esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Principles  
### Basic Operating Principle of External Cavity Diode Lasers  
The external cavity structure refers to the construction of a passive cavity outside the laser diode (LD) using components such as planar mirrors and gratings. Some of the emitted light is reflected into the active region of the LD, creating a composite cavity structure. Diode lasers employing this cavity are collectively known as external cavity diode lasers (ECDLs) [17]. The most fundamental structure, utilizing only planar mirrors to form the external cavity, has been proven in both research and practical applications. Combining an external cavity mirror with the diode laser chip is an effective method for reducing linewidth. The basic model of an ECDL is illustrated in Figure 1[18; 19]. In the figure, \(r_{1}\), \(r_{2}\), and \(r_{3}\) represent the reflectivities of the cavity surfaces for the optical field, \(l\) denotes the length of the LD chip (inner cavity length), and d represents the distance from the chip's right facet to the plane mirror of the external cavity (external cavity length). More complex external cavity structures can be built based on this fundamental model.  
This type of ECDL still conforms to the Fabry-Perot (F-P) cavity characteristics, where the laser longitudinal modes formed within the F-P resonant cavity have a fixed correspondence with the cavity length, expressed as \(\Delta\lambda=\lambda 2/(2 nL)\). Here, \(\Delta\lambda\) represents the spacing between laser longitudinal modes, L denotes the resonant cavity length, and \(n\) is the refractive index inside the cavity. In other words, under a constant refractive index and wavelength conditions, longer cavity lengths result in narrower spacing between longitudinal modes. For a passive F-P external cavity, the longitudinal mode linewidth \(\delta_{\lambda}\) satisfies the following relationship [20]:  
\[\delta_{\lambda}=\frac{\Delta\lambda}{\pi\sqrt{R}/(1-R)} \tag{1}\]  
where \(R\) is the cavity surface reflectivity. From this equation, it can be observed that both the cavity surface reflectivity and the cavity length are inversely related to the linewidth of the passive cavity. Therefore, by appropriately increasing the cavity length and cavity surface reflectivity, the linewidth of the corresponding resonator-formed laser longitudinal modes can be reduced.
# Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks
## 3 Results
#### Comparison with New Scenarios  
The purpose of this section was to validate the adaptability of the proposed CFC3 PSO clustering method to WSN deployments that were not included in the optimization process. Therefore, Tables 7 and 8 show the results for a new set of simulations with the BS located at coordinates (75, 75) and (125, 50), respectively. All other parameters remained unchanged (see Section 2.4).  
As can be seen from the results in both tables (Tables 7 and 8), the proposed method CFC3 PSO continued to perform better than any other tested algorithm, significantly improving on the two more complex ones (CRT2 FLACO and ICUU). Therefore, the proposed approach proved to be a good solution even for WSN deployments not included in its optimization.
# Synthesis of Si-Fe Chondrule-like Dust Analogues in RF Discharge Plasmas
## 3 Sample Characterisation
### SEM, TEM, and Elemental Analysis  
The morphological properties of the synthesised iron-containing materials were studied by scanning electron microscopy (SEM, Quanta 3D 200i, FEI Inc., Valley City, ND, USA) with a spatial resolution of 3 nm at an operating voltage of 30 kV in secondary electron imaging (SEI) mode. Chemical composition analysis was assessed by energy-dispersive X-ray spectrometry (EDS, EDAX, Mahwah, NJ, USA) coupled with SEM. In SEM-equipped EDS, atoms on a surface are excited by an electron beam, emitting X-rays of specific energies [28].  
The morphology of the nanoparticles was analysed by transmission electron microscopy (TEM, JEM-1400 plus, JEOL Inc., Tokyo, Japan). For SEM analysis, the synthesised particles are deposited on the quartz glass, whereas, for TEM, they are deposited on the surface of copper grid (Ted Pella Inc., Redding, CA, USA, TEM Grids, #12563-CU).  
\begin{table}
\begin{tabular}{c c} \hline
**Operating Parameters** & **Values** \\ \hline Accelerator voltage, U & 4 kV \\ Discharge current, \(I_{max}\) & 0.1 MA \\ Pulse duration, \(\tau\) & 150 μs \\ Pressure, p & 0.06 torr \\ \hline \end{tabular}
\end{table}
Table 1: Operating parameters of the plasma accelerator [25].
## 3 The Proposed Architecture
### The Framework of the Proposed Architecture  
In order to understand network status and predict potential situations that might disrupt a network, an Endsley situational awareness model, proposed by [PERSON] et al. (2018), was applied as a starting point for a knowledge-based cognitive system. In the Endsley situational awareness model, the role of a domain knowledge base in various modules of the cognitive process is missing.  
Figure 3 illustrates the framework of the proposed architecture. The arrow from the knowledge base module to the training algorithms implies that rules from the knowledge  
Figure 2: The hierarchical architecture of satellite networks.  
base are fed to the training algorithms to produce the heuristic strategies that are used to reduce the speed of training. Using the knowledge base rules in the pre-processing component enables us to improve its performance and sense-check the results of this module. The numbers in bubbles correspond to sub-section numbers in Section 3.3 (The Modules in the Knowledge-Based Cognitive Architecture).  
In our work, a knowledge-based cognitive architecture is proposed by applying the knowledge base to various stages of the cognitive process in the Endsley situational awareness model. The proposed architecture includes five functional modules: (1) a flexible prediction and pattern recognition module that enables addition and extension of existing machine learning techniques for anomaly detection--this supports the ability to configure the architecture based on user needs and support offline algorithm training process; (2) a knowledge base module made up of rules derived from the expert's domain knowledge of satellite networks; (3) the data pre-processing module, which has the capacity to remove noise, perform feature extraction or dimension reduction, etc.; (4) the working memory module which is used for storing the data collected from the networks to be monitored and models produced by the machine learning algorithms; and (5) an inference engine with an interface that produces (visual) outputs and allows user involvement in the decision making.
# Theoretical Zero-Thickness Broadband Holograms Based on Acoustic Sieve Metasurfaces
## 4 Discussion
### Robustness  
To verify the robustness of the proposed hologram, three cases that may cause errors are calculated using numerical simulation based on the combination of the FEM and the BEM. These three cases are, respectively, small displacements of holes from their ideal places, a dispersion of hole size, and an incidence wave with a wrong angle. In the case of small displacements of holes from their ideal places, every hole position is added a random error within \(\pm 0.1\lambda_{0}\) in the \(x\)-direction and \(y\)-direction, respectively. Figure 7a-c show the calculated images for the three frequencies of \(f_{0}/2\), \(f_{0}\), and \(2f_{0}\) in this case. It can be seen from the figures that the images are almost identical to Figures 4c and 5a,f but without the error. The RMSEs between the images and the desired image (Figure 2a) are 0.2012, 0.1788, and 0.1841, respectively. The RMSEs increase only slightly (rates of increase are less than 3.7%) compared to the non-error case. In the case of the dispersion of hole size, a random error within \(\pm 0.1d\) is added to the diameter of every hole. Figure 7d-f show the calculated images for the three frequencies of \(f_{0}/2\), \(f_{0}\), and \(2f_{0}\) in this case. It can be seen from the figures that the images are almost identical to Figures 4c and 5a,f but without the error. The RMSEs between the images and the desired image (Figure 2a) are 0.2009, 0.1780, and 0.1868, respectively. The RMSEs increase only slightly (rates of increase are less than 3.5%) compared to the non-error case. In the case of the incident wave with a wrong angle, the plane wave that was originally incident parallel to the \(z\)-axis is inclined to the \(y\)-direction by \(5^{\circ}\). Figure 7g-i show the calculated images for the three frequencies of \(f_{0}/2\), \(f_{0}\), and \(2f_{0}\) in this case. Since the incident direction of the plane sound wave is inclined, the position of the image is shifted by \(z\tan 5^{\circ}\). The RMSEs between the images and the desired image (Figure 2a) are 0.2062, 0.1873, and 0.1868, respectively. The increases in RMSEs are also not significant (rates of increase are less than 6.8%) compared to the non-error case. To sum up, the proposed hologram offers a high level of robustness.
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 3 Proposed Method
### Appearance Feature Extraction Module  
To extract appearance features, we utilize prominent backbone architectures, including ResNet-50 [45], the OSNet framework [16], precision-optimized MGH [7], and the high-dimensional PiT model [10]. These models yield an appearance vector (\(\mathbf{z}_{\text{appearance}}^{i}\)) encoding the visual traits of an individual (\(i\)) from tracklet \(\mathbf{A}^{i}\):  
\[\mathbf{z}_{\text{appearance}}^{i}=\phi^{\text{appearance}}(\mathbf{A}^{i}) \tag{3}\]  
In developing GAF-Net, we chose to focus on a subset of architectures for appearance feature extraction that includes state-of-the-art models and specific architectures offering unique advantages for video-based person re-identification. This decision balances computational efficiency with high performance in real-world scenarios. We focused on the last three architectures--OSNet [16], MGH [7], and PiT [10]--due to their unique capabilities in addressing specific challenges in appearance-based person re-identification. The OSNet architecture is lightweight yet powerful, designed for person Re-ID with an emphasis on omni-scale feature learning, which efficiently captures and integrates multi-scale feature representations. By incorporating OSNet, we aim to enhance the scalability and speed of GAF-Net, making it particularly suitable for scenarios where resources are limited but high accuracy is required. Following OSNet, we explored the MGH architecture, noted for its ability to manage complex relationships within data. Hypergraphs in MGH extend traditional graph-based methods by enabling higher-order connections among data points, which improves the handling of variations in pose, scale, and occlusion in videos. Lastly, the PiT model achieved top performance on the iLIDS-VID dataset [5], utilizes transformers to achieve rich contextual understanding and fine-grained feature extraction. This is crucial for distinguishing subtle differences in appearance and setting new benchmarks in the field. Our aim is to demonstrate the impact of incorporating gait information and using different fusion schemes to optimize these appearance-based architectures for improved re-identification performance.
## 3 Semi-Passive Systems  
Semi-passive systems are characterized by having tags powered exclusively or partly by batteries and the absence of active radios, relying on a passive reflection modulation to communicate with the interrogator [17]. When compared with passive tags, semi-passive have a much higher computational  
Figure 11: Classification of smart materials.  
Figure 12: Encoding based on the phase modulation. Adapted from [105].  
potential, however at the cost of having a battery that eventually needs to be recharged and replaced. These characteristics are especially interesting in quality-oriented tracking and tracing [106]. Similary to passive solutions, semi-passive uses backscatter techniques to communicate, and can use WPT or EH to charge its batteries. Besides these technologies already approached in Section 2, semi-passive tags can have methods to receive information and specially designed WUR solutions to use their energy in the most efficient way possible.  
Regarding possible applications, semi-passive tags can be applied to monitoring food conditions during the whole supply chain. It is of particular interest in food that needs to be in controlled environments and is very sensitive to temperature or humidity variations. Besides that, these devices can also be beneficial in medicines or other medical products that need strict monitoring, or to any product that needs continuous monitoring and communication at specific locations [106].
## 4 Experimental Results
### Multi-Attribute Recognition on the CMU Multi-PIE Dataset
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Representation Method** & **Identity** & **Expression** & **Gender** & **Race** & **Age** \\ \hline Single task learning & 0.48 & 11.48 & 0.77 & 0.78 & 1.12 \\ \hline G+R+A & - & - & 0.17 & 0.26 & 0.47 \\ \hline G*R*A & - & - & 0.16 & 0.25 & 0.47 \\ \hline E+G+R+A & - & 10.30 & 0.24 & 0.24 & 0.50 \\ \hline E+G*R*A & - & **9.51** & 0.14 & 0.21 & 0.36 \\ \hline E*G*R*A & - & 9.52 & 0.30 & 0.46 & 0.79 \\ \hline I+G+R+A & 0.49 & - & 0.15 & 0.19 & 0.41 \\ \hline I+G*R*A & 0.45 & - & 0.13 & 0.20 & 0.37 \\ \hline I+E+G+R+A & 0.46 & 10.35 & 0.14 & 0.19 & 0.39 \\ \hline I+E+G*R*A & **0.36** & 9.56 & **0.10** & **0.17** & **0.29** \\ \hline I+E*G*R*A & 0.47 & 9.65 & 0.18 & 0.26 & 0.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Recognition error (%) of five facial attributes under different representation methods. expression as an independent task and the other three attributes were correlated (E+G*R*A in Table 2), and this is consistent with what was revealed in the previous dual task (Table 1) and three task experiments. For the next step, we combined identity with gender, race, and age recognition. As with the results from Table 2, we could confirm that identity, as an independent task, helped overall recognition performance of three other recognition tasks, which is also consistent with the results in the previous experiments. The reason why we did not conduct the joint relationship learning with identity and other tasks is that the number of output nodes becomes too large when a novel output node is created for the joint representation which may lead to overfitting the problem. Lastly, we implemented multi-task learning with five tasks all together by applying the appropriate combination of the output representation methods, and confirmed that we can get a considerable performance improvement on all attributes except for expression. Through the whole experiment, we found the best output representation for multi-attribute recognition of facial images, and confirmed our proposed output representation methods can improve the performance of all recognition tasks when properly combining them.
# Low-Cost SDR for GNSS Interference Mitigation Using Spatial Diversity Techniques +
## 3 Exploiting Spatial Diversity for Interference Mitigation
### Prewhitening Beamformer
\[\mathbf{w}_{PREW}=\mathcal{P}\bigg{\{}\widetilde{\mathbf{R}}_{\mathbf{x}}\bigg{\}} \tag{11}\]  
Again, the weights presented in Equation (11) have been obtained with no more information than that extracted from the received signal, resulting in another _blind_ approach.
###### Abstract  
A comprehensive validation of data acquired by different myocardial perfusion imaging (MPI) systems was performed to evaluate contrast, self-attenuation properties, and perfusion detection capability. An anthropomorphic phantom with a myocardial insert and perfusion defect was used to simulate \({}^{99 mTc}\)T-tetrofromism distribution. Different _MPI systems_ were evaluated: a SPECTC system with iterative reconstruction algorithms and resolution recovery (IRR) with/without scatter correction (SPECT-IRR-SC and SPECT-IRR), and a cardio-centric IQ SPECT/CT system with IRR, with/without scatter and attenuation corrections (IQ-IRR-SC-AC and IQ-IRR). The image quality was assessed through physical descriptors: the contrast between the left ventricular (_LV_) wall and _LV_ inner chamber (\(C_{LV/LVIC}\)), intrinsic contrast (_IC_), and net contrast (_NC_). \(C_{LV/LVIC}\) was found to be superior for IQ-IRR-SC-AC. The _IC_ results showed non-uniformity of the signal intensity in the _LV_ wall for the SPECTC systems. The lowest _IC_ values were obtained for IQ-IRR-SC-AC, except for septal position, where an underestimation of the signal intensity was revealed. The _NC_ was found to be the highest for IQ-IRR-SC-AC and SPECT-IRR-SC. Additionally, for IQ-IRR-SC-AC, the _NC_ increased in posterior and septal positions compared to IQ-IRR, enabling better perfusion detection capability over short-axis images. IQ-IRR showed performances comparable to SPECT-IRR. The characterization and evaluation perfusion detection capability of the _MPI systems_ enabled the investigation of the systems' performance and limitations.  
medical physics; SPECT; hybrid imaging; myocardial perfusion imaging; image quality; scatter correction; attenuation correction; iterative reconstruction with resolution recovery  
1  
[PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] Evaluation of Single-Photon Emission Computed Tomography Myocardial Perfusion Detection Capability through Physical Descriptors. _Appl. Sci._**2024**, _14_, 5283. [[https://doi.org/10.3309/app1412583](https://doi.org/10.3309/app1412583)]([https://doi.org/10.3309/app1412583](https://doi.org/10.3309/app1412583))
# Satellite On-Board Change Detection via Auto-Associative Neural Networks
## 2 Materials and Methods
### Dwt  
The DWT algorithm was chosen as a benchmark technique for performance analysis because it is broadly used for image compression purposes, for example, in the JPEG2000 compression standard and also for remote sensing images in most Earth observation satellites [19; 33]. The two-dimensional DWT decomposes the input image along the rows and along the columns into high frequency and low frequency parts, called low-low (LL), low-high (LH), high-low (HL), and high-high (HH) sub-bands; thus, each decomposed level consists of four sub-bands of coefficients. The LL sub-band represents the low pass filtering along rows and columns of the input image, and it is recursively used to obtain the subsequent level of decomposition, until reaching the predefined number of decomposition level. The n-th decomposition level is the approximation of the input image [29; 34].
###### Abstract  
The estimation of extreme ocean wave heights is important for understanding the ocean's response to long-term changes in the ocean environment and for the effective coastal management of potential disasters in coastal areas. In order to estimate extreme wave height values in the Northwest Pacific Ocean, a 100-year return period were calculated by applying a Peak over Threshold (PoT) method to satellite altimeter SWH data from 1992 to 2016. Satellite altimeter SWH data were validated using in situ measurements from the leodo Ocean Research Station (IORS) south of Korea and the Donghae buoy of the Korea Meteorological Administration (KMA) off the eastern coast of Korea. The spatial distribution and seasonal variations of the estimated 100-year return period SWHs in the Northwest Pacific Ocean were presented. To quantitatively analyze the suitability of the PoT method in the Northwest Pacific, where typhoons frequently occur, the estimated 100-year return period SWHs were compared by classifying the regions as containing negligible or significant typhoon effects. Seasonal variations of extreme SWHs within the upper limit of 0.1% and the PoT-based extreme SWHs indicated the effect of typhoons on the high SWHs in the East China Sea and the southern part of the Northwest Pacific during summer and fall. In addition, this study discusses the limitations of satellite altimeter SWH data in the estimation of 100-year extreme SWHs.  
satellite altimeter; significant wave height (SWH); extreme SWH; Peak over Threshold (PoT) method; typhoon; leodo Ocean Research Station (IORS)
# Metrics-Based Comparison of OWL and XML for Representing and Querying Cognitive Radio Capabilities
## 4 Problem Formulation
### Problem Formalization  
#### 4.1.1 Notations  
For the purpose of formalizing the problem, the following notations are introduced.  
* \(D=\{D_{1},D_{2},\ldots,D_{n}\}\)--set of \(n\) devices
* \(D^{xml}=\{D_{1}^{xml},D_{2}^{xml},\ldots,D_{n}^{xml}\}\)--set of \(n\) descriptions of device capabilities represented in XML
* \(B=\{B_{0},B_{1},\ldots,B_{k}\}\)-set of \(k+1\) knowledge representations of \(D\) in OWL, where \(B_{0}\in B\) is the most complete representation and \(B_{i}\in\{B_{1},\ldots,B_{k}\},1\leq i\leq k\) are progressively less complete representations s.t. each \(B_{i}\) does not include at least one type of OWL 2 RL axiom with respect to \(B_{i-1}\)
* \(D^{owl}=\{D_{1}^{owl},D_{2}^{owl},\ldots,D_{n}^{owl}\}\)--set of \(n\) descriptions of device capabilities from \(D\) in RDF/OWL
* \(Q=\{Q_{1},Q_{2},\ldots,Q_{m}\}\)--set of \(m\) requests (queries) against device capabilities from \(D\)
* \(Q^{xml}=\{Q_{1}^{xml},Q_{2}^{xml},\ldots,Q_{m}^{xml}\}\)--set of \(m\) requests expressed in XQuery
* \(Q^{owl}=\{Q_{1}^{owl},Q_{2}^{owl},\ldots,Q_{m}^{xml}\}\)--set of \(m\) requests expressed in SPARQL
* \(f^{xml}\)--function that takes a set of device descriptions and a query in XML and returns a set of devices that match the query: \[f^{xml}:(2^{D^{xml}},Q^{xml})\to 2^{D}\] (1)
* \(f^{owl}\)--function that takes a set of device descriptions, a background knowledge representation and a query in OWL and returns a set of devices that match the query):  
Figure 2: Visual representation of the evaluation process.  
\[f^{outl}:(2^{D^{val}},B,Q^{outl})\to 2^{D} \tag{2}\]
* \(g^{xml}\)--ground truth function that takes a set of device descriptions and a query in XML and returns a set of devices that the query is expected to return: \[g^{xml}:(2^{D^{val}},Q^{xml})\to 2^{D}\] (3)
* \(g^{owl}\)--function that takes a set of device descriptions and a query in OWL and returns a set of devices that the query is expected to return: \[g^{owl}:(2^{D^{val}},Q^{owl})\to 2^{D}\] (4)
* \(m^{xml}\)--function that compares two functions, a query function and a ground truth function in XML, and returns the value of the metric from [0, 1]: \[m^{xml}:(f^{xml}(2^{D^{val}},Q^{xml}),g^{xml}(2^{D^{xml}},Q^{xml}))\to[0,1]\] (5)
* \(m^{owl}\)--function that compares two functions, a query function and a ground truth function in OWL, and returns the value of the metric from [0, 1]: \[m^{owl}:(f^{goal}(2^{D^{val}},B,Q^{owl}),g^{owl}(2^{D^{val}},Q^{owl}))\to[0,1]\] (6)  
The metrics \(m^{xml}\) and \(m^{owl}\) introduced above are just patterns used for defining concrete metrics. The concrete metrics formalization and definitions are presented in Section 4.2.