esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive Linear Programming Algorithm with Parameter Learning
## 4 A Test Problem
### The Hot Rod-Rolling Process Chain  
Hot rod rolling is a multi-stage manufacturing process in which a reheated billet, slab, or bloom produced after the casting process is further thermo-mechanically processed by passing it through a series of rollers [36]. During the thermo-mechanical processing, there is an evolution of microstructure of the material, in this case, steel. Columnar grains in the cast material are broken down to equiaxed grains. Along with the evolution of grain size, there is a phase transformation of the steel. The phase transformation is predominant during the cooling stage that follows the hot rod-rolling process chain. The transformation of the austenite phase of steel to other phases like ferrite, pearlite, or martensite takes place during this stage. The final microstructure of the material after the rolling and cooling process defines the mechanical properties of the product. Many plant trials are required to produce a new steel grade with improved properties and performance. These trials are usually expensive and time-consuming. Hence, there is a need to address the problem from a simulation-based design perspective to explore solutions reaching multiple conflicting property/performance goals. The requirement is to produce steel rods with improved mechanical properties such as yield strength (YS), tensile strength (TS), and hardness (HV). These mechanical properties are defined by the microstructure after cooling, which includes the phase fractions (ferrite and pearlite phases are only considered in this problem), pearlite interlamellar spacing, ferrite grain size, and chemical compositions of steel. [PERSON] et al. [36] identify the microstructural requirements after the cooling stage to meet the mechanical properties of the rod. The microstructural requirements are to achieve a high ferrite fraction value, low pearlite interlamellar spacing, and low ferrite grain size values within the defined ranges. The requirement is to carry out the integrated design of the material and the process by managing the cooling rate (cooling process variable), final austenite grain size after rolling (rolling microstructure variable), and the chemical compositions of the material. Hence, we explore the solution space of the defined variables using ALPPL to meet the target values identified for the microstructure after the cooling stage such that the mechanical property requirements of the steel rod are met. Our focus in this paper is to use this example in improving the solution algorithm rather than the design of the material and the manufacturing. The initial design formulation of the problem is shown as follows.
## 4 Results and Discussions
* Impact: The effect of the interference on 5G networks and IoT services. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \begin{table}
\begin{tabular}{l l l l l l} \hline
**Interference** & **Frequency** & **Bandwidth** & **Power** & **Impact** & **Reference** \\ \hline Atmospheric Absorption & 24–40 GHz & Narrowband & Low & Attenuation & [101] \\ \hline Free Space Path Loss & All & All & Low & Attenuation & [102] \\ \hline Reflection & All & All & Low & Multipath Fading & [103] \\ \hline Refraction & All & All & Low & Path Bending & [104] \\ \hline Diffraction & All & All & Low & Path Bending & [105] \\ \hline Scattering & All & All & Low & Multipath Fading & [106] \\ \hline Rain Fade & 10–100 GHz & Wideband & High & Attenuation & [107] \\ \hline Multipath Fading & All & All & Low & Intersymbol Interference & [108] \\ \hline Co-Channel Interference & All & All & High & Reduced Signal Quality & [109] \\ \hline Adjacent Channel Interference & All & All & High & Reduced Signal Quality & [110] \\ \hline Interference from Other Radios & All & All & High & Reduced Signal Quality & [111] \\ \hline Interference from Other IoT Devices & All & All & Low & Reduced Signal Quality & [112] \\ \hline \end{tabular}
\end{table} 
Table 12: Future research challenges in 5G for IoT services. \begin{table}
\begin{tabular}{l l l l} \hline
**Issues** & **Methodologies** & **Advantages** & **Limitations/Future Work** \\ \hline Interference & Dynamic Spectrum & Better spectrum & Developing efficient and scalable DSA (Dynamic utilization, & Spectrum Access) and cooperative sensing \\  & Sensing & increased reliability & & cooperative sensing \\ \hline Security & Authentication, & Secure & & Developing lightweight \\  & encryption & communication, & and energy-efficient \\  & & protecting against & & security mechanisms \\ \hline Energy & Power & & Longer battery life, & \\ Efficiency & management, & improved system & & algorithms for resource \\  & resource allocation & capacity & & allocation and power \\ \hline Scalability & Network slicing, & Better resource & & Developing efficient \\  & virtualization & utilization, & & network slicing and \\  & virtualization & improved service & & virtualization techniques \\  & & quality & & for massive IoT \\ \hline Latency & Edge computing, & & & \\  & network & & communication & \\  & architecture & delay, improved & & network architecture for \\  & architecture & & performance & IoT services \\ \hline \end{tabular}
\end{table} 
Table 13: Interference characteristics in 5G networks and IoT services.
## 2 Methods  
### Setup  
The JACO 2 (Kinova, Boisbriand, Quebec, Canada), a 6 degrees of freedom (DoF) robot arm, is attached to a motorized wheelchair and equipped with a RealSense d435 Red Green Blue-Depth (RGBd) camera (Intel, Santa Clara, CA, USA), as shown in Figure 1. The robot arm features an underactuated 2-Finger, 4-DoF gripper designed for grasping tasks. Each finger can be opened and closed with a single command, and the angle of the joint between the two links is dependent on the closure of the fingers and cannot be actuated manually. Control of the arm is facilitated through a fully integrated inductive tongue computer interface (TCI) [14]. The TCI provides semi-autonomous control, allowingthe user to give inputs with their tongue via an inductive plate placed on the roof of the mouth, as illustrated in Figure 2.  
The interface used is multimodal, allowing the user to control the end effector via a virtual joy-stick-like environment and switch modes to semi-autonomously grasp objects. Grasping intent prediction is accomplished by identifying the closest object to the palm-side of the gripper [5]. A grasping point is then determined by approximating the shape of the object with a virtual cylinder. While the user retains control over the grasping process and can signal to grasp or release via the TCI, they also have the ability to interrupt the process at any given time. The remaining path planning and grasping of the object are executed autonomously. Previous experiments revealed instances of slippage occurring during both the initial lifting of the object and while the object was already in-hand. With our proposed method, we aim to react to these slip events using the existing setup, avoiding the need to incorporate additional sensors and complexities into the system. This work aims to evaluate the proposed slip detection method, since there are already publicly available datasets published, we did not conduct experiments of our own, which would involve the TCI. To evaluate the performance of the proposed method, we conducted tests on a dataset consisting of 28 daily objects, each lifted 30 times [9], resulting in a total of 840 frame sequences.  
Figure 1: The JACO 2 robot arm attached to a wheelchair. A RealSense D435 Red Green Blue-Depth (RGBd) camera is attached to the end effector, which is holding a tea carton. The robot arm is controlled semi-autonomously by the user via a tongue–computer-interface (TCI).  
Figure 2: The tongue–computer interface individually fit for each user. Two conductive plates serve as interfaces. One to move the robot arm, the other to act as a menu to switch between manual and semi-autonomous mode. More detail on the TCI can be found in [14].
# Design of a Cell Phone Lens-Based Miniature Microscope with Configurable Magnification Ratio
## 5 Conclusions  
Cell phone microscopes have been widely researched, the applications of which are still limited by the following factors, including inferior image quality, fixed magnification, inconvenient operation, etc. In this paper, we proposed a cell phone lens-based miniature microscope with a configurable magnification ratio. Inheriting the advantages of low costs and direct access to the internet via cell phone, the proposed miniature microscope simultaneously provides users with the ability of adjustable magnification ratios, truly easy deployment and even an array of applications in the field. These improvements are significant for the field applications of cell phone microscopes.  
On the basis of the proposed scheme, by switching three camera lens objectives and applying the digital zooming function of the cell phones, a set of miniature microscopes with magnification ratios continuously configurable among 0.8\(\times\)-11.5\(\times\) was built. The imaging tests exhibit that the proposed miniature microscope can provide high-quality microscopic imaging, with a maximum resolution of up to 575 lp/mm and a maximum FOV of up to 7213 \(\times\) 5443 \(\upmu\)m. In addition, by moving the tube lens module of the cell phone microscope out of the cell phone body, the built miniature microscope is as compact as a cube with a side length of 15 mm, profoundly advancing the operational experience. We believe that the proposed scheme has addressed the mentioned limitations of the cell phone-based microscopes and users may find it easier to learn and practice with such miniature microscopes. With the launch of more powerful cell phone cameras these days, we can foresee that more powerful miniature cell phone microscopes will be produced and put into use.  
Investigation, X.W. and X.T.; methodology, X.W. and X.T.; resources, X.W.; supervision, X.W.; validation, X.W. and X.T.; writing--original draft, X.T.; writing--review and editing, X.W. and X.T. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Potential applications based on the proposed miniature microscope. (**A**) Real-time microscopic wireless observation of the enclosed cell culturing process. (**B**) Scheme of a low-cost total internal reflection miniature microscope. (**C**) Scheme of a low-cost structured illumination miniature microscope for enhanced resolution.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 61505107 and by the Shanghai Science and Technology Innovation Action Plan, grant number 19511104600.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used to support the findings of this study are included within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios
## 2 Materials and Methods
### Optimization Problem Formulation
#### 2.1.5 Measured Strains and Temperature Approximation  
Due to the finite length of the sensor, it is assumed that the response of a sensor under given load case \(R_{det_{i}}\) is the average value of the measured physical quantity at all points on the sensor line within the structure (Figure 5). To simulate it in the objective function evaluation, a finite number of \(v\) points is assumed, and the response of the sensor is approximated by the mean value of the responses \(R_{g}\), where \(g=1,2,\ldots,v\).  
\[R_{det_{i}}\ \ =\frac{1}{\int\limits_{\text{P}_{1}}^{\text{P}_{2}}R_{q}\text{d }q}\approx\frac{1}{v}\sum_{g=1}^{v}R_{g} \tag{10}\]  
In the computations of objective function, the \(R_{det_{i}}\) values are computed using a numerical model of the structure under load with a relatively dense grid of points with known values of the measured physical quantity for every load case (in this paper, the grid of points of known field values is the mesh of the finite element model). The field values  
Figure 4: Illustational example of objective function evaluation.  
\(R_{g}\) are interpolated using the known values \(R_{k}\) in the set closest points of the nearest data points to \(g\).  
\[R_{g}=\sum_{k\in\text{closest\_points}}w_{k}\cdot R_{k} \tag{11}\]  
where \(w_{k}\) are weights that are assigned inversely proportional to these distances between points \(g\) and \(k\):  
\[w_{k}=\frac{1/(distance_{k}+e)}{\sum_{j\in\text{closest\_points}}1/(distance _{k}+e)} \tag{12}\]  
In this interpolation, a very small number \(e\) is added to the denominator to avoid division by zero.
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 4 Photocatalytic Oxidation
### Airflow Rate  
Airflow rate is another critical factor affecting photocatalytic oxidation. Increased airflow can reduce the residence time of molecules, resulting in decreased photocatalytic efficiency 6. For example, one study found that increasing airflow from 100 to 300 mL/min decreased the conversion rates of toluene, ethylene, and propylene when using TiO\({}_{2}\) nanoparticles (Figure 4) [96].  
Figure 3: Degradation of formaldehyde using g-C\({}_{3}\)N\({}_{4}\)-TiO\({}_{2}\) considering different residence times [95].  
Figure 2: (**a**) Removal efficiency of formaldehyde using Ti1 MS2-550 and TiO\({}_{2}\) increasing the relative humidity [94] and (**b**) degradation of formaldehyde using g-C\({}_{3}\)N\({}_{4}\)-TiO\({}_{2}\) in different conditions of relative humidity [95].  
However, increased airflow also enhances mass transfer between the catalyst and pollutants, potentially increasing conversion rates by reducing concentration gradients between the catalyst bulk and its surface. The effects of airflow rate on pollutant removal are complex and can be categorized into three main conditions:  
_Low Airflow Rate_: High residence time leads to enhanced photocatalytic oxidation due to sufficient contact between the catalyst and pollutants, indicating that mass transfer limits degradation.  
_Intermediate Airflow Rate_: Variations in airflow rate have minimal impact on pollutant removal efficiency.  
_High Airflow Rate_: Increased airflow reduces residence time, decreasing the efficiency of the photocatalytic process [91].
# Non-Thermal Methods for Ensuring the Microbiological Quality and Safety of Seafood
## 6 Electrolyzed Water--EW
### Effects of EW on the Quality of Fish and Seafood  
The treatment of fish and seafood products with various types of EW has been extensively studied with great antimicrobial results, as observed above. Still, food safety has to be accompanied by quality and sensory retention. When [PERSON] et al. (2014) [197] investigated the effect of acidic EW ice, a relatively new concept, on the quality of shrimp (_L. vannamei_) in dark condition, they reported that AEW ice could be a proper post-harvest treatment for preserving the quality of seafood in dark condition. The raw shrimp results showed that AEW ice, besides its bactericidal effect on TVC, inhibited the pH changes, the formation of total volatile basic nitrogen (TVB-N), and the activity of Polyphenol oxidase (PPO) after 6 days of storage. These results in agreement with [PERSON] et al. (2013) [228] indicate that AEW ice can serve as an innovative method to retain the quality of shrimps. In addition, lower TVB-N, trimethylamine (TMA), and thiobarbituric acid reactive substance (TBARS) values also reported for raw puffer fish (_Takifugu obscurs_) stored at 4 \({}^{\circ}\)C after treatment with weakly acidic EW (WAEW) and modified atmosphere packaging (MAP) [229]. Overall, [PERSON] et al. (2020) [229] concluded that WAEW and MAP (60% CO\({}_{2}\); 5% O\({}_{2}\); 35% N\({}_{2}\)) significantly extended the shelf life and maintained the better quality of the farmed puffer fish. [PERSON] et al. (2017) [203] similarly reported increased shelf life of Southern Australian King George Whiting (KGW) and Tasmanian Atlantic Salmon (TAS) refrigerated (4 \({}^{\circ}\)C) fillets by 2 and 4 days, respectively, after treatment with pH neutral EW solution of 15 and 50%. Finally, numerous studies indicating the potential of using different types of EW on fish and seafood products to retain their main physicochemical [14; 199; 216; 223; 229; 230; 231] and sensory characteristics [198; 205; 215; 218] playing an essential role in consumer acceptance.
## 2 Materials and Methods
### Ground Data  
First, 96 sample points were designed. After field investigations, 86 samples were sampled in Kenli county, considering factors such as soil type, vegetation type, and land use. The sample area was set to 30 m \(\times\) 30 m to match the pixel area of Landsat 8 OLI data, with a sampling depth of 0-20 cm, conducted from 11 October to 16 October 2016. All sampling points were located as far from roads and buildings as possible. The distance between adjacent sampling points was approximately 4.5 km, sufficient to avoid spatial dependence among samples. In this study, all georeferencing work was conducted using the U.S. Global Positioning System (GPS), with a handheld GPS device used to determine field coordinates and record surrounding environmental information. It is worth noting that in recent years, China's BeiDou Navigation Satellite System (BDS) has been widely  
Figure 1: Location of the study area and 86 sampling points. Red dots mean the sampled points. The data sources were Landsat 8 OLI Collection 2 Level-2 image taken on 15 January 2016 from the United States Geological Survey ©, [[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)) (accessed on 20 October 2024).  
adopted for high-precision positioning and remote sensing applications in China due to its accuracy advantages. In future research, the adoption of BeiDou will be considered.
## 6 Future Outlook and Recommendations
### Call for Multidisciplinary Collaboration  
Given the rapid pace of technological change, future research should engage diverse stakeholders: network engineers, AI ethicists, curriculum designers, psychologists, and legal experts. Joint initiatives can explore how to optimize 5G capacity for specialised education or how to embed cultural competencies into AI-driven tutoring systems. In parallel, consortia focused on bridging digital divides might investigate cost-sharing models, open source platforms, and skill-development workshops for teachers in underprivileged regions.  
Ultimately, the synergy of AI and 5G in education transcends mere hardware upgrades; it hinges on a well-coordinated triad of pedagogical alignment, solid infrastructure, and nuanced policy-making. Recent demonstrations show that advanced telecom features alone do not guarantee deeper learning unless they are matched by flexible curricula, continuous faculty development, and ethically sound data governance [6]. By unifying these strands, stakeholders can ensure that the next wave of digital transformation will provide equitable access, holistic skill development, and long-term societal benefits.
## 3 Results
### Data Generation Results  
We evaluate how similar the generated signatures produced by our proposed method are to the original signatures. Since the dataset included in the CEDAR signature lacks crucial information, such as pen pressure, signing time, and speed variations, only visual comparisons can be performed. Visual comparison elements include line flow, thickness, signing style, and characteristic curves or angles. Figure 5 shows how similar the original and generated signatures can appear from a macroscopic perspective.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Input Image Size (H, W) & (128, 256) \\ Optimizer & Adam \\ Optimizer Learning Rate & \(2\times 10^{-4}\) \\ Number of Time Steps (T) & 1000 \\ Beta Schedule & Linear \\ Beta Start & \(1\times 10^{-4}\) \\ Beta End & 0.02 \\ Noise Schedule Type & Linear \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used in the training process of the improved U-Net-based model.  
We observed that the signatures generated using our proposed method closely mimic the curves and line patterns found in the original signatures used for training. Figure 6a,b illustrates the similarities between the original and generated signatures for two different signature types. The blue boxes and arrows in Figure 6a,b highlight the regions where the original and generated signatures exhibit a high degree of similarity. We found that the curves, the width of the ovals corresponding to the handwritten signatures, and the shape of the serifs were well preserved.  
The visual elements of the generated signatures were primarily composed of multiple features observed from various original signatures. This indicates that the proposed method can generate new signatures that incorporate the characteristics of original signatures rather than creating exact replicas. The orange boxes in Figure 6a,b highlight the flow of lines that were not observed in the original signatures. In Figure 6a, a blurred and roughly drawn line progression is observed, while in Figure 6b, although the curve's swoop and dot positions are similar, a new form with a shorter lower curve length can be observed.  
Figure 5: Comparison of the original signatures with signatures generated using the proposed method.  
Figure 6: Detailed comparison of original signatures and generated signatures.
## 2 The Evolution and Trends of 6G Network Architecture
### The Design Fundamentals of 6G Networks
#### 2.1.4 360-Cybersecurity and Privacy-by-Engineering Design  
While 5G has prioritized security considerations in terms of protocols and architecture, there has been a gap in standardization efforts concerning the security of embedded code executing system components. This gap has resulted in various security vulnerabilities. Moving forward, there will be a comprehensive cybersecurity approach, addressing security holistically from end to end and considering both top-down perspectives (architecture and protocols) and embedded software. The design will incorporate privacy-by-engineering principles, integrating mechanisms into protocols and architecture to ensure inherent privacy features. For instance, devices such as security cameras will only be permitted to stream video footage if they meet specific privacy requirements at the networking and contextual levels, ensuring they undergo certified privacy evaluation.