esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Multilayer Measurement Methodology
### Objectives and Requirements  
While IEEE 802.11 ad provides multi-Gbps communication performance, the communication quality is vulnerable due to its high frequency of 60 GHz compared to the existing 2.4/5 GHz Wi-Fi networks. To spread unlicensed IEEE802.11 ad networks, measurement indicators that are relatively easy to understand, even for non-specialists, are necessary. Thus, this paper proposes a measurement methodology that considers indicators on multilayers. In addition, the following two requirements should be satisfied to avoid the need for expensive equipment and advanced expertise:  
1. Utilizing open-source measurement tools;
2. Employing easy-to-understand indicators.  
As described in Section 2, the existing studies are based on the various viewpoints of their research. However, a low-cost and easy-to-understand measurement concept is necessary to build and manage a stable IEEE 802.11 ad network because non-specialists, such as end-users, may be involved in network management. Therefore, as for requirement (1), open-source tools are strong candidates for low costs. Additionally, as for requirement (2), easy-to-understand indicators at multiple layers, such as throughput, delay, and signal strength, are desired to understand communication performance in an IEEE 802.11 ad network.
# Resilience and Resilient Systems of Artificial Intelligence: Taxonomy, Models and Methods
## 7 Conclusions  
### Summary  
This survey provides a review of multiple publications related to AI resilience. Although concepts of AI and resilience are well known by now, they are still actively evolving in terms of methodology, models, and technologies. Until the last decade, these concepts developed independently and had very little overlap--an unnatural state of affairs considering how close the concepts are in their essence. AISs have to be resilient by definition--similar to the \"traditional\" (non-AI) systems they need to take into account a variety of constantly changing factors. Among them are changing parameters of the information and physical environment, evolving requirements and the occurrence of unspecified failures caused by hardware and software faults and cyber-attacks.  
However, compared to \"traditional\" systems, where resilience is thought of primarily in terms of proactive failure and intrusion tolerance, AISs can adapt their properties and algorithms to unusual conditions more \"naturally\". This dictates that they are developed, trained, and applied accordingly. This article attempts to systematize a variety of specific mechanisms for ensuring the resilience of AI and AISs.  
The ontological and taxonomic diagrams of AIS vulnerabilities and resilience were constructed to systematize knowledge about this topic. The approaches and methods of ensuring specific stages of resilience to handling faults, drift and adversarial attacks have been analyzed. The basic ideas and principles of measuring, certifying and optimizing AIS resilience are considered. It had been shown that particular resilience properties can be built into cutting edge AIS, by implementing some of the techniques discussed.  
The issue of AIS resilience to various kinds of perturbations is extremely relevant, especially in security and military installations, disaster management complexes, police and judicial practices, finance, and health systems. Analysis of AIS applications has shown that inadequate AIS resilience can have direct or indirect negative impacts on health, mortality, human rights, and asset values.  
From the perspective of the management dimension, the following two main recommendations can be formulated related to AIS resilience:  
* technical specifications of forthcoming AIS for safety, security, human rights and trust critical domains, should be include resilience capabilities to mitigate all relevant disturbing influences;
* customers, owners, developers, and maintainers of AIS should be taken into account that the system may degrade when faced with a disturbance, and the system needs certain resources and time to recover, adapt and evolve.  
At the beginning of the study, we identified three research questions. We will now summarize the answers to these questions based on the results of the analysis above.
# Extending Appearance Based Gait Recognition with Depth Data
## 3 Image Processing and Feature Extraction
### Extraction of Silhouettes  
To extract silhouettes of people walking from RGB images, different approaches are used in gait recognition methods. Silhouettes are obtained using background subtraction and foreground detection methods, combined with some form of thresholding. Background subtraction represents a technique for segmenting out objects of interest in a scene [12]. Methods such as Gaussian mixture models (GMM) [13; 14], background modeling, and subtraction by codebook construction, kernel density estimation (KDE) [15], and others are typically used. Foreground and background separation is different in the case of depth images. Detailed information on background extraction from depth images can be found in [16].  
Rarely, the person is walking at the same exact distance from the camera. If the subject's distance from the camera varies, as is usually the case, then the silhouette size varies and should be normalizedfirst. The extracted silhouettes are therefore all resized to the same size. The region of interest (ROI) is typically defined calculating a bounding box around a silhouette, and all the silhouettes are then scaled into uniform size [5; 17]. The horizontal position is placed so that the torso in each frame is approximately at the same location.
## 2 Experimental Protocols
### Ice Melting Protocol Using the AS6170 Test  
We followed the AS6170 test method standard [20] for the ice melting test (Figure 1).  
First, a Petri dish was filled with 60 mL of ASTM D1193 type IV water and then cooled at \(-10\)\({}^{\circ}\)C. After at least 8 h, the Petri dish and resulting ice were weighed using a calibrated scale (820 g max capacity, \(\pm 0.001\) g accuracy). Then, 5 g of RDP was evenly deposited onto the ice surface. After 1, 2, 3, 4, 5, 10, and 30 min, the melted ice (brine) was removed using compressed air, and the Petri dish was reweighed after each removal to determine the mass  
Figure 1: The ice melting test for a runway de-icing product (RDP) following the AS6170 standard. Panel (**A**) depicts the initial ice sample in a Petri dish and the placing of the RDP (sodium formate) onto the ice sample. In panel (**B**), the RDP and melted ice are removed using compressed air [20].  
of the melted ice. Each time interval was treated as a separate measurement and repeated three times.  
To gain a deeper understanding of product performance, we used a thermal camera (Optris Pi450i, Optris, Berlin, Germany) with its corresponding software (Optris PIX Connect Rel.1.2.1030.0) to obtain an overall view of the Petri dish and ice. The camera had an uncooled 382 \(\times\) 288-pixel microbolometer frame plane array detector and was equipped with an 18\({}^{\circ}\)\(\times\) 14\({}^{\circ}\) field of view (FOV) Ge lens. The camera was positioned approximately 40 cm away from the target and was turned on at least 30 min before measurements to ensure thermal equilibrium. The camera had a Noise Equivalent Temperature Difference (NETD) of 60 mK without further specifications provided by the manufacturer.  
Additionally, an aluminum foil mirror was placed on the ice surface in the camera's FOV. The foil served to correct the radiative environment and allow for thermometric analysis using thermal images. Thermal images were recorded at 80 Hz in video mode, capturing temperature measurements during the ice-melting process. The entire test was repeated three times for reliability and consistency.
# Distribution System State Estimation Using Model-Optimized Neural Networks
## 3 Neural Network-Based State Estimation
### Implementation to State Estimation  
This paper takes off from a previous work shown in [36], where the standard MSE loss function has been replaced with the WLS loss function, which is used traditionally by power system state estimation to help the neural network model learn which of the measurements to trust more. By coming off of Equation (2), the weight, \(W\), is used with predetermined weights that correspond to the measurement devices known to the operator. The resulting equation is shown in Equation (14)  
\[W=\frac{1}{\sigma} \tag{13}\]  
\[\min J=\sum_{i=1}^{n}\frac{\left(z_{i}-h_{i}(x)\right)^{2}}{\sigma_{i}^{2}} \rightarrow\left[z-h(x)\right]^{T}\cdot\frac{1}{\sigma}\cdot\left[z-h(x)\right] \tag{14}\]  
Note that, in this paper's notation, the system states \(x_{i}\) are the neural network's target prediction, \(\hat{g}_{i}\), and the system measurements \(z_{i}\) are the neural network's inputs, \(x_{i}\). The paradigm this paper takes is inferring the system state given the known system states some time steps before as shown in Figure 3, i.e., approximating \(h(x)\) and learning to consider or reject \(\epsilon\). Furthermore, reference [36] used a special kind of neural network structure to improve the accuracy of estimation. This is the long short-term memory (LSTM) neural network, which is an improved variation of the recurrent neural networks (RNNs). It works better for time series data by taking into account the measurements observed some few time steps prior and introduces new weights that correspond to remembering, forgetting, and passing certain information from the input data. These weights are what sets it apart from standard fully connected feed-forward or multilayer perceptron (MLP) networks.  
For this paper, the LSTM networks are sequentially stacked with three hidden LSTM layers and a fully connected output layer as shown in Figure 4. The network structure may vary depending on the scale of application. The activation function for the layers is the default linear (or pass-through) activation. This is possible because the measurements are already acquired using the per-unit system; thus, the numbers are already within manageable ranges and typical machine learning problems such as the vanishing and exploding gradient are prevented. Furthermore, the learning rate, \(\alpha\), is also set to be reasonably small, at 0.001. The loss functions, both MSE and WLS, are minimized using the adaptive moments optimization algorithm.
## Materials and Methods
### Data
#### 2.1.2 Earth Gravitational Model 2008  
In this study, the Earth Gravitational Model (EGM) 2008 is used to calculate the geoid undulation for correcting the reflection reference surface. The spatial resolution of the EGM2008 is about 5\({}^{\prime}\) (about 9 km), and the geoid undulation propagated standard deviation is 10.925 cm [37]. This study uses the highest-spatial-resolution model which is interpolated to a 1\({}^{\prime}\)\(\times\) 1\({}^{\prime}\) grid, the interpolation error does not exceed \(\pm\)1 mm [38].
## 2 Materials and Methods
### Anticoagulation  
#### 2.4.1 Hemolysis Rate  
The hemolysis experiments were done in accordance with ISO 10993-4:2009 and ASTMF756-00. The fresh whole blood was obtained legally from a healthy volunteer and mixed with 3.8 wt % sodium citrate solution with the ratio of 9:1. The blood was centrifuged at 1500 rev/min to obtain the red blood cells, and then 2 mL red blood cells was diluted to get 2% erythrocyte suspension using physiological saline solution. The sample was incubated 1 h at 37 \({}^{\circ}\)C with 10 mL erythrocyte suspension. After taking out the sample, the medium was centrifuged at 3000 rev for 10 min. The absorbance of the supernatant was measured at 540 nm by a microplate reader (Eons, Bio-Tek, Winooski, VT, USA). For controls, the mixture of 2 mL erythrocyte and 98 mL physiological saline was used as the negative control, and the mixture of 2 mL erythrocyte and 98 mL ultrapure water was used as the positive control. The hemolysis ratio was calculated according to the following formula:  
\[R=(A-CI)/(C2-CI)\times 100\%,\]  
where \(R\) is the hemolysis ratio (%), \(A\) is the absorbance of the sample, \(C1\) is the absorbance of the negative control (%), and \(C2\) is the absorbance of the positive control.
## 3 Experimental Results and Discussion
### Load-Carrying Capacity  
Table 4 shows the attained ultimate load, percentage of load difference against specimen CS, and mid-span deflection in all the specimens. The attained ultimate loads for all the specimens are compared in Figure 5. The ultimate load capacity of specimen CS is 240 kN. It is highlighted in a red dashed line in Figure 5.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Specimen** & **Ultimate Load (kN)** & **Comparison with Specim CS (\%)** & **Mid-Span Deflection at Ultimate Load (mm)** \\ \hline CS & 240 & - & 7.27 \\ \hline FP-1.5 & 206 & \(-\)14.2\% & 18.43 \\ FP-2.0 & 225 & \(-\)6.3\% & 18.39 \\ FP-2.5 & 223 & \(-\)7.1\% & 18.77 \\ \hline HP3-1.5 & 245 & +2.1\% & 6.79 \\ HP3-2.0 & 247 & +2.9\% & 8.53 \\ HP3-2.5 & 242 & +0.8\% & 6.27 \\ \hline HP4-1.5 & 235 & \(-\)2.1\% & 7.32 \\ HP4-2.0 & 242 & +0.8\% & 7.53 \\ HP4-2.5 & 225 & \(-\)6.3\% & 7.38 \\ \hline HP5-1.5 & 230 & \(-\)4.2\% & 6.38 \\ HP5-2.0 & 235 & \(-\)2.1\% & 8.57 \\ HP5-2.5 & 225 & \(-\)6.3\% & 7.98 \\ \hline \end{tabular}
\end{table}
Table 4: Experimental results of ultimate load and deflection.  
Figure 4: Four-point loads testing setup.
# Non-Contact Measurement of Small-Module Gears Using Optical Coherence Tomography
## 2 Methods and Experiments
### Factors Affecting Optical Coherence Tomography Gear Measurement
#### 2.2.3. Gear Roughness  
When the surface roughness of gear is different, the reflection of the incident light from the gear surface will be different. The smaller the surface roughness of the gear, the better the imaging quality of the tooth surface. The reason is that light scattering will occur on the rough surface, and thus, it will affect the accuracy of the OCT measurement. Figure 8 shows two OCT images for two gears with different roughnesses. The roughness for the gear in Figure 8a is smaller than that in Figure 8b. However, from the figure, we cannot differentiate the OCT image quality, due to the roughness effect. Figure 8c,d shown the corresponding FFT (Fast Fourier Transform) results of the spectrogram at the dash line in Figure 8a,b. The peak amplitude for Figure 8c is 62.42, which is larger than the value of 56.42 for Figure 8d. However, as can be seen from the figures, these FFT results of the spectrogram contained some noise. There could be some solutions to increasing the signal-to-noise ratio (SNR) of the OCT system, for example, increasing the intensity of incident light, and adding a short focal length convex lens that is closer to the gear surface, to collect the scattered light, etc.  
Figure 8: OCT images of two gears with different roughnesses: (**a**) low level of roughness; (**b**) high level of roughness; (**c**) and (**d**) are the corresponding FFT results of the spectrogram at the dashed line in (**a**) and (**b**).  
Figure 7: (**a**) The tooth profile of the gear with negative modification coefficient; (**b**) the photo of the gear with negative modification coefficient.
# Towards Understanding the Function of Aegerolysins
## 2 Aegerolysins
### Bacterial Aegerolysins from Proteobacteria  
#### 2.6.1 Aegerolysins from _Alcaligenes faecalis_  
Strains of the rod-shaped _Alcaligenes_ species are found in soil, water, and in the environment associated with humans. The Gram-negative species _Alcaligenes faecalis_ [PERSON] (1919) was first discovered in feces and named. Opportunistic infections in humans, usually urinary tract infections, are generally considered non-pathogenic. Nematicidal activity against _C. elegans_ and _Meloidogyne incognita_ has been reported [41].  
The 16 kDa unit of the two-component insecticidal protein (**AfIP-1A**) is an aegerolysin which was identified in _A. faecalis_[143]. The structure of this aegerolysin was solved (PDB ID: 5V3S) [143]. **AfIP-1A/AfIP-1B** represents as a two-component insecticidal protein complex that exhibits high activity against the western corn rootworm. Transgenic maize plants expressing **AfIP-1A/AfIP-1B** show strong protection against rootworm damage [143]. Similarly to **Cry34 Ab1/Cry35 Ab1** binary toxins from _B. thuringiensis_, **AfIP-1A** binds directly to insects' intestinal brush border membranes, whereas **AfIP-1B** does not. Binding of **AfIP-1B** occurs only in the presence of **AfIP-1A**, and is accompanied by the presence of stable, high molecular weight oligomers of **AfIP-1B** observed on denaturing protein gels. **AfIP-1A/AfIP-1B** complexes form pores in artificial lipid membranes [144], and likely share the same binding sites as **Cry34 Ab1/Cry35 Ab1** in brush border membranes of the western corn rootworm midgut epithelium [144]. The coordination of **AfIP-1B** binding by **AfIP-1A**, the similar structures of PDB ID: 5V3S (**AfIP-1A**) and PDB ID: 4 JOX (**Cry34 Ab1**), and their shared binding sites in western corn rootworm intestinal tissues and cross-resistance suggest a similar role of **AfIP1A** and **Cry34 Ab1** in receptor recognition and docking site for their cognate partners **AfIP-1B** and **Cry35 Ab1**, respectively [144].  
In contrast to _B. thuringiensis_, where **Cry34 Ab1** and **Cry35 Ab1** are plasmid-encoded, the two genes for **AfIP-1A** and **AfIP-1B** are chromosomally encoded as neighboring genes. Unlike other bicomponent toxin gene pairs identified in fungi, these two genes are oriented in the same direction in _A. faecalis_ strains GCA_003521065 and GCA_005311025, whereas they are absent in three other strains: GCA_003813085, AN70, and YBY.
## 3 Results
### Resilient Modulus Test Results  
Figure 11 illustrates the results of the resilient modulus of the CBA mixture. The results demonstrated a consistent decline in the resilient modulus values as the CBA content increased. The resilient modulus denotes the capacity of a mixture to return to its original shape and properties after the release of a load [66]. The results indicated that the CBA-0 mixture showed the greatest resilient modulus compared to the mixtures with CBA content. The resilient modulus for the CBA-0 mixture was 8351, as shown in Figure 11. It was observed that the resilient modulus of the CBA mixture demonstrates a negative correlation with the increase in CBA content. The decrease in the resilient modulus value in the CBA-10 and CBA-20 mixtures was minimal. According to the normalised data presented in Table 8, the resilient modulus of CBA-10 and CBA-20 decreased by 0.01 (a 1% reduction) and 0.07 (a 7% reduction) compared to the control mixture, respectively.  
An increase in the CBA content consistently led to lower resilient modulus values. As presented in Table 8, The CBA-30, CBA-50, CBA-70, and CBA-100 mixtures exhibited a gradual decrease in their resilient modulus values, which was demonstrated by normalised values of 0.89 (11% reduction), 0.86 (14% reduction), 0.84 (16% reduction), and 0.83 (17% reduction), respectively, compared to the control mixture. The resilient modulus, representing the elastic stiffness component, indicated that a lower value implies that increasing the CBA content reduces the elastic stiffness under repeated loads. This phenomenon is due to the high porosity and low density of CBA particles, which decrease the elastic stiffness of the asphalt mixture. The increased porosity reduces the aggregate structure's density, thereby reducing the mixture's resilience under repeated loading. Consequently, the mixture demonstrates a decreased resilient modulus, suggesting reduced stiffness.
## 2 GRU-HMM Fault Prediction Model
### Hidden Markov Fault Prediction Model  
The HMM fault prediction model uses monitoring data and sets the initial model parameters to maximize the conditional probability of the observed sequence using the Baum-Welch algorithm [29,30]. The HMM fault prediction model can be formulated as follows: \(\lambda=(\pi,A,B)\). It contains two state sets and three probability sets. On-site research and literature analysis indicates that the most common reason for failure of the CRH5 gearbox is universal joint and gear wear. Considering this characteristic, the HMM fault prediction model is modeled as follows:  
* The hidden state \(Q\) is defined as \(Q=\{\)_normal state_, _universal joint degradation_, _driving gear degradation_, _driven gear degradation_\(\}\), representing the hidden fault states of the CRH5 gearbox; here, the hidden state \(N=4\).
* Observable state \(O\), denoted as \(O=\{o_{1}\), \(o_{2}\), \(\ldots\), \(o_{N}\}\), where \(o_{i}\)\(\epsilon\) (\(v_{1}\), \(v_{2}\), \(\ldots\), \(v_{t}\)), is a dataset for monitoring the operating status of the CRH5. Here, observable state \(v_{t}\) is a feature vector capturing the data characteristics of the train running up to the moment t under the four hidden states mentioned above.
* Initial vector \(\pi\), \[\prod=(\pi_{1},\pi_{2},\pi_{3},\ldots,\pi_{N})=\frac{C(i)}{\sum\limits_{S=1}^{ N}C(s)},\pi_{i}=P(q_{i}=s_{i}),1\leq i\leq N\] (1) indicates the probability that the degraded state of the component occurs at the initial time, where \(C(i)\) is the probability that the initial state is \(q_{i}\).
* State transition matrix \(A=\left\{a_{ij}\right\}_{N*N}\), \[a_{ij}=P(q_{i+1}=s_{j}|q_{i}=s_{i})=\frac{A_{ij}}{\sum\limits_{S=1}^{N}A_{is}},1\leq i,j\leq N\] (2) denotes the transfer probability of the gearbox component from state \(s_{i}\) to state \(s_{j}\).
* Confusion matrix \(B\), \[B=\left\{b_{j}(v_{i})\right\}_{N*M^{\prime}}b_{j}(v_{i})=P(v_{i}|q_{i}=s_{j})= \frac{B_{ij}}{\sum\limits_{S=1}^{N}B_{is}},1\leq j\leq N,1\leq i\leq M\] (3) indicates the probability of occurrence of eigenvector \(v_{i}\) in state \(s_{i}\) at train operating moment \(t\).  
The HMM fault prediction model is based on the sequence \(O\) and the initial model \(\lambda_{0}=(\pi,A,B)\). The parameters \(\pi_{i}\), \(a_{ij}\), and \(b_{j}(v_{i})\) are continuously improved and revalued to obtain the new model \(\overline{\lambda}=(\overline{\pi},\overline{A},\overline{B})\), bringing \(P=P(O|\overline{\lambda})\) to a point of convergence.