esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Set-Up  
The digestion process was conducted in anaerobic reactors with full mixing and an active volume of 4.0 dm\({}^{3}\) (total volume: 5.0 dm\({}^{3}\)) (Figure 2). The reactors were kept in an insulated, heated chamber. The temperature in the reaction chambers was maintained at 38 \({}^{\circ}\)C. The starting concentration of anaerobic sludge in the anaerobic chambers was approximately 4.0 gTS/dm\({}^{3}\). The start-up took 60 days, during which the reactors were filled with the inoculum, then fed with a continuous supply of the feedstock. After start-up, the organic load rate (OLR) was around 2.0 gVS/dm\({}^{3}\)-d. The hydraulic retention time (HRT) was 40 d in all of the variants. The experiment was run for 80 days, with samples taken every 4 days. The study design is presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Unit**} & \multicolumn{6}{c}{**Variant**} \\ \cline{3-8}  & & **1—** & **2—** & **3—** & **4—** & **5—** & **6—MA0 +** & **1** \\  & & **MA100+MS0** & **MAS0+MS20** & **MA60+MS40** & **MA40+MS60** & **MA20+MS80** & **MS100** & **Inoculum** \\ \hline Dry matter (TS) & \% & \(10.4\pm 1.5\) & \(12.0\pm 1.4\) & \(14.0\pm 1.2\) & \(17.1\pm 1.1\) & \(21.8\pm 1.0\) & \(30.2\pm 0.9\) & \(3.8\pm 0.2\) \\ Dry organic matter (VS) & \% TS & \(87.7\pm 1.1\) & \(88.9\pm 0.9\) & \(90.2\pm 0.7\) & \(91.4\pm 0.5\) & \(92.6\pm 0.4\) & \(93.8\pm 0.2\) & \(68.5\pm 2.5\) \\ Total nitrogen (TN) & mg/gTS & \(46.0\pm 3.9\) & \(39.0\pm 3.3\) & \(32.0\pm 2.7\) & \(25.1\pm 2.1\) & \(18.1\pm 1.5\) & \(11.1\pm 0.9\) & \(33.1\pm 3.4\) \\ Total phosphorus (TP) & mg/gTS & \(4.4\pm 0.9\) & \(4.0\pm 0.8\) & \(36.0\pm 0.7\) & \(3.2\pm 0.5\) & \(2.8\pm 0.4\) & \(2.4\pm 0.3\) & \(1.7\pm 0.2\) \\ Total carbon (TC) & mg/gTS & \(464\pm 25\) & \(463\pm 23\) & \(462\pm 20\) & \(462\pm 18\) & \(461\pm 15\) & \(460\pm 13\) & \(309\pm 28\) \\ Total organic carbon & mg/gTS & \(437\pm 20\) & \(438\pm 19\) & \(439\pm 18\) & \(440\pm 17\) & \(440\pm 16\) & \(441\pm 15\) & \(199\pm 34\) \\ C.N ratio & - & \(9.5\pm 0.4\) & \(11.2\pm 0.7\) & \(13.7\pm 0.9\) & \(17.5\pm 1.2\) & \(24.3\pm 1.4\) & \(39.6\pm 1.7\) & \(9.4\pm 0.1\) \\ pH & - & \(8.1\pm 0.8\) & \(7.8\pm 0.6\) & \(7.4\pm 0.5\) & \(6.9\pm 0.4\) & \(6.0\pm 0.2\) & \(4.6\pm 0.1\) & \(7.2\pm 0.3\) \\ Protein & \% TS & \(28.7\pm 2.5\) & \(20.9\pm 2.1\) & \(13.1\pm 1.7\) & \(24.8\pm 1.4\) & \(17.0\pm 1.0\) & \(9.2\pm 0.6\) & \(20.7\pm 2.8\) \\ Lipids & \% TS & \(20.0\pm 1.4\) & \(12.9\pm 1.2\) & \(5.8\pm 1.0\) & \(16.4\pm 0.9\) & \(9.3\pm 0.7\) & \(2.2\pm 0.5\) & \(3.1\pm 0.5\) \\ Sugars & \% TS & \(15.8\pm 2.6\) & \(33.7\pm 2.3\) & \(51.5\pm 2.0\) & \(24.8\pm 1.6\) & \(42.6\pm 1.3\) & \(60.4\pm 1.0\) & \(1.6\pm 0.4\) \\ \hline \end{tabular}
\end{table}
Table 1: Profile of the feedstock digested in different variants and anaerobic sludge (inoculum).  
Figure 2: The set of anaerobic reactors used in the experiments: (**a**) technical diagram; (**b**) general view; (**c**) thermally insulated chamber with a heating system.
## 4 Permafrost Subsurface and Land Surface Features
[72; 73] used Landsat-5 TM, as well as Radarsat-2 (C-band SAR) for permafrost modeling. A land cover map was produced, and values for peat thickness and vegetation height were associated with the resulting classes. Ground temperatures have been up-scaled using an ecotype land cover map (from among other sources, Landsat, stratified NDVI and unsupervised classification of [PERSON] et al. [74]) on a regional scale [75]. The available classes were grouped into four clusters representing certain ground temperature ranges in a permafrost transition zone in Alaska. Ecotypes with thick moss and organic layers dominate the group with lowest temperatures, whereas thinner organic layers can be found at the warmer sites. Sites with mean annual ground temperature above zero degrees are sites with alder, spruce and also burned areas. A potential information source on organic layer properties is also C-band SAR, as measurements under frozen conditions reflect soil organic carbon content north of the treeline [76]. Extent mapping of peatland with C-band SAR (unfrozen period) has been shown in numerous studies (e.g., [77; 78]). It is assumed that the near surface is comparably wet. An alternative approach is to take advantage of the backscatter signal arising from frozen surface conditions, which can provide general information on wetland distribution with up to four classes [79]. Land cover serves also as input for permafrost modeling, including semi-empirical equilibrium models [1]. It has strong implications for the small-scale distribution of the snow cover. [PERSON] et al. [1] reclassified a MODIS land cover product into high vegetation/forest (\"International Geosphere-Biosphere Programme\" (IGBP) Classes 1-9, 11) and bare ground/low vegetation (IGBP Classes 10, 15, 16). Areas with bare ground or low vegetation are locations where strong redistributions of snow due to wind drift can occur. Snow cover is more uniform in areas with trees and high vegetation. In some areas the model failed to reproduce the permafrost patterns. Especially the patchiness of shrublands cannot be resolved. [PERSON] et al. [1] suggest that subsurface properties should also be considered to better estimate the active layer damping factor. [PERSON] [80] has defined the class'shrub' as vegetation that is less than 0.5 m tall. This specification is supposed to reflect certain properties, such as trapping by snow, where the threshold represents the approximate height boundary between shrubs that extend significantly above tussocks and shrubs that grow primarily between tussocks [80]. To better understand changes in permafrost temperatures, information on snow cover extent and depth, including redistribution potential and rival traps, is imperative. Not unexpectedly this demands clear delineation of assorted vegetation types through appropriate thematic detail.
## 3 Results and Discussion
### Antibacterial Performance
These results are consistent with the fact that combining metal or metal oxides with Gr, rGO, or graphene can result in a synergetic effect of the composite by increasing the inhibition zone and the toxicity of the matrix (GO) and, therefore, preventing bacterial infection. The results reported in the literature on ZnO NPs [83] and GO/ZnO and RGO/ZnO nanocomposites [84] against _E. coli_ have proven that increasing the amount of ZnO in the GO/ZnO composite resulted in increased inhibition zone, which is similar to our finding in the present manuscript. The improved antibacterial properties of ZnO/GO nanocomposites were attributed to the combined activation of mechanisms of Zn ions from ZnO and the physical interaction between the GO sharp edge and _E.coli_[84]. In fact, the addition of metallic ions (+) to GO, rGO and/or graphene will definitely improve the antibacterial properties of these materials due to the synergetic effect of metallic ions when combined with GO or rGO [85; 86]. It is important to note here that the GO/Zn (OH)\({}_{\rm x}\) composites synthesized in this work can eventually be applied to a bacterially contaminated environment. In fact, the GO and GO/Zn (OH)\({}_{\rm x}\) nanocomposite can be applied to a bacterially contaminated environment as an antibacterial soap or oil since ZnO and Zn (OH)\({}_{\rm x}\) cannot dissolve in water. Zinc oxide is not soluble in water; thus, it requires oil or other such carrier agents to function effectively. The nanocomposite can also be used as wipes or in the form of particles dispersed in  
Figure 11: Inhibition zone against _E. coli_ bacteria of GO/Zn (OH)\({}_{\rm x}\) nanocomposites with different coating amounts of Zn (OH)\({}_{\rm x}\): (**A**) 0%, (**B**) 25%, (**C**) 50%, and (**D**) 75 wt. %. gel. However, we are still in the early stages of applying the material as a commercial antibacterial agent. This requires more work and research to achieve this goal.
## 4 Mobility Model of the MS
### Optimization Model  
We present in the following the optimization model used to obtain the optimal path trajectory of the MS. The function \(f\) in Equation (2) represent the objective function which minimizes the tour length of the MS in term of distance:  
\[f:\min\sum_{i,j\in\mathcal{C},j\
eq j}^{n}c_{ij}d_{ij} \tag{2}\]  
Subject to:  
\[0\leq d_{ij}\leq 1\forall i,j\in C;\,i,j=1,\ldots,n;\,i\
eq j;\] (i)  
\[\sum_{i=1}^{n}d_{ij}=1;\forall i,j\in C;\,i\
eq j;\] (ii)  
\[\sum_{j=1}^{n}d_{ij}=1;\forall i,j\in C;\,j\
eq i;\] (iii)  
\[\sum^{n}d_{ij}\leq n-1;\forall i,j\in C;i\
eq j;\] (iv)  
\[E_{i}\geq E_{min};\forall i\in C;\] (v)  
where \(C\) designates the set containing \(n\) cluster heads in the wireless sensor network, \(c_{ij}\) represents the distance between two cluster head nodes (the distance between cluster head \(i\) and cluster head \(j\)). \(d_{ij}\) is a binary value (0 or 1) which indicates whether there is a displacement from cluster head \(i\) to cluster head \(j\) in the optimal path or trajectory which is defined in constraint (i).  
Constraint (ii) makes the MS visits only one CH \(j\) after it visits a CH \(i\). The MS visits a CH \(j\) and it must come from only one CH \(i\) which is expressed in constraint (iii). Constraint (iv) ensures that the tour of the MS is fully connected, and it visits all CHs in the network. Constraint (v) is defined to ensure that the CH to be visited have enough energy to transmit data toward the MS.  
In Equation (1), \(c_{ij}\) is calculated by Equation (3):  
\[c_{ij}=\sqrt{(w-u)^{2}+(z-v)^{2}}\forall i,j\in C,i\
eq j.\,\forall(w,z),(u,v) \in\mathbb{R}^{2} \tag{3}\]  
where \((w,z)\) and \((u,v)\) are the coordinates of the cluster head \(i\) and \(j\) respectively in two dimensions zone (2D).  
In Equation (2), \(d_{ij}=\left\{\begin{array}{cc}1,&\text{If there is a displacement from $i$ to $j$ in the optimal path.}\\ 0,&\text{else.}\end{array}\right.\)
# LightSeg: Local Spatial Perception Convolution for Real-Time Semantic Segmentation
## 4 Experiments
### Effective Receptive Field Analysis  
The receptive fields of RegSeg and LightSeg were visualized to demonstrate how our network effectively addresses the \"Gridding Effect\" [12] issue in RegSeg.  
The visualization results are shown in Figure 11; the darker colors indicate that the coordinates are focused within the receptive field of the corresponding feature kernel. Based on the visualization results, the following conclusions can be drawn:  
* The D Block in RegSeg effectively increases the receptive field, but also causes sparse gradients due to its dilated convolution structure, resulting in the \"Gridding Effect\".
* Both LightSeg and RegSeg exhibited relatively small ERFs in the early stages of the network.
* In Stage 3, LightSeg showed a global receptive field while still maintaining a strong focus on local regions.  
Figure 11: Effective receptive field (ERF) Analysis on Cityscapes. The depth of color represents the degree of influence of the input image on the target value. A darker color indicates a significant influence, while a lighter color indicates a minimal impact on the target value.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Model** & **Extra Data** & **FPS \(\uparrow\)** & **mIOU \(\uparrow\)** \\ \hline STDC2-Seg [37] & IM & 152.2 & 73.9 \\ GAS [38] & - & 153.1 & 72.8 \\ CAS [39] & - & 169 & 71.2 \\ VideoGCRF [40] & C & - & 75.2 \\ BiSeNetV2 [41] & C & 124 & 76.7 \\ BiSeNetV2-L [41] & C & 33 & 78.5 \\ DDRNet-23 [36] & C & 94 & 80.1 \\ RegSeg [10] & C & 70 & 80.9 \\ \hline LightSeg & C & 99.4 & 77.1 \\ \hline \end{tabular}
\end{table}
Table 5: Comparison with state-of-the-art methods on Camvid, IM: ImageNet,C: Cityscapes.
###### Abstract  
(1) Background: Cough is a major presentation in childhood asthma. Here, we aim to develop a machine-learning based cough sound classifier for asthmatic and healthy children. (2) Methods: Children less than 16 years old were randomly recruited in a Children's Hospital, from February 2017 to April 2018, and were divided into 2 cohorts--healthy children and children with acute asthma presenting with cough. Children with other concurrent respiratory conditions were excluded in the asthmatic cohort. Demographic data, duration of cough, and history of respiratory status were obtained. Children were instructed to produce voluntary cough sounds. These clinically labeled cough sounds were randomly divided into training and testing sets. Audio features such as Mel-Frequency Cepstral Coefficients and Constant-Q Cepstral Coefficients were extracted. Using a training set, a classification model was developed with Gaussian Mixture Model-Universal Background Model (GMM-UBM). Its predictive performance was tested using the test set against the physicians' labels. (3) Results: Asthmatic cough sounds from 89 children (totaling 1192 cough sounds) and healthy coughs from 89 children (totaling 1140 cough sounds) were analyzed. The sensitivity and specificity of the audio-based classification model was 82.81% and 84.76%, respectively, when differentiating coughs from asthmatic children versus coughs from 'healthy' children. (4) Conclusion: Audio-based classification using machine learning is a potentially useful technique in assisting the differentiation of asthmatic cough sounds from healthy voluntary cough sounds in children.  
cough; asthma; machine learning; computer assisted diagnosis Article
## 3 Case Study 1: Robo-Chef
#### Synergy Opportunities for Robo-Chef  
We divide the opportunities for Robo-chef improvements into two parts: (1) conversational capabilities, (2) fundamental AI capabilities.  
While Robo-chefs currently do not possess the same conversational capabilities as service systems such as chatbots, it is inevitable that its maturity trajectory will eventually lead to the addition of these capabilities. Therefore, we start from discussing the improvement opportunities for Robo-chef that come from conversational capabilities.  
Synergy potential of Conversational AI capabilities:  
1. Speech communication potential: the Robo chef could get instructions from the human chef while the human chef is busy; this has the potential for activating the robo-chef without immediate closeness and while the chef's hands are occupied in other activities. Speech instructions are easy and flowing, with the ability for passing much more information and exerting tighter control on the robo-chef; this would make the robo chef more flexible and easier to guide.
2. Gesture recognition: collaborative work with humans is the hallmark of the new digital age. Therefore, a robo chef should be able to collaborate with a human chef. Using gesture recognition, the human chef can efficiently signal the robo--chef several important instructions such as: (1) stop current activity, (2) increase or decrease the flames, (3) operate faster or slower, (4) wait, (5) start stirring etc.
3. Face and sentiment recognition: These abilities are currently missing form Robo-chefs, but greatly improve the conversational capabilities of many other service AI systems (e.g., chatbots). Thus, they hold the same potential for Robo-chefs. Synergy potential of fundamental AI Capabilities  
1. Computer vision potential: certain robo-chefs currently have ability to verify the readiness of the food they cook and its location; however, there is no evidence of using vision capabilities for identifying silverware and cookware and bringing them to the cooking arena, as well as removing them for washing and cleaning.
2. Navigation potential: If robo-chefs could navigate the clattered kitchen, this would enable them to move from place to place, to bring pots, pans mixers and other large cookware from different parts of the kitchen; this dramatically increases the robochet's independence and efficiency.
3. Machine learning (ML): ML is a very efficient tool for identifying patterns and their effects, Identify the correct combination of dosages that would make a recipe a great success. ML can identify repeating situations and can infer when and what to do, to improve the process. For example: (1) when to bring hot or cold water to the chef, (2) when to bring salt or certain spices to the chef, (3) when to change the oven temperature and how to control it, etc.
## 6 Conclusions  
This study presents a surrogate-assisted multi-objective optimization framework for IPMSMs under limited sample constraints, addressing critical challenges in computational efficiency, variable selection, and Pareto solution prioritization.  
The proposed framework integrates three core components to systematically optimize IPMSM performance. First, a dual-strategy variable importance analysis combining RF and ANOVA is employed to identify key design parameters. RF focuses on the contribution of design variables to the model's predictions, while ANOVA examines the sensitivity of design variables to optimization objectives, ensuring that the selected variables (flux barrier thickness \(b_{r}\), PM thickness \(b_{m}\), and PM width \(l_{m}\)) possess both predictive utility and high sensitivity. Next, a GRNN surrogate model is constructed using LHS optimized by the Morris-Mitchell criterion. GRNN's single hyperparameter architecture simplifies training and enhances efficiency under limited samples, achieving high prediction accuracy (\(R^{2}>0.96\)). Finally, an MOPSO algorithm generates Pareto-optimal solutions, and an entropy-weighted distance metric objectively selects the final design by quantifying tradeoffs between normalized objectives, eliminating reliance on subjective expert judgment.  
The optimized IPMSM demonstrates significant performance improvements over the prototype. Finite element analysis validates a 4.62% increase in average output torque (from 154.96 N-m to 162.12 N-m), a 0.15% enhancement in efficiency (from 98.05% to 98.20%), and a 10.48% reduction in torque ripple (from 36.13 N-m to 32.35 N-m). The surrogate model predictions align closely with simulation results, exhibiting relative errors below 2.92%, confirming the framework's robustness. These enhancements are attributed to optimized magnetic flux concentration and reduced leakage through strategic adjustments to \(b_{r}\), \(b_{m}\) and \(l_{m}\), validated by increased air gap flux density and improved pole-arc characteristics.  
In conclusion, this study advances surrogate-assisted optimization for IPMSMs, providing a systematic, data-driven framework that balances computational efficiency, predictive accuracy and engineering practicality.  
Conceptualization, M.H. and Z.L.; methodology, M.H. and Z.W.; software, M.H. and Z.W.; validation, Z.W.; formal analysis, M.H.; investigation, M.H. and Z.W.; resources, Z.L.; data curation, Z.W.; writing--original draft preparation, M.H. and Z.W.; writing--review and editing, Z.L.; visualization, M.H. and Z.W.; supervision, Z.L.; project administration, M.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data availability Statement: Data are contained within this article.  
The authors declare no conflicts of interest.
###### Abstract  
In engineering practice, properly characterizing the spatial distribution of soil liquefaction potential and induced surface settlement is essential for seismic hazard assessment and mitigation. However, geotechnical site investigations (e.g., cone penetration test (CPT)) usually provide limited and sparse data with high accuracy. Geophysical surveys provide abundant two-dimensional (2D) data, yet their accuracy is lower than that of geotechnical investigations. Moreover, correlating geotechnical and geophysical data can effectively reduce site investigation costs. This study proposes a data-driven adaptive fusion sampling strategy that automatically develops an assessment model of the spatial distribution of soil liquefaction potential from spatially sparse geotechnical data, performs monitoring of liquefaction-induced settlement, and integrates spatiotemporally unconstrained geophysical data to update the model systematically and quantitatively. The proposed strategy is illustrated using real data, and the results indicate that the proposed strategy overcomes the difficulty of generating high-resolution spatial distributions of liquefaction potential from sparse geotechnical data, enables more accurate judgment of settlement variations in local areas, and is an effective tool for site liquefaction hazard analysis.  
liquefaction potential; cone penetration test; Bayesian compressive sampling; information entropy; data fusion; differential settlement +
Footnote †: journal: Applied Sciences
# Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector
## 4 Results and Discussion
### Experiment 1: Impact of Pretraining on Performance
#### 4.1.2 Discussion  
The comparison of Tables 2 and 3 indicates that the indicators were improved in the case of the extremely scarce training sets, except for the slight decrease in the precision rate of \"mild\". The recall rate of \"debris\" was increased by approximately 10%. Table 4 also shows that mF1 and mAP are increased by approximately 10%. The overall performance of the model is generally improved.  
The recall rate of the \"debris\" class can be greatly improved because the features of the ruins are simple, and the features can be extracted well by the CNN. However, the characteristics of the \"mild\" class are complex, and the structure, position, and angle of the house are all factors that affect feature extraction. Therefore, many samples are needed to improve this situation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **mF1** & **mAP** \\ \hline
**SSD** & 51.95 & 39.01 \\
**SSD\_CAE** & 56.025 & 44.83 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of the SSD and SSD_CAE methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Label** & **Debris** & **Mild** & **Miss** & **Total** & **Recall** \\ \hline
**Debris** & 60 & 25 & 54 & 139 & 43.165\% \\
**Mild** & 31 & 42 & 15 & 88 & 47.727\% \\
**False detection** & 5 & 4 & - & & - \\
**Total** & 96 & 71 & & - \\
**Precision** & 62.500\% & 59.155\% & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of the single-shot multibox detector (SSD) method in the test dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Label** & **Debris** & **Mild** & **Miss** & **Total** & **Recall** \\ \hline
**Debris** & 74 & 28 & 37 & 139 & 53.237\% \\
**Mild** & 28 & 43 & 17 & 88 & 48.863\% \\
**False detection** & 10 & 3 & - & - \\
**Total** & 112 & 74 & & - \\
**Precision** & 66.071\% & 58.108\% & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of the SSD-convolution auto-encoder (CAE) method in the test dataset.