esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Visualization Studies
#### 3.3.2 Model-Agnostic Visual Explanations  
Local interpretable model-agnostic explanations (LIME) is a visualization tool proposed by [29]. It helps to provide a qualitative interpretation of the relationship between perturbed input instances and the model predictions. The input image is divided into contiguous superpixels and a dataset of perturbed input instances is constructed by turning on/off these interpretable components. The perturbed instances are weighted by their similarity to the explained instance. The algorithm approximates the CNN by a sparse, linear model that is weighted only in the neighborhood of the explained predictions. An explanation is generated in the form of superpixels with the highest positive weights that demonstrate the discriminative ROI localized by the model to classify the image to its expected class. Let \(k\in\mathbb{R}^{d}\) be the explained instance, and \(k^{\prime}\in\{0,1\}^{d}\), the binary vector that denotes the presence/absence of a superpixel. Let \(g\in G\) denote the explanation where \(G\) is a class of interpretable linear models. Let \(\gamma(g)\) denote the complexity measure associated with the explanation \(g\in G\). The value \(\gamma(g)\) denotes the number of non-zero coefficients for the linear model. Let \(m\): \(\mathbb{R}^{d}\rightarrow\mathbb{R}\) denote the explained model and \(m(k)\), the probability that \(k\) belongs to a given class. Let \(\Pi_{k}(x)\) denote the measure of proximity between the instance \(x\) to \(k\) and \(P(m,\,g,\,\Pi_{k})\) denote the loss of \(g\) toward approximating \(m\) in the neighborhood defined by \(\Pi_{k}\). The value \(P(m,\,g,\,\Pi_{k})\) is minimized and the value of \(\gamma(g)\) remains low enough for interpretability. Equation (7) gives the explanations produced by LIME.  
\[\beta(k)=\operatorname*{argmin}_{g\in G}P(m,\,g,\,\Pi_{k})+\gamma(g) \tag{7}\]  
The value \(P\left(m,g,\,\Pi_{k}\right)\) is approximated by drawing samples weighted by \(\Pi_{k}\). Equation (8) shows an exponential kernel defined on the L2-distance function (\(\mathfrak{J}\)) with width \(\mathbf{\epsilon}\). For a given input perturbed sample \(b^{\prime}\in\{0,1\}^{d^{\prime}}\) containing a fraction of non-zero elements, the label for the explanation model \(m(b)\) is obtained by recovering the sample in the original representation \(b\in\mathbb{R}^{d}\) as shown in Equation (9).  
\[\Pi_{k}^{b}=\exp\left(-J\,\frac{(y,b)^{2}}{\mathbf{\epsilon}^{2}}\right) \tag{8}\]\[P(m,\ g,\ \Pi_{k})=\sum_{b,b\in B}\Pi_{k}^{b}(m(b)-g(b^{\prime}))^{2} \tag{9}\]  
LIME provides explanations that help to make an informed decision about the trustworthiness of the predictions and gain crucial insights into the model behavior.
# Spatiotemporal Prediction and Proactive Control Method for Excavation-Induced Wall Deflection
## 3 Case Study  
### Project Overview  
The proposed ST-GAT-based model is evaluated through the Sanbao Station excavation project, which is a subway station located in Hangzhou, China. Figure 6 shows the specific location of the project. Sanbao Station is a high-risk deep excavation project, which is classified as a Grade 1 excavation based on the Chinese national code [50]. The excavation depth ranges from 23.85 to 25.41 m with complex surrounding conditions, including underground pipelines and challenging geological features. The area is densely populated with residential buildings, schools, and intricate road networks, making deformation control essential due to the proximity to sensitive infrastructure such as schools and subway tunnels. The main structure of the station is 175 m long. The standard section is 22.79 m wide, while the width at the end shaft is 27 m. The top slab is covered by approximately 3.05 m of soil, and the base of the foundation at the station center is buried at a depth of 30.85 m. At the end shaft, the foundation reaches a depth of 32.41 m. The main enclosure structure of Sanbao Station consists of 1200 mm thick diaphragm walls, which is supported by four levels of concrete struts and five levels of steel struts.
# A Lightweight Secure and Energy-Efficient Fog-Based Routing Protocol for Constraint Sensors Network
## 4 Results
This is due to the fact that SEFR increases the utilization of data links efficiently and maintains a load of transmitted packets in a balanced manner. Also, the multi-facet QoS factors including packet queuing rotate the data forwarders and decrease the number of route request-response messages, which results in efficient load balancing. Also, existing solutions frequently generate bogus route discoveries packets and make the data links more congested. The proposed SEFR protocol makes use of secret numbers using the Blum Blum Shum algorithm and avoids the chances for malicious nodes to integrate the false information in actual data packets. As a result, the proposed SEFR protocol minimizes the unnecessary data traffic on the selected links and makes the communication more flexible and trusted. Figure 5: Network throughput with varying number of nodes. Figure 6: Communication overhead with varying number of nodes.
# An Ensemble Feature Selection Approach to Identify Relevant Features from EEG Signals
## 3 Results
### Stability EFS  
To determine the reliability of the implemented EFS method, the subset of features generated to support the classification of epileptiform events was evaluated. First, the EFS method was used 10 times to generate 10 relevant features with 10 different random samples from the dataset. Then, the 10 subsets generated were compared according to the Jackar index to determine the difference between them.  
For 10 executions, the EFS method obtained the same subset of relevant features: 'F1', 'F11', 'F15', 'F22', 'F28', 'F32', 'F36', 'F40', 'F49', 'F53', 'F55', 'F57', 'F60', 'F61', 'F70', 'F71', 'F74', 'F76', 'F77', 'F78', 'F85', 'F86', 'F92', 'F95', 'F97', 'F99', and 'F106', which means that the stability measured by the Jackar index is perfect in the 100% of the cases evaluated.  
Considering the previous, it is concluded that at least for datasets with complete and correctly balanced data, such as the one used in this test, the EFS method implemented achieved 100% stability.
# FtsZ Interactions and Biomolecular Condensates as Potential Targets for New Antibiotics
## 1 Introduction
Another mechanism might be to hyperstabilize or promote inappropriate bundling of FtsZ polymers, which would perturb polymer dynamics and \"freeze\" them in nonfunctional states [48]. In contrast to targeting of FtsZ polymerization, the heterotypic interactions of FtsZ with other proteins and its indirect binding to the membrane have been relatively under-explored as drug targets. Inhibiting these protein-protein interactions may help to reduce the impact of resistance [1; 50]. Furthermore, because FtsZ's binding partners are not as well conserved, inhibition of these interactions would be more species specific [48]. The reported tendency of FtsZ complexes to undergo crowding-induced dynamic condensation may provide an additional opportunity for therapeutic intervention. Interestingly, condensate formation is often observed in cells growing under stress conditions and thus correlates with the emergence of persisters, a sub-population of dormant cells that survive antibiotic treatment [45; 51]. Research on the druggability of FtsZ requires suitable methodology to identify drug candidates and to determine their mechanisms of action. Decades of research into how FtsZ functions in cell division have provided numerous approaches to assess its self-association and its direct or indirect interaction with protein partners, nucleic acids, or the membrane that may be adapted for this purpose. Simple assays amenable to high throughput screening are useful for initial identification of lead compounds. Further elucidation of mechanisms, required for a drug to be approved [52; 53], would benefit from orthogonal approaches [54] and reconstruction in cell-like systems to evaluate the effects of crowding, compartmentalization, and the membrane surface on these mechanisms [55; 56; 57; 58]. Such assay systems are particularly useful in cases where the targeted protein complexes mainly assemble in crowded conditions only found inside the cell and/or on lipid membranes. Reflecting the growing interest in FtsZ as a target for the development of new antibiotics, comprehensive reviews have been very recently published dealing with descriptions of the families of candidates already identified and their mechanisms of action, mainly involving the alteration of FtsZ self-association to form GTP induced polymers [1; 4; 59; 60; 61]. Consequently, here we will focus on areas less covered, including the assays that have been used, or are potentially useful, to identify new anti-FtsZ compounds. We will discuss how such assays can help evaluate the molecular mechanisms of drug candidates in dilute solution and through biochemical reconstruction in minimal membrane systems. We will also highlight the importance of the self-association of FtsZ in the GDP bound state and its interactions with division regulators in crowding conditions leading to higher order membraneless structures, possibly related with persistent states and hence of interest for further understanding of antibiotic resistance mechanisms.
## 2 Materials and Methods
### The 3 PG-MT-LSTM Hybrid Modelling Approach  
#### 2.2.1 3-PG Model  
The 3-PG (Physiological Principles in Predicting Growth) model is a process-based model that simulates forest growth and yield with a monthly time scale [19]. It is widely used for predicting biomass production and carbon sequestration in forest ecosystems. The model uses environmental variables such as temperature, precipitation, and solar radiation to predict forest growth. The model illustrates key physiological and ecological processes such as photosynthesis, respiration, and nutrient cycles in forest production. The key process formulation of the model is shown in Equation (1).  
\[GPP=\alpha_{Cx}\times FPAR\times PAR\times f_{T}\times f_{N}\times f_{F}\times f _{age}\times min\{f_{VPD},\ f_{SW}\} \tag{1}\]  
Among them, \(GPP\) refers to the gross primary productivity of forest stands; \(\alpha_{Cx}\) refers to the quantum efficiency of the vegetation canopy, which is the number of CO\({}_{2}\) molecules assimilated by vegetation per absorbed light quantum; \(FPAR\) (Fraction of photosynthetically active radiation absorption) refers to the proportion of photosynthetically active radiation absorbed by vegetation; \(PAR\) (Photosynthetically active radiation) refers to the radiation that drives photosynthesis; and \(f_{T}\), \(f_{N}\), \(f_{F}\), \(f_{age}\), and \(f_{VPD}\) represent the correction factors for temperature, soil fertility, frost, stand age, vapor pressure deficit, and soil moisture, respectively.  
Compared with other models, the 3-PG model is relatively simple to use and requires fewer input parameters. In addition, the model is available in multiple open-source versions such as EXCEL [40], Python [41], and R. We followed the method of 3-PG and used the 'r3 pg' package of R program [42] to estimate biomass [43]. In this study, we used the Morris method to analyze the sensitivity of physiological and ecological parameters involved in the 3-PG model. The Morris sensitivity analysis method facilitates a global sensitivity analysis of parameters by calculating model outputs by changing only one input value between successive simulation runs [44]. Considering that forest growth is a dynamic process, we re-fitted and optimized the model parameters (i.e., phased localization model) after the thinning events and changes in the structure of the stand age groups. Finally, we used the built-in biomass allocation module in the 3-PG model to simulate the allocation of stand biomass among leaves, stems, and roots. The estimated values were used as inputs for the multi-task LSTM.
## 1 Introduction
To address these challenges, this paper introduces a novel soft measurement method, DBN-ACO-SVR, leveraging the synergistic potential of a deep belief network (DBN) and a support vector regression (SVR) network, enriched by system identification techniques so as to precisely predict the crystal diameter. We begin by addressing the delay effect of the temperature and crystal pulling speed on the tail diameter growth, utilizing a backpropagation (BP) neural network based on the mean impact value (MIV) method to determine the optimal delay time. This critical step ensures the synchronization of the process variables with the actual growth dynamics of the crystal tail. At the heart of our method is the DBN network, which leverages the temperature and crystal pulling speed as inputs to capture intricate relationships between process variables and the tail diameter, and offers a sophisticated predictive model. To refine this model, we augment the DBN network with an SVR network, enhancing its linear regression capabilities and infusing robustness against non-linear variations in the data. The ant colony optimization (ACO) algorithm is employed for fine-tuning the SVR network's parameters, ensuring accuracy and adaptability under varying data conditions. Extensive computational experiments benchmark our DBN-ACO-SVR network against traditional models, demonstrating a superior capability in addressing the delay problem and achieving precise tail diameter predictions. The contributions of this study are manifold and significant. Primarily, it pioneers a soft measurement technique for the tail diameter in the Czochralski process, a task previously marred by technical and practical challenges. This technique not only enhances the accuracy, but also negates the need for direct measurement methods that are often cumbersome and inefficient. The study further elucidates the relationship between process variables (temperature and pulling speed) and the tail diameter by quantifying their impact through the MIV method. This contributes to a deeper understanding of the crystal growth process. Moreover, the integration of DBN and SVR networks, optimized by ACO, marks an innovative approach in the realm of predictive modeling, offering a novel solution to a longstanding problem in semiconductor manufacturing. This research, therefore, holds substantial promise for advancing the precision and efficiency of monocrystalline silicon production, with potential ramifications in the semiconductor and solar photovoltaic industries.
## 4 Vibration Characteristics for Bearing Clearance Monitoring
### Influence of Rotational Speed  
To study the influence of rotational speed, simulations under 500 and 1000 rpm were carried out to make a comparison with 1500 rpm based on the spectral centroid values of the low and high frequency bands in the load direction, as shown in Figure 18.  
Figure 16: Spectral centroid: (**a**) low and (**b**) high frequency band in the load direction, (**c**) low and (**d**) high frequency band in non-load direction.  
Figure 17: (**a**) RMS and (**b**) spectral centroid values of bearing test rig data.  
As can be seen, under different rotational speeds, apart from fluctuations under a relatively light load, the spectral centroid showed an uptrend with the increase in clearance in general. In addition, the spectral centroid of the low frequency band in the load direction had a better performance in describing the clearance variations. However, it should be noted that, the rotational speed had an influence on the location of the resonance peak. Taking a load of 800 N as an example, the resonance peak shifted with the increase in rotational speed.
## 3 The Proposed PCA-LSTM Model  
As described above, the basic knowledge regarding PCA and LSTM networks was introduced to set up the proposed PCA-LSTM model in this section. Considering mechanism and human experience, the PCA procedure will bring about a more accurate variable analysis for the practical multi-sensors system. The time-series data of effective variables are subsequently learned in LSTM network to output the target prediction.
###### Abstract  
Lane occupancy is a crucial indicator of traffic flow and is significant for traffic management and planning. However, predicting lane occupancy is challenging due to numerous influencing factors, such as weather, holidays, and events, which render the data nonsmooth. To enhance lane occupancy prediction accuracy, this study introduces a fusion model that combines the CT-Transformer (CSPNet-Attention and Two-stage Transformer framework) with the Temporal Convolutional Neural Network-Long Short-Term Memory (TCN-LSTM) models alongside the Variational Mode. This includes a long-term lane occupancy prediction model utilizing the Variational Mode Decomposition (VMD) technique. Initially, the Variational Mode Decomposition decomposes the original traffic flow data into multiple smooth subsequences. Subsequently, each subsequence's autocorrelation and partial correlation coefficients ascertain the presence of seasonal characteristics. Based on these characteristics, the CT-Transformer and TCN-LSTM models process each subsequence for long-term lane occupancy rate prediction, respectively. Finally, predictions from both models are integrated using variable modes to derive the ultimate lane occupancy predictions. The core CT-Transformer model, an enhancement of the GBT (Two-stage Transformer) model, comprises two phases: autoregressive and prediction. The autoregressive phase leverages historical data for initial predictions inputted into the prediction phase. Here, the novel CSPNet-Attention mechanism replaces the conventional attention mechanism in the Encoder, reducing memory usage and computational resource loss, thereby enhancing the model's accuracy and robustness. Experiments on the PeMS public dataset demonstrate that the proposed model surpasses existing methods in predicting long-term lane occupancy, offering decent reliability and generalizability.  
late occupancy prediction; time-series prediction; Transformer; TCN-LSTM; Variational Mode Decomposition; CSPNet-Attention +
Footnote †: journal: Medical Image Processing  
2024 May 2024  
18 June 2024
## 4 Discussion
### Future Research  
Future research should prioritize the standardization of mechanical testing protocols, including consistent span lengths, deflection levels, and loading procedures, to improve the comparability of results across studies. Many existing investigations lack clinical simulationof intraoral conditions, such as thermal cycling, humidity, and repeated insertion/removal, all of which significantly influence material performance in vivo.  
Material-specific findings in this review also suggest new directions for investigation. For example, TPU-based aligner materials like Zendura demonstrated increased modulus after thermoforming [33] and reduced stress decay compared to PET-G counterparts [19], indicating promising mechanical behavior. However, these results remain limited to a few studies, and broader comparisons across brands and manufacturing protocols are needed.  
Despite their widespread clinical use, proprietary multilayer materials, such as SmartTrack(r) (Align Technology, Santa Clara, CA, USA), are yet to be independently evaluated due to trade secrecy, creating a critical evidence gap. Similarly, three-layer sheets, such as Zendura FLX, have only recently begun appearing in the literature and warrant further investigation.  
Lastly, the emergence of 3D-printed aligners, such as Graphy TC-85 [27], introduces a new class of materials with distinct properties. Research into the long-term mechanical performance, durability, and force consistency of 3D-printed systems remains sparse but essential as they gain popularity in clinical practice.  
Together, these directions highlight the need for research that not only standardizes test protocols but also embraces emerging materials and designs that reflect the evolving landscape of clear aligner therapy.
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
## 7 Evaluation
### Experimental Setup
#### 7.1.2 Byzantine Attacks  
In our experiments, the following Byzantine attacks were considered:  
* **Label Flipping Attack:** By changing the label of the training sample to another category, the label flipping attacker misleads the classifier. To simulate this attack, the labels of the samples on each malicious \(\mathbf{MC}_{k}\) are modified from \(l\) to \(N-l-1\), where \(N\) is the total number of labels and \(l\in\{0,1,\ldots,N-1\}\).
* **Backdoor Attack:** Backdoor attackers force classifiers to make bad decisions about samples with specific characteristics. To simulate the attack, we choose \(6\times 6\) pixels as the trigger, and randomly select a number of training images on each malicious \(\mathbf{MC}_{s}\), and overwrite the \(6\times 6\) pixels at the top left of each selected images. Then, we reset the labels of all the selected images to the target class.
* **Arbitrary Model Attack:** The model attacker arbitrarily tampers with some of the local model parameters. To reproduce the arbitrary model attack, malicious \(\mathbf{MC}_{k}\) select a random number as its \(j\)th local model parameter.
* **Untargeted Model Poisoning:** The model attacker constructs the toxic local model that is similar to benign local models but causes the correct global gradient to reverse, or is much bigger (or smaller) than the maximum (or minimum) benign local models.
## 2 Characterization of Echo Envelope Fluctuation Features  
### Relation of Echo Envelope Fluctuation with Carrier Frequency of Incident Pulse  
Normally, based on the echo highlight model, an echo is considered the superposition of echo highlights and background. In this study, the concept underlying the echo highlight model has been generalized: the echo is the supposition of highlights from scatterings on the target, and the scatterings are equally spaced according to the carrier frequency of the incident pulse. For the linear dimension, the complex echo can be expressed by [20]  
\[y(t,\theta)=\sum\limits_{i=1}^{N}a_{i}(\theta)\cdot x(t).\exp[j(2\pi f(i-1)\tau_ {\theta})] \tag{1}\]  
where \(\theta\) is the azimuth of the incident signal; \(a_{i}(\theta)\) is the strength coefficient of the echo highlight, assumed equal spaced distribution of scatter on the target; \(x(t)\) is the incident signal; \(N\) is the number of echo highlights; \(\tau_{\theta}\) is the difference in time between the echo highlight and the start of the echo signal, i.e.,  
\[\tau_{\theta}=\frac{L\cos(\theta)}{(N-1)C} \tag{2}\]  
where \(L\) is the length of the target, \(C\) is the sound speed in water. Assuming equal strength coefficients of echo highlights, namely \(a_{i}(\theta)=a(\theta)\), the frequency domain expression of (1) is as below [21]  
\[\begin{array}{ll}s(f,\theta)&=a(\theta).X(f).\sum\limits_{i=1}^{N}\exp(j2\pi f (i-1)\tau_{\theta})\\ &\qquad=a(\theta).X(f).h(f,\theta)\end{array} \tag{3}\]  
where  
\[\begin{array}{ll}h(f,\theta)&=\sum\limits_{i=1}^{N}\exp(j2\pi f(i-1)\tau_{ \theta})\\ &\qquad=\frac{\sin(\pi fN\tau_{\theta})}{\sin(\pi f\tau_{\theta})}\end{array} \tag{4}\]  
In (3) and (4), \(h(f,\theta)\) receives the maximum value when \(f\tau_{\theta}=m,\ (m=1,\ 2,\ 3,\ldots)\). The echo envelope forms dips and peaks correspondingly. As per the theoretical expression above, the spacing of echo highlights relates to the carrier frequency, and when the carrier frequency increases, a higher frequency modulation of the echo envelope forms, presenting more detailed information on the target. It also implies that when the carrier frequency changes, the characteristics of the echo envelope modulation change accordingly, representing the features of a real complex target.