esa-sceva/satcom-chunk-collection huggingface dataset:
# Skull Thickness Calculation Using Thermal Analysis and Finite Elements
###### Abstract  
In this study, the skull bone thicknesses of 150 patients ranging in age from 0 to 72 years were calculated using a novel approach (thermal analysis), and thickness changes were analyzed. Unlike conventional thickness calculation approaches (Beam Propagation, Hildebrand), a novel heat transfer-based approach was developed. Firstly, solid 3D objects with different thicknesses were modeled, and thermal analyses were performed on these models. To better understand the heat transfer of 3D object models, finite element models (FEM) of the human head have been reported in the literature. The FEM can more accurately model the complex geometry of a 3D human head model. Then, thermal analysis was performed on human skulls using the same methods. Thus, the skull bone thicknesses at different ages and in different genders from region to region were determined. The skull model was transferred to ANSYS, and it was meshed using different mapping parameters. The heat transfer results were determined by applying different heat values to the inner and outer surfaces of the skull mesh structure. Thus, the average thicknesses of skull regions belonging to a certain age group were obtained. With this developed method, it was observed that the temperature value applied to the skull was proportional to the thickness value. The average thickness of skull bones for men (frontal: 7.8 mm; parietal: 9.6 mm; occipital: 10.1 mm; temporal: 6 mm) and women (frontal: 8.6 mm; parietal: 10.1 mm; occipital: 10 mm; temporal: 6 mm) are given. The difference (10%) between men and women appears to be statistically significant only for frontal bone thickness. Thanks to the developed method, bone thickness information at any desired point on the skull can be obtained numerically. Therefore, the proposed method can be used to help pre-operative planning of surgical procedures.  
ANSYS; skull thickness; thermal image analysis; finite element Article
## 4 Conclusions  
In this study, a novel structured mesh-type Pd/Al\({}_{2}\)O\({}_{3}\)/Al catalyst was developed by anodic oxidation technique. The pore structures of the \(\gamma\)-Al\({}_{2}\)O\({}_{3}\)/Al supports were designed by acid pore-widening treatments. Firstly, the pore physical properties and chemical properties of supports and catalysts were observed. Secondly, we tested nitrobenzene liquid-phase hydrogenation performance over granular and structured mesh-type Pd/Al\({}_{2}\)O\({}_{3}\)/Al as well as different acid pore-widening treatment structured mesh-type Pd/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\)/Al catalysts. Eventually we had further investigated the deep reason behind the differences in catalytic activity between granular and structured mesh-type catalysts by calculation. The main conclusions are listed as follows.  
1. Acid pore-widening treatments can improve the specific surface area and the pore size of the \(\gamma\)-Al\({}_{2}\)O\({}_{3}\)/Al supports. The HNO\({}_{3}\)/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\)/Al support with HNO\({}_{3}\) pore-widening treatment has the largest specific surface area, enlarging from 70 m\({}^{2}\)/g to 80 m\({}^{2}\)/g, and the largest pore size, enlarging from 3.7 nm to 4.6 nm.
2. The Pd/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\)/Al catalysts with different acid pore-widening treatments contributes to the increased catalyst loading, more Pd\({}^{0}\) content, and better dispersion of the metal-active particles. The Pd/( HNO\({}_{3}\))\(\gamma\)-Al\({}_{2}\)O\({}_{3}\)/Al prepared by support with HNO\({}_{3}\) pore-widening treatment has the largest active metal Pd loading, enlarging from 1.82% to 1.95%, the smallest particle size, reducing from 103 nm to 41 nm, and the largest Pd\({}^{0}\) content, enlarging from 52.1% to 58.5%, resulting in the highest nitrobenzene conversion rate, increasing from 67.2% to 74.3%.
3. Compared to the granular catalyst, the structured mesh-type catalyst with higher porosity and more regular pore structures exerts a lower pressure drop. This facilitates diffusion of the H\({}_{2}\) through nitrobenzene solution to the active sites of the catalyst surface. Consequently, better conversions and yields of the structured mesh-type catalyst can be obtained in the nitrobenzene liquid-phase catalytic hydrogenation reaction.  
Writing--original draft, H.T.; Conceptualization, H.T.; Methodology, H.T., Q.S. and Z.X.; Validation, H.T. and H.L.; Investigation, H.T., Q.S., Z.X. and H.L.; Writing--review and editing, Q.Z.; Project administration, Q.Z.; Supervision, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no competing financial conflicts of interest.
# Dynamic Prediction Model for Progressive Surface Subsidence Based on MMF Time Function
## 2 Models and Methods
### The Existing Time Functions and Their Flaws  
#### 2.2.1 Knothe Time Function  
In the early 1950s, Polish scholar [PERSON] proposed the hypothesis of surface subsidence coefficient based on the results of soil compaction experiments. He assumed that the rate of subsidence is proportional to the difference between the final subsidence \(W_{0}\) and the dynamic subsidence \(W(t)\) at the time \(t\)[33], shown in Equation (1):  
\[\overset{\bullet}{W}(t)=\frac{dW(t)}{dt}=c\cdot[W_{0}-W(t)] \tag{1}\]  
where \(c\) is related to overburden lithology. \(W_{0}\) is the final subsidence.  
According to Equation (1) and considering \(W(0)=0\), \(W(t)\) is:  
\[W(t)=W_{0}(1-e^{-ct}) \tag{2}\]  
Figure 2: Surface movement process of a point. (The initial phase is phase I. The active phase is divided into phase II and phase III based on the maximum subsidence velocity. The weakening phase is phase IV.)  
The expressions of \(v(t)\) and \(a(t)\) are shown in Equations (3) and (4), respectively.  
\[v(t)=\frac{dW(t)}{dt}=W_{0}\cdot e^{-ct} \tag{3}\]  
\[a(t)=\frac{d^{2}W(t)}{dt^{2}}=-W_{0}\cdot c^{2}e^{-ct} \tag{4}\]  
Considering W\({}_{0}\) is 1500 mm, the influence coefficient \(c\) is 0.04 and the curves of the Knothe time function are plotted in Figure 3a,b, respectively.  
As can be seen from Figure 3a, the shape of subsidence-time curve is not the \"S\" shape and cannot reflect the three phases of surface subsidence completely. According to Figure 3b, it can be seen that the surface subsidence velocity is not \(0\rightarrow+v_{\max}\to 0\), while the subsidence acceleration is not \(0\rightarrow+a_{\max}\to 0\rightarrow-a_{\max}\to 0\). Therefore, Knothe time function cannot effectively describe the dynamic subsidence.
###### Abstract  
Water-use efficiency (WUE) is a critical variable describing the interrelationship between carbon uptake and water loss in land ecosystems. Different WUE formulations (WUEs) including intrinsic water use efficiency (WUE\({}_{i}\)), inherent water use efficiency (IWUE), and underlying water use efficiency (uWUE) have been proposed. Based on continuous measurements of carbon and water fluxes and solar-induced chlorophyll fluorescence (SIF) at a temperate forest, we analyze the correlations between SIF emission and the different WUEs at the canopy level by using linear regression (LR) and Gaussian processes regression (GPR) models. Overall, we find that SIF emission has a good potential to estimate IWUE and uWUE, especially when a combination of different SIF bands and a GPR model is used. At an hourly time step, canopy-level SIF emission can explain as high as 65% and 61% of the variances in IWUE and uWUE. Specifically, we find that (1) a daily time step by averaging hourly values during daytime can enhance the SIF-IWUE correlations, (2) the SIF-IWUE correlations decrease when photosynthetically active radiation and air temperature exceed their optimal biological thresholds, (3) a low Leaf Area Index (LAI) has a negative effect on the SIF-IWUE correlations due to large evaporation fluxes, (4) a high LAI in summer also reduces the SIF-IWUE correlations most likely due to increasing scattering and (re)absorption of the SIF signal, and (5) the observation time during the day has a strong impact on the SIF-IWUE correlations and SIF measurements in the early morning have the lowest power to estimate IWUE due to the large evaporation of dew. This study provides a new way to evaluate the stomatal regulation of plant-gas exchange without complex parameterizations.  
solar-induced chlorophyll fluorescence; water use efficiency; leaf area index; observation time; regression analysis 2018 10 796 doi:10.3390/rs10050796
# Research on O-ring Dimension Measurement Algorithm Based on Cubic Spline Interpolation
###### Abstract  
Current O-ring dimension measurement algorithms based on machine vision are mainly whole-pixel level algorithms, which have the disadvantage of a low measurement accuracy. In order to improve the stability and accuracy of O-ring dimension measurement, a sub-pixel edge detection algorithm based on cubic spline interpolation is proposed for O-ring dimension measurement. After image pre-processing of the O-ring graphics, the whole-pixel-level O-ring edges are obtained by using a noise-resistant mathematical morphology method, and then the sub-pixel edge contours are obtained using a sub-pixel edge detection algorithm based on cubic spline interpolation. Finally, the edge curve is fitted with the least squares method to obtain its inner and outer diameter as well as the size of the wire diameter. The experimental data show that the algorithm has a mean square error of 4.8 \(\upmu\)m for the outer diameter and 0.18 \(\upmu\)m for the wire diameter. The outer diameter error is kept within \(\pm\)100 \(\upmu\)m and the wire diameter error can be kept within \(\pm\)15 \(\upmu\)m. Compared with the whole pixel algorithm, the measurement accuracy has been greatly improved.  
O-ring dimension measurement; morphology; cubic spline interpolation; sub-pixel edge detection [PERSON] et al. 2021  
[PERSON], [PERSON], [PERSON], [PERSON], Y. Research on O-ring Dimension Measurement Algorithm Based on Cubic Spline Interpolation.  
_Appl. Sci._**2021**, _11_, 3716. [[https://doi.org/10.3390/app11083716](https://doi.org/10.3390/app11083716)]([https://doi.org/10.3390/app11083716](https://doi.org/10.3390/app11083716))
## 1 Introduction
How to improve the quality of satellite remote sensing imaging and provide accurate data for satellite navigation [10], surface observation [11], mapping [12], and military reconnaissance [13] has become a recent research hotspot. In this paper, we propose a dual-path generative adversarial network model algorithm which can be applied directly to the super-resolution reconstruction of satellite remote sensing images for improved quality and performance. Specifically, our system includes a dual-path convolution operation in the generator structure, a feature mapping attention mechanism to extract important feature information from a low-resolution image, an enhanced deep convolutional network to extract the deep feature information of the image, and a construction layer to fuse the deep feature information and the important feature information together for the final output. The improved algorithm structure consisting of a loss function and discriminator also achieves a relatively optimal balance between the output image and the output of the discriminator, thus bringing the final result closer to human perception.
# Human-Centered AI for Migrant Integration Through LLM and RAG Optimization
## 3 Holistic Optimization of LLM- and RAG-Based HCAI Systems
### Environmental Impact of Computational Processes  
The determination of the environmental impact of the computational processes is influenced by several factors. The factors we will analyze include the energy consumption of the CPU, GPU, TPU, and RAM, which allow for determination of the carbon footprint of the computation [11; 12].  
Calculation of the total power consumption of all active devices (\(E_{\text{Total}}\)) is achieved by multiplying the power consumption of the devices by their charging times (see Equation (4)).  
The power calculation for all CPU devices is denoted by \(E_{\text{CPU}}\) (Equation (5)). TDP represents the specific power consumption of the equivalent CPU model under long-term load, and \(W_{\text{CPU}}\) is the total load of all processors. The total power consumption of all active GPUs is denoted by \(E_{\text{GPU}}\). TDP\({}_{\text{GPU}}\) is the specific power consumption of the equivalent GPU model, and \(W_{\text{GPU}}\) is the total load of all graphics processing units (Equation (6)). The total power consumption of all active TPUs, denoted by \(E_{\text{TPU}}\), calculates the total power of the active TPUs. It sums the product of each TPU's \(V_{i}\), \(C_{i}\), and usage \(T\), as well as its sequential energy consumption TPUec\({}_{i}\) (Equation (7)). The power consumption of the RAM is denoted by \(E_{\text{RAM}}\) (Equation (8)), where \(M_{\text{RAM}_{i}}\) denotes allocated memory (GB) [60; 61]:  
\[E_{\text{Total}}=E_{\text{CPU}}+E_{\text{GPU}}+E_{\text{TPU}}+E_{\text{RAM}} \tag{4}\]\[E_{\text{CPU}}=\text{TDP}\int_{0}^{n}W_{\text{CPU}}(t)dt \tag{5}\]  
\[E_{\text{GPU}}=\text{TDP}_{\text{GPU}}\int_{0}^{n}W_{\text{GPU}}(t)dt \tag{6}\]  
\[E_{\text{TPU}}=\sum_{i=1}^{n}(V_{i}\cdot C_{i}\cdot T_{\text{usage}})+\sum_{i=1} ^{n}(\text{TPUec}_{i}) \tag{7}\]  
\[E_{\text{RAM}}=0.375\int_{0}^{n}M_{\text{RAM}_{i}}(t)dt \tag{8}\]  
The calculation of CO\({}_{2}\)e equivalent emissions is crucial for a holistic understanding of environmental impact. This calculation encompasses energy consumption, emission intensity (denoted by \(\gamma\)), and power usage effectiveness (PUE), offering more profound insight into the sustainability of AI practices. This comprehensive approach includes not only the total energy consumption but also other key parameters, resulting in the equivalent CO\({}_{2}\)e rate, as shown in Equation (9):  
\[\text{CO}_{2}\textbf{e}=\gamma\cdot\text{PUE}\cdot E_{\text{Total}} \tag{9}\]  
This provides a measure of the carbon emissions associated with the AI operation, enabling a deeper understanding of their sustainability and eco-friendliness.
## 4 Experimental Results and Discussion  
In this section, we use a dataset derived from sea trials to build predictive models. Specifically, the dataset captures the rotational angle of the regulatory unit's mass during the steady-state gliding phase, hereinafter referred to as the rudder angle, and determines the mean rudder angle across both the descent and ascent phases. First, we detail the acquisition and preprocessing of the raw data. Then, we employ five established predictive algorithms, including Informer, Autoformer, VMD-SSA-LSSVM, VMD-LSTM, and DFFormer, to train and forecast the rudder angle from the training dataset. The validation set is used to validate and evaluate the performance of these methods. After a comprehensive comparison and analysis of the predictive results from various methods, DFFormer is conclusively identified as the optimal forecasting method for UGs.
# Study of Diagnostic Accuracy: Fundus Photography vs. Optical Coherence Tomography
## 2 Materials and Methods
### Test Procedure  
Participating subjects were given a verbal and written explanation of the purpose of the study and the methods to be used. Written informed consent was obtained from all the participants.  
The steps commonly used to carry out tests in an optometry clinic in its usual practice were followed, in order to achieve the greatest possible affinity with reality and, in this way, to calculate the precision of eye examinations in adults. Therefore, complete and detailed eye examinations were performed (corrected distance visual acuity (CDVA) and refraction, PF, macular and papilar OCT). The diagnostic accuracy assessment included components such as background information and images, and it was conducted in a conventional optometry clinic. The examinations were performed by an optometrist external to this study in order to avoid bias.  
The proposed methodology can be divided into three main steps: an assessment step, an analysis step, and a diagnostic precision calculation step. During the assessment step, the two groups of evaluators (ophthalmologists and optometrists) evaluated and classified the posterior poles of the patients according to the information given in each stage (FP and FP + OCT). During the analysis step, the classifications made by each group were compared. In the last step, all the information obtained was compiled and calculations were conducted.
## 3 Background
### Conventional Authentication Methods  
Conventional authentication methods, such as password-based authentication, are the most widely used. However, as mentioned, there are three main types of authenticators, which we will describe in the following.  
The category of authenticators \"something you know\" is based on some secret that the user needs to remember. This can be implemented in many variants, but it most commonly uses passwords/passphrases (e.g., a Facebook account) or numeric PINs (e.g., for usinga credit card). Other forms include specific swiping patterns, pairs of a question and a corresponding secret answer, etc. This authentication category is generally vulnerable to man-in-the-middle attacks, observation attacks, brute force, etc. Furthermore, since the user often chooses the secret, it frequently suffers from low entropy and easily identifiable patterns, such as short passwords with simple-to-remember sequences, which are consequently easy to guess [13; 19].  
The category \"something you have\" is a physical authenticator that the user possesses. The most common form of such a physical authenticator is security keys, where the implementation is based on time-based One Time Passwords (TOTPs), sometimes also called One Time Passwords (OTPs) [13; 19]. Other forms of physical authenticators are based on hardware keys, leveraging certificates or public-private key pairs, RFID cards, smart cards, etc. All of these physical authenticators are especially vulnerable to theft or accidental loss [18; 22].  
\"Something you are\" relies on biometric authentication, which could be the fingerprint authentication of a laptop, the facial recognition of a phone, or even an iris scan [19]. Unlike the other two authentication forms, this form does not require an authenticator's knowledge or physical possession, and while biometric authentication is convenient, it also has a few drawbacks. Some biometric authentication implementations can easily be spoofed with, for example, a picture of a person or a rubber clone of the fingerprint shape of a person, etc. Furthermore, biometric authentication usually requires storing more extensive data (e.g., 3D shape of a person's face for facial recognition, etc.), making the authentication system less scalable [23; 24]. Furthermore, there is a big issue regarding the privacy of biometric authentication. Since the characteristics of a person like the facial structure or the fingerprint cannot be changed, a copy or clone of the person's relevant characteristics, like a 3D mask or a fingerprint clone, poses a great danger to the security of the authentication process, since there is no way for the person to change the 'authenticator' (i.e., in this case, the fingerprint or the face, etc.). Therefore, when using biometric authentication, the privacy and secure storage of the relevant biometric data is paramount [25].
## 4 Deep Reinforcement Learning-Based Resource Allocation Policy  
In this section, we first reformulate the joint power and bandwidth allocation problem, which is aimed at maximizing the long-term data transmission rate of the system as the framework of an MDP. Then, we apply a DRL approach to solve the problem, in which the agent (i.e., the CBS) learns to create the optimal resource policy via trial-and-error interactions with the environment. One of the disadvantages of reinforcement learning is that the high computational costs can be imposed due to the long time learning process of a system with high state space and action space. However, the proposed scheme requires less computation overhead by adopting deep neural networks, as compared to other algorithms such as value iteration-based dynamic programming in partially observable Markov decision process (POMDP) framework [20] in which the transition probability of the energy arrival is required for obtaining the solution. Thus, the complex in formulation and computation can be relieved regardless of the dynamic properties of the environment by using the proposed scheme, as compared to POMDP scheme.  
Furthermore, the advantage of a deep reinforcement learning scheme as compared with the POMDP scheme is that the unknown harvested energy distribution can be estimated to create the optimal policy by interacting with the environment over the time horizon. In addition, the proposed scheme can work effectively in a large-state-and-space system by adopting deep neural networks. However, other reinforcement learning schemes such as Q-learning [38], actor-critic learning [39] might not be appropriate to large-state-and-space systems. In the proposed scheme, a deep neural network was trained to obtain the optimal policy where the reward of the system converges to optimal value. Then, the system can choose an optimal action at every state according to that policy learned from the training phase without re-training. Thus, deep actor-critic reinforcement learning can be more applicable to the wireless communication system.
## 2 Background and Definitions  
It has become increasingly important to maintain maritime communication as oceanic activities have increased dramatically, including naval shipping and logistics, offshore oil exploration, wind farms, fishing, and tourism, among others [9]. There are three broad types of maritime communication systems: 1. radio communication, 2. satellite communication (SATCOM), and 3. shore-based mobile communication. Below are the three most significant communications challenges currently faced by the maritime industry:  
* Range: A Wi-Fi signal can travel approximately 330 feet (100 m) from an on-land base station, but this may be affected by obstructions between the base station and the receiver, which limits their reliability. Even though Wi-Fi appears to be an economical and fast networking solution, its use is limited by its range of restrictions.
* Latency: There has been considerable effort toward solving the latency problem. While it is not possible to completely eliminate latency, significant improvements have been made to minimize the latency [8; 10]. Using satellites requires sending data as radio waves from ground equipment (transmitter) to a satellite (in space), which is then re-transmitted by the satellite to a receiving ground equipment (receiver). While this process occurs at the speed of light, the distance the data must travel and a small amount of processing of signal at the satellite result in latency.
* Service Demands: With advances in networking, IoT, and smart technologies, the demand for data services is at an all-time high [11]. The provision of network connectivity on a cruise ship requires robust and flexible technology, not only for safety, communication, and navigational purposes but also for recreational and business purposes. As a result of this increase in service demands, reliable, flexible, robust, and agile maritime networks have become an essential component of maritime infrastructure [8].