esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Bone Grafts and Substitutes for Bone Repair
### Natural Bone Grafts and Substitute Materials
#### 4.1.3 Xenografts  
Xenografts are materials that are derived from a genetically different species from the host species.  
In dentistry, the most common source of xenograft material is deproteinized bovine bone tissue that is commercially available as Bio-Oss. Bovine bone is treated to produce a hydroxyapatite-based porous material that contains only the inorganic component of bovine bone tissue. The resulting porous structure closely resembles that of human bone and can provide good mechanical support and stimulate the healing process by osteoconduction. This porous structure facilitates the development of new blood vessels through angiogenesis, which underlies the formation of new bone tissue [50]. Bone xenografts of bovine origin have been used extensively in procedures for lifting the floor of the maxillary sinus and obtaining implant support due to their superior stability and low immunogenicity (Figure 1) [51; 52].  
Statistical studies have suggested that the effectiveness of Bio-Oss in stimulating the formation of new bone tissue is similar if not superior to autografts [53; 54]. It was also found that the volume and quality of the newly obtained bone tissue allow for the foreseeable simultaneous placement of the implants, thus performing procedures for augmentation of the maxillary sinus in one stage [55]. Clinically, Bio-Oss has been shown to be a valuable bone replacement material, providing good-quality newly formed bone structures and promising rates of long-term survival of inserted dental implants [56]. Of course, there are other commercial products, such as OsteoGraf, Cerabone [57] or Lumina-Porus [58], which have very similar structures and biochemical properties favorable to human bone and which can act as an effective osteeconductive graft [59].  
Another xenograft material with promising results in the recent studies is chitosan, which is a polymer derived from crustacean exoskeletons capable of stimulating bone regeneration by providing a structural skeleton that supports osteoblastic activity, mineralization of bone matrix and induction of mesenchymal cell differentiation into osteoblasts [60]. Due to its poor mechanical properties, chitosan is usually combined with other materials in order to obtain the desired properties. However, its structural versatility and hydrophilic surface make this material a viable alternative to autografts and allografts [61].  
In the following, some phytogenic materials acting as bone tissue substitute biomaterials that are preferred in the field of dentistry will be presented, given the smaller volume of the bone defect compared to orthopedic applications.
# Measurement of Dielectric Properties of Thin Materials for Radomes Using Waveguide Cavities
## 4 Conclusions  
Permittivity, loss tangents, and noise results were obtained for widely available fabrics with the intention to use them as radomes within the frequency range of about 1 to 6 GHz. Good agreement between full wave simulation and the simplified equations gave confidence in the application of those equations to the measurements. These measured results, performed with WR650 and WR137 waveguide resonant cavities and shim iris diameters of 12.2 mm and 7.2 mm, respectively, showed encouraging outcomes for three fabrics. This was observed through the comparison with two reference materials previously used for antenna array radomes. These results confirm that cost-effective, widely available materials could be appropriate in radome applications. Factors that impacted measurements were also identified and discussed, with possible solutions provided. Further testing of these materials within different environments and at higher frequencies will further identify their appropriate usage. The orientation of these materials in the y-z plane of the cavity will allow for measurements to be obtained at higher frequencies using higher-order modes with the same setup we used in this article. This exemplifies how this newly proposed orientation can make this commonly used method even easier for numerous measurements.  
Conceptualization, D.B.H. and B.M.; methodology, D.B.H. and B.M.; software, D.B.H., B.M. and T.D.; validation, T.D. and B.M.; formal analysis, T.D.; investigation, T.D. and B.M.; resources, D.B.H. and B.M.; data curation, T.D.; writing--original draft preparation, T.D.; writing--review and editing, T.D., D.B.H., B.M. and S.L.S.; visualization, T.D.; supervision, D.B.H. and S.L.S.; project administration, B.M.; funding acquisition, T.D. and S.L.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Australian Government Research Training Program Scholarship and CSIRO PhD Top-Up Scholarship.  
Data Availability StatementData will be made available upon reasonable request to the corresponding author.
## 3 Results
### Approach Duration  
There was a significant group-by-approach phase interaction for approach duration. All groups had a similar approach duration during the approach to target 1 (diplegic CP; 1.03 \(\pm\) 0.16 s, hemiplegic CP; 1.08 \(\pm\) 0.12 s, TD children; 1.10 \(\pm\) 0.12 s), but approach duration to target 2 increased in children with diplegic CP (1.31 \(\pm\)0.33 s) whilst decreasing in children with hemiplegic CP (1.04 \(\pm\) 0.12 s) and TD children (1.03 \(\pm\) 0.11 s).  
Figure 3: Raincloud plots [32,33] displaying the central tendency (mean \(\pm\) 1 SD), probability distribution (split-half violin), and jittered dot plots (individual participant means) for each of the two target approaches for head-to-trunk (**top row**) and head-to-lab (**bottom row**) velocity inflection counts. \(\ddagger\) = a significant main effect of approach. \(\lx@sectionsign\) = a significant group-by-approach interaction.  
Figure 4: Raincloud plots [32; 33] displaying the central tendency (mean \(\pm\) 1 SD), probability distribution (split-half violin), and jittered dot plots (individual participant means) for both target approaches combined for the maximum (**top row**), minimum (**middle row**), and variability (**bottom row**) of head-to-trunk resultant, sagittal, frontal, and transverse plane angular velocity. * = significant main effect of group between children with diplegic CP and children with hemiplegic CP. \(\dagger\) = a significant main effect of group between children with diplegic CP and TD children. \(\ddagger\) = a significant main effect of approach. \(\lx@sectionsign\) = a significant group-by-approach interaction. NB: Bonferroni-corrected \(p\)-values represent significant post-hoc pairwise comparisons.  
Figure 5: Location of the foot center for all participant groups, relative to the center of the target in (**a**) target 1 and (**b**) target 2, representing both the mean (filled shapes) and dispersion of foot placement bias (unfilled shapes). Positive anteroposterior and mediolateral values indicate the foot was placed anterior and lateral to the target center, respectively.
# Design of a 90 GHz SOI Fin Electro-Optic Modulator for High-Speed Applications
## 5 Conclusions  
In this work, a MOS-based modulator has been proposed. The modulator employs a standard structure from the electronics domain, the 'fin' gate structure. This makes the modulator easy and inexpensive to fabricate. We showed how the design parameters--such as the doping concentration, the waveguide dimensions, and the silica thickness--can be altered to get different variants of the proposed modulators. The performance metrics such as the insertion loss, the extinction ratio, the modulation speed and the voltage of operation can then be customized to make the modulator suitable at different points of a communications network. The very high bandwidth of 90 GHz is introduced to match the 5G network requirements at the backhaul truck. This way, silicon photonics can be easily integrated with the prospective networks for high speed communications and the internet of things.  
Conceptualization, M.G. and M.F.; Methodology, H.M., M.G., and M.F.; Software, H.M. and M.E.S.; Validation, M.G., M.F., and W.F.; Formal analysis, H.M. and M.G.; Investigation, H.M.; Resources, M.G. and M.F.; Data curation, H.M. and M.G.; Writing--original draft preparation, H.M. and M.G.; Writing--review and editing, M.G., M.F., M.E.S., and W.F.; Visualization, H.M.; Supervision, W.F.; Project administration, M.G. and M.F.  
This research received no external funding.  
The authors declare no conflict of interest.  
The simulation settings in this work are:  
**Device:**  
Simulation window \(2D\ x-normal\) & \(y=4\)\(\upmu\)m & \(z=2\)\(\upmu\)m  
Mesh Size & Minimum edge length = 5 nm & Maximum edge length = 100 nm  
Temperature & 300 K  
Temperature Dependence & Isothermal  
Material Parameters  
Silicon & Permittivity = 11.7 & Work function = 4.59 eV & \(ni=1.5e10\)  
Mobility physical models & Auger Radiative trap-assisted (SRH)  
Oxide SiO2 & Permittivity = 3.9  
Aluminum & work function = 4.28 eV  
**MODE:**  
Simulation window: & 2D x-normal & \(y=4\)\(\upmu\)m & \(z=2\)\(\upmu\)m  
Temperature: & 300 K  
Mesh: & 5 nm inside the core & 10 nm elsewhere  
Boundary conditions: & PML  
Wavelength: & 1.55 um
# Integrated Sensing and Communication Using Random Padded OTFS with Reduced Interferences
## 3 The Random Padded OTFS
### RP-OTFS Transmitting and Receiving Process  
The principle of the OTFS-based communication system with random padding was recently proposed [22; 23; 24; 25; 26], and is explained in Figure 3. The \(K\) parameter marked on the top of the picture and the repeating OFDM-pilots in the Doppler direction will be discussed in the next section.  
In the RP-OTFS transmitter, the data part of the TD \(\mathbf{S}\) matrix is obtained by the inverse Zak transform of the DD data matrix \(\mathbf{X}\) that carries transmitted bits (computed as IDFTs of the rows of the matrix \(\mathbf{X}\)--see Figure 2). However, in contrast to all known types of OTFS, the pilot samples \(p_{k}\) are inserted directly into the matrix \(\mathbf{S}\) with \(s_{k}\) data in the TD  
Figure 3: Principle of the RP-OTFS-based communication system.  
domain as some of its additional first rows: an additional prefix. Then, samples of the \"hybrid\" TD-domain matrix \(\mathbf{S}\) containing alternately blocks of pilot and data samples are transmitted column by column through a communication channel.  
In the RP-OTFS receiver, all operations are performed in reverse order. First, the signal samples obtained (\(q_{k}\)--pilot, \(r_{k}\)--data) are put into columns of a receiver matrix in the TD domain. The matrix has dimensions \(M\times N\). Then, the cyclic prefixes of the OFDM-pilots are removed: the \(L\) first elements of each matrix column. The next elements \(L\) are used for the estimation of the instantaneous CIR, as explained in [26]. This is conducted using any ordinary OFDM CIR estimation technique [34].  
The received samples \(r_{k}\) create in the RP-OTFS receiver the matrix \(\mathbf{R}\) with dimensions \((M-2L)\times N\). The \(\mathbf{R}\) contains only data samples in the TD domain. Pilot samples are used only for the estimation of the momentum CIR and are excluded from further processing dealing with bit decoding. Performing DFT on rows of \(\mathbf{R}\) brings the signal back to the DD domain and, as a result, gives the matrix \(\mathbf{Y}\). The initial CSI estimation is conducted separately for each OFDM-like pilot symbol. At this stage, the instantaneous CSI estimations are acquired. Later, they are processed, and the final CSI estimate is obtained.
# Location Adaptive Motion Recognition Based on Wi-Fi Feature Enhancement
## 4 Enhanced Convolution Transformer Network
### Model Overview  
The human motion distribution data extracted from CSI is used as the input of the convolution enhanced Transformer model, as shown in Figure 3. The main target is to classify. Thus, we only use the Encoder component in the Transformer model. Firstly, the input data is processed by the multi-head self-attention module [42] after the location code. A weighted sum of the values is output, where the weight assigned to each value is computed by the dot product of the query with the corresponding key.  
\[\text{Attention}(Q,K,V)=\text{softmax}\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}} \bigg{)}V \tag{19}\]  
where \(Q\in R^{L\times d_{k}},K\in R^{L\times d_{k}}\), \(V\in R^{L\times d_{v}}\), \(d_{k}\), \(d_{v}\) are queries, keys, values, the dimension of \(Q\) and \(K\), and the dimension of \(V\), respectively. \(Q\), \(K\), and \(V\) are obtained by linear variation of the input data.  
\[Q=X\mathcal{W}_{Q} \tag{20}\]  
\[K=X\mathcal{W}_{K} \tag{21}\]  
\[V=X\mathcal{W}_{V} \tag{22}\]  
where \(\mathcal{W}_{Q}\in R^{d_{(}in)\times d_{K}},\mathcal{W}_{K}\in R^{d_{(}in) \times d_{v}}\), \(\mathcal{W}_{w}\in R^{d_{(}in)\times d_{v}}\) are learnable weight parameters. \(d_{(}in)\) is the dimension of the input vector. To concentrate information from different subspaces, the queries, keys, and values use different linear variations methods to change h times (h-head). The output of the projection is wired and projected again to produce the final output  
\[\text{MultiHead}(Q,K,V)=[\text{head}_{1},\dots,\text{ head }_{h}]\mathcal{W}_{O} \tag{23}\]  
\(\text{head }_{i}=\text{Attention}\Big{(}X\mathcal{W}_{Q}^{i},X\mathcal{W}_{K}^{i},X \mathcal{W}_{V}^{i}\Big{)},\mathcal{W}_{O}\in R^{hd_{m}\times d_{k}}\) is the final projection matrix.. Multi-head self-attention module makes the Transformer learn characteristic from different subspace. In order to facilitate the residual connection, \(d_{i}n=h*d_{v}\). After residual connection and normalization, this structure can make the backward and forward propagation of information smoother and alleviate the gradient explosion problem.  
Figure 3 shows the entire network structure. Furthermore, the main part is the Transformer. Different from the usual Transformer, we use a dynamic convolution module  
Figure 3: Convolutional Feature Augmentation Transformer Model Architecture.  
instead of PFFN to capture feature information at different scales and strengthen the model's performance. Meanwhile, we use Gaussian Prior function to enhance the ability to express human actions' temporal features and retain the data sequence. Finally, extract the corresponding action features for action classification.
## 3 Results
### Time Cost Analysis  
The time cost of the proposed method is analyzed for several of records from the MIT-BIH DB (360 Hz) using Matlab 2019 in Win 10 with Intel i7 4-core CPU and 8G RAM. Figure 16 shows the calculation time per 1000 iterations for 11 seconds of Record 108, 111, 118, 217, and 214. The respective average processing time is 33.81 ms for Record 108, 33.18 ms for Record 111, 32.94 ms for Record 118, 33.00 ms for Record 217, and 31.26 ms for Record 214. The total processing average time for an 11 second period is less than 32.84 ms, which implies that the proposed method is suitable for real-time ECG signal processing.
# Handover Management for Drones in Future Mobile Networks--A Survey
## 7 Future Directions
### New Cellular Technologies  
New challenges have emerged as a result of the latest generation of cellular technology, leading to an increase in network heterogeneity. For example, the implementation of 5G and 6G mobile networks will contribute to an increase in mobility issues. This is because these technologies will mostly operate based on high-frequency bands, which will lead to reducing the cell coverage. This, in turn, will increase the handover probability. This will be even greater in the case of drones, because drones move in three dimensions with high movement speeds and mostly with LoS connections. Thus, the handover probability will definitely increase further. Moreover, future mobile networks will be characterized as ultra-dense heterogeneous networks. Various mobile technologies will be deployed as overlapping with each other's. This also will increase the handover probability, especially if drones have the capabilities to be connected to more technologies. Thus, effective and more intelligent handoff algorithms must be implemented to resolve these challenges. With the launch of various cellular technologies, drones can be used to enhance 5G spectral efficiency. Although the application of drones in 5G networks is still at its infancy stage, interest in such integration is rapidly growing.
## 2 AMTB Filter without Need for Clutter Density
### Generation of Potential Birth Objects  
In this step, we generate the potential birth objects based on the unused measurements at steps \(k\) and \(k-1\). A potential birth object is formed if the two picked measurements satisfy the given speed gating criterion.  
Let \(\mathbf{Z}_{k-1}^{u}=\left\{z_{f,k-1}^{u}\right\}_{f=1}^{M_{k-1}^{u}}\) and \(\mathbf{Z}_{k}^{u}=\left\{z_{g,k}^{u}\right\}_{g=1}^{M_{k}^{u}}\) denote two sets of unused measurements at time steps \(k-1\) and \(k\). We pick observations \(\mathbf{z}_{f,k-1}^{u}\) and \(\mathbf{z}_{g,k}^{u}\) from sets \(\mathbf{Z}_{k-1}^{u}\) and \(\mathbf{Z}_{k}^{u}\), respectively, and then test whether they satisfy (39).  
\[v_{\min}<\frac{\left\|z_{g,k}^{u}-z_{f,k-1}^{u}\right\|_{2}}{T}<v_{\max} \tag{39}\]  
where \(T\) is the scan period, and \(v_{\min}\) and \(v_{\max}\) are two speed thresholds. A potential birth object is detected if \(\mathbf{z}_{f,k-1}^{u}\) and \(\mathbf{z}_{g,k}^{u}\) satisfy (39). The mean vectors of the potential birth object at the time steps \(k-1\) and \(k\) according to the least squares technique [25] are:  
\[\mathbf{m}_{i,k-1}^{b}=\left(\Lambda_{1}^{T}\Lambda_{1}\right)^{-1}\Lambda_{1}^{T} \left[\mathbf{z}_{f,k-1}^{u}\right] \tag{40}\]  
\[\mathbf{m}_{i,k}^{b}=\left(\Lambda_{2}^{T}\Lambda_{2}\right)^{-1}\Lambda_{2}^{T} \left[\mathbf{z}_{f,k-1}^{u}\right] \tag{41}\]  
where  
\[\Lambda_{1}=\begin{bmatrix}1&0&0&0\\ 0&0&1&0\\ 1&T&0&0\\ 0&0&1&T\end{bmatrix},\ \Lambda_{2}=\begin{bmatrix}1&-T&0&0\\ 0&0&1&-T\\ 1&0&0&0\\ 0&0&1&0\end{bmatrix} \tag{42}\]  
Its error covariance at time steps \(k\) is:  
\[\mathbf{P}_{i,k}^{b}=\left(\Lambda_{2}^{T}\Lambda_{2}\right)^{-1}\Lambda_{2}^{T} \begin{bmatrix}\mathbf{R}_{k-1}&\mathbf{0}\\ \mathbf{0}&\mathbf{R}_{k}\end{bmatrix}\Lambda_{2}\left(\Lambda_{2}^{T}\Lambda_{2} \right)^{-1} \tag{43}\]  
Assume that potential birth object \(i\) is generated based on measurements \(\mathbf{z}_{f,k-1}^{u}\) and \(\mathbf{z}_{g,k}^{u}\). This potential birth object is given by:  
\[\left\{\varepsilon_{i,k}=g,\mathbf{m}_{i,k-1}^{b},N(\mathbf{x}_{i,k};\mathbf{m}_{i,k}^{b}, \mathbf{P}_{i,k}^{b})\right\} \tag{44}\]  
where \(g\) denotes the index of measurement \(\mathbf{z}_{g,k}^{u}\). \(\varepsilon_{i,k}=g\) implies that measurement \(\mathbf{z}_{g,k}^{u}\) is used to form potential birth object \(i\).  
The set of potential birth objects can be acquired by repeating the above process and is given by:  
\[\left\{\varepsilon_{i,k},\mathbf{m}_{i,k-1}^{b},N(\mathbf{x}_{i,k};\mathbf{m}_{i,k}^{b}, \mathbf{P}_{i,k}^{b})\right\}_{i=1}^{N_{k}^{b}} \tag{45}\]  
where \(N_{k}^{b}\) denotes the number of potential birth objects.  
Algorithm 3 gives the pseudo-code for the generation of potential birth objects.
# Towards a Single Integrative Metric on the Dynamics of Social-Environmental Systems
### IMoSES Calculation and the Intermediate Variables  
IMoSES and associated intermediate variables are based on six input variables: land area (A) of the administrative unit (e.g., country), GDP, POP, carbon price (USD Mg), GPP, and PET (potential evapotranspiration) associated with the administrative unit. Multiple intermediate variables can be calculated to reflect synchronized SES functioning, such as GDP\({}_{\text{pc}}\), GPP\({}_{\text{pc}}\), POP\({}_{\text{d}}\) and ecosystem water use efficiency (WUE). We used the historical input data (1992-2016) of Mongolia and Inner Mongolia for the calculations and values of IMoSES and the intermediate variables (Figure 1).  
M Mongolia (1.57 million km\({}^{2}\)) and Inner Mongolia (1.15 million km\({}^{2}\)) are jurisdictions with similar ecological systems but contrasting socioeconomic systems on the Mongolian Plateau. The political separation of the two in the 1920s, coupled with Chinese and Soviet influences, has caused a significant divergence in their human demographic and socioeconomic conditions [20]. The divergence of these SESs was attributed to the collapse of the USSR in 1991 and the rapid economic development of China since the mid-1990s [20; 25; 34; 39]. During 1992-2016, the population and GDP of Inner Mongolia (IM) and Mongolia (MN) grew similarly, with approximately 10-fold and 7-fold higher levels in IM, respectively. The GPP and PET of IM are much higher than those of MN, though the patterns of interannual variation are similar. As a result of these differences, the population density of MN is \(\sim\)8% of IM. Interestingly, the GDP\({}_{\text{pc}}\) of MN before 1996 was higher than that of IM, but the relatively slow growth in MN resulted in a difference of 6486 USD lower than IM in 2016. Due to the high population density, GPP\({}_{\text{pc}}\) of IM is \(\sim\)16% of MN. Finally,  
Figure 6: Boxplots of IMoSES at the prefectural level for the four political entities (PEs) in 2016 (**a**) and the PE mean values of HDI and SDI in 2015 (**b**). Note the similar values between IM and MN but different values between KZ and UZ.  
the ecosystem water use efficiency of IM is much higher than that in MG. In 2016 WUE was 0.37 for IM and 0.23 for MN. When the input variables are applied for calculating IMoSES for IM and MN, consistently higher values appear for MN than IM. In 2016, IMoSES of MN was 413.0 USD\({}^{2}\) pers\({}^{-2}\) yr\({}^{-2}\) mm\({}^{-2}\) versus 160.4 USD\({}^{2}\) pers\({}^{-2}\) yr\({}^{-2}\) mm\({}^{-2}\) for IM. More importantly, the differences in temporal changes of IMoSES for the two SESs seemed very different from those of all input and intermediate variables (Figure 1). If higher IMoSES values indicate better SES functioning, then MN had been performing consistently higher than IM, with the difference growing in the most recent decade. However, this conclusion could not be made from any input and intermediate variables.