esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment
### Datasets  
The dataset plays an essential role in evaluating the performance of the proposed framework. We test the proposed method on the two datasets: HAM10000 and ISIC 2019.  
HAM10000 is the dermatoscopic database, which uses a benchmark dataset [75]. This database comprises more than 10,000 dermatoscopic pictures obtained from many people worldwide. The HAM10000 database also holds metadata formats like comma-separated-values data (.CSV), containing gender, age, and cell class. This dataset consists of seven different types of skin diseases: actinic keratoses and intraepithelial carcinoma (AKIEC), basal cell carcinoma (BCC), benign keratosis-like lesions (BKL), dermatofibroma (DF), melanoma (MEL), melanocytic Nevi (NV), and vascular lesions (VASC). The principal problem of the HAM10000 database is classes imbalance and the irregular distribution of skin disease numbers. NV class exceeds 70% of the total image numbers. This factor influences the training and creates an extreme imbalance database. The second large class is BKL, with approximately 13% of the pictures. The other classes contribute a minority number of the images. Especially, less than 2% of the total images belong to the DF class, which is the most difficult class for prediction. Figure 4 shows some sample images from the HAM10000 dataset. HAM10000 dataset is a part of the ISIC 2018 challenge with three tasks: lesion boundary segmentation (task 1), lesion attribute detection (task 2), disease classification (task 3).  
Figure 4: Sample skin lesions in the HAM10000 dataset. Type 1: MEL; Type 2: NV; Type 3: BCC; Type 4: AKIEC; Type 5: BKL; Type 6: DF; Type 7: VASC.  
The second dataset is the ISIC-2019 [76], consisting of 25,331 dermoscopic pictures belonging to eight categories: AKIEC, BCC, BKL, DF, MEL, NV, VASC, and squamous cell carcinoma (SCC). ISIC-2019 data are obtained from the following sources: BCN_20000 Dataset, HAM10000 Dataset, and MSK Dataset. The ISIC-2019 challenge has only one task: disease classification. Table 2 gives the class distribution of the two datasets, HAM10000 and ISIC-2019.
## 4 Discussion
### Limitations and Future Directions  
One key limitation of this study is the material assumptions used in the finite element model. All structures--excluding the PDL--were modeled as homogeneous, isotropic, and linearly elastic materials for computational simplicity. While this approach is commonly applied in FEA studies, it does not fully capture the nonlinear and anisotropic behavior of biological tissues. In addition, the PDL was modeled as a nonlinear hyperelastic material to simulate its immediate mechanical response. However, its viscoelastic behavior--characterized by stress relaxation and creep under sustained loading--was not included. This may limit the accuracy of predictions regarding long-term tooth movement [23]. Future studies should incorporate viscoelastic PDL modeling to better approximate biological conditions [42].  
Moreover, a uniform aligner thickness of 0.5 mm was assumed in the model. However, this does not reflect the variable thickness introduced by thermoforming in clinical settings, where commercial aligners typically range between 0.6 and 0.75 mm. In addition, the time-dependent mechanical behavior of thermoplastic aligners--such as stress relaxation and material creep--was not considered. These dynamic properties may lead to force decay over time and should be incorporated into future models to improve the clinical accuracy of aligner simulations [20; 43].  
This study is also limited by its simulation-based design. Patient-specific anatomy, soft tissues, and occlusal loads were not included. These may involve clinically relevant variables such as individual differences in bone density, crown height, saliva lubrication, and temperature, which can affect aligner biomechanics in vivo.  
Only upper molars were assessed, without evaluating adjacent teeth or anchorage effects. However, clinical studies, such as that of [PERSON] et al. [44], have demonstrated that maxillary molar distalization with clear aligners can lead to measurable anchorage loss at the premolar and canine levels, particularly in the absence of auxiliary anchorage. The exclusion of anterior segments in our model may thus underestimate the biomechanical impact on the full arch.  
Additionally, the influence of aligner wear time, patient compliance, and attachment wear should be examined to improve clinical translation. This study also did not include a staging protocol or simulate sequential tooth movements. Future research should consider time-based staging sequences to reflect the progressive biomechanics of clinical aligner treatment. Moreover, while this study focused on isolated parameters in a controlled simulation, future models may benefit from incorporating clinically oriented guidelines--such as attachment design indications or trimline selection criteria--to enhance practical applicability.
## 4 Experimental Verification
### Accuracy Evaluation of Clock Synchronization  
Figure 2 shows the evaluation results of the clock synchronization and depicts an error of clock synchronization between the master and the slaves. In this experiment, the system consists of four PCs, and three errors of clock synchronization between the master and the slaves \(1/2/3\) were examined. Figure 2a,b illustrates the error from the starting point of the clock synchronization and the error at the steady state, respectively.  
From the result shown in Figure 2, because the error of the clock synchronization was approximately 0.05 ms, we confirmed that the clock synchronization with sufor high-speed sampling rate was achieved. It can be considered that the high-speed sensing with the sampling rates of 0.2, 0.5, and 1 ms can be achieved. The feasibility of the high-speed sampling rates of 0.2, 0.5, and 1 ms is verified in Section 4.3. The accuracy of the clock synchronization is considered to be improved by tuning the parameters inside PTP.
# Improvement of Power Converters Performance by an Efficient Use of Dead Time Compensation Technique
## 3 Proposed Dead Time Compensation Technique
### Dead-Time Compensation for PWM Inverter
Additionally, there is a dependence on the current direction; when altered from load to positive terminal, the output is \(\frac{V_{dc}}{2}+u_{d}\), while changes to \(-\frac{V_{dc}}{2}+u_{ce}\) for the opposite current direction. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Time (T)**} & **Ideal O/P** & **Ideal O/P Voltage** & **Actual O/P** & **Dead-Time** \\  & **Voltage/Actual Voltage** & **Voltage** & **Error** \\ \hline \(t_{0}-t_{1}\) & \(D_{2}/D_{2}\) & \(-V_{dc}/2\) & \(-V_{dc}/2-u_{d}\) & \(u_{d}\) \\ \(t_{1}-t_{2}\) & \(S_{1}/D_{2}\) & \(V_{dc}/2\) & \(-V_{dc}/2-u_{d}\) & \(V_{dc}+u_{d}\) \\ \(t_{2}-t_{3}\) & \(S_{1}/S_{1}\) & \(V_{dc}/2\) & \(V_{dc}/2-u_{ce}\) & \(u_{ce}\) \\ \(t_{3}-4\) & \(D_{2}/D_{2}\) & \(-V_{dc}/2\) & \(-V_{dc}/2-u_{d}\) & \(u_{d}\) \\ \(t_{4}-t_{5}\) & \(D_{2}/D_{2}\) & \(-V_{dc}/2\) & \(-V_{dc}/2-u_{d}\) & \(u_{d}\) \\ \(t_{5}-t_{6}\) & \(S_{2}/S_{2}\) & \(-V_{dc}/2\) & \(-V_{dc}/2+u_{ce}\) & \(-u_{ce}\) \\ \(t_{6}-t_{7}\) & \(D_{1}/D_{1}\) & \(V_{dc}/2\) & \(V_{dc}/2+u_{d}\) & \(-u_{d}\) \\ \(t_{7}-t_{8}\) & \(D_{1}/D_{1}\) & \(V_{dc}/2\) & \(V_{dc}/2+u_{d}\) & \(-u_{d}\) \\ \(t_{8}-t_{9}\) & \(S_{2}/D_{1}\) & \(-V_{dc}/2\) & \(V_{dc}/2+u_{d}\) & \(-V_{dc}-u_{d}\) \\ \(t_{9}-t_{10}\) & \(S_{2}/S_{2}\) & \(-V_{dc}/2\) & \(-V_{dc}/2+u_{ce}\) & \(-u_{ce}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The error between the ideal voltage and the actual voltage.
# Cloning and Characterization of Two Iridoid Synthase Homologs from _Swertia Mussotii_
## 2 Results
### Cloning, Sequencing and Phylogenetic Analysis of SmIS1 and SmIS2 cDNAs
However, SmIS1 belonged to another subgroup that clustered with _C. acuminata_ CaIS (AON76722.1), _L. japonica_ LiJS (AMB61018.1), _C. roseus_ CrIS (AFW98981.1), _O. europaea_ Oels (ALV83438.1), _D. purpurea_ P5 bR2 (ACZ66261.1), and _D. lanta_ P5 bR2 (ADL28122.1). This result is similar to that reported by [PERSON] et al. [17], who suggested that iridoid synthases potentially originated from an ancestor exclusively common to Asterids.
# Additively Manufactured Polymers for Electronic Components
## 1 Introduction
The transfer of pixels in a solid phase involves irradiating a thin layer of an absorbing material (the donor) with a pulsed laser through a transparent substrate. This interaction generates high local pressure, ejecting small pixels from the donor onto a nearby target substrate (the receiver). Transparent layers can also be transferred using a dynamic release layer (DRL) between the substrate and the transferable film [42]. The principle behind LIFT is based on using a laser pulse to generate a rapid pressure or thermal effect in a thin donor layer. This causes a small amount of material to be ejected and deposited onto a nearby surface, without any contact between nozzle and substrate. This mechanism allows for high-resolution patterning and is particularly useful for complex or viscous materials that are otherwise hard to print. The shape and size of the ejected material are affected by the dimensions and the features of the incident laser spot [43]. Considering all of these AM processes, this review provides a comprehensive overview of polymers in AM for electronics, assessing the advantages, limitations, and applications of various polymer classes, including ICPs, ECPs, and insulators. It also highlights the potential of 3D-printed polymers in electronics and suggests future research directions in the field. Figure 1: Schematic representing the AM technologies mentioned in this review: (**a**) vat polymerisation [23], (**b**) powder bed fusion [23], (**c**) inkjet printing [27], (**d**) direct ink writing [23], (**e**) aerosol jet printing [32], and (**f**) laser-induced forward transfer technology [41].
## 5 Conclusions  
A novel downlink PD SISO-NOMA communication scenario for TI employing multiple sensors and actuators, collectively treated as users, and a base station is proposed in this paper. We have developed an analytical system model comprising SINR, achievable sum rate, fair power allocation (PA) coefficients and latency for SISO-NOMA users to study the system performance. The system model is validated by simulation scenarios with varying path loss exponents and fixed PA coefficients. We have evaluated and validated the analytical model by simulation. A higher BER is achieved for SISO-NOMA users than for the OMA scheme due to additional signal processing to decode and perform Successive Interference Cancellations (SICs) for received signals. In contrast, the achievable sum rate and latency trends for the proposed PD SISO-NOMA have outperformed the OMA scheme for higher SNR. The outage probability of SISO-NOMA is also analysed with varying fixed PA coefficients. Finally, to promote dynamic PA and user fairness of the proposed PD SISO-NOMA scheme, we have compared the outage probability, achievable sum rate and latency for fixed and derived fair PA coefficients. Thus, it maximised the spectral efficiency while minimising the power consumption and latency by efficiently utilising the available spectrum/resources. Incorporating zero forcing-based beamforming and a round-robin scheduling process in 4 \(\times\) 4 MIMO-NOMA has outperformed SISO-NOMA in terms of achievable sum rate and latency. Proposing a joint dynamic user clustering, beamforming and scheduling process for MIMO-NOMA and developing a deep-learning-based NOMA algorithm are suggested as future research work.  
Conceptualisation, V.F.; methodology, V.F. and N.I.S.; discussion, V.F., N.I.S. and A.A.-A.; implementation, V.F.; validation, V.F. and N.I.S.; empirical evaluation, V.F. and N.I.S.; writing--original draft preparation, V.F.; writing--review and editing, V.F., N.I.S. and A.A.-A.; project supervision, N.I.S. and A.A.-A.; correspondence author, N.I.S.; responded to reviews, V.F. and N.I.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.
## 2 Structural Features and Methodology
### Computational Details
#### 2.3.2 Basis Sets  
For a simple all-silica structure, we utilized the Gaussian basis sets optimized for molecular calculations, developed by [PERSON] and [PERSON] [41], with the double zeta valence polarized (DZVP) for the Si and the O atoms.  
The molecularly optimized (MOLOPT) basis sets that come with the distribution of CP2K were originally spherical Gaussian primitives with diffuse functions (this is crucial for weak interactions) and small exponents; however, they are contracted into tighter functions, are highly transferable, and lead to overlap matrices that are well-conditioned, although they are incomplete. In addition, we have different types for these basis sets, namely, m-SZV(1_s1p/1s_), m-DZVP (_2s2p1d/2s1p_), m-TZVP (_3s3p1d/3s1p_), m-TZVP(_3s3p2d/3s2p_), and  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Cell** & \(\mathbf{a}\) **(Å)** & \(\mathbf{b}\) **(Å)** & \(\mathbf{c}\) **(Å)** & \(\mathbf{\beta}\) (\({}^{\circ}\))** \\ \hline Experimental Agoura & 17.646 & 17.898 & 7.397 & 116.22 \\ \hline Calculated Agoura with PBE & 17.696 & 17.746 & 7.458 & 116.67 \\ \hline Calculated Agoura with PBE-D3 & 17.711 & 17.781 & 7.436 & 116.59 \\ \hline Experimental Siusi & 17.637 or 17.641 & 18.024 or 18.031 & 7.399 or 7.402 & 116.23 or 116.26 \\ \hline Calculated Siusi with PBE & 17.688 & 17.877 & 7.416 & 116.09 \\ \hline Calculated Siusi with PBE-D3 & 17.652 & 17.861 & 7.401 & 116.11 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison between lattice cell parameters for [PERSON]’s experimental clinoptiolites [21] and our calculated all-silica model: Agoura clinoptiolite experimental sample, PBE- and PBE-D3-calculated Agoura model, Alpe Di Siusi experimental clinoptiolite sample for which [PERSON] noted two measurements, and, finally, the PBE- and PBE-D3 calculated Siusi model.  
m-TZV2 PX (\(3s3p2d1f3s2p1d\)). These basis sets were optimized in molecular calculations, hence the m- prefix, employing the PBE density functional. For our system, we performed extensive convergence testing on those available basis sets and included not only the valence electrons but also the semi-core electrons in the calculations. The following basis sets performed the best regarding computational efficiency: 1. Si atoms (\(3s^{2}\),\(3p^{2}\)): TZVP; 2. O atoms (\(2s^{2}\),\(2p^{4}\)): DZVP; 3. Al atoms (\(3s^{2}\),\(3p^{4}\)): TZVP; 4. Na atoms (\(2s^{2}\),\(2p^{6}\),\(3s^{1}\)): SZV; 5. K atoms (\(3s^{2}3p^{6}\),\(4s^{1}\)): TZVP; and 6. Ca atoms (\(3s^{2}\),\(3p^{6}\),\(4s^{2}\)): DZVP with an extra d-orbital. As Ca\({}^{2+}\) cations were added to the system, a noticeably poor convergence behavior in the SCF loop was observed. Hence, the basis sets of the double zeta type were optimized. This is discussed thoroughly in the Supplementary Material. In addition, the reader can find sample input parameters for CP2K in the Supplementary Material.
# Trends and Technological Challenges of 3D Bioprinting in Cultured Meat: Technological Prospection
###### Abstract
According to the standard of the American Society for Testing and Materials (ASTM), 3D printing is mainly based on the following processes. In 3D bioprinting, the primary methods are extrusion-based, jetting-based, and vat photopolymerization-based bioprinting [13]. In extrusion-based bioprinting, the biohik is deposited with high precision, obtaining customized 3D structures with good structural integrity due to the continuous deposition of filaments. The entire process of bioprinting is carried out under the control of a computer [14]. Jetting-based bioprinting can produce ink droplets with controllable size and low volume, depositing the ink in specific locations with high precision and without contact. Employing this technique, it is possible to use a variety of biomaterials as well as the incorporation of living cells [15]. Finally, Vat polymerization-based bioprinting is an emerging technology in the biofabrication of scaffolds applied in tissue engineering, used for its high resolution compared to other bioprinting technologies [16]. Nowadays, companies have been contributing to advancing studies related to the biofabrication of cultured meat using the 3D bioprinting technique. For example, 3D bioprinting Solutions, in partnership with KFC (Kentucky Fried Chicken), plans to produce and market cultured nuggets. 3D Bio-Tissues Ltd (3 DBT) has also been partnered with CPI (Independent Center for Technological Innovation) and the United Kingdom government's High-Value Manufacturing Catapult to improve cell culture media for the cultured meat industry. Japan's Nissin Food Holdings is partnering with the University of Tokyo to develop printed meat cubes [17]. In addition to cultured meat, a wide variety of food types have been successfully manufactured using 3D printing technology, including chocolate, cakes, and breads [18]. As cultured meat production via Bio 3D becomes more commonplace, the research investment in this area also increases. For this reason, a comprehensive technological prospection of the scientific literature related to cultured meat produced via 3D bioprinting is of great interest to the scientific community. Technological prospecting can use bibliometric analysis as support for exploring and analyzing large volumes of data that can be used to identify emerging trends, research components, and patterns of collaboration on a specific research topic. Thus, it is possible to obtain a comprehensive view of a field of knowledge and to identify and characterize knowledge gaps, how they can be filled, and what kind of information may be missing. The bibliometric methodology applies quantitative techniques focused on analyzing variables related to publication and citation metrics [19,20]. Therefore, this study aimed to carry out a technological prospection of recent trends on cultured meat produced by 3D bioprinting, to investigate related research topics, and to illustrate the advances and challenges which have emerged in this area of research in recent years.
# A Novel Spaceborne Sliding Spotlight Range Sweep Synthetic Aperture Radar: System and Imaging
## 2 Continuous Varying Pulse Interval Technique
Notice that \(PI_{0}\) should be both large enough to enclose all the echoes from the ROI strip and small enough to avoid the Doppler aliasing. The detailed method of calculating \(PI_{0}\) will be discussed in Section 4. Based on a given \(PI_{0}\), the PI number \(M\) between the transmitting and receiving should then be calculated as  
\[M=\left\lfloor\frac{2R_{\text{c0}}}{cPI_{0}}\right\rfloor \tag{3}\]  
where \(c\) denotes the speed of light and the operator (\(\cdot\)) computes the maximum integer not larger than the input number. Then by using the stop-go model, the CBSR at the \(m\)th sample yields  
\[R_{\text{c}}(m)=\frac{c}{2}\Bigg{(}\sum_{p=0}^{M-1}PI(m+p)+\frac{PI(m+M)}{2} \Bigg{)} \tag{4}\]  
By substituting Equation (2) into Equation (4), the latter can be rewritten in the matrix formation as  
\[A\mu=B \tag{5}\]  
where  
\[\left\{ \begin{array}{l}A=\left(a_{\text{mi}}\right)_{\text{N}_{\text{a}}\times 6 }\\ B=\left(b_{\text{m}}\right)_{\text{N}_{\text{a}}}\\ \mu=\ \ \left[\mu_{5}\ \ \ \mu_{4}\ \ \ \mu_{3}\ \ \ \mu_{2}\ \ \ \mu_{1}\ \ \ \ \mu_{0}\right]^{\text{T}}\end{array} \right. \tag{6}\]  
where \(N_{\text{a}}\) denotes the azimuth sample number. The elements of Matrix \(A\) and \(B\), denoted by \(a_{\text{mj}}\) and \(b_{\text{m}}\), yield  
Figure 3: Expected Continuous Varying Pulse Interval (CVPI) based echo distribution. \[\left\{ \begin{array}{l}a_{\text{mj}}=\sum\limits_{p=0}^{M-1}t^{6-j}(m+p)+\frac{t ^{6-j}(m+M)}{2}\\ b_{\text{m}}=\frac{2R_{\text{c}}(m)}{c}\end{array} \right. \tag{7}\]  
The vector \(\mu\) can be solved out based on the least-square method. Note that the pulse intervals are no longer uniform, the azimuth sampling time \(t\) should be calculated iteratively as  
\[t(m)=\left\{ \begin{array}{l}t_{0}+\sum\limits_{k=1}^{m-1}PI(k),\quad m>1\\ t_{0},\quad m=1\end{array} \right. \tag{8}\]  
where \(t_{0}\) denotes the start time of the data acquisition, followed by a PI equaling to \(PI_{0}\).
# A Survey of Secure Time Synchronization
## 3 Current Time Synchronization Methods
### Network Time Protocol (NTP)  
The NTP uses several algorithms to reduce jitter, increase the robustness, and avoid improperly operating servers which allows it to provide accuracies of low tens of milliseconds on WANs and sub-milliseconds on LANs [36].  
Synchronizing Node A and node B involves an exchange of packets, Node A stores its local time in the request packet \(T1\)[41]. Then, Node B time stamps it as \(T2\) according to the current local time on arrival of the request and sends a reply, \(T3\), which includes the current local time of departure of the packet. When Node A receives the reply, it is stamped as \(T4\), as shown in Figure 3[42]. Then, the NTP calculates the clock offset and roundtrip delay as below, respectively [41; 42; 43]:  
\[\text{offset} = \frac{(T_{2}-T_{1})+(T_{3}-T_{4})}{2} \tag{4}\] \[\text{delay} = (T_{4}-T_{1})-(T_{3}-T_{2}) \tag{5}\]  
The NTP does not consider energy consumption and keeps the processor busy to frequently discipline the oscillator, which is not possible in sensor nodes because sensor nodes have limited resources and cannot spend all the CPU cycle on time synchronization [43].  
Figure 2: GPS time synchronization.
## 3 Results and Discussion
### TPCB's Effect on the Pavement Performance of an Asphalt Mixture
#### 3.4.3 Low-Temperature Performance of Mixture  
In order to investigate the TPCB and AHO effect on the low-temperature anti-cracking performance of asphalt mixtures, the flexural strength, flexural strain, and flexural modulus were tested with a low-temperature bending test. Figure 9 gives the results of a low-temperature bending test of GF70 and TPCB/GF70 asphalt mixtures.  
From Figure 9, we see that the AC-13 mixture has a larger flexural strain than the AC-20 mixture. Because of the softening effect of the 1.2% dosage of AHO and 0.1% higher oil aggregate ratio, the flexural strain experienced by the TPCB/GF70 asphalt mixtures is much greater than that of the GF70 asphalt mixtures, and the flexural moduli of TPCB/GF70 asphalt mixtures are lower than those of the GF70 asphalt mixtures. The flexural strain of the AC-13 mixture with TPCB is 7.9% larger than that of the AC-13 mixture without TPCB, and the flexural strain of the AC-20 mixture with TPCB is 18.3% bigger than that of the AC-13 mixture without TPCB. So it is feasible to improve the low-temperature performance of TPCB-modified mixtures by adding AHO and only a 0.1% higher oil aggregate ratio.