esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Computational Extended Orthogonal Matched Filter Structure for Multiuser Detection
###### Abstract  
This research provides a novel extended orthogonal matched filter (EOMF) structure that supports multiuser detection. In addition, this structure is designed to suppress increases in computational complexity and circuit scale. In coming years, the number of Internet of Things (IoT) applications that use wireless ad hoc networks is expected to increase. To realize a highly reliable wireless ad hoc network, it is essential to introduce a method for cancelling the interference from other users. This research adopts a method based on code division multiple access (CDMA). However, a CDMA-based method has difficulty with deteriorating performance due to the near-far problem and the increase in the amount of interference as the number of users increases. Another problem is that the spreading sequence of each user is unknown in a wireless ad hoc network. The EOMF effectively removes interference while solving the above problems by combining an OMF and an adaptive array antenna. In this research, an EOMF structure with multiuser detection and lower computational complexity than the conventional EOMF is proposed. By the derived formulas and numerical examples, the proposed structure reduces the computational complexity by approximately 75% compared to the conventional EOMF and obtains sufficient bit error ratio (BER) performance.  
Keywords:wireless ad hoc network; orthogonal matched filter; adaptive array antenna; multiuser detection; low computational complexity +
Footnote †: journal:
# Integrated Information in Relational Quantum Dynamics (RQD)
## 8 Limitations and Future Directions
### Potential Improvements and Future Research  
* **Polynomial-time approximations.** Develop matrix product state (MPS) or tensor network estimators \(\tilde{\Phi}_{\chi}(\rho)\) that truncate bond dimension \(\chi\) to achieve \(|\tilde{\Phi}_{\chi}-\Phi|\leq\varepsilon(x)\) in time \(\text{poly}(n,\chi)\).
* **Beyond bipartitions.** Generalize to _multipartition_ measures by replacing QJSD with Renyi-regularized divergences that admit analytic gradients. This would allow a variational search over \(k>2\) blocks without exhaustive enumeration.
* **Directional integrated information.** Introduce an ordered-partition variant \[\Phi_{\rightarrow}(\rho)\ =\ \min_{A\to B}D^{Q}_{JS}(\rho\|\rho_{A} \otimes\rho_{B|A})\,\] where \(\rho_{B|A}\) is defined via a suitable quantum conditional map (e.g., Petz recovery). This would capture causal asymmetry.
* **Continuous-variable extension.** Replace QJSD by the Bures or Hellinger distance on Gaussian states to define \(\Phi_{\text{CV}}\), computable via symplectic spectra. Such a generalization would cover bosonic modes in quantum optics.  
* **Heuristic and learning-based search.** Employ machine learning models or sampling algorithms to predict high-\(\Phi\) cuts, reducing the search space from \(\binom{n}{\lfloor n/2\rfloor}\) to \(\operatorname{poly}(n)\) candidates in practice.
* **Robust resource-theoretic framework.** Embed \(\Phi\) into a full resource theory of quantum holism, identifying free operations and monotones that allow for closed-form bounds or monotonicity under restricted classes of noise.  
Addressing these points will be the subject of forthcoming work and is expected to broaden the applicability of \(\Phi\) to larger systems, more general settings, and richer operational tasks.
## 3 System Model
### Distribution of Serving BS Distance  
After a typical UE type is classified according to the user association strategy, the probability density function (PDF) of distance \(r\) between this typical UE and its serving BS can be obtained according to Lemma 2 as below.  
**Lemma 2**.: _On the basis of user association probability deduced in Equation (5), the probability density function (PDF) \(f_{l}(r)\) of the distance \(r\) between the typical UE \(l\) and its serving BS can be derived as:_  
\[f_{l}(r)=\left\{\begin{aligned} &\frac{2\pi r\lambda_{m}}{A_{pm}}\exp \left[-\pi r^{2}\left(\lambda_{m}+k_{c}^{2}\lambda_{p}\right)\right],\,\text{ if }l=pm\\ &\frac{2\pi r\lambda_{p}}{A_{pp}}\left\{\exp\left[-\pi r^{2}\left( \lambda_{m}/k_{e}^{2}+\lambda_{p}\right)\right]-\exp\left[-\pi r^{2}\left( \lambda_{m}/k_{p}^{2}+\lambda_{p}\right)\right]\right\},\text{ if }l=pp\\ &\frac{2\pi r\lambda_{m}}{A_{um}}\left\{\exp\left[-\pi r^{2}\left( \lambda_{m}+k_{c}^{2}\lambda_{p}\right)\right]-\exp\left[-\pi r^{2}\left( \lambda_{m}+k_{c}^{2}\lambda_{p}\right)\right]\right\},\text{ if }l=um\\ &\frac{2\pi r\lambda_{p}}{A_{up}}\exp\left[-\pi r^{2}\left( \lambda_{m}/k_{p}^{2}+\lambda_{p}\right)\right],\text{ if }l=up\end{aligned}\right.. \tag{6}\]  
Proof.: The proof is shown in Appendix B.
## 5 In-Line Application of LIBS in Industrial Production
### Refractory Materials  
Many industrial processes at high temperature such as metal making, furnace annealing, and sintering require high-temperature stable refractory products such as bricks, etc. The reuse and recycling of spent refractory materials have high potential to reduce the production of waste and the consumption of primary raw materials. The estimated amount of spent refractories is up to 28 million tons per year [461]. For high-grade recycling, the different types of refractory materials have to be identified and sorted with respect to their chemical composition and impurities have to be removed. The refractory materials are modified on the surface due to the interaction with the high-temperature processed material (e.g., liquid steel). Therefore, the surface layer is not representative for the bulk. LIBS has been used for the analysis of spent refractory materials [353,354]. When several laser pulses are applied on the same position of the specimen the contamination layers on the surface can be removed and the composition of the bulk material retrieved [208].  
A demonstrator of a LIBS-based spent refractory sorting machine is shown in Figure 15a. The LIBS sensor is installed above the conveyor belt. An end-of-life refractory brick with a modified surface layer and unmodified bulk is shown in Figure 15b. Three LIBS measurement spots are marked with a white rectangle.  
The demonstrator succeeded in sorting 30 tons of mixed bricks (doloma, magnesia, and alumina) at a throughput of 10 tons/h (1 brick per second). The sorting accuracy was validated by analysis of the output fractions (magnesite and dolomite). For all oxides analyzed (CaO, MgO, SiO\({}_{2}\), Fe\({}_{2}\)O\({}_{3}\), Al\({}_{2}\)O\({}_{3}\)) the targeted composition of sorted fractions was reached (small exceedance for SiO\({}_{2}\)) [208].
# UAV Formation Trajectory Planning Algorithms: A Review
## 3 Traditional Algorithm
### Probabilistic Roadmap Algorithm  
The Probabilistic Roadmap Algorithm (PRM) is a method based on graph search, which converts continuous space into discrete space. Trajectory planning is mainly divided into two stages. In the offline learning stage, a large number of robot pose points are randomly sampled; then, neighbor nodes are searched and connections are established to construct a landmark map. In the online query phase, a feasible trajectory is searched from the landmark map using a heuristic search algorithm based on the starting point, target point and landmark map information. Figure 9 is a trajectory diagram of a roadmap algorithm.  
[PERSON] et al. (2018) proposed a multi-trajectory PRM-based planning method by establishing a parameter to define three different modes, so that different UAVs in the UAV formation can achieve different mission goals.
# An 8 kb RRAM-Based Nonvolatile SRAM with Pre-Decoding and Fast Storage/Restoration Time
## 3 nvSRAM Peripheral Circuit Design  
### Decoding Circuit  
In this paper, a pre-decoding design is proposed to process the address signal by stages [27]. The first stage performs preprocessing, and then the second stage drives the output signal. Since the capacity of the memory is 8 kb, there are 10 address decoding signals DIN0-DIN7, and eight input and output signals, I/O DIN0-DIN7 and DOUT0-DOUT7, respectively. Considering the need to share the SRAM address signal for decoding control of RRAM and the read-write driving ability of RRAM, the memory array is divided into 128 rows and 64 columns. The control of 128 row address signals requires 7-bit address signals (AIN0-AIN6). The column decoding circuit is jointly controlled by the address signal and I/O signal. AIN7-AIN9 can output eight groups of control signals through decoding of 3-8 decoder. Each group of signals controls an 8-bit row at the same time, and then controls different column signals through eight input or output signals, so as to complete the decoding operation of address rows and columns.  
The row decoding circuit is shown in Figure 5, which consists of a three-input NAND and inverter. The NAND and inverter form the second-stage decoding circuit, which drives WL through a large inverter to improve the driving ability of WL. Because each row of the memory array shares the WL signal, it needs a row decoding circuit with large driving capacity to drive a whole row of WL signals. When the input signals A, B and C are high,  
Figure 4: Data restoration operation.  
Figure 3: Data storage operation.  
and the NAND outputs low, the subsequent inverter outputs high to drive WL. When the input signal changes from A to XA <0:7>, and the input signal is high in turn while B and C are also high, WL <0:7> will be output in turn. Therefore, when XA <0:7>, XB <0:3> and XC <0:3> decode together, 128 WL decoding signals <0:127> can be generated.  
As shown in Figure 6, the column decoding circuit is mainly composed of two transmission gates and two PMOS for pre-charge [28]. The two transmission gates are controlled by the same set of YT and YN. The input of the transmission gate is BL and BLB, and the output is DL and DLB. DL and DLB are connected to the read-write control circuit. EQB is the gate control signal of MP6 and MP5, which controls the pre-charge switch of BL and BLB. When YT is low and YN is high, both transmission gates are on. While EQB is low, BL and BLB are charged to high. When EQB is high, the pre-charge process ends. Because there are 128 memory cells on the same BL, the pre-charge PMOS needs a larger size to ensure that the BL and BLB can be pulled up to high under large load. When the system reads and writes, it can control different column units by selecting different groups of YN and YT are generated by address and clock control signals.
## 3 Stress-Strain Relationship of Basalt-Fiber-Reinforced Loess
### Effect of Fiber Length on Strength  
Figure 4 shows the stress-strain curves for reinforced loess with different _FL_ (4, 8, 12, and 16 mm) and _FC_ (0.2%, 0.4%, 0.6%, and 0.8%) under 50 kPa cell pressure. It can be found that the peak strength of reinforced loess was significantly higher than that of the unreinforced soil, and gradually increased with the increase in _FL_, which exhibited a different trend compared to reference [10]. When the _FC_ was 0.2%, the peak strength of the reinforced soil at 4, 8, 12, and 16 mm _FL_ increased by 36.38%, 69.95%, 73.97%, and 78.87% compared to the unreinforced soil, respectively. When the _FC_ was 0.8%, the peak strength of the reinforced soil with different _FL_ improved by 123.23%, 166.45%, 175.52%, and 179.01% compared to the unreinforced soil. It can be revealed that the peak strength  
Figure 3: Effect of fiber content on stress–strain curves under 50 kPa cell pressure: (**a**) _FL_ = 4 mm; (**b**) _FL_ = 8 mm; (**c**) _FL_ = 12 mm; (**d**) _FL_ = 16 mm.  
of the reinforced soil with _FL_ of 12 and 16 mm increased slightly compared to reinforced soil with an _FL_ of 8 mm. For the actual engineering of subgrade backfill, the _FL_ can be chosen to facilitate the construction. The primary reasons for the above phenomenon is that the contact points between the fiber and soil particles gradually increased with the increase in _FL_, and the tensile effect of a single fiber is more obvious. When the sample is subject to shear loading, the bridging effect of the fiber can form a wider force transmission system, which results in the improvement of the uniformity and integrity of the sample. The transfer effect between the fibers and soil particles occurs, thereby increasing the shear strength of basalt-fiber-reinforced loess.
# An Energy-Efficient and Obstacle-Avoiding Routing Protocol for Underwater Acoustic Sensor Networks
## 5 Performance Evaluation
### Evaluation with Different Parameters
It is very difficult to replace the batteries for the sensor nodes in UASNs, so extending the network lifetime is important. In this paper, the network life cycle is defined as the duration from the network startup to the first sensor node exhausting its energy. The formula for the network lifetime is expressed as:  
\[NL=\frac{\sum_{k=1}^{T}(EX_{k}-ST_{k})}{T} \tag{13}\]  
where \(EX_{k}\) and \(ST_{k}\) are the time at which the first sensor node exhausting its energy at the \(k\)th simulation run and the time at the start of the \(k\)th simulation run, respectively. In this simulation, the results are averaged from a total of 50 runs. The average network lifetime was measured for the EOAR protocol and compared to the HHVBF and VHGOR protocols, as shown in Figure 10. It can be clearly seen from the figure that the network lifetime of EOAR and VHGOR are longer than that of the HHVBF protocol. This is because the HHVBF protocol only depends on the depth information of the candidate forwarding relays to select the best forwarding relay that forwards the packet, whereas the EOAR and VHGOR protocols consider the residual energy of the candidate forwarding relay in selecting the best forwarding relay. In addition, the EOAR protocol can also reduce packet collisions by setting the waiting time, so EOAR has a higher network life time than VHGOR. Figure 10: Average network lifetime versus number of nodes. Figure 9: Energy consumption versus simulation time.
## 1 Introduction
(2) The QoS routing method evaluates the QoS of different routing paths and selects the one that can provide the best service for a source-destination pair. However, the major issue with the QoS routing method is it is difficult to achieve multi-constraint routing, which is a well-known non-deterministic polynomial-hard (NP-hard) problem. If services are not distinguished, services with high QoS needs and high priority cannot be handled in time, resulting in network performance deterioration. Therefore, it is critical to design a routing algorithm that considers the dynamic features of satellite network structure, varied QoS needs of different traffic, and traffic load and capacity of different links [11]. On the other hand, the mentioned works only evaluate their proposed routing algorithms via simulations, which may not be feasible to be implemented in a real satellite network. In order to bridge the gap between simulation and real satellite networks, an SDN-based semi-physical simulation platform is built to evaluate satellite networking and routing in this paper. The major contributions of the paper are summarized as follows:A novel label-based Consultative Committee for Space Data Systems (CCSDS) advanced orbiting system (AOS) data link layer protocol, optimized OSPF protocol, constraint shortest path first-traffic engineering (CSPF-TE), and constraint-label distribution protocol (CR-LDP) protocol are designed for integrated satellite-terrestrial networks (ISTNs). In addition, a simulation platform is built to verify the performance of label-switching-based elastic ISTNs with no restrictions on simulated nodes based on a software environment, i.e., INTEL VxWorks, an embedded real-time operating system;  
We develop the southbound interface of simulated nodes (act as controllers) in INTEL VxWorks to communicate with field-programmable gate array (FPGA) boards (which act as switches) to fully verify the performance of label-switching-based elastic ISTNs, which follows and expands the recommended standard of the CCSDS AOS protocol [12,13];  
The programming codes running in the INTEL VxWorks simulated node could be easily applied to different micro-chip architectures according to the board support packages, e.g., Acorn RISC Machine (ARM), performance optimization with enhanced RISC-performance computing (PowerPC), and microprocessor without interlocked piped stages (MIPS), thus having high compatibility. The remainder of the paper is structured as follows. In Section 2, principles and methods of SDN-based ISTNs are introduced. Sections 3 and 4 give an explicit description of the experimental results in terms of software simulation platform and semi-physical simulation platform, respectively. Finally, concluding remarks and future work are given in Section 5.
# Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series
## 3 Data and Methods  
Land-based pond aquaculture objects can be identified and distinguished from other artificial or natural water bodies (such as rivers, lakes, reservoirs) with radar satellite data on the basis of different backscatter intensities on their structural components (low backscatter on smooth water surface, strong backscatter on surrounding dams, dikes) in combination with size and shape features. The data and methodological workflow for the mapping of coastal aquaculture in this study is based in parts on the processing steps published in [32]. The method was adapted and further developed in order to enable large-scale continental mapping: the processing of the satellite data and the segmentation and vectorization of the aquaculture objects including temporal features were carried out in the JavaScript API of the cloud computing platform Google Earth Engine (GEE). Further post-processing workflows and statistical analysis were carried out with the free and open source software QGIS and with the Python libraries pandas, geopandas, and geofeather (for more details, please see Appendix A).
# Computer Vision and Image Processing Approaches for Corrosion Detection
## 2 Background
### Corrosion Detection Process
#### 2.3.3 Image Segmentation  
The third process is image segmentation, which plays a prominent role in corrosion detection. The corroded regions in an image are detected and isolated in accordance with similarities in discontinuity and criteria. Examples of segmentation techniques include region-based, edge detection, threshold-based, clustering, pattern matching, and artificial neural network (ANN) segmentation.  
* Region-based: This segmentation technique locates connected pixels of similar colors and intensities in an image. Normally, similar pixel regions are classified using some predefined rules between adjacent pixels. The segmented regions are obtained from the neighboring pixels and they are connected to the seed pixels. This technique works better with high contrast images.
* Edge detection: This technique detects object boundaries by locating and identifying points of brightness discontinuities or sharp changes in an image. These points are also called the image edges. Widely used edge detection methods include Canny edge, Prewitt edge, Sobel edge, and Laplacian edge methods. The aim of edge detection methods is to reduce the probability of detecting wrong edges in an image by optimizing edge detection algorithms.  
Figure 2: Corrosion detection processes.  
* Threshold-based: This segmentation method converts color or grayscale images into binary images based on a threshold value. The threshold value is obtained from the original image's pixel intensity. The pixels in the original images are converted for easier image analyzation. This method separates the desired object from the background pixels.
* Clustering: This technique partitions an image into clusters or groups by identifying the relationship between adjacent pixels in an image. Then, groups of similar patterns are clustered to explore during data analysis. The most used clustering methods are the fuzzy c-means, k-means, improved k-means, and hierarchical methods.
* Pattern matching: Pattern matching, or template matching, is a technique that allows template localization in images. The patterns in images are matched to each other in terms of their special features by restricting the searching region. The corroded region can be segmented by matching a predefined template with the small parts of an image. Examples of pattern matching include Hamming distance and Harris corners.
* ANN segmentation: This method uses an encode-decoder structure for the segmentation of 3D images [12]. It works with the height, width, and channel number. The first two dimensions represent the image resolution. Moreover, the third dimension represents the red, green, and blue channels. This segmentation technique uses machine learning in its segmentation process.
## 5 Data Analysis
### Path Analysis  
The different teacher interventions and their influence on the learning performance were considered, for which a PA was chosen. Before the PA, the correlations for the variables of teacher intervention and learning performance were tested. This information was the base input to build the empirical model. It was found that all the correlations were positive and more significant than 0.3 but less than 0.8 (Table 5), which indicates a direct relationship between the different variables. In addition, all correlations were statistically significant (p < 0.1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable Code** & **Mean** & **Standard Deviation** & **Minimum** & **Maximum** \\ \hline
**Student** & & & & \\ \hline VS1 & 19.6629 & 8.43861 & 17.70 & 45.10 \\ VS2 & 60.4714 & 20.64098 & 21.00 & 101.00 \\ VS3 & 290.1286 & 150.75337 & 19.00 & 523.00 \\ \hline VS4 & 204.9857 & 71.73997 & 38.00 & 430.00 \\ VS5 & 80.7214 & 15.89561 & 60.00 & 99.00 \\ \hline
**Professor** & & & & \\ \hline VT1 & 121.2857 & 21.59317 & 89.00 & 164.00 \\ VT2 & 13.2571 & 2.83705 & 10.00 & 22.00 \\ VT3 & 18.3143 & 1.73313 & 18.00 & 20.00 \\ VT4 & 11.7143 & 1.28698 & 11.00 & 17.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Descriptive statistics for the study variables.  
The model tested is shown in Figure 1 with its predictive observable variables (PA) and its mediating or indirect effects. For the statistical significance of the t indices, p < 0.05 was taken as a reference. In addition, multiple fit statistics and indices were calculated to test the theoretical model [30; 31; 32], the chi-square statistic, the Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), Square Root Mean Residual (SRMR), and the Goodness-of-Fit Index (GFI). Thresholds indicating good fit have been established for these indices. The authors [36] have suggested an adequate fit when the CFI and GFI are greater than 0.90, along with an RMSEA and SRMR less than 0.08, and an excellent fit if they are greater than 0.95 or less than 0.05 according to the indices indicated. All analyses were performed with AMOS 25 (Analysis of Moment Structures by IBM SPSS) [37].  
The results obtained after identifying and estimating the model (Figure 1) evaluated the quality of the data's fit to the proposed theoretical model using the thresholds described above as references. In other words, the model was interpreted globally together with the contrast of the hypotheses. The model tested did not show satisfactory values for the model [37], with the data collected \(\chi^{2}{}_{(3)}\)= 11.32; p < 0.05; GFI = 0.942; AGFI = 0.711; IFC = 0.942; RMSEA = 0.202; AIC = 35.36; ECFI = 0.519).