esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of the Tyres Deformation on the Vehicle Stability  
The above-presented equations have general validity, since they relate the instantaneous spatial position of the \(CoG\) of a vehicle under test with the possible gatherings of the presented equipment (i.e., the weights supported by each quadrant). In a real case, the \(CoG\) position is the result of both the spatial distribution of the vehicle masses and the consequent possible deflection of the elastic elements involved in supporting the vehicle itself (e.g., shock absorbers, tyres). Indeed, the deflection of these elastic elements can cause a translation downstream of the \(CoG\) and/or an additional pitch and roll angle, greater than the corresponding angles imposed by the inclination of the support surface, having again the effect of moving downstream the \(CoG\). The above-exposed mathematical model, useful for the prediction of the weight loaded on each support, can, therefore, be further completed to take into account the presence of any elastic elements by iteratively correcting the spatial position of the \(CoG\) to keep into account the deformable elements, as described hereinafter. The starting point is the result of the application of the equations written under the hypothesis of infinite stiffness of the elastic elements, useful to describe the very first distribution of the weights on the supports. Subsequently, on the basis of these first weights and of the equations that express the deflection of these elastic elements as a function of the applied load [45], it is possible to correct the position of the \(CoG\), hence to recalculate the position of the \(CoG^{\prime}\) and the distribution of the weights on the supports, and again the deflection of the elastic elements until arriving at a numerical convergence of the results. The viscous damping component of these deformable media is not taken into account here because the measurement methods shown above are static or quasi-static.  
Figure 12: Distances of the \(CoG^{\prime}\) from the front/rear axles, and from the left and right wheels.  
In particular, most of the agricultural tractors still in use today (the average age is very high, especially in Italy [46; 47]) are not equipped with shock absorbers (the comfort of the operator is delegated entirely to the suspension seat). Therefore, the only elastic elements that can be taken into account for these machines are the pneumatic tyres. The tyres are subject to:  
* a load perpendicular to the support plane, which causes a flattening of the tyres (i.e., they undergo a decrease in height), thus causing the chassis having a rigid rotation downstream as a consequence of the increase in the roll and pitch angles;
* a load parallel to the supporting plane; in particular, the component of this load parallel to the axis of the wheel (called: the lateral horizontal load) is the one of greatest interest, as it is responsible for a lateral deformation of the tyre and, therefore, it causes a translation of the entire chassis downstream.
## 5 Conclusions  
In FMT modulation UWA communications, although the span of ISI has been reduced through band splitting, there is still a shortened ISI which needs to be suppressed. The performance of traditional adaptive equalization commonly exploited in FMT modulation UWA communications is limited when the effect of ISI spans tens of symbols or large constellation sizes are used. In order to further suppress the ISI with relatively low computation complexity, the FMT modulation UWA communication using low-complexity CE-based MMSE turbo equalization is proposed in this paper.  
Figure 11: Scatterplots for subband four and subband seven of the proposed method with 8 PSK mapping: (**a**), (**b**), (**e**) and (**f**) the scatterplots indicating the estimated symbols after equalization; (**c**), (**d**), (**g**) and (**h**) the scatterplots indicating the mean of the symbols after decoding.  
Figure 10: Scatterplots for subband four and seven of the proposed method with BPSK mapping: (**a**) and (**b**) the scatterplots indicating the estimated symbols after equalization; (**c**) and (**d**) the scatterplots indicating the mean of the symbols after decoding.  
The proposed method is analyzed in theory and verified by simulation analysis based on a measured channel response, and a real trial carried out in a pool with multipath propagation. Benefitting from the error correction of ECC, the information exchanged between decoding and equalization, and the iterative process, the ISI can be effectively suppressed. The results show that the proposed method achieves better communication performance at a higher bit rate than the FMT modulation UWA communication using traditional MMSE adaptive equalization.  
Conceptualization, L.S.; data curation, G.Z.; formal analysis, M.W.; funding acquisition, L.S.; investigation, L.H.; methodology, L.S.; project administration, L.S. and H.L.; resources, M.W.; software, G.Z.; supervision, H.L.; validation, L.S.; visualization, L.S.; writing--original draft, L.S.; writing--review and editing, L.S.  
The research of this paper is supported by the Shandong Provincial Natural Science Foundation, China (Grant No. ZR2017 MD019), the China Postdoctoral Science Foundation (Grant No. 2017M621248), the Shanghai Aerospace Science and Technology Innovation Foundation, China (Grant No. SAST2017-001), and the Experimental Technology Research Foundation of Qufu Normal University, China (Grant No. SJ201723).  
The authors declare no conflict of interest.
## 3 Results
### Pelvis Dataset Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MAE** & **PSNR** & **Pearson** \\ \hline
**2D** & **210.51 \(\pm\) 31.19** & **22.89 \(\pm\) 1.391** & **0.465 \(\pm\) 0.089** \\ \hline
**3D-32** & 220.55 \(\pm\) 33.40 & 22.53 \(\pm\) 1.371 & 0.419 \(\pm\) 0.082 \\ \hline
**3D-16 av** & 214.21 \begin{table}
\begin{tabular}{c c c c} \hline  & **MAE** & **PSNR** & **Pearson** \\ \hline
**2D** & \(35.24\pm 3.15\) & \(38.41\pm 1.006\) & \(\textbf{0.632\pm 0.0455}\) \\ \hline
**3D-32** & \(35.71\pm 3.74\) & \(37.20\pm 1.25\) & \(\textbf{0.614\pm 0.067}\) \\ \hline
**3D-16 av** & \(\textbf{35.04\pm 3.54}\) & \(37.47\pm 1.17\) & \(\textbf{0.628\pm 0.062}\) \\ \hline
**3D-16** & \(35.88\pm 3.77\) & \(\textbf{37.03\pm 1.253}\) & \(\textbf{0.613\pm 0.064}\) \\ \hline \end{tabular}
\end{table} 
Table 26: Pelvis dataset Soft-tissue with Atrous-net. Mean Absolute Error, PSNR and Pearson coefficient from Atrous-net using different convolutions and reconstructions in the Pelvis data set within the soft tissue. \begin{table}
\begin{tabular}{c c c c} \hline  & **MAE** & **PSNR** & **Pearson** \\ \hline
**2D** & \(36.40\pm 3.45\) & \(36.01\pm 1.05\) & \(0.584\pm 0.047\) \\ \hline
**3D-32** & \(36.28\pm 3.56\) & \(36.78\pm 1.090\) & \(0.589\pm 0.067\) \\ \hline
**3D-16 av** & \(\textbf{35.26\pm 3.39}\) & \(\textbf{37.21\pm 1.069}\) & \(\textbf{0.611\pm 0.063}\) \\ \hline
**3D-16** & \(35.78\pm 3.79\) & \(37.58\pm 1.164\) & \(0.607\pm 0.070\) \\ \hline \end{tabular}
\end{table} 
Table 27: Pelvis dataset Soft-tissue with Residual-net. Mean Absolute Error, PSNR and Pearson coefficient from Residual-net using different convolutions and reconstructions in the Pelvis data set within the soft tissue. \begin{table}
\begin{tabular}{c c c c} \hline  & **MAE** & **PSNR** & **Pearson** \\ \hline
**2D** & \(\textbf{55.78\pm 20.32}\) & \(30.520\pm 2.405\) & \(\textbf{0.009\pm 0.133}\) \\ \hline
**3D-32** & \(57.78\pm 23.070\) & \(\textbf{30.86\pm 2.490}\) & \(-0.128\pm 0.088\) \\ \hline
**3D-16 av** & \(57.51\pm 23.25\) & \(30.96\pm 2.50\) & \(-0.127\pm 0.102\) \\ \hline
**3D-16** & \(57.19\pm 23.09\) & \(30.93\pm 2.50\) & \(-0.121\pm 0.104\) \\ \hline \end{tabular}
\end{table} 
Table 24: Pelvis dataset Fat with Residual-net. Mean Absolute Error, PSNR and Pearson coefficient from Residual-net using different convolutions and reconstructions in the Pelvis data set within the fat. \begin{table}
\begin{tabular}{c c c c} \hline  & **MAE** & **PSNR** & **Pearson** \\ \hline
**2D** & \(\textbf{55.78\pm 20.32}\) & \(30.520\pm 2.405\) & \(\textbf{0.009\pm 0.133}\) \\ \hline
**3D-32** & \(57.78\pm 23.070\) & \(\textbf{30.86\pm 2.490}\) & \(-0.128\pm 0.088\) \\ \hline
**3D-16 av** & \(57.51\pm 23.25\) & \(30.96\pm 2.50\) & \(-0.127\pm 0.102\) \\ \hline
**3D-16** & \(57.19\pm 23.09\) & \(30.93\pm 2.50\) & \(-0.121\pm 0.104\) \\ \hline \end{tabular}
\end{table} 
Table 25: Pelvis dataset Soft-tissue with U-net. Mean Absolute Error, PSNR and Pearson coefficient from U-net using different convolutions and reconstructions in the Pelvis data set within the soft tissue.
# Development of the First Portuguese Radar Tracking Sensor for Space Debris
## 2 Implemented System
### Radar Site and Antenna  
We obtained a Vertex RSI C band, 9-meter Cassegrain Antenna originally used for Satellite Communications in the Azores Tereceira Island, Portugal. The antenna was moved to Pampilhosa da Serra, Portugal, a site with a low Radio Frequency Interference (RFI) environment [26]. New foundations, electrical power and lightning protection were installed at this new site. The mounting pedestal underwent a major adaptation in order to comply with a fast, continuous azimuth antenna rotation. Originally intended for radio  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **ATLAS** & **TIRA** & **BIRALET** & **HAX** & **Haystack** \\ \hline
**Operating Frequency** & 5.56 GHz & 1.333 GHz & 410 MHz & 16.7 GHz & 10 GHz \\
**Peak power** & 5 kW & 1 MW & 4 kW & 50 kW & 250 kW \\
**Waveform type** & Pulsed & Pulsed & Continuous & Pulsed & Pulsed \\
**Antenna Gain** & 46 dB & 49.7 dB & 13 dB (TX), 47 dB (RX) & 63.64 dB & 67.23 dB \\
**Antenna Beamwidth** & 0.73\({}^{\circ}\) & 0.50\({}^{\circ}\) & 30\({}^{\circ}\) (TX), 0.8\({}^{\circ}\) (RX) & 0.1\({}^{\circ}\) & 0.058\({}^{\circ}\) \\
**Receiver Bandwidth** & 80 MHz & 250 kHz & 5 MHz & 1 MHz & 1 MHz \\
**Topology** & Monostatic & Monostatic & Bistatic & Monostatic & Monostatic \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between ATLAS and other tracking radars.  
Figure 1: ATLAS system block diagram.  
astronomy surveys, this facility represents a step forward in our technological development and evolution of high-performance space radar instrumentation systems.  
The major improvements have given the station space telemetry and tracking capabilities. These improvements will represent one of Portugal's main support pilot facilities for space monitoring and will provide support to current and future space missions. The upgrade approach manages to automatically, and quickly, attain the best antenna steering strategy to successfully track the objects.  
The data output capabilities have been upgraded for wide bandwidth. These capabilities are now available to users on site and remotely via a high throughput connection. Extensive user support will be provided for these new facilities, including assistance, monitoring and control, antenna interfaces, and output interfaces. Operation and data reduction is also available for object tracking.  
The antenna is depicted in Figure 2 and specifications are detailed in Table 2.
## 3 Results
### Composition of Spinach Bacterial Community  
Overall, a total of 1,093,364 ASVs were observed, with a maximum of 113,737 and minimum of 39,474 reads. After removing uncharacterized phyla and contaminants and normalizing the data to the lowest number of reads (1000), the total number of ASVs reduced to 383,290 with a minimum of 1044 (observed for samples washed with ECAS at 85 mg/L of FAC on day 0) and a maximum of 50,871 (observed for samples washed with ECAS at 50 mg/L of FAC on day 5). The above reads were assigned to 12 distinct phyla, with the majority identified as Proteobacteria (2949 distinct ASVs), followed by Bacteroidetes (1876 ASVs), Actinobacteria (756 ASVs) and Firmicutes (396 ASVs). All other phyla had \(\leq\)8 ASVs (Table 1) and were excluded from further analysis [27] as the percentage abundance of these phyla were approximately 0.1% which would not affect the biological interpretation. All ASVs were assigned to one of 65 bacterial family identified and 84% of reads were further assigned to different bacterial genus with 158 genera identified. The five most abundant families identified were _Micrococaceae_ (28.2%), _Clostridiales Family XII_ (19.7%), _Flavobacteriaceae_ (17.9%), _Pseudomonadaceae_ (12.8%), and _Burkholderiaceae_ (10.1%). The five most abundant genera identified were _Exigubacterium_ (19.7%), _Flavobacterium_ (17.7%), _Arthobacter_ (15.4%), _Pseudomonas_ (12.6%), and _Paeniglutamicibacter_ (10.3%) (Table S1).  
The overall relative abundances (RA) of phyla observed for all types of sanitization wash are presented in Figure 1a, and the relative abundances for the samples collected immediately after treatment (day 0) as well as on day 5 and day 10 after storage at 4 \({}^{\circ}\)C are presented in Figure 1b. On day 0, the phyla Proteobacteria had the highest RA (\(0.36\pm 0.07\)), while the phyla Actinobacteria had the lowest RA (\(0.18\pm 0.05\)). On day 5, Proteobacteria was still the most abundant phyla (\(0.35\pm 0.08\)), whereas phyla Bacteroides was the least abundant (\(0.09\pm 0.01\)). However, on day 10, Actinobacteria was the most abundant phyla (\(0.34\pm 0.05\)) and Firmicutes was the least abundant phyla (\(0.10\pm 0.05\)). The relative abundances of bacterial taxonomy at order level for sanitization wash types and sampling days are presented in Supplementary Figure S1.  
\begin{table}
\begin{tabular}{c c c} \hline
**Phylum** & **Phyla Abundance** & **Percentage Abundance** \\ \hline Actinobacteria & 756 & 12.59 \\ Bacteroidetes & 1876 & 31.25 \\ Deinococcus-Thermus & 7 & 0.12 \\ Firmicutes & 396 & 6.60 \\ Fusobacteria & 4 & 0.07 \\ Patesobacteria & 8 & 0.13 \\ Plancromycetes & 7 & 0.12 \\ Proteobacteria & 2949 & 49.13 \\ \hline \end{tabular}
\end{table}
Table 1: Abundance and percentage abundances of phyla present in spinach leaf samples identified from 16S rRNA gene sequences analyzed using DADA2 package in R and taxonomic assignment performed according to the SILVA rRNA database.
## 4 Proposed Scheme
### Complexity Analysis of the Proposed Scheme  
The proposed resource management and binary power control scheme has computational complexity of \(O[K\{N+(M\times F)\}]\) for estimating resource reuse partner for all CUs and DPs. The computational complexity of the proposed scheme is relatively small. Hence, the proposed scheme reduces the implementation cost by proper designing D2D communications. Moreover, as the value of \(F\) increases, system capacity of our proposed scheme increases significantly. We can conclude that for real system deployment, the proposed resource management and power control scheme potentially increase the system capacity with least interference overhead.
# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
## 4 Results
### Evaluation Parameters  
For each of the evaluation videos, the amount of data sent, the time delays in sending the data, and the frames per second (fps) were compared for two scenarios. The first scenario was through the proposed method, where portions of frames were sent for most of the time. On the other hand, in the second scenario, all frames were sent, with no ROMs, which scenario was called herein All Frames. Because, for confidential transmission of data, cryptography was employed, the bit-rate increase as a result of the application of cryptography was also compared for the proposed method with and without encryption. Lastly, results for the two video quality metrics, namely SSIM and PSNR (see Section 4) are discussed.
# VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management
## 4 Proposed Strategy
### Slice Creation by Machine Learning Approaches  
The field of artificial intelligence has made extensive use of machine learning in a variety of research applications that concentrate on system characterization. Machine learning can play a vital role in the implementation of a resource provisioning mechanism for network slicing. Machine learning algorithms can be classified into multiple categories, such as supervised, unsupervised, and reinforcement learning. A supervised learning approach employs a labelled data set for model training. The system knows both the input data and the desired output data to be produced in a supervised manner. Different machine learning algorithms such as K-Nearest Neighbour, Support Vector Machine, Naive Bayes and Random Forest are used to perform classification [34; 35; 36]. Figure 2 shows the workflow of supervised machine-learning-based classification. As shown in Figure 2, supervised learning algorithms divide the nodes into the eMBB, mMTC,  
Figure 1: Proposed 5G mobile network orchestration for network slicing.  
and uRLLC slices after receiving KPIs from the network nodes. Each node's KPIs are contained in a single database, which must be split into two separate datasets for training and validation. Classification of slices necessitates training the ML algorithm with a collection of characteristics from multiple services. Since the predictive model can provide results for new data after training, this allows for new known input values (KPIs) and new unknown slices to be assigned. Therefore, once the ML algorithm has been trained, the predictive model may correctly forecast or classify the requests for service. The ML algorithm is used to classify network slices in Algorithm 1. These three types of slices will be sorted using different methods of Supervised Machine Learning (SM) classification.  
```
0:\(G^{PI}\)
0:\(eMBB,mMTC,uRLLC\)
1: Represent eMBB, mMTC, uRLLC as an empty vector of length vs.
2: Represent node, k = 0
3: Receive the node characteristics of \(N^{PI}\)
4: Do the supervised ML Classification()
5:while\(k\leq\sigma\)do
6:\(X_{Predict}\) of k
7:\(Y_{Predict}\)=Predict the slice of \(X_{Predict}\)
8:if\(Y_{Predict}\)=EMBB then
9: eMBB \(\leftarrow\) node k
10:else if\(Y_{Predict}\)=mMTC then
11: mMTC \(\leftarrow\) node k
12:else
13: uRLLC \(\leftarrow\) node k
14:endif
15: k = k + 1
16:endwhile
17:return eMBB, mMTC, uRLLC slices
```  
**Algorithm 1** Slice classification in 5G
# Adaptive Modulation Tracking for High-Precision Time-Delay Estimation in Multipath HF Channels
## 2 HF Time-Delay Estimation Signal Model  
### HF Channel Model  
The selection of the HF channel model significantly influences how closely the modeled channel approximates the actual channel characteristics. The Watterson model [12; 13], a mathematical representation of the narrowband HF channel derived from empirical data, has been extensively utilized and is recommended by the ITU as a standard model. The parameters of this experimental simulation conform to the conditions of the Watterson model, thereby enabling the simulation of the HF channel using this model. The Watterson model is characterized as a Gaussian scattering tap gain delay line model, with its system block diagram illustrated in Figure 1:  
In Figure 1, \(i\) denotes the ith propagation path, \(\tau_{i}\) represents the time delay associated with the ith path, \(n\) is the total number of paths, and \(G_{i}(t)\) signifies the stochastic gain process for the ith path. The input signal is first fed into a tap delayer, where each tap can be regarded as a propagation path through the ionosphere, and the multipath effect is simulated by varying the value of the time delay \(\tau_{i}\). Then, different \(G_{i}(t)\) modulate the amplitude and phase of the delayed signal to simulate Doppler shift and Doppler frequency spreading. Finally, the delayed and modulated individual signals are aggregated, with noise and interference added to produce the output signal [14].  
The channel transfer function of the Watterson channel model is expressed as follows:  
\[h(f,t)=\sum_{i=1}^{T}G_{i}(t)e^{-j2\pi ft} \tag{1}\]  
where \(T\) is the number of paths for multipath propagation; \(G_{i}(t)\) is the channel gain function for the ith path, which is used to represent the effects of small-scale fading characteristics such as Doppler spreading, multipath fading, and Doppler frequency shift experienced by HF signals during transmission through the channel. \(G_{i}(t)\) can be represented by a zero-mean complex Gaussian function:  
\[G_{i}(t)=G_{ia}(t)\exp(j2\pi f_{ia}t)+G_{ib}(t)\exp(j2\pi f_{ib}t) \tag{2}\]  
where \(G_{ia}(t)\) and \(G_{ib}(t)\) are two mutually independent, complex Gaussian smooth stochastic processes with each state experienced; they are mutually independent orthogonal components with mean values of 0 and have equal root mean square values and spectra. The exponential factor is the Doppler shift of the two magneto-ionic components. The variance of \(G_{ia}(t)\) and \(G_{ib}(t)\) is the size of the Doppler expansion formed by the  
Figure 1: Block diagram of the Watterson model system.  
two magneto-ionic components, consisting of two mutually independent real Gaussian processes:  
\[G_{ia}(t)=g_{ia}(t)+j\hat{g}_{ia}(t) \tag{3}\]  
\(g_{ia}(t)\) and \(\hat{g}_{ia}(t)\) have the same power spectral density.  
The HF channel propagation patterns in practice are correlated and the statistical properties of the stochastic processes are time-varying, so the Watterson model is not applicable to bandwidths greater than 10 kHz and durations greater than 10 min.
# Deep Learning for Sarcasm Identification in News Headlines
## 3 Materials and Methods
### Sarcasm Detection  
The News Headlines dataset, comprising 26,709 headlines, includes 14,985 neutral headlines from Huff-Post and 11,724 satirical headlines from The Onion. We sorted headlines in ascending order according to length and removed text in square brackets. We removed punctuation and numerals from the text and converted the headlines to lowercase. The dataset has an average headline length of 12.5 words and a standard deviation of 4.3 words, as presented in Table 2. The dataset features three columns: the headline, a binary indicator of whether the headline is sarcastic (1 for sarcastic, 0 for non-sarcastic), and the source organization (HuffPost or The Onion). Table 3 presents information on the Sarcasm Corpus V2 dataset, which consists of 9386 headlines, of which 4693 are sarcastic and 4693 are non-sarcastic. We divided the dataset into three tasks: Generic Sarcasm (GEN), Hyperbole Sarcasm (HYP), and Rhetorical Sarcasm (RQ). Figure 2 provides a graphical representation of the distribution of sarcastic and non-sarcastic headlines in the dataset.  
In Figure 3, words are depicted as clouds, wherein the size of each cloud corresponds to the relative frequency of occurrence of the corresponding word compared to the other words.  
The dataset comprises three features:  
* \"Headline\": Textual information from which we need to determine whether the headline is sarcastic.
* \"is_sarcastic\": Output column, which indicates the sarcasm status of the headline, where 1 represents sarcasm, and 0 represents non-sarcasm.
* \"article_link\": We removed unnecessary links from the dataset, which was then divided into two categories:
* 0 represents non-sarcastic headlines
* 1 represents sarcastic headlines  
Figures 4 and 5 show the number of headlines (non-sarcastic and sarcastic) with the increasing length of headlines.