esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Disclosure to an IoT Conversational Agent: Effects of Space and User Context on Users'
## 2 Theoretical Background and Research Hypotheses  
### Information Types for Self-Disclosure to IoT-CAs  
#### 2.1.1 Self-Disclosure to IoT-CAs  
Self-disclosure is the process of revealing thoughts, emotions, attitudes, or other personal information to others [7; 8]. Social penetration theory [2] asserts that as relationships develop, self-disclosure moves from relatively nonintimate to more intimate levels. Self-disclosure is not limited to human-human interaction. People can engage in self-disclosure in human-computer interaction (HCI). This can be explained by the computers are social actors (CASA) paradigm, in which people respond to computer technologies as social actors, applying interpersonal social rules to their interactions with computers [9; 10]. [PERSON] [11] found that users were much more likely to engage in self-disclosure when a text-based computer initiated the disclosure process by disclosing some information about itself first (e.g., the computer's abilities or configuration) and then followed a socially appropriate sequence of disclosure by gradual escalation from nonintimate to intimate disclosure requests. [PERSON] and [PERSON] [12] were the first to use a structured disclosure (i.e., question and answer) procedure, which has been widely used in interpersonal self-disclosure to help people engage in verbal and intimate self-disclosure, with an IoT-CA. They pointed out that people exhibited a close relationship with an IoT-CA when they were engaged in intimate self-disclosure with it.
# A Review on Vibration Control Using Piezoelectric Shunt Circuits
## 2 Types, Applications, and Recent Advances of Piezoelectric Shunt Circuits
### Quadratic and Cubic Nonlinear Shunt Circuits
The saturation phenomenon causes an energy exchange by creating a threshold for one of the modes and transferring the energy to the other mode that is not directly excited. The type of nonlinearity coupling between the modes and tuning the frequency ratio between them determines the type of internal resonance in the system. For instance, 2:1 and 3:1 internal resonance can be achieved for systems with quadratic and cubic nonlinearities, respectively. A hybrid nonlinear vibration attenuation approach is proposed by [PERSON] et al. [102; 103; 104; 105]. A quadratic nonlinearity was added to a shunt circuit to be tuned to obtain a 2:1 internal resonance between the mechanical resonance and the electrical resonance. This created a nonlinear antiresonance and a saturation phenomenon, thus allowing the mechanical amplitude to be independent of the forcing amplitude above a threshold. [PERSON] et al. [106] exploited the idea of the saturation phenomenon via the introduction of the quadratic nonlinearity into the system and investigated the nonlinear vibration suppression of a cantilever beam using a tuned shunt circuit. Some of the results of this reference will be presented in the following sections.
## 2 Related Work
Furthermore, it also addresses how to convey more important data to sink nodes and how to extend the network's life. The AUV path is essential for complete data collection and to balance energy utilization by sensors in UWSNs. There is no standard mechanism for selecting the best path for AUV rotation to collect data and consume less energy. To fill this gap, an AUV-based pattern for selecting the best path and less energy consumption protocol has been proposed [10]. In [9], the authors propose an AUV-aided data gathering routing protocol (AEDG), which divides nodes into two categories: GNs (near the AUV) and member nodes (not near the AUV) (MNs). The GNs receive data from the MNs depending on RSSI values, and the GNs send collected data to a moving AUV. This routing technique increases packet delivery rates while reducing node energy consumption. The \"hot region\" problem is caused by the AUV's fixed journey path, which means that nodes along the path consume energy more quickly.
## 3 Analysis of Bibliographic Results on Road Network Pricing
### Keyword Analysis Using Thematic Maps  
The keyword analysis (Figure 6) showed that congestion pricing featured highly, which demonstrates its perceived importance as an effective tool for travel demand management [57]. The convergence of keywords showed the interaction of topics such as acceptability, equity, transport policy, value of travel time, congestion management, and sustainability when considering the value of pricing as an effective strategy to encourage commuters and travellers to adjust their travel behaviour [58]. This analysis captured how studies on mode choice and stated preference were also considered [59] and modelled, e.g., [60; 61]. The issue of equity, which has always been considered a critical aspect of road network pricing, also featured in the papers [16]. The literature on the topic shows that equity problems will persist unless the revenue raised from road pricing is used to expand public transport and improve access [62]. The convergence of keywords also showed formations comprising heterogeneity, elastic demand, cost-benefit analysis, and social welfare considerations, which relate to the economic aspects of congestion pricing. The heterogeneity issue is concerned with improving the modelling assumptions by recognising that road users are not homogenous groups of travellers and that income and other characteristics have direct influence on travel behaviour, route, and mode choice [63; 64]. Similarly,  
Figure 5: Geographic distribution of research activities.  
other studies considered improving the cost-benefit analyses for use in identifying optimal charges for congestion pricing to maximise system performance [65; 66].  
Other formations include mode choice, route choice [67], departure time choice, value of travel time, incentives, and willingness to pay for road pricing [43; 49; 68; 69]. These formations dealt with behavioural research and the factors influencing travellers on using or avoiding areas with road charges. Big data, simulation and modelling, intelligent transport systems, and electronic road pricing were also formations that reflected literature dealing with road pricing technologies. Modern congestion pricing schemes comprise advanced technologies and use of communications and positioning solutions for travel distance/duration calculations [24]. Big data comprising travel flow, speed, route, and mode choice behaviours are used to develop models for simulating impacts of congestion pricing under different pricing scenarios [70; 71]. Finally, surveys of stated preferences and discrete choice modelling are formations that reflect literature related to research questions that explored governance, policy implementation, public opinion, and user acceptance [72]. The above keyword analysis is directly related to answering **Q-3**.
## 4 Experimental Results
### Evaluation Metrics  
For the evaluation, different matrices have been used in this research work, such as Intersection over Union (_IoU_), Precision (_P_), Recall (_R_) and Accuracy (_ACC_). These requirements are as defined as follows:  
\[IoU=\frac{TP}{TP+FP+FN}\]  
\[P=\frac{TP}{TP+FP}\]  
\[R=\frac{TP}{TP+FN}\]  
\[ACC=\frac{TP+TN}{TP+TN+FP+FN}\]  
wherein _TP_ (True Positive) denotes the number of correctly recognized foreground pixels (interest region), the number of background pixels accurately recognized as a background is referred to as _TN_ (True Negative) (skin region), the number of background pixels labelled as the foreground is known as _FP_ (False Positive) and the number of foreground pixels incorrectly labelled as background is known as _FN_ (False Negative).  
The ratio of overlapping and union areas between the expected segmentation mask and the ground truth mask is represented by _IoU_. The _IoU_ metric measures how similar the prediction mask is to the ground truth mask. The percentage of correctly identified pixels in the total number of pixels is represented by _ACC_. The fraction of successfully segmented foreground pixels versus the total number of foreground pixels is represented by recall (_R_).
## 4 Case Study- Business Process Innovation and Improvement
### Problem Resolution Steps Adopted  
Currently, there are four main steps followed by the help-desk process of the fuel retailing company with their semi-automatic job tracking system. When an incident is reported by a customer from a petrol service station, the following steps are undertaken by the help desk department:  
1. Level-1 staff manually logs the complaint and checks the request. Common problems are resolved by Level-1 staff who will guide the customer over phone to troubleshoot and close the job.
2. If the problem is not resolved, Level-1 staff creates a problem ticket in the job tracking system, assigning a priority and the system automatically allocates it to an available Level-2 staff.
3. Level-2 Staff assigned to the problem tries to resolve it and sends the resolution to level-1 staff. However, if the problem requires a technician to fix the problem, an external contractor is hired.
4. The contractor assigned to the job visits the station to fix the problem and sets the job status when the problem is solved and the Level-2 staff forwards the resolution to Level-1 staff, who communicates it via email to the customer and closes the job.  
The help desk department of the petrol service station company consists of five Level-1 and three Level-2 staff. The hourly rate of level-1 staff is $40, and level-2 staff is $60. Whenever the incident is a common problem that could be fixed remotely, such as troubleshooting with POS system, computer login or software issues, the help desk staff are able to resolve the service request successfully. However, certain problems that require a domain expert are channeled to a technician, who is hired as an external contractor, to visit the service station to diagnose and resolve the problem. Overall, the company wishes to improve their processes and enhance their efficiency in terms of customer service and optimise on the costs involved.  
In many countries, petrol service stations are moving towards running most of their operations in an unmanned basis 24/7 and are required to ensure customer safety and support. Hence, a systematic approach for a business process re-engineering is warranted. The steps given in the proposed BPM framework were applied for the abovementioned case study in order to illustrate how it can lead to BPI.
## 2 Materials and Methods
### Measurement of the Force Components Acting on the Model and Corresponding Wind Speed  
The sensors for the air pressure and velocity measurements were placed in front of the model, 600 mm above the aerodynamic tunnel floor. They are visible in Figure 11 (the vertical rod installed on the movable table). The measurements of the forces and moments were performed with the use of the aerodynamic balance device that was placed under the plate of the movable table. The nominal wind speed was assumed to be equal to 15 m/s.  
A diagram of the measuring system is depicted in Figure 12. The model of the studied truss (1) was installed onto the aerodynamic balance (2). The balance was wired to an amplifier module with a data acquisition system (3) that collects aerodynamic force values. The angular position of the model relative to the x, y plane was changed using a turntable with a stepper motor (4), and thus, the angle of attack of the wind was changed. The rotation of the stepper motor was controlled by a motor step control system (5). The turntable is a part of the aerodynamic balance, and its axis of rotation coincides with the axis of the balance. The wind speed was obtained based on pressure measurements provided by sensor Pitot pipes (6) located on the x, z plane, which were connected to the pressure scanner (7). The wind speed sensor (8) and thermo-anemometer AMD 2000 (9) were used for the wind speed measurements. Devices (3, 7, and 9) are linked with the multifunctional measuring card PCI DaqBoard/2000 (10), which was connected to a personal computer (11).  
The sampling time of the wind speed measurement was equal to 5000 ms. Since the model of the investigated truss was installed 300 mm over the aerodynamic tunnel floor, it can be assumed that there was a uniform wind speed profile between the circular plates (Figures 3 and 5). The turbulent air flow was introduced by the set of \"spires\", which was installed at the beginning of the aerodynamic tunnel. On the other hand, the \"quasi-laminar\" airflow was obtained in the absence of the spires.  
Below are the formulas for determining the coefficient of aerodynamic drag \(C_{X}\), the coefficient of the lateral aerodynamic force \(C_{Y}\), and the aerodynamic coefficient of torque \(C_{MZ}\):  
\[C_{X}=\frac{2F_{x}}{\rho V^{2}A_{ref}},\ C_{Y}=\frac{2F_{y}}{\rho V^{2}A_{ref}},\ C_{MZ}=\frac{2M_{z}}{\rho V^{2}A_{ref}b_{ref}} \tag{7}\]  
where \(F_{X}\) is the aerodynamic drag (N); \(F_{Y}\) is the lateral force (N); V is the average wind speed (m/s); \(\rho\) is the mass air density; \(A_{ref}\) is the effective area of one of the supporting structures of the model, i.e., the area of the shadow normally projected by its members on a plane parallel to the wall; and \(b_{ref}\) is the conventionally accepted characteristic dimension. Table 3 shows the appropriate reference values for the force coefficients.  
Figure 11: Model of the truss of the tower inside the aerodynamic tunnel during the tests.
# Object Tracking Algorithm Based on Multi-Layer Feature Fusion and Semantic Enhancement
## 4 Summary
Embedding the mask learning process into the training pipeline to achieve end-to-end optimization and enable the adaptive adjustment of the mask threshold. Instead of relying on manually set fixed thresholds, the model will be able to dynamically learn the optimal masking strategy for different scenarios during training, further improving the flexibility and generalization capability of the algorithm. Through these improvements and optimizations, the generalization ability, adaptability, and robustness of the proposed algorithm are expected to be further enhanced, providing stronger support for practical applications in complex and dynamic environments. While the proposed object tracking algorithm demonstrates promising performance in various challenging scenarios, it is important to recognize its potential societal impacts, particularly concerning privacy and ethical risks. Object tracking technology, if misused, can lead to unauthorized surveillance, privacy infringement, or other malicious applications. To mitigate such risks, it is imperative that the development and deployment of object tracking algorithms strictly adhere to relevant legal frameworks, ethical guidelines, and responsible research practices. Future work should also consider incorporating privacy-preserving mechanisms, such as differential privacy or federated learning, to reduce the possibility of misuse in sensitive application domains. Furthermore, we emphasize that the research in this paper was conducted for general-purpose and positive applications, including intelligent transportation, robotics, and sports analytics. We strongly oppose any use of the proposed technology for illegal or unethical purposes. By acknowledging these potential risks and taking proactive measures, we aim to promote the sustainable and responsible development of object tracking technologies. Conceptualization, J.W. ; methodology, J.W. ; software, Y.W. ([PERSON]) and D.Y. ; validation, Y.W. ([PERSON]) and Y.Q. ; formal analysis, J.W. and D.Y. ; investigation, [PERSON]; resources, [PERSON] ([PERSON]) and D.Y. ; data curation, [PERSON] and Y.Q. ; writing--original draft preparation, [PERSON] and Y.W. ([PERSON]); writing--review and editing, [PERSON]; visualization, [PERSON] and Y.W. ([PERSON]); supervision, [PERSON] and W.H. ; project administration, [PERSON], [PERSON] ([PERSON]), and Y.Q. ;funding acquisition, [PERSON] and W.H. All authors have read and agreed to the published version of the manuscript. This research is supported by the National Natural Science Foundation (NNSF) of China (NO.62127809) and the National science basic research program of Shaanxi (2024 JC-YBMS-552). Not applicable. The original data presented in this study are openly available in Transitpp at [[https://pan.baidu.com/s/1](https://pan.baidu.com/s/1) BKVfZwQBS67 dK1_OJolwUw?pwd=lp5s]([https://pan.baidu.com/s/1](https://pan.baidu.com/s/1) BKVfZwQBS67 dK1_OJolwUw?pwd=lp5s) (accessed on 1 January 2025). The authors declare no conflicts of interest.
# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
## 3 Experiment and Analysis
### Comparison of Different Input Features  
In this section, we compare the performance and computational complexity of different input features (such as spectral features and Log-Mel spectral features) and classification networks. The relevant results are summarized in Tables 2 and 3, with anomaly scores calculated using the K-Means method.  
In terms of performance across individual machine types, as shown in Table 2, SpecMFN demonstrates strong performance across several device types, particularly Fan, Pump, Slider, and ToyCar, where it achieves AUC and pAUC scores of 99.39% and 98.15%, 96.72% and 91.76%, 99.78% and 98.82%, and 97.06% and 90.14%, respectively. These results underscore SpecMFN's strong ability to extract deep features and detect anomalies across various machines. In contrast, LogMel-MFN performs best on the Valve device, with AUC and pAUC scores of 99.02% and 95.01%. While ASDNet performs slightly worse on Valve than LogMel-MFN, it outperforms LogMel-MFN on most other devices, indicating a more balanced overall performance. Notably, compared to SpecMFN, ASDNet produces similar results across most devices and even surpasses SpecMFN on the Valve and ToyConveyor devices.  
From an overall performance perspective, the difference between SpecMFN and ASDNet is minimal. SpecMFN achieves an AUC of 94.36% and a pAUC of 88.60%, while ASDNet scores 94.42% and 87.18%, respectively. Both models perform similarly across most devices, significantly outperforming LogMel-MFN (AUC of 91.46% and pAUC of 84.43%).  
This suggests that both SpecMFN and ASDNet are highly effective at capturing anomalous features in devices and exhibit strong robustness across a wide range of device types. Since both models use spectral features as input, the results further validate the effectiveness of spectral features for anomaly detection in this study. SpecMFN's high average pAUC score underscores its superior detection precision and exceptional ability to accurately identify anomalous patterns across diverse devices, making it particularly well suited for high-sensitivity and robust anomaly detection tasks in demanding real-world applications.  
In terms of computational complexity, as shown in Table 3, SpecMFN requires the most computation and has the highest parameter count among the models compared, which allows it to deliver superior detection performance, particularly excelling in pAUC for high-sensitivity applications. In contrast, ASDNet achieves 85.4 M FLOPs and 0.51 M parameters, which are only 46.4% and 48.6% of SpecMFN's values, respectively, and 61.0% and 58.6% of LogMel-MFN's values. While maintaining nearly the same accuracy as SpecMFN, ASDNet significantly reduces computational complexity, making it an efficient and practical choice for deployment in resource-constrained environments such as embedded devices or real-time applications.
## 2 Tomography Theory and Method
### Optimal Solution Strategy for Multi-GNSS Troposphere Tomography Modeling
Judging from the inequality \(\chi_{0_{tp}}^{2}\leq\chi_{0.1}^{2}\), if true, we assumed that the unit weight variances of different tomographic equations are statistically equal, otherwise, continue the following produces. * Calculate the updated weightings for different tomographic equations based on the following formula: \[\textbf{P}_{tp}^{j}=\frac{c}{\vartheta_{0_{tp}}^{2}}\cdot\textbf{P}_{tp}^{(j-1)} (tp=gps,gionass,bds,H,V,P),\] (13)  
where \(j\) is the number of iteration times while \(c\) is an arbitrary value. In our test, \(c=\hat{\sigma}_{0_{gps}}^{2}\) is determined.
# Decision-Maker's Preference-Driven Dynamic Multi-Objective Optimization
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**DMOOP** & \(\mathbf{n_{t}}\) & \(\mathbf{\tau_{t}}\) & **PM** & **PPA** & **DPA** & **RSTFRA** & **PM** & **PPA** & **DPA** & **RSTFRA** \\ \hline fda5 & 10 & 4 & acc & 0.6404 & 0.5962 & **0.5380** & stab & **0.0000** & **0.0000** & **0.0000** \\ fda5 & 10 & 4 & acc & **0.6751** & 0.7731 & 0.7557 & stab & 0.1793 & 0.1457 & **0.0884** \\ fda5 & 10 & 4 & acc & **0.4445** & 0.4619 & 0.6266 & stab & 0.4251 & 0.4261 & **0.2747** \\ fda5 & 10 & 4 & acc & 0.2282 & 0.2270 & **0.1946** & stab & 0.4743 & 0.5300 & **0.4098** \\ \hline fda5 & 10 & 5 & acc & **0.2683** & 0.4080 & 0.8316 & stab & **0.0000** & **0.0000** & **0.0000** \\ fda5 & 10 & 5 & acc & **0.4298** & 0.6803 & 0.8570 & stab & 0.1992 & 0.1543 & **0.0168** \\ fda5 & 10 & 5 & acc & **0.5140** & 0.6998 & 0.8183 & stab & 0.2266 & 0.1100 & **0.0883** \\ fda5 & 10 & 5 & acc & **0.5382** & 0.6913 & 0.7904 & stab & 0.2461 & 0.1599 & **0.1331** \\ \hline fda5 & 10 & 2 & acc & **0.6239** & 0.6727 & 0.9835 & stab & **0.0000** & **0.0000** & **0.0000** \\ fda5 & 10 & 2 & acc & **0.8799** & 0.8925 & 0.9526 & stab & 0.0985 & 0.0860 & **0.0000** \\ fda5 & 10 & 2 & acc & **0.6285** & 0.6549 & 0.9234 & stab & 0.3367 & 0.2923 & **0.0227** \\ fda5 & 10 & 2 & acc & **0.4285** & 0.4320 & 0.9186 & stab & 0.4402 & 0.4291 & **0.0455** \\ \hline fda5 & 1 & 4 & acc & **0.9984** & 1.0000 & 1.0000 & stab & **0.0000** & **0.0000** & **0.0000** \\ fda5 & 1 & 4 & acc & 0.9986 & **0.9955** & 0.9970 & stab & **0.0014** & 0.0045 & 0.0030 \\ fda5 & 1 & 4 & acc & 1.0000 & **0.9991** & **0.9991** & stab & **0.0000** & 0.0009 & 0.0009 \\ fda5 & 1 & 4 & acc & 0.9996 & **0.9945** & 0.9956 & stab & **0.0004** & 0.0055 & 0.0044 \\ \hline fda5 & 1 & 5 & acc & 1.0000 & 0.9993 & **0.9958** & stab & **0.0000** & **0.0000** & **0.0000** \\ fda5 & 1 & 5 & acc & 0.9953 & 0.9915 & **0.9746** & stab & **0.0047** & 0.0085 & 0.0254 \\ fda5 & 1 & 5 & acc & 1.0000 & **0.9971** & 0.9976 & stab & **0.0000** & 0.0029 & 0.0024 \\ fda5 & 1 & 5 & acc & 0.9963 & 0.9962 & **0.9793** & stab & **0.0037** & 0.0038 & 0.0207 \\ \hline fda5 & 1 & 2 & acc & 1.0000 & 0.9993 & **0.9948** & stab & **0.0000** & **0.0000** & **0.0000** \\ fda5 & 1 & 2 & acc & 0.9963 & 0.9990 & **0.9781** & stab & 0.0037 & **0.0010** & 0.0219 \\ fda5 & 1 & 2 & acc & 1.0000 & 0.9993 & **0.9980** & stab & **0.0000** & 0.0007 & 0.0020 \\ fda5 & 1 & 2 & acc & 1.0000 & 0.9987 & **0.9853** & stab & **0.0000** & 0.0013 & 0.0147 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: acc and stab for each DMOA for FDA5 with various frequency and severity of change.
## 3 Results and Discussion
### Calibration Results and Modelling
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Calibration Performance**} \\ \hline Component & RMSECV & Bias & Slope & Intercept & R\({}^{2}\) \\ \hline Humidity & 0.653 & 0.000 & 1.000 & 0.000 & 0.829 \\  & & Validation performance & & & \\ \hline Component & RMSEP & Bias & Slope & Intercept & R\({}^{2}\) \\ Humidity & 0.698 & 0.066 & 0.894 & 7.110 & 0.676 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Prediction results of the humidity percentage in pomace for the calibration and validation model. Figure 6: Measured vs predicted values for oil content in the pomace: (**a**) calibration set; (**b**) validation set. Blue dots: measured vs predicted values for each sample. Dashed line: trend line of PLS model. The results highlight the robustness of the NIR approach based on a PLS calibration model to monitor the industrial olive oil process. Figure 7: Measured vs predicted values for humidity in pomace: (**a**) calibration set; (**b**) validation set. Blue dots: measured vs predicted values for each sample. Dashed line: trend line of PLS model.