esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Insertion Loss Measurement by Field Tests  
A wheelset drop test was performed on the connection line of Xuzhou East Station (China) to obtain rail and sleeper acceleration data. The wheelset weighs 1.2 tons and is lifted and dropped from a 25 mm height over the rail surface onto the mid-span between two sleepers. The equipment used for lifting and dropping the wheelset is shown in Figure 20A. The test segments consist of two ballasted tracks, one with and another without the ballast mat, on a concrete rail bridge. The track structure consists of a CHN60 rail, a type II elastic fastener, a type III concrete sleeper and a ballasted track bed with and without the ballast mat. Accelerometers are fixed on the rails and sleeper, as shown in Figure 20B,C. The accelerometers on the rail and sleeper can measure accelerations up to 1000 g and 100 g, respectively. The sampling rate was chosen as 10 kHz.  
The insertion loss for each ballasted track component is calculated as the difference in the results of the one0 third octave analysis of track vibration between the tracks with and without a ballast mat. A positive insertion loss means that the installation of the ballast mat can reduce track vibration, whereas zero or negative insertion loss means that the installation of ballast mat increases track vibration. The comparison of the measured insertion losses and the numerical results for both the rail and the sleeper are shown in Figure 21.  
Figure 19: Insertion loss of rail, sleeper, and ballast.  
Figure 20: Field test spot and equipment.
# Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks
###### Abstract  
Flying ad-hoc networks (FANETs) are a very vibrant research area nowadays. They have many military and civil applications. Limited battery energy and the high mobility of micro unmanned aerial vehicles (UAVs) represent their two main problems, i.e., short flight time and inefficient routing. In this paper, we try to address both of these problems by means of efficient clustering. First, we adjust the transmission power of the UAVs by anticipating their operational requirements. Optimal transmission range will have minimum packet loss ratio (PLR) and better link quality, which ultimately save the energy consumed during communication. Second, we use a variant of the K-Means Density clustering algorithm for selection of cluster heads. Optimal cluster heads enhance the cluster lifetime and reduce the routing overhead. The proposed model outperforms the state of the art artificial intelligence techniques such as Ant Colony Optimization-based clustering algorithm and Grey Wolf Optimization-based clustering algorithm. The performance of the proposed algorithm is evaluated in term of number of clusters, cluster building time, cluster lifetime and energy consumption.  
F 19 March 20181413390/s18051413 10.3390/s18051413  
FANET; routing; clustering; transmission range optimization; energy optimization
## 4 Conclusions  
The best tuning method was the maintained oscillation with the lowest error values (IAE, ISE and ITAE) in the specific response, compared to the rest of tuned controllers. However, PID-MO showed unstable control at low H2S IL of the tested profile. The anoxic BTF under PI-MO controller action was able to work at low EBRT (from 117 s down to 67 s), with outlet H2S concentration below 15 ppmV, which extends the use of biogas for a more profitable approach, such as fuel cell, rather than burning in an engine system. This work also showed the usefulness of feedforward control for nitrite dosage in anoxic BTF, even at a varying H2S IL profile (extremely changing conditions). In addition, this control strategy allowed to reduce H2S outlet concentration peaks and the emitted H2S mass by 26.18% and 2.6%, respectively.  
Research design, F.A., M.R. and D.C.; methodology, F.A. and M.R.; investigation, J.B. and F.A.; writing--original draft preparation, J.B; writing--review and editing, M.R.; supervision, D.C. and M.R.; funding acquisition, D.C. and M.R.  
This research was funded by \"Ministerio de Economia y Competitividad\", grant number CTM2012-37927-C03/FEDER \"Monitoring, modelling and control towards the optimization of anoxic and aerobic desulfurizing biotrickling\".  
The authors declare no conflict of interest.  
Figure 5: Profiles along experimental tests. (**a**) H2S\({}_{\text{out}}\) concentration. (**b**) Inlet molar ratio N:S\({}_{\text{in}}\). Blue lines: controlled experiments; dashed black lines: H2S\({}_{\text{in}}\); red lines: experiments without control.
# ZigBeeNet: Decrypted Zigbee IoT Network Traffic Dataset in Smart Home Environment
## 4 Fundamentals of Zigbee  
Zigbee is a widely used wireless communication protocol tailored for low-power, low-bandwidth devices. Its primary applications are in the Internet of Things (IoT), smart home automation, industrial control systems, and wireless sensor networks. This section provides an in-depth look into the fundamental aspects of Zigbee, including its architecture, network formation, data transmission, and security mechanisms. The discussion is divided into three key subsections: Zigbee Network Structure, Zigbee Communication Process, and Zigbee Data and Security.
# Analysis of the Dynamic Characteristics of Coaxial Counter-Rotating Planetary Transmission System
## 4 Analysis of Dynamic Response Characteristics of the Transmission System
### Analysis of Planet Gear Vibration Displacements  
The vibration of the planet gears has the potential to change both the internal and external forces involved in the meshing process. Hence, it is imperative to study the vibrational characteristics of the planet gears.
# Cyclic Consistent Image Style Transformation: From Model to System
## 4 Method
### Generator  
The architecture of the generator network in the C3 GAN model is illustrated in Figure 4. The generator begins with an initial convolutional layer (Conv2d) with a kernel size of \(7\times 7\), a stride of 1, 3 input channels (corresponding to the RGB channels of the image), and 64 output channels. After passing through this layer, the output feature map size becomes \(250\times 250\times 64\), calculated as \((256-7+1)\times(256-7+1)\times 64\).  
Following this, the generator processes the feature map through a series of residual blocks, which maintain the spatial dimensions \((W,\,H)\) while increasing the number of channels. The model typically uses 6 ResNet blocks, each doubling the number of channels. As a result, after passing through all residual blocks, the number of channels increases from 64 to 1024, calculated as \(64\times 2^{4}\).  
Subsequently, the generator employs upsampling layers (ConvTranspose2d) to enlarge the spatial dimensions of the feature map while reducing the number of channels by half.  
Figure 3: Training of C3 GAN Model.  
With two default upsampling layers, the spatial size expands from 250 to 1000, while the number of channels decreases from 1024 to 512, and finally to 256.  
The final Conv2d layer reduces the number of channels to match the output image's required channels, typically 3, corresponding to the RGB color channels of the output image.
# A Time-Varying Filter for Doppler Compensation Applied to Underwater Acoustic OFDM
## 4 Validation Using Real Data  
Two major deployments for different applications were used to confirm the performance of multicarrier transmission in mobile conditions and to assess the performance of the Doppler compensation technique discussed in Section 3.2. In a first instance, in Section 4.1, an ultra-sonic telemetry link is presented and the reliability of the receiver deployed is compared to that of the software model for different system parameters. Then, in Section 4.2, a long-range communication deployment is described, and the receiver reliability is compared for different mobile conditions.
## 1 Introduction
IL-1 family has at least 11 cytokines; clustered on the long arm of chromosome 2q, and the three most studied members are IL-1A, IL-1B, and IL-1 RN genes, which encode the agonistic proteins IL-1\(\alpha\) and IL-1\(\beta\), and IL-1 receptor antagonist (IL-1 Ra), respectively [17]. The effect of IL-1 is determined by the balance between IL-1\(\alpha\), IL-1\(\beta\), and IL-1 Ra through competitive binding of IL-1 Ra to the IL-1 receptor to block the activity of IL-1\(\alpha\) or IL-1\(\beta\). IL1 is strongly induced by lipopolysaccharidesaccharides from the cell walls of Gram-negative bacteria and acts either directly or indirectly to initiate and amplify inflammatory responses through inducing expression of a substrate of effectors including cytokines/chemokines and matrix metalloproteinases [18]. The variations of IL-1 gene cluster, including IL-1A and IL-1B genes, and the variations of IL-1 RN are the most commonly studied functional polymorphisms for peri-implantitis. Many studies have investigated different single nucleotide polymorphisms (SNPs) in the IL-1 genes as a risk factor for peri-implantitis. Among them, the IL-1A \(-\)889 C/T (rs1800587) and IL-1B +3954 C/T (rs1143634) have been mostly investigated [19]. The IL-1B-511 (rs16944) is also studied in some studies. These polymorphisms are characterized by the substitution of cytosine with thymine in the DNA sequence, which has been demonstrated to be associated with directly changed levels of gene expression and secreted cytokines,respectively [20]. In the IL-1 RN gene, there is a genetic polymorphism located in intron 2 which is composed of a variable number of tandem repeats (VNTR) of 86 base pairs length. Several studies have analyzed the relationship between these polymorphisms and peri-implantitis. However, studies have yielded conflicting results on this issue [21; 22; 23; 24; 25; 26]. A clarification of the genetic basis associated with peri-implant pathology could be used to predict peri-implantitis occurrence and to improve treatment and monitoring of patients with dental implants [21]. Most of these studies had a relatively small sample size and thus had insufficient statistical power to detect the genetic associations. Some of them do not refer to confounding variables such as periodontal condition, ethnicity and smoking habits. Furthermore, many studies, including published systematic reviews, assess the relationship of these polymorphisms with the peri-implant disease, pooling in the same group patients with bone loss, implant loss, and peri-implantitis [27; 28; 29]. However, peri-implant tissue health can exist around implants with variable levels of bone support [6]. In addition, an implant can fail without having an associated chronic inflammatory reaction as occurs in peri-implantitis. Therefore, we performed a systematic review and meta-analysis, quantitatively synthesizing previous studies, to evaluate the association of common functional polymorphisms in the IL1 and IL-1 RN genes with susceptibility to peri-implantitis.
## 4 The Vehicle Trajectory Prediction Model  
To effectively aggregate the interaction features between the target vehicle and the surrounding environment and thereby achieve more reliable trajectory prediction, an attention-based encoding-decoding framework for trajectory prediction is proposed. As illustrated in Figure 1, the whole framework can be divided into the vehicle-vehicle interaction module, trajectory encoding module, and decoding module.
# Experimental Study on a Granular Material-Filled Lining in a High Ground-Stress Soft-Rock Tunnel
## 4 Study of the Mechanical Properties of Different Filling Materials
### Analysis of the Supporting Effect of Compressible Granular Filling
#### 5.1.3 Comparative Pressure Analysis  
The optimum bottom pressure of the supported beam without the sand filling was 56.34 kN. The maximum bottom pressure of the supported beam with the sand filling and before relief pressure was 29.41 kN. The maximum bottom pressure of the supported beam with the sand filling and after relief pressure was 12.34 kN. The pressure reduction rate of supported beams with the sand filling and before pressure relief and without sand filling was 47.8%. The pressure reduction rate of the sand filling before pressure relief and after pressure relief and without sand filling was 78%.  
By comparing the deflection and pressure of the supported beam without the sand filling and the supported beam with the sand filling, it can be seen that the deflection and pressure of the supported beam with the sand filling were significantly lower than those of the supported beam without the sand filling. The reason is that sand mobility homogenizes the supporting force. Sand absorbs pressure generated by the deformation of the surrounding rock. This reduces the deflection and pressure on the second lining, which provides a better yielding-pressure effect. By comparing the deflection and pressure of the supported beam with sand applied before pressure relief and after pressure relief, it can be seen that the deflection and pressure of the supported beam after pressure relief were significantly reduced because the discharge of compressible granular filling sand released part of the pressure applied by the surrounding rock, which further decreases the deflection and pressure on the secondary lining. The pressure which the compressible granular filling sand was able to absorb and release from the surrounding rock in the  
Figure 24: Comparison of deflection changes.  
middle of the supported beam was higher than that at both ends. It tended to decrease gradually from the peak in the middle towards both ends.
## 3 Experimental Characteristics of Multicolored Pulses
### Experimental Setups
After that, a pair of chirped mirrors (GDD) was used to compensate for the dispersion induced by the BK7 glass and other components. Then, the laser beam was split into two parts, Beam 1 and Beam 2, with a beamsplitter (BS). Beam 1 first propagated through a short-pass filter (F1, cut-off wavelength of 800 nm), was then focused into the nonlinear medium by a concave mirror (M4) with a focal length of 500 mm. Beam 2 was spectrally filtered with a long-pass filter (F2, cut-on wavelength of 800 nm), and then focused into the nonlinear medium by another concave mirror (M3) with a focal length of 500 mm. The beam diameters of both Beam 1 and Beam 2 were \(\sim\)300 \(\upmu\)m on the 1 mm thick diamond plate. As no hollow fiber or gas chamber is used, the Type-2 experimental setup occupied half the space of a Type-1 setup. In the experiment, the diameters of the incident beams at the position of the nonlinear media were measured using a CCD camera (BeamStar FX33, Ophir Optronics: Jerusalem, Israel). The pulses were characterized using the XFROG or SHG-FROG technique and retrieved with a commercial software package (FROG 3.0, Femtosoft Technologies: Chennai, India). The spectra of the pulses were measured with a commercial spectrometer (USB4000, Ocean Optics: Dunedin, FL, USA). To avoid optical damage, the intensities of the two pump beams on the surface of nonlinear media surface were set at least one order of magnitude lower than the damage threshold for all the media used. Neither damage nor supercontinuum generation were observed in any experiment.
## 4 Experiments and Analysis
### Effectiveness of Emotion Feature Similarity Evaluation  
The emotion feature encoding network constructed in this paper aimed to accurately extract and encode emotional features from speech. By comparing cosine similarities between emotion feature vectors, corresponding evaluation scores were derived, providing strong support for the quantitative analysis of emotional features. The data distribution is depicted in Figure 5, with the central horizontal line representing the average data distribution. Figure 5 illustrates that the distribution of emotion scores calculated using cosine similarity is relatively broad, while the distribution of teacher ratings is comparatively narrow. This further confirms that the former can provide a more objective assessment of students' spoken emotional features, whereas the latter may be influenced by encouragement or other factors, reducing the occurrence of very low or perfect scores, which is more in line with practical application requirements. Therefore, during the training stages of the emotion evaluation network, a two-stage approach involving coarse-tuning and fine-tuning was employed, harnessing the advantages of both scores to compensate for their respective limitations.  
Figure 4: Emotion recognition model evaluation results.
## 4 Methodology
### Population Representation  
Most population-based algorithms start by generating a population randomly and then applying a sequence of rules to it during the optimization process, aiming to produce better generations. In our research, every solution is depicted as a binary vector consisting of either 0 s or 1 s, with a size of d, which corresponds to the number of variables (features) within the initial dataset. Each binary digit symbolizes an individual attribute. Note that the last feature (\(F_{d}\)) is the class label, which is always selected. Figure 2 shows an illustration of such a representation of different solutions in a population for FS.  
Precisely, the value of the \(i_{th}\) attribute (or feature) is \(x_{i}\), which denotes whether this feature is selected in \(X\) or not. If it is equal to 1, this means that it will be selected; otherwise, it will not. In the majority of optimization algorithms, the process begins by iteratively  
Figure 2: Representation of two solutions (vectors of features) for FS.  
refining solutions towards the current best one. The initial population is not formed through prior knowledge but, rather, through randomness, essentially a random guess. Such a guess could potentially be distant from the optimal solution because of the absence of prior information at the start by generating high-quality solutions at the beginning. To solve such a problem, opposition-based learning (OBL) is used. To enhance the diversity and quality of the initial population, the OBL technique generates solutions in a direction opposite to the initially generated ones. The main idea of OBL is introduced in [113]. In particular, the OBL of the value \(x\) is the opposite direction denoted by \(\tilde{x}\). The OBL mechanism can be defined as follows:  
\[\tilde{x}_{i}=U_{i}+L_{i}-x_{i} \tag{6}\]  
For binary problems like FS, \(0/1\) knapsack [114], \(U_{i}\) is set to \(0\), and \(L_{i}\) is set to \(1\). The previous equation Equation (6) can be reformulated as follows:  
\[\tilde{x}_{i}=1-x_{i} \tag{7}\]  
After calculating OBL, each solution will be compared with its opposite, based on the given fitness function, \(f\), and the better solutions will be kept. The previous procedure can be formulated as follows:  
\[x_{i}=\begin{cases}\tilde{x}_{i},&f(\tilde{x}_{i})>f(x_{i})\\ x_{i},&otherwise\end{cases} \tag{8}\]  
For example, assume that we have a maximization problem, and two solutions, \(X_{1}\), \(X_{2}\), whose their fitness values are given via \(1.49\) and \(1.56\), respectively, and their opposites, \(\tilde{X}_{1}\), \(\tilde{X}_{2}\), have fitness values of \(1.39\) and \(1.62\). Then, based on Equation (8), \(X_{1}\) will remain, and \(X_{2}\) will be replaced with \(\tilde{X}_{2}\) because \(f(X_{1})\) is better than \(f(\tilde{X}_{1})\), and \(f(\tilde{X}_{2})\) is better than \(f(X_{2})\). Note that here, \"better\" means \"higher\" since the fitness function is used for the aforementioned maximization problem in Section 4.1.
# An Approach to Dynamic Sensing Data Fusion
## 3 Sensor Data Acquisition System  
### System Composition  
W5 is an experimental system for verifying sensor networks, which has the advantages of convenient operation, high accuracy, reliable transmission, and accurate positioning. W5 has been proven to be stable, functional, and expansible in multiparty testing and teaching, and it can play a good platform role in practical research. This training platform provides full ecological practice training for sensing equipment, which involves many contents, such as the Internet of Things technology, electronic engineering, computer engineering, mobile internet, communication engineering, biomedical electronics, and so on [18; 19; 20; 21; 22; 23; 24; 25]. It is especially suitable for Internet of Things, electronic engineering, computer software, medical electronics, and communication engineering. An image of the system is presented in Figure 2.  
The sensor network of W5 consists of ground control station, remote control adapter, radio control (RC) receiver, RC controller, data radio, autopilot, and so on, as shown in Figure 3.  
Figure 3: W5 sensor network.  
Figure 2: W5 experimental platform.  
In Figure 3, the components of the W5 system are divided into two classes: sensor parts and controlled parts. The controlled parts include autopilot, airborne data radio, and so on, while the sensor parts include ground control station, RC receiver, RC controller, remote control adapter, ground data radio, and so on.  
In the W5 system, there are three wireless transmission paths: the first is the wireless transmission path between RC controller and RC receiver, whose frequency is 2.4 GHz; the second is the wireless transmission path between ground data radio and airborne data radio, whose frequency is 433 MHz; and the third is the wireless transmission path between the video receiver on ground and the video capture card onboard, whose frequency is 5.8 GHz.  
There are three functional modules in the W5 system: (1) remote control module, i.e., the data link consisting of RC remote controller, RC receiver, remote control adapter, ground data radio, and airborne data radio, whose function is to provide the interfaces for users to remotely control the controlled objects; (2) planning and management module, i.e., the data link consisting of ground control station, remote control adapter, ground data radio, airborne data radio, and autopilot, whose function is route planning, task management, and automatic control; (3) video collection module, i.e., the data link consisting of the video receiver on the ground part and the video capture card on the airborne part, whose function is video collection, video display, and video recording.
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 3 Performance Evaluation of Infrared and Visible Image Fusion  
For the quality of image fusion, there are two methods of evaluation: 1. Qualitative assessment, i.e., subjective evaluation method, refers to the visual analysis by a group of observers to match the fused image with the source image, with the visual observation of the human eye as the main body. 2. Quantitative assessment, i.e., objective evaluation method; this refers to the method of quantitative evaluation of the fused image quality by calculating a specific numerical index from the fused image with some specific algorithms. In the following, these two types of methods will be elaborated on in detail [68].