esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results
### Quantitative Results
#### 6.1.2 Self-Reported Measure (UES-SF)
The participants in both conditions reported similar levels of Aesthetic Appeal (Inactive: \(M=3.87\), \(SD=0.94\); Active: \(M=3.85\), \(SD=0.71\)), and no significant difference was found (\(p=0.950\)). The effect size was negligible (\(d=-0.02\)), with very low statistical power to detect such a small effect (\(1-\beta=0.06\)). Finally, for _Reward Factor_, four outliers were excluded from the Inactive group, while no outliers were detected in the Active group, resulting in a final sample of 20 participants in the Active condition and 16 in the Inactive condition. Shapiro-Wilk tests indicated significant deviations from normality in both groups (Inactive: \(W=0.808\), \(p=0.003\)  
Figure 6: Bar chart with the average scores and SDs of the total user engagement measured by the UES-SF. Significance level is indicated as \({}^{*}\) for \(p<0.05\). Active: \(W=0.822\), \(p=0.002\)). Given the non-normal distributions and unequal group sizes, a non-parametric Mann-Whitney test was conducted. The participants in the Inactive condition reported slightly higher reward scores (\(M=4.63\), \(SD=0.27\)) than those in the Active condition (\(M=4.43\), \(SD=0.66\)), but this difference was not statistically significant (\(p=0.789\)). The effect size was negligible (\(r=0.05\)), with low statistical power to detect the effect (\(1-\beta=0.07\)). Figure 7 displays the distribution of scores for each UES-SF dimension across the conditions. A summary of the main results across all the engagement measures, including sample sizes, means, standard deviations, significance levels, and statistical power, is provided in Table 3.
## 3 Recommended Architecture for Industrial Software Service Components
### Relationship of EIAFM to Existing FM-Based Models  
The mathematical formulation of the model described in Sections 4.2 and 4.3 can potentially encapsulate the connection between existing FM-based models and EIAFM. Table 1 compares the models from five aspects; notably, EIAFM is the only model that captures explicit and implicit higher-order interactions and the different importance of second-order feature interactions at the same time. EIAFM improves on xDeepFM and AFM while reducing the complexity of the model through the summing-pooling operation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & **Order-One** & **Order-Two** & **Explicit High-Order** & **Implicit High-Order** & **Distinguish the Importance** \\  & **Features** & **Features** & **Features** & **Features** & **of Order-2 Feature Interaction** \\ \hline
**basicFM** & \(\checkmark\) & \(\checkmark\) & \(\times\) & \(\times\) & \(\times\) \\ \hline
**DeepFM** & \(\checkmark\) & \(\checkmark\) & \(\times\) & \(\checkmark\) & \(\times\) \\ \hline
**AFM** & \(\checkmark\) & \(\checkmark\) & \(\times\) & \(\times\) & \(\checkmark\) \\ \hline
**xDeepFM** & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\times\) \\ \hline
**EIAFM** & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Contrasting FM-Based Models.
## 2 Materials and Methods
### User Acceptance and Usage
The results of this structure with a hybrid cloud, and a mix of the private and public environment, were:  
* fast implementation--in a week we decided on the cloud provider, created needed infrastructure, and installed all applications;
* no procurement--cloud computing allows the billing method of pay-as-you-go, plus using an open-source model made the system license-free;
* access to all services 24/7 regardless of the location and the time (the user needs only an internet connection);
* controlled access--the system allows only authorized users to access data based on defined rules and roles; and
* data protection--sensitive data are not stored in the public cloud, they stayed in the original official store, and only aggregated on street or settlement levels for analytical purposes. Figure 3: Epidemic Location Intelligence System (ELIS). (**a**) Architecture; (**b**) Use Case. In general, the portal, as a web user interface, is an access point to many resources. It provides an environment for users to access data. The portal provides links to other websites and includes various security measures, including access rights to the portal or parts of it. It can be personalized to fit the needs of the user in terms of functionality and appearance. Its development aims to promote the interoperability of data and services, which is achieved through the acceptance and implementation of the standards. Interoperability is largely achieved through standardized geoportal architecture. Users are separated into two main categories: GIS data administrators and Case data ones (Figure 2(b).). Along with choosing the required components to develop and deploy the web system, there are considered technical standards that allow interaction between these components [24]. Open Geospatial Consortium (OGC) defined standards like Geography Markup Language (GML), which is based on Extensible Markup Language (XML), data identification such as the Catalog Service for the Web (CSW), data provision services such as the Web Mapping/Feature/Coverage Service (WMS/WFS/WCS), and spatial processing such as the Web Processing Service (WPS) specification, based on the Hypertext Transfer Protocol (HTTP). To integrate geospatial data and services with web-based distributed applications, the OGC specified the Web Service implementation standards (OWS) interface.
# On Implementing a Two-Step Interior Point Method for Solving Linear Programs
## 1 Introduction
### Contribution  
Motivated by these ideas, this paper makes the following contributions:  
* To solve optimization problems using the IPM, the search direction plays an important role. We use the idea proposed in [20] for determining the search direction. The main goal of this paper is to extend the strategy proposed in [20] to IPMs. This paper introduces a two-step method that calculates the inverse of the matrix only once to determine the search direction in each iteration. The next goal of this article is to demonstrate how this method can be integrated with the feasible interior-point algorithm for linear optimization problems to enhance the efficiency of the IPM. We combine the technique described in [20] with the algorithm presented in [8], resulting in the development of two new algorithms.
* To demonstrate the efficiency of the new method, we implement the algorithms and solve a collection of NETLIB test problems. The results show that the proposed method can improve the algorithm's efficiency by 27% in terms of the number of iterations and 18% in terms of CPU time. The implementation on a Graphics Processing Unit (GPU), a specialized processor designed to accelerate graphics rendering and computational tasks, shows a reduction in the number of iterations and GPU time compared to the classical algorithm by 39% and 30%, respectively.  
The paper is organized as follows: Section 2 briefly recalls the basics of the IPMs and the central path for solving linear programs. We explain the two-step algorithm proposed here next. Section 3 reports the numerical results on a collection of LP instances from NETLIB for CPU and GPU implementations of the algorithm. We end with concluding remarks in Section 4.  
In this paper, we use the following notational conventions. The Euclidean norm of a vector is denoted by \(\|.\|\). The non-negative and positive orthants are represented by \(\mathbb{R}_{+}^{n}\) and \(\mathbb{R}_{++}^{n}\) respectively. The vectors \(xs\) and \(\frac{x}{s}\) indicate coordinate-wise operations on the vectors \(x\) and \(s\), with components \(x_{i}s_{i}\) and \(\frac{x_{i}}{s_{i}}\) respectively. We also use diagonal matrices \(X\) and \(S\) with entries \(x\) and \(s\) respectively. For any variable \(v\) belonging to \(\{x,y,s\}\), subscripts denote the \(n^{th}\) iteration value. For instance, \(x_{n}\) refers to the value of \(x\) after \(n\) updates. If the current iteration is \(n\), we use \(v\) instead of \(v_{n}\) and \(v_{+}\) instead of \(v_{n+1}\) for \(v\in\{x,y,s,\tilde{x},\tilde{y},\tilde{s},\hat{x},\hat{y},\hat{s}\}\). Here, \(x\) refers to the current point and \(x_{+}\) refers to the updated value of \(x\).
# Quantitative Assessment of Upper- Limb Volume: Implications for Lymphedema Rehabilitation?
## 6 Clinical Applications and Implications
### Patient Education and Compliance  
Volumetric assessment methods offer visualizations of the limb shape and contours, enhancing patient understanding of their condition [84]. Three-dimensional representations or graphical depictions of the limb's volume changes can help patients visualize the impact of lymphedema on their body and the progress made through treatment [9, 74]. This visual feedback can motivate patients to adhere to their treatment regimens, improving compliance and treatment outcomes.  
Furthermore, volumetric assessment provides objective measurements that can be used to educate patients about the effectiveness of their treatment. Showing patients the quantitative changes in limb volume over time can instill confidence in the treatment process and reinforce the importance of self-care practices [85]. By actively involving patients in monitoring their limb volume, they become partners in their own care, potentially impacting treatment engagement and empowerment [85].
# Comparative Theoretical Analysis of Halden Reactor Creep Tests on M5 Cladding Material
## 2 The TRANSURANUS Code  
The TRANSURANUS code [10; 11; 12; 13] is a widely used computational tool developed for the analysis of fuel behavior in nuclear reactors. It was initially designed to predict the thermal and mechanical behavior of fuel rods in light water reactors (LWRs) under both normal operation and transient conditions. Over the years, its scope has expanded to encompass various types of fuel and cladding materials, as well as different reactor designs, including fast reactors and experimental setups like those at the Halden reactor. The code provides detailed simulations of fuel rod performance by accounting for a wide range of physical processes, including fuel swelling, fission gas release, creep, and mechanical interactions between the fuel and cladding.  
One of the key strengths of TRANSURANUS lies in its ability to simulate complex time-dependent phenomena, such as the in-reactor creep behavior of cladding materials under varying stress, temperature, and irradiation conditions. This makes it particularly useful for assessing the long-term structural integrity of cladding materials like M5, which are subjected to high neutron flux and mechanical stress during reactor operation. The code is frequently updated to incorporate the latest experimental data and to improve its predictive capabilities, which are essential for ensuring the safety and efficiency of nuclear reactors. In the present study, TRANSURANUS is employed to simulate the creep behavior of M5 cladding material under the specific operational conditions of the IFA-617 test, enabling a detailed evaluation of the combined effects of stress, temperature, and neutron flux on the cladding's performance. For the present work, the v1m1j23 version of the code was used.
## 1 Introduction  
Satellite-based positioning has gained a lot of attention due to its integration into many aspects of our daily activities. The Global Positioning System (GPS) has become a crucial part of numerous devices and applications, such as routing, vehicular Internet of Things (IoT), rescue and emergency services and future self-driving. One of the most well-known emerging applications nowadays that rely mainly on GPS is the autonomous vehicles in which there is a pivotal need for accurate and undisturbed navigation solutions. In such applications, a failure of the positioning service will potentially result in serious safety issues. Furthermore, countless current applications rely mainly on GPS such as intelligent transportation, tracking people, devices, pets and nearby area searching such as museums, oil stations, hospitals and restaurants. The widespread use and popularity of applications that demand GPS have led to the need for reliable, uninterrupted and accurate positioning and navigation services, which was the primary motivation for this research. Additionally, the flexibility associated with software receivers' use compared to hardware receivers was another stimulus for the research in this paper.  
GPS signals are received by receiver antennae below the noise floor, which makes them highly susceptible to in-band intentional, unintentional and malicious interference, even with the fact that GPS signals employ the direct sequence spread spectrum (DSSS) modulation technique, which gives them inherent robustness to interference signals. Jamming is considered one of the significant threats to users of GPS services [1; 2] because it can severely affect the GPS receiver, causing unreliable and erroneous navigation solutions, and a loss of integrity and availability of satellite signals. In some cases, jammers could cause the receiver to undergo a complete outage. Moreover, high-power jammers can hinder the usage of GPS over a large area in the vicinity of the jammer [3; 4].  
This paper investigates the performance of a scalar- and vector-based software-defined receiver (SDR) in harsh navigation environments to provide a robust, continuous and reliable performance that can be further integrated with other navigation sensors to improve the overall system performance. The performance is assessed in highly degraded signal environments in which the signal power is highly attenuated and in the presence of interference signals.  
In this paper, several quasi-real static and dynamic simulated scenarios are collected and processed by the SDR in both scalar and vector modes for performance comparison purposes. In these scenarios, the signal strength of one or more satellites is attenuated by 10-20 dB to assess the performance of the receiver in degraded conditions. The results show the superiority of vector tracking (VT) loops compared to scalar tracking (ST) loops in terms of bridging momentary outages in one or more satellites and re-locking to satellites' signals in the presence of jamming.
## 2 Methodology
### Adsorption Experiment  
Considering that the average flow velocity of groundwater is quite slow, only 1-2 m per day, all adsorption experiments in this study do not include external factors (such as shaking, stirring, etc.) after the adsorbent addition. Instead, the sorbent is allowed to react with the water sample during the precipitation process without agitation to achieve experimental conditions similar to those in the groundwater environment.  
One microloliter of TCE stock solution (\(\geq\)99.5% purity, CAS Number 79-01-6, Merck, Germany) and PCE stock solution standards (\(\geq\)99.5% purity, CAS Number 127-18-4, Merck, Germany) were added to 1 mL of methanol solvent (CAS Number 67-56-1, Avantor, USA) and diluted to make a TCE standard stock solution with a concentration of 1424 mg L\({}^{-1}\) and a PCE standard stock solution with a concentration of 1625 mg L\({}^{-1}\). In the high-concentration adsorption experiment, 400, 450, 500, 550, 600, 650 and 700 \(\mu\)L of TCE and PCE standard stock solutions were added to 100 mL of ultrapure water. After shaking evenly, 10 g of silica gel was added and allowed to stand for 5, 10, 20, 30, 60 and 120 min, and then the TCE and PCE contents in the water were measured after the adsorption process. In addition, in the low-concentration adsorption experiment, 40, 45, 50, 55, 60, 65 and 70 \(\mu\)L of TCE and PCE standard stock solutions were added to 100 mL of ultrapure water. After shaking evenly, 10 mg of activated carbon was added and allowed to stand for 5, 15, 30, 60, 120, 180 and 300 s, and then the TCE and PCE contents in the water were measured after the adsorption process. When the adsorption experiment was carried out, the room temperature was controlled under 20 \({}^{\circ}\)C to avoid the loss of TCE/PCE due to evaporation. In order to avoid the adsorption phenomenon caused by the glassware/vial/cap, there will be a control group for each adsorption experiment; that is, in the solution of known concentration, without adding the adsorbent, and finally getting on the computer and calculating the recovery rate and then controlling the recovery rate above 90%.
# Modeling and Numerical Computation of the Longitudinal Non-Linear Dynamics of High-Speed Elevators
## 2 Elevator System Dynamics Modeling  
This paper considers structural dynamics modeling methods, including centralized mass and distributed mass models, to study elevator system dynamics. Focusing solely on the longitudinal motion of each component, this paper employs the centralized mass model. Due to the numerous degrees of freedom in the dynamics model, this paper utilizes the finite element method to initially establish dynamics models for individual subsystems. These models are then centralized to derive the overall system's dynamics model.  
The high-speed elevator system is partitioned into eight subsystems: a car and frame subsystem, four sections of wire rope (a car side traction rope subsystem, a car side tensioning rope subsystem, a counterweight side traction rope subsystem, and a counterweight side tensioning rope subsystem), a traction subsystem, a counterweight subsystem, and a tensioning subsystem. These subsystems share degrees of freedom. The car and frame subsystem, traction subsystem, counterweight subsystem, and tensioning subsystem are linear, while the traction rope and tensioning rope are non-linear. For ease of subsequent model integration and numerical solution, the linear and non-linear subsystems are separately and independently managed. This ensures that during numerical computation, only the contribution of non-linear subsystems to the dynamics model is updated. Figure 1 depicts the process of establishing the dynamics model, encompassing degree of freedom selection, subsystem decomposition, subsystem dynamics modeling, and integration of the overall dynamics model. Within this, the subsystem dynamics modeling process comprises linear and non-linear subsystem dynamics modeling.  
Figure 1: Flowchart of the dynamics model.  
After integration, the overall dynamics equation of the elevator model can be expressed as follows:  
\[\begin{array}{l}M\ddot{x}(t)+\dot{C}\dot{x}(t)+Kx(t)=F\\ M=M_{linear}+M_{no-linear}\\ C=C_{linear}+C_{no-linear}\\ K=K_{linear}+K_{no-linear}\end{array} \tag{1}\]  
where \(M\) represents the equivalent mass matrix of the elevator system model, which can be expressed as:  
\(C,K\) is the equivalent damping matrix and the equivalent stiffness matrix of the elevator system model, similar to the structure \(M\). Figure 2a depicts the overall dynamics model of the high-speed elevator, while Figure 2b illustrates the mechanical model of the high-speed elevator.  
Figure 2: High-speed elevator model. (**a**) Overall model of high-speed elevator dynamics; (**b**) high-speed elevator mechanical model.
# ECC: Passenger Counting in the Elevator Using Commodity WiFi
## 5 Discussion  
ECC is still researching prototype implementations that need further improvements before it can finally be deployed for the intended application of elevator safety. The factors that affect the system performance have been summarized in the following fields:  
* A general way of device deployment is required to make sure of WiFi signal coverage and the verification of our system in more types of elevator environments. In the experiments, the device was deployed in positions where it could achieve maximum coverage. However, it is impossible to deploy these devices inside an elevator in an actual environment. In addition, the materials and structures of some elevators are different, which will lead to differences in the mathematical relationship between the variation of CSI and the crowd number. The variation of the features may also be different in different cases.
* In the experiments, the testers are relatively static, but people usually perform some small activities, such as raising their hand, touching their nose, turning around or talking to each other, etc. These activities may affect the strength and path number of signal propagation that may have different effects on the system. To solve the above problems, the researchers have designed two improvement schemes and will try to implement each scheme:
* The researchers first consider using a smaller alternative hardware device, which can be deployed on the top of the elevator, like a camera, without affecting the normal use of the elevator. Then, the device number and the deployment will be adjusted according to the different elevators and verify the system performance.
* More CSI propagation path information (e.g., the phase, etc.) will be introduced into the multi-dimensional feature to improve the resolution of the crowd when the CSI is interfered with by human activities, thereby improving the estimation accuracy and strengthening the practical applicability of the system. In addition, there also is a problem with the experimental hardware devices; of course, this problem is not limited to our system. Currently, many wireless sensing research based on commercial WiFi devices use network interface cards, e.g., 5300 and 9300 series NICs. However, the CSI can be accessed through software only in a limited number of chip-set  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Procedure** & **Processing Time (s)** \\ \hline CSI Segmentation & 0.29 \\ Stream Selection & 0.62 \\ Noise Filtering & 0.45 \\ Feature Extraction & 1.73 \\ Passenger Counting & 0.38 \\ Total overhead & 3.47 \\ \hline \hline \end{tabular}
\end{table}
Table 2: System overhead of the main steps.  
ranges with modified drivers and firmware. If the chip-set manufacturers can expose CSI externally in a wider range of products, it will greatly accelerate our research progress in the field of wireless sensing.