esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 3 Decolorization Methods
### GD Method  
This method converts color images to grayscale images with a focus on preserving and enhancing contrast [21]. The real-time performance is one of the main contributions of GD. The focus is on enhancing the visibility of equiluminant chromatic contrasts in grayscale images, which are often ignored in conversion methods that use only luminance information.  
Image sampling and dimensionality reduction together capture the color differences that contribute to perceived contrast. Gaussian pairing is used for image sampling, which allows for a more representative analysis of color contrasts by pairing pixels based on a displacement vector drawn from a bivariate Gaussian distribution. With this, the blurring effects that can occur with fixed neighborhood sampling can be avoided. Predominant component analysis, a technique proposed in the paper, is used for dimensionality reduction. It differs from principal component analysis because it optimizes differences between observations, and not their variability. It enables the discovery of the color axis that best represents chromatic contrasts lost when relying solely on luminance channels for grayscale mapping. The method ensures that when colors in the original image are the same, their grayscale counterparts are also the same. This global consistency enhances the interpretability of the images.
# Low-Rate Non-Intrusive Load Disaggregation with Graph Shift Quadratic Form Constraint
## 4 Conclusions and Future Work  
In this paper, a novel GSQF-APD algorithm is proposed, which is essentially a GSP-based hybrid NILM. As the experiment results show, adding the graph shift quadratic form constraint to the MF problem leads to significant improvement in classification accuracy compared with the graph Laplacian quadratic form constraint in GLQF-APD. In addition, we discuss the PCEC value of GSQF-APD and GLQF-APD using three houses power measurements from REDD dataset. The results also confirm that our method is robust with a short training phase. Moreover, we demonstrate that GSQF-APD outperforms the state-of-the-art NILM approaches, such as HMM-based algorithm, DT-based algorithm and FHMM-based algorithm. One essential limitation in power disaggregation research is the lack of a comprehensive dataset. Thus we focus on the low-rate NILM, and experiments show that our proposed algorithm works well with data sampled at a frequency of about once a minute.  
Further work will include reducing computational complexity and improving event detection via pre-processing (denoising and filtering). Furthermore, we will attempt to decrease the number of _FP_ and _FN_ by exploiting time intervals as a feature. It may be possible to solve the problem of one state change lasting longer than the sampling duration and multiple consecutive events being identified.  
This work is supported by the \"Fundamental Research Funds for the Central Universities\" (Project No. 2018 MS001). [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the simulation; [PERSON] and [PERSON] analyzed the results; [PERSON] and [PERSON] wrote and revised the paper.  
The authors declare no conflict of interest.
## 3 Testing with a Simple Room Case
### Test Method
#### 3.3.1. Based on Simulation Data and 2D Simulations  
Based on the simulation data using 2D simulations for inverse prediction, the output Pareto set is shown in Table 2:  
As an inverse prediction based on simulation data, the true Pareto set should be a single point ([PERSON] obtained unbiased results when using the genetic algorithm and simulation data in his inverse prediction), and the above results indicate that the method can only provide approximate results. One reason is that the objective functions established from the velocity and temperature data at the above positions are all very small in some directions. In the vicinity of the correct boundary condition, smaller \(V_{in}\) leads to an increase in temperature, and smaller \(T_{in}\) leads to an increase in the buoyancy force (increasing the velocity, especially in the Y-direction), resulting in very small values of \(F_{1}\) or \(F_{2}\) in a direction. This direction is different for \(F_{1}\) and \(F_{2}\), so it is easy to produce non-dominated solutions when sampling near the correct boundary condition.  
In addition, because of the errors of the OK models and the use of continuous populations (continuous optimization) when using NSGA-II, it was difficult to select the correct parameter combination directly. However, the current Pareto Frontier is close enough to  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \(\mathbf{V_{in}}\) **(m/s)** & \(\mathbf{T_{in}}\) **(\({}^{\circ}\)C)** & \multirow{2}{*}{\(\mathbf{F_{1}}\)} & \multirow{2}{*}{\(\mathbf{F_{2}}\)} & \multirow{2}{*}{\(\mathbf{F^{\prime}}\)} \\  & **(Deviation)** & **(Deviation)** & & & \\ \hline \multirow{2}{*}{1} & 0.57103 & 15.0359 & 0.000151 & \multirow{2}{*}{0.001755} & 0.000953 \\  & (0.18\%) & (0.24\%) & (min) & & (min) \\ \multirow{2}{*}{2} & 0.55203 & 14.9596 & \multirow{2}{*}{0.005745} & 0.000493 & \multirow{2}{*}{0.003119} \\  & (3.15\%) & (0.29\%) & & & \\ \hline \hline \end{tabular} Notes: Based on simulation data and 2D simulations.  
\end{table}
Table 2: Pareto set and the objective function values.  
Figure 5: The change curve about \(F^{\prime}_{min}\) (**(Part A)** based on simulation data and 2D simulations; (Part B) based on experimental data and 3D simulations).  
the true Pareto Frontier. When using the boundary condition corresponding to \(F^{\prime}_{min}\) as the final result (representing the simultaneous use of velocity and temperature data for inverse prediction), result 1 in Table 2 is already very close to the correct boundary condition.
# Exhaustive Variant Interaction Analysis Using Multifactor Dimensionality Reduction
## 2 Materials and Methods
### Multifactor Dimensionality Reduction  
MDR is a non-parametric statistical method for detecting and characterizing nonlinear interactions. It follows a naive Bayes approach, building a probabilistic classifier for each variant-variant interaction and finding the best combinations in terms of prediction. The higher the prediction power of a pair is, the stronger its association with the disease is. The steps of the method can be found in Figure 1 and are described as follows.  
1. The first step consists of a \(k\)-fold cross-validation and is performed to avoid potential over-fitting. The dataset is subdivided into \(k\) parts, where \(k-1\) data parts are used for training and one part is used for testing. Since we generate \(k\) different distributions of the dataset, the following steps are going to be performed \(k\) times: one for each distribution.
2. In the second step, for each variant pair, we cross-tabulate genotypes for cases and controls using only the individuals from the training dataset. This generates two factor tables (variant-variant): each one has three classes corresponding to the variant genotypes (AA, Aa, aa). In the end, we have a nine-cell table with two dimensions: the number of cases and the number of controls.
3. In step three, each of the tables is going to be transformed into a one-dimensional space using the case-control ratio from the training distribution set as a threshold \(T\). The cells where the ratio of cases to controls is greater than \(T\) are going to be classified as 'high risk'; the other cells are classified as 'low risk'. As a result of this step, we reduce the dimension of the problem from two classes to one.
4. In step four, each of the variant-variant tables is used to classify the individuals of the training set. The classification is done as follows: for each individual, we extract the variant-variant genotypes and determine class of the multifactor table it belongs to. Then, if the class corresponds to a high-risk cell, we classify the individual as a case. Otherwise, we classify it as a control. After classifying every individual, we compare the predicted classes with the original labels, obtaining the misclassification error of the variant-variant table.
5. In step five, the model with the best misclassification error is selected, and the prediction power of the model is estimated using the independent test data and determining the percentage of patients correctly classified. After these steps are repeated for each of the possible cross-validation sets, the best and most consistent variant-variant combinations are selected. This means picking the ones that appear the most times in the cross-validation sets as a top predictor pair.
## 3 Results and Discussion
### Case 1  
In this case, which is directly taken from the company, 43 types of products that are contained within 64 containers will be scheduled in 4 weeks. The quantity of each product inside each container is presented in Figure 4. It is also known that the load factor of all products is similar, i.e., \(l_{1}=l_{2}=\ldots=l_{43}=1\). This case is solved using the proposed method described in Section 2 above using \(\alpha\) equal to 0.005.  
The results of the proposed method are presented in Figure 5, which are the value of decision variables \(x\) and \(y\). The decision variable \(x\) represents the delivery schedule of containers. The interpretation of the results in Figure 5 are as follows: Containers C1, C2,..., C8 are scheduled to be delivered at week 4; Containers C9, C10,..., C13 are scheduled to be delivered at week 1; Container C14 is scheduled to be delivered at week 3; and so on. Meanwhile, the decision variable \(y\) represents the production schedule of products. Our interpretation of the results in Figure 5 is as follows: Products P1, P2,..., P14 are scheduled to be produced at week 4; Products P15, P16,..., P21 are scheduled to be produced at week 1 and week 3; Products P24 and P25 are scheduled to be produced at week 4; and so on.  
A summary of the weekly decision variables and weekly production load are presented in Table 1. As seen in Table 1, all 64 containers have been scheduled completely within four weeks. Meanwhile, 43 products are scheduled to be manufactured within the next four weeks. It is also found that five products are scheduled for two distinct weeks, which are P22, P23, P28, P33, and P42. As a result, the total number of products, which is the objective function of this mathematical model, equals 48. Since the \(\alpha\) value is very small, the weekly production load is similarly comparable, in which the production load is in the range of  
Figure 4: Quantity of products inside containers, Case 1, in the form of an Excel spreadsheet.  
1255 to 1265. The production load range is consistent with the definition of constraints (7) and (8). Following those constraints, the load must be within the range of 1252.7 to 1265.3.
# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 3 Applications
### LoRaWAN Network Server  
The Network Server is the heart of the LoRaWAN protocol, where all MAC functionality controlled by the Network Server. Network Server is the termination point of the LoRaWAN MAC layer for the end-nodes and responsible for [51; 55];  
* Managing all MAC layer functionality,
* Managing Join Request and Join Responses and Roaming
* End-node address control,
* Frame counter and authentication controls,
* Acknowledgment mechanism,
* Data rate adoption (or ADR)
* Data delivery to Application Servers,
* Data delivery to end-devices from Application Servers.  
This section will embrace different LoRaWAN Network Servers and tools available today.  
Growth in the IoT ecosystem led the increase in the communities where individual volunteers share their knowledge, interest, infrastructure (internet connection) and other resources... etc. with public usage. The Things Network (TTN) is one of the well known IoT community which aims to build open global LoRa community to provide wide coverage [53].  
TTN has both free and commercial solutions, however, providing free Network Server took the attention of hobbyists, researches and makers to dive into LoRaWAN quickly. The community has help and support documents, forums and volunteers that help each other. Also, TTN Network Server is available as open source [152] to start private LoRaWAN Networks.  
An alternative open protocol OpenChirp is presented by [PERSON] et al. [153], which has a purpose to provide simplified LoRaWAN network services such as; data serialization, meta-data, and time series data storage.  
A flexible open source LoRaWAN Network Server project is sponsored by CableLabs [154], which has Web user interfaces and also RestFul API support for network management.  
Besides open source solutions, there are serveral commertial products available through companies Actility [155], Ublsoftware [156] and LORIOT [157].
# STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction
## 3 Dataset and Methodology
### Spatial-Temporal Self-Attention Graph Convolution Networks (STA-GCN)
#### 3.3.3 Spatial Self-Attention  
Since the spatial-temporal information output from the time module has already undergone convolution on the time axis, each vector in the time dimension can represent the spatial-temporal information of a convolution-length-time segment. The dependency relationship between each traffic node for each time segment can also be captured through self-attention mechanism. The formula for computing spatial self-attention is shown in Equation (5).  
\[A^{S}=\textit{softmax}\left(\frac{Q^{S}\big{(}K^{S}\big{)}^{S}}{\sqrt{d^{S}}} \right)V^{S} \tag{5}\]  
where \(Q^{S}\in\mathbb{R}^{L_{Q}\times d^{S}}\), \(K^{S}\in\mathbb{R}^{L_{k}\times d^{S}}\), and \(V^{S}\in\mathbb{R}^{L_{V}\times d^{S}}\) refer to the time-series query matrix, key matrix, and value matrix, respectively. These matrices are used in the self-attention mechanism to capture the dependency relationship between traffic-flow features at different time steps. The query matrix A contains the queries that are compared with the keys in the key matrix B, and the value matrix C contains the values corresponding to the keys. The softmax function performs a normalization operation on the temporal dependencies at each time step. The \(\sqrt{d^{S}}\) scaling factor \(\sqrt{d^{S}}\) is applied to prevent significant differences in the probability distribution of the matrix resulting from the dot product of points \(Q^{S}\) and \(\big{(}K^{S}\big{)}^{T}\) after passing through the softmax function. After obtaining the attention-probability matrix, a linear layer is applied to obtain \(f^{S}\in\mathbb{R}^{N\times d}\), which is then residually connected with \(f\in\mathbb{R}^{N\times d}\) and normalized along the \(N\times d\) dimension. Finally, the output is passed through a ReLu activation function.
# Towards Adaptive Multipath Managing:
## 4 Performance Evaluation
### Simulation Results
lowered the MPTCP throughput performance; and (iv) the out-of-order data arrival would actually degrade the performance in most cases because of the path quality differences. Still, the MPTCP-LM\({}^{3}\) scheme's cumulative average throughput was 27.97% higher than that of the baseline MPTCP and 3.68% higher than that of MPTCP + (PU)\({}^{2}\)M\({}^{2}\).
# Bottom-Up Model of Random Daily Electrical Load Curve for Office Building
###### Abstract  
In the design stage of energy systems in buildings, accurate load boundary conditions are the key to achieving energy supply and demand balance. Compared with the building cold and heat load, the generation of building electrical load has stronger randomness, and the current standard electrical load calculation method cannot reflect this feature. Therefore, this paper proposes a bottom-up high time resolution power load generation method for office buildings. Firstly, the non-homogeneous Markov chain is used to establish the random mobility model of personnel in office buildings, and the building electrical appliances are divided into four categories according to the different driving modes of personnel to electrical appliances in office buildings. Then, based on the personnel mobility model, the correlation between the use of electrical appliances in office buildings and the personnel in the room is established to construct the random power simulation model of different types of electrical appliances. Finally, the electric load of different types of electrical appliances is superimposed hourly to generate a random daily load curve. In order to verify the validity of the model, an office building is simulated and compared with the measured electrical load value. The verification results show that the model well reflects the daily distribution characteristics of electric load. The simulation value and the measured value are used for statistical analysis. The evaluation results show that the correlation between the simulation value and the measured value is high, which further illustrates the validity and accuracy of the model.  
bottom-up; electrical load; Markov chain; Monte Carlo +
Footnote †: journal: Applied Sciences
## 2 Data and Methods
### Data Sources  
#### 2.2.1 Site Data  
The situ observation data for this study were obtained from the Land-Atmospheric Interaction Observation Stations constructed by the Institute of Desert Meteorology, China Meteorological Administration, Urumqi. There were three stations (Table 1), each equipped with EC systems, radiation observation systems, and gradient tower systems.  
Figure 1: (**a**) Specific locations of the Tianshan Mountains, Ulan Usu Station, Ulastai Station, and Kelameili Station in Xinjiang. (**b**) Schematic representation of the elevations of the study area. (**c**) Schematic representation of the land use types at the study area.  
The EC system consisted of an open-path CO\({}_{2}\)/H\({}_{2}\)O infrared gas analyzer (LI7500, LicCor, Lincoln Nebraska, IA, USA) and an ultrasonic three-dimensional anemometer (CSAT3, Campbell Scientific, Logan, UT, USA). The Ultrasonic anemometer can accurately measure pulsating wind speed and acoustic virtual temperature in three different directions, with measurement accuracies of \(\pm\)4.0 cm/s and \(\pm\)2.0 cm/s. The data acquisition frequency is 10 Hz/20 Hz and the data output interval is 30 min.  
The open-path LI7500 infrared gas analyzer provides accurate measurements of CO\({}_{2}\) concentration and water vapor density in the atmosphere, with measurement accuracies of \(\pm\)0.01 mmol/mol and \(\pm\)0.15 mmol/mol. The data output interval is also 30 min.  
The data used in this study were continuous observation data in 2020, with observation times synchronized to the local time. All measured data were output through a data collector (CR3000, Campbell Scientific, Logan, UT, USA) at time frequencies of 10 s, 1 min, 30 min, and 1 h. The acquisition frequency of the radiation and gradient observation systems was 1 Hz, whereas that of the EC covariance system was 10 Hz.
## 5 Model Application
### Results of ANN Based on Real GPS Data  
In our experiment, the trained model obtained according to extracted historical features was used to predict link travel time based on real GPS data and different datasets were used to verify our proposed model. As depicted in Figure 9, the correlation between true link travel time and predicted link travel time corresponded to different testing datasets. The horizontal axis represents true link travel time while the vertical axis denotes predicted link travel time. The linear correlation coefficient R value indicates the correlation between true link travel time and predicted link travel time. As we can see in Figure 9,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Total Data** & \begin{tabular}{c} **Training** \\ **(Percentage)** \\ \end{tabular} & \
\begin{tabular}{c} **Validation** \\ **(Percentage)** \\ \end{tabular} &
\begin{tabular}{c} **Testing** \\ **(Percentage)** \\ \end{tabular} & **RMSE** & **MAPE** \\ \hline \multirow{4}{*}{20/8} & 1849 (89\%) & 208 (10\%) & 20 (1\%) & 17.9606 & 12.46\% \\  & 1766 (85\%) & 208 (10\%) & 104 (5\%) & 26.5225 & 15.90\% \\ \cline{1-1}  & 1662 (80\%) & 208 (10\%) & 208 (10\%) & 28.4815 & 15.61\% \\ \cline{1-1}  & 1454 (70\%) & 208 (10\%) & 417 (20\%) & 23.0533 & 15.02\% \\ \hline \hline \end{tabular}  
* RMSE, Root Mean Square Error; MAPE, Mean Absolute Percentage Error.  
\end{table}
Table 10: Training, evaluation and testing dataset 1.  
with an increasing quantity of data, the R value trended downward. Table 10 shows the prediction accuracy corresponding to different testing data. The value of MAPE becomes slightly bigger with an increase in the amount of data from 1 to 5% of the total data. As shown in Figure 9a, it reaches the best prediction effect from the perspective of MAPE when the data were 1% of the total data. The mean absolute percentage error was 12.46% and the root mean square error was 17.96 s. However, the MAPE values corresponding to different testing data obtained values closer to each other when the testing data were 5%, 10% and 20%, as shown in Figure 10: 15.90%, 15.61%, and 15.02%, respectively.  
Figure 10: The Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) of predicted link travel time corresponding to different data percentage.  
Figure 9: Correlation of link 82 between true link travel time and estimated link travel time.
## 3 Results  
Out of 54 patients, 7 patients were excluded because they did not meet eligibility criteria (4 underwent previous surgery; 4 aged more than 80 years). Therefore, a total of 47 patients (mean aged 68.8 \(\pm\) 13.7 years, body mass index: 28.2 \(\pm\) 4.9 kg/m\({}^{2}\)) were included and their baseline characteristics are depicted in Table 1.  
We observed a statistically significant reduction in pain at the 1-month evaluation (T1) (NPRS: 7.48 \(\pm\) 1.74 vs. 3.63 \(\pm\) 1.68; \(p\) < 0.001).  
Moreover, all patients showed a significant decrease in disability at T1 as suggested by the reduction of KOOS score from 21.45 \(\pm\) 7.33 to 44.97 \(\pm\) 10.77 (\(p\) < 0.001). Considering the KOOS domains, there was a statistically significant difference at T1 (\(p\) < 0.001) in all except for the \"symptoms domain\" that did not significantly differ. In addition, a significant improvement in terms of HR-QoL was recorded in both EQ-5D (0.48 \(\pm\) 0.06 vs. 0.67 \(\pm\) 0.07; \(p\) < 0.001) and EQ-VAS (49.4 \(\pm\) 4.3 vs. 79.4 \(\pm\) 5.8; \(p\) < 0.001). Further details are shown in Figure 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Patients (\(n\) = 47)** \\ \hline Age (years) & 68.8 \(\pm\) 13.7 \\ BMI (kg/m\({}^{2}\)) & 28.2 \(\pm\) 4.9 \\ Sex (female/male) & 28/19 \\ Smoke (habitual smokers) & 6 (12.7) \\ \(\geq\)3 alcohol units/day & 2 (4.2) \\ Caucasian ethnicity & 45 (95.7) \\ Occupation (yes) & 8 (14.9) \\ \hline \hline \end{tabular} Continuous variables are expressed as means \(\pm\) standard deviations, categorical variables are expressed as counts (percentages).  
\end{table}
Table 1: Anamnestic and demographical characteristics of the study population.  
Adherence to the exercise training program was over 80% of the sessions proposed in 28 participants. Furthermore, no major adverse events were reported, while few minor adverse events occurred as vagal reactions (\(n=2\)), minor bleeding (\(n=17\)), and temporary local hypoesthesia (\(n=29\)). However, all these adverse events were self-limiting and did not require any medical intervention.