esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploration of Multi-Scale Reconstruction Framework in Dam Deformation Prediction
## 2 Materials and Methods
### VMD-Based Decomposition Technology  
VMD is a new type of self-adaptable signal decomposition technology with preset scale. It can decompose a real-valued signal into K modal components around the center frequency. The process of signal decomposition is the process of solving the variational problem, as shown in Formula (1):  
\[\left\{\begin{array}{rl}min\\ \{u_{k}\},\{\omega_{k}\}\\ \end{array}\right.=\{\sum\limits_{k}||\partial_{t}\Big{[}\Big{(}\delta(t)+ \frac{j}{\pi t}\Big{)}u_{k}(t)\Big{]}*e^{-j\omega_{k}t}||_{2}^{2}\} \tag{1}\] \[\sum\limits_{k}u_{k}=f\]  
where \(\{u_{k}\}=\{u_{1},u_{2},\cdots u_{k}\}\) are the K modal components, \(\{\omega_{k}\}=\{\omega_{1},\omega_{2}\cdots\omega_{k}\}\) are the center frequency of each modal component, \(f\) is the original signal, and \(\delta_{t}\) is the pulse function. In order to obtain the optimal solution of the constrained variational problem, the Lagrange multiplication operator \(\lambda(t)\) and the quadratic penalty factor \(a\) are introduced to transform the constrained variational problem into an unconstrained variational problem. The extended Lagrange function is expressed as:\[L(\{u_{k}\},\{\omega_{k}\},\lambda)=a\sum_{k}||\partial_{t}\bigg{[}\bigg{(} \delta(t)+\frac{j}{\pi t}\bigg{)}u_{k}(t)\bigg{]}*e^{-j\omega_{k}t}||_{2}^{2}+||f (t)-\sum_{k}u_{k}(t)||_{2}^{2}+\langle\lambda(t),f(t)-\sum_{k}u_{k}(t)\rangle \tag{2}\]  
Alternate direction operator multiplication is used to obtain the saddle point of the above Lagrangian function, then the optimal solution can be obtained. Specific steps are as follows:  
1. Initializing \(\{u_{k}^{1}\}\), \(\{\omega_{k}^{1}\}\), \(\lambda^{1}\), \(n\gets 0\) and converting each parameter from time domain to frequency domain.
2. \(n\gets n+1\). On the non-negative frequency domain interval, \(\omega\geq 0\), for \(k=1:\) K, updating \(\hat{u}_{k}\), \(\omega_{k}\): \[\hat{u}_{k}^{n+1}(\omega)=\frac{\hat{f}(\omega)-\sum_{i<k}\hat{u}_{i}^{n+1}( \omega)+\hat{\lambda}^{n}(\omega)/2}{1+2a\big{(}\omega-\omega_{k}^{n}\big{)}^{ 2}}\] (3)  
\[\omega_{k}^{n+1}=\frac{\int_{0}^{\infty}\omega\Big{|}\hat{u}_{k}^{n+1}(\omega )\Big{|}^{2}d\omega}{\int_{0}^{\infty}\Big{|}\hat{u}_{k}^{n+1}(\omega)\Big{|} ^{2}d\omega} \tag{4}\]
3. For all \(\omega\geq 0\), updating \(\hat{\lambda}^{n}\): \[\hat{\lambda}^{n+1}(\omega)=\hat{\lambda}^{n}+\tau(\hat{f}(\omega)-\sum_{k} \hat{u}_{k}^{n+1}(\omega))\] (5)  
In the above Formulas, \(\hat{u}_{k}^{n+1}(\omega)\), \(\hat{f}(\omega)\) and \(\hat{\lambda}^{n+1}(\omega)\) are the Fourier transforms of \(u_{k}^{n+1}(\omega)\), \(f(\omega)\) and \(\lambda^{n+1}(\omega)\), respectively.
4. Judging whether the convergence condition (judgment accuracy) is satisfied, if the following Formula is satisfied, stop the iteration, otherwise return to step (2). When the entire period is over, the result is output, and K eigenmode components (IMF) are obtained. \[\frac{\sum_{k}||\hat{u}_{k}^{n+1}-\hat{u}_{k}^{n}||_{2}^{2}}{||\hat{u}_{k}^{n }||_{2}^{2}}<\varepsilon\] (6)
## 4 Analysis
### Total Benefit of Demand Sharing  
We first analyze the medical alliance that generates the maximum benefit possible from demand sharing. The objective is to maximize the total revenue with three optimal prices (i.e., \(p_{1},p_{2},p_{12}\)) as follows. \[\pi_{c}(p_{1},p_{2},p_{12})=p_{1}\lambda_{1}+p_{12}\lambda_{12}+p_{2}\lambda_ {2}. \tag{4}\]  
In (4), we can see that the direct benefit of demand sharing is \(p_{12}\lambda_{12}\), since, otherwise, this part of patients cannot get service. However, the waiting time will increase if HS would serve more patients; this may drive the HS to set a lower price to compensate its own patients (from Region-2) for the extra delay. This may cause a revenue reduction (lower \(p_{2}\)) in the Market-2. Hence, whether demand sharing can raise total revenue depends on HS's market characteristics. Proposition 1 specifies a lower threshold for the HS service rate, which provides the condition for revenue gain via demand sharing. Proofs of propositions and corollaries are in the Appendixes A and B. Proposition 1: _There exists a threshold_  
\[\underline{\mu_{2}}=\Lambda_{2}+\frac{c_{1}}{2V_{2}}+\sqrt{\frac{c_{1}^{2}}{4V _{2}^{2}}+\frac{c_{2}\Lambda_{2}}{V_{2}}}, \tag{5}\]  
_such that if and only if \(\mu_{2}>\underline{\mu_{2}}\), then the medical alliance via demand sharing will result in a revenue gain compared to the independent hospitals without demand sharing._  
Proposition 1 shows that even if the HS has some idle capacity, the demand sharing may not be necessarily beneficial. For the demand sharing to be feasible, the HS's idle capacity(service rate) must exceed a threshold, that is, \(\mu_{2}>\underline{\mu_{2}}\). The threshold \(\underline{\mu_{2}}\) implies a condition as per which the HS's revenue loss, as a result of lowering the price \(p_{2}\) for its region, must be equal to its revenue gain, as a result of serving more patients from the HD's region. Given \(\mu_{2}>\underline{\mu_{2}}\), wherein the demand sharing is beneficial, we derive the optimal pricing strategy with the medical alliance. Intuitively, with more idle capacity, the HS will serve more patients who make the switch to increase total revenue. HD can at least share patient flow with an arrival rate of \(\Lambda_{12}^{0}=\Lambda_{1}-\lambda_{1}^{0}\), which does not affect its optimal pricing strategy in Region-1. If HD shares a demand that is larger than \(\Lambda_{12}^{0}\), then the demand sharing may result in a new mismatch between demand and supply. Hence, whether to share a demand that is larger than \(\Lambda_{12}^{0}\) would depend on the trade-off between the revenue loss caused by the new mismatch and the revenue gain from HS's higher utilization. Anextreme case would be if HS has infinite service rate, then it may be optimal for HD to share all of its demand. The following proposition provides an upper threshold for the HS service rate that can characterize the optimal strategies if the two hospitals cooperate and make an alliance.
# System Design and Experimental Study of a Four-Roll Bending Machine
## 5 Experimental Validation
### Arc Integrity Validation  
In conventional roll bending processes, formed profiles commonly exhibit significant geometric residual straight edges at both ends. This phenomenon arises from the incomplete wrapping effect at the contact area between the upper roll and the workpiece. During operation of traditional roll bending machines, the length of the unformed end regions of the profile can reach up to 300 mm, as shown in Figure 19a. This parameter is influenced by the coupling effects of the machine's structural parameters and the material's mechanical properties. The fully servo-controlled four-roll bending machine developed in this study offers high precision, enabling real-time pose control during the rolling process. During the pre-bending stage, dynamic adjustment of the main spindle feed and side roll displacement significantly suppresses the geometric residual straight edges. Based on the experimental data from the profile rolling tests mentioned earlier, the maximum residual straight edge at the ends of the profiles formed using this machine is merely 40 mm, representing an 86.67% reduction compared to conventional equipment. This effectively ensures  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Serial Number** & **Preset Radius (mm)** & **Actual Radius (mm)** & **\(\Delta\) Radius (mm)** & **Error (\%)** & **Average Error (\%)** \\ \hline
1 & 1500 & 1512.61 & 12.61 & 0.840 & \\
2 & 2000 & 2021.82 & 21.82 & 1.090 & \\
3 & 2500 & 2523.95 & 23.95 & 0.950 & 0.798 \\
4 & 3000 & 3018.50 & 18.50 & 0.610 & \\
5 & 3500 & 3517.50 & 17.50 & 0.500 & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Analysis of Experimental Results for Rolling Accuracy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Serial Number** & **Preset Radius (mm)** & **Actual Radius (mm)** & **\(\Delta\) Radius (mm)** & **Error (\%)** & **Average Error (\%)** \\ \hline
1 & 1030 & 1085.12 & 55.12 & 5.35 & \\
2 & 970 & 1020.23 & 50.23 & 5.18 & \\
3 & 2015 & 2070.47 & 55.47 & 2.75 & 3.676 \\
4 & 1985 & 2040.09 & 55.09 & 2.78 & \\
5 & 3020 & 3090.10 & 70.10 & 2.32 & \\ \hline \hline \end{tabular}
\end{table}
Table 11: Conventional Roll Bending Machine Roll Bending Results.  
the geometric integrity of continuously bent profiles, meeting the stringent requirements for high-precision profile forming in aerospace and other advanced manufacturing fields.
### Proposed Methodologies to Apply Concurrent Engineering  
In general terms, concurrent engineering is an approach applicable according to the needs and the desired scope. The methodologies are classified into three application levels according to the desired goals:  
* Focused on integrating design and the process, which consists of improving a process through the teamwork of those involved.
* Concurrent design assisted by computer, engineering, and manufacturing methods, which consists of supporting the integration of design through products and process models and databases.
* Fully address the product's life cycle; this includes the design of a product, its manufacturing processes, and its disposition.  
Each project where CE can be applied is different, so the tools required to implement the methodology may vary. [PERSON] et al. [19] proposed a systematic approach, which included concurrent work from the development of the product, starting from the con cept generated by the marketing department to the product's distribution and sale. [PERSON] and [PERSON] [20] proposed an implementation method for implementing CE for SMEs through seven stages. [PERSON] [21] presented the PACE (Practical Approach for Concurrent Engineering) methodology. Its development consists of a seven-stage implementation framework that includes various activities, focusing on the product born as an idea and is transformed from a series of resources to meet its requirements or characteristics. [PERSON] et al. [22] proposed a model to integrate the product design information, design process, and knowledge.  
An analysis of the models for the implementation of CE in SMEs showed that, although they offer a way to guide efforts, in their current form, they are complex or limited, which makes them challenging to implement. The proposals of [PERSON] et al. [19] and [PERSON] [21] were elaborated on based on a bibliographical review, which left a gap between theory and practical application. [PERSON] and [PERSON] [20] indicated that the model should be adapted according to the company and its characteristics. [PERSON] et al. [9] focused on team and workgroup forming in the concurrent product development process's loops. [PERSON] et al. [22] presented an approach on collaborative network design, which is appropriate for CE but restricting of the work environment, and it does not provide SMEs a methodology. [PERSON] et al. [23] presented the CEPRA (Concurrent Engineering in Practice) approach, which aims to bring CE to SMEs by personal consulting and a software system simultaneously, but this is not open access. [PERSON] et al. [24] exposed a framework that integrates engineering methods with Web-based software tools, which could be complex for SMEs with limited IT and software resources. Moreover, it was found that SMEs work in line with CE's concept in the new product development process, although they do not understand what CE is about [25].  
To help to fill this research gap, we deliver a new CE methodology for SMEs. The proposed approach and implementation's explanation can overcome the shortcomings mentioned above with today's work with others' CE models.
## 2 Collected Data and Research Method  
### Data Collection and Dimensionality Reduction  
The data collection was carried out through a survey applied to \(N=205\) (two hundred and five) Brazilian industrial companies. In this collection step, the variables were associated with representative questions, structured on a semantic differential scale, in which 1 represents \"Strongly Disagree\" and 7 means \"Strongly Agree\". These variables were divided into input variables that are related to the implemented CP practices (adapted from [3], and output variables that quantify the impact of the adopted CP strategies, in terms of the environmental, product, and economic performance of the manufacturing process. The validation of the data collection process is detailed in [2], where SEM was used to establish a correlational framework between the constructs (latent variables). The observable variables, that is, the ones that can be directly obtained from the survey, are denominated manifest variables. In our case, we considered 18 input and 17 output manifest variables. However, the high number of variables may lead to a model with an elevated number of degrees of freedom (DoFs), which would require a prohibitively large data sample to be trained. A widely accepted solution is to combine sets of related manifest variables with latent variables [35]. In this way, the number of input variables was reduced from 18 manifest variables to 3 latent variables, whereas the number of output variables decreased from 17 to 3. In machine learning terminology, the reduction of the amount of input and output variables is denominated dimensionality reduction, and it represents a critical step in many data processing applications. This dimensionality reduction generally uses some correlation or independence analysis to filter out redundant information, leading to more efficient modeling. In our case, the amount of sample data is not sufficiently high to implement sophisticated methods, such as independent component analysis (ICA), so we adopted a simpler approach, in which the latent variable is computed by averaging the associated manifest variables. Table 1 lists the input latent and manifest variables, alongside their respective survey statements, whereas equivalent information for the output variables is given in Table 2.
## 2 Related Work
It was demonstrated that the resource allocation based on LP-relaxation proposed in the work leads to a distribution that is very close to the calculated optimal one. The authors in [13] draw attention to the problem of downlink multicast data transmission in large MIMO systems. This paper notes that serving a large number of users in one multicast group can degrade system performance because the overall multicast data rate is limited by the rate of the user with the worst signal-to-noise ratio (SNR). To solve this problem, it has been proposed to dynamically divide the set of serving users into multiple multicast groups using the spatial degrees of freedom offered by a large number of transmit antennas. User grouping patterns and co-channel beamforming vectors of these groups were also jointly developed. The scheme proposed by the authors provided a significantly higher performance of the average total speed in comparison with the previously known schemes. Differently from the studies presented above, in our paper, we concentrate on estimating the required density of NR BS deployments as a function of UE density and their service requirements. To achieve this goal, we combine the tools of stochastic geometry and queuing theory. Particularly, the overall approach consists of a multicast grouping scheme and evaluation of the VR enhancement layer drop probability. Finally, we develop the algorithm allowing us to assess the required inter-site distance between NR BS that is further utilized the assess the density of NR BSs.
## 4 Power Loss Evaluation  
Usually, power losses cause two consequences; one is impairing the converter efficiency, and the other is heat generation. Therefore, heat must be removed from the semiconductor devices and never be higher than a specific junction temperature (T)) [21; 22]. The allowed junction temperature in a switching device module is usually up to 125 \({}^{\circ}\)C. In order to simulate the losses dissipation in the power electronic converter, the PLECS toolbox is used for developing the thermal model of the VSC, and to calculate conduction loss, which is given by:  
\[P=\frac{1}{T}\int_{0}^{T}(v_{CE}(t)i_{c}(t)+r_{ce}i_{c}^{2}(t))dt \tag{27}\]  
where \(v_{C_{E}}\) and \(r_{c_{e}}\) can be determined graphically from the converter datasheets. During the commutations, losses appear due to the switching pattern. The expression of the switching loss is given by:  
\[P_{on+off}=\frac{\sqrt{2}\cdot i_{rms}}{\pi}\cdot f_{sw}\cdot V\cdot K \tag{28}\]  
where \(K\) is the reference value as it can be derived from the converter datasheet. The power losses should be dissipated away in order to keep the semiconductor devices at a safe temperature. Thermal resistance exists due to the internal structure of the materials. Practically, two factors should be considered. Firstly, the thermal resistance must have a low value. Secondly, the cooling system must be working efficiently. The cooling system, and heat sink are simulated in this work through the PLECS toolbox and all equivalent thermal resistances are added, where a 40 \({}^{\circ}\)C temperature is considered.
# Affordable Broadband with Software Defined IPv6 Network for Developing Rural Communities
## 2 Broadband Policy and Network Deployment Initiatives of Nepal
With the deployment of nation-wide ICT infrastructure, the government has to come up with additional measures to reduce the digital divide and gap in internet access and utilization between groups [8]. Hence, the increase in ICT accessibility to rural areas becomes the basic requirement that these projects are trying to address. Additionally, content creation, delivery, and security will become other concerns, together with the greening of network infrastructure for the ecofriendly environment and long term sustainability of service providers and societies [5].
# Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses
## 5 Discussion
### Power Consumption Analysis  
In the current setup we have used the battery of 15,000 mAh. It lasts for 12-13 weeks, with message frequency of five minutes and configuration as mentioned in Table 3. We did the power measurement to see the exact consumption using the Keysight N6705B power analyzer as shown in Figure 13. The letters marked in the Figure 13, represents different section of the message such as 'A': sensor reading; 'B': radio transmission time; 'C': one second time before opening first receive slot; 'D': next one second time before opening second receive slot; 'E': radio receive slot; 'F': one full message marked between two markers. The measurement for the power consumption is given in Table 6. Later, we will use this baseline power consumption analysis to compare the power consumption overhead for onboard computation (to minimize message frequency) Vs sending each message; also, the power analysis of integrating different ML algorithms.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Phase** & **Time** & **Avg. Current (mA)** \\ \hline Sensor reading & 82.28 ms & 13.24 \\ Radio transmission & 249.8 ms & 46.08 \\ Inactive mode & 5 min & 6.01 \\ One total message & 2.3 s & 10.92 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Power consumption analysis for LoRaWAN message.  
Figure 12: Messages along with signal strength (RSSI) inside greenhouse.
# Adaptive Hierarchical Sliding Mode Control for Double-Pendulum Gantry Crane Based on Neural Network
## 1 Introduction
Validating through comparative simulations that the proposed RBF-HSMC strategy significantly outperforms conventional SMC and standard HSMC in both swing suppression and chattering reduction, confirming its superior performance and robustness. The remainder of this paper is organized as follows. In Section 2, the dynamic model of the double-pendulum gantry crane is established based on [PERSON]'s equations, laying the foundation for subsequent controller design. Section 3 introduces the theory of RBF neural networks and hierarchical sliding mode control, followed by a detailed description of our proposed adaptive hierarchical sliding mode control strategy based on RBF neural networks and its applicability to the double-pendulum crane control problem to address the research questions of this paper. In Section 4, we describe the simulation and validation of the algorithm. We conduct simulation tests on the designed controller and compare its performance with traditional sliding mode control and hierarchical sliding mode control under identical operating conditions to verify the rationale of the proposed algorithm. In Section 5, we summarize the work of this paper and demonstrate the advantages of the proposed algorithm in terms of swing suppression, positioning accuracy improvement, and robustness enhancement. Compared with traditional control methods, the proposed method exhibits a better control performance and stronger robustness. The proposed method is particularly suitable for industrial scenarios with high requirements for control accuracy and stability, such as performing precise lifting tasks in precision manufacturing, automated assembly, or environments with parametric uncertainties and external disturbances. Furthermore, while other studies have integrated neural networks for crane control, the nature of this integration is a key differentiator. Some approaches utilize a 'pure' neural network controller, which relies entirely on the network for control action, or combine neural networks with PID-like structures. In contrast, our work presents a hybrid framework that synergistically combines the RBF network with the robust HSMC backbone. This integration is designed to leverage the strengths of both methodologies: HSMC provides a guaranteed stable framework and fast transient response, while the RBF network learns to actively compensate for unmodeled dynamics and disturbances online. This compensation is crucial for reducing the chattering inherent in standard HSMC and improving the final control precision. Therefore, the novelty of our approach lies not in the individual components, but in this specific, synergistic integration of an adaptive RBF network within a hierarchical sliding mode framework tailored to solve the double-pendulum crane problem.