esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Verification of the Digital Twin Model
### Verification of the Uplink Field Conformity  
The OAU and the balise in this verification are the same as the antennas in Table 2. The difference is that this verification is to test the uplink signal transmission characteristics.  
Some typical parameters of the uplink receiving antenna matching circuit obtained from the manufacturer are shown in Table 3. With these parameters, the signal power of the uplink receiving antenna port can be calculated according to (13).  
The uplink field conformity test configuration is shown in Figure 14. The signal generator sent the telegram signal with the center frequency of 4.2 MHz to the balise, and the attenuator was used to isolate and protect the signal generator. We adjusted the output power of the signal generator and observed the power meter A to make the uplink signal current intensity on the balise transmit antenna reach 59 mA. The 27 MHz tele-powering transmitting antenna was impedance-matched with a 50-ohm load. We attained the signal power of the uplink receiving antenna port by power meter B.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(Q_{\text{r}}\) & \(A_{\text{z}}\) & \(\Delta z\) (mm) & \(\Delta y\) (mm) \\ \hline
3 & 0.5 & 340 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Some typical parameters of the OAU and the passive balise (Part B).  
The test scenario was consistent with Figure 12. During the measurement process, the current intensity of the uplink signal on the balise transmitting antenna remained the same. The signal power of the onboard receiving antenna port at each position needed to be measured and recorded. The rest of the measurement steps were consistent with the measurement scheme of uplink field conformity in the test specification. The results of measurement and calculation are shown in Figure 15. The cause of the sidelobe effect in Figure 15 is the same as that in Figure 13, which is also by the magnetic field distribution characteristics of the rectangular antennas. Compared with Figure 13, Figure 15 adopts a logarithmic coordinate system so that the sidelobe effect appears more prominent. The envelope of the calculation result curve was very similar to that of the measurement result curve, and the error between the measurement and calculation results in the main lobe zone was within 2 dB.  
The Pearson correlation coefficient, Spearman rank correlation coefficient, and maximum mutual information coefficient were 0.9943, 0.9971, and 0.9999, respectively, for the calculation curve and the measurement curve. This shows that the proposed electromagnetic coupling calculation model can accurately predict the uplink field conformity measurement results.  
The tele-powering field conformity test and the uplink field conformity test are respectively for downlink and uplink of the BTS. Based on the verification of these two aspects, the validity and accuracy of the electromagnetic coupling model of the BTS established in this paper can be fully affirmed.
## 3 Data and Methods
### Mechanisms for Monitoring and Evaluating the Learning Experience  
The monitoring and evaluation of the Ecuadorian training workshop experience involved various methods. Participation was tracked through attendance lists and active tracking during sessions, with the goal of achieving 80% daily attendance and completion of at least four of seven posit.Cloud practical exercises, evidenced by activity hours on the  
Figure 5: Interferometric SAR (InSAR) exercise outputs performed in Ecuador workshop: (**a**) Subsidence map generated using SNAP (Sentinel Application Platform) with processed Sentinel-1 data from the period 24 December 2016 to 17 January 2017. The yellowish color indicates uplift, while the bluish colors represents subsidence in cm. (**b**) Sentinel-2 natual color image (31 July 2017) showing the area of interest in Zaruma, Ecuador. (**c**) Collapsed building due to subsidence in Zaruma’s urban area. Image reproduced with permission from [PERSON] et al. [48].  
platform. Weekly evaluations tested knowledge with nine questions per test, requiring a minimum score of 70% to meet the training objective. Two brainstorming sessions via Jamboard reinforced concepts, enabling participants to share insights on Copernicus Services across topics like air quality, climate monitoring, disaster management, and resource management. Weekly satisfaction surveys with Likert-scale questions assessed quality, relevance, learning methods, guidance, and overall satisfaction. Data processing, detailed in Appendix D, highlighted areas for improvement by filtering high-value responses while addressing positively skewed results.  
In the case of the Bolivian panel discussions, due to their format and the short duration of the sessions, satisfaction surveys and evaluations were not used. Instead, attendance lists were used to measure the level of interest in specific topics. Additionally, notes on the participants' opinions and the interest observed in open-ended questions in assessment of the survey of needs to assess the impact of the talks. This approach helped us to understand the relevance and impact of the panel discussions on the stakeholders involved.  
One year after conducting events in Ecuador and Bolivia, post-event interviews with participants were conducted to gain deeper insights into participants' learning experiences, focusing on five key areas: application of knowledge, practical use of Copernicus tools, local impact and collaboration, long-term evaluation, and suggestions for improvement. Participants were asked questions about how they applied the acquired knowledge in their work, specific problems and case studies addressed with Copernicus tools, the benefits to their institutions, and recommendations for future training programs. The interviews, lasting 30-40 min, provided qualitative data that complemented the quantitative evaluation methods, enabling a more comprehensive assessment of the workshop's impact. Details of the interview questions can be found in Appendix E.
# Compact Fuzzy Systems Based on Boolean Relations
## 4 Compact Fuzzy System Architectures Based on Boolean Relations
### Compact System with Architecture I  
In order to establish a system with architecture I, the coding shown in Table 15 is used.  
According to the previous analysis, from Table 15 is found that:  
\[Y_{m}=A_{1,m}(x_{1})\wedge A_{2,m}(x_{2})\wedge\cdots\wedge A_{p,m}(x_{p}) \tag{40}\]  
For the implementation using fuzzy sets the conjunction is made with the product t-norm and each set \(A_{p,m}\) has a membership function associated \(\mu_{p,m}\). Taking \(m=1\) a partial virtual output of this inference process can be seen in Figure 7, where \(v_{1}\) corresponds to the respective virtual actuator.  
Using \(M\) virtual actuators with their respective activation functions, it is found the scheme shown in Figure 8. A constant virtual actuator is used to complement the design \(v_{c}\) which corresponds to an encoding with an activation function \(Y_{c}\) always active, that is, with values of \(1\) in their respective column.  
In this way, the expression for the inference process of this system is:  
\[f=v_{c}+\sum_{m=1}^{M}v_{m}\left[\prod_{p=1}^{N}\mu_{p,m}(x_{p})\right] \tag{41}\]  
This scheme can be employed to systems identification since it has a structure similar to that conventionally used in radial-based neural networks when used to \(\mu_{p,m}\) Gaussian membership functions [60].  
The structure of Figure 8 can be interpreted as a neuro-fuzzy network. The respective membership value for each input is obtained in the first layer. The second layer corresponds to the product of the values obtained from the membership functions, that is, the calculation of \(Y_{m}\), while \(Y_{m}v_{m}\) (virtual actuators multiplied by the respective activation functions) is obtained in the third layer. Finally, in layer four it is performed the summation of all \(Y_{m}v_{m}\) obtaining the inference output \(f\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \(A_{1,m}(x_{1})\) & \(A_{2,m}(x_{2})\) & \(\cdots\) & \(A_{p,m}(x_{p})\) & \(Y_{m}\) \\ \hline
1 & 1 & \(\cdots\) & 1 & 1 \\
0 & \(X\) & \(\cdots\) & \(X\) & 0 \\ \(X\) & 0 & \(\cdots\) & \(X\) & 0 \\ \(X\) & \(X\) & \(\cdots\) & 0 & 0 \\ \hline \end{tabular}
\end{table}
Table 15: Coding for compact systems with architecture I.  
Figure 7: Partial virtual output of the inference process.  
Application for Dynamic Systems  
In order to carry out the application of this configuration for dynamic systems, delays can be used for the input signal and the feedback of the system output. As example, Figure 9 shows a configuration with one input delay and two for the output feedback. This fuzzy system can be used for identification or control.
# Scenario-Based Configuration Refinement for High-Load Cellular Networks: An Operator View
## 3 High Population Density Scenario
### Dual-Band Networks  
The dual-band policy with \(F\) and \(D\) frequency bands is believed to be an effective approach to increase the network capacity. As we know, \(F\) and \(D\) frequency bands are operating at 1.9 GHz and 2.6 GHz. \(F\) frequency band is adopted to improve the network coverage due its relatively low path loss. Moreover, \(F\) frequency band can achieve better network coverage than \(D\) frequency band, which is widely used in the residential areas of cities. On the other hand, \(D\) frequency band is utilized to support telephone traffic in hot spots of cities. We investigate the switch policy in the dual-band network with \(F\) and \(D\) frequency bands. Intuitively, the priority of user reselection in \(D\) frequency band networks is higher than that in \(F\) frequency band networks and UE choose to stay in the \(D\) band cell with the highest priority. The other key configurations of user reselection are reported in Table 7. It is worth noting that dual-band networks with \(D1+D2\) and \(E1+E2\) do not exploit user reselection due to its high complexity of networking operation (\(D1:2575\sim 2595\) MHz, \(f_{c}=2585\) MHz; \(D2:2594.8\sim 2614.8\) MHz, \(f_{c}=2604.8\) MHz; \(E1:2320\sim 2340\) MHz, \(f_{c}=2330\) MHz; \(E2:2339.8\
# Evaluation of Mixed Traffic Flow Efficiency and Safety on Hard-Shoulder-Running Freeways
## 2 Materials and Methods  
### Data and Study Area  
As shown in Figure 1, we select a realistic scenario which is located in the Yinbai Expressway (G69) Fuling East interchange section. The studied road section is about 1.43 km, with two main lanes and one hard shoulder. The maximum speed limit on this road is 120 km/h. After analyzing the real traffic data of this road section, three different levels of traffic demands have been generated. The free flow is about 1100 pcu/h/lane; the saturated flow is about 1600 pcu/h/lane; and the congested flow is about 2000 pcu/h/lane.
## 4 Application Design and Implementation
### Message Structure and Dissemination Protocol  
#### 4.2.1 Cellular Domain  
Updated traffic information is retrieved by PUs via an HTTP GET request sent to the AS through the cellular network--requests may be periodic or triggered by a user. Important parameters, such as the username, the password and the geographical coordinates (latitude and longitude), of the requesting node are included in the GET request by the X-NETAD client. The AS needs the usernames and passwords of the nodes for authentication purposes. The traffic information provided by the AS is geographically relevant, taking advantage of the PUs' coordinates. If the authentication information provided is correct, the AS replies to PUs by sending the archive containing the local traffic update.
## 5 Experimental Results and Discussion
### Throughput  
Throughput is a measure of the efficiency of the communication system in transmitting data; it is commonly used in networking and communication systems and can be measured in different layers and different ways. For this purpose and to validate the proposed ACO-PT, throughput was measured and compared to state-of-the-art algorithms ACO-FL, RACO, and AODV-ACO. Figure 8a shows that the proposed ACO-PT achieved a greater average throughput of 90.2 kbps with 200 cars than the competing algorithms, which achieved an average throughput of 62.1-68.5 kbps. When the number of cars was increased to 400 vehicles, the suggested ACO-PT, which obtained 78.2 Kbps, was not greatly affected. Figure 8b depicts the competing algorithms, which yielded 50-62.1 kbps. The usage of a congestion-avoidance module and an autonomous detection module resulted in greater throughput. An average throughput can be obtained as follows:  
\[\begin{array}{l}T_{P}=\frac{(P_{s}*P_{i})}{P_{dt}}\\ T_{Pa}=\frac{V_{ts}*T_{P}}{P_{dt}}\end{array} \tag{38}\]  
where \(T_{P}\) is the throughput of the vehicles, \(P_{s}\) is the number of successful packets, \(P_{si}\) is the size of the delivered packet, \(P_{dt}\) is the total time for delivery of the packets, \(V_{t}\) is the total number of vehicles, \(P_{dtt}\) is the total time for the delivery of packets for all vehicles, and \(T_{Pa}\) is the average throughput.
## 3 Approximate Inverse Kinematics Solution for Imposed End-Effector State  
The proposed control method for the position and orientation of a trunk-type robot with three inextensible segments consists of four main steps:  
1. Trunk-type robot bent spine curve approximation in the 3D Cartesian coordinate system.
2. The bending angles \(\theta_{n}\) calculation for each robot segment.
3. Segments' endpoints' x, y, and z coordinates in the Cartesian coordinate system, and sections' endpoint orientation computation.
4. Robot metal wire (tendon) lengths' calculation according to robot spine curvature and segments' orientations.  
Sections 3.1-3.4 explain the above steps in more detail. In the first part, the primary purpose is to get a curve in 3D space such that its length would be equal to the robot spine length, which is predefined and fixed. Here, the soft constraint condition is the goal position, and the mandatory constraint condition is the orientation. The approximate curve endpoint's orientation must be the same as the preset orientation (as a unit vector). However, the curve endpoint must be as close as possible to the predefined robot's end-effector point. Then, further calculations will involve estimation of the approximate target position and mandatory target orientation.  
In the second and third steps, the top hard constraint condition is that the robot segment shape has to consist of a regular circle arc form. The approximate robot spine will be the prerequisite for the next curve composed of arcs connected in series in these sections. Figure 2(a) depicts bending angles \(\theta_{n}\), segments endpoints \(P_{n}\), and segments orientation \(\overset{\wedge}{\mathbf{s}}_{\text{orient}}\) unit vectors found in this approach.  
In the final step, the second and third steps' expressions lead to the estimation of the final segment cables' lengths. Computed robot tendon lengths are necessary for the robot segment position and orientation control (Figure 2(b)).
# Distribution of Operating Costs Along the Value Chain of an Open-Pit Copper Mine
## 2 Materials and Methods
### Estimation of Operating Costs
#### 2.4.8 Flotation  
The flotation circuit operated in three stages: rougher, scavenger, and cleaner. The circuit received an average annual feed rate of 28,568,816.33 tons, equivalent to an average of 3450.34 t/h. The slurry consisted of 30% solids and had a density of 1.225 t/m\({}^{3}\).  
In terms of flotation performance, 92% of copper (Cu) was recovered at an average concentrate grade of 28% Cu.  
The flotation operating cost was estimated using the following equations:  
\[K_{Ft}=K_{Fmr}+K_{Flab}+K_{Fenc}+K_{Fepd}+K_{Fcr}+K_{FO} \tag{15}\]  
\[OPEX_{F}=\frac{K_{Ftc}}{FR} \tag{16}\]  
where \(K_{Ft}\) is the total cost of flotation (USD/year); \(K_{Fmr}\) is the cost of maintenance and repairs (USD/year); \(K_{Flab}\) is the labor cost inclusive of salaries and wages for flotation operators and technicians (USD/year); \(K_{Fenc}\) is the cost of energy consumption relating to flotation (USD/year); \(K_{Fapd}\) is the cost of equipment depreciation (USD/year); \(K_{Fcr}\) is the cost of flotation consumables and reagents (USD/year); \(K_{FO}\) is the other costs (USD/year); \(OPEX_{F}\) is the operating cost associated with flotation (USD/ton); \(K_{Ftc}\) is the total cost inclusive of the 10% contingency (USD/year); and \(FR\) is the feed rate (tons/year).
## 2 Materials and Methods
### Definition of the Regions of Interest (ROIs)  
After the thermal images acquisition, the body of the athletes was divided into several regions of interest (ROIs). The definition of ROIs largely affects the result and it varies among different studies [37]. Indications regarding how to choose these areas [38] and to calculate properly the temperature of a specific region [1] have been often proposed. The different clothing of the fencers and the consequent diverse emissivity make it impossible to use standard shapes for choosing the ROI.  
The ROI associated with a specific part of the body was selected considering a representative area of each body region. Each ROI was defined through the software Infrared Report Express-PCE, which provides also the calculation of the maximum, minimum and mean temperature of the ROI.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Technical Specifications** **PCE TC-30 Thermal Camera** \\ \hline Resolution of the sensor & 80 pixels \(\times\) 80 pixels \\ Measurement range & 0–250 \({}^{\circ}\)C \\ Display & LCD (320 pixels \(\times\) 240 pixels) \\ Thermal sensibility & 80 mk \\ Frequency & 50 Hz \\ Operative Temperature & 0–50\({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 2: Technical specifications of the infrared thermal camera PCE TC-30. Data provided by [35].  
For this research, as it was focused on the temperature evolution on the athletes' bodies, only the mean temperature of the selected area was considered, which was evaluated as the arithmetic mean of all the pixels inside the ROI. Particular attention was given to ensure that each ROI was the same for all the subjects and that the pixels along the contour line were excluded from the calculation of the temperature of the ROI.  
In particular, this research was focused on evaluating the mean temperature of the whole body over the fencing uniform, calculated with the definition of ROIs shown in Figure 1a-b and on the estimation of the mean temperature of the single body parts, defined by the ROIs shown in Figure 1c. Moreover, as fencing is not a sport in which muscles work symmetrically, the ROIs associated with the left and the right part were considered separately. The head was not included in the ROIs, as athletes use a protective mask during fencing. As it was not possible to distinguish the anterior to the posterior part of the hands, only the anterior part was considered.
## 3 Results and Discussion
### IoT bioreactor Performance
#### 3.1.1 Biogas Production Monitoring of the IoT Bioreactor
The results in Figure 5 indicate a statistically significant difference in time intervals between biogas counter activations in the four days after the production decrease compared to the preceding four days. This can also be seen by observing the time intervals' values (42a-45a) after the production decrease on day 42, which increased compared to the 38a-41b period. This increase (statistical difference) persisted until 45a, whereas afterwards it was even more eminent (45b). Notably, this pattern provides a valuable signal to the bioreactor's operator that AD may be facing an operational disruption. Given the IoT protocol's capability to support real-time notifications, the bioreactor operator could be promptly alerted via email or another digital channel. Such timely notifications would enable rapid corrective actions informed by further analytical data, potentially preventing the extended biogas production decline observed in the following days. Figure 5: Biogas counter hit intervals (min) over eight consecutive days before and after day 42, when a reduction (<11%) in biogas production was observed. Subscripts “a” and “b” indicate 12-h intervals following substrate addition. Different letters above bars indicate statistical significance between groups (\(p>0.05\)).
# X-Reality Museums: Unifying the Virtual and Real World Towards Realistic Virtual Museums
## 4 Case Study: XR Natural History Museum
### XR Systems and Applications  
As illustrated in Figure 3, there are two types of interactive systems that can coexist in the same physical space, lending full support the Pleistocene Crete exhibition. First, the virtual experience, which constitutes a fully immersive, small museum room, where fossils and reconstructed skeletons of the creatures (based on hypothesized remains not yet found, or making up for the fact that animal remains may have been moved to museums abroad) are either mounted, or shown as dig sites. The user can navigate the room and view an abundance of information shown either in textual, image, audible or video form, borrowing real elements from the museum's audiovisual material (Figure 4a). However, users can opt to \"travel\" back in time, and view each individual animal in a fully animated, lifelike reconstruction, roaming a virtual recreation of the animal's habitat that completely transforms the environment around the user (Figure 4b). As such, users can observe the different ways these animals moved, and thus gain a far greater insight on the morphological conditions of the environment that allowed each animal to thrive in Crete at various time periods during the Pleistocene era. A \"timeline\" interactive User Interface feature further enables users to \"travel\" to the various time periods corresponding to the calculated eras where each animal lived, and visualize the changes via various effects (i.e., a fast-forward montage of evolution). Narrative elements can further be infused into the virtual exploration, allowing the user to view interactions between animals spanning the same time period, as if partaking in a nature documentary television series [74] (e.g., witnessing the growth of a specific Mammuthus creticus [75], or Athene cretensis [76] preying on a herd of Candiacervus [77]).  
Second, the substitutional reality experience serves as an augmented approximation of the aforementioned virtual case. In this experience, the real, physical area of the museum fossil room is setup to accommodate the XR experience, allowing the real (or plaster-built) animal remains to be subjected to a diminished reality effect. As the real-world animal skeleton cast disappears, a lifelike, moving reconstruction of the animal takes its place, and is allowed to roam the physical space around the user. Elements of natural interaction (involving gestures, movement, body postures and object manipulation) can be embedded in the experience, allowing the animal to realistically react to users' attempts to touch it, while also utilizing spatial mapping and surface understanding to allow the holographic creature to avoid collisions with other bystanders, furniture, and overall museum infrastructure.