esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Radio Frequency Identification for URLLC in Industrial IoT Networks
## 4 Transmission Performance Analysis of RFI-Aided URLLCs with Massive MIMO
### Error Probability Bound for Random Channels
\begin{split}\mathbb{P}&\Big{[}\sum_{m=1}^{n}I_{s}( x(m),y(m))\leq\log\frac{e^{nR}-1}{u}\Big{]}\\ &=e^{n[\kappa(\xi)+\xi R]}\Big{[}\Theta_{n,\widehat{\xi}}(\zeta) +\Theta_{n,\widehat{\xi}}(1-\zeta)+o\left(\frac{1}{\sqrt{n}}\right)\Big{]} \end{split}  \tag{27}\]  
where  
\[\Theta_{n,\zeta}(u)\triangleq e^{n\frac{u^{2}}{2}x^{n^{\prime}}(\xi)}Q\Bigl{(} u\sqrt{nx^{\prime\prime}(\zeta)}\Bigr{)} \tag{28}\]  
where \(\mathcal{O}\Bigl{(}\frac{1}{\sqrt{n}}\Bigr{)}\) denotes residual error. The cumulant-generating function of saddle-point approximation can be described as \(-\kappa(\zeta)\). We assumed that \(k=e^{nR}\) for \(R>0\). Furthermore,the right-hand side part of (27) corresponds to [PERSON]'s error exponent for the wrong decoding application. \[P_{error}=\left\{ \begin{array}{ll}e^{n\left[\log\mathbb{E}\left[e^{-\gamma_{s} \left(x(m),y(m)\right)}\right]+R\right]}\left[\widetilde{\Theta}_{n}(1,1)+ \widetilde{\Theta}_{n}(0,-1)+\mathcal{O}\left(\frac{1}{\sqrt{n}}\right)\right] &\zeta>1,R<R_{critical}\\ 1-e^{n\left[\kappa\left(\zeta\right)+\zeta R\right]}\left[\Theta_{n,\zeta}(- \zeta)-\Theta_{n,\zeta}(1-\zeta)\right.+o\left(\frac{1}{\sqrt{n}}\right)\right],&\zeta<0,R>I_{s}\\ \text{where}\\ \end{array} \right. \tag{29}\]  
\[\widetilde{\Theta}_{n}(a_{1},a_{2})=e^{na_{1}\left[R_{critical}-R+\frac{e^{n \prime}(1)}{2}\right]}\times\mathcal{Q}\left(a_{1}\sqrt{n\kappa^{n\prime}(1)} +a_{2}\frac{n(R_{critical}-R)}{\sqrt{n\kappa^{n\prime}(1)}}\right) \tag{30}\]  
and \(R_{critical}=-\kappa^{\prime}(1)\) represents the critical system rate. The subexponential factor is estimated by saddle-point approximation to acquire accurate error probability [16]. From (29), we can find that separating [PERSON]'s error exponent is the main process of saddle-point approximation. Then, the Berry-Esseen central-limit theorem is applied to calculate factors before the exponential term. \(P_{error}\) represents the error probability for finite block-length transmission.
## 2 Related Work
An additional evaluation of various MFs was conducted to facilitate a simulation of inflation growth utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS). This network structure employs a modified set of parameters to ascertain the overall input-output behavior. Tests were performed to identify the most suitable MF, which included triangular, trapezoidal, and GBell MFs. Among the three techniques evaluated, only the Gbell function did not yield a convergent graph, whereas the most favorable error rate was achieved with a triangular MF [16]. Conversely, in contrast to the knowledge of the modeler, certain studies employ computational methods for selecting MFs in fuzzy logic. Genetic algorithms, founded on genetic rules and natural selection principles, are utilized across various applications, including modifying MF shapes. This methodology can identify the optimal solution; while triangular MFs are commonly employed, it remains suitable for MFs defined by a mathematical model [17]. Another recognized technique for identifying MFs is the tabu search algorithm, which is regarded as an intelligent optimization strategy to navigate local minima. Simulation results demonstrate the efficacy of this approach. Nevertheless, the performance can be influenced by the calibration of the control parameters, including the type of move, algorithmic conditions, initial solutions, and stopping criteria [18]. The evaluation methodologies delineated herein seek to enhance the quality of their respective tasks, including forecasting and, in certain instances, data description pertaining to fuzzy clustering [19]. However, the fundamental architecture remains unaltered; therefore, it is pertinent to assess the core functions. This assessment may be accomplished by testing the outputs of a system while systematically varying the core functions. The literature review in this section substantiates the notion that multiple factors continue to underlie the enhancement of fuzzy systems.
# Analysis and Research on Secondary LT Coding Anti-Eavesdropping Scheme Based on LT Code Degree-1
## 2 LT Code Anti-Eavesdropping Method
### Anti-Eavesdropping Method Based on Degree-1 Secondary LT Coding  
Based on the anti-eavesdropping channel premise of Figure 1, the source first LT encodes the overall symbols and then continues LT encodes the degree-1 symbols produced by it again, as shown in the literature [23]. The method is known as double LT code because it goes through two LT encoding processes. Figure 2 depicts its scheme model.  
In Figure 2, the source [PERSON] first sends the LT-a code under the deletion channel from \(G\) of Equation (4), and sends the non-degree-1 encoding symbol of the LT-a code to [PERSON] through the main channel. [PERSON] receives it and stores it for later use. Meanwhile, [PERSON] performs a secondary LT encoding of degree-1 symbols of the LT-a code, gets the LT-b code and continues to send it to [PERSON]. When [PERSON] receives the LT-b code, LT-b decoding begins, and continues until ACK2 is sent to [PERSON] at the conclusion of decoding. [PERSON] receives the feedback and instead sends a non-degree-1 symbol code of the LT-a code to [PERSON]. At this point, [PERSON] executes LT-a decoding using the recovered degree-1 symbols and the received LT-a code. [PERSON] instantly stops transmitting any encoded data after the feedback message ACK1 is sent to it once all the decoding is finished. Meanwhile, during [PERSON]'s decoding process, [PERSON] eavesdrops on the encoded symbols through the eavesdropping channel, and similarly decodes the degree-1 symbols after the LT-b code is decoded along with the LT-a code. However, due to the randomness of the fountain code, there is a difference between the main channel and the eavesdropping channel environments, which creates uncertainty in the eavesdropper's decoding, causing a difference in the degree-1 symbols decoded by [PERSON] and [PERSON], and an even greater difference when continuing to decode with the LT-a code. The probability of [PERSON] not yet completing the decoding increases when [PERSON] finishes the decoding.  
Figure 1: Eavesdropping channel model based on fountain code.  
Figure 2: Double LT code anti-eavesdropping model based on degree-1.
# Simultaneous Instance and Attribute Selection for Noise Filtering
## 2 Materials and Methods
### Performance Measures  
We analyze the performance of the different methods using three magnitudes: instance retention ratio, feature retention ratio, and classifier error. Let \(T\) be the instances in the training data described by a set of features \(A=\{A_{1},\dots,\ A_{n}\}\), and let \(P\) be the set of instances returned by the simultaneous instance and feature selection algorithm, described by a set of features \(B\subseteq A\). The instance retention ratio is given by:  
\[InstanceRetention=\frac{|P|}{|T|} \tag{1}\]  
The feature retention ratio is given by:  
\[FeatureRetention=\frac{|B|}{|A|} \tag{2}\]  
Finally, the classifier error is computed as the ratio of incorrectly classified testing instances. Let \(X\) be the testing set, where each instance \(x\in X\) has a true class label \(\alpha(x)\). The classifier error is computed as:  
\[Error=\frac{|\{x\in X\ |\ \alpha(x)\
eq clasif(x)\}|}{|X|} \tag{3}\]  
where \(clasif(x)\) is the class label assigned by the corresponding classifier.  
The main problem we are addressing is to obtain a reduced dataset by selecting a relevant set of features and instances without sacrificing classifier accuracy.  
Considering these three objectives, we treat the methods' performance results as a multi-objective optimization problem with three functions to minimize instance retention, feature retention, and classifier error.  
We execute the experiments on a laptop with a Windows 8 operating system, an AMD Sempron SI-42 processor at 2.1 GHz, and usable RAM of 2.75 GB, and under low priority. This is why we cannot compute the time used by the compared algorithms.
## 4 Discussion
### Effect of Displacement  
Keeping other parameters constant, the displacement ratio of fracturing fluid was changed sequentially to 1, 1.1, 1.2, 1.3, and 1.4 to study the effect of fracturing fluid displacement on the morphology of fracture propagation. The simulation results are shown in Figures 10 and 11.  
From Figures 10 and 11, it can be observed that, under the same injection time, as the displacement of fracturing fluid increases, the width, height, and half-length of the edge fracture increase at the end of the fracturing process, while the width of the mid fracture decreases and the height and half-length increase. Normally, most of the injected fracturing fluid is used for the extension and propagation of fractures, with  
With the increase in displacement, the volume of fracturing fluid injected into the formation within the same time period is larger, resulting in more fluid loss into the formation. This leads to a greater interaction between the fractures. The larger displacement will increase the length and width of the fractures, while the squeezing effect of the edge fracture on the mid fracture becomes more pronounced. As a result, the width of the mid fracture decreases, the half-length increases, and the difference in half-length between the mid fracture and edge fracture also increases. However, the degree of fracturing in the mid fracture is not as significant as in the edge fracture.
# Advanced Technology in ATSC3.0: Boosting Data Signal Rates Using Polarization Properties and FSS +
###### Abstract  
The ATSC 3.0 standard supports ultra-high-definition broadcasting and introduces transmitter identification (TxID) to enable robust operation in single-frequency networks (SFNs). TxID typically relies on long spreading codes to minimize interference, but this approach limits the achievable data transmission rate. To address this limitation, we propose a novel scheme that combines cross-polarization discrimination (XPD) and frequency symbol spreading (FSS) to reduce interference from additional data on standard broadcast signals without increasing the system complexity. In the proposed system, vertically polarized antennas transmit standard broadcast signals, while horizontally polarized antennas transmit additional data. FSS utilizes orthogonal Hadamard codes in the frequency domain to enhance signal robustness in multipath fading environments. The simulation results demonstrate improved bit error rate (BER) and throughput under varying XPD conditions (5-15 dB), with further gains achieved through the use of longer spreading codes and adaptive modulation. The proposed method requires only minor hardware modifications and is fully compatible with existing ATSC 3.0 infrastructure.  
ATSC 3.0; TxID; XPD; FSS Article
# Line Matching Based on Viewpoint-Invariance for Stereo Wide-Baseline Aerial Images
## 5 Discussion
### The Effect of Building Distribution on Line Matching  
It is worth mentioning that internal flat areas were chosen (the quadrilateral areas formed by green lines and the image boundary) in the last three image pairs and the outer fluctuant areas with higher elevation difference (the remaining regions outside the quadrangles) as the studied objects. Mismatched line segments of these two different areas are counted from the matched images, as shown in Figure 16. First of all, it can be seen that the numbers of total matched segments in the outer areas are far less than those in the internal areas, but their incorrect rates are as follows. For image pair 2, the number of totally mismatched line segments is 38, and the mismatched number in the outer area is 25, occupying 65.8% of the total incorrect matches. For image pair 3, the total incorrect matches are 52, and incorrect matches in the outer area are 43, occupying 82.7%. For image pair 4, the total incorrect matches are 42, and incorrect matches in the outer area are 31, occupying 73.8%.  
It can be seen that the incorrect rates are higher in the fluctuant area than those in the flat area, which is very low. This proves that the rectification effect is obvious for the flat area using the perspective projection model. This can greatly eliminate the distortion of the viewing angle on this flat projection surface, thus improving the line matching effect of the building edges. Although incorrect rates of the fluctuant regions on the rectified images are high, the matching effects are still improved relative to the original images. For these three image pairs, the number of correct matches in fluctuant regions is respectively increased from 21 to 90 in image pair 1, from 0 to 97 in image pair 2, and from 0 to 100 in the image pair 3, as shown in Figure 17.
## 4 Experimental Results
### Effects on Parallel/Distributed Environments  
In this subsection, we performed experiments with a large dataset in distributed settings making use of data parallelism. Data parallelism is a deep learning method that replicates the model to multiple machines when a dataset is large and performs training by dividing a batch of data into each machine, calculating gradients in parallel, and then  
Figure 9: Effects of the bundle_ratio on training accuracy as time progresses.  
aggregating them [24]. As the shuffling scheme proposed in this article deals with the order in which to train the dataset on a single machine each epoch, it can be performed after data parallelism is adopted.  
To see the effects of our scheme on parallel/distributed environments, we performed experiments with a large dataset SUN397 as the number of nodes increases. Note that SUN397 (Scene Understanding 397) is an image database that contains 397 categories of scenes and 108,754 images in total [25].  
After assigning a subset of the dataset to each node, we selected data to be trained through local shuffling and examine the performance of the buffer cache during training for five epochs. The size of the buffer cache in this experiment is set to the 50% of the full dataset size, and LRU is used as the caching algorithm. Table 1 shows the hit ratio of the buffer cache with the proposed scheme and the original random shuffling method. As shown in the table, the proposed scheme consistently performs better than the method that does not use it, implying that it scales well. Specifically, the hit rate is in the range of 11.39% to 12.70% in original shuffling, whereas the proposed scheme shows the hit rate of 36.14% to 37.76% regardless of the number of nodes used.
## 5 Energy Optimization Approach Based on MAC/Routing Cross-Layer (EOAMRCL)
### Incorporating Node Paring in CSMA/CA and NAV (MAC Layer)  
In our protocol, we make several key assumptions: the nodes in the network are randomly dispersed, and at each iteration, all the sensors gather data and transmit it to a central BS. The sensor nodes relay their position data to the BS, enabling the formation of clusters. Within these clusters, nodes that are within the intra-cluster transmission range and of the same application type are connected in pairs based on minimal distance, using broadcast matching information shared with all nodes in the network (Figure 1). This connectivity ensures that nodes become aware of one another's presence and positions, facilitating efficient communication within the network.  
A significant feature of our approach is the alternation between \"Sleep\" and \"Wake\" modes within a single communication period. In sleep mode, nodes conserve energy by  
Figure 1: Clustered network topology.  
not communicating with CHs, resulting in minimal energy dissipation (Figure 2). This reinforces the energy-saving benefits of the proposed approach. Unpaired nodes, however, operate continuously in active mode until their energy is depleted, highlighting the critical importance of energy conservation in the network. If the initial node in a pair is closer to the sink than its associated node, it will transition to wake-up mode, also known as active mode. During active mode, the node collects data from its environment and transmits it to the CHs. Meanwhile, the associated node's transceiver will enter sleep mode and remain powered off during this time, conserving energy. In subsequent iterations, nodes in active mode will switch to sleep mode, and those in sleep mode will become active, ensuring a balanced energy consumption across the network.  
Figure 3 shows the integration of a sleep mode into the CSMA/CA mechanism. This integration primarily aims to enhance energy efficiency, which is crucial for the longevity of WSNs. By incorporating sleep mode, nodes can significantly reduce their energy consumption when they are not actively transmitting or receiving data. This approach helps in maintaining the balance of energy consumption across the network. Nodes can be paired to alternate their active and sleep cycles, ensuring that no single node is overburdened, thereby preventing early depletion of individual nodes' energy resources.  
Figure 3: Integration of sleep mode in CSMA/CA mechanism.  
Figure 2: Node paring.  
In traditional CSMA/CA, the Network Allocation Vector (NAV) is used to indicate the duration that the channel will be occupied. This helps prevent collisions by informing nodes of the ongoing transmission duration. In the proposed CSMA/CA mechanism with node pairing and sleep scheduling, the NAV must be adapted to account for the new sleep/activate mode and node pairing dynamics. Below is a conceptual update to the NAV mechanism (Figure 4).
# Acceleration of a Production-Level Unstructured Grid Finite Volume CFD Code on GPU
## 4 Data Locality Optimization
### Grid Reordering  
Due to the irregular shape of the unstructured grid, the numbering index span between adjacent grid elements is large. Therefore, when accessing data, the caching system may not be able to effectively cache the required data, resulting in low memory access efficiency. Reordering the grid elements is a way to improve the access pattern in face-based loops by minimising the distance between memory references when gathering and scattering data from and to pairs of cells that share a face [27]. Moreover, it does not alter the shape and distribution of the grid, so it will not affect the reliability of CFD calculations. In this work, three reordering methods, including Cuthill-McKee [28], space-filling curve [29], and graph partitioning [30], were implemented. Figure 5 shows the system matrix form obtained by reordering a grid using different algorithms, where the non-zero block of the matrix is represented by a filled rectangle. It can be seen that after reordering, the index between adjacent grid cells is closer, and the bandwidth of the matrix is significantly reduced. Among the three methods, the Cuthill-McKee method had the best result. Detailed comparisons of the three reordering methods will be provided later.  
We renumbered the cells using the above bandwidth-minimization techniques then subsequently renumbered the faces according to the minimum cell number on each face, as shown in Figure 6. All of these renumbering algorithms are of complexity O(N), or at most O(Nlog(N)), and are well worth the effort [31].  
Figure 4: Hyperplane partition for unstructured grid. (**a**) Preliminary partition into hyperplanes. (**b**) Final groups. The numbers in the figure represent the number of the group. Elements belong to the same group are marked with same colour.
## 2 Materials and Methods
### Data
#### 2.2.2 TROPOSIF from TROPOMI  
We used TROPOSIF from the TROPOMI spectrometer onboard the Sentinel-5 Precursor satellite as our _SIF_ data source [23]. The Sentinel-5 Precursor has a wide swath of 2600 km and a 16-day orbital period with an overpass time of 13:3 with the ability to provide nearly daily global coverage. Only TROPOSIF L2B data are available at [[https://s5p-troposif.novelits.fr](https://s5p-troposif.novelits.fr)]([https://s5p-troposif.novelits.fr](https://s5p-troposif.novelits.fr)) (accessed on 20 December 2021) from May 2018 to November 2021. The TROPOSIF instantaneous estimates are derived from a data-driven approach based on a linear forward model fitting Top of Atmosphere (TOA) radiance in the far-red spectral region including SIF_735 from window 743-758 nm and SIF_735 from window  
Figure 1: The land cover types of Henan Province. Here, the land cover types are from the MCD12Q1.006 land cover dataset (International Geosphere-Biosphere Programme (IGBP) classification scheme) for the year 2019. The abbreviations are as follows: DBF, deciduous Broadleaf Forests; MF, Mixed Forests; CSH, Closed Shrublands; OSH, Open Shrublands; WSA, Woody Savannas; SAV, Savannas; GRA, Grasslands; WET, Permanent Wetlands; CRO, Croplands; URB, Urban and Built-up Lands; CNV, Cropland/Natural Vegetation Mosaics; PSI, Permanent Snow and Ice; BAR, Barren; WAT, Water Bodies.  
735-758 nm [10]; the daily average SIF_Corr_735 and SIF_Corr_743 were also created using [PERSON]'s method [6] to account for variations in overpass time, length of day, and solar zenith angle. Compared to the earlier _SIF_ observation satellite instruments, TROPOMI has relatively high spatial and temporal resolution and a dense sampling strategy. Because SIF_Corr_743 (SIF_743) is more robust to atmospheric effects compared to SIF_Corr_735 (SIF_735) [10], we selected SIF_Corr_743 as our model output variable. TROPOSIF data were spatially and temporally aggregated to 10 km 16-day grid cells to match the aggregated MODIS data. To control _SIF_ data quality and reduce uncertainties, the grid cells with less than five TROPOSIF soundings and the grid cells labeled as urban and built-up lands, permanent snow and ice, barren, and water bodies were also excluded. For a detailed description of TROPOSIF data, please refer to [[https://s5p-troposif.noveltis.fr](https://s5p-troposif.noveltis.fr)]([https://s5p-troposif.noveltis.fr](https://s5p-troposif.noveltis.fr)) (accessed on 20 December 2021).