esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The goal treatment of underground metal mines is an important link in mining, and it is particularly important to master the laws of overlying rock strata and surface movement of goaf. In this paper, Persistent Scaterer Interferometric Synthetic Aperture Radar (PS-InSAR) technology is used to monitor the surface subsidence of the Taibao lead-zinc mine, and the surface subsidence laws of goaf-closure, partial-filling, and full-filling treatments are analyzed by the time-series method. The findings indicate that the surface subsidence of the closed goaf is solely governed by the pillars, with the quality of these pillars playing a pivotal role in controlling such subsidence. Factors like stope span also influence the surface subsidence of partially filled goaf. Prior to compaction, it is primarily the pillars that control surface subsidence; however, after compaction, filling and pillars jointly regulate this phenomenon. Notably, in filled goaf, the quality of both roof and pillars significantly impacts surface subsidence. Before compaction occurs, control over surface subsidence is not evident, yet post-compaction, the filling is effective and tends to stabilize this process. The research findings are significant in enhancing goaf's treatment efficacy, mitigating surface damage and minimizing ecological environmental impact.  
PS-InSAR; underground mine; mine goaf management; overlying rock layers; surface subsidence +
Footnote †: journal: Applied sciences
## 5 Conclusions  
A highly linear and low noise receiver front-end is proposed for dual antenna systems (for Full duplex and FDD) that is based on an active self-interference canceler with a 5-bit resolution to implement an active programmable vector modulator. To meet the FD requirements, the SI cancellation  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **This Work** & [18]b **JSSC \({}^{\circ}\)14** & [20]b **ISSCC \({}^{\circ}\)15** & [21]b **ISSCC \({}^{\circ}\)15** & [12]b **ESSCIRC \({}^{\circ}\)15** \\ \hline \hline
**FDD/FD** & **FDD** & **FDD** & **FDD/FD** & **FD** & **FD** \\ \hline \multirow{2}{*}{**Technology**} & 40 nm & 65 nm & 65 nm & 65 nm & 40 nm \\  & CMOS & CMOS & CMOS & CMOS & CMOS \\ \hline
**Frequency** & 1.5-2.5 GHz & 0.5-1.5 GHz & 0.8-1.4 GHz & 0.15-3.5 GHz & 1–3 GHz \\ \hline
**NF w/o canc** & 3 dB & 4.2-5.6 dB & 4.8-5.8 dB & 6.3 dB & 4.8-5.8 dB \\ \hline
**NF w/canc** & 3.9-4.6 dB & 5-6.4 dB & 5.3/7 dB & 10.3–12.3 dB & 5.3/7 dB \\ \hline
**IP3 RX** & 14 dBm & 12 dBm & 17/\(-\)22 dBm & 16.2 dBm \({}^{\text{a}}\) & 18 dBm \\ \hline
**Eff. IIP3** & 35 dBm & 33 dBm & 27/2 dBm & 19 dBm \({}^{\text{a}}\) & N/A \\ \hline
**SI** & \textgreater{}30 dB & 30 dB & 20 dB & 27 dB & 30 dB \\
**Cancellation** & /250 \({}^{\circ}\)-10 \({}^{\text{d}}\) MHz & & /25 MHz & & /4 MHz \\ \hline
**Max TX Leak.** & \(-4\) dBm & \(+2\) dBm & \(-8\) dBm & 1.5 dBm & \(-20\) dBm \\ \hline \multirow{2}{*}{**Power**} & 14 mW RX & 83 mW RX 72 & 69 mW RX & 23-56 mW & 11 mW RX \\  & 11 mW conc & mW conc & 91 mW/path & & 0.6 mW/Cac \\ \hline \multicolumn{5}{c}{\({}^{\text{a}}\) In-band IIP3; \({}^{\text{b}}\) measured results; \({}^{\text{c}}\) without delay between transmitter (TX)/RX; \({}^{\text{d}}\) with 2 ns group delay.} \\ \end{tabular}
\end{table}
Table 1: Performance comparison.  
Figure 18: Receiver P1 dB compression with and without the canceler: (**a**) In-band compression at 11 MHz; (**b**) Out of band compression at 100 MHz as function of input power.  
can be improved in the digital domain [10]. The proposed canceler strongly attenuates the TX leakage of up to \(-4\) dBm of power at the input of the LNA, with benefits for the entire receiver in terms of IIP3, IIP2 and reciprocal mixing. A cross-modulation mechanism of the canceler was identified and strongly suppressed thanks to resistive feedback. The effective OOB IIP3 of the receiver was improved from 14 dBm with the canceler disabled to 35 dBm with the canceler enabled. Compared with previous implementations, lower power, lower noise, and higher IIP3 with respect to self-interference was achieved.  
[PERSON] is the main author and responsible for writing the paper. [PERSON] was responsible for the review and supervising of the paper.  
The authors declare no conflict of interest.
# Thrust and Pressure Control in a Solid Propulsion System via Reinforcement Learning
## 3 Simulation and Experimental Results
### Performance of the Opening Closed-Loop Age  
The control effectiveness of the nozzle opening closed-loop agent was tested by recording displacement tracking, as shown in Figure 11. The closed-loop control showed an error of less than 1% at the step stable position and demonstrated excellent response time and anti-overshoot characteristics.  
The adjustment time, amplitude, and phase lag under different valve opening commands were tested, as shown in Tables 3 and 4.  
\begin{table}
\begin{tabular}{c c} \hline
**Command Type** & **Adjustment Time (t\({}_{90}\))/ms** \\ \hline
0–1 & 37 \\
0–0.75 & 29 \\
0–0.5 & 22 \\
0–0.25 & 22 \\
1–0 & 37 \\
0.75–0 & 29 \\
0.5–0 & 26 \\
0.25–0 & 25 \\ \hline \end{tabular}
\end{table}
Table 3: Control performance for step valve opening commands.  
Figure 10: Control performance by thrust distribution agent: (**a**) \(y\)-axis, (**b**) \(z\)-axis, (**c**) pitch-axis, (**d**) roll-axis, (**e**) yaw-axis, and (**f**) influence on pressure.  
In step curve tracking, the reinforcement learning algorithm controlled overshoot effectively, keeping it below 1% in all scenarios. The full-stroke step response time was \(\leq\)40 ms. For continuous curve tracking, the full-stroke 20 Hz amplitude attenuation was \(\leq\)40%, with a phase lag of 144\({}^{\circ}\).
# Linguistic Interval-Valued Spherical Fuzzy Soft Set and Its Application in Decision Making
## 4 Application of Linguistic Interval-Valued Spherical Fuzzy Soft Set
### Multi-Attribute Decision Making  
**Definition 18**: _For a linguistic interval-valued spherical fuzzy soft set \((\bar{F},E)\), \(U=\{x_{1},x_{2},\ldots,x_{n}\}\), \(E=\{e_{1},e_{2},\ldots,e_{m}\}\), \(\overline{S}=\{s_{a}|s_{0}\leq s_{a}\leq s_{h},\alpha\in[0,h]\}\), \(\omega\)\(=\{\omega_{1},\omega_{2},\ldots,\omega_{m}\}\) contains the weighted values of every parameter \(e_{j}\). \(\forall e_{j}\in E\), \(\bar{F}_{x_{i}}(e_{j})=([s_{a_{j}},s_{b_{j}}],[s_{c_{j}},s_{d_{j}}],[s_{e_{j}}, s_{o_{j}}])\), \(i\in[1,n]\), \(j\in[1,m]\), which represents the membership degree of object \(x_{i}\) for parameter \(e_{j}\). The linguistic weighted choice value \((\omega c_{i})\) for each object \(x_{i}\) is defined as_  
\[\begin{array}{l}\omega c_{i}=\left[\omega c_{i}^{\mathrm{L}},\omega c_{i}^{ \mathrm{L}}\right]\\ =[\sum\limits_{j=1}^{m}\omega_{j}\left(\sqrt{\frac{h^{2}+\left(e_{j}\right)^{2 }-\left(e_{j}\right)^{2}}{3}}\right),\sum\limits_{i=1}^{m}\omega_{j}\left( \sqrt{\frac{h^{2}+\left(b_{j}\right)^{2}-\left(f_{j}\right)^{2}}{3}}\right) \right]\end{array} \tag{19}\]  
_where \(\omega c_{i}^{\mathrm{L}}\) is the lower linguistic weighted choice value for \(x_{i}\) and \(\omega c_{i}^{\mathrm{L}}\) is the upper linguistic weighted choice value for \(x_{i}\)._  
**Definition 19**: _For a linguistic interval-valued spherical fuzzy soft set \((\bar{F},E)\), \(U=\{x_{1},x_{2},\ldots,x_{n}\}\), \(E=\{e_{1},e_{2},\ldots,e_{m}\}\), \(\overline{S}=\{s_{a}|s_{0}\leq s_{a}\leq s_{h},\alpha\in[0,h]\}\). \(\omega c_{i}=\left[\omega c_{i}^{\mathrm{L}},\omega c_{i}^{\mathrm{L}}\right]\) is the linguistic weighted choice value for \(x_{i}\). The linguistic weighted overall choice value \(\omega c_{i}^{\mathrm{overall}}\) for \(x_{i}\) is defined as_  
\[\omega c_{i}^{\mathrm{overall}}=\omega c_{i}^{\mathrm{L}}+\omega c_{i}^{ \mathrm{L}} \tag{20}\]  
That is to say, \(\omega c_{i}^{\mathrm{overall}}\), as the linguistic weighted overall choice value for \(x_{i}\) is the sum of the lower linguistic weighted choice value and the upper linguistic weighted choice value for \(x_{i}\).  
Based on the above given definitions, we describe our proposed MADM Algorithm 1 as follows.  
**Example 2**.: _We use \((\tilde{F},E)\), \(U=\{h_{1},h_{2},h_{3},h_{4}\}\), \(E=\{e_{1},e_{2},e_{3}\}\) shown in Table 1 and set \(\omega=\{\omega_{1},\omega_{2},\omega_{3}\}=\{1/3,\,1/3,1/3\}\) to demonstrate how Algorithm 1 chooses the best teacher considering three parameters. For each \(x_{i}\in U\), the algorithm determines the linguistic weighted choice value using Formula (19), the linguistic weighted overall choice value using Formula (20). It then returns \(x_{k}=\Big{\{}x_{k}\in U\big{|}max_{x_{k}\in U}\Big{\{}\omega c_{i}^{overall} \Big{\}},\ 1\leq k\leq n\Big{\}}\). Table 2 shows the results of Algorithm 1 based on the above settings. The algorithm returns \(x_{1}\) as the best teacher._
# Daily Flood Monitoring Based on Spaceborne GNSS-R Data: A Case Study on Henan, China
## 2 Materials and Methods
### SMAP Data  
The comparison product used in this paper comes from the SMAP satellite. The SMAP satellite mission was launched by NASA in January 2015. The SMAP satellite is equipped with active radar and passive radiometer sensors, and its revisit period is 2-3 days. By measuring brightness as well as temperature and performing geophysical inversion to obtain global soil moisture information, it can provide users with global soil moisture estimates [34].  
The observations of SMAP are mainly divided into data collected by passive L-band radiometer and those gathered by active L-band radar. The GPS L1 frequency band is approximately 1.58 GHz and the operating frequency of the SMAP radiometer is approximately 1.41 GHz. These two types of data have similar frequencies and sensitivity to the land surface [35], and therefore, SMAP data are used for analysis and comparison with CYGNSS data.  
SMAP products use EASE-grid 2.0 to record data. The active radar fails after two months in orbit, and the spatial resolution of the microwave radiometer measurement data is approximately 40 km. Thus, the SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture (Version 7) product is used in this paper. The data period is from 1 July to 15 August 2020.
## 2 Results and Discussion
### Effect of Various Experimental Parameters on RAFT Polymerization of DMAEMA
#### 2.1.2 Kinetics of DMAEMA Polymerization in the Presence of CPTCD  
The performance of the radical polymerization with CPTCD as RAFT agent was explored. DMAEMA was polymerized at 70 \({}^{\circ}\)C with AIBN as the initiator, and the feed molar ratio of reactants is DMAEMA:CPTCD:AIBN = 150:1:0.25. The time-conversion and the pseudo first-order kinetic plots are shown in Figure 2A. The linearity of the pseudo first-order kinetic plot suggests that there is a constant radical concentration throughout the RAFT polymerization and the polymerization occurs in  
Figure 1: The gel permeation chromatograph (GPC) of poly (\(N\),\(N\)-dimethylaminoethyl methacrylate) (PDMAEMA) with different reversible addition-fragmentation chain transfer (RAFT) agents (DMAEMA:DBTTC/BDATC/MTTCD/CPTCD:AIBN = 100:1:0.25, reaction time = 6 h).  
a controlled manner. Linear growth of the molecular weight with the increase of monomer conversion was shown in Figure 2B. The PDI increases slightly with the conversion but remains in the range of 1.25-1.37. These results confirm that the polymerization of DMAEMA by the RAFT process in the presence of CPTCD follows a virtually \"living\" polymerization mechanism.
###### Abstract  
This study compares the physicochemical properties of _Royal Gala_ apple snacks fresh and processed using different methods: solar-dried (SD), oven-dried at 65 \({}^{\circ}\)C (OD65\({}^{\circ}\)) and 85 \({}^{\circ}\)C (OD85\({}^{\circ}\)) and two commercial brands (CC--commercial apple C and CF--commercial apple F). Evaluated parameters included color, microstructure, acidity, sugar content, phenolic compounds, antioxidant activity, and the presence of heat-induced compounds such as 5-hydroxymethylfurfural (5-HMF) and acrylamide. Commercial samples showed more pronounced color changes and a denser microstructure, with higher browning index (BI) values. The ratio of soluble solids to titratable acidity, an indicator of sensory acceptance, was more influenced by drying methods than temperature. Total phenolic content was highest in fresh apples (123.68 mg GAE/100 g d.m.) and decreased across all drying methods, particularly in solar-dried (SD) samples (78.57 mg GAE/100 g d.m.). Antioxidant activity followed a similar trend, although SD samples performed better than expected, likely due to the retention of certain bioactive compounds. Fresh apples had the highest sugar content (43.25 mg/100 g d.m.), followed by CC (33.81 mg/100 g d.m.), OD65\({}^{\circ}\) (33.37 mg/100 g d.m.), CF (31.56 mg/100 g d.m.), OD85\({}^{\circ}\) (25.92 mg/100 g d.m.) and SD (25.01 mg/100 g d.m.). Commercial samples were sweeter and darker, with detectable levels of 5-HMF and acrylamide. The findings highlight that drying method significantly affects snack quality. While solar- and oven-dried samples better preserve bioactive compounds, industrial processes enhance sweetness and visual appeal but increase the formation of potentially undesirable compounds. These results are valuable for both consumers and producers aiming to balance sensory quality with nutritional and food safety considerations in dried apple products.  
type: principles, phenolic compounds; antioxidant capacity; microstructure; 5-HMF; acrylamide +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Electrodeposition of Co\({}_{3}\)Se\({}_{4}\)  
Prior to electrodeposition, the substrates were cleaned using a standard protocol, which involves treating the substrates with acetone, 2-propanol and deionized water, respectively, using a bath sonicator (10 min each). For electrodeposition, substrates of planar silicon, nanostructured Si (NSi) and FTO were used to get the best possible results from the three electrodes. An aqueous solution containing 20 mM Na\({}_{2}\)SeO\({}_{3}\) (0.101 g), 20 mM Co(CH\({}_{3}\)COO)\({}_{2}\) (0.146 g) and 100 mM LiCl (0.0121 g) were placed in a 30 mL container. The solution was kept near pH 2 by adding dilute HCl. The deposition was conducted at room temperature in a single compartment glass cell using a three-electrode configuration. Electrodeposition was performed with a CHI 7008E (CH Instruments, Inc., Austin, TX, USA, 2018) potentiostat. Graphite rod was used as a counter electrode and Ag/AgCl (3M NaCl) was used as the reference electrode for the electrodeposition. The chronoamperometric deposition was conducted at \(-\)0.8 V for varying deposition times (5, 10, or 15 s) and we determined that the optimal time was 15 s. A thin film of Co\({}_{3}\)Se\({}_{4}\) co-catalyst was formed on the surface of the working electrodes.
# Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
## 7 Discussion
### Resource Usage  
The hardware used varies from basic laptops with low-end configurations to high-performance computing (HPC) facilities and cloud setups, as can be seen in Table 11. For instance, the majority of the studies used personal computers or laptops with Intel Core i5 or i7 processors and 8-32 GB of RAM. While these setups are sufficient for small datasets and general machine learning models, they struggle to handle large datasets or deep, computationally intensive models. Alternatively, highly resource-consuming systems such as HPC clusters or systems with components with GPUs such as NVIDIA Tesla or RTX4070 Ti will be in a position to offer researchers an accelerated method of dealing with big datasets along with computer-resource-intensive models such as CNNs and LSTMs.  
One of the most noticeable trends is the dominance of Windows operating systems in most experiments, with some experiments being conducted using Ubuntu or other Linux operating systems in instances where cloud or server-based setups were used. Programming environments are largely comprised of Python-based libraries (e.g., Scikit-learn and TensorFlow), indicating the dominance of Python as an ML research standard language. Other approaches, such as using Rust in the interest of efficiency, were reported but less common.  
The choice of infrastructure can significantly impact the scalability, efficiency, and overall feasibility of experiments. HPC or cloud-based experiments benefit from minimized computation time and task parallelization. This is particularly evident in experiments based on deep learning frameworks or utilizing large datasets such as BoT-IoT and TON_IoT. However, personal computer or laptop experiments rely on straightforward models such as Decision Trees or Random Forests due to hardware limits.
# Optimal Pattern Synthesis of Linear Array Antennas Using the Nonlinear Chaotic Grey Wolf Algorithm
## 6 Data Analysis
### Array Factor of a 20-Element Uniform Linear Array Antenna with a Lower Notch

Table 5: Results for a 20-element linear array antenna. In this design example, the NCGWO algorithm exhibited significant advantages in suppressing the PSLL and achieving a greater depth in the notch. The NCGWO algorithm also demonstrated better optimization capabilities than other algorithms.
# LSTM-DQN-APF Path Planning Algorithm Empowered by Twins in Complex Scenarios
## 4 Twin System Modeling and LSTM-DQN-APF Algorithm  
**Validation Analysis**  
To verify the performance of the LSTM-DQN-APF algorithm in complex urban scenarios, a \"perception-simulation-decision-control\" closed-loop virtual-physical twin platform was first constructed, integrating physical drones, digital city models, and a twin service layer. Next, real-time synchronization of virtual and physical data was achieved through the UDP/MAVLink [27] protocol and the ROS framework. Finally, a three-level progressive experiment validated that, under the empowerment of the twin platform, the \"prediction-driven-decision optimization\" architecture effectively enhanced the algorithm's adaptability and robustness in complex environments.
# Spatiotemporal Road Traffic Anomaly Detection: A Tensor-Based Approach
## 3 Background
### Tensors  
Tensor \(\mathcal{T}\) is defined as multi-dimensional array \(\mathcal{T}\in\mathbb{R}^{N_{1}\times N_{2}\times...\times N_{M}}\), where \(M\) represents the order of the tensor (number of dimensions). A vector is then represented with the first-order tensor, matrix with the second-order tensor, and three or more order tensors are called higher-order tensors [35]. For the analysis of the road traffic spatiotemporal data, most authors use a third-order tensor composed using \(origin\times destination\times time\) and \(profile\times roadsgements\times time\) where profile represents the speed or volume time series on the observed road network segment. Notations and abbreviations are adopted from [PERSON] and [PERSON] [35].  
The decomposition method used in this article is the CANDECOMP/PARAFAC (CP) in its non-negative form. The CP decomposition factorizes a tensor into a sum of component rank-one tensors. For tensor \(\mathcal{T}\) CP is the following:  
\[\mathcal{T}\approx\sum_{r=1}^{R}a_{r}\circ b_{r}\circ c_{r} \tag{2}\]  
where \(R\) is a positive integer that represents the decomposition rank. Then, rank one components can be expressed as factor matrices \(\mathbf{A}\in\left(\begin{array}{cc}\mathbf{a}^{(1)}&\mathbf{a}^{(2)}&...&\mathbf{a}^{(R)} \end{array}\right)\), \(\mathbf{B}\in\left(\begin{array}{cc}\mathbf{b}^{(1)}&\mathbf{b}^{(2)}&...&\mathbf{b}^{(R)} \end{array}\right)\), and \(\mathbf{C}\in\left(\begin{array}{cc}\mathbf{c}^{(1)}&\mathbf{c}^{(2)}&...&\mathbf{c}^{(R)} \end{array}\right)\).  
Most of the authors predefined a tensor rank based on the underlying knowledge of the phenomena that is observed [16]. The Core Consistency Diagnostic (CORCONDIA) [36] method is used in this paper
## 5 Conclusions  
In this study, the RSRs performance at the abutment transition zone of the railway was evaluated. Notably, this subgrade was reinforced with cement-treated gravel and a geogrid. The conclusions from the analysis results of the long-term measurement of settlement and horizontal earth pressure are as follows:  
1. During the stabilization period, the net settlement of the reinforced backfill converged at the early stage of subgrade construction, and the subsequent increase in settlement was dominated by that occurring in the ground. The rate of total net settlement of the  
Figure 11: Variation of geogrid strain over time: (**a**) 2 nd layer (short geogrid); (**b**) 5 th layer (short geogrid); (**c**) 10 th layer (long geogrid); (**d**) 13 th layer (long geogrid); (**e**) 16 th layer (long geogrid).  
reinforced backfill was 0.12%. In the RSR, because the reinforced backfill supported the rigid facing wall, additional settlement occurred as a result of the wall load and rearrangement of soil particles owing to rainfall. The additional settlement caused by the wall load and rainfall was 0.06% and 0.02%, respectively. The results confirm the low compressibility of the RSR reinforced with cement-treated gravel and geogrid.
2. The horizontal earth pressure was reduced to a level of 54-63% of Rankine's active earth pressure. In view of the decrease in active earth pressure, the shear strength of the cement-treated gravel was considered similar to that of granular soil with a friction angle of 45-50\({}^{\circ}\) for practical designs. In particular, the higher the shear strength, the higher the stiffness encountered in the soil would be; thus, the low compressibility of the RSR could be confirmed indirectly.
3. The measured maximum geogrid strain was equivalent to 0.784%, which indicated the stability of the RSR in the event of pullout and rupture of the geogrid. Upon the completion of the backfill, the geogrid deformation resulting from the increase in the overburden load was approximately negligible.  
Conceptualization and methodology, D.S.K.; validation, D.S.K. and S.J.; investigation, S.P. and U.K.; resources, S.P. and U.K.; data curation, S.P., D.S.K. and U.K.; writing--original draft preparation, S.P. and U.K.; writing--review and editing, D.S.K. and S.J.; supervision, D.S.K.; project administration and funding acquisition, D.S.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant from the R&D Program (Development of long-life technology for ballasted tracks using EPDM rubber pad, PK2204B1I) of the Korea Railroad Research Institute.  
The authors declare no conflict of interest.