esa-sceva/satcom-chunk-collection huggingface dataset:
# Crude Oil Price Forecasting Model Based on Neural Networks and Error Correction
## 4 Application Analysis
### Application  
Accurate and efficient crude oil price forecasting is essential for strategic planning across multiple stakeholders. Our proposed model shows significant potential for application in crude oil price forecasting. Similarly, this principle remains valid when confronting unforeseen contingencies in the crude oil market. For instance, supplementary experiments reveal that, even under pronounced price volatility triggered by geopolitical conflicts, the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Forecasting Models**} & \multicolumn{3}{c}{**Training Time (s)**} \\ \cline{2-4}  & **\#1** & **\#2** & **\#3** \\ \hline TCN & 45.32 & 47.81 & 26.93 \\ GRU & 19.21 & 18.71 & 7.34 \\ ESN & 8.13 & 7.25 & 4.15 \\ PSO-TCN-GRU-ESN & 87.75 & 91.54 & 53.51 \\ GA-TCN-GRU-ESN & 79.82 & 81.61 & 45.32 \\ SARSA-TCN-GRU-ESN & 100.23 & 105.36 & 58.86 \\ EWT-SARSA-TCN-GRU-ESN & 1185.91 & 1290.49 & 659.19 \\ EWT-SARSA-TGE-ELM & 1194.16 & 1297.93 & 663.46 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Computation time for different models.  
model sustained exceptional predictive accuracy. Consequently, the proposed forecasting framework allows energy companies to predict future price trends, manage price risks judiciously, maintain stable production costs, and ultimately improve profitability. Furthermore, because crude oil prices serve as a pivotal commodity in the futures market, their fluctuations substantially affect the stock market and other speculative investments. Timely adaptation to these variations can help the investment sector optimize asset allocation strategies and enhance capital efficiency. Lastly, accurate crude oil price forecasts offer vital insights for governmental policy-making, helping policymakers promptly address environmental pollution and energy scarcity.
# Analyzing Data Reduction Techniques: An Experimental Perspective
## 4 Proposed Taxonomy
Numerosity data reduction techniques [10; 25; 26] reduce the amount of data by capturing the overall trend or patterns of the data. These techniques aim to represent concise and summarized data while preserving its essential characteristics and patterns. Unlike lossy compression, numerosity data reduction techniques do not intentionally discard data details but summarize them to make them more manageable and efficient. There are two main types of numerosity data reduction techniques: parametric and non-parametric. \(\bigcirc\)  Parametric techniques [31] rely on pre-existing data models, such as Linear Regression and Log-Linear, to estimate the data and reduce its quantity. \(\bigcirc\)  Non-parametric techniques [32; 33], on the other hand, focus on creating compressed versions of the data while preserving essential characteristics and patterns. Examples of non-parametric algorithms include Simple Random Sampling, K-Means, and data aggregation [9; 25; 26; 32]. Figure 1: Data reduction techniques’ taxonomy. The choice between lossy compression and numerosity data reduction techniques depends on the desired trade-off between data size reduction and retained accuracy, and the nature of the data. If accuracy is crucial, a lossy data compression algorithm such as Random Projection might be the best choice. It attempts to preserve the pairwise distances or similarities between data points as much as possible during the dimensionality reduction process. A numerosity data reduction technique such as Linear Regression may be the best choice when the final size is the selection criteria. Table 2 provides examples of different data reduction algorithms classified using our proposed taxonomy. The data reduction algorithms in bold are those used in the experimental evaluation.
# Deep Learning-Based Signal Detection for Underwater Acoustic OTFS Communication
## 4 Numerical Results
### Robustness Analysis with UIWA Non-Ideal Channel Estimation  
In the actual UWA communication system, many uncertainties will make the estimated CSI non-ideal. It can be seen from the literature [35] that when the system obtains the CSI with errors, the performance of the system will degrade. In this subsection, the impact of non-ideal channel estimation on the proposed model will be analyzed. Assume that the channel estimation errors follow a Gaussian distribution with a zero mean and a certain variance.  
The KAU1 experimental channel dataset was used to evaluate the multiple signal detection methods with channel estimation error. In the simulation, the variance of the channel estimation error was set to 0.1.  
As shown in Figure 13, with the channel estimation error, the BER performances of conventional signal detection methods obviously became worse. In the experimental channel, the BER of ZF and LMMSE almost increased to the error floor, and the BER of MP increased by up to two orders of magnitude.  
The BER performances of DL-based signal detection methods degraded less sharply than those for the conventional methods. As shown in Figure 13, compared with the two DL-based signal detection methods, SC-CNN-BiLSTM-based signal detection could recover data more accurately with the channel estimation error. In the KAU1 experimental channel, at a BER of \(2\times 10^{3}\), the FC-DNN, 2D-CNN and SC-CNN-BiLSTM methods with channel estimation error had 3 dB, 2 dB and 1.5 dB SNR losses compared with the corresponding signal detection method with ideal channel estimation. The robust performance of SC-CNN-BiLSTM signal detection can be attributed to the proposed cascaded network, where the SC-CNN provides information fusion from the interaction of the current layer and the previous layer for more effective signal feature extraction, and BiLSTM can continuously  
Figure 12: BER performance comparison for KAU1 experiment.  
store and extract valid information with symbols that are sequentially transmitted from the past to the future.
_Article_  
**Cartilage Differentiation of Bone Marrow-Derived Mesenchymal Stem Cells in Three-Dimensional Silica Nonwoven Fabrics**  
**[PERSON] 1, [PERSON] 2,\({}^{\dagger}\), [PERSON] 3, [PERSON] 2, [PERSON] 3 and [PERSON] 1,4,\({}^{\star}\)\({}^{\copyright}\)**  
1 Graduate School of Science, Tokyo University of Science, 1-3 Kagurazaka, Shinjuku-ku, Tokyo 162-8601, Japan; [EMAIL_ADDRESS]  
2 Department of Industrial Chemistry, Faculty of Engineering, Tokyo University of Science, 12-1 Ichigayafunagawara-machi, Shinjuku-ku, Tokyo 162-0826, Japan; 1 [EMAIL_ADDRESS] (K.I.); [EMAIL_ADDRESS] (M.H.)  
3 Japan Vilene Company Ltd., 7 kita-Tone, Koga, Ibaraki 306-0213, Japan; [EMAIL_ADDRESS] (K.S.); [EMAIL_ADDRESS] (M.K.)  
4 Department of Applied Chemistry, Faculty of Science, Tokyo University of Science, 1-3 Kagurazaka, Shinjuku-ku, Tokyo 162-8601, Japan  
5 Correspondence: [EMAIL_ADDRESS]; Tel.: +81-3-5228-8265  
+ Present address: Faculty of Engineering, Yokohama National University, 79-5 Tokiwadai, Hodogaya-ku, Yokohama 240-8501, Japan.  
Received: 24 June 2018; Accepted: 16 August 2018; Published: 18 August 2018  
**Featured Application: Silica nonwoven fabrics are materials with mechanical strength and cellular functions for potential cartilage regeneration applications.**  
**Abstract:** In cartilage tissue engineering, three-dimensional (3D) scaffolds provide native extracellular matrix (ECM) environments that induce tissue ingrowth and ECM deposition for in vitro and in vivo tissue regeneration. In this report, we investigated 3D silica nonwoven fabrics (Cellbed(r)) as a scaffold for mesenchymal stem cells (MSCs) in cartilage tissue engineering applications. The unique, highly porous microstructure of 3D silica fabrics allows for immediate cell infiltration for tissue repair and orientation of cell-cell interaction. It is expected that the morphological similarity of silica fibers to that of fibrillar ECM contributes to the functionalization of cells. Human bone marrow-derived MSCs were cultured in 3D silica fabrics, and chondrogenic differentiation was induced by culture in chondrogenic differentiation medium. The characteristics of chondrogenic differentiation including cellular growth, ECM deposition of glycosaminoglycan and collagen, and gene expression were evaluated. Because of the highly interconnected network structure, stiffness, and permeability of the 3D silica fabrics, the level of chondrogenesis observed in MSCs seeded within was comparable to that observed in MSCs maintained on atelocolagen gels, which are widely used to study the chondrogenesis of MSCs in vitro and in vivo. These results indicated that 3D silica nonwoven fabrics are a promising scaffold for the regeneration of articular cartilage defects using MSCs, showing the particular importance of high elasticity.  
**Keywords:** cartilage tissue engineering; mesenchymal stem cells; 3D scaffold; electrospun nanofiber; silica nonwoven fabrics
## 7 5G Radio Propagation Mechanisms
### Atmospheric Gaseous Losses/Atmospheric Attenuation  
When signal propagation at the millimeter waves passes through the atmosphere, they get absorbed by water vapor, oxygen, and other gaseous constituents in the atmosphere, thus attenuating the received signal [166].  
* Rain losses: Since radio wavelengths and droplets are essentially the same size, rain also attenuates mmWave propagation. As a result, the signal quality is affected when the radio waves are scattered as they encounter raindrops [167].
* Foliage losses/Vegetation attenuation: Foliage losses and reduced vegetation foliage losses at FR2 frequencies are very damaging, in contrast to the microwave, where they have little impact. Since millimeter waves are far more susceptible to attenuation by foliage, this sensitivity typically grows as the signal's route through the foliage is extended.
* Free-space path loss: Path loss occurs when clear routes are used for communication between the transmitter and receiver. Although mmWave has been said to have a substantial free-space path loss, studies have shown that this is only true if the gain of the mmWave antenna is considered to be frequency-independent [123; 168]. When the antenna area is constant at one link end, path loss becomes frequency-independent, and when the antenna area is constant at both link ends, path loss reduces since higher frequencies enable higher antenna gain for a constant area.
* Outdoor-to-indoor penetration: Higher frequencies result in greater attenuation of wall and window penetration. Mobile network users are more likely to be found indoors. Steel concretes, brick buildings, and energy-efficient windows all have attenuation values between 20 and 40 dB, whereas post and dry-wall dwellings have a value of less than 10 dB [169; 170]. Both attenuation and penetration loss increase as the carrier frequency increases. In [170], the average attenuation and penetration loss of ceiling, clear glass, drywall, and plywood were plotted for 28, 39,120, and 144 GHz.  
As presented in Figure 15, their result showed a clear increase in attenuation and penetration loss with an increase in frequency.
## 4 Experiments and Evaluation  
For experiments and evaluation, we used simulations and compared between LEACH and our proposed work. For the simulation, we considered MATLAB software. Here, we discuss the simulation parameter and results in Table 28.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Alternatives** & **N1** & **N2** & **N3** & **N4** \\ \hline N1 & (1,1,1) & (1,1,3/2) & (1,3/2,2) & (1,1,3/2) \\ N2 & (2/3,1,1) & (1,1,1) & (1,1,3/2) & (1,3/2,2) \\ N3 & (1/2,2/3,1) & (2/3,1,1) & (1,1,1) & (1,1,1) \\ N4 & (2/3,1,1) & (1/2,2/3,1) & (1,1,1) & (1,1,1) \\ \hline \end{tabular}
\end{table}
Table 26: Pair wise comparison matrix of four alternative nodes based on CH.  
Figure 3: Cluster head Selection algorithm.  
\begin{table}
\begin{tabular}{c c} \hline
**Alternatives** & \(I_{i}\) \\ \hline N1 & 0.29 \\ N2 & 0.27 \\ N3 & 0.22 \\ N4 & 0.22 \\ \hline \end{tabular}
\end{table}
Table 27: Find normalized weight of alternatives to find the best strategy.  
Figure 8: Overhead.  
Figure 6: Lifetime.  
Figure 7: Energy Consumption.  
_Stability:_ The stability of the network is depicted in Figure 5. The first node dies in 170 rounds of LEACH (approximately), and the first node dies in 530 rounds of the fuzzy AHP. However, in the proposed fuzzy ANP it dies around 1600 round (approximately) as shown in the figure below. Thus, the proposed F-ANP technique performs well.  
_Life Span:_ Life span of the whole network is depicted in Figure 6. In fuzzy AHP, the last node dies in approximately 1100 rounds; in comparison it takes approximately one thousand rounds of LEACH and it occurs at 2400 rounds in the proposed fuzzy ANP mechanism. This is due to the multi-criteria.  
_Energy Consumption:_ The per round energy of the network is recorded in Figure 7. The proposed method uses less energy than fuzzy AHP because of the changing cluster leader.  
_Overhead:_ The overhead of the signal is shown in Figure 8, in which it is minimized in our proposed method compared to the traditional LEACH and fuzzy AHP.  
_Throughput:_ The throughput of the network is depicted in Figure 9, which indicates that the throughput is high for our proposed mechanism in comparison to others because the fuzzy ANP is adopted in multi-hop.
## 2 Preliminaries  
### D CML System  
The mathematical structure of the two-dimensional coupled map lattices (2D-CML) system is expressed as:  
\[\begin{array}{l}x_{n+1}(i,j)=(1-\varepsilon)f[x_{n}(i,j)]+\\ \frac{\varepsilon}{4}\{f[x_{n}(i+1,j)]+f[x_{n}(i-1,j)]+f[x_{n}(i,j+1)]+f[x_{n} (i,j-1)]\},\end{array} \tag{1}\]  
where \(n=1,\,2,\,3,\,\cdots\,\) represents the time dimension. \(i=1,\,2,\,3,\,\cdots\), \(R\) and \(j=1,\,2,\,3,\,\cdots\), \(L\) denote the row and column indexes of the system, respectively. The number of lattices is \(R\times L\) in the system. \(\varepsilon\) (\(\varepsilon\in(0,1)\)) indicates the coupling coefficient. The periodic boundary condition of the system is denoted as:  
\[\left\{\begin{array}{l}i-1=R,i=1\\ i+1=1,i=R\\ j-1=L,j=1\\ j+1=1,j=L\end{array}\right., \tag{2}\]  
\(f(x)\) denotes the logistic map:  
\[f(x)=\mu x(1-x), \tag{3}\]  
where \(\mu\) (\(\mu\in(0.4]\)) indicates the control parameter. The state of \(f(x)\) is chaotic when \(\mu\) is between 3.57 and 4.
## 4 Measurement Setup and Dataset
### Dataset and Preprocessing
#### 4.2.3 Measurement Noise Extraction  
As mentioned before, this work claims for generalizability with respect to the background scene and application. In order to fulfill this requirement, only precipitation-induced noise is processed. As elaborated in Section 3.2, it is generally occurring rather close to the sensor aperture in the case of Flash LiDAR sensors. Following that, the major part of the noise was found to occur at a distance of \(L_{n,k}\leq L_{\text{crop}}=1\,\text{m}\). Accordingly, only false-positive detections at a distance smaller than--and just above--the sensor's specified minimum working distance of \(L_{\text{min}}=0.5\,\text{m}\) are considered; hence, data are mostly discarded on the application side. In case of the SUT, \(L_{\text{min}}\) is mainly dictated by the collinear orientation of transmitter and receiver and the resulting distance in which a full crossover between transmitter beam and receiver frustum is guaranteed [40; 41].  
Due to design-related close-distance backscattering sensitivity, it was found that the information content of close-approximity noise is, opposed to full PSPD prediction [18], sufficient to characterize the precipitation precisely by its respective SYNOP class and rate.  
This does not only allow for an application-agnostic deployment, but also for performant noise extraction through simple distance-based cropping of \(\mathbf{I}_{\mathbf{k}}\) and \(\mathbf{L}_{\mathbf{k}}\). Consequentially, for each pixel \((u,v)\), the noise frames \(\mathbf{I}_{\mathbf{n},\mathbf{k}}\) and \(\mathbf{L}_{\mathbf{n},\mathbf{k}}\) can be found by (see also Figure 1):  
\[I_{n,k}(u,v)=\begin{cases}I_{n,k}(u,v),&\text{for }L_{n,k}\leq L_{\text{crop}}\\ 0,&\text{otherwise}\end{cases} \tag{11}\]  
\[L_{n,k}(u,v)=\begin{cases}L_{n,k}(u,v),&\text{for }L_{n,k}\leq L_{\text{crop}}\\ 0,&\text{otherwise}\end{cases} \tag{12}\]
# PAM-UNet: Enhanced Retinal Vessel Segmentation Using a Novel Plenary Attention Mechanism
## 2 Methods
### Overall Network Architecture  
PAM-UNet uses a network architecture of encoder and decoder, as shown in Figure 1. First, the original color fundus image is preprocessed and cropped to a suitable size for input to the network. In order to preserve more high-level semantic information and prevent the network from overfitting, PAM-UNet adds DropBlock_Diagonal to all the convolutional blocks except BottleNeck (n.b., DropBlock_Diagonal convolution block is abbreviated as Diag_Conv in the following). In the encoding stage, two Diag_Conv blocks are passed through(2\"Diag_Conv means two Diag_Conv are used), followed by Max-Pooling with a step size of \(2\times 2\) to reduce the feature map size to half of its original size. The above operation is repeated three times and the final feature map size obtained in the encoding stage is one eighth of the original map size. Contrary to references [33; 34; 35], the plenary attention mechanism is not within the skip connections, but instead is at the bottleneck of the network. The spatial features in the meaningful channels are paid attention to by the PAM in the BottleNeck block of the network. In the decoding stage, the feature map is enlarged to twice its original size by two Diag_Conv, bilinear interpolation upsampling steps. The above operation is repeated three times to restore the feature map to the original image size. At the same time, in order to preserve more contextual information to enhance the vessel segmentation effect, the feature maps obtained from each stage of the decoder are merged. A convolution operation is performed on the low-dimensional feature maps obtained from each stage of the decoder to match the number of channels with the last stage of the decoder. Then, using bilinear interpolation, these are upsampled to match the resolution with the last stage of the decoder. The resulting feature maps obtained are added through convolution and upsampling to obtain the final feature map. Finally, a \(1\times 1\) convolution operation is used to obtain the segmentation result.
## 4 Materials and Methods
### GC-MS Analyses of EO Compositions  
The EOs were diluted in methanol (1:20 \(v/v\)) before the GC analysis. GC analyses were performed on an Agilent 6890 5973 N, GC-MS system (Agilent Technologies, Palo Alto, CA, USA), provided with a quadrupole mass filter for mass spectrometric detection. GC separation was accomplished by a DB1-MS column (0.25 mm \(\times\) 60 m, 0.5 \(\upmu\)m film thickness, J&W; Agilent Technologies). The following chromatographic conditions were applied: injection of 1 \(\upmu\)L volume, with a split ratio of 50:1 and injector temperature of 250 \({}^{\circ}\)C; the oven temperature program selected from 60 \({}^{\circ}\)C to 200 \({}^{\circ}\)C at 4 \({}^{\circ}\)C min\({}^{-1}\) and then to 280 \({}^{\circ}\)C (5 min) at 50 \({}^{\circ}\)C min\({}^{-1}\); and the He carrier gas constant flow set at 1.5 mL min\({}^{-1}\) corresponding to a linear velocity of 32 cm s\({}^{-1}\). The MS detector setting parameters were an electronic impact ionization mode set at 70 eV and the transfer line, source and quadrupole temperatures set at 300, 230, and 150 \({}^{\circ}\)C, respectively. For MS detection, the full scan mode was applied by selecting the mass range of 33-300 amu. The identification of EO constituents was performed by a comparison of the linear retention indices (LRI) and mass spectra of the chromatographic peaks with those obtained from a standard solution of pure reference compounds when commercially available (purchased from Merck, Sigma-Aldrich, Milan, Italy) (Table S1). Linear retention indices were determined by analyzing, under the same conditions used for the EOs, a standard solution of C7-C30 saturated alkanes and according to the equation proposed by [PERSON] and [PERSON]. When a pure compound was not available, tentative identification was based on the comparison of determined linear retention indices with those reported in the literature [55], in the NIST Chemistry WebBook database [56] and by a comparison of mass spectra with those reported in the NIST/EPA/NIH Mass Spectral Library (Version 2.4, 2020). The percent content of the compounds was determined from their peak areas in the GC Total Ion Current profile. Only compounds at a level higher than 0.05% were quantified. Each oil sample was analyzed in duplicate.
## 4 Enabling Multi-RAT Indoor Handover
### Proposed Handover Scheme
In [23], various context-aware handover schemes are discussed and compared. In particular, some works utilized the positioning system, such GNSS as the context parameter, whereas others measured the RSSI values and used them as the context parameter. In a way, the comparison between RSSI-based handover and LHO (in the same environment) is fair enough, since they merely utilized different context parameters for triggering the handover procedure. Nevertheless, LHO is in general more difficult to implement than the RSSI-based handover since it requires more information about the environment. Our contribution in this work is to demonstrate the potential performance gain when utilizing the estimated locationinformation for the handover, hence the LHO, further enhancing the unification of positioning and communication technologies in the HetNets.
# Trajectory Optimization for UAV-Aided IoT Secure Communication Against Multiple Eavesdroppers +
## 5 Conclusions  
In this paper, we investigate trajectory optimization to enhance physical layer security in a UAV-aided IoT communication system. We formulated the problem of optimizing the UAV's trajectory to ensure secure transmission in the presence of multiple eavesdroppers. A novel set of trajectory increment variables was introduced to replace the discretized UAV trajectory coordinates, thereby transforming the original non-convex problem into a sequence of convex subproblems. These non-differentiable sub-problems were then equivalently converted into standard convex forms using hypograph theory. Finally, we solved these problems using the CVX toolbox. The simulation results show how the UAV adjusts its trajectory to approach the communication destination while avoiding eavesdroppers under different system parameters. The significant impact of trajectory optimization on enhancing physical layer security performance is clearly verified. Moreover, we found that the achievable secrecy rate converges rapidly, and a higher maximum UAV speed benefits the UAV-aided IoT secure communication system.