esa-sceva/satcom-chunk-collection huggingface dataset:
# Visible Spectrum and Infra-Red Image Matching: A New Method
## 3 Proposed Method
### Mn-Sift  
To compute MN-SIFT [17] descriptors, a circular region around each detected SIFT feature point is cropped from the image. The radius of the region is proportional to scale (\(\sigma\)) of the SIFT feature point. The region is sub divided into \(4\times 4\) location bins as shown in Figure 2. The bins are denoted as \(H_{r,c}\) where \(r,c=1,2,3,4\). The region is convolved with kernels to obtain derivatives \(F_{h}\) and \(F_{v}\) along horizontal and vertical directions, respectively. The gradient magnitude (\(\Omega\)) and gradient orientation (\(\beta\)) are calculated at each pixel location \((x,y)\) as:  
\[\Omega(x,y) = \  
where \(mod(.)\) represents a modular operator i.e., the modular operation on \(\beta(x,y)=-\pi/2\) produces \(L(x,y)=6\). Then a feature histogram \(h_{r,c,t}\) is computed for each location bin as follows:  
\[h_{r,c,t} = \sum_{x=l_{c}}^{u_{c}}\sum_{y=l_{r}}^{u_{r}}\tilde{\Omega}(x,y) \delta(L(x,y)=t), \tag{7}\]  
where \(t=0,1,...,7\) and \(\delta(.)\) is defined as:  
\[\delta(z) = \begin{cases}1&\text{z is true}\\ 0&\text{otherwise}\end{cases}. \tag{8}\]  
The histograms are concatenated over all the location bins \(H_{r,c}\) to obtain MN-SIFT descriptor.
## 5 Conclusions  
In soil incubation, the N release of AN and AW was not only similar to that of CAN, but it also manifested a positive priming effect on soil treated with these RDFs and their respective blends. The effect of RDFs on lettuce growth indicated a positive fresh yield and nutrient concentration results in the case of AN and AW fertilization, whereas CaE and both blends demonstrated poor performance in comparison to the synthetic N fertilizer CAN. The Na toxicity induced by CaE fertilization led to a low N uptake and, subsequently, a lower yield seen in these treatments. This also impacted the attempt at optimization of NPK by blend formation. The NFRV results exhibited that AN and AW both had high N replaceability potential over the synthetic CAN. Overall, the current study gave a positive insight into RDFs such as AN and AW, while raising further research questions about CaE and opening new investigative avenues into its suitability towards less (salt; Na) sensitive plants. Emphasis is laid on the full field validation of the tested RDFs in diverse environmental conditions and, soil and crop types for data collation on their agronomic properties and environmental impacts. In parallel, the development of fertilizer management strategies for novel products such as CaE and AW needs to be set in motion in order to generate market for these products.  
The conceptualization of the experiment was conducted by [PERSON], [PERSON] and [PERSON]; [PERSON] and [PERSON] conducted the experiments, analyzed the data, and wrote the manuscript. [PERSON], [PERSON], [PERSON] and [PERSON] reviewed the manuscript and supervised the work. E.M. approved the manuscript for publication. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was conducted as a part of the ReNu2 Farm project and funded by INTERREG NORTH-WEST EUROPE PROGRAMME, grant number NWE601.  
**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. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; in the decision to publish the results.
# Can People Really Do Nothing? Handling Annotation Gaps in ADL Sensor Data
## 3 Annotation Gaps  
At the core of activity recognition systems, multi-modal data, which is acquired by various sensors, represents the main ingredient for recognition algorithms. Typically, sensors are installed around target premises or house to \"continuously\" monitor the household activities. These sensors are physically installed in locations, which guarantee a collection of data assisting activity recognition algorithms. To provide such assistance, collected data must achieve a tradeoff between distinction and compactness. In other words, the collected data should be as compact as possible to get rid of any reading redundancy [27]. Yet, the collected readings should contain data that can be used to clearly distinguish between different activities. Types of sensors, readings resolution, installation positions, and operation environments usually contribute to the achievement of such tradeoff.  
Used sensors may be visual, such as cameras of different modalities, or non-visual such as temperature and motion sensors. The scope of this paper is handling the data acquired by non-visual sensors. Typically, non-visual sensors provide digital and/or analog readings 24/7. The richness of such data could be an advantage in many cases and a source of challenge in other cases [27]. One of the challenges created by the big data size shows up in feeding such data into supervised learning-based frameworks. In particular, getting a ground truth label for every single instance in the acquired data is an extremely exhaustive process, if not impossible. This is because of the dependence on common manual labeling procedures.  
A human annotator finds difficulties in giving a crisp activity label to each single reading instance due to several reasons such as the following:  
1. There is a usual delay between acquisition and annotation. This results in missing specific labels of some activities.
2. By nature, many activities lack clear in-between boundaries determining their beginnings and their ends.
3. A household himself/herself is often the annotator. He/She usually gives labels to activities on fuzzy basis depending on his/her memory of the starts and the ends of the activities. Supported by the aforementioned annotation delay, this leads to consequent annotation inaccuracy.  
All these factors lead the annotator to leave some unlabeled gaps surrounded by well-determined activity readings. For training processes, these gaps represent a source of modeling inaccuracy. Since it is extremely difficult to avoid the creation of such label gaps in the training data, we investigate various approaches to handle them during the training process. In the next section, we propose three different paradigms to handle these gaps.
## 5 Results
### Test-Case 3: Aircraft Wing System  
The third test-case is another common benchmark in the literature on pole assignment. It consists of an aircraft wing under an air stream, as proposed in [15; 28; 29]. The system matrices are:  
\[\mathbf{M}=\left[\begin{array}{ccc}17.6&1.28&2.89\\ 1.28&0.824&0.413\\ 2.89&0.413&0.725\end{array}\right]\mathbf{C}=\left[\begin{array}{ccc}7.66&2.45 &2.1\\ 0.23&1.04&0.223\\ 0.6&0.756&0.658\end{array}\right]\mathbf{K}=\left[\begin{array}{ccc}121&18.9 &15.9\\ 0&2.7&0.145\\ 11.9&3.64&15.5\end{array}\right]\mathbf{b}=\left[\begin{array}{c}1\\ 1\\ 1\\ \end{array}\right]. \tag{32}\]  
The system is unstable, and the stiffness and damping matrices are both asymmetric, and are hence decomposed into a symmetric and an asymmetric part: **K=K\({}_{s}\)+K\({}_{a}\)** and **C=C\({}_{s}\)+C\({}_{a}\)**.  
The control design task prescribed to assign the dominant complex conjugate pole pair \(\mu_{1,2}=-1.500\pm 3.000i\), while the remaining closed-loop poles \(\mu_{i}\) must belong to the LMI region proposed in [15], which is a disk of radius equal to 1 and center at coordinates (\(-3\),0).  
The gain \(k_{0}\), computed by means of the receptance method for asymmetric systems (see Section 3.2), is unable to stabilize the unstable open-loop system, leading to an unstable right half complex conjugate pole pair. Finally, the gain \(k\) computed through the second stage can stabilize the system and cluster the unassigned poles into the prescribed LMI region. The gains are reported in Table 6 while the open-loop and closed-loop poles are reported in Table 7 and shown in Figure 9. It should be pointed out that, unlike [15], the method proposed here is able to modify the open-loop pole in \(-0.918\pm 1.7606i\), by moving it, for example, to \(-1.500\pm 3.000i\). This modification is effective in speeding up the closed-loop system response due to the increase of the pole natural frequency compared to the open-loop poles. This is a relevant feature and novelty of the method proposed in this paper, compared to the methods already available in the literature.  
Figure 8: Maximum singular value: traditional PPP and the proposed method (test-case 2).
## 3 Multi-Objective Optimization Model for WNN-Based PI Construction
### WNN-Based PI Construction  
WNN is a neural network based on the wavelet transform (WT). It is powerful for frequency component analysis and suitable for signals which are composed of high-frequency components with short duration and low-frequency components with long duration [20; 21]. A brief review of WT is described as follows:  
Assuming that \(f(t)\) is a square integral function, it can be expressed as:  
\[f(t)=\iint W(a,b)\psi(\frac{t-b}{a})dbda \tag{7}\]  
where \(W(a,b)\) is the continuous wavelet transformation of \(f(t)\) and defined as:  
\[W(a,b)=\int f(t)\psi^{*}_{a,b}(t)dt \tag{8}\]  
with:  
\[\psi^{*}_{a,b}(t)=\frac{1}{\sqrt{a}}\psi(\frac{t-b}{a}) \tag{9}\]  
where \(a\) and \(b\) are the scaling parameter and shifting parameter, respectively.  
\(X_{i}=\{x_{1},\cdots,x_{i},\cdots,x_{N}\}\) is the \(N\) input sample of WNN, \(\omega_{is}\) is the input parameter between the input layer node and the hidden layer node, and \(\beta_{sj}\) is the output parameter that connects the hidden layer and the output layer. The calculation equation of the output of the hidden layer is as follows:  
\[g(s)=\psi\Bigg{(}\left(\sum_{i=1}^{N}\omega_{is}x_{i}-b_{s}\right)/a_{s} \Bigg{)},\quad s=1,2,\cdots k \tag{10}\]  
where \(g(s)\) is the output of the \(s\) node of the hidden layer, \(\psi\) is the small wave basis function, and \(k\) is the number of hidden layer nodes of the WNN. The calculation equation of the output layer is as follows:  
\[Y_{j}=\sum_{s=1}^{k}\beta_{sj}g(s),s=1,2,\cdots k \tag{11}\]  
Then, the \(M\) output \(Y_{j}=\{y_{1},\cdots,y_{j},\cdots,y_{M}\}\) of the WNN is designed as:  
\[y_{j}=\sum_{s=1}^{k}\beta_{sj}\psi\Bigg{(}\left(\sum_{i=1}^{N}\omega_{is}x_{i }-b_{s}\right)/a_{s}\Bigg{)} \tag{12}\]The upper and lower bounds of the prediction interval of wind power are directly given by the dual-output WNN, which effectively avoids the complex process of the traditional interval prediction method for the probability error analysis. Thus, a symbolic WNN-based on PIs' construction model is shown in Figure 1.  
In order to avoid the irrational effect of the number of input and hidden layer nodes on the prediction results, the C-C phase space reconstruction method is used to determine the number of input layer nodes \(N\). According to the Kolmogorov theorem, the number of hidden layer nodes K is determined, and the structure of the PI model for the WNN is N-K-2. The Morlet wavelet with strong adaptive ability is selected as the wavelet basis function, and the equation is as follows:  
\[\psi(t)=\cos(1.75t)\cdot e^{-t^{2}/2} \tag{13}\]  
By choosing suitable input parameters, output parameters, scaling parameters, and shifting parameters of the WNN, effective and reliable PIs of wind power will be constructed.
# Efficient Secure Routing Mechanisms for the Low-Powered IoT Network: A Literature Review
## 8 Categories of Network Attacks in WSN-IoT Layers
### Data Attacks  
(1) Data breach  
A data breach occurs when private, secure, or confidential information is stored, distributed, accessed, stolen, or used by someone who cannot do so. Memory leakage is the disclosure of personal, critical, or confidential information [17].  
(2) Unauthorized access  
Unauthorized access occurs when a person uses another person's account or other methods to access a website, software, server, facility, or other equipment. Giving access to registered users and refusing entry to unauthorized users is what access management entails. Malicious users may obtain data control or privileged access through unauthorized access [96].  
(3) Data inconsistency  
In the WSN-IoT, \"data inconsistency\" is a phrase used to describe an attack on data honesty that results in data inconsistency in transit or data kept in a central database [56].
# Damage Identification Method Using Additional Virtual Mass Based on Damage Sparsity
###### Abstract  
Damage identification methods based on structural modal parameters are influenced by the structure form, number of measuring sensors and noise, resulting in insufficient modal data and low damage identification accuracy. The additional virtual mass method introduced in this study is based on the virtual deformation method for deriving the frequency-domain response equation of the virtual structure and identify its mode to expand the modal information of the original structure. Based on the initial condition assumption that the structural damage was sparse, the damage identification method based on sparsity with \(l_{1}\) and \(l_{2}\) norm of the damage-factor variation and the orthogonal matching pursuit (OMP) method based on the \(l_{0}\) norm were introduced. According to the characteristics of the additional virtual mass method, an improved OMP method (IOMP) was developed to improve the localization of optimal solution determined using the OMP method and the damage substructure selection process, analyze the damage in the entire structure globally, and improve damage identification accuracy. The accuracy and robustness of each damage identification method for multi-damage scenario were analyzed and verified through simulation and experiment.  
Keywords:structural health monitoring (SHM); damage identification; virtual mass; sparse constraint; IOMP method +
Footnote †: journal: Applied Sciences
# Simulative and Experimental Investigation of Vibration Transfer Path at Gearboxes
## 2 Test Case  
The FZG back-to-back test rig is shown in Figure 1. The data on the modification of the used test rig have already been explained in a previous work [19]. The test rig mainly consists of a drive gearbox, a test gearbox with a center distance of 112.5 mm each, and a motor. The gear parameters are displayed in Table 1.  
With the back-to-back setup, the test rig can be operated with one motor. Based on simulations, it can be stated that the long shafts between the gearboxes decouple the vibration excitation of the gearboxes [23]. Therefore, only the vibration excitation of the test gearbox can be measured. Torque is applied to the system by using an external load with a lever arm. The test rig offers variable speeds up to 3000 rpm and torques up to 1000 Nm at this setup. In the test gearbox, a helical gear pair and tapered roller bearings of  
\begin{table}
\begin{tabular}{c c c c} \hline
**Gear Parameter** & **Symbol** & **Unit** & **112.5 Helical Gear Set** \\ \hline Center distance & a & mm & 112.5 \\ Number of teeth (pinion/wheel) & z\({}_{1}\)/\({}_{2}\) & - & 22/24 \\ Normal module & m\({}_{\text{n}}\) & mm & 4.25 \\ Transverse module & m\({}_{\text{t}}\) & mm & 4.86 \\ Normal pressure angle & \(\alpha_{\text{n}}\) & In \({}^{\circ}\) & 20 \\ Helix angle & \(\beta\) & In \({}^{\circ}\) & 29 \\ Profile shift coefficient (pinion/wheel) & x\({}_{1}\)/\({}_{2}\) & - & 0.100/0.077 \\ Pitch diameter (pinion/wheel) & d\({}_{1}\)/d\({}_{2}\) & mm & 106.90/116.62 \\ Tip diameter (pinion/wheel) & d\({}_{\text{a1}}\)/d\({}_{\text{a2}}\) & mm & 117.70/127.30 \\ Transverse contact ratio & \(\varepsilon_{\alpha}\) & - & 1.5 \\ Overlap ratio & \(\varepsilon_{\beta}\) & - & 1.0 \\ Face width & b & mm & 27.6 \\ \hline \end{tabular}
\end{table}
Table 1: Gear parameters, according to [PERSON] [22].  
Figure 1: FZG-gear test rig based on ISO 14635-1 [20], source [21] CC-BY 4.0.  
type FAG 32310-A [24] with an axial preload of 10 \(\upmu\)m are used. To avoid disturbances, the tests are performed after a sufficient run-in period of the gear set.  
In this work, the influence of different torques and rotational speeds on the peak-to-mean ratio of the gear mesh frequency is discussed. The peak-to-mean ratio of the gear mesh frequency in this work can be calculated with Equation (1). In a previous work [9], the influence of the rotational speed on the peak-to-mean ratio for the vibration amplitudes in the frequency domain has already been investigated. The result shows that the peak-to-mean ratio of the measurement at the rotating sensor increases compared to the measurement at the housing sensor when the rotational speed decreases. In this work, the influence of the torque will also be discussed.  
\[\text{peak}-\text{to}-\text{mean ratio}=\frac{\text{amplitude at gear mesh frequency}}{\text{mean value of spectrum}} \tag{1}\]
## 2 Materials and Methods
### Count of Endophytic Bacteria Colonising Root  
The ability of _Bacillus subtilis_ and _Pseudomonas fluorescens_ endophytes to colonise white lupine roots was investigated by means of fluorescent in situ hybridisation (FISH). First, 5 mL of saline was poured on the test material (0.5 g of the plant root crushed in a mortar). Next, 0.05 mL of the resulting suspension was collected for further analysis by applying it to the surface of microscope slides using the breed chamber, and then it was fixed with a 4% PFA solution (paraformaldehyde). In order to clean the applied material from grease and proteins of the tested bacteria, the microscope slides with the biomaterial were rinsed in a PBS solution three times. A 0.5% Triton solution was then added and they were rinsed with the PBS solution three times again. Next, the microscope slides were placed in an alcohol series (70%, 80%, 96%), following which a 70% formamide solution was added, and a genetic probe at a concentration of 25 ng \(\upmu\)L\({}^{-1}\), dedicated to _Bacillus subtilis_ (5\({}^{\prime}\)-/Cy3/TAC CGC CCT ATT CGA ACG GTA C-3\({}^{\prime}\)) ([PERSON] et al., 2016) and a probe at a concentration of 0.5 ng \(\upmu\)L\({}^{-1}\) dedicated to _Pseudomonas fluorescens_ (5\({}^{\prime}\)-ACT ACC AGG CAG ATT CCT AGG CA-/Cy3/-3\({}^{\prime}\)) were applied (19). The probes were labelled with fluorescent dyes, i.e., Cy5 for bacteria of the _Bacillus_ genus and Cy3 for bacteria of the _Pseudomonas_ genus, and suspended in a solution consisting of: 5M NaCl, 1M Tris/HCl, 10% SDS, and ddH\({}_{2}\)O. After 24 h incubation, the presence of bacteria was determined on the basis of an image observed with a Zeiss Imager M1 fluorescence microscope.  
The count of endophytic bacteria colonising the roots of white lupine was assessed with the Zeiss Imager M1 fluorescence microscope. After hybridisation, the number of bacterial cells labelled with the genetic probe was counted under the microscope in the field of view by making ten gradual shifts diagonally across the smear. Two smears were prepared for each experimental variant. The count of bacteria was determined according to the following formula:  
\[X=\frac{13,500\times\text{A}}{20}\]  
where: \(X\)--the count of root-coloning bacteria; 13,500--the area of the field of view of the fluorescence microscope; A--the total number of cells in all fields of view; 20--10 shifts \(\times\) 2 smears.  
The value calculated with the formula above (\(X\)) was multiplied by 100. The result was the count of bacteria in 1 mL of the suspension obtained by grinding 0.5 g of the white lupine root.
# 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
#### 4.5.4 Standardization and Interoperability  
With the growing number of IoT devices, standardization and interoperability become increasingly crucial. E-learning in particular illustrates the predictability of this perspective: as IoT wearable devices such as smartwatches are widely used in education, it will be essential to establish strict security and privacy standards to ensure that all relevant devices and programs adhere to these privacy guidelines. In addition, the interoperability of IoT systems is demonstrated through the use of intelligent whiteboards and virtual reality (VR) technologies that enhance the interactivity of the learning experience and emphasize the value of seamless integration across devices and platforms [105]. Future research will concentrate on creating common frameworks and protocols to guarantee the efficient resource utilization across various IoT systems. This involves aligning security standards, communication protocols, and interoperability standards among devices to streamline complexity across the IoT ecosystem and enhance the system's overall security. These emerging trends will offer innovative solutions for energy-aware security in the IoT and propel the entire field toward greater intelligence, sustainability, and security.  
Open research questions encompass balancing the costs and benefits of energy-harvesting technologies, addressing security challenges in dynamically changing environmental contexts, and striking a balance between flexibility and consistency in the standardization process. Exploring these questions will pave the way for future research directions, advancing the development of IoT energy-sensing security.
## 1 Introduction
During Si ingot slicing for wafer manufacturing, a large quantity of waste Si powder is produced. If the waste Si can be reused to fabricate Si anodes, it can significantly lower the production cost of LIBs and will be environmentally beneficial. In a recent attempt, we succeeded in the fabrication of large-area 1D Si micropillars on copper foils by using nanosecond-pulsed laser irradiation, and the pillar-on-Cu structures were demonstrated to be useable as LIB anodes with a capacity about seven times higher than a graphite one [39]. This is an efficient and environmentally friendly process compared with chemical growth methods. If this method can be optimized and applied to the mass production of Si anodes for LIBs, the industrial silicon waste will be reduced, and, at the same time, a new manufacturing technology for high-performance LIB anodes may become possible. However, in this process, the relationships among the laser irradiation condition and the resulting pillar size, shape, distribution density, and electrochemical property are not yet clear. In addition, how the pillar morphology and property change during long-term electrochemical cycling is unknown. In this study, Si micropillars with tunable sizes were fabricated from Si waste powders using a nanosecond pulsed laser under various conditions, and the role of micropillar size on the electrochemical performance of the Si-on-Cu anode was systematically examined. The dependence of rate capability and cycling performance on the size/shape of micropillars was investigated. Furthermore, the morphological change of Si micropillars during electrochemical cycling was characterized to understand the mechanism of capacity decay during long-term cycling. The findings from this study could provide guidelines for fabricating high-performance and reliable LIB anodes from Si waste.
## 3 Security and Privacy-Preserving Solutions for IoE
### Blockchain-Based Security Solutions  
Blockchain enables IoE to establish a decentralized and secure data-sharing infrastructure. While traditional security solutions are managed by central authorities, blockchain secures data through its Distributed Ledger Technology (DLT) structure [71].