esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Intelligent Fuzzy Controller for a Two-Axis Solar Tracking System
###### Abstract  
This paper proposes the development of a two-axis sun tracking solar energy system using fuzzy logic as intelligent quality policy. To achieve maximum efficiency for solar panels, it is necessary to follow the sun's path in the sky. Therefore, the architecture for the two-axis sun tracking solar energy system uses software to control the hardware. The hardware comprises (i) solar cells; (ii) lead-acid batteries; (iii) a gear box; (iv) a stepping motor; and (v) a light detection circuit, while the software comprises (i) a detection system; (ii) a fuzzy tracking controller; and (iii) a database system. A fuzzy logic controller is designed as the software architecture of the system to decide the timing for tracking the sun. The nearest position that results in receiving direct sunlight is obtained from the database. Our system is fully automatic in a changing environment and takes into account meteorological changes and the effects of the external environment arising from a malfunction. This approach reduces the number of starting motors and results in smaller energy loss in cloudy, cloud mask, or unstable weather conditions.  
solar tracking system; fuzzy logic controller; gear box +
Footnote †: journal: Applied Sciences
## 5 Simulation Results and Discussion
### Nonlinear Load Harmonic Spectrum  
The study's harmonic power flow types were employed for network distribution in three-phase balanced and unbalanced categories. At the specified time domain, the coordinating force was applied. Connectivity was also achieved in a number of other harmonic modes. The previous harmonic list employed in the research included seven spectra. Figure 19 depicts the harmonic spectra in the form of the voltage, current, and nonlinear properties of the PCC employed in this investigation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach’s** & & **Advantages** & **Disadvantages** \\ \hline \multirow{6}{*}{Modified Predictor Method} & 1. & Provided good & \multirow{2}{*}{1.} & \multirow{2}{*}{Heavy calculations, not suitable for fast, time-varying systems.} \\  & & performance (THDu), & & \\ \cline{1-1}  & & especially when having & & \\ \cline{1-1}  & & very small changes \(\Delta V\), & & \\ \cline{1-1}  & & at location B. & & \\ \cline{1-1}  & & Strong robustness, low & & \\ \cline{1-1}  & & requirements for & & \\ \cline{1-1}  & & model accuracy. & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: _Cont._  
Figure 19: Harmonic spectra of voltage in PCC for (**a**) no control, (**b**) predictor controller, and (**c**) fuzzy controller methods.
## 2 Methodology
### Guided Diffusion Model with Transformer
#### 2.2.2 Transformer-Based Data Restoration Architecture  
As shown in Figure 4, to acquire high-quality future FEMR signal time-frequency data in each iteration of prediction tasks, we integrated a transformer-based data restoration architecture within the guided diffusion model. This addition is specifically designed to restore the missing pixels in the predicted FEMR signal data.  
The transformer-based data restoration architecture adopts a residual in residual (RIR) structure, comprising three key components: shallow feature extraction, deep feature extraction, and image reconstruction. The shallow characteristics extraction part uses a \(3\times 3\) convolution layer to extract shallow characteristics from the low-quality input image as Equation (9):  
\[F_{0}=H_{SF}\big{(}I_{LQ}\big{)} \tag{9}\]  
where \(F_{0}\) is the shallow characteristics and \(I_{LQ}\) is low-quality (LQ) input image.  
The deep characteristics extraction part utilizes a residual hybrid attention group (RHAG) to process intermediate characteristics and a final \(3\times 3\) convolution layer to aggregate information as Equation (10):  
\[F_{DF}=H_{DF}(F_{0}) \tag{10}\]  
where \(H_{DF}\) consists of RHAG and one \(3\times 3\) convolution layer.  
The image reconstruction part combines shallow and deep characteristics and reconstructs the high-quality (HQ) image using a reconstruction module with pixel-shuffle for super-resolution tasks or two convolutions for same-resolution tasks as Equation (11):  
\[I_{HQ}=H_{R\alpha}(F_{0}+F_{DF}) \tag{11}\]  
where \(H_{R\alpha}(\cdot)\) denotes the reconstruction module.  
The residual hybrid attention group (RHAG) comprises two main components: hybrid attention blocks (HAB) and overlapping cross-attention blocks (OCAB). The HAB module integrates self-attention and channel attention mechanisms, enabling the activation of a broader range of input pixels and boosting reconstruction performance. It employs a large window size for window-based self-attention and incorporates a channel attention block (CAB) to dynamically adjust feature scaling. On the other hand, the OCAB module improves cross-window interactions by applying overlapping windows to key and value features, ensuring that the query can access richer contextual information. Figure 4 shows transformer-based data restoration architecture specifically.  
Figure 3: Schematic of guided diffusion model: (**a**) data splitting: split the FEMR signal data from \(x_{t-1}\) to \(x_{t+1}\) into conditioning known data from \(x_{t-1}\) to \(x_{t}\) and future data \(x_{t}\) to \(x_{t+1}\); (**b**) diffusion process: encode the data through the future data encoder \(\varepsilon\), and tractable noise is added to the input data during the diffusion process; (**c**) prediction process: the U-Net-based prediction process calculates the characteristics similarity between known and future data, and the trained model can predict future data from known data conditions; and (**d**) data stitching: stitch the data from \(x_{t-1}\) to \(x_{t+1}\) and \(x_{t-1}\) to \(x_{t}\) to obtain the predictions.
## 3 Results and Discussion
### Amino Acid and Peptide Profile
#### 3.2.2 Peptide Profile
No significant differences were seen between fish species in CONC samples, which was expected since all the raw materials belonged to the same Gadidae family. As can be seen from the SEC chromatograms (Figure 2), compounds with a molecular weight greater than 10 kDa comprised the most prominent fraction in the CONC samples (Figure 3). Both CONC1 and CONC2 had almost identical MW distribution profiles, while CONC3 had a lower proportion of compounds with molecular weight < 1 kDa compared to the other protein concentrates. It should be noted that the amount of dissolved protein obtained by Bio-Rad DC Protein Assay (Bio-Rad Laboratories, Hercules, CA, USA) for the fish concentrate samples was very low: 0.3 mg/mL, 0.4 mg/mL, and 0.6 mg/mL for CONC1, CONC2, and CONC3, respectively. Thus, obtained SEC profiles represent dissolved protein, not the total protein. However, it was found that the protein solubility for CONC samples was very low. Therefore, the obtained SEC results correspond well to the experiments performed in aqueous solutions. The most prevalent fraction in both hydrolysate samples was 1-10 kDa. With HYDR1, compounds above 10 kDa were almost negligible, while with HYDR2 they were moderately present at about 15% of total protein (Figure 3). In the HYDR1, the proportion of compounds below 1 kDa, which correspond to peptides of fewer than ten amino acids or free amino acids, was the highest among the examined samples. Surprisingly, in HYDR2 their proportion was even lower than in CONC samples. The results show that in sample HYDR1 the enzymatic treatment effectively hydrolyzed the fish proteins into peptides, having MW < 1 kDa. In general, bioactive peptides that are suitable for use in therapeutic foods primarily have a molecular weight of less than 1 kDa [48]. Although the proportion of > 10 kDa in the COLL1 sample was of the same order of magnitude as in the CONC samples, the SEC profile was different. It lacked the major peak near the void volume typical of CONC samples (Figure 3). In addition, there were practically no compounds smaller than 1 kDa in the COLL1 samples. Figure 1: The reducing SDS-PAGE image of commercial fish protein concentrates, hydrolyses, and hydrolyzed collagen. To lines 1–6 was pipetted 20 \(\upmu\)L of solution and to lines 7–12 25 \(\upmu\)L of solution. Line numbers represent samples: 1 and 7 = CONC3, 2 and 8 = CONC1, 3 and 9 = CONC2, 4 and 10 = HYDR1, 5 and 11 = HYDR2, 6 and 12 = COLL1. Fish protein concentrates = CONC1–3, fish protein hydrolyses = HYDR1–2, hydrolyzed fish collagen = COLL1. Standard is abbreviated as std.
# A Location and Velocity Prediction-Assisted FANET Networking Scheme for Highly Mobile Scenarios
## 5 Simulation and Analysis
### Comparisons with Existing Methods  
We also compare with the existing algorithms to validate our proposal. Specifically, three different algorithms are compared, i.e., (1) Method 1: the conventional k-means algorithm is used. Note that the number of clusters is set to a constant of 4 during the whole simulation process. Moreover, the distance between nodes consists of their relative speed difference [25]. (2) Method 2: the method is similar to Method 1, but the distance between nodes does not consider the speed information [26]. (3) Method 3: the method is similar to Method 1, but it uses the k-means++ algorithm [27].  
Figure 11a plots the stability CDF results of these 4 different algorithms during the whole simulation process at a speed of 30 m/s. It can be observed that these four algorithms show approximate performance, where Method 2 presents a slight advantage. Nonetheless, more comparisons using different metrics are needed to verify our proposal. Figure 11b plots the ratio CDF of the changed edges, where three different methods mentioned above are compared with our proposal. Generally, a smaller value of the ratio of changed edges implies a well-predicted network graph with respect to the actual network graph, resulting in fewer communication overheads consequently. From this figure, it can be seen that our proposal is slightly lower than these three algorithms, proving a good evolution performance.  
The computational complexity of these four algorithms is compared in Table 3. The conventional \(k\)-means algorithm has a complexity of \(\mathcal{O}(kdN)\) for each iteration, where \(N\) is the number of nodes, \(D\) is the number of dimensions, and \(k\) is the number of clusters. As such, the computational complexity of Method 2 is half of Method 1 since the data dimension only considers the distance. The \(k\)-means++ algorithm owns a higher complexity since it calculates the selection probability for all nodes during the centroid initialization. The complexity of our proposal lies between Method 1 and Method 3, where parameter \(\alpha\in(0,1)\) means the proportion of the \(k\)-means procedure during the whole simulation process. Furthermore, we obtain the time consumption results of these algorithms via the Matlab platform running on a computer with an Intel(R) Core(TM) i7-9750H [EMAIL_ADDRESS] GHz. The corresponding is also listed in Table 3. Note that calculating the number of clusters is omitted to highlight the performance of clustering algorithms herein.
## 6 Experimental Scheme Design and Optimization Results Discussion  
### Experiment Design Based on Taguchi Method  
The Taguchi method is an effective mathematical-statistical method, which can mine the information of the whole parameter space through a little number of experiments. The basic steps for using the Taguchi method for experimental design are as follows:  
1. Identifying the objectives: In the Taguchi method, the specific optimization objective should be defined first, which determines the objective function and influences the classification of variables into controllable factors and noise factors.
2. Determining the objective function: Divide the objective function according to three different types: nominally the best, the smaller the better, and the larger the better.
3. Selection of controllable factor and noise factor: In the Taguchi method, the controllable factor is determined during the design process, while the noise factor varies according to the conditions of users.
4. Selecting an orthogonal array: The orthogonal array can obtain quasi-optimal results without spending a lot of time and uses independent factor impact analysis to reduce computational optimization.
5. Simulation and analysis: The analysis phase of the simulation consists of converting the raw data of the objective function into the computation of a representative signal-to-noise ratio, which serves as a measurement tool to determine robustness and is an important factor in optimizing design parameters.  
Through the study of the 6-UPS parallel mechanism, five design variables, as shown in Table 2, are determined, which are the main factors affecting the load-bearing capacity and motion range of the mechanism. When different levels of each factor are combined,  
Figure 10: Variation curve of the objective function with the initial height \(H\) between the mobile and fixed platform.  
the mechanism's performance will change. Therefore, the optimization of the 6-UPS parallel mechanism is a multi-objective, multi-factor, and multi-level problem. The Taguchi experimental design used in this article includes five factors and four levels L\({}_{16}\) (4\({}^{5}\)); the levels of every factor are shown in Table 4; and the experimental layout and experimental results are shown in Table 5.
# Combining Optimization and Simulation for Designing a Robust Short-Sea Feeder Network
## 5 Computational Study
### Determining Global Estimated Optimal Routes
#### 5.3.2 Best Routes with and without Weather Uncertainty
This is beneficial during the winter season since the weather conditions are rougher, and a greater buffer against delays is needed to avoid duration violations. Conversely, during the summer season, the weather conditions are better, resulting in a shorter main route with corresponding longer daughter routes to reduce operational cost.
###### Abstract  
In this study, four optical techniques--Optical Coherence Tomography, Sidestream Darkfield Microscopy, Laser Speckle Contrast Imaging, and Fluorescence Angiography (FA)--were compared on performing an intraoperative quantitative perfusion assessment of the gastric conduit during oesophagectomy. We hypothesised that the quantitative parameters show decreased perfusion towards the fundus in the gastric conduit and in patients with anastomic leakage. In a prospective study in patients undergoing oesophagectomy with gastric conduit reconstruction, measurements were taken with all four optical techniques at four locations from the base towards the fundus in the gastric conduit (Loc1, Loc2, Loc3, Loc4). The primary outcome included 14 quantitative parameters and the anastomic leakage rate. Imaging was performed in 22 patients during oesophagectomy. Ten out of 14 quantitative parameters significantly indicated a reduced perfusion towards the fundus of the gastric conduit. Anastomotic leakage occurred in 4/22 patients (18.4%). At Loc4, the FA quantitative values for \"T1/2\" and \"mean slope\" differed between patients with and without anastomic leakage (\(p=0.025\) and \(p=0.041\), respectively). A quantitative perfusion assessment during oesophagectomy is feasible using optical imaging techniques, of which FA is the most promising for future research.  
optical imaging; fluorescence imaging; fluorescence angiography; indocyanine green (ICG); optical coherence tomography (OCT); laser speckle contrast imaging (LSCI); esophagectomy; gastric conduit; Sidestream Darkfield Microscopy (SDF); multispectral
# Data Exchange in Cluster Structure for Longevity of IoT
## 3 Proposed Property-Based Data Transfer
### System Operation Process
#### 3.3.2 Setup-Cluster Phase  
In the \"setup cluster phase\", the member nodes choose the most adjacent header node based on their locations. The member nodes utilize the ID information of cluster headers received in the advertisement phase. Finally, each member node involves in the cluster of header node they chose. Subsequently, the property information of the things is exchanged to support the cluster-property-based data transfer. If the memory of the things is sufficiently large, the information of all the things deployed in the IoT network can be stored successfully. However, generally, since the things in IoT have limited computing resources, they cannot store the information of all the things. Therefore, in the CPDT, each header node stores the property information of its member nodes. Furthermore, the header node also stores the information of properties retained by the other clusters to support the cluster-property-based data transfer.  
Figure 2: Advertisement phase sequence diagram.  
In this study, to choose the most adjacent cluster header, RSSI is exploited. The RSSI indicates the strength of the radio signal received in the communication module. The strength of RSSI is stronger as the distance between things is lesser, and vice versa. Based on this characteristic of RSSI, the distance between things is calculated relatively [31].  
Figure 3 shows the sequence diagram of the setup cluster phase. The following are the steps of this phase.  
1) Among header nodes, if the header node of cluster A is the closest header, the member node receives the strongest RSSI value from this header node. The member node transmits a message to the header of cluster A to participate in this cluster. This message includes the property of the member node.  
2) The header node stores the property information of participating member nodes and allocates slot numbers. The slot number indicates the order in which data is collected from the member nodes by the header node. The slot number is also transmitted to the member node. Otherwise, if the member node is closest to cluster B, the strongest RSSI value is detected from the header of cluster B. This node participates in cluster B, as shown in Figure 3.  
3) When all the corresponding member nodes are involved in a cluster, the configuration of a cluster is complete. Subsequently, the cluster header broadcasts the property information of the member nodes. The other cluster headers receive the broadcasted message and store the property information included in the message. Based on the stored property information, when the data interchanges between clusters are executed, only the clusters with the necessary property information are chosen, and the issued queries are sent to them.
## 2 Materials and Methods
### Partial Least Squares Regression (PLSR) Models  
Partial Least Squares Regression was chosen for its ability to handle highly collinear spectral data by compressing multiple data points into fewer representative factors, known as LVs. These LVs capture the main variance in the data while minimising redundancy, allowing for effective model calibration. Calibration equations were specifically developed for the chemical constituent and FA fractions.  
The calibration dataset was used to build the PLSR model by applying five-fold cross-validation, a method particularly well suited for the relatively small dataset size and the presence of repeated measurements. This approach divides the calibration data into five subsets, with four subsets used for model training and the remaining subset for validation in each iteration. This method ensures that all samples are included in cross-validation phases [37].  
The optimal calibration models were selected based on key performance metrics reflecting the robustness of each model. Subsequently, the PLSR model was used in the external validation dataset to estimate the performances of the prediction models.  
This study considered several goodness-of-fit statistics, including the coefficients of determination for calibration, cross-validation, and prediction (R\({}^{2}\)c, R\({}^{2}\)cv, and R\({}^{2}\)p,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **NIRFlex 500 BUCHI** \\ \hline Sensor & Extended range InGaAs (temperature controlled) \\ Spectrum range & 1000–2500 nm \\ Numerical resolution & 4 cm\({}^{-1}\) (with boxcar apodisation) \\ Average optical resolution & HWHN 3.25 nm \\ Supply & 100–230 VAC \(\pm\) 10\%, 50/60 Hz, 350 W \\ Type of measurements & Reflectance/transmittance \\ Measurement cells (NIRFlex Solids) \\ \multicolumn{2}{c}{FT-NIR} \\ Measurement geometry & Internal and automatic \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical specifications of the NIRS instrument used in the study: NIRFlex 500 BUCHI (benchtop).  
respectively). The standard errors of calibration (SECs), cross-validation (SECVs), and prediction (SEPs) were also evaluated, with the ideal being as low as possible to indicate the high predictive accuracy of the model. The Residual Predictive Deviation (RPD = SD/SECV or SEP) was also calculated, with its classifications outlined in Table 2. In addition, the range error ratio (RER = Range Of Reference Values/SECV or SEP) was calculated [37]. A high R\({}^{2}\), an RPD value greater than 3, and an RER value greater than 10 suggested that the model retains good predictive capabilities across different datasets [39].
# A Study of the Behavior and Responsibility of Slovak Drivers, Especially in Case of Fatigue
## 3 Results
### Interior Temperature  
Study shows (Table 5) that 73.3% (1066) of all drivers regulate the interior temperature of the vehicle during fatigue, while 90% of these 73.3% drivers reduce the interior temperature to increase concentration and attention. This confirmed a relatively common phenomenon of lowering the temperature of the vehicle interior when drivers feel tired. Moreover, only 3% of all participating drivers assume that interior temperature does not affect driver fatigue.  
Finally, 97% of all drivers consider the optimum temperature in the vehicle interior to be in the range of 18-24 \({}^{\circ}\)C. More specifically, (20-22 \({}^{\circ}\)C) 64% of drivers, (23-24 \({}^{\circ}\)C) 21% of drivers and (18-19 \({}^{\circ}\)C) 12% of drivers. Two percent of drivers reported the optimum temperature t > 25 \({}^{\circ}\)C and 1% of drivers reported t <17 \({}^{\circ}\)C as the ideal temperature.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Professional Driver**} & \multirow{2}{*}{**Total**} \\ \cline{3-4} \cline{6-6}  & & **No** & & **Yes** & \\ \hline \multirow{4}{*}{Attitude to fatigue} & I often find myself fired behind the wheel & Count & 28 & 18 & 46 \\  & \% & 2.5\% & 5.5\% & 3.2\% \\ \cline{2-6}  & It is not always possible to avoid fatigue behind the & & & & \\  & wheel, especially due to responsibilities and time & Count & 445 & 172 & 617 \\  & schedule, so sometimes I drive tired & \% & 39.3\% & 52.8\% & 42.3\% \\ \cline{2-6}  & I never drive tired & Count & 164 & 36 & 200 \\  & \% & 14.5\% & 11.0\% & 13.7\% \\ \cline{2-6}  & I avoid fatigue behind the wheel & Count & 495 & 100 & 595 \\  & \% & 43.7\% & 30.7\% & 40.8\% \\ \hline \multirow{4}{*}{Total} & Count & 1132 & 326 & 1458 \\  & \% & 100.0\% & 100.0\% & 100.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Do you drive during fatigue? Attitude to fatigue among PD and other drivers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Age**} & \multirow{2}{*}{**Total**} \\ \cline{3-4} \cline{6-6}  & & **Up to 25 Years** & **26–35 Years** & **36–50 Years** & **51+ Years** & \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Car interior temperature \\ Regulation \\ in case of driver fatigue \\ \end{tabular} } & No &
\begin{tabular}{c} Count \\ \% \\ \end{tabular} & 295 & 60 & 23 & 10 & 388 \\  & \% & 32.3\% & 17.5\% & 14.2\% & 27.8\% & 26.7\% \\ \cline{2-6}  & Yes & Count & 620 & 282 & 139 & 26 & 1066 \\  & Yes & \% & 67.8\% & 82.5\% & 85.8\% & 72.2\% & 73.3\% \\ \hline \multirow{2}{*}{Total} & Count & 915 & 342 & 162 & 36 & 1454 \\  & \% & 100.0\% & 100.0\% & 100.0\% & 100.0\% & 100.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Car interior temperature regulation in case of driver fatigue and driver’s age.  
Moreover, 95% of all respondents stated that temperature is a subjective and not a decisive factor in fatigue but with associated factors such as sleep deprivation, untreated sleep disorder or pre-trip fatigue can affect the driver.