esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### CycleGAN-Based Voice Conversion  
One way of training voice conversion systems requires the use of parallel training data consisting of identical transcription utterances from different speakers. However, collecting such data is expensive. The CycleGAN-based voice conversion algorithm uses two pairs of generator and discriminator for training without such parallel data. One pair converts the spectral feature from a source speaker to that of a target speaker and the other pair converts vice versa. Using both generators, it is possible to convert the spectral feature from the source speaker into that of the target speaker and then to convert it back to that of the original source speaker. Two constraints are given in the training process so that the speaker identity of the spectral feature is changed to the target speaker while the linguistic content of the utterance is preserved. The first constraint is that the spectral features before and after the cyclic conversion must have the same linguistic content, which is enforced by a cycle consistency loss [24]. The second constraint is that the speaker identity of the resulting spectral features from each generator must be indistinguishable from that of the target speaker, which is enforced by an adversarial loss from the discriminators [5].  
Figure 2 illustrates the training process of the CycleGAN-based mapping model. Source speaker \(A\)'s waveform \(w^{A}\) is converted to spectral feature \(s^{A}\) using spectral analysis, and the spectral feature is used as input into the generator model:  
\[s^{A}=F\Big{(}w^{A}\Big{)}\, \tag{1}\]where \(F\) represents the spectral analysis model. The resulting spectral feature \(s^{A}\) is converted into \(s^{A\to B}\) through the generator model where the identity of the speaker is changed from speaker \(A\) to speaker \(B\):  
\[s^{A\to B}=G_{A\to B}\big{(}s^{A}\big{)}\, \tag{2}\]  
where \(G_{A\to B}\) represents the generator model that converts the speaker identity of the input spectral feature from speaker \(A\) to speaker \(B\). The spectral feature \(s^{A}\) of the source speaker \(A\) is converted to \(s^{A\to B}\); then, it is converted back to \(s^{A\to B\to A}\). The cycle consistency loss is measured between \(s^{A}\) and \(s^{A\to B\to A}\) to ensure the linguistic content preservation. Similarly, spectral feature \(s^{B}\) from the target speaker \(B\) is converted to \(s^{B\to A}\); then, it is converted back again to \(s^{B\to A\to B}\), which provides another cycle consistency loss. The discriminators \(D_{A}\) and \(D_{B}\) provide the adversarial losses of \(s^{B\to A}\) and \(s^{A\to B}\), respectively.  
The recently proposed MaskCycleGAN-VC [11], which is a variant of CycleGAN-based voice conversion, achieved a state-of-the-art performance by using masked spectral features. The generator learns to restore the masked spectral parts, which improves the robustness of the generator. However, it should be noted that MaskCycleGAN-VC is a standalone mapping method that is not jointly optimized with the analysis stage nor with the reconstruction stage.
# A Survey of Ledger Technology-Based Databases
## 4 Permissioned Blockchain Based Databases
### BigchainDB
#### 4.2.2 Transaction Processing  
BigchainDB's transaction processing is the simple order-then-commit, as can be seen in Figure 4. It is similar to order-then-execute transaction processing. Firstly, received transactions are ordered by Tendermint, then, based on the answer from Tendermint, the block is committed to the local MongoDB by each server, and each server sends a response back to their own Tendermint component.  
**Order phase:** Clients can communicate with BigchainDB via APIs. Gunicorn web server, inside the server node, accepts HTTP requests. Then, as mentioned, the Tendermint instance receives the message. Messages are then sent through the Tendermint Broadcast API. If all validation has occurred, consensus is reached by Tendermint. All transactions in the memory pool will be made part of the block and a block will be successfully created, following which all servers vote on the validity of the block.  
**Commit phase:** Consensus having been reached between all Tendermint component of servers, BigchainDB commits the block to the local MongoDB database. BigchainDB does not store transactions which failed validation and does not provide information about them in APIs. In Tendermint both failed and successful transactions are included in the current blocks.  
**Checkpoint phase:** Each server indicates to their own Tendermint component via a response message the successful commit of the block to their local MongoDB database.
## 2 Materials and Methods
### Mathematics Foundations
#### 2.1.4 Logistic Regression  
The mathematical foundations of logistic regression [21] are as follows:  
_Sigmoid function:_ The sigmoid function maps any actual number to a value between 0 and 1. This makes it particularly useful in machine learning for binary classification problems, where the output needs to be a probability of belonging to a particular class. It transforms the linear combination of input features into a probability score. The formula for the sigmoid function is:  
\[\sigma(z)=1/(1+e^{\prime}(-z))\]  
where:  
\(\sigma(z)\) is the sigmoid function output.  
\(z\) is the linear combination of features (\(z=w1\times x1+w2\times x2+\ldots+wn\times xn+b\)).  
\(e\) is Euler's number.  
_Cost function (cross-entropy loss)_: The cost function in logistic regression quantifies the difference between the predicted probabilities and the actual class labels. A common choice for this cost function is the cross-entropy loss, which penalizes the model for incorrect predictions. The formula for the cross-entropy loss (binary classification) is:  
\[J(w,b)=-[y\times log(\sigma(z))+(1-y)\times log(1-\sigma(z))]\]  
where:  
\(J(w,b)\) is the cost function.  
\(y\) is the actual label (0 or 1).  
\(\sigma(z)\) is the predicted probability.  
\(w\) are the model weights.  
\(b\) _is the bias_.  
_Gradient descent_: Gradient descent is an algorithm for minimizing the cost function by iteratively adjusting the model's parameters in the direction of the steepest descent. The update rule for gradient descent is:  
\[w\_new=w\_old-\alpha\times\
abla J(w,b)\]  
\[b\_new=b\_old-\alpha\times\
abla J(w,b)\]  
where:  
\(w\_new\) and \(b\_new\) are the updated weights and bias.  
\(w\_old\) and \(b\_old\) are the current weights and bias.  
\(\alpha\) is the learning rate.  
\(\
abla J(w,b)\) is the gradient of the cost function.
## 4 The Cooperative Optimization Algorithm
### Evaluation Component
By factorizing \(W\) along the \(r\) dimension and the \(v\) dimension on the basis of a feature set \(F=\{1,\ldots,\phi\}\), we obtain an SPR/feature matrix \(\xi=(\xi_{r,i})_{r\in R,i\in F}\) with \(\xi_{r,i}\in\mathbb{R}\) and a location/feature matrix \(v=(v_{v,i})_{v\in V,i\in F}\) with \(v_{v,i}\in\mathbb{R}\). Feature vectors \(\xi_{r}\) describe the SPR \(r\) in terms of abstract features, while feature vector \(v_{v}\) reflect the characteristics of locations \(v\). In general, it is expected that SPRs with similar needs will have similar feature vectors in \(\xi\), and locations with similar suitability characteristics will have similar feature vectors in \(v\). The number of features \(\phi\) is hereby a parameter that is chosen, e.g., in dependence of an estimation of the overall number of different service point requirements, and we assume it is considerably smaller than the overall number of SPRs, as well as the number of locations \(n\). As unknown suitability values are more likely zero than being greater than zero, we set the bias target \(\hat{w}=0\). Having obtained matrices \(\xi\) and \(v\), an unknown value of \(W\) is approximated by the dot product of the respective feature vectors rounded to the nearest of the five discrete suitability values we defined, i.e.,  
\[g_{\Theta}(r,v)=\left\lfloor 4\cdot\xi_{r}v_{v}^{\mathrm{T}}+0.5\right\rfloor/4. \tag{16}\]  
The trainable parameters of \(g_{\Theta}\) are, therefore, \(\Theta=(\xi,v)\). Our loss function for the matrix factorization is  
\[\min\ \sum_{(r,v)\in K}\Bigl{(}w_{r,v}-\xi_{r}v_{v}^{\mathrm{T}}\Bigr{)}^{2}+ \alpha\sum_{(r,v)\
otin K}(\xi_{r}v_{v}^{\mathrm{T}})^{2}+\lambda(\|\xi_{r}\|^ {2}+\|v_{v}\|^{2}), \tag{17}\]  
and randomized block coordinate descent [47] is used to minimize it.
## 3 Results and Discussion
### Phase and Microstructures  
The phase composition of starting nHAp powder and the CaSO\({}_{4}\) scaffolds with different contents of nHAp was analyzed by XRD (Figure 2). It could be found in the patterns that the scaffolds were composed of HAp and CaSO\({}_{4}\) crystalline phase by comparison with the standard XRD pattern of HAp (JCPDS 72-1243) and CaSO\({}_{4}\) (JCPDS 70-0909). There were no other phases generated during the sintering process, which was in accordance with previous results of another researcher [28]. It was demonstrated that nHAp remained stable in the matrix, which could be compatible with CaSO\({}_{4}\).  
In order to analyze the nHAp distribution in the CaSO\({}_{4}\) matrix, morphologies of the scaffolds at high magnifications were obtained by SEM (Figure 3). HAp nanoparticles were distributed as  
Figure 1: (**a**) Front view; (**b**) top view; and (**c**) enlarged view of the calcium sulfate/nano-hydroxyapatite (CaSO\({}_{4}\)/nHAp) scaffold.  
Figure 2: X-ray diffraction (XRD) patterns of (**a**) starting nHAp powder and the scaffolds with different contents of nHAp: (**b**) 0 wt %; (**c**) 5 wt %; (**d**) 10 wt %; (**e**) 15 wt %; and (**f**) 20 wt %.  
primary particles in the microstructure of scaffolds containing 5, 10, and 15 wt % nHAp (Figure 3b-d). The particle size of some randomly selected HAp nanoparticles in these scaffolds was all in conformity with the particle size of starting nHAp powder. As the nHAp further increased to 20 wt %, agglomerates of the nanoparticles were observed (Figure 3e). [PERSON] et al. also obtained similar distribution regularity of nanoparticles in the microstructure by adding nHAp into calcium hydroxide [29]. Fine ceramic particles tend to incorporate together under adhesion forces between particles, such as van der Waals forces or electrostatic forces.
# An Energy-Saving Forwarding Mechanism Based on Clustering for Opportunistic Networks
## 6 Performance Evaluation
### ANOVA--Delivery Probability of Messages--Reality Scenario  
The results for ANOVA in the Reality scenario are described in Table 10.  
There is at least one routing algorithm with relevant performance differences in message delivery probability for the variation in the battery ranges. By doing the \(F\) test, using 5% of significance, we have \(F>\)_Fcritical_, being \(F=414.05\) for the battery range and \(F=299.04\) for the routing algorithms, values respectively higher than the Fcritical of each one.  
Hence, it is reasonable to state the impact of each battery range on the delivery probability of messages. These results also support the authors' findings in [14], which evaluated the impact of low cooperation on network performance.  
Thus, such as the analysis made in the Infocom scenario, the null hypothesis H\({}_{0}\) is rejected where all averages are equal. We then applied the Tukey test and statistically calculated the variations.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Variation** & **SQ** & \(gl\) & \(MS\) & \(F\) & \(p\)**-Value** & **F-Critical** \\ \hline Battery Range & 1.077451696 & 3 & 0.359150565 & 414.0508078 & 4.656932 \(\times 10^{-59}\) & 2.688691468 \\ Routing Algorithm & 0.518781447 & 2 & 0.259390724 & 299.0415415 & 9.261242 \(\times 10^{-45}\) & 3.080386863 \\ Battery \(\times\) Routing & 0.574260028 & 6 & 0.095710005 & 110.3403658 & 9.81607 \(\times 10^{-44}\) & 2.183656883 \\ Algorithm & 0.093679955 & 108 & 0.000867407 & & & \\ Inside & 0.07988524 & 119 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Analysis of variance vs. delivery rate in Reality scenario.  
Figure 8: Network overhead vs. battery ranges in Reality scenario.
## 4 Materials and Methods
### Immune Profile  
The immune profile was evaluated using a 10-color Gallios cytometer (Beckman Coulter, Brea, CA, USA). Due to the COVID-19 pandemic, whole blood was collected from only 14 of the 31 patients. In fact, during the pandemic, all hospitals in Italy decided to stop collecting fresh whole blood samples unless absolutely necessary to avoid the possible risk of infection of healthcare workers. All samples were collected in BD Vacutainer(r) K2E (EDTA) (BD 367525) and stored at room temperature for less than 2 h to enable the counting of populations with short half-lives, such as neutrophils and eosinophils [60]. To determine absolute leukocyte counts from fresh peripheral whole blood samples, we used Trucount(tm) Absolute Counting Tubes (Becton Dickinson, Franklin Lakes, NJ, USA, 663028). In total, 100 \(\upmu\)L of freshly isolated blood was stained using the recommended volume of the following specific antibody reagents: CD15-FITC (BD-555401), CD19-PE (BioLegend-302208), HLA-DR-PE-CF594 (BD-562208), CD16-PE-Cy7 (BD-557744), CD33-APC (BD-551378), CD45-APC-AI700 (Coulter-A79390), CD14-APC-Fire750 (BioLegend-367120), CD56-BV421 (BD-562751), and CD3-BV510 (BD-563109), as well as FcR Blocking Reagent (Miltenyi) to avoid non-specific signals, and incubated at 4 \({}^{\circ}\)C in the dark for 30 min. NH\({}_{4}\)Cl (ACK) was added to eliminate red blood cells and incubated for 10 min at room temperature, being kept in darkness throughout this stage. Finally, cells were acquired via flow cytometry, in which the optical alignment and fluidics of the instrument were routinely checked using Flow-Check Fluorospheres (Beckman Coulter, A63493), while Flow-Set Fluorospheres (Beckman Coulter, A63492) were used to control light scatter and fluorescence intensity. Data were analyzed using FlowJo software (v.10.8.0, FlowJo, FranklinLakes, NJ, USA). The panel used allowed simultaneous quantification of 31 immune subsets, as previously described [61], including neutrophils (CD16+ CD15+), eosinophils (CD16\(-\) CD15+), T cells (CD3+) and their activation states (CD3+ HLA-DR+), B cells (CD19+ HLA-DR+), NK cells (CD3\(-\) CD56+) and their subsets (based on differential expression of CD16 and CD56), NKT-like cells (CD3+ CD56+), three monocyte subsets (differentiated by CD14 and CD16), and myeloid-derived suppressor cells (MDSCs) (via expression of HLA-DR-/low CD33+ and differential expression of CD14 and CD15).
## 5 Feasibility Study Through System Simulations
### Mechanisms to Optimize the Models and Minimize the Error Distance
#### 5.7.1 Mechanism 1: Selection of the NN Structure
Instead, Mechanism 1 comprises a process that requires the execution of independent simulations several times to tune the NN structures, according to the results observed in previous simulations. This process is not presented as part of the simulation process in the diagram. In contrast, Mechanisms 2 and 3 are actually programmed as part of the simulation process. Mechanisms 2 and 3 react in real time during the execution of the simulation to find the value of the weights that minimizes the selected optimization metric of the model. We carried out extensive simulations with different NN structures and sets of hyperparameters; however, we cannot guarantee that the NN structures that we found (presented in the next subsection) are the best ones that exist. The NN structures reported are the best that we were able to find. However, we explain next that the NN structure found for the pattern-matching methods 1-SingFingIn-PosOut and 2-SingFingIn-AggPosOut is suitable and enough to show that the positioning error decreases when introducing spatial side information, independently of the existence of a better-performing NN structure. To explain the reasoning, we need to anticipate from the results presented in Section 6 that the pattern-matching method 2-SingFingIn-AggPosOut performs better than the method 1-SingFingIn-PosOut. Therefore, there is a gain in terms of minimizing the error distance when using spatial side information. Pattern-matching methods 1-SingFingIn-PosOut and 2-SingFingIn-AggPosOut implement the same NN structure. The method 2-SingFingIn-AggPosOut can be interpreted as the execution of the method 1-SingFingIn-PosOut \(N_{ST}\) times for all of the sensors in the 2D sensor array (or sub-tiles in the tile), as discussed in Section 4.4.2. Then, the gain observed with the method 2-SingFingIn-AggPosOut is attributed to the aggregation of \(N_{ST}\) position estimates, and in this case, it is independent of the selected NN. That is, the gain comes from the contribution of the spatial side information. If there is an NN structure for the method 1-SingFingIn-PosOut that produces a better result than that presented here, then it is expected that in the aggregation process of the method 2-SingFingIn-AggPosOut, the result will be also better than that presented here. Thus, if there are enhancements introduced by the selected NN structure, the end result showing that there is a gain with spatial side information does not change. With the NN structure found by us, we can come to the conclusion confirming that spatial side information contributes to a gain in terms of minimizing the positioning error.
## Materials and Methods
### DCMD Experiments  
DCMD experiments were carried out under controlled conditions using a custom built lab-scale DCMD setup. The description of the set-up and its operation can be found elsewhere [13]. The feed- and permeate-side liquids were 35 g/L NaCl solution and DI water, respectively. Both were circulated at a constant flow rate of 25 L/h (corresponding to a linear flow velocity of 0.08 m/s in the DCMD chamber). During each experiment (~3 h), the feed and permeate temperatures were maintained at 70 and 25 \({}^{\circ}\)C, respectively. Permeate overflow was continuously measured by means of a microbalance and the membrane flux was calculated from this value.  
The salt rejection (SR) was calculated based on the following formula:  
\[\text{SR }(\%)=\left(1-\frac{C_{\text{permeate}}}{C_{\text{Feed}}}\right) \times 100\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Range** \\ \hline Polyvinylidene fluoride (PVDF) concentration & 8\%, 10\%, 12\% and 15\% (\(w/w\)) \\ Relative humidity & N/A *, 37\% (room relative humidity), 60\% and 80\% \\ Exposure time & 0 *, 2, 5, 10, 30, 60 and 120 min \\ Casting thickness & 250, 500, 750 and 1000 \(\upmu\)m \\ \hline \hline \end{tabular}  
* Not Applicable: For non-solvent-induced phase separation (NIPS) process.  
\end{table}
Table 1: Summary of membrane preparation parameters.  
where \(C_{\text{feed}}\) and \(C_{\text{permeate}}\) are the salt concentrations in the feed and permeate streams, respectively (g/L).
# A Framework for Cloud Based E-Government from the Perspective of Developing Countries
## 3 Cloud Based E-Government Framework
### Cloud Based G2G E-Government Framework Stages and Assimilation Process
#### 3: Define the Electronic transaction processing model  
In the absence of the payment gateway, transaction processing model needs to be identified. It is imperative for e-government systems to have electronic transaction processing mode in place in order to achieve service automation. These models are based on the available technology in the particular country context. One of the possible solutions of the financial transaction could be collaborating with the private banks or other businesses to handle the monetary transaction. Generally private banks now days offer e-banking services which can be used to deal with payment processing by embedding their services within e-government portals.
# Research on Prediction of TBM Performance of Deep-Buried Tunnel Based on Machine Learning
## 3 Prediction Model of TBM Penetration Rate Based on Machine Learning
### Prediction Model of TBM Penetration Rate Based on Support Vector Regression
#### 3.3.2 SVR Parameter Setting  
In the support vector regression (SVR) algorithm, the kernel function determines the result of sample transformation. Therefore, the choice of kernel function is one of the important factors affecting the accuracy of the SVR model. The prediction accuracy and generalization ability of the support vector regression model also depend on the selection of loss function parameter \(\varepsilon\) and penalty parameter \(C\)[24]. Therefore, it is necessary to adjust and test \(\varepsilon\), \(C\) and \(K\left(x_{\rm i},x_{\rm i}\right)\) to obtain good prediction performance and the generalization ability of the SVR model.  
The most commonly used kernel functions of the SVR are the radial basis function (RBF) and polynomial kernel function (Poly). The penalty parameter \(C\) is generally between 0.1 and 100, and the loss function parameter \(\varepsilon\) is generally between 0.01 and 0.1. To determine the best prediction model, the statistical section is divided into 170 driving units, in which 110 groups of data are used as training sets to train the SVR model, and 30 groups of data are used as test sets to measure the prediction performance of the model under different parameter settings. The SVR models with different combinations of kernel functions, penalty parameter \(C\) and loss function parameter \(\varepsilon\) are compared. The SVR models with different kernel functions, \(C\) and \(\varepsilon\) combination are numbered. A and B, respectively, mean RBF and Poly functions are selected as kernel functions. The value of \(C\) is in the range of 0.1-100 in steps of 10; the value of \(\varepsilon\) takes 0.05 as the step, takes the number between 0.01 and 0.1, and the number is 1-3. The mean square error (MSE) and mean absolute percentage error (MAPE) are used to evaluate the prediction accuracy of the model. The comparison results are shown in Figure 10. It can be seen from Figure 10 that when the kernel function is the RBF function, C is 10 and \(\varepsilon\) is 0.1, the SVR model obtains the minimum mean square error and mean absolute percentage error, and the prediction effect is the best.
## 4 Discussion
### Antimicrobial Activity  
The use of different enzymes and hydrolysis times are crucial in the production of antibacterial peptides [20]. The activity of these antimicrobial peptides allows the selective disruption of cell membranes, which leads to membrane permeabilization, depolarization, dissipation of electrochemical gradients, and eventual cell death [54]. Microbial cell walls are covered by polyanionic molecules such as lipoteichoic acids in Gram-positive bacteria, while in Gram-negative bacteria the peptidoglycan layer is thinner and is surrounded by two phospholipids membranes, with the outer membrane being topped by lipopolysaccharidesaccharides [55]. Therefore, the effectiveness of the porcine liver hydrolysates could be due to the cationic and hydrophobic properties of the peptides, which allow the reaction with the internal cytoplasmic anionic membranes of the microbes, causing their breakdown [10].  
The hydrolysates obtained in this study showed low activity. For Gram-negative bacteria this could be due to the high resistance of these strains, which is greater than the Gram-positive bacteria [56]. Bromelain was the enzyme that resulted in hydrolysates with the highest antimicrobial activity, followed by alcalase. A similar behavior was observed by [PERSON] et al. [20], who noticed higher activities of bromelin and no activity against Gram-negative bacteria.  
On the contrary, papain did not show activity against the bacterial strains evaluated. These outcomes disagree with the findings reported by [PERSON] et al. (2010), who utilized 3 enzymes to evaluate the antimicrobial capacity of protein hydrolysates obtained from the same matrix. They studied the type of enzyme and the effect of hydrolysis time on the inhibition of \(L\). _monocytogenes_, _E. coli_, and _S. aureus_. They achieved the greatest inhibition using trypsin and papain, while with alcalase the effect obtained was lower.