esa-sceva/satcom-chunk-collection huggingface dataset:
# Wideband Lorenz Resonance Reconfigurable Metasurface for 5G+ Communications
## 4 Conclusions  
In this paper, a wideband MSRRA realization with low energy consumption is proposed targeted at the center frequency of 28 GHz. We have designed a circuit for providing DC-bias voltage for the varactors, which can greatly reduce the loss of the RF current and the influence of the bias circuit on the radiating element. Additionally, an EC model for an MSRRA unit cell is also introduced. Guided by the EC model, we designed a simple unit cell structure suitable for operating at a millimeter-wave frequency band. The proposed MSRRA system can cover a dynamic reflection phase range of over 300\({}^{\circ}\) from 26.10 GHz to 29.93 GHz. The single-beam bandwidth performance of the proposed MSRRA phototype is also measured as 1.85 GHz. The proposed MSRRA can fully cover the whole n257 channel of 5G NR. For future 5G+ and 6G applications, the proposed MSRRA prototype has a broad application prospect.  
[PERSON]. ([PERSON]) designed the structure of the MSRRA, proposed the EC model for the unit cell, designed the experiments, analyzed the experimental data, and wrote the paper; [PERSON]. ([PERSON]) examined and approved the structure of the MSRRA, the EC model, and the paper. [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the experimental data and approved the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China under Grant 2020 YFB1806405 and the Peng Cheng Laboratory under Grant PCL2021A04.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Conclusions  
The iterative coded CDMA receivers with extrinsic feedback will not benefit from use of the weighted delay filter. Indeed, should a weighted delay filter be implemented, the filter will pick a scaling factor of \(\delta_{2}=0\), and automatically ignore data from previous cycles. The reason for this behaviour is outlined in Section 2.2, where it is shown that when extrinsic feedback is used data from previous cycles is simply a scaled, noisy version of the data from the current cycle and hence no benefit is to be realised from its utilisation. Iterative coded CDMA receivers with APP feedback however will see a dramatic performance improvement via the use of a weighted delay filter, and will significantly outperform a traditional pure extrinsic feedback implementation. In an APP implementation, the optimal weighting factors are not 0 and 1. The key difference between the two scenarios is the residual feedback in the APP implementation. The existence of the residual feedback has a significant effect on the statistics at the output of the PIC, and as shown in Sections 3.2 and 3.3, and makes calculation of these statistics very challenging. The residue term is shown to be a key difference between the APP and extrinsic feedback systems with respect to the benefits to be had by weighted delay filtering. In Section 3.3 it is shown that the notion of \"previous data being a scaled, noisy version of data from the current cycle\" does not hold with APP feedback because of the residue information. Empirical results are given in Section 4.1 which verify that the residue is the key difference between the PIC statistics for APP and extrinsic data. It is shown in Section 4.2 that the residue is indeed substantial and hence, cannot be ignored in any rigorous analysis of an APP feedback system.  
* The iterative receivers with suboptimal components may not always yield the best results.
* For a coded CDMA system with extrinsic feedback, no benefit will be realised by weighted delay filtering.  
Figure 8: System Capacity, N = 10, \(E_{b}/N_{0}\) = 6 dB, \(L\) = 1000.  
* Determining the PIC output statistics analytically in the case of APP feedback remains an open problem.
* The conclusion can be made that for a given values of SNR = 6 dB, and a processing gain of 10 dB, the system load increases to approximately 42% using the weighted delay filtering.
* Further noted that WDF outperforms MRC giving a value of 0.8 dB.  
Conceptualization, A.A.; M.T. methodology, validation, A.A.; M.T.; formal analysis, writing--original draft preparation, writing--review and editing, A.A. and M.T.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number (PSAU/2023/01/224253).  
Not applicable.  
The author declares no conflict of interest.
## 3 Results  
Different CTA imaging protocols were successfully tested on the 3D printed model. All measurements were performed by two observers, with excellent correlation between the observers. Table 1 shows the SNR measurements at images acquired with different CTA protocols. SNR measurements did not vary significantly when the kVp value range was close such as a comparison between 80 and 100 kVp or between 100 and 120 kVp (\(p>0.05\)), regardless of the pitch values. However, the measurements showed that at pitch 1.5, 2.0, and 2.5, SNR at 120 kVp was significantly higher than 80 kVp (\(p<0.05\)) in both true and false lumens. SNR showed no significant differences between different kVp when the pitch value was 1.2. At low pitch values such as 1.2 and 1.5, a decrease in kVp from 120 to 80 led to a significant effective dose reduction by more than 20%.  
Unlike kVp, a variation of pitch values can significantly affect SNR measurements. When the kVp value is kept constant, SNR showed significant differences with different pitch values. SNR measured significantly higher in images acquired with low pitch values such as 1.2 and 1.5, compared to those acquired with the high 2.0 and 2.5 protocols (\(p<0.05\)), regardless of the location of measurements. SNR measurements showed that image quality significantly dropped when the pitch value increased from 1.5 to 2.0 in all kinds of kVp protocols (\(p<0.05\)). SNR dropped 30% when pitch value was raised from 1.2 to 2.5 at 80 kVp, and 20% at 120 kVp. In contrast, SNR dropped only 3.9% when kVp was reduced from 120 to 80 at pitch 1.2, and 15.9% at pitch 2.5.  
Under low pitch protocols such as 1.2 and 1.5, there were no significant differences in SNR measurements between the proximal and middle descending aorta. In contrast, at high pitch value protocols such as 2.0 and 2.5, the proximal descending aorta tended to have a higher image quality than the middle descending aorta, especially in the false lumen. Figure 5 is an example showing the reformatted images of these CTA protocols. When kVp decreased to 80, image noise was increased with the use of high pitch value protocols such as 2.0 and 2.5, as shown in Figure 5. However, the shape of the stent graft and the location of the intimal flap are clearly displayed in these images, despite the use of low-dose protocols.
# A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
## 2 Materials and Methods
### Combined Anomaly and Trend Detection Model  
The objective of CATS is the reliable detection of trends and anomalies in industrial robot gear health indicator data. In the following, the assumptions that the system is based on, are defined. Then, the system itself is presented.
## 4 Calibration and Correction of the Additive Phase
### Discussion  
With the data presented here, it can be established that the additive phase piston detected in the optical trap is a consequence of changes in the immersion oil. Although in Section 3.1.1 (Figure 6d), the refractive index shows a Gaussian behavior, in Section 4.1 (Figures 16 and 17), a quasi-constant phase shifting appears throughout the field of view, due to the difference of orders of magnitude of the observed areas (mm and \(\upmu\)m, respectively). This phase piston is attributed to a thermal effect caused by laser radiation when  
Figure 19: Paint damage (in phase terms) when the laser IR radiates at: **(a)** 110; **(b)** 140; **(c)** 175; **(d)** 205; and **(e)** 230 mW.  
Figure 20: Measurement correction of the phase map at 205 mW of power. Left side (from 0 to 4.78 \(\upmu\)m over the y-axis) corresponds to the uncorrected phase and the right side (from 4.78 to 10.27 \(\upmu\)m) is the corrected measurement.  
transmitted through immersion oil. Although the oil's morphological change (volume) does not usually affect the experiment directly, because it does not touch the sample under test, the change in temperature implies a variation in the refractive index and indicates an alteration in the beams focal point at the exit of the immersion objective used. In the experiments described in Sections 3.1.1 and 3.2, the effect of a specific wavelength was analyzed, this was only to understand the oil behavior; if other wavelengths were used, it is suggested that there would be similar results, but with different ratios. Also, the results could vary with a focused beam instead of the collimated beam used in those experiments. However, the calibration and phase correction were done with the trap's IR laser. In the case of a proper digital correction, the laser characteristics and the oil response to its exposure must be known.
# Simulation of the Influence of Wing Angle Blades on the Performance of Counter-Rotating Axial Fan
## 4 Analysis of Optimization Results
### Q Isosurface Analysis
Therefore, in the actual design and use of the fan, the generation of blade shedding vortices should be avoided as much as possible. Figure 7: \(Q\) isosurface distribution. (**a**) First-stage blade of prototype fan; (**b**) first-stage blade of wing angle fan; (**c**) second-stage blade of the prototype fan; and (**d**) second-stage blade of wing angle fan. Since the \(Q\) isosurface method can only observe the distribution of the core position of the blade vortex, it cannot obtain the strength of the vortex. For this reason, this paper uses the analysis method provided by [PERSON] [21] to determine the magnitude of the vortex intensity. The method found through experiments that the vortex intensity is inversely related to the static pressure, that is, the lower the static pressure at the trailing edge of the blade, the higher the vortex intensity. Additionally, the lowest area of its static pressure is the core area of the vortex. In order to obtain the distribution of the blade shedding vortex intensity, take the vicinity of the trailing edge of any blade of the two-stage impeller as the starting point, and set three planes A, B, and C at equal intervals from near to far, as shown in Figure 8, where the interval between two adjacent planes is 0.25 L. By observing the static intensity distribution cloud diagrams of these three planes, the strength of the shedding vortex at the trailing edge of the blade can be seen. Figure 9 shows the static pressure distribution of planes A, B, and C in the first and second blade trailing edge regions of the prototype fan and the angle blade fan, respectively. The arrows in the figure indicate the direction of rotation of the impeller, and the dashed lines indicate the trajectory of the blade shedding vortex. The contours are discontinuous in both figures, which is caused by errors in the interface surface during data transmission. It can be seen from the figure that the static pressure intensity distribution of the prototype blade in section A is significantly smaller than that of the angle blade, and the range of the lowest static pressure zone at section C is also larger than that of the angle blade, which indicates both the strength and range of the shedding vortex of the prototype blade are greater than those of the angle blades. This is because the wing angle structure divides the blade into upper and lower parts at the wing angle position, and the leading edge of the upper half is at a certain angle to the incoming flow direction. This causes the airflow passing through the upper half of the blade to flow toward the tip of the blade after reaching the leading edge of the blade during the rotation of the blade. This destroys the conditions for generating the tail shedding vortex, thereby reducing its vortex intensity and range.
## 2 Materials and Methods  
Data for children and adolescents aged 11-17 years, affected by moderate-to-severe AD and/or severe asthma treated with dupilumab, were collected through standardized questionnaires and scores. Lung function testing was acquired from our Pediatric Allergy and Pulmonology Unit in Chieti. AD and asthma diagnoses were formulated by a pediatric based on clinical presentation, personal history and spirometry according to the U.K. working party's diagnostic criteria for AD and Global Initiative for Asthma (GINA) guidelines [3; 16]. The U.K. working party's diagnostic criteria require an itchy skin condition with three or more of the following: history or visible flexural involvement, a history of asthma/ allergic rhinitis, a history of generalized dry skin or onset of rash under the age of 2 years [16]. GINA guidelines require two pivotal elements: a history of variable respiratory symptoms and demonstration of variable expiratory airflow limitation [3]. Dupilumab was administered subcutaneously [SC] according to the disease and the weight of the patients. Patients continued using moisturizers, topical and/or systemic treatments for AD and inhaled corticosteroids (ICSs) associated or not with long-acting beta-agonists (LABAs) or leukotriene receptor antagonists for asthma. The following data were collected for a descriptive analysis: age, gender, medical history, clinical phenotype of AD, asthma clinical and spirometric parameters, comorbidities (atopic and non-atopic), other ongoing treatments and adverse events (AEs). Disease severity was assessed at baseline and after week 12 (12W) of dupilumab using, for AD: EASI (Eczema Area and Severity Score) with range 0-72 and SCORAD (Severity Scoring of Atopic Dermatitis) with range 0-103, both describing the extent (area) and severity of AD; P-NRS (Pruritus Numerical Rating Scale (P-NRS) and sleep (S)-NRS) with range 0-10; c-DLQI score (Children's Dermatology Life Quality Index) with range 0-30; for asthma: ACT (asthma control test), ranging from 5 (poor control of asthma) to 25 (complete control of asthma), with higher scores reflecting greater asthma control; spirometric parameters, such as FVC (forced vital capacity) and FEV1 (forced expiratory volume in the first second).
# Mycoremediation as a Potentially Promising Technology: Current Status and Prospects--A Review
## 2 Classification of Fungi, Bacteria, and Microalgae Species and Their Remediation Performance
### Soil Fungi  
Zygomycota, Chytridiomycota, and Ascomycota are the soil fungi that comprise this complex group. These groups play essential roles in nitrogen and carbon cycling and soil organic matter degradation, the most critical elements of soil ecology [55]. Typically, they are saprophytes, non-ligninolytic organisms that can degrade cellulose [74]. Genera in this category include _Trichoderma_, _Stachybotrys_, _Rhizopus_, _Phelbia_, _Penicillium_, _Paicolomyces_, _Mortierella_, _Geomyces_, _Fusarium_, _Engyodontium_, _Cladosporium_, _Cunningham_, _Beauveria_, _Apergillus niger_, _Alternaria_, _Microsporum_, _Allescheriella_, and _Acremonium_[68]. Non-ligninolytic fungi produce monooxygenase, an extracellular enzyme that degrades PAHs by hydroxylation [75]. The fungi serve as mediators for soil bioremediation and also tolerate a variety of contaminants, such as endosulfan, chlorinated biphenyl (CBA), PAHs, and PCBs. The fungus degraded the resistant polymers in further degradation phases. Consequently, these fungi are categorised as xenobiotic-degrading fungi, and their multispecies consortia demonstrate their exceptional effectiveness in soil bioremediation under laboratory conditions [76]. In a study conducted by [PERSON] et al. [77], researchers found that _Apergillus sclerotiorum CBMAI 849_ and _Mucor racemosus CBMAI 847_ could metabolize between 50% and 90% of pyrene and benzo[a]pyrene. Table 2 lists recent species of fungi and their biodegradation performance under different environmental conditions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Fungal Species**} & **Remediation Methods** & **Pollutants** & **Experimental Conditions** & **Degradation Environnment** & **Treatment (mg/L)** & **Removal/Cptake** & **Ref.** \\ \hline _Fusarium proliferation_ & & & Incubation conditions: 5 days, & & & \\ CF2 & Degradation & (Insecticdes) & media—mineral slat at 26 \(\,\mathrm{\SIUnitSymbolC}\), & & & \\ CF2 & & (Insecticdes) & shaker speed—110 rpm and & & & \\ _Tinea versicolor_ & Biosorption & Ketoprofen & 100 \(\,\mathrm{\SIUnitSymbolC}\) of fungi incubation were & & & \\ _Staphylococcus_ & & & Diazion & injected for 21 days at 25 \(\,\mathrm{\SIUnitSymbolC}\) & & \\ _succinis_ HLI-10, & & & under 150 rpm shaking speed. & & & \\ _Aspergillus niger_ & & Degradation & (Organophosphorus & & \\ MK640786 & & & & & \\ _Eleurrotus trirus_, & & & & & \\ _Tranetes hirsuta_ & & & & & \\ _Apergillusterreus_ & & & & & \\ _Cylindorezphalum_ & & & & & \\ _aurelium_ & & & & & \\ _Lasioidplodia_ sp. & Degradation & & & & \\ _Talamomyces_ & & & & & \\ _amestololike_, & & & & & \\ _Penicillium lhatlevili_, & & & & & \\ _Penicillium ctrirum_, & & & & & \\ _Gongronella bulteri_ & & & & & \\ _Talamomyces islandicus_ & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Biodegradation performance of recent fungal species under varying experimental conditions.
# Effects of Pulsed Electric Field on Oil Extraction Rate and
## 4 Conclusions  
In this paper, nanosecond PEF was employed to treat the peony seeds with varying particle sizes, investigating the impact of particle size and PEF parameters on oil yield, and analyzing the changes in tocopherol content in peony seed oil. Several conclusions were obtained as follows: (1) oil yield is correlated with the particle size of peony seeds, as smaller particles result in a higher oil yield; (2) electric field strength and voltage parameters significantly influence oil yield; higher voltage frequencies do not promote an increase in oil yield, while an elevation in electric field strength leads to a peak value of oil yield; and (3) compared to the control group, tocopherol content exhibits extremely significant levels at larger particle sizes, but no significant differences at a particle size of 60-mesh.  
Overall, treating peony seed particles with PEF proves advantageous for enhancing oil yield by increasing the permeability of the cells, and further improves tocopherol content in oil. However, specific alterations depend on both the characteristics of peony seeds and PEF parameters. Therefore, when practically applying this technique, it is essential to select specific parameter ranges. In industrial applications, PEF is a solvent-free processing method that operates at non-thermal conditions. The equipment structure is simple, and the initial investment cost is low (not exceeding 1000 CYN/kg processing capacity). Based on an analysis of oil yield and nutrient content, it can be concluded that PEF demonstrates favorable cost-effectiveness in practical applications.  
Conceptualization, W.X. and Z.C.; methodology, Y.T., J.L. ([PERSON]) and Z.C.; investigation, W.X., Y.T. and Z.C.; resources, W.X. and Z.C.; data curation, W.X. and J.L. ([PERSON]); writing--original draft preparation, W.X., Y.T. and J.L. ([PERSON]); writing--review and editing, Z.C.; supervision, Z.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The authors are grateful to Engineer [PERSON] of Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences for his help and helpful discussion on HLPC data testing. The authors declare no conflicts of interest.
# Semi-Supervised Medical Image Classification Based on Attention and Intrinsic Features of Samples
## 6 Conclusions  
In the present work, we have studied the problem of semi-supervised learning in medical image classification that aims to reduce the manual labeling of medical image data. We introduced the self-attention mechanism into the backbone network to focus more on the features that are more relevant to the current task and used the improved version of focal loss to narrow the gap between wrongly classified and correctly labeled samples at the supervision loss point and recommended the intrinsic feature consistency loss of the sample to make more effective use of the unlabeled data. Compared with existing semi-supervised learning methods, our experimental results for two datasets show that our method achieved 94.02% and 72.03% on AUC and Sensitivity indicators, but its Accuracy and Specificity scores were not so good. This shows that our method still has some shortcomings, but it is worth discussing exploring the intrinsic characteristic relationships among samples and controlling the weighting of sample loss by introducing a super-parameter that varies with the performance of the classifier model. In addition, the proposed consistency loss and supervised loss functions of sample intrinsic characteristics can also be combined with other semi-supervised methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter (lp)**} & \multicolumn{4}{c}{**Metrics**} \\ \cline{2-5}  & **AUC** & **Sensitivity** & **Accuracy** & **Specificity** \\ \hline
0.6 & 92.24 & 68.34 & 91.06 & 89.65 \\
0.7 & 92.70 & 69.75 & 91.54 & 90.86 \\
0.8 & 93.55 & 71.54 & 91.98 & 91.24 \\
0.9 & 93.86 & 71.87 & 92.24 & 91.66 \\
1.0 & 94.02 & 72.03 & 92.61 & 91.78 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effects of different lp parameter values on the performance of our semi-supervised model on the ISIC 2018 dataset.  
**Author Contributions:** Conceptualization, Z.Z. and W.W.; methodology, Z.Z. and C.L.; software, Z.Z. and C.L.; validation, Z.Z. and W.W.; writing--original draft preparation, Z.Z. and W.D.; writing--review and editing, Z.Z. and K.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Cold Boot Attacks on the Supersingular Isogeny Key Encapsulation (SIKE) Mechanism
## 5 Evaluation of Our Key-Recovery Algorithm  
Throughout this section we assess our key-recovery algorithm. We first give a detailed account of how we compute the success rates for our key-recovery algorithm considering several scenarios, each with a different set of SIKE parameters, and finally show how to integrate a quantum key search algorithm (QKEA) to our key-recovery algorithm to improve its overall performance.