esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Performance Metrics and Environmental Conditions for 5G MIMO OTA
## 3 Experimental Results and Analysis
### Influence Analysis of Different Average Manners  
For a comprehensive analysis, the measured raw data of both TRMS and MARSS are averaged pursuant to both TRMS and MARSS manners, i.e., the averaging shall be done as an inverse linear average and a linear average, respectively. As can be seen from Equations (1) and (3), the average methods of TRMS and MARSS favor toward the peak (like traditional TRP/TIS) and the null (emphasize the defect) of the 12 azimuth positions, respectively. Therefore, the influence from SIR substitution/penalty is less in TRMS due to favor in peak than in MARSS manner.  
Figure 7: A comparison between TRMS and MARSS for the UMi channel model.  
Figures 8 and 9 illustrate the impact of different average methods to the same set of raw data. In Figure 8, different average methods are applied to TRMS raw data of UMi and UMa models under 3 GPP UE noise-limited environmental condition, respectively. In Figure 9, similar processing is performed under CTIA interference-limited environmental condition. Under either combination of environmental condition and channel model, a maximum discrepancy of no more than 0.3 dB can be observed, which suggests that different average methods adopted in TRMS and MARSS, i.e., inverse linear average and linear average, have little influence on the discrepancy in ability to distinguish between well- and poorly performing DUTs.  
Figure 8: A comparison between the different average manners for (**a**) the UMi and (**b**) the UMa channel models under 3 GPP UE noise-limited environmental condition (TRMS raw data).  
In conclusion, TRMS under UE noise-limited environmental condition has a stronger ability to distinguish between well- and poorly performing DUTs than MARSS under CTIA interference-limited environmental condition. This discrepancy originates from the environmental condition itself. Under interference-limited environmental condition, UE MIMO OTA throughput performance is evaluated in terms of the minimum SIR at a relatively high downlink signal power from the emulator while the level of an AWGN signal (which is used to emulate co-channel interference in an actual network) is varied. The initial SIR is set to 30 dB. On the contrary, under noise-limited environmental condition, UE MIMO OTA throughput performance is evaluated in terms of the minimum downlink power at a low downlink signal level. In such an environmental condition, DUT's low-signal performance is typically limited by its internal noise floor while antenna efficiency is the dominant role in TRMS. The measured SIRs under noise-limited environmental condition are around 20 dB level at all thresholds, which are 100 dB higher than the measured minimum downlink signal power in amplitude. Accordingly, the transformation of weak signals from a linear domain to a logarithmic domain will create much greater divergence across samples.
# Experimental Study on Drying Characteristics of Alfalfa Hay Bales Using Hot Air Convection
## 3 Results and Discussion
### Overall Trend of the Decline in the Moisture Content of Hay Bales
There is no significant relationship between the initial moisture content and the drying rate. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Treatment**} & \multirow{2}{*}{\begin{tabular}{c} **Drying Form of** \\ **Hay Bales** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Drying** \\ **Duration (h)** \\ \end{tabular} } & \multirow{2}{*}{**Hay Bales**} & \multirow{2}{*}{\begin{tabular}{c} **Initial** \\ **Weight (kg)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Final** \\ **Content** \\ **(\%(w.b.))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Moisture** \\ **Content** \\ **(\%(w.b.))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Final** \\ **Content** \\ **(\%(w.b.))** \\ \end{tabular} } \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{\begin{tabular}{c} Two bales heated \\ from top and bottom \\ \end{tabular} } & \multirow{2}{*}{2} & Upper bale & 34.96 & 27.058 & 34.78 & 26.94 \\ \cline{3-8}  & & & Lower bale & 32.92 & 29.522 & 31.54 & 26.71 \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{\begin{tabular}{c} Two bales heated \\ from top and bottom \\ \end{tabular} } & \multirow{2}{*}{3.5} & Upper bale & 24.23 & 33.97 & 18.45 & 14.00 \\ \cline{3-8}  & & & Lower bale & 25.06 & 31.65 & 22.7 & 24.91 \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{\begin{tabular}{c} Two bales heated \\ from top and bottom \\ \end{tabular} } & \multirow{2}{*}{2} & Upper bale & 29.09 & 27.86 & 26.3 & 20.58 \\ \cline{3-8}  & & & Lower bale & 29.53 & 26.34 & 24.9 & 13.41 \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{\begin{tabular}{c} Two bales heated \\ from top and bottom \\ \end{tabular} } & \multirow{2}{*}{3} & Upper bale & 27.78 & 27.02 & 24.01 & 16.40 \\ \cline{3-8}  & & & Lower bale & 27.98 & 26.34 & 24.43 & 16.01 \\ \hline \multirow{2}{*}{5} & \multirow{2}{*}{\begin{tabular}{c} Two bales heated \\ from top and bottom \\ \end{tabular} } & \multirow{2}{*}{4} & Upper bale & 17.62 & 32.02 & 13.61 & 16.90 \\ \cline{3-8}  & & & Lower bale & 19.85 & 29.27 & 16.00 & 14.00 \\ \hline \multirow{2}{*}{6} & \multirow{2}{*}{\begin{tabular}{c} Two bales heated \\ from top and bottom \\ \end{tabular} } & \multirow{2}{*}{5} & Upper bale & 40.03 & 26.46 & 34.98 & 18.28 \\ \cline{3-8}  & & & Lower bale & 42.55 & 30.16 & 39.50 & 25.71 \\ \hline \multirow{2}{*}{7} & \multirow{2}{*}{\
\begin{tabular}{c} One bale heated from \\ top and bottom \\ \end{tabular} } & \multirow{2}{*}{4} & \multirow{2}{*}{Bale} & 39.3 & 26.07 & 33.52 & 13.96 \\ \cline{3-8}  & & & & & & & \\ \hline \multirow{2}{*}{8} & \multirow{2}{*}{
\begin{tabular}{c} One bale heated from \\ left and right \\ \end{tabular} } & \multirow{2}{*}{4} & \multirow{2}{*}{Bale} & 40.18 & 26.46 & 33.78 & 13.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Effect of drying process on moisture content.
# Additive Manufacturing of Variable Density Lenses for Radio Frequency Communications in X-Band
## 3 Horn Antenna and Gradient Index Lens Design
### Electromagnetic Design  
#### 3.1.1 Horn Antenna  
The radiating element is located in the focal point of the set. For the type of communication services the set was intended for (satellite), the whole system would have to handle high power and require low loss; the optimal technology in that case would be waveguide, either rectangular or circular. Traditionally, horn antennas have been used together with waveguides due to ease of integration and high efficiency. In this case, a conic horn was designed to achieve a good balance between illumination efficiency and spillover efficiency. The criterion adopted was that the electromagnetic field level radiated by the horn at the edges of the lens should be 10 dB below the maximum, ensuring an optimal balance between illumination efficiency and spillover efficiency, thereby maximizing the total efficiency of the system. The sketch with the dimensions of the horn is presented in Figure 8a,b, and the values are provided in Table 3.  
Figure 7: Variable density sample measurements at 24.5 GHz.
# One-Class SVM Model-Based Tunnel Personnel Safety Detection Technology
## 5 Experiments
### Data Enhancement Experiments  
In order to investigate whether the experimental results of the original data after data augmentation have any effect, this subsection conducts an experimental investigation. The main purpose of data augmentation is to make the model training better, i.e., to adopt a suitable data augmentation method when the original data may be too smooth or the amount of data is small. Therefore, the following experiments are designed in this paper to investigate the effect of data augmentation. The control variable method is used to design the validation experiments. First, the datasets 1, 2 and 3 are divided in an 8:2 ratio into the training set and the test set, respectively. Then, the following four experiments are performed to verify the effect of data augmentation.  
1. No processing is performed on the three datasets and the OC-SVM model is used for experimental testing.
2. Data augmentation is performed on the training set part of the three datasets, keeping the test set unchanged, and the OC-SVM model is used for training and testing.
3. Data augmentation is performed on the test set part of the three datasets, keeping the training set unchanged, and the OC-SVM model is used for training and testing.
4. The test and training sets of all three datasets are expanded, and the OC-SVM model is used for training and testing.  
For the four experiments, which are denoted by No  
The experimental results are shown above, and it can be seen that the best experimental results occurred in No. 4 in all plots, i.e., the data generalization ability is stronger and the trained model performance is better after data augmentation on the training and test sets, while comparing the results of the training and test sets, the model has overfitting and underfitting. No. 1 has no data augmentation, and the remaining No. 2, 3 and 4 results are analyzed consistently. The models perform around 90% on datasets 1, 2 and 3 with better results after data enhancement. Usually, the evaluation index values of the test set of the model after data augmentation are improved, the evaluation index values of the training set are decreased and the generalization ability of the model is enhanced.
## 2 Existing Approaches
### Studies on Misbehaviour  
At present, there are different security mechanisms for resisting node misbehaviour problems. When a specific node starts to exhibit misbehaviour, it drastically decreases the communication performance of the wireless network. During node misbehaviour, the node usually violates the assigned routing scheme and adopts an unprogrammed ruleset, which could be detrimental to the entire communication system. The recent work by [PERSON] et al. shows the adoption of vulnerable conditions in mobile ad hoc networks [28].  
The study introduced an authentication mechanism to mitigate node misbehaviour using a collaborative approach of diffusing information about the local selfish node. [PERSON] and [PERSON] [29] developed a voting game-based scheme to identify node misbehaviour. The technique implements a single-stage Bayesian game for capturing information related to node uncertainties. [PERSON] et al. [30] presented a solution for relay node misbehaviour in the IoT environment. This model discusses an intrusion detection system to securely uplink and downlink communication between relay nodes and IoT networks. A study on misbehaviour detection was also carried out by [PERSON] and [PERSON] et al. [31] using a supervised machine-learning scheme in the IoT context. A similar line of work was also carried out using prototyping by [PERSON] et al. [32], where a ruleset of behaviour is formulated for IoT, followed up by using the Kalman filter for data estimation. [PERSON] et al. [33] presented a significant detection model for secure communication among mobile nodes in a vehicular network. The model evaluates the weight of dynamic trust for time-varying misbehaviour while the trusted vehicle is selected along with the differential allocation of resources. A similar direction of work toward vehicular networks was formulated by [PERSON] et al. [34]. The integrated usage of reputation and learning schemes is also reported to be used for misbehaviour detection as per the work of [PERSON] et al. [35]. The model implements evidence-based theory using reputation value to ascertain secure communication among the vehicles. Studies on misbehaviour detection found insecure communication can be mitigated using the detect-before-decoding principle, as noted in the work of [PERSON] and [PERSON] [36].
## 2 Scenario and Ray-Tracing Simulation Setup
### Ray-Tracing Simulation Setup for Electronic Wave Bands  
We used Wireless InSite(Remcom, State College, PA, USA) software tools for channel modeling at electronic wave bands [29]. Wireless InSite(R) is a suite of ray-tracing models and high-fidelity electromagnetic equation solvers for the analysis of site-specific radio wave propagation. There are five main steps for channel modeling in Wireless InSite(R), including  
Figure 1: Layout of the industrial scenario.  
features, waveforms, antennas, transmitters and receivers, and outputs. In the following subsection, we will explain the main settings for each step in the simulation study.  
The first step was the feature setup, which built the three-dimensional model of the industrial environment in the software. The computer-aided design (CAD) models for both the non-metallic reflectors (floor, ceiling, and windows) and the metal instruments were imported to model the realistic industrial scenario. Then, the surface materials of each object could be selected from the database. Secondly, for the waveform setup, we used a sinusoid waveform with carrier frequencies of 3.5 GHz, 6 GHz, 28 GHz, 60 GHz, 220 GHz, 260 GHz, and 300 GHz.  
For the antenna, we chose to utilize an isotropic antenna instead of an omnidirectional antenna. The reason was that an omnidirectional antenna produces an omni-pattern in the azimuthal XoY plane with a null on the Z-axis. When the transmitter and the receiver are not placed at the same vertical height, it is very likely that only a small amount of the signal can be received or even that no signal can be received. Meanwhile, an isotropic antenna provides a uniform field strength in all directions.  
For the transmitter, it was placed on a passable path near the wall and is shown by the red star in Figure 1. The distance between the transmitter and the wall was 2 m, and the height of the transmitter was 1.60 m. For the receivers, these were placed on the LOS route with a height of 1.27 m, and the route is shown by the red square. The interval between two adjacent points was 0.50 m. A total of 50 points were arranged.  
As outputs, Wireless Insite(r) could generate the received power, the path loss, and the channel impulse response (CIR) automatically by selecting those items as the intended results in the output window, which could be used to calculate the channel characteristics.
## 2. Mature Technologies for Water and Wastewater Remediation
### Ultraviolet Radiation
#### 2.3.3 Advantages and Disadvantages of UV Radiation  
While UV radiation poses many advantages, its disadvantages are more prominent in the context of tertiary wastewater treatment. Firstly, UV radiation does not improve the odour, taste or clarity of water [31]. It is unable to oxidise inorganic contaminants, such as iron and manganese [30] and can only oxidise some organic contaminants at very high concentrations [32]. Endocrine-disrupting chemicals (EDCs) are also not easily oxidised and require significantly high doses. While the oxidation of organics and EDCs may be possible, inorganic contaminants (which do not degrade) tend to absorb UV radiation and prevent the radiation from reaching the aforementioned contaminants. Thus, UV radiation is not feasible for the wastewater considered in this review. Finally, UV is a point source disinfectant, much like ozone, meaning that it cannot provide long-lasting disinfection. The method is therefore unfeasible for water distribution systems.  
Despite UV radiation being unsuitable, for this review, its advantages should not go unappreciated: it is faster than chlorination, cheaper than ozonation [33] and has a power rating that is comparable to that of a light bulb [34]. It is highly effective at destroying virtually all pathogens and does not form DBPs when it reacts with water of a good quality (i.e., low organic content). Finally, UV radiation equipment requires frequent, but relatively easy and inexpensive maintenance [30].
## 3 Results
### Comparison with the Classical Convolutional Neural Network (CNN) Model  
To validate the advantage of using the Xception network instead of a conventional CNN, we compare our method with an existing study using the classical CNN model [19]. VGG16 is a widely used CNN model for image classification [27]. [PERSON] et al. fine tuned a pre-trained VGG16 model for zebrafish larvae microscopic image classification [19]. Their experiments were also based on the same ground-truth dataset from the [PERSON] group. In their experiment, the network was trained to classy different combinations of five, eight and all phenotypes, respectively. They aimed to validate the classicization performance for different numbers of target phenotypes. The combination of five phenotypes included hemostasis, necrosed yolk sac, edema, short tail and normal. The combination of eight phenotypes included the normal, dead, down-curved tail, chorion, up-curved tail, short tail, edema and necrosed yolk sac.  
To conduct a fair comparison, we followed the same experiment setup of [PERSON] et al. [19] That is, our Xception network was also trained to classify the same combinations of five, eight and all phenotypes, respectively. The averaged classification accuracy of each combination is compared with [PERSON]'s accuracy in Table 3. For all the combinations, the Xception network resulted in better accuracy than the VGG16 network. When more phenotypes are included in the combination, the improvement of accuracy becomes larger. This result means that the Xception network is more suitable for multi-phenotype classification and better suits the multi-label nature of the zebrafish larvae.  
Besides the VGG16-based method of [PERSON] et al., we further compared our method with transfer learning using three other popular CNN models including AlexNet [24], VGG19 [27], and GoogLeNet [28] which were all pretrained with the ImageNet data. For a fair comparison, these networks were also validated on the combinations of five, eight and all phenotypes, respectively. The validation results are reported in Table 3 as well. For the five-phenotype combination, VGG19 achieved the best accuracy (0.88) while our Xception-based method yielded suboptimal accuracy (0.86). For both the eight- and all-phenotype combinations, our method was the most accurate.
# Advances in Improving Energy Efficiency of Fiber-Wireless Access Networks: A Comprehensive Overview
## 4 Energy Efficiency Analyses of Radio-over-Fiber Networks
### Approaches for Improving the Energy Efficiency of Cloud Radio Access Networks
#### 4.2.1 Energy-Saving Potential of Cloud Radio Access Network Architecture
Additionally, the C-RAN has an architecture suitable for the implementation of cooperative techniques such as coordinated multi-point (CoMP) processing technology. The cooperation of multiple BBUs in the large cloud BBU pool using CoMP technology enables the sharing of different system information in the cloud. This can improve the spectral and EE of the RAN and also can contribute to alleviating inter-cell interference (ICI) of the densely deployed small cells [86]. Therefore, through the implementation of the cloud-computing paradigm, the C-RAN architecture is able to aggregate multiple BS resources into a central BBU pool in the cloud. According to ref. [90], with such centralized architecture, it is possible to optimize multi-cell cooperation processing, which results in more energy-efficient operation of RAN than those having decentralized BBUs in the cloud. Therefore, full implementation of the C-RAN architecture can bring considerable potential energy savings to mobile network operators in the future.
## 5 Conclusions  
Virtual planning allows orthognathic surgeons to predict possible complications and potential difficulties, and to accurately design a precise and adequate surgical plan preoperatively.  
The differences found in this study between the surgical simulation and the actual postoperative results were less than 1 mm for most cephalometric landmarks, indicating overall acceptable predictability of the virtual surgery. The highest differences were found in the anteroposterior direction, where splints may be less accurate. Overall, differences in the mediolateral direction were the lowest and could be interpreted as very good accuracy when positioning the midline for the mandible and maxilla. In general, distances in the mandible were lower compared to the maxilla, meaning higher accuracy. It should be considered that the maxilla is positioned manually and that a mandible-first sequence was used.  
In conclusion, virtual surgical planning in orthognathic surgery offers predictable results. All distances were lower than 1 mm. Accuracy was greater when positioning the maxillomandibular complex in the mediolateral direction, whilst accuracy in the sagittal plane and vertical direction was lower, especially in the maxilla, similar to other studies.  
Conceptualization, M.M.P.M., A.G.V., M.B.B. and I.A.N.; Data curation, M.M.P.M., A.G.V., I.A.N. and A.M.S.; Methodology, M.M.P.M., A.G.V., J.G.D.d.C., M.B.B. and A.M.S.; Resources, J.G.D.d.C. and J.L.d.C.P.d.V.; Software, M.M.P.M., A.G.V., M.B.B. and A.M.S.; Supervision, A.G.V., J.G.D.d.C. and J.L.d.C.P.d.V.; Validation, J.L.d.C.P.d.V.; Writing--original draft, M.M.P.M. and I.A.N.; Writing--review & editing, A.G.V., J.G.D.d.C. and J.L.C.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of Hospital La Paz (CEI/CEIm).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Data available on request due to ethical restrictions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Rolling Bearing Incipient Fault Detection Based on a Multi-Resolution Singular Value Decomposition
## 2 Overview of the Method of Multi-Resolution Singular Value Decomposition  
### Decomposition Process of MRSVD  
Inspired by the multi-resolution analysis idea of wavelet analysis and the construction principle of matrix dichotomy recursion, a new SVD-based method that is MRSVD is proposed [46]. The specific steps of the algorithm are as follows and the decomposition process is shown in Figure 1.  
**Step 1.** A Hankel matrix \(\mathrm{H}_{0}\) is constructed with two rows for a given signal \(\mathrm{A}_{0}=(x_{1},x_{2},\ldots\ x_{N})\), i.e., \(\mathrm{H}_{0}=\left(\begin{array}{c}x_{1},x_{2},\cdots,x_{N-1}\\ x_{2},x_{3},\cdots,x_{N}\end{array}\right)\).  
**Step 2.** An SVD is performed on the matrix \(\mathrm{H}_{0}\), and on the two obtained singular values that are denoted as \(\sigma_{a1}\) and \(\sigma_{d1}\), with \(\sigma_{a1}>\sigma_{d1}\). Two signal components can be obtained from \(\sigma_{a1}\) and \(\sigma_{d1}\) via the principle of the SVD matrix's dichotomy recursive algorithm (see the Section 2.2), and these two signal components are denoted as \(\mathrm{A}_{1}\) and \(\mathrm{D}_{1}\). In terms of the contribution to the original signal, \(\mathrm{A}_{1}\) and \(\mathrm{D}_{1}\) have different weights. The \(\mathrm{A}_{1}\) signal has a larger contribution to the original signal. It is the main component of the original signal and it reflects the main profile obtained from the original signal during the current signal decomposition. Similar to the approximate signal in wavelet analysis, the \(\mathrm{A}_{1}\) signal is called as SVD approximate signal. In addition, the \(\mathrm{D}_{1}\) signal reflects the details obtained from the original signal during the current signal decomposition. The \(\mathrm{D}_{1}\) signal is similar to the detail signal in the wavelet analysis, so it is called the SVD detail signal.  
**Step 3.** The detail signal \(\mathrm{D}_{1}\) is preserved, and the construction of the 2-row Hankel matrix \(\mathrm{H}_{1}\) is continued for the \(\mathrm{A}_{1}\) approximate signal in order to obtain the next level singular value decomposition. Consequently, a series of SVD detail signals and approximate signals can be obtained using the recursion decomposition layer by layer.