esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Indium sulfide (In\({}_{\mathrm{2}}\)S\({}_{\mathrm{3}}\)) is one of the materials, besides CdS and Zn(O,S), which is already used as a buffer layer in chalcopyrite-type solar modules [1; 2]. Due to environmental concerns and the political decision to avoid Cd in solar modules, In\({}_{\mathrm{2}}\)S\({}_{\mathrm{3}}\) could be a candidate of choice to apply as a buffer layer for Cu(In,Ga)Se\({}_{\mathrm{2}}\) (CIGS) thin-film solar cells instead of the established CdS. In,S\({}_{\mathrm{y}}\) layers for the application as CIGS buffers can be grown by almost all available deposition techniques, including atomic layer deposition (ALD), thermal evaporation, chemical bath deposition (CBD), ion layer gas reaction (ILGAR), spray pyrolysis, and sputtering [3; 4; 5]. A value of 18.2% (with anti-reflective coating) was reported by [PERSON] et al. for a CIGS cell with thermal evaporated In\({}_{\mathrm{x}}\)S\({}_{\mathrm{y}}\) buffer [6], 17.9% (certified) was achieved with a 30 cm \(\times\) 30 cm module with thermal evaporated In\({}_{\mathrm{x}}\)S\({}_{\mathrm{y}}\):Na buffer by the company Avancis [7], 16.4% were published for a cell with an ALD In\({}_{\mathrm{x}}\)S\({}_{\mathrm{y}}\) buffer [4], 16.8% (in-house) and 16.1% (certified) were achieved by [PERSON] et al. with an ILGAR In\({}_{\mathrm{x}}\)S\({}_{\mathrm{y}}\):Cl buffer [8], and 14.3% for a 225 cm\({}^{\mathrm{2}}\) cell with a reactively sputtered In\({}_{\mathrm{x}}\)(O,S)\({}_{\mathrm{y}}\) buffer fabricated by the company Midsummer [2].  
The sputtering approach can be easily implemented in an industrial environment, similar to the sputtering of the molybdenum back contact, high-resistive layers and transparent conductive oxides used as front contact. Furthermore, the method would be favorable compared to ALD, thermal evaporation, or growth by CBD, due to the very fast deposition times of approximately one minute or less; compared to ten or even more minutes to deposit a 20-60 nm thick In\({}_{\rm x}\)S\({}_{\rm y}\) buffer layer. This \"dry\" sputtering process could be a candidate for an application in a roll-to-roll coater with a flexible substrate or the implementation in an in-line deposition machine without breaking the vacuum after CIGS absorber deposition.  
We present our development of very fast rf-magnetron sputtered In\({}_{\rm x}\)S\({}_{\rm y}\) buffer layers deposited at different substrate temperatures, show the efficiency results of In\({}_{\rm x}\)S\({}_{\rm y}\)-buffered cells with open-circuit voltage (\(V_{\rm OC}\)) values close to reference cells with CBD-CdS buffer, and discuss the influence of substrate temperature during In\({}_{\rm x}\)S\({}_{\rm y}\) sputtering. In addition, a detailed materials characterization of our various In\({}_{\rm x}\)S\({}_{\rm y}\) buffer layers including chemical depth profiles for the analysis of the In\({}_{\rm x}\)S\({}_{\rm y}\) buffer and CIGS/In\({}_{\rm x}\)S\({}_{\rm y}\) interface reveal a sodium accumulation in the In\({}_{\rm x}\)S\({}_{\rm y}\) buffer and at the CIGS/buffer interface and a copper depletion of the CIGS surface with higher substrate temperatures.
## 3 Results and Discussion
### Life Stages and Clustering  
According to the results of the PCA analysis, 30 principal components were needed to explain 70% of all the variance that the gene count matrix was able to capture (see Figure 1a). As a result, data projected on these components and subsequently subjected to spectral embedding revealed clear contiguity of the major life stages as well as tight clustering of intra-stage groups (i.e., prepupa, pupa, and three instar larval stages) (Figure 1b).  
Expression data were then processed by WGCNA that was run with default parameters: the power that produced a higher similarity with a scale-free network was 9 (Figure 2a left), and clustering was performed using the resulting TOM by merging modules whose expression profiles were very similar as measured by the correlation of their eigengenes (Figure 2b).  
Figure 1: (**a**) Cumulative variance explained as the number of principal components increases; (**b**) Spectral embedding of the expression values of all Drosophila melanogaster samples labeled with the corresponding life stage.  
Originally, the detected modules were 28; merging the ones whose distance was less than 0.2 gave us the 17 clusters considered in this work.
# A Carrier Estimation Method Based on MLE and KF for Weak GNSS Signals
## 5 Simulation Results
### Monte Carlo Simulations for Sensitivity and Accuracy
It is suitable for some land applications such as indoor pedestrian navigation and vehicle navigation. Figure 9: Performance comparison in pedestrian-level dynamic circumstances. (**a**) Tracking probability; (**b**) Frequency error; (**c**) Bit error rate. FPLL: two-order FLL-assisted three-order PLL.
# On Evaluating IoT Data Trust via Machine Learning
## 4 Features
### Feature Space Visualization  
We visualize the correlation features (as described in Section 4.1) that we extract from the Intel Lab dataset. We augment the dataset by adding untrustworthy data synthesized through both RWI and Drift methods. We utilize the uniform manifold approximation and projection (UMAP) algorithm [20] for the visualization of the feature values, which originally reside in a seventeen-dimensional feature space, in a two-dimensional embedding subspace (plane). We show the results in Figure 4. The figure also includes the original data instances that are conspicuously erroneous or abnormal. We consider these instances to be untrustworthy and label them as untrustworthy outliers.  
Note that UMAP makes three assumptions about the data, namely, (i) the data are uniformly distributed on a Riemannian manifold; (ii) the metric, which quantifies the distance between data points, is locally constant; and (iii) the manifold representing the underlying structures of the data is locally connected. UMAP preserves the local structure of the data as it produces data embedding, where the relationships and clusters in the original data are maintained. This embedding aids in visually identifying clusters and understanding the organization of the data.  
In Figure 4, the untrustworthy data instances synthesized by the Drift method appear to be further apart from the trustworthy data instances in the correlation feature space compared with the untrustworthy data instances synthesized by the RWI method. This implies that the RWI synthetic data more closely resemble the original trustworthy data compared with the Drift synthetic data. Therefore, RWI synthetic data are potentially more realistic, but also more challenging to distinguish from the trustworthy data. In addition, there appear to be regions in the correlation feature space where the trustworthy data do not mix with the synthesized untrustworthy data. Classifying the trustworthiness of data is likely to be easier in regions where the trustworthy data and untrustworthy data form separate clusters, as opposed to regions where the two types of data are mixed and do not exhibit distinct clusters.
###### Abstract  
The capability to orient the solar arrays of a spacecraft toward the Sun is an ultimate asset for any attitude determination and control subsystem (ADCS). This ability should be maintained in any operative circumstance, either nominal or off-nominal, to avoid the loss of the entire space-borne system. The safe mode implementation should guarantee a positive power generation from the solar arrays, regardless of the health status of the satellite platform. This paper presents a solar array pointing algorithm, to be executed on-board, with a minimal set of sensors and actuators. In fact, the sensors are limited to the solar arrays, exploiting the current/voltage sensing capacity of the electrical power subsystem to measure the Sun angle with respect to the arrays normal, and to the angular rates sensors. The actuators are required to provide a torque only along two axes and, thus, a reduced actuation capacity is still manageable by the proposed algorithm. The paper describes the algorithm, both in the Sun direction determination and in the Sun pointing control capacity. The achieved performance is outlined, considering either an ideal system or a realistic one, being the latter affected by sensors and actuators limitations. The actuation by means of momentum exchange devices or magnetic torquers is discussed, with the purpose to prove the wide applicability range of the presented algorithm, which is capable to guarantee solar array orientation with a minimal hardware set.  
title: **Keywords:** attitude determination and control; solar arrays pointing; reaction wheels; magnetic torquers; sun angle; angular rates +
Footnote †: journal: _Algorithms_
## 3 Data Processing
### Report Generation  
Different dissemination strategies (mail, FTP...), with several formats (plain text, XML, PDF, Google Earth, etc.) can be configured. The reports are automatically generated after receiving SAR products and can be easily distributed to different operators or integrated in different platforms. Final reports contain information regarding ship location, detection confidence, ship category, classification confidence, heading, length, breadth and the normalized RCS values for the three bow, middle and stern sections. The user can also configure filters in order to reduce the information that will be reported, or the way it is displayed. By customizing the information contained in the report, operators can focus only on their areas of interest or can define alarms, i.e. particular situations that comprise a set of conditions that are met (_short ship cruising at certain direction with no AIS_).  
The following figure shows two reports generated for an image of the Strait of Gibraltar. At the left, the image presents ship detections with the associated confidence level (green, yellow and red for high,  
\begin{table}
\begin{tabular}{|l l l l|} \hline
**Category SAR** & **Category AIS** & **Ship name AIS** & **Check** \\ \hline
**Coaster** & **Fast Ferry** & **Avemar dos** & \\
**Oil Tanker** & **Container** & **MSC Levina** & \\ \hline
**Bulk** & **Bulk** & **Maersk Nagoya** & \\ \hline
**Goods Cargo** & **Oil Tanker** & **Hekabe** & \\
**Oil Tanker** & **Oil Tanker** & **Omega Theodore** & \\ \hline \end{tabular}
\end{table}
Table 2: SAR-AIS category cross-correlation for detections performed in an ENVISAT image.  
Figure 7: Illustrative description of the SAR-AIS route tracking algorithm.  
medium and low confidence, respectively) while, at the right, the layer displays the associated category via a color code where black means unclassified. With these layers, data reports can be filtered according to user preferences and the alarms defined. All of this improves decision making.
## 4 Application
### Time Series Analysis  
The predictive models are applied to data from the city of Barcelona's traffic situation information in March 2022. Due to the large number of monitored sections, it is important to know whether their behavior is similar. For this purpose, 10 sections belonging to the same street are chosen. We would like to highlight that, even though the sections belong to the same street, there are times when the traffic conditions are not similar, even with changes depending on whether it is a weekend or a working day.  
Table 1 shows the output of the time series models. The first two columns identify the section and the fitted model. The subsequent five columns display the coefficients, while the last two columns present the performance measures AIC and BIC. It is not necessary to differentiate the series in any case because the models are stable. Table 2 shows the Mean Error (ME) and the Root-Mean-Square Error (RMSE) of each model. The tests of independence (Box-Ljung test for residuals), homoscedasticity (Box-Ljung test for squared residuals), and normality (Shapiro-Wilk normality test) are applied to each of the models, and their p-values are shown. None of the models complies with the initial assumptions, which leads us to confirm that these models do not explain the behavior of the data very well. In conclusion, due to the high non-linearity and complexity of the traffic flow, classical methods cannot make good predictions; moreover, they do not fit well with the space-time structure of the traffic data. For this reason, in the next subsection, a machine learning method is applied.  
Figure 3: Geospatial traffic state map. Traffic state at 9:00 (**left**) and at 15:00 (**right**).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Section** & **Model** & **ar1** & **ar2** & **ar3** & **ma1** & **ma2** & **AIC** & **BIC** \\ \hline
1 & ARIMA(2,0,2) & 1.568 & \(-\)0.655 & & \(-\)0.839 & \(-\)0.315 & 934.270 & 961.940 \\
2 & ARIMA(3,0,1) & 0.067 & 0.824 & \(-\)0.211 & 0.965 & & 105.650 & 133.320 \\
3 & ARIMA(2,0,1) & 1.799 & \(-\)0.872 & & \(-\)0.910 & & \(-\)39.040 & \(-\)15.980 \\
4 & ARIMA(2,0,2) & 1.668 & \(-\)0.742 & & \(-\)0.639 & \(-\)0.193 & 1126.190 & 1153.860 \\
5 & ARIMA(2,0,2) & 1.506 & \(-\)0.583 & & \(-\)0.622 & \(-\)0.249 & 1469.640 & 1497.310 \\
6 & ARIMA(1,0,1) & 0.638 & & & 0.259 & & 1172.080 & 1190.520 \\
7 & ARIMA(2,0,1) & 1.785 & \(-\)0.850 & & \(-\)0.900 & & 69.040 & 92.100 \\
8 & ARIMA(1,0,0) & 0.589 & & & & 1138.790 & 1152.620 \\
9 & ARIMA(2,0,0) & 0.788 & \(-\)0.227 & & & 1541.360 & 1559.800 \\
10 & ARIMA(2,0,1) & 1.778 & \(-\)0.838 & & \(-\)0.867 & & \(-\)33.030 & \(-\)9.970 \\ \hline \end{tabular}
\end{table}
Table 1: Output of time series by section.  
Figure 4: Heatmap traffic state map. Traffic state at 9:00 (**left**) and at 15:00 (**right**).
# Interpretable Variational Graph Autoencoder with Noninformative Prior
## 4 The Proposed NPIVGAE
### Loss Function  
The proposed NPIVGAE takes the noninformative prior as the prior distribution of the latent variables \(\mathbf{Z}\) to minimize the influence of the prior on the posterior. Since we still assume that the variational posterior \(q(\mathbf{Z}|\mathbf{X},\mathbf{A})\) is a normal distribution \(\mathcal{N}(\mathbf{\mu},\mathbf{\sigma})\), the corresponding noninformative prior is \(p(\mathbf{Z})\infty\frac{1}{\sigma^{2}}\) (i.e., uniform distribution, which is regarded as noninformative) according to Section 3.1.  
By minimizing the negative evidence lower bound (ELBO), we define the loss function of the noninformative prior-based interpretable variational graph autoencoder:  
\[\mathcal{L}=E_{\mathbf{Z}\sim q(\mathbf{Z}|\mathbf{X},\mathbf{A})}[\log\frac {q(\mathbf{Z}|\mathbf{X},\mathbf{A})}{p(\mathbf{Z})}]-E_{\mathbf{Z}\sim q( \mathbf{Z}|\mathbf{X},\mathbf{A})}[\log p(\mathbf{A}|\mathbf{Z})]. \tag{10}\]  
After deduction (please refer to Appendix A for the detailed derivation), we can get the final objective function:  
\[\mathcal{L}=\sum_{i=1}^{n}\sum_{j=1}^{d}\left(\log\frac{1}{\sqrt{2\pi}}+\log \sigma_{ij}-\frac{1}{2}\right)-\sum_{i=1}^{n}\sum_{j=1}^{n}E_{\mathbf{Z}\sim q (\mathbf{Z}|\mathbf{X},\mathbf{A})}[\log p(a_{ij}|\mathbf{z}_{i},\mathbf{z}_ {j},\mathbf{\Pi})]. \tag{11}\]  
Our model exploits stochastic gradient descent to minimize the objective function and optimize the parameters. Like VGAE, the reparameterization trick is also leveraged for training in this paper to achieve the gradient update of the mean and variance.
# Robust Burst Detection Algorithm for Distributed Unique Word TDMA Signal
## 4 Performance Analysis  
### Robustness Analysis  
From Equations (7) and (8), it can be seen that the frequency offset is converted to fixed phase offset \(\phi^{\mathrm{i}\omega_{0}\mathrm{T}}\) and \(\phi^{\mathrm{i}(I_{0}+1-N)\omega_{0}\mathrm{T}}\) by DUWDC algorithm, and two phase offsets are eliminated by modular operation, respectively, which will not affect correlation results. It shows that DUWDC inherits anti-frequency offset performance of TDC algorithm perfectly.  
Hou\({}_{1}\) and Hou\({}_{2}\) are the existing DUW detection algorithms, and their algorithm formula is as follows:  
\[corr_{\mathrm{Hou}_{1}}=\left|\sum\limits_{i=0}^{p-1}\sum\limits_{k=IN+1}^{( i+1)N-1}r_{0}^{*}(k)r_{0}(k-1)\{a^{*}(k)a(k-1)\}^{*}\right| \tag{10}\]  
\[corr_{\mathrm{Hou}_{2}}=\left|\sum\limits_{i=1}^{p-1}\sum\limits_{k=IN}^{(i+1 )N-1}r_{0}^{*}(k)r_{0}(k-N)\{a^{*}(k)a(k-N)\}^{*}\right| \tag{11}\]  
It can be seen from Equations (10) and (11) that Hou\({}_{1}\) and Hou\({}_{2}\) both use similar dual correlation algorithm, but their delay rules are different from the algorithm in this paper. The number of points involved in the correlation operation between receiving sequence and local sequence is also different, and of Hou\({}_{1}\) and Hou\({}_{2}\) it is \((N-1)P\) and \((P-1)N\), respectively. It can be seen from Equation (7) that the number of points involved in the correlation operation of DUWDC is \(N\times P-1\). \(N\) and \(P\) depend on the distribution structure of DUW. When the ratio of \(N\) and \(P\) changes, the number of correlation operation points of DUWDC remains the same, but of Hou\({}_{1}\) and Hou\({}_{2}\) it is variable, as shown in Figure 8. This is where we assume that the total length of DUW is 48 symbols (\(N\times P=48\)), and N is equal to 2, 4, 6, 12 and 16, respectively.  
Generally, the longer the sequence involved in the correlation operation is, the better the correlation effect [18]. Therefore, the advantage of DUWDC over Hou\({}_{1}\) and Hou\({}_{2}\) is that the change of UW distributed structure cannot affect detection performance, and the number of points involved in correlation operation has been maximized, and correlation effect is better.  
Figure 8: The relationship between the number of correlation operation points and UW distribution structure.
## 3 Test of Masonry Prisms Strengthened with SFRM Overlay
### Result of Tests for Masonry Prisms
#### 3.2.3. Diagonal Tension Test Result
Above all, masonry prisms strengthened with SFRM exhibit much more deformation capacity compared to those strengthened with plain mortar commonly for both masonry materials. Representative damage states of specimens after the termination of diagonal tension tests are represented in Figure 17. Failure in diagonal tension tests of masonry prisms is generally classified into three types: diagonal tension failure along the force direction, sliding failure along the horizontal mortar joint, and composite failure in which both diagonal tension failure and sliding failure occur in combination. According to [PERSON] and [PERSON] (2011) [1], the compressive strength of the joint mortar higher than the compressive strength of the brick tends to result in diagonal tension failure or composite failure, while low compressive strengths of the joint mortar lead to sliding failure. In this study, the compressive strengths for joint mortar, concrete bricks and red clay bricks are 33, 29.1 and 23.2 MPa, respectively, from which diagonal tension failure or composite failure are anticipated, as observed in Figure 17a. In case of the specimen CB0 strengthened with mortar overlay without steel fibers, brittle failure occurred in both mortar overlay and the masonry, as shown in Figure 17b. On the other hand, for the specimen CB1.3 strengthened with SFRM, the overlay detached as the load reached the peak in a similar way to the compression tests, and the condition of the SFRM at the termination of the test was good with few cracks, as shown in Figure 17c. The damage state of the masonry beneath the mortar overlay was difficult to identify visually, so the mortar overlay was taken off after the termination of the test. From the investigation, it was observed that diagonal tension failure had occurred in the masonry prism between the two mortar overlays, as shown in Figure 18. From the above observations, it is supposed that masonry prisms strengthened with SFRM overlay failed in diagonal tension after the detachment of the overlay. Figure 16: Shear stress–strain curves from diagonal tension tests for concrete brick prism. (**a**) CB0. (**b**) CB1.0. (**c**) CB1.3. Figure 15: Shear stress–strain curves from diagonal tension tests for red clay brick prism. (**a**) RB0. (**b**) RB1.0. (**c**) RB1.3. Therefore, strengthening effects are expected to be improved, as in the compression test, if the integrity between the masonry and SFRM is improved.
# A Novel Drone Design Based on a Reconfigurable Unmanned Aerial Vehicle for Wildfire Management
## 6 PULSAR Applications in Forest and Wildfire Management  
The use of UAVs for research and operations on wildland fires has been rapidly accelerated by advancements in aircraft systems, sensor technologies, telemetry, and computing capabilities. Drones have evolved beyond mere observational tools, transforming into versatile instruments with the ability to revolutionize the landscape of wildfire research and operational strategies. Beyond the various configurations of the UAV, this article also explores its ability to accommodate a range of payloads, thereby addressing crucial aspects of wildfire and prescribed fire science. In particular, the drone's capabilities extend across three vital categories: (i) detailed forest mapping, (ii) edge computing and the generation of cartographic products on the fly, as well as (iii) detection and tracking of elements such as people, animals, and vehicles.
## 1 Introduction
A finite element T-channel trapping model was developed by designing and optimizing the channel's geometry to produce an efficient single cell trapping system. The model is developed based on the hydrodynamic flow resistance (\(Rh\)) manipulation between the main channel and trap channel to achieve successful trapping. A proof of concept demonstration for cell trapping in the T-channel model is presented in the simulation analysis and experimental work using HUVEC cell aggregate. The developed finite element T-channel trapping model could be used for designing and optimizing the trapping of various types of cells of different sizes. Numerical simulations were carried out to evaluate the channel's trapping efficiencies for different trap hole sizes and \(Rh\) of the main channel to \(Rh\) of the trap channel (\(Rh_{\text{Main}}/Rh_{\text{Trap}}\)) ratios. Cell aggregate trapping has been performed experimentally to support the simulation findings.