esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Fire-Resistance Tests  
The fire resistance of the samples was assessed using cone calorimetry, a widely recognized method for evaluating fire behavior. It is essential to note that there is no consensus within the scientific community for interpreting cone calorimeter measurements [39]. Various parameters, such as mass loss, heat release rate (HRR), effective heat of combustion, combustion efficiency, and gas flows, are considered and discussed.  
The fire behavior in different stages can be characterized using the following properties [39]:  
1. Ignition 1. Ignitability; 2. Flamability;
2. Developing fire 1. Flame spread; 2. Heat release; 3. HRR;
3. Fully developed fire 1. Heat penetration; 2. Fire penetration/fire resistance; 3. Heat release/fire load.  
Building fire regulations and test methods vary across countries, even within the European Union [40]. There is no direct correlation between cone calorimeter test results and EU regulations. Nonetheless, the HRR, specifically the difference in the HRR curves between treated and untreated materials [41], is particularly relevant for characterizing the fire-retardant properties of a specific treatment.  
It is important to note that due to variations in the impregnation process (non-uniform liquid flow leading to unequal immersion of surfaces), samples with effective impregnation, identifiable by their intense brown coloration, exhibited superior results in fire-resistance tests. In contrast, parameters of less-treated specimens resembled those of untreated ones.  
In Figure 7, the heat release rates of untreated (a) and treated (b) black alder wood samples are presented.  
A significant decline in the graph of treated wood is evident. This indicates that after the initial ignition of the sample surface, the heat release substantially diminished. This effect can be attributed to two key factors:  
1. **Capping effect:** The concrete cork applied to the wood tracheids acts as a barrier, impeding heat flow into the material.
2. **Endothermic release of carbon dioxide:** The decomposition of the concrete material leads to the endothermic release of carbon dioxide, which, in turn, cools the fire front and obstructs the access of atmospheric oxygen.  
Similar results were obtained in a previous study, where precipitation of CaCO\({}_{3}\) was studied in the veins of poplar wood pretreated with NaOH and then subjected to sequential impregnation with sodium alginate, CaCl\({}_{2}\), and (NH\({}_{4}\))\({}_{2}\)CO\({}_{3}\) solutions. The treated wood showed significantly lower HRRs in comparison with untreated wood as well as improvements in other fire-resistance characteristics. The authors also attributed these results to the formation of an adiabatic char layer on the surface of the wood, slowing down the heat transfer through the charcoal layer [42].  
The graphs of different samples exhibit noticeable differences based on the depth of penetration by the treating agent. A significant proportion of the specimens displayed minimal effects, highlighting the influence of treatment depth on fire-resistance outcomes.
# Comprehensive Evaluation of NIMBY Phenomenon with Fuzzy Analytic Hierarchy Process and Radar Chart
## 1 Introduction
However, the above method can not enable visualization to reflect certain characteristics of high-dimensional data or the overall relationship between data. The radar chart method is a multi-variable analytical method for comprehensive evaluation that can visually reflect the relative strengths and weaknesses of the evaluation system on the trend of each indicator [33]. [PERSON] et al. [34] combined the AHP and entropy weight method based on the radar chart method to solve the comprehensive evaluation problem of power transmission and transformation projects. [PERSON] et al. [35] used the fuzzy analytic hierarchy process and improved radar method to comprehensively evaluate and rank the braking performance of three types of hydrodynamic retarders. Therefore, in order to solve the dilemma caused by the NIMBY phenomenon of transmission and transformation projects, a fuzzy analytic hierarchy process method based on improved an radar chart is suggested. For this aim, six units and thirty-one elements are proposed for constructing a hierarchical structure of performance evaluation. Then, the expert evaluation method, the radar chart method and the minimum area method are used to determine the weights of the element and unit layers. Finally, a transmission and transformation project in Xi'an City, China, is used as an example to verify the correction of the risk model and its evaluation method. The detailed contributions and novelty of this paper are as follows:  
1. Based on the laws and regulations that should be followed in the transmission and transformation project in China, a risk-evaluation index system of the NIMBY phenomenon that can accurately assess the risk model of transmission and transformation project is derived;
2. The fuzzy analytic hierarchy and radar chart methods are applied to calculate the weights of the evaluation criteria. The imprecise decision maker's judgments are represented as fuzzy numbers rather than exact numerical values;
3. The proposed method is feasible and effective, which has been verified by the transmission and transformation project in Xi'an City. It further provides suggestions based on the research results for risk evaluation and serves as a reference for future research in this field.
## 4 Cluster-Based Distributed Compressed Sensing for QoS Routing
### Distributed QoS Routing
#### 4.3.1 Energy Consumption  
The model in [36] is used for quantifying the energy dissipation of data communication. Suppose that one sensor node transmits \(l\) bits of data over a distance \(d\) to another node. The energy consumption for data transmission is  
\[E_{t}\left(l,d\right)=l\cdot E_{elc}+\varepsilon_{amp}\cdot l\cdot d^{\alpha} \tag{21}\]  
while the energy consumption for receiving these bits is  
\[E_{r}\left(l,d\right)=l\cdot E_{elc}. \tag{22}\]  
The electronics energy, denoted by \(E_{elc}\), is the energy needed by the transceiver circuitry to transmit or receive one bit, whereas \(\varepsilon_{amp}\) is a constant for communication energy. The total energy consumption for transmitting and receiving \(l\) bits over a distance \(d\) is given by  
\[E\left(l,d\right)=E_{t}\left(l,d\right)+E_{r}\left(l,d\right)=2\cdot l\cdot E_{ elc}+\varepsilon_{amp}\cdot l\cdot d^{\alpha}. \tag{23}\]  
Apart from the energy consumed by data transmission and reception, the energy consumed by in-network processing is also considered. This part of energy consumption can be modeled as a function of supply voltage. Suppose the execution of a task consisting of \(N_{cyc}\) clock cycles, the energy consumption for processing is estimated as  
\[E_{proc}\left(N\right)=N_{cyc}C_{total}V_{dd}^{2}+V_{dd}\left(I_{0}e^{\frac{V_ {dd}}{n\Omega_{T}}}\right)\left(\frac{N_{cyc}}{f}\right). \tag{24}\]The first term in (24) is the switching energy, where \(C_{total}\) is the total capacitance switched by the computation per cycle, and \(V_{dd}\) is the supply voltage. The second term stands for the leakage energy, where \(f\) is the clock speed, and \(I_{0}\), \(n\) are processor-dependent parameters [37].
# Free Vibration Analysis of a Thermally Loaded Porous Functionally Graded Rotor-Bearing System
###### Abstract  
The present work deals with natural and whirl frequency analysis of a porous functionally graded (FG) rotor-bearing system using the finite element method (FEM). Stiffness, mass and gyroscopic matrices are derived for porous and non-porous FG shafts by developing a novel two-noded porous FG shaft element using Timoshenko beam theory (TBT), considering the effects of translational inertia, rotatory inertia, gyroscopic moments and shear deformation. A functionally graded shaft whose inner core is comprised of stainless steel (SS) and an outer layer made of ceramic (ZrO\({}_{2}\)) is considered. The effects of porosity on the volume fractions and the material properties are modelled using a porosity index. The non-linear temperature distribution (NLTD) method based on the Fourier law of heat conduction is used for the temperature distribution in the radial direction. The natural and whirl frequencies of the porous and non-porous FG rotor systems have been computed for different power law indices, volume fractions of porosity and thermal gradients to investigate the influence of porosity on fundamental frequencies. It has been found that the power law index, volume fraction of porosity and thermal gradient have a significant influence on the natural and whirl frequencies of the FG rotor-bearing system.  
porosity; functionally graded rotor; finite element method; non-linear temperature distribution; power law; whirl frequency Article
## 3 Concept and Architecture of DigiHoriRobot
### Virtual Layer and Synchronization  
The Virtual Layer consists of dynamic, AI-enhanced models of the crops, greenhouse environment, and robotic system. These models are continuously updated with real-time sensor data, enabling the predictive simulation of future states, fault diagnostics, and virtual testing of robotic actions. The robot's virtual agent mirrors its kinematic chain, task states, and interaction constraints, supporting anticipatory planning and online decision refinement.  
Synchronization between the physical and virtual domains is structured through a nine-layer IoT architecture inspired by FIWARE-compliant systems [14]:  
1. Device Layer: Incorporates all edge-level sensors and actuators embedded within the greenhouse and the robotic system.
2. Communication Layer: Utilizes heterogeneous networking protocols (e.g., Wi-Fi, Ethernet, 4G/5G) for resilient and low-latency data transmission. Communication between the physical entities (e.g., dual-arm robot, cameras, and sensors) and higher-level services is facilitated by ROS 2 interfaces for perception and control, as well as IoT agents using MQTT for structured context data exchange.
3. IoT Service Layer: Employs middleware (e.g., Orion Context Broker) for context-aware data aggregation and control dispatch.
4. Digital Twin Management Layer: Handles the instantiation, storage, and synchronization of digital counterparts, including environmental models and plant lifecycle representations.
5. IoT Process Management Layer: Specifies task execution logic, procedural dependencies, and event-based triggers through workflow engines.
6. Service Organization Layer: Performs the orchestration and composition of services across abstraction levels, enabling scalability and modularity.
7. Security Layer: Ensures system integrity and data privacy through identity management, authorization, and authentication.
8. Management Layer: Oversees configuration, resource allocation, fault detection, and system status monitoring.
9. Application Layer: Provides a user-friendly interface for farmers and stakeholders to interact with the system.
## Example Analysis  
### Example and Data Description  
In this paper, data of three northern Chinese cities of Shandong Province for one year were selected for simulation analysis. Although the model EN-GMDH proposed in this paper is used to predict the hourly load of the next day, data of different seasons are selected to verify the generalization performance of this model. Due to the similar climate of the spring and autumn and the short seasonal feature time in these cities, we selected three months of data from each year to represent spring (autumn), summer, and winter. A total of nine data sets were generated in three locations, which are represented by L1 to L9. With one hour of data as a group, there are nine data sets, 270 days, and 6480 h series. Each set of data consists of 11 input variables, including 10 characteristic variables composed of meteorological factors, meteorological accumulation factors, and holiday factors, and 1 load value as the observation value. The annual hourly load of the three cities is shown in Figure 3.  
According to the grouping rule of the GMDH algorithm, 75% of the input data is used as the training set and 25% as the testing set. This paper uses MATLAB (R2017b, MathWorks, Natick, MA, USA, 2017) to build the prediction model and used MATLAB's lasso.m function. The software operating environment uses a quad-core processor and 8 GB of memory. To improve the speed of operation, this paper uses the MATLAB parallel toolbox to run the program.  
The hourly data set used in this paper is composed of 11 input variables: extreme wind speed, maximum wind speed, hourly rainfall, average temperature, maximum temperature, relative humidity, minimum humidity, _THI_ of the previous day, _THI_ of the previous two days, holidays, and load value. All of the data types are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Variables** & **Categories** & **Introduction** \\ \hline
1 & & extreme wind speed \\
2 & & maximum wind speed \\
3 & & hourly rainfall \\
4 & meteorological factors & average temperature \\
5 & & maximum temperature \\
6 & & relative humidity \\
7 & & minimum humidity \\ \hline
8 & meteorological & \(THI_{1}\) \\
9 & accumulation factors & \(THI_{2}\) \\ \hline
10 & holiday factors & Differentiating holidays, weekends, and working days by numerical values. \\ \hline
11 & load & Each set of characteristic variables corresponds to a load value. The load value of the training set is used for training the model, and the load value of the testing set is used for testing the error. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Categories of input variables  
Figure 3: (**a**) Industrial city, (**b**) small-scale city, and (**c**) medium-sized city. The first load fluctuation point in the figures is the time of Chinas biggest festival Chinese New Year.
## 3 Preparation for Fault Localization
### Identifying the Set of Updated Addresses  
This section describes the final preparation step for fault localization. In the second step of Figure 6, we identified the set of updated addresses based on the acquired memory information. This information is captured \(N\) times with \(P\) cycles targeting the program with execution time \(T\), and is composed of \(M\) addresses; \(P\) refers to a period of the main task in the system. A frame (\(F_{k}\)) is defined as the set of memory information captured each time. Furthermore, whether each address is updated is determined by the change of the value at that address. That is, an address is updated only if its value changes from the previous frame to the present one. The definition of terms related to this is as follows:  
\[\text{Memory Address Set }(\text{A})\ \equiv\left\{a\ |\ 0\leq a<M;\ a\ \text{is an address}\right\}; \tag{1}\]  
\[\text{Frame }(F_{k})\equiv\left\{V_{a,k}\right|\ V_{a,k}\ \text{is the value of the address }a\ \text{at time }P\times k, \tag{2}\]  
\[a\in A,k\ \text{is a frame number, }P\ \text{is a period of the system main task}\ ;\]  
\[\text{Memory Update }(U_{a,k})=\ \left\{\begin{array}{l}1,\ V_{a,k}\ \
eq\ V_{a,k-1}\\ 0,\ V_{a,k}\ =\ V_{a,k-1}\end{array}\right.\ for\ a\ \in A,\ k\ \text{is a frame number}. \tag{3}\]  
The update information calculated in this way can be used for fault localization in a failed test. This is because test failure is closely related to the test-script inputs, and it is possible to find the address estimated as being at fault by processing the update frequency approximately simultaneously with an input being set. In a previous study [7], a range with a relatively high input quota based on the update frequency of each address (i.e., a range in which the number of updated addresses increases and remains above average) was defined as an input-driven update range (IDUR). The updated addresses in this range can be regarded as being related to the faults generated in the test cases. This is because IDUR is a range in which the number of update addresses increases while the output changes in response to the input. In this paper, we use the IDUR method to calculate the FS.
## 1 Introduction
This model encompasses a generalized analytical expression for the FSIR, which accounts for Doppler shift effects and distortions in illumination geometry induced by the flight path angle as well as for pronounced mis-pointing angles in oblique flight scenarios. The Gaussian approximation of the antenna mis-pointing angles is considered. The resulting delay-Doppler map (DDM) obtained by three-fold convolution is governed by six altimetric parameters: the epoch \(\tau\), significant wave height, Pu, flight path angle, and on-track and cross-track mis-pointing angles. Compared to satellite altimeters, the lower altitude of airborne altimeters results in a narrower Doppler beam, leading to a decrease in the precision of traditional delay compensation methods. A novel delay compensation method based on sinc interpolation for SAR altimeters with vertical movement is proposed to obtain the multilook echo, which can optimally handle non-integer delays and maintain the signal frequency characteristics. The validity of the proposed model is ascertained through the performance of the widely adopted least squares (LS) algorithm for altimetric parameter estimation [22; 31]. We propose a five-parameter estimation strategy in which the antenna mis-pointing angle and flight path angle are updated with each iteration. The effectiveness of the proposed model and algorithm is demonstrated in ideal conditions, with the results confirming our model's ability to accurately estimate the altimetric parameters.
# The Efficiency of Calcium Oxide on Microbial Self-Healing Activity in Alkali-Activated Slag (AAS)
## 3 Results and Analysis
### XRD  
Figure 7 indicates the formation of calcite within C5 and Ca-5, confirming the occurrence of the microbiological reactions within the AAS matrix. Calcium silicate hydrates (C-S-H) appeared to be more dominant in Ca-5 when compared to calcium aluminate silicate hydrates (C-A-S-H). The high leaching rate of Ca\({}^{2+}\) ions in the AAS mix enhanced the formation of the hydration product C-S-H. Feldspar (KAlSi\({}_{3}\)O\({}_{8}\)) and relevant families, like anorthoclase (Na, K)(SiAlO\({}_{5}\)) and albite (NaAlSi\({}_{3}\)O\({}_{8}\)), were recognized in the XRD profile of Ca-5, which was not seen in the other AAS mixes. Ca\({}^{2+}\) ions were highly detected in the formation of C-S-H and CaCO\({}_{3}\), which linked the Feldspar together. Moreover, reactions took place between the other available ions, such as calcium, sodium, silicon, aluminum, oxygen, and potassium, forming an anorthoclase compound [48]. Since the formation of C-S-H became more dominant in this mix, the production of portlandite and calcite could be expected to link the feldspar together. Both of them are brittle and weak compounds that could be easily broken. Nevertheless, feldspar has a higher strength than calcite. Linking feldspar with calcite (CaCO\({}_{3}\)) bicarbonate would weaken the resistance against flexural and bending loading. Thus, the decrease in the mechanical properties of Ca-5 could be associated with the formation of portlandite, which provided more spaces for crack propagation due to its large crystalline structure [48].
# Programmable Deterministic Zero-Copy DMA Mechanism for FPGA Accelerator
## 2 Zero-Copy DMA Technology Overview
### AF_XDP Redirection  
AF_XDP is a socket address family built on XDP (eXpress Data Path) [22; 23], which realize zero-copy by calling the XSK (AF_XDP Socket) interface. XSK uses a pair of data structures, Rx and Tx, in user space, each pointing to a memory area called umem (user space memory). Each umem has two rings, the Fill ring and the Completion ring. The application fills an empty descriptor into the fill ring of Rx. When a packet arrives, the kernel pops a descriptor from the Fill ring. The DMA controller copies the packet to the memory pointed to by the descriptor and queued the descriptor into the Completion ring when finished. Afterward, the application retrieves the data through the descriptor in the Completion ring. The Fill and Completion rings work in the same way when sending packets using Tx queue.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Framework** & **Multqueue** & **Multcore** & **Kernelbypass** & **Driver** & **I/O Sys\_Call** & **License** & **API** & **Thought/Core** \\ \hline _PF\_RINGZC_ & _Yes_ & _Yes_ & _Yes_ & _ZC driver_ & _Custom_ & _Proprietary_ & _libpcap_ & \(\approx\)_DPDK_ \\ _Netmap_ & _Yes_ & _No_ & _Yes_ & _Linux patch_ & _Standard_ & _BSD_ & _libc_ & \(<\)_DPDK_ \\ _AF\_XDP_ & _Yes_ & _Yes_ & _No_ & _Linux driver_ & _Standard_ & _MIT_ & _libbpf_ & \(<\)_DPDK_ \\ _DPDK_ & _Yes_ & _Yes_ & _Yes_ & _UIO_ & _Custom_ & _BSD_ & _PMD_ & / \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of Four Zero-copy DMA Technologies.  
AF_XDP relies on the Linux kernel to achieve zero-copy while maintaining compatibility with existing operating systems. Its zero-copy implementation is similar to Netmap in that it initializes a shared memory area between the kernel and user space. However, there are two additional limitations to using AF_XDP. One is that the development of XDP programs needs to rely on restricted libc libraries (i.e., libbpf). Second, the NIC still generates frequent hardware interrupts, and the interrupt livelock issues are not fundamentally solved.
# Usability and Affects Study of a Virtual Reality System Toward Scorpion Phobia Exposure Therapy
## 1 Introduction
Similarly, augmented reality has also been used as an approach to support the treatment of specific phobias in small animals such as scorpions [35]. However, most arachnophobia VRET studies lack usability evaluations and biofeedback. Some of the works related to usability studies are presented in [36; 37], where the use of a spider instead of a scorpion makes a difference. On the other hand, biofeedback, including the integration of real-time biofeedback [38] of physiological variables such as electrodermal activity and heart rate, ensures effective implementations and applications [39], but these works are not related to scorpion phobia. To address the limitations and opportunities mentioned above, in this work our aim was to develop and evaluate the usability and affect impact of a virtual reality exposure therapy system toward scorpion phobia, using a framework based on the Methodology for the Development of Virtual Reality Educational Environments (MEDEERV), which was originally published in 2018 [40]. The research questions that we addressed for this purpose were as follows: 1.--How does the proposed framework make it possible to develop and evaluate a virtual reality exposure therapy toward the treatment of scorpion phobia? and 2.--What is the result of the evaluation of the usability and emotional impact of the virtual reality system toward scorpion phobia exposure therapy? The main contribution of this work is a framework that integrates usability testing, affect measurement, and real-time heart rate biofeedback in a virtual reality system toward exposure therapy for scorpion phobia.