esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Selection of Evaluation Methods  
ESV is a prevalent issue, and many different assessment methods have been created, making it a valuable tool for land-use planning and evaluation [50; 51]. Different ESV evaluation models have different parametric needs. Different factors, such as market price, value coefficient, and land-use data, will affect the ESV and its accuracy [52; 53]. Considering the above facts, in our research, we chose the equivalent factor method based on [PERSON] and [PERSON] et al. [3; 20], which is widely used in China. The equivalent factor method evaluates the ecosystem service function value of the major crops in the study region based on land-use data, requiring fewer data and a shorter evaluation time. Although the equivalent factor method cannot account for spatial heterogeneity, and coefficient selection is subjective, we can alter the coefficient depending on the current circumstances in the research region to improve the accuracy of the results to some extent [54; 55]. The correction of the coefficient is affected by factors such as grain prices and the GDP in different years in the region. The average value of grain prices in the study period was used in this paper, and a more reasonable and accurate method is an aspect which could benefit from further improvement.  
The province of Shaanxi is in northern and western China. The vegetation varieties in northern and southern Shaanxi, which are influenced by geography and climate, are significantly varied, as are the land-use patterns. Most existing research [27; 37; 56] has focused on determining the ESV in northern Shaanxi, but in this work, we used the same index method across the entire province of Shaanxi. The evaluation process is characterized by certain inaccuracies, which is a concern that should be addressed in the future. The ecological environment is a complex, diverse, and ambiguous system. The evaluation method for ESV is neither scientific nor complete, and the results are questionable. More scientific and perfect evaluation systems should be investigated in the future.
## 5 Discussion
### Innovation, Limitations, and Further Research  
In this paper, the performance of the coupled model formed by standard artificial intelligence models, pre-processing techniques, and the quantile mapping post-processing method was evaluated. The QM-EMD-Elman model for monthly runoff prediction in the karst areas was constructed. The EMD pre-processing technique can be used to generate a smooth series, which effectively reduces the effects of non-linearity and non-stationarity in the original series. Additionally, the QM post-processing method can be used to remove the negative values from the prediction results, which can effectively control the output errors. The proposed QM-EMD-Elman model was more effective in capturing the runoff influence patterns, and can be used for monthly runoff predictions in karst areas.  
The restricted information limits the research process, and the study of runoff prediction can be further improved in the following aspects in future research.  
Due to the data, the conclusions and results of this study have limited portability and may not be directly transferred to other watersheds.  
The pre-processing technique used in the study divides the data into high-frequency components, low-frequency components, and trend terms by frequency. Then the different components were uniformly studied for prediction using the same model, and the results were summed afterward [53]. In future studies, different models can be used to predict different frequency components to find the best model for each component.  
Changes in runoff have become more complex due to the effects of climate change and human activities. Nevertheless, artificial intelligence models cannot adequately consider these two factors to predict runoff. Therefore, there is a need to further study how models can fully integrate factors such as climate and human activity.
# Reliability Enhancement Driven by ANN for Lighting Control System in Highway Tunnels
## 5 Model Training of Tunnel Lighting ANN
### Process of Model Training
#### 5.2.2 Simulation of inside color temperature  
This simulation result in Figure 13 shows the following:  
1. Actual inside color temperature of the threshold zone is 100% consistent with the prediction of the training model, that is, the color temperature classifier has a 100% recognition rate.
2. In this variable color temperature lighting regulation strategy, the color temperature of the threshold zone only switches among limited enumerated values (3500 K and  
Figure 12: Simulation result for inside luminance.  
6000 K). If the actual color temperature needs to be changed (for instance, from 3500 K to 4000 K), the corresponding network model can be trained and switched instantly.
3. With the co-training process of color temperature recognition as a discrete parameter and luminance fitting as a continuous parameter, color temperature classification recognition and luminance fitting maintain relative independence and have no adverse impact on each other.
4. Color temperature transitions twice between 6000 K and 3500 K, 3500 K lasts roughly for the midday duration. During this time window, drivers are prone to struggle with visual fatigue; this transition of color temperature between the threshold zone and open space greatly helps to stimulate drivers' excitement and make them concentrate on driving carefully, keeping the driving process safe and smooth in the tunnel area.
## Abbreviations  
\begin{tabular}{l l} \(q\) & Lagrangian coordinates \\ \(t\) & Translation vector \\ \(\theta\) & Unit quaternion vector \\ \(R\) & Rotation matrix \\ \(\overline{n}\) & Local position vector \\ \(r\), \(v\), \(a\) & Position, velocity and acceleration vector in the ground reference frame \\ \(n_{B}\), \(n_{dof}\), \(n_{c}\) & Number of bodies, degrees of freedom and constraints \\ \(C\) & Constraint equations \\ \(C_{q}\) & Constraint (Jacobian) \\ \(\vartheta\) & Rotation axis unit vector \\ \(q_{dof}\) & Degrees of freedom vector \\ \(M\) & Mass matrix \\ \(Q\) & Generalised force vector \\ \(\Lambda\) & Lagrange multipliers \\ \(I_{q}\) & Coordinate change matrix \\ \(l_{t}\) & Tendon length \\ \(l_{m}\) & Muscle fibre length \\ \(v_{m}\) & Muscle fibre deformation velocity \\ \(w_{m}\) & Muscle width \\ \(a_{p}\) & Muscle fibre pennation angle \\ \(F_{CE}\) & Muscle contractile element force \\ \(F_{PE}\) & Muscle passive element force \\ \(F_{m}\) & Muscle force \\ \(l_{0}\), \(\alpha_{0}\) & Optimal muscle fibre length and the related pennation angle \\ \(F_{0}\) & Maximum isometric force \\ \(l_{mt}\), \(v_{mt}\) & Musculotendon length and deformation velocity \\ \(\mathbf{x}(u,v)\), \(f(\mathbf{x})\) & Surfaces' parametric and implicit equations \\ \(s\) & Curvilinear coordinate \\ \(c\) & Curve \\ \(\hat{t}\), \(\hat{n}\), \(\hat{b}\) & Tangent, normal and binormal unit vectors \\ \(\mathbf{\Phi}_{q}\) & Muscle (Jacobian) \\ \(a\) & Muscle activation vector \\ \(\Lambda_{r}\), \(\Lambda_{s}\) & Lagrange multipliers related to the rheonomic constraints (or residual actuators) and to the scleronomic constraints (or joint reactions) \\ \end{tabular}
# Fusion of Telecommunications and IT Services Boosted by Application Programming Interfaces
## 4 Fusion of Telco and IT Services
### Nokia TAS Open APIs  
A robust, easy to use set of APIs is the key to unlocking innovation and exciting developers. Nokia's API-enabled 5G core product functions can be exposed securely to the public domain over the Nokia Network Exposure Function (NEF), which also acts as the API Gateway. The Nokia Telephony Application Server (NTAS) provides OMA-based RESTful Network Open APIs as future-proof functionality. The API-based solution of Nokia TAS can be seen in Figure 6.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Telco and IT Domains** & **References** \\ \hline
2G, GSM & [2; 3; 6; 7; 8; 10; 11; 13; 33; 34; 35; 45; 46; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 8  
The API functionality is enabled for 2G, 3G, 4G, 5G, VoWiFi, and fixed networks; hence, it is seamlessly available for operators with Fixed-Mobile Convergence efforts. It is also the key enabler of the Telco-IT fusion, as can be seen in Figure 7.  
Benefits of the API Framework  
With their solution principles, the API-based services provide benefits, such as the following:  
* Faster time to market is possible for complex services compared to the traditional approaches; quick development is feasible with a large third-party developer base, using feature-rich APIs and libraries (they also unlock the Internet of Things and social media);
* Opening the IT domain for Telco can allow IMS services to become more sophisticated. The services are delivered with orchestrated service execution in a cloud-native fashion; serverless technologies and service mesh mode are also feasible;
* The 3 GPP IMS AS interworking is ensured on Southbound Interfaces;
* A rich catalog of Open and RESTful Northbound Interfaces is available. For instance, the Nokia TAS has detailed Open API Swagger Documentation for third-party developers. Swagger is also used by 3 GPP to describe the 5G interfaces. The Swagger definition text file describes the complete interface. Both sides of the interfaces can be tested easily based on the Swagger definition. Public testing tools for functional and load tests are available, and it enables collaborative API design (via GitHub). Automatic documentation and code generation are also possible. All in all, this API yields lower efforts for the operators and Nokia as well;
* The OpEx saving is also important with software reuse, including lower maintenance costs [17].  
Figure 6: Application programming interface-based solution in Nokia Telephony Application Server.  
Figure 7: Fusion of IT and telco worlds.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 2 Technologies for LV Smart Metering--Background Information
### Wired Technologies
#### 2.1.4 Fiber Optic Communications  
Fiber optics provide a high-capacity, high-speed data exchange transmission media that is widely used in backbone transmission networks. At present time the high installation costs associated to its deployment makes it only a suitable solution _in situ_ ations where high data transmission rates are required [26].
## 5 Performance Evaluation  
To reduce the variability of the prediction performance, a cross-validation test is usually used to evaluate the performance of the proposed system. It is one of the most popular methods to evaluate a model's prediction performance. If a model was trained and tested on the same data, it would easily lead to an overoptimistic result. Therefore, the better approach, the holdout method, is to split the training data into disjoint subsets.  
As it is a single train-and-test method, the error rate we got resulted from an 'unfortunate' split. Moreover, in some cases of a lack of samples, we cannot afford the luxury of setting. The drawbacks of the holdout can be overcome with a family of resampling methods, called cross-validation. Two well-known kinds of cross-validation are leave-one-out cross-validation (LOOCV) and \(k\)-fold cross-validation.  
In \(k\)-fold cross-validation, the total samples are randomly partitioned into \(k\) groups, which have the same size. Of the \(k\) groups, one group is for testing the model, while the remaining (\(k-1\)) groups are used as training data. This process is repeated \(k\) times (the folds) until all groups are tested. Then, the results from the \(k\) experiments can be averaged to produce a single estimation. Thus, the true accuracy is estimated as the average accuracy rate  
\[Acc=\frac{1}{k}\sum_{i=1}^{k}Acc_{i}. \tag{42}\]  
The advantage of this method is that all samples are used for both training and validation, and each observation is used for validation only one time. Although 10-fold and 5-fold cross-validation are commonly used, in general \(k\) is an unfixed parameter.  
Leave-one-out cross-validation could be considered a degenerate case of \(k\)-fold cross-validation where \(k\) is the total number of samples. Consequently, for a data set with \(N\) samples, LOOCV performs \(N\) experiments. In each experiment, only one sample is used for testing, while \(N\)-1 samples left are for the training process.  
In this research, we use 10-fold cross-validation for performance evaluation. The classification results in four kinds of value: true positive (TP), true negative (TN), false positive (FP), and false negative (FN) (shown in Table 3).  
Where 'true' and 'false' are intended for result correction, while 'positive' or 'negative' signifies the tumor is either HCC or liver abscess. Based on the information, we calculate the accuracy factors as in Equation (43), which describes the performance of classifiers  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}}. \tag{43}\]
# Smart Clothing Framework for Health Monitoring Applications
## 3 Proposed Framework for Smart Clothing (Leggings)  
This section offers a proposed framework for developing smart clothing with a specific example of leggings for health and fitness monitoring. The framework shows in detail the development of the smart clothing and how it can be used for health-monitoring purposes. Application scenarios, design requirements, textile materials, and sensor materials, including their integration process, are described elaborately. In addition, the sensing mechanism, data collection (bio signals) and processing, data analysis and decision-making process for monitoring health securely are presented.  
Current smart clothes are not free from shortcomings, such as potential discomfort from electronic hardware integrated with fabric, washability, durability and proneness to motion issues, resulting in a lack of accuracy in sensing and tracking human health condition and fitness. Several frameworks for smart clothing have been proposed in the literature to monitor and track health conditions. These frameworks focus on either smart clothing sensors and textile materials, mechanism and integration [2; 71; 72; 73] or data collection techniques, analysis methods, cloud services and displaying the decision [32; 36; 74; 75; 76] separately. However, no integrated complete framework for smart clothing is available. In this paper, a framework has been proposed for developing smart leggings in Figure 8 by combining the design criteria, textile materials, sensor materials, sensor integration with textile and decision support system. Further details of the proposed framework are presented in the following sections.
## 1 Introduction
#### Projectile Penetration Process Parameters  
The penetration process of projectile and soil target are classified into three groups, namely, non-penetrating projectile (impact of soft projectile into solid rocks), penetrating projectile (rigid projectile penetration into the softer soil by shearing, pulverizing, and compressing), and impact in water (penetration in highly saturated alluvial soils under effect of incompressibility of soil and the viscosity of water). The impacting objects on target media may be of free falling or rigid projectiles type. Target media may be naturally occurring media such as a geological shield or manufactured media such as concrete/metal shield. The static and kinematic properties of projectiles are characterized by the shape of tip, diameter of shank (D), and length (L) and velocity (v) of projectiles, which is classified as hypervelocity (v \(>\) 3 km/s), ultra-ordnance (1.5 \(<\) v \(<\) 3 km/s), ordnance (500 \(<\) v \(<\) 1500 m/s), and sub-ordnance (v \(<\) 500 m/s). They also defined the target materials and thicknesses of the target (h) as thin (h/D \(<\) 1), intermediate (1 \(<\) h/D \(<\) 10), and semi-infinite (h/D \(>\) 10) [13]. The mechanisms of penetration resistance are dependent on interactive characteristics of impacting objects and target media such as particle size of soil media [24], bulk packing fraction [25], penetration depth [17], intruder shape [2], and penetration velocity [26]. An enormous number of studies on projectile penetration area have been published in last 50 years dealing with different process parameters such as different target materials, various geometry and velocity of impacting objects, and different investigation procedures. Though many researchers have devoted their work to summarize the projectile penetration studies in general, dedicated reviews of recent publication dealing with projectile penetration into soil media are scarce. An extensive and dedicated literature survey for the response of soils media to penetration, categorizing the applied methodologies into the analytical, computational, and experimental methods, have been presented in the following paragraph. This review studies would provide updates on current and future research directions in the field of soil media penetration.
# Thermal Environment Prediction for Metro Stations Based on an RVFL Neural Network
## 3 Measurement Process
### Results and Analysis  
#### 3.4.1 Effects of Training Parameters  
With the RVFLNN, the predicted temperatures were made closer to the experimental temperatures by adjusting the number of hidden layer nodes, \(L\), and the regularization coefficient, \(\lambda\). In this paper, the model accuracy was evaluated using the average relative error, \(E\). This can be calculated with the following equation:  
\[E=\frac{1}{N}\sum_{i=1}^{N}\left|\frac{T_{\text{sim},i}-T_{\text{exp},i}}{T_{ \text{exp},i}}\right| \tag{10}\]where \(T_{exp,i}\) and \(T_{sim,i}\) are the experimental data and prediction data, respectively; and \(N\) is the total number of sampling points. Table 1 shows the error versus the number of hidden layer nodes from 20 to 1000.  
\(E_{\text{train}}\) and \(E_{\text{validation}}\) are the average relative errors of the training data and the validation data. After the initialization of RVFLNN parameters, the number of hidden layer nodes was adjusted. It can be seen from Table 1 that when the number of hidden layer nodes gradually increases from 20 to 1000, the training error continuously decreases. However, when the hidden layer nodes reach about 800, 200, and 100 for model S2, S3, and S4, respectively, the test error begins to increase or fluctuate. Therefore, we can draw a conclusion that the generalization effect of RVFLNN will be good when the respective number of hidden layer nodes for the three different RVFLNN models is 800, 200, and 100. Thus, we determined the number of hidden layer nodes.  
For the RVFLNN, after determining the hidden layer nodes, we adjusted the regularization coefficients and the ranges of \(w\) and \(b\). Taking the RVFLNN model S3 as an example, we can observe from Table 2 that no matter what the range of \(w\) and \(b\) is, the validation error will reach the minimum when the regularization coefficient is about 0.5 or 1. Moreover, the range of \(w\) and \(b\) has no significant effect on the minimum error. In conclusion, the system performance will be better when the regularization coefficient is finally determined as 1 and the range of \(w\) and \(b\) is [\(-\)1, 1].
# The Campus as a Smart City: University of Malaga Environmental, Learning, and Research Approaches
## 3 UMA IoT and Telecommunication Infrastructure
### Smart Meters and Irrigation Control System  
UMA Smart-Campus deployments initiated with a limited set of smart meters, smart sensing, and actuation boxes measuring multiple magnitudes related to the management and maintenance of the Smart-Campus' electricity, water consumption, well water extraction, fire sensors, and irrigation.  
These devices were equipped with a heterogeneous set of radio access and wired technologies (Wi-Fi, Zigbee, and Ethernet). They were distributed throughout all the campus, each with their own management platform and, in the case of Zigbee, dedicated radio GWs. The data were accessible across a deployment-specific OAM platform, where the information was available in real-time and stored for further processing and analysis.  
In addition to these meters, an irrigation control system was implemented. This consisted of multiple stations deployed widely through the campus and equipped with weather and soil-related sensors and caudal control actuators. Data from such sensors allowed optimized irrigation based on measured humidity, temperature, or other physicochemical parameters of the ground. Additionally, it provided an indication of when it was suitable to irrigate the remaining areas not equipped with IoT nodes (based on the common parameters of weather and type of soil), how much water was required, or if plants needed additional care. Depending on their location, the irrigation stations were connected to the general network by different RATs, such as Wi-Fi, cellular (LTE), and/or a low-power wide-area wireless (LPWA) proprietary technology. These stations--also managed by their own dedicated platform--helped to improve the vegetation care and their sustainability, and supported turning most green spaces of the campus into a Living-Lab where students and researchers could investigate vegetation-environment relationships.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
\begin{tabular}{l} LoRaWAN \\ \end{tabular} \\ \hline \hline \end{tabular}  
* Long-Term Evolution (LTE), UMA Heterogeneous Network (UMAHetNet), Radio Access Technology (RAT), evolved packet core (EPC), low-power wide-area wireless (LPWA), Measuring Mobile Broadband Networks in Europe (MONROE).  
\end{table}
Table 2: Internet of Things (IoT) main elements and communication technologies.
## 2 Finite Element Analysis for Bridge Structures with Damage and Deterioration
### Detecting Slab Deterioration Using Vibration-Based MEH  
It is observed in Figure 11 that the generated power increases at the deteriorated panels and decreases at the other five panels for nearly all of the cases in three modes of vibration. However, the generated power at the observation points depends on material and structural conditions; hence, it is almost impossible to detect the most deteriorated panel by a direct comparison between the generated powers on different panels. In the case that all panels are intact, their generated powers shall keep in a ratio as presented for Case 0 in Figure 11. Herein, a power generation index \(I_{j}\) (\(j=1\) to 6)  
Figure 11: Output power generated by MEH (Slab model).  
is proposed as follows for the \(j\)-th test panel to normalize this location-dependent factor, so as to fairly evaluate the health state of panels at any location.  
\[I_{j}=\frac{\sum_{i=1}^{6}\frac{P_{j}}{P_{j}}}{6}\text{ for }j\ =\ 1,\ 2,\ 3,\ 4,\ 5,\ 6 \tag{6}\]  
where \(P_{i}\) is the power generated from Panel \(i\) in the intact case (Case 0) and \(P_{j}\) the power generated from Panel \(j\) in the test case (could be intact or damaged). Generally, all modes could be taken to calculate \(e_{j}\) and to identify damage. Herein, the 3 rd mode was selected for its larger power being generated and its outcome is illustrated in Figure 12.  
Table 5 shows the power generation indices and the modified power generation for Cases 1, 2, and 3, where Panels #1, #2, and #3 are deteriorated, respectively. The results display that all deteriorated panels are pointed out with larger modified power generation, qualifying our proposed index.