esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Cluster Method  
In this paper, the K-means clustering method was used for research on index thresholds and the classification of indicators. Clustering factors should select indicators that have a significant impact on the classification of a hierarchical structure. At the same time, they need to be easy to operate, collect, and quantify. Based on the above analysis, this paper selected average acceleration, standard deviation of acceleration, average jerk, and standard deviation of jerk as clustering factors of driving style. The whole algorithm was implemented in Python. The detailed algorithm steps are shown below:  
1. Selection of \(K\)-value  
Select the number \(k\) of classes to be clustered and choose \(k\) centroids. \(K\) represents the number of classes of data classification. In this paper, three classes of driving style are defined based on previous studies, so the value of \(k\) is 3.
2. Distance calculation  
For each sample point, find the nearest centroid, and the closest point to the same centroid is a class, thus completing a clustering. The \(K\)-means algorithm uses Euclidean distance to represent the distance between the same kind of sample points. The closer the distance is, the higher the similarity of the sample points is.  
The principle of the \(K\)-means algorithm is as follows:  
For \(N\) sets of data, \(\mathrm{G}=\{x_{1},x_{2},\ldots,x_{N}\}\), \(x_{1},x_{2},\ldots,x_{n}\) are the parameters that affect the classification of driving styles in each segment. \(x_{i}=\{\)average acceleration, standard deviation of acceleration, average jerk, standard deviation of jerk\(\}\). Let \(K=3\)Set up \(K\) clustering centers and calculate the Euclidean distance between data and clustering centers. The data belong to the nearest clustering centers.  
Define cluster centers \(\{R_{m}\}\), where \(1<m<K\). Define \(f_{nk}\) as a category determination variable. If \(x_{n}\) belongs to class m, then \(f_{nk}=1\), and if \(x_{n}\) does not belong to class \(m\), then \(f_{nk}=0\).  
Objective function is expressed as:  
\[\mathrm{D}=\sum_{n=1}^{N}\sum_{m=1}^{K}f_{nk}\,||x_{n}-R_{m}\,||^{2} \tag{7}\]  
* Determine whether the class situation of the sample points before and after clustering is the same; if it is, the algorithm terminates, otherwise it enters Step 4.  
When the objective function is the smallest, the clustering results are obtained. The clustering center is  
\[R_{m}=\frac{\sum_{n}f_{nk}x_{n}}{\sum_{n}f_{nk}} \tag{8}\]  
* For the sample points in each class, calculate the centroids of these sample points as the new centroids of the class, and continue to Step 2.
# Managing Access to Confidential Documents: A Case Study of an Email Security Tool
## 5 Results
### Sharing Confidential Information  
We wanted our participants to explore the security options that the email security tool offers and to be able to compose an email with it easily before evaluating their interactions with the encrypted email when sharing their sensitive information. We first asked them to watch a video (see Appendix B for the video transcript) that included security- and privacy-related information about the investigated email security tool (Virtru), the importance of using it, and a demonstration of adding its extension to their Google Chrome browser. After performing this task, the participants added the email security extension to their Google Chrome browser. Then, they were asked to compose an email using this extension and think aloud while performing this task. All participants performed this task without any difficulties. After the participants sent their emails, they received a notification informing them they had successfully sent their first encrypted email with this email security tool.  
To answer the research question, (_\"How comfortable are users with sharing various types of confidential information using this email security tool, namely medical, tax, and employee?\"_) the participants were provided with three hypothetical scenarios to understand (1) how they utilized security features to manage the access control for the encrypted email and (2) how they rated their satisfaction and concern once they shared their confidential information via standard email and encrypted email. First, the participants downloaded three documents (employee information form, medical history form, and W-9 form) on their personal devices. These forms had been filled out with artificial information as an example based on the given scenario. Therefore, we asked the participants to imagine that the information in these forms, such as the social security number (SSN), belonged to them. The order in which the three scenarios were presented to the participants was randomized.
## 3 System Architecture of the Decentralized Framework
### Healthcare Emergency Aggregator
A local database serves as a temporary cache for incident data, enabling rapid access and updates during an incident's active phase. When an incident is closed, the HEA consolidates all related information, including timestamps, resource allocations, stakeholder actions, and blockchain transaction receipts, into a coherent record. These data are then transferred to remote cloud storage, which serves as the permanent repository for historical data analysis and reporting, offering scalability, redundancy, and accessibility. To further enhance data security and trust, HEA uses blockchain technology to save hashes of incident data. Such hashes, being immutable, would prove whether the exhibited data were the original data, or, vice versa, if hashes do not match, that the data were altered. After receiving critical data, the information is stored in the local database and then passed to the blockchain module for subsequent aggregation and submission. Likewise, when an incident is closed, all associated data are aggregated and their corresponding hashes are submitted to the blockchain. In the event that a blockchain transaction does not receive confirmation, the affected data batch and its hash remain securely stored in the local database, and the HEA subsequently reissues the hash-writing request to the blockchain module. This mechanism leverages the inherent resilience of blockchain technology, ensuring that, despite any transient failures, the hash writing is ultimately and reliably completed. Once a blockchain transaction is completed, a receipt is generated and attached to the incident record to facilitate later verification. This dual-layered storage strategy, combining cloud storage with blockchain solutions, balances scalability with security. It reinforces the reliability and integrity of the emergency response system, fostering stakeholder confidence and ensuring compliance with legal and regulatory requirements.
## 4 Experimental Results and Discussion
### Effect of the Longitudinal Tilt Angle of the Secondary System on Thermal Efficiency  
Figure 17 shows the thermal efficiency for the longitudinal tilts under study and the power not converted into electricity by the photovoltaic cells of 400 (W).  
Figure 17 suggests the following conclusions:  
1. The trend obtained with useful heat (see Figure 15) is also obviously true for thermal efficiency.
2. The thermal efficiency is always below 0.6. As wind speed increases, this value decreases. This effect is more pronounced as the longitudinal tilt angle increases.
3. When the wind speed is 1.05 (m/s), the thermal efficiency ranges between 0.5 and 0.4. For wind speeds of 2.55 (m/s), it ranges between 0.4 and 0.3. For wind speeds of 4.99 (m/s) and 10.03 (m/s), the thermal efficiency is between 0.3 and 0.2. This is valid for all locations. The lowest thermal efficiency is obtained in Almeria (location with the highest latitude).  
Figure 15: Useful heat for power not converted into electricity by the PV cells of 400 (W).  
Figure 18 shows the thermal efficiency for the longitudinal tilts under study and power not converted into electricity by the photovoltaic cells of 300 (W). The conclusions drawn in Figure 17 are applicable to Figure 18.
# Optimization Algorithms for Scalable Stream Batch Clustering with k Estimation
## 3 Scalable Evolutionary Clustering Algorithms
### Isesc-Cd  
_Incremental Scalable Evolutionary Stream Clustering with Change Detection_ (ISESC-CD) [13] is an extension of ISESC with one additional heuristic component, an attempt to avoid unnecessary executions of full clustering. The change-detection component (CD) resides between the incremental update and full-clustering components, as shown in Figure 6. This component addresses five types of data stream changes detected by the incremental update component: (i) the displacement of an existing cluster in space; (ii) the emergence of a new cluster; (iii) the split of an existing cluster caused by the arrival of objects that leads two or more parts of the cluster in different directions; (iv) the disappearance or expiration of an  
Figure 5: Representation of boundaries used to detect significant change for evidence criterion.  
existing cluster according to its \(ts\) and the time window; (v) the merging of two existing clusters, as they incrementally overlap each other. The role of CD is to distinguish the displacement of the clusters (case i) from the others, as cases ii and iii increase the number of clusters \(k\), while cases iv and v decrease it. Applying \(k\)-means is enough to deal with cluster displacements, while the full-clustering component is required to estimate models with variations of \(k\).  
The model's change is detected by comparing the current model with two derivations of it. The first derivation results from adding the farthest object from the centroids of the model as a new centroid, increasing \(k\) by one. The second results from merging the closest pair of clusters according to its boundaries (the distance between the centroids minus the sum of their radii), decreasing \(k\) by one. Both derivations and the current model are fine-tuned by distributed \(k\)-means, and then compared among themselves in terms of SS index values. If the current model is the best evaluated, it is preserved. Otherwise, significant evidence of change is detected, and the full-clustering component builds a new model.  
Although the full-clustering component is essentially SF-EAC, other clustering algorithms may be considered since the incremental update and change-detection components work regardless of the full-clustering and vice versa. This structure, named Incremental Scalable Stream Clustering with Change-Detection (ISSC-CD), allows the exploration of new batch stream clustering algorithms from non-stream ones.
## 2 Materials and Methods
### In-Vitro Setup
#### 2.1.2 Radial and Deep Palmar Arch Phantoms  
Since the aim of this study was to acquire and analyse PRV information obtained from PPG signals from this in-vitro model, two independent tissue phantoms were used to simulate the points of measurement of these PPG signals. The inclusion of the phantoms allowed for a better positioning of PPG sensors and a more accurate replication of tissue surrounding the arteries.  
Figure 2 illustrates the design of the built phantoms. In both cases, the phantoms consisted of a clear, hollow cuboid with a length of \(L\) = 60 mm, a width of 15 mm, height of \(H\) = 5 mm, and wall thickness of \(T\) = 1.5 mm. These cuboids were 3D printed using clear resin (Formlabs, Somerville, MA, USA) and filled with clear polydimethylsiloxane (PDMS) with 3% catalyst, which has been shown to replicate mechanical properties of skin [15], and a single custom-made tube was passed through each of the phantoms, to replicate the radial and deep-palmar arch arteries. In the case of the radial artery, this was located closer to the edge of the phantom, since the radial artery is relatively superficial in the wrist. Hence, \(x_{1}<x_{2}\) for the radial phantom. On the contrary, and since the deep palmar arch phantom was replicating a finger, the vessel passing through this phantom was located approximately in the vertical centre of the cuboid, with \(x_{1}=x_{2}\). In both cases, the inner diameters \(d_{r}\) and \(d_{p}\) had the values selected and shown in Table 1. The vessels used for the simulation of these arterial segments were custom-made following the methodology proposed by [PERSON] et al. [15,16].  
\begin{table}
\begin{tabular}{l l l} \hline
**Vessel Segment** & **Length (mm)** & **Inside Diameter (mm)** \\ \hline Aorta & 120 & 20 \\ Subclavian artery & 80 & 10 \\ Brachial artery & 200 & 4 \\ Radial artery & 220 & 2.3 \\ Deep palmar arch arteries & 250 & 1.6 \\ Venous system & 220 & 2.3 \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of arterial and venous segments replicated on the in-vitro setup.  
Figure 1: Diagram of the designed rig for the simulation of the upper-body circulatory system, which consists of a commercial pump that replicates the heart activity; aorta, subclavian, brachial, radial and deep-palmar arch arterial segments; a venous reservoir; and a single vein replicating the venous return. Pressure sensors were placed to measure the pressure of the fluid flowing through the system, and silicon, custom-made phantoms were used for the placement of photoplethysmography (PPG) sensors.
## 3 Results and Analysis
### Operational Constraints Analysis  
While exhibiting superior technical performance, OMRoadNet's operational deployment confronted three critical constraints requiring engineering considerations. Hardware dependency analysis revealed nonlinear growth in GPU memory consumption with the input scale, where processing 512 \(\times\) 512 pixel tiles demanded 8.4 GB VRAM, escalating to  
Figure 7: SNR frequency distribution of the open-pit mine road test dataset and evaluation metrics of OMRoadNet extraction results under different SNR intervals.  
34.7 GB for 1024 \(\times\) 1024 images--exceeding capacities of 23% developing-country mining compunavigation systems, as per ICMM 2023 reports.  
Environmental conditions significantly influencd model reliability, particularly in ultra-high-dust environments (PM\({}_{10}\)\(\geq\) 800 ug/m\({}^{3}\)). Empirical tests demonstrated that spectral distortions in VIS-NIR bands induced 9.7% F1-score degradation, necessitating adaptive histogram matching during inference. Dynamic occlusion by haul trucks introduced transient feature loss, with single-vehicle passes obscuring up to 42% of the road surface in LiDAR validation data. However, OMRoadNet's self-training mechanism mitigated this through multi-temporal pseudo-label fusion, recovering 68.9% of occluded segments across consecutive image acquisitions.  
Economic benchmarking against conventional LiDAR solutions revealed substantial cost advantages, with OMRoadNet's cloud-based deployment reducing per-kilometer surveying costs by USD 47.3 (78% reduction) compared to terrestrial LiDAR's USD 18.7/km operational expense. Nevertheless, the framework's current implementation requires manual intervention for cross-sensor calibration (WorldView-3 \(\leftrightarrow\) UAV) and pseudo-label validation, consuming 12-15 person-minutes per square kilometer--a bottleneck demanding future automation through active learning paradigms. The 12-15 min/km\({}^{2}\) of manual time refers only to the manual verification process of the pseudo-label results. This process does not include any form of manual annotation.
## 4 Simulation Results and Discussion  
The WFSim is implemented using MATLAB. First, a virtual wind farm with two turbines is studied with 3D to 10D spacing between the turbines, and an increase in power is estimated when the WRC approach stated in Section 3.1.2 is applied for different spacing. Second, when the optimal WRC and AIC procedures described in Section 3.1 are utilized, a real-life offshore wind farm with 20 turbines is investigated.
# An Overview of Nanofiltration and Nanoadsorption Technologies to Emerging Pollutants Treatment
## 2 Emerging Pollutants
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Compound** & **CAS Number** & **Emerging** & **Coherence** & \begin{tabular}{c} **Maximum** \\ **Concentration** \\ **(ng L\({}^{-1}\))** \\ \end{tabular} & **Reference** & \begin{tabular}{c} **Recommendeded** \\ **Limit U.S EPA** \\ \end{tabular} & \
\begin{tabular}{c} **Recommendeded Limit** \\ **Europe Union** \\ \end{tabular} &
\begin{tabular}{c} **WHO** \\ [32] \\ \end{tabular} \\ \hline \multirow{4}{*}{Stimulants} & \multirow{4}{*}{58-08-2} & \multirow{4}{*}{Caffeine} & Drinking waters & 2-5845 & [33] & & & & \\ \cline{3-8}  & & & & 2743 & [8] & & & & \\ \cline{3-8}  & & & Surface water & 262-11048 & [34] & & & & \\ \cline{3-8}  & & & 1040 & [35] & - & - & - & - \\ \cline{3-8}  & & & 19-127,000 & [33] & & & & \\ \cline{3-8}  & & & Groundwater & 262 & [8] & & & & \\ \cline{3-8}  & & & & 2-53 & [33] & & & & \\ \hline \multirow{4}{*}{Plasticzer} & \multirow{4}{*}{80-05-7} & \multirow{4}{*}{Bisphenol A} & Drinking waters & 1-178 & [33] & & \multicolumn{2}{c}{Substance subject to} & & \\ \cline{3-8}  & & & Surface water & 29.5 & [35] & & \multicolumn{2}{c}{review for possible} & & \\ \cline{3-8}  & & & Surface water & 2-13,016 & [33] & & \multicolumn{2}{c}{-} & & \\ \cline{3-8}  & & & Groundwater & 2-6453 & [33] & & \multicolumn{2}{c}{hazardous substance [36]} & & \\ \hline \multirow{4}{*}{Pesticide} & \multirow{4}{*}{1912-24-9} & \multirow{4}{*}{Arrazine} & Drinking waters & 1-687 & [33] & & & & \\ \cline{3-8}  & & & Surface water & 42-1 & [35] & \multicolumn{2}{c}{0.003 mg L\({}^{-1}\)[37]} & \multicolumn{2}{c}{0.6 g L\({}^{-1}\)} & \multicolumn{2}{c}{100 ug L\({}^{-1}\)} \\ \cline{3-8}  & & & Surface water & 1-611 & [33] & & \multicolumn{2}{c}{0.003 mg L\({}^{-1}\)[37]} & \multicolumn{2}{c}{0.2 g L\({}^{-1}\)[38]} & \multicolumn{2}{c}{100 ug L\({}^{-1}\)} \\ \cline{3-8}  & & & Groundwater & 2-5 & [33] & & & & \\ \hline \multirow{4}{*}{Pesticide} & \multirow{4}{*}{121-75-5} & \multirow{4}{*}{Malathion} & \multirow{4}{*}{Surface water} & \multirow{4}{*}{50.4} & \multirow{4}{*}{[35]} & & & & No established & reference value \\ \cline{3-8}  & & & & & & & & & \\ \cline{3-8}  & & & & & & & & & \\ \cline{3-8}  & & & & & & & & \\ \hline \multirow{4}{*}{Pesticide} & \multirow{4}{*}{330-54-1} & \multirow{4}{*}{Duron} & \multirow{4}{*}{Surface water} & \multirow{4}{*}{11.7} & \multirow{4}{*}{[35]} & & & & & \\ \cline{3-8}  & & & & & & & & & \\ \cline{3-8}  & & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Reference values of concentration of priority pollutants and admissible concentrations in water resources.
Magnetic resonance imaging (MRI), with a spatial resolution equivalent to \(1.0\times 1.0\times 2.0\) mm\({}^{3}\), a highly enhanced contrast resolution with advanced techniques such as ICE-DICE54, and sensitivity and specificity with different values depending on the disease under consideration, appears to have a diagnostic accuracy of 93.6% in the detection of recurrence at the primary site [6]. PET-CT with 18F-FDG, lastly, has a spatial resolution equal to 4.1 mm, an improved contrast resolution with the use of specific algorithms, such as \"Q.Clear\", that increase the signal-to-noise ratio by 22%, variable sensitivity, and specificity, so that the diagnostic accuracy is estimated to be 96.8% for detection of recurrence at the primary site [7]. If imaging fails to discover a primary lesion, an EUA examination and direct laryngoscopy are performed, with biopsy sampling--as mentioned above--and bilateral palatine tonsillectomy, lingual tonsillectomy, and submission to histological examination, which achieves a successful identification of occult primary tumor in 20-50% of CUP cases, and becomes significantly higher by employing techniques such as transoral laser microsurgery (TLM) and trans-oral robotic surgery (TORS), reaching more than 90% of primary diagnosis. Since a CUP of the head and neck region (HNCUP), presenting with cervical lymphadenopathy, is increasingly linked to the oropharyngeal primary subsite of onset, the implication of human papillomavirus (HPV) is parallelly being established. Thus, HPV and Epstein-Barr virus (EBV) status should be sought [8]. However, diagnostic workups do not identify the primary site in approximately 40-50% of CUP cases. Thus, the subsite of origin remains uncertain [9]. A head and neck CUP mandates a prompt, organic, and efficient diagnostic pathway for the identification of the primary carcinoma to improve the possibility of tumor control and patient outcome with an early, conformal, and tailored treatment, while reducing morbidity related to an unspecific medical therapy for failure to identify tumor markers, due to the low likelihood of surgical resolution and to a wider-volume radiation therapy necessary to try to cover the hidden primary tumor [10]. Identifying the primary tumor allows its congruent staging, the study of its histo-cytopathological features and markers, and clinicopathologic behavior; this contributes to optimal management and postoperative surveillance, with the possibility of eligibility for subsequent-line therapies in case of local failure. In light of the above, the challenging goal and urgency of having a codified diagnostic pathway that maximizes outcomes within the tightest possible timeframe become glaring.
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 3 Performance of Broadband Power Line Communication--Related Works
### Simulation Goals  
There is significant reason to prefer BB-PLC to NN-PLC. BB-PLC holds potential for high performance broadband IP networks to support the evolution of all Smart Grid applications (real-time, security). On the other way, the key question for BB-PLC is: _How will the high performance (defined according to standards and vendor solutions) be influenced by distance, noise, and frequency?_  
This article focuses on Smart Metering deployment in Europe, therefore the European LV topology was considered. The LV topology is typically radial with many branches and average length of LV lines is 150-400 m [5; 8]. Also, different types of LV cable design for underground or overhead installation were considered. For that reason, another key question for BB-PLC is: _How will the throughput be influenced by the topology and physical characteristics of the power line?_  
The key questions in the issue of PLC/BPL evaluation for deployment for Smart Metering are:  
* Is it possible to design replicable and repeatable methodology for the evaluation of PLC technologies for Smart Metering deployment?
* Is it possible to perform experimental evaluation of PLC using a simulation tool and find the limits of particular PLC technologies, which will correspondent with real deployment? Therefore, the main goals of simulation are to find:
* The impact of topological parameters and physical characteristics of the power line on throughput (channel link capacity).
* The impact of noise on throughput (channel link capacity).
* The impact of repeaters on throughput and reliability of communication.
* Communication distances without repeaters.