esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Nonuniform Background Elimination
### Gray-Scale Morphological Operation
Since the size of \(B_{b}\) in the erosion process is larger than the size of \(\Delta B\) in the dilation process \((Q>L)\), it reduces the nonuniform background region that needs to be eliminated. Consequently, we reuse \(B_{b}\) to execute a dilation operation on image \(F_{2}\) as shown in Equation (13). In short, the nonuniform background region that needs to be eliminated will not change. \[F_{3}=F_{2}\oplus B_{b}=\max\{F_{2}(i-m,j-n)+B_{b}(m,n)\big{|}(i-m),(j-n)\in D _{F_{2}},(m,n)\in D_{B_{b}}\} \tag{13}\]  
The above is the case when targets are fully or partially in the definition domain of the inner structural operator \(B_{i}\). However, if there are no target pixels in the definition domain of \(B_{i}\), at this time, the relationship of this substitution is uncertain. Therefore, we take the minimum value of \(F_{3}\) and the original image \(F_{0}\) as shown in Equation (14). \[F_{4}=\min(F_{0},F_{3}) \tag{14}\]  
Figure 6: Morphological operation results. (**a**) Original real surveillance image; (**b**) dilation operation result after Equation (11); (**c**) erosion operation result after Equation (12); and (**d**) minimum value result after Equation (14). The result is shown in Figure 6d, where \(F_{4}\) is the final nonuniform background needing to be eliminated. Based on the IBSGM method, we can eliminate nonuniform backgrounds accurately while retaining stars and space targets.
## 3 Methodology
### Performance Measurement Metric  
The evaluation metric that can most intuitively indicate the performance of a model is accuracy. Accuracy represents the fraction of the total samples that was correctly classified by the classifier, i.e., the ratio of classifying true as true and false as false correctly. However, when the number of data per class is imbalanced as in this study, the accuracy is relatively high when predicting the classes of large numbers, but the performance for the classes with small numbers is low. Therefore, in this study, the F1 score is used as a performance metric. Before explaining the F1 score, it is necessary to understand precision and recall. Precision, also known as the true positive rate, refers to the proportion of positive classes actually predicted positive. Figure 3 is a confusion matrix and Equation (1) is the equation for precision. Here, TP stands for true positive, denoting the number of predictions where the classifier correctly predicts the positive class as positive. FP stands for false positive, denoting the number of predictions where the classifier incorrectly predicts the negative class as positive. Recall is also called the true positive rate and refers to the ratio of all negative samples correctly predicted as negative by the classifier. In the case of precision, what the classifier classifies as true is the criterion, whereas in the case of recall, the actual true is the criterion. Recall is calculated as in Equation (2), where FN is an abbreviation for false negative, which represents the number of predictions where the classifier incorrectly predicts the positive class as negative. The F1 score used as a performance measurement metric in this study is calculated as the harmonic mean of precision and recall. The formula is given in Equation (3).  
\[\frac{TP}{(TP+FP)} \tag{1}\]  
\[\frac{TP}{(TP+FP)} \tag{2}\]  
\[2\times\frac{Precision\times Recall}{Precision+Recall} \tag{3}\]  
Figure 2: Architecture of risk factor recognition model.
# A Review of the Synthesis and Applications of Polymer-Nanoclay Composites
## 2 Synthesis
### In-Situ Polymerization Method
#### 2.3.1 Surface-Initiated Controlled/Living Radical Polymerization (SI-CLRP)
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nanoclay** & **Polymer** & **Clay Structure** & **Reference** \\ \hline MMT & 2,2,2-trifluoroethyl methacrylate & Exfoliated and intercalated & [35] \\ Fe-kanemite & PPy & Exfoliated & [111] \\ MMT & Bisphenol A polycarbonate & Exfoliated & [112] \\ Vinyl clay & PS & Intercalated & [113] \\ Sepiolite & PP & Exfoliated and intercalated & [101] \\ MMT & PP & Exfoliated & [114] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Polymer/nanoclay prepared by the melt-blending method and dispersion states. PPy: polypyrrole; PP: polyppropylene.
## 8 Challenges, Future Directions, and Lessons Learned
### Future Directions
Integration of this novel technology into the 5G network is, thus, an area that requires more research effort [210].
# An Efficient Adaptive Data-Link-Layer Architecture for LoRa Networks
## 6 Conclusions  
In this work, the design and implementation of a data-link-layer architecture based on the LoRa physical layer was presented. The proposed solution is able to manage the spreading factor and transmission power dynamically according to the measured link quality, ensuring efficient utilization of the device and ISM band resources. Along with it, a LoRa parameter calculator was developed to aid the user to configure the physical layer parameters in accordance with the necessities of the target application.  
The developed architecture poses itself as a viable and low-cost alternative to the LoRaWAN stack, where some of its important features are replicated, such as the ability to efficiently manage the network and end node resources. It also upgrades the P2P LoRa link by controlling the end nodes' link budget dynamically and individually, enhancing network robustness by adding downlink ACK packets and allocating resources for contention-free data transmission, so that network performance is not degraded with an increasing number of nodes while complying with the ISM band restrictions.  
Even though the developed work is promising, some improvements can be made, such as the adaptation of the proposed architecture to a mesh network topology for increased network coverage and robustness. The integration of the packet forwarder into the LoRaWAN stack may also provide some advantages, such as easing the interoperability with already-developed applications that use this stack and extending the network coverage. The integration of security mechanisms in the data link protocol should also be a relevant step to improve its usefulness.  
Conceptualization, M.C., S.F.L. and J.A.A.; methodology, M.C. and S.F.L.; software, M.C.; validation, M.C.; investigation, M.C. and S.F.L.; resources, S.F.L.; writing--original draft preparation, M.C., S.F.L. and J.A.A.; writing--review and editing, M.C., S.F.L. and J.A.A.; supervision, S.F.L.; funding acquisition, S.F.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by FEDER through COMPETE2020--Programa Operacional Competitividade e Internacionalizacao (POCI) grant number POCI-01-0145-FEDER-028247 and by FCT--Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.  
The data presented in this study are available within this article.  
Figure 12: Relation between coding rate and ToA for SFs 7 through 9 (with Bw = 125 kHz).  
Figure 13: Relation between coding rate and ToA for SFs 10 through 12 (with Bw = 125 kHz).
# Advancing Nighttime Object Detection through Image Enhancement and Domain Adaptation
## 4 Discussion
### Datasets  
#### 4.4.1 Comparative Experiments on the ExDark [49] Datasets  
We compared our method with state-of-the-art domain adaptation methods, including TDD [33], 2 PCNet [41], and UMT [47], is shown in Table 2. Compared to the TDD [33] method, our method improves the average precision (AP) by 8.09%. Although our AP is 6.15% lower than last year's SOTA algorithm 2 PCNet [41], we outperform 2 PCNet [41] in terms of average precision for small objects (APs), medium objects (APm), and large objects (API). Notably, we achieve a substantial improvement of 69.88% in APs for small objects. We also conducted a visual comparison of the ExDark dataset, as shown in Figure 4.  
Based on the visual results shown in Figure 4, we observe clear advantages of our method in detecting small objects and reducing false positives. The TDD method exhibits a significant number of false positives in this environment, while 2 PCNet demonstrates complete insensitivity to small objects at night. In comparison to TDD and 2 PCNet, our approach exhibits stronger adaptability in low-light conditions, particularly in accurately identifying small objects within complex backgrounds. This is attributed to the effective image enhancement provided by the LightImg module and the application of consistency learning strategies, which enable the model to maintain high detection accuracy across different scenarios.
## 3 DC-SoC Consortium Chain Network Model
### Data Dissemination
#### 3.3.2 Dynamic Network Self-Healing  
Dynamic changes are inevitable during network operation, such as peers entering an inactive state or new peers requesting to join the network. Instability in the network naturally arises from changes in peer states, prompting DC-SoC to implement measures to maintain the stability of the propagation structure. Collectively, these maintenance measures constitute self-healing mechanisms.  
Blockchain networks typically utilize a heartbeat mechanism to monitor peer offline status [29], and DC-SoC adheres to this practice. However, unlike traditional approaches, we posit that most offline peers are only temporarily in a pseudo-dead state and will return to normalcy within a period of time. Hence, to minimize network overhead, DC-SoC designates the offline peer \(a\) as deactivated, temporarily removing it from the propagation structure. If the heartbeat mechanism detects that peer \(a\) is back online within a fixed time period, it reinstates its propagation relationship within the cluster class in which it is located, instead of directly rejoining the network. If peer \(a\) does not come back online after the fixed time period, it is formally expelled from the network.  
Changes in intra-cluster propagation relationships resulting from changes in peer states also require attention. If the deactivated peer \(a\) is a core peer, it becomes essential to transfer its core neighborhood affiliation to another peer within the same neighborhood and inform all parent peers of peer \(a\) in the propagation structure about the change in the core relationship of that neighborhood. The new core peer is then determined through an election mechanism, with users having the option to select either voting or automatic election. If the automatic election is used, the peer with the largest social value with peer \(a\) is selected as the new core peer. Upon peer _a_'s return online within a predefined timeframe, it assumes the role of a regular leaf peer within the corresponding neighborhood. In the case of leaf peers, when a new peer requests to join the network, it is initially added randomly to a neighborhood within a cluster. During the subsequent network update phase, this new peer will be allocated to the appropriate cluster class and neighborhood.
# Improving Low Earth Orbit (LEO) Prediction with Accelerometer Data
## 2 LEO Orbit Prediction Procedure
Strategy 1: Accelerometer data are used with the calibration parameters of biases and scale factors in each axis for one arc, which are named as \(\mathrm{B}_{X}\), \(\mathrm{B}_{Y}\), \(\mathrm{B}_{Z}\), \(\mathrm{K}_{X}\), \(\mathrm{K}_{Y}\), and \(\mathrm{K}_{Z}\). For simplicity and easy reading, ACC_B_K is used in the following study referring to this strategy. Strategy 2: Accelerometer data are used with the calibration parameters of biases in each axis every 60 min, which are named as \(\mathrm{B}_{X}\), \(\mathrm{B}_{Y}\), and \(\mathrm{B}_{Z}\) and scale factors for three directions are equal to one. This strategy is called ACC_B in the following. Figure 2: Timeline of the data process. Strategy 3: Empirical force models are used with the dynamic coefficients of Ca, Sa, Cc, and Sc for along- and cross-track directions (cosine/sine), and a scale factor for the atmospheric drag every one cycle per revolution. EMP is used referring to this strategy in the following. Strategy 1 (ACC_B_K) and 2 (ACC_B) are different at the estimation of calibration parameters of the accelerometer, which are based on the GFZ GRACE Level-2 processing standards document [30; 31]. Strategy 3 (EMP) was conducted to show the precision of predicted LEO orbits with empirical force models. Actually, there is a shortcoming with the method using accelerometer data, for which orbit prediction must be conducted onboard, since real time accelerometer data should be acquired. The empirical method is simple that one can conduct the orbit prediction at the analysis center, then uplink the LEO orbits to the corresponding LEO satellites. The batch least square approach was used to estimate the LEO initial position and velocity as well as dynamic parameters. The force models, observation models, and the parameters to be estimated in LEO POD are listed in Table 1.  
As recorded by the GRACE Science data system monthly report, the Disabling of Supplemental Heater Lines (DSHL) of GRACE-A would cause temperature control on accelerometer to be stopped. The cool down of the accelerometer caused the accelerometer biases to change and the reheating of the accelerometer returned the accelerometer biases to near nominal values after some days, which would affect POD by using accelerometer data. For this reason, these days are excluded in the following analysis, such as the data from 17 th to 21 st January (DOY: 017 to 021, 2007) and from 22 nd to 26 th November (DOY: 326 to 330, 2007). We used the slide window process to simulate the real time process. The length of slide window is equal to the length of prediction time. So, we have 24 sets of results in one day if the length of prediction time is one hour.
###### Abstract  
Since battery systems typically account for over 40% of the cost of an electric vehicle, their mid-life replacements are exceptional. Therefore, the battery's lifespan must exceed that of the vehicle. To ensure long-term and safe use, accurate state-of-charge (SOC) estimation must be maintained throughout the battery's lifespan. This requires appropriate updates to parameters, such as capacity, in the battery model. In this context, dual extended Kalman filters, which simultaneously estimate both states and parameters, have gained interest. While existing reports on simultaneous estimators seemed promising, our study found that they performed well under low levels of battery aging but encountered issues at higher levels. Accurately reflecting the actual physicochemical changes of the parameters in aging cells is challenging for two reasons: the limited number of measurements of terminal voltage available for numerous parameters, and the weak observability of the capacity. Therefore, we combined the simultaneous estimator with a capacity estimator operated separately during charging and a sequential estimator specialized for an enhanced self-correcting model, achieving SOC accuracy within 5% even when the SOH decreased by 30%. However, there is still much work to be carried out to implement sequential estimators in battery management systems operating in real time with limited computational resources.  
lithium-ion battery; battery management system; battery aging; enhanced self-correcting model; dual extended Kalman filter; state of charge; capacity
## 6 Conclusions, Future Works and Limitation
### Limitations and Open Research Issues
* Flexible lightweight phase reconfiguration: The challenge of CSI increases as the transverse size of the IRS increases. Putting cost and computational complexity into account, it is important that flexible and low-complexity algorithms are designed for an IRS.
# Prediction Model for Shield Tunneling Roll Angle and Pitch Angle: A PCA-PSO-LGBM Approach
###### Abstract  
Accurately predicting shield roll angle and pitch angle based on different geological conditions and excavation parameters is significant for adjusting the shield's posture in advance and ensuring safe excavation. Based on this, this paper proposed a prediction model using Principal Component Analysis (PCA)-Particle Swarm Optimization (PSO)-Light Gradient Boosting Machine (LGBM). The composite strata's primary physical and mechanical parameters were obtained through the strata area weighting method, combined with selected adjustable excavation parameters to form the input parameters of the model. The results showed that the PCA-PSO-LGBM model performs exceptionally well in predicting both roll angel and pitch angle, with mean absolute errors of only 0.068\({}^{\circ}\) and 0.048\({}^{\circ}\), respectively, and determination coefficients reaching 0.98 or higher for both. A comparative validation with other models confirmed the superiority of the PCA-PSO-LGBM model. Additionally, the proposed strata area weighting method comprehensively considered the influence of geological factors on the roll angle and pitch angle. Sensitivity analysis revealed that the pitch angle is generally more sensitive to excavation parameters than the roll angle. During the adjustment of shield tunnelling parameters, priority should be given to adjusting hingged pull, hingged thrust, lower cylinder propulsion pressure, and cutterhead speed.  
roll angle; pitch angle; principal component analysis; particle swarm optimization; light gradient boosting machine; composite strata +
Footnote †: journal: Applied sciences  
Article
# Advances and Challenges of Corrosion and Topology Detection of Grounding Grid
## 1 Introduction
Intertwined old and new grids result in a large deviation between the drawing and the actual layout of grounding network, which contributes to the difficulty of fault diagnosis and state assessment of a grounding network. For the grounding project of newly built substations, effective detection methods to judge whether the grounding grid is constructed strictly according to the design drawings are not well-developed. For the fault diagnosis methods of grounding grids such as network method, determining the topological structure of grounding grids in order to establish the diagnosis equation is necessary. Therefore, a considerable amount of attention has been paid to the diagnosis technology of grounding grid topology. Accordingly, the electromagnetic field method, transient electromagnetic method (TEM), and some image processing technologies have been developed in recent years.
## 5 Test Results
### Acquisition Performance Test  
Firstly, we compare the sensitivity performance of the proposed acquisition method with the standalone conventional half-bit acquisition method. According to the analysis in the last section, we select a coherent integration time of 200 ms with two non-coherent combinations for the LEO-assisted method. The total integration time of the two methods is set to 400 ms. A statistical analysis of 100 times detection probability simulations is shown in Figure 14. It can be seen that the proposed acquisition method can achieve a detection probability of 90% even at C/N0 = 15 dB-Hz, which is consistent with the performance analysis result in Figure 11. It also can be seen that the proposed method outperforms the traditional method by about 8 dB. According to the performance analysis of Section 4, the 8 dB gain mainly comes from two aspects. First, without the navigation bit, the longest integrated time in the traditional method is limited to 10 ms, which means 200 ms integration time in total, resulting in 3 dB loss in C/N0 gain. Moreover, the C/N0 in the proposed method is 10 dB higher than that in the traditional method before non-coherent integration, which causes about 5 dB squaring loss in the weak C/N0. Therefore, the profits of the assisted acquisition method on sensitivity is consistent with the analysis above.
# Analysis and Experimental Test of Electrical Characteristics on Bonding Wire
## 2 Bonding Wire Model
### Simulation Model of Single Gold Bonding Wire  
The simulation model of single bonding wire (as shown in Figure 3 was composed using S-loop wire, microstrip lines, chips, substrate and ground. In practice, the bonding wire was embedded in a plastic body. The plastic material was not taken into consideration in order to simplify this model. In actual work conditions, bonding wire usually connects the chip I/O pin to the other chip pins or the external PCB board. Therefore, the two ends of the bonding wires were connected to different materials. This meant that the dielectric constant and the dielectric thickness were different. As the simulation frequency is up to GHz level, Rogers RO4350B was chosen as the substrate in order to reduce substrate dielectric loss. The materials of each part in the simulation model are shown in Table 1. The ambient temperature can be maintained at a constant and the loss factor of materials ignored as well.  
Figure 1: Wire bonding (WB) package structure.  
Figure 2: S-loop wire geometric parameters.
# Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion
###### Abstract  
When extracting land-use information from remote sensing imagery using image segmentation, obtaining fine edges for extracted objects is a key problem that is yet to be solved. In this study, we developed a new weight feature value convolutional neural network (WFCNN) to perform fine remote sensing image segmentation and extract improved land-use information from remote sensing imagery. The WFCNN includes one encoder and one classifier. The encoder obtains a set of spectral features and five levels of semantic features. It uses the linear fusion method to hierarchically fuse the semantic features, employs an adjustment layer to optimize every level of fused features to ensure the stability of the pixel features, and combines the fused semantic and spectral features to form a feature graph. The classifier then uses a Softmax model to perform pixel-by-pixel classification. The WFCNN was trained using a stochastic gradient descent algorithm; the former and two variants were subject to experimental testing based on Gaofen 6 images and aerial images that compared them with the commonly used SegNet, U-NET, and RefineNet models. The accuracy, precision, recall, and F1-Score of the WFCNN were higher than those of the other models, indicating certain advantages in pixel-by-pixel segmentation. The results clearly show that the WFCNN can improve the accuracy and automation level of large-scale land-use mapping and the extraction of other information using remote sensing imagery.  
convolutional neural network; image segmentation; multi-scale feature fusion; semantic features; Gaofen 6; aerial images; land-use; Tai'an 25 November 2019; Accepted: 6 January 2020; Published: 8 January 2020