esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### TBVI Results  
According to Formula (1), the TBVI of the official-style buildings (O-TBVI) and the TBVI of the folk-style buildings (F-TBVI) at each location were calculated, and the TBVI of the Chinese traditional-style building (TBVI) was equal to the sum of the O-TBVIs and F-TBVIs. Figure 6 shows the TBVI results of the Chinese traditional-style buildings for all locations in the study area. The TBVI ranged from 0 to 1, with a mean of 0.137. The red dot indicates the largest TBVI, while the green dot indicates the smallest TBVI. Generally, the values of the TBVI were most pronounced inside the Second Ring Road, first decreasing from the Second Ring Road to the Fourth Ring Road, and then increasing toward the Fifth Ring Road to beyond the Fourth Ring Road. The spatial distribution of the Chinese traditional-style buildings within the Second Ring Road was aggregated overall, and the northern area had a higher concentration than the southern area. However, the spatial distribution was dispersed in the region outside the Second Ring Road.  
The results of the O-TBVI and F-TBVI were shown in Figures 7 and 8. The Moran's I values of the O-TBVI, F-TBVI and TBVI in the study area are shown in Table 4. In the study area, Moran's I of the Chinese traditional-style building's TBVI, the official-style building's O-TBVI and the folk-style building's F-TBVI were 0.27, 0.09 and 0.28, respectively. The results indicated that the spatial distribution of the Chinese traditional-style buildings and its two subcategories presented aggregation and had a positive spatial correlation, in which the positive spatial correlation of the folk-style was the largest. The Moran's I values of the TBVI within the Second Ring Road, between the Second Ring Road (excluding the Second Ring Road) and Third Ring Road, between the Third Ring Road (excluding the Third Ring Road) and the Fourth Ring Road, and between the Fourth Ring Road (excluding the Fourth Ring Road) and the Fifth Ring Road were 0.47, 0.13, 0.033, and 0.145, respectively, which decreased from the Second Ring Road to the Fourth Ring Road, but the trend toward the Fifth Ring  
Figure 6: The overall spatial distribution of the traditional building view index (TBVI) of the Chinese traditional-style buildings.  
Road was increasing. The positive spatial correlation of the TBVI was the largest within the Second Ring Road, while it had the smallest positive correlation from the Third Ring Road to the Fourth Ring Road. The spatial correlation patterns of the O-TBVI and F-TBVI were the same as those of the TBVI. In particular, Moran's I of the O-TBVI was lower than that of the F-TBVI (Table 5), indicating that the Chinese traditional-style buildings that pedestrians perceived from the street level were mainly folk-style buildings.
# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 4 Experimental Evaluation
### Ablation Studies
#### 4.3.2 Effect of Multi-Source Graph Fusion  
The Multi-Source Graph Fusion (MSGF) module contributes significantly to our framework's performance, improving ZSD mAP from 60.3% to 62.1%. This improvement highlights the importance of integrating multiple knowledge sources (Food Safety Knowledge Graph, Hyperclass Graph, and Co-occurrence Graph) for effective zero-shot detection. The individual contribution of each graph source is analyzed in the lower part of Table 6, showing that the Food Safety Knowledge Graph provides the most significant benefit (62.5% mAP) among the three sources.
## 3 Results
### Comparison with Other ET Partitioning Methods  
Due to the unavailability of observed E or T data at the 16 cropland sites, we used three other ET partitioning methods (Z16, N18, and Y22) to indirectly validate the accuracy of our ET partitioning method based on the XGBoost model (hereafter, X24). As shown in Figure 5, the T estimation results of X24 are broadly consistent with the T results of three other methods in the overall pattern, with correlations ranging from 0.83 to 0.91. However, the estimated T values reported in X24 are slightly lower than that of N18 but obviously higher than those of Z16 and Y22. When compared to the ensemble mean values of T from the three other ET partitioning methods (T\({}_{\text{Mean}}\) in Figure 5d), the correlation between T\({}_{\text{X24}}\) and T\({}_{\text{Mean}}\) can reach up to 0.90, which further emphasized the reliability of the XGBoost model in E prediction and T/ET estimation of crops.  
The seasonal variations in the daily T values from the four ET partitioning methods (Z16, N18, Y22, and X24) are further examined at four cropland sites (Figure 6). Despite the diversity of crop species, the daily T time derived from the four ET partitioning methods show similar seasonal trajectories. All of them reach the highest T values during the peak growing seasons of crops. Especially in DE-Rus and FR-Gri, which planted sugar beet and winter wheat, respectively, the seasonal variations in the daily T values from the four ET partitioning methods are very similar, suggesting the consistencies of the four methods in T estimation. While in DE-Kli, T values from Z16 are clearly lower than those from X24,  
Figure 5: Comparison of estimated daily T of the X24 method with three other methods: (**a**) T\({}_{\text{X24}}\) compared to T\({}_{\text{Z16}}\), (**b**) T\({}_{\text{X24}}\) compared to T\({}_{\text{N18}}\) (**c**) T\({}_{\text{X24}}\) compared to T\({}_{\text{Y22}}\), and (**d**) T\({}_{\text{X24}}\) compared to T\({}_{\text{Mean}}\). The T\({}_{\text{Mean}}\) is the mean of the T estimated by the other three methods (Z16, N18, and Y22).  
Y22, and N18. In the paddy rice flux site US-Twt, N18 significantly overestimated the T values in the early and late stage of the crop growing season, while X24 underestimated T during the peak growing season, compared to Z16 and Y22. This highlights the substantial uncertainty in T estimation for paddy rice cropland.
## 2 Methodology
### Classical EM Algorithms  
The EM algorithm consists of four phases, i.e., particle initialization, local search, charge and force calculation, and moving particle along the direction of the resultant force. The flowchart of the algorithm is shown in Figure 2.  
Step 1: Particle initialization. Initialization is performed to randomly select a certain number of points from the known feasible domain as the initial particles, then calculate the objective function value \(f(x_{i})\) for each particle, and denote the particle with the best objective function value as \(x_{best}\).  
Step 2: Local search. The local search is performed on a single particle and is used to improve the solution already searched by the population. The local search used by the basic EM algorithm is the simplest linear search, where each dimension of the population's current optimal particle \(x_{best}\) is searched in a certain step size, and the current optimal particle is updated once a better solution is found.  
Step 3: Charge and force calculation. Firstly, the charge \(Q_{i}\) of particle i is calculated as in Equation (4), and then, the resultant force \(F_{i}\) of each particle is calculated by imitating the formula of force in electromagnetic field (see Equation (5)). Of the two particles, the particle with the smaller value of the objective function attracts the other particle and, vice versa, repels it.  
\[Q_{i}=exp\left\{-n*\frac{f(x_{i})-f(x_{best})}{\sum\limits_{k=1}^{m}[f(x_{k}) -f(x_{best})]}\right\},\forall i. \tag{4}\]  
As can be seen from Equation (4), the smaller the value of the objective function, the greater the charge carried by the particles and the stronger their attraction. The charge carried by each particle in the population is not positive or negative, which is different
## 2 Materials and Methods
### Composition of the GCIM  
The GCIM is mainly composed of four primary materials: graphite polystyrene particles (GPPs), glass microspheres (GMs), cement, and silica fume (SF) [31; 32; 33]. It has excellent insulation performance and can meet Class A fire protection requirements, making it widely used in the field of external wall insulation in buildings. GPPs are the main insulating aggregate in the GCIM. As shown in Figure 2, the GPPs are spherical after foaming treatment, with a size ranging from 1 to 5 mm, gray in color, and characterized by low TC and good fire resistance. GMs, as an additive material, primarily enhance the compressive strength of the material. Additionally, they have a low TC, ranging in size from a few micrometers to several hundred micrometers. The inclusion of cement and SF serves a dual purpose: acting as a binder and improving the overall mechanical performance and durability of the insulation material.  
GPPs are the primary insulating and fireproof material, derived from improvements made to polystyrene particles by coating them with graphite powder to enhance their fire resistance. According to literature research, the TC of the four components of the GCIM is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Components** & **GPPs** & **Cement** & **GMs** & **SF** \\ \hline TC (W/m\(\cdot\)K) & 0.0376 & 0.453 & 0.046 & 0.151 \\ VRs & \(\Psi_{1}\) & \(\Psi_{2}\) & \(\Psi_{3}\) & \(\Psi_{4}\) \\ \hline \hline \end{tabular} Note: \(\Psi_{1},\Psi_{2},\Psi_{3}\) and \(\Psi_{4}\) respectively represent the volume ratios (VRs) of the four component materials.  
\end{table}
Table 1: TC of the component materials.  
Figure 1: The research framework.  
The GCIM is composed of four main components. These four raw materials are mixed with water at room temperature, and additives are added to form insulation aggregates. The mixture is then shaped using molds and placed in a drying machine until it reaches the required strength, after which it is cut to the desired dimensions. The specific processing workflow is illustrated in Figure 3.
# Chalcogenide Thin Films--Holographic Media for Augmented Reality Devices
## 2 Modeling Results
### Efficiency-Thickness Modeling
To reach the minimal possible DE for input HOE (10%), the least possible \(d\) should be 0.9 \(\mu\)m for \(\Delta n=0.15\) and 2.9 \(\mu\)m for \(\Delta n=0.05\). 4. To fulfill the \"TIR window\" (which, in case of waveguide with \(n_{0}=1.9\), is 54 deg), \(d\) should be from 0.5 \(\mu\)m (for \(n_{0}=1.8\)) to 0.65 \(\mu\)m (for \(n_{0}=2.2\)). We can make the following conclusions from the modeling results:  
Figure 3: The dependence of maximum diffraction efficiency (**a**) and FWHM of angular selectivity contour (**b**) on ChG layer thickness for different average refractive indices and refractive index modulations. Probing beam is s-polarized. 1. The desired HOE thicknesses range is from 0.5 to 1 \(\upmu\)m. Less will be not selective enough (diffraction bleaches will start to occur), and more will be too selective (>2 exposures will be needed to fulfill the \"TIR window\"). 2. Until the waveguides with \(n_{0}\) > 1.9 are made, there is no need for material with \(n_{0}\) > 1.9 since it does not provide any huge benefits (the TIR window for waveguides with \(n_{0}\) = 1.8 is 50 deg). 3. The \(\Delta n\) = 0.05 can be enough for the output, but for effective input, it should be \(\Delta n\) > 0.1. Moreover, \(\Delta n\) parameter is the key one in terms of the performance increase. 4. It is necessary to make at least two exposures for each HOE since brightness uniformity will not be good enough with single exposure (for single exposure, the brightness fall will be about 50% on the edges and for two exposures, about 10%). Correspondingly, the ChG material for HOE recording should have the following parameters:  
1. \(n_{0}\) > 1.8 (1.9 is preferred; >1.9 is also considered but not necessary). 2. \(\Delta n\) > 0.1 (>0.15 is preferred). It is a key parameter. 3. \(d\) from 0.5 to 1 \(\upmu\)m.
4. Ability to record multi-exposure HOEs.
# A Review of the Scheduling Problem within Canadian Healthcare Centres
## 4 Hospital Scheduling in Depth
### Outpatient Scheduling
[PERSON] and [PERSON] [46] tried several measures of schedule performance and found that depending on which was used, the results varied greatly. The five that were settled on were:  
* Cost of client waiting time. * Cost of waiting and server idle time. * Cost of waiting and overtime. * Cost of waiting, idle, and day end time. * Cost of waiting, idle, and overtime. One of their main priorities was using these metrics to determine now scheduling patterns. They found that the best schedules tended to have a constant distribution throughout and this was called the dome pattern. A dome pattern holds a value near constant for a majority of the range of the independent variable, causing the graph to visually display a rise, a plateau, and a fall. By creating a constraint to represent the dome, they believed that it would serve as a useful pattern for scheduling. Adding in constraints to represent this pattern only reduced evaluations by 0.22% proving its legitimacy. To further test this pattern they experimented with patient no-shows and found that on average patient no-shows only led to a 0.19% drop in performance on average. It also faired well when given variable session length. [PERSON] et al. [49] focus on a different area of scheduling. They worked in tandem with the Nephrology Division at The Ottawa Hospital to schedule home dialysis machines for patients with kidney failure. Their contribution is a decision support system built upon a mixed integer programming model. The ILP creates the schedules which are passed onto the nurses to help inform their itineraries. Home dialysis scheduling offers unique challenges not present in any work discussed this far as it has to incorporate constraints such as travel time and distance. The decision support system incorporates breaks, overtime, cost, and lunches among other considerations. Their model was tested and evaluated using data supplied from The Ottawa Hospital and it was found to improve work distributions and minimize cost, and distance.
# A Review of Physical Layer Security Techniques for Internet of Things: Challenges and Solutions
## 2 Literature Review of PLS Techniques for IoT
### PLS Schemes for IoT: State of the Art
#### 2.2.1 Artificial Noise Injection  
The principle of the artificial noise (AN) injection approach is to simultaneously send the information-bearing signal and the AN to degrade the performance of the eavesdropper. The information-bearing signal and the AN are injected into the range space and the null space of the legitimate user's channel matrix, respectively. In this manner, the AN only deteriorates the eavesdropper but has little detrimental impact on the legitimate receiver [27]. AN injection is an effective means to create the channel quality advantage for the legitimate transmission link. However, most of the AN-based PLS schemes rely on the the deployment of multiple antennas at the transmitter [28; 29], which violates the low-cost and small-size requirements of IoT devices. To address this issue, the cooperative AN injection becomes a promising solution to ensure the IoT transmission security. Ref. [30] studied secure downlink transmission from a controller to an actuator with the help of a cooperative jammer to fight against multiple passive and non-colluding eavesdroppers. In [31], a wireless powered jammer is employed to improve the secrecy rate of an orthogonal frequency division multiplexing (OFDM) system, where the jammer operates with the harvest-then-jam protocol. In [32], a power allocation policy was developed to maximize the secrecy information rate while maintaining the harvested energy requirement of the energy receiver. The above works [30; 31; 32] are concerned about the design of the cooperative AN injection strategies. To reveal the security performance achieved by these strategies, [PERSON]. [33] analyzed the secrecy outage probability of the system with multiple cooperative jammers and multiple eavesdroppers. The combination of AN injection with other secure transmission techniques can further improve the system security performance. [PERSON]. [34] integrated the AN injection into the fountain coding based secure transmission framework. By transmitting the AN and the useful signal over two orthogonal channels, the intercept probability can be significantly lowered down and the legitimate user's delay performance is guaranteed as well. [PERSON]. [35] combined the compressive sensing technique with the AN injection approach, thus improving the system security performance while reducing the feedback overhead.
## 2 Materials and Methods
### Search Strategy  
A standardized electronic literature search strategy was conducted across six databases--Google Scholar, PubMed, Embase, Cochrane, Scopus, and Web of Science--covering studies published between 2009 and September 2024. The search strategy applied Boolean operators and MeSH terms where applicable, using the following keyword combination: ('Badminton' AND ('biomechanics' OR 'kinetics' OR 'kinematics') AND ('shoe' OR'shoes' OR 'footwear') AND ('midsole' OR'shoe heel' OR'sole' OR 'inside' OR'stiffness' OR 'torsional stiffness' OR 'lateral-wedge hardness' OR'shoe last' OR 'toe wedge' OR 'heel to toe drop' OR 'foot morphology' OR 'cushioning' OR 'energy return')).  
In addition to database searches, manual screening of reference lists from relevant journals, including _Footwear Science_, was performed to identify additional studies that met the inclusion criteria.  
The selection of 2009 as the starting point was based on significant advancements in sports footwear technology, particularly in midsole cushioning, gender-specific designs, and biomechanical testing methodologies. Older studies may not reflect contemporary badminton shoe designs and were therefore excluded. For this revision, the search was extended to May 2025 to include the latest advancements, ensuring the review reflects state-of-the-art research.  
The study selection process adhered to PRISMA guidelines and is illustrated in Figure 1.  
A total of 445 records were identified through database searches. After removing 33 duplicates, 412 records were screened by title and abstract. Of these, 363 were excluded due to irrelevance or methodological issues. The full texts of 15 articles were assessed for eligibility, and 10 studies were included in the final analysis.  
Given that most existing studies focus on male athletes, this review did not exclude studies based on gender. However, considering the biomechanical disparities in foot morphology, load distribution, and movement strategies between male and female players, the potential influence of gender differences on the interpretation of results was emphasizedin the discussion. Future research should prioritize gender-balanced samples and sex-stratified biomechanical analyses to refine personalized footwear recommendations.
###### Abstract  
This research aims to evaluate the reliability indices of a public address system (PAS) through the Markovian approach. Many organizations and businesses use this system to address or broadcast a message or pass on important information to the huge gatherings assembled at big premises. This system is the nerve of these organizations and businesses. The major components of a PAS are a microphone, mixer, amplifier, and speaker. These components should work in harmony with one another to execute the intended task. Any failure in these components leads to big issues for the public, and they may miss very important information. Therefore, the reliability assessment of this system is of utmost importance. The authors used the Markovian decision process to model the PAS by analyzing the various failure rates and repairs of the components. The explicit expressions for reliability, availability, and MTTF have been obtained for clear understanding about the PAS behavior with time as well as different failures. The sensitivity analysis of reliability is performed as well to determine the critical components of the system. The obtained results show that the reliability of the PAS at 2000 operated hours is 0.8. Also, the finding reflects that the PAS reliability is much sensitive with the failure rate of microphone, mixer, and amplifier.  
public address system; microphone; mixer; amplifier; reliability indices; Markov process Article
## 5 Results and Discussion
However, these settings can be disabled when the monitoring is conducted remotely from home, but these functions can be enabled when it is deployed in public care areas like hospitals to track and trace events in and around the patient for their safety and security. Figure 10 shows a warning message, while Figure 11 shows the alert message that is triggered when someone comes too close to the device, and Figure 12 shows the activation of the camera when someone attempts to take or move the IoMT device. These systems are necessary to give alerts on disturbances to the surroundings and also connect with the concerned stakeholders of the user. * Visualisation of the Collected Data: The health and well-being environmental data that are collected from IoMT sensors can be viewed by the stakeholders through the IoMT server and web server. The screenshots of the temperature reading and moisture level of the skin when holding the sensors were collected using temperature and humidity sensors, and the results are shown in Figure 13. The spikes in the results are the results of blowing warm air through the mouth, which guarantees proper working of the system. The readings are taken from a snapshot record from 11:51:33 (AM) to 12:58:17 (PM), and the readings are taken every 5 s and updated on the server only when there is a change in the reading value; however, the IoMT client pushes the last recorded data even if there is no change if the time lapse over 5 min to ensure that the connection is live. Figure 10: Warning message so that the device is not moved. Figure 14 shows the measuring and monitoring of the air quality and the movement of people around the monitoring system. The positive incremental spikes from the normal reading in the air quality show that the air quality was decreased, which happened when five people sat around the IoMT sensor (more released CO2), and the lower reading value of air quality occurring towards the end of the reading shows that the air quality  
Figure 11: Warning when coming too close. Figure 12: Capturing evidence if the system is moved. Figure 13: The body temperature and moisture level. was improved, which was triggered by the opened windows. The distance between the proximity sensor and the wall was measured as 110 cm, and the spikes are the result of introducing a human hand movement towards the proximity sensor at different times. The light and noise levels were measured and recorded, as shown in Figure 15. The reading above 600 Lux is due to the introduction of more lighting around the sensor, and the reading going down is due to the closing of the window curtains. The normal lab sound record is 65 Db, the spikes are due to the noise of the servers running in the lab room, and the higher spikes are due to the introduction of a random human noise.