esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Hybrid Beamforming Scheme for Millimeter-Wave Massive-MIMO 5G Wireless Networks
## 3 Proposed Algorithm
### Bit Error Rate (BER) Analysis  
To evaluate the robustness of the proposed scheme, this section compares the BER of the proposed algorithm with the existing reference algorithms under different SNR values. The results are illustrated in Figure 8. As can be seen from the results of Figure 8, the proposed algorithm showed better BER performance and was similar to the optimal fully digital precoding algorithm. This means that the proposed algorithm gave good channel quality and better QoS to subscribers as compared with existing algorithms. It also gives important evidence that the proposed algorithm is robust to channel impairments, such as cochannel interference and noise accumulations, as compared with existing algorithms.
## 3 Results
### Phylogenetic Analysis of RcTPS Genes in R. chinensis  
To investigate the classification and evolutionary relationships between RcTPS proteins, an unrooted phylogenetic tree was constructed based on alignments of the TPS proteins of \(R\). _chinensis_, \(A\). _thaliana_, \(S\). _Iycopersicum_ and \(M\). _domestica_ using the neighboring (NJ) method in MEGA 7.0 (Figure 3). The 49 _RcTPS_ genes were classified into five subfamilies (TPS-a, TPS-b, TPS-c, TPS-g, TPS-e/f) based on phylogenetic analysis. Among the 49 RcTPS proteins, 48 members (RcTPS01 to RcTPS48) with high similarity could be classified as type I TPSs, whereas the remaining RcTPS49, which clustered in the same branch as AfTPS31, was classified as a type II TPS. Six TPS members (RcTPS09, RcTPS13, RcTPS19, RcTPS20, RcTPS33, RcTPS46) formed the TPS-b subfamily, 4 members (RcTPS35, RcTPS37, RcTPS40, RcTPS41) formed the TPS-g subfamily, 4 members (RcTPS02, RcTPS29, RcTPS31, RcTPS47) formed the TPS-e/f subfamily, and the remaining 34 members formed the TPS-a subfamily. The classification results were consistent with the previous report in Arabidopsis [18].  
Figure 2: Localization of the 49 TPS genes in R. chinensis 7 chromosomes. The scale bar on the left indicates the size of the chromosome. Gene name colors indicate different subgroups, and red lines indicate tandem repeats.
# Recycled Carbon Fibers with Improved Physical Properties Recovered from CFRP by Nitric Acid
###### Abstract  
To effectively reuse a large amount of Carbon Fiber Reinforced Plastics (CFRP) waste, the carbon fibers should be able to be recovered without degrading their quality. In this report, we developed a new approach to recover carbon fibers from CFRPs with improved physical properties compared to virgin carbon fibers with an environmentally friendly recycling method using nitric acid. Following the decomposition of the CFRP waste in nitric acid at 80 \({}^{\circ}\)C, both recycled carbon fibers and decomposed resin were recovered. The obtained recycled carbon fibers showed 1.4 times higher tensile strength and 2.2 times higher interfacial shear strength to resin compared to virgin carbon fibers. TEM-EDX analysis showed a decrease in the abundance of voids existing in the carbon fiber surface layer and new polar functional groups caused by nitric acid existing inside the voids, leading to increased tensile strength. Furthermore, XPS analysis showed that the interfacial shear strength improved due to the formation of new polar functional groups due to nitric acid. The possibility of applying recycled carbon fibers to CFRP products was shown by elucidating the mechanism that expressed its physical properties during the recycling process, leading to a novel approach to realizing closed-loop recycling.  
CFRP; carbon fiber; closed loop recycling; epoxy resin; nitric acid Article
## 6 Experimental Verification
### Speed Regulation Performance Tests  
To demonstrate the advantages of the proposed scheme for HPMSM further, speed regulation experiments with and without the proposed scheme are carried out, as shown in Figure 21.  
Comparing Figure 21a,b, it can be seen that without filtering high-frequency harmonics, the HPMSM shows a large speed fluctuation reaching the set speed of 2000 rpm, and the speed regulation performance is poor. But when the proposed harmonics suppression scheme is adopted, the speed fluctuation is significantly reduced, and the speed regulation performance is enhanced. Figure 21c supplements the result of the HPMSM with a sudden speed dropping from 8000 rpm to 6000 rpm. After the sudden speed change, HPMSM responds quickly with low overshoot and small steady-state error, which validates its excellent speed regulation performance.
# MPVT: An Efficient Multi-Modal Prompt Vision Tracker for Visual Target Tracking
## 2 Materials and Methods
### Fully Connected Head Network Module  
Traditional models such as OSTrack [32] use convolutional head networks for classification and regression, which have strong inductive biases, such as locality assumptions and translation invariance. The local receptive field of convolutional kernels limits the global feature interaction ability. For this reason, a fully connected head network is utilized in this study, which entirely uses multi-layer perceptrons for classification and regression.  
In this module, a multi-task joint loss function, \(\mathcal{L}_{total}=\mathcal{L}_{cls}+\mathcal{L}_{box}\), is used to train the model. The classification loss \(\mathcal{L}_{cls}\) adopts binary cross-entropy loss (BCE) and can be defined by  
\[\mathcal{L}_{cls}=\frac{1}{N_{pos}}\sum_{i,j}[-\overline{y}_{i,j}\log\hat{y}_ {i,j}-(1-\overline{y}_{i,j})\log(1-\hat{y}_{i,j})], \tag{14}\]  
where \(\overline{y}_{i,j}\) is the IoU value of the predicted box \(B_{i,j}^{pred}\) and the true label \(B^{gt}\) for each foreground point, \((i,j)\). \(\hat{y}_{i,j}=\mathbb{C}[i,j]\in[0,1]\) is the predicted score of the classification branch, and \(N_{pos}\) is the total number of foreground points.  
The regression loss \(\mathcal{L}_{box}\) adopts generalized IoU (GIoU), which can provide effective gradients even if the predicted and label boxes do not overlap. It can be defined as  
\[\mathcal{L}_{box}=\frac{1}{N_{pos}}\sum_{i,j}\Pi_{y_{i,j}=1}\Big{(}1-\text{GIoU }(B_{i,j}^{pred},B^{gt})\Big{)}, \tag{15}\]  
where II is the indicator function, which only calculates the loss of foreground points.
## 2 Construction Time-Cost Dynamic Optimization Model
### Dynamic Optimization Model  
In this study, the net present value (\(Pc\)) of the cost is taken as the economic evaluation index; the direct costs are paid in one lump sum based on the intermediate time points of each work; the indirect costs are calculated as interest at the end of each interest time; and the project rewards and punishments are calculated as interest at the end of each project time. The dynamic optimization model for construction time-cost is:\[\min P_{C}=\sum_{i=1}^{n}c_{i}(1+r)^{-(ES_{i}+t_{i}/2)}+C_{2}\times\frac{ \left(1+r\right)^{T}-1}{r\left(1+r\right)^{T}}+C_{3}\times\left(1+r\right)^{-T} \tag{6}\]  
\[\text{s.t.}\left\{\begin{array}{l}t_{i},ES_{i}\geq 0\\ t_{iM}\leq t_{i}\leq t_{iN}\\ c_{iN}\leq c_{i}\leq c_{iM}\\ ES_{i}=\max\{ES_{h}+t_{i}\},ES_{1}=0\\ T=\max\{ES_{k}+t_{k}\}\end{array}\right. \tag{7}\]  
In Equations (6) and (7): \(ES_{i}\) is the earliest start time for work \(i\), \(ES_{1}\) is the earliest start time for work without prior work, \(ES_{h}\) is the earliest start time of the immediate work of work \(i\), \(ES_{k}\) and \(t_{k}\) are the earliest start time and work time of the work without immediate work, \(r\) is the discount rate, and other parameters are the same as above.
## 6 Conclusions  
In this manuscript, we presented CAVeCTIR, a novel approach that could support the cybersecurity stakeholders (e.g., experts and organizations) during post-incident forensic analysis. CAVeCTIR seeks, collects, analyzes, and correlates cyber threat intelligence (CTI) reports that record detected malicious activities on connected and autonomous vehicles (CAVs) in Internet-of-Vehicles (IoV) environments. CAVeCTIR takes advantage of machine learning techniques (e.g., real-time clustering analysis) to find similarities between new and recorded CTI reports. Hence, cybersecurity stakeholders could gain access to previous knowledge, identify common patterns, and adopt similar techniques and tools during the post-incident investigation. Therefore, it could drive a more effective and accurate forensics analysis and, thus, speed up the attack attribution process. CAVeCTIR helps cybersecurity stakeholders to mitigate the impact of the detected malicious activities quickly and effectively, and eventually, prevent future similar activities in IoV environments. While CAVeCTIR focuses on CAVs and IoV, it could also be applied to other application domains, such as Industrial Internet-of-Things (IIoT), e-banking, and safety-critical systems. However, it might need appropriate adjustments and adaptations to fit the unique features and address the challenges of each application domain.  
Conceptualization, G.E.R., C.K., C.A. and D.S.; methodology, G.E.R., C.K. and C.A.; software, G.E.R.; validation, G.E.R., C.K. and C.A.; formal analysis, G.E.R., C.K. and C.A.; investigation, G.E.R., C.K. and C.A.; resources, C.A., A.K. and D.S.; data curation, G.E.R., C.K. and C.A.; writing--original draft preparation, G.E.R., C.K. and C.A.; writing--review and editing, G.E.R., C.K., C.A., A.K. and D.S.; visualization, G.E.R. and C.K.; supervision, C.A. and D.S.; project administration, C.A., A.K. and D.S.; funding acquisition, C.A., A.K. and D.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by European Union (EU) Horizon 2020 research and innovation programme--nIoVe: A Novel Adaptive Cybersecurity Framework for the Internet-of-Vehicles under grant agreement No 833742.  
Not applicable.  
The authors declare no conflict of interest. The co-funding body had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript (main text, tables, figures):  
\begin{tabular}{l l} CAV & Connected and Autonomous Vehicle \\ IoV & Internet of Vehicles \\ IOCs & indicators of compromise \\ CAN bus & Controller Area Network bus \\ GNSS & Global Navigation Satellite System \\ DoS & Denial of Service \\ IMU & Inertial Measurement Unit \\ OBU & On-Board Unit \\ RSU & Road-Side Unit \\ \end{tabular}
## 3 Case Study
### Result Analysis
#### 3.3.2 Comprehensive Evaluation of Node Importance
* Level 2 (moderate-ranking nodes, orange squares): These nodes hold intermediate ranking values, reflecting their transitional function within the station network. They are mainly distributed along arrival and departure tracks, locomotive waiting tracks, and safety tracks, indicating their supporting role in train movements;
* Level 3 (low-ranking nodes, blue diamonds): These nodes have the lowest ranking values, signifying their limited influence in network operations. They are primarily positioned on freight tracks, pull-out tracks, and certain auxiliary routes, where connectivity is less critical compared to the main operational nodes. The spatial distribution pattern reveals that nodes with higher rankings are concentrated in station throat areas, reinforcing their dominant role in network functionality and train dispatching operations. In contrast, lower-ranking nodes are more dispersed, particularly in less active track segments, suggesting a clear structural hierarchy within the railway station network.
###### Abstract  
Accurate and quantitative assessment of the impact of natural environmental changes and human activities on total suspended solids (TSS) concentration is one of the important components of water environment protection. Due to the limits of traditional cross-sectional point monitoring, a novel water quality evaluation method based on the Markov model and remote sensing retrieval is proposed to realize the innovation of large-scale spatial monitoring across administrative boundaries. Additionally, to explore the spatiotemporal characteristics and driving factors of TSS, a new three-band remote sensing model of TSS was built by regression analysis for the inland reservoir using the synchronous field spectral data, water quality samples and remote sensing data in the trans-provincial Hedi Reservoir in the Guangdong and Guangxi Provinces of South China. The results show that: (1) The three-band model based on the OLI sensor explained about 82% of the TSS concentration variation (\(R^{2}~{}=~{}0.81,N~{}=~{}34,~{}p\) value \(<0.01\)) with an acceptable validation accuracy (\(RMSE~{}=~{}6.24\) mg/L, \(MRE=18.02\%\), \(N=15\)), which is basically the first model of its kind available in South China. (2) The TSS concentration has spatial distribution characteristics of high upstream and low downstream, where the average TSS at 31.54 mg/L in the upstream are 2.5 times those of the downstream (12.55 mg/L). (3) Different seasons and rainfall are important factors affecting the TSS in the upstream cross-border area, the TSS in the dry season are higher with average TSS of 33.66 mg/L and TSS are negatively correlated with rainfall from upstream mankind activity. Generally, TSS are higher in rainy seasons than those in dry seasons. However, the result shows that TSS are negatively correlated with rainfall, which means human activities have higher impacts on water quality than climate change. (4) The Markov dynamic evaluation results show that the water quality improvement in the upstream Shijiao Town is the most obvious, especially in 2018, the improvement in the water quality level crossed three levels and the TSS were the lowest. This study provided a technical method for remote sensing dynamic monitoring of water quality in a large reservoir, which is of great significance for remediation of the water environment and the effective evaluation of the river and lake chief system in China.  
Keywords:total suspended solids; progress degree; Markov model; remote sensing; river chief system +
Footnote †: journal: Sensors
# Comparative Study and Overview of Field-Oriented Control Techniques for Six-Phase PMSMs
## 1 Introduction
A method involving two individual current controllers, without a harmonics elimination technique, was compared to the VSD method with harmonics elimination in [24]. The proposed results showed the same harmonics behavior of both techniques. This paper demonstrates the inability to achieve natural harmonics reduction when using the method involving two individual current controllers, especially with reduced load. Automotive market target applications such as steering control or main traction drive do not operate at the rated power level all the time. A working cycle of these applications includes operation below the rated power, where the pure two-controller method will not fulfill the best qualitative parameters of the drive, since phase current harmonics lead to additional losses. This paper summarizes the characteristics, advantages, and disadvantages of the abovementioned methods through a comparison, mainly from the software control application point of view. In Sections 2 and 3, the control methods are introduced. In Section 4, further details, drawbacks, and benefits are discussed. Lastly, the pros and cons are summarized in a discussion. Experimental verification is demonstrated in the setup depicted in Figure 1a, whereas the phase configuration of the six-phase PMSM is sketched in Figure 1b. Multiple unconnected research articles have preferred six-phase machines with a \(30^{\circ}\) shift between a star-connected winding with unconnected neutral points as the most beneficial option. The airgap space harmonic content is significantly reduced, as well as the ripple component, in terms of torque and speed, due to the \(30^{\circ}\) shift winding configuration [1; 2]. This configuration of the six-phase machine with a mechanical shift between three-phase sets of windings different from \(0^{\circ}\) or \(60^{\circ}\) is often called an asymmetrical six-phase machine. In addition, the machine used in this experiment has a \(30^{\circ}\) shift between both sets of star-connected three-phase windings, with a so-called dual-star configuration, which means that each three-phase star has a separate neutral point. If these points are joined together, the whole machine has one common neutral point for all six phases, which is called a single-star configuration. The machine was supplied by two standard three-phase converters, with one common DC bus. Both converters were controlled by one microprocessor _MPC5643L (NXP)_. Currents were sensed using shunt resistors, and a resolver was used as a speed sensor.
# On Transform Domain Communication Systems under Spectrum Sensing Mismatch: A Deterministic Analysis
## 4 Simulation Results  
In this part, the number of spectrum bins is set to \(N=256\). 256-ary CCSK modulation and QPSK modulation are introduced, respectively. Based on the above analysis, the mismatch factor is set to \(\alpha=(N_{\text{C}})^{2}/N_{Tx}N_{Rx}\). Meanwhile, a random spectral bins availability method is assumed to obtain a better BER performance. For the multipath Rayleigh fading channel, a typical COST207 RAx6 channel is used, the Mismatch Factor is shown in Table 1, which are used as simulation parameters. More details are available in [18].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Channels** & **AWGN Channel** & **COST207 RAx6 Channel** \\ \hline Modulation model & CCSK/QPSK & CCSK/QPSK \\ \hline \multirow{3}{*}{Mismatch Factor (\(\alpha\))} & 0 & 0 \\  & 25\% & 25\% \\  & 50\% & 50\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.
## 4 Materials and Methods
### Sorption Studies  
Sorption studies were systematically performed in batch systems. A fixed volume of the solution (V, L) containing a given concentration of metal (C\({}_{0}\), mmol L\({}^{-1}\)) was mixed with a fixed amount of the sorbent (m, g). The experiments were carried out at room temperature (i.e., 20 \(\pm\) 1 \({}^{\circ}\)C) under agitation (at 210 rpm). The initial pH was controlled by sulfuric acid and NaOH solutions (1/0.1 M). Although the pH was not controlled during sorption, the equilibrium pH (i.e., pH\({}_{\text{eq}}\)) was systematically monitored at the end of the experiments. At given contact times (for uptake kinetics) or at equilibrium (24-48 h), the samples were collected by filtration (and/or magnetic separation), and the residual metal concentration (C(t) or C\({}_{\text{eq}}\), mmol L\({}^{-1}\)) was analyzed by inductively coupled plasma atomic emission spectrometry (ICPS-7510, Shimadzu, Tokyo, Japan). The mass balance equation enables the calculation of the sorption capacity (q, mmol g\({}^{-1}\)): q = (C\({}_{0}-\)C\({}_{\text{eq}}\)) \(\times\) V/m. The same protocol was used for the experiments involving the presence of different metals orsalt (i.e., Na, Cl, etc.). For the study of the cerium desorption, the metal-loaded sorbents (collected from the uptake kinetics experiments) were also processed in batch systems in the presence of 0.2 M HCl solutions. Water-rinsing steps were systematically applied between the sorption and desorption operations. The sorption tests were triplicated. The extensive experimental conditions are systematically reported in the captions of the figures.  
In the final application, the cerium recovery was investigated using MC-UR/S in pretreated solutions resulting from the leaching of an Egyptian ore (sulfuric acid concentrated solutions). A series of pre-treatments were processed on the acidic leachate, including U recovery using the Amberlite IRA-400 ion exchange resin (in fixed-bed column), pH control (at pH 4 for the partial precipitation and co-precipitation of the base metals), and REE recovery using the Dowex 50 X8 ion exchange resin. The outlet of the resin column was controlled at pH 5 (to precipitate mainly Al(III)) before carrying out the sorption tests on this raffinate using MC-UR/S (at different pH values) in an agitated reactor. The detailed presentation of the experimental procedures is reported in the Supplementary Materials.
## 5 Case Study
### Analysis of Day-Ahead Optimization Scheduling Strategies for Different Types of Pumped Storage
The peak-valley difference of thermal power output in a day is 282 MW, which indicates a significant reduction in the peak regulation pressure of thermal power compared to the system without PS. The dispatch results in Figures 4 and 5 exhibit similar characteristics. Figure 4 reveals that P1, P2, P3, and P4 belonging to FS-PSUs can only pump at their maximum pumping power of 99 MW. However, Figure 5 shows that at certain times, they can operate below this maximum, for example, at the 8 th hour, the pumping power of P1 belonging to VS-PSUs is 72 MW, reflecting the limitations of FS-PSUs in accurately absorbing surplus wind power. VS-PSUs, with the capability to pump at any rate between 80% and 100% of their maximum pumping power, provide enhanced flexibility and relatively accurate control over the absorption of surplus wind energy. Figure 6 demonstrates that during some specific hours, T-PSUs operate in a simultaneous pumping and generating mode. In the first hour, P4 pumps at 99 MW and G4 generates at 71 MW, so the actual pumped power of PS is 28 MW. This approach accurately matches the actual pumped amount with the surplus wind power to prevent issues related to over-pumping or under-pumping. (3) The distribution of pumped storage output in configuration schemes 2 to 8. Figure 7 visually compares the pumping and generating outcomes of different pumped storage types in configuration schemes 2 to 8. Among them, each configuration scheme shows the pumping and generating results of all pumped storage units obtained from the 24 h optimal scheduling. It is evident that configuration schemes 6, 7, and 8 (equipped with T-PSUs) offer broad output range in the pump mode, ranging from 0 MW to 99 MW, as T-PSUs can regulate the rotation of the water turbine in the pump mode to adjust pumping capacity extensively. Configuration schemes 2 to 5 (equipped with FS-PSUs or VS-PSUs, without T-PSUs), however, have more limited output distribution due to their technical constraints. Among them, FS-PSUs can only pump at 99 MW, and the VS-PSUs can pump from 72 MW to 99 MW. The dispatching results of the different types of pumped storage in Figure 7 show that the T-PSUs have the widest adjustment range in the pump mode, followed by VS-PSUs, and the narrowest adjustment range is of FS-PSUs. The larger adjustment range also makes it possible for the T-PSUs to achieve the optimal dispatch output.
# Integrating Machine Learning Algorithms: A Hybrid Model for Lung Cancer Outcome Improvement
## 2 Methodology
### Applied Algorithms for Predicting Lung Cancer
#### 2.12.6 Gradient Boosting  
Boosting algorithms work by combining many simple models, called weak learners, which are only slightly better than guessing randomly. By combining these weak models step by step, they create a much stronger and more accurate model. Gradient Boosting is a boosting algorithm often used for regression problems. It improves the prediction model by adding minor corrections at each step, building the final solution as a weighted combination of these corrections [37].  
\[\hat{y}_{i}^{(M)}=\sum_{m=1}^{M}\lambda h_{m}(x_{i}) \tag{7}\]  
where \(\hat{y}_{i}^{(M)}\) is the predicted output for the \(i\)-th data point after \(M\) iterations, \(\sum_{m=1}^{M}\) denotes the summation notation, indicating that the terms from \(m=1\) to \(M\) are to be added together, \(\lambda\) is a weight or coefficient that adjusts the contribution of each function \(h_{m}(x_{i})\), and \(h_{m}(x_{i})\) is the output of the \(m\)-th function (or model) for the \(i\)-th input data point \(x_{i}\).