esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Structure of Rubber Ring for Hydraulic Buffer Seal Based on Numerical Simulation
## 5 Discussion
### Evaluation of Pre-Compression Rate and Hydraulic Pressure  
The results of pre-compression rate experiment show that simply increasing the section diameter of the rubber ring cannot effectively increase the contact peak pressure, and properly increasing the pre-compression rate is effective for increasing the contact peak  
Figure 12: The force between piston rod and rubber ring in the installation mode RA is shown in (**a**). In the installation mode RL and GL, the interaction force is the same, and is shown in (**b**).  
pressure. [PERSON]'s research shows that the relationship between compression stiffness and pre-compression rate is approximately a fifth power, so the compression stiffness of different section diameters are approximately equal under the same pre-compression rate. As shown in Figure 9, the contact peak pressure of simulation from 1M to 5M is concentrated in a narrow band. And the first-degree polynomial is used to fit the function of pre-compression rate to contact peak pressure, and the Pearson's value is above 0.92. Obviously, the contact pressure increased by the pre-compression rate is higher than that by the section diameter.  
The influence of hydraulic pressure on the contact peak pressure of the rubber ring is caused by the influence of compression stiffness. In the working mode, the compression stiffness of the rubber ring is affected by the pre-compression rate and the hydraulic pressure, so the compression stiffness of the rubber ring under the hydraulic pressure is larger than that under the pre-compression rate. Accordingly, the contact peak pressure increases with the increase of hydraulic pressure. This point is also reflected in the fitting formula at groove side. The increase rate of contact peak pressure by hydraulic pressure (1.2) is greater than that by pre-compression rate (0.28). Correspondingly, 0.99 to 0.31 at piston rod side.  
Although increasing the hydraulic pressure can improve the sealing pressure, but in engineering applications, the amplitude of hydraulic pressure is determined by the working load, and it cannot always be maintained at high value. Therefore, increasing the pre-compression rate is still an effective way to improve the sealing pressure.
## 3 The Role of Organizational Theories in Blockchain Literature in the Context of LSCM  
In presenting our study results, we present the identified organizational theories, and discuss their application in current blockchain literature in LSCM. Our analysis of the 22 articles revealed that blockchain literature in LSCM is linked to six organizational theories, namely: agency theory (with six articles), information theory (three articles), institutional theory (two articles), network theory (six articles), resource-based view (four articles) and transaction cost analysis (eight articles). Notably absent theories include game theory, as well as path dependency theory, which usually are also linked to blockchain technology [25; 26]. A summary of the results from this review is provided in Table 4, where the theories are identified, basic concepts are presented, recent literature displayed and relevant blockchain-specific research questions are formulated. The next subsections will present and discuss the organizational theories: the basic outline of each section begins with the theory definition and their core constructs. Subsequently, the use of organizational theories in blockchain literature in LSCM are presented, followed by possible future theory applications for blockchain. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Theory** & **Conceptualization** & \begin{tabular}{c} **Current Blockchain-Related** \\ **Study and Theory Application** \\ \end{tabular} & \begin{tabular}{c} **Future Blockchain** \\ **Research Questions** \\ \end{tabular} \\ \hline Agency Theory & \begin{tabular}{c} Agency theory posits \\ that the interests of \\ owners and managerial \\ agents routinely diverge, \\ and that managers can \\ use the inherent \\ information asymmetry \\ to inflict agency costs to \\ a firm [27; 28] \\ \end{tabular} & \
\begin{tabular}{c} (1) Blockchain can help enhance \\ trade contract efficiency and \\ harmonize conflicting goals [29] \\ (2) Agency theory was used to \\ examine opportunistic behavior \\ and asymmetry in the distribution \\ of information between the agent \\ and the principal entail agency \\ costs in a blockchain system [30] \\ (3) Agency mechanisms as \\ useful to complement or \\ substitute transactional and \\ relational approaches in \\ blockchain scenarios [31] \\ \end{tabular} &
\begin{tabular}{c} What incentives drive \\ blockchain applications? \\ How does blockchain \\ affect the relation \\ between principal and \\ agent? \\ How does blockchain \\ affect trust within \\ business relationships? \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Organizational theories in blockchain literature in the context of logistics and supply chain management (LSCM).
# Fast 50 Hz Updated Static Infrared Positioning System Based on Triangulation Method
## 1 Introduction
### Triangulation Positioning Method  
The triangulation method determines the location of a point by measuring angles from known base stations located at the ends of a fixed line. The sought point is the third vertex of a triangle with one known side and two known angles. The unknown angle can be calculated using the knowledge of two known angles, as the sum of the angles of a triangle is 180 degrees. The unknown sides can be calculated algebraically.  
The use of Delaunay triangulation algorithms can extend the localization method. These algorithms have been applied to localize mobile robots in forests [23] and to plan routes for mobile robots in logistics warehouses and manufacturing workshops [24]. A dynamic localization method can be one possible approach for measuring the robot's position [25]. The mobile robot's navigation node can be determined by measuring the RSSI [26]. Robot self-localization can be achieved by integrating trilateration and triangulation using RF sensor networks and fuzzy inference [27]. A localization model for the passive positioning of an unmanned aerial vehicle (UAV) was developed using the sine theorem, the formula for the distance between two points, and the circumferential angle theorem [28]. The triangulation method is effective in determining the distance to clouds for UAVs [29]. Triangulation is a crucial subsystem in a navigation system that reconstructs an observed 3D point using cameras with known internal parameters and positions [30]. To enhance the positioning accuracy of the triangulation method and increase its robustness against inaccuracies in received signal strength measurements, an alternative option is to use a deep neural network based on LoS classification [31]. When utilizing the triangulation method, it may be beneficial to consider using the existing infrastructure of Wi-Fi routers and mobile hotspots. A precise positioning system can be achieved by using a multi-node triangulation algorithm that takes into account the correlation between RSSI values and transmitter-receiver distance, along with a coordinate system model to determine the optimal receiver coordinates [32]. If measurements in a localization system are obtained discontinuously, such as in laser localization, an extended Kalman filter can be used. The state vector consists of angular measurements, which are dynamically estimated based on their changes. This approach eliminates approximation errors in the correction phase [33].
# Ear Centering for Accurate Synthesis of Near-Field Head-Related Transfer Functions +
###### Abstract  
The head-related transfer function (HRTF) is a major tool in spatial sound technology. The HRTF for a point source is defined as the ratio between the sound pressure at the ear position and the free-field sound pressure at a reference position. The reference is typically placed at the center of the listener's head. When using the spherical Fourier transform (SFT) and distance-varying filters (DVF) to synthesize HRTFs for point sources very close to the head, the spherical symmetry of the model around the head center does not allow for distinguishing between the ear position and the head center. Ear centering is a technique that overcomes this source of inaccuracy by translating the reference position. Hitherto, plane-wave (PW) translation operators have yield effective ear centering when synthesizing far-field HRTFs. We propose spherical-wave (SW) translation operators for ear centering required in the accurate synthesis of near-field HRTFs. We contrasted the performance of PW and SW ear centering. The synthesis errors decreased consistently when applying SW ear centering and the enhancement was observed up to the maximum frequency determined by the spherical grid.  
head-related transfer functions; ear centering; acoustic centering; translation operator; spherical Fourier transform; distance-varying filter +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Performance Evaluation of Routing Protocols for Underwater Wireless Sensor Networks
## 1 Introduction
The paper also uses the cooperative method to transmit data between the sender and the receiver. In RACE-SM, data are forwarded both directly and through multiple hops. EERBCR is another state-of-the-art routing protocol that uses sink mobility and cooperative methods for data transfers from source to destination. EERBCR divided the whole area into four regions and assigned a sink node in each area, which moved around the region and sensed the packets from the sensor node. The EH-UWSN is an improved cooperative routing scheme designed to solve energy-consumption issues in the harvesting environment. In EH-UWSN, data are transferred through relay nodes from source to destination. MuLsi-Co is an improved version of the MuLsi routing protocol. MuLsi is a non-cooperative routing algorithm that transfers data through a single relay node. However, MuLsi-Co uses a cooperative routing scheme for data transmission from sender to receiver. This study analyses the performance metrics of recently designed routing protocols for UWSNs, such as RACE-SM, EERBCR, EH-UWSN, MuLSi-CO, MuLSI, GCORP, and EEDG. Different parameters of the mentioned routing protocols were examined and evaluated. The contributions of this paper are concise, as follows:  
* A brief overview of RACE-SM, EERBCR, EH-UWSN, MuLSi-Co, MuLSi, GCORP, and EEDG are introduced. * Simulation analysis of seven currently designed routing protocols. This analysis examines the impact of the mentioned routing protocols on end-to-end delay, energy consumption, number of live nodes, and PDR. The remaining paper is arranged as follows. Section 2 discusses the literature review, and in Section 3, the simulation results are presented. Finally, the conclusion is explained in Section 4.
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
## 5 Results
### Limitations and Future Work  
Although the proposed method, integrating OCSVM, dAE, and DBSCAN, demonstrates superior performance across most metrics, it does have inherent limitations and shortcomings. The method's efficacy is validated on the CSECIC-IDS2018 and CICIDS2017 datasets. However, its performance may vary on other datasets or in different domains, underscoring the need for additional validation across diverse datasets to ensure broad applicability. Moreover, the effectiveness of the proposed method hinges significantly on meticulously tuning parameters for each algorithm, a task that can consume considerable time. This research focuses on detecting emerging zero-day attacks, utilizing a binary classification algorithm. Future  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Metrics** & **CIC-IDS2017** & **CSECIC-IDS2018** \\ \hline Prevention rate (PR) & 1.000 & 1.000 \\ Priority based blocking rate (PBBR) & 1.000 & 1.000 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of MITRE ATT&CK-based prevention model.  
Figure 21: Success rate of MITRE ATT&CK-based prevention model.  
research can expand on this work by exploring multiclass classification techniques. It should prioritize optimizing the method to minimize computational complexities, validating it across diverse datasets, simplifying parameter tuning, improving scalability, and ensuring reliable performance in varied and dynamic environments.
## 3 Results and Discussion
### Field Performance and Adaptive Control Challenges  
The experimental and simulation results demonstrate the superiority of the proposed VSMC method with RLS-based adaptive estimation over PI and conventional SMC in terms  
Figure 18: Compares the potassium nitrate fertilizer distribution deviation between the Proportional–Integral (PI) control method and the variable-parameter sliding mode control (VSMC) method: (**a**) PI control method and (**b**) VSMC method.  
of response speed, steady-state accuracy, and robustness to load disturbances. However, several practical limitations and uncertainties should be acknowledged.  
The control performance relies on the accuracy of the system model, particularly the relationship between motor dynamics and fertilizer flow. While the RLS observer effectively estimates inertia and load torque, unmodeled nonlinearities (e.g., hydraulic resistance, pump wear, or sensor drift) may degrade performance over time. Future work could integrate hybrid modeling (e.g., physics-based and data-driven) to improve adaptability.  
The RLS algorithm assumes quasi-static parameter variations, making it less effective under highly dynamic conditions (e.g., sudden pipe blockages or rapid EC fluctuations). In such cases, the observer's convergence time (0.0085 s in simulations) may lag behind real-world disturbances. Combining RLS with robust filtering (e.g., Kalman or Ho) could mitigate this issue.  
The experiments were conducted under controlled disturbances (step torque changes), but actual field conditions involve stochastic factors (e.g., soil heterogeneity and varying water pressure). While VSMC reduced potassium nitrate deviation (SD: 0.59 vs. PI's 2.14), long-term validation across diverse terrains is needed.  
VSMC's adaptive gains improve responsiveness but may amplify noise in low-signal environments (e.g., noisy EC sensors). A sensitivity analysis of observer tuning parameters (forgetting factor and covariance matrix) would help to balance robustness and noise immunity.  
Despite these limitations, the VSMC method significantly advances precision in water-fertilization by dynamically adjusting to operational changes. Its real-time adaptability makes it a promising solution for sustainable agriculture, though further field trials are recommended to refine robustness under extreme conditions.
## 4 Conclusions  
The empirical correlations were developed for in situ determination of the solid volume fraction from the measured pressure drop across the orifice and flow rate of R134a hydrate slurry. The conclusions drawn from the experimental results are as follows:  
1. R134a hydrate slurry of the specified solid volume fraction can be formed within the flow loop chilled at 275 K by removing an appropriate amount of water, and simultaneously injecting the pertinent amount of R134a. The uncertainty in the thus-obtained solid volume fraction is smaller than 9%, with the largest contribution originating from the uncertain hydration number.  
2. The velocity of slurry flow can be estimated from the rotation speed of the slash plate pump with the same uncertainty as employed for the volume flow meter after calibration.  
The near linear relationship between \(\ln(\text{K})\) and \(\ln(Re_{MR})\) was recognized with the average value of slope \(m\) to be \(-0.616\). However, the nonlinear nature \(Re_{MR}\) with respect to \(\phi_{\text{s}}\) inevitably makes the solution procedure for \(\phi_{s}\) iterative. The evaluation of pressure loss (\(\Delta P_{Ld}\)) caused by the straight pipe also requires a priori knowledge of \(\phi_{\text{s}}\).  
3. The short span pressure difference (\(\Delta P_{12}\)) between the pressures measured at 50 mm upstream and 100 mm downstream of the 10 mm bore orifice were regressed as linear functions of \(\phi_{\text{s}}\) to yield the empirical correlation, Equation (15), which showed excellent reproducibility.  
4. The uncertainty is less than 12% of the thus-determined solid volume fraction, which includes about 7.2% of biased uncertainty in obtaining the raw solid volume fraction data, and about 4.6% of random error.  
Figure 12: Reproduced solid volume fraction using Equation (16) for the 15 mm bore orifice.  
**Author Contributions:** Experiments, M.U. and Z.R.; conceptualization, Z.R.; analysis and data curation, M.U. and Z.R.; supervision, K.S. and M.H.S.; writing original draft, M.U.; reviewing and editing, M.H.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Dongguk University Research Fund of 2019 and by the Korean government Ministry of Oceans and Fisheries (No. 20110141, \"Development of key technology in seawater desalination using gas hydrate process\").  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Research on Short-Term Traffic Flow Combination Prediction Based on CEEMDAN and Machine Learning
## 4 Combined Model Construction and Case Analysis Based on CEEMDAN and Machine Learning
### Case Studies
#### 4.2.3 Outcome Evaluation Indicators  
The establishment of the outcome evaluation indicators is an indispensable part of traffic flow forecasting, and its significance is as follows:  
(1) When the traffic flow of a certain section of the road is predicted, the overall advantages and disadvantages of the prediction results can be directly reflected, and the purpose of verifying the validity of the model is achieved;  
(2) When we use different models to predict traffic flow, we can also visually compare the prediction effects of different models through evaluation indicators so as to quickly judge the advantages and disadvantages of the prediction models.  
In this study, the short-term traffic flow prediction model was evaluated by the mean squared error (_MSE_), the mean absolute error (_MAE_), the root mean square error (_RMSE_), and the decision coefficient (_R\({}^{2}\)_) [25]. The calculation formula is as follows:  
\[E_{MSE}=\frac{1}{N}{\sum\limits_{i=1}^{N}{\left({y_{i}-\overset{\wedge}{y_{i} }}\right)^{2}}} \tag{13}\]  
\[E_{MAE}=\frac{1}{N}{\sum\limits_{i=1}^{N}{\left|{y_{i}-\overset{\wedge}{y_{i} }}\right|}} \tag{14}\]  
\[E_{RMSE}=\sqrt{\frac{1}{N}{\sum\limits_{i=1}^{N}{\left({y_{i}-\overset{\wedge }{y_{i}}}\right)^{2}}}} \tag{15}\]  
\[R^{2}=1-\frac{\frac{1}{N}{\sum\limits_{i=1}^{N}{\left({y_{i}-\overset{\wedge }{y_{i}}}\right)^{2}}}}{\frac{1}{N}{\sum\limits_{i=1}^{N}{\left({\overline{y_ {i}}-y_{i}}\right)^{2}}}} \tag{16}\]  
In the above equation, \(E_{MSE}\) is the mean square error, \(E_{MAE}\) is the mean absolute error, \(E_{RMSE}\) is the root mean square error, \(R^{2}\) is the decision coefficient, \(N\) is the total predicted time length, and \(y_{i}\), \(\overset{\wedge}{y_{i}}\), and \(\overline{y_{i}}\) are, respectively the measured value, predicted value, and the average value of traffic flow. The smaller the values of \(E_{MSE}\), \(E_{MAE}\), and \(E_{RMSE}\), and the smaller the deviation between the predicted results and the actual traffic data, the better the model performance. \(R^{2}\) is the correlation between the prediction results and the actual results, and its value is between 0 and 1. The closer it is to 1, the better the fitting regression effect is and the better the model is. Therefore, the optimal prediction model should simultaneously meet the two conditions of the minimum error and the maximum decision coefficient.
# IoT and Blockchain Integration: Applications, Opportunities, and Challenges
## 6 Security Assurances and IoT Threat Model
### IoT Threat Model
#### 6.2.3 Threat and Attacks on Integrity  
Events such as theft of a node, unauthorized control of a node, MIM attack, insertion of a rouge sensor node, unapproved modification of input and output data, etc., may alter the integrity of data and operation of nodes in an IoT. A vital node on the sensor layer be physically stolen from the current network and may be accessed and controlled on a different network. Unauthorized node control may result from theft of a node or an attack such as man-in-the-middle (MIM) that causes the communication between nodes to replay [12; 40]. Due to an insecure channel, a MIM attack can mimic the communication and ultimately compromise the integrity of the message being exchanged between two nodes. Inserting a rouge sensor in an IoT can produce faulty data values that can negatively impact decision-making in different domains utilizing IoT [43]. Unapproved input and output data modification relevant to precision agriculture IoT, smart home, and industrial IoT, among others, can cause massive economic, environmental, and trade disruptions. Artificial intelligence and machine learning techniques have been adopted for the advanced operation of agriculture, industry, and home systems. These techniques feed on the massive data collected by an IoT. However, there is a higher risk of using insufficiently modeled algorithms, and biased predictive models for decision supports [50]. The compromised models can produce unintended and adverse effects in the current and future operations of the IoT systems [43; 51].