esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Section
### Spatial Analysis  
We examined associations between land cover and CO\({}_{2}\) mixing ratios for 2 different sectors around the Worcester tower site. Each sector spanned 180 degrees, with the \"East\" sector extending from 0 to 180 degrees, with 0 degrees being polar north. A categorical variable representing the wind sector was then appended to the CO\({}_{2}\) dataset according to observed wind direction, so that each CO\({}_{2}\) observation was associated with one of the two sectors. Since the wind direction observations at Worcester and Boston could have been adversely affected by micrometeorology, they were validated by data from the NOAA affiliated Boston Logan International and Worcester Regional Airport weather stations. For our land cover analysis, we used MassGIS' 2005 impervious surface and land use layers [53], which are based on 0.5 m resolution digital orthoimagery and accessor's parcel information, as well as a 2010-2011 30 m summertime cloud-free EVI layer generated from LandSat Thematic Mapper data [50]. The ISA layer defined an impervious surface as one covered by buildings, parking lots, roads, and compacted, man-made soils. The land cover layer originally contained 40 categories, which were combined into 8 categories including wetland, water, forest, residential, agricultural, open and successional, commercial and industrial, and other developed (including roads). We calculated ISA, land cover, and EVI within a 5 km radius of the tower site for each sector (Figure 7) using the ArcGIS 10.0 software [74]. To address a finer spatial domain around the Worcester tower site, we repeated this analysis for 8 sectors representing 45 degree wind fields around the tower (e.g., 0 to 45 degrees, 45 to 90 degrees, _etc._).  
We also used land cover to determinate a first order estimate of the CO\({}_{2}\) mixing ratio source area at each site. For example, within the 9 \(\times\)9 pixel area seen in Figure 2, land cover around the Worcester tower appeared to be dominated by vegetation, despite the presence of downtown Worcester. Yet, there was a statically significant difference in CO\({}_{2}\) mixing ratios between easterly and westerly sectors at the Worcester tower site, presumably due to local emissions sources. Consequently, we assumed that the source area at Worcester (and those at the Harvard Forest and Boston sites) fell primarily within the 5 km radius around each tower since it was this approximate spatial scale that clearly highlighted site specific differences in land cover.
## 3 Elastographic Methods
### Two-Dimensional Shear Wave Elastography (2D-SWE)
#### 3.3.2 Reproducibility and Variability of Liver Stiffness Estimates  
The intra-operator reproducibility of LSMs was found to be excellent, comparing the average of 3, 5 or 7 measurements to the average of 15 considered as the reference, with intraclass correlation coefficient (ICC) of 0.944 (95% CI: 0.899-0.972), 0.958 (95% CI: 0.923-0.978) and 0.969 (95% CI: 0.945-0.982), respectively, in free-breathing status. Results were very similar in the group of patients studied with breath-hold: ICC = 0.937 (95% CI: 0.887-0.978), ICC = 0.938 (95% CI: 0.876-0.981), and ICC = 0.941 (95% CI: 0.878-0.983) for the average of 3, 5 and 7 measurements, respectively [74]. An excellent correlation of repeated measurements made by each of three operators was also reported in another study, with intra-operator ICCs ranging from 0.93 to 0.96 [73]. This study also investigated inter-observer agreement in 39 randomly selected children (9 controls, 16 patients without biliary atresia (BA) and 14 with BA. Very good reproducibility was found among the three operators (ICC = 0.98; 95% CI: 0.96-0.99), and the Bland-Altman analysis also showed that the interobserver agreements within each pair of operators were good [73].  
Another study on NASH patients with various stages of liver fibrosis (F0: \(n\) = 5; F1: \(n\) = 16; F2-3: \(n\) = 14) showed that the inter-observer agreement between two operators was excellent, as indicated by an ICC for absolute agreement of 0.95 (95% CI: 0.90, 0.97) [12]. Using the SI as an indicator of unreliable measurements, it has been found that an intra-operator ICC of 0.87 (95% CI, 0.74 to 0.94) in the free-breathing group increased to 0.99 (95% CI, 0.97 to 0.99) when the SI was used. Similarly, the ICC of 0.95 (95% CI, 0.90 to 0.98) in the breath-holding group increased to 0.99 (95% CI, 0.99 to 0.99) when the SI was used [74].
###### Abstract  
The present study reports the tribological properties and residual stress of titanium chromium nitride (TiCrN) coatings. Thin films of TiCrN were deposited on tungsten carbide substrates at 400 \({}^{\circ}\)C in a vacuum of 5 \(\times\) 10\({}^{-6}\) mbar using the cathodic-arc physical vapor deposition technique with chromium variation. X-ray diffraction (XRD) spectroscopy was employed to probe the structures of the deposited thin films. The phase constituent was found to gradually shift from cubic TiN to cubic CrN. Both the hardness and elastic modulus of the sheet changed from 29.7 to 30.9 GPa and 446 to 495 GPa, respectively. The biaxial compressive residual stress after an initial absolute scan in the range of 30-100\({}^{\circ}\) was determined using XRD (d-sin\({}^{2}\)\(\Downarrow\) method). These mechanical and tribological properties of films were investigated with the help of instrumented nanoindentation and a ball-on-disk tribometer wear test. The wear test indicates that the TiCrN thin film, featuring a Cr/Ti ratio of 0.587, exhibits superior wear resistance and maximum compressive residual stress in comparison to other thin films.  
Keywords: titanium chromium nitride (TiCrN) thin film; cathodic-arc physical vapor deposition (CAPVD); tribological properties; nanoindentation; residual stress +
Footnote †: journal: Applied Sciences
# Efficient and Secure Management of Medical Data Sharing Based on Blockchain Technology
## 4 Medical Data Provenance Methods
### Data Structure of Medical Data and Operational Behaviors  
The medical data and operational behaviors data stored on the blockchain are the foundation for medical data provenance. Combining the characteristics of the business and the requirements for provenance, we designed the data structure of medical data and operational behaviors as shown in Tables 1 and 2.  
Figure 3: Example of medical data provenance.  
Based on the structures of medical data and operational behaviors, the original medical data, intermediate results, and operational behaviors can be organized into a directed acyclic graph (DAG) structure as shown in Figure 3, which facilitates the implementation of subsequent provenance algorithms. Next, we introduce two provenance methods: the naive provenance method and the DAG-based provenance method.
## 3 Approach
### Update of DDPG-DMP
```
Given the actor and critic networks \(\mathbf{\pi}_{\mathbf{\phi}}(\cdot)\), \(Q_{\mathbf{\theta}}(\cdot)\) and the corresponding target networks \(\mathbf{\pi}_{\mathbf{\phi}^{-}}(\cdot)\), \(Q_{\mathbf{\theta}^{-}}(\cdot)\)  for\(t=0\) to \(T\) with interval \(K\)do # Interact phase  Observe state \(\mathbf{s}_{t}\)  Decide the parameters of DMP and adaptive inverse controller \([\mathbf{\psi},\mathbf{\rho}]=\pi_{\mathbf{\phi}}(\mathbf{s}_{i})\)  Generate \(m\)-step DMP trajectory using Equation (14): \([\mathbf{y}_{j},\mathbf{y}_{j},\mathbf{\dot{y}}_{j}]_{j=1}^{m}\)  Obtain the \(K\)-step control sequence using the adaptive inverse controller \([\mathbf{a}_{t},\mathbf{a}_{t+1\cdots},\mathbf{a}_{t+K-1}]=f_{\rho}([\mathbf{y}_{j},\mathbf{\dot{y }}_{j},\mathbf{\dot{y}}_{j}]_{j=1}^{m})\)  Sequentially conduct a \(K\)-step control sequence with exploration noise and stored samples \(\{\mathbf{s}_{t+k},\mathbf{a}_{t+k},\mathcal{R}_{\mathbf{s}_{t+k}g_{t+k}^{\prime}}^{ \mathbf{a}_{t+k}},s_{t+k}^{\prime}\}_{k=0}^{K-1}\) to memory buffer \(\mathcal{D}\) # Update phase  Sample mini-batch of \(N\) whole trajectories with \(K\)-step samples from \(\mathcal{D}\)  Process them to \(N\) samples with accumulated reward using Equation (19): \([\mathbf{s}_{n},\mathbf{a}_{n},\mathcal{R}_{n}^{\text{traj}},\mathbf{s}_{n}^{\prime}]_{n=1} ^{N}\), where \(\mathcal{R}_{n}^{\text{traj}}=\sum_{k=0}^{K-1}\mathcal{R}_{n}^{\mathbf{a}_{n+k}g_{ t+k}^{\prime}}\) # Calculate the loss function and update critic networks: \(\mathcal{L}(\mathbf{\theta})=\frac{1}{N}\sum_{n=1}^{N}(\mathcal{R}_{n}^{\text{ traj}}+\gamma\mathcal{Q}_{\mathbf{\theta}^{-}}(\mathbf{s}_{n}^{\prime},\mathbf{\pi}_{\mathbf{\phi}^{-}}( \mathbf{s}_{n}^{\prime}))-Q_{\mathbf{\theta}}(\mathbf{s}_{n},\mathbf{a}_{n}))^{2}\) # Update actor networks with the following gradient: \(\
abla_{\mathbf{\phi}}\leftarrow\frac{1}{N}\sum_{n=1}^{N}\
abla_{\mathbf{a}_{n}^{ \prime}}Q_{\mathbf{\theta}}(\mathbf{s}_{n},\mathbf{a}_{n}^{\prime})\big{|}_{\mathbf{a}_{n}^{ \prime}=\pi_{\mathbf{\phi}}(\mathbf{s}_{n})}\
abla_{\mathbf{\phi}}\pi_{\mathbf{\phi}}(\mathbf{s}_ {n})\) where \(\mathbf{a}_{n}^{\prime}\) indicates the first-step output action of \(\pi_{\mathbf{\phi}}(\mathbf{s}_{n})\) and adaptive inverse controller # Smooth update \(\mathbf{\theta}^{-}\leftarrow\eta\mathbf{\theta}+(1-\eta)\mathbf{\theta}^{-}\), \(\mathbf{\phi}^{-}\leftarrow\eta\mathbf{\phi}+(1-\eta)\mathbf{\phi}^{-}\). return
```  
**Algorithm 1**Learning and Update Processes of the Proposed DDPG-DMP
## 1 Introduction
Three-dimensional printing techniques such as fused deposition modeling and selective laser sintering allow for the creation of AFOs with mechanical properties similar to traditional carbon-fiber AFOs [23; 24]. Higher satisfaction and comfort have been reported for 3D-printed AFOs compared to conventional ones, particularly in terms of weight and ease of use [21; 22]. Furthermore, 3D printing facilitates rapid prototyping and design iterations, allowing for real-time adjustments based on patient feedback or clinical observations. This feature contrasts with the lengthy and iterative nature of traditional orthosis fabrication. The capacity for on-demand production and decentralized manufacturing also enhances accessibility, particularly in remote areas. Additionally, the ability to use a wide variety of materials allows for orthoses to be created with specific mechanical properties which can replicate or improve upon traditional AFOs [24]. The reduction in material waste and the ability to digitally store orthotic designs, thus reducing time and costs in case a replacement AFO is required, also allow for more efficient and less expensive orthotic care. Recently, a novel 3D-printed PD-AFO made from fiberglass-reinforced polyamide has been developed for individuals with foot drop dissatisfied with off-the-shelf devices. This PD-AFO has been reported to improve gait parameters and ankle kinematics compared to no-AFO conditions and to be more comfortable than standard polypropylene leaf spring AFOs [17]. These AFOs can be individually dimensioned to achieve the desired stiffness and, due to the high properties of the polyamide-based composite material, the calf-shell can sustain large angular displacements without plastic deformation and without the need for torsion springs, e.g., as in [3]. Thus, it is plausible that some elastic energy can be stored and released in the stance phase of gait. While AFOs are designed primarily to prevent excessive plantarflexion during the swing phase, understanding PD-AFO energetics is crucial for optimizing design, especially when ankle plantarflexor muscles are compromised. Therefore, this pilot study aimed to (1) investigate if any elastic energy is absorbed/released by the novel custom PD-AFO and (2) determine the timing of energy absorption/release during gait. In order to address these aims, the kinematics of the novel 3D-printed dynamic custom AFO were recorded in a population of individuals with foot drop during gait.
###### Abstract  
The comparison of the angular light-scattering method (ALSM) and the spectral extinction method (SEM) in solving the inverse problem of aerosol size distribution (ASD) are studied. The inverse problem is solved by a SPSO-DE hybrid algorithm, which is based on the stochastic particle swarm optimization (SPSO) algorithm and differential evolution (DE) algorithm. To improve the retrieval accuracy, the sensitivity analysis of measurement signals to characteristic parameters in ASDs is studied; and the corresponding optimal measurement angle selection region for ALSM and optimal measurement wavelength selection region for SEM are proposed, respectively. Results show that more satisfactory convergence properties can be obtained by using the SPSO-DE hybrid algorithm. Moreover, short measurement wavelengths and forward measurement angles are beneficial to obtaining more accurate results. Then, common monomodal and bimodal ASDs are estimated under different random measurement errors by using ALSM and SEM, respectively. Numerical tests show that retrieval results by using ALSM show better convergence accuracy and robustness than those by using SEM, which is attributed to the distribution of the objective function value. As a whole, considering the convergence properties and the independence on prior optical information, the ALSM combined with SPSO-DE hybrid algorithm provides a more effective and reliable technique to obtain the ASDs.  
a _compatiation_
# Network Tower Sharing Analysis in Greece: A Structure-Conduct-Performance Approach
## 1 Introduction
Can the SCP paradigm provide valuable information (at strategic and decision-making levels) regarding network tower-sharing in Greece? * How can market structure and conduct affect MNO's performance and especially their profitability? * Do government policies play a significant role in market restructuring? * Can specific performance variables be proposed when a network tower-sharing strategy is concerned? * Which are the perspectives of network tower-sharing analysis in the Greek telecommunication industry according to the SCP paradigm? In the paper's following sections, an SCP model in its extended form is constructed, while an analysis on how structure, conduct, and performance interrelate with each other, is carried out. The SCP model is employed to explain how strategic decisions regarding tower infrastructure development were formed in the period between 2013-2022, as a result of a series of interactions and feedback effects, amongst market structure, MNO's conducts and performances, resulting in strengthening competition and reshaping market structure with the entrance of a new player in the Greek mobile market, an independent TowerCo. The above-mentioned framework enriches our knowledge of international tendencies, competition, domestic growth potentialities, operators' concentration, and sharing practices for the development of telecom infrastructures in Greece. This framework is directly related to the overall infrastructure-sharing services provided in Greece, as a result of the joint influence of existing market structure and market conducts on the tower-sharing prices and costs, the content of the associated infrastructure-sharing contracts, the basic conditions or the government policies affecting commercialization and the class of different buyers of tower spaces. The paper concludes with presenting a basically qualitative, explanatory interpretive analysis on the perspectives of network tower-sharing analysis in the Greek telecommunication industry, including policy recommendations for the near future and thoughts on future research, too. Moreover, it sheds light on how a competitive advantage/disadvantage (e.g., based on cost-sharing) of key market players, is possible to affect the structure of the market and, in some cases, the conduct and the performance.
## 5 Conclusions  
The new module, which was officially introduced by the OHDSI community in the OMOP CDM v5.4 release, is a great addition to the field of joint observational cancer research. It is currently the only CDM in the field of clinical research that includes a comprehensive standardized terminology of cancer representation and allows time-dependent episode-based modeling of disease progression. In addition, by mapping to the OMOP ontology, the source data can be enriched with additional information. This increases its application and evaluation possibilities. Furthermore, unified semantics offers the easy implementation of an AI-federated algorithm pipeline.  
Nevertheless, many terminologies were included in the Standardized Vocabulary Metadata Repository that are rarely used or not used at all in the European or German areas, limiting the mapping success. This gap should be closed in the coming years to guarantee the mappability of different oncological data sources to the CDM. Especially, the inclusion of the basic oncology dataset (ADT/GEKID) in the standardized vocabulary would considerably facilitate and expand data harmonization between German cancer registries and enable joint analyses.  
Additionally, it should be considered that duplicate patients can also occur in distributed research networks. These can only be clearly identified via record linkage. Therefore, future research should especially consider how to establish record linkage within the CDM across distributed research networks without contradicting the country-specific data protection regulations in place, potentially through the use of superior pseudonyms, and prepare the essential steps to enable precision medicine and precise oncology.  
**Author Contributions:** Conceptualization, S.B. and J.C.; methodology, S.B. and J.C.; software, S.B.; validation, S.B.; formal analysis, J.C.; resources, S.B., F.U.; data curation S.B. and J.C.; writing--original draft preparation, J.C.; writing--review and editing, S.N., S.B., F.U., C.S.; visualization, J.C.; supervision, S.B.; project administration, C.S. and S.B.; funding acquisition, C.S. and S.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Informed Consent Statement:** Broad consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Data sharing not applicable.  
**Acknowledgments:** Special acknowledgment to the Clinical Cancer Registry team. Without the administrative and technical help of the whole team this study would not have been feasible.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Identifying At-Risk Students for Early Intervention--A Probabilistic Machine Learning Approach
## 3 Materials and Methods
### Data Preprocessing
#### 3.2.6 Handling Class Imbalance  
The problem of identifying at-risk students was approached as a binary classification task, wherein students deemed at-risk were assigned a classification of 0, and those not at-risk were assigned a classification of 1. However, the data exhibited a significant class imbalance--a minimum of 76% of students passed each subsequent assessment. As it is well known, class imbalance occurs when one class has significantly more samples than the other [24]. To mitigate this issue, the synthetic minority oversampling technique (SMOTE) was employed in this study. SMOTE was used to oversample the minority class by creating synthetic examples, which in turn enabled matching the number of samples in the majority class. The SMOTE technique can be likened to a form of data augmentation [24].
# Downregulation of Squalene Synthase Broadly Impacts Isoprenoid Biosynthesis in Guayule
###### Abstract  
Production of natural rubber by _Paruthenium argentatum_ (guayule) requires increased yield for economic sustainability. An RNAi gene silencing strategy was used to engineer isoprenoid biosynthesis by downregulation of squalene synthase (SQS), such that the pool of farnesyl diphosphate (FPP) substrate might instead be available to initiate natural rubber synthesis. Downregulation of SQS resulted in significantly reduced squalene and slightly increased rubber, but not in the same tissues nor to the same extent, partially due to an apparent negative feedback regulatory mechanism that downregulated mevalonate pathway isoprenoid production, presumably associated with excess geranyl pyrophosphate levels. A detailed metabolomics analysis of isoprenoid production in guayule revealed significant differences in metabolism in different tissues, including in active mevalonate and methyleryththird phosphate pathways in stem tissue, where rubber and squalene accumulate. New insights and strategies for engineering isoprenoid production in guayule were identified.  
Parthenum argentatum; guayule; squalene synthase; squalene; natural rubber
## 2 Characterization and Measurement of Blade Twist  
### Definition of Twist Angular Position of the Profile  
The profile is a closed two-dimensional blade contour with a special aerodynamic performance, which consists of a convex profile, a concave profile, a leading edge arc and a trailing edge arc [15; 16]. The twist angular position of the profile characterizes the absolute torsion level of profile relative to the axial line of engine in the space, and its geometric definition is shown in Figure 1.  
In Figure 1, the profile chord line refers to the common tangent of the leading and trailing edges of the profile, and the twist angular position \(\psi\) of the profile is defined by the angle between the profile chord and the axial line of engine. As can be seen from Figure 1, the registration of the engine axis and extraction of the profile chord line are key issues for measuring the twist angular position of profile.
## 2 Force Analysis and Power Calculation
### Climb Phase  
The climb phase involves the UAV ascending from ground level to its designated cruising altitude at a specified climb angle and climb rate. During this phase, the forces acting on the UAV can be simplified into components along the vertical and horizontal directions, corresponding to the UAV's climb trajectory.  
During steady, straight-line climbing, the forces acting on the UAV in both the vertical and horizontal directions are balanced. In this phase, the UAV's motor operates at a constant power output to maintain the steady climb.  
Figure 1: An UAV flight profile diagram.  
(1) Force analysis:  
\[\left\{\begin{array}{l}L_{c}=W\cdot\cos\gamma,\ \textit{Vertical direction}\\ T_{a,c}=D_{c}+W\cdot\sin\gamma,\ \textit{Parallel direction}\end{array}\right. \tag{3}\]  
where \(\gamma\) is the UAV's climb angle.  
(2) Power situation:  
\[P=P_{c,r} \tag{4}\]  
where \(P_{c,r}\) is the rated power during the UAV's climb process.  
If the UAV performs an unsteady straight-line climb, the force and power conditions can be described as follows:  
(1) Force analysis:  
\[\left\{\begin{array}{l}L_{c}=W\cdot\cos\gamma,\ \textit{Vertical direction}\\ F_{a,c}=T_{a,c}-D_{c}-W\sin\gamma,\ \textit{Parallel direction}\end{array}\right. \tag{5}\]  
(2) Power situation:  
\[P=P_{c}=T_{a,c}\cdot V(t) \tag{6}\]  
If the UAV performs an unsteady, non-straight-line climb, the force and power conditions are as follows:  
(1) Force analysis:  
\[\left\{\begin{array}{l}F_{a,c,y}=L_{c}-W\cos\gamma(t),\ \textit{Vertical direction}\\ F_{a,c,x}=T_{a,c}-D_{c}-W\sin\gamma(t),\ \textit{Parallel direction}\end{array}\right. \tag{7}\]  
(2) Power situation:  
\[P=P_{c}=T_{a,c}\cdot V(t) \tag{8}\]
# An Enhanced Energy Balanced Data Transmission Protocol for Underwater Acoustic Sensor Networks
## 4 EBET and EEBET: Proposed Techniques
### Route Establishment
2:TotalEnergyLevels \(\leftarrow\) m
3:UnitEnergyLevel \(\leftarrow\)\(E_{o}/m\)
4:RelayType \(\leftarrow\)\(\mu\)
5:return TRUE
6:end procedure
7:procedure:NEIGHBORQUERYRECEIVED
8:\(\lambda\gets d(i,j)\)
9:\(\varsigma\gets d(j,s)\)
10:\(N_{j}\leftarrow\)\(\alpha|\lambda-O_{t}|+\alpha\varsigma\)
11:\(\psi\gets min(N_{j})\)
12:SendNeighborFoundAck.Id = IdOfRelayNode
13:SendNeighborFoundAck()
14:return TRUE
15:end procedure
16:procedure:OneEnergyLevelConsumed
17:ControlMessageSend()
18:if\(EL_{s}<EL_{p}\)then
19:\(SendNeighborFindingMessage.EL=EL_{i}\)
20:SendNeighborFindingMessage()
21:NeighborFoundAck = IdOfHighRLNode
22:RelayType = \(\sigma\)
23:endif
24:end procedure
25:if\(EL_{s}>=EL_{p}\)then
26:RelayType = \(\mu\)
27:end procedure
28:endif
```  
**Algorithm 1** : EBET