esa-sceva/satcom-chunk-collection huggingface dataset:
# Trade-Off Analysis of Hardware Architectures for Channel-Quality Classification Models
## 5 Results
### Resources Comparison  
The number of resources utilized by the four developed ML algorithms is summarized in Table 3. It is essential to mention that the number of features could impact the number of resources employed. For example, for the implemented MLP model, there are 15 multiplier and 15 adder modules per neuron due to the 15 features. If more or fewer features are required, the number of modules will increase or decrease in proportion.  
In terms of resources, the best results were achieved by the Decision Tree Algorithm with a depth of five, since it has the highest efficiency (9.83 Mbps/LUT), throughput (3148.76 Mbps), and frequency (323.31 MHz) and the lowest number of LUTs (320), FFs (812), DSPs (0), and period (3.09 ns). Its latency is 23 and was overcome only by two clock cycles (21) by Logistic Regression; however, the difference is small. The worst results belong to MLP since it has the lowest throughput (249 Mbps) and efficiency (0.006 Mbps/LUT) and the highest latency (317), LUTs (36825), FFs (79465), and DSPs (439).  
For the comparison of metrics, in balanced accuracy, the highest score was achieved by the Decision Tree with a depth of 6, with 95.26, followed closely by the Decision Tree with a depth of 5 with 95.01. The lowest result was achieved by SVM with 92.06. However, the difference between the highest and lowest result is slight, with only 3.2. For sensitivity, the highest score (97.48) was obtained by MLP. The Decision Tree with a depth of 6 obtained 94.88, whereas the lowest belongs to SVM with 92.99. The difference of values between MLP and SVM is 4.49. For specificity, the highest score belongs to the Decision Tree with a depth of five with 96.91, followed closely by the Decision Tree with a depth of six with 95.64; the lowest result belongs to MLP with 90.57; the difference between the highest and lowest result is 6.34. We noticed that MLP has the highest imbalance for recognizing classes, since it has the highest sensitivity (true positive rate) but the lowest specificity (true negative rate). Decision Tree and CNN can recognize the negative class better than the positive class, whereas SVM recognizes the positive class better than the negative class. We remark that sensitivity and specificity are used for calculating balanced accuracy; for this reason, only balanced accuracy is used for trade-off comparison of the following sub-section.
## 3 Results
### General Model
#### 3.1.2 Fragility Curves for Ductility  
To construct fragility curves for the different mechanical properties in the presented study, we first chose the corresponding probability distributions and their coefficients of variations. Such data were needed for enlarging the database by using LHS.  
For the F/T cycles, CoV was determined based on the different ages of the test specimens (see the description of the different sample ages) and the unreliability of a realistic description of the F/T cycles by the standard compared to the actual course in reality. This was a rough estimate, which is intentionally conservative, as separate experimental investigations would be needed for a more accurate estimate. The selected CoV value was 0.333 (i.e., 50 cycles/150 cycles). It contains equal proportions of epistemic (relatively (un)realistic protocol of F/T cycles compared to the real course in nature; different ages of samples in testing) and aleatory uncertainties (uncertainties in the implementation of protocols and measurements)!  
For the mortar type, a relatively large value of 0.5 was deliberately chosen for the corresponding CoV. In this way, we consciously increased the influence of one mortar on another and consequently \"smoothed\" the influence of an individual mortar on the considered mechanical characteristics. Note that the fuzzy logic approach was simulated at this stage (the term simulation refers to the use of a Gaussian distribution instead of the triangular or trapezoid distribution commonly used and the standard fuzzy logic approach). At the expense of lower nonlinearity, we thus obtained more meaningful trends with the F/T cycles for the considered mechanical characteristics in both types of mortars. CoV contains mostly epistemic uncertainties due to simplifications of the influence of the mortar type.  
Figure 3 shows the ductility fragility curves for walls M1 and M3a after 0, 50, and 150 F/T cycles, respectively. For comparison, the empirical (thin line), based on CAE analysis, and the corresponding theoretical fragility curves (bold line) obtained as cumulative distribution functions (cdf) are shown. For theoretical curves, the normal distribution was considered. An excellent match between the empirical and theoretical curves was observed, which is undoubtedly also due to the expansion of the database with the LHS method, where normal distributions were taken into account for all individual variables.  
Figure 2: Mean and Mean \(\pm\) Standard deviation regression curves for ductility, \(\mu\), for two types of mortar. Dashed line represents Median. Corresponding expressions for Mean are also presented.
## 4 Discussion
Other studies reported bacterial consortia composed of _Pseudomonas_, _Curtobacterium_ and other genera (containing from 3 to 12 strains) for the germination of cotton seeds [134], the protection of tomato plants against the phytopathogen _Pseudomonas syringae_ pv. _tomato_[135], and the bioremediation of petroleum [136]. In addition, bacteria of the _Pseudomonas_ and _Curtobacterium_ genera are the most abundant in ecosystems related to poplar, willow, grapevine, rice, and nectarine plants [137; 138; 139; 140], showing a close relationship to diverse plants. Bacterial consortia to alleviate the cold stress in plants have been scarcely reported. The bacterial consortium composed of _Bacillus amyloliquefaciens_ Bk7 and _Brevibacillus laterosporus_ B4, which solubilizes phosphates and produces IAA and siderophores, protects and improves the growth of rice under cold stress [28]. The rice seedlings inoculated with this consortium 1 week before the chilling stress (5 \({}^{\circ}\)C for 24 h) improves survival rate, plant height, and shoot number. The bacterial consortium of _Bacillus cereus_ AR156, _Bacillus subtilis_ SM21, and _Serratia_ sp. XY21, which produces IAA and has antagonistic activity against phytopathogenic INA bacterium _P. syringae_, fungi, and oomycetes, improves the survival rate of tomato seedlings subjected to chilling stress (4 \({}^{\circ}\)C for 7 days) [29]. The selected bacterial consortium formulated with _Pseudomonas_ sp. TmR5a and _Curtobacterium_ sp. BmP22c, which exhibits several plant growth-promoting and protective activities including plant growth-promoting under chilling stress (<15 \({}^{\circ}\)C), is an attractive candidate for future applications in agriculture. Additional assays will be carried out to study its protective effect on plants under freezing stress, which are required prior to its evaluation on the field against frost.
Initially introduced in [18], it is a converter whose main advantage over other high voltage-gain converters is that their capacitors sustain a low voltage compared to  
Figure 1: The standard step-up or boost topology. the output voltage. On the other hand, the traditional boost converter and other similar converters require at least one capacitor to sustain the output voltage, which is the largest voltage in a step-up converter. Moreover, the output capacitor usually stores a high amount of energy since the stored energy in a capacitor is proportional to the square of its voltage. Therefore, it would be desirable to develop boost converters in which capacitors block a voltage lower than the output voltage, which is the advantage of the LVC converter [18]. The LVC converter has two capacitors in series with the input power source to provide the output port, which reduces its voltage rating. The converter was introduced in [18] with the traditional single switching-function pulse width modulation (PWM) scheme. The study in [18] presented the LVC converter as an advancement over another high-gain converter [19]. It also compared the LVC with two similar converters and found that it used less stored energy in capacitors while still meeting the output voltage ripple requirements. Reducing the stored energy in capacitors is desirable as the capacitor size is proportional to the energy they store [20; 21; 22]. This work explores a different operation of the LVC converter with a different PWM scheme, which consists of two switching signals (instead of one) shifted in phase 180\({}^{\circ}\). The PWM scheme affects the voltage ripple at the converter's output port. The results presented in this article demonstrate that the LVC converter operating with the proposed scheme has a reduced voltage ripple at its output port (compared with the previous operation), while using the same capacitance in capacitors and other parameters (inductance in inductors, switching frequency, etc.). This article briefly introduces the LVC converter first on the former operation. Then, the converter dynamic mathematical model is presented, including calculating the voltage ripple at the converter output port in the proposed and previous operations. Finally, the proposal is verified through experimental results.
## 4 Results
### Simulation Result Limitations  
#### 4.4.1 Limitation on the Number of Hops  
In this paper, simulations of communicating nodes above four hops away are not presented, as the delivery rate for such communications decreases dramatically. This is illustrated in Figure 12, which reports the packet delivery ratio results for communicating pairs of nodes located about about five to six hops apart. It can be seen that at the end of the transmission period, both S2 and S3 delivery rates were lower than 70%, with those of CBL-OLSR being up to 50%.
# Powdered Foods: Structure, Processing, and Challenges: A Review
## 6 Improvements and Challenges on Powdered Foods
### Agglomeration Process and Fluidized Bed Technology  
To enhance rehydration properties, the agglomeration process, which is known for the various functionalities it imparts to the final product, is defined by the joining of particles to form large agglomerates. This process can improve powder characteristics, from its rehydration properties (wettability, dispersibility, and solubility) to flow, storage, and other properties. This improvement is achieved by changing the powder properties, including increasing particle size and porosity, reducing the bulk density, and avoiding the caking and dustiness process during storage [55].  
Agglomeration can occur in two ways, either by applying pressure or via wet controlled growth agglomeration. Pressure agglomeration occurs via simple compression of the particulate material, causing adhesion between the particles via van der Walls forces, requiring that the particles deform with the application of pressure to reduce the distance between the surfaces [56].  
However, wet controlled growth agglomeration is a more complex process, as it occurs in several stages. The first step (wetting and nucleation) consists of the application of  
Figure 4: Pretreatment methods applied to foods to reduce drying time.  
water to the dry particle and its homogeneous distribution within the powder, besides the initial formation of a stable structure between the water and the solid [56; 57]. The second stage (consolidation and coalescence) results in the production of larger particles via collision and interaction between smaller particles, strengthening the formed particles via the migration of liquid to the surface of the agglomerate. Finally, the last stage, attrition and breakage, consists of the rupture and separation of certain particles until a balance between the formation and rupture of these agglomerates is achieved, and consequently the final drying occurs to remove the liquid from the agglomerates, as well as reducing the temperature below the glass transition temperature (Tg) to avoid the caking effect on storage [56; 57].  
Handling powdered food presents a major challenge in production, and the agglomeration process can modify the physical properties of the powder, being able to change the particle size, flowability, and water content [58]. The fluidized bed process can impart these properties when applied below a spray-dryer, in which the liquid is applied under intense heat and constant agitation, resulting in high mass transfer [59].  
The agglomeration process can be undesirable (e.g., the caking effect), or, when applied in a controlled way in which materials with desired porosity and density are obtained, it can lead to improved instant properties [60]. Fluidized bed agglomeration technology is not recommended for foods with a high concentration of carbohydrates and organic acids, since the low glass transition temperature can create high stickiness and reduced yield [61].
# A Deep Learning-Based Approach for Extraction of Positioning Feature Points in Lifting Holes
## 2 Methods  
### Overview  
Considering the difficulty of directly obtaining corner points on the contours, firstly, target contour is obtained from original images. Then, VLFGM is utilized to determine the geometric shape of the rectangular hole, using the vertices of the fitted shape as feature points for image matching. Additionally, to achieve sufficient adjustment by utilizing an adequate number of points, we calculate the centroid coordinates of the lifting holes, which hold significant importance and can even be applied on depth images. The workflow of this study is as follows (Figure 1):
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 6 Nanoparticle Toughening Method  
Nanoparticle toughening, characterized by its synergistic effect of simultaneously reinforcing and toughening polymers, has emerged as a prominent research focus in materials science. Nanoparticles refer to ultrafine materials with particle sizes ranging from 1 to 100 nm, bridging the dimensional gap between atomic/molecular scales and macroscopic systems [97]. Owing to their large specific surface area, elevated surface atomic occupancy ratio, and enhanced chemical reactivity, nanoparticles can establish robust chemical bonding with epoxy resin matrices This interfacial interaction facilitates the formation of an ideal interface that induces crack blunting and hinders crack propagation, thereby achieving the goal of toughening [98; 99].
## 2 The Use of GO and FA in the Cement Mortar
### Mixing Process  
The very high characteristic surface area of GO particles, together with the unique properties of nanometer-sized GO particles, makes GO dispersion more difficult. Therefore, unique dispersion methods, such as mixing using ultrasonic devices, may be required, which significantly increases the fabrication cost. To ensure that the mixed samples have uniform material composition and that GO particles are dispersed in the mortar with a high degree of stability, this study proposes a mixing process based on reference to previous research and experience from the test mixing process, as follows:  
* Firstly, during the GO suspension preparation, the GO was diluted with 30% of the necessary volume of water, followed by the addition of the PCE additive, and stirred at high speed to disperse the GO particles evenly for 3 min;
* Batch mix cement, fly ash, sand for 3 min;
* Add 70% of water into the mixing batch, mixing the cement mixture, fly ash, and water for 2 min;
* Next, add the prepared GO mixture into the mixing batch and mix for 2 min until the mixture is homogeneous;
* Rest the mixer for 1 min, and then mix again for 2 min to avoid loss of slump and ensure mixture uniformity.  
The mixing diagram of material components is shown in Figure 2 below:  
Figure 1: The effect of PCE in GO dispersion: (**a**) the effect of mixing order on the degree of GO dispersion (2018); (**b**) an image of GO after being dispersed in water and PCE.  
* PCE: A mixture of water-based modified polycarboxylate from Sika, which is light brown in color.
## 8 Query/Data Protection Mechanism
### Simulated Real-World Deployment  
We then set up a more realistic network with multiple senders and one receiving more, with a fixed sending rate of _four_ 100-byte packets per second (both encryption and MAC are enabled). We chose this sending rate (400 bytes/s per patient) based on the following considerations. The ECG data in the long-term ST database was sampled at 250 Hz. Allotting 2 bytes for each sample, this results in 500 bytes per second of raw data. However, we found that 500 bytes of raw ECG data can be reduced to about 260 bytes using S-LZW [34], a compression scheme specifically designed for the resource constraints of wireless motes. Compression would thus allow ample space for ECG data as well as other types of patient data to be sent. Note that other patient data (e.g., body temperature) could be sampled at a much lower rate than the ECG data.  
Figures 9 and 10 show the throughput and packet loss as measured at the receiving mote (mote \(B\) in Figure 7) and at the PC. Any difference between the two curves in each figure indicates that packets were dropped by the serial connection between the receiving mote and the PC. We found that a single receiving mote is able to reliably handle 10 senders, with a packet loss of under 1%. At 11 senders and above, the receiving radio is unable to keep up with the incoming packets.  
Figure 8: Throughput as a function of packet size and encryption.  
Figure 9: Aggregate throughput at receiving mote and PC as a function of the number of senders (sending rate = 4 packets/sec).
## 4 Modeling and Implementation  
### Basics of Bayesian Networks  
A Bayesian network is the graphical representation of the influence of one event, one fact, or one variable on another. It is a directed acyclic graph (DAG) composed of parent and child nodes (existing real events) modeled as random variables, and oriented arrows that represent the causal relationship  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Mean \(\mathrm{C_{s}}\)** & **Standard Deviation \(\mathrm{C_{s}}\) (\% \(\mathrm{Cl^{-}}\))** & **Mean \(\mathrm{D}\)** & **Standard Deviation \(\mathrm{D}\)** \\  & **(\% \(\mathrm{Cl^{-}}\))** & & **(mm\({}^{2}\)/day)** & **(mm\({}^{2}\)/day)** \\ \hline Unloaded & 0.28 & 0.05 & 0.94 & 0.52 \\ Static load & 0.27 & 0.06 & 1.31 & 0.60 \\ Cyclic load & 0.30 & 0.05 & 1.65 & 0.77 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean and standard deviation of chloride ingress model parameters for different loading conditions.  
between the nodes. To each child node is associated a conditional probability with respect to its parent node and the set of nodes is defined on the probabilized space (\(\Omega,\mathbf{X}\)) such that:  
\[\mathrm{P}(\mathbf{X})=\mathrm{P}(\mathrm{X}_{1},\ldots,\mathrm{X}_{\mathrm{N} })=\prod_{\mathrm{i}=1}^{\mathrm{N}}\mathrm{P}\big{(}\mathrm{X}_{i}\big{|} \mathrm{pa}\left(\mathrm{X}_{\mathrm{i}}\right)\big{)} \tag{5}\]  
where \(\Omega\) is the samples space, \(\mathbf{X}\) is the set of random variables \(\{\mathrm{X}_{1},\ldots,\mathrm{X}_{\mathrm{N}}\}\), and \(\mathrm{pa}(\mathrm{X}_{\mathrm{i}})\) is the set of parents of the \(\mathrm{X}_{\mathrm{i}}\) nodes (for more information see [48]). Figure 3 illustrates a simple three-node Bayesian network corresponding to three random variables \(\mathrm{Y}\), \(\mathrm{X}_{1}\), and \(\mathrm{X}_{2}\) where \(\mathrm{X}_{1}\) and \(\mathrm{X}_{2}\) are the child nodes of the parent node \(\mathrm{Y}\). The joint probability of the events \((\mathrm{Y},\mathrm{X}_{1},\mathrm{X}_{2})\) of this network is given as the product of conditional probabilities:  
\[\mathrm{P}(\mathrm{Y},\mathrm{X}_{1},\mathrm{X}_{2})=\mathrm{P}(\mathrm{X}_{1 }\mathrm{Y})\mathrm{P}(\mathrm{X}_{2}\mathrm{Y})\mathrm{P}(\mathrm{Y}) \tag{6}\]  
where \(\mathrm{P}(\mathrm{X}_{\mathrm{i}}\mathrm{|}\mathrm{Y})\) is the conditional probability of \(\mathrm{X}_{\mathrm{i}}\) knowing \(\mathrm{Y}\).  
Bayesian networks are useful to update probabilities in the network by integrating new information of observed variables called evidences. In our study, the methodology will allow us to integrate information from experimental trials to characterize random variables.
## 2 Materials and Methods
### Evaluation Methods  
This paper uses classic evaluation metrics including the Confusion Matrix, Accuracy, Precision, Recall, and F1 Score. The Confusion Matrix provides an intuitive representation of classification results. Accuracy is the percentage of correctly predicted samples out of the total number of samples. Precision indicates the percentage of actual positive samples among those predicted as positive by the model. Recall represents the percentage of actual positive samples that are predicted as positive. The F1 Score is the harmonic mean of Precision and Recall. Their mathematical expressions are shown in the following equations. Due to the significant differences in the number of samples across each class in the dataset, we compute the weighted average of Precision, Recall, and F1 Score for each class to evaluate the overall performance of the model.  
\[\text{Acc}=\frac{TN+TP}{TP+TN+FN+FP}\times 100\% \tag{4}\]  
\[\text{Precision}=\frac{TP}{TP+FP}\times 100\% \tag{5}\]  
\[\text{Recall}=\frac{TN}{TN+FP}\times 100\% \tag{6}\]  
Figure 5: Structure of multi-head attention layer.  
\[\text{F1}=\frac{2\times(\text{Precision}\times\text{Recall})}{\text{Recall}+\text{ Precision}}\times 100\% \tag{7}\]  
where \(TP\) denotes true positives (where both the true label and the prediction are correct). \(FP\) denotes false positives (where the true label is incorrect but the prediction is correct). \(FN\) represents false negatives (where the true label is correct but the prediction is incorrect). \(TN\) denotes true negatives (where both the true label and the prediction are incorrect).
# Seismic Resilience Evolution of Shield Tunnel with Structure Degradation
## 1 Introduction
(2020) conducted a full-scale experiment on the longitudinal joints of shield tunnels; according to the test results, the deformation resistance and load variation rule of tunnel longitudinal joints are obtained to analyze the resilience evolution of longitudinal joints in tunnels. Currently, there is a lack of resilience assessment methods for shield tunnels considering the effects of seismic loads. Additionally, many resilience assessment indicators are based on the deformation of tunnel structures, which may not adequately reflect the resilience of shield tunnels under short-term dynamic loads. Therefore, the mechanical mechanisms and evolution patterns of resilience indicators for shield tunnel structures are not yet clear, which limits their widespread application in engineering. In this study, a refined numerical simulation model of shield tunnels was established based on the finite element method. The mechanical responses of shield tunnel structures under the combined effects of seismic loads and bolt preload loss are analyzed. Furthermore, various indicators were selected to establish a resilience evolution assessment model that encompasses the normal state, affected state, and recovered state. This study can provide a valuable reference for the seismic resilience assessment of shield tunnels.