esa-sceva/satcom-chunk-collection huggingface dataset:
# SCM-IoT: An Approach for Internet of Things Services Integration and Coordination
## 3 Methodology
### Platform Independent Computational Model
#### 3.2.3. Collect  
As indicated in the conceptual model, the collection is a service that allows all the notebook sheets that meet a pre-established condition to be retrieved. The representation of this service corresponds to the CPN pattern outlined in blue, as shown in Figure 4a,b. The service request begins with the insertion of the \"COLLECT\" token in place S. This initial marking causes the transition to be enabled. CS that, when triggered, inserts a unitary token in place CE1. The CC transition is enabled with this marking, thereby initiating a sequence of actions to carry out the collection. The first action is to remove the entire content of the HC place, which is where the collected data reside. The second action is to initialize the place U with all the identifiers corresponding to each notebook page. The third and last action consists of copying in the destination place HC in each notebook page  
Figure 3: Subscription for notification service behavior. In (**a**), transition N is enabled by the marking, showing only one subscriber in place US. In (**b**), a notification for the subscriber has been deposited in place NC.  
that satisfies the collection condition \"[\(p\) = SI]\" of transition C as shown in Figure 4a. The three previous actions are carried out sequentially in an indivisible way to guarantee that all the data in the collection comply with the established condition. Figure 4b shows the marking after repeatedly firing transition C until it is no longer enabled when no more pages are found that satisfy the collection condition.  
The abstract behavior of the editor services described in the CPN formalism allows representing the main features of the system application. The platform-independent computational model is a prototype that is further detailed into a platform-dependent computational model, according to the technological aspects of the infrastructure available.
## 4 Cma  
[PERSON]'s 1965 study, \"Introduction of modal expansion in electromagnetic scattering on resonance region\", was the first to examine the fundamental theory of CMA [21; 22]. The total current that flows through a conducting or radiating structure generated by an incoming electromagnetic field may be described as the weighted sum of N transverse eigencurrents (J\({}_{i}\)) that are geometrical and material reliant while being self-reliant on the excitation vector, according to a key concept of CMA theory [23; 24]. The total current can be expressed as in Equation (6)  
\[\mathrm{J}=\sum_{i=n}^{N}\beta_{i}\,\mathrm{J}_{i} \tag{6}\]  
where \(\beta_{i}\) is modal weight coefficients that calculate the impact of each eigencurrent on the overall current. Because each eigencurrent generates its electric field, the overall current generates the total radiated electric field. The modal significance (\(MS\)) is a significant metric of the CMA that determines each mode's maximum normalized current strength and influences the radiation properties of that particular mode [25]. The MS can be calculated using Equation (7)  
\[MS=\left|\frac{1}{1+j\lambda_{i}}\right| \tag{7}\]  
where \(\lambda_{i}\) is the eigenvalue of the \(i\)th mode and is related to the eigencurrents using a method of moment matrix (\([Z]\)), as follows:  
\[[Z]=[R]+j[X] \tag{8}\]  
\[[X]\,\mathbf{J_{i}}\,=\lambda_{i}\,[R]\,\,\mathbf{J_{i}} \tag{9}\]  
where \(R\) and \(X\) depict the real and imaginary part of the impedance \(Z\). The eigenvalues help analyze the capacitance and inductance values; their significance is listed in Table 4. The phase difference in the electric field and surface current of the antenna is described by the characteristic angle (CA). The CA (\(\alpha_{i}\)) can be computed using Equation (10)  
\[\alpha_{i}\,=180^{\circ}-tan^{-1}\lambda_{i} \tag{10}\]the eigencurrent, which generates the overall current and the radiated electric field. The surface current distribution over these frequencies in different modes is illustrated in Figure 14.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Resonant** & **Modes** & **Significance** & **Eigenvalue** & \
\begin{tabular}{c} **CA** \\ **(Degrees)** \\ \end{tabular} &
\begin{tabular}{c} **Nature of the** \\ **Mode** \\ \end{tabular} \\ \hline
3.7 GHz & Mode1 & 0.7 & 1.09 & 132 & Inductive \\  & Mode2 & 0.08 & \(-\)11.80 & 265 &
\begin{tabular}{c} Capacitive \\ (Nonsignificant) \\ \end{tabular} \\
6.8 GHz & Mode1 & 0.98 & \(-\)0.21 & 192 & Capacitive \\  & Mode2 & 0.89 & \(-\)0.50 & 207 & Capacitive \\  & Mode1 & 0.97 & \(-\)0.24 & 193 & Capacitive \\
11.5 GHz & Mode2 & 0.99 & \(-\)0.07 & 183 & Capacitive \\  & Mode3 & 0.80 & \(-\)0.74 & 216 & Capacitive \\  & Mode4 & 0.89 & 0.48 & 153 & Inductive \\ \hline \hline \end{tabular}
\end{table}
Table 5: CMA of the projected antenna design.  
Figure 13: CMA of the projected two-port dual band-notch UWB antenna (**a**) CA (**b**) MS (**c**) eigenvalues (**d**) modal coefficient.
# rSEM: System-Entropy-Measure-Guided Routing Algorithm for Industrial Wireless Sensor Networks
## 2 System Model and Previous Work
The set \(\zeta\) is the set that includes nodes that have not become cluster head in the final \(1/Per\) slots. Figure 1: The simulation scenario discussed in this paper. In order to avoid such isolated nodes becoming cluster heads, Ref. [10] proposed the RED \(\backslash\)_LEACH algorithm to improve LEACH. The specific improvement is that the distance from the cluster head to the sinknode is lower than the average distance from all the sensors to the sinknode. The location of the cluster heads is closer to the sinknode, which can obviously improve the power performance of LEACH. However, the nature of cluster head elections is still randomization. In order to further improve the power performance of the LEACH algorithm, some optimization methods are needed to replace the LEACH random cluster head election. The advantages and disadvantages of the above two algorithms are self-evident. The Dijkstra algorithm focuses on energy consumption, while LEACH focuses on load balancing and the network delay. This paper focuses on the problem of how to reduce the power consumption as much as possible on the premise of ensuring a low delay. Therefore, the rSEM algorithm proposed in this paper is suitable for networks sensitive to both the delay and power consumption, which also accords with the future industrial network development trend [1]. In the next section, the details of the rSEM algorithm are presented.
# A Survey of CNN-Based Network Intrusion Detection
## 3 Background of Study
### IDS Datasets
#### 3.4.5 CSE-CIC-DS2018  
The Amazon Web Services platform developed the CSE-CIC-DS2018 [107] dataset, which displays network behavior in real time and includes multiple intrusion states. There are several intrusion profiles in the dataset that can be used for security purposes. These can be applied to a variety of topologies and network protocols. The dataset has 80 statistical properties, including volume, number of bytes, and packet length, all of which have been calculated in both forward and backward modes. Researchers can easily access the dataset online, with around 5 million records. It covers seven fundamental types of attacks, namely, Heartbleed, Infiltration, Brute-force DOS attacks, DDOS attacks, Brute-force SSH, Botnet, and web attacks [109, 110].
# RBF Neural Network Based Backstepping Control for an Electrohydraulic Elastic Manipulator
## 3 Control Design
### Backstepping Sliding Mode Control
\begin{array}{ll}\dot{V}_{2}&=-k_{1}s_{1}^{2}+s_{1}\Big{(}\delta_{1}(t)-\eta _{1}\ \tanh\!\left(\frac{s_{1}}{\phi_{1}}\right)\Big{)}+g_{1}s_{1}e_{3}+s_{2} \Big{(}g_{0}u+f_{0}+\delta_{2}(t)-\dot{x}_{3d}+\lambda_{2}e_{3}\Big{)}\\ &=-k_{1}s_{1}^{2}+s_{1}\Big{(}\delta_{1}(t)-\eta_{1}\ \tanh\!\left(\frac{s_{1}}{ \phi_{1}}\right)\Big{)}+g_{1}s_{1}e_{3}-k_{2}s_{2}^{2}+s_{2}\Big{(}\delta_{2}( t)-\eta_{2}\ \tanh\!\left(\frac{s_{2}}{\phi_{2}}\right)\Big{)}-g_{1}s_{1}s_{2}\\ &=-\sum\limits_{i=1}^{2}\Big{(}k_{i}s_{i}^{2}+s_{i}\Big{(}\delta_{i}(t)-\eta _{i}\ sign\!\left(\frac{s_{i}}{\phi_{i}}\right)\Big{)}+s_{i}\eta_{i}\Big{(} sign\!\left(\frac{s_{i}}{\phi_{i}}\right)-\tanh\!\left(\frac{s_{1}}{\phi_{1}} \right)\Big{)}\Big{)}-\lambda_{2}g_{1}s_{1}z_{3}\\ &\leq-\sum\limits_{i=1}^{2}\Big{(}k_{i}s_{i}^{2}+(\delta_{i}(t)-\eta_{i})|s_{ i}|\Big{)}-\lambda_{2}g_{1}s_{1}z_{3}+\varepsilon_{d}\leq-\zeta^{T}\Xi\zeta+ \varepsilon_{d}\\ &\leq-\lambda_{\min}\Big{(}\Xi\Omega^{-1}\Big{)}V_{2}+\varepsilon_{d}\\ \end{array}  \tag{30}\]  
where \(\Xi=\left[ \begin{array}{ccc}k_{1}&0&\frac{\lambda_{2}g_{1}}{2}\\ 0&k_{2}&k_{2}\lambda_{2}\\ \frac{\lambda_{2}g_{1}}{2}&k_{2}\lambda_{2}&k_{2}\lambda_{2}\end{array} \right]\), \(\|\sum\limits_{i=1}^{2}s_{i}\eta_{i}\Big{(}sign(s_{i})-\tanh\!\left(\frac{s_ {i}}{\phi_{i}}\right)\Big{)}\|_{\infty}\leq\varepsilon_{d}\) is a positive constant, and \(\lambda_{\min}((. ))\) is the minimum eigenvalue of matrix \((.)\). When Assumption 1 is satisfied, and the parameters \(k_{1},k_{2}\), and \(\lambda_{2}\) are chosen so the matrix \(\Xi\) is a semi-positive definite matrix, the controlled system will be uniformly asymptotically stable [29]. In practice, the boundaries of the uncertainties, \(\delta_{i}(i=1,2)\), are difficult to determine. If the robust gains chosen are smaller than the boundaries of the uncertainties, then the stability and robustness are not assured. In another way, if the robust gains selected are significantly larger than the boundaries, then the stability and robustness are guaranteed, but the chattering effects can happen and affect the control performance.
# Long-Term Occurrence and Fate of Microlastics in WWTPs: A Case Study in Southwest Europe
## 3 Results and Discussion
### Release of MPs to the Environment  
Several studies reported the environmental incidence of MPs emitted to the environment by WWTPs. As far as we know, until now, there have been seven works published that analysed the incidence of MPs in Spanish WWTPs [10; 17; 45; 66; 78; 79; 80]. The values found in the present work (average values of 16.1 \(\pm\) 3.3 MPs/L and 1.1 \(\pm\) 0.3 MPs/L in influent and effluent, respectively) were within the ranges reported by the previous works (between 2.7 MPs/L and 645 MPs/L in influent and 0.31 MPs/L and 16 MPs/L in effluent).  
Considering the MP concentration detected in the influents (between 11.4 and 23.8 MPs/L) and the volume of wastewater that arrives at the WWTP (an average value between 4089 m\({}^{3}\)/day and 5570 m\({}^{3}\)/day) (Table S5), it can be estimated that between 5.57\(\cdot\)10\({}^{7}\) and 1.27\(\cdot\)10\({}^{8}\) microplastics enter into the facility each day. Since the removal efficiency of the studied facility is 92.9 \(\pm\) 2.1%, approximately between 2.50\(\cdot\)10\({}^{6}\) and 6.98\(\cdot\)10\({}^{6}\) microplastics per day are emitted to the environment. For example, [PERSON] et al. (2020) [17], who analysed a WWTP five times larger than that studied here, estimated that around 3\(\cdot\)10\({}^{8}\) microplastics per day are release into the Henares River (Madrid), even though the WWTP reduce the MP concentration by 93%. This highlights the importance of WWTPs as source of MPs released into the environment.  
In dry sludge samples, an average value of 24.0 \(\pm\) 8.6 MPs/g is found, value lower than those values reported by other authors that analysed sewage sludge samples in the same country (Spain) (between 50 and 165 MPs/g) [17; 27; 78]. Considering that the MP concentration detected in the sludge (between 12.0 and 39.4 MPs per gram of dry sludge) and the kg of sludge generated in the WWTP (values between 1764 kg and 3976 kg)  
Figure 7: Abundance of microplastics in sludge samples according to (**a**) the shape and (**b**) chemical composition.  
(Table S6), it can be estimated that around between and 4.23\(\cdot\)10\({}^{7}\) and 9.54\(\cdot\)10\({}^{7}\) microplastics are entrapped in sludge. Thus, the subsequent management of the sludge is a determinant step to avoid the release of these MPs to the environment.
## 4 Deep Learning Methods for HDR Reconstruction
### HDR Video Reconstruction
#### 4.5.1 Optical Flow-Based Approaches  
Currently, state-of-the-art methods for HDR video reconstruction mostly comprise models relying on optical flow alignment. [PERSON] et al. [30] introduced the first end-to-end model of this kind, consisting of a flow network to align the LDR frames and a weight network to merge them. [PERSON] et al. [31] advanced alignment by incorporating deformable convolutions after optical flow alignment. To further reduce flow estimation errors in these methods, [PERSON] et al. [32] proposed an approach with a more robust optical flow estimation, surpassing other SoTA methods in public benchmarks [31] while also being the first to reconstruct HDR video in real time. Figure 11 illustrates this model architecture, featuring an optical flow network with large multi-size convolution kernels, an HDR-domain alignment loss to supervise the flow network, and a sophisticated training scheme combining real video datasets (Vimeo-90K) and synthetic ones (Sintel).
## 5 Discussion
### Limitations  
This study forecasts the seasonal and annual GPP from 2021 to 2100 across four SSP scenarios using a CNN model to identify significant relationships between environmental factors and GPP. However, challenges remain in understanding the biochemical mechanisms through which environmental changes impact vegetation growth. Nutrient availability is crucial for vegetation growth but has not been considered in the development of more effective ML models. The accuracy of GPP forecasts is largely dependent on the quality of the input data, with potential discrepancies arising from the GPP NIRv dataset, which relies on satellite-based near-infrared reflectance and may not accurately capture the historical GPP. Additionally, the inherent uncertainty in future climate projections complicates GPP predictions, as even advanced ESMs struggle to accurately simulate future climatic conditions. The vegetation dynamics in the region are likely influenced by a combination of climatic and human factors. Human activities, including afforestation and ecological restoration, can enhance GPP and improve vegetation's carbon sequestration capacity, contributing to the goals of \"carbon peaking\" and \"carbon neutrality\". Studies suggest that the contributions of climate and human activities to GPP increases are comparable, accounting for 48-56% and 44-52%, respectively [54]. Future research should focus on the interactions between natural processes and anthropogenic influences on vegetation growth on the Tibetan Plateau.  
Integrating physical models with ML approaches to simulate GPP presents a promising avenue to enhance the predictive accuracy and understanding of vegetation dynamics. Physical models, grounded in ecological and biological principles, provide a mechanistic understanding of the processes influencing GPP, including photosynthesis, respiration, and nutrient cycling. On the other hand, ML models excel in capturing complex, non-linear relationships within large datasets, making them highly effective in identifying patterns and trends that may not be apparent through traditional modeling approaches. By combining these two methodologies as a physics-informed ML model [55; 56], one can leverage the strengths of each, using physical models to inform the structure and parameters of ML algorithms, while employing ML to refine and improve the physical model predictions based on empirical data. This hybrid approach can also enhance the ability to incorporate diverse data sources, including remote sensing, ground-based measurements, and historical climate records, thereby enhancing the predictive power of GPP simulations. Additionally, integrating these methodologies may facilitate a more comprehensive understanding of the interactions between environmental factors, vegetation dynamics, and anthropogenic influences. Future studies should explore specific frameworks and case studies that exemplify this integration, ultimately contributing to more robust and adaptable models for the prediction of GPP in diverse ecological conditions.
# Building Information Modelling and Energy Simulation for Architecture Design
## 3 From BIM to BEM: Advantages and Limitations  
The study and the implementation of methods to transform a \"BIM\" to a \"BEM\" are a topic always more common in the scientific community. Indeed, BEM has a large number of applications in the most varied cases of energy analysis. Moreover, as said, the available tools have limitations (sometimes highly restrictive). For this reason, technicians are not encouraged to use them. The main identified problem is the transmission of data between the BIM modelling tools and the energy simulation tools. It limits the possibility to operate with the least possible number of digital models.  
The main formats are:  
* The IFC is the standard format for exchanging information in BIM modelling. It is the only format to have a certification. By using it, it is possible to upload most of the information relating to a building with the exception of data relating to energy analysis (such as occupancy profiles, data relating to external and internal temperatures, systems, etc.);
* the gbXML is a format based on the IFC; it contains all the energy information. It was developed specifically to operate in this field and it is the most popular among energy analysis software. Indeed, some software only allows the import of this format to the detriment of IFC (IES-VE, EnergyPlus, eQUEST).  
It is therefore understood that, based on the software used, working times can increase or be reduced to a minimum. It is possible that, once the BIM model of a building is completed, it is exported to software for energy analysis. Sometimes, it happens that it does not interpret the geometries well or even fails to import a lot of information previously entered, effectively making the work already done useless. In this case, the technician has to manually enter each missing item or, in the worst case, he/she has to build a model specifically for the energy analysis. This procedure can be more or less long depending on the building under consideration. This certainly differs from the BIM methodology aim. Indeed, it provides for a faster and interoperable workflow by means of a single model. The most commonly used choice in the architectural-energy field is to proceed with the architectural design and postpone the energy analysis at the end with a separate and specific calculation. Often this process leads to a meaningless analysis as this is ascertained, which was designed with no room for improvement [26]. Obviously, the process to be adopted should be the opposite: Support the design with energy analysis from the early stages (which by its nature will be a summary, as you will not yet have all the parameters to be able to make a more complex analysis) and direct the project towards a more eco-sustainable way. With this in mind, obviously, a real transition from BIM to BEM is required, instantaneous and managed by a single program. It is based on the digital model that is being built. Consequently, the BEM should be automatically updated. This procedure should reduce the time, the energy spent to work, and should allow a more energy-saving design.
###### Abstract  
In this paper, an optimal direct instantaneous torque control scheme using terminal sliding mode control is proposed to achieve better control performance for switched reluctance motors. To obtain faster convergence speed and better chattering suppression, the terminal sliding mode speed controller based on an advanced reaching law is proposed. Moreover, the hybrid wolf optimization algorithm is employed to tune the adjustable parameters for optimum performance. The experimental results show that the torque ripple range of the proposed scheme can be reduced by 25% and 17% under 0.3 Nm compared with PI and conventional sliding mode controllers. Meanwhile, the transient response can be improved by 50% and 31% in the initial stage, respectively. The proposed controller can perform well in aspects of speed tracking, torque ripple suppression, dynamic performance, and anti-interference capability of the system, which was validated by experimental results under different conditions.  
switched reluctance motors; direct instantaneous torque control; sliding mode control; parameter optimization
###### Abstract  
The multimode underwater acoustic communication network is a novel form of underwater acoustic communication that adjusts its communication mode to enhance overall performance. Current performance analysis methods are primarily applied to single-mode networks and assume uniform communication capability across all nodes, making them unsuitable for multimode networks. This paper investigates the multimode communication of the physical layer, considering factors such as the marine environment, the node transmitting sound source level, and the transmitting distance. A decoding conflict model is proposed to support multimode concurrent transmission scenarios. The communication mode is designed to be compatible with the channel and node characteristics. Additionally, using a Markov decision process, this paper establishes a performance evaluation and analysis model for multimode underwater acoustic networks to determine throughput performance limits in real underwater environments. Simulations across various scenarios validate that the throughput performance limits obtained by this method are more accurate under multimode networks, with an improvement in accuracy of over 67.5% compared to existing methods.  
Markov decision process; multimode underwater acoustic network; network throughput performance