esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of Motion Data Low-Pass Filtering Methods in Machine-Learning Models
## 2 Materials and Methods
### Filtering Methods  
Zero-lag Butterworth low-pass filters with different parameters were applied to the raw kinematic 3D data (marker coordinates in the \(x,y,z\) directions) and gait characteristics were extracted from these filtered data. In accordance with previous studies [12; 17; 23], three orders (2, 4, and 6) along with nine cutoffs (4 to 20 Hz, in increments of 2) were selected for each type of filter. In total, 27 (3 \(\times\) 9) different filters were used in this study.
## 2 Mathematical Model
### Mooring System Modelling  
#### 2.4.1 Quasi-Static Mooring Model  
The quasi-static mooring model shown in Figure 3a applies the multi-segmented, quasi-static (MSQS) approach [8] to obtain the mooring force. Each mooring force consists of two parts, including the pre-tension and linearized mooring force. The total mooring force is the sum of each mooring force.  
Figure 3b shows the catenary solution of each quasi-static mooring line. [PERSON] et al. [13] derived the solution of the catenary equation for a single line. The motion equation of each catenary section can be expressed as follows:  
\[H_{2}=AE\sqrt{\bigg{(}\frac{T_{2}}{AE}+1\bigg{)}^{2}-\frac{2 wZ_{2}}{AE}}-AE \tag{19}\]  
\[X_{2}=\frac{H_{2}}{w}\text{sinh}^{-1}\bigg{(}\frac{wL}{H_{2}}\bigg{)}+\frac{H_ {2}L}{AE} \tag{20}\]  
Figure 3: Description of the quasi-static mooring model: (**a**) the schematic diagram of quasi-static model; (**b**) the catenary solution of each quasi-static mooring line. \(L_{B}\) means the length of the un-stretched mooring cable on the seabed; \(\beta\) is the slope of seabed; and \(S\) is the un-stretched length of a catenary segment from its top right end \((X_{2},\;Z_{2})\).  
\[V_{2}=wL \tag{21}\]  
\[T_{2}=\sqrt{{H_{2}}^{2}+{V_{2}}^{2}} \tag{22}\]  
where \(L\) is the un-stretched suspend length from the origin to the point \((X_{2},~{}Z_{2})\); \(w\) is the submerged weight per unit length; \(T_{1}\) and \(T_{2}\) represent the tension force; \(V_{1}\) and \(V_{2}\) denote the vertical component of the tension force; \(H_{1}\) and \(H_{2}\) mean the horizontal component of the tension force; and \(AE\) is the stiffness per unit length.  
In addition, the stretched length of the suspended catenary mooring line can be expressed as:  
\[L^{\prime}=L+\frac{1}{2 wAE}\bigg{\{}wL\sqrt{{H}^{2}+{(wL)}^{2}}+{H}^{2}ln\bigg{[} wL+\sqrt{{H}^{2}+{(wL)}^{2}}\bigg{]}-{H}^{2}ln|H|\bigg{\}} \tag{23}\]  
Given that the un-stretched length of a catenary segment from its top right end \((X_{2},~{}Z_{2})\) is \(S\), \(S\) is shorter than the theoretical un-stretched suspended length \(S\). When the tension force at the top right end is known, the position of the bottom left end of this segment can be written as:  
\[X_{1}=\frac{H}{w}ln\frac{V_{2}+T_{2}}{V_{1}+T_{1}}+\frac{HS}{AE} \tag{24}\]  
\[Y_{1}=\frac{V_{2}+V_{1}}{T_{2}+T_{1}}S+\frac{V_{2}+V_{1}}{2 AE}S \tag{25}\]  
The horizontal and vertical components of the tension forces at the left-hand end are  
\[H_{1}=H \tag{26}\]  
\[V_{1}=V_{2}-wS \tag{27}\]  
\[T_{1}=\sqrt{{H}_{1}^{2}+T_{1}^{2}} \tag{28}\]  
In addition, the stretched length of the catenary segment is defined as:  
\[S^{\prime}=S+\frac{1}{2 wAE}\bigg{(}V_{2}T_{2}-V_{1}T_{1}+{H}^{2}ln\frac{V_{2} +T_{2}}{V_{1}+T_{1}}\bigg{)} \tag{29}\]  
Finally, the mooring force \(F_{M}\) obtained from the quasi-static mooring model is simply represented as:  
\[F_{M}=T_{2}-T_{1} \tag{30}\]
# IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses
## 2 Taxonomy of Deep Learning for IoT-IDS Logic
### Deep Learning (DL) Approaches  
DL algorithms can be organized into three different types, as shown in Figure 1, based on their functionality and structure: (1) supervised, (2) unsupervised, and (3) hybrid (semi-supervised) algorithms. The common supervised, unsupervised, and hybrid algorithms are all used to protect IoT systems. Supervised algorithms include Deep Neural Network (DNN) [30], Convolutional Neural Network (CNN) [31], Deep Belief Network (DBN) [32], Recurrent Neural Networks (RNN) [33], Bi-Directional RNN (Bi-RNN) [34], Long-Short-Term Memory (LSTM) [35], and Gated Recurrent Neuro Networks (GRU) [36]. Unsupervised algorithms comprise Deep Restricted Boltzmann Machine (DBM) [37] and Autoencoder Neural Network (AE) [38]. The Generative Adversarial Network (GAN) is an example of the hybrid approach. DL Algorithms contain a sequence of many common hidden layers. Artificial neural networks (ANNs), usually simply called neural networks (NNs), are computing systems vaguely inspired by the biological neural networks that constitute animal brains. ANN is considered the simplest neural network. DNN is considered the most complicated neural network due to multiple \"hidden\" layers. DNNs are widely used in various applications, such as network security, image recognition, and speech-recognition systems. All methods can be used for binary classification, multi-classification, and prediction as well as relatively high feature extraction to enable data reduction and faster convergence times [39; 40]. To improve accuracy and to obtain a low false-negative rate, one must employ feature engineering. This considers techniques, such as converting non-numeric to numeric, normalization, and scaling, during the deep learning model development phase. Section 5.3 deeply examines recent studies and findings for the DL class of algorithms.
###### Abstract  
With the increasing demand for expected data volume daily, current telecommunications infrastructure can not meet requirements without using enhanced technologies adopted by 5G and beyond networks. Due to their diverse features, 5G technologies and services will be phenomenal in the coming days. Proper planning procedures are to be adopted to provide cost-effective and quality telecommunication services. In this paper, we planned 5G network deployment in two frequency ranges, 3.5 GHz and 28 GHz, using a mixed cell structure. We used metaheuristic approaches such as Grey Wolf Optimization (GWO), Sparrow Search Algorithm (SSA), Whale Optimization Algorithm (WOA), Marine Predator Algorithm (MPA), Particle Swarm Optimization (PSO), and Ant Lion Optimization (ALO) for optimizing the locations of remote radio units. The comparative analysis of metaheuristic algorithms shows that the proposed network is efficient in providing an average data rate of 50 Mbps, can meet the coverage requirements of at least 98%, and meets quality-of-service requirements. We carried out the case study for an urban area and another suburban area of Kathmandu Valley, Nepal. We analyzed the outcomes of 5G greenfield deployment and 5G deployment using existing 4G infrastructure. Deploying 5G networks using existing 4G infrastructure, resources can be saved up to 33.7% and 54.2% in urban and suburban areas, respectively.  
5G; mixed radio cell structure; fixed wireless access; metaheuristic algorithm; optimization
# Multi-Backup Beams for Instantaneous Link Recovery in mmWave Communications
## 3 System Model
### Signal Model  
Consider that MS and BS entities operate in LoS settings in urban outdoor environments composed of various objects in the proximity of the MS. In addition, assume a full-duplex division duplexing (FDD) channel of reciprocal channel state information (CSI) at both entities. Then, the downlink (DL) received signal profile at the MS, \(y_{\text{an}}\), is expressed as  
\[\text{y}_{\text{bb}}^{\prime}=P_{\text{tr}}U_{\text{MS}}^{\text{H}}V_{\text{BS }}\text{Hz}\ +\ U_{\text{MS}}^{\text{H}}w, \tag{7}\]  
where \(P_{\text{tr}}\), \(\mathbf{H}\), \(z\), and \(w\) denote in order the transmitted signal power, the power complex channel, the reference control signal, and the additive white Gaussian noise (AWGN), i.e., \(w\ \sim N(0,\ \sigma_{w}^{2})\), where \(\sigma_{w}^{2}\) is the noise variance.
# Influence of Cracking on Oxygen Transport in UHPFRC Using Stainless Steel Sensors
## 1 Introduction
Water and oxygen are the main oxidant agents that are involved in the reduction-oxidation reaction of rebar corrosion [12; 13], and crack geometry and pattern will affect their transport inside the concrete matrix. One of the few studies performed on FRC in micro-cracked state [14] reports that FRC improves the durability in load levels between 30 and 50% of the ultimate strength, which reduces the permeability to water and delaying corrosion initiation, with more delay with higher contents of fibers. Similarly, it has also been reported [15] that the presence of fibers contributes to a more generalized corrosion on rebars instead of pitting corrosion, which produces a fast section loss in the pitting zone. A very effective way of detecting and characterizing the cracks produced in a FRC concrete element could be voltammetric sensors, since these sensors are able to detect variations of water or oxygen availability [12; 16]. Voltammetric sensors are based on the application of a potential signal that changes with time. These sensors use different sweeping range potentials, depending on the agent to be detected. Each potential produces a different intensity of electrical current, which is associated with a reaction in which the agents of interest (oxygen, water, chlorides, etc.) are involved. These agents can be quantified through the analysis of the obtained signals. These sensors can be used as an indicator of cracking, damage in the cover, or other deterioration processes, since the availability of the agents inside concrete varies depending on the presence of cracks and concrete permeability. A recent study [17] verified the capability of a voltammetric stainless steel sensor to detect the oxygen availability variations in the surroundings of the sensor inside the hardened concrete matrix through the Id.O\({}_{2}\) parameter. In the current study, the viability of this sensor to detect the availability of oxygen is analyzed in four different concrete types: (1) Standard Quality Concrete, (2) High Performance Concrete, (3) Ultra-High Performance Fiber-Reinforced Concrete, and (4) Ultra-High-Performance Fiber-Reinforced Concrete functionalized with nanofibers and a self-healing promoter. The first two concrete types will display a cracking pattern that corresponds to single cracks, while the last two types will show multi-cracking behavior. Three states have been compared (uncracked and two crack levels of different severity) with the aim of evaluating whether UHPFRCs working in the cracked state (with multiple cracks) present reduced oxygen presence as compared to traditional concrete types, since oxygen is one of the main elements involved in the corrosion processes.
## 2 Framework of RUL Prediction Based on TCNMABG
### Signal Acquisition and Data Preprocessing  
#### 2.2.1 Vibration Signal Acquisition of Rolling Bearing  
The vibration signals of rolling bearings are important to determine the degradation characteristics, including wear, cracks, and so on. By monitoring and collecting the vibration signal of the rolling bearing, it can effectively evaluate the operating state of the equipment and predict the remaining useful life. However, it is difficult to accurately extract the effective features because of the influence of strong background noise and other factors in the signal acquisition process. Therefore, it is essential to use the denoise method to reduce the noise in the collected vibration signals.
## 4 Discussion
### Uncertainties and Future Perspectives  
The default values of the parameters in terrestrial ecosystem models are typically based on field measurements, literature, assumptions, or calibrations [15; 16], and some parameters are associated with substantial uncertainty. The uncertainty in these parameters can lead to significant uncertainty in modeled fluxes [27; 29]. Evaluating the uncertainty of simulated fluorescence and photosynthesis is a persistent challenge. Several important factors can affect the SIF and GPP model performance. Aside from the uncertainty of model parameters, the uncertainty of flux tower GPP estimation (e.g., [27; 68], SIF retrieval uncertainty (e.g., [12; 13]), the mismatch of the spatial scales between satellite observation (e.g., the coarse resolution of GOME-2 SIF measurements), and flux tower footprint can also introduce uncertainty to the simulated SIF and GPP. For example, different NEE partitioning approaches will induce uncertainty in GPP estimates [68]. Our study found that the model parameters optimized with OCO-2 SIF were able to better estimate SIF than those optimized with GOME-2 SIF since the use of OCO-2 SIF observations can substantially reduce the scale mismatch between satellite grid cells and the flux tower footprint than the use of GOME-2 SIF data.  
In this study, we only assessed the capacity of the OCO-2 SIF measurements and flux tower data in constraining the photosynthesis and SIF parameters within the SCOPE model with version 1.7. The recently released SCOPE 2.0 is likely to improve the modeling of SIF and photosynthesis. In addition, new SIF observations from the TROPOspheric Monitoring Instrument (TROPOMI), which provides global SIF measurements on a daily basis, might be valuable for constraining parameters because of its temporal continuity [69]. However, it should also be recognized that its grid cell (3.5-15 km \(\times\) 7 km) is larger than the typical footprint of flux towers, leading to a sizeable scale mismatch. The next generation SIF mission, the FLEX ([70]), will provide SIF data with a spectral range between 500 and 789 nm, temporal continuity, and small grid cells (300 m \(\times\) 300 m), and these data will have greater potential for SIF and GPP modeling and parameter estimation than the currently available SIF observations. Additionally, the SCOPE model is based on a 1-D radiative transfer model (i.e., PROSAIL), and the model is thus limited to homogeneous vegetation canopies, and cannot reflect the inhomogeneity in vegetation properties of complex canopy (e.g., LAI in horizontal direction) well. The 3-D radiative transfer model (e.g., DART) can be expected to be used in the future for the SIF estimation of the vegetation with more complex canopies.
## 5 Results and Analysis
### Comparative Evaluation Across Models  
To compare the overall performance of each model, average scores were calculated. A random sample of 100 questions was drawn from the experimental items used in Sections 5.2 and 5.3. The additional comparative evaluation included three GPT-based engines--GPT-3.5-turbo, GPT-4-turbo, and GPT-4o--as well as a non-GPT model, Claude 3 Opus.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics** & **Four-Character Idioms** & **Synonyms** & **Antonyms** & **Homophones** \\ \hline Accuracy & 88.0 & 84.0 & 86.5 & 90.5 \\ \(\text{PA}_{C,-E}\) & 0.0 & 13.0 & 10.5 & 0.5 \\ \(\text{PA}_{-C,E}\) & 1.5 & 0.5 & 1.0 & 0.0 \\ Error Rate & 10.5 & 2.5 & 2.0 & 2.5 \\ No Explanation Rate & 0.0 & 0.0 & 0.0 & 6.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of GPT-4o across different question types, including accuracy and explanation evaluation (unit: %).  
As shown in Table 4, GPT-4o exhibited the highest performance in terms of cultural understanding. The model achieved accuracy rates of 89%, 84%, 86%, and 93% for Question Types 1 through 4, respectively, maintaining stable performance above 84% across all categories. Notably, its overall average score of 88.0 was the highest among the evaluated models, suggesting that GPT-4o demonstrated the strongest ability to interpret cultural context, even when compared with the other GPT-based models.  
In contrast, GPT-3.5-turbo recorded the lowest overall average score at 48.75. Its performance was particularly weak on antonyms and homophones, with accuracy rates of 24% and 7%, respectively. Although the model was prompted to provide explanations, it responded only with the selected choice number, omitting the requested justifications.  
GPT-4-turbo yielded a mid-range performance, with an overall average of 78.0. However, it showed comparatively lower performance on the synonym task, with an accuracy rate of 75%.  
Claude 3 Opus, the only non-GPT model included in the comparison, achieved the highest score of 97% on the homophone task. However, it demonstrated lower accuracy on synonyms (55%) and antonyms (45%). A notable issue with Claude was the frequency of incorrect explanations: Out of 100 questions, 35 explanations were incorrect for synonyms and 48 for antonyms, despite the model selecting the correct answer choices. Furthermore, during the antonym evaluation, [PERSON] occasionally generated and answered additional questions beyond those specified in the prompt. All responses referring to these unauthorized \"The Second Question\" items were evaluated as incorrect. [PERSON] autonomously generated 21 additional questions. Excluding those, it produced correct explanations for 18 questions from the original prompt.  
Taken together, GPT-4o demonstrated consistently high and reliable accuracy across all four types of culturally grounded Hanja questions. These results suggest that GPT-4o is the most culturally adept model among those tested, exhibiting the most refined reasoning abilities in the context of Hanja literacy evaluation.
# A Comprehensive Digital Model Approach for Adaptive Manufacturing Systems
## 1 Introduction
### Aim of Paper  
From the analysis of existing knowledge and literature, several key points can be discerned:  
* Emphasis is placed on swiftly responding to customer demands. Customers now seek not only to select from offered products but also to configure products themselves. This inclination toward personalized products necessitates changes in manufacturing systems to accommodate such demands [36; 37].
* Achieving the cost of a mass-produced product for personalized goods is challenging due to the high costs of product variations. Adaptive manufacturing aims to imbu production systems with flexibility and adaptability at the operational level. It seeks to enhance efficiency and reduce costs by responding to changing market conditions [38].
* An adaptive enterprise is better positioned to exploit fleeting opportunities and rapid shifts in customer requirements. To qualify as an adaptive manufacturing entity, specific conditions must be met, including adaptability, which involves responding based on \"if-then-else\" rules, which entails preparing potential scenarios and alternative strategies using \"what if...\" scenarios, and ultimately expressing and processing knowledge [39].
* Companies are focusing on developing new technologies that bolster manufacturing system flexibility. Adaptive manufacturing systems must learn to effectively utilize available technologies. The enterprise itself is considered a network integrating advanced technologies, computers, communication systems, management strategies, and cognitive agents (whether human or advanced intelligent systems). These agents are capable not only of overseeing processes and products but also of generating novel behavior to adapt to dynamic markets.
* Several manufacturing systems exhibit varying degrees of adaptability. Reconfigurable manufacturing systems are one example, while further development leads to the emergence of competency islands within manufacturing systems [40].
# Diamond-Like Carbon (DLC) Coatings: Classification, Properties, and Applications
## 2 Classification of DLC Coatings
### Processing  
The vapor deposition method is one of the well-known procedures applied to blend indistinct C. Vaporization methods are essentially isolated into two ways: (1) PVD and (2) CVD. As non-ionized particles are added to the film development to increase the voltage to the substrate and do not cause any changes in the structure of C: H, it is regularly added to the plasma gas for forming a-C: H films. The C bond, with around 30% ionized C particles, flows toward the substrate [51]. It additionally consists of a large number of small C particles.  
In the CVD cycle, hydro C gases, for example, acetylene and methane are deteriorated synthetically and dense into the C or hydrogenated C films on the substrate. For deterioration, plasma response measures are presented, in which the dehydrogenation of the hydro C particles continues to diminish the disintegrated stress. High-plasma-density PECVD sources are proposed to create sp\({}^{3}\)-rich films; these sources incorporate electron cyclotron reverberation (ECR) [52], the plasma bar source (PBS) [53], and electron cyclotron wave reverberation (ECWR). These methods are completed under generally low weights [54]. In this way, the ionization capacity of gas atoms by electron sway and speeding up the level of the ionic species around the substrate is high.
## 3 Results and Discussion
### Spectral Characterization of the Breakfast Cereal Samples  
Examples of NIR spectra obtained from the ground and intact breakfast cereal samples are shown in Figure 2. These spectra show evidence of characteristic absorption bands of various vibrational modes, which are identified based on the prior studies in the literature [39; 40; 41; 42]. The prominent absorption bands in the NIR spectra are centered at 2278, 2091, 1940, 1792, and 1463 nm (Figure 2). In particular, the bands at 2278 nm and 2091 nm correspond to aliphatic C-H bonds of carbohydrates and O-H fundamental vibrations, respectively, which are associated with crystalline sugar, particularly sucrose. The band at 1940 nm corresponds to a combination of O-H bending and stretching, while the bands near 1792 nm and 1463 nm relate to C-H overtone and the first overtone of O-H stretching, respectively.  
Figure 2 also compares spectral differences between high sugar (50.3 g/100 g) and low sugar content (3.8 g/100 g) breakfast cereals in both ground and intact forms. The spectral differences between the high sugar content and low sugar content cereals are predominantly located at 2091 and 1940 nm. Absorbance at 2091 nm is greater in high sugar content samples in both ground and intact forms, indicating higher carbohydrate levels associated with the sugars. Absorbance at 1940 nm is greater in the low sugar content sample (Figure 2).  
The visual comparison between the ground and intact cereal samples spectra revealed that the intact cereal samples had a higher degree of noise and slightly less intense bands, likely due to greater scattering of light. Even though the spectral band intensity was slightly lower, the differences between high and low total sugar content in intact cereal were still visually detectable (Figure 2).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **g/100 g Cereal** & **Minimum** & **Maximum** & **Average** & **STDEV *** \\ \hline \multirow{4}{*}{Snack food company} & Sucrose & 0.5 & 25.9 & 12.2 & 7.8 \\  & Glucose & 1.1 & 3.9 & 2.3 & 0.6 \\  & Fructose & 1.5 & 4.2 & 2.8 & 0.7 \\  & Total & 3.7 & 32.0 & 17.0 & 8.9 \\ \hline \multirow{4}{*}{Grocery stores} & Sucrose & 0.9 & 61.0 & 21.5 & 15.3 \\  & Glucose & 0.9 & 3.4 & 2.1 & 0.7 \\ \cline{1-1}  & Fructose & 1.3 & 6.7 & 3.6 & 1.3 \\ \cline{1-1}  & Total & 3.7 & 67.2 & 27.6 & 16.1 \\ \hline \hline \end{tabular}  
* standard deviation.  
\end{table}
Table 1: Individual and total sugar content for industry-provided and purchased breakfast cereal samples measured by high-performance liquid chromatography (HPLC).