esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment  
### Datasets  
The proposed method was tested on four datasets acquired by MLS in different indoor scenes, as shown in Figure 16. Table 1 lists the technical specifications of system, and the Table 2 details the specifications of the point clouds. The first dataset, the ISPRS Benchmark Data [50], was captured using a handheld scanner, Zeb-Revo, in one of the buildings at the Technical University of Braunschweig, Germany. The data were acquired from across two floors connected via a staircase. The point clouds and trajectories are shown in Figure 16a. The walls had different thicknesses, the ceilings were of different heights, and the level of point cloud quality was high. The second dataset was captured in the 14 th floor of the Technology Building of Shenzhen University, using our own developed backpack laser  
Figure 15: Signal intensity simulation. (**a**) Setting three base stations. (**b**) Multipath signal propagation. (**c**) Horizontal profile of signal intensity.  
scanning (BLS), which contains a 16-beam 3D laser scanner. The location was in a corridor with glass walls and contains a number of moving objects; the collected point clouds had a high level of noise, as shown in Figure 16b. The third and fourth datasets were acquired on a corridor and a parking lot by the backpack mapping system of Xiamen University (shown in Figure 16c,d). This laser scanning system [37] contains two 16-beam laser scanners and can obtain higher quality 3D point cloud data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **ZEB REVO** & **BLS (Shenzhen University)** & **BLS (Xiamen University)** \\ \hline Max range & 30 m & 100 m & 100 m \\ \hline Speed (points/sec) & \(43\times 10^{3}\) & \(300\times 10^{3}\) & \(300\times 10^{3}\) \\ \hline Horizontal Angular Resolution & \(0.625^{\circ}\) & 0.1–0.4\({}^{\circ}\) & 0.1–0.4\({}^{\circ}\) \\ \hline Vertical Angular Resolution & \(1.8^{\circ}\) & \(2.0^{\circ}\) & \(2.0^{\circ}\) \\ \hline Angular FOV & \(270\times 360^{\circ}\) & \(30\times 360^{\circ}\) & \(2\times 30\times 360^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical specifications of the laser scanning system.  
Figure 16: The experiment data. (**a**) Benchmark point clouds and trajectories acquired by handheld laser scanning (HLS), ZEB-REVO. (**b**) Point clouds acquired by Shenzhen University (BLS) system of Shenzhen University. (**c**) A closed-loop corridor by BLS system of Xiamen University. (**d**) Parking lot by BLS system of Xiamen University.
## 2 Materials and Methods
### Method Validation  
HPLC-MS. Linearity was verified by generating calibration curves for each reference standard across five successive dilutions of the stock solution. Seven concentrations of the standard steroids (ranging from 0.1 to 20 ng/mL, see Section 2.2) were injected, and calibration graphs were plotted using the peak area of each analyte obtained from mass chromatogram (SIM mode) versus the concentrations. Linearity was evaluated on real spiked samples at the same concentrations. Sensitivity as limit of detection (LOD, signal/noise = 3) and limit of quantitation (LOQ, signal/noise = 10) were obtained by triplicate injections of serial dilution of stock standard solutions and real samples. To investigate the accuracy of the method, recovery studies were performed by spiking real samples with low (0.3 and 0.5 ng/mL), medium (10 ng/mL), and high (20 ng/mL) concentrations of standard steroid solutions. After treating the sample as described in Section 2.2, the obtained results of the analysis were compared to those of the standard solutions at the same concentration. Matrix effect (ME) was also determined through adding different concentrations of standards (low, medium, and high) to the solutions obtained after sample extraction. ME% was calculated as the percent ratio between the response in spiked samples subtracted by the standard response. Precision was evaluated through analyzing three sets of standard solutions at four different concentrations three times a day (intra-day) for three consecutive days (inter-day), and the corresponding relative standard deviations (CV%) were calculated.  
ELISA. Calibration curves were generated by spiking known amounts of the target analytes in two different matrices obtained after the pretreatment described in Section 2.2. Calibration graphs were plotted using the B/B\({}_{0}\) ratio, in which B represents the signal obtained for a specific target analyte concentration and B\({}_{0}\) is the maximum signal measured in the absence of the target analyte against the logarithm of the target concentration and fitting the experimental data with a four-parameter logistic equation. The LODs of the assay were calculated as the concentration, yielding a B/B\({}_{0}\) corresponding to that of the blank minus three times its standard deviation. These calibration curves were then compared with a calibration curve obtained by spiking each corticosteroid in a mixture composed of 80% PBS 0.1 M and 20% THF in order to evaluate the matrix effect. To investigate the recovery obtained with the proposed procedure of sample pretreatment, recovery studies were performed by directly spiking the cosmetic products with low, medium, and high concentrations of each corticosteroid.
###### Abstract  
Medicanes, a type of strong hurricanes/cyclones occurring in the Mediterranean, can be the source of major geohazard events in Mediterranean coastal and inland areas. Medicare Ianos that hit Greece during 17-19 September 2020 caused widespread damage, with numerous landslides and floods being the most prominent. Following the landfall of Medicare Ianos, a series of field surveys were launched together with rapid response through satellite imagery. We focused on two of the areas most affected by Medicare Ianos, Cephalonia island and Karditsa, Thessaly, both in Greece. A rapid landslide inventory for the Karditsa region was prepared using Copernicus Sentinel-2 satellite imagery, the first of its kind for a severe weather event in Greece. The mountainous area of Karditsa region in western Thessaly experienced the unprecedented number of 1696 landslides, mapped through satellite imagery and examined in the field. Cephalonia Island experienced a smaller number of landslides but damaging debris flows and severe structural damages. The rapid landslide inventory was then compared to new methods of automated landslide mapping through change detection of satellite imagery.  
Keywords:landslides; landslide inventory; rapid mapping; remote sensing; Sentinel-2; Ianos; Medicare; Greece +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### Comparison with Other P300 Detectors
#### 4.3.4 Results  
We compare the proposed P300 detector (Kernel FDA+SVM) with other detectors in different numbers of intensification rounds \(N\), and the experimental results are listed in Table 5. The average classification accuracies of \(K\)-NN, SVM, and Kernel FDA + SVM are the same as those in Figure 8. In each condition of \(N\), the highest average classification accuracy is marked in bold. It can be observed from Table 5 that kernel FDA + SVM outperforms the widely used FLDA and its extension version BLDA in all cases, _i.e._, \(\forall N\). It is interesting to further compare FLDA and BLDA. It can be found that BLDA achieves higher accuracies than FLDA for all cases. This comparison demonstrates that BLDA is better than FLDA for P300 detection. Nevertheless, this comparison could be a bit unfair because the within-class scatter matrix in FLDA did suffer from the SSS problem. In the experiment, each data has a dimension of 250 because two EEG channels (Pz and Cz) are used and one 500 ms epoch from each channel has 125 sample points (sampling frequency = 250 Hz). On the other hand, the training data is only 200 in number due to the use of two-fold cross validation (original data size for each subject is 400). Obviously, the number of training data is smaller than the data dimension. To prevent the SSS problem, we add a small value (0.0001) to every diagonal element of the within-class scatter matrix to make the matrix invertible, as mentioned in Section 4.3.1. This simple method, however, is not the best way to address the SSS problem. To improve the classification accuracy of FLDA, more effective solutions to SSS problem should be introduced in the future, such as the direct LDA proposed in [33].  
Among the 10 different cases, the results also show that kernel FDA + SVM performs better than BLDA in all cases, and better than gradient boosting with OLS in eight cases (except \(N\) = 9 and \(N\) = 10). Although the classification accuracies of gradient boosting with OLS are higher than those of kernel FDA + SVM as \(N\) = 9 and 10, the differences in the two cases are only 0.2% and 0.1%, respectively. Nevertheless, for a P300-based BCI, a smaller number of intensification rounds is preferred because the smaller the number of rounds, the faster the response of the BCI. When \(N\) is lower than 9, it can be seen that the differences are much larger. For example, Kernel FDA + SVM outperforms gradient boosting with OLS by 4.6% when \(N\) is 5, which demonstrates that Kernel FDA + SVM is more suitable than the gradient boosting with OLS method for a P300 BCI where a fast response is required.
# Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForest
###### Abstract  
The Amazon biome is frequently targeted by illegal activities, with clandestine mining being one of the most prominent. Due to the dense forest cover, criminals often rely on covert aviation as a logistical tool to supply remote locations and sustain these activities. This work presents an enhancement to a previously developed landing strip detection algorithm tailored for the Amazon biome. The initial algorithm utilized satellite images combined with the use of Convolutional Neural Networks (CNNs) to find the targets' spatial locations (latitude and longitude). By addressing the limitations identified in the initial approach, this refined algorithm aims to improve detection accuracy and operational efficiency in complex rainforest environments. Tests in a selected area of the Amazon showed that the modified algorithm resulted in a recall drop of approximately 1% while reducing false positives by 26.6%. The recall drop means there was a decrease in the detection of true positives, which is balanced by the reduction in false positives. When applied across the entire biome, the recall decreased by 1.7%, but the total predictions dropped by 17.88%. These results suggest that, despite a slight reduction in recall, the modifications significantly improved the original algorithm by minimizing its limitations. Additionally, the improved solution demonstrates a 25.55% faster inference time, contributing to more rapid target identification. This advancement represents a meaningful step toward more effective detection of clandestine airstrips, supporting ongoing efforts to combat illegal activities in the region.  
onvolutional neural networks; deep learning; landing strip detection
# A New In Vitro Model to Evaluate Anti-Adhesive Effect against Fungal Nail Infections
###### Abstract  
Nail fungal infection is often mistakenly considered a minor issue or a purely esthetic problem that is not worth solving. However, onychomycosis has been demonstrated to have a negative impact on a patient's social life. Therefore, given the poor efficacy of various therapy types, there is strong interest in exploring new methods for evaluating antifungal treatments. As such, the aim of this work was to develop a new protocol, using bovine hoof membranes as a model of the human nail to evaluate the capability of a product claiming to prevent fungal adhesion, which is the first step of the infection. In this work, two specific and representative fungal strains, _Trichophyton rubrum_ and _Candidia albicans_, were used. In order to evaluate the possible protective activity of a product against fungal contamination of the nail plate, it was first necessary to test the affinity of the hoof membranes to be contaminated by the fungi. Then, a pharmaceutical product and a base coat were tested as a positive and negative control, respectively, by introducing the membranes (anti-fungal, basic or no treatment and single vs. multiple treatments) into the fungal suspensions for three different contact times (15 min, 5 h and 24 h). The results showed that the more significant antiadhesive effect (AE) was obtained against _Trichophyton rubrum_ than against _Candidia albicans_. Furthermore, taking into account the results obtained at all testing times, 5 h appeared to be the best time for testing the antiadhesive activity. The results obtained after three treatments with drugs and on washed membranes, in comparison to one single application of antifungal product (AP), demonstrated clearly that the drug was able to penetrate deeper into the membranes to exert itself, even after washing and also after only 15 min of contact. Thus, hoof membrane has been shown to be a valuable in vitro model for this kind of product assessment.  
antifungal effectiveness; bovine membrane; _Trichophyton rubrum_; nail infection; _Candidia albicans_
## 4 Experimental Results
### TetraOpt for Hyperparameter Optimization on NATS-Bench
The inertia weight (\(\mathbf{w}\)) was set to 0.7, balancing the influence of a particle's previous velocity, while the cognitive (c1) and social (c2) weights were both set to 1.5, encouraging particles to explore both their own best-known positions and those of their neighbors. The results of the six experiments demonstrate that TetraOpt achieved a higher maximum accuracy in five out of the six experiments compared to the other algorithms. Among the competing methods, Particle Swarm Optimization (PSO) [6] and Bayesian Optimization [4] also showed competitive performance, reaching results close to those of TetraOpt in some cases. Figure 5: Maximum accuracy against algorithm accumulated runtime for TetraOpt and other optimization algorithms across six NATS-Bench experiments. (**a–f**) corresponds to specific dataset and training duration. Total algorithm runtime evaluations also showed that TetraOpt finds configurations with high accuracy with lower or comparable-to-competitor-method runtime, which is critical in real-world applications, where computational resources are often constrained. Unlike some competing algorithms, TetraOpt avoids getting stuck in local optimum and continues to identify better configurations with increasing number of steps. These results highlight the algorithm's robustness, its efficiency in balancing accuracy and runtime, and its effectiveness in finding global optima.
# Efficient Capture of Sr\({}^{2+}\) Ions by a Layered Potassium Neodymium Phosphate
## 3 Results and Discussion
### Removal of Sr\({}^{2+}\) from Aqueous Solutions
#### 3.2.3 pH-Dependent Experiments  
Taking into account that the radioactive waste liquid is treated at different degrees of acidity and alkalinity [14], and the pH of the solution has a great influence on the adsorption process [60], it is not negligible to study the Sr\({}^{2+}\) adsorption of K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\) under different pH values. Therefore, we investigated the Sr\({}^{2+}\) adsorption of K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\) in the pH range 3.07-12.17 (Figure 3a, Table S4).  
After strenuous stirring of the Sr\({}^{2+}\) solutions (pH = 3.07-12.17) for 12 h, K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\) was not dissolved. In the powder x-ray diffraction (PXRD) patterns of the sample after stirring in different pH solutions, the characteristic peaks can well match with those of the pristine K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\) (Figure 3b). This confirms that the structure of K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\) did not collapse during the experiments. At the pH of 3.07, the leaching percentage of Nd (%) (Equation (S9), Figure S1) was 3.76%. It is worth noting that the leaching percentage of Nd (%) was less than 0.5% in the pH range from 7.00 to 12.17. These results demonstrate that the structure of K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\) can maintain good stability during the adsorption process. In neutral to strong alkaline solutions with the initial Sr\({}^{2+}\) concentration (C\({}_{0}\)\({}^{\rm Sr}\)) of about 4.5 mg/L, the \(R^{\rm Sr}\) values were greater than 99.7%, and the distribution coefficient values (\(K_{\rm d}\)\({}^{\rm Sr}\), Equation (S10)) were greater than 10\({}^{5}\). Especially at the pH of 8.07, the \(R^{\rm Sr}\) reached 99.93% and \(K_{\rm d}\)\({}^{\rm Sr}\) was up to 1.46 \(\times\) 10\({}^{6}\) mL/g, which was greater than those of many adsorbents, such as 3D-K\({}_{2}\)Ce(PO\({}_{4}\))\({}_{2}\) (-8.0 \(\times\) 10\({}^{3}\) mL/g), 2D-K\({}_{2}\)Zr(PO\({}_{4}\))\({}_{2}\) (-3.0 \(\times\) 10\({}^{4}\) mL/g) and 2D-HUO\({}_{2}\)PO\({}_{4}\)\(\cdot\)3H\({}_{2}\)O (6.90 \(\times\) 10\({}^{3}\)) (Table 2). It is generally believed that, when \(K_{\rm d}\) reaches 10\({}^{4}\), the adsorbent has an excellent affinity for the targeted ions. This shows that K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\) can not only effectively remove Sr\({}^{2+}\) in the pH range from 3.07 to 12.17, but it also has a high affinity for Sr\({}^{2+}\) under neutral and alkaline conditions.  
Figure 3: (**a**) The effect of different pH on the Sr\({}^{2+}\) adsorption of K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\) studied by plotting the relationship of \(K_{\rm d}\)\({}^{\rm Sr}\) (mL/g) (column) and \(R^{\rm Sr}\) (line chart) vs. pH values; (**b**) PXRD patterns of K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\) samples before and after Sr\({}^{2+}\) adsorption in the pH range from 3.07 to 12.17.
## 3 Re-Usable Re-Constructible Sensor Device  
Figure 2 illustrates the basic architecture of the proposed sensing device. It contains a flat-pack that can be assembled as a sensor housing. Inside the housing, an embedded electronics PCB is placed. This must be as flat as possible or have multiple flat parts connected by flexible wires, such as AWG 28-grade wires. A serial cable connects the sensor to the user's PC. The device may also be connected to a Wi-Fi network router.  
Due to the combination of electronic components and cardboard, this project operates at the intersection of typical product categories. Previous studies have explored personal health and fitness, office electronics (to understand the category-specific domain), and flat-packed cardboard furniture (to understand the material and production potential).  
The majority of personal health and fitness products, as well as office electronics, were typically housed in plastics, silicones, and metals. However, the lifespan of these materials does not correlate to their psychological and technological obsolescence, which, on average (using mobile phones, for example), is every 2-3 years [35]. While robust and sleek, these materials do not lend themselves to repair or recycling, and they typically do not invite the user into the construction process.  
Within this category-specific domain, Fairphone [36; 37] and DIY electronics kits such as Toy Bricks and Arduino kits [38] were seen as the most relevant to the user-built model that this project was pursuing. Fairphone is a mobile phone that allows users to repair and refurbish the components when they need replacement. It is durable, modular, and allows for customization. Electronic Toy Bricks are similarly modular and designed to engage children to learn how electronic components work in a safe environment. Arduino kits take a DIY approach to electronics, incorporating microcontrollers that enable the coding of software in conjunction with the electronics. The simplicity and legibility of these precedents were seen as key takeaways for this project.  
While many examples of flat-packed furniture could have been included in this precedent study, the comparison between cardboard and timber helped narrow the focus. Pakt by [PERSON] [39] was one such example, which took the form of an accordion folder that, when expanded, functions as a low table. Lightbox by [PERSON] [40] combines packaging and product into a single form that, when constructed, becomes a hanging light. Clear visual instructions enabled users to engage with the product's building with no prior experience needed. Both of these examples employ a modular approach with intuitive assembly, increasing the likelihood of success for the user. The successful use of cardboard for interior products reinforced the potential for its use in personal health and fitness, as well as office electronics.  
Previous studies have concluded that flat-pack approaches must be fail-proof in assembly for the intended audience. Modular components, intuitive assembly, and comprehensive instructions would increase the likelihood of assembly success.
# Multi-Decision Dynamic Intelligent Routing Protocol for Delay-Tolerant Networks
## 2 Routing Decision in Double Q-Learning Environment
### Double Q-Learning Algorithm in DTNs
During the message delivery process from source nodes to destination nodes, the entire network provides the necessary information for message forwarding. Therefore, in this paper, we consider the entire mobile network as a reinforcement learning environment, where nodes that relay messages are treated as intelligent agents. The action space of these agents consists of forwarding data packets to the next-hop nodes, and selecting the appropriate next-hop to forward the message is considered an action choice in reinforcement learning. All nodes in the network can serve as storage nodes for data packets, so the collection of all nodes in the network forms the state space of the agents. When a message is successfully forwarded to the next-hop node, the agent receives an immediate reward value from the environment, which is used for updating the Q-values. Figure 1 depicts a typical reinforcement learning example within a delay-tolerant network. In this illustration, a message resides at node \(a\), which can be regarded as a state. \(m_{d}^{i}\) represents message \(i\) whose destination node is \(d\). Nodes \(b\), \(c\), and \(e\) represent the next action choices made by node \(a\) to forward \(m_{d}^{i}\) to the destination node \(d\). The Q-table stores the Q-values associated with node \(a\) by choosing nodes \(a\), \(b\), and \(e\) as forwarding nodes for \(m_{d}^{i}\). When the message eventually reaches its destination node \(d\), we update the rewards associated with node \(a\) of choices of nodes \(a\), \(b\), and \(e\) as forwarding nodes for \(m_{d}^{i}\). In the proposed protocol, each node in the network stores and maintains two Q-tables, denoted as \(Q^{A}\) and \(Q^{B}\). These Q-tables consist of columns and rows, storing reward lists for the best actions for each state. When a node in the network begins to move without having forwarded any messages yet, it initializes the corresponding Q-values in its Q-tables to 0 upon encountering other nodes. Whenever a message is successfully relayed to the destination node by a node that has relayed the message, it updates its two Q-tables and shares its local information only with neighboring nodes. The two Q-values stored in the respective Q-tables are used for predicting the optimal action for the next step and predicting the optimal action for the termination state (successfully forwarding the message to the destination node). One Q-value is used for selecting the best action, and the other is used to estimate the maximum Q-value. Double Q-learning is employed to address the problem of overestimation that may lead to routing local optima. The two Q-values change with variations in the network's topology and node states, allowing the proposed algorithm model to adapt to highly dynamic environments. Figure 1: Q-learning in DTN.