esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 A reliability Evaluation Framework Based on DBNs
### Proposed Computational Framework  
In Figure 5, a computational framework is presented to illustrate the approach of utilizing the DBN to estimate the reliability of a cable-supported bridge. Detailed descriptions of the followed computational steps are summarized below.  
In the first step, the critical random variables, such as the cable and concrete elasticity modulus, and the cross-sectional inertia moments of girders and pylon elements, are selected. The training samples should be selected according to the probability distribution of these random variables, which will be used to train the mechanical behavior of the bridge structure.  
In the second step, a parametric finite element model is developed using a commercial software, such as ANSYS or MIDAS. It is recommended to use ANSYS for the FE modeling, since its APDL code can be connected with MATLAB script files.  
In the third step, the DBNs are trained the generated samples as the input data and the FE results as the output data. The number of hidden layers should be optimized based on the production accuracy of the DBNs. If the accuracy is not sufficient, the number of training samples will be increased. This will improve the prediction accuracy, but will increase the computational cost.  
Figure 5: Flowchart of evaluating system reliability of a cable-stayed bridge via DBNs.  
In the fourth step, a large number of samples are generated by using the Monte Carlo simulation (MCS) according to the probability distribution of the random variables. Subsequently, the load effect of the critical components is predicted taking into consideration the samples of the random variables. Therefore, structural reliability can be estimated by counting the number of failure samples.  
In the fifth step, the load-carrying capability of the residual structure is checked with consideration of the potential failure of one of the critical components. The system failure criterion is defined as when the structural system has lost its load-carrying capacity, or when the structural deflection is so large enough that it cannot carry the external load. Such critical states are extremely dangerous for in-service bridges, since the bridge is possible to collapse at any time.  
In the sixth step, the components that are likely to fail are deleted or replaced based on the criterion of plastic or brittle failure. For example, cable failure is brittle, and the corresponding updating approach is to delete the failed cables. On the other hand, the bending failure of a girder is plastic, and thus the corresponding updating approach is to add a plastic hinge at the failure point. Subsequently, the structural system is updated, and the process goes back to the second step.  
Finally, based on the above steps, all failure sequences are screened out. The correlation coefficient matrix can be estimated for the system reliability evaluation, and the system event tree can be estimated considering the critical failure sequences. Eventually, the system reliability can be assessed based on the series-parallel connection criterion.
# Optimization of Active Distribution Network Operation with SOP Considering Reverse Power Flow
## 3 Actual Maximum Transfer Capacity of Reverse Power Flow in the Distribution Network
### Constraints
#### 4.3.4 OLTC Operating Constraints  
\[U_{m,i,t}=U_{m,j,t}k_{m,ij,t} \tag{51}\]\[k_{m,ij,t}=k_{m,ij,0}+K_{m,ij,t}\Delta k_{m,ij} \tag{52}\]  
\[\sum_{t=1}^{24}\left|K_{m,ij,t}-K_{m,ij,t-1}\right|\leq\Delta K_{m,ij,max} \tag{53}\]  
\[K_{m,ij,min}\leq K_{im,j,t}\leq K_{m,ij,max} \tag{54}\]  
In the distribution network \(m\), \(k_{m,ij,t}\) represents the transformation ratio of the OLTC between nodes \(i\) and \(j\) during time period \(t\). \(k_{ij,0}\) denotes the original transformation ratio. \(K_{m,ij,t}\) signifies the tap position of the OLTC during time period t. \(K_{ij,min}\) and \(K_{m,ij,max}\) are the minimum and maximum tap positions, respectively. \(\Delta K_{m,ij,max}\) constrains the maximum number of tap operations of the OLTC within one scheduling cycle.
## 3 Construction of Coherent Integration Time Optimization Model
### The Relationship between Model Parameters and Coherent Integration Time
#### 3.2.1 Altimetric Sensitivity  
As discussed in [28], the complex waveform is composed of useful signal and noise components  
\[y(nT_{c},\tau)=y_{\text{s}}(nT_{c},\tau)+y_{\text{nd}}(nT_{c},\tau)+y_{\text{ nr}}(nT_{c},\tau)+y_{\text{ndr}}(nT_{c},\tau) \tag{16}\]  
where \(y_{\text{s}}(t,\tau)\) is the cross-correlation value for the useful signal term, and \(y_{\text{nd}}(t,\tau).y_{\text{nr}}(t,\tau)\) and \(y_{\text{ndr}}(t,\tau)\) are the cross-correlation values of the direct and reflected noise terms.  
Assuming that the signal component has no correlation with the noise components, the power waveform can be expressed as [29]:  
\[Z(\tau)=\left\langle\left|y_{\text{s}}(nT_{c},\tau)\right|^{2}\right\rangle+ \left\langle\left|y_{\text{nr}}(nT_{c},\tau)\right|^{2}\right\rangle+\left \langle\left|y_{\text{nd}}(nT_{c},\tau)\right|^{2}\right\rangle+\left\langle \left|y_{\text{ndr}}(nT_{c},\tau)\right|^{2}\right\rangle \tag{17}\]  
The components above can be interpreted as the expression for the coherent integration time [18; 20]:  
\[\left\langle\left|y_{\text{s}}(nT_{c},\tau)\right|^{2}\right\rangle=2P_{ \text{d}}\iint\frac{2\Pi G_{\text{r}}(\widetilde{n})\psi_{0}}{4\pi R_{\text{ r}}^{2}(\widetilde{p})R_{\text{r}}^{2}(\widetilde{p})}\times\Lambda^{2}( \Delta\tau)\times\text{sinc}^{2}(\Delta fT_{\text{c}})d^{2}p \tag{18}\]  
where \(B\) represents the equivalent noise bandwidth of the receiver, \(T_{\text{rec\_d}}\) and \(T_{\text{rec\_r}}\) represent the equivalent input noise temperature of the up-looking and down-looking chains, \(P_{\text{r}}\) represents the total power of the reflected signal at the input of the correlator, \(P_{\text{d}}\) represents the total power of the direct signal at the input of the correlator, \(P_{\text{t}}\) represents the power of the transmitted signal, and \(G_{\text{r}}\) represents the antenna gain [30; 31]. Based on Equations (17) and (18), the altimetric sensitivity can be represented by the coherent integration time [5].
## 1 Introduction
Black arrows represent the flow of electricity, while green arrows depict the communication links. Control data exchange occurs throughout the grid structure using the communication network. networks [16]. The majority of papers overlook the impact of background traffic on the communication network between NMG endpoints. We address this gap by measuring the RTTs of NMG control data using a real hardware testbed under varying network congestion levels. We investigate whether background traffic adversely affects the QoD of NMG control data. To this end, we inject background traffic with varying bit rates into Layer 3 (L3) of the open systems interconnection (OSI) network model between NMG endpoints. We hypothesise that background traffic with high bit rate variance leads to RTTs surpassing industrial latency requirements. To set the context for our empirical evaluation of RTT in NMGs, we begin by highlighting the limitations of existing approaches that focus on one-way delay measurement or simulation-based studies. To this end, in Section 2 we classify the different types of NMG. In Section 3, we discuss current QoD measurement papers in the area of NMGs. This establishes the need for a more realistic assessment of QoD under dynamic network conditions. However, it is crucial to note that this paper is not a review article; rather, it presents a novel, empirical study using a real hardware testbed to measure RTTs under varying levels of background traffic. This background allows us to then bridge the gap between theoretical models and practical NMG deployments, offering insights into the impact of  
Figure 3: A typical communication between NMG endpoints, \(i\) and \(j\), involves the transmission of control data and ACK messages. Figure 2: NMGs enable two-way electricity and information transmissions. Black arrows represent electricity flow, and red arrows indicate information flow. A networked microgrid controller (NMGC) is employed to monitor and control NMGs. real-world network congestion on control data transmission. We explore the different types of communication networks deployed to support NMGs in Section 4 and present real-life NMG deployments to highlight the usage of communication technologies in NMG projects. In Section 5, we present the NMG testbed design and provide RTT measurements derived from various experimental cases to examine our hypothesis. The aim of this work is to empirically measure RTT in a realistic, hardware NMG testbed. The main contribution is the RTT measurements, not the testbed's design. Finally, in Section 6, we present future work and challenges in NMGs.
## 2 Background
### Machine Learning Application for Stuttering Detection
The authors declared that, at the time of publication of the paper, they achieved the highest score on the UCLASS dataset. The values of the F1-score were 0.27, 0.16, 0.46, and 0.63 for repetitions, prolongations, blocks, and fluent speech, respectively. Another paper that used deep learning was published by [PERSON] and colleagues [1]. In their work, they employed data from the UCLASS (Release Two) dataset, 25 recordings of children reading the fairy tale \"[PERSON] the Rat,\" 14 recordings of people reading the text \"One more week to Easter,\" and 27 recordings from other sources. They investigated two methods for classifying speech disorders, conditional random fields (CRF), and bidirectional long short-term memory (BLSTM) to detect stuttering events in transcriptions of stuttering speech. The researchers obtained the highest averaged F1 measure of 0.76 for the BILSTM model for six different subclasses of speech disorders. Another publication is the paper by [PERSON] and colleagues [3]. They created an automated system for detecting stuttering and selecting possible therapies based on classification results. They consider prolongations, repetitions, and abnormal pauses as classes of speech disorders. The authors created and released their own data labels, classifying passages as prolongations or repetitions. They performed segmentations on each audio sample using a Python language library, then calculated a 13-dimensional MFCC-based feature vector. The authors noticed that repetitions differ strongly from prolongations, so they trained two separate models. Both used an architecture built from modified recurrent blocks, i.e., a gated recurrent convolutional neural network (GRCNN). They introduced several different types of machine learning models. The best was GRCNN, trained separately for prolongations and repetitions, achieving accuracy results of 95% and 92%, respectively. However, these were the results obtained on a validation set. There are several publications that measure model performance on the SEP-28k dataset. [PERSON] et al. [45] used the wav2 vec2 model and achieved an F1-score equal to 0.22 for blocks, 0.69 for interjections, 0.41 for prolongations, 0.43 for sound repetitions, and 0.46 for word repetitions. An essential element of their publication is that they added semi-automatically generated samples to the SEP-28k dataset to address the class imbalance problem. [PERSON] et al. [46] investigated the impact of multi-task and adversarial learning. They achieved F1-scores equal to 0.32, 0.34, 0.12, 0.51, 0.71 for repetitions, prolongations, blocks, interjections, and general class. The same team of scientists published another paper related to stuttering detection [47]. They managed to achieve a mean F1-score for all classes in SEP-28k equal to 0.46 by addressing the class imbalance problem with augmentations and weighting the contribution of classes in the loss function.
# Flocculation Harvesting Techniques for Microalgae: A Review
## 3 Bio-Flocculation
### Algal-Algal Bio-Flocculation  
The use of a self-aggregating alga (or its extracellular polymeric substance, EPS) to harvest non-flocculating, target alga is known as algal-algal bio-flocculation. Significant research has been conducted in this field so far. Self-aggregation has been observed for many algal species such as _Ankistrodesmus falcatus_, _C. vulgaris_ JSC-7, _E. texensis_, _S. obliquus_ AS-6-1, _Scenedesmus_ sp. BH, and _T. Succiia_[39; 46; 59]. A self-aggregation-inducing EPS was purified from _S. obliquus_ AS-6-1 and identified as a cell-wall-associated polysaccharide (consisting of glucose, mannose, galactose, rhannose, and fructose) [39]. The auto-flocculating ability of _E. texensis_ could be attributed to the glycoprotein-type EPS, based on scanning electron microscopy (SEM) and cell surface property analyses. EPS is also considered to play an important role in the harvesting of non-self-aggregating _Chlorella_ spp. [46]. Similarly, the EPS-rich culture of the self-aggregating _Coelastrum_ cf. _pseudomicporum_ resulted in a high harvesting efficiency of 96.8% for _Scenedesmus_ sp. after treatment for 4 h [54]. It should also be noted that algal-algal bio-flocculation is species-specific. For instance, the self-flocculating _S. obliquus_ AS-6-1 resulted in a harvesting efficiency of 80-85% for two freshwater algae, _C. vulgaris_ and _S. obliquus_. However, when applied to the marine alga _N. oceanica_ DUT01, the bio-flocculation efficiency was significantly reduced to 60% [39]. The algal-algal bio-flocculation process could be enhanced by optimization of environmental conditions such as pH; for example, maintaining the pH at 4.5 enabled a harvesting efficiency of over 90% for _C. zofingiensis_ and _C. vulgaris_, using three self-aggregating algae, namely _C. nivale_, _C. ellipsoideum_, and _Scenedesmus_ sp. [59].  
The utilization of self-aggregating algae in the bio-flocculation process can have operational advantages over bacterial and fungal methods in terms of microbial contamination and post-purification processes. The biochemical and physiological mechanisms need to be clearly elucidated to improve the sustainability of the algal-algal bio-flocculation process. In addition, if a low-cost algae mass-cultivation process is realized, this bio-flocculation technique can be applied in order to commercialize the algal bio-refinement.
## 3 Results and Discussion  
Preliminary Selection of Potential Bacterial Strains to Be Protective Cultures and Characterization of Their Antimicrobial Activity  
The CFSs of 200 strains of potential protective cultures were subjected to the agar well diffusion assay against the indicator micro-organisms. Figure S1 shows the inhibition zones obtained with CFSs derived from cultures that grew normally. Approximately 60% out of all the CFSs exhibited a certain antimicrobial activity, and those strains are depicted in Figure 1. These selected CFSs were more active against Gram-positive indicator micro-organisms, particularly _L. monocytogenes, Bacillus cereus_, and _L. parabuchneri_. The observed inhibition halos ranged from 0.92 to >25 mm. Among all the strains under consideration, three representatives of LAB (_Pediococcus acidilactici_ CNTA 1059, _Lactiplantiobacillus plantarum_ CNTA 600, and _Levilactobacillus brevis_ CNTA 1374) and one from _Bacillus_ sp. (_Bacillus subtilis_ CNTA 517) were chosen due to their heightened antimicrobial activity against _L. monocytogenes_ and _L. parabuchneri_, exceeding a 15 mm inhibition halo (Figure 1). These strains were isolated from an asparagus by-product (_P. acidilactici_ CNTA 1059), homemade _chorizo_ (_L. plantarum_ CNTA 600 and _L. brevis_ CNTA 1374), and an altered paint (_B. subtilis_ CNTA 517).
# Separable Data-Hiding Scheme for Encrypted Image to Protect Privacy of User in Cloud
## 3 Proposed Scheme
### Image Encryption Procedure  
Assume that the protected image \(I\), sized \(M\times N\), is divided into non-overlapping blocks of size \(B\times B\), and that each block \(x_{i}\) is measured by BCS to obtain measurement vector \(y_{i}\) using Equation (3),  
Figure 3: Detailed process of the proposed system.  
Figure 2: Example of the application of the proposed scheme in cloud computing.  
i.e., \(\mathbf{y_{i}}=\left[y_{i}^{1},y_{i}^{2},\ldots,y_{i}^{m}\right]^{\mathrm{T}}\), where \(m\) is the number of measurements in one block. Take every \(\mathbf{y_{i}}\) in order into the overall matrix \(y=\left[y_{1},y_{2},\ldots,y_{l}\right]\), where \(l=\frac{M\times N}{B^{2}}\) is the number of blocks.  
\[\mathbf{y_{i}} =\mathbf{\Phi_{B}}\mathbf{x_{i}}, \tag{3}\]  
where \(\mathbf{\Phi_{B}}\) is a block measurement matrix with the size of \(m\times B^{2}\). Then a random number \(R\) was used to rearrange each element of the measurement matrix in order to distribute its energy.  
Equation (3) shows that the randomness of the measurement matrix destroys the correlation between the pixels of the image block, so all measurements obtained in vector \(\mathbf{y_{i}}\) are completely independent of each other. Here, \(\mathbf{\Phi_{B}}\) and \(R\) were used as the encrypted key \(K_{1}\).
# A New Method for Large-Scale Landslide Classification from Satellite Radar
## 2 Materials and Methods
### Case Study: The 2015 Gorkha Earthquake
#### 2.2.2 SAR Data and Processing  
C-band Sentinel-1a SAR imagery acquired on descending track 19 (dashed red line, Figure 1) was used in this study. Acquisition dates are shown in Figure 6. This includes a long time-series of pre-event imagery, which is required for sibling calculation in the new method presented in Section 2.1.4. The occurrence of the Gorkha earthquake early in the lifetime of Sentinel-1a and prior to the launch of Sentinel-1b meant that there were not sufficiently regular data acquisitions before the earthquake on ascending track 085 (Figure 1). Ascending imagery was therefore not used in this study. Similarly, descending track pre-seismic imagery of east of Kathmandu on track 121 (Figure 1) was acquired prior to the earthquake less frequently than on track 19 and so is not used here.  
Interferograms and coherence maps were produced for consecutive date pairs using the LiCSAR software package [33], which uses GAMMA software to process Sentinel-1 single look complex data. A 1-arcsecond digital elevation model (DEM) derived from Shuttle Radar Topography Mission data [45] was used in this processing. The raw SAR data had a pixel size in the radar coordinate system of 2.3 m \(\times\) 14.0 m (range \(\times\) azimuth). Images were multilooked by a factor of five in range and one in azimuth, giving a pixel size of 12 m \(\times\) 14 m.  
Boxcar coherence was calculated using pixels within a 3 \(\times\) 3 pixel window. To calculate the sibling-based coherence, we used the RapidSAR algorithm of [PERSON] [44]. For every pixel, a window of 41 \(\times\) 41 pixels was searched and between 15 and 100 pixels were identified which behaved similarly in terms of amplitude and amplitude variability throughout 11 pre-event images acquired up until 5 April 2015 (see Figure 6 for acquisition dates).  
Once the three classification surfaces had been calculated according to the methods outlined in Section 2.1, these were converted to a geographic coordinate system, with a pixel size of 20 m \(\times\) 22 m, which reflects the multilooked resolution of the data. We carried out the analyses presented in Section 3 in this coordinate system. Each classifier surface was then rescaled to produce a surface with values between 0 and 1, where 1 was most likely to be a landslide.
# Regression of Likelihood Probability for Time-Varying MIMO Systems with One-Bit ADCs
## 2 System Model
### Receiver
This is because the difference between the initially obtained channel estimate \(\hat{\mathbf{H}}[0]\) and true channel \(\mathbf{H}[n]\) increases as the time index \(n\) increases.
# Seismic Response of Container Crane under Near-Field and Far-Field Ground Motions
## 4 Results and Discussions
### Portal Drift  
The portal drift of the scale container crane was measured at location D1 (landside portal) and D2 (seaside portal) using a camera, as shown in Figure 2. The horizontal displacements at location D1 and D2 relative to the shake table in the trolley boom direction are defined as the portal drift. They are expressed in Equation (3):  
\[Portal\ drift=Ab_{portal\_disp}-Ab_{shake\_disp} \tag{3}\]  
where \(Ab_{portal\_disp}\) is the absolute displacement at location D1 and D2 obtained from the camera; \(Ab_{shake\_disp}\) is the absolute displacement at a location on the shake table obtained from the camera. In this study, the absolute displacement of the location D1, D2, and the point on the shake table was determined from a video recorded with the camera using the Kanade-Lucas-Tomasi face detection and tracking algorithm in Matlab software [34; 35; 36].  
The values of the portal drift of the crane model are shown in Figure 8. Despite the PGA of the near-field ground motion for the shaking table test being lower than that of the far-field ground motions, the portal drift under the near-field Gazli USSR Iran earthquake was higher than that of far-field earthquakes (Nahanni Canada and Mineral Virginia earthquakes). In detail, the seaside portal drift of the near-field Gazli USSR Iran earthquake (8.1 mm) was 25% and 43% higher than that under the far-field earthquakes, Nahanni Canada (6.1 mm) and Mineral Virginia (4.6 mm), respectively. The landslide portal drift under the Gazli USSR Iran (8.2 mm) was also 27% and 44% higher than that under the Nahanni Canada (6.0 mm) and Mineral Virginia (4.6 mm) earthquakes, respectively. Therefore, the near-field ground motion caused larger portal drift than the far-field ground motions. Figure 9 shows the portal drift time history for the three input ground motions. It is observed that there were no differences between seaside and landslide portal drift in this study. The positive values indicate movement in the seaside direction relative to its original position, while the negative values indicate movement in the landslide direction relative to its original position.  
Figure 7: The maximum internal forces in the seaside leg: (**a**) Axial force; (**b**) Moment about the x-axis; (**c**) Moment about the y-axis.  
Figure 8: Comparison of portal drift for near-field and far-field earthquakes.  
Figure 9: _Cont_.