esa-sceva/satcom-chunk-collection huggingface dataset:
# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 3 Methods
### Identify the Physiological Feature Cluster by KNN  
To address the issue of individual physiological differences affecting the cross-pilot accuracy of model, we employed a differentiated processing method for various patterns of physiological features, processing different categories of features with models tuned to specific parameters. All collected feature data are clustered into four classes using spectral clustering. The KNN algorithm is then used to determine the cluster of the input features. Based on model performance, K = 7 is set, meaning that the 7 nearest feature points to the input data are selected. This nearest neighbor selection is based on Euclidean distance calculations. Subsequently, the most frequent feature category (1-4) among these 7 points is chosen as the cluster for the input data, and an appropriate ensemble learning model is selected based on this category for further processing.
## 3 Design and Implementation of the Algorithm
### Expert System
The similarity \(\sigma(u_{1},u_{2})\) of the user \(u_{1}\) and the matched user \(u_{2}\) is defined as:  
\[\sigma(u_{1},u_{2})=\sum\limits_{k=1}^{N}\sigma_{k}(u_{1},u_{2}) \tag{11}\]  
The smaller the \(\sigma(u_{1},u_{2})\) is, the greater the similarity of the user \(u_{1}\) and the matched user \(u_{2}\) will be. During the process of population evolution, the user with preferences most similar to the current user preferences is searched in the knowledge base at each evolution. The final fitting parameters of the matched user are added to the current evolution population to replace the worst individual, which hastens the population evolution and decreases user fatigue. (2) Evaluation of the population evolution  
During the process of population evolution, the degree of evolution of the current population is continuously evaluated. The diversity of the population has a great influence on the algorithm performance, which is an important measure of the evolutionary state of the genetic algorithm. If an extremum exists, the same or similar individuals will be produced. It worsens the diversity of the population, which influences the efficiency of the genetic operation and the ability to explore other extreme regions [31]. The population diversity \(d\) is defined as follows:  
\[d=1-\frac{1}{0.5\times N\times L}\sum\limits_{k=1}^{N}\sum\limits_{j=1}^{L} \left|q_{k}^{j}-0.5\right| \tag{12}\]  
where \(q_{k}^{j}=\frac{1}{M}\sum\limits_{i=1}^{M}g_{ik}^{j}\) represents the probability that the \(i\)-th gene in the \(k\)-th channel is 1. Here, the range of \(d\) is \(0\leq d\leq 1\). The larger the value of \(d\) is, the better the diversity of the population will be. If the diversity of \(d\) is larger than the threshold \(D\) with a value of 0.5, the crossover probability increases by 5% based on Formula (5) with an upper limit of 0.95. The mutation probability increases by 0.5% based on Formula (6) with an upper limit of 0.3. Otherwise, the crossover probability and mutation probability are obtained according to Formulas (5) and (6). After many tests, some parameters are obtained as shown in Table 1. \begin{table}
\begin{tabular}{c c} Maximum crossover probability & 0.95 \\ Maximum mutation probability & 0.3 \\ Population size \(M\) & 8 \\ Maximum genetic generation & 20 \\ Fitness function & Five levels, hyper-geometric operator \\ Diversity threshold \(D\) & 0.5 \\ \end{tabular}
\end{table} 
Table 1: Settings for the algorithm parameters.
## 3 Experimental Setup  
### Datasets  
To validate the effectiveness of the model, we use mainstream farmland change datasets, including the CLCD and HRSCD datasets.  
CLCD: The CLCD dataset is a public farmland dataset collected by the Gaofen-2 satellite in 2017 and 2019, as shown in Figure 2. It contains 600 pairs of farmland change samples from Guangdong Province, with a size of 512 \(\times\) 512 pixels and a spatial resolution ranging from 0.5 m to 2 m. The dataset includes various land types such as buildings, roads, lakes, and bare soil. Each pair of sample images is associated with a binary label indicating farmland change. The samples are divided into training, validation, and test sets in a 6:2:2 ratio, resulting in sizes of 360, 120, and 120, respectively.  
HRSCD: The HRSCD dataset consists of 291 pairs of RGB aerial images, each with a size of 10,000 \(\times\) 10,000 pixels and a spatial resolution of 0.5 m, as shown in Figure 3. The dataset provides corresponding land cover information, including five land cover categories: artificial surfaces, agricultural areas, forests, wetlands, and water bodies. All images were acquired from urban and rural areas in Rennes and Caen, France. To obtain more refined information on cropland changes, we reclassified the original land cover labels of the bi-temporal images, assigning a label of 1 to agricultural areas and 0 to all other categories. The cropland change annotations were subsequently derived by comparing the reclassified labels from the two time periods. Given the large size of the original images, they were cropped into 4398 pairs of 512 \(\times\) 512 pixel image patches for cropland change detection. These image patches were further split into training, validation, and test sets in a ratio of 6:2:2.  
Figure 2: CLCD dataset.
# Research and Design of a Decentralized Edge-Computing-Assisted LoRa Gateway
## 3 Design of the EC-Assisted LoRa Gateway
### Distributed Task Execution Mode in the LoRa Gateway
The resources for \(EN_{k}\) employed in the edge network distribution process associated with the cloud are defined as the degree of fairness \(fdENk(t)\) of \(k\)th \(EN_{k}\) at time \(t\). \[fdEN_{k}(t)=\frac{\int_{0}^{t}REN_{k}(t)dt}{\int_{0}^{t}\max\{DEN_{k}(t),SEN_{ k}(t)\}dt} \tag{5}\]  
The degree of network fairness \(fdEN_{k}(t)=1\) indicates that all needed network resources have been allotted to perform the \(k\)th \(EN_{k}\) at time \(t\). Less than 1 in disparity \(fdEN_{k}(t)\) indicates an unfair execution. For competent administration, we propose that significant jobs at the edge be spread over a group \(G_{i}^{e}(EN)\), where \(i\) denotes the number of groups and e denotes edge nodes. \(EN\) is associated with a group job that has to be executed. As an organizer, \(E^{o}\) is a representation of an edge node that will receive and process the \(EN_{k}\). The edge node of the organizer will interact with other edge nodes in the group to complete the assigned task and provide output to the end nodes. In the \(E^{o}\) edge node group, each edge node acquires the available LoRa gateway resources based on the task. Our proposed latency-aware algorithm (LAA) executes in a decentralized way. An edge node joins the edge node group when it accepts handling a task, so that the action may be completed in distribution mode. To enable distributed processing of the \(E^{o}\), the \(E^{o}\) node will broadcast to the edge environment \(Q\) to establish a \(G_{i}^{e}(EN)\). Our proposed LAA algorithm is shown in Algorithm 1. First, in steps 3-7, The \(E^{o}\) will broadcast a grouping message for adding edge nodes to the pair queue \(Q\). After the grouping is completed, in steps 9-15, the LoRa gateway will perform tasks on the edge node elements added to the queue \(Q\). Then, in steps 18-21, the nearby edge nodes in the grouping are monitored and are included in the group when the nearby nodes are available. Throughout the process, the task processing is cyclically performed on all edge nodes that join the group. ```
1:Input:\(E^{o}\), \(G(EN)\), \(Q\), \(Q_{free}\), \(\sum EN\), \(\sum ae_{i}\) Output:\(G_{i}^{o}(EN)\)
2:for\(E^{o}\in Q\)do
3:\(Q_{free}\gets E^{o}\);
4:\(E^{o}\) send a request to join \(u(u\in Q)\);
5:\(E^{o\prime\prime}\) response neighborhood edge node \(u^{\prime}(u^{\prime}\in Q)\)
6:for\(u^{\prime}\in Q\)do
7:\(Q_{free}\gets u\)
8:endfor
9:while\(f4 EN_{k}\
eq 1\)do
10:if\(Q_{free}\
eq\phi\)then
11:for\(e_{i}\in Q_{free}\)do
12:if\(\sum EN\cap\sum ae_{i}\
eq\phi\)then
13:\(e_{i}\) pull \(\delta_{i}\)
13:\(\sum EN=\sum EN-\sum ae_{i}\)
14:\(G_{i}^{o}(EN)\gets e_{i}\)
15:endif
16:endfor
17:else
18:for\(e_{i}\in G(EN)\)do
19:if\((e_{n}\in e_{i})\cap G(EN)==\phi\)then
20:\(e_{i}\) send \(Rrs\) to \(e_{n}\)
21:\(G(EN)\gets e_{n}\)
22:endif
23:endfor
24:endif
25:endwhile
26:\(G_{i}^{o}(EN)\gets G(EN)\)
27:endfor
28:return\(G(EN)\)
```  
**Algorithm 1** Pseudocode of the LAA algorithm.
# DDS: A Delay-Constrained Duty-Cycle Scheduling Algorithm in Wireless Sensor Networks
###### Abstract  
Since sensor nodes usually have a large duty cycle interval to prolong network lifetime, duty-cycled wireless sensor networks (WSNs) can suffer from a long end-to-end (E2E) delay. Because delay-sensitive applications have a certain E2E delay requirement, a lot of studies have tried to tackle the long E2E delay problem. However, most existing studies focused on simply reducing the E2E delay rather than considering the delay bound requirement, which makes it hard to achieve balanced performance between E2E delay and energy efficiency. Although a few studies took into consideration both the delay bound requirement and energy consumption, they required specific node deployment or strict time synchronization between nodes in the network. In order to address the limitations of the existing studies, we propose a delay-constrained duty-cycle scheduling (DDS) algorithm. The objective of DDS is to achieve low energy consumption while satisfying the delay bound requirement in various node deployment scenarios depending on user demands. First, based on network topology information collected by the sink, one-hop delay distribution is derived as a function of the duty cycle interval. Then, the E2E delay distribution is estimated using the Lyapunov central limit theorem, which allows each node group to have a different delay distribution. Finally, the duty cycle interval is determined using the estimated E2E delay distribution such that energy consumption is minimized while meeting the delay bound requirement. Practical WSN deployment scenarios are considered to evaluate the proposed algorithm. The simulation results show that DDS can guarantee the given delay bound requirement and outperform existing algorithms in terms of energy efficiency.  
wireless sensor networks; duty cycle interval; delay distribution; delay requirement; delay bound Article
# A Study of Order Modification for Reactive Power Support by Wind Farm during Communication Failure
## 3 Case Studies
### Simulation Results
#### 3.2.2 Imposing Communication Failure  
Without the central controller, each wind turbine has to follow the previously generated references. An emergency stop for reactive power can be applied, but this kind of solution is unable to handle the changed condition, as shown in Figure 9. Cases B and C deal with communication problems, rendering the controller unusable. The changed voltage condition cannot affect individual references, even though the upper controller still modifies the values. Figure 10 shows the (a) reactive power supply modification, and (b) related voltage condition, at the same section under the proposed method. Each wind turbine measures the variation condition, and modifies its own references, using the imposed control delay (in this case, 0.1 s).  
Figure 8: Reactive power supply from array 1 and voltage condition in case A, with (**a**) reactive power supply, and (**b**) voltage variation.  
Figure 10: Reactive power supply from array 1 and voltage condition in case B—modified algorithm, with (**a**) reactive power supply, and (**b**) voltage variation.  
Figure 9: Reactive power supply from array 1 and voltage condition in case B—conventional algorithm, with (**a**) reactive power supply, and (**b**) voltage variation.  
The modified references, as described above, follow the imposed proportional value, which is generated from the loss minimization plan (the closer wind turbines receive more references than those at a greater distance). According to the modified references, the additional reactive power supply is processed, and the voltage condition is saturated to a reasonable value, within 0.5 s. Because every wind turbine accepts the modified condition appropriately, the rated voltage level at the connection point is easily recovered.
## 2 Material and Methods
### Flow Control Equation  
Reservoir dam failure is a complex fluid dynamic process, often characterized by localized turbulence and significant flow deformation. To accurately capture the three-dimensional flow field of the dam breach flood, this study employs the FLOW-3D hydrodynamic model to simulate and calculate the flood evolution process following a reservoir dam failure [24, 25]. Post-processing techniques are then applied to extract relevant flood evolution data. The model utilizes the continuity equation and the momentum equation as the governing equations for fluid motion, as presented in Equations (1) and (2). The RNG laden dam-break flow is shown in Equation (3).  
Continuity equation:  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot(\rho u)=0 \tag{1}\]  
Momentum equation:  
\[\rho[\frac{\partial u}{\partial t}+(u\cdot\
abla)u]=-\
abla p+\
abla\cdot \tau+\rho f \tag{2}\]Governing equations of the RNG \(k\)-\(\epsilon\) turbulence model:  
\[\begin{array}{l}\frac{\partial k_{T}}{\partial t}+\frac{1}{V_{F}}\left(uA_{x} \frac{\partial k_{T}}{\partial x}+\upsilon A_{y}\frac{\partial k_{T}}{\partial y }+wA_{z}\frac{\partial k_{T}}{\partial z}\right)=P_{T}+G_{T}+DIff_{T}-\epsilon _{T}\\ \frac{\partial e_{T}}{\partial t}+\frac{1}{V_{F}}\left(uA_{x}\frac{\partial k_ {T}}{\partial x}+\upsilon A_{y}\frac{\partial e_{T}}{\partial y}+wA_{z}\frac{ \partial e_{T}}{\partial z}\right)=\\ \frac{CDIS1+\epsilon_{T}}{k_{T}}.(P_{T}+CDIS3\cdot G_{T})+DIff_{\epsilon}-CDIS2 \frac{\epsilon_{T}^{2}}{k_{T}}\end{array} \tag{3}\]  
In these equations, \(\rho\) is the fluid density; \(t\) is time; \(u\) is the velocity field; \(P\) is pressure; \(\tau\) is the stress tensor; and \(f\) represents external forces. \(k_{T}\) is the turbulent kinetic energy; \(\epsilon_{T}\) is the turbulent dissipation rate; \(P_{T}\) is the production of turbulent kinetic energy due to velocity gradients; \(G_{T}\) is the production of turbulent kinetic energy due to buoyancy; \(D\)_lift\({}_{T}\)_ is the turbulent diffusion term; \(CDIS1\) is 1.42; \(CDIS3\) is a model constant; \(CDIS2\) is the shear rate function; and \(D\)_lift\({}_{\epsilon}\)_ is the diffusion term.
## 3 Methods
### Ensembles Trees: Bagging Algorithm  
Bootstrap Aggregation or Bagging ensemble is a technique to combine multiple ML algorithms to make a single model with more accurate predictions than an individual model [39; 40]. The Bagging Ensemble technique can be used for base models that have low bias and high variance, typically the Decision Trees algorithm. Otherwise, Decision trees are sensitive to the specific data on which they are trained. If noise is added to the training dataset the resulting decision tree can be quite different and, in turn, the predictions can be quite different. Typically, the Random forest algorithm has high variance [37], but when the bootstrap aggregation technique is applied the variance can be reduced.  
The training algorithm for random forests applies the general technique of bootstrap aggregating, or bagging, to tree learners. Given a training dataset \(X=x1,\ x2,\ldots,xn\), with responses \({PL_{ML}}={pl_{1}},\ {pl_{2}},\ldots,\ {pl_{n}}\), bagging repeatedly (B times) results in selection of a random sample with replacement of the training set, and fits trees to these samples. For each \(b\) (\(b=1,2,\ldots,B\)), a dataset is created with input \(X_{b}\) and output \(PL_{ML_{b}}\), and a \(f_{b}\) function is obtained from the training process with \(X_{b}\), \(PL_{ML_{b}}\). After training, a test for unseen samples \(\mathsf{x^{\prime}}\) can be made by averaging (\(PL_{ML^{\prime}}\) in Equation (3)) the predictions from all the individual regression trees on \(\mathsf{x^{\prime}}\).  
\[PL_{ML^{\prime}}=\frac{1}{B}\sum_{b=1}^{B}f_{b}\big{(}\mathsf{x^{\prime}} \big{)} \tag{3}\]  
As this bootstrapping procedure decreases the variance of the model, this means that while the predictions of a single tree can be highly sensitive to noise in its training dataset, the average of many trees will not while the trees are not correlated. The problem is solved simply by training many trees on a single training dataset, such that, at the end, it gives strongly correlated trees. Additionally, an estimation of the prediction's uncertainty can be made based on the standard deviation (see Equation (4)) of the predictions from all the individual regression trees on \(\mathsf{x^{\prime}}\). The number of samples/trees, B, is a free parameter and an optimal number of trees B can be found using k cross-validation [38]. The accuracy achieved in this research was obtained bagging the random forest algorithm with \(B=k=5\), minimum leaf size equal to 8, and the number of learners equal to 30.  
\[\sigma=\sqrt{\frac{\sum_{b=1}^{B}(f_{b}(x^{\prime})-PL_{ML^{\prime}})^{2}}{B- 1}} \tag{4}\]
# A Survey of Methods and Technologies for Congestion Estimation Based on Multisource Data Fusion
## 2 Background
### Congestion Problem and Impact on Society
To mitigate the congestion problem, traffic engineers use a variety of solutions, such as congestion pricing, encouraging multimodal and public transport, and developing Intelligent Transport Systems (ITS) applications [17; 18]. [PERSON] [19] argues that switching to the sharing economy and relinquishing private car ownership, or switching to connected autonomous vehicles, can potentially reduce congestion. A different strategy to reduce congestion can be achieved by adopting teleworking [20], which includes various programsand activities that substitute physical travel with new telecommunication technologies. Teleworking is defined as work performed from a distance, typically online, which eliminates travel to work offices. This can be tested especially in times of COVID-19 [21], where a complete or partial lockdown causes a reduced number of trips to work offices. In addition, the authors in [22] suggest that moving consumers from physical stores to online stores can potentially alleviate traffic congestion by preventing consumers from driving to physical stores.
## 2 Research Background
### Physical Comfort Evaluation  
Physical effort assessment mainly refers to the study of the postures assumed by the operators during the work shift and the efforts in handling of loads, tools, and equipment, depending on the features of the operating spaces and the workstation layout. Postural risk generally considers manual handling of loads and repetitive movements, favoring the onset of muscle-skeletal disorders and pathologies, the so-called work-related musculoskeletal disorders (WMSDs) [20]. For these reasons, many methods have been developed to assess the factors related to physical risk exposure. Among these factors, we can find the frequency of movements and their duration, use of tools, awkward postures, postural loading, effect of vibration, and so on [21]. The reference legislation for assessing the risk related to the assumption of incongruuous postures is wide and includes different aspects [22; 23; 24; 25]. Among the several available, the most applied methods are the Rapid Upper Limb Analysis (_RULA_, which includes particular attention to the neck, trunk, shoulders, arms and wrists) [26], the Rapid Entire Body Assessment (REBA, that considers all the body parts, included the inferior ones) [27], the Ovako Working posture Analysis System (OWAS, which consists of a four-digit number, representing the: (1) back; (2) arms; (3) lower limbs; and (4) handled load classification) [28]. Also, the Occupational Repetitive Actions (OCRA) analysis considers the frequency and repetitiveness of movements, use of force, type of posture, recovery period distribution and additional factors, and provides two separate indices (shoulder and elbow/wrist/hand) for each of the right and left sides of the body [29]. The NIOSH equation allows the assessment of the risks involved in material handling tasks (for loads \(\geq\) 3 kg) [30]. The acquisition of the data necessary to perform the analysis has been based, for decades, on the direct observation of the operator during the execution of a specific task, by an experienced ergonomist. The spread of motion capture devices has then been exploited to automate this phase, with considerable benefits in terms of time, costs, and accuracy of results [31].
## 5 Experimental Analysis
### Regularization Method  
Is regularization item useful for learning SRL model? One of the most common problems data science professionals face is to avoid overfitting. Regularization is a technique that makes slight modifications to the learning algorithm such that the model generalizes better. Thus, it improves the model's performance on the unseen data as well. In this section, we employ the regularized state representation (_ell1_ norm and _ell2_ norm regularization terms on SRL neural networks weight) to learn navigation strategies in the mobile robot navigation with random target task. We contrast the performance of different regularization methods and analyze the results. \(\ell_{1}\) norm and \(\ell_{2}\) norm are the most common types of regularization. \(\ell_{2}\) norm is also known as weight decay as it forces the weights to decay towards zero (but not exactly zero); In \(\ell_{1}\) norm, we penalize the absolute value of the weights. Unlike \(\ell_{2}\), the weights may be reduced to zero here. Hence, it is very useful when we are trying to compress our model. Otherwise, we usually prefer \(\ell_{2}\) over it. These update the general cost function by adding another term known as the regularization term. \[\mathcal{J}=L(x,\hat{x})+\lambda R(w), \tag{17}\]  
where \(L\) is the regular loss function, \(R\) is the regularization term, and \(\lambda\) is the scaling parameter in front of the regularization term to adjust the trade-off between the two objectives. Due to the addition of this regularization item, the values of weight matrices decrease because it assumes that a neural network with smaller weight matrices leads to simpler models. Therefore, it will also reduce overfitting to quite an extent. **Results:** In the mobile robot one-random-target task, we test several regularization scaling parameter \(\lambda\) for \(\ell_{1}\) norm and \(\ell_{2}\) norm from \(10^{-1}\) to \(10^{-6}\) and choose the best one, which is \(10^{-6}\) for \(\ell_{1}\) norm and \(10^{-3}\) for \(\ell_{2}\) norm. As it shows in Figure 10, \(\ell_{2}\) regularized SRL is second to ground truth, while \(\ell_{1}\) is the worst. \(\ell_{1}\) regularization in SRL can generate adequate sparsity. In comparison, \(\ell_{2}\) regularization, which is easily solved by the ridge regression, is utilized to achieve faster and robust learning. \(\ell_{1}\) and \(\ell_{2}\) regularizations have their contributions for robust sparse representation. \(\ell_{1}\) regularization is the preferred choice when having a high number of features as it provides sparse solutions. Although \(\ell_{1}\) regularization technique has a computational benefit since zero-coefficient features can be avoided, it is discovered that \(\ell_{1}\)-norm based SRL is not necessary to produce positive results as expected.