esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Structures and Computational Methods  
The perovskite \(ABZ_{3}\) is a cubic structure with a space group of \(Pm3m\). The \(B\)-site cations correspond to six anions to form cuboctahedra, which are chemically inert ions arranged in a cuboctahedron. The \(B\)-site and \(Z\)-site refer to the atomic arrangement in the unit cell of cations and anions, respectively. The Wyckoff position for the \(A\)-site cation is located at the corner (0, 0, 0), that of the \(B\)-site cation is located at the center (1/2, 1/2, 1/2), and those of the \(Z\)-site anions are located at face-center (1/2, 1/2, 0), (1/2, 0, 1/2), and (0, 1/2, 1/2). Studies have reported that, under experimental conditions, lead halide perovskites have a simple cubic phase at high temperatures. In this study, the structural and electronic properties of CsPbBr\({}_{x}\)I\({}_{3-x}\) (\(x\) = 0, 1, 2, and 3) perovskites were calculated using density functional theory as implemented in the Vienna ab initio simulation package (VASP) [29; 30] along with the Perdew-Burke-Ernzerhof (PBE) GGA [31] for the exchange-correlation functional. The \(\Gamma\)-centered Monkhorst-Pack scheme with a \(k\)-mesh of \(12\times 12\times 12\) was used for the first Brillouin zone integration. The self-consistent total energy criterion was set to 1.0 \(\times\) 10\({}^{-6}\) eV. The plane wave cutoff energy was set to 350 eV. The equilibrium lattice constant of the cubic perovskite structure was obtained from the minimum of the total energy as a function of the lattice constant. The SOC effect was considered after the equilibrium lattice constant was found. To identify the surface conduction band of CsPbBr\({}_{x}\)I\({}_{3-x}\) perovskites, we prepared a supercell containing 10 formula units with a vacuum spacing of approximately 20 A, which is wide enough to decouple the interlayer interaction. The surface atoms were fixed (i.e., with no further structural optimization possible) to correspond with the one in the bulk form. The atomic structure of CsPb\(X_{3}\) (\(X\) = Br and I) bulk is shown in Figure 1a, and schematics of two initial surface structure models--specifically with one Pb\(X_{2}\)- and Cs\(X\)- and two Pb\(X_{2}\)-terminated slabs as shown in Figure 1b,c, respectively--were constructed from known bulk geometries to help clarify which plane contributes to the surface conduction bands. The nonpolar supercells contained 10 formula units with a vacuum of 20 A, which is large enough to help obtain the surface conduction bands.  
We studied the electric polarization caused by the movement of the Pb atom in the \(z\)-direction. Displacement of Pb atom in the \(z\)-direction breaks the inverse space symmetric of the CsPbBr\({}_{x}\)I\({}_{3-x}\), resulting in a finite electric polarization. In addition to using the band inversion, the so-called Chern number or topological number \(Z_{2}\) can be used to prove the characteristics of TIs. We calculated \(Z_{2}\) by considering the parity of the wave function at specific \(k\) points reported by [PERSON] and [PERSON] [10].
# Low Complexity List Decoding for Polar Codes with Multiple CRC Codes
## 4 Simulation Results and Analysis
Figure 5: Comparison of the proposed and conventional 2-CRC schemes for \(N=1024\), \(R=1/2\), \(L=32\) and \(P=16\); (a) block error rate; (b) complexity ratio. Figure 6: Comparison of the proposed and conventional 2-CRC schemes for \(N=4096\), \(R=1/2\), \(L=32\) and \(P=16\); (a) block error rate; (b) complexity ratio. Figure 7: The complexity ratio of the proposed and conventional 3-CRC schemes for \(R=1/2\), \(L=32\) and \(P=16\); (a) \(N=1024\); (b) \(N=4096\). Figure 7 depicts the complexity ratio of the proposed and conventional 3-CRC schemes. The performances for the 3-CRC schemes are not included in this paper because they are virtually the same as for the 2-CRC scheme. For the 3-CRC scheme, the complexity reduction from the early stopping is smaller than the 2-CRC scheme because the CRC of four bits located in the middle of vector \(\mathbf{u}\) can not eliminate most incorrect paths. Especially, if \(L=32\) and a CRC of four bits is used, then \(32/(2^{4})=2\) paths survive on average, which implies a lower probability of early decoding termination than the 2-CRC scheme. However, if the ID-SCL decoding is applied, there is more complexity reduction at the high SNR. For \(N=1024\), about 12% _points_ (about 9% _points_) are reduced compared to U-3 CRC (U-3 CRC with ID), and about 11% _points_ (about 9% _points_) for \(N=4096\) compared to U-3 CRC (U-3 CRC with ID). Note that, for the proposed \(J\)-tuple CRC scheme, the complexity reduction can be increased, while keeping the performance level by selecting \(\mathcal{A}_{g}\) rigorously, not based on a threshold \(\sigma_{\text{Th}}\). This paper provides a simple and systematic algorithm that minimizes the complexity and performance loss by adjusting the single parameter \(\sigma_{\text{Th}}\).
## 5 Performance Analysis
### Evaluation and Simulation Results  
We tested EdgeGuard on the MIMIC-III dataset in a simulated IoMT environment. Our evaluation focused on model accuracy, communication efficiency, security robustness, and resource utilization in four key aspects.  
The experimentation environment maintains consistent conditions for both implementations, utilizing the MIMIC-III dataset with identical preprocessing steps and evaluation metrics. This setup ensures a fair comparison while highlighting EdgeGuard's enhanced security and efficiency features.  
\begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Value** \\ \hline _System Configuration_ & \\ \hline Number of Edge Devices & 50–500 \\ Number of IoMT Sensors & 100–2000 \\ CPU Cores per Server & 40 \\ RAM per Server & 128 GB \\ GPU Memory & 32 GB \\ \hline _Federated Learning Parameters_ & \\ \hline Train/Test Split & 80:20 \\ Local Epochs & 10–50 \\ Batch Size & 64 \\ Communication Rounds & 1–300 \\ Client Selection Rate & 0.8 \\ Malicious Devices & (10\%, 20\%,..., 50\%) \\ \hline _Optimization Parameters_ & \\ \hline Base Learning Rate & 0.01 \\ Optimizer & SGD with Momentum (\(\beta=0.9\)) \\ Learning Rate Scheduler & Cosine Annealing \\ Weight Decay & \(1\times 10^{-4}\) \\ Gradient Clipping & 1.0 \\ Early Stopping Patience & 10 epochs \\ Momentum & 0.9 \\ \hline _Blockchain Parameters_ & \\ \hline Block Generation Rate (\(\lambda\)) & \{0.1, 0.3, 0.5, 0.7\} \\ Consensus Algorithm & Proof of Work \\ Gas Limit & 6,721,975 \\ Block Time & 15 s \\ Smart Contract Version & Solidity v0.8.0 \\ \hline _Network Parameters_ & \\ \hline Bandwidth Range & 1–10 Mbps \\ Latency Range & 10–100 ms \\ Packet Loss Rate & 0.1–1\% \\ Network Topology & Star \\ \hline _Dataset Configuration_ & \\ \hline Training Set & 80\% \\ Validation Set & 10\% \\ Test Set & 10\% \\ Time Window & 24 h \\ Sampling Rate & 5 min \\ \hline _Security Parameters_ & \\ \hline Differential Privacy \(\varepsilon\) & 0.1–1.0 \\ Privacy Budget \(\delta\) & \(10^{-5}\) \\ Encryption Method & AES-256 \\ Key Length & 2048 bits \\ \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.
## 4 Protocol for Source Location Protection Based on Limited Flooding
### _The Shortest Path Routing_  
After the _h_-directed routing, each packet will be routed following the shortest path from the phantom location to the base station. Specifically, the current sensor will always send the packet to a neighbor who has a smaller \(D_{s}\) than the \(D_{s}\) of the current sensor. The packet transmission process will be repeated until the packet reaches the base station.
# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## Abbreviations  
\begin{tabular}{l l} Extraction result & Data determined as green tide pixels from RS images \\ Accumulative cover area & The accumulation cover area of extraction result \\ \(f\) & The _NDVI_ statistic sample frequency \\ \(H\) & The equation for the second-order Gaussian fitted curve of \\ \(NDVI\) values & \\ \(TP\) & True positive (TP) indicates the number of correctly classified \\  & green tide pixels \\ \(FN\) & False negative (FN) indicates the number of green tide pixels that are \\  & misclassified as background \\ \(FP\) & False positive (FP) represents the number of background pixels that \\  & are misclassified as green tide pixels \\ \(TN\) & True negative (TN) indicates the number of correctly classified \\  & background pixels \\ Precision & The percentage of extracted green tide elements (all elements with a \\  & predicted value of 1) that are accurate \\ Recall & The percentage of green tide elements (all elements with a true value \\  & of 1) extracted from the Defender \\ F1-score & Summed average of Precision and Recall \\ Acc & The area consistency coefficient \\ Coverage rate & The roughly distribution of green tide in the selected area \\ \(y1\) & The \(y\) coordinates of the same \(x\) value point (band 2) on the black \\  & dotted line \((y=ax+b)\). \\ \(y2\) & The mean reflectance of band 2 (Green band) of each RS data \\ \(d\) & The difference between \(y2\) and \(y1\) (\(d=y2-y1\)) \\ \(y^{\prime}\) & Gaussian fitted curve formula for the first-order derivative of \\  & the growth curve \\ \(R^{2}\) & Fitting coefficient of determination of the growth curve \\ \(P\) & The percentile corresponding to the time of start and dissipation, i.e., the statistic related to the percentage of area integrated with time (days) \\ \(S_{a}\) & The integral area of the first derivative of the growth curve for day \(x_{a}\) \\ \(S_{all}\) & The integral area of the first derivative of the growth curve for the \\  & whole year \\ \(g_{2}\) & The kurtosis coefficient of \(y^{\prime}\) \\ \(ss\) & The standard deviation of \(y^{\prime}\) \\ \end{tabular}
# Detection of Threats to Farm Animals Using Deep Learning Models: A Comparative Study
###### Abstract
To summarize the specific contributions of this article, the following important points shall be overviewed:  
* The primary contribution of this article is the utilization of deep learning-based object detection technologies to protect livestock from potential threats and improve farm safety. * Critical emphasis was placed on the ability to effectively distinguish various animal species concerning the characteristics of the model parameters. This provides valuable insights into model selection and adjustments for future studies. * We comprehensively examined the capabilities of advanced deep learning models such as YOLOv8, Yolo-NAS, and Fast-RNN to detect elements that may threaten livestock. These technologies' effectiveness in agricultural practices can potentially revolutionize farm management and animal protection strategies. * We validated the superior capacity of the proposed system via a series of experiments in association with real traces of datasets. * Detection studies were conducted on specific species, encompassing both wild and domestic animals. A notable aspect of this research is the amalgamation of farm and wild animals within a unified dataset, which enables their integration into comprehensive image detection studies. The forthcoming sections of the present article are organized as follows. The recent research field about the scope of novel technologies in observing and monitoring agriculture and wildlife is described in Section 2. The third section aims to provide the essential definitions of the system models, covering the properties of the datasets, the details of the DL approaches, the experimental environment, and the evaluation metrics. Section 4 presents the performance results to demonstrate the operating basis of the proposed idea. A discussion to highlight the main findings along with a clear conclusion is given in Section 5.
# NMR-MPar: A Fault-Tolerance Approach for Multi-Core and Many-Core Processors
## 3 N-Modular Redundancy and M-Partitions Approach
### Description of the N-Modular Redundancy and M-Partitions Approach  
The partitioning principle is used to create different partitions that co-exist in the same device. This is typically done by the hypervisor, which provides virtual CPUs to each partition. In contrast, the NMR-MPar approach proposes a physical resource distribution to each partition, to minimize the propagation of faults producing dysfunctions in other resources or cores. Each partition can be setup as a mono- or multi-core running on different multiprocessing modes: Asymmetric Multi-Processing (AMP) or Symmetric Multi-Processing (SMP) mode with different programming models: bare-metal, OpenMP, Portable Operating System Interface uniX (POSIX), etc. Consequently, there is a considerable versatility for the system configuration that is enhanced by the number of cores comprising the device. It is important to note that each partition can be configured in bare-metal or with an independent OS. In bare-metal, no OS is used, then the programmer uses the functions provided by the manufacturer to access hardware resources. There is no abstraction layer from the hardware architecture.  
Furthermore, it is proposed for critical functions that N partitions with the same configuration participate in an N-Modular redundancy system. Thus, several partitions execute the same application, and the results are used by a voter to build a fault-tolerant system. The voter system can include one or more cores of the device, or an external one if desired. Depending on the partition of the device, several N-modular redundancy systems may run on it concurrently.  
Figure 1 illustrates an example of this approach implemented on a multi-core having 16 processor cores. The example establishes seven partitions (\(M=7\)). Partitions P0-P2 are quad-core, while P3-P6 are mono-core. The quad-core partitions are part of a TMR system. Each one of them runs in SMP mode executing the parallel Application 1. On the other side, mono-core partitions (P3-P5) are part of another TMR running Application 2. The last mono-core partition P6 is the voter of both TMRs. The latter is configured in bare-metal to reduce the use of resources, minimizing the impact of faults.  
By using this approach, temporal and spatial isolation of the applications is guaranteed by this type of partitioning. The security of each partition against unauthorized access and data modification can be obtained by the configuration of the system.
# Circle-U-Net: An Efficient Architecture for Semantic Segmentation
## 1 Introduction  
Convolutional neural networks (CNN) have achieved state-of-the-art experiments in many computer vision tasks, such as attribute recognition, object detection, semantic segmentation, labels optimizing and so on [1; 2; 3; 4]. Semantic segmentation is of paramount importance in the computer vision field because of its applications in box-supervised and texture features. Mask R-CNN [5] and U-Net [6] are used in semantic segmentation.  
Mask R-CNN was developed by adding a fork, which aimed at predicting and generating a high-quality segmentation mask. [PERSON] et al. [7] used Mask R-CNN to automatically detect and sort IWPs in the North Slope of Alaska. Mask R-CNN is widely applied in the remote sensing domain. [PERSON] et al. [8] proposed an adaptive Mask R-CNN in multi-class objects, detected in a remote sensing domain. [PERSON] et al. [9] created polygons using segmentation algorithms based on Mask R-CNN. [PERSON] et al. [10] proposed HTMask R-CNN, which is based on Mask R-CNN. HTMask R-CNN could adopt the features of single-object segmentation from Mask R-CNN. Mask R-CNN and U-Net [6] are widely applied in object segmentation. However, there are some limitations to U-Net. For example. the mIoU (mean Intersection of union, mIoU) is low and is unable to detect 2, 4 and 8 classes.  
In this paper, we propose a new type of U-Net [6], called Circle-U-Net. Circle-U-Net has 101 layers and is inspired by a residual net from deep learning and a circle from geometry. We prove that Circle-U-Net can segment objects better than other networks, due to both its depth and residual layer; the more profound the network model, the more incredible because it can segment. Moreover, only in a deeper network can we say thatadditional functionalities, such as residual layers, attention layers, are helpful. The residual layers that form a circular pattern suggest that the recurring patterns vividly help the network understand specific parts. Our contributions can be summarized below.  
1. We put forward a Circle-U-Net network with a circle connect model, which exceeds the performance of the attention mechanism. Our network improves 0.78 mIoU than adding the attention mechanism to our model. The circle connects model is robust and capable in object segment, which performs better than most state-of-the-art experiments.
2. Circle-U-Net cannot detect 2 classes, while some networks cannot see 2, 4 and 8 classes. In other words, Circle-U-Net has high power to detect than other networks.
3. We prove that the proposed method has a better performance in comparison with the state-of-the-art networks. Furthermore, we organize the rest of this paper as follows: Section 2 describes related works and Section 3 reviews the proposed Circle-U-Net structure in detail. Experimental results and comparisons are described in Section 4, followed by conclusions in Section 5.
# Spatial Autocorrelation Incorporated Machine Learning Model for Geotechnical Subsurface Modeling
## 2 Study Area and Methodology
### Data Pre-Processing
#### 2.3.2 Data Transformation  
In this study, data transformation techniques were used to improve features significant to the machine learning model development. Min-max normalization is applied in this study for transformation of location information features, GSE, GWL, and percent finer than 0.075 mm sieve. The following is the data normalization formula, where \(s_{norm}\) is the normalized value of variable \(v\), \(s\) is the initial value of variable \(v\), \(s_{min}\) is the minimum value of variable \(v\) and \(s_{max}\) is the maximum value of variable \(v\)[5].  
\[s_{norm}=\frac{s-s_{min}}{s_{max}-s_{min}}. \tag{2}\]  
Log transformation technique is applied for the transformation of SPT-N values. Log transformation is performed by replacing each data point \(s\) with the transformed value \(s_{trans}\), where 1 is mean to prevent log(0) condition for every 0 value in the transformed dataset.  
\[s_{trans}=log(s+1). \tag{3}\]  
For the Soil Classification attribute, the data need to be transformed by assigning each soil type category with an integer value. In this study, ordinal encoding is used to transform the data from categorical data to numerical data. The assigned integers for each category are 1 for SM, 2 for ML, and 3 for CL.
# AgriMFLN: Mixing Features LSTM Networks for Sentiment Analysis of Agricultural Product Reviews
## 3 Materials and Methods
### Research Methods
#### 3.3.1 LSTM Layer  
LSTM [46] is consistent with the typical RNN framework, but it calculates the hidden state using distinct methods. LSTM can learn long-term dependence, solve the problem of gradient explosion and gradient disappearance in the process of long sequence training, and is suitable for classification and prediction tasks based on time series data, with better performance in longer sequences.  
The LSTM model consists of a series of repeated memory units, each of which contains three gates with different functions. Each block includes one or more self-connected memory cells and three multiplicative units--the input, output, and forget gates--that continuously analogize the actions of writing, reading, and resetting the cells [47].  
The structure of such a unit is shown in the following Figure 1.  
The specific calculation formula is as follows:  
The \(f_{t}\) is the forget gate:  
\[f_{t}=\sigma\Big{(}W^{f}x_{t}+U^{f}h_{t-1}+b_{f}\Big{)} \tag{1}\]  
The \(i_{t}\) is the input gate:  
\[i_{t}=\sigma\Big{(}W^{i}x_{t}+U^{i}h_{t-1}+b_{i}\Big{)} \tag{2}\]  
The \(C_{t}\) represents the state value of the current time in the memory cell:  
\[C_{t}=f_{t}\times C_{t-1}+i_{t}\times\widetilde{C}_{t} \tag{3}\]  
The \(o_{t}\) is the output gate:  
\[o_{t}=\sigma(W^{o}x_{t}+U^{o}h_{t-1}+b_{o}) \tag{4}\]  
The \(\widetilde{C}_{t}\) represents the candidate memory cell status at the current time step:  
\[\widetilde{C}_{t}=\tan\h(W^{n}x_{t}+U^{n}h_{t-1}) \tag{5}\]  
Figure 1: The structure of the LSTM unit.  
\(h_{t}\) represents the hidden layer state at time \(t\):  
\[h_{t}=o_{t}\times\tanh\text{h}(C_{t}) \tag{6}\]  
\(W\) and \(U\) represent the weight matrix, \(b\) represents the deviation vector, \(\sigma\) represents the sigmoid activation function, and \(x_{t}\) represents the input vector at a time.
# Preliminary Results of an Astri/UWM EGNSS Receiver Antenna Calibration Facility
## 9 Conclusions and Future Work  
The paper addresses the initial results of a project aimed at the development and implementation of the field calibration procedure for a multi-frequency and multi-system GNSS. Based on the \"Hannover\" concept, an absolute antenna phase center calibration algorithm was investigated and a GNSS receiver antenna calibration procedure has been implemented. Robotic arm rotation and tilting scenarios ensured a homogenous observation distribution with regard to the antenna hemisphere, which is critical for high precision antenna phase center parameter estimation.  
The analyses of the internal accuracy show that the dPCCs do not exceed 2 mm in the whole antenna hemisphere. The most significant differences occur at low zenith angles (from 0\({}^{\circ}\) to 30\({}^{\circ}\)), which is mainly caused by the differences in PCO vertical components. Significant differences can also be observed in some azimuthal directions for high zenith angles from 80\({}^{\circ}\) to 90\({}^{\circ}\), where dPCCs exceed 2 mm. The reason for the observed phenomenon could be higher noise which is typical for the low elevation angle signals.  
When analyzing the external accuracy of achieved results for the full PCCs model, it can be seen that differences are slightly higher than in the case of internal accuracy and exceed 3 mm in some antenna hemisphere regions. As previously described, some larger differences (reaching 1.5 mm) occur at low zenith angles. Some noticeable differences in the order of 3-4 mm can also be found close to horizontal reception angles. Nevertheless, the achieved results demonstrate consistent performance for a Astri/UWM calibration strategy and calibration procedure; however, some aspects (e.g., low elevation PCCs modeling) need to be further investigated in the future.  
Conceptualization, K.D., J.R. and P.W.; methodology, K.D., J.R., M.S. and P.W.; algorithms, K.D., J.R. and M.S.; software, J.R., D.K., K.D., M.S., D.T. and W.J.; robot adaptation, M.S., B.W., J.R. and R.B.; robot mounting, M.S., R.B. and J.R.; measurements, K.D., J.R., M.S., D.K., D.T., P.G., B.W., R.B. and G.K.; receivers management, P.G.; validation, J.R., K.D., D.T., G.K. and K.S.; writing--original draft preparation, K.D.; writing--review and editing, J.R., P.W., W.J. and K.D.; visualization, K.D.; supervision, P.W.; project administration, K.B., T.G., J.R., P.W., J.J. and G.F.-C.; funding acquisition, K.B., T.G., J.R., P.W. and K.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Space Agency (ESA Contract No: 4000125961/18 /NL/CBi).  
Not applicable.  
Raw data were generated at UWM. Derived data supporting the findings of this study are available from the corresponding author [PERSON] on request.  
The authors declare no conflict of interest.