esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The integration of artificial intelligence in grading clinical reports within the context of OSCEs is a promising tool. It can provide consistent and objective results while alleviating some of the inherent challenges of manual correction, such as the subjectivity and fatigue of human evaluators.  
Formulating assessment items plays a crucial role in the effectiveness of automated grading. Ambiguous or low-quality evaluation items can hinder the performance of both AI and human graders.  
A hybrid model, where AI and human evaluators collaborate, emerges as the most practical and ethical approach. Such a model maintains human oversight throughout the process, ensuring that ethical considerations are upheld, especially given the high-stakes nature of exam scores on students' futures.  
Future studies should focus on refining AI algorithms to better handle nuanced language and complex medical scenarios, as well as exploring ways to optimize hybrid models. Expanding the scope of research to diverse institutional contexts will be crucial to validate the generalizability of these findings. By advancing these efforts, AI can be effectively integrated into the educational landscape, ensuring both efficiency and fairness in student evaluations.  
Conceptualization: D.L., M.T.A., C.T.C. and M.V.V.G. Methodology: D.L., M.T.A. and C.T.C. Software: L.M.R.C. Validation: K.D.S.S., J.D.J., M.P.C., L.M.R.C., J.J.G.M., F.J.G.S., A.L.G.-A., R.R.G., R.D.L., Y.T.-C. and M.V.V.G. Formal analysis: J.J.G.M., F.J.G.S., A.L.G.-A. and R.R.G. Investigation: D.L., M.T.A., C.T.C., L.M.R.C., J.J.G.M., F.J.G.S., A.L.G.-A., R.R.G., R.D.L., Y.T.-C. and M.V.V.G. Resources: [PERSON], F.J.G.S. and A.L.G.-A. Data curation: L.M.R.C. Writing--original draft: [PERSON], M.T.A. and C.T.C. Writing--review and editing: [PERSON], [PERSON], M.P.C., [PERSON], [PERSON], F.J.G.S., A.L.G.-A., R.R.G., R.D.L., Y.T.-C. and M.V.V.G. Visualization: L.M.R.C. Supervision: [PERSON], [PERSON] and M.V.V.G. All authors have read and agreed to the published version of the manuscript.  
The funding for this study was provided by the Spanish Society for Medical Education (SEDEM) through Grant 3/2024.  
The study was conducted in accordance with the guidelines of the Declaration of Helsinki, and the requirement for IRB approval was waived.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** Some of the authors of this study ([PERSON], [PERSON], [PERSON], and [PERSON]) are owners of the company DIGIT-ECOE S.L., which provided the software support used for the development of the exam. However, this article is not intended to promote the applications of DIGIT-ECOE, and the authors do not receive any commercial benefit from its mention in the study. The artificial intelligence technology used for exam correction is ChatGPT-4, owned by OpenAI, which is not related in any way to DIGIT-ECOE. Therefore, the authors declare no conflicts of interest that could influence the results or interpretations of this work.
# A Decision Framework for Choosing Telecommunication Technologies in Limited-Resource Settings
## 5 Networks in the Design Phase
### Papua New Guinea  
A network has been proposed as a means of distributing learning materials to primary and secondary schools, as well as offering IEEE educational courses to community members in an area surrounding a plantation in Madan, a Western Highlands province of Papua New Guinea (PNG). Madan is located 20 km east of Mount Hagen, 1 km north of the Highlands Highway and 20 km west of the border with Jiwaka Province. The network, centered on the plantation owner's house, is to include 35 schools and a health center, all located within a 10 km radius [41]. WiLDNet is under consideration as a technology.  
The Western Highlands of PNG is the most heavily populated density of roughly 90 per square km. The area in question is even more densely populated (135/km\({}^{2}\)), although is poor, with an average income barely above the chronic poverty level. The area is served by a 3G provider that employs low orbit, low latency satellite linkage. Most of the Highlands province is limited to 2G services,but the wealthier population near the Highlands Highway deems 3G provisioning economically viable. The 3G is sufficiently robust so as to provide hot spot service that will support Skype and other videos.  
The value proposition is that schools would be willing to pay for dissemination of learning materials, and that community members will pay for postgraduate learning opportunities. The only partner that has so far been considered is the Madan Plantation on which the equipment is to be located. No discussions with government, communities, or 3G providers have yet been initiated. According to the proposed framework, collaboration with the local carrier would be the most efficient choice.
## 2 Related Work
This assumption is not feasible in GPS-denied zones at large ranges, because even if we know the pixel-to-meter conversion, it is impossible to guarantee that the elevation of the terrain remains constant in real reconnaissance or surveillance applications. In [41], a template-matching algorithm is used to compute the pose estimation of a UAV based only on images. However, there is no onboard computation due to insufficient computational resources, which reduces the autonomy of the system. Additionally, the experiments presented are performed only in urban environments, which are rich in features, and, similarly to the previous work, assume that the scale between pixels and meters remains constant. Other work based on satellite images is presented in [42]. Here, the approach is not limited to urban environments, but it has two main disadvantages in contrast with this study: firstly, it is tested at lower altitudes and shorter ranges, and secondly, it relies only on images and is unable to estimate coordinates if there are no matches or if, for some reason,it stops receiving photos. As a result, it does not have a subprocess that allows it to continue estimating the position if it stops finding correspondences; in addition, the matcher they use is not rotation-invariant, which is desirable when the photographs will not be aligned with the satellite map. In [43], the authors present a visual odometry approach based on satellite images. However, instead of capturing real images, they simulate capturing frames by cropping them from a satellite image and simulate flights at altitudes of 200 and 300 m. Another drawback is that the range of these simulations extends up to 3 km and is limited to straight lines, which is not feasible for real-world applications. The main contribution of this work is an algorithm that reduces accumulated errors in GPS coordinate estimation using aerial and satellite images. The algorithm can be executed online and onboard the UAV and it has been tested on a BVLOS fixed-wing UAV operating at altitudes exceeding 1000 m and ranges over 15 km. Additionally, a key advantage of this system is its versatility, since it can be implemented on any UAV without relying on the dynamic model of the vehicle, unlike other methods that require inertial sensor fusion.
# Lift-Thrust Integrated Ducted-Grid Fusion Configuration Design for a Ducted Fan Tail-Sitter UAV
###### Abstract
[PERSON] [15] developed an unsteady method to evaluate the lip vortex effect on the ducted fan under the crosswind conditions, and the results indicated that the dynamic aerodynamic phenomena was generated by the lip vortex with four frequencies. [PERSON] [16] developed a disk model based on inner flow field information and blade element theory to simulate the ducted fan during the transition period. The study indicated that the increase in the pitch rate of the tilt ducted fan delays the flow field separation, and increases the lift and drag of the ducted fan system. [PERSON] [17] designed a planar particle velocimeter system to investigate the new duct aerodynamic performance in hover and edgewise modes, and built a radial equilibrium-based fan aerodynamic model also integrated to CFD simulation. [PERSON] [18] described the CFD method to evaluate the aerodynamic and aeroacoustic performances on the electric ducted fans. The CFD results demonstrated that changing the design flow coefficient changes the proportions of endwall and blade profile loss, with the \(d=0.75\) design providing the optimum balance. In summary, a great deal of research work has been conducted on the aerodynamic aspects of a tail-sitter ducted propeller power plant. These studies focus on the coupled optimization design between ducted propellers, rapid and accurate evaluation methods, etc., or consider the unsteady aerodynamic characteristics of propellers during conversion flight. Unfortunately, even though the ducted fan has been developed over decades, few relevant studies can be found with propellers functioning as lift components during the level flight phase. In other words, current ducted propellers are used only as power components during the droop land cruise multi-mode flight process, without considering their lift-thrust coupling characteristics, which also limits the range of applications of the ducted fan. In this study, we face the multi-mode design of lift-thrust integration in flight for a ducted fan tail-sitter UAV, combining the duct wall with the multi-channel grid to form a new asymmetric duct shape, using the difference of force components without affecting the thrust properties of the duct itself, in order to achieve the form of vertical takeoff and landing developing thrust characteristics, and level flight integrating lift-thrust. By using the CFD method, the aerodynamic mechanism of the improved design is analyzed, and the influence law of design parameters is studied to form a practical design system of the lift-thrust integrated ducted fan.
## 4 Conclusions  
These results indicated that the electron transmittance of CNT triode field emission structures was determined by the aperture ratio of the grid. A larger aperture ratio of the grid resulted in higher electron transmittance; however, it also caused the cathode electron divergence to increase, resulting in an enlarged electron beam spot size. It was necessary to strike a balance between electron transmittance and the size of the electron beam spot rather than barely increase the grid aperture ratio. For the CNT triode field emission structure described in this work, to obtain a large anode current and small electron beam divergence, achieving a trade-off between high electron transmittance and small electron beam spot size, the aperture ratio of the grid can be kept between 75% and 85%. Compared with existing reports, this study not only presents high electron transmittance but also achieved a balance between electron transmittance and electron beam spot size. This study, which focused on determining the optimal grid aperture ratio for achieving a balance between transmittance and electron divergence, could provide a reliable reference for applications related to cold cathode X-ray tubes.  
Figure 6: Fluorescence images of the electron beam spot and their variations at different voltages and aperture ratios. (**a**) Fluorescence images at anode voltages (Va) of 1.5 kV, 2.0 kV, 3.0 kV, 4.0 kV, 5.0 kV, 6.0 kV, 7.0 kV, and 8.0 kV. (**b**) Variations in electron beam spot area when the anode voltage increases from 1.5 kV to 8.0 kV. (**c**) Fluorescence images at gate voltages (Vg) of 1.5 kV, 1.6 kV, 1.7 kV, 1.8 kV, 1.9 kV, 2.0 kV, 2.1 kV, and 2.2 kV. (**d**) Variations in electron beam spot area when the gate voltage increases from 1.5 kV to 2.2 kV. (**e**) Fluorescence images at aperture ratios of 51%, 61%, 65%, 70%, and 85%. (**f**) Variations in electron beam spot area when the aperture ratio increases from 51% to 85%.  
**Author Contributions:** Conceptualization, Y.L. ([PERSON]) and Z.L.; data curation, X.L. and X.S.; formal analysis, X.L.; funding acquisition, S.F., Z.L. and L.Z.; investigation, A.W., K.C. and S.Z.; methodology, Y.L. ([PERSON]) and X.L.; project administration, S.F., Z.L. and L.Z.; resources, Z.L. and L.Z.; software, Y.L. ([PERSON]); supervision, C.L.; validation, X.S.; writing--original draft, Y.L. ([PERSON]); writing--review and editing, Z.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Key R&D Program of China (Grant No. 2021 YFC2802000), Natural Science Foundation of China (Grant Nos. 52372141, 52072084, 51925203, 5222207), Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB3600000), and Key Area Research and Development Program of Guangdong Province (Grant No. 2020B0101020002).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Optimization of Cluster Head Selection
### Node Sleep Scheduling Algorithm
The node self scheduling algorithm is described as follows:  
Step 1. Send a PIM message, and collect information from neighbor nodes;  
Step 2. To determine whether redundant nodes are possible. If there is a redundant node, then there is a delay for a period \(T_{off}(i)=\mu(1-\left|E_{i}-E_{res}(i)\right|/E_{res}(i))t_{i}\). Otherwise, the node is marked as an active node;  
Step 3. The possible redundant nodes monitor the communication channel in the delay period \(T_{off}(i)\). If a SSM message is received, then mark the sending node dormancy, and delete it from the active node list. The node will re-judge whether it is still a possible redundant node. If it is a redundant node, it continues to monitor the communication channel, otherwise, it is immediately marked as an active node;Step 4. At the end of the delay \(T_{off}(i)\), it sends a SSM message to the related neighbor nodes, and enters into the dormant preparation period, and continues to monitor the communication channel. During this period DCHSby if a SSM message is received, the sending node dormancy and mark from the active sending nodes in a node list is deleted. If it is a redundant node, it continues to monitor the communication channel, otherwise, it is immediately marked as an active node;  
Step 5. At the end of the sleep preparation period, the transfer to a dormant state will be immediately started.
###### Abstract  
Corrosion of the reinforcement is a major degradation mechanism affecting durability and safety of reinforced concrete (RC) structures. As the corrosion process starts internally, it can take years before visual damage can be noticed on the surface, resulting in an overall degraded condition and leading to large financial costs for maintenance and repair. The acoustic emission (AE) technique enables the continuous monitoring of the progress of internal cracking in a non-invasive way. However, as RC is a heterogeneous material, reliable damage detection and localization remains challenging. This paper presents extensive experimental research aiming at localizing internal damage in RC during the corrosion process. Results of corrosion damage monitoring with AE are presented and validated on three sample scales: small mortar samples (scale 1), RC prisms (scale 2), and RC beams (scale 3). For each scale, the corrosion process was accelerated by imposing a direct current. It is found that the AE technique can detect damage earlier than visual inspection. However, dedicated filtering is necessary to reliably localize AE events. Therefore, AE signals were filtered by a newly developed post-processing protocol which significantly improves the localization results. On the smallest scale, results were confirmed with 3D micro-CT imaging, whereas on scales 2 and 3, results were compared with surface crack width measurements and resulting rebar corrosion levels.  
acoustic emission; concrete; corrosion; damage detection; localization Article
## 4 Mean Squared Prediction Error  
To make the desired connection of entropy rate power with the log likelihood ratio, we now develop some well-known results in statistical time series analysis. As before, we start with a discrete-time stationary random process \(\mathbf{X}\) with autocorrelation function \(\phi(k)\) and corresponding power spectral density \(\Phi(\omega)\), again without the Gaussian assumption. It can be shown that the minimum mean squared prediction error (_MMSPE_) for the one-step ahead prediction of \(X_{m}\), given all \(X_{i},i\leq m-1\), can be written as [6; 8]  
\[MMSPE=\exp\Biggl{\{}\frac{1}{2\pi}\int\limits_{-\pi}^{\pi}\log\Phi(\omega)d \omega\Biggr{\}}, \tag{9}\]  
which, as was first observed by [PERSON] and [PERSON], is the same expression as for the entropy rate power \(Q\) of the signal [13].  
The entropy rate power is _defined_ by [PERSON] to be the power in a Gaussian signal with the same differential entropy as the original signal. The signal being analyzed is not assumed to be Gaussian, but to determine the entropy rate power, \(Q\), we use the signal differential entropy, whatever distribution and differential entropy it has, in the relation for a Gaussian process. It is tempting to conclude that, given the _MMSPE_ of a signal, we can use the entropy rate power connection to the differential entropy of a Gaussian process to explicitly exhibit the differential entropy of the signal being analyzed as \(h(p)=\frac{1}{2}\log(2\pi eQ)\); however, this is not true and this conclusion only follows if the underlying signal being analyzed is Gaussian.  
Explicitly, [PERSON]'s definition of entropy rate power is not reversible. [PERSON] stated clearly that entropy rate power is a _derived_ quantity [9]. For a known differential entropy, we can calculate the entropy rate power as in [PERSON]'s original expression. If we start with the _MMSPE_ or a signal variance, we cannot obtain the _true_ differential entropy from the Gaussian expression as above. Given an _MMSPE_, however, what we do know is that there exists a corresponding differential entropy and we can use the _MMSPE_ to define an entropy rate power in terms of the differential entropy of the original signal. We just cannot calculate the differential entropy using the Gaussian expression.  
While the expression for the _MMSPE_ is well-known in digital signal processing, it is the connection to the entropy rate power \(Q\) and thus to the differential entropy of the signal, which has not been exploited in digital signal processing, that opens up new avenues for digital signal processing and for interpreting past results. Specifically, we can interpret comparisons of _MMSPE_ as comparisons of entropy powers, and thus interpret these comparisons in terms of the differential entropies of the two signals. This observation provides new insights into a host of signal processing results that we develop in the remainder of the paper.
## 2 Methods  
### Measurement Method of Vehicle Noise Spectrum Data  
The measurements were conducted on the roadside of seven typical dry roads in Guangzhou, China, with a surface constructed of asphalt concrete. The parameters of surface could refer to the Chinese standard JTG F40-2004 (Technical Specification for Construction of Asphalt Pavements) [33]. To avoid possible disturbances, the samples were collected in places far away from intersections, rivers, and populated areas. The chosen urban roads have the following speed limits: 2 roads with 70 km/h, 3 roads with 60 km/h, 1 road with 50 km/h, and 1 road with 40 km/h. The spectrum was captured when the A-weighted sound pressure level during a vehicle pass-by was at its maximum, and the sampling frequency of sound level meter was 23 Hz, i.e., 23 spectra per second. Only one vehicle was measured at a time. In Figure 1, the locations of the measurement sites are shown. Both sites were 1.2 m high and 6 m to the roadside with a distance of 50 m to each other. In the experiment, the subject vehicles only moved from left to right alone. As the single vehicle passed by measurement site 1, the noise spectrum data were recorded by a digital noise recorder. The vehicle speed had little variation between points 1 and 2 while traveling on a long-straight road with a very sparse traffic flow. The vehicle speed was detected by a laser speedometer at measurement site 2.  
In this paper, the vehicles are classified into three categories: heavy cars, middle cars, and light cars. The 1/3 rd-octave-band sound pressure level (SPL) and the total SPL are included in the noise spectral data. All the noise data are A-weighted.  
In this paper, 1351 sets of valid data were collected, among which 973 sets were light car data, 166 sets were middle car data, and 212 sets were heavy car data. The distribution of the samples is shown in Table 1.
# A Review of In-Situ and Remote Sensing Technologies to Monitor Water and Sanitation Interventions
## 2 Sensing WASH
### Water Service Delivery and Use
In two recently funded USAID programs in Ethiopia [43] and Kenya [44], satellite-network-connected sensors were installed on remote, electrically powered boreholes to monitor functionality and water service delivery to thousands of customers. It was hoped that the resulting data would inform decisions regarding when to dispatch technicians, order repair supplies, and initiate other responses as needed.
## 3 Encapsulation Protocol
### IPSec  
**Intro**  
Machine Learning  
An important part of reading ciphertext is extracting sensitive features that are different from plaintext. With EFM (Estimated Feature Method), [PERSON] [32] selected 29 strong correlation features with thresholds greater than 0.7 and then compared the accuracy of SVM, Naive Bayes, and C4.5. The best EFM using SVM was found to identify IPSec tunnel traffic with 97.2% accuracy.  
IPSec tunnel traffic can also be used to identify protocols and applications at a fine-grained level. [PERSON] [32] approximated each encrypted tunnel traffic feature through Gaussian distribution by using a Naive Bayes classifier. When mixed with HTTP, FTP, SMTP and SSH passenger protocols, this method improves IPSec tunnel traffic protocol identification accuracy by 28.5%. In order to reduce computation, [PERSON] [33] first used C4.5 to classify the encryption types of tunnel traffic. The author then used a small number of data packets to represent the flow characteristics and combined SVM to identify the tunnel traffic applications. It is possible to achieve 92.5% accuracy when using private datasets. KNN was chosen by [PERSON] [46], a decision followed by binary classification of tunnel traffic noise and then multi-classification of tunnel traffic. In private datasets, TMT-RF achieves the best classification performance of 93% by dividing overlapping traffic into multiple segments and making predictions. It does not take time to find the dividing point but divides the traffic into many segments and makes predictions.
###### Abstract  
A method which utilizes a Kerr phase-interrogator to measure the group birefringence dispersion (GBD) of a polarization-maintaining fiber (PMF) is systematically studied in this paper. The differential group delay of two sinusoidally modulated optical signals (SMOSs) polarized along the principal axes of the PMF is measured by a Kerr phase-interrogator, which leads to the group birefringence of the PMF. As the laser wavelength of the SMOSs varies, the group birefringence as a function of the laser wavelength is obtained, and the GBD is calculated as the derivative of the group birefringence with respect to the laser wavelength. The proposed method is experimentally demonstrated by characterizations of a Panda PMF with high GBD and an elliptical core PMF with low GBD, and its performance is analyzed. The proposed method eliminates the impact of the laser coherent length and allows for characterizing the GBD of PMFs that are tens of kilometers long.  
polarization-maintaining fiber; group birefringence dispersion; Kerr effect +
Footnote †: journal: Applied Physics Letters