esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Discussions  
This section explains the estimation of the efficiency of the proposed method in terms of the life of the network, the stability period, the residual energy, the packets received per round and the total packets received at the sink. To verify the new system, the latest state-of-the-art schemes are extensively analyzed. The underwater routing schemes DBR [10], EEDBR [30], and SEEC [9] were taken into account for this reason. We have modeled these protocols by using Matlab 2017b/Simulink software, and the simulation cycle consists of 3500 rounds with the same set of environmental parameters to provide reliable performance. In a 100 m \(\times\) 100 m underwater area, the total number of 100 sensor nodes is deployed randomly. Initially, each node contains 5 joules of energy. Each sensor node can transmit in the range of 50 m. The depth threshold is set to 15 m. The proposed protocol is evaluated based on the following metrics:  
Figure 3: Network Flow Chart.  
The proposed scheme is evaluated considering the following standard evaluation metrics:
* Network Lifetime: These metrics indicate how long the network nodes operate. This refers to the cumulative number of rounds when the network field's nodes are both alive and working. To consider the efficacy of a system, this metric is essential as energy is the primary concern of UWSNs. In the underwater world, the aim of most mechanisms is to prolong the lifespan of the network. Basically, it depends on energy consumption.
* Throughput: Throughput is called the efficient reception of the data packet at the sink. Data transmission to the sink depends on the number of nodes being involved. More active nodes contribute sufficient packets to the sink, which increases the network throughput.
* Packets received per round: This review shows the ratio and the capacity of packets received at the sink in the unique round to accept the data packets. If the ratio exceeds the threshold for sending packets, the packet will be dropped. Residual Energy: Residual Energy is the difference between the initial energy and total energy consumed by each node after data packets have been transmitted and received.
## 2 Methods and Tools
### Very High-Resolution Imagery  
VHR imagery has significantly advanced Earth observation since the early 2000s, providing unparalleled detail for monitoring small-scale surface changes. Spatial resolution has progressed from approximately 2 m with IKONOS (1999) and QuickBird (2001) to as fine as 30 centimetres with modern satellites like WorldView-3 (2014) and WorldView-4 (2016). These improvements have enhanced the ability to study dynamic geological phenomena, particularly in regions experiencing active tectonic and hydrological processes, such as the Dead Sea [24; 25].  
Radiometric capabilities have evolved alongside spatial resolution. Earlier systems captured imagery in four spectral bands (RGB and NIR), while modern satellites, including Pleiades-1A/1B and GeoEye-1, now offer up to eight spectral bands, incorporating additional shortwave infrared (SWIR) bands. This enables researchers to detect subtle differences in surface materials, vegetation, and water content, providing a more comprehensive understanding of environmental and geological changes [26].  
These advancements are critical in the Dead Sea, where sinkholes, shoreline retreat, and surface deformations pose significant risks. VHR imagery allows precise mapping of small-scale features such as cracks and sinkholes, linking their formation to subsurface dissolution and groundwater salinity gradients. Studies using WorldView-2, Pleiades, and drone-based imagery have demonstrated the capability to identify sinkholes, analyze their spatial patterns, and assess their relationship with anthropogenic and natural processes [1; 26; 27; 28].  
Historical data available in Google Earth and Bing have amplified the utility of VHR imagery. These tools complement commercial satellite data by enabling near-real-time monitoring of shoreline changes, sinkhole development, and land use dynamics [21; 26]. Researchers have integrated these resources to track shoreline retreat, understand industrial impacts, and support hazard mitigation efforts [21].  
The combined use of data from IKONOS, QuickBird, WorldView, Pleiades, and GeoEye satellites has transformed the understanding of the Dead Sea's dynamic landscape. The high spatial and temporal resolutions achieved by these tools enable the detection of subtle surface changes, providing critical insights into environmental impacts and supporting the development of risk reduction strategies [26].  
Looking forward, continued advancements in satellite technology and improved accessibility to high-resolution datasets hold promise for further enhancing these capabilities. The integration of VHR imagery with other remote sensing and geophysical methods will continue to support researchers and policymakers in addressing the environmental and geotechnical challenges of rapidly evolving regions like the Dead Sea.
# An Intelligent Optimized Route-Discovery Model for IoT-Based VANETs
###### Abstract
However, many optimization algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO), and cuckoo search optimization (CSO), have been converted from continuous space to discrete space to address this IoT-based route-discovery process [3; 11]. Parameter tuning and conversion from continuous space to discrete space has made algorithms achieve poor convergence due to the imbalance between intensification and diversification. In addition, it also lacks selection and elitist procedure during the search process. In this paper, we propose a Java macaque optimization algorithm that has been copied from the behavior of Java macaques, which combines genetic and swarm behavior to achieve an efficient route-discovery process. The novel contributions of this proposed intelligent approach are described as follows:  
* We introduced a novel optimization algorithm based on the behavior of Java macaque monkeys for balancing a search operation. The balance is achieved by modeling the natural behavior of Java macaques in terms of multi-group behavior, multiple search agents, social hierarchy-based selection strategy, mating, male replacement, and learning process. * A multi-group population with multiple search agents of male and female monkeys helps with the exploration of different search spaces and in maintaining diversity. * This algorithm uses the dominance hierarchy-based mating process for exploring complex search spaces. * To address communication issues in a multi-group population, Java monkeys have a unique behavior called male replacement. * The exploitation phase of the proposed algorithm is achieved by the learning process. * This algorithm uses the multileader approach for male and female search agents such as alpha male and female in each group, and maintains the global best male and female agents. Thus, the multileader approach assists in the smooth transition from exploration phase to exploitation phase [12]. Furthermore, the social hierarchical-based selection strategy improves the maintenance of both converged and diverse monkeys in each group, as well as in the population. Further in this paper, Section 4 provides a brief behavior analysis of the Java macaque monkey. Section 5 formulates the algorithmic modeling of the Java macaque based on behavior analysis.
## 2 System Model and the Correction of Offset, Gain, Time Skew, and Bandwidth Mismatch  
### System Model  
Figure 1 shows the abstract view of an \(M\)-channel time-interleaved analog-to-digital conversion system. Additionally, ADC\({}_{0}\) is assumed as the reference ADC. When there is an offset, gain, and time skew in the TI-ADCs system, the discrete-time Fourier Transform (DTFT) of the \(i\)-th channel output is represented as follows [7]:  
\[Y_{i}(j\omega)=g_{i}e^{-j\omega\Delta\tau_{i}}X_{i}(j\omega)+o_{i}, \tag{1}\]  
here \(X_{i}(j\omega)\), \(o_{i}\), \(g_{i}\) and \(\Delta\tau_{i}\) are respectively the DTFT of the input signal before distortion, the offset, the gain, and the time skew in the \(i\)-th channel.  
The inverse discrete-time Fourier transform (IDTFT) of \(e^{-j\omega\Delta\tau_{i}}\) can be expressed as follows:  
\[h_{\Delta\tau_{i}}[k]=\frac{\sin\left(\pi\left(k-\frac{\Delta\tau_{i}}{T_{s}} \right)\right)}{\pi\left(k-\frac{\Delta\tau_{i}}{T_{s}}\right)}. \tag{2}\]  
When there are bandwidth mismatches in this TI-ADCs system, the DTFT of the output in the \(i\)-th channel can be represented as follows:  
\[Y_{i}(j\omega)=g_{i}e^{-j\omega\Delta\tau_{i}}H_{\omega_{j}}(j\omega)X_{i}(j \omega)+o_{i}, \tag{3}\]where \(H_{\omega_{i}}(j\omega)\) is the frequency response of bandwidth mismatch. Additionally, the IDTFT of \(H_{\omega_{i}}(j\omega)\) can be approximated by a single-pole low-pass filter as follows [21]:  
\[H_{\omega_{i}}(j\omega)=\frac{1}{1+j\frac{\omega}{\omega_{\rm bi}}}, \tag{4}\]  
where \(\omega_{\rm bi}\) is the cutoff frequency of the sample and hold circuit of the \(i\)-th channel.
# A Mathematical Model for Efficient and Fair Resource Assignment in Multipath Transport
## 4 Performance Metrics
### Aggregate Allocation  
The aggregate allocation which is a metric for the network utilization efficiency is the sum of the throughputs achieved by all (sub)flows in the network:  
\[Alloc=\sum_{[s,t]\in K}\varphi^{st}. \tag{28}\]
# Three-Dimensional Human Head Reconstruction Using Smartphone-Based Close-Range Video Photogrammetry
## 2 Materials and Methods
### Setup and Data Collection  
The performance of the 3D head reconstruction improvements was tested on the mannequin head. Comparative evaluation of the algorithms requires a reference head model and test models. The capturing of the head was performed differently for the creation of the reference model and for the test models. Imaging of the mannequin head for the reference model was performed in such a setup that it would allow for the creation of a high-quality 3D model. Imaging setup for the test models was determined by the need to compare the performance and expose the properties of the 3D reconstruction algorithms while applying the algorithms in real-world scenarios.  
Firstly, the mannequin head was prepared for capturing and photogrammetry by giving it a faint texture; because the mannequin's skin was very smooth and even, without any pattern compared to a real face's skin, which has a texture, the face of the mannequin was covered with faint glitter makeup. The presence of the texture is necessary for the successful matching of image patches during the reconstruction. The given makeup can be observed in the images of Figure 5a.  
The pictures of the mannequin for the construction of the 3D reference head model were taken using a Nikon D3200 Digital SLR camera. The photographs were taken in an environment where the lighting of the dummy was uniform and adequate. Shooting settings: image resolution was set to the maximal \(6016\times 4000\) pixels, the photo quality was set to maximal, flash was turned off, focal length was kept fixed (focal length 18 mm), focal ratio f/3.5, exposure time 1/500 s. During all shooting, the mannequin's head was kept steady, without turning on the base, keeping the background neutral and without changes.  
The videos for the creation of the test models were acquired using the smartphone Samsung Galaxy S10+ standard Camera App. For the comparative evaluation of the algo  
Figure 6: Facial feature points detected in one of the images (plotted as red dots) **(a)** and projected onto the reconstructed 3D model of the mannequin’s head (sparse point cloud) **(b)**. For the alignment of the experimental models (sparse point cloud) to the reference model (dense point cloud), only facial feature points presented in **(b)** are used.  
rithms, 19 videos were taken. Acquisition conditions were varied while taking individual video footages: changing the orientation of the smartphone, changing lighting conditions, stationary or varying background, frame rates of 30 or 24 frames/second, frame size \(3840\times 2160\) pixels, and changing mannequin makeup for more or less glitter. The average length of the videos was \(51.5\pm 16.7\) s. The movement pattern of the phone while capturing was the same for all videos--a zigzagging sideways movement while moving slowly from top to bottom, trying to imitate an effort to make a selfie video that captures one's head from all sides as wide as possible from reaching with a hand.
## 2 Research Trends of Carbon-Capturing Concrete  
The generation amount of blast-furnace slag, a representative industrial byproduct, has been continuously increasing with the accelerated development of the steel industry. If blast-furnace slag powder is used as a raw material of concrete by mixing with cement, approximately 40 million tons of cement per year can be reduced as well as CO\({}_{2}\) emissions [14; 15].  
To reduce CO\({}_{2}\) emissions, the main cause of GHGs, the construction industry is striving to develop environment-friendly concretes. Consequently, cementless binders that use industrial byproducts such as blast-furnace slag, flay ash, and metakaolin instead of ordinary Portland cement (OPC) are being actively researched in and outside the country. Technologies related to cementless binders include geopolymer technology using alkaline reaction with clay minerals and alkali-activated binder technology that develops hardened body by stimulating the latent hydraulic activity of slag byproducts [14; 15].  
The economic efficiency of 50 MPa or lower alkali-activated concrete is moderately higher than that of the equivalent OPC, but the cost of alkali-activated concrete for high-strength concrete is known to be lower by approximately 10-40% compared to that of the OPC concrete with replaced silica fume [14; 15].  
Furthermore, the alkali-activated concrete can have advantage over the OPC concrete in terms of economic aspect because it uses industrial byproducts, which do not require a firing process for cement production. Moreover, using an alkali stimulant that is easy to handle can further improve economic efficiency. Therefore, the development of a carbon-capturing concrete through the research of geopolymer concrete and alkali-activated concrete of a non-plastic inorganic binder is expected to effectively reduce CO\({}_{2}\) emissions [14; 15].
## 3 Model Formulation
### Model Development
#### 3.4.1 Objective Function Analysis of Model
In an actual distribution process, the distribution vehicles may not arrive on time for various reasons, this will incur a penalty cost. The concept of a time window is introduced. As the timing for first-stage distribution is flexible, we only consider the time window requirements of the customers in the second-stage distribution. The time window is divided into a hard time window and a soft time window. Considering the characteristics of cold-chain distribution, we calculated the penalty cost associated with the hard time window. Assuming that the earliest service time allowed by customer \(i\) is \(ET_{i}\), the latest service time allowed by customer \(i\) is \(LT_{i}\), the service time window required by the customer \(i\) is \([ET_{i},LT_{i}]\). The penalty function equation can be expressed as follows [33]:  
\[C_{4}=P(t)=\left\{ \begin{array}{ll}M&t<ET\\ 0&ET\leq t\leq LT\\ M&t>LT\end{array} \right.\]  
where \(M\) is infinite. \(t\) is the time when the vehicle arrives at the customer. \([ET,LT]\) is the service time window required by the customer. The cost at the transfer station mainly consists of the time cost incurred in the transfer of goods from large refrigerated vehicles to small refrigerated vehicles. Therefore, the transfer cost can be expressed as follows:  
\[C_{5}=\lambda_{1}\sum_{g\in M_{1}}Z_{g}\frac{W_{g}}{v_{3}} \tag{6}\]
# Multistage Smoothing Compressor for Multistep Pulse Compressors
## 3 Multistage-Smoothing-Based MPC
An induced spatial chirp width of approximately 5 to 10 mm will not affect the main amplification. In fact, inducing a spatial chirp width of about 5 mm in one direction (X or Y) is enough to improve the LSIM of the amplified beam and protect the largest and most expensive crystal. Note that the beam smoothing at this stage is not essential because usually a 4f-system-based spatial filter is used before the main amplifier, and it is optional according to the requirements and conditions of every laser system. The whole optical scheme of an MS-MPC is shown in Figure 5; it includes a pre-compressor, a main compressor, and a post-compressor. Since the induced spatial chirp can smooth the laser beam in both the near field and far fields, the MS-MPC will significantly improve the safety of the important and expensive optics, and then increase the long-term operational efficiency of the laser system. Here, the PS_I, together with BE_1, will protect the largest crystal in the main amplifier; the spatial chirp induced by PS_II will protect DM_2, G1 and other important optics after PS_II. The spatial chirp induced by the asymmetric four-grating compressor will protect G4 and DM_3 directly. Furthermore, the DM_1 ensures that the wavefront before PS_II is perfect; the DM_2 guarantees the reflective wavefront by G1 is flat; the CP will compensate the wavefront distortions from G2 and G3, which is optional; and, finally, the DM_3 will be well controlled to obtain the highest focal intensity on the focal point FP.
## 1 Introduction  
Association between removable partial dentures (RPD) and implants is gathering more and more interest in clinical practice in dentistry, especially for class I and II Kennedy situations, aiming to avoid displacement of the distal parts of the classical dentures toward mucosal support and to improve masticatory efficiency and comfort [1; 2; 3; 4]. The extensive coverage of the major connectors used for classic class II Kennedy RPDs can be avoided using a unilateral removable partial denture, but in the absence of the cross-arch stabilization these connectors are offering, the denture can be easily dislodged during functions [5]. Proper design with splinting abutments using crowns with milled oral surfaces and interlocks and, if possible, posterior implant support is required in order to avoid complications.  
There are studies in the literature demonstrating that using an extra-coronal resilient attachment can reduce the stress on the terminal abutment because it distributes more loads onto the distal edentulous ridge [6]. Excessive movements of a unilateral RPD cause bone resorption, which, in turn, contributes to accelerating the loss and amplifying denture movements. It is necessary to identify and evaluate methods to prevent these movements.  
The aim of this study is to investigate the von Mises stress distribution in two similar unilateral RPD designs, in order to assess whether adding a posterior implant in the distal part of the edentulous ridge improves the functionality of the denture and, subsequently, contributes to a decrease in the rhythm of the bone resorption. The null hypothesis was that stress distribution in two unilateral RPDs could be similar if supported by a posterior implant in the distal part and without the implant.
## 3 Improved LEACH Protocol
### Parallel Transmission Based on Scheduling for Cluster Head Nodes and Sensor Nodes
#### 3.3.1 Scheduling for Cluster Head Nodes  
The ideal scheduling for cluster head nodes is that the packets sent by the cluster head nodes are received sequentially by the base station with minimum intervals between packets, as shown in Figure 2, and the optimal times of arrival of the \(Q\) cluster head nodes should be given by:  
\[t_{i}^{RX}=\left\{\begin{array}{ll}d_{1},&i=1\\ t_{i-1}^{RX}+t_{bs}^{p}+t_{guard},&i=2,\ldots,Q\end{array}\right. \tag{12}\]  
where \(d_{1}\) denotes the transmission delay from the first cluster head node to the base station, \(t_{i}^{RX}\) denotes the arrival time of the packet from the \(i\)th cluster head node to the base station, \(t_{bs}^{p}\) denotes the duration of the base station receiving a packet, and \(t_{guard}\) denotes the guard time slot, respectively. And \(t_{guard}\) is designed to account for the uncertainty in the propagation delay estimates, ensuring that even if there is a slight delay in the signal reaching its destination, it will not interfere with the subsequent transmission slot. Then, the transmission time \(t_{i}^{TX}\) of the \(i\)th cluster head node is given by:  
\[t_{i}^{TX}=t_{i}^{RX}-d_{i},\;\;i=1,\ldots,Q \tag{13}\]where \(d_{i}\) denotes the transmission delay of the cluster head node Si. Note that if \(d_{i}\) is large, \(t_{i}^{TX}\) may be negative in (13). In this case, the actual transmission time of each node should be adjusted as follows:  
\[t_{i}^{TX\prime}=t_{i}^{TX}-min\Big{(}t_{j}^{TX}\Big{)}\;\;i,j=1,\ldots,Q \tag{14}\]
## 3 Phenology-Based Crop Identification
### Derivation of Phenological Metrics
Figure 4: The asymmetric logistic curve and characteristic points. Figure 5: Average Normalized Difference Vegetation Index (NDVI) series of sampling points for maize and sunflower and logistic curve fitting results in 2012. The coefficient of determination (\(R^{2}\)) for the fitting were both over 0.9 for maize and sunflower, which indicated that this asymmetric logistic curve fitted the crop phenological cycle in the Hetao Irrigation District fairly well. Comparison of the fitting curves for maize and sunflower showed that the two curves were distinct in the left side (crop growth period), while almost coinciding in the right side (crop senescence period). Therefore, three key NDVI characteristics, NDVI values at the left inflection point with maximum growth rate and the peak point and their difference, and corresponding phenological metrics were selected as possible indexes to differentiate maize and sunflower (Table 3). From Figure 5, the maize NDVI reached its maximum value of 0.54 at the 224 th day in the year (_t_max), and achieved its maximum growth rate at the 190 th day (_t_inf_) with the corresponding NDVI of 0.38 (NDVI_inf), while for sunflower, NDVI reached its maximum value of 0.52 at the 227 th day, and achieved its maximum growth rate at the 203 th day with NDVI_inf = 0.37. There was no clear difference between _t_max of maize and sunflower, but t_inf of maize was almost half a month earlier than the sunflower. Accordingly, maize had a longer fast growth period (FGP) than sunflower, with FGP of maize and sunflower of 34 days and 25 days, respectively. Consequently, a combination of appropriate NDVI characteristics and FGP would be effective to identify maize and sunflower in the Hetao Irrigation District.