esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Radiomics and ML in ASCC
### Pet-Ct  
For PET-CT, several functional parameters, alone or in combination with clinical parameters, have previously been linked to recurrence. For instance, in a monocentric trial, [PERSON] et al. included 93 ASCC patients from the prospective ANCARAD trial (NCT01937780) who benefited from baseline PET-CT imaging (and at two weeks of treatment for 39 patients). Alongside available clinical data, several PET parameters were investigated: standard uptake volume (SUV) max/peak/mean, metabolic tumor volume (MTV), total lesion glycolysis (TLG) and a proposed Z-normalized combination of MTV and SUV peaks (ZMP). In the bivariate analysis, HPV status was the most independent predictor in combinations with N3 stage, ZMP, TLG, and MTV (\(p<0.02\)). The 18-F-fluoro-2-deoxyglucose (FDG)-PET parameters at two weeks into radiotherapy decreased by 30%-40% of the initial values, but this decrease failed to improve the prediction models, thus questioning the utility of an intermediary PET-CT after two weeks of treatment when using classical PET parameters [33].  
[PERSON] et al. prospectively included 19 ASCC patients and performed PET-CT assessments before and 12 weeks following CRT. Six VOI were extracted and analyzed: five based on SUV (maximum, mean, median, standard deviation and peak), MTV and TLG. Exact logistic regression and ROC AUC analyses were completed. Two PET-CT parameters were found to be associated with recurrence: MTV bounded by a threshold of 41% maximum SUV on the pre-CRT PET-CT predicted for any recurrence (\(p=0.03\), ROC AUC 0.89), and median SUV within a VOI bounded by an SUV of three on post-CRT PET-CT correlated with local recurrence (\(p<0.01\), ROC AUC 1.00 with a median SUV threshold of 3.38) [34].  
The only found research using radiomics for PET-CT in ASCC patients was a monocentric retrospective trial trying to predict PFS, that included 189 non-metastatic ASCC patients [35]. A single operator semi-autonomously segmented the primary tumor and associated lymph nodes on baseline PET-CT, and voxels with an SUV greater than 1.5 times the mean liver SUV were included in the final VOI. First, second and third-order features were extracted using the LifeX software [36]. Elastic net regularization and feature selection were used for logistic regression model generation on a randomly selected training cohort and applied to a validation cohort. Three models were created and evaluated using ROC-AUC analysis: a clinical prognostic factors model (age, sex, tumor and nodal stage), a radiomics model and a combined model. GLCM \"entropy\", neighborhood grey-level different matrix (NGLDM) \"busyness\", minimum CT value (lowest Hounsfield unit within the lesion) and standardized MTV were selected for inclusion in the prognostic model, alongside tumor and nodal stage. The combined model performed best for PFS prediction comparatively with the clinical and radiomics model, with AUCs of respectively 0.738, 0.602 and 0.660 on the validation set. This highlights the usefulness of combining both imaging and clinical parameters for increased prediction performance.
# Antioxidant, Antibacterial, and Cosmeceutical Potential of Four Common Edible Mushrooms
## 3 Results and Discussion
### Antibacterial Activity
coli_, and _P. aeruginosa_, with MIC values of 13.49, 13.49, and 28.34 mg/mL, respectively. [PERSON] et al. [19] also evaluated the antimicrobial effect of ethanolic extracts from _L. edodes_ and _A. bisporus_ against _S. aureus_ and _E. coli_. The authors found that _L. edodes_ extract was the most efficient against the bacteria under analysis, with MIC values of 1.56 mg/mL and 100 mg/mL for _S. aureus_ and _E. coli_, respectively, while _A. bisporus_ obtained a MIC value of 200 mg/mL for both bacteria. In our investigation, _L. edodes_ did not show such a great result against _S. aureus_, showing a MIC of 20 mg/mL for both MS and MR strains. However, we obtained a better result against _E. coli_ with a MIC value of 20 mg/mL. In another study by [PERSON] [53], the antimicrobial effect of ethanolic extracts of _L. edodes_ and _A. bisporus_ against _K. pneumoniae_, _S. aureus_, and _A. buumannii_ was evaluated. The author demonstrated that the extracts of _L. edodes_ were more effective, showing MIC values ranging from 5.9 to 6.01 mg/mL, while the extracts of _A. bisporus_ displayed MICs ranging from 7.21 to 9.54 mg/mL [53]. According to [PERSON] et al. [29], the ethanolic extracts of _P. ostreatus_ and _L. edodes_ were more efficient than those of _A. bisporus_ in inhibiting the growth of MR and MS _S. aureus_, with MIC values of 2.5 mg/mL and 10 mg/mL, respectively. In this study, the extract of _A. bisporus_ showed the same MIC for MS _S. aureus_ but not for MR _S. aureus_ (20 mg/mL). Regarding the effect of the extracts against _P. aeruginosa_, only _B. edulis_ was able to inhibit its growth (MIC = 20 mg/mL); our results are in agreement with the findings of [PERSON] et al. [29]. Several studies evaluate the antimicrobial effect of mushroom extracts [54], and the results vary considerably among them. There are many factors that could be responsible for this discrepancy, such as the mushroom origin, the extraction methodology, which includes per se many variables (ratio, solvent, temperature, and the type of assisted extraction, among others), and the nature of the bacteria, which can be from a culture collection or from clinical isolates with an antibiotic resistance profile. In our study, the edible mushroom _B. edulis_ was the only one that inhibited the growth of all the bacterial isolates tested. Since mushrooms have bioactive compounds that, combined with conventional therapies, can have synergistic effects in cancer cell signaling pathways [55; 56], it is possible that _B. edulis_ could also enhance the effect of antibiotics or other agents used to treat microbial infections.
## 3 Proposed Method
### The Framework of IDP-YOLOV9  
This study optimized the original network architecture to address the issues of error accumulation in the parameter estimation process, which can lead to incomplete image processing and distortion. To overcome these challenges, we employed a parallel architecture that combined the TRA image dehazing, deraining, and desnowing modules with the adaptive image enhancement module (DLIE). The resulting features were then integrated with the YOLOV9 detection network, creating an end-to-end object detection algorithm suitable for power scene images captured by drones under adverse weather conditions. The DLIE module consisted of five adaptable image enhancement parameters, essentially functioning as pixel-level filters. These parameters included the following: white balance (WB), which eliminated color deviations caused by atmospheric light; gamma correction, which restored details in darker areas; contrast enhancement, which improved overall visibility in regions affected by heavy fog, raindrops, or snowflakes; hue adjustment, which emphasized the overall atmosphere or produced specific effects; and image sharpening, which effectively enhanced the visual clarity of dehazed, derained, and desnowed images.  
Figure 1 illustrates the IDP-YOLOV9 network architecture. This comprehensive approach ensures more accurate and reliable object detection in power scene images captured under adverse weather conditions.  
Figure 1: (**a**) Network architecture diagram of IDP-YOLOV9; (**b**) parallel framework diagram of TRA  
(Three-Weather Removal Algorithm) module and DLIE (Deep Learning-based Image Enhancement) module; (**c**) TRA module for Image DeFogging, DeRaining, and DeSnowing; (**d**) DLIE module for Image Enhancement.
## 3 Proposed Security Protocol for Initial Attach in LTE
### Initial Attach with GUTI  
After the IMSI is safely transmitted, UE, eNB, MME and HSS carry out up to the \"AS security setup\" process during the \"ECM connection establishment\", \"authentication\", \"NAS security setup\", \"location update\" and \"EPS session establishment\" processes.  
After the \"AS security setup\", the eNB encrypts RNTI to MME using the secret key of the \"AS security setup\" to allocate the RNTI to the UE. The MME encrypts the transmitted RNTI to RN\({}_{UE\_2}\) saved in the \"ECM connection establishment\" process to transmit to the eNB, and the eNB allocates the RNTI by transmitting the relevant value to the UE.  
Initial attach with GUTI is the initial attach process of the case in which the UE that successfully performed an initial attach with the IMSI process re-accesses, due to a series of events. f() functions that are used in Section 3.2, are as the following formula.  
f() = hash( Expansion P-Box( hash( S-Box(RN\({}_{UE\_1}\)), S-Box(RN\({}_{MME\_1}\) ), NAS seq. no. ) ) )
## 3 AUV-Aid Underwater Data Gathering Problem
### Motivation and Overview  
Leveraging the sub-sea MI communication model in [27], as shown in Figure 2, the MI channel capacity can exceed \(5\times 10^{6}\) bps. Moreover, we have built up a sub-sea MI testbed as shown in Figure 3. A data transmission based on Quadrature Phase Shift Keying (QPSK) modulation scheme with symbol rate equal to 100 kHz is demonstrated in Figure 4. Limited by the size of the water tank, we were only able to separate the transmitter and receiver at the distance of 0.7 m. The testing results show that the data transmission is 100% successful. Thus, leveraging the advantages of reliable and high speed MI communication, long distance acoustic communication and the mobility of AUVs, UWSNs can dramatically reduce energy cost on data gathering with high reliability.  
Figure 2: Channel capacity of subsea MI (magnetic-induction) wireless communications with transmission distance = 1 m and coil radius = 0:05 m.  
Consider an AUV and many sensors form a underwater wireless sensor network. Each sensor is anchored to the target area for ocean monitoring/sensing applications. Both magnetic-induction (MI) and acoustic communication modules are equipped to every sensor for short distance high speed communication and long distance low speed communication, respectively. We assume that the perceived data are periodically gathered and delivered to the designated surface station. During each period, the AUV travels from the surface station, dives into the water and visits a subset of the sensors for data collection, brings the collected data back to the surface station, and prepares for the  
Figure 4: QPSK (quadrature phase shift keying) data transmission results for sub-sea MI (magnetic-induction) wireless communications.  
Figure 3: Sub-sea MI communication testbed.  
next round data collection. Since the MI communication range is quite short, the AUV should visit the sensor position exactly for MI communication. These sensors that are not visited by the AUV transmit their sensing data to chosen sensors via acoustical channels.  
Suppose there are \(S=\{s_{1},s_{2},\cdots,s_{n}\}\) sensors, and the \(i\)-th sensor \(s_{i}\) is anchored at the coordinate \(c_{i}=[x^{i},y^{i},z^{i}]\). Let \(M=\{m_{1},m_{2},\cdots,m_{k}\}\) denote the acoustic sub-channel set. The transmission range on the \(m\) acoustic sub channel is \(m^{i}\), and the interference range of this channel is \(m^{i}\). The neighbor sensor set for each sensor can be obtained according to their coordinates and communication range. For simplicity, in this paper, we assume the AUV's speed is constant at \(V\), and the MI communication rate is constant at \(r_{mi}\).
## 5 Case Studies
### Inertia Response Analysis of Wind Farms
#### 5.3.1 Comparison of Different Wind Speeds  
The equivalent virtual inertia of the wind farm under operating condition 1, evaluated using the proposed inertia assessment method, is shown in Figure 8.  
The findings from the inertia assessment conducted using the proposed methodology exhibit a strong correlation with the simulation values across a range of wind speeds. Furthermore, the results reveal that the equivalent virtual inertia of the wind farm is contingent upon wind speed, exhibiting an increase in response to rising wind velocities.  
Figure 8: Simulated and evaluated inertia values at different wind speeds.  
Figure 7: Partial identification of data acquisition: (**a**) rotor speed operating data; (**b**) grid angular frequency operating data; (**c**) power of inertia control operating date.
## 2 Methodology
### Uncertainty Propagation Analysis
#### 2.4.2 Unscented Transform  
The intuition upon which the Unscented Transform technique is based is that it is simpler to approximate the distribution of the inputs rather than approximate the model used to generate the output distribution. The approximation of the input distribution is obtained by generating a set of sigma points that represent the probability distribution of the input and which are used to feed the exact model. According to the literature [1], \(2d+1\) sigma points are sufficient to obtain a good representation of the input and of the mean and variance of the output, where \(d\) represents the dimension of the input perturbed data. The UT generally requires fewer simulations than the PCE, but it gives a limited amount of information (namely, the first two moments of the output distribution), assuming a Gaussian distribution for the output. However, in some cases, it could be sufficient to know the mean and the variance of an output distribution, making the UT an interesting technique for preliminary evaluations.  
Among the several algorithms presented in the literature, here, the so-called Generalized Unscented Transformation [27] has been employed since it allows us to deal with constrained sigma points like those generated from the uniform distributions in Equations (3) and (4) for the DTT FDU input parameters at hand, avoiding having non-physical sigma points outside of the prescribed distributions.  
In this work, the UT has been employed to check that at least the first two moments calculated with the PCE are correct to add an additional benchmark in the EM model in which the comparison to the MC was not viable due to the excessive computational time of the detailed model.
## 2 Materials and Methods
### Methodology
#### 2.3.1 Model Assumptions and Workflow  
The preprocessing, fusion, and extraction of DN values in this study were all performed using ArcGIS Pro 3.0.  
The model is constructed based on the following core assumptions:  
1. Night-time light intensity positively correlates with residential occupancy;
2. Non-residential light interference (e.g., roads, factories) can be effectively excluded via POI/AOI data;
3. The DN threshold for fully occupied areas can be accurately calibrated through sampling. The workflow consists of four key stages (Figure 2):
## 2 Materials and Methods
### Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDX) Analysis  
A scanning electron microscope was used to examine the surface microstructure and was used to determine the degree of damage on the ceramic surface after exposure to gastric acid juice. SEM analysis was carried out on an FEI Quanta 250 microscope (Eindhoven, The Netherlands). SEM analysis parameters were HV mode, 30 kV, ETD (Everhart-Thornley detector for secondary electrons), with two magnification orders, one for a general overview (100\(\times\)) of the image/measurements and another for a higher surface topography (5000\(\times\)) for regions of interest. The observations were made before and after immersion. EDX analysis (energy dispersive X-ray spectroscopy) was carried out with an EDAX system (Apollo X detector, Ametek, Mahwah, NJ, USA). For better conductivity and to acquire high resolution SEM imaging, each specimen was covered with gold: 4 nm/deposition, 3 times, using an AutoAgar Sputter Coater (Agar Scientific Ltd., Essex, UK). The identified chemical species were expressed as atomic relative percent (At%) and weight relative percent (Wt%).
# Movable Platform-Based Topology Detection for a Geographic Routing Wireless Sensor Network
## 3 Workflow
### Node Couple Capture  
The results obtained with RSSI are greatly affected by environment in realities and the practical error might be up to 50% [35]. Therefore, we measured many times and positioned nodes with error cancellation method. We defined two nodes that communicate in network as one node couple. Supposing the movable platform is U, and it enters WSN area randomly at constant speed V, when air data packets are captured, platform U keeps moving rectilinearly for distance L, which is within the communication range of the node couple. During this period, the platform receives interaction data packets of node couples without stop, supposing the platform identifies signal source direction at precision 10-20\({}^{\circ}\) (device costs restrict signal direction recognition precision of the platform). Each time when data are captured, conversion is made with Equation (1) according to signal intensity and the intensity and distance standards that are videotaped in advance for this area, and a 10-20\({}^{\circ}\) fan area, with a radius of d, will be formed according to the position that platform U is at when it receives data packets. The value of distance d calculated will only be larger than d', the actual distance from platform to node (during actual work, signal intensity that the platform receives is smaller than the signal intensity actually received in theory, due to such factors as obstacle, air and electromagnetic interference). Therefore, all nodes detected are within the fan area marked by the platform. When the platform is beyond the communication area, the intersection of all fan areas is roughly the position of nodes. When two positions of the node couple are acquired, it is determined that data packet type is the command sent by station or source nodes according to the MAC frame information of data packets, and the node closer to station is determined according to node receiving and sending behaviors in the node couple (shown in Figure 6).  
Figure 5: System Structure.
## 3 Experimental Setup
### Test Conditions
#### 3.6.8 Thermal Energy Chosen for the Test  
The thermal power not converted into electricity by the \(PV\) system (see Equation (5)) depends on the solar irradiance and electrical efficiency of the \(PV\) system, which in turn depend on the ambient temperature and solar irradiance. These parameters depend on the location, day, and time chosen (see Figure 11). Therefore, the choice of thermal energy not converted into electricity by the \(PV\) system depends on multiple parameters. The thermal power not converted into electricity by the \(PV\) system chosen was 300 and 400 (W). These values were chosen for the following reasons:  
1. The concentration factor of the \(LCPV\) system is 10.41. To determine this value, the parameters of the primary reflector system shown in Table 1 and the dimensions of the prototype were used. With the incident solar irradiance at the Cairo and Almeria locations, a thermal power not converted into electricity by the PV system of approximately 300 (W) is obtained.
2. The choice of 300 (W) allows the Medellin, Bangkok, Morelia, and Karachi locations to increase the \(LCPV\) concentration factor in future studies.
3. The choice of 400 (W) allows all locations under study to be able to increase the \(LCPV\) concentration factor in future studies.  
Figures 13 and 14 show the results of the average water temperature in the flat tube obtained in the tests, the ambient temperature at which the test was performed, and the average temperature of the PV system obtained using Equations (6)-(10). From the test, \(T_{w}\) and \(Q_{conv}\) are obtained, and by means of Equations (8) and (7), \(T_{AC}\) is obtained. Once \(T_{AC}\) is known, by means of Equation (6), \(T_{PV}\) is obtained. On the other hand, once \(T_{PV}\) is known, \(Q_{radPV}\) can be determined by Equation (9). \(Q_{convPV}\) can be obtained by subtracting the losses \(Q_{cond}\) and \(Q_{radPV}\) from \(Q_{th}\).  
Figure 12: Simplified heat transfer scheme.  
Figure 14: Temperatures for power not converted into electricity by the PV cells of 400 (W).  
Figure 13: Temperatures for power not converted into electricity by the PV cells of 300 (W).
## 2 Emerging Applications of 6G
### Industry 4.0 and Industry 5.0  
Industry 4.0 has transformed the traditional workflow of factory settings by integrating connectivity, IoT, and intelligence [15]. Within the Industry 4.0 factory setting, activities such as managing environmental conditions of the production line such as temperature and humidity using IoT technologies can tolerate comparatively higher latency and packet loss rate values, such as 50-100 ms and \(10^{-3}\), respectively [16]. However, activities that deal with real-time machine and robot handling will require more stringent latency, as low as 25 u, due to precision and health and safety requirements. Moreover, the recent discussion on Industry 5.0 has taken another step towards a fully connected industrial environment, with digital twins, human-centric communication, and artificial intelligence (AI) [17]. The Industry 5.0 environments are expected to collaborate seamlessly with humans, which requires low latency and high data rate connectivity for the monitoring, and edge computing and AI on-site for data processing.
# Legumes as Functional Food for Cardiovascular Disease
## 3 Legumes as Functional Food for Cardiovascular Diseases
### Cardiovascular Benefits of Soybean 7S \(\beta\)-Conglycinin
#### 3.1.3 Soybean Lunasin  
Another important soybean peptide that has reported anti-inflammatory, antioxidant, anti-hypertensive, hypocholesterolemomic, and anti-cancer benefits is lunasin. Lunasin is a 43-amino acid (SKWQHQQDSCRKQLQGVNLTPCEKHIMEKIQGRGDDDDDDDDD) (MW = 5.5 kDa) peptide derived from the methionine-rich Gm2S-1 gene encoding the 2S albumin found in the soybean cotyledon [259; 272; 273; 274; 275; 276]. In soybeans, lunasin was found to constitute 4.4-70.5 mg/soybean protein [259]. Lunasin was found to be highly bioavailable and heat-resistant [275]. Though this protein was originally found in soybean, it has been later detected in cereals and pseudocoreals including wheat, barley, rice, rye, triticale, and amaranth [278; 279; 280; 281].  
Lunasin's hypocholesterolememic effect is associated with reduced HMG-CoA reductase gene levels, thus, reducing cholesterol biosynthesis. Furthermore, lunasin reduced HMG-CoA expression and induced LDLR production leading to increased LDL binding causing enhanced clearance of plasma LDL-C as shown in HepG2 liver cells. Lunasin was also found to reduce LDL-C levels in pigs [151].  
Lunasin was also found to possess anti-oxidant properties by scavenging peroxyl and superoxide radicals in vitro [282] and to be able to chelate Fe\({}^{2+}\) ions, thereby, preventing hydroxyl radical formation through a Fenton reaction [30; 283]. Lunasin was also found to protect cell viability and antioxidant defenses in human Caco2 cells treated with hydrogen peroxide and _tert_-butyl hydroperoxide after 24 h [282].  
Lunasin was also found to have anti-inflammatory effects as shown by the inhibition of various pro-inflammatory biomarkers such as the IL6 and IL-1\(\beta\) interleukins, TNF-\(\alpha\), NF-\(\kappa\)B, cyclooxygenase 2 (COX2), prostaglandin E\({}_{2}\) (PGE\({}_{2}\)), and inducible nitric oxide synthase (iNOS) by activating various inflammatory pathways such as AKT/NF-\(\kappa\)B in macrophages [284; 285]. [PERSON] et al., (2010) found that the RGD tripeptide motif from lunasin binds to the \(\alpha\)V\(\beta\)3 integrin and is involved in macrophage aggregation during inflammation that might inhibit pro-inflammatory pathways [286].