esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning Reveals Migmatic Fertility of Skarn-Type Tungsten Deposits
## 5 Discussion
### Reliability of Apatite as an Indicator of Magmatic Fertility  
The results indicate that the RF model, based on concentrations of 18 trace elements in apatite, achieved high predictive performance, with F1-scores ranging from 0.92 to 0.93 and accuracies between 0.91 and 0.92. Furthermore, the ROC curves revealed outstanding model performance, achieving an AUC value of 0.98. The confusion matrix demonstrated stable recall rates of 0.91 and 0.92 for barren and fertile categories, respectively, and after threshold adjustment, recall for the fertile class increased to 0.98. These robust results strongly support apatite as a reliable indicator for evaluating magmatic fertility.  
The source of tungsten-mineralized granites is typically derived from metamorphosed sedimentary rocks (e.g., S-type granites) or partially melted crustal material. These magmas generally exhibit relatively low oxygen fugacity (i.e., low oxidation states), creating favorable conditions for tungsten enrichment [60]. Tungsten-mineralized granites often demonstrate high degrees of magmatic differentiation [61; 62], especially prominent in A-type granites characterized by elevated fluorine concentrations and higher zircon saturation temperatures [63]. Geochemically, these granitic bodies display distinctly negative \(\epsilon\)Nd(t) values, suggesting derivation from older crustal components [64]. In tungsten-mineralized granites, apatite commonly exhibits low Eu/Eu\({}^{\star}\) ratios accompanied by elevated Ga concentrations, indicative of strongly reduced magmatic environments [65]. Meanwhile, the tungsten-mineralized granites display steep light-REE enrichment (La/YbN > 100) with pronounced negative Sr anomalies, signatures of plagioclase-suppressed, highly oxidized differentiation that predisposes fluids to precipitate scheelite during late-stage exsolution [6; 66]. Moreover, these granitic intrusions frequently coexist spatially with tin mineralization, particularly in the Nanling Range, where tungsten-mineralized intrusions are extensively distributed in the central and eastern segments and associated with granites originating from metamorphosed sedimentary rocks [67].
# Fuzzy-PID-Based Atmosphere Packaging Gas Distribution System for Fresh Food
###### Abstract
[33] designed an intelligent greenhouse control system which utilized fuzzy logic control of temperature, humidity, and CO\({}_{2}\) concentration, resulting in a 22% reduction in total energy consumption and a 33% reduction in water consumption during testing. [PERSON] [34] designed an industrial robot control system that adjusts the direction and joint angle of an end effector, and the performance was found to be superior than that of the traditional PID controller. In addition, various studies have been conducted using fuzzy PID control technology for other applications. For example, [PERSON] et al. [35] designed an automatic oil flow adjustment system with a fuzzy PID controller, which achieved automatic adjustment of oil output flow and was found to perform better than a cascade PID controller. Given the adaptability of fuzzy PID algorithms to time-varying control systems and mathematical models, this paper proposes an atmosphere packaging gas distribution system for fresh food based on a fuzzy PID control algorithm for specific application scenarios. The system's mathematical model was constructed using the step response method under a given set of conditions, and the gas distribution control process was optimized. The control strategy's effectiveness in improving the performance of the gas distribution monitoring system was verified through comparative simulation experiments of the fuzzy PID control and traditional PID control algorithms. The gas distribution process could be remotely monitored by using mobile devices with a fourth-generation mobile communication technology standard (4G) network.
## 2 Problem Formulation and Motivations  
This section briefly introduces the general sparse recovery framework, upon which we develop our algorithms. As stated before, various applications can be represented by the linear model as Equation (2) shows.  
When considering the practical applications, sparse recovery techniques require low-complexity algorithms just as some kinds of greedy methods which are suitable for real-time implementation. However, the performances of greedy methods are compared unfavorably with optimization based methods (e.g., using \(l_{1}\) norm, reweighted \(l_{1}\) norm, \(l_{p}\) norm, smoothed \(l_{0}\) regularization). Recently, [PERSON] _et al._ have proposed the homotopy DCD method with convex regularizations, including \(l_{1}\) and reweighted \(l_{1}\) norms, which have shown a high recovery performance and relatively low complexity. Moreover, most of the operations in the algorithms are additions, therefore, they are very suited for the hardware implementation of real-time operating systems. Motivated by the idea of homotopy DCD method and the fact that non-convex regularization usually yields a sparser solution than any convex penalty for a given residual energy (see Figure 1 for example), this paper proposes a fast and accurate sparse signal reconstruction by homotopy DCD technique with non-convex regularizations. Three typical non-convex penalty functions [12,41] are considered, _i.e._, the first order rational function penalty, the logarithmic penalty and the \(l_{p}\) penalty, respectively. With the corresponding penalties, we have achieved the derivation of the HR-DCD, Hlog-DCD and H\(l_{p}\)-DCD algorithms in the next section.  
Since **x** is usually distributed continuously in the corresponding space in many applications, the off-grid problem is likely to exist. For example, in the radar imaging field, the reflectivity centers of the scatterers are generally not located at exact on-grid spatial positions illustrated in Figure 2, which means the measurement matrix should cover the basis vectors corresponding to off-grid scatterers. As conventional CS-based methods do not consider the off-grid effect, therefore the related sparse reconstruction techniques suffer unacceptable degradation in image quality. This paper has utilized the grid refinement approach [27] shown in Figure 3 combined with the HR-DCD, Hlog-DCD and H\(l_{p}\)-DCD algorithms to alleviate the impact of off-grid problem (see details in Section 3.2). Experimental results in Section 4 are provided to demonstrate the performance improvement of the proposed algorithms.  
Figure 1: Examples of different penalty functions.
# Therapeutic Applications of Halloysite
## 2 Nanoclay Types  
### Montmorillonite  
Montmorillonite is an abundant phyllosilicate clay material composed of layered silica sheets. Each layer consists of two sheets, octahedral and tetrahedral sheets [11]. The octahedral sheet is aluminum and magnesium bonded with six oxygen and a hydroxyl  
\begin{table}
\begin{tabular}{|c|c|c|c|} \hline
**Clay Type** & **General Formula** & **Structure** & **Therapeutic Applications** \\ \hline
**Bentonite** & Al\({}_{2}\)H\({}_{2}\)Na\({}_{2}\)O\({}_{13}\)Si\({}_{4}\) & Layered & Absorption, detoxification, drug delivery, filler \\  & & & Absorption, biotin additive \\
**Halloysite** & Al\({}_{2}\)Si\({}_{2}\)O\({}_{5}\)(OH)\({}_{4}\) & Nanotube & bioremediation, detoxification, drug delivery, filler, tissue engineering \\  & & & Absorption, drug delivery, filler, tissue engineering \\
**Kaolinite** & Al\({}_{2}\)H\({}_{4}\)O\({}_{5}\)Si\({}_{2}\) & Layered & Absorption, drug delivery, filler, tissue engineering \\  & & & Bioink additive, bioimaging, drug delivery, tissue engineering \\
**Laponite** & H\({}_{12}\)Li\({}_{2}\)Mg\({}_{16}\)Na\({}_{2}\)O\({}_{72}\)Si\({}_{24}\) & Discoidal & drug delivery, tissue engineering \\  & & & Antimicrobial, drug delivery, filler, tissue engineering \\ \hline \end{tabular}
\end{table}
Table 1: Major types of clay nanoparticles.  
Figure 1: Ceramic nanoparticle types and their morphologies.  
group. The tetrahedral sheet is composed of linked silicon-oxygen tetrahedral and bonds with octahedra. It can be modified chemically to form nanocomposites. The loosely packed silicates layers can let water infiltrate the sheets, and the clay can swell. It also has excellent cation exchange capacity, producing nanocomposites from the naturally occurring montmorillonite [11; 12]. Montmorillonite is the main constituent of bentonite clay.  
As a nanoclay, montmorillonite can have particle length and breadth between 1.5 um to 1/10 th of a micron. The pore diameter is small compared to the length of the particle (\(\sim\)1 nm). Refs. [11; 12] Because of its property to hold water and hydrophilic molecules, montmorillonite nanocomposites have been used as a filler material to make bioactive scaffolds. As montmorillonite also enhances the mechanical properties of the scaffold materials, it has been used as an additive to hydrogel or polymer scaffolds [11; 12; 13]. Montmorillonite has been combined with different scaffold materials such as chitosan, methyl methacrylate, gelatin, starch, and polycaprolactone for tissue engineering applications [14; 15; 16]. Montmorillonite nanoclay composite scaffolds have been studied for their applications in bone tissue engineering [15; 16], controlled drug delivery [17], and wound healing [18; 19].
# Carrier Phase Dual One-Way Ranging Method Based on a Frequency Hopping Signal
## 5 Simulation Results of the FH-DOWR System
When the CNR is 40 dB-Hz, the probability of correct integer ambiguity resolution is 89.2% and the accuracy of the carrier phase measurement also decreases. However, it still has a ranging accuracy smaller than the 1% wavelength level, with a maximum ranging error of 0.307 mm and an RMSE better than 0.0679 mm. As the CNR increases, the probability of correct ambiguity resolution reaches 100% at 70 dB-Hz, the maximum measurement accuracy error decreases to 17.5 \(\upmu\)m, and the RMSE is 2.28 \(\upmu\)m.  
However, under frequency division conditions, there is a difference in the hardware time delay between the two frequency channels. The parts that have the greatest impact on the consistency of the channel delay and the variation in the channel group delay are the RF and IF receiving and transmitting units. In the RF channel, all devices used are analog devices; therefore, the RF channels in different devices have different group delay characteristics. However, in actual environments, changes in environmental temperature and signal level can affect the characteristics of the entire channel, ultimately leading to changes in the results of high-precision ranging. Considering that the delay deviation between two channels under frequency division conditions is 1 ps, a ranging error of 0.29 mm occurs. Therefore, how can the consistency of multiple RF channels be ensured under frequency division conditions? For carrier phase measurements in an FH-DOWR system, as the signal switching time increases, the carrier phase ranging performance improves. Figure 15 shows the ranging performance under a CNR of 60 dB-Hz, with a signal switching time of 10 ms-210 ms during the frequency hopping period. Figure 15: Analysis of the carrier phase ranging performance in different hopping periods: (**a**) measurement accuracy with carrier phase measurement fitting; (**b**) measurement accuracy without measurement value fitting; and (**c**) probability of a successful calculation with and without fitting. Even if the switching time increases, fitting is still an effective method for improving the system's ranging accuracy and success rate of ambiguity resolution. An increase in the frequency hopping period will improve the accuracy of the frequency hopping carrier phase ranging. For a CNR = 60 dB-Hz, when the hopping period at each frequency point is increased from 10 ms to 210 ms, the ranging accuracy decreases from 0.0278 mm to approximately 0.00126 mm.
# Post-Earthquake Fire Resistance in Structures: A Review of Current Research and Future Directions
## 5 Methodology
### Structural Analysis
#### 5.2.5 Structural Response Analysis  
The structural response under fire loading is analyzed with emphasis on displacement profiles, fire resistance time, and critical temperature thresholds. Nonlinear geometric effects are incorporated using large-displacement formulations to capture progressive loss of load-bearing capacity under extreme thermal conditions [37; 53; 54].
## 2 Problem Statement
### Considered Factors  
Three factors are ignored when employing the \"first come, first served\" scheduling principle:  
1. [label=(0)]
2. Berth preference: Randomly allocating berths can increase ship operation time, resulting in disrupted berthing plans for other ships, an increased risk of waterway congestion, and reduced port operational efficiency.
3. Seagoing ship inspections: Although the inspection itself does not charge fees, the freight forwarder must move their goods to the inspection area, split them apart, and repack them as required. The shipping company must bear the costs associated with the dock and labor resources as well as the labor utilized during this process, and, if the inspection takes too long, the shipping schedule will be delayed. For the customs, maritime, and border inspection departments, a low inspection rate may pose significant regulatory risks, whereas a high inspection rate may seriously affect customs clearance efficiency.  
Figure 2: Operational procedures for seagoing ships subject to inspection.  
Planning period: The port conducts preliminary scheduling based on the expected arrival time of each ship at the anchorage. Owing to various factors such as weather, manpower, and resources, ships often arrive early or late at the port, resulting in significant discrepancies between the actual and forecast arrival times [15]. The berth utilization rate is an excellent indicator of the level of port services within a certain period, and is often calculated based on a longer planning period. However, if scheduling is based on a shorter period of time, directly adjusting the original plan to accommodate the change in the arrival time of a ship may cause a chain reaction in multiple subsequent planning periods, requiring arriving ships to be rescheduled and causing considerable inconvenience to port work. Simultaneously, short-term scheduling is not conducive to evaluating berth utilization.  
Therefore, this paper developed a ship scheduling optimization model using a seven-day planning period to help the port perform global control, macro-scheduling, and long-term planning. The restrictive conditions for ship scheduling, including berth preference and ship inspection factors, were determined based on the scheduling situation at the port. Next, the waterway and berth collaborative scheduling process was reconstructed and an objective function established to minimize the berth deviation cost and total scheduling time while maximizing berth utilization. Finally, the resulting optimized NSGA-III algorithm was used to solve the mathematical model and obtain an optimal solution set describing the optimal collaborative port waterway and berth scheduling scheme.
# Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management
## 1 Introduction
In 2017, a first attempt was made to combine passive and active satellite data to create regional scale inland excess water maps. Results of IEW detection based on the combined satellite data sources were promising but were still quite error prone [10]. Therefore, the algorithms were improved and fully automated. Here, we present a new workflow that is capable of fully automatic processing of freely available data of Sentinel-1A, Sentinel-1B, Sentinel-2A and Sentinel-2B into weekly inland excess water maps at regional or national scales. We also present validation of the results based on independent high-resolution satellite data and aerial photographs.
## 2 System Model and Proposed Scheme
### Proposal: Blind Adaptive Array with Subcarrier Transmission Power Assignment
#### 2.2.1 Constant Modulus Algorithm (CMA)  
\(\mathbf{W}_{\text{CMA}}(n)\) defined as the \(L\)-by-1 CMA weight vector at the \(n\)-th subcarrier. CMA adapts the array output signal, \(y(n,i)\), in order to yield a constant envelope by minimizing the following cost function:  
\[\begin{array}{rcl}Q(\mathbf{W}_{\text{CMA}}(n))&=&\text{E}\left[||y(n,i)|^{p}- \sigma^{p}|^{q}\right]\\ &=&\text{E}\left[\left||\mathbf{W}_{\text{CMA}}^{H}(n)\mathbf{X}(n,i)\right|^{ p}-\sigma^{p}\right|^{q}\right]\,\end{array} \tag{4}\]  
where \(\text{E}[.]\) and \((.)^{H}\) denote ensemble mean and Hermitian transposition, respectively. \(\sigma\) is a constant envelope value and generally takes the value of one. \(p\) and \(q\) are positive integers, and take a value of one or two, respectively.  
Figure 1: System model.  
Figure 2: Concept of blind adaptive array with subcarrier transmission power assignment (STPA-BAA).  
SIR: signal-to-interference power ratio; CMA: constant modulus algorithm; PI: power inversion.  
A number of algorithms were investigated to minimize Equation (4). We adopt least square CMA (LS-CMA) [15], which is known to exhibit superior convergence performance. CMA weight at the \(m\)-th iteration order, \(\textbf{W}_{\text{CMA}}^{(m)}(n)\), is updated in the following manner:  
\[\begin{array}{ll}\textbf{W}_{\text{CMA}}^{(m+1)}(n)&=\textbf{W}_{\text{ CMA}}^{(m)}(n)-\left[\sum_{i=1}^{S}\textbf{X}(n,i)\textbf{X}^{H}(n,i) \right]^{-1}\sum_{i=1}^{S}\textbf{X}(n,i)\left[y^{*}(n,i)-\delta^{*}(n,i)\right] \\ &=\left[\sum_{i=1}^{S}\textbf{X}(n,i)\textbf{X}^{H}(n,i)\right]^{-1}\sum_{i=1 }^{S}\textbf{X}(n,i)\delta^{*}(n,i),\\ \\ &\delta(n,i)=\frac{\sigma}{|y(n,i)|}y(n,i),\\ \\ \textbf{W}_{\text{CMA}}^{(0)}(n)=\left[\begin{array}{cccc}1&0&\cdots&0 \end{array}\right]^{T},\end{array} \tag{5}\]  
where \(p=1\) and \(q=2\). \(m\) (=0, 1, 2,...), \(S\), and \((.)^{*}\) denote the index of CMA iteration, the index of sample, and complex conjugate, respectively. With the CMA, the desired signal must be captured at the initial array output. It indicates that the SIR\({}_{n}\) of the initial array output must be larger than 0 dB.
## 2 TCN-BiLSTM Predictive Model
### TCN-BiLSTM Prediction Method
Since minority class faults typically have fewer samples and may occur at specific time points, BiLSTM can capture these critical pieces of information through its bidirectional structure, thereby enhancing the detection capability for minority class faults. By stacking multiple layers, the BiLSTM enhances its capacity to learn complex fault patterns. Moreover, for different application scenarios, the model leverages hyperparameter optimization (based on the Optuna framework) to fine-tune critical parameters such as the number of hidden units, learning rate, and regularization, effectively improving the model's robustness and adaptability. Both the TCN and BiLSTM layers in this model are designed as multi-layer structures. By conducting hyperparameter optimization tailored to specific problems, the model not only utilizes the TCN layer to capture multi-scale temporal features but also employs the BiLSTM layer to further extract long-term dependencies from the data. This complementary deep learning framework integrates features across different levels, significantly enhancing the model's generalization and overall adaptability.
# Designing Dual-Function Nanostructures for Water Purification in Sunlight
## 1 Introduction
In addition, the high electrical conductivity of graphene can compensate the low electrical conductivity of LDHs. Furthermore, the common disadvantage of aggregation processes of both graphene and LDHs can be avoided through controlling the combination process between them. In this trend, authors have attempts to explore the ability of LDHs based on zinc cations to be active in the visible light because zinc oxide is one of the most familiar semiconductors in the optical materials. However, the results were not satisfactory because of the weak response of the charged sheets of LDHs to visible light [15]. [PERSON] et al. [14] studied the photocatalytic properties of Zn-Al LDHs before and after combining with carboxyl graphene (CG) in presence of visible light. They reported that the photocatalytic activity of both pure Zn-Al LDHs and the CG/LDHs nanohybrid was not satisfactory probably because of the low photocatalytic activity of Zn-Al LDHs. However, they indicated that the CG/LDHs after calcination showed higher photocatalytic activity for decomposition of orange G dye because LDHs converted to oxides form and lost its nanolayered structures. [PERSON] et al. [15] indicated that a disappearance of 98% of phenol and a total photodegradation of p-cresol were obtained after 6 h and 4 h of UV-light illumination in presence of the calcined products of Zn-Al-Fe LDHs. In the same trend, 99% degradation of Orange G dye was achieved within 100 min. of solar radiation using the oxides form of Ni-Zn-Al LDHs [16]. By using UV-light, [PERSON] et al. [17] reported that Zn-Al LDHs were active for photocatalytic degradation of methyl orange and fast green dyes. Recently, [PERSON] et al. [18] have used graphene oxide to enhance the visible-light photocatalytic activity of the oxides form of Zn-Al LDHs. They reported that 3.3 wt.% of graphene oxide increased the photocatalytic activity of the mixed metal oxides of Zn-Al LDHs to be 90.2% of methyl orange removal. According to the previous results, the researchers focused their studies on using the pure Zn-Al LDHs or the combined with other species as precursors for metal oxides to build effective photocatalysts avoiding their nanolayered structures. In the current study, nanolayered structures of Zn-Al LDHswere prepared and doped by graphene oxide to build effective nanostructures in sunlight. To maximize the efficiency of these nanostructures, different percentages of graphene oxides have been used for improving surface and optical properties of Zn-Al LDHs. Surface texture of the prepared nanostructures was measured and compared with the pure Zn-Al LDHs. In addition, the optical properties of the prepared materials were determined by UV-Vis spectroscopy. In addition, the photocatalytic activity of the prepared nanostructures was investigated through degradation of green dyes using sunlight. Furthermore, kinetic study was investigated for the prepared nanocomposites and compared with the pure Zn-Al LDHs.