esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Security Threats
### Implementation
#### 4.4.6 Identity Theft/Multiple Identities  
Unique, unambiguous and tamper-proof identities are required for securing not only the negotiation phase of a spectrum sharing protocol, but also that of rule conformance [51]. Otherwise, an identity theft and/or spoofing attack may pose a significant threat to the operation of most modern DSA networks.  
A misbehaving node has strong motivation to steal or then mimic an unsuspecting competitor's identity. To begin with, this attack can enable an adversary to hide his malicious behavior behind someone else. As a result, not only the former will avoid being detected and punished, but also the latter will be accused for rule violations he never committed. Additionally, the attacker may perform identity spoofing to increase his own benefit without cost and degrade the performance of his competitors. As outlined above, spectrum can be shared in more than one dimension. After that, the resulting spectrum slices are assigned to network nodes in an optimal manner. If a selfish node achieves to successfully spoof someone else's identity, he will automatically be enabled to exploit hisvictim's spectrum opportunities. Sooner or later, the nodes composing such a DSA will stop trusting the sharing mechanism and start searching for new alternatives.  
Additionally, an attacker may attempt to simultaneously spoof more than one identity at a time for exactly the same reasons as above. In this case, however, not only would the possible adversary's benefits be increased, since access opportunities of more victims would be illegally exploited cumulatively, but also their malicious behavior would be masked in a more efficient manner. The impact of such a combined spoofing attack would be greater than before, causing bigger problems inside an underlying DSA network.  
Even worse, an attacker can continuously change the spoofed identity equally dividing the caused disturbance to his competitors, making it harder to be detected.
# Highly Reliable Fuzzy-Logic-Assisted AODV Routing Algorithm for Mobile Ad Hoc Networks
In order to determine a route of high reliability and low latency in the topology of dynamic rapid changes, we propose to use fuzzy logic based on the AODV of the shortest path to consider multiple parameters to enhance routing reliability and delay concurrently. In addition, considering route constructed by more stable and more surplus energy nodes can maintain longer activity period to achieve reliable transmission of data packets. Therefore, delay, stability, and residual energy are selected as the evaluation of node reliability. In this paper, we propose a highly reliable fuzzy-logic-assisted AODV routing (FL-AODV) algorithm. The main contributions of this paper are summarized as follows:  
* Reliability of route is described by delay, stability, and remaining energy of nodes. Fuzzy logic is used to fuzzy the considered performance metrics and thus to realize the uncertainty conversion between fuzzy language concepts and quantitative values. * Based on node reliability, the fuzzy-logic-assisted AODV routing algorithm is proposed. The node with the largest reliability is added to the routing table during route discovery process with AODV to determine a route with high reliability and low latency. Simulation experiments have been implemented to verify the effectiveness of the proposed algorithm. The rest of this paper is organized as follows. In Section 2, we introduce the system model of this paper and review the traditional AODV. In Section 3, we describe the basic concepts and fuzzification process of normalized delay, stability, and residual energy. In Section 4, we compare the performance of the fuzzy logic algorithm and the fuzzy-logic-assisted AODV algorithm with the traditional AODV and present the simulation results.
## 4 Results Analysis
### Summary and Outlook
#### 4.1.2 Enhance Interactivity and Personalized Experiences  
By designing multi-user interaction modes such as virtual team exploration and interactive quiz games, visitors are encouraged to share information and learn from each other. Meanwhile, the spatial layout of exhibition areas is optimized by placing interactive installations in easily observable and accessible locations, while creating an immersive cultural atmosphere through technologies like augmented reality (AR) or virtual reality (VR) to enrich visitors' sensory experiences during interactions. Additionally, a personalized recommendation system is introduced to provide customized content based on users' interests and browsing history, making the interactive installations more engaging and educationally valuable. Through a user feedback collection mechanism, the design of interactive installations is continuously improved to meet the needs of diverse visitors, thereby enhancing user loyalty and the effectiveness of cultural dissemination.  
This study employs distributed cognition and perceived value theory as its framework to systematically analyze the influencing mechanisms behind the sustained use of digital interactive installations at the Capital Museum. The results indicate that optimizing user experience requires balancing technical usability, content value, and social interaction, with satisfaction serving as the core driver of user retention. Although the study identifies key factors influencing sustained use and yields relatively comprehensive findings, certain limitations and shortcomings remain. Moving forward, the digital transformation of museums should transcend mere technological accumulation and focus on the deep integration of \"human-technology-culture.\" Through scientific evaluation and continuous innovation, digitally enhanced experiences that combine educational significance with emotional resonance can be created, ultimately achieving sustainable cultural heritage dissemination and public engagement.
# A Mixed Reality Interface for a Digital Twin Based Crane
## 2 Materials and Methods
### Mixed Reality Application for the Overhead Crane
#### 2.3.3 Registration Module  
The registration module placed the holograms at the designed positions in the physical environment regardless of the user's initial location. This way, users would not necessarily start the application from a certain place at the lab, thus benefiting from enhanced mobility and flexibility.  
Workflow of Registration Procedure  
The second page of instructions guided users to complete the registration step. In this step, users were instructed to walk towards and look closely at the _image target_ print at the lab wall, until a green bounding box appeared and stably aligned to the _image target_. The holograms of the fixed and movable targets as well as the interface adjustment toggles then appeared at the designed locations, which would stay even when users moved their gaze away from the _image target_.  
Development of Registration Module  
The registration module was built with the Vuforia AR SDK in Unity, leveraging its features of image target, device tracking, and extended tracking. Firstly, we imported the _image target_ database processed by Vuforia as well as the spatial mapping mesh of the lab scanned by HoloLens camera into the Unity project. In addition, we placed a bounding box hologram around the _image target_, which served as an indicator to help users understand if they completed the registration step successfully. Figure 6 shows the spatial mapping mesh, holograms and _image target_ in the Unity scene.  
Meanwhile, we printed this _image target_ without changing its scale and ensured that its size matched the database size configured in the Unity project. We then fixed the _image target_ print to the lab next to the crane, while its corresponding database in the Unity project was placed with approximately the same pose (i.e., both position and orientation) relative to the spatial mapping mesh of the lab as the _image target_ print relative to the physical lab environment. Finally, from the HoloLens view, we checked the alignment between the spatial mapping mesh and the surroundings in the physical world and fine-tuned the relative pose of the _image target_ in the Unity project accordingly. The alignment was only required to be at an approximate level since the mesh only functioned as a spatial reference that facilitated the process of locating the holograms. Once the scene was constructed, the mesh was deactivated and eventually not included in the application deployed to the HoloLens.  
Figure 7 illustrates the spatial relationship in the registration setup. The HoloLens with the device camera functioned as a tracker that constantly accessed the pose of the _image target_ with Vuforia extended tracking feature. The poses of holograms were pre-calibrated with respect to the _image target_. By concatenating the holograms pose relative to the _image target_ and the _image target_ pose relative to the HoloLens, the poses of the holograms with respect to the HoloLens device were derived.
# The Use of Autogenous Teeth for Alveolar Ridge Preservation: A Literature Review
## 4 Demineralized Dentin Matrix (DDM)
### Preclinical Studies
#### 4.1.1 Histologic and Histomorphometric Outcomes  
A histologic and morphometric analysis showed that human DDM integrated newly formed bone after 14 days showing histological features of mature bone, proving its osteoconductivity [45; 46].
## 3 Research Methods
### Spatial Autocorrelation  
Spatial autocorrelation is employed to assess whether significant relationships exist between the attributes of geographic locations and those of their neighboring areas [44], and it serves as a key method for identifying patterns of spatial heterogeneity and urban spatial structure [22]. Spatial autocorrelation analysis, which includes both global and local autocorrelation, is a fundamental statistical tool in spatial analysis [45]. Global spatial autocorrelation analysis helps identify the overall patterns in how phenomena are distributed across a region and measures the extent and type of spatial dependence throughout the entire study area. [PERSON]'s I index, a widely used measure of spatial autocorrelation, is expressed as follows:  
Figure 2: Study framework.  
\[I=\frac{\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}w_{ij}(x_{i}-\overline{x})(x_{j }-\overline{x})}{s^{2}\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}w_{ij}} \tag{1}\]  
\[s^{2}=\frac{1}{n}\sum\limits_{i=1}^{n}(x_{i}-\overline{x})^{2} \tag{2}\]  
In the equation, \(n\) stands for the total count of units; \(x_{i}\) and \(x_{j}\) refer to the vitality values of spatial units \(i\) and \(j\), respectively; \(\overline{x}\) denotes the mean vitality value; and \(w_{ij}\) signifies the weight between \(i\) and \(j\) in the weight matrix, assuming a value of \(1\) when the spatial units are adjacent and \(0\) otherwise. Moran's I statistic spans from \(-1\) to \(1\). A positive value indicates an aggregation trend, a negative value indicates a dispersion trend, and the magnitude represents the intensity of the pattern; an absolute value of \(0\) indicates a random distribution.  
Local spatial autocorrelation measures the strength of the association between a given spatial unit and its surrounding neighbors. This analysis is instrumental in detecting local instability across different spatial locations and uncovering spatial heterogeneity within the data. Local Moran's I (LISA) measures spatial autocorrelation locally [46], with its formula given as follows:  
\[I_{i}=\frac{n(x_{i}-\overline{x})\sum\limits_{j=1}^{n}(x_{i}-\overline{x})}{ \sum\limits_{i=1}^{n}(x_{i}-\overline{x})^{2}} \tag{3}\]  
In the formula, \(w_{ij}\) represents the row-normalized form of the spatial weight matrix. Using Local Indicators of Spatial Association (LISA) metrics, four distinct categories of hotspot and coldspot areas emerge: low-value clustering regions (Low-Low, LL) and high-value clustering regions (High-High, HH), along with low-high (LH) and high-low (HL) spatial outlier clusters.
# Unsupervised SAR Imagery Feature Learning with Median Filter-Based Loss Value
## 5 Proposed Pipeline  
The graph in Figure 6 presents the whole training pipeline. Original scans (in.ntif,.tif and.cos file format) were trimmed to \(1024\times 1024\) pixels (including down-scaled from bigger areas) (described in the Section 3). Images prepared in this way are split in train/val/test and saved for all trainings, according to the description in Section 3).  
The training process starts with the loading of a batch of images, which are randomly rotated/mirrored and can be trimmed to augment the database (Section 2.1). These images are supplied to the input and output batch generators.  
The images are supplied as input (X) for neural network training and images are passed through without modification or a pseudo-random Gaussian noise is applied to them. The purpose of the noise application is to perform statistical analysis (see Section 6.2). The networks were not trained with the noise, as only inference was performed for the algorithm analysis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Neural Network**} & \multicolumn{5}{c}{**Effect of Noise on**} \\ \cline{2-6}  & \multicolumn{2}{c}{**Unfiltered MSE**} & \multicolumn{2}{c}{**Unfiltered MAE**} & \multicolumn{2}{c}{**Filtered MSE**} & \multicolumn{2}{c}{**Filtered MAE**} \\ \hline \multirow{2}{*}{Compressing CNN} & Without filters & 0.0026 & 0.0059 & 0.0376 & \\ \cline{2-6}  & With \(\times\) 7 Filter & & & 0.0349 & 0.1175 \\ \hline \multirow{2}{*}{Fully CNN} & Without filters & 0.0020 & 0.0044 & & \\ \cline{2-6}  & With \(\times\) 7 Filter & & & 0.0341 & 0.1302 \\ \hline \multirow{2}{*}{Compressing CNN with SSIM metric} & Without filters & 0.0001 & 0.0001 & 0.0349 & \\ \cline{2-6}  & With \(\times\) 7 Filter & & & 0.0263 & 0.0361 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simple analysis of the error change due to noise application. Green background determines the better results, while gray font means that the neural network was not trained for this task.  
Figure 6: Graph presenting the used pipeline for autoencoders training and testing. Orange color represents training with the backpropagation, purple presents the described processes.  
The labels or reference values (y) also have the following two paths: pass unmodified to perform classic training, perform filtering for the described training improvement. Passing without any alternation was used to create reference networks, which are juxtaposed in the Results Section. The applied filtering is described in Section 2.6. The filtering improves the achieved metrics as described in the Results Section (Tables 1 and 2).
## 3 Testing Wide-Sense Stationary Assumption Fulfilment
As it is shown in Figure 1, to calculate \(K\) realisations of \(h(t,\tau)\), each of which has \(N\) samples, the N-point DFT algorithm should be run \(2K\) times.To be more precise, one DFT is used for frequency domain transition, and one inverse DFT is needed to return to the time domain. Next, the autocorrelation functions (ACFs): \(R_{II}^{h}\) and \(R_{QQ^{\prime}}^{h}\), and cross-correlation functions (CCFs): \(R_{IQ}^{h}\) and \(R_{QI}^{h}\) are also calculated using DFT. There are \(2N\) calls of K-point DFT in the case of calculation of ACFs, and \(3N\) calls of DFT of the same size in the case of calculation of CCFs. The difference is due to the fact that in the case of ACF, the DFT is calculated for one input vector, and in the case of CCF, it is calculated for two different input vectors. In order to determine the correlation coefficients, \(3N\) DFT calls are needed, each of the size \(2K-1\), resulting from the size of input ACFs and CCFs. In the case of calculating the \(c_{IQ}\) and \(c_{IQQI}\) coefficients directly from the complex envelope \(y(t)\) of the received signal, the matched filtering step is omitted and there is \(2K\) of \(N\)-point DFTs less to calculate. Figure 1: Process of calculating cross-correlation coefficients based on the impulse response \(h(t,\tau)\) of \(K\) realisations in \(t\) domain and \(N\) samples in \(\tau\) domain.
# Comparison of Microbial Gene Diversity in Grassland Topsoil Depending on Soil Quality
## 2 Materials and Methods
### Sample Pretreatment and NGS Analysis  
The total nucleic acid was extracted from the soil samples following the manufacturer's instructions with a Fast DNATM Spin Kit for soil (MP Biomedicals, Solon, OH, USA). To analyze the genetic diversity of prokaryotes and eukaryotes, the V4 region of the bacterial 16S rRNA gene was amplified using barcode-attached 515F (5'-GTG YCA GCM GCC GCG GTA A-3') and 806R (5'-GGA CTA CTH GVGG TTW TCT AAT-3'), with the extracted nucleic acid as a template. Similarly, the barcode-attached 1391F (5'-GTA CAC ACC GCC CGT C-3') and EukBr (5'-TGA TCC TTC TGC AGG TTC ACC TAC-3') were used to amplify the fungal 18S rRNA gene [21]. The amplified products were identified using a gel documentation system (BioRad, Hercules, CA, USA) after running through a 2% agarose gel, followed by purification using a QIAquick PCR purification Kit (Qiagen, Hilden, Germany) and analysis of microbial gene diversity using the Illumina MiSeq platform (Illumina Inc., San Diego, CA, USA). Quantitative Insights into Molecular Ecology (QIME) software was used for comparison at levels from phylum to species through data trimming and analyzing the alpha diversity. Excluding chimera, short length, low-quality, primer mismatch, and non-target reads, 208,499-406,826 bacterial reads (262,851 on average) and 54,165-312,367 fungal reads (159,150 on average) were analyzed from the NGS baseline data (data not shown).
# Analysis and Optimization of the Stray Capacitance of Rogowski Coils
## 3 Stray Capacitance of Rogowski Coils
### Discussions of the Factors Affecting Stray Capacitance
#### 3.3.3 Measures to Reduce Stray Capacitance  
Based on the above results, there exists a tradeoff regarding the selection of the turn-to-turn distance \(p\) and the distance \(h\) between the turns and shielding shell. Increasing \(p\) or decreasing \(h\) can reduce \(C_{tt}\) but increase \(C_{ts}\). On the other hand, adjustment of \(p\) changes the number of turns \(N\), which is directly related to the self-inductance \(L_{c}\) and mutual inductance \(M\). As for \(h\), this parameter is limited by space; thus, it cannot be chosen freely. Similarly, small winding conductor radius \(r_{c}\), wire insulation thickness \(\delta\), and \(e_{r}\) are favorable for reduction of both capacitance \(C_{tt}\) and \(C_{ts}\), but they are restricted in practical applications. For example, reducing \(r_{c}\) leads to increased internal resistance and spoils the performance of Rogowski coils.  
Last but not least, \(C_{tt}\), \(C_{ts}\), and \(C_{AB}\) are proportional to \(l_{t}\); hence, shortening the length of the single-turn winding \(l_{t}\) to simultaneously reduce these three parameters is favorable from the perspective of minimizing the stray capacitance. Since \(l_{t}\) is approximately equal to the perimeter of the skeleton cross-section \(l\), \(C_{AB}\) can be optimized by deliberate design of the cross-section of the skeleton of Rogowski coils so that \(l_{t}\approx l\) is minimized.  
It is worth emphasizing that the optimization must be implemented under the condition of invariant magnetic flux through the cross-section, or in other words, guaranteeing constant mutual inductance, so that the low-frequency performance of Rogowski coils is not affected.
# A Study on the Roof-Cutting and Pressure Releasing Technology of Roof Blasting
## 4 Engineering Applications of Hard Roof Blasting
### Testing Program
#### 4.2.2 Parameter Optimization of Blasting  
(1)(3) Optimize borehole spacing  
The spacing between adjacent boreholes was calculated from the radius of the fracture zone during a single-hole blasting using the theoretical calculation formulation. Generally, the crack area is more prominent when the two holes are detonated simultaneously rather than during differential initiation. Furthermore, the stress wave of the two boreholes is superimposed and canceled during the propagation process. The size of the crack area was obtained through a test explosion experiment [25]. The calculation results show that the theoretical calculated fracture radius is about 13.39% larger than the numerical simulation, and the radius of the fracture zone does not exceed 5 m. Combined with the analysis of the scope of the crushing area, the effective blasting range of the 75 mm coil is about 5 m. Therefore, the spacing between two boreholes is set at about 10 m in line with the theoretical basis.
# On Parameter Estimation for Bandlimited Optical Intensity Channels
## 3 Modified Cramer-Rao Lower Bound
### Computation of the Modified CRLB
Therefore, regarding the signal amplitude, we have  
\[\mathrm{MCRLB}(A)\,=\,\frac{1}{J_{1,1}}=\frac{A^{2}P_{0}^{2}}{L\,\gamma_{s} \bigg{\{}g(0)\,+2\,\mu_{a}^{2}\sum\limits_{i=1}^{L-1}\left(1-\frac{i}{L}\right) g(iT)\bigg{\}}}, \tag{16}\]  
whereas for recovery of the symbol timing, the theoretical limit develops as  
\[\mathrm{MCRLB}(\tau)\,=\,\frac{1}{J_{2,2}}=-\frac{P_{0}^{2}}{L\,\gamma_{s} \bigg{\{}\ddot{\bar{g}}(0)\,+2\,\mu_{a}^{2}\sum\limits_{i=1}^{L-1}\left(1- \frac{i}{L}\right)\ddot{\bar{g}}(iT)\bigg{\}}}. \tag{17}\]  
To the best of the author's knowledge, it is the first time that the MCRLB has been derived for a PAM constellation and a pulse shaping governed by an SRC or SDJ function, as it would be needed for a bandlimited optical intensity link. It is to be noticed that (16) and (17) apply in the same way to SRC as well as SDJ pulses. Finally, we recall that \(\mu_{a}=0\) for zero-mean symbol alphabets like \(M\)-ary phase shift keying (PSK) or quadrature amplitude modulation (QAM) schemes; taking this into account, together with \(P_{0}=1\) and \(g(t)\) as a simple raised cosine function, the relationships characterize the conditions for an RF link [11].