esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Stepwise LDA Based on Volatile Compounds Analysis  
Classification of honey samples was carried out using the supervised method of LDA. GC analysis has shown that samples of different botanical origins present both similarities and differences in their volatile composition. Therefore, in order to reach the optimum discrimination results and develop a robust statistical model, it was essential to remove the redundant variables and identify the most significant variables that best discriminate the fir, thyme, pine, and citrus honey samples. For this purpose, stepwise variable selection was applied based on the Mahalanobis distance. Six volatile compounds, octane, 2-phenylacetaldehyde, 1-nonanol, methyl 2-hydroxybenzoate (methyl salicylate), 2-(4-methylcyclohex-3-en-1-yl)-propanal (p-menth-1-en-9-al), and nonanoic acid, were selected from the stepwise algorithm as the most significant for the botanical differentiation of honey samples. We observe that the stepwise procedure eliminated the furan derivatives, which mainly emanate from thermal processing and storage conditions [38, 39, 40, 41] and are not related to botanical origin [38, 42]. Utilizing the above-selected set of six volatile compounds, LDA was performed where the application of the Wilks \"Lambda statistical test for the calibration model evaluation\" proved the existence of a significant difference between the means vectors of the four monofloral honey groups (Wilks' Lambda = 0.027 with \(p<0.05\) for the first; 0.164 with \(p<0.05\) for the second and 0.656 with \(p<0.05\) for the third canonical discriminant function). The calibration model was also confirmed by the eigenvalues. According to the eigenvalues, the first two discriminant functions were significant in the differentiation of honey samples. The first discriminant function recorded the higher eigenvalue (5.142), whilst the second was lower (3.008) and the third was significantly lower (0.524). The canonical correlation for the first discriminant function was estimated at 91.5%, the second at 86.6%, and the third at 58.6%. The percentage of correct classification of honey samples was 85.5%, while using the cross-validation method enabled 82.3% of samples to be accurately discriminated (Table 2). In total, from the 26 \"unknown\" honey samples, 23 (88.5%) were correctly classified. Classification results indicate that the difference among the rate of correct classification, cross-validation, and external validation set is minimal, which proves the robustness of the statistical model since a large difference indicates the poor performance of the model. Although other studies on botanical discrimination of Greek honey [19, 20] present a long distance among pine and fir honey at the discriminant scatter plot. In our study (Figure 6), we observed that fir and pine honey have a small centroid distance, while thyme honey is closer to the pine centroid when compared to the citrus honey that is concentrated farther away. Our findings are confirmed by the literature, as pine and fir honey are honeybee honey share a number of similarities [43].
## 5 Materials and Methods
### Analyses  
Because of the great variability of messages in a conversation, it is not possible to directly link the delay between messages to the cognitive cost required to process them. Indeed, the part of the delay of interest to us (linked to the cognitive cost of understanding the message and preparing a reply to it) can be overshadowed by other factors that have a strong effect on the response times of participants. Thus, it was important to reduce this noise before doing any further analysis.  
A factor of noise that can be clearly identified is the length of sentences. Indeed, longer messages take more time to read, and writing long replies also takes more time. Eliminating these two aspects as much as possible greatly diminishes the noise in collected data.  
To do this, we used a linear regression model taking into account the length of the experimenter's message (\(L_{e}\)) and the length of the participant's reply (\(L_{p}\)) to predict the theoretical delay in normal conversations (\(D\)). Just as in [32; 33], we used a simple linear model here to define the average reading and writing speed of participants, as the number of turns during a conversation was generally not enough to get a good model for each individual participant.  
\[D=wL_{e}+xL_{p}+yL_{e}L_{p}+z \tag{1}\]  
This allowed us to calculate \(\Delta d\): the difference between the observed response time (\(d\)) and the theoretical delay (\(D\)), which gave us a measure where the noise from the length of the messages was filtered out.  
\[\Delta d=d-D \tag{2}\]  
This process is similar to calculating residual reading times (see [36; 37], for examples), with the distinction that we were also taking into account residual writing times in the calculation of \(\Delta d\).  
It is this difference that was then analyzed using a type III ANOVA, with the order and the type of conversation as the two factors. The data were tested for normality, and the outliers were cleaned (beyond 3 standard deviations above or below the mean, which removed 10 out of 873 measures).
# Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks
###### Abstract  
Technological advantages in energy harvesting have been successfully applied in wireless sensor network environments, prolonging network's lifetime, and, therefore, classical networking approaches like information dissemination need to be readdressed. More specifically, Probabilistic Flooding information dissemination is revisited in this work and it is observed that certain limitations arise due to the idiosyncrasies of nodes' operation in energy harvesting network environments, resulting in reduced network coverage. In order to address this challenge, a modified version of Probabilistic Flooding is proposed, called _Robust Probabilistic Flooding_, which is capable of dealing with nodes of about to be exhausted batteries that resume their operation after ambient energy collection. In order to capture the behavior of the nodes' operational states, a Markov chain model is also introduced and--based on certain observations and assumptions presented here--is subsequently simplified. Simulation results based on the proposed Markov chain model and a solar radiation dataset demonstrate the inefficiencies of Probabilistic Flooding and show that its enhanced version (i.e., Robust Probabilistic Flooding) is capable of fully covering the network on the expense of increased termination time in energy harvesting environments. Another advantage is that no extra overhead is introduced regarding the number of disseminated messages, thus not introducing any extra transmissions and therefore the consumed energy does not increase.  
wireless sensor networks; energy harvesting; Markov chain; Robust Probabilistic Flooding +
Footnote †: journal: Sensor and Actuator Networks  
_Article_  
Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks
# The Latest Advances in Wireless Communication in Aviation, Wind Turbines and Bridges
## 4 WSN Systems in Different Applications: Aerospace, Bridge Structures, and Wind Turbines
### WSN in Wind Turbines  
Wind Energy has helped to achieve the 20% target set out in the EU's renewable energy directive and will continue contributing to the new targets for 2030. Wind energy turbines urgently need new methodologies for design and performance analysis and wind farm operation to keep up with market demand for ever-larger turbines [82].  
A Wireless Sensor Network for wind turbines should be considered in two areas, which correspond to the market domains. The first concerns the wind turbine of large output on-shore and off-shore considered as a single unit, but predominantly the second area is wind turbine farms which are the main objective (Figure 12). In this market sector, technology is developed by large companies. Hence, it is strictly regulated and rather resistant to any new concepts and ideas from outside this community. Only direct cooperation with such companies may provide an opportunity for implementation of improvements.  
Figure 11: Hardware units of SHM-A(**left**) and SHM-W board wireless smart sensors (**right**) [79].  
Figure 12: Wind turbine farm [82].  
The second sector concerns small and mini installations. This wind turbine market gives a lot of freedom because the type of wind turbine type is not specified, and the turbine applications have various objectives. Small or prosumer turbines (vertical axis) owners may be satisfied with a very limited data transfer. Often, energy production counter is sufficient.
# Understanding and Classifying Permanent Denial-of-Service Attacks
## 4 Design of the Framework
### Recovery Action  
PDoS attacks can be classified further into Direct PDoS and Extended PDoS attacks based on their lasting effects as follows:  
* Physical Restart: the simplest form of recovery, often applicable in cases where the impact is minimal and temporary. Attacks causing temporary system unresponsiveness or minor disruptions typically fall under this category.
* Factory Reset: More severe attacks, leading to significant configuration or system changes that may require a factory reset. This involves restoring the system to its original state as defined by the manufacturer. While more time consuming than a physical restart, it can allow partial data recovery or at least the restoration of the device functionality.
* Device Replacement: The most extreme form of recovery. Device replacement is necessitated when the attack causes irreversible damage to the hardware or firmware, rendering the device unusable. This type of recovery is the most resource intensive, both in terms of cost and time. It is generally reserved for attacks of the highest severity, such as those causing permanent damage to critical components.  
These distinctions help in understanding the longevity and potential repercussions of an attack, allowing for tailored response strategies.
# Par3 DNet: Using 3 DCNNs for Object Recognition on Tridimensional Partial Views
## 3 Proposal
### Training Data and Preprocessing  
As there is a lack of large-scale datasets of real-life objects with known poses, we adopted ModelNet10 [18]. This dataset is composed of 4900 3D CAD models of objects. They are categorized in 10 different classes: toilet, table, desk, sofa, night stand, monitor, dresser, chair, bed and bathtub. The models are manually aligned so all of them share the same pose. The authors of the dataset provide both training and test splits too. This dataset was chosen because the amount of samples is enough to train our architecture without requiring too much time. The Figure 3 shows random samples of ModelNet10.  
As mentioned before, our approach is able to classify point clouds that depict a range of objects provided only by a partial view of it, similarly as the data provided by commercial sensors. In this way, the point clouds do not represent the complete object, but the part of it that is facing the sensor, as depicted in Figure 4. As expected, the inner artifacts and the back of the object are missing in the point clouds.  
Figure 3: Some samples of the CAD models that compose the ModelNet10 dataset.  
Figure 2: Some samples of random point clouds converted to the input voxel representation. Note that the representation does not cover the full object, but only the part that would be facing the sensor.  
In order to use the ModelNet10 dataset, which are CAD models, to our purpose, we first need to extract the point clouds of the objects as if they were captured by an actual 3D sensor. To do that, we located the CAD objects in the center of a tessellated sphere. Each corner of that tessellated sphere yields a virtual 3D camera which captures the tridimensional data of the object from different points of view. This way, we get 42 different views of each CAD object. It is worth noting that the views are, in fact, point clouds. This process is shown in Figure 5.  
As a result, there are some views, namely point clouds, that are not interesting because it is unlikely to find that view in the wild. For instance, the point clouds that depict the underneath of the objects are discarded, and not considered for training nor for testing. Thus, we only consider the remaining 25 most relevant views. Finally, we got a dataset comprised of more than 76,000 training samples and about 18,000 testing samples. The distribution of samples per category can be seen in Figure 6. It is worth noting that, at this point, a sample is actually a point cloud that depicts a partial view of a ModelNet10 CAD object.  
Figure 4: Samples of color images and the corresponding point clouds as provided by a Kinect. Note that the point clouds only depict the part of the object that is facing the camera.  
Figure 5: In order to get the samples, we located each CAD object inside a tessellated sphere (**left**). Each corner of the tessellated sphere has a virtual 3D camera that provides a partial, point cloud view of the object (**right**).
## 7 Concluding Remarks  
To summarize, we would like to emphasize, once again, that the radiative properties and efficiency of light emission in semiconductor nanostructures are determined not only by the radiative transitions, but also by various non-radiative processes which can be even more intense than the radiative transitions themselves. As a result, in these cases, the radiative recombination can be significantly suppressed. On the other hand, even the non-radiative processes can be used in a constructive way. For instance, the exciton migration is accompanied by a non-radiative energy transfer (FRET) that can be channelled in a desired direction via creating a certain \"architecture\" of the nanocrystals in the ensemble, which allows to concentrate and illuminate the energy inside a given area [58; 191] or direct it to a certain layer of a funnel-type heterostructure of QD monolayers [231]. Exciton energy transfer and emission with upconversion because of simultaneous absorption of optical phonons has been suggested for QD-assisted cooling [232]. The multi-exciton effects make it possible to efficiently transform the absorbed photons into the rising number of electron-hole pairs capable of participating in the electric current. Such processes underlie the operating principle of photosynthetic light harvesting systems [233] and can be mimicked in solar cells [234].  
The exciton transport can be strongly enhanced if mediated by polaritons arising under strong coupling of the excitons to light in microcavities [235; 236]. Combining entities supporting localized excitons, such as QDs, dye molecules or J-aggregates, with microcavities in order to achieve the strong coupling regime is an important area of research envisaging various applications [41], in particular, in the context of controllable quantum emitters. A plasmonic surface considered in Section 6 can be seen as a near-field microcavity if the lossy channels are not the dominating ones. A detailed discussion of the strong coupling between surface plasmon-polaritons and quantum emitters can be found elsewhere [237].  
Conceptualization, writing--review and editing, V.A.B. and M.I.V. All authors have read and agreed to the published version of the manuscript. Funding from the Ministry of Science and Higher Education of the Russian Federation (State Assignment No 0729-2020-0058), the European Commission within the project \"Graphene-Driven Revolutions in ICT and Beyond\" (Ref. No. 696656), from the Portuguese Foundation for Science and Technology (FCT) in the framework of the PTDC/NAN-OPT/29265/2017 \"Towards high speed optical devices by exploiting the unique electronic properties of engineered 2D materials\" project and the Strategic Funding UID/FIS/04650/2019 is gratefully acknowledged. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.
# Transformer-Based Distillation Hash Learning for Image Retrieval
## 2 Related Works
Facebook proposed a visual Transformer-based image retrieval model [12]. The model uses a visual Transformer to generate image descriptors and trains the model with a metric learning objective. The metric learning objective combines contrast loss with a differential entropy regularizer. In the Google Landmark Recognition 2021 Kaggle competition, [PERSON] et al. [34] proposed an effective end-to-end network architecture for large-scale landmark recognition and retrieval. The network architecture combines DLOG (orthogonal Local and Global) [35] and the hybrid Swin-Transformer model [36] and uses a predictive retrieval approach. For each query image, the method uses L2 to normalize the cosine similarity between image descriptors and then searches for the most similar image in the indexed image database. Although many Transformer-based models have been proposed to solve computer vision tasks such as image retrieval, existing Transformer models are usually huge and computationally expensive, e.g., the base ViT model [11] requires 18 billion FLOPs to process the images. The knowledge distillation algorithm [13] is a technique that can compress a large network into a small efficient network and obtain comparable performance. The knowledge distillation algorithm usually uses a teacher network (large model) to teach a student network (small model), transferring the knowledge from the teacher network to the student network so that the performance of the student model is as close as possible to the performance of the teacher model. In 2015, [PERSON] et al. [13] first proposed the concept of knowledge distillation (KD) in neural networks, using the output logits of the teacher network or the integrated network (many teacher networks) and applying these logits to train fast small networks. [PERSON] et al. [37] used not only the final output logits of the teacher network, but also its intermediate hidden layer parameter values (intermediate representations) to train the student network. [PERSON] et al. [38] introduced multi-step knowledge distillation in teacher networks and student networks with teaching assistant networks. Knowledge distillation can be used for many networks (e.g., from large to small networks, from single networks to integrated networks, from CNNs to Transformer, etc.). More relevantly, [PERSON] et al. [39] used distillation learning to fuse multi-modal transformers for a sketch-based image retrieval task. Few studies are currently related to knowledge distillation on Transformer-based image hashing learning networks. Our work focuses on using Transformer to obtain hash codes with a global view and high uniqueness and using knowledge distillation to obtain lightweight and efficient models that can be deployed in practice.
## 3 Results  
### Relationships between GPP, EVI2 and Environmental Variables  
Daily EVI2 from Sentinel-2 MSI and MODIS all showed linear relationships with flux tower-derived GPP (R\({}^{2}\) = 0.45-0.93, Table 2). There was no tendency for stronger relationships with higher resolution data. MODIS EVI2 showed stronger relationships than Sentinel-2 at four sites; however, at the evergreen forest site SE-Htm, the relationships with EC GPP were considerably weaker than for Sentinel-2 (R\({}^{2}\) = 0.45 and 0.56). Comparing the two MODIS resolutions, the higher spatial resolution EVI2\({}_{\rm 250m}\) provided stronger relationships with flux tower GPP at the agricultural site SE-Lnn. At this site and the deciduous forest site DK-Sor, daily EVI2 at all spatial resolutions from both Sentinel-2 MSI and MODIS showed very strong relationships with flux tower GPP (R\({}^{2}\) > 0.85).  
The environmental variables showed lower correlations with GPP than EVI2 for five of the eight sites. T\({}_{a}\) had higher R\({}^{2}\) than PPFD at most sites, expect at the agricultural site SE-Lnn and the evergreen forest site SE-Htm. For PPFD, R\({}^{2}\) values were lower at high latitudes than at low latitudes, and values at the forest sites were higher than values for the agricultural and wetland sites. VPD showed the weakest relationships among all three environmental variables at all sites. In DK-Sor and SE-Nor, however, the relationships between VPD and GPP were relatively strong, with R\({}^{2}\) values of 0.57 and 0.53, respectively. Spring 2017 was relatively dry in the study areas and for SE-Nor summer 2017 remained dry which increased the sensitivity to VPD. Otherwise, shallow soil depth at these sites might affect the water storage capacity. Despite this, the influence of VPD seemed to be low for most of the sites so only EVI2, T\({}_{a}\) and PPFD were chosen for the final regression models, because of their stronger relationships with GPP.
# Smart Healthcare Network Management: A Comprehensive Review
## 3 Smart Healthcare Remote Monitoring Contexts
### Possible Contexts (Atypical)  
These contexts should be determined based on the patient's preferences and health condition. They may include travel-related locations such as airplanes, ships, or international destinations. When a patient travels outside their home country, maintaining connectivity to the healthcare system becomes essential. To address this, the service broker should facilitate seamless integration by connecting the patient to a local service provider in the visiting country. This connection can be established using the patient's mobile phone or a dedicated device that links them to the home network via the local provider.
## 4 Data Quality Checks and Experimental Corrections
### Geometric Position (for AC-2 TsAGI Data)  
Although geometric positions of the microphones in AC-2 TsAGI and FL-17 CARDC were deliberately chosen as close to each other as possible, distances from the landing gear to the microphones in AC-2 TsAGI and FL-17 CARDC differ, as can be seen from Figures 2b and 4c. The data from AC-2 TsAGI are therefore corrected to the distances of FL-17 CARDC using the geometric spreading law.  
Moreover, the shear layer reflection introduces some differences in radiation angles between the two facilities. To account for the difference in radiation angles, the noise level of the results from AC-2 TsAGI are interpolated with respect to azimuthal angle for each frequency, which allows the interpolated spectra to be obtained at azimuthal angles \(\theta_{0}\), corresponding not to the values of AC-2 but to the values of FL-17. An example of such an interpolation is provided in Figure 12.  
As a result of the procedure, noise levels of small-scale landing gears are determined in the position and distances corresponding to the microphones in FL-17.