esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Perturbative Estimates of the \(B\) Operator  
Our perturbative analysis of the \(B\) operator will utilize the following partitioning of the Hamiltonian (2) into 0-th order part \(H_{0}\) (assumed to be diagonal) and perturbation \(W\)  
\[H(\lambda)=H_{0}+\lambda W \tag{1}\]  
where \(H_{0}\) and \(W\) are generally defined as  
\[H_{0}=\sum_{p,q}(h_{q}^{p}-v_{q}^{p})a_{p}^{\dagger}a_{q}=\sum_{p}\epsilon_{p} a_{p}^{\dagger}a_{p} \tag{2}\]  
and  
\[W = V_{1}+V_{2}\, \tag{3}\] \[V_{1} = \sum_{p,q}v_{q}^{p}a_{p}^{\dagger}a_{q}\,\] (4) \[V_{2} = \frac{1}{4}\sum_{p,q,r,s}v_{rs}^{pq}a_{p}^{\dagger}a_{q}^{\dagger }a_{s}a_{r}. \tag{5}\]  
At this point, we assume the diagonal form of the \(H_{0}\) operator without specifying the form of the \(V_{1}\) operator, which can generally be a spatial/spin symmetry-breaking operator. In particular, we are not assuming that \(e_{p}\)'s are Hartree-Fock spin-orbital energies, which would be challenging in situations when Hartree-Fock external orbitals are degenerate. These problems can be addressed by the proper definition of \(H_{0}\) (or by the properly designed gauge transformation mentioned earlier).  
For simplicity, we assume that the perturbative expansion for the \(B\) operator is convergent and takes the form  
\[B=\sum_{i=0}^{\infty}\lambda^{i}B^{(i)}\, \tag{100}\]  
where \(i\) refers to the order of perturbative expansion. Introducing (100) into sufficiency conditions (24) (similar analysis is valid for the variant described in Corollary 1 or for the Hamiltonian \(\widehat{H}_{\text{C}}\) (40))  
\[\widehat{\mathcal{P}}_{\text{e}ad}(e^{\sum_{i=0}\lambda^{i}B^{(i)}}(H_{0}+ \lambda W)e^{-\sum_{i=0}\lambda^{i}B^{(i)}})=0\, \tag{101}\]  
We get, using BCH expansion, equations for perturbative components of the \(B\) operator. For example,  
* **0-th order:** \[\widehat{\mathcal{P}}_{\text{e}od}([H_{0},B^{(0)}])=0\,\] (102) where we utilized the fact that \(H_{0}\) is a diagonal operator (\(\widehat{\mathcal{P}}_{\text{e}od}(H_{0})=0\)), which leads to \(B^{(0)}=0\).
* **1-st order:** \[\widehat{\mathcal{P}}_{\text{e}ad}(W-[H_{0},B^{(1)}])=0\,\] (103) which yields one- and two-body components only.
* **2-nd order:** \[\widehat{\mathcal{P}}_{\text{e}ad}(-[H_{0},B^{(2)}]+\frac{1}{2}[[H_{0},B^{(1) }],B^{(1)}]-[W,B^{(1)}])=0\,\] (104) which generates lowest-order three-body interactions (stemming from the \([W,B^{(1)}]\) term).  
As a consequence of the non-linear character of the expansion (101), higher orders of perturbation theory generate higher-rank many-body components. It is interesting to notice that the rank of excitation vs. its perturbation order is essentially the same as in the standard SR-CC theory.
## 3 Results  
### Projection Profile and Energy Spectrum  
Figure 3 shows the simulated photon counts reaching the detector panel along the anteroposterior (AP) direction of the body-equivalent water phantom and the adult abdomen using different bowtie filters. The photon counts of each channel were further normalized to the photon counts at the boundary of the detector plane where no beam passed through the subject. As shown in Figure 3, compared with using the SB or without any attenuation filter, the modulation of photon numbers among different channels of the detector was the best when using the FDB. This indicates that the FDB can efficiently improve the fluence modulation performance of the CBCT.  
Figure 4 shows the simulated X-ray energy spectra of the CBCT at the project center using different attenuation filters. First, we used the software of Spektr 3.0 to generate the preliminary spectra of X-ray originating from the CT tube without the bowties. Then, based on the preliminary spectra and configurations of the bowtie  
Figure 3: Normalized photon counts reaching the detector panel along the AP direction using different bowties for scanning (**a**) body-equivalent water phantom and (**b**) an adult abdomen.  
of the CBCT were further simulated by using the GGEMs, a GPU Geant4-based Monte Carlo simulation. As shown in Figure 4, SB hardly changed its original energy spectrum; however, FDB significantly changed the original distribution of the energy spectrum, and the average energy was much higher. The result implies that the radiation dose absorbed by the patients and the image quality might change when using FDB.
## 3 Simulation Analysis Using SSA for GREATEM Signal De-Noising
### Parameter Selection  
The singular spectrum analysis method has two parameters: the window length M and the effective singular value order. Whether the user can appropriately select two parameters is the key factor to determining the de-noising effect of the SSA for GREATEM data.  
Figure 1: The calculated GREATEM response of uniform half-space.
# A Review of Deep Learning Based Speech Synthesis
## 2 An Overview of Speech Synthesis
### Traditional Speech Synthesis Technology
#### 2.3.1 Concatenative Speech Synthesis  
The waveform concatenation based synthesis method directly concatenates the waveforms in the speech waveform database and outputs a continuous speech stream. Its basic principle is to select the appropriate speech unit from the pre-recorded and labeled speech corpus according to the context information analyzed from the text input, and concatenate the selected speech unit to obtain the final synthesized speech. With the guidance of the context infomation, the naturalness of the synthesized speech has been improved greatly.  
There are two different schemes for concatenative synthesis: one is based on linear prediction coefficients (LPCs) [21], the other is based on PSOLA. The first method mainly uses the LPC coding of speech to reduce the storage capacity occupied by the speech signal, and the synthesis is also a simple decoding and concatenation process. The speech synthesized by this method is very natural for a single word because the codec preserves most of the information of the speech. However, since the natural flow of words when people actually speak is not just a simple concatenation of individual isolated speech units, the overall effect will be affected by the concatenative points. To address this problem, PSOLA, which pays more attention to the control and modification of prosody, has been proposed. Different from the former method, PSOLA adjusts the prosody of the concatenation unit according to the target context, so that the final synthesized waveform not only maintains the speech quality of the original pronunciation, but also makes the prosody features of the concatenation unit conform to the target context. However, this method also has many defects: (1) as stated in Section 2.2, the quality of the synthesized speech will be affected by the pitch period or starting point; and (2) the problem of whether it can maintain a smooth transition has not been solved. These defects greatly limit its application in diversified speech synthesis [22].
# AT-Text: Assembling Text Components for Efficient Dense Scene Text Detection
## 3 The Proposed Method
### Pipeline  
The overview of our pipeline is illustrated in Figure 2b. This pipeline is mainly implemented by three parts: a segmentation model for exploiting discriminative text semantics, a text components model for detecting parts of words/text lines, and an assembly model for generating bounding boxes of text instances.  
Figure 2a displays a common workflow of some component-based methods. Based on the assumption that strokes of characters are approximate stable regions with a uniform intensity feature, MSERs detector is used to generate connected components, which are subsequently processed by a classifier (e.g., a shallow CNN classifier) to filter out non-text components. However, the remaining components are still redundant for the construction of words/text lines. As shown in [PERSON] et al. (2014), the true text lines accounting for the final text lines is only 9%. Thus, it requires complicated and costly post-processing to remove false-positive components--e.g., obtaining whole character components ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2018; [PERSON] et al., 2018). We observed that the false positive components are those containing part of the text and part of the background, which are difficult to accurately predict with a classifier.  
To deal with the trouble of removing above redundant components, AT-text carefully selects the candidate components to directly supply a parsimonious text component set for the assembly of words/text lines. The candidate components are divided into three categories: Only-Text, Text-Background, and Non-Text, as shown in Figure 2b. Only-Text means that the components are enclosed completely in the text regions. Text-Background components cover part of the text regions and part of the background. Non-Text components are enclosed completely in background. The Only-Text is treated as positive sample, while Text-Background and Non-Text are treated as negative samples to be discarded directly. To this end, we employ a segmentation model to generate text regions mask for selecting the candidate components. Different from previous works, our approach takes advantage of the deep segmentation model to filter out false positive components without multiple steps and complicated manual rules.
## 7 RNN-Based Optimization of Mirror Placement for Maximizing SNR in ADRs
### Computational Complexity of the RNN Algorithm  
For \(T\) time steps, the computational complexity of the RNN algorithm has the following features:  
* **Hidden State Calculation (\(\mathbf{h}_{t}\)):*
* Matrix operations: \(O(n_{h}\cdot n_{x}+n_{h}^{2})\).
* Evaluation of activation function: \(O(n_{h})\).
* **Output Calculation (\(\mathbf{o}_{t}\)):*
* Matrix operations: \(O(n_{o}\cdot n_{h})\).
* Evaluation of activation function: \(O(n_{o})\).
* **SNR and Loss Function Evaluation:*
* SNR computation: \(O(N_{\text{receivers}})\).
Loss calculation: \(O(1)\).
* **Weight Updates (Backpropagation):*
* Gradient computation for weights (\(W_{h}\), \(U_{h}\), \(W_{o}\)): \(O(n_{h}^{2}+n_{h}\cdot n_{x}+n_{o}\cdot n_{h})\).  
Then, we add all operations for each time step, and we can obtain the following expression:  
\[O(T(n_{h}\cdot n_{x}+n_{h}^{2}+n_{o}\cdot n_{h}+N_{\rm receivers})). \tag{49}\]  
Finally, for \(N_{\rm epochs}\) epochs, the total computational complexity is as follows:  
\[O(N_{\rm epochs}T(n_{h}\cdot n_{x}+n_{h}^{2}+n_{o}\cdot n_{h}+N_{\rm receivers} )). \tag{50}\]
## 3 Results and Discussion  
### Identification Components of Wendan Pomelo Peel  
The ion chromatograms in positive ion mode obtained by HPLC-MS/MS for the pericarp of four different origins of Wendan pomelo peel are shown in Figure 2. The chemical composition results are presented in Table 2. A total of 20 compounds (Figure 3) were detected in the samples from the four origins, with 8 compounds (chlorogenic acid (CP4), rutin (CP5), genipin-\(\beta\)-gentiobioside (CP9), narirutin (CP10), hesperidin (CP11), neohesperidin (CP12), meranzin hydrate (CP13), and isoquercitrin (CP18)) common to all four samples. A further two compounds were identified, but their names remain unknown. The major compounds were coumarins and flavonoids. The compound meranzin hydrate (CP13), which belongs to the coumarin compounds, exhibited the highest peak area in the chromatogram. This compound was first discovered in the root of Prangos ferulacea in 1972 [16] and is commonly studied in the context of Chaihu-Shugan-San [17]. It was the first time that this compound was discovered in Wendan pomelo peel. The flavonoid compounds with higher peak areas in the chromatogram were identified as rutin (CP5), narirutin (CP10), hesperidin (CP11), neohesperidin (CP12), and kaempfer (CP14). There were significant differences in peak areas among samples from different origins, with the proportions of coumarin compounds and flavonoid compounds in the Zhangzhou samples being similar, while the coumarins were much more abundant than the flavonoids in the samples from Xuzhou and Meizhou.  
Figure 2: The ion chromatogram of Wendan pomelo peel from different regions.  
Significant variations were observed in the chemical composition of samples from different origins. For instance, the sample from Zhejiang, Taizhou (W3), exhibited the highest number of detected compounds, with a total of 16 compounds. The sample from Guangdong, Meizhou (WF4), had the lowest number of detected compounds, with 11 compounds identified only. For these compounds, CP2, CP3, CP6, and CP16 were exclusively found in the sample from Taizhou, while CP14 was only detected in the sample from Zhangzhou, and CP15 was present solely in the sample from Xuzhou. Compound CP8 was not detected in the sample of Meizhou. Previous studies suggested that variations in plant chemical composition may be attributed to ecological differences [18; 19]. Differences in chemical composition were observed in samples of the same plant species collected from different regions or even different locations within the same region, as described by [PERSON] et al. [20].  
Figure 3: The structural formula of compounds.
## 4 Results and Discussions
### Validity of Non-Local Block  
To evaluate the validity of the non-local block in our model, we conduct a comparison between the non-local block and 3D convolution, which is a widely used strategy in video processing. This part is performed on an inpainting task to evaluate the global and local performance. Based on the trained encoder-decoder model, we replace the non-local block with a 3D convolution layer and train the encoder-3D-Conv-decoder model for 100 epochs. The loss of the generator during the 100 epochs is shown in Figure 8.  
The non-local block always gains a lower loss than the 3D convolution. The non-local block performs better as long as the non-local block has a larger respective field. Although the 3D convolution can combine the information from different frames, it is only able to combine the local features, which are limited by the size of the convolution kernel. However, the non-local block explores the relationship between different frames and positions at the pixel level by combining non-local spatio-temporal information. As a result, the non-local block has a stronger ability to find the useful information from distant frames and positions.  
In addition, we are also inquisitive about how many non-local blocks are sufficient for this task. We adopt one, three, and five non-local blocks, respectively, in our encoder-nonlocal-decoder model and train the model adequately. The comparison result of the test datasets is shown in Table 7. We find that the performance becomes better when more blocks are used. Nevertheless, the calculation quantity also increases dramatically; the five-block version requires about 1.2 h for each epoch during training. The three-block version gains a similar performance with only 35 min. Therefore, we take up the three-block version in our work.
# A Head Control Strategy of the Snake Robot Based on Segmented Kinematics
## 2 The Strategy of the Head Control
### Choosing the Dexterous Workspace Center  
In the following subsection, the center of the dexterous workspace for the head part is needed to estimate the end frame of the neck part. According to the structure of the snake robot in our lab, the link lengths from the first link to the sixth link for the head part are all 0.0865 m, and the length of the seventh link is 0.046 m. The workspace whose reachability index equals 100 called dexterous workspace, shown in Figure 6. We can find that it is not reasonable if we treat the centroids of the dexterous workspace as the center of the dexterous workspace. By looking at Figure 6, the dexterous workspace can be divided into three parts based on its distribution. To find a reasonable center point in the dexterous workspace, the k-means++ method is adopted [26; 27]. The k-means algorithm uses a heuristic iteration process to divide the sample set into several clusters and make sure that the error sum between every sample and centroids in the same cluster has minima. Assuming that the sample set has \(k\) clusters \(C_{1},C_{2},C_{3},\ldots,C_{k}\), then error sum \(E\) can be calculated [26; 27],  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**DOF**} & \multicolumn{5}{c}{**Reachability Index**} \\ \cline{2-6}  & **[0, 20]** & **[20, 40]** & **[40, 60]** & **[60, 80]** & **[80, 100]** \\ \hline
6 & 29.82\% & 31.28\% & 28.14\% & 10.76\% & 0\% \\
7 & 8.22 \% & 15.87\% & 16.96\% & 25.80 \% & 33.15\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reachability index of the head part.  
Figure 4: (**a**) the workspace of the head part of the snake robot with 6 DOFs (**b**) the sectional view of the workspace. The color bar uses 20 kinds of gradient color to indicate the reachability measure.  
Figure 5: (**a**) the workspace of the head part of the snake robot with 7 DOFs (**b**) the sectional view of the workspace. The color bar uses 20 kinds of gradient color to indicate the reachability measure.  
\[E=\sum_{i=1}^{k}\sum_{x_{i}\in C_{i}}\mathrm{d}(x_{i}-c_{i}) \tag{16}\]  
\[c_{i}=\frac{1}{\mathrm{size}(C_{i})}\sum_{x_{i}\in C_{i}}x_{i} \tag{17}\]  
where \(c_{i}\) is the centroids of the cluster \(C_{i}\) and function \(\mathrm{d}\) denotes the distance between two sample based on squared Euclidean distance. The size function returns the number of the samples in the cluster. k-means++ algorithm optimizes the selection of the original centroids of the clusters based on the k-means algorithm before the iteration. We choose the k-means++ algorithm to find the center of the dexterous workspace.  
The k-means++ method divides the workspace into 3 clusters, and every cluster has a centroid. The results are shown in Figure 7. The centroids of the cluster 3 is chosen as the center of the dexterous workspace.
# AMBIQUAL: Towards a Quality Metric for Headphone Rendered Compressed Ambisonic Spatial Audio
###### Abstract  
Spatial audio is essential for creating a sense of immersion in virtual environments. Efficient encoding methods are required to deliver spatial audio over networks without compromising Quality of Service (QoS). Streaming service providers such as YouTube typically transcode content into various bit rates and need a perceptually relevant audio quality metric to monitor users' perceived quality and spatial localization accuracy. The aim of the paper is two-fold. First, it is to investigate the effect of Opus codec compression on the quality of spatial audio as perceived by listeners using subjective listening tests. Secondly, it is to introduce AMBIQUAL, a full reference objective metric for spatial audio quality, which derives both listening quality and localization accuracy metrics directly from the B-format Ambisonic audio. We compare AMBIQUAL quality predictions with subjective quality assessments across a variety of audio samples which have been compressed using the Opus 1.2 codec at various bit rates. Listening quality and localization accuracy of first and third-order Ambisonics were evaluated. Several fixed and dynamic audio sources (single and multiple) were used to evaluate localization accuracy. Results show good correlation regarding listening quality and localization accuracy between objective quality scores using AMBIQUAL and subjective scores obtained during listening tests.  
virtual reality; spatial audio; Ambisonics; audio coding; audio compression; Opus codec; MUSIRA; audio quality; QoE +
Footnote †: journal: Medical and Electronic Engineering  
1
# MPC for Optimized Energy Exchange between Two Renewable-Energy Prosumers
## 2 Problem Formulation  
The MPC algorithm is designed to maximize the use of renewable power for the two loads, minimizing the trading that occurs with the rest of the network or other retail energy suppliers. This means that the algorithm seeks to isolate the coupled prosumers as much as possible from the rest of the energy network. The following subsections define the MPC problem, including a battery storage model, state-space formulation, objective function, a reference trajectory based on forecast information, and all system constraints.
# Shape and Weighting Optimization of a Subarray for an mm-Wave Phased Array Antenna
###### Abstract  
This paper discusses how to optimize the weighting of individual subarrays to derive the low sidelobe level (SLL) based on quadratic programming (QP) and how to derive QP parameters to ensure that the objective function is composed of the quadratic function form, with the actual number identical to the standard objective function of QP. Next, in order to analyze the SLL, a \(24\times 24\) phased array antenna was compared with 96 transmit-receive modules (TRMs) attached only to the subarray stage and a phased array antenna with 576 TRMs attached to all radiating elements without a subarray. Optimized weighting was applied to the array antennas with a subarray, and Taylor weighting was applied to the array antennas without a subarray. The number of TRMs used in the phased array antenna with the optimized weighting was reduced by 83.3% compared to the phased array antenna in which TRMs were attached to all radiating elements. The SLL and the half-power beamwidths (HPBWs) of the two antennas were practically identical in a narrow beam-scanning environment. Finally, an array pattern (AP) in which mutual coupling between the radiating elements was considered was calculated to verify the optimized weighting. Moreover, the optimized weighting was applied to CST Microwave Studio (an EM full-wave simulation) to compare the results from the AP calculation and a simulation. It was confirmed that the two results above are largely indistinguishable. The analysis found that the HPBW is \(3.6^{\circ}\times 3.6^{\circ}\) and the SLL is \(-26.18\) dB from AP calculations in the boresight direction. When each \(5^{\circ}\) beam was scanned at the azimuth and elevation, the corresponding HPBW values were \(3.7^{\circ}\times 3.7^{\circ}\) and \(3.7^{\circ}\times 3.7^{\circ}\) and the SLLs were \(-22.70\) dB and \(-24.44\) dB according to the AP calculations.  
Keywords:phased array antenna; subarray; quadratic programming; aperiodic array; weighting optimization; low sidelobe level +
Footnote †: journal: Applied Sciences