esa-sceva/satcom-chunk-collection huggingface dataset:
# Sea Ice Detection Based on Differential Delay-Doppler Maps from UK TechDemoSat-1
## 2 Theory and Method
### Data Processing
#### 2.3.3 Differential DDM (dDDM) and overall normalization  
Let \(\overline{DDM_{1}^{i}}(\tau,f)\) and \(\overline{DDM_{2}^{i}}(\tau,f)\) be two adjacent normalized DDMs from sea ice field, let \(\overline{DDM_{1}^{w}}(\tau,f)\) and \(\overline{DDM_{2}^{w}}(\tau,f)\) be two adjacent normalized DDMs from sea water field. Then dDDMs between ice and ice, ice and water, water and water can be obtained according to Equation (4). The corresponding equations are shown below:  
\[\left\{\begin{array}{rcl}\text{DDM}_{D}^{ii}(\tau,f)&=&\overline{DDM_{1}^{i}} (\tau,f)-\overline{DDM_{2}^{i}}(\tau,f)\\ \text{DDM}_{D}^{iw}(\tau,f)&=&\overline{DDM_{2}^{i}}(\tau,f)-\overline{DDM_{1}^ {w}}(\tau,f)\\ \text{DDM}_{D}^{ww}(\tau,f)&=&\overline{DDM_{1}^{w}}(\tau,f)-\overline{DDM_{2}^ {w}}(\tau,f)\\ \text{DDM}_{D}^{ww}(\tau,f)&=&\overline{DDM_{2}^{w}}(\tau,f)-\overline{DDM_{1}^ {z}}(\tau,f)\\ \end{array}\right.. \tag{9}\]  
Considering that the values of \(\text{DDM}_{D}(\tau,f)\) are relatively small, we normalized the various dDDMs by \(\left|DDM_{D}(\tau,f)\right|_{\text{max}}\), where \(\left|DDM_{D}(\tau,f)\right|_{\text{max}}\) is the maximum absolute value of obtained dDDMs. Since it is useful to know what \(\text{DDM}_{D}(\tau,f)\) looks like, examples of \(\text{DDM}_{D}(\tau,f)\) associated with the four different scenarios are shown in Figure 4. Clearly, these four pictures are significantly different from each other, providing the basis to identify the transition type. To remain consistency and avoid performance degradation, the same normalization with the same maximum value is applied to all the dDDMs of the specific dataset.  
Figure 4: Normalized dDDMs for four different observation situations: (**a**) water-water transition, (**b**) water-ice transition, (**c**) ice-ice transition and (**d**) ice-water transition.
## 3 Methods
### Network Layer
#### 3.1.6 Applicational Potential  
The EU-SENSE system is designed to enhance chemical hazards detection capabilities. It can be used during chemical, biological, radiological, and nuclear (CBRN) incidents. Such incidents result from a release of various agents, and require appropriate hazard management, including the employment of special units to combat the situation, warning and reporting activities [39; 40; 41; 42]. CBRN defence is the umbrella term for all these situations [43].  
According to ATP-45(D), chemical agents are likely to be used to produce casualties (non-persistent effect) or to contaminate ground and/or equipment (persistent effect) [34].  
Moreover, the type of chemical agent is related to target conditions. For example, forward troops may be subjected to a quick dispersion vapour such as hydrogen cyanide in a ground attack. Mobile units are vulnerable to an attack with a fast-acting nerve agent, such as sarin or soman. In airfields and depots, persistent agents, which contaminate surfaces and produce a downwind hazard, can be used. Naval forces at sea are likely to be attacked by thickened or persistent agents such as TGD or HL (mustard-lewisite mixture). Ships near shore are at risk from vapour hazards associated with attacks on land targets [44]. The EU-SENSE system (Figure 1), with its unique sensor node (Figure 2), aims to serve as a versatile tool detecting various chemical agents in different conditions.  
Recipients of the system are CBRN practitioners, being, among others, first responders (medical personnel, fire brigade, military personnel) who constitute a specialized group to arrive on the scene [45] and commanders who manage the crisis.
## 3 Results and Discussion  
### Identification of Microorganisms in the Extraction Area and Juice Purification  
The output of the metagenomic sequencing of the duplicates sampled on two consecutive days in an Austrian sugar factory over the beet sugar process was in total approx. 22.1 million reads with a length of 300 bp. The reads and operational taxonomic units (OTU) per sample are tabularized in Table 1. The majority of total bacteria sequences over all samples were from the phyla _Thermus_ (60%), followed by _Frimicutes_ (29%) and _Proteobacteria_ (6%). The _Frimicutes_ contained mostly the class _Bacilli_ (99%), which are divided in _Bacillus_ (79%) and _Lactobacillus_ (21%). The different conditions in the production process of beet sugar strongly influence the bacterial community. In the extraction area, bacteria belonging to the phylum _Frimicutes_ were mainly detected. In juice purification, bacteria belonging to the phylum _Deinococcus-Thermus_ were predominant. In the regenerate/eluat, which is used as fresh water in the extraction tower, bacteria belonging to the phyla _Proteobacteria_ and _Actinobacteria_ were primarily observed (Table 2).
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 4 Energy-Aware Security Mechanisms in IoT
### Future Directions and Open Research Questions
#### 4.5.2 Context-Aware Security  
In the constantly changing environment of the IoT, context-aware security policies address the changing threats and challenges by utilizing contextual information about the surrounding environment to make security decisions [103]. Dynamic and smarter security measures should be proposed in response to dynamic changes in device states, environmental conditions, and threat levels. Sensors are the bridge for IoT devices to interact with the external environment, such as temperature, humidity, light, and motion, which can be used to collect context-awareness information. This involves assessing the context around the device in real-time through sensing technologies (e.g., image recognition, sensor data) and adapting security policies accordingly. For example, this includes increasing the level of security in high-risk environments while reducing power consumption in low-risk environments.
# An Advanced DEM-FEM Method for Herringbone Gear in Shot Peening
## 3 Advanced DEM-FEM Modeling for Shot Peening
### Verification of the Model  
Experimental validation was conducted on the calculation results at the pitch circle of the herringbone gear. Under the same shot peening process parameters, the surface roughness and residual stress were compared between experimental measurements and simulation calculations. The surface micro-topography of the shot peened tooth surface of the herringbone gear from experimental measurements and simulation calculations is shown in Figure 15. The morphological features of the simulation and experiment are basically consistent, but there are still some differences. Specifically, the surface topography measured experimentally has more high-frequency micro-peaks and micro-valleys. The experimental measured surface roughness Sa is 0.57 \(\upmu\)m, and the surface roughness Sa obtained from simulation calculations is 0.64 \(\upmu\)m, with a relative error of 12.3%. The simulation results and experimental results show good consistency.  
The residual stresses obtained from experimental measurements and simulation calculations are depicted in Figure 16. The surface residual stresses from experimental measurements and simulation calculations were \(-\)830 MPa and \(-\)835 MPa, respectively, with a relative error of 0.6%. Both the experimental and simulation results indicated that the maximum residual stress occurred at a depth of 30 \(\upmu\)m. The maximum residual stress measured experimentally was \(-\)1250 MPa, while the simulation calculated a value of \(-\)1350 MPa, yielding a relative error of 8%. The simulation results for residual stress exhibited good agreement with the experimental findings.  
Figure 14: Initial morphology setting process.  
Figure 15: Comparison of experimental and simulated surface topography. (**a**) Experimental result. (**b**) Simulation result.
###### Abstract  
Enhancing lung cancer diagnosis requires precise early detection methods. This study introduces an automated diagnostic system leveraging computed tomography (CT) scans for early lung cancer identification. The main approach is the integration of three distinct feature analyses: the novel 3D-Local Octal Pattern (LOP) descriptor for texture analysis, the 3D-Convolutional Neural Network (CNN) for extracting deep features, and geometric feature analysis to characterize pulmonary nodules. The 3D-LOP method innovatively captures nodule texture by analyzing the orientation and magnitude of voxel relationships, enabling the distinction of discriminative features. Simultaneously, the 3D-CNN extracts deep features from raw CT scans, providing comprehensive insights into nodule characteristics. Geometric features and assessing nodule shape further augment this analysis, offering a holistic view of potential malignancies. By amalgamating these analyses, our system employs a probability-based linear classifier to deliver a final diagnostic output. Validated on 822 Lung Image Database Consortium (LIDC) cases, the system's performance was exceptional, with measures of 97.84%, 98.11%, 94.73%, and 0.9912 for accuracy, sensitivity, specificity, and Area Under the ROC Curve (AUC), respectively. These results highlight the system's potential as a significant advancement in clinical diagnostics, offering a reliable, non-invasive tool for lung cancer detection that promises to improve patient outcomes through early diagnosis.  
Keywords:lung cancer diagnosis; computed tomography (CT) scans; 3D-local octal pattern (LOP); 3D-convolutional neural network (CNN); probability-based linear classifier; lung image database consortium (LIDC) +
Footnote †: journal: _Algorithms_
## 3 Results and Discussion
### Impact Abrasion Resistance of Concrete by Waterborne Sand Flow  
Figure 10 shows the relationship between the w/cm ratio and impact abrasion loss. For the concrete impacted at 45\({}^{\circ}\), when the w/cm ratio increased from 0.28 to 0.36 and then to 0.50, the total impact abrasion loss at 2 h increased by approximately 25% and 72%, respectively. For the concrete impacted at 90\({}^{\circ}\), increasing the w/cm ratio from 0.28 to 0.36 and then to 0.50 resulted in wear resistance improvements of nearly 26% and 88%, respectively. These results reveal that concrete of low strength can be worn easily by water jets and can subsequently develop additional porosity, constituting an undesirable cycling effect. By contrast, preparing concrete with a low w/cm by adding silica fume as micro-filler and pozzolanic material can substantially reduce the overall porosity and pore sizes of the concrete, in addition to strengthening the bond between particles of the hydrated matrix [14]. Concrete prepared with a low w/cm ratio performs more favorably in resisting impact abrasions. Additionally, the impact abrasion loss was influenced by the impact angle in this study. At the end of 2 h of testing and increasing the w/cm ratio from 0.28 to 0.36 and then to 0.50, the impact abrasion loss of the concrete impacted at 90\({}^{\circ}\) was nearly 69%, 85%, and 86% higher than that of the concrete impacted at 45\({}^{\circ}\), respectively (Figure 11). This may explain why the hydraulic pressure and its associated particle prising action on the concrete impacted at 90\({}^{\circ}\) were higher than those of the concrete impacted at 45\({}^{\circ}\), thus increasing the impact abrasion loss. Moreover, Figure 11 shows an improvement in resistance to the impact abrasion for the steel fiber-reinforced concrete relative to that of the reference mixture with the same w/cm ratio. For the fiber-reinforced concrete impacted at 45\({}^{\circ}\) and 90\({}^{\circ}\), the impact abrasion loss at 2 h increased by nearly 8% and 9%, respectively, relative to that of the reference mixture (C36). This may be because the addition of steel fibers and silica fume resulted in increased tensile and bending strength.  
During the impact abrasion test, concrete abrasive wear occurred readily in the small holes and pits. The IAR was also influenced by the aggregate, regardless of whether it was peeled off. When the aggregate was removed through prising, the IAR increased. Consequently, the IAR trend was nonregular (Figure 12). For the concrete made with a low w/cm ratio (0.28) and impacted at 45\({}^{\circ}\) and 90\({}^{\circ}\), the IAR stabilized after 1 h of the test. This can be attributed to the fact that a low w/cm ratio results in a denser and stronger matrix, improving the homogeneity of concrete and thus leading to a stable abrasion rate. Regarding the other types of concrete, during the impact abrasion test, removing the aggregate engendered an increase in abrasion loss and consequently increased the IAR.  
Figure 11: Total impact abrasion loss of concrete at 2 h.
## 2 Functional Attributes of PGPR and Their Mechanisms
### Biofertilization
Various mechanisms are used by KSB, such as the synthesis of organic acids (e.g., oxalic acid, citric acid, succinic acid, tartaric acid, and \(\alpha\)-ketoglucic acid), which can affect the dissolution of K-containing minerals by decreasing the pH of the environment as well as by attaching the polysaccharides to the mineral surface [51; 52]. The complexation of metal ions (e.g., Fe\({}^{2+}\), Al\({}^{3+}\), and Ca\({}^{2+}\)) and proton supply are other mechanisms of KSB to enhance the dissolution of K compounds [52]. It has been reported that zinc (Zn) deficiency can be addressed by applying a type of PGPR known as zinc solubilizing bacteria (ZSB), which can mobilize Zn complexes and solubilize insoluble Zn forms in the soil, including ZnO, ZnCO\({}_{3}\), and Zn\({}_{3}\)(PO\({}_{4}\))\({}_{2}\), through various mechanisms [27]. These PGPR evade organic acids and phenolic and flavonoid compounds in the rhizosphere, resulting in the sequestration of Zn cations, lowering the pH of the rhizosphere and, consequently, increasing the soluble form of Zn and the ratio of Zn\({}^{2+}\) to organic Zn ligands [53]. In fact, Zn absorption is mainly affected by soil pH, in which Zn easily adsorbs on cation exchange places at high pH levels while being replaced by CaCl\({}_{2}\) at low pH levels [27]. On the other hand, it has also been suggested that the high Zn mobilization in soils in the presence of high levels of low molecular weight organic acids, phenolics, siderophores, and other bacterial metabolites mostly depends on the complex capacity of these metabolites compared to their ability to acidity the rhizosphere [53; 54]. Such complex capacity is raised at high levels of soil pH due to the high concentration of deprotonated carboxylic and phenolic moieties, which are more potent Lewis bases in reacting with metal cations. Moreover, some organic molecules possessing more than one acidic moiety (e.g., citric acid) show a greater complexing capacity at high pH levels and, therefore, can form polydentate complexes with cations possessing more than one positive charge, such as Fe\({}^{3+}\) and Zn\({}^{2+}\), when all the acidic functional groups are consecutively deprotonated [17; 54]. Several studies have clearly shown the favorable effects of PGPR on nutrient uptake and plant production. For instance, [PERSON] et al. [52] reported an increment in P and K uptake by rice plants, influenced by three PGPR strains (_Pantoea ananatis_, _Rahnella aqutilis_, and _Enterobacter_ sp. ), of up to 35-77% in leaves, 17-53% in stems, and 25-75% in roots, as well as plant height (+11-15%) and biomass (+27-65%), depending on the PGPR strain. Moreover, some researchers have documented that the application of PGPR treatment promoted plant growth by improving leaf photosynthetic efficiency by up to 19, 12, 12, 16, and 20% in durum wheat [55], rice [56], eggplant [57], cucumber, and pepper [58], respectively, compared to nontreated control plants, which could lead to higher CO\({}_{2}\) assimilation [59] and enhanced grain yield [56].
# Theory of Edge Effects and Conductance for Applications in Graphene-Based Nanoantennas
## 2 Optical Conductance of a Terminated Graphene Sheet
### The Limit of an Infinite Sheet  
In this subsection we show that our model reduces to the familiar Drude relation for the conductivity in an infinitely wide sheet. Taking the limit \(L\to\infty\) and using the following transformation \((2L)^{-1}\!\!\sum_{n}(\ldots)\to(2\pi)^{-1}\int_{-\infty}^{\infty}(\ldots)dk_{x}\), Equation (16) yields  
\[\begin{array}{l}\sigma^{Intra}(x)\approx-\frac{ie^{2}\
u_{c}^{2}}{2\pi( \widetilde{\mu}\omega+i0)}.\\ \int\limits_{-\infty}^{\infty}\int\limits_{-\infty}^{\infty}\frac{\partial f( \varepsilon)}{\partial\varepsilon}\Bigr{|}_{\varepsilon=\varepsilon_{x}= \varepsilon_{t}(k_{y})}\Bigl{[}1-\tfrac{1}{2}\cos(k_{x}(2x-L))-\tfrac{1}{2} \cos(k_{x}(2x+L))\Bigr{]}dk_{x}dk_{y}.\end{array} \tag{22}\]  
Figure 2: Illustration with respect to the zero-temperature approximation. Black lines correspond to confined modes, red line corresponds to the edge mode, and blue line corresponds to the first confined mode. The black point corresponds to the critical wavenumber in which the mutual transformation of the edge mode to the first confined mode takes place. The dotted horizontal line corresponds to the given electrochemical potential. The values \(k_{yn}\) denote the roots of Equation (17).  
Introducing the polar variables \(k_{x}=\widetilde{z}\sin\Phi\), \(k_{y}=\widetilde{z}\cos\Phi\), we obtain \(dk_{x}dk_{y}=\widetilde{z}d\widetilde{z}d\Phi\). Using the zero temperature approximation, we exchange the Fermi distribution derivative by the Dirac delta function and integrate over \(\widetilde{z}\), so that Equation (22) becomes  
\[\begin{array}{c}\sigma^{Intn}(x)\approx\frac{ie^{2}\mu}{2\pi^{2}H^{2}(\omega+ \imath 0)}.\\ \int\limits_{0}^{2\pi}\Bigl{[}1-\frac{1}{2}\cos(\widetilde{\mu}(2x-L)\cos\Phi )-\frac{1}{2}\cos(\widetilde{\mu}(2x+L)\cos\Phi)\Bigr{]}\ \ d\Phi.\end{array} \tag{23}\]  
Furthermore, employing the following addition theorem for the Bessel functions [52]  
\[e^{\mp i\rho\cos\Phi}=\sum\limits_{m=-\infty}^{\infty}i^{\mp m}J_{m}(\rho)e^{ \pm i\pi\Phi}, \tag{24}\]  
it is possible to integrate Equation (23) and obtain  
\[\sigma^{Intn}(x)\approx\frac{ie^{2}\mu}{\pi\hbar^{2}(\omega+\imath 0)}\biggl{[}1- \frac{1}{2}J_{0}(\widetilde{\mu}(2x-L))-\frac{1}{2}J_{0}(\widetilde{\mu}(2x+L) )\biggr{]}. \tag{25}\]  
If the observation point \(x\) (under the assumption of large \(L\)), is placed rather far from the edges, the arguments of Bessel functions become large, so that the asymptotic relations [52]. In this case, the last two terms in (25) become indefinitely small. The first term, which is exactly equal to the Drude conductivity \(G^{Drude}=ie^{2}\mu/\pi\hbar^{2}(\omega+\imath 0)\), becomes dominate over the whole area of the ribbon excluding the narrow vicinities of the edges. Thus, it is demonstrated that in this limit our model asymptotically reduces to the classical expression for the Drude conductivity.
## 1 Introduction
According to the above analysis, our method prepares anchor boxes in two steps: assigning the ground-truth boxes to different feature levels and selecting positive locations in the feature maps for anchor shape predictions; and determining the shape targets of the anchor boxes at different positions, with the anchor boxes achieving the maximum IoU scores with the ground-truth boxes. After obtaining a set of high-quality predicted anchor boxes, we propose the feature enhancement module to enhance the semantic features of the objects and develop detection performance. We implement the dynamic anchor on RetinaNet (DA-RetinaNet) with the anchor generator and the feature enhancement module. The anchor generator uses the feature maps from the feature pyramid (FPN [19]) to predict the optimal anchor shapes without any pre-defined boxes. Therefore, DA-RetinaNet avoids all hyper-parameters of the anchor boxes. In addition, as the scales and aspect ratios of the anchor boxes are learnable rather than fixed, the detector makes it easier to handle tall or wide objects. In the experiments, the predicted anchors as the detection results achieved 26.5% Average Precision (AP) on  
Figure 1: This is the schematic diagram of a set of pre-defined anchor boxes. There are nine anchors with different scales and aspect ratios. The red box represents the anchors and the blue box represents the ground-truth box. COCO2017 minival, proving the effectiveness of our scheme. The proposed dynamic anchor can lead to significant improvements on the anchor-based detectors. On COCO test-dev [20], DA-RetinaNet improved AP by 2.1% while using ResNet-50 [21] as the backbone, and DA-RetinaNet achieved 40% AP with ResNet-101, outperforming the baseline and guided anchoring [22]. The main contributions of this paper can be summarized as follow:  
* We propose an alternative anchor strategy (dynamic anchor) to automatically generate anchor boxes without any hyper-parameters. Compared with the pre-defined anchor scheme, dynamic anchor would generate anchor boxes that are specific to the objects and avoid careful parameter tuning;
* We propose a feature enhancement module, which takes advantage of the high IoU scores of the predicted anchors. The module enables the network to focus on the region of anchors and extract more precise semantic features;
* We propose a quality branch, which is used to predict the quality scores of the predicted anchors. By investigating the influence of the anchor boxes with different quality, the scores are used to repress the low-quality anchors. The code will be available at [[https://github.com/LX-SZY/Dynamic-Anchor](https://github.com/LX-SZY/Dynamic-Anchor)]([https://github.com/LX-SZY/Dynamic-Anchor](https://github.com/LX-SZY/Dynamic-Anchor)) (accessed on 3 March 2022).