esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Security Requirements for Enhancing VMF Authentication and Research Contribution  
As such, the following approach to improving the VMF authentication scheme in a military CNR network based on a wireless terminal device is required.  
* The feasibility of implementing a lightweight authentication and integration scheme to improve the existing VMF terminal authentication, such as a hash chain, must be analyzed. The network configuration in CNRs with hash chain-based authentication schemes must be similar to that of the dynamic military ad hoc network-based mobile ad hoc network (MANET) and vehicle ad hoc network (VANET) in the cell planning area.  
* Cryptographic hash chain-based authentication and transmission integrity approaches must be considered. Interoperability within the next-generation NCW system, and technical adaptability and continuity in CNR networks with TDL, should also be ensured, and there should not be any deviations from the specifications of the existing VMF military standard.
* Rather than simply assembling the conceptual technology suggested by previous hash chain-based studies, a comparative analysis based on the pros and cons, differences, and limitations of each study should be considered to suit VMF-based CNR networks.
* Active security requirements that consider various military network scenarios must be established. Examples include a hash chain-based authentication, including a time-based one-time password (T-OTP) keyless point, low latency and transaction, robustness to external and internal auth attacks, a specialization of various FBCB2-based All-IP CNR network types, the introduction of re-authentication and revocation steps in the authentication scheme, and deep interoperability. Functional support and availability for rapidly changing battlefield environments and secure lightweight communication for covert activities are also required.  
Accordingly, in this study, an authentication scheme to be employed in rapidly changing VMF-based environments is proposed. It is based on the cryptographic hash chain-based authentication technology that includes T-OTP. In particular, assuming a flexible CNR network environment, the hash chain structure is applied to various military ad hoc networks in dynamic cell planning areas, and its effectiveness is analyzed. Based on the final established authentication scheme within the military ad hoc network and the multi-factor hash chain structure, an improved lightweight authentication scheme is proposed to satisfy the demands of a rapidly changing battlefield network and any additional security requirements based on VMF standards.
## 3 Research Methodology
### Performance Metrics
#### 3.1.2 Delay  
The average End-to-End Delay (EED) is a measurement of the time it takes for packets to travel from the source to the destination node's application layer. Due to the delays at various stages, the sending time of each data packet is subtracted from the receiving time at the destination node, and the result is divided by the total number of received packets. For this reason, network quality-of-service (QoS) is affected by the delay, and it is an important criterion in designing and measuring the performance of a communication network. As network delay is related to time, it is expressed in millisecond units. The average end-to-end delay is the sum of the processing, queuing, and transmission time of a packet in a network. It can be expressed as follows:  
\[D_{end-to-end}=\sum_{i=1}^{n}\frac{T_{r}(P_{i})-T_{s}(P_{i})}{P_{i}} \tag{2}\]  
where \(D_{end-to-end}\) is the end-to-end delay; \(n\) denotes the total number of received packets, \(P_{i}\) indicates the current received packet, \(T_{r}(P_{i})\) is the receiving time for \(P_{i}\), and \(T_{s}(P_{i})\) is the sending time of the packet \(P_{i}\).
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & **Season** & & & **Annual** & **Season** & & & **Annual** \\ \hline \hline
**Year** & **DJF** & **MAM** & **JJA** & **SON** & **Values** & **DJF** & **MAM** & **JJA** & **SON** & **Values** \\ \hline \multicolumn{10}{c}{_T\({}_{a}\)_(°C)**} \\ \hline
2008 & 2.55 & 11.30 & 20.08 & 10.90 & 11.29 & 3.65 & 9.69 & 18.09 & 12.25 & 11.12 \\
2009 & 0.87 & 12.45 & 20.02 & 11.49 & 11.28 & 3.70 & 10.53 & 17.65 & 12.56 & 11.24 \\
2010 & 0.75 & 10.91 & 20.10 & 10.13 & 10.27 & 3.80 & 9.59 & 17.69 & 11.42 & 10.31 \\
2011 & 0.24 & 11.65 & 20.47 & 10.14 & 11.04 & 2.20 & 9.82 & 18. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**(a) Eigenvalues of the Correlation Matrix**} \\ \hline
**Principal** & **Eigenvalue** & **Difference** & **Proportion of Variance** & **Cumulative Proportion** & \\
**Component (PC)** & & & & & \\ \hline _PC1_ & 3.0422 & 1.3715 & 0.507 & 0.507 & \\ _PC2_ & 1.6706 & 0.7073 & 0.278 & 0.786 & \\ _PC3_ & 0.9633 & 0.7660 & 0.161 & 0.946 & \\ _PC4_ & 0.1973 & 0.0945 & 0.033 & 0.979 & \\ _PC5_ & 0.1028 & 0.0791 & 0.017 & 0.996 & \\ _PC6_ & 0.0237 & - & 0.004 & 1.000 & \\ \hline \multicolumn{6}{c}{**(b) Relation between the Physical Driver Variables and the Principal Components**} \\ \hline \multicolumn{6}{c}{**Principal Components (Eigenvectors)**} \\ \hline
**Variable** & _PC1_ & _PC2_ & _PC3_ & _PC4_ & _PC5_ & _PC6_ & **Uneexplained** \\ \hline \(P\) & 0.5313 & \(-0.1376\) & \(-0.1502\) & \(-0.4509\) & 0.6785 & 0.1121 & 0 \\ \(T_{a}\) & 0.3025 & 0.6464 & 0.0065 & 0.2386 & 0.1597 & \(-0.6389\) & 0 \\ \(T_{s}\) & 0.3513 & 0.5572 & 0.2789 & \(-0.2349\) & \(-0.3487\) & 0.5581 & 0 \\ \(R_{s}\) & \(-0.3844\) & 0.4151 & \(-0.4819\) & 0.2968 & 0.4023 & 0.4446 & 0 \\ _SWC_ & 0.4925 & \(-0.2747\) & 0.1317 & 0.7710 & 0.0573 & 0.2587 & 0 \\ \(ASWC_{4 br-Sgr}\) & \(-0.3352\) & 0.0694 & 0.8062 & 0.0468 & 0.4769 & 0.0565 & 0 \\ \hline \multicolumn{6}{c}{**(c) Correlation between the NEE and the Principal Components (PCs)**} \\ \hline \multicolumn{6}{c}{**NEE**} & **PC1** & **PC2** & **PC3** & **PC4** & **PC5** & **PC6** \\ \hline _NEE_ & 1 & & & & & \\ _PC1_ & 0.3391 & 1 & & & & \\ _PC2_ & \(-0.0284\) & 0 & 1 & & & \\ _PC3_ & \(-0.6233\) & 0 & 0 & 1 & & \\ _PC4_ & 0.9562 & 0 & 0 & 0 & 1 & \\ _PC5_ & \(-0.1664\) & 0 & 0 & 0 & 1 & \\ _PC6_ & \(-0.0355\) & 0 & 0 & 0 & 0 & 1 \\ \hline \end{tabular} \({}^{\text{*}}\) Statistically significant at \(p<0.1\).  
\end{table} 
Table 16: Principal Components Analysis (a and b) and the resulting correlation between _NEE_ and Principal Components (c). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{13}{c}{_DBH_} & \multicolumn{13}{c}{_Year (at the End of the Season)_} \\ \cline{3-13} \multicolumn{13}{c}{_Spikes_} & \multicolumn{1}{c}{_(em)_} & **2007** & **2
## 2 Related Works
### Tracking Data Analysis
#### 2.1.2 Acquisition Methods  
The International Federation of Football Association (FIFA) has classified three technologies as electronic performance and tracking systems (EPTS). EPTS technologies are differentiated into Global Navigation satellite Systems (GNSS), Local positioning systems (LPS), and Optical-based systems (OPTS) [6, 21].  
A GNSS system requires athletes to wear GNSS receivers. The athlete's position information can be determined in real time by measuring the distance from the satellite system to the receivers. Even though the reliability and accuracy of GNSS has improved, there are still many drawbacks when changes of direction happen frequently or targets move in curve at a high speed.  
LPS system emerged as a solution to improve player-tracking accuracy and make it possible in indoor conditions. The LPS system [22] installs multiple signal receiving stations around the stadium and requires players wearing signal generators. By calculating the time differences between signals reaching different stations, the LPS can figure out the relative position of the players in real time.  
In recent years, the use of OPTS has gradually become mainstream. One reason is that this kind of system provides the only non-intrusive solution for tracking both players and the ball simultaneously without physical intervention by technical equipments. A second advantage is that there is no data loss due to sensor failure, since data can always be restored from video footage.  
Optical-based systems can be categorized into three kinds. Static, dynamic/moving and mixed cameras. The extracted data from each setup category was obtained through multiple cameras [23]. Among them, the multi-view fixed recording system presents a series of advantages. Firstly, ambiguities, particularly during crowded, cluttered or occluded scenes can be reduced, and the analysis results (e.g., player labeling) can be improved. Moreover, the whole field can be covered the entire time, 3D information of the scene can also be retrieved, and the accuracy of localization can be improved by collaboration among multiple views, which is required for detecting events such as offside moments. This makes these setups more suitable for game analysis.  
Whether it is a wearable system or an optical tracking system, the existing solutions are generally expensive. The prohibitive price makes it almost impossible to apply trajectory tracking technology to non-professional players. Fortunately, the rapid developments of deep learning and wide-angle camera technology in recent years promise to change this situation. The single-view-based multi-target tracking technology is becoming more and more mature, and is gradually applied to various sports scenes. Based on this, we proposed a new NPSPT system based on a fixed wide angle camera which covers the whole field.
## 2 Materials and Methods
### Post Processing RF Element Data  
Plane wave imaging allows for the conversion of unfocused element data into beamformed RF data at any given location within the region insonified. In this way, not only the axial and lateral grid ratio can be tailored to optimize the displacement estimation accuracy [41], but also the spacing of the beamforming grid can be adjusted to facilitate a fast combination of the steered acquisitions used in the process of displacement compounding as described before in [23]. This beam-forming approach was also used in this study for a steering angle \(\alpha\) equal to \(19.47^{\circ}\). An additional orthogonally steered grid (\(90^{\circ}\)) was added to enable a match with the \(0^{\circ}\) grid of the other probe. For all four beamforming grid orientations, a single displacement grid was defined. Figure 4 shows the grid layout for both probes, indicating the displacement grid defined by the coinciding points of all grid definitions. The spacing of the beamforming grids and the displacement grid (coinciding points) is listed in Table 1. Element data were beamformed using Delay And Sum (DAS) beamforming assuming a speed of sound of 1540 m/s, a dynamic focus F = 0.875, and Hamming apodization window in receive. Please note that the resolution is specified in terms of dx, dy, and dz in Table 1, in which x is oriented in the lateral direction, y is oriented in the axial direction, and z is oriented in the elevational direction.  
Figure 3: Schematic of the temporal acquisition protocol applied using an interleaved dual probe setup.  
B-mode representations of the beamformed RF-data of the external and internal carotid artery are shown in Figure 4 for both probes and for steered (\(\alpha=\pm 19.47^{\circ}\)) and non-steered (\(\alpha=0.0^{\circ}\)) transmissions. The orthogonal positions of both probes relative to each other allows one to utilize the axial zero-degree estimated displacements of probe A as lateral displacement estimates for probe B and vice versa as depicted in the last column of Figure 4. In this way, highly accurate estimates of the lateral displacments can be obtained, because native RF-phase information can be used in the estimation process, which is normally absent when using a single probe. Finally, the 2D beamformed RF data were stacked according to their elevational position to create a volume matrix of RF data over time. Please regard video S1 (Multi-perspective_3D_Bmode.mp4) for a dynamic B-mode presentation of the carotid artery phantom for both probes.
## 2 Materials and Methods
### Wireless Sensor Network
#### 2.1.3 Data Storage  
The master node plays a crucial role in sending all the collected measurements to the web platform for storage. These data are transmitted using a POST request to a designated endpoint on the platform.  
Upon receiving an incoming request, the web platform follows a sequence of steps. It starts by processing the content and confirming its compatibility with the correct method. If the content is in JSON format, it is sanitized for any potential issues. Then, the credentials of the master node are verified, and the incoming measurements are assessed.  
If a measurement is beyond the acceptable range (where any parameter contains 0 or 65,535 or is outside the sensor's measuring range), it is disregarded because it is considered an outlier. Concurrently, the metadata of the sensor node are updated to reflect an online status, along with an indication of a \"bad measurement\" and a timestamp. This information is useful for troubleshooting purposes. When all checks pass successfully, the measurement is stored in the database. The node's metadata are adjusted accordingly, and this process continues for the remaining measurements.  
At the end of the process, the web platform acknowledges the successful receipt of the data by sending a confirmation message back to the master node, ensuring data integrity and reliable transmission.  
This systematic approach ensures that data remain accessible for future retrieval and in-depth analysis. It guarantees secure storage in a centralized database, accessible from anywhere and at any time.
# Using Statistical Shape Models to Optimize TKA Implant Design
## 3 Results  
### Generation of the SSM  
#### 3.1.1 Selection of Input Data, Impact of Pathology  
The numerical color map enabled to visualize the differences between the models (Figure 2). 'OA-SSM' and 'healthy SMM' differed on average by 1 mm. Some differences were visible on the distal and posterior condyles, as well as on the shape of the articular surface of the patella.  
The \(t\)-test performed on the combined SSM showed significant differences (\(p<0.05\)) for 3 modes for the femur (mode 4, 10 and 15), 5 modes for the tibia (mode 3, 9, 10, 12 and 19) and 3 modes for the patella (mode 6, 17 and 63).  
This pilot study led to the conclusion that the SSM used to represent the arthritic population should preferably be created exclusively from CT scans of arthritic patients if the number of available datasets is sufficient.  
Figure 2: Comparison between average SSMs of the healthy and arthritic bones (femur/tibia/patella). Positive values mean the average arthritic model is larger than the average healthy model.
## 3 Feature Extraction and Application to Floor Classification and Landmark Detection
### Semisupervised Discriminant Analysis
#### 3.2.1 Generalized Eigenvalue Problem  
Remember that \(y=[y_{1},\ldots,y_{d}]^{\mathrm{T}}\in R^{d}\) is the set of the raw Wi-Fi RSSIs. The feature set is given \(z\in R^{r}\) (\(1\leq r\leq d\)), which is the resultant low-dimensional set by a feature extraction implementation. Let \(T\in R^{d\times r}\) be a transformation matrix; then, it has the following relationship,  
\[z=T^{\mathrm{T}}y, \tag{13}\]  
where \(\cdot^{\mathrm{T}}\) represents the transpose operation. Both FDA and PCA solve the following optimization problem,  
\[T^{\mathrm{opt}}=\mathrm{argmax}_{T}\left[\mathrm{tr}\left(T^{ \mathrm{T}}BT(T^{\mathrm{T}}CT)^{-1}\right)\right], \tag{14}\]  
where \(B\) is a quantity we want to increase, \(C\) is a quantity to be hopefully decreased, and \(\mathrm{tr}(\cdot)\) represents the trace of a matrix.  
Further, suppose that \(\{\lambda_{i}\}_{i=1}^{d}\) is the set of generalized eigenvalues and \(\{\varphi_{i}\}_{i=1}^{d}\) is the set of the corresponding generalized eigenvectors. Then, the generalized eigenvalue problem can be defined as  
\[B\varphi=\lambda C\varphi. \tag{15}\]  
The generalized eigenvectors are orthogonal to matrix \(C\):  
\[\varphi_{i}^{\mathrm{T}}C\varphi_{j}=0, \tag{16}\]  
for \(i\
eq j\), and the eigenvectors are normalized:  
\[\varphi_{i}^{\mathrm{T}}C\varphi_{i}=1\ \ \mathrm{for}\ i=1,\ 2,\ \cdots,\ d. \tag{17}\]  
Additionally, assume that the eigenvalues are organized in descending order:  
\[\lambda_{1}\geq\lambda_{2}\geq\cdots\geq\lambda_{d}. \tag{18}\]  
Finally, the transformation matrix can be summarized as  
\[T^{\mathrm{opt}}=\left(\sqrt{\lambda_{1}}\varphi_{1}|\cdots| \sqrt{\lambda_{r}}\varphi_{r}|\cdots|\sqrt{\lambda_{d}}\varphi_{d}\right). \tag{19}\]  
From Equation (19), it can be seen that the influence of the transformation becomes deemphasized as the dimension order increases, because the eigenvalues and eigenvectors are decreasing.
## 2 Theoretical Concept
We are talking about costs in the context of dynamic time warping because the triangle inequality is a necessary requirement for being a distance. In [49], the authors show that the corresponding estimator is asymptotically normal and converges to the true shift function as the sample size per subject goes to infinity. In case we observe structural breaks, things become more complex and the outlined statement cannot be kept. In the 21 st century, research has focused either on developing a generalized model framework or on optimizing the computation run time. With the exception of [9], the setting of model parameters plays a central role in all contributions--criticism of arbitrariness and data snooping is omnipresent. Within the framework of generalization [33; 34] universalize the optimal search by including the Boltzmann factor proportional to the exponent of the global imbalance of this path. In [50], the authors provide a symmetric variant for determining the time-dependent mapping. Finally, ref. [9] quantifies the optimal lead-lag structure between two time series under the assumption that there is no structural break in the data set. Within the framework of optimization, ref. [51] implement a modification of the dynamic time warping, which uses a higher-order representation of data. In [48; 52], the authors recursively project an alignment path calculated at a coarse resolution level to the next higher level and then refine it. In [53], the authors exploit the possible existence of inherent similarity between two time series. In [54], the authors present a memory-restricted alignment procedure and [55] use an upper bound estimation to prune unpromising warping alignments. In recent times, machine learning methods are used to identify Granger causality, e.g., ref. [56] for univariate MIDAS regressions and [57] for VAR processes. Following [58], Granger causality and dynamic time warping are mechanisms to find possible causal links in temporal data. Of course, there also exist some differences between both [59; 60]. First, Granger causality uses a statistical hypothesis test to judge whether a time series is causally affected by another time series based on predictability. In contrast, dynamic time warping is a method that calculates an optimal match between two given sequences with certain restriction and rules. Second, the Granger causality may lose its power in the context of structural breaks and drifts because the model residuals are not be stabilized. Dynamic time warping can handle such situations better. Third, Granger causality is a well-established method from econometrics that can identify relationships between temporally offset causes and effects when the offsets are fixed. In contrast, dynamic time warping addresses uneven temporal offsets. Figure 2: Sakoe-Chiba band (**left**) and Itakura parallelogram (**right**). The constraint regions (grey) represent the environment in which the optimal causal path may run.
###### Abstract  
Traditional natural river sand is used as a fine aggregate for concrete, but due to the severe environmental situation in recent years, many places have asked for a ban or restriction on the extraction of river sand. This has resulted in an increasing demand for concrete using machine-made sand instead of natural sand. The estimation and prediction of the compressive strength of concrete is very important in civil engineering applications. In this investigation, a Box-Behnken test model was established to analyze the effect of stone powder (SP), pulverized fuel ash (PFA), and silica fume (SF) contents on the compressive strength of manufactured sand concrete using response surface methodology (RSM). A prediction model for the compressive strength of manufactured sand concrete was developed using multiple regression analysis with SP, PFA, and SF content as factors and compressive strength as the response value. In addition, the interaction of stone powder (SP), pulverized fuel ash (PFA), and silica fume (SF) content was analyzed according to the response surface and contour. The investigation showed that for single factors, SP had the greatest effect on the compressive strength of the manufactured sand concrete, with PFA having the second greatest effect, and SF the least; for the interactions, SP and PFA had the most significant effect, and the interaction between SP and SF and PFA and SF had the same effect on the compressive strength.  
manufactured sand concrete; RSM; SP; PFA; SF; compressive strength Article Research on Compressive Strength of Manufactured Sand Concrete Based on Response Surface Methodology (RSM). _Appl. Sci._**2022**, 12, 3506. [[https://doi.org/10.3390/app12073506](https://doi.org/10.3390/app12073506)]([https://doi.org/10.3390/app12073506](https://doi.org/10.3390/app12073506))