esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Extended Fouling Study  
The membranes chosen for extended fouling study were the larger pore sized 200 kDa, 20 kDa, 8 kDa and 4 kDa cut-off membranes. The permeate flux reduces with time as the membrane foils and asymptotes or equilibrates to a certain flux called the critical flux [22]. Figures 8 and 9 depict the fouling of 200 kDa and 20 kDa cut-off membranes at 100 psi and 8 kDa and 4 kDa cut-off membranes at 350 psi, respectively. The flux reduces exponentially with time for first few minutes, and then it starts to equilibrate to a critical flux with increase in time. Therefore, the raw data was modelled with exponential fouling models, and the parameters of the model were determined from least sqaures, and are shown in Table 5. It is seen that the 200 kDa cut-off membrane has the highest critical flux (asymptote) of 95.4 \(\pm\) 0.84 L/(m\({}^{2}\)h) which is related to the biggest pore size. The 20 kDa cut-off membrane has the lowest critical flux of 62.01 \(\pm\) 2.48 L/(m\({}^{2}\)h) and fouling rate of 0.11 \(\pm\) 0.02 h\({}^{-1}\) among the four membranes studied. Lower fouling rates relate to longer lifetimes of the membrane; thus, the 20 kDa cut-off membrane is desirable, even though its critical flux is lower.
## 2 Materials and Methods
### The Proposed BO-Stacking-TabNet Model
#### 2.5.2 Attentive Transformer Module  
The Attentive Transformer is one of the core components of the TabNet model. Its primary function is to perform dynamic feature selection through the attention mechanism, thereby selecting the most relevant subset of features at each decision step based on the input data.  
The primary task of the Attentive Transformer is to select the most important subset of features at each decision step, thereby efficiently utilizing the input features in the multi-step decision process of TabNet. This enhances the model's feature selection capability, significantly improves inference efficiency in high-dimensional data scenarios, effectively selects a small number of features, reduces redundant computations, enhances the model's  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Indicators** & **Description** & **Formula** & **Reference** \\ \hline \multirow{3}{*}{Landsat9 Bands} & \multirow{3}{*}{SR\_B1} & Band 1 (ultra blue, coastal & Landsat9 Band 1 resolution: 30 m, & \\  & & aerosol) surface reflectance & wavelength: 0.435-0.451 μm & https://developers. \\  & & & Landsat9 Band 2 resolution: 30 m, & google.com/earth- \\  & & & wavelengths: 0.452-0.512 μm & engine/datasets/catalog/catalog/ \\  & & Band 3 (green) & Landsat9 Band 3 resolution: 30 m, & LANDSAT LC09\_CO2\_T1 \\  & & & surface reflectance & wavelength: 0.533-0.590 μm & \multirow{3}{*}{(accessed on 13 June 2024)} \\  & & & Landsat9 Band 4 resolution: 30 m, & \\  & & & surface reflectance & wavelength: 0.636-0.673 μm & \\  & & & & surface reflectance & wavelength: 0.851-0.879 μm \\  & & & 6 (shortwave infrared 1) & Landsat9 Band 6 resolution: 30 m, & \\  & & surface reflectance & wavelength: 1.566-1.651 μm & \\  & & & & surface reflectance & wavelength: 1.565-1.651 μm & \\  & & & 7 (shortwave infrared 2) & Landsat9 Band 7 resolution: 30 m, & \\  & & surface reflectance & wavelength: 2.107-2.294 μm & \\ \multirow{3}{*}{Vegetation index} & \multirow{3}{*}{NDVI} & Normalized Differences & \(\frac{(P_{MIN}+P_{RED})}{(P_{NIR}+P_{RED})}\) & [40] \\  & & Vegetation Index & \(\frac{(P_{MIN}+P_{RED})}{P_{NIR}+P_{RED}}\) & [41] \\  & & Enhanced Vegetation Index & \(2.5\times\frac{P_{MIN}+P_{RED}}{P_{NIR}+P_{RED}}\) & [42] \\  & & & \((1+0.5)\times\frac{P_{MIN}+P_{RED}}{P_{NIR}+P_{RED}}\) & [43] \\  & & & \(\frac{P_{MIN}+P_{RED}}{P_{RED}}\) & [43] \\  & & & & \\  & & DVI & Differences Vegetation Index & \(\rho_{NIR}-P_{RED}\) & [44] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Indicators for field-scale cultivated land quality assessment based on Landsat 9 satellite remote sensing.  
Figure 3: Module structure of the Feature Transformer.  
interpretability, and improves the model's generalization ability. The specific structure of the Attentive Transformer module is shown in Figure 4.
# Application-Oriented Data Analytics in Large-Scale Metal Sheet Bending
## 4 Methods and Results
### Physical Considerations behind the Analyzed Signal  
The collected signal for the curving of the component includes periods of inactivity and multiple curving cycles, as shown in Figure 2, so initially, the physical phenomena needed  
Figure 2: Preliminary analysis on the dataset acquired from the bending machine.  
to be isolated. Initially, the algorithm uses the Boolean signals to create a Boolean variable \"Active,\" which is true if any of the columns \"H_MP_AV,\" \"H_MP_RE,\" or \"H_F_On\" is true and false otherwise. It is considered that for there to be activity, one of the motors must be running. Then, the \"timestamp\" is converted into a date and time format with the string representation of the \"Active\" column. Finally, a scatter plot is created that shows the activity of the process over time.  
The curving of the component is achieved through successive bending of the metal sheet as it rotates with the rollers, so bending occurs along the entire length of the metal sheet. Bending occurs when the rear roller moves upward and the motor rotates. The pressure will never be zero, as the hydraulic axis must exert force even without deformation, just to support the weight of the rear roller. Therefore, the evolution of the signals during bending should be the following: the rear roller (Z_CPD) should have incremental upward displacement, pressure (Z_CPD_P) should increase, and rotation (Z_MP_V) should be negative when rotating in that direction. If the motor rotation changes direction, there will be no bending.  
Measurements of the position of the rear roller and the activity and inactivity times of the curving process are provided for the component's curving. The measurement position of the rear roller varies between 562 and 572 mm in different components depending on the thickness. The process activity times range from 15 min to nearly 1 h, while the process inactivity times range from a few minutes to over 2 h. These data can be useful for the analysis and optimization of the component's curving process. The variability in the process may be due to the manual nature of the operation and the on-site inspection carried out by hand. These methods may be more prone to errors and variations compared with automated processes monitored by sensors. Additionally, the corrections and controls made on the machine may be less precise than controls performed through automated systems. In general, the lack of automation and reliance on human intervention can contribute to process variability and therefore a higher amount of undesired inactivity time.
## 7 Conclusions
\begin{tabular}{l l} ICT & Information and Communication Technologies \\ EC & Edge Computing \\ VN & Vehicular Networks \\ RSU & Road Side Units \\ BS & Base Stations \\ VUs & Vehicular Users \\ ITS & Intelligent Transportation System \\ IoT & Internet of Things \\ IoV & Internet of Vehicles \\ V2V & Vehicle-to-Vehicle \\ V2R & Vehicle-to-Road Side Units \\ V2I & Vehicle-to-Infrastructure \\ V2S & Vehicle-to-Sensors \\ V2P & Vehicle-to-Person \\ VEC & Vehicular Edge Computing \\ TN & Terrestrial Networks \\ NTN & Non-terrestrial Networks \\ LAP & Low Altitude Platforms \\ HAP & High Altitude Platforms \\ ML & Machine Learning \\ MEC & Mobile Edge Computing \\ NR & New radio \\
3 GPP & 3 rd generation partnership project \\ UAV & Unmanned Aerial Vehicle \\ NFV & Network Function Virtualization \\ SDN & Software-defined Networking \\ CDN & Content Delivery Network \\ NAT & Network Address Translation \\ VMs & Virtual Machines \\ COTS & Commercial Off-the-shelf \\ VNF & Virtual Network Function \\ KPI & Key Parameter Index \\ QoS & Quality of Service \\ DRL & Deep Reinforcement Learning \\ DNN & Deep neural Network \\ FL & Federated Learning \\ MDP & Markov Decision Process \\ LOS & Line of Sight \\ NLOS & Non Line of Sight \\ OBU & On-board Unit \\ FedAvg & Federated averaging \\ eMBB & enhanced Mobile Broadband \\ URLLC & Ultra-reliable Low Latency Communications \\ \end{tabular}
# A Review of PCI Express Protocol-Based Systems in Response to 5G Application Demand
## 4 Technical Strategy Using PCI Express, CXL, USB, NVMe, and Security
### Technical Strategy through Security  
TPM with security IC has been a major feature of recent applications. Security solutions in PC systems focus on TPM and chipset security solutions using SHA 256 and RSA 2048 with Public Key Cryptography Standards (PKCS). We reviewed the security solutions that have entered the market since 2019. In software security, SHA256, SHA384, RSA 1024, 2048, and 3096 are included in the product plans. Silicon data security solutions were delivered in 2019. Chipset security is based on different strategies to work on multiple security solutions. Security solutions and strategies have undergone key changes since 2019 at different levels, which were based on risk analysis of current security systems in the environment of cloud computing and storage access and protection. For quantum computing, suggested solutions from the NSA have influenced security solutions. There are multiple algorithms, including RSA 3072-bit the larger SHA-384, AES-256, Diffie-Hellman (DH) 3072-bit, or the even larger ECDH with NIST P-384 and ECDSA with NIST P-384. The NSA also suggests enhancing some algorithms that have been used in previous designs [4].  
Secure solutions are based on the operating system, and the operating systems have a strategy using open-source kernels and virtual machine design architectures on CPU core design topology. These changes in the system rely on more secure solutions using hardware and software.
# Design of Miniaturized Dual-Band Microstrip Antenna for WLAN Application
## 4 Design of Dual-Band Microstrip Antenna
### Improved Dual-Band Microstrip Antenna  
In order to eliminate the third frequency point, we now improve the antenna structure shown in Figure 14. We open two symmetrical folding shape slots on the basis of the above antenna, the improved antenna structure as shown in Figure 20. The length of slotted horizontal section is L2, the length of collapsible sections is L3, and the length of vertical component is W2. In Figure 20, the length of dielectric slab is Ls = 55 mm, the length of square patch of antenna is L0 = 25 mm, and the size of ladder shaped groove is L1 = 14 mm, W1 = 4 mm. The size of the folding groove is L2 = 8 mm, W2 = 4.5 mm, L3 = 7 mm, and the distance from the patch center to the feed point is xp = 4.5 mm. According to the parameters of the improved antenna, the simulation results are as follows.  
Figure 21 presents the \(S_{11}\) parameter curve of the improved antenna. The antenna consists of two resonant frequencies--2.43 GHz (2.4 GHz-2.46 GHz) and 5.2 GHz (5.16 GHz-5.4 GHz). The bandwidth, that when the return loss of the antenna is less than \(-\)10 dB, are 60 MHz (2.4 GHz-2.46 GHz) and 240 MHz (5.16 GHz-5.4 GHz), which can basically meet the demand of WLAN in the two frequency bands. Figure 22 is the curve of voltage standing wave ratio (VSWR) of the improved antenna. In the two frequency bands above, the VSWR is less than two, which  
Figure 19: \(S_{11}\) parameter curve of the antenna with a stepped groove.  
Figure 20: Improved antenna structure.  
indicates there exists positive transmission characteristics. Figure 23 illustrates the directional pattern of the antenna at the far field. It can be seen that the directional pattern of the antenna is better in the 2.43 GHz, but it becomes worse in the 5.2 GHz. Figure 24 is the gain of the antenna. The gain is greater than zero in the working band of the antenna, and the gain can achieve 4 dB in most of the frequency bands. In addition, the size of the antenna is 25 \(\times\) 25 mm, which realizes the miniaturization of the antenna, and ensures the antenna gain, so that it has excellent performance.  
Figure 23: The directional pattern of the improved antenna (a) 2.43 GHz; (b) 5.2 GHz.  
Figure 21: \(S_{11}\) parameter curve of the improved antenna.  
Figure 22: VSWR curve of the improved antenna.
# Exploring CeO\({}_{2}\)-Doped Co/SBA-15 Catalysts for Acetic Acid Oxidative Steam Reforming
## 2 Materials and Methods
### Catalyst Characterization  
All the samples were characterized by multiple procedures. ICP-OES analysis was performed to measure the Ce and Co contents in the synthesized catalysts, employing an Agilent ICP-OES 5800 VDV spectrophotometer (Agilent, Santa Clara, CA, USA). Before analysis, the catalysts were dissolved by acid digestion (H\({}_{2}\)SO\({}_{4}\)\(\cdot\)HF mixture). The textural properties were evaluated based on N\({}_{2}\) physisorption at 77 K using a Micromeritis TRISTAR 3000 sportometer (Micromerities, Norcross, GA, USA). Specific surface areas and pore sizes were estimated using the BET method and the BJH formula, respectively. X-ray diffractograms were acquired on a Philips X'PET PRO diffractometer (Philips, Eindhoven,The Netherlands) equipped with Cu K\(\alpha\) radiation. Mean crystallite sizes were calculated using the Scherrer equation.  
\[\mathrm{d=}\frac{\mathrm{K\cdot\lambda}}{\beta\mathrm{-}\mathrm{cos}\theta} \tag{11}\]  
where \"d (nm)\" refers to the mean crystallite diameter, \"K\" is the Scherrer constant (0.9), \"\(\lambda\)\" is the X-ray wavelength (Cu-K\(\alpha\) at 0.15406 nm), \"\(\beta\)\" refers to the FWHM (full width at half maximum) of the highest intensity diffraction peak, and \"\(\theta\)\" indicates the diffraction half-angle.  
To assess the catalyst's reducibility, H\({}_{2}\)-TPR analyses were performed on a Micrometrics in Situ Catalysts Characterization System (ICCS, Micromerities, Norcross, GA, USA), where a reducing gas mixture flowed through the sample (10% H\({}_{2}\)/Ar, 60 mL/min) while heating at 10 \({}^{\circ}\)C/min up to 900 \({}^{\circ}\)C.  
The morphology of the prepared materials was evaluated using TEM in a 120 kV JEOL JEM-1400 FLASH microscope (JEOL, Tokyo, Japan); the samples were previously dispersed in acetone. Using a [PERSON] series UV-Vis-NIR spectrophotometer (Agilent, Santa Clara, CA, USA) at wavelength range of 200-800 nm, the CeO\({}_{2}\) oxidation stages on the synthesized materials were evaluated. Finally, TGA and Raman spectra were used to assess the coke deposition on the used catalysts. For that purpose, a TGA-DSC Mettler Toledo thermobalance (Mettler Toledo, Greifensee, Switzerland) employing airflow at a 5 \({}^{\circ}\)C/min heating rate until 1000 \({}^{\circ}\)C and a Horiba LabRam HR evolution spectrometer (HORIBA Scientific, Kyoto, Japan) with a 532 nm laser beam (nominal power 200 mW) were utilized.
# Google Gemini's Performance in Endodontics: A Study on Answer Precision and Reliability
## 1 Introduction  
Access to large language models (LLMs) is transforming several scientific disciplines by providing a wide range of support tools. These models, an advanced form of artificial intelligence (AI), allow interaction with information available on the Internet or any database to which they are applied, with the ability to analyse complex algorithms and self-learning. We are entering a new era in medicine. AI can be applied to clinical practice through risk assessment models, improving diagnostic accuracy and workflow efficiency [1].  
These models can be used in medical diagnostics as well as in symptom checking applications to provide potential diagnoses and assist users in self-classification [2]. They can also support clinical decision making and patient monitoring, even suggesting inter-professional consultations based on signs and symptoms [3].  
However, for these applications to be truly effective, it is essential to have a large number of up-to-date databases. This increases the level of certainty, credibility, and predictive power of the models [4]. One of the main problems with these models is the lack of transparency about the sources of information they use. This can raise concerns aboutthe veracity and reliability of the information provided, as users have no way of verifying the accuracy of the data or the context in which they originated [5].  
There are numerous tools on the market. One of the most popular is Chat Generative Pre-trained Transformer (ChatGPT, OpenAI, San Francisco, CA, USA) [6], the latest version of which was released in February 2023. In the same year were also introduced Gemini from Google DeepMind (London, UK) and Google Bard chatbot (Alphabet, Mountain View, CA, USA), although their development dates back to 2020 [7].  
Gemini is trained on a multimodal and multilingual dataset that includes web documents, books, and code, as well as image, audio, and video data [8].  
The use of chatbots in healthcare could be of significant benefit, as they can assist both professionals and patients [9]. Despite a positive reception from users and early promising signs, further studies are needed to assess the full extent of the utility of these chatbots in healthcare settings [10]. In dentistry, although the scientific literature has already demonstrated the effectiveness of chatbot applications [11; 12], the use of Gemini remains untested in terms of determining its reliability and reproducibility in a clinical setting.  
Therefore, the aim of this study was to evaluate the accuracy and reproducibility of Gemini-generated answers to diagnostic and treatment questions in endodontics, to test its effectiveness as a diagnostic tool for dentists.
## 4 Evaluation
### Results and Discussion  
In the subsequent section, we discuss the simulation results, focusing on the consumer Quality of Service (QoS) evaluated through four key performance indicators: average Input loss, average Data packet delay, average path stretch, and handoff latency. Additionally, we explore the network cost to support producer mobility in the DMMS, which is evaluated using signaling overhead performance. Furthermore, we also study how the number of Anchors in the network impacts the consumer QoS. In addition, we discuss the limitations of our strategy that can be explored for further research.
## 4 Discussion  
Because exhaust gas temperature varies significantly when the engine loads change, it is essential to study the potential of the WHR system under different engine loads. The layout sharing a common shaft is considered for its simple structure, especially for transportation. R1233 zd and R134a are used in the ORC and VCC, respectively. By considering the influence of the engine load on the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Belt Drive** & **Common Shaft** \\ \hline ORC working fluid & R245 ca & R1233 zd \\ VCC refrigerant & R134a & R134a \\ Cooling capacity at 263K [kW] & 9823 & 6902 \\ ORC evaporation pressure [kPa] & 3500 & 900 \\ Energy efficient ratio (_EER_) & 2.74 & 3.06 \\ Power output of expander [kW] & 3584 & 2180 \\ Total exergy destruction [kW] & 7482 & 8132 \\ ORC thermal efficiency [\%] & 13.79 & 9.10 \\ Exergy efficiency [\%] & 13.10 & 9.50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimal operational conditions at 263 K VCC evaporation temperature.  
Figure 13: Effect of condensation temperature on exergy efficiency in the case of sharing a common shaft.  
operation of the ORC-VCC WHR system, an evaluation of the performance was conducted in this paper with respect to the cooling output at the same temperature. The following analysis is based on the flowing operating conditions. The ambient temperature and the sea water (heat sink) are both 298 K, the refrigeration temperature \(T_{15}\) is 263 K, which is low enough for both air conditioning and food preservation. The condensation ranges from 309 to 313 K, and the low temperature is limited by the heat sink temperature. The ORC evaporation pressure is the optimal pressure of 900 kPa listed in Table 1.  
Figure 14 shows the cooling capacity of ORC-VCC when the engine is operated under different loads. It is noted that a higher cooling output could be achieved when increasing the engine. The waste heat recovery is closely related to the thermal properties of the heat source. As shown in Figure 2, the heat energy available for recovery increases with the engine load. The higher the temperature, the better the quality of the heat energy, and there is a peak temperature at 40% MCR. These two factors resulted in an upward trend for the ORC power output, and a sharp change appeares at 40% MCR. This variation trend is then transmitted to the cooling capacity in VCC, as it is powered by the ORC expander.  
Figure 14 also shows that the cooling capacity increased with the condensation temperature. However, the condensation temperature is limited by the parameters of sea water, including its temperature and flow rate. The operational condition of the combined cycle could be controlled by adjusting the ORC operation pressure and the flow rate of the heat sink and heat sources according to the cooling demand on ship.
## 5 Signal Feature Extraction Methods for Different Vessels
### Feature Extraction Based on Multi-Scale Permutation Entropy  
Due to the varying complexities of time series signals from different types of vessels in the same maritime area, this paper utilizes multiscale permutation entropy (MPE) to describe the complexity of time series at different scales, as shown in Figure 12. With an embedding dimension of 4 and a time delay of 1, the X-axis represents the time scale, and the Y-axis represents the entropy values corresponding to each time scale.  
Figure 12 shows that with the increase in time scale, Vessel B and Vessel C fluctuate within a certain range, and the fluctuation amplitude is insignificant. In contrast, the fluctuation amplitude for Vessel A is quite noticeable. There are certain differences among the three signals at different time scales, with the largest difference observed at time scales 42 and 43.  
Table 2 analyzes the entropy values of the signals from the three vessels at time scales 42 and 43. Within the two sets of time scales, Vessel A exhibits significant differences in entropy values compared to the other two vessels, while the difference between Vessel B and Vessel C is slight. Looking at the overall data, the differences are not very pronounced, and separability requires further analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Vessel A** & **Vessel B** & **Vessel C** \\ \hline Highest Energy Level & IMF2 & IMF2 & IMF3 \\ Average Energy & 0.04727 & 0.0653 & 0.0048 \\ PE Values & 0.6187 & 0.9883 & 0.3934 \\ \hline \hline \end{tabular}
\end{table}
Table 1: PE values of components for three different types of vessels.  
Figure 12: MPE values for different vessels.
## 5 Investigation and Results  
In this section, we apply _message alignment_ and the proposed tree-like node structure during the construction of information-bottleneck decoders for irregular LDPC codes. The investigated irregular code is taken from [3], where several irregular LDPCs relevant for optical communications were proposed and compared. We provide numerical simulations for our proposed information bottleneck decoder and several reference systems assuming transmission over an additive white Gaussian noise (AWGN) channel with BPSK modulation. We compare the decoder in terms of decoding performance, computational complexity, and memory demand.