esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Setup
### Pre-Processing  
Data preparation is critical in any text classification task. For this study, the incident descriptions in our datasets were highly unstructured with no specific formatting (i.e., entirely dependent on the user's view). Therefore, we developed a preprocessing pipeline to standardize the input text and remove unnecessary noise. We started with the noise entity removal for HTML tags, stop words, punctuation, white-spaces, and URLs. Next, we normalized the data using the standard NLTK toolkit [53]. During the normalization, we performed tokenization, lemmatization, stemming, and sentence segmentation. For punctuation removal, we used a regular expression, in which we removed all the values other than alphabetic words (i.e., command _r'[A-Za-z]'_), then converted all the words into lowercase representations. We assigned these lower-case words as tokens using _WordPunctTokenizer_. The function tokenized a text into a sequence of alphabetic and non-alphabetic characters following _regexp_. Unnecessary white spaces, which were the byproducts of the upper-to-lower-case conversion, were removed. Lastly, manual cleaning of these sequences using the _join_ and _strip_ functions was performed. The resulting datasets were split as follows: 70% for training and 30% for testing. For training, we further split the training dataset by reserving 20% of the training dataset (from 70% of the original portion) for validation (which is necessary for DL-based models).
## 4 Performance Analysis and Simulation Results
### Improvement of the Energy Efficiency
#### 4.1.2 RPC Scheme  
As shown in (12), if the PAPR reduction technique is applied, \(\eta\) can increase. If we decide to fix the transmission power \(P_{tx}^{ref}\), we can reduce the PA power consumption to \(P_{PA}^{RPC}=\frac{P_{PA}^{ref}}{\eta^{PAPR}}\) by reducing the supply voltage of PA. When the RPC scheme is applied, therefore, the relative EE with RPC scheme, \(EE_{r}^{RPC}\) can be expressed as follows:  
\[\begin{array}{rcl}EE_{r}^{RPC}&=&EE^{RPC}/EE_{ref}\\ &\approx&\frac{\frac{\delta{\rm{B}}K}{\frac{P_{tx}^{ref}}{\eta^{PAPR}}+P_{R}} \cdot\cdot\cdot\left[\log_{2}\left(1+\frac{P_{tx}^{ref}N_{t}}{(t+N0)\delta{\rm{B }}K}\right)\right]}{\frac{\delta{\rm{B}}K}{\frac{P_{tx}^{ref}}{\eta^{ref}}} \cdot\cdot\cdot\left[\log_{2}\left(1+\frac{P_{tx}^{ref}N_{t}}{(t+N0)\delta{\rm{B }}K}\right)\right]}\\ &=&\frac{P_{tx}^{ref}}{\frac{\eta^{ref}}{\eta^{ref}}+P_{R}}\\ &=&\frac{\eta^{PAPR}(P_{tx}^{ref}+\eta_{ref}P_{R})}{\eta_{ref}(P_{tx}^{ref}+ \eta^{PAPR}P_{R})}.\end{array} \tag{20}\]  
In contrast with the case of ITXP, in the case of RPC, the log terms are cancelled. Note that the relative EE with RPC scheme is independent of the precoding methods.
# Energy Efficient Determinism in WSN through Reverse Packet Elimination
###### Abstract  
Recently, the industrial wireless sensor network (WSN) has gained attention as a complement to wired networks due to its flexibility and lower installation cost. We present a novel Reverse Packet Elimination (RPE) algorithm implementation at the IPv6 over the TSCH mode of IEEE 802.15.4e (6 TSCH) stack that increases reliability without significantly increasing energy consumption. RPE increases the reliability while conserving energy by transmitting a cancellation packet from the sink towards the sender to reduce unnecessary packets. The evaluation utilized mainly the 6 TiSCH Simulator, with additional analytical assessments. We present several evaluation scenarios and compare WSN with and without RPE. In a WSN where each link had a packet reception rate of 70%, RPE increased the reliability with 11.8%. Furthermore, the average latency decreased with 39.1%. The average energy consumption increased with 19.8% when utilizing RPE. However, the network lifetime, i.e., the time before the first node experiences battery depletion increases slightly, which is a significant improvement compared to alternative replication mechanisms.  
Keywords:6 TiSCH; packet replication; TSCH; DetNet; WSN; IWSN; IoT +
Footnote †: journal: Sensors
# Advances in Simulating Radiative Transfer in Complex Environments
## 2 Model Description  
In the following, the old averaged view factor concept used in ENVI-met is described (Section 2.1) and the advancements of the new indexed view sphere (IVS) algorithm are laid out in detail (Section 2.2). To evaluate the advantages of the new IVS module, a proof-of-concept simulation is conducted comparing the results of both concepts (Section 2.3).
## 5 The Importance of VR Audio  
Also known as virtual audio, spatial audio, and immersive audio [41], 3D audio includes two forms of playback based on binaural audio and speaker playback. The former is widely used in VR devices, usually called binaural audio. The basic principle of binaural audio is to simulate the sound field generated by a sound source at a certain point in space in two ears such that the listener can have a sense of where the source was emitted from. This technology is also known as binaural acoustic technology [42; 43]. Because of the inherent characteristics of the human auditory system, there are often certain differences between the sound that people subjectively feel and natural sound. The effect of the human auric and other structures on sound waves can be seen as a filter, called head-related transfer function (HRTF). HRTF simulates the human ear's perception of sound direction and distance in space, which plays an important role in the generation of binaural virtual audio.  
With the development of virtual reality devices, more and more immersive applications are emerging. Although most research is focused on how to improve the resolution and frame rate of frames, hearing plays as equally an important role as vision in providing immersion [44; 45]. Audio is one of the important outputs of electronic games [44]. In first-person shooting games, players need to identify the position of the enemy according to the direction of the sound for an effective reaction. In addition, game audio can play an important technical role in providing basic user feedback by providing player behavior confirmation or warning of in-game activities. Research in [46] showed that in a shared space, with the inclusion of spatial audio and video, users can identify speakers better, retain more information, and have an increased comprehension from video conference meetings.  
As is indicated in [47], sound may have a greater influence on immersion than immersion has on images. Sound is one of the most important sources of human cognition. VR/AR content matching of audio effects has a great impact on enhancing users' memory of content. [PERSON], an American audiovisual educator, put forward the theory of a \"tower of experience\" in his book _Audio-Visual Methods in Teaching_. This theory considers how human experience is derived. Figure 2 shows the top three layers in [PERSON]'s tower of experience (the main theoretical basis of audiovisual education). The third row from the top representsaudio/recording/photos, and the layer above is visual signals and language symbols, indicating that the combination of listening and audition is more conducive to memory and understanding of knowledge.  
The demand for VR will increase along with the improvement of hardware, and the demand for immersive audio is also growing. It is pointed out in [49] that we need better authoring tools to support the creation of high-quality immersive audio works, regardless of whether the creators understand the underlying principles of audio, just as many video creators do not understand the underlying codec of a video.
## 3 Results and Discussion
### Three-Dimensional Laser Scanning Model and the Roughness of the Phosphogypsum Slope  
The surface morphology of the four simulated phosphogypsum slopes after a rainfall erosion test can be scanned by a 3D laser scanner to obtain the point cloud and elevation information of the model. The simulated slope model is presented in Figure 10. As shown in the color bar, dark blue represents the top of the slope. As the elevation decreases, the blue will lighten and then turn dark red. The structure of the S1 was destroyed and the loose particles under the hardened layer were lost, but the hardened layer had not fallen off, so 3D scanning cannot obtain the information relating to the hollow bottom. With this condition, the bottom should be filled with a plane when processing the point cloud, which resulted in the simulated erosion degree, meaning that the erosion volume was smaller than the actual situation.  
The elevation information of the simulated surface could be observed from the model analysis. The maximum elevation difference of the S1 model was 28 mm, and the red color  
Figure 10: Three-dimensional laser scanning model of slope ((**a**): S1; (**b**): S2; (**c**): S3; (**d**): S4).  
Figure 9: pH of outflow solution with time.  
shows that the erosion depth had reached the bottom of the tray and that the slope had been completely destroyed, which was consistent with the real condition. The models of the S2, S3, and S4 slopes did not show any red parts, which indicated that no deep erosion occurred in the model. The maximum height difference of S2 was 4 mm, and the color of the surface was almost blue and changed gradually, which shows that the change in the erosion elevation was slight. The maximum elevation difference of S3 was 7 mm. There were many light blue or white blocks on the surface, which were consistent with the erosion pits in the actual slope. The elevation difference of the S4 slope was approximately 6 mm, and the light blue areas were arranged in strips to form gullies.  
Roughness measures can quantitatively evaluate the erosion degree of slope. The actual area and projected area of the surface can be obtained by importing the point cloud for analysis. The higher values of RS are associated with the rougher surface. The increase in the actual area indicates that the deeper the erosion depth, the greater the degree of erosion. Each artificial simulated slope is divided into several parts to calculate the roughness and maximum erosion depth, as shown in Figure 11.  
It can be seen that the roughness of S1 was 1.87, and the maximum erosion depth was 28.54 mm, which was much greater than that of the microbially modified slope because some part of the slope was collapsed, so the different degree of each part of S1 is large. As for the microbially treated slopes, the erosion degree of S2 was the smallest and most uniform. The roughness of S3 was slightly larger than that of S4, but S4 has the largest erosion depth of the three, and the result was the same as the relationship of the actual measured erosion weight among four slopes.
## 2 Methods
### Case 2: Low-Latitude Region  
#### 3.2.1 Experimental Region and Data  
The experimental region with low latitude is located in Thailand, which is a country at the center of the Southeast Asian Indochinese peninsula (Figure 9a). Two L-band full-polarimetric ALOS/PALSAR images acquired on 23 March 2010 and 29 March 2011 were collected in this region (Table 1). As Thailand is located at a latitude of 15\({}^{\circ}\) N, which is directly under the north equatorial ionospheric anomaly region, the phenomenon of ionospheric irregularities often occurs in this area. Figure 9b,c show the International Global Navigation Satellite System (GNSS)Service (IGS) global vertical total electron content (VTEC) maps at 16:00 (UT) on 23 March 2010 and 29 March 2011 (3 min after the SAR time), respectively. It is clearly observed that the collected SAR images (black rectangle) lie in this ionospheric anomaly region at the SAR-acquired time. Thus, it is predicted that the generated interferogram from these two images will be contaminated by the ionosphere.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(\mathbf{\alpha}_{0}\) & \(\mathbf{\alpha}_{1}\) & \(\mathbf{\alpha}_{2}\) & \(\mathbf{\alpha}_{3}\) & \(\mathbf{\beta}_{0}\) & \(\mathbf{\beta}_{1}\) & \(\mathbf{\beta}_{2}\) & \(\mathbf{\beta}_{3}\) & \(\mathbf{\beta}_{4}\) \\ \hline \(-1.625\) & \(-0.0026\) & \(-0.0055\) & \(9.321\times 10^{-6}\) & \(-15.504\) & \(-0.016\) & \(-0.0125\) & \(4.49\times 10^{-8}\) & \(7.97\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Estimated model parameters used to remove the ionospheric errors.  
Figure 8: Comparison among the interferometric phase in the profile A-A’ in Figure 7a,c,d.
## 1 Introduction
[20] proposed a rolling bearing fault diagnosis method based on variational mode decomposition and a grey wolf algorithm optimized extreme learning machine. These interpretation methods of rolling bearing fault features improved the detection speed but did not effectively improve the accuracy. [PERSON] et al. [21] introduced a new machine learning approach, called deterministic learning, for training soft robot models using radial basis function neural networks, based on which methods for machine fault detection are proposed. Using real engineering cases as the carrier, [PERSON] and [PERSON] et al. [22; 23; 24] proposed the method of optimizing the signal analysis model and proposed the idea of fault detection. [PERSON] et al. [25] demonstrated, through extensive simulations, the effectiveness of STARNet in detecting untrusted sensor flows in both unimodal and multimodal environments, which is used to judge some fault signals. However, due to the severe lack of label data sets, it is difficult for existing autoencoders to extract a large number of effective fault features. This article provides a new idea for traditional and complex fault diagnosis methods of rolling bearings, returning to the natural attributes of faults to realize fault diagnosis. This paper defines fuzzy mapping with new connotation features. Based on the original control advantages, combined with the fuzzy inference system, there is no need to establish a complex mathematical model for the rolling bearing; based on the constructed fuzzy mapping, a new difference algorithm and product algorithm are proposed, which are simple in operation and do not need to conduct data training. According to the calculation results of the former, a maximum method for individual fault diagnosis is proposed according to the \"maximum approximation principle\", and an indirect method for group fault diagnosis is proposed according to the \"minimum difference approximation principle\". The algorithm proposed is based on the defined set operation principle as a scientific theoretical basis. It provides a new way of thinking for fuzzy information control theory to deal with the actual fault problem with fuzzy signal, which can realize the fault diagnosis of rolling bearings more quickly and effectively.
# Unsupervised Image Classification Based on Fully Fuzzy Voronoi Tessellation
## 5 Discussion
### Robustness Analysis  
To further demonstrate the classification performance and anti-noise robustness of the proposed algorithm, taking Figure 6(c1) as an example, images with different proportions of salt and pepper noise are generated and tested, as shown in Figures 13 and 14. In Figure 13a-e, their noise ratios are 0.02, 0.04, 0.06, 0.08, and 0.1. Figure 14 shows their classification results. As the proportion of noise increases, the noise in the classification results of the FCM, FCM_S, FGFCM, and RFLICM algorithms also increases. Due to the influence of noise, GKWFLICM and MSHLICM easily get stuck in local optima. Thus, the classification results are extremely unstable. For VT-HMRF-FCM, its boundaries will become rough with the increase in noise, until it is difficult to distinguish small objects. For the proposed algorithm, the classification effect is almost unaffected when the noise is less than 0.06. When it is greater than 0.06, even if there are some misclassifications in the boundary, the classification performance is still better than other algorithms.  
Table 7 shows the quantitative evaluation of classification results, where OA\({}_{\text{r}}\) represents the OA index of classification results, and OA\({}_{\text{b}}\) represents the OA index of boundaries. Their changes are visualized in Figure 15. As the proportion of noise increases, each of the accuracy indicators decrease to varying degrees. Among them, FCM has the highest descent speed. GKWFLICM and MSHLICM have extremely unstable accuracy. The accuracy of VT-HMRF-FCM and the proposed FVT-FCM have always been at a high level. Especially when the noise is greater than 0.08, the proposed algorithm is more stable, as shown in Figure 15a,b. In addition, the OA of boundaries of the proposed algorithm are always greater than VT-HMRF-FCM, as shown in Figure 15c.  
Figure 14: Classification results of images with noise.  
Figure 15: Accuracy curve with the change in noise proportion.
# A Fast and Accurate Method for Computing the Microwave Heating of Moving Objects
## 3 Results and Discussion
### The Influence of Rotational Speed on the Microwave Heating
Additionally, the maximum temperature of the potato with a stationary turntable is higher than all those with the rotating ones, and the minimum temperate is far lower, which leads to severe heating unevenness. Figure 11: Simulated temperature distributions on the top surface of the cubic potato after microwave heating for 24 s with different rotation speeds (top view, unit: \({}^{\circ}\)C). Figure 10: \(|\mathrm{S_{11}}|\) computed by the proposed method at different speeds. Thirdly, to show how the rotational speed affects the efficiency of microwave heating, we also calculate the average temperature profile of the potato. As shown in Figure 13, the average temperature of the speed 5 rpm is slightly higher than that of the other two speeds, suggesting the most efficient microwave heating among these speeds. This is also due to the time-dependent S\({}_{11}\) shown in Figure 10. As we know, \(1-|\)S\({}_{11}|^{2}\) is proportional to the effective output microwave power of the waveguide. According to the data of S\({}_{11}\) the microwave energy absorbed by the potato can be estimated by an integral of \(1-|\)S\({}_{11}|^{2}\) over time. The calculated integrals show that the potato rotating at the speed of 5 rpm absorbs the most energy, agreeing well with the average temperature presented in Figure 13.  
Finally, to reconfirm the most suitable speed obtained above, we also study the effects of these three speeds, i.e., 10 rpm, 5 rpm, and 2.5 rpm, on microwave heating of a cylindrical potato (10 mm (height) \(\times\) 30 mm (radius)) centered in the turntable with the same microwave cavity. The total heating time is also set to 24 s and the mesh with 161,619 quadratic tetrahedral elements and the rotational angle of 15\({}^{\circ}\) are selected for the computation. Figure 14 compares the temperature field distribution on the top surface of the cylindrical potato after heating 24 s at different rotational speeds. The temperature distribution patterns of cylindrical potato are also similar under these three speeds with \"hot ring\" on the middle and \"cold spot\" in the center. Moreover, to quantitatively compare the effects of rotational speeds, the minimum, maximum, and average temperatures of the cylindrical potato heated with these  
Figure 12: The minimum (**a**) and maximum (**b**) temperatures of the cubic shaped mashed potato heated with different speeds. Figure 13: The average temperatures of the cubic potato heated with different rotational speeds. three different speeds are also given in Figure 15. It clearly shows that the average temperature at the speed 5 rpm is slightly higher than that at the other two speeds, which reconfirm that 5 rpm is indeed a good choice for improving the temperature uniformity with high energy efficiency.
## 4 Conclusions and Future Work  
In conclusion, this paper presents a novel approach for estimating ground plane radiation intensity around 5G base stations using a random forest regression model. We explored the key characteristics of machine learning in the estimation process. In practice, the proposed method effectively provides accurate electric field intensity estimates at the  
\begin{table}
\begin{tabular}{c c c} \hline
**Machine Learning Method** & **RMSE** & **MAPE (\%)** \\ \hline BPNN & 1.2192 & 11.95 \\ \hline Linear Regression & 2.8198 & 64.69 \\ \hline Random Forest Regression & 0.5930 & 6.99 \\ \hline \end{tabular}
\end{table}
Table 10: The RMSE and MAPE of various machine learning methods.  
ground plane given the necessary inputs such as transmit power, antenna gain, distance between the base station and terminal, number of 5G terminals, terminal service modes, and the environmental complexity surrounding the 5G base station. The accuracy of the model is validated by an MAPE of 5.98% and an RMSE of 0.5928. This method can be applied to enhance electromagnetic wave coverage for 5G base stations and improve the precision of electromagnetic radiation level estimates. Communication network engineers can utilize this technique in the design and optimization of cellular networks. However, the data collected may also be influenced by the antenna's orientation. If the antenna is rotated to a different direction, the individual readings could vary significantly. Alternatively, it may be necessary to shield the antenna's radiation using different materials, which could be explored in future studies. More specific suggestions for overcoming the current model's limitations (like addressing antenna orientation or shielding methods) would help guide further research. Expanding the dataset to include data from other regions or considering the dynamic nature of 5G service deployment could provide a more comprehensive outlook.  
Conceptualization, O.J.F.; methodology, O.J.F.; software, T.S.; validation, O.J.F. and T.S.; formal analysis, O.J.F.; investigation, O.J.F.; resources, T.S.; writing--original draft preparation, O.J.F.; writing--review and editing, O.J.F. and T.S. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by the University of Johannesburg.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.