esa-sceva/satcom-chunk-collection huggingface dataset:
# Pulse Peak Delay-Total Focusing Method for Ultrasonic Tomography on Concrete Structure
## 5 Conclusions  
A new TFM method called PPD-TFM is proposed to compensate for the long pulse duration of the MIRA device applied to concrete structures. The results show that the PPD-TFM have higher accuracy and array performance than the TFM and SAFT-C by using the modified ultrasonic time-of-flight values considering pulse peak delay. The PPD-TFM calculates a PPD using a simulated frequency response function of a transducer comprised in the MIRA and an electronic input signal. The calculated PPD is added to the delayed time term in the algorithm. The results of the PPD-TFM were compared with the existing TFM method and the SAFT-C method by using simulated ultrasonic array data and experimental data. The following conclusions were drawn from the results and observations.  
1. The PPD-TFM improved the accuracy of the image reconstruction algorithm on concrete structures. The proposed method even showed better accuracy than the SAFT-C embedded in the MIRA device. In addition to the higher accuracy, the PPD-TFM showed better performance than the existing TFM and even the SAFT-C method in terms of API.
2. A high input frequency could result in an accurate B-scan result even if the PPD-TFM is not applied. However, in the case of the MIRA device that was developed for concrete structures, a low-frequency input pulse is inevitable to avoid severe signal attenuation by concrete aggregates. In addition, a fixed range of frequency bandwidth for the DPC transducer in the MIRA device makes the usage of high-frequency input out of the bandwidth ineffective. Therefore, the calibration of the PPD like the PPD-TFM is essential for the MIRA device.
3. Although the proposed PPD-TFM provides a high-quality image, the image can be impaired by surface noise especially if the reflector is close to the surface. If surface noise removal techniques are adapted in the PPD-TFM, a better B-scan with reduced surface noise could be expected.  
Conceptualization, H.K.; methodology, H.K.; software, H.K.; validation, H.K., C.J. and W.J.C.; formal analysis, H.K.; investigation, H.K.; resources, H.K., C.J. and W.J.C.; data curation, H.K.; writing--original draft preparation, H.K.; writing--review and editing, H.K.; visualization, H.K.; supervision, H.K.; project administration, C.J. and W.J.C.; funding acquisition, C.J. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Received Signal by A1040 MIRA for Pair of 1 st and 8 th Transducers.  
**Funding:** This research was supported by a grant from a Strategic Research Project (Non-Destructive Evaluation (NDE) Technology for PSC Structures (PSC Stethoscope), Project # 20210158-001) funded by the Korea Institute of Civil Engineering and Building Technology (KICT).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Classification Models for Nitrogen Concentration in Hop Leaves Using Digital Image Processing
## 5 Conclusions  
Thus, it can be concluded that image quality had a direct impact on hop leaf classification regarding nitrogen concentration, with high-definition images improving model performance, achieving AUC values ranging from 0.76 to 0.85 and sensitivity between 56% and 79.2%, while images of lower resolution, though slightly inferior (AUC between 0.70 and 0.74), still provided promising classification results. The quadratic discriminant analysis (QDA) model exhibited the best performance, standing out even when using a reduced number of explanatory variables and achieving superior AUC, indicating good discriminative capacity, although still inferior to other studies in the literature focused on disease and nutritional classification in plant leaves.  
The key contribution of this study is demonstrating that accurate nitrogen classification can be achieved using a simplified set of image-derived features and a straightforward model, enabling easy application while minimizing the need for complex preprocessing.  
For future research, integrating data from different image sources, including additional variables such as spectral and morphological characteristics, and evaluating advanced classifiers, such as neural networks and support vector machines (SVM), are suggested, as they may offer complementary alternatives in leaf nutritional analysis. Additionally, the development of practical tools, such as applications for nutritional classification, could expand the applicability of the results in hop cultivation management in Brazil.
## 6 Reinforcement of Concrete Beams with Fe-SMA
### Shear Performance Enhancement  
Shear cracks often occur in concrete beams under load. Reinforcements shall be used to delay the occurrence of cracks, to reduce the crack width in the normal use stage, and to avoid the shear damage of concrete beams due to insufficient bearing capacity.  
[PERSON] et al. [46] initially employed Fe-SMA rods to externally reinforce the shear cracks of a bridge in Michigan, USA. The Fe-SMA rods were anchored to the concrete beam through a bolted connection device. The average crack width was reduced from 0.55 mm to 0.32 mm after reinforcement (reduced by about 40%), effectively improving the shear bearing capacity. However, due to the need to move and expand the anchor holes, the target prestress (176 MPa) of the Fe-SMA rods was not reached, and the final prestress was 120 MPa after activating Fe-SMA.  
[PERSON] et al. [15] surrounded the concrete beam without stirrups with the Fe-SMA strips and activated Fe-SMA to apply circumferential restraint, as shown in Figure 19. After strengthening, the number of shear cracks was reduced, the occurrence of shear cracks was postponed, and the beam deflection was significantly reduced. The shear strength of the strengthened beam increased by 65%, and the failure mechanism changed from brittle shear failure to ductile bending failure.  
Figure 17: External installation modes: (**a**) mechanical connection, (**b**) surrounding beam surface.  
Figure 18: Internal installation modes: (**a**) NSM technology, (**b**) embedding Fe-SMA bars/strips.  
[PERSON] et al. [111] employed the Fe-SMA strips to repair the T-shaped concrete beam of 5.5 m in length and 0.55 m in height, where the Fe-SMA strips, processed into U-shape, were anchored to the beam by bolts (as shown in Figure 20a), and the shear carrying capacity increased by 30% after reinforcement. [PERSON] et al. [33] embedded U-shaped ribbed Fe-SMA bars into the sprayed mortar layer to reinforce the T-shaped beam of 5.2 m in length and 0.75 m in height (as shown in Figure 20b). After reinforcement, the rigidity of the beam was increased, the stress of the internal stirrups was decreased, the number and width of cracks were reduced, and the interface between the original concrete layer and the new sprayed layer was not damaged. It shows that the above two shear reinforcement methods are reliable.
# A Novel Indoor Localization Method Based on Image Retrieval and Dead Reckoning
## 4 Vision-Aided PDR System Design
### Vision-Aided PDR Fusing
There is no special requirement for selecting correction points, but VPR should be used after pedestrians crossing floors. The whole localization process of V-PDR is shown in Figure 8.  
For location identification across floors, a new decision mechanism needs to be added here. The VPR location labels include the location in one floor and which floor it is. Besides, areas where floor transitions may occur are labeled. Note that the floor change here involves the floor change of stairs and elevators. A simple PDR system cannot judge the change of floors, thus the VPR positioning result is used for floor prediction. In addition, the scenes of the stairwell and the elevator doorway on each floor are probably similar; thus, the VPR cannot be identified only once. When entering the area where the floor may change, a new judgment mechanism is added. VPR judgment is performed here multiple times. Only when three consecutive floors are on the same floor does it judged that the floor has changed. With the above algorithm, the V-PDR can be used to solve the accumulative error due to PDR not being able work in a three-dimensional environment. When there is a large error in PDR or VPR, it can be corrected by posterior. Only when \(\xi_{2}\geq\left\lVert L_{i}^{v}-L_{i}^{p}\right\rVert\geq\xi_{1}\) is the PDR corrected. This is already a correction process for outliers to ensure that the system positioning is not affected when the error is too large. Especially when abnormal conditions occur, to obtain reliable positioning results, the posterior needs to be processed in the subsequent positioning. Three consecutive VPR positioning distances and PDR measured distances are judged to determine whether there is an error. \[\left\lVert\frac{|L_{i}^{v}-L_{i-1}^{v}|}{|L_{i}^{p}-L_{i-1}^{p}|}-1\right\rVert \leq 0.03 \tag{17}\]  
Through Equation (17), whether outliers appear can be judged. If a positioning error occurs, the VPR is called multiple times in a short period during the operation. If Equation (17) holds, the positioning is corrected to the current positioning. With this method, rare errors are corrected. Note that all this is based on the fact that our PDR and VPR accuracy is high enough.
## 4 An Architecture for IoT and Multimedia Data Handling
### Downstream Data Flow Management  
The downstream data is handled by the proper caching management in the traversed network devices. The end-users always need the requested content within a shorter time. Therefore, our goal is to keep the expected user data towards the edge of the users. We have more caching capabilities in our EC devices that are used for caching the most popular content for the nearby end-users. The efficient in-network caching mechanism is used for the more popular content caching and the less popular contents are cached in the remote caching devices or the servers of the content service providers. The content popularity measurement procedure is described in the Section 4.1.1. The clustering mechanism is applied to the core network to handle the downstream data. The clustered network solves the content request message flooding issue and it provides an efficient routing mechanism. The core network is represented by a graph (G). Individually, the devices of the graph are denoted as a node. Algorithm 1 is used to make a cluster-based network and it provides us with a set of clusters. The cluster list, C_List, is used to make the cluster head for each cluster. In a cluster, each node is represented as a cluster node and the cluster head knows all the information (e.g., the available cache memory and the total cache memory of each node, available cached content at each node within the cluster, etc.) of the cluster node including the neighboring cluster head information. The content request messages handling mechanism is also changed at each cluster node. Section 4.1.4 covers the content access mechanism.  
```
Input: All the nodes of the core network a graph, G Output: A set of clusters
1 Definition of used variables:
2 C\({}_{i}\) = \(k\)th Cluster;
3 Neighbor(n) = Include all the neighbors of node n;
4 FINISH(n) = It indicates whether the node n is included in a cluster or not.
5
6 Initialization:
7 For node 1 to N, FINISH(n) = FALSE; N represents the total number of nodes.
8 C_List = \(\mathcal{O}\);
9 k = 1;
10
11 Begin;
12 Randomly choose a node p among N nodes from Graph G
12 For each node q in Neighbor(p)
13 if q \(\
ot\in\) C\({}_{i}\)\(=\) C\(\cup\) {q};
15 FINISH(q) = TRUE;
16
17 else
18 if Neighbor(p) = \(\mathcal{O}\)
19 C\({}_{i}\) = C\(\cup\) {p};
20 FINISH(p) = TRUE;
21 else
22 C_List = C_List \(\cup\) C\({}_{i}\);
23 k = k + 1;
24 while (n in 1 to N, FINISH(n) = TRUE)
25
26 End;
27 return C_List;
```  
**Algorithm 1**An algorithm to make clusters (G).
# Shared eHMI: Bridging Human-Machine Understanding in Autonomous Wheelchair Navigation
## 5 Conclusions and Future Works
### Limitations  
Experimental Design:  
1. Experimental method: The reliance on online video simulations deprived participants of a holistic experience. Despite potential congruence in results, participants missed out on sensory experiences, such as touch and sound, which would accompany direct interaction with the autonomous driving and projection interface.
2. Brief interface adaptation time: Although information prioritization and simplification were considered in the design, the brief exposure to interface information could lead to a high cognitive burden for first-time users, especially elderly wheelchair users.
3. Incomplete scenario simulation: The experiment used an abstract general road scenario with common obstacles and pedestrians, but it overlooked specific challenges such as steps, dense crowds, and diverse road users, possibly affecting user engagement and perceptions of practicality and safety. Additionally, the simulation platform shown in videos operated slowly, impacting the views of users proficient in wheelchair use. Nevertheless, we recommend starting at a slower pace until users build confidence in the system.  
Equipment Limitations:  
Due to current projector brightness limitations, visual effectiveness may be compromised in brightly lit environments.
## 3 Experimental Section
### Comprehensive Performance Analysis of UOCC System
#### 3.5.4 Comparison of Rate and Distance between Different Systems  
Table 4 shows the comparison of the data rate and communication distance between the our proposed RES-based UOCC system and the GS-based UOCC or land-OCC systems proposed in other publications.  
According to Table 4, our proposed scheme can achieve a faster data rate over a relatively long distance and has high robustness. We believe that this data rate may enough to send secure identification or authorization information for autonomous underwater vehicle (AUV) interconnection or send position information in an underwater navigation system.  
Figure 13: BER performances in a dense bubble moving scene: (**a**) \(K\) = 5 and the bubble source moves left and right in the image, (**b**) \(K\) = 10 and the bubble source moves left and right in the image, (**c**) the air pump is located in the center of the image and \(K\) varies from 5 to 10.
# Contactless Palm Vein Recognition Based on Attention-Gated Residual U-Net and ECA-ResNet
## 1 Introduction
To mitigate this challenge and attain more accurate segmentation outcomes with minimal computation, we integrated an attention gate mechanism with the U-Net model. Inspired by the residual U-Net [10], the segmentation model introduces residual learning into each convolutional block. The efficient channel attention residual network (ECA-ResNet) solves the second problem by enhancing the channel-wise information in the feature maps as well as suppressing useless features, which overcomes the problem of rotation, position translation, and scale transformation. This recognition model uses ResNet as the backbone for solving the vanishing or exploding gradients' problem where the repeated multiplication in the back-propagation may cause the gradient to be infinitely small. The combined loss function proposed in this paper addresses the issue of inter-class similarity and intra-class compactness for palm vein recognition systems, where only a few samples are available for each class. The main contributions of this work can be summarized in four points:  
* We propose a deep-learning-based approach for both palm vein segmentation and recognition with promising identification and verification results;
* With the advantages of the attention gate, we propose an attention-aware residual U-Net segmentation model to learn domain-specific features such as vein vascular structure from low contrast and blurry palm images, allowing for more precise authentication performance;
* We propose a light-weight attention-aware feature extractor for palm vein recognition that can efficiently extract palm vein features without any extra computational overhead;* We propose the most effective loss function for palm vein discriminative learning by combining state-of-the-art loss functions, such as ArcFace, focal loss, and triplet loss. The rest of this paper is systematized as follows. Section 2 discusses the related work. Section 3 presents the proposed methodology for palm vein recognition. Section 4 and Section 5 describe the experiments and results. Finally, Section 6 consists of the conclusion.
# Broadband Holography via Structured Black Silicon Nano-Antennas
## 1 Introduction  
Holography has been largely exploited in a vast range of fields, such as imaging [1; 2; 3; 4; 5], synthetic optical elements [6], data storage [7], metrology [8], and anti-counterfeiting [9; 10].  
Computer-generated holograms reconstruct 2D, as well as 3D objects, calculating the required interference pattern both in the phase and amplitude based on light propagation simulations [11; 12; 13; 14].  
The recent progress in laser and computer technology allows us to fully take advantage of Computer-Generated Holograms (CGH). CGH has the capability to reconstruct 2D, as well as 3D objects by numerically generated patterns based on the intensity and phase profiles of the object that do not need to exist in reality [15; 16; 17]. Currently, the realisation of efficient and miniaturised holograms passes through complicated fabrication processes and sometimes expensive materials, such as gold, that are difficult to scale up [18; 19; 20; 21].  
In this work, we present a new efficient method to fabricate a broadband intensity binary hologram using a single inexpensive dielectric material. We leverage on the possibility to selectively turn different areas of a Si wafer into a series of Si nanoantennas, which can change the reflectivity of the Si to obtain a completely dark material [22].  
This technique was inspired by the fabrication of Black Silicon BSi [23; 24; 25], that was created by patterning the entire surface of the sample. The fabrication involves a photolithography step to transfer the interference pattern on the Si sample, and a subsequent dry etching process which generates a local pattern of Si nanotips that behaves as broadband nanoantennas on specific areas of the sample. This process changes the effective refractive index of the Si, opening up the possibility of obtaining surfaces with very low reflectivity. Currently, BSi is mostly being used as a bulk material, and there have been no applications in holography.  
Herein, we present our experiments in which we created different holographic images, and we show their broadband response by illuminating them with three different wavelengths from 440 nm to 632 nm. Our results enable the possibility to develop a cost-effective and large-scale platform to fabricate broadband binary amplitude holograms.
# Linked Open Government Data: Still a Viable Option for Sharing and Integrating Public Data?
## 6 What Factors Are Holding back the Spread of LOGD?
### Data Privacy and Security  
Government datasets often include sensitive or personally identifiable information, which poses a significant challenge in the context of open data initiatives. As [PERSON] et al. [73] have pointed out, data providers are tasked with finding a delicate balance. They must make data as freely available as possible while also respecting individuals' right to privacy. This balance is crucial to _maintain public trust and ensure legal compliance_, especially when dealing with datasets that contain personal information. [PERSON] [78] brings attentionto additional complexities in the collection and processing of private and, in particular, personal and sensitive data. Under the current EU General Data Protection Regulation ([[https://gdpr-info.eu/](https://gdpr-info.eu/)]([https://gdpr-info.eu/](https://gdpr-info.eu/)) (accessed on 1 February 2024)) (GDPR), any information relating to an identified or identifiable natural person is subject to protection. This regulation imposes stringent technological and organizational requirements for data processing, including the need for pseudonymization and restrictions on profiling individuals. These requirements, although designed to protect personal data, can potentially discourage or complicate the reuse of such data in LOGD projects.
# Optical Chirality Determined from Mueller Matrices
## 2 Materials and Methods
### Instrumentation  
Mueller-matrix spectroscopic ellipsometry (MMSE) data were recorded in transmission mode at normal incidence with a dual rotating compensator ellipsometer (RC2, J. A. Woollam Co., Inc., Lincoln, NE, USA) with a beam diameter of 3 mm in the spectral range of 210-1690 nm. Focusing probes to reduce the beam diameter to below 100 \(\upmu\)m were used for the measurements on beetle elytra. Complementary measurements to measure incomplete (three rows only) Mueller matrices were performed with a rotating-analyzer ellipsometer equipped with a compensator (VASE, J. A. Woollam Co., Inc.).  
For the measurements on the glucose solution, a glass cuvette with path length of 13.8 mm was used. The cuvette was made of 1-mm thick fused quartz. With an empty cuvette, normalized Mueller-matrix elements within \(\pm\)0.005 of their nominal value (zero or unity of the identity matrix) were obtained in the spectral range of 300-1690 nm. Below 300 nm, deviations up to \(\pm\)0.015 were seen in some elements at the shortest wavelengths. When the cuvette was filled with glucose solution, the transmitted irradiance was low for longer wavelengths, and only data in the spectral range 210-1000 nm are presented.  
Data analysis was performed with CompleteEASE and WVASE (J. A. Woollam Co., Inc.). The latter program was employed for modeling using the Tellegen constitutive relations. Model data were fitted to experimental data using nonlinear regression whereby best fit parameter values and their confidence limits were obtained. The confidence limits are defined as \(SCL_{i}\sqrt{MSE}\), where \(SCL_{i}\) is the standard 90\(\%\) confidence limit for the fit parameter \(i\) determined from the diagonal of the covariance matrix of the fit, and \(MSE\) is the mean-squared error. Differential decomposition was performed with MATLAB. The decomposition code included an unweighted five-point moving average filter for spectral filtering to reduce noise.