esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Consumption Analysis of LPWAN Technologies and Lifetime Estimation for IoT Application
## 6 Battery Life Estimation  
Having prior knowledge of device lifetime when operated from a battery can be beneficial during the design phases of the network. Battery selection can then be made to meet the device lifetime requirement. Based on the data produced in the energy consumption analysis, we estimated a theoretical lifetime for all the four LPWAN modules for different battery capacity. A plot of the estimated life and battery capacity is shown in Figure 21a. The graph is intended to assist a developer to decide the battery capacity for a particular communication protocol and device lifetime. However, it must be noted that the data presented are assuming the ideal operating conditions. Factors like temperature and humidity can have an impact on battery capacity and the leakage current of the electronics used as discussed in Section 5. Moreover,we have considered only the radio transmission and reception processes, and no other activities are considered for the lifetime estimation. Nevertheless, one should be able to estimate the approximate actual life time by adding the energy consumption of the other required states to the base model. An example calculation for LoRa is listed in Table 7. We notice that LoRaWAN and DASH7 have got better energy consumption performance with extended lifetime compared to NB-IoT and Sigfox. We also analyzed the trade-off in lifetime calculation with variation of message frequency while keeping the battery capacity constant (2500 mAh) as shown in Figure 21b. This can be useful to look over the different lifetime of the end-device while sending message every 1,2,3,4 and 5 min respectively.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Process** & **Value** \\ \hline Transmission interval & 300 s \\ Number of packets per day & 288 \\ Battery Capacity & 133,200 J (10,000 mAh) \\ Energy spent in one transmission & 39.73 mJ \\ Total energy spent in 288 transmission per day & 11.44 J \\ Current consumption in sleep mode & 0.08 mA \\ Energy spent in sleep in a day & 25.37 J \\ Total Energy spent in a day & 36.82 J \\ Expected device life time & 9.9 Years \\ \hline \hline \end{tabular}
\end{table}
Table 7: Battery life estimation for LoRaWAN end-device.  
Figure 21: Lifetime estimation for different LPWAN technologies: (**a**) Different battery capacities; (**b**) Different frequency of message transmission (every 1,2,3,4,5 min with constant 2500 mAh battery).
###### Abstract  
Alginate is one of the most utilized biopolymers for the encapsulation of polyphenols throughout ionic gelation. For improvement in the encapsulation of polyphenols, other biopolymers and/or fillers can be employed. The purpose of this study was to include pullulan and/or disaccharides in an alginate encapsulation mixture to monitor whether we would achieve higher encapsulation of chokeberry juice polyphenols. Alginate hydrogel beads were used as controls, and through the results for total polyphenol and proanthocyanidin contents, concentrations of individual polyphenols, and antioxidant activities, it can be observed that pullulan and/or disaccharides had an impact on the encapsulation of these bioactives. Alginate/pullulan hydrogel beads had the highest contents of total polyphenols and proanthocyanidin's (8.60 g/kg and 2.37 g/kg, respectively), whereas alginate/trehalose hydrogel beads had the lowest (5.50 g/kg and 1.16 g/kg, respectively). All hydrogel beads, except alginate/pullulan/sucrose, had higher anthocyanin (cyanidin-3-galactosidase and cyanidin-3-arabinoside) contents than alginate beads (404.37 mg/kg and 89.97 mg/kg, respectively), but the most efficient combination for encapsulation of chokeberry anthocyanins was alginate/pullulan (477.32 mg/kg and 109.60 mg/kg, respectively). The highest concentration of neochlorogenic acid was determined in controls (260.14 mg/kg), while the highest concentration of chlorogenic acid in alginate/pullulan/sucrose beads (229.51 mg/kg). Quercetin-3-glucoside was evaluated as having the highest concentration in alginate/pullulan hydrogel beads (35.45 mg/kg). The data obtained through this study highlight the importance of the composition of an encapsulation mixture in order to achieve high encapsulation of chokeberry juice polyphenols. High encapsulation efficiency was obtained for anthocyanins, especially when pullulan was used in combination with alginate.  
alginate; pullulan; sucrose; trehalose; encapsulation; chokeberry juice polyphenols; antioxidant activity Article
## 3 Results and Discussion  
### 3.1 4-n-NP Degradation in Solution by Various Bacterial Strains  
4-n-Nonylphenol (10 mg L\({}^{-1}\)) biodegradation curves were obtained over 28 days using bacterial strains previously isolated from sewage sludge through 4-n-NP enrichment cultures. The bacterial strains selected were _Aeronous salmonicida_ CN1, _Acaligenes faecalis_ CN8, _Enter_obacter bugandensis_ CN13, _Pseudomonas putida_ CN14, _Pseudochromatum saccharolyticum_ CN15, Ochrobactrum anthropi _CN16, and_ Shewanella putrefaciens _CN17._ Bacillus safensis CN12 was included as a comparison. Except for _O. anthropi_ and _P. putida_, which had been described as NP-degrading bacteria [46; 47], the rest of the bacterial strains were observed as possible NP degraders for the first time in our laboratories. The biodegradation curves and kinetic parameters for all these bacterial strains are shown in Figure 1 and Table 1, respectively. Except for the control and _B. safensis_ CN12 that fitted to an SFO model, the HS or FOMC model accurately fitted the experimental data of 4-n-NP biodegradation curves by isolated strains, showing a good fit regarding the tabulated chi-square (\(\chi^{2}\)) values. The R\({}^{2}\) values were over 0.93 except for _P. saccharolyticum_ CN15 (Table 1). Up to 74-75% of the 4-n-NP was degraded by _S. putrefaciens_ CN17 and _A. faecalis_ CN8, with a DT\({}_{50}\) of about 9-10 days, in comparison to the control, which showed a DT\({}_{50}\) of 129 days and only degraded 14.7% of the 4-n-NP. Also, _P. putida_ CN14 and _E. bugandensis_ CN13 showed a good extent of degradation (66.5 and 63.0%, respectively) and a DT\({}_{50}\) of only 10.5 and 11.7 days. The other three bacterial strains studied achieved an extent of biodegradation of <51% after 28 days. Special mention should be made to _A. salmonicia_, which showed the highest initial degradation rate after 2 days, but it subsequently reached a plateau at around only 40% degradation. All the bacterial strains tested showed lower degradation capacity than the previously studied _B. safensis_ CN12 strain [36], since it was able to remove 100% of the 4-n-NP (10 mg L\({}^{-1}\)) from the solution in about 7 days, with a DT\({}_{50}\) value of only 0.9 days (Table 1).  
Figure 1: 4-n-NP biodegradation in solution by various bacterial strains isolated from sewage sludge. * _B. safensis_ CN12 reported by [PERSON] et al. [36].
# Collision Avoidance Using Finite Control Set Model Predictive Control for Unmanned Surface Vehicle
## 3 Collision Avoidance Based on the Finite Control Set Model Predictive Control Method
### Finite Control Set Model Predictive Control of CAS  
The FCS-MPC is an optimal control algorithm [19]. Based on the USV input function combination (control set) being limited, through the prediction model, the output of the CAS is calculated by using the traversal method in a control cycle, and the combination of the input function that makes the designed evaluation function optimal is applied to the actuator of USV.  
Figure 3 shows the principle of the CAS based on FCS-MPC. The USV prediction model \(f_{p}\{X,S\}\) is constructed according to the relationship between the state quantity \(X\) and input the function combination \(S\) of USV. At time \(t_{k}\), use the state quantity \(X(t_{k})\) and the \(i\)-th input function combination \(S_{i}\) to calculate the predicted value of the state quantity \(X_{pi}(t_{k+j})\) at the next prediction horizon under the action of \(S_{i}\), that is \(X_{pi}(t_{k+j})=f_{p}\{X(t_{k}),S_{i}\}\), \(i=1,2,\cdots,c\), where \(c\) is the number of all valid input function combinations for the USV, \(j=1,2,\cdots,t_{p}\), and \(t_{p}\) is the prediction horizon. The subscript \(pi\) of the variable symbol refers to the predicted value under the action of the \(S_{i}\). According to the requirements of CAS, i.e., attainability, safety, stability and rapidity, the evaluation function \(f_{e}\{X_{pi},S_{i}\}\) of the system is constructed. Finally, the control behavior combination \(S_{o}(o\in\{1,\cdots,c\})\) that makes the function \(f_{e}\) optimal is applied to the vessel.
# XTTS-Based Data Augmentation for Profanity Keyword Recognition in Low-Resource Speech Scenarios
## 2 Proposed Methods
### Proposed Classification Models  
#### 2.3.1 CNN-Based Algorithm (Proposed-I)  
The Proposed-I model is a CNN-based classification model using preprocessed speech features with an input size of 80 \(\times\) 39, comprising six convolutional layers and  
Figure 4: MFCC architecture diagram. The distinct colors represent different Mel filter banks utilized in the feature extraction process.  
Figure 5: Padding and segmentation process diagram. (Yellow represents features post-MFCC, blue indicates regions with particularly strong energy).  
fully connected layers to 16 output classes, as shown in Figure 6. The model applies a convolution operation, defined in (2), to transform input feature map \(X\) into output feature map \(Y\) using weights \(W_{m,n,k}\) for the \(k\)-th channel and bias \(b_{k}\). ReLU activation, defined in (3), is applied after each convolution to enhance nonlinearity and mitigate vanishing gradients. The architecture includes six convolutional layers with 32, 32, 64, 32, 16, and 3 filters, respectively, using 3 \(\times\) 3 kernels, stride 1, and padding 1 to preserve feature map dimensions, followed by fully connected layers for classification, as detailed in Table 1. This design effectively captures local speech patterns, improving keyword recognition in small-sample, multi-class scenarios.  
\[Y_{i,j,k}=\sum_{m=0}^{M-1}\sum_{n=0}^{N-1}X_{i+m,j+n}\cdot W_{m,n,k}+b_{k} \tag{2}\]  
\[f(x)=max(0,x) \tag{3}\]
## 3 Overall Design of the Communication Channel Reconstruction
### Protection Principle  
The differential protection is commonly based on phasor calculation and makes a tripping decision by comparing the differential current and restraint current in phasor form. There is also a sampled-value-based differential protection adopting instantaneous values. During the steady states, the root-mean-square values of differential and restraint currents does not vary with the time window'sshifting. However, the requirements of the data packet and capacity are different for these two kinds of differential protection, which will be discussed in the following section.
## 3 Validation of Proposed CEDM
### Proposed CEDM for Estimating Improvement Range  
Based on the testing and simulated results, the soil properties representing different dense degrees were obtained, as shown in Table 2. Because the radii of tamper and crater depths during DC process were known, the ellipsoid parameters of the proposed CEDM for different compacted zones could be solved using the solution procedure in Figure 2.  
Figure 7 shows the simulated dry density and the predicted improvement range of the soil foundation after the first, third, fifth, and seventh tampings. It can be seen that the HCZ and WCZ improvement zones of the soil foundation under DC can both be well fitted by an ellipse. The fitting degrees of HCZ and WCZ under different tamping times are all above 90%. For HCZ, the ellipsoidal parameters after the first tamping are as follows: a center point location of \(-8\) cm; a major axis of \(8\) cm; and a minor axis of \(11\) cm. With increases in tamping times, the center point location of the HCZ ellipse moves down from \(-8\) cm to \(-15\) cm, while its major and minor axes remain unchanged. This indicates that the improvement effect of HCZ under DC mainly develops in the vertical direction without volumetric expansion. For WCZ, after the first, third, fifth, and seventh tampings, the center point locations of the ellipses are \(-18\) cm, \(-23\) cm, \(-25\) cm and \(-28\) cm, respectively. At the same time, the minor axis of the WCZ ellipse increases from \(12\) cm to \(14.5\) cm, and its major axis increases from \(20\) cm to \(23.5\) cm. This indicates that WCZ expands in both the vertical and horizontal directions under successive tampings, with an average area expansion rate of approximately 7%. However, this trend of volumetric expansion generally slows with increases in tamping times. It should be noted that because the proposed CEDM assumes that the edge of the densification ellipsoid passes through the corner point of the tamper, an elliptical function tends to underestimate the extent of soil improvement extent in this area. Nevertheless, the depth and lateral extent of the simulated improvement can be well predicted with the HCZ and WCZ ellipses. These results verify the applicability of the proposed CEDM.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(\omega\) (\%) & \(\rho\) (\%/m\({}^{3}\)) & \(E\) (kPa) & \(c\) (kPa) & \(\varphi\) & Dense Degree \\ \hline
32.1 & 1850 & 5000 & 8 & 12.5 & Loose \\
18.4 & 2100 & 10,000 & 8 & 30 & Dense \\ \hline \end{tabular}
\end{table}
Table 2: Soil properties with different dense degrees.  
Figure 6: Comparisons of testing and simulated dynamic stress in the (**a**) vertical and (**b**) horizontal directions after the first tamping.
# Enhancing the 3 GPP V2X Architecture with Information-Centric Networking
## 3 NDN for V2X Communications  
Several ICN architectures have been proposed thus far that share the same principles at the network layer but differ in the implementation details [3]. Among them, NDN [10] has been mainly considered as the reference architecture for vehicular environments, thanks to its effective data delivery mechanism and robust security support [11]. Research works have been dealing with NDN-based V2X networks, mainly focused on name-based forwarding strategies. In the following, after summarizing the basics of the NDN architecture, we give an overview of the current state of the art about NDN for V2X networking.
# Leveraging Artificial Intelligence in Blockchain-Based E-Health for Safer Decision Making Framework
## 2 Background Study
### Technological Background
#### 2.2.1 Blockchain Technology  
The term \"Blockchain\" started to be popularly used to refer to the technology presented in [PERSON]'s paper. Although it was considered a breakthrough in technology, concepts of Blockchain, such as cryptography and hashing, were explored way before Bitcoin's publication [24].  
Blockchain has many definitions, but it can be defined as the technology that uses block-type data structure to store data, uses consensus algorithms to generate and update the distributed ledger, and uses encryption to ensure security during transmission [25]. To be put in other words, Blockchain is a Peer to Peer (P2P) network, where nodes share a distributed ledger [26].  
\begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt}} \hline
**Ref.** & **Summary** & **Limitation** \\ \hline
[14] & Combined Blockchain and ML to design a more efficient COVID-19 supply chain system. & Separate integration of ML modules. No focus on security/privacy \\
[12] & Designed DeepRing, where each NN layer is presented as a Blockchain block to protect against tampering attacks. & Not suitable for other none NN-based ML models \\
[21] & Developed dynamic malware detection system based on behavioral logs using Deep learning and Blockchain & No consideration to poisoning attacks prevention. No privacy consideration \\
[22] & Used DanKu protocol to build a malware detection system on Blockchain & Resource wastage due to using DanKu protocol. No consideration to poisoning attacks \\
[18] & Used FL with Blockchain to mitigate end-point corruption attacks. & The consensus committee is only one member who is considered non-hostile \\ \hline \end{tabular}
\end{table}
Table 1: Summary of existing solutions incorporating ML with Blockchain and their limitations.  
Blockchain protects the integrity of the ledger content through hashing; each block's hash connects with the previous block, and a small change in any block's content will not go unnoticed [11]. Consensus algorithms are rules and agreements which the P2P network uses to draw verdicts on the new block's validity to the ledger. Nodes calculate a cryptography challenge, called a nonce, to prove the block's validity. Once the nonce is validated, the new block is added. The previous operation is also called mining. Different consensus algorithms rely on various intensives to encourage mining nodes [26].
# Speech Emotion Recognition Based on Two-Stream Deep Learning Model Using Korean Audio Information
## 2 Materials and Methods
### Deep Learning Models  
#### 2.2.1 One-Dimensional Audio Signal-Based LSTM and Bi-LSTM Models  
LSTM is a type of RNN that iterates and maintains the information obtained from a previous step. It is a deep learning model explicitly designed to solve the long-term dependency problem of an RNN and can perform learning that requires a long dependency time. LSTM was first introduced by [PERSON] and [PERSON] [17] and has been widely used across various fields owing to its advancements and popularity achieved.  
All RNNs have a chain-like form that repeats neural network modules, and the repeated modules consist of one neural network layer. As shown in Figure 4, LSTM has a chain-like form; however, each repeated module has four layers exchanging information with each other in a specific manner. The core idea was designed based on a cell state. The cell state functions as a conveyor belt, and thus operates the entire chain continuously by applying a small linear interaction. It ensures that information flows without being altered. Furthermore, LSTM can add or remove certain elements to or from the cell state, which is controlled by a \"gate\" structure. The gate is an additional measure for delivering information and consists of a sigmoid layer and pointwise operation.  
Unlike the original LSTM, Bi-LSTM receives input from both directions and can utilize the information of both directions. LSTM can only perform forward learning in a sequential manner, whereas Bi-LSTM can learn forwards and backwards.  
Figure 4: LSTM architecture.  
Figure 3: Visualization of log-mel spectrograms for eight emotions.  
In Figure 5, the architecture of the Bi-LSTM indicates that the forward-direction LSTM in pink blocks is in parallel with the backward-direction LSTM in purple blocks. Bi-LSTM adds one more LSTM layer that reverses the direction of information flow. That is, the input sequence flows in the backwards direction in the additional LSTM layer. Then, the outputs of two LSTM layers are combined through various methods, including mean, sum, multiplication, and concatenation. Bi-LSTM is generally used when the task order must be designated. This type of network can be used for text classification, speech recognition, and prediction models.
# Cyclic Consistent Image Style Transformation: From Model to System
## 5 Experiment
### Time-Efficiency Comparison against SOTA Methods  
In evaluating the performance of different GAN-based models for image style transfer, inference time is a critical metric, especially for applications requiring real-time processing. Table 3 presents the inference times (in milliseconds) for various models at an input-output resolution of 512 \(\times\) 512.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Per-Pixel ACC** & **Per-Class ACC** & **Class IOU** \\ \hline CoGAN & 0.38 & 0.12 & 0.06 \\ BiGAN/ALI & 0.21 & 0.07 & 0.03 \\ SimGAN & 0.24 & 0.11 & 0.05 \\ GAN & 0.22 & 0.05 & 0.01 \\ cGAN & 0.57 & 0.22 & 0.16 \\ L1+CNN & 0.42 & 0.15 & 0.11 \\ L1+GAN & 0.58 & 0.19 & 0.14 \\ L1+cGAN & 0.60 & 0.22 & 0.16 \\ Pix2 Pix(baseline) & 0.62 & 0.21 & 0.16 \\ CycleGAN(ours) & **0.76** & **0.28** & **0.19** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of various image style transfer systems.  
Among the models compared, our proposed C3 GAN demonstrates the fastest inference time of 6240 ms, marginally outperforming other models, including cGAN and L1 + cGAN, which exhibit times of 6260 ms and 6480 ms, respectively. Traditional models like CoGAN and SimGAN show significantly longer inference times, at 8260 ms and 7900 ms, respectively, reflecting their higher computational demand.
## 2 Methods: Experiment and Numerical Simulation
### Boundary and Initial Conditions  
The Star CCM provides solutions to the unsteady RANS equations discretized through the finite volume method (FVM), meanwhile, the volume of fluid (VOF) method is applied to capture the free surface interface between air and water. All boundary settings and initial conditions are depicted in Table 4. Besides, a snapshot of the self-propulsion simulation with the rotating rudders and propeller is illustrated in Figure 5. This scenario is set up to observe the movement of rudder and propeller conveniently, and generate a simulation animation if necessary.  
Figure 5: The computing scenarios of the self-propulsion simulation with the rotating rudder. The light blue area in the upper sub-figure represents the free water surface, while the red area in the right bottom sub-figure represents the water with the volume fraction of 1 in the volume of fluid (VOF) method.  
Based on the research ideas of verification and validation, the accuracy and feasibility of numerical simulation under the equivalent working conditions of model test are verified firstly. Then, using the same numerical simulation method and grids, another two simulations with rudder steering or propeller rotating is further carried out respectively, to verify the viewpoint of this paper. In summary, the numerical simulation conditions involved in this piece of work are listed in Table 5, with being divided into three categories, i.e., A, B, C. They are all simulated at the longitudinal blown ballast position of 362 mm where an excessive roll is prone to occur.  
Taking self-propulsion simulation as an example, at the beginning of the simulation, the submarine is set to straight motion with a fixed depth of 8 m (the height of keel to the free surface), while the propeller revolutions per minute (rpm) is manually adjusted to obtain overall axial force equilibrium. The other 5 DOF motions are released when the initial velocity maintains around the target speed of 1.15 m/s at an appropriate propeller spin rates, while propeller speed remains constant in the remaining phase. After released, the submarine will gradually rise to the surface due to the buoyancy greater than the gravity, and continue to advance for a while on water surface under the action of propeller trust. The computation time step is 0.01 s, with 20 times iterations for each time step. Simulation results indicate average \(Y+\) values increase from 30 at the beginning and reach to its peak value 80 when the submarine is moving at maximum axial velocity of 1.5 m/s.