esa-sceva/satcom-chunk-collection huggingface dataset:
# Fingerprint Classification Based on Multilayer Extreme Learning Machines
## 5 Results and Discussion
### Hyperparameter Optimization
Furthermore, the Cappelli99a [42] method, which segments OMs into areas with similar orientations and compares them to predefined templates for each fingerprint class (arch, left loop, right loop, tented arch, and whorl) using a defined cost function, exhibits additional limitations. The class with the lowest segmentation cost is selected, and the resulting orientation map is used to populate the second part of the feature vector, centered on the Poincare-determined core point. This approach is more prone to class confusion compared to Hong08 and Liu10. Experimental evidence from previous studies has consistently identified the Hong08 descriptor as the most reliable and effective option [10; 13; 34]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline (a) Original ELM & \multicolumn{2}{c}{Capelli02} & \multicolumn{2}{c}{Hong08} & \multicolumn{2}{c}{Liu10} \\ \cline{2-7}  & \(N_{1}\) & Exac & \(N_{1}\) & Exac & \(N_{1}\) & Exac \\ \hline Default & 1000 & 0.8256 & 2000 & 0.9302 & 750 & 0.8939 \\ \hline HQNoPert & 1000 & 0.8477 & 2000 & 0.9512 & 750 & 0.9085 \\ \hline VQAndPert & 1000 & 0.7635 & 2000 & 0.9044 & 750 & 0.8559 \\ \hline \multirow{2}{*}{(b) ELM-M2} & \multicolumn{2}{c}{Capelli02} & \multicolumn{2}{c}{Hong08} & \multicolumn{2}{c}{Liu10} \\ \cline{2-7}  & \(N_{1}/N_{2}\) & Exac & \(N_{1}/N_{2}\) & Exac & \(N_{1}/N_{2}\) & Exac \\ \hline Default & 1000/1000 & 0.8604 & 1000/1000 & 0.9604 & 1000/1000 & 0.9045 \\ \hline HQNoPert & 1000/1000 & 0.8826 & 1000/1000 & 0.9730 & 1000/1000 & 0.9154 \\ \hline VQAndPert & 1000/1000 & 0.7864 & 1000/1000 & 0.9418 & 1000/1000 & 0.8603 \\ \hline \multirow{2}{*}{(c) ELM-M3} & \multicolumn{2}{c}{Capelli02} & \multicolumn{2}{c}{Hong08} & \multicolumn{2}{c}{Liu10} \\ \cline{2-7}  & \(N_{1}/N_{2}/N_{3}\) & Exac & \(N_{1}/N_{2}/N_{3}\) & Exac & \(N_{1}/N_{2}/N_{3}\) & Exac \\ \hline Default & 1000/250/1000 & 0.8609 & 250/250/1000 & 0.9634 & 250/250/250 & 0.9143 \\ \hline HQNoPert & 1000/1000/1000 & 0.8872 & 250/250/750 & 0.9740 & 250/250/250 & 0.9249 \\ \hline VQAndPert & 500/250/1000 & 0.8003 & 750/750/750 & 0.9471 & 500/500/500 & 0.8786 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Numbers of hidden neurons that maximize the prediction of the (a) original ELM, (b) ELM-M2, and (c) ELM-M3.
## 10 Simulation Results and Discussion  
In this section, we describe the simulation results and the discussion for the validation of our proposed energy management algorithms in the TOU pricing environment. We consider a single household with various types of appliances having variable energy consumption requirements. EMC is installed inside the home, which directly takes energy price signals from the utility company via the smart meter. Based on appliance energy consumption and user comfort requirements, EMC transmits the energy-efficient schedules to all types of appliances via the two-way communication module. Widely-used communication protocols, such as ZigBee, Wi-Fi, Bluetooth, _etc._, are used for this purpose. For the sake of simplicity, we assume six appliances that are mainly used in a normal household. All appliances are controlled and operated automatically. Each appliance has different lengths of operation time and starting time bounds. We consider scheduling time horizon T = 24 h, so that the user can solve the optimization problem to calculate the overall electricity bill. We simulate different scenarios for electricity cost saving and user comfort perspectives. For the WDO algorithm, the initial population size, the number of bits, _etc._, are shown in Table 4. Simulations are conducted in MATLAB. Some appliances can be used more than once by the residential users during 24 h. However, these appliances are shiftable throughout the time slots of a day. Table 1 shows the total appliances, energy consumption and operation intervals. Initially, we simulate WDO for all types of appliances without taking into account the capacity constraints and then simulate K-WDO. As discussed earlier, the TOU pricing signal is used, which is shown in Figure 6, and obtained from the New York ISOwebsite. Appliance energy consumption data are obtained from [49].
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## Appendix A Topology-Based Routing Protocols  
Below is the list of conventional topology-based routing protocols for FANETs [19; 36; 39] with respect to their proposed time.  
1994 Destination Sequence Distance Vector (DSDV) [175]  
1996 Dynamic Source Routing (DSR) [176]  
1998 Zone Routing Protocol (ZRP) [177]  
1998 Temporally Ordered Routing Algorithm (TORA) [178]  
1999 Ad hoc on Demand Vector (AODV) [179]  
1999 Hybrid Routing Protocol (HRP) [180]  
2000 Fisheye-State Routing (FSR) [181]  
2000 Multicast Ad hoc on Demand Vector (MAODV) [182]  
2001 Optimised Link State Routing (OLSR) [183]  
2002 Data-Centric Routing (DCR) [184]  
2003 Sharp Hybrid Adaptive Routing Protocol (SHARP) [185]  
2004 Topology Broadcast based on Reverse-Path Forwarding (TBRPF) [186]  
2007 Load, Carry and Delivery (LCAD) [131]  
2007 Time Slotted Ad hoc on Demand Vector (TS-AODV) (TS-AODV) [187]  
2008 Better Approach to Mobile Ad Hoc Network (BATMAN)[188]  
2008 Modified Optimised Link State Routing (MOLSR) [189]  
2008 Hybrid Routing based on Clustering (HRC) [190]  
2010 Directional Optimised Link State Routing (DOLSR) [191]  
2011 Cartography-Enhanced Optimised Link State Routing (CE-OLSR) [192]  
2011 Better Approach to Mobile Ad Hoc Network-Advanced (BATMAN-ADV) [193]  
2011 BABEL [194]  
2012 Contention Based Optimised Link State Routing (COLR) [195]  
2012 Ad hoc on Demand Vector Security (AODVSEC) [196]  
2012 Mobility Prediction Clustering Algorithm (MPCA) [197]  
2013 Predictive Optimised Link State Routing (POLSR) [198]  
2013 Hybrid Wireless Mesh Protocol (HWMP) [199]  
2013 Rapid-reestablish Temporally Ordered Routing Algorithm (RTORA) [200]  
2014 Mobility and Load aware Optimised Link State Routing (ML-OLSR) [201]  
2014 Multi-Level Hierarchical Routing (MLHR) [20]  
2017 UAV-assisted routing (UVAR) [202].
# Impact of Putting on Traffic Safety: A Synthesis of Research Findings
## 3 Results and Discussion
### Study Location
#### 3.2.2. Rut Depth Limits  
Table 5 presents the rut severity classification adopted by multiple highway agencies, as reported by [PERSON] et al. [37] and [PERSON] et al. [8]. Generally, ruts with depths lower than 10-13 mm are considered low severity, while ruts with depths higher than 19-25 mm are classified as high severity. However, these limits do not account for the impact of the rutting based on other interrelated factors such as weather conditions and speed. As shown  
Figure 8: Visual representation of reported accident rates associated with rut depth from studies conducted in (**a**) North America, (**b**) Europe, (**c**) Asia, and (**d**) Australia.  
Figure 9: Road accident rate per 100 MVkm in multiple countries [55].  
in the aforementioned discussion, rutting is more dangerous when the pavement is wet, the road is curved, the driving is at low illumination, and the driving trajectory is across the rut groove. Simulation studies have also clearly shown that rutting poses more risk when driving at higher speeds. Therefore, it is crucial to establish rut depth limits considering these various conditions.  
Table 6 presents the rut limits suggested or induced by multiple studies. [PERSON] et al. [42] suggested limiting the rut depth to about 7.6 mm, although highway agencies often consider the upper bound of the low rut severity to be higher than this value. [PERSON] et al.'s [11] results suggested a limit that largely corresponds to the upper limit of low-severity ruts used by multiple highway agencies, as presented in Table 5. [PERSON] et al. [37] recommended different rut limits for various driving speeds on wet pavement. For high-speed roads (>90 km/h), the rut depth should be kept below 5 mm during the rainy season. However, for urban roads where the speed is less than 70 km/h, the rut depth can be tolerated up to 25 mm. For dry pavement, they suggested limiting the rut depth to 20 mm for roads with a speed limit of 100 km/h. [PERSON] et al. [48] extended the analysis by considering skid number (SN) in their simulations, recommending a rut depth limit of 6 mm for roads with an SN of 30 at 60 km/h under wet conditions. For roads with an SN of 25 at the same speed, they suggested a higher limit of 25 mm, reflecting the combined influence of surface friction and rut depth on safety. These studies collectively highlight the need for context-specific rut depth limits, accounting for factors such as speed, road conditions, and environmental conditions to ensure optimal safety.
# Reconstructing Polarization Multiplexing Terahertz Holographic Images with Transmissive Metasurface
## 2 Theoretical Analysis
Thus, the intensity distributions on the focal plane can be calculated by using the Fresnel-Kirchhoff diffraction integral [44; 45]:  
\[U(x,y,z)=\frac{1}{i\lambda}\iint\limits_{S}U(r_{0})\frac{e^{i\lambda r}}{r} \left[\frac{\cos\left(\overrightarrow{n},\mathbf{r}\right)-\cos\left( \overrightarrow{n},\mathbf{r}_{0}\right)}{2}\right]\text{d}S. \tag{3}\]  
In other words, the quality of the focal point is evaluated and optimized by using the Fresnel-Kirchhoff diffraction integral [45] according to the intensity distributions on the focal plane. When a THz beam with specific polarization propagates through the metalens, a target image is reconstructed on its focal plane. The schematic representation of the transmissive all-dielectric metasurface for the polarization multiplexing holograms is shown in Figure 1. The incident THz beam can be focused by the metalens at each focal point with the nominated location on the focal plane to form the target holograms. Particularly, different holographic images are generated under different incident polarizations. When left-handed circularly polarized (LCP) light illuminates the metalens, the reconstructed image in the upper panel of Figure 1 is letters \"EL\". Alternatively, when the incident lights are with right-handed circularly polarized (RCP), pattern of numbers \"17\" is presented in the lower panel of Figure 1. Consequently, by controlling the polarization of incident lights, independent images can be reconstructed on the focal plane of the metalens. Moreover, the retrievedpartial phase profiles are also given in Figure 1, where the required phase profiles are imposed by rotating the meta-atoms with angle \(\theta_{i,j}(x,y)=\pm\varphi_{i,j}(x,y)/2\), where \"+\" and \"\(-\)\" correspond to the incident LCP and RCP lights, respectively.
## 4 Conclusions  
This work shows that FTIR spectra in the high wavenumber range (2750-3000 cm\({}^{-1}\)) are able to discriminate two different types of cells, as HUKE and SH-SYSY cells. In this spectral range, the FTIR spectra are mainly characterized by peaks due to the asymmetric and symmetric stretching mode of the CH\({}_{3}\) groups of cellular proteins and lipids (at about 2956 and 2870 cm\({}^{-1}\), respectively), as well as by bands assigned to the asymmetric and symmetric stretching of the CH\({}_{2}\) groups of lipids (at 2924 and 2850 cm\({}^{-1}\), respectively). In particular, the obtained results suggest that the lipid content, assessed with respect to the protein content, is larger in HUKE cells than in SH-SYSY cells.  
The discrimination between the two types of cells occurs regardless of the substrate on which they were grown. In particular, thick glass slides, thin glass coverslip, CaF\({}_{2}\) crystals, and low-e slides were tested. By considering the results of PCA and ratiometric analysis, best discrimination occurs for cells grown on thick glass and low-e slides. The results obtained for thick glass slides are interesting because of the physical sturdiness, low cost, and large availability of such substrates; in fact, they are routinely used in clinical environment for cytological and histological diagnosis. The proposal to consider FTIR spectra in the high wavenumbers range for discriminating different types of cells has previously been reported by other authors [16; 17]. Our findings not only support such a proposal, but also they are an important step towards the application of FTIR micro-spectroscopy for clinical diagnosis, particularly because they were obtained by a benchtop FTIR microspectrometer, which can be easily integrated into hospital environments. The next step to better promote the use of FTIR in diagnostics should concern the possibility of discriminating with this technique cells of the same type but with different degrees of pathology.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/24/8825/s1](http://www.mdpi.com/2076-3417/10/24/8825/s1)]([http://www.mdpi.com/2076-3417/10/24/8825/s1](http://www.mdpi.com/2076-3417/10/24/8825/s1)). Figure S1: Distribution of the FTIR spectra of HUKE cells grown on different substrates. Figure S2: Distribution of the FTIR spectra of SH-SYSY cells grown on different substrates.  
Conceptualization, V.C. and G.P.; methodology, G.P. and M.L.; investigation, G.P. and M.L.; data curation, G.P.; writing--original draft preparation, G.P. and M.L.; writing--review and editing, V.C.; supervision, V.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# An Enhanced Aircraft Carrier Runway Detection Method Based on Image Dehazing
## 4 Experiment Results and Discussions
### Dehazing Experiment
#### 4.1.1 Parameter Settings  
To accelerate the training speed of the model and obtain better generalization performance, we use the AdamW optimizer to train the model. The optimizer parameters \(\beta_{1}=0.9\) and \(\beta_{2}=0.999\) are set using the currently popular parameter settings. Considering the high total number of iterations used in our training, we set the weight decay parameter of the optimizer to \(4\times 10^{-3}\). The initial learning rate is set to \(5\times 10^{-4}\), and the learning rate is reduced by cosine annealing strategy gradually descending to \(1\times 10^{-7}\) within 600 K iterations. The batch size is set to 16 and the input image size is split to \(512\times 512\). The loss function is the Peak Signal to Noise Ratio loss(PSNRLoss) [34] function. The training process is conducted on a single NVIDIA RTX 4090 GPU with a 13 th Gen Intel(R) Core(TM) i9-13900 KF CPU. The network framework is implemented using PyTorch 1.11.0, CUDA 11.3 and CUDNN 8.6.2.  
In the simulation environment, to obtain the dataset for training the model, we control the CUAV to fly near the deck position and capture 600 pairs of clear and hazy images using the onboard camera. Among these, we selected 421 pairs as the training set and the remaining 179 pairs as the test set. Each image in the dataset has a size of \(1280\times 720\) pixels. To reduce GPU memory consumption during training, we divided each image into eight equally sized sub-images of \(512\times 512\) pixels, with overlapping regions between adjacent sub-images. Through this operation, the number of image pairs in both the training and test sets is increased to 3368 and 1512 pairs, respectively. Additionally, we employed image augmentation techniques such as horizontal flipping and vertical symmetry to enhance the diversity of the dataset and improve the generalization ability of the trained model. The metrics including Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM) [35], and loss functions during the training process are shown in Figures 10(a), 10(b) and 10(c), respectively.  
Figure 11: Reference values of various metrics during dehazing model training.
# Optimization of Additive and Current Conditions for Void-Free Filled Through-Silicon Via
## 1 Introduction  
To meet the lightweight, high-speed signal transport, and low-power consumption trends of the electronic packaging industry, devices require miniaturization and high-density packaging through compact packaging technology [1; 2]. Therefore, chip fabrication and interconnection form an advanced spectrum in the microelectronics industry, where specifications for high performance and miniaturization of semiconductors continue to change every 18 months [3]. A candidate to meet such compact chip packaging technology demands is 3D chip stacking, because of its advantage of volume reduction compared to conventional package-on-package technology [1; 2; 4]. As demonstrated in Figure 1, there are two types of 3D lamination processes. Figure 1 is a schematic diagram of wire bonding. The stacked chips on the printed circuit board (PCB) are connected by wire as shown in fig1a.; 3D lamination through wire bonding can be used to improve the performance of the device. However, there is a limit to miniaturization as space is required to prevent signal noise and interference between the chip and the wire [5]. To overcome these disadvantages, technical developments to commercialize 3D integrated circuits using through-silicon vias (TSVs) have been actively studied in recent years [6]. A TSV is a high-performance vertical electrical connection technique used as an alternative to wire bonding and fillip chips to create 3D integrated circuits. As presented in Figure 1b, TSVs directly connect between stacked chips. Compared to other methods such as wire bonding technology, theinterconnect and device density are higher while the length of the connections is shorter [7]. To achieve the characteristics of TSVs, it is important to completely fill the TSV hole without voids [2; 5; 8; 9].  
In this study, the results before and after pretreatment were compared, and the optimal conditions for void-free filling were determined by varying the concentrations of additives poly(ethylene glycol) (PEG), bis-(3-sodiumsulfopropyl disulfide) (SPS), Janus Green B (JGB), as well as current parameters.
## 3 Network Protocol
### External Routing Protocol  
Currently, there is no common standard for space-based communications. To establish communication links with other space-based devices, a public IP is required, which is where the EGP protocol comes into play.  
Figure 3: Representation of interconnections among ABR, LSR and PMN swarm.  
Building on the Link State (LS) routing algorithm and scaling up within the network that reaches all celestial bodies within the asteroid belt, interconnection links are proposed to be established through router satellites ABR. These router satellites will be responsible for constructing the algorithm's database, generating their respective routing tables using the Dijkstra algorithm [8] to find the shortest path to all possible destinations within the network, and determining which interface to use to reach them. Lastly, they will update the network topology as it changes, regenerating routing tables when the state changes.
# Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey
## 4 Typical Detection Pipeline
### Regions of Interest  
ROIs, also known as region proposal, is regarded as the first and most vital step in a detection system [59]. Some image processing techniques are applied at this stage for ease of finding ROIs [23]. The region proposals have been typically visible-based approaches, such as monocular or stereo cameras. The features such as edges, lines, patterns are then extracted and processed through to the classifier to determine the class of the object (e.g., whether the object is human or not). ROIs are proposed in an image to detect potential pedestrians/cyclists in the scene. Approaches that can be used for finding ROIs include sliding window, selective search [60] and locally decorrelated channel features (LDCF) [44].  
In the sliding window approach, a window is scanned both vertically and horizontally to extract candidate regions. These regions may be different scales as the pedestrians can be of varying sizes. No positive regions are discarded as all the regions are fed into CNN. This will provide high accuracy but with a higher level of computational complexity due to the large number false positives [44]. Selective search uses a coarse filter for detecting class-independent regions [60]. This has been successfully used with CNNs for feature extraction and classification [51]. The approach reduces the number of regions proposed, reducing the computational costs. LDCF can detect pedestrians with high accuracy [61]. To further improve, this approach is coupled with a neural network [44], where a large number of regions are produced, each with a confidence value. The confidence value refers to the likelihood that a pedestrian is contained in the frame. This provides for a trade-off of accuracy and efficiency of the detector.  
The sliding window approach is the simplest technique and is adaptable for use with various aspect ratios and scales [44]. However, more complex algorithms can lead to lower the number of ROIs, reducing the number of false positives. This also reduces the computational costs of the overall detection system.
# Properties and Mechanism of Hydration of Fly Ash Belite Cement Prepared from Low-Quality Fly Ash
###### Abstract  
Fly ash belite cement (FABC) is predominantly composed of \(\alpha^{\prime}_{\text{L}}\)-C\({}_{2}\)S and C\({}_{12}\)A\({}_{7}\). It is prepared from low-grade fly ashes by hydrothermal synthesis and low-temperature calcination methods. The formation, evolution process, and microstructure of FABC hydration productions were studied in this work, and the ultimate aim is to give a theoretical foundation and technological support for the application of the new cementitious material made of low-quality fly ash. The results showed that the optimal amount of gypsum was about 7% of cement by weight. The 3-day and 28-day compressive strength of cement pastes with 7% gypsum was 13.6 and 60.2 MPa, respectively. Meanwhile, the 28-day flexural and compressive strengths of mortars with 7% gypsum were 4.6 and 25.9 MPa, respectively. The early hydration heat release rate of this low-temperature calcined cement was higher compared with that of high-temperature calcined cement as Portland cement. FABC hydration pastes contained mostly C-S-H, etmitigite (AFt), unreacted mullite, and quartz. It was significantly different from Portland cement in that no calcium hydroxide [Ca(OH)\({}_{2}\)] was observed in the hydration products of different ages because all Ca(OH)\({}_{2}\) formed in the hydration reaction could react completely to generate AFt. The ratio of harmful pores (d \(\geq\) 50 nm) reached 55.04% after 3-day hydration. However, it decreased to 6.71%, which was lower than that of Portland cement pastes (35.72%) after 28-day hydration. In the later hydration period from 3 to 28 days, the strength developed rapidly, and a compact microstructure appeared in the hardened paste due to the presence of pores less than 20 nm in diameter.  
b 2020; 10, 7026; doi:10.3390/app10207026