esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Validation  
The developed NR NF/FFT techniques have been experimentally validated at the UNISA Antenna Characterization Laboratory. In such a facility, an anechoic chamber (8 m \(\times\) 5 m \(\times\) 4 m) is available for NF and FF measurements, depending on the FF distance requirements. The chamber is paneled with pyramidal absorbers, which guarantee, in the X band, a background noise level of less than \(-\)40 dB. A vertical scanner and three turntables equip a versatile NF measurement system (see Figure 3), which allows one to execute NF measurements with planar-spiral, helicoidal, and spherical-spiral scans, besides those with plane-polar, cylindrical, and spherical scans. The vectorial network analyzer ANRITSU 37247C by Anritsu Corporation, Kanagawa, Japan, is used to perform accurate amplitude and phase measurements. A further turntable, allowing direct FF measurements of AUTs whose sizes are small in terms of wavelength, or of radar cross section measurements of electrically small targets, is also available.  
The AUT employed in the experimental proofs is a resonant slotted waveguide array, working at 10 GHz, obtained by cutting eight slots with rounded ends in the large walls of a tapered rectangular waveguide. It is fed using a coaxial to rectangular waveguide transition and has been mounted so that its axis coincides with the \(z\) one and its large walls  
Figure 3: Sketch of the UNISA NF measurement system.  
are parallel to the \(x=0\) plane. Moreover, an open-ended WR90 rectangular waveguide is employed as a probe.  
The MI-3000 software by NSI-MI Technologies, Atlanta, GE, USA, which implements the traditional spherical NF/FFT technique [15], is used to evaluate the FF patterns from both the directly acquired massive NF data and those obtained by interpolating the NR NF samples. Furthermore, for both experimental test scenarios, the AUT is shifted along the \(z\)-axis by a distance \(d_{s}\) of 5.37\(\lambda\).
## 3 Methods  
### Raw Records Processing and Seismic Noise Spectrograms Calculation  
First of all, the reading, conversion and pre-processing of the OBS seismic records are performed. After reading from the flash memory of the seismic recorder, the raw records are contained in files of specific binary format with the samples of five channels: a hydrophone, three components of a velocimeter, and a channel of accurate time samples. Next, the raw  
\begin{table}
\begin{tabular}{l l} \hline Number of analog channels & 4 (basic), 8 (extended) \\ \hline Number of digital channels & 1 (for reference to absolute time) \\ \hline Sample rates & 20, 25, 40, 50, 80, 100, 160, 200, 400, 800 \\ \hline Time synchronization & GPS interface \\ \hline Temperature stability of the quarts generator & \(\pm\)5 \(\times\) 10\({}^{-9}\) \\ \hline Dynamic range & 85–90 dB \\ \hline Memory & SD card up to 64 Gb \\ \hline Power supply voltage & 6.5–32 V \\ \hline Power consumption & 20 mA (12 V) \\ \hline \end{tabular}
\end{table}
Table 4: Main characteristics of the URS-S recorder.  
\begin{table}
\begin{tabular}{l l} \hline Pressure range & 100 m (dBar)–maximum accuracy \\ \hline Initial accuracy & \(\pm\)0.05\% FS (full scale) \\ \hline Resolution & \textless{}0.001\% FS \\ \hline Typical stability & 0.05\% FS \\ \hline Depth rating & 750 m \\ \hline Storage & 240 M readings \\ \hline Clock drift & \(\pm\)60 s/year \\ \hline Power & 8 AA cells \\ \hline \end{tabular}
\end{table}
Table 5: Main characteristics of the RBR virtuoso[3] wave recorder (single channel logger).  
binary files are batch converted into one of the generally accepted seismological formats, miniSEED, using Matlab scripts and some Seisan conversion utilities [28]. After that, the records are ready for seismological analysis.  
By using seismic noise spectrograms, we assessed the temporal variability of observations in order to distinguish the effects of ice cover, wind-generated gravity waves, infragravity waves, and atmospheric parameters, such as atmospheric pressure and wind speed. Spectrograms of the seismic records were calculated with standard functions of the Matlab environment, which implement short-time Fourier transform [29; 30; 31; 32] with the use of a sliding Hamming window and 50% overlap between segments. The records were preliminary averaged in 1-s long windows. Calculations on long time series from different source files were performed sequentially with subsequent concatenation of the resulting array. A spectrogram of the sea level records in the range of periods from 30 to 300 s was calculated with [PERSON]'s method [33], using the Kaiser-Bessel spectral window two days long, with 50% overlap.
# A Low-Cost Sensorized Vehicle for In-Field Crop Phenotyping
## 2 Materials and Methods
### Phenomobile Multispectral Camera and Operation  
To perform prototypical in-field operations, phenomobile was equipped with the MAIA (Eoptics S.r.l, Trento, Italy) multispectral camera. MAIA is a multispectral camera providing simultaneous acquisition of high-resolution images (f 1208 \(\times\) 960 pixel) at dif  
Figure 13: General scheme of the system.  
ferent wavelengths in the visible (VIS) and near-infrared (NIR) regions (390-950 nm). The MAIA multispectral camera is based on an array of nine sensors (see Figure 1), each with its relevant band-pass filter that precisely defines the radiation range to be detected. The filter set chosen matches the bands of the Sentinel-2(tm) (SN-2) satellite. Given Q, the quantum efficiency of the sensors i-th sensor (i = 1,...,9) and T, the transmittance of the corresponding filter (both quantities normalized to 1), the sensitivity S of the i-th sensor is given by  
\[\text{S}=\text{Q}\cdot\text{T}. \tag{1}\]  
Figure 14 shows the sensitivity of the 9 sensors as a function of the wavelength in nanometers.  
Table 1 reports the main characteristics of the 9 sensors of the MAIA multispectral camera in terms of central wavelength (CWL), bandwidth (BW), and color.  
The MAIA multispectral camera was connected to the phenomobile by means of a dedicated Gimbal 3D operating with 3 degrees of freedom (\"yaw\", \"roll\", and \"pitch\") at a ground sampling distance (GSD) of 2 m. Furthermore, the system is also implemented with a dedicated RTK (Real-Time Kinematics) base station for precise positioning of around 2 cm. The images obtained from the 9 sensors were co-registered by means of \"MultiCam Stitcher Pro\" proprietary software v. 1.4 (SAL Engineering, Ravenna, Italy; 3D Optical Metrology--Bruno Kessler Foundation (FBK), Trento, Italy) and from the obtained multi-tif images, the Normalized Difference Vegetation Index (NDVI) and Generalized Difference  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **CWL (nm)** & **BW (nm)** & **Color** \\ \hline S1 & 443 & 20 & Violet \\ S2 & 490 & 65 & Blue \\ S3 & 560 & 35 & Green \\ S4 & 665 & 30 & Red \\ S5 & 705 & 15 & Red Edge 1 \\ S6 & 740 & 15 & Red Edge 2 \\ S7 & 783 & 20 & NIR 1 \\ S8 & 842 & 115 & NIR 2 \\ S9 & 865 & 20 & NIR 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of optical bands detected by the MAIA multispectral camera sensors.  
Figure 14: Sensitivity of the MAIA multispectral camera sensors (**on the left**) and their position (**on the right**) with respect to the acquisition direction (black arrow).  
Vegetation Index (GDVI) were calculated averaging the signals from the S7 and S8 NIR sensors as  
\[\text{NDVI}=((\text{S8}+\text{S7})/2-\text{S5})/((\text{S8}+\text{S7})/2+\text{S5}), \tag{2}\]  
\[\text{GDVI}=((\text{S8}+\text{S7})/2-\text{S3})/((\text{S8}+\text{S7})/2+\text{ S3}). \tag{3}\]
## 3 Results and Discussion
### Compressive Strength
#### Influence of Efficiency Factor  
The results displayed in Figure 12 show that Mix 6, with a k-factor of 1, presents a lower compressive strength at all ages of testing when compared with Mix 3, produced with an efficiency factor of 0.6.  
The compressive strength of concrete with additions depends on the activity index of the addition. Given that the rate of strength development of concrete with GGBS is lower than that of Portland cement-only concrete, an efficiency factor concept was developed. In addition, the compressive strength of concrete highly depends on the water/cement ratio. Therefore, one way to achieve the same strength when using additions is to decrease the water/binder ratio. This means that, in order to achieve similar compressive strength at 28 days, for the same water content, the mass of addition used must be larger than the mass of cement that it is replacing. Thus, according to the k-factor concept, the mass of addition (A) replaces a mass of kA of Portland cement. In this approach, instead of using the water/binder ratio, an equivalent water/cement ratio is used, which is defined by Equation (1) [7]. A k-factor of 1 means that the addition replaces the exact same amount of cement, which is the case for Mix 6. A k-factor lower than 1 means a larger amount of binder material and, therefore, a lower effective water/binder ratio (Mix 3). In this research, a k-value of 0.6 was used, as prescribed in [5] for GGBS additions. Therefore, even though the equivalent water/cement ratio is 0.45 for both mixes, for Mix 3 (with an efficiency factor of 0.6), the water/binder ratio is 0.39, whereas for Mix 6 (with a k-factor of 1) the water/binder ratio is 0.45. Given that the compressive strength decreases with an increase  
Figure 12: Compressive strength development of concrete with 50% GGBS, air = 4.5%, cured at 20 \({}^{\circ}\)C and with different efficiency factors.  
in water/binder ratio [4], Mix 6 would theoretically show lower compressive strength, which is in accordance with the results obtained.  
The results presented show, once again, effectiveness of the use of the efficiency factor concept in reducing the influence of the addition of GGBS in the compressive strength of concrete at all ages.
## 2 Thematic Review and Analysis
### Control Strategies
#### 2.3.3 Low-Flow Operation  
Low-flow conditions, often resulting from reduced return temperatures and increased temperature differentials at the end-user site, can be managed using the 'Kiruna Method'. This method involves either increasing the supply temperature while decreasing the flow rate or expanding the size of room heaters to maintain consistent heat output [13; 17; 23; 29; 54; 98; 99]. [PERSON] [100] provides data on temperature balancing for a low-flow operation with an 80/30 \({}^{\circ}\)C temperature scheme. Maintaining low-flow operation requires careful management of the pump minimum flow rate and the efficiency of control valves, such as substation control valves and thermostatic radiator valves.  
[PERSON] [23] identifies a potential issue with 2-way control valves, such as thermostatic radiator valves, which may fail to achieve the necessary minimal valve opening (referred to as 'rangeability') during low-demand conditions. This limitation can result in higher-than-expected return temperature levels. In contrast, the low-flow thermostatic valves discussed by [PERSON] and [PERSON] [101] (citing [102]) are engineered for reliable performance under low-flow conditions.  
Figure 8: Illustrations of various weather compensation curves sourced from the literature: [PERSON] et al. [96], [PERSON] et al. [88], [PERSON] et al. (Sc: A and Sc: B denote scenarios for the same district) [94], [PERSON] et al. (for Skogás DH in Sweden and Cheongju DH in Korea) [11], and [PERSON] and [PERSON] (for indirect substations with an optimized supply temperature of 80 \({}^{\circ}\)C aimed at minimizing return temperature) [97]. Dashed lines indicate expected or resultant return temperatures, while solid lines represent supply temperatures, with both types of lines depicted in the same color.  
[PERSON] [17] emphasizes that low-flow strategies can effectively achieve low return temperatures, provided that adequate hydraulic balancing and proper component design are maintained. In this approach, radiators must rapidly adapt to changes in flow rates and utilize internal heat gains. However, unlike high-flow systems, low-flow systems are more susceptible to local network deviations that can elevate return temperatures. To mitigate these issues, [PERSON] recommends careful commissioning of valve settings, selecting a pump with a steep characteristic curve, and maintaining a low differential pressure. These measures are intended to minimize the adverse effects caused by open valves within the local network [17].  
[PERSON] et al. [103] highlight an issue with ultra-low-flow operation, where the unintended mixing of supply medium with radiator water at the radiator inlet can lead to a reduction in radiator heat output.
## 4 Real-Time Implementation of an Auralization System
### Translation of the Acoustic Field  
It is important to point out that the reconstructed field will be exact for the target field if an integral plane wave expansion (Equation (1)) is used for the synthesis. This outcome means that the translation operator will lead to the correct field regardless of the location where the translation is intended. However, if the plane wave expansion is approximated by a finite sum, as in Equation (8), the reconstructed field will contain errors, and the translation operator will lead to the correct field only in the area where the discretized plane wave expansion matches the target field. An indication of the amount of translation that can be applied before noticeable sound artefacts occur can be estimated from Equation (14) as:  
\[r_{t}=\left(\frac{(\sqrt{L}-1)\lambda}{2\pi}\right)-r_{l}. \tag{21}\]  
where \(r_{t}\) is the translation distance, \(L\) is the number of plane waves used for the synthesis, \(\lambda\) is the wavelength and \(r_{l}\) is the radius of the listener's head (e.g., 0.1 m) where it is desirable that the sound field is reproduced accurately. Indeed, \(r_{l}\) must be taken into consideration in order to preserve the binaural cues. An example of the translation radius is given in Figure 16, in which \(R(\omega)=\frac{(\sqrt{L}-1)\lambda}{2\pi}\).  
Figure 15: General architecture for a real-time auralization based on the plane wave expansion.
# Collaborative Optimization of Aerodynamics and Wind Turbine Blades
###### Abstract  
This paper explores the application of multidisciplinary design optimization to the blades in horizontal-axis wind turbines. The aerodynamics and structural performance of blades are considered in the optimization framework. In the aerodynamic discipline, class function/shape function transformation-based parameterized modeling is used to express the airfoil. The Wilson method is employed to obtain the aerodynamic shape of the blade. Computational fluid dynamics numerical simulation is performed to analyze the aerodynamics of the blade. In the structural discipline, the materials and ply lay-up design are studied. Finite element method-based modal analysis and static structural analysis are conducted to verify the structural design of the blade. A collaborative optimization framework is set up on the Isght platform, employing a genetic algorithm to find the optimal solution for the blade's aerodynamics and structural properties. In the optimization framework, the design variables refer to the length of the blade chord, twist angle, and lay-up thickness. Additionally, Kriging surrogate models are constructed to reduce the numerical simulation time required during optimization. An optimal Latin hypercube sampling method-based experimental design is employed to determine the samples used in the surrogate models. The optimized blade exhibits improved performance in both the aerodynamic and the structural disciplines.  
wind turbine blades; aerodynamic characteristics; structural characteristics; multidisciplinary design optimization; numerical simulation Article
# Affinity Propagation Clustering Using Path Based Similarity
## 2 Method
### Affinity Propagation Clustering
However, if there are two or more exemplars suitable for the same cluster, it is difficult to identify the optimal exemplar. To address this issue, APC introduces a damping factor \(\lambda(\lambda\in[0,1))\) and updates \(\mathbf{R}\) and \(\mathbf{A}\) as follow:  
\[\mathbf{R}^{(t)}(i,j)=(1-\lambda)(\mathbf{S}(i,j)-max_{h\
eq j} \left\{\mathbf{A}^{(t-1)}(i,h)+\mathbf{S}(i,h)\right\})+\lambda\mathbf{R}^{(t- 1)}(i,j) \tag{5}\]  
\[\mathbf{A}^{(t)}(i,j)=(1-\lambda)min\left\{0,\mathbf{R}^{(t)}(j,j)+\sum_{h \
eq i,h\
eq j}max\left\{0,\mathbf{R}^{(t)}(h,j)\right\}\right\}+\lambda \mathbf{A}^{(t-1)}(i,j) \tag{6}\]  
\[\mathbf{A}^{(t)}(i,i)=(1-\lambda)\sum_{h\
eq i}max\left\{0,\mathbf{R}^{(t)}(h,i)\right\}+\lambda\mathbf{A}^{(t-1)}(i,i) \tag{7}\]  
APC iteratively updates \(\mathbf{R}\) and \(\mathbf{A}\) using Equations (5)-(7). If \(\mathbf{A}(i,j)+\mathbf{R}(i,j)\) is the maximum and \(i=j\), then \(\mathbf{x}_{i}\) is an exemplar; otherwise, \(\mathbf{x}_{j}\) is the exemplar and \(\mathbf{x}_{i}\) is assigned to the cluster whose exemplar is \(\mathbf{x}_{j}\). Equations (5)-(7) describe the basic procedure of APC. After finding out exemplars, APC assigns non-exemplar samples to the closest cluster whose exemplar has smallest Euclidean distance from them. However, Euclidean distance does not work well on samples distributed with complex structure, as our illustrative examples demonstrated in the next subsection. To mitigate this problem, we propose a path-based similarity to explore the underlying structure between samples and incorporate the similarity into APC to assign samples to their respective clusters.
## 4 Terminal Load and Communication
### Analysis of Terminal Communication Overhead  
In the FL-AHNEAP method, end users need to interact with the cloud. According to Algorithm 1, the communication items are mainly six transformation matrices, the dimensions of which are mainly related to the characteristic dimension of the node features, the node embedding dimension, and the edge in the network. We selected the matrix with the largest dimension for analysis. It can be drawn from the Reference [2] that there are three edge types in the network. The attribute feature dimension is set to 50, and the node embedding can be set to 20 according to experiments; then, there are up to floating-point type elements in a matrix. Five matrix parameters need to be passed, so a total of floating-point type elements need to be passed. A floating-point type occupies 8 bytes, so it occupies a total of about 14 KB. We exploit the array form in Python to store six matrices, and then store the six matrices in the same dictionary. We store the structure and data of the dictionary in a file, and the final file size is about 33 KB; that is, the interaction required for each communication is a file of about 33 KB. For users, the communication overhead of each round is almost negligible.
# Internet-of-Things and Information Fusion: Trust Perspective Survey
## 8 Internet-of-Things Trust Models
### QoS-Driven Trust Models  
There are some trust models considering only QoS trust attributes. [PERSON] et al. [84] proposed a trust model based on fuzzy logic where each node has a table for maintaining a data forwarding information acquired by overhearing activities of its neighbors. Their study considers only three QoS metrics including energy consumption,end-to-end packet forwarding ratio, and packet delivery ratio.  
[PERSON] et al. [85] and [PERSON] et al. [86] proposed a centralized trust model and only considered QoS trust attributes in three network layers. The aim of trust model in the first layer, sensor layer, is to identify a subset of nodes for providing services based on their trust scores. Its aim in the second layer, core layer, is to acquire optimal routes in the network based on some attributes (e.g., historical trust, risk, service capability, ability of anti-attack, and recommended experience). While the aim in the third layer, application layer, is to select candidate trusted methods for data processing and storage based on some control attributes such as service efficiency, service history, and service risk.  
[PERSON] et al. [87] proposed a distributed trust model based on only direct information acquired by direct communication between IoT nodes. This work considers only service quality attribute. Their model assigns positive trust value to the node that fulfills a required service and negative trust value to the node that refuses to cooperate. The objective of their model is to mitigate OOA by utilizing a reward and punishment paradigm. Also, one QoS attribute is considered and no social attributes in their model. Later, [PERSON] et al. [88]. extended their previous work presented in [87] by adding recommendations from their neighbors in the trust computation. The computation of the node' trust involves two QoS trust attributes namely service quality and recommendation. They investigated the effectiveness of their model in the presence of only BMA.  
[PERSON] et al. [89] proposed a trust model for access control based on fuzzy logic considering the experience, knowledge, and recommendation QoS attributes. For the calculation of trust value, the model assigns linguistic values (e.g., good, average or bad) to the QoS attributes. To achieve access control in IoT, the model maps the acquired fuzzy trust values to access permission.  
[PERSON] et al. [66] proposed an autonomic trust management framework based on a feedback control loop to assess the trust level considering only four QoS trust attributes namely, availability, response time, reliability, and capacity. The acquired trust values are disseminated in a centralized manner periodically.  
[PERSON] et al. [63] proposed a trust management system for IoT considering only service quality trust attribute with different context information. Their model assigns reputation score to cooperating nodes based on different context and different functions and uses a centralized manager to maintain all reputation reports sent by service providers after each transaction.
## 4 Results and Discussion
### ANOVA Tests Rating Differences  
The ANOVA tests were applied to assess whether there are differences among passenger ratings across months. The only significant difference found was the ratings in August across the years prior to 2019. The results are significant across aspects from terminal cleanliness to airport staff except airport shopping, as shown in Table 1, where \"***\" and \"***\" indicates \(p\)-value \(\leq\) 0.01 and 0.001 respectively.  
These low \(p\)-values (except for airport shopping) indicate strong evidence against the null hypothesis, which states there is no difference in ratings across years. This confirms that there are indeed differences in how these categories were rated in different years during August.  
Figure 9: Pearson correlation coefficients between various airport aspects.  
Table 2 shows the ANOVA results for airport rating categories for each month from the year 2019 onward. All categories across any month from 2019 onward show a statistically significant difference in ratings with \(p<0.05\).  
These results indicate significant variability in how each aspect was rated across different years, suggesting that perceptions and perhaps the actual quality of services and facilities at airports are changing over time. The low \(p\)-values confirm that these differences are statistically significant, pointing to real changes rather than random fluctuations. These findings could serve as a basis for further investigation into what specific changes occurred over these years that have influenced passenger ratings.