esa-sceva/satcom-chunk-collection huggingface dataset:
# The Utilization of Artificial Neural Network Equalizer in Optical Camera Communications +
## 3 ANN Equalizer
\right], \tag{7}\]  
\[\mathbf{v}^{(m)}=[v_{1}^{(m)}\quad v_{2}^{(m)}\quad\ldots\quad v_{N_{m}}^{(m)}]^{ T}. \tag{8}\]  
Considering the \(N_{0}\times 1\) input vector, \(N_{M}\times 1\) output vector, \(\mathbf{o}^{(0)}=\mathbf{x}\) and \(\mathbf{o}^{(M)}=\mathbf{y}\), the following observation vector \(o^{(m)}\) is given by:  
\[\mathbf{x}=[x_{1}\quad x_{2}\quad\ldots\quad x_{N_{0}}^{(m)}]^{T}, \tag{9}\]  
\[\mathbf{y}=[y_{1}\quad y_{2}\quad\ldots\quad y_{N_{M}}]^{T}. \tag{10}\]  
Therefore,  
\[\mathbf{o}^{(1)}=f\Big{[}\mathbf{W}^{(1)}\mathbf{x}+C^{(1)}\mathbf{v}^{(1)}\Big{)}, \tag{11}\]  
\[\mathbf{o}^{(2)}=f\Big{[}\mathbf{W}^{(2)}\mathbf{o}+C^{(2)}\mathbf{v}^{(2)}\Big{)}, \tag{12}\]  
\[\ldots\]  
\[\mathbf{y}=f\Big{[}\mathbf{W}^{(M)}\mathbf{o}^{(M-1)}+C^{(M)}\mathbf{v}^{(M)}\Big{)}. \tag{13}\]  
MLP will record its trained information in \(w_{kn}^{(m)}\) and in the threshold factors \(\mathbf{v}_{n}^{(m)}\), since \(C^{(m)}\) is given as a constant for all layers (i.e., set as \(C^{(m)}=1,m=1,\ 2,\ldots,M\)). Resilient back-propagation (RBP) is a supervised back-propagation (BP) training method, which updates the weights to converge more rapidly than the standard BP training technique [26]. Figure 3 depicts a single neuron for the case where the layers are interconnected  
Figure 3: A structure of the \(k\)th neuron in the layer \(m\). with different weight coefficients. The RBP adjusts the MLP weights to reduce the error cost function \(E_{n}\) as given by [25]:  
\[E_{k}= ||d_{k}-y_{k}||_{2}, \tag{14}\]  
where \(d_{k}\) and \(y_{k}\) are the ideal and actual received symbols, respectively. It should be noted that, for the training sequence, \(d\) is known. Each iteration of the RBP algorithm has a dynamic step size, which varies based on the magnitude of the gradient descent of \(E_{k}\).
## 4 Discussion
### DHA Yield and Fatty Acid Profile of S. limacinum  
DHA productivity is closely related to low dissolved oxygen levels ranging from 10% to 30% [76; 77]. However, high oxygen levels are favorable for biomass productivity [9] ranging from 40-50%. This is in line with the results of the present study, since compared to earlier studies [33; 36; 62], the biomass productivity observed was substantially high (49.2 \(\pm\) 2.7 g L\({}^{-1}\)) followed by low DHA levels (760 \(\pm\) 85 mg L\({}^{-1}\)). With regards to the maximum DHA yield, which was achieved at elevated concentrations, this could be attributed to increased Fe concentration, since fatty acid desaturases are iron contained enzymes responsible for sequential addition of double bonds to saturated fatty acids in order to produce DHA [72]. Furthermore, it has been suggested that iron concentration is closely related to PUFA synthesis in fungi [70].  
The fatty acid profile of _S. limacinum_ at varying concentrations of effluent, contained primarily 18:0 (71-80% TFA), 16:0 (17-21% TFA) with DHA constituting only a small fraction (3-7% TFA). Under ideal conditions and optimized culture media, according to the literature the main fatty acids of _Schizochytrium_ sp. are 16:0, DPA and DHA [78]. According to recent evidence, fatty acid synthesis in thraustochytrids involves two independently operating pathways. The first pathway requires molecular oxygen and produces short chain fatty acids 16:0 that can further elongate to 18:0. The second pathway, being oxygen independent and responsible for the production of DHA and DPA is the polyketide synthase (PKS) pathway [79]. A possible explanation for the elevated fraction of 16:0 and 18:0 fatty acids found in our study could be due to high oxygen levels, which upregulated the FAS pathway at the expense of DHA production through the anaerobic PKS pathways. This is in line with other research where high dissolved oxygen levels increased the fraction of short chain fatty acids [80], indicating a strong dependence on the abiotic environment besides optimal nutrient concentrations.  
Although lipid productivity and DHA yield was very low compared to previous studies, the utilization of crude glycerol and effluent digestates as alternative nutrient sources can be successfully implemented in thraustochytrids fermentation media under optimized culture conditions. Furthermore, the medium composition suggested in the present study can also serve as an alternative economic solution for the seed culture in order to substitute conventional media, which primarily contain glucose, yeast extract and trace elements, since in this production, stage lipid productivity is irrelevant.
# Optimization Method for Stiffened-Plate Layout in Box Structures Based on Load Paths
## 2 Structural Optimization Method Based on Load Path  
The method proposed in this paper consists of three steps. Firstly, an envelope model of a given structure is established based on the working conditions and functional characteristics, and a functional cross-section decomposition of the structural model based on the load distribution is performed to achieve dimensionality reduction. Then, a finite element model (FEM) is established, and the load path theory is used to visualize the load path. Finally, the design guidelines for the external shape and stiffened plate layout are established by analyzing the load transfer law. The design process of each step is described in detail in this section, and the proposed method is validated by the example in the next section.
###### Abstract  
The interferometric imaging radar altimeter (InIRA) aboard the Chinese Tiangong-2 space laboratory is the first spaceborne imaging radar working at low incidence angles. This study focuses on the retrieval of significant wave heights (SWHs) from InIRA data. The retrieved SWHs can be used for correcting the sea state bias of InIRA-derived sea surface heights and can supplement SWH products from other spaceborne sensors. First, we analyzed tilt, range bunching and velocity bunching wave modulations at low incidence angles, and we found clear dependencies between the SWH and two defined factors, range and azimuth integration, for ocean waves in the range and azimuth directions, respectively. These dependencies were further confirmed using InIRA measurements and collocated WaveWatch III (WW3) data. Then, an empirical orthogonal SWH model using the range and azimuth integration factors as model inputs was proposed. The model was segmented by the incidence angle, and the model coefficients were estimated by fitting the collocation at each incidence angle bin. Finally, the SWHs were retrieved from InIRA data using the proposed model. The retrievals were validated using both WW3 and altimeter (JASON2, JASON3, SARAL, and HY2A) SWHs. The validation with WW3 data shows a root mean square error (RMSE) of 0.43 m, while the average RMSE with all traditional altimeter data is 0.48 m. This indicates that the InIRA can be used to measure SWHs.  
Tiangong-2 space laboratory; interferometric imaging radar altimeter (InIRA); significant wave height (SWH); empirical model; retrieval Article  
* 2021
## 3 Results and Discussion  
All fungi used in this study had conidia with viability greater than 90%. The isolates of _B. bussiana_, _M. anisopliae_, and _I. funososrosea_ were pathogenic for _T. molitor_, with significant variation between their results (F\({}_{15.79}\) = 25.650; \(p\) = 0.000) (Table 1). The insect average mortality treated with isolates SE104, SE106, SE107, SE109, SE111, Psi, 1260, 1258, SE201, and SE202 did not differ significantly, with the mortality rate around 98%-69.1%. The other isolates had a confirmed mortality rate of less than 65%. The virulence of the fungi isolates against _T. molitor_, accessed through the lethal time 50 (LT\({}_{50}\)), also showed significant variation. The lowest time estimates for the confirmed average mortality of insects were observed by the action of isolates SE301 (_I. fumosrososea_), 1260 (_B. bussiana_), and SE202 (_M. anisopliae_) (LT\({}_{50}\) = 4 days), being the most isolated virulent. Isolates 447 and SE101 (both _B. bussiana_) and SE201 (_M. anisopliae_) promoted slower mortality on _T. molitor_, with LT\({}_{50}\) varying between 7 and 9 days. The insects treated with the other isolates showed an average lethal time between 5 and 6 days. Pathogenicity and virulence evaluations are commonly used in insect pathology to analyze the pathogen-host interaction between entomopathogenic fungi in insects, observing the potential for colonization and mortality caused by the fungus. Some species of insects are considered good models for pathogenicity analysis, and _T. molitor_ (Coleoptera: Tenebrionidae) is one of the insects most used for this owing to its easy handling and low cost for establishing crosses [16; 17]. A previous study demonstrated the presence of variation in the mortality of _T. molitor_, which may be associated with the use of different species or isolates of entomopathogenic fungi (Table 2). In this report, _B. bussiana_ isolates promoted a mortality rate of _T. molitor_
# Instantaneous Beam Prediction Scheme against Link Blockage in mmWave Communications
### Motivations and Contributions  
Overall, existing schemes feature major limitations. First, the deployment of mmWave in non-standalone (NSA) modes by relying on legacy sub-6 GHz bands weakens mmWave networks as independent technology in 5 GC. Namely, this conflicts with the objectives of the FR2 NR of 5G systems that operate mmWave bands as a standalone (SA) network. Additionally, surveyed schemes feature high computational complexity due to the increased requirements in hardware, such as cameras and reflectors, redundant arrays for redundant beam establishments and continuous beam redundancy, which all exhaust network resources. Other limitations include indoor implementation and marginal enhancements to existing conventional methods. Furthermore, they lack comprehensive models for the recovery times and power consumption levels in the control plane. This is a key performance metric to meet the ultra-low latency requirements in 3 GPP specifications and meeting realistic power demands at the MS. Hence, work is needed to investigate the delays in standalone beamforming-based mmWave networks.  
Along these lines, this paper proposes a novel beam recovery scheme in highly directional standalone (SA) mmWave networks that is projected for the second phase of 5G systems. The scheme achieves near-instantaneous recovery times, low power consumption, and energy-efficient levels without relying on out-of-band information or any auxiliary information. This is realized at power-efficient beamformers at the MS, based on uniform circular arrays (UCA) to account for the sparse channel structure.  
This paper is structured as follows. First, Section 2 surveys key recovery schemes due to beam blockage. Section 3 presents the system model composed of the beamforming architecture, blockage, and signal models. Section 4 introduces the beam recovery scheme using deep learning. Section 5 presents the simulation results and performance evaluation. Finally, concluding remarks and future directions are presented in Section 6.
# Predicting Tensile Strength for Prestressed Reinforced Concrete-Driven Piles
## 5 Analysis of Pile-Induced Tensile Stress and Strength  
For the original pile design with four cables that showed cracks during construction, the tensile strengths were calculated using the AASHTO equation (Equation (1)) and the proposed equation (Equation (4)) for comparison. The results are imposed in Figures 5 and 6, where the red lines represent the tensile strength obtained using the AASHTO equation (Equation (1)), which produced the value of 4.44 MPa; the orange lines represent the tensile strength using the proposed equation, which produced the value of 7.06 MPa. This value of 7.06 MPa is within the higher range of the measured tensile stress (6.3 MPa to 8.1 MPa) and, as a result, a large number of the piles of this initial design were damaged. Compared with the measured data and the observed cracks, the AASHTO equation underestimated the tensile strength of the piles because the equation predicted a tensile strength of 4.44 MPa, which is smaller than the measured tensile stress along the length of the pile segments, which means that the cracks would have been along most of the length of the pile segments. The proposed approach predicts a tensile strength of 7.06 MPa, which is actually more accurate because the cracks appeared at locations where the measured induced stresses were larger than the predicted strength.  
For the revised design of the piles with eight cables, the tensile strength obtained by using the AASHTO equation is 6.89 MPa, which is smaller than the maximum induced tensile stress of 8.1 MPa, and cracks would have occurred. However, with this revised design of eight cables, there were no cracks observed on the piles. The proposed approach's calculated tensile strength for the eight-cable piles is 9.5 MPa, shown by the green lines in Figures 5 and 6. This value is larger than the induced tensile stress in the piles, predicting no cracks. The calculated factor of safety is 1.17, and these piles were successfully constructed without tensile cracks. For eight-cable piles, if a design did not account for the tensile strength of the concrete component as recommended by most standards, then the pile tensile strength would be 5.12 MPa, which is smaller than the induced tensile stress during driving, and cracks would have occurred.  
For this case study, the proposed approach predicts the pile tensile strength (and, therefore, the cracks) more accurately, with the calculated tensile strength about 38% to 59% higher than the values from AASHTO [5] for designs of four and eight cables, respectively (Table 3). These quantities are significant but may vary, depending on the used compression strength of the concrete and the pile configurations; however, based on the analysis using measured data and field observations, the proposed approach is reliable to use for estimating the tensile strength of prestressed concrete piles. This approach can provide a better design for the prestressed concrete pile at the construction stage, resulting in cost savings.
###### Abstract  
Soil moisture mapping at a high spatial resolution is very important for several applications in hydrology, agriculture and risk assessment. With the arrival of the free Sentinel data at high spatial and temporal resolutions, the development of soil moisture products that can better meet the needs of users is now possible. In this context, the main objective of the present paper is to develop an operational approach for soil moisture mapping in agricultural areas at a high spatial resolution over bare soils, as well as soils with vegetation cover. The developed approach is based on the synergic use of radar and optical data. A neural network technique was used to develop an operational method for soil moisture estimates. Three inversion SAR (Synthetic Aperture Radar) configurations were tested: (1) VV polarization; (2) VH polarization; and (3) both VV and VH polarization, all in addition to the NDVI information extracted from optical images. Neural networks were developed and validated using synthetic and real databases. The results showed that the use of a priori information on the soil moisture condition increases the precision of the soil moisture estimates. The results showed that VV alone provides better accuracy on the soil moisture estimates than VH alone. In addition, the use of both VV and VH provides similar results, compared to VV alone. In conclusion, the soil moisture could be estimated in agricultural areas with an accuracy of approximately 5 vol % (volumetric unit expressed in percent). Better results were obtained for soil with a moderate surface roughness (for root mean surface height between 1 and 3 cm). The developed approach could be applied for agricultural plots with an NDVI lower than 0.75.  
Keywords:neural networks; soil moisture mapping; Sentinel 1&2; SAR; the C-band; NDVI; agricultural areas +
Footnote †: journal: remote sensing
## 2 Mechanisms Specific to QoS and Multicast in Ad Hoc Networks
### Mechanisms for Resource Estimation
#### 2.5.2.2 Indirect Estimation (End-to-end Probing)  
The alternative for estimation is to probe the network and measure the end-to-end performance from source to receiver as in [30] or from source to intermediate nodes as in [28]. In the former case, the source sends probes to the destinations. As soon as the destination receives the probe packets, it measures the achieved quality. If the difference between the quality of probe packets and required QoS (known prior) is within an accepted threshold, the receiver accepts the whole transmission; otherwise, it refuses. The method also introduces the priority of packets. The priority of the probe packets is lower than that of QoS multicast packets and higher than that of best effort packets. This priority assignment ensures that the probing traffic does not affect the existing QoS flows. An example of this mechanism is in M-CAMP [30]. In the later case, the sender also sends probe packets to the destinations. When a node receives a probe packet, it checks the QoS constraints and compares the metric (e.g., delay and  
Figure 2: Classification of available resource estimation mechanisms.  
cost) of the current probe packet with the previous probe packets. If the QoS constraints are satisfied and the metric of the new probe is better than the previous probes metric, the node change its predecessor to the node that the new probe packet came from. This strategy is called \"the best predecessor replacement strategy\". LACMQR [28] uses this technique for multicast routing.
###### Abstract
The use of the PLC approach in the classroom makes it even more essential to analyze student behavior in relation to learning outcomes. The major contributions of this paper are as follows:  
* We have designed an efficient keyframe selection method based on the K-means and the sum of absolute differences (SAD) of the L channel in the LUV color space to extract keyframes from a random length of video clips. The proposed method guarantees the extraction of a unique sequence for any given video clip. * Four features are fused to characterize the behavior type efficiently. High-level features are extracted from the natural RGB images, while motion features are extracted from the optical flow. To overcome problems regarding various luminance conditions and data deficiency, features from Lab color components and data augmented (DA) are also extracted and fused with the other high-level features. We propose this multi-feature fusion, ensuring that the feature extraction process captures the most relevant information from the video keyframes covering spatial and temporal features. * To achieve efficient classification, we introduce an innovative hybrid network that combines a bi-directional long short-term memory (BiLSTM) model with an attention mechanism (AT) to classify student behavior in a classroom setting effectively. The BiLSTM component excels at capturing long-range temporal dependencies within the student behavior data. Simultaneously, the integrated AT empowers the model to identify and prioritize the most relevant data segments for precise classification. Our architectural enhancements include the use of 512 LSTM cells, equally distributed into 256 cells each for both forward and backward directions. This architecture is robust and can support 7168 feature dimensions in each of the ten keyframe feature fusions. The remainder of this paper is organized as follows: Section 2 reviews the related literature regarding student behavior in the classroom. Section 3 describes the materials and methods, including the student behavior data collection, keyframe extraction, and selection methods, the multiple pretrained models for feature extraction and the fusion approach, and the proposed overall framework using a deep sequence model based on the BiLSTM-AT network to classify students' behaviors. Section 4 describes the experimental setup, dataset setup, and evaluation metrics. Section 5 presents the experimental results and discusses the findings. Section 6 concludes the paper and presents directions for future work.
# Design of a Configurable Spike-Encoding Circuit Based on Focal Plane Array
## 2 System Requirement and Coding Theory
### Spike FPA System
#### 2.2.1 Accelerated Mode  
In accelerated mode, the luminance of each pixel is encoded using a coarse single spike, and a shallow SNN is used to construct the image processing block, imitating the saccade principle in biology to locate the area where the target is situated. This design serves to increase the transmission rate and processing efficiency of image data; however, at the expense of image resolution. In the neurons of the subsequent neural network, such pulses are received using leaky integrated-firing neurons (LIF). Each LIF neuron corresponds to a randomly divided area of the array. The output layer uses a classification voting mechanism to determine the area whose brightness is higher than the design threshold. The output area's coordinates and the mode-switching signal are fed back to the array reader, as depicted in Figure 3. In their research, [PERSON] and [PERSON] have developed an SNN model derived from ANN that can accomplish nearly the same performance as ANN in target detection and segmentation tasks [14].
## 4 Analysis of Marginal Spectrum Change of an Active Lamb Wave  
### Preliminary Analysis of the Marginal Spectrum  
The instantaneous frequency is defined by HHT theory as a derivative of the instantaneous phase [20]. An analysis of the experimental results shows that the superposition of the scattering signal could cause instantaneous phase change, and thus the instantaneous frequency would also change. The experimental results of the signal with a center frequency of 150 kHz, which is shown in Figure 4,are presented in Figure 5. Figure 5 aI shows the time sequence of the Lamb wave packet before and after the damage (10 signals for each condition). Figure 5 aII displays the instantaneous frequency obtained from the signal that is presented in Figure 5 aI. Figure 5 aII shows that there was an obvious frequency change in the interval from 2.5 us to 3.5 us. Following Equation (5), the marginal spectrum was defined as the integral of the IMF's Hilbert spectrum over time, describing the relationship between the signal energy and its instantaneous frequency. When the instantaneous frequency changed, the energy distribution also changed. Therefore, the damage scattering waves could change the distribution of the Lamb wave marginal spectrum in the frequency domain. Figure 5 bI shows the IMFs obtained by decomposing the signal presented in Figure 5 aI by the EMD. Figure 5 bI shows that the energy was mainly concentrated in the first few order IMFs. Considering that the EMD decomposition was performed from high frequencies to low frequencies, the other order IMFs did not represent effective frequency components, and their energy was relatively small. In this work, only the first three order IMFs were used to determine the marginal spectrum. Figure 5 bII shows the obtained marginal spectrum, presenting the change before and after damage.  
The explanation given above is only a preliminary illustration that the damage scattering signal can change the distribution of the Lamb wave marginal spectrum in the frequency domain. A detailed explanation of the causes is given below.