esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions
### Implications for Practical Application  
The findings from this study have practical implications for the development of NIR-based fish safety testing tools. The high accuracy achieved by the CNN model, particularly at the stomach region, suggests that such models could be integrated into rapid, non-destructive testing devices. These devices could offer food safety authorities and industry stakeholders a cost-effective alternative to traditional laboratory methods, allowing for on-site testing and quicker decision-making regarding fish safety.  
However, the study also points to limitations, such as the relatively small dataset and the use of a low-cost NIR scanner, which may affect the generalizability of the results. Despite efforts to mitigate this through techniques like dropout and SMOTE, the potential for overfitting in CNN models is another consideration that warrants further exploration.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 3 Wi-Fi 6 Overview
### Novel Features
#### 3.1.5 Modulation Techniques  
Wi-Fi 6 defines the 1024-QAM modulation that supports the transmission of ten bits at a time, increasing the data rate by 25% compared to Wi-Fi 5. This modulation scheme is more sensitive to noise so it can be applied only if the channel quality is sufficiently good. Combinations of Modulation and Coding Schemes (MCSs) are defined by an index. Due to the novel modulation in Wi-Fi 6, two novel MCS indices are defined.  
Furthermore, Wi-Fi 6 benefits from an optional modulation named Dual Carrier Modulation (DCM) to combat interference for long-distance transmission [5; 24]. The modulation improvements are explained in Section 8.
# A Trust-Based Predictive Model for Mobile Ad Hoc Network in Internet of Things +
## 3 Proposed Protocol
### Trust Generation
#### 3.2.1 Direct trust calculation  
1. **Trust Initiation:** Along with bootstrapping the IoT-MANET, CH and cluster members initiate the trust calculation. Once the trust value has aged, the trust revocation procedure is restarted and the direct trust calculation takes place.
2. **Evidence Collection:** In this phase both CH and cluster members collect the Good and Bad evidences, as described in Section 3.
3. **Trust Aggregation:** In this phase each member and CH of a cluster collects and stores collective data for all good and bad events. A reputation system-based on the \(\beta\) probability density function has been used, in order to calculate direct trust. The \(\beta\) function represents probability distributions of binary events (either good or bad). It is a mathematical method for combining feedback and expressing reputation ratings. A brief description of \(\beta\) distribution is presented in Appendix A.  
Suppose that node \(X\) has collected information about positive (good) and negative (bad) behaviors about node \(Y\). Good behavior is represented by \(\alpha\), and \(\beta\) represents bad behavior. The posterior probabilities of trust value can be predicted using the \(\beta\) distribution function, which is presented in Equation (1). The \(\beta\) distribution can be expressed using the \(\Gamma\) function as  
\[f(p|\alpha,\beta)=\frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha)+\Gamma(\beta)}p^{ (\alpha-1)}(1-p)^{(\beta-1)} \tag{1}\]  
where \(0\leq p\leq 1\), \(\alpha\), \(\beta>0\), with the restriction that the probability variable \(p\
eq 0\) if \(\alpha<1\), and \(p\
eq 1\) if \(\beta<1\).  
It is an uncertain probability and the expected probability of trust value is \(E(p)\). Therefore, it can be said that the relative frequency of outcome is most likely to be \(E(p)\). The direct trust of a node under review can be calculated using Equation (2).  
\[E(p)=\alpha/(\alpha+\beta) \tag{2}\]  
Each node calculates and stores the trust value about its neighboring nodes. Here, \(\alpha\) and \(\beta\) correspond to the accumulated number of good behavior and bad behavior. Initially, the direct trust value of each node is set to 0.5 as there are no recorded observations or evidences about a node under review. Thus, we start with \(\alpha=\beta=1\), which makes both the values of expectation and the direct trust equal to 0.5. Here, we train the Beta function according to [27].
## 3 Results and Discussion
A deeper analysis of the IMC layer thicknesses can be seen in Figure 11. Considerable IMC thickness differences have been found between the soldering technologies, which correlate well with the \"time above liquidus\" during the soldering process. Time above liquidus means the time that the solder joint spends in the liquid state during the reflow process, practically the time above the liquidus point of the applied solder alloy. In our case, it was the time above 217 \({}^{\circ}\)C (SACX0307 solder alloy). The time above liquidus values can be calculated from the thermal profiles (Figure 3) for all samples. The longer liquid state of the solder results in a thicker IMC layer [15]. If we compare the thermal profiles (Figure 3) with the average IMC thicknesses (Figure 11), it can be concluded that they correlate well. Usually, the IMC layer was a little thicker at the bottom side of the solder joint than at the upper side. The only exception was the batch oven. Interestingly, the \(R_{th}\) values (Figure 5) are inversely proportional to the IMC layer thicknesses (Figure 11). The lowest \(R_{th}\) values were measured in the case of the batch oven (\(\sim\)15 K/W) with the thickest IMC layers  
Figure 9: An exemplary “sandwich structure” of the solder joints under IGBT soldered in batch oven (SEM micrograph). (bottom and upper together over 5 \(\upmu\)m). The highest \(R_{th}\) values were measured in the case of the vapour phase and hot air soldering (\(\sim\)18K/W) with the thinnest IMC layers (lower and upper together around \(\sim\)3 \(\upmu\)m). The specific thermal properties of the bulk SnAgCu solders and the Cu\({}_{6}\)Sn\({}_{5}\) IMC are around 55-60 W/mK [24] and 34 W/mK [25]. However, recent studies have proved that bonded Cu-Sn IMC joints made by solid-liquid interphase diffusion could reach specific thermal conductivity of between 120 and 140 W/mK [26; 27]. Therefore, the role of the IMC layer in the heat transfer of solder joints needs further and deeper investigation, since it is surely not negative, as our results also showed. Figure 10: SEM micrographs of the solder joints under the IGBT: (right bottom side of the solder joint, left upper side of the solder joint): (**a**) batch oven, (**b**) 3-zones tunnel oven, (**c**) 4-zones tunnel oven, (**d**) vapour phase oven, (**e**) hot air soldering.
## 5 Simulation Results and Analysis
### Coverage Probability  
We exhibit the statistics for the probability of coverage of all user equipment in the area in Figure 14. With 50, 100, and 200 items of user equipment in the vicinity, we mimic the ICBM-UAV. Additionally, the ICBM-UAV was compared to distributed and centralized methods. In Figure 14, the centralized algorithm performance is worse than distributed and ICBM-UAV. As for the ICBM-UAV scheme at 50 UE scenario, the coverage probability is a little less than the distributed algorithm, as the affected area is larger than the number of UE distributed in the area. With the increase in UE in areas, with 100 and 200, the probability of covering UE gets better, as \(\tau=0.02\), which shows that its ICBM- UAV scheme satisfies the required coverage probability threshold, which is set as 98%.  
Figure 12: Packet delivery ratio without obstacles.  
Figure 13: Packet delivery ratio with obstacles.
## 4 Different Application Models of Wireless Power Transfer Technologies
### Application with a Mobile Wireless Charging Vehicle (WCV): Static Nodes vs. Mobile WCV  
Unlike the first model, this case scenario shows a mobile WCV which periodically visits a sensor network with the sole aim of charging the batteries of the sensor nodes wirelessly. This is depicted in Figure 3. After the visit, the WCV returns to its service station for servicing which may be either recharging or battery replacement and prepares for another possible periodic visit.  
Figure 2: Wireless Charging Station (WCS) with Mobile Nodes. A node continuously receives energy once it is within the charging coverage of the WCS.  
Proposed Model: Mobile Data Collectors (Data Mules) Used as Wireless Mobile Battery Chargers (MBCs)/Power Transmitters  
Figure 4 shows the architecture of a multi-node wireless mobile energy transmitter which opportunistically traverses the IoT sensor network to recharge the batteries of the sensors. The model shows a data mule which traverses the network for the purpose of data collection and energy transmission. Unlike the previous models, this model exploits the advantages inherent in vehicular networks for opportunistic data collection and power transmission to the sensor-nodes. In this scheme, we assume a uniform charging for all the sensor-nodes within the vicinity and coverage area of the wireless energy transmitter. Considering some trade-off factors, this model explores the energy provisioning process to the sensor-nodes based on their inter-meeting time with the energy transmitters. We shall be investigating the impact of inter-meeting time between the energy transmitters and the nodes on the efficiency of WPT based on different trade-offs. We will also consider the effect of increasing the sensor-nodes on the average energy charged by each sensor.  
Figure 4: Architecture of a multi-node wireless mobile battery charger (MBC) which doubles as mobile data collector and wireless energy transmitter. The MBC opportunistically traverses the IoT network to collect data and recharge the batteries of the sensor-nodes.  
Figure 3: A model of wireless charging vehicle which periodically visits a WSN to recharge the batteries of sensors.
# Numerical Algorithm for Dynamic Impedance of Bridge Pile-Group Foundation and Its Validation
###### Abstract  
The characteristics of bridge pile-group foundation have a significant influence on the dynamic performance of the superstructure. Most of the existing analysis methods for the pile-group foundation impedance take the trait of strong specialty, which cannot be generalized in practical projects. Therefore, a project-oriented numerical solution algorithm is proposed to compute the dynamic impedance of bridge pile-group foundation. Based on the theory of viscous-spring artificial boundary, the derivation and solution of the impedance function are transferred to numerical modeling and harmonic analysis, which can be carried out through the finite element method. By taking a typical pile-group foundation as a case study, the results based on the algorithm are compared with those from existing literature. Moreover, an impact experiment of a real pile-group foundation was implemented, the results of which are also compared with those resulting from the proposed numerical algorithm. Both comparisons show that the proposed numerical algorithm satisfies engineering precision, thus showing good effectiveness in application.  
numerical algorithm; pile-group foundation; bridge; dynamic impedance; viscous-spring artificial boundary; harmonic analysis Article
## 5 Evaluation Protocol and Results
### Evaluation Protocol
#### 5.1.2 Performance Metrics  
To measure the performance of the proposed method, we used three commonly used metrics: sensitivity, specificity, and accuracy [16; 18; 19]. Sensitivity is the rate at which people with schizophrenia are correctly diagnosed. Specificity is the rate of healthy controls that are correctly diagnosed. Accuracy is the rate of all subjects that are correctly classified, either healthy or patients, among all examined subjects. The effectiveness of classification methods depends on providing high values of these metrics.  
Figure 4: Learning curves of accuracy (**left**) and loss (**right**).
# Evaluation of Remote Sensing-Based Evapotranspiration
## 3 Methodology
### Model Calibration  
Model calibration is carried out through \(\varepsilon\)-NSGAII, which has been coupled with DHSVM by developing an automatic calibration model (\(\varepsilon\)P-DHSVM) in the authors' previous study [41]. Daily time series of runoff from the Jinhua hydrological station and three remote sensing-based ET datasets including PML, FLUXCOM, and GLEAM are used for parameter calibration and validation. Based on the data availability, all observed time series data are divided into calibration (2004-2008) and validation (2009-2010) periods.  
Four different model calibration schemes are used to realize the aims (mentioned in the last paragraph of Section 1) of this study: (1) Scheme I--parameter calibration with only runoff; (2) Scheme II (PML)--parameter calibration using both runoff and ET based on PML dataset (ET\({}_{\text{PML}}\)); (3) Scheme II (FLUXCOM)--parameter calibration using both runoff and ET based on FLUXCOM dataset (ET\({}_{\text{FLUXCOM}}\)); (4) Scheme II (GLEAM)--parameter calibration using both runoff and ET based on GLEAM dataset (ET\({}_{\text{GLEAM}}\)).  
In Scheme I, the KGE and percentage of relative bias (PBIAS) of runoff are used as objective functions. In all three variants of Scheme II, both KGE and PBIAS of runoff and ET are used as objective functions. Moreover, the performance of calibrated and validated results is also assessed with other evaluation indices, such as mean bias (ME), root mean square error (RMSE), and Pearson's correlation coefficient (R).  
The parameters used for model calibration are based on the two-step global sensitivity analysis results of DHSVM at the Jinhua River Basin [30]. Eight highly sensitive parameters, shown in Table 1, are used for model calibration in this study. Detailed procedures of the two-step global sensitivity analysis are omitted here, and interested readers can refer to [30].
# Research on Real-Time Model of Turboshaft Engine with Surge Process
## 3 Real-Time Surge Model of Turboshaft Engine
### Stability Analysis of MG Model
With the increase of the \(B\) parameter, the limit cycle increases rapidly, then decreases slowly in the direction of pressure rise coefficient, and the direction of flow coefficient tends to be stable. When the \(B\) parameter is large, the mass flow coefficient is smaller than 0, which means that backflow occurs and the model enters a deep surge. Based on the above analysis, \(B_{cr}\) is determined by the compressor characteristics and the throttle opening: if the throttle opening is reduced, \(B\) decreases, and if the throttle opening is increased, \(B\) increases accordingly. When the \(B\) parameter is greater than \(B_{cr}\), the MG model surges meanwhile \(\Phi\) and \(\Psi\) pulsate, and the larger the \(B\) parameter, the larger the pulsation amplitude, the lower the pulsation frequency. Therefore, the process of MG model's stable state--surge state--stable state in and retreating can be simulated by the following steps:  
Step 1. Set the initial \(B\) parameters and throttle valve opening, run the MG model. Step 2. Decrease the throttle opening \(\gamma_{T}\) so that \(B_{cr}<B\), MG model goes into surge state. Figure 5: Variation of \(B_{cr}\) with \(\gamma_{T}\). Figure 6: The variation of the limit cycle with \(B\) parameter: (**a**) Mass flow instability coefficient; (**b**) Pressure rise instability coefficient. Step 3. Adjust the \(B\) parameter to simulate different pulsation amplitudes and frequencies. Step 4. Increase the throttle opening \(\gamma_{T}\) so that \(B_{cr}>B\), MG model return to the stable state.
# Enhanced Beacons Dynamic Transmission over TSCH
## 5 Experimental Results and Discussion
### A Linear Network Scenario  
In a second scenario, the behavior of the association process of a linear topology network is evaluated as Figure 8 shows. In this case, the network has a coordinator node and three linearly distributed nodes that seek to associate. The coordinator node's coverage area is highlighted in green. Each join-seeker node can only listen to EBs generated from its adjacent nodes. In this way, beacons transmitted by node 2 take one hop to reach the coordinator, whereas beacons transmitted by node 4 need to take three hops to reach the coordinator. From now on, the number of hops necessary to reach the coordinator will be called the distance from the coordinator node.  
To evaluate the impact of EBDT-TSCH in linear networks, a comparison is made using the default method in Contiki-NG, which follows the 6 TiSCH minimal configuration. Figure 9 shows the average association time as a function of distance to coordinator node for three configurations being EBDT-TSCH with \(\beta=0.8\), EBDT-TSCH \(\beta=1.8\), and default minimal 6 TiSCH. The number of available channels is set to 16 and \(Teb\) is set to 4 seconds.  
As shown in Figure 9, the average association time increases linearly as the network depth increases. The results show a reduction in the association time between nodes with the proposed EBDT-TSCH mechanism, compared to the default 6 TiSCH minimal configuration. The implementation of the proposed algorithm decreases the average association time of the node located three hops away from the coordinator by 18.33% and  
Figure 8: Linear network scenario. The nodes are on a line and each node can only communicate with its adjacent nodes. Node 1 is the network coordinator and nodes 2–4 are join-seekers. The green circle depicts the transmission range of node 1, while the grey circle depicts the interference range.  
Figure 9: Association time in a linear network topology.  
29.46% for \(\beta=0.8\) and \(\beta=1.8\), respectively, compared to the results obtained with minimal 6 TiSCH default implementation.  
The improvement provided by EBDT-TSCH allows for faster network formation, which results in earlier data packet transmission. The decrease in network formation time is also reflected in energy consumption during network formation. This can be observed in Figure 10, which shows the energy consumption of the network, which is the sum of the average energy consumption of each node during network formation. It is lower for the proposed method when compared with default implementation of minimal 6 TiSCH, reducing it up to 18.25% and 30.16% for \(\beta=0.8\) and \(\beta=1.8\), respectively.
## 5 Research Methodology
### Data Collection Parameters
#### 5.3.2 Bandwidth Usage  
Network bandwidth is the maximum data rate or capacity at which data can be transferred within a network [36]. It is determined by the number of data that can be transmitted over a blockchain network within a specific time period. Bandwidth is commonly measured in bits per second (bps) [36; 37]. The network bandwidth plays a critical role in determining the speed at which sensor data can be transmitted and received over the blockchain network [37]. Consequently, evaluating the blockchain network's bandwidth is an essential factor in assessing network scalability.
# LaRoAwan and Urban Waste Management--A Trial
## 2 LoRa Technology--Long Range
### LoRa  
The main innovations introduced by LoRa are low-power consumption and long-range, thus providing a basis for creating a LPWAN (Low Power Wide Area Network) to support the Internet of Things (IoT) and associated smart city applications.  
When operating in an unlicensed band, LoRa technology competes with all existing communications in the same band. However, it also allows any entity to install and operate its network without the need for additional licensing, also implying that limitations on the number of messages transmitted are introduced by regulatory domains. In the European case, the limitation is imposed by a duty cycle, which represents the activity rate of the radio channel, with a value of 1% (in most frequencies), indicating that a device cannot transmit for more than 1% in a given period.  
LoRa technology is patented and owned by Semtech, Camarillo, California, United States, which is the main manufacturer of the radio components, however, it is supported by the LoRa Alliance organization, composed of several manufacturers and integrators, from where the standardization of the LoRaWAN protocol arose, which allows the creation of a network structure based on LoRa transmission technology.  
LoRa technology uses a modulation scheme with spectrum spreading, of the CSS type (Chirp Spread Spectrum), using spreading factors between 7 and 12, depending on the transmission rate and signal level of the connection, spreading factor 12 being the one that introduces better guarantees of the signal reaching the destination, but also the one that has a lower transmission rate, and higher chances of suffering a collision [3]. Additionally, in order to optimize the reception of the signal, an error correction mechanism is used, and the Link Budget of a LoRa transmission can reach values of 168 dB [4]. The Link Budget is the determining factor when considering the network coverage of a device in any environment.
# A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues
## 3 Indicative Use Cases
### IoT in Agriculture  
Due to the large area coverage of agriculture and the variety of production objects, central cloud storage and processing of related data might significantly increase the computational load and violate latency requirements. Therefore, the IEC continuum can be beneficial for large-scale data processing and process optimization. In this context, the use of IoT devices in agriculture allows the development of processes on farms that can reduce operational costs, improve the effective use of resources, and reduce the amount of used plant protection products [62]. IoT sensor nodes can be placed in various locations of the agricultural area and measure a variety of diverse parameters, such as humidity, temperature, etc. Therefore, planting and harvesting procedures can be optimized. Other advanced applications include the usage of the so-called weeding robots that can use digital image processing to look through the images of weeds in their database to detect similarities with crops and weed out or spray them directly with their robotic arms. In the same context, harvesting robots may be used to pick crops directly from the field, thus solving the problem of labor shortages. Finally, UAVs can also be used for a variety of applications, such as high-definition two-dimensional (2D) images, weeding, etc. [63].  
It becomes apparent from the above that IoT usage in agriculture can support a variety of applications with diverse requirements. These applications can be classified in terms of latency as non-critical (e.g., optimal harvesting period) as well as time-critical (e.g., field robots or UAVs on the field). In the second case, edge servers in close proximity to IoT gateways can facilitate efficient data processing via task offloading. In the same context, decision-making procedures should be expanded to a variety of field operations that include a huge amount of heterogeneous data. Therefore, decentralized ML approaches can effectively support latency/privacy critical scenarios. Finally, uninterrupted connectivity in real-time conditions and continuous monitoring of device operation when ultra-low latency is required can be supported by the concept of subnetworks, as previously mentioned. This case can be highly applicable when the agricultural field covers a wide territory and short-range network connectivity is required.