esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Proposed Classification
### Classification of RL-Based Routing Methods with Regard to Routing Algorithm
The most important challenge in these approaches is to obtain accurate location information because, if the location of the nodes is not available and/or is not accurately calculated, these protocols have weak performance. In these methods, all nodes are equipped with GPS, which requires a lot of bandwidth. - **Cluster-based routing protocols:** In these methods, vehicles have different responsibilities on the network [28; 49]. Therefore, vehicles are categorized into different groups called clusters. In each group, a cluster head node (CH) manages the cluster and inter-cluster communication. These routing methods greatly lower the number of control messages and prevent network congestion [50]. Therefore, they are scalable. However, the challenges of this type of routing approaches, especially for dynamic networks such as VANET, are CH selection and cluster management. - **Topology-based routing protocols:** In these approaches, topological information of nodes is used for transmitting data packets in the network [51; 52]. They create a suitable path before starting the data transfer procedure. Topology-based routing methods are classified into three groups: - _Proactive routing methods:_ These approaches are also known as table-driven protocols. In this technique, each vehicle transfers the newest routing information to other vehicles and does not consider whether they have data packets for sending? The routing information is kept in the routing tables of vehicles and is regularly refreshed and shared with network nodes. The proactive routing is not suitable for VANETs because they cannot well react against repeated topological changes and have high route breakage [53]. - _Reactive routing methods:_ These approaches are on-demand. In thses schemes, a vehicle begins the route discovery process only if it has a data packet, which must be delivered to the destination and there is no path for this work. In these protocols, vehicles maintain only the routing information about valid paths. As a result, a path maintenance system checks valid paths and eliminates invalid paths. When the network topology is updated, the failed paths will be eliminated and the route discovery process begins again. The reactive routing protocols are more efficient in terms of bandwidth consumption compared to proactive routing methods because routing tables are updated periodically [53]. - _Hybrid routing protocols:_ It combines proactive and reactive approaches and is suggested to overcome their weaknesses. The hybrid routing method reducescommunication overhead compared to proactive routing protocols and improves delay in the path discovery process compared with reactive routing schemes. They are especially suitable for large-scale networks [53].
# A Hands-On Laboratory for Intelligent Control Courses
## 2 Fuzzy Logic Background
### Operations with Fuzzy Sets  
Certain operations can be performed between this type of set. The main ones are equality, inclusion, union, intersection and complement. These operations work in a similar way to operations on classic sets. The equality operation described in Equation (3) states that two fuzzy sets \(A\) and \(B\), defined in the same universe \(X\), are equal if they have the same membership function. Inclusion states that, given two fuzzy sets \(A\) and \(B\), \(A\) is a subset of \(B\)\((A\subseteq B)\) if its membership function takes smaller values. This is described in Equation (4).  
\[\mu_{A}(x)=\mu_{B}(x)\quad\forall x\in X \tag{3}\]  
\[\mu_{A}(x)\leq\mu_{B}(x)\quad\forall x\in X \tag{4}\]  
If there is a value \(x\in X\) such that \(\mu_{A}(x)\
eq\mu_{B}(x)\), then it is defined as \((A\subset B)\).  
For the union operation, the generalized form is taken, which is a triangular conorm, the well known T-conorm (\(\bot\)), and can be expressed according to Equation (5).  
\[\mu_{A\cup B}(x)=\bot\ (\mu_{A}(x),\mu_{B}(x)) \tag{5}\]  
A T-conorm must satisfy the following properties \(\forall a,b,c,d\in[0,1]:\)  
Commutativity: \(\bot\ (a,b)=\bot\ (b,a)\).  
Monotonicity: \(\bot\ (a,b)<\bot\ (c,d)\) if \(a\leq c\) and \(b\leq d\).  
Identity element: \(\bot\ (a,0)=a\).  
Associativity: \(\bot\ (a,\bot\ (b,c)=\bot\ (\bot\ (a,b),c.\)The T-conorm that corresponds to fuzzy sets is the maximum, therefore Equation (5) is rewritten to obtain Equation (6).  
\[\mu_{A\cup B}(x)=max(\mu_{A}(x),\mu_{B}(x)) \tag{6}\]  
For the intersection operation described in Equation (7), the triangular norm is needed, also called T-norm(\(T\)).  
\[\mu_{A\cap B}(x)=T(\mu_{A}(x),\mu_{B}(x)) \tag{7}\]  
For the T-norm, the conditions that must be met \(\forall a,b,c,d\in[0,1]\) are the following:  
Commutativity: \(T(a,b)=T(b,a)\).  
Monotonicity: \(T(a,b)<T(c,d)\) if \(a\leq c\) and \(b\leq d\).  
Identity element: \(T(a,0)=a\).  
Associativity: \(T(a,T(b,c)=T(T(a,b),c\).  
The T-norm that corresponds to fuzzy sets is the minimum. Equation (8) is obtained once this t-norm is substituted in Equation (7).  
\[\mu_{A\cap B}(x)=min(\mu_{A}(x),\mu_{B}(x)) \tag{8}\]  
For the complement operation (\(\mu_{\overline{A}}\)), the following axioms have to be satisfied.  
\(\mu_{\overline{A}}\) is a continuous function.  
Monotonicity: If \(a\leq b\), then \(\mu_{\overline{A}}\geq\mu_{\overline{B}}\).  
Involution: \(\mu_{\overline{FA}}=A\)  
The complement can be defined as in the classical sets described in Equation (9).  
\[\mu_{\overline{A}}=1-\mu_{A}(x) \tag{9}\]
## 4 Flying Base Station Channel Capacity and Positioning  
We define the _flying base station channel capacity_ as the reliable communication rate a flying base station can provide to the user. This definition builds on [PERSON]'s concept of channel capacity, which determines the reliable communication rate a channel can offer. Due to its reliance on the stationary base station (i.e., the flying base station can operate in conjunction with, but not without, a stationary base station), the flying base station channel capacity quantifies the potential channel performance when a flying base station supplements the stationary base station to better serve the user. The flying base station, with the objective of providing connectivity, moves and controls its location with respect to the user and the stationary base station to best serve the user. We, therefore, analyze the ideal positioning of the flying base station with respect to the stationary base station's location in Section 4.1. Assuming the ideal position of the flying base station with respect to the stationary base station, we quantify the flying base station's channel capacity given its power budget (\(\eta\)) and the stationary base station's channel condition in SNR (SNR\({}_{SU}\)) in Section 4.2.
## 3 Results and Discussion
### Further Experimental Validation
#### 3.2.2 Quality Assessment
Then, it was followed by a gradual increase until the end of the procedure. The hue angle was initially 39.19, representing a yellowish red color. Its value decreased significantly to 26.92 at 170 min, indicating a shift towards a redder hue, followed by an increase to 30.40 by the end of the osmotic dehydration. The overall decrease in hue angle suggests that the strawberries became redder during the dehydration process, which was consistent with the increase in the a* value. The appearance of the osmotically treated strawberry slices at different immersion times is shown in Figure 7.  
In conclusion, osmotic dehydration seems to effectively preserve the color of strawberries, with initial darkening followed by partial lightness recovery and an overall increase in redness. The results align with previous research indicating that OD generally preserves color due to reduced air exposure and minimal heat impact [62]. The variations in color parameters depend on factors such as the concentration of osmotic solutions, temperature, and pretreatment methods [54,63]. Finally, the addition of calcium chloride as a firming agent may also contributed to the color retention by interacting with cell wall components [60,64].
# The Multiband over Spatial Division Multiplexing Sliceable Transceiver for Future Optical Networks
###### Abstract  
In the last 15 years, global data traffic has been doubling approximately every 2-3 years, and there is a strong indication that this pattern will persist. Hence, also driven by the emergence of new applications and services expected within the 6G era, new transmission systems and technologies should be investigated to enhance network capacity and achieve increased bandwidth, improved spectral efficiency, and greater flexibility to effectively accommodate all the expected data traffic. In this paper, an innovative transmission solution based on multiband (MB) over spatial division multiplexing (SDM) sliceable bandwidth/bitrate variable transceiver (S-BVT) is implemented and assessed in relation to the provision of sustainable capacity scaling. MB transmission (S+C+L) over 25.4 km of 19-cores multicore fibre (MCF) is experimentally assessed and demonstrated achieving an aggregated capacity of 119.1 Gb/s at 4.62 \(\times\) 10\({}^{-3}\) bit error rate (BER). The proposed modular sliceable transceiver architecture arises as a suitable option towards achieving 500 Tb/s per fibre transmission, by further enabling more slices covering all the available S+C+L spectra and the 19 cores of the MCF.  
adaptive loading; spatial division multiplexing; multiband transmission; sliceable transceiver +
Footnote †: journal:  
Article
# Potential and Limitations of Open Satellite Data for Flood Mapping
## 3 Materials and Methods
### Data Processing, Analysis and Validation
#### 3.3.3 Semi-Automatic Mapping Based on Multispectral Image Processing
Specifically, we created a Boolean raster map based on the relationship AND/OR between SAR- and optical-based flood maps.
## 4 Services for Smart Cities: The ICT-Centric Implementation  
The fundamental ICT layer consists of a sensor grid in the city, where all points of interest are to be monitored and controlled. These sensors include installed CCTV cameras, natural parameter sensors, RFID tags, mobile devices, embedded SIMS, and actuators. These points of interest could include traffic intersections, bus stops, railway stations, utility poles, and water supply lines. These sensor/device arrays are further connected to wireless or wireless communication devices, such as routers/switches, which serve as gateways for further devices that acquire and evaluate data as part of an overall monitoring and control system [10].  
Communication networking technologies provide the infrastructure for smart cities so that all devices, computers, and physical users can establish reliable and secure communication links. This includes mostly terrestrial (wired and wireless) but also satellite networks. The network-defined mode of smart cities encompasses all range groupings, from personal and home/local area networking to metropolitan/wide area networking [11].  
The availability of real-time data and information in various verticals is very important for the success of smart city designation and implementation. This ubiquitous availability requires setting up advanced data centers and cloud infrastructure that integrates different smart city applications [12].  
High-performance data analytics using advanced AI algorithms aided by machine learning are also key technologies for smart city applications, as decisions need to be made in real time across different domains. This can include understanding the current state of a system and predicting the future state through predictive analytics. Fuzzy logic analysis may need to be employed for different parameters [13].  
Finally, an integrated management and administration center is meant to provide a single interface for assessing all information at the city level. These command and control centers do not imply that only one will be applicable to a single city, but many smaller ones could feed the necessary information to its different nodes [14]. Thus, scalability in terms of population density, area coverage, and aggregation of services and collected data will be the determining factors as to the hierarchy of the different gateways and command centers. The municipal character of a smart city designation implies that the city council can be the top administrative authority [15].
## 3 Results and Discussion
### IPIF Model's Results
[59], where error rate \(\alpha\) is fixed during the training step and PIF only predicts intervals for the chosen \(\alpha\) error rate, our model does not have this limitation. However, there are other authors in literature, such as [PERSON] et al. [58], who have proposed an IPIF whose \(\alpha\) error rate can be modified for similar energy demand and prediction horizon boundary conditions. [PERSON] et al. only provide reliability error metrics such as PICP\({}_{\alpha}\) and CE\({}_{\alpha}\) in their study; Table 12 summarizes [PERSON] et al. 's results and our results (averaged from Table 9 (\(F=1560\))) for those reliability indexes. From the results in Table 12, we can conclude that for similar prediction horizon conditions ([PERSON] et al. forecaster's makes predictions 10 min ahead, whereas in this study the forecaster makes predictions 15 min ahead), similar relative energy demand evolution along days ([PERSON] et al. energy demand varies in a range from 1500 to 3000 kW, whereas in this study it varies from 2500 to 6000 kW) and similar input parameters ([PERSON] et al. forecaster's uses Month, Day, Hour, Minute, Day Type, Outdoor air temperature, Outdoor air relative humidity and previous load values as input parameters, in this study the forecaster uses season, time of day and previous load values) our forecaster is a slightly improvement over the IPIF proposed by [PERSON] et al. [58]. The main difference between both forecasters relies on the input parameters selection; [PERSON] et al. introduced meteorological parameters as input parameters, whereas in this study they are discarded based on the results obtained in a \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(\alpha\)} & \multicolumn{2}{c}{**Proposed IPIF**} & \multicolumn{2}{c}{**[PERSON] et al.’s IPIF**} \\ \cline{2-5}  & 100 \(\times\)**PICP\({}_{\alpha}\)** & 100 \(\times\)**CE\({}_{\alpha}\)** & 100 \(\times\)**PICP\({}_{\alpha}\)** & 100 \(\times\)**CE\({}_{\alpha}\)** \\ \hline
0.05 & 97.40 & 2.40 & 94.69 & \(-\)031 \\
0.10 & 93.23 & 3.23 & 90.06 & 0.06 \\
0.15 & 90.10 & 5.10 & 85.62 & 0.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison of computed error metrics’ comparison for our proposed IPIF and [PERSON] et al.’s IPIF. Figure 5: Predicted intervals and actual energy demand evolution for 8 February 2018 scenario (\(\alpha=0.15\), \(F=1540\)). previous study [60]. The results of [60] demonstrated that the introduction of meteorological parameters reduces the accuracy of final forecaster for chosen prediction horizon.
# Analyzing the SAR in Human Head Tissues under Different Exposure Scenarios
## 3 Results and Discussion  
### SAR Laboratory Measurements  
SAR measurements are conducted in the laboratory using specialized equipment and techniques, following a series of steps. These steps can be summarized as the preparation phase, exposure settings, and SAR measurements. During the preparation phase, a phantom is used and filled with liquid of different properties specific to each frequency, such as 900 MHz and 1800 MHz. Liquid calibration is performed to ensure accurate measurements. Additionally, a noise experiment is carried out to establish a baseline. Next, the antenna is positioned on the phantom, and power adjustments are made to simulate realistic usage conditions. This step ensures that the electromagnetic field emitted by the antenna accurately represents the output of the device under test (DUT). Moving on to the exposure settings stage, measurements are taken on the twin phantom's right and left sides. The DUT is set to active mode, and surface scanning starts. Four tests are conducted during scanning, starting from the right cheek and the phantom's 15\({}^{\circ}\) angled position. The same procedure is then applied to the left phantom. Next, the location with the highest SAR value is determined, and two additional volume scans are conducted at this specific position. Each measurement is completed in approximately 20 min, and this measurement procedure ensures a comprehensive and reliable assessment of SAR levels.  
In this study, after determining the compliance of the liquids with the standards, SAR measurements are performed for 900 the MHz and 1800 MHz frequencies in the laboratory. The output power values of the generators were set to six different levels for 900 MHz and five different levels for 1800 MHz, representing the low, moderate and high doses of electromagnetic exposure. The power values used in the laboratory measurements for 900 MHz and 1800 MHz and the measured electric field strength (E), and SAR values are provided in detail in Table 5. Average 10g SAR values (both laboratory and simulation) and corresponding E for 900 MHz and 1800 MHz are shown in Figure 4 and saved for future reference. As seen from the table and figure, a number of the SAR values are above the general public exposure limits set by ICNIRP [1]. The SAR value measured at 4 W power for 900 MHz and at 1.25 and 1.5 W power for 1800 MHz exceeds the 2 W/kg value determined by ICNIRP.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & Power (W) & 0.5 & 1 & 1.5 & 2 & 3 & 4 \\ \cline{2-8}  & E (V/m) & 2.89 & 13.75 & 19.18 & 27.33 & 38.01 & 50.18 \\ \cline{2-8}  & SAR 10g (W/kg) & 0.0077 & 0.1752 & 0.3410 & 0.6919 & 1.3386 & 2.3327 \\ \cline{2-8}  & Power (W) & 0.25 & 0.5 & 1 & 1.25 & 1.5 & - \\ \cline{2-8}  & E (V/m) & 10.29 & 21.68 & 35.50 & 42.64 & 47.45 & - \\ \cline{2-8}  & SAR 10g (W/kg) & 0.1468 & 0.6629 & 1.7483 & 2.5222 & 3.1227 & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: The power, E and SAR values of the laboratory measurements at 900 MHz and 1800 MHz.
# A Reverse Shortest Path Tree-Based Multicast Joining Node Selection Method
## 3 Reverse Shortest Path Tree Based MJN Selection Method  
Our RSPT-based MJN selection method can be divided into two parts. The first part involves constructing the RSPT, while the second part involves utilizing network load state information collected by RSPT to select the MJN.
# A Framework for the Design of Privacy-Preserving Record Linkage Systems
## 4 Discussion
### Elucidation of Existing Systems  
The usage of this framework can help with creating easy-to-understand explanations of the architectures of existing PPRL systems, as well as identifying the roles different parties play in the system and detailing their responsibilities. As an example, we can take the PPRL system currently in use by N3C to facilitate linkages for clinical research. When applying the framework, we can map roles to the different parties in the system; i.e., the clinical sites are data controllers, the Regenstrief Institute acts as the Honest Broker, Datavant is the Tokenization provider, N3C hosts the data platform and provides data governance for the system, researchers who apply for research access are data users, the National Center for Advancing Translational Sciences (NCATS) privacy office acts as the privacy officer overseeing the system, and Datavant and Regenstrief Institute provided their expert evaluation for the system [13; 27]. After mapping out the roles, our framework also helps clarify the data transfers between different parties, which allows us to create a simple data flow diagram for the N3C ecosystem using the architecture presented in Figure 2 (shown in Figure 4). The N3C PPRL system also has many of the privacy-protecting system controls listed in Section 3.3 (as well as additional controls for government data systems) [14]. Thus, with this framework, we are able to reduce the complexity of the N3C PPRL system, gaining a clear understanding of the different parties' roles and responsibilities, what data flows between these different parties, and how privacy is protected while data gets shared, linked, and analyzed.
## 3 Performance Verification and Results
### Results
#### 3.2.2 Experiment Two
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**H-Index**} & \multicolumn{2}{c}{**V-Index**} & \multicolumn{2}{c}{**Reaction Time**} & \multicolumn{2}{c}{**Completion Time**} & \multicolumn{2}{c}{**SCM EMG**} & \multicolumn{2}{c}{**Masseter EMG**} \\ \hline H-index & 1 & & & & & & & & & \\ V-index & 0.911 & 1 & & & & & & & & \\ Reaction Time & 0.415 & 0.239 & 1 & & & & & & & \\ Completion Time & 0.760 * & 0.892 * & 0.316 & 1 & & & & & & \\ SCM EMG & 0.826 * & 0.938 * & 0.228 & 0.980 * & 1 & & & & \\ Masseter EMG & 0.886 * & 0.925* & 0.394 & 0.891 * & 0.888 * & 1 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The correlation amongst variables. Figure 13: Comparison of performance for male and female subjects: (**a**) reaction time; and (**b**) total time taken. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{\begin{tabular}{c} **Approx.** \\ **Turns** \\ \end{tabular} } & \multicolumn{2}{c}{**Reaction Time**} & \multicolumn{2}{c}{**Completion Time**} & \multicolumn{2}{c}{**SCM Input**} & \multicolumn{2}{c}{**Masseter Input**} & \multicolumn{2}{c}{\
\begin{tabular}{c} **Trajectory/** \\ **Path Length** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Total** \\ **Objects** \\ \end{tabular} } \\ \hline
**h** & **v** & **Time (s)** & **std** & **Time (s)** & **std** & **Sum** & **std** & **Sum** & **std** & **Sum** & **std** & \\ \hline
3 & 1 & 2.54 & 1.30 & 23.74 & 14.90 & 119.09 & 75.75 & 198.79 & 56.46 & 722.54 & 283.49 & 2 \\
3 & 6 & 2.30 & 0.75 & 92.08 & 58.11 & 405.34 & 203.46 & 598.90 & 286.29 & 8329.42 & 5655.32 & 3 \\
6 & 7 & 2.67 & 1.96 & 146.50 & 96.43 & 707.53 & 464.89 & 748.29 & 479.92 & 12263.92 & 9157.81 & 3 \\
8 & 5 & 3.10 & 1.89 & 80.94 & 56.13 & 380.11 & 221.90 & 671.95 & 172.68 & 7579.69 & 5995.52 & 3 \\
5 & 3 & 1.66 & 0.53 & 57.76 & 46.76 & 333.12 & 263.26 & 477.12 & 332.44 & 5359.19 & 4410.02 & 3 \\
6 & 4 & 2.72 & 0.95 & 71.45 & 37.37 & 310.37 & 158.64 & 725.07 & 369.32 & 6735.52 & 3802.65 & 3 \\
13 & 13 & 2.03 & 1.26 & 138.77 & 29.20 & 768.13 & 216.44 & 975.86 & 417.63 & 14840.91 & 3427.49 & 5 \\
15 & 11 & 3.26 & 2.00 & 125.29 & 44.17 & 671.08 & 330.30 & 960.57 & 414.29 & 12489.97 & 3938.40 & 5 \\
13 & 11 & 3.15 & 1.85 & 154.70 & 86.62 & 725.67 & 513.16 & 1298.74 & 971.09 & 15180.07 & 7197.53 & 5 \\
16 & 16 & 2.70 & 1.90 & 156.81 & 39.77 & 850.41 & 287.54 & 1430.92 & 692.21 & 19860.39 & 5027.02 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overall performance of all subjects. In Table 4, scene difficulty measured by completion time increases with increasing v-index. In the test, the v-index and completion time were found to be strongly correlated, (r(8) = 0.892) compared to that of h-index (r(8) = 0.790), which remained significant. The turning index (h and v indices) positively correlated with the input command, with the v-index strongly correlated with the two indices. The input command was associated with the expected completion time (r = 0.98 and 0.891 for SCM and Masseter EMG, respectively).