esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Problem Statement
### Formal Security Model  
The real-or-random (RoR) model is employed to provide the formal security proof of this work. The model is a well-known tool used to analyze the probability of the adversary breaking the cryptographic schemes [35]. In the model concerned, there exist two communicating parties, namely patient \(P\) and provider server \(S\), which is consistent with the entities of the proposed protocol. They carry out the communications via an open internet channel. In the model, \(\mathbb{C}\) is a protocol challenger, and \(M\) is a message communicated by \(P\) and \(S\). \(\mathcal{A}\) would execute the following queries to launch various attacks.  
* \(\textit{Send}(\mathbb{C},\,M)\): \(\mathcal{A}\) is allowed to request \(M\) to \(\mathbb{C}\); \(\mathbb{C}\) replies to \(\mathcal{A}\) in accordance with the rules of the proposed protocol.
* \(\textit{Execute}(P,S)\): This passive attack allows \(\mathcal{A}\) to eavesdrop on the message communicated by \(P\) and \(S\).
* \(\textit{Reveal}(\mathbb{C})\): In this attack, \(\mathcal{A}\) attempts to retrieve the session key generated by \(\mathbb{C}\) based on the rules of the protocol.
* \(\textit{Corrupt}(P,x)\): In my proposed protocol, this query returns the password of the patient, the biometrics of the patient and the parameters stored on the smart card and the device to \(\mathcal{A}\) if \(x=1\), \(x=2\) and \(x=3\), respectively.
* \(\textit{Test}(\mathbb{C})\): This query allows \(\mathcal{A}\) to request the session key from \(\mathbb{C}\); \(\mathbb{C}\) replies to \(\mathcal{A}\) based on the probabilistic outcome of the coin \(c\) tossed.  
**Definition 4**: _Let \(\textit{Adv}_{\mathbb{C}}^{DNAHC}\) be the advantage of \(\mathcal{A}\) running in polynomial time in semantically breaking the security system of the proposed protocol, where \(\textit{NDAHC}\) denotes the protocol (for DNA-based U-healthcare). We obtain \(\textit{Adv}_{\mathbb{C}}^{DNAHC}=|2 Pr[c^{\prime}=c]-1|\), where \(c^{\prime}\) is the guessed bit of the session key._
# The eIDAS Regulation: A Survey of Technological Trends for European Electronic Identity Schemes
## 9 Lessons Learned
### Privacy Considerations
#### 9.2.1 General Considerations  
In the following, we highlight how the \"Accountability\" and \"Federated or Direct Login\" implementation choices can affect privacy level of the eID schemes.  
* **Accountability**: in some use cases, the service providers may need additional information to precisely identify users to provide access to their resources. Thus, based on the form of identifier (e.g., pseudonymized identity) that is considered in the eID schemes, there could be greater accountability or privacy;
* **Federated or Direct login**: Federated solution means a centralized system exists to handle the identification and authentication. While it can simplify the management of authentication for the service providers, it will come with the cost of user tracking across the system if no other measures against that are in place. In the case of direct login, there is not a single entity that can know about a user's authentication actions. Thus, in this way, it can increase the user's privacy by preventing an entity from learning about all the user's activity across different service providers. However, this comes with the cost of handling the authentication and all the relevant overhead that comes with it on service providers. Furthermore, it has the problem of interoperability issues with the other eID schemes. Regarding interoperability, the eIDAS framework implements a proxy to solve the problem, with the cost of creating a federation at the eIDAS level [27].
# Application of the Extended Reality Technology for Teaching New Languages: A Systematic Review
## 3 Results
### Facilitating Language Learning  
Eleven articles reported that MR technology has been a successful tool to facilitate the process of learning amongst educational institutions [24; 25; 26; 27; 28; 29; 30]. [PERSON] [26] states that the use of computer game-based lessons positively impacts vocabulary acquisition, vocabulary learning, vocabulary retention and writing tasks. [PERSON] and [PERSON] [30] explored the effects of MR technology on students' learning performance and revealed that MR technology has a notable positive effect on language learning and achievements due to the nature of its flexibility, efficiency, and accessibility. Meanwhile, [PERSON] et al. [31] showed that most students found MR technology enjoyable and effective, because it could present a real appearance like learning in a class environment and created a feeling of engagement. [PERSON] and [PERSON] [27] believe that MR technology can facilitate collaborative learning and increase students' fluency in a language, leading to a better understanding of the education materials. Furthermore, [PERSON] et al. [28] claim that the MR technology improves the quality of the learning experiences and supports students' motivation and creative self-sufficiency.  
Figure 2: Distribution of the selected papers by the year of publication.
# Double-Shot Transfer Learning for Breast Cancer Classification from X-Ray Images
## 2 Materials and Methods
### Dataset Description
#### 2.1.1 CBIS-DDSM Dataset  
DDSM is a public resource for providing the research community with mammographic images to facilitate and enhance the development of computer algorithms and training aids in order to develop an effective CAD system. It is a collaborative work between Massachusetts General Hospital, Sandia National Laboratories, and the University of South Florida Computer Science and Engineering Department [17]. Curated breast imaging subset of DDSM (CBIS-DDSM) is an updated version of the DDSM. This dataset contains normal, benign, and malignant cases with verified pathology information. The CBIS-DDSM collection contains a subset of the DDSM data organized by professional radiologists. It also contains bounding boxes, pathological diagnosis, and ROI segmentation for training data. After eliminating the corrupted and noisy images as shown in Figure 1, the number of images has been reduced to 7277 images of abnormal cases [18; 19]. These abnormal images include 4009 benign and 3268 malignant cases.
## 4 Results  
### Initial Sensitivity Analysis  
The sensitivity of GPP varied across the six model parameters listed in Table 1, with the phosphorus resorption coefficient of leaf (rpleaf) being the most influential, followed by the empirical parameter relating plant phosphorus uptake rate to labile pool size in the soil (kpup) (Figure 2a). Other parameters had only slight or negligible influence on GPP, including rpwood, rroot, kp biotochem, kplasor and psorbmax. Since rpleaf is used to calculate pleaf, which regulates stomatal conductance and photosynthesis, we focused on rpleaf as the target parameter to be adjusted.  
Figure 2: Sensitivity of (**a**) GPP and (**b**) biomass to variation in six key parameters and (**c**) four main meteorological factors. Red points represent mean values of the sensitivity index, and the dotted line describes where y = 0. In panel (**a**,**b**), rpeaf, rpwood, and rroot are phosphorus resorption coefficients of leaf, wood and root, respectively; kp, an empirical parameter for phosphorus limitation on net ecosystem productivity (NPP); kpbiochem, an empirical constant for biochemical phosphorus  
mineralization; kplabsor, an empirical parameter for describing the equilibrium between labile and reabsorbed phosphorus; kpup, an empirical parameter relating plant phosphorus uptake rate to labile pool size in the soil; and psychmax, the maximum amount of absorbed phosphorus. In panel (**c**), the sensitivity of GPP (blue boxes) and amount of leaf phosphorus (red boxes) to variations in the indicated variables are shown.  
For total biomass, the sensitivity analysis results of the parameters indicate the rpleaf is the most sensitive parameter (Figure 2b), and its value is about 0.74. The other parameter kpup is less sensitive to biomass than GPP. The remaining parameters including rpwood, rproot, kp, kkpiochem, kplasor and psychmax are less sensitive to total biomass. This also provides a basis for us to determine the adjustment parameter rpleaf.  
With respect to GPP, the mean values of SI were all greater than zero for the four meteorological factors (Figure 2c): precipitation, 3.11; near-surface temperature, 1.87; vapor pressure, 0.80; and cloud cover, 0.17. It could be concluded that for the majority sites, the precipitation was the most sensitive meteorological factor to GPP and there was a significant correlation between them. At all sites, GPP generally increased as precipitation increased. The absolute values of maximum reductions of GPP under \(-20\%\) were greater than the absolute values of maximum increases under \(+20\%\) for the two precipitation gradients tested. This suggests that simulated GPP was more sensitive to drought, less sensitive to vapor pressure, and was not substantially affected by cloud cover.  
Also, in Figure 2c, with respect to the amount of leaf phosphorus, mean SI values were again positive (vapor pressure, 0.63; precipitation, 0.53; cloud cover, 0.06), except for near-surface temperature (\(-3.79\)), and this factor was the most influential on pleaf.
## 2 Materials and Methods
### Test Equipment and the Procedure  
The testing method allows for continuous mass measurement of the test specimens during the experiment that generates parameters such as mass loss and relative mass loss rate over time. The test equipment consists of the parts depicted in Figure 2.  
The specimens were attached horizontally. During the experiment they were exposed to a flame heat source applied perpendicularly from below. The used fuel was propane-butane gas. The flame height on the burner was set to 5 cm. The mouth of the burner was positioned at a distance of 4 cm from the specimen (1 cm of flame extending into the specimen).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Thermowood from** & **Thermowood from** & **Untreated Wood** \\  & **Prism** & **the Floor** & **U\(-\)R** \\ \hline
**Exposed side-Radial** & TP\(-\)R & TF\(-\)R & U\(-\)R \\ \hline
**Exposed** & TP\(-\)T & TF\(-\)T & - \\
**side-Tangential** & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Designation of individual specimen groups.  
Figure 1: Tested specimens and the direction of flame.  
During the experiment, the mass was continuously recorded at 10 s intervals. The accuracy of the mass measurement was ensured by Mettler Toledo MS16025/M01 (Greifensee, Switzerland) scales to the hundredths of grams. Accurate measurement times at set intervals and automatic recording of masses were provided by BalanceLink 4.2.0.1 (Mettler Toledo, Greifensee, Switzerland). The exposure time of the specimens to the flame was 10 min. Afterwards, the flame was extinguished while the mass was still continuously recorded for 3 min after its extinguishing.  
In addition, the temperatures on the unexposed specimen sides were recorded with a digital thermometer at times of 2, 4, 6, 8, 10, and 13 min during the experiment.
# New Candidate Multicomponent Chalcogenide Glasses for Supercontinuum Generation
## 1 Introduction
While not exclusive to the application of SC generation, this glass system illustrates how morphology and microstructural tailoring with a secondary phase of known chemistry, refractive index, size, and size distribution can result in composites with broadly tailorable linear and nonlinear optical and physical properties within a narrow composition space. GAP-Se materials have recently been shown to exhibit desirable transparency over a wide range of wavelengths from 1 to 12 \(\upmu\)m, high linear/nonlinear refractive indices, and expansive property tunability via composition alloying and microstructural tailoring [56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66]. However, like most covalently-bonded ChG made from large ions, weak(er) bonding (as compared to oxides) can lead to reduced thermal/mechanical properties, photosensitivity when pumped with near bandgap light, and low(er) laser damage resistance at the benefit of broad IR transparency [19; 27]. Following a short review of the state-of-the-art understanding of key optical properties requisite for SC applications, we present examples highlighting the novelty and scalability of our approach based upon planar bulk and film forms of GAP-Se glasses. We discuss where our model system stands in terms of their attributes compared to other material systems as an example to exploit performance optimization for potential use in planar SC applications.
## 3 Labyrinthine Slit Perforations in a Rigid-Porous Matrix
Also shown (dash dot lines) are predictions for these materials without perforation. Figure 2: (**a**) Plot of the modulus of the pressure diffusion correction function, \(|F_{d}(\omega)|\), against frequency for a vertical-wall labyrinthine slit (\(w\) = 1 mm, \(b\) = 3 mm, \(l\) = 45 mm, \(N\) = 10) in a slanted slit pore matrix (0.04 mm wide slits at 70\({}^{\circ}\) to the normal, porosity 0.9). (**b**) Plot of the corresponding imaginary part of the double porosity bulk modulus. The absorption peaks at 240 Hz in Figure 4a and 170 Hz in Figure 4b correspond to the layer thickness being 1/28 th and 1/40 th of the incident wavelength, respectively. Also shown are absorption spectra predicted for the labyrinthine slit perforations in a non-porous rigid solid structure (broken black lines in Figure 4a,b) which result in rather narrow absorption peaks. For a horizontal-wall configuration such as that shown in Figure 1a, if the wall width, labyrinthine slit width and slit folding number and hence the corresponding layer thickness, \((N+1)w+Nb\), are fixed, then the tortuosity and hence the frequency of the lowest-frequency absorption peak are determined by the wall length. In a vertical-wall configuration such as that shown in Figure 1b, if the wall width, wall length, and labyrinthine slit width and hence the layer thickness, \(l+b+2w\), are fixed, then the tortuosity and hence the frequency of the lowest-frequency absorption peak are dominated by the number of folds in the slit, N. These influences are illustrated in Figure 5a,b. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Material/Parameter** & \(\Omega_{m}\) & \(\sigma_{m}\)**kPa s m\({}^{-2}\)** & \(T\) & \(\mathbf{k_{0}\)**m\({}^{2}\)** & \(\Lambda\)**\(\mu\)m** & \(\Lambda^{\prime}\)**\(\mu\)m** \\ \hline rockwood & 0.94 & 135 & 2.1 & \(3.3\times 10^{-9}\) & 49 & 166 \\ melamine foam & 0.99 & 12 & 1.01 & \(1.5\times 10^{-9}\) & 100 & 400 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: JCAL model parameter values assumed for rockwood [2] and melamine foam [9]. Figure 4: Normal incidence absorption spectra predicted for a 5 cm thick hard-backed inclined slit matrix (0.04 mm wide slits at 70\({}^{\circ}\) to the normal, porosity 0.9) with (solid lines) and without (dotted lines) (**a**) a horizontal-wall labyrinthine slit perforation (\(w\) = 2 mm, \(b\) = 4 mm, \(l\) = 40 mm, \(N\) = 8) and (**b**) a vertical-wall labyrinthine slit perforation (\(w\) = 1 mm, \(b\) = 3 mm, \(l\) = 45 mm, \(N\) = 10). The dash-dot lines represent predictions for the labyrinthine slit perforations in a non-porous solid matrix. In a horizontal-wall labyrinth with fixed \(w\), \(b\) and \(N\), increasing the wall length, \(l\), is predicted to decrease the frequency of the lowest-frequency absorption peak but to decrease its magnitude also. Similarly, in a vertical-wall labyrinth with fixed \(w\), \(b\) and \(l\), increasing the slit folding number, \(N\), is predicted to decrease the frequency and magnitude of the lowest-frequency absorption peak.
# A Survey on Multi-User Conversational Interfaces
## 4 Capturing Multi-User Conversational Interfaces
### Application Domains and Examples
#### 4.1.4 Tutoring  
Another discipline in MUCIs involves educational settings. [PERSON] et al. [24] observe a tutoring scenario in which two students interact with two embodied AI agents within a virtual world called 'Cognitive Immersive Language Learning Environment'. This interaction serves the purpose of familiarising students with vocabulary and cultural knowledge in a playful manner. As a specific use case, the authors modelled a learning scenario where users are sent to virtualised street markets in China.  
[PERSON] et al. [25] also discuss the integration of embodied dialogue tutors in an educational context. Here, their aim is to facilitate MU discussions, encourage collaboration, and ensure equal participation among children. They also assess children's responses to text-based questions, providing insights into their comprehension and engagement. In the created educational setting, a tutor agent engages in a conversation with two children. The tutor asks questions related to texts the children have studied and assesses their reading comprehension through these questions. Additionally, the tutor promotes collaboration between the children and assists them in finding the correct answers within the text. It is important to note that the integration described in the paper is currently a conceptual framework designed primarily for data collection purposes, with no real system implementation in place at the time of publication.  
Moreover, [PERSON] et al. [26] describe a tutoring system to overcome the fear of public speaking using a virtual reality environment. Here, a participant is standing on a stage and tasked to speak in front of a mixed audience consisting of humans and computer-controlled avatars.  
To help learners develop their argumentation skills effectively, a tutoring system called Metalogue has been developed to provide real-time feedback during debates [27; 28]. This feedback enhances the learning experience by adapting posture, volume, and tone adjustments. The system generates various performance analyses while ensuring that real-time feedback is delivered in a balanced manner to prevent cognitive overload. A reflection dashboard allows for detailed review and analysis of learner performance, fostering awareness of the specific aspects and behaviours being trained.
###### Abstract  
Studies examining the effect of operator experience on the accuracy of static guided implant surgery have used postoperative computed tomography (CT) images to measure the error, with inconsistent results. The purpose of this study was to try to clarify this issue by using a measurement method based on the postoperative optical scan. Thirty dentists were divided into an experienced group and an inexperienced group. On a partially edentulous mandibular model in the manikin head, each dentist placed three implants via the stereolithographic (SLA) surgical guide. The implant positions were identified by a desktop scanner and compared with the planned positions using a metrology software program. No statistically significant differences were observed for any of the measured positional and angular deviations of the three implant sites between experienced and inexperienced operators (\(p>0.01\)). All the mean values of deviations of the inexperienced group, except the depth deviation, were less than the experienced group. Implants inserted by dentists under 40 years old had significantly better accuracy than senior doctors in the global deviation at implant apex (\(p=0.006\)). Within the limits of this study, we concluded that operator experience is not a critical factor in achieving the accuracy of guided implant surgery via the tooth-supported SLA surgical guide. Large deviations could occur even with the aid of the SLA surgical guide, and care must be taken to avoid errors for both experienced and inexperienced operators.  
stereolithographic surgical guide; optical scan; 3D-planning; accuracy; computer-aided design/computer-aided manufacturing (CAD/CAM); guided implant surgery; operator experience
# An Active-Clamp Forward Inverter Featuring Soft Switching and Electrical Isolation
## 3 Operation Principles
### State 4 (\(t_{3}<t<t_{4}\), Figure 6d)  
At the time \(t=t_{3}\), the leakage inductor current \(i_{Lr1}\) is charging the clamp capacitor \(C_{C1}\) via the anti-parallel diode \(D_{a1}\) of the auxiliary switch \(S_{a1}\), so the voltage across \(S_{a1}\) is zero. The equivalent circuit is shown in Figure 6d. Since the anti-parallel diode \(D_{a1}\) is turned on, the gate driving signal of the auxiliary switch \(S_{a1}\) must be provided during this state to achieve ZVS. At this time, the primary voltage \(v_{p1}\) is zero. The clamp-capacitor voltage \(v_{cc1}\) and the leakage inductor current \(i_{Lr1}\) can be expressed as  
\[v_{cc1}(t)\ =\ V_{in}-[V_{in}-v_{cc1}(t_{3})]\cos\omega_{3}(t-t_{3})+i_{Lr1}(t_{3} )Z_{3}\sin\ \omega_{3}(t-t_{3}), \tag{16}\]  
\[i_{Lr1}(t)\ =\ i_{Lr1}(t_{3})\cos\omega_{3}(t-t_{3})+\frac{V_{in}-v_{cc1}(t_{3} )}{Z_{3}}\sin\ \omega_{3}(t-t_{3}), \tag{17}\]  
where \(\omega_{3}\ =\ \frac{1}{\sqrt{L_{r1}C_{s1}}}\), and \(Z_{3}\ =\ \sqrt{\frac{L_{r1}}{C_{s1}}}\). When \(i_{Lr1}\) is equal to \(i_{Lm1}\), the forward diode current \(i_{D1}\) drops to zero, and this state ends.
# Modeling and Calculation of Limit Magnitude Detection of Orbital Optoelectric Tracking System
## 3 The Limit Magnitude Detection Capability of the Orbital Optoelectric Tracking System Modeled  
The detection capability of the orbital optoelectric tracking system is mainly affected by the target and noise, and the following gives the optoelectric imaging information characteristic model from these two aspects with the limiting magnitude.
## 4 Results
The results obtained for low modulus ratio shows that decreasing the borehole depth has no effects on hard rock, whereas, increased borehole spacing values were obtained for both soft rock and normal rock when the borehole depth value was decreased up to 1.2 m. So in case of low modulus ratio, the borehole depth value of 1.2 m is proposed to be used in soft rock and normal rock for utilizing increased borehole spacing values as compared to those typically used. The results for the case of average modulus ratio generally suggest that by employing 1.2 m borehole depth, increased borehole spacing values can be used in soft rock, normal rock, and hard rock. So in case of average modulus ratio, it is proposed to use 1.2 m borehole depth in all three types of rock. The effect of decrease in borehole depth on the maximum borehole spacing results in case of high modulus ratio was observed to be less than the cases of other two modulus ratio, for all three types of rock because the maximum borehole spacing values were obtained to be same when borehole depths of 1.3 and 1.2 m were used. Based on this observation, it is proposed that either 1.3 or 1.2 m borehole depth values can be used in case of high modulus ratio for soft rock, normal rock, and hard rock in order to utilize increased borehole spacing. Figure 13: Comparison of maximum borehole spacing values at different intact rock strengths (borehole depth = 1.4 m). Figure 16: Effect of borehole depth on maximum borehole spacing values at different intact rock strengths (average modulus ratio). Figure 14: Rock splitting model using constant borehole spacing (0.7 m) with three different borehole depths, (a) borehole depth = 1.4 m; (b) borehole depth = 1.3 m; (c) borehole depth = 1.2 m.  
Figure 15: Effect of borehole depth on maximum borehole spacing values at different intact rock strengths (low modulus ratio).