esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Predictive Models Developed Using ANN
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Data Training Percentage**} \\ \cline{2-4}  & **70\%** & **80\%** & **90\%** \\ \hline B1–H1 & 5.4039439 & \(-0.1157967\) & 4.5469032 \\ \hline I1–H1 & \(-15.0766425\) & \(-5.3827556\) & \(-11.0499554\) \\ \hline I2–H1 & 1.5627953 & \(-3.4223409\) & 2.7245021 \\ \hline I3–H1 & 2.7587930 & 2.9422321 & 0.9683699 \\ \hline I4–H1 & \(-0.1562694\) & 4.3960790 & \(-1.9540237\) \\ \hline B1–H2 & \(-13.793552\) & \(-9.118127\) & \(-3.2660749\) \\ \hline I1–H2 & 6.031370 & 2.836462 & 0.7803872 \\ \hline I2–H2 & 6.958133 & 12.101105 & 6.6604275 \\ \hline I3–H2 & 4.942339 & 1.334247 & \(-0.7337680\) \\ \hline I4–H2 & 7.859338 & 9.306791 & 5.1178065 \\ \hline B1–H3 & \(-4.721011\) & 4.775554 & \(-8.241048\) \\ \hline I1–H3 & 4.605214 & \(-5.185507\) & 1.584072 \\ \hline I2–H3 & \(-5.536911\) & 7.562261 & 6.583553 \\ \hline I3–H3 & \(-13.412774\) & 8.626503 & \(-2.312179\) \\ \hline I4–H3 & 10.487666 & \(-9.557800\) & 6.642880 \\ \hline B1–H4 & \(-2.739654\) & 10.686570 & \(-3.080241\) \\ \hline I1–H4 & 9.506876 & \(-1.389448\) & \(-6.917437\) \\ \hline I2–H4 & 6.985396 & \(-7.260299\) & \(-1.723676\) \\ \hline I3–H4 & \(-4.915390\) & 4.507227 & \(-2.475820\) \\ \hline I4–H4 & \(-5.293412\) & \(-11.680316\) & 10.804223 \\ \hline B1–H5 & 10.342958 & 2.1681860 & 5.354134 \\ \hline I1–H5 & \(-5.340765\) & \(-11.4221402\) & \(-2.957952\) \\ \hline I2–H5 & \(-12.073148\) & \(-0.8285786\) & \(-7.420634\) \\ \hline I3–H5 & \(-1.495051\) & \(-5.1982137\) & \(-1.183222\) \\ \hline I4–H5 & \(-9.681449\) & 9.6726127 & \(-4.575078\) \\ \hline B1–H6 & 1.1958247 & 4.9131114 & 5.565350 \\ \hline I1–H6 & \(-12.1059627\) & \(-13.9610034\) & \(-4.021247\) \\ \hline I2–H6 & 0.0344864 & 2.4310158 & 5.553954 \\ \hline I3–H6 & \(-9.2652208\) & 2.2366086 & 5.455334 \\ \hline I4–H6 & 12.0185146 & 0.2850172 & \(-10.527717\) \\ \hline B2–O1 & 14.765571 & \(-17.24445758\) & \(-0.62485862\) \\ \hline H1–O1 & \(-6.662708\) & 6.63597309 & 0.90165390 \\ \hline H2–O1 & \(-10.577831\) & 9.95768136 & 5.92878925 \\ \hline H3–O1 & \(-7.284002\) & 3.98047085 & \(-9.87260412\) \\ \hline H4–O1 & \(-8.744731\) & 9.88497394 & 0.08648525 \\ \hline H5–O1 & \(-12.319315\) & \(-0.06539451\) & \(-7.63236367\) \\ \hline H6–O1 & \(-4.108802\) & \(-7.477772434\) & \(-0.07664491\) \\ \hline B2–O2 & \(-8.337048\) & 6.135447 & \(-3.107592\) \\ \hline H1–O2 & \(-9.444444\) & \(-1.290546\) & \(-9.655582\) \\ \hline H2–O2 & \(8.975756\) & \(-10.614987\) & \(-6.024728\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The biases and weights of the ANN model developed using tanh activation function. Below were the tables of performance measurements of both models. 1. ANN model developed using logistic activation function  
Based on Table 7, it is shown that the ANN model with 70% training data yielded the highest accuracy (90.91%), sensitivity (91.11%), and specificity (95.51%).
## 6 Conclusions  
FM consists of complex operations and activities. Therefore, BI&A are heavily used in FM to find out hidden relationships and to perform more comprehensive FM performance analysis. Although BI&A presents a promising feature to measure the success of FM, existing healthcare FM KPIs are crude. To measure efficiency of these operations and activities, this study aims to identify and rank healthcare FM KPIs.  
In the study, a literature review was performed to identify FM KPIs. As a result of the literature review, one hundred sixty-seven FM KPIs were identified under six groups. These FM KPIs were shortlisted by the expert. As a result of the shortlist, ninety-eight FM KPIs were found to be usable in healthcare FM performance benchmarking. These FM KPIs were evaluated by ten FM experts and survey results were analyzed with the AHP-PROMETHEE method to obtain the importance order of FM KPIs. Analysis results showed that fifty FM KPIs are very dominant against the remaining forty-eight FM KPIs.  
According to analysis results, \"Prediction of Resource/Energy load profile (E4)\", \"Prediction of indoor air parameters and indoor quality (U2)\", \"Number of accidents per year (S2)\", \"Preventive maintenance ratio (M6)\", \"Condition index (P5)\", and \"O&M cost (including depreciation and amortization costs) per building area/per patient bed (L17)\" were found to be the most important FM KPIs under their groups. Analysis results were also compared with existing studies. This showed that there are some similarities and differences between China, Hong Kong, and Turkey. However, differences can be related to the usage of limited FM KPIs in the China and Hong Kong cases.  
This study provides insights about BI&A-related FM healthcare KPIs and their ranks specific to Turkish healthcare facility management. These FM KPIs give clues about BIM information types to perform performance management with BIM and obtain dynamic data requirements of BIM. Furthermore, the determination of FM KPIs will increase awareness of facility managers in the selection of FM tools for healthcare facilities.  
The presented results are based on questionnaire data originating in Turkey. Moreover, comparisons were made between China, Hong Kong, and Turkey. However, considered KPIs that are used in the China and Hong Kong cases are lower than the number of used KPIs in this study. Therefore, there are some limitations that are worth noting. As a further study, the FM platform (works with BIM, database, Big Data Engine--Hadoop) will be developed by considering this study's results for FM KPIs, which have positive phi value and FM information query requirements.  
Conceptualization, G.D., Z.I. and Y.A.; investigation, G.D., Z.I. and Y.A.; writing--original draft preparation, G.D., Z.I. and Y.A.; writing--review and editing, G.D., Z.I. and Y.A.; supervision, Z.I. and Y.A.; project administration, Z.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 The Motion State Estimation Method
### Moving Object Segmentation  
After estimating the ego-motion, the moving objects within the point cloud are segmented using the model illustrated in Figure 2. After obtaining the multi-frame point cloud data that eliminate ego-motion, the point cloud is projected into a range image with tighter information (Section 3.3.1). The range image is differenced to obtain the differential image, and the differential image is incorporated into the range image to form a 5D range image (Section 3.3.1). The 5D range image is then fed into a CNN segmentation model for moving object segmentation [4].
## 3 P-MAC Design Integrated with Routing
### Network Model  
P-MAC is proposed for WSNs deployed for rare events detection with prompt reporting. Applications including fire or other hazards detection fall into this category. Such a network consists of many sensor nodes randomly deployed in a sensing area with one (or a few) sink(s) collecting information for an outside system. All nodes are assumed to be homogeneous with the same transmission range.
# UV-Induced Aging in Thermochromic Pigment-Integrated Food-Grade Polymers: A Performance Assessment
## 3 Results
### Visual Property Results  
Figure 4 displays the visual changes observed in impact test specimens of the PP98/TP2 and PP92/TP8 blends before and after UVA exposure. These samples were aged using the RS 559-934 chamber, which simulates indoor UV conditions. With an apparent shift from deep violet to mild pink and beige tones, Figure 4 shows a steady loss of color intensity throughout both formulations. This diminished color intensity indicates pigment degradation and polymer surface oxidation over time [73]. The more noticeable fading observed in specimens exposed to longer exposure times supports the time-dependent character of photo-induced discoloration [74].  
Figure 5 presents the same formulations aged under outdoor-simulated (QUV/Solar Eye) UVA conditions. Compared to the previously indoor-aged specimens, the QUV-exposed samples exhibit more significant discoloration, particularly after 1000 h, with some specimens losing most of their original chromatic intensity (Figure 5F). Notably, a square-shaped region of preserved color is visible in samples (B) and (E). This localized retention of the original hue is attributed to the sample mounting method during UV exposure.  
As shown in Figure 6, the test specimens were secured in metal frames prior to aging in the QUV weathering chamber. These frames partially covered sections of the specimen surfaces, creating a shadowed region shielded from direct UVA radiation. There is a clear visible contrast between the exposed and protected portions due to the covered areas not experiencing the same level of photodegradation. This phenomenon emphasizes how  
Figure 4: Visual appearance of the PP98/TP2 and PP92/TP8 impact test specimens before and after UV exposure: (**A**) PP98/TP2 prior to UV exposure; (**B**) PP98/TP2 after 500 h of simulated indoor UV exposure; (**C**) PP98/TP2 after 1000 h of simulated indoor UV exposure; (**D**) PP92/TP8 prior to UV exposure; (**E**) PP92/TP8 after 500 h of simulated indoor UV exposure; (**F**) PP92/TP8 after 1000 h of simulated indoor UV exposure; simulated indoor UV exposure.  
crucial consistent sample exposure is in UV aging studies. It supports the finding that changes in surface color are closely related to the total amount of UV light received [75; 76].  
Overall, the visual results correlate well with the quantitative colorimetric data (\(\Delta E^{*}_{\text{ab}}\) values), confirming that both formulations--especially the more pigment-rich PP92/TP8--are susceptible to UV-induced discoloration [77]. These observations underline the need for effective UV stabilization strategies in thermochromic systems intended for environments with prolonged light exposure.
# The Long-Term Consequences of Forest Fires on the Carbon Fluxes of a Tropical Forest in Africa
## 4 Discussion
Some studies related to fire consider not only the loss of biomass but also the change in species composition [64; 65]. In this simulation study, a significant change in species composition was also observed. The forest without disturbance was dominated by climax species; in the simulated forest with fire, the proportion of pioneer species increased. Thus, the species composition looked much more diverse. This was evaluated by calculating the normalized Shannon Index: in the undisturbed forest it was 30%, and in the fire-disturbed forest (fire events every 4 years), it reaches up to 80%. This means that fire events can increase functional diversity. In this study the relationship between fire frequency and species diversity was also investigated, which is also a topic of the intermediate disturbance hypothesis (IDH) [66]: \"The highest diversity of tropical rainforest trees should occur... with smaller disturbances that are neither very frequent nor infrequent\". According to this theory, ecosystems respond to light but regular disturbances with increasing species diversity. Species diversity shows a maximum at medium disturbance intensity. According to the IDH, however, species diversity declines again when disturbances are intense. This study shows a maximum of species diversity when 2-3 forest fires per decade occur in a region. Empirical studies in a forest-grassland ecotone showed similar results with a maximum of heterogeneity at five fire events per decade [65]. This simulation study at Mt. Kilimanjaro revealed that the investigated forest shows responses to disturbances similar to the IDH. Physiological trade-offs between different tree species--here the trade-off between growth and mortality--might be an important factor for this phenomenon. However, it must also be noted that with 2-3 fires per decade, the tropical forest has a much lower biomass level than an undisturbed forest, with negative consequences for its carbon balance.
## 6 Results and Discussions
### System Performance
The reason is that steam is condensed to become freshwater after exhausting from the SF steam turbine, and the steam is originated from the vapor at the outlet of TF turbine. In this way, lower wellhead pressure means higher vapor quality of the geothermal water, which would increase the vapor quality at TF turbine outlet. On the other hand, lower separating temperature also leads to higher vapor fraction at the outlet of TF turbine, revealing that freshwater generation is an advantage of total-flow expansion system. Freshwater of the TF-SF system is thus increased by the combination of these two factors. For example, a low wellhead pressure of 436.9 kPa can lead to a higher freshwater amount of 4.7 kg/s, which accounts for 30.8% of the total wellhead flowrate, but at higher wellhead pressure of 940.5 kPa, a low freshwater amount of 2.5 kg/s would be reached, which only accounts for 17.9% of the total wellhead flowrate.
# Contour Mission Flight Planning of UAV for Photogrammetric in Hillside Areas
## 3 Simulation Test
### Case 2: Maolin District
#### 3.2.3 Elevation Difference in the Same Image  
Figure 7c shows that 75% of the contour-type images have less than a 30 m elevation difference, while 35% of the grid-type images have less than a 30 m elevation difference.  
The rugged sloping terrain in Case 2 is more undulating than in Case 1. The results of both terrain simulations show that the contour-type can better match the user's setting conditions and effectively reduce the effect of topography on altitude. Because of the high correlation between altitude and overlap rate, contour-type mission planning can maintain the same height and help to keep the overlap. The contour calculation process considers the amount of elevation variation, which can effectively control the elevation change within the image coverage.  
Figure 7: Case 2: Analysis of route planning results. (**a**) Image shooting distance, (**b**) overlap rate, (**c**) elevation difference.
## 5 Security Analysis
### Decryption  
Data generators encrypt the file (\(E=(\{E_{1 ij},E_{2 ij},E_{3 ij},E_{4 ij}\}_{j\in[1,n]})\)) with the suggested framework. They then transmit the ciphertext (\(E\)) to the FS in order to exchange sensingdata. The \(dr\) uses its private key (\(x\)) to decrypt \(Sp_{ggg}\) and retrieve the symmetric key (\(SS_{agg}\)) after receiving the result \((E^{1}_{agg},E^{2}_{agg})\) from the FS. After that, the \(dr\) computes \(d_{i}=SS_{agg}\oplus E^{1}_{agg}\) to retrieve the sensing data. Therefore, the suggested plan prevents a third party from decrypting the ciphertext.
## 3 Data and Methods
### Vegetation Indices
#### 3.2.3 Gcc  
The green chromatic coordinate (GCC) index is based on the visible spectral wavebands using red, green, and blue bands with no information from the infrared band. The GCC is calculated as the ratio of green to the sum of the three bands (Table 2). It is correlated with the amount of green vegetation tissue, and is widely used in proximal sensing such as in-field PhenoCams [41; 42] and digital camera [43; 44]. The GCC is also used in vegetation monitoring from unmanned aerial vehicles (UAV) because of the relatively low cost RGB sensors mounted on UAVs [45; 46; 47].  
The use of the GCC also expands the possibility of using images from Planet Doves prior to January 2016 when they acquired images only in the RGB spectral bands (since early 2015).
## 4 Experiments
### Implementation Details
#### 4.2.2 Environment and Experimental Setup  
Except for special instructions, all experiments were trained and inferred using Python 3.8 and MMRate 0.3.4 with PyTorch, the Windows 11 operating system, and an NVIDIA GeForce RTX 3090 GPU. All hyperparameters trained follow the default MMRate configuration. The training batch size was set to 2 using the AdamW optimizer. The binary cross-entropy classification loss and the L1 regression loss function were used. In the classifier learning stage, extra freeze training for 12 epochs and the classification loss function are referred to in Section 3.4.2. The training and validation sets of DOTA were used as training data for fair comparison with other models in MMRate. Unless otherwise specified, all models in the comparative experiment are default references to the author's paper or publicly available data on GitHub. The backbone network LSKNet was pre-trained with 300 epochs on ImageNet. Due to the original author using 4 GPUs for the experiment and the lack of single-scale training results in the paper results, the learning rate was set to \(5\times 10^{-5}\) after multiple optimizations to find the best learning rate, and a fair comparison was conducted on a single GPU. Finally, LSKNet-T was used as the baseline model and subjected to a fair comparison.
## 2 Continuous Wave Data and Uniqueness in Parameter Recovery
For the source-detector distances of \(\rho=10\), \(20\), \(30\), \(40\), \(50\) mm, and wavelengths \(\lambda=650\) to \(850\) nm, in steps of \(10\) nm, the boundary intensity is simulated using NIRFAST [15], which is an open-source finite-element-based model for simulation of light propagation in biological tissue. Figure 2 shows the spectral derivative of intensity data for two distinct parameter sets corresponding to \(\alpha=1\) and \(0.5\), which are clearly coinciding with each other and their corresponding intensity data is shown in \[-\
abla^{2}\Psi+\eta\Psi=\frac{q_{0}}{\gamma}. \tag{1}\]0  
As evident in Figures 2 and 3, while the intensity data remains distinct for two different ground-truth parameters, the derivative data shows very little variation and therefore demonstrates the non-uniqueness of derivative of intensity data. Hence, in this work, only the intensity data is considered as the measurement for the recovery of absolute tissue parameters. Although uniqueness in terms of intensity data is established from above, one should remember that in a practical scenario, finite independent measurements, noise in the measured data, any mismatch in the assumed model, or the stopping criterion of an iterative fitting method algorithm, can lead to potential recovery errors. \[-\
abla^{2}\Psi+\eta\Psi=\frac{q_{0}}{\gamma}. \tag{1}\]1 Intensity data at \(\alpha=1\) and \(0.5\). \[-\
abla^{2}\Psi+\eta\Psi=\frac{q_{0}}{\gamma}. \tag{1}\]2 1 st Spectral derivative of intensity data at \(\alpha=1\) and \(0.5\).
Even with the automatization and efficiency of recent equipment, several problems are still prevalent and hinder data quality. Recurrent issues, such as decomposition during distillation, incomplete recovery of the amines, interference of the reagents, among others, contribute to bias between results obtained by the different methods, and overestimation of the measured compounds [2; 4]. The comprehensive and versatile headspace solid-phase microextraction (HS-SPME), a solvent-free extraction technique that incorporates extraction and concentration in one single step, has demonstrated unique capabilities for the quantitative analysis of volatile components in a wide variety of food samples [13; 14; 15]. However, as with other methods, there is still a lack of standardization in the extraction protocol, fiber selection, and conditions for GC-MS analysis (Table S2, Supplementary Material). The analytical quality-by-design (AQbD) approach has been recognized as a reliable methodology for the development of analytical measuring systems and promoted by several international regulators, such as the International Conference on Harmonization [16]. The AQbD principals started on risk management, which initiates with the understanding of the control of the system process and product, through predefined objectives to promote the overall quality [17; 18; 19]. The application to the analytical methods is supported by the following four key steps: (i) Selection of the analytical target profile (ATP), (ii) definition of a method operable design region (MODR), (iii) integration of risk assessment, and (iv) multivariate statistical analysis. All of them contribute to the overall knowledge of the method and quality verification [17; 18; 19]. The AQbD concept is commonly applied to pharmaceutical development and was recently introduced in the analysis of foods, reinforcing its contribution to the methods harmonization [20; 21; 22]. The purpose of this study was to use the AQbD approach as a powerful strategy to determine the optimal analytical conditions for the quantification of TMA and DMA levels in _Sparus aurata_ (gilthead seabream, GSB) with origin from aquaculture, by HS-SPME/GC-MS methodology. The optimization approach will allow the selection of the best points in the MODR for HS-SPME and GC-MS analysis. The HS-SPME/GC-MS results will be compared with data from physical, microbial, and sensory analyses. Additionally, the rejection day for the gilthead seabream will be estimated based on the data results from all the analyses.
## 3 Results and Discussion
### Effect of the Zigzag Ratio on the Friction Factor  
The effect of zigzagging the rib heights on the friction factor is shown in Figure 9. The friction factor values decrease considerably as the \(\mathrm{e_{2}/e_{1}}\) ratio decreases. At \(\mathrm{e_{2}/e_{1}}\) values of 0.25 and 0.5, the friction values decrease by 27 and 25%, respectively, compared with the equal rib height case, \(\mathrm{e_{2}/e_{1}}=1\), respectively. The highest friction factors occur at \(\mathrm{Re}=10\),000, and then they decrease up to \(\mathrm{Re}=30\),000 for each value of \(\mathrm{e_{2}/e_{1}}\). After \(\mathrm{Re}=30\),000, the behavior of the friction factor changes according to the values of \(\mathrm{e_{2}/e_{1}}\). The friction factor increases after \(\mathrm{Re}=30\),000 for \(\mathrm{e_{2}/e_{1}}=0.25\) and 0.5, remains constant at \(\mathrm{e_{2}/e_{1}}=0.75\), and continues decreasing at \(\mathrm{e_{2}/e_{1}}=1\). However, approximation of the behavior of the friction factor shows that it does not change with Re for \(\mathrm{Re}>20\),000.  
Figure 7: Change in the average Nusselt number (Nu\({}_{\mathrm{av}}\)) with the rib height ratio (\(\mathrm{e_{2}/e_{1}}\)) at two Re number values and a Pr number of 7.0.