esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Drone Design Based on a Reconfigurable Unmanned Aerial Vehicle for Wildfire Management
## 4 Electronics Design
### Air-Frame Selection and Validation Board/Circuit  
The frame recognition board has been built around an ARM Cortex M0+ microcontroller (STM32G0) and features 20 red LEDs divided into four groups of 5 LEDs (A, B, C, D), with each group representing the four pairs of arms, a green status LED, three blue LEDs for airframe indication, a preset selection button, and a link LED (Figure 3). Users can select a frame by pressing the button, cycling through presets: 1 for QUAD (quadcopter), 2 for OCTO (octocopter), and 3 for QUAD-COAXIAL (co-axial quadcopter).  
After selecting a frame, the board indicates (using red LEDs) all the required connection positions for the chosen frame for 1 s. Then, it constantly checks for the actual connections required, indicating (in red) all connections that need to be altered.  
The red LEDs of each group correspond to the four motor positions and to the locking pins of the joints. A red LED ON indicates that there is a faulty condition (a connection that should be made is not present or a connection that should not be made is present), relative to the selected air frame. For example, if frame 3 (QUAD-COAXIAL) is selected, the system expects to see Motors 3 and 4 of each group connected. If any of these motors are not connected to the corresponding ESC, its LED will be ON, indicating that the motor must be connected. Also, if any of motors 1 and 2 in any group are connected, the corresponding LED will also be ON, indicating that this motor needs to be removed.  
Additionally, there are 12 locking pins, 3 in each pair of arms, that ensure that the joints of the arms are immobilized. Each of the four LEDs marked with the letter P checks the three locking pins. The system requires all three pins to be in place for the LED to turn OFF. These locking pins close a circuit through a limit switch attached to each joint of the UAV.  
If all connections are correct, all red LEDs turn OFF and the board communicates with the flight controller. The board then loads the selected frameset to the flight controller and restarts the flight controller system. The status LED flashes green. The board waits for the flight controller to initialize with the new airframe, and the status LED becomes a steady green. The system is then ready for flight.  
In case of a power off-on cycle, the frame recognition board loads the last preset.
## 5 Materials and Methods
### Data Preprocessing  
The preprocessing phase aimed to clean, transform, and prepare the dataset for training the LSTM model and evaluating the performance of genetic algorithms for route optimization. The raw dataset contained millions of taxi trip records with diverse attributes, including timestamps, locations, and trip metadata. To enhance the dataset for analysis, the following preprocessing steps were performed:
## 3 Methods and Materials
### Joint Inversion as a Tool for Velocity Models Validation
Hence, the effect originating from residual, fast-changing values of gravity anomalies was distributed near the surface whereas the slow-changing values and regional trends were distributed at depth. During the simultaneous procedure of data integration, the SJI was run in order to construct the Multiparameter Geophysical Model (MGM), which was a distribution function of density and interval velocity parameters measured in the wells. As a result, separate distributions of parameters were obtained from the MGM although this was a rather serious simplification of the joint inversion procedure, which required additional parameterization. In practice, it was possible to apply additional weights, which defined the impact strength of one method on another [30; 32]. In fact, a model based on one algorithm affected the inversion solution in the other algorithm. Weighting procedure could be used also at the level of datasets where their fitting was analyzed, i.e., a hierarchy was established in which a parameter is to be fitted with the lowest error [30; 32]. Such a weighting procedure is used, among others, in joint inversion of data acquired from refraction seismic and gravimetry. Generally, the seismic method was weighted as the leading one because gravity is based upon the distribution of the potential field, which cannot directly determine the vertical variability of density in geological formations [33]. The simultaneous inversion allowed automatic modification of the density field based on seismic tomography in the shallow zone, which was crucial to PreSDMwhereas the gravity inversion enabled the researcher to verify the model and to supplement geophysical information in places where seismic survey could not be run in deeper parts of geological formations. When the weights are determined, parameters such as resolution, sensitivity and prospection depth of each method must be considered [30; 32; 34]. The well-known weighting function \(W(z)\) is used for this purpose described below:  
\[W(z)=\frac{1}{\left(z+z_{0}\right)^{\frac{\beta}{2}}} \tag{3}\]  
where: \(z\)--depth, \(z_{0}\) and \(\beta\)--parameters depended on model parameters [32].
###### Abstract  
This study aims to optimize the conditions for \"Genovese\" basil (_Ocimum Basilicum_) germination and growth in an indoor environment suitable for horticulture through a synergic effect of light and fertilizers addition. In fact, several studies determined that specific light conditions are capable of enhancing basil growth, but this effect is highly dependent on the environmental conditions. In this study, the effect of different light sources was determined employing a soil with a negligible amount of fertilizer, demonstrating substantial improvement when light-emitting diode (LED) lights (hyper red and deep blue in different combinations) were applied with respect to daylight (Plants height: +30%, Total fresh mass: +50%). Thereafter, a design of experiment approach has been implemented to calculate the specific combination of LED lights and fertilizer useful to optimize the basil growth. A controlled-release fertilizer based on nitrogen, phosphorus, and potassium (NPK) derived from agro-residues was compared with a soil enriched in macronutrients. The results demonstrate significant improvements for the growth parameters with the employment of the controlled-release NPK with respect to enriched soil combined with a ratio of hyper red and deep blue LED light equal to 1:3 (Total fresh mass: +100%, Leaves number: +20%).  
basil; design of experiments; valorization strategies +
Footnote †: journal: Applied Sciences
# Dilated Filters for Edge-Detection Algorithms
## 3 Methodology
### Guo-Hall Algorithm  
The Guo-Hall algorithm [31] is one of the most efficient thinning algorithms and is based on a subiteration approach of alternatively deleting north and east and then south and west boundary pixels. If we have one group of 8-connected 1s around a position P, see Figure 4, the deletion of P will not break the connectivity of the elements in a 3 \(\times\) 3 window. This approach on thinning will preserve the connectivity properties of an image.  
For an input binary image, let the object to be thinned be represented by a set \(S\), and the background and holes in the image be represented by a complement set \(\bar{S}\)[31].  
Thinned lines should be a curve or a union of curves, which are referred to as medial curves. A set of pixels \(G\), is curve-like if most of the pixels of \(G\) have exactly two 8-neighbors in \(G\), a few pixels in \(G\) are end-points (with one 8-neighbors in \(G\)) or branch points (more than two 8-neighbors in \(G\)) [31].  
Let us define the following: \(C(P_{1})\) is defined as the number of distinct 8-connected components of 1s in the 8-neighborhood; \(\wedge\) as the AND operator; \(\vee\) as the OR operator; \({}^{-}\) as a logical complement. A variable \(N(P_{1})\), defined in Equation (1), helps with the detection of end-points as well as achieving thinner results. The \(N_{1}(P_{1})\) and \(N_{2}(P_{1})\) are defined in Equations (2) and (3). Each break the ordered set of \(P_{1}\)'s neighboring pixels into four pairs of adjoining pixels and count the number of pairs that contain one or two 1 s [31].  
\[N(P_{1})=min[N_{1}(P_{1}),N_{2}(P_{1})] \tag{1}\]  
\[N_{1}(P_{1})=(P_{9}\lor P_{2})+(P_{3}\lor P_{4})+(P_{5}\lor P_{6})+(P_{7}\lor P _{8}) \tag{2}\]  
\[N_{2}(P_{1})=(P_{2}\lor P_{3})+(P_{4}\lor P_{5})+(P_{6}\lor P_{7})+(P_{8}\lor P _{9}) \tag{3}\]  
The algorithm that is applied for each pixel \(p(i,j)\) is defined in Algorithm 1. The conditions in the algorithm are necessary for preservation of local connectivity when \(P_{1}\) is deleted and avoids deletion of pixels in the middle of medial curves.  
Figure 4: (a) 3 \(\times\) 3 window showing 8-neighborhood of a pixel; (b) 4-connectivity; (c) 8-connectivity.  
```
1:while points are deleted do
2:for all pixels p(i, j) do
3:if odd iterations then
4:if\(C(P_{1})=1\ AND\ 2\leq N(P_{1})\leq 3\ AND\ (P_{2}\lor P_{3}\lor\bar{P_{5}})\lor P_{4}=0\)then
5: Delete pixel p(i, j)
6:endif
7:else
8:if\(C(P_{1})=1\ AND\ 2\leq N(P_{1})\leq 3\ AND\ (P_{6}\lor P_{7}\lor\bar{P_{9}})\wedge P_{8}=0\)then
9: Delete pixel p(i, j)
10:endif
11:endif
12:endfor
13:endwhile
```  
**Algorithm 1** Guo-Hall Algorithm.
# Toward a Detailed Evaluation of Wireless Industrial Data Distribution Approaches +
## 2 Related Works
### On-Site Deployment Evaluations  
The on-site deployments within factories are not uncommon. The extent of the deployments, as well as the selected technologies, greatly vary. For example:  
* In [25], the authors present a network setting which is built in a collaborative robotics experimental facility. The system wirelessly connects a dual-arm robot and a mobile robot that collects and supplies components to the dual-arm robot.
* In [26], the authors explore the communication quality achieved by TSCH in a setup that includes real motes exposed to a realistic interfering industrial traffic.
* In [27], the authors introduce the design of a general yet accurate and reproducible measurement setup that will be exploited to assess the performance of the main open-source industrial implementations.  
Last but not least, in [28], the authors explore the impact of rate adaptation algorithms on Wi-Fi-based factory automation systems.  
Although the above works are valuable in their own implementation limits, they select isolated industrial application concepts. This observation leads us to the conclusion that there is a research gap in the literature which refers to the focus on deployments (of both stationary and mobile scenarios) with IEEE 802.15.4 combined with fog nodes and robotic elements.  
Table 1 is displaying a comparison among the reported works and highlighting the main contributions of this paper.
## 3 Results
### Bouguer Gravity Anomalies at Different Depths
Furthermore, in the mid-Himalayas between 75\({}^{\circ}\) E and 90\({}^{\circ}\) E, the anomaly values are close to zero, while the Bangong-Nujiang suture zone (BNS) and its northern lithosphere show significant positive anomalies. This suggests the existence of the subducted Indian plate. Compared with the overlying crust, the materials are obviously redistributed and readjusted at a depth of 136 km in Figure 2g. The Bouguer gravity anomalies are significantly related to the spatial distributions of major tectonics. There are two significant positive gravity anomalies in this pattern. One corresponds to the Tarim basin, and the other corresponds to the Indian plate close to the WHS that forms an evident wedging structure. The northern boundary of the latter is the Shyok suture zone (SSZ), and its western boundary is highly in accordance with the Chaman fault (CF) and Quetta valley. The CF is widely recognized as a tectonic boundary between the Indian plate and Eurasian plate in this region. These two positive regions seem to collide with each other, and the NW-SE gradient belt of negative gravity anomalies between them is intensely squeezed. This negative anomaly continuously covers the entire Pamir, western Tian Shan and western Tibet. In the Tian Shan range, this negative gradient belt is cut off by the Talas-Fergana fault (TF), and in Tibet, it tends in the direction of the Karakoram fault (KF), which separates the Pamir Plateau and Tibetan Plateau structurally. Farther east, this negative gradient belt is divided into two narrower belts, the Yalu-Zangbo suture zone (YZYS) and western Jinshajiang suture zone (JS). In addition, the Shan Plateau and Sichuan-Yunnan tectonic zone show significant negative Bouguer gravity anomalies. Near the EHS, the positive anomalies form a narrow belt distributed along the JIF, NjF and BFB, and the southern end of the YGF also exhibits significant positive anomalies. Figure 2h shows the wavelet approximation of the entire study area, which relates to the material distribution in the deep Earth. Compared with those in the overlying lithosphere, the significant negative Bouguer gravity anomalies further tend to the northeast, and the negative gravity anomalies of the NW Pamir and Himalayan tectonic zones are close to zero. This reveals that crustal and mantle materials are squeezed into the Tarim basin and northern Sichuan-Yunnan tectonic zone, where the characteristics of negative anomalies are evident.
# Two-Stage Fusion-Based Audiovisual Remote Sensing Scene Classification
## 4 Experiments
### Image Experiments  
This experiment did not establish a new image scene classification model. We chose to use the ViT-B/16 classification model, which is effective in the field of image classification, with an appropriate number of model parameters. Here, we compare the results of the ADVANCE scheme, the SoundingEarth scheme, and the Vision Transformer architecture. In addition, the pretrained parameters of ImageNet-21K were loaded onto the ViT-B/16 (except the fully connected layer). During fine-tuning, we normalized all images and used random repositioning cropping, random horizontal flipping, random adjustment of image brightness and contrast, and other data-enhancement techniques.  
The visual pathway in the ADVANCE solution employs a pre-trained ResNet-101 from AID. Likewise, the second solution utilizes a ResNet architecture, but with a distinction: it employs pre-trained ResNet-18 and ResNet-50 models using the SoundingEarth dataset. In the third approach, we opted for the ViT-B/16 for comparative purposes.  
Table 3, shows the results of different image system methods. We can observe that the method of the SoundingEarth scheme architecture (\(86.92\%\pm 0.16\) on the F1-score) performed 14.07% higher than the baseline (ADVANCE) scheme (\(72.85\%\pm 0.57\) on the F1-score). One of the most-straightforward explanations is that, despite their models being pre-trained on large-scale aerial image datasets, the dataset scale varies. The SoundingEarth scheme employs a dataset that is five-times larger than that of ADVANCE (AID comprises over 10,000 aerial scene images, while SoundingEarth boasts more than 50,000 overhead imagery samples). The Vision Transformer introduces a self-attention mechanism, which can effectively capture different positions in the image dependencies between and model global information. Although it was not pre-trained on a large-scale aerial scene images dataset, it still showed its efficient scene classification performance (\(87.61\%\pm 0.41\) on the F1-score).  
The confusion matrix for our image classification model is shown in Figure 8. Scene classification using images is better than scene classification using audio. The figure shows that, first, there were much fewer test samples that were incorrectly classified, and in some scenes (like beach, forest, harbor, residential, and train station), there were very few misclassified samples. Second, the scenes farmland, orchard, and shrubland were where  
Figure 7: Confusion matrix for our audio classification model AiT, with true labels on the vertical axis and predicted values on the horizontal axis.  
the misclassified test samples were most-frequently found. In the scene, eight samples of farmland were incorrectly classified as grassland, nine samples of orchard were incorrectly classified as forest, and eight samples of shrubland were incorrectly classified as forest. Eight samples from sports land were incorrectly classified as residential for the same issue, and six samples from lake were incorrectly classified as harbor.
# A Distance-Encoded Bloom Filter for Fast NDN Name Lookup
## 2 Related Works and Background
### Related Works
proposed a name lookup architecture utilizingthree types of Bloom filters: level-VBF, face-VBF, and en-BF [13]. The vectored-Bloom filter (VBF), originally introduced for IP lookup by [PERSON] et al. [26], enables rapid output port retrieval using multiple Bloom filter vectors. Some approaches addressed name lookup by incorporating hash tables and tries [14; 21; 22]. [PERSON] et al. proposed a hybrid model that utilizes fixed-sized counting Bloom filters for higher-level components and variable-sized tries for lower-level components to optimize NDN lookup efficiency [14]. [PERSON] et al. proposed constructing hash tables at each component level and using a Patricia trie instead of chaining to resolve hash table bucket collisions [21]. [PERSON] et al. introduced a hybrid lookup scheme combining a level-priority trie with a hash table-based approach [22]. The level-priority trie is implemented through programming Bloom filters, called _l_-BF and _p_-BF. [PERSON] et al. proposed machine learning-based index structures that predict the position or existence of data by learning their distribution [23]. Building on this idea, [PERSON] et al. enhance name lookup by applying the learned indexes to Bloom filters [24]. Since neural network-based classifiers may produce false negatives unlike standard Bloom filters, a backup Bloom filter is employed to handle such cases. Similar to conventional methods, multiple accesses are still required to find the longest matching prefix, and this approach does not address this issue. [PERSON] et al. also proposed a neural network-based name lookup scheme [25]. In their approach, the input name string is split into fixed-length segments to be fed into neural network models and processed through a layered architecture of multiple sub-range models. All these neural network-based approaches face challenges with incremental FIB updates.
## 3 Results and Discussion
### Optimization of Enrichment Parameters  
To achieve the optimal CE of pathogenic _Listeria_ spp., we optimized the key parameters including dosage of PEI-MNPs, incubation time, and magnetic separation time. The results presented in Figure 4a-c, depict the CE increase with the increase in PEI-MNPs dosage, incubation time, and magnetic separation time. When the dosage of PEI-MNPs was 25 \(\upmu\)g,  
Figure 3: CE of _L. monocytogenes_ under different conditions. (**a**) CE of _L. monocytogenes_ captured by MNP and PEI-MNPs, (**b**) Images of _L. monocytogenes_ colonies captured by MNP and PEI-MNPs on BHI agar. (**c**) Effects of pH on the CE by PEI-MNPs. (**d**) Effects of ionic strength on the CE by PEI-MNPs. *** represents \(p\) < 0.001.  
then the co-incubation time of bacteria with PEI-MNPs was 7 min, and the magnetic separation time was 3 min, the CE of PEI-MNPS on pathogenic _Listeria_ spp. mixture (10\({}^{4}\) CFU/mL of pathogenic _Listeria_ spp.) reached a plateau (over 96%) (Figure 4a-c). Under the above optimization conditions, the CE of PEI-MNPs for different concentrations of the pathogenic _Listeria_ spp. mixture in PBS and lettuce was evaluated (Figure 4d). The CE of PEI-MNPs for pathogenic _Listeria_ spp. mixture from 10\({}^{1}\) CFU/mL to 10\({}^{4}\) CFU/mL was over 90%. The CE decreased significantly due to the low dosage (25 \(\upmu\)g) of PEI-MNPs which was not enough to capture a high concentration of pathogenic _Listeria_ spp. mixture (10\({}^{5}\) CFU/mL to 10\({}^{6}\) CFU/mL). When the dosage of PEI-MNPs increased to 100 \(\upmu\)g, the CE of pathogenic _Listeria_ spp. mixture in PBS and lettuce was increased to 59.5 \(\pm\) 2.8% and 44.1 \(\pm\) 1.9% (Figure S2), respectively. Considering the potential number of pathogenic _Listeria_ spp. contamination in lettuce [35], PEI-MNPs have great potential for application in real food samples.
## 5 Numerical Examples
### Measurement Verification  
In the measurement experiment, the hardware devices used are the DecaWave Mini2016 suite, including the anchor and tag nodes, already described in detail in Section 2. The experimental scene is an empty hall of 10 m by 18 m. During the testing process, no relevant personnel entered. The data sampling interval of the positioning system is about 0.28 s. The tag node is mounted on the top of the helmet, which is placed at test point at the height of 30 cm in a static situation and is worn on the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **The Statistics** & **Test 1** & **Test 2** & **Test 3** & **Test 4** & **Test 5** & **Test 6** \\ \hline \multirow{2}{*}{X} & positional bias before correction & 0.05 & \(-\)0.05 & \(-\)0.01 & 0.02 & 0.17 & \(-\)0.08 \\  & corrected positional bias & 0.05 & \(-\)0.05 & \(-\)0.01 & 0.02 & 0.17 & \(-\)0.08 \\ \hline \multirow{2}{*}{Y} & positional bias before correction & \(-\)0.05 & \(-\)0.03 & \(-\)0.02 & 0.01 & \(-\)0.13 & \(-\)0.11 \\  & corrected positional bias & \(-\)0.05 & \(-\)0.03 & \(-\)0.02 & 0.01 & \(-\)0.13 & \(-\)0.11 \\ \hline \multirow{2}{*}{Z} & positional bias before correction & 0.01 & 0.11 & 0.06 & 0.10 & 1.28 & 1.69 \\  & corrected positional bias & 0.01 & 0.04 & 0.006 & \(-\)0.01 & \(-\)0.07 & 0.21 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The statistics of bias correction (cm).  
Figure 5: The hypothesis test based on Mahalanobis distance.  
human head at a height of 196 cm in a dynamic situation. The anchor nodes are placed on a tripod of the same antenna height. The static and dynamic tests analyses are as follows.