esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Data Collection
### Multi-Camera Injection Analysis System
#### 2.1.3. Reverse Blood Detection Devices  
'Reverse blood', also referred to as 'backflow' or'reflux', is a phenomenon frequently encountered in medical procedures such as blood withdrawal or IV initiation [27]. Figure 4 shows the reverse blood during an injection.  
This phenomenon results from pressure fluctuations within the blood vessel upon puncture, which temporarily leads to the opposite direction of blood flow [28]. The occurrence of reverse blood is typically regarded by healthcare professionals as indicative of a successful operation. In this experiment, we aim to detect reverse blood to evaluate the success of the injection, for which we have developed a hardware device employing a photo sensor. The operational principle of this device is illustrated in Figure 5.  
A working principle diagram of the reverse blood detection device is shown in Figure 5a. When either no injection training is being conducted or the needle has not  
Figure 4: Reverse blood in injection process. After puncturing the blood vessel, some blood flows back into the needle.  
Figure 5: Reverse blood detection devices.  
Figure 3: General camera for detecting the moment of injection stabbing.  
yet punctured the skin, no blood flows within the blood vessel, and, hence, the photo sensor reliably outputs a stable signal by sensing the LED located on the opposite end of the needle tube, as shown in Figure 6 from 0 s to 17 s. Nevertheless, the figure reveals the presence of extraneous disturbances, stemming from ambient illumination and interference originating from the VICON system's IR camera.  
Once the needle is inserted into the vein, however, the reverse blood takes place, wherein the blood flows back into the syringe via the needle, resulting in a discernible alteration in the signal transmitted by the photo sensor, as depicted in Figure 6, spanning the time interval of 17 s to 22 s. The oscillations observed during this phase are a product of simulating a regular pulsation in the human body. It is worth noting that, through numerous preliminary experiments, we have ascertained that all saturation signal variations, due to this remarkably rapid transformation, surpass a threshold of 100, as delineated in Table 2. Therefore, we were able to determine the exact point in time when the reverse blood occurred and record the exact time of detection.  
As illustrated in Figure 5b, we have designed the sensor hardware keeping in mind the need to avoid some disturbance to the operator's posture during the injection process. Hence, we have employed 3D printing technology to miniaturize the photo sensor and fix it onto the injection needle. Furthermore, to counteract occasional noise interference, we have wrapped the sensor with black tape to mitigate any possible light interference.
###### Abstract  
This study evaluates the antibacterial and anti-biofilm effect of erbium:yttrium aluminum garnet (Er:YAG) laser treatment on a tooth root model infected with _Enterococcus faecalis_. Background: New treatment options are required to overcome endodontic infection in periapical tissue. Studies using Er:YAG during endodontic treatment yielded promising results regarding anti-biofilm/ antimicrobial effects. Methods: The root canals of 80 teeth were incubated with _E. faecalis_ for 4 weeks, allowing biofilm formation in the root canals, then divided into one control group and seven treatment groups that were exposed to Er:YAG laser using a side-firing spiral Endo tip, 2.5% sodium hypochlorite (NaOCI), 17% EDTA solutions alone or combined. The number of bacteria in each sample was determined by counting the number of colony-forming units (CFU) and was statistically compared. Results: Er:YAG laser, NaOCI and EDTA treatments alone caused a \(76.0\pm 5.7\%\), \(98.0\pm 0.6\%\) and \(69.0\pm 9.1\%\) reduction, respectively, in CFU. Combining the laser treatment with NaOCI, EDTA or both, caused a further reduction in the bacterial load by \(99.77\pm 0.14\%\), \(93.4\pm 1.6\%\) and \(99.95\pm 0.04\%\), respectively. Conclusions: Er:YAG laser treatment showed significant antibacterial effect on the experimental groups, while combination with NaOCI and EDTA provided the most efficient conditions for achieving antibacterial effect against _E. faecalis_, in the tooth root model.  
authentical; bacteria; biofilm; EDTA; _E. faecalis_; irrigation solution; Er:YAG laser; NaOCI; side-firing spiral endo tip
## 5 Results
### Availability and Latency  
From all of the scenarios, the update interval for the NLP was at a minimum of 5 s, and was typically at 5 s or 20 s. This is due to the limited network search rate inherent in the Android devices associated with power savings. Even when only Wi-Fi or only cell positioning was used, the same update rate was observed. For the total number of access points required for cell positioning, the authors looked at the minimum number of access points observed when only cell positioning was used. For Wi-Fi positioning, the minimum number of access points required for altitude measurements was assessed, as altitude is available only when Wi-Fi positioning is available. Consequently, a minimum of two cell  
Figure 9: Observed network location error versus the android estimated error level. For horizontal accuracies, the Wi-Fi errors matched the android estimates well, but the errors exceeded the estimates significantly for the urban scenario when only cell positioning was used. For vertical accuracies, the Wi-Fi errors were well bounded for the suburban scenario, but were underestimated for the urban environment.  
towers were required for cell positioning, and only a single Wi-Fi access point available in the fingerprinting database was required for Wi-Fi positioning.  
In order to obtain the NLP solution, the access point data needs to be transmitted from the smartphone, computed over the server using the access point fingerprinting, and sent back to the user. This implies that there is a latency between the data collection and PVT computation. In order to estimate this, the time stamps for the GNSS and NLP solutions from the Android device were assessed. Both position fixes provide UTC and receiver time stamps. Although it is difficult to know the timing truth, as GNSS time has on a nanosecond-level accuracy, GNSS time was assumed to be the truth [11]. Also, as the receiver time is common for both location engines, it was used as a comparison basis. When the two location engines were compared, NLP was slower by 1.13 s RMS, which is the location engine latency.
## 1 Introduction
More recent works used MR Dixon images as input to the network, which are the images typically acquired for AC in commercial PET-MR scanners. Dixon sequences include 4 images: water, fat, in phase and out-of-phase. In addition, the work by [30] provides better bone depiction than Dixon images by adding a Zero-echo time MRI volume. They used a 3D U-net architecture with transposed convolutions as up-sampling layer. In contrast, the work by [31] proposed a 2D U-net architecture with transposed convolutions as up-sampling layer using only Dixon images. In this case, Dixon-Vibe images from the Pelvis were utilised as input to the network in order to generate the corresponding pseudo-CT. Therefore, the input to the network was composed by 4 channels corresponding to the 4 volumes of the Dixon image. This proposal generated a whole pseudo-CT volume in around 1 min. All these approaches suggested different network architectures that were trained with different pipelines and using either 3D patches or 2D slices as input. Unfortunately, it is hard to assess which approach would perform better in different situations, as they were tested in different anatomies, with different sequences and over different subjects due to the lack of a common database to compare their results. This paper provides an overview of the different DCNN architectures investigated in the past years for the generation of pseudo-CTs. In order to do this, a simple pipeline with the MAE as training loss function is proposed. The addition of a more sophisticated loss, adversarial refinement or a post-processing should improve the results of all architectures in a similar way for all considered cases. Every architecture reviewed in this paper is tested with 2D and 3D schemes. On one hand, the 2D versions of the networks use 2D MR slices as input and employed 2D convolution filters. Thus, the pseudo-CT generated by this scheme is composed by slices. On the other hand, the 3D schemes consist in 3D patches from the MR volumes used as input and 3D convolution filters in the convolution layers. This scheme generates a pseudo-CT composed by 3D patches. Additionally, different ways of combining these patches were explored to generate the pseudo-CT through the 3D schemes. For the evaluation of the different architectures and schemes, two datasets were employed. The first one contains 3D-T1 MRI volumes of the head, paired with their corresponding CT scans. The second database contains Dixon-VIBE volumes of the pelvis, paired with their corresponding CT scans. With these two datasets we aim to explore how the networks perform with different MR sequences and anatomical distributions. This paper is divided into 4 sections. The Sections 2.1 and 2.2 gives an overview of the datasets and the image pre-processing. The Section 2.3 describes the architectures and depicts the training pipeline for 2D and 3D inputs and filters. The Section 3 shows the results of the different architectures as a function of the datasets and the schemes. Finally, a discussion about the findings and conclusions are presented.
## 1 Introduction  
Submarine channels are major conduits for terrigenous sediments to enter the deep sea [1]. Coarser-grained deposits filled in the channels can form high-quality petroleum and gas hydrate reservoirs [2]. Therefore, research on the sedimentary characteristics and evolution processes of submarine channels holds great significance for enhancing our understanding of the regional geological evolution of the host continental margin and guiding hydrocarbon exploration [3; 4; 5; 6].  
The Bounty Channel, located in the eastern continental margin of New Zealand's South Island, comprises one of the world's longest submarine channel systems (Figure 1A). The channel system begins at a water depth of \(\sim\)250 m on the shelf edge and extends eastward to the Bounty Trough at \(\sim\)5200 m water depth. The total length of the channel system is approximately 1200 km. It traverses the axis of Bounty Trough, serving as a conduit for terrigenous sediments transported from South Island to the Southwest Pacific abyssal plain (Figure 1A) [7; 8]. Therefore, the Bounty Channel should witness the tectonic and sedimentary evolution processes of South Island and the adjacent continental margin. This channel system consists of three main tributaries (named C1-C3) at its head, which merge downstream before confluence into a trunk channel. The latter leads to a terminal submarine fan, i.e., the Bounty Fan (Figure 1) [9; 10]. Previous studies have presented a preliminary description of the morphology and sedimentary characteristics of the trunk channel and the terminal fan [7; 8; 9; 11; 12], but less attention has been paid to the tributaries at the head of the channel system. In this study, we used high-quality two-dimensional multichannel seismic data to investigate the sedimentary characteristics and evolution processes of two of the three tributaries (C1 and C2) of the Bounty Channel and to explore the controlling factors.
## 1 Introduction
KD [11] extracts essential information from a complex teacher model and transfers it to a more compact student model, enhancing efficiency without compromising performance. KD techniques have been extensively studied for image classification tasks; however, their direct application to pixel-level tasks through classification-based KD methods often yields suboptimal results. Imposing the strict alignment of the coarse feature maps between teacher and student models can impose restrictive constraints, neglecting structured relationships among pixels and reducing overall efficiency. Unlike previous studies that ignore dataset insufficiency, we introduce a few-shot learning framework that integrates self-training with noise and dense-pixel KD. Our approach yields superior performance in both smoothness and detail sharpness while utilizing the same limited dataset (Figure 1f). We train a teacher model on limited labeled data, ensuring a noise-free training process to produce reliable pseudo-labels for the unlabeled data, which retain a high accuracy and remain closely aligned with the true labels. To enhance the student model's training, we introduce noise augmentation, incorporating both labeled and unlabeled data. We apply Gaussian blur to RGB images and introduce stochastic perturbations to sparse depth data. The training process follows an iterative scheme, where the student model transitions into a teacher role to generate pseudo-labels, while a newly initialized student model is trained to improve performance. To sustain the teacher model's high performance, dense-pixel KD is employed, leveraging its acquired knowledge as a supervisory signal for the student model's training [11]. The primary contributions of our study are as follows:  
1. We propose a novel few-shot learning framework for depth completion. This framework utilizes advanced depth completion techniques to produce dense depth maps from datasets that are either incomplete or contain missing data. 2. We employ noise augmentation and KD techniques, utilizing the deep regression network NLSPN to capture and infer depth-related features from limited datasets. Our approach improves the student model's resistance to noise, strengthening its overall performance and generalization abilities. 3. Our experimental results on the NYUv2 dataset show that the proposed method substantially enhances the performance of the student model compared to current techniques. Even when limited to only 10% of the training data, our method improves the accuracy from 97.1% to 99.7%, matching the performance achieved with the complete training set. The remainder of this paper is organized as follows. Section 2 reviews related research, Section 3 details the methodology, Section 4 presents the experimental results and analysis, and Section 5 concludes the study.
# An Innovative Internal Calibration Strategy and Implementation for LT-1 Bistatic Spaceborne SAR
## 4 Temperature Compensation
### Results  
Through the ground temperature tests of the internal calibrator, antenna calibration networks, and through-cabin coaxial cables, temperature compensation data were obtained. The phase compensation errors and amplitude compensation errors of each calibration equipment are shown in Table 5. The measurement errors of each equipment can be considered as uncorrelated random variables, and the total measurement error is the root mean square value of these error terms.  
It can be seen that the amplitude compensation errors of the calibration equipment are relatively small, and the phase compensation errors are all around 0.8\({}^{\circ}\), further calculation shows that the system temperature compensation accuracy is 0.10 dB/1.38\({}^{\circ}\), so the \(K_{T}\) value in the system internal calibration strategy is obtained.
## 3 Data Records and Procedures  
Given the absence of a public dataset encompassing a broad range of subjects' stature variability, it was necessary for us to gather a substantial dataset of vital signs. This study received approval from the Ethics and Deontology Committee of the University of Aveiro, Portugal (No. 29-CED/2021), and was conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants. The data collection took place in a single unaltered environment: an 18 square meter room. The setup involved the Bio-Radar system described earlier, positioned on a plastic table. Styrofoam plates were utilized to adjust the height of the antennas to match the stature of the subject being tested. Figure 5 provides an example of the setup with two volunteers--a woman (Figure 4(a)) and a man (Figure 4(b)). The data collection involved 92 subjects (46 males and 46 females) aged between 18 and 50 years old with Body Mass Index (BMI) values ranging from 17.5 to 40 kg/m2 and Chest Wall Perimeter (CWP) measurements between 77 and 129.5 cm.
# A TDD Framework for Automated Monitoring in Internet of Things with Machine Learning +
## 3 Research Background
### Iso25010  
There are development guidelines to be followed in order to ensure that the IoT system delivers the needed level of trust and reliability, with non-functional requirements to be fulfilled. By definition, the non-functional requirements of a system are what the system is, not what the system does. The non-functional requirements must be evaluated under a quality model that assures the quality in a degree that the system can satisfy the needs and demands of its stakeholders. The ISO/IEC model defines the standard of non-functional requirements and guarantees that each of the stakeholders needs are covered.  
The ISO25010 standard defines a quality model composed of eight quality characteristics: functional suitability, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability. The performance efficiency considers the time behavior, resource utilization and capacity of a system. The maintainability characteristic has the modularity, reusability, analysability, modifiability, and testability characteristics.  
The development process must be planned in order to be compliant with the ISO/IEC 25010 [6] from the beginning, combining characteristics with agile development methods to deliver fault tolerant, reliable, and trustworthy software solutions. As an example of the implementation of a software engineering tactic to control a non-functional requirement of an IoT system, consider the Markov analysis for availability evaluation and impact of preventive and corrective maintenance procedures at device and local network levels based on real testbed data from local and Internet-level ping procedures [27].  
The proposed framework aims to enhance the testability, which is a characteristic of the maintainability non-functional requirement of ISO25010 standard. The study cases reported in the next sections investigated the automated monitoring of the resource utilization, characteristic of the performance efficiency non-functional requirement.
# The MINLP Approach to Topology, Shape and Discrete Sizing Optimization of Trusses
## 6 Conclusions
The comparison of the obtained results with results available from literature proved the proposed MINLP synthesis to be a competitive optimization technique. Some important conclusions regarding the buckling constraints of compressed bars also need to be emphasized. Not only do the buckling constraints represent a vital step of the design process, these constraints also have a crucial influence on the progress of topology optimization. When a truss is optimized, considering the stress and displacement constraints only, it is advisable for the low-stressed bars from the initial topology to be removed from the structure. By introducing buckling constraints, however, the lower stressed (mostly tensioned) bars from the initial topology are often a better choice for selection, while the higher stressed (mostly compressed) ones are rejected. Thus, it is not advisable to remove the low-stressed bars from the initial topology when the buckling constraints are considered. Rigorous topology such as that proposed in this paper should be employed to obtain optimal solutions. A considerably greater improvement in results (reduction in structural weight) was additionally achieved by the introduction of the shape optimization into the synthesis. Conceptualization, S.S., Z.K. and S.K. ; methodology, S.S., Z.K. and S.K. ; formal analysis, S.S., Z.K. and S.K. ; writing--original draft preparation, S.S., Z.K. and S.K. All authors have read and agreed to the published version of the manuscript. The authors acknowledge the financial support from the Slovenian Research Agency (grant numbers P2-0129 and P2-0032). Not applicable. The data presented in this study are available upon request from the corresponding author. The authors declare no conflict of interest. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Optimization** & \multirow{2}{*}{**MINLP Iteration**} & \multirow{2}{*}{**Value of Objective Function**} & \multirow{2}{*}{**No. of Active Bin. Var.**} & \multirow{2}{*}{**CPU Time (s)**} \\ \cline{5-6}
**Cycle** & & & & & \\ \hline \multirow{5}{*}{\(n=4\)} & \multirow{5}{*}{T} & 17 & 16. MILP & 30,427.5500 & 8 & 0.050 \\  & & & 17. NLP & 35,270.9973 & (\(z_{1}{}^{t}\)\(>z_{\text{OPT}}\)) & 0.148 \\  & & 18 & 17. MILP & 32,423.2100 & & 0.060 \\  & & 18. NLP & 35,590.9738 & (\(z_{2}{}^{t}\)\(>z_{\text{OPT}}\)) & 0.082 \\  & & 19 & 18. MILP & 34,956.8920 & & 0.080 \\  & & 19. NLP & 34,630.1695 & (\(z_{3}{}^{t}\)\(>z_{\text{OPT}}\)) & 0.082 \\  & & 20 & 19. MILP & 35,625.8000 & & 0.081 \\  & & 20. NLP & 34,950.1460 & (\(z_{4}{}^{t}\)\(>z_{\text{OPT}}\)) & 0.117 \\  & & 21 & 20. MILP & 42,980.0600 & & 0.090 \\  & & 21. NLP & 35,309.5418 & (\(z_{5}{}^{t}\)\(>z_{\text{OPT}}\)) & 0.109 \\  & & & & & \(\sum\) = **3.344** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Convergence of the Modified OA/ER algorithm and the **MTP** strategy, example **TC10d** (topology and shape optimization, with buckling constraints).
# Using Drones to Estimate and Reduce the Risk of Wildfire Propagation in Wildland-Urban Interfaces
## 2 Materials and Methods
### Data Generated from the Aerial Images  
The captured images for each use case were processed by Open Drone Map (ODM) software v3.5.4 [22] to generate a high-resolution orthophoto map and a 3D point cloud file for each use case, which are needed by the CHAMELON bundles to extract information about trees and vegetation. Figure 7 illustrates the orthophoto (left) and 3D point cloud (right) of the Castanheira use case.  
Figure 6: The mission planner module.  
ODM was unable to determine the 3D point cloud of some trees, as the large number of similar trees near each other means that it is difficult to extract unambiguous geo-referenced features. A methodology using LiDAR to enhance the model would probably provide better results.