esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Things and Distributed Computing Systems in Business Models
## 4 Theoretical Framework
### IoT and CDS Impact on Business Models  
#### 4.4.1 Improved Data and Workflow Management  
Integrating IoT and Distributed Computing Systems significantly enhances business data and workflow management. IoT devices are equipped with various sensors and connectivity capabilities, continuously collecting vast amounts of data from different aspects of operations [38]. This on-time collection of information eases the monitoring processes, tracks performance, and identifies inefficiencies that might go unnoticed. For instance, in a manufacturing setting, IoT instruments can check the status of machinery, track production output, and detect deviations from standard operating conditions [39]. This immediate visibility into operations enables proactive maintenance, reducing downtime and improving overall productivity. Distributed computing systems further improve data and workflow management by providing the necessary infrastructure to work out the large volumes of information built by IoT computing [40; 41]. The decentralized approach minimizes latency and ensures that data can be processed close to its source, which is crucial for opportune analytics and decision-making. Businesses can thus leverage distributed systems to handle complex datasets, perform advanced analytics, and derive actionable insights quickly.  
Moreover, the synergy between IoT and distributed systems facilitates seamless integration and coordination across different departments and processes within an organization. Workflow management systems powered by these technologies can automate routine procedures, such as reporting and scheduling that enable employees to focus on more strategic tasks [42; 43]. For instance, IoT devices can automatically update inventory levels in a warehouse management system, triggering reorders when stock runs low [44]. Distributed systems ensure this information is immediately available across the supply chain, enabling timely and efficient responses. In addition, integrating IoT and distributed systems supports advanced data management strategies, such as edge computing and real-time data streaming, in which information is constantly shared with the data center [45]. This approach is key with regard to just-in-time operations, for instance when it comes to robotics [46]. Real-time data streaming, facilitated by distributed systems, allows businesses to analyze data as it arrives, providing up-to-the-minute insights and enabling rapid responses to changing conditions.
## 2 Materials and Methods  
It is necessary to state that the spatial and temporal coordinates in the following equations are normalized by \(k_{0}^{-1}=\lambda_{0}/2\pi\) and \(\omega_{0}^{-1}=\lambda_{0}/2\pi c\), where \(\lambda_{0}=1\) um is the wavelength of the laser and \(c=2.998\times 10^{8}\) m/s is the light speed. The schematic diagram of the NITS process is shown in Figure 1. In a tightly focused Gaussian laser field, the electric field \(\mathbf{E}\) and magnetic field \(\mathbf{B}\) which satisfies [PERSON]'s equations can be expressed as follows [19]:  
\[\mathbf{E}=\sqrt{\epsilon}[(i/k)\
abla(\
abla\cdot\mathbf{A})+ik\mathbf{A}] \tag{1}\]  
\[\mathbf{B}=\
abla\times\mathbf{A} \tag{2}\]  
where \(\epsilon\) is the dielectric constant. In the Lorentz gauge, the vector potential \(\mathbf{A}\) obeys the following inhomogeneous wave equation:  
Figure 1: A schematic of nonlinear inverse Thomson scattering and temporal–spatial distribution. A series of laser parameters such as laser intensity \(a_{0}\) are determined by the laser generator. The normalized initial energy of the electrons (normalized by the energy of rest electron) is determined by the drift velocity of the electrons when they leave the electron accelerator, which is shown by \(\gamma_{0}=1/\sqrt{1-v^{2}/c^{2}}\). The greater the velocity when they leave, the greater the energy. \[\
abla^{2}A+k_{0}^{2}A=-(4\pi/c)J \tag{3}\]  
Considering the electromagnetic field in vacuum, Equation (3) can be reduced to the following:  
\[\
abla^{2}A+k_{0}^{2}A=0 \tag{4}\]  
In the Cartesian coordinate, the circularly polarized laser can be decomposed into a pair of linearly polarized lasers with a phase difference of \(\pi/2\), whose polarization plane are perpendicular to the y-axis and x-axis, respectively. Thus, the electromagnetic field of the circularly polarized laser can be decomposed as \(\mathbf{E}=\mathbf{E_{xy}}+\mathbf{E_{yp}}\) and \(\mathbf{B}=\mathbf{B_{xy}}+\mathbf{B_{yp}}\). The Gaussian laser field in this model has the spot size of \(2w_{0}\) (\(\upmu\)m), pulse width of \(L\) (\(\upmu\)m), and the intensity of \(I\) (\(\mathrm{W/cm^{2}}\)), so we can make the definition that  
\[E_{0}=a_{0}\frac{w_{0}}{w}\mathrm{exp}\left(-\frac{\eta^{2}}{L^{2}}\right) \mathrm{exp}\left(-\frac{\rho^{2}}{w^{2}}\right) \tag{5}\]  
where \(a_{0}=\sqrt{I\lambda_{0}^{2}/1.384}\times 10^{-9}\) indicates the peak amplitude, \(w=w_{0}\sqrt{1+z^{2}/z_{0}^{2}}\) is the waist radius at \(z\) in which the Rayleigh distance \(z_{0}=k_{0}w_{0}^{2}/2\). The longitudinal distance is \(\eta=z-t\), and the transverse distance is \(\rho=\sqrt{x^{2}+y^{2}}\). Under the circumstance of a tightly focused laser [20; 21], the expression of the electromagnetic field must undergo high-order expansion, accurate to the diffraction angle \(\epsilon=w_{0}/z_{0}\). [PERSON] et al. [20] derived \(A\), \(B\) and \(E\) in the x-axis linearly polarized laser field from Equations (1)-(4). The electromagnetic field expression of the y-axis linearly polarized laser field is proposed by [PERSON] [21] and [PERSON] [22] using symmetry expression.
# Assessment of Carbon Emissions at the Logistics and Transportation Stage of Prefabricated Buildings
## 2 Research Boundary and Key Parameters  
### Calculation Boundary of Carbon Emissions [9]  
Carbon emissions are greenhouse gas emissions caused by the entire process of extracting, producing, and using energy [10]. The main greenhouse gases are methane (CH\({}_{4}\)), nitrous oxide (N\({}_{2}\)O), and hydrofluorocarbons (HPCs). In this study, carbon dioxide (CO\({}_{2}\)) equivalent was used as the calculation boundary for greenhouse gases used for carbon emission calculations.  
In this study, the transportation phase of the assembled building, which includes the transportation of general building materials and prefabricated components, was examined. The study boundary of building material transportation is the process of transporting building materials from the production site to the construction site. The inherent carbon emissions include direct carbon emissions from the transportation process and indirect carbon emissions from the production process of the consumed energy [11]. The study boundaries of prefabricated component transportation include the loading of transportation vehicles at the factory, transportation from the factory to the construction site, and unloading at the construction site. In this phase, we do not consider the environmental impact associated with moving large pieces of equipment into and out of the plant on the construction site or the secondary transportation that comes from construction work.
## 5 Proposed Framework for Maintenance Scheduling
### Maintenance Scheduling with Dynamic PM Scheduling (PMD)  
Consider a scenario where no RUL prognostic model exists for a given component and its replacement is currently scheduled at equally spaced intervals, \(T_{R}\). The proposed dynamic approach allows the first \(B_{a}\) tools to reach failure. The \(T_{R}\) parameter is initially set as the minimum time between failures observed for these tools and is dynamically updated after \(B_{a}\) components are replaced, following these rules: If none of the components within the \(B_{a}\) set have failed before being replaced, the new replacement interval, \(T_{Ri+1}\) is adjusted to be \(T_{Ri+1}=T_{Ri}*\alpha\), where \(\alpha>1\). Conversely, if at least one component fails before being replaced, \(T_{Ri+1}\) will be set to the shortest failure time observed for the last set of components, as depicted in Figure 4. A search grid on the parameters \(B_{a}\) and \(\alpha\) is performed in the training data to find the optimal values based on the cost obtained for different values of these parameters.  
Figure 4: Illustration of the proposed dynamic PM policy.  
Figure 3: Illustration of 3 hypothetical scenarios when scheduling maintenance based on the k-LSTM-GFT.  
This adaptive updating of the replacement interval balances the exploitation of each component's lifetime and the number of failures that occurred. The time between failures of a given component may depend on the degradation level of adjacent systems or changes in working conditions. The dynamic updating of the time between interventions allows these aspects to be considered since it is updated based on the last replacements.  
In this case, the slots during which a maintenance action is triggered \(d_{T}\) are known in advance since \(T_{Ri}\) is predetermined. Consequently, the cost \(C_{\text{os}}\) is only incurred when a failure occurs. As a result, the cost of this approach is calculated as follows:  
\[C_{PMD}=\sum_{i=1}^{n}\frac{C_{p}}{Nc_{i}}\chi(d_{Fi}\geq T_{Ri})+\frac{C_{c}+C _{os}}{Nc_{i}}\chi(d_{Fi}<T_{Ri}). \tag{3}\]  
If the replacement occurs before the failure, only the cost of a preventive action \(C_{p}\) is present; otherwise, there is a cost of a corrective action \(C_{c}\) and the out-of-stock cost \(C_{\text{os}}\) since the replacement was not planned.
## 2 System Model  
This section presents an overview of the system setup at the physical layer for the reliable transmission of data across a wireless channel prone to noise effects. Figure 2 describes the system model for channel encoding at the transmitter and universal decoding at the receiver. The input to the encoder is a vector \(\mathbf{u}\) of K number of message bits.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Symbols and Abbreviations** & **Meaning** & **Application** \\ \hline \(\pi\), \(\pi^{-1}\) & Permutation, inverse permutation & Automorphism ensemble decoding \\ \(p\) & Permutation & Ordered statistics decoding \\ \(u\) & Codeword & Encoder input \\ \(\hat{x}\) & Estimated codeword & Decoder output \\ \(e,s\) & Error vector, syndrome & Checking validity of a codeword \\ \(T\) & Transpose of a vector & Parity-check equation \\ \(z\) & Noise sequence & GRAND \\ \(\alpha\) & Confidence value & Soft decoding \\ ms, \(\mu\)s & Millisecond, microsecond & Measurement of latency \\ \(E_{b}/N_{0}\) & Ratio of energy per bit to noise power & Normalized SNR measure \\ \(C\) & Codebook/code & Set of valid codewords \\ \(G,H\) & Generator matrix, parity-check matrix & Encoding, decoding of codeword \\ eMBB & Enhanced mobile broadband & 5G new radio \\ mMTC & Massive machine type communication & 5G use case \\ SNR & Signal-to-noise ratio & Measure of channel quality \\ BER, BLER, FER, WER & Bit error rate, frame error rate, block error rate, word error rate & Reliability metrics \\ AWGN & Additive white Gaussian noise & Simulation channel \\ BPSK & Binary phase shift keying & Modulation technique for simulation \\ N, K, R & Code block length, information block & Code parameters \\  & length, code rate & \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of symbols and abbreviations.  
After encoding, redundant bits get added to generate vector \(\mathbf{x}\) of N bits. Under a BPSK modulation scheme, the modulator maps the encoded vector \(\mathbf{x}\) onto the symbol vector \(\mathbf{s}=\{+1,\,-1\}\). In a point-to-point communication link for an AWGN channel, the received vector is obtained as \(\mathbf{y}=\mathbf{s}+\mathbf{n}\), where \(\mathbf{n}\) is the Gaussian noise vector. Universal decoding is applied to the demodulated vector \(\mathbf{y}\). Universal decoders like GRAND are designed to decode any linear block code by systematically guessing the noise patterns instead of estimating every codeword bit, whereas OSD starts each decoding operation by permuting received symbols in a declining order of reliability. Ensemble decoding (AED) involves running several identical low-complexity decoders in parallel.
# Scenario-Based Emergency Material Scheduling Using V2X Communications
## 4 Experiment
### Scenario Coefficient  
We calculated the scenario coefficients based on the SEMS evaluation indicators and the AHP method with the data collected by V2X communications and data from the websites [34; 35]. The scenario coefficients for Chengdu's, Shifang's and Jiangyou's demand for gauze, tents and water are 0.095, 0.057, 0.064, 0.195, 0.157, 0.165, 0.112, 0.074 and 0.081, respectively.
## 4 Related Work
### Comparison of Related Approaches and Identification of Research Gaps  
All previously presented approaches that integrate active responses in the presence of intrusions for IAS are compiled and rated in Table 1 according to their fulfillment of the requirements presented in Section 3. As it can be observed, none of these approaches are capable of fulfilling all these requirements.  
Contributions that present approaches that support active responses that affect network traffic or communication are often ambiguous with regards to whether or not they are capable of maintaining the operational requirements of IAS (i.e., \(R3\)). Similar ambiguity is found with regards to multi-platform support and interoperability with preexisting solutions (i.e., \(R4\)) in contributions presenting approaches that support active responses that affect individual IAS components and configurations. This occurs, as the mechanisms to execute active responses are often system- or device-specific.  
Furthermore, it can be observed that approaches that support active responses that affect network traffic or communication are more likely to integrate concepts related to the ISA/IEC 62443 series of Standards (i.e., \(R2\)). This higher compliance is supported, as many security components that aid in the execution of active responses already consider some of the concepts and trends related to industrial networks integrated in these Standards. On the other hand, approaches that support active responses that affect individual IAS components and configurations often neglect these aspects.  
Moreover, although automatic and semi-automatic execution of active responses is supported by most of these approaches, they provide low configurability (i.e., \(R1\)). This occurs due to the predominant preference of integrating model-based approaches that allow for decreasing their configuration efforts.  
Thus, a research gap is the lack of a security solution capable of providing configurable and automatic active protection against intrusions that ensures the correct operation of the underlying automation system while taking into consideration the following: current architectural and security trends defined by the ISA/IEC 62443 series of Standards, multi-platform support and interoperability with other security solutions.
# On Optimal Cell Flashing for Reducing Delay and Saving Energy in Wireless Networks
## 4 Simulation Results
### Optimal \(\beta^{*}\)  
Figure 3 shows the optimal \(\beta^{*}\) that minimizes \(f(\beta)\) as cell radius changes. According to Proposition 1, when cell radius is small, i.e., in the interference limited regime, \(\beta^{*}\) is 1 regardless of \(\eta\). This is because, when cell radius is small, the effect of interference power is dominant, so cell flashing reduces both the average delay and energy consumption in the interference limited regime. However, as the cell radius increases, the system operates more and more in a noise-limited regime, and thus \(\beta^{*}\) decreases, and eventually \(\beta^{*}\) becomes zero (unless \(\eta\) is infinite). This implies that cell flashing is not suitable for conventional macro cells albeit it can improve edge user performances. For a given cell radius, as \(\eta\) increases, more emphasis is put on energy conservation, and thus \(\beta^{*}\) increases, i.e., cell flashing becomes more beneficial as expected.  
Figure 4 shows the relations between the average delay and the average energy consumption as cell flashing ratio \(\beta\) varies when cell radius is 500 m, 1200 m, 1500 m, respectively. When cell radius is 500 m (i.e., \(\beta^{*}\) is 1), we can see that the average energy consumption decreases when the average delay decreased. This means that cell flashing reduces delay and energy consumption simultaneously. When cell radius is 1200 m (i.e., \(0<\beta^{*}<1\)), average delay is minimized at some \(\beta\). When cell radius  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Transmission power (\(P_{b}\)) & 5 W \\ Operation power (\(P_{b}\)) & 103 W \\ Sleep power (\(P_{s}\)) & 69 W \\ Average File Size & 10 Mbit \\ Channel bandwidth & 20 MHz \\ Noise power spectral density & \(-174\) dBm\(/\)Hz \\ Path loss factor & 3.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
is 1500 m, there is a tradeoff between minimizing delay and energy consumption, i.e., to save energy, the delay needs to get longer.
## 4 Integration of Biosensors
### Node Configuration
#### 4.2.1 Biosensors Node Configuration
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Aims and Contributions** & **Methodology** & **Biosensors Used** & **Evaluation Metrics** \\ \hline \multirow{9}{*}{[56]} & & The systems employed & & \\  & & deep learning algorithms & & \\  & To develop a deep learning & integrated with IoT & & \\  & IoT system for real-time & technology for real-time & & \\  & remote monitoring and & data collection, & & \\  & early detection of & transmission, analysis, and & & \\  & health issues. & alert generation to enable & & \\  & & early detection of & & \\  & & health issues. & & \\ \hline \multirow{9}{*}{[57]} & To advance healthcare & The focus was on & & \\  & monitoring through & designing and & & \\  & integration of the IoT with & implementing an & & \\  & a specific focus on ECGs & end-to-end solution for & AD8232, ML390614, & Heart signals, heart rate, blood oxygen levels, and \\  & and vital signs, offering a & capturing, analyzing, and & & \\  & holistic & displaying physiological & & \\  & end-to-end solution. & & & \\ \cline{1-1}  & & Designing and integrating & & \\  & & IoT technologies for & & \\  & & capturing asthma-related & & \\  & To design a monitoring & data, developing a remote & & \\  & system that allows doctors & monitoring platform, & & \\  & to monitor asthmatic & conducting data analysis, & & \\  & patients from a & and ensuring ethical & & \\  & remote area. & considerations to create an & & \\  & & effective IoT-based remote & & \\  & & asthma patient & & \\  & & monitoring system. & & \\ \hline \multirow{9}{*}{[59]} & & Developed a non-invasive & & \\  & To develop a digital & & & \\  & system using a pulse & & & \\  & oximeter and fuzzy logic & & & \\  & for early prediction and & & & \\  & screening of hypoxemia, & & & \\  & utilizing SpO2 values. & & & \\  & & & \\  & & & & \\  & & & \\ \hline \multirow{9}{*}{[60]} & Addressing the increased & Employing photoelectric & & \\  & risk of cardiovascular & & & \\  & diseases in an aging & & & \\  & population and & & & \\  & individuals with chronic & & & \\  & diseases, with a focus on & & & \\  & continuous vital sign & & & \\  & monitoring and real-time & & & \\  & health status awareness. & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of biosensor patient monitoring systems based on the IoT.
## 4 Experiments
### Ablation Experiment  
This section will delve into the analysis of the main components in the model architecture, aiming to scientifically deconstruct and comprehensively evaluate how each core component independently and cooperatively affects the accuracy and operational efficiency of the model's fault diagnosis through rigorous ablation experiments. Specifically, this experiment focuses on the effectiveness of the periodic two-dimensional transformation method and its performance in improving diagnostic sensitivity, as well as the enhancement effect of residual structure design on model learning ability.  
The experimental results in Table 5 demonstrate the importance of residual design and periodic two-dimensional transformation methods in improving the accuracy of model fault diagnosis and reveal the complex interaction mechanism between the two. When only implementing residual design, the average diagnostic accuracy of the model reached 96.52%, indicating the core value of residual structure in promoting deep network information transmission and alleviating gradient vanishing problems.  
Although the effectiveness of the periodic two-dimensional transformation method weakens when not used in conjunction with residual modules, its average accuracy reaches 95.33%. Although this value is slightly lower than when the two modules are used in combination, it is still significantly higher than the baseline level without any advanced modules, effectively verifying its hypothesis that the complex two-dimensional transformation mechanism enhances the model's ability to capture high-order features of time series. The periodic two-dimensional transformation method expands the adaptability  
Figure 7: Output feature visualization of the four stages.  
and understanding depth of the model to input data diversity, supplementing the breadth and depth of the model's feature representation learning. Even without the assistance of residual design, it demonstrates its independent performance.  
It is worth noting that by combining the robustness of residual design with the feature enhancement ability of the periodic two-dimensional transformation method, the model achieved the highest diagnostic accuracy in all four operating conditions, indicating a significant synergistic effect of these two designs in improving model performance. The residual path maintains the information fluency and learning efficiency of deep networks, while the periodic two-dimensional transformation method further unlocks hidden patterns and subtle structures in the data. The two complement each other and jointly promote the development of the model towards deeper and wider dimensions of learning. Therefore, when designing similar deep learning models, effectively integrating residual design and periodic two-dimensional transformation methods, and optimizing for the characteristics of different datasets, is expected to improve the overall structure and performance of the model.
###### Abstract  
Multi-platform satellite-based precipitation gridded estimates are becoming widely available in support of climate monitoring and climate science. The characterization of the performances of these emerging Level-4 products is an active field of research. This study introduced a simple Gaussian mixture model (GMM) to characterize the distribution of uncertainty in these satellite products. The following three types of uncertainty were analyzed: constellation changes-induced uncertainties, sampling uncertainties and comparison with rain-gauges. The GMM was systematically compared with a single Gaussian approach and shown to perform well for the variety of uncertainties under consideration regardless of the precipitation levels. Additionally, GMM has also been demonstrated to be effective in evaluating the impact of Level-2 PMW rain estimates' detection threshold definition on the constellation changes-induced uncertainty characteristics at Level-4. This simple additive perspective opens future avenues for better understanding error propagation from Level-2 to Level-4.  
precipitation; satellite; uncertainty analysis; error model; Gaussian mixtures Article