esa-sceva/satcom-chunk-collection huggingface dataset:
# Partial Fake Speech Attacks in the Real World Using Deepfake Audio
## 6 Amazon Alexa (Mobile App)  
Finally, we carried out a partial fake speech attack on Alexa, a virtual assistant software that is widely used on Amazon Echo devices and as a mobile app on multiple mobile phone brands. Alexa depends on pre-defined voices to perform various sensitive and secure transactions, such as purchases, calling, and messaging [20]. It also handles other secure elements in smart homes, such as opening the main or garage door, switching off CCTV, and more. Therefore, it is crucial to determine how Alexa responds to partial fake speech.  
**Participants.** We recruited 14 participants for our study through an online crowdsourcing platform called Prolific ([[https://www.prolific.co/](https://www.prolific.co/)]([https://www.prolific.co/](https://www.prolific.co/)) (accessed on 1 August 2024)). All the participants self-identified as native English speakers and were currently residing either in the United Kingdom or the United States. Half of the participants identified themselves as female, while the other half identified as male. We only included participants who were 18 years of age or older. The survey was designed to take approximately 5 min to complete, and we compensated the participants with $1 for their time.  
**Experiment Setup.** We evaluated the effectiveness of a DNN-based PF speech attack on [PERSON] using the attack success rate (ASR). To conduct this evaluation, we asked the participants to read and record six different sentences. Out of these six sentences, four were used for the enrolment of each participant's real voice \(\mathbf{RB}\) to Alexa, while the remaining two sentences were used to clone their voice \(\mathbf{RF}\), saying the same sentences and then create the PF speech \(\mathbf{RP}\) to perform the attack and evaluate [PERSON]'s responses. Then, we used ElevenLabs [15], an online commercial platform, to clone voices and create text-to-speech audio. After that, we replaced the original segments with the fake ones using Audacity. Lastly, we registered each speaker using the four recorded sentences in the Alexa (iPhone) mobile app version and performed the attack by re-playing the two PF sentences listed below:  
* [PERSON], can you recognise my voice?
* [PERSON], who am I?  
The first sentence contained ('my voice') as the fake segment, while we used the wake-up word ('[PERSON]') as the fake segment in the second sentence.  
**Results.** We found that Amazon Alexa was able to identify all 14 participants when they used partial fake speech. The success rate of this attack was 100% since [PERSON] did not reject any of the partial fake speech. This result reveals a significant issue with [PERSON]'s pre-defined voices, especially when it comes to secure and sensitive transactions.
# Steel Surface Defect Diagnostics Using Deep Convolutional Neural Network and Class Activation Map
## 3 Proposed Method
### Network Architecture  
The descriptions of the proposed CNN with CAM architecture are presented in Figure 4 and Table 1. Inspired by VGG-Net introduced by [PERSON] et al. [28], we built a VGG-like CNN model that has a total of 17 layers with different types of operations, i.e., convolution, max pooling, global average pooling and dense. In particular, the model is comprised of five convolutional blocks, where each convolutional block refers to a combination of two consecutive convolutional layers and a max-pooling layer. Two convolutional layers in the block share the same kernel size of \(3\times 3\) and the number of channels. It is proved that taking advantage of the small size of the filters makes receptive fields more simple, where it is possible to increase the depth of the architecture [28]. Also, we utilized batch normalization and rectified linear unit (ReLU), which are appended between every convolutional layer. The former is used to normalize each training mini-batch for resolving overfitting problems as a regularizer [29], while the latter serves as a nonlinear activation function. Besides, the global average pooling (GAP) is taken into account to visualize activation maps for understanding the discriminative results of the proposed network. Finally, the latter part of the network is fully connected layers and an output layer with six neurons, corresponding to the number of steel surface defects categories.  
Figure 3: Schematic flow chart of research outline.
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 6 Performance Evaluation
### Communication Cost  
In our scheme, the size of a smart meter identity ID, a Chebyshev polynomial, a random number, a hash output, a timestamp, and a ciphertext in the symmetric encryption and decryption algorithm is assumed to be 64, 160, 64, 160, 16, 128 bits, respectively. In the process of key agreement, only two information interactions are carried out. Firstly, the smart meter sends \(\{PID_{i},M_{1},M_{3},M_{4},t_{1}\}\) to the data concentrator, with the communication cost of \((64+160+160+160+16)=560\) bits; secondly, the data concentrator sends \(\{M_{5},M_{7},M_{8},t_{2}\}\) to the smart meter, with the communication cost of \((160+160+160+16)=496\) bits. The total communication cost in the key agreement process of the proposed scheme is 1056 bits.  
The communication costs between the scheme and other representative schemes are shown in Table 3. Figure 6 shows the comparison of communication costs among different schemes, indicating that the proposed scheme has the lowest communication cost.
###### Abstract  
Concrete production requires a significant amount of natural resources, with aggregates comprising between 55% and 80% of the total volume. However, the over-exploitation of natural aggregates has led to the exploration of alternative materials for use in concrete production. In this study, crushed hazelnut shells were investigated as a partial replacement for fine aggregate, addressing the problem of natural resource depletion and offering a second use for this important agricultural waste product available in Chile. Hazelnut shells were incorporated in percentages of 2.5%, 5%, and 10% by weight of sand for water/cement ratios of 0.4 and 0.5. The compressive strength at 7 and 28 days and bending strength at 28 days were determined, alongside physical properties such as the workability, temperature, air content, fresh density, and hardened density of the concrete. Our findings showed that replacing 2.5% of the fine aggregate with hazelnut shells led to a higher compressive strength at 28 days, exceeding the strength of the standard specimens by 9.5%, whereas replacing 5% of the fine aggregate led to the highest bending strength, exceeding the strength of the standard specimens by 3.5%. Moreover, the 0.4 w/c ratio consistently led to better results for both compressive and bending strength, with fewer and lower reductions in mechanical strength compared to the standard mixture. Our results suggest that concrete mixes with hazelnut shells as a replacement for fine aggregate at a percentage of up to 2.5% can be used in construction systems with a compression strength lower than 17 MPa, and mixtures with up to 10% hazelnut shell replacement can be used in structures with tensile bending stress requirements lower than 6 MPa. Overall, the use of hazelnut shells as a partial replacement for fine aggregate in concrete production presents an environmentally friendly and cost-effective solution for the construction industry.  
concrete production; natural aggregates; alternative materials; hazelnut shells; concrete strength; sustainable construction +
Footnote †: journal: Applied Sciences  
Article Influence on Physical and Mechanical Properties of Concrete Using Crushed Hazelnut Shell  
[PERSON], [PERSON], [PERSON] *(c), [PERSON] and [PERSON]  
Civil Engineering Department, Faculty of Engineering, University of Santiago of Chile (USACH),  
Santiago 9170022, Chile; [EMAIL_ADDRESS] (N.G.C.); [EMAIL_ADDRESS] (G.M.A.); [EMAIL_ADDRESS] (S.G.S.); [PERSON](J.C.P.)  
* Correspondence: [EMAIL_ADDRESS]
## 5 Numerical Simulation
### Boundary Conditions and Fundamental Equations
#### 5.2.2 Boundary Conditions  
(1) The inlet boundary conditions.  
The inlet conditions for this numerical simulation are set as fixed-pressure boundary conditions based on the required grouting pressure in the experiment.  
(2) The outlet boundary conditions.  
In this experiment, there is no need to consider the outflow of the slurry from the porous medium. Therefore, the outlet boundary conditions can be set as zero-pressure boundary conditions.  
\[-\phi\rho u=0 \tag{27}\]  
To ensure a closer match between the simulation results and the experimental measurements, the developed numerical simulation program was configured with geological parameters and slurry parameters that aligned with the parameters used in the indoor experiments. These parameters are detailed in the tables mentioned in previous sections (Tables 1-3). Taking Condition S1 as an example, Figure 12 illustrates the profile of random porosity and permeability obtained from the numerical simulation.  
Figure 12: Schematic diagram of stochastic porosity versus permeability of S1 formation.  
From Figures 12-14, it is evident that the Newtonian fluid exhibits a spherical diffusion pattern both when considering and not considering porous media with different particle size distributions. When considering porous media with different particle size distributions, the diffusion radius of the Newtonian fluid is smaller than that in porous media without considering particle size distributions.
# Adaptive Software Defined Equalization Techniques for Indoor Visible Light Communication
## 3 Methods
### Least Mean Squares  
The LMS algorithm is very often used for adaptive filtering, as it is very simple and not time demanding. This algorithm is a stochastic gradient descent adaptive method based on Wiener filtering theory, stochastic averaging, and the least squares method. Calculation of the linear adaptive algorithm coefficients is given by Equation (3), where \(\mathbf{x}\) is the input signal, \(\mathbf{w}\) represents the impulse characteristic of the filter, and \(\mu\) is the step size. Step size \(\mu\) is one of the main parameters. When the value of \(\mu\) is too low, it takes a very long time to find the optimal solution of the adaptive filter. On the other hand, when the value of \(\mu\) is too high, the adaptive filter becomes unstable. Therefore, it is very important to find a compromise between the speed and stability of the adaptive algorithm convergence [33; 34; 35].  
For optimization, a random starting point is selected in the coefficient space, and then by subsequent steps, the optimum point is reached. To reach optimization, the method of steepest descent is very often used. The implementation of the LMS algorithm is basically composed of three steps. In the first step, the finite impulse response (FIR)filter output value is calculated by Equation (1). Then, the estimated error value is calculated based on Equation (2). In the last step, the values of the FIR filter weights are updated with respect to the next iteration of Equation (3) [33; 34; 35].  
\[y(n)=\sum_{i=0}^{\mathrm{N}-1}\mathbf{w}(n)\mathbf{x}(n-i)=\mathbf{w}^{ \mathrm{T}}(n)\mathbf{x}(n). \tag{1}\]  
\[e(n)=d(n)-y(n). \tag{2}\]  
\[\mathbf{w}(n+1)=\mathbf{w}(n)+2\mu e(n)\mathbf{x}(n). \tag{3}\]
# SP-YOLOv8s: An Improved YOLOv8s Model for Remote Sensing Image Tiny Object Detection
## 4 Experimental Evaluation
### Experimental Environment and Datasets  
All experimental evaluations presented in this research paper were performed within a consistent experimental environment, employing Ubuntu 20.04 as the operating system, an Intel(R) Xeon(R) Silver 4214R CPU operating at 2.40 GHz, an NVIDIA Tesla T4 GPU with 16 GB of memory. The stochastic gradient descent algorithm was utilized to optimize the loss function. During training, an initial learning rate of 0.01 was employed, and the learning rate adjustment strategy followed a cosine annealing algorithm.  
The proposed approach was evaluated using the recently introduced AI-TOD aerial image micro-object detection dataset. The images in this dataset possess dimensions of 800 \(\times\) 800 pixels, while the dimensions of the network's input image were modified to a resolution of 640 \(\times\) 640 pixels. The dataset encompasses eight distinct object categories, namely, aircraft, bridge, storage tank, ship, swimming pool, car, pedestrian, and windmill, totaling 700,621 object instances. To ensure comprehensive evaluation, the dataset was partitioned into training and test sets, following an 8:2 ratio. Consequently, the training set consists of 11,214 samples, while the test set contains 2804 samples. It should be noted that the training and test data are strictly independent, ensuring unbiased evaluation. Notably, the AI-TOD dataset differs from other benchmark datasets, as its largest object measures less than 64 pixels. Furthermore, approximately 86% of the objects in the dataset are smaller than 16 pixels, with an average object size of approximately 12.8 pixels. It is worth mentioning that certain object classes, such as swimming pools and windmills, exhibit significantly fewer instances compared to more prevalent classes like vehicles and boats. This class imbalance is commonly observed in aerial image datasets, such as DOTA [53] and DIOR [54].  
Figure 4 shows some images of the AI-TOD dataset (Figure 4a) and some images of the TinyPerson dataset (Figure 4b).
# Landsat Surface Product Validation Instrumentation:
## 3 ASD and UAS Technology Assessment
### Accuracy
#### 3.2.7 Accuracy Summary  
To summarize the results for the accuracy assessment, the first point that needs to be made is that the traceability of the absolute calibration is a primary factor. Instruments that have been calibrated along a path consistent with the calibration of the targets under measurement tend to exhibit greater accuracy. For the case of the BigMAC campaign, this effect appears to be in the order of 0.02 reflectance units.  
For the case of ASD-based measurements, absolute accuracy in the order of 0.02-0.03 reflectance units in the VSWIR seems achievable for bright targets (\(>\)0.30 reflectance units). Conversely, dark target results were somewhat mixed but tend to indicate accuracy in the order of 0.002 reflectance units. UAS accuracy measurements, as achieved for the BigMAC, were the lowest due to the traceability path inherent in the calibration of those sensors. With targets that are large with respect to the UAS instrument's GSD, accuracy potential  
Figure 14: Reflectance measurement accuracy using the Permalflect 55% panel.  
Figure 15: Reflectance measurement accuracy using the Permalflect 5% panel.  
may be in a range from 0.02 to 0.04 reflectance units. For all instruments, it should also be noted that the reflectance of the surrounding area should also be similar to the target for the greatest accuracy. If the dissimilarity is too great, scattering from the surroundings will affect the accuracy measurements.  
From an operational land surface reflectance product validation perspective, normally, targets will be large (relative to Landsat GSD), homogeneous areas. Thus, some of the difficulties present with small human-made reflectance panels will be mitigated. With the goal of a high degree of automation, it will likely be necessary to gather ground truth information on a regular basis to assess the stability of the deployed instruments. An example would be to send a team in the field with an ASD-based system and measure surface reflectance. Results from BigMAC indicate that accuracy using this approach, which has become essentially a de facto standard in the calibration community, will be approximately 0.02 reflectance units and could serve as an adequate baseline for the quality assessment of future land surface products.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 8 Challenges and Opportunities
### Latency and Bandwidth
#### 8.1.2 Bandwidth Limitations  
Bandwidth limitation is the maximum rate at which data can be transmitted over a network. In the metaverse, bandwidth determines how much data can be exchanged between users and servers at any given time [246]. High-quality virtual experiences in the metaverse require transmitting large volumes of data, including high-resolution graphics, audio, and real-time interactive elements. When bandwidth is limited, these data transmissions can become bottlenecked, leading to slower loading times, reduced graphical fidelity, and overall degraded user experience.  
The impact of bandwidth limitations on the metaverse is particularly significant during peak usage times or in areas with poor network infrastructure [247]. Users may experience lag, buffering, and disconnections, disrupting the immersive experience and hindering interactions within the virtual world [248]. Bandwidth constraints can also limit the scalability of the metaverse, as the infrastructure may struggle to support many concurrent  
Figure 10: Metaverse applications - bandwidth versus latency.  
users. To mitigate these issues, advancements in network technologies, such as fiber optics and 5G, are being deployed to increase bandwidth capacity [124]. Additionally, data compression and content delivery networks (CDNs) optimize data transmission and reduce the strain on bandwidth, ensuring a smoother and more reliable metaverse experience.  
Table 1 highlights the progression of VR technologies by comparing bandwidth requirements, latency, and supported resolutions based on the latest published research. Wi-Fi 6E supports up to 8K resolution with bandwidth of up to 2.4 Gbps and approximately 20 ms latency, leveraging the 6 GHz band for enhanced performance in crowded environments [249]. Wi-Fi 7 further improves, offering bandwidth of up to 46 Gbps and latency of around 10 ms, also supporting up to 8K resolution with multi-link operations and enhanced spectrum efficiency [250]. Wi-Fi 8 is expected to make these features a lot better by using millimeter-wave technology to reach data rates up to 100 Gbps and latency levels below 1 ms. It will also support 8K and higher with features like beamforming and multiple access point (AP)-coordination [251; 252]. Moreover, 5G networks provide ultra-fast speeds of 1-10 Gbps and latency between 1-10 ms, capable of smoothly streaming up to 8K VR content, especially in mobile environments [253]. The combination of NGCodec with 5G optimizes bandwidth usage through advanced video compression, reducing latency to under 5 ms while supporting up to 8K resolution [254]. Finally, FPGA-based VR streaming uses re-configurable hardware for fast video encoding and decoding, with latency below 1 ms and support for up to 8K resolution. This makes sure that users can interact and the system runs smoothly in real-time [255; 256].
## 1 Introduction
Thus, model-based approaches can be exploited, in which data from analytical and numerical models can be used as preliminary information for SHM system training and optimization; however, they require that the model be fast enough to allow for multiple simulations of the system in different conditions of interest, including both environmental influences and different damage configurations. In this scenario, a recent method for relatively fast modeling of transmissions [10] and prediction of vibration-based signal features that relies on a hybrid analytical-numerical approach is used in this study. It combines a traditional finite element method (FEM) to simulate the system deformations with an analytical Hertzian solution of the contact between gears or rolling elements. In this way, it allows the numerical simulation of very complex systems without geometrical simplifications, with a good accuracy and with a reasonable computational effort. With this approach, many different vibrational spectra can be generated for a healthy condition and with the presence of different sources of damage. The same hybrid approach has been tested on planetary transmissions of wind-turbine gearboxes by the NREL (National Renewable Energy Laboratory--USA), in which the entire multistage transmission was modeled and promisingly validated with a test rig owned by the NREL [11]. After generating a database of simulated signals in healthy and faulted conditions, made realistic by adding noise extracted from real sensor data, damage identification was performed in this study based on machine-learning algorithms, particularly artificial neural networks (ANNs). Specific ANNs, in the form of multilayer perceptrons (MLPs) have been trained to detect, localize, and quantify pitting damage over the transmission's gear teeth, verifying the performance of the model-based strategy for the global damage identification hierarchical structure defined by [PERSON] [12], thus paving the way toward future implementation of prognostic algorithms for predictive maintenance. The paper is structured as follows: The modelling approach is presented in the next section, with focus on the application scenario, with details of the hybrid model in both the healthy and damaged conditions, and examples of validated simulated signals. The results of damage identification algorithms for detection, localization, and assessment are provided in a separate section. A conclusion section completes the paper.
## 3 Transmission Protocol Configuration and Resource Allocation Scheme
### Traffic Flow Resource Allocation Algorithm  
#### 3.2.1 Maximization of Network Utility  
In this paper, through optimizing the transmission power \(P_{0}^{l}\) of HAPS on LP-ABS and the resource allocation \(s_{i}\) of each traffic flow, the network utility can be maximized under the condition of resource quantity constraint. Therefore, the resource allocation problem of user traffic flows can be expressed mathematically as  
\[\max\{\sum\limits_{i=1}^{|\Gamma^{d}|}U_{i}^{d}(R_{i}^{d})+\sum\limits_{k=1}^{| \Gamma^{u}|}U_{k}^{u}(R_{k}^{u})\} \tag{19}\]  
The constraints are  
\[\begin{array}{l}R_{i}^{d}=\alpha_{i}^{n,d}c_{i}^{n,d}+\alpha_{i}^{l,d}c_{i}^{ l,d},\forall i\in\Gamma^{d}\\ R_{i}^{u}=\alpha_{i}^{n,u}c_{i}^{n,u}+\alpha_{i}^{n,u}c_{i}^{l,u},\forall i\in \Gamma^{u}\\ \sum\limits_{i=1}^{|\Gamma^{d}|}\alpha_{i}^{n,d}\leq\Phi^{n,d};\\ \sum\limits_{i=1}^{|\Gamma^{u}|}\alpha_{i}^{n,u}\leq\Phi^{n,u};\\ \sum\limits_{i=1}^{|\Gamma^{d}|}\alpha_{i}^{n,d}+\sum\limits_{i=1}^{|\Gamma^{ u}|}\alpha_{i}^{l,u}\leq\Phi^{l};\\ \Phi^{n,d}+\Phi^{n,u}+\Phi^{l}=1;\end{array} \tag{20}\]  
where \(U\Big{(}a_{i}^{d}c_{i}^{d}\Big{)}\) and \(U(\alpha_{i}^{u}c_{i}^{u})\) correspond to the utility functions of user \(i\) downlink and uplink traffic flow, respectively. \(U\Big{(}a_{i}^{d}c_{i}^{d}\Big{)}\) represents the utility value of user \(i\)'s downlink traffic flow when the reachability rate is \(a_{i}^{d}c_{i}^{d}\), and \(U(\alpha_{i}^{u}c_{i}^{u})\) represents the utility value of user \(i\)'s uplink traffic flow when the reachability rate is \(a_{i}^{u}c_{i}^{u}\). HAPS configures downlink transmission on LP-ABS, so \(\alpha_{i}^{n,u}=0\). In addition, if user \(i\) does not generate downlink traffic flow, \(\alpha_{i}^{n,d}=\alpha_{i}^{l,d}=0\). If the user \(i\) does not generate uplink traffic flow, then \(\alpha_{i}^{n,u}=\alpha_{i}^{l,u}=0\).  
The specific form of the utility function is determined by the service type of the corresponding traffic flow, so the objective function of the optimization problem is the sum function of multiple S-type functions and multiple concave functions, that is, a non-concave function. Maximizing network utility is a non-concave problem that is difficult to solve directly. In addition, the resource allocation result \(s_{i}\) of the traffic flow generated by user \(i\) is affected by the transmission protocol configuration \(\Big{(}\Phi^{n,u},\Phi^{n,d},\Phi^{l}\Big{)}\), and \(\Big{(}\Phi^{n,u},\Phi^{n,d},\Phi^{l}\Big{)}\) is affected by the power \(P_{0}^{l}\). Therefore, there is a coupling relationship between \(\alpha_{i}\) and \(P_{0}^{l}\), which makes maximizing network utility more challenging to solve.