esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Related Studies  
In recent years, there were many studies focusing on the implementation of SQRD. In reference [15; 16; 17; 18; 19; 20; 21], the SQRD is performed directly on the complex-valued matrix \(\mathbf{H}\) for the complex-valued MIMO detection model. However, the subsequent MIMO detection in a complex-valued model is more complicated than the one in a real-valued model [22]. Thus, many studies [3; 8] develop SQRD based on conventional RVD [23], with which the complex-valued model in Equation (1) is converted into a real-valued model as follows  
\[\left[\begin{array}{c}\Re(\mathbf{y})\\ \Im(\mathbf{y})\end{array}\right]=\left[\begin{array}{cc}\Re(\mathbf{H})&- \Im(\mathbf{H})\\ \Im(\mathbf{H})&\Re(\mathbf{H})\end{array}\right]\cdot\left[\begin{array}{c} \Re(\mathbf{s})\\ \Im(\mathbf{s})\end{array}\right]+\left[\begin{array}{c}\Re(\mathbf{n})\\ \Im(\mathbf{n})\end{array}\right]. \tag{4}\]  
Before performing iterative sorting and GR operations, the \(N\times N\) complex channel matrix is firstly converted into its real counterpart of size \(2N\times 2N\) with conventional RVD [23]. As the size of the matrix is enlarged by four times, the number of sorting procedures and operations of GR both dramatically increase, which leads to huge hardware overhead and lengthy processing latency. The design in reference [9] adopts a SQRD algorithm based on a modified RVD and utilizes the symmetry of adjacent columns of the RVD matrix to reduce the number of CORDIC operations as well as sorting procedures. However, the computational complexity of this scheme still stays at a relatively high level and thereby brings about considerable hardware cost. To alleviate this problem, we propose an efficient SQRD algorithm, which can significantly reduce the computational complexity.
## 3 Results
### Rheological Studies  
The viscoelastic properties of the silica sols prepared using ionic liquid were studied by time-dependent rheological measurements at 25 \({}^{\circ}\)C. The time-dependent rheological curves are given in Figure 3. All the prepared gel samples exhibited a G' \(>\) G\({}^{\prime\prime}\) trend, which is the characteristic feature of a strongly viscoelastic gel system [70]. The gelation time was defined as the time required to reach a constant storage modulus G' of a viscoelastic material [71]. We noted the gelation time as the maximum time required to reach a constant modulus value. In the case of gel prepared without ionic liquid (IL 0), the sol took more time to become a gel and the gelation time was higher (t\({}_{\text{gel}}\) = 1800 s). The addition of ionic liquid enhances the cross-linking, thereby decreasing the gelation time, which can be seen from gelation time values for the gel prepared with IL. The decrease in gelation time was observed for imidazolium ionic liquid bearing organically modified silica (ORMOSIL) compared with pure silica [72]. Moreover, DNA ionogels containing IL (1-ethyl-3-methyl imidazolium chloride) show a decreasing trend in gelation time from 400 to 250 s for  
Figure 2: N\({}_{2}\) adsorption/desorption isotherm plot for the prepared gel samples.  
1% and 5% IL concentrations, respectively [73]. [PERSON] et al. [74] reported that the addition of a higher polymer concentration and lower gelation temperature allows faster gelation time and rate in the gelation kinetics of polybenzimidazole in phosphoric acid solution. The gelation time for the gel prepared with a higher IL (IL 10.0) content was 1100 s. The gel prepared with IL (10 wt %) showed a higher G' value than that of gel without ionic liquid. This can be attributed to the enhancement of cross-linking and ionic interaction between silica particles and IL. Further, we could observe a very steep rise in the complex viscosity (\(\eta^{*}\)) of the silica sol system prepared using ionic liquid. Similar results were reported by [PERSON] et al. [75] for the sol solution of low-density aerogel. Overall, it is shown that silica ionogels with tunable gelation time can be prepared by varying different concentrations of ionic liquid. The gelation time is a measure of the elastic and viscous behavior of a viscoelastic material. The introduction of an ionic liquid promotes the formation of a more interconnected silica network structure. This clearly proves the strong interaction between silica particles and ionic liquid, which enhances network formation [76].
# A Secure Blockchain-Based Authentication and Key Agreement Scheme for 3 GPP 5G Networks
## 6 Performance Evaluation
### Discussion of the Results  
The performance evaluation shows that when there are DoS or DDoS attacks in the 5G networks, the 5 GSBA is the better protocol with a higher performance in terms of a stable authentication delay and a high authentication success rate. The only limitation of the 5 GSBA would be the slightly added overhead in computational overhead, communication overhead, and energy consumption compared with the 3 GPP 5G-AKA [4]. However, given that there will be an exponential growth of 5G mobile devices in the future, the risk of DoS and DDoS attacks will become more prominent. Thus, we believe that these imperceptible overheads are justifiable to safeguard future 5G networks.
# Sentence Representation Method Based on Multi-Layer Semantic Network
## 5 Results and Discussion
### Results
#### 5.1.4 Semantic Relevance Analysis  
In addition to quantitative analysis of the model, to test whether the model can capture multi-layer semantic information, qualitative analysis, namely semantic correlation analysis, is carried out. Figures 5 and 6 show the results of visualizing the semantic relevance of the sentence \"a person is training his horse for a competition\" by using the BiLSTM model [42] and our sentence representation method based on the multi-layer semantic network.  
The darker color indicates that the correlation between the two words is more important to the representation of the sentence. Compared with Figures 5 and 6, the multi-layer semantic representation network not only focuses on the relationship between \"a person\", but also pays attention to other levels of information, such as \"trialing his horse\" and \"for a competition\" due to the existence of the multi-attention mechanism. This shows that the sentence representation method based on the multi-layer semantic network can express more comprehensive and accurate sentence information.
## 2 Backgrounds
### Data Rebalancing Methods
#### 2.1.2 Under-Sampling Methods  
Under-sampling methods eliminate the samples of the majority class. This might cause the loss of information of the data, which led the under-sampling method to be less popular than the over-sampling of [PERSON] et al. [38].  
* Random removal (RREM)--A sample of the majority class is randomly selected for removal.
* Neighborhood cleaning rule (NCL) [39]--A sample of the majority class is selected by [PERSON]'s edited nearest neighbor rule [40] or a simple 3-nearest-neighbors search [36] for removal.
## 6 Transceiver Architectures' Classification and Basic Concepts
### 2D-Based Architectures  
There are enormous opportunities when the exploited DoFs are two rather than one. Based on this, there is a unique window of opportunity to seize the offered advantages while a self-beating direct detection scheme is still maintained. There are a number of 2D-based architectures, and some of the potential ones are presented.
# An Improved PSO Algorithm and Its Application in GNSS Ambiguity Resolution
## 3 Experiments and Result Analysis
### Validation of the IPSO-AR Algorithm with Known Baseline  
To further validate the effectiveness of IPSO-AR, we select one baseline with known baseline length and use two GPS receivers. We obtain observations for approximately 2 h at the sampling interval of 1 s and approximately 7000 epochs. The truth value of the baseline length is exactly known with 10.332 m. Then, we postprocess the baseline observation data epoch by epoch by using the real-time kinematic (RTK) model. The proposed IPSO-AR method is used in data processing, and the IPSO-AR parameter setting is illustrated in Table 4.  
Figure 11 shows the visible DD satellite number and ADOP value with respect to epochs. Figure 12 shows the assumption time of IPSO-AR with respect to epochs. In most cases, the ADOP value will decrease when the corresponding satellite number increases at some epochs. The redundant observation equation will also increase with the increment in satellite number. Theoretically, the precision of the corresponding DD ambiguity float resolution will improve. Figure 12 shows that at epochs with an increase in satellite number, the corresponding efficiency of IPSO-AR is relatively low. This result proves once again that the efficiency of IPSO-AR is mostly dependent on dimension instead of the precision of DD ambiguity.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ADOP**} & \multicolumn{4}{c}{**Dimensions**} \\ \cline{2-5}  & **[1,5]** & **(5,9]** & **(9,12]** & **-** \\ \hline (0,0.1] & \(n=30\)\(s=10\) & \(n=60\)\(s=20\) & \(n=90\)\(s=30\) & - \\ (0.1,0.5] & \(n=30\)\(s=10\) & \(n=60\)\(s=20\) & \(n=90\)\(s=30\) & - \\ (0.5,2) & \(n=60\)\(s=20\This experiment verifies the correct rate of IPSO-AR. The series of baseline length differences between the fixed solution (by IPSO-AR method) and truth value is shown as the blue line in Figure 13. An incorrect solution to integer ambiguity may cause large baseline length error. To verify whether the fixed ambiguity calculated through the IPSO-AR method is correct, we randomly add an integer value cycle on one DD satellite at every epoch. The integer value randomly varies from \(-1\) to \(1\). The corresponding series of baseline length differences is represented by a green line in Figure 13. Given only one cycle error on the DD ambiguity at many epochs, the corresponding baseline length will deviate from the true value by \(10\) cm. Meanwhile, all the baseline length errors calculated by IPSO-AR are on the mm level, as represented by the blue line in Figure 13. This result indicates that the estimated DD ambiguity float solutions are all correct. In other words, the correct rate of IPSO-AR in this test is \(100\%\).  
Figure 11: Visible DD satellite number and ADOP value.  
Figure 12: Search time of IPSO–AR.
# Calculation of View Factors for Building Simulations with an Open-Source Raytracing Tool
## 3 Validation of RADIANCE Generated Results against Analytical Solutions
### Differential Element to Rectangle in a Plane at 90\({}^{\circ}\) to Plane of Element  
Figure 12 shows the view factor calculation for a differential element and a rectangle. The rectangle is in a plane that is oriented at 90\({}^{\circ}\) to the plane of the element.  
The analytical expression for \(F_{dA_{1}-A_{2}}\) in this scenario, provided in [33] and adapted from [34], is:  
\[F_{dA1-A2}=\frac{1}{2\pi}\left\{\tan^{-1}\left(\frac{1}{C}\right)-\frac{C}{Y} \tan^{-1}\left(\frac{1}{Y}\right)\right\} \tag{8}\]  
where \(A=a/b;C=c/b;Y=\sqrt[3]{A2+B^{2}}\). For values of \(a=1\), \(b=1\), \(c=1\) as per the dimensions in Figure 12a, the analytical result rounded off to four decimal places is 0.0557. As shown in Figure 12b this is equal to the result calculated through RADIANCE.
## 2 Data and Methods
### Data Sets
#### 2.2.3 In Situ Measurements  
We first considered the autonomous in situ measurements recorded at the Pauillac station concurrently with Pleiades imagery. The Pauillac station is located along the western shore of the estuary by the port of Pauillac (Figure 2) and is part of the MAGEST network ([[http://mages.oasu.u-bordeaux.fr/](http://mages.oasu.u-bordeaux.fr/)]([http://mages.oasu.u-bordeaux.fr/](http://mages.oasu.u-bordeaux.fr/))), a high-frequency monitoring program of water quality [40; 41]. Every 20 minutes, water temperature, salinity, and turbidity are measured at a 1-m depth, providing quasi-synchronous measurements with the Pleiades data. The two turbidity measurements recorded before and after Pleiades data acquisition times were linearly interpolated to the satellite overpass times.  
The SeaSWIR 2013 dataset (simultaneous in situ measurements of water reflectance and turbidity at the Pauillac station [42; 43]) was used for the calibration of the water turbidity algorithm and validation of Pleiades-retrieved water reflectances.
# Research and Conceptual Design of Sensor Fusion for Object Detection in Dense Smoke Environments
###### Abstract  
In this paper, we propose a conceptual framework for a sensor fusion system that can detect objects in a dense smoke environment with a visibility of less than 1 m. Based on the review of several articles, we determined that by using a single thermal IR camera, a single Frequency-Modulated Continuous-Wave (FMCW) radar, and multiple ultrasonic sensors simultaneously, the system can overcome the challenges of detecting objects in dense smoke. The four detailed methods proposed are as follows: First, a 3D ultrasonic sensor system that detects the 3D position of an object at a short distance and is not affected by temperature change/gradient; Second, detecting and classifying objects such as walls, stairs, and other obstacles using a thermal IR camera; Third, a 2D radial distance measurement method for a distant object using an FMCW radar; Fourth, sensor fusion for 3D position visualization of multiple objects using a thermal IR camera, 3D ultrasonic sensor system, and FMCW radar. Finally, a conceptual design is presented based on the proposed methodologies, and their theoretical usefulness is discussed. The framework is intended to motivate future research on the development of a sensor fusion system for object detection in dense smoke environments.  
sensor fusion; dense smoke; thermal IR camera; ultrasonic sensor; FMCW radar
# Stochastic Extreme Wind Speed Modeling and Bayes Estimation under the Inverse Rayleigh Distribution
## 4 A Bayes Method of Inference for the Risk Index Based upon the IRD Model
### The Case of a Gamma Prior PDF on the IRD Parameter \(\alpha\)--Posterior Analysis
\begin{table}
\begin{tabular}{l|c c c c c c c c} \hline \hline
**Parameters** & **n = 3** & **n = 10** & **n = 30** & **n = 50** & **n = 3** & **n = 10** & **n = 30** & **n = 50** \\ \hline  & \multicolumn{6}{c}{**Case A11**} & \multicolumn{6}{c}{**Case A12**} \\ \hline _AV(R)_ & 0.0733 & 0.0735 & 0.0733 & 0.0734 & 0.0735 & 0.0734 & 0.0733 & 0.0735 \\ _AV(\(\alpha\))_ & 14.9964 & 15.0061 & 14.9767 & 15.9998 & 14.9929 & 14.9980 & 14.990 & 15.0057 \\ _AV(\(\phi\))_ & 2.4969 & 2.5000 & 2.4986 & 2.5003 & 2.5022 & 2.4982 & 2.4999 & 2.5010 \\ _AV(RL)_ & 0.0057 & 0.0115 & 0.0001 & 0.0471 & 0.0068 & 0.0108 & 0.0471 & 0.0470 \\ _AV(RB)_ & 0.0730 & 0.0730 & 0.0732 & 0.0733 & 0.0730 & 0.0731 & 0.0732 & 0.0733 \\ _RMSEL_ & 0.0732 & 0.0661 & 0.0542 & 0.0460 & 0.0740 & 0.0663 & 0.0543 & 0.0467 \\ _RMSEB_ & 0.0050 & 0.0049 & 0.0048 & 0.0047 & 0.0050 & 0.0049 & 0.0049 & 0.0049 \\ _RARE_ & 17.7987 & 16.1190 & 12.6195 & 10.2275 & 17.8265 & 16.2862 & 12.3797 & 10.2125 \\ _RMKE_ & 21.8762 & 9.5639 & 5.3210 & 8.8966 & 36.5525 & 11.0236 & 6.3284 & 8.2127 \\ _REFF_ & 14.7407 & 13.3886 & 11.2272 & 9.7079 & 14.7653 & 13.5250 & 10.9862 & 9.6030 \\ \hline \hline \multicolumn{10}{c}{**Case A21**} \\ \hline _AV(R)_ & 0.0733 & 0.0735 & 0.0734 & 0.0734 & 0.0734 & 0.0734 & 0.0734 & 0.0735 & 0.0735 \\ _AV(\(\alpha\))_ & 14.9964 & 15.0061 & 14.9767 & 14.9497 & 15.0027 & 15.0038 & 15.0051 & 14.9998 \\ _AV(\(\phi\))_ & 2.4969 & 2.5000 & 2.4986 & 2.4993 & 2.4980 & 2.5000 & 2.5007 & 2.5003 \\ _AV(RL)_ & 0.0057 & 0.0115 & 0.0001 & 0.0466 & 0.0064 & 0.0115 & 0.0291 & 0.0471 \\ _AV(RB)_ & 0.0730 & 0.0730 & 0.0732 & 0.0733 & 0.0730 & 0.0731 & 0.0732 & 0.0733 \\ _RMSEL_ & 0.0732 & 0.0661 & 0.0546 & 0.0475 & 0.0743 & 0.0662 & 0.0542 & 0.0470 \\ _RMSEB_ & 0.0050 & 0.0049 & 0.0049 & 0.0049 & 0.0050 & 0.0050 & 0.0049 & 0.0048 \\ _RARE_ & 17.7987 & 16.1190 & 12.6776 & 13.3687 & 17.9172 & 16.0920 & 12.5001 & 10.3649 \\ _RMKE_ & 21.8762 & 9.5639 & 8.9712 & 11.8046 & 40.5468 & 11.0576 & 8.3189 & 10.7958 \\ _REFF_ & 14.7407 & 13.3886 & 11.2468 & 9.7686 & 14.9604 & 13.3499 & 11.0788 & 9.7256 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation results for cases Aij. Cases **Aij** correspond to \"low risk\" values (E[\(R\)] \(\approx\) 0.0735). **Cases **Bij** correspond to \"medium risk\" values (E[\(R\)] \(\approx\) 0.3585). **Cases **Cij** correspond to \"high risk\" values (E[\(R\)] \(\approx\) 0.8167).
# Near-Real-Time Detection of Insect Outbreaks in Urban Trees Using a PlanetScope Time Series
## 4 Discussion
This would have resulted in a reduction in crown thickness, with all that this entails as previously discussed. This type of information is essential to better understand the results in more detail. More emphasis should be placed on regular data collection. The absence of a historical series, even for field data, represents a significant obstacle to the evaluation and effective utilization of these data. Although remote sensing remains a fundamental tool for the study of tree health, it is important to acknowledge certain inherent limitations. First, although high-resolution images were employed in this study, it remains challenging to work on the scale of the individual tree. The canopy size of _Pinus pinea_ can vary significantly, ranging from approximately 2 m in younger trees to 10-12 m in mature trees [72]. Using imagery with a spatial resolution of 3 m, single-tree analysis would be subject to significant errors, as accurately distinguishing individual canopies would be challenging. Overcoming this limitation would require the acquisition of very high spatial resolution spectral data, which currently necessitates specialized equipment, such as sensors mounted on drones. However, detailed monitoring at the single-tree scale still represents a major challenge for remote sensing techniques. In the future, once these limitations are addressed, the use of high-resolution vegetation indices could become a pivotal tool for precisely analyzing the activity and health conditions of individual trees [73]. Consequently, large areas are typically assessed as single entities [74]. Moreover, as remote sensing employs radial images, only the crowns can be assessed when studying trees [48]. This ultimately gives rise to two issues. First, as in the case of _T. parvicornis_, if damage commences in the lower part of the crown, it will only be possible to discern the infestation at an advanced stage. Second, a reduction in reflectance resulting from the desiccation of the crown may be obscured by the reflectance of the background (if other species on the ground are present). Background is a confounding factor in the interpretation of remote-sensing results because it can alter the spectral signal. Elements such as soil, water, artificial structures, or other vegetation cover can reflect light in similar ways or overlap with the signal of the target of interest [75]. This overlap makes it difficult to isolate the spectral behavior of vegetation and can compromise the accuracy in identifying specific conditions. In the future, by testing the use of different spectral indices and increasing the information obtained in the field, it would be possible to reduce the influence of the background, improving the reliability of the results.
# Hierarchical Guided-Image-Filtering for Efficient Stereo Matching
## 2 Proposed Method
### The Cost Aggregation Based on the Pervasive Guided-Image-Filtering (PGIF)
 \tag{5}\]  
We have:  
\[\begin{array}{l}a^{*}_{d,0}(p)=\frac{\sum\limits_{q\in\Omega_{0}(p)}\big{[}a _{0}(p,q)\cdot I_{L,0}(q)\cdot C_{d,0}(q)\big{|}}{\sum\limits_{q\in\Omega_{0}( p)}\omega_{0}(p,q)}-\frac{\sum\limits_{q\in\Omega_{0}(p)}\big{[}a_{0}(p,q) \cdot I_{L,0}(q)\big{|}}{\sum\limits_{q\in\Omega_{0}(p)}\omega_{0}(p,q)}- \frac{\sum\limits_{q\in\Omega_{0}(p)}\big{[}a_{0}(p,q)\cdot C_{d,0}(q)\big{|} }{\sum\limits_{q\in\Omega_{0}(p)}\omega_{0}(p,q)}\] \[b^{*With the convention that \(q=(i,j)\) and \(p=(x,y)\), the weighting factors, \(\overline{W}_{i,x,0}^{H}\) and \(\overline{W}_{j,y,0}^{V}\), can be recursively calculated as:  
\[\overline{W}_{i,x,0}^{H}=\left\{ \begin{array}{cc}\prod\limits_{u= \min(i,x)+1}^{\max(i,x)}\exp\Big{(}\frac{f([I_{L,0}(u,j)-I_{L,0}(u-1,j)])}{-\beta }\Big{)},&i\
eq x\\ 1,&i=x\end{array} \right. \tag{8}\] \[\overline{W}_{j,y,0}^{V}=\left\{ \begin{array}{cc}\prod\limits_{u= \min(j,y)+1}^{\max(j,y)}\exp\Big{(}\frac{f([I_{L,0}(x,u)-I_{L,0}(x,u-1)])}{- \beta}\Big{)},&j\
eq y\\ 1,&j=y\end{array} \right.\] \[\text{with }f(\xi)=\left\{ \begin{array}{cc}0,&\text{if }\xi=0\\ 1,&\text{if }\xi>0\end{array} \right.\]  
where the parameter \(\beta\) is a constant factor. By this scheme, the weight \(\omega_{0}(p,q)\) depends on both the spatial and intensity differences, and the contribution of any pixel located at \(q\) to the pixel located at \(p\) can be effectively involved. Besides, the introduction of function \(f\) alleviates the effects of abrupt change in intensity, enhancing the immunity to noise caused by the recursive calculation.
## 3 System Design
### Comparison of ILDM and PIM-SM
Since the mapping of MSEID2 was not stored in NRS, NRS returned the Enable-state candidate core NA2 (arrow 28) and added the mapping of MSEID2 and NA2. After receiver3 joined the multicast service MSEID2, Router2 forwarded the multicast packets to receiver3 based on MSEID2 (arrows 29-31). Compared with PIM-SM, ILDM could effectively avoid core overload by using NRS to dynamically allocate a low-load core to each multicast service.
# Coexistence Scheme for Uncoordinated LTE and WiFi Networks Using Experience Replay Based Q-Learning
## 1 Introduction
Furthermore, the proposed coexistence strategy utilizes an experience replay technique to train a model that determines the optimal configuration, making it an excellent coexistence solution for co-located networks with dynamic traffic loads. The rest of this paper is structured as follows. Section 2 examines some recent studies on the coexistence of LTE and WiFi while Section 3 describes the definition of the problem addressed. In Section 4, the architecture of coexistence schemes in uncoordinated LTE and WiFi is described. The proposed coexistence mechanisms are discussed in Section 5. Section 6 evaluates the performance of the proposed coexistence mechanisms. Finally, Section 7 presents the conclusion of this work and outlines related future works. Table 1 summarizes the notations used in this article. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Notation** & **Description** & **Notation** & **Description** \\ \hline \(LTE_{\text{Of}}\) & LTE offered data rate & \(\varepsilon\) & Exploration/exploitation policy value \\ \hline \(LTE_{\text{Ob}}\) & LTE obtained throughput & \(N_{\varepsilon}\) & Number of iterations to update \(\varepsilon\) \\ \hline \(LTE_{\text{Tar}}\) & LTE target throughput & \(f_{\varepsilon}\) & Reduction factor of \(\varepsilon\) \\ \hline \(LTE_{\text{Ref}}\) & LTE reference throughput & \(\varepsilon_{\text{min}}\) & Minimum value of \(\varepsilon\) \\ \hline \(LTE_{\text{State}}\) & LTE state & \(R_{f}\) & Record of experiences in a fixed traffic load \\ \hline \(WiFi_{\text{State}}\) & WiFi state & \(R_{E}\) & Set of record of experiences for dynamic traffic \\ \hline \(WiFi_{\text{Off}}\) & WiFi offered data rate & \(N_{E}\) & Buffer size of experience replay \\ \hline \(W_{\text{S}}\) & WiFi saturated state & \(WiFi_{\text{Ob}}\) & WiFi obtained throughput \\ \hline \(W_{\text{U}}\) & WiFi unsaturated state & \(WiFi_{\text{Tar}}\) & WiFi target throughput \\ \hline \(\gamma\) & Discount factor & \(WiFi_{\text{Ref}}\) & WiFi reference throughput \\ \hline \(\eta\) & Learning rate & \(N_{\text{b}}\) & Number of LTE blank subframes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of notations used.