esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Fn Application and Involvement in Biomaterials and Medical Device Engineering
### Potential for the Use of Fn in Medical Applications  
The sections above present different state-of-the-art strategies developed to generate Fn implants that can be considered for potential medical applications. It is evident that, from synthetic or recombinant fragments to whole Fn purified from animal or human plasma, these strategies have biological potential for directing tissue responses and promoting repair in the human body.  
Recently, the use of Fn-modified biomaterials in surgical applications has been reported in different studies, especially in animal models (from rodents to dogs) [119; 120; 121]. Some opportunities can also be explored for human applications, and the clinical use of Fn-modified biomaterials can now be seriously considered. However, in these cases, aspects beyond the biological tissular response benefits need to be considered, such as concerns about safety and regulatory affairs.  
Synthetic or recombinant strategies using Fn fragments are powerful; however, as explained above, the use of whole animal or human Fn may represent a pertinent alternative for fully harnessing the potential of this molecule. With produced fragments, the issues of immunocompatibility and the risk of contamination are less prominent. Nevertheless, in the case of animal or human purified molecules, the control of the source and the origin of the material have to be carefully considered, and questions about quality assurance and regulatory affairs are central. For human medical applications, we can envision, as developed previously with platelet-rich plasma, the use of Fn purified from the patient themself. This approach necessitates some optimization of the purification process at the bedside. Allogenic xenogenic sources may also be considered; however, in that case, the perfect control of the purification and the analysis of the elimination of immunological markers and viruses would be essential.
# Guaranteeing QoS for NOMA-Enabled URLLC Based on \(\kappa\)-\(\mu\) Shadowed Fading Model
## 2 System Model
According to \(\gcd(p,q)=1\), both \(p\) and \(q\) can be 1._  
_Then, the MGF of \(\zeta_{k}\) can be simplified to_  
\[\mathcal{M}_{\zeta_{k}}(s)=\frac{\mathcal{A}_{k}\mathcal{D}_{k}^{\mathcal{A}_ {k}\mathcal{K}_{k}}}{\Omega_{k}^{\mathcal{A}_{k}\mathcal{K}_{k}}\Gamma( \mathcal{K}_{k})}\sum\limits_{v=1}^{V}\frac{c_{v}}{\omega_{v}^{\mathcal{A}_{k} \mathcal{K}_{k}}}\frac{1}{(-s)^{\mathcal{A}_{k}\mathcal{K}_{k}}}\mathrm{G}_{1, 1}^{1,1}\bigg{(}\left(\frac{\mathcal{D}_{k}}{\omega_{v}\hat{\Omega}_{k}} \right)^{\mathcal{A}_{k}}\frac{1}{-s}\bigg{|} \begin{array}{c}\Delta(1,1- \mathcal{A}_{k}\mathcal{K}_{k})\\ \Delta(1,0)\end{array} \right). \tag{13}\]  
Proof. : Please refer to Appendix B. Based on Equation (6) and Theorem 1, the statistical characteristics of the PPSNR in the CF mMIMO system are provided in the following corollary. **Corollary 1**. : _Due to \(\hat{\gamma}_{k}\approx\hat{\rho}_{u}\xi_{k}\), the PDF of the PPSNR \(\hat{\gamma}_{k}\) at the BBU is derived as_  
\[f_{\hat{\gamma}_{k}}\big{(}z;\mathcal{A}_{k},\mathcal{K}_{k},\hat{\rho}_{u} \hat{\Omega}_{k}\big{)}\cong\sum\limits_{v=1}^{V}c_{v}\frac{\mathcal{A}_{k} \mathcal{D}_{k}^{\mathcal{A}_{k}\mathcal{K}_{k}}}{\left(\omega_{v}\hat{\rho}_{u} \hat{\Omega}_{k}\right)^{\mathcal{A}_{k}\mathcal{K}_{k}}\Gamma(\mathcal{K}_{k}) }z^{\mathcal{A}_{k}\mathcal{K}_{k}-1}\exp\left(-\left(\mathcal{D}_{k}\frac{z} {\omega_{v}\hat{\rho}_{u}\hat{\Omega}_{k}}\right)^{\mathcal{A}_{k}}\right). \tag{14}\]  
_The CDF of \(\hat{\gamma}_{k}\) is given by_  
\[F_{\hat{\gamma}_{k}}\big{(}z;\mathcal{A}_{k},\mathcal{K}_{k},\hat{\rho}_{u}\hat{ \Omega}_{k}\big{)}\cong\sum\limits_{v=1}^{V}c_{v}\frac{\gamma\Big{(}\mathcal{K} _{k^{\prime}}\left(\mathcal{D}_{k}\frac{z}{\omega_{v}\hat{\rho}_{u}\hat{\Omega}_ {k}}\right)^{\mathcal{A}_{k}}\Big{)}}{\Gamma(\mathcal{K}_{k})}. \tag{15}\]  
_The corresponding MGF of \(\hat{\gamma}_{k}\) can be expressed as_  
\[\begin{array}{l}\mathcal{M}_{\zeta_{k}}(s)\\ =\frac{\mathcal{A}_{k}\mathcal{D}_{k}^{\mathcal{A}_{k}\mathcal{K}_{k}}}{\Omega_{k }^{\mathcal{A}_{k}\mathcal{K}_{k}}\Gamma(\mathcal{K}_{k})}\sum\limits_{v=1}^{V} \frac{c_{v}}{\omega_{v}^{\mathcal{A}_{k}\mathcal{K}_{k}}}\frac{1}{(-s)^{ \mathcal{A}_{
# Hybrid Precoder Using Stiefel Manifold Optimization for Mm-Wave Massive MIMO System
## 2 System Model  
Let us consider a hybrid precoding millimeter-wave system for a single user as shown in Figure 2. It consists of \(\rm{N_{T}}\) antennas and \(N_{RFT}\) RF chains on the transmitter side and \(\rm{N_{R}}\) antennas and \(N_{RFR}\) RF chains on the receiver side. Each user transmits \(N\)s data streams from the base station on each subcarrier with the help of \(N_{T}\) antennas. The user symbols, RF chains, and antenna terminals are bounded as  
\[N_{S}\leq N_{RFT}\leq N_{T}\ \text{and}\ N_{S}\leq N_{RFR}\leq N_{R}\]  
The hardware structure consists of a digital baseband precoder, \(F_{BB}\) (\(N_{RFT}\times N_{S}\)), that is followed by an analog RF precoder, \(F_{RF}\) (\(N_{S}\times N_{RFT}\)), at the transmitting end and an analog RF combiner, \(W_{RF}\) (\(N_{R}\times N_{RFR}\)), followed by a digital baseband combiner, \(W_{BB}\) (\(N_{RFR}\times N_{S}\)). Therefore, the transmitted signal of each user can be expressed as  
\[x(N_{T}\times N\text{s})=(F_{RF}*F_{BB})*S,\]  
where \(\text{S}(N_{S}\times 1)\) is the symbol vector of each user. For the given system, the normalized transmit power constraint is imposed and is given by \(\left|\left|\left(F_{RF}*F_{BB}\right)\right|\right|_{F}^{2}=N_{S}\).  
More significantly, the analog precoder consists of several phase shifters, so there is a need to adjust the phase of the transmitted signals. Hence, \(F_{RF}\) should gratify the element-wise unit modulus constraints given by  
\[\left|\left(F_{RF}\right)_{ij}\right|=1\]  
Channel matrix \(H\) of size \(N_{R}\times N_{T}\) having an L propagation path using the clustered channel model is modeled as  
\[H=\sqrt{\frac{N_{T}N_{R}}{L}}\ \sum\
olimits_{l=1}^{L}\alpha_{l}\mathbf{a}_{R} \left(\theta_{l}^{R}\mathcal{O}_{l}^{R}\right)\mathbf{a}_{T}^{H}\left(\theta_{l}^ {T},\mathcal{O}_{l}^{T}\right) \tag{1}\]  
where \(\alpha_{l}\) is the complex gain of the lth propagation path and vectors \(\mathbf{a}_{R}\left(\theta_{l}^{R}\mathcal{O}_{l}^{R}\right)\mathbf{a}_{T}^{H}\left( \theta_{l}^{T},\mathcal{O}_{l}^{T}\right)\) are the normalized array response vectors, with \(\left(\theta_{l}^{R}\mathcal{O}_{l}^{R}\right)\) and \(\left(\theta_{l}^{T},\mathcal{O}_{l}^{T}\right)\) being the azimuth (elevation) angles of the receiver and of the transmitter, respectively. These parameters depend on the structure of the antenna array.  
Figure 2: Hybrid precoding in a millimeter system.  
In matrix form, \(\mathbf{H}\) is represented as  
\[\mathbf{H}=\sqrt{\frac{N_{T}N_{R}}{L}}\left[a_{R}\left(\theta_{1}^{R}\right)\ a_{R} \left(\theta_{2}^{R}\right)\dots\dots\dots a_{R}\left(\theta_{l}^{R}\right)* \begin{bmatrix}a_{1}&\dots&0\\ \vdots&\ddots&\vdots\\ 0&\dots&a_{l}\end{bmatrix}*\begin{bmatrix}a_{T}\left(\theta_{1}^{T}\right)\\ \vdots\\ a_{T}\left(\theta_{1}^{T}\right)\end{bmatrix}\right. \tag{2}\]
## 4 Evaluation of Experimental Results
### Investigation of Surface Roughness by Experimental Design for Different Process Parameters
Within the current parameter ranges, the following combination is considered optimal for minimizing surface roughness:  
\[f=0.05\frac{mm}{rev}\quad v_{v}=0.9817\frac{m}{s}\qquad F=80\ N\]  
Figure 12 presents the experimental results for improvement ratios of two- and three-dimensional surface roughness indicators, similar in structure to the previous figure. Equations (17)-(20) show the corresponding empirical formulas. Again, all four graphs display nearly identical characteristics. \[ \begin{split} IRa=-617.3081+7828.9877\cdot f+844.3904\cdot v_{v} +7.092\cdot F_{v}-9699.8958\cdot f\cdot v_{v}-78.2321\cdot f\cdot F_{v}\\ -8.8816\cdot v_{v}\cdot F_{v}+99.34\cdot f\cdot v_{v}\cdot F_{v} \end{split}  \tag{17}\]  
\[ \begin{split} IRq=-731.7867+9167.8887\cdot f+997.4297\cdot v_{v} +8.3297\cdot F_{v}-11622.7857\cdot f\cdot v_{v}-92.6757\cdot f\\ \cdot F_{v}-10.5401\cdot v_{v}\cdot F_{v}+119.6249\cdot f\cdot v _{v}\cdot F_{v}\end{split}  \tag{18}\]  
Figure 11: Variation of surface roughness indicators as a function of technological parameters: (a) \(\text{R}_{a}\); (b) \(\text{R}_{\text{q}}\); (c) \(\text{S}_{\text{a}}\); (d) \(\text{S}_{\text{q}}\). \[ISA=-470.0568+5651.476\cdot f+650.9899\cdot v_{v}+5.3679\cdot F_{v}-687 1.8305\cdot f\cdot v_{v}-51.8225\cdot f\cdot F_{v} \tag{19}\] \[-6.5457\cdot v_{v}\cdot F_{v}+64.2584\cdot f\cdot v_{v}\cdot F_{v}\] \[ISq=-638.927+7604.1146\cdot f+872.1778\cdot v_{v}+7.2023\cdot F_{v }-9575.5471\cdot f\cdot v_{v}-72.8039\cdot f\cdot F_{v}\] (20) \[-8.9562\cdot v_{v}\cdot F_{v}+92.9142\cdot f\cdot v_{v}\cdot F_{v}\]  
Based on the improvement ratios, it can also be concluded that in most cases, the higher burnishing force results in better surface roughness improvement when compared to the lower force during diamond burnishing. However, the combination of lower feed rate and higher burnishing speed again proves to be an exception, significantly improving the surface quality. This confirms that within the examined parameter ranges, the best surface roughness is achieved with the following combination:  
\[f=0.05\frac{mm}{rep}\quad v_{v}=0.9817\frac{m}{s}\qquad F=80\ N\]  
Figure 12: Variation of improvement ratios as a function of technological parameters: (**a**) _IRa; (**b**) _IRa; (**c**) _ISa; (**d**) _ISq._  
With the technological parameters \(f=0.05\) mm/rev and \(v_{v}=0.9817\) m/s, the optimal choice was a burnishing force of 83...89 N (optimization of surface roughness metrics) and 86...90 N (optimization of improvement factors), which is close to the 80 N determined using the factorial experimental design. The difference may be due to the different methodology, since during the experimental design we examined all parameter combinations simultaneously, while in the Kraljic matrices we first divided the data into \"burnishing speed + feed\" pairs and selected from them using the trend lines of the matrix.
## 4 Experimental Results
### Comparative Study between the Spectral Clustering Algorithms
Figure 18: Comparing the average runtimes of the Evolutionary Spectral Clustering (ESC) and the Efficient Evolutionary Spectral Clustering (EESC) algorithms for PCQ and PCM frameworks. Figure 19: Clustering results for time periods from \(t=2\) to \(t=4\) for the IND. FCM and PCQ methods in the case of time period = 10 min. Next, we set the time period to 20 min. We consider the neighborhood of a link in both space and time, and we make a duplicate of the links at time period \(t-1\). In fact, we consider the graph with link speeds at time period \(t\) and the graph with link speeds at time period \(t-1\). The length of the snake is set to 30%, and we choose \(\phi=0.7\) in order to assure the maximum mean values of CCD (Figures 20 and 21). Figure 22 shows the number of clusters obtained by applying the density peak method for time periods \(t=1\) to \(t=24\). Figure 21: The mean values of connected clusters’ dissimilarity versus the weight coefficient \(\phi\) in the case of time period = 20 min. Figure 20: The mean values of connected clusters’ dissimilarity versus snake length computed in the percentage of the size of the network of roads in the case of time period = 20 min. Figure 23 shows the partitioning results for time periods \(t=2\) to \(t=4\). It is clear that the PCQ framework gives more stable and consistent cluster results than the PCM and IND methods. Finally, we set the time period to one hour. We consider the neighborhood of a link in both space and time, and we make a duplicate of the links at time period \(t+1\) and \(t-1\). The length of the snake is set to 15% of the network size in order to minimize the computation complexity, and we choose \(\phi=0.7\). Figure 24 shows the partitioning results for time periods \(t=2\) to \(t=4\). It is clear that, for all experiments, the PCQ framework gives more stable and consistent cluster results compared with the PCM and the IND methods. These experiments, based on real data collected from Amsterdam city, demonstrate that, compared to traditional clustering methods, our efficient evolutionary spectral clustering algorithm has the advantage of finding directional congestion within stable and consistent clusters, which allows us to design real-time traffic control schemes--specifically, hierarchical perimeter control approaches to alleviate or postpone congestion. Figure 22: The number of clusters obtained by applying the density peak method for each time period in the case of time period = 20 min. Figure 23: Clustering results for time periods from \(t=2\) to \(t=4\) for the ND, PCM and FCO methods in the case of time period = 20 min. Figure 24: Clustering results for time periods from \(t=2\) to \(t=4\) for the IND, FCM and PCQ methods in the case of a time period equal to one hour.
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 6 Three-Dimensional Spatial Representation and Annotation in Mixed Reality
### Attaching Annotations to Real-World Objects  
Once the 3D position of each vial is determined, the system needs to make sure that the virtual labels and bounding boxes remain correctly aligned with their corresponding physical objects. Unlike traditional AR overlays, which are simply drawn on the screen, our approach anchors each annotation to a fixed position in the real-world coordinate system.  
To achieve this, we employ a transformation matrix that maps the detected 2D position of a vial onto the MR world space:  
\[P_{MR}=T_{camera}^{world}\cdot P_{2D} \tag{11}\]  
In Equation (11), \(P_{2D}\) is the position of the detected object in the camera frame, and \(T_{camera}^{world}\) is the transformation matrix that converts the local camera coordinates into global world coordinates. The matrix \(T_{camera}^{world}\) is derived using the camera calibration process, which combines intrinsic parameters (such as focal length, principal point, etc.) and extrinsic parameters (such as rotation and translation between the camera and the world coordinate system). This calibration is achieved using depth sensor data and the spatial mapping system available in the HoloLens 2. By obtaining a 3D mesh of the environment, the spatial mapping allows for the accurate positioning of objects in the real world, even as the user moves or adjusts their viewpoint. Once a vial is detected and labeled, its annotation remains fixed in space, even as the user moves around. This approach eliminates common issues such as floating or drifting labels, which can make MR applications difficult to use in real-world settings.
## 3 Results and Discussion
### Changes in Taste Compounds in Cooked Marinated Pork Knuckle During Processing  
There are 17 free amino acids in Table 2, including 7 essential amino acids. The major free amino acids are glutamic acid, leucine, valine, proline, alanine and phenylalanine, and the T4 stage contained approximately 61% of the total free amino acids in its composition, which involved three essential amino acids. It has been reported that the content of Lys,  
Figure 3: Variable importance projection (VIP) scores (**A**) and the heat map of volatile compounds (**B**) in cooked marinated pork knuckle during processing. Glu and Thr in black pork is higher than that of ordinary pork, and these three amino acids play an important role in the formation of flavour [3]. The content of amino acids rises with the progress of protein hydrolysis. Among them, the concentration of Glu, which is a typical amino acid that contributes to umami taste, was relatively high. Furthermore, the amount of Glu consistently increased as the heating process continued. Glu and Asp have an acidic taste; when sodium salts are present, they provide a strong umami taste [6]. Moreover, the contents of sweet amino acids Ala and Thr were relatively high in the T1 stage. From Figure 4A, all free amino acid contents were high in the T3 stage, and the rich taste of the sample was provided by brine soup. Bitter and sweet amino acids are the most abundant in the brine soup, followed by umami amino acids, and astringent amino acids are the least abundant due to their unfavourable contribution to the taste. The desirable taste of cooked marinated pork knuckle could be attributed to these factors. During the processing, free amino acids participated in the Maillard reaction and form different flavour precursors, which are added to the flavours of meat products. Similar findings reported by [PERSON] et al. [25] found that hydrolysis of proteins and peptides into amino acids due to thermal treatment, and the free amino acids would be released into pork cooking juice. For example, Met is a dominant precursor substance for sulphur-containing volatile compounds, which could contribute to the development of meaty and soy-like flavours. In addition to their sweet characteristics, Ala, Gly, Ser, and Thr can undergo Streker degradation to become aldehydes, thereby stimulating the production of highly aromatic pyrazines. Val, Leu, Ile, and Phe are degraded by Streker to form branched chain aldehydes and aromatic aldehydes.
## 3 Results
### Enzyme Activity Assays of MlongTPS29  
Limonene is an important precursor of the essential oil components of the genus _Mentha_, whose synthesis is catalyzed by limonene synthase (LS). In order to identify the candidate LS in _M. longifolia_ genome sequence, LSs of _M. spicata_ and _M. piperita_ were used as queries to BLAST in _M. longifolia_ TPSs. As a result, a candidate LS-coding gene, _MlongTPS29_, was identified in _M. longifolia_ genome sequence. The coding sequence of _MlongTPS29_ is 1800 bp, which is the same as that for the _LS_ homologs in _M. spicata_ and _M. piperita_. Multiple sequence alignment also showed that MlongTPS29 was considerably similar to the LS of _M. spicata_ and _M. piperita_ (Figure S1). Both the sequence length and sequence similarity indicate that _MlongTPS29_ is complete. This gene was cloned and then subjected to assay its catalytic activity. The recombinant MlongTPS29 was heterologous expressed in _E. coli_ and used to construct the reaction in vitro. After adding GPP as a substrate, GC-MS analysis showed that the limonene could be detected in the MlongTPS29 group, while no limonene was detected in the empty pET28a group (Figure 6). This result indicates that MlongTPS29 could catalyze the production of limonene from GPP.
#### 2.1.3 Data Collection with the h-LIBS Device  
All standard samples, except NIST612 which is a glass and can be analyzed directly, were in powder form and thus required pressing, using a RETSCH PP 25 press (Dusseldorf, Germany), into pellets prior to being analyzed by h-LIBS (see Supporting Information for further experimental details). Archaeological samples were analyzed in selected areas of the artifacts to minimize problems related to surface roughness or microstructural variations.  
Spectral data were collected from both standard and archaeological samples using a Z-903 h-LIBS analyzer (SciAps Inc., Andover, MA, USA) equipped with a 1064 nm Nd:YAG laser covering the 180-950 nm spectral range. In order to enhance the signal-to-noise ratio, spectra were acquired under argon gas (\(\sim\)12 atm) using an argon purge included in the system. For each sample, the LIBS spectrum was obtained, averaging 96 spectral profiles recorded on 6 different locations (16 profiles per location) with a spot size of about 100 \(\upmu\)m. The penetration depth was not measured, but the literature reports that it is material-dependent, being around 10 \(\upmu\)m for a laser shot [31; 38]. If required by the operator, averaging of the spectral profiles was automatically performed by the acquisition software of the equipment. Some random checks were performed to evaluate the repeatability of the spectral profiles. The laser pulsed at a frequency of 1 Hz, and each pulse lasted for 1 ns, delivering 5-6 mJ to the sample. The delay time was 650 ns over a 3 ms integration time. The collected spectra were arranged in a matrix (S\({}_{40,23401}\)) with 23,401 variable columns  
Figure 3: Archaeological samples collected from the Archaeological Park of Tindari (Italy) along with photographic scale. Each colored segment represents 1 cm length \(\times\) 0.5 cm height, providing a reference for size. (F): front side, (B): back side.  
(emissions in arbitrary units as a function of wavelength) and 40 rows. This matrix is composed of two sub-matrices:  
* X17,23401, containing 17 spectra collected from standard samples (i.e., one for each standard sample) for model calibration and validation;
* A23,23401, consisting of the 23 spectra collected from 9 archaeological samples (being some spectra collected in replicates, see Table 1) with unknown elemental concentration.
## 3 Materials and Methods
### Low-Cost Nodes for Precision Agriculture Deployments  
Nowadays, there is a wide variety of low-cost nodes that can be employed for PA purposes using WiFi communication. Some of the most utilized ones are presented in Table 2. In the scenario contemplated in this paper, low-cost nodes are utilized both for the field monitoring and for the gateway mounted on the drone. As can be seen, the characteristics of the nodes can vary greatly. The weight and size of the nodes should be considered to determine if the chosen drone would be affected by it or if it is able to carry the node without interfering with its flight. If no sensors are connected to the node carried by the drone and big processing capacity is not necessary, nodes such as the WEMOS MINI D1 would be best to reduce the energy consumption due to its low weight and size.  
Figure 1: (a) Flight plan with straight lines, (b) Flight plan circling the nodes.  
Other wireless technologies such as LoRa and 5G would reduce the need for using drones for data collection or completely eliminate it in small fields due to their wider range. However, these technologies are more costly, and therefore, they are less accessible for farmers with lower budgets.  
Furthermore, multiple parameters can affect the communication system, such as the transmitted power, the protocol, or the link budget. Regarding the transmitted power parameter, it would fundamentally affect the drone coverage, as it would consume a greater amount of energy, and its flight capacity would be reduced. Using different protocols would affect the total connection time since, depending on the transitions between different states, which are associated with the actions implemented by the nodes, the connection time necessary between the nodes and the drone can be altered. On the other hand, the link budget would be defined as an addition of transmitted power and gains minus the losses. The received power is based on the losses, which occur when the signal is transmitted through the medium and can be affected by aspects such as the weather or the objects that interrupt the line of sight between the emitter and the receiver. However, in the studied scenario, there are no objects between the emitter and the receiver, and the drone would not fly with adverse weather conditions. Therefore, in this scenario, the losses would be constant. Lastly, the transmitted power and the gains will be based on the antennas and their configuration and, therefore, they would not vary unless different types of antennas or nodes with their configuration were used in order to increase the transmitted signal.