esa-sceva/satcom-chunk-collection huggingface dataset:
# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 2 AIoT in PV Systems: Background
### Artificial Intelligence Techniques
#### 2.1.1 Machine Learning  
Machine learning (ML) is a subset of Artificial Intelligence that enables systems to learn from data and improve their performance over time without being explicitly programmed. It focuses on developing algorithms that can identify patterns and make decisions with minimal human intervention [17].  
ML is broadly categorized into the following paradigms, each suited to specific types of problems:  
* **Supervised Learning:** Models are trained on labeled datasets, where each input has a corresponding output. This approach is ideal for both regression and classification tasks [14]. In solar panel monitoring, it can be used to classify panel conditions (e.g., normal, dusty, or damaged) based on labeled historical data.
* **Semi-Supervised Learning:** This hybrid approach leverages a small amount of labeled data alongside a large volume of unlabeled data. The model learns correlations between labeled and unlabeled instances, then uses the labeled data to guide the labeling of the remaining dataset [18]. This is particularly useful in PV system analysis, where labeled fault data may be limited, but abundant sensor or image data are available.
* **Unsupervised Learning:** This approach works with unlabeled data to discover hidden patterns or groupings, using techniques such as clustering and dimensionality reduction [19]. It is suitable for anomaly detection or grouping similar fault types in PV systems without requiring labeled data.
* **Reinforcement Learning:** Models learn by interacting with an environment and receiving feedback through rewards or penalties [20]. This method can be applied to optimize cleaning schedules for solar panels or manage adaptive control of energy flow in solar farms.  
Figure 4 outlines the key ML algorithms commonly associated with each learning paradigm.  
A typical machine learning workflow is illustrated in Figure 5. It begins with data collection and preprocessing to ensure data quality and consistency. The process continues with identifying the problem type classification or regression, which determines the selection of an appropriate algorithm. The dataset is split into training and test sets. Model selection is carried out using cross-validation and hyperparameter tuning. This procedure can be repeated for different machine learning algorithms, allowing performance metrics to be compared. The model with the highest evaluation score is then selected as the final candidate.  
Figure 4: Categorization of ML algorithms by paradigm.
# Integrated Components and Solutions for High-Speed Short-Reach Data Transmission
## 2 Transmitter for Short-Reach Transmission Systems
### Directly Modulated Laser
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Tx** & **\(\Lambda\)** & **MF** & **BW** & **Rate** & **Reach** & **Fiber** & **FEC** & **Left.** \\
**Device** & **(nm)** & & **(GHz)** & **(Gbit/s)** & **(m)** & **Type** & **Limit** & **Ref.** \\ \hline MM-VCSEL & 850 & PAM-4 & 22 & 112 & 200 & OM4-MMF & \(2\times 10^{-4}\) & [34] \\ MM-VCSEL & 850 & PAM-4 & \(-\)28 & 168 & 50 & OM5-MMF & \(>1\times 10^{-6}\) & [35] \\ MM-VCSEL & 850 & PAM-4 & 25 & 112 & 200 & OM4-MMF & \(42\times 10^{-4}\) & [36] \\ MM-VCSEL & 850 & PAM-4 & 25 & 200 & 100 & OM4-MMF & \(4\times 10^{-3}\) & [37] \\ MM-VCSEL & 850 & PAM-4 & 20 & 100 & 100 & OM3-MMF & \(3.8\times 10^{-3}\) & [38] \\ SM-VCSEL & 850 & DMT & 26 & 161 & 10 & OM4-MMF & \(2.7\times 10^{-2}\) & [39] \\ SM-VCSEL & 850 & DMT & 25 & 112 & 300 & OM4-MMF & \(3.8\times 10^{-3}\) & [40] \\ SM-VCSEL & 1543 & PAM-4 & \(-\)23 & 100 & 1000 & SMF & \(3.8\times 10^{-3}\) & [41] \\ SM-VCSEL & 1543 & PAM-4 & 22 & 100 & 2500 & SMF & \(3.8\times 10^{-3}\) & [42] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
###### Abstract  
Recently, simultaneous wireless information and power transfer (SWIPT) systems, which can supply efficiently throughput and energy, have emerged as a potential research area in fifth-generation (5G) system. In this paper, we study SWIPT with multi-user, single-input single-output (SISO) system. First, we solve the transmit power optimization problem, which provides the optimal strategy for getting minimum power while satisfying sufficient signal-to-noise ratio (SINR) and harvested energy requirements to ensure receiver circuits work in SWIPT systems where receivers are equipped with a power-splitting structure. Although optimization algorithms are able to achieve relatively high performance, they often entail a significant number of iterations, which raises many issues in computation costs and time for real-time applications. Therefore, we aim at providing a deep learning-based approach, which is a promising solution to address this challenging issue. Deep learning architectures used in this paper include a type of Deep Neural Network (DNN): the Feed-Forward Neural Network (FFNN) and three types of Recurrent Neural Network (RNN): the Layer Recurrent Network (LRN), the Nonlinear AutoRegressive network with eXogenous inputs (NARX), and Long Short-Term Memory (LSTM). Through simulations, we show that the deep learning approaches can approximate a complex optimization algorithm that optimizes transmit power in SWIPT systems with much less computation time.  
harvested energy; SWIPT system; neural network; power-splitting scheme; deep learning; power minimization +
Footnote †: journal: Applied Sciences
## 1 Introduction
It is important, therefore, to look for methods to improve the nanoscale morphology of the CNT film and to improve the smoothness of the film to increase CNT/Si junction density. One such option is to focus on orienting the CNTs within the film to produce aligned films. Aligned CNT films do not suffer the same hole issues and provide an overall better coverage over the silicon. Many methods have been suggested and implemented for forming smooth, aligned CNT films such as physical shearing from highly concentrated CNT suspensions [18; 19; 20; 21], physical shearing of dry CNT films [22; 23], growth of upright CNTs followed by vertical collapse [24; 25], drawing from highly aligned arrays [26], deposition of aligned CNTs using Langmuir-Blodgett [27] or Langmuir-Schaefer [28] deposition, horizontal growth on a patterned substrate [29], and self-assembly of CNTs from evaporation of suspension [30; 31], drop-casting [32], and deposition on a slide pulled from solution [33]. This paper looks at a simple, novel method to produce flat, aligned CNT films over a large area through the use of ultra-slow filtration, first examined by [PERSON] et al. [34]. It has been suggested that, in suspension, the CNTs will naturally align within the fluid, and a slow deposition process will allow this alignment to be maintained in the produced film and thus lead to the formation of smoother films. Slow filtration is also less labour-intensive and does not require the use of dangerous chemicals normally used to align CNTs. This would make slow filtration--a preferable method on both a cost and environmental basis. For CNT alignment to occur in slow filtration three conditions must be satisfied. The surfactant concentration must be below the critical micelle concentration (CMC). The CNT concentration must be below a threshold value. The filtration process must be controlled at a low speed [34]. The alignment of the CNTs in suspension is driven by electrostatic interactions between anisometric particles in a colloidal solution as investigated by [PERSON] [35]. [PERSON] et al. determined that long MWCNTs form a lyotropic liquid crystalline phase [36] and [PERSON] et al. used this property to form rows of aligned CNTs via solvent evaporation [31]. In this work, we produce and compare CNT films filtered quickly under vacuum and filtered slowly under gravity. We applied these films in CNT/Si solar cell devices. The films produced using gravity were smoother but more inhomogeneous over a large scale meaning solar cells using these films yielded more inconsistent results.
## 3 Results  
Table 2 lists the comparison results of the average Top-1 accuracy on Dataset 1. Our method achieves a Top-1 accuracy of 87.07% on the macro average and 88.04% on the weighted average. Moreover, as shown in Figure 4, according to the confusion matrix of different models, our method has the highest recognition accuracy in multiple categories, especially in the C2 category Debris Quartz Sandstone, where the accuracy reaches 87.67%.  
We further conducted a detailed performance analysis of recall, precision, and F1-scores. The experimental comparison provided in Table 3 shows that our model has the highest recall, precision, and F1-scores in both the macro average and weighted average. Compared with the original Swin Transformer, it improves the F1-scores of all categories except for C3, C10, and C12.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **ResNet152** & **ViT** & **Swin Transformer** & **Ours** \\ \hline macro avg & 86.65\% & 85.00\% & 84.50\% & 87.07\% \\ weighted avg & 86.17\% & 84.45\% & 86.32\% & 88.04\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The average Top-1 accuracy on Dataset 1.  
Figure 3: Flowchart of the training and evaluation process for each model in this study.  
Figure 4: Confusion matrices of four models, where (**a**) is ViT, (**b**) is Swin Transformer, (**c**) is ResNet152, and (**d**) is our improved model. C1–C14 are named according to the order of rocks in dataset1 in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Categories Metrics (\%)**} & \multicolumn{4}{c}{**Models**} \\ \cline{2-5}  & **ResNet-152** & **ViT** & **Swin Transformer** & **Ours** \\ \hline \multirow{4}{*}{C1} & Precision & 0.9018 & 0.8938 & 0.8870 & 0.8898 \\  & Recall & 0.9266 & 0.9266 & 0.9358 & 0.9633 \\  & F1-score & 0.9140 & 0.9099 & 0.9107 & 0.9251 \\  & Precision & 0.8048 & 0.7137 & 0.7552 & 0.7742 \\ \multirow{4}{*}{C2} & Recall & 0.7717 & 0.8311 & 0.8311 & 0.8767 \\  & F1-score & 0.7879 & 0.7679 & 0.7913 & 0.8223 \\ \multirow{4}{*}{C3} & Precision & 0.9600 & 0.9412 & 1.0000 & 0.9245 \\  & Recall & 0.8889 & 0.8889 & 0.8889 & 0.9074 \\ \multirow{4}{*}{C4} & F1-score & 0.9231 & 0.9143 & 0.9412 & 0.9159 \\  & Precision & 0.5484 & 0.5200 & 0.5385 & 0.7083 \\ \multirow{4}{*}{C5} & Recall & 0.4595 & 0.3514 & 0.3784 & 0.4595 \\  & F1-score & 0.5000 & 0.4194 & 0.4444 & 0.5574 \\ \multirow{4}{*}{C5} & Precision & 0.8544 & 0.8515 & 0.8218 & 0.8700 \\  & Recall & 0.8381 & 0.8190 & 0.7905 & 0.8286 \\ \multirow{4}{*}{C6} & F1-score & 0.8462 & 0.8350 & 0.8058 & 0.8488 \\  & Precision & 0.7407 & 0.8077 & 0.8182 & 1.0000 \\ \multirow{4}{*}{C7} & Recall & 0.9524 & 1.0000 & 0.8571 & 1.0000 \\  & F1-score & 0.8333 & 0.8936 & 0.8372 & 1.0000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of recall, precision, and F1-scores on Dataset 1.
## 4 Discussion
### Heart Development in C10-Treated Zebrafish Embryos  
By conducting C10 treatment, PE occurred in all C10-exposed zebrafish embryos at a concentration of 1.0 mg\(\cdot\)L\({}^{-1}\). The occurrence of this edema may be related to heart malformation. Therefore, a transgenic zebrafish line, _Tg(cmlc2:EGFP)_, was utilized to find the relationship between C10 treatment and heart malformation. Many studies have supported the relationship between PE occurrence and heart malformation [16; 22]. C10 caused slight differences in cardiac development when compared with the control, resulting in decreasing heartbeat rates at 48 and 72 hpf and then maintaining normal heartbeat rates at 96 hpf in C10-treated embryos. Based on these results, C10 showed no strong developmental toxicity on heart formation.  
Using RT-qPCR analysis, the expression of eight genes was examined in C10-treated embryos, and _nppa_ and _cacna1c_ genes were significantly up- and down-regulated, respectively. In the _nppa_ gene expression, C10-treated embryos exhibited a 2.3-fold increase and encoded natriuretic peptide A, which restricted the atrial location during embryonic development [23]. The deficiency of the _nppa_ gene is related to the failure of blood pressure [24], so a higher expression in C10-treated embryos may cause heart dysfunction as our study observed differential heartbeat rates. In the PS-treated zebrafish embryos, the _nppa_ gene was highly expressed by 8.6-fold when compared with the control group [16].  
However, the decreased expression level of the cacna1 gene in C10-treated zebrafish embryos was observed, and this gene downregulation may be related to the dysfunction of heart contraction caused by calcium homeostasis impairment because it encodes a voltage-dependent calcium channel. Recently, a similar result has been reported in the bisphenol A-treated zebrafish embryo as an impairment of cardiac contraction and relaxation by the downregulation of the cacna1 gene [25].  
Other genes were not differently expressed in relation to C10 treatment in zebrafish embryos. In zebrafish embryos treated with ginger oil, very low teratogenic effects were observed in the treatment range of 1 to 10 mg\(\cdot\)L\({}^{-1}\). Thus, ginger oil does not have embryotoxicity and teratogenicity at treated concentrations of less than 10 mg\(\cdot\)L\({}^{-1}\)[20]. The C10 compound is a major component of massoia EO; however, this compound shows high embryotoxicity as 100% of zebrafish embryos died at 2.0 mg\(\cdot\)L\({}^{-1}\) when compared with ginger oil, and it exhibits heart dysfunction until 72 hpf after C10 treatment.
## 2 Results and Discussion
### Spatial Context  
The spatial concept of videoconferencing refers to the geographic distance between the individuals offering and receiving medical information and knowledge, offering the opportunity for a widespread community of practitioners. GPA describes how collaborative work not only offers knowledge but also provides specialists with opportunities to follow up with patients that the specialists have previously treated.  
GPA: \"When the patient is transferred to us from XX [hospital], the hospital's responsibility is not complete. They follow up on their patients (...). The professional development goes both ways...\"  
Specialist A and Specialist B support GPA's opinion regarding the two-way outcome of the collaborative work. The discussions via videoconference allow the GP to be a part of a larger collegium, a community of several knowledge specialties. As SA describes, multidisciplinary consultations also contribute something for the specialists.  
SA: \"We feel that it provides some service to them, but there are also two sides. It is a very nice way to discuss, which is actually easier than by phone because you are following a complete collegium. There are different specialties here, so it is easier for anyone to give, shall we say, an answer that is helpful, than if it had only been one person on the phone.\"  
SB: \"It creates a kind of community when meeting every day (...) an expanded collegium, a kind of mutual training meeting, in which we help each other. When transferring or referring, it is also quite natural to request to know what has happened [with the patient].\"  
SA: \"Several approaches might be good. The medical problems are often complex and diffuse. We contribute to one another from a broad professional perspective.\"  
Regular meetings provide an extended medical community for medical problem-solving and for following up. The regularity makes the collaborative work a two-way achievement.
# Optimization of Virtual Loudspeakers for Spatial Room Acoustics Reproduction with Headphones
## 2 Virtual loudspeaker Position Optimization
### Spatial Downsampling  
The optimization process described in Sections 2.1 and 2.2 is already a working solution, which finds the optimized virtual loudspeaker positions from the SDM data. However, the process is slow due to the amount of data. The complexity of K-means algorithm is directly proportional to the number of data points clustered. As an IR of a regular concert hall is approximately two seconds long, there are 96,000 data points after SDM analysis when using 48 kHz sampling rate. Finding the optimal positions over the whole data is possible, but requires long computation time to complete. However, a considerable speedup is possible by reducing the number of data points. If the reduced data are used to find a coarse approximation of the positions, and the whole data are only used to fine-tune the result, a considerable speedup may be achieved. Here, this data reduction step is called spatial downsampling.  
The implementation of spatial downsampling is similar to direction weighting and initialization of loudspeaker positions described before. The SDM data points are condensed to equidistant point grid on the unit sphere. The difference is that the energy of each data point is distributed between the three closest grid points in order to get more accurate approximation of the surrounding energy field. The distribution is calculated by determining barycentric coordinates of the data point with respect to those three closest grid points. The reduced point grid is then used instead of SDM data to initialize and optimize the virtual loudspeaker positions. After the optimization algorithm has converged, the reduced data are replaced by the original SDM data and the final optimized positions are fine-tuned from the reduced data optimum. The processing time is shortened due to reduced data, but the result is the same as without the reduction due to the fine-tuning step.  
**3. Perceptual Evaluation with a Listening Test**  
The performance of the virtual loudspeaker location optimization and its effect on the perceived spatial sound reproduction was evaluated with a subjective listening test. The aim of the listening test was to examine how perceivable the differences are in two typical use cases of the SDM-based spatial sound. The first case was a stereo music played in a dry studio control room and the second case was an orchestra music performance set to the acoustics of a concert hall. It should be noted that the first case has two sound sources in a small space and the second one has 24 sound sources in a large space.
# Quantification and Correction of Wave-Induced Turbulence Intensity Bias for a Floating LIDAR System
## 2 LIDAR Theory
### WindCube Principle  
#### 2.2.1 Line-of-Sight Radial Wind Speed Measurement  
The five laser directions of the WindCube v2(r) are maintained for a duration of 0.8 s for the non-vertical beams and 1 s for the vertical position. During a \(LOS\) measurement, repeated laser pulses are sent from the LIDAR to the particles in the atmospheric boundary layer. As the time of light propagation is the unit used to evaluate the distance from the LIDAR to the particle backscattering the laser, the duration of the laser pulse induces an uncertainty in the position of the particle within the probe volume [19]. In the case of the WindCube, the pulses have a duration of 175 ns. Therefore, for each pulse, the backscattered signal is temporally sliced into range gates and a weighting function is applied to each of the sliced signals to take into account the filtering effect exerted by the probe volume on the measured radial wind speed [14]. At the end of a \(LOS\) measurement, a mean radial wind speed and a carrier to noise ratio (CNR) are determined at several altitudes using the maximum likelihood estimator (MLE) method to the averaged spectrum of all the pulses [20].
# Digital Mapping of Soil Organic Matter in Northern Iraq: Machine Learning Approach
## 4 Results and Discussions  
### Descriptive Statistics  
Descriptive statistics for both SOM and remote sensing data are presented in Table 2. The SOM content ranged from 0.1% to 6.18%, with a mean value of 1.81%. The mean value was slightly higher than the standard deviation (1.22%), suggesting that more data points were clustered around the mean, resulting in a higher overall average. The variability of SOM was classified using the [PERSON] [43] approach and was found to be high, with a coefficient of variation (CV) of 67.45%. This high variability may be attributed to the varying topographies and climatic conditions of the area. NDVI had the next highest CV (31.57%), likely due to the increase in vegetative cover and plant residue that remained after the harvesting process in August and September. BI values ranged from 0.06 to 0.19, with a CV of 25.11%. [PERSON] et al. [44] confirmed that BI was strongly correlated with farmland SOM in autumn. The DEM had the lowest CV (11.35%), mainly because of the significant amount of human interference in the topography of Batifa, particularly agricultural engineering activities.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Variables** & **Min** & **Max** & **Median** & **Mean** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **CV\%** \\ \hline
**SOM\%** & 0.10 & 6.18 & 1.69 & 1.81 & 1.22 & 67.45 \\
**DEM** & 633.00 & 1028.00 & 772.00 & 752.91 & 85.46 & 11.35 \\
**NDVI** & 0.12 & 0.46 & 0.17 & 0.19 & 0.06 & 31.58 \\
**SAVI** & 0.09 & 0.30 & 0.12 & 0.13 & 0.03 & 27.34 \\
**BI** & 0.06 & 0.19 & 0.12 & 0.12 & 0.031 & 24.41 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of SOM and auxiliary data.
# Identification of Damage in Beams by Modal Curvatures Using Acoustic Beamformers
## 2 Application of MFP Algorithms to Damage Identification
### Minimum Variance Distortionless Response (MVDR) Beamformer  
To suppress sidelobes and then improve estimates, adaptive methods were introduced. In adaptive methods, data are weighted with weights which depend on the data themselves. One of the most effective methods among the adaptive algorithms is the minimum variance distortionless response processor (MV, MVD, or MVDR), which is utilized in many different applications [29; 30; 31].  
The MVDR processor looks for a replica vector \(\boldsymbol{\chi}_{i,MVDR}^{T}\) such that when applied to CSDM, the beamformer output is minimized, with the exception of the look direction. That is [32],  
\[\begin{array}{ll}\text{minimize}&\boldsymbol{\chi}_{i,MVDR}^{T}\mathbf{K}_ {i}\boldsymbol{\chi}_{i,MVDR}\\ \text{subject to}&\boldsymbol{\chi}_{i,MVDR}^{T}\bar{\boldsymbol{\chi}}_{i}( \mathbf{x})=1.\end{array} \tag{3}\]  
This is achieved by minimizing the following functional:  
\[F=\boldsymbol{\chi}_{i,MVDR}^{T}\mathbf{K}_{i}\boldsymbol{\chi}_{i,MVDR}+ \lambda(\boldsymbol{\chi}_{i,MVDR}^{T}\bar{\boldsymbol{\chi}}_{i}(\mathbf{x}) -1), \tag{4}\]  
where \(\lambda\) is the Lagrange multiplier. The minimization procedure [32] provides  
\[\lambda=-2\Big{(}\bar{\boldsymbol{\chi}}_{i}(\mathbf{x})^{T}\mathbf{K}_{i}^{- 1}\bar{\boldsymbol{\chi}}_{i}(\mathbf{x})\Big{)}^{-1}\Rightarrow\boldsymbol{ \chi}_{i,MVDR}=\frac{\mathbf{K}_{i}^{-1}\bar{\boldsymbol{\chi}}_{i}(\mathbf{x })}{\bar{\boldsymbol{\chi}}_{i}(\mathbf{x})^{T}\mathbf{K}_{i}^{-1}\bar{ \boldsymbol{\chi}}_{i}(\mathbf{x})} \tag{5}\]  
in which the dependence of the sought weights on the received data is apparent. By inserting Equation (5) into the objective function of Equation (3), we obtain  
\[B_{MVDR,i}(\bar{\mathbf{x}})=\Big{[}\bar{\boldsymbol{\chi}}_{i}^{T}(\mathbf{ x})\mathbf{K}_{i}^{-1}\bar{\boldsymbol{\chi}}_{i}(\mathbf{x})\Big{]}^{-1}, \tag{6}\]  
which requires the solution of the forward problem, and the received data. The drawback of this expression is that the CSDM is usually ill conditioned, and therefore its inverse is not reliable. There are, of course, a few ways o solve this problem, such as truncated singular value decomposition or the use of diagonal loading for the regularization of CSDM. We use the latter and write  
\[\bar{\mathbf{K}}_{i}=\mathbf{K}_{i}+\epsilon\mathbf{I}, \tag{7}\]  
where \(\mathbf{I}\) is an \(m\times m\) identity matrix and \(\epsilon\) is a scalar, which is at the same time sufficiently large to ensure that \(\bar{\mathbf{K}}_{i}\) is invertible, and small enough not to cause a blurring effect on the beamformer. In our case, we took \(\epsilon=10^{-6}\times\text{tr}\mathbf{K}_{i}\).