esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Organized Fast Routing Protocol for Radial Underwater Networks
## 2 Related Work
### Energy Efficient Protocols  
The Information-Carrying Routing Protocol (ICRP) [15] is an energy-efficient, real-time, scalable routing protocol. The source node checks the existing route to the destination when it has packets to send. If there is no existing route, it initiates a route formation process by broadcasting the data packet, which also carries the route discovery message. All the nodes that broadcast the data packet also maintain the reverse path. Route discovery is required every time the lifetime of the route expires. The Reliable and Energy Balanced Routing (REBAR) protocol [35] adaptively changes the broadcast domain to balance the energy consumption among the nodes. Nodes near the sink have a smaller radius to decrease their chances of being involved in routing, which increases their working life.  
The Energy-Efficient Routing Protocol (EUROP) [36] reduces energy consumption by reducing the number of broadcast messages. EUROP uses pressure sensors in order to measure the depth, thus avoiding the need for hello messages. In the Power Efficient Routing (PER) protocol [37], the forwarding node is selected on the basis of distance, angle between the neighboring nodes, and residual energy. A node forwards the packet to the next node if the number of received duplicates does not exceed the limit. Compared to DBR, its energy consumption is lower. In the Energy-Efficient Depth-Based Routing (EEDBR) [38] protocol, the nodes share their depth and residual energy information with their neighbors. Packets are held by nodes for a certain time based on the residual energy, to avoid duplicates and balance the energy costs of packet forwarding. The nodes having less energy will wait longer. The Reliable Energy-Efficient Routing Protocol [39] takes into account link quality, physical distance, and residual energy. The three metrics are computed and shared by all the nodes.  
The Delay-Aware Energy-Efficient Routing Protocol (DEEP) [40] is a 3-D routing protocol based on energy efficiency and collision rate. There is no handshake mechanism in DEEP. Relay nodes are selected on the basis of link quality and successful delivery ratio. A sequence number is used to discard duplicate packets. In the Channel Aware Routing Protocol (CARP) [41], the next forwarding node is selected on the basis of hop count and residual energy. Every node knows its hop distance from the sink node. The transmitting node broadcasts a PING packet to find the next forwarding node. If the hop count of the receiving node is less than the transmitting node, it sends back a PONG packet with its information.  
The Energy Efficient and Collision Aware (EECA) [42] multipath routing algorithm is based on finding two collision-free routes using constrained and power adjusted flooding. Multipathpower-control transmission (MPT) helps to deliver the packet with certain end-to-end packet error rate and minimum transmission power.
# An Analysis of Rock Bolt Dynamic Responses to Evaluate the Anchoring Degree of Fixation
## 2 Materials and Methods
### Instrumentation  
Vibrations were collected using a NI-DAQ 6210 data logger linked to a PC (Figure 8) with LabView 2019 software (National Instruments 11500 N Mopac Expwy, Austin, TX 78759-3504, USA).  
A sampling frequency of 5000 Hz and a time window of 2 s were adopted. The amplitude scale ranged from \(-10\) V to 10 V.  
Low-cost sensors were adopted (piezoelectric analog shock sensors), characterized by low sensitivity (5 mV/g) but with a wide bandwidth (from 10 Hz to 10 kHz). This guaranteed good detection accuracy because the main resonant frequencies reflected the response of the system.  
The preliminary tests using low-cost sensors were performed on rock bolts: steel rods 140 cm in length with a diameter of 2.2 cm.  
Measurements of vibration were performed on several rock bolts in the mine by exciting them with a Schmidt hammer and observing the vibration using shock sensors located on the head of the rock bolt under test. Shots were repeated at least five times for each tested bolt. A calibration test was performed on a free bar 1.4 m in length with a  
Figure 7: Example of a testing method using accelerometers anchored to the protruding head of a rock bolt (**A**). A hammer is used to excite a mechanical impact on the head (**B**); the vibration is then transferred (**C**) and recorded on a computer. Courtesy of [PERSON].  
diameter of 0.022 m by exciting the protruding part of the bar five times and the bottom part of the bar five times.
## 5 Discussion
Engineering design philosophy, which is based on determining the demand on an element or structure and designing that element or structure with the capacity to withstand that demand, includes many different steps in the process. As a rule, the final design or product is also different and generally depends on the designer and the regulations considered despite having the same initial requirements. The cause lies in the previously mentioned epistemic and aleatory uncertainties, which are always present in the design as well as in the phenomenon under consideration. Consequently, accounting for these uncertainties in models appears to be a necessity, and not just a reason to expand the database to produce more robust models. Basically, design in general, as well the bridge design, is an iterative process. Compliance with technical regulations for structure, aesthetic criteria, and wishes of investors must be harmonized. For this purpose, the structure of the DB ANN assistant is very important,because in each step and iteration, a different structure of the model may appear, which depends on the number of input and output parameters considered. For the purpose of additional improvement of the above-discussed issues, further explicit research is needed. It should include considering additional parameters, expanding the primary database with new sample bridges, determining the reliability of the results, optimizing smoothing parameters, and explaining the obtained results, among others. All of this can be summed up as follows:  
* The first and last span could be added as a first additional input parameter, which has a significant impact on the quantity of cables in the intermediate spans. * As additional input parameters, the maximum internal static quantities (bending moments and transverse forces) could be used. * Both test cases in the presented study, demonstrating the application of the ANN BD assistant, are from a set of implemented projects from another project office that was not included in the primary database. It is anticipated that each researcher will be able to supplement the primary database with data from other implemented projects and from different investors' libraries as the research progresses. Following an analysis of parameters from such an extended database, new mechanical and/or structural parameters (in addition to those mentioned above) and new discrete parameters will be introduced to differentiate between different designers and engineers, project offices, and schools. * A larger number of implemented projects included in the primary database will allow for a more accurate analysis of epistemic and aleatory uncertainties, allowing for more efficient extension of the primary database and thus even better ANN BD assistant predictions. * In order to avoid using CAE ANN application as a black box, additional research is needed to explain the obtained results (e.g., [37]). This could help to avoid the use of physically impossible results in ANN.
###### Abstract  
In this paper, we present a reliable and robust method for magnetomotive optical coherence tomography (MM-OCT) imaging of single cells labeled with iron oxide particles. This method employs modulated longitudinal and transverse magnetic fields to evoke alignment and rotation of anisotropic magnetic structures in the sample volume. Experimental evidence suggests that magnetic particles assemble themselves in elongated chains when exposed to a permanent magnetic field. Magnetomotion in the intracellular space was detected and visualized by means of 3D OCT as well as laser speckle reflectometry as a 2D reference imaging method. Our experiments on mesenchymal stem cells embedded in agar scaffolds show that the magnetomotive signal in rotational MM-OCT is significantly increased by a factor of \(\sim\)3 compared to previous pulsed MM-OCT, although the solenoid's power consumption was 16 times lower. Finally, we use our novel method to image ARPE-19 cells, a human retinal pigment epithelium cell line. Our results permit magnetomotive imaging with higher sensitivity and the use of low power magnetic fields or larger working distances for future three-dimensional cell tracking in target tissues and organs.  
optical coherence tomography; cell imaging; dynamic contrast agents; magnetic particles; laser speckle; fluorescence microscopy +
Footnote †: journal: Applied Physics Letters
The experiments hypothesize that more precise and reliable predictions regarding the link quality in complex wireless communication scenarios can be achieved using LTCs. The proposed LTC-based Multi-Link Prediction model seeks to enhance the accuracy and efficiency of link quality predictions by leveraging recent developments in artificial intelligence technologies. The experiments are intended to validate the effectiveness of LTCs in a practical simulation environment to confirm the hypothesis that LTCs address the challenges associated with predicting link quality in dynamic wireless communication systems. This research contributes to the evolution of predictive modeling in wireless communication, paving the way for robust and adaptive systems in the future as wireless communication systems evolve towards 6G networks and beyond. In the case of mmWave frequencies, it is expected that the distance between cellular base stations remains consistent. With carrier frequencies progressing towards terahertz, the wider bandwidth of the channel allows for covering similar distances. This is achieved through the exponential growth in antenna gains as frequencies rise, assuming the physical antenna remains unchanged [12]. The LSTM model is impacted by certain shortcomings when used for link prediction for EHF and THF: LSTM outcomes tend to be less accurate, particularly in scenarios where prediction involves data that are not continuous. EHF and THF waves are prone to attenuation due to the oxygen and other matter in the atmosphere. The waves are absorbed by certain compounds such as water vapor in the atmosphere. The higher the frequency, the greater the absorption. Also, as mentioned earlier, EHF and THF waves propagate only along the Line-of-Sight paths. They cannot bend around obstacles. Due to the varying distances between the transmitter, receiver, and any obstructing objects present on each link, the signal could be choppy. Consequently, for such scenarios, when the LSTM model is applied, the results may not be accurate to the desired extent. In scenarios where there are more links between the transmitter and the receiver, the effectiveness of an LSTM model might diminish, resulting in suboptimal predictions [13; 14]. The overall mobile handover operation (HO) probability with respect to both horizontal as well as vertical hand-off needs to be characterized to attain specific mobile coverage probability in mmWave and sub-terahertz networks [15].
## 3 Results Simulation Analysis and Optimization
### Optimization of Stress-Applying Parameters  
To optimize the structure size of the stress-applying area, we analyzed the influence of \(r_{1}\), \(h\), and \(\theta\) on \(\Delta n_{\text{eff}}\), \(B_{\text{m}}\), and bending loss. Figure 8 shows the colormaps of min \(\Delta n_{\text{eff}}\) versus \(r_{1}\) and \(h\) with \(\theta\) set as \(60^{\circ}\), \(90^{\circ}\), and \(120^{\circ}\), respectively. The influence of structure parameters of optical fiber \(r_{1}\), \(h\), and \(\theta\) on bending loss along the direction of the \(x\)-axis and \(y\)-axis at bending radius \(R\) = 30 mm are shown in Figure 9. It can be clearly seen that the error range gradually decreases with the increase of angle \(\theta\). \(B_{\text{m}}\) increases with increasing \(h\) and decreasing \(r_{1}\). The increase in the area of the bow-tie stress-applying areas has a significant effect on improving the birefringence of the optical fiber. As shown in Figure 8b, within the region of 7 \(\upmu\)m \(\leq h\leq\) 15 \(\upmu\)m and 16 \(\upmu\)m \(\leq r_{1}\leq\) 26 \(\upmu\)m, the proposed fiber continues to support 22 eigenmodes with a min \(\Delta n_{\text{eff}}\) larger than 2 \(\times\)\(10^{-4}\). To obtain a larger min \(\Delta n_{\text{eff}}\), lower bending loss, and larger manufacturing error range with reasonable parameter values, we finally choose the red point \(\theta\) = 90\({}^{\circ}\), \(r_{1}\) = 20 \(\upmu\)m, \(h\) = 11 \(\upmu\)m as the target fiber structure size. At this point, the min \(\Delta n_{\text{eff}}\) value is 3.29 \(\times\)\(10^{-4}\), and modal birefringence \(B_{\text{m}}\) = 3.28 \(\times\)\(10^{-4}\) N/m\({}^{2}\).  
Figure 6: Variations of (**a**) \(n_{\text{eff}}\) and (**b**) \(\Delta n_{\text{eff}}\) as a function of \(\Delta n_{1}\).
# Deceptive Jamming Detection for SAR Based on Cross-Track Interferometry
## 5 Experimental Analysis
### Interferometric Phase Analysis  
A terrain with mountainous areas is considered. The system parameters are listed in Table 1. The processing procedures involve SAR imaging, relevant registration, and interferometry. Finally, the interferometric phase with deceptive jamming is derived.  
Since the original echo signal and the deceptive jamming signal are received by the two antennas simultaneously, simulation of SAR echo signals with such jamming is first performed. Without loss of generality, only the original master image is shown in Figure 6a. We choose Figure 6b as the deceptive jamming template according to the prior knowledge about targets around the coastal area. Assuming that the jamming to signal ratio (JSR) is 0 dB, the master image with the traditional SAR deceptive jamming transmitted by a single jammer is shown in Figure 6c. Through co-registration and comparing both complex images, the resultant interferometric phase is obtained and depicted in Figure 7b and the interferometric phase without jamming is shown in Figure 7a. By comparing Figure 7a,b, the deceptive part in the interferogram is clearly visible. As can be seen, the false phase of the deceptive target shows as a constant in the interferogram.  
Figure 5: Range frequency of the interferometric phase in Figure 2b.
# Low-Altitude Sensing Model: Analysis Leveraging ISAC in Real-World Environments
## 1 Introduction
Furthermore, this mode is relatively easy to implement, requiring no complex protocol interactions or coordination mechanisms between nodes. It is also easier to upgrade and deploy on the existing base station equipment, effectively reducing system complexity and costs, and providing a more reliable and feasible technical foundation for the construction of low-altitude networked sensing models.
## 7 Results
### HBO Study Analysis
2007 [43]} & \(11\times\) Debridement & & & & \\  & \(1\times\) Resection & & & & \\  & \(4\times\) just HBO & & & & \\ \hline \multirow{3}{*}{[PERSON] et al. 2011 [44]} & Unclear & NR & NR & \begin{tabular}{c} Stage \(0\times 4\) \\ Stage \(1\times 1\) \\ Stage \(2\times 5\) \\ Stage \(3\times 3\) \\ \end{tabular}  \\ \cline{3-6}  & & SPC (surgery + ABX + MW) \(\times\) 21 & & & \\ [PERSON] et al. 2012 [45] & (Control Group) & & & \\  & HBO + SPC (surgery + ABX + MW) & & & \\  & \(\times\) 25 (Study Group) & & & \\ \hline \multirow{3}{*}{[PERSON] et al. 2015 [46]} & Drug Holiday + ABX + surgery & 20 & 10 & Stage 2 \\ \cline{1-1} \cl \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**Follow-up Time**} & \multicolumn{2}{c}{**Type of Special Investigation**} & \multirow{2}{*}{**Treatment Complications during**} & \multirow{2}{*}{**MRON Status after Treatment**} \\  & & **Used during Patients’ Follow-up** & & & **the Study** \\ \hline [PERSON] et al. 2006 [41] & NR & NR & Patient developed acute otitis media & \\  & & & & (HBO interrupted and re-started) & \\ \hline [PERSON] et al. 2007 [42] & 9 months & \(\text{CT}\times 1\) & - & Complete resolution \\  & & \(\text{NR}\times 1\) & & \\ \hline [PERSON] et al. 2007 [43] & From 1 to 32 months & NR & \begin{tabular}{c} \(\text{PC}\times 1\) \\ Multiple surgery \(\times 2\) \\ HBO therapy more than one time \(\times 4\) \\ \end{tabular} & \begin{tabular}{c} Remission 8 (50\%) \\ Stable 2 (12.5\%) \\ Progression 6 (37.5) \\ \end{tabular} \\ \hline [PERSON] et al. 2011 [44] & NR & NR & NR \\ \hline [PERSON] et al. 2012 [45] & 24 months & NR & \
\begin{tabular}{c} Fatality \(\times\) 3 (after 3 months); \\ HBO declared \(\times\) 1; \\ Immediate Crossover to HBO Group \(\times\) 2; \\ Late Crossover to HBO \(\times\) 3 \\ \end{tabular} &
\begin{tabular}{c} NR \\ \end{tabular} \\ \hline [PERSON] et al. 2015 [46] & NR & NR & - & Complete resolution \\ \hline \hline \end{tabular}
\end{table} 
Table 10: HBO analysis of the MRON) status at the end of follow-up; not reported (NR), poor patient compliance (PPC), computer tomography (CT). * RCT [PERSON] et al. 2012 [45] showed no statistically significant improvement in cure rate compared to placebo but there was improvement in secondary outcome.
# Analysis of Tidal Accelerations in the Solar System and in Extrasolar Planetary Systems
## 2 Materials and Methods
### Tidal Accelerations  
Tracking the orbital motion of celestial bodies based on their orbital elements allows the calculation of the positions of the object for discrete time steps and the determination of the three-dimensional distance vectors between the objects. Using a minimalistic set of physical and geometrical parameters of the objects, we calculate the tidal acceleration vectors acting on the surface of one object. After this step, for easier evaluation, the distance vectors were normalized and multiplied by the absolute value of the tidal acceleration \(A\) acting on the surface of the influenced object facing the influencing objects following Equation (1).  
\[A=\frac{GM}{r^{2}}\left(\frac{1}{\left(1-\frac{R}{r}\right)^{2}}-1\right) \tag{1}\]  
\(M\) is the mass of an influencing object; \(r\) is the distance between the objects, and \(R\) is the radius of the influenced object. Considering also the influences of a changing orbit, we use \(e\) (eccentricity), as well as parameters for the orientation of the objects in space and amongst each other, e.g., \(i\) (inclination) and \(\Omega\) (longitude of the ascending node). This allows a computation of acceleration vectors in 3D caused by every object in the system on the investigated one. Adding up these acceleration vectors takes into account that the tidal effects caused by several objects can intensify or partially cancel each other out, due to the different orientations of the tidal acceleration vectors acting on the objects. Determining the vector difference between these \"total\", the summed up tidal accelerations for the different time steps also gives a rough estimate of the deformation of the objects and may serve therefore as an indicator for the possible tidal heating on their inside.
## 2 Materials and Methods
### PCA Model  
PCA is a classical statistical method that aims to transform the attributes of a dataset into a new set of uncorrelated attributes called principal components (PCs). One of the main uses of PCA is to reduce the dimensionality of a dataset in such a way that it retains as much of the variability of the original dataset as possible [27]. Dataset \(X\) with a \(m\) number of variables and a \(n\) number of observations can be expressed as a matrix with \(m\) row-vectors each with length \(n\), as shown in Equation (1) [28].  
\[X=\begin{pmatrix}x_{11}&x_{12}&\ldots&x_{1n}\\ x_{21}&x_{11}&\ldots&x_{2n}\\ \vdots&\vdots&\ddots&\vdots\\ x_{m1}&x_{11}&\ldots&x_{mxn}\end{pmatrix}=\begin{pmatrix}X_{1}\\ X_{2}\\ \vdots\\ X_{m}\end{pmatrix} \tag{1}\]  
The matrix \(X\) is required to linearly transform into \(Y_{mxn}\) matrix by means of a \(P_{mxn}\) matrix with raw vectors \(p_{1},\ p_{2},\ p_{3},\cdots,\ p_{m}\) and column vectors of \(X\) matrix \(x_{1},\ x_{2},\ x_{3},\cdots,\ x_{m}\), as can be seen in Equation (2).  
\[Y_{mxn}=P_{mxn}X=\begin{pmatrix}p_{1}.x_{1}&p_{1}.x_{2}&\ldots&p_{1}.x_{n}\\ p_{2}.x_{1}&p_{2}.x_{2}&\ldots&p_{2}.x_{n}\\ \vdots&\vdots&\ddots&\vdots\\ p_{m}.x_{1}&p_{1m}x_{2}&\ldots&x_{mxn}\end{pmatrix}=\begin{pmatrix}X_{1}\\ X_{2}\\ \vdots\\ X_{m}\end{pmatrix} \tag{2}\]  
The projection of the original dataset \(X\) on the columns of \(P\) is achieved through the dot product \(p_{1}\): \(x_{1}\) showed in Equation (2). The rows of matrix \(P\) are a new basis that signifies the principal component directions.  
In our case, the results obtained from absorption spectra of samples represent the matrix \(X\), which means that from each 64 ternary dye solution sample UV-Vis spectrophotometric measurement, we obtain a vector with 350 characteristics; the aim of PCA method is to reduce the number of characteristics data to their essential features, which are principal components (PCs). UV-Vis spectra of the 64 samples were handled with the library of Numpy package of the programming language Python.  
PCA method was carried out with the use of the sklearn.decomposition module included in the package of the language programming Python.
# Homomorphic Encryption Based Privacy-Preservation for IoMT
## 3 Proposed Scheme
### Workflow of the Proposed Scheme
#### 3.2.1 Assigning Clusters  
We assume that the local physical edge nodes, such as a base station, rely on the vast computing capabilities of the expansive cloud computing environment. Physical edge nodes have limited resources to compute extensive data shared by IoMT devices in the healthcare network. The objective is to design several virtual nodes with equal computing for data computation using the homomorphic secret sharing scheme. The process of cluster generation is explained as follows:  
1. Initially, all VENs are generated and dispersed randomly to cover the entire coverage area of a hospital. Each VEN contains and stores the distance from the virtual node to the central physical base station. Each VEN has equivalently distributed computing resources received from the cloud environment services.
2. Several clusters \((C_{n})\) are designed for each group of IoMT devices and their respective VENs. Each cluster includes a boundary area that consists of selected VENs and sensors using k-nearest neighbors (KNN) based on a preset distance. The objective is to prevent any two clusters from including the same sensor device in their group.
3. Geolocation data of each device and their distance from the central base station is used to include it in the cluster. Each cluster has a unique identity \((C_{1},\ C_{2},\dots\ldots\ C_{n})\) and each device registered with the cluster cannot join another cluster.
4. Communication overhead is a problem when dividing devices into multiple clusters. Each device may transmit data using VENs with a longer distance from the physical base station than other VENs. A centroid (_cen_) node is selected as a  
Figure 2: Workflow of the proposed scheme.  
midpoint in the cluster using geolocation data and ensure each device \((d_{1},d_{2},\ldots,d_{i})\) is equidistant from the centroid. Each centroid is a part of the set of VENs \((V_{1},V_{2,\ldots},V_{i})\) included in each cluster and is represented as \(VEN\in(V_{1},V_{2,\ldots},V_{i})\). The centroid VEN is selected as the cluster head and provides an equivalent data communication distance for each IoMT device in the cluster.