esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Networks and Protocols of Communication
### Network and Communications Protocols for IoT Applications
#### 3.3.3 IEC 61850  
The IEC 61850 standard is certainly the main reference for substation automation. It has been designed to establish guidelines for the virtual modeling of substations, standardizing methods of communication between devices, and defining system performance requirements. The IEC 61850 is an open standard for Ethernet communication between substations, ensuring interoperability between the various Intelligent Electronic Devices (IED) used. The IEC 61850 standard is not defined as a communication protocol, but defines a set of protocols to be used in electrical power projects, among them are Simple Value, Generic Object Oriented Substation Event (GOOSE), Time Sync, Manufacturing Message Specification among others [39; 40].  
The GOOSE protocol has been designed specifically for applications in substations. The goose protocol has horizontal communications since messages remain at the same level. GOOSE messages have very strict time requirements because they are often associated with critical applications and replace control cables. As a result, the GOOSE protocol is mapped directly to the link layer, which provides greater speed in communication. The GOOSE protocol does not have any method of confirming a receipt of the message, prioritizing the speed of communication. Therefore, to achieve the appropriate level of reliability, the protocol uses a specific relay scheme, which consists of successive repetitions of the message over a short period. This mechanism is triggered as a result of an event when there is variation in the state of some variable. The GOOSE communication mechanism, where T0 represents the transmission time during stable conditions (without any variation), usually around 1 second. T1, T2, and T3 represent transmission times right after an event This scheme ensures the reliability of communication because if the first message does not reach its destination for some reason, subsequent transmissions will have a high probability of reaching the destination. In the implementation process, T1 = T2 = 4 ms and T3 = 8 ms; these values are considered to be typical of this protocol [39; 41].  
There are several other communication protocols that have been used to create the so-called smart homes. The smart home concept is based on the interaction between services and resources. This idea results from the convergence of several areas: entertainment, security, energy management, and healthcare. The smart home paradigm can be the answer to these demands, as long as the residence is equipped with technologies that observe the inhabitants and offer proactive services that can provide comfort, security, energy savings, sustainability, and home care. Some of the most widely used communication protocols for these applications are Power Line Communication (PLC) Bluetooth Low Energy, ZigBee, Meter Bus, Z-Wave and they have technological updates.
# A Road Crack Detection Method Based on Residual and Attention Mechanism
## 2 Proposed Method  
### Unet Model  
The Unet model is a widely used segmentation network in the field of medical images, which uses multi-scale features for semantic segmentation. Figure 1 shows its basic network structure. It can be seen that the Unet network is based on encoder and decoder structures. The Unet network generates feature maps with low resolution and high-dimensional semantics through convolution and pooling operations during the encoder stage. The decoder stage restores the feature map to its original resolution through continuous convolution and upsampling operations, ultimately obtaining the segmentation result. However, the model ignores local features of key edge information, resulting in lower universality of the network. Therefore, this article improves the feature extraction part of the network by adding residual structures with better feature extraction capabilities and introduces the CBAM attention module to allow the model to focus more on crack details.
# Evaluation of Tip Loss Corrections to AD/NS Simulations of Wind Turbine Aerodynamic Performance
## 4 Applying Corrections to AD/NS Simulation
### Application of Glauert Tip Loss Factor \(F\)
#### 4.1.2 Glauert-B Correction  
[PERSON] [42] compared Equations (32) and (33) to the corresponding baseline equations without factor \(F\). The baseline equations are  
\[\frac{a}{1-a}=\frac{\sigma(C_{l}\cos\phi+C_{d}\sin\phi)}{4\sin^{2}\phi}, \tag{46}\]  
\[\frac{a^{\prime}}{1+a^{\prime}}=\frac{\sigma(C_{l}\sin\phi-C_{d}\cos\phi)}{4 \sin\phi\cos\phi}. \tag{47}\]In order to distinguish from the parameters in the baseline equations, we here rewrite Equations (32) and (33) to  
\[\frac{F\tilde{a}}{(1-\tilde{a})}=\frac{\sigma\Big{(}\tilde{C}_{l}\cos\tilde{ \phi}+\tilde{C}_{d}\sin\tilde{\phi}\Big{)}}{4\sin^{2}\tilde{\phi}}, \tag{48}\]  
\[\frac{F\tilde{a}^{\prime}}{(1+\tilde{a}^{\prime})}=\frac{\sigma\Big{(}\tilde{C }_{l}\sin\tilde{\phi}-\tilde{C}_{d}\cos\tilde{\phi}\Big{)}}{4\sin\tilde{\phi} \cos\tilde{\phi}}. \tag{49}\]  
The implementation of [PERSON] [42] adopts the following equations which implies an assumption of \(\phi=\tilde{\phi}\) which in fact does not accurately hold,  
\[\frac{a}{1-a}=\frac{\sigma(C_{l}\cos\phi+C_{d}\sin\phi)}{4\sin^{2}\phi}=\frac{ F\tilde{a}}{(1-\tilde{a})}, \tag{50}\]  
\[\frac{a^{\prime}}{1+a^{\prime}}=\frac{\sigma(C_{l}\sin\phi-C_{d}\cos\phi)}{4 \sin\phi\cos\phi}=\frac{F\tilde{a}^{\prime}}{(1+\tilde{a}^{\prime})}. \tag{51}\]  
The corrected interference factors were, thus, determined by  
\[\tilde{a}=\frac{a}{F(1-a)+a}, \tag{52}\]  
\[\tilde{a}^{\prime}=\frac{a^{\prime}}{F(1+a^{\prime})-a^{\prime}}. \tag{53}\]  
The tip loss correction was completed as the above functions for \(\tilde{a}\) and \(\tilde{a}^{\prime}\) were used to replace Equations (9) and (10).
###### Abstract  
(1) Introduction: This review aimed to synthesize the significant literature addressing digital techniques for producing removable partial denture (RPD) metal frameworks, focusing on oral fit outcomes. (2) Material and Methods: A systematic review of the Web of Science and Pubmed databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. The selection was for original articles in English containing relevant data on RPD metal frameworks produced with digital techniques, including study characteristics, digital techniques, and fit assessment methods and outcomes. (3) Results: From the 967 search studies, 405 were duplications, and 521 were excluded after screening against set criteria. A manual search included 21 studies resulting in 62 papers for review. Extra-oral was more frequently used than intra-oral scanning. The computer-assisted design was the most applied digital technique. Additive manufacturing was preferred to milling for direct and indirect fabrication of frameworks. Fit assessments were based on qualitative measures, but quantitative evaluation showed acceptable clinical fit for RPDs made by digital protocols. (4) Conclusions: The combination of direct metal additive manufacturing with conventional impression was the most used protocol and included better qualitative and quantitative fit outcomes than the other digital protocols.  
removable partial denture; framework production; digital; CAD-CAM; fit accuracy
###### Abstract  
This paper presents an approach to enhancing the clarity and intelligibility of speech in digital communications compromised by various background noises. Utilizing deep learning techniques, specifically a Variational Autoencoder (VAE) with 2D convolutional filters, we aim to suppress background noise in audio signals. Our method focuses on four simulated environmental noise scenarios: storms, wind, traffic, and aircraft. The training dataset has been obtained from public sources (TED-LIUM 3 dataset, which includes audio recordings from the popular TED-TALK series) combined with these background noises. The audio signals were transformed into 2D power spectrograms, upon which our VAE model was trained to filter out the noise and reconstruct clean audio. Our results demonstrate that the model outperforms existing state-of-the-art solutions in noise suppression. Although differences in noise types were observed, it was challenging to definitively conclude which background noise most adversely affects speech quality. The results have been assessed with objective (mathematical metrics) and subjective (listening to a set of audios by humans) methods. Notably, wind noise showed the smallest deviation between the noisy and cleaned audio, perceived subjectively as the most improved scenario. Future work should involve refining the phase calculation of the cleaned audio and creating a more balanced dataset to minimize differences in audio quality across scenarios. Additionally, practical applications of the model in real-time streaming audio are envisaged. This research contributes significantly to the field of audio signal processing by offering a deep learning solution tailored to various noise conditions, enhancing digital communication quality.  
speech enhancement; noise suppression; deep learning; variational autoencoders
## 3 Performance Analysis  
The instantaneous electrical SNR of DF relayed cooperative link \(\mathcal{S}-\mathcal{R}_{m}-\mathcal{D}\) is given by  
\[\Psi_{SR_{m}D}=\min(\Psi_{SR_{m}},\Psi_{R_{m}D}). \tag{9}\]  
where \(\Psi_{SR_{m}}\) and \(\Psi_{R_{m}D}\) represent the instantaneous SNRs of \(\mathcal{S}-\mathcal{R}_{m}\) and \(\mathcal{R}_{m}-\mathcal{D}\) links, respectively, and are computed as  
\[\Psi_{SR_{m}}=0.25h_{SR_{m}}^{2}I_{SR_{m}}^{2}\psi_{\circ}, \tag{10}\]  
and  
\[\Psi_{R_{m}D}=0.25h_{R_{m}D}^{2}I_{R_{m}D}^{2}\psi_{\circ}. \tag{11}\]  
Here, \(\Psi_{\circ}=\frac{h_{\circ}^{2}P_{\circ}^{2}}{\sigma_{R}^{2}}\) is the average SNR, \(I_{SR_{1}}=I_{SR_{2}}=\ldots=I_{SR_{m}}\), and \(I_{R_{1}D}=I_{R_{2}D}=\ldots=I_{R_{m}D}\). The channel fading coefficients of \(\mathcal{SR}_{m}\) links \(\{h_{SR_{m}}\}_{m=1}^{M}\) are assumed to be iid log-normal random variables, \(\ln(h_{SR_{m}})=\mathcal{N}(\mu_{SR},\sigma_{SR}^{2}),\forall m=1,\ldots,M\). Similarly, the channel fading coefficients of \(\mathcal{R}_{m}\mathcal{D}\) links \(\{h_{R_{m}D}\}_{m=1}^{M}\) are iid and log-normally distributed, \(\ln(h_{R_{m}D})=\mathcal{N}(\mu_{RD},\sigma_{RD}^{2}),\forall m=1,\ldots,M\). Moreover, \(\{h_{SR_{m}}\}_{m=1}^{M}\) and \(\{h_{R_{m}D}\}_{m=1}^{M}\) are considered to be independent random variables. Using the properties of log-normal random variables [38] ( [Lemma 1]), it can be shown that \(\Psi_{SR_{m}}\) and \(\Psi_{R_{m}D}\) are also log-normally distributed.  
\[\ln(\Psi_{SR_{m}}) \sim\mathcal{N}(\mu_{\Psi_{SR}},\sigma_{\Psi_{SR}}^{2}), \tag{12}\] \[\ln(\Psi_{R_{m}D}) \sim\mathcal{N}(\mu_{\Psi_{RD}},\sigma_{\Psi_{RD}}^{2}), \tag{13}\]  
where \(\mu_{\Psi_{j}}=\mu_{j}+\ln(0.25I_{j}^{2}\Psi_{\circ})\), and \(\sigma_{\Psi_{j}}^{2}=4\sigma_{j}^{2}\) for \(j\in\{\mathcal{SR}_{m},\mathcal{R}_{m}\mathcal{D}\}\). In best relay selection, the only relay that forwards the received signal to the destination node \(\mathcal{D}\) is the one for which \(\Psi_{SR_{m}D}\) is maximum. The e2e SNR for the best relay selection scheme is given by  
\[\Psi_{e2e}=\max_{m=1,M}\Psi_{SR_{m}D}. \tag{14}\]  
Figure 1: UWVLC cooperative communication systems. (**a**) Horizontal. (**b**) Vertical.
## 2 Background and Related Work
### Lstm RNN  
In an LSTM RNN, the recurrent cell is modified into the reset gate for Equation (3), forget gate for Equation (4), candidate memory cell for Equation (6), and output gate Equation (7). The internal diagram of an LSTM  
Figure 1: Internal structure of a Vanilla Recurrent Neural Network Unit.  
input gate, \(i_{t}=\sigma\)\([U_{th}^{l}\ h_{t-1}^{l}+W_{ix}^{l}\ x_{t}^{l}+diag[p_{i}^{l}]\ c_{t-1}+b_{i}]\)  
\[\text{forget gate, }f_{t}=\sigma\ [U_{fh}^{l}\ h_{t-1}^{l}+W_{fx}^{l}\ x_{t}^{l}+ diag[p_{f}^{l}]\ c_{t-1}+b_{f}] \tag{4}\]  
\[g_{t}=\tanh\ [U_{c}^{l}\ h_{t-1}^{l}+W_{cx}^{l}\ x_{t}^{l}+b_{c}^{l}] \tag{5}\]  
\[\text{memory cell, }c_{t}=f_{t}^{l}\bigodot\ c_{t-1}^{l}+f_{t}^{l}\bigodot\ g_{t}^{l} \tag{6}\]  
\[\text{output gate, }o_{t}=\sigma\ [U_{dh}^{l}\ h_{t-1}^{l}+W_{ox}^{l}\ x_{t}^{l}+ diag[p_{o}^{l}]\ c_{t-1}+b_{o}^{l}] \tag{7}\]  
\[\text{recurrent hidden state, }h_{t}=o_{t}^{l}\bigodot\tanh c_{t}^{l} \tag{8}\]  
\[\text{where, }x_{t}^{l}=\begin{cases}h_{t}^{l-1}\ (\text{hidden temporal }\text{features}),&l>1\\ input\ features,&l=1\end{cases} \tag{9}\]  
\[\text{and final output at the last layer will be }y_{t}=\phi[W_{yh}h_{t}+b_{y}] \tag{10}\]  
\(\phi\) represents the output activation function, for example, a softmax activation. \(\bigodot\) represents element-wise multiplication. Peep-hole connections (\(diag[p]\)) are optional for each of the gates. In all our experiments, we used single-layer structures without any peep-hole connections. The recurrent weight matrix \(U_{hh}\) of an LSTM is obtained by vertically stacking the four-gate, recurrent weight matrices as \([U_{lh},U_{oh},U_{fh},U_{ch}]^{T}\); likewise, the nonrecurrent weight matrix \(W_{hx}\) is obtained by vertically stacking the four-gate, nonrecurrent weight matrices as \([W_{ix},W_{ox},W_{fx},W_{cx}]^{T}\).
# Multi-Dimensional Sparse-Coded Ambient Backscatter Communication for Massive IoT Networks
###### Abstract  
In this paper, we propose a multi-dimensional sparse-coded ambient backscatter communication (MSC-AmBC) system for long-range and high-rate massive Internet of things (IoT) networks. We utilize the characteristics of the ambient sources employing orthogonal frequency division multiplexing (OFDM) modulation to mitigate strong direct-link interference and improve signal detection of AmBC at the reader. Also, utilization of the sparsity originated from the duty-cycling operation of batteryless RF tags is proposed to increase the dimension of signal space of backscatter signals to achieve either diversity or multiplexing gains in AmBC. We propose optimal constellation mapping and reflection coefficient projection and expansion methods to effectively construct multi-dimensional constellation for high-order backscatter modulation while guaranteeing sufficient energy harvesting opportunities at these tags. Simulation results confirm the feasibility of the long-range and high-rate AmBC in massive IoT networks where a huge number of active ambient sources and passive RF tags coexist.  
Keywords:RF energy harvesting; ambient backscatter communication (AmBC); sparse code multiple access (SCMA); modulation and coding scheme; iterative decoder +
Footnote †: journal:
## 4 Results
### Numerical Simulation Results of Slope Deformation and Failure
#### 4.5.4 Dynamic Deformation Characteristics  
To visually examine the deformation and damage patterns within the model, we activated the large deformation parameters in FLAC3D for simulation. As depicted in Figure 29a, an initial small bulging deformation emerges at the simulated slope corner. As the simulation progresses, the displacement values gradually increase, and the step surface undergoes deformation, presenting a warping shape at the front end of the upper step of the slope body (Figure 29b). With ongoing calculations, the model's deformation continues to intensify, involving deformation at the top and local stratigraphic misalignment near the slope's corner, a characteristic pattern of buckling in steep terrain (Figure 29c).  
An analysis of Figures 13-15 shows that distinct extrusion and deformation features appear at the foot of the pit slope. Local shallow landslides manifest in the middle of the slope, with numerous tensile cracks developing at the slope's summit and minor-scale misalignment occurring along the edge. Figure 29 illustrates that the deformation characteristics in this numerical simulation broadly align with those observed in the field, rendering the simulation process more plausible.
## 5 Experimental Evaluation
### Performance Metrics  
To validate the performance of the FT-CID model, the experimental model employs the widely used evaluation metrics described below.  
Precision: the ratio of the total number of samples correctly classified as intrusions relative to the total number of samples detected as intrusions expressed as a percentage.  
Precision = \[\left[\frac{\text{True Positive}}{\text{True Positive}+\text{False Positive}}\right]\]  
Recall: the ratio of the total number of samples correctly classified as intrusions relative to the total number of actual intrusions expressed as a percentage.  
\[\text{call}=\left[\frac{\text{True Positive}}{\text{True Positive}+\text{False Negative}}\right]\]  
F-measure: the harmonic mean of precision and recall expressed as a percentage.  
\[\text{F}-\text{measure}=2\times\frac{\text{Precision}\times\text{Recall}}{ \text{Precision}+\text{Recall}}\]  
Intrusion detection accuracy: the overall accuracy of correctly identified normal and abnormal traffic samples expressed as a percentage.  
\[\text{Accuracy}=\left[\frac{\text{True Positive}+\text{True Negative}}{ \text{True Positive}+\text{True Negative}+\text{False Negative}}\right]\]  
False-Positive Rate: the ratio of legitimate samples incorrectly classified as intrusions relative to intrusions incorrectly classified in the corresponding attack category expressed as a percentage.  
\[\text{False Positive Rate}=\left[\frac{\text{False Positive}}{\text{False Positive}+\text{True Negative}}\right]\]  
where:  
* True positive (TP) = number of samples correctly detected as intrusion samples;
* True negative (TN) = number of samples correctly detected as normal samples;
* False positive (FP) = number of samples incorrectly detected as intrusion samples;
* False negative (FN) = number of samples incorrectly detected as normal samples.
## 2 Related Work
### Hardware Configuration and Energy Efficiency for IoT Secure End Nodes  
There are different approaches when trying to improve the energy efficiency of IoT deployments that target hardware platforms and their configuration. One option is to develop mechanisms that allow for reducing the time end nodes are in an active state [39]. When it is possible to put the nodes to sleep, the reduction in the up-time can yield interesting results on energy consumption, but this is not applicable when real-time constraints or constant availability are required. Some authors investigated energy harvesting techniques [40; 41; 42], which allow IoT end devices to obtain energy from their environment, and thus remove the need for external power sources and increase battery replacement periods. Other works are focused on improving the network routing protocols of IoT networks to accelerate communications, which has a positive effect on energy consumption [43]. These approximations, although practical in some scenarios, do not address the security mechanisms required to deploy secure IoT networks.  
Regarding the modification of the actual hardware configuration parameters of IoT end nodes for improving their energy efficiency, there are few examples in the literature. For instance, the authors of [44] presented an interesting approach that consists of using Dynamic Voltage Scaling (DVS) to improve the energy efficiency of IoT control devices. The proposed scheduling algorithm allows for obtaining significant energy consumption gains while maintaining real-time capabilities. Another example of hardware configuration for improving the energy efficiency of IoT end nodes is presented in [8], where the authors provided energy consumption values for different memory mappings when executed on Ferroelectric RAM (FRAM)-based IoT devices. While both approximations could yield significant energy reductions, they fall short in their applicability: they are complex to implement, platform-dependant and application-specific, narrowing the application of the obtained conclusions to specific hardware platforms and use cases. It is also worth mentioning that the impact of cryptographic algorithms on the energy consumption of IoT end devices can be estimated by means of proper mathematical models [45; 46].
# A Connected and Autonomous Vehicle Reference Architecture for Attack Surface Analysis
## 3 A CAV Reference Architecture: Components and Related Attack Surfaces
### CAV Reference Architecture
#### 3.1.1 Wireless Communications  
Vehicles are currently or expected to be equipped with multiple antennas in order to communicate over different wireless protocols. This includes antennas for (i) receiving audio over AM, FM or DAB radio, (ii) receiving and transmitting IEEE 802.11 WiFi, (iii) bidirectional V2X communications over IEEE 802.11p and (iv) bidirectional cellular antennas (such as 4G). It may also be the case that Internet of Things (IoT) technologies such as IEEE 802.15.4 or ZigBee are included to facilitate interoperability with IoT networks. Many of the systems in the CAV will interact with the communications due to the need to coordinate with nearby vehicles or to provide services to the vehicle's users. As communications are the primary way in which vehicles will exchange information, they will be the avenue through which most attacks are launched. These attacks may try to compromise or interfere the way in which packets are communicated or compromise the components to which the packets are forwarded.  
\begin{tabular}{l} \hline \hline \multicolumn{2}{c}{**Example Attacks**} \\ \hline \(\bullet\) DoS V2X communications [37] \\ \(\bullet\) Eavesdrop \\ \(\bullet\) Replay \\ \(\bullet\) MiTM Intercept \\ \(\bullet\) Incorrect handling of malicious packets (e.g., DAB [38]) leading to RCE \\ \(\bullet\) Context information leakage (e.g., location, identity [39]) \\ \(\bullet\) Sybil Attacks [40] \\ \(\bullet\) Colluding to defeat agreement protocols [41] \\ \(\bullet\) Wormhole (Relay) Attack [42] \\ \hline \hline \end{tabular}