esa-sceva/satcom-chunk-collection huggingface dataset:
# DeepGOMIMO: Deep Learning-Aided Generalized Optical MIMO with CSI-Free Detection
###### Abstract
In [21], a machine learning method was proposed for quality of transmission prediction of unestablished lightpaths. In [22], a convolutional neural networks-based error vector magnitude estimation scheme was proposed for fast and accurate signal quality monitoring in coherent optical communications. In [23], a long-short-term-memory (LSTM) algorithm was proposed to mitigate transmission impairments of 4-level pulse-amplitude modulation (PAM4) produced by silicon-microring modulator. Most recently, deep learning techniques have been further introduced in OWC systems for binary signaling design [24], mitigation of both linear and nonlinear impairments [25], energy-efficient resource management [26], and so on. More specifically, a ZF-based deep neural network (DNN)-detection scheme has been proposed for MIMO detection in GOMIMO systems [27]. The obtained results in [27] show that the ZF-DNN detector can achieve comparable BER performance as the optimal joint ML detector with greatly reduced computational complexity. Nevertheless, the ZF-DNN detector takes the ZF equalized signal as its input, which requires accurate channel state information (CSI), i.e., the MIMO channel matrix, to successfully perform ZF equalization. Although CSI can be estimated by using training symbols [28], training-based instantaneous channel estimation inevitably causes both the loss of achievable data rate and the increase of communication time delay. In this paper, to address the disadvantages of CSI-based ZF-DNN detection due to the requirement of instantaneous CSI for ZF equalization, we for the first time propose a DeepGOMIMO framework for GOMIMO systems where CSI-free MIMO detection is achieved by a novel DNN detection scheme. By adding a specially designed preprocessing module before the feedforward DNN module, CSI-free detection can be successfully enabled for GOMIMO systems. The key difference between our previous work [15] and this current work can be described as follows: our previous work [15] mainly proposed four OFDM-based GOMIMO schemes, whereas this current work proposes a CSI-free DNN detection scheme for PAM-based GOMIMO systems. Numerical simulations are extensively conducted to evaluate the performance of the proposed CSI-free DNN detector, which is also compared with other three benchmark schemes including the joint ML detector, the ZF-ML detector and the ZF-DNN detector. Our simulation results verify the advantages of the proposed CSI-free DNN detector in comparison to other benchmark schemes in GOMIMO systems. To the best of our knowledge, it is the first time that a CSI-free DNN detection scheme is proposed and evaluated in detail for PAM-based GOMIMO systems. The rest of this paper is organized as follows. In Section 2, we describe the mathematical model of a general GOMIMO system. In Section 3, we introduce four detection schemes for GOMIMO systems. Detailed simulation setup and results are presented in Section 4. Finally, Section 5 concludes the paper.
## 2 Measurement System Composition and Principles
 \tag{1}\]  
Using the initial position of the laser tracking device coordinate system as the reference coordinate system, the rotation relationship between the laser tracking device coordinate system and the target coordinate system can be calculated through changes in the rotation matrices, as shown in the following equation:  
\[\frac{L}{T}\mathbf{R}=\frac{L}{C}\mathbf{R}\times\frac{C}{T}\mathbf{R} \tag{2}\]  
Expressed in the form of a \(3\times 3\) matrix  
\[\frac{L}{T}\mathbf{R}= \begin{pmatrix}r_{11}&r_{12}&r_{13}\\ r_{21}&r_{22}&r_{23}\\ r_{31}&r_{32}&r_{33}\end{pmatrix}  \tag{3}\]  
Using the mathematical relationships between rotation matrices and Euler angles, one can calculate the azimuth angle (rotation around the X-axis), elevation angle (rotation around the Y-axis), and roll angle (rotation around the Z-axis) of the target in the laser tracking device coordinate system. \[ \begin{split}\alpha&=-\arcsin(r_{23})\\ \beta&=\arctan(\frac{r_{13}}{r_{33}})\\ \gamma&=\arctan(\frac{r_{12}}{r_{22}})\end{split}  \tag{4}\]  
In the aforementioned attitude dynamic measurement system, \(\frac{C}{T}\mathbf{R}\)'s data consist of two parts: first, attitude tracking measurements of the target in the camera coordinate system obtained through visual means, resulting in attitude angle 1; second, integrated measurements of the target's attitude relative to its initial position using the IMU, followed by refinement with real-time relative pose information from vision to obtain attitude angle 2 in the camera coordinate system. The visual system is able to provide highly accurate target attitude information due to its superior spatial resolution, especially achieving high precision for targets at short distances. The IMU provides real-time acceleration and angular velocity information, offering significant advantages in tracking fast-moving targets. However, the visual system may experience interference from factors like ambient light and occlusion during prolonged tracking, slightly compromising its dynamic performance compared to the IMU. Therefore, effectively integrating multi-source sensor data using a combination of visual and IMU-based measurements to enhance system dynamic performance is a critical focus of the subsequent research.
# GIS-Based Cluster and Suitability Analysis of Crop Residues: A Case Study in Yangon Region, Myanmar
## 3 Results
### Cluster Analysis
#### 3.1.2 Township-Based Clusters Analysis of the Feedstock of the Rice Straw  
According to step one, \"Spatial Autocorrelation (Global Moran's I)\", the z-score is 6.56, and the \(p\)-value is less than 10% Figure 4. The result indicates that the cluster pattern exists in 43 townships. Then, step two of the tool \"Calculate Distance Band from Neighbor Count\" finds 6948.84 m as the average distance among 43 townships. Then, step three of \"Incremental Spatial Autocorrelations\" ensure the result in the peak distance of 20,846.51 m as the radius parameter that promotes the township-based cluster Figure 5. Finally, step four distinguishes township-based clusters of high values (High-High Cluster), clusters of low values (Low-Low Cluster), outliers in which a high value is surrounded primarily by low values (High-Low Cluster), and outliers in which a low value is surrounded primarily by high values (Low-High Cluster) Figure 6, Table 2.  
Figure 3: Crop Residue Generation from 43 Townships.  
Figure 4: The result of Spatial Autocorrelation (Global Moran’s I).  
Figure 5: The result of Incremental Spatial Autocorrelations.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Township Name** & **Cluster Type** & **Rice Straw Generation (ton)** & **Electricity Potential (MW)** \\ \hline
**Kungyangon** & Not Significant & 285,000.43 & 27.43 \\ \hline
**Thongwa** & Not Significant & 381,007.22 & 36.67 \\ \hline
**Hmawbi** & Not Significant & 11,308.38 & 1.09 \\ \hline
**Hlegu** & Not Significant & 292,719.14 & 28.17 \\ \hline
**Twantay** & Not Significant & 291,133.19 & 28.02 \\ \hline
**Shwepyithar** & Not Significant & 274.16 & 0.03 \\ \hline
**Taikkyi** & Not Significant & 170,932.38 & 16.45 \\ \hline \end{tabular}
\end{table}
Table 2: The Results of Township-Based Clusters Analysis of the Feedstock of the Rice Straw.
## 4 Quality Assessment Methodology
### Validation Methods
#### 4.2.2 Satellite Product Intercomparison  
The intercomparison of satellite products must account for the differences in the spatial and temporal characteristics of the different datasets. Consequently, a spatial support area and temporal support period were defined consistent with the C3S SA characteristics (Table 1). Thus, the MCD43A4 C6 products were re-sampled to Plate Carree using a 1 km spatial sampling grid. Satellite products  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Quantity** & **Validation Metric** \\ \hline
**Completeness** & Gap size distribution (spatial and temporal) \\  & Gap length \\ \hline
**Precision** & Median 3-point difference (intra-annual precision) \\  & Median absolute deviation (inter-annual precision) \\ \hline
**Accuracy** & Median Error \\  & Box-plots of the bias per albedo range \\ \hline
**Uncertainty** & Root mean square deviation (RMSD) \\  & Scatter-plots of match-ups (MAR Linear models and correlation) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Validation metrics for product validation.  
were compared using the closest date of their different temporal composites, using the 10 day temporal frequency of C3S SA products.  
The completeness and precision of the different products were evaluated and compared, as well as the temporal consistency among the several products, which was qualitatively assessed per biome type. The uncertainty estimates from the intercomparisons provided an overall figure of the spatio-temporal consistency between products. Scatter-plots and the associated metrics between pairs of products were analyzed and complemented with box-plots of the bias per bin albedo value. Our analysis focuses on the consistency during the 6-month overlap period between PROBA-V SA v1.0 and SPOT/VGT SA v1.0, whereas the intercomparison with MCD43A3 C6 was conducted using one year (2014) of data.
## 4 The Proposed Real-Time Monitoring System
### Developed ANN Model  
Here, neural networks are employed to predict the status of cardiac patients, as depicted in Figure 1. An artificial neural network (ANN) with one fully connected hidden layer and an output layer of three classes was utilized. The hidden layer is a single layer since the inputs of the network are extracted features that do not require deep analysis to be trained for making correct decisions and therefore the structure of the developed ANN is not complicated. For model training and performance evaluation, Stochastic Gradient Descent with Momentum (SGDM) of 0.9 provided the best fine-tuning, the learning rate was set to be 0.001, and maximum epochs of 60 were configured for the model to terminate the training. The training time per iteration is 14 s, and thus the performance was found to incur 829 s for completing all epochs, which confirms the straightforward training process of the developed ANN classifier. However, we could not determine how to construct an optimized number of neurons in the hidden layer, so modeling the dataset required intensive examinations of different design structures. Thus, a trial and error technique was used to determine the required number of neurons in the hidden layer. We started with from 10 to 70 neurons, and it turned out that the accurate solution can be realized when the number of neurons in the hidden layer equals 37, illustrated in Figure 2. As shown in Figure 3, we conducted various setups of the loss function for model fine-tuning via applying adaptive optimizers like Adam, SGD with momentum, etc., associated with increasing the number of neurons to more than 37; however, we found out that does not improve the accuracy (98.4% was the best achieved accuracy), and instead, it becomes worse.  
Additionally, the developed ANN model, containing 37 neurons, is specifically designed for a task-oriented dataset involving cardiac patients. Therefore, if the dataset type changes, the ANN classifier must be retrained to adapt and optimize for the new data. For this reason, we switched to come up with a more fine-grained model that can deliver higher prediction accuracy while incurring less intensive computational complexity, i.e., a lightweight model. Next, we discuss the proposed encoding-based machine learning model.  
Figure 1: State transition diagram for the cardiac patient’s status condition.
# A Review: Thermal Stability of Methylammonium Lead Halide Based Perovskite Solar Cells
## 2 Structural Stability of Perovskite
The structural data of various methylammonium lead trihalides (MAPbX\({}_{3}\); X = I, Cl, Br) [33; 34] are summarized in Table 1. As indicated in Table 1, both MAPbBr\({}_{3}\) and MAPbCl\({}_{3}\) crystallize in the cubic phase at room temperature, while MAPbI\({}_{3}\) needs to be heated to a temperature higher than 323 K to allow the transition from a tetragonal to a cubic phase. Interestingly, many studies on MAPbI\({}_{3}\) materials report that the tetragonal phase of MAPbI\({}_{3}\) can still exist even after heating at temperature of 373 K [4; 35; 36; 37]. This suggests that the tetragonal phase is surprisingly stable in the thin films, but also emphasizes an ambiguity about the exact phase transition temperature and the nature of the phase transition between tetragonal and cubic phases. Figure 1: (**a**) The unit cell of cubic perovskite, where the blue spheres at lattice corners are A cations, the green sphere at the center is a B cation, and the red spheres at the lattice faces are X anions. (**b**) Another illustration of BX\({}_{6}\) octahedral network, where B cations are surrounded by X anions. (**c**) A tilted BX\({}_{6}\) octahedral structure due to non-ideal size effects and additional factors. Reprinted from [23], with permission from Elsevier.
# Orbit Determination of Korean GEO Satellite Using Single SLR Sensor
###### Abstract  
Geostationary Earth Orbit (GEO)-Korea Multi-Purpose Satellite (KOMPSAT)-2B (GK-2B) is a Korean geostationary Earth orbit (GEO) satellite that is scheduled to be launched in 2020 for meteorological and ocean monitoring. While the primary orbit determination (OD) for GK-2B is by ground-based radar observations and the expected orbit precision is less than 1 km, a satellite laser ranging (SLR) technique has been selected as a subsidiary OD method to verify/complement/enhance primary OD results. In general, the available time and equipment for observing GEO satellites with SLR are limited. Furthermore, because the optical sensors mounted on GK-2B may be defected by laser, only a domestic single SLR station would obtain the tracking data. This research presents the mitigation of these drawbacks to improve orbit precision. Observation data generation and the associated OD of GK-2B are performed by considering numerical SLR data analysis on Compass-G1, a Chinese GEO navigation satellite, and Chinese SLR station at Changchun. With the OD performed for two scenarios with the varying number of observations, the 3D position error is 24.01 m when 13 observations per day are obtained, while the error becomes 43.46 m when 9 observations per day are obtained. To verify these results, the OD of Compass-G1 using actual SLR data from Changchun station is performed to yield 31.89 m for 3D error, which is favorable compared with the external precise ephemeris by GeoForschungsZentrum (GFZ) analysis center. Therefore, the OD based on single SLR station is applicable to estimating the orbit within less than 100 m.  
GK-2B; Compass-G1; geostationary Earth orbit (GEO); orbit determination (OD); satellite laser ranging (SLR) +
Footnote †: journal: Sensors
## 4 Validation of the Simulation Framework  
This section focuses on the methodology followed to validate the simulation framework through real measurement data from the demonstrations. The measurements were also used to improve the inputs to the simulation model to have a better representation of the UCs; these improved inputs are then used for the simulation. Measurement data from Osnabruck and Gothenburg were used in the validation process. The quality of the data from the two sources were different. The Gothenburg dataset consists of continuous measurement values sampled at 20 Hz directly from the vehicle's CAN-bus. The data from Osnabruck, extracted from the CBO's cloud server, were only available at intermittent intervals. Thus, the two cases were handled differently.  
The validation and tuning process addressed the following features:  
* The EMS: The energy recovery system was tuned to align the traction energy profile with the measurement data. The regenerative braking system (RBS) is a proprietary system for many OEMs; thus, assumptions were made during model development.
* The charging management system (CMS): The cutoff between the constant current (CC) mode and the constant voltage (CV) mode, and the current decay parameter during the CV mode were tuned based on the measurement data. These parameter values are also not forthcoming by the OEMs.
* The passenger load estimation: Passenger load inside the bus is the one aspect that could not be automatically measured and requires manual counting; thus, it is usually ignored. Instead, some simulations involved an intricate passenger model based on the passenger appearance rate at each bus stop as a function of time [12], which is modeled on actual bus traffic data by the CBO. Others use agent-based modeling whereby each passenger is a unique object that has \"preferences\", such as drop in point, drop off point, and waiting time [13]. In [14], a cellular automata model is utilized to study behavioral characteristics of bus passengers boarding and alighting behavior. There are also certain cases where a fixed load was assumed within the bus, based on passenger load factor [15], when the passenger load is ancillary to other considerations. The UC simulations carried out in [5] assumed a random passenger profile as a function of time within the bus cabin; however, for this validation, the passenger inside the bus was estimated based on the measured SoC profile.
# 3D Monitoring of Buildings Using TerraSAR-X InSAR, DInSAR and PolSAR Capacities
## 3 Measuring the Height by Interferometry
### Methodology
#### 3.1.2 Unwrapping the Fringes  
To obtain an interferometric result, \(\phi\) the phase difference from the bottom to the top of the buildings, has to be estimated. Since the height of the studied buildings is often higher than the ambiguity height \(-\frac{2\pi}{k_{z}}\), the phase difference needs to be unwrapped.  
In this study, the unwrapping has been performed on the whole building at ones by a comparison between the measured phase and a modeled phase [23]. The phase to unwrap is modeled by a linear phase having the same starting and ending point but a different number of fringes, and thus a different slope. The estimated number of fringes is then selected as the one maximizing the correlation between the measured and the modeled phases  
This methodology enables to take into account the whole building during the phase estimation and not only some selected pixels in the image as it can be done in Permanent Scatterers (PS) framework [24]. Nevertheless, to guarantee a high accuracy of the phase, reference point at the bottom and the top of each building are chosen as Permanent Scatterers (PS), using a 0.5 threshold on the dispersion index \(\mathcal{D}_{\mathcal{A}}\) for all the buildings. The dispersion index has been computed on the 98 images composing our interferometric dataset.  
Due to the lack of PS at the very bottom or very top of buildings, we have restricted the monitoring of the interferometric phase over a range of 85 m for the Cristal Tower, 90 m for the Keller Tower, and 68 m for the Mirabeau Tower. The Eiffel Tower is only monitored between the second floor and the antenna leading to a monitored height of 177 m. These heights are obtained by a monoscopic projection, defined by the vertical projection \(N_{p}\) of the number of pixels between the PS: \(h=N_{p}\delta_{r}\tan(\theta)\). They will be refereed as monoscopic reference to be compared by the interferometric estimation. For the Cristal Tower, the limitation of the monitored height is due to the strong echoes at the base of the building whose secondary lobes leak the other pixels as it can been seen in Figure 3c. The base of the Keller Tower, represented in Figure 3d, has a different structure with a very low backscattering leading to the absence of PS.  
Except 3 dates for the Eiffel Tower and 5 dates for the Cristal Tower, the phase could be unwrapped without user intervention (for the Eiffel Tower, the dates are the 15 November 2010, 20 January 2011 and 23 July 2012. For the Mirabeau Tower the 5 January 2010, 19 November 2008, 16 December 2011, 24 March 2013 and 23 July 2012). No averaging was needed, for the Eiffel, Keller and Mirabeau Towers. For the Cristal Tower, a five pixels azimuth boxcar filter has been used to estimate the phase. Avoiding averaging preserve the phase precision of the very bright scatterers present on the buildings.
## 3 Smart Metering in Germany
### Configuration of SMGWs  
The functionality described in the technical guidelines is divided into network (i.e., LMN, HAN, or WAN) specific use cases. Furthermore, communication scenarios are specified as implementations of the use cases, where each communication scenario defines specific requirements. An exemplary WAN use case is \"administration and configuration\", which is executed by the WAN communication scenario \"management\". Furthermore, tariff use cases (\"Tarifanwendungsfall\", TAF) define rules that specify which data are measured, the time of measurement, how this data are processed, when they are transmitted, and whom they are sent to. TAF2, for example, reflects a variable time-based tariff.  
Figure 1: Communication in the local metrological network (LMN), home area network (HAN), and wide area network (WAN) based on [6; 26].  
In order to set up all these parameters, there are several types of configuration profiles. WAN and HAN communication profiles define the parameters for the communication with the SMGW in the corresponding communication networks. They also reference a specific communication scenario. Meter profiles configure the communication with metering devices. The processing of meter data is set up in evaluation profiles, which each specify one of the available tariff use cases and comprise data transmission specification. Finally, proxy profiles link WAN and HAN communication profiles to determine which DERs and EMPs are able to use the transparent data tunnel between HAN and WAN [6].