esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
Sometimes this process is implemented while also taking into account previous evidence, such as images or drawings of the initial design, the logical continuity of the design or similar patterns found replicated in different places on the same art-piece, etc. The wood-carver contributes significantly to this work as well, and especially, does not leave any destroyed or missing points/parts without the necessary correction, based again on the initial design of the original work. Figure 11: A pattern of the original gilded Bema door (**left**) and its corresponding copy in oak wood (**right**). Figure 10: Copy of the whole Bema door leaf (**left**) made of oak wood and the whole image of the final version of the Bema door copy in oak wood bearing all the attached decorative parts (**right**). The wood-carving tool traces do not constitute a problematic issue, rather a desirable one that, of course, cannot be precisely replicated (even by the same wood-carver), although the tools used in wood-carving have been more or less the same for thousands of years. In general, the wood-carver in the last step of reproduction process bestows it with this special unique appearance of the handmade work. The contribution of the human factor (the wood-carver's view and artistic skills) may have slightly varied some curves or other points, always respecting the initial design and detailed images of the original Bema door elements. The reproduction of precise copies of old wood-carved icons or parts of them, exclusively in the traditional way, is a very difficult process, as it requires many hours of laborious specialized work of an experienced and talented wood-carver and correspondingly, very high cost [28]. The machining, combined with the digitization of the original object and the correct reproduction of the wood-carving patterns in the produced copy, contributes significantly to the reduction in the wood-carver's working time and the corresponding total cost of reproduction [28]. Of course, the level of resolution and choice of material determine the final cost of the copy as well [29]. Particularly, for each of the two leaves of the Bema door, the machining process on the CNC machine lasted about 7 h (keeping in mind the step of 3D digitization that lasted 4 h for each leaf and the scanning data processing that lasted one week for the whole Bema door), followed by four more days of manual work by the wood-carvers to refine it. Without the CNC machine, applying only the manual wood-carving, it would take twice as long. In the present project, the enormous importance of the coexistence of human contribution and CNC machine involvement towards a successful, easy, quick and of low-cost reproduction of high cultural value and heritage wood-carvings was highlighted.
## 1 Introduction
However, it is difficult to select highly universal optimal features, and inappropriate feature selection may lead to shadow misdetection or omission. Moreover, they are unable to accurately differentiate between water bodies, low-reflectance objects, and shadows due to the problem of spectral similarity. Deep learning-based shadow detection methods recognize shadow regions by learning shadow patterns in images [23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. Commonly used neural network structures for shadow detection include Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and networks based on attention mechanisms. Instead of manually designing shadow features as in traditional methods, deep learning methods enable the discovery of high semantic features through complex and dense network connections. [PERSON] et al. [36] proposed the first Aerial Imagery dataset for Shadow Detection (AISD) and, accordingly, proposed a Deeply Supervised convolutional neural network for Shadow Detection (DSSDNet), which solves the problem of insufficient shadow feature extraction by adopting the encoder-decoder residual structure. [PERSON] et al. [37] proposed a Multi-Resolution Parallel Fusion Attention Network (MRPFANet), which improves the ability to extract spatial information and shadow features from images by incorporating a cross-space attention module and a channel attention module. In recent research, deep models have been used to segment shadows in videos [38], and deep models that use the Transformer architecture as a backbone network [39; 40] are also being used more often for shadow detection in images. However, although deep learning methods show high robustness in specific scenes, they require massive amounts of training data as input, and the cost of manually labeling is too high. Additionally, the extraction of deep features is not fully sufficient, as high-level features still tend to mix with low-level features, making it challenging to distinguish independent shadow features. In this paper, we propose a shadow detection method that combines topography and spectra (CTS). Based on the spectral attributes used to detect shadows, we exploit the terrain information of the elevation data to avoid the misdetection caused by spectral similarity. In cases where the image contains large areas of dark objects mixed with shadows, such as vegetation and water bodies, CTS can accurately identify shadows, leading to a significant improvement in detection performance compared to existing methods. The rest of this paper is organized as follows. In Section 2, the relevant theories and the proposed framework are described in detail. Section 3 presents the experimental results with a comparison. In Section 4, the experimental parameters are analyzed. The conclusions are provided in Section 5.
## 5 Conclusions  
GAC as well as weak and strong base anion exchange resins were successfully applied for the separation of lactic acid from a model fermentation broth at a pH above and below the \(pK_{a}\) (3.86) value of lactic acid. The temperature experiments showed that increasing the temperature from 303.15 to 353.15 K decreased the lactic acid recovery efficiency by 50% and 13% for RLX425 resin and GAC, respectively, whereas the recovery efficiency remained unchanged for the AMB400 resin. From the thermodynamic analysis, the lactic acid adsorption onto the adsorbents suggests a physical adsorption mechanism for both GAC and RLX 425 and ion exchange for the AMB400 resin. The equilibrium adsorption data for the lactic acid were adequately explained by the Langmuir isotherm model, indicating a monolayer adsorption process. Lactic acid recovery was found to be well represented by pseudo-second order kinetics. Among the different adsorbents tested, the strongly basic resin (AMB400) showed good lactic acid recovery from the model fermentation broth in terms of adsorption kinetics, adsorption capacity and thermodynamic feasibility.  
This work was funded by the Erasmus Mundus Joint Doctorate program ETeCoS\({}^{3}\) (Environmental Technologies for Contaminated Solids, Soils and Sedimenters) under the EU grant agreement FPA No. 2010-0009. This work was further supported by the Italian Ministry of Education, University and Research (MUR) through the project \"Sfruttamento Integrato di Biomasse Algali in Filiera Energetica di Qualita (SIBAFEQ)\" (PON01_02740) and by the CNR through the project \"Energia da FOnt Rinnovabili (EFOR)\". The authors would like to thank the laboratory staff of UNESCO-IHE for their analytical support.  
[PERSON] carried out the experiments and bibliographic search; [PERSON], [PERSON], and [PERSON] performed the data analysis and wrote the manuscript; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] corrected the manuscript and improved the discussion.  
The authors declare no conflict of interest.
## 3 Methodology
### Multi-Task Optimization
The router computes a gating weight \(g_{i,m}\) for each expert \(E_{m}\) based on the input feature vector \(\mathbf{z}_{i}\):  
\[g_{i,m}=\frac{\exp\left(\mathbf{W}_{g}^{(m)}\mathbf{z}_{i}+b_{g}^{(m)}\right) }{\sum_{k=1}^{M}\exp\left(\mathbf{W}_{g}^{(k)}\mathbf{z}_{i}+b_{g}^{(k)}\right)}, \tag{27}\]  
where \(\mathbf{W}_{g}^{(m)}\) and \(b_{g}^{(m)}\) are the learnable weights and bias for expert \(E_{m}\) in the router. These gating weights determine the contribution of each expert to the final output. The output of the MoE module is computed as a weighted sum of the experts' outputs:  
\[\mathbf{o}_{i}=\sum_{m=1}^{M}g_{i,m}\cdot E_{m}(\mathbf{z}_{i}), \tag{28}\]  
where \(E_{m}(\mathbf{z}_{i})\) is the output of expert \(E_{m}\) for the input \(\mathbf{z}_{i}\). Each expert is implemented as a feedforward neural network with independent parameters, enabling specialization on different subsets of the input features. To ensure efficient utilization of experts, a sparsity-promoting regularization term is added to the loss function. This term minimizes the entropy of the gating weights:  
\[\mathcal{L}_{\text{sparsity}}=-\frac{1}{N}\sum_{i=1}^{N}\sum_{m=1}^{M}g_{i,m} \log g_{i,m}. \tag{29}\]  
By encouraging sparse activation, the model avoids over-reliance on a single expert and effectively utilizes multiple experts. The final training objective combines the Multi-Task Optimization loss and the sparsity regularization:  
\[\mathcal{L}=\mathcal{L}_{\text{multi-task}}+\gamma\mathcal{L}_{\text{sparsity }}, \tag{30}\]  
where \(\gamma\) controls the weight of the regularization term. The MoE module, as shown in the figure, includes a router network that selects experts dynamically based on the input characteristics. This selection process ensures that the model adapts to diverse tasks, such as deformation prediction, environmental impact assessment, and temporal trend analysis. By dynamically selecting relevant experts, the MoE module provides the following advantages: it enhances specialization by enabling certain experts to focus on specific data patterns, improves efficiency by activating only a subset of experts for each input, and increases robustness through diversity among the experts. This adaptive mechanism aligns with the overarching goal of accurately assessing railway deformation risks under varied conditions.
###### Abstract  
Rate-distortion optimization (RDO) plays an essential role in substantially enhancing the coding efficiency. Currently, rate-distortion optimized mode decision is widely used in scalable video coding (SVC). Among all the possible coding modes, it aims to select the one which has the best trade-off between bitrate and compression distortion. Specifically, this tradeoff is tuned through the choice of the Lagrange multiplier. Despite the prevalence of conventional method for Lagrange multiplier selection in hybrid video coding, the underlying formulation is not applicable to 3-D wavelet-based SVC where the explicit values of the quantization step are not available, with on consideration of the content features of input signal. In this paper, an efficient content adaptive Lagrange multiplier selection algorithm is proposed in the context of RDO for 3-D wavelet-based SVC targeting quality scalability. Our contributions are two-fold. First, we introduce a novel weighting method, which takes account of the mutual information, gradient per pixel, and texture homogeneity to measure the temporal subband characteristics after applying the motion-compensated temporal filtering (MCTF) technique. Second, based on the proposed subband weighting factor model, we derive the optimal Lagrange multiplier. Experimental results demonstrate that the proposed algorithm enables more satisfactory video quality with negligible additional computational complexity.  
rate-distortion optimization; mode decision; scalable video coding; Lagrange multiplier; 3-D wavelet-based SVC; motion-compensated temporal filtering +
Footnote †: journal:  
0000-0001-11
# Collusion Attack Resilient 3D Mesh Watermarking Based on Anti-Collusion Fingerprint Code
## 4 Experimental Results
### Experimental Results of [PERSON]'s Code-Based Scheme  
For the experiments with [PERSON]'s fingerprint code, we used two parameter sets to generate the fingerprint code matrix: parameter set \(1\) (\(\epsilon=0.1,c=3\), length \(=2072\)), parameter set \(2\) (\(\epsilon=0.3,c=4\), length \(=1926\)). In addition, we generated 100 watermarked models by embedding a fingerprint code for each recipient of the models. Then collusion attacks were performed 100 times using a set of randomly selected watermarked models for each iteration. For the collusion attack for our tests, we averaged a set of selected models and obtained one attacked model. We also conducted experiments by varying the number of selected models \(k\) (\(1\leq k\leq c\)). Robustness for collusion attack was evaluated by the number of times that tracing all attackers was successful.  
Figure 8 shows robustness to the combination of noise addition and collusion attack. We added Gaussian noise \(\mathcal{N}(0,\sigma)\) to the averaged model, where \(\sigma=0.01,0.001\), or \(0.0001\), and traced the attackers using the proposed detector. The performance of the proposed method for the noise strength \(\sigma<0.001\) was acceptable compare to the results without noise attack. On the other hand, in the experiments with strong noise (\(\sigma\geq 0.01\)), the accuracy of our method decreased. Therefore, similar to the the result shows that the proposed method was robust to the combination of a small amount of added noise and collusion attack. Compared to the GD-PBIBD-based scheme, [PERSON]'s code-based scheme requires more bit capacity to carry the fingerprint code. This aspect results reduction in the ratio between imperceptibility and robustness for the fingerprinting scheme. Although Tardos's fingerprint code outperforms the other anti-collusion codes in many applications, it does not work well in some cases of the mesh fingerprinting such as the high number of accused users, and the environment in which noise can be added.  
Figure 8: _Cont._  
Figure 7: Accuracy of the proposed method with collusion attack and noise addition.
## 1 Introduction
As it was expected, specimens with a crack opening of 0.5 mm showed a higher creep coefficient compared to those with a lower initial crack opening. The effect of fiber geometry and content, concrete compressive strength, maximum aggregate size, and flexural load on the creep of steel fiber-reinforced concrete (SFRC) in a cracked state was studied [21]. Notched specimens were tested under flexural load during 90 days. Crack width, opening rates, and creep coefficients at 14, 30, and 90 days were obtained. Based on these data, semi-empirical equations were obtained for these parameters. It was demonstrated that fiber slenderness and content can significantly modify the flexural creep behavior of SFRC. The authors have proposed to use two-layer concrete beams as effective bending elements, consisting of normal-strength concrete (NSC) in the tensile zone and steel-fibered high-strength concrete (SFHSC) in the compressed one. The idea of TLBs can be used as a way for retrofitting or strengthening along with other known methods, like external retrofitting sub-structures [22]. Using TLBs is effective for decreasing the cracking load, similar to replacing prestressing steel with prestressing carbon fiber-reinforced polymer in new prestressed reinforced concrete (RC) structures [23], etc. The two-layer beams (TLBs) were theoretically and experimentally analyzed. At the first stage of the experimental research, specimens with a length of 70 cm were investigated. At further stages, 3 m long simple supported TLBs and continuous two-span 4 m long TLBs with two equal spans were tested [24; 25]. Later, 3 m and 8 m long prestressed TLBs were experimentally studied [26]. The obtained results have proved that concrete TLBs are very effective bending elements. Although the creep effect is considered in the design codes and modern software [27], there are no accurate dependences for linear and non-linear creeps of TLBs, including such aspects as interaction between the layers. Another interesting topic is how creep changes the concrete modulus of elasticity, the energy of which is dissipated by the concrete section at linear and non-linear creeps. Predicted creep values are important for accurate design of concrete elements at service and even at ultimate limit states. Linear and non-linear creeps were analyzed and an algorithm, allowing consideration of the creep effect was proposed [12]. The methodology is based on structural phenomenon [28] and new theoretical concepts, using just one empirical coefficient in the case of non-linear creep.
# Continuous Camera-Based Premature-Infant Monitoring Algorithms for NICU
## 2 Related Works
The proposed multi-task deep learning algorithms performed three tasks that provided essential information for the automatic extraction of vital signs in a hospital environment: (1) the detection of the patient in the video frame, (2) the automated segmentation of skin areas, (3) and the detection of time periods during which clinical interventions were performed by the attending hospital staff. An automated pipeline of respiration estimation in NICU was introduced by [5]. They compared the traditional optical flow-based respiration signal extraction to the solution based on DeepFlow which is an algorithm for motion matrix estimation proposed by [32]. This is an optical flow extended by a descriptor matching algorithm that builds upon a multi-stage architecture with interleaving convolutions and max pooling-a construction akin to deep convolutional networks. In the referred comparison, the DeepFlow-based solution outperformed the traditional OpticalFlow-based solution evaluated on their dataset which contains records from the infant's whole body with the camera facing from the head down to the feet.
## 5 Experiments
### Controllers Optimization
#### 5.2.1 Fuzzy controller
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input 1** & **Input 2** & **Output** \\ \hline \multirow{4}{*}{\begin{tabular}{c} MF parameters \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Second MF \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Second MF \\ \end{tabular} } \\  & & \(-1<a_{1}<-0.1\) & \(-1<a_{1}<-0.4\) \\  & & \(b_{1}=-0.2\) & \(b_{1}=-0.2\) \\  & & \(-0.1<c_{1}<0\) & \(-0.2<c_{1}<0\) \\  & Second MF & Third MF & Third MF \\  & \(-1<a_{1}<0\) & \(-0.09<a_{2}<0\) & \(-0.1<a_{2}<0\) \\  & \(b_{1}=0\) & \(b_{2}=0\) & \(b_{2}=0\) \\  & \(0<c_{1}<1\) & \(0<c_{2}<0.10\) & \(0<c_{2}<0.1\) \\ \hline \multirow{4}{*}{\begin{tabular}{c} MF parameters \\ \end{tabular} } & \multirow{4}{*}{\
\begin{tabular}{c} Fourth MF \\ \end{tabular} } & Fourth MF \\  & & \(0<a_{3}<0.10\) & \(0<a_{3}<0.1\) \\  & & \(b_{3}=0.2\) & \(b_{3}=0.2\) \\  & & \(0.1<c_{3}<1\) & \(0.09<c_{3}<1\) \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Third MF \\ \end{tabular} } & Fifth MF & Fifth MF \\  & \(a_{2}=0\) & \(a_{4}=0.1\) & \(a_{4}=0.09\) \\  & \(0<b_{2}<0.5\) & \(0.2<b_{4}<0.4\) & \(0.2<b_{4}<0.6\) \\  & \(c_{2}=d_{2}=1\) & \(c_{4}=d_{4}=1\) & \(c_{4}=d_{4}=1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 24: Boundary T1 membership functions parameters of the vector. Figure 8: Representation of the individuals for the T1 FIS. It is noticeable in Figures 13 and 15 that the proposed approach obtains a better performance than the conventional approach. However, in order to validate this, a statistical test is realized. Based on the parameters of the z-test statistic presented in Section 5.1 and Equation (24), the results of the Z-values are presented in Table 27.  
Figure 16: Comparison of the surface for each method for the motor position controller: (a) DE with noise algorithm; (b) ST2 FDE with noise algorithm. Figure 14: DE and ST2 FDE with noise. Figure 15: Comparison of the surface for each method for the motor position controller: (a) DE without noise algorithm; (b) ST2 FDE without noise algorithm. The Z-values obtained for the statistical test demonstrate the improvement of the proposed approach with respect to the original method.
## 3 System Architecture  
This section begins by presenting the delay-sensitive service scheduling architecture based on ICN and SDN stateful programmable data plane technology, followed by an explanation of the functions of each entity and a detailed description of the implementation of network nodes. Finally, the overall service scheduling mechanism under this architecture is summarized.
## 1 Introduction
The most commonly used are: factorial design, response surface methodology (RMS), Plackett-Burman and Box-Behnkena (BBD) design, central composite design (CCD), artificial neural networks (ANN) and Taguchi methods [6; 7]. Factorial design and central composite plans have been used, among others, to establish optimal conditions for the biosynthesis of pyruvate acid by _Torulopsis glabrata_[8], arginine deaminase by _Enterococcus faecium_[9], as well as carboxymethyl-cellulose and pro tease by _Bacillus subtilis_[10]. Proliferation methods of probiotic microorganisms such as _Lactobacillus rhamnosus_[11]_Bacillus coagulans_[12], and _Bifidobacterium longum_[13] have also been developed. Methods by Plackett-Burman and Box-Behnkena have been used to establish the optima composition of the medium for the production of succinic acid by _Corynebacterium glutamicum_[14], as well as the fungal biomass of _Pleurotus_ spp., which has the ability to degrade lignin [15]. Artificial neural networks have been applied, among others, to determine the medium composition for the production of the lipopeptide iturin A by _Bacillus subtilis_, and vitamin C [16]. Both iturin A and vitamin C are obtained from a two-stage fermentation process operated with a mixture of microorganisms containing _Bacillus subtilis_ and _Ketoglonicigenin vulgare_ strains [17]. In recent years, a steady growth in interest has been observed in the optimization of the process parameters by mathematical methods. The Taguchi method, used in this study for instance, implies the existence of an ideal, infinitely regular output factor level, and simultaneously assumes that any deviation from the optima value of operating variables results in a squared loss of quality [18]. This method, used in practice for culture medium optimization, can easily determine how the biomass concentration or process efficiency is affected by each medium component at its various concentrations. It is confirmed in literature that data obtained with this methodology allow for taking action with the aim of selecting the optimal parameters of a culture. The Taguchi method has been applied to optimize conditions in the production of the enzymes protease, produced by _Bacillus subtilis_[19], and lactase, by _Pleutrotus ostreatus_[20]. No investigations with the Taguchi method have been performed to optimize a plant-based medium used for the efficient production of microbial biomass. Hence, the aim of this work was to use the Taguchi approach to optimize the composition of the plant-based medium used for the cultivation of the biomass containing six bacterial strains, which are components of the deodorizing consortium. The scope of the research included optimization of the composition of the plant-based medium for the production of the above-mentioned biomass in a parallel-culture microbiomeactor under laboratory conditions. The biomass yield thus obtained from the optimized medium was compared with the yield obtained from a commercial medium. Finally, the bacterial biomass yields achieved in laboratory conditions were confirmed in pilot and industrial-scale experiments.
# Super-Resolution Remote Imaging Using Time Encoded Remote Apertures
## 1 Introduction
### Our Contribution  
In this work, we propose a novel super-resolution imaging approach that reconstructs scenes from internally scattered light while using their time response. This temporal signal is not subject to diffraction. We show that the resolution of our method is independent of distance and aperture size, and we can reconstruct the scene up to Euclidean congruence. We demonstrate the capabilities of the novel approach while using simulated and experimental data.