esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
A measurement and process control strategy is presented based on an organizational structure that includes pre-processing, in situ defects or fault detection, in situ continuous feedback control, and signature deviation control. When this organizational structure of [PERSON] et al. and the individual components are examined in more detail, a concept for the in situ defect detection component can be developed on the basis of the current study. For this defect detection concept, certain parameters, as shown in Figure 1, need to be defined and implemented in order to be integrated and processed within an overall process control structure. Figure 1: Concept for in situ defect or fault detection, including sensor concept and defect detection, either by exceeding defined thresholds with a specific measurement method, e.g., laser profilometry, or via image recognition based on deep learning, e.g., anomaly detection. As a non-part of this study, the connection to the next conditional points of a complete process monitoring is then presented. The parameters that are responsible for the defect and the derivation of suitable countermeasures and continuous interaction with in situ process control to modulate process inputs based on measurements. Regarding the failures and defects that can occur in the processing of polyamides as well as metallic powders, there are synergies as well as independent effects. In addition to similar defects like curling, shrinkage, coating failures, part shifting, particle drag, powder contamination, and powder short feed, the manufacturing of metal powders might lead to additional defects like cracks, spatters, and porosity [25; 33; 34]. Figure 2 shows the effect of part shifting during the ongoing sintering process in the FORMIGA P 110 PBF-LB/P system via the EOS (Krailling, Germany) used for this work. As can be clearly seen, this event immediately leads to a complete stoppage of the build job. Previous work has shown several different experimental approaches for the detection of defects in both metallic and plastic powder processing [24; 25; 28; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45]. However, despite the promising results, these are always scientific experiments with expensive measuring instruments or complex experimental set-ups that are not yet suitable for industrial applications. In this paper, a new approach to coating defect detection is proposed by installing a cooled camera system inside the PBF-LB/P system and evaluating the data via deep learning using a custom convolutional neural network (CNN). Since the artificial and reproducible induction of various defects in the field of powder bed fusion represents a major challenge without direct process termination [24; 25; 35], this approach represents a new experimental solution.
## 2 Materials and Methods
### The Prediction Model of ECRNet
#### 2.3.2 Modified Neural Network
The formula for calculating the feature value score is as Equations (11) and (12):  
\[s=\frac{1}{m}\sum_{i=1,j=1,2,\cdots,9}^{m}L(x_{t},x_{k}) \tag{11}\]  
\[L(x_{t},x_{k})=\frac{1}{2}|y-f(x_{t})|^{2} \tag{12}\]  
\(s\) represents the value score of each feature, and \(L(x_{t},x_{k})\) represents the loss value predicted by the random shuffle model for the feature \(k\). The value score of each feature obtained by calculation is shown in Figure 7:  
Through the normalization formula, the value score of each feature is mapped to the range of (0,1) to initialize the parameters of the Proactive Modulation module. The normalization formula is as Equation (13):  
\[s^{\prime}=\frac{s_{i}-\text{min}(s_{i})}{\text{max}(s_{i})-\text{min}(s_{i})} \tag{13}\]  
2. Response Modulation Module  
The input of the Response Modulation Module's different part from the Proactive Modulation Module is the \(x_{t}\) obtained from the feature selection in the feature factor data set  
Figure 7: Characteristic value score of deep ocean layer marine environment. \(X_{t}\), and then one decides whether to enter the response modulation module by activating the function \(s(z)\). The formula of \(s(z)\) is as Equation (14):  
\[s(z)= \begin{cases}0,&z<-200\\ 1\,,&z\geq-200\end{cases}  \tag{14}\]  
It can be seen from Figure 8 that the Response Modulation module is mainly composed of the reward function and suppression function. The first step of it is the same as the Proactive Modulation module to obtain the position state information \(h_{t}\) at the current time as Equation (7). The next step is to process and evaluate location information \(h_{t}\) and \(y_{t}\) by two steps. The first operation is handled by a reward function \(i_{t}\) to obtain compensating information \(I_{t}\). The formula is as Equation (15):  
\[ \begin{split} I_{t}=x_{t}*i_{t}\\ i_{t}=\sigma(W_{i}\cdot[x_{t},x_{t-1}]+b_{i}),\end{split}  \tag{15}\]  
\(i_{t}\) is a number between 0 and 1. The closer it is to 1, the greater the degree of reward for \(x\), and the more information retains from \(h_{t}\). The next operation is handled by the suppression function \(f_{t}\) to obtain the information \(F_{t}\). The formula is as Equation (16):  
\[ \begin{split} F_{t}=x_{t}*f_{t}\\ f_{t}=\sigma\Big{(}W_{f}\cdot[x_{t},x_{t-1}]+b_{f}\Big{)},\end{split}  \tag{16}\]  
The \(f_{t}\) also is the value between 0 and 1. The closer it is to 1, the more information in \(y_{t}\) is suppressed and ignored and the less information is learned and retained by the network. Finally, the updated information \(I_{t}\) and \(F_{t}\) is selected through the output function \(o_{t}\) to obtain the final modified prediction result. This section introduces the framework and prediction process of AUV drift track prediction based on a modified neural network. The structure and prediction principle of each module of the modified neural network is introduced in detail. Then, in the next section, we will verify the improvement of the accuracy of the prediction results based on our mothed through experiments.
# Evaluation of Ionospheric Delay Effects on Multi-GNSS Positioning Performance
## 4 Results and Discussion
### Performance of Single-Frequency SPP
Vertical error: the horizontal and vertical axes represent the universal time (unit: hour) and the error of the U component (unit: m), respectively.
## 2 Related Works
### Performance Measure Approach (PMA) for Reliability Analysis  
Performance measure approach (PMA), which is also called inverse reliability analysis method, generally refers to the problem of solving the value of the limit state function under the given reliability index [18]. In the reliability based design optimization, the inverse reliability analysis problem used to evaluate the constraints is usually expressed in the mathematical form as shown in Equation (9).  
\[\begin{array}{l}\min g(\mathbf{u})\\ \text{s.t.}\ \|\mathbf{u}\|=\beta\end{array} \tag{9}\]  
where \(\mathbf{u}\) is the design variable in the standard normal space, and \(g(\mathbf{u})\) is the limit state function in the standard normal space, \(\beta\) represent the specified reliability index. The practical meaning of Equation (9) is to find the point on the specified hypercircular surface that makes the limit state function get the minimum value in the standard normal space. The specified hypercircular takes the coordinate origin of standard normal space as its center and the specified reliability index \(\beta\) as its radius. The point which makes the limit state function minimum is also called most probable point (MPP) of PMA. PMA plays an important role in reliability based design optimization. Through reliability analysis based PMA, the relationship between deterministic constraints and reliability constraints can be established, which provides a strong support for the decoupling of reliability analysis and optimization calculation [6].  
The essence of inverse reliability analysis is to solve the optimization problem shown in Equation (9). In order to explain its true meaning more clearly, we will take the two-dimensional (containing two variables) problem as an example. For two-dimensional variable problems, the practical significance of the problem described by Equation (9) can be shown in Figure 1.  
Figure 1: PMA theory in standard normal space.  
It can be seen from the figure that the essence of MPP solved by inverse reliability analysis in standard normal space is the intersection of the specified circle and isoline of state function which has the smallest value and the curve with the smallest value is tangent to the specified circle.  
At the same time, when dealing with non-normal aleatory variables, PMA involves fewer nonlinear changes. At the same time, in multidisciplinary system optimization, the PMA method does not need to calculate the specific reliability value and only needs to evaluate the value of limit state function, so the PMA method has high robustness and efficiency for a multidisciplinary system.
## 3 Results
### Effect of Individualized Configuration (LIAC vs. Individually Configured model (IND))  
#### 3.2.1 Aspect 1 and 2: Interaction between Oar Blade and Water Surface  
The _lme-2_ showed a main effect of _Configuration_ for the horizontal movements of the oar blade above the water surface (Q1: \(F_{C}(1,\ 10)=6.01\); \(p=0.034\) and Q2: \(F_{C}(1,\ 10)=5.56\); \(p=0.041\)) (Table 2). The realism rating value for the forces of oar blade sliding (Q1) increased by 11% compared to the LIAC model (\(\mu_{IND}^{\text{Q1}}=6.60\)). Rating of the braking forces increased by 16%. Regarding the water surface interaction forces resulting from the vertical movement of the blade above surface, there was a trend for a main effect of the _Configuration_ for the Question 3 (Q3: \(F_{C}(1,\ 10)=4.86\); \(p=0.052\)). The rating value for the Question 3 increased by 10%. The _Configuration_ did not yield any significance for the Question 4 although the rating value increased by 13%.  
2.2 Aspect 3 and 4: Interaction of Oar Blade and Virtual Water during Sub-Phases of the Rowing Cycle  
The _lme-2_ showed a significant main effect of the _Configuration_ for all four sub-phases of the rowing stroke (for Question 5, 6, 7, and 8, Table 2). Compared to base LIAC model, the configured LIAC model, i.e., IND, was rated more realistic for the drive (Q5: \(F_{C}(1,\ 10)=6.13\); \(p=0.032\)), recovery (Q6: \(F_{C}(1,\ 10)=54.44\); \(p<0.001\)), catch (Q7: \(F_{C}(1,\ 10)=7.21\); \(p=0.023\)), and release (Q8: \(F_{C}(1,\ 10)=6.95\); \(p=0.025\)). The rating values of the drive, recovery, catch, and release were increased by 13%, 28%, 12%, and 17%, respectively.
# Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
###### Abstract
The RF aided method can discriminate sea ice from water with a success rate up to 98.03% validated with the collocated sea ice edge maps from the special sensor microwave imager sounder (SSMIS) data provided by the Ocean and Sea Ice Satellite Application Facility (OSISAF). [PERSON] et al. [40] applied the NN algorithm for sea ice concentration and sea ice extent sensing using GNSS-R data from the FFSCat mission [40]. [PERSON] et al. [30] initially exploited the implementation of the classification and regression tree (CART) algorithm for sea ice classification using GNSS-R observables derived from GNSS-R DDM. The results showed that the FYI and MYI can be classified with an accuracy of 70% and 82.34% respectively. In order to illustrate the ML for sea ice sensing based on GNSS-R, relevant information about the above-mentioned studies is presented in Table 1.  
As one of the most powerful ML algorithms, RF has been widely applied for remote sensing image classification [33; 42; 43]. However, RF has not been considered for classifying FYI and MYI using spaceborne GNSS-R data. In addition, the SVM-based method showed great potential in sea ice detection and classification in some previous studies [33; 38]. Although SVM has been applied to sea ice classification using SAR images [33], the application of SVM to GNSS-R sea ice classification has not been investigated. Therefore, RF and SVM classifiers are adopted in this study to develop algorithms for sea ice classification. The purpose of this research is to demonstrate the feasibility of spaceborne GNSS-R to classify sea ice types using ML classifiers. The spaceborne GNSS-R dataset and reference sea ice type data from the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) OSISAF [44] used in this study is firstly described in Section 2. Then, the theoretical basis and the proposed method for sea ice classification are described with details in Section 3. The sea ice classification results are presented in Section 4 and discussed in Section 5. The conclusions are finally addressed in Section 6.
# Effect of an Organic Rectorite on the Properties of a Waste Engine Oil-Modified Asphalt
## 3 Results and Discussion
### Low-Temperature Rheological Properties
#### 3.4.2 Creep Rate  
Figure 7 presents the test results of the creep rate for base asphalt and OREC + WEO composite-modified asphalt at different OREC content levels to further assess the impact of OREC on the low-temperature performance of WEO-modified asphalt. From the graph, it can be observed that compared to the base asphalt, WEO-modified asphalt exhibits a 65.7% increase in creep rate at \(-18\)\({}^{\circ}\)C. This indicates that WEO-modified asphalt has greater stress relaxation ability at low temperatures, resulting in better low-temperature cracking resistance performance compared to the base asphalt. With the addition of OREC, the creep rate of OREC + WEO composite-modified asphalt gradually decreases. As the OREC content increases from 0% to 2%, the creep rate of the modified asphalt decreases from 0.406 to 0.257, a reduction of 36.7%. This result suggests that adding OREC reduces the stress relaxation ability of WEO-modified asphalt, thus adversely affecting its low-temperature cracking resistance performance. Additionally, from the graph, it can be seen that when the OREC content reaches 2%, the corresponding composite-modified asphalt has a creep rate only 4.9% higher than the base asphalt. This result further confirms that the low-temperature cracking resistance performance between the composite-modified asphalt with 2% OREC content and the base asphalt does not show a significant difference. Based on the decreasing trend of the creep rate shown in the graph, it can be predicted that as the OREC content continues to increase, the low-temperature cracking resistance performance of the composite-modified asphalt may decrease below that of the base asphalt, which is consistent with the analysis results of the S value. Therefore, from the perspective of low-temperature cracking resistance performance, it is recommended to use an OREC content which does not exceed 2%.
# An Adaptive Hierarchical Hybrid Multicast Based on Information-Centric Networking
## 5 Evaluation
### Flow State  
Secondly, we evaluated the number of multicast forwarding states that the router needs to maintain in each multicast approach. We compared the maximum number of multicast forwarding states among all routers in different multicast methods. The experimental results are shown in Figure 9.  
Figure 8: The comparison of the control overhead. (**a**) The comparison of the control overhead with different thresholds; (**b**) The comparison of the control overhead with different numbers of receivers; (**c**) The comparison of the control overhead with different numbers of groups; (**d**) The CDF of the control overhead in each multicast method.  
Figure 9a shows that the number of forwarding states of the router in AHHM gradually increases as the threshold increases. When the threshold is increased, more groups use the stateful mode to establish multicast communication, which increases the number of forwarding states that routers need to maintain. Figure 9b,c shows that the number of multicast forwarding states in AHHM, SSM, and BIER all increases with the number of receivers and groups. The CDF of the number of forwarding states for each router in each approach is shown in Figure 9d. These experimental results indicate that the multicast forwarding state in AHHM is more than that in BIER but less than that in SSM.  
By analyzing the experimental data, we can find that the number of multicast forwarding states in AHHM using the nearest selection strategy increased by 57.2% on average compared with BIER but decreased by 51.4% on average compared to SSM. This is because SSM needs to establish a stateful delivery tree for each group, and each on-tree router needs to maintain a forwarding state. BIER only needs edge routers instead of intermediate routers to maintain the multicast forwarding state. AHHM can adaptively use the stateful mode on routers with fewer forwarding states and the stateless mode on routers with more forwarding states.  
Figure 9: The comparison of multicast forwarding state. (**a**) The comparison of the number of multicast forwarding states with different thresholds; (**b**) The comparison of the number of multicast forwarding states with different numbers of receivers; (**c**) The comparison of the number of multicast forwarding states with different numbers of groups; (**d**) The CDF of the number of multicast forwarding states in each multicast method.  
forwarding states. Therefore, compared with SSM, the forwarding states maintained in each router in AHHM are greatly reduced.  
In addition, we can also find that the number of multicast forwarding states in AHHM using the minimum cost selection strategy is reduced by 12.1% on average compared with AHHM using the nearest selection strategy. This is because the minimum cost selection strategy can select the MJN with a smaller PS to join by considering the NFT load of each node on the path from the MJN to the RER.
## 3 The Circuit-Partitioning Algorithm Based on Partition Connectivity Clustering and Tabu Search
### Refinement Partitioning Algorithm Based on Tabu Search
#### 3.4.1 Neighborhood Solution-Generation Rule  
Given a vertex \(v\) from a subset \(G_{m}\), the gain for each other subset \(G_{n}\) is calculated by Formula (7).  
Let \(P_{G}^{k}=\{G_{1},G_{2},\ldots,G_{k}\}\) be a \(k\)-way partition of the graph \(G\), and \(G_{\max}\) be the subset with the maximum weight, where \(G_{\max}=\max_{i\in(1\to k)}\{A_{G}^{i}\}\). Two neighborhood solution-generation rules are discussed as follows.  
Neighborhood Solution-Generation Rule R1: Move a vertex \(v_{m}\) with the highest gain. The specific operation is as follows: First, randomly select a subset \(G_{m}\) (\(m\
eq\max\)). Then, choose the vertex \(v_{m}\) with the highest gain for subset \(G_{m}\) from subset \(G_{c}\in\{G_{i}\in P\mid A_{G}^{i}>A_{G}^{m}\}\). Finally, move the selected vertex \(v_{m}\) to the target subset \(G_{m}\).  
Neighborhood Solution-Generation Rule R2: Move the vertex \(v_{m}\) with the highest gain and its corresponding target subset \(G_{m}\) according to Neighborhood Solution-Generation Rule R1. Then, randomly select another target subset \(G_{n}\in P\), where \(n\
eq m\) and \(n\
eq\max\) (with \"\(\max\)\" denoting the subset with the highest gain). Next, select the vertex \(v_{n}\) with the highest gain from subset \(G_{c}\). Finally, move \(v_{m}\) to target subset \(G_{m}\) and move \(v_{n}\) to the newly selected target subset \(G_{n}\).  
R1 moves a single vertex to both optimize load balancing and reduce cut edges. It is suitable for situations where there are few cut edges but uneven load distribution, allowing for fine-grained optimization of partition quality, particularly in terms of load balancing. R1 can quickly refine the partition, avoiding unnecessary vertex movements, and is both simple and efficient in operation.  
On the other hand, R2 focuses more on reducing cut edges by moving two vertices. The first vertex not only optimizes load balancing but also reduces cut edges, while the second vertex mainly serves to further reduce cut edges. R2 can significantly decrease the number of cut edges for large-scale cut edge problems. The movement of the second vertex does not need to consider load balancing, making it more focused on reducing cut edges, thereby leading to a significant reduction in the number of cut edges.  
By constraining vertices to migrate from higher-weight subsets to lower-weight subsets, these neighborhood generation rules R1 and R2 gradually guide the search towards balanced partitions and reduce cut edges. They also ensure that no single subset has excessively high weight.
###### Abstract  
Laryngeal squamous cell carcinoma (LSCC) constitutes a noteworthy subset of head and neck cancers, contributing to about 4.5% of all malignancies. Its clinical behavior and characteristics exhibit variations contingent upon the specific anatomical site affected, with the glottis, supraglottis, and subglottis emerging as the most prevalent locations. Notably, squamous cell carcinoma represents a predominant histological type, accounting for 85% to 95% of all laryngeal cancers. The gender disparity is evident, with a higher incidence among males, exhibiting a ratio of 3.9:1. Moreover, disparities among racial groups are observed, as African American patients tend to manifest the condition at a younger age, coupled with lower overall survival rates compared to their Caucasian, Hispanic, and Asian counterparts. The primary etiological factors implicated in the onset of laryngeal cancer are tobacco and alcohol consumption, with a direct correlation to the intensity and duration of usage. Importantly, the risk diminishes gradually following cessation, necessitating a substantial period of at least 15 years for a return to baseline rates. Given the diverse nature of laryngeal SCC, treatment modalities are tailored based on the specific site and stage of the disease. Therapeutic interventions, such as radiotherapy, transoral laser microsurgery, open horizontal partial laryngectomy, or total laryngectomy, are employed with the overarching goal of preserving organ function. This study delves into the intricate realm of laryngeal SCC, specifically exploring the involvement of heat shock proteins (HSPs) in disease progression. This research meticulously examines the expression levels of Hsp10, Hsp27, Hsp60, and Hsp90 in dysplastic and benign tissue samples extracted from the right vocal cord, utilizing immunohistochemistry analysis. The focal point of the investigation revolves around unraveling the intricate role of these molecular chaperones in tissue differentiation mechanisms and cellular homeostasis, particularly within the inflammatory milieu characteristic of the tumor phenotype. The findings from this study serve as a robust histopathological foundation, paving the way for more in-depth analyses of the underlying mechanisms governing the contribution of the four chaperones to the development of squamous cell carcinoma in the larynx. Additionally, the data gleaned from this research hint at the potential of these four chaperones as valuable biomarkers, not only for diagnostic purposes but also for prognostication and ongoing patient monitoring. As our understanding of the molecular intricacies deepens, the prospect of targeted therapeutic interventions and personalized treatment strategies for laryngeal SCC becomes increasingly promising.  
**Keywords:** chaperone system; molecular chaperones; Hsp10; Hsp27; Hsp60; Hsp90; vocal cords; squamous cell carcinoma; immunohistochemistry; HSDPs in carcinogenesis; HSP biomarkers