esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Review of Coefficient of Variation for Resistance and for Loads
### Permanent Loads  
The permanent loads acting on a structure consists of the self-weight of structural and nonstructural members. The name permanent load indicates that the load is varying insignificantly over time; however, its intensity is uncertain. Following JCSS [8], the uncertainty associated with the self-weight of steel components is predominantly due to the uncertainty in the cross-sectional area. Both the density and the length dimensions of structural members made of steel may be assumed to be deterministic. For structural components made of concrete, the uncertainty associated with the density is dominating, while geometry should play a minor role. The self-weight of nonstructural components may be made of a material that is different to the structural components. Hence, the uncertainty associated with the permanent load of steel structures could increase due to possible nonstructural member weight contributions, and vice versa in the case of concrete structural components. Due to these different effects, a coefficient of variation for the total permanent load equal to 0.10 is proposed in the literature [8; 9]. Because individual permanent loadings are additive, the variability of the total permanent load is less than that of the individual items; it also suggests that the central limit theorem applies. Hence, permanent loads are commonly assumed to be closely approximated by the normal distribution, typically with a mean equal to the nominal load, and a COV of 0.05-0.10. Nominal loads can be simply defined as volume, with dimensions as shown on the plans, multiplied by density. Variability in the total permanent load often appears to be due mainly to non-structural components rather than the variability of the load-bearing materials themselves [12]. [PERSON] [16] suggests the COV for permanent loads for bridges in the range of 0.08-0.1. Further, it was suggested in [16] that because of the different degrees of variation in different structural and nonstructural elements, it is convenient to break up the total dead loads into two components: the weight of factory-made elements (steel, precast concrete members) and the weight of cast-in-place concrete members. In addition, for bridges, a third component of the dead load is the weight of the wearing surface (asphalt).
## 2 Materials and Methods
### Experimental Data and Remote Sensing Imagery
#### 2.2.2 Water Quality Data  
Water quality was represented by the TSS concentration that was measured by the weighing method according to the Chinese national standard of GB11901-89. The TSS concentrations were produced by dividing the mass of the TSS by the volume of the filtered water sample, among which the quality of suspended matter was obtained by placing the filtered sample in an oven at 40-80 \({}^{\circ}\)C for 8 h. It is worth noting that when we calculated the volume of the water sample, the water sample filtered by a GF/F filter with a pore size of 0.47 \(\upmu\)m was used.  
Following the characteristics of the TSS in the reservoir area and actual needs, and referring to the Chinese Standards for Pollutants in Municipal Wastewater Treatment Plants (GB18918-2002), the concentration of TSS is divided into four levels for analysis as follows (Equation (2)).  
\[\text{TSS}(\text{mg}\text{L})\left\{\begin{array}{ll}\text{I}&\quad 0\leq \text{TSS}<20\\ \text{II}&\quad 20\leq\text{TSS}<30\\ \text{III}&\quad 30\leq\text{TSS}<50\\ \text{IV}&\quad\text{TSS}\geq 50\end{array}\right. \tag{2}\]
## 3 Results and Discussion
### Calibrations  
The calibration regression equation for the antero-posterior and medium-lateral axis of \(300\) mm \(\times\) 250 mm and \(600\)\(\times\) 300 mm force plates is depicted in Figure 5a-d. Results evidenced the previously noticed linearity (\(R^{2}\) ranging between 0.97 and 0.99) and non-meaningful cross-talk  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strain Gauges** & **Variable** & \(\mathbf{300\times 250}\) **mm (\(\mu\varepsilon\))** & \(\mathbf{600\times 300}\) **mm (\(\mu\varepsilon\))** \\ \hline
1 & Lateral moment & \(-\)4.54 & 78.13 \\
2 & Vertical force & 84.66 & 301.17 \\
3 & Lateral moment & \(-\)23.73 & 157.09 \\
4 & Lateral moment & \(-\)149.79 & 108.67 \\
5  
between orthogonal axes (small and negligible; \(<\)5%) when quantifying any couple of force plate output signals (cf. [20; 21]). Calibration results of 300 mm \(\times\) 250 mm force plates are depicted considering the forces applied on handgrips positioning previously simulated.  
Figure 5: _Cont._  
Figure 6 presents vertical force, antero-posterior and lateral moment calibration (in normalised strain measure) for the underwater 600 mm \(\times\) 300 mm force plate, evidencing linearization and cross-talk \(\sim\)5% between orthogonal sensors (cf. [18; 20]). The force plate design used has revealed greater sensibility in responses to the vertical loads, corroborating Lywood [21].  
Figure 5: Force plates calibration results: 300 mm \(\times\) 250 mm force plate antero-posterior force (**a** panel), medium-lateral force (**b** panel), 600 mm \(\times\) 300 mm force plate antero-posterior force (**c** panel), and medium-lateral force (**d** panel).  
Repeatability for loads above 300 N and below 40 N has revealed within 5% and 10-15% with a 95% confidence level. The \(\overset{\rightarrow}{COP}\) locus was more uncertain farther from the platform centre with a reasonable radial around centre dependency.
## 1 Introduction
This paper presents the multi-agent platform HADES, developed in collaboration with the Cartagena Port Authority, in Spain. HADES has two objectives: (i) to foster coordination among the different port agents and terminals and (ii) to solve the BAP problem in multi-client liquid bulk terminals. HADES adds to the literature about BAP problems by focusing on multi-client liquid bulk terminals, making coordination among the different port agents easier to achieve and providing improvements to the value chain associated with trade, which results in more efficient, sustainable, and competitive logistic chains. Specifically, HADES is a software tool for the optimal operation of liquid bulk terminal management developed by E-lighthouse. This tool is based on an optimization model for the allocation of liquid bulk berths to reduce congestion and vessel waiting time. Based on information technologies, HADES facilitates coordination among the different port agents. It allows the three main agents of the logistic-port community: terminal operators/owners, shipping agents, and port authorities to communicate. By means of a user-friendly interface, all the agents involved in the process share the data relevant to optimal quay management, supervised by port authorities. This project has been promoted by the Cartagena Port Authority. Since 2016, an increase in delays at numerous liquid bulk berths (for petroleum, petroleum products, oil, and chemical products, among others) in the Escombreras Basin has been detected. These delays have increased the number of vessels having to anchor as well as their anchoring time due to congestion. In fact, an analysis of the multi-client E010 dock, with data from January 2010 to August 2020, estimates that anchoring congestion was the reason for an annual average anchoring time of 6.3 h per call (over an average call time of 45.28 h), with an estimated cost of 500,000 EUR per year in ship freight. The document is organized as follows. In Section 2 a literature review of temporal coordination procedures and BAP models is described. Section 3 presents the HADES tool. Section 4 provides the algorithm applied to solve the berth allocation problem, considering the restrictions related to liquid bulk. The real application to the liquid bulk terminal in the Port of Cartagena (Spain) is shown in Section 5. Finally, in Section 6, conclusions are drawn, together with a discussion about the future research lines of HADES.
# Demand-Response Control in Smart Grids
## 4 Comparison between Centralized and Distributed Control for Demand Response
### Centralized Control for Demand Response  
Centralized control is a method of demand response that uses a centralized system to manage energy usage. This system is typically operated by a utility or other energy provider, and it allows the provider to monitor and adjust energy usage to meet demand.  
The system can be used to reduce peak demand, reduce overall energy consumption, or both. It can also be used to provide incentives for customers to reduce their energy use during peak times.  
In a centralized solution, the DR procedure is controlled by a central controller, who collects request data from customers and then decides for the demand schedule.  
The provider agent updates its energy supply strategies in each step according to the consumer agents' electricity consumption approaches, while the consumer agent \(i\) regulates its power consumption in a function of real-time costs. We present the common model for centralized control in Algorithm 1 [28].  
```
1: Initialization.
2: For each time \(t=1\), \(t<=24\); \(t++\) do:
3: Collect the initial requested power (\(\pi_{i,t}^{\min}\), \(\pi_{i,t}\), \(\pi_{i,t}^{\max}\)) from every consumer agent \(i\in N\).
4: Update the total load \(\pi_{t}\) accordingly.
5: Compute the new price, \(\lambda_{t}\)
6: Broadcast the new price, \(\lambda_{t}\), to all the consumer agents.
7: Receive the updated requested power (\(\pi_{i,t}^{\min}\), \(\pi_{i,t}\)) from each consumer agent \(i\in N\).
8: End for.
9: End algorithm.
```  
**Algorithm 1**Executed by the Provider Agent.  
Running Example  
In Figure 2, we provide simulation data and evaluate the algorithm's performance. In our simulation model, the consumers' number is assumed to be 3. To cover the full-time cycle, the 24 hours of the day are represented by 24 time slots. In each time slot, the minimum and maximum required power of all users vary, and the minimum generating capacity is guaranteed to meet the minimum power requirements. Figure 2 shows the simulation results of the power consumed by each one of the three consumers based on the demand-response algorithm.  
Figure 3 depicts the simulation results for the whole power allocated to all the consumers. As expected, both curves relative to the overall energy consumption of the users and the targeted production capacity of the power supplier coincide because of the live interaction between the consumers and the energy provider. The overall power consumption and generating capacity are limited by all users' minimum and maximum overall energy needs within each time interval.  
Figure 2: The total power consumed with the applied demand-response algorithm.
# Full-Field Strain Determination for Additively Manufactured Parts Using Radial Basis Functions
## 3 Evaluation of Strain Determination Using RBFs
### Real Application Example of a Tube under Tension and Torsion  
In our final example, we performed tension and torsion experiments at real tube-like specimens. The specimens were produced by a WAAM-process using the gas metal arc welding material EN ISO 14341-A G 50 7 M21 4 Mo (C 0.09, Mn 1.95, Si 0.7, Mo 0.5), see Figure 5 (left).  
The tube-like specimens are welded onto round solid sections that act as specimen holders (diameter 32 mm, possible clamping length \(L_{C}=55\) mm). Two specimens remain in their original shape and two specimens are machined off (outer diameter 27.2 mm) in order to obtain a smooth surface on the outside, see Figure 5 (right). It is not possible to reach the inside to machine off the inner surface. Thus, the tubes are still rough on the inside. The length between the full material clamping region is \(L_{0}\approx 82\) mm. The machine's traverse displacement is \(\overline{u}(t)=K_{U}t\), \(K_{U}=0.01\) mm s\({}^{-1}\), for tension, and a torsional angle of \(\overline{\theta}(t)=K_{\theta}t\), \(K_{\theta}=0.01\) \({}^{\circ}\)s\({}^{-1}\), for torsion. The 3D-DIC-system ARAMIS 12M (which uses the software ARAMIS Professional 2020 from GOM GmbH, Brunswick, Germany) is chosen to identify the 3D coordinates of the data points in the reference and all current configurations, to ensure that the new strain determination tools can be used. These data points no longer have equidistant spacing. Depending on the number of pixels (px) to build up the facet data in ARAMIS, the results can differ. In the case here, 9 px for the facet size and 6 px for the point distance are chosen. Figure 10 (left) shows the evaluation region of the rough surface in the reference state, where the data points were taken, the RBC-concept was applied, and the points were provided to the pre- and post-processor GiD [30]. The evaluation region is approximately \(L_{0}=80\) mm long and has an opening angle of approximately 90\({}^{\circ}\). In the subsequent investigations, inverse multi-quadrics combined with monomials are chosen with a shape factor of \(c_{\text{IMQ}}=0.3\) mm. If one increases the number of pixels, the geometric results change and are smoothed, see Figure 10 (right). Here, the facet size is 19 px and the point distance is 15 px. In the following example, a short discussion of the effects on the strain results is added.
## 4 Case Study
### Data Classification  
As explained in Section 3, training NLU services on custom tasks requires a training dataset of labeled examples. The creation of this dataset, given that requests were not classified, was done manually. In the classification phase, we randomly extracted 1000 requests and showed them one by one to an operator who performed the manual classification using a command line interface. A tuple made by the request and the label is then saved in the dataset. All the sentences extracted from the conversations were anonymized. This process was also made by the operators who performed the classification, in fact when the text shown to the operator contains sensitive data, such as names, addresses, telephone numbers, etc., the operator has to replace this information with aliases which cannot lead to the identification of the user. To replicate a real interaction with a user, no other pre-processing operations (such as removing typos or special characters) were performed.  
During the creation of the dataset, some requests were rejected. The most common cases are requests that were references to emails or phone calls that occurred previously between users and operators; meaningless requests, when the users typed in some text in the chat just to try it. In total, 33.1% of requests were excluded.  
As we show in Figure 3, the examples belonging to the subgroup of the principal intentions are 551. One-hundred eighteen requests were too specific and could not be linked to an intention but, instead, they could only be directly satisfied by the tutors. A small set of requests (37) consists of text with a higher number of characters than the sentence length constraint imposed by the platforms, and that is why these examples were removed from the dataset. Therefore, the actual number of examples that can be used to build the training set and the test set is 514. The selected intentions are: I1, Human Operator; I2, Greetings; I3, Stopping the course; I4, Generic Issue; I5, Everything is ok; I6, Correction; I7, Number of attempts; I8, Certificates; I9, Deadlines. While there are common intentions such as initial and final greetings or simply the confirmation that the issues presented have been solved, some of them are quite specific for our application, such as requests of stopping an e-learning course or asking about the release of the certificate, the correction date, etc.  
Figure 4 shows the distribution of the 514 sentences classified over the intentions selected, which ranges from a minimum of 22 up to 190.  
Figure 3: Requests made to the e-learning platform between 1 January 2018 and 31 December 2019.
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 1 Introduction
### Contributions of This Paper  
Numerous studies in the literature have reviewed energy management strategies for extending the lifespan of WSNs [34; 36; 37; 38]. However, due to the unique topology of LWSNs, the challenges and solutions pertinent to LWSNs differ from those of conventional WSNs [25]. Consequently, strategies effective for conventional WSNs may not be directly applicable to LWSNs. Hence, there is a need to explore energy management strategies specifically tailored to prolong the lifespan of LWSNs. To the best of our knowledge, no existing study has comprehensively evaluated energy management strategies for enhancing the lifespan of LWSNs. This paper aims to address this gap by first providing a comprehensive review of energy management strategies used in traditional WSNs and then evaluating their suitability and impact in LWSNs. It highlights the ineffectiveness of certain energy management techniques designed for traditional WSNs when used in the context of LWSNs.  
The contributions of this paper are threefold:  
1. Novel Classification and Evaluation of Energy Management Techniques for LWSNs: This paper provides a comprehensive taxonomy of energy management techniques specifically tailored to LWSNs. Unlike traditional WSNs, LWSNs possess unique structural and operational challenges, particularly in critical applications such as border surveillance, pipeline monitoring, powerline monitoring, etc. This research is among the first to categorize and evaluate energy conservation strategies, balancing methods, and energy replenishment solutions within this context, providing a roadmap for engineers and researchers working on green LWSN deployments.
2. Significance in Prolonging the LWSN's Lifespan: Extending the operational lifespan of battery-powered LWSNs is crucial due to the high cost and logistical challenges associated with battery replacement in remote or inaccessible areas. This study goes beyond reviewing energy management strategies; it also offers insights into the effectiveness of each technique under the constraints of LWSNs. By identifying the techniques that significantly reduce energy consumption and by evaluating their feasibility in LWSNs, this study contributes essential knowledge that will enhance the reliability and sustainability of such networks.
3. Practical Implications for a Green LWSN Design: The findings of this research contribute directly to the design of eco-friendly LWSNs by minimizing reliance on battery replacements and reducing the environmental impact. This study identifies specific energy management techniques that are both feasible and effective in LWSNs. The proposed taxonomy and analysis offer practical guidelines for network designers to develop energy-efficient solutions in environmentally sensitive and economically challenging regions.
# Is Satellite Ahead of Terrestrial in Deploying NOMA for Massive Machine-Type Communications?
## 3 From S-MIM to F-SIM
### F-SIM Link Layer  
The F-SIM link layer specifies the state machine of the modem. When there are no data to transmit, the terminal transmit chain is completely off, thus saving on power consumption. The terminal logs into the network with a single transmission (logon request) which is valid for hours. The hub continuously transmits signaling information, which is shared by all terminals, in order to support a large number of terminals. F-SIM is designed to transport native Internet Protocol (IP) packets. The link layer on the terminal side  
Figure 3: The uplink burst and its constituent parts.  
Figure 2: F-SIM transmitter functional block diagram.  
encapsulates each IP packet into one or more fragments according to its length. Each fragment is sent separately, with a minimal encapsulation. On the hub side, fragments are reassembled after demodulation, and the resulting IP packet is routed according to normal IP routing policies. The specifications support the deployment of separate IP address spaces, i.e., each customer of the platform can freely use the entire address space, with their own routing rules, without any conflict with other customers.  
Two important tasks are performed by the link layer:  
* Power spreading optimization--The overall throughput of the system is optimized when packets are received at the hub with different power levels, ideally a uniform distribution if expressed in dBm. The algorithms in the F-SIM link layer therefore randomly adjust the outgoing packet power, within the available link margin, to ensure this property [17].
* Congestion control--when the system approaches saturation, signaling information is generated by the hub and used by the terminals to slow down or stop transmission for low-priority services.  
One interesting feature of F-SIM is that different spreading factors are supported for the same channelization, which allows adjusting the uplink speed to the current link conditions. The terminal continuously monitors the forward link received power level, in order to compute the expected return link budget. When a packet is ready to be sent, the most appropriate spreading factor is selected, e.g., maximizing speed while guaranteeing that the link budget closes.  
Spreading factors can also be allocated statically to certain services. In fact, the overall network performance is increased if many terminals use a large spreading factor. Therefore, if the uplink speed is not important for a certain use case, a service could be allocated to a higher spreading factor.
# An Intelligent Wireless System for Field Ecology Monitoring and Forest Fire Warning  
Yili Zheng  
School of Technology, Beijing Forestry University, Beijing 100083, China; [EMAIL_ADDRESS] (Y.Z.); [EMAIL_ADDRESS] (W.L.); [EMAIL_ADDRESS] (S.L.); [PERSON]DRESS] (R.Y.)  
Yandong Zhao  
School of Technology, Beijing Forestry University, Beijing 100083, China; [EMAIL_ADDRESS] (Y.Z.); [EMAIL_ADDRESS] (W.L.); [EMAIL_ADDRESS] (S.L.); [PERSON]DRESS] (R.Y.)  
Yaandong Zhao  
School of Technology, Beijing Forestry University, Beijing 100083, China; yandongzh@bjfu.  
Class 4 consists in building designated ecological monitoring stations in the forest region [14; 15; 16] to automatically measure the ecological microenvironment data of farmland and forests. This method is widely used because of its advantages, such as high accuracy, saving of labor, and capacity to continuously monitor for a long time. Many ground-based ecological monitoring stations are built to collect the visual or infrared images and microclimate data for forest fire warnings [17; 18; 19]. Based on static and dynamic analysis of chromatic changes, histogram changes, or luminance changes in images, forest fires can be detected and classified. Different sensor data are used to reduce or eliminate the false alarm rate. In some remote regions or regions without mobile phone signal, this method cannot achieve an effective real-time transmission of data and images, which becomes a bottleneck for its actual application in monitoring field ecology.  
The Beidou Satellite, developed by China, is a global satellite navigation system capable of providing positioning and navigation, two-way short message communication, and precise timing services at any location and at any time within the coverage area, thus effectively solving the problem of data communication in remote regions and regions with no or weak mobile phone signal [20; 21; 22].  
Based on the communication of China's Beidou Satellite and on GPRS communication, this paper develops a real-time monitoring network system for field ecological parameters and achieves real-time monitoring of various parameters such as atmosphere, soil, light, and plants of the forest as well as remote transmission, cloud storage, and query of data. The system serves the acquisition and analysis of ecological big data in remote regions and regions with strong or weak mobile phone signal.