esa-sceva/satcom-chunk-collection huggingface dataset:
# Occlusion-Robust Multi-Target Tracking and Segmentation Framework with Mask Enhancement
## 3 Methodology
### Occlusion-Aware Mask Propagation Network  
Existing occlusion perception methods rely primarily on traditional bounding box modeling [1]. These approaches use techniques such as IoU, confidence scores, and regression models to detect occlusion states and integrate target states into the appearance model in order to optimize tracking performance. However, these approaches require manually designed templates and hyperparameters such as thresholds and result in complex models that lack robustness.  
To solve these issues, this section details an occlusion-aware mask propagation network that overcomes the three main sources of noise in congestion tracking: occlusion between objects, occlusion between objects and scenes, and mutual interference between targets.
## 4 Results and Discussion  
This section presents the results and discussions concerning the application of the proposed method, considering the initial calculations to define the stability criteria. After calculating the SLSa for each cluster of each solution up to \(k=9\), it is possible to represent the values in a graph (see Figure 6), starting from a solution with two clusters in the left corner and ending with a solution with nine clusters in the right corner. Clusters with low SLSa values are colored with a shade of red according to their instability.  
In Figure 6, the black lines represent the number of clients belonging to one cluster that is assigned to another cluster in the next solution; thick lines indicate a larger amount, and many lines to the left of a cluster indicate that it was generated from several others.  
Cluster number 3 in the three-cluster solution has a high level of instability since it was created from the data in clusters 1 and 2 in the previous solution (effectively representing half of each cluster in the previous solution). Other clusters follow the same behavior: more specifically, the clusters created from a new solution (the last clusters in each column) are most often the product of joining parts of other clusters.  
After solution 6, almost all the clusters in the following solutions present some amount of instability, being formed from two or more clusters in previous solutions with a few exceptions. Of the candidate solutions (4, 5, and 6) only solution 6 presents a satisfactory distribution of stable clusters, with five clusters having only one parent in the previous solution.  
For better visualization, Figure 7 shows the transition of clusters along the different candidate solutions. Cluster 5 (in orange) was created in solution 5 from data coming from clusters 2 (in red) and 3 (in green). Similarly, cluster 6 (in cyan) in solution 6 was created from half of the data from cluster 4 (in purple), which consequently was shifted towards cluster 1 (in purple), resulting in the apparent \"junction\" between two halves of clusters.  
Figure 6: SLSa solutions of K-means with \(k=2\) to \(k=9\).  
Customers in cluster 6 (in cyan) are completely absorbed into another cluster in the smaller solutions, despite having unique characteristics such as having all three RFM attributes high compared to the rest of the clusters. Therefore, the solution with six clusters was chosen because it satisfactorily represents all customer types present in the dataset, as well as having an acceptable overall stability (above 0.95) and a tolerable instability per cluster (only one cluster formed from shifts).
## 2 Materials and Methods
### PCR Amplification of Efflux Pump Genes and Regulatory Proteins  
The bacterial DNA of MDR _P. aeruginosa_ was extracted as previously reported but with slight modifications [16]. The extracted DNA was used as a template for amplifying the efflux pump gene and regulatory proteins. The following efflux pump genes (_MexA_, _MexB_, _OprM_, _MexC_, _MexD_, _OprJ_, _MexX_, _MexY_, _OprN_) and specific regulatory proteins (_MexR_, _MexZ_, _NalC_, _NalD_, _NfAB_) were assayed in multidrug-resistant _P. aeruginosa_ isolates using gene-specific primers. PCR reactions were made up of a total volume of 25 \(\upmu\)L of PCR mixture with 5 \(\upmu\)L of DNA, 1 \(\upmu\)L of each forward and reverse primer, 12.5 \(\upmu\)L of PCR master mix and 5.5 \(\upmu\)L of nuclease-free water. The PCR products were visualised using 1.5% agarose gel electrophoresis (AGE) under a UV transilluminator (Alliance 4.7, UVITEC, Cambridge, UK). The primer sequences are listed in Table 1.  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Gene** & **Primer** & **Sequence (5\({}^{\prime}\)-3\({}^{\prime}\))** & **Product Size (bp)** & **Reference** \\ \hline _MexA_ & _MexA_-F & CGACACAGGCCTGAGCAAGCAC & 256 & [17] \\ _MexB_ & _MexA_-R & GGAGACCTTGCCCGCTTGGC & 242 & [18] \\ _MexB_ & _MexB_-F & CCGTAATCCGACCTGATG & 242 & [18] \\ _OprM_ & _OprM_-R & CTAGTGTGTGCAAAAGCTGCATCC & 186 & [19] \\ _MexC_ & _MexC_-F & GTACGCGCGCGTCATGCAGGGTIC & 164 & [17] \\ _MexD_ & _MexC_-_R & TTACTGTTGGGGGGCAGGGTAC & 656 & [19] \\ _MexD_ & _Mex D_-F & AGGTGATCAAGGATTGCACA & 851 & [19] \\ _OprJ_ & _OprJ_-F & CACGCGAAGAGAGTTC & 851 & [19] \\ _OprJ_ & _OprJ_-R & CATCGAAACAGGCCGACATT & 842 & [19] \\ _MexX_ & _Mex X_-F & CTATCGGCTACCCGAC & 842 & [19] \\ _MexY_ & _Mex Y_-F & ATGCTTACCTCTACCTCTCCA & 834 & [19] \\ _MexY_ & _Mex Y_-R & GACGTTTCCCACCAGGATT & 971 & [19] \\ _OprN_ & _OprN_-F & CTGAACCAGTTGTTGTTGAACAGGT & 971 & [19] \\ _MexR_ & _MexR_-F & CTGGATACACCATTATTACA & 503 & [20] \\ _NfxB_ & _NfxB_-F & ACGCGAGGCCAGTTTTCT & 731 & [20] \\ _NfxB_ & _NfxB_-R & ACTGATTCCGAGGTCG & 814 & [21] \\ _NalD_ & _NalD_-F & GCGGCTAAAATTCGGTACACT & 789 & [22] \\ _MexZ_ & _MexZ_-F & TATGATTCGCGGCGCTTTC & 862 & [23] \\ \hline \end{tabular}
\end{table}
Table 1: Primer sequences of efflux pump genes and regulatory proteins of _P. aeruginosa_.
# Resilient Formation Reconfiguration for Leader-Follower Multi-UAVs
## 5 Articulation of Formation Reconfiguration Strategies for Multi-UAVs with Variable Swarm Scale
### Design of Formation Reconfiguration Strategy for Multi-UAVs under Leader Disengagement  
With respect to the leader-follower control architecture, the leader is critical to the stable flight of the multi-UAVs. Once the command capability of the leader is lost for malfunction or destruction, the multi-UAVs can be at risk of losing control and collapse. As such, in the case of leader departure, the hierarchical BRB performance assessment model for UAVs in Section 4 is utilized to select a new leader. Then, update the swarm communication topology and the desired distance of each follower with respect to the leader. Finally, reform the intended shape under the formation control protocol. The formation reconfiguration strategy for leader disengagement can be further summarized as the following steps, with the renewal of the swarm communication topology illustrated in Figure 4.  
**Step 1.** Select a new leader based on the hierarchical BRB performance assessment model for UAVs, which is noted as NEW_LEADER.  
**Step 2.** If NEW_LEADER has two neighbors, disconnect the bidirectional connection between NEW_LEADER and the neighbor with smaller number, and then, establish a bidirectional connection between the node and the last node of the path starting with NEW_LEADER.  
Figure 4: The renewal of the swarm communication topology under leader disengagement.  
**Step 3.** Change the bidirectional connection between NEW_LEADER and the current neighbor to a directed connection incident on NEW_LEADER.  
**Step 4.** If the current number of NEW_LEADER is odd, establish a unidirectional connection from NEW_LEADER to node 2, otherwise establish a unidirectional connection from NEW_LEADER to node 1.  
**Step 5.** If the difference in length between the two paths starting from NEW_LEADER is 2, disconnect the bidirectional connection between the end node of the longer path and its neighbor, and then, establish a bidirectional connection between the node and the end node of the shorter path.  
**Step 6.** Update the amount of followers inside the multi-UAVs by N=N-1.  
**Step 7.** Update the number of nodes from 0 to N.  
**Step 8.** Update the desired distance of nodes 1 to N with respect to node 0, and then reform the desired geometry under the formation control law (30).
## 2 Materials and Methods
### Research Method
#### 2.3.3 Adsorption Kinetics Model  
Data analysis and model fitting were performed using Origin software (2022) v.9.9.0.225,  
Pseudo-First-Order Adsorption Kinetic Model: The pseudo-first-order adsorption kinetic model, introduced by [PERSON], is designed to describe single-layer adsorption predominantly accomplished through boundary diffusion. It effectively captures the rapid reaction phase of the adsorption process, not the entire process. The specific expression is as follows:  
\[Q_{t}=Q_{e}\Big{(}1-e^{-k_{1}t}\Big{)}, \tag{5}\]  
In the equation above, \(Q_{t}\) represents the adsorption capacity at time \(t\) (mg/g). \(Q_{e}\) represents the adsorption capacity at equilibrium (mg/g). \(k_{1}\) is the pseudo-first-order rate constant (1/h).  
When plotting \(\frac{Q_{t}}{Q_{e}}\) against \(t\), a linear fit with a correlation coefficient approaching 1 indicates conformity to the pseudo-first-order kinetic model. The slope of the line allows for the calculation of the pseudo-first-order rate constant (\(k_{1}\)), while the intercept provides the adsorption capacity at equilibrium.  
* Pseudo-Second-Order Adsorption Kinetic Model  
The pseudo-second-order adsorption kinetic model, developed by [PERSON] and [PERSON], primarily considers chemical adsorption limitations and provides a comprehensive representation of the entire adsorption process. Its mathematical expression is  
\[\frac{t}{Q_{t}}=\frac{1}{k_{2}Q_{e}^{2}}+\frac{t}{Q_{e}}, \tag{6}\]  
Among these equations, \(k_{2}\) represents the pseudo-second-order rate constant (g/mg-h).  
Upon plotting \(\frac{t}{Q_{t}}\) against \(t\), the slope of the fitted line yields the value of \(k_{2}\), while the intercept provides the adsorption capacity at equilibrium. These models offer valuable insights into the material's adsorption kinetics, shedding light on the complex mechanisms governing the process.
# Double-Layer Compressive Sensing Based Efficient DOA Estimation in WSAN with Block Data Loss
## 4 Experimental Results  
In this section, a series of numerical simulation results is presented to illustrate the DOA estimation performance achieved by DCS-DOA and DCS-DDOA. We use CS-DOA as a baseline, where DOA estimation is implemented under the single-layer CS framework. The simulations are performed using MATLAB2010b running on an Intel Core i5-4460, 3.20-GHz processor with 12 GB memory, under Windows 7.
## 2 Related Work
### Noise and Outlier Reduction Using Robust Principal Component Analysis  
At its core, a PCA involves identifying a subspace of a specified dimension that offers an optimal approximation to given data. This is applicable regardless of whether the data are vectors, matrices, or tensors. When applied to data influenced by noise or outliers, the process becomes a robust PCA (RPCA) [28]. Adopting tensor representations and methodologies produces superior results when extracting multidimensional structural features compared with matrix-based approaches. Despite the presence of noise or sparse outliers, this approach to robust tensor data recovery has demonstrated its effectiveness across fields such as SAR imaging [29], hyperspectral image compression [30; 31], video sequence background subtraction, image and video denoising [32; 33], and wireless communication [34]. With the vast amounts of multi-dimensional data produced by large-scale heterogeneous IoT systems, tensor-based PCAseffectively reveal correlations among multiple attributes. This substantially boosts data reconstruction efficiency in tandem with the recognition of spatiotemporal correlations within individual attributes.  
Similar to TC, the TRPCA requires a decomposition method to discover the low-rank structure of data to reduce noise and outliers. Decomposition using t-SVD is computed more efficiently than other methods such as CP decomposition, Tucker decomposition, and high-order SVD (Ho-SVD). Furthermore, t-SVD has been utilized in TRPCAs [23; 33].  
Similar to TC, discovering the low-rank data structure is essential for reducing noise and outliers in a TRPCA. To achieve this, a decomposition method is required. t-SVD is a more efficient decomposition method than CP decomposition, Tucker decomposition, and Ho-SVD. It is commonly used in TRPCAs [23; 33].  
It has been observed in various fields that singular values within the RPCA and TRPCA receive weighted shrinkage based on their importance. Several methodologies have been employed to achieve this goal. For instance, a weighted nuclear norm with a truncated SVD was utilized for noise reduction in hyperspectral images [35]. The weighted tensor nuclear norm was used in conjunction with a higher-order RPCA (HoRPCA) for similar purposes [36]. Furthermore, the weighted-tensor p-norm technique, which employs t-SVD, was used to mitigate noise in videos, images [33], and hyperspectral images [37]. These cases highlight the varying shrinkages of singular values based on their importance.  
The RPCA and TRPCA aim to recover noise and outliers by identifying low-ranking structures [28]. Therefore, enhancing their ability to manage missing values is essential.
## 2 Material and Methods
### Experimental Setup and PA Module
#### Mist Couplant Delivery  
During automated phased array inspection, the prerequisite requirement for reliable ultrasonic data collection is to ensure that the wedge is in contact with the surface along with uniform couplant distribution on the surface. The spray of water is a sufficient form of couplant for the elastomer wedge on a flat surface. However, the fuselage of the aircraft is curved, smooth, and painted, which results in any sprayed water droplets to coalesce and drip down the surface. It was seen experimentally that fine mist of water instead of spray using a appropriate nozzle ensures uniform distribution of mist on the surface under any orientation during inspection. A self-priming micro-diaphrag pump was used to generate a fine mist of water using a nozzle with an orifice of 0.1 mm instead of spray during the inspection. The pump outlet was controlled by a flow control valve through a TEE connector to reduce the flow rate and provide a 50 PSI constant pressure water for generating a fine mist. The other end of TEE was connected to a hose to recirculate the extra water back to the water reservoir. It was observed that fine mist adheres to the painted surface, irrespective of the surface's orientation. The mist is uniformly distributed with less than 2 mL of water consumed, for a scan area of the size of a coupon 150 \(\times\) 100 mm, thus minimizing the amount of couplant water near the VR.  
Positioning Device  
For maximum signal amplitude during the reception, the PA transducer-wedge assembly must maintain both normal incidence to the surface and constant contact between the wedge and inspected surface, throughout the scan in all orientations (vertical, horizontal, and inverted). This requirement is achieved by the customization of a spring-loaded positioning fixture with three degrees of freedom (one vertical and two rotations) suitable for flat and curved surfaces. This gimbaled probe holder also ensures adherence in the case of the inspection of curved surfaces (e.g., aircraft fuselage).
# Secure and Efficient Deduplication for Cloud Storage with Dynamic Ownership Management
## 5 The Proposed Secure Deduplication Protocol  
In this section, we propose a secure deduplication protocol based on the secure data sharing in cloud (SeDaSC) protocol [5]. The proposed protocol has the following key characteristics. First, our protocol ensures both computational efficiency for the client and data privacy. By building upon SeDaSC, we alleviate the client from complex operations, entrusting these tasks to a cryptographic server (CS). In the initial SeDaSC protocol, there was a challenge in terms of exposing plaintext to the CS. However, our proposed protocol addresses this concern by introducing message-locked encryption (MLE), making it possible to deduplicate encrypted data. Within our proposal, we employ hash and convergent encryption with tag check (HCE2) within the context of MLE. Second, our protocol includes a feature for dynamic ownership updates. When owners upload or revoke their data, the cloud storage re-encrypts the data using a group key, which is shared by clients who own the same data. This group key is generated by the cloud storage when the owner group changes, and then it is distributed by both the CS and the cloud storage. Table 1 provides a description of the notation used in this paper.  
Figure 1: System model.
###### Abstract  
Zone refining, as the currently most common industrial process to attain ultrapure metals, is influenced by a variety of factors. One of these parameters, the so-called \"zone length\", affects not only the ultimate concentration distribution of impurities, but also the rate at which this distribution is approached. This important parameter has however neither been investigated experimentally, nor ever varied for the purpose of optimization. This lack of detections may be due to the difficult temperature measurement of a moving molten area in a vacuum system, of which the zone refining methodology is comprised. Up to now, numerical simulation as a combination of complex mathematical calculations, as well as many assumptions has been the only way to reveal it. This paper aims to propose an experimental method to accurately measure the molten zone length and to extract helpful information on the thermal gradient, temperature profile and real growth rate in the zone refining of an exemplary metal, in this case aluminum. This thermographic method is based on the measurement of the molten surface temperature via an infrared camera, as well as further data analysis through the mathematical software MATLAB. The obtained results show great correlation with the visual observations of zone length and provide helpful information to determine the thermal gradient and real growth rate during the whole process. The investigations in this paper approved the application of an infrared camera for this purpose as a promising technique to automatically control the zone length during a zone refining process.  
zone refining; zone length; growth rate; infrared camera; aluminum; fractional crystallization; high purity; ultrapure aluminum; zone melting +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### pH, Viscosity, Drug Content, and Percentage Entrapment Efficiency of ETB-NEG  
ETB-NEG was prepared and evaluated for pH, viscosity, drug content, and percentage entrapment efficiency. The pH of the prepared ETB-NEG was 5.53 \(\pm\) 0.32 and was in good match with that of the skin acid mantle [38]. Thus, any pH-related skin irritation may be precluded from the prepared ETB-NEG. Meanwhile, the viscosity of the ETB-NEG was 572.9 \(\pm\) 3.45 cps. This value was in good agreement with the viscosity (504 \(\pm\) 21.4 cps) of a reported curcumin-loaded NEG for wound healing application [39].  
The drug content was determined to be 96.54 \(\pm\) 1.26%, whereas the entrapment efficiency was 93.18 \(\pm\) 1.69%. The drug content is a measure of drug loading and can influence the amount reaching the systemic circulation. It can also influence the drug release pattern from the NEG. The drug content near to 100% can be expected in the formulations such as NE and NEG. Low chances of drug loss are responsible for such high drug loading behavior. The entrapment efficiency is also influenced by the drug loading and drug content. Therefore, the high entrapment efficiency observed for ETB-NEG is justified. Most of the reported NEG formulations provide such high values for drug content and entrapment efficiency [38; 39].
Specifically, the main objective of this study is to assess the response of the TEROS 21 probe in terms of the suction measurement range and equilibrium time, comparing its performance with established methods, such as the FP technique [36; 37; 38] and HCTs [25]. Following the approach of [PERSON] et al. [7], this experimental study also examines the influence of the initial moisture conditions of the TEROS 21 porous stone (wet or dry) on the measured suction values. These initial conditions could impact in-situ measurements, potentially leading to the misinterpretation of the SWRC during drying or wetting processes. Finally, since the TEROS 21 also incorporates a sensor to log temperature (T), the temperature readings are compared with those obtained from a thermocouple sensor to further corroborate the consistency of the probe.
## 4 Discussion
### The Influences of [Ca2+]i and \(\mathrm{I_{CaL}}\)  
Our results demonstrate that the alternans caused by rapid heart rate yields a periodic L-S-L-S variation in the AP of cardiomyocytes in ischemic regions. The variation in [Ca2+]i at the end-diastole is reversed compared to the AP due to decreased calcium re-uptake in cardiomyocytes under MI. The expression of SERCA on the SR is highly dependent on cellular metabolism. The reduction in ATP during acute ischemia lowers the energy available to SERCA, leading to decreased electrical conductivity [32; 33]. Furthermore, SERCA activity is regulated by phospholamban (PLB). During MI, the dephosphorylation of PLB impairs SERCA dynamics, reducing the calcium handling capacity of SR [34]. This dysfunction in calcium re-uptake disrupts intracellular calcium homeostasis during diastole, preventing Ca2+ absorption by the SR and thereby increasing [Ca2+]i.  
[PERSON] et al. reported that higher [Ca2+]i during diastole reduced the availability of the \(\mathrm{I_{CaL}}\) channel [35], which plays a crucial role in SR calcium release. Concurrently, increased [Ca2+]i enhances the closing of the ff-gate, promoting \(\mathrm{I_{CaL}}\) inactivation and further hindering the Ca2+ passage. Although \(\mathrm{I_{CaL}}\) plays a minor role during phase 0 of the ventricular myocyte AP, its activation is essential from the beginning of the AP, and reduced \(\mathrm{I_{CaL}}\) impairs AP formation during the plateau phase. In fact, the overall AP amplitude decreases with the partial inactivation of \(\mathrm{I_{Na}}\)[36] as the elevated extracellular potassium ion concentration from MI alters the potassium equilibrium potential, partially depolarizing the RMP. This explains the raised RMP under alternans observed in our results. Previous in vivo and animal studies have shown that RMP depolarization is associated with susceptibility to alternans [37; 38]. In addition to potassium-related factors, the application of a high-frequency pacing protocol can also increase RMP [39] due to the inability of SR to fully compensate for rapid calcium cycling, leading to an increase in the intracellular calcium level and the acceleration of the sodium-calcium exchanger. The influx of sodium ions consequently depolarizes RMP [40]. As the d-gate activation characteristic is voltage-dependent, its function is affected. In addition to the transmembrane voltage, which is the main factor controlling the opening of the L-type calcium channel, the reduced \(\mathrm{I_{CaL}}\) shortens the plateau duration and overall APD. This collective reduction in AP amplitude and APD accelerates the onset of repolarization, inhibiting d-gate opening. Such inhibition, combined with increased ff-gate closing, decreases GCOT, consequently limiting \(\mathrm{I_{CaL}}\) flow. Eventually, a positive feedback loop develops between \(\mathrm{I_{CaL}}\), AP, the d-gate, and GCOT.