esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Impact Localization from Fluid-Pipe Coupled Vibration under Noisy Environment
## 3 Results and Discussion
### Data Preparation and System Characterization
It is noteworthy that the coupled wave propagating from an impact source has a band-limited feature in CWT, which is clearly distinguishable from the burst noise of which the ridge value increases along the scale parameter. On other hand, the CWT for the S2 signal has the maximum peak near the scale parameter of 1200, and its ridge line continues to the highest scale with less value than the peak. The signals are from an impact severe enough to be detected by the vibration sensors. Therefore, the resonant features are clearly seen in the CWT. The maximum scale values at two places in Figure 9 are not the same, and the scales correspond fairly well to the frequency bands in Figure 7b. In addition, the pipeline has its own structural damping,  
Figure 9: Continuous wavelet transform of the signals measured at sensing points: S1 (**a**) and S2 (**b**). proportional to a power law dependence on frequency [42]. This means that low-frequency components propagate longer than higher-frequency ones. Therefore, the frequency at S2 is lower than that at S1. This implies that the frequency responses of the coupled waves induced at different locations could have different frequency bands. This leads us to determine a scale band by which to effectively observe resonant features of the coupled waves. The band we set here for the piping system was from 300 to 1600, which corresponds to 30-170 Hz in the frequency domain. This comes from the observation based on the frequency characteristics of the system in Figure 7, and the high ambient background noise level at a low frequency (< 25 Hz). The maximum scale here was set to 2048 (=211) to cover the low frequency (25 Hz), and to identify and remove the burst noises.
# Joint Extraction of Multiple Relations and Entities from Building Code Clauses
## 2 Methodology
### Joint Extraction Model
#### 2.2.2 Augmented Character Embedding  
Word embedding is a technique for representing characters/words as real-valued vectors of high density and low dimensionality. Vectors representing a collection of words or characters with similar semantic meanings exhibit high cosine similarity. For example, the cosine value of the angle between the vector for \"door\" and that for \"window\" is 0.72, indicating high semantic similarity. In this way, the sequence of discrete characters forming a sentence or even a paragraph can be transformed into a numeric representation for further processing by deep learning algorithms.  
Unlike written English, written Chinese does not use delimiters between words. Incorrect word segmentation may lead to erroneous entity recognition and information extraction failure. Therefore, for pragmatic reasons, each Chinese character is frequently treated as an atomic linguistic unit. However, one drawback of general character embedding is the lack of word information expressed, especially in the context of building code analysis, which involves dozens of characteristic technical terms. Thus, an augmented embedding method is designed to enrich Chinese characters with word information (see Figure 3, Part 1). Specifically, the Building Information Model Classification and Coding Standard (GB/T 51269-2017) [32] is utilized as the terminology reference for deriving the prior semantic information that needs to be represented by word-level embedding vectors. These word-level vectors can then be integrated into character-level vectors. In this way, the resulting vectors can express characters with richer semantics.  
Formally, for a given clause \(S=\{c_{1},c_{2},\ldots,c_{n}\}\), where \(c_{i}\) represents the \(i\)-th character in \(S\), the input representation for each character \(\mathbf{x}_{i}\in\mathbb{R}^{d_{c}}\) is as follows:  
\[\mathbf{x}_{i}=\left[\mathbf{e}^{c}\left(c_{i}\right)+\mathbf{W}_{T}\mathbf{e}^{w} \left(\mathbf{word}\left(c_{i}\right)\right)\right], \tag{1}\]  
where \(\mathbf{e}^{c}\) and \(\mathbf{e}^{w}\) denote a pretrained character embedding lookup table and a pretrained word embedding lookup table, respectively. The formula \(\mathbf{word}\left(c_{i}\right)\) represents the word to which character \(c_{i}\) belongs, which is implemented based on the Jieba segmentation library. \(\mathbf{W}_{T}\in\mathbb{R}^{d_{c}\times d_{w}}\) is a trainable transformation matrix for aligning the dimensionality of a word vector (\(d_{w}\)) with that of a character  
Figure 2: Framework of the joint extraction model.  
vector (\(d_{c}\)). Finally, the sequence of characters that forms a raw clause can be expressed as a vector matrix, where each column represents the character vector \(\mathbf{x}_{i}\) associated with the corresponding position in the sentence.
# Shear Capacity of Textile-Reinforced Concrete Slabs without Shear Reinforcement
## 2 Experimental Investigation on Shear Capacity
### Reinforcement
Conventional spacers made from fiber-reinforced mortar or plastic could function as a suitable alternative but were avoided here to minimize the risk of floating up of the reinforcement and to eliminate a possible influence on the cracking process. Figure 2: Reinforcement layout for test series: (**a**) Planar biaxial grid made of carbon with epoxy impregnation; (**b**) formwork with bottom reinforcement layer projecting beyond the end; (**c**) cross-section of the three specimen types. The material characteristics of non-impregnated textiles and textiles with partial impregnation (surface coating) needed to be determined with composite specimens via uniaxial tensile tests [10; 61; 62; 63]. For fully impregnated textiles with a homogenous stress distribution over the yarn area and simultaneous activation of all filaments, the properties could be determined on the textile without surrounding concrete [64]. Table 2 lists the material properties. The ultimate stress and modulus of elasticity were analyzed in uniaxial single yarn tests according to the setup described in [65], where individual yarns were extracted from the hardened grid and connected to the testing machine with an variable pressure along the clamping length. Note that, due to the well-known statistical effects for a bundle of linear-elastic yarns with brittle failure, the strength of a single yarn does not equal the strength of the fabric [66; 67]. This can be taken into account by a reduction factor which depends on the number \(n\) of yarns [68]. For the reinforcement used in this study, a reduction factor of 0.85 for \(n=\infty\) was proposed by [PERSON] [64]. The bond properties of the reinforcement were analyzed in a companion investigation [69]. In contrast to non-impregnated textiles or textiles with soft impregnation, the full and hard epoxy impregnation led to form closure with longitudinal splitting of the concrete as a bond failure mechanism, rather than pull-out or jamming of the yarns. The mean length required for full anchorage was determined to be 78 mm for a concrete cover of 20 mm in the same cementitious matrix with equal tensile and compression strengths, as used in the present paper [69]. A free length of 50 mm behind the supports on both ends of the specimens proved to be sufficient to anchor the respective forces from shear and bending. All layers of reinforcement continued to the end of the specimen. Up to the point of ultimate failure, no longitudinal cracks ran up to the supports or up to the ends of the specimen. This allowed for the conclusion that no anchorage failure occurred in this study.
# Exploiting Outage and Error Probability
## 4 Simulation Results and Discussions
### Network Performance Parameters--Discussion
#### 4.2.5 Packet Acceptance Ratio  
Figure 12 demonstrates another parameter, i.e., packet acceptance ratio. E-ACE outperforms EEDBR with packet acceptance ratio close to 98%. Whereas, EEDBR has the lowest packet acceptance ratio (approximately 28%). This means that less than half of the packets are successfully delivered to sink and rest of the packets are dropped. ACE shows acceptance ratio of 95% and CARQ shows  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Serial. No.**} & \multirow{2}{*}{**Protocol Name**} & \multicolumn{6}{c}{**Dead Nodes**} \\ \cline{3-8}  & & **0** & **50** & **100** & **150** & **200** & **250** \\ \hline
1 & E-ACE & 247 & 182 & 102 & 26 & 3 & 0 \\
2 & ACE & 239 & 166 & 46 & 15 & 3 & 0 \\
3 & EEDBR & 72 & 59 & 17 & 2 & 1 & 0 \\
4 & CARQ & 146 & 119 & 29 & 15 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Throughput vs. dead nodes.  
Figure 12: Packet acceptance ratio.  
acceptance ratio of 58%. After 230 s, there is a drop off in acceptance ratio of ACE, E-ACE and CARQ. This drop is due to two reasons; (1) less relays are available to provide cooperative retransmissions and (2) due to fading consideration, the channel conditions are not suitable to achieve BER more than the threshold even after two retransmissions.  
More acceptance ratio means more reliable network. In later time slots, EEDBR has more packet acceptance ratio because it has more alive nodes to forward data to sink. Thus, the retransmission mechanism clearly enhances network performance in terms of reliability.
## 2 Materials and Methods
### Detailed Landcover Map Update Methodology
#### 2.3.4 Landcover Misclassification Check and Quality Assurance  
To ensure proper application of the proposed method to a new urban area nonpoint source pollution management program, its accuracy should be assured. Therefore, an error detection table was prepared using the quality assurance checklist proposed in the landcover map guidelines. Samples were extracted from the classified landcover items by random sampling per landcover class and their accuracy was rated by superimposing ortho-aerial photos and using the portal site map service. Accuracy testing was performed on both object boundaries (represented by polygons) and attributes, and either error was rated as misclassification. Errors were defined as \"misclassification\" (wrong attribute), \"non-classification\" (missing attribute data), \"boundary correction\" (wrong boundary contour), and \"other\" errors. For quality assurance (QA), the samples selected were 10% of the polygons, relative to the total number of objects. The error rate of the detailed classification by landcover map item is expressed by Equation (1), in which the overall error rate was obtained by averaging the error rates of all items. Items with error rates within 5% of the overall error rate were determined as the final products, and the first and second update processes were repeated until the cut-off error rate of 5% was reached.  
\[R_{e}(\%)=\frac{E_{m}+E_{u}+E_{b}+E_{o}}{O_{i}}\times 100 \tag{1}\]  
\(R_{e}\): Error rate  
\(E_{m}\): Number of misclassifications  
\(E_{u}\): Number of non-classifications  
\(E_{b}\): Number of boundary corrections  
\(E_{o}\): Number of other errors  
\(O_{i}\): Number of QA control samples  
**3. Results and Discussion**  
The landcover classification change rates after the first and second update processes are compared in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \begin{tabular}{c} **Landcover Map after** \\ **the First Update** \\ **(Change Rate Relative** \\ **to the Existing** \\ **Landcover Map)** \\ \end{tabular} & \
\begin{tabular}{c} **Landcover Map after the Second Update** \\ **Change Rate Relative** \\ **to the Updated** \\ **Landcover Map** \\ \end{tabular} &
\begin{tabular}{c} **Landcover Map after the Existing** \\ **Landcover Map** \\ \end{tabular} \\ \cline{2-5}
## 5 Discussion
### Potential Future Research  
In this paper, we describe a method that considers the precision of each individual annotator for each landmark separately, thus allowing the best annotators to be used to determine the optimal location of individual landmarks. One aspect of the described method is that the iterative annotator precision computation/annotator removal process repeats until the final and most precise annotator remains to form the final landmark selection. While not included within this article, a number of modifications may be further studied to improve the final result.  
* The termination of the iterative loop: throughout this study, we removed the least precise annotator on each iteration until only one annotator remained. In datasets where there is a high level of disagreement between annotators, the termination criteria could be adjusted to try and improve the overall result, e.g., terminating the iteration when two or three annotators remain. Such an adjustment could allow for underperforming annotators to be removed while allowing for a degree of consensus amongst the higher performing. Other means of termination also include stopping when the derived landmark position stops moving within a defined amount.
* Different stating position seeds: in this study, we used the global mean position for each landmark as the initial seed to determine annotator precision. Other seeding methods may also be useful for further study, such as using the expert annotator's mean selection or randomly selecting one selected landmark location and repeating Algorithm 1 multiple times.
* A weighted averaged based on annotator precision was specifically selected as the update mechanism, as higher-performing annotators contribute more to the overall result. The effect/impact of alternative update mechanisms could be investigated.
# New Security Improvements in Next-Generation Passive Optical Networks Stage 2 +
###### Abstract  
Passive optical networks are currently the most promising solution for access networks. These networks rely on broadcast signal distribution in the downstream direction and unicast signal transmission in the upstream direction. The upstream direction is controlled by optical line termination (OLT). The broadcast transmission method increases security vulnerability because the attacker is able to connect his/her modified optical network unit (ONU) to the free port of the splitter (commonly in the basement). We present the concept for the activation process of ONUs based on physical unclonable function (PUF) for next-generation passive optical networks stage 2 (NG-PON2). The use of PUF increases security in the NG-PON2. Furthermore, the registration identifier (ID) is not stored in a nonvolatile memory, in comparison with the common solution defined by the International Telecommunication Union (ITU) recommendation G.989.3. An attacker cannot perform a reverse engineering attack to obtain the registration ID. For this reason, the attacker cannot clone an ONU. We proposed security improvements that involve authentication, encryption, integrity protection, and data origin verification methods in the NG-PON2. Our model uses the standard implementation of the transmission convergence layer of NG-PON2 with the new physical layer operations, administration, and maintenance (PLOAM) messages. The recommendation G.989.3 allows specifying own PLOAM messages since not all IDs are used in the current specification.  
Keywords:NG-PON2; physical unclonable function; transmission convergence layer; PLOAM messages; security +
Footnote †: journal: Applied Physics Letters B
## 2 Materials and Methods
### Antioxidants Evaluation  
The antioxidant capacity of the AgNPs and flaxseed extract was evaluated using the DPPH free radical scavenging technique, with ascorbic acid serving as the positive control. In all, 1 mL of biosynthesized AgNP solution was diluted with methanol to acquire different concentrations (10-100 \(\mu\)g/mL), then mixed with 2 mL of DPPH solution (0.1 mM), and the standard solution of ascorbic acid was used as a positive control. The mixture was then agitated and kept at room temperature in the dark. The measurements of radical scavenging activity were determined by measuring the absorbance (A) of each sample with a UV-visible spectrophotometer at 517 nm, after 1 h of incubation. Calculating the percentage inhibition (1%) using the following equation:  
\[\text{Inhibition \%}\,=\,\frac{\text{(A of Control- A of Sample)}}{\text{A of control}}\times 100\]
# All about Delay-Tolerant Networking (DTN) Contributions to Future Internet
## 3 Related Work
### Internet of Things and Smart Cities
#### 3.3.3 MANETs, VANETs, V2X, WSN  
A significant category where DTN protocols find extended applications is VANETs [131]. VANETs enable communication between vehicles and roadside infrastructure to enhance road safety, traffic efficiency, and provide infotainment services. To this end, DTN protocols provide resilience to VANET infrastructures, which is an important aspect due to the obstacles, signal interference, or network congestion that they may encounter.  
Similarly, Mobile Ad Hoc Networks (MANETs) [132], i.e., decentralized mobile networks of autonomous agents that collaborate and communicate, are commonly employed in scenarios where traditional infrastructure is lacking or impractical. One key benefit gained from DTN utilization is resilient communication. In this context, DTN protocols enable communication even in challenging and dynamic environments encountered by MANETs, such as intermittent connectivity, node mobility, and disruptions. In addition, the store-and-forward mechanism of DTN allows for efficient message delivery, as messages are relayed opportunistically through intermediate nodes. This is particularly beneficial in MANETs, where varying communication ranges and node densities are common issues.  
Moreover, as mentioned in [133], DTN's decentralized nature aligns well with the autonomous operation of agents in MANETs, thus enabling self-organizing and self-healing communication without relying on fixed infrastructure. Dynamic routing is another advantage, as DTN protocols adapt routes based on real-time network conditions, thereby making them suitable for MANETs where network topology and connectivity can change rapidly.  
Furthermore, several works on DTN-related solutions for emergency situations have been proposed. In [134], the authors propose LoRAgent, a DTN-based location-aware system and geospatial routing mechanism using LoRa technology to provide decentralized communication and message forwarding. Moreover, in [135], a mobile cloud computing system for information exchange among isolated shelters via mobile vehicle server is introduced using IBR-DTN and DTN2. In addition, [136] provides an overview regarding the utilization of UAVs in V2X applications, e.g., as access point carriers [137].
## 4 Experimental Results
### Object-Oriented Level SLAM System Test  
In this study, based on a robust object pose estimation algorithm, two lightweight ways of cubes and ellipsoids are used to express objects in the scene, and an object-oriented 3D semantic map is constructed. Compared with the sparse point cloud map constructed with feature points as geometric features in ORB_SLAM2, the object-oriented semantic map constructed in this study can better express the semantic information in the scene.  
Figure 15 shows an example of the running results of the object-oriented SLAM system in a real scene. Specifically, Figure 15a shows the ORB feature points recognized in the image, the 2D bounding boxes of objects, and the semantic labels. For the ORB feature points in the 2D bounding boxes of different types of objects, the system displays them in different colors, e.g., the feature points outside the bounding boxes of objects are displayed in green. Figure 15b shows the pose estimation of objects in the image. In the visualization interface, the system represents the object in the form of an ellipsoid. Figure 15c shows the object-oriented 3D semantic map constructed by the system. Based on the ORB_SLAM2 sparse point cloud map, the semantic information of the object is integrated. It can be seen  
Figure 14: AR registration based on SLAM map reloading: (**a**) SLAM map in online positioning mode; (**b**) 3D model of initial placement; (**c**) 3D model after rotation, translation, and scaling; (**d**) SLAM map in offline positioning mode; (**e**) 3D model after loading the map.  
that the objects in the scene can be accurately estimated, and these objects are mapped into the 3D semantic map, which verifies the accuracy and robustness of the system.
Sensors_**2014**, _14_, 5622-5643; doi:10.3390/s140305622  
_Series of_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**An Analytical Model for the Performance Analysis of Concurrent Transmission in IEEE 802.15.4**  
**[PERSON] \({}^{1,*}\), [PERSON] \({}^{2}\) and [PERSON] \({}^{3}\)**  
\({}^{1}\) Vestel Electronics, 45030, Manisa, Turkey  
\({}^{2}\) Institute of Electronics, Computer and Telecommunication Engineering (IEIIT),  
National Research Council (CNR), 40126, Bologna, Italy; E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) Electrical, Electronic, and Information Engineering (DEI), University of Bologna,  
40126, Bologna, Italy; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +90-236-233-01-31; Fax: +90-236-233-38-68.  
_Received: 4 November 2013; in revised form: 12 March 2014 / Accepted: 14 March 2014 / Published: 20 March 2014_  
**Abstract:** Interference is a serious cause of performance degradation for IEEE802.15.4 devices. The effect of concurrent transmissions in IEEE 802.15.4 has been generally investigated by means of simulation or experimental activities. In this paper, a mathematical framework for the derivation of chip, symbol and packet error probability of a typical IEEE 802.15.4 receiver in the presence of interference is proposed. Both non-coherent and coherent demodulation schemes are considered by our model under the assumption of the absence of thermal noise. Simulation results are also added to assess the validity of the mathematical framework when the effect of thermal noise cannot be neglected. Numerical results show that the proposed analysis is in agreement with the measurement results on the literature under realistic working conditions.  
**Keywords:** concurrent transmissions; capture effect; IEEE 802.15.4