esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Simulation and Sensor Performance Evaluation of Space-Based Infrared Point Target Group
## 4 Simulation Experiments and Validation
### Simulation Analysis
#### 4.2.4 Speed Analysis  
Since STK did not provide specific implementation details, we compared the original method mainly in terms of simulation time cost. We used the parallel implementation for the computation of the positions and gray values of the stars and targets. Although the time complexity was kept as O(n), the engineering goal of saving time in the simulation phase was achieved. Meanwhile, we used C#'s asynchronous approach to allow the simulation system to perform subsequent computations, and also implemented noise templates to pre-generate noisy data in scenarios requiring higher output performance, further reducing the time cost. We evaluated the performance of the simulation system under different satellite constellations and sensor parameters using the Monte Carlo method; Table 7 shows the rate of simulated image generation at different resolutions. Our system demonstrated a remarkable 186-fold improvement in CPU-based simulation of the output of MSX satellite sensors for target imaging when compared to STK. This satisfied the observation of targets by 20 sensors in the satellite constellation at the same moment, surpassing the number of target-visible satellites in the above case of an STSS-like constellation. Despite a decline in the frame rate of the system after the resolution increase, it maintained 32 FPS at a resolution of 1 K \(\times\) 1 K, fulfilling the real-time simulation requirement. This advancement significantly enhances the usability of STK in infrared simulation and improves the viewing experience. In the future, we will continue to optimize the simulation process and use GPU-based methods to refine the simulation requirements at high resolutions.  
Figure 11: Sat47 observes SNR changes for various types of targets.  
Figure 12: Effect of targets micromotion on SNR.
# Design of an Integrated System for Spaceborne SAR Imaging and Data Transmission
## 2 Construction of Mathematical Models
### Algorithms
#### 2.3.1 Alternating Direction Method of Multipliers
FBMC physical layer: A primer. _PHYDYAS_**2010**, _25_, 7-10. * _[PERSON] et al. (2020)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Development of flat panel active phased array antennas using 5G silicon RFICs at Ku- and Ka -Bands. _IEEE Access_**2020**, \(8\), 192669-192681. [CrossRef]
* _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON] Multi-user OFDM adaptive resource allocation research based on clustering idea. _Appl. Electron. Tech._**2019**, _45_, 63-66. * _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON] Optimal power allocation for the joint radar and communications with OFDM waveform transmission. In Proceedings of the 2021 CIE International Conference on Radar (Radar), Haikou, China, 15-19 December 2021; pp. 2999-3003. **Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
# A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR
## 6 Conclusions and Future Directions  
The final feature vector for the BoVW model contains no information regarding the distribution of visual words in the 2D image space. Due to this reason, the performance of a computer vision application suffers, as spatial information of visual words in the histogram-based feature vector enhances the performance of image retrieval. This paper presents a novel approach to image representation to incorporate the spatial information to the inverted index of the BoVW model. The spatial information is added by calculating the global relative spatial orientation of visual words in a transformation-invariant manner. This is established by computing the geometric relationship between pairs of identical visual words with respect to the centroid of an image. The experimental results and quantitative comparisons demonstrate that our proposed representation significantly improves the retrieval performance in terms of precision and recall values. The proposed approach outperforms other concurrent methods and provides competitive performance as compared with the state-of-the-art approaches.  
Furthermore, the proposed approach is not confined to the retrieval task but can be applied to other image analysis tasks, such as object detection. This is because we incorporate the invariant spatial layout information into the BoVW image representation, thereby ensuring seamless application of follow-up techniques.  
In future, we would like to enhance the discriminative power of the proposed approach by extracting rotation invariant low-level features at descriptor level. We intend to create a unified representation, tolerant to all kinds of layout variances. As the proposed method has shown excellent results on five image benchmarks, in future we aim to apply a pre-trained deep convolution neural network for the computation of histogram of visual words for learning of classifier to a large scale image dataset. Combining our image representation with a complementary absolute feature extraction method and enriching it with other cues such as color and shape is another possible direction for future research.  
Conceptualization, B.Z., R.A. and N.A.; Data curation, B.Z., R.A. and N.A.; Formal analysis, B.Z., R.A. and N.A. ; Investigation, B.Z., R.A., N.A. and S.H.D.; Methodology, B.Z. and N.A.; Project administration, N.A., M.S., S.H.D. and N.I.R.; Resources, M.K.I., M.S., S.H.D. and N.I.R.; Software, B.Z., N.A. and M.S.; Supervision, R.A. and N.A.; Validation, B.Z., N.A., M.K.I. and N.I.R.; Visualization, B.Z., M.K.I, S.H.D. and N.I.R.; Writing--original draft, B.Z., R.A., N.A., M.K.I, M.S., S.H.D. and N.I.R.; Writing--review and editing, R.A., N.A., M.K.I, M.S., S.H.D. and N.I.R.  
This research received no external funding.  
The authors declare no conflicts of interest.  
Figure 21: Average precision comparison of the proposed RGSIR with the state-of-the-art approaches for the Corel-1K image benchmark.
# Introduction to Photonics: Principles and the Most Recent Applications of Microstructures
## 7 Waveguides and Applications
Because of this peculiar property, waveguides are formed through inducing of line imperfections in photonic crystal structures [112; 113]. These line faults are used to guide light from one place to another. The defects are guided inside the photonic band gap through the streamline via the total internal reflection principle. Because of the asymmetrical boundary, this streamlining creates backscattering, which causes slow light phenomena. Slow light causes optical signals to compress in space, and this enables interaction between light and matter and allows miniaturization. Presentation of photonic crystal waveguides is given in Figure 15a. A variety of dielectric slab materials of the high refractive index can be used to produce photonic crystal slabs. A typical example of these photonic crystals is based on polymethyl methacrylate (PMMA) prepared by [114]. A photonic crystal slab based on Si\({}_{3}\)N\({}_{4}\) has been demonstrated to work in the visible region of the electromagnetic spectrum. Moreover, photonic crystals based on InP/InGaAsP structure have been prepared with a slight loss [115]. Several types of research are on-going for the potential applications of photonic crystals. Most frequent among them are related to the photonic integrated circuits. The introduction of defects can be achieved through the photonic band gap. Instead of guiding light through total internal reflection, it could be conducted using line defects in photonic crystals. The use of a photonic bandgap to guide light allows for small bending loss even when the bending angles are large. In the area of sensors, photonic crystals have been widely used in the field of sensors. A photonic crystal slab provides sensitivity to the photonic band gap. Some of these sensors can be designed to detect pressure using a GaAs/AlGaAs slab [116].
## 8 Conclusions
### Nomenclature
\begin{tabular}{l l} \(\varepsilon_{A_{i,\
u}}\) & geometric error of \(i\)-th facet \(A_{i}\) in \(y\) direction \\ \(w_{i,j}\) & weight of the vertex \\ \(w_{j}^{k}\) & weight assigned in the \(j\)-th neuron of the \(k\)-th layer to the input from \(i\)-th neuron of the \\ \(W_{i}\) & convolution kernel of the \(i\)-th layer namely assigned weight \\ \(\
abla T\) & temperature gradient (K-\(mm^{-1}\)) \\ \(\sigma\) & hyperbolic tangent Tanh activation function \\ \(\sigma_{s}\) & Stefan constant \(\left(W\cdot m^{-2}\cdot K^{-4}\right)\) \\ \(\rho\) & material density (kg\(\cdot m^{-3}\)) \\ \(\varepsilon\) & target emissivity \\ \(\rho_{r}\) & reflectivity \\ \(\tau\) & transmissivity \\ \(\sum\ L_{i}\) & total servo trajectory \\ \(\
abla_{\theta_{z}}\) & gradient solving operator of discriminator \(D\) \\ \(\
abla_{\theta_{z}}\) & gradient solving operator of generator \(G\) \\ \(h\) & thermal convection coefficient \(\left(W\cdot m^{-2}\cdot K^{-1}\right)\) \\ \(k\) & thermal conductivity \(\left(W\cdot m^{-1}\cdot K^{-1}\right)\) \\ \(k_{1}\), \(k_{2}\) & thermal conductivities of two contacted objects \(\left(W\cdot m^{-1}\cdot K^{-1}\right)\) \\ \(k_{x}\),\(k_{y}\),\(k_{z}\) & thermal conductivities along the directions of \(\times\), \(y\), \(z\) axes \(\left(W\cdot m^{-1}\cdot K^{-1}\right)\) \\ \(p\) & cross-sectional perimeter of filament (mm) \\ \(\dot{q}\) & heat density inside the object \(\left(W\cdot m^{-3}\right)\) \\ \(q_{f}\) & input heat of extruded filament (kJ) \\ \(q_{f+df}\) & conduction heat from extruded filament to interactive element (kJ) \\ \(q_{A}\) & convection heat to ambient air (kJ) \\ \(q_{S}\) & convection heat to substrate (kJ) \\ \(t\) & interaction time (s) \\ \(T\) & temperature (K) \\ \(T_{1}\),\(T_{2}\) & temperatures of two contacted objects (K) \\ \(T_{s}\) & boundary surface temperature (K) \\ \(T_{\infty}\) & final temperature when time tends to infinity (K) \\ \(\Delta q\) & internal energy change of filament microelement (kJ) \\ \(\
abla T_{n}\) & temperature difference (K) \\ \(\alpha\) & thermal diffusivity (m\({}^{2}\cdot s^{-1}\)) \\ \(R_{i}\) & receptive field of the \(i\)-th layer \\ \(R_{i-1}\) & receptive field of the \(\left(i-1\right)\)-th layer \\ \(k_{i}\) & convolution kernel size of the \(i\)-th layer \\ \(s_{l}\) & convolution kernel stride of \(l\)-th layer \\ \(d_{i}\) & deviation between the i-th localized measured point and the design surface S \\ \(\overline{p}_{i}\) & homogeneous coordinate of \(P_{i}^{*}\) \\ \(\overline{p}_{i}\) & homogeneous coordinate of the measured point \(P_{i}\)' \\ T & transformation matrix; \\ \(\alpha\), \(\beta\), \(\gamma\) & angles rotating the measuring coordinate system around the X, Y, and Z axis, respectively \\ \(\Delta x\), \(\Delta y\), \(\Delta z\) & translations along the X, Y and Z axes, respectively \\ \end{tabular}
# SQRT: A Secure Querying Scheme of Routing Table Based on Oblivious Transfer
## 2 System Model
### The Directory Server Queries and Returns the Status Information of the Routing Node  
After obtaining the \(M_{k}\) of the user equipment \(UE_{k}\), the DS calculates the \(S_{ij}\) for the routing node \(j,j=1,\,2,\,\ldots,\,n\),  
\[\begin{split} s_{ij}&=\mathbf{M}_{k}\cdot(\mathbf{d}_{u}- \mathbf{d}_{v})^{T}\\ &=\left[\begin{array}{ccc}b_{11}\cdot q_{1}^{k}&\cdots&b_{1m} \cdot q_{m}^{k}\\ \vdots&\ddots&\vdots\\ b_{w1}\cdot q_{1}^{k}&\cdots&b_{wm}\cdot q_{m}^{k}\end{array}\right]\cdot \left(\begin{array}{c}d_{u1}-d_{v1}\\ \vdots\\ d_{um}-d_{vm}\end{array}\right)\end{split} \tag{2}\]  
where \(u\) and \(v\) represent different routing nodes, respectively, i.e., \(u\), \(v=1,\,2,\,\ldots,\,n\), and \(u\
eq v\).  
The DS returns the routing node information \(S^{\prime}_{ij}\) to the user equipment \(UE_{k}\), which can be obtained according to the received \(S_{ij}\)  
\[\begin{split} S^{\prime}_{ij}&=(s_{1},\cdots,s_{w})\cdot S_{ ij}\\ &=(s_{1},\cdots,s_{w})\cdot\left[\begin{array}{ccc}b_{11}\cdot q_{1}^{k}& \cdots&b_{1m}\cdot q_{m}^{k}\\ \vdots&\ddots&\vdots\\ b_{w1}\cdot q_{1}^{k}&\cdots&b_{wm}\cdot q_{m}^{k}\end{array}\right]\cdot \left(\begin{array}{c}d_{1}^{u}-d_{1}^{v}\\ \vdots\\ d_{um}^{u}-d_{m}^{v}\end{array}\right)\\ &=\sum\limits_{i=1}^{w}s_{a}\cdot b_{a1}\cdot q_{1}^{k}\cdot(d_{1}^{u}-d_{1}^{ v})+\cdots\sum\limits_{a=1}^{w}s_{a}\cdot b_{am}\cdot q_{m}^{k}\cdot(d_{m}^{u}-d_{m}^{v})\\ &=\sum\limits_{i=1}^{w}q_{i}^{k}\cdot d_{i}^{u}-\sum\limits_{i=1}^{w}q_{a}^{k} \cdot d_{i}^{v}\\ &=\sum\limits_{i=1}^{w}\left[\sum\limits_{i=1}^{w}\left(q_{i}^{k}-d_{i}^{u} \right)^{2}-\sum\limits_{i=1}^{w}\left(q_{i}^{k}-d_{i}^{v}\right)^{2}\right] \end{split} \tag{3}\]This paper is based on the distance \(dist_{i}^{k,j}\) between \(q_{i}\) and \(d_{j}\) to the query routing nodes. With a flowchart, Figure 3 shows that the DS selects nodes with the number of \(N_{k}+\Delta\) from the number of \(n\) routing nodes that meet the \(U\!E_{k}\) requirements. The specific method steps are shown in Algorithm 1. The DS queries the elements in the routing request set of the user equipment \(U\!E_{k}\) through Algorithm 1, compares them with the routing node status information in the routing table database, and looks for the nearest \(N_{k}+\Delta\) routing nodes between them. Algorithm 1 describes how the directory server feeds back the \(N_{k}+\Delta\) routing nodes that meet the requirements of the routing node request of the user equipment.
# Review: A Survey on Objective Evaluation of Image Sharpness
## 2 Evaluation Methods and Analysis
### Learning-Based Methods
#### 2.3.3 Dictionary Learning-Based Methods  
Dictionary learning-based methods are used in image definition evaluation to a certain extent, which are often combined with sparse learning/clustering algorithms.  
[PERSON] et al. [80] presented a no-reference SPArse Representation based image sharpness (SPARISH) index. The blurred image is represented as a block using a dictionary; the blocked energy is calculated using sparse coefficients; and the sharpness evaluation score is obtained by normalizing the energy value using a pooling layer. The method is insensitive to the training image and can be used to improve the evaluation of the sharpness of the image using a dictionary. [PERSON] et al. [81] proposed a no-reference image sharpness measurement method based on a sparse representation of structural information. In this method, a learning dictionary is used to encode the patch of the blurred image, and a multi-scale spatial maximum pool scheme is introduced to obtain the final sharpness score.  
[PERSON] et al. [82] proposed a blind image quality evaluation method based on high-order statistical aggregation (HOSA). This method extracts local normalized image blocks as local features through regular grid and constructs a codebook containing 100 codewords through K-means clustering. Each local feature is assigned to several nearest clusters, and the higher-order statistical differences between local features and corresponding clusters are aggregated as the global evaluation results. [PERSON] et al. [83] proposed a no-reference image evaluation method based on an optimized multilevel discriminant dictionary (MSDD). MSDDs are learned by implementing a label consistent K-SVD (LC-KSVD) algorithm in a phased mode.  
The dictionary learning method is to establish the transfer relationship between the image features and the dictionary and then matches them with the dictionary to obtain the image evaluation results.
## 2 Impedance-Based Model of Multi-Converter Grid-Tied System
### Equivalent Model of VSC
 \tag{5}\]  
Considering one sampling period (\(T_{\text{s}}\)) for computational delay and a half-sampling period (0.5 \(T_{\text{s}}\)) delay related to the pulse width modulator [17], the Pade approximation is used to replace the control delay link, and its equivalent transfer function is [18]  
\[G_{\text{del}}(s)=e^{-1.5T_{\text{s}}s}\approx\frac{1-0.75T_{\text{s}}s}{1+0.75 T_{\text{s}}s} \tag{6}\]  
where \(T_{\text{s}}\) represents the sampling interval. According to Equations (1)-(6), the small-signal mathematical model of VSC can be obtained as shown in Figure 2. The transfer function model of each link is shown in the following equation:  
\[G_{\text{ays1}}(s)= \begin{bmatrix}0&-\frac{H_{\text{pl}}(s)I_{0}}{s+H_{\text{ pl}}(s)U_{\text{pccd},s}}\\ 0&\frac{H_{\text{pl}}(s)I_{0}}{s+H_{\text{pl}}(s)U_{\text{pccd},s}}\end{bmatrix}  \tag{7}\]  
\[G_{\text{ays2}}(s)= \begin{bmatrix}0&-\frac{H_{\text{pl}}(s)v_{\text{pccd},s}} {s+H_{\text{pl}}(s)U_{\text{pccd},s}}\\ 0&\frac{H_{\text{pl}}(s)v_{\text{mm},s}}{s+H_{\text{pl}}(s)U_{\text{pccd},s}} \end{bmatrix}  \tag{8}\]  
\[G_{i}(s)= \begin{bmatrix}H_{\text{cl}}(s)&0\\ 0&H_{\text{cl}}(s)\end{bmatrix}  \tag{9}\]  
\[G_{\text{de}}(s)= \begin{bmatrix}G_{\text{del}}(s)&0\\ 0&G_{\text{del}}(s)\end{bmatrix}  \tag{10}\]  
where \(G_{\text{ays1}}(s)\) and \(G_{\text{ays2}}(s)\) are correction matrices for current and modulation signal, respectively, taking into account PLL. \(G_{\text{i}}(s)\) represents the current loop matrix. \(G_{\text{de}}(s)\) represents the delay loop matrix. According to Figure 2 and Equations (7)-(10), the impedance of the VSC with PLL working under a closed-loop condition is  
Figure 2: The small-signal model of VSC controlled in the \(dq\) frame. \[\mathbf{Y}_{\text{vsc}}(s)=[\mathbf{I}_{2}+\mathbf{Y}_{\text{L}}(s)\mathbf{K}_{\text{PWM}}\mathbf{G}_{ \text{de}}(s)\mathbf{G}_{\text{i}}(s)]^{-1}\cdot\mathbf{Y}_{\text{L}}(s)[\mathbf{I}_{2}-\bm {K}_{\text{PWM}}\mathbf{G}_{\text{de}}(s)\cdot(\mathbf{G}_{\text{ays2}}(s)+\mathbf{G}_{ \text{i}}(s)\mathbf{G}_{\text{ays1}}(s))] \tag{11}\]  
where \(\mathbf{I}_{2}\) is a second-order unit matrix.
# Green Hydrogen Storage in an Underground Cavern: A Case Study in Salt Diapir of Spain
## 3 Methodology and Results  
### Storage Design  
The design of the cavities must ensure their stability, tightness to the stored gas, acceptable surface subsidence, and the environmental safety of the surroundings. These requirements have to be met both during the leaching process for cavern excavation and during gas injection and extraction operations. The permeability of the salt rock is mainly affected by the stress conditions and the operating pressure requirements of the storage gas [7]. In addition, from an economic point of view, the optimization of its shape and size is sought in order to store the largest amount of gas. The storage capacity is calculated on the basis of thermodynamic considerations according to the specific data of the storage facility [12].  
Salt caverns intended for storage must be carefully designed according to the specific properties of the salt and the precise operating circumstances.  
The estimated cavern construction time of 1-3 years [18] and up to 5 years [11] varies according to the dimensions to be achieved, the structure and composition of the salt rock, and the leaching method employed [19]. To carry out the leaching process, the drilling of a well is required, which, after a specific and adequate completion, will constitute the \(\mathrm{H_{2}}\) injection well in the cavern.
# Data-Driven Construction of User Utility Functions from Radio Connection Traces in LTE
## 3 Trace Collection Process  
Monitoring the QoE of individual users can only be done by collecting QoS indicators for each connection. Such a piece of information is only available in connection traces, containing signaling messages (a.k.a. events) exchanged between every single piece of user equipment (UE) and base station. The structure of events consists of a header and a message container made up of different attributes, referred to as event parameters. The header provides general information (e.g., timestamp, base station, user, event type, among others), whereas attributes stored in the message container are specific to the event. Depending on the network entities involved, events can be external or internal. External events consist of signaling messages exchanged through network interfaces via standard protocols [26; 27; 28], whereas internal events store vendor-specific information about the performance of the base stations (known as evolved Nodes B (eNBs) in LTE). Events selected by the network operator are registered in a Data Trace File (DTF) for each cell, which is. generated after each reporting period (currently, 15 min). Two types of DTFs are distinguished: UE Traffic Recording (UETR) and Cell Traffic Recording (CTR) [29]. UETRs gather events from a specific users identified by International Mobile Subscriber Identity (IMSI), while CTRs store cell performance information by monitoring many anonymous connections [30]. In this work, CTRs are used to collect QoS indicators that reflect the average performance of each cell in the network.  
A high-level view of the architecture for trace reporting in LTE can be found in [30]. The operator starts the trace collection process by preparing a Configuration Trace File (CTF) in the Operations Support System (OSS). A CTF consists of (a) the event(s) to be monitored, (b) the particular UE(s) or ratio of anonymous users to be monitored, (c) the Reporting Output Period (ROP), (d) the maximum number of traces activated simultaneously in the OSS and (e) the time period when trace collection is enabled. Once trace collection is enabled, UEs transfer their event records to their serving eNB. After finishing the ROP, DTFs are generated by the eNB and then sent to the OSS asynchronously.  
Trace files are binary files encoded in ASN.1 format [29]. Trace decoding is performed by a parsing tool that decodes, synchronizes and correlates events to extract the information contained in fields and compute the required network indicators, as described later.