esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The Switching Strategy  
Under a certain speed level, the DPTC algorithm can reduce copper loss and achieve effective commutation. If the speed limit is exceeded, the delayed demagnetization caused by the optimization operation may increase the copper loss. However, if this speed limit is determined, the DPTC algorithm provides the optimization operation over the entire allowable range. The rate of change \(\partial\varphi/\partial\theta\) of flux linkage relative to the rotor position is an important criterion for evaluating the torque-speed performance [13]. The maximum \(\partial\varphi/\partial\theta\) is denoted as  
\[\max\frac{\partial\varphi}{\partial\theta}=\max\left\{\frac{\Delta\varphi_{in} }{\Delta\theta},-\frac{\Delta\varphi_{ou}}{\Delta\theta}\right\} \tag{29}\]  
where \(\Delta\varphi_{ou}\) is the decreasing flux linkage of the outgoing phase, and \(\Delta\varphi_{in}\) is the increasing flux linkage of the incoming phase.  
The maximum value of \(\partial\varphi/\partial\theta\) during commutation is mainly determined by the demagnetization ability of the outgoing phase. The maximum demagnetization voltage is represented by the case of \(s=-1\) in (4). Therefore, the maximum permissible speed of the algorithm can be expressed as  
\[\omega_{\max}=\frac{V_{dc}+i_{ou}R}{\max(-\frac{\Delta\varphi_{ou}}{\Delta \theta})} \tag{30}\]  
When this maximum speed is exceeded, undesired negative torque is generated and copper loss is increased. To avoid this case, it is necessary to ensure that the flux linkage of the outgoing phase at the aligned position is zero under the action of the maximum demagnetization voltage. In the three-phase \(12/8\)-pole SRM, the alignment position is \(45^{\circ}\). Therefore, according to the phase flux linkage \(\varphi_{ou}(k)\), phase current \(i_{p}(k)\) and rotor position \(\theta_{ou}(k)\), the speed limit condition is finally defined as  
\[\omega_{max}(k)=(V_{dc}+i_{ou}(k)R)\frac{45^{\circ}-\theta_{ou}(k)}{\varphi_{ ou}(k)} \tag{31}\]  
The maximum permissible speed of the proposed DPTC algorithm is provided by (31). When the rotor speed satisfies the constraint \(\omega(k)<\omega_{max}(k)\), the commutation operation is provided by the DPTC algorithm. When the constraint is not satisfied, i.e., \(\omega(k)\geq\omega_{max}(k)\), the delayed demagnetization will increase the copper loss. In this case, the commutation operation switches to the regular PWM-DITC strategy to demagnetize the outgoing phase as quickly as possible. Through this switching strategy, the generation of negative torque can be avoided and the optimization of copper loss is guaranteed. Please note that the trigger condition is based on a speed hysteresis, which avoids frequent switching of the strategies caused by speed fluctuation.
# Lip2 Speech: Lightweight Multi-Speaker Speech Reconstruction with Gabor Features
## 4 Gabor-Based Speech Reconstruction Network  
Gabor lip features offer valuable insights into lip shape and motion during speech production, suggesting the theoretical feasibility of generating speech signals from lip movements. In this paper, two novel speech reconstruction systems are introduced, based on distinct visual features. The first model, GaborCNN2 Speech, enhances the baseline LipAudSpec model. It replaces mouth image inputs with Gabor lip images to simplify the input, and reduces the number of CNN layers from 7 to 1. The CNN output is then fed into an LSTM network to capture temporal sequence information, generating an autoencoder spectrogram, which is subsequently converted into an audio signal. The second model, GaborFea2 Speech, uses Gabor features as inputs, further reducing input complexity and eliminating all CNN layers. The features are directly input into an LSTM network to produce the autoencoder spectrogram. Figure 12 shows the overall model architecture, where (i) presents the Lip2 AudSpec baseline model, which is the CNN-based model used by [PERSON] et al. [2], and is used here for comparison; (ii) displays our proposed GaborCNN2 Speech model, which is fully introduced in Section 4.1; and (iii) illustrates our proposed novel GaborFea2 Speech which uses the Gabor feature extraction presented in Section 3.3 as the input to the simplified model, which will be introduced in Section 4.2. In this work, an LSTM-based model has been chosen for several reasons: Firstly, it mirrors the approach of [PERSON] et al. [2], who use an LSTM for their baseline model, and allows for a consistent comparison. In addition, previous research by the authors has identified that LSTM-based approaches can achieve good results when using Gabor features for speech recognition [6; 11], and so this approach is used in this paper.  
Figure 11: Example of mouth ROI, showing height, width, area, and intensity.
# Common-Ground Photovoltaic Inverters for Leakage Current Mitigation: Comparative Review
## 5 Conclusions  
This paper introduces a comprehensive review of the common-ground converters reported in the literature to eliminate leakage current produced due to the stray capacitances in PV systems. The generation mechanism of leakage current in the PV system is illustrated. Accordingly, it was illustrated the effectiveness of such topologies compared to other methods to mitigate the leakage current. The common-ground topologies are classified into current-source and voltage-source topologies, which are further divided into buck, boost, and buck-boost converters. The following characteristics are determined for each converter to define its advantages and disadvantages to be used in PV systems: (1) number of components (semiconductors and passive components), (2) number of produced voltage levels, (3) the capability to offer continuous input current, (4) symmetry of the current path during positive and negative half-cycles, and (5) the reported efficiency for each converter. Accordingly, the advantages and disadvantages are identified for each converter. In view of the required features for PV systems along with the associated advantages and disadvantages of the different CG converters, six CG converters shown in Figure 3a, Figure 5a,e,i, and Figure 6d,o are selected as suitable candidates for cost-effective and efficient operation of PV systems. Finally, some research topics have been highlighted for future development.  
**Author Contributions:** Conceptualization, M.A.G.; methodology, M.A.G., M.O., M.A. and A.I.; software, M.A.G. and A.I.; validation, M.A.G., M.O., A.I. and M.A.; formal analysis, M.A.G. and A.I.; investigation, M.A.G., M.O., A.I. and M.A.; writing--original draft preparation, M.A.G., M.O. and A.I.; writing--review and editing, M.A. and R.K.; visualization, M.A.G., A.I. and M.A.; supervision, M.O. and R.K.; project administration, M.O. and R.K.; funding acquisition, M.O. and R.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was co-funded by Aswan Power Electronics Applications Research Center (APEARC)-Scientific Excellence Center in Photovoltaic Energy, Aswan University, for their immense support under STDF grant ID: 41945.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Forest Volatile Organic Compounds and Their Effects on Human Health: A State-of-the-Art Review
## 3 Results
### Limonene
#### 3.5.5 Other Pharmacological Activities  
According to a review by [PERSON] and colleagues analyzing data from experiments with animal models, limonene may be a potential metabolic agent, capable of improving hyperglycemia, pancreatic \(\beta\)-cell mass, lipid blood levels, and liver fat accumulation [24]. Limonene could also be a solvent for cholesterol, and it has been used to dissolve gallstones after direct infusion into the gallbladder [117]. Limonene may even exert protective and healing effects in experimental models of gastric ulcer and colitis, possibly by promoting mucus secretion and PGE2 production [24]. In in vitro settings with Rolf B1.T cells, limonene can decrease levels of neuropeptide Y, the most important appetite regulator [118]. It finally shows moderate antifungal and antibacterial activities, more pronounced in Gram-positive strains [116]. A summary of preclinical evidence of limonene biological activity has been reported in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Functional Response**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Molecules/Mechanisms**} & \multirow{2}{*}{**Ref.**} \\  & & **Involved in Targeted Pathways** & \\ \hline \multirow{8}{*}{**Anti-inflammatory Antioxidant**} & Murine raw 264.7 cell line & TNF-\(\alpha\), IL-1, IL-6 & [119] \\ \cline{2-4}  & Human chondrocytes & NF-kB, NO, iNOS, p38, JNK & [70] \\ \cline{2-4}  & Human lens epithelial cells & ROS, CASP, MAPK, Bcl-2/Bax & [120] \\ \cline{2-4}  & Human neuroblastoma cells & LC3, clonogenic capacity & [121] \\ \cline{2-4}  & Fruit fly & NO & [84] \\ \cline{2-4}  & BALB/c mice & NO & [122] \\ \cline{2-4}  & BALB/c mice & Catalase, peroxidase & [123] \\ \cline{2-4}  & BALB/c mice & IL-5, IL-13, MCP-1, TGF-\(\beta\) & [124] \\ \cline{2-4}  & BALB/c mice & NF-kB, p38, JNK, ERK & [125] \\ \cline{2-4}  & Wistar rats & NF-kB, COX-2, iNOS & [126] \\ \cline{2-4}  & Swiss mice & IL-1\(\beta\) & [127] \\ \cline{2-4}  & Sprague–Dawley rats & COX2, ERK, iNOS, MMP-2, MMP-9, PGE, TGF-\(\beta\) & [128] \\ \cline{2-4}  & BALB/c mice & Apoptosis-related genes & [129] \\ \cline{2-4}  & Swiss mice & Oxidative stress & [130] \\ \hline \multirow{8}{*}{**Anxiolytic Antidepressant Sedative**} & ICR mice & Elevated plus maze test & [90] \\ \cline{2-4}  & Mice & Sleeping time & [88] \\ \cline{2-4}  & Swiss mice & MBT assay, anxiolytic effect & [131] \\ \cline{2-4}  & Rats & Locomotion, dopamine level & [132] \\ \cline{2-4}  & Rats & Immobility in forced swim test & [133] \\ \cline{2-4}  & CUMS mice & Body weight, sucrose preference, mobility & [134] \\ \cline{2-4}  & _Mus musculus_ albino mice & Elevated Plus Maze (EPM) test & [135] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Preclinical evidence of limonene biological activity.
## 5 The Striations Interference Phenomenon and Its Formation Mechanism
### The Phenomenon of Striations Bending and Its Generation Mechanism  
In the simulation results for the monostatic configuration (Figure 3), the striations were straight in all frequency bands. In Section 4, the striations obtained from the monostatic configuration experiments were also straight. However, in the bistatic configuration (Figure 4), the simulation striations became bent at every frequency band. In order to explain the phenomenon, we started with the definition of the waveguide invariant. From Equation (5), we obtained the relationship between \(\omega\) and \(\tau\), that is:  
\[\omega=\frac{1}{\gamma}\frac{\Delta\omega}{\Delta\tau_{\rm b}}\tau_{\rm b}. \tag{6}\]  
The invariants \(\gamma\) and \(\Delta\omega/\Delta\tau_{\rm b}\) were constants, and the relationship between \(\omega\) and the distance r was linear, indicating that the striations shape in the range-frequency spectrogram was approximately consistent with the target motion trajectory. This was verified by the simulation for two scenarios (Figures 3 and 4). The specific factor was determined by the value of the invariant and \(\Delta\omega/\Delta\tau_{\rm b}\). The white dotted line in Figures 4a, 7b and 8b were the striations predicted by Equation (6). The prediction based on the invariant showed good agreement with the interference pattern obtained by the mathematical model of the sound field interference.  
The acoustic path in active sonar had two parts: the source to the target path and the target to the receiver path. For bistatic configuration, the source and the receiver were at two separate locations, and there was a big difference between the distance from the source to the target and the distance from the target to the receiver. The variation of the total distance was nonlinear. When the two paths were combined, the motion trajectory appeared curved. The shape of the striation was consistent with the target trajectory, so the striations appeared the bending phenomenon. However, the devices were in the same  
Figure 8: Comparison between the experiment result and the simulation result at 40\(-\)80 kHz: (**a**) simulation result; (**b**) experiment result; (**c**) the motion trajectory.  
location in the monostatic configuration, so the variations of the distance from the target to the source and from the target to the receiver were the same. The variation of the total distance was linear, so the striation patterns were straight. Figures 7 and 8 show the experimental striations had a slight bending compared with the simulation results, because during the lake test, the target sphere could not move completely along the specified straight line due to the actual conditions. The sound source, the receiver, and the target were no longer strictly distributed according to the monostatic configuration. The nonlinear variation of the distance made the striations slightly curved.
# Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
## 3 From Machine Learning to Deep Learning
### Deep Learning Frameworks  
There are many deep learning libraries and frameworks to simplify the deep learning implementation processes. Most of these libraries work with different programming languages and are built with GPU acceleration. The most commonly used deep learning frameworks are  
TensorFlow: TensorFlow, developed by Google, is the most common deep learning library [46]. It supports python, C, Java, and GO programming languages. Many high level programming interfaces, like Keras, Luminoth, and TensorLayer, are built on TensorFlow. TensorFlow enables computation graphs on CPUs, GPUs, and even on mobile devices.  
Caffe(2): Berkeley AI Research has developed a dedicated deep learning framework named Caffe2 [47]. Caffe2 has become a very flexible framework that enables users to build highly efficient models. Caffe(2) allows neural networks to be trained on multiple GPUs within distributed systems and supports deep learning implementations on mobile operating systems, such as iOS and Android.  
(Py)Torch: Originally, (Py)Torch was developed in Lua language, but later an improved Python version was released. (Py)Torch [48] is a lightweight toolbox that can run on embedded systems, such as smartphones, but lacks comprehensive documentations.  
Theano: Theano [49] is a Python library that provides both GPU and CPU modes. It allows the efficient optimization and evaluation of computations involving multi-dimensional data. It provides both GPU and CPU modes.  
MXNET: MXNET [50] is a flexible deep learning library that provides interfaces for multiple languages (e.g., C++, Python, Matlab, R, etc.). MXNET provides fast numerical computation for both single machine and distributed ecosystems. It wraps work flows that are commonly used in deep learning into high-level functions, such that standard neural networks can be easily constructed without substantial coding effort.  
Chainer: Chainer [51] is a Python-based, flexible deep learning framework. It provides dynamic computational graphs, as well as object-oriented high-level APIs to build and train neural networks. It supports CUDA and cuDNN using CuPy for high performance training and inference.  
Microsoft Cognitive Toolkit/CNTK: Microsoft Cognitive Toolkit/CNTK [52] is known for the easy and flexible training of deep learning models. CNTK is an open source library, and it is supported by different interfaces such as Python, C++, and the command line interface.  
Keras: Keras [53] is an easy-to-use, simple deep learning library for building deep learning models by stacking multiple layers. Keras is part of TensorFlow's core API group. Keras is written in Python and is capable of running on top of TensorFlow, CNTK, or Theano. It runs seamlessly on CPUs and GPUs.
## 6 Discussion  
The different indicators considered above were evaluated and compared at different positions (see Figure 1a), as shown in Table 4. In particular, \(PMV\) and associated \(PPD\) values were tabulated together with the critical values of the local discomfort indices found in the four stations. The last column of the table contains the overall environment class obtained combining the different indicator categories according to [21]. Figure 16: Winter heating condition. \(PMV\) (top subfigures) and relative \(PPD\) (bottom subfigures) distributions along the office obtained with low input air velocity (Case E) and high input air velocity (Case F). Figure 15: Summer cooling condition. \(PMV\) (top subfigures) and relative \(PPD\) (bottom subfigures) distributions in the office obtained for low input air velocity and no solar load (Case A), high input air velocity and no solar load (Case B), low input air velocity and solar load (Case C), high input air velocity and solar load (Case D). During summer, when no solar load was accounted for, both the minimal and the maximal velocities of the discharged air resulted in a slight cold sensation. The higher the input air velocity was, the higher the \(PPD\) and DR values were. Overall, all the stations were Class C for Cases A and B. On the other hand, when a solar load was applied, the minimal inlet air velocity could not deliver enough fresh air to Stations 1 and 2, leading to a slight hot sensation. In addition, high values of floor temperature were found near Stations 1 and 3, leading to \(PD_{f}\) values higher then 10% (Class C). Station 2 had the highest value of vertical air temperature difference found in this study, about 7.6 \({}^{\circ}\)C, far above the maximal allowable value of 3 \({}^{\circ}\)C (Class C). The remaining stations had a high vertical air temperature gradient as well, which brought them into Class B. By increasing the input air velocity, the situation changed, and an overall cold sensation was felt except for Station 3. This station was placed right next to the window, so that it could capture more thermal radiation. During winter, a much more uniform distribution of \(PMV\) and \(PPD\) values was found within the room. High \(DR\) values were avoided given the buoyancy effects on discharged air flows. Thus, the classification was mostly driven by the values of \(PMV\) and \(PPD\), since no local discomfort was detected in the room. By increasing the discharged air velocity, \(PMV\) values were improved, while \(PD_{v}\) values associated with vertical temperature gradients rose by about 5 \({}^{\circ}\)C in each working station.
# A High Latitude Model for the E Layer Dominated Ionosphere
## 3 Modeling Approach
### Model Coefficient Computation
#### 3.2.1 E Layer Observable Model Coefficients  
For a considered E layer observable, such as \(NmE\), \(hmE\) or \(wvE\), we computed the model coefficients based on all those profiles of the modeling dataset that correspond to an ELDI event. To improve the quality of the computed coefficients, we first reduced the number of outliers of the E layer observable. Such outliers may arise, for example, during the derivation of the observable values from the original profiles. Therefore, as part of a percentile pre-filtering, we set up a histogram of the used observable values and then excluded those profiles whose observable value fell in the lower or upper 10% of the histogram.  
To compute the internal coefficients, we considered each model term as independent from all the other terms by analogy with [PERSON] et al. [19] and Hoque and Jakowski [20; 21]. Regarding this, we set up a cumulative histogram of the used values of the corresponding model driver and divided it into 20 bins in such a way that each bin contained approximately the same number of profiles. Next, we computed the median of the driver values and the median of the observable values for every bin. Subsequently, we fit the considered model term to the resulting 20 data points by means of nonlinear least squares fitting in order to estimate all its coefficients. From this step, we kept only the internal coefficient values as fixed results.  
To determine the external coefficients of the full model equation, we directly fit the equation to the model observable-model driver combinations of the pre-filtered ELDI profiles, without performing any additional binning beforehand.
## 5 Safety and Bioavailability of PS  
Despite the medical properties of PS, information on its toxicity and bioavailability is limited. [PERSON] et al. [85] found that the LD\({}_{50}\) of the aqueous whole plant extract of PS was more than 10 g/kg through the oral route in rats [85], while that of the methanolic leaf extract of PS was more than 5 g/kg in mice [150]. The aqueous leaf extract of PS did not cause subacute toxicity in Sprague Dawley rats at 2 g/kg based on their haematological profile and liver and kidney histology [151]. Similarly, [PERSON] et al. [152] reported that the LD\({}_{50}\) of ethanolic extracts of PS fruit and leaves was more than 2 g/kg, and no adverse behavioural change, signs of toxicity, and mortality were observed [152].  
PS has been found to contain several biologically active amides, including pellitorine, surmentine, and sarmentosine. According to [PERSON] et al. [153], pellitorine was found in the intestinal wall, liver, lungs, kidney, and heart after an oral administration of the ethanolic extract of PS fruit at a dose of 500 mg/kg, whereas sarmentine was only found in the intestinal wall and heart of male Sprague Dawley rats. Sarmentosine, on the other hand, was not detected in plasma, tissues, or urine, but was found in faeces. According to their findings, pellitorine and sarmentine, two bioactive markers of PS, have good oral bioavailability and different tissue affinities, and are excreted in urine as metabolites, whereas sarmentosine has a low oral bioavailability and is not absorbed in the intestine [153]. These findings will aid in interpreting the efficacy of PS in preclinical studies and predicting human pharmacokinetics using the scaling technique. A search on [[https://clinicaltrials.gov/](https://clinicaltrials.gov/)]([https://clinicaltrials.gov/](https://clinicaltrials.gov/)) (accessed on 30 August 2021) using the term \"_Piper sarmentosum_\" yielded no results as there were no clinical trials available for PS. Hence, the effects of PS in humans cannot be validated.
# A Novel FFT_YOLOX Model for Underwater Precious Marine Product Detection
## 2 Methods  
### Overview of YOLOX  
The YOLOX model is derived from the YOLO family, which is a one-stage object detection algorithm. The YOLOX framework mainly includes YOLOX-s, YOLOX-m, YOLOX-l and YOLOX-x models. Generally, YOLOX uses the modified CSPDarknet53 as its backbone for feature extraction, PANet as its neck for feature fusion, and a novel anchor-free algorithm as its detection head. Since it is the most popular and convenient one-stage detector, we selected it as our baseline. Furthermore, we chose YOLOX-s model to detect underwater objects, due to its low computation cost but competitive precision. In order to pursue better performance of precious marine product detection, we modified the backbone of the original YOLOX-s to have fewer model parameters but higher accuracy.
## 3 Methodology
### Validation and Comparison of the Results Obtained by the Models  
The validation of the success and predictive performance of the three models was performed based on the receiver operating characteristic (ROC) curves [61; 62; 63; 64; 65]. The estimated AUC values range between 0.50 and 1.00 and can be classified based on a quantitative-qualitative classification scheme as follows: 0.5-0.6 (poor), 0.6-0.7 (average), 0.7-0.8 (good), 0.8-0.9 (very good), and 0.9-1 (excellent) [66]. In addition to the AUC values, two evaluation statistics, namely standard error (SE) and confidence interval (CI) at 95%, were also estimated. The best model has the smallest standard error, and the narrowest CI [67; 68].