esa-sceva/satcom-chunk-collection huggingface dataset:
# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 4 Results
### Configuration of the Simulation Environment
#### 4.2.7 Configuration of the Data Collection Optimization Model  
We use a Gurobi commercial optimizer to solve the data collection optimization. We use the Gurobipy python library to solve the problem of optimization of data collection by calling a python script for solving the optimization from NS3 script at run time to provide a solution based on the metadata collected by the controller. The metadata collected by the controller is saved in a Comma Separated Values (CSV) file to be read by the python script as input to the optimization model. Similarly, once a solution is found by the optimizer, it will be saved in another CSV file to be read by the controller in the NS3 environment. In a simulation run, we call the optimization solver at the rate of nominal optimization frequency (\(f^{\prime}\)) if and only if the entropy change in the network is greater than a threshold entropy change, compared to the last time the network was optimized. In this research, we set \(k=1\) in Equation (12) such that the nominal optimization frequency (\(f^{\prime}\)) is the same as the data collection frequency (\(f\)). We set the communication cost per byte from the \(i^{th}\) node to neighbor \(m\) (\(C_{im}\)) as 1 for wired communication, and 2 for DSRC communication [56]. The communication cost per byte for an agent (\(C_{i}\)) is set to the highest value of 40, as it involves cellular communication, which is usually 20 times the cost per byte of DSRC communication [57]. In order for the broadcasting delay from the \(i^{th}\) node to the \(m^{th}\) neighbor (\(\mathcal{D}_{im}\)) comparable with the set communication cost per byte values, we calculate all delay parameters in milliseconds. The agent nodes' combined communication (\(\mathcal{B}_{i}\)) is input to the objective function in Equation (11) in milliseconds (typical values lie in the range 2 to 60). Therefore, to give equal priority for agents combined communication cost and overhead minimization (term 2 and term 3 in the objective function in Equation (11)) compared with the broadcasting nodes cost minimization (term 1 in the objective function whose typical values lie in range 1 to 30), \(C_{CN}\) is set to two and both \(C_{CA}\) and \(C_{O}\) are set to one. For a maximum of 256 nodes in the vehicular network, the solution time for solving the IQP model using the Gurobi IQP solver on average is only 40 ms. Thus, for very large-scale networks, it can be solved in parallel such that the solution time will not be much larger. Therefore, a heuristic solution mechanism for this problem is not needed in this context. We summarize the main simulation parameters as shown in Table 2.
# Hierarchical Active Tracking Control for UAVs via Deep Reinforcement Learning
## 3 Methodology
### Reward Engineering  
Figure 4 shows the aerial view and abstraction of the training environment. The y-axis is set to the head direction of the quadrotor, and the x-axis is set from the quadrotor's right to left. The coordinate of the target in this quadrotor's body frame is denoted as \((dx,dy)\); then, the yaw angle \(\varphi\) and distance \(\rho\) between the target and quadrotor can be calculated as follows:  
\[\begin{array}{ll}\varphi&=\arctan(dx/dy)\\ \rho&=\sqrt{dx^{2}+dy^{2}}\end{array} \tag{11}\]  
We want the quadrotor to follow the target at a fixed distance \(\rho_{d}\) while being oriented toward the target; thus, the following naive reward function is proposed, which encourages \(\rho=\rho_{d}\) and \(\varphi=0\):  
\[r=\alpha_{1}\exp(-\left|\frac{\rho-\rho_{d}}{\beta_{1}}\right|)+\alpha_{2}\exp (-\left|\frac{\varphi}{\beta_{2}}\right|) \tag{12}\]  
where \(\alpha_{1}>0,\alpha_{2}>0,\beta_{1}>0,\beta_{2}>0\) are tunable hyperparameters. \(\beta_{1},\beta_{2}\) control the gradient of the first and second parts of the exponential reward function, respectively, and \(\alpha_{1},\alpha_{2}\) are their coefficients of combination.
###### Abstract  
Three-dimensional (3D) bioplotting has been widely used to print hydrogel scaffolds for tissue engineering applications. One issue involved in 3D bioplotting is to achieve the scaffold structure with the desired mechanical properties. To overcome this issue, various numerical methods have been developed to predict the mechanical properties of scaffolds, but limited by the imperfect representation of one key feature of scaffolds fabricated by 3D bioplotting, i.e., the penetration or fusion of strands in one layer into the previous layer. This paper presents our study on the development of a novel numerical model to predict the elastic modulus (one important index of mechanical properties) of 3D bioplotted scaffolds considering the aforementioned strand penetration. For this, the finite element method was used for the model development, while medium-viscosity alginate was selected for scaffold fabrication by the 3D bioplotting technique. The elastic modulus of the bioplotted scaffolds was characterized using mechanical testing and results were compared with those predicted from the developed model, demonstrating a strong congruity between them. Once validated, the developed model was also used to investigate the effect of other geometrical features on the mechanical behavior of bioplotted scaffolds. Our results show that the penetration, pore size, and number of printed layers have significant effects on the elastic modulus of bioplotted scaffolds; and also suggest that the developed model can be used as a powerful tool to modulate the mechanical behavior of bioplotted scaffolds.  
3D bioplotting; additive manufacturing; mechanical behavior; elastic modulus; finite element modeling; layer penetration; alginate
###### Abstract  
Recombination of charge carriers can affect the response of ionization detectors used for the dosimetry of radiotherapy fields. In this work, we present a method for correcting online the recombination effects in arrays of ionization chambers irradiated with time-varying dose rates. The method is based on the characterization of the dose rate/recombination response of the detector, and the measurement of the instant ionization current in the detector, rather than the integrated charge. The proposed method was investigated with simulations of the response of different air and liquid ionization chambers in situations where recombination can be large. In addition, we experimentally investigated the application of the method with an in-house-developed liquid-filled ionization chamber. The proposed online correction method can compensate for recombination losses and seems feasible to implement in the software of ionization arrays/detectors used for the dosimetry of radiotherapy fields.  
recombination; ionization chamber; FFF; UHDR +
Footnote †: journal: Medical Physics and Biomathematics, Instituto de Investigacion Sanitaria de Santiago (IDIS), 15706 Santiago de Compostela, Spain
In this work, we present a method for correcting online the recombination effects in arrays of ionization chambers irradiated with time-varying dose rates. The method is based on the characterization of the dose rate/recombination response of the detector, and the measurement of the instant ionization current in the detector, rather than the integrated charge. Recombination in ionization chambers irradiated with dynamic fields has been studied by other authors. In particular, [PERSON] theoretically studied recombination in an ionization chamber irradiated by a swept beam [19]. [PERSON] et al. studied recombination in ionization chambers irradiated by Tomotherapy dynamic beams and developed a dynamic method of correction based on the calculation (with Monte Carlo) and integration of dynamic dose rates in the detector [20].
# Comparative Analysis of Chatbots Using Large Language Models for Web Development Tasks
## 1 Introduction  
Thanks to developments in natural language processing (NLP) and artificial intelligence (AI), chatbots are becoming more and more common in a variety of entities [1]. More specifically, chatbots that utilize large language models (LLMs) have been shown to be exceptionally skilled at recognizing and creating content that is human-like [2]. These models--including Copilot, GPT-3.5 from OpenAI, and others--are trained on enormous volumes of data, which allows them to accomplish a variety of tasks, like writing essays, answering questions, or creating code [3].  
Chatbots have become useful in the field of web development [4]. These AI technologies are widely used by developers to obtain code snippets, debug issues, and simplify development processes. Chatbots that are based on LLMs can help with creating HTML structures, creating CSS, writing JavaScript, and connecting backend features with databases like MySQL and PHP. The need to assess these chatbots' ability to handle difficult programming needs grows with the complexity of web development activities.  
Although LLM-based chatbots show potential, their efficiency in real-world coding jobs has to be methodically verified. Reliable tools that not only produce precise code but also comprehend the jobs' context, guarantee security, and preserve usability have become vital for developers. Recent research [5; 6; 7] has investigated these chatbots' overall performance; however, little is known about how well they work in particular, multi-step web development scenarios. This assessment is necessary for developers to select the best chatbot for their purposes and identify areas where these AI tools may be enhanced.
## 1 Introduction
### Literature Review
Considering that track geometry condition indicators are continuous variables and using discretized state levels to describe the deterioration process may not ensure accuracy, some scholars have employed a continuous state-space stochastic process method, that is, the linear Wiener process method, to describe the deterioration of condition indicators, such as the standard deviation of the longitudinal level over a track segment [26; 27]. However, the above methods all assume that the deterioration process of track geometry conditions has a Markov property--that is, that the future state only depends on the current state and is independent of past states. However, not all infrastructural deterioration processes satisfy this assumption. Whether the deterioration process of the track geometry condition satisfies this assumption requires a large amount of researchfor verification [6]. In their research on the degradation of highly reliable products, [PERSON] and [PERSON] [28] and [PERSON] and [PERSON] [29] noted that degradation rates often vary with time or stress levels, exhibiting nonstationary characteristics. To address this issue, they proposed a novel class of Wiener degradation models incorporating time-varying parameters to capture the dynamic evolution of degradation rates. These studies offer a fresh modeling perspective that potentially overcomes the limitations associated with the Markovian assumption when applied to predicting track geometry conditions.
## 2 Materials and Methods  
The proposed framework for the multi-objective, multivariate optimization of GGI, integrating Tech-R and Oper-R, is depicted in Figure 1. The framework comprises three sequential task phases, each represented by a distinct column.  
Phase 1: GREI Design Optimization (Column 1)  
This phase focuses on the design considerations for GREI based on a specified design rainfall event. Key parameters include the physical layout of the drainage network, terrain characteristics, topography, and drainage ancillaries. The primary objective is to minimize the LCC associated with the GREI while establishing a foundational system configuration.  
Phase 2: GGI Integration and Baseline Assessment (Column 2)This phase consists of two interrelated sub-processes, as follows:  
(a) Incorporate a selected number and configuration of GI elements into the GREI to form the GGI. The GI is strategically arranged to minimize the overall LCC of the integrated system;  
(b) Conduct hydraulic and hydrological simulations using the same design rainfall as in Phase 1, while imposing predefined Tech-R and Oper-R parameters. These simulations establish the baseline peak discharge response and the associated LCC for the GGI.  
Phase 3: Resilience Optimization (Column 3)  
The final phase seeks to optimize the resilience performance of the GGI under varying Tech-R and Oper-R parameters. This involves evaluating the system's adaptive capacity to manage uncertainties and stressors, such as operational and environmental variations.  
A management matrix is developed to organize the optimized configurations of GGI based on selected Tech-R and Oper-R parameters. The matrix can be structured to rank configurations by increasing LCC, degree of decentralization, or operational efficiency. This reflects the evolving complexity of base infrastructure design, operation, and maintenance as a function of Tech-R and Oper-R interactions. The methodology is further elaborated in the subsequent sections, with a case study of Zhujiang New Town provided to illustrate its application and validate the framework.
## 3 Results and Discussion
### System Suitability  
System suitability for method A with 200 IU/mL Hum R showed a Rt of 13.3 min for A21 with a %RSD = 0.41 (\(n\) = 6). For method B with 250 IU/mL Hum R a Rt of 16.6 min with a %RSD of 1.49 (\(n\) = 6) was observed. %RSD for peak areas were found to be 0.41 and 0.45, respectively. Both methods were within the \(\pm\)2% acceptance criteria indicating suitability of the corresponding system. The capacity factor and resolution were both \(>\)2 and the tailing factor \(<\)2 which was within accepted the criteria.
# Cryptographic Protocol with Keyless Sensors Authentication for WBAN in Healthcare Applications
## 4 Test and Results
### Test  
The security tests were performed using the automatic protocol verification tools described above. Both Scyther and AVISPA tools verified the security of the protocol. Both tools were downloaded from their official web pages. Both tools are used in a Linux environment. In the case of Scyther, a Linux distribution must be installed; therefore, Ubuntu 21 was used as the operating system, and, as it was also required, Python 2.7 was installed. For its part, AVISPA also uses an Ubuntu environment; however, this environment only can be run on a virtual machine. In order to use the tools, it is necessary to encode the protocol. Each tool has its own language, Scyther uses the SPDL language, and AVISPA uses the HLPSL language. Once the protocol described in the previous section had been codified, the tests were carried out on each tool.  
In Scyther, the tool checks for claims searched for in the protocol. In our case, we want the protocol to comply with the entity authentication properties provided by _Niagree_ and _Nisync_. Hence, for the _secrecy_ of some of the values used, for example, the key, we look for the secrecy property. The tool will then verify that the described protocol complies with these properties. For this, the tool is configured to search for all possible attacks.  
The results of the tool helped determine if the protocol has security gaps and can be attacked. The results are displayed with the claim made followed by the verification result. If an \(OK\) is observed, then it would mean that the property has no attack; otherwise, Fail would mean that the property has failed, and the number of attacks that can be made on this property will be displayed in the comments. If this happens, a button is displayed showing the attack tree. If this is the case, the protocol must be adapted to cover such gaps.  
Unlike Scyther, AVISPA does not require the properties to be checked to be specified, but the mode in which the protocol is to be checked must be specified. Therefore, once the protocol is encoded, it is fed into the tool and checked in all modes it allows. The results of the tool show a summary indicating whether the protocol is \(safe\) or \(unsafe\). This result can be translated as to whether the protocol is secure or insecure. In the same way, it shows the  
Figure 12: AVISPA tool architecture.  
execution times, and in case the protocol is not secure, the tool allows the user to see the attack carried out on the protocol step by step. Similarly, the user can see how the protocol behaves between entities.
# One-Dimensional Nanostructure Engineering of Conducting Polymers for Thermoelectric Applications
## 2 D-Conducting Polymers and Their Thermoelectric Properties  
Recently, considerable progress has been achieved in the construction of 1D nanostructures (such as nano/microfibers, nanowires, nanotubes, nanorods, etc.) with polymer-based materials for thermoelectric applications. In this section, we mainly introduce the synthetic approaches towards various 1D nanoscaled conducting polymers (including PEDOT, PPy, PANI and PTh derivatives) as well as their thermoelectric performance enhancement resulted from the unique structures.
## 4 Immersive Environment Architecture
### The Assessment Mode  
The Assessment Mode is another significant component of the immersive environment architecture, designed to evaluate the user's progress, competencies, and outcomes in a comprehensive and objective manner. It acts as a reflective phase, where the system measures the effectiveness of learning and training interventions while providing detailed feedback for both users and stakeholders, such as educators or therapists. By combining structured evaluation tasks with advanced analytics, this mode ensures that the immersive environment serves not only as a tool for skill development but also as a reliable mechanism for monitoring growth and identifying areas for improvement.  
Very important to the Assessment Mode are evaluation tasks, which form the basis of performance evaluation. These tasks are carefully designed to test specific knowledge and skills acquired during the Learning and Training Modes. They encompass a variety of formats, ranging from interactive problem-solving activities to scenario-based simulations. For instance, a user might be tasked with resolving a conflict between virtual characters in a social-emotional training program or completing a procedural task in a simulated workplace. Each task is scored against predefined criteria such as accuracy, response time, and efficiency, ensuring an objective measurement of user performance. The scoring system is transparent, providing users with clear benchmarks to understand how their actions align with expected outcomes.  
Another key aspect of the Assessment Mode is its ability to measure therapeutic or developmental benefits. Beyond evaluating task-specific skills, the Assessment Mode tracks broader cognitive, emotional, and behavioral improvements resulting from interactions within the immersive environment. For example, a user participating in a rehabilitation program might have their progress monitored through metrics such as reaction times, emotional stability during stress-inducing scenarios, or changes in engagement levels over time. Feedback in the Assessment Mode is both immediate and detailed, providing users with actionable insights into their performance. The system delivers feedback through multiple channels, such as visual summaries, verbal reports, or interactive dashboards. For example, a user might receive a detailed breakdown of their strengths and weaknessesafter completing a simulation, with recommendations for areas requiring further practice. This feedback loop not only helps users track their progress but also motivates them to continue improving by highlighting their achievements.  
Additionally, the Assessment Mode incorporates qualitative and quantitative metrics to provide a holistic view of user performance. While scored tasks and system-generated data offer objective measures, qualitative insights--such as user reflections, behavioral observations, or recorded interactions--add depth to the evaluation. This combination ensures that the assessment captures not only measurable outcomes but also the nuanced aspects of user engagement and behavior.
# Quantitative Assessment Methods of Early Enamel Caries with Optical Coherence Tomography: A Review
## 3 Preparation of Artificial Carious Lesions  
The study of early caries using OCT requires tracking of the caries process, but natural early caries takes 6-18 months, and there are many influencing factors. Therefore, the preparation of artificial caries using isolated teeth is the main way to study early caries. The key to preparing artificial caries is to simulate the chemical and microbial environment of natural caries formation. There are two main methods to prepare artificial caries: chemical and biofilm methods. Chemical methods can be used for enamel surface demineralization by adjusting the chemical state and pH of a using a partially saturated acidic buffer [37; 38] or an acid gel [39]. This method is easy and the most widely used, while a biofilm method is performed by placing a cariogenic suspension on the surface of the tooth, altering the metabolic environment of the bacteria and creating a carious sample that more closely resembles natural caries [40; 41]. However, the biofilm method is generally used for studies related to the pathogenesis of dental caries because it is difficult to control the experimental conditions.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 7 Challenges and Future Research Directions
### Mobility Management  
Having a realistic mobility model for UAVs in FANETs is a crucial task. It represents the position, acceleration, and speed of UAVs in different time instances [24; 25; 160]. Although there are some mobility models defined for MANETs and VANETs which may be applicable to FANETs as well, the difference between FANETs and MANETs/VANETs should be further taken into account to have more FANET-oriented models. These differences include but are not limited to 3D moving area, power supply, safety, freedom in speed and directions, and so on.
###### Abstract  
Overhead transmission line conductors are usually helically stranded. The current-carrying section is made of aluminum and/or aluminum alloys. Several factors affect their electrical resistance, such as the conductivity of the conductor material, the cross-sectional area, the lay length of the different layers of aluminum, and the presence of a steel core used to increase the mechanical strength of the conductor. The direct current (DC) and alternating current (AC) resistances per unit length of stranded conductors are different due to the effect of the eddy currents. In steel-reinforced conductors, there are other effects, such as the transformer effect due to the magnetization of the steel core, which make the AC resistance dependent on the current. Operating temperature also has an important effect on electrical resistance. Resistive losses are the main source of heating in transmission line conductors, so their temperature rise is highly dominated by such power losses, making it critical to know the value of the AC resistance per unit length when applying dynamic line rating (DLR) methods. They are of great interest especially in congested lines, as by applying DLR approaches it is possible to utilize the full line capacity of the line. This paper highlights the difficulty of accurately calculating the electrical resistance of helically stranded conductors, especially those with a magnetic core, and the importance of accurate measurements for the development of conductor models and DLR approaches.  
transmission lines; conductors; stranded conductors; DC resistance; AC resistance; transformer effect; power loss