esa-sceva/satcom-chunk-collection huggingface dataset:
# Coordinated Optimization of Feeder Flex-Route Transit Scheduling for Urban Rail Systems
## 5 Result Analysis
### Case Analysis
In Figure 6, \"Equal weights with original departure interval\" represents the total operating cost of the system under the default weight values, which aligns with the data presented in Table 2. \"Attach importance to passengers with original departure interval\" refers to the system operating cost at the original departure interval under adjusted weight values. \"Attach importance to passengers with short departure interval\" represents the system operating cost when the departure interval and average vehicle running time are reduced accordingly. As the passenger weight coefficient increases, the system operating cost under normal conditions also rises. At this stage, decreasing the departure interval, increasing the departure frequency, and expanding the number of operating vehicles significantly reduce passenger waiting and late arrival times, leading to a corresponding decrease in total system cost. However, as the number of vehicle departures increases, the corresponding operating costs also rise, limiting the extent of overall system cost reductions. Figure 5: Comparison of average vehicle running time changes under different demand levels. Figure 6: Comparison of system operation cost changes under different demand levels. Specifically, the routine simulation verification is based on a passenger demand level of 50 people per hour, as shown in Figure 7. In the figure, red dots represent origins where passengers transfer from flex-route transit to the subway, while green dots indicate destinations where passengers transfer from the subway to flex-route transit. The subway arrives and departs from the interchange station every 10 min between 7:00 and 7:59, while flex-route transit departs from the feeder area boundary at 7:00. The departure interval and corresponding system operating parameters are computed by prioritizing passenger-related considerations. Based on passenger reservation data and relevant parameters, demand allocation is performed to derive the collaborative optimization scheme for vehicle running path and timetable scheduling, as presented in Table 3.  
Figure 7: Relative position of flexible demand points in feeder service area. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Vehicle Shifts**} & \multicolumn{2}{c}{**Departure Time of**} & \multirow{2}{*}{**Vehicle Running Path**} & \multicolumn{2}{c}{**Cumulative Number**} \\  & **Interchange Station** & & **of Passengers** \\ \hline
1 & 7:13 & 14-2-13-10-9-0-50-32-35-26-38-49 & 11 \\
2 & 7:23 & 6-3-12-25-0-41-29-44-40 & 8 \\
3 & 7:33 & 23-16-5-11-1-0-42-33-36-28 & 9 \\
4 & 7:43 & 18-4-7-19-15-17-0-27-46-43-30-48-47 & 12 \\
5 & 7:53 & 20-21-24-8-0-37-31-39-34-45 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Operation path, service time, and service demand of feeder flex-route transit.
## 3 Results and Discussion
### New Technologies in Ecosystem Delimitation  
LiDAR data proved to be valuable for delimiting the habitats in which the edges were associated with variations in relief, enabling up-to-date characterization of the lake surface with an uncertainty of around 4%, compared to remote sensing techniques, except in the case of the Louro lake. However, lake zones should be defined by taking other data sources into account, because LiDAR analysis may include part of the areas around the lake where there is little variation in relief [56]. This was clear in the Vixan lake where the TRI values of the cropped area were similar to those of the lake surface.  
To improve the management of these environments, detailed studies should be conducted to monitor the seasonal and annual variations in the lake systems. Field surveys should be carried out to analyze water and sedimentary parameters. The use of Unmanned Aerial Vehicles (UAVs) could be valuable for quantifying spatial variations and for estimating vegetation growth and other related parameters [57; 58; 59; 60].
# Improvements of the xAAL Home Automation System
## 3 The xAAL Devices Behavior and xAAL Schemas  
xAAL devices are described by so-called schemas. Schemas are documents specifying attributes, notifications and methods API.  
Note that the idea of specifying home automation devices by schemas is inspired by the UPnP approach [24].  
Thanks to that, UPnP entities could discover new entering devices, ways to interact with them, and possibly dynamically build user interfaces (for this latter point, UPnP schemas usually pointed to some Microsoft DLL installers or Internet Explorer plugins to be installed on the user's PC, which is not very secure).  
The other advantage of a formal specification of home automation devices is for software production. Having a clear API specification may help to automatically build skeletons of code. That was the case with the XDR (External Data Representation) for Sun's RPC (Remote Procedure Call) since the middle of the 1990s. Now there is a renewal with OpenAPI and other RESTful API description languages: during the first age of Web technologies, communications were rarely clearly specified since developers were used to manage both sides (the server code, plus the client in the shape of JavaScript code). Nowadays, with REST architecture principles, the web client is not necessarily the end-user web browser but sometimes a third-party application. Within the Web community, this revives needs towards clear API specifications.  
The home automation community does have the same needs.  
Objectives:  
* xAAL devices should be specified in a clear way for others to know how to interact with them, what to provide, what to expect.
* Those specifications should describe devices in a generic and in an abstract way (e.g., all thermometers should have the same behavior, even if a manufacturer adds blinking nice LEDs on, etc.).
* Those specifications should allow extensions to describe devices more sophisticated than the basic ones of the same family, without breaking the basic behavior one may expect for devices in this family, in a backward compatibility way.
* Those specifications should follow a strict syntax to be parsed by software for automatic treatments (e.g., building skeletons of software, or automatic discovering of peers API).
* The specification document should be readable by a human (with some software programming skills).
## 4 Object-Oriented Classification Process
### Validation of the Fm Method  
In order to test the effectiveness of the Fm method, we selected different research areas and GF-2 images and carried out experiments. The classification results and overall accuracy with different classifiers are shown in the Figure 8.  
Figure 8: _Cont._  
As shown in Table 5, Fm achieves better classification results in the different study area. The overall accuracy of Fm with CART, RF, KNN, and SVM classifiers were 88.67%, 92.04%, 91.08%, and 88.68%, respectively. The kappa coefficient also reached 0.8545, 0.8979, 0.8852, and 0.8546, respectively. The overall accuracy of RF and KNN is better than that of the CART and SVM, which is consistent with the experimental results above. Verified experiments show that Fm can effectively reduce the dimensionality of high-dimensional data and obtain the optimal feature subset. RF and KNN classifiers are more suitable for image classification combined with Fm.
## 3 Results
### Salinity--Commercial Variety  
In the Treemap diagram in Figure 7, the mean values of all treatments of each agronomic trait are given without salt addition and at salt concentrations of 80 mM NaCl and 160 mM NaCl. The results showed that at 80 mM NaCl salinity level, the biopriming treatment (T3) gave the highest mean values for the following traits: FWS (25.7%), SL (33.5%), and BIO (26.2%), with statistically significant differences from the no-priming treatment (T1) only regarding the SL. The biopriming/hydropriming treatment (T4) gave the highest values, with statistically significant differences from the no-priming treatment (T1), FWR (36.9%), RL (70.5%), and R/S (25.3%). The hydropriming treatment (T2) gave the lowest values across all treatments, except for the RL trait, where it showed a statistically significant difference compared to the unprimed (T1) treatment. At 160 mM NaCl, the no-priming (T1) treatment gave the highest values regarding the DWS, SL, and BIO. Subsequently, the biopriming treatment (T3) improved the following traits: FWS (7.7%), as well as FWR (40.4%) and RL (50.4%), with statistically significant differences from the no-priming treatment (T1). Finally, the biopriming/hydropriming treatment (T4) gave the highest values for the traits R/S (37.2%) and DWR (13.2%) with statistically significant differences from the no-priming treatment (T1) only for the root/shoot ratio.  
Figure 6: Treemap plot for the mean value of the treatments, each trait separately, for the two varieties. Different Latin letters within the squares indicate significant differences according to the Least Significant Difference (LSD) test at \(p<0.05\), among the eight treatments (four for each variety). T1: no-priming, T2: hydropriming, T3: biopriming, T4: hydropriming/biopriming, FWR: Fresh Weight Root, DWR: Dry Weight Root, FWS: Fresh Weight Shoot, DWS: Dry Weight Shoot, RL: Root Length, SL: Shoot length, BIO: Biomass, R/S: Dry Weight Root/Dry Weight Shoot.  
Comparing the treatments among salinity levels, the biopriming treatment (T3) significantly reduced DWS, SL, and BIO. The biopriming/hydropriming treatment (T4) gave reduced values for all traits without statistically significant difference. The hydropriming treatment (T2) gave the lowest values across all treatments at the high salinity level.  
Finally, the unprimed (T1) treatment either maintained or increased the mean values for each trait, and for stem length (SL), it exhibited a statistically significant increase in its mean value (Figures S4, S5 and S8).
## 1 Introduction  
The environmental pollution situation has changed from bad to worse in recent decades, which is obviously due to the rapid growth of urbanization and industrialization. Nanostructured semiconductor photocatalysts have an essential role in energy and environmental applications, such as water splitting and organic pollutants degradation [1; 2; 3]. Cadmium sulfide (CdS) has been given tremendous attention among various photocatalysts due to its unique characteristics, such as its catalytic active nature under visible light and suitable band gap (2.4 eV), which may be employed for the production of chemical energy from solar energy under sun light irradiation [4]. Due to these functional properties CdS has a great potential to be used in numerous applications, such as electronics, ceramics, optics, and catalysis [5; 6]. However, CdS, like other metal sulfides photocatalysts, is unstable under light irradiation, a phenomenon known as photocorrosion or photodissolution, where S\({}^{2-}\) in CdS is oxidized by the photo-generated holes accompanied with elution of Cd\({}^{2+}\)[7].  
The photocatalytic activity and stability of CdS can be improved by enhancing the separation capacity of the photogenerated electron-hole pairs. Therefore, much effort has been directed at restraining the photocatalytic instability of CdS. This was mainly achieved either through metal doping [8; 9], or by coupling CdS with a semiconductor having suitable conduction and valance band potentials, such as PdS, TiO\({}_{2}\), ZnO [10], ZnS, LaMnO\({}_{3}\), and ZrO\({}_{2}\)[11]. Zn doped CdS solid solutions with different morphologies were reported [12; 13; 14], which have achieved greater photocatalytic efficiency than pure CdS for dye degradation. The Zn 3d incorporation into CdS crystal structure can enlarge the absorption edge and may lead to new properties and applications in the photocatalysis [15; 16; 17; 18; 19; 20; 21]. On the other hand, most reports are mainly focused on decorating or coupling CdS with semiconductors, such as TiO\({}_{2}\), to improve the photocatalytic performance of CdS [22; 23; 24]. Indeed, such modifications have resulted in the increased photocatalytic activities due to the efficient separation of photo-induced electrons and holes occurring during the photocatalysis process [25; 26]. Herein, we report the synthesis of P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite by one-step low temperature hydrothermal method, as well as its photocatalytic studies for the degradation of Rh-B. The P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite is an efficient adsorbent and it shows high photocatalytic activity and stability in the organic dyes degradation, e.g., (Rh-B). The outstanding performance suggests that the flower-like nanostructure of Zn\({}_{0.15}\)Cd\({}_{0.85}\)S could be useful as sites for the depositing small amounts of P25 in the petals of the P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite, which facilitates the charge transfer process that is responsible for the high photocatalytic activity and stability of CdS nanoparticles.
## 4 Discussion
### Linear Predictive Model
The line graph in Figure 8b shows the following:  
* At a maximum power of 5100 W and a minimum assisting gas pressure 0.45 bar an average cutting width of 0.35 mm is obtained;
* At a minimum power of 4900 W and a minimum assisting gas pressure 0.45 bar a cutting width of approximately 0.34 mm results;
* The cutting width increases linearly with the laser power because the laser energy supplied to the material causes an excess heat, which changes the size of the cutting width under low pressure conditions. The cutting width decreases linearly with the increase of the auxiliary gas pressure at the same time with the laser power level of minimum up to the value of 0.33 mm;
* The cutting width 0.33 mm is obtained at power value of 4900 W and auxiliary gas pressure of 0.55 bar;
* The cutting width increases to values close to 0.34 mm when the gas pressure is maximum 0.55 bar and the laser power increases to a maximum value of 5100 W. Increasing the density of the energy flow contributes to the increase of the melting mass, the heat entering the material increases, which is why the width of Kerf increases;
* There is a similarity between the prediction and the linear regression model at gas pressure values of 0.52 bar at the same time as the laser power of 5000 W. The response surface graph for the linear regression model is a flat surface describing the cutting width perturbation. There are differences for average values of laser power and low pressure between the anticipated and the calculated model. Such differences occur due to the deviation of the values from the measurements compared to the average value. A possible cause may be the degree of heating of the steel plate. Another cause may be the low speed level, which causes a longer irradiation time of the steel plate, having the effect of increasing the cutting width. Increasing the cutting speed to the maximum level causes a short irradiation time and a lower heat absorption, and finally a minimum cutting width. There is a similarity between the prediction and the linear regression model at gas pressure values of 0.52 bar and laser power of 5000 W. The regression model is different from the anticipated one due to the estimation of the cutting width. Possible causes would be technical, standard error, standard deviation, plate temperature gradient, pressure gradient and melt mass. The mathematical model more accurately estimates the width cut by its small variations and errors to an average value. The predictive model indicates lower values of the cut width due to the correlation between the laser energy and the heat flow distribution through the material at high values of the cutting speed. The disturbed values of the cutting width are obtained when the values of the laser power are antagonistic to those of the assist gas pressure. The two investigated models lead to the same value of the cutting width <0.34 mm, a good agreement due to the influence factors set at values close to the environment.
## 2 Background Knowledge
### Network Reliability  
The network design problem is to arrange these components such that a given set of traffic requirements are met at the lowest cost. This problem is known as network optimization, and concerns throughput, delay and reliability. Reliability is the probability that a network will perform satisfactorily for a given period of time when adopted under specified operating condition. The topological connectivity generally determines the network reliability. This study focuses on individual elements. Figure 5 shows the series-connected units.  
If the units do not interact, then the failures are independent, and the system reliability \(R_{s}(t)\) denotes the product of the reliabilities of the individual constituent units. The function is set as follows:  
\[R_{s}\left(t\right)=\prod_{i=1}^{k}R_{s}\left(t\right)=\prod_{i=1}^{k}\left[1- F_{s}\left(t\right)\right]\]  
Figure 4: _Cont._  
Figure 5: Network elements linked with serial configuration.  
In this function, \(R_{i}(t)\) and \(F_{i}(t)\) are the reliability and failure distribution functions, respectively, of the \(i\)th system unit.  
In a parallel network, a number of similar individual components are connected in parallel. Figure 6 shows a parallel network with \(k\) units. To derive the reliability function \(R_{p}(t)\) for the parallel network, the following two assumptions are made: (1) all units are active and share the network load, and (2) all components are statistically independent. For no identical components, the failure distribution function \(F_{p}(t)\) at time \(t\) is given by:  
\[F_{p}\big{(}t\big{)}=\prod_{i=1}^{k}F_{i}\big{(}t\big{)}\]  
where \(F_{i}(t)=1\)-\(R_{i}(t)\) denotes the failure distribution of the \(i\)th component.  
Since \(R_{p}(t)+F_{p}(t)=\)1, the parallel-configuration reliability is given by  
\[R_{p}\big{(}t\big{)}=1-F_{p}\big{(}t\big{)}=1-\prod_{i=1}^{k}\big{[}1-R_{i} \big{(}t\big{)}\big{]}.\]  
The sensor network has highly constrained resources such as storage, computation capability, radio model and energy, and these limitations affect the routing mechanisms that can be efficiently deployed. Sensor nodes have many failure modes. Each failure degrades the network performance. An excellent and complete routing protocol and algorithm for handling reliability routing path of wireless sensor computing can be obtained by combining the advantages and disadvantages of the systems described in the above related works. Therefore, this study proposes a novel mechanism that adopts a hybrid cluster-based routing protocol for sensor computing to select the best reliable routing path.
# Background Subtraction for Moving Object Detection in RGBD Data: A Survey
## 6 Comparisons
### Comparisons on the MULTIVISION Kinect Dataset  
Performance comparisons on the MULTIVISION Kinect dataset are reported in Table 4. Here, values for the DECB and the CB4D algorithms by [PERSON] et al. [58] and the Codebook algorithm using only color (CB) and only depth (CB-D) are those reported in [58]. Values for the RGBD-KDE algorithm by [PERSON] et al. [80] and the KDE algorithm using only color (C-KDE) and only depth (D-KDE) are those reported in [80]. Values for the RSBS (Random Sampling-based Background Subtraction) algorithm by [PERSON] et al. [19] and the SAFBS algorithm by [PERSON] et al. [75] are those reported by their authors.  
It can be observed that in general, depth alone (i.e., CB-D and D-KDE) allows achieving results better than color alone (i.e., CB and C-KDE), being insensitive to illumination variations (e.g., in sequences ChairBox and Hallway) and color camouflage (e.g., in sequence Hallway). The exception clearly holds for the case of depth camouflage, as in sequence Wall (see Figure 2). For all the sequences, the combined use of both information allows in general to achieve comparable or better performance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Video** & **Method** & \(\mathbf{F_{1}}\) & \(\boldsymbol{\sigma_{\mathbf{F_{1}}}}\) & **Video** & **Method** & \(\mathbf{F_{1}}\) & \(\boldsymbol{\sigma_{\mathbf{F_{1}}}}\) \\ \hline \multirow{8}{*}{ChairBox} & CB & 0.847 & 0.057 & Hallway & CB & 0.555 & 0.189 \\  & C-KDE & 0.881 & 0.062 & & C-KDE & 0.632 & **0.052** \\  & CB-D & 0.854 & 0.058 & & CB-D & 0.770 & 0.097 \\  & D-KDE & 0.933 & 0.047 & & D-KDE & **0.923** & 0.072 \\  & DECB & 0.914 & **0.027** & & DECB & 0.783 & 0.187 \\  & CB4D & 0.886 & 0.050 & & CB4D & 0.617 & 0.190 \\  & RSBS & 0.895 & - & & RSBS & 0.843 & - \\  & RGBD-KDE & **0.962** & 0.032 & & RGBD-KDE & 0.873 & 0.061 \\  & SAFBS & 0.910 & - & & SAFBS & 0.745 & - \\ \hline \multirow{8}{*}{Shelves} & CB & 0.699 & 0.192 & Wall & CB & 0.843 & 0.108 \\  & C-KDE & 0.885 & **0.012** & & C-KDE & 0.918 & 0.054 \\  & CB-D & 0.835 & 0.137 & & CB-D & 0.595 & 0.414 \\  & D-KDE & 0.709 & 0.110 & & D-KDE & 0.665 & 0.178 \\  & DECB & 0.848 & 0.128 & & DECB & **0.938** & 0.029 \\  & CB4D & 0.711 & 0.205 & & CB4D & 0.868 & 0.054 \\  & RSBS & 0.900 & - & & RSBS & - & - \\  & RGBD-KDE & **0.921** & 0.033 & & RGBD-KDE & 0.886 & **0.009** \\  & SAFBS & 0.894 & - & & SAFBS & 0.930 & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance results of various background subtraction methods on the RGBD videos of the MULTIVISION Kinect dataset. and \(\sigma_{F_{1}}\) are the mean and the standard deviation over four GT masks for each video. In boldface, the best results for each metric and each sequence.
## 7 Evaluation Results  
A routing method with the highest bundle arrival rate is not necessarily the best, because it is necessary to consider how big the relaying loads on all nodes are. Therefore, we compared the proposed routing with the existing routing methods in terms of not only the bundle arrival rate, but also the number of copies, the number of hops and the maximum required buffer size.
## 4 Algorithm Design
### Hybrid Ant Colony Optimization Algorithm
#### 4.2.4 Pheromone Update  
Pheromone update involves both accumulation and evaporation. During their traversal, ants deposit pheromones on paths, while the pheromone concentration gradually evaporates over time. This evaporation mechanism introduces a forgetting effect, preventing the algorithm from prematurely converging to a local optimum and enhancing its exploration capability. Let \(\rho\ (0<\rho<1)\) denote the evaporation factor, representing the rate of pheromone decay. After each iteration, the pheromone concentration on each path is updated, and the concentration at time \(t+1\) is given by  
\[\left\{\begin{array}{l}\tau_{ij}(t+1)=(1-\rho)\cdot\tau_{ij}(t)+\Delta\tau_{ ij}(t)\\ \Delta\tau_{ij}(t)=\sum\limits_{k=1}^{m}\Delta\tau_{ij}^{k}\end{array}\right. \tag{43}\]  
In the above equation, \(\Delta\tau_{ij}^{k}\) represents the pheromone deposited by ant \(k\) on the path between nodes \(i\) and \(j\). Summing the pheromone contributions from all ants \(k\), the total pheromone increment on the path is calculated as \(\Delta\tau_{ij}(t)\). The calculation of \(\Delta\tau_{ij}^{k}\) is as follows:  
\[\Delta\tau_{ij}^{k}=\left\{\begin{array}{l}\frac{Q}{L_{k}},\qquad\text{ ant $k$ from node $i$ to $j$}\\ 0,\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\text{\emph{other}}\end{array}\right. \tag{44}\]  
Figure 11: The Change Infrastructure operator.  
Figure 10: The Remove Infrastructure operator.  
This study employs the ant-cycle model, where pheromone updates occur only after ant \(k\) completes a full route. This approach enhances exploration and prevents premature convergence to local optima by avoiding excessive pheromone accumulation. In the equation above, \(Q\) is a constant, and \(L_{k}\) represents the total cost of the route constructed by the \(k\)-th ant.
# A Comprehensive Study on EDFA Characteristics: Temperature Impact
## 3 Results and Discussion
### Optimum Fiber Length Variation with Temperature  
The optimum fiber length, L\({}_{\text{opt}}\), investigation shows its dependency on several parameters such as input signal power and input pump power as follows.