esa-sceva/satcom-chunk-collection huggingface dataset:
# A Program Model of Fuzzy Interpreted Petri Net to Control Discrete Event Systems
## 3 The Formal Basis and the Conception of FIPN
### The Program Model of the FIPN--Functions  
In this subsection, all functions that belong to the program model of the FIPN are presented. These functions are created based on Definitions 2 and 3. They specify some principles of model operation and can be divided into four groups:  
* the functions that calculate the increment of the degree to which the condition corresponding to the transition is satisfied;
* the functions that check if the transitions are granted to be fired or lose the concession;
* the functions that calculate the new marking of places;
* the functions that set output variables based on the new marking of places. All functions are more clearly described below.  
2.1 The Calculation of the Degree to which the Condition Corresponding to the Transition Is Satisfied  
To limit the values from sensors, the function \(Bound\) is used. This function changes each value to the range \([0,1]\). The limit of each input variable \(in_{i}\in\mathit{In}^{P}\), that represents the value from the sensor corresponding to the transition \(t_{i}\in T\) is as follows:  
\[Bound(in_{i})=\left\{\begin{array}{ll}1.0&\text{for }in_{i}\geq 1.0\\ 0&\text{for }in_{i}\leq 0.0\\ in_{i}&\text{in other case}\end{array}\right. \tag{9}\]  
Based on (9), the function \(CalcDet\) is implemented. This function calculates the increment \(det_{i}\) of the degree to which the condition corresponding to the transition \(t_{i}\in T\) is satisfied (in two subsequent cycles). It is implemented in the following way:  
\[CalcDet(tet_{i},in_{i})=\left\{\begin{array}{ll}Bound(in_{i})-tet_{i}& \text{for }Bound(in_{i})\geq tet_{i}\\ 0&\text{in other case}\end{array}\right. \tag{10}\]  
where: \(tet_{i}(tet_{i}\in\mathit{Tet}^{P})\) denotes the variable that stores the degree to which the condition corresponding to the transition is satisfied.
## 3 Simulation Results  
### Test-case Scenario Definition  
The LVC algorithm was tested in a model of a real Portuguese LV distribution network. The network is composed by 241 buses and 239 lines, and has a total of 129 consumers. The single-line diagram of the simulation network can be seen in Figure 11 of the Appendix section. The MV/LV transformer does not have OLTC capabilities and a DSO-owned 27 kW/30 kWh energy storage device is located at node 117. HEMS were stochastically distributed to around 30% of the LV customers, simulating consumer led installation of these devices. To simulate a network with a high penetration level of renewable energy, it was considered that every consumer has a small microgeneration unit installed. The installed capacity of the generation unit was determined as a function of the consumer's contracted power, according to the criteria listed in Table 2.  
In Table 3, is provided a summary with the number and contracted power of clientes with and without HEMS, as well as the microgeneration installed capacity.
# Camera-Based Continuous Heart and Respiration Rate Monitoring in the ICU
###### Abstract  
We provide new insights into the performance of camera-based heart and respiration rate extraction and evaluate its usability for replacing spot checks conducted in the general ward. A study was performed comprising of 36 ICU patients recorded for a total time of 699 h. The 5 beats/minute agreement between camera and ECG-based heart rate measurements was 81.5%, with a coverage of 81.9%, where the largest gap between measurements was 239 min. The challenges encountered in heart rate monitoring were limited visibility of the patient's face and irregular heart rates, which led to poor agreement between camera- and ECG-based heart rate measurements. To prevent non-breathing motion from causing error in respiration rate extraction, we developed a metric which was used to detect non-breathing motion. The 3 breaths/minute agreement between the camera- and contact-based respiration rate measurements was 91.1%, with a coverage of 59.1%, where the largest gap between measurements was 114 min. Encountered challenges were the morphology of the respiration signal and irregular breathing. While a few challenges need to be overcome, the results show promise for the usability of camera-based heart and respiration rate monitoring as a replacement for spot checks of these vital parameters conducted in the general ward.  
remote photoplethysmography; heart rate; respiration rate; contactless monitoring; video; optical flow 2025
## 3 The Proposed Model
### Left-Context with BERT-Based Model  
#### 3.1.1. _Bert_ Pre-Trained Model  
BERT (Bi-directional Encoder Representations from Transformers) is a neural network architecture based on the transformer architecture, which was introduced in [36]. This architecture is designed to model sequences of data, such as natural language text, and has been used in a variety of natural language processing tasks, including machine translation [37; 38], language modeling [39], and chatbot [40]. In order to understand the relationship between two sentences, the BERT training process also utilizes next sentence prediction. A pre-trained model with this understanding is relevant for tasks like question answering. During training, the model receives input pairs of sentences and learns to predict if the second sentence is the next one in the original text.  
Figure 1 shows an overview of the BERT architecture. The input text is first tokenized and embedded using BERT's token embedding layer. The position embedding layer adds positional information to the input tokens, allowing the model to distinguish between the order of the tokens. On a high level, the BERT block is a stack of multiple Transformer encoder blocks, each of which is composed of multi-head attention layer and positionwise layer (shown in the right part of the figure). The multi-head attention layer allows the model to weigh the importance of different words in the input text based on their relevance to each other. In contrast, the feed-forward layer transforms the weighted input into a fixed-length vector representation. This process is repeated for each transformer block in the stack, and the output embedding layer generates the final vector representation of the input text, which is then used for downstream tasks such as text classification or question answering. In this study, we use the pre-trained BERT model to downstream our task.
# A Heterogeneous Battlefield Situation Information Sharing Method Based on Content
## 4 Construction of the Unified Battlefield Situation Information Space
### Aggregation of Internal Description Information about Entities
Suppose \(k_{2}\geq k_{1}\), then \(T_{2}^{\prime}(e_{i})=T_{1}^{\prime}(e_{i})\oplus T_{M}(m_{2})\), and we get Equation (6), where the \({}^{\prime\prime}\oplus^{\prime\prime}\) operator represents the operation of tree merge:  
\[T_{2}^{\prime}(e_{i})=(EC_{i}(EC_{i+1}(...(EC_{i+k_{2}}(P_{2}^{\prime}S_{c_{i+k _{1}}}^{\prime\prime},P_{2}^{\prime}A_{c_{i+k_{2}}}^{\prime\prime}),P_{2}^{ \prime}S_{c_{i+k_{2}-1}}^{\prime\prime},P_{2}^{\prime}A_{c_{i+k_{2}-1}}^{ \prime\prime})...),P_{2}^{\prime}S_{c_{i}}^{\prime\prime},P_{2}^{\prime}A_{c_{ i}}^{\prime\prime})) \tag{6}\]  
where \(\{P_{2}^{\prime}S_{c_{j}}^{\prime\prime}=P_{2}S_{c_{j}}^{\prime\prime}\cup P_{ 1}^{\prime}S_{c_{j}}^{\prime\prime},\,P_{2}^{\prime}A_{c_{j}}^{\prime\prime}=P _{2}A_{c_{j}}^{\prime\prime}\cup P_{1}^{\prime}A_{c_{j}}^{\prime\prime}|j\in[ i,\,i+k_{2}]\}\wedge\{P_{1}^{\prime}S_{c_{l}}^{\prime}=\varnothing,\,P_{1}^{ \prime}A_{c_{l}}^{\prime}=\varnothing|I\in(i+k_{1},i+k_{2}]\}\). By that analogy, when message \(m_{g}\) arrives, \(T_{g}^{\prime}(e_{i})=T_{g-1}^{\prime}(e_{i})\oplus T_{M}(m_{g})\), and we get Equation (7)  
\[T_{g}^{\prime}(e_{i})=(EC_{i}(EC_{i+1}(...(EC_{i+k_{g}}(P_{g}^{\prime}S_{c_{i+k _{g}}}^{\prime\prime},P_{g}^{\prime}A_{c_{i+k_{g}}}^{\prime\prime}),P_{g}^{ \prime}S_{c_{i+k_{g}-1}}^{\prime\prime},P_{g}^{\prime}A_{c_{i+k_{g}-1}}^{ \prime\prime})...),P_{g}^{\prime}S_{c_{i}}^{\prime\prime},P_{g}^{\prime}A_{c_{ i}}^{\prime\prime})) \tag{7}\]  
where, \(\{P_{g}^{\prime}S_{c_{j}}^{\prime\prime}=P_{g}S_{c_{j}}^{\prime\prime}\cup P_{ g-1}^{\prime}S_{c_{j}}^{\prime\prime},\,P_{g}^{\prime}A_{c_{j}}^{\prime\prime}=P _{g}A_{c_{j}}^{\prime\prime}\cup P_{g-1}^{\prime}A_{c_{j}}^{\prime\prime}|j\in[ i,\,i+k_{g}]\}\wedge\{P_{g-1}^{\prime}S_{c_{l}}^{\prime\prime}=\varnothing,\,P_{g-1}^{ \prime}A_{c_{l}}^{\prime\prime}=\varnothing|I\in(i+k_{g-1},\,i+k_{g}]\}\). If \(\forall j\in[i,\,i+n]\), \(P_{g}^{\prime}S_{c_{j}}^{\prime}=S_{c_{j}}^{\prime}\) and \(P_{g}^{\prime}A_{c_{j}}^{\prime}=A_{c_{j}}^{\prime}\) are satisfied, then \(T_{g}^{\prime}(e_{i})=T(e_{i})\) can be obtained. From the above analysis, it can be seen that the process of information fusion is actually the process of constantly updating and expanding the information tree describing \(e_{i}\) in the UIS and ultimately making the information describing \(e_{i}\) complete, i.e., all the information describing the states and attributes of \(e_{i}\) have specific values. Subsequent messages are a partial replacement process for the existing information of a certain state or attribute. Therefore, information can be fused based on the concept-logic tree so as to reduce information redundancy, which is useful to eliminate the information search time.
# Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset
## 3 Application of the Proposed Approach to Insulator Detection
### Training and Test Dataset  
The training and test datasets included images taken during the flight of the UAV along the high-voltage line. For data acquisition, a GoPro Hero 4 camera was used. All the images of the training and test datasets were manually tagged, thus obtaining a prepared training dataset and a ground truth for the test set. For each image, a separate XML file was generated. The file contained coordinates (x_min, y_min, x_max, y_max) of the regions of interest (ROIs), defining the positions of objects in the scene. Figure 2 presents examples of tagged images from the flight along the high-voltage line. The whole dataset contained 343 photos. In total, 3138 ROIs containing the insulator were marked. The test dataset consisted of 283 images (2560 ROIs), and the entire training dataset contained 60 images (578 ROIs). Additionally, the training dataset was divided into smaller sets of images, i.e., five, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, and 60 frames.
## 6 Case Study and Discussion
### Comparison  
Optimization results of system allocation are demonstrated in Table 1. Results of costs and the net exchanged energy between microgrids and the main grid in four cases are demonstrated in Table 2. The total costs and costs of the three microgrids in the four cases are listed. In addition, the costs of allocation and dispatching are also calculated and listed in the table. According to the figures in Section 6.3 and the data in the tables, conclusions can be drawn as follows:  
1. After optimization, the net exchanged energy between microgrids and the main grid decreases, which means that the microgrids becomes more independent from the main grid.
2. Microgrids purchase energy from the main grid in load valley hours when electricity prices are low and sell energy to the main grid in load peak hours when prices are high, which contributes to the peak shaving of the main grid.
3. In Cases 3 and 4, numbers of PVs in RMGs dramatically decrease, while number of PVs in OBMG is greatly increased. The reason is that for RMGs, the EV charging load is mainly distributed during night and the output of PVs at night is zero. Therefore, PV modules in RMGs are reduced to save installation costs.
4. The total costs in Cases 2 and 3 are lower than that in Case 1. In Case 2, the numbers of EVBCDIs are optimized. Since redundant installation of EVBCDIs is cut down, the allocation is more proper and total cost is reduced. Similarly, in Case 3, the installation of RESs is optimized. The installation capacities of RESs are optimally configured according to the demand of the different kinds of microgrids, and the total cost is decreased compared to Case 1.
5. In Case 4, economic dispatching and capacity allocation of RESs and EVs in the multi-microgrid system are optimized synergistically. Though the allocation cost of RESs is slightly increased, the dispatching cost of the system is significantly reduced because of the cooperative optimization of economic dispatching and capacity allocation for RESs and EVs. The total cost in Case 4 is the lowest of all four cases. The integration efficiency of RESs and EVs is improved, and more environmental benefits are achieved.  
Figure 9: Dispatching results in Case 4: (**a**) Dispatching profiles of RMG1; (**b**) Dispatching profiles of OBMG.
## 4 Current Status and Identified Needs
### Level 2 Land Surface Reflectances
#### 4.2.2 Cloud Mask Assessment within CMIX
The take-home message from CMIX was that our ability of inter-comparing cloud-masks is currently hampered by the lack of a community-agreed cloud definition and an associated reference dataset. In the ideal case, this reference dataset should be collected from independent ground-based measurements and should provide a physically based measure of the cloud properties, such as the optical thickness. One cost-effective solution, based on the use of ground-based fish-eye cameras, was recently proposed by NASA and initially used within CMIX [135]. In the absence of independent datasets, there is an urgent need for an accurately labelled ground-truth dataset, specifically tailored to the validation needs. In order to address the remaining needs and gaps, a follow-up CMIX-II initiative is being defined by ESA in coordination with the CEOS-WGCV.
## 3 Conceptual Framework for the Classification of Sleep Stages  
Figure 2 shows the conceptual framework for sleep stage classification. Researchers in this area use various datasets, such as Sleep-edf [35], Sleep-edfx [36], MASS [37], MIT-BIH [38], ISRUC-Sleep [39], SHHS [40], UCD [41], and PhysioNet Challenge [42]. In the preprocessing step, datasets are cleaned to exclude missing values and eliminate noise, artefacts, and other distortions. The most common preprocessing methods are filtering [43], normalization [44], and signal conditioning [45]. After that, the feature selection step is used to select the most important features that have a significant impact on the model's performance. The two common methods for feature selection are feature engineering and statistical methods. Most of the existing work on sleep stage classification employs feature engineering methods, such as fast Fourier transform (FFT) [46], wavelet transform (WT) [47], short-time Fourier transform (STFT) [48], Pan-Tompkins algorithm [49], and temporal decomposition (TD) [50]. Common statistical methods include dynamic wrapping, dispersion entropy, max, mean, skew, and variance features [51]. However, recently, some studies proposed to use raw data as input without feature engineering or statistical methods to reduce the complexity of the proposed model. This approach allows deep learning algorithms to learn directly from raw data [52].  
The next step is the data splitting strategy, which includes cross-validation and random splitting approaches used to split datasets into training and testing sets. The cross-validation approach divides the dataset into 3, 5, or 10 parts to evaluate the classification models [53]. The random splitting approach employs a small fraction of the testing set to evaluate the model (e.g., 70% training set, 15% validation set, and 15% testing set). In addition, some studies use the above splitting strategies with either a subject-wise or non-subject-wise approach. In a subject-wise approach, the training and testing sets do not share any patient's recording samples. Therefore, the patients whose recordings are used in the training set are excluded from the testing set. In contrast, a non-subject-wise approach uses the same patients' recording samples in the training and testing sets.  
The final step involves classification using machine learning or deep learning models. In the sleep stage classification models, three types of categorizations for the sleep stages have been used. In the first type, a binary classification has been implied, distinguishing between wake stage (W ) and sleep stage (combining REM and NREM). In the second type, it categorises sleep into three stages: wake (W), non-rapid eye movement (NREM), and rapid eye movement (REM), while the third type involves categorization into five stages: W, N1, N2, N3, and REM. However, it is important to note that binary and three-sleep stage  
Figure 2: Conceptual framework for the classification of sleep stages.  
classifications do not completely align with traditional AASM guidelines, while a five-sleep stage classification is consistent with the AASM guidelines.
# Technologies and Principles of Hot Recycling and Investigation of Preheated Reclaimed Asphalt
## 2 Theoretical Formulation of the Problem
### Model of Technologies of RAP Recycling
}\)--superheating temperature of virgin mineral materials (aggregates), \({}^{\circ}\)C; \(T_{RHMA}\)--required temperature of recycled hot mix asphalt, which depends on the grade of the bitumen binder, \({}^{\circ}\)C; \(S\)--ratio of RAP batching mass in the recycled hot mix asphalt mixture, in units; \(T_{RAP}\)--temperature of RAP, \({}^{\circ}\)C; and, \(W_{RAP}\)--moisture (water content) of RAP, in units. This formula (1) contains four variables, two of which (\(T_{RHMA}\), \(S\)) show the temperature of RAP and component composition, whereas other two (\(T_{RAP}\), \(W_{RAP}\))--show RAP temperature and moisture. Experimental investigations showed that RAP may absorb from 0.7 to 6.5% of moisture. The average made up 3.40%. Therefore, it is recommended to store it in warehouses. It is likely that not all humidity is removed from RAP in a parallel drum. The quantity and temperature of a batched. RAP, which depends on its transfer type impacted by the production technology of RHMA in AMP, allows to calculate the temperature at which virgin aggregates shall be heated. According to formula (6), it was identified how the temperature required for heating virgin aggregates varies when the moisture of RAP varies from 0 to 8%, and its quantity in RHMA varies from 5 to 50% (Figure 4). When making calculations, it was assumed that the initial temperature of RAP is equal to ambient temperature of 15 \({}^{\circ}\)C (Figure 4a). RAP was heated in a parallel drum to the working temperature of 100\({}^{\circ}\)C (Figure 4b), and it also contained some residual temperature. Figure 4: \(Cont.\)  
It was estimated that if RAP content in RHMA mixture is 50% and RAP moisture is 2%, then the temperature required to heat virgin aggregates when producing 160 \({}^{\circ}\)C RHMA with RAP heated in a parallel drum is 262 \({}^{\circ}\)C, and with non-heated RAP-even 354 \({}^{\circ}\)C. The difference made up 92 \({}^{\circ}\)C.
# Linking Remotely Sensed Carbon and Water Use Efficiencies with In Situ Soil Properties
## 2 Materials and Methods
### Satellite Data  
The MODIS 250 m resolution EVI (MOD13Q1) and NDVI (MOD13Q1) data and 500 m resolution ET data (MOD16A2; hereafter ET) were obtained from the USGS Earth Explorer website ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)), accessed on 10 April 2018) for the study sites for the year 2017. MODIS 250 m resolution GPP (8-days; hereafter GPP\({}_{\rm 250}\)) and NPP (annual; hereafter NPP\({}_{\rm 250}\)) for the year 2017 were acquired from the Terra Numerical Simulation Group (NTSG) site ([[http://files.ntsg.umt.edu/data/NTSG_Products/MOD17/MODIS_250/](http://files.ntsg.umt.edu/data/NTSG_Products/MOD17/MODIS_250/)]([http://files.ntsg.umt.edu/data/NTSG_Products/MOD17/MODIS_250/](http://files.ntsg.umt.edu/data/NTSG_Products/MOD17/MODIS_250/)), accessed on 10 April 2018). We also acquired Landsat-derived GPP (16 days; hereafter GPP\({}_{\rm 30}\)) and NPP (annual; hereafter NPP\({}_{\rm 30}\)) data [40] at 30 m resolution from the NTSG website ([[http://files.ntsg.umt.edu/data/](http://files.ntsg.umt.edu/data/)]([http://files.ntsg.umt.edu/data/](http://files.ntsg.umt.edu/data/)) Landsat_Productivity/, accessed on 10 April 2018).  
We also used the GPP product (GOSIF GPP) based on SIF derived from the Orbiting Carbon Observatory-2 (OCO-2). GOSIF is a global, OCO-2 based SIF product, while GOSIF GPP is based on GOSIF and linear relationships between GPP and SIF [65; 66]. Both products are available at [[http://globalecology.unh.edu](http://globalecology.unh.edu)]([http://globalecology.unh.edu](http://globalecology.unh.edu)), accessed on 13 May 2021. GOSIF GPP data, refer to as GPP\({}_{\rm SIF}\) hereafter were resampled from 0.05\({}^{\circ}\) to 250 m spatial resolution using the bilinear interpolation method to match the resolution of GPP\({}_{\rm 250}\).  
CUE based on each of the data inputs was calculated as follows:  
\[{\rm CUE}_{\rm 250}=\frac{{\rm NPP}_{\rm 250}}{{\rm GPP}_{\rm 250}} \tag{1}\]  
\[{\rm CUE}_{\rm 30}=\frac{{\rm NPP}_{\rm 30}}{{\rm GPP}_{\rm 30}} \tag{2}\]  
\[{\rm CUE}_{\rm SIF}=\frac{{\rm NPP}_{\rm 250}}{{\rm GPP}_{\rm GOSIF}} \tag{3}\]  
where \({\rm CUE}_{\rm 30}\) is CUE estimated from Landsat 30 m GPP and NPP, \({\rm CUE}_{\rm 250}\) is CUE derived from MODIS 250 m GPP and NPP, and \({\rm CUE}_{\rm SIF}\) is CUE based on MODIS 250 m NPP and GOSIF GPP. Similarly, WUE for each of the data sets was calculated as:  
\[{\rm WUE}=\frac{{\rm GPP}_{\rm 250}}{{\rm ET}} \tag{4}\]  
\[{\rm WUE}_{\rm SIF}=\frac{{\rm GPP}_{\rm GOSIF}}{{\rm ET}} \tag{5}\]  
We used GOSIF GPP along with MODIS NPP to calculate CUE and WUE to explore how SIF-based GPP can be used to examine the relationships of WUE and CUE with soil properties.