esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Related Work  
Few recent studies have thoroughly analyzed and compared robotic simulation platforms from diverse perspectives. For example, [PERSON] et al. [12] discussed how simulation systems can be used to model robotic actuators, sensors, environments, and human interactions. They emphasized that effective simulation requires accurately representing four key physical elements: rigid bodies, deformable bodies, fluids, and granular materials. However, their study primarily focused on the physics of simulation, overlooking critical aspects for building Industry 4.0/5.0 applications, such as connectivity and interoperability. Similarly, [PERSON] et al. [13] analyzed physics-based simulators across various autonomous robotics sub-domains, including mobile ground robotics, soft robotics, medical robotics, marine robotics, and aerial robotics, as well as applications such as robotic manipulation and learning. Their study provided a comparative analysis of key simulator capabilities, with a strong emphasis on sensors, actuators, and environmental modeling. [PERSON] et al. [10] reviewed commercial simulation software for digital twin development in manufacturing, analyzing eight simulation tools. They identified five key requirements for simulation in HRC: human operator simulation, robot agent simulation, ergonomic analysis, time measurement, and data exchange/interoperability. Unlike their study, which focused solely on commercial simulation software, our analysis includes commercial, free-to-use, and open-source platforms. Furthermore, we emphasize simulators that support ROS, enabling seamless connectivity with robotic modules developed by the research community, industry, and broader innovation ecosystems. [PERSON] et al. [11] conducted a systematic review of ROS-enabled simulation platforms, focusing on AI-driven wheeled mobile robots (WMRs). Their analysis covered WMR applications, perception and control tasks, commonly used platforms, AI-enhanced simulation tools, and human-in-the-loop (HITL) testing. Their comparison between simulators emphasized physics engines, programming language support, and open-source availability. In contrast, our study extends this evaluation to Industry 4.0/5.0 applications.  
Table 1 summarizes the key differences between previous surveys in robotics simulation and this study. As shown in the table, there is a clear need for an updated systematic review that holistically analyzes the use of robotics simulators for the development and  
testing of Industry 4.0/5.0 applications from a holistic perspective. Therefore, this article aims to fill this research gap.
# A Color Matching Method for Mosaic HY-1 Satellite Images in Antarctica
## 2 Data and Methods
### Image Color-Matching Process  
The study of color matching in this paper consists of three main parts: (1) HY-1 CZI data collection and preprocessing, (2) single-field image nonuniformity luminance correction, and (3) multi-image color matching is performed based on color segmentation matching methods. The color matching workflow of the HY-1/CZI satellite mosaic image in the Antarctic region is shown in Figure 3:  
Figure 3: Image color matching workflow.  
Figure 2: Multi-image color matching processing.
# Using Convolutional Neural Networks for Blocking Prediction in Elastic Optical Networks
## 5 Results  
This section describes the results of the model evaluation. The goal of the designed models is to classify the status of the network status snapshots between the _leading-to-blocking_ or _not-leading-to-blocking_ and label them accordingly. Proper metrics need to be  
Figure 10: Training and validation accuracy in the proposed CNN 2D Model.  
considered to evaluate the models. Distinguishing between the two statuses of the network is a binary classification problem. Therefore, a binary classifier metric is beneficial.  
The performance of the proposed CNN 1D and CNN 2D models in the blocking prediction is presented in Table 2. In this table, for comparison purposes, Support Vector Machine (SVM) and K Nearest Neighbor (KNN) algorithms are computed and compared with the proposed CNN models. _Accuracy_ is the ratio of the number of correct predictions over the total number of predictions. The accuracies are also depicted in Figure 11.  
The best model among all is the proposed CNN 2D model, with an accuracy of 92.17%, followed by the SVM, the CNN 1D, and the KNN. The 2D CNN outperforms with superior classification power compared to the 1D CNN.
# A New Approach to Defining Uncertainties for MODIS Land Surface Temperature
## 3 Validation  
### Site Descriptions  
The GlobTemperature MODIS LST products are validated against several permanent stations of the Surface Radiation (SURFRAD) network to assess quality. These stations have been used in numerous LST validation exercises [6; 61; 62; 63]. Table 1 (reproduced from [1]) details the location, altitude and surface characteristics of each of the seven sites.  
For the seven SURFRAD stations, ground-based LSTs are obtained from incoming and outgoing infrared (IR) radiance measurements made by upward and downward pointing Eppley Precision Infrared Radiometers respectively together with the associated broadband emissivities (BBE). The instruments measure hemispherical IR radiances between 4-50 \(\upmu\)m with a spati  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Site** & **Latitude** & **Longitude** & **Elevation** & **Surface Type** & **Site** \\ \hline Bondville & 40.05155 & \(-\)88.37325 & 230 m & Grassland & Bondville \\ Desert Rock & 36.62320 & \(-\)116.01962 & 1007 m & Arid shrub land & Desert Rock \\ Fort Peck & 48.30798 & \(-\)105.10177 & 634 m & Grassland & Fort Peck \\ Goodwin Creek & 34.2547 & \(-\)89.8729 & 98 m & Grassland & Goodwin Creek \\ Penn State & 40.72033 & \(-\)77.93100 & 376 m & Cropland & Penn State \\ University & 43.73431 & \(-\)96.62334 & 473 m & Grassland & Sioux Falls \\ Sioux Falls & 40.12557 & \(-\)105.23775 & 1689 m & Sparse grassland & Table Mountain \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the in situ stations of the Surface Radiation (SURFRAD) network.  
Figure 2: Aqua MODIS Level-2 land surface temperature (LST) data and pixel uncertainty estimates for 1 June 2010 at 10:50: (**a**) LST; (**b**) total random component; (**c**) locally correlated atmosphere component; and (**d**) locally correlated surface component. Note, the cloud mask is not applied in deriving the Level-2 output but is made available as a separate field in the product.  
of approximately \(70\times 70\) m and are re-calibrated on an annual basis [64]. Further details of the instrumentation and calibration can be found in [65].  
To calculate the ground-based LST, we first estimate the BBE using the corresponding monthly emissivities of the CIMSS Baseline Fit Emissivity Database applied in the linear equation given by [66]:  
\[BBE=0.068+0.045\varepsilon_{6}+\ 0.297\varepsilon_{7}+0.215\varepsilon_{8}+0.372 \varepsilon_{9} \tag{15}\]  
We then use the BBE following the Stefan-Boltzmann law to convert the measured upwelling and downwelling radiances to in situ LST using the following formula:  
\[LST=\sqrt[4]{\frac{L_{\uparrow}-(1-BBE)L_{\downarrow}}{\sigma}} \tag{16}\]  
where \(L_{\uparrow}\) is the upwelling radiance, \(L_{\downarrow}\) is the upwelling radiance, and \(\sigma\)_is_ the Stefan-Boltzmann constant. This is the same method as used in previous validation studies with SURFRAD such as [34; 67].
## 1 Introduction
### Motivation  
Nevertheless, despite the efforts in standardization to include codebook-based MIMO techniques in the last releases, the information provided in the above TSs is very limited to support a clear understanding of the precoding matrices design. For this reason, several books have compiled the information on 5G-NR standardization and have exposed it more clearly [2; 3; 4; 29; 30; 31; 32]. In [2; 3], a description of the CSI acquisition, the main physical reference signals, and the general procedure of the design of the precoding matrices are covered. A study about the implementation of Type I single-panel, Type I multi-panel, and Type II is given in [30; 31]. The improvements introduced in Release 16 and 17 are described in [29; 32]. However, a detailed analysis matching the theory with the implementation stages in the TSs is missing.  
Although several studies have been proposed to evaluate the 5G-NR performance under a realistic 3 GPP channel model, the new sets of features in recent releases are described mainly focusing on the services, technology, and use cases [33; 34]. In [35], authors propose a system-level simulator for evaluating the 3 GPP performance in the 5G use cases according to the requirements defined by the International Telecommunication Union (ITU). However, in all these previous works, an analysis of the precoding matrix design and the SE bounds that can be attained in SU-MIMO or MU-MIMO scenarios is missing. This study is required to provide valuable insights for system designers about the implementation and performance of a practical deployment of a 5G-NR network. Therefore, to fill this gap in the scientific literature, an initial approach consisting of a detailed description of the 5G-NR precoding matrix design and its performance in SU-MIMO systems is addressed in [36]. However, this paper is limited to the assumption of perfect channel knowledge and the effect of channel estimation errors is not taken into account. Also, simulation results were obtained with a tapped delay line (TDL) model that does not properly represent the spatial characteristic of the channel. Moreover, an MU-MIMO scenario is not considered, which restricts its applicability in dense networks. Therefore, motivated by these preliminary results, the present work provides a complete and comprehensive guide for the implementation of the 5G-NR precoding matrices for both SU and MU-MIMO scenarios, even considering channel estimation impairments and channel models aware of spatial characteristics. To the best of our knowledge, the proposed analysis is missing in the literature.
## 3 Results
### Statistical Analysis
#### 3.4.2 Accuracy Assessment  
The dNBR and RdNBR analysis of Landsat imagery with MTMF shows a heterogeneous fire affected area in the northeast and southeast part. In the southwest part, the RdNBR detects the more homogeneously burnt surface. The dNBR analysis had an overall accuracy of 84.4% and a Cohen's Kappa coefficient of 0.82 (Table 3). The RdNBR analysis had an overall accuracy of 89.4% and a Cohen's Kappa coefficient of 0.82 (Table 4). The user's accuracy for both classification categories was the highest (98%) for the high burn severity class and the lowest (40%) for the unburnt/no change class. Results of the producer's accuracy demonstrate difficulty in detecting lower burn severity classes compared to classes of higher burn severity.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ground Cover** & **MTMF Scores** & **NBR Values** & **dNBR Values** & **RdNBR Values** \\ \hline Scorched & & & & \\ vegetation (\%) & 0.72 & 0.70 & 0.75 & 0.76 \\ Soil cover (\%) & 0.34 & 0.55 & 0.51 & 0.53 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pearson correlation coefficients between ground values (\(n=40\)) and image pixels.  
Figure 7: Scatterplots showing comparison of MTMF scores with ground data: (**a**) MTMF scores versus scorched vegetation cover in %, (**b**) MTMF scores versus soil cover in %.
###### Abstract  
The development of innovative transformation techniques for various wastes generated by the agri-food industry is one of the goals of sustainable waste management oriented toward \"zero-waste\" approach. This includes the production of bulk and fine chemicals, bioactive compounds, enzymes and functional materials from various waste. The present research shows the possibility of the production of value-added products from eggshell waste using chemical transformation by acetic and \(\circ\)-phosphoric acid at a laboratory scale level. Eggshell-derived calcium acetate monohydrate and calcium hydrogen phosphate, both of high purity, as well as corresponding eggshell membranes as valuable by-products, were produced. Chemical transformation of 100 g of eggshell waste with 10% (\(w/v\)) acetic acid resulted in 111.41 \(\pm\) 2.13 g of calcium acetate monohydrate and 2.90 \(\pm\) 0.14 g of eggshell membranes, with a chemical yield in calcium acetate monohydrate of 79.16 \(\pm\) 1.98%. On the other hand, the transformation of 100 g of eggshell waste in 15% (\(w/v\)) \(\circ\)-phosphoric acid yielded 77.06 \(\pm\) 6.21 g of calcium hydrogen phosphate and 2.94 \(\pm\) 0.08 g of eggshell membranes, with a chemical yield in calcium hydrogen phosphate of 71.36 \(\pm\) 5.58%. Eggshell-derived calcium hydrogen phosphate met all prescribed criteria for food-grade additive, as determined by physicochemical analysis. On the other hand, calcium acetate monohydrate met the majority, except water insoluble and formic acid and oxidizable impurities content. Based on the obtained results, it can be concluded that the proposed eggshell waste transformation process by two different acids might be useful for the production of calcium acetate and calcium hydrogen phosphate as food-grade additives, as well as eggshell membranes as valuable by-products.  
eggshell waste utilization; acid treatment; calcium acetate; calcium hydrogen phosphate; eggshell membranes +
Footnote †: journal: Applied sciences
## 5 PM-DQN Based Joint Routing and Resource Allocation Algorithm
### Prioritized Memories Deep Q-Network
```
1:Input: mini-batch \(k\), step-size \(\eta\), replay period \(C\), memory size \(N\) and erasing parameters \(a,b,\lambda\)
2: Initialize replay memory \(D=\phi\) and the number of replayed experience \(K_{0}=0\)
3: Initialize \(Q\) function with random weights \(\theta\) and target \(\hat{Q}\) function with weights \(\theta^{-}=\theta\)
4:For\(\textit{eposide}=1,M\)do
5: Initialize state \(s_{1}\)
6:For\(t=1,T\)do
7: With probability \(\varepsilon\) select a random action \(a_{t}\)
8: otherwise select \(a_{t}=\text{argmax}_{a}Q(s_{t},a;\theta)\)
9: Execute action \(a_{t}\) and observe reward \(r_{t}\), state \(s_{t+1}\)
10: Store transition \((s_{t},a_{t},r_{t},s_{t+1},\delta_{t})\) in \(D\)
11: Sample mini-batch of transitions \((s_{i},a_{i},r_{i},s_{i+1},\delta_{i})\) from \(D\)
12: Set \(y_{i}=\left\{ \begin{array}{c}r_{i}\quad\text{if episode terminates at step }\mathrm{i}\ +1\\ r_{i}+\gamma\text{max}_{\theta^{-}}\hat{Q}(s_{i+1},a^{\prime};\theta^{-})\quad \text{otherwise}\end{array} \right.\)
13: Perform gradient descent on \((y_{i}-Q(s_{i},a_{i};\theta))^{2}\) with respect to the network parameters \(\theta\)
```  
**Algorithm 1**Prioritized Memories Deep Q-learning  
Figure 4: Implementation of Prioritized Memories. Figure 5: Variation trends of the transition quantities in PM-DQN.
# Impact of Mega Constellations on Geospace Safety
## 2 Models and Assumptions  
### Orbital Flux Calculation and Collision Probability Model  
The MASTER-8 model flux calculation utilizes a similar approach to gas dynamics theory, where space debris travels through the particle-filled Earth space environment as if the surface were sweeping through a static gas-filled space container, called a \"bin\", as depicted in Figure 3. As shown below, Earth orbital space is divided into an infinite number of similar space containers, with \(\Delta t\) indicating the container height, \(\Delta\alpha\) the container longitude span, and \(\Delta\delta\) the latitude span.  
The total space flux is descripf by Equation (1)  
\[F=\sum_{i}F_{i}=\sum_{i}\left(\sum_{bin}q_{bin}\times p_{t}\times\Delta v_{p}\right) \tag{1}\]  
\(F_{i}\) denotes the flux within a single space, \(q_{bin}\) denotes the spatial density at rest, \(p_{t}\) means the target residence probability within the \"bin\", and \(\Delta v_{p}\) denotes the relative velocities of space objects and static objects. The flux distribution of the spacecraft's operational orbit can be obtained from the evolved orbital object distribution, and Equation (2) can be applied to determine the average number of collisions \(n\)[10].  
\[n=F\times A_{c}\times T \tag{2}\]  
\(F\) is the flux, calculated from MASTER-8, \(T\) is the time frame, and \(A_{c}\) is the cross-sectional area of the collision, which in this paper is the cross-sectional area of the Starlink satellite in orbit.  
From the average number of collisions \(n\), collision probabilities can be calculated using Poisson statistics, \(m\) means the number of times a collision event occurs.  
\[P_{i=m}=\frac{n^{m}}{m!}e^{-n} \tag{3}\]  
Figure 3: The spatial pattern of the bin.
# Li2O-Based Cathode Additives Enabling Prelithiation of Si Anodes
## 3 Results and Discussion
### Performance of Li\({}_{2}\)O + Co\({}_{3}\)O\({}_{4}\) @1100 rpm Electrode
The Li\({}_{2}\)O + Co\({}_{3}\)O\({}_{4}\) @1100 rpm electrode was then paired with a Si anode to test its full cell performance. The areal capacities of anode and cathode used for full cells are shown in Figure S6. The first-cycle voltage profiles of the baseline and Li\({}_{2}\)O + Co\({}_{3}\)O\({}_{4}\) @1100 rpm full cells are presented in Figure 2b, and their continued cycling results are shown in Figure S7. During the first charge, additional capacity from Li\({}_{2}\)O was observed at voltages lower than 4 V, demonstrating the successful performance of this electrode in full-cell configuration. Up to 4.1 V in full cells, 1035 mAh/g\({}_{\text{Li2O}}\) (or 546 mAh/g\({}_{\text{Li2O}+\text{Co}_{3}\text{O}}\) considering the total mass of Li\({}_{2}\)O and Co\({}_{3}\)O\({}_{4}\)) of capacity was obtained from the additive. However, one notable behavior observed from both half cells and full cells was the lower capacity of Li\({}_{2}\)O + Co\({}_{3}\)O\({}_{4}\) @1100 rpm electrode in the following discharge compared to that of the baseline electrode. For example, the discharge capacity of the baseline and the Li\({}_{2}\)O + Co\({}_{3}\)O\({}_{4}\) @1100 rpm electrodes was 187 and 180 mAh/g\({}_{\text{SNMC}}\), respectively, in half cells (Figure 2a), and 179 and 160 mAh/g\({}_{\text{SNMC}}\), respectively, in full cells (Figure 2b). The greater difference in the discharge capacity between the two electrodes in full cells likely resulted from the higher upper cutoff voltage. When the half cells were charged up to 4.7 V (Figure S3), the discharge capacity of the baseline and Li\({}_{2}\)O + Co\({}_{3}\)O\({}_{4}\) @1100 rpm electrodes was 229 and 211 mAh/g\({}_{\text{SNMC}}\), respectively, which is a larger difference (18 mAh/g\({}_{\text{SNMC}}\)) compared to the 4.3 V charged cells (7 mAh/g\({}_{\text{SNMC}}\)). Such capacity fade was observed from all additive-containing electrodes examined in this work, regardless of the presence of Co\({}_{3}\)O\({}_{4}\), indicating a general degradation mechanism induced by Li\({}_{2}\)O. To evaluate the morphological integrity of the cycled electrodes, SEM images were obtained (Figure S8a). However, there was no noticeable change in the electrode structure. To examine the potential degradation of other cell components impacting the cell performance, the cycled electrode was retrieved and reassembled into a half cell with fresh electrolyte and Li metal (Figure S8b). However, the capacity was not recovered in this case either. These results suggested that the degradation arises from chemical changes happening to the electrode during the charge. Thus, we performed XPS analysis on the cycled electrodes to understand the source of this phenomenon.
## 4 Validation of the Rotational Speed Estimation Principle Using GAD  
### Experimental Setup  
In order to verify the correctness and effectiveness of the proposed rotational speed estimation methodology, a motor-shaft-disc experimental setup is established. The setup mainly consists of a converter, a motor, a shaft, a disc, an encoder, etc., as illustrated in Figure 4. One in situ accelerometer is mounted on one end of the disc with the sensitive direction in the tangential direction. The mounting radius of the in situ accelerometer is approximately 20 mm. The selected accelerometer type is ADXL 1001 from Analog Devices in the Norwood, MA, USA, the measuring range is \(\pm\)100 g and the sensitive value is 20 mv/g. In the experiment, an E6 HZ-CWZ6C incremental encoder of OMRON from Kyoto, Japan is used to record the rotation angle in this paper, which is mounted on the end of the extension shaft. The encoder line is 360 P/R. The rotational speed is controlled by adjusting the converter.  
Two tests are conducted based on the experimental setup: Test 01 involves the startup procedure, where the initial motor speed gradually increases from 0 r/min to 700 r/min within a time span ranging from 1.5 s to 9.9 s. Conversely, Test 02 focuses on the run-down procedure, during which the initial motor speed decreases from 620 r/min to 0 r/min within a time frame varying from 1.5 s to 8.5 s. The sampling frequency for the encoder is set as 204.8 kHz to guarantee the high-resolution accuracy of the encoder. And the sampling frequency for the in situ accelerometer is set as 4096 Hz. The sampling time length for both of the two measurement quantities is set as 10 s.
## 3 Physical Layer Design
### Link Budget Analysis  
In this section, we estimate the Bit Error Rate (BER) that can be sustained by using the TX described here in typical application environments. We evaluate the BER for a range of path loss from 25 to 50 dB corresponding to typical intra and inter-chip communication distances [11]. The received signal power is given by:  
\[\mathrm{P_{R}}=\mathrm{P_{T}}-\mathrm{PL} \tag{2}\]  
Here, \(\mathrm{P_{R}}\) and \(\mathrm{P_{T}}\) are the received and transmitted power respectively and PL is the path loss. At the receiving side we assume a non-coherent OOK receiver as these are most power efficient [68]. Considering Additive White Gaussian Noise (AWGN) at the receiver, the overall Noise Floor, \(\mathrm{N_{Floor}}\) of such a receiver is given by,  
\[\mathrm{N_{Floor}}=10\log(\mathrm{kT})+10\log(\mathrm{BW})+\mathrm{NF}. \tag{3}\]  
where, \(\mathrm{k}\) is the Boltzmann constant, \(\mathrm{T}\) is the absolute temperature, \(BW\) is the bandwidth and NF is the Noise Figure of the receiver. We have considered an NF of 2.8 dB for the LNA designed in [68]. Therefore, from (2) the \(\mathrm{N_{Floor}}\) of the receiver is \(-67.8\) dBm at 300 K for a BW of 16 GHz. However, in addition to thermal AWGN, Inter-symbol Interference (ISI) can impact the reliability of the mm-wave link. Multipath channels existing in such environments as well as bandwidth limitation of the transceiver, the antennas and the channel, cause ISI. In the absence of thorough multipath channel models at 60 GHz in multi-chip environments as discussed in Section 3.1, we only consider bandwidth limited ISI in our link budget analysis. From Figure 10, the transmitter output is 450 mVpp and 67 mVpp when it is transmitting a '1' and a '0' respectively when measured at the center of the pulses. Therefore, the on-off ratio of overall OOK transmitter, \(\mathrm{ROOK}\) is 16.54 dB. Therefore, the value of bandwidth-limited ISI noise, \(\mathrm{N_{ISI}}\) is dependent on path loss and is given by,  
\[\mathrm{N_{ISI}}=\mathrm{P_{T}}-\mathrm{ROOK}-\mathrm{PL}. \tag{4}\]  
The received Signal to Interference and Noise Ratio (SINR) is given by,  
\[\mathrm{SINR}=\mathrm{P_{R}}-\mathrm{N_{Floor}}-\mathrm{N_{ISI}} \tag{5}\]  
For non-coherent OOK demodulation, the BER is given by [69],  
\[\mathrm{BER_{OOK}}=\frac{1}{2}\exp\biggl{(}-\frac{1}{2}\mathrm{SINR}\biggr{)} +\frac{1}{4}\mathrm{erfc}\biggl{(}\sqrt{\frac{1}{2}\mathrm{SINR}}\biggr{)} \tag{6}\]  
where \(\mathrm{erfc}(.)\) is the complimentary error function. Figure 12 shows the BER achieved with this TX for a wide range of path loss values with and without ISI (for comparison). Consideration of multipath channel models may have a significant effect on this link budget analysis.  
Figure 12: BER variation with path loss: with and without ISI.