esa-sceva/satcom-chunk-collection huggingface dataset:
# A Secure Auditable Remote Registry Pattern for IoT Systems
## 4 The Secure Auditable Registry Based On Message-Oriented Middleware (SARMOM) Pattern
### Consequences  
After applying the pattern, we can expect different consequences, both positive and negative. Among the positive consequences, we can highlight the fact that we can solve our original problem with additional qualities, such as scalability, performance, ease of evolution and management of a large number of concurrent producers. More specifically, the SAR is the core to achieving the requirements of secure system registry and auditability, while the MOM is the key to achieving the applicability of the concept of the SAR to our target scenarios. Other positive consequences of the application of the SARMOM pattern include the following:  
* Enabling asynchronous messaging from producers to the registry through the MOM allows the system to accept high rates of input messages regardless of the consumption capability of the registry. It also improves integrity and fault tolerance.
* MOM is normally an externally developed system, which frequently has control over the policies for evolution of features and security patching.
* The use of the SAR as the source of information for other SUD functions makes the system less vulnerable to data-alteration attacks.
* Likewise, centralizing the sourcing of information in the registry facilitates the adoption of centralized and consistent access control for the different SUD functions (clients).
* Centralizing the sourcing of system events in the registry facilitates the adoption of AI-powered monitors for early detection and prevention of attacks.
* The architecture of the pattern fosters the decoupling of components, thus facilitating maintenance and evolution.
* The intermediation of the MOM makes the registry independent from the source data formats. In particular, most MOM implementations provide the means to add specific interfaces (connectors).  
On the downside, we can highlight the following considerations:  
* It becomes necessary to install, configure and manage a relatively complex infrastructure, such as the MOM.
* The fact that the MOM is normally an externally developed system reduces the control over the policies for evolution of features and security patching and creates a dependency that may affect our system in case the MOM is discontinued.
* Despite the security mechanisms adopted, the SAR becomes a single point of failure and a single point of attack.
* If not carefully designed, the centralization of the sourcing of information through the SAR may have a negative impact on the performance.  
It is important to note that most of the negative considerations can be reduced or eliminated through careful design and integration strategies.
## 4 Evaluation
### Discussion
#### 4.3.5 Blockchain-Based Storage, Validation, and Consensus  
The proposed framework ensures efficient and secure storage and validation of the global model using blockchain technology. Instead of storing the entire model, only a hash of the global model is recorded on the blockchain to ensure integrity while minimizing storage requirements. For instance, in our experiments with CIFAR-10, the model size was approximately 2 MB, and the hash sufficiently captured its integrity. Blockchain transactions, including model updates, typically take about 15 s to be recorded, leveraging Ethereum's average block time. Validation of these transactions is conducted by decentralized blockchain nodes, which use cryptographic signatures to ensure tamper-proof updates. A lightweight DPoS consensus mechanism further optimizes computational efficiency, balancing security and scalability for IIoT applications. This design ensures the framework remains robust, secure, and practical for real-world deployment.
_Article_  
**Leak Detection in Water-Filled Small-Diameter**  
**Polyethylene Pipes by Means of Acoustic**  
**Emission Measurements \({}^{\dagger}\)**  
**[PERSON] \({}^{\star}\), [PERSON] and [PERSON]**  
Department of Engineering for Industry (DIN), University of Bologna, V.le del Risorgimento 2, Bologna 40136, Italy; [EMAIL_ADDRESS] (M.T.); [EMAIL_ADDRESS] (A.R.)  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +39-0543-374-440  
\({}^{\dagger}\) A preliminary and partial version of this paper was presented at the 8 th International Conference on Urban Rehabilitation and Sustainability (URES'16), Rome, Italy, 21-23 October 2016.  
Academic Editors: [PERSON] and [PERSON]  
Received: 24 November 2016; Accepted: 16 December 2016; Published: 22 December 2016  
**Abstract: The implementation of effective strategies to manage leaks represents an essential goal for all utilities involved with drinking water supply in order to reduce water losses affecting urban distribution networks. This study concerns the early detection of leaks occurring in small-diameter customers' connections to water supply networks. An experimental campaign was carried out in a test bed to investigate the sensitivity of Acoustic Emission (AE) monitoring to water leaks. Damages were artificially induced on a polyethylene pipe (length 28 m, outer diameter 32 mm) at different distances from an AE transducer. Measurements were performed in both unburied and buried pipe conditions. The analysis permitted the identification of a clear correlation between three monitored parameters (namely total Hits, Cumulative Counts and Cumulative Amplitude) and the characteristics of the examined leaks.**  
**Keywords: leak detection; Acoustic Emission; service pipe; customer connection; water supply network**
## 3 Performance Analysis of NOMA-Enabled HSTRNs
### Outage Probability
#### 3.2.1 DF Protocol  
Let us denote \(\Psi_{i}=2^{\frac{2R_{i}}{\Gamma-\chi}}-1\) as the threshold SNRs of \(i\)-th user to decode \(x_{i}\). Then, the outage probability of \(D_{1}\) is determined by  
\[\begin{split}\mathcal{P}_{out,D_{1}}^{DF}&=\Pr \left(\min\left(\Gamma_{R\to x_{1}}^{DF},\Gamma_{D_{1}\to x_{1}}^{DF}, \Gamma_{D_{2}\to x_{1}}^{DF}\right)<\Psi_{1}\right)\\ &=1-\Pr\left(\Gamma_{R\to x_{1}}^{DF}>\Psi_{1},\Gamma_{D_{1} \to x_{1}}^{DF}>\Psi_{1},\Gamma_{D_{2}\to x_{1}}^{DF}>\Psi_{1}\right).\end{split} \tag{18}\]  
**Proposition 1**.: _The closed-form outage probability of signal \(D_{1}\) can be expressed by_  
\[\begin{split}\mathcal{P}_{out,D_{1}}^{DF}&=1-\sum_{ i_{1}=0}^{m_{\mathrm{SG}}-1}\cdots\sum_{i_{M}=0}^{m_{\mathrm{SG}}-1}\Theta \left(M\right)\sum_{n_{1}=0}^{m_{1}N_{1}-1}\sum_{n_{2}=0}^{m_{2}N_{2}-1}\frac{ \left(\beta-\delta\right)^{n_{1}+n_{2}-\Delta}}{n_{1}!n_{2}!\Lambda_{1}^{n_{1 }}\Lambda_{2}^{n_{2}}}\left(\frac{\kappa_{1}}{\rho_{S}\phi}\right)^{n_{1}+n_{ 2}}\\ &\times\Gamma\left(\Delta-n_{1}-n_{2},\frac{\kappa_{1}\left(\beta -\delta\right)}{\rho_{S}},\frac{\left(\Lambda_{1}+\Lambda_{2}\right)\left( \beta-\delta\right)\kappa_{1}}{\Lambda_{1}\Lambda_{2}\phi\rho_{S}}\right).\end{split} \tag{19}\]  
Proof.: Please refer to Appendix A for detailed proof of derivation of Equation (19).  
Next, we derive the outage probability of \(D_{2}\) which is given by  
\[\begin{split}\mathcal{P}_{out,D_{2}}^{DF}&=\Pr \left(\min\left(\Gamma_{R\to x_{2}}^{DF},\Gamma_{D_{2}\to x_{2}}^{DF} \right)<\Psi_{2}\right)\\ &=1-\Pr\left(\Gamma_{R\to x_{2}}^{DF}>\Psi_{2},\Gamma_{D_{2} \to x_{2}}^{DF}>\Psi_{2}\right).\end{split} \tag{20}\]  
Replacing Equations (2) and (8) into Equation (20), we get  
\[\mathcal{P}_{out,D_{2}}^{DF}=1-\Pr\left(\gamma_{SR}>\kappa_{2},\left\|\mathbf{ h}_{2}\right\|^{2}>\frac{\kappa_{1}}{\tilde{\gamma}_{SR}\phi}\right), \tag{21}\]  
where \(\kappa_{2}=\frac{\Psi_{2}}{\Sigma_{2}}\). Following the approach of Appendix A, the closed-form outage probability of \(D_{2}\) can, thus, be obtained as  
\[\mathcal{P}_{out,D_{2}}^{DF}=1-\sum_{i_{1}=0}^{m_{\mathrm{SG}}-1}\cdots\sum_{i _{M}=0}^{m_{\mathrm{SG}}-1}\Theta\left(M\right)\sum_{n_{2}=0}^{m_{2}N_{2}-1} \frac{\left(\beta-\delta\right)^{n_{2}-\Delta}}{n_{2}!}\Big{(}\frac{\kappa_{2} }{\Lambda_{2}\rho_{S}\phi}\Big{)}^{n_{2}}\Gamma\left(\Delta-n_{2},\frac{\kappa _{2}\left(\beta-\delta\right)}{\rho_{S}},\frac{\kappa_{2}\left(\beta-\delta \right)}{\Lambda_{2}\phi\rho_{S}}\right). \tag{22}\]
## 4 Conclusions  
1. The vertical confinement has an obvious effect on the decay rate of the mean centerline velocity. When the confined width changes from 15 to 5, the speed is reduced by 20%. The slower decay rate can be explained by the conservation of mass which demands an increase in the jet velocity compared to the free jet because the cross-sectional area of the jet becomes constrained by the boundaries.
2. The vertical confinement has a significant effect on the jet spread. With the decrease of the confined width, the jet has a tendency to spread horizontally. The confinement has a profound effect on the axial velocity profiles in the vertical plane. As the flow goes downstream, the jet spreads to the boundaries and the axial velocity above and below the jet centerline increases rapidly.
3. The vertical confined region induces a space hysteresis effect which changes the location of the transition region moving downstream. With the enlarging of the confined width, the position moves back.
4. The fluid could form a negative pressure region near the confined wall, and negative pressure will lead to the trend of inward movement of the wall, rather than outward expansion.  
This work was supported by Research Program supported by the National Key Basic Research Program of China (No. 2014 CB239203), the National Natural Science Foundation of China (No. 51474158), China. [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] supervised the projects and the students with contributions to guide the research program; [PERSON] wrote the paper. The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  
Figure 13: Radial turbulent intensity of the vertical plane (X-Z).
# A Communication-Efficient Secure Routing Protocol for IoT Networks +
## 4 Proposed Method
### Route Maintenance Phase  
The constructed routes must be maintained periodically, as the communication quality of the routes may fluctuate over time based on various factors, such as multipath fading, interference, and attacks involving packet dropping or delaying. Thus, we set up an optional packet for route maintenance.  
Consider a situation where node \(\mathsf{N}_{i}\) on the route detects that a node on the route cannot connect and communicate with the destination node \(\mathsf{D}\). In this situation, \(\mathsf{N}_{i}\) generates a route error message (RERR) and sends it to the upstream node. The format of the RERR in the proposed method is expressed as follows:  
\[\text{RERR}_{\mathsf{N}_{i}}=(\text{RERR}_{\text{AODV}},\,\text{ID}_{\mathsf{ N}_{i+1}},\,\,\text{LT})_{\text{Si}_{\mathsf{N}_{i}}} \tag{10}\]  
The address of the destination node \(\mathsf{D}\) of the disabled route is written to the RERR, and each node that receives the RERR discards the route to that destination from its table. Subsequently, the node relays the RERR to its upstream node. In addition, the signature of the source of the RERR is added to the packet and verified by the receiver. To prevent a specific RERR from flowing to the network, the packet has a valid time; a node that receives a packet for which the valid time has expired discards the packet and does not forward it. In addition, the detection of route abnormalities is performed based on the packet transmission rate (PSR) using an acknowledgment packet (ACK), as in [18].  
After the route is established, an ACK is sent back to the source when the destination receives an information packet from the source. Each node then calculates whether the ratio of the number of ACKs actually received to the number of messages sent is below a threshold \(r_{\text{ACK}}\). The packet transmission rate is calculated for each fixed number of transmitted messages \(M\), that is, if \(\frac{N_{\text{ACK}}}{\text{M}}<r_{\text{ACK}}\) (where \(N_{\text{ACK}}\) is the number of received ACKs), then the node generates the RERR.
# Accurate RANS Simulation of Wind Turbine Stall by Turbulence Coefficient Calibration
## 1 Introduction
Their results showed that stall prediction depends on the simulation of transition, but it is still not accurate enough. The problem also appears in RANS simulations of the well-known NREL Phase VI rotor [20] and MEXICO rotor [21]. A large number of simulations, including those in the blind comparison [22], were carried out for the NREL Phase VI rotor using various solvers such as OVERFLOW-D [23],elsA [24], EllipSys3D [25], CFX [26], FLUENT [27], THETA [28], etc. The MEXICO rotor was simulated by participants of the Mexnext project [29] and researchers using a variety of RANS codes [30; 31; 32]. Many of the above simulations failed to accurately predict the stall. The inaccuracy of stall prediction remains an unsolved problem [33; 34]. Stall frequently occurs on stall-regulated wind turbines. Also, it may occur on variable-pitch wind turbines during operation in yaw or in extreme wind conditions, and particularly on the blade inboard sections where blade incidence angle is usually high. Therefore, the accurate stall prediction is of great significance to wind turbine load computation, as well as the optimal design of a wind turbine. The present study is devoted to achieving accurate stall prediction in wind turbine RANS simulations. Firstly, the similarity between airfoil simulation and rotor simulation is investigated, which makes it feasible to obtain accurate rotor simulation by making a calibration in airfoil simulation. Then, a coefficient \(\beta_{\infty}^{*}\) of the SST turbulence model is calibrated according to airfoil experimental data. Finally, the calibrated \(\beta_{\infty}^{*}\) is applied to rotor simulation, and more accurate prediction of stall is realized. The above approach enhances the potential of RANS in accurate simulation of the wind turbine aerodynamic performance.
# High-Tolerance Grating Couplers for Vertical Backside Coupling
## 4 Conclusions and Outlook  
In summary, we have introduced a compact focused vertical backside-coupling GC (VBGC) based on binary blazed gratings. In contrast to traditional GCs, it efficiently couples the light from a four-core MCF coming from the backside of a photonic chip into a waveguide in order to achieve the integration of PICs on the tip of an optical fiber, making it well suited to optical sensing. By using a PSO algorithm, a CE of 40% was reached at a center wavelength of 1550 nm, with a minimum feature size larger than 200 nm. The designed device also features high fabrication tolerance with a decrease of only 3% in CE for the sub-grating and a grating period variation within a \(\pm 30\) nm range. Such a GC exhibits a high misalignment tolerance, reducing the precision required during transfer of the chip on the underneath multi-core optical fiber. A comparison of vertical GCs is listed in Table 1, showing that the proposed VBGC achieved a good balance in performance metrics. Finally, the fabrication of such GC structures on an SOI platform is readily available through commercial foundries or through our in-house facilities (at TU/e). We believe that the combination of these new VBGCs, optimized ring resonators, and our efficient novel fiber-tip transfer method [4; 5; 7; 8] can meet the demands for miniaturized, multiplexed (several sensors on single device), and high performance optical sensors.  
Figure 7: (a) Fiber core misalignment tolerance with the designed GC. (b) Impact on the CE of angular misalignment between fiber’s core and GC.  
**Author Contributions:** [PERSON] and [PERSON] supported the team with the analytical model, [PERSON], [PERSON] and [PERSON] performed the simulations, [PERSON] wrote the initial draft, [PERSON], [PERSON], [PERSON] and [PERSON] carried out review and editing, [PERSON] managed funding acquisition. All authors have read and agreed to the published version of the manuscript.  
**Funding:** National Science and Technology Council, Taiwan (111-2923-B-006-002-MY4, 111-2221-E-006-052-MY3) and by NWO project \"PicProbe\", grant n. 19717.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
###### Abstract  
Shenzhen, a coastal city, has changed from a small village to a supercity since the late 1980s. With the rapid development of its population and economy, ground disasters also occur frequently. These disasters bring great harm to human life and surface architecture. However, there is a lack of regular ground measurement data in this area. Permanent scatterer interferometry (PSI) technology can detect millimeter deformation of urban surface. In this paper, the building height and deformation from 2008 to 2010 in the Futian District of Shenzhen are obtained by using this technique alongside high-resolution TerraSAR-X data. For a visual expression of the result, we export the permanent scatterer (PS) points on buildings to Google Earth for 3D visualization after ortho-rectification of the PS height. Based on the Google Earth 3D model, the temporal and spatial characteristics of the building deformation became obvious. The InSAR measurements show that during the study period, the deformation rates of the Futian area are between \(-10\) and \(10\) mm/year, and deformation is mainly distributed in a few buildings. These unstable activities can be attributed to human activities and the natural climate, which provides a reference for the local government to carry out a survey of surface deformation, as well as the monitoring and management of urban buildings, in the future.  
PSI; seasonal displacements; 3D model; Shenzhen +
Footnote †: journal: Applied Sciences
## 5 Conclusions and Future Work  
Based on the normal behavior model, this paper studies the status monitoring of the pitch motor in an electronic motor. Considering that the state of the motor varies with time, the SCADA data are used to train an echo state network to predict the temperature of the pitch motor. Then, the residual analysis technology is used to detect the sudden changes in its running state. Particularly, the EWMA technique is used to set the alarm limit lines of each parameter. The experimental results show that this method can effectively identify and provide an early warning about the faults of the pitch motor.  
As for the status monitoring problem of the pitch motor, one of its core ingredients is a time series prediction task. In recent years, some advanced deep neural networks such as LSTM (long-and-short term memory) [21], temporal convolutional networks [22] and the transformer model [23] have exhibited excellent performance on this type of forecasting task since they can more effectively capture the long-term effect of input features to the target variable. Thus, it will be interesting to investigate how well they will perform in solving the tasks of status monitoring and early warning. We will pursue this in one of our near future works.  
Conceptualization, C.Z.; methodology, C.Z. and L.Y.; software, C.Z.; validation, L.Y. and L.Q.; formal analysis, C.Z.; investigation, L.Y.; resources, L.Y.; data curation, C.Z.; writing--original draft preparation, C.Z. and L.Y.; writing--review and editing, L.Q.; visualization, L.Y.; supervision, L.Q.; project administration, L.Y.; funding acquisition, L.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China grant number [2018 YFF01012001] and the National Natural Science Foundation of China [61635003].  
Not applicable.  
Due to confidential reason, the experimental data cannot be made public and the data are available from the corresponding author.  
The authors are grateful to reviewers and editors for their useful comments to improve the paper.  
The authors declare no conflict of interest.  
Figure 7: Pitch motor fault early warning.
## 2 Previous Works
stressed that although it is important to identify whether the flow is malignant or benign, it is also desirable to be able to detect the specific type of attack evidenced by the flows. This implementation is proposed as work to be done in new DDoSNet implementations. [PERSON] [11] used the CIC dataset, the same dataset used in our research. [PERSON] proposed the evaluation of three models, which use concepts such as dense neural networks, self-encoders, and Pearson's correlation coefficient (PCC). Dense neural networks are made up by layers whose neurons are fully connected be- tween each layer, i.e., each neuron in each layer receives information from all neurons in the previous layer and sends information to all neurons in the next layer [15]. In this case, the PCC attempts to measure linear correlation between two attributes of the dataset. Once PCC is applied to the attributes, a value is obtained between \(-1\) and \(1\). There is no linear correlation if the value is \(0\), \(1\) if the linear correlation is totally positive, and \(-1\) if there is totally negative linear correlation [16]. In [PERSON] et al. 's work, five major processes were executed: analysis and engineering of dataset attributes, dataset training, adjustments based on results of the training, testing with information not previously seen, and comparison with traditional ML methods. The three resulting models perform a binary classification of the flow, i.e., like [PERSON] et al. [10], the model is limited to binary classification as benign or malignant. The composite models can be understood as a disaggregation of the proposed steps. That is, the initial model is a dense neural network, the second a dense neural network with an implemented autoencoder, and the third a dense neural network with an autoencoder that receives information subsets as input data which through the PCC analysis does not include attributes that have strong linear correlation. In the third model from [PERSON] et al., the training is executed with a subset of the dataset that only contains one type of attack and based on the PCC analyses, the attributes of the subset that contain a PCC value greater than 0.9 and less than \(-0.9\) are eliminated. The three proposed models give different results depending on the attack classification as malignant or benign. In general, the model constructed only with dense neural networks generally attains the best score among the three proposed models, followed by the dense neural network with autoencoding and, lastly, the dense neural network with autoencoder and PCC analysis. The limitations of the existing works relay on the classification score and its behavior on each label. The models' score tends to variate considerably between the labels with a model's scores varying between the 0.5 and 0.9 on average.