esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
## 4 Results  
For experiments, the IoT testbed comprised of two Intel NUC Core i5-6260U mini computers connected via an IEEE 802.11 (wi-fi) connection. The operating system on both computers was the Ubuntu 16.04 64-bit variety of Linux and device specifications included 4 GB RAM and 1.80 GHz processor. Both sender and receiver side software codes were implemented with the OpenCV library in the Python programming language, as previously mentioned. All communication was carried out through User Datagram Protocol (UDP) sockets [63]. Results were taken from stored videos, as mentioned already in Section 3. A diagram of the communication setup is given in Figure 5. Results were acquired through application of the proposed technique on the five previously-mentioned test video sequences. Five parameters were computed, comprising of the amounts of data transferred, delays in data transmission, bit-rates, and two measures of video quality, namely Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index [64].  
Figure 5: Testbed communication setup.
# Enhancing the 3 GPP V2X Architecture with Information-Centric Networking
## 4 Including NDN in the 3 GPP V2X Architecture
### Application Scenarios  
The focus of this study is on a class of V2X applications that show _non-safety_ requirements, i.e., they do not have the URLLC constraints of the class of road-safety applications. The applications of interest are delivered via NDN over the PC5 and/or the LTE-Uu radio interfaces of the LTE and beyond cellular system. Examples of non-safety applications include the retrieval of space/time-relevant traffic efficiency and convenience information, e.g., a road congestion status, the pollution level in an urban area, and the high-definition map used by an autonomous vehicle.  
On the contrary, safety message dissemination, such as cooperative awareness message (CAM) and event-triggered warning message, relies on other well-established non-IP protocols defined by other SDOs such as ETSI and IEEE (e.g., WSMP).
## 5 Hand Position Detection  
After showcasing our ability to predict the locations accurately using an ideal robot arm, we took it a step further and conducted a more intricate experiment with a real human hand. This experiment posed challenges due to the natural shaking and trembling of the human hand, differences in hand sizes, and variability in positioning between each trial.
## 3 Results and Discussion
### Validation of the Quantification Method for the Volatile Compounds
In the case of CS3 and CS4 the addition of rosemary and thymus increased considerably the number of VOCs as can be observed in Table 3. The volatile profiles of CS3 and CS4 were noticeable due to the presence of seven common compounds that are linked to the presence of thymus and rosemary in each formulation (linalool, 4-terpineol, borneol L, \(\alpha\)-terpineol, 1-methyl-4-(1-methylethyl)-benzene, \(\beta\)-caryophyllene and delta-cadinene) [43; 44]. However thymol and 2-methyl-5-(1-methylethyl)-phenol were only detected in _softrio_ number 3 linked with thymine ingredient [44] whereas 8 additional compounds were characterized in _softrio_ number 4 (camphene, myrcene, eucalypt, camphor, \(\alpha\)-copaene, \(\alpha\)-humulne and \(\alpha\)-amorphene) that are expected to be extracted from rosemary. Most of these compounds are phenolic terpen with known antioxidant properties [29]. As some of the volatiles identified in this study are terpenoids or phenols with antioxidant capacity a cluster analysis was applied to the volatile data trying to classify the samples based on their dissimilarities in VOCs. The dendrogram obtained is shown in Figure 4 and shows three groups at a rescaled distance of 15. One of the groups was constituted by S1 and the other one by CS4, and a third group was formed by the samples CS1 and CS5 being the most similar, followed by CS2 and CS6. These dissimilarities are the same found before when the antioxidant capacity was considered. CS4 is the sample with the higher TPC and antioxidant capacity which agrees with the sample with the greater amount of antioxidant volatiles such as \(\alpha\)-terpineol, camphor, myrcene and \(\alpha\)-copaene. Additionally, S1 presents sulfur-containing compounds (di-2-propenyl disulfide and di-2-propenyl trisulfide) that have antioxidative properties [45] and are not present in the cooked samples which make this sample different to the others. Finally, CS3 is the most different sample in the third group. CS3 contains some volatiles such as thymol and 2-methyl-5-1-methyl ethyl-phenol with antioxidant properties not present in the rest of the samples.
## 5 Discussion
### Effect of Experimental and Simulation Parameters
#### 5.1.4 Comparison with Other Research  
When comparing our results with other research, it is evident that the interaction between pressure, speed, and offset is critical in achieving the desired mechanical properties through deep rolling. Similar studies, such as those by [PERSON], [PERSON], et al. [32], also highlight the need for optimizing all three parameters together rather than focusing on just one. For instance, [PERSON]'s research suggests that increasing pressure while maintaining a high speed and small offset results in the most effective combination for inducing deep compressive stresses without causing surface damage. In contrast, our findings diverge slightly from [PERSON] et al. [33], who found that larger offsets (up to 0.5 mm) could still produce uniform stress distributions, albeit with more surface roughness. Their research implies that there may be a trade-off between surface smoothness and stress uniformity when altering the offset. In our case, the 0.1 mm offset provided smooth surfaces and even stress profiles, which may be better suited for applications requiring high surface quality, such as in railway rail treatments. In summary, the pressure, speed, and offset used in this study were optimized to ensure uniform compressive residual stresses and controlled deformation in both the experimental and simulation setups. Our findings align with much of the existing literature, confirming that the interaction of these parameters is crucial for the success of the deep rolling process. The results also suggest that FEA is an effective tool for predicting the outcomes of deep rolling processes, as the simulated results closely matched the experimental data across all parameters.
## 2 Related Works
### Enhancement of Sand Dust Images  
The primary task of enhancing dust-laden images is to address issues such as severe color shifts and decreased contrast. Some commonly used color correction algorithms like the Retinex algorithm [19], Grey World algorithm [20], Histogram Equalization [21], and Wavelet Transform [22] were found ineffective in directly enhancing dust-laden images based on experimentation. [PERSON] et al. [23] attempted a combination of global fuzzy enhancement and constrained histogram equalization, which improved the contrast after enhancing dust-laden images, but the color correction effect was not ideal. [PERSON] et al. [24] proposed a method that employs constrained contrast adaptive histogram equalization to enhance contrast in dust-laden images, followed by gamma correction for normalization, and finally obtains the enhanced image based on the Grey World principle. While this method exhibited good results, it struggled with color restoration in severely degraded images. Another category of methods relies on image restoration and frequently depends on the Atmospheric Scattering Model (ASM). In the domain of image dehazing, the ASM model has been widely employed. In the context of enhancing dust-laden images, many methods still rely on ASM. [PERSON] et al. [25] proposed a dust-laden image enhancement algorithm based on ASM that reverses the blue channel. This method performed well in removing dust, but its effectiveness diminished when handling images heavily contaminated with dust.  
Currently, dust-laden image enhancement algorithms based on deep learning have garnered significant attention in the academic community. However, the development of deep learning in the field of dust-laden image enhancement has not reached an ideal state due to the lack of large-scale publicly available standard datasets for dust-laden images. Currently, the primary approach involves training networks using synthesized dust-laden images and then testing them using real captured dust-laden images. Nonetheless, this method has evident drawbacks as synthetic images cannot fully capture the distinctive features of real dust-laden images. Some researchers have attempted to address the data scarcity issue through methods such as transfer learning [26] and unsupervised training [27], achieving some degree of success.
# Effective Receiver Design for MIMO Visible Light Communication with Quadrichromatic LEDs
## 1 Introduction  
Visible light communication (VLC) with many attractive features has recently gained much attention because of the increasing demand for seamless mobile data connectivity [1; 2]. To further improve the transmission rate of VLC systems, multiple input and multiple output (MIMO) has also been widely investigated and demonstrated to be a highly effective technique that can be employed in VLC systems [3].  
Meanwhile, light emitting diodes (LEDs) and photo detectors (PDs) are utilized as transmitters and receivers in general VLC systems, respectively. Multi-color red/green/blue (RGB) LED with better color rendering index and multi-channel parallel transmission can provide a higher transmission rate with a lower error rate in comparison with ordinary LED [4]. Moreover, to improve the color qualities of white illumination light; quadrichromatic LED (QLED), usually with red/amber/green/blue (RAGB) colors, has been suggested as an alternative to RGB LED [5]. Color shift keying (CSK) is a modulation method that exploits the diversity of a multi color LED to modulate the data with the intensity signals of different colors [6]. The authors in [7] recently proposed an enhanced CSK with QLED and considered various illumination qualities.  
Moreover, the general communication through VLC channels mainly depends on the availability of line-of-sight (LOS) link. Various studies [8; 9] have shown that the orientation and the mobility of the receiver are the two most important factors affecting the quality of the LOS link in VLC. The hemispheric receiver (HR) is one of the most representative angle diversity receivers (ADR) [10] to achieve highly uncorrelated MIMO-VLC channels through altering the orientation angles of the PDs. Since the HR has a fixed value of the PDs orientation, it can not guarantee a good performance in all scenarios of receiver locations.  
In this paper, we focus on designing the receivers of the MIMO-VLC systems that are composed of QLEDs. The optimization problem of maximizing the minimum Euclidean distance (ED) of the received signal that considers the normal vectors of the receivers under constraints is solved with the help ofthe Taylor expansion method. Moreover, a two-stage iterative algorithm is proposed to determine the optimal orientation with various initial input solutions. The simulation results demonstrate that the proposed method provides a better bit error rate (BER) performance in comparison with the conventional one, where the fixed orientation of the receiver is employed.  
Notation: Throughout this paper, \(\mathbf{I}_{x}\) and \(\mathbf{1}_{x\times y}\) denote an \(x\times x\) identity matrix and an \(x\times y\) matrix with all elements as one, respectively. Furthermore, \((.)^{T}\) denotes transpose, \(\|.\|\) stands for the Frobenius norm. \(\otimes\) and \(\circ\) denote the Kronecker product and the element-wise product, respectively. The operator \(\mathrm{vec}\) (\(\mathbf{X}\)) is a column vector resulting from stacking all the columns of \(\mathbf{X}\).
# Modeling a Wave Energy Harvester for Supplying Data Buoys
###### Abstract  
An energy harvester scavenging the kinetic energy of fluctuating waves for supplying small sea monitoring buoys is studied and tested. The harvester exploits a magnetic cylinder that rolls on a track due to the pitching motion of the buoy. The electromagnetic coupling between the rolling magnet and pairs of coils placed along the track generates an electromotive force used to supply a DC load through a bridge rectifier. The considered harvester is characterized by low-cost, simplicity, lightness and efficiency. An analytical model of the harvester is presented to investigate its operating conditions and to predict its nonlinear dynamic behavior. The operating mode of the energy harvester named bang-bang is studied in depth as it allows maximizing the extracted power, and analytical equations that characterize the behavior of the harvester in this operating mode are deduced. A prototype of the energy harvester was built and tested in order to identify the model parameters and to validate the theoretical results.  
energy harvester; low-power wave energy converter; wireless sensor networks +
Footnote †: journal: Applied
# Advanced DSP for Coherent Optical Fiber Communication
## 4 SDM and Frequency Domain Equalization (FDE)
We have experimentally demonstrated an RLS FDE approach for MDM with good performance. The block diagram of the proposed algorithm and experimental results are shown in Figures 15 and 16 [60]. Figure 14: Complex multiplies per symbol vs. filter tap length. Figure 15: Block diagram of recursive least square (RLS) frequency domain equalization (FDE) for m-dimension system operating on two samples per symbol.
# Geometric Accuracy Evaluation Method for Subway Stations Based on 3D Laser Scanning
## 4 Case Study
### Structural Column Accuracy Evaluation  
In this study, 50 structural columns in a subway station are evaluated. Since the structural columns and the four planes of the structure columns have been segmented into independent objects during point cloud structure element extraction, each structural column is used as a unit in this step of the evaluation. By horizontal slicing, four edge lines to the east, west, south, and north of the structural column were extracted. Then, the four corner points fitted from the four edges were considered to be the four corner points of the structural column in the as-built data. In the case that the point cloud and BIM are moved to the same survey coordinate system, the one-to-one correspondence of corner points can be achieved.  
To clearly identify each structural column, the structural columns are numbered using the grid. Among them, the structural columns that are not set on central axis B are indicated using axes A and C according to the north-south orientation of their location, respectively. All structural columns are quantitatively evaluated using the two metrics (\(SCDM,ADD\)) proposed in this paper, with the average value of the deviations of the four column angles used as the final indicator. According to the construction requirements, the deviation of structural columns should not exceed 30 mm and the angular deviation should not exceed 2\({}^{\circ}\).  
The experimental results of the structural column are shown in Figure 22. Table 2 summarizes a display of columns in the point cloud and BIM that exceed or do not exceed the tolerance. The 2D planes of the structural columns are obtained by projecting the point cloud of the first floor on the XOY plane, and the pink rectangles represent the structural column models at the design phase. Table 3 lists the \(SCDM\) and \(ADD\) results of structural columns that exceed the tolerances. Figures 23-25 present three typical cases of relatively large deviations. The \(SCDM\) and \(ADD\) of all structural columns are shown in Figure 26. The method proposed in this paper can solve the problem of accuracy evaluation of different structures, as shown in Figures 16-22.  
Figure 22: (a) Visualization results of the 3D deviation of structural columns; (b) histogram of the error distribution of the structural columns.  
Figure 23: Structural column C-2: (**a**) 3D deviation visualization results; (**b**) error distribution histogram.  
Figure 24: Structural column B-18: (**a**) 3D deviation visualization results; (**b**) error distribution histogram.  
Figure 25: Structural column B-35: (**a**) 3D deviation visualization results; (**c**) error distribution histogram.
# Method for Detecting Underwater Microbubbles Using Dual-Mode Fusion of Laser Polarization
## 2 Dual-Mode Fusion Detection Mechanism
### Image Fusion and Feature Extraction
After the fusion, we again calculate the line polarization degree after the scatter calibration process in the image, as shown in the literature [20]:  
\[P_{x}^{\prime}(x,y)=\frac{L_{0}^{\prime}-L_{90}^{\prime}}{L_{0}^{\prime}+L_{90} ^{\prime}} \tag{9}\]  
\(P_{x}^{\prime}(x,y)\) is the degree of linear polarization at an optical scattering within the image, \(L_{0}^{\prime}\) and \(L_{90}^{\prime}\) are the light intensity of the fused image in the direction of \(0^{\circ}\) and \(90^{\circ}\). From the literature [20], it is known that the intensity of the light in the image is linearly correlated with the grayscale and from Equation (6) each parameter of the fused image is also linearly correlated with the polarization and the non-polarized image; therefore  
\[P_{x}^{\prime}(x,y)=\frac{a(x,y)(G_{P0}-G_{P00})}{a(x,y)(G_{P0}+G_{P00})+2b(x,y )G_{np}} \tag{10}\]  
where \(G_{P0}\) and \(G_{P00}\) are the grayscale in the \(0^{\circ}\) and \(90^{\circ}\) directions of the polarized image, and \(G_{nP}\) is the grayscale of the non-polarized image, so the line polarization in a certain scattering region can be expressed as follows:  
\[P_{x}^{\prime}=\frac{\sum\limits_{x=1}^{W}\sum\limits_{y=1}^{H}P_{x}^{\prime}( x,y)}{W\cdot H} \tag{11}\]  
\(W\), \(H\) denote the width and height of the fused image respectively.
## 2 Methodology Framework  
The model architecture is proposed based on a lot of related works [23; 24] and a different number of convolutional layers, different kernel sizes, and different number nodes in fully connected layers that have been tested to yield the current model. The MI-CNN model proposed in this paper is shown in Figure 2 and can be divided into three parts:  
1. CNN-based ground motion feature extraction According to [PERSON] et al. [23], CNN can well extract the time-/frequency-domain features of ground motions. Therefore, this study adopts CNN to extract the ground motion features. Specifically, the amplitudes of all ground motion sequences were normalized to improve the performance of CNN. The normalized ground motion sequence is adopted as Input_A, and fed into seven convolutional and pooling layers, followed by two fully connected layers, through which the ground motion sequence is transformed into 512 feature parameters. The kernel sizes decrease from 11 to 3 for the seven convolutional layers. The ReLU activation function is adopted for convolutional and fully connected layers. The Softmax activation function is used for the last fully connected layer to obtain the probabilities of different damage states.
2. Processing of building attribute data (\(T_{1}\) and \(FL\)) and PGA  
Consider that the ground motion sequences are normalized in Input_A and the amplitude of ground motion (PGA) together with building attribute data are Input_B. In this study, \(T_{1}\) and \(FL\) are utilized as building attributes to capture the nonlinear seismic performance of RC frame buildings. Other attributes can also be utilized to characterize the building properties more comprehensively. For example, the stiffness, mass, height, and designed strength for each story of a building are also very good building attributes. Nevertheless, the size of the training set is exponential to the number of building attributes considered; thus, only two building attributes (\(T_{1}\) and \(FL\)) are adopted in this work to avoid too many samples in the training set. Subsequently, Input_B is fed into three fully connected layers to obtain the other 512 feature parameters.  
Parameter integration and damage prediction  
In this part, the model concatenates all 1024 feature parameters obtained from the first and second parts, and the output is the probability of every damage state through four fully connected layers. The dropout technique is adopted to avoid overfitting and the dropout rate is set to 0.5 [25].  
Sample Generation and Model Training
# Multicriteria Decision Making and Its Application in Geothermal Power Project
## 2 Literature Review
The studied model was built based on GIS and a combination of the weighted linear combination (WLC) method and MCDM methods. The authors applied the studied method to a case study in the Sulaimaniyah Governorate of Iraq to demonstrate its feasibility. [PERSON] and [PERSON] (2019) introduced an MCDM-based approach to the urban drainage management plans evaluation process. The proposed framework was built based on adaptive AHP, Entropy, and TOPSIS methods. A real-world case study was tested where the studied framework was applied to evaluate four urban drainage plans for Tehran. [PERSON] et al. (2019) proposed a GIS-based MCDM model to calculate potential locations for a solid waste incineration power plant. The authors utilized the fuzzy analytic network process (fuzzy ANP) method to calculate the evaluation criteria weights and decision-making trial and evaluation laboratory (DEMATEL) technique to determine interrelation between the criteria. The studied model was demonstrated through a realistic case study in northern Iran. [PERSON] et al. (2019) introduced an integrated fuzzy MCDM approach to support a landfill location selection. The proposed model was based on FAHP and fuzzy TOPSIS. An illustrative case study was performed to demonstrate the calculation process of the model. [PERSON] et al. (2020) proposed a dynamic modelling of the reinjection process in a binary cycle geothermal power plant. In this work, the author introduces a new Antoine-based correlation for a water-CO2 mixture. [PERSON] et al. (2020) studied the modeling of a steam-washing scrubber for solid removal in geothermal powerplants. The authors also validated industrial sizing correlations data for scrubbers in geothermal applications. As a review of the literature shows, several MCDM models have been put forth to support location evaluation and selection processes in renewable energy, but very few hybrid MCDM models have been created to address the location selection for Geothermal Power Plants, particularly in a fuzzy decision-making environment. In this research work, the authors introduce a fuzzy Multicriteria Decision-Making Model, including the methods of fuzzy analytic hierarchy process (FAHP) and the Weighted Aggregated Sum Product Assessment (WASPAS), for a geothermal power plant site selection in Vietnam. The FAHP can be applied for ranking alternatives, but the number of alternatives selected is practically limited because of the number of pair-wise comparisons that need to be made, and a disadvantage of the FAHP approach is that the input data, expressed in linguistic terms, depend on the experience of decision makers, and thus involves subjectivity. This is the reason why we proposed the WASPAS model for ranking alternatives in the final stage. Additionally, WASPAS is presented to reaffirm it as a systematic method and solve the disadvantages of the FAHP model mentioned above.