esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight CoAP-Based Bootstrapping Service for the Internet of Things
## 2 State of the Art
### Bootstrapping in IoT
As a consequence, the solution is devoid of the AAA's architecture scalability. Moreover, it does not use EAP and it only allows one authentication mechanism, relegating the usability to small/medium scale deployments, such as residential networks. The _Open Mobile Alliance_ (OMA) [67] defines a protocol for managing IoT infrastructures called _OMA Lightweight Machine to Machine_ (OMA LWM2M). Among other aspects, bootstrapping is defined using CoAP and DTLS. The architecture consists of three entities, _LWM2M Client, LWM2M Server_ and _Bootstrap Server_. It is specified that the Client and the Server share credentials with the Bootstrap Server in order to authenticate, but it is not specified how the credentials are configured. The _IPSO Alliance_[68] defines the use of CoAP as application protocol, using DTLS to protect the devices with sensible resources such as actuators unless other underlying security mechanism are used. No further considerations are added to the security and bootstrapping landscape. Finally, the _World Wide Web Consortium_ W3C [69] considers the case of security on the smart object bootstrapping outside its scope, proposing the adoption of other security implementations such as the _ARM Trust Zone_[70], although there is a proposal aimed at using of GBA [71]. Thus, these solutions do not support large scale deployments, specially for the lack of identity federation support. However, we envisage the necessity of scalable systems where smart objects from different vendors and organizations can interoperate in large scale scenarios, for example, smart cities.
## 5 Results
### Robustness to Parameter Variation  
Second, the developed control system is tested by applying it to the nonlinear model of the triple inverted pendulum given in Equation (3), with perturbed uncertain parameters, \(I_{1}\), \(I_{2}\), and \(I_{3}\) = nominal value + 10%, \(C_{1}\), \(C_{2}\), \(C_{3}\), \(C_{m1}\), and \(C_{m2}\) = nominal value + 15%. The three angles respond as shown in Figure 11, and the phase-plane trajectories of the angles and their derivatives are shown in Figure 12.  
Figure 10: Nominal system torque: (**a**) first motor; (**b**) second motor.  
Figure 11: Perturbed by parametric uncertainty-system response: (**a**) first link’s angle; (**b**) second link’s angle; (**c**) third link’s angle.  
Figure 12: Perturbed by parametric uncertainty-system phase-plane trajectory: (**a**) (\(\theta_{1}\), \(\dot{\theta}_{1}\)); (**b**) (\(\theta_{2}\), \(\dot{\theta}_{2}\)); (**c**) (\(\theta_{3}\), \(\dot{\theta}_{3}\)).  
The first and third angles overshoot by 1.299 and 22.47 deg, respectively, in the H\({}_{2}\) control system, more than in the hybrid H\({}_{\infty}\)/SMC system. Only the second angle overshoots higher in the hybrid H\({}_{\infty}\)/SMC system, by 1.503 deg. The difference in errors between the two systems are found to be 0.004, 0.05, and 0.027 deg.  
The three phase-plane trajectories clearly depict the fluctuations at the start of the response in the H\({}_{2}\) control system.  
The applied torques are shown in Figure 13. The start-up torques \(t_{m1}\) and \(t_{m2}\) in the H\({}_{2}\) control system are again higher than in the hybrid H\({}_{\infty}\)/SMC system, but by 8.227 and 10.2 N.m, respectively, this time. On the other hand, the differences in steady torques are 0.13 and 0.38 N.m. In this case, the H\({}_{2}\) controller fluctuates significantly at the beginning.
# Dispersion Management and Pulse Characterization of Graphene-Based Soliton Mode-Locked Fiber Lasers
## 1 Introduction
This has led to research progress on tuning the net cavity dispersion for optimizing the output pulse between negative and normal dispersion regime [37], as well as within the stretched-pulse regime [38] in thulium- and holmium-doped fiber lasers, respectively. In this regard, there is still room for knowledge enhancement to further investigate the similar tuning effect in other dynamics of fiber lasers, particularly within the anomalous dispersion regime. We are interested in investigating the dispersion behavior in graphene-based soliton mode-locked erbium-doped fiber lasers (EDFL), and its influence on the pulse performance by tailoring the cavity length. For this purpose, the length of single-mode fibers (SMF) in the cavity is reduced gradually from 4 m to 0 m while keeping other fiber lengths constant. The mode-locked operation is enabled by graphene/PMMA thin film as SA, which is prepared by using a simple transfer procedure of the thin-film on a fiber ferrule. The laser and pulse characteristics are investigated within the net cavity dispersion ranging from \(-0.033\) to \(-0.121\) ps\({}^{2}\), which falls in the anomalous dispersion regime. Pulse duration as short as 710 fs is generated from this proposed laser at the net dispersion closest to zero, while the maximum energy of 599.52 pJ is obtained at the highest value of the anomalous dispersion.
# BPMNE4 IoT: A Framework for Modeling, Executing and Monitoring IoT-Driven Processes +
## 7 End-User Study
### Analysis of the Results
#### 7.4.2 Workload  
After completing the modeling task with either one of the two languages (i.e., BPMN 2.0 vs. BPMNE4 IoT), the participants were asked to fill out the NASA-TLX questionnaire to assess the individual task load perceived by them. Note that this questionnaire captures the perceived mental, physical and temporal demand as well as performance, effort and frustration on a 20-point scale, where the highest scores represent the worst results, i.e., these properties are rated on a scale from very low (0) to very high (20). The performance is rated on a scale ranging from very good (0) to very bad (20) [61]. Table 6 shows the obtained  
Figure 20: Means and standard deviation of task completion time in minutes.  
values (average (Avg), median (Med), standard deviation (SD), best result (Best), and worst result (Worst) columns). Figure 21 compares the TLX means and standard deviation of BPMNE4 IoT and BPMN 2.0.  
Due to the higher complexity of representing the IoT involvement with BPMN 2.0, its limited suitability to represent IoT-specific behavior (e.g., continuous pull interaction), as well as the ambiguity of how to describe IoT aspects in process models, the overall task load and the individual factors are significantly higher for BPMN 2.0 than for BPMNE4 IoT (cf. Table 6). As BPMN 2.0 is a general-purpose language that does not allow for the explicit modeling of IoT aspects, the study participants had to find workarounds to express the IoT devices and the desired process behavior. This task was overwhelming for study participants without experience in IoT-intensive domains (e.g., smart home, smart factory and smart health).  
Although all participants had never applied BPMNE4 IoT before and received only a short training of about 45 min, for the given task, the workload with BPMNE4 IoT was lower than with BPMN 2.0. The better results for BPMNE4 IoT can be explained with the ability of BPMNE4 IoT to represent the IoT specifics (e.g., sensor pull interaction in a loop, asynchronicity and parallelism) and IoT behavior (e.g., IoT-driven business rules). Furthermore, BPMNE4 IoT conforms to the BPMN standard, making the extension intuitive for BPMN process modelers. The results of the NASA-TLX (cf. Table 6 and Figure 21) reinforce these statements and confirm the Hypothesis H2.
## 1 Introduction
In this study, we were able to improve the spectral bandwidth of seismic data from the Inas Field, offshore Malay Basin, by using simple harmonic extrapolation principles, which included a combination of spectral decomposition and seismic inversion fundamentals. The technique applies a sparsity constraint that is unbiased against layer thickness [33]. It thus produces inverted earth models containing reliably thinner layers than would a conventional spare-spike inversion [17]. The bandwidth extension algorithm is fully described and tested in [17] and theoretical aspects of the validity of bandwidth extension are discussed and demonstrated in [19]. This was used to improve the resolution limit of seismic data below the generally accepted tuning thickness, where seismic amplitude and frequency response are more sensitive to thin beds [34; 35; 36; 37]. The resulting broadband seismic data was spectrally decomposed to reveal the geomorphic and geologic features contained in the analysis layer conspicuously hidden by the original bandlimited data. These revealed features aided the interpretation and inference of the paleo-geology of the study area.
## 3 Results
### Diagnosis
#### 3.1.3 Biopsies  
In 2011, [PERSON] et al. [41] compared the performance of ANN, trained with clinical and pathological data from 3025 patients, and Logistic Regression (LR) to predict Trans Rectal Ultrasound (TRUS) biopsy outcomes. No difference was observed between both models (55 vs. 57%, respectively), to discriminate histological groups (LR-ANN respectively): benign outcomes were correctly identified (86-88%) and CS PCa was well classified in 65-66%. However, more recently, an ANN outperformed a model based on LR to predict PCa at biopsies [42]. Using this model, clinicians may avoid biopsies for one in two patients. However, the ANN missed 16% of all PCa of which 6% were clinically significant.  
ANN has also wide applications in Natural-Language Processing (NLP), to extract data from reports. [PERSON] et al. [43] have shown how this technological breakthrough could be useful with pathological reports. In their study, using 100 RARP pathology reports, extraction of pathological or staging information was feasible and reached an accuracy of nearly 100% for all characteristics: histologic subtype (99.0%), perineural invasion (98.9%), TNM stage (98.0%), surgical margin (97.0%), and dominant tumor size (95.7%). The execution time for NLP was under 1 s, while manual reviewers took more than 3 min per report.
# Theoretical Framework for Characterizing Strain-Dependent Dynamic Soil Properties
## 3 Results and Analysis  
### Examples  
Resonant column test (RCT) characterizes the resonance frequency and damping ratio as a function of shear strain. The shear modulus is obtained from the resonance frequency using the characteristic equation derived from the linear vibration of the column-mass system. In fact, the hysteretic nonlinear nature of the vibration problem is ignored on the calculation of the shear modulus. Thus, this study uses damping ratio data to avoid data interpretation errors. The linear damping ratio \(\zeta^{L}\) is experimentally constrained below elastic threshold regime. Meanwhile, the nonlinear damping ratio is defined by using the least-squares method:  
\[E(\alpha)=\text{min}\sum_{i}\left[\zeta_{i}^{Exp}-\zeta_{i}^{Model}(\alpha) \right]^{2} \tag{13}\]  
Figure 2 presents fitted damping ratio and slices of the error surface presenting the inevitability of the hysteretic nonlinear parameter.  
Figure 3 shows the data points and fitted models for damping ratios and the estimated values are tabulated in Table 1. Since, the \(\alpha\) values are on the order of \(10^{2}\) and the maximum shear strain is less than \(10^{-3}\), \(\alpha\gamma\) is at most \(10^{-1}\), which proves the inequality in Equation (6). Higher confinement stress extends the elastic threshold region with lower values. In addition, clay is expected to be lower nonlinear than sand. These results show trend of the \(\alpha\) values, implying that the \(\alpha\) parameter involves  
Figure 2: Determination of the hysteretic nonlinearity parameter \(\alpha\) using the damping ratio measured from resonant column test (RCT) and the proposed model (sand data from 16 \(\sim\) 18 in Table 1): (**a**) Least-squares fitting of the model to measured damping ratio; (**b**) error function (L2 norm).  
physical information about the soil fabric changes. With the fitted parameters, the secant shear modulus data was compared with the proposed model (Figure 4). As the shear strain levels are increased beyond the elastic threshold strain, the conventional method predicted larger reductions in the shear modulus. These deviations show that the use of the linear characteristic equation produces data interpretation errors on the shear modulus. However, larger reductions in the shear modulus would be more conservative data for design purpose. Figure 5 shows the hysteresis curves, corresponding backbone curves, and the instantaneous tangent shear moduli for sand under confinement stresses, 100 and 400 kPa. The instantaneous tangent shear moduli have a bow-tie shape with end-point discontinuities, thereby, showing that the shear modulus is dependent on the loading path and exhibits a significant drop with the strain amplitude again due to the strain softening effect. The secant modulus at the maximum strains is superimposed at its tangent counterpart. Note that the tangent modulus at \(\gamma=0\) is equal to the average of two secant modulus at each shear strain amplitude.  
\begin{table}
\begin{tabular}{c c
## 2 Method
### Theoretical Basis of EFSDAF
#### 2.2.5 Final Prediction of the Landsat-Like Image Using Neighborhood in a Sliding Window  
Theoretically, the final prediction of Landsat-like image \(F_{tp}\big{(}x_{ij},y_{ij},b\big{)}\) at \(T_{P}\) can be obtained by the following equation.  
\[F_{tp}\big{(}x_{ij},y_{ij},b\big{)}=F_{B}\big{(}x_{ij},y_{ij},b\big{)}+\Delta F _{f}\big{(}x_{ij},y_{ij},b\big{)} \tag{20}\]  
However, uncertain errors are inevitable in EFSDAF because of the complexity of the calculation process and the characteristics of pixel-by-pixel. In addition, the final prediction will lead to block effects because the residual distribution is for the MODIS pixels. In this study, EFSDAF adopts the same solution as STARFM, which uses the information of neighborhood to reduce the uncertainties and smooth final prediction in a sliding window. The final predicted Landsat-like image \(F_{fp}\big{(}x_{ij},y_{ij},b\big{)}\) can be calculated by Equation (21), and detailed information can be found in STARFM [7].  
\[F_{fp}\big{(}x_{ij},y_{ij},b\big{)}=F_{B}\big{(}x_{ij},y_{ij},b\big{)}+\sum \limits_{k=1}^{n}w_{k}\times\Delta F_{f}\big{(}x_{ij},y_{ij},b\big{)} \tag{21}\]  
where \(n\) is the number of similar pixels for Landsat pixels in a sliding window, and \(w_{k}\) is the weight for each similar pixel.
## 4 Results  
### Design Results  
Following the WMO recommendations described earlier and inspired by the state-of-the-art projects, the OSWS prototype was developed. The final result is shown in Figure 9, illustrating the size measurements. All the electronic components that handle and manage the system are placed inside this case.  
Moreover, Figure 10 illustrates the proposed solution; the photos show the weather station attached to a 1.65 m aluminum base powered by the internal Li-ion battery.  
Figure 8: Distance between OSWS and Purple Air sensor.  
Figure 9: OSWS size measurements.  
Figure 3 shows three sensors in the sensing system that measure air temperature, relative humidity, atmospheric pressure, CO\({}_{2}\) concentration, and PM 1, PM 2.5, and PM 10 concentration. According to the WMO, the sensors should be exposed to the air but protected from rain or direct sunlight. Figure 11 shows how the sensors are distributed along the case, which has IP65 protection, keeping the internal electronic components away from dust and water, and the accessory at the top of the device is designed to protect the sensors from rain and sun.  
Figure 11 presents the following components:  
1. Accessory for rain and sun.
2. BME280 measures temperature, relative humidity, and atmospheric pressure.
3. SCD30 for CO\({}_{2}\) concentration measurement.
4. PMS5003 for PM (PM 1, PM 2.5, and PM 10) concentration.
5. IP65 case.  
Figure 10: OSWS mounted in a 1.65 m height base.  
Moreover, the HMI, shown in the diagram in Figure 3, was implemented as illustrated in Figure 12, where:  
1. Indicator LEDs. Green: if the system is measuring. Yellow: if there is some trouble with a sensor.
2. 2.4\" Display (Figure 13) showing: 1. Live measurements. 2. Interval selected. 3. SD status (inserted or removed). 4. Sensing platform status (measuring or not).
3. Interval selector: used to configure the interval measuring time: 1. 30 s 2. 1 min 3. 10 min 4. 30 min 5. 1 h 6. 12 h 7. 24 h 8.  
Two microprocessors and a wireless board were installed to handle all the sensors and the HMI. This solution prevents overheating a single MCU and provides an easier way to modularize the sub-systems. Following this design, two PCBs were designed: main PCB (Figure 14) and second PCB (Figure 15).  
The main PCB includes:  
1. The main MCU that manages the sensors stores the data in the SD card, controls the timestamp obtained from the RTC, and sends the data shown through the display.
2. SD module controlled with SPI protocol.
3. Button for removing or inserting the SD safely.
4. 3V Coin battery to power the RTC even when the primary power source is disconnected.
5. Voltage regulators.
6. JST connectors for a secure component connection.  
On the other hand, the second PCB includes:  
1. MCU that manages the 2.4\" display and controls the WiFi board.
2. JST connector for communication with the main PCB.
3. WiFi board (ESP8266) that sends the data via the internet to ThingSpeak.
4. Headers to attach the display.
## 2 Analysis of Demand for a Cross-Regional Operation and Maintenance Service Platform for Harvesters
### Analysis of Requirements  
At present, in view of the formation of cross-regional operation service teams of combine harvesters, there is a lack of overall planning and systematization for support maintenance services. The sudden failure of a combine harvester can affect the harvesting efficiency of agricultural machinery operators and the harvest time of field crops, and there is a lack of unified workflow management and data flow management among the various maintenance resources. At the same time, due to the migration of harvester operation service teams and the lack of information resources with respect to service sites and maintenance stations in new locations, it is difficult to quickly solve fault problems and effectively obtain information regarding the availability of maintenance personnel, maintenance stations and spare parts. Due to the lack of maintenance experience among operators, they are only able to seek professional harvester maintenance personnel for the purpose of solving the problem. With the mastery of information regarding service providers possessed by agricultural machinery enterprises, as well as the growing service teams of combine harvesters, the increasing demand for information fusion of various service resources, the interoperability between various vertical systems is becoming increasingly necessary, and it is increasingly urgent that the system is able to communicate and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Agricultural Dispatch Platform** & **System Function** & **Source in Literature** \\ \hline \multirow{3}{*}{Farm management system platform} & The adoption of the new agricultural system model created a market for services and applications available to farmers. & [20; 21] \\ \cline{1-1}  & Job scheduling method can be carried out & [22; 23; 24] \\ \cline{1-1}  & & through the system platform. & \\ \hline \multirow{3}{*}{Agricultural Internet of Things platform} & Realized the networking management and control of each module in the new agricultural scene. & [25; 26; 27] \\ \cline{1-1}  & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Research and design related to agricultural machinery management platforms.  
share data seamlessly, realize information sharing, and achieve information consistency. As shown in Figure 1, the sensor monitoring devices installed on combine harvesters produced by agricultural machinery enterprises and the existing CDMA/GPRS network technology can be used for the transmission of fault information, service resource information analysis, and the timely transmission of maintenance work orders, thus providing a convenient service platform for cross-regional operation service teams for combine harvesters, and effectively solving the problem of performing maintenance work for the cross-regional operation of combine harvesters in China every year.
## 3 The F-Channel Platform
### Coordination Module  
Within the F-channel Coordination module for providing balancing and congestion management services, following the successful integration of the results of OneNet in the F-channel environment, TSOs and DSOs have interfaces for Flexibility Register and TSO-DSO Coordination. These two subsystems interface all market participants, i.e., the FSPs, market operators, metering data administrators, and consent administrators. Data are exchanged at TSO level for interoperability towards the distribution system. It is not foreseen that any market data are shared directly between SOs. Grid data are, on the other hand, shared directly between the SOs to meet requirements related to the existing practices related to the network security and operational stability of regular checks. TSO and DSO are able to share information related to future flexibility needs, calls for flexibility tenders, flexibility purchase offers, grid data for qualification and bid optimization, flexibility requirements that are checked against operational and asset-based constraints, and activation requests within their systems. Within the Coordination module, the auction submodule with all its components is considered.