esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Control Methodology
### Reconfigurable Learning Control Algorithm (RLCA)
the power of the exponential term \(-\alpha_{S}(t)\beta\) is negative definite, the term \(e^{(-\alpha_{S}(t)\beta t)}\) decays to zero as time progresses. To ensure that the power of the exponential term \(-\alpha_{S}(t)\beta\) remains strictly negative, a small offset \(\Delta=0.01\) is added in \(\alpha_{S}(t)\beta\), turning it into \(-(\alpha_{S}(t)\beta+\Delta)\). This arrangement prevents it from reaching zero under every operating condition. The updated control law is shown in Equation (31). \[u(t)=e^{(-(\alpha_{S}(t)\beta+\Delta)t)}\;u(0)+\int\limits_{0}^{t}\Bigl{(}e^{ (-(\alpha_{S}(t-p)\beta+\Delta)(t-p))}[\alpha_{M}(p)\gamma\;u_{m}(p)+\alpha_{L} (p)\delta\;h(p)]\Bigr{)}dp \tag{31}\]  
This behavior signifies that the system is exponentially stable, as the output diminishes over time. Based on this condition, the finalized RLCA control law is presented in Equation (32). \[\alpha_{S}(t)=\text{sech}(\mu\ e_{\theta}(t)),\ \alpha_{L}(t)=1-\alpha_{S}(t),\ \alpha_{M}(t)=2\alpha_{L}(t)\alpha_{S}(t) \tag{27}\]0  
Under nominal conditions, the system remains stable as it tracks the LQI controller, whose asymptotic stability has been rigorously established in the preceding section. To prevent the actuator from saturation under large servo requirements caused by external disturbances, the control signal \(u(t)\) is bounded between 0 and 10 V, by using the saturation function of the following form. \[10\;sat(u(t))=\left\{ \begin{array}{c}10\;V,\;u(t)>10\;V\\ u(t),\;0\leq u(t)\leq 10\;V\\ 0,\;u(t)<0\end{array} \right. \tag{33}\]  
The combination of these terms allows the controller to dynamically adjust damping stiffness and response speed in reaction to disturbances while maintaining closed-loop stability. The RLCA updates the weighting of the three control terms constituting the LCA, solving the algebraic equations (for adaptive weight modulation) numerically at each sampling interval. This self-learning approach strengthens the UAV's damping control strength and improves its transient recovery speed against external disturbances during the longitudinal motion. The schematic of the RLCA law designed for UAV's longitudinal motion control, as per the formulations in Equations (27) and (28), is shown in Figure 7.  
Figure 7: Schematic of RLCA framework for UAV’s longitudinal motion control.
# Crystal Plasticity Finite Element Modeling on High Temperature Low Cycle Fatigue of Ti2 AlNb Alloy
## 3 Model Parameters Identification and Validation
### Crystal Plasticity Finite Element Model
#### 3.2.2 Identification of Material Parameters  
The material parameters of O phase and \(\beta\) phase used in current CPFE model are shown in Table 4, respectively. The elasticity constants are cited from Chu [33], Fan [34] and Fu [35]. However, the elasticity constants of Ti2 AlNb alloy at 550 \({}^{\circ}\)C have not been reported so far. Therefore, in this paper, the elasticity constants at 550 \({}^{\circ}\)C are obtained by revising the values at room temperature. According to the previous studies, the crystal structure of O phase is orthorhombic structure with cmcm symmetry. At room temperature the activated slip systems are <100> [001] and <110> [110], and the slip resistance of <110> [110] is greater than <100> [001] [36]. When loading temperature is 550 \({}^{\circ}\)C, more slip systems are activated, but new slip systems are not all activated through whole cycle deformation. Therefore, contributions from other slip systems will be added to <100> {001} and <110> {110}. The crystal system of \(\beta\) phase is BCC, so the slip system of \(\beta\) phase is <111> {110}.  
The parameter fitting procedures are shown as follows. First, the parameters at room temperature are used as the initial data and the approximate range of each parameter was obtained. Then, the parameters were adjusted to fit the uniaxial tension simulation and experimental curves. Finally, a cyclic loading simulation was performed using the parameter set of uniaxial tension. Due to the hysteresis loop reaching the stable stage after 10 cycles, the model parameters were adjusted based on the steady-state hysteresis loop. The true stress-strain curve is shown in Figure 7. It can be seen that the simulation results fit with the test results.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Parameter** & **Unit** & \multicolumn{2}{c}{**Value(O \(|\beta\))**} \\ \hline \multirow{3}{*}{Elastic constants} & C\({}_{11}\) & GPa & 184 & 135 \\  & C\({}_{12}\) & GPa & 86.2 & 113 \\  & C\({}_{44}\) & GPa & 49 & 55 \\ \hline \multirow{3}{*}{Flow parameters} & \(\hat{\gamma}_{0}\) & s\({}^{-1}\) & 120 & 120 \\  & \(F_{0}\) & kJ/mol & 150 & 250 \\  & \(\tau_{0}\) & MPa & 200 & 200 \\ \hline \multirow{3}{*}{Hardening parameters} & \(h_{B}\) & MPa & 1000 & 950 \\  & \(r_{D}\) & MPa & 8 & 10 \\ \cline{1-1}  & \(S_{sat}\) & MPa & 250 & 160 \\ \cline{1-1}  & \(S_{0}\) & MPa & 300 & 225 \\ \cline{1-1}  & \(h^{\alpha\beta}\) & MPa & 350 & 350 \\ \hline \end{tabular}
\end{table}
Table 4: Material parameters of the crystal plasticity-based formulations for Ti2 AlNb.  
Figure 7: Crystal plasticity simulation and experimental results: (a) 2% uniaxial tension; and (b) steady-state hysteresis loop at 1.4% cycle deformation.
# Combating Fake News with Cryptography in Quantum Era with Post-Quantum Verifiable Image Proofs
## 6 Cryptographic Foundations and Preliminaries
### CRYSTALS-Dilithium: A Post-Quantum Digital Signature Scheme  
CRYSTALS-Dilithium is a lattice-based digital signature scheme designed for post-quantum cryptography, based on the hardness of the Module-LWE and Module-SIS problems. These problems are considered quantum-resistant and form the basis for the scheme's security. The key generation process involves the creation of a secret key and a corresponding public key through a trapdoor function that utilizes a structured lattice. Signature generation employs a combination of Gaussian sampling and rejection sampling, ensuring that the signature size remains compact, while satisfying security requirements. During signature verification, the verifier checks the validity using lattice-based linear algebra operations, ensuring the authenticity of the signature [18].  
Dilithium's efficiency is optimized by balancing security and performance. The key sizes are smaller than those of RSA with comparable security levels, and its signing and verification processes are more efficient than those of hash-based signature schemes. The use of bounded noise sampling ensures that signatures remain small, while the rejection sampling technique prevents the generation of invalid signatures. In terms of performance, Dilithium achieves practical speeds for both signing and verification in software and hardware implementations, making it suitable for a wide range of applications, including secure communication protocols (TLS), blockchain, and code signing.  
Despite these advantages, the scheme faces challenges, such as larger key and signature sizes compared to traditional RSA and the need for further resistance to side-channel attacks, particularly in hardware implementations. Future work will focus on improving these aspects and optimizing Dilithium for use in resource-constrained environments, like IoT devices. With its strong security foundations and efficiency, CRYSTALS-Dilithium is positioned to play a key role in post-quantum cryptographic standards.
## 2 Materials and Methods
### Color Analysis  
Instrumental color measurements was performed on raw, cooked and cooled samples, using Chroma Meter CR-400 (Konica Minolta, Osaka, Japan) with a port (\(\O\) 8 mm) and the D65 illuminant, and standard observer 2\({}^{\circ}\). The equipment was calibrated before measurement using a white standard plate with Y = 89.3, x = 0.3159, and y = 0.3225. Measurements were made in eight replicates on the surface of broccoli florets, bean pods and beet slices. The color parameters were expressed as \(L^{*}\) (100 lightness/darkness 0), \(a^{*}\) (+\(a\) redness/greenness -\(a\)), and \(b^{*}\) (+\(b\) yellowness/blueness -\(b\)) in the CIE Lab system (The International Commission on Illumination). To better highlight differences in color, the indexes of color saturation (parameter \(C^{*}\), Equation (1)) and hue (\(h^{\circ}\)--hue angle, Equation (2), when \(a^{*}\) < 0 and \(b^{*}\) > 0) were calculated according to the formula [14]:  
\[C^{*}=\sqrt{a^{*2}+b^{*2}} \tag{1}\]  
\[h^{\circ}=arctan\left(\frac{b^{*}}{a^{*}}\right)+180^{\circ} \tag{2}\]  
Total color difference \(\Delta E\) (Equation (3)) was calculated, using raw vegetable as a reference, according to the equation [15]:  
\[\Delta E^{*}=\sqrt{\left[\left(\Delta L^{*}\right)^{2}+\left(\Delta a^{*} \right)^{2}+\left(\Delta b^{*}\right)^{2}\right]} \tag{3}\]
# Vehicle Interior Noise Prediction Based on Elman Neural Network
## 2 Establish a Finite Element Model
### Modal Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Modal Frequency Order** & **Frequency/Hz** & **Modal Shape** \\ \hline First order & 27.04 & Transverse local modal at the \\  & & front of the roof \\ Second order & 38.60 & First-order \\  & & torsional modal \\ Third order & 45.66 & Lateral local modal of \\  & & water tank bracket \\ Fourth order & 50.26 & Vertical local modal at the \\  & & front of the roof \\ Fifth order & 54.05 & First-order \\  & & bending modal \\ Sixth order & 59.92 & C-pillar lateral \\  & & local modal \\ Seventh order & 67.20 & Vertical local modal at the \\  & & front of the roof \\  & & B-pillar \\  & & local modal \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The first eight modal frequencies and vibration shapes of the body-in-white. Figure 5: Modal shape of front windshield. within the frequency range determined by the above references. The first-order torsional modal frequency and the lateral local modal frequency of the front water tank bracket are very close to the modal frequencies obtained from the test of some vehicle models in the reference, and the difference in frequency is 0.2 Hz and 1.37 Hz, respectively. The above results show that the modal frequency range of the finite element model in this article is correct, and the finite element model can simulate the low-frequency vibration characteristics of the vehicle structure. The data obtained by the finite element calculation can be used as the training set of the Elman neural network to predict the noise inside the vehicle.
## 2 Reliability Analysis Method
### Method Process  
The reliability analysis of engineering structures is carried out using the \"ISSA-Kriging-MCS + FEM\" analysis framework. The relevant process of the entire calculation method is shown in Figure 2, and the main calculation steps are as follows:  
(1) Regarding the experimental design, the average value of random variables is taken as the center, and based on the LHS principle, 200 sample points are extracted to generate the initial sample group.  
(2) A training sample set is generated. The initial random variable sample group is input into the finite element numerical analysis model, and the output variable result is obtained to form the initial training sample set.  
(3) The ISSA is used to optimize the relevant parameter \(\theta\) of the Kriging model. The ISSA-Kriging surrogate model of the structural performance function is constructed from the initial sample point set and its corresponding real function value set.  
(4) The reliability index and failure probability of the ISSA-Kriging surrogate model are calculated using the MCS algorithm.  
(5) It is determined whether the convergence condition is satisfied. It is calculated whether the difference in the reliability index in the two iterations is less than the allowable error value of 0.001. If the conditions are met, the final result is output. Otherwise, skip to step (3).  
Figure 1: LHS process diagram.
## 3 Results
### Real Brain Image Data Studies
#### 3.2.1 3D Segmentation of the CC  
A volumetric region of interest (ROI) from the middle of the brain is chosen as input to the K-means and sFCM (with parameters used \(p=2\) and \(q=1.5\)) algorithms. The ROI is clustered into five clusters, with CC being one of those clusters, using the root Euclidean metric. To visualise the CC, the cluster labels are binarised (i.e., cluster labels for the CC cluster are all 1 and 0 is used for labels in the other four clusters). Examples of the results of the segmentation of the CC using K-means and sFCM are shown in Figure 7. To evaluate how much better sFCM is in reducing the noise around the CC as compared to K-means, the number of voxels (nv) that are considered as noise is calculated (i.e., the voxels around the CC that have the same cluster labels as the CC but are not actually part of the CC).  
The Wilcoxon Signed-Rank test is used to test the significant difference in nv values produced by using K-means and sFCM. The results show that the nv values are significantly  
\begin{table}
\begin{tabular}{c c c c} \hline
**Method** & **Dist (\(D_{1}\)\(D_{2}\))** & **Dist (\(D_{1}\)\(D_{3}\))** & **Dist (\(D_{2}\)\(D_{3}\))** \\ \hline Euclidean & 3.3689 \(\times\) 10\({}^{-19}\) & 2.2948 \(\times\) 10\({}^{-18}\) & 1.5564 \(\times\) 10\({}^{-18}\) \\ Log Euclidean & 42.1681 & 51.4121 & 5.9165 \\ Root Euclidean & 1.2426 \(\times\) 10\({}^{-9}\) & 4.7398 \(\times\) 10\({}^{-9}\) & 2.3534 \(\times\) 10\({}^{-9}\) \\ \hline \end{tabular}
\end{table}
Table 4: The distances between the tensors.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Tensor** & \(\lambda_{1}\) & \(\lambda_{2}\) & \(\lambda_{3}\) & **FA** \\ \hline \(D_{1}\) & 0.1537 & 0.0192 & 0.000028 & 0.936 \\ \(D_{2}\) & 0.1714 & 0.0171 & 0.0036 & 0.937 \\ \(D_{3}\) & 0.1588 & 0.0206 & 0.0040 & 0.919 \\ \hline \end{tabular}
\end{table}
Table 3: Eigenvalues of \(D_{1}\), \(D_{2}\), and \(D_{3}\) (\(\times\)10\({}^{-8}\)).  
smaller using sFCM as compared to K-means (see Table 5) for both the baseline and post baseline data. This confirms that the use of sFCM instead of K-means significantly reduced the amount of noise in these images.
## 6 Recent Studies on CRSN Routing
### Reconfigurable Joint Route and Spectrum Brokering  
This category of routing protocols is characterized by the ability to recover from changes in the spectrum caused by PU arrival. The probabilistic routing protocol based on priori information (PRP) [34] expands upon the Dijkstra routing algorithm by introducing a routing metric that enables the nodes to select channels and routes based on the documented performance of the channels during previous transmissions. The metric is formulated based on naive Bayes inference and uses an m-estimate probability to make the route decisions more realistic. The source node first broadcasts a route discovery packet. This packet is disseminated across all of the nodes to the sink. This packet enables individual nodes to calculate the cost function of choosing any of its neighbors based on the formulated routing metric. At this stage, channels are tagged to neighbor nodes. During the process of routing, when the PU arrives, the affected node can easily change path without jeopardizing the entire routing process. Notwithstanding the ability of the network to reconfigure the route, the energy required to implement the Dijkstra routing algorithm can prove to be non-trivial.
## 4 Materials and Methods
### Dataset  
For this study, we meticulously curated a dataset by harnessing the received power data, alongside the development of a comprehensive system model and scenario simulations using MATLAB. Subsequently, we implemented a deep learning model through Python. Our investigative journey involved a series of experiments and simulations, which were meticulously designed to capture both the authentic and spoofed signals originating from various spoofer locations. The data collection process unfolded as the UAV spoofer executed randomized maneuvers around the designated target node, covering a radius of approximately 100 m. Furthermore, we introduced variations in the spoofer's transmission power, thus effectively diversifying our dataset. Additional adjustments were made to the transmission power of the nodes, as well as variations in the inter-node distances.  
Our dataset encompasses a set of seven distinct input features and two critical output variables, as meticulously delineated in Table 2. These input features are leveraged by our deep learning models to serve a dual purpose: first, to effectively discern the authenticity of the received signals, and, second, to accurately estimate the \((t_{prop})\) of the spoofer. Upon the reception of any signal, the AP meticulously extracts the relevant feature values, which are seamlessly integrated into the model's analytical framework. The dataset used for testing and training the model comprises precisely 2000 samples for testing and 8000 samples for training. This dataset has been thoughtfully curated to ensure balance, whereby it contains an equal number of authentic and spoofed flight times in the scenario.
###### Abstract  
Unsupervised anomalous sound detection, especially self-supervised methods, plays a crucial role in differentiating unknown abnormal sounds of machines from normal sounds. Self-supervised learning can be divided into two main categories: Generative and Contrastive methods. While Generative methods mainly focus on reconstructing data, Contrastive learning methods refine data representations by leveraging the contrast between each sample and its augmented version. However, existing Contrastive learning methods for anomalous sound detection often have two main problems. The first one is that they mostly rely on simple augmentation techniques, such as time or frequency masking, which may introduce biases due to the limited diversity of real-world sounds and noises encountered in practical scenarios (e.g., factory noises combined with machine sounds). The second issue is dimension collapsing, which leads to learning a feature space with limited representation. To address the first shortcoming, we suggest a diffusion-based data augmentation method that employs ChatGPT and AudioLDM. Also, to address the second concern, we put forward a two-stage self-supervised model. In the first stage, we introduce a novel approach that combines Contrastive learning and masked autoencoders to pre-train on the MIMI and ToyADMOS2 datasets. This combination allows our model to capture both global and local features, leading to a more comprehensive representation of the data. In the second stage, we refine the audio representations for each machine ID by employing supervised Contrastive learning to fine-tune the pre-trained model. This process enhances the relationship between audio features originating from the same machine ID. Experiments show that our method outperforms most of the state-of-the-art self-supervised learning methods. Our suggested model achieves an average AUC and pAUC of 94.39% and 87.93% on the DCASE 2020 Challenge Task2 dataset, respectively.  
anomalous sound detection; masked autoencoders; Contrastive learning; Variance-Covariance Regularization; diffusion-based data augmentation
## 4 Proposed Routing Algorithm for Wireless Sensor Networks
### Cluster Formation  
To enable cluster formation and maintenance, all nodes keep the information about their neighbors in their neighbor table. Let t be the period of time between the Hello broadcasts. When a node first switches on, it first listens to Hello packets on the broadcast channel. If any other node on the broadcast channel is already advertising itself as a cluster-head (status of node = cluster-head), the new node saves the heard cluster-head ID in its cluster-head ID field and changes its status to member. At any point in time, a node in the mobile network associates itself with a cluster-head. The cluster-heads are identified by the cluster-head ID. Otherwise, the new node becomes cluster-head. The cluster-head is responsible for the cluster and periodically sends a Hello Message.  
_Strategy for Cluster-head forwarding_  
If (Packet_received)  
If (type_packet == \"d\")  
If ( know_path_sink) Send_packet_r()  
else Send_packet_s()  
else if (type_packet == \"r\")  
if (idpacket == myid)  
If (know_sink) Send_packet_sink()  
else Relay_packet_r()  
else if (type_packet == \"t\")  
if (idpacket == myid)  
else Relay_packet_t()  
else if (type_packet == \"s\")  
if (idpacket == myid)  
if(know_sink) Send_packet_sink()  
else Relay_packet_s()  
else if (type_packet == \"z\")  
register_address_sink()  
else Lora_cbf()  
When a cluster member receives a Hello message, it registers the cluster-head and responds with a reply Hello message. The cluster-head then updates the Cluster Table with the address and status of every member in the cluster. When a member receives a Hello packet from a different cluster-head, it first registers the cluster-head, but the member does not modify its cluster-head ID until the expiration time for the field has expired. Before the member rebroadcasts the new information, it changes its status to a gateway. After receiving the Hello packet, the cluster-heads update the Cluster Table with the information about the new gateway.  
_Strategy for Gateway forwarding_  
If (Packet_received)  
if (type_packet == \"r\")  
if (idpacket == myid)  
If (know_sink) Send_packet_sink()  
else Relay_packet_r()  
else if (type_packet == \"t\")  
if (idpacket == myid) Do_task()  
else Relay_packet_t()  
else if (type_packet == \"s\")  
if (idpacket == myid)  
if (know_sink) Send_packet_sink()  
else Relay_packet_s()  
else Lora_cbf()  
If the cluster-head source wants to send a message to the sink, it first checks its routing table to determine if it has a \"fresh\" route to the sink. If it does, it first seeks its Cluster Table to determine the closest neighbor to the sink. Otherwise, it starts the discovery process.
## 5 Conclusions  
The overall HetNet efficiency has been analyzed by means of stochastic geometry modeling of UE and BS locations as mutually independent and homogeneous PPPs.  
After the according model validation by means of the ns-3 network simulator, it came out that heterogeneous cellular networks to a large extent satisfy customer requirements for high spectral, energy, and deployment efficiency.  
The analysis and simulation results imply that the introduction of small cells in heterogeneous networks with any chosen distribution of base stations has a major positive impact on energy and SE, as well as on deployment cost reduction.  
Hence, reducing distances between BSs and UEs in state-of-the-art mobile networks--LTE and LTE-A, in particular--enables rationalizing and redistributing signal coverage towards areas of increased traffic demand and is definitely a very good strategy, which needs to be complemented with a number of other consideration factors such as: RF interference and impairments, traffic flow and frequency resources (bandwidth and channel allocation), whose management has to ensure the projected QoS level, complexity reduction, and fairness.  
Conceptualization, J.M., and V.L.; methodology, J.M. and V.L.; software, J.M.; validation, J.M., V.L., and A.L.; formal analysis, J.M. and A.L.; investigation, J.M.; resources, J.M. and A.L.; data curation, J.M.; writing--original draft preparation, J.M; writing--review and editing, A.L. and V.L.; visualization, J.M.; supervision, A.L.; project administration, A.L.; funding acquisition, J.M. All authors have read and agreed to the published version of the manuscript.  
Figure 5: SE vs. transmit powers ratio.