esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on a Random Mask Infection Countermeasure against Double Fault Attacks
## 4 Experimental Analysis
### Comparison of Existing Countermeasures  
In Table 2, we briefly compare some countermeasures against fault attacks mentioned in this paper and indicate the possible threats that they may suffer from power attacks, as well as fault attacks, such as double-fault attacks, single-byte fault attacks, and higher fault attacks.  
Papers [11; 14; 23; 25] only assume single-fault attacks and, thus, neither can defend against double-fault attacks. Paper [11] improves the performance of AES circuits in defending against byte fault attacks by redesigning matrix for mix-column module, and successfully increases the exhaustive complexity of byte fault attacks to \(2^{116}\). In [14], four  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number** & **Diffusion Results** \\ \hline  & P: f3 01 a6 8a 9e 9f fa 50 84 45 81 d9 e2 90 d8 18 \\  & K: 09 36 3c fc fc 8d 9c 33 f4 74 27 e da b2 c2 82 \\  & M: 02 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 \\ \hline
1 & f4 5b 3b 13 63 a1 c2 c0 63 b5 d8 16 63 a0 c2 e3 \\ \hline
2 & 22 ab 41 b0 18 e5 af 9d 30 e 32 67 02 cfd 43 \\ \hline
3 & ae 3b ce 64 c3 67 30 d5 81 66 74 ga 6c 36 42 6b \\ \hline
4 & 08 88 e4 23 d4 89 3c 67 d4 89 3c 67 d4 89 3c 67 \\ \hline
5 & 86 1e 3c 78 f0 91 53 e3 5d 7a 46 8c 69 d2 d5 db \\ \hline
6 & c7f 8e fd de cd eb a7 3f 7e fc 89 63 c6 fd 8b \\ \hline
7 & 7d e2 36 Nc 95 9d 32 67 95 9d 32 66 95 9d 32 ce \\ \hline
8 & ed ab 27 4e 9c 49 92 55 aa 25 4a 94 59 b2 15 2a \\ \hline
9 & 54 a8 21 42 84 79 f2 95 5b be 16 3a 74 e8 a1 33 \\ \hline
10 & 66 cc e9 a3 37 6e dc c9 e3 b7 1f 3e 7c f8 80 74 \\ \hline
11 & e6 bd 0b 16 2c 58 b0 11 22 34 61 c2 f5 9b 47 8e \\ \hline
12 & 6d da c5 fb 87 7f fe 8d db 6d d6 dcb e7 bf 0f 1e \\ \hline
13 & 3c 78 f0 91 53 a6 3d 7a f4 99 43 86 7d fa 85 7b \\ \hline
14 & f6 9d d4 b9 5d ba 05 a0 14 28 50 a0 31 62 c4 f9 \\ \hline
15 & 83 77 ee ad 2b 56 ac 29 52 a4 39 72 e4 b9 03 06 \\ \hline
16 & 18 30 60 f1 93 57 ee 2d 5a b4 19 32 64 c8 e1 b3 \\ \hline
17 & 17 2e 5c b8 01 02 71 e2 b5 1b 34 68 d0 cf 5e 09 \\ \hline
18 & 12 24 90 51 a2 35 6a d4 d9 e3 f7 94 f4 9e 4d 9a \\ \hline
19 & 45 a8 65 ca e5 b0 07 0e 1c 38 70 eb 11 32 64 c6 \\ \hline
20 & 98 41 82 75 ea e3 b7 6 ec a9 23 46 Nc 69 d2 c7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Diffusion simulation results.  
countermeasures were proposed, and they were all based on Hamming code and parity bits. These carry the risk of being exploited by power analysis (PA) attacks. In [25], the authors utilized randomly added virtual round transformation and redundant computation based on the infection countermeasure to resist fault attacks, but there is a risk of suffering from single-byte fault attacks because the same unknown mask is used in the infection process. The second-order infection mechanism proposed in [29], although it can defend against double-fault attacks, is subject to the risk of triple-fault attacks (three cryptographic circuits injecting the same fault at the same time) because of the use of three cryptographic circuits.
# The Effect of Ageing on Chemical and Mechanical Properties of Asphalt Mortar
###### Abstract  
Asphalt mortar is a mixture of bitumen, filler, and sand. Mortar plays an important role in asphalt mixtures as it serves as the adhesive between the coarser aggregates. Due to the effect of bitumen ageing, the chemical and mechanical properties of asphalt mortar evolve with time. The mortar becomes more brittle and prone to cracking, thus leading to inferior pavement performance. In this study, Fourier transform infrared (FTIR) spectrometry was used to quantify changes in the chemical functional groups related to ageing and to calculate the carbonyl and sulfoxide indices. In addition, frequency sweep tests and uniaxial tension tests were performed by means of dynamic shear rheometer (DSR) tests to determine evolution of the stiffness and strength due to ageing. Two different oven ageing protocols were used to evaluate the effect of fine mineral particles on bitumen ageing. The protocols differed with respect to the order of ageing and mixing of the constituents. The results showed that both the chemical and mechanical properties of mortars significantly changed with ageing. Specifically, the carbonyl index, stiffness, and strength of the mortar increased. Under the same ageing conditions, a higher ageing level was observed for mortars produced by first mixing and then ageing compared to the mortars produced by mixing aged bitumen with filler and sand. This could be due to the presence of sand and filler particles, which resulted in an increased length of diffusion paths and consequently a slower ageing process.  
Keywords:asphalt mortar; ageing; FTIR; stiffness; strength +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Experiments on Mixed Datasets
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **OCIL** & **K-Prototype** & **The Proposed Algorithm** \\ \hline Statlog (Heart) & 0.1891 \(\pm\) 0.0029 & 0.2192 \(\pm\) 0.0662 & 0.1606 \(\pm\) 0.0288 \\ Hepatitis & 0.2065 \(\pm\) 0.0000 & 0.2065 \(\pm\) 0.0000 & 0.1810 \(\pm\) 0.0074 \\ Cylinder Bands & 0.2743 \(\pm\) 0.0861 & 0.2852 \(\pm\) 0.0749 & 0.2676 \(\pm\) 0.1168 \\ Australian & 0.2579 \(\pm\) 0.1286 & 0.2218 \(\pm\) 0.0678 & 0.2136 \(\pm\) 0.0866 \\ Dermatology & 0.1953 \(\pm\) 0.0510 & 0.3063 \(\pm\) 0.0792 & 0.1855 \(\pm\) 0.0555 \\ Zoo & 0.1449 \(\pm\) 0.0376 & 0.1578 \(\pm\) 0.0520 & 0.1318 \(\pm\) 0.0341 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of cluster accuracy for the proposed algorithm with OCIL and K-Prototype on mixed datasets. Zoo dataset have only one numerical attribute, calculating the correlation for numerical attribute is meaningless. Comparing Tables 2 and 3, it can be seen that the Dermatology dataset, with a good clustering result, has a strong correlation between dependencies and weights for categorical attributes. In addition, the Australian dataset has a strong correlation not only for categorical attributes but also for numerical attributes; this dataset also has a good clustering result. However, in the Hepatitis dataset it has a strong correlation for categorical attributes, but has a weak correlation for numerical attributes. There are only six numerical attributes and 13 categorical attributes in the Hepatitis dataset. The influence of category attributes is much greater than that of numerical attributes. So, the clustering result of Hepatitis dataset is not good, which does not violate the theory in the article. Therefore, a good clustering result may be obtained due to the reasonable weight assigned to each attribute by the proposed similarity measurement.
# A Sparse Autoencoder-Based Unsupervised Scheme for Pump Fault Detection and Isolation
## 2 Fault Detection and Isolation Scheme
### Online Fault Detection Phase
#### 2.2.2 Fault Isolation  
After a fault being detected, it is necessary to identify the most relevant variables related to the fault. This can be achieved by a reconstruction-based contribution map and the variables with larger contributions are responsible for the fault.  
The \(Q\) statistic (also called squared prediction error, SPE) is widely used in process control for condition monitoring data [34; 35; 36]. The traditional \(Q\) statistic contribution plot can be calculated by Equation (11) [37]:  
\[Q_{i}=(x_{i}-\hat{x}_{i})^{2} \tag{11}\]where \(\hat{x}_{i}\) is the reconstructed value of \(x_{i}\) by the SAE model.  
The contribution plot is a one-dimensional plot, which only examines the contributions at one time point (one observation), and multiple contribution plots are needed to illustrate multiple observations in time series data. In contrast, a two-dimensional contribution map [38] stacks multiple observations into one image to clearly illustrate the contribution of the variables over the entire faulty data times series, which enables the fast identification of faulty variables within large heterogeneous data sets. Therefore, a two-dimensional \(Q\) statistic contribution map is applied.
#### 1.1.2 Fes
Combining VR and FES, the study aims to create a synergistic effect where VR enhances the patient's psychological and cognitive engagement while FES ensures effective muscle activation and motor recovery. The decision to focus on VR and FES, rather than combining them with other treatment methods, is driven by the need to systematically explore and evaluate the efficacy of this specific combination, as it holds significant potential for creating more personalized and effective rehabilitation programs. This focused approach allows for a clearer understanding of how these technologies can be integrated and optimized, potentially leading to more impactful and targeted interventions in stroke rehabilitation. The current understanding of the effectiveness, potential challenges, and facilitators of using VR and FES for upper limb rehabilitation in stroke patients is limited. Despite the growing interest in physical approaches, there is still a notable gap in the literature regarding identifying effective rehabilitation methods and interventions for the most effective treatment following a stroke. We believe that combining VR and FES could offer a more effective rehabilitation approach for helping post-stroke patients recover upper extremity motor function compared to conventional therapies or using VR or FES alone. Specifically, we expect that integrating these technologies will enhance motor function, increase patient engagement, and improve outcomes in upper limb rehabilitation. This systematic literature review aims to address this research gap by examining the characteristics and clinical outcomes of studies focusing on rehabilitation through VR and FES and by identifying the current state of research and rehabilitation systems that use customized exercises for each patient, combining virtual reality technology with functional electrical stimulation devices. The potential impact of this research on stroke rehabilitation is significant, as it could lead to the development of more effective and personalized treatment methods.
## 4 Discussion
### Influences of Projectile-Target Ratio  
Figure 11 presents the influences of the projectile-target ratio on the dimensionless crater depth, diameter, volume, and the crater \(H\)/\(D\) ratio. The dimensionless crater depth, diameter, and volume increase with the increase of \(\rho_{\rm p}\)/\(\rho_{\rm t}\), as shown in Figure 11a-c. These three dimensionless crater parameters positively correlate with \(\rho_{\rm p}\)/\(\rho_{\rm t}\).  
The crater \(H\)/\(D\) ratio increases slightly (the slope is only 0.0041) with the increase of \(\rho_{\rm p}\)/\(\rho_{\rm t}\), as shown in Figure 11d. This implies that the positive correlation between the \(H\)/\(D\) ratio and the \(\rho_{\rm p}\)/\(\rho_{\rm t}\) ratio is relatively weak. However, the experimental results of [PERSON] [33] and [PERSON] [34] show that the positive correlation between the \(H\)/\(D\) ratio and the \(\rho_{\rm p}\)/\(\rho_{\rm t}\) ratio is stronger. The difference of the correlations may be caused by the reason that the range of \(\rho_{\rm p}\)/\(\rho_{\rm t}\) (1.71-6.86) in this study is wider than those in the experiments by [PERSON] [33] (\(\rho_{\rm p}\)/\(\rho_{\rm t}\) = 1.05-2.9) and [PERSON] [34] (\(\rho_{\rm p}\)/\(\rho_{\rm t}\) = 1.37-4.23).  
Figure 10: The influences of the impact velocity on crater depth, diameter, volume, and crater \(H\)/\(D\) ratio: (**a**) dimensionless crater depth versus impact velocity; (**b**) dimensionless crater diameter versus impact velocity; (**c**) dimensionless crater volume versus impact kinetic energy; (**d**) crater \(H\)/\(D\) ratio versus impact velocity.
# Application-Specific Integrated Circuit of an Inter-IC Sound Digital Filter for Audio Systems
## 2 Materials and Methods
### Digital Filter Architecture  
There are multiple recommended FIR architectures in the open literature [8], and some of them are intended to minimize the hardware complexity of FIR filters for applications with constrained resources [9]. This section describes the hardware architecture and design considerations of the digital filter module, as shown in Figure 1.  
The word length of the input, the output, the filter coefficients, and the filter order were fixed to \(K+1=16\), because the flow control is deeply attached to the audio codec (the device that encodes analog audio as digital signals and decodes digital back to analog) word length.  
Using the word length of \(K+1=16\) bits (including the sign bit) and assuming the considerations described in Appendix 1 of [9], the signal-to-quantization-noise-power ratio (\(SQNR\)) in decibels is given by:  
\[SQNR=10\log_{10}\frac{\left(2^{K-2}\right)^{2}}{\left(1/12\right)} \tag{3}\]  
which corresponds to the typical value for high-definition audio.  
Table 1 describes the type and functionality of all the filtering module signals. The ASIC can be seen as an I2S filtering unit. The module receives a 16-bit serial stereo input and should output a signal with the same format; therefore, there is a deserializer (M2) saving the serial input in the inner registers, and the input values in said registers will be filtered (M3) based on the selected filters from the filter bank (M1), which can be written through an independent I2C interface (M5); finally, the output values will go through a deserializer (M4), returning it to its original format. Both I2S_ADC and I2S_DAC, input and output, respectively, will share the same control signals, I2S_CLK and I2S_LR. The output is synced with its corresponding LR input; the timing and input-output delay based on each filter mode can be seen in Figures 5 and 6.  
Figure 1: Filtering module architecture block diagram.
## 3 Results
### Assessment of NDZ and Comparison of Existing Islanding Detection Strategies
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Refs** & **Concept** & **Method** & **NDZ** & \begin{tabular}{c} **Detection** \\ **Time** \\ \end{tabular} & \
\begin{tabular}{c} **Algorithm** \\ **Complexity (E/M/T)** \\ \end{tabular} &
\begin{tabular}{c} **Power Quality** \\ **Issues** \\ \end{tabular} \\ \hline Proposed & Voltage phase angle variation & Passive & Near zero & \(\leq\)0.093 s & Easy & No \\
[6] & Voltage and frequency variation & Passive & zero & \(<\)0.3 s & Medium & No \\
[36] & Rate of change of exciter voltage & Passive & zero & \(<\)2 s & Medium & No \\
[37] & Rate of change of reactive power & Passive & very small & 0.10 s & Medium & No \\
[38] & ROCOF and Voltage-frequency & Passive & very small & \(\leq\)0.17 s & Tough & No \\
[30] & Rate of change of frequency over & Passive & small & 0.2 s & Medium & No \\ reactive power & & Passive & small & \(\leq\)0.8 s & Medium & No \\
[31] & Reactive power variation & Passive & small & \(\leq\)0.8 s & Medium & No \\
[32] & Negative sequence voltage \& & Passive & zero & 0.02 s & Tough & No \\ current & & Passive & small & 0.02 s & Tough & No \\
[33] & Transient index voltage & Passive & small & 0.02 s & Tough & No \\
[34] & Positive sequence voltage & Passive & small & 0.06 s & Tough & No \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of existing islanding detection strategies. Figure 12: NDZ plot of the proposed strategy.
## 3 Preliminaries
### The Energy Model  
To calculate the energy consumption for transmitting a \(k\) bit data packet at a distance \(d\) can be calculated through the energy model given in [2] as:  
\[E_{Tx}(k,d)=\left\{\begin{array}{ll}E_{elec}k+kE_{fs}d^{2}&ifd<d_{0}.\\ E_{elec}k+kE_{mp}d^{4}&ifd\geq d_{0}.\end{array}\right. \tag{1}\]  
Figure 1: An overview of two-layer hierarchy of the proposed model.  
where \(E_{elec}\) is the energy consumed per bit to run the transmitter or the receiver circuit, \(E_{amp}d^{4}\) and \(E_{fs}d^{2}\) symbolize the coefficient of transmit amplifier for free space model, while \(d\) signify the distance between sender and receiver. Furthermore, the coefficient of transmit amplifier for multi-path model is \(E_{amp}d^{4}\). When a FN receives a \(k\) bit data packet, the energy consumed by the FN is computed using the following expression:  
\[E_{Rx}(k)=kE_{elec}. \tag{2}\]  
In underwater, the attenuation of the signal depends on both the frequency \(f\) and the distance \(d\). Therefore, SNR for a low bandwidth signal with the frequency and unit transmission power can be represented through \(\rho(d,f)\). An acoustic channel with a distance \(d\), frequency \(f(KHz)\) and the spreading factor \(F\) can be computed using [3; 40; 41; 42; 43; 9] through following expression as:  
\[A(d,f)=A_{o}[a(f)^{d}]d^{F}. \tag{3}\]  
where \(A_{o}\) and \(A\) represent the normalization constant and signal attenuation function, respectively. \(F\) is a spreading factor, its value depends upon the environment as:  
1. F = 1 for shallow water environment.
2. F = 1.5 for practical environment.
3. F = 3 for the deep water environment.  
The co-efficient of absorption \(a(f)\) can be computed by employing the Thorp formula as:  
\[10 loga(f)=(0.11f^{2})\times\frac{1}{1+f^{2}}+(44f^{2})\times\frac{1}{4200+f}+ 2.75f^{2}\times 10^{-4}+0.003\quad if(f>0.4). \tag{4}\]  
Here the \(a(f)\) is considered in \(dB/Km\) for the calculation purpose.  
\[10 loga(f)=0.002+(0.11f)\times\frac{1}{1+f}+0.0011f\quad if(f<0.4). \tag{5}\]  
Now the energy consumption between a transmitter and receiver FN for transmitting a \(k\) bit data packet at a distance \(d\) with the transmitting frequency \(f\) is computing using [3] as:  
\[E_{Tx}(k,d)=\left\{\begin{array}{ll}E_{elec}k+ka(f)^{d}d^{2}&ifd<d_{0}.\\ E_{elec}k+ka(f)^{d}d^{4}&ifd\geq d_{0}.\end{array}\right. \tag{6}\]
## 1 Introduction
We also address the risks--model inversion, data poisoning, and Sybil attacks--and the defenses needed to keep FL systems secure. This survey offers a comprehensive examination of federated learning (FL), a decentralized machine learning approach that enhances data privacy and reduces communication overheads. It covers both the theoretical underpinnings and practical applications of FL in sectors like healthcare and IoT, addressing challenges such as data security, regulatory compliance, and model optimization. This paper stands out by exploring FL algorithms, frameworks, and security risks while proposing mitigation strategies. Its holistic approach caters to both beginners and experts, providing valuable insights to leverage FL's potential in real-world applications and guiding future research in this evolving field. Our paper's major goal is to present a comprehensive overview of federated learning (FL), including the current achievements, problems, and future applications in areas such as IoT, healthcare, and data privacy. While survey articles typically summarize the state of the art, our purpose goes beyond simply gathering information. Our paper's specific goal is to examine and highlight existing difficulties in FL, as well as presenting a road map for future study. The research questions (RQ) that we examine are as follows. RQ1: How can federated learning (FL) strike a balance between data privacy, security, and communication costs while retaining model performance? This point is critical since FL aims to keep data decentralized while maintaining high accuracy and security standards. Understanding how to ensure this equilibrium can help to guide future algorithmic advancements. RQ2: What security problems, such as adversarial attacks, must be addressed to ensure the viability of FL deployment in real-world applications? With the rising number of security threats in decentralized systems, particularly in vital sectors such as healthcare, this paper aims to identify the limitations of the present security solutions and consider how future work can overcome these. These questionsnot only correlate with this paper's primary thesis, but they also demonstrate the survey's structure by focusing on crucial areas of FL that are yet to be examined. This paper is structured as follows. In Section 2, we introduce federated learning by covering its background and foundational concepts. Section 3 explores the opportunities, challenges, and applications of FL across various domains. Section 4 categorizes the types of FL, such as horizontal and vertical FL. Section 5 examines the key attributes and characteristics of FL, like privacy and scalability. Section 6 discusses the algorithms and frameworks that underpin FL. Section 7 addresses potential attack strategies and mitigation techniques. Section 8 provides a critical discussion of the findings, and Section 9 concludes with future implications and directions for research.
## 3 Results and Discussion
### Feasibility of Applying Circular Economy Strategies to Different Building Types
#### 3.2.1 Storage Buildings  
Storage buildings were ranked first in the feasibility of applying CE principles, with a total score of 28 indicating a strong consensus on their suitability for incorporating CE strategies. The primary rationale behind this high-ranking relates to the simplicity and flexibility of storage facility designs. Participant A1 noted that \"_Storage buildings are highly feasible for circular strategies because their layouts are very simple. They are easier to convert or adapt to other uses due to their open-plan design. Even reused materials can be utilized within them. I believe storage buildings are the simplest type of buildings, and their simplicity makes them more feasible for adopting circular strategies. That's why I consider storage buildings to be the most feasible option_\". This was supported by C3, who mentioned that \"_In my view, less complicated designs are usually attached to storage buildings, which means that standard materials can be used, as well as reused/recyclable ones. Simple designs can easily facilitate the adoption of CE principles, such as reused materials and modular construction. Adaptability can be achieved easily, as the structure can be used for different purposes over time, which meets the requirements of the CE agenda_\". Additionally, D1 commented that \"_The straightforward design of storage facilities reduces complexities when disassembling and repurposing materials. Also, the lack of complex internal finishes in storage buildings simplifies the incorporation of recyclable and reusable materials_\".
# A Hybrid DNN Multilayered LSTM Model for Energy Consumption Prediction
## 3 Proposed Model
### The DNN Multilayered LSTM Model
#### 3.2.2 Multilayered LSTM Model
The value includes the weight between the memory unit and the input data and the weight between the memory unit and the hidden layer. * Equation (6), \(h_{t}=o_{t}\odot tanh(c_{t})\), is the final equation that determines the final output at any given time t for the LSTM unit. The model's final output is related to Equations (3) and (5), which are the output value and the current memory value, respectively. Combining the DNN module, which is composed of one layer, with the multilayered LSTM model, which is made up of three LSTM layers, improves prediction accuracy. The dataset goes through cleaning first, in which the missing values are replaced with null and mean values, and then the outliers are removed. Noise is also filtered from the data, and the output from this module is passed on to the DNN layer. The weighted inputs produced as the outputs of the DNN layer are then transferred to the LSTM multilayers. The first layer computes the data and passes them on to the second layer, which refines the output, thus obtaining more accurate predictions. The output from the multilayered LSTM model is then passed on to the fully connected layer, in which the energy consumption values are generated.