esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The Jiaojia gold deposit in the Jiaodong Peninsula, located in the northwestern part of the Jiaodong gold province in eastern China, has a gold reserve of over 300 t. Gold mineralization in Jiaojia deposit occurred in three stages: (1) The Pyrite-Quartz-Sericite Stage (Stage I) developed primary minerals that included quartz, sericite, and a small amount of anhedral pyrite, appearing as disseminations within milky quartz and foliated sericite. (2) The Quartz-Pyrite Stage (Stage II) developed quartz that appears smoky gray and pyrite that appears with a euhedral cubic morphology, with crystal faces oriented in a longitudinal pattern. Native gold occurs as fracture filling in pyrite. (3) The Quartz-Polymetallic Sulfides Stage (Stage III) developed polymetallic sulfides, including pyrite, chalcopyrite, galena, sphalerite, and magnetite. Native gold filled the pyrite fractures and was enclosed within the pyrite. (4) The Quartz-Carbonate Stage (Stage IV) developed the main minerals of quartz and carbonate, with scattered occurrences of pyrite. In situ geochemical analysis of pyrite, the main gold-carrying mineral from mineralization Stages I to III in the Jiaojia gold deposit, was conducted, including major element, trace element, and sulfur isotope analyses. The \(\delta^{34}\)S values of Jiaojia pyrite range from 4.5 to 8.0%. Pyrite in Stage I (Py I) has \(\delta^{34}\)S values ranging from 4.5 to 7.4%, with an average of 6.4%. Pyrite in the Stage II (Py II) has \(\delta^{34}\)S values ranging from 5.9 to 8.0%, with an average of 6.8%. Pyrite in Stage III (Py III) has \(\delta^{34}\)S values ranging from 6.4 to 7.9%, with an average of 7.4%. Combined with the C-D-O-He isotopes, the ore-forming fluids of the Jiaojia gold deposit likely originated from subducted oceanic plate-related metasomatized mantle. The Co/Ni ratios of Jiaojia pyrite range from 0.50 to 1.47 in Stage I, 0.27 to 1.69 in Stage II, and 0.58 to 295 in Stage III. The Cu/Au ratios in the Jiaojia pyrite in all mineralization stages were >1. These geochemical features imply that the ore-forming fluids of the Jiaojia gold deposit were in a medium- to low-temperature reducing environment, with temperatures gradually decreasing from ore Stages I to III. The increase in Co and As in the pyrite of Stage III implies that gold precipitation resulted from fluid immiscibility caused by a decrease in pressure and temperature and an increase in the oxygen fugacity of the ore-forming fluid.  
**Keywords:** Jiaojia gold deposit; geochemistry of pyrite; source of ore-forming fluids; gold precipitation
# Energy Communities Design Optimization in the Italian Framework
## 3 Case Study
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Typical Day 1**} & \multicolumn{2}{c}{**Typical Day 2**} & \multicolumn{2}{c}{**Typical Day 3**} & \multicolumn{2}{c}{**Typical Day 4**} & \multicolumn{2}{c}{**Typical Day 5**} \\  & **[N = 117]** & **[N = 124]** & **[N = 118]** & **[N = 3]** & **[N = 3]** \\ \cline{2-10}  & **Peak** & **Total** & **Peak** & **Total** & **Peak** & **Total** & **Peak** & **Total** & **Peak** \\  & **[kW]** & **[kWh]** & **[kW]** & **[kWh]** & **[kW]** & **[kW]** & **[kW]** & **[kW]** & **[kW]** & **[kWh]** & **[kW]** \\ \hline
**Old couple 1** & 0.0 & 0.0 & 0.0 & 0.0 & 0.95 & 9.2 & 0.0 & 0.0 & 4.00 & 39.0 \\
**Old couple 2** & 0.0 & 0.0 & 0.0 & 0.0 & 0.95 & 9.2 & 0.0 & 0.0 & 4.00 & 39.0 \\
**Young couple 1** & 0.0 & 0.0 & 0.0 & 0.0 & 1.05 & 9.2 & 0.0 & 0.0 & 4.00 & 35.2 \\
**Young couple 2** & 0.0 & 0.0 & 0.0 & 0.0 & 1.04 & 9.2 & 0.0 & 0.0 & 3.97 & 36.2 \\
**Family 1** & 0.0 & 0.0 & 0.0 & 0.0 & 1.48 & 13.8 & 0.0 & 0.0 & 6.00 & 56.1 \\
**Family 2** & 0.0 & 0.0 & 0.0 & 0.0 & 1.36 & 13.8 & 0.0 & 0.0 & 5.50 & 56.1 \\
**Office 1** & 0.0 & 0.0 & 0.0 & 0.0 & 1.61 & 11.1 & 0.0 & 0.0 & 6.43 & 44.6 \\
**Office 2** & 0.0 & 0.0 & 0.0 & 0.0 & 1.29 & 8.9 & 0.0 & 0.0 & 5.14 & 35.6 \\
**Office 3** & 0.0 & 0.0 & 0.0 & 0.0 & 1.93 & 13.4 & 0.0 & 0.0 & 7.71 & 53.5 \\
**Conditioninium** & 0.0 & 0.0 & 0.0 & 0.0 & 0.00 & 0.0 & 0.0 & 0.0 & 0.00 & 0.0 \\ \hline
**Yearly total** & & 0 & & 0 & & & & & & &Two electrification scenarios have been considered:  
A--the condominium can install up to 100 m\({}^{2}\) of PV panels on the roof; the cooling demand is satisfied by individual air conditioning units and the heating demand is satisfied by a centralized system fed by a natural gas boiler;  
B--like A, but the natural gas boiler can be substituted by an electric heat pump (HP), coupled with thermal energy storage (TES). For each electrification case, the energy community configuration (EC)--which in this case, according to the EU nomenclature, it should be named group of jointly acting renewable self-consumers--is compared to the current Italian economic support scheme for PV energy production named Scambio Sul Posto (SSP): such an option allows the prosumer to inject the energy surplus in the grid, and to have it back (when it is required) at given selling/purchase prices [22]. Within the EC configuration, there is the possibility to share the PV electricity production between all residents, through the distribution grid, hence using the so-called virtual model [23]. The quota of shared electricity is incentivized with the feed-in-tariff foreseen by ARERA resolution 318/2020 [6] and GSE rules [23], whereas the amount that is injected into the grid and not shared is remunerated at the hourly zonal price. Within the SSP configuration, the condominium acts as a group of single users who interact with the public grid independently. The combination of the two electrification scenarios (A and B) and the two configurations (SSP and EC) gives rise to four cases: A-SSP, A-EC, B-SSP, and B-EC.
## 3 The High-Rate Sealed MRPC  
### Structural Design  
As shown in Figure 4, each gas stack of a sealed MRPC will form a sealed chamber with gas inlets. In operation, the working gas flows into the sealed chamber through one of the inlets and out by the other. To obtain a higher rate capability, the new sealed prototype should be assembled with low-resistive glass. Based on the same idea of enclosure as before, the sealing frame has been designed and produced by 3D Printing [21]. However, the low-resistive glass plate, with 0.7 mm thickness, is weaker in mechanical strength compared with the float glass; thus, it cannot be used as the panel for the sealed chamber. For this reason, the PMMA plates of 1 mm in thickness are used instead. On the inner surface of each panel, a square-shaped groove is reserved for the placement of HV conductor, as shown in Figure 4. The prototype has the same geometry as CBM-MRPC2 mentioned in Table 1, but the total gas volume is as low as \(\sim\)170 mL. Thus, faster gas exchange can be expected. In former tests on the sealed prototype with float glass [12], the HV application could be safely started within 2 h after we purged the chamber with a 20 mL/min gas flow. This favorable feature has been maintained on the high-rate sealed MRPC prototype.
## 3 Results and Discussion
### Multivariate Analysis  
Before performing any chemometric analysis, the raw spectra needed to be corrected because they contained fluorescence, as in Figure 2a. For this purpose, the spectra underwent a baseline correction with a seventh-order polynomial fitting [41; 42], resulting in the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Assignment 1** & **Literature** & **Fructose** & **Glucose** & **Sucrose** & **Cherimoya** & **Soursop** & **Pineapple** \\ \hline \(\beta\) (CH\({}_{2}\)) & 1460 [37] & 1473.93 & 1471.74 & 1472.81 & 1464.22 & 1467.44 & 1464.22 \\ \hline \(\rho\) (CH\({}_{2}\)) & 1346 [37] & 1356.80 & 1359.73 & 1355.73 & 1355.73 & 1352.50 & 1353.58 \\ \hline \(\
u\) (CC), \(\
u\) (CO), \(\beta\) (COH) & 1142 [37] & 1133.37 & 1133.37 & 1131.22 & 1134.44 & 1133.37 \\ \hline \(\
u\) (CC), \(\
u\) (CO) & 1074 [37] & 1080.73 & 1081.81 & 1080.73 & 1078.58 & 1085.03 & 1085.03 \\ \hline \(\
u\) (CH) & 924 [38] & 932.49 & 929.27 & 933.57 & 928.20 & 928.20 & 918.53 \\ \hline \(\
u\) (CH) & 867 [38] & 859.45 & 853.00 & 860.52 & 855.15 & 854.08 & 851.93 \\ \hline \(\beta\) (CCO) & 596 [39] & 594.12 & & 595.20 & 601.64 & & 595.12 \\ \hline \(\beta\) (CCO), \(\beta\) (CCC) & 527 [39] & 536.12 & & 537.19 & 536.12 & 535.04 & 535.04 \\ \hline \(\beta\) (CCC) & 465 [39] & 456.63 & 455.55 & 457.70 & 453.40 & 455.55 & 455.55 \\ \hline \(\beta\) (CCO),\(\beta\) (CCO),\(\beta\) (OCO) & 424 [38] & 416.88 & 415.81 & 416.88 & 417.95 & \\ \hline \end{tabular} \({}^{1}\)\(\
u\)–stretching, \(\beta\)–in-plane bending.  
\end{table}
Table 1: Assignments of Raman peaks for sugar standards and cherimoya, soursop, and pineapple.  
Figure 1: Raman spectra of glucose, fructose, and sucrose in cherimoya, soursop, and pineapple.  
spectra of Figure 2b, where the characteristic peaks of the, soursop, and pineapple chemical composition are distinguished. Raw spectra are available in the Supplementary Data File.
# An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication
## 1 Introduction
In summary, we demonstrate that the proposed autoencoder-based SC model can be used in task-oriented M2M communication to convey the semantics of the image under a limited system of resources. The rest of the paper is organized as follows: Section 2 provides a brief review of the relevant academic literature, covering the theoretical aspects, the use of ML techniques in SC, and the application of autoencoders in image-processing tasks, as well as existing SC-based architectures and their limitations. Section 3 introduces our proposed autoencoder-based SC system, followed by an analysis of the results in Section 4. Finally, conclusions and future work are discussed in Section 5. Figure 1: Semantic communication system.
## 3 Results  
### Handling of the Robot Arm and Navigation Assisted Orthognathic Surgical System  
To assess the handling and maxillary repositioning accuracy of the robot arm and navigation assisted orthognathic surgical system, 10 experiments were conducted using full skull phantom models (Sawbones; Pacific Research Laboratories Inc., Vashon, WA, USA). The surgical plans implemented in this study included the commonly used surgical plans such as antero-posterior reposition, midline correction, canting correction, and impaction of posterior maxilla (Table 1). Since the accuracy was evaluated after a series of procedures including Le Fort I osteotomy, recognition and removal of bone interference and repositioning of maxillary bone segment, the accuracy evaluation was performed once for each experiment. For all experiments, the end-effector could reach its planned position based on the spatial relationship between the maxilla and the end-effector. Using the end-effector's sliding movement, the maxillary segment-split complex was joined to the end-effector of the robot arm even though bone interference was present between the maxillary segment and the upper maxilla. With the up and down sliding movement of the maxillary segment, the bone interference could be identified and removed until the end-effector's slider reached its initial position. After complete removal of the bone interference, it was confirmed using the navigation system that the maxillary segment was in its target position.  
Figure 5: Identification (**A**) and removal (**B**) of the bony interference. (**C**) Final position of the maxilla according to the surgical plan.
# The Sun and Space Weather
## 5 Solar Connection to Geomagnetic Storms
### Coronal Holes and Geomagnetic Storms  
SIRs often act like CMEs in their Earth impact, causing geomagnetic storms of intensities up to \(\sim\)150 nT [115; 140; 141]. Figure 14 shows a low-latitude coronal hole that resulted in a HSS with a speed of \(\sim\)750 km/s. The HSS caused a CIR in which the density attained a peak value of \(\sim\)30 cm\({}^{-3}\). The Bz component was relatively large (\(-\)20 nT) and resulted in a geomagnetic storm with Dst = \(-\)119 nT. Investigating the geoeffectiveness of 866 SIRs during 1995-2016 [141], it was found that about half of them (52%) caused some level of geomagnetic storms (Dst \(\leq-\)30 nT). The number of SIRs causing geomagnetic storms rapidly decreases with storm intensity: minor (\(-\)50 nT \(<\) Dst \(\leq-\)30 nT), moderate (\(-\)100 nT \(<\) Dst \(\leq-\)50 nT), and intense storms (Dst \(<-\)100 nT) are caused by 240 (28%), 187 (22%), and 26 (3%), respectively. Although weak, the SIR storms occur more frequently than the ICME storms and hence are very important for space weather (e.g., [142]).  
Recent investigations have shown that the space weather due to CIRs are milder in SC 24 [143; 144; 145; 146]. [PERSON] et al. [144] reported that the number of intense geomagnetic storms caused by CIRs has dropped by 75% in SC 24. [PERSON] et al. [146] report that stream SIR/HSSs are 20-40% less geoeffective during cycle 24 than during SC23. The speed and magnetic field strength in cycle 24 are smaller than the corresponding values in cycle 23 and hence the weaker geomagnetic storms.
# Unsupervised Learning-Based Spectrum Sensing Algorithm with Defending Adversarial Attacks
## 6 Conclusions  
The spectrum sensing algorithms based on deep learning have significantly improved the spectrum sensing performance. However, due to the vulnerability of neural networks, even small primary user adversarial attack perturbations can lead to a sharp decline in algorithm performance. To improve the robustness of the spectrum sensing model for FGSM and PGD attacks, the USDAA algorithm was proposed. In the unsupervised pre-training stage, contrast loss and reconstruction loss are used to extract features from unlabeled samples to obtain the feature extractor. Then, the USDAA defense model is obtained by fine-tuning the feature extractor and classification layer using a small number of labeled samples. Experimental results demonstrate that the USDAA algorithm outperforms the DAED algorithm in terms of defense effectiveness and robustness to attack perturbations. However, PGD and FGSM are both gradient-based attack algorithms, but not all attacks depend on gradients. Therefore, a way to improve the defense capability of spectrum sensing algorithms for non-gradient attacks needs further research.  
Figure 8: Robustness of PGD defense model to attack perturbations.  
**Author Contributions:** Conceptualization, X.L.; methodology, X.L.; software, X.L. and S.D.; validation, Z.Z. and S.D.; formal analysis, Z.Z.; investigation, S.D.; resources, Z.Z.; writing original draft preparation, X.L.; writing review and editing, X.L. and Z.Z.; visualization, S.D.; supervision, Z.Z.; project administration, Z.Z.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Research on Intelligent Spread Spectrum Anti-interference Technology in Complex Electromagnetic Environment, U19B2016 National Natural Science Foundation of China.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding authors upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Three-Way Alignment Improves Multiple Sequence Alignment of Highly Diverged Sequences
## 3 Results
### Accuracy of the Guide Tree Affects the Accuracy of the Final Tree from MSA  
We evaluated the hypothesis that the quality of guide trees directly influences the phylogenetic accuracy by directly examining the association in RFd between the guide tree and the final phylogenetic reconstruction from PhyML. For the 8-taxa tree, we combined results from two simulations (symmetric and asymmetric trees) and the two types of guide trees (the L-INS-i and three-way alignment approaches), so that there are 200 guide trees and 200 PhyML trees from the resulting MSA. There is a strong association in RFd between the guide tree and the PhyML-reconstructed final tree (Figure 3). When the guide tree has an identical topology as the true tree (RFd = 0 between the two), the resulting PhyML-reconstructed tree also tend to have the topology of the true tree; when the guide tree deviates much from the true tree, so does the resulting PhyML-reconstructed tree (Figure 3).  
We have done the same for 16-taxa trees (Figure 4), including the fast but inaccurate FFT-NS-1 and FFT-NS-2 options in MAFFT, in addition to the L-INS-i and three-way alignment approaches. For each of these approaches, we combined the results from four simulations (the symmetric and asymmetric trees and the half-symmetric and half-asymmetric trees). Thus, each sub-figure in Figure 4 includes 200 guide trees and 200 PhyML trees. It is clear that the two fast and inaccurate options (that generate guide trees from six-tuple similarities) produced poor guide trees (large RFd values), as well as the final PhyML trees from the resulting MSA (Figure 4A,B) relative to the L-INS-i approach that generated the guide tree from local pairwise alignment (Figure 4C) or to the three-way alignment approach (Figure 4D). However, for all the four approaches, the guide tree quality strongly affects the accuracy of the final PhyML tree (Figure 4). The relationship between the guide tree RFd and PhyML tree RFd are all highly significant (\(p<0.0001\)).  
Figure 3: Relationship between RFd of a guide tree and RFd of the corresponding PhyML tree from the resulting MSA, based on the 8-taxa symmetric and asymmetric trees. A bubble plot was used because many points overlap each other. The relationship is highly significant (\(n=200\), \(r=0.83143\), \(p<0.0001\)).
## 5 Discussion
### Discussion Research Objective One: Design of Open-Text Items for Assessing \"Selecting dLMs\"
Again, this could be caused by factors such as the university curriculum and coursework [14; 74] or voluntary participation [21; 79]. The latter is supported by the high percentage of the category of \"no reasoning\" (33% in Study 2, see Figure 3). However, it is noteworthy that there is a downward trend in the proportions of \"no reasoning\" and \"generic reasoning\", paralleled by a rise in TCK-x and TPK-x reasoning, with the academic progression of participants (see Figure 4). Participants in semesters 7 and higher use a higher proportion of TCK-x than TPK-x reasoning, compared to pre-service teachers in semesters 1-2. This may be an outcome of their university education, which prepared them to be more cognizant of the implications of dLMs for their teaching subject, mathematics. The fact that TPACK reasoning, requiring the evaluation of dLMs from a learning content (TCK) and pedagogical (TPK) perspective, remained relatively flat (see Figures 3 and 4) aligns with the findings by [PERSON] and [PERSON] [80]. They stated that TPACK does not develop spontaneously. Especially for pre-service teachers, it requires reinforcement and training to evaluate dLMs from both the learning content (TCK) and the pedagogical (TPK) perspective, thus the TPACK perspective, as well as considering situational and infrastructure factors. However, one must be careful with these interpretations of the results, due to the composition of the sample and the reasoning possible with the specific dLM1 (see Appendix A.1). Also, the wording of the items could have been the cause for the low proportion of TPACK reasoning, as no particular type of reasoning, i.e., pedagogical (TPK) or learning content (TCK), or a minimum number of reasons, was specified. Additionally, it is worth noting that none of the participants cited the unavailability of digital technology required for using dLMs in classrooms as part of their reasoning, as has been done in other studies. This could be because the wording of the items implied it, or due to the participants' understanding of the level of available infrastructure in education in the local context. To summarize, regarding research questions 2.1-2.3 and the overall research aim, we can conclude the following for the new four-item instrument for assessing \"selecting dLM\". (1) The instrument enables the differentiation of different levels of \"selecting dLMs\". (2) The relationship between content knowledge (items 1-3) and sound reasoning (item 4) is not as strong as suggested by the literature. (3) The analytics of the reasoning (items 4) enables the identification of areas in which educational institutions are effective and ineffective in their teaching regarding the facet of \"selecting dLMs\".
## 2 Materials and Methods
### DNA Sequencing and Analysis  
PCR products were prepared for sequencing using ExoSAP-IT (USBiochemicals, Cleveland, OH, USA). The nucleotide sequences of the PCR products were determined at the Oklahoma Medical Research Foundation (OMRF) in Oklahoma City, Oklahoma. Sequencer (Windows version 4.2; Gene Codes Corp., Ann Arbor, MI, USA) was used to analyze the chromatograms and produce a consensus sequence. The sequence of the 165 rRNA gene and nitrate reduction/denitrification functional genes were compared to the GenBank database using BLASTN searches (Basic Local Alignment Search Tool) [53]. The taxonomic assignation of the 165 rRNA gene sequence was determined by the Naive Bayesian rRNA Classifier of the Ribosomal Database Project II (RDP) [54]. The 165 rRNA sequences were deposited in GenBank under accession numbers JQ917765-JQ917839. Sequences confirmed as genes of nitrate reduction/denitrification were deposited in GenBank under accession numbers OR416960-OR416973.  
165 rRNA gene sequences of the 75 strains were clustered as operational taxonomic units (OTUs) at an overlap identity cut-off of 98% by MOTHUR software ver1.23 [55]. Unique sequences were aligned against the SILVA reference alignment database [56] using the NAST-aligner [57] and subsequently screened for chimeras using UChime [58]. A distance matrix was generated and used to cluster sequences into OTUs at 98% similarity.  
A representative sequence from each OTU was assigned a taxonomic classification based on the naive Bayesian classifier [54]. The representative OTUs, together with closely related sequences from the GenBank database, were aligned by Clustal-X software version 1.81 [59]. As implemented in CLUSTALX, the neighbor-joining method of [PERSON] (1987) [60] was used to construct the phylogenetic tree among the aligned sequences. Distances were calculated as % divergence between all pairs of sequences from the multiple alignments, alignment positions with gaps were excluded from the analysis, and the Kimura two-parameter distance correction for multiple substitutions was applied [61]. The support for the tree branches was estimated from 1000 bootstrap replicates [62]. Only bootstrap values greater than 700 are shown on the phylogenetic trees.