esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Natural Language Processing Tools in Orthopaedic Surgery: A Scoping Review
## 3 Results
### Audit  
Five studies explored the use of NLP for auditing within orthopaedic surgery. [PERSON] et al. used NLP algorithms to extract data from TKR operative reports and compared its accuracy to manual chart review [21]. They assessed the category of knee arthroplasty (total, unimportant, patellofemoral), laterality, constraint type, patella resurfacing and implant model. These showed an accuracy of 98.3%, 99.5%, 99.2%, 99.4%, and 99.9%, respectively. [PERSON] et al. completed a similar study using THR operation reports, assessing NLP accuracy in identifying the operative approach (antendrolateral, direct anterior, posterior), fixation (uncemented, cemented, hybrid and reverse hybrid) and bearing surface (metal on polyethylene, ceramic on polyethylene, metal on metal, ceramic on ceramic) [22]. They identified an accuracy of 99.2%, 90.7% and 95.8%, respectively. They also validated these results externally using operative reports from a different institution and found an accuracy of 94.4%, 95.6% and 98%, respectively. [PERSON] et al. later applied it to a further 39 operative THR reports from a private practice \"OrthoCarolina\" to refine the algorithm and validate its use externally on a broader scale, they identified an accuracy of 100% for operative approach, fixation and bearing surface compared to manual chart review [23].  
More broadly, [PERSON] et al. applied NLP algorithms to 1000 randomly selected operative and hospital notes for patients undergoing a primary arthroplasty [24]. They used preoperative, operative and postoperative variables in their NLP algorithm, showing accuracy of 96.3%, sensitivity of 95.2% and specificity of 97.4%. The algorithm was better at detecting structured data, i.e., range of motion, as opposed to unstructured author-dependent information, i.e., written complications. This was compared to the gold standard manual chart review. Finally, [PERSON] et al. utilised a pre-trained in-house NLP engine to categorise 5397 radiological reports (hand/wrist, elbow, shoulder, ankle, knee, pelvis/hip) to identify the incidence and age distribution of fractures during the COVID-19 pandemic [25]. In their study, the NLP engine achieved an F1 score of 0.81 when benchmarked against manual human annotation indicating that there was sound evidence to use these technologies for epidemiological studies, auditing of cases and real-time monitoring of fractures. Overall, these studies indicate that NLP has a promising role in data extraction from joint registries and can contribute to auditing and research processes.
# Fast Format-Aware Fuzzing for Structured Input Applications
## 3 Methodology
### I2S Recognizer
#### 3.2.1 De Bruijn Sequence  
We briefly present the concept of the de Bruijn sequence, its unique property, and the main idea of applying it to recognizing I2S dependencies.  
The de Bruijn sequence [21] is a kind of pseudo-random sequence [22], which is in one-to-one correspondence with Euler cycles in the de Bruijn graph [23]. Given a \(t\)-sized alphabet \(A=\{s_{0},s_{1},\ldots,s_{k-1}\}\), a \(k\)-ary, \(n\)-order de Bruijn sequence over \(A\) is a sequence \(\vec{a}=\{a_{0},a_{1},\ldots,a_{k^{n-1}}\}\) for which all the \(n\)-tuples are contained in a \(k^{n}\)-period of \(\vec{a}\). It also means that every \(n\)-tuple occurs once and only once in a \(k^{n}\)-cycle of the de Bruijn sequence, as a substring of consecutive symbols. Here, we denote all the \(k\)-ary, \(n\)-order de Bruijn sequences as \(B(k,n)\). For example, \(00010111\), \(01011100\), \(00111010\) are three de Bruijn sequences of \(B(2,3)\) over alphabet \(A=\{0,1\}\). It is straightforward to observe that the offset of each three-length substring is unique in the entire sequence within period \(2^{3}=8\). Likewise, \(aaabaacabbabcacabbbcccc\) is a de Bruijn sequence of \(B(3,3)\) over \(A=\{a,b,c\}\), where the length of substrings such as \(aba\), \(ccca\) is no less than three and is unique in the entire string within period \(3^{3}=27\).  
For an I2S field, the corresponding operand of the \(cmp\) instruction is direct-copy-related [24], meaning that it can be considered as a substring of the test case. In a de Bruijn sequence, each different subsequence has its unique offset. Therefore, a fast mapping table from \"substring\" to \"offset\" can be created, enabling matching the I2S field from its operand quickly, rather than costly traditional pattern matching across the whole test case.
# Blending Sea Surface Winds from the HY-2 Satellite Scatterometers Based on a 2D-Var Method
## 4 Results  
In this section, the 2D-Var approaches with different background error correlation functions are tested in order to look for an optimal configuration for blending the HSCATs winds. Hereinafter, the results derived from different 2D-Var schemes are denoted by the used error correlation functions, namely GSF and EBEC, respectively. Section 4.1 illustrates a typical 2D-Var experiment with different settings, and then a general verification is presented in Section 4.2.
## 4 Establishing the Data Fusion Kalman Filter Equations  
### State Equation  
By combining the flexural deformation angle models, the flexible lever arm model, and the error equations of the SRINS, we can establish the state matrices for three sets of SRINS data fusion as follows:  
\[\mathbf{X}=\left[\begin{array}{ccc}&\mathbf{X}_{SRINS_{1}}&\delta\mathbf{r}_{2}{}^{p_{1 }}&\mathbf{\theta}_{2}{}^{p_{1}}&\mathbf{\mu}_{2}{}^{p_{1}}&\mathbf{X}_{SRINS_{2}}&\delta \mathbf{r}_{3}{}^{p_{1}}&\mathbf{\theta}_{3}{}^{p_{1}}&\mathbf{\mu}_{3}{}^{p_{1}}&\mathbf{X}_{ SRINS_{3}}\end{array}\right] \tag{26}\]  
In the equations, \(\mathbf{X}_{SRINS_{1}}\) represents the 15-dimensional state matrix of \(SRINS_{2}\), and \(\mathbf{X}_{SRINS_{3}}\) represents the 15-dimensional state matrix of \(SRINS_{3}\). \(\delta\mathbf{r}_{2}{}^{p_{1}}\) represents the lever arm errors between \(SRINS_{1}\) and \(SRINS_{2}\), and \(\delta\mathbf{r}_{3}{}^{p_{1}}\) represents the lever arm errors between \(SRINS_{1}\) and \(SRINS_{3}\). \(\mathbf{\theta}_{2}{}^{p_{1}}\) represents the deformation angles between \(SRINS_{1}\) and \(SRINS_{2}\), and \(\mathbf{\theta}_{3}{}^{p_{1}}\) represents the deformation angles between \(SRINS_{1}\) and \(SRINS_{3}\). \(\mathbf{\mu}_{2}{}^{p_{1}}\) represents the deformation angle rates between \(SRINS_{1}\) and \(SRINS_{2}\), and \(\mathbf{\mu}_{3}{}^{p_{1}}\) represents the deformation angle rates between \(SRINS_{1}\) and \(SRINS_{3}\). Therefore, a 63-dimensional state transition matrix can be established as follows:  
\[\mathbf{F}=\left[\begin{array}{ccc}\mathbf{F}_{RINS_{1}}&\mathbf{O}_{15\times 48}\\ \mathbf{O}_{9\times 15}&\mathbf{F}_{r\theta 2}&\mathbf{O}_{9\times 39}\\ \mathbf{O}_{15\times 24}&\mathbf{F}_{RINS_{2}}&\mathbf{O}_{15\times 24}\\ \mathbf{O}_{9\times 39}&\mathbf{F}_{r\theta 3}&\mathbf{O}_{9\times 15}\\ \mathbf{O}_{15\times 48}&\mathbf{F}_{RINS_{3}}\\ \end{array}\right] \tag{27}\]  
In the formula, \(\mathbf{O}_{i\times j}\) represents an \(i\times j\) zero matrix, \(\mathbf{F}_{SRINS_{1}}\) represents the state transition matrix of \(SRINS_{1}\), \(\mathbf{F}_{SRINS_{2}}\) represents the state transition matrix of \(SRINS_{2}\), and \(\mathbf{F}_{SRINS_{3}}\) represents the state transition matrix of \(SRINS_{3}\). \(\mathbf{F}_{r\theta 2}\) and \(\mathbf{F}_{r\theta 3}\) represent the state transition matrices of the lever arms and flexural deformation angles between \(SRINS_{1}\) and \(SRINS_{2}\), and \(SRINS_{1}\) and \(SRINS_{3}\), respectively. Additionally:  
\[\mathbf{F}_{r\theta i}=\left[\begin{array}{ccc}\mathbf{O}_{3\times 3}&\mathbf{O}_{3\times 3}&-(r_{i}^{p _{1}}\times)\\ \mathbf{O}_{3\times 3}&\mathbf{I}_{3\times 3}&\mathbf{O}_{3\times 3}\\ \mathbf{O}_{3\times 3}&\mathbf{F}_{r\theta i32}&\mathbf{F}_{r\theta i33}\\ \end{array}\right]\]\[\begin{array}{c}\mathbf{F}_{r\theta i32}=\left[\begin{array}{ccc}-\beta_{ix}^{2}&0&0 \\ 0&-\beta_{iy}^{2}&0\\ 0&0&-\beta_{iz}^{2}\end{array}\right]\\ \\ \mathbf{F}_{r\theta i33}=\left[\begin{array}{ccc}-2\beta_{ix}&0&0\\ 0&-2\beta_{iy}&0\\ 0&0&-2\beta_{iz}\end{array}\right]\\ \\ i=2,3\end{array}\]
## Results and Discussion
### Formation of the BCGCuO Electrolyte Film on a LNFO\({}_{\rm P}\) Substrate  
To check if the effect of the LBNO\({}_{\rm nc}\) sublayer on the preservation of barium in the deposited BCGCuO electrolyte layer exists, the deposition of BCGCuO was performed on the LNFO\({}_{\rm P}\) substrate without the LBNO\({}_{\rm nc}\) sublayer. The EPD mode used was similar to that previously applied to obtain the BCGCuO/LBNO\({}_{\rm nc}\)/LNFO\({}_{\rm P}\) half-cell (U = 80 V, deposition time 1 min). The thickness of the electrolyte BCGCuO (10 \(\upmu\)m) was obtained after three cycles of deposition/sintering on an LNFO\({}_{\rm P}\) substrate (intermediate sintering between cycles were carried out at temperatures of 1200 \({}^{\circ}\)C, 2 h; 1350 \({}^{\circ}\)C, 2 h; and final sintering at 1450 \({}^{\circ}\)C, 2 h). Sintering at all the stages, including intermediate stages, was carried out in a protective filler, as it was suggested in [21].  
After the final sintering, barium was not detected in the electrolyte layer, thereby repeating the results obtained by sintering the BCGCuO electrolyte of the same thickness on the LBNO\({}_{\rm nc}\) sublayer, i.e., in the structure of BCGCuO/LBNO\({}_{\rm nc}\)/LNFO\({}_{\rm P}\). It can be concluded that the prevailing reason for the loss of barium may be associated with its diffusion into the substrate. However, due to low film thickness, evaporation can also have an influence on Ba loss, even during the sintering performed in the protective filler. In order to increase the thickness of the BCGCuO coating, four additional deposition/sintering cycles were carried out (intermediate sintering between the cycles was carried out at temperatures of 1200 \({}^{\circ}\)C, 2 h; 1200 \({}^{\circ}\)C, 2 h; 1350 \({}^{\circ}\)C, 2 h; final sintering--1450 \({}^{\circ}\)C, 2 h) with a total thickness of about 28 microns. Sintering at all stages was performed with a bulk of barium carbonate. According to the EDX data, Ba content in the electrolyte layer was \(\sim\)19.3 at.%, while La and Gd content were 1.3 and 1 at.%, respectively, due to interdiffusion which occurred between the LNFO  
Figure 7: SEM images of the BCGCuO/LBNO\({}_{\rm nc}\)/LNFO\({}_{\rm P}\) half-cell with the BCGCuO electrolyte after 5 cycles of deposition/sintering: the electrolyte surface (**a**); EDX mapping electrolyte surface images—integrated (**b**) and obtained for the individual elements (**c**); the integrated EDX mapping image of the BCGCuO/LBNO\({}_{\rm nc}\)/LNFO\({}_{\rm P}\) half-cell cross-section taken after the electrochemical study (**d**).  
substrate and the BCGCuO electrolyte layer. The content of Ba and Gd turned out to be higher in comparison with the BCGCuO/LBNO\({}_{\text{nc}}\)/LNFO\({}_{\text{P}}\) sample, which is probably due to the greater thickness of the coating.
###### Abstract  
Food-grade animal by-products are a source of bioactive peptides that could be used as natural antioxidant compounds. Until now, only few studies have put attention to the research of the most effective enzyme(s), to the antioxidant power of collagen peptides in food matrix and to the consideration of a mineralized collagen tissue such as bone, in particular the bovine one. Hence, this study aimed to investigate the effect of enzymes of different origins (animal, plant and microbial) and the effect of processing parameters such as the enzyme-collagen ratio, the steam sterilisation, the concentration of peptides and the mixing of peptides obtained by different enzymes on their antioxidant activity. Low molecular weight peptides (<3000 Da) were produced by the hydrolysis of bovine bone collagen by bromelin, cathepsin B and collagenase B. The antioxidant activity towards lipids peroxidation in meat and butter and the quenching capacity toward free radical were assessed. The bone minerals calcium, magnesium and phosphorous were also present in solution. Peptides-minerals complexes showed a significant antioxidant activity, which depended on the enzyme and on the test used to measure the antioxidant power; collagenase B showed the highest antioxidant capacity, which was further improved by peptides mixing and concentration; sterilization had no adverse effect on the antioxidant capacity. The results of this study can corroborate that food-grade animal by-products, such as the bovine bone, could be valorised for high-value food and health application, which can contribute to a circular use of the agro-food resources.  
collagen peptides; natural antioxidants; animal by-products valorization; bone minerals +
Footnote †: journal: Applied Sciences
# Heat Transfer Performance of Fruit Juice in a Heat Exchanger Tube Using Numerical Simulations
## 2 Numerical Simulations  
### Case Study and Mesh Generation  
A smooth curved tube heat exchanger made of stainless steel with an inner diameter of 18 mm and a wall thickness of 1 mm (see Figure 1) was used. The total length of the tube was approximately 1.6 m, comprising two equal straight pipe sections of 0.75 m long and an elbow with a radius of 0.047 m and 0.148 m arc length. Two different \(HTF\)s were studied: liquid water and a commercially available fruit juice. First, the numerical scheme and the computational mesh were validated comparing the numerical results in turbulent regime obtained with water as the \(HTF\), using widely recognised correlations available in the literature. The same mesh was then used to obtain the results of the non-Newtonian fruit juice in laminar regime. In both cases, to create a 3D mesh, the meshing process was performed with ANSYS Workbench v. 19.2 (ANSYS, Inc., Canonsburg, PA, USA) [21].  
Regarding the simulations using water as the \(HTF\), an unstructured grid was defined with a refined mesh near the wall to ensure that the laminar viscous sub-layer was captured (see Figure 2). In this case, the Realizable \(k-\epsilon\) turbulence model with enhanced wall treatment was used to model the turbulence. The selected model is one the most commonly used to model the turbulence, considered consistent with the physics of turbulent flows, and characterised by its robustness and accuracy in heat transfer applications [21; 22]. Several studies have used this model, especially in cases which analysed heat transfer in helically coiled tube heat exchangers [18] or studies related to analysing spirally corrugated tubes [23]. According to this model, the quality of the mesh near the wall was defined considering parameter \(y^{+}\), which is defined as a non-dimensional distance to describe the fineness of the mesh for a specific flow condition [24]. Parameter \(y^{+}\) is defined as follows:  
\[y^{+}=\frac{y\,u_{\tau}}{v} \tag{1}\]  
Figure 1: Case study.  
where \(y^{+}\) is the dimensionless distance to the wall, \(y\) is the distance to the wall, \(u_{\tau}\) is the friction velocity and \(\
u\) is the kinematic viscosity.  
A grid independence analysis was carried out to examine the influence of the mesh size for the turbulent numerical simulations using water. Three unstructured grids with a refined mesh near the wall were used for the numerical simulations, representing a total of 402,000 (Grid 1), 510,000 (Grid 2) and 703,000 (Grid 3) tetrahedral cells. In total, 14 layers were defined at each grid and the first layer thickness yielded values of 0.06, 0.01 and 0.007 mm at Grids 1-3, respectively.  
Regarding the simulations using fruit juice as the \(HTF\), the grid used was the one selected in the previous simulations carried out under turbulent regime.
# Reconditioning of Diamond Coated Tools and Its Impact on Cutting Performance for CFRP Laminates
## 2 Materials and Methods  
### Experimental Procedures  
In this study, as shown in Figure 1, the substrate pretreatment (WCE), B-MCD deposition (CVD), CFRP cutting (CT), and film decoating (RIBE) were conducted repeatedly on one-shot cemented carbide drills in order to emulate the practically viable tool life cycle (TLC). The tool substrate and the film properties were investigated at each step using a combination of surface characterization techniques such as scanning electron microscopy (SEM), confocal scanning laser microscopy (CSLM), and Raman spectroscopy. Moreover, the cutting performance was evaluated during the CT through flank wear and CFRP hole quality assessment. The evaluation was performed for three consecutive TLCs, including the TLC0 (WCE0, CVD0, and CT0)--a newly deposited tool, the TLC1 (RIBE1, WCE1, CVD1, and CT1)--a first-time reconditioned tool, and the TLC2 (RIBE2, WCE2, CVD2, and CT2)--a second-time reconditioned tool.  
Figure 1: Tool life with multiple reconditioning cycles.  
The CT0, CT1, and CT2 machining were interrupted prior to the major tool damage occurrence in order to limit the wear to the B-MCD film and not the WC-Co substrate. Due to this tool-saving approach, no tool regrinding was necessary.
# Generative Adversarial Network for Overcoming Occlusion in Images: A Survey
## 5 GAN in Amodal Completion
### Amodal Appearance Reconstruction  
Recently, there has been a significant progress in image inpainting methods, such as the works in [65; 74]. However, these models recover the plausible content of a missing area with no knowledge about which object is involved in that part. On the contrary, amodal appearance reconstruction (also known as amodal content completion) models require identifying individual elements in the scene, and recognizing the partially visible objects along with their occluded areas, to predict the content for the invisible regions.  
Therefore, the majority of the existing frameworks follow a multi-stage process to address the problem of amodal segmentation and amodal content completion as one problem. Therefore, they depend on the segmentator to infer the binary segmentation mask for the occluded and non-occluded parts of the object. The mask is then forwarded as input to the amodal completion module, which tries to fill in the RGB content for the missing region indicated by the mask.  
Among the three sub-tasks of amodal completion, GAN is most widely used in amodal content completion. In this section, we present the usage of GAN in amodal content completion for a variety of computer vision applications.
## 1 Introduction
To the best of our knowledge, no existing framework integrates AR with adaptive accessibility while utilizing methods and tools from game-learning analytics. Our framework addresses this gap by enabling the automated tracking of user interaction data, mapping user abilities and/or preferences to interaction parameters, and dynamically adjusting accessibility settings to improve usability for individuals with various disabilities and of different ages. Furthermore, while accessibility in AR-based games is an emerging field, existing solutions often lack real-time adaptability and personalized accessibility adjustments. Our approach fills this gap by introducing a methodology for automated data collection, storage, and analysis of interaction parameters within the accessibility context, allowing for customized content and interaction mechanisms based on individual needs. Additionally, the flexibility of our framework allows its application beyond AR-based serious games, extending to digital games in general and contributing to broader digital inclusion efforts. The sections of this paper are organized as follows. Section 2 introduces the topic of user needs in AR in the context of accessibility as well as investigates the current state in the field of adaptation to the user in AR and tracking user interactions in serious games. In Section 3, we propose a model for tracking user interactions in AR-based serious games in the context of accessibility. Section 4 then presents the methodology for automated collection, storage and analysis of user interactions. The implemented technology platform that is based on the proposed model and methodology is then described along with the proposed framework and its key parts. An experiment with user testing is described in Section 5 along with a discussion of the results. Conclusions and future research are presented in Section 6.
## 3 Results and Discussion
### The AVISPA Results
The secret key is computed \(SK_{i}=A_{th}^{*}\oplus h(PID_{i}^{*}\parallel PWR_{i}^{*}\parallel TID_{i}) \oplus h(\sigma_{i}^{*}\parallel R_{i}^{*})=h(HGW_{ID}\parallel PID_{i} \parallel HGW_{MSK})\), and it needs \(PID_{i}\), \(R_{i}^{*}\), and \(\sigma_{i}^{*}\) only known to the user. If the adversary somehow reveals the secret key of both user and gateway, he/she also needs to know \(PID_{i}\), which is protected using a one-way hash function, and the random number \(R_{i}^{*}\). The complexity of guessing the secret key and the random number chosen by the user or sensor node in polynomial time using any powerful computer is amazingly massive and almost impossible. As a result, the proposed scheme preserves forward secrecy. * Mutual authentication: The proposed scheme provides mutual authentication because the agriculture professional sends the login message, say \(M_{1}=[SN_{ID},TID_{i},\) \(CT_{i}]\), to the HGWN via a public channel. Upon receiving message, the HGWN verifies the \(TS_{1}\) by selecting a new timestamp \(TS_{2}\) to check the freshness \(|TS_{2}-TS_{1}|\leq\triangle T\), where \(\triangle T\)is allowed transmission delay. Furthermore, it decrypts \(CT_{i}=D_{SK_{i}}[HGW_{ID}^{*},SN_{ID}^{*},W_{i}^{*},N_{i}^{*},TS_{1}^{*}]\), where \(D_{SK_{i}}\) depicts the decryption of a symmetric key using the key \(SK_{i}\). After retrieving the information, HGWN verifies the timestamp \(|TS_{1}^{*}-TS_{1}^{*}|\leq\triangle T\), where \(TS_{1}^{*}\) is the message receiving time. If it holds, it checks \(HGW_{ID}^{*}\
eq HGW_{ID}\), and \(SN_{ID}^{*}\
eq SN_{ID}\), and if these parameters are valid, it computes \(W_{i}^{**}=h(PID_{i}\parallel TID_{i}\parallel N_{i}^{*})\) based on the stored \(PID_{i}\), and \(TID_{i}\), then checks \(W_{i}^{**}\
eq W_{i}^{*}\). If it does not hold, it terminates the session. The verification will fail here since the validation depends on the one-way hash function. Therefore, mutual authentication is provided in the proposed scheme among all the participants. * Multi-gateway supports: In the proposed scheme, multi-gateways (e.g., HGWN and FGWN) are registered with SA to enable agriculture professionals to authenticate to a sensor node with other fields. When the HGWN receives the login message, it checks if the HGWN database contains \(SN_{ID}\), and performs HGWN authentication (Case 1); otherwise, it performs FGWN authentication (Case 2). Therefore, the proposed scheme supports multi-gateway authentication.