esa-sceva/satcom-chunk-collection huggingface dataset:
# Nickel (Ni\({}^{2+}\)) Removal from Water Using Gellan Gum-Sand Mixture as a Filter Material
## 3 Results and Analysis
### Isotherm and Kinetics of Ni\({}^{2+}\) Adsorption Mechanism Control  
The change in hydraulic conductivity with gellan gum content is shown in Figure 7. The hydraulic values of 0 HBL specimens were approximately 10\({}^{-5}\) m/s regardless of  
Figure 6: SEM-EDX spectra of (**a**) pure gellan gum and (**b**) nickel adsorbed gellan gum.  
the magnitude of flow rate running through the specimens. Since HBLs were installed into the soil column, the initial hydraulic conductivity became lower than that of the hydraulic conductivity of the soil column without HBL. This was observed owing to the water absorption of GG. Furthermore, the hydraulic conductivity decreased with time due to the pore-clogging effect of GG [9] as GG interacted with water. The hydraulic conductivity decreased to 5.46 \(\times\) 10\({}^{-6}\), 3.73 \(\times\) 10\({}^{-6}\), and 6.02 \(\times\) 10\({}^{-7}\) m/s for 0.5, 1, and 2% GG, respectively, at a flow rate of 0.2 mL/min. Regarding the flow rate of 0.04 mL/min, the initial hydraulic conductivity (i.e., 1.32 \(\times\) 10\({}^{-5}\) m/s) decreased to 2.28 \(\times\) 10\({}^{-6}\), 8.27 \(\times\) 10\({}^{-7}\), and 4.19 \(\times\) 10\({}^{-7}\) m/s for 0.5, 1, and 2% GG, respectively.  
The presence of meniscus water and free water in the sand column [29] caused the hydraulic behavior. The free water could be easily pushed out under the pressure-induced movement of the nickel-contaminated water, which formed water paths within the sand column. The formation of water paths also depended on the flow rate. The formation of water pathways was fast, with an increase in the flow rate. Therefore, the hydraulic conductivity values were 1.96 \(\times\) 10\({}^{-5}\) m/s and 1.31 \(\times\) 10\({}^{-5}\) m/s for flow rates of 0.2 mL/min and 0.04 mL/min, respectively.  
As the HBL was installed, the water absorption of GG formed the hydrogel matrix in the pore space of HBL and caused the pore-clogging effect [9], which was essential to control the movement of the injected flow (nickel-contaminated water) in HBL. Furthermore, the flow rate affected the interaction between water molecules and GG hydrogel. A low injected flow rate (0.04 mL/min) could promote the pore-clogging effect earlier than a high injected flow rate (0.2 mL/min). With the occurrence of pore-clogging, the interaction between nickel and adsorbents increased.
## 4 Verification and Visualization
### Verification

Table 2: Classification performance comparisons between the optimized VGG model and contrastive Convolutional Neural Networks (CNNs). Figure 5: Experimental results of the welding area defect dataset: (a) training and validation accuracy; (b) training and validation loss. time is long because of its larger size. Considering the cost of training in an industrial environment (time-consumption and computer performance), we adopted a pre-training approach to optimize these CNNs. We modified AlexNet, Resnet-50, and Densenet-121 by replacing the FC layers and changing the nodes, as mentioned in Section 3.1. The three optimized models achieved the accuracy of 98.40% (Pre-AlexNet), 99.87% (Pre-Resnet), and 99.87% (Pre-Densenet-121), respectively. Despite the high accuracy, Pre-Resnet-50 and Pre-Densenet-121 require huge memory consumptions, which an industrial computer cannot afford. Thus, Pre-Resnet-50 and Pre-Densenet-121 cannot be used in an industrial environment. Additionally, we compared the training time of these models for the Q-D classifications task. AlexNet took approximately 30 hours, and Pre-AlexNet was not considered for the time and model size evaluation because it behaved poorly for the three-classifications. Regarding VGG-16, Resnet-50, and Densenet-121, they could not run on an industrial computer. Eventually, they took approximately three hours, four hours, and seven hours, respectively, using three 2080 Ti GPU. Particularly, MobileNetV3-Large improved its performances and became smaller and faster than MobileNetV2 [31]. Nevertheless, in our case, the optimized VGG-16 model had smaller size and faster predict time compared to MobileNetV3-Large model. Our optimized model was derived from the available Python Keras library using a TensorFlow backend on an industrial computer with i5-4460 CPU, and it took approximately one hour to train. From the comparisons of these figures, our pre-trained method greatly saved training time and prediction time. Comparatively, the optimized VGG-16 model could be tested and applied quickly in industrial production, and its efficiency was greatly higher than that of using other CNNs. Additionally, the optimized VGG-16 model had a lower fault positive rate; therefore, it is more suitable to be used in an industrial environment.
# Vibration Energy Harvesting from Raindrops Impacts: Experimental Tests and Interpretative Models
## 3 Experimental Results
### Time-Domain Analysis  
Figure 3 shows examples of the voltage signals measured in the two sets of tests, both with an empty and full spoon. The data selected to represent the indoor scenario correspond to signals with a peak-to-peak voltage close to the mean of all recorded impacts; conversely, the tests selected to represent the outdoor scenario are the ones with the highest peak-to-peak voltage. The reason for this choice resides in the different dispersion of the indoor and outdoor results (see the Statistical Analysis section below).  
Figure 3: Typical voltage signals obtained from the impact of a drop on the empty and full spoon. (**a**) Indoor tests, (**b**) outdoor tests. The voltage was measured for 3 s from the trigger (0.05 V), with a sampling frequency of 2 kHz.  
Figure 2: Raindrops collected on a rainy day (outdoor tests). The ruler on the bottom is used only to give an order of magnitude.  
Indoor and outdoor tests share some common qualitative features. In both testing scenarios, when the drop impacts on the empty spoon the first voltage peaks (positive and negative) are the largest peaks (in absolute value) recorded in the entire response. Conversely, when the drop impacts on the full spoon, the second peaks (positive and negative) are the largest (in absolute value) peaks in the recorded voltage. Additionally, in both indoor and outdoor tests the presence of the pool of water in the spoon allows the generation of a higher voltage. All these effects can be justified by the modified impact dynamics. Finally, all signals are dominated by a single harmonic component: for this reason, the dominant frequency is one of the parameters used to characterize the harvester response.  
Figure 4 shows an in-depth analysis of the signals of Figure 3, according to the methodology described in Section 2. In particular, three parameters are computed from the response to each drop impact: the peak-to-peak voltage, the dominant frequency, and the damping ratio. In addition to the qualitative characteristics discussed previously, it can be noted that the additional mass consisting of the water contained in the spoon causes a lower dominant frequency in the case of the full spoon compared to the case of the empty spoon. The dominant frequency was consistent between indoor and outdoor tests. Moreover, the full spoon caused a higher damping ratio (+43% in the indoor tests, +30% in the outdoor tests). Both indoor and outdoor tests with the empty spoon showed damping ratios that were roughly constant in time. Conversely, owing to the specific waveform, the tests with the full spoon exhibited a roughly constant damping ratio only if the first cycles of vibration were neglected.  
Figure 4: Analysis of the voltage signals. (**a**) Indoor tests, empty spoon; (**b**) outdoor tests, empty spoon; (**c**) indoor tests, full spoon; (**d**) outdoor tests, full spoon.
# DFD-SLAM: Visual SLAM with Deep Features in Dynamic Environment
## 4 Results
### Test on TUM-VI Dataset
Finally, we found that the DFD-SLAM system, which employs bag-of-words descriptions, consistently outperforms ORB-SLAM3 with activated loop closure detection across several sequences. This underscores the effectiveness of our front-end tracking approach. The experiment demonstrates that our tracking and loop closure strategies significantly improve the system's performance. In pure monocular scenarios, we found significant accuracy discrepancies between DFD-SLAM and ORB-SLAM3 in the _Magistrale1_ sequence with and without loop closure detection enabled. We have already demonstrated that DFD-SLAM's deep feature strategy and frame rotation estimation strategy yield excellent tracking performance without loop closure. Furthermore, to investigate the substantial improvement brought by loop closure detection, we designed more precise experiments. Specifically, we statistically compared the number of loop closures between the two methods, with DFD-SLAM experiencing five loop closures and ORB-SLAM3 only encountering two loop closures, as shown in Figure 6. Figure 6a-e represent the scenes where DFD-SLAM detects loop closures during camera traversal, while Figure 6f,g represent the loop closure scenes encountered by ORB-SLAM3. Our approach is able to detect more loop closures at intersections and corners in scenes where features are not distinctly clear. The global descriptors based on deep features offer advantages over bag-of-words descriptors in such scenarios. The superior descriptive capability of global descriptors significantly enhances DFD-SLAM's loop closure detection in environments with less distinctive features, thereby markedly improving system performance. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**TUM-VI (ATE)**} \\ \cline{2-10}
**Sequence** & \multicolumn{1}{c}{**DFD-SLAM**} & \multicolumn{1}{c}{**DFD-SLAM (BOW)**} & \multicolumn{1}{c}{**DFD-SLAM (HFNet)**} & \multicolumn{3}{c}{**ORB-SLAM3**} \\ \cline{2-10}  & **Without** & **With** & **Boost** & **With** & **Boost** & **Without** & **With** & **Boost** \\  & **Loop** & **Loop** & **Loop** & **Loop** & **Loop** & **Loop** & **Loop** & **Loop** \\ \hline Magistrale1 & 5.724 & 4.427 & 0.227 & 0.417 & **0.927** & 12.943 & 9.897 & 0.235 \\ Magistrale2 & 1.153 & 0.879 & 0.238 & 0.716 & **0.379** & 1.120 & 0.757 & 0.324 \\ Magistrale4 & 1.013 & 0.523 & 0.484 & 0.761 & 0.249 & 3.376 & 0.893 & **0.735** \\ Magistrale5 & 2.119 & 2.012 & 0.050 & 1.472 & **0.305** & 2.547 & 2.539 & 0.003 \\ Magistrale6 & 3.587 & 3.226 & 0.101 & 2.124 & **0.408** & 4.568 & 4.035 & 0.117 \\ average & 2.719 & 2.213 & 0.220 & 1.098 & **0.454** & 4.911 & 3.624 & 0.282 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation of loop closure in TUM-VI.
# Optimizing CNN-LSTM for the Localization of False Data Injection Attacks in Power Systems
## 4 Simulation Experiments  
### Simulation Experiment Settings  
In this research, the experimental setup was executed on an Ubuntu 20.04 operating system platform, configured with an Intel Core i7-10700 CPU (Intel, Santa Clara, CA, USA), an NVIDIA Quadro RTX 6000 GPU, and 32 GB of RAM (NVIDIA, Santa Clara, CA, USA). Throughout the experimental procedures, Python version 3.8.10 and TensorFlow version 2.13.0 were utilized for computational operations.  
To validate the proposed GA-CNN-LSTM-based FDIA detection method for power systems, simulation experiments were conducted on the IEEE bus test systems. The topological structure and parameter information were obtained from MATPOWER [26]. The load data utilized in the experiments were from the \"household_power_consumption\" data set [27], which collects the electricity consumption records of a single household for more than four years.  
In the simulation experiments, we utilized the node voltage amplitude and phase angle as state variables and considered the active power at the nodes and the active power of the branches as the measured values. Taking the IEEE 14-bus system as an example, the measurement vector included a total of 34 dimensions, and its topology is shown in Figure 6. First, we obtained the true value of the measurement vector from MATPOWER and simulated the actual measurement error by adding Gaussian noise with a standard  
Figure 5: Flow chart of the genetic algorithm optimization model’s hyperparameters.  
deviation of 0.05. We then added the attack vectors to the actual measurement vectors to form the attacked measurement vectors and generated corresponding classification labels for each vector. For the IEEE 14-bus test system and the IEEE 118-bus test system, we generated 50,000 and 12,000 compromised measurement vectors and divided them into training and testing sets in a 4:1 ratio. To enhance the convergence velocity and mitigate the risk of overfitting, we employed the mini-batch gradient descent method for training the network. Within each batch, 7/10 of the data was allocated to the training set, while 3/10 was designated for the validation set. Due to the imbalance of the false data injection attack dataset, we used the Weighted Cross Entropy Loss function as the loss function for model training [28]. By assigning higher weights to the attack data, the model's ability to identify attack data was effectively enhanced.
# Algorithm for Option Number Selection in Stochastic Paired Comparison Models
## 1 Introduction
In these studies, the numbers of options were fixed before data collection, based on preliminary empirical observations. If we fix the number of options, then the data will be available to us in a corresponding structure. It seems natural to use a fixed number of options during an evaluation. But data can also be transformed. In the past, ties were transformed into half a win and half a loss. Which option number is the best choice for an evaluation? Is this method better than an evaluation using a three-option model? Is it possible that by using a different number of options and transforming the data, we could obtain a model that fits the data better? In which cases is this possible? What can be said about the choice of the number of options? After studying the literature, we did not find an answer to this question. We intend this paper to fill this gap. As a result, we develop an algorithm by which the appropriate number of options can be selected. This algorithm relies on both theoretical and experimental aspects. Theoretical aspects concern evaluability considerations, while experimental aspects concern more accurate estimations and are supported by a large number of computer simulations. This paper is structured as follows: In Section 2 we introduce a general model allowing for \(s\) options with an MLE parameter estimation. Then, we turn to the theoretical aspects of evaluability. First we investigate the relationship between two- and four-option models, and then the general case of \(s\) and \(s+2\) follows. The preliminary results for the case \(s=3\) and \(s+2=5\) are contained in the conference paper [50]. We can see that if data are evaluable in the \(s\)-option model and the data's transformation satisfies a natural condition, then the data can also be evaluated in the \((s+2)\)-option model. After these theoretical considerations, in Section 4 we present the results of a large number of computer simulations. The runs investigate how often the \(s\)-option model (\(s=2,3\)) and the \((s+2)\)-option model (\(s+2=4,5\)) are evaluable. If both are evaluable, we investigate which one fits better with the data, and that is the one worth using for evaluations. We define a measurement for fitting which helps us decide on the choice of the number of options. In the subsequent section, we elaborate on and present an algorithm for performing the decision process, and the paper then concludes with our final remarks.
## 3 Results
### Demonstration on a Case Study
#### 3.2.2 Bayesian Calibration of the Reduced Single DoF Reference Model  
The reduced single DoF BWBN model was used to validate the entire procedure in the first phase. Tables 5 and 6 report the prior information on model parameters and the results of the Bayesian inversion, respectively. Figure 10 depicts the posterior distributions of the parameters, while Figure 11 depicts the velocity response of the oscillator once the model has been calibrated. From the inspection of the latter, it can be concluded that uncertainties on the input parameters do not affect the response prediction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Mean** & **Std. Dev.** & **(0.05–0.95) Quant.** & **MAP** \\ \hline \(k_{i}\) (N/m) & \(5.7\times 10^{2}\) & \(0.55\times 10^{-1}\) & \((5.7\)–\(5.7)\times 10^{2}\) & \(5.7\times 10^{2}\) \\ \(\beta\left(\text{m}^{-1}\right)\) & \(34\) & \(0.26\) & \((34\)–\(35)\) & \(34\) \\ \(\gamma^{*}\) & \(-0.47\) & \(0.007\) & \((-0.49\)—\(-0.46)\) & \(-0.48\) \\ \(N\) & \(0.98\) & \(0.15\) & \((0.96\)–\(1)\) & \(1\) \\ \(\delta_{A}\) & \(4.2\times 10^{-5}\) & \(6.8\times 10^{-6}\) & \((2.8\)–\(5.3)\times 10^{-5}\) & \(3.8\times 10^{-5}\) \\ \(\sigma^{2}\) & \(1.2\times 10^{-10}\) & \(3.6\times 10^{-11}\) & \((0.52\)–\(1.7)\times 10^{-10}\) & \(3.5\times 10^{-11}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Marginal posterior distribution of the BWBN model parameters for the case study.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(k_{i}\) (\(N/m\)) & \(5.735\times 10^{8}\) \\ \(\beta\left(1/m\right)\) & \(35.01\) \\ \(\gamma\) & \(-16.68\) \\ \(N\) & \(1\) \\ \(\delta_{A}\) (\(1/f\)) & \(1.812\times 10^{-6}\) \\ \(m\) (\(kg\)) & \(573\),\(459\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Reference model parameters.  
Figure 9: Reference models. (**a**) Structural model; (**b**) acceleration of the first floor.  
Moreover, the value of the degrading term \(\delta_{A}\) turns out not to influence the response prediction. This is due to the fact that during the earthquake shaking (PGA \(\approx\) 1 m/s\({}^{2}\)) the structure exhibited a low level of damage [44].  
Both parametric and non-parametric degradation models have been used herein. Two models per each family are first mathematically introduced, then insights into the accuracy afforded by each model are provided.  
Figure 11: Velocity response prediction of the BWBN model for the case study.  
Figure 10: Posterior samples of the BWBN model parameters for the case study. The vertical lines denote the mean of the distribution (in yellow) and the maximum a posteriori estimate (MAP) (in red).
# Current Knowledge on the Fungal Degradation Abilities
## 4 Enzymatic Action

Table 3: Overview of fungal species for which isolates have been identified as displaying ligninolytic abilities in biodeteriorative plate essays. biodeterioration processes by the action of deteriorating enzymes [75; 76]. In particular, the silks fibroin and sericin can both be the target of microbial attack [77]. Moreover, wool keratains can also be the target of attack by microbes [77]. Evaluation of fibroinolytic and keratinocyte action in cultural heritage scenarios has been helpful to study mummies, funeral clothes and accessories biodeterioration [78; 79; 80]. Moreover, fungal chitinolytic and pectinolytic action has also been pinpointed as a threat to Ancient Yemeni mummies preserved with diverse organic compounds [81]. Additionally, esterase action profiling has also been helpful to study isolates retrieved from wax seals, air, textiles and human remains [82; 83; 84]. Fibironolytic screening can be conducted using fibroin agar, with the fibroinolytic action being evaluated by the isolates ability to grow in the culture-amended plates [85]. Moreover, keratinocyte action can be evaluated using feather broth and keratin medium and positive ability can be verified by media turbidity changes [79; 85]. On the other hand, chitinolytic activity can be evaluated using powdered chitin agar [86] and pectinolytic activities can be evaluated with media containing pectin [87]. Both these deteriorative activities can be estimated and quantified [81]. Additionally, esterase action can be studied using Tributyrin agar and Tween 80 agar [83; 84; 88]. Their action can be detected by the development of clear zones (Tributyrin agar) or by the precipitation of insoluble salts and compounds (Tween 80) around colonies. As occurring with ligninolytic action, data regarding filamentous fungi fibroinase and keratinocyte action is still infrequent. Twenty-three species were found to be able of fibroinolytic activity, while more than twenty-five were found to have keratinocyte action. Again, _Apergillus_ and _Penicillium_ species also dominate these biodeteriorative profiles (Tables 4 and 5). Moreover, various _Alternaria_ species also displayed putative keratinocyte abilities. On the other hand, chitinolytic abilities have been identified for _Apergillus niger_ and _Penicillium_ sp., while pectinolytic action has been identified in a slightly more diversified range of fungal genera and species (_Apergillus candidus_, _Mucor circinelloides_, _Penicillium echinulatum_, _Scopularopsis koningii_, _Stachybottys charatum_ and _Trichoderma hamatum_) [81]. In parallel, fifty species have been identified as displaying estereolytic action, with a great dominance of _Apergillus_ and _Penicillium_ species (Table 6). Understandably, the detection of these fungal species on crypt environments, human remains, buried materials, mummies, wax seals, textiles and clothes denotes a putative threat to these materials [3].
## 4 Conclusions  
In this study, Ni/Al RMFs were used as a heating source for joining epoxy matrix CFRP and aluminum alloys. Dense and reliable joints were obtained and the highest shear strength of 9.5 MPa was reached using Ni/Al RMFs with the thickness of 96 \(\upmu\)m under the pressure of 5 MPa. The total reaction time was less than 100 ms. Ni/Al RMFs provide a novel method for joining CFRP and aluminum without significant power requirements or the weight of equipment transported into space. Laser direct joining of CFRPs to aluminum alloys was also investigated. Femtosecond pretreatment on the surface of CFRPs and aluminum alloys proved to be efficient for improving the tensile strength of the joints. The highest tensile strength of 20 MPa was obtained when using a 150 W diode laser for joining, with the help of 300 \(\upmu\)m PC sheets. These studies may pave the way for the development of light-weight materials, CFRPs and aluminum alloys for joining and repairing in space or underwater.  
Conceptualization, Y.M., A.H.; Methodology, Y.M., D.B.; Software, J.H., Y.Y.; Validation, Y.M., A.H.; Investigation, Y.M.; Writing--original draft preparation, Y.M.; Writing--review and editing, Y.M., D.B., H.L., A.H.; Supervision, H.L., A.H.; Project administration, A.H.  
This work was funded in part by the National Natural Science Foundation of China (NSFC) under grant numbers 51575016 and 51475007.  
We also acknowledge the Joint Institute of Advanced Materials for use of their electron microscopy and X-ray diffraction equipment.  
The authors declare no conflict of interest.  
Figure 11: Fracture surface of the joint with 20 MPa tensile strength on (**a**) the aluminum side; (**b**) the CFRP side.  
Figure 10: Tensile strength of joints using laser joining with different laser powers.