aug) is proposed to solve the class imbalance problem. The experimental results on the KITTI 3D object dataset demonstrate that the proposed ASCA-PointPillars algorithm significantly boosts the recognition performance and RS-Aug effectively enhances the training effects on an imbalanced dataset.  
autonomous vehicle; adaptive scale pillars (ASP); correlative point attention (CPA); object detection; LiDAR point cloud; random sampling data augmentation algorithm (RS-Aug)