esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results and Discussion
### Energy Consumption Comparison  
We also compare TSR and TSBR on their energy consumption performance. Figure 10 shows the standard deviation of the first layer node consumption change with increased node number. The results show that all three protocols consume more energy if node number is increased, but we can see clearly that the extent of the increase of TSBR is much lesser than for TSR. Therefore, we can confirm TSBR improves the equilibrium. The advantages perform better with increased number of nodes. Furthermore, TSBNC further decreases the consumption based on TSBR.  
Figure 11: Consumption of each node for different node numbers.  
Figure 10: Standard deviation of first layer consumption.  
We also confirm the advantages of TSBR and TSBNC in Figure 11, which shows how the consumption of sending one packet changes with increased node number. The increase of TSBNC is the slowest among the three protocols. TSBR's performance is worse and TSR's performance is the worst among the three protocols. The results prove TSBNC has excellent advantages when equalizing the node consumption.
# Study on Preparation and Processing Properties of Mechano-Chemical Micro-Grinding Tools
## 4 Simulation Analysis of Grinding Temperature
### Temperature Distribution Characteristics  
The grinding temperature comes from the mutual friction between the two contact surfaces. Due to the fast friction speed, the temperature rise rate is higher than the heat loss rate caused by the heat conduction of the silicon wafer and the micro-grinding tool itself. Figure 10 shows the temperature distribution of the abrasive surface, and the grinding time is 1 s, 2 s, 3 s, and 5 s, respectively. As can be seen from Figure 10, the maximum temperature of the contact point between the silicon wafer and the abrasive is 57.4 \({}^{\circ}\)C, 70.5 \({}^{\circ}\)C, 88.0 \({}^{\circ}\)C, and 144 \({}^{\circ}\)C. The simulation results show that as the grinding time increases, the temperature generated by the contact surface of the abrasive and the silicon wafer also increases. Moreover, it can also be seen from Figure 10 that the heat source is mainly concentrated on the edge of the abrasive, which is mainly due to the high linear velocity at the edge of the abrasive and the longer friction distance at the same time. As a result, the temperature at the edge of the abrasive is higher than in the internal area, and material removal is more likely to occur.  
Due to the low hardness and small particles of the abrasives and additives, they are prone to crushing and heat generation during grinding. The instantaneous maximum heat generated by the powder crushing caused by friction between the two contact surfaces can be calculated by Equation (14).  
\[Q=\frac{0.236\mu WV}{l\left[K_{A}+0.88K_{B}\left(\frac{V_{I}}{K_{B}}\right)^{0. 5}\right]} \tag{14}\]  
where \(\mu\) is the sliding friction coefficient, \(W\) is the vertical weight of the contact point, \(V\) is the sliding speed, \(l\) is the perimeter of the contact area, and \(K_{A}\) and \(K_{B}\) are the thermal conductivity of the two contact objects.  
Figure 9: Simplified finite element model of micro-grinding tool and silicon wafer.  
Substituting the relevant data into the calculation Equation (14) shows that a high temperature of more than 1000 \(K\) can be generated during grinding, and the temperature threshold for solid phase reaction between cerium oxide and single-crystal silicon can be reached [16]. Therefore, based on the above analysis, it can be preliminarily concluded that under the test conditions in this paper, the self-made micro-grinding tools can undergo solid-solid phase chemical reactions when grinding silicon wafers.
# Inter-Annual Variability of Peatland Vegetation Captured Using Phenocam- and UAV Imagery
## 4 Discussion
### Monitoring Vegetation Phenology
#### 4.1.2 UAV Data  
A high temporal resolution can be challenging to achieve with UAV surveys due to weather constraints. However, UAV data, when calibrated correctly and following a robust workflow, do provide spatially geo-referenced data of high spectral resolution and quality. The multispectral Parrot Sequoia camera setup employed in the present study acquired data in both the Red-Edge and NIR bands, which are more sensitive to physiological changes than bands in the visible spectrum. Analysis found that UAV-derived VIs centred on these bands (e.g., reNDVI1) were more sensitive to the impacts of abiotic change on the canopy. During dry spells, we observed reductions in UAV-derived VIs equating to around 40% of the seasonal amplitude in greenness, which were not visible in the phenocam data. Incorporating data from the short-wave infrared (SWIR) region may improve our ability to study the impact of drought events on vegetation, and such VIs have been successfully used to detect moisture stress in peatland environments, both as a whole and in particular species such as _Sphagnum_[95; 96]. The use of additional VIs may improve the correlation between phenology and flux datasets, although this could not be tested with our presented setup. Finally, the future use of UAVs in peatland phenology research at a range of sites could improve understanding of the impact of short- (e.g., response to weather events) and long-term environmental change (e.g., response to climate change) on peatland vegetation.
## 4 Agent Training with Markov Decision Process (MDP)
### Design of the MDP Model
#### 4.1.2 Analysis of the MDP Model Based on the Dynamic Bayesian Network  
To further analyze this MDP model, Figure 6 illustrates the corresponding dynamic Bayesian network (DBN) model that visually captures the temporal dependency relationships between states, actions, and rewards during the positioning process. This figure explicitly shows how the agent's weighting decisions \(A(t)\) at each timestep influence both the immediate reward \(R(t)\) and the state transition \(S(t)\to S^{\prime}(t)\), while maintaining the Markov property where each state depends on its immediate predecessor. The DBN structure particularly highlights the sequential nature of the proposed AWR-PF, where epoch-specific observations progressively refine the positioning solution through the agent's learned weighting policy.  
The MDP's state transition mechanism can be comprehensively illustrated through the example of \((S(1),A(1),R(1),S^{\prime}(1))\). The agent initially constructs the complete state \(S(1)\) by integrating: (1) \(S_{\text{signal}}(1)\), which includes SNR and Doppler frequency from the first epoch; (2) \(S_{\text{epoch}}(1)\), containing the satellite identifier and cumulative observation count; (3) \(S_{\text{satellite}}(1)\), comprising ephemeris age, azimuth/elevation angles, and satellite-receiver distance; and (4) \(S_{\text{GDOP}}(1)\), obtained from the covariance matrix.  
Upon receiving \(S(1)\), the agent generates weighting decision \(A(1)\), which is applied in the weighted RLS positioning. The resulting position estimate yields positioning error \(e(1)\) through comparison with the true position, enabling reward computation \(R(1)\), which combines both absolute (\(k_{1}\)-weighted) and relative (\(k_{2}\)-weighted) error improvements from the initial error \(e(0)\). Then the agent utilizes \(R(1)\) to optimize its parameters. The subsequent state \(S^{\prime}(1)\) (equivalent to \(S(2)\)) is then assembled from the second epoch's corresponding measurements, maintaining the temporal progression of the positioning process.  
This transition logic iterates sequentially until terminal conditions are met. At the final timestep \(T\), after determining \(A(T)\) and computing \(R(T)\), the system enters termination state \(S_{\text{end}}\)--a null state indicating episode completion when all epoch weights have been decided. The complete cycle demonstrates how the MDP model achieves progressive positioning optimization through intelligent epoch weighting.  
Figure 6: The diagram of the dynamic Bayesian network (DBN) corresponding to the proposed MDP model. Solid arrows indicate direct operations, while dashed arrows indicate indirect influences.
## 2 Concrete Strength Estimation Using a PZT Sensor  
### Piezoelectric Sensor  
Piezoelectric materials can convert the mechanical energy into electric signals and vice versa [21; 22]. Thus, PZT materials can be used and embedded in concrete to induce and detect wave propagation and vibrations [5; 17; 18; 23]. Through different approaches, the piezoelectric sensors can use to estimate the strength of concrete. By creating ultrasonic waves and observe the dynamic effects of the structure to these waves to analyze the EMI of excitation [5; 16]. The ultrasonic wave response and EMI can be acquired due to the unique properties of PZT [13].  
The material electrical behavior is linear:  
\[\begin{array}{l}D=\varepsilon E\\ D_{i}=\epsilon_{ij}E_{i}\end{array} \tag{1}\]  
Here, \(E\) is the electric field strength and \(D\) is the displacement of the electric charge density; the factor of proportionality relating the electric displacement and electric field strength is defined as the medium's dielectric constant. Additionally, \(\varepsilon\) is the permittivity of a free body. Equation (1) also shows the tensor of the displacement of the electric charge density.  
Additionally, according to [PERSON]'s law:  
\[S=sT \tag{2}\] \[S_{i}=S_{ij}T_{j}\]  
Combining Equations (1) and (2) yields:  
\[\begin{array}{l}S=s^{E}T+dE\\ D=dT+\varepsilon^{T}E\end{array} \tag{3}\]  
Here, the strain is shown by \(S\), similarly \(s^{E}\) represents the compliance under a constant electrical field, \(\varepsilon^{T}\) is the dielectric constant of PZT under a constant stress, and \(T\) is the stress. The combined equation can cover the strain and electric field strength with the other factors.  
As mentioned earlier, PZT can interconvert electrical and mechanical energies [14; 22]. Thus, PZT can be used as an actuator and a sensor [15]. Here, PZT was embedded in concrete and was used to create vibrations and produce guided ultrasonic waves; it also worked as a sensor to sense the EMI and dynamic response of the host structure [5; 15; 17]. For data acquisition, the sensor had to be installed before pouring concrete. To secure the sensor and prevent it from being damaged during concrete casting, a protection cover of Styrofoam was used, as shown in Figure 1.  
The size and properties of PZT used in our experiments are shown below in Table 1.  
In the table above, the terms \(d_{33}\) and \(d_{31}\) represent the piezoelectric charge constant while \(k_{33}\) and \(k_{31}\) represent the piezoelectric coupling factor.
# A History of Channel Coding in Aeronautical Mobile Telemetry and Deep-Space Telemetry
## 3 Deep-Space Telemetry
### In Search of the Missing 2 dB--Turbo Codes
A particularly noteworthy program is _New Horizons_, launched in January 2006 to explore the Pluto-Charon system in the Kuiper belt. The Pluto encounter occurred in July 2015 [90]. After the Pluto encounter, the _New Horizons_ mission was extended to study a Kuiper belt object then known as (486958) 2014 MU\({}_{69}\), now called Arrokoth. The Arrokoth encounter occurred in January 2019. A block diagram of the _New Horizons_ telemetry system is illustrated in Figure 18. A real-time telemetry data link was not possible during an encounter. During an encounter, the spacecraft was positioned for scientific data collection. When the spacecraft was positioned for scientific data collection, the high-gain antenna was usually not pointed to Earth. Consequently, scientific measurements were stored in onboard solid-state memory. The data were encoded using the CCSDS rate-1/6 turbo code [91]. The encoded data modulated a 25 kHz subcarrier [91]. The subcarrier phase-modulated an X-band carrier [92] to produce a coded PCM/BPSK/PM signal. At Pluto, a bit rate of about 1 kbits/s was possible using one of the two redundant 12-W TWTAs. Using both TWTAs, the bit rate increased to 2 kbits/s [93]. The encoder used for the telemetry link is the rate-1/6 turbo code using \(k=1748\) bits. During the Pluto encounter, _New Horizons_ collected about 6.25 GB of data [94]. The dataset was transmitted to Earth after the encounter at an average rate of 2 kbits/s using both TWTAs. The downlink took 15 months and was completed at the end of October 2016 [90]. The Arrokoth encounter took place in January 2019 and it took 20 months to downlink all the data at about 1 kbit/s. _New Horizons_ was the first to image Pluto and other Kuiper belt objects.
## 4 Discussion  
In previous studies in which three-axis accelerometer sensors were used to recognize activities, the sensors were attached to smartphones, wearable devices, body parts, etc. and the data were analyzed [27; 28; 29; 30; 31; 32].  
In the existing studies on user activity recognition via three-axis accelerometer sensors, the determinations were made using only the size of the data of the three-axis accelerometer sensor, however, it is necessary to classify data by using several statistical methods regarding the sizes of the vertical and horizontal components and the directionality of the smartphone [27].  
However, when activities cannot be accurately identified using only the size of the data of the three-axis accelerometer sensor, the FFT can be used to differentiate clearly between static activities for which the data are confusing such as \"sit\" and \"stand\" [28]. The data acquired in dynamic environments such as vehicles were simultaneously combined with the activity data as they were obtained. The chaotic data with overlapping pattern ranges were reanalyzed by performing an FFT, and the common patterns of specific unique frequencies were used as features to classify activities posture [29]. FFT analysis, in which points in time with sudden irregular patterns are recognized as state changes, is seen as a very suitable method for analyzing three-axis accelerometer sensor data [30].  
In this study, a safety helmet with a three-axis accelerometer sensor attached was developed for worker activity analysis. The mean recognition rate for properly wearing, improperly wearing, and not wearing the proposed safety helmet is increased from 17.09% to 96.95% between before applying FFT and after applying FFT, when the data classification is made by the RandomTree algorithm of Weka. In the proposed method, data obtained when the helmet is worn properly with the chinstrap correctly fastened, and not worn are employed to differentiate among properly wearing, improperly wearing, and not wearing the helmet.
## 1 Introduction
In addition, existing research on the health assessment of mechanical equipment primarily focuses on real-time data, often neglecting to incorporate historical maintenance records as a reference dimension into the health assessment framework. As equipment ages, its performance naturally degrades, and the probability of failure increases. By analyzing the time between failures of failed equipment in similar working conditions and establishing the reliability model to assess the equipment's failure probability, the health assessment can be dynamically corrected based on the reliability analysis. This approach ensures that the health assessment results more precisely reflect the actual performance degradation of the equipment. To address the aforementioned issues, this paper proposes a rolling bearing health assessment method integrating MSET and reliability analysis. The framework comprises four key phases: (1) Constructing condition-specific health baselines through MSET-based historical memory matrix optimization; (2) Adopting Mahalanobis distance metric for multidimensional degradation quantification; (3) Establishing health score mapping through nonlinear function design; (4) Implementing reliability-driven dynamic correction with service mileage integration. The principal advances distinguishing this research include:  
1. A condition-robust deviation quantification mechanism that synergizes MSET's nonlinear modeling with Mahalanobis distance's covariance sensitivity, resolving Euclidean metric's limitations in handling correlated multivariate bearing parameters and speed variation interference. 2. A reliability-informed health evaluation paradigm that dynamically calibrates degradation assessment through statistical failure patterns, achieving service-mileage adaptive precision improvement beyond fixed-parameter assessment frameworks. 3. Comprehensive validation through Case Western Reserve University bearing datasets demonstrates two advantages: (1) Inherent resistance to rotational speed fluctuation interference during operation. (2) Physically consistent monotonic degradation progression mapping across fault evolution phases. The remaining sections are structured as follows: Section?? details MSET-based baseline modeling, Section?? presents the reliability-integrated correction methodology, Section?? validates the approach through CWRU experimental analysis, and Section?? concludes with a research outlook.
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 5 Open Research Challenges and Future Directions  
While ViMindXAI provides a promising architectural foundation and illustrates its applicability through conceptual case scenarios, its transition to real-world deployment still requires addressing several unresolved challenges. These include real-time scalability, privacy-preserving learning, explainability, and the integration of emerging technologies. Overcoming these limitations is essential to enable secure, efficient, and intelligent video analytics in high-demand and sensitive domains. These challenges not only shape the roadmap of ViMindXAI but also highlight persistent research gaps across the broader landscape of video analytics.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Criteria/Layer** & **FALKON [115]** & **SIAT [116]** & **SurveilEdge [149]** & **VIDEX [130]** & **ViMindXAI (Proposed)** \\ \hline
**Architecture Type** & Centralized & Cloud-Centric & Edge Computing & Hybrid Cloud-Edge & Hybrid Cloud-Edge (Cognitive) \\  & Basic edge input; limited models support & Standard ingestion (Kda/NiFi) & YOLO-based detection + task allocation & Parallel object/anomaly threads & Grid A (YOLO-bus-tiny), denoising, ONNX models, FL at acquisition layer \\ \multirow{2}{*}{**Federated Learning and Privacy (DAL/DaAL)**} & Minimal FL, no privacy focus & FedAvg at cloud layer only & FL partially applied & Lossed coordinated FL & Full FL (FedAvg/Prox), PETs, Zero Trust sync \\  & HDFS-based batch & HDFS-HBase architecture & Local DB (SQLite) only & Unspecified backend storage & Delta Lake, Iceberg, Redis, Hybrid SS3/Ceph \\ \multirow{2}{*}{**Multimodal Semantic Indexing (DIL)**} & Basic temporal-spatial & Partial Elastic-based indexing & Edge-level metadata tagging & Metadata-based object search & CLIP, VideoBERT, FAISS, GraphDB \\  & & & & & & \\ \multirow{2}{*}{**Graph-Based Reasoning (DIL)**} & \multirow{2}{*}{Not supported} & Not supported & Not supported & Lightweight reasoning & flagp-based modeling \\  & & & & & & \\ \multirow{2}{*}{**Explainable AI (DaAL)**} & \multirow{2}{*}{Not integrated} & Some dashboard visualizations & Cloud-side & Parallel logic with XAI tools (GradCAM, SHAP) & XAI tools (StAP) \\  & & & & & & \\ \multirow{2}{*}{**Compliance and Security (DaAL)**} & \multirow{2}{*}{Standard encryption only} & Limited PEs in FL logic & Device-local privacy only & Basic authentication & GDPR/HPDA \\  & & & & & & \\ \multirow{2}{*}{**Inmensive and Explainable UI (UEL)**} & Basic dashboard visualization & Grafana/Kibana UI & 2D dashboard UI views & & Atta, Zero Trust \\  & & & & & & \\ \multirow{2}{*}{**Natural Language Analytics (UEL)**} & \multirow{2}{*}{Not supported} & Not supported & Not supported & Not supported & Natural language querying via GPT and semantic \\  & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparative Analysis of ViMindXAI and Existing VBDA Platforms.
## 5 Numerical Simulation and Discussion  
### Simulation Setup  
The studied FIBC was developed using the Simscape library in MATLAB/Simulink 2019b. The actual and nominal parameters of the FIBC are presented in Tables 1 and 2, respectively.  
To verify the effectiveness of the proposed control algorithm, some cascade control approaches are employed for comparison as follows:  
1. C1 (Proposed control approach): The GSTA-based current controller, whose control parameters are designed as \(\lambda_{1}=\lambda_{2}=1\), \(\alpha_{1}=\alpha_{2}=10^{-4}\), \(\sigma_{1}=2\), and \(\sigma_{2}=1\). The GSTA-based observer is developed with the chosen observer bandwidth as \(\omega=250\) and the parameters \(\eta_{1}=2\), and \(\eta_{2}=1\). The observer-based proportional controller is established, and the control gain is selected as \(\beta_{0}=800\).
2. C2 [39]: The conventional STA-based controller is developed for the inner current loop with the control parameters selected as the same as the C1 controller. A conventional ESO is adopted to estimate the total disturbance in the voltage loop with the observer bandwidth as the same as in the proposed method for a fair comparison. The control framework and control parameters of the voltage outer loop are similar to the proposed control scheme.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Parameter** & **Value** & **Unit** & **Parameter** & **Value** & **Unit** \\ \hline Input Voltage & \(10\sim 20\) & \(V\) & Capacitance & \(1000\times 10^{-6}\) & \(F\) \\ Inductance & \(400\times 10^{-6}\) & \(H\) & Capacitor Resistance & \(0.04\) & \(\Omega\) \\ Inductor Resistance & \(0.4\) & \(\Omega\) & Load & \(n/a\) & \(A\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: System parameters of the studied EHSS.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Parameter** & **Value** & **Unit** & **Parameter** & **Value** & **Unit** \\ \hline Input Voltage & \(16\) & \(V\) & Capacitance & \(1000\times 10^{-6}\) & \(F\) \\ Inductance & \(400\times 10^{-6}\) & \(H\) & Capacitor Resistance & \(0\) & \(\Omega\) \\ Inductor Resistance & \(0\) & \(\Omega\) & Load & \(1\) & \(A\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Nominal system parameters of the studied EHSS for control system design.  
C3 [38]: The conventional STA-based controller is developed for the inner current loop with the control parameters selected as the same as the GSTA-based controller of the C1 controller. A second-order ESO with a similar observer bandwidth is employed to cope with the generalized disturbance in the outer loop, whose structure and parameters are similar to the proposed control algorithm.
4. C4: The cascade control with two PI controllers, whose control parameters are carefully tuned by the trial-and-error method as \(K_{cP_{1}}=K_{cP_{2}}=0.15\), \(K_{cI_{1}}=K_{cI_{2}}=15.5\) for the inner current controller and \(K_{vP}=0.02\), \(K_{vI}=15.5\) for outer voltage controller.