esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Digital Signal Processing (DSP)
the number of channels without a hit and the MCM average value. Both parameters are very useful for DSP testing and debugging. The SALT generates exactly one data packet every clock cycle. To enable tracking and loss detection in the Data Acquisition (DAQ) system, each packet contains four least significant bits of Bunch Crossing Identification (BXID) counter in the first field of its header. The sole exception is the synchronisation packet (Sync) which contains the full 12-bit BXID value. This high priority packet, similarly to other special packets like HeaderOnly and BxVeto, causes the data to be dropped. The packet type depends mainly on the current TFC command, but the number of hits and free memory space (the next element in the data processing chain) are also taken into account. When a Normal packet is expected but the number of hits (nHits) is larger than 63, a BusyEvent is created instead. The packet builder checks whether Normal or NZS packets will fit into memory, and if not, a BufferFull or BufferFullIN status is created, respectively. The on-chip memory (MEM) collects built data packets. Their size may vary in consecutive clock cycles, from one (e.g., HeaderOnly) to 67 (NZS) 12-bit words. Therefore, the memory works as a derandomising buffer. In the same clock cycle, the serialiser receives a constant amount of data, which size depends on the number of active data links. More details of the memory design, including the feedback to the packet builder, can be found in [25].
# IFF-Net: Irregular Feature Fusion Network for Multimodal Remote Sensing Image Classification
## 2 Materials and Methods  
The primary focus of this section lies in the network architecture of IFF-Net, specifically designed for the classification of multimodal remote sensing data. As illustrated in Figure 1, IFF-Net comprises three distinct modules: feature extraction via residual blocks, irregular feature propagation, and a feature normalization and calibration module. The extraction of features primarily relies on the utilization of residual blocks, which facilitate nonlinear feature propagation within redundant channels when the judgment factor of the BN layer falls below a predefined threshold value \(\theta\). The data at position \(\gamma_{\textit{basic}}\) are transmitted to the present channel when \(\gamma_{\textit{basic}}<\theta\), where \(\gamma_{\textit{basic}}\) represents the judgment factor of the \(c^{\textit{th}}\) channel. Ultimately, the feature normalization and calibration module primarily aims to incorporate identifiable characteristics for feature classification.
## 2 Materials and Methods
### Finite-Difference Discretization  
We apply a finite-difference method to solve Equation (3) on staggered grids (Figure 1). The discretized electric-field components are defined on cell edges and magnetic field components are defined on cell faces [30]. The finite difference discretization for Equation (3) with the complete wave number is similar as that for the static approximation. By applying appropriate boundary conditions, the finite-difference discretization of the Equation (3) results in a linear system:  
\[\mathbf{AE}=\mathbf{B} \tag{7}\]  
with  
\[\mathbf{A}=\mathbf{C}^{\dagger}\mathbf{C}+diag  
where **E** is the discretized vector electrical field. Since **E** is defined on edges, we need to volume average the conductivities of four cells surrounding each edge to get the edge conductivity \(\overline{\sigma}\). **C** is the discrete representation of the curl operator, which maps cell edges to cell faces, and \(\textbf{C}^{\textsf{t}}\) is the adjoint of the discrete curl operator, which maps cell faces back to cell edges. **B** includes the applied boundary condition and source terms for specific EM problems (e.g., for MT only boundary terms are non-zero).  
Equation (7) can be solved by either direct or iterative solvers. However, if the size of Equation (7) is significantly large, e.g., for typical MT problems, direct solvers can be prohibitive in terms of both computing time and memory cost, and iterative solvers (such as bi-conjugate gradient (BiCG) used in this paper) are exclusively used. However, the convergence of solving Equation (7) iteratively can be slow due to the abundant null space of the double-curl operator. This problem can be alleviated by the application of preconditioners. As \(\omega\) becomes very small, such as for long period MT explorations, the numerical solution cannot adequately model the charges accumulated at the discontinuities of conductivity, and this can pose a challenge for iterative solvers to converge. This is commonly improved by enforcing the static divergence correction:  
\[\
abla\cdot\sigma\textbf{E}=0. \tag{9}\]
# Distributed Localization with Complemented RSS and AOA Measurements: Theory and Methods
###### Abstract  
Remarkable progress in radio frequency and micro-electro-mechanical systems integrated circuit design over the last two decades has enabled the use of wireless sensor networks with thousands of nodes. It is foreseen that the fifth generation of networks will provide significantly higher bandwidth and faster data rates with potential for interconnecting myriads of heterogeneous devices (sensors, agents, users, machines, and vehicles) into a single network (of nodes), under the notion of Internet of Things. The ability to accurately determine the physical location of each node (stationary or moving) will permit rapid development of new services and enhancement of the entire system. In outdoor environments, this could be achieved by employing global navigation satellite system (GNSS) which offers a worldwide service coverage with good accuracy. However, installing a GNSS receiver on each device in a network with thousands of nodes would be very expensive in addition to energy constraints. Besides, in indoor or obstructed environments (e.g., dense urban areas, forests, and anyons) the functionality of GNSS is limited to non-existing, and alternative methods have to be adopted. Many of the existing alternative solutions are centralized, meaning that there is a sink in the network that gathers all information and executes all required computations. This approach quickly becomes cumbersome as the number of nodes in the network grows, creating bottle-necks near the sink and high computational burden. Therefore, more effective approaches are needed. As such, this work presents a survey (from a signal processing perspective) of existing distributed solutions, amalgamating two radio measurements, received signal strength (RSS) and angle of arrival (AOA), which seem to have a promising partnership. The present article illustrates the theory and offers an overview of existing RSS-AOA distributed solutions, as well as their analysis from both localization accuracy and computational complexity points of view. Finally, the article identifies potential directions for future research.  
distributed localization; hybrid localization; fifth generation (5G); Internet of Things (IOT); wireless sensor network (WSN); received signal strength (RSS); angle of arrival (AOA) +
Footnote †: journal: Applied Physics Letters  
17 November 2019; Accepted: 24 December 2019; Published: 30 December 2019
## 3 Results
### Mortar Proprieties
#### 3.1.2 Microstructure Properties  
The effect of the SS-PCM incorporation on the hardened mortar microstructure was analyzed by scanning electron microscopic (SEM) observations (Figure 4). The morphology of 28 days aged mortars (M0, M5, M10 and M15) was observed for 2 scales: large field (a), and low field (b).  
The observation of the large field images (a) shows clearly the macroporosity increasing with the SS-PCM incorporation. As a consequence, the SS-PCM addition should generate the modification of the pore size distribution. This should be confirmed by the mercury intrusion porosimetry test. The high macroporosity visible in M15 matrix (a) should have an effect on mechanical and thermal performance.  
There is difference in morphology between M5, M10 and M15 noted in the low field images (b). Furthermore, [PERSON] et al. (2018) observed the partial dissolution of SS-PCM in the cement paste during the mixing phase. This should generate chemical interactions between cement grains and soluble part of SS-PCM, which influences the cement hydration, hence the morphology. The centrifugation extraction of the interstitial fluid of the fresh mortar without (M0) and with SS-PCM (M5, M10 and M15), then ICP-OES analysis (Inductively Coupled Plasma Optical Emission spectroscopy) of extracted solutions would allow to quantity the dissolved part of SS-PCM in each fresh mortar.  
The microstructure of M10 (low field), with needles sharped hydrates looking like ettringle, is very different to others mortars. There also seems to be C-S-H and ettringlets phases visible in the M15 matrix. It is easier to distinguish hydrates in an aerated matrix, such as M15 matrix (less dense, less volume congestion). The hydrates in the other matrices (M0, M5, M10) are less visible because of their high density (volume congestion). Additional EDS on metallized samples are necessary to confirm the hydrate identification.  
Figure 3: Slump and spread diameter of SS-PCM composite mortars.
## 2 Materials and Methods
### Total Lipids and Fatty Acid Profile  
Lipid extraction from breast muscle was performed according to the chloroform: methanol extraction procedure [27]. Fatty acids (FA) were quantified as methyl esters (FAME) using a gas chromatograph GC Trace 2000 (ThermoQuest EC Instruments, Milan, Italy) equipped with a flame ionization detector (260 \({}^{\circ}\)C) and a fused silica capillary column (Zebron ZB-88, Phenomenex, Torrance, CA, USA) 100 m \(\times\) 0.25 mm \(\times\) 0.20 \(\upmu\)m film thickness. Helium was used as a carrier gas. The column temperature was held at 100 \({}^{\circ}\)C for 5 min, then raised 4 \({}^{\circ}\)C/min up to 240 \({}^{\circ}\)C and maintained for 20 min. The individual fatty acid peaks were identified by comparison of retention times with those of known mixtures of standard fatty acids (37 Component FAME MIX and docosapentaenoic acid (cis-7,10,13,16,19), Supelco, Bellofonte, PA, USA) run under the same operating conditions. Results are expressed as percentages of the total FA identified. To assess the nutritional implications, the ratio of n-6 PUFA to n-3 PUFA (n-6/n-3) and the ratio of polyunsaturated fatty acids (PUFA) to saturated fatty acids (SFA) (P/S) were calculated. Moreover, to evaluate the risk of atherosclerosis and the potential aggregation of blood platelets, the atherogenic index (AI) and the thrombogenic index (TI) were calculated according to the formulas suggested by [PERSON] and [PERSON] [28].
# Location-Based Relay Selection in Full-Duplex Random Relay Networks
## 2 System Model  
We consider a spectrum-sharing relay network in which all nodes utilize the same spectrum for communication. This network is illustrated in Figure 1. Also, the notations used in this paper are summarized in Table 1.  
We assume that the transmitter node sends signals to the receiver node with equal transmission power. In addition, we consider path loss and Rayleigh fading for the propagation channel model.  
Let us assume that the transmitter nodes are distributed according to a Poisson point process (PPP) with a spatial density of \(\lambda_{\text{Rx}}\) over a finite two-dimensional plane [34]. Each transmitter node establishes a communication link with its corresponding receiver node. It is also assumed that some direct links between the transmitter node and receiver node experience outages due to unexpected channel impairments. To ensure a reliable communication link, those transmitter nodes exploit a relay node selected from a set of candidate relay nodes, which are distributed according to a PPP with a spatial density of \(\lambda_{\text{Rx}}\).  
We assume that the relay nodes adopt the DF protocol to convey the data from the transmitter node to the receiver node. In addition, each relay node is equipped with SIC capability, which facilitates FD transmission. Hence, relay nodes can simultaneously transmit the signal received from the transmitter node to the receiver node while receiving the signal from the transmitter node by performing SIC.  
During FD transmission, the relay node suffers from residual SI due to imperfect SIC. Regarding the residual SI, we consider a constant value in this paper for simplicity of analysis [35]. However, note that the analysis of its impact can be extended by averaging over the distribution of the residual SI.  
Transmitting nodes, including transmitter nodes and relay nodes in FDRRNs, generate interference to the receiving nodes at each relay hop. Let us assume that the transmitting nodes of the first and second relay hops follow a PPP \(\mathbf{\Pi}_{1}\) with a spatial density of \(\lambda_{1}\), and a PPP \(\mathbf{\Pi}_{2}\) with spatial densities of \(\lambda_{1}\) and \(\lambda_{2}\). Additionally, we assume that the interference is independent of different times and locations [36].
# Analysis of Precise Orbit Determination for the HY2D Satellite Using Onboard GPS/BDS Observations
## 2 Materials
### Data Collection  
For evaluating the POD performances of HY2D, we selected spaceborne BDS data from July 6 to July 19, and GPS data from July 20 to August 10 to conduct experiments. Here, Figures 2 and 3 present the number of BDS and GPS satellites observed on a day, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Date** & **Time** & **Roll** & **Pitch** & **Yaw** \\ **(Year/Month/Day)** & **(Hour/Minute/Second)** & **(deg.)** & **(deg.)** & **(deg.)** & **(deg.)** \\ \hline
2021/7/10 & 19:20:56.322 & \(-\)0.0770 & \(-\)0.0385 & +88.0330 \\
202  
As can be shown from Figure 2, most of the epochs have at least four BDS satellites available, and there are an average of five satellites. However, the smallest is 2, and the biggest is 9. As can be seen from Figure 3, compared to BDS, the usability of satellites for GPS is much better, and most epochs have more than five GPS satellites, and the maximum is up to 10 GPS satellites. This is mainly attributed to the GPS constellation consisting of 32 MEO satellites, more than the current BDS constellation, and the fact that most BDS, GEO and IGSO satellites are located within the Asia-pacific region.
# Uncertainty in Automated Ontology Matching: Lessons from an Empirical Evaluation
## 6 Discussion
### Granularity  
First of all, the different granularity characterizing the raw datasets definitely plays a role, as the performance of the current ontology matching tools generally decrease when dealing with ontologies addressing a different level of abstraction/detail.  
An example is reported in Table 11, where country names (e.g., _Sudan_) and parts of countries (e.g., _South Sudan_) coexist. In the first example in the table, \"Sudan\" in \(\mathcal{O}_{1}\) is considered equivalent to \"Sudan\" in \(\mathcal{O}_{2}\), which is false in this case; indeed, Sudan in \(\mathcal{O}_{2}\) is actually North Sudan, and Sudan in \(\mathcal{O}_{1}\) is the former Sudan that is composed of both the northern and southern parts. The entities \"Sudan\" and \"South Sudan\" from \(\mathcal{O}_{2}\) should rather be sub-entities of \"Sudan\" from \(\mathcal{O}_{1}\) (using a _subsumption_ relation, not an _equivalence_ relation). Ontology matching tools cannot identify such _subsumption_ correspondences between entities.  
Similar errors may be hard to predict, as \(\mathcal{O}_{1}\) and \(\mathcal{O}_{2}\) do not present different granularity in general terms. A more appropriate level of data semantics may contribute to mitigate uncertainty by enabling ontology matching tools to work on a more extensive and accurate knowledge-base.
# Improving the Accuracy of Predicting Bank Depositor's Behavior Using a Decision Tree
## 4 Discussion
[15] proposed a model based on a SMOTE algorithm to handle imbalanced datasets. SMOTE is an oversampling technique and it was applied on the minority class in order to create an increment of the data. After the SMOTE method was applied on the imbalanced dataset, an increment in the minority class was accompanied by no change in the majority class. The results showed that, the SMOTE method can solve the imbalanced data problem. The results from comparing the proposed methods with J48, rotation forest, Bayes net, Naive Bayes, MLP and RBF neural network were presented, and J48 achieved 89.43% accuracy on the bank direct marketing dataset [11]. The achieved metrics are compared with the ones obtained by other studies in Table 4.  
In summary, the contribution of this research is as follows: we addressed the imbalanced issue of the dataset and improved prediction results after selecting 10% of the full dataset [11]. A resampling technique was used to handle this problem and complexity of data computing. After using a supervised resampling technique, the minor class increased (over sampling) and the major class decreased (under sampling) at the same time. This technique was effective for the J48 classifier of the bank direct marketing imbalanced dataset [11] to more accurately predict the resample technique, combined with feature selection, and applied to find the minimum subset of features that captures the related attributes of a dataset in order to enable sufficient classification. Feature selection is a technique used to remove the irrelevant features that are not practical for making the learning algorithm operate faster and more efficiently [29]. We identified the most promising predictive input: 12 features out of the 20 input from J48 model. As a result, the dimension reduction increased the effectiveness of the three metrics that we used in this paper. Then, we could further improve the accuracy rate and even the highest speed of prediction. Finally, we highlight that, from a managerial perspective, our best approach using J48 with resamplingand feature selection resulted in the lowest values of false positives and false negatives. This is especially important in the telemarketing business, as false positives represent cases in which calls are made (consuming both time and money) needlessly, since the contacted customers did not subscribe to the product, whereas false negatives are even worse for marketing managers, since these represent cases where the model suggested that the customers would not subscribe to the deposit, which was actually not true, and implies the loss of business opportunities.
# Image Quality and Information Parameters of Electronic Portal Imaging Devices
## 2 Materials and Methods
The SNFR can be derived straightforwardly from the CTF and NPS and operates as a comprehensive image quality index associated with the signal-to-noise ratio in the spatial frequency domain [3]. \[\mathrm{SNR(f)}=\frac{\left(\mu\mathrm{CTF}_{\mathrm{norm}}(f)\right)^{2}}{ \mathrm{NPS(f)}} \tag{5}\]  
According to [PERSON]'s theory, the image IC was calculated using the MTF and the NPS [9; 10]. \[\mathrm{IC}=\pi\underset{0}{\overset{\infty}{\underset{0}{\int}}}\log_{2} \Biggl{\{}1+\frac{\left[\mathrm{\mu\cdot MTF(f)}\right]^{2}}{\mathrm{NPS(f)}} \Biggr{\}}\mathrm{fdf} \tag{6}\]  
The SNFR and IC are holistic parameters calculated by means of the MTF, CTF and NPS. They both provide information about the SNR in the spatial frequency domain, thus enabling the determination of the imaging capabilities of the EPID. The phantom used in this work does not incorporate enough spatial frequencies to accurately calculate the MTF of the system. A method proposed in the literature [3] has been used where the CTF values, calculated by Equation (2), were extrapolated at higher spatial frequencies. This method allowed for an approximation of the MTF by using Equation (1), hereafter referred to as the aMTF [3] and the subsequent calculation of the IC. In general, these methods permit a comparative evaluation of EPID imaging performance under different source-to-phantom distances, while maintaining the stability of MUs and DRs, thus providing important information about system functionality and image quality in order to benefit different radiotherapy treatments.
# A Congestion Control Strategy for Power Scale-Free Communication Network
## 3 Congestion Control Strategy for the Power Scale-Free Network
### Critical Packet Generation Rate  
In order to characterize the network capacity, which describes the phase transition of data flow, a related model has been proposed to describe the network congestion, as shown in Equation (5).  
\[\left\{\begin{array}{c}\mathrm{R}_{\mathrm{c}}=\min\{\mathrm{R},\,\eta>0\} \\ \eta=\lim\limits_{\mathrm{t}\to\infty}\frac{\overline{\mathrm{c}}}{\mathrm{R}} \frac{<\Delta\mathrm{W}>}{\Delta\mathrm{t}}\end{array}\right. \tag{5}\]  
where \(\Delta\mathrm{W}=\mathrm{W}(\mathrm{t}+\Delta\mathrm{t})-\mathrm{W}(\mathrm{t})\), \(\mathrm{W}(\mathrm{t})\) represents the total number of packets within the network at time t, \(<\Delta\mathrm{W}>\) indicates the average over time windows of width \(\Delta\mathrm{t}\) and \(\overline{\mathrm{C}}\) is the average delivery capacity of all nodes. Critical packet generation rate \(\mathrm{R}\mathrm{c}\) is an important parameter to measure network capacity. In the free flow state, due to the balance of created and removed packets, order parameter \(\eta\) is around zero. With increasing packet generation rate \(\mathrm{R}\), there will be a critical value of \(\mathrm{R}\mathrm{c}\) that characterizes the phase transition from free flow to congestion. When \(\mathrm{R}\) exceeds \(\mathrm{R}\mathrm{c}\), the packets are accumulated continuously in the network, \(\eta\) will become constantly larger than zero, the inflow will exceed the outflow and some nodes in the network begin to become congestion. The network capacity can be measured by the maximal generation rate \(\mathrm{R}\mathrm{c}\) at the phase-transition point [21]. When a greater critical packet generation rate is reached, it shows that the integrated routing can optimize the traffic distribution and alleviate the number of congested packets effectively, as well as reach a much higher network capacity.  
Figure 7: Delivery path selection of global dynamic routing in different load conditions. (**a**) Initial load of nodes; (**b**) changed load of nodes.