esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Assessment of the Models' Level of Detail and Feature Recognizability
#### 3.4.1 Target Recognition  
Planar and three-dimensional targets can be useful for the Stencil and Zeb-Revo dataset geo-referencing even though, at present, only the Pegasus post-processing workflow can consider ground control points.  
A simplified qualitative comparison of the target recognition in our test can be done by observing Figure 17, which shows a 20 cm \(\times\) 20 cm black and white target attached to a wall. In the Stencil and Zeb-Revo datasets (for both paths), it is totally impossible to recognize this planar target, while it can just about be identified in the Pegasus dataset only (Figure 17, second row). The clouds in Figure 17 also highlight the big differences in dataset resolution and clearly show the direction of the acquired profiles. It is important to stress that, for all of the datasets, the resolution varies greatly on the surfaces, being higher along the scanned profiles and lower between one profile and the next.  
Figure 17: Various datasets in correspondence to a black and white target on a wall: in addition to the difficulty in recognizing the target, it is visible how the resolution along the vertical and horizontal directions changes dramatically.
# Joint SAR Image Time Series and PSInSAR Data Analytics: An LDA Based Approach
###### Abstract
The literature calls them Persistent Scatterers (PS) and they could establish a reference, nonvarying class, especially in built-up areas [12; 13]. Often, a range-azimuth pixel from a focused SAR image can comprise the summed contributions of multiple scatterers. If a pixel presents a constant response across the SAR images dataset, it is highly probable that it contains a single, dominant target. Those points are characterized by high temporal correlation, being ideal for differential interferometry applications because they present low values of the residual component of the interferometric phase, which also offers the possibility of a more accurate atmospheric phase screen estimation. Moreover, those points are highly coherent even in image pairs with perpendicular baselines values above the critical one, which lowers the constraints on the SAR images dataset's selection. The PS-InSAR method [12; 13] was formulated based on those principles. This algorithm conducts an interferometric phase regression analysis in points unaffected by temporal decorrelation and is able to generate digital elevation models with submeter accuracy and estimate linear deformation rates with millimetric precision. The stable temporal behavior of the targets analyzed within the PS-InSAR method should also be observed by the LDA classification algorithm. Consequently, all those scatterers may be included in a separate evolution class or should form multiple, separate classes. Starting from this hypothesis, the authors exploit those two envisaged components, LDA and PS-InSAR, in order to initiate an exploratory analysis of spatiotemporal SAR data with the clear purpose of discovering hidden semantics to support monitoring applications. The proposed approach was demonstrated on two SAR image time series. They were acquired using ERS and Sentinel-1 sensors over the areas of Buzau city and Constanta city, Romania, respectively.
## 2 Overview of Network Automation and Enabling Technologies
### Application of Traditional Machine Learning and Deep Learning as a Cognitive Component of Agents
#### 2.3.4 CNN-Based Network Traffic Predictor Agent  
CNN is a kind of feed-forward neural network that has major application in image classification [27]. It has also applications in time series, where we can utilize it for network traffic prediction. It consists of an input layer, convolution layer, pooling layer, fully connected layer (as hidden layers), and an output layer. The convolution layer is a good substitute for a fully connected layer, as it is scalable to massive datasets. The role of the convolution layer is to reduce the dimension of the input images without losing critical features, allowing the network to be deeper and learn more easily.  
A part of a convolution layer is the kernel/filter that has smaller dimensions than the input image and moves over the image and performs matrix multiplication until the entire image is traversed. This process produces a convoluted feature output, extracting high-level features of the image, such as edges. The next layer is called the pooling layer. The pooling layer is in charge of reducing the spatial size of the convoluted feature output even more, using two operations: max pooling and average pooling. Max pooling returns the maximum value of the calculated convoluted features corresponding to a portion of the image. Average pooling returns the average of all values generated by the kernel/filter. Finally, a fully connected layer (multilayer perceptrons) is added to classify the images using the softmax classification technique.
# Recent Advances and Insights into Bromelain Processing, Pharmacokinetics and Therapeutic Uses
## 2 Physicochemical Properties of Bromelain  
Pineapple-stem bromelain belongs to the class of \(\alpha+\beta\) proteins that are characterized by a single polypeptide chain, either 211 or 212 amino acids in length, and have an estimated molar mass of 22.8 kDa. The amino acid sequences of bromelain are homologous to those of papain, chymopapain and actinidin [13]. A single oligosaccharide chain has been reported to be conjugated to this polypeptide, making the molar mass of stem bromelain around 23.8 kDa [14].  
Many studies have shown that the proteolytic activity of bromelain is only partly connected to its pharmacological effects, suggesting that evaluating the whole phytocomplex, including the non-protein factors [7], is of great importance. The most prominent cysteine endopeptidase in pineapple tissues, other than stem and fruit bromelain, are anania and comosain, whose physicochemical characteristics are summarized in Table 1[15; 16].  
Bromelain remains intact for a relatively long time when stored at \(-20\)\({}^{\circ}\)C, while its proteolytic activity is lost, via bromelain degradation, when it is heated to 100 \({}^{\circ}\)C for 10 min [15; 17]. [PERSON] and colleagues have demonstrated that the maximum activity of glycosylated bromelain occurs at pH 7.0 and 30 \({}^{\circ}\)C [18]. The activity of the protease complex decreases by 17% at 40-60 \({}^{\circ}\)C and, upon lowering the pH to 3.0-4.0, the optimum temperature was found to decrease with a higher sensibility for the deglycosylated rather than the glycosylated form [18].  
[PERSON] et al. have investigated the activity of bromelain after storage at 30 \({}^{\circ}\)C for 30 days, and 4 \({}^{\circ}\)C for 60 days. Their results show a retention of activity after storage of 22 \(\pm\) 2% and 44 \(\pm\) 2% under these respective conditions, highlighting that there is an inverse correlation between storage temperature and the retention of enzymatic activity [19]. As for pH, bromelain activity was shown to increase up to pH 7.0, showing a decreasing trend thereafter [20].
## 1 Introduction
Some research contributions have introduced surrogate modeling techniques into the probabilistic framework for quantifying the failure probability of bridges under flood hazards to overcome this issue [22; 23]. Yet, there is limited research on the fragility modeling of MAB subjected to flood and scour effects using surrogate modeling. The present work proposes a consistent framework to derive fragility curves associated with a failure mode of MAB under flood hazard using surrogate models for reducing computational costs. The framework includes random variables that affect both the bridge structural capacity and scour geometry. The proposed approach uses two-dimensional limit analysis models to predict ultimate load-carrying capacity, which has been effectively used in combination with probabilistic analysis for safety verification of MAB [9; 21; 24]. Moreover, a parametric analysis is introduced into the framework to investigate the influence of the geotechnical properties of the riverbed and MAB backfill. This type of parametric analysis may serve as a starting point for network-level fragility analysis, where all the assets and their effects under a flood hazard can be efficiently considered, thus supporting the overall decision making and bridge management. For calibration and validation purposes, the proposed framework was used to obtain a flood fragility surface for a Portuguese MAB using river discharge as the hazard intensity measure associated with a given set of flow angles of attack. This paper is divided into five sections. Section 2 presents the proposed framework for parametric fragility assessment of MAB under local scouring and the information regarding the case study used for the demonstration of the methodology. Section 3 describes the probabilistic analysis of local scour, bridge reliability, and sensitivity analysis, as well as information regarding the surrogate model implemented for the fragility analysis. Section 4 provides a parametric analysis based on variations in the geotechnical properties. Section 5 presents the conclusions drawn during this research and states the possible follow-up studies.
## 2 System Model
### Parameter Estimation Based on CNR
Bringing Formula (3) into (5) yields:  
\[CNR\approx pu+pg\cos(\omega t+\varphi_{0}+\varphi)+pw=D+A\cos(\omega t+\phi)+W=s (t;D,A,\phi)+W \tag{6}\]  
where \(D\) is the constant component of the CNR, \(A\) is the varying component of the CNR, and \(W\) is the zero mean additive white Gaussian noise with variance \(\sigma_{CNR}^{2}\). We represent the CNR measurement value as \(\mathbf{x}=[CNR(0),CNR(T_{0}),CNR(T_{1}),\cdots,CNR((N-1)T_{0})]^{T}\), where \(T_{0}\) is the sampling interval. By using MLE to calculate the variables \(\boldsymbol{\theta}=[D,A,\phi]\), a cost function is established as follows:  
\[J(\boldsymbol{\theta})=\sum_{n=0}^{N-1}[CNR(nT_{0})-D-Acos(\omega mT_{0}+\phi) ]^{2}=\sum_{n=0}^{N-1}[CNR(nT_{0})-D-Acos(\phi)cos(\omega nT_{0})+Asin(\phi) sin(\omega nT_{0})]^{2} \tag{7}\]  
According to the MLE, it can be obtained that \(\hat{\phi}\sim N(\phi,\sigma_{\phi}^{2})\)[18]:  
\[\hat{\phi}=\arctan\frac{-\sum\limits_{n=0}^{N-1}CNR(nT_{0})\sin(n\omega T_{0 })}{\sum\limits_{n=0}^{N-1}CNR(nT_{0})\cos(n\omega T_{0})} \tag{8}\]  
\[\sigma_{\phi}^{2}=\frac{N\sigma_{CNR}^{2}}{2\bigg{[}\bigg{[}\sum\limits_{n=0} ^{N-1}CNR(nT_{0})cos(n\omega T_{0})\bigg{]}^{2}+\bigg{[}\sum\limits_{n=0}^{N- 1}CNR(nT_{0})sin(n\omega T_{0})\bigg{]}^{2}\bigg{]}} \tag{9}\]  
Without considering multipath interference, for navigation signals from satellite \(i\) and satellite \(j\), the CNR can be used to obtain the corresponding \(\hat{\phi}_{i}\sim N(\phi_{i},\sigma_{\phi j}^{2})\) and  
Figure 3: FRPAs gain graph [18]. \(\hat{\phi}_{j}\sim N(\phi_{j},\sigma_{\phi_{j},j}^{2})\) of the two sets of signals. Assuming the azimuth angles of satellite \(i\) and satellite \(j\) relative to the rotating antenna are \(\varphi_{i}\) and \(\varphi_{j}\), then:  
\[G_{R}=f(\varphi,\theta,\beta,\omega,t)\approx u+g\cos(\omega t+\varphi_{0}- \varphi)+w \tag{3}\]0  
Therefore, \(d\hat{\phi}_{ij}\sim N(d\varphi_{ij},\sigma_{\phi,j}^{2}+\sigma_{\phi,j}^{2})\), and it can be standardized as:  
\[G_{R}=f(\varphi,\theta,\beta,\omega,t)\approx u+g\cos(\omega t+\varphi_{0}- \varphi)+w \tag{3}\]1  
The above is the analysis result when the navigation signal is real. When there is a spoofing signal, the above formula will not conform to the standard normal distribution.
# A Comparative Study on Violent Sloshing with Complex Baffles Using the ISPH Method
## 5 Analyses and Discussion
### Position Height of Two Horizontal Baffles  
In this section, the liquid sloshing in a tank with two horizontal baffles installed on different positions under different excitation frequencies are compared. The configuration of the tank and baffles has been shown in Figure 5c, where the two horizontal baffles are installed at a normalized distance of 0.117-0.250 from the tank bottom. Figure 8 shows the maximum impact pressure at P2 computed by ISPH for the various installation heights under a range of non-dimensional frequencies. It can be seen that the effect on the liquid sloshing pressure improves obviously with an increase in the height placement of the horizontal baffles. It also shows that installing the horizontal baffles on the side wall near the water surface is the most effective way to reduce the impact pressure. This is evidenced by the fact that the evolution of the sloshing loads appears to be quite stable and the impact pressure is nearly kept at a constant, as shown in Figure 8, for the curve with normalized installation height of 0.250. Compared with the sloshing tank without any baffle, the impact pressure can be reduced by around 40%.
# A 920-MHz Dual-Mode Receiver with Energy Harvesting for UHF RFID Tag and IoT +
## 4 Proposed Dual-Mode Receiver
### Demodulator  
The demodulator circuit is based on a low-pass filter (LPF) and a hysteresis comparator, as shown in Figure 13. Because the low-pass filter has a smaller cutoff frequency, and in order to reduce the area of the circuit, the MOS capacitor with larger unit capacitance is used instead of the conventional MIM capacitor. The calculation of the low-pass filter cutoff frequency is \(f_{C}=1/\left(2\pi RC_{MOS}\right)\). In this design, the typical values of total resistance and capacitance are 500 k\(\Omega\) and 10 pF.  
The demodulation and digitization of the signal is achieved by the comparator. Therefore, the comparator needs to be designed as a hysteretic comparator structure because of the slowly changing input signals and noisy environments. As shown in Figure 13, positive feedback is introduced in the circuit through the source-level cross-coupling structure of the \(M_{4}\) and \(M_{5}\)[21].  
Due to the fully differential structure of the hysteretic comparator, the second-stage amplifier is added and realizes the differential to single-ended output function through a current mirror \(M_{7}\). At the same time, the gain of the comparator is further improved. The hysteresis range is designed as 40 mV to provide good immunity to the fluctuations of noise and glitches.
# Prediction of Compressive Strength Loss of Normal Concrete after Exposure to High Temperature
## 4 Results and Discussion
### Vim
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**T (\({}^{\circ}\)C)**} & **Compressive** & **P** & **P** & **P** \\  & & **Strength (MPa)** & **(Measured, \%)** & **(Predicted by BP, \%)** & **(Predicted by PSO-BP, \%)** & **(Predicted by RF, \%)** \\ \hline \multirow{4}{*}{1} & 20 & 75.61 & – & – & – & – \\  & 200 & 69.45 & \(-\)8.14 & \(-\)4.61 & \(-\)5.57 & \(-\)5.13 \\  & 400 & 62.59 & \(-\)17.22 & \(-\)14.37 & \(-\)15.83 & \(-\)16.98 \\  & 600 & 45.36 & \(-\)40.00 & \(-\)37.42 & \(-\)41.23 & \(-\)39.61 \\  & 800 & 26.61 & \(-\)64.81 & \(-\)64.14 & \(-\)65.03 & \(-\)62.03 \\ \hline \multirow{4}{*}{2} & 20 & 48.16 & – & – & – & – \\  & 200 & 44.24 & \(-\)8.14 & \(-\)8.27 & \(-\)7.99 & \(-\)9.01 \\  & 400 & 38.42 & \(-\)20.22 & \(-\)16.68 & \(-\)21.07 & \(-\)20.66 \\  & 600 & 26.48 & \(-\)45.02 & \(-\)37.69 & \(-\)47.51 & \(-\)47.22 \\  & 800 & 14.99 & \(-\)68.88 & \(-\)65.36 & \(-\)69.32 & \(-\)70.23 \\ \hline \multirow{4}{*}{3} & 20 & 40.42 & – & – & – & – \\  & 200 & 35.38 & \(-\)12.47 & \(-\)13.15 & \(-\)11.73 & \(-\)12.40 \\  & 400 & 30.01 & \(-\)25.76 & \(-\)27.39 & \(-\)25.82 & \(-\)24.92 \\  & 600 & 22.17 & \(-\)45.15 & \(-\)47.86 & \(-\)46.22 & \(-\)42.79 \\  & 800 & 11.64 & \(-\)71.19 & \(-\)70.37 & \(-\)69.61 & \(-\)68.11 \\ \hline \multirow{4}{*}{4} & 20 & 48.16 & – & – & – & – \\  & 200 & 42.72 & \(-\)11.31 & \(-\)10.96 & \(-\)12.62 & \(-\)11.80 \\  & 400 & 37.78 & \(-\)21.56 & \(-\)22.92 & \(-\)22.56 & \(-\)23.19 \\  & 600 & 29.37 & \(-\)39.01 & \(-\)43.83 & \(-\)38.91 & \(-\)38.93 \\  & 800 & 11.76 & \(-\)75.57 & \(-\)77.17 & \(-\)77.14 & \(-\)74.32 \\ \hline \multirow{4}{*}{5} & 20 & 48.16 & – & – & – & – \\  & 200 & 44.11 & \(-\)8.41 & \(-\)7.59 & \(-\)10.23 & \(-\)6.99 \\  & 400 & 38.89 & \(-\)19.25 & \(-\)16.00 & \(-\)23.23 & \(-\)20.20 \\  & 600 & 29.94 & \(-\)37.83 & \(-\)36.47 & \(-\)39.67 & \(-\)38.90 \\  & 800 & 15.70 & \(-\)67.39 & \(-\)64.00 & \(-\)68.22 & \(-\)66.39 \\ \hline \multirow{4}{*}{6} & 20 & 48.16 & – & – & – & – \\  & 200 & 44.24 & \(-\)8.14 & \(-\)8.14 & \(-\)8.19 & \(-\)9.13 \\  & 400 & 39.67 & \(-\)17.63 & \(-\)16.10 & \(-\)18.11 & \(-\)15.19 \\  & 600 & 29.15 & \(-\)39.46 & \(-\)34.44 & \(-\)40.34 & \(-\)38.92 \\  & 800 & 17.21 & \(-\)64.27 & \(-\)61.42 & \(-\)63.28 & \(-\)64.02 \\ \hline \multirow{4}{*}{7} & 20 & 48.16 & – & – & – & – \\  & 200 & 44.96 & \(-\)6.63 & \(-\)8.21 & \(-\)7.91 & \(-\)7.24 \\ \cline{1-1}  & 400 & 38.67 & \(-\)19.70 & \(-\)21.03 & \(-\)21.19 & \(-\)18.93 \\ \cline{1-1}  & 600 & 26.63 & \(-\)44.69 & \(-\)46.11 & \(-\)47.91 & \(-\)44.93 \\ \cline{1-1}  & 800 & 15.29 & \(-\)66.24 & \(-\)68.31 & \(-\)69.01 & \(-\)67.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Compressive strength loss percentages measured and predicted.
# The Soundscape Hackathon as a Methodology to Accelerate Co-Creation of the Urban Public Space
## 3 The Soundscape Hackathon Event
### Equipment
#### 3.4.1 Art-Science-Interaction Lab (ASIL)  
The 'Art-Science-Interaction Lab' measures 10 m \(\times\) 9.5 m \(\times\) 6 m and features a 62 loudspeaker system. The room is acoustically treated to reduce reverberation. In the lower corners, four Martin Audio CX18 sub-woofers are placed; along the walls and ceiling 58 Martin Audio CDD6 loudspeakers are placed, which have a coaxial design and a wide dispersion angle (110\({}^{\circ}\)). The first loudspeaker ring is located at 2 m height and consists of 34 speakers (cone-to-cone distance 92 cm). The second ring is located at 4 m height and has 14 speakers (cone-to-cone distance 184 cm). The ceiling array features 10 evenly distributed speakers (cone-to-cone distance 240 cm). Figure 2 visualizes the structure. Participants of the hackathon were provided with a list of speaker coordinates in Euclidean (XYZ) and spherical (AED) space [33]. All speakers are powered by Powersoft Ottocanali 4K4+DSP amplifiers, located in an adjacent room. Finally, audio connection to the amplifiers is performed using Audinate's DANTE audio over IP (AoIP) protocol [34]. Participants could connect to the system using one CAT6 Ethernet cable in conjunction with the Dante Virtual Soundcard for 64 discrete audio channel output; or a USB3 soundcard (64-channel RME Digiface Dante interface). Mapping the computer's output channels to the speakers was done in the Dante Controller matrix interface.
## 2 Related Work  
### AR Navigation  
Presently, there are many related studies and applications for using AR for positioning and navigation functions. For example, visual tags which are pasted in various places can be scanned by smartphones to determine one's current location [8] and can be used in the library to help readers locate the bookshelf [9] and to access books metadata [10], or can be used to label misplaced books on the shelf [11; 12], or to provide a library instruction for students [13]. But in order to counteract these visual tags' vulnerability to ambient light and shadows, a hybrid Bluetooth positioning and visual tag scanning method has been developed. Bluetooth positioning is used to determine the general position. Visual tags can then be subsequently scanned to refine the positioning. This has significantly improved indoor navigation and positioning [14]. In recent years, researchers have successively proposed to combine AR with WiFi positioning technology [15], or with the accelerator camera and compass function in smartphone [16], or even built a HyMoTrack system [17; 18] to develop indoor navigation systems.  
To date, AR's spatial positioning technology has made breakthroughs, mainly in Apple's ARKit and Google's Tango and ARCore. Apple's ARkit can be used with CoreLocation for GPS outdoor positioning and road navigation. Tango technology needs to be supplemented with depth photographic lenses and motion capture photographic lenses. Tango's core technologies include motion tracking, area learning, and depth perception, which jointly allows for the scanning of a 3D space to create a point cloud to facilitate spatial positioning. Google's ARCore [19] includes motion tracking, environmental understanding, and light estimation, which can detect a mobile phone's direction of movement, and identify surrounding features such as the ground and walls as well as the position and brightness of virtual objects. Using this technology, a user can perform virtual object positioning without frequently scanning visual tags. With ARKit and ARCore's current AR spatial positioning technology, these applications can be used on a regular smartphone to execute physical space positioning without additional hardware. Even without installing additional positioning hardware, it can achieve a more accurate indoor position than Beacon [20]. With the gradual development of the two technologies and the popularity of smartphones, there are already relevant examples of using these two technologies to construct indoor navigation [21] and extended applications. For example, [PERSON] and [PERSON] developed mobile AR indoor navigation system (MARINS) using a smartphone as a device to guide users to exits in a dark environment with the path only illuminated by the phone camera's LED [22].
## 3 Methods
### Bidirectional RNNs  
Bidirectional Recurrent Neural Networks (BiRNN) [26] are made up by two separate recurrent hidden layers that scan the input sequence in the opposite direction. The two hidden layers are connected to the same output layer, so the context information can be accessed in two opposite directions. The context information actually used by the network is learned during network training and it does not need to be specified in advance. Therefore, contexts are learnt independently of each other. This point has been elaborated in [27]. Figure 3 shows the structure of a simple bidirectional recurrent neural network.  
The BiRNN forward process is the same as the unidirectional RNN. The hidden layer at the time t of the forward RNN is affected by the hidden layer at time t - 1 of the forward RNN and the input x(t) at time t. The hidden layer at time t of the reverse RNN is affected by the t + 1 hidden layer of the reverse RNN and the input x(t) at time t.The RNNs in two different directions do not share the cell state value. The status value of the cell output by the forward RNN is only transmitted to the forward RNN. Similarly, the status value of cell output by the reverse RNN is only transmitted to the reverse RNN. The forward and reverse RNNs are not directly connected.  
The input of each time node is respectively transmitted to the forward and reverse RNNs. During the specific calculation, BiRNN first scans the entire sequence. It calculates the hidden state of the forward RNN along the direction from 1 to T, and it then calculates the hidden state of the reverse RNN from T to 1. According to their internal states to output, the forward and reverse outputs are combined and connected to the BiRNN output nodes to synthesize the final output. The current time node loss value is calculated during training. The reverse transmission process of BiRNN is similar to that of RNN, which is trained by the Back Propagation Through Time (BPTT) algorithm [28]. The algorithm first calculates the error of the output layer, and then respectively passes the error along T to 1 and 1 to T to the hidden layers of the forward and reverse RNN, and the model parameters are optimized to the appropriate values, according to the gradient.