esa-sceva/satcom-chunk-collection huggingface dataset:
# Multimodal Network for Object Detection Using Channel Adjustment and Multi-Scale Attention
## 3 Methodology
### Dynamic Channel Adjustment  
Building upon the success of channel interaction strategies in multimodal learning [30], we propose a dynamic channel adjustment (DCA) mechanism to enhance cross-modal feature fusion. The DCA adaptively recalibrates and exchanges channel-wise features between modalities through learnable attention weights, effectively capturing complementary information while suppressing redundant features.  
Given the feature maps \(x_{1}\in\mathbb{R}^{H\times W\times C}\) and \(x_{2}\in\mathbb{R}^{H\times W\times C}\) from two modalities at stage \(s\), we first apply Batch Normalization (BN) to each channel to stabilize the feature distribution:  
\[\hat{x}_{m,s,c}=\frac{x_{m,s,c}-\mu_{m,s,c}}{\sqrt{\sigma_{m,s,c}^{2}+\epsilon} },\quad m\in\{1,2\} \tag{1}\]  
where \(\mu_{m,s,c}\) and \(\sigma_{m,s,c}^{2}\) denote the mean and variance of the \(c\)-th channel for modality \(m\) at stage \(s\), and \(\epsilon\) is a small constant for numerical stability. The normalized features are then scaled and shifted using learnable parameters:  
\[\hat{x}_{m,s,c}=\gamma_{m,s,c}\hat{x}_{m,s,c}+\beta_{m,s,c} \tag{2}\]  
where \(\gamma_{m,s,c}\) and \(\beta_{m,s,c}\) are the affine transformation parameters for channel-wise adjustment.  
To enable dynamic channel interaction, we generate modality-specific attention weights through a shared \(1\times 1\) convolutional layer followed by a sigmoid activation:  
\[g_{m}=\sigma(W_{g}*\hat{x}_{m}),\quad m\in\{1,2\} \tag{3}\]  
where \(W_{g}\in\mathbb{R}^{1\times 1\times C\times C}\) denotes the convolutional kernel, and \(\sigma(\cdot)\) is the sigmoid function that maps weights to the range \([0,1]\).  
The channel exchange operation is then performed as a weighted combination of the normalized features:  
\[x_{1,c}^{\prime}=g_{1,c}\odot\hat{x}_{1,c}+(1-g_{1,c})\odot\hat{x}_{2,c} \tag{4}\]  
\[x_{2,c}^{\prime}=g_{2,c}\odot\hat{x}_{2,c}+(1-g_{2,c})\odot\hat{x}_{1,c} \tag{5}\]  
where \(\odot\) denotes element-wise multiplication. This formulation ensures that each channel in the output features \(x_{1}^{\prime}\) and \(x_{2}^{\prime}\) incorporates complementary information from both modalities while preserving the original spatial dimensions \(H\times W\times C\).  
Figure 1: The overall architecture of the proposed MNCM framework consists of two parallel ResNet-based image feature extraction networks, processing input features from each modality. The extracted features are then fed into the DCA-MAAM module. In the DCA-MAAM module, the dynamic channel adjustment and multi-scale activated attention mechanisms are applied. The blue box with three small squares represents sliding convolutions with different dilation rates of 1, 2, and 3, capturing features at multiple receptive field scales.
## 2 Related Work
### Noise Reduction Methods for Anomaly Detection
#### 2.2.6 Logistic Regression  
The logistic regression is a model that can obtain a relatively sophisticated classification result, unlike other models in that it can calculate the weight for the classification result by expressing the classification result as a probability between 0 and 1. This method reflects the overall trend of data that is changed in real-time and can perform more sophisticated noise detection in the noise reduction process of detecting new noise data [29].
## 3 Feature Extraction
### Time Domain Feature Extraction
#### 3.2.2 Mean Absolute Value (MAV)  
The mean absolute value (MAV) feature represents one of the most direct measures of signal amplitude and is a commonly used time-domain feature. When the signal manifests in the form of a Laplacian random process, MAV serves as the maximum likelihood estimate of its signal amplitude. Its computational formula is as follows:  
\[MAV=\frac{1}{N}\sum_{i=1}^{N}|x_{i}| \tag{4}\]  
Here, \(N\) represents the number of sample points in a given channel of the electromyographic signal, and \(x_{i}\) denotes the energy value (amplitude) of a signal sample point, where \(i=1,2,\ldots,N\).
## 6 Numerical Example: Trajectory Tracking of a Wheeled Mobile Robot
### Simulation Results  
Simulations results with the proposed controller (64) are illustrated in Figure 2. We can confirm that the tracking error \(e\) converges to \(0\) quickly, and hence, trajectory tracking is achieved by Figure 1(a)-c.  
For comparison, we also ran computer simulations with the other tracking controllers mentioned in Section 6.3. The comparative simulation results are summarized in Figure 3. We can confirm that the controllers (10) and (103) achieve trajectory tracking by Figure 2(a). According to Figure 2(c), we can also see that the Sontag-type controller (10) violates the convex input constraint (85) for \(\tilde{u}\) (i.e., the original norm constraint (82) for \(u\)) while the proposed controller satisfies it. Although the (unconstrained) Sontag-type controller (10) achieves the best performance in terms of tracking error convergence, the proposed con troller (64) also achieves good performance under the input constraint (see Figure 3b). To improve the convergence speed of the proposed controller in a small neighborhood of \(e=0\), it is important to design the function \(C\) in (64) more carefully.  
Figure 2: Simulation results with the proposed controller (64). (**a**) Tracking error; (**b**) trajectory; (**c**) control input.  
To see the advantage of considering convex input constraints, we then compare the proposed controller with the norm-constrained tracking controller (103). According to Figure 3a,b, we can confirm that the proposed controller achieves better tracking performance than (103). This is because the proposed controller is based on the minimization of \(\dot{V}(t,e,\bar{u})\) subject to the original convex constraint (85), while the controller (103) is based on the conservative norm constraint (102). In other words, the proposed convex constraint-based approach can use the control input more efficiently than the norm-based one. We can observe this in the enlarged section of Figure 3c.  
Figure 3: \(Cont.\)
# A Deep Learning Approach for Maximum Activity Links in D2D Communications
## 6 Performance Evaluation
In Figure 9, we show the change in the number of D2D links with respect to the change of D2D distribution radiuses and base station transmit powers. It can be seen from Figure 9 that when the base station transmit powers are 0.5w and 1w, the D2 Ds device distribution range increases and the number of D2D links decreases slowly because the number of potential devices involved in D2D communication increases with the growth of the distribution radius, thus causing severe interference. In this case, the transmission distance of D2D links increased, and the receiving signals became easily affected by other D2D devices and the eNB activity. When the transmit power of the eNB was 1.5w, the amount of D2D links declined slightly and then turned to grow slowly. The overall change was not significant because the distance between the D2D devices became larger as the distribution radius increased. During signal transmission, the amount of potential D2D links grows as the distribution radius increases. That is because D2D equipment can potentially be located farther from the eNB, and the interference from the eNB can be reduced. Figure 8: The impact of activity on device-to-device (D2D) links under different SINRs and different base station powers. Figure 9: The number of active D2D links under different radii and different base station powers.
# A New Approach to Production Process Capability Assessment for Non-Normal Data
## 6 Research Results
### Normality Analysis and Transformation for Machine 3  
Figure 4 shows the distribution of treatment time for actual values and post-transformation values for machine 3. In addition, Table 4 shows the basic statistics and results of the Jarque-Bera test for both real and transformed data. As with Machine 1 and Machine 2, the \(p\)-value (0.0000) for the actual data clearly indicates the non-normality of the data distribution. Additionally, the value of skewness (1.6794) shows their asymmetry, while the value of kurtosis (3.2309) indicates the presence of extreme values (Figure 4).  
As in the case of machine 1 and 2 using the proposed power transform, the \(p\)-value (0.0172) for the transformed data clearly indicates that the distribution of the data is close to a normal distribution, whereas the value of skewness (\(-0.0189\)) and the value of kurtosis (\(-0.9942\)) also show no major asymmetry or outliers. The value of the transformation parameter for machine 3 is \(\lambda=-0.3279\). For the transformed values, the interval \(6-sigma\) is (\(1.7249\); \(6.3091\)), while after the inverse transformation it is (\(0.0036\); \(0.1897\)).  
Figure 4: Densities for real time and time after transformation for machine 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**Statistics** & **Treatment Time** & **Treatment Time Transformation** \\ \hline mean & 0.0363 & 57.5813 \\ st. deviation & 0.0213 & 24.1257 \\ min & 0.0180 & 11.3862 \\
1Q & 0.0229 & 41.3097 \\
3Q & 0.0390 & 76.1573 \\ max & 0.1199 & 100.4055 \\ skewness & 2.0826 & \(-0.0974\) \\ kurtosis & 4.3153 & \(-0.9380\) \\ JB statistic & 94.4231 & 2.4092 \\ \(p\)-value & \(<\)0.0001 & 0.2998 \\ USL & 0.1200 & 11.3771 \\ \hline \end{tabular}
\end{table}
Table 3: Based statistics and result of Jarque–Bera test.
# Feature Detection of Focused Plenoptic Camera Based on Central Projection Stereo Focal Stack
## 3 Feature Detection Based on CPSFS
### Calculation of Plenoptic Disc Data Based on Two-Layer CPSFS  
Based on Equation (10), we can get the two parallax \(\epsilon_{Q}^{1},\epsilon_{Q}^{2}\) of \(Q\) in two different refocus planes with plenoptic disc radius \(R_{F}^{1}\) and \(R_{F}^{2}\). Furthermore, the \(R_{Q}\) can be obtained by  
\[R_{Q}=(R_{F}^{2}\epsilon_{Q}^{1}-R_{F}^{1}\epsilon_{Q}^{2})/(\epsilon_{Q}^{1}- \epsilon_{Q}^{2}). \tag{12}\]  
In practice, we use SIFT detector (not limited to SIFT) to extract features in the central projection stereo refocused image pair and perform feature matching along the s-axis. Let the detected features be \(C^{L},C^{R},C^{\prime L}\), and \(C^{R}\) in the two layers, as illustrated in Figure 5b. Thus, we have \(\epsilon_{Q}^{1}=C_{s}^{L}-C_{s}^{R}\) and \(\epsilon_{Q}^{2}={C^{\prime}}_{s}^{L}-{C^{\prime}}_{s}^{R}\). Due to error detection and mismatching, we should limit the \(\epsilon_{Q}^{1}\) and \(\epsilon_{Q}^{2}\) within the valid range. Therefore, we restrict \(|R_{Q}-R_{F}|<\theta_{R}\) and have \(|\epsilon_{Q}|<8d{r_{mi}}\theta_{R}/(3\pi)\) based on Equation (11).  
Then, the matched feature pairs \(C^{L},C^{R}\) and \(C^{\prime L},C^{\prime R}\) from different layers in CPSFS should be matched. This is rather simple for CPSFS. As illustrated in Figure 5b, point \(G\) and \(G^{\prime}\) are located in the center of relevant matched feature pairs \(C^{L},C^{R}\) and \(C^{\prime L},C^{\prime R}\). According to Equation (6), we can have  
\[(Q_{u}^{S},Q_{v}^{S})=\frac{1}{d}(\frac{C_{s}^{L}+C_{s}^{R}}{2},\frac{C_{t}^ {L}+C_{t}^{R}}{2})=\frac{1}{d}(\frac{C^{\prime L}_{s}+C^{\prime R}_{s}}{2}, \frac{C^{\prime L}_{t}+C^{\prime R}_{t}}{2}). \tag{13}\]  
Therefore, given a fixed \(d\), the plenoptic disc data of SIFT feature point \(Q\) can be calculated by Equations (12) and (13). The descriptor of \(Q\) is the average value of the descriptors of \(C^{L},C^{R},C^{\prime L}\), and \(C^{\prime R}\). In order to ensure the number and accuracy of the detected features, the two layer of the CPSFS should be relatively close to the actual imaging point \(Q\).
# Numerical Simulation Analysis of Concrete-Filled Circular CFRP-Steel Middle Long Columns
## 5 Analysis of ANSYS Calculation Results
### The Results of Ultimate Bearing Capacity in ANSYS
#### 5.2.3 The Influence of Circular Steel Pipe Wall Thickness  
It can be seen from Table 6 that the ultimate bearing capacity of single-layer CFRP concrete-filled circular steel tubular columns with 3.5 mm wall thickness is 8.53% higher than that of 2.5 mm, that of 4.25 mm is 18.6% higher than that of 3.5 mm, and that of 4.25 mm is 9.28% higher than that of 3.5 mm. This indicates that the ultimate bearing capacity of the samples increases as the steel tube wall thickness increases. The influence of the wall thickness is greater than that of concrete strength grades but less than that of the number of CFRP layers.
# Retrofitting of Steel Structures with CFRP: Literature Review and Research Needs
## 6 Conclusions  
A state of knowledge on the application of the CFRP in retrofitting steel elements as well as the influencing parameters is presented. Based on the findings obtained from the available literature, the following conclusions can be drawn:  
* Using CFRP with higher elastic modulus results in an increase in CFRP/steel bond strength and contributes to an improvement of the performance of retrofitted steel components by increasing structural load-carrying capacity and flexural strength.
* Applying adhesives with higher tensile modulus generally results in enhanced bond strength of the steel/CFRP interface. Nevertheless, adhesives with nonlinear properties can yield higher failure loads than linear adhesives with an even higher tensile modulus.
* As for bond-slip models, studies have shown that in linear adhesive materials, triangular bond-slip curves are obtained, whereas in nonlinear adhesives, the bond-slip relationship tends to follow a trapezoidal curve.
* Proper surface preparation of steel substrates is crucial for achieving a strong bond strength. Mechanical treatments like grit blasting improve surface roughness and chemical bonding.
* Increasing the elastic modulus of CFRP reinforcement could lead to an improvement in the fatigue life of specimens. Indeed, it has been found that the fatigue life of a steel structure can be enhanced by either applying prestressing to the steel details or by increasing beam stiffness by using UHM CFRP or adding CFRP layers.
* Regarding the performance of prestressed CFRP, experimental results indicate that prestressing can reduce the stress intensity factor and confine crack growth by applying compressive forces to the edges of cracks in notched steel elements. Therefore, the use of prestressed CFRP patches could enhance the effectiveness of CFRP rehabilitation systems.
* Finally, experimental studies of anchorage systems have shown that using anchorage techniques can help delay crack propagation and thereby prolong fatigue life in strengthened steel specimens. The crack mouth opening displacement could also be reduced as a result of using anchorage systems.  
This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Notations**  
\begin{tabular}{l l} \(b_{p}\) & Width of CFRP plate \\ \(b_{s}\) & Width of steel plate \\ \(E_{a}\) & [PERSON]'s modulus of adhesive \\ \(E_{p}\) & [PERSON]'s modulus of CFRP plate \\ \(E_{s}\) & [PERSON]'s modulus of steel plate \\ \(h_{b}\) & Height of beam \\ \(f_{a}\) & Tensile strength of adhesive \\ \(f_{p}\) & CFRP tensile strength \\ \(f_{y}\) & Steel yield stress \\ \(f_{u}\) & Steel ultimate stress \\ \(G_{a}\) & Shear modulus of adhesive \\ \(G_{f}\) & Interfacial fracture energy \\ \(L_{e}\) & Effective bond length of CFRP plate \\ \(L_{span,b}\) & Span length of steel beam \\ \(N\) & Number of interfaces working in parallel \\ \(P_{u}\) & Ultimate load (bond strength) \\ \(t_{a}\) & Thickness of adhesive layer \\ \end{tabular}
# Impact Wear Behavior of the Valve Cone Surface after Plasma Alloying Treatment
## 3 Results and Discussion
### Microhardness and Adhesion  
The hardness test results are summarized in Table 3. According to the data, the average surface hardness of the valve substrate is 377.1 HV\({}_{0.5}\), while that of the CrN coating reaches 903.1 HV\({}_{0.5}\). This significant increase (by approximately 2.4 times) can be attributed to the fact that chromium as a transition metal element can form an interstitial phase crystal structure with highly electronegative non-metallic nitrogen. In this structure, the chromium atoms are organized into a face-centered cubic configuration, and the nitrogen atoms are mainly arranged in the gaps within the octahedron, resulting in strong atomic bonding and, consequently, high hardness [22].  
The adhesion test results are presented in Figure 7. At the initial stage of testing, the scratch depth was shallow, and the indenter slid smoothly on the coating surface without generating any acoustic signals. Once the load increased to 72 N, the coating underwent local rupture, and acoustic signals were generated, indicating that the Lc value of the coating was approximately 72 N. With a further increase in the load, the acoustic signals fluctuated continuously in the form of peaks, demonstrating the constant cracking and peeling off of the CrN coating.  
Figure 6: Section of CrN coating. (**a**) Section morphology; (**b**) element distribution.  
Figure 7: Acoustic signals of CrN coating.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Point 1** & **Point 2** & **Point 3** & **Point 4** & **Point 5** & **Average** \\ \hline Substrate & 371.4 & 378.6 & 380.3 & 369.5 & 385.7 & 377.1 \\ CrN & 914.6 & 922.0 & 879.1 & 905.3 & 894.7 & 903.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hardness test results of samples (HV\({}_{0.5}\)).
## 5 Numerical Results
### Indoor Temperatures  
In all simulations, the indoor air temperature starts at 20 (\({}^{\circ}\)C) and increases logarithmically (Figure 9). It is observed that the indoor temperatures depend on the ventilation flow, with lower temperatures obtained at higher flow rates. Although the amount of fuel is not high, temperatures rise rapidly when ventilation does not work (CALIB1), approaching 100 \({}^{\circ}\)C. In CALIB 2, temperatures exceed 80 \({}^{\circ}\)C during the analyzed period. In CALIB1 and CALIB2, in less than three minutes, the temperatures exceed 60 \({}^{\circ}\)C, and the interior conditions are no longer suitable for survival and evacuation of the people.  
We can observe that ventilation rates that are too low do not significantly improve the indoor conditions (CALIB2). At high ventilation rates (CALIB3), the air temperature does not exceed 60 \({}^{\circ}\)C, maintaining good conditions for people evacuation. We conclude that mechanical ventilation affects indoor temperatures. Moreover, the mechanical ventilation extracts polluted air and brings fresh air with higher oxygen concentration, maintaining good conditions for people evacuation. However, the negative effect of the high oxygen concentration is that it sustains the fire.  
The current regulations [26] establish a correlation between the hot exhaust air flow and surface of the fan-protected space. The indoor air temperature represents the result of the heat gains (heat release from the fire) and heat loss (through air exhaust and through walls). Therefore, another useful conclusion might be to improve the current norms, so that the exhaust air flow should also depend on the fire heat release or the combustible materials in the room.
# Food and Drinking Water as Sources of Pathogenic Protozoans: An Update
## 7 Entamoeba spp
### Epidemiology of Entamoeba spp.  
E. histolytica has been listed as one of the top 15 causes of diarrhea in the first 2 years of life. Breastfeeding is also protective against this parasite, which affects about 50 million people, causing 100,000 deaths annually worldwide, although it is more common in tropical and subtropical countries. Entamoeba spp. prevalence ranged between 0.43% in Belgium and 82.64% in Malaysia [7]. Infections most often occur in people living in communities, and contaminated food is the most common method of transmission, especially in cases of inadequate hygiene of food handlers [187].  
In Iraq, _E. histolytica_ infection is endemic and prevalence does not vary seasonally. Real-time PCR showed prevalences of 35.0% for _E. histolytica_ and 18.8% for _E. dispar_[204]. The prevalence of _E. histolytica_ in 1-to-10-year-old children from Sulaimani Province, Iraq, was 19.3% and significantly associated with raw vegetable consumption [205].  
In Abuja, Nigeria, the prevalence of _E. histolytica_ in children was 12% [181]. In northern South Africa, _E. histolytica_ was significantly associated with diarrhea and more prevalent among HIV patients [190]. A prevalence of 19.8%, was found among school children in Arsi Town, Ethiopia [206] and Perak, Malaysia among children aged between 7 and 12 years old; the overall prevalence of _Entamoeba_ complex infections was 21.3%, of which _E. moshkovskii, E. dispar_, and _E. histolytica_ represented 10.7%, 9.0%, and 5.0%, respectively [207]. Handwashing habits were significantly associated with _E. histolytica_ infection [206; 207].  
A systematic review and meta-analysis based on 36 studies from Brazil, India, Ethiopia, Cote D'Ivoire, Kenya, Lesotho, Mexico, Vietnam, Colombia, Ecuador, Cuba, Chile, Cambodia, Iraq, Nigeria, South Africa, Uganda, and Yemen found that the prevalence of intestinal _Entamoeba_ spp. infection was positively, though not significantly, correlated with the lack of safe drinking water and significantly correlated with the lack of toilets [208].  
In an outbreak involving 250 cases in IdahoI Juye Bozorg village, Iran, the source of infection was network drinking water contaminated by sewage pipe erosion [209]. _E. dispar_ was reported to be a pioneering agent in biofilm formation, as observed in an industrial wastewater deep injection disposal well in Florida, where it was the most abundant microorganism. This aspect is probably implied in the occurrence and persistence of _Entamoeba_ spp. in water [210].
## 3 Results and Discussion
### Consistency of Fresh Mixture  
Table 3 presents the results of consistency tests of Formulas R1 and R3. In Formula R1 using cement CEM 52.5 R, it is clear that 25% replacement of pure mixing water with waste sludge water from the concrete plant reduces the diffusion value by 7.5%, 50% replacement reduces the diffusion value by 16.9%, 75% the replacement reduces the diffusion value by 23.1% and 100% replacement reduces the diffusion value by 31.3%. When using cement CEM II/B-LL 32.5 R (Formula R3), it is clear that 25% replacement of pure mixing water with waste sludge water from the CP decreases the diffusion value by 7.3%, 50% replacement reduces the diffusion value by 14%, 75% replacement reduces the diffusion value by 22%, and 100% replacement reduces the diffusion value by 26.8%.  
Figure 1: Mineralogical analysis of solids sample (Stage 1) acquired from waste sludge water [%].  
Figure 2: Mineralogical analysis of solids sample (Stage 2) acquired from waste sludge water [%].  
Figure 3 presents the results of a linear regression of diffusion values taking into account the percentage replacement of pure mixing water with waste sludge water from the CP of Formulas R1 and R3. The coefficient of determination R\({}^{2}\) is 0.9973 for Formula R1 and 0.9956 for Formula R2. Based on these values of the coefficient of determination (values approaching the value of 1), it can be stated that the equations presented in the graphs (see Figure 3) can be used for extrapolation of diffusion value in the interval of 0-100% replacement of mixing water with waste sludge water from the CP. Figure 3 also clearly shows that the increasing replacement of pure mixing water with waste sludge water from the CP reduces the diffusion value. The reduction is due to an increasing proportion of fine particles that are present in the sludge water from the CP. The increasing content of fine particles is directly dependent on the increasing replacement of pure mixing water with waste sludge water from the CP. This is in line with the results of an experimental research dealing with the use of fine proportions of recycled aggregate in the production of concrete [16]. It has been established that the increasing proportion of fine particles in concrete mixture reduces the consistency of fresh concrete mixture.