esa-sceva/satcom-chunk-collection huggingface dataset:
# Super-Resolution Reconstruction of an Array Lidar Range Profile
## 3 Experimental Results
### Real Detecting Data Experiment and Result Analysis
#### 3.2.2 Outdoor Scene Experiment and Result Analysis  
In order to further verify the reliability of the method, under the settings of the same radar system experimental parameters, the detection imaging of the outdoor scene at a distance of 280 m from the lidar was carried out, and the detection targets included pedestrians riding bicycles, SUV cars, and sedans, in which the SUV cars were in the state of being obscured. Figure 8 shows the 400% super-resolution imaging effect.  
From Figure 8b,c, it can be seen that the interpolation-based super-resolution range profile of the target edge and contour details are fuzzy, and when the target and the target or target and background distance values are similar, for example, pedestrians riding bicycles and in close proximity of the side car, the pedestrian is easily interfered with by the bicycle and the car, resulting in the reduction in the accuracy of the extracted feature information. Figure 8f clearly shows the target contours of pedestrians, bicycles, sedans, and SUVs, and it can be seen in Table 7 that the proposed method improves the AG by at least 4.15% and the ES by at least 6.73% compared to the above-compared methods.  
As the super-resolution of the range profile increases, the retention of both the detail and edge information of the image by the super-resolution method decreases, but the POCS-based super-resolution reconstruction method still outperforms the interpolation method for the retention of the detail and edge information of the image at the same magnification.  
Figure 8: Super-resolution rendering of the outdoor scene range profile with (**a**) the original outdoor scene range profile; (**b**) bilinear outdoor scene range profile; (**c**) bicubic outdoor scene range profile; (**d**) POCS outdoor scene range profile; (**e**) POCS [17] outdoor scene range profile; and (**f**) the proposed outdoor scene range profile.  
By analyzing the effect of the reconstructed image and objective evaluation indexes, it is proved that the method in this paper has a better edge detail retention ability than bilinear interpolation, bicubic interpolation, and the POCS super-resolution reconstruction methods and meets the demand of the super-resolution reconstruction of range profiles.
# Low Discrepancy Sparse Phased Array Antennas
## 2 Sampling Points on a Planar Aperture
### Low Discrepancy Sampling Approaches
#### 2.2.3 Sobol Sampling  
The \(j\)th coordinate of the \(i\)th point, \(\text{p}(i,j)\), in a Sobol sequence [44; 45; 46; 47], is given by  
\[\left\{\begin{array}{ll}0,&i=1\\ \gamma_{i}(1)v_{j}(1)\oplus\gamma_{i}(2)v_{j}(2)\oplus\ldots,&i>1.\end{array}\right. \tag{11}\]  
Here \(\oplus\) is the bitwise exclusive or operator, \(\gamma_{i}(n)\) are the binary digits of the integer \(i-1\), and \(v_{j}(n)\) are direction numbers that are generated from primitive polynomials in \(\mathbb{Z}_{2}\). The direction numbers depend on the coordinate \(j\) and are obtained from primitive polynomials according to the \(i\)th dimension. The detailed process to determine these direction numbers can be found in [48]. Sobol sampling is hierarchical, since it does not depend on the total number of elements and adding additional elements does not require recalculation of element locations. For two-dimensional sampling with Sobol sequence, one option is to use the \(i\) points for one axis, and the \(j\) points for the orthogonal axis. This is similar to Hammersley sampling. Alternatively, one can use two Sobol sequences for the axes, similar to Halton sampling, which will improve the equidistribution.
# Label-Free Model Evaluation with Out-of-Distribution Detection
## 3 Methods
### Energy-Based OOD Detection  
Since unlabeled test sets collected in real-world environments may have semantic shift, the unlabeled test set may contain OOD images. Obviously, the classifier \(f\) always gives wrong results for classifying the OOD data. However, the representation of the dataset does not reflect the effect of OOD data well, which can lead to AutoEval producing an overly optimistic evaluation of the classifier's performance on the dataset that includes OOD data. We attempt to mitigate the influence of OOD data through the OOD detection. Specifically, we detect each input image by the OOD detection and use the AutoEval method to calculate the accuracy of the ID data.  
In order to ensure the accuracy of OOD detection, we use energy-based OOD detection. Energy-based OOD detection uses the energy function instead of the softmax function to identify OOD data [12]. The method uses an energy score derived from the discriminant  
Figure 2: The transformed image in the meta-dataset. The source images are from the training set. These images have the same label as the source image.  
model to determine whether the sample is OOD. The density function of the discriminant model can be expressed as an energy function as:  
\[p(x)=\frac{e^{-E(x;f)/T}}{\int_{x}e^{-E(x;f)/T}} \tag{5}\]  
where \(Z=\int_{x}e^{-E(x;f)/T}\); then, take the logarithm of the above formula to obtain:  
\[\log p(x)=-E(x;f)/T-\log Z \tag{6}\]  
Thus, \(E(x;f)\) is linearly aligned with the log-likelihood function. Low energy means high likelihood (ID), and high energy means low likelihood (OOD). We can set a threshold \(\tau\) to classify:  
\[G(x;\tau,f)=\left\{\begin{array}{l}0,if-E(x;f)<\tau\\ 1,if-E(x;f)>\tau\end{array}\right. \tag{7}\]
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 5 Algorithms for VLC-Based IPS  
By leveraging the existing techniques, VLC-based IPSs offer a cost-effective and energy-efficient approach to accurately locate and track objects or individuals within indoor environments. However, the development of efficient algorithms for VLC-based IPSs faces various challenges. The complexity of such algorithms depends on the intricacies of channel modeling, signal processing, and positioning techniques. Channel modeling involves understanding the behavior of light propagation in indoor environments, which can be affected by factors such as reflection, diffraction, and interference. Signal processing techniques are crucial for extracting useful information from the received light signals, considering the noise and interference present in the environment. Several approaches can be proposed to optimize VLC-based IPSs. This section provides a comprehensive review of the extensively researched algorithms. We have collected relevant research papers from Google Scholar in recent years, and the statistical data are shown in the above Figure 3.
# Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study
## 3 Qualitative Comparison
### Main Idea of the Frameworks  
Each of the data-gathering frameworks has unique properties that are the main performance-controlling features of that scheme. The main ideas of the techniques reviewed in this study are compared in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Protocol** & **Ref.** & **Main Idea** \\ \hline \multirow{2}{*}{TEEN} & [56] & A \(H_{T}\) and \(S_{T}\) value are used by the sensor nodes for transmitting data to the CH that reduces the number of data transmissions \\  & [57] & A chain-based mechanism is used for one data transmission per round to the BS through data fusion among sensor nodes \\ \multirow{2}{*}{HEED} & [58] & A distributed clustering method is used that considers node residual energy and proximity for CH selection \\  & [60] & A tree-based protocol that dynamically adapts the routing path \\  & & based on the transmission cost \\  & & A clustering method is used in which CHs are selected based on residual energy and distance, and a relay CH is selected that is closest to the sink \\ \multirow{2}{*}{IoJHR} & [62] & Several virtual grids are created with one CH in each grid which forwards data to the mobile sink when it is closest \\  & [63] & Clustering is performed by PSO and the CH is selected based on the maximum residual energy in each cluster \\ \multirow{2}{*}{NDCMC} & [64] & Clustering and CH selection is done based on node density; additionally, VH is also used for data gathering \\  & [65] & A suboptimal solution is given for joint optimization of the UAV trajectory and node wake-up scheduling \\ \multirow{2}{*}{UAVCDG} & [66] & CDG is performed to reduce the volume of data transmission and the UAV trajectory is minimized \\  & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main ideas of the investigated WSN data-gathering frameworks.
## 4 Results and Discussion  
Figure 6 shows the experimental results for the fixed-pressure control, cooling-load-estimation control, and cooling-load-estimation-with-interlocking-fans control methods. With regard to compressor operating frequency fluctuations resulting from an outdoor temperature of 30 \({}^{\circ}\)C, the operating frequency fluctuation range decreased in the fixed-pressure control, cooling-load-estimation control, and cooling-load-estimation-with-interlocking-fans control methods. Particularly, in the fixed-pressure control method, the on/off operation of the compressor was repeated seven times. This repetitive on/off operation in the fixed-pressure control method resulted in an excessive cooling capacity. Figure 7 shows that the temperature in the room was 1.1 \({}^{\circ}\)C lower than the set temperature. In the cooling-load-estimation control method, the error between the indoor room temperature and target value was 0.1 \({}^{\circ}\)C, and the fan temperature control in the cooling-load-estimation-with-fan control method could maintain the room temperature at the level of the set temperature.  
In conclusion, obtaining a result in which the temperature in the indoor room can be stably maintained at the same level of the set temperature by determining the cooling load and receiving the feedback of the difference between the set and evaporation temperatures to fluctuate the indoor fan in consideration of the change in the compressor operating frequency is possible.  
Figure 8 displays the energy consumption according to the control method. Specifically, 882.0 W was used in the fixed-pressure control method; 567.7 W was consumed in the cooling-load-estimation control method; and 527.7 W was consumed in the fan-load-control condition of the cooling-load-estimation control method.  
Figure 5: Flowchart of the cooling load estimation control with the fan-control logic.  
Figure 9 shows the total power consumption for 2 h after the start of the operation according to the control method for environmental temperatures of 27 \({}^{\circ}\)C, 30 \({}^{\circ}\)C, and 35 \({}^{\circ}\)C. Power consumption was reduced in the fixed-pressure control, cooling-load-estimation control, and cooling-load-estimation-with-fan control methods, sequentially. The greater the cooling load, such as when the environment temperature was 35 \({}^{\circ}\)C, the greater the benefit of energy efficiency through control.  
Figure 6: Operating Hz of the cooling-load-estimation-with-fan control method (ambient temperature 30 \({}^{\circ}\)C).  
Figure 7: Room temperature in the cooling-load-estimation-with-fan control method (ambient temperature 30 \({}^{\circ}\)C).  
ergy savings effect of 16.1% compared with the fixed-pressure control method and 1.2% compared with the cooling-load-estimation control method.
# Ultrasonic VMCF Tests on Very Large Specimens with Risk-Volume Up to 5000 mm+
## 3 Results
### Experimental Calibration  
Figure 3 reports the stress-amplitude variation between Gage A and Gage B, computed through Finite Element Analysis (\"FEA\" in Figure 3) and measured with strain pages (\"Experimental\" in Figure 3). In particular, the ordinate axis reports the stress amplitude measured by Gage B (\(s_{\text{B}}\)), normalized with respect to the stress amplitude measured by Gage A (\(s_{\text{A}}\)). For the experimental data, the stress amplitude was computed through a sinusoidal interpolation of the acquired signal, whereas, for the FEA model, the stress along the gage length (1.5 mm, subdivided in 140 points) was considered. Figure 3b shows the data points acquired with Gage A and Gage C for an input voltage amplitude of 1.4 V (inducing a maximum stress amplitude of about 105 MPa). The acquired data points were interpolated with a sinusoidal function.  
According to Figure 3a, the measured \(s_{A}\) was close to the measured \(s_{\text{B}}\), and the difference between the experimental and the FEA ratio \(s_{A}\)/\(s_{\text{B}}\) was limited (i.e., the experimental average value was equal to 0.997, and the FEA average value was equal to 1.006). The scatter of the ratio \(s_{A}\)/\(s_{\text{B}}\) was within a limited range that was smaller than \(\pm\) 3%, and this was mainly due to the signal noise. It is worth noting that the ratio was above 1, since the stress amplitude slightly increases from the specimen half length, as already shown in [6]. This first calibration confirmed the uniformity of the stress distribution within the risk-volume and validated the FEA model considered for the design of the Gaussian specimen. Moreover, Figure 3b confirms that bending loads are not present even in Gaussian specimens characterized by very large risk-volumes. The two sinusoidal fitting functions overlap. The 90% confidence intervals for the amplitude, equal to [105:107] MPa for Gage A and to [105:106] MPa for Gage C, and for the phase, equal to [149:155] rad for Gage A and to [150:155] rad for Gage C, overlap. This is a further confirmation that the risk-volume was subjected to pure tension-compression stress, with no bending loads. It is worth noting that this test was also carried out at larger stress amplitude and, even in this case, no bending loads were found.
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 4 Energy Conservation Techniques
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Energy Conservation Techniques**} & **Strategy Implemented for Achieving Energy Savings** & \begin{tabular}{c} **Targeted Components for** \\ **Energy Reduction** \\ \end{tabular} & \
\begin{tabular}{c} **Layer of** \\ **Implementation** \\ \end{tabular} &
\begin{tabular}{c} **Sulability** \\ **in LWSN** \\ \end{tabular} \\ \hline \multirow{4}{*}{Energy-Efficient Sensing} & \multirow{4}{*}{Hierarchical Sensing} & Reduces energy consumption by trading-of accuracy for energy efficiency. It involves using low-power sensors to acquire coarse-grained information about the sensing field, with the power-hungry and more accurate sensors only activated when an event is detected. & Sensor & Application & Yes \\ \cline{2-6}  & \multirow{4}{*}{Adaptive Sampling} & Reduces energy consumption by taking advantage of temporal and/or spatial correlations between sensed data. & Sensor and Radio & Application & Yes \\ \cline{2-6}  & \multirow{2}{*}{Model-Based Active Sensing} & Reduces energy consumption by minimizing the number of data samples collected and transmitted. & Sensor and Radio & Application & Yes \\ \hline \multirow{4}{*}{Energy-Efficient Routing} & \multirow{4}{*}{Cluster-Based Routing} & Reduces energy consumption by minimizing data & & & \\  & & transmissions thanks to data fusion, reduces communication range by limiting communication within the cluster, limits energy-intensive data fusion and coordination tasks to cluster heads, and selectively powers off other nodes in the cluster. & Network energy & Network & Yes \\ \cline{2-6}  & \multirow{2}{*}{Data-Centric Routing} & \multirow{2}{*}{Energy savings by eliminating data redundancy during transmission throughout the network.} & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & \multirow{2}{*}{Geographic Routing} & Reduces transmission energy by preferably using the shortest distance between nodes when maintaining or establishing a routing table. & & & \\ \hline \multirow{4}{*}{Topology Control} & \multirow{4}{*}{Location-Driven} & Reduces energy consumption by dynamically adapting the network topology based on the application needs and location of sensor nodes so as to allow network operations while minimizing the number of active nodes. & Network energy & Data Link & No \\ \cline{2-6}  & \multirow{2}{*}{Connection-Driven} & Reduces energy consumption by dynamically & & & \\  & & deactivating some nodes while maintaining network operations and connectivity. & & & \\ \hline Mobility-Based & Mobile Sink & Energy savings thanks to reduced link errors, contention overhead, and forwarding range during communication. & Radio & Network/Application & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._
# Valorization of Agri-Food Waste into PHA and Bioplastics: From Waste Selection to Transformation
## 3 Production of Bioplastics from Volatile Fatty Acids
Several species of algae can carry out this biosynthesis with different VFAs as a carbon sources, such as _Chlorella sorokiniana_ (CCAP 211/8K), which uses acetate and butyrate [124]; _Microactinium internum_ F014, which uses acetic, propionic, and butyric acids [125], and _Chlorella protothecoides_ FACHB-3, which can use a mixture of VFAs [126]. Additionally, in the case of yeasts, VFAs are regarded as a prospective hydrocarbon source for lipid production, offering a viable alternative to traditional hydrocarbon sources such as glucose. This is attributed to their transient metabolic pathways and elevated theoretical conversion efficiency [127]. Acetic acid can be converted into lipids by oleaginous yeasts and is related to acetyl-CoA metabolism. The formation of new fatty acids is contingent upon the uninterrupted provision of acetyl-CoA, which can be achieved by the cultivation of oleaginous yeasts on acetic acid as a carbon source [128]. Following the uptake of the acid, it is converted to acetyl-CoA, with the surplus being utilized for intracellular lipid accumulation. The oily yeast genera include _Yarrowia_, _Candidia_, _Rhodotorula_, _Rhodosporidium_, _Cryptococcus_, _Trichosporon_, and _Lipomyces_[129]. The Section 3.1 addresses the bacterial production of bioplastics, from VFAs as carbon source, but mainly focuses on PHA production.
# Evaluation of Optimal Visible Wavelengths for Free-Space Optical Communications
## 1 Introduction
This study aims to address that gap by designing and implementing a modular laboratory platform capable of evaluating the optical and modulation performance of three visible-wavelength laser diodes--650 nm, 532 nm, and 405 nm--under consistent experimental conditions. The system includes an Atmega328p microcontroller (Microchip Technology Inc., Chandler, AZ, USA) for precision current and frequency control, a Visual Basic-based graphical interface for real-time adjustments, and a silicon photovoltaic cell that serves as both a detector and an energy-harvesting component in the FSO link. The investigation pursues two core objectives: (1) to experimentally compare the optical power output and quantum efficiency of red, green, and violet laser diodes across varying current levels and modulation frequencies; and (2) to identify the wavelength that delivers the most practical and energy-efficient performance for short-range point-to-point FSO communication. By providing controlled, side-by-side evaluation of visible wavelengths, this study highlights the significance of wavelength selection in the design of energy-efficient, low-cost optical wireless systems. The findings contribute to the development of compact FSOsolutions tailored for indoor and urban environments, where short-range, high-reliability links are essential. The remainder of this paper is organized as follows: Section 2 presents the theoretical background on atmospheric attenuation, laser diode efficiency, and modulation techniques. Section 3 describes the experimental methodology and testbed architecture. Section 4 details and analyzes the results, including trends in optical power, diode efficiency, and modulation performance, as well as comparing our findings with prior studies and linking them to theoretical trends predicted in atmospheric models. Section 5 concludes the study and outlines directions for future work.
## 3 Results
### Equivalent Stress  
#### 3.3.1 Equivalent Stress on a Screw  
The stress applied to the screw was calculated for each motion using FEA. Figure 6 shows the screw stress in each case. According to [PERSON] et al.'s study [36], in the case of two-segment fixation (T12-L2) in the fractured spine, the stress received by a CS was higher than that received by a PS when lateral bending was performed. Conversely, in other motions, the stress received by a PS was higher than that received by a CS. As shown in Figure 6, our study also showed the same stress tendency. The average stress generated by each motion was named as the motion average stress, and the stress level for each fixing method was compared using the motion average stress. The highest motion average stress was 321.6 MPa, which was observed in T10-T11-L1 extension using a PS in the normal state. In addition, the low motion average stress levels in T11-L1-L2 using a PS and in T11-L1-L2 using a CS were 69.0 MPa and 86.9 MPa, respectively. The stress reduction rate based on the screw type in the same fixation method in the fractured state is described below. In the case of T11-L1 fixation, the motion average stress of the CS was 87.2 MPa, which was 19.3% lower than that using a PS. Notably, the lowest value of single motion was 48.9 MPa, which was observed in the lateral bending motion with a PS. In the case of T10-T11-L1 fixation, the motion average stress of the PS was 87.4 MPa, which was 24.3% lower than that of the CS. In the case of T11-L1-L2 fixation, the motion average stress of the PS was 69.0 MPa, which was 20.6% less than that of the CS. In the case of T10-T11-L1-L2 fixation, the motion average stress of the CS was 53 MPa, which is 27.4% lower than that of the PS, and this reduction rate was the highest among all cases. The motion average stress in the CS exceeded that in the PS in the fixation of three segments or more because high stress occurs in the part where the vertebral arch edge and screw head make contact due to the trajectory characteristics of a CS.  
Figure 6: Equivalent stress applied to the screw via pure moment: (**a**) equivalent stress of screw in flexion, (b) equivalent Scheme 11. L2, T10-L11-L1, T11-L1-L2, T10-T11-L1-L2.