esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Factors Influencing Depression  
Out of 462 nurses, 168 experienced depression, which is equivalent to 36.4%. Table 3 shows the findings of the binomial logistic regression analysis with depression. The model had a reasonable level of explanatory power (Nagelkerke value = 0.107), and the goodness of fit was appropriate based on the Hosmer-Lemeshow test (\(\chi^{2}\) = 2.989, significance value = 0.886). The prediction accuracy of the model was 67.7%.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variables** & **N** & **Complaint Rate (\%)** & **B** & **\(p\)-Value** & **OR** & \multicolumn{2}{c}{**95\% C.I. for OR**} \\  & & & & & & **Lower** & **Upper** \\ \hline Age (years) & & & & 0.031 \({}^{*}\) & & & \\ \(<\)40 (ref) & 214 & 7.9\% & & & & & \\ \(40\)–49 & 151 & 0.7\% & \(-\)2.868 & 0.008 \({}^{*}\) & 0.057 & 0.007 & 0.480 \\ \(\geq\)50 & 97 & 11.3\% & \(-\)0.254 & 0.647 & 0.775 & 0.261 & 2.308 \\ Establishment & & & & & 0.085 & & \\ Clinic (ref) & 232 & 2.6\% & & & & & \\ Hospital & 230 & 10.0\% & 1.125 & 0.085 & 3.079 & 0.855 & 11.085 \\ Working hour/week & & & & 0.006 \({}^{*}\) & & & \\ \(<\)41 (ref) & 281 & 3.9\% & & & & & \\ \(\geq\)41 & 181 & 9.9\% & 1.511 & 0.006 \({}^{*}\) & 4.530 & 1.556 & 13.183 \\ Patient lifting/carrying & & & & & 0.041 \({}^{*}\) & & & \\ \(<\)2 h (ref) & 338 & 2.7\% & & & & & \\ \(2\)–4 h & 95 & 9.5\% & 1.109 & 0.067 & 3.033 & 0.926 & 9.930 \\ \(\geq\)4 h & 29 & 37.9\% & 1.766 & 0.017 \({}^{*}\) & 5.847 & 1.363 & 25.081 \\ Upper limb pain & & & & 0.022 \({}^{*}\) & & & \\ No (ref) & 342 & 2.0\% & & & & & \\ Yes & 120 & 18.3\% & 1.372 & 0.022 \({}^{*}\) & 3.943 & 1.218 & 12.769 \\ Lower limb pain & & & & 0.039 \({}^{*}\) & & & \\ No (ref) & 392 & 2.8\% & & & & & \\ Yes & 70 & 25.7\% & 1.203 & 0.039 \({}^{*}\) & 3.331 & 1.061 & 10.461 \\ Constant & & & \(-\)5.308 & \(<\)0.001 \({}^{*}\) & 0.005 & & \\ \hline \hline \end{tabular}  
* * significant at 0.05, ref = reference, OR = odds ratio, C.I. = confidence interval.  
\end{table}
Table 2: Results of binary logistic regression analysis with anxiety.  
In Table 3, the factors associated with depression were weekly working hours (\(p=0.042\)), exposure to infection (\(p=0.005\)), and complaints of lower limb pain (\(p=0.011\)). Nurses who worked more than 41 h per week were 1.576 times more likely to complain of depression than nurses who worked less than 41 h. Nurses exposed to infection for more than 4 h were 3.147 times more likely to experience depression than those exposed for less than 2 h. Nurses who complained of lower limb pain were 2.028 times more likely to have depression than those who did not.
# Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping
## 3 Results
### Classification  
#### 3.2.1 Predictor Variables and Selection  
A set of 163 variables grouped in four categories has been used as predictors: 13 environmental variables (three geographic, four geomorphological, three climatic, and three soil properties); 124 spectral variables (80 spectral bands, 44 spectral indices); and, 26 temporal variables (10 temporal statistics, 16 phenological metrics) (see Table A1 for the complete list). After the variable selection procedure, a total of 68 variables have been retained as predictors for the RF classification (see Figure 4). Successively, some of the predictors excluded in the previous step (i.e., elevation, normalized annual temperature, minimum value of LAI, maximum value of LAI, and latitude) were reconsidered in the classification procedure of the four RF models (see Figure 4). All of the selected variables reported a higher importance value than the shadow features generated by the Boruta algorithm.  
Figure 3: (**a**) Forest cover area corresponding to the forest mask produced in the ancillary data; (**b**) Spatial distribution of vegetation habitat dataset categorized in EUNIS II hierarchical level (T1: broadleaved deciduous; T2: broadleaved evergreen; and, T3: needleleaved forest habitat-type).
## 4 Results and Experimental Evaluation
### Quantitative Results  
Effect of Sampling Rate on Prediction Accuracy  
Table 1 presents the test MSE for all models across different sampling rates. Lower values indicate better performance.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **SR = 1** & **SR = 2** & **SR = 4** \\ \hline StochasticTrajectory & 0.0000678 & 0.000849 & 0.0218 \\ LSTM & 0.000163 & 0.00152 & 0.0212 \\ Attention & 0.000229 & 0.00225 & 0.0238 \\ SimpleRNN & 0.00313 & 0.00653 & 0.0390 \\ GRU & 0.000136 & 0.00137 & 0.0177 \\ MLP & 0.000561 & 0.00551 & 0.0434 \\ Physics & 0.0404 & 0.0899 & 0.401 \\ KalmanFilter & 0.229 & 0.144 & 0.401 \\ ARIMA & 0.0889 & 2.28 & 1.85 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test MSE comparison across models and sampling rates.  
Deep Learning Approaches: Our StochasticTrajectory model demonstrates superior performance at higher temporal resolutions (SR = 1 and SR = 2), achieving MSEs of 0.0000678 and 0.000849, respectively. This reflects its ability to effectively model the inherent uncertainty in general aviation trajectories. Among conventional recurrent models, GRU exhibits the most consistent performance across all sampling rates, achieving the best overall result at SR = 4 (MSE = 0.0177). This suggests that GRU's simpler gating mechanism provides better generalization on sparse data typical in general aviation environments. LSTM performs well but shows slightly higher error rates than both StochasticTrajectory and GRU, while SimpleRNN and MLP demonstrate substantially higher errors, particularly at lower sampling rates.  
Traditional and Physics-Based Methods: Statistical and physics-based approaches show clear limitations in modeling general aviation trajectories, with performance strongly dependent on data density. LinearRegression achieves competitive performance only at the highest sampling rate (SR = 1, MSE = 0.0000663), likely capturing short-term linear segments of flight, but degrades rapidly as the data becomes sparser (SR = 4, MSE = 0.0551). The substantial deterioration of ARIMA, Physics, and KalmanFilter models across all sampling rates, with errors orders of magnitude higher than deep learning approaches, underscores the fundamental limitations of these methods for general aviation applications.  
Impact of Sampling Rate: All models exhibit performance degradation as the sampling rate decreases, reflecting the increasing difficulty of trajectory prediction with sparse temporal data. However, deep learning approaches, particularly StochasticTrajectory and GRU, demonstrate greater resilience to reduced data density compared to traditional methods. This characteristic is crucial for practical applications in general aviation, where available data may vary in quality and frequency.  
These results demonstrate that our stochastic model not only captures uncertainty effectively but also maintains competitive or superior accuracy compared to deterministic approaches, particularly at lower sampling rates where data are more dense and patterns are more discernible.
## 3 Interoperability Issues
### IFC and CityGML Structure  
The IFC structure is made up of entities (constructive, geometric or basic elements), rooted or not rooted, which the BIM software transforms into layers and subsequently into parameters. Rooted entities are part of the category _IfcRoot_ and have a concept of identity, which is, a set of attributes such as name and description, while entities that are not rooted do not have an identity and their instances exist only if related to a rooted instance.  
_IfcRoot_ is divided into:  
- IfcObjectDefinition, that concerns presence and types of material objects;  
- IfcRelationship, that concerns relationship between objects;  
- IfcPropertyDefinition, that concerns properties dynamically extensible on objects.  
_IfcObjectDefinition_ is divided into:  
- IfcObject, concerning the presence of the object from a physical point of view;  
- IfcTypeObject, concerning information on the type of object.  
They are both divided into six categories that answer the questions \"Who? Why? What? Where? When?\" and \"How?\", namely IfcActor, IfcControl, IfcGroup, IfcProduct, IfcProcess, IfcResource. It is then the duty of IfcRelationship to identify the relationships between multiple objects.  
Figure 8: (**a**) The overlapped point cloud on the wall. (**b**) The subtraction of the volume for narrowing the thickness of the wall.  
In particular, IfcProduct represents entities by providing information on description, representation and spatial arrangements of the elements. Indeed it is divided into: ifAnnotation, IfcElement, IfcGrid, IfcPort, IfcPoxy, IfcSpatialElement, IfcStructuralActivity e IfcStructuralItem. IfcElement classifies elements into 10 categories, like IfcBuildingElement, IfcCivilElement, IfcElementComponent, and so on.  
On the other hand, the scheme proposed by CityGML (Figure 9) consists in:  
* LoD 0 requires the creation of a 2.5D territorial information system. The city model is divided into buildings, transport, topography, hydrography and vegetation.
* LoD 1 is meant for the creation of a 3D territorial information system. In this case a model composed only by blocks is required, without the roof structures. The CityGML scheme foresees the introduction of solid geometries.
* LoD 2 is meant for the creation of a textured model, with differentiated roof structures. The scheme proposed by CityGML adds to the information on the solid geometries in LoD1, the information regarding the surfaces. The division into roofs and walls is thus obtained.
* In order to model a building, LoD 3, which is composed by the construction of a nearly detailed architectural model, is required. The CityGML scheme for LoD 3 foresees the insertion of objects (CityObject), such as doors and windows, on surfaces.  
Specifically, these objects are made up of primitive, complex and aggregate geometries and the latter ones are part of the \"MultiSurface\".
# Post-Quantum Authentication in the MQTT Protocol
###### Abstract  
Message Queue Telemetry Transport (MQTT) is a common communication protocol used in the Internet of Things (IoT). MQTT is a simple, lightweight messaging protocol used to establish communication between multiple devices relying on the publish-subscribe model. However, the protocol does not provide authentication, and most proposals to incorporate it lose their lightweight feature and do not consider the future risk of quantum attacks. IoT devices are generally resource-constrained, and postquantum cryptography is often more computationally resource-intensive compared to current cryptographic standards, adding to the complexity of the transition. In this paper, we use the postquantum digital signature scheme CRYSTALS-Dilithium to provide authentication for MQTT and determine what the CPU, memory and disk usage are when doing so. We further investigate another possibility to provide authentication when using MQTT, namely a key encapsulation mechanism (KEM) trick proposed in 2020 for transport level security (TLS). Such a trick is claimed to save up to 90% in CPU cycles. We use the postquantum KEM scheme CRYSTALS-KYBER and compare the resulting CPU, memory and disk usages with traditional authentication. We found that the use of KEM for authentication resulted in a speed increase of 25 ms, a saving of 71%. There were some extra costs for memory but this is minimal enough to be acceptable for most IoT devices.  
Message Queue Telemetry Transport (MQTT); postquantum authentication; Internet of Things (IoT) +
Footnote †: journal: Cybersecurity and Privacy  
0
Footnote 0: Copyright** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Multi-Granularity Dilated Transformer for Lung Nodule Classification via Local Focus Scheme
## 3 Methods
### Network Architecture  
Figure 3 is the overview of the Multi-Granularity Dilated Transformer. Unlike previous work (CNNs-based) to extract semantic features, we use the proposed 4\(\times\) Deformable Dilated Transformer as our backbone, which can capture the more discriminative long-range global understanding. Following the backbone, we leverage global pooling and dual sub-pooling to obtain the coarse-grained global information and local features of lung nodules. Global pooling can help model obtain a representation of the statistical description of lung nodules, and this operation can guide the other two branches to learn a better representation. As for dual sub-pooling, with our intention to further seek more specific structural features, we empirically split the learned feature maps into two parts based on human biological structure (i.e., left lung and right lung) and perform pooling on each part, respectively. Notably, we use the proposed Local Focus Scheme (LFS) to mine fine-grained local features by modeling grouped channel-wise topology. Specifically, Regularized Channel-wise Grouped Attention in LFS first extracts the fine-grained channel-wise relations. Then, we use the KeyMask to discard a channel ranking in the top 1% of importance, forcing our model to learn the more discriminative local representation. We train the above three branches simultaneously, thus the model can learn multi-granularity features and has a more robust representation ability.  
Figure 3: Illustration of the proposed Multi-Granularity Dilated Transformer. We use the proposed Deformable Dilated Transformer as our backbone and then leverage the global pooling, dual sub-pooling, and LFS to jointly capture multi-granularity features.
# LDS Realization of High-Q SIW Millimeter Wave Filters with Cyclo-Olefin Polymers
## 3 Filter Design  
### Filter Synthesis  
The SIW filter is obtained by forming cavities inside the substrate, and coupled by inductive posts, whose equivalent models were presented by [PERSON] [13]. The synthesis is fully described in chapter eight of Reference [14]. Previous inductive post filters have been built in our laboratory using other materials [3; 4]. Here we chose a six-pole Chebyshev SIW filter with a narrow relative bandwidth, under 10%. This filter operates at 37.5 GHz and we synthesized two electrical responses with relative bandwidths 2.5% and 4.5%, respectively. The order of the filter is arbitrary. It was chosen at six, in order to be sufficiently representative in our demonstration.
## 3 Results and Discussion
### In Vitro Experiments
#### 3.2.3 Morphological Changes of _A. flavus_ Spores  
Figure 4 presents representative SEM micrographs of _A. flavus_ spores subjected to different treatments. The results show that spores treated with light had lower density and some cell wall deformations. On the other hand, non-treated (control) and curcumin-treated cells showed higher density and hyphal filament with no apparent deformations with the normal globular shape. In contrast, photosensitized spores showed the least cell density with some distortions in cellular shape, where the extent of damage was enough to cause cell death. Some cells in the photosensitization treatment still showed normal shape which substantiates the fact that some of the cells were still viable in vitro. The reduction in cell density due to photosensitization has been previously reported in _Candida_ spp. [40,41].  
Figure 3: (**a**) Decay of DPBF absorbance upon illumination (EOH-CUR = curcumin in ethanol); (**b**) Decay of DPBF absorbance upon illumination (CUR-PG = curcumin in 50% propylene glycol); (**c**) Kinetic decay of DPBF absorbance in ethanolic curcumin solution (second-order kinetics); (**d**) Kinetic decay of DPBF absorbance in curcumin-propylene glycol solution (first-order kinetics). Data are means \(\pm\) SE (\(n\) = 3).  
The cell wall is dynamic and undergoes changes in response to oxidative stress. ROS generated during photosensitization react with macromolecules such as proteins, polysaccharides, lipids and nucleic acids causing molecular changes that can negatively affect cellular physiology and morphology [42]. Deformations of the cell wall may result in intracellular content leakage, causing the cell shrinking and ultimately cell death [43] which has been reported for several microorganisms such as _Trichophyton rubrum_[29], _Escherichia coli_[43], and _Candida albicans_[44].  
It was observed that light treatment alone was able to cause deformations of the cell wall in some spores, which is in agreement with previous reports [29]. Nevertheless, [PERSON] et al. [29] reported that changes caused by light treatment on _T. rubrum_ spores were reversible, and the spores exhibited normal growth after eight days of incubation. This could explain the observed normal growth of light treated spores.
## 3 Analytical Solutions for Beams with One, Two, and Three Equal Spans  
The application of the presented theoretical procedure will be demonstrated on beams with span \(L\), specifically for a beam with three equal spans (\(L=3l\)), a beam with two equal spans (\(L=2l\)), and a beam with a single span (\(L=l\)).  
Analysis was conducted for three characteristic cases of additional mass configurations. The first case involves a uniformly distributed mass \(\mu_{q}\) over length \(\lambda\), the second case concerns the approach of a uniformly distributed mass \(\mu_{q}\) on the beam, and the third case examines the impact of a concentrated mass \(m\). The dynamic models of those three cases are further presented in Sections 3.1-3.3.  
Sinusoidal vibrations were considered, with the following eigencharacteristics applying to all three beams:  
* The eigenmode shape function for mode \(r\), which satisfies boundary conditions: \[V_{r}(x)=sin\frac{r\pi x}{l},\] (19)
* Natural frequency of the \(r\)-th mode: \[\omega_{r}=\frac{r^{2}\pi^{2}}{l^{2}}\sqrt{\frac{EI}{\mu}},\] (20)* Generalized mass of the \(r\)-th mode: \[M_{r}=\mu\int_{0}^{L}sin^{2}\frac{r\tau xx}{l}dx=\mu\frac{L}{2},\qquad L=nl, \qquad n=1,2,3.\] (21)  
For a beam with a single span, eigencharacteristics (19)-(21) apply to all modes, while for beams with two and three equal spans, they pertain to the fundamental mode and higher modes only of sinusoidal nature. Since emphasis is placed on the fundamental mode, the analysis for higher modes of non-sinusoidal nature is omitted here.
## 3 Results  
### Effect of BFRT in KOA  
Research in strength training physiology has shown overwhelming evidence that hypertrophic muscle adaptations can be induced at much lower exercise intensities than the intensities assumed to be crucial to muscle mass gains by combining LLRT with blood-flow restriction [18]. For example, the systematic review (SR) and meta-analysis (MA) from [PERSON] and colleagues [19] demonstrated that BFR with LLRT results in similar increases in muscle mass compared to HLRT, which is supported by the SR from [PERSON] et al. in 2017 [17]. In line with these studies, a recent study by [PERSON] and colleagues showed the additional effect of BFR in 112 patients with KOA, as the BFR group significantly improved in terms of strength, range of motion (ROM), quality of life (QoL), and ability of daily living (ADL) compared to a control group [14].  
Beside resistance training, which is the preferred exercise modality to induce muscle hypertrophy and -strength, it appears that enhanced levels of metabolic accumulation and ischemia during BFRT, combined with aerobic activities at low intensities (e.g., walking, cycling, rowing), also increase muscle volume and strength compared to aerobic exercises without BFR [20; 21; 22]. For example, the study by [PERSON] et al. (2010) indicated a significant increase in strength (11%) and thigh muscle mass volume (10.7%) after 6 weeks of walking 20 min 5\(\times\)/week, whereas no significant increases in the group without BFR were found [21]. This is particularly interesting for the KOA population, which is encouraged to perform joint-friendly aerobic activities like walking and cycling on a regular basis to safeguard joint load-bearing capacity and a healthy body mass index (BMI). As these activities alone have little effect on muscle strength and size, the implementation of BFR would allow KOA patients to achieve strength and muscle mass increments whilst maintaining joint loading tolerance and a healthy cardiovascular profile.  
In terms of pain, [PERSON] et al. (2018) showed that the pain score according to the WOMAC subscale within KOA patients was reduced significantly in both the LLRT-group and low load-BFRT group compared to HLRT, although positive muscle adaptations in terms of hypertrophy and strength were only found in the latter two [23]. In line with these findings, [PERSON] et al. (2016) found that in patients with KOA, LL-BFRT resulted in similar benefits in function and quadriceps strength compared to HLRT, with HLRT inducing higher levels of anterior knee pain during training sessions compared to LL-BFRT [24]. Therefore, evidence suggests that BFRT is a more effective training strategy than LLRT alone in individuals with KOA, as it appears to replicate the improvements in strength and muscle growth seen with HLRT without the need for high intensities, which might increase pain, discomfort, and reduced adherence to therapy.
###### Abstract  
We adapted a fast Fourier transform-based Beam Propagation Method (FFT-BPM) to investigate waveguide discontinuities in plasmonic waveguides. The adaptation of the FFT-BPM to treat transverse magnetic (TM) fields requires the circumvention of two major difficulties: the mixed derivatives of the magnetic field and waveguide refractive index profile in the TM wave equation and the step-like index change at the transverse metal-dielectric boundary of the plasmonic guide and the transverse boundaries of the dielectric waveguide as well. An equivalent-index method is adopted to transform TM fields to transverse electric (TE) ones, thus enabling the benefit of the full power and simplicity of the FFT-BPM. Moreover, an appropriate smoothing function is used to approximate the step-like refractive index profile in the transverse direction. At the junction plane, we used an accurate combined spatial-spectral reflection operator to calculate the reflected field. To validate our proposed scheme, we investigated the modal propagation in a silicon waveguide terminated by air (like a laser facet in two cases: with and without a coating layer). Then we considered a subwavelength plasmonic waveguide (metal-insulator-metal MIM) butt-coupled with a dielectric waveguide, where the power transmission efficiency has been calculated and compared with other numerical methods. The comparison reveals good agreement.  
plasmonics; photonics; bi-directional BPM; optical propagation; TM fields; FFT-BPM; plasmonic waveguide; butt-coupler; sensors; reflection formalism +
Footnote †: journal: Applied Physics Letters