esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hybrid Topology Architecture
### Implementation
#### 3.2.5 Database Module  
The proposed solution of hybrid network topology coexistence for LoRaWAN requires the installation of a database paired with its control module to allow communication with the other elements of the proposal, referred to as the database module for simplicity. As the data format in the LoRaWAN network is JSON, the popular open source NoSQL MongoDB database [40] was chosen for the implementation. This database includes free and paid versions of the software that vary according to features and storage capacity. The implementation of the database module only requires basic functionalities. Therefore, the free option meets all our requirements.  
The database is used by the topology manager module. This module creates an MQTT client that connects to the MQTT broker and subscribes to the MQTT topics where the gateway posts all the messages it sends to the LoRaWAN server. In our proposed solution, this corresponds to the intermediate topic. Once subscribed, the message is decoded and the resulting JSON message is parsed. The link quality metrics stored in the message, RSSI and SNR, are extracted from the message and stored in the database. The type of event and end device identification are also stored with the link quality metrics. This information is obtained from the decoded message as well. Lastly, the messages are posted at the default MQTT topic, that is, _us915_0/gateway/l.GatewayID 1)/event/l.EventType 1)_. Data stored in the database can be accessed later and requested by the topology manager to decide on the topology update.  
In our implementation, the MongoDB database was installed in a local server at Santiago de Cali University. However, it could be configured to any machine as long as the connection string to the database is updated accordingly.
# Hybrid Dual-Polarization Synthetic Aperture Radar
## 4 Performance in Earth Applications
### Results from RISAT-1 HCP Data  
RISAT-1 (2012-2018) included HCP as an operational mode, the first Earth-orbiting mission to do so. RISAT-1 demonstrated that the HCP mode is compatible with ScanSAR [56], thus making available polarimetric imagery having swath widths of 223 km, a nearly ten-fold improvement over the 25-km width for RISAT-1's QP mode. A study using data from the HCP mode of RISAT-1 and the quad-pol mode of Radarsat-2 using near-contemporaneous coverage from the Greenland ice sheet [57] found the comparisons to be favorable. Several different land classification approaches included in studies by [PERSON] et al. using RISAT-1 data [58] yielded excellent informative results. [PERSON] showed that, for soil moisture applications, HCP \"works well\" [59], and [PERSON] [60] found that compact polarimetry \"works well\" for maize crop discrimination. [PERSON] et al. [61] achieved \"similar results\" when comparing classifications derived from RISAT-1 HCP data and HCP data emulated from Radarsat-2 for sea ice applications. [PERSON] et al. [62] applied unsupervised model-based classification techniques on the Stokes parameters from the RISAT-1 HCP mode to yield \"very good\" results.
# Application of Dynamic Time Warping to Determine the Shear Wave Velocity from the Down-Hole Test
## 4 Methodology
### Signal Quality Assessment  
Despite the adequate preconditioning of signals, it has been found that some of them are still of a poor quality, which makes it impossible to determine the difference in the wave arrival times or makes the results less reliable. Poor-quality signals have high-level signal noise, a lack of a characteristic maximum amplitude, as well as other qualities that make one of the waves not similar to the other from the same sample. For this reason, it was decided to test the quality based on the similarity of the waves recorded using both geophones. Both signals are expected to have similar shapes, but differ in their timing. Significant deviations in the waveform shape could indicate the presence of disturbances, which may compromise the ability to compare the signals and ascertain differences in the wave arrival times. The similarity between the signals was measured using the Dynamic Time Warping (DTW) distance measure, where a greater similarity indicates the better quality and higher accuracy of the final result. To standardize the DTW measure across signals of varying lengths, the distance value was divided by the measurement length N. Additionally, in order to normalize the measures of all the results, the value was divided by the maximum value occurring in all the analyzed cases (M). Considering the above, the following measure of the quality of signals is proposed in relation to their similarity:  
\[\mathrm{Q}=1-\left(\frac{\mathrm{DTW(s_{1},s_{2})}}{\mathrm{N}}\cdot\frac{1}{ \mathrm{M}}\right). \tag{14}\]  
An example of the determined Q quality values for the left shear wave and right shear wave depending on their depth is presented in Figure 4. The quality is also illustrated with the proposed color scale, which is used in the next part to compare the results of the estimated wave speed with the quality of the signals.  
Figure 4: An example of signal quality comparison based on depth measurements of left and right shear waves (the color scale in the background also corresponds to the quality values).
# Improvement of Buildings' Air Quality and Energy Consumption Using Air Purifying Paints
## 2 Materials and Methods  
### Materials  
Chemicals for the preparation of VISIONS photo-powders were purchased from Aldrich and were of an analytical reagent grade. Titanium (IV) oxysulfate hydrate (\(\geq\)29% Ti, TiO\({}_{4}\) xH\({}_{2}\)O), iron (III) nitrate nonahydrate (\(\geq\)98%, Fe(NO\({}_{3}\))\({}_{3}\cdot\)9H\({}_{2}\)O), manganese(II) acetate tetrahydrate (\(\geq\)99%, (CH\({}_{3}\)COO)\({}_{2}\)Mn\(\cdot\)4H\({}_{2}\)O), cobalt(II) acetate tetrahydrate (98%, (CH\({}_{3}\)COO)\({}_{2}\)Co\(\cdot\)4H\({}_{2}\)O), copper acetate hydrate (98%, (CH\({}_{3}\)COO)\({}_{2}\)Cu\(\cdot\)xH\({}_{2}\)O), nickel acetate tetrahydrate (98%, (CH\({}_{3}\)COO)\({}_{2}\)Ni\(\cdot\)4H\({}_{2}\)O), aluminum nitrate nonahydrate (\(\geq\)98%, Al(NO\({}_{3}\))\({}_{3}\cdot\)9H\({}_{2}\)O), sodium hydroxide (\(\geq\)98%, NaOH), and ammonium hydroxide solution (25% NH\({}_{4}\)OH) were applied with no further purification.  
Chemicals for the preparation of the photo-paint were the above-described VISIONS photo-powder at 20%, Vinyl VeoVA 10 copolymer emulsion, non-photocatalytic TiO\({}_{2}\) (Ti Pure 902+), high-boiling-point organic solvents used as plasticizers, special organic polymers like polyacrylate salts used as dispersing and wetting agents, modified cellulosic additives used as thickeners and antisettling agents, organic biocides based on isothiazole-none mixtures used as in can biocidal protection, silicon-based additives used as defoamers, calcium carbonates used as fillers, and other additives for viscosity and stability adjustment.
## 4 Experiment and Results
### Machine-Learning Algorithm
#### 4.3.1 Structure Feature Model  
We firstly built a detection model with the structure and metadata. We then tested the base samples and the baseline model. As shown in Table 6, the random forest achieved the highest accuracy while the naive Bayes model showed fair performance. Table 7 shows the results of classifying base samples and adversarial samples into this detection model with the structure and metadata. For the base samples, RF and naive Bayes models showed good performance. However, the machine-learning algorithms using the structure feature set were vulnerable for adversarial samples.
## 4 Discussion
### The Design Rationale of the GS Constellation Structure
This intricate trade-off is achieved through the iterative process of the PO algorithm, performed with the appropriate SNR value. As shown in Figure 6a, 13 dB is selected as the SNR for PO iteration of 32 QAM, and the designed constellation structure not only achieves the approach from the cross type to the square, but also ensures the Euclidean distance between adjacent constellation points, so the MI performance can be significantly improved in the entire SNR range. In contrast, if the SNR chosen for the PO iteration is too high (e.g., 25 dB), the GS process will make the constellation structure of the design approximate a square by sacrificing the minimum Euclidean distance excessively, as shown in Figure 11a. As can be seen from the performance comparison in Figure 11b, the constellation structure in Figure 11a incurs an SNR penalty of approximately 0.35 dB at the 20% SD-FEC threshold, compared to the constellation structure in Figure 5a. Figure 10: Measured magnitude of residual phase error versus the number of iterations, when the PCPE algorithm is applied to the ideal R-32 QAM and GS-32 QAM constellations that suffer only from a \(\pi/6\) phase shift. Overall, according to the results shown in Figures 6 and 11, the constellation structures given in Figure 5 exhibit good universality, and the MI performance close to the theoretical value can be obtained under different SNRs. Therefore, it is recommended to select the SNR value corresponding to the FEC threshold for constellation design when performing the PO iteration, to ensure that the designed constellation structure can achieve reliable transmission performance under different channel conditions.
## 4 Results and Discussion
### Watermark Robustness
#### 4.2.4 Resistance to Compound Attack  
Figure 8 shows the distribution of the _NC_ values for three types of watermarks in ZC and SZ under multiple compound attacks. The overall distribution trends under random compound attacks as shown in Figure 8a and b are similar, showing a downward trend with increased attack numbers. The _NC_ values for both the DTM-based zero-watermarks and dual zero-watermarks roughly distribute between 0.5 and 1, whereas the SVD-based zero-watermarks have a range from 0 to 1. The _NC_ values for the dual zero- watermarking scheme are significantly higher than those of both the feature vertex watermark and the non-feature vertex. Figure 9 shows that the ratio distributions of the watermarked images have _NC_ values greater than 0.8 under compound attacks. As the attack numbers increase, the probability for detecting high-quality watermark images decrease and the chances to detect valid watermark information, even with eight attacks, is still non-negligible.  
Figure 7: The _NC_ change curves under increased levels of (**a**) object addition and (**b**) object deletion.
## 4 Experiment Results and Discussion
### Dataset Description
The attributes for each day data file include [_appName_, _totalSourceBytes_, _totalDestinationBytes_, _totalDestinationPackets_, _totalSourcePackets_, _sourcePayloadAsBase64_, _sourcePayloadAsUTF_, _destinationPayloadAsBase64_, _destinationPayloadAsIITF_, _direction_, _sourceTCPFlagsDescription_, _destinationTCPFlagsDescription_, _Source_, _protocolName_, _sourcePort_, _destinationPort_, _startDateTime_, _stopDateTime_, _Tag_]. In the preprocessing dataset, the *XML file is read and converted to *.CSV file. Each attribute in the XML file is a column in the CSV file. The [_Tag_] feature is the output which contains target values. The other features are input features as well as input values. A cross-validation technique is used to split the preprocessing dataset that randomly followed the ratio of 75% and 25%, respectively. Table 1 presents a description of the number of training and testing data of ISCX dataset. Further adjustments were made to make the data fit for use. Reduction of the number attributes from all the possible attributes have to be carried out. The following attributes were chosen for the experiment: [_appName_, _totalSourceBytes_, _totalDestinationBytes_, _totalDestinationPackets_, _totalSourcePackets_, _direction_, _sourceTCPFlagsDescription_, _destinationTCPFlagsDescription_, _source_, _protocolName_, _sourcePort_, _destination_, _destinationPort_, _startDateTime_, _stopDateTime_, _Tag_]. Some accumulative or redundant attributes such as [_sourcePayloadAsBase64_, _sourcePayloadAsUTF_, _destinationPayloadAsBase64_, _destinationPayloadAsUTF_] were removed.
## 1 Introduction
Although GAN has a wide range of applications in the sample augmentation field, they mainly focus on domains such as face attribute transformation and landscape color transformation. In the industrial defect detection field, due to difficulties such as lack of defect samples, low visibility of defects, irregular shape, unknown type, etc., existing GAN-based augmented samples are difficult to meet the task requirements of high accuracy and high speed at the same time. Therefore, designing a GAN model that can synthesize realistic and diverse defect samples with high fidelity and efficiency is a challenge in industrial defect detection. To address this challenge, we propose a perceptual capsule cyclic generative adversarial network (PreCaCycleGAN) for industrial defect sample augmentation, which aims to learn a more realistic distribution of industrial defect data. Our method leverages CycleGAN's framework of bi-directional mapping and cyclic consistency loss and enhances it with least-squares loss and perceptual loss function. Moreover, our method adopts an optimized generator structure with U-Net and DenseNet modules, and a capsule network with perspective invariance, to further improve the generator's ability to learn the features of industrial defect samples. The main contributions of our model are shown below:  
1. We design a generator model with U-Net network structure [19] and DenseNet [20] modules to enhance the feature propagation and feature reuse of defects. This can solve the gradient disappearance problem of deep networks and add perceptual loss functions to enhance the feature and semantic information of generated images;
2. We use cyclic consistency loss, identity mapping loss, and least squares loss to construct an adversarial training framework to achieve random changes in defect location and shape, ensure the consistency between the generated samples and the real samples in the non-defective region, improve the similarity between the generated samples and the real samples, and avoid the mode collapse and gradient vanishing or oscillation problems;
3. We design a discriminator model with PatchGAN [21] and capsule network [22] using dynamic routing protocols dual branches after the initial feature extraction, which can effectively extract and retain the detailed features of defective samples, identify the local and overall features of the samples, and improve the authenticity and diversity of industrial defect generation samples;
4. We compare our method with other generation algorithms and validate it in the actual industrial manufacturing defect detection model. We prove that our method has the optimal performance improvement for the actual industrial manufacturing defect detection model and can effectively increase the generalization ability of the defect detection model.
## 4 Conclusions  
The workability of the mixtures tends to diminish when the content of biomass fly ash (BFA), as received and sieved, increases, which leads to difficulties in the mixing process. Consequently, mortars with increasing levels of BFA, presented lower density, higher water absorption, and lower compressive and flexural strengths due, not only to the lower Ordinary Portland Cement (OPC) content but also to the higher porosity.  
The use of grounded BFA (with narrower particle size distribution and a smaller number of elongated particles) attenuates this trend by promoting a better homogenization of the mixtures and improving the compactness of the mortars. After curing, the obtained specimens present densities and water absorption values closer to the standard composition, even when a higher OPC percentage is replaced (67 wt.%).  
Figure 10: View of the samples after 0, 5, 15 and 25 freeze–thaw cycles.  
The obtained results show that the partial substitution (17 wt.%) of OPC, used in a commercial screed mortar formulation, by BFA is possible. The mechanical strength of the developed product and of the standard are similar. Moreover, the developed specimens are resistant to 25 freeze-thaw cycles.  
The use of BFA, as an OPC substitute, in non-structural construction materials is a way to reduce these cement-based materials' environmental footprint and, at the same time, contributing to the development of a circular economy. Furthermore, economic benefits can also be achieved through the elimination of the landfilling costs of BFA and by decreasing the raw materials costs.  
Conceptualization, M.P.S.; methodology, M.N.C., M.P.S. and J.A.L.; validation, M.N.C., D.M.T., M.P.S., L.A.C.T. and J.A.L.; investigation, M.N.C. and M.P.S.; resources, M.P.S., L.A.C.T. and J.A.L.; data curation, M.N.C. and D.M.T.; writing--original draft preparation, M.N.C.; writing--review and editing, M.N.C., D.M.T., M.P.S., L.A.C.T. and J.A.L.; supervision, M.P.S.; project administration, M.P.S., L.A.C.T. and J.A.L.; funding acquisition, M.P.S., L.A.C.T. and J.A.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Portugal 2020 through the European Regional Development Fund (in the frame of Operational Competitiveness and Internationalization Program) in the scope of the project INPACTUS, POCI/01/0247/FEDER/21874 and in the scope of the project CICECO, UIDB/50011/2020 and UIDP/50011/2020 cofinanced by national funds through the FCT/MEC. Thanks are due to FCT/MCTES for the financial support to CESAM (UIDP/50017/2020 and UIDB/50017/2020) through national funds.  
Not applicable.  
The authors gratefully acknowledge Weber Saint-Gobain Portugal and The Navigator Company for providing the Ordinary Portland Cement, sands and limestone and fly ash from the fluidized bed biomass boilers (BFA1 and BFA2), respectively.  
The authors declare no conflict of interest.
## 1 Introduction  
According to the World Health Organisation, ischaemic heart disease and stroke are nowadays leading causes of death globally [1]. Cardiovascular disease (CVD) progression is dramatically increasing every year and thus many efforts to improve risk predictors are needed. The assessment of cardiovascular risk can be done from a wide variety of information and parameters derived from patients' history such as individual age, gender, smoking status, blood pressure, body mass index (BMI), or metabolic parameters (i.e., glucose and cholesterol levels) [2]. However, this information is not always updated, centralized or available.  
Coronary artery calcium (CAC) is a clinically validated strong marker of a CVD [3]. CAC scoring has proved to be a consistent and reproducible method of assessing risk for major cardiovascular outcomes, especially useful in asymptomatic people for planning primary prevention [4]. Nevertheless, CAC score obtention needs the use of CT scans, which are expensive and involve radiation risks.  
Retinal Fundus Imaging (RFI) provides relevant information about the vascular system of the eye. There is previous evidence that RFI can be a predictor of CVD [5; 6; 7; 8; 9], kidney chronic disease [10] or dementia [11]. Preceding research have shown that capillary vascular and foveal area are the most prevalent regions in predicting the CAC scoring from RFI [12]. In addition, RFI is usually acquired on a regular basis from patients that suffer type 2 diabetes, being CVD one of the most common morbidity [13].  
Deep Learning (DL) algorithms have obtained significant improvements in almost all the applications of computer vision [14; 15], including medical imaging applications. Particularly, automated retina image classification outperformed human accuracy in diabetic retinopathy diagnosis [16]. This paper proposes to apply DL, more concretely a convolutional neural network (CNN), to the prediction of CAC score from RFI. Using data gathered in a previous project [13] we conjecture that a CNN can predict whether a patient may be diagnosed with a CAC score greater than 400, i.e., the threshold defined by cardiologists to indicate a very high risk of CVD disease. In addition, we explore the use of two clinical variables, age and presence of diabetic retinopathy, which previous studies suggest that are highly correlated with CAC \(>400\)[13]. The experiments show that both informational cues are complementary, and when properly combined, they can significantly increase the precision or recall of the prediction. We validate the proposal in two different applications, clinical diagnosis and large-scale retrieval. The results show that RFI can be used for CVD risk prediction with low-cost image acquisition, which may have a significant impact in the strategies for selecting those patients in whom the screening of CVD should be prioritized.
## 5 Conclusions  
The current study aimed to examine the possibility of utilising sub-metered electricity and water consumption with data mining methods to determine the occupancy of the office. Two approaches were tested: supervised and unsupervised data mining methods. The supervised method was done using a Random Forest algorithm, while the unsupervised method used the clustering \(k\)-means algorithm.  
The results of this study have shown that it is possible to predict the number of people on a larger office floor with the explained variance at 96% using sub-metered electricity and water consumption with the supervised method. Best results came with using all available predictors, followed by electricity consumption used for lighting and sockets and then by water consumption. The lowest-performing result was by using lighting electricity as the main predictor, but still it had a decent explained variance of 93%. Furthermore, with the help of data sufficiency and generalisation analysis, the performance of the method was tested during different periods and with different training lengths. This analysis showed that performance is at a similar level as with the main testing period during the regular working weeks, while the lower performance of the model was for irregular weeks, such as during vacations. The analysis has also shown that the minimum recommended length of the training dataset should be between one and three months. Since the continuous collection of occupancy ground truth in a building, has its earlier mentioned problems, this study has also explored the way of using the unsupervised method with consumption data to find insight into the office occupancy.  
With the unsupervised method, it was possible to cluster the days into three occupancy levels: high, medium and low without the need for ground truth. This clustering was possible by using the sub-metered office equipment (from sockets) electricity consumption. The ground truth was used only for the verification of the method. On the other hand, using the same method with the lighting electricity consumption and the water consumption did not give satisfactory results when comparing their clusters to the ground truth.  
Conceptualization: D.S. Methodology: D.S. Formal analysis: D.S. Investigation: D.S. Data curation: D.S. Writing--original draft preparation: D.S. Writing--review and editing: [PERSON], [PERSON] Supervision: [PERSON], [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by Business Finland through the project Smart Otaniemi.  
The authors would like to thank Aalto University and Granlund Oy for making this research possible.