esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Deep Learning Based Speech Synthesis
## 4 Deep Learning Based Speech Synthesis
### Speech Synthesis Using Deep Mixture Density Networks
#### 4.3.1 Mixture Density Networks  
Mixed density networks (MDNs) [45] can not only map input features to GMM parameters (such as the mixture weights, mean and variance), but also give the joint probability density function of \(y\) given input features \(x\). The joint probability density function is expressed as follows:  
\[p(y|x,M)=\sum_{m=1}^{M}w_{m}(x)\cdot N\big{(}y;\mu_{m}(x),\sigma_{m}^{2}(x) \big{)}, \tag{1}\]  
where \(M\) is the number of mixture components, and \(w_{m}(x)\), \(\mu_{m}(x)\) and \(\sigma_{m}^{2}(x)\) are the mixture weights, mean and variance of the \(m\)-th Gaussian component of GMM, respectively. The parameters of the GMM can be calculated based on MDN with Equations (2)-(4):  
\[w_{m}(x)=\frac{exp\big{(}z_{m}^{(w)}(x,M)\big{)}}{\sum_{l=1}^{M}exp\big{(}z_{l }^{(w)}(x,M)\big{)}}, \tag{2}\]  
\[\sigma_{m}(x)=exp\big{(}z_{m}^{(\sigma)}(x,M)\big{)}, \tag{3}\]  
\[\mu_{m}(x)=z_{m}^{(\mu)}(x,M), \tag{4}\]  
where \(z_{m}^{(w)}(x,M)\), \(z_{m}^{(\mu)}(x,M)\) and \(z_{m}^{(\sigma)}(x,M)\) are the excitation of the MDN output layer corresponding to the mixture weights, mean and variance of the \(m\)-th Gaussian component, respectively. Finally, given the input/output pair of the training data in Equation (5), the model is trained by maximizing the log likelihood of \(M\), which is expressed as Equation (6):  
\[D=\Big{\{}\Big{(}x_{1}^{(1)},y_{1}^{(1)}\Big{)},...,\Big{(}x_{T(1)}^{(1)},y_{ T(1)}^{(1)}\Big{)},...,\Big{(}x_{1}^{(N)},y_{1}^{(N)}\Big{)},...,\Big{(}x_{T(N)}^{(N)},y_{ T(N)}^{(N)}\Big{)}\Big{\}}, \tag{5}\]  
\[\hat{M}=\operatorname*{arg\,max}_{M}\sum_{n=1}^{N}\sum_{t=1}^{T(n)}logp\Big{(} y_{t}^{(n)}|x_{t}^{(n)},M\Big{)}, \tag{6}\]  
where \(N\) is the number of sentences and \(T(n)\) is the number of frames in the \(n\)-th sentence.
# Current Trends in Adjuvant Therapies for Medication-Related Osteonecrosis of the Jaw
## 3 Results and Discussion  
### Teriparatide  
Teriparatide (TPTD) is a human recombinant peptide consisting of the first 34 amino acids, or the bioactive part, of the human parathyroid hormone. It stimulates osteoblastic bone formation, improving bone quality and mass [14]. Because of this property, it was approved as the first anabolic agent for treating osteoporosis in men and postmenopausal women. Intermittent administration of TPTD causes an early decrease in mRNA levels of the receptor activator of nuclear factor-kappa B ligand, and an increase in osteeoprotein mRNA, enhancing bone formation in vitro [16]. A case study showed that by promoting bone reformation and anabolic activity, the physiologic mechanism of TPTD, although unknown, is effective in treating MRONJ [17]. The effects of TPTD on MRONJ were also evaluated in a rat model. Better bone formation and density were observed in the TPTD-treated group versus the control, suggesting that TPTD is a beneficial treatment modality for MRONJ [18]. Other animal studies also support TPTD as an effective treatment for the disease [19; 20].  
Although the positive effects of TPTD in treating MRONJ are unequivocal, inadequate dosage, duration, and timing of TPTD administration can cause unpredictable side effects. For example, the continuous administration of TPTD increases the incidence of osteosarcoma in rats [21; 22]. Although concerning, it is questionable whether this result applies to humans due to interspecific and dosing (timing, duration, or level) differences between humans and rats. Temporary adverse events such as nausea, malaise, and renal dysfunction were reported in some patients who received TPTD [23], increasing attention to the proper dosage, duration, and timing of TPTD treatment. Low dosage and intermittent administration of TPTD promote the maturation of osteoblast precursors and the differentiation of lining cells into osteoblasts. Moreover, this dosage and type of administration helps increase bone turnover via stimulation of new bone generation as a part of bone metabolism [24]. Administering TPTD before or after surgery positively affects the prevention of MRONJ and recovery [25; 26].  
Therefore, appropriate low-dose TPTD before or after surgery is an effective adjuvant treatment for MRONJ. Although there is no standard protocol for TPTD administration, low-dose TPTD appears to improve prognosis as reported in the known case reports, which are summarized in Table 1.
## 3 Results
### Sorption Tests
#### 3.2.3 Uptake Kinetics
Figure 5: Uptake kinetics for Ni(II), Zn(II) and Hg(II) using G@MChs for single-component solutions— modeling with the PFORE (C\({}_{0}\): 0.3 mmol L\({}^{-1}\); pH\({}_{0}\): 5.5; pH\({}_{\text{eq}}\): 5.75 (Ni), 5.80 (Zn), and 6.01 (Hg); Sorbent dose, SD: 1 g L\({}^{-1}\); T: 21 \(\pm\)1 \({}^{\circ}\)C; time: 300 min (equilibrium reached before 120 min); agitation speed: 150 rpm). Multi-component solutions: A complementary study of the uptake kinetics was performed on multi-component solutions (with equimolar concentrations) (Figure 6, Figures S8 and S9, and Table 2). The equilibrium time is unchanged (i.e., 60 min). Apparently, the presence of competitor ions strongly affects the kinetic behavior of Ni(II) and Zn(II); the profiles for these two metal ions almost overlap (contrary to mono-component profiles, Figure 5). The sorption capacities at equilibrium are quasi halved and the apparent rate coefficients reach intermediary values (0.068-0.062 min\({}^{-1}\)) compared to mono-component kinetics. In the case of Hg(II), the impact of the other metal ions has a limited effect on sorption capacities at equilibrium (q\({}_{\rm eq}\) decreases from 0.26 to 0.21 mmol Hg g\({}^{-1}\); i.e., \(-\)18%). This is a first indication of the preference of G@MChs for Hg(II) vs. Ni(II) and Zn(II) (see below for extended study of selectivity issues). In addition, the apparent rate coefficient (i.e., k\({}_{1}\)) slightly increases compared with mono-component solutions from 0.077 to 0.92 min\({}^{-1}\). Surprisingly, the modeling of kinetic profiles with the Crank equation shows an increase in the apparent diffusion coefficient (especially for Ni(II) and Hg(II), where the D\({}_{\rm e}\) values are doubled). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Parameter** & **Ni(II)** & **Zn(II)** & **Hg(II)** \\ \hline Experimental & q\({}_{\rm eq,exp.}\) (mmol g\({}^{-1}\)) & 0.148 & 0.210 & 0.263 \\ \hline \multirow{3}{*}{PFORE} & q\({}_{\rm eq,1}\) (mmol g\({}^{-1}\)) & 0.150 & 0.210 & 0.258 \\  & k\({}_{1}\times 10^{2}\) (min\({}^{-1}\)) & 4.99 & 9.13 & 7.70 \\  & R\({}^{2}\) & 0.995 & 0.968 & 0.985 \\  & AIC & \(-\)109 & \(-\)78 & \(-\)85 \\ \hline \multirow{3}{*}{PSORE} & q\({}_{\rm eq,2}\) (mmol g\({}^{-1}\)) & 0.184 & 0.243 & 0.297 \\  & k\({}_{2}\) (L mmol\({}^{-1}\) min\({}^{-1}\)) & 0.270 & 0.422 & 0.310 \\  & R\({}^{2}\) & 0.986 & 0.955 & 0.991 \\  & AIC & \(-\)94 & \(-\)73 & \(-\)93 \\ \hline \multirow{3}{*}{RIDE} & D\({}_{\rm e}\times 10^{12}\) (m\({}^{2}\) min\({}^{-1}\)) & 3.38 & 4.80 & 1.99 \\  & R\({}^{2}\) & 0.979 & 0.946 & 0.984 \\ \cline{1-1}  & AIC & \(-\)85 & \(-\)71 & \(-\)84 \\ \cline{1-1} \cline{2-5}  & D\({}_{0}\times 10^{8}\) (m\({}^{2}\) min\({}^{-1}\)) & 3.97 & 4.22 & 5.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ni(II), Zn(II) and Hg(II) uptake kinetics using G@MChs for single-component solutions— parameters of the models.
# Nutrient Sensing and Biofilm Modulation: The Example of L-arginine in _Pseudomonas_
###### Abstract  
Bacterial biofilm represents a multicellular community embedded within an extracellular matrix attached to a surface. This lifestyle confers to bacterial cells protection against hostile environments, such as antibiotic treatment and host immune response in case of infections. The _Pseudomonas_ genus is characterised by species producing strong biofilms difficult to be eradicated and by an extraordinary metabolic versatility which may support energy and carbon/nitrogen assimilation under multiple environmental conditions. Nutrient availability can be perceived by a _Pseudomonas_ biofilm which, in turn, readapts its metabolism to finally tune its own formation and dispersion. A growing number of papers is now focusing on the mechanism of nutrient perception as a possible strategy to weaken the biofilm barrier by environmental cues. One of the most important nutrients is amino acid L-arginine, a crucial metabolite sustaining bacterial growth both as a carbon and a nitrogen source. Under low-oxygen conditions, L-arginine may also serve for ATP production, thus allowing bacteria to survive in anaerobic environments. L-arginine has been associated with biofilms, virulence, and antibiotic resistance. L-arginine is also a key precursor of regulatory molecules such as polyamines, whose involvement in biofilm homeostasis is reported. Given the biomedical and biotechnological relevance of biofilm control, the state of the art on the effects mediated by the L-arginine nutrient on biofilm modulation is presented, with a special focus on the _Pseudomonas_ biofilm. Possible biotechnological and biomedical applications are also discussed.  
arginine; biofilm; _Pseudomonas_; nutrients; c-di-GMP; ArgR; RmcA; metabolism
## 5 Hierarchical Control Development
### MPPT Controller
#### 5.1.2 DC-DC Full Bridge Converters Controllers Development  
The DC-DC full bridge converter model obtained in (2) is evaluated as a static model, i.e., with all the variables in steady state, and the obtained result is this kind of PEC is a linear system; then, a linearization procedure is not necessary. Once again, same control linear technique is used to design a feedback state controller, its structure is similar that shown in Figure 6, but, in this case, only two variables are feedbacked (\(v_{C2}\) and \(i_{L}\)). Controller structure includes a gain-integrator trajectory that leads steady state error to zero and other gain trajectories to feedback system state. Furthermore, it is necessary to calculate gain values \(K_{I}\), \(K_{1}\) and \(K_{2}\); they are defined using the Ackermann method. Gains before mentioned are part of the gain vector given as:  
\[\mathbf{K_{a}}=\begin{bmatrix}K_{1}&K_{2}&K_{I}\end{bmatrix}\]  
The main parameters to design desired polynomial are overshoot percentage \(\%OS\) and settle time \(T_{s}\). Now, one auxiliar pole in needed to balance system order and gains vector \(\mathbf{K_{a}}\). The product of desired polynomial evaluated in augmented system matrix and controllability matrix results values for gain matrix \(\mathbf{K_{a}}\). The selected values for desired polynomial for DC-DC converter 400 W at 48 V are: \(\%OS=0.1\%\) and \(T_{s}=700\)\(\upmu\)s. In order to obtain 1.2 kW DC-DC full bridge converter gain values, the selected parameters values are: \(\%OS=0.1\%\) and \(T_{s}=1.9\) ms. For bidirectional power transfer between buses, the step-down version 3.3 kW 380 V to 190 V DC-DC full bridge converter takes the following parameters for desired polynomial are: \(\%OS=0.1\%\) and \(T_{s}=1\) ms. The step-up version 3.3 kW 190 V to 380 V uses next parameters for this desired polynomial \(\%OS=1\%\) and \(T_{s}=2\) ms. In all cases, auxiliar pole is placed five times real part of the complex conjugate value.  
To obtain the gains for the full bridge DC-DC converters for the charge-discharge of the ESS in the primary bus; the first one for battery charge, 2.5 kW and 380 V to 250 V, uses the following parameters to define the desired polynomial: \(\%OS=1\%\) and \(T_{s}=1.5\) ms. On the other hand, the converter that manages the battery discharge, 2.5 V and 250 V to 380 V, uses the following parameters to define the desired polynomial: \(\%OS=1\%\) and \(T_{s}=2.3\) ms. Finally, for the charge-discharge of ESS in the secondary bus. The first converter that manages charge cycles, 1.25 kW and 190 V to 125 V, uses the following parameters to define the desired polynomial: \(\%OS=1\%\) and \(T_{s}=1.1\) ms. The converter that perform discharge events for batteries uses the following parameters for desired polynomial: \(\%OS=1\%\) and \(T_{s}=1.6\) ms. Again, the auxiliary pole is placed five times the real part of the complex conjugate of the dominant pair. Results appear in Tables 8 and 9.  
Behavior of DC-DC full bridge converter, all versions, performing in close loop and facing load changes are presented and commented in next section.
## 3 Ways to Assess Psoriasis Severity and Therapeutic Strategies for Psoriasis Treatment
### New Therapeutic Approaches in the Treatment of Psoriasis
#### 3.3.2. Biologic Drugs as a New Era of Psoriasis Treatment
The efficacy of risankizumab is often compared to ustekinumab-a human IgG monoclonal antibody that binds to the p40 subunit common to both IL-12 and IL-23 [27]. Risankizumab does not affect IL-12, which promotes the development of Th1 lymphocytes responsible for the immune response and suppression of inflammatory lesions [40]. Its selectivity for IL-23 makes it more advantageous for long-term therapy of psoriasis [43]. Studies have shown that this drug has greater efficacy compared to ustekinumab, adalimumab and secukinumab [38]. Another relatively new human monoclonal antibody showing an identical mechanism of action to risankizumab is tildrakizumab. This drug has high efficacy, a low relapse rate, and is safe for the treatment of moderate to severe psoriasis [44].
# Research on Grain Moisture Model Based on Improved SSA-SVR Algorithm
## 2 Data Sources and Processing  
### Data Sources  
The samples selected for the experiment were long-grain rice sold in the northeast region. Moisture content was measured using a homemade resistance grain moisture meter and validated by the oven-drying method at 105 \({}^{\circ}\)C (GB/T5009.3-2003 [14]) for the actual moisture content of the samples [15, 16]. At 30 \({}^{\circ}\)C, the relationship between the resistance of long-grain rice as measured by the moisture meter and corresponding voltage and moisture content was determined. Subsequently, moisture content was tested with a single-grain automatic moisture meter, model PM-2500, produced by Kett Corporation of Japan. The voltage values corresponding to these moisture readings were used as the modeling set from the fitting curve of the homemade meter. The homemade meter voltage values were used as the prediction set to train the model [17, 18].
# An Efficient Algorithm for Mapping Deep Learning Applications on the NoC Architecture
## 3 Multilevel Task Mapping for NN Applications
### Level 2 Mapping: Neurons Mapping on the Cores  
In the second step, we map the neurons to a particular processor core based on the assigned NoC region in level-1 mapping. Our goal in this step is to select a particular core to minimize the energy consumption and communication latency of that region. This process is explained in Algorithm 3. The idea of the proposed algorithm is obtained from [39], which tries to optimize the global objectives based on a game theoretic approach. This will have a positive impact as the hot-spots in the NoC are minimized, leading to a better solution, which is explained in Figure 4. In this figure, we show that the neurons of a task are assigned to a particular processing element to reduce the communication energy consumption, which is a major source of energy depletion in the NoC. Furthermore, we also have to evenly distribute the neurons on all the available cores to have an even energy consumption profile. If the neurons are assigned to a single processor, then it would reduce the communication energy but it would create the energy hot-spots. On the other hand, if the neurons are evenly distributed, then it would increase the communication energy but it would minimize the hot-spots in the energy consumption. This will eventually increase the lifetime of the processor.
## 5 Experiment Analysis
### Loss Tolerance Test  
In this section, we discuss the impact on the CCES scheme when suffering from packet loss caused by DoS or a bad network environment. Netem is a network simulation function module provided by Linux 2.6 and above, used to simulate complex network environments, like low-bandwidth, transmission delay, packet loss, etc. Command #_tcqdiscaddkeeth0 rootnetemloss1%_ means randomly discard 1% of data packets. We set the packet loss rate (PLR) in the range of (0%, 40%) and the final results are shown in Figure 7a.  
There are five schemes in the test: the [PERSON] scheme in [37] (regarding the value difference among the \(SrcPorts\) as the covert channel), the scheme without end spreading (i.e., loading \(M\) directly over end information), and the proposed CCES schemes with three different values of \(e\). As shown in Figure 7, since it merely relies on the relationship among \(SrcPorts\) and lacks loss tolerance, the [PERSON] scheme has the worst robustness with the increase in PLR. Although the scheme without end spreading performs better than [PERSON]'s due to the independence among end information, the BER still rises rapidly under the higher PLR. However, the CCES schemes achieve better robustness, especially in the higher end spreading degree, which benefits from the inherent loss tolerance. Therefore, the test results are consistent with the theoretical analysis above.  
Figure 6: The end information entropy range of different \((n,e)\). (**a**) SrcPort entropy variation with different end spreading degrees. (**b**) DstIP entropy variation with different end spreading degrees. (**c**) DstPort entropy variation with different end spreading degrees.  
Anti-Interference Test  
To defend against the attack intended to obstruct the covert communication, the covert channel ought to be equipped with robust anti-interference capability. In this section, SYN-Flood, a typical kind of DoS attack, was launched by Hping3 to build a network environment glutted with irrelevant packets. The attack strength of SYN-Flood is from \(0(Mbps)\) to \(50(Mbps)\), and the final performance of the five schemes is shown in Figure 7b.  
For the [PERSON] scheme, the irrelevant packets set off a chain reaction with ease due to a lack of filtering mechanisms; thus, the BER increases dramatically. On the contrary, since the usage of framing flags and verification field, the BER of the CCES scheme is far below that of others, which indicates great anti-interference.  
Although a larger \(e\) can improve performance, it also brings a significant drawback: a substantial reduction in communication efficiency. As \(e\) increases, the amount of redundancy or spreading grows, resulting in a lower effective data transmission rate.
# Transcranial Direct Current Stimulation in the Treatment of Chronic Knee Pain: A Scoping Review
## 3 Results
### Direct Comparison: tDCS vs. Sham Control  
A pivotal aspect of understanding the efficacy of any treatment is comparing it with a control, often a sham procedure. In the context of tDCS, the majority of studies have reported favorable outcomes. [PERSON] et al., 2018 [16]: This study stands out for its meticulous observation of pain thresholds. The research authors found notable increases in heat and pressure pain thresholds, as well as conditioned pain modulation, suggesting a direct impact of tDCS on pain perception mechanisms. [PERSON] et al., 2021 [20]: Targeting elderly subjects, this study delved into the effects of tDCS on individuals with a dysfunctional descending pain inhibitory system. The results were promising in the short term, with significant pain reduction, but these effects waned during follow-up, indicating the need for sustained treatments or complementary therapies. [PERSON] et al., 2021 [30]: In a broader age range of 30 to 70 years, this study uniquely combined tDCS with acetaminophen administration. While knee pain reduction was evident post treatment and at a three-month follow-up, the lack of significant difference between active and sham groups calls for further investigation.
# Fast Fourier Asymmetric Context Aggregation Network:
## 4 Experiments
### Implementation Details  
In our experiments, training is conducted using a fixed learning rate of \(1\times 10^{-4}\), with the Adam optimizer ( \(\beta_{1}=0.5,\beta_{2}=0.999\) ) to optimize the network parameters until the model converges. We initialized the loss weights in our experiments based on the settings in Refs. [56; 61; 79] as the weights in these papers performed well on similar tasks. To further optimize, we performed a grid search within a 50% range of the initial weights to ensure the selection of the most appropriate weight combination, ultimately determining the final weights. The loss weights in the experiments are set as \(\lambda_{g}=0.1,\lambda_{d}=0.5,\lambda_{1}=0.5\), \(\lambda_{2}=50\), and \(\lambda_{3}=6\). Considering a balance between performance and computational cost, we set the number of FF-CDRB blocks in the FCAN as \(R=6\) and the number of encoding and decoding blocks as \(N=4\). While deeper network architectures and additional context aggregation operations may lead to improved performance, they would also increase computational resource consumption.  
Additionally, we follow the operations in [56; 80], where randomly sized texture patches are cropped from the clothing areas as texture patches. To ensure fairness in the experiments, the same random seed is used across all experiments to guarantee consistency in the texture content.  
FCAN was trained for 100 epochs with a batch size of 16 on the SKFashion and VITON-HD datasets, and for 100 epochs with a batch size of 8 on the Fashion-Gen dataset.  
We adopted the texture patch size specified in [56], setting 60-70 pixels as the standard texture patch size. Based on this standard, the texture patch size was reduced by approximately 20-30% and 40-50% to represent medium-sized and small-sized texture patches, respectively. Extensive experiments were conducted under varying texture patch sizes to evaluate the performance of different generation methods under diverse conditions.  
The primary software environment used in the experiments is Python 3.8 (Ubuntu 20.04) and PyTorch 2.0.0. The main hardware environment consists of a single RTX 4090 (24 GB) GPU, Xeon(R) Platinum 8352 V CPU, and 120 GB of memory.
# IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data
## 3 Methods
### Prediction Models-MLT Models and Naive Approach
#### 3.2.4 Multilayer Perceptron (MLP)  
MLP is a feedforward artificial neural network, consisting of neurons fully connected through adaptable synaptic weight [31]. In this study, the MLP model consisted of an input layer, an output layer, and two hidden layers comprising 100 nodes each. MLP training involved pairing input and target values. During forward propagation, input features traverse the network, generating predictions at the output layer. The disparity between observed and predicted values is calculated and this information is then propagated backwards [32]. The Adam optimizer was employed to adjust the weights between neurons. The iterative process was repeated for a maximum of 500 iterations until the model converged. We compared MLPs fitted with linear (in MLP1) and nonlinear (in MLP2) activation functions. The nonlinear transfer function was Rectifier Linear Unit (f(x) = max(0,x)) from Scikit Learn. The linear transfer function was Identity (f(x) = x) from Scikit Learn.