esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Parametric Study of S-Band Antenna
an optimal separation suitable for the resonant size of the two patches, the lower and the parasitic. The shape, depth, and positioning of the notches influence the current distribution on the patches, thus affecting the impedance matching, AR bandwidth, and polarization characteristics. The introduction of lateral slots or notches on the edges of the patch modifies the surface currents and fields generated on the antenna [32]. These slots affect the patch's symmetry, and with proper placement and size, they generate a current distribution that facilitates the excitation of orthogonal field modes. It has been established that these two orthogonal modes possess a 90-degree phase difference and that when these two modes are of equal magnitude, circular polarization is achieved [14; 16]. Furthermore, it has been demonstrated that the axial ratio (AR) bandwidth for circular polarization improves when the size of the slots is increased. The incorporation of notches along the edges of the patch results in the creation of additional electromagnetic coupling points between regions of the lower and parasitic patches, thereby enhancing the coupling between the orthogonal modes within the patches. This, in turn, enables the antenna to maintain circular polarization efficiently over a broader frequency range [33; 34]. As demonstrated in Figure 7b, an enhancement in impedance matching capability is observed with an increase in notch depth (DNP), leading to an expansion in the frequency response. However, when the depth exceeds a certain threshold, these notches have a detrimental effect on the coupling between the patches, necessitating the calculation of design values through fine optimization. The AR bandwidth (Figure 8b) also improves with increasing depth (DNP and DNL), but only up to a certain notch depth value, after which it worsens, reducing the bandwidth. Finally, increasing notch length (LNP and LNL) improves the AR bandwidth (Figure 8a), and therefore, notches with a high length are chosen. Consequently, the length of the notches of the designed antenna is considerably larger than that of conventional circular patches based on lateral slots. The variation in the size and position of the notches, the size of patches, and the thickness of the dielectrics in a stacked patch antenna can have a significant effect on the  
Figure 8: Variation in axial ratio with the size of the notches; (**a**) LNP (length); (**b**) DNP (depth). Figure 7: \(|\)S11\(|\) variation: (**a**) H6 (h—rohacel2); (**b**) depth notch parasitic (DNP). axial ratio and the impedance matching and, therefore, on the polarization characteristics of the antenna. As a result, a thorough optimization process is imperative to ascertain the optimal values of the antenna parameters, thereby ensuring low axial ratios, optimal circular polarization, and the attainment of the desired bandwidth and resonant frequency (see Table 1).
## 4 Existing Jamming Attack Detection Methods and Their Analysis
### Conclusions from Study of Existing Methods  
Some major deductions from the discussion of various jamming-detection methods and their comparative study are: that none of the methods, except [PERSON]'s method, is capable of discriminating various types of jamming; that they all tax the scarce resources of the nodes; that the decision is taken by the nodes based on only its own parameters and those of its neighbors (if spared by the jammer to get these) without being aware of the global scenario, and that their detection-quality is highly dependent on the ability of the victim nodes to continue communicating with their neighbors or the base station despite adverse jamming conditions. The existing methods are capable of detecting all jammers, including military warfare jammers; yet they are not suitable for information war because they are unable to grade the intensity of jamming experienced by different victim nodes, which is vital for further decision making by the battle field commander (or the base station). These inferences lead us to conclude that the existing methods are vulnerable to different jamming attacks under organized information warfare and, as such, there is a need to devise a method to obviate the vulnerabilities and  
\begin{table}
\begin{tabular}{|p{113.8 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|} \hline
**Comparison parameter** & **[PERSON] _et al._** & **[PERSON] _et al._** & **[PERSON] _et al._** & **[PERSON] _et al._** & **[PERSON] _et al._** \\ \hline Jamming detection done by & Individual nodes & Individual nodes & Individual nodes & Individual nodes & Individual nodes \\ \hline Requirement for sampling and threshold fixing & Not required & Required & Required & Required & Required \\ \hline Requirement for neighborhood check or threshold fixing & Required & Required & Required & Required & Required \\ \hline Requirement to communicate with neighbors during jamming & Required & Required & Required & Required & Required \\ \hline Requirement to communicate with base-station to report jamming & Required & Required & Required & Required & Required \\ \hline Ability to discriminate different types of jamming & Able & Unable & Unable & Unable & Unable \\ \hline Node over-load assessment (1: minimum, 5: maximum) & 2 & 5 & 1 & 3 & 4 \\ \hline Accuracy assessment (1: most accurate, 5: least accurate) & 3 & 5 & 2 & 4 & 1 \\ \hline Speed assessment (1: fastest, 5: slowest) & 3 & 5 & 4 & 1 & 2 \\ \hline \end{tabular}
\end{table}
Table 1: A comparative study of existing jamming detection methods.  
improve the detection quality with the added ability of quantifying the intensity of jamming at different nodes.
## 4 Discussion  
To identify how the proposed non-contact method compares to the conventional methods [11; 12; 13; 14], we analyzed the same specimen using a contact ultrasonic transducer instead of the LDV. Figure 7 depicts the visual results of the ultrasonic-wave propagation for the CFRP quasi-isotropic specimen using the conventional method. The contact ultrasonic receiver had a resonant frequency of 200 kHz (M204A, Fuji Ceramics, Fujinomiya, Japan) and was glued to the specimen surface at the position of the CW laser illumination. The received signals were amplified by a preamplifier (A1201, Fuji Ceramics, Fujinomiya, Japan) and bandpass-filtered from 50 to 400 kHz before being stored on the computer.  
The sensitivity of the transducer is high enough for the S\({}_{0}\) mode to be observed. Similarly to the result shown in Figure 4, a phase delay is observed when the A\({}_{0}\) mode reaches the damage. However, the visualized incident wave of the A\({}_{0}\) mode is displayed as a continuous wave rather than a pulsed  
Figure 5: Lamb wave propagation in the impacted carbon fiber reinforced polymer (CFRP) quasi-isotropic laminate without signal averaging. (**a**) 30 \(\upmu\)s; (**b**) 40 \(\upmu\)s; (**c**) 50 \(\upmu\)s; (**d**) 60 \(\upmu\)s.  
Figure 6: Effects of the averaging times on the ultrasonic images at 55 \(\upmu\)s for carbon fiber reinforced polymer (CFRP) quasi-isotropic laminate. (**a**) non-averaged; (**b**) 5 times; (**c**) 10 times; (**d**) 30 times.  
wave, causing the wave reflected from the damage to interact with the incident wave, obstructing its clear identification. Figure 8 compares the waveforms received by both methods when the pulsed laser illuminates the position shown by the black dot in Figure 7a. As expected, using the LDV unit, the incident wave of the A\({}_{0}\) mode has a pulsed shape; therefore, the wave reflected from the damage can be identified. In contrast, when using the contact transducer, the S\({}_{0}\) mode is observed and followed by a ringing phenomenon in the A\({}_{0}\) mode due to the resonance of the piezoelectric transducer. Due to the ringing, the reflected wave cannot be identified. This ringing often causes difficulty in detecting reflected waves from small defects or damage, and additional filtering becomes necessary for detailed analysis of the damage. In contrast, the LDV purely measures the out-of-plane displacement of the pulsed ultrasonic wave and thus does not cause such ringing. It should be noted that this is another significant advantage of the proposed non-contact method for easy inspection.
# On the Design and Implementation of a Blockchain-Based Data Management System for ETO Manufacturing
## 1 Introduction  
Engineer-to-order (ETO) production means that products are engineered and produced after orders have been received. This production method helps to meet the exact specifications of customers [1]. With the increasing personalized needs of customers, more and more enterprises have adopted the production model of ETO for their development, which accounts for an increasing proportion in the manufacturing industry. The ETO production mode is a highly discrete production type and is the most complex in the manufacturing environment. ETO manufacturing faces many challenges in production [2; 3; 4; 5]. Customers' needs are personalized and diversified, making it impossible to reuse the manufacturing process of the produced products. Furthermore, the relevant information about the new product can only be determined after the design drawings are released, including material code, BOM, and process route. This delay also directly leads to the lack of effective control of the collaborative cost of production. In addition, enterprises in the ETO production model have many discrete workshops for processing semi-finished products. Therefore, to meet the customer's customized needs for products and the traceability of the production process, it is necessary to ensure the accuracy of product processing and the credibility of the production process information after the product is completed.  
Through production process data management, not only can the production process of orders be tracked in real-time, but production problems can also be found in time and improved through data analysis. Therefore, the validity of data in manufacturing is crucial to improving production quality and efficiency. Due to the above reasons, eachchange in the process data or transformation event should be traceable in a record that cannot be deleted or changed. Building efficient multi-department collaboration to achieve credible and traceable production process data, and traceability of the production process, has always been a key research issue in the manufacturing field. The blockchain has the characteristic that data cannot be tampered with in a distributed environment, and is thus highly suitable for data management scenarios in manufacturing [6; 7; 8; 9; 10; 11].
## 3 Materials and Methods
### Characterization Methods  
#### 3.3.1 X-ray Photoelectron Spectroscopy (XPS)  
X-ray photoelectron spectroscopy (XPS) measurements were conducted using a Kratos AXIS Supra system from Manchester, UK. The system was equipped with a 500 mm Rowland circle monochromatic Al K-\(\alpha\) (1486.6 eV) source, along with a hemispherical analyzer (HSA) and a spherical mirror analyzer (SMA). Data were collected using a spot size hybrid slot (300 \(\upmu\)m \(\times\) 700 \(\upmu\)m), and all survey spectra were obtained in the \(-\)5-1200 eV binding energy range with 1 eV steps and a pass energy of 160 eV. The analysis utilized an accelerating voltage of 15 keV and an emission current of 15 mA.
# Low-Loss Active Grid Impedance Cancellation in Grid-Connected Inverters with LCL Filter
## 3 System Control and Modelling with the Proposed Compensator  
According to Figure 6b, the operation principle of the proposed inverter system includes (1) output voltage regulation of the LV-VSC and (2) grid current regulation of the GC-VSI. Figure 8 presents the details of such control mechanism. In the following subsection, regulation of the LV-VSC and the system modeling will be introduced.  
Figure 6: Configuration of the proposed grid impedance cancellation. (a) Control block diagram; (b) Circuit diagram.  
Figure 7: Circuit illustration of the proposed low voltage source converter (LV-VSC) to generate \(u_{o,\text{B}}\).
## 2 Preliminaries
### The MM\({}^{*}\) Model  
The MM model is based upon comparison. It conducts a diagnosis by comparing the responses of processor execution testing. The set of all comparisons performed by system \(G=(V(G),E(G))\) can be inscribed as a multiple graph \(M=(V(G),P)\). \(V(G)\) is the set of processors in the system. \(P\) denotes the set of edges and \(P=(x,y)_{z}\). \((x,y)_{z}\) denotes a comparison: the comparison processor \(z\) sends the same test to the two compared processors \(x\) and \(y\) and compares their responses. Side \((x,y)_{z}\in P\) is equivalent to \((x,z)\in E\) and \((y,z)\in E\), where \(x,y\in N_{G}(z)\).  
A symptom of a system is denoted by the symbol \(\sigma\), defined as the set of all comparison results in the system \(G\). A set of all nodes are tested under a \(\sigma\) faulty syndrome, then the set is called a fault set \(F\). Two faulty sets \(F_{1}\), \(F_{2}\) are distinguishable if \(\sigma(F_{1})\cap\sigma(F_{2})=\emptyset\); otherwise, they are indistinguishable. The following are sufficient conditions for two fault sets \(F_{1}\) and \(F_{2}\) to be distinguishable pairs under the MM\({}^{*}\) model. Let \(F_{1}\bigtriangleup F_{2}\) denote the symmetric difference between \(F_{1}\) and \(F_{2}\), i.e., \(F_{1}\bigtriangleup F_{2}=(F_{1}-F_{2})\cup(F_{2}-F_{1})\).  
**Lemma 1** ([24]).: _Let \(G=(V(G),E(G))\) be a multiprocessor system. For any two distinct sets \(F_{1}\), \(F_{2}\subseteq V(G)\), \(F_{1}\) and \(F_{2}\) are distinguishable under the MM\({}^{*}\) model if and only if one of the following conditions is satisfied:_  
1. _There are two vertices_ \(v\)_,_ \(w\in V(G)-F_{1}\cup F_{2}\)_, and there is a vertex_ \(u\in F_{1}\bigtriangleup F_{2}\) _such that_ \((u,w)\in E(G)\) _and_ \((v,w)\in E(G)\)_._
2. _There are two vertices_ \(u\)_,_ \(v\in F_{1}\cup F_{2}\)_, and there is a vertex_ \(w\in V(G)-F_{1}\cup F_{2}\) _such that_ \((u,w)\in E(G)\) _and_ \((v,w)\in E(G)\)_._
3. _There are two vertices_ \(u\)_,_ \(v\in F_{2}\cup F_{1}\)_, and there is a vertex_ \(w\in V(G)-F_{1}\cup F_{2}\) _such that_ \((u,w)\in E(G)\) _and_ \((v,w)\in E(G)\)_._  
Figure 1 shows an illustration for Lemma 1.
# Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets
## Methodology
### Image Classification
\[MNDWI=\frac{b_{green}-b_{swir}}{b_{green}+b_{swir}} \tag{4}\]  
where \(b_{green}\) and \(b_{swir}\) are the green and shortwave infrared bands, respectively. Land-cover classification accuracies have been improved with the conjugative use of optical and microwave datasets [39; 43]. In the current study, it is proposed that the integration of the aforementioned optical indices with temporal microwave data could provide additional information that would improve overall classification accuracy. The 5 Sentinel-1A images from the optimal polarization and the NDVI and MNDWI images were finally layer stacked via bilinear interpolation to produce a 7-band dataset as follows: 9 June image (b1), 8 July Image (b2), 1 August image (b3), 25 August image (b4), 18 September image (b5), 3 August NDVI image (b6) and 3 August MNDWI image (b7). The collected training polygons as stated in Section 2.4 were used to extract backscatter profiles of the targeted land-cover categories using the optimal polarization. Average backscatter values in decibels (dB) were calculated for each training polygon in each class. Then the average temporal backscatter profile was calculated for each land-cover class. Based on the temporal backscatter of the investigated land-cover classes at the optimal polarization and the spatial distribution of NDVI and MNDWI values, a decision rule mapping algorithm is proposed and validated in this study.
# Robust Visual Tracking via Patch Descriptor and Structural Local Sparse Representation
## 1 Introduction
Inspired by the success of deep convolutional neural networks (CNNs) in object recognition and detection, several CF based trackers have been proposed to exploit CNN features [22; 23; 24]. In this paper, we pay attention to devising an effective appearance model and alleviating the model drift. Based on structural local sparse representation in [9], we design the patch descriptor to reflect the degree, to which each patch is contaminated with noise caused by appearance changes. Then, we model the appearance of an object by the combination of the patch descriptor and the patch sparse coefficients. Finally, we carry out tracking within the particle filter framework. Furthermore, we design an outlier ratio to describe the outlier degree of a target object. When the outlier ratio is larger than the threshold, we stop updating the template to alleviate the model drift. The rest of this paper is organized as follows. Section 2 reviews the related work. In Section 3, we first describe target region division, then introduce structural local sparse representation, and finally detail the design of patch descriptor. In Section 4, we present our tracking method within the particle filter framework. Section 5 gives the template update scheme. Experimental results and comparisons are shown in Section 6. This paper concludes with Section 7.
# Scaling of FAPAR from the Field to the Satellite
## 4 Results and Analysis
### Temporal Corrections
#### 4.1.3. Comparing Temporal Interpolation Methods  
The _in-situ_ data after temporal normalization were fitted using a logistic model respectively, which is further applied to temporal interpolation. The derived parameters of \(b_{1}\) and \(b_{2}\) in Equation (7) ranged 12.20-16.52 and \(-\)0.097-\(-\)0.070, individually. The mean of the residual errors of logistic fitting were 0.003, with STD = 0.065. To illustrate the feasibility of the logistic model, the linear and cubic fittings were also performed for comparison. The \(R^{2}\) and RMSE were calculated to measure the goodness of fit.  
As shown in Figure 9, the logistic fit derived the higher \(R^{2}\) values and lower RMSE values than the cubic fit does, in 76.9% and 69.2% of the _in-situ_ sites respectively. Compared to the linear model, the logistic model performed significantly better evaluated in both \(R^{2}\) and RMSE at all sites. For logistic, linear and cubic models, the mean of \(R^{2}\) values was calculated as 0.978, 0.937 and 0.969 respectively, while that of RMSE was 0.070, 0.090 and 0.075. Although the values of the cubic model were very close to those of the logistic model, the logistic model was much more stable, with \(R^{2}\) and RMSE ranging 0.951-0.994 and 0.040-0.107, while the \(R^{2}\) and RMSE values of the cubic model ranged 0.914-0.996 and 0.027-0.150. In short, the logistic interpolation is preferred as it maintains stable and good performance in fitting existing data series.  
Figure 8: Validation of the temporal normalization method using _in-situ_ FAPAR data.  
Figure 9: Comparison of the fitting results from logistic, linear and cubic interpolations in: (**a**) correlation coefficient (\(R^{2}\)); and (**b**) RMSE.
# An Energy Efficient Sink Location Service for Continuous Objects in Wireless Sensor Networks
## 3 Proposed Scheme
### Origin-Based Continuous Object Tracking(Ocot)
#### 3.2.2 Boundary Detection and Tracking  
A continuous object gets a great number of sensor nodes activated so it is more efficient to gather detection information only from boundary nodes rather than all activated sensor nodes. Figure 11 illustrates tracking a boundary of a continuous object where black, hatched, and white cluster heads/nodes are activate, boundary, and inactive status respectively. Figure 12 illustrates the state transition diagram of cluster heads. When a sensor node detects a continuous object in its sensing range, it communicates with one-hop neighboring sensor nodes to ask them about whether they detected the continuous object. If the sensor node has one or more one-hop neighboring sensor nodes with no detection of the continuous object, then it acknowledges that it becomes a boundary node. Boundary nodes have responsibility to report the detection information to its cluster head. Otherwise, a sensor node does not report the detection information even it is activate node, since the boundary of the continuous object already moved through its local area. For example, node \(A\) in Figure 11 was a boundary at time \(T_{1}\) so it reported the detection information to its cluster head. Over time, the boundary has moved. At time \(T_{2}\), node \(A\) is activate node because its all neighbors detect the continuous object, so it does not report data to the cluster head. The number of boundary nodes \(K\) can be estimated by calculating boundary area. For example, the continuous object has circular shape,  
Figure 10: Construction of the origin-based grid structure.  
\[K=\rho\left(1-\frac{\pi(R-r)^{2}}{\pi R^{2}}\right) \tag{3}\]  
where \(\rho\) is the node density, \(R\) is the radius of the continuous object, and \(r\) is the communication range of sensors. If \(R\) is 100 m and \(r\) is 15 m, the 27.75% of detecting sensors become boundary nodes.
## 6 Frequency Tracking
### Proposed Adaptive RFO Tracking Method  
The above methods have a common drawback, which is that too many multiplications cause an excessive burden on the NB-IoT terminals. As the frequency tracking process is a continuous process during the whole communication transmission, we propose an adaptive RFO tracking solution which can effectively reduce the number of multiplications. Considering the features of NB-IoT, our tracking algorithm can be implemented with the following two steps:  
1. Under the condition that initial synchronization and cell search have been accomplished, and NPSS, NSSS and NPBCH have been regenerated. Then, we define the symbol space vector as \(\mathbf{D}=[d_{1},d_{2}\cdots d_{J}]\), where \(J\) denotes the number of available correlation pairs. Then, all the possible correlation pairs are computed beforehand. \[U_{i}^{j}=S_{p,f_{f}}^{*(m_{i})}(k)S_{p,f_{f}}^{(m_{i+d_{j}})}(k).\] (39) These precalculated signals can be represented by \(U_{i}^{j}\), and \(I_{j}\) denotes the number of correlation pairs when the correlation space is \(j\) symbols.  
Once RFO tracking needs to be conducted, the following estimator is applied.  
\[\hat{\varepsilon}_{RFO}=\frac{N}{2\pi D(N+N_{g})}\,\angle\,\left(\sum_{j=1}^{b} \sum_{i=1}^{I_{j}}\left(R_{p,f_{f}}^{(m_{i})}(k)R_{p,f_{f}}^{*(m_{i+d_{j}})}(k)U _{i}^{j}\right)\right). \tag{40}\]  
The adaptive factor is \(1\leq j_{0}\leq J\), which can be adjusted according to the current SNR.  
By means of preprocessing, the proposed method can save nearly one-third multiplications compared with [28]. Besides, the adjustable number of correlation pairs can maintain a balance between performance and power consumption.