esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Results
Zeros in the matrix indicate cases where the models did not make predictions for those particular probabilities. \[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{4}\]9 Confusion matrix of ML and DL models of tumor recurrence dependent variable with LDA. \[Precision=\frac{TP}{TP+FP} \tag{5}\]0 ROC curve of ML models (**a**) and ROC curve of DL models (**b**) of tumor recurrence dependent variable with LDA. Figures 20 and 21 depict the loss and accuracy function graphs of the RNN and LSTM models, which are deep learning models for the cause of death dependent variable. The loss function demonstrates a declining trend as the epoch value increases, indicating a reduction in the model's error over the course of training. As can be seen from the Figures 20 and 21, a very low loss and high accuracy are observed for the test set. Figure 22 shows the ROC curve of different ML and DL algorithms for the cause of death dependent variable. Figure 22a shows the performance of various machine learning models, while Figure 22b compares two different neural network models (LSTM and RNN). The Logistic Regression, SVM, and Random Forest models show high performance when  
\[Precision=\frac{TP}{TP+FP} \tag{5}\]1 RNN loss (**a**) and accuracy (**b**) function graphs of the cause of death dependent variable with LDA. \[Precision=\frac{TP}{TP+FP} \tag{5}\]2 Confusion matrix of ML and DL models of cause-of-death dependent variable with LDA. \[Precision=\frac{TP}{TP+FP} \tag{5}\]3 LSTM loss (**a**) and accuracy (**b**) function graphs of the cause of death dependent variable with LDA. looking at the ROC curve. KNN and DTC demonstrate low performance. While LSTM is among the best DL models, it is also the model with the highest accuracy among all models. It was able to correctly separate all positive classes without any false positive rate.
## 4 Understanding Centralised Identity Systems
### RADIUS: Remote Authentication Dial-In User Service  
RADIUS is a protocol designed to provide centralised authentication, authorization, and accounting (AAA) services for users accessing network resources, such as VPNs and WLANs [30]. It operates over the User Datagram Protocol (UDP), using port 1812 for authentication and port 1813 for accounting. RADIUS follows a client-server architecture, where the Network Access Server (NAS) functions as a client, routing user access requests to the RADIUS server [31].  
The centralised server manages the user's identity, the extent of their access, and logs all activities. Communication between the client and server is secured using a shared secret and a private key, unique to each pair, which encrypts the communication between them. The protocol employs various types of packets, each distinguished by a unique code. For instance, Code 1 represents an access request from the client requesting authentication, Code 2 indicates successful authentication, and Code 3 is used to deny access [32]. Each packet contains fields such as Code, Identifier, Length, Authenticator, and several other attributes [31; 33].  
When a client seeks authentication, it sends an Access-Request packet containing the username and password, encrypted using a pseudo-random Request Authenticator, the shared secret, and MD5 hashing. The server decrypts the password and, based on the validation result, responds with Code 1, 2, or 3, indicating the status of the authentication. RADIUS sends accounting packets to log session data and acknowledges them with Accounting-Response packets. It is versatile and can be integrated with EAP, LDAP, and Active Directory [33]. RADIUS is widely deployed in enterprise environments, using implementations like FreeRADIUS, Cisco Secure ACS, and Microsoft's IAS/NPS to support wired, wireless, and remote access needs [30; 31; 34].  
RADIUS: Security and Limitations  
The security of RADIUS is primarily dependent on the use of a shared secret key and the proper generation of the Request Authenticator. However, as cyber threats have evolved, the use of a stream cipher with MD5 for password encryption is now considered weak, exposing the protocol to potential attacks [35; 33]. Attackers can exploit Access-Request packets to target the shared secret, and if they obtain both the Access-Request and server response, the Response Authenticator can be compromised. Additionally, insufficient randomness in the Request Authenticator can lead to both active and passive attacks, such as Denial of Service (DoS) [33].
# Non-Contact and Non-Intrusive Add-on IoT Device for Wireless Remote Elevator Control
## 5 Experimental Application Results
### Evaluation of the Latency of the IoT Device  
Figure 14 shows the evolution of the latency of the IoT device evaluated using the ping software utility to test the reachability of the IoT device placed inside a car of the elevator with the doors closed. This software utility is available for virtually all operating systems that provide network access. Ping measures the round-trip time for messages sent from a host to a destination device and echoed back to the host. Figure 14 shows the latency of the IoT device evaluated at the different floors of the building. The results show the following packet loss depending on the floor: 26% on Floor \(-1\), 0% on Floor 0, 0% on Floor 1, 0% on Floor 2, and 0% on Floor 3. These packet loss results agree with the results obtained in the previous sections when accessing the IoT device of the elevator at the basement floor of the building. Regarding the packets successfully received, the average latency of the IoT device evaluated in the WiFi network of the building was 266 ms.
###### Abstract  
The path planning algorithm not only ensures that the mobile robot can avoid obstacles to reach the target point at a safe speed but also ensures that the mobile robot can quickly adapt to the complex changing environment. In this paper, the existing path planning algorithms of mobile robots are analyzed, and then the fusion path planning algorithm is studied. The main work is summarized as follows: A* algorithm is used to complete global path planning and path smoothing, the basic principle of dynamic window method (DWA) is studied, and the dynamic constraints of mobile robots are discussed. The shortcomings of the dynamic window method, i.e., that the dynamic window method does not have the ability to self-learn and self-adapt in the dynamic and unknown environment, are analyzed through simulation experiments. In addition, by studying the basic principle of deep reinforcement learning, the essence and characteristics of DWA algorithm and DQN algorithm are analyzed, which provides ideas for the fusion path planning algorithm based on DQN and DWA. Finally, to cope with the complex and changeable environment and improve the real-time obstacle avoidance ability of mobile robots, a fusion path planning algorithm based on DQN and DWA is proposed. First, the dynamic window method is used to limit the driving of the mobile robot directly to the velocity space. Then, a deep Q network is designed and trained to approximate the state-action value function of the mobile robot, then dynamically interact with the environment to adjust the robot's moving trajectory in real time, and finally, find the optimal path for the robot. The simulation results show that the fusion path planning algorithm proposed in this paper ensures that the mobile robot has strong generalization ability and robustness under the complex, variable, and dynamic unknown environment. Compared with the existing DWA and DQN algorithms, the proposed fusion path planning algorithm achieves better path planning performance with less training times, shorter computation time, and faster convergence speed.  
* +
Footnote †: journal: Applied sciences
## 2 Model for Ultrasonic Guided Wave Ray Tomography
### Conventional Model
Suppose that the group velocity in each grid is constant, Equation (2) can be discretized as:  
\[\tau_{guri}=\sum_{j=1}^{N^{2}}l_{ij}s_{j}\left(i=1,2,\cdots,M^{2}\right), \tag{3}\]  
where \(l_{ij}\) is the length of the \(i\)th propagation path in the \(j\)th grid, as shown in Figure 1, \(s_{j}=\frac{1}{c_{guri}}\) represents the slowness in the \(j\)th grid. For \(N\times N\) grids and \(M\times M\) propagation paths, Equation (3) can be rewritten as the following linear equation:  
\[\mathbf{T}=\mathbf{LS}, \tag{4}\]  
where \(\mathbf{T}=\left(\tau_{guri}\right)_{M^{2}\times 1}\) is the column vector of travel-time, \(\mathbf{S}=\left(s_{j}\right)_{N^{2}\times 1}\) is the column vector of slowness, and \(\mathbf{L}=\left(l_{ij}\right)_{M^{2}\times N^{2}}\) is the length matrix of the propagation paths divided into pieces by the grids. Equation (4) is known as the conventional model for ultrasonic guided wave ray tomography. By solving Equation (4), one can obtain the slowness of each grid. Based on the slowness obtained, the thickness mapping is performed. Additionally, it is obvious that the more the number of grids is, the higher the spatial resolution is. For high spatial resolution, \(N^{2}\) is much larger than \(M^{2}\). Unfortunately, when \(N^{2}>M^{2}\), Equation (4) is underdetermined. This means that the vector \(\mathbf{S}\) cannot uniquely be determined by solving Equation (4). Even so, an iterative technique called the SIRT is constantly used to do so because another alternative is unavailable so far. The SIRT is started with an initial guess of the solution and then iteratively updates the solution until some criteria are met. It is known that the solution obtained by the SIRT can only be regarded as one of all possible solutions of the underdetermined equation. This is the main reason believed why the thickness mapping reconstructed by the SIRT contains so many artifacts.
###### Abstract  
Thermal conductivity is a key property in many applications from electronic to informatics. The interaction of fillers with Sylgard 184 was studied; this study explores new composites and the influence of metal particles (copper and nickel), carbon-based materials (carbon nanotubes and carbon black), and ceramic nanoparticles (boron nitride) as fillers to enhance thermal properties of silicon-based composites. The effect of the fillers on the final performances of the composite materials was evaluated. The influence of filler volume, dimension, morphology, and chemical nature is studied. Specifically, FT-IR analysis was used to evaluate curing of the polymer matrix. DSC was used to confirm the data and to further characterize the composites. Thermo-mechanical properties were studied by DMTA. The filler morphology was analyzed by SEM. Finally, thermal conductivity was studied and compared, enlightening the correlation with the features of the fillers. The results demonstrate a remarkable dependence among the type, size, and shape of the filler, and thermal properties of the composite materials. Underlining a that the volume filler influenced the thermal conductivity obtaining the best results with the highest added volume filler and higher positive impact on the k of the composites is reached with large particles and with irregular shapes. In contrast, the increase of filler amount affects the rigidity of the silicon-matrix, increasing the rigidity of the silicon-based composites.  
Keywords:thermal conductivity; metallic fillers; carbon filler; ceramic fillers; silicon composites +
Footnote †: journal: Applied Sciences
# Low-Noise Programmable Voltage Source
## 3 Test of the Designed Voltage Source
### Test Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Transistor 2 SK3557-6**} & \multicolumn{2}{c}{**Transistor IF9030**} \\ \cline{2-5}  & **Vout = 50 mV** & **Vout = 1 V** & **Vout = 50 mV** & **Vout = 1 V** \\ \hline VSD at 1 Hz & 3 nV/Hz & 3 nV/Hz & 3 nV/Hz & 3 nV/Hz \\ \hline VSD at 10 Hz & 1 nV/Hz & 0.7 nV/Hz & 0.8 nV/Hz & 0.6 nV/Hz \\ \hline VSD at 100 Hz & 0.8 nV/Hz & 0.5 nV/Hz & 0.7 nV/Hz & 0.4 nV/Hz \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Noise performances of the constructed low-noise voltage source. Figure 5: Voltage spectral density for four paralleled 2 SK3557-6s (**a**) and two paralleled IF9030s (**b**) as a \(T_{1}\) in Figure 3 with \(R_{\text{s}}=100\)\(\Omega\). Measurements with the inputs of the amplifiers shorted (“GND”) and with a 100 \(\Omega\) resistor as a device-under-test (DUT), obtained with the very same measurement setting, are also shown for reference. As expected, the configuration with two parallelized IF9030 JFETs results in less noise for the same output voltage (\(R_{S}\) = 100 \(\Omega\) in both cases). The results of cross-correlation noise measurements also show that below 1 Hz the voltage noise appears to be independent on the bias point and on the nature of the output transistors, thus suggesting that in this region it is the residual noise from the DA converter that sets the ultimate noise performances. Moreover, in this region some correlated components coming from measurement setup might also appear. With \(V_{B2}\) = 6.4 V, the maximum voltage that can be obtained at the output with \(R_{S}\) = 100 \(\Omega\) is 3.3 V (the DAC was programmed to \(\pm\)3 V range to operate with lowest possible voltage noise at its output [33]). While reaching excellent noise performances was the main goal of this work, in measurement applications the stability of the output voltage is also a key parameter. To test for stability, in the configuration with the 2 SK3557-6, we set an output voltage of 1 V and after waiting for the stabilization of the system, we monitored the output voltage for three hours in the conditions used for noise measurements (Figure 6). The voltage was monitored by a KEITHLEY 2002 multimeter (measurement range: 2 V; measurement resolution: 10 nV). Over a period of three hours, the voltage remained within \(\pm\)25 \(\mu\)V of the set voltage (1 V). During the test, the external temperature was monitored and remained within \(\pm\)0.1 \({}^{\circ}\)C of the initial value. While the measured stability can be regarded as sufficient for many applications, better temperature stabilization and larger capacity batteries (to avoid excessive discharge in a given time) can be used to improve stability of the output voltage.
## 4 Results and Discussion
### Truck Productivity  
As mentioned in Equation (1), truck productivity will rely on the tonnages moved and the overall cycle time. From Figure 11, the fall in productivity between January and April occurred as a consequence of the increment in the overall cycle time by six minutes. Moreover, the tonnages moved in the months after April were maintained almost at a constant rate with minimal variation in the overall cycle time. Consequently, the truck productivity behaved similarly during those months.
# A Lightweight Fault Diagnosis with Domain Adaptation for Defected Bearings
## 4 Conclusions  
In this paper, we proposed a lightweight fault diagnosis framework that integrates time-frequency analysis, deep learning, and model compression techniques for bearing fault diagnosis in resource-constrained environments. By leveraging the S-transform for high-resolution time-frequency representation and MobileNet as an efficient backbone network, the framework achieves robust fault detection while maintaining computational efficiency. The application of knowledge distillation further compresses the model, enabling real-time deployment on edge devices. Additionally, domain adaptation techniques enhance the model's generalization across varying operating conditions. Experimental results demonstrate the framework's effectiveness in achieving high diagnostic accuracy with reduced computational overhead, making it a practical solution for industrial applications.  
Nonetheless, it is found that the current work still has limitations, including: (1) Dependency on high-quality vibration signals, which may degrade with sensor placement errors; (2) Limited validation under extreme conditions (e.g., <100 RPM or >3000 RPM); and (3) Computational overhead for very large-scale systems (>1000 bearings). To guide future work, we propose the following: (1) Incorporating active learning to reduce labeling costs, (2) Extending the framework to multi-sensor fusion, such as thermal and acoustic. (3) Optimizing the distillation process for heterogeneous edge devices. These points provide concrete directions for improvement in the future.  
Each author contributed extensively to the preparation of this manuscript. Writing--original draft preparation, X.J.; Investigation, Y.Z.; Writing--review and editing, X.L. and Z.M.; Funding acquisition, F.X. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Natural Science Foundation of China under Contract No. 5247514 and No. 52005442, and the Zhejiang Provincial Natural Science Foundation of China under Contract No. LY22E050014.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 2 Review of LRB Design Characteristics  
In previous studies, the lead inserted small-sized LRBs have been designed with 100 mm outer diameter and a design vertical inertia load of 10 kN. In this design, the lead plug with a 21.5 mm diameter is inserted into the LRB center position to take the role of energy dissipation. Ultimately this is expected to reduce the maximum shear displacement of LRB during the seismic events.  
The total rubber height is 20 mm with appropriate shape factors of \(S_{1}=9.9\) and \(S_{2}=5.0\), where \(S_{1}\) represents the ratio of the area of the rubber plate where the surface pressure applies and the free surface area where the rubber plate; \(S_{2}\) represents the ratio of the diameter and the total rubber height.  
The design shear displacement of LRB is 35 mm corresponding to 175% shear strain of LRB based on the total rubber height of 20 mm. To accommodate the beyond design basis earthquake, the 70 mm corresponding to 350% shear strain of LRB is set to increase the seismic fragility capacity for the peak ground motions.  
Table 1 presents the summary of the design parameters of the lead inserted small-sized LRB to be carried out by the shaking table tests to identify its seismic isolation performance characteristics.  
The LRB dynamic characteristics corresponding to the design parameters are presented in Table 2. As shown in the table, the horizontal seismic isolation frequency at the design shear displacement level is 2.3 Hz for a design and the identified frequency by the quasi-static tests is 2.25 Hz [21]. The critical damping ratio is 25.5% for a design and the identified value by the quasi-static tests is 24.1%. For equivalent bi-linear characteristics of LRB, the designed tangential stiffness and the characteristic strength are 123 kN/m and 3 kN, respectively, and the identified values by the quasi-static tests are 122 kN/m and 2.72 kN, respectively.
# A QoS-Enabled Medium-Transparent MAC Protocol for Fiber-Wireless 5G RAN Transport Networks
## 4 Results
Figure 8c,d present the delay performance under the three PS execution policies for the EXP and BE flows, respectively. By means of Figure 8c, it can be seen that for very-low-load values (0.1) the enhanced priority policies produced marginal benefits compared to the No priority policy. This was due to the fact that, in very-low-load conditions, the default priority scheme at the creation of the PS suffices to produce adequate prioritization as the low packet generation means that only a few EXP packets will arrive during an SF and will have to wait for the next PS. However, as load increased, we witnessed significant performance gains produced by the enhanced priority schemes, reaching up to 62.25% for the EXP priority and up to 64% for the Pre-emption policy vs. the equivalent No priority results. This performance improvement stemmed from the fact that under the EXP priority scheme, an EXP packet does not have to wait for the next PS to be transmitted and can, instead, take over the transmission slot of the next BE packet, while under the Pre-emption policy, an EXP packet can even interrupt the on-going transmission of a BE packet and gain even further performance improvement. By means of Figure 8d, it can be seen that the opposite was true for the BE flows, as the enhanced priority PS execution policies produced higher delays for the BE packets as the prioritization schemes further stalled their transmission. Therefore, it became evident that the enhanced priority policies could produce significant delay reductions for delay-sensitive traffic, making them more suitable for use with delay-bounded traffic such as FH or URLLC.
# A Stackelberg Game Approach for Price Response Coordination of Thermostatically Controlled Loads
## 3 Stackelberg Game Coordination
### Stackelberg Game  
Since there exists a hierarchy among players in Stackelberg games, leaders are in a position to enforce their strategies on the followers. In this leader-follower competition, the followers find the best response function first, i.e., getting to know how they will respond once they observe the strategies of leaders. The leaders are aware of the fact that each follower will choose its best response with respect to the leaders strategies. Hence, the leaders are able to maximize their payoffs anticipating the predicted response of the followers. This is actually observed by the followers to adapt their expected strategy accordingly as a response.  
We introduce a one-leader, \(N\)-follower Stackelberg game to characterize the electricity transaction process between EECC and TCL users, where EECC serves as the leader and TCL users act as followers. Thus, the system proceeds by the following two stages:  
* Stage I: Each TCL user \(i\) implements the best response function with respect to the broadcasted price \(p\) from EECC.
* Stage II: EECC optimizes the broadcasted price \(p^{*}\) considering TCL users' best response \(\mathbf{u}^{*}(p)\) at Stage I.  
Then observing EECC's best strategy, each TCL user \(i\) determines its optimal energy demand \(u_{i}^{*}\) under the broadcast price \(p^{*}\) from Stage II. Based on the above set-up, the optimization problem can be formally formulated as the following:* Leader level: \[p^{*}=\underset{p\in\mathcal{P}}{\arg\max}\ S_{E}(p;\mathbf{u}^{*}(p))\] (17)
* Follower level: \[u_{i}^{*}(p)=\underset{u_{i}\in\mathcal{U}_{i}}{\arg\min}\ C_{i}(u_{i};p)\] (18)  
The optimal strategies of the game take the form of the Stackelberg equilibrium [30; 31]. At the equilibrium, the leader's strategy \(p^{*}\) is a solution to the optimization problem specified in (17) based on the best strategy trajectories \(\mathbf{u}^{*}(p)\) of the followers. Each follower's strategy is also a solution to (18) when it is informed of the equilibrium strategy of the leader. The optimal strategies \(u_{i}^{*}(p^{*}),i\in\mathcal{N}\) therefore constitute the equilibrium for all the followers.  
Then, we have the following definition of the Stackelberg equilibrium [18; 22].  
**Definition 1** (Stackelberg equilibrium).: _The strategy \((p^{*},\mathbf{u}^{*})\) is a Stackelberg equilibrium if it satisfies:_  
\[S_{E}(p^{*};\mathbf{u}^{*}(p^{*}))\geq S_{E}(p;\mathbf{u}^{*}(p)), \tag{19}\]  
\[C_{i}(u_{i}^{*};p^{*})\leq C_{i}(u_{i};p^{*}),\text{ for all }i\in\mathcal{N}. \tag{20}\]