esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Opposite Normalized Trust-Region Reflective (ONTRR) Algorithm
### Overall Performance  
For all twelve of the cases in Tables 4 and 5, a Friedman test with a confidence of 0.05 is used to evaluate the overall performances of the five compared algorithms. The Friedman test ranks each indicator and then gives the average rank of each algorithm. It is clear from the left of Figure 8 that the average rank of OINTRR is better than that of ABC-TRR. For the five IEF-based cases excluding TDM, it is evident from the right of Figure 8 that OINTRR still ranks the best on average, followed by ABC-TRR, ISCE, EHA-NMS and R\({}_{cr}\)-IJADE. These results confirm that the proposed OINTRR outperforms other algorithms in extracting the most accurate parameter values of IEF- and LWF-based solar cell models.  
tional effort among all the compared algorithms. (2) In terms of convergence speed and average accuracy of 1000 independent runs, the proposed OUTRR surpasses other algorithms in most cases. The average convergence improvement is 3.03 times in comparison to the best-performing algorithms reported in the literature. (3) In terms of statistics stability of 1000 independent runs, the sum of ranks of standard deviation achieved by the proposed OUTRR is much less than that of the other algorithms. (4) In terms of overall performance, the average rank of OUTRR is better than those of the other algorithms. Given these superiorities, the proposed OUTRR is expected to be the most promising option for solar cell parameter extraction.  
In this paper, the proposed OUTRR algorithm is only used to extract the model parameters from the measured \(I\)-\(V\) data under the given operating condition. For future work, it will be extended to explore the dependence of extracted parameters on the input irradiances and temperatures for fault detection, maximum power point tracking and energy yield predictions of PV systems and solving other complex problems.  
X.G.: Conceptualization, Funding acquisition, Methodology, Supervision, Validation, Visualization, Writing--original draft, Writing--review & editing; Q.H.: Formal analysis, Investigation, Data curation; S.Y.: Methodology, Formal analysis, Investigation; K.Z.: Conceptualization, Funding acquisition, Software, Formal analysis, Writing--original draft, Writing--review & editing. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Key Science and Technology Foundation of Henan Province [Grant No. 222102240031 and 222102240102] and the Scientific Research Foundation of the Higher Education Institutions of Henan Province [Grant No. 22B430017].  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Radiation-Assisted Synthesis of Polymer-Based Nanomaterials
## 6 Controlling Radiation-Induced Grafting on Nanoscale
### Fuel Cell Membranes  
Polymer electrolyte membranes are one of the key components in electrochemical power sources. Fluorinated polymers with their unique properties have been the material of choice to be used as membrane materials. Despite their superior properties, however, they are nonconducting, which is remedied by radiation-induced grafting of polyelectrolytes. A recent review addresses the advances in radiation-induced grafting techniques for the preparation of polymer electrolyte membranes for fuel cell, battery and supercapacitor applications [76].  
Proton-conducting poly(ethylene-alt-tetrafluoroethylene) (ETFE) based membranes were prepared by gamma irradiation grafting of styrene with further sulfonation to make the polystyrene grafts conductive [77].  
Track-etched polymer thin films were also used in developing proton exchange membranes with high conductivity by radiation-induced grafting. The cylindrical pores in TeMs provide short pathways for the proton transfer. Radiation-induced grafting of polyelectrolytes from the inner surfaces of nanopores increases the durability of the pore-filled membranes by eliminating the risk of leaching of physically trapped polyelectrolytes during application. PVDF based proton exchange membranes were prepared by ion-track grafting via a similar approach. Gamma-irradiated track-etched PVDF films were grafted with pure styrene and subsequently sulfonated to form conductive domains within PVDF [78]. Novel proton exchange membranes were prepared via radiation-induced grafting of styrene from ETFE film in the presence of a RAFT agent. Both the molecular weight and polydispersity of graft chains were controlled via RAFT mechanism. Controlling of graft copolymerization enhanced the structural uniformity and performance in terms of proton conductivity compared to similar ETFE membranes prepared by conventional method [79]. It is anticipated that the use of RAFT polymerization in radiation-induced grafting techniques will open the door for designing new tunable membranes in a controlled way by a one-step method under mild conditions.
# Lifetime Maximization via Hole Alleviation in IoT Enabling Heterogeneous Wireless Sensor Networks
###### Abstract  
In Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs), there are two major factors which degrade the performance of the network. One is the void hole which occurs in a particular region due to unavailability of forwarder nodes. The other is the presence of energy hole which occurs due to imbalanced data traffic load on intermediate nodes. Therefore, an optimum transmission strategy is required to maximize the network lifespan via hole alleviation. In this regard, we propose a heterogeneous network solution that is capable to balance energy dissipation among network nodes. In addition, the divide and conquer approach is exploited to evenly distribute number of transmissions over various network areas. An efficient forwarder node selection is performed to alleviate coverage and energy holes. Linear optimization is performed to validate the effectiveness of our proposed work in term of energy minimization. Furthermore, simulations are conducted to show that our claims are well grounded. Results show the superiority of our work as compared to the baseline scheme in terms of energy consumption and network lifetime.  
Keywords:Internet of Things Wireless Sensor Networks (IoTWSNs); traffic load; energy consumption; energy hole; network lifetime; End2 End Delay (E2 ED); routing; linear programming +
Footnote †: journal: Sensors
# WLAN Aware Cognitive Medium Access Control Protocol for IoT Applications
## 5 Proposed WLAN Aware Cognitive MAC (WAC- MAC) Protocol  
In this section, we explain the proposed WLAN Aware Cognitive MAC (WAC-MAC) protocol for IoT nodes to improve energy efficiency and data throughput. WAC-MAC protocol modifies the superframe structure of IEEE 802.15.4 to incorporate sensing time slots for increased reliability. Moreover, an adaptive wake-up scheduling mechanism is introduced to improve the end-to-end delay of IoT data.
# Monitoring of Structures and Infrastructures by Low-Cost GNSS Receivers
## 2 Concepts
### Absolute and Relative Positioning  
When we speak about coordinates, we should always specify what kind of coordinate system we are talking about (in our case a Cartesian system) and the physical reference to which the axes are attached. In fact, in principle we could estimate the coordinates of pointsin the same system in which the orbits of the GNSS satellites are computed, namely the International Terrestrial Reference System [21,22]. This is also called, improperly, absolute positioning. Nevertheless, coordinates of this kind are somehow confusing since they include geophysical motions, such as those of plate tectonics, which are of no interest for our problem. In fact, as already recalled, we want to study local phenomena and, in particular, how the structure moves with respect to its close environment and how points on the structure move with respect to each other. This because deformation implies a stress that, in the long run, can weaken the resilience of the structure. To achieve both results, one can include in the monitoring network a station placed outside the structure in such a way that it is not affected by the same forces causing the structural deformation. Of course, this reference point is usually chosen to be stable with respect to its environment. This point is called master station \(M\) and is used as the origin of our local reference system. Then, by treating the GNSS data in a suitable modality, called relative positioning, we can derive coordinates of the points on the structure in this local reference system.  
This approach, though elementary, has the advantage of allowing the elimination of a number of effects related to the transmission of signals from satellites to receivers and thereby increasing the accuracy of the coordinate estimates.  
Of course, in this setting, any anomalous motion of the reference station \(M\) when the origin of the reference system is attached to it will result in a parallel and opposite inverse translation of the monitored points. We first notice that this is a rigid motion that does not affect the estimate of the strain tensor on the structure. Moreover, the same station \(M\) can be, in turn, monitored by a larger positioning service present in the area if there are permanent stations linked to the ITRS that are not too far away. A design of a star-shaped network is so established, which is typical for monitoring schemes (see Figure 1).  
Indeed, according to the practical case in consideration, the scheme could be amplified and replicated with more stars. One has to remark that usually the distances between bases in permanent stations are farther than the distances between stations in the monitoring network. Since shorter distances between bases provide more accurate positioning, it is useful to fix the local reference system by a master station \(M\) close to the monitored structure [23].
## 3 Key Techniques of near Real-Time Remote Sensing
### Random Access Technology of Remote Sensing Satellites  
In the NRRS architecture based on satellite Internet, remote sensing satellites receive shooting requirements via inter-satellite links, and send back the remote sensing data with high capacity. As shown in Figure 5, when transmitting data, the remote sensing satellite first establishes the connection with an LEO communication satellite through the omni-directional narrow-band link, and facilitates the establishment of high-speed laser links [33]. The high-capacity remote sensing data are relayed by other LEO communication satellites and transmitted back to the ground through feeder links of the gateway station.  
Due to the fact that the satellites are moving at high speed in orbit, access to the satellite Internet of remote sensing satellites requires addressing some key techniques, such as rapid capture and tracking of laser terminals, robust chain building, data-link switching, etc.  
Figure 4: Terminal access process under random access mode.
# A Multi-Stage Method for Logo Detection in Scanned Official Documents Based on Image Processing
## 3 Materials and Methods
### C4.5 Decision Tree  
Logo classification could be performed with numerous expert systems, including neural networks or Bayesian networks. However, in the case of neural networks, they were initially discarded because they require a very large number of images for training, increasing the complexity of learning. Logo detection offers good results using simpler classifiers such as the C4.5-type decision tree [36], as can be seen in this research. The configuration of the C4.5 classifier was as follows: a confidence factor of 0.25 and with a minimum number of instances per leaf of 2. We selected a decision tree, introducing the above attributes, for this problem because it is fast and can perform well in bi-class problems. Bayesian networks were also considered promising for solving our problem; however, they performed worse for our particular problem than a C4.5 decision tree, both in terms of precision, which was 3% worse, and recall, which was 2% worse.  
We chose a low value for the confidence factor to prune the tree more aggressively. Higher values for this confidence factor led to keeping more leaves and branches in the tree, and that did not contribute to its predictive accuracy. For comparison purposes, we chose the well-known SVM bi-class classifier. It is a powerful tool for solving bi-classification problems and also allows a direct comparison against the also bi-class C4.5 classifier [37]. A support vector machine (SVM) with a radial basis function kernel had a 2% worse precision and recall. The radial basis function was chosen because of its outperformance with respect to other classical kernels (linear, polynomial, and sigmoidal) [38]. The regularization cost parameters involved in the SVM have been set after experimentation to a value of 2000, i.e., a value high enough, as reported in the literature [39]. There is no consensus or unique criterion in the scientific community to set the value of this parameter.  
The steps of our proposed logotype-detection algorithm are represented in Figure 5, where the image in Figure 5a is the input document. In Figure 5b, we obtain the result after computing regions of interest marked in light brown, with the algorithm explained in Section 3.3. In Figure 5c, we applied the logo-detection method (Section 3.4) exclusively to the regions of interest, where the candidates are marked in red. These candidates are extracted in Figure 5d. Finally, in Figure 5e, we obtain the valid logo using the feature vector described in Section 3.5. Figure 6 shows different documents, whereas, in Figure 6a,the possible logos are marked in red and in Figure 6b,c, the final result of the logos detected is shown.
# Flame Retardance and Smoke Suppression of CFA/APP/LDHs/EVA Composite
###### Abstract  
A new intumescent flame-retardants (IFR) system including the charing-foaming agent (CFA), ammonium polyphosphate (APP) and modified-layered double hydroxides (LDHs) with different transition metals (Ni, Co, Cu) were used in the ethylene vinyl acetate (EVA) matrix. Both the limiting oxygen index and the vertical burning tests indicate that the CFA/APP system and LDHs have significant synergistic flame retardant effects. The morphology of combustion residues indicates that the many pores of residues can prevent the melt dripping. The thermal analysis shows that the flame retardants obviously enhanced the thermal degradation temperature of ethylene-based chains of the composites. The cone calorimeter test reveals that the CO\({}_{2}\) and combustion residues have an important influence on the reduction of heat release rate (HRR), the smoke production rate (SPR), the production rate and the mean release yield of CO. The composite containing Cu (ELDH-Cu) delivers an 82% reduction in peak heat release rate, while ELDH-Ni has the best CO suppression among all composites. This work not only confirms the flame retardance and smoke suppression of CFA/APP/LDH/EVA, but also provides an effective method for producing new flame retardants and smoke suppressants.  
composite; layered double hydroxides; flame retardant mechanism; production rate of CO\({}_{2}\); mean release yield of CO\({}_{2}\)
## 2 System Model
### Mutualism Scenario  
The HT correctly declares the presence of the BS with probability \(P(H_{1})P_{d}\). Since the spectrum is busy, the HT transmits the BS's signal by backscattering it back to the IR. Thus, HT acts as a relay by assisting the BS. According to the Shannon channel capacity, the number of bits reaching the IR per bandwidth in the mutualism scenario is written as the equation below.  
\[\begin{split} R_{\rm{s_{1}}}&=k_{\rm{BS}}\;\beta_{ 1}\log_{2}\Biggl{(}1+\frac{P_{\rm{BS}}|g_{\rm{BS-IR}}|^{2}}{\sigma_{\rm{IT}}^{ 2}}\Biggr{)}+\beta_{1}\log_{2}\Biggl{(}1+\frac{P_{\rm{BS}}\;z|g_{\rm{BS-HT}}\;g _{\rm{HT-IR}}|^{2}}{\sigma_{\rm{IT}}^{2}}\Biggr{)}\\ &+k_{\rm{HT}}\;\gamma\beta_{2}\log_{2}\Biggl{(}1+\frac{P_{\rm{ active}}^{1}|g_{\rm{HT-IR}}|^{2}}{\sigma_{\rm{IT}}^{2}}\Biggr{)},\end{split} \tag{4}\]  
where \(k_{\rm{BS}}\in(0,1]\) is the active data transmission efficiency of the BS, and \(k_{\rm{HT}}\in(0,1]\) is the active data transmission efficiency of the HT. \(\sigma_{\rm{IT}}^{2}\) is the noise power at IR. In Equation (4). Let us define the number of bits transmitted to the IR during \(\beta_{1}\) as \(R_{\rm{s_{1}}}^{\rm{a}}\), and the number of bits transmitted to the IR during \(\beta_{2}\) as \(R_{\rm{s_{1}}}^{\rm{b}}\).  
\[R_{\rm{s_{1}}}^{\rm{a}}=k_{\rm{BS}}\;\beta_{1}\log_{2}\Biggl{(}1+\frac{P_{\rm {BS}}|g_{\rm{BS-IR}}|^{2}}{\sigma_{\rm{IT}}^{2}}\Biggr{)}+\beta_{1}\log_{2} \Biggl{(}1+\frac{P_{\rm{BS}}\;z|g_{\rm{BS-HT}}\;g_{\rm{HT-IR}}|^{2}}{\sigma_{ \rm{IT}}^{2}}\Biggr{)}. \tag{5}\]  
\[R_{\rm{s_{1}}}^{\rm{b}}=k_{\rm{HT}}\;\gamma\beta_{2}\log_{2}\Biggl{(}1+\frac{ P_{\rm{active}}^{1}|g_{\rm{HT-IR}}|^{2}}{\sigma_{\rm{IT}}^{2}}\Biggr{)}. \tag{6}\]  
Figure 3: Four different operating modes for the HT.
## 2 Experimental Procedure
### Electrode Design  
In this work, the electrical discharge machining was performed with conventional flushing of the copper electrode; the properties of the electrode are shown in Table 2. The conventional electrode was cylindrical in shape (CE), as shown in Figure 2a. The newly design shape electrode was created in the flank area in the region close to the sparking area (Step Cylinder Electrode, SCE), which is vital for self-flushing in the electrical discharge machining process for deep hole drilling, as shown in Figure 2b. For this design, we expect that the step shape of the electrode will increase the escape area for bubble flows, and the debris removed from the sparking area will lead to improvements in machining performance. Both sets of electrodes were produced by a computer numerical control turning machine (PC TURN50, EMCOTRONICS, Austria). The electrode designed in this work can be compared to the conventional electrode consisting of a shank electrode and body electrode, which is shown in Figure 2a. The newly designed electrode consists of a shank electrode, neck electrode, and body electrode, as shown in Figure 2b. The diameter of the electrode in the experiment was varied 3, 6, 9, and 12 mm, and the total length of any electrode was 103 mm, which was controlled in the range of \(\pm 10\)\(\upmu\)m. A feature of the workpiece included the design of a splice by couple plate, which is shown in detail in Figure 2c. The machining test was used in order to evaluated machining performance for any electrode designed, this was repeated 3 times with the new electrode.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Metric Value** \\ \hline Young’s Modulus (GPa) & 128 \\ Poisson’s ratio & 0.34 \\ Electrical resistivity (\(\upmu\Omega\) cm) & 1.673 \\ Density (g/cm\({}^{3}\)) & 8.96 \\ Melting point (\({}^{\circ}\)C) & 1085 \\ Hardness (HV) & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of the copper electrode.  
Figure 2: Schematic of copper electrode and AISI P20 workpiece. (**a**) Conventional electrode (CE); (**b**) step cylindrical electrode, SCE; (**c**) feature of the workpiece by section view.
# DOA Estimation of GNSS Signals Based on Deconvolved Conventional Beamforming  
[PERSON]  
1 College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China; [EMAIL_ADDRESS].  
sparse signals through optimization algorithms for DOA estimation. These methods can achieve accurate DOA estimation at lower sampling rates but are sensitive to signal sparsity representation and the choice of optimization algorithms.  
DOA estimation methods based on deep learning [12; 13; 14], e.g., Convolutional Neural Networks (CNN) [15] and Recurrent Neural Networks (RNN) [16], estimate DOA by learning signal features from large datasets. However, these methods require extensive training data and computational resources.  
The Direction Lock Loop (DiLL) method [17; 18] leverages the symmetrical properties of array pattern diagrams to stabilize DOA parameter tracking through loop design, significantly reducing computation, and it is applied in GNSS signal DOA estimation. However, this method requires coarse estimation information of DOA parameters and has limited resolution, making it difficult to distinguish multiple nearby signal sources.  
To address these limitations, a DOA parameter estimation method based on decon-volved conventional beamforming is proposed. Firstly, a conventional array pattern is constructed in the correlation domain. The array pattern has translation invariance and can be equivalent to the convolution of the basis function and the impulse response function of the signal incident direction. Then, the deconvolution method is used to estimate the precise DOA parameters of the signal sources. The processing frequency of the proposed method is the same as the loop update frequency of the GNSS receiver, typically in the order of milliseconds, making it suitable for both static and dynamic scenarios. However, it cannot be used concurrently with array interference suppression beamforming techniques. This is primarily because such techniques can cause distortions in the array beam pattern, which may lead to inaccurate estimations.  
The contributions of the proposed method are listed as follows: (1) it can achieve accurate blind estimation of multiple signal sources without requiring a known number of targets; (2) matrix inversion is not necessary, resulting in significantly lower computational complexity; (3) precise DOA estimation can be achieved even in low-signal-to-noise-ratio conditions and with a small number of elements in the array.  
The remainder of this paper is organized as follows. Section 2 presents the signal model. Section 3 presents the proposed DOA estimation method. The performance of the proposed method is analyzed in Section 4. Section 5 concludes this paper.
## 5 Our Approach
### Our Evolving Method  
The evolving clustering approach _eClustering_ was proposed in 2004 [47] and it is based on the calculation of the density of the different samples using RDE (which was explained in Section 4.2). _eClustering_ does not require either the number of clusters to be pre-specified or any other user- or problem-specific parameters. It is on-line, one-pass, non-iterative, recursive, prototype-based, and the procedure of _eClustering_ can start from scratch. In addition, in this approach, the same algorithm is extended to detect callers' outliers. This approach proposes to consider an observation as an outlier if its density is lower than 3 \(\sigma\).  
To understand how this approach works, the structure of _eClustering_ extended to detect outliers is summarized as follows:  
```
Read the current observation (\(x_{k}\)) IF \(k\) = 1 THEN  Initialize (the first observation is read) ELSE  Calculate recursively the data density of \(x_{k}\) (\(D_{k}\)) //eClustering:  Update recursively the density of the prototypes IF (\(D_{k}\) > \(D_{max}\)) THEN  a new prototype is created IF (\((x_{k}-x^{*})\) < \(\sigma/2\)) THEN  the corresponding prototype is removed \(x_{k}\) is assign to the nearest cluster //eDetectingOutliers: IF (\(D_{k}\) < 3\(\sigma\)) THEN \(x_{k}\) is an outlier END IF
```  
**Algorithm 1**eClusteringAndOutliersDetection
## 3 Experimental Results and Analysis
### Real Signal Experiments
The positioning errors in the ENU directions are also reduced by 44.9%, 47.1%, and 47.4%, respectively. Moreover, ADBO dramatically decreases the execution time from 13.74 s to 1.95 s, resulting in an 85.8% reduction in computational cost. These results indicate that the search capability of the MRG method is inadequate to realize the performance potential of DPE for the same  
Figure 12: Dataset Characteristics in the dynamic receiver scenario. (**a**) Sky plot. (**b**) \(C/N_{0}\) for each satellite. computational cost, leading to lower positioning accuracy compared with the 2 SP method. In contrast, the ADBO algorithm significantly accelerates the search process for navigation solutions, making it a more efficient solution for the DPE-SI framework.