esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Trust Modeling and Robust Aggregation in Federated Learning  
Security threats in federated learning primarily stem from clients uploading malicious or low-quality updates that degrade global model performance. To mitigate this, trust modeling and robust aggregation have become central research topics.  
In trust modeling, various methods have been proposed to evaluate client credibility from different perspectives. FedTrust (2018) calculates trust scores based on similarity among uploaded models and adjusts aggregation weights accordingly. TrustFL (2018) dynamically adjusts client weights based on performance fluctuations on a public validation set and consistency of feature representations. [PERSON] et al. (2018) constructed a composite trust scoring system using multi-dimensional indicators to reflect long-term behavioral stability and reliability.  
At the same time, robust aggregation algorithms provide effective defenses against poisoning attacks. Methods such as the Krum (2018) and the Trimmed Mean (2018) eliminate outlier gradients or select consistent subsets of updates to enhance robustness. However, most rely on static thresholds or distance-based filtering, which struggle to adapt to dynamic client behaviors and often overlook the strategic interactions among participants.  
Recently, some works have attempted to couple trust mechanisms with robust aggregation. [PERSON] et al. [36] introduced update correlation analysis for dynamic detection of collusive poisoning, and [PERSON] et al. [37] modeled the trust learning process as a Markov decision process to recognize potential attack states. However, these approaches still neglect client responses to incentive feedback. Without a proper incentive regulation mechanism, the effectiveness of trust scoring and aggregation strategies can be compromised.  
This work proposes a soft trust filtering mechanism that introduces a smoothing suppression function during aggregation to avoid penalizing edge clients with behavioral fluctuations. Moreover, the trust evaluation is coupled with the incentive allocation function to form a \"high trust-high incentive-high participation\" positive feedback loop, thereby enhancing adaptive defense and strategy stability.
# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 2 Important Applications of UAVs in Smart Cities
### Environmental Monitoring and Conservation  
Drones are used for wildlife monitoring, forest conservation, and environmental research. They can collect data on wildlife populations, track animal movements, and monitor environmental changes [23].
## 3 Results  
The application of the here described GA-adaptive TM led to the estimation of a set of 13 parameters fully specifying the selected template model over time (i.e., frame after frame).  
As an example, the first two parameters obtained after the first GA step are the coordinates of the outer hexagon in the search space. These are reported in Figure 9. As can be seen from the picture, the \(X_{c}\) coordinate shows some jumps. These can be put in relation with the autofocus. In the same way, it is easy to notice that the coordinate \(Y_{c}\) is subject to a variation of the first-order statistics (i.e., the mean value) over time. This is highlighted by the estimation of the moving average (order: 50 samples) reported in yellow in the graph.  
Similar graphs are available for all the 13 optimized parameters. The position of the inner hexagon center and the 8.8 logo vertex were then used to compute the angular position of the fan over time. As can be seen in Figure 10, the angle increments over time are quite uniform if the green, red and magenta points are not considered. In order to get a more consistent estimate, these points should be compensated. As introduced, the green points (360\({}^{\circ}\) error) can be avoided by performing and \"unwrap\" of the angle signal, while the red and magenta points correspond to a wrong localization  
Figure 9: Outer hexagon coordinates over time (frame index). The moving average of the estimated parameter \(Y_{c}\) is added in yellow. N.B., The selected \(y\) coordinate ranges from the top to the bottom of the displayed image (e.g., in Figure 6), differently from the standard.  
of the 8.8 logo, which is confounded with the JD logo, so that the 180\({}^{\circ}\) error can be distinguished and compensated.  
From the compensated angular increments over time, \(\Delta\alpha\), it is easy to obtain the IAS estimate by normalizing this signal with respect to the constant \(\Delta t=1/f_{s}\), as indicated in Equation (14).  
For the sake of comparison, Hilbert phase demodulation was performed on the running-mean-removed \(Y_{c}\) signal so as to produce a second IAS estimate (Equation (17)). The two estimates were then lowpass filtered to produce a more physically reasonable result. The IAS signals after a FIR1 lowpass filter (order: 50 samples, cutoff: \(0,1\,f_{s}/2\)) is reported in Figure 11.  
As can be noticed in Figure 11, the two methods lead to overlapping results. This, then, increases the confidence in the reliability and accuracy of the proposed video-tachometer procedure for the IAS estimation.  
Figure 11: Final Instantaneous Angular Speed (IAS) estimates from the SURVISHNO video.  
Figure 10: Fan angular position variation (angular increments \(\Delta\alpha\)) over time (frame index). In green are the points for the “unwrap”, in red and magenta are the errors to be compensated (8.8 logo missed).
## 3 Background
### Security Requirements  
Security has become a critical issue in the IoT due to the large amount of data being transmitted, the different technologies used, and the development of cloud computing systems. IoT systems are particularly vulnerable when data are transmitted from a user interface to a cloud-based service, as attackers can use loopholes in transmission links to manipulate the data. Moreover, they can take charge of cameras, the brake system as well as the alert system. In such a case, the people inside the vehicle have no control over what the vehicle can do. This is what makes IoV a high-risk technology. Despite efforts to address the challenges presented by IoV, new and more sophisticated challenges keep popping up. The various security threats are a result of the frequent availability of large amounts of data in IoV. Among the many devices and nodes involved in IoV are data, access points, base stations, sink nodes, and backbone points, making data collection security crucial. To guarantee data security, IoV experts must ensure the data are authenticated and made confidential. Other requirements include integrity, authorization, and nonrepudiation [57].  
Security requirements are measures for identifying how secure an IoV network is. While IoV is vulnerable to multiple threats, researchers have identified some of the most vulnerable areas and how they can be protected from network attacks [57; 58; 59; 60]. Below are some of the security aspects that must be considered. Figure 1 illustrates the security requirements including authentication, confidentiality, availability, data integrity, nonrepudiation, access control, privacy, and real-time guarantees.
## 4 Diagnosis of Dentin Hypersensitivity  
### Clinical Examination  
The accurate diagnosis of DH demands a substantial amount of time, primarily because it involves obtaining a thorough dental history and relies on the process of eliminating other analogous conditions. In the course of the clinical examination, patients are questioned about discomfort experienced when consuming hot, cold, iced, acidic, or soft foods and beverages. Additionally, patients are inquired about whether symptoms manifest during brushing or emerge subsequent to a dental restoration procedure. In instances of pain, patients are further queried about specific attributes of the pain, encompassing its location, character, intensity, timing, and the particular stimulus that triggers it [36].  
Subsequently, the patient's personal habits (including the consumption of acidic foods and beverages, as well as excessive brushing) and prior dental interventions (such as teeth scaling, other periodontal treatments, bleaching, and fillings) are assessed. Furthermore, the presence of corrosion, receding gums, and the exposure of cervical dentin is examined. As long as symptoms of DH are present, it becomes essential to differentiate and diagnose other forms of orofacial pain, such as pulp inflammation, periodontal discomfort, tooth fractures, microleakage, atypical odontalgia, and more [36]. Following this step, a specific clinical assessment of DH is conducted by gently probing the exposed dentin in the mesial-distal direction. To confirm the diagnostic outcome, a second stimulus is typically employed, often utilizing an air stream from an air syringe [4; 37].  
Once the diagnosis is confirmed, patients are provided with instructions to mitigate predisposing factors such as acidic foods and drinks, as well as incorrect brushing techniques [38; 39]. Treatment then commences with agents that alleviate sensitivity, either at the dentist's office or through at-home methods [14]. Individuals suffering from DH often recognize a diminished quality of oral health compared to the general population. Employing a questionnaire specifically focused on the impact of DH on patients' quality of life would significantly enhance the disease diagnosis process within routine dental practice.  
Regrettably, the two most widely recognized and validated questionnaires employed in various clinical studies focusing on oral health consist of a minimum of 48 questions each [40; 41]. Given the extensive nature of these questionnaires and their practical challenges, there have been recommendations to employ shorter versions consisting of around 12 to 14 questions, referred to as the Oral Health Impact Profile \(-14\) and the General Oral Health Assessment Index [42; 43].
## 2 Analysis of Crack Initiation Pressure of Inclined Crossing Borehole Wall
### Analysis of Crack Initiation Pressure of Multi-Layered Coal
#### 2.3.1 Analysis of Cracking Pressure When Cracking Occurs along Radial Direction of Borehole  
The cracking of the surrounding rock starts from the wall surface of the borehole. According to Equation (3), the tangential stress along the radial wall of the borehole section under the triaxial stress and water injection pressure can be obtained, as follows:  
\[\begin{array}{l}\sigma_{\theta}=-p+\Big{[}\Big{(}\sigma_{H}\cos^{2}\alpha+ \sigma_{h}\sin^{2}\alpha\Big{)}\cos^{2}\beta+\sigma_{v}\sin^{2}\beta+\sigma_{ H}\sin^{2}\alpha+\sigma_{h}\cos^{2}\alpha\Big{]}\\ -2\Big{[}\big{(}\sigma_{H}\cos^{2}\alpha+\sigma_{h}\sin^{2}\alpha\big{)}\cos^ {2}\beta+\sigma_{v}\sin^{2}\beta-\sigma_{H}\sin^{2}\alpha-\sigma_{h}\cos^{2} \alpha\Big{]}\cos 2\theta\end{array} \tag{4}\]  
It is assumed that the maximum horizontal principal stress is consistent with the coal seam tendency, while the minimum horizontal principal stress is consistent with the coal seam trend. The azimuth of the borehole is assumed to crack along the axial layer of the borehole when the coal is fractured, and the influence of the radial stress of the borehole can be ignored.  
\[\sigma_{\theta}=\big{(}\sigma_{H}\cos^{2}\beta+\sigma_{v}\sin^{2}\beta+\sigma_ {h}\big{)}-2\Big{(}\sigma_{H}\cos^{2}\beta+\sigma_{v}\sin^{2}\beta-\sigma_{h} \big{)}\cos 2\theta-p \tag{5}\]  
When \(0\leq\theta\leq 180^{\circ}\), only \(\theta=0\) and \(\theta=90^{\circ}\) result in an extreme tangential stress value. Therefore, the tangential stress is considered only with the direction angle and extreme values in both cases.  
\[\left\{\begin{array}{l}\sigma_{\theta=0}=3\sigma_{h}-\sigma_{H}\cos^{2}\beta- \sigma_{v}\sin^{2}\beta-p\\ \sigma_{\theta=90}=3\big{(}\sigma_{H}\cos^{2}\beta+\sigma_{v}\sin^{2}\beta\big{)} -\sigma_{h}-p\end{array}\right. \tag{6}\]For the borehole wall to crack, \(|\sigma_{\theta}|>\sigma_{t}\) must be satisfied, where \(\sigma_{t}\) is the tensile strength of the coal seam. Because each coal layer has a different tensile strength, \(\sigma_{ti}\) is considered as the tensile strength of the \(i\)th coal layer, \(\min\{\sigma_{ti}\}\) is the minimum coal layer tensile strength, \(1\leq I\leq n\), and \(n\) is the coal layer number. Then, the cracking pressure must satisfy the following conditions:  
\[\left\{\begin{array}{l}p_{\theta=0}>3\sigma_{h}-\sigma_{H}\cos^{2}\beta- \sigma_{v}\sin^{2}\beta+\min\{\sigma_{ti}\}\\ p_{\theta=90}>3\left(\sigma_{H}\cos^{2}\beta+\sigma_{v}\sin^{2}\beta\right)- \sigma_{h}+\min\{\sigma_{ti}\}\end{array}\right. \tag{7}\]  
Therefore, when only the radial cracking is analyzed, the cracking pressure of the borehole wall can be expressed as follows:  
\[p_{1}=\min\left\{\begin{array}{l}3\sigma_{h}-\left(\sigma_{H}\cos^{2}\beta+ \sigma_{v}\sin^{2}\beta\right)+\min\{\sigma_{ti}\},\\ 3\left(\sigma_{H}\cos^{2}\beta+\sigma_{v}\sin^{2}\beta\right)-\sigma_{h}+\min \{\sigma_{ti}\}\end{array}\right\} \tag{8}\]
## 5 Performance Analysis
### Numerical Simulation  
To verify the performance of ACE-FHBOC, a computer simulation is carried out based on MATLAB. As is shown in Figure 11, the original navigation signal is first generated. Second, we add noise and interference signals to the original signal. Then, after filtering and sampling, the digital intermediate frequency (IF) signal is generated. Finally, the IF signal is processed by a software-defined receiver (SDR) that is developed based on a generic GNSS SDR [30]. Table 4 lists the main parameters of the simulated signal. According to the acquisition and tracking results of the SDR, we discuss the multiplexing performance, the probabilities of false locking on ACF side peaks, the tracking performance, the anti-narrowband interference performance, and the multipath performance for ACE-FHBOC(24:1:1,1), ACE-FHBOC(24:1:6,1), ACE-BOC(12,1), and ACE-BOC(15,10).  
Figure 11: Simulation scheme.  
Figure 10: PSDs of the Type-II and Type-III example signals.
# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 9 Future Trends in Fault Location Estimation  
As the transmission network is growing with complexity and inadequate measurements are expected to be common. Wide-area methods would be employed largely for fault location finding in the near future. However, machine learning algorithms have more compliance and less affected by line parameters as compared to TW or impedance-based approaches. Increased involvement of communication and computation is foreseen in power systems. Thus machine learning including deep learning methods should be explored for future fault location findings.
# Tensegrity Applications to Architecture, Engineering and Robotics: A Review
## 4 Robots and Electromechanical Engineering
### Bio-Inspired Robots  
Tensegrity spines, as part as models of human anatomy (which will be exposed later), have been developed by [PERSON] [101] and [PERSON] [102]. These spines have been referenced in later work in the robotics industry (Figure 33).  
[PERSON] is considered to be the originator of the biotensegrity concept [103], having published several papers on the matter. In 2011, [PERSON] proposed a tensegrity-truss system to model the spine (Figure 33a), distributing the loads through the system only in tension or compression. His model was nondependent on gravity to hold it together and could stand static and dynamic forces.  
Figure 32: **(a)** Prototype of the underwater robot proposed by [PERSON] [98]. **(b)** The bio-tensegrity fish robot by [PERSON] et al. [100]; top view: the robot in disassembled condition and bottom view: final assembly of the system.  
Just a little later, [PERSON] stated that \"each vertebral body in a spine bears a formal topological resemblance to a stellated tetrahedron. A sequence of these tetrahedrons suspended as a tensegrity mast can illustrate the equivalent spinal properties of load bearing combined with flexibility and rotation\". He developed several spine models [101] based on previously invented masts. One of his models is built from four strings composing a tensegrity saddle joint, and another four strings running along the outside (Figure 33b). All eight strings were being actuated.  
Based on these works, the NASA Tensegrity Robotics Toolkit (NTRT) [104] developed a technique for the rapid design of tensegrity robots via simulation. They used multiple designs of tetra-spines to build their hardware prototype that employs 12 DC motors, 12 load cells and vectran cables (Figure 34a).  
Figure 34: Cont.  
Figure 33: (**a**) [PERSON]’s tensegrity model of the spine [102]. (**b**) Model of a tetrahedral vertebral mast built by [PERSON] [101].  
In 2021, [PERSON] et al. [105] proposed a tensegrity spine robot also inspired on the vertebrates' spine. To replicate a soft spine structure, this robot is supported in a tension network and connected by springs (Figure 34b). In the paper, the authors mention how \"this robot has the advantages of lightweight, compliance, and a high strength-to-weight ratio, which suggest good environmental adaptability and manipulation controllability\", properties that make it suitable for potential application prospects in space debri capturing.  
Later, [PERSON] et al. [106] presented a form-finding method for three different variations of k = 1 biotensegrity spine models (Figure 34c). They proposed an algorithm capable of simulating the shape change via four states. The algorithm can search for the optimal forced elongation of elements in tension and guarantee that they do not become slack after the configuration changes.
# Recent Progress on Underwater Wireless Communication Methods and Applications
###### Abstract  
The rapid advancement of underwater wireless communication technologies is critical to unlocking the full potential of marine resource exploration and environmental monitoring. This paper reviews recent progress in three primary modalities: underwater acoustic communication, radio frequency (RF) communication, and underwater optical wireless communication (UWOC), each designed to address specific challenges posed by complex underwater environments. Acoustic communication, while effective for long-range transmission, is constrained by ambient noise and high latency; recent innovations in noise reduction and data rate enhancement have notably improved its reliability. RF communication offers high-speed, short-range capabilities in shallow waters, but still faces challenges in hardware miniaturization and accurate channel modeling. UWOC has emerged as a promising solution, enabling multi-gigabit data rates over medium distances through advanced modulation techniques and turbulence mitigation. Additionally, bio-inspired approaches such as electric field communication provide energy-efficient and robust alternatives under turbid conditions. This paper further examines the practical integration of these technologies in underwater platforms, including autonomous underwater vehicles (AUVs), highlighting trade-offs between energy efficiency, system complexity, and communication performance. By synthesizing recent advancements, this review outlines the advantages and limitations of current underwater communication methods and their real-world applications, offering insights to guide the future development of underwater communication systems for robotic and vehicular platforms.  
under water communication; multimodal communication; emerging applications
## 3 Materials and Methods
### Experimental Setting
#### 3.2.2 Experimental Phases  
The experimental evaluation comprises three phases, focusing on dataset-specific performance and comparative analysis with other data generation methods:  
* Phase 1: Individual Dataset Performance Evaluation. In this phase, models are trained and tested separately on TalkMoves and EduTalk to establish baseline performance. This step highlights differences in annotation quality between manually labeled data and EduDCM-generated data. The evaluation metrics include accuracy, F1 score, and recall.
* Phase 2: Combined Dataset Evaluation. The EduTalk and TalkMoves datasets are mixed in ratios (e.g., 3:7, 1:1) to create combined test sets. This phase evaluates the compatibility of EduTalk-generated data with high-quality TalkMoves annotations and the models' generalization capabilities across mixed datasets.
* Phase 3: Data Generation Method Comparison. In this phase, EduDCM is compared against two prominent data generation approaches. The first is Template-Based Generation [24], which creates synthetic dialogues using predefined templates and expert-defined rules. While structured, this method is labor-intensive and commonly used in early natural language generation tasks. The second approach is Few-Shot GPT Data Generation [25], leveraging GPT-3.5 in a few-shot setup to generate datasets based on limited annotated examples, relying on the model's capability to extrapolate patterns from minimal inputs. Datasets generated by these methods are evaluated on identical models and reviewed by domain experts across three dimensions: (1) the accuracy of annotations, measured by the proportion of correctly labeled utterances; (2) the coverage of pedagogical categories, indicating the extent to which all categories are sufficiently represented; (3) the consistency of annotation style, reflecting the coherence and uniformity in annotation patterns.
## 2 Inter-DCI Link Modeling and OP Estimation  
Nowadays, inter-DCI links are supported by SM-SCFs, single-core EDFA-based optical amplification and CD compensation using DCFs [31; 32]. To support a future significant link capacity increase, our proposal is to deploy weakly-coupled MCFs in such links.  
In IM-DD inter-DCI links fully supported by multi-core technology, the signals generated by several optical transmitters, each one feeding a different core, would be spatially multiplexed by a multiplexer (MUX) before launching the signals along the MCF. With CD compensation performed in the optical domain, we may suppose that, at the MCF output, an MC-DCF would compensate the CD induced in each core by the MCF. Afterwards, a MC-EDFA would compensate the link losses and a spatial demultiplexer (DeMUX) would physically separate each core. The signal transmitted in each core would be photodetected and processed by an individual DD optical receiver. However, this inter-DCI link architecture can be seen as a future solution as, to the best of our knowledge, the MC-DCF is not an available technology.  
As an alternative solution, which can be deployed with today's technology and that has a low use of MCF technology, the inter-DCI link has a spatial DeMUX at the MCF output which allows for compensating the core CD and link losses on a core-by-core basis using SC-DCFs and SC-EDFAs, respectively. Afterwards, each signal associated with each core is photodetected and processed by an individual DD optical receiver.  
In the following, we consider the alternative solution for the inter-DCI link architecture. As the spatial MUX and DeMUX insertion losses can be as low as 1 dB [24; 35; 36], those losses are considerably lower than the MCF and DCF losses, particularly for the longer inter-DCI link lengths, and we neglect them in comparison with the MCF and DCF losses. In addition, the level of ICXT originated in the spatial MUX and DeMUX may be of the order of \(-55\) dB [37], which is significantly lower than the MCF ICXT levels presented in Section 3 corresponding to the maximum acceptable OP. Hence, in this work, we assume that the spatial MUX and DeMUX ICXT can be neglected relative to the MCF ICXT.  
In the remainder of this section, the equivalent model of the inter-DCI link is described and the method used to estimate the OP is presented.