esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Biochars as Amendment for Greenhouse Media for Seeding Production
#### 3.2.2 Basil Seedling Growth
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Mixes \({}^{\mathbf{z}}\)** & **Root Length (cm)** & **Root Surface Area** & **Average Diameter** & **Number of Tips** \\  & & **(cm\({}^{\mathbf{2}}\))** & **(mm)** & \\ \hline SBB:PM:P(10:60:30) & 121 \(\pm\) 13** & 16 \(\pm\) 1** & 0.43 \(\pm\) 0.03 & 480 \(\pm\) 42** \\ SBB:PM:P(30:40:30) & 295 \(\pm\) 523** & 34 \(\pm\) 11** & 0.37 \(\pm\) 0.01 & 819 \(\pm\) 88** \\ SBB:PM:P(50:20:30) & 433 \(\pm\) 23** & 51 \(\pm\) 6** & 0.37 \(\pm\) 0.01 & 1408 \(\pm\) 235** \\ SBB:PM:P(70:0:30) & 617 \(\pm\) 92** & 72 \(\pm\) 22** & 0.37 \(\pm\) 0.01 & 1204 \(\pm\) 118** \\ SBB:PM:P(10:00:0) & 841 \(\pm\) 95** & 97 \(\pm\) 15** & 0.37 \(\pm\) 0.02 & 1584 \(\pm\) 163** \\ HB:PM(10:90) & 331 \(\pm\) 29** & 40 \(\pm\) 7** & 0.39 \(\pm\) 0.01 & 873 \(\pm\) 45** \\ HB:PM(30:70) & 757 \(\pm\) 67** & 88 \(\pm\) 19** & 0.37 \(\pm\) 0.01 & 1758 \(\pm\) 177** \\ HB:PM(50:50) & 793 \(\pm\) 145** & 96 \(\pm\) 35** & 0.39 \(\pm\) 0.01 & 1761 \(\pm\) 167** \\ HB:PM(70:30) & 690 \(\pm\) 44** & 85 \(\pm\) 6** & 0.39 \(\pm\) 0.01 & 1446 \(\pm\) 194** \\ Control & 1181 \(\pm\) 67 & 145\(\pm\) 21 & 0.39\(\pm\) 0.02 & 3001 \(\pm\) 214 \\ \hline \hline \end{tabular} Note.: \({}^{\mathbf{z}}\) SBB = sugarcane bagasse biochar; HB = mixed hardwood biochar; P = perlite; PM = peat moss; Control = commercial propagation substrate. Numbers in parentheses indicate the ratio of different components, by vol.  
\end{table} 
Table 3: Root growth of basil seedlings grown in different mixes (\({}^{\mathbf{*}}\), \({}^{\mathbf{**}}\) indicate a significant difference from the control using Dunnett’s test at \(p\leq 0.05\) and \(p\leq 0.01\), respectively). Figure 5: The correlations between total fresh weight **(TFW, a)**, total dry weight **(TDW, b)**, growth index **(GI, c)** and biochar incorporation rate and the SPAD **(d)** of basil seedlings grown in biochar-amended mixes. SBB = sugarcane bagasse biochar; HB = mixed hardwood biochar; CS = commercial propagation substrate. SBB (10%, 30%, 50%, 70% and 100%; by vol.) incorporated with 30% perlite with the rest being peat moss; HB (10%, 30%, 50% and 70%; by vol.) incorporated with peat moss. (\({}^{\mathbf{*}}\), \({}^{\mathbf{**}}\) indicate a significant difference from the control using [PERSON]’s test at \(p\leq 0.05\) and \(p\leq 0.01\), respectively).
###### Abstract  
Silicon carbide (SiC) nanowhiskers (NWs) constitute an important type of optical and structural materials. Herein, SiC NWs were successfully combustion synthesized (CSed) in a Si-C-N system using tungsten (W) as a catalyst. Scanning electron microscopy, transmission electron microscopy, and X-ray diffraction were used to characterize the SiC NWs. Results of morphological characterization indicated that the W-catalyzed CSed SiC NWs products were fluffy from surface to the core, and they were about several hundred micrometers in length with diameters less than 1 \(\upmu\)m. For the comprehensive understanding of the initial growing progress of W-catalyzed CSed SiC NWs, the absorption behavior of C, N, and Si atoms on the crystal planes of W (100), W (110), and W (111) surfaces was investigated by using first-principles calculations. The calculated surface energy (\(E_{\text{surf}}\)) of the studied W surfaces and the absorption energy of C, N, and Si atoms on different sites, indicate that the C atom has a priority to sink to the nanometer catalysts grain of W, and the pre-sunk C atom then reacts with Si atom to form NWs.  
Keywords:optical materials; combustion synthesis; silicon carbide nanowhiskers; first-principles calculations +
Footnote †: journal: Applied Physics Letters
## 3 Results and Discussion
### Spectral Consistency
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**BAND \#** & **BAY** & **BRO** & **CNS** & **EHLERS** & **GRS** & **IMVM** & **MIHS** & **PANSHARP** & **PCA** & **RCS** & **WAVELET** \\ \hline
1 & 0.610 & 0.008 & 0.049 & 0.719 & 0.569 & 0.973 & 0.721 & 0.614 & 0.307 & 0.921 & 0.548 \\
2 & 0.524 & 0.004 & 0.046 & 0.532 & 0.472 & 0.972 & 0.569 & 0.539 & 0.278 & 0.869 & 0.786 \\
3 & 0.473 & 0.009 & 0.046 & 0.345 & 0.475 & 0.972 & 0.375 & 0.491 & 0.308 & 0.776 & 0.866 \\ \hline AVERAGE & 0.536 & 0.007 & 0.047 & 0.532 & 0.505 & **0.972** & 0.555 & 0.548 & 0.298 & 0.856 & 0.733 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Spectral consistency: Erratum Relative Global Adimensionnel de Synthese (ERGAS). RASE results show the average performance of the fusion algorithm in spectral bands and ideal values should be as small as possible. The results show that the IMVM fusion method produced the best results followed by the RCS method. The PCA method produced the worst results. The RMSE results are reflective of the average spectral distortion arising from the image fusion and the results are indicative of spectral quality in homogeneous zones in the image. The domain for RMSE value ranges from zero to infinity and lower values close to zero are considered ideal and reflective of high quality. The best results were produced by the IMVM method followed by the RCS method. The worst results were produced by PCA and the Brovey method. The results indicate the fusion quality over the whole image by showing difference in variances relative to the original one. The metric reveals a decrease or increase of information content as a result of the pansharpening process. The results are considered ideal positive when the information content decreases and undesirable when the information content increases. The ideal value should be close to 0. The IMVM pansharpening method produced the best results. The Brovey transform method ranked second and PCA had the worst performance. The results are reflective of difference between the original image and fused image and the ideal value should be as small as possible. The IMVM method showed the best results followed by the RCS method. The Brovey transform method showed the worst performance.
## 2 Materials and Methods
### Dataset Composition  
To train the proposed object detection model, areas of the images showing specific behaviors were labeled with bounding boxes and the cattle behaviors were categorized into five classes: resting, communion, feeding, drinking, and eating. These five behavioral classes were further categorized into two groups: independent and interacting. Independent behavior included resting, drinking, and eating, which can be observed independently in cows and calves. Interacting behavior comprised communion and feeding behaviors, which involve interactions between cows and calves. Sample images depicting each behavior class are shown in Figure 5. The behaviors for each class were classified as follows: Resting involved the cattle sitting with their bellies touching the ground. Communion involved the cow's mouth touching the calf's body, or vice versa. Feeding involved the calf placing its head on the cow's teat and drinking milk. Drinking involved passing the entire head over the fence toward the water trough and drinking water. Finally, eating involved placing their heads in the feed trough.  
Among the five behavior classes, this study focused on the communion class. Because cattle are social animals, the bond formation between cows and calves is crucial; cattle that lack adequate bonding experience stress. Interactions between cows and calves can strengthen this bond and potentially aid in calming calves [18]. Therefore, bonding behavior is a vital indicator for analyzing cattle behavior.  
Figure 5: Classification and sample images of each behavior class.  
After labeling, the data were divided into training, validation, and test sets in a ratio of 6:2:2. However, in a deep learning model, employing multiple images from a single video across different datasets can result in overfitting. Therefore, we ensured that the images extracted from a video were included in only one dataset to avoid duplicates among the training, validation, and test sets. Table 1 presents the dataset configurations and Table 2 lists the number of labels for each class.
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 3 Overview of Network Protocols
### Application Layer  
The application layer sits at the top of the communication protocol suite, irrespective of the prevailing network environment or conditions. It is an abstract layer that contains a combination of protocols to facilitate and define how applications communicate with others. In addition, these protocols specify how data is formatted, transmitted, and received. Hypertext transfer protocol (HTTP), File Transfer Protocol (FTP), Simple Mail Transfer Protocol (SMTP), and Domain Name Server (DNS) are some of the most popular TCP/IP application protocols. Most TCP/IP application protocols operate on a client-server model, in which a client device sends a request to the server, and the server acknowledges and responds with the required information [49]. This method works well with a typical client-server architecture. Meanwhile, the demands for real-time communication in the IoT domain require more efficient application protocols. Constrained Application Protocol (CoAP), Message Queuing Telemetry Transport (MQTT), Advanced Message Queuing Protocol (AMQP), Extensible Messaging, Presence Protocol (XMPP), Data Distribution Service (DDS), and WebSocket have been developed and upscaled to address these fast-paced communications in IoT applications by providing publish-subscribe or request-reply communication architecture which can reduce latency and provide communication improvements in IoT systems [50]. Some of these IoT protocols support different QoS, ensuring that communication in the IoT environment receives the necessary performance level to meet operational requirements, including interoperability and scalability.
# A Review of Recent Advances in Roll Stability Control in On-Road and Off-Road Vehicles
## 4 Roll Stability Control in Road Vehicles
### Experimental Validation of Roll Stability for Road Vehicles
#### 4.3.2 Real-Vehicle Tests  
Real-vehicle testing is a critical step in validating the effectiveness of active roll stability control systems in real-world driving environments. The primary objective is to assess the actual impact of control strategies on vehicle roll dynamics, analyze the response time, stability, and robustness of control algorithms under complex conditions, and calibrate the intervention effects of various active actuators. Compared to simulation and HIL testing, real-vehicle testing can reveal interference factors, such as tire nonlinearities, aerodynamic disturbances, and road surface irregularities, which are difficult to replicate in models. This allows control strategies to be more closely aligned with real driving conditions. In recent years, researchers have conducted systematic real-vehicle testing studies on single-actuator control strategies, including active braking, active steering, and active suspension, as well as multi-actuator integrated control strategies.  
To verify vehicle dynamic models and control strategies, several studies have conducted thorough real-vehicle and scaled-platform experiments. [PERSON] et al. [74] validated a lateral dynamic model enhanced by neural networks using real-vehicle data collected via a GNSS/IMU navigation system and Correvit sensor. Tests under random paths and varying speeds showed that adaptive lateral stiffness modeling improved accuracy by compensating for tire nonlinearities while maintaining generalization. [PERSON] et al. [57] tested an MPC-based control strategy on a four-wheel-drive Chevrolet Equinox using dSPACE and high-precision GPS/IMU. During double-lane changes on dry asphalt (\(\mu\) = 0.9), the system prioritized torque redistribution and activated differential braking only when necessary, effectively reducing intervention frequency and enhancing control efficiency. [PERSON] et al. [23] validated an integrated rollover control strategy via a three-axis emergency maneuver at 25 km/h. The system, with inertial navigation and active hydraulic actuators, achieved reductions in the lateral load transfer ratio (10.9-14.3%), lateral acceleration (22.8-27.4%), and roll angle (25.2-29.1%), confirming improved stability. For scaled-platform tests, [PERSON] et al. [53] verified a vehicle model and electro-hydraulic suspension control using a pure electric prototype vehicle. LQR-based control achieved roll angle and suspension deflection reductions of over 25% in double-lane and sinusoidal maneuvers, with simulation errors below 5%. Overall, these experiments demonstrate that diverse rollover prevention strategies can significantly enhance safety while maintaining dynamic performance across various platforms.
# Blockchain Applications in Education: A Systematic Literature Review
## 3 Results
### Summary of Contributions
Several initiatives to issue and verify academic certificates based on blockchain are described and analyzed by [PERSON] et al. [37], who also compare their security requirements and propose their own model using the Hyperledger Fabric Framework. [PERSON] et al. [38] make a compilation of educational applications where blockchain can be implemented and propose a solution to the problems of online education based on a custom blockchain technology: full record of learning trajectory, trusted certification of learning results and decentralized sharing of education resources. Based on the European Credit Transfer and Accumulation System (ECTS) concept, [PERSON] et al. [39] design and prototype a system for higher education in which academic institutions transfer credits called EduCTX to students for the courses that they successfully accomplish. The platform is based on the ARK blockchain [40], where the peers are higher education institutions that collaborate to maintain the running of the initiative. The article by [PERSON] et al. [41] designs an Ethereum-based framework to secure educational data from modifications. An intelligent agent autonomously monitors all of the information and protects data from malicious access-privileged users or attackers. Edoublocs, a project to register on an Ethereum-based blockchain students' activities, is introduced by [PERSON] and [PERSON] [42]. This information is utilized by teachers to assess student progress and design personalized learning itineraries. [PERSON] et al. [43] model a Tangle-based platform to issue, manage and check the authenticity of academic certificates. Tangle [44] is a distributed ledger technology built on Directed Acyclic Graphs (DAG) with some special features that provides a highly scalable architecture, zero fees and achieves a quantum resistant security. Another system based on blockchain for cooperative education is designed by [PERSON] et al. [45]. With their proposal, undergraduate students already in the labor market can integrate their job activity as part of their educational program. To accomplish this, they design a digital E-Portfolio where information and acquired digital competences are counterfeit-proof stored on the blockchain to be shared among communities of higher education institutions and third parties (e.g., employers). In [46], [PERSON] models a future of education based on blockchain where universities must collaborate to remain competitive, allowing students to have a real authentic learning curriculum [47], receive meaningful feedback, improve the learning process by applying skills and communicating with others. As a general approach in all of the documents reviewed, the authors include a model of the described initiative with a higher or lower level of detail, as discussed below. Nineteen papers (67%) refer a prototype. In nine cases (32%), additional information or experimental results are included.
## 1 Introduction  
Highly water-soluble derivatives of camptothecin, a type of quinoline alkaloid (irinotecan and topotecan), are currently known to have a broad spectrum of anticancer activity such as in lung cancer, colorectal cancer, ovarian cancer, and malignant lymphoma [1; 2; 3; 4; 5; 6; 7]. Its derivatives are the most clinically used because they bind to type I topoisomerase and inhibit recombination with DNA, inhibiting DNA synthesis, causing cancer cell apoptosis, and resulting in an extremely potent anticancer activity [4]. Irinotecan is biotransformed because of the action of carboxylesterase in SN-38 (7-ethyl-10-hydroxy-20(S)-camptothecin), which lacks a substituent at the 10-position on the piperidine ring. Additionally, the cytotoxicity of SN-38 is 100-1000 times greater than that of irinotecan (Figure S1) [8; 9; 10]. However, it causes serious side effects, and there is demand for developing a mechanism such as a drug delivery system (DDS) to deliver specific drugs and reduce dosages [11; 12; 13; 14; 15; 16]. For example, a liposomal irinotecan called Onivyde has been reported and approved for medical use [17; 18].  
Previously, we have reported that thrombin-binding aptamer (TBA) is selectively and efficiently incorporated into the gel through thrombin during the fibrin gel formation known as the blood coagulation reaction [19; 20; 21; 22; 23; 24]. In the fibrin gel formation process, first, thrombin cleaves FpA and FpB from the A\(\alpha\) and B\(\beta\) chains located at the N-terminus of fibrinogen to produce fibrin monomers [25]. The resulting fibrin monomer engulfs thrombin and polymerizes to form a gel. Therefore, it was suggested that by using this phenomenon named selective oligonucleotide entrapment in fibrin polymers (SOEF), the desired substance or functional group could be introduced into the fibrin gel with TBA [26].  
Recently, it has been shown that fibrin plays an important role when cancer cells metastasize or invade other organs, and that fibrinogen is abundant in the cancer stroma [11]. When considering SOEF,the bifunctional aptamer (bApt), which is a combined 29-mer TBA and camptothecin binding aptamer, can deliver and condense camptothecin as a potent anticancer drug around cancer cells and efficiently suppress their cell growth (Figure 1). Previously, we developed a camptothecin (CPT)-binding modified DNA aptamer (CMA-70), which is a base-modified DNA aptamer obtained by the systematic evolution of ligands via the exponential enrichment (SELEX) method targeting the camptothecin derivative (CPT1) (Figure 1) [27; 28; 29]. Therefore, the ability to selectively introduce CPT1 into fibrin gel using bApt as a drug carrier under the SOEF phenomenon provides a new strategy for cancer chemotherapy [30]. However, as a shortcut to the practical application of the said DDS, it is desirable to use an approved anticancer drug such as irinotecan, and thus, this study was conducted.
# Revisiting the Role of Knee External Rotation in Non-Contact ACL Mechanism of Injury
## 1 Introduction
There is a plethora of studies simulating the ACL response to different conditions after a reconstruction procedure [31]. However, accurate knee models are needed that mimic the average native ACL anatomy and the expected behaviour to common external forces that play a role during ACL injury (including ER). The simulation of soft tissues is a complex process due to the typical anisotropy of such biological materials. The soft tissue is, most of the time, simulated as an isotropic material (mainly the cartilage) or as a spring structure (mainly the ligaments) [31; 32; 33], which is not suitable for stress distribution calculation on the ACL [31]. The geometry of the ACL is often modelled as a spring structure [31] with a single bundle [31; 34], which is not fit to test the response of the ACL to different loading conditions. The use of two bundles is important to best mimic the native anatomy of the ACL, especially considering each bundle may play a different role [35]. Moreover, most models collect data from a single individual/specimen that, although true for that single subject, is probably not adequate to be generalized to an average individual (lack of external validity). Therefore, it would be more accurate to use average data for both bone and soft structures, but especially to pinpoint the exact ligament insertions. The present work focuses on a realistic geometry modelling that provides a detailed ACL structure with its two bundles and the surrounding connective tissue (instead of simulating the ACL as a single-spring structure). The material properties definition of the ACL model was applied to the bundles with the Holzapfel-Gasser-Ogden (HGO) to include the fibres and obtain a more realistic behaviour. The current study objective was to develop a realistic FEM model of the ACL (two bundles and exact morphometry of insertion points to the average population) and investigate which knee kinetics components result in a higher impact on ACL stress and strain during a simulated (FEM) ACL noncontact injury mechanism. We hypothesize that, contrary to the common literature, the tibial ER will have a more predominant impact than IR in ACL stress and strain.
## 3 Finite Element Analysis Results
### Quenching Analysis  
Building upon the heating analysis detailed in Section 3.1, quenching analysis was performed using a thermal-structural coupled method. Localized phase transformations can induce non-uniform volume changes and residual stresses, leading to structural deformation. This phenomenon is particularly significant in induction hardening, where rapid heating and cooling generate microstructural changes that influence mechanical stability [19]. Figure 8a presents the results under actual operational conditions, while Figure 8b shows the outcomes under ideal conditions, where the material was heated above 1000 \({}^{\circ}\)C and cooled within 11 s. A comparison of these two conditions reveals that, under actual operational conditions, the heating temperature did not exceed 1000 \({}^{\circ}\)C, and the cooling duration surpassed 12 s, failing to meet the criteria for martensitic transformation. As a result, the actual conditions exhibited a significantly lower martensite ratio compared to Figure 8b. As shown in Figure 9, the martensite ratio had a direct impact on the hardness distribution. The hardness results demonstrate that an increase in the martensite ratio corresponded to higher hardness values. Figure 9a illustrates the distribution under ideal conditions. Under actual conditions, the lower martensite content led to a less uniform hardness distribution, which could negatively affect the mechanical properties of the material. Additionally, transformation-induced plasticity (TRIP) effects may contribute to additional strain in regions where the martensite fraction is inconsistent. This transformation-induced strain can locally alter stress distributions, exacerbating residual stresses and deformation [20]. Following the quenching process, product distortion was evaluated to analyze its impact on assembly accuracy. The displacement results are shown in Figure 10. As indicated in Figure 10a, the z-axis displacement was most significant at Point P, measuring 0.94 mm. The cross-sectional analysis at Point P, presented in Figure 10b, reveals a height discrepancy relative to the design dimensions. Localized phase transformations occurring at Point P may have led to differential volume expansion and contraction, contributing to distortion. Point P corresponds to a welding section that connects to another component, and if the  
Figure 7: Temperature analysis of CTBA with and without skin effect: (**a**) with skin effect (**b**) without skin effect.  
height difference is not corrected, it may result in misalignment and potential welding defects. This could compromise the structural integrity of the product.