esa-sceva/satcom-chunk-collection huggingface dataset:
# Extraction of Novel Bioactive Peptides from Fish Protein Hydrolysates by Enzymatic Reactions
## 5 Characterization of Novel Bioactive Peptides
### Purification and Identification of Peptides Using One-Dimensional Separation Systems
#### 5.1.2 Gel Filtration (Size-Exclusion Chromatography)  
Gel filtration is a widely used technique that determines the size of proteins, and it is considered an effective method that allows the separation of proteins and other biological molecules with a high yield [74,107]. This separation technique is widely utilized in studying fish proteins, such as in the recent work of [PERSON] et al. [108], in which the authors purified and identified a novel ACE-I peptide from _Lepidotrigla microtera_, using gel filtration chromatography (Sephadex G-15 column) as a separation technique, and five peptide fractions with potent activities were separated. On the other hand, the authors [PERSON] et al. [109] also utilized gel filtration chromatography (Sephadex G-15 column) to purify a novel ACE-I peptide from _Enteromorpha clathrata_ or Ulva seaweed protein hydrolyses. Furthermore, the authors identified the peptide as Pro-Ala-Phe-Gly and indicated that the novel peptide was non-competitive in its inhibitory kinetic mechanism. [PERSON] et al. [110] purified five novel peptides from spotless smooth-hound (_Mustelus griesus_) muscle, using gel filtration as one of the consecutive chromatographic methods employed for the ethanol-soluble proteins with potent antioxidant activities. Besides utilizing the separation method in protein fractionation, the work of [PERSON] et al. [111] utilized gel filtration (Sephadex G-100) as a consequent method for the purification of digestive enzyme alpha-amylase from blue crab (_Porturus segnis_) viscera hydrolyses. It is important to note that peptides and enzymes from marine and aquatic resources could be purified using separation techniques such as gel filtration, and this is a necessary additional method prior to peptide characterization and identification. The aid of this method could provide purified biological peptides without compromising their functionality.
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 3 How IoMT Works
### Technologies
#### 3.1.3 Zigbee  
In order to reach devices farther away, Zigbee devices use a mesh network of intermediary devices to relay data across long distances. Zigbee technology is much faster than Bluetooth technology for data communication. Nowadays, Zigbee technology is mostly used with IoT technology to enhance the benefits of IoT. It is also used in medical services to deliver data on time and accurately. It uses 128-bit encryption for the security of transmission.
###### Abstract  
Background: Endovascular aneurysm repair (EVAR) has become the primary choice of treatment for abdominal aortic aneurysm (AAA) and preventive intraoperative AAA sac embolization (SE) is an effective way to reduce endoleaks after EVAR. Preoperative planning of AAA SE has not been standardized yet, and typically requires a quite long and operator-dependent procedure using standard EVAR planning software. Methods: We introduce 'EVAR-eaSE', a software tool that we have developed to help vascular surgeons to plan the optimal SE procedure during EVAR, providing semi-automatic AAA segmentation and 3D modeling starting from patient scans, and then computing the aneurysm volumes. A preliminary accuracy evaluation on radiological phantoms, and a comparison with the commercial 3 Mensio software for EVAR planning are carried out. Results: EVAR-eaSE software showed a good level of accuracy in aneurysm volume reconstruction and calculation (mean difference of 2.5% between real and segmented phantom volumes). A strong positive correlation (r = 0.940, r = 0.954, \(p<\) 0.001) was found between EVAR-eaSE computation of AAA Lumen and AAA Total volumes and the same measurements obtained with 3 Mensio, for a database of 26 AAA cases. Conclusions: EVAR-eaSE demonstrated to be an easy-to-use, objective and accurate tool to assist the vascular surgeons in preoperative planning of SE during EVAR.  
aneurysm; EVAR; embolization; 3D modeling; image segmentation; preoperative planning
## 2 Methodologies
### Mathematical Model of Real-Time Single-/Dual-Frequency PPP  
The measurements used for RT-PPP are pseudo-range (\(P\)) and carrier-phase (\(L\)), which can be expressed as [29; 30],  
\[P_{j}=\rho_{r}^{s}+c(t_{r}-t^{s})+I_{r,j}^{s}+T_{r}^{s}+c(b_{r,j}-b_{j}^{s})+ \varepsilon_{P,j} \tag{10}\]  
\[L_{j}=\rho_{r}^{s}+c(t_{r}-t^{s})-I_{r,j}^{s}+T_{r}^{s}-\lambda_{j}(N_{j}+B_{ r,j}-B_{j}^{s})+\varepsilon_{L,j} \tag{11}\]  
where \(\rho_{r}^{s}\) indicates the geometric distance from the satellite \(s\) to the receiver \(r\); \(t_{r}\) and \(t^{s}\) are the receiver clock and the satellite clock offset, respectively; \(I_{r,j}^{s}\) and \(T_{r}^{s}\) are the ionospheric delay and tropospheric delay, respectively; \(b_{r,j}\) and \(b_{j}^{s}\) are the DCB of the pseudo-range on receiver and satellite, respectively; \(\lambda_{j}\) and \(N_{j}\) are wavelength and ambiguity of carrier-phase; \(B_{r,j}\) and \(B_{j}^{s}\) denote the Un-calibrated Phase Delay (UPD) on the satellite and receiver, respectively; \(\varepsilon_{P,j}\) and \(\varepsilon_{L,j}\) are observing noise of pseudo-range and carrier-phase, respectively.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 2 UAV Tracking System Overview
### Steering System
#### The mechanical steering system  
The mechanical steering system is the most energy-consuming unit of tracking system because it needs energy to drive the motors and overcome the torque [16].The energy loss is seen in the form of heat energy which are dissipated in the metallic enclosures. There are several types of motors in use for the UAV tracking system, namely (i) stepper motors [17; 18]; (ii) DC motors [19; 20]; (iii) geared DC motors [21; 22]; (iv) brushless DC (BLDC) motors [23; 24]; and (v) brushless AC (BLAC) motors [25]. It has been reported that the steeping motor is the most effective in designing steering antenna [26]. Whereas, DC motors are characterized as having low life and low heating of the armature windings. However, they are very cheap. Furthermore, the mechanical steering operational principle is based on closed-loop and open-loop mechanisms. In a closed-loop mechanism, there is a feedback system to control the motors while open-loop depicts the absence of feedback mechanism effectively there is no control system. Consequently, the tracking unit control relies on positioning sensors, such as gyroscope and inertial measurement unit (IMU), for guidance [27]. This type of control mechanism is suitable for a land to mobile satellite services and it is not affected by shadowing and blocking signal components.
## 3 Results and Discussion
### Corrosion Resistance Performance
The value of C\({}_{\rm dl}\) correlates with the thickness of the protective layer, as expressed using the following equation:  
\[\rm C_{\rm dl}=\frac{\varepsilon\ \varepsilon_{\circ}A}{d} \tag{4}\]  
where (\(\varepsilon\)) represents the local dielectric constant of a solution, (\(\varepsilon_{\circ}\)) is the permittivity of free space, (d) is the protective layer thickness, and (A) is the electrode surface area [63]. Furthermore, the variable n, representing phase displacement, provides information regarding the surface characteristics of the coating; a lower n value implies a rougher surface, while a higher value indicates a smoother, more uniform surface [64,65]. As evidenced in Table 3, the highest n value was found at a 25% concentration for the copolymer compound, whereas the maximum n value for the magnetite compound was observed at a 20% concentration. These atypical or irregular n values could be due to variations in the coating properties at different concentrations. Changes in thickness, uniformity, and porosity can cause deviations from the expected linear trends. The higher n values imply a denser adsorption of inhibitor particles on the alloy surface, subsequently slowing down the alloy's corrosion process, and enhancing the inhibition efficiency, with an increase in the inhibitor weight percentage. It is important to note, however, that such irregularities in n and Q values emphasize the complexity of the system, and could also hint at variations in the coatings' physical properties. These unexpected trends underscore the necessity for further microlevel investigations of coatings, to better understand and optimize their performance.
## 6 Simulation Results
### Analysis of the Crack Detection Results of the OMP Algorithm
#### 6.2.3 Analysis of Results for Different Spatial Sample Sizes (I)  
Then, the performance of CS-based crack detection with different spatial sample sizes was analyzed. The range of values for the spatial sample number I is from 100 to 3000. The resulting crack position and size estimation errors relative to I are shown in Figure 9. The simulation results demonstrate that the crack detection performance improves as the number of spatial samples increases.  
The detection error for multiple cracks is greater than that for a single crack. The chosen spatial sample size I also has a substantial impact on the simulation results, as increasing the number of spatial samples can improve the accuracy of localization detection simulation to some extent.
# Communication-Efficient Vertical Federated Learning
## 4 Experimental Setup
### Datasets  
We tested our proposed method on several datasets in Table 1 from the UCI repository, as well as other publicly available sources. One of the criteria for selecting the datasets was that they be publicly available. Second, the size of the datasets was taken into account. We chose the datasets in such a way that the number of samples as well as the features ranged from small to large, so that the robustness of the proposed method could be evaluated.  
* **Adult Income Dataset:** The adult income dataset [29] includes two labels: whether or not a person earns more than $50,000 per year.  
Figure 3: Undercomplete Autoencoder.  
* **Hear Disease Dataset:** The dataset [30] contains medical information of patients across different hospitals. Predictions are made between patients with or without heart disease.
* **Wine Quality Dataset:** The dataset [31] contains chemical properties of red and white variants of the Portuguese \"Vinho Verde\" wine. Classification are made between red and white wine.
* **Rice MSC Dataset:** The dataset [32] contains morphological and shape features of five types of rice varieties. Classification of the rice variety is to be performed.  
All the datasets were pre-processed accordingly taking into account factors such as missing values, duplication, and class imbalance. Over-sampling was used to balance datasets with imbalanced classes. Moreover, One-Hot-Encoding was performed on the categorical features of the datasets (e.g., _Adult Income Dataset_). To simulate a vertically federated environment, the datasets were vertically divided into nearly equal three partitions, each representing data for 3 clients. It is assumed that one of the clients (guest party) has the labels or ground truth. We used the following datasets to perform the evaluation.
# V2X Communication over Cellular Networks: Capabilities and Challenges
## 3 3 GPP's V2X Architecture
In [24], various solutions have been proposed for providing multicast services. One of them is Multimedia Broadcast Multicast Services (MBMS), which has been used in E-UTRAN. According to the latter, multiple UEs receive the same content from a BS. However, such an approach requires multiple enhancements in the network so as to (a) enable the UEs to obtain the multicast IP, (b) enable the BS to transmit this content to multiple UEs, and (c) handle multicast session control. Note that in Figure 1 we have included the MBMS for reasons of completeness, although the exact solution will be specified in Release 17. As we will discuss later, the specific choices made for multicasting and broadcasting may have a significant impact on the communication delay. The network functions that are responsible for the control of the multicast are the Session Management Function (SMF) and the Broadcast Multicast Service Center-Control  
Figure 1: 5G-V2X network architecture described by the 3 rd Generation Partnership Project (3 GPP) [23,24]. Plane Function (BMSC-CPF). The SMF supports the MBMS bearer sessions and the IP multicast addresses allocation. BMSC-CPF connects with the V2X and provides the Temporary Mobile Group Identifier (TMGI) needed for the multicast communication. In addition, it manages the MBMS session and location-related information. For the user plane, the BMSC-User Plane Function (UPF) connects with V2X AS and transmits the MBMS-related data. In [26], 5 GAA has analyzed the aspects of predictive QoS (for 5G systems). Considering that V2X applications in the vehicle can request one or more communication links with a V2X AS, the latter can allocate various QoS levels to these links. However, due to varying network conditions, unanticipated QoS degradation may occur. In such cases, if the network can identify the QoS degradation in advance, it may enable the network or application to act proactively. Therefore, the notion of the \"In-advance QoS Notification\" (IQN) has been proposed. Finally, 5 GAA has investigated in detail the support of communicating vehicles that are being served by different operators. Therefore, it has analyzed specific solutions for direct or infrastructure-based communication among vehicles [22]. In these cases, some form of inter-operator collaboration is needed, at least for the very demanding scenarios. This may include RAN sharing, regional roaming, local breakout points, or the dynamic exchange of configuration parameters. The selection of the communication interface and the type of communication (i.e., unicast, broadcast, or multicast) have a significant effect on the experienced delay, but also on the needed network capacity, as we will explain in detail in the following sections.
## 4 Results
### Influence of the Dip Angle of Rock Layers on the Stability of an Anti-Dip Interbedded Rock Slope
#### 4.2.2 Analysis of Failure
As the dip angle of the rock strata increases, the gravitational moment changes for each rock stratum, leading to a transformation from shear-sliding failure to toppling-sliding failure for the slope.
# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 5 Simulation Results and Discussion
### Simulation Environment  
In order to evaluate the performance of our proposed system, we conducted simulations with various parameter settings. In particular, we considered a sensing period length of \(N\) samples, \(S\) SU devices and \(M\) antennas in each SU device. We assumed that the PU signal was an independent and identically distributed (i.i.d.) Gaussian random vector with zero mean and signal variance \(\sigma_{x}^{2}\).  
For the PU-SU channel models, we assumed  
* noise signals \(w_{s}^{m}\) as i.i.d. Gaussian random vectors with zero mean and variance \(\sigma_{w_{s}^{m}}^{2}\) that add up the PU signal at the SU receivers;
* gains \(h_{s}^{m}\) following a Rayleigh distribution with parameter \(\sigma_{h}\).  
Here, \(s\in[1,\dots,S]\) identifies each specific SU and \(m\in[1,\dots,M]\) identifies a specific antenna in the SU.  
The signal generation process begins by converting the desired SNR from decibels to a linear scale and by generating, if it is in the active state, the transmitted signal of the PU for a specific sensing period. For each antenna of each SU, the transmitted PU signal will be scaled using the specific gain of the related channel \(h_{s}^{m}\). The resulting power of the received signal, \(RP_{s}^{m}\), is then computed using the squared magnitude of each scaled signal sample. The power of the noise is determined based on the desired SNR, and Gaussian noise with the related variance \(\sigma_{w_{s}^{m}}^{2}\) is generated. The scaled noise is finally added to the scaled signal, resulting in a new signal that reflects fading and the specified SNR. Moreover, we assumed the channels to be time-invariant during each sensing period.  
To model the bidirectional transmissions between the SUs and the fusion center, we assumed channels that exhibit a fixed BER value with no automatic repeat request (ARQ) or forward error correction (FEC) mechanisms at the data link layer.  
The environment parameters used in the simulations are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Description** & **Value** \\ \hline N & Number of samples in a sensing period & 64 \\ S & Number of cooperating SUs & 4 \\ M & Number of antennas on each SU & 4 \\ \(x[n]\) & PU samples & Gaussian random variables \\ \(\mu_{x}\) & PU sample mean & 0 \\ \(\sigma_{x}^{2}\) & PU sample variance & 1 \\ \(h_{s}^{m}\) & Path gain at antenna \(m\) of SU \(s\) & Rayleigh random variable \\ \(\sigma_{h}\) & Scale parameter of the Rayleigh distribution & \(\frac{\sqrt{2}}{2}\) \\ \(w_{s}^{m}[n]\) & Noise samples at antenna \(m\) of SU \(s\) & Gaussian random variables \\ \(\mu_{w}\) & Noise sample mean & 0 \\ \(\sigma_{w_{s}^{2}}^{2}\) & Noise sample variance & Evaluated according to desired \\  & & SNR and current \(RP_{s}^{m}\) \\ SNR & default SNR at SU & \(-14\) dB \\ BER & Bit error rate for service messages & \(10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Environment parameters.
## 2 Background and Related Literature
### CNNs for Semantic Segmentation of Remote Sensing Images
Within the decoders, a feature-merge module focuses on the positions of prominent objects. As a result, the final output includes the identified aircraft. The authors of [39] developed a multi-scale residual U-Net with an attention (MSRAU-Net) scheme for multi-scale aircraft segmentation in remote sensing photos to solve this issue for U-Net if used for multi-scale segmentation. Two types of attention components, two adjusted Respaths, and a multi-scale convolution component were created and added to MSRAU-Net to gather the multi-scale features and improve the efficiency of the features' fusion. MSRAU-Net performs better than the other networks, especially when it comes to recognizing tiny aircraft, according to the trials conducted on the ISI dataset. MSRAU-Net obtained an F1 score of 93.10% and an accuracy of 93.12%. The authors claim that MSRAU-Net performed better than FCN, U-Net, AU-Net, and MultiResUNet [39]. Using the U-Net model, the study demonstrated a real-time method for object segmentation in aerial drone photos. The model's efficiency is increased by the use of data augmentation and transfer learning approaches. Several base architectures, such as VGG 16, ResNet-50, and MobileNet, were tested using the U-Net segmentation model. The model's performance is enhanced by data augmentation, as demonstrated by the experimental findings, which yield a segmentation accuracy of 92% for VGG-16, 93% for ResNet, and 95% for MobileNet [40]. As demonstrated in Table 1, our model surpassed all existing models in the domain of detecting and segmenting moving aircraft. This highlights the superior performance of our proposed approach compared to previous methods. Our research stands out from previous work by employing the U-Net architecture for aircraft recognition through semantic segmentation. Additionally, our study may represent the first attempt to utilize the DLRSD specifically for the development of a semantic segmentation model tailored for aircraft recognition using the U-Net architecture. This innovative approach highlights the potential of U-Net in the context of aircraft image segmentation and opens new possibilities for enhancing the accuracy and efficiency of remote sensing applications.
## 4 PSO Based on the PSO-BP Model
### Model Parameter Setting and Data Processing
#### 4.3.2 Network Architecture Design  
1. BP Neural Network Architecture Determination  
A three-layer feedforward network was adopted as shown in Figure 12, comprising an input layer, a hidden layer, and an output layer. The core of neural network modeling lies in determining the weight configurations between the hidden layer and output layer, a process fundamentally based on supervised learning for parameter optimization. Given initial weights, input independent variables (wheat straw contact parameters) undergo forward propagation through the network to generate pile angle predictions. Errors are calculated by comparing predictions with measured values, triggering backpropagation to adjust weight parameters. Forward propagation computes the mapping from input to output, while backpropagation iteratively updates weights based on error gradients. One forward-backward propagation cycle constitutes a training iteration; through multiple iterations, predictions gradually converge toward true values. Weight updates during backpropagation rely on the error function and the \"gradient descent\" method. Related mathematical proofs involving partial differentiation, matrix operations, etc., are complex and lengthy, and have been fully derived elsewhere (omitted in this paper).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Original** & **Range Normalized** \\ \hline Straw–straw rolling & [0.14, 0.20] & [0, 1] \\ friction coefficient & [0.43, 0.47] & [0, 1] \\ Skr surface energy (J\(\cdot\)m\({}^{2}\)) & [0.01, 0.05] & [0, 1] \\ \hline \hline \end{tabular}
\end{table}
Table 12: Significance parameter normalization.  
This study configured the input layer with significant parameters screened through Plackett-Burman experiments in Section 3.1 (rolling friction coefficient between wheat straw forage particles, static friction coefficient between wheat straw forage and steel plate, JKR surface energy). Thus, the number of input neurons was n1 = 3. The output layer consisted of the dependent variable (pile angle). Through preliminary modeling experiments, the hidden layer neuron count was set to 7. The trainlm function was selected as the training function for the BP neural network, and the ReLU function was chosen as the hidden layer activation function, forming a 3 \(\times\) 7 \(\times\) 1 three-layer BP neural network.  
2. Regularization Configuration Dropout layer: Dropout rate 0.2; L2 weight penalty: Coefficient \(\uplambda\) = 0.001.