esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The current study examined the possibility of using hyperspectral imaging to identify the growth time of _A. flavus_ and _A. parasiticus_. The conclusions from the study are the following:  
1. Band math and PCA on hyperspectral image can be used to classify fungi with distinct characteristics. A band ratio of 446 nm and 460 nm classified _A. flavus_ and _A. parasiticus_ on day 1. The image at the band of 520 nm classified _A. parasiticus_ on day 6. The 5-dimensional score plot of PC\({}_{2}\) to PC\({}_{6}\) gave an indication of clusters fungi in the same incubation time, with the exception of _A. flavus_ on day 3 and day 4 and _A. parasiticus_ on day 2 and day 3.
2. The SVM model that is based on full spectrum data can be used to classify fungal growth time. On the full spectral, PC\({}_{2}\) to PC\({}_{6}\) 2-6 and the SVM method could be used to classify fungi with different incubation times. The overall classification accuracies were 92.59% and 100% for _A. flavus_ and _A. parasiticus_, respectively.
3. Optimal wavelengths selected by the CARS method can be used to a build an optimized SVM model for identifying the growth of _A. flavus_ and _A. parasiticus_. Nine (402, 442, 487, 502, 524, 553, 646, 671, and 760 nm) and seven (461, 538, 542, 742, 753, 756, and 919 nm) were selected for _A. flavus_ and _A. parasiticus_. The accuracies of the optimal wavelengths SVM models were 83.33% for _A. flavus_ and 98.15% for _A. parasiticus_. The predicted map indicated that the predicted fungal growth days were basically consistent with the actual situation.  
This study is supported by the National Natural Science Foundation of China (No. 31772062) and Fund for Less Developed Regions of the National Natural Science Foundation of China (No. 61462074).  
[PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 4 Research Opportunities in O-RAN
### Big Data Collection for Machine Learning  
Given its openness, using O-RAN for big data collection in the context of ML offers substantial advantages. O-RAN promotes interoperability among different vendors' network components, allowing for diverse data sources, which is vital for ML model training and accuracy. It also fosters vendor neutrality, reducing lock-in and enabling network operators to select the best-suited hardware and software components, enhancing data collection capabilities. The scalability of O-RAN accommodates the growing volume of data generated by modern networks and encourages innovation by enabling custom software applications, including data collection and analytic tools tailored to specific ML use cases. O-RAN development can further advance with the more data available for ML, including use of synthetic data from network simulators [82]. Simulation tools such as OpenRAN Gym [83] and WiThRay [84] offer data generation for ML training when empirical data are insufficient or difficult to collect. However, data may be out-of-distribution due to different domains, and new learning techniques should be explored to tackle the out-of-distribution issue [85].
# A More Efficient Conditional Private Preservation Scheme in Vehicular Ad Hoc Networks
## 8 Conclusions  
In this paper, we proposed a new optimized protocol called MECPP based on ECPP for secure vehicular communications. The proposed MECPP scheme achieves mutual authentication between the RSUs and the vehicles in an anonymous manner before temporary certification. Particularly, the MECPP scheme not only provides the security and privacy protection to vehicles, but also provides a fast tracking mechanism to reveal the real identity of the malicious vehicles. In addition, the performance analysis section shows that the proposed scheme outperforms the ECPP scheme in terms of computational cost, communication overheads and storage overhead.  
As future work, we propose to continue to optimize the latency, such as reducing communication overhead. Furthermore, we will aim to develop a new batch authentication scheme to simultaneously authenticate the vehicles in order to avoid the computation burden in large vehicular clouds.  
[PERSON] conceived the ideas. [PERSON] performed the theoretical results and carried out the numerical simulation supervised by [PERSON].  
This research received no external funding.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Scheme** & **Stored Message about Temporary Anonymous Key** & **Size of Stored Message** \\ \hline \(ECPP\) & \(PID_{i},T_{i},Y,R_{2},\sigma_{1}\) & 64 bytes \\ \(MECPP(Proposed)\) & — \\ \hline \hline \end{tabular}
\end{table}
Table 6: Storage cost between the proposed scheme and ECPP.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Scheme** & **Sending the Signature Message** & **Size of Signature Message** \\ \hline \(ECPP\) & \((\sigma_{M},Y,T_{i},Cert_{t_{i}})\) & 120 bytes \\ \(MECPP(Proposed)\) & \((R,s_{r},Cert_{t_{i}})\) & 108 bytes \\ \hline \hline \end{tabular}
\end{table}
Table 5: Communication overhead between the proposed scheme and efficiency conditional privacy preservation (ECPP).
# A Formula-Structure-Embedding-Based GNN for Formula Equivalence Determination
## 3 Method  
To address the limitations of existing methods in extracting structural features from mathematical formulas, this paper proposes a multi-level node feature embedding based GNN for formula equivalence determination. The model uses multi-level node feature embedding to capture structure features from different levels, providing a strong support for equivalence determination.  
The model follows a typical encoder-processor-decoder architecture (see in Figure 1). The encoder consists of a formula standardization module, a multi-level node feature embedding module, a node-level embedding module that encodes each node's representation, and a graph-level embedding module that captures the overall structure of the formula graph. The processor includes both graph-level and node-level similarity comparison modules, which determine formula equivalence from both global and local perspectives. Specifically, the graph-level similarity comparison module adopts the corresponding module of SimGNN to evaluate the overall structural similarity between formulas. The node-level similarity comparison module incorporates an attention mechanism that dynamically adjusts interaction weights between nodes, enabling more accurate determination of key structure components. Finally, in the decoder, the model employs a cross-attention mechanism to integrate similarity vectors from both levels and outputs the final equivalence prediction. The integration of formula standardization, multi-level node feature embedding, and attention mechanisms together enhances the model's capability to effectively extract formula structure information and improve its scalability and accuracy in practical applications.
## 3 Materials and Methods
### Performance Metrics for Cluster Analysis  
We used the Silhouette Score and Davies-Bouldin Index to evaluate the quality of clustering results. The Silhouette Score measures how similar a data point is to its own cluster (cohesion) compared to other clusters (separation). It lies in the range of \(-1\leq\) Silhouette Score \(\leq 1\), where negative values indicate possible misclassification, 0 suggested overlapping clusters, and values closer to 1 reflect well-separated and compact clusters. The Davies-Bouldin Index evaluates clustering quality by measuring the average similarity between each cluster. Its value is always zero or positive, where lower values represent tight, well-separated clusters, while higher values represent loose clustering.
# A Simple Technique for Studying Chaos Using Jerk Equation with Discrete Time Sine Map
## 7 Discussion  
Nonlinear dynamical systems with complex and irregular behavior can be characterized using chaotic time-series analysis. This method aims to study characteristics of the deterministic dynamical systems with different tools, describe them with a proper model, and predict future values. Since all practical systems are nonlinear, the analysis of a nonlinear dynamical system involves linearization. But, these hidden non-linearities produce strange behavior. Most of the engineering curriculum emphasizes the analysis of a system based on linear system theory [36], thereby oversimplifying the wide range of fascinating dynamic responses.  
During the last century, many chaos theorists highlighted the educational importance of chaotic dynamical systems to pay off the conventional linear thinking established through prolonged learning techniques [43; 44; 45]. Nonlinear systems can exhibit complicated dynamics that disobey almost every law studied under linear systems. Because of several equilibrium points for different initial conditions, it becomes impossible to predict their long-term behavior. This property of the nonlinear system, which happens to be considered noise as it appears random in the time domain, can find its application in almost every scientific field. Overall, chaos theory enables us to embrace all the weirdness of life rather than suppressing it.  
In this paper, a novel chaotic system is presented based on the simple jerk equation. The discrete sine map was added as a nonlinear function to the jerk equation and approximated using the Taylor series expansion. The proposed system simulation was carried out in MATLAB/NI Multisim first and later verified by hardware implemented using simple Op-Amps with analog multipliers.
###### Abstract  
Due to the differences in mineral processing techniques, the grain-size of tailings used in the construction of a tailings pond is not commensurate. It has been determined that the hydrodynamic characteristics of mudflow resulting from the failure of tailings dams are directly influenced by grain-size, solids concentration, and the surface roughness of gully and impoundment geometry. However, the behavior and influence of the grain size of mudflow resulting from a tailings dam failure have not been sufficiently examined. To investigate the effect of grain size on the hydrodynamic characteristics of mudflow surging from tailings dam failure, the law of mudflow evolution, the change of dynamics pressure, and the velocity distributions of mudflow have been obtained via a series of flume experiments utilizing three types of grain size tailings (\(d_{50}\) = 0.72 mm; \(d_{50}\) = 0.26 mm; \(d_{50}\) = 0.08 mm, respectively). This study proves conclusively that with an increase in grain size, the peak value of mudflow depth notably decreases in the same section. Furthermore, it has been noted that both the velocity and the dynamic pressure raise significantly, wherein the velocity displays two distinct primary stages; namely a rapid reduction stage and a slow reduction stage. This research provides a framework for the exploration of the effect of grain size on the hydrodynamics of slurry surging from a tailings dam failure, and all presented results provide an indispensable tool in terms of the accurate assessment of potential damage in the case of a prospective impoundment failure.  
Keywords:tailings dam; dam-break; hydrodynamics; grain-size effect; experimental test +
Footnote †: journal: Applied Physics Letters
# Effective Management System for Solar PV Using Real-Time Data with Hybrid Energy Storage System
## 5 Results and Discussion
### Real-Time HIL Results  
In the simulated model, the PV system rating of 40 V and the battery of ratings 48 V are considered. The IBC and bidirectional converters are operated to interlink the PV and batteries with the DC grid. The DC link reference voltage is 80 V and a variable source is connected to the DC link. The Real-Time HIL Simulator results are presented in Figures 13-15. The irregular nature of the PV current profile varying between instants \(t_{1}\)-\(t_{10}\) by 4 A/div is shown in Figure 13a, and Figure 13b shows the randomly varying load current profile by 4 A/div. During the variable PV and/or load conditions (\(P_{pv}>P_{l}\) or \(P_{pv}<P_{l}\)), there is a necessity to sustain power stability and provides fast DC voltage regulation into the system. At time \(t_{3}\), the load current is suddenly decreased by 2 A/div and at instant \(t_{5}\) suddenly increased by 4 A/div; in this period the drop of the bus voltage is much less when compared to boost converter.  
The battery-1 current is shown in Figure 14b. From Figure 14b, the difference between the \(P_{pv}(t)\) and \(P_{s}(t)\) can be seen. Figure 14c shows the battery-2 current. The battery-2 current varies according to the \(P_{pv}(t)\) and \(P_{l}(t)\). The battery-2 can not only preserve charging or discharging conditions, it can also sustain the power stability at the DC link. The SC current is shown in Figure 14d and it decreases the current stress on the batteries by responding to transients in the system. Figure 14a shows the constant DC link voltage. At instant \(t_{1}\)-\(t_{3}\), the load current is constant by 4 A/div, thus during constant load conditions, there is no variation in the bus voltage. From \(t_{5}\)-\(t_{7}\), the load current is increased by 2 A/div and PV current rises at instant \(t_{5}\) by 4 A/div and reduces after instant \(t_{5}\) to \(t_{7}\) by 4 A/div; at these instants, the voltage drop is less than 2 A/div and also maintained the stable DC bus voltage. Hence, it is clear that even sudden a change in load also does not affect the steady DC link voltage. The SOCs of battery-1 and battery-2 is shown in Figure 15a,b. The SOC of batteries will respond according to the charging and discharging conditions of battery-1 and battery-2.
# Incorporating Setup Effects into the Reliability Analysis of Driven Piles
## 4 Reliability Analysis
### The Effect of \(M_{\rm setup}\) on Reliability Index  
In this subsection, the effect of \(M_{\rm setup}\) on the reliability index of driven piles considering setup effects is studied. The \(\lambda\) and COV of loads and resistances for driven piles in Table 2 were used, and the value FOS = 3.0 and \(\rho=3.69\) were obtained from the analysis of the first two subsections. Reliability analysis was performed for the increase factor \(M_{\rm setup}\) ranging from 0 to 2.0. In this part, the correlation between \(R_{0}\) and \(R_{\rm setup}\) was not taken into account. Figure 3 shows the reliability indices corresponding to \(M_{\rm setup}\) of driven piles.  
The results show that in clay and sand, the reliability indices of driven piles increase with rising \(M_{\rm setup}\), and the growth rates decrease slowly. Additionally, it also can be seen that reliability indices of the driven pile in clay are larger than those in the sand; the difference between them is about 15.5% for a given \(M_{\rm setup}\). This is because the soil around the pile will be disturbed in the process of pile driving, which will lead to the dissipation of pore water pressure and the consolidation of soil. Then, the degree of consolidation of soil is affected by the cohesion of soil, so the corresponding reliability index of different soil will be different.  
Figure 3: Reliability indices with \(M_{\rm setup}\) for driven piles in clay and sand.
## 4 Mean Squared Performance of Individual MISO APA Filters
### Energy Conservation Relation for MISO Filters  
We can reformulate the update equation of the \(j^{\text{th}}\) MISO filter (4), \(j=1,2\), in terms of the weight error vector (12):  
\[\widetilde{\mathbf{w}}_{n}^{(j)}=\widetilde{\mathbf{w}}_{n-1}^{(j)}-\mu_{j} \mathbf{X}_{n}^{(j),T}\left(\delta_{j}\mathbf{I}+\mathbf{X}_{n}^{(j)}\mathbf{ X}_{n}^{(j),T}\right)^{-1}\mathbf{e}_{n}^{(j)}. \tag{25}\]  
We left-multiply both sides of (25) by \(\mathbf{X}_{n}^{(j)}\):  
\[\mathbf{X}_{n}^{(j)}\widetilde{\mathbf{w}}_{n}^{(j)}=\mathbf{X}_{n}^{(j)} \widetilde{\mathbf{w}}_{n-1}^{(j)}-\mu_{j}\mathbf{X}_{n}^{(j)}\mathbf{X}_{n}^ {(j),T}\left(\delta_{j}\mathbf{I}+\mathbf{X}_{n}^{(j)}\mathbf{X}_{n}^{(j),T} \right)^{-1}\mathbf{e}_{n}^{(j)} \tag{26}\]  
in which we replace the expressions of the a priori and a posteriori estimation errors, respectively (14) and (15), thus yielding:  
\[\mathbf{e}_{p}^{(j)}=\mathbf{e}_{a\bar{n}}^{(j)}-\mu_{j}\mathbf{X}_{n}^{(j), T}\left(\delta_{j}\mathbf{I}+\mathbf{X}_{n}^{(j)}\mathbf{X}_{n}^{(j),T}\right)^{-1} \mathbf{e}_{n}^{(j)}. \tag{27}\]  
Assuming that \(\mathbf{X}_{n}^{(j)}\mathbf{X}_{n}^{(j),T}\) is invertible, we can rewrite (27) as:  
\[\left(\delta_{j}\mathbf{I}+\mathbf{X}_{n}^{(j)}\mathbf{X}_{n}^{(j),T}\right)^ {-1}\mathbf{e}_{n}^{(j)}=\frac{1}{\mu_{j}}\left(\mathbf{X}_{n}^{(j)}\mathbf{X} _{n}^{(j),T}\right)^{-1}\left(\mathbf{e}_{a\bar{n}}^{(j)}-\mathbf{e}_{p}^{(j)} \right). \tag{28}\]  
Replacing (28) in (25) and rearranging appropriately, we obtain:  
\[\widetilde{\mathbf{w}}_{n}^{(j)}+\mathbf{X}_{n}^{(j),T}\left(\mathbf{X}_{n}^ {(j)}\mathbf{X}_{n}^{(j),T}\right)^{-1}\mathbf{e}_{a\bar{n}}^{(j)}=\widetilde {\mathbf{w}}_{n-1}^{(j)}+\mathbf{X}_{n}^{(j),T}\left(\mathbf{X}_{n}^{(j)} \mathbf{X}_{n}^{(j),T}\right)^{-1}\mathbf{e}_{p}^{(j)}. \tag{29}\]  
We evaluate the energy of (29), thus obtaining the energy conservation relation for the \(j^{\text{th}}\) MISO filter:  
\[\left\|\widetilde{\mathbf{w}}_{n}^{(j)}\right\|^{2}+\mathbf{e}_{a\bar{n}}^{(j),T}\left(\mathbf{X}_{n}^{(j)}\mathbf{X}_{n}^{(j),T}\right)^{-1}\mathbf{e}_{a \bar{n}}^{(j)}=\left\|\widetilde{\mathbf{w}}_{n-1}^{(j)}\right\|^{2}+\mathbf{e} _{p}^{(j),T}\left(\mathbf{X}_{n}^{(j)}\mathbf{X}_{n}^{(j),T}\right)^{-1} \mathbf{e}_{p}^{(j)} \tag{30}\]in which no approximations are used, but it is an exact relation showing how the energies of the weight-error vectors at two successive iterations are related to the weighted energies of the a priori and a posteriori estimation error vectors [42].
## 2 Mechanism Overview
The incentives in a multi-signature wallet require \(m\)-out-of-\(n\) signatures to become redeemable, where \(m\) is the minimum number of signatures required, \(n\) is the total number of account holders of the wallet, and both \(m\) and \(n\) are determined when the wallet is created. Apart from the vehicles receiving these incentives, the account holders of a multi-signature wallet also include one or two verifiers and a mediator. The multi-signature wallets are designed in a cascading manner, such that the frozen incentives can only become redeemable when the user passes the route validation checkpoints along the selected route in order. When the vehicle is around a checkpoint on the selected route, instead of using GPS information (which can be easily forged by malicious vehicles), it needs to follow the proposed route validation scheme to generate a position proof with the assistance of witness vehicles (nearby vehicles willing to sign on the position proof) and send it to roadside units (RSUs) to verify. The RSUs in the network will verify the proof independently and  
Figure 2: Conceptual structure of the privacy-preserving incentive mechanism for collaborative routing. reach a consensus following the practical byzantine fault tolerance (PBFT) algorithm. If the position proof is valid, a digest of the proof (which is a fixed-size representation of the contents of the proof) will be included in the current block of a consortium blockchain. Meanwhile, the verifier at the checkpoint will sign two multi-signature transactions: one makes the frozen incentives associated with the current checkpoint redeemable for the user; the other one is for the frozen incentives associated with the next checkpoint, which will remain frozen for the time being and requires one more signature to be redeemable. The user does not have to redeem the redeemable incentives (by signing their own signature on the multi-signature transactions and sending them out) at the time he/she receives them. Note that the transactions will be included in the consortium blockchain only when the incentives are redeemed. Therefore, users can hide their trajectories on the blockchain by redeeming the incentives from the checkpoints from multiple trips in arbitrary order. Leveraging the tamper resistance of the on-chain information while keeping the sensitive information off-chain, the proposed incentive mechanism eliminates both direct privacy leaks (e.g., sharing the value of time in computation) and indirect privacy exposure (e.g., historical trajectories inferred from the transaction pattern or plain position proofs recorded in the blockchain). It also achieves high-standard security in terms of potentially malicious behavior from both the user side and the infrastructure (i.e., RSUs).
## 5 Analysis of Prominent Clustering Routing Protocols in WSNs
### Cluster-Construction Based Clustering Routing Protocols
#### 5.1.5 TL-LEACH  
Two-Level Hierarchy LEACH (TL-LEACH), introduced by [PERSON] _iet al_. [19], is an extension to the algorithm of LEACH. TL-LEACH uses the following two techniques to achieve energy and latency efficiency: randomized, adaptive, self-configuring cluster formation and localized control for data transfers. In TL-LEACH, a CH collects data from MNs as original LEACH, but instead of transmitting data to the BS directly, it uses a part of CHs that lies between the CH and the BS as a relay station.  
TL-LEACH introduced two-level hierarchy as shown in Figure 5: top CHs called primary cluster heads (CH\({}_{i}\)), second level represented from secondary cluster heads (CH\({}_{i\!j}\)) and ONs. The algorithm is composed from four basic phases: advertisement phase, cluster setup phase, schedule creation and data transmission. In the first phase, each node decides whether it become a primary CH, secondary CH or ON in each round which is the same as that of LEACH. If a node is elected a primary CH, it must advertise other nodes. The mechanism used in this phase is carrier sense multiple access (CSMA). Thereafter, secondary CH nodes send the advertisement to the ONs. In this phase, each secondary CH decides to which primary CH it belongs and sends an advertisement message to its primary CH. In the same way, each ON must decide which secondary CH it belongs to and informs it through an opposite message. In the third phase, each primary CH creates a TDMA schedule assigning each node in its group a slot to transmit. Each primary CH chooses a CDMA code and informs all the nodes at second level in its group to use this code. In the same way, each secondary CH can transmit this information to ONs in its group using both the code and the schedule from the primary CH. In the last phase, clusters are created and each node can transmit in respect to the TDMA schedule decided by its primary CH.  
The advantages of TL-LEACH are as follows: (1) TL-LEACH uses random rotation of local cluster BSs, _i.e._, primary CHs and secondary CHs, which can bring about better energy load distribution across the network; (2) TL-LEACH uses localized coordination, which is conductive to scalability and robustness in the network; (3) Compared with LEACH, the scheme of two-levels clustering leads to less average transmission distance, and less nodes are required to transmit far distances to the BS via TL-LEACH. This effectively reduces the total energy consumption.  
However, there exist a few disadvantages of TL-LEACH as follows: (1) Despite that the average transmission distance is decreased in comparison with LEACH, the two-hop inter-cluster routing of TL-LEACH is still not applicable to large-range networks, because it uses only two hops for data transmission from sources to the BS, and long-distance communications can breed much energy consumption; (2) CH election without energy considerations assumes an ideal homogeneous network and can not ensure real load-balancing in case of nodes with different amount of initial energy.