esa-sceva/satcom-chunk-collection huggingface dataset:
# Modelling and Measuring Trust in Human-Robot Collaboration
## 5 Discussion
Variables such as pupil dilation or changes in breathing patterns play a fundamental role in implementing personalized trust detection models, but lack significance in the generalist model. Conversely, signals like brain activity in the parietal-occipital lobe (electrodes PO7 and PO8) contribute to the generalist model but have very little influence on personalized models. Although the cause of these phenomena has not been studied in detail, it can be inferred that signals included in the generalist model exhibit similar behavior in most individuals, but these changes are not necessarily the most sensitive to variations in trust situations. Having outlined the scope of the results obtained, it is important to acknowledge that while this research may not fully capture the entirety of trust dynamics, it offers valuable insights that could have a significant impact if implemented in industrial environments. The findings of our study underscore the significance of initial interactions in shaping the trajectory of trust dynamics in Human-Robot Collaboration (HRC). While it is essential to recognize the critical role of first interactions in trust formation, it is equally crucial to acknowledge the potential challenges posed if the machine's trustworthiness diminishes over time. Monitoring trust variations in real time may offer insights into detecting deteriorating robot behavior. By incorporating mechanisms to detect and respond to changes in trust, developers can proactively address potential issues and maintain user confidence in robotic systems. These responses to changes in trust may include automatic modifications in robotic behavior, such as reduced operation speed, increased safety distance, and additional communication and feedback channels, such as alarms or flashing lights. Additionally, another noteworthy aspect to consider is how the robot's behavior might adapt if it receives indications that it is not trusted by the human user in its actions. This feedback loop could serve as a basis for extending trust models and developing more sophisticated algorithms for adaptive robot behavior. By incorporating feedback mechanisms that allow robots to adjust their actions based on user trust levels, we could potentially enhance the overall user experience and foster stronger collaboration between humans and robots in various contexts. In summary, our study has clarified the intricate interplay of factors influencing trust in HRC, emphasizing the importance of initial interactions, the need for the real-time monitoring of trust dynamics, and the potential for adaptive robot behavior. These insights offer valuable guidance for developers and researchers seeking to design reliable, robust, and trusted human-robot collaborative environments.
## 3 Experimental Setup
### The Thermal Subsystem  
In order to examine the performance of the secondary system at various longitudinal inclination angles, a thermal subsystem was constructed. The construction of the thermal subsystem used to dissipate the heat generated in the secondary system was based on ISO 9806: 2017--Test methods for solar thermal collectors [42]. The main objective of the thermal subsystem is to simulate a thermal load that allows the heat generated in the secondary system to be removed. The schematic diagram of the thermal subsystem is shown in Figure 3, and a photograph of this subsystem is shown in Figure 8. The thermal subsystem comprises a pump, an expansion tank, a valve, a heat exchanger, a fan, a pressure gauge, an air bleeder valve, a temperature sensor, and a flow meter. The connection of these devices can be seen in Figure 3. The thermal subsystem comprises a closed circuit with a centrifugal pump taking the water from the outlet of the secondary system where the heating process takes place and pushing it through a heat exchanger, finally returning it to the secondary system inlet. As this is a closed circuit, a heat exchanger (radiator with fan) must be installed to transfer the heat recovered from the secondary system to the environment. Two temperature sensors are also available to record the secondary system inlet and outlet water temperatures. A pressure gauge and a flow meter are present to ensure the circuit pressure and the water flow rate remain constant during the experimental campaign. The fact that the recommended volumetric flow rate for liquid heating solar collectors is between 10 and 50 (L/h) [43] and that thermosyphon-driven liquid-heated solar collectors can reach up to 55 (kg/h) of water mass flow rate [44] was taken into account. The power consumed by the metering equipment, the auxiliary equipment, and centrifugal pump were disregarded as they fall outside the scope of this energy analysis. A constant volumetric water flow rate of 0.5 (L/min) was established for all tests.  
Figure 7: Photographs of the inclination system.
## 3 Results
### Effect of the Rake Angle on the Cutting Force  
The effect of the rake angle was also investigated by the analysis of the mean cutting force and peak cutting force.  
Figure 11 plots the mean cutting force with respect to the rake angle. When the rake angle increases from 0\({}^{\circ}\) to 5\({}^{\circ}\), the cutting force decreases slightly. At a cutting depth of 3 mm, it reduced by 0.15 kN from 2.59 kN to 2.44 kN and by 0.06 kN from 0.94 kN to 0.88 kN the for UCS 50 MPa and decreased UCS 30 MPa rocks, respectively (Figure 11a). Similarly, at a cutting depth of 9 mm, it reduced from 10.18 kN to 9.07 kN and 5.25 kN to 5.11 kN for the UCS 50 MPa and UCS 30 MPa rocks, respectively (Figure 11c).  
However, when the rake angle increased above 5\({}^{\circ}\), the mean cutting force increased. When the rake angle increased from 5\({}^{\circ}\) to 15\({}^{\circ}\) at a cutting depth of 6 mm, the mean cutting force of the UCS 40 MPa and UCS 20 MPa rocks increased from 3.53 kN to 5.07 kN and 1.42 kN to 2.12 kN, respectively (Figure 11b).  
The peak cutting force exhibits similar trends (Figure 12). At a cutting depth of 6 mm, the cutting force of the UCS 50 MPa rock decreases from 11.55 kN to 9.38 kN as the rake angle increases from 0\({}^{\circ}\) to 5\({}^{\circ}\). In addition, when the rake angle increases from 5\({}^{\circ}\) to 15\({}^{\circ}\), the peak cutting force increases to 12.85 kN (Figure 12b).  
This phenomenon also appears to be caused by friction between the cutting tool and rock. Figure 13 shows the friction surface of the cutting tool after cutting, which increases slightly with the rake angle. The details of the mean and peak cutting forces according to the rake angle are listed in Table 4.  
In summary, the cutting force decreased with increasing clearance angles. In particular, when the angle increased from 0\({}^{\circ}\) to 5\({}^{\circ}\), the cutting force decreased significantly. Furthermore, when it exceeded 10\({}^{\circ}\), the cutting force converged to a constant value. However, the cutting force increased with the rake angle. A slight decrease occurred when increased from 0\({}^{\circ}\) to 5\({}^{\circ}\), but an increase was observed after that. However, the effect of the rake angle was significantly lower than that of the clearance angle.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**UCS** **(MPa)**} & \multirow{3}{*}{\(p\) **(mm)**} & \multicolumn{6}{c}{**Clearance Angle (Deg.)**} \\ \cline{3-10}  & & & **0** & & **5** & & **10** & & **15** \\ \cline{3-10}  & & **F\({}_{\rm c}\)**Figure 11: Relationship between the mean cutting force and rake angle at cutting depths (\(p\)) of: (a) 3 mm, (b) 6 mm, and (c) 9 mm.  
Figure 12: Relationship between the peak cutting force and rake angle for depths (\(p\)) of: (a) 3 mm, (b) 6 mm, and (c) 9 mm.
## 4 Assessment of Key Parameters  
The previous outcomes are herein organized in order to propose analytical formulations that may be used as reference for the design procedures that may be implemented in international seismic codes for structures [32; 33] and bridges [34; 35]. The previous outcomes may be compared with existing case studies, such as [36; 37; 38], that demonstrated that the effects of SSI depend on the fundamental period of the entire system (soil + structure) T\({}_{1}\) that needs to be compared with the main frequency of the earthquake. Therefore, herein several analyses of dependencies have been performed. The main goal is to aggregate the results in a synthetic formulation that allow to describe in a closed-form the relationships between the key parameters: the fundamental period of the structure (T\({}_{S}\)), the ratio between the isolated and the structural period (_n_) and the shear wave velocity of the soil layer (V\({}_{S}\)). In this regard, the comparison between various n values (i.e., Figures 5 and 6) have been considered and the function \(\mathcal{F}\) representing the period elongation (as in the previous Equation (1)) was derived:  
\[\mathcal{F}:\frac{T_{1}}{T_{S}}=\mathcal{F}(n,\ T_{s},V_{s}) \tag{8}\]  
The interpolations of the results were considered to derive a common relationship that describes the function \(\mathcal{F}\).  
Parameter \(\alpha\) (3) may be rewritten as:  
\[\alpha=\left(\frac{T_{s}\cdot V_{s}}{4H}\right)^{2} \tag{9}\]  
In order to represent the condition under which SSI effects become important: big values of \(\alpha\) represent the cases with rigid soil and deformable structures, with that the effects of SSI are not important, as shown in [22]. Small values of \(\alpha\) represent those cases where the mutual role of soil deformability and rigid structures may have significant effects on the dynamic properties (such as the fundamental periods) of the entire system (structure + isolation + soil).  
For each value of n, it is possible to calculate a limit value of \(\alpha\) (named \(\Omega\)) that may be considered a pivotal value for separating the cases in two parts. In particular, the parameter d was calculated as 0.324, 0.226 and 0.174, respectively for n = 2, n = 3 and n = 4.  
Figure 6: Vs = 150 m/s, comparison between: n = 2, n = 3 and n = 4.  
Therefore, the pivotal value d was assessed with this formulation that depend on the isolation ratio only:  
\[\Omega=0.725\cdot(n)^{-0.897} \tag{10}\]
# FREDY: Federated Resilience Enhanced with Differential Privacy
## 2 Related Work and Backround  
### Federated Learning  
Federated Learning [1] adopts a distributed paradigm for the learning process of Machine Learning models, implementing collaborative training among a set of nodes (participants). Each node in FL trains its own local model based on their own data, which are kept private and not disclosed to the peer nodes in the FL system. In this way, FL does not involve any centralized training, but rather the locally trained models are aggregated into a global model at a centralized server. Figure 1 presents the Federated Learning process. The process starts by initiating the training of the initial global model at the central server for \(n\) federated rounds. For each round, the server sends a copy of this model to the clients of the FL system, where it is trained using the client's local data. Then, the updated model weights are sent back to the central server and the global model is updated by aggregating each client's weights. The most commonly used aggregated function [4; 32; 33; 34; 35; 36] is the averaging function of the weights, and it is defined as follows:  
\[W_{t}^{global}=\frac{1}{K}\sum_{k=1}^{K}W_{t}^{k}, \tag{1}\]  
where \(W_{t}^{k}\) is the updated weights from the client \(k\) and \(K\) it the total number of clients at round \(t\). Therefore, through this process, machine learning models can be derived, exploiting the aggregated knowledge of a number of dispersed nodes, without disclosing the data to the peers, thus ensuring data privacy and saving communication overheads.
## 1 Introduction
### New Research Scheme Proposal
The possible reason behind this observation is that numerical simulations are based on mathematical assumptions and approximations of the complicated realistic flow physics, and therefore contain the inherent inaccuracies. Moreover, the computational cost is high for such a simulation approach, and the solution always comes out from the program after a long time of calculation. On the other hand, we also simulated several sinusoidal wave flow fields and found that most simulated results across the computational domain agree well with the simulation target, suggesting the good ability of the numerical platform in simulating rather simple flow events. In recent years, a multiple-fan actively controlled wind tunnel called TJ-6 has been constructed at Tongji University in Shanghai, China, as shown in Figure 2. TJ-6 has 120 independently controlled highly sensitive fans arranged in a 10 \(\times\) 12 matrix, with a test section size of 1.8 \(\times\) 1.5 m and an applied wind speed range of 2.0-20 m/s. However, preliminary investigations have also revealed that TJ-6 cannot easily simulate realistic target velocity histories with sharp velocity changes (typical non-stationary velocity histories), as achieved by [PERSON] et al. [24]. After technical condition evaluations, it was found that the fans in TJ-6 wind tunnel neither do have sufficient stiffness to accurately deliver the targeted complicated non-stationary flows. However, it was also noted that TJ-6 can accurately simulate longitudinal velocity histories that vary according to basic sinusoidal functions, as reported in ref. [27]. According to ref. [27], basic sinusoidal functions are those that can be mathematically expressed as \(y=A\sin(\omega x+\varphi)\), while senior sinusoidal functions are those obtained via the linear superposition of several basic sinusoidal functions, e.g., the function \(y=A_{1}\sin(\omega_{1}x+\varphi_{1})+A_{2}\sin(\omega_{2}x+\varphi_{2})\). In sum, the research scheme proposed in Section 2.1 cannot be undertaken as planned on a commercial CFD platform or in a TJ-6 wind tunnel due to the incapability of both numerical and physical approaches. However, shall we use the abilities of the commercial CFD platform and TJ-6 wind tunnel in accurately simulating the simple sinusoidal wave flow fields to subtly address the aforementioned technical issue of simulating the complicated non-stationary flow fields? This question is answered via in-depth numerical simulations below based on the specific case study of Pengcheng cooling tower. Figure 2: TJ-6 actively controlled wind tunnel.
## 2 Materials and Methods
### Structure of the Water Distribution Network  
The Farsala water supply system relies on groundwater abstraction through vertical boreholes. The system includes three wells/pumping stations in the north, with one of them being in continuous operation and two used as backups, ensuring a maximum pumping capacity of approximately 270 m\({}^{3}\)/h.  
Due to the lack of enclosed facilities, these wells are only protected by aluminum roofs, making them more susceptible to operational and maintenance issues. Water is stored at a primary storage tank (PST) with a capacity of 1000 m\({}^{3}\). Given the limited number of active wells, maintaining a stable water supply remains a challenge, particularly during peak demand periods. For this reason, the stored water is led to two other locations closer to the city and then distributed across the network. The external aquduct connects these storage units to the internal distribution network through a 200 mm PVC pipe.  
The PST is connected to the city's reservoir, which consists of two identical reinforced concrete cisterns at an altitude of 196 m, with water transfer facilitated through a 315 mm PVC pipe extending for 4823 m. Each cistern has a height of 3.5 m, and both are housed within a single facility. Similarly, the water is then led to the main balancing tank (MBT) located at an altitude of 231 m at the foothills of the mountain, which comprises two twin semi-underground cisterns of 400 m\({}^{3}\), and is distributed from the hilly heights to the plains of the water network.  
The water distribution network is classified as a mixed system, combining elements of both radiant and closed-loop networks. However, much of the existing infrastructure is in a deteriorated state, leading to frequent leaks and breakages, up to 1200 incidents per year in total, and 800 of them refer to leakages. The pipeline network comprises three materials, including high-density polyethylene (HDPE), polyvinyl chloride (PVC-U), and ductile iron. Pipe diameters range from 90 mm to 315 mm, with primary transmission lines supplying water from tanks and reservoirs to the distribution network.  
A major limitation of the Farsala water network has been the historical absence of a centralized data management system. Until 2022, the network had no digital mapping or structured database, making operational assessments and maintenance planning difficult. The Municipal Water Supply and Sewerage Company of Farsala, established in 2000, has been facing severe understaffing issues most of the years it has operated. To address data gaps and improve network monitoring, a SCADA telemetry system was implemented in 2022. The SCADA telemetry system provides real-time data on pressure levels, flow rates, and storage capacities, allowing for better operational control and loss management.
# Salp Swarm Algorithm Optimized A* Algorithm and Improved B-Spline Interpolation in Path Planning
## 2 Path Planning Algorithm  
### The A* Algorithm  
The A* algorithm is a heuristic search algorithm proposed by [PERSON] et al. in 1968 [25]. It combines the optimality guarantee of Dijkstra's Algorithm with the efficiency of greedy search. Unlike traditional non-heuristic search methods, the A* algorithm considers both the estimated distance from the current node to the target and the actual cost to reach the node while exploring the path. A combined priority score, \(f(n)=g(n)+h(n)\), is computed to intelligently guide the search process. This mechanism enables the A* algorithm to ensure optimal solutions while significantly improving search efficiency, which is why it has been broadly utilized in various fields such as path planning, game AI, and robot navigation.  
However, despite its excellent performance in many scenarios, the A* algorithm still has some limitations. First, its computational time can be long, particularly in large maps where the algorithm must expand a large number of nodes and maintain open and closed lists, which increases the computational burden significantly. Second, the performance of the A* algorithm is critically determined by the design of the heuristic function \(h(n)\). If the heuristic function is unacceptable or inconsistent, the algorithm may fail to find the optimal solution or experience a significant drop in efficiency.
## 4 Case Study
### Comparative Study  
This paper presents the novel PCA-LSTM method, which combines advantages of PCA and deep learning algorithm LSTM to manage big time-series data in operation monitoring systems. We compare the proposed method with other prediction methods including traditional PCA-LSTM and LSTM using the same dataset as Section 4.2 for further analysis. The results are shown in Figures 12 and 13.  
Figure 11: The learning result of cross validation.  
Figure 12: The training result of comparative study.  
It is obvious in Figure 12 that the proposed method works better with a satisfactory error range. LSTM shows the maximum deviation because there is no variable selection before of the prediction process. Although it is a powerful tool to deal with the big series data for its special gate control function, it cannot give consideration to the variable analysis.  
In Figure 13, it is easy to find that the novel PCA-LSTM performs better than both traditional PCA-LSTM and LSTM in the test. The yellow line in the figure marks the proposed PCA-LSTM, which has the lowest mean average error of 0.9213%. The green line marks the traditional PCA-LSTM, which shows a mean average error of 1.5301%. However, LSTM shows the worst result with a MAE (mean average error) of 2.0269%. The results differences are caused mainly because of the variable selection for the prediction model. As the input of the data-driven model, variable selection should be more focused with human knowledge and experience.  
From a practical point of view, the comparative results are also analyzed by different evaluating indicators such as MAE (mean average error), R\({}^{2}\) (coefficient of determination), and RMSE (root mean square error). As shown in Table 2, all of the models show a good performance with the coefficient of determination, which can explain the LSTM effectiveness. However, for the root mean square error, the proposed method takes on a better stability in the prediction result. The comparative results indicate that the control of input is essential to the machine learning methods.
## 5 Experimental Analysis and Discussion
### QoS in Different Routing Protocols Corresponding to Mobility Models  
The performance of different routing protocols through different mobility models is analyzed. There are various factors, namely the number of nodes, and energy consumption  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Sr. No** & **Type of Parameters** & **Value** \\ \hline
1. & Number of ’n’ body nodes & \(25,50,75\) \\
2. & Network initial energy (Joule) & \(1\) j/node \\
3. & Propagation delay & End to end delay \\
4. & Probability loss & \(0.33\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation performance parameters.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Node Id** & **X-Coordinates** & **Y-Coordinates** \\ \hline
1. & 1.055 & 4.0667 \\
2. & 2.003 & 3.223 \\
3. & 5.220 & 4.22 \\
4. & 1.002 & 3.3332 \\
5. & 2.222 & 3.2223 \\
6. & 3.554 & 2.3332 \\
7. & 1.225 & 5.5552 \\
8. & 3.222 & 4.442 \\
9. & 1.222 & 3.3336 \\
10. & 1.3335 & 2.3335 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Coordinates of x, y coordinator with different nodes.  
with joule rate, to analyze the performance. Different QoS parameters for analyzing the routing parameters are as follow: