esa-sceva/satcom-chunk-collection huggingface dataset:
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 3 MAC Layer and Beamforming for mmWave
### Beamforming  
Mobile devices are highly prone to experience interference, and by nature mmWaves experience high blockage which significantly impacts the spectrum sharing and accessing. To overcome these interference and blockage issues, it is necessary to search for an alternate channel that is directed, and not blocked. Directed communication, referred to as beamforming, is the most widely used scheme in mmWave as discussed in Section 1. Moreover, beamforming requires continuous switching and scheduling, and transmission and reception beam operation can be regulated on the MAC layer [32]. In general, to support a densely populated indoor WPAN with more users, indoor concurrent beamforming is very crucial.  
Concurrent beamforming protocols rely on receivers that detect the signal-to-interference plus noise ratio (SINR) [32]. Figure 2 presents a four-phase single-link beamforming scheme that can schedule the link transmission of wireless devices. Some mmWave MAC layer beamforming protocols can work efficiently by eliminating the need for the angle of departure (AOD) and angle of arrival (AOA) [33]. Moreover, it has been established that a MAC protocol that utilizes spatial properties could significantly boost the mmWave cellular network capacity [14]. Network architectures such as WPANs and wireless local area networks (WLANs) permit non-line of sight (NLoS) communication. However, the above-mentioned phenomenon complicates the cellular network architecture in terms of the mmWave MAC design [34].  
From the base station (BS), the signals are propagated directly towards the users. In general, an MIMO antenna array is used to propagate a signal in a specific direction, and a beamforming protocol is required in the MAC layer of a system to choose the optimal transmission and reception beams based on the selection measure. In an indoor environment, in which the traffic is high because of numerous devices, beamforming can significantly reduce collisions among the signals and increase the quality of service (QoS)  
Figure 2: Single-link beamforming.  
of the connected devices. However, the previously mentioned deafness problem is also prevalent in beamforming techniques, which are discussed in terms of MAC layer solutions in the next section.
## 4 Results and Analysis
### Image Quality with Different Focus Depth  
The images of defects with different focus depths at the buried depth of 70 mm are selected to evaluate the image quality and verify the performance of the DAS_VCF algorithms. The defects are selected with the green rectangle, and located in the center, as shown in Figures 7c and 8a. What is more, the area of interest region is 2.8 \(\times\) 1.1 mm while calculating the SNR, i.e., the area of the green rectangle. According to Equation (8), the SNR of defects with different focus depths is calculated. The SNR of cracks with DAS and DAS_VCF algorithms are shown in Table 4.  
Both SNR of cracks at the buried depth of 70 mm with DAS and DAS_VCF algorithms increase with the focus depth increasing, as shown in Table 4. When the sound beam is focused at the depth of 30 mm, the focusing ability decreases with the propagation of the sound. Due to the target defect at the buried depth of 70 mm is far from the focusing range, the SNR of the traditional S\(-\)scan image is 11.24 dB and the lowest. Outside the focus region, the phase alignment of the echo signal from the target defect is low but still higher than the spurious signal. Hence, VCF weighting improves the SNR of the DAS image, according to the phase difference. When focusing depths at 60 mm and 70 mm, the focusing ability of the sound beam at the target defect is strong, and the SNR of the same algorithm is almost the same and reaches the maximum. Compared with DAS algorithms, in the case of the same focus depth with the same buried depth, the SNR of the crack is improved in images obtained by DAS_VCF algorithms.
## 5 Discussion
Whether the location of maximum subsidence corresponds to the location of lowest groundwater level is an interesting question and it can help us understand the mechanism of land subsidence caused by groundwater pumping. The rapid declining level of the middle and deep aquifers can result in land subsidence due to the damage to the balance of the hydraulic structure [33; 34]. In Zhanjiang city, the water level of middle-deep and deep aquifers started declining in the late 1980s and resulted in two obvious subsidence zones centered on XiaShan district [2; 32] until 2001. The lowest point of groundwater of the first subsidence zone is generally consistent with the center of subsidence bowl 5 and 6 in Figure 7a. For the second subsidence zone, the cone of bowl 1 in Figure 7 is located within the place whose drawdown depth is less than 10 m during the observation time span [4], but not in the center. So we can conclude that the location of maximum land subsidence is often, though not always, associated with maximum decline of the groundwater level, whereas sometimes it may be located away from the center of the groundwater depression. The rapid land subsidence along the coastline reveals the potential disaster. The coastline of the LZP is long enough (1180 km) to support complex ecosystems, including mangroves, coral reefs, bedrocks, and fluvial deltas [1]. However, the rapid land subsidence caused by aquaculture facilities near the coastline not only leads to intrusion of the saltwater and coastline erosion but also damages the ecosystems [35; 36]. Fortunately, owing to the slow tectonic uplift of the peninsula, until now only a small part of the coastline has undergone intrusion [23; 32]. However, the land subsidence may spread to the rest of the coastal areas in future if not addressed. Furthermore the sea level of the South China Sea rises 2.6 mm/year [1] and it may aggravate the pollution of shallow-water near the coast. Taking no action, the land subsidence (32 mm/year) of coastline will reach 64 cm and the sea level will rise 5.2 cm in 20 years, which will lead to an area disappearance of 55,000 m\({}^{2}\) for every 100 km coastline, and will devastate the ecosystem along the coastline as well as the aquaculture economy. Therefore, grasping the spatial patterns, e.g., the deformation time-series in a wide coverage, and progress of these hazards caused by natural and anthropogenic process, is particularly important to
# Segmentation of Liver Tumors by Monai and PyTorch in CT Images with Deep Learning Techniques
## 3 Materials and Methods
### Preprocessing of CT Image Data
#### 3.3.4 Image Normalization  
Intensity normalization is a method applied to mitigate variations in patient data in image intensity distribution within the dataset used, as explained in Ref. [29]. Our aim in using this particular technique is to enhance the amount of patient data, improve the comparability, and improve the accuracy of the segmentation results. For this reason, intensity normalization is applied to liver CT scan results. In medical image analysis, intensity values are used to differentiate between different types of tissues or structures. However, due to differences in acquisition protocols and imaging equipment, the intensity values of images can vary significantly. Intensity normalization aims to address this issue by adjusting the intensity values of images to a common scale. There are various methods of intensity normalization, including linear and nonlinear methods. Linear methods include scaling and normalization based on statistical measures such as the mean and standard deviation, while nonlinear methods involve more complex transformations of the intensity values. In the context of liver CT scan results, intensity normalization can help enhance and improve segmentation results by reducing variability in the intensity values of liver tissues. This can make it easier to differentiate between the liver and surrounding tissues and improve the accuracy of liver volume measurements or the segmentation of liver tumors.
## 1 Introduction
### Background and Motivation
#### 1.1.3 Purpose and Objectives  
This study addressed two key concerns: (1) What are the main benefits and challenges entailed in utilizing eye tracking as a supporting tool for educators to develop lesson plans and manage instructional resources during instruction? (2) How can the practical observations conducted in this activity contribute to applying eye tracking in educational contexts? Therefore, this research aims to investigate the feasibility of eye tracking in one-on-one classes across different disciplines. Through this approach, we seek to gain insights into the benefits and difficulties of developing class materials and using instructional equipment to understand how eye tracking could benefit educational settings. To the best of our knowledge, this work represents the first attempt to integrate teachers' perspectives on students' real-time gaze data to guide instruction during class.
# A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification
## 3 Materials and Methods
### Model Architectures
#### 3.3.1 Transformer Models  
Transformers are suitable for high-performance tasks as they can handle large volumes of data and complex structures efficiently. The workflow of the transformer model used in this study is shown in Figure 8.  
Figure 6: Code snippet for calling the tokenizer from the pretrained model.  
Figure 7: Code snippet for character encoding method used in the DL models.  
AraBERT is a transformer model pretrained specifically for Arabic [60]. It was developed to address the challenges of Arabic NLP, particularly the language's intricate morphology and dialectal variations. AraBERT has demonstrated outstanding performance in various tasks, including sentiment analysis [61]. Built on the original BERT architecture, AraBERT significantly enhances the precision and efficacy of Arabic NLP applications.  
AraELECTRA is another transformer model fine-tuned for Arabic language [42], extending the ELECTRA architecture. Unlike conventional masked language models, ELECTRA employs a discriminator network that distinguishes between real and replaced tokens generated by a generator. This approach improves training efficiency while maintaining performance.  
CAMeLBERT is a robust Arabic transformer model designed to process different Arabic language forms, including dialectal Arabic, Classical Arabic, and Modern Standard Arabic (MSA) [43]. Unlike AraBERT, CameLBERT is trained on a more diverse corpus, enabling it to capture stylistic nuances in poetry. Its BERT-based architecture is enhanced through extensive fine-tuning for a better comprehension of poetry forms and morphological patterns.  
MARBERT and ARBERT are advanced transformer models tailored for Arabic NLP tasks [62]. These models extend the BERT architecture with a bidirectional attention mechanism, allowing them to evaluate context by examining both left and right surroundings. MARBERT was trained on a large corpus comprising various Arabic dialects and MSA, while ARBERT focused primarily on MSA.  
Arabic-BERT further expands the BERT architecture by focusing on Arabic-specific linguistic features, particularly those of MSA [63]. All the pretrained transformer models used in this study consist of 12 hidden layers and 12 attention heads in their architecture.
## 4 Numerical Examples
### EPSD Model  
Equation (5) gives the simulation formula of two-sided EPSD. The relationship between the two-sided EPSD and one-sided EPSD is as follows:  
\[S_{f}(\omega,t)=2S_{ff}(\omega,t)=2\cdot|A(\omega,t)|^{2}\cdot S_{f_{0}f_{0}}(\omega) \tag{35}\]  
in which \(S_{f}(\omega,t)\) represents the one-side EPSD of a non-stationary stochastic process. Unless otherwise specified, the following EPSD graphs are all one-sided EPSD.  
The time-frequency non-uniform modulation function \(A(\omega,t)\) used in Equation (35) is taken here as follows [42]:  
\[A(\omega,t)=\exp\left(-\eta_{0}\frac{\omega}{\omega_{a}\ t_{a}}\right)\cdot \left[\frac{t}{c}\exp\left(1-\frac{t}{c}\right)\right]^{k} \tag{36}\]  
in which \(\omega_{a}=\omega_{g}\), \(t_{a}=T\), and \(T\) is the duration time of seismic ground motion. \(\eta_{0}\) is the frequency modulation factor. \(c\) is related to the peak acceleration. The values of \(k\) affect the shape of the modulation function. The parameters in Equation (36) take the following values: \(t_{a}=T=30\) s, \(\eta_{0}=\leavevmode\
obreak\ \leavevmode\
obreak\ 0.15\), \(c=\leavevmode\
obreak\ \leavevmode\
obreak\ 9.0\), and \(k=2.0\). Figure 4 shows the modulation function.  
Commonly used PSD models include the Kanai-Tajimi spectrum [43; 44], the Clough-Penzien spectrum [42; 45], and some improved models [46; 47] based on these two spectra. In this article, the Clough-Penzien spectrum will be adopted here and the expression of the Clough-Penzien spectrum is  
\[S_{f_{0}}(\omega)=\frac{\omega_{g}^{4}+4\zeta_{g}^{2}\omega_{g}^{2}\omega^{2} }{\left[\omega^{2}-\omega_{g}^{2}\right]^{2}+4\zeta_{g}^{2}\omega_{g}^{2} \omega^{2}}\times\frac{\omega^{4}}{[\omega^{2}-\omega_{f}^{2}]+4\zeta_{f}^{2} \omega_{f}^{2}\omega^{2}}S_{0} \tag{37}\]  
in which \(\omega_{g}\) and \(\zeta_{g}\) denote the dominant frequency and damping ratio, respectively, of site soil. \(\omega_{f}\) and \(\zeta_{f}\) are parameters that regulate the low-frequency component of seismic acceleration. \(S_{0}\) is the intensity factor, which depends on the peak ground acceleration (PGA) [48] and is written as follows:  
\[S_{0}=\frac{\vec{u}_{\max}^{2}}{\gamma^{2}\left[\tau\omega_{g}\left(2\zeta_{g }+\frac{1}{2\zeta_{g}}\right)\right]} \tag{38}\]  
Figure 4: Time–frequency modulation function.  
where \(\overline{a}_{\max}\) is the average PGA and \(\gamma\) is the peak value factor. In Equations (37) and (38), the values of these parameters are taken as follows: \(\omega_{g}=11.42\,\mathrm{s}^{-1}\), \(\zeta_{\mathrm{g}}=\zeta_{f}=0.72\), \(\omega_{f}=0.1\,\mathrm{s}^{-1}\), \(\omega_{g}=1.142\,\mathrm{s}^{-1}\), \(\overline{a}_{\max}=196\,\mathrm{cm}/\mathrm{s}^{-2}=0.2\) g, and \(\gamma\ =\ 2.65\). Figure 5 shows the Clough-Penzien spectrum using the above parameters. Figure 6 gives the EPSD model using the above modulation function and PSD.
# Leaf-Level Spectral Fluorescence Measurements: Comparing Methodologies for Broadleaves and Needles
## Materials and Methods
### Description of Methods  
#### 2.2.1 Optical Chamber  
The optical chamber (OC) is a customized setup originally developed to meet three requirements: (i) facilitate the arrangement of different types of leaf samples in the cuvette, where samples can be carefully attached with the help of magnets or tape; (ii) tightly accommodate an off-the-shelf reflectance probe fiber holder (see details below), so that it can be removed and placed again to measure exactly the same leaf area; and (iii) allow measuring both ambient and liquid nitrogen temperature (77 K) ChlF spectra of the same sample area, where liquid nitrogen is poured into the chamber to reduce leaf temperature to 77 K. The setup can be used for measuring directional reflectance and ChlF of leaves in the backscatter direction both at nadir (with specular reflectance component but enhanced ChlF signal), and at 45\({}^{\circ}\) (no specular reflectance but slightly smaller ChlF signal). We here selected the nadir arrangement to add further differentiation from the FluoWat method. Besides the elements listed in Table 1, the setup (Figure 3a) consisted of a 3-D printed PLA plastic cuvette (see Supplementary Material, Figure S2 for cuvette design). A series of detachable metal plates were used to facilitate the arrangement of multiple samples, the use of magnets, and for added chamber protection at 77 K. A fiber holder (Ocean Optics(r), RPH-1) was used to keep the bifurcated reflectance probe (OceanOptics(r)R600-7-VIS-125F) at a constant position in the chamber. A multimode round fiber optic bundle cable (BF20 HSMA, ThorLabs(r), Newton, NJ, USA) connected the light source to the filter  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Integration Time** & **Optical Chamber** & **FluoWat** & **Integrating Sphere** \\ \hline \multirow{2}{*}{_short_} & Integration time (ms) & 14 & 100 & 1000 \\ \cline{2-5}  & Number of averaged spectra & 25 & 10 & 10 \\ \hline \multirow{2}{*}{_long_} & Integration time (ms) & 300 & 15,000 & 60,000 \\ \cline{2-5}  & Number of averaged spectra & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Integration times and spectral averaging used in the optical chamber, Fluowat, and integrating sphere protocols.  
Figure 2: Flux diagram of the experimental protocol used for acquiring leaf ChIP and reflectance (R) spectra. Note the combination of long and short integration times (IT). See Section 2.2. for further details on each method.  
carrier (OceanOptics(\(\updelta\)FHS-UV). Samples (leaf or needles) were arranged and fixed on the metal plates using a non-fluorescent black tape (see Figure 1 and Supplementary Material, Figure S1). Plates with leaf samples were then dark acclimated for approx. 30 min.
## 2 Thermal and Strength Analysis of Compression and Tension Steel Elements in Fire
### Thermal Analysis
#### 2.1.1 Element Temperature Increment without Thermal Insulation  
For unprotected steel elements, the temperature increment can be estimated according to the formula:  
\[\Delta\theta_{a}=k_{sh}\frac{A_{m}/V}{c_{a}\rho_{a}}\dot{h}_{net,d}\Delta t_{fi}, \tag{2}\]  
where:  
\(k_{sh}\)--correction factor for the shadow effect (dependent on the section factor);  
\(A_{m}/V\)--section factor for unprotected steel members according to Table 4.2 PN-EN 1993-1-2, where \(A_{m}\) is the surface area per unit length, and \(V\) is the member volume per unit length;  
\(c_{a}\)--specific heat of steel;  
\(\dot{h}_{net,d}\)--net heat flux per unit area according to [29];  
\(\Delta t_{fi}\)--time interval (not greater than 5 s);  
\(\rho_{a}\)--the unit mass of steel.
## 3 Scalable Dynamic Multi-Agent Byzantine Consensus
### System Model  
In large-scale distributed systems, hierarchical technology and agent technology are the basic technologies for large-scale computing and complex communication. In the near future, permissioned blockchains will be applied extensively. First, we propose the SDMA-PBFT model, which is based on the concepts of hierarchical and agent technologies.  
In our proposed method, we divide the system into layers, each of which consists of various areas. An agent will serve as the primary in every area. The PBFT consensus algorithm will be conducted in the area. Simultaneously, the level of division is dynamic and the system view may be changed.  
The hierarchical dynamic multi-agent model for a permissioned blockchain system is illustrated in Figure 2.  
Figure 2: Multi-agent PBFT model for a blockchain system.  
The following questions must be answered when applying this model in practice:  
How should the layers be divided? Into how many layers should the system nodes be divided? What are the constraints?  
How does the system select agent nodes? Is there a constraint on the selection of agent nodes? What conditions must an agent satisfy?  
How large should the area be? In each area, is the size fixed or variable? How is the size determined?  
Does the system satisfy the characteristics of a global system? Is it possible to ensure that all nodes participate in voting?  
These questions will be answered in the following subsection.
# Vehicle Lane Change Models--A Historical Review
## 2 Vehicle Lane Change Models
### Cognitive Models of Lane Change  
In the lane change cognitive models, lane change behavior is dependent on the driver's decisions, which are made according to their cognition, understanding, and judgment. Accordingly, a lane change model is established to describe how drivers perceive environmental information, make lane change decisions, and perform lane change behaviors.  
Figure 4: Reinforcement learning in simulation of connected driving vehicles. On the left is the deep reinforcement learning agent, and on the right is the driving simulation environment, where the driving simulation inputs state and reward functions into the deep reinforcement learning agent, and the deep reinforcement learning agent feeds actions back into the driving simulation environment [110].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Paper** & **Input Variables** & **Algorithm** & **Key Issues to Be Resolved** \\ \hline
[108] & Time series historical location & & Accurate estimation of vehicle \\  & information of the subject’s vehicle and & HRC LSTM & following and lane-changing behavior at the same time \\ \hline
[6] & US-101 segments of NGSIM data & P-LRLP & Estimate the collision risk level of lane \\ \hline
[109] & Movement and combined lane change & & In the context of sharing traffic \\  & data for freeway exit ramp entry & GCN + DQN & information and issuing control \\  & sections & & movement commands, the safety and \\  & sections & & mobility of intelligent vehicles can \\  & & & be improved \\ \hline
[110] & Relative distance, relative speed, and & & Use reinforcement learning to facilitate \\  & relative lane position & DRL + end-to-end framework & the safe and efficient movement of \\  & relative lane position & & connected autonomous vehicles in a \\  & & & simulated environment \\ \hline \end{tabular}
\end{table}
Table 5: Input parameters of different control lane change models and key problems to be solved.  
There are three common cognitive models: decision tree, artificial neural network, and information processing.
# Model for Determining Parking Demand Using Simulation-Based Pricing
## 3 Results and Data Analysis  
For the proposed model, data were selected regarding the accumulation \(A_{t}\) by user category from Formula (3) over a single workday divided into hourly intervals, which were then used to calculate occupancy \(Occ_{t}\) using Formula (6). Data was collected both before and after the price increase. The analysis identified the composition of garage user categories. Figure 2 shows a comparison of overall occupancy (\(Occ_{t}\)) by hour for two days of the same date before and after the price increase.  
Figure 2: Comparison of parking space occupancy in Gorica PG before and after the price change.  
The implemented price increase resulted in a reduction in garage occupancy across all hourly intervals. The adjustment of the pricing policy was prompted by overcapacity observed between 09:00 and 13:00, during which Gorica PG operated at over 95% occupancy. Following the adjustment, occupancy levels consistently remained below the 95% threshold. Subsequent analysis focused on the user composition and the structural shifts in user categories following the price change. Notably, despite the price increase, demand from STU rose, which stands in contrast to the core assumption embedded in Formula (1). This trend is illustrated in Figure 3.  
After the price increase, \(A_{DMPt}\) user demand also rose, as shown in Figure 4.  
In accordance with the hypothesis (1), \(A_{CMPt}\) user numbers decreased, shown in Figure 5.  
Figure 4: Increase in \(A_{DMPt}\) after the price increase in Gorica PG.  
Figure 3: Increase in \(A_{STU}\) after the price increase in Gorica PG.  
The rise in STU after the price adjustment occurred because the garage had been operating beyond its effective capacity before the change, which kept demand suppressed. The significant decrease in CMP users opened parking spaces which allowed more STU to park despite the elevated parking price. The system displays a chaotic demand regime because unmet demand becomes active when availability limitations ease as shown in Figures 3-5.