esa-sceva/satcom-chunk-collection huggingface dataset:
# Review: A Survey on Objective Evaluation of Image Sharpness
## 2 Evaluation Methods and Analysis
### Spectral Domain-Based Methods
#### 2.2.2 Wavelet Transform-Based Methods  
The wavelet transform method can obtain the evaluation results by using the localization characteristics of the image, and its process is shown in Figure 5, which is more suitable for the global or local evaluation of the image. In Figure 5, H and L represent the high-pass filter and the low-pass filter, respectively. The high-pass filter and the low-pass filter are used to extract edge features and image approximation, respectively.  
Figure 4: The general framework of spectral domain-based methods.  
Figure 5: The process of wavelet decomposition and reconstruction.  
[PERSON] et al. [63] proposed a no-reference blur image evaluation method based on the wavelet transform, which extracts the high-frequency components of the image and obtains edge-defined evaluation results by analyzing multi-resolution decomposition. [PERSON] et al. [64] presented a global and local sharpness evaluation algorithm based on fast wavelet, which decomposes the image through a three-level separable discrete wavelet transform and calculates the logarithmic energy of wavelet sub-bands for obtaining the sharpness of the image. [PERSON] et al. [65] proposed a method to evaluate the image sharpness of strong local phase coherence near different image features based on complex wavelet transform. [PERSON] et al. [66] proposed a fast blind image sharpness/ambiguity evaluation model (BISHARP). The local contrast information of the image was obtained by calculating the root mean square of the image. At the same time, the diagonal wavelet coefficients in the wavelet transform were used for ranking and weighting to obtain the final evaluation result. [PERSON] et al. [55] proposed a no-reference stereo image quality assessment model based on quaternion wavelet transform (QWT), which extracted a series of quality-aware features in QWT and MSCN coefficients of high-frequency sub-bands and finally predicted the sharpness score.  
The spectral domain-based methods decompose the image into high-frequency and low-frequency components or sub-images of different resolution layers. They then use different functions to process these sub-images so that complex edge information can be extracted more clearly. However, the spectral domain-based methods require converting image information from the spatial domain to the spectral domain, which greatly increases the computational complexity.  
The advantages and disadvantages of the different methods based on the spatial domain/spectral domain are shown in Table 1.
## 3 Results and Discussion
This unequivocally shows that the stability of the SiO\({}_{2}\)-CsPbI\({}_{3}\) PeQDs against polar solvent is greatly enhanced. Therefore, the SiO\({}_{2}\)-CsPbI\({}_{3}\) PeQDs have exhibited optical properties and outstanding stability against air, heat, and polar solvent due to the encapsulation of SiO\({}_{2}\) through the hydrolysis of APTES. Figure 5: (**a**) XRD patterns of the CsPbI\({}_{3}\) PeQD films before and after 7-day storage under ambient air atmosphere (insets show the photographs under daylight irradiation); green stars (\(\star\)) are used to mark yellow phase (\(\$\)) CsPbI\({}_{3}\). The evolutional PL spectra of (**b**) OAm- and (c) SiO\({}_{2}\)-CsPbI\({}_{3}\) PeQD solutions under continuous heating at 100 \({}^{\circ}\)C in air, and (**d**) showing the corresponding normalized PL. With the excellent optoelectronic properties and great stability in mind, we anticipate that the SiO\({}_{2}\)-CsPbI\({}_{3}\) PeQDs could be ideal candidates as down-conversion emitters. To verify this point, we mixed CsPbI\({}_{3}\) PeQDs and PMMA, which were dropped onto the purple LED chip (395 nm). Figure 7a,b show the EL spectra of the OAm- and SiO\({}_{2}\)-CsPbI\({}_{3}\) PeQDs under different driving currents (20-200 mA), respectively. It can be seen that the EL intensity increases with driving current, with the corresponding EL spectra remaining intact and below the upper limit. We therefore set up a driving current of 50 mA to investigate the stability under continuous power on, and the evolutional EL spectra were recorded. Figure 7c,d show the evolutional EL spectra of the OAm- and SiO\({}_{2}\)-CsPbI\({}_{3}\) PeQDs under a driving current of 50 mA, respectively. These spectra show that as time increases, both the EL intensities decrease gradually over a period of 120 min. For the OAm-CsPbI\({}_{3}\) PeQDs, EL intensity dropped significantly to 38% of the initial value, while for the SiO\({}_{2}\)-CsPbI\({}_{3}\) PeQDs, it only fell to 77% of the initial value. These results verify that the SiO\({}_{2}\)-CsPbI\({}_{3}\) PeQDs have excellent stability in practical applications, which coincides with the above results of the enhanced stability against air, heat, and polar solvent. Figure 6: The evolutional PL spectra of (**a**) OAm- and (**b**) SiO\({}_{2}\)-CsPbI\({}_{3}\) PeQD solutions (with hexane:ethanol = 10:1), (**c**) corresponding normalized PL intensity, (**d**) daylight photographs taken at different times, and (**e**) UV light photographs taken at 0 and 40 min. Figure 7: The EL spectra of (**a**) OAm- and (**b**) SiO\({}_{2}\)-CsPbI\({}_{3}\) PeQD LED under different driving currents (20–200 mA). The evolutional EL spectra of (**c**) OAm- and (**d**) SiO\({}_{2}\)-CsPbI\({}_{3}\) PeQD LED under a driving current of 50 mA, with insets showing the LED device with current off (bottom) and on (upper), and (**e**) showing the corresponding normalized EL intensities.
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
## 7 Challenging Issues and Research Directions  
Modeling wireless channels for over-the-sea wave propagation is a relatively new research topic. In this section, important challenging issues for modeling a wireless channel for over-the-sea communication are presented. We also elaborately discuss the research directions that can be helpful for researchers if they intend to design a new channel model.
# Wideband Lorenz Resonance Reconfigurable Metasurface for 5G+ Communications
## 3 Fabrication and Measurement
### Measurement of the Proposed MSRRA  
The MSRRA was measured using a far-field testing system in an anechoic chamber. The block diagram and test scene of the experimental scheme are shown in Figure 5. Two wideband horn antennas, whose working frequency band is from 26 GHz to 40 GHz, are employed as the feed to illuminate the MSRRA aperture and as the receiver to capture the electromagnetic wave reflected from the MSRRA aperture. The two antennas were placed perpendicular to MSRRA, both connected to a Keysight Company model N5525B PNA Network Analyzer (PNA), whose working frequency band is from 10 MHz to 50 GHz.  
The aperture diameter of the proposed MSRRA is 64 mm with a corresponding far-field distance of approximately 850 mm. Therefore, the feed horn antenna is placed 900 mm from the center of the turntable and the receiver horn antenna is placed 1000 mm from the center. The receiver horn antenna is mounted at a position higher than the feed horn antenna, and the geometric center of the line connecting the center of the receiver horn antenna and the feed horn antenna is aligned with the phase center of the MMRRA. When the turntable is rotated to 0 degrees, the feed horn antenna will not block the reflected beam from the MSRRA.  
The phase shift from the bias voltage of the varactor of the MSRRA was measured, and can reach over 300 degrees from 26.10 GHz to 29.93 GHz, as shown in Figure 6.  
The beam-scanning performance at 28.5 GHz of the MSRRA was also measured using the far-field testing system in an anechoic chamber. Some representative measured scan beams with main beam directions from \(-45^{\circ}\) to \(45^{\circ}\), stepped by \(1^{\circ}\),  
Figure 5: Block diagram of experimental scheme.  
Figure 6: MSRRA measurement and simulation results in marked lines and solid lines; they show a good degree of agreement with each other.  
Owning to the well-performing MSRRA unit cell and the effectively optimized phase distribution, well-defined scan beams are successfully obtained in the prototype.  
The bandwidth performance of the MSRRA prototype was also measured. As shown in Figure 8, it is clear that, within \(-3\) dB gain, and within the single beam direction, the beam from 27.05 GHz to 28.9 GHz remains very close; the bandwidth of the single beam is 1.85 GHz.  
Figure 8: Bandwidth of single beam within \(-3\) dB gain. (**a**) From 27.05 GHz to 28.9 GHz, the main beam remains very close. (**b**) Zoom in on the dashed circle of (**a**).  
Figure 7: Far-field measurement and simulation results for several steering beams at 28.5 GHz. They show a good degree of agreement with each other. The direction of the reflect single beam is \(-33^{\circ}\) (light blue line), \(-13^{\circ}\) (pink line), \(0^{\circ}\) (red line), \(13^{\circ}\) (blue line) and \(33^{\circ}\) (green line).
## 2 Power Domain-NOMA  
Generally, PD-NOMA has achieved improved spectral efficiency (SE), enabling users to share resources efficiently. More commonly, a basic two-user downlink NOMA scheme is considered in much of the research, where two users are allocated such that the user U1 is deployed near the base station with high channel gain and U2 is far from the base station, experiencing a relatively low channel gain. Different powers are superimposed upon each other at the transmitter's end for users U1 and U2 as shown in Figure 1. The transmitter assigns high power to the weak user due to its high path loss component and comparatively low power to the strong user. The strong signal receiver will have a higher signal-to-noise ratio than the weak user, implying that the strong user can decode and subtract its signal more efficiently. The robust user's signal at the weak side is considered noise, as its transmission power is lower than the weak user's signal. Therefore, the weak user can decode its signal without performing SIC.  
Thus, grouping or clustering active users in the same resource block is necessary in PD-NOMA and is usually performed as two or multiple users per cluster. The selection in the basic NOMA scheme, with single-antenna BS and users, depends on the rapid channel gains, and the users are ranked accordingly to allow proper SIC decoding, which tends to improve as the channel gain increases, unless the channel gain of the weak user is minimal [29]. Overall, the optimal user clustering requires a thorough search and might not be reasonable for practical systems and networks with many users [30]. Therefore, researchers resort to low complexity solutions to solve the user clustering problem through heuristic algorithms, leading to unexpected results. Instead, the NOMA clustering problem includes the following [29]: A large number of user decoding is considered to be complex, and therefore it is required to limit the exhaustive search to a much smaller region of the achievable set; this will simplify the problem and can lead to the optimum result.  
Figure 1: PD-NOMA with 2 users.
# An Improved Neural Network Cascade for Face Detection in Large Scene Surveillance
## 3 Cascade Framework with Head-Shoulder Information  
### Overall Framework  
In this part, we first use a simple example to depict our idea. We can see from Figure 1 that if we only look at the \"face region\", we can hardly classify whether it is a face or not. However, when we observe the \"head with shoulder\" region around the \"face region\", we can recognize it as a face. Similarly, we can see from Figure 1 that we are likely to classify the \"face region\" as a face if we only look at the \"face region\", but when we observe the \"head with shoulder\" region, we know that it is only a crater.  
Therefore, the overall purpose of our improved framework is to incorporate head with shoulder information into our cascade. The main framework of our face detector is shown in Figure 2. It consists of two parallel CNN cascaded networks. The \"small size cascade\" is used for detecting the faces with a scale smaller than \(20\times 20\) pixels, and a small-scale network is preferable for speed because the number of sliding windows is large for small face detectors. The \"big size cascade\" is used for detecting faces with a scale larger than \(20\times 20\) pixels. The latter cascade takes a longer time to process one sliding window because the network size is larger and more accurate. \"NMS\" means non-maximum suppression, and the details of each block in Figure 2 are discussed in the following subsections.  
Each block in Figure 2 is a module of a convolutional neural network. Blocks named Face-12-small and Face-12-big are face detectors, and their detailed frameworks are illustrated in Figure 3. Block Shoulder-24-1 and Shoulder-24-2 are used to reject false positives in \"small size cascade\", and block Com-24-1 and Com-24-2 are used to reject in \"big size cascade\". There detailed structures are shown in Figures 4 and 7, respectively. We use two modules in each cascade for rejection because such a hierarchy can improve efficiency, as early rejections do not need to be fed into later stages. Blocks Reg-12 and Reg-24 are used to regularize bounding boxes for larger face detectors, as they are more sensitive to the positions. Their structure is presented in Figure 8.  
Given a test image, we first create two image pyramids as the input of the two CNN cascaded networks. After the evaluation of the two parallel CNN cascaded networks using sliding windows, we merge the two detections results. Because our cascade approach takes head and shoulder information into consideration, we name it \"HS-cascade\". As it aims to solve surveillance problems, we do not consider face rotations.  
Figure 2: The overall framework of our proposed face detection algorithm, which consists of two parallel CNN cascaded networks for detecting different scales of faces. NMS, non-maximum suppression; Reg, regularize.
# The Effect of Apple Juice Concentration on Cider Fermentation and Properties of the Final Product
## 1 Introduction  
Cider is a beverage produced by fermenting apple juice. The juice for the fermentation can be obtained in two different ways--either by having the juice freshly pressed and clarified or by reconstituting a juice concentrate to desired properties. The use of juice concentrates in the cider industry possesses several economic advantages, especially where mass production is concerned. Concentrates are available in large quantities, easily transportable (stable), and more affordable when purchased in bulk. However, freshly pressed apple juice allows more options due to blending juices from different apple varieties whereas the choice between different available apple cultivar concentrates is limited.  
The specific definition for cider is country dependent and also legislation on cider differs--some countries have strict legislation and cider is well defined, whereas other countries are still developing specific legislation for cider. In Estonia, cider can be prepared from apple juice obtained from freshly pressed apples or apple juice concentrate with no particular limit on the percentage of apple components [1]. On the other hand, for example, French cider production policy declares that the product named \"cider\" must derive from the fermentation of fresh apple juice or a mixture of several juices. In this instance, apple juice may be partially obtained from the concentrate only if the latterdoes not exceed 50% by volume [2]. In the UK, the pre-fermentation mixture should contain at least 35% apple juice [3].  
The number of studies on cider properties and their development during fermentation has recently increased to a considerable extent. Most of these studies, however, have been largely focused on fermentation management aspects--the use of different apple varieties, ripening stage, yeast strains, and nitrogen supplementation [4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19]. There are no studies available that would directly compare freshly pressed apple juice and the concentrate made from the same juice pressing batch. This kind of research could potentially help to evaluate the effect of the juice concentration process on the fermentation and properties of the final product. Despite the economic advantages of the use of commercial apple juice concentrates in the cider industry, the definitive research on the differences in the performance is yet to be properly explored.  
The purpose of this research was to provide a comparison of fresh apple juice and concentrate as a substrate for cider fermentation based on yeast performance and properties of fermented cider in terms of fermentation kinetics, volatile ester production, and off-flavor development potential.
# Intelligent Design of Pavement Concrete Based on RSM-NSGA-III-CRITIC-VIKOR
## 3 Results and Analysis
### Global Optimization Using NSGA-III  
Based on NSGA-III, this study performs multi-objective optimization of pavement cement concrete performance indexes with a response surface model as the input objective function; the optimization objectives are to maximize R\({}_{\mathrm{f}}\)/R\({}_{\mathrm{c}}\) (0.125-0.182), minimize porosity (2.68-8.55%), and minimize shrinkage (0.042-0.050%)--the specific parameters of NSGA-III are shown in Table 7. The Pareto front obtained through the NSGA-III algorithm is shown in Figure 13, clearly illustrating the trade-offs among the optimization objectives. The results indicate that, under different weight combinations, increasing R\({}_{\mathrm{f}}\)/R\({}_{\mathrm{c}}\) is often associated with fluctuations in porosity and shrinkage. Maximizing R\({}_{\mathrm{f}}\)/R\({}_{\mathrm{c}}\) typically results in an increase in porosity, but it remains within an acceptable range (below 8.55%). Conversely, as shrinkage approaches its minimum, R\({}_{\mathrm{f}}\)/R\({}_{\mathrm{c}}\) may decrease to a critical threshold (>0.125). Further analysis demonstrates that the optimized solution set effectively balances the conflicts among the three objectives, providing comprehensive guidance for concrete performance design.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of generated reference points & 50 \\ Population size & 100 \\ Maximum number of generations & 1000 \\ Crossover rate & 0.9 \\ Mutation rate & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 7: NSGA-III parameter settings.  
Figure 13: Pareto optimal front.
# AIR-YOLOv3: Aerial Infrared Pedestrian Detection via an Improved YOLOv3 with Network Pruning
## 3 Air-YOLOv3
### Bounding Box Clustering  
YOLOv3 is the quintessence of the YOLO series. The input image is divided into many grids, and every grid corresponds to predict three boxes. The prediction box of each object is deformed from the anchor box, which is clustered from the ground truth box of the \(\mathcal{D}_{train}\). Specifically, during the model training, if the center of the prediction box is located in one grid cell, that grid cell is responsible for predicting. The box with the largest IOU (intersection over union) in the proposal boxes is used for predicting it. Furthermore, the final object bounding boxes are obtained by NMS (non-maximum suppression).  
The prediction box is shown in Figure 2. \(t_{x}\), \(t_{y}\) are the predicted offsets. \(t_{w}\), \(t_{h}\) are the predicted scale parameters. The width and height of the bounding box prior are \(p_{w}\), \(p_{h}\), and the coordinates of the upper left corner of the grid cell are \(c_{x}\), \(c_{y}\).  
This anchor-based method can effectively constrain the prediction box's spatial scope, and add the prior knowledge to predict the box, which is conducive to converging the model. In origin YOLOv3, the \(K\)-means algorithm clusters nine anchor boxes through VOC and COCO image samples. These image samples contain multi-scale objects, which are not suitable for infrared aerial pedestrian data. Therefore, we clustered anchor boxes again according to the self-built aerial dataset to improve the matching degree between the prior boxes and pedestrian objects.
# Dynamic Group Management Scheme for Sustainable and Secure Information Sensing in IoT
## 3 Secure Dynamic Group Management Scheme
### Group Sensor Authentication and Secure Sensor Information Transmission
The resulting value of calculating ID and the remainder bit stream not used as seed of transmitted value in the challenge/response process of the first round becomes a \(n\) bit stream. This bit stream is again sub-divided into a bit stream of each \(n\) bits. The second challenge/response process verifies the response time in a sensor node. The point P1 above the polynomial is transmitted via challenge/response process. After the challenge/response process is completed, P2 and P3 undergo an XOR operation with the remainder bit and random number not used in the second challenge/response process and then they are transmitted. After transmitting all the seed values, a sensor node derives a polynomial for restoring the secret value S. As shown in the operation process in CH, a sensor node generates a polynomial by using the ID, random number, secret key and filter function. After this, it derives the secret value S by substituting the received P1, P2 and P3 in the polynomial. The derived S will be utilized as the encryption key to transmit sensor information. After the first sensor node authentication and key derivation process are completed, the second sensor node will be processed. At this time, the value used in the first sensor node is transmitted to the second sensor node as though it is a random number, in order to guarantee that all the authentication and key derivation processes are continuous.