esa-sceva/satcom-chunk-collection huggingface dataset:
# Task Scheduling Mechanism Based on Reinforcement Learning in Cloud Computing
## 4 QMTSF Algorithm  
### Priority-Based Task Allocation Algorithm  
Each server in the cloud computing environment maintains a server task queue (SQ), represented as \(SQ_{1},SQ_{2},\ldots,SQ_{i},\ldots,SQ_{n}\), which receives tasks assigned from the cloud data center. Each server has a status table Status, which includes information such as the task type priority tuple, the number of tasks waiting to be assigned in the server, the total number of tasks assigned to VMs but not yet executed, and the average processing time of tasks in the server. The cloud task scheduler assigns tasks to the appropriate SQ based on the task status in the GQ and the status table information Status of all servers, which then wait in the SQ for further task scheduling. For any server i, we use \(Waiting\_D_{i}=|SQ_{i}|\) to indicate the number of tasks waiting in the queue and \(Waiting\_P_{i}\) to indicate the total number of tasks assigned to VMs but not yet executed; \(Task\_Type\_Prio_{i}\) is a task type priority tuple, and \(Ave\_Time_{i}\) is the average processing time of completed tasks (including waiting time and processing time). Whenever a task is completed in server i, \(Ave\_Time_{i}\) is dynamically updated. The workflow for task allocation to hosts in the cloud data center follows Algorithm 1:  
Figure 2: QMTSF stage two: task scheduling.  
In line 9 of the above algorithm, the expected completion time of each task j assigned to server i needs to be calculated; the formula for calculating this time is as follows:  
\[Time\_E_{j}^{i}=(Waiting\_P_{i}+Waiting\_D_{i}+1)*Ave\_Time_{i}\]  
The estimated completion time for a task is related to the number of tasks waiting in the SQ and the number of tasks assigned but not yet processed. Here, \(Ave\_Time_{i}\) is the average processing time of tasks completed on server \(i\), including the waiting time and processing time. This time is updated automatically whenever a task is completed on the VM of server i. This design avoids the need to calculate the tasks in each VMQ, saving processing time on task assignment.  
In line 12 of the algorithm, if ServerList is empty the system considers assigning the task to a suboptimal server, that is, \(Task\_Type\_Prio_{i[1]}==Task\_Type_{j}\). In this case, lines 4 to 15 are executed again; if no suboptimal host is found, the task assignment fails and no lower priority server is considered. The task is reinserted at the end of the GQ and waits for the next assignment. Although task assignment failures do occasionally occur in practice, the probability is very low. The optimal and suboptimal cases can already cope with most environments; therefore, based on the actual situation and from the perspective of time saving, we do not consider assigning servers below the suboptimal level.
# Analysis of Cultivated Land Productivity in Southern China: Stability and Drivers
## 2 Materials and Methods
### Research Methodology
#### 2.3.3 Cultivated Land Landscape Pattern Metrics  
The terrain of Poyang Lake region is complex, which affects the utilization efficiency of cultivated land. To evaluate cultivated land use, quantitative index analysis is essential. Patch size (_AREA_MN_) is a key measure of cultivated land fragmentation, and a large cultivated land area is conducive to structural stability, mechanization, and improving productivity [36]. The scattered cultivated land in hilly areas restricts the mechanization and scale of agriculture. Fractal dimension (_FRACT_) was introduced to quantify the complexity of cultivated patches. The higher the value, the more complex the shape, which hindered mechanization and scale [37]. The nearest neighbour distance (\(ENN\)) reflects the patch isolation degree, reveals the degree of cultivated land intensification, and facilitates cultivated land planning [25]. By comprehensively using these indicators, the present situation of cultivated land utilization in Poyang Lake region was comprehensively analyzed. The raster data of cultivated land were divided by county, and the landscape pattern index was calculated using Fragstats 4.2 software [38]. The specific formula and description are shown in Table S1.
## 2 Wireless Multimedia Sensor Networks
### Real-Time Video Data  
Most applications of WMSNs require delivery of real-time video data. To put it straight, WMSNs need to send video data to the sink before a deadline and the ratio of packet loss should not exceed a defined threshold. Apparently, stringent QoS demands in terms of delay and reliability will be imposed on WMSNs as a result. Nevertheless, for a resource constrained network with large volume of multimedia data to transmit, providing guarantees on such QoS requirements is rather a challenge. In order to sum up existing solutions, here we outline the fundamental ways of achieving QoS provisions.
## 2 Overview of Supplementary Damping Systems
### Principles of Energy Dissipation Mechanisms
#### 2.2.3 Friction Damping  
Friction damping relies on the conversion of mechanical energy into heat through the relative motion of sliding surfaces. The damping force is governed by [PERSON]'s law of friction:  
\[\text{F}=\upmu\cdot\text{N}\cdot\text{sign}(\text{v}) \tag{3}\]  
where \(\upmu\) is the coefficient of friction, N is the normal force, v is the relative velocity, and sign is the signum function. Key characteristics of friction damping include:  
Displacement-dependent behavior.  
Potential for stick-slip phenomena.  
Wear and degradation over time.
## 3 Context Modeling
### Context Model
#### 3.2.2 Location Class  
A _location_ can be either _indoors_ or _outdoors_. Although other inheritance approaches are valid for the definition of the location object, such as building, floor, corridor, and room scheme presented in SeCoMan ontology [44], this work argues that conceptually, there is no need to integrate such differences in the location class. A location may belong to other locations, thus modeling the inheritance functionality. Apart from the self-referential relation of _belonging_, a location may also be _adjacent_ to other locations, modeling spatial proximity. A thing can be located to a location which may be determined with high accuracy (e.g., a room, or an exact geolocated point), or with lower accuracy (e.g., a building, or a district), depending on the availability of sensory data and necessity. Multiple things may be located in the same locations, adjacent locations or locations of the same hierarchy (e.g., building), allowing inferences on proximity and interaction.  
A location has a _position_, which may be defined by absolute measurements such as exact geo-positioning and effective radius for points or geo-constrained spaces for outdoors areas, or by relative positioning such as the indoors rooms, corners, areas. The different subclasses of position allow the modeling of both indoors locations whose absolute position is not applicable (e.g., floors with same geolocation) and outdoors locations that can be easily defined by absolute positioning systems (e.g., GPS). A location may have many _profiles_ with either a _general_ scope to model various properties and requirements, or an _authorization_ scope to model accessibility and privileges.  
Finally, a location may have an _environment_, which models the environmental parameters such as temperature, humidity, etc. It is a derived relation since it is computed by the fusion of sensory measurements that are related to that location. Thus, the calculation of the temperature of a room is based on the data collected from relevant sensors that are located in the room, either fixed or mobile from the users also present inside. Figure 3 illustrates the location class.  
Figure 2: Thing class.
By controlling the vibration of the truss in the highly sensitive direction, the vibration response of the lens system in the highly sensitive direction can be suppressed. At present, the research on the coupling of vibration active control technology into Optomechanical structure has not been put forward. The existing research has only analyzed the beam pointing stability by assuming that the structural vibration is known [29], but has not analyzed the structural transmission characteristics of the structure under excitation. Due to the high precision requirement of vibration control in the field of precision Optomechanical, such as PW laser facility, the active vibration control technology, should be coupled into the Optomechanical system when modeling. Micro vibration is the main factor that affects the optical axis deviation of the system. In order to make the spot converge in the ideal position, this paper takes PW laser facility space filter Optomechanical system as the research object, analyzes the beam imbalance principle of the Optomechanical system, designs the split support scheme of long-distance optical spatial filter, identifies the modal parameters through a vibration test, constructs the Optomechanical structure equivalent model, and further constructs the Optomechanical coupled vibration active control system. The active control parameters are introduced into the beam transmission matrix of the Optomechanical coupling system, and the beam transmission model is modified. Through the first stage coupling of active control parameters and structural response and the second stage transfer of structural response and beam direction, the point-to-point link between active control parameters and beam pointing is realized. The active controller and voice coil motor active actuator are designed. The formula of output light angle response peak value is deduced. The influence of excitation amplitude, frequency, and phase on the peak value of output optical angle response is analyzed. The main factors affecting the beam pointing are found. The algorithm flow of the active control system is designed. The control rules are established, and the control effect is verified. The convergence characteristics of the beam pointing accuracy of the active control system compared with the noncontrol system is studied. It can be extended to PW facility full link space filter and similar system, which has certain engineering value.
## 2 Materials and Methods
### Data Extraction, Missing Part Simulation, and Dataset Division  
The proposed methodological framework to impute missing parts of UAV orthomosaics required that the UAV and satellite images were closely aligned. Therefore, we first checked if the UAV, PS, and S2 data were entirely co-registered to each other. Next, we resampled the UAV (0.08 m), PS (3 m), and S2 (10 and 20 m) images using bilinear interpolation to the spatial resolution of 1 m to ensure better integration between the remotely sensed data. The spatial resolution of 1 m was chosen based on the spatial resolution required in typical PA applications, the greatest common divisor of the spatial resolutions (3, 10, and 20 m) of the optical satellite imagery, and the computational costs involved in the proposed imputation framework. A spatial resolution of 1 m is suitable for several PA applications that use equipment that cannot deal with sub-meter spatial resolutions, such as variable-rate seeding and fertilization [32; 33]. After resampling the images, we selected 220,000 points regularly distributed based on a 10 m \(\times\) 10 m grid. The selected points corresponded to the centroid of around 10% of the total number of pixels of the image. Those points were considered the original sample to calibrate the imputation models and avoid a large amount of data derived from the entire image that could decrease the computing efficiency of the proposed framework. For each one of the 220,000 points, we extracted the spectral bands and NDVI values on the UAV, PS, and S2 images.  
The parallel path planning system commonly adopted to collect UAV images combined with unexpected flight situations (such as sudden wind gusts) may affect the perfect overlap of the images, resulting in missing parts in the UAV orthomosaic in the shape of strips. Thus, we simulated three levels of artificial missing parts in the UAV orthomosaic in strips, corresponding to gaps of 10% (MP-10%), 30% (MP-30%), and 50% (MP-50%) of the orthomosaic total area (Figure 4). Considering the missing part levels and the 220,000 points (original sample), we defined the training and testing datasets for data modelling. From the selected 220,000 points, those not matching the missing parts of the UAV orthomosaic were used to train the imputation models, i.e., 198,000 points for MP-10%, 154,000 points for MP-30%, and 110,000 points for MP-50%. The points matching the missing parts of MP-10%, that is, the same 22,000 points were used in the testing dataset for the performance evaluation of all three levels of missing parts (MP-10%, MP-30%, and MP-50%).  
Figure 3: Flowchart for UAV and satellite imagery processing and filling in the missing parts in UAV orthomosaics based on the RF algorithm and ancillary data derived from satellite image.
## 3 Methodology
### Data Normalization and Outlier Detection  
To ensure consistency across the seventeen wells (1260 data records), a normalization step was applied to the variables (FV, FD, and MFV) prior to model development. This step helped eliminate well-specific variations in measurement scales, allowing the ML model to identify consistent patterns across all wells [26]. Crucial steps in the ML model development include data division and selecting an optimal train-to-test ratio. Random division prevents data leakage, and sensitivity analysis determines the ideal ratio for a reliable and generalizable model. Outlying data records were detected and removed to prevent noise and bias, ensuring optimal prediction performance [26]. For this purpose, the Mahalanobis distance (MD) technique is used to identify outliers in the training subset during the ML model configuration [35]. Initially, ML models were established using all available data records to predict the FV. For each data point, a prediction error (Err) was computed (Equation (1)) by comparing measured (\(FV_{m_{i}}\)) and predicted (\(FV_{p_{i}}\)) values. MD values were calculated for each data record (Equation (2)), and outliers were identified based on a predetermined threshold. If the MD value exceeded the threshold, the data point was flagged as a potential outlier and removed from the training dataset unless a valid justification supported its retention [35].  
\[Err_{i}=FV_{m_{i}}-FV_{p_{i}} \tag{1}\]  
\[MD_{i}=\sqrt{Err_{i}\times\left(Cov(Err)\right)^{-1}\times{Err_{i}}^{T}} \tag{2}\]  
The MD outlier detection analysis was conducted using 10-fold cross-validation to:  
* Provide a robust and unbiased estimate of the model's performance using distinctive groups of data records.
* Minimize overfitting risks by identifying diverse outliers.
# Characteristics and Applications of Spatiotemporally Focused Femtosecond Laser Pulses
## Appendix  
The propagation of the spatiotemporally-focused pulses can be derived based on Fresnel diffraction integral theory [18]. The intensity distribution of the laser after dispersed by the gratings can be described as:  
\[E_{1}(x,y,\omega)=E_{0}\text{exp}[-\frac{(\omega-\omega_{0})^{2}}{\Delta \omega^{2}}]\text{exp}\left\{-\frac{[x-\alpha(\omega-\omega_{0})]^{2}+y^{2}}{ {w_{\text{in}}}^{2}}\right\}, \tag{17}\]  
where \(E_{0}\) is the field amplitude, \(\omega_{0}\) is the center frequency, \(\Delta\omega\) is the bandwidth, \(\alpha\) is the linear offset of each spectral components, \(\omega_{\text{in}}\) is the beam waist of input laser pulses. After passing the focusing lens, the laser field is:  
\[E_{2}(x,y,\omega)=E_{1}\text{exp}[-ik\frac{x^{2}+y^{2}}{2f}], \tag{18}\]  
where \(k\) is the wave vector, and \(f\) is the focal length.  
Under the paraxial approximation, after propagating a distance from the lens, the laser field can be written as:  
\[E_{2}(x,y,z,\omega)=\frac{\text{exp}(ikz)}{i\lambda z}\int\int_{-\infty}^{ \infty}E_{2}(\xi,\eta,\omega)\text{exp}[-ik\frac{(x-\xi)^{2}+(y-\eta)^{2}}{2 z}]d\xi d\eta, \tag{19}\]  
By performing a Fourier transform, the laser intensity in the space and time domains can be obtained by:  
\[I(x,y,z,t)=\left|\int_{-\infty}^{\infty}E_{2}(x,y,z,\omega)\text{exp}(-i \omega t)d\omega\right|^{2}. \tag{20}\]
## 7 Networks
### Solitary States
#### 7.3.3 Noise Induced and Controlled Chimera  
In [152], Rybalova et al. studied (116) in more detail, establishing that the SSC regime can be induced by multiplicative noise  
\[\sigma=\sigma_{0}(1+\sqrt{2D\xi^{i}}), \tag{129}\]  
introduced into the nonlocal coupling coefficient of this network, where \(\xi^{i}\) is a random process distributed uniformly in the interval \([-1,1]\) and \(D\) is the noise intensity.  
In this case, bistability emerges in the phase space of each individual oscillator. Because the initial conditions are set randomly for all the ensemble elements, the appearance of solitary states and SSCs becomes possible. However, their detailed studies showed that bistability can be caused by both the noise perturbation and by the signals from \(P\) neighboring oscillators on each individual oscillator of the ensemble (the second term in (116)).  
More importantly, they established that the structure of the basins of attraction essentially depends on how far the ensemble oscillator in the solitary state regime is located from the phase chimera. The basin of attraction of the attractor corresponding to the solitary state regime gradually narrows as the distance between this oscillator and the phase chimera grows. Moreover, when an amplitude chimera exists in the considered network, the riddling effect takes place, which increases the probability of switching the phase trajectories into the solitary state and the SSC. The fact that the basins of attraction for the SSC decrease with the distance from the SSC cluster explains the existence of a finite incoherence cluster in space.  
Instead of multiplicative noise, Rybalova et al. [153] numerically studied the spatiotemporal dynamics of ring networks of coupled discrete-time systems in the presence of additive noise. They explored the robustness of chimeric states with respect to noise perturbations for two ensembles in which the individual elements are described by either logistic maps or Henon maps in the chaotic regime. The influence of noise on the behavior of solitary states was investigated for a ring of nonlocally coupled Lozi maps  
\[\left\{\begin{array}{l}x_{n+1}^{i}=f(x_{n}^{i},y_{n}^{i})+\frac{g}{2}\sum_ {j=i-P}^{i=i+P}\Bigl{[}f(x_{n}^{j},y_{n}^{j})-f(x_{n}^{i},y_{n}^{i})\Bigr{]}+ D\xi_{n}^{i},\quad i=1,2,\ldots,N,\\ y_{n+1}^{i}=bx_{n}^{i},\end{array}\right. \tag{130}\]  
where \(\xi_{n}^{i}\) is a random process distributed uniformly in the interval \([-1,1]\) and \(D\) is the noise intensity.  
Numerical simulations were performed for a set of different noise realizations and random initial conditions to provide reliable statistical data. The type of dynamics of the considered networks was quantified using a cross-correlation coefficient. They found that there is a finite and sufficiently wide region with respect to the coupling strength and the noise intensity in which the probability of observing chimeras and solitary states is high.
## 3 Proposed Algorithm
### Definition of Outlier Detection  
Outlier detection, without supervision, employs some criteria to find outlier candidates which deviate from major normal points. We have \(n\) data points \(X_{1},\ X_{2},\ldots,X_{n}\in R^{d}\), which are sampled independently and identically distributed. We use the matrix \(\mathbf{X}\in R^{n\times d}\) as the notation of the entire dataset, which is formed by stacking each data point's vectors as rows. After giving \(\mathbf{X}\), an outlier detector obtains an outlier score \(o_{i}\in R\) for each data point \(x_{i},\ 1\leq i\leq n\). Data points with higher outlier scores are more likely to be outliers.
## 4 Results and Discussion
As a result of the calculations, \(\sim\)1081.3 total yields in tonnes were estimated. The total yield we acquired from the official sources reported a yield of roughly around 1103.2 tonnes. The model we developed acquired low RMSPE (0.01985) by only using RS data and GIS applications. In contrast, the [PERSON] study's findings showed that the formula for harvest calculation applied to highly productive farmland had a high accuracy of 96% by using RS data only for calculating NDVI and SI indices [19]. The accuracy of yield estimations was still high (85%) but significantly lower for low-productive agriculture with this method [19]. The remaining data were obtained from laboratory analyses. Consequently, using only RS data and GIS methods, [PERSON]'s approach this research is based on, and the MEDALUS model we modified--forecasted agricultural yield with the greatest of ease, considering high accuracy (98.03%), low cost, and high availability. A non-linear positive correlation between NDVI and SQI has been detected. Thus, if SQI in the soil is showing high trends before the plantation, NDVI and therefore, LAI values are also going to be high. \begin{table}
\begin{tabular}{c c} \hline \hline  & \(\mathbf{YIELD_{mean}}\) \\ \hline \hline \(\mathrm{Y_{mean}}\) & 0.029730528 \\ \hline Pixel count & 3637 \\ \hline \(\mathrm{Y_{total}}\), tonnes & 1081.299 \\ \hline RMSPE, \% & 1.985 \\ \hline \(\mathrm{Validity_{mean}}\), \% & 98.0326 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Total Yield, Validity rate, and RMSE are shown in the table. Figure 6: Yield gradient using TICI. Considering the approach in this article as an option, more detailed analyses in this field can be conducted using a wide range of data. GIS and RS technologies can be valuable tools for addressing complex agricultural concerns. The primary reason for development is that modern techniques such as GIS and RS can contribute significantly to the management of timely, accurate, cost-effective, and repetitious agricultural data [43]. Modern agriculture can benefit greatly from the application of GIS and RS technologies for monitoring crop development, detecting afflicted areas, assessing soil quality status, estimating yields, and visualizing the actual topography of crop fields, among other spatial analyses.