esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Analysis of PTBO and a Comparative Study
### The Differences between PTBO and Other Algorithms
#### 4.3.2 The Differences between PTBO and SMS  
SMS (States of Matter Search) is a nature-inspired optimization algorithm based on the simulation of the states of matter phenomenon [35]. Specifically, SMS is devised by considering each state of matter at one different exploration-exploitation ratio by dividing the whole search process into three stages, i.e., gas, liquid, and solid.  
Although the sources of inspiration for PBTO and SMS are similar, which are taken from a physical phenomenon about the states of matter, the evolution processes of PTBO and SMS are completely different. The evolution process of SMS is as follows. At first, all individuals in the population perform exploration in the mode of the gas state. Then, after 50% of the iterations, the search mode is changed into the liquid state for 40% of the iterations, i.e., the search between exploration and exploitation. Finally, the evolutionary process enters the stage of exploitation (liquid state) for 10% of the iterations. However, in PTBO, the three phases are coexistent in the entire search process. In other words, the three different phases of individuals execute dynamically different search tasks according to their phase in each generation. Hence, the implementation about the balance of exploration and exploitation between PTBO and SMS is completely different. Besides, the operators of PTBO and SMS are also completely different. In summary, it can be said that there are fundamental differences between PTBO and SMS.
## 4 Simulation Results
### Analysis of Positioning Errors for Dynamic Points  
Under pseudorange observation errors of approximately 1 m, 3 m, 5 m, and 7 m, the dynamic point positioning map is shown in Figures 26-29. In Figures 26-29, distinct positioning trajectories can be observed. Although the trajectory positioning diverges as the noise variance increases, the trajectory points remain within a reasonable range.  
Figure 28: Error characteristics of dynamic point positioning (pseudorange observation error approximately 5 m).  
Figure 27: Error characteristics of dynamic point positioning (pseudorange observation error approximately 3 m).  
Figure 29: Error characteristics of dynamic point positioning (pseudorange observation error approximately 7 m).  
The positioning deviations for dynamic points are shown in Figures 30-33. When the pseudorange observation error is approximately 1m, 80% of the horizontal positioning deviations are within 0.8871 m, 80% of the vertical positioning deviations are within 0.9698 m, and 80% of the total positioning deviations are within 1.2421 m. When the pseudorange observation error is approximately 3 m, 80% of the horizontal positioning deviations are within 1.2388 m, 80% of the vertical positioning deviations are within 1.2903 m, and 80% of the total positioning deviations are within 1.6331 m. When the pseudorange observation error is approximately 5 m, 80% of the horizontal positioning deviations are within 1.4159 m, and 80% of the total positioning deviations are within 1.9555 m. When the pseudorange observation error is approximately 7 m, 80% of the horizontal positioning deviations are within 1.4008 m, 80% of the vertical positioning deviations are within 1.7396 m, and 80% of the total positioning deviations are within 2.3351 m.  
Figure 30: Dynamic point positioning deviation distribution.  
Figure 31: Dynamic point positioning deviation distribution.
## 4 Discussion
### Clinical Implications and Future Directions  
The proposed AI system is integrated into a web-based interface, enabling real-time radiographic analysis and structured diagnostic support. This technology has the potential to improve clinical workflows by standardizing the evaluation of plain radiographs, reducing interobserver variability, and expediting decision making. The ability to upload images, annotate lesion locations, and download automated reports contributes to its usability in both high-volume and resource-limited settings.  
Future iterations of this platform could benefit from the integration of multimodal data--such as probe-to-bone test results, inflammatory biomarkers, and clinical photographs--through ensemble learning strategies. Such integration may enhance both the sensitivity and specificity of the system, broadening its utility at different levels of care.  
Although artificial intelligence has been widely explored in fields such as oncology, musculoskeletal imaging, and cardiology [11; 12; 13; 16; 30; 43; 44], its application to diabetic foot infections, and particularly to diabetic foot osteomyelitisitis detection, remains limited. This study contributes to filling that gap by validating a deep learning model tailored specifically for radiographic assessment of diabetic foot osteomyelitisitis. As such, it provides a foundation for future prospective validations and implementation studies.  
Further research is required to explore the performance of AI-based diagnostic systems across diverse healthcare settings, including primary care and community health environments. To enhance generalizability, future iterations of the model should incorporate a greater proportion of negative cases and be prospectively validated in lower-prevalence populations. Integrating multimodal clinical data--such as ulcer duration, anatomical location, laboratory markers, and probe-to-bone test results--may also improve specificity. Moreover, evaluating the integration of these tools into real-world clinical practice, considering usability, interpretability, and clinician trust, will be essential to ensure successful adoption. In the long term, AI-driven decision support systems may assist clinicians in achieving earlier and more accurate diagnoses of DFO, ultimately contributing to the prevention of major amputations and a reduction in diabetes-related morbidity and mortality.
# A Clustering WSN Routing Protocol Based on k-d Tree Algorithm
## 3 Background and Preliminaries
### LEACH-C Protocol  
The LEACH-C protocol is a centralized version of the LEACH protocol, and, therefore, it uses the same phases of the LEACH protocol, which are configuration and transmission phase forming rounds. During the LEACH-C configuration phase, each node in the network sends a packet to the \(\text{Sink}/\text{BS}\) node that contains the location and power level. The \(\text{Sink}/\text{BS}\) node calculates the average energy value of all the nodes, selected as possible node CH, only the nodes with more energy than the average value of the energy of all the nodes in the network.  
The \(\text{Sink}/\text{BS}\) node uses an annealing algorithm for the formation of clusters [43; 44]. The other LEACH-C operations are the same as those of LEACH and the results show that LEACH-C has energy improvements on LEACH [45], for the following reasons:  
* The \(\text{Sink}/\text{BS}\) node is static and is the one that organizes. the roles of each node in the network, centralizing information and cluster formation.
* When clusters are formed, they do not communicate between nodes to save energy.
* The \(\text{Sink}/\text{BS}\) node in the configuration phase establishes the CH nodes beforehand; therefore, the network can use energy more effectively.
## 4 Data Analysis and Findings
### Hypotheses Testing Measurements
#### 4.3.1 Significance Measurement  
According to [PERSON] et al. [47], most researchers rely on the \(p\)-values and \(t\)-values to assess the probabilistic significance and confidence cut-off levels of the dependent variables. Indeed, \(p\)-values are regarded as determinants of significance through probabilistic error calculation between two variables. Moreover, \(p\)-values should achieve a maximum error threshold of 0.05, equivalent to 5%, where \(p\) < 0.05 determines a significant causal relationship between two variables. It should be noted that \(p\)-values are calculated in conjunction with \(t\)-values representing the cut-off degree at which the strength of significance is determined [47]. Therefore, for hypotheses testing at 5% error (\(p\) < 0.05) or 1% error (\(p\) < 0.01), the \(t\)-value should not fall below 1.96. Accordingly, a 5000 resample bootstrapping calculation method was engaged by Smart PLS 3.0 software to compute the significance.
# Automatic Detection of Marine Litter: A General Framework to Leverage Synthetic Data
## 4 Discussion
Currently, our debris patches are only composed of one material. Moreover, the S2 images that we tested on are of artificial plastic targets. However, as previously stated, the real life patches tend to be formed of multiple materials. A model of mixed-material patches could therefore enhance the performances of classifiers in real situations. This is why experiments with real targets of different types and of different ages should be seen as complementary to the simulation based approach in this paper. These experiments should be used both to enrich the simulation tool and to test the resulting machine learning algorithms. Finally, when trained on simulated data, classifiers, such as the Random Forest we tested, can prove viable for marine litter detection. Currently, we trained the Random Forest on a dataset pixel by pixel and used it to predict the presence of plastic or other material, but it could also be trained to classify the exact material present in a pixel. Moreover, other classifiers could be trained to predict on an image by image basis. A particularly interesting one could be a Convolutional Neural Network (CNN) [43]. Adding new features such as bands combination with indexes such as those in [8] is another option for investigating automated marine debris detection. Such classifiers, with a great number of parameters, typically require large datasets to be trained and this is where our framework, with refined parameters, could bring the most added value. In the future, we believe that a mixed approach, one that combines new data collection campaigns and machine learning algorithms trained on synthetic data will lead to a dramatic improvement in the current state of the art of remote sensing marine debris detection. The main advantages of using a physical approach for the generation of synthetic data is that, compared to the other approaches presented in the introduction, it is easy to reproduce with openly available resources. Furthermore, the bias introduced by our simplifications can be controlled by adapting the physics of the model, as previously discussed. We have faith in a symbiotic relationship, where new in situ data is used to validate the various detection algorithms and where synthetic data is used to inform and guide future data collection and cleanup campaigns.
# Unraveling Patient Safety Culture Trends in U.S. Hospital Settings: A Yearly Retrospective Analysis
## 3 Results
### Annual Changes in Patient Safety Culture
PSC dimensions: D1: Overall Perceptions of Patient Safety; D2: Frequency of Events Reported; D3: Supervisor/Manager Expectations and Actions Promoting Patient Safety; D4: Organizational Learning; D5: Teamwork within Units; D6: Communication Openness; D7: Feedback and Communication about Error; D8: Nonpumptive Response to Error; D9: Staffing; D10: Management Support for Patient Safety; D11: Teamwork across Units; and D12: Handoffs and Transitions. The results revealed significant regional variations in U.S. healthcare PSC over the years, as shown in Table S1, Supplementary Materials. The South, followed by the Central region, consistently achieved significantly higher scores than other regions, with the highest scores observed in D5 (Teamwork within Units), D3 (Supervisor/Manager Expectations), and D4 (Organizational Learning), averaging between 75% and 83%. In contrast, the Northeast and West reported lower scores, particularly in D8 (Nonpunitive Response to Error) and D12 (Handoffs and Transitions), averaging between 42% and 45%. Overall, regional and dimensional differences were statistically significant (\(p<0.001\)). Over the years, PSC analysis highlighted trends across hospitals of varying sizes and types. In smaller hospitals (6-49 beds), D5 (Teamwork within Units) and D3 (Supervi  
Figure 8: Trends in positive safety grades and event reporting (2013–2020). Figure 7: Yearly changes in safety grade distribution and reported events. sor/Manager Expectations) remained consistently high (84-86%), while D8 (Nonpunitive Response to Error) and D12 (Handoffs and Transitions) showed lower scores, averaging 52% and 57%, respectively. D4 (Organizational Learning) remained relatively strong, above 74%. The scores exhibit greater variability in medium-sized hospitals (50-199 beds). D3 and D5 remained relatively stable (80-82%), while D8 and D12 continued to show lower scores (44-51%). Additionally, D5 and D10 (Management Support for Patient Safety) experienced a slight downward trend in recent years. Regarding hospital type, non-teaching hospitals consistently achieved significantly higher scores than teaching hospitals, which exhibited more variability and a decline in recent years (Table S1, Supplementary Materials).
# Fabry-Perot Interferometer Based on Suspended Core Fiber for Detection of Gaseous Ethanol
## 4 Conclusions  
In summary, a novel optical fiber tip sensor, based on a Fabry-Perot interferometer comprised by a small section of SCF spliced to a standard SMF, was developed for the detection and monitoring of volatile organic compounds in the gas phase. The device experimental characterization to different concentrations of ethanol/water mixtures revealed a linear response through the entire range, attaining a maximum sensitivity of 3.9 pm/wt.%. To assess the temperature influence on its response, a thermal characterization was performed, revealing a sensitivity of 9.7 pm/\({}^{\circ}\)C, thus achieving a cross-sensitivity of 2.49 wt.%/\({}^{\circ}\)C. Despite the existence of a small cross-sensitivity, any issues could be addressed in the future by adding a temperature sensor (e.g., a fiber Bragg grating), or by assuring thermal stabilization of the environment. Therefore, the reduced footprint, electric passivity linear response, stability, and sensitivity to VOCs concentrations make the developed sensor a strong candidate to monitor several applications comprising the detection of VOCs molecules in the gas phase, such as the different stages of the production of several wines and distilled beverages.  
Conceptualization, T.P., M.S.F. and A.S.N.; methodology, T.P., M.S.F. and A.S.N.; validation, T.P., M.S.F. and A.S.N.; formal analysis, T.P., M.S.F. and A.S.N.; investigation, T.P., M.S.F. and A.S.N.; resources, J.B. and J.K.; data curation, T.P., M.S.F. and A.S.N.; writing--original draft preparation, T.P. and M.S.F.; fiber drawing: J.B. and J.K.; writing--review and editing, T.P., M.S.F., J.B. and J.K.; supervision, M.S.F.; project administration, M.S.F.; funding acquisition, M.S.F., J.B. and J.K. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the project AROMA, funded by FEDER, through CENTRO2020-Programa Operacional Regional do Centro, CENTRO-01-0145-FEDER-031568, and by national funds (OE), PTDC/EEI-EEE/31568/2017, UIDB/50025/2020 & UIDP/50025/2020, through FCT/MCTES. The work of [PERSON] was supported by the research fellowship CEECIND/00777/2018. The work was also funded by the German Federal Ministry of Education and Research (BMBF). \"The Innovative Growth Core TOF\" (Tailored Optical Fibers, FKZ 03 WKCV03E) as well as the bilateral cooperation FCT/DAAD (FLOW, Project ID: 57518590).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 7: Sensor wavelength shift with temperature. The blue circles represent the experimental data, and the dashed blue line represents the linear fit applied for the temperature range between 25.0 and 87.0 \({}^{\circ}\)C.
## 2 Description of the Dynamic Model  
Consider the mechanical system depicted in Figure 1a, which represents a vibrating setup of an exciter driven by the induction motor and three CRs placed inside the cavities of a rigid platform. The rigid platform is elastically attached to a fixed support by four weightless springs in a symmetrical arrangement. The induction motor is installed in the center of mass or centroid of the rigid platform, and three cavities are uniformly distributed in a circumference of the rigid platform. In the process of operation, the cylindrical rollers absorb vibration energy from the rigid platform, which becomes a rotational motion along the surface of the cavities and thus automatically tracks the rotating exciter. The coordinate system is shown in Figure 1b. It consists of a fixed frame denoted by \(oxy\), a non-rotating moving frame denoted by \(Gx^{\prime}y^{\prime}\) and a moving frame (attached at the rigid platform) denoted by \(Gx^{\prime\prime}y^{\prime\prime}\). The rigid platform can vibrate in horizontal and vertical directions and swing about its center of mass, its movements are described by coordinates \(x\), \(y\) and \(\psi\), respectively. The motion of an exciter is described by angle \(\varphi_{1}\) and the rotation angle of CR \(i\) is denoted by \(\varphi_{Ri}\). In the moving frame \(Gx^{\prime\prime}y^{\prime\prime}\), the coordinates of the mass center of an exciter and three CRs, \(\mathbf{x}_{E}^{\prime\prime}\) and \(\mathbf{x}_{Ri}^{\prime\prime}\) (\(i=1,\ 2,\ 3\)), can be written as:  
\[\mathbf{x}_{E}^{\prime\prime}=\left\{ \begin{array}{c}r\cos\varphi_{1}\\ -r\sin\varphi_{1}\end{array} \right\}\]  
\[\mathbf{x}_{Ri}^{\prime\prime}=\left\{ \begin{array}{c}l_{0}\cos\beta_{i}+r _{Ri}\cos\varphi_{Ri}\\ l_{0}\sin\beta_{i}-r_{Ri}\sin\varphi_{Ri}\end{array} \right\},\ i=1,\ 2,\ 3 \tag{1}\]  
In the fixed frame \(oxy\), the coordinates of the mass center of an exciter and three CRs, \(\mathbf{x}_{E}\) and \(\mathbf{x}_{Ri}\) (\(i=1,\ 2,\ 3\)), can be expressed as:  
\[\mathbf{x}_{i}=\mathbf{x}+\mathbf{R}\mathbf{x}_{i}^{\prime\prime},\ i=E,\ R_{1},\ R_{2},\ R_{3} \tag{2}\]  
Figure 1: The draft of a vibrating system with one exciter and three CRs: (**a**) dynamic model; (**b**) coordinate systems. The expression of the kinetic energy is given as follows:  
\[ \begin{array}{l}T=\frac{1}{2}m(\dot{x}^{2}+\dot{y}^{2})+\frac{1}{2}J_{n}\dot{y }^{2}+\frac{1}{2}J_{1}\dot{\phi}_{1}^{2}+\frac{1}{2}m_{1}\dot{x}_{E}^{\rm T} \dot{x}_{E}\\ +\frac{1}{2}\sum\limits_{i=1}^{3}m_{Ri}\dot{r}_{Ri}^{2}\dot{\phi}_{Ri}^{2}+ \frac{1}{2}\sum\limits_{i=1}^{3}m_{Ri}\dot{x}_{Ri}^{\rm T}\dot{x}_{Ri}+\frac{1 }{2}\sum\limits_{i=1}^{3}I_{Ri}\dot{\phi}_{Ri}^{2}.\end{array}
# Label-Setting Algorithm for Multi-Destination \(K\) Simple Shortest Paths Problem and Application
###### Abstract  
The \(k\) shortest paths problem finds applications in multiple fields. Of particular interest in the transportation field is the variant of finding \(k\) simple shortest paths (KSSP), which has a higher complexity. This research presents a novel label-setting algorithm for the multi-destination KSSP problem in directed networks that obviates repeated applications of the algorithm to each destination (necessary in existing deviation-based algorithms), resulting in a significant computational speedup. It is shown that the proposed algorithm is exact and flexible enough to handle several variants of the problem by appropriately modifying the termination condition. Theoretically, it is also shown to be faster than state-of-the-art algorithms in sparse and dense networks whenever the number of labels created is sub-polynomial in network size. A heuristic method and optimized data structures are proposed to improve the algorithm's scalability and worst-case performance. The computational results show that the proposed heuristic provides two to three orders of magnitude computational time speedups (29-1416 times across different networks) with negligible loss in solution quality (maximum average deviation of 0.167% from the optimal solution). Finally, a practical application of the proposed method is illustrated to determine the gravity of an edge (relative structural importance) in a network.  
k applications KSSP, label-setting algorithm, loopless paths; network optimization; edge importance; edge ranking
As BLE signals are also significantly attenuated when they hit an obstacle like concrete walls in indoor environments [16], the applicability of BLE-based proximity sensing for construction worker hazard alert systems needs to be tested in real-world settings to provide clearer empirical evidence for such a system's viability and possibly reveal the potential issues to be addressed before such systems can be implemented in construction sites. To address this research need, this paper aims to investigate the impact of different beacon sensor deployment locations on the location where a worker would receive a proximity-based hazard notification in a concrete building structure. This goal was achieved by conducting an experimental study at a typical concrete structure building construction site and marking the locations at which a BLE proximity sensing-based hazard notification is received on a mobile phone through a cloud-based hazard notification system. As environmental factors and random effects affect the exact location of the BLE signal and mobile data reception, the data collection was repeated so that the notification reception locations were visualized as a heat map, which was then qualitatively analyzed for ascertaining the impact of the beacon sensor locations on hazard notification performance. The rest of the paper is organized as follows. First, the relevant literature is reviewed, primarily focusing on three areas of research: construction worker hazard recognition and risk perception, site hazard information communication, and sensor-based site hazard alarm systems. The subsequent section will explain the detailed design of the BLE-based hazard notification cloud system used in this study. Next, the experimental design and the case site are explained, followed by the results. Lastly, the implications of the experiment results and findings are discussed, and the paper concludes.