esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification of Industrial Heat Source Production Areas Based on SDGSAT-1 Thermal Infrared Imager
## 1 Introduction
[PERSON] [22] and [PERSON] [24] used the Landsat 8/9 thermal infrared sensor (TIRS) to construct thermal anomaly indices, such as the heat island intensity index and the heat island radiation index, to distinguish much smaller IHSs. However, it is still challenging to identify IHSPAs precisely. Sustainable Development Science Satellite 1 (SDGSAT-1) launched on 5 November 2021, and its thermal infrared sensor (TIS) with a 30 m spatial resolution is a significant addition of data for Earth observation because of its highest spatial resolution in the civilian field and three-band advantage [28]. It has been widely used for target detection and monitoring, such as ship detection [29, 30], sea surface temperature retrieval, monitoring near nuclear power plants [31], and arctic sea ice lead monitoring [32]. Therefore, nighttime SDGSAT-1 TIS data were first introduced to identify IHSPAs and improve the recognition accuracy and granularity of IHSs. To distinguish IHSPAs from the background, SDGSAT-1 TIS and Landsat 8 OLI data were used to extract muti-features that combine temperature features and optical features. Then, an IHSPA identification model using the multi-features extracted from SDGSAT-1 TIS and Landsat OLI data and support vector machine (SVM) was proposed. First, three brightness temperatures and four thermal radiation indices using SDGSAT-1 TIS and Landsat OLI data were designed to enlarge the temperature difference between the IHSPAs and the background in IHS areas. Then, 10 features combined with three indices from Landsat OLS images with the same spatial resolution (30 m) and stable data were extracted. Second, an IHSPA identification model based on SVM and multi-feature extraction was constructed to identify IHSPAs. Our results showed that our new model can detect IHSPAs and obtain much smaller IHSs. Finally, the IHS objects were manually delineated and verified using the identified IHSPAs and Google Earth images. This new way to detect IHSPAs can obtain higher-spatial-resolution emissions of IHSs on a large scale and help decision makers target environmental monitoring, management, and decision making in industrial plant processing. The study area, data sources, and methods are all described in Section 2. The results of the identification utilizing various feature combinations and approaches are examined and discussed in Section 3. The results identified in this paper and the existing IHS data are compared, the study's limitations are discussed, and prospects for future work are outlined in Section 4. Finally, a summary is given in Section 5.
# Image Mosaicing Applied on UAVs Survey
## 3 Stitching Methods
### Feature-Based: Global Single Transformation  
The feature descriptors must have different characteristics and must be found throughout the image so that the points of coincidence in both images are distinguished. There must be a high number of descriptors; in case of geometric changes, the identifiers can relate images efficiently. Among the most used feature algorithms are the Harris Corner Detector [62], FAST [63], ORB [64], BRIEF [65], BRISK [66], SIFT [67], and SURF [32].
## 4 Results and Discussion
### Analysis of Pore-Throat Utilization Characteristics of Cores with Different Permeabilities
In miscible flooding, the interfacial tension is 0, and CO2 only needs to overcome seepage resistance to enter the tiny pores; CO2 is more able to enter the pore-throat, and part of the large pore-throat in the <3.7 nm interval is effectively used (for miscible flooding, it is also challenging to use the more minor pore-throat in the <3.7 nm interval). With the increase of permeability, the pore-throat size becomes larger, so the utilization of the <10 nm pore-throat (especially the <3.7 nm pore-throat) increases and the sweep degree and displacement efficiency are significantly improved. Because the proportion of tiny pore-throats in different cores varies greatly, the utilization degree of <3.7 nm pore-throats in miscible flooding is high and dispersed. As shown in Figure 16, for the two core groups with maximum pore-throat radii of >2.5 \(\upmu\)m (D1, D2, D3) and <0.5 \(\upmu\)m (D5, D6, D7), in immiscible flooding, the utilization degree of <10 nm pore-throats increases with the increase of permeability, while the utilization degree of >10 nm pore-throats decreases. With the increase of permeability, the proportion of >10 nm pore-throats increased, so the proportion of CO2 passing through >10 nm pore-throats during displacement increased (with lower seepage resistance), and the degree of utilization increased. This results in a decrease in the proportion of CO2 used to overcome capillary pressure in the <10 nm pore-throat and a reduction in the utilization of the <10 nm pore-throat. On the contrary, with the increase of permeability in miscible flooding, the pore-throat utilization rate in the total pore diameter of both groups of cores increases. Figure 15: Line chart of pore-throat utilization degree of 7 cores under miscible flooding and immiscible flooding.
## 3 Methodology
### Auto-Correlation in the Time-Series  
As explained earlier, assessing the degree of auto-correlation of the time series in our data set is a key task in forecasting. As data in a time series is a sequence of values, it would be interesting to know how much does the observation at time \(t\) depend on the one at time \(t+1\).  
With a very simple experiment we scatter plot the observation \(y\) at time \(t\) and at time \(t+1\) in a cumulative way for all the time series in the grid. The plot is shown in Figure 4, and it shows a strong linear correlation along the main diagonal, but there is also a weak one below the main diagonal and a non-linear one above it. These correlations in the data are important for a forecasting model. The more the model will be able to capture such linear and non linear relationships the more it will be successful on forecasting them.  
Following this approach we correlate the observations at time \(t\) with those at time \(t+1\), \(t+2\),..., \(t+n\), and we plot this auto-correlation as in Figure 5, which shows a strong correlation up to 15-18 steps. This means that lagged data can indeed be used as features to perform forecasting. However, other than linear relationships, there are also non-linear dependencies between the observed variable and its lags. For example, as mentioned above, the auto-correlation scatter plot in Figure 4 shows that besides the main diagonal there are two other directions along which the observed variable at time \(t\) correlates with its lagged value at \(t+1\).
# Iterative QR-Based SFSIC Detection and Decoder Algorithm for a Reed-Muller Space-Time Turbo System
## 3 The Novel Iterative QR-Based SFSIC Detection and Decoder Algorithm
### RM-Turbo Iterative Decoder (Outer Decoder)
#### 3.2.2 Engendering Extrinsic Information and Making Hard Decisions  
Meanwhile, the horizontal decoder will use \(L_{e^{T}}^{\mid}(i_{n},m,q)\) in place of \(L_{a}^{-}(i_{n},m,q)\) after the first iteration, the transpose of which, computed by the vertical decoder is, in turn:  
\[L_{e}^{\mid}(i_{n},m,q)=L_{2}^{\mid}(i_{n},m,q)-L_{c}\cdot L^{\mid}(d_{i_{n},m },q)-L_{e^{T}}^{-}(i_{n},m,q) \tag{20}\]  
where \(L^{\mid}(d_{i_{n},m},q)\), estimated by the outer decoder, denotes the received de-multiplexed soft output of the vertical decoder, associated with the information bit \(u_{i_{n},m}\); \(L_{e^{T}}^{-}(i_{n},m,q)\) is the transpose of the \([i_{n},m]\)th APP LLR extrinsic information \(L_{e}^{-}(i_{n},m,q)\) of the horizontal decoder; \(L_{2}^{\mid}(i_{n},m,q)\) is the APP LLR associated with the information bit \(u_{i_{n},m}\) in the \(q\)th iteration, which can be defined in the manner similar to Equations (18) and (19). Note that, in Equation (20), the APP LLR extrinsic information \(L_{e}^{\mid}(i_{n},m,q)\) is obtained in such a way that subtracting the terms, namely,\(L_{c}\cdot L^{\mid}(d_{i_{n},m},q)\) and \(L_{e^{T}}^{-}(i_{n},m,q)\), removes the effect of the current information bit \(d_{i_{n},m}\) from \(L_{2}^{\mid}(i_{n},m,q)\), leaving only the effect of the parity constraints, thus providing an independent estimate of information bit \(d_{i_{n},m}\) to the horizontal decoder, in addition to the received soft channel LLR \(L_{c}\cdot L^{-}(d_{i_{n},m},q)\). The iterative process is usually terminated after a predetermined number of iterations, when the soft-output value stabilizes and changes slightly between successive iterations.  
Furthermore, Equations (17) and (18) can be relatively straightforwardly generalized to analyze and discuss the soft parity bits streams. Then, we multiplex the soft systematic bit streams and the soft parity bit streams in the same way as that in the RM turbo encoding [4; 17] to obtain the encoded bits' extrinsic information which is sent to the iterative QRSFSIC detector as an a priori LLR for the next iteration.  
With the increasing number of iterations, the contribution of extrinsic information from the inner and outer decoders to the improvement of performance of the receiver diminishes. Finally, the availability vanishes, that is to say the receiver converges. At this time, the vertical decoder will not only combine both extrinsic information values in computing the soft output \(L_{2}^{\mid}(i_{n},m,q)\), but was also make the difficult decision after de-multiplexing to obtain the estimated \(\hat{u}_{i_{n},m}\) of the information bit.
## 2 Methods
### Evaluation Metrics
#### 2.3.2 Secondary Evaluation: BERTScore  
For secondary evaluation, we employ BERTScore [25] using XLM-RoBERTa [26] as the underlying multilingual model for contextual embeddings. The BERTScore calculation process involves several steps. Each token in generated and reference summaries is converted to contextual embeddings. A similarity matrix is constructed using cosine similarity between all token pairs. The Hungarian algorithm performs optimal matching for maximum similarity pairing. Finally, score aggregation computes the harmonic mean of precision and recall.  
The mathematical formulation is:  
\[\text{BERTScore}_{\text{F1}}=\frac{2\times(\text{Precision}\times\text{Recall} )}{\text{Precision}+\text{Recall}} \tag{4}\]  
where:  
\[\text{Precision} =\frac{\sum\text{max}_{\text{similarity}}(\text{gen\_token}_{i} )}{|\text{generated\_tokens}|} \tag{5}\] \[\text{Recall} =\frac{\sum\text{max}_{\text{similarity}}(\text{ref\_token}_{j} )}{|\text{reference\_tokens}|} \tag{6}\]XLM-RoBERTa's cross-lingual training ensures multilingual robustness through consistent semantic similarity assessment across languages, reduced bias toward lexical overlap, and contextual understanding preservation across different linguistic contexts.
# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## 5 Discussion
### Green Tide Source Identification  
In addition to _U. prolifera_ green tide, Sargassum gold tide also occurred frequently in the Yellow Sea, especially in the southeastern Yellow Sea [42; 43]. Therefore, it cannot be ruled out that the green tide source found in the Southeastern Yellow Sea is Sargassum.  
According to the identification method described in Section 3.6, we counted the spectral reflectance of the extracted green tide pixels and calculated the mean value of each band. As shown in Figure 16a,c,d shows that 99% of \(d\) (the green part of the pie chart) was positive, indicating that the majority of our extracted green tide pixels matched this spectral property of _U. prolifera_ with a reflection peak near the green band. The reflection peak in Figure 16b was lower than in Figure 16b on the solid black line was not very pronounced, so we selected the pixels that \(d>0\) and drew as a red solid line to demonstrate the presence of the reflection peak near the green band, and thus prove the existence of _U. prolifera_. The pixel proportion of \(d>0\) was about 50% (the green part of the pie chart). The reflection peak near green band (550-570 nm) in Figure 16b is not as obvious as the other ones (Figure 16a,c,d), which was mainly affected by Sargassum.  
Figure 16: Spectral information of extracted green tide pixels for the source found in the southeastern Yellow Sea (blue box in Figure 6) for 2008, 2017, 2020, and 2021 (where N is the number of pixels).  
Figure 15: Source site statistics 2008–2022. (**a**) shows the source sites of green tide blooms over the years, and (**b**) shows the results of k-means clustering, which divided the green tide source into two clusters: the Subei shoal (red box) and the southeastern Yellow Sea (blue box).  
Therefore, the green tide source of the southeastern Yellow Sea (blue box in Figure 15) was sufficiently possible to exist and should be taken seriously.  
As seen in Figure 14, 2015, 2019, and 2021 were the three years with the largest green tide blooms in the Yellow Sea, and it was found that the green tide generally reached its maximum cover area in mid-to-late June to early July, which was consistent with previous studies [44; 45; 46]. In addition, the duration of the three years was relatively long, consistent with [PERSON] et al., 2022.
# Insights on Osmotic Tolerance Mechanisms in _Escherichia coli_ Gained from an _rpoC_ Mutation
## 3 Results and Discussion
### Impact of the rpoC Mutation in MG1655  
To determine whether the _rpoC_ mutation exhibits a similar benefit to osmotic stress tolerance in other _E. coli_ K-12 strains, the _rpoC_ mutation was reconstructed in MG1655 to generate strain EYG1. The osmotic tolerance of MG1655 and EYG1 were assessed in M9 with or without challenge with 0.6 M NaCl (shown in Figure 10A,B, respectively). In contrast to BW25113, the _rpoC_ K370_A396 dup mutation did not confer a benefit to MG1655 under osmotic stress. Though the growth of both MG1655 and EYG1 were slower than BW25113 in M9 in the absence of hyperosmotic stress (Figure 10A), their growths were nearly identical to EJW3 under osmotic stress (Figure 10B), suggesting that the inherent  
Figure 8: Growth kinetics of overexpression strains in BW25113 background. (**A**) M9. (**B**) M9 supplemented with 0.55 M NaCl. Error bars are standard deviations.  
Figure 9: Growth kinetics of knockout strains in BW25113 background. (**A**) M9. (**B**) M9 supplemented with 0.6 M NaCl. Error bars are standard deviations.  
tolerance of MG1655 to NaCl was higher than BW25113, and led us to use a higher NaCl concentration with subsequent assays with MG1655.  
As we found tryptophan supplements to benefit BW25113 in the presence of hyperosmotic challenge, we also assessed its potential benefit in MG1655. In the presence of 0.7 M NaCl, addition of tryptophan to the medium increased the osmotic tolerance of MG1655 and EYG1 (Figure 11A). The deletion of _bolA_ also improved the growth of MG1655 under osmotic stress with 0.7 M NaCl (Figure 11B,C), which suggests that although the impact of the _rpoC_ mutation on osmotic tolerance appears to be strain specific, some of the findings based on BW25113 also applies to MG1655.  
We also compared the transcriptional profiles between MG1655 and EYG1 (MG1655 background) in M9 with or without 0.6 M NaCl challenge to identify any transcriptional regulatory differences caused by the _rpoC_ mutation between MG1655 and BW25113 background. The microarray data showed that the _rpoC_ mutation caused significantly different transcriptional perturbation between these two strain backgrounds, with few similarities shared between strains and conditions. As mentioned previously, most genes upregulated in strain EJW3 under osmotic stress are related with amino acids metabolism; however, in EYG1 (MG1655 background), most amino acids metabolism and transport related genes perturbed by the _rpoC_ mutation were downregulated (Table S10). This difference between strains EJW3 and EYG1 likely explains the different physiological impacts of the _rpoC_ mutation on osmotic tolerance between BW25113 and MG1655.
# Design of Two Compact Wideband Monopoles Through Loading with Linear Approximated Lumped Components
## 1 Introduction
Since the loaded resistance would absorb part of the power on the antenna, a traveling wave is established on the antenna, which would result in an expansion of the bandwidth [28; 29]. Inductance or capacitance loading is also used to alter the reverse current on the monopole to obtain a wideband characteristic [30; 31; 32]. To alter the reverse current effectively, a parallel RLC circuit is fairly suitable since the current would vary intensely when the circuit is in resonance. Moreover, the resistance in the circuit can absorb power to some extent, which might benefit the port characteristics of the antenna. Refs. [33; 34; 35; 36] present many strategies to determine the position and element value of the parallel RLC circuit. However, all of these discussions are based on an assumption that all element values of the parallel RLC circuit are constant, which might fail in the ultra-wideband range. To realize the required wideband characteristic, tedious tuning on the prototype is unavoidable. In this paper, two monopoles in a compact structure are presented with ultra-wideband characteristics through loading with a parallel RLC circuit. All elements are modeled as linear frequency-dependent elements to model practical component behaviors. To simulate the antenna across an ultra-wide bandwidth, the antenna is simulated using the method of moments (MoM) with an asymptotic waveform evaluation (AWE) sweeping process for efficiency compared with sweeping discrete points one by one across such a wide band. The parallel RLC circuit with linear frequency-dependent elements is further modeled in the AWE loading model. The lower monopole also loads a hat in spokes to obtain a good port match at the lowest side of the concerned band. Moreover, this hat also acts as an elevated ground for the upper monopole. The outer conductor of the coaxial feed line for the upper monopole is designed to act as a loaded inductor for the lower monopole since this feed line has to travel through the lower monopole. In addition, this feeding line with a ferrite bead functions as part of an attenuator for the lower monopole. With these shared structure designs, the two monopoles are designed in a compact dual-band radiation system. The positions and element values of the parallel RLC circuit are determined by genetic algorithm (GA) optimization. Then, the optimized antenna is verified through experiments.
## 4 Methodology
### Sequential Optimization Problems
#### 4.2.1 Optimal Selection of Technology Set
For the sake of notation, we introduce also the risk vector located to the \(i\)th technology as the row vector \(\mathbf{X}_{i}=(X_{i1}\ X_{i2}\ \ldots\ X_{iM})\), the risk matrix \(\mathbf{X}=(\mathbf{X}_{1}\ \mathbf{X}_{2}\ldots\ \mathbf{X}_{N})^{T}\), and the communication protocol matrix \(\mathbf{Y}=(\mathbf{Y}_{1}\ \mathbf{Y}_{2}\ldots\ \mathbf{Y}_{N})^{T}\). We recall that the parameters \(M\), \(N\), and \(L\) are the total number of SRNG events, technologies, and protocols respectively. The first set of \(M\) constraints ensures that if a particular technology belongs to the optimal solution, it is the only one exposed to the SRNG event \(r\). We note that these constraints do make the problem unfeasible but they yield a monoculture network. In addition, the second set of, at most \(\binom{N}{2}\), constraints ensures that technologies are not orthogonal in terms of communication protocols, that is, there must exist at least one shared communication protocol between technologies \(i\) and \(j\), should they appear in the optimal solution. (Consequently, such constraint was formulated in terms of the dot product between the communication protocol vectors associated to every pair candidate technologies.) Finally, we mention that the failure probability associated to the \(r\)th SRNG event can be computed in practice from the risk matrix as:  
\[P_{r}\triangleq\frac{\sum_{k=1}^{N}X_{kr}}{\sum_{k=1}^{N}\sum_{s=1}^{M}X_{ks}}, \tag{5}\]  
which means that such probability is given by the frequency of occurrence of a SRNG event among the available technologies in the market.
# An Improved Single-Epoch Attitude Determination Method for Low-Cost Single-Frequency GNSS Receivers
## 2 GNSS Attitude Determination
### GNSS Single-Baseline Compass Model  
For single-baseline double-difference carrier phase and code observations, whose ionosphere error, troposphere error and clock errors become negligible with short baselines, the linear form of GNSS observations is given by the following:  
\[E(y)=Az+Gb,D(y)=Q_{y},z\in Z^{n},b\in R^{3} \tag{1}\]  
where \(y\) is GNSS the double-differenced observation, \(A\) is the design matrix of the double-differenced ambiguities, \(z\) is the double-differenced ambiguity vector, \(G\) is the matrix of the differenced unit line-of-sight vectors. \(E(\cdot)\) and \(D(\cdot)\) represent the mean value and variance, respectively. \(Q_{y}\) is the variance of the double-differenced observation \(y\). In general, the least-squares estimation or Kalman filter is used to derive the float double-differenced ambiguity \(z\) and its variance \(Q_{z}\). A second least-squares estimation is further utilized to fix ambiguities by the following:  
\[\widetilde{z}=\argmin_{z\in Z^{n}}\lVert\hat{z}-z\rVert_{Q_{z}}^{\widetilde{ \phantom{z}}},\ \widetilde{b}=\hat{b}(\widetilde{z}) \tag{2}\]  
\[\hat{b}(\widetilde{z})=\hat{b}-Q_{\hat{b}\hat{z}}Q_{z}^{-1}(\hat{z}-z) \tag{3}\]  
\[Q_{\hat{b}(\widetilde{z})\hat{b}(\widetilde{z})}=Q_{\hat{b}}-Q_{\hat{b}\hat{ z}}Q_{z}^{-1}Q_{\hat{z}\hat{b}}\]  
where \(\widetilde{z}\) is the fixed ambiguity vector, \(\widetilde{b}=\hat{b}(\widetilde{z})\) and \(Q_{\hat{b}(\widetilde{z})\hat{b}(\widetilde{z})}\) are the ambiguity-fixed baseline vector and its variance, respectively. \(Q_{\hat{z}}\) and \(Q_{\hat{b}\hat{z}}\) are the variance of float ambiguity and its covariance between the baseline vector, respectively. The most classic ambiguity resolution method is the LAMBDA method.  
If the baseline vector is known, Equation (1) turns into the following:  
\[E(y)=Az+Gb,D(y)=Q_{y},z\in Z^{n},b\in R^{3},\lVert b\rVert=l \tag{4}\]Different to the LAMBDA method, the integer least-squares principle, with quadratic equality constraints, is used to formulate the following cost function [33]:  
\[\min_{z\in Z^{n}}\left(\|\hat{z}-z\|_{Q_{z}^{-}}^{2}+\min_{b\in R^{3},\|b\|=l}\| \hat{b}(z)-b\|_{Q_{z}^{-}}^{2}\right) \tag{5}\]  
If we have the uncertainty of baseline length \(D(l)\), the GNSS compass model is read as the following:  
\[E(\text{y})=\text{Az}+\text{Gb},\text{D}(\text{y})=\text{Q}_{y},z\in Z^{n},b \in R^{3},E(l)=\ \|b\|,D(l)=\sigma_{l}^{2} \tag{6}\]  
Then, the ambiguity objective function with another nonlinear least-squares problem is presented as the following:  
\[\min_{z\in Z^{n}}\left(\|\hat{z}-z\|_{Q_{z}^{-}}^{2}+\min_{b\in R^{3}}H(z,b) \right)\text{ with }H(a,b)=\left\|\hat{b}(z)-b\right\|_{Q_{z}}^{2}+\sigma_{l}^{-2}(l-\|b\|) \tag{7}\]  
This minimization problem can be resolved with the weighted constrained C-LAMBDA method, which has been described in detail in [PERSON] [34].
# The Impact of Antenna Height on 3D Channel:
## 1 Introduction
We conclude that the AS and the DS reach their maximum or minimum value when the antenna height is half of the building's height, except the AS under the crossroad scenario. This paper is organized as follows. In Section 2, we briefly introduce the IRLA and the experiment scenarios. In Section 3, we derive the PDFs of EAoD and EAoA under experiment scenarios through simulations. Meanwhile, the AS and DS are also analyzed. Finally, some conclusions are drawn in Section 4.
## 4 Simulation Results and Discussion  
The proposed optimal covariate predictor and DA-ECMS were validated through simulations performed in MATLAB/Simulink R2023a on a PC with an Intel (R) Core (TM) i5-9300H @ 2.40 GHz and 16 RAM (Intel, Santa Clara, CA, USA).