esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Recently Raman spectroscopy has increasingly been recognized as a reliable quality control tool due to its ability to non-destructively probe adulteration related changes in olive oil with high chemical specificity and without requiring any reagents or sample preparation. Several studies have explored the potential of Raman spectroscopy to identify the nature of adulterants and their concentrations in spiked EVOO (i.e., EVOO spiked with edible oils such as sunflower, corn, soya, rapeseed, hazelnut and olive pomace oil) [6; 8; 13; 17; 18; 19; 20]. However, the studies so far have focused on diluting the EVOO with just one component [6; 8; 13; 17; 18; 19; 20; 21; 22; 23]. In a real-world scenario, the possibilities for diluting EVOO are in principle infinite and the authenticity has to be investigated without having any prior knowledge about how many adulterants have been added and the nature of them. Hence determination of adulteration of olive oils regardless of adulterant vegetable oils (i.e., binary, ternary and quaternary mixtures) was carried out using near-infrared spectroscopy in conjunction with genetic inverse least squares multivariate calibration method [22]. Rather than quantifying each adulterant, quantification of adulteration (i.e., amount of any cheaper oil blended with EVOO) is important and has been accomplished from a mixed group of oil samples (i.e., binary mixture of EVOO with three different edible oils) using Raman spectroscopy together with Bayesian least squares support vector machines [8]. As a consumer, quantification of purity is the primary interest (how much real EVOO is present in the bottle of oil labelled as EVOO). It is also much easier and practical to develop cheap and fast tools that can quantify the purity (the amount of EVOO) regardless of the number of adulterants that have been added. In this study, we explored the potential of Raman spectroscopy with a simple partial least squares (PLS) method to quantify the purity of olive oil from up to four different kinds of dilutions at the same time i.e.,quaternary mixtures (EVOO + rapeseed + corn + soybean oil). Raman spectroscopy in combination with a simple PLS model is able to quantify the purity of olive oil regardless of number of adulterants that has been added (binary + ternary + quaternary mixtures). Note that the spectral library was obtained with different measurement parameters (power levels and acquisition times). As we know the nature of the adulterants in this study, non-negative least squares method [27] was implemented to quantify the concentrations of EVOO and each adulterant in the spiked EVOO. As this study focused on determining the purity of olive oil irrespective of blended with one or more adulterants, the potential variations in the chemical composition of EVOO associated with cultivar, region, altitude, time of harvest, soil conditions, extraction process, as well as ageing and oxidation associated with storage conditions such as exposure to oxygen, light, and temperature will be incorporated in the future study.
# Failure Prediction of the Rotating Machinery Based on CEEMDAN-ApEn Feature and AR-UKF Model
## 1 Introduction
Aiming at shortcomings of existing failure prediction methods, this paper proposes an effective method to track the degradation process and predict the remaining useful life of the rotating machinery. The main innovation of this paper is summarized as follows. 1. The complete ensemble empirical mode decom-posetion with adaptive noise (CEEMDAN), which can overcome shortages of EMD and EEMD on modal aliasing and calculation efficiency, is adopted to decompose the original vibration signals of the rotating machinery into a number of IMFs and a residual. 2. The maximal information coefficient (MIC), which can reveal the degree of association between two variables, is utilized to select eligible IMFs and get the reconstructed signals. 3. The approximate entropy (ApEn) is employed to achieve fault features of the reconstructed signals and track the degradation process of the rotating machinery. 4. A remaining useful life (RUL) prediction model by combing the auto regress (AR) model and the unscented Kalman filter (UKF) algorithm is developed to achieve the RUL of the rotating machinery accurately. The paper is organized as follows. Following with the introduction, Section 2 describes details of the proposed failure prediction method. Section 3 gives the experimental results to show the effectiveness of the proposed method. The final section summarizes the conclusions drawn from the results.
# Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks
## 5 Results and Analysis
### Results
#### 5.1.2 Adjusting the Number of Hidden Neurons  
To investigate the impact of varying the number of hidden neurons on the network's performance, we conducted experiments using different configurations. To ensure consistency, the distribution of the entire learning dataset in the subsequent results was kept consistent with the default values (70% for learning, 15% for validation, and 15% for testing), while only the number of hidden neurons was altered. Table 6 below highlights the network accuracy.
## 2 Wave Scattering Problem of Soil-Tunnel Interaction under Oblique Incidence of P-SV Wave  
The soil-tunnel interaction system used for seismic analysis of underground long lined tunnel is shown in Figure 1a. The site is divided into the underlying half space bedrock and overlying soil layer. The interface between the soil and the tunnel is fully bonded. In addition, at the interface nodes, the displacement and stress are continuous. The geometry and material properties remain consistent along the longitudinal direction of the tunnel. The underlying half space bedrock, overlying soil layer and tunnel are assumed to be homogeneous, isotropic and linear elastic material. The surface traction and initial stress of the tunnel is zero.  
In this study, the seismic wave is input from underlying half space with any angle in the form of a plane wave. The tunnel is subjected to earthquake loading by compression wave and shear wave. In the Cartesian coordinate system \((x,y,z)\), as shown in Figure 1b, \(\theta_{v}\) is defined as the incident angle between the \(x^{\prime}\) axis and the \(z\) axis. \(\theta_{t}\) is the conversion angle between the \(x^{\prime}\) axis and the \(x\) axis in the \(x\)-\(o\)-\(y\) view in Figure 1c. The wave propagation direction is \(x^{\prime}\) axis.
## 3 Method
### T\({}_{roi}\): Transfer Learning with RoI  
Medical images have crucial areas, or RoIs, that should be focused on based on the task purpose, such as lesion classification and segmentation. Ref. [9] proposed improving the classification performance using the extracted RoI. Inspired by this study, the transfer learning approach with RoIs was used and evaluated on the three applications in this study. This RoI approach is frequently used for classification tasks, but in this study, the object detection and segmentation tasks, including classification, were also performed, so an approach using segmentation appropriate to each application's medical images was introduced. The RoI training model is based on a U-Net structure that can improve segmentation performance, but the encoder of the U-Net structure uses the baseline model of the application. Hence, if the baseline model for cervical cancer classification is ResNet50, the U-Net for the RoI learning approach consists of a ResNet50 encoder and a decoder using an up-sampling layer, as shown in Figure 5. In the RoI learning, the application dataset is used as the input, and the output is a segmented object area. Also, in the fine-tuning stage, the application dataset is used as the input, and the output is a class type. We trained the model with end-to-end learning to output the RoI first, then used the encoder's weight for transfer learning and experimentally fine-tuned the model to evaluate the task performance. This transferred U-Net encoder was used to improve the classification performance of the application. The approach using RoI is denoted as \(T_{roi}\), and the performance was measured for the three applications. The total number of parameters of the segmentation model is 28,976,386.  
Figure 4: \(T_{sil}\) Model: Patch self-supervised learning model based on ResNet50 for cervical cancer classification.  
Figure 5: \(T_{roi}\) Model: U-Net based on a ResNet18 variant for pressure ulcer segmentation. For \(T_{roi}\) training, the encoder and decoder depend on the baseline model’s number of convolution blocks for each application.
# E-MQTT: End-to-End Synchronous and Asynchronous Communication Mechanisms in MQTT Protocol
## 5 Experimental Results
### Energy Consumption  
Finally, we conducted measurements on the battery usage of the publisher to estimate the energy consumption of MQTT and E-MQTT. Figure 18 illustrates the battery usage results, indicating that the overall battery usage was nearly identical for MQTT and E-MQTT. However, when the number of subscribers exceeded 60, the MQTT publisher experienced a slight increase in battery consumption (3%) due to the higher number of packet exchanges, as discussed in Section 3.1. As the battery level information provided by the operating system was in units of 1%, we could not measure more accurate values in floating point numbers. However, we could estimate that a large number of packets  
Figure 17: Comparison of packet size results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**QoS** & \
\begin{tabular}{c} **Number of** \\ **Subscriber IDs** \\ \end{tabular} &
\begin{tabular}{c} **PUBLISH** \\ **(Bytes)** \\ \end{tabular} \\ \hline
2 & N/A & 12 \\ \hline  & 0 & 14 \\  & 1 & 16 \\  & 2 & 18 \\  & 3 & 20 \\
3 & 4 & 22 \\ (E-MQTT) & 5 & 24 \\  & 10 & 34 \\  & 20 & 54 \\  & 40 & 94 \\  & 80 & 174 \\ \hline \hline \end{tabular}
\end{table}
Table 3: PUBLISH packet sizes according to the number of subscriber IDs.  
also affected energy consumption. On the other hand, the battery usage of the E-MQTT publisher remained consistent (2%) since it exchanges the same number of packets regardless of the number of subscribers. Notably, both synchronous and asynchronous E-MQTT demonstrated equivalent battery usage. This implies that the additional overhead involved in the synchronization management of the synchronous E-MQTT has minimal impact on energy consumption.
# Design of a Fixed IF Down-Conversion Double-Balanced Mixer for UHF Band Applications
## 2 The Design Methodology  
A double-balanced mixer optimally combines RF and LO signals while inhibiting significant LO components appearing at the IF output. The method utilizes two cross-coupled single-balanced networks to eliminate in-phase and out-of-phase local oscillator components prior to the current-to-voltage conversion stage. Figure 1 illustrates the configuration of the Gilbert cell used for this mixer; the differential output current \(i_{out}\) can be written as follows:  
\[i_{out}=i_{m}-i_{n}=I_{RF}\text{tanh}\bigg{(}\frac{v_{RF}}{2V_{T}}\bigg{)} \text{tanh}\bigg{(}\frac{v_{LO}}{2V_{T}}\bigg{)} \tag{1}\]  
Taking into account both large and small signal components in the \(i_{out}\) of the RF input pair and calculating the output currents, we obtain the following:  
\[i_{m}=\bigg{(}\frac{I_{RF}}{2}\bigg{)}+(i_{rf})\text{tanh}\bigg{(}\frac{v_{LO }}{2V_{T}}\bigg{)} \tag{2}\]  
\[i_{n}=\bigg{(}\frac{I_{RF}}{2}\bigg{)}-(i_{rf})\text{tanh}\bigg{(}\frac{v_{LO }}{2V_{T}}\bigg{)} \tag{3}\]  
Figure 1: Double-balanced Gilbert-cell mixer.  
From Formulas (2) and (3), the differential output current can be written as  
\[i_{out}=\frac{i_{m}-i_{n}}{2}=i_{rf}\text{tanh}\bigg{(}\frac{v_{LO}}{2V_{T}}\bigg{)} \tag{4}\]  
The double-balanced mixer output current expression does not include the large-signal component, \(I_{RF}\), according to Formula (4). Within differential pair matching limits, the double-balanced topology effectively eliminates the DC component of bias current at the IF port; however, the presence of extra devices at the RF port may result in a higher noise figure.
# Numerical Simulation and Optimization of the Airflow Field of a Forage Drum Mower
## 3 Material and Method
### Orthogonal Experiments
#### 3.2.2 Measurement Methods of Evaluation Indexes  
Through the post-processing module of CFX software, measurement points were set in the airflow field generated by numerical simulation. Besides, the data on airflow velocity were counted, while the magnitude of the gathering, lifting, and feeding airflows was calculated.  
The measurement points of the gathering airflow velocity were set directly in front of the two drums (Figure 4) to measure the horizontal velocity of the airflow gathering towards the middle. The velocity of the gathering airflow was calculated as follows:  
\[v_{g}=\frac{\sum\limits_{i=1}^{n}{(v_{1i}+v_{2i})}}{2n} \tag{10}\]  
where, \(v_{g}\) denotes the velocity of the gathering airflow, in m/s; \(v_{1i}\) represents the velocity of the airflow at the \(i\)th measurement point on line L\({}_{1}\) in the +X-direction, in m/s; \(v_{2i}\) indicates the velocity of the airflow at the \(i\)th measurement point on line L\({}_{2}\) in the -X-direction, in m/s; \(n\), which was set to 50, refers to the number of measurement points evenly distributed on line L\({}_{1}\) and L\({}_{2}\), respectively.  
With the measurement points for the velocity of the lifting airflow set in the middle position in front of the cutter (Figure 5), the velocity of vertical upwards airflow was measured. The velocity of the lifting airflow was calculated as follows:  
\[v_{l}=\frac{\sum\limits_{i=1}^{n}{v_{3i}}}{n} \tag{11}\]  
where, \(v_{l}\) denotes the velocity of the lifting airflow, in m/s; \(v_{3i}\) represents the velocity of the airflow at the \(i\)th measurement point on line L\({}_{3}\) in the +Y-direction, in m/s; \(n\), which was set to 50, refers to the number of measurement points evenly distributed on line L\({}_{3}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{5}{c}{**Factors**} \\ \cline{2-5}
**Level** & **A** & **B** & **C** & **D** \\  & **Disc Speed** & **Blade Twist Angle** & **Disc Tilt Angle** & **Number of Guide Plates** \\  & **(r/min)** & **(Degrees)** & **(Degrees)** & **Number of Guide Plates** \\ \hline
1 & 1500 & 0 & 0 & 0 \\
2 & 1750 & 4 & 4 & 2 \\
3 & 2000 & 8 & 8 & 4 \\ \hline \end{tabular}
\end{table}
Table 1: Factors and Levels.  
Figure 4: Measuring positions of the gathering airflow.  
The measurement points for the velocity of the feeding airflow were set in the middle position of the two conical drums (Figure 6) to measure the horizontal velocity of the airflow feeding backward. The velocity of the feeding airflow was calculated as follows:  
\[v_{f}=\frac{v_{P1}+v_{P2}+v_{P3}}{3} \tag{12}\]  
where, \(v_{f}\) denotes the velocity of the feeding airflow, in m/s; \(v_{P1}\) represents the velocity of the airflow at point P\({}_{1}\) in the \(-\)Z-direction, in m/s; \(v_{P2}\) represents the velocity of the airflow at point P\({}_{2}\) in the \(-\)Z-direction, in m/s; \(v_{P3}\) represents velocity of airflow at point P\({}_{3}\) in the \(-\)Z-direction, in m/s.
# Initialize GroupKFold for cross-validation group_kfold = GroupKFold(n_splits = 5)
## 6 Discussion
### Practical Applications  
Our research findings offer several practical applications for organizations seeking to optimize information dissemination in social networks.  
For marketing strategists, our results suggest focusing resources on maximizing spread probability rather than targeting specific network structures. This can be achieved throughengaging content design, incentivized sharing mechanisms, and reduced friction in the sharing process. The optimal spread probability of 0.3-0.4 identified in our study provides a concrete target for campaign designers.  
For platform developers, the importance of timing in diffusion suggests implementing real-time monitoring systems focused on early diffusion speed. By identifying peak diffusion speed timing, platforms can better predict which content will achieve widespread coverage. This insight could improve content recommendation algorithms and trending topic identification.  
For crisis communication managers, our finding that constant delay mechanisms perform slightly better than alternatives suggests implementing consistent, predictable information release schedules during emergencies. This supports the work of [PERSON] et al. (2021) [8], who emphasized the importance of warning timeliness in crisis information diffusion.  
For community managers, particularly on platforms like Reddit, our analysis indicates that discussion-type networks generally achieve higher coverage than non-discussion networks. This suggests the importance of investing in the moderation and facilitation of meaningful discussions rather than simply broadcasting information.
## 3 Results and Discussion
### Optimum Moisture Content and Maximum Dry Density  
The variations of OMC and MDD with fly ash content and soil are presented in Table 2. As seen from this table, the OMC varied from 15 to 20.5% and the MDD varied from 1648 to 1823 kg/m\({}^{3}\). The original soil had a higher OMC than that of other mixtures. However, the mixture with a 40% fly ash content had the highest MDD compared to the original soil and the other mixtures. This could because poorly graded soil could have gaps in the particle distribution and fly ash particles may have filled the gaps, establishing a better connection of soil particles due to an abundance of fly ash particles that increased the maximum dry density up to certain percentage of fly ash content, and this process was called the \"optimum fly ash-soil mixing ratio\". The theoretical analysis of the decrease in the MDD for 50% and 60% fly ash content could be due the fine particles and the light weight of Class F fly ash compared to the SC soil that was used in the study, which was also concluded in a study by [PERSON] [7] for Class C fly ash and CL soil. The curing process could play a role here as well for the high MDD at 40% fly ash content, whereas it is supposed to be less than that for 50% and 60% fly ash contents to maintain the trends mentioned in several studies [2; 6; 8; 16]. Beyond this fly ash content, there was a decrease in the maximum dry density, however, it was still higher than the maximum dry density of the original soil.
## 3 Improved Radial Movement Optimization  
### Concepts of IRMO  
The IRMO algorithm [31; 32; 33; 34], which can quickly solve multidimensional objective functions of nonlinear constraint problems, is a global optimization algorithm that further improves the data structure of the traditional radial movement optimization (RMO) algorithm [34]. The IRMO algorithm simulates a group of particles [Xij] moving in a gradually shrinking solution space. In IRMO, one particle position, which contains the information of variables, represents a solution vector referring to a complete HDD trajectory in this study. All of the solution vectors form a solution matrix [Xij]. After the expression of the objective function and the range of the variables are determined, a group of initial particles are generated randomly in the IRMO algorithm. The position of each particle is evaluated by the value of the fitness function (the value of the sum of squares _SOS_ or total length \(L\)); therefore, the local optimal solution _Rbest_ (radial best) and global optimal solution _Gbest_ (global best) can be obtained by one-by-one comparisons. The particle corresponding to the best value of the function is chosen as the initial center. To avoid the algorithm overreliance on the central particle and the loss of potential good particles of the previous generation, which contribute to deviation from the optimization direction of the global optimal solution, IRMO proposed generating prepositioned particles [Yij]. The fitness values of [Yij] are compared with those of [Xij], pre-_Rbest_ and pre-_Gbest_ to evaluate the optimal information that will be updated or persist. As the iteration progresses, the solution space (ranges of variables) will be narrowed so that the central particle, determined by _Rbest_ and _Gbest_, will move toward the optimum. When the positions of the particles are narrowed to the smallest solution space, the optimum solution is output. The principle of updating the central particle is shown in Figure 4.
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 2 Overview of RPL
### Route Construction for Point-to-Multipoint and Point-to-Point Traffic Flows  
To construct and maintain downward routes from a DODAG root toward leaves, RPL nodes send destination advertisement object (DAO) messages upward. Each node sends a DAO message to one of its DAO parents which are in the node's DODAG parent set. An RPL instance may choose between two modes for maintaining downward routes. One is \"storing\" mode where a node stores a downward routing table for its sub-DODAG. The other mode is \"nonstoring\" mode where nodes do not store downward routing tables and packets are routed with source routes constructed by the DODAG root. Hence, in nonstoring mode, the DODAG root is expected to store source routing information learned from DAO messages.  
In RPL, point-to-point traffic uses both upward routes and downward routes. A point-to-point packet flows towards a DODAG root until it reaches an ancestor that has a known route to the destination. If nodes cannot store routes, the common ancestor may be the DODAG root. The main drawback of point-to-point routing specified by the RPL standard is that the routes from the source to the destination can be suboptimal and congestion can take place near the root. Also, in contrast with the proactive nature of RPL, applications such as home automation and building automation require a  
Figure 1: An illustration of upward routing in RPL.  
device to communicate with another device on demand. To address the drawbacks, an extension of RPL, P2P-RPL [11; 12], has been proposed as an experimental standard. P2P-RPL provides a reactive mechanism. When a node (source) needs to communicate with another node (destination), the source initiates the route discovery process by forming a temporary DAG rooted at itself. While this DAG exists, the routes are discovered and installed. The installed routes remain for the specified lifetime even though the temporary DAG no longer exists. The DIOs used to form the temporary DAG are identified using the MOP (mode of operation) indicating P2P route discovery mode. A P2P mode DIO carries the destination address, the nature of the route to be found (i.e., hop-by-hop or source routes), etc. Propagation of P2P mode DIO messages can be limited by using aggregated routing metrics and constraints carried in the DIO messages.