esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Aiming at the fact that the existing motor control system is not suitable for the working conditions of electric CM, a vector control strategy based on fuzzy PI of the PMSM is proposed and used in a positive flow system of a CM. Its feasibility was verified through simulation and test. The main conclusions are as follows:  
1. Compared with the traditional PI control, when using the fuzzy PI control, the motor speed output overshoot is smaller, the response is faster, the load disturbance has less affection, and the stability is higher;
2. When the fuzzy PI control is used, the electric motor output torque overshoot is slightly increased, but the responsiveness and stability are significantly improved;
3. The simulation results show that when the load is variable, the fluctuation of motor speed was approximately 1.5% with fuzzy PI, and 2.5% with traditional PI;
4. The test results show that when the load fluctuates drastically, the electric motor speed fluctuation is within 3.5%, and the steady-state error is only approximately 0.3%.  
CM has the characteristics of severe load fluctuation, so it is necessary to avoid the interference of fluctuation to CM mobility. The vector control system of permanent magnet synchronous motor based on fuzzy PI proposed in this paper is suitable for electric CM with large load fluctuation and complex working conditions. In the next step of work, it will be tested on the actual excavator under the actual working conditions to verify its responsiveness, stability, and anti-interference ability. In addition, the calculation burden caused by the fuzzy control algorithm will be studied in depth.  
Conceptualization, Z.L. and T.L.; methodology and software, Y.Z.; formal analysis, Q.C. and W.G.; investigation, S.F. and Y.Z.; writing--original draft preparation, Q.C. and H.R. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The authors declare no conflict of interest.
# Stripe-Assisted Global Transformer and Spatial-Temporal Enhancement for Vehicle Re-Identification
## 4 Experiment
### Comparisons with State-of-the-Art Methods
#### 4.3.2 Comparisons on VehicleID  
Table 2 compares the performance of the SOTA methods on VehicleID. Our SaGT method continued to achieve competitive performance on three subsets of VehicleID. In particular, our SaGT method achieved the highest mAP and Rank-1 on the small subset, the highest mAP and second highest Rank-1 on the medium subset, and the third highest mAP and Rank-1 on the large subset. Compared with other methods that performed well on VeRi, our method was superior on VehicleID. This can be attributed to the fact that VehicleID provides more training data, promoting our model to learn more patterns. Additionally, when vehicle images encounter drastic viewpoint changes in VehicleID, we  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & **Type** & **Reference** & **mAP** & **Rank-1** \\ \hline SN [13] & GF & TNNLS’22 & 75.70 & 95.10 \\ VARID [12] & GF & TITS’22 & 79.30 & 96.00 \\ VAT [18] & GF & IPM’22 & 80.40 & 97.50 \\ MsKAT [32] & GF & TITS’22 & 82.00 & 97.10 \\ \hline PGAN [15] & LF & TITS’22 & 79.30 & 96.50 \\ DGPM [14] & LF & TITS’21 & 79.39 & 96.19 \\ LCG-CGT [36] & LF & ICTAI’22 & 79.70 & 97.00 \\ HPGN [37] & LF & TITS’22 & 80.18 & 96.72 \\ DFR [38] & LF & PR’22 & 84.47 & 93.02 \\ DSN [39] & LF & PR’23 & 76.30 & 94.80 \\ SFMNet [40] & LF & IJCNN’23 & 80.00 & 97.00 \\ GiT [31] & LF & TIP’23 & 80.34 & 96.86 \\ SOFCT [22] & LF & TITS’23 & 80.70 & 96.60 \\ MART [41] & LF & TITS’23 & 82.70 & 97.60 \\ \hline DPGM-ST [14] & LF and ST & TITS’21 & 82.17 & 98.45 \\ DFR-ST [38] & LF and ST & PR’22 & 86.00 & 95.67 \\ \hline Baseline & GF & Ours & 78.38 & 95.71 \\ SaGT & GF & Ours & 80.67 & 96.96 \\ SaGT-ST & GF and ST & Ours & **86.59** & **98.75** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The performance (%) comparison on VeRi-776. The best performance is marked in bold.  
employed local features to assist SaGT in learning a reliable whole representation, which brought better overall performance than MsKAT and DFR.
# A Novel Ensemble Belief Rule-Based Model for Online Payment Fraud Detection
## 5 Experimental Study
### Complexity Improvements  
In this section, we will present the improvements made by the proposed ensemble BRB method over traditional BRB models in terms of model complexity, using mathematical formulations.  
The complexity of a BRB model is primarily determined by the number of rules it contains. In this case, we compare the complexities of the proposed ensemble BRB model and the traditional BRB model by calculating their respective numbers of rules.  
Given a dataset with \(M\) attributes, where \(i\)-th attribute has \(A_{i}\) referential values, the number of rules \(n\_rules\) in a traditional BRB model can be calculated using Equation (23) as follows.  
\[n\_rules=\prod_{i=1}^{M}A_{i} \tag{23}\]  
In the method proposed in this paper, the number of rules is calculated as shown in Equation (24).  
\[n\_rules=n\times\prod_{i=1}^{2}A_{i} \tag{24}\]  
Figure 11: Performance of ensemble BRBs under different sampling ratio in Dataset #1.  
Figure 12: Performance of ensemble BRBs under different sampling ratio in Dataset #2.  
where \(n\) represents the number of weak BRBs in the ensemble BRB.  
Plugging in the values from the fraud detection dataset used in this paper, where \(M\) is 30, \(A_{i}\) is 4 uniformly and \(n\) is 7, the complexities of the two models are calculated as follows.  
The number of rules of the traditional BRB is 430, while the number of rules of the ensemble BRB is \(7\times 4^{2}=112\). It can be seen that in building the traditional BRB model, the combinatorial explosion problem arising from the large number of attributes in the fraud detection dataset makes it difficult to construct the model. On the other hand, the proposed method of combining the ensemble BRB with feature selection can effectively avoid the combinatorial explosion problem, reducing the modeling difficulty of BRB models when dealing with high-dimensional data.
## 7 Challenges and Limitations
### Unpredictable RF Energy Availability  
The unpredictable and dynamic nature of ambient RF energy sources poses another significant limitation. In urban areas, RF power densities are relatively high due to dense cellular, Wi-Fi, and broadcast infrastructures. However, in rural, suburban, or industrial environments, RF energy availability fluctuates greatly and may drop to levels insufficient for practical energy harvesting [36]. A recent field measurement campaign indicated that RF power densities can vary by two to three orders of magnitude within the same urban area depending on time, location, and user density [108]. This variability makes it challenging to design harvesters that can reliably operate under all environmental conditions without supplemental energy storage or hybrid systems. Additionally, the sporadic availability of RF signals raises concerns about the stability of critical applications such as biomedical implants or industrial IoT sensors, where continuous power supply is mandatory. These fluctuations are caused by factors such as dynamic user behaviour, time-varying transmission loads, and physical obstructions that cause multipath fading and shadowing. As a result, designing effective harvesters requires practitioners to capture ambient energy when available but also store it efficiently during surplus conditions. Potential technical solutions include integrating advanced energy storage devices (like supercapacitors or micro-batteries) that can buffer energy during periods of high RF availability. Another approach is the use of hybrid energy-harvesting systems that combine RF with other renewable sources such as solar or vibrational energy, thus smoothing out the intermittency associated with any single source. Furthermore, adaptive algorithms that predict RF energy availability based on historical data can be implemented to dynamically adjust power management and load conditions, ensuring more reliable operation in environments with highly variable RF signals.
## 3 The Proposed Method
In addition, a thematic map of the relevance of pleasant/unpleasant emotions is constructed. Our method is shown structured in pseudocode in Algorithm 1. ```
1. Select the N spatial entities in the area of study
2. Create the dictionary of emotional words
3. Create the emotional relevance fuzzy partition on the domain [0, 1]
4. For each spatial entity e\({}_{i}\) = 1, 2,..., N
5. Extract the data stream given by reviews and comments from the web network
6. Execute the text parsing component
7. Create the i\(th\) document D\({}_{i}\)
8. Next i
9. Execute the term filtering component on the corpus of documents D = {D1, D\({}_{2}\),..., D\({}_{N}\)}
10. Execute the TF-IDF calculus component
11. For each document d\({}_{i}\) i = 1, 2,..., N
12. Execute the document classification component
13. Join the data with relevance of each emotion category to the i\(th\) spatial entity
14. Next i
15. For each emotion category c\({}_{j}\) j = 1, 2,..., M
16. Construct the thematic map of the emotion category c\({}_{j}\)
17. Next j
18. Construct the pleasant/unpleasant emotions thematic map
```  
**Algorithm 1** Emotional relevance of service facilities assessment  
We tested our framework to analyze the relevance of pleasant and unpleasant emotions expressed in reviews and comments on the Web by users who attended theatrical performances in theaters located in the municipality of Naples (Italy). For each spatial entity, the relevance of the sixteen primary and secondary emotional categories of [PERSON]'s wheel were assessed. The following section presents and discusses the test results.
# A Novel Adaptive Gain of Optimal Sliding Mode Controller for Linear Time-Varying Systems
## 4 Conclusions  
In this study, a new optimal control law was formulated using the sliding mode controller, whose gains are adjustable and of which the control performances were evaluated by adopting several illustrative examples. In the formulation of the controller, the objective function of the Bolza-Meyer criterion was adopted to make the value of the gain larger than 1. This is a salient point to resolve some drawbacks conventional optimal controllers in which the values of the definite symmetry matrices are less than 1. By adopting the adjustable gain values, the non-negative definite symmetric matrix function of the Meyer term is reduced and hence the energy consumption for control action is also decreased. In order to verify the benefits of the proposed optimal control law with the new gain  
Figure 16: Vibration control results at the driver position under random step wave road excitation: (**a**) displacement, (**b**) velocity.  
Figure 17: Power spectral density of the proposed control system under random step wave road excitation.  
function matrix, five illustrative examples are adopted and control performances are evaluated based on three criteria: control gain index, main input control index, and the state index through the power spectral density (PSD) figures. After showing excellent control performance of the proposed controller in simple systems, it was applied to vibration control of the vehicle seat suspension system subjected to both the random bump road excitation and random step wave excitation. It was shown that unwanted vibration at the driver position is well controlled by implementing the proposed optimal controller showing the low power and high stability. The results prove that the proposed gain function and chosen matrices can satisfy for the systems as given Table 1. Therefore, the results presented in this work are self-explanatory justifying that the proposed optimal control will be applied to diverse linear time-varying systems subjected to disturbances. It was finally remarked that as a second phase of this work, an experimental implementation through a real time feedback control will be undertaken by adopting the seat suspension system treated in this work.  
Conceptualization: D.X.P and S.-B.C., formula analysis: V.M. and D.X.P. and data curation: V.M. and D.X.P, writing and editing: D.X.P. and S.-B.C., review and funding acquisition: S.-B.C.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2017R1A2B3003026). This financial support is gratefully acknowledged.  
The authors declare no conflict of interest.
# Research on Wear Detection of End Milling Cutter Edge Based on Image Stitching
## 4 Results and Discussion
### Algorithm Test Analysis
#### 4.4.2 Digital Microscope Tool Wear Verification  
The image processing algorithm is used to obtain the minimum circumscribed rectangle of the divided wear area. First, the cutting edge is reconstructed by rotating the cutting edge to the horizontal according to the angle of the helical edge. Column scanning is performed on the wear image segmented by FCN, and the Minimum Bounding Rectangles method is used to envelop the wear area, and the width of the rectangle is the wear value VB. The most significant wear amount among the four side edges is taken as the wear value of the end mill, as shown in Figure 13. We stitched the wear images in the test set, and the FCN segmentation model and the precision optical microscope were used to detect the maximum wear value VB. The maximum wear value VB of the side edge and the error rate are detected, as shown in Figure 14. It can be seen from the image that the tool wear VB value identified by the FCN segmentation model is almost the same as a result detected by the digital microscope, and the error rate is below 10%. It fully demonstrates the accuracy and reliability of the FCN segmentation model for detecting the wear of the end mill.  
Figure 14: Comparison of the maximum wear values measured by the two methods.  
Figure 13: The minimum circumscribed rectangle of side edge wear area. (**a**) The first edge; (**b**) The second edge; (**c**) The third edge; (**d**) The fourth edge.
# Blockchain Interoperability for Future Telecoms
## 2 Study Background
### Benefits of Blockchain Interoperability in Telecommunications  
The following benefits provided by blockchain can be considered the most critical:  
* Enhanced security: Blockchain technology can provide a secure platform for telecommunication transactions by encrypting data and ensuring their integrity through the use of a tamper-proof hashing mechanism. DID management can be utilized alongside a ZKP approach to ensure secure authentication, providing protection against eavesdroppers and man-in-the-middle attacks. The implementation of smart contracts enhances and allows for the automation of security policies across various components of the mobile network, such as ISAC.
* Improved interoperability: Blockchain technology allows for seamless data transfer and communication between telecommunication networks, enabling better integration and collaboration among service providers with decentralized decision-making using smart contracts.
* High transparency: Integrated distributed ledger technologies make all transactions traceable and transparent to the parties involved when necessary.
* Increased operational efficiency: Blockchain technology can streamline and automate telecom processes, including supply chain management, by reducing manual intervention and improving operational efficiency.
* Cost efficiency: Blockchain technology can lower costs for telecommunications providers and their end users by eliminating the need for intermediaries and reducing transactional friction.
* Enhanced customer experience: Blockchain interoperability can enable seamless roaming services for customers, allowing them to connect to different networks, particularly 6G 3D communications, without disruptions or complicated billing processes.
* Facilitated billing and settlement: Blockchain technology, with its immutable and fully transparent features, can enable more efficient and accurate billing and settlement processes in the telecommunications sector, thereby effectively reducing disputes and delays.
# A Novel Consistent Quality Driven for JEM Based Distributed Video Coding
## 4 Performance Evaluation  
Several experiments have been performed to illustrate the effectiveness of the proposed JEM-DVC architecture. In this work, the performance of the JEM-DVC codec in terms of objective, subjective, and consistent quality evaluations is compared with that of the relevant benchmarks, including the H.265/HEVC based DVC (H.265-DVC) [9] with H.265-DVC reference code in [27] and H.264/AVC based DVC or DISCOVER DVC [10] codecs. The DISCOVER DVC is one of the most efficient DVC solutions in literature and it is also the most widely used as the reference benchmark for evaluating the performance of a DVC codec.  
For evaluation, the proposed JEM-DVC codec is tested on several video sequences, including _Foreman_, _Soccer_, _Coastguard_, and _Hall Monitor_. These test sequences are in 4:2:0 YUV format with CIF (\(352\times 288\)) resolution. The size of a GOP is set to 2 (\(N_{GOP}=2\)). In our experiments, several values of QPs and QMs determined in the KF-Q and WZF-Q methods are employed to verify the PSNR performance of the key and WZ frame coding, respectively.  
The performance of the proposed codec, including: (1) Key frame coding evaluation; (2) Consistent video quality evaluation; and, (3) Overall rate-distortion performance evaluation, are described in detail in the next subsections, as follows:
## 5 Discussion
### Management Implications and Future Researches  
Forest managers are often concerned about the ability of ecosystems to sequester and store carbon. Long-term net flux of carbon between terrestrial ecosystems and the atmosphere have been dominated by changes in both forest area and biomass that result from management and regrowth [52]. The process presented in this study which provides a tool for understanding rates of forest AGB accumulation at small temporal scales can provide managers with valuable information about the carbon dynamics of an ecosystem.  
In addition, this model may provide a basis to understand how ecosystems respond to disturbance, such as wildfire, as well as estimate their potential to emit carbon to the atmosphere. Wildfires can consume substantial proportions of AGB in some ecosystems [66; 67], and estimates of AGB may offer  
Figure 9: The relationship between field-based LAI and AGB. Inset (**a**–**c**) represented needle leaf, broadleaf and mixed leaf forests, respectively.  
a basis on which to approximate potential emissions from fire. Moreover, quantifying the accumulation of AGB due to regrowth after wildfire can provide insights into the recovery of forest ecosystems and the key factors regulating biomass accumulation [68].  
This research can help identify factors that drive accumulation of AGB in a forest ecosystem, which will improve our understanding of how changes in climate and fire regimes affect the carbon balance [68]. Future research is needed to address where AGB accumulation is occurring in forest ecosystems, as there are considerations for forest management such as managing wildfire hazards, depending on the strata (for example: ladder fuels or upper canopy) where AGB is amassing.
## 4 Results
### Additional Observations on Robustness, Generalizability, and Limitations  
#### 4.4.1 Robustness and Reliability of AUC Values  
To assess the robustness and reliability of the reported AUC (Area Under the Curve) values, multiple validation techniques were employed. Additional analyses were conducted using bootstrapping and stratified K-Fold cross-validation. In the bootstrapping analysis, the AUC ranged from 0.88 to 0.93 across 1000 iterations, with a mean AUC of 0.91 and a 95% confidence interval of [0.89, 0.92]. Similarly, in the stratified K-Fold cross-validation (K = 10), the AUC ranged from 0.87 to 0.92 with a mean of 0.90, further reinforcing the reliability of this metric. These additional analyses consistently indicated high AUC values, comparable to those reported in the main experiments. Furthermore, the experiment was repeated under identical conditions. A consistent pattern of results was observed across both the initial and repeated experiments. The high AUC values obtained suggest the model's strong ability to distinguish between the classes, even if other performance metrics may appear modest.
## 2 Materials and Methods
### Governing Equations  
#### 2.3.1 Mathematical Model of Flow Field  
In fluent simulation, the steady-state calculation of the fluid module is mainly based on the rotational flow inside the box when dry air is introduced into the secondary chamber by the dehumidifier. Therefore, a realizable \(k-\varepsilon\) model is used. The model should include gravity conditions according to the actual situation. The main governing equations of its mathematical model can be described as:  
\[\frac{\partial\rho}{\partial t}+\rho(u\cdot\
abla)u=\
abla\cdot\left[-pI+( \mu+\mu_{T}).\left(\
abla u+(\
abla u)^{T}\right)\right]+F+\rho g, \tag{1}\]  
\[\frac{\partial\rho}{\partial t}+\rho\
abla\cdot(u)=0, \tag{2}\]  
\[\frac{\partial(\rho k)}{\partial t}+\rho(u\cdot\
abla)_{\rm k}=\
abla\cdot \left[\left(\mu+\frac{\mu_{t}}{\sigma_{k}}\right)\
abla_{\rm k}\right]+G_{k}- \rho\varepsilon, \tag{3}\]  
\[\frac{\partial(\rho\varepsilon)}{\partial t}+\rho(u\cdot\
abla)_{\varepsilon }=\
abla\cdot\left[\left(\mu+\frac{\mu_{t}}{\sigma_{k}}\right)\
abla_{ \varepsilon}\right]+\rho C_{1}E\varepsilon-\rho C_{2}\frac{\varepsilon^{2}}{ k+\sqrt{v\varepsilon}}, \tag{4}\]  
where \(\rho\), \(t\), \(u\), \(p\), \(g\), \(l\), \(\mu\), \(T\), \(F\), \(k\), \(\varepsilon\), and \(G_{\rm k}\) are the density, the time, the vector, the pressure, the gravitational constant, the identity matrix, the fluid dynamic viscosity, the temperature, the volume force vector, the kinetic energy of the flow field, the dissipation rate of the flow field, and the generation term of turbulent kinetic energy k caused by buoyancy, respectively; \(\sigma_{k}\), \(\sigma_{\varepsilon}\), \(C_{1}\), \(E\), and \(C_{2}\) are all empirical constants.  
Figure 3: (a) Ventilating pipes layout 1 and 2; (b) ventilating pipes layout 3 and 4.