esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This paper addresses the effect of using three PCMs (salt, paraffin and milk fat) as thermal regulators for cooling of electronics packaging. Different investigations employing empty HS and HS filled with PCM under NV and FV are tested. The experiments are conducted at four heat loads intensities ranging from 4027 W/m\({}^{2}\) to 9748 W/m\({}^{2}\). It is observed that under natural ventilation up to 6 W heat load, HS only can maintain device temperature below safety limit (60 \({}^{\circ}\)C) for only 5 min. When PCMs are included in HS, for the same heat load up to 6 W, the time for device to operate under safety temperature extends to 15 min. Under forced ventilation, the HS only can operate the device under safety temperature for 5 min up to 10 W, for 15 min up to 6 W and for 60 min up to 4 W only. When the PCMs are included in the HS under forced ventilation, the device can be maintained below safety temperature by all the three PCMs for 5 min up to 10 W, for 15 min up to 8 W by paraffin and milk fat while up to 10 W by salt hydrate and for 60 min up to 6 W by all of the PCMs. It emphasizes that the PCM amount needs to be optimized for a certain duration of temperature control at various heat loads. Comparison between experimental results and numerical predictions yields a very good agreement as represented by \(R^{2}\) and RMSE varying in the range of 0.93-0.98 and 1.9-2.5 \({}^{\circ}\)C, respectively. A parametric study is conducted through the validated simulation model concluding that a right selection of the melting point, thermal conductivity and density can remarkably improve temperature control performance of the PCMs. The following general conclusions are drawn from the presented research:  
1. A time lag in temperature rise ensues due to increased heat absorption by PCMs.
2. PCMs under both NV and FV achieve lower temperature than the HS-only at all tested heat loads.
3. At higher heat fluxes, PCM melts faster implying that the effective time for temperature control is reduced. Consequently, an additional amount of PCM would be required for prolonged operation.
4. No clear phase transition boundaries could be traced for milk fat due to its wider melting range contrary to salt hydrate and paraffin.  
Figure 14: The effect of PCM melting characteristics on the HS temperature drop: (**A**) with a fixed start of melting and increasing melting range; and (**B**) with a fixed melting range and shifting melting start.  
**Acknowledgments:** The authors would like to acknowledge the support from the UAE University Research Affairs through Grant No. G00001181.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Multi-Object Deep-Field Digital Holographic Imaging Based on Inverse Cross-Correlation
## 2 Principle
### Introduction of Image Cross-Correlation  
Image cross-correlation is a prevalent technique in the realms of signal processing and image analysis. It can be used to compare and analyze the degree of similarity or correlation between two signals or images, and it is widely utilized in various domains, such as object detection and recognition [22; 23; 24; 25; 26], image registration [27; 28; 29; 30], motion tracking [31; 32; 33; 34], image matching [28; 35; 36; 37; 29], and numerous other applications. It is also widely used in incoherent coded aperture correlation holographic imaging [38; 39; 40].  
Image cross-correlation is based on a cross-correlation algorithm that is used in signal processing, one that calculates the sum of the products between pixels in the overlapping portion of a sliding window and the image itself, whereby the window traverses both images (where the sliding window serves as the template image). The outcome is a newly derived image matrix that is termed the cross-correlation image or cross-correlation matrix. Each pixel's value within this cross-correlation matrix represents the similarity between the original image and the template image within the overlapping region at the corresponding location. A greater resemblance in pixel distribution between the template image region and the original image leads to higher values in the corresponding pixels of the cross-correlation matrix.  
In two-dimensional images, similarity is defined as in Equation (3), where \(\gamma\) is the cross-correlation matrix of the image; \(I_{x,y}\) and \(T_{u,v}\) are the weighting cross-correlation image and the template image, respectively; \(I(x,y)\) denotes the light intensity value of the weighting image at pixel \((x,y)\); \(\tilde{I}_{u,v}\) is the average value of \(I(x,y)\) in the range of the template image \(T_{u,v}\); and \(\tilde{I}\) is the template average.  
\[\gamma=\frac{\sum_{xy}[I(x,y)-\tilde{I}_{u,v}][T(x-u,y-v)-\tilde{I}]}{\left\{ \sum_{xy}[I(x,y)-\tilde{I}_{u,v}]^{2}\sum_{xy}[T(x-u,y-v)-\tilde{I}]^{2}\right\} ^{1/2}} \tag{3}\]Generally, the method of finding the maximum value of the cross-correlation matrix \(\gamma\) is used for image alignment, whereas, in this paper, we use the process of finding the position of object edges by locating the pixel points under a certain threshold of \(\gamma\); we call this the inverse cross-correlation operation. In contrast to direct edge extraction algorithms, this method proves more suitable for cases where the diffraction information of an object is intertwined and mixed with the background diffraction fringes. In addition, the edges of the object cannot be easily extracted.
# Voxel-Based Navigation: A Systematic Review of Techniques, Applications, and Challenges
## 6 The Potential Applications of Voxel-Based Navigation
### Voxel-Based Navigation for Autonomous Driving  
Autonomous driving has become a highly active research field. However, in autonomous driving, voxel-based navigation systems still face significant challenges in managing and processing large-scale voxel data. One key issue during voxelization is the loss of detail, especially when modeling complex environments. The complexity of urban roads, building details, and terrain variations require high-resolution voxel models. Low-resolution voxels may fail to accurately represent the position or structure of obstacles, thus reducing the precision of path planning. While reducing voxel size can mitigate the loss of detail, it significantly increases the computational load and memory consumption, posing challenges for real-time navigation tasks, where rapid updates and environmental analysis are crucial. Overly fine-grained voxel models can overwhelm computational resources, hindering real-time processing and even compromising safety. To address these challenges, common solutions include sampling techniques and parallel computing. For example, ref. [112] proposed a collaborative framework where multiple devices work together to construct voxel-based indoor maps. However, the applicability of this framework to both indoor and outdoor modeling remains limited. With the rapid development of quantum computing, future research could explore its application in voxel-based urban environment modeling to further improve data-processing efficiency.
## 2 Materials and Methods
### Data Acquisition and Data Fusion
#### 2.2.2 Data Fusion  
1. Data Partitioning  
After finishing data acquisition, the data set obtained by the variable temperature experiment was divided into training sets and test sets. The training set accounted for 70% of the whole data set, while the remaining data set (30%) was used for testing.  
2. Data Preprocessing  
The training and test sets needed to be preprocessed to speed up the training before being sent into the network. The collected data were normalized according to Equation (2) so that different indicators could be in the same dimension. This step could make sure that the input values were in the range of 0-1 to effectively avoid some small values being overwhelmed by large values and to minimize error in the correction process:  
\[x_{i}^{*}=\frac{x_{i}-x_{min}}{x_{max}-x_{min}} \tag{2}\]  
where, \(x_{i}^{*}\)--normalized value; \(x_{i}\)--the ith sensor input; \(x_{min}\)--minimum of sensor input; \(x_{max}\)--maximum of sensor input.  
3. Modeling Prediction  
According to the prediction model constructed in Section 2.1.3, the normalized data were sent into the well-trained BP neural network, the network outputted the predictions, and then renormalization to obtain the results of the remaining shelf-life prediction. The prediction results were presented in the quality perception interface of the CCT monitoring platform for managers to make decisions.  
The processing flow of data acquisition and data fusion is shown in Figure 5.  
Figure 5: The processing flow of data acquisition and data fusion.
## System Description  
### System Model  
A relay transmission system model consists of a source (S), a destination (D), a relay (R) and an eavesdropper (E), as depicted in Figure 1. S wants to communicate with D in the presence of E. The S broadcasts signals with average power \(P_{S}\). R receives signals from S with DF strategy.
## 2 Datasets
### CaliPSO CALIOP Data  
The CALIOP is a multiwavelength (532 and 1064 nm) polarization-sensitive lidar for the global profiling of aerosols and clouds. CALIPSO is the NASA A-train LEO satellite constellation that crosses the equator at approximately 1:30 P.M. (LST), indicating that CALIPSO can provide nearly simultaneous and collocated observations with Aqua. Generally, the CALIOP Version 4.2 (V4.2) L2 products are considered to perform better than the previous versions owing to the improvements in detecting the surface, revising the aerosol lidar ratio, and updating the aerosol subtype of the troposphere. The aerosol profile product (L2_05 kmAPro) provides CALIOP AOD retrievals with atmospheric volume description (AVD) by vertically integrating the extinction coefficient at 532 nm from the aerosol profiles. Pixels are screened over regions where no surface (AVD = 5) is detected within the whole profile and no aerosol particle layer (AVD = 3) appears within 250 m above the surface to remove the unreliable AOD caused by the gradually decaying lidar echo signal through clouds or heavy aerosols [51; 52; 53]. Additionally, the vertical feature mask (L2_VFM) product integrates the entire spatial range of the CALIOP profile and provides optimal layer characterization; it helps build a series of identification flags (cloud/aerosol) and provides guidance for controlling the quality of CALIOP and Aqua-MODIS AOD retrievals under cloud contamination. These two products are downloaded from the Atmospheric Science Data Center (Hampton, VA, USA) at [[https://eosweb.larc.nasa.gov/](https://eosweb.larc.nasa.gov/)]([https://eosweb.larc.nasa.gov/](https://eosweb.larc.nasa.gov/)) (accessed on 5 March 2022).
# Cooperation-Assisted Spectrum Handover Mechanism in Vehicular Ad Hoc Networks
###### Abstract  
This paper proposes a cooperation-assisted spectrum handover (CASH) scheme in vehicular ad hoc networks (VANETs). In CASH, each vehicle uses cognitive radio technology to collect the surrounding spectrum information, computes the stability of the spectrum, exchanges neighbor information by vehicle-to-vehicle collaboration technology, and then executes a partial prescan mechanism to reduce the handover delay. The proposed method can improve the spectrum utilization, shorten the delay time of the spectrum handover, and reduce the total number of handovers. Finally, the simulation results show that the proposed method outperforms other existing schemes. In spectrum utilization, CASH is about 20% better than that of the traditional full scan method. In average scan time, the time of CASH is about 5 times less than the time of the traditional full scan method. In total number of handovers, CASH improves the performance of the full scan scheme by 33%.  
cooperation-assisted; spectrum handover; vehicular ad hoc network; prescan +
Footnote †: journal:  
0000-0001-1998-22001  
0000-0001-1998-22001
## 3 Simulation and Analysis
### Wind Speed  
Ocean dynamics processes are the cause of SSR, among which sea surface wind is the most important process. The sea surface wind field is an important physical parameter for the interaction between the ocean and the atmosphere, and it is the main driving force of upper seawater movement. The trend of normalized SG radiance at different SVA is simulated considering sea surface wind speed (\(W\)) in the range of [1 m/s, 15 m/s] and in steps of 2 m/s, as shown in Figure 6.  
At high sea surface wind speeds, SSR is high. The simulation results show that the overall SG radiance is also stronger at high wind speeds. With the SG radiance threshold (0.004), the range of SVA that can be used for the SSR estimation model was found to increase with increasing wind speed. At \(W\) = 15 m/s, the range of available SVA is about \(-41^{\circ}\leq\theta\leq 36^{\circ}\), and at \(W\) = 1 m/s, the range of available SVA is very narrow at \(-7^{\circ}\leq\theta\leq 2^{\circ}\).  
At different wind speeds, the distribution of \(\Delta\sigma_{0}^{2}\) under different SVA combinations is shown in Figure 7, and the statistical results of \(\rho_{10}\) and \(\rho_{20}\) are shown in Figure 8. According to Figure 7, the SSR estimation model becomes more effective as the wind speed increases. In addition, an optimal combination of SVA could be always selected at all wind speeds. According to Figure 8, when \(W\) = 1 m/s, \(\rho_{10}\) and \(\rho_{20}\) are close to 0. When \(W\) gradually increases, the values of \(\rho_{10}\) and \(\rho_{20}\) increase gradually, but the increase trend of \(\rho_{20}\) is greater than that of \(\rho_{10}\). At \(W\) = 15 m/s, \(\rho_{10}\) is about 12.9% and \(\rho_{20}\) is about 16.2%. This shows that, the number of available SVA combinations is small at low wind speeds, but it is large at high wind speeds.  
Figure 6: Normalized SG radiance (\(I\)) simulated under each SVA (\(\theta\)) at different wind speeds.  
Figure 8: Statistical results of \(\rho_{10}\) and \(\rho_{20}\) at different wind speeds (\(W\)).
## Appendix A Azimuthal Averaging  
We took advantage of the spherical symmetry of the problem and azimuthally averaged the flow fields. We transformed Cartesian coordinates to spherical coordinates: \(P(x,y,z)\to P(r,\theta,\phi)\). Consider a spherical shell volume \(\delta V\) at radius \(R_{n}\):  
\[\delta V=\left[\iint(R_{n}d\theta)(R_{n}sin\theta d\phi)\right]\delta r=4\pi R _{n}^{2}\delta r \tag{1}\]  
Let us say that the shell thickness is equal to the cell size, \(\delta r=\Delta\). Points within a shell are denoted by \(P_{n}(R_{n},\theta,\phi)\), while flow field variables within a shell are denoted by \(\Phi_{n}(R_{n},\theta,\phi)\). Then, one can define a mean flow field variable by  
\[\overline{\Phi(R_{n},t)}=\frac{1}{\delta V(R_{n})}\iiint\Phi(R_{n},\theta, \phi,t)dV\cong\frac{1}{N}\sum_{1}^{N}\Phi_{n} \tag{2}\]Given the mean \(\Phi(R_{n},t)\), one can then define the fluctuation by  
\[\overline{\Phi^{\prime}(R_{n},t)^{2}}=\frac{1}{\delta V(R_{n})}\iint\!\!\!\iint \!\!\!\left[(R_{n},\theta,\phi,t)-\overline{\Phi(R_{n},t)}\right]^{2}\!\!\mathrm{ d}V\!\equiv\frac{1}{N}\sum_{1}^{N}\left[\Phi_{n}-\overline{\Phi(R_{n},t)} \right]^{2} \tag{11}\]  
Then the root mean squared (rms) fluctuation becomes  
\[\Phi^{\prime}(R_{n},t)_{rms}=\sqrt{\Phi^{\prime}(R_{n},t)^{2}} \tag{12}\]  
The accuracy of the azimuthally averaged variables depends on the ensemble size, \(N\). The number of points in the ensemble is shown in Table 11.  
For radii outside of 5 cm, one has \(10^{5}\) samples, which gives a good average. For radii inside 5 cm, there may not be enough points to give an accurate average.
# Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links
## 2 High Spectral Efficiency State-of-the-Art Techniques  
In order to push the data rate beyond the limited BW of the opto-electronic components, two common approaches are serial transmission with channel equalization at the Rx, and the use of high spectral efficiency modulation techniques such as multiple sub-carrier modulation [8]. For instance, the use of on-off keying (OOK), pulse-amplitude modulation (PAM), and pulse-position modulation (PPM) with frequency-domain equalization (FDE) was considered in [9; 10]. Several works also have considered the use of O-OFDM for UWOC links, with the advantages of using a simple single-tap equalizer to equalize the frequency-selective aggregate channel. These latter approaches have been primarily applied experimentally in clear underwater environments with a LD Tx and DCO-OFDMsignaling. For instance, a data rate of 1.45 Gbps over a range of 4.8 m was reported in [11] based on pre-emphasized O-OFDM symbols and using an avalanche photo-diode (APD) at the Rx, attaining a bit-error-rate (BER) of \(9.1\times 10^{-4}\). A similar approach was used in [12] while adjusting the LD bias, where a data rate of 4.8 Gbps with a BER of \(2.6\times 10^{-3}\) was reported over 5.4 m. Using a power-loading technique [13] at the Tx and a simple PIN PD with a lens at the Rx, a data rate of 1.3 Gbps over 6 m was achieved in [14] with a BER of \(2\times 10^{-3}\). In [15], using power-loading and pre-emphasizing at the Tx and a PIN PD with a lens at the Rx, a 12.4 Gbps link was established over 1.7 m. Furthermore, using an LED and without power-loading, [16] demonstrated a 161 Mbps data rate over 2 m using a PIN PD and a pair of focusing lenses, achieving a BER of \(2.5\times 10^{-3}\).  
On the other hand, downlink/uplink transmission was considered in [17] over 26 m, including a 5 m air and a 21 m water channel, where a 5.5 Gbps data rate with BER\(\approx 2\times 10^{-3}\) was achieved using power-loading at the Tx with an APD at the Rx. This experiment was repeated in [18] using an SiPM with a plano-convex lens at the Rx, where a data rate of 312 Mbps was achieved with a BER of \(\sim\)\(3\times 10^{-3}\).  
As mentioned in the previous section, we consider the three techniques of DCO, ACO, and LACO-OFDM and investigate their suitability for use in an SiPM-based UWOC system.
## 3 Energy-Efficient Optimal Power Allocation
### Energy-Efficient Optimal Power Allocation for Non-Real-Time Applications
#### 3.1.1 Average Transmit Power Constraint
By definition of \(\eta_{k+1}\), we have \(E[\log_{2}(1+\frac{P_{k}^{k}}{N_{\rm s}})]=\eta_{n+1}E(\xi P_{t}^{k}+P_{c})\), thus \(T(\eta_{n})=E[\log_{2}(1+\frac{P_{k}^{k}}{N_{\rm s}})]-\eta_{n}E(\xi P_{t}^{k }+P_{c})=(\eta_{n+1}-\eta_{n})E(\xi P_{t}^{k}+P_{c})>0\). Again using \(E(\xi P_{t}^{k}+P_{c})>0\), we have \(\eta_{n+1}>\eta_{n}\). Then we prove \(\lim_{n\to\infty}\eta_{n}=\eta^{*}\). From theorem in [26], we have \(T(\eta^{*})=0\), if \(\lim_{n\to\infty}\eta_{n}=\eta^{\circ}\
eq\eta^{*}\), we must have \(\eta^{\circ}<\eta^{*}\). By constructing a sequence \(\eta_{n}^{\circ}\) such that \(\lim_{n\to\infty}T(\eta_{n}^{\circ})=T(\eta^{\circ})=0\), and using Lemma 1, we have  
\[L\left(P_{t},\tau,\mu\right)=\mathrm{E}\left[\log_{2}\left(1+\frac{P_{r}}{N_{ s}}\right)\right]-\eta\mathrm{E}\left(\xi P_{t}+P_{c}\right)-\tau\left[ \mathrm{E}\left(P_{t}\right)-P_{uv}\right]-\mu\left[\mathrm{E}\left(P_{i} \right)-I_{th}\right], \tag{10}\]1  
which is a contradiction. Hence \(\lim_{n\to\infty}T(\eta_{n})=T(\eta^{*})\). Considering the continuous property of \(T(\cdot)\), we have \(\lim_{n\to\infty}\eta_{n}=\eta^{*}\).
# FLINT: Flows for the Internet of Things
## 2 Case Study--Port of the Future
### Heterogeneous Low Power Wide Area Networks (LPWANs)  
The typical size of a port requires wireless technologies that can transmit traffic over large distances. Low Power Wide Area Networks (LPWANs) can span very large areas, however are characterized by severe bandwidth constraints [9]. Traffic towards the wireless network is often restricted by duty cycle limitations and must be operated using different transmission schemes. Furthermore, incorporating multiple wireless networks into a single platform requires some sort of routing. This can be achieved with a central entity that stores the last active network and the properties of all available networks.  
Due to their bandwidth constraints, LPWANs often use proprietary payload encoding formats. This limits the application portability and interoperability among systems. In order to solve these issues, the Internet Engineering Task Force (IETF) LPWAN Working Group (WG) developed the Static Context Header Compression (SCHC) technique. This standard uses a _static_ context to represent the most common Internet Protocol Version 6 (IPv6), User Datagram Protocol (UDP) and Constrained Application Protocol (CoAP) patterns of the sensor node, known to both the end-device and the gateway. Every flow is distinguished by means of a unique identifier that precedes the payload of the message. This identifier is used by the translating gateway to perform the decompression of the message [10].  
This way, very constrained devices can establish an end-to-end IPv6 link over a low bandwidth technology. However, LPWAN systems typically consist of a star topology in which an intermediary captures packets from receiving gateways to remove duplicates. The SCHC router [11] (i.e., the edge router that (de)compresses packets from the IPv6 network) must therefore implement an abstraction to capture packets from the intermediary. Typical examples of such abstractions include the Message Queuing Telemetry Transport (MQTT), the Hyper Text Transfer Protocol (HTTP) and the Advanced Message Queuing Protocol (AMQP). These observations indicate the need to incorporate abstractions for different wireless networks and devices that have multiple network interfaces and their mapping to standardized outputs.
## 2 Materials and Methods
### Earth Observation: Open Resources of the Spanish National Geographic Institute  
LiDAR data acquisitions and orthophotogrammetric surveys of the Spanish National Geographic Institute (IGN-Spain) were used. LiDAR data for the entire surface of Spain are freely available and constitute an exceptional resource for aerial archeology prospection, as evidenced by their increasingly frequent use. In recent years, several archeological sites have been identified using LiDAR data from the National Aerial Orthophotography Plan of Spain (PNOA), including prehistoric settlements [23] and Roman camps [24], among others. The project researchers also used LiDAR data for the geoarcheological characterization of the site [25] and to identify large Roman construction sites [26].  
From 2013 to 2014, IGN-Spain carried out a LiDAR acquisition process for the whole region of Andalusia, which completed the general LiDAR-PNOA acquisitions obtained for all of Spain, beginning in 2010. LiDAR-PNOA point clouds with a density of 0.5 points/m\({}^{2}\) and an altimeter accuracy of about \(\pm\)20 cm of the root mean square error (RMSE) were acquired. The points were classified automatically. The LiDAR data files are presented in Log ASCII Standard (LAS) format or the Log ASCII Zipped (LAZ) format. The data are accompanied by some Supplementary Files, including a decompression tool and visualization of the LAZ and LAS files, metadata, specifications, and a file of acquisition dates. The LAZ file can be downloaded directly from the IGN-Spain website [27].  
Updated orthoimages of Spain and Andalusia were also acquired by IGN-Spain. This geographic documentation allows several applications for the aerial detection of archeological sites in the landscape of the Alto Guadiato Valley. The digital orthophotos of PNOA flights have a pixel size of 0.25 m or 0.50 m and can be downloaded from the IGN-Spain website in Tagged Image File Format (TIFF), together with the corresponding georeferencing World File Format (TFW) files. The PNOA orthomosaics were generated by sheets at 1/50,000 and are of maximum resolution and maximum topicality in Enhanced Compression Wavelet (ECW) format. All technical parameters are available on the website [28].