esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Molybdenum Re-Os and zircon U-Pb isotopic data are first obtained from the stockwork and disseminated-style gold-bearing ores and the fine-grained granite hosting these ores in the Xiawolong gold mine, respectively, which is located within the Muping-Rushan gold metallogenic belt, eastern Jiaodong Peninsula, so as to illustrate the genesis of gold mineralization and its implication for exploration. Four molybden samples yield a well-defined Re-Os isochron age of 118.4 \(\pm\) 2.5 Ma (2\(\sigma\)), which is identical to the weighted average Re-Os model age of 118 \(\pm\) 1.7 Ma (2\(\sigma\)). Integration of the new geochronologic data with those reported recently from the other gold mines in the Muping-Rushan gold metallogenic belt suggests that a discrete gold event occurred in Xiawolong ca. 4 m.y. older than that for the other gold mineralization at ca. 114 Ma in eastern Jiaodong. In addition, two fine-grained granite samples, measured using the LA-ICP-MS zircon U-Pb method, produce the first precise ages of 118 \(\pm\) 2 to 117 \(\pm\) 2 Ma (2\(\sigma\)), identical to the molybden Re-Os ages, within the margin of error and obtained in this study. The fine-grained granite has a similar lithology and emplacement age as those of the medium-grained monzogranite consisting of the marginal facies of the Sanfoshan batholith, and is considered to be the crystallization products of Sanfoshan granitic magma in the late stage. Combined with the previous S-Pb-D-O isotope, fluid inclusion and geological studies, which suggest that the ore-forming fluid of Xiawolong gold mineralization is from magmatic water, and the identification that the magnetite coexists with the gold-bearing pyrite and molybden in the gold ores, which indicates a high oxygen fugacity (fO\({}_{2}\)) of both the magma and resultant hydrothermal fluids, it is logical to infer that the Xiawolong gold deposit is genetically in relation to the Sanfoshan granitic magmatism, which is high in fO\({}_{2}\) and rich in Au at the magmatic-hydrothermal transition stage, and the change in fO\({}_{2}\) mostly likely makes a significant contribution to the precipitation of Au. This result reveals that the late-stage gramitic magma with high fO\({}_{2}\), which is crystallized into the fine-grained granite, probably is also rich in Au, except the W-Mo-Cu-Zn-U-Be-Li-Nb-Ta-Sn-Bi-elements. Therefore, based on the extensional tectonic regime for the early Cretaceous Jiaodong gold deposits, we propose that gold exploration in the Jiaodong should not only focus on the fault-hosted Au but also on the fine-grained granite-hosted Au around the apical portions of the late Early Cretaceous small-granitic intrusions with high fO\({}_{2}\). This model could also be important for prospecting in other gold ore districts, which have a similar tectonic setting.  
molybdenum Re-Os; Xiawolong deposit; East China; Muping-Rushan; zircon U-Pb
# CF-mMIMO-Based Computational Offloading for UAV Swarms: System Design and Experimental Results
## 4 Experimental Validation  
To validate the proposed CF-mMIMO-based UAV swarm communication and computation framework, we developed a hardware prototype system comprising a cloud-edge collaborative computing platform and a UAV terminal layer, as shown in Figures 2 and 3, respectively. This platform supports real-time task migration, low-latency data exchange, and distributed control in dynamic swarm scenarios.
# ResnetCPS for Power Equipment and Defect Detection
###### Abstract  
Routine visual inspection is fundamental to the preventive maintenance of power equipment. Convolutional neural networks (CNNs) substantially reduce the number of parameters and efficiently extract image features for classification tasks. In the actual production and operation process of substations, due to the limitation of safety distance, camera monitoring, inspection robots, etc., cannot be very close to the target. The operational environment of power equipment leads to scale variations in the main target and thus compromises the performance of conventional models. To address the challenges posed by scale fluctuations in power equipment image datasets, while adhering to the requirements for model efficiency and enhanced inter-channel communication, this paper proposed the ResNet Cross-Layer Parameter Sharing (ResNetCPS) framework. The core idea is that the network output should remain consistent for the same object at different scales. The proposed framework facilitates weight sharing across different layers within the convolutional network, establishing connections between pertinent channels across layers and leveraging the scale invariance inherent in image datasets. Additionally, for substation image processing mainly based on edge devices, smaller models must be used to reduce the expenditure of computing power. The Cross-Layer Parameter Sharing framework not only reduces the overall number of model parameters but also decreases training time. To further enhance the representation of critical features while suppressing less important or redundant ones, an Inserting and Adjacency Attention (IAA) module is designed. This mechanism improves the model's overall performance by dynamically adjusting the importance of different channels. Experimental results demonstrate that the proposed method significantly enhances network efficiency, reduces the total parameter storage space, and improves training efficiency without sacrificing accuracy. Specifically, models incorporating the Cross-Layer Parameter Sharing module achieved a reduction in the number of parameters and model size by 10% to 30% compared to the baseline models.  
power system; substation; ResNet; cross-layer parameter sharing +
Footnote †: journal: applied sciences  
0
Footnote †: journal: applied sciences  
0
Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 1 Introduction
Both, the choice of transformation and the method of aligning the vertical motion network are individual for each area and depend on the number of common points, spatial resolution, data quality, etc. A parametric method was used to align vertical movement networks from leveling data in [11] and a robust method was used to align vertical movement networks from GNSS data in [27]. Depending on the study, tide gauges on the southern Baltic coast [28] or a selected point in central Poland [11] were taken as reference points. The choice of reference point(s) is an authoritative choice; its location has a strong influence on the error distribution in the network. The adopted vertical movement at the reference point only affects the magnitude of relative movements. However, if several points are adopted as a reference and their vertical movements are known, then each point affects the determined relative movements to some extent. Relative vertical movements of the Earth's crust from repeated precision leveling measurements [11], and absolute vertical movements determined from coordinates of permanent GNSS stations of the Polish ASG-EUPOS network [5] were used as data. The area of Poland lies in central Europe, influenced by glacial isostatic adjustment, with a minor influence of tectonic and geological movements, mainly caused by mineral extraction [5]. Such conditions limit the possibility of generalizing the results to seismically active regions. Nevertheless, they provide greater certainty regarding the results obtained. One of the main crustal and lithospheric boundaries in Europe, known as the Teisseyrre-Tornquist Zone--TTZ [29,30]--passes through Poland. It separates the Precambrian crystalline crust of the East European platform and its oldest cover from the crust of the Palaeozoic platform. A very detailed description of this zone can be found in [29]. Relative vertical movements vary in Poland from 0 mm/yr near the Carpathian Mountains to \(-\)3.5 mm/yr in the rest of the area. Point-wise, they are higher, e.g., \(-\)6 mm/yr--caused by the strong impact of the flooded salt mine in the city of Inowroclaw. Relative vertical movements refer to changes in the level of the Baltic Sea, where average changes in sea level are from 2 mm/yr to 3 mm/yr. Ellipsoidal (absolute) movements determined from the PPP measurement technique range from \(-\)3 mm/yr to +3 mm/yr.
## 3 Materials and Methods
### Headspace Solid-Phase Microextraction-Gas-Chromatography-Mass Spectrometry (Headspace-SPME-GC-MS)
#### 3.2.2 Analysis by GC-MS  
An Agilent 6890 GC coupled with a 5973i mass spectrometer (Agilent Technologies, Palo Alto, CA, USA) was used. The GC was equipped with a HP-5 MS capillary column (30 m \(\times\) 250 \(\upmu\)m i.d. \(\times\) 0.25 \(\upmu\)m, Agilent Technologies). Helium was the carrier gas with a constant flow of 1 mL/min to the column. The initial oven temperature was at 40 \({}^{\circ}\)C, holding for 2 min, then raised to 150 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min; and finally raised to 280 \({}^{\circ}\)C at 15 \({}^{\circ}\)C/min, holding for 2 min. The injection port was in splitless mode. The mass detector was operated at 150 \({}^{\circ}\)C in electron impact mode at 70 eV. The ion source temperature was at 230 \({}^{\circ}\)C and the transfer line temperature was at 250 \({}^{\circ}\)C. The chromatograms were recorded by monitoring the total ion currents in the 15-450 mass range. MS was detected with 2 min solvent delay. Analysis of the sample at each condition was repeated 1 times. C6-C23 \(n\)-alkanes were run under the same chromatographic conditions in order to calculate the retention indices (RI) of detected compounds. Compounds were identified by comparing their mass spectra with those contained in the NIST08 database, and confirmed by comparison of the retention times of the separated constituents with those of the authentic samples and by comparison of retention indexes (RIs) of the separated constituents with the RIs reported in the literature.
## 4 Results Analysis
### Effect of Physical Additives on the Test Accuracy of SBS Content  
4.3.1. Experimental Results and Analysis  
Four groups of 4% SBS-modified asphalt with compatibilizers 1 and 3 are tested by the infrared spectrum. The test results are shown in Table 4 after calculating the peak area ratio and substituting them into the standard curve in Figure 4. Considering the adding of additives, the actual SBS content needs quality correction.  
After considering the influence caused by the quality of the compatibilizer, the actual SBS modifier content is shown in Table 4: the content of No. 1 compatibilizer was 2.7%, resulting in an actual SBS modifier content of 3.89%, and the actual errors of the four groups respectively were \(-\)0.05%, \(-\)0.05%, 0.00%, and +0.04%, and all of them are within \(\pm\)0.1%. The content of No. 3 compatibilizer was 3.0%, resulting in the actual SBS modifier content of 3.88%, and the actual errors of the four groups respectively were \(-\)0.10%, +0.10%, +0.09%, and +0.01%, and all of them are within \(\pm\)0.1%. It proves that the addition of compatibilizer has no effect on the content determination accuracy of the SBS modifier when using the peak area ratio A966/(A966 + A1377).
## 3 Experimental Results
### Impact of Speech Rate  
Figure 12 shows the PCC and RMSE of air traffic control speech with various speech rates. Figure 12 demonstrates that the proposed model maintains good performance when the speech rate is medium.  
Figure 11: Prediction results of air traffic control speech database with different SNRs.  
Figure 12: Prediction results of air traffic control speech database with different speech rates.
# A Power Monitor System Cybersecurity Alarm-Tracing Method Based on Knowledge Graph and GCNN
## 3 Results and Discussion
### Validation of the Effectiveness of the PowerCyberSec Knowledge Graph  
First, using the PowerCyberSec alert information segmentation technique based on the Jieba word segmentation library, sentences in the PowerCyberSec alert traceability sample set are split into word forms. Next, based on semantic matching technology, alert entities are extracted and text triples are generated. From 70,107 word-form PowerCyberSec alert messages, 12,914 alert entities were extracted, combined with \"possesses\" and \"belongs to\" relationships to generate 23,930 text triples. Further, a KG is constructed based on PowerCyberSec alert text triples and the Neo4j database. Nodes and connections are created in the Neo4j graph database to build a PowerCyberSec alert KG with 12,914 nodes. Finally, using the spatial knowledge embedding model, a 50-dimensional feature vector is generated for each alert entity. According to Equations (1) and (2), 70,107 adjacency matrices \(A\) and feature matrices \(X\) are constructed, and according to the vectorization method, 70,107 alert label vectors \(Y_{\text{Label}}\) are obtained.  
To verify that the proposed PowerCyberSec alert KG and knowledge representation techniques can achieve precise storage and characterization, this section compares models M0, M5, and M6, with the results shown in Table 2. Among these, M5 and M6 use CNN for traceability, with the main difference being the feature representation of alert information using either TF-idf text vectorization or the proposed KG construction and spatial knowledge embedding model. As shown in Table 2, when using TF-IDF text vectorization to construct input features for a convolutional neural network, it completely misjudges alerts related to routine equipment operations. In contrast, M6, which uses KG construction for input features, can compensate for this deficiency. Even under highly imbalanced data conditions, M6 can maintain traceability for low-sample alerts. Meanwhile, M0, which employs GAT for traceability and strategies for handling imbalanced samples, achieves the highest traceability accuracy. These results validate the effectiveness of the proposed KG construction and spatial knowledge embedding model for accurate storage and representation of alert information.
## 5 GaoFen-3 Test Data Processing Results
### Test Information and Data  
On September 8, 2016, in the Ordos microwave calibration field in Inner Mongolia, an external field calibration experiment was conducted and a polarized ground receiver was installed. For the H and V polarized pulse signals alternately emitted by the GF-3 SAR satellite, the relevant parameters of the test are as follows:  
* PRF = 2792.176270 Hz;
* eqvPRF = 1396.088135 Hz;
* centerLookAngle = 29.360000\({}^{\circ}\);
* azimuth emission pattern \(-\)3 dB beamwidth = 0.188\({}^{\circ}\);
* Fs = 66.66667 MHz;
* Bandwidth = 60 MHz;
* pulsewidth = 24.99 us;
* satVelocity = 7567.397210 m/s;
* groundVelocity = 6738.85526 m/s;
* averageAltitude = 1228.350098 m;
* nearRange = 874,626.096672 m.  
The location of the ground receiver is:  
* Latitude = 39.2411\({}^{\circ}\) N;
* Longitude = 107.4662\({}^{\circ}\) E;
* Altitude = 1320.60 m.  
The antenna pattern parameters of the ground receiver is:  
* receiving pattern \(-\)3 dB beamwidth = 13.6\({}^{\circ}\);
* receiving pattern \(-\)0.5 dB beamwidth = 5.6\({}^{\circ}\).  
The data of the ground receiver is:  
* samplingrate = 300 MHz;
* RangeofSat2 Receiver = 882,300.41 m.  
The H-polarized data recorded by the ground receiver are shown in Figure 7 below. The sampling rate of the receiver is 300 MHz. The recorded number of complete H-polarized transmit pulses is 349 and the length of time is about 0.25 s. It can be seen from the figure that the main lobe portion of the SAR antenna beam is received. The SAR azimuth emission pattern beamwidth and the ground receiver receiving pattern beamwidth data show that the latter is more than 72 times that of the former, so the modulation effect of the receiver pattern can be ignored in the receiver data processing.
## 3 Results
### Volatile Aroma Compound Profiles
#### 3.1.11. Odor Activity Values (OAV)  
Table 2 lists the average odor activity values (OAV) of the volatile aroma compounds found in the investigated EVOOs, calculated as the ratios of their concentrations and odor perception thresholds available in literature. For thirteen compounds average OAV higher than 1 was observed in at least one of the monovarietal EVOOs implying their direct influence on the aroma. The compound with the highest OAV was (Z)-3-hexenal, followed by 1-penten-3-one and (E)-2-hexenal, while other compounds exhibited much lower OAVs. (Z)-3-hexenal was potentially the most important odorant in all the investigated monovarietal EVOOs except I. bjelica and Leccino in which 1-penten-3-one was dominant.
# Big Data Perspective and Challenges in Next Generation Networks
## 3 Data Analytics Perspective on Next Generation Networks
### Big Data in Next Generation Networks  
Given the massive advancement in cellular technology and increase in mobile devices, congestion of mobile networks is not unexpected. The resultant big data poses challenges to be processed as it is significantly different from ordinary data. As mentioned above, in next generation networks, number of devices will increase by a factor of one thousand [49; 50]. It becomes discernible that the size of data in 5G networks will be massive as well as diverse in nature. Consequently, one would need to explore solutions for handling such a big data for optimizing 5G networks. The huge flood of data will create hindrance in fulfilling the core requirements and features of 5G networks. Thus, it is very difficult to efficiently deploy 5G networks without handling big data issues. There are big data analytics techniques which can be used for handling such type of data. We discuss these in the subsequent sections.
## 2 LiCSAR System Architecture
### LiCSAR FrameBatch Processing
#### 2.3.3 FrameBatch Post-Processing  
The final stage consists of the following operations: a gap-filling script checks for non-existing wrapped and unwrapped interferograms and runs parallel jobs to generate InSAR products in standard combinations (4 preceding epochs, based on a set of successfully generated RSLCs) and a geocoding script georeferences InSAR products in the georeferenced Tagged Image File Format (GeoTIFF) in the WGS-84 coordinate system and their previews in Portable Network Graphics (PNG) or Keyhole Markup language Zipped (KMZ) formats. An optional routine stores relevant files in the internal LiCSAR storage and the public LiCSAR folder and cleans the temporary processing directory and LiCSBatch cache.  
We have developed a specific interferogram quality check routine that is applied before the automatic data store. In this routine, we identify artifacts such as SD estimation errors and coregistration issues using a line detection algorithm. We also employ a normalised mean coherence and fraction of number of unwrapped pixels w.r.t. existing set of interferograms, in order to identify corrupted interferograms by a predefined threshold value.
## 4 Conclusions  
This paper has illustrated a UAV-assisted cellular data network model based on an IRS and introduced the SER as the evaluation standard for signal quality. At the same time, we extended the original model to build a model for the optimal deployment position of the UAV. Through the derivation of the formula, we obtained the expression of the SER of the signal received at the UAV end and conducted a simulation analysis by changing various parameters in the original model. After determining the optimal value of each parameter, we also obtained the lowest SER of the signal received at the UAV end when the UAV is directly above the IRS. After that, the expression of the received signal and path loss formula at the UAV were used to derive the expression of the SER of signal received at the USER end in the model of the optimal UAV deployment position. Meanwhile, a simulation test of the optimal UAV deployment position was carried out. Finally, the optimal UAV deployment position was obtained through graphic simulation, determination of the intersection point, function fitting, and an extreme value operation. This not only ensures that the coverage users can get good signals, but also achieves the goal of maximizing coverage.  
In real life, it is a feasible choice to use an IRS deployed in a building cluster and a UAV deployed at a low altitude as the transmission medium of a cellular data network. In terms of cost, this model will not cause excessive cost consumption when applied to real life. In terms of energy efficiency, the simulation results show that the model can achieve better signal transmission. In terms of feasibility, the deployment of an IRS in a building complex and the deployment of a UAV at a low altitude meet the needs of real life. If the practice proves that this model is suitable for real life, deploying a network in a city with numerous and dense buildings will have a very significant effect.  
Future research can also be carried out on the probability of interruption of signal transmission and the received power of the signal terminal in order to optimize and improve the relevant model. As an efficient communication system model, UAV-assisted cellular data relay communication based on an IRS is expected to provide users with more efficient and convenient services.  
Conceptualization of this study, Methodology, Software, Writing--Original draft preparation, T.L.; Funding acquisition, Writing--review, Data curation, M.M.; Investigation, M.X.; Formal Analysis, Y.H.; Resources, Y.F.; Supervision, R.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 62271190.