esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Resource Management in Heterogeneous Network (RM HetNet)
### Power Allocation (PA)
#### 4.1.2 Energy Efficiency (EE)
The problem of maximizing energy efficiency is formulated with certain maximum power limit constraints and predetermined quality-of-service requirements. An algorithm based on gradients is utilized to find a solution to the power allocation problem. In order to achieve the highest possible level of energy efficiency, an algorithm for iterative joint user association and power allocation has been proposed. In the simulation results, as the number of small cell users increased, the EE of the system improved efficiently. Nonetheless, all the small base stations used a single frequency band to provide users. Hence, inter-cell interference occurs. The authors in [92] investigated the problem of maximizing EE in a hybrid-powered HetNet, where data and energy are coordinated between BSs. Intending to decrease grid power consumption (GPC), they propose a combinatorial optimization algorithm to increase EE in the system. Because of the formulation complexity, both the Lagrange dual decomposition and the metaheuristic method were incorporated into the solution to tackle the issue. In addition, the Dickelbach algorithm is used to guarantee rapid convergence. The simulation results demonstrate that during times of harvested energy scarcity when BSs are cooperating to share channel information and energy, the GPC is reduced by nearly 20%, and EE is increased by around 10%. However, user association requires attention and consideration. In [93], the authors investigate the power allocation-based interference mitigation issue of multi-hop D2D links and cellular users (CUs) to enhance the EE of the cell with guaranteeing the network QoS constraints. They suggested a joint power allocation and channel assignment scheme. Binary particle swarm optimization is used for power allocation. The proposed scheme determines unique power values for each multi-hop D2D link to enhance network performance and energy efficiency. The results show that the proposed scheme improves EE by providing the best possible channel and power allocation for the CUs and multi-hop D2D users. Both uplink and downlink scenarios have been modeled and analyzed using our scheme, with the uplink scenarios yielding consistently better results for the proposed schemes. Nonetheless, multi-cell and multi-sharing resource allocation in the HetNet are not considered.
# Dynamic Energy Management Strategy of a Solar-and-Energy Storage-Integrated Smart Charging Station
## 4 Smart EMS Control
### Dynamic Energy Management System Strategy
#### 4.1.1 Off-Peak Daytime Hours (Summer 6:00-16:00/Non-Summer 6:00-15:00)  
The primary source of power for the charging station is PV energy. When the PV generation exceeds the power demand of the station, the excess energy is supplied to the station's needs, and the surplus is exported to the grid. However, if the exported power to the grid exceeds the contractual capacity, priority is given to charging the ESS. Once the ESS reaches its maximum capacity, any additional solar power generation is curtailed to prevent wastage, a process known as 'curtailment for protection.' Conversely, during periods when PV generation falls short of the station's power needs, the station first draws additional power from the grid, staying within the contracted capacity limits. Only if this grid-supplied power proves insufficient will the station then draw from the ESS. For a detailed illustration of this energy management process, please refer to Figure 3.
## 4 Problem Solution and Analysis
### The Solution of the Converted LP  
To solve Equation (19), the Lagrange multiplier method is used. The Lagrange function is  
\[\begin{array}{ll}&L(x,f,R,\gamma,\delta,\zeta,\theta,\lambda,\mu,\rho)\\ &=\sum_{r}\sum_{j}\sum_{k}(x_{1i}*T_{1i}+x_{2i}*T_{2i}+x_{3i}*T_{3i})+\sum_{i }\gamma_{1i}\Big{(}x_{1i}^{2}-x_{1i}\Big{)}+\sum_{i}\gamma_{2i}\Big{(}x_{2i}^ {2}-x_{2i}\Big{)}\\ &+\sum_{i}\gamma_{3i}\Big{(}x_{3i}^{2}-x_{3i}\Big{)}+\sum_{i}\delta_{i}(x_{1i} +x_{2i}+x_{3i}-1)+\sum_{i}\zeta_{i}(\sum_{i\in Nb\left(j,K\right)}\sum_{k=1}^{ K}x_{1i}*f^{ONU}_{ji}-F^{ONU}_{j})\\ &+\sum_{i}\theta_{i}(\sum_{j}\sum_{i\in Nb\left(j,K\right)}\sum_{k=1}^{ K}x_{2i}*f^{OLT}_{ri}-F^{OLT}_{r})+\sum_{i}\lambda_{i}(\sum_{r}\sum_{j}\sum_{i \in Nb\left(j,K\right)}\sum_{k=1}^{K}x_{3i}*f^{Int}_{i}-F^{Int}_{\text{C}})\\ &+\sum_{i}\mu_{i}(\sum_{r}\sum_{j}\sum_{i\in Nb\left(j,K\right)}\sum_{k=1}^{ K}x_{3i}*f^{Int}_{i}-F^{Int}_{\text{C}})+\sum_{i}\rho_{i}(\sum_{\text{C}}x_{3i}*R^{F}_{ Ci}-R^{Int})\end{array} \tag{20}\]  
The first-order derivatives of the Lagrange function with respect to \(x,f,R,\gamma,\delta,\zeta,\theta,\lambda,\mu\), \(\rho\), are easy to get. Let these first-order derivatives equal 0. By solving these equations,their optimal values can be obtained. The optimal values are denoted as \((x^{*},f^{*},R^{*},\gamma^{*},\delta^{*},\zeta^{*},\)\(\theta^{*},\lambda^{*},\mu^{*},\rho^{*})\).  
\[\left\{\begin{array}{l}\frac{\partial L}{\partial x}=0,\frac{\partial L}{ \partial f}=0,\frac{\partial L}{\partial R}=0,\frac{\partial L}{\partial \gamma}=0,\frac{\partial L}{\partial\delta}=0\\ \frac{\partial L}{\partial\zeta}=0,\frac{\partial L}{\partial\theta}=0,\frac{ \partial L}{\partial\lambda}=0,\frac{\partial L}{\partial\mu}=0,\frac{ \partial L}{\partial\mu}=0\end{array}\right. \tag{21}\]  
as Equation (19) is an LP with inequality constraint conditions. The result \((x^{*},f^{*},R^{*},\gamma^{*},\)\(\delta^{*},\delta^{*},\theta^{*},\lambda^{*},\mu^{*},\rho^{*})\) should meet the Karush-Kuhn-Tucker (KKT) conditions, as follows.  
\[\begin{split}&\delta^{*},\zeta^{*},\theta^{*},\lambda^{*},\mu^{*},\rho^{*}\geq 0\\ &\delta^{*}(x_{1i}+x_{2i}+x_{3i}-1)=0\\ &\zeta^{*}(\sum_{i\in\text{N}eb(j,K)}\sum_{k=1}^{K}x_{1i}*f^{ ONU}_{ji}-F^{ONU}_{j})=0\\ &\theta^{*}(\sum_{j}\sum_{i\in\text{N}eb(j,K)}\sum_{k=1}^{K}x_{2i}*f ^{OLT}_{ri}-F^{OLT}_{r})=0\\ &\lambda^{*}(\sum_{r}\sum_{j}\sum_{i\in\text{N}eb(j,K)}\sum_{k=1}^{ K}x_{3i}*f^{Int}_{i}-F^{Int}_{C})=0\\ &\mu^{*}(\sum_{r}\sum_{j}\sum_{i\in\text{N}eb(j,K)}\sum_{k=1}^{ K}x_{3i}*f^{Int}_{i}-F^{Int}_{C})=0\\ &\rho^{*}(\sum_{C}x_{3i}*R^{F}_{Ci}-R^{Int})=0\end{split} \tag{22}\]
# IoT Privacy and Security Challenges for Smart Home Environments
## 4 Security Threats in the Smart Home
### Vulnerabilities  
A significant vulnerability is networked system accessibility. Because modern Smart Home systems are connected to the Internet, attacks can be conducted remotely, either by direct access to networked control interfaces, or by downloading malware to devices.  
System physical accessibility is also an issue. For both wireless and power-line carrier technologies, the networks can be physically accessed from outside the house, even if the house itself is securely locked.  
The next vulnerability is constrained system resources. Device controllers have traditionally been small 8-bit microcontrollers with very limited computational and storage resources, limiting their ability to implement complex security algorithms.  
System heterogeneity is a vulnerability. Devices come from many manufacturers, with different networking standards and different software update capabilities. Often the devices have little or no documentation about their internal software, operating systems, and installed security mechanisms.  
Fixed firmware is another issue. There are very few smart home appliances which provide any regular software update service to patch security vulnerabilities. One suspects that there is currently little incentive to continually patch software to stay ahead of security vulnerabilities for devices costing a few dollars.  
Slow uptake of standards is a vulnerability. While some proprietary systems, such as a health monitoring sub-system, may have well-designed standards-compliant security, most current Smart Home devices implement few, if any, security approaches.  
We consider the largest vulnerability to be the lack of dedicated security professionals who can manage the complexities of a Smart Home network. Few households can afford professional ongoing home network management assistance. Instead, amateur householders need to be able to self-manage their systems simply, safely and securely.
## 4 Results and Discussion
To ensure that the SVM can learn and capture link properties from any a small amount of training data [12], it is important, that besides a sufficient number of training symbols all classes are uniformly distributed in the training set. For example, with the amount of 512 training symbols and 64 different classes it is not guaranteed that each class is included in the training data, if a randomly generated training sequence is used. Concerning the NLVE, the training is based on the amount of inter symbol interference which is independent on the training data itself. Here, a certain number of symbols is necessary to estimate the coefficients correctly. In case of the NLVE[4,2,5], the training length of 512 is not sufficient to determine the coefficients correctly. However, if a certain number of training symbols is used, the channel estimation of the NLVE can improve its performance barely, even if more training data is used as it can be seen for the reduced NLVE[4,2,3]. While the plain NLVE and SVM structures saturated fast, the results with combined NLVE and SVM based detection are quite remarkable. Figure 6: BER as a function of the launch power at 100 km for NLVE equalization with optimal and reduced number of coefficients in combination with SVM based detection. Figure 7: **(a)** Shows the BER as function of the number of training symbols for 100 km dispersion unmanaged transmission at 5 dBm launch power. **(b)** Shows the the corresponding constellation diagram for NLVE[4,2,5] & SVM trained with 1024 symbols and **(c)** Shows the constellation diagram for NLVE[4,2,5] & SVM trained with 3072 symbols.
# A Two-Phase Approach for Predicting Highway Passenger Volume
###### Abstract  
With the continuous process of urbanization, regional integration has become an inevitable trend of future social development. Accurate prediction of passenger volume is an essential prerequisite for understanding the extent of regional integration, which is one of the most fundamental elements for the enhancement of intercity transportation systems. This study proposes a two-phase approach in an effort to predict highway passenger volume. The datasets subsume highway passenger volume and impact factors of urban attributes. In Phase I, correlation analysis is conducted to remove highly correlated impact factors, and a random forest algorithm is employed to extract significant impact factors based on the degree of impact on highway passenger volume. In Phase II, a deep feedforward neural network is developed to predict highway passenger volume, which proved to be more accurate than both the support vector machine and multiple regression methods. The findings can provide useful information for guiding highway planning and optimizing the allocation of transportation resources.  
intercity transportation; highway passenger volume; urban attributes; two-phase approach
## 5 Discussion
### Comparison with Existing Frameworks  
The AI-of-Oz framework aligns conceptually with the DesignFusion framework [25] and Siricharoien's AI-UCD framework [14] in utilizing generative AI to augment UCD processes. All three frameworks leverage generative AI's capability to dynamically support the design and evaluation phases and promote iterative feedback and real-time adaptability.  
However, the key differentiation lies in the frameworks' goals, the extent of live interactivity, and their validation processes.  
The DesignFusion framework primarily targets the early conceptual design stage, integrating multiple generative AI models to explore design ideas and conceptual alternatives. Its distinctive feature is the structured decomposition of design tasks into sub-components, facilitated by multi-step generative reasoning [25]. While this framework significantly empowers designers by generating diverse conceptual artifacts, it does not explicitly support immediate prototype adjustments during real-time interactions with end-users. DesignFusion thus excels in structured ideation and exploration but lacks the direct user-testing adaptability featured prominently in our framework.  
Conversely, the AI-UCD framework emphasizes a comprehensive integration of AI throughout all stages of the UCD process. It systematically covers user research, AI integration objectives, interface design, iterative feedback loops, and rigorous validation procedures with ethical audits and performance benchmarking [14]. Unlike the DesignFusion framework, this framework places significant importance on continuous validation and ethical considerations, underscoring user satisfaction and accessibility. However, the framework's process does not inherently facilitate real-time dynamic modifications during user interactions. Instead, it relies on a structured iterative cycle where user feedback informs subsequent design refinements rather than immediate adjustments within the same testing session.  
The AI-of-Oz framework merges elements of existing frameworks by applying generative AI within live-prototyping to facilitate immediate adjustments during user testing. It extends the Wizard-of-Oz prototyping concept by replacing the human wizard entirely with generative AI models, allowing designers to dynamically alter prototype components instantly in reaction to user feedback. This approach enables in-session iterations through a dual-interface design: one for participants and a hidden interface for designers to allow real-time control over generative prompts and outcomes. The framework emphasizes adaptability during live user interactions more directly than the existing frameworks, making it particularly useful for exploring user responses to immediate interface adjustments or new feature explorations within a single user-testing session.
## 1 Introduction
### Stating the Problem, Motivation, and Objective  
The special cold-start case that is described above is a very common issue in personalized advertisement systems. Often no data about the user are available. For example, a user does not give consent of his/her data due to privacy reasons, most apps do not require an account for using them, etc. Imagine a scenario where a user downloads an application, rejects giving personal info (e.g., location, cookies, etc.) and starts using it. Lots of (special cold-start/frozen user) situations like this arise and the applications face issues to display related advertisements. This is called the \"frozen user problem\" in the literature [8; 9]. The problem is that the state-of-the-art personalization models cannot be applied directly to provide accurate suggestions. Often a popular or a random advertisement is shown.  
The motivation of this work is to explore this frozen user problem and the aim is to propose and implement a novel approach for designing personalization systems that can handle the frozen user problem and decide to display or not a related advertisement to him/her. Section 2 below describes some state-of-the-art solutions to the cold-start problem in the advertisement domain and the contribution of this work. Section 3 describes the design approach in detail. The approach tries to alleviate the \"frozen user problem\". Section 4 provides details about the experiments that were conducted, the validation and the implementation of the approach (concerning every step). Finally, Section 5 concludes the paper, discusses the findings, and pinpoints future directions.
## Materials and Methods
### MEA Performance Tests (ADLFC)  
A lab-made Ni/C [2] and Pt/C (46.7%, Tanaka Co., Tokyo, Japan) were chosen as anode and cathode electrocatalysts, respectively. The electrocatalyst solutions were directly sprayed on the surface of the membranes (3 \(\times\) 3 cm\({}^{2}\); OH-form), and the total loading amount of Ni and Pt was revealed to be about 2 mg cm\({}^{-2}\) and 1 mg cm\({}^{-2}\), respectively. As a binder in the electrocatalyst links, commercially available anion-exchange ionomer (AS-4, Tokuyama Co., Tokyo, Japan) was utilized. The catalyst-coated membrane (CCM) was inserted between two sheets of carbon fiber composite paper (TGP-H-060, Toray Inc., Tokyo, Japan) used as a gas diffusion layer (GDL). The CCM and GDLs were then assembled with a clamping pressure of about 5.5 MPa. The current-voltage (\(I\)-\(V\)) polarization characteristics of the prepared MEAs were investigated using a single cell, having an effective area of 9 cm\({}^{2}\) at 60 \({}^{\circ}\)C. Liquid fuel (4 M N\({}_{2}\)H\({}_{4}\)/4 M KOH) and humidified O\({}_{2}\) gas (of 100% relative humidity (RH)) were fed to the anode and cathode at the flow rate of 5 mL min\({}^{-1}\) and 500 sccm, respectively. In addition, for the \(I\)-\(V\) polarization curve measurement, a current was stepped up by 0.01 A and then maintained for 30 s at each step to gain a stable response.
# Extended-Reach Drilling (ERD)--The Main Problems and Current Achievements
## 3 Drilling Fluids for Extended-Reach Wells
### Barite Sag  
Barite sag occurs when the mud is not circulated for a long time. However, it has been shown that a barite sag can form during circulation and can be thicker than when the flow is static. This process can be accelerated by slow circulating rates, casing running and wire line logging. In extended-reach wells, barite deposition can lead to wellbore mudlosses, mud weight fluctuations, a stuck pipe and wellbore instability. This is una-voidable but can be managed through a combination of good operating procedures and drilling fluid design. A low shear rate viscosity is a critical factor in achieving good hole cleaning and avoiding barite settling [18]. In drilling ERWs, barite sag can be minimized or mitigated by increasing the low shear rate viscosity (LSRV) of the drilling fluid, rotating the drill pipe and using micronized weight materials.
## 2 Experimental Setup and Methods
### Measurement Methods  
In this experiment, NO\({}_{2}\)\({}^{-}\), NO\({}_{3}\)\({}^{-}\), and NH\({}_{4}\)\({}^{+}\) were considered the main nitrogen fixation products in the liquid phase, which were measured by UV-visible spectrophotometry [22], and comparison with the results of ion chromatography (IC-100, Qingdao ShengHan Inc., Qingdao, China) proved the reliability of the above tests. Standard curves of NH\({}_{4}\)\({}^{+}\) and NO\({}_{x}\) in the liquid phase products are referenced to Figure S1. Voltage-current curves and Lissajous figures of the WF-DBD were recorded by a digital oscilloscope (MDO3024, Tektronix Inc., Beaverton, OR, USA), and the data were measured by a high-voltage probe and a current probe. The power consumption of the WF-DBD was calculated using the Lissajous method. The spectral information of the WF-DBD was collected and transmitted to the spectrometer (AvaSpec Multi-Channel Spectrometer, AVANTES, Apeldoorn, The Netherlands) via an optical fiber equipped with a fibre-optic collimator (UV-74, AVANTES, Apeldoorn, The Netherlands).
## 5 Experimental Validation (Simulated)
### Simulation Scope and Practical Modeling Boundaries  
Given the scale and the complexity of the framework and the application, several assumptions were necessary to practically simulate the framework operations, including the following:  
* Quantum-resistant cryptography was conceptually integrated but not practically implemented. This is because there is not a mature quantum-computing simulation tool available yet.
* The blockchain component is known to introduce delays. So to simplify and speed up the work, the process was simplified to a conceptual representation with a fixed delay overhead. These represent the transaction and identity verification overheads. Thus, complexities associated with full blockchain network simulations are avoided, especially since it is already established that Blockchain will introduce known delays.
* Federated learning and Digital Twins were assumed to have constant communication overhead and synchronization delays. These were almost negligible on the operation of the framework, so they were represented as fixed overheads in the model.
* Threats were modeled based on established statistical profiles rather than actual cyber-attacks. So assumptions were made based on employing an exponential distribution for response times and a uniform distribution for detection accuracy.
## 3 Results and Discussion
### Comparision to SIMCA and PLS-DA Model for Direct Multiclass Classification  
In order to further validate the efficiency of this hierarchical classification strategy, a SIMCA, PLS-DA classification model designed directly for classifying five classes had been also developed, respectively. Likewise, these two models continued to use the same preprocessing method of the first derivative (second polynomial and a filter width of 15) after mean centering, and the same Venitian blinds method for cross-validation. The predictive performances of these three classification methods had been evaluated using the classical binary classification metrics shown in Table 5.  
It could be found in Table 5 that each classification metric was not less than 0.95 for each class in the hierarchical classification scheme, which was a really satisfying performance compared to other two schemes, which both had poor performance on two or more classes. For example, the fatal flaw of the SIMCA multiclass classification scheme used here was the predictive performances on precision for four classes (less than 0.8) except for that of the MS1 class, which means that there existed great errors of false positive on assigning unseen instances of these four classes. This situation was also expressed in the process of classifying test samples of the MS4 class in the PLS-DA multiclass classification scheme. This scheme failed especially in the performance on sensitivity for the MS3 class. With the sensitivity value of 0.25, there were three times the number of misclassified samples in the MS3 class that was than those correctly classified as the MS3 class.