esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Downward/Upward Routing Protocol for Mobile-Sensor Networks
## 4 Simulation Environment and Performance Evaluation
### Simulation Results
#### 4.2.1 Packet-Delivery Ratio  
Packet delivery ratio is the ratio between the number of received data packets at the destination and the number of generated packets by the source nodes. Figure 6 shows packet delivery ratio based on different traffic patterns.  
Figure 5(a) shows that ADUP outperforms AODV and Flooding in terms of packet-delivery ratio for upstream traffic. ADUP has about 10% improvement over AODV. This is mainly due to the fact that, in upward routing, ADUP only needs to update next-hops rather than whole path routes, which helps adapt faster to mobility. Moreover, ADUP and AODV are much better than Flooding because it uses broadcasting as a basic traffic pattern, which causes serious network congestion, especially when the number of senders is high.  
Figure 5(b) shows that ADUP is much better than AODV and Flooding for downstream traffic. This is mainly due to the fact that benefiting from upstream traffic ADUP allows the sink to update the path to reach any node in the network in a timely manner. Results also show that the packet-delivery ratio of AODV decreases when the number of nodes increases. The reason is that, with more nodes, upstream traffic is higher, which causes more congestion and a higher risk of collisions. Affected by upstream traffic, the downstream traffic of AODV would lose more packets during transmission. However, unlike AODV, the packet-delivery ratio of Flooding on downstream traffic first increases for 40 nodes, and then decreases for 60 nodes. This is mainly due to the fact that Flooding uses broadcasting rather than unicasting. When the number of nodes increases, broadcasting would help the same packet being relayed more times, which would increase the opportunity of successfully sending a packet to reach the destination. This allows Flooding to increase its packet-delivery ratio of downstream traffic with 40 nodes. However, the downstream traffic of Flooding is also affected by the  
Figure 6: Packet delivery ratio.  
upstream traffic. When there is serious network congestion caused by the upstream traffic of 60 nodes, the packet-delivery ratio of downstream traffic decreases.
# Region-Based Automated Localization of Colonoscopy and Wireless Capsule Endoscopy Polyps
###### Abstract  
The early detection of polyps could help prevent colorectal cancer. The automated detection of polyps on the colon walls could reduce the number of false negatives that occur due to manual examination errors or polyps being hidden behind folds, and could also help doctors locate polyps from screening tests such as colonoscopy and wireless capsule endoscopy. Losing polyps may result in lesions evolving badly. In this paper, we propose a modified region-based convolutional neural network (R-CNN) by generating masks around polyps detected from still frames. The locations of the polyps in the image are marked, which assists the doctors examining the polyps. The features from the polyp images are extracted using pre-trained Resnet-50 and Resnet-101 models through feature extraction and fine-tuning techniques. Various publicly available polyp datasets are analyzed with various pertained weights. It is interesting to notice that fine-tuning with balloon data (polyp-like natural images) improved the polyp detection rate. The optimum CNN models on colonoscopy datasets including CVC-ColonDB, CVC-PolypHD, and ETIS-Larib produced values (F1 score, F2 score) of (90.73, 91.27), (80.65, 79.11), and (76.43, 78.70) respectively. The best model on the wireless capsule endoscopy dataset gave a performance of (96.67, 96.10). The experimental results indicate the better localization of polyps compared to recent traditional and deep learning methods.  
colonoscopy; wireless capsule endoscopy; polyps; localization; segmentation; deep learning +
Footnote †: journal: Medical Imaging  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 2 Materials and Methods
### Spatial Coherence  
Spatial coherence (also called spatial correlation) is a measure of the coherence of the ultrasound beam reflected by a diffuse scattering medium. The VCZ theorem [2] states that spatial coherence can be obtained by computing the Fourier transform of the field intensity. Hence, in a classic B-mode scan, where dynamic focusing is only applied in reception, spatial coherence reaches its maximum value at the transmit focus, where the beam is narrower; moreover, the coherence function has a triangular shape, being the field a \(sinc^{2}\) for a uniformly weighted aperture. Away from the focus, the beam widens and spatial coherence decreases. The same effect is observed in all those cases in which a decorrelation occurs, e.g., because of the presence of acoustic and/or electronics noise, beam sidelobes, and aberrations, etc. [9; 20; 23].  
For each discrete time sample \(t\), we define the normalized spatial covariance as:  
\[C_{N}(l,t)=\frac{C(l,t)}{C(0,t)}=\frac{\sum\limits_{n=1}^{N-l}\sum\limits_{t= 1}^{l_{2}}s_{n}(t)s_{n+l}(t)}{\sum\limits_{n=1}^{N}\sum\limits_{t=1}^{l_{2}}s_{ n}^{2}(t)} \tag{1}\]  
where \(s_{n}(t)\) are the backscattered radio-frequency (RF) signals received by each \(n\)-th transducer of the \(N\)-element receive aperture and focused (delayed). \(l\) is an integer number representing the spatial lag, i.e., the number of elements between the couple of multiplied signals \(s_{n}(t)\) and \(s_{n+l}(t)\) (\(l=0\dots\ N-1\)), whose product is integrated over a short time interval \(t=[t_{1};t_{2}]\). \(C(l,t)\) is the spatial covariance at the \(l\)-th lag; the normalization factor (denominator of Equation (1)) is given by the zero-lag covariance \(C(0,t)\), as in [3].  
Thus, a metric which accounts for the total spatial coherence \(SC(t)\) (similar to what is proposed in [10] for SLSC imaging) can be computed as:  
\[SC(t)=\sum\limits_{l=0}^{N-1}C_{N}(l,t). \tag{2}\]
# A Novel Carbon Fiber Composite Material for the Simulation of Damage Evolution in Thick Aquifers
## 4 Simulation Test of Damage and Fracture in Extremely Thick Sandstone Aquifers
### Damage Distribution Characteristics of a Very Thick Sandstone Aquifer  
Figure 12 illustrates the \(D\) variation pattern of different measurement lines within the thick sandstone aquifer during the mining process. According to the results shown, after the workforce advancement, varying degrees of damage and failure occurred in different layers within the thick sandstone aquifer. Specifically, the damage and fracturing were most severe in the center of the workface. The maximum \(D\) values for measurement lines 3, 4, and 5 above this area were 0.43, 0.19, and 0.12, respectively. It is noteworthy that the damage and failure regions of the thick key layer correspond to its deformation characteristics, with the area experiencing the greatest deformation also being the most severely damaged.  
The damage distribution characteristics of the thick aquifer at the same stratum level show that the central region experiences consistent damage and fracturing, while the damage decreases rapidly towards the sides, with the outermost areas almost unaffected by mining activities, resulting in a damage coefficient \(D\) of 0. Taking Line 3 at the bottom of the thick aquifer as an example, in the central region from 45 to 90 m, the damage values remain around 0.43. In the ranges from 20 to 45 m and 90 to 120 m, the damage values quickly decrease from 0.43 to 0.04. In the ranges of 0 to 20 m and 120 to 150 m, the areas are almost unaffected by mining, with damage values approaching 0.
# CONDA-PM--A Systematic Review and Framework for Concept Drift Analysis in Process Mining
## 2 Fundamentals
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Case ID**} & \multirow{2}{*}{**Event ID**} & \multicolumn{4}{c}{**Properties**} \\ \cline{3-6}  & & **Timestamp** & **Activity** & **Resource** & **Cost** \\ \hline \multirow{4}{*}{7} & 3581 & 30 December 2018 14:02:26 & Enter Loan Application & [PERSON] & 170 \\ \cline{2-6}  & 3582 & 8 January 2019 10:02:15 & Retrieve Appliciant Data & [PERSON] & 97 \\ \cline{2-6}  & 3583 & 15 January 2019 15:08:09 & Compute Installments & [PERSON] & 250 \\ \cline{2-6}  & 3584 & 22 January 2019 19:20:10 & Notify Rejection & [PERSON] & 60 \\ \hline \multirow{4}{*}{55} & 3600 & 30 February 2019 08:44:05 & Enter Loan Application & [PERSON] & 54 \\ \cline{2-6}  & 3601 & 10 March 2019 10:15:25 & Retrieve Appliciant Data & [PERSON] & 47 \\ \cline{2-6}  & 3602 & 15 March 2019 11:33:29 & Compute Installments & [PERSON] & 210 \\ \cline{2-6}  & 3603 & 22 March 2019 13:30:44 & Notify Eligibility & [PERSON] & 64 \\ \cline{2-6}  & 3604 & 30 March 2019 09:10:50 & Approve Complex Application & [PERSON] & 120 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._and functions assigning each event to its activity class [5]. A Representative Execution Graph (REG) corresponds to an aggregation of execution graphs. It is used to pinpoint differences between instances of the same business process [5]._  
**Definition 6** (Behavioural profiles). : _A behavioural profile is a representation of relations between every pair of events or activities appearing in an event log [6]._  
Relations can be causality, direct succession, parallelism, or no-direct succession. A footprint of an event log is one example of a behavioural profile. It represents ordering relations between activities or events in a given event log. These ordering relations can capture certain patterns, like directly-follows (\(a>b\)), causality (\(a\to b\)), choice (\(a\#b\)), and parallelism (\(a||b\)). For example, consider an event log L = \([<a,,b,c,d>^{3},<a,c,b,d>^{2},<a,e,d>]\).The footprint for the given log would be similar to the one in Table 2.  
**Definition 7** (Procedural and Declarative process models). : _procedural process models represent a conceptual view of how activities should be carried out. Meanwhile, declarative process models provide a formalisation of the undesired behaviour through defining a set of constraints._  
Unlike procedural models, the sequence at which activities are executed is not rigid in declarative models [7]. DECLARE is one of the widely used declarative process specification languages [8].
# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 2 Related Work
### Limitations of Existing Approaches  
Despite recent progress, current systems continue to face key challenges. High false alarm rates, especially in vision-only setups that misinterpret rapid but non-dangerous movements, limit trust and usability [10]. Furthermore, the lack of contextual awareness often leads to inappropriate or delayed responses, as most systems do not verify the user's condition after a detected event [51].  
Privacy concerns also persist, particularly in camera-based monitoring systems, which are frequently perceived as intrusive. This perception significantly reduces user acceptance, especially in private home settings [14].  
Limited Real-World Testing remains a major barrier. Many fall detection models are trained and evaluated using simulated datasets or controlled laboratory conditions, typically involving young, healthy participants. This limits their generalizability to real-world scenarios, where elderly users exhibit more diverse and unpredictable movement patterns. The absence of benchmark datasets containing real falls from elderly individuals further complicates performance validation [10].  
Hardware limitations and usability issues present challenges in adoption, particularly for frail users or low-resource environments [52].  
To mitigate these issues, our system introduces several practical features that prioritize reliability, privacy, and real-world feasibility:  
* It uses two cameras, which improves spatial coverage and reduces blind spots, leading to more reliable fall detection. Unlike many existing solutions that rely on a single camera, this dual-camera approach enhances spatial robustness and reduces the risk of occlusion-related misdetections;
* Local processing avoids dependence on cloud services and enhances data security;
* Importantly, the system does not store any recorded images or videos, directly addressing one of the main privacy concerns associated with vision-based monitoring.  
The design of our system highlights privacy, practicality, and user acceptability, establishing a solid foundation for future real-world deployment, despite its current evaluation being conducted in laboratory using a simulated dataset.
# Deep Lifelong Learning Optimization Algorithm in Dense Region Fusion
## 3 Materials and Methods
### Algorithm Principle in DLLO-DRF
#### 3.2.4 Complexity Analysis  
In DLLO-DRF, primary variables include \(m\) (number of training stages) and \(n\) (number of fusion parameters). It contains two components: lifelong learning training and dense region fusion. The time complexity of obtaining parameters and partitioning them is \(O(m)\) and \(O(n)\) for a single model, resulting in a total of \(O(mn)\). Spatial complexity, dependent on model number and size, is also \(O(mn)\).
## 1 Introduction
### Motivation and Major Contributions of the Paper  
Based on the above discussion, the literature review demonstrates that adequate work has been carried out regarding BESS planning for voltage control. Nevertheless, there are some shortcomings that remain unresolved. Therefore, the objective of this study is to address these issues by providing a reliable control method for the voltage profile in LV distribution networks embedded with high residential rooftop PV units. The proposed model is called the open architecture model, meaning that other sources can be added if the user wants. In this study, voltage control is accomplished by developing a bi-level optimization approach consisting of upper and lower optimization levels. In the upper-level optimization, the Neural Network Optimization Algorithm (NNA) is used while the lower-level optimization is represented using LP. The proposed voltage mitigation strategy helps the distribution system planners decide on the location where the available BESS should be installed, the capacity of each BESS, and the optimal charging and discharging characteristics of the BESS in order to overcome the overvoltage and undervoltage problems. The main contributions of the paper compared to other studies can be summarized as follows:  
* Since most of the BESS-controlling strategies are based on local information, they did not provide a system-level optimality guarantee. That is, the optimum amount of active power to be stored by each installed battery in the network is absent. This study tackles this point by providing a network-level optimization for BESS operation in grid-connected solar PV.
* Most of the studies use analytical approaches and local information to control the operation of the batteries. This research work, on the other hand, provides a charging/discharging strategy, taking into consideration all batteries in the distribution system to improve the voltage profile.
* In addition, different daily charging/discharging strategies have been proposed in the literature. Some of them proposed a load-following control method, while others utilized a droop-based method. Therefore, the proposed method examines a new controlling approach that shows its effectiveness.
* In addition, most papers have assumed predetermined sizes, numbers, and BESS locations in the distribution network. Their assumption may not be ideal since considering different sizes, numbers, or locations could result in better operation scenarios. Results indicate that the proposed voltage strategy is useful in the optimal planning of the BESS in the network.  
The rest of this paper is structured as follows: Section 2 describes the overall framework of the proposed voltage management approach and BESS modeling. NNA is explained in Section 3. In Section 4, a case study with analytical findings is presented. In Section 5, the study is completed by summarizing the main conclusions and the importance of the work.
## 2 Basic Theory  
### Local Mean Decomposition (LMD)  
As a signal-adaptive time-frequency-processing method realized through an iterative process [16], local mean decomposition (LMD) constructs the local mean and local envelope function of the signal, and separates it into the multiplication product of the envelope signal representing instantaneous amplitude and pure frequency-modulation signal, and then obtains a series of product-function (PF) components [22]. The local mean decomposition of the original signal can reduce the interference signal and obtain a more effective real signal.  
The main calculation flow of LMD is as follows. First, we should find the extreme value of signal \(x(t)\) and calculate the local mean and local envelope function by moving average. Then, the local mean function is separated and the residual signal is demodulated by pure frequency-modulation. We should accumulate all local envelope functions \(a_{p}(t)\) and the first pure frequency-modulation signal \(s_{p}(t)\) to obtain the first PF component \(F_{PFp}(t)\), as shown in Equation (1). Finally, the residual signal \(u_{k}(t)\) is decomposed. The above stepsneed to be calculated iteratively, so as to convert the original signal into \(n\) PF components and one residual component \(u_{k}(t)\), as expressed in Equation (2).  
\[F_{PFp}(t)=a_{p}(t)s_{p}(t) \tag{1}\]  
\[x(t)=\sum\limits_{p=1}^{n}F_{PFp}(t)+u_{k} \tag{2}\]
# Algorithm for Determining the Position of a Ship Hull-Cleaning Robot
## 1 Introduction
First, we present a positioning algorithm that integrates kinematic models with corrective techniques based on machine learning and probabilistic filtering. Second, we demonstrate how deep learning architectures can predict corrective displacements by leveraging temporal patterns in sensor data, thereby mitigating the cumulative error typically associated with odometry. Finally, our extensive experimental evaluations provide insight into the performance trade-offs between different correction frequencies and sensor fusion strategies, forming the optimal integration of these methods in real-world hull-cleaning applications. In the following sections, we survey related work on hull-cleaning robot positioning, detail the methodologies adopted, including machine learning algorithms, Kalman filters, and deep learning approaches, and present our experimental setup and results.
# Conjugation of Urokinase to Water-Soluble Magnetic Nanoparticles for Enhanced Thromolysis
## 4 Conclusions  
The synthesis and characterization of magnetic carrier drug by conjugating urokinase onto PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs are discussed in detail. The drug carrier of water-soluble PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) particle was first prepared into nano-scale monodisperse, and then successfully covalently bound with urokinase for thrombolysis. In vitro experiments show that alternating magnetic field can trigger urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs to release drugs. Under the action of static magnetic field, the thrombol efficiency of nano-drugs can be improved by a factor of two. When a coupling magnetic field combined with static and alternating current magnetic fields is applied, the efficiency is increased by a factor of four. This indicates that, in addition to the traditional static magnetic field used for targeted thrombolysis, the use of coupled magnetic fields may be a new option to enhance thrombolysis with magnetic carrier drug NPs. In addition, toxicity assay shows that the urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs have good biocompatibility and low cytotoxicity. This implies that the prepared magnetic carrier NPs may be used in vivo experiments in the future. However, further studies have to be done to transition these NPs in vivo test before the clinical use for thrombolysis events.  
[PERSON] conceived the experiments and wrote the paper; [PERSON] and [PERSON] contributed materials preparation and analysis tools; [PERSON] contributed the idea and revised the manuscript; [PERSON]. ([PERSON]) and [PERSON] ([PERSON]) analyzed the data.  
This research was funded by the State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology.  
Figure 9: Comparison of wet weight of thrombus with time in solutions of saline, 1 mg/mL pure urokinase, 0.7 mg/mL urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs, and 1 mg/mL urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs inside the mimic blood vascular system, wherein the nanoparticles are operated under the action of AC magnetic field with a frequency of 50 Hz and without AC magnetic field.  
**Acknowledgments:** The authors gratefully acknowledge the support of the State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, China.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Materials and Methods
### Data Collection  
Both the University of Pannonia and University Debrecen were the sites of measurements. The spatial skills of 61 students were measured using the Gear VR version at the former university, and 240 students participated using the LG 20M37A (19.5\") desktop display at the latter university [42]. Information technology (IT) and non-IT students used the Gear VR version, while architectural/civil engineering and mechanical engineering students used the desktop display version.  
The tests were constructed as the following: students had to solve 10 problems of each test type in sequential order. After they finished, they could rest, if they wanted. Then, they had to solve another 10 problems of each test type. Afterward, they could rest, and then, another 10 problems of each test type had to be solved. The reason why these tests had to be solved three times is that the display parameters were using a randomization technique: two or three display parameters were randomized before starting each test type. While this could result in smaller sample sizes, a large number of results could be achieved with it in the end. It should be noted that both the problems and their answers were also randomized, thus eliminating the possibility that the students remember them.  
Since one Gear VR device was available at the University of Pannonia, students had to come sequentially for the measurements. As each person took around 30-50 min to complete all tests, 3 weeks were needed to measure the spatial skills of everyone. Testing was easier at the University of Debrecen: a computer laboratory with a capacity of 20 people was used. Therefore, the students were grouped into 20 groups. All students were informed of the tests, and every one of them gave oral consent before the measurements commenced, although their names were not recorded. It is impossible to identify them based on the logged data alone.  
The application logs the following information about the students while respecting their anonymity: their age, gender, primary hand, what their major is, and their number of university years. These previous user characteristics are not focused on in this study. It also logs the test type, number of correct answers, and completion times. Naturally, the display parameters are also logged: the contrast ratio, whether there are shadows in the scene, virtual camera type, its rotation, and its field of view. It was not necessary to log the various display devices because each version logs the information into a different.csv file. Therefore, it is possible to identify them later.