esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data
## 3 Approach
### Reference Pressure Extraction
#### 3.3.2 GPS Uncertainty  
Another characteristic of the OITransition is the rapid increase of the GPS uncertainty. As a result, in our approach, we recorded the GPS uncertainty with a sampling frequency of proximately 1 Hz. As can be seen in Figure 5, at the moment of the transition (after the 80 th sample), the GPS uncertainty increased from less than 10 m to almost 60 m. Hysteresis thresholding [23] was applied for the maximization of the margin between low GPS accuracy (indoors) and high-accuracy data (outdoors) for better classification. More specifically, GPS uncertainty was first smoothed via a Gaussian smoothing filter. Then multiple hysteresis thresholding was applied in order to enhance the margin and hence the accuracy of the OITransition classification. The approach can be seen in Figure 6. As can be seen in the figure, raw GPS uncertainty (red line) was first smoothed with a Gaussian filter. Then hysteresis thresholding was applied to the smoothed signal (magenta line).  
OITransition Detection and Histogram Analysis  
Before hysteresis thresholding was applied, the raw GPS uncertainty signal was smoothed via a Gaussian smoothing filter. Then multiple hysteresis thresholding was applied to enhance differences between segments of the signal that belonged outdoors or indoors. This approach is detailed explained in Section 3.3.1 and visualized in Figure 6.  
For the identification of an OITransition in the data segment, as well as for the definition of the threshold in the hysteresis thresholding, histogram analysis was applied in the entire GPS uncertainty signal segment. As can be seen in Figure 7, the frequency of different uncertainty radii is visualized in the histograms. As can be seen in Figure 7, the histogram forms a bimodal pattern when an OITransition occurs. This is a recognizable characteristic of a segment of uncertainty data that contains  
Figure 5: GPS uncertainty data from five outdoor–indoor transitions (OITransitions). As can be seen, at the moment of the transition after the 100 th sample, the uncertainty rapidly increased.  
Figure 6: Smoothing and hysteresis thresholding of raw GPS uncertainty signal.  
an OITransition. Once a transition is identified, the two peaks of the signal are used as the upper and lower thresholds in the hysteresis thresholding algorithm.  
On the other side, as can be seen in Figure 7a,b, the histograms show a more symmetric pattern, which is an indication that the data are extracted from a single place; this place is either indoors or outdoors. More specifically, as can be seen in Figure 7b, the GPS uncertainty is high--more than 20 m--which is an indication that the particular segment has been extracted from exclusively indoor locations. On the other side, as can be seen in Figure 7a, the GPS scores a low uncertainty--less than 15 m--which is an indication that the data are extracted from exclusively outdoor locations.
# Influence of Water Addition on the Latent Heat Degradation of Sodium Acetate Trihydrate
## 5 Conclusions and Outlook  
This study was dedicated to the analysis of the aging of sodium acetate trihydrate. It was found that the reference sample (SAT60%) had lost 10% of its latent heat quantity, from 235 to 215 J\(\cdot\)g\({}^{-1}\) after about 35 cycles. After these cycles, the evolution seemed asymptotic,  
Figure 16: Visual appearance of (a) SAT60% after 35 cycles at 70 \({}^{\circ}\)C, (b) the same sample after 46 more cycles until 80 \({}^{\circ}\)C and (c) SAT58% after 41 cycles until 70 \({}^{\circ}\)C and 10 cycles until 80 \({}^{\circ}\)C.  
reaching a constant value that was supposed to be close to the final latent heat after aging. Besides, some crystallization occurred at a lower temperature and were considered as singular behaviors. The supercooling degree of these solidifications increased throughout cycles that may be considered as a factor of aging, unwanted for applications. Indeed, a reduction of the SAT quantity at stoichiometric concentration would result in a degradation of the properties. This would limit the use of this material as a storage medium.  
Therefore, the addition of water as a solution to aging was investigated. In contrary to our expectations, the sample containing water in excess (SAT58%) was less stable after 35 cycles. Indeed, the latent heat had lost 25% of its initial value moving from about 225 to 170 J\(\cdot\)g\({}^{-1}\). In addition, the achievement of the stability is slower, as the exponential fit gives a final value of 96 J\(\cdot\)g\({}^{-1}\) (vs. 213 J\(\cdot\)g\({}^{-1}\) for SAT60%).  
Moreover, preliminary experiments have also shown that the agitation of the sample could \"reset\" the value of latent heat. This agitation could be induced by any mechanical movements in the liquid, such as shaking, bubbling, or ultrasound that may also present interest as nucleation triggering techniques. Indeed, the limitation of the supercooling degree is generally targeted in phase change materials to improve their heat release capability.  
Finally, for an application, the use of a high and narrow container could induce a more rapid aging behavior. Indeed, after the separation of the two species (sodium acetate and water) during heating, it may be more difficult to put them in contact during cooling. It would then be recommended to use a container of low height (h < 5 cm) to limit the separation of species due to gravity to facilitate the rehydration of the SAT.  
Conceptualization, N.B., U.S. and L.Z.; methodology, N.B., U.S. and L.Z.; investigation, N.B. and L.Z.; writing--original draft preparation, N.B.; writing--review and editing, N.B., U.S. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data available on request.  
The authors declare no conflict of interest.
# GraphMS:Drug Target Prediction Using Graph Representation Learning with Substructures
## 3 Our Approach
### Problem Formulation  
GraphMS predicts unknown DITs through a heterogeneous graph associated with drugs and targets.  
Definition 1: (Heterogeneous graph) A heterogeneous graph is defined as a directed or undirected graph \(G=(V^{N},E^{R})\), where each node \(v\in V\) and each edge \(e\in E\). Each node \(v\) in the node set \(V\) belongs to the object type \(N\), and each edge \(e\) in the relationship set \(E\) belongs to the object type \(R\). The type of node \(N\) includes drugs, targets and diseases. The type of relation \(R\) includes protein-disease-interaction, drug-protein-interaction, drug-disease-interaction, drug-drug-interaction, protein-protein-interaction, drug-structure-similarity and protein-sequence-similarity.  
In our current model, each node only belongs to a single type and all edges are undirected and non-negatively weighted. We adopted the heterogeneous graph that was built in our team previous study. The data example of the heterogeneous graph is shown in the Figure 2.  
```
0: A Heterogeneous Graph \(G=(V^{N},E^{R})\)
0: Drug-Protein Reconstruction Matrix \(M\)
1: Perform feature integration on nodes whose node type \(N\in\{drug,protein\}\) according to Equation (1);
2: Select the relationship type \(R\in\{drug-drug,protein-protein\}\) and convert to homogeneous graphs \(G_{d},G_{p}\) ;
3:whilenot Convergencedo
4: Shuffle \(X\) on row dimension to obtain \(\widetilde{X}\) ;
5:for node type \(N\in\{drug,protein\}\)do
6:\(h^{N}=Softmax\ (A^{N}X^{N}W_{1}^{N})\) ; //Obtain Node-level Representation (Positive)
7:\(\widetilde{h}^{N}=Softmax\ (A^{N}\widetilde{X}^{N}W_{1}^{N})\) ; //Obtain Node-level Representation (Negative)
8:\(s^{N}=Pool\ (\frac{1}{n}\sum_{l=1}^{n}h_{l}^{N})\) ; //Obtain Graph-level Representation
9: Partition \(G_{d},G_{p}\) nodes into k subgraphs \(G_{1}^{N}\), \(G_{s_{1}}^{N}\), \(\cdots\), \(G_{s_{k}}^{N}\) by METIS separately;
10:for all each subgraph do
11: Form the subgraph \(G_{s_{k}}\) with nodes and edges into \(\left\{(X_{s_{1}}^{N},A_{s_{1}}^{N}),(X_{s_{2}}^{N},A_{s_{2}}^{N}),\cdots,(X_{s _{k}}^{N},A_{s_{k}}^{N})\right\}\) ;
12: Shuffle other nodes except nodes of the current subgraph and select k nodes randomly to obtain the corresponding negative subgraph \((\widetilde{X}_{s_{i}}^{N},\widetilde{A}_{s_{i}}^{N})\) ;
13:\(g^{N}=Pool\ (GCN_{sub}(X_{s}^{N},A_{s}^{N}))\) ; //Obtain Substructure-level Representation (Positive)
14:\(\widetilde{g}^{N}=Pool\ (GCN_{sub}(\widetilde{X}_{s}^{N},\widetilde{A}_{s}^{N}))\) //Obtain Substructure-level Representation (Negative) ;
15:endfor
16:\(Embedding^{N}=concat(h^{N},(concat(g_{1}^{N},g_{2}^{N},\cdots,g_{k}^{N}))^{T})\) ;
17:endfor
18:\(U=Embedding^{drug},\quad V=Embedding^{protein}\) ;
19: Compute the final loss and update parameters according to Equation (14);
20:\(M=UW_{3}(VW_{4}^{T})\) ;
21:endwhile
22:return\(M\)
```  
**Algorithm 1** GraphMS  
Figure 2: The data example of the heterogeneous graph
## 4 Pig Slurry, from Biowaste to Bioenergy: Searching for Management Solutions  
The pursuit of economic circularity and the growing significance of industry synergy has led to the search for new strategies of rerouting pig slurry (PS) management on livestock farms. PS is decreasingly viewed as biowaste without value and has come to be considered a resource with the potential to recover nutrients which could provide agronomic valorisation, facilitating the efficient recycling of plant nutrients [38; 39] and the production of bioenergy with little or no global warming potential, such as biogas/biomethane [39; 40]. Furthermore, according to [PERSON] et al. [2], manure should not be viewed as waste but as an essential resource that may be used to reduce GHG emissions by producing bioenergy. To that effect, the EC Regulation nbr. 1069/2009 refers to biogas production as a management example for manure [40]. [PERSON] et al. [41] investigated the biogas potential of farm manure (cattle, pigs, sheep/goats, and poultry) for the whole of Europe and concluded that 942 million tonnes could be collected, giving an estimated theoretical biogas potential of 43 billion m\({}^{3}\) biogas. In 2016, an estimated 53.3 million tonnes of manure was used for biogas plants, producing around 4 TWh of dung-based power, of which about 16% was pig manure [42]. [PERSON] and [PERSON] et al. [24] report that in Brazil, appropriate manure management resulting from pork production through biogas technology covers the energy consumption of 5 million people.  
Back in 2012, [PERSON] et al. [43] reported that the growing trend of intensive and concentrated farming activities in limited areas had led to an increased focus on pig slurry treatment in Europe. [PERSON] et al. [38] state that rerouting manure to AD facilities would offer a way to improve control and could redistribute nutrients more effectively from surplus to deficiency areas or farther afield. Furthermore, it would offer the chance to promote nutrient recovery and collection.  
With the proper management strategies, the adverse effects caused by the volume of pig slurry generated in livestock farms would be considerably reduced, depleting odours and GHG emissions, increasing nutrient recycling and recovery, and boosting agro-energy transition. On the other hand, improper management can have negative effects on the environment, increasing the risk of air and water pollution as well as the potential for several pathogens to contaminate food crops [39].  
In Portugal, PS from swine farms is the second most produced livestock effluent, representing 17% of the total livestock effluents [44]. In the scope of the National Renewable Energy Action Plan launched by the Directorate-General for Agriculture and Rural Development, livestock effluents are prioritised in terms of their destination: (1) agronomic valorisation; (2) organic valorisation in composting units; (3) organic and energy recovery in biogas units; (4) energy recovery in combustion or co-incineration units; (5) treatment in wastewater treatment plants; and finally, (6) landfill disposal after pressure sterilisation.
# Deceptive Jamming Detection for SAR Based on Cross-Track Interferometry
## 2 Principle of Deceptive Jamming Detection  
In this section, the model for the InSAR deceptive jamming scenario is first introduced, followed by the signal model of deceptive jamming and real signals. Then, the interferometric phase differences between false targets and the surroundings are analyzed. Although, in general, the deceptive scene may be comprised of many false point targets, for simplicity and without loss of generality, an arbitrary single point target is only considered in the following.
# Unraveling the Biosynthesis of Carvacrol in Different Tissues of _Origanum vulgarae_
## 3 Discussion
### Transcriptomic Insights into the Tissue-Specific Mva/Mep Pathways in O. vulgar 'HS'  
All terpenids are derived from two universal precursors of IPP and its isomer DMAPP, which are biosynthesized in plants via two independent MEP and MVA pathways [20]. In this study, STs presented more highly expressed MVA genes than other tissues and most MEP pathway genes presented higher expression levels in SEs and BRs, followed by LEs and PEs, than in STs in _O. vulgar_ 'HS' plants. These 'upstream pathway' genes presented the tissue-specific expression patterns; of these, several vital genes, such as _IDI, AACT, HMGR, HDR_, and _HDS_ family genes, were predicted to play significant roles in generating the metabolic products of carvacrol and p-cymene. Supported by previous research, the IDI activity is of critical importance in the MVA and MEP pathways, and ensures an optimal IPP/DMAPP ratio for terpenoid precursors and/or to fuel export from plastids to cytosol [25]. The HMGRs were regarded as the rate-limiting enzymes regulating the products of mevalonate [25]. The HDRs convert HMBPP into a combination of both IPPand DMAPP, with a stoichiometry of about 5:1 [26]. Tissue transcriptomic analysis revealed higher MEP gene expression in carvacrol-rich tissues, which suggests their significant involvement in carvacrol biosynthesis.
## 4 Simulation and Experimental Analysis  
In this section, we evaluate the performance of the proposed multi-layer stacked bidirectional LSTM neural network for short-term load forecasting, and the key parameters of the model are discussed as well. Moreover, the comparison between the proposed method and previous work are also assessed. All models were executed in a computer with a CPU clock speed of 3.0 GHz and 8 GB of RAM. The hidden layer of the proposed model is 100, the hidden node is 8, the initial value of training learning rate is 0.01, and the number of model training iterations is 100.
## 3 Forensic Human Identification through the Analysis of Biomedical Images
### AI-Based Approaches for Traumatism and Pathology Analysis  
Over the recent years, the success of ML in general, and DL in particular, to classify images has caused great interest in its application to medical image analysis in several relevant fields, including the detection of skin cancer [116], gastrointestinal lesions [117], diabetic retinopathy [118], mammographic lesions [119] or lung nodules [120]. However, a representative pathological example that is of high interest, but also rare, is bone lesions [121]. To our knowledge, there are just a few works in the field of orthopaedics related to the application of DL to detect bone lesions or pathologies in X-ray images.  
[PERSON] et al. [122] extracted 256,000 wrists, hand, and ankle radiographs from Danderyd's Hospital and identified 4 classes: fracture, laterality, body part, and exam view. Then, they evaluated the diagnostic accuracy of 5 openly available deep networks adapted to this task. All networks exhibited an accuracy of at least 90% when identifying laterality, body part, and exam view. The final accuracy for fractures was estimated at 83% for the best performing network.  
[PERSON] et al. [123] evaluated the ability of AI techniques for detecting and classifying proximal humerus fractures using plain anteroposterior shoulder radiographs. The evaluated dataset was composed of 1891 images (1 image per person) of normal shoulders (n = 515) and 4 proximal humerus fracture types (greater tuberosity, 346; surgical neck, 514; 3-part, 269; 4 part, 247) classified by 3 specialists. They trained a ConvNet after augmenting the training dataset. The ability of the ConvNet was measured by top-1 accuracy in comparison with humans (28 general physicians, 11 general orthopedists, and 19 orthopedists specialized in the shoulder) to detect and classify proximal humerus fractures. Their results showed 96% accuracy, for distinguishing normal shoulders from proximal humerus fractures, and 65-86% accuracy for classifying the fracture type.  
In [PERSON] et al. [124], the authors address the problem of classifying bone lesions from X-ray images by increasing the small number of positive samples in the training set. They propose a generative data augmentation approach based on a cycle-consistent generative adversarial network that synthesizes bone lesions on images without pathology. They pose the generative task as an image-patch translation problem that they optimize specifically for distinct bones (humerus, tibia, and femur). In experimental results, they confirm that the described method mitigates the class imbalance problem in the binary classification task of bone lesion detection. They show that the augmented training sets enable the training of superior classifiers achieving better performance on a held-out test set. Additionally, they demonstrate the feasibility of transfer learning and apply a generative model that was trained on one body part to another.
## 3 Model Setup  
The cavity is an efficient flame-holding structure in the scramjet engine combustor [37] and is illustrated in Figure 2. In this study, a typical cooling channel around the leading edge of the cavity was selected as the study object. The geometry of the cooling channel for numerical simulation is shown in Figure 3[38]. The inside and outside dimensions of the rectangular channel were 2 mm \(\times\) 2 mm and 4 mm \(\times\) 4 mm, respectively. Therefore, its hydraulic diameter \(D_{h}\) was 2 mm. The rectangular cooling channel consisted of four parts. The first part (Part I) was a straight section with a length of \(30D_{h}\) to provide fully developed turbulent flow. The second part (Part II) was a straight section with a length of \(10D_{h}\) to provide a developed thermal boundary layer. The third part (Part III) was a \(90^{\circ}\) curved channel with a radius of \(25D_{h}\). The last part (Part IV) was a straight outlet section with a length of \(10D_{h}\), which was used to eliminate the curvature effect. The heating section (Part II, Part III, and Part IV) was heated from the concave side, with a constant heat flux of 5 MW/m\({}^{2}\) imposed on the external wall. The inlet mass flow rate of hydrocarbon fuel was 1 g/s, and the inlet temperature was 700 K. The back pressure was set to be 3 MPa.
## 5 Case Study
### Metaheuristic Optimization  
As explained before, the metaheuristic optimization of the inductance selection is made through the geo-inspired Earthquake Algorithm. In this case, the \(x_{i}\) positions represent the inductor values to be evaluated.  
Therefore, Figure 6 shows the basic scheme of the EA, with the architecture adapted for the optimization through the \(G_{vd}\) and \(G_{id}\) transfer functions. Where the evaluation block in the flowchart corresponds to the simulation of the current and voltage performance with the inductor to be tested. After the simulation of both transfer functions, Equation (5) is applied in order to evaluate the performance through time.  
The results of each position (each position from EA is an inductance value), are classified regarding their behavior against Equation (5). The classification leads to knowing which inductance achieved the better result and, therefore, to update the global best solution in case of having a new one.  
The EA was implemented using 100 epicenters, for 100 iterations as the maximum number of iterations, the epicenters were initialized as a random population in the range between 0.1 [uH] and 1000 [uH], obeying the order of the initial value proposed for the inductor.  
Additionally, to eliminate some of the uncertainty from the optimization process, the earthquake algorithm was performed four times, with the purpose of having three validation runs for the first response. Accordingly, Figure 13 shows the evolution of the error through the iterations, comparing the first obtained response with the validation trials.  
Thus, the convergence plot in Figure 13 shows uniformity in all the trials, ensuring and validating accuracy and consistency of the results, therefore the selected inductor from the optimization process is taken from the average value of the four results.  
Moreover, since this work presents a novel design methodology for the inductance selection in DC-DC converters, an experimental testbed was design for the validation of the proposal. Hence, the following section shows and details the schematic and the Printed Circuit Board (PCB) designed for the task.
# CEMB-YOLO: Efficient Detection Model of Maize Leaf Blight in Complex Field Environments
## 2 Related Work  
### Object Detection Algorithm Based on CNN  
In 2012, [PERSON] et al. proposed AlexNet [9], a deep convolutional neural network-based image classification system. AlexNet achieved remarkable results in the ImageNet image classification competition, causing CNNs to gain significant attention. Object detection algorithms based on deep neural networks have advanced rapidly since then.  
There are two types of CNN-based object detection algorithms: two-stage detection based on candidate regions and one-stage detection based on regression. The R-NN (R-CNN [10], Fast R-CNN [11], Faster R-CNN [12]) series is a representative two-stage algorithm series that can achieve better detection accuracy but is far from real-time in terms of speed. Single-stage algorithms represented by the SSD series [13; 14; 15] and YOLO series [16; 17; 18] have comparatively fewer parameters and superior real-time performance but inferior detection accuracy.
## 4 Result
### Ricean K-Factor  
The K-factor is used to model Ricean fading, a common statistical way of representing fading with and without a strong LoS component. When no LoS component exists, K becomes 0 and behaves similar to Rayleigh fading channel characteristics. The Ricean K-factor is the ratio between the LoS and the reflected signal power.  
As shown in Figure 7, the average Ricean K-factors obtained from our measurements with the omnidirectional antenna are 3.29-3.64 for our rural-like crossings (#1, #2, and #5) and 3.55-4.25 for our suburban-like crossings (#3 and #4) with a normal distribution. With the bidirectional antenna, we obtained 6.20-7.47 for our rural crossings and 5.77-6.98 for our suburban crossings, with a normal distribution. Interestingly, the average Ricean K-factors for rural-like and suburban-like crossings are not much different. Additionally, the constancy for each crossing is similar even with using different antennas. The average of Ricean K-factor when using an omnidirectional antenna is slightly lower than with a bidirectional antenna. From this behavior, we assume a communication system using a bidirectional antenna may face less of a fading effect than when using an omnidirectional antenna.  
Reference [7] suggests a Ricean K-factor of 7.6 for a general LoS highway environment. The authors of [7] categorized road crossings in suburban environments with vehicular traffic (average speed of 30 km/h) and, without traffic or a smaller number of vehicles around; the evaluated K-factors were 3.7 and 4.5, respectively. Despite our initial assumptions of some crossings being in a rural area and others in suburban environments, our measurement results indicated that all five crossings with an omnidirectional antenna fall into the suburban category, and with a bidirectional antenna fall into a general LoS highway category.