esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Methods
### Earthquake Detection, Phase Picking, Association and Location
The red dashed line represents the averaged P-wave velocity from [PERSON] et al. [25], which is used for absolute localization with Hypoinverse, while the blue solid line indicates the P-wave velocity model from [PERSON] et al. [25], used for relative localization with HypoDD.
## 4 LBV of SAFs
### LBV of Bio-jet Fuel
[PERSON] et al. observed an LBV\({}_{\text{max}}\) biased towards rich conditions when studying palm methyl esters (PMEs) using the stagnation method [36]. The PMEs' LBV was found to be closer to that of diesel but lower than Jet A's LBV. [PERSON] et al. determined the LBV of a bio-jet fuel primarily composed of ethers, esters, alcohols, and ketones derived from rice husk using the spherical flame method at temperatures of 358, 388, and 418 K [65]. The LBV of this fuel lay between those of its two main components, ethanol and ethyl acetate. Additionally, research has investigated the influence of blending ratios of bio-jet fuels on aviation fuel LBV performance. [PERSON] et al. studied the effect of lignocellulosic-biomass pyrolysis oil (mainly composed of anisole, 4-methylanisole, and ethylvalerate) on the LBV of commercial gasoline using the Bunsen flame burner method and found minimal impact on commercial gasoline LBV even with 10% alternative fuel blending under testing temperatures of 400-470 K and pressures of 0.1-0.8 MPa [66]. [PERSON] et al. measured the LBV of a blend of algae-derived biocroude with RP-3 aviation kerosene over temperature ranges of 420-480 K and pressure ranges of 1-3 atm, indicating that the blending ratio has a greater influence on LBV than temperature and pressure [62]. To support the airworthiness certification of SAFs and assess their combustion performance and safety in practical applications, we tentatively propose recommending standard characterization protocols: (1) The T\({}_{0}\) of unburned fuel/air mixtures should be set at 400/425/450/475/500/550 K, with this wide range designed to simulate real scenarios such as cold starts, low-temperature ignition, and high-altitude reignition conditions; (2) the P\({}_{0}\) of unburned fuel/air mixtures should be set at 0.25/0.5/0.75/1/2/5 atm to cover extreme flight conditions, particularly for fighter aircraft; (3) the equivalence ratio should be tested across 0.6-1.5 at intervals of 0.5 to thoroughly simulate combustion performance under both low-emission and high-power modes while exploring engine safety boundaries; (4) the maximum blending ratios for SAFs from different production processes and feedstocks should follow ASTM D7566, for instance, FT-SPK and HEFA-SPK capped at 50% and SIP and HC-HEFAs limited to 10%; (5) within the equivalence ratio range of 0.9-1.2, the LBV performance gap between maximum-blend SAFs and conventional jet fuel should not exceed 5%, while remaining below 7.5% for \(\Phi\) < 0.85 or \(\Phi\) > 1.2; and (6) under identical conditions with equivalent additive content (e.g., water, methane, ammonia, LNG), the LBV performance deviation between maximum-blend SAFs and conventional jet fuel should remain around 7.5%.
## 3 Testbed Description
### Scenarios  
The evaluation of the routing protocols was conducted in both indoor and outdoor static scenarios, enabling a comprehensive analysis of WMN performance across different environmental conditions. In the indoor scenario depicted in Figure 4, the number of WMN nodes was increased from two to eleven Raspberry Pi 4 nodes, distributed across several offices within the university.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Parameters** & **Outdoor Experiment** & **Indoor Experiment** \\ \hline Numbers of nodes & 11 & 2, 5, 8, 11 \\ \hline Measured parameters & Bandwidth & Bandwidth, Packet Delivery Ratio, Jitter \\ \hline Duration of the experiment & 1000 s & 24 h \\ \hline Transport layer protocol & UDP \\ \hline Operating system on the nodes & Raspbian GNU/Linux 11 (bullseye) \\ \hline Linux version & Linux 6.1.21-v8+ \\ \hline BATMAN-ADV version & batchl version—debian-2020.4-2, Batman-adv version—2022.3 \\ \hline OLSR version & Pre-0.9.9 \\ \hline Babel version & 1.13.1 \\ \hline \end{tabular}
\end{table}
Table 1: Experimental parameters.  
Figure 3: Raspberry Pi 4 Model B node (**a**) internal appearance and (**b**) external appearance.  
In Figure 4, the orange nodes represent the endpoints to which the data packets were routed, while the blue nodes signify the routers within the network responsible for transporting the data from the sender to the receiver. These nodes were located on the two most remote points of the network from each other, with an intermediate'mesh' network between them.  
The outdoor scenario, represented in Figure 5, involved a WMN consisting of eleven Raspberry Pi 4 nodes allocated along the roads throughout the university campus.  
In Figure 5, the laptop nodes represent the endpoints to which data packets were routed, while the Raspberry Pi 4 nodes function as the routers within the network, responsible for transporting data from the sender to the receiver. These nodes were situated at the two most distant points within the network, with an intermediate mesh network positioned between them. The experimental development scenarios consider the impact of nearby wireless systems and Wi-Fi access points on signal strength.
## 5 Further Application Examples
### k-Sat  
Let us recall some well-known concepts about Boolean formulas. Such a formula is a CNF (Conjunctive Normal Form) formula if it is the conjunction (logical AND) of _clauses_, where each clause is the disjunction (logical OR) of _literals_. A literal is either a Boolean variable or its negation. A formula is a \(k\)-CNF formula if every clause contains \(k\) literals. We assume that the same variable cannot occur multiple times in a clause. The problem called \(k\)-SAT is this: given a \(k\)-CNF formula, is it satisfiable? This is a well-known NP-complete problem, but in some cases, the LLL allows us to quickly show that certain \(k\)-CNF formulas are satisfiable.  
Let us say that two clauses in a \(k\)-CNF formula overlap, if there is a variable occurring in both (regardless of whether the variable is negated or not in the clauses). We can show via the LLL that if any clause overlaps with at most \(2^{k-2}\) other clauses in a \(k\)-CNF formula \(\Phi\), then \(\Phi\) is satisfiable.  
Assign random truth values to the variables independently. Let \(C_{i},\ i=1,\ldots,n\), be the clauses and let \(A_{i}\) denote the event that \(C_{i}\) is not satisfied by the random truth assignment. There are \(2^{k}\) possible truth assignments to the \(k\) literals in \(C_{i}\), and only one of them makes\(C_{i}\) false - the one in which all literals of \(C_{i}\) are false. Therefore, \(\Pr(A_{i})=2^{-k}\). Furthermore, observe that each event \(A_{i}\) is mutually independent of the set of those \(A_{i}\)'s that correspond to clauses which do not overlap with \(C_{i}\). Consequently, the maximum degree \(d\) in the dependency graph of the events \(A_{1},...,A_{n}\) satisfies \(d\leq 2^{k-2}\). Therefore, we can write  
\[4\Pr(A_{i})d\,\leq\,4\cdot 2^{-k}2^{k-2}\,=\,1,\]  
implying  
\[\Pr(A_{i})\leq\frac{1}{4d}\quad\ \ (\forall i).\]  
Then, we can conclude from the LLL that \(\Pr(\overline{A}_{1}\ldots\overline{A}_{n})>0\) holds, which means that at least one of the random truth assignments satisfies _all_ clauses--that is, \(\Phi\) is satisfiable. It is interesting to note that this does not depend on \(n\), the number of clauses, and it is also independent of the number of variables. Thus, by means of the LLL, we have proved the following result:  
Theorem 5.1: _If in a k-CNF formula \(\Phi\) any clause overlaps with at most \(2^{k-2}\) other clauses, then \(\Phi\) is satisfiable._  
For example, if each clause contains \(k=12\) literals, and each clause overlaps with at most \(2^{k-2}=1024\) other clauses, then the formula is satisfiable, no matter how many clauses and variables it has.
# Off-Cloud Anchor Sharing Framework for Multi-User and Multi-Platform Mixed Reality Applications
## 1 Introduction
In addition, the proposed system also uses the Mixed Reality Toolkit (MRTK) [22], an open-source set of tools provided by Microsoft, designed to simplify the development of MR applications by offering a collection of pre-built components. This is Microsoft's recommended development tool for creating MR applications, particularly optimized for HoloLens 2 smart glasses. Thus, the multi-user cross-platform application can be executed both from desktop computers and MR devices. The developed game consists of a collaborative experience in which users work together in a fast-food restaurant in a similar way to games like Overcooked [23]. Players in multiple remote locations need to collaborate in real-time to face challenges such as preparing salads or hamburgers, making sure these are delivered on time while not burning the kitchen in the process. Moreover, in order to achieve more dynamic experiences, an Internet of Things (IoT) module has also been designed to allow the application to interact with real-world objects [24]. In this particular use case scenario, an IoT button is used to activate a fire extinguisher in the event that a frying pan bursts into flames. Specifically, this article includes the following main contributions:  
* It details the design and development of a novel spatial anchor sharing system: a system that locally stores and synchronizes spatial anchors between devices, removing reliance on third-party or cloud services. * It provides cross-platform compatibility. The proposed solution seamlessly integrates diverse devices, including Microsoft HoloLens 2, desktop computers and IoT devices, enabling real-time multi-user collaboration. * To illustrate the use of the proposed solution, a practical implementation is described (a multiplayer collaborative game showcasing the framework capabilities), which serves as a reference for future applications in XR development. In order to facilitate the reproducibility of the experiments and the extension of the proposed framework, the development is available as open-source code [25]. The rest of this paper is structured as follows. Section 2 overviews the state of the art on MR multi-user or multi-platform studies. Section 3 details the design and implementation of the proposed multi-user multi-platform collaborative MR framework. Section 4 illustrates with a practical use case how the proposed framework can be used for developing multi-platform MR applications for multi-user experiences, while Section 5 details the conducted experiments and analyzes the obtained results. Finally, Section 7 is dedicated to the conclusions.
# Impact of Load Variation on the Accuracy of Gait Recognition from Surface EMG Signals
## 3 Results
### Results of the Mixed-Load Evaluation  
In order to study the influence of the training set composed from the data from parts of loads on gait recognition accuracy, 15 training models were constructed for comparison. We gathered all the data together as a test set at each speed (group 3 at V3, group 5 at V5, group 7 at V7), and freely combined the four kinds of data from the four loads as a training set. In all, we obtained 15 different training sets, which were composed of the data from one load (four kinds), two loads (six kinds), three loads (four kinds), or four loads (one kind). The single accuracy of gait recognition and the average accuracy of gait recognition for all the same kinds of loads at each speed are shown in Figure 6.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **V3** & **V5** & **V7** & **Total** \\ \hline Intra-load accuracy (mean \(\pm\) sd) & 88.61 \(\pm\) 3.96 & 94.25 \(\pm\) 2.71 & 92.56 \(\pm\) 2.14 & 91.81 \(\pm\) 3.69 \\ Inter-load accuracy (mean \(\pm\) sd) & 65.11 \(\pm\) 9.30 & 71.20 \(\pm\) 7.47 & 71.96 \(\pm\) 4.86 & 69.42 \(\pm\) 7.86 \\ Overall accuracy (mean \(\pm\) sd) & 70.98 \(\pm\) 13.30 & 76.96 \(\pm\) 12.19 & 77.11 \(\pm\) 10.15 & 75.02 \(\pm\) 12.05 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Intra-load, inter-load, and overall accuracy of gait recognition (%) (mean \(\pm\) sd) across the three speeds  
Figure 5: Accuracy of gait recognition (%) matrices under three speeds. Each value in the matrix represents the accuracy of gait recognition from the specified training loads and testing loads (vertical and horizontal axis, respectively). The whiter color means a higher gait recognition accuracy.  
We noted that when any data set from one load was used as a training set, and the data group at the same speed as the testing set, the average accuracy of the gait recognition was the poorest (64.76% at V3, 62.80% at V5, and 61.67% at V7). When any of the data from two loads was used as a training set, the average accuracy of the gait recognition was significantly higher (77.10% at V3, 75.51% at V5, 72.36% at V7) than that which was obtained when the training set data was from one load. When any of the three data sets from the three loads was used as a training set, the average accuracy of the gait recognition (86.6% at V3, 86.48% at V5, and 82.54% at V7) was more agreeable, which was much higher than the first two. When the training set consisted of the data from all four loads, the accuracy of the gait recognition (94.91 at V3, 94.5% at V5, and 91.60% at V7) was the highest in all the cases. The results imply that when the training set data is taken from more types of loads at the same speed, the gait recognition accuracy is better.
## 2 Prediction of Tire Pattern Noise Based on System Identification
### Optimal Adaptive Filter Theory  
The adaptive filter was developed considering the \"adaptive linear combiner\" shown in Figure 3.  
The adaptive filter output at discrete time \(k\) is defined by the linear convolution sum,  
\[y=\mathbf{x}^{T}\mathbf{w}=\mathbf{w}^{T}\mathbf{x} \tag{1}\]  
where \(\mathbf{x}=\) input force vector; \(\mathbf{w}=\) tap weight vector of adaptive filter \(f\); \(y=\) predicted noise which is output sequence of filter.  
To optimize the filter, we chose to minimize the mean square value of the estimate error \(e\), which is defined as follows  
\[e=d-y \tag{2}\]  
where \(d\) is the desired response or target data. We may define the _cost function_ as the mean squared error (\(\xi\)), which is evaluated by taking the expectation of the square of the error (e).  
\[\xi=E\Big{[}\big{(}e^{2}\big{)}\Big{]}=E\Big{[}\big{(}d^{2}\big{)}\Big{]}+ \mathbf{w}^{T}\mathbf{R}\mathbf{w}-2\mathbf{p}\mathbf{w} \tag{3}\]  
where \(\mathbf{R}=E[\mathbf{x}\mathbf{x}^{T}]\) and \(\mathbf{P}=E[d\mathbf{x}]\). \(\mathbf{R}\) is the auto correlation matrix of input vector, and \(\mathbf{P}\) is the cross-correlation vector between the input vector and the desired value. This equation is a quadratic equation in terms of the weight vector (\(\mathbf{w}\)). Figure 4 shows a typical performance surface for a filter of length (\(M=2\)). The gradient of the mean square error, \(\
abla(\xi)\), can be obtained by differentiating Equation (3) with respect to the weight vector \(\mathbf{w}\) to obtain the column vector  
\[\
abla(\xi)=\frac{\partial(\xi)}{\partial\mathbf{w}}=-2e\,\mathbf{x}=2\mathbf{ R}\mathbf{w}-2\mathbf{p} \tag{4}\]  
The _cost function_ (\(\xi_{k}\)) attains its minimum value when the gradient of this quadratic equation is zero:  
\[\mathbf{W}^{\mathrm{o}}=\mathbf{R}^{-1}\mathbf{p}. \tag{5}\]  
Figure 3: Linear combiner tap delayed adaptive filter.  
This is the \"Wiener\" optimal filter. The optimal tap-weighting vector of the adaptive filter \(f\) is shown in Figure 2.
# A High-Robust Sensor Activity Control Algorithm for Wireless Sensor Networks
## 4 Methods
### Transform Procedure of the State of Sensors  
Each sensor was set with a weight value of \(W_{i}\), which was between 0 and 1. Before the system began to operate, the initial weight value of each sensor was set to 0. The weight value, \(W_{i}\), affects the state of the sensor in the next round. In the transformation procedure of the state of the sensors, the sensor generates a probability value \(r\) between 0 and 1. The state transition rules are described as follows:  
1. When \(r\) greater than or equal \(W_{i}\), the sensor either remains in the sleep state or transitions from the active to the sleep state.  
Figure 6: The active sensor performs a state transition according to the difference value \(d\) in the feedback packet.  
2. When \(r\) is less than \(W_{i}\), the sensor remains active or transitions from the sleep to active state. In other words, a sensor node with a higher weight value, \(W_{i}\), will have a higher probability of being in the active state. We implemented the SACA and set the activation parameters to 0.0001. Figure 7 shows the execution result when the total number of sensors is 100 and the target value is set to 35.  
We summarize the characteristics of ACK and SACA. SACA and ACK only receive packets from active sensors. The sleep sensors do not have to transmit data packets, and the sleep sensors are in a low power consumption state to avoid power waste. The difference between ACK and SACA is that the ACK algorithm uses a finite state machine with a fixed probability value as the probability that the sensor is in an active state. In SACA, each sensor is set with a weight value, which adjusts the probability of active and sleep modes according to the change in the number of active sensors and the target value.
## 2 Materials and Methods
### Induction of osteoarthritis (OA) and SJFE Administration  
After the cellular experiment, SJFE was tested for its anti-inflammatory properties in an arthritis model in rats.  
All rats were anesthetized with isoflurane, hair was removed from both knees, and hairless areas were disinfected. Eight rats were assigned to the sham control group, where saline (50 \(\mu\)L) was injected into the joint space of both knees. Forty rats were assigned to the OA group, where 50 \(\mu\)L of monosodium iodoacetate (MIA) (60 mg/mL in saline) was injected into the left knee, and saline (50 \(\mu\)L) was injected into the right knee. After the procedure, Ketoprofen (2 mg/kg) was administered subcutaneously to relieve any pain. These 40 OA-induced rats were divided into 5 groups (8 rats/group), and each group were orally administered a vehicle (saline), SJFE (50, 100, or 200 mg/kg), or methyl sulfonyl methane (MSM) (150 mg/kg), respectively, once a day for 4 weeks. SJFE and MSM were dissolved in saline, and 10 mL/kg of each of these solutions was given for the final doses to be as above. MSM was used as a positive control, which is known as an anti-inflammatory and anti-arthritic agent [15]. During 4-week experiment, the conditions of the rats were observed every day, and the body weight of all rats was measured once a week for 4 weeks. The following experiments were performed to evaluate the anti-osteoarthritis effect of SJFE.
## 9 Conclusions  
With the evolution of airborne collision avoidance systems from ACAS II to the ACAS X family of standards, there has been a clear shift towards integrating multiple sensing systems to enhance situational awareness and mitigate the risk of collisions. The ACAS X family targets both UAVs and manned aircraft, considering that the safe integration of UAVs into the airspace is particularly problematic. This development along with the ongoing demand for efficient and robust communication by means of compact hardware inspired the utilization of M\({}^{3}\)PAs for aerial applications, because this advanced antenna class is able to emulate antenna arrays at a smaller form factor and lower weight. Towards this goal, this paper introduced a direction finding MUSIC-based algorithm to estimate the DoA of received signals using an M\({}^{3}\)PA. The measured gain patterns of an M\({}^{3}\)PA designed for integration on a UAV or manned aircraft were used in a numerical simulation to prove the validity of the proposed algorithm. Furthermore, the results of the conducted numerical simulations were verified experimentally. For that purpose, measurements were conducted in an anechoic chamber, using an M\({}^{3}\)PA prototype and an SDR. Both numerical and experimental results show good performance of the suggested algorithm in combination with the M\({}^{3}\)PA under investigation. Hence, M\({}^{3}\)PAs represent, along with suitable signal processing techniques, a promising candidate for enhancing the safety and efficiency of airborne applications, both for UAVs and manned aircraft.  
Conceptualization and methodology, S.A.A. and N.L.J.; experimental verification, N.L.J.; writing--draft preparation, S.A.A.; writing--review and editing, S.A.A., N.L.J. and P.A.H.; supervision and funding acquisition, P.A.H. All authors have read and agreed to the published version of the manuscript.  
This research has been funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) within the framework of the Master360 program under research grant 20D1905.  
Not applicable.  
Data are contained within the article.  
The authors would like to express their gratitude to [PERSON] and [PERSON] at Leibniz University Hannover, Germany, for providing the M\({}^{3}\)PA antenna measurements, facilitating access to their laboratories, and providing assistance with the conducted experiments. Their efforts and support are highly appreciated.  
The authors declare no conflicts of interest.
# Topological and Fractal Analysis of Nanostructured Metal-Dielectric Films
## 2 Materials and Methods  
Ultrathin films of silver and gold were produced by the thermal evaporation of metal in vacuum conditions at a pressure of 10\({}^{-6}\) torr and a speed of 0.01...0.02 nm/s. Substrates from glass, maintained at a temperature of 290 K, were chosen for this process. Mass thickness \(d_{m}\) is an important practical parameter of the films, which corresponds to the thickness of a solid, homogeneous metal layer of the same mass as the experimental film. The bulk thickness \(d_{m}\) of the films was controlled during sputtering using a quartz crystal microbalance. For structural studies, a set of silver films with mass thicknesses between 1.0 and 6.0 nm, significantly below the percolation threshold of approximately 18 to 20 nm, was deposited [41; 42]. Gold films were fabricated within the thickness range of 1.0 to 3.0 nm, as films with \(d_{m}\geq 5.0\) nm transitioned into a percolating state. For some additional studies, samples with larger mass thicknesses were also used, namely 10 nm and 17.8 nm for silver and 4, 6, and 8 nm in the case of gold films. Silver and gold with a purity of 99.99%, obtained from Sigma-Aldrich, were used for thermal evaporation. During metal deposition, island structures are created, presenting with different characteristics depending on the substrate and its temperature. These structures can be called metal-dielectric composites with a fractal order [43].  
Morphological analyses of the metallic film surfaces were performed using Solver P47 PRO (NT-MDT) with NSG10 cantilevers possessing a tip curvature radius of 6-10 nm. The studies were conducted in tapping mode, with a scan rate between 0.5 and 2 lines per second (LPS) so as not to damage the surface of the film.  
The initial processing and image reconstruction of the AFM data were performed using Gwyddion [44], a freely available and open-source modular software suite under the GNU General Public License. This software is specifically designed for the visualization and analysis of data acquired via scanning probe microscopy (SPM) techniques. Python 3.11 scripts were used all further data processing and parameter determination, such as roughness, particle size, and fractal dimensions. In addition to the implementation of our own algorithms, the scripts also use the NumPy 2.1.0 and OpenCV 3.2 libraries. Gwyddion 2.67 was also used to visualize some resulting surfaces.