esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of High-Sensitivity Tracking Techniques for Satellite Navigation Signals
## 3 Results
Overall, in terms of sensitivity metrics, vector tracking outperforms scalar tracking, and closed-loop tracking outperforms open-loop tracking. These findings provide a solid basis for selecting high-sensitivity tracking techniques suitable for complex environments. Figure 14: Tracking error standard deviation vs. C/N\({}_{0}\) for different tracking methods. Figure 13: Positioning error vs. C/N\({}_{0}\).
## 4 Data Silos and Fragmentation:
### Main Contribution  
The main contribution of this research lies in the development of a robust framework is that addresses critical challenges in unlocking revolutionary medical insights while upholding privacy and security standards. The framework offers several key contributions:  
1. _Privacy-Preserving Collaborative Learning:_ The proposed framework enables healthcare institutions to collaborate and train machine learning models on locally stored EMR data without sharing sensitive patient information. This privacy-preserving approach ensures compliance with data protection regulations and fosters trust among stakeholders [36].
2. _Secure and Transparent Data Aggregation:_ By leveraging blockchain technology, the framework establishes a decentralized network where data contributors can securely share and aggregate their EMR data. The transparent nature of the blockchain ensures the integrity and traceability of the data aggregation process, mitigating concerns of data tampering and manipulation [21].
3. _Enhanced Data Integrity:_ The blockchain infrastructure guarantees the immutability and tamper-proof nature of the EMR data and the trained machine learning models. This enhances data integrity and instills confidence in the reliability of the insights derived from the framework [23].
4. _Consensus Mechanisms and Smart Contracts:_ The framework incorporates robust consensus mechanisms and smart contracts to facilitate trust among participants and establish a fair reward system. This incentivizes active participation and encourages data contribution, leading to a more comprehensive and diverse dataset for training models [37].
5. _Advancement in Healthcare Analytics:_ The proposed BEFL-EMR framework paves the way for advanced healthcare analytics by leveraging the collective intelligence of distributed EMR data. It enables the discovery of valuable medical insights that can drive evidence-based decision-making, improve patient outcomes, and advance medical research in various domains [38].
* _Scalability and Applicability:_ The framework is designed with scalability in mind, allowing for the inclusion of numerous healthcare institutions and accommodating diverse types of EMR data. This ensures its applicability across different healthcare settings and facilitates broader adoption within the medical community [24]. The main contribution of this research is the comprehensive development and validation of the proposed framework, which addresses the challenges of privacy, security, data integrity, and scalability in unlocking revolutionary medical insights. By enabling collaboration, preserving privacy, and ensuring transparency, this framework has the potential to transform the landscape of healthcare analytics and contribute to significant advancements in medical research and decision-making.
# SKVOS: Sketch-Based Video Object Segmentation with a Large-Scale Benchmark +
## 4 SKVOS Model
### Loss Function  
Binary Cross-Entropy Loss (\(\mathcal{L}_{BCE}\)) is a widely used training loss function in segmentation tasks. It is defined by the following formula:  
\[\mathcal{L}_{BCE}=-\frac{1}{\mathcal{N}}\sum_{i=1}^{\mathcal{N}}\bigl{(}M_{i} \log(\hat{M}_{i})+(1-M_{i})\log(1-\hat{M}_{i})\bigr{)}, \tag{16}\]  
where \(M_{i}\) represents the ground truth label of the target object (0 or 1), \(\hat{M}_{i}\) denotes the predicted probability of a pixel belonging to the target object, and \(\mathcal{N}\) is the total number of pixels. Additionally, we employ the temporal consistency loss \(\mathcal{L}_{OCL}\), designed in Section 4.6, to ensure that the features learned for the same target remain consistent across the temporal domain. Thus, by combining \(\mathcal{L}_{BCE}\) and \(\mathcal{L}_{OCL}\) as the overall supervisory signal, the network is trained to generate binary segmentation maps (foreground target and background) for  
Figure 7: Training cycles in video of Sketch-Anchored Temporal Contrastive Learning. The arrows show the temporal relationships between objects across frames, and the boxes indicate the objects’ positions in the video frames. Different colors correspond to different objects: red for \(O^{1}\), yellow for \(O^{2}\), and green for \(O^{3}\).  
each frame. The overall spatiotemporal objective function for video segmentation, \(\mathcal{L}\), is defined as follows:  
\[\mathcal{L}=\mathcal{L}_{BCE}+\lambda\mathcal{L}_{OCL} \tag{17}\]  
where \(\lambda\) is a weighting factor to balance these two loss terms.
###### Abstract  
Granite is widely used in laboratory rockburst simulations due to its exceptional strength, brittleness, and uniform composition. This study employs a true triaxial loading system to replicate asymmetric stress states near free surfaces, allowing precise control of three-dimensional stresses to simulate strain-mode rockbursts. Advanced monitoring tools, such as acoustic emission (AE) and high-speed imaging, were used to investigate the evolution process, failure mechanisms, and monitoring strategies. The evolution of strain-mode rockbursts is divided into five stages: stress accumulation, crack initiation, critical instability, rockburst occurrence, and residual stress adjustment. Each stage exhibits dynamic responses and progressive energy release. Failure is governed by a tension-shear coexistence mechanism, where vertical splitting and diagonal shear fractures near free surfaces lead to V-shaped craters and violent rock fragment ejection. This reflects the brittle nature of granite under high-stress conditions. The AE monitoring proved highly effective in identifying rockburst precursors, with key indicators including quiet periods of low AE activity and sudden surges in AE counts, coupled with 'V-shaped' b-value troughs, offering reliable early warning signals. These findings provide critical insights into strain-mode rockburst dynamics, highlighting the transition from elastic deformation to dynamic failure and the role of energy release mechanisms.  
strain-mode rockburst; true triaxial loading; acoustic emission monitoring; failure mechanisms; deep rock engineering Article
# Fabricating a Novel Three-Dimensional Skin Model Using Silica Nonwoven Fabrics (SNF)
## 2 Materials and Methods
### Preparation of Skin Models  
#### 2.3.1 Preparation of a Single Material Skin Models with Immortalized Cells or Primary Cells  
We prepared four types of models: Epidermal, SNF epidermal, SNF dermal/epidermal, and Collagen dermal/epidermal. SNF were punched in 11 mm diameter circles using a hole punch and attached to a cell culture insert (Corning Inc., NY, USA) with porous polyethylene terephthalate (PET) track-etched membranes (membrane pore size: 0.4 \(\upmu\)m). The cell culture insert, attached to 12-well plate (VIOLAMO, IKEDA SCIENTIFIC Co., Ltd., Tokyo, Japan), was then filled with 100 \(\upmu\)L 70% ethanol for 10 min. After the ethanol was removed, 100 \(\upmu\)L of phosphate-buffered saline (PBS, Thermo Fisher Scientific K. K., Tokyo, Japan) was added for 10 min and removed. This was repeated thrice. The number of fibroblasts [34] and keratinocytes [35] were adjusted to the cell density in a previous study. We seeded 5.0 \(\times\) 10\({}^{5}\) NIH3T3 cells in 400 \(\upmu\)L D-MEM medium on the SNF and poured 1 mL D-MEM outside the cell culture insert for the SNF dermal/epidermal model. We cultured the model for 5 days while changing the medium every 2 or 3 days. After 5 days, 2.5 \(\times\) 10\({}^{5}\) HaCaT/400 \(\upmu\)L assay medium was seeded on the SNF in the cell culture insert. For the Collagen dermal/epidermal model, 5.0 \(\times\) 10\({}^{5}\) NIH3T3/10 \(\upmu\)L D-MEM were prepared. At 4 \({}^{\circ}\)C, 400 \(\upmu\)L of 3 mg/mL collagen solution, 50 \(\upmu\)L of 10 \(\times\) Ham's medium, and 50 \(\upmu\)L of 50 mM reconstitution buffer were mixed together. From this mixture, 390 \(\upmu\)L was taken out and 10 \(\upmu\)L of the NIH3T3 cell suspension was added. Next, 400 \(\upmu\)L of this mixture was poured onto the cell culture insert. After incubation at 37 \({}^{\circ}\)C with 5% CO\({}_{2}\) for 1 h until the collagen gelated, 2.5 \(\times\) 10\({}^{5}\) HaCaT/400 \(\upmu\)L assay medium were seeded on the collagen in the cell culture insert. After HaCaT seeding, the process was the same for all models. One milliliter of assay medium was poured outside the insert. After 24 h, the medium in the insert was removed and the culture was started at the air-liquid interface. The medium was changed every 2 or 3 days for 14 days. Excluding the process of seeding NIH3T3 from the SNF dermal/epidermal model would give the SNF epidermal model, and excluding SNF would give the Epidermal model (Figure 1).
# Performance Analysis of Soft-Switching FSO/THz-RF Dual-Hop AF-NOMA Link Based on Cognitive Radio
## 3 Performance Analysis  
### CR System  
For the analysis in this section, the OP will be derived. The underlay CR we consider is the PowIntSCSI model with a constrained power budget at ST and computed statistical CSI at ST, which increases the practical application effect of the link [26]. Specifically, this model is restricted by both the maximum relay transmit power (i.e., \(P_{Peak}\)) and the maximum tolerable interference power of PN (i.e., \(I\)). In such a scenario, the quality of service (QoS) for the PU can be safeguarded through statistical constraints, ensuring that the probability of R causing interference to the PU surpassing the interference threshold \(I\) remains below the predetermined threshold \(\delta\). The transmitted power at R is defined as \(P_{R}\), which is obtained as  
\[\Pr\!\left(|h_{RP}|^{2}P_{R}>I\right)\leq\delta\implies 1-F_{|h_{RP}|^{2}}\!\left(\frac{I}{P_{R}}\right)\leq\delta \implies P_{R}\leq\frac{I}{\Gamma_{|h_{RP}|^{2}}(1-\delta)}\,, \tag{14}\]  
where \(F_{|h_{RP}|^{2}}^{-1}(x)=-\Omega_{P}\mathrm{ln}(1-x)\) represents the inverse function of \(F_{|h_{RP}|^{2}}(x)\). Thus, the transmission power at R can be expressed as [26]  
\[P_{R}=\mathrm{min}\!\left\{P_{Peak},\frac{-I}{\Omega_{P}\mathrm{ln}\,\delta} \right\}=\begin{cases}P_{Peak},&\text{if }\ P_{Peak}\leq\frac{-I}{\Omega_{P}\mathrm{ln}\,\delta}\\ \frac{-I}{\Omega_{P}\mathrm{ln}\,\delta},&\text{otherwise.}\end{cases} \tag{15}\]In the subsequent derivation, \(P_{R}\) will take the smaller value in (15) to represent the transmit power at \(R\). As \(I\) keeps increasing, \(\frac{-I}{\Omega_{\text{p}}\ln\delta}\) will increase and \(P_{R}\) will be equal to maximum relay transmission power \(P_{Peak}\) at \(R\).
# Blockchain Technology in Healthcare: A Comprehensive Review and Directions for Future Research
## Internet of Medical Things
### Healthcare IoT and Medical Devices  
IoT is an association of computing devices having unique identifiers, capable of transferring data over a network using Internet protocol without any man-machine interactions. Such a powerful seamless interaction makes IoT play a crucial role in the healthcare system [136]. Wearable and medical devices are currently at the heart of IoMT [137]. One major constituent of IoMT in the healthcare industry is wireless body-area network (WBAN) systems. WBAN are becoming a significant enabling technology for a wide variety of applications, especially in medical and healthcare environments for remote monitoring of physiological parameters [138; 139; 140; 141]. For instance, people can use IoT devices to remind them about appointments, changes in blood pressure, calories burnt and much more [142; 143]. In the following discussion, we provide a review of current work on the integration of blockchain with IoMT.  
[PERSON] et al. [144] introduced the integration of WBANs with blockchain smart contracts for a secured real-time patient monitoring and medical interventions system. The study proposes the integration of blockchain to execute smart contracts that would evaluate information collected by a patient's IoT healthcare devices based on customized threshold values. This is done to overcome the problem of logging transfer of data transactions in an IoT healthcare system. [PERSON] et al. [145] introduced an intelligent dyslexia analytics solution where a decentralized big data repository is used to store, and then share with healthcare communities, groups, and individuals using blockchain. Mobile Multimedia Health data is captured during dyslexia testing and stored in a decentralized big data repository, which can be shared for further clinical research and statistical analysis. [PERSON] et al. [146] introduced structural health monitoring by using both IoT and blockchain technology. They proposed a novel system where locally centralized and globally decentralized distribution is activated by dividing them into edge and core network. Therefore, it enhances the efficiency and scalability of the blockchain system. [PERSON] et al. [57] introduced a model for using a securely PSN based healthcare. The primary challenge of this model is to ensure security while sharing data among PSN nodes. To address this problem, the authors developed two protocols. The first protocol designed is an improved version of IEEE802.15.6, which establishes secure links with unbalanced computational requirements for mobile devices and resources-limited sensor nodes. However, the second protocol used blockchain technique to share health data among PSN nodes. [PERSON] et al. [147] developed a framework tamper-resistant mobile Health system using blockchain technology, in order to ensure fidelity of records. The aim of this study was to develop a mobile health system for cognitive behavioral therapy for insomnia using a smartphone application.  
Figure 5: Internet of medical things (IoMT) in blockchain.
## 3 Proposed Procedure
### Texture Data Acquisition and Preprocessing
#### 3.2.1 Texture Acquisition  
The first stage of the workflow involves collecting texture data from various public data sources. These data primarily consist of orthoimages, satellite imagery, and aerial photographs provided by national operating platforms. Data are collected in various formats considering scalability and compatibility, where map sheet numbers can be interpreted to define precise geographical ranges of each tile, allowing texture alignment based on converted coordinates.
## 4 Description of the Experiment
### Evaluation Metrics Setting  
Spearman's correlation coefficient is a commonly used similarity measure algorithm for evaluating the relevance of a non-linear relationship between two variables. Compared with other similarity measures such as cosine similarity, Minkowski distance, and [PERSON]'s correlation coefficient, [PERSON]'s correlation coefficient has the following characteristics:  
1. Non-parametric approach: the Spearman correlation coefficient is a non-parametric statistic that does not depend on the specific form of the distribution of the data, which  
Figure 1: Illustration of the EdaCSE algorithm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**System Development** & **Development** & **CUDA** & **GPU** & **CPU** & **Memory Size** \\
**Environment** & **Environment** & **Version** & **A5000** & **A5000** & **A5000** \\ \hline Ubuntu20.04 & Pycharm2020.1 & 11.3 &
\begin{tabular}{c} NVIDIA RTX \\ A5000 (24 G) \\ \end{tabular} & AMD EPYC 7551P & 64 G \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental hardware and software configuration.  
makes it effective in assessing correlation on a wide range of data types (continuous, discrete, ordered, etc.).
2. Suitable for ordered data: the Spearman correlation coefficient is particularly suitable for ordered or ranked data, and it is able to alleviate the limitations of other measurement algorithms on this type of data by comparing the rank order of the variables.
3. Evaluation of non-linear relationships: Unlike the Pearson correlation coefficient, which applies only to linear relationships, the Spearman correlation coefficient is able to capture the correlation of non-linear relationships, which makes it more flexible in practical applications and allows for a more comprehensive description of the interrelationships between variables.  
Due to the applicability and flexibility of [PERSON]'s correlation coefficient, [PERSON]'s correlation coefficient was chosen as the evaluation metric for the subsequent experiments in this paper.  
The [PERSON]'s correlation coefficient is calculated as shown in Equation (3), which takes values between \(-1\) and \(1\), where \(-1\) indicates a perfect negative correlation, \(1\) indicates a perfect positive correlation, and \(0\) indicates no correlation. Positive values indicate that as one variable increases, the value of the other increases; negative values indicate that as one variable increases, the value of the other decreases. The Spearman correlation coefficient can be used to measure the degree of similarity between two sentence vectors.  
\[\rho=1-\frac{6\sum\mathrm{d}_{\mathrm{i}}^{2}}{\mathrm{n}(\mathrm{n}^{2}-1)} \tag{3}\]  
where \(\mathrm{d}_{\mathrm{i}}\) denotes the difference in ranks between the ith elements of X and Y, and n denotes the length (i.e., the number of elements) of X and Y.
# Joint SAR Image Time Series and PSInSAR Data Analytics: An LDA Based Approach
## 2 Evolving Structures in SAR Imagery
Once the three-level hierarchy is defined, a new hidden variable, called topic, is emphasized by the LDA model. Each document will be represented as a random mixture over topics, and each topic is characterized by a distribution over words. Considering that \(\alpha\) and \(\beta\) are the main parameters of these distributions and \(\theta\)-Dir (\(\alpha\)), we can compute the probability of each visual word \(W\) to be included in the topic \(z\), based on the joint distribution of words and topics:  
\[p(\theta,z,W|\alpha,\beta)=p(\theta|\alpha)\prod_{n=1}^{N}p(z_{n}|\theta)p(w_ {n}|z_{n},\beta) \tag{1}\]  
Taking into consideration all the values of \(\theta\) and \(z_{i}\), we can compute the marginal distribution of a document based on equation:  
\[p(W|\alpha,\beta)=\int p(\theta|\alpha)\left(\prod_{i=1}^{N}\sum_{z_{i}}p(z_{ i}|\theta)p(w_{i}|z_{i},\beta)\right)d\theta \tag{2}\]  
Figure 1: Proposed approach to extract categories of evolution from SAR SITS. Furthermore, the probability of the corpus is measured as the product of the marginal probabilities of single documents:  
\[p(D|\alpha,\beta)=\prod\limits_{d=1}^{m}\int p(\varphi_{d}|\alpha)\left(\prod \limits_{n=1}^{n_{d}}\sum\limits_{z_{dn}}p(z_{dn}|\theta_{d})p(w_{dn}|z_{dn}, \beta)\right)d\theta_{d} \tag{3}\]  
where \(m\) is the number of documents in the corpus and \(n_{d}\) the number of words in a document. LDA is a generative process where the distribution of words, documents, and corpus over the topics are incremented step by step until they converge to an acceptable error. New semantic rules were defined and unique groupings of basic elements emerged as topics relying on the temporal features. The modeling results in a scene classification, where each topic represents a category of evolution.
# A Scalable Solution to Detect Behavior Changes of Elderly People Living Alone
## 5 Discussion
The presence of some peers with limited skills at operating digital equipment does not improve the performance of the system in detecting deviations of the ADL. However, knowing that there are human observers in the system might bring the users a valuable psychological benefit, and might mitigate the feelings of abandon, loneliness, and depression that are frequent among the elderly people living alone ([49]). It is likely that, after some time of mutual monitoring, some of the participants will want to meet each other and extend the area of mutual support. Moreover, if the members of a P2P monitoring group are acquaintances, or live in the same neighborhood, they can take direct actions to support a peer who needs assistance. The main limitation of our method is that it does not consider the intervals when the monitored persons are away from home, and this can lead to false alerts. However, this issue can be easily solved with a simple magnetic door contact at the main entrance and an additional software filter for false alerts (e.g., by using the algorithm described in Ref. [40]). Further work is needed for the adjustment of the time slices used for building the activity maps--the one-hour intervals were arbitrarily chosen and may not be optimal. In practice, it may be useful to consider longer time slices and uneven in length (e.g., early morning from 6.00 am to 9.00 am, morning hours from 9.00 am to 1.00 pm, afternoon from 1.00 pm to 6.00 pm, evening from 6.00 pm to 11.00 pm, and nighttime from 11.00 pm to 6.00 am). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Criteion’Study**} & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] et al.** & **This Work** \\  & **Ref. [28]** & **Ref [40]** & **Ref. [41]** & \\ \hline \multirow{2}{*}{**Complexity/Cost**} & High & High & High & Low \\  & Low & Low & Low & High \\  & High & High & High & High \\  & Low & Medium & Medium & High \\  & Low & Medium & Medium & High \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the proposed solution with other related work.
# RSSI and Device Pose Fusion for Fingerprinting-Based Indoor Smartphone Localization Systems
## 5 Discussion
In conclusion, the proposed framework provides consistent accuracy improvement across the board; it also comes at a low computational and memory cost and has low overall privacy risk. The accuracy improvement vs. timing performance trade-off illustrated by the results in Table 4 shows that studying the optimization of classification algorithms for the implementation of the framework (which, as mentioned in Section 1.2, was beyond scope of this study) is a good direction for further research. Developing the framework on a system with wireless communication protocols that enables more expansive deployment of anchors (e.g., in a multistorey building) is also an avenue for future work. **Author Contributions:** I.M.K. : Conceptualization, Methodology, Software, Investigation, and Writing: A.T.: Conceptualization and Reviewing: A.A.-H.: Methodology and Reviewing; K.S. : Reviewing; W.S.T.R. : Conceptualization and Reviewing. All authors have read and agreed to the published version of the manuscript. **Funding:** This study was supported by the Australian Government, Department of Industry, Innovation and Science grant AEGP000053 and the Australian Government Research Training Program scheme. **Data Availability Statement:** Data are available upon reasonable request from I.M.K. **Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Mesh Independence Verification
#### 3.1.2 Size of Parcels Independent Verification  
Under the condition of fixed time steps, the initial gas in the regenerator is divided into 65, 130, 195 parcels, and the corresponding parcel sizes are 1, 0.5 and 0.333 mm, respectively. Then, simulations for a few cycles are performed, and the predictions of cold end velocities are compared, which enables us to make a proper choice of parcel meshing.  
Figure 5 illustrates that, when the initial gas parcels inside the regenerator increase from 65 to 130, the predictions of the cold end velocity are converged when the initial parcel number is 130; the predictions show little change with further increase of the initial parcel number beyond 130. Therefore, the parcel meshing of 130 is chosen in the following simulations.