esa-sceva/satcom-chunk-collection huggingface dataset:
# An Accurate Jacobian Matrix with Exact Zoeppritz for Elastic Moduli of Dry Rock
## 3 The Partial Derivatives of P- and S-Wave Velocities with Respect to Dry Rock Moduli  
To accurately invert the dry rock parameters, the most important step is to derive the Jacobian matrix (partial derivatives of PP and PS reflection coefficients with respect to dry rock moduli). Letting \(m_{j}\) represent the unknown parameters \(K_{d1}\), \(K_{d2}\), \(\mu_{d1}\) and \(\mu_{d2}\), the partial derivatives of the Zoeppritz equations with respect to \(m_{j}\) can be expressed as [PERSON] et al., [11]  
\[A\frac{\partial R}{\partial m_{j}}=-\frac{\partial A}{\partial m_{j}}R+\frac{ \partial B}{\partial m_{j}} \tag{8}\]  
Equation (8) shows matrix A depends on the incidence angle. The reflectivity \(R\) can be calculated using Zoeppritz Equations (1) and (2). The two remaining parameters needed to be calculated are \(\frac{\partial A}{\partial m_{j}}\) and \(\frac{\partial B}{\partial m_{j}}\). The following sections will derive these two partial derivatives.  
To derive \(\frac{\partial A}{\partial m_{j}}\) and \(\frac{\partial B}{\partial m_{j}}\), intermediate partial derivatives of P- and S-wave velocities need to be derived first. Using chain rules and Equation (3), the partial derivatives of P-wave velocity with respect to elastic moduli \(K_{d}\) and \(\mu_{d}\) of dry rock are:  
\[\frac{\partial\,v_{p1}}{\partial\,K_{d1}}=\frac{1}{2v_{p1}\rho_{e1}}\Bigg{[}1 -\frac{1}{b_{k1}^{2}}\Bigg{(}\frac{2}{K_{s1}}\Bigg{(}1-\frac{K_{d1}}{K_{s1}} \Bigg{)}b_{k1}-\frac{1}{
## 3 Rapid Spin Initialization with Optical Fields
### Time-Dependent Control
The common final value \(\rho_{22}(T)=0.9963\) exceeded the final value \(0.9945\) found using constant Rabi frequency \(\Omega=5.17\gamma\) in the second column of Figure 3. In the last column of Figure 6, we plot contours of \(\rho_{22}(T)\) for a positioning error \(|\delta R|\leq 1.5\) nm around the nominal value \(R=3.5,6,7.5\) nm of each case and an additive constant error in the control \(0\leq\delta\Omega/\gamma\leq 1\). The final value \(\rho_{22}(T)\) was quite robust with respect to the amplitude error, while values \(\rho_{22}(T)\geq 0.994\) were found for a positioning error between \(0.5-1\) nm, depending on the case. Note that, with respect to positioning error, the case with a bad quality semiconductor layer at \(R=7.5\) nm (Figure 6i) was more robust than that of a medium quality layer at \(R=6\) nm (Figure 6f), which in turn was more robust than the case with a good quality layer at \(R=3.5\) nm (Figure 6). Such a behavior can be explained by inspecting the diagrams of Figure 2, which show the dependence of Purcell factors versus the distance \(R\). The slope in these plots corresponds to the sensitivity of Purcell factors in changes of \(R\), i.e., to positioning errors. It was obvious that for the cases of interest, highlighted with red stars, a smaller slope corresponded to the more robust situations. Similar results were also obtained for different host dielectric material permittivities (not shown here). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Coefficients** & **Good** & **Medium** & **Bad** \\ \hline \(a_{0}\) & 102.1785 & 101.6637 & 101.5777 \\ \(a_{1}\) & \(-476.4625\) & \(-475.7275\) & \(-475.5947\) \\ \(a_{2}\) & 642.1003 & 644.4623 & 644.8125 \\ \(a_{3}\) & \(-333.5799\) & \(-337.1246\) & \(-337.6715\) \\ \(a_{4}\) & 57.9093 & 59.0719 & 59.2532 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Optimal coefficients \(a_{k},k=0,1,\ldots,4\) for the polynomial control (23) and the three examples that we consider: good quality MoS\({}_{2}\) layer at \(R=3.5\) nm, medium quality MoS\({}_{2}\) layer at \(R=6\) nm, and bad quality MoS\({}_{2}\) layer at \(R=7.5\) nm. In all the cases, the duration is set to \(T=2/\gamma=1.097\) ns.
## 3 Results
### Characteristics of the Included Studies
#### 3.1.3 Outcomes  
In 12 out of 18 studies, the balance of participants in bipedestations was analysed [25; 26; 27; 29; 30; 31; 32; 33; 34; 39; 40; 41; 42], in seven studies, gait was analysed [30; 36; 37; 38; 40; 41; 42] and, in 17 studies, aspects related to balance during functional tasks were analysed [25; 26; 27; 39; 42; 43; 44; 37].  
Data related to maintaining bipedestations were registered using instrumentation systems, such as the Wii Balance Board(r)[26; 33; 39; 42], pressure disc [25; 40] and dynamometric platforms [27; 29; 30; 31; 41]. Only four studies used the Steptest observational tests [26], the Static Balance Index (SBI) [31] and Postural Assessment Scale (PASS) [31; 32].  
In order to assess balance during functional tasks, the following observational scales were used: the Berg Balance Scale (BBS) in 11 studies [27; 28; 30; 31; 32; 34; 35; 36; 37; 39; 42], the Timed Up and Go test (TUG) in 12 studies [25; 26; 27; 39; 30; 31; 35; 37; 39; 40; 41; 42], the Functional Reach Test (FRT) in eight studies [26; 29; 31; 33; 34; 35; 39; 42] and the Falls Efficacy Scale International (FES-I) in two studies [26; 29].  
On the other hand, the variables related to gait were registered in four studies using the 10-metre walk test (10 MWT) [36; 37; 40; 42], the 6-min walk test (6 MWT) [30; 40] and the Dynamic Gait Index (DGI) [38; 42].  
Four studies included possible adverse effects derived from the intervention [26; 29; 34; 37], which only were evidenced in three of them [26; 29; 34]. In one study, four EG and two CG participants experienced a fall during treatment, but they were inconsequential. Those in the EG also manifested an increase in pain, more so than those in the CG; however, this pain did not last more than 24 h. Likewise, an intergroup difference existed as regards the sensation of fatigue experienced by the EG participants in comparison to the CG participants after each session [26]. In other study, two CG participants reported increased knee pain when training, and three EG participants reported increased spasticity when playing with the Wii Balance Board(r), but this symptom subsided when they played other games [29]. In the other one, both groups experienced pain, hypertonia and dizziness and were greater in the EG compared to the CG [34].
## 3 Results and Discussion
### Limitations of this Study  
This study shows PyC, EC concentration, split time and their relationships based on the analysis of samples collected for about 45 days, which is short to address seasonality on the relationships. The samples were collected at a university in the middle of Korea. Thus, the EC fractions obtained in this study reflect only a specific situation in Korea. Samples from various locations need to be analyzed in order to understand the spatial variation of PyC, EC, split time and their relationships, which is beyond the scope of this study.
## 3 Constellation Station Maintenance Method  
After the satellite is inserted into the initial orbit, it runs in the nominal orbit. Due to the influence of atmospheric drag perturbations, the orbital altitude of the satellite keeps decreasing and the operation period keeps becoming shorter within one orbital period. As a result, the actual phase of the satellite will deviate from the nominal value (theoretical value). To maintain the constellation configuration and phase requirements, the semi-major axis of the orbit needs to be corrected regularly. The frequency of correction mainly depends on the atmospheric density (related to the solar activity cycle), the satellite's windward area, and the satellite phase drift range constraints. At present, the common method of constellation station position maintenance is the phase-holding ring method.
## 4 Simulation Results and Analysis  
This section includes the simulation outcomes of both the DMR, CoDMR and some existing routing algorithms, all simulated in MATLAB. The DMR and CoDMR schemes are compared with the CoDBR [13] and DBR [28] protocols. The reason behind this comparison is that,that the CoDBR is a cooperation based-based routing scheme just like like CoDMR except that the former uses two relays while the latter uses one relay node. Moreover, the DBR is a non-cooperative single-path path -routing scheme, and the DMR is also non-cooperative single- path scheme and DBR uses low depth nodes to reduce delay while DMR uses minor sink nodes to avoid propagation delay due to multi-hopping. The network contains of 500 sensor nodes, which are distributed in arbitrarily in the ocean environment. The network is a 3D sphere having with each side 500 m. Initially, all the nodes in the underwater acoustic network possess with 10 J energy. The acoustic link quest UWM 1000 modem is used in the network. The total bandwidth is 30 kHz, while the transmission range is 100 m. The transmission power is 2 W and the receiving power is 0.1 W. The size of data packet is 1600 bitsand transmitting rate is 10 kbps. The simulations results are averaged over 50 runs as the behavior of each scheme ceased to fluctuate after 50 runs. The below Table 3 shows the simulation parameters.
## 5 Case Study: Smart Conservation of Cultural Heritage
### Reasoning in a Smart Cultural Heritage Space  
A hybrid reasoning system was implemented based on the generic reasoning architecture presented in Figure 7. The reasoning system was executed on a machine running macOS Catalina, equipped with a six-core i7 processor and 16 GB RAM. The reasoning scripts were coded in Python 3.7, including libraries, such as _scapy_ for packet manipulation, _numpy_ for computing, _sklearn_ for machine learning, _pandas_ for data manipulation and _pymongo_ for database connectivity. The inference process is composed of three autonomous modules.
# TSNetIQ: High-Resolution DOA Estimation of UAVs Using Microphone Arrays
## 7 Conclusions  
This study proposes a new architecture, termed TSNetIQ, to execute more accurate DOA estimations of UAVs, which directly predicts continuous angle values between \(0^{\circ}\) and \(360^{\circ}\), bypassing the limitations of angle discretization commonly found in classification models. This regression-based method not only avoids boundary errors but also offers a higher angular resolution and a lighter model size. The TSNetIQ model integrates the strengths of Transformer and CNN, along with an SE module, to enhance feature extraction and improve estimation accuracy. Unlike previous methods that rely on traditional covariance matrices or spectral decomposition, our model directly utilizes the \(IQ\) components of signals, making the training process more efficient. A simulation environment resembling an anechoic chamber was used to simulate sound source reception at different random angles by rotating the microphone array. This setup generates diverse datasets, providing a more realistic and interpretable training process. Extensive experiments under different SNRs, array element numbers, and sampling frequencies demonstrate that TSNetIQ outperforms conventional CNN and ResNet architectures, showing superior accuracy and robustness, especially in dynamic environments with varying noise conditions.  
The code repository is openly available in Github at [[https://github.com/saltyfish2003/TSNetIQ.git](https://github.com/saltyfish2003/TSNetIQ.git)]([https://github.com/saltyfish2003/TSNetIQ.git](https://github.com/saltyfish2003/TSNetIQ.git)) (accessed on 17 July 2025).  
Conceptualization, J.S. and T.J.; methodology, K.Z.; software, K.Z.; validation, K.Z., T.J. and S.X.; formal analysis, T.J. and K.Z.; investigation, T.J. and K.Z.; resources, S.X.; data curation, S.X. and Z.L.; writing--original draft preparation, T.J. and K.Z.; writing--review and editing, J.S.; visualization, K.Z., T.J. and Z.L.; supervision, J.S.; project administration, J.S.; funding acquisition, J.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 23 KJA510004, and in part by the Key Research and Development Program of Wuhan under Grant 2023020402010590.  
Not applicable.  
Data Availability Statement: Data is unavailable due to privacy.  
Author Jinlong Sun was employed by the company Tongding Interconnection Information Company, Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Materials and Methods  
### Experimental Materials and Design  
The test site was in Tongyu County, Baicheng City, Jilin Province (122\({}^{\circ}\)02\({}^{\prime}\) E, 44\({}^{\circ}\)13\({}^{\prime}\) N, 160 m above sea level). The amaranth variety was K472, which was provided by Daxin Ecological Technology Co., Ltd. (Suzhou, China). Wheat bran was provided by Tianfeng Chemical Technology Co., Ltd. (Jinan, China). Lactic acid bacteria (Chikusou-1) were provided by SNOW BRAND SEED Co., Ltd., Sapporo, Japan. The strain number was _Lactiplantiobacillus plantarum_ LP1. Cellulase, _Acremonium cellulase_, was provided by Meiji Seika Pharma Co., Ltd., Tokyo, Japan, Lot No.: ACCF-6940.  
A two-factor completely randomized experimental design was used for this trial. Factor one was the moisture content (MC). The theoretical MC of the silage materials after mixing was 60%, 65%, and 70%, and the amaranth to wheat bran mixing ratios were calculated as 70%:30% fresh weight (FW), 76%:24% (FW), and 83%:17% (FW), respectively. Factor two was the additive (A) type, and four additive combinations were designed under each MC condition: (1) no additive (control); (2) lactic acid bacteria (L, with a live count of 5 \(\times\) 10\({}^{7}\) colony forming units (CFUs) g\({}^{-1}\) FW); (3) cellulase (E, with an enzyme activity of 4.8 \(\times\) 10\({}^{3}\) U g\({}^{-1}\) FW); and (4) a mixed preparation of lactic acid bacteria and cellulase (M, with a live count of 5 \(\times\) 10\({}^{7}\) CFUs g\({}^{-1}\) FW and enzyme activity of 4.8 \(\times\) 10\({}^{3}\) U g\({}^{-1}\) FW). Both lactic acid bacteria and cellulase were added at 50 mg kg\({}^{-1}\) FW. Amaranth was planted in June 2020, and then harvested in September 2020, when it reached maturity. Amaranth was moved according to the 5-point sampling method and dried for 1 day after moving. Amaranth was chopped to 1-3 cm and mixed thoroughly and evenly, and then the appropriate number of samples was selected for testing. The different additive solutions were sprayed evenly on the mixed silage and mixed thoroughly, and the same dose of distilled water was added to the control group. After mixing well, it was packed into 140 mm \(\times\) 200 mm polyethylene vacuum packing bags with 300 g per bag, and 6 replicates were designed for each group. Then, vacuum treatment was performed, and the samples were stored at ambient temperatures (21-25 \({}^{\circ}\)C) for 60 days to prepare amaranth and wheat bran (AWB) mixed silage.
## 3 Clustering Model for UAV Systems  
### Network Topology Model  
The network topology model of the UAV system is shown in Figure 1. In this system, the ground control station (GCS), UAV, and jammer are deployed. And it is assumed that each UAV integrates an inertial measurement unit (IMU), Global Positioning System (GPS) chipset, and a communication module to accurately measure speed and position information, while also enabling information exchange capabilities. During the task execution, UAVs are grouped into clusters according to the proposed clustering algorithm. The nodes participating in the task are divided into multiple clusters through a clustering algorithm, and different clusters are assigned to perform different tasks. Each cluster consists of different roles: the cluster head and the cluster members. Cluster members are further categorized into regular members and those with backup cluster head functions. The cluster head is responsible for managing clusters, communicating between different clusters, and communicating with external devices such as ground control centers. Cluster members can not directly communicate with nodes outside the cluster and must complete information collection and processing through cluster head forwarding. When the cluster head loses its working ability, the backup cluster head will take over its functions. After clustering, a dual power mode is adopted for cluster head nodes. Cluster head nodes use higher communication power for inter-cluster communication and lower power for intra-cluster communication to improve resource utilization and ensure successful information exchange.  
Figure 1: System model.
## 3 Results  
### Fabrication and Energy Band of Solar Cells with All-Carbon Electrodes  
First of all, to evaluate the feasibility of carbon-based film materials as all-carbon electrodes, CVD graphene and CNT films were used to fabricate OPV cells. The mixture of C\({}_{60}\)-based derivative (phenyl-C61-butyricacid methyl ester: PCBM) and P3 HT (poly(3-hexyl)thiophene) [39] was employed as an active layer to absorb incident radiation and generate excitons. The schematic diagram of our devices is shown in Figure 1a. Graphene films were firstly transferred onto a flexible PEN wafer as bottom electrodes, and then ZnO nanoparticles were spin-coated as an electron transport layer [51]. In order to gain homogeneously dispersed nanoparticles, this step was repeated twice. After the active layer and hole transport layer of poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) were coated, CNT or G films were laminated as the top transparent electrode. In theenergy band diagram shown Figure 1b, the work functions of CNT and graphene films are set to 4.7 eV, depending on their thickness and purity [36; 52]. On the top side, i.e., the anodes, the energy band of CNT and graphene films is near to that of ITO, which is slightly lower than the highest occupied molecular orbital (HOMO) of PEDOT:PSS. This match is favorable to extract and collect holes to the external circuit. The whole energy band diagram theoretically indicates that it is viable to use all-carbon electrodes in this structure. Semitransparent and flexible solar cells with all-carbon electrodes are shown in Figure 1c,d. It is indispensable to use transparent and flexible PV devices in some applications, such as power-generating windows and portable electronic devices. So, there are structural advantages to using two-dimensional carbon nanomaterials as electrodes in solar cells.
###### Abstract  
Background: Temporary anchorage devices (TADs) have reduced the need for anterior segmental osteotomy (ASO) in Class I patients; however, ASO remains relevant in Class II patients. As previous studies were conducted before the usage of TADs, updated guidelines are required to determine the relationship between surgery and orthodontic treatment. We aimed to evaluate the hard tissue changes following ASO versus non-ASO (NASO) orthodontic treatment utilizing TADs with premolar extraction. Methods: This retrospective clinical study involved 67 patients diagnosed with skeletal Class II malocclusion. The patients were categorized into the ASO (n = 31) and NASO (n = 36) groups. Cephalometric analyses were conducted to compare initial and final records to assess the treatment effects. Results: Pre- and post-treatment changes in the ASO and NASO groups exhibited significant differences in \(\Delta\)SNA, \(\Delta\)SNB, \(\Delta\)ANB, \(\Delta\)A to N-perp, \(\Delta\)B to N-perp, \(\Delta\)Pog to N-perp, and \(\Delta\)A to FH (\(p<0.05\)). Both groups demonstrated normalized ANB, indicating significant skeletal improvement in Class II malocclusion. Specifically, the change in FMIA (\(\Delta\)L1 to FH [\({}^{\circ}\)]) was 4.98\({}^{\circ}\) greater in the NASO group than in the ASO group (\(p<0.05\)). Conclusions: Considering the IMPA change, NASO was recommended for treatment choice when a reduction of more than 10\({}^{\circ}\) was achievable.  
anterior segmental osteotomy; extraction; skeletal class II malocclusion; IMPA 2025 15 4849
## 4 Discussion
However, all the considered variables of the FHC (GFHC, FHHNC, FDFH, SDFH) were associated with an OR greater than 1. The highest OR was observed with the FHHNC (OR = 1.871). Almost all the performed studies on this issue demonstrated similar results [41; 42; 43; 44]. [PERSON] et al. found that the OR of GFHC was 1.1 and the OR of the family history of oropharyngeal cancers was 1.2 [21]. In a multicenter study, the estimated OR was 2.6 for family history of oral and pharyngeal cancer [19]. In a study, the ORs were calculated and pooled from 12 studies. It was found that the OR of FHHNC was 1.7 [44]. [PERSON] et al. found that the Relative Risk (RR) of developing a HNC was 1.97 in patients with FDFH and the RR increased to 3.65, in case the FDFH was of head and neck cancer [43]. In another study, the OR was 1.28 in patients with a FDFH of any type of cancer. The risk increased with the increase of affected relatives number [41]. In the literature, there is a controversy to consider the FHC as a risk factor of oral cancer or not. Some authors were against because there were no hereditary cancer syndromes that showed oral cancers except some Cowden Syndrome cases [6]. Others suggested considering the FHC as a risk factor of oral cancer. This suggestion was based on the presence of case reports with multiple affected members, the presence of epidemiological studies that indicate the presence of a familial tendency to oral cancer, the presence of increased risks associated with polymorphic genes implicated in the metabolism of alcohol and tobacco and the presence of increased risks associated with genes implicated in DNA repair maintenance of genetic stability [19]. Many studies showed the familial aggregation of oral cancer. It was attributed to be possibly due to the presence of genetic factors or environmental factors that make the relatives having similar reflection to specific risk factors such as tobacco and/or alcohol [19; 20]. This explanation of the familial aggregation could be observed in a study, which showed that the risk of oral cancer increased in patients with a family history of lung cancers that is highly associated with tobacco risk factor. However, the risk of oral cancers did not increase in patients with a family history of other cancers associated with tobacco or alcohol such as esophagus or liver cancers [19; 28; 45]. Some limitations were observed in our study that should be acknowledged. First, the study was a hospital-based study where the control group consisted of attended patients to our department. Second, the data of family history were collected through the self-recall method. According to the literature, the self-recall of the family history of HNC or other cancers would be more accurate in patients with oral cancers than that of control cancer free patients [19]. Third, the exact site of cancer for both GFHC and FHHNC was not provided. Therefore, the association of the family history of each specific site with oral cancer could not be evaluated.