esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results

\begin{tabular}{c c c c c c} \hline
**EEG Indices** & **Site** & **Before Inhalation (\(\mu\)V\({}^{2}\))** & **During Inhalation (\(\mu\)V\({}^{2}\))** & **\(t\)-Test** & **\(p\) Value * \\ \hline \multicolumn{6}{c}{**Both Genders**} \\ \hline \multirow{2}{*}{RST} & Af4 & 0.2598 \(\pm\) 0.0519 & 0.1882 \(\pm\) 0.0395 & 2.363 & 0.029 \\  & Cp5 & 0.2838 \(\pm\) 0.0674 & 0.2084 \(\pm\) 0.0461 & 2.231 & 0.038 \\ \hline RMT & Cp5 & 0.3897 \(\pm\) 0.0969 & 0.2926 \(\pm\) 0.0750 & 2.143 & 0.045 \\ \hline \multirow{2}{*}{RSMT} & Af4 & 0.5810 \(\pm\) 0.1191 & 0.3760 \(\pm\) 0.1004 & 2.388 & 0.027 \\  & Cp5 & 0.6735 \(\pm\) 0.1557 & 0.5010 \(\pm\) 0.1165 & 2.448 & 0.024 \\ \hline \multicolumn{6}{c}{**Women**} \\ \hline \multirow{2}{*}{RST} & Af4 & 0.3588 \(\pm\) 0.0891 & 0.2453 \(\pm\) 0.0729 & 2.383 & 0.041 \\  & Cp5 & 0.4377 \(\pm\) 0.1140 & 0.2955 \(\pm\) 0.0838 & 2.627 & 0.027 \\  & Cp6 & 0.4045 \(\pm\) 0.0959 & 0.3210 \(\pm\) 0.0923 & 2.299 & 0.047 \\ \hline \multirow{6}{*}{RMT} & O2 & 0.6098 \(\pm\) 0.1883 & 0.4806 \(\pm\) 0.1747 & 2.569 & 0.030 \\  & F8 & 0.4973 \(\pm\) 0.1614 & 0.3731 \(\pm\) 0.1426 & 2.569 & 0.030 \\  & Cz & 0.4589 \(\pm\) 0.1431 & 0.3278 \(\pm\) 0.1397 & 2.424 & 0.038 \\  & Pz & 0.4835 \(\pm\) 0.1337 & 0.3313 \(\pm\) 0.1046 & 3.276 & 0.010 \\  & Af4 & 0.4475 \(\pm\) 0.1200 & 0.3385 \(\pm\) 0.1129 & 2.399 & 0.040 \\  & Fc2 & 0.4240 \(\pm\) 0.1502 & 0.3252 \(\pm\) 0.1340 & 2.690 & 0.025 \\  & Cp5 & 0.6034 \(\pm\) 0.1633 & 0.4183 \(\pm\) 0.1376 & 2.870 & 0.018 \\  & Cp6 & 0.5189 \(\pm\) 0.1448 & 0.3897 \(\pm\) 0.1414 & 2.463 & 0.036 \\ \hline \multirow{2}{*}{RSMT} & Cz & 0.7992 \(\Figure 2: The t-mapping of EEG changes (absolute waves) before and during the inhalation of geosmin in both genders. Arrows show the significant changes in the region during the inhalation of geosmin.
## 2 Materials and Methods
### Evaluation
#### 2.2.2 Real Data  
DW-MRI images of five Head-and-Neck (HN) patients affected by oropharyngeal tumor (three males and two females) and four pelvic patients affected by rectal tumor (two males and two females) were considered as examples of IVIM acquisitions for tumor diagnosis. They were collected from retrospective studies approved by the local ethics committee and already published in previous works [14; 15; 19].  
DW-MRI images were acquired using a 1.5 T system (Optima MR 450w, GE Healthcare, Milwaukee, WI, USA) and obtained by single-shot spin-echo echo-planar imaging with: acquisition matrix equal to \(128\times 128\); in-plane resolution equal to 1.094 \(\times\) 1.094 mm\({}^{2}\); TR/TE equal to 4500 ms/72 ms for HN and 3500 ms/77 ms for pelvis; slice thickness equal to 4 mm for HN and 5 mm for pelvis; same \(b\)-values as in the simulated images. The estimated average \(SNR\) of the acquired images was equal to 80 and 50 for the HN and pelvis, respectively; in this way, we were able to test the performance of the methods at different \(SNR\) values also in the real setting.  
Three different tissues were considered in the two datasets: masticatory muscle, parotid gland and tumor for HN; internal obturator muscle, prostate and rectal tumor for pelvis. An average of 4-5 slices containing the three structures of interest were selected for each patient, excluding basal and apical slices to avoid partial volume effects, for a total of 39 two-dimensional images (21 for HN and 18 for pelvis). In the two female pelvic patients, one structure (the prostate) was not considered.
# Multi-Pedestrian Tracking Based on KC-YOLO Detection and Identity Validity Discrimination Module
## 3 Multi-Pedestrian Tracking Method Based on IVDM
### Construction of KC-YOLO Detection Model  
We propose a model called KC-YOLO, which is applied to complex scenes in surveillance video and uses the YOLOv5 [30] detection model as the base algorithm.  
The core steps of the KC-YOLO model are as follows:  
* Determine the optimal anchor frame that is compatible with the input pedestrian image;
* Extract the deep features of the pedestrian image through the KC-YOLO network. Use the attention mechanism to highlight its salient information and achieve adaptive feature refinement.  
We introduce the CBAM into the backbone and neck parts of the detection network for the following reasons: the backbone part is the key part for extracting pedestrian features, while the neck part fuses the features and sends them to the head for prediction, and the introduction of the CBAM here can improve the feature extraction ability of the network more effectively. The structure of the improved KC-YOLO network is shown in Figure 2.  
Concat is primarily responsible for combining addition and residual convolution operations. Through feature fusion, it allows the detection network to simultaneously utilize the extracted shallow and deep features. The main purpose of the upsample structure is to perform upsampling operations; CBL is a convolutional block. Within CSP2_1, the input feature map is divided into two parts. One part is processed through a subnetwork, while the other part undergoes further processing directly. These two sets of feature maps are then concatenated and used as input for the next layer. By combining the features processed by the subnetwork with those processed directly, a series of convolution operations are performed. This approach effectively integrates low-level detail features with high-level abstract features, thereby improving the feature extraction efficiency.
## 3 Materials and Methods
### HPLC-DAD-MS\({}^{n}\) Analysis of LE, DM and UM  
An Agilent 1260 chromatograph with an autosampler and a diode array detector (DAD), interfaced with a Varian MS 500 ion trap mass spectrometer, was employed to quantify cannabinoids and phenolic compounds in the LE, DM, and UM of the 9 hemp commercial varieties. For the analysis of flavonoids and minor compounds, the column was an Agilent Eclipse XDB C18 (3.0 m \(\times\) 150 mm \(\times\) 3.5 \(\mu\)m), and the mobile phase was represented by a mixture of 1% formic acid in water (A) and acetonitrile (B). At the beginning, the gradient was 95% A, and in 30 min reached 100% B, with a flow rate of 0.4 mL/min. Data were collected by DAD in the \(\lambda\) range of 200-400 nm. The mass spectrometer was provided with an electrospray ion (ESI) source, which was employed in negative ion mode. The MS parameters were the following: spray chamber temperature, 45 \({}^{\circ}\)C; needle voltage, 4700 V; capillary voltage, 85 V; RF loading, 80%; nebulizing gas pressure, 25 psi (nitrogen); drying gas pressure, 15 psi; drying gas temperature, 300 \({}^{\circ}\)C. Spectra were acquired in the 50-1000 \(m\)/\(\underline{\times}\) range. Chromatograms were acquired from the turbo data depending on the scanning (TDDS) mode, allowing the generation of fragmentation spectra for the most intense ionic species. For the analysis of cannabinoids, an Agilent XDB (4.6 m \(\times\) 250 mm \(\times\) 5.0 \(\mu\)m) was used as the stationary phase. The gradient of elution was performed using water 1% formic acid (A) acetonitrile (B) and methanol (C). The gradient started with 30% A and 70% B, and in 20 min arrived at 70% B and 30% C; in 23 min it reached 100% C and stayed isocratic up to 33 min. Data were detected with DAD in the \(\lambda\) range of 200-400 nm. ESI-MS spectra were collected in positive ion mode for the neutral cannabinoids, and in negative ion mode for the acidic forms. Compounds were identified based on \(m\)/\(\underline{\times}\) values and retention times, and by comparison with authentic standards. For quantitative purposes, standard solutions in concentration from 0.1 to 100 \(\upmu\)g/mL were set up to develop the calibration curves. For this purpose, CBD, cannabidiolic acid (CBDA), THC, and 8-9-tetrahydrocannabinolic acid (THCA) were used for cannabinoids' quantification, while rutin and quercetin-3-O-glucose were selected for phenols content determination. For the analyses, LE, DM and UM samples were finely ground and about 200 mg were weighed and extracted with 25 mL of MeOH/H\({}_{2}\)O 70:30.  
The extracts were sonicated in an ultrasound bath for 15 min and centrifuged, and then the supernatants were taken and inserted into HPLC vials.
# Multi-Step Traffic Speed Prediction Based on Ensemble Learning on an Urban Road Network
## 3 Prediction Methodology
### Feature Construction  
Notably, representative features determine the performance of forecasting modeling. In order to determine the appropriate model inputs, this study chooses initially the ten spatiotemporal candidate variables of travel speed and flow for correlation analysis, as shown in Table 1, which involve the time of day, day of week, and upstream and downstream connected links. Correlation analysis is a statistical analysis method that studies the correlation between two or more random variables. Among them, the Pearson correlation coefficient proposed by Pearson in 1895 is one of the most influential coefficients in correlation analysis to select the final representative features for the prediction model [35].
## 1 Introduction
[31] using a loudspeaker as a pulsation inducer in flat film cooling have indicated that cooling jet pulsation can impact the film cooling efficiency. [PERSON] and [PERSON] [32] employed numerical simulation to demonstrate that a high-frequency pulsating jet exhibits higher cooling efficiency, unlike low- to medium-frequency cooling jet pulsation. In the operational environment of film cooling, the unsteady flow inside gas turbines causes the shedding wake of upstream blades to periodically affect downstream blades, thereby impacting film cooling efficiency [33; 34]. Experimental studies by [PERSON] et al. [35] in a low-speed wind tunnel have revealed that the influence of the mass flux ratio on the film cooling effectiveness diminishes under high turbulence intensity and unsteady wake conditions across many areas of the blade surface, and that the film cooling effectiveness decreases as the wake Strouhal number increases. Thus, optimizing film cooling performance under the combined effects of pulsating jets and wakes has become a key challenge in current research. For instance, [PERSON] et al. [36] studied the impact of pulsating injection and mainstream attack angle on film cooling performance through experimental and numerical simulations, finding that increasing the attack angle and pulsation frequency significantly improves average film cooling effectiveness. [PERSON] et al. [37] explored the effects of vortex generators and pulsation on compound-angle film cooling, showing that pulsation can enhance cooling performance under specific conditions. [PERSON] et al. [38] found that combining impingement and film cooling technologies significantly improves overall blade cooling efficiency, with pulsating jets playing a critical role in enhancing the cooling effect. [PERSON] et al. [39] conducted studies on the influence of similar flow conditions on film cooling performance, providing additional references for pulsating jet cooling technology research. Although significant progress has been made in film cooling technology, many challenges remain due to the complex interaction mechanisms among various factors. The interplay between pulsating jets, wakes, and other flow phenomena has not yet been fully understood. Optimizing the film cooling structure under various operational conditions to further enhance cooling efficiency remains an ongoing challenge. This study aims to investigate the film-cooling characteristics of pulsating jets under wake disturbances through numerical simulations and to explore the impact of wake interactions on film-cooling effectiveness in gas turbines, providing theoretical support for optimizing film-cooling technologies. In this study, we propose that the integration of periodic wake patterns with a pulsating jet may enhance the efficiency of film cooling. The effectiveness of this integration is anticipated to depend on the blowing ratio and the phase lag. We will rigorously evaluate this hypothesis through extensive numerical simulations and a thorough analysis of the resulting data.
## 3 Results
### Spatial Variability
#### 3.2.1 Gldas  
The daily precipitation estimates by GLDAS had the highest correlation and Willmott coefficients in GBC with r = 0.46 and d = 0.62 and the lowest in PRR with r = 0.35 and d = 0.55. The highest RMSE was obtained with the daily precipitation estimates in MTP with an RMSE of 12.88 mm day\({}^{-1}\) and the lowest was in CCR (RMSE = 10.05 mm day\({}^{-1}\)) (Figure 6A). The monthly precipitation estimates had highest correlation and Willmott coefficients in GBC (r = 0.92 and d = 0.96) and the lowest, but significant, in CBA (r = 0.82 and d = 0.90). The highest error in the monthly precipitation estimates was obtained in CCR with an RMSE = 75.12 mm month\({}^{-1}\) and the smallest was in CCR (RMSE = 53.83 mm month\({}^{-1}\)) (Figure 6B). The annual precipitation estimates showed higher variability in the correlation and Willmott coefficients in different locations, with the highest values of r = 0.89 and d = 0.88 and the lowest error of RMSE = 100.51 mm year\({}^{-1}\) obtained in GBC (Figure 6C).  
The daily precipitation estimated by GLDAS in MTP had the highest performance as indicated by the categorical indicators with CSI = 0.55, FAR = 0.04%, BIAS = 0.59, and POD = 0.57). It was noticed that the resulted categorical indices tended to decrease with increasing latitude, until reaching the lowest values in CCR of CSI = 0.32, POD = 0.32, FAR = 0.06, and BIAS = 0.35) (Figure 6D).  
Figure 5: Average values with the corresponding \(\pm\) Confidence Interval (CI) of (**A**) daily (mm day\({}^{-1}\)), (**B**) monthly (mm month\({}^{-1}\)), and (**C**) annual (mm year\({}^{-1}\)) precipitation as measured by conventional weather stations and estimated by the GLDAS, MERRA, TRMM, GPM, and GPCP products in the state of Mato Grosso, Brazil. For (**A**), the daily values for the entire study period were averaged, while for (**B**), the total monthly values for all months during the study period were averaged, and the annual values in (**C**) were the annual values averaged for all years during the study period.
## 3 Calculation Results and Discussion
### Hybrid System Off-Design Performance Evaluation under Variable Operation Parameters  
This study primarily focuses on the design point, where optimal performance is established. To gain a deeper understanding of the system's behavior and assess its performance under less-than-optimal conditions, an off-design analysis was conducted. This analysis investigates the impact of varying critical parameters on achieving the best performance. Specifically, the selected parameters for analysis are the reformer O/C ratio, SOFC fuel utilization, and HPC outlet split ratio. By exploring these variable parameters, this study aims to evaluate the system's performance when these key factors deviate from their optimal values.  
By conducting the off-design analysis, this study aims to gain insights into how the system performs when operating away from its optimal design point. This analysis provides valuable information on the system's robustness and flexibility in accommodating variations in critical parameters, which is crucial for real-world applications where operating conditions may deviate from the ideal design point. This analysis mirrors the assessment of the main hybrid system performance parameters outlined in Table 2.  
This study offers a unique perspective on enhancing aircraft endurance, range, and reducing emissions through the application of the SOFC/GT system. Given the novelty of this concept, certain system sensitivities remain unexplored, especially in the context of aircraft application. The careful consideration of the three mentioned parameters during system design is crucial for achieving optimal performance at the design point. Neglecting these aspects may incur costs and hinder the attainment of increased endurance, range, and reduction in emissions.
# Interplanetary Student Nanospacecraft: Development of the LEO Demonstrator ESTCube-2
## 2 ESTCube-2 Mission
### Satellite Design
#### 2.3.2 Avionics Stack Systems
A 1/2.5-inch ON Semiconductor MT9P031 5 Mp monochrome CMOS image sensor and a high-resolution imaging lens with a 16 mm focal length and F/1.2 aperture are used to obtain images of the stars. Combining the sensor and optics yields a 21.5\({}^{\circ}\)\(\times\) 15.7\({}^{\circ}\) field of view. The image sensor and FPGA require specific voltage levels. Therefore, the voltage conversion is conducted locally on the ST PCB with switching or low-dropout regulators, depending on the current levels and input/output voltage differences.
## Materials and Methods  
### Field Measurements  
We observed the CO\({}_{2}\) flux and multi-angle canopy spectral reflectance of a sub-tropic mature evergreen coniferous forest stand on a tower at Qianyanzhou Experimental Station (QYZ) in the southeast of China. The CO\({}_{2}\) flux was measured using eddy covariance (EC) method at 39.6 m height flux tower [75]. Half-hourly flux data was provided and processed to derive GPP from measured NEE with daytime ecosystem respiration (R\({}_{e}\)) estimated with an empirical equation fitted using nighttime NEE and temperature measurements [76; 77], as follows:  
\[GPP=NEE+R_{e} \tag{1}\]  
Multi-angle spectral data were acquired from iAMSPEC system mounted at 31 m on the same tower for EC measurements [73; 78], which could concurrently measure incoming solar irradiance and reflected canopy radiance every 2-3 s all day using a dual-channel spectrometer Unispec-DC (PP Systems, Amesbury, MA, USA). A rotator device PTU-D46 (FLIR Systems, Goleta, CA, USA) was used to change view angles to obtain multi-angle canopy reflectance. The view azimuth angle changed at 10\({}^{\circ}\) angular step width with in a range from 45\({}^{\circ}\) to 325\({}^{\circ}\) (defined from geodetic north), and an observation cycle was completed in 15 min with four view zenith angles (firstly fixed at the instantaneous solar zenith angle to finish a horizontal cycle and then at (37\({}^{\circ}\), 47\({}^{\circ}\), 57\({}^{\circ}\)) or (42\({}^{\circ}\), 52\({}^{\circ}\), 62\({}^{\circ}\)) for each view azimuth angle (backward- and forward-looking, changing alternatively) alternatively between two 15-min cycles). After dark current (DC) and calibration of sensors, canopy reflectance (R\({}_{\text{can}}\)) was calculated as observed canopy reflected radiance divided by solar radiance:  
\[R_{can}=\frac{(R_{r}-R_{rDC})\times(R_{i}^{\prime}-R_{iDC}^{\prime})}{(R_{i}- R_{iDC})\times(R_{r}^{\prime}-R_{rDC}^{\prime})} \tag{2}\]  
where \(R_{i}\) and \(R_{r}\) are the irradiance and the radiance of the canopy sensor, respectively; the single quote mark means data measured from the reference target. Then PRI was calculated as [73; 78]:  
\[PRI=\frac{R_{531}-R_{570}}{R_{531}+R_{570}} \tag{3}\]  
The canopy-level big-leaf PRI (PRI\({}_{\text{b}}\)) was simply the arithmetic average of all 2-3 s intervals of observed multi-angle PRI values when the view zenith angle is less than 63\({}^{\circ}\) within half an hour to match the temporal scale of half-hourly LUE.  
Available bioclimatic measurements were also made around the same flux tower, mainly including photosynthetically active radiation (PAR, MJ/m\({}^{2}\)/hh), air temperature (Ta, \({}^{\circ}\)C), vapor pressure deficit (VPD, kPa), relative humidity (Rh), wind direction, wind speed (m/s) and precipitation (mm). The green leaf area index (LAI) of the canopy was 5.8 for the entire study period, and the clumping index (\(\Omega\)) was 0.57 estimated from hemispherical photos for this forest. Data from day of year (DOY) 101 to 275 in 2013 were used in this study.
# Resource Management and Secure Data Exchange for Mobile Sensors Using Ethereum Blockchain
## 4 Methodology
### MS Operation Module
Also, it directly contributes to introducing security loopholes into the N2S mode of communication. This means attackers can leverage a victim MS and traverse the entire network structure. Hence, verifying each MS's legitimate identity is essential before authorizing the node to permit caching or allow data exchange operations. To render an effective transmission, an MS must have access to the resource-level information stored within the cache of another MS or AP while performing its mobility. The proposed scheme introduces controlled constraints to realize the complexity associated with the data transmission process. The first constraint attribute chosen is the transmission criticality coefficient \(\alpha\), which the user with MS defines. The second constraint attribute is the data size \(ds\), and the third constraint is the permissible latency of transmission \(pl\). The constraint attributes differ from each other. It is based on the computational effort required to process varied forms of data transmission with various content to be exchanged. For this purpose, the GN provides resources (especially channel capacity), and different costs are assigned according to the demands of the data transmitted and exchanged by the MS.
# The Potential of Focusing Acoustic Retroreflectors for Architectural Surface Treatment
## 3 Results
### Large Ideal Reflector Array
#### 3.1.2 Spatial Distribution of Reflected Energy  
The four types of patches had markedly different effects on the spatial distribution of reflected sound. Figure 7 shows results for the 1.2 m patches, which were good performers for both focusing and non-focusing space-reversal configurations. The simple reflector reflected sound quite evenly across the receiver plane, with no sound concentration zones. The focusing patches created strong zones of sound concentration, which were mostly not at the source location. The depicted 2.4 \(\times\) 2.4 m receiver square aligned with four 1.2 m patches, the natural foci of which were in the center of each quadrant. The non-focusing space-reversal patches usually concentrate sound in the vicinity of the source. The sound field is patterned from the superimposed projections of multiple square reflectors, and in many cases the source position is not at the sound field maximum. On the other hand, in most cases the focusing space-reversal patches have a maximum at or close to the source. The contrast across the receiver square is consistently more than 20 dB for focusing space-reversal patches.  
Further results for non-focusing and focusing space-reversal patches are shown in Figure 8 for small (0.6 m) and large (2.4 m) patch sizes. Focusing had little effect on the reflected sound field when the patch size was small, but had a strong effect when the patch size was large. For the non-focusing 2.4 m space-reversal patches, the reflected sound field was not noticeably concentrated at the source, but the reflected energy level is considerably greater than the simple specular case. Of course, it should be borne in mind that the receiver square was the same size as the 2.4 m patches, so the nine projected patch images tend to fill much of the receiver square. By contrast, the focusing 2.4 m space-reversal patches exhibited a strong concentration of reflected energy onto the source.  
Figure 7: Spatial distribution of reflected energy level over a 2.4 \(\times\) 2.4 m square for 25 source positions 2 m from the 7.2 \(\times\) 7.2 m reflector, with 1.2 m patches. The top left quadrant shows the simple reflector; the top right quadrant shows a focusing array; the bottom left quadrant shows the 1.2 m space-reversal array; and the bottom right quadrant shows the focusing space-reversal array. Source locations are indicated by the green circles (the source for the bottom left subplot of each quadrant is outside of the receiver square). Values are speech-A-weighted.