esa-sceva/satcom-chunk-collection huggingface dataset:
Herein, the scanning parameters were optimized through theoretical considerations and experimental implementation to dramatically reduce the number of scanning positions and thereby the acquisition time while maintaining high contrast and uniform spatial resolution. In particular, we studied the influence of the rotation step on the spatial resolution and image contrast. The system was characterized in vitro with imaging phantoms and the performance was compared to synthetic aperture scanning with a translation-only scan to highlight the superior image quality of this new, more complex scanning scheme.
# Biological Production of 3-Hydroxpropionic Acid: An Update on the Current Status
###### Abstract  
The production of high added-value chemicals from renewable resources is a necessity in our attempts to switch to a more sustainable society. 3-Hydroxpropionic acid (3 HP) is a promising molecule that can be used for the production of an important array of high added-value chemicals, such as 1,3-propanediol, acrylic acid, acrylamide, and bioplastics. Biological production of 3 HP has been studied extensively, mainly from glycerol and glucose, which are both renewable resources. To enable conversion of these carbon sources to 3 HP, extensive work has been performed to identify appropriate biochemical pathways and the enzymes that are involved in them. Novel enzymes have also been identified and expressed in host microorganisms to improve the production yields of 3 HP. Various process configurations have also been proposed, resulting in improved conversion yields. The intense research efforts have resulted in the production of as much as 83.8 g/L 3 HP from renewable carbon resources, and a system whereby 3-hydroxypropionitrile was converted to 3 HP through whole-cell catalysis which resulted in 184.7 g/L 3 HP. Although there are still challenges and difficulties that need to be addressed, the research results from the past four years have been an important step towards biological production of 3 HP at the industrial level.  
3 -hydroxypropionic acid; metabolic engineering; building-block chemicals; glycerol; platform chemicals; _Klebsiella pneumoniae; Escherichia coli; Saccharomyces cerevisiae_
# A Domain-Oriented Entity Alignment Approach Based on Filtering Multi-Type Graph Neural Networks
###### Abstract  
Owing to the heterogeneity and incomplete information present in various domain knowledge graphs, the alignment of distinct source entities that represent an identical real-world entity becomes imperative. Existing methods focus on cross-lingual knowledge graph alignment, and assume that the entities of knowledge graphs in the same language are unique. However, due to the ambiguity of language, heterogeneous knowledge graphs in the same language are often duplicated, and relationship triples are far less than those of cross-lingual knowledge graphs. Moreover, existing methods rarely exclude noisy entities in the process of alignment. These make it impossible for existing methods to deal effectively with the entity alignment of domain knowledge graphs. In order to address these issues, we propose a novel entity alignment approach based on domain-oriented embedded representation (DomainEA). Firstly, a filtering mechanism employs the language model to extract the semantic features of entities and to exclude noisy entities for each entity. Secondly, a Structural Aggregator (SA) incorporates multiple hidden layers to generate high-order neighborhood-aware embeddings of entities that have few relationship connections. An Attribute Aggregator (AA) introduces self-attention to dynamically calculate weights that represent the importance of the attribute values of the entities. Finally, the approach calculates a transformation matrix to map the embeddings of distinct domain knowledge graphs onto a unified space, and matches entities via the joint embeddings of the SA and AA. Compared to six state-of-the-art methods, our experimental results on multiple food datasets show the following: (i) Our approach achieves an average improvement of 6.9% on MRR. (ii) The size of the dataset has a subtle influence on our approach; there is a positive correlation between the expansion of the dataset size and an improvement in most of the metrics. (iii) We can achieve a significant improvement in the level of recall by employing a filtering mechanism that is limited to the top-100 nearest entities as the candidate pairs.  
domain knowledge graph; entity alignment; embedded representation; filtering mechanism +
Footnote †: journal: Applied Sciences
## 6 Thematic Taxonomy of UWSNs
### Applications  
UWSN technology can replace traditional approaches by offering real-time monitoring, an onshore system to control underwater appliances remotely, and advanced devices for data recording. Commonly, UWSN applications are composed of three categories: scientific, industrial, and military and security (see Figure 8). In the military, sensor nodes are used to detect the movement of enemies and their location. It can be applied to monitor ports and harbors, conduct border surveillance, identify underwater mine locations, and detect enemy submarines. In the case of natural disasters, sensor nodes can detect marine environments by performing seismic monitoring in advance of disasters. A wide range of applications requires rapid developments in standards and technologies to support and enhance the growth of new applications. While there are many different applications, this section presents a survey of recent developments in the domain of UWSN applications, that are assisting in scientific, industrial, and defense and disaster prevention activities.
# Resource- and Neighbor-Aware Observation Transmission Scheme in Satellite Networks
## 5 Evaluation Results  
For performance evaluation, we compare the proposed scheme, RNA-OTS, with the following three schemes: (1) GROUND, where the observation satellite directly transmits its observation to the ground station with/without compressing the observation; (2) RELAY, where the observation satellite always exploits the relay satellite with/without compressing the observation; and (3) RAND, where the observation satellite randomly chooses the action. The performance metrics are the average delay \(\zeta_{D}\) to deliver the observation to the destination, the average utilization \(\zeta_{C}\) of the computational resource, and the average utilization \(\zeta_{S}\) of the storage resource.  
The default parameter settings are as follows: The number of observation satellites is 5. The average inter-observation time \(1/\lambda^{M}\) is set to \([2,5]\) (s), where \([a,b]\) is a random value between \(a\) and \(b\). The average duration of the disconnection (connection) between the ground station and the observation satellite is set to \([5,20]\). The target utilization, \(\theta_{C}\) and \(\theta_{S}\), for the computational and storage resources is set 0.05 and 0.01, respectively. The latency \(L_{T_{0,C}}^{W}\) (\(L_{T_{0,C}}^{C}\)) between the observation satellite and the ground station without the compression (with the compression) is set to 1 (0.5) (s). The latency \(L_{T_{I_{R,C}}}^{W}\) (\(L_{T_{R,C}}^{C}\)) between the relay satellite and the ground station without the compression (with the compression) is set to 1 (0.5) (s). The latency \(L_{T_{0,R}}^{W}\) (\(L_{T_{0,R}}^{C}\)) between the observation satellite and the relay satellite without the compression (with the compression) is set to 0.1 (0.05) (s). The unit latency \(L_{Q}\) to handle one observation in the relay satellite is set to 0.01 (s).
# Secure Transmission in mmWave Wiretap Channels: On Sector Guard Zone and Blockages
## 2 System Description and Performance Metrics
### Performance Metrics
#### 2.2.1 Secrecy Outage Probability and Connection Outage Probability  
If the perfect security of confidential information can not be guaranteed, that is, a portion of the confidential information sent from the transmitter is decoded by at least one eavesdropper, the secrecy outage event takes place, the SOP is written as  
\[p_{so}=\Pr\left(\gamma_{E}>2^{R_{B}-R_{S}}-1\right), \tag{2}\]  
where \(\gamma_{E}\) denotes signal-to-interference-plus-noise received by the eavesdropper. Adopting Wyner code, \(R_{S}\) and \(R_{B}\) are the confidential information rate and codeword transmission rate, respectively, where \(R_{B}\geq R_{S}\)[15; 38].  
If the confidential information cannot be decoded without error at the legitimate receiver, the connection outage event occurs, then the COP can be expressed as  
\[p_{co}=\Pr\left(\gamma_{D}<2^{R_{B}}-1\right), \tag{3}\]  
where \(\gamma_{D}\) denotes signal-to-interference-plus-noise received by the legitimate receiver.
## 3 Experiment and Analysis  
### Experiment Implementation  
The laboratory experiments were conducted in the anechoic chamber to validate the proposed method. The experimental layout is shown in Figure 7. The laptop was the control center to manage the audio system for simulating a real environment. Two omnidirectional speakers were connected to the laptop as the speech and noise sources, respectively, using an INTERM L-2400 power amplifier. The microphone array consisted of 6 MEMS microphones, distributed uniformly in a circle with a radius of 0.1 m. The printed circuit board (PCB) of the SCIEN company was connected to the microphones to realize the signal amplification function, the A/D conversion function, and transfer the digital signal to the laptop for recording. The signal was processed by the MATLAB software. To simplify the experiment, the speakers and the microphone array were deployed in the same horizontal plane. This means that the elevation angles of the incident signals (desired signal and noise signal) were \(90^{\circ}\).  
Based on the common far-field equation, expressed as in Equation (29), the minimum distance between the microphone array and the sound source was calculated to ensure that the position of the speakers satisfied the far-field condition. Assuming that the sound speed \(c\) is 343 m/s, the maximum frequency \(f\) was 4000 Hz, and the diameter of microphone array \(L\) was 0.2 m. Hence, the minimum distance \(d\) was approximately 0.933 m. Thus, the speakers were deployed about 2.5 m away from the microphone array, considering the space of the anechoic chamber and the minimum distance \(d\). The azimuth angles of the speech signal source and the noise signal source were \(0^{\circ}\) and \(90^{\circ}\), respectively.  
\[d=2*L^{2}/\lambda=2*L^{2}*f/c \tag{29}\]  
Figure 6: Block diagram of the GSC-SN-MCC method.  
The speech of a female was selected randomly from the TIMIT database [32] as the desired signal. White noise in the NOISE-92 database [33] was chosen as the noise signal. The power of the noise signal was adjusted to simulate different SNR conditions. The sampling rate of the microphone array was 16,000 Hz, and the received signal was filtered using a lowpass filter, of which the cutoff frequency was 4000 Hz. The high sampling rate could increase the time resolution of the system and exhibit the phase difference between microphones more obviously. Because most of the energy of speech is concentrated in the low and medium frequency bands, the received signal was filtered by a lowpass filter to reduce the calculation burden of the hardware, while most of the speech information was preserved.
# Experimental Investigation on Flow-Induced Rotation of Two Mechanically Tandem-Coupled Cylinders
## 2 Theoretical Basis  
### Principle of Flow-Induced Rotation  
Figure 1 is a simplified schematic diagram of the flow-induced rotation (FIR) system. As shown in Figure 1, the double cylinders are mechanically coupled and arranged in series. When the water flows through the FIR system, the fluid force, \(F_{fluid}\), forces the tandem-coupled double cylinders to rotate around its geometric central axis, \(O\). The motion of the cylinders drives the upper rotary shaft with the pivot point, \(P\), to rotate synchronously through the transmission device, composed of the transmission belt and the synchronous wheel. The upper rotary shaft, \(P\), is connected to a hollow vertical rod through a three  
way shaft sleeve to ensure that they also rotate synchronously. Two tension springs are connected to this hollow vertical rod by using a spring connector to generate the restoring force. \(F_{k}\). Note that the spring connector can slide freely on the vertical rod.  
In order to facilitate the establishment of a mathematical model of the FIR system, the original system was divided into three subsystems: pendulum system, transmission system and restoration system.
## 6 Bending Analysis of mm-Wave Linear MIMO Array Antenna  
The deformation of the antenna structure can alter its properties. To examine the S-parameter behavior of the MIMO antenna, a bending analysis along the x-direction was conducted, as shown in Figure 15. Additionally, Figure 16 presents the \(|\)S\({}_{11}\)/S\({}_{22}\)/S\({}_{33}\)/S\({}_{44}\) | curves plotted for different bending radii along the x-axis. Figure 16 shows that the resonance dips are slightly altered up to Rx = 40 mm, and the measured bending results match the simulated results. When bending in the y-direction is analyzed as depicted in Figure 17, it is observed that again a small change in resonance frequencies is observed even at the worst case when Ry = 25 mm (Figure 18). Moreover, the realized gains and radiation efficiencies are calculated at Ry = 25, 50, and 75 mm, and the corresponding results are depicted in Figure 19. Slight variations in peak gains and efficiencies are observed due to the change in the overall impedance of the MIMO antenna, which result from alterations in current density in the deformed structure of the MIMO design. This analysis indicates that the proposed mm-wave MIMO array antenna is suitable for 5G, wearable, and medical applications.  
Figure 14: Simulated and measured CCL of MIMO antenna.  
Figure 13: Calculated TARC of linear MIMO array antenna.
## 3 Proposed Methods
### Stereo Taillight Matching
#### 3.2.3 Matching Classifier using Gradient Boosted Tree  
In our context, the dataset is sensitive to outliers as long as we combine the information from two images captured from different cameras, and these cameras may have little mismatches in the specifications. Furthermore, the dataset is imbalanced, as the number of cases when two taillights do not match is much larger than that when two taillights match. The gradient boosted tree [30]is a machine learning model that is well known for its superiority compared to other approaches when dealing with inaccurate, unbalanced data without explicit rules [31; 32; 33]. This is because it can generate the most optimized fit from the predicted output to the actual output by observing the complicated relations between relevant features. Therefore, to overcome the limitations mentioned earlier, the gradient boosted tree was chosen as an architecture of the taillight matching classifier.  
The intuition behind the gradient boosted tree is a decision tree, which aims to identify the contribution of each input feature on the classification result by considering several training samples. From a beginning decision tree with a very bad error, the new decision tree is constructed based on improving the error from the previous tree. This process is repeated a specific number of times \(m\) until the best decision tree is obtained, which is a gradient boosted tree, as illustrated in Figure 18.  
In this paper, the training set, including the feature vector _matchVec_ and the expected _status_ of multiple taillight combinations (\(L_{i}\), \(R_{j}\)), is used to train the gradient-boosted-tree-based matchingClassifier. All parameters relevant to the training are shown in Table 1.  
After training matchingClassifier, relative feature importances, which measure the influence of each particular feature when compared with each other one upon constructing the boosted tree, were obtained as shown in Figure 19. The intuition behind the relative feature importance is explained in detail in [34].  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Component** & **Value** \\ \hline Number of trees \(m\) & 70 \\ Maximum depth of the tree & 5 \\ Number of batches per layer & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter setup for training the matching classifier.  
Figure 18: Gradient boosted tree construction.  
The figure reveals that NCC and HPR are the two main factors in constructing matchingClassifier. Specifically, NCC approximately contributes 49.2%, whereas HPR contributes 26.44% of the overall feature contributions. Besides, the relative feature importances of VPR, WR, and HR were respectively determined as 9.91%, 9.56%, and 4.89%.
# Superimposed Training Combined Approach for a Reduced Phase of Spectrum Sensing in Cognitive Radio
## 3 SCAR-Phase of Spectrum Sensing
### Second Sample Processing Period: Enhanced ST-Based Detection
#### 3.2.2 ST-Det Probability of Detection  
The probability of detection of the ST-Det, denoted by \(P_{\rm d}^{st}\) is defined as the probability of deciding \(\mathcal{H}_{1}\) when \(\mathcal{H}_{1}\) is true, i.e.,  
\[P_{\rm d}^{st}\triangleq\Pr\{\Phi_{\rm ST}(\mathbf{x})>\lambda_{st}^{\prime}| \mathcal{H}_{1}\}=\int_{\lambda_{st}^{\prime}}^{\infty}p_{\Phi_{\rm ST}}(x| \mathcal{H}_{1})dx, \tag{33}\]  
where \(p_{\Phi_{\rm ST}}(x|\mathcal{H}_{1})\) is the PDF of the test statistic \(\Phi_{\rm ST}(\mathbf{x})\) when \(\mathcal{H}_{1}\) is true. Next, \(P_{\rm d}^{st}\) is obtained by analyzing the statistical properties of \(\Phi_{\rm ST}(\mathbf{x})\) under \(\mathcal{H}_{1}\). Using the CLT, it can be shown that  
\[\Phi_{\rm ed}\sim\mathcal{N}\left(N\theta^{2}(\sigma_{d}^{2}+\sigma_{t}^{2})+ N\sigma^{2},\ 2N\theta^{2}\sigma_{t}^{2}(\theta^{2}\sigma_{d}^{2}+\sigma^{2})+N(\theta^{2} \sigma_{d}^{2}+\sigma^{2})^{2}\right) \tag{34}\]  
and  
\[\[PERSON]_{\rm t}\sim\mathcal{CN}\left(N\theta e^{-j\phi}\sigma_{t}^{2},\ N\sigma_ {t}^{2}(\theta^{2}\sigma_{d}^{2}+\sigma^{2})\right), \tag{35}\]  
when \(\hat{\tau}=\tau\) modulo-\(P\). Hence, \(|\Phi_{t}|^{2}\) follows a non central chi squared distribution with two degrees of freedom and a non centrality parameter given by \(\rho=2N\theta^{2}\sigma_{t}^{2}/(\theta^{2}\sigma_{d}^{2}+\sigma^{2})\). Therefore, under hypothesis \(\mathcal{H}_{1}\), the ST-Det test statistic can be written as:  
\[\Phi_{\rm ST}(\mathbf{x})=\psi_{1}\mathcal{G}+N\theta^{2}(\sigma_{d}^{2}+ \sigma_{t}^{2})+N\sigma^{2}+v_{1}\mathcal{X}, \tag{36}\]  
where \(\psi_{1}=\sqrt{2N\theta^{2}\sigma_{t}^{2}(\theta^{2}\sigma_{d}^{2}+\sigma^{2 })+N(\theta^{2}\sigma_{d}^{2}+\sigma^{2})^{2}}\), \(v_{1}=N\alpha(\theta^{2}\sigma_{d}^{2}+\sigma^{2})\), \(\mathcal{G}\sim\mathcal{N}(0,1)\) and \(\mathcal{X}\) is a non central chi squared r.v with two degrees of freedom and non centrality parameter \(\rho\). By assuming independence amongst these random variables the \(P_{\rm d}^{st}\) is found as:  
\[P_{\rm d}^{st}=\int_{0}^{\infty}\int_{\lambda_{st}^{\prime}}^{\infty}\frac{1} {\psi_{1}\sqrt{2\pi}}e^{-\frac{1}{2}\left(\frac{\bar{\sigma}-\varsigma}{\bar{ \psi}_{1}}\right)^{2}}\frac{1}{2}e^{-\frac{1}{2}(\mathcal{X}+\rho)}I_{0}(\sqrt {\rho\mathcal{X}})d\mathcal{G}d\mathcal{X}, \tag{37}\]  
where \(\varsigma=N\theta^{2}(\sigma_{d}^{2}+\sigma_{t}^{2})+N\sigma^{2}+v_{1} \mathcal{X}\) and \(I_{0}(\cdot)\) is the modified Bessel function of the first kind and order 0. Since finding a closed-form solution of (37) makes the analysis more involved, the \(P_{\rm d}^{st}\) is found by noting that the inner integral is the Q-function and then evaluating the resulting outer integral by relying on numerical methods, e.g., the trapezoidal method over a large number of evenly spaced points.
## 3 SIF-Controlled In-Situ Fatigue Experiment
### Comparison to FEM Simulation  
As observed in the FEM simulations presented in Section 2, a relationship between the length of a fatigue crack which releases an AE signal and the frequency domain peaks of that signal is predicted. As a primary example of the comparison of the experimental signals to the FEM simulated signals, AE hit #455 from fatigue cycle range 44-48 kcycles, as presented in Figure 12, along with its corresponding CWT intensity plot. This signal is representative of all of the signals in the data set, which show similar characteristics. The CWT is a method of summarizing the time and frequency domain information into an intensity plot, which becomes convenient when building artificial intelligence capabilities for signal analysis. In the CWT of signal #455, we see the frequency peaks at 409.1 kHz and 127.8 kHz manifest in colormap intensity peaks at those frequency values. These frequency peaks match well with the FEM simulations of an 8-mm crack AE signal, which predicts frequency peaks at 127.5 kHz and 410 kHz, as seen in Figure 12.  
These results are very encouraging for both the experimental work done and the parameters involved in creating an accurate finite element model. Ultimately, the good match  
Figure 11: Experimental acoustic emission signals captured at a crack length of 8 mm: (**a**) #532, 44-48 kcycles, (**b**) #652, 44-48 kcycles, (**c**) #494, 44-48 kcycles, and (**d**) #446, 44-48 kcycles.  
Figure 12: Time domain, frequency domain, and Choi-William transform of experimental signal #455, 44-48 kcycles (approx. 8 mm) compared to the FEM simulation of an 8-mm crack length AE signal.  
observed between the FEM simulation and experimental signals mean the FEM results may be utilized to further crack the length prediction efforts of artificial intelligence techniques.