esa-sceva/satcom-chunk-collection huggingface dataset:
# Entropy Wavelet-Based Method to Increase Efficiency in Highway Bridge Damage Identification
## 5 Experimental Results and Analysis
Figure 28: Entropy–CWT diagram, plotting the average SEI for the CWT coefficients considering all measurement points (sensors S1–S12 in Figure 4) for the experimental case of the real-life RPB with a removed cable and a scale range from 1 to 1000. The below plot focuses on the above plot, showing the entropy increments resulting from damage. Even though promising results have been achieved in this specific scenario, further research is necessary in order to enhance the robustness of the proposed method. In this regard, it is important to explore other case studies that address the following scenarios: (i) sensor placement does not align with or is far away from the damage zone, (ii) varying quantities of sensors are to be employed, and (iii) a wider range of damages, such as additional cable removal, corrosion, loosened bolts, among others, are present. This calibration of the proposal under these circumstances will allow for the investigation of diverse scenarios where sensor triggering may occur. Currently, the performance of the proposed method can be compromised in these scenarios, as its accuracy in both damage detection and localization heavily relies on the quantity and positioning of the sensors. For instance, a limited number of sensors diminishes the system's capability of detecting vibrations associated with damage across the entire bridge, and reduces the resolution for determining or identifying the damage location, especially in the context of a large bridge, such as the one analyzed in this work. Hence, real-world experiments, considering other conditions (additional types of damage and alternative sensor orientations or directions), are essential for validating and calibrating structural health monitoring strategies, but conducting analyses on actual bridges under both healthy and damaged conditions poses significant challenges in terms of human resources, infrastructure, and financial constraints. In addition to the previously mentioned issues, the obtaining of the vibrational signature of a new bridge, through considering all available vibration sensors and their locations, will play a crucial role in assessing the accuracy and sensitivity of the proposed method for detecting and locating various types of damage. This effort will be always supported by modeling and computational simulations. Figure 29: Values of maximum CWT coefficient considering the average of the 30 respective maximum values of each sensor/condition with a scale range from 200 to 1000 (solid circles) for (**a,c**) healthy RPB and (**b,d**) damaged RPB with a removed cable. For the first two plots (**a,b**), the same vertical scale is considered for identifying damage quickly. Fore the last two plots (**c,d**), the vertical scale is auto-adjusted for both scenarios (healthy and damage) to visualize the corresponding minimum and maximum values obtained for the respective 30 tests of each case (horizontal solid lines).
## 4 Risk Analysis for Current Protocols
### Automated Identification System (AIS)
#### 4.4.4 Replay/Man-in-the-Middle  
We further suggest that a man-in-the-middle attack can be performed either through software or radio frequency. An attacker can eavesdrop on a communication between vessels and replace AIS information. Similarly, for radio frequency, an attacker can override a legitimate AIS signal with a fake one. Regardless of the attack method, the recipient only acquires the modified AIS message from the attacker [37].
## 3 Results and Discussion
### In Situ Hydrolysis of the Oriented Films  
Hydrolysis of the NBA moieties occurs under acidic conditions [31; 32; 33]. We previously reported that photoalignment of copolymerhacrylate with NBA2 and benzoic acid side groups and subsequent annealing under humid air generates an oriented film with hydrolyzed phenylalanine moieties [28]. The benzoic acid side groups act as an acid catalyst. Immersing the oriented **P4** film in an acetic acid solution generates hydrolysis because **P4** does not include an acidic moiety. However, hydrolysis does not occur when using only water (Figure S4a).  
Polarized absorption spectrum changes of oriented **P4** films before and after hydrolysis are shown in Figure 4a,b. Hydrolysis was carried out by immersing in an acetic acid/water solution [(a) 1/5 (pH = 1.5) or (b) 1/3 (pH = 1.3) (vol/vol)] at 50 \({}^{\circ}\)C for 10 min, respectively. After hydrolysis, the absorption band at 325 nm decreases, and the absorption maxima shift to a shorter wavelength due to the formation of 4-aminophenyl benzoate mesogenic side groups. The FTIR spectrum decreases at 1621 and 1590 cm\({}^{-1}\) (N=C vibration), and new absorptions appear at 3385 and 3361 cm\({}^{-1}\) (Ph-NH\({}_{2}\) vibration) (Figure 4c). In these cases, the DH (\(D_{260}\)) values are 60% (0.64) and 95% (0.49) after hydrolysis when using 1/5 and 1/3 solution, respectively. The higher DH and lower \(D_{260}\) values using 1/3 solution indicate that the hydrolysis is fast but the orientation structure gradually collapses when using a higher acidity solution. Additionally, the DH (\(D_{260}\)) value increases (decreases) as the acetic acid concentration or immersion time for the hydrolysis increases (Figure S4b,c). Nevertheless, the film maintains its orientation structure, even though most of the mesogenic groups are hydrolyzed (Figure S4c). This is because the hydrolyzed **P4** film displays LC characteristics upon hydrolysis at 50 \({}^{\circ}\)C. This characteristic is confirmed by the change in the transmittance of the hydrolyzed film between crossed polarizers upon heating, exhibiting thermal stability up to 110 \({}^{\circ}\)C (Figure 5).  
Figure 3: **(a,b)** Changes in the polarized absorption spectrum of **P4** films upon exposure to linearly polarized 313 nm light. Exposure energy and the annealing conditions are (a) 5 and (b) 30 J/cm\({}^{2}\) and 120 \({}^{\circ}\)C for 10 min, respectively. (c,d) Thermally stimulated \(D\) value change for the exposed **P4** films (c) as a function of exposure energy of linearly polarized 313 nm light when the annealing temperature is 120 \({}^{\circ}\)C and (d) as a function of annealing temperature when the exposure energy is 3 J/cm\({}^{2}\).
# Utilizing Energy Storage for Reliability Solutions in Active Distribution Systems
## 4 Case Studies and Discussion
### End-User Reliability Profile with ESS
A case study was conducted by varying hardware unavailability of ESS and total damage cost incurred to the customer connected at IL9. Figure 7 shows an increase in damage cost with the increase in ESS unavailability. The hardware availability depends on the maintenance practices, configuration of ESS, failure characteristics of components, etc. Results in Figure 7 provide useful information in investing in ESS component reliability. Apart from using ESS in microgrid mode taking up critical loads in case of utility supply disturbances (as described above), other means of reliability solutions, especially for voltage sag, are also in practice in industries. The injection of series voltage or series voltage by extending the existing  
Figure 5: Improvement in the reliability profile of IL9 with ESS. Figure 6: Impact of (**a**) ESS state of charge; (**b**) the rated discharge duration on the unavailability of IL9. power electronic interface used for DG operation help reduces the impact of voltage sag [7]. Power electronic-based devices such as FACTs, STATCOM, SVC, DVR, TVR, etc., can also be used to mitigate voltage sag of the overall system as well as individual buses [5; 6]. Their effectiveness to mitigate voltage sag depends on rating and location. Although such practices are effective to mitigate voltage sag, they do not contribute to mitigating interruptions.
## 3 Results
### Differences in Physical Performance Variables Based on Maturity Status and Gender
#### 3.4.1 Interaction Effects of Chronological Age and Maturity Factors  
The results demonstrated a significant interaction between age and maturity in certain anthropometric variables, including standing height, sitting height, leg length, fat-free mass, wingspan, and leg muscle volume (as shown in Table 5). In contrast, no age-by-maturity interactions were observed for any physical performance parameters (\(p>0.05\)) (refer to Table 6). \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Physical Performance Parameters** & **Gender** & **Chronological Age** & \(p\) \\ \hline VO\({}_{\text{2 max}}\) (ml\(\cdot\)min\({}^{-1}\cdot\)kg\({}^{-1}\)) & Boys & 0.506 & 0.001 \\  & Girls & 0.346 & 0.001 \\ MAV (km\(\cdot\)h\({}^{-1}\)) & Boys & 0.506 & 0.001 \\ SJ height (cm) & Girls & 0.353 & 0.001 \\  & Boys & 0.590 & 0.001 \\ SJ height (cm) & Girls & 0.217 & 0.001 \\  & Boys & 0.581 & 0.001 \\  & Girls & 0.253 & 0.502 \\ SJ height-S (cm) & Boys & 0.448 & 0.001 \\  & Girls & 0.267 & 0.001 \\  & Boys & 0.443 & 0.001 \\ CMJ height-S (cm) & Girls & 0.276 & 0.011 \\  & Boys & 0.660 & 0.001 \\ SJ Power (W\(\cdot\)kg\({}^{-1}\)) & Girls & 0.205 & 0.002 \\  & Boys & 0.686 & 0.001 \\  & Girls & 0.195 & 0.003 \\ SJ Power-S (W\(\cdot\)kg\({}^{-1}\)) & Boys & 0.501 & 0.001 \\  & Girls & 0.188 & 0.004 \\ CMJ Power -S (W\(\cdot\)kg\({}^{-1}\)) & Boys & 0.509 & 0.001 \\  & Girls & 0.124 & 0.061 \\ Five-Jump test (m) & Boys & 0.744 & 0.001 \\  & Girls & 0.338 & 0.001 \\
5 m sprint time (s) & Boys & \(-\)0.275 & 0.001 \\  & Girls & \(-\)0.298 & 0.001 \\
10 m sprint time (s) & Boys & \(-\)0.349 & 0.001 \\  & Girls & \(-\)0.417 & 0.001 \\
20 m sprint time without the ball (s) & Boys & \(-\)0.550 & 0.001 \\  & Girls & \(-\)0.090 & 0.173 \\
20 m sprint time with the ball (s) & Boys & \(-\)0.210 & 0.001 \\  & Girls & \(-\)0.465 & 0.001 \\ Agility test (s) & Boys & \(-\)0.573 & 0.001 \\  & Girls & \(-\)0.396 & 0.001 \\ Flexibility (cm) & Boys & 0.250 & 0.001 \\  & Girls & 0.252 & 0.001 \\ \hline \hline \end{tabular} Note: VO\({}_{\text{2 max}}\): maximum oxygen uptake; MAV: maximum aerobic velocity; SJ: squat jump; CMJ: counter movement jump; height-S: counter movement jump with arm swing; Power-S: relative power with arm swing.  
\end{table} 
Table 4: Coefficients of correlation between performance parameters and chronological age.
## 5 Secure Key Agreement and Authentication Protocol for VCC
### Message Signature and Message Confirmation Phase  
If the \(v_{i}\) wants to send information to the concerned RSU, \(v_{i}\) must sign the message using the session key and sends it to the \(RSU_{j}\). Additionally, \(RSU_{j}\) checks whether the message is legitimate or not. If the message is legitimate, \(RSU_{j}\) validates the message and sends it to a cloud server. The detailed steps are as following and are shown in Figure 7.  
**Step 1:**: for signing the information \(M_{i}\), \(v_{i}\) computes \(\sigma_{i}=SK\oplus h(M_{i}||T_{1})\) and \(M^{{}^{\prime}}_{i}=SK\oplus M_{i}\oplus T_{1}\oplus ID_{RSU_{j}}\) and sends the message \(\{\sigma_{i},M^{{}^{\prime}}_{i},T_{1}\}\) to the concerned \(RSU_{j}\).
**Step 2:**: after receiving the message, \(RSU_{j}\) extracts information \(M_{i}=SK\oplus M^{{}^{\prime}}_{i}\oplus T_{1}\oplus ID_{RSU_{j}}\), computes \(\sigma^{{}^{\prime}}_{i}=SK\oplus h(M_{i}||T_{1})\) and checks whether \(\sigma_{i}\) and \(\sigma^{{}^{\prime}}_{i}\) are equal or not. If they are the same, \(RSU_{j}\) uses the information \(M_{i}\) for the future computations. Additionally, generally for batch verification, \(RSU_{j}\) inspects the reaction by a following equation:  
\[(\sum_{i=1}^{n}v_{i}\cdot\sigma_{i})=\sum_{i=1}^{n}v_{i}\cdot SK\oplus\sum_{i= 1}^{n}v_{i}\cdot h(M_{i}||T_{1})\]
# A Fully Integrated Low-Power Multi-Mode RF Receiver for BDS-3/GPS
## 3 Systematical Design of the Receiver
### Filter Order  
The frequency response of the IF bandpass filter (BPF) is determined by IMRR, the in-band flatness. The designed IF BPF adopts the Butterworth type with excellent in-band flatness. The desired Butterworth filter order can be calculated by (1).  
\[N=int\left[\frac{log_{10}^{\left(2\times 10^{\left(AL/10\right)}-1\right)}}{2 log_{10}^{\left(ff/f_{c}\right)}}\right]+1, \tag{1}\]  
where \(N\) is the order, and \(f_{c}\) is the \(-3\) dB bandwidth frequency, which in this design is equal to 2.046 MHz. \(f\) is the frequency difference between a specific frequency (\(f_{sp}\)) and the central frequency. AL is the out-of-band attenuation at \(f_{sp}\). Considering the flicker noise and DC offset, the central frequency of this paper is 4.092 MHz [20; 21]. For instance, the difference between the mirror frequency and the center frequency is 8.184 MHz. In other words, for IMRR, the frequency difference \(f\) is 8.184 MHz. As mentioned in Section 2, an IMRR of 25 dB is required. It means that at the mirror frequency, the out-of-band attenuation AL is 25 dB. So, according to (1), the needed filter order N is 3 to meet the IMRR requirement.
# Fully Open-Source Meeting Minutes Generation Tool
## 5 Evaluations
### Performance of Information Extraction Function
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Topic**} & \multirow{2}{*}{**Topic**} & \multirow{2}{*}{**Topic**} & \multirow{2}{*}{**Topic**} & \multirow{2}{*}{**Topic**} & \multirow{2}{*}{**Topic**} & \multirow{2}{*}{**Topic**} & \multirow{2}{*}{**Topic**} & \multirow{2}{*}{**Topic**} & \multirow{2}{*}{**Topic**} \\  & & & & & & & & & & & & & \\ \hline \multirow{4}{*}{**ROUGE-1**} & Slide 1 & 27.4\% & 49.9\% & 33.9\% & 44.4\% & 49.9\% & 49.9\% & 59.5\% & 46.1\% & 0\% & 35.2\% \\  & Slide 2 & 36.7\% & 45.7\% & 4.1\% & 60.8\% & 42.4\% & 69.1\% & 60.2\% & 46.8\% & 47.4\% & 33.5\% \\  & Slide 3 & 68.4\% & 30.6\% & 45.3\% & 43.7\% & 52.9\% & 29.8\% & 37.9\% & 38\% & 50.6\% & 31.9\% \\  & Slide 4 & 43.2\% & 48.1\% & 51.7\% & 55.3\% & 32.9\% & 41\% & 72.7\% & 46.8\% & 43.9\% & 55.7\% \\  & Slide 5 & 41.5\% & - & 46.3\% & 38.2\% & 19\% & 39.6\% & 62.7\% & 45.3\% & 43.9\% & 49\% \\ \hline \hline \multirow{4}{*}{**ROUGE-2**} & Slide 1 & 14\% & 19.6\% & 6.3\% & 9.8\% & 21.8\% & 27.1\% & 40.7\% & 20.3\% & 0\% & 22.9\% \\  & Slide 2 & 5.9\% & 9.5\% & 21.7\% & 33.3\% & 16.2\% & 44.7\% & 33.6\% & 22.2\% & 19.1\% & 12.8\% \\  & Slide 3 & 37.5\% & 8.6\% & 6.8\% & 18.8\% & 23.2\% & 9.3\% & 14.7\% & 17.1\% & 28.8\% & 15.5\% \\  & Slide 4 & 13.1\% & 20.8\% & 17.9\% & 20.2\% & 11.7\% & 8.1\% & 44.4\% & 15.9\% & 20.6\% & 37.8\% \\  & Slide 5 & 18.9\% & - & 16.2\% & 8.8\% & 5.8\% & 10.7\% & 20.3\% & 21.9\% & 10.2\% & 29.8\% \\ \hline \hline \multirow{4}{*}{**ROUGE-L**} & Slide 1 & 23.5\% & 38.4\% & 22.6\% & 37\% & 45.8\% & 49.9\% & 59.5\% & 46.2\% & 0\% & 35.2\% \\  & Slide 2 & 22.9\% & 25.7\% & 39.5\% & 55.7\% & 34.3\% & 64.2\% & 55.9\% & 36.3\% & 27.4\% & 19.1\% \\ \cline{1-1}  & Slide 3 & 50.6\% & 27\% & 34.6\% & 37.4\% & 44.1\% & 20.8\% & 31\% & 30.9\% & 45.8\% & 23.9\% \\ \cline{1-1}  & Slide 4 & 23.4\% & 40.5\% & 34.4\% & 33.8\% & 21.1\% & 24.6\% & 62.3\% & 36.3\% & 26.3\% & 39.3\% \\ \cline{1-1}  & Slide 5 & 23.7\% & - & 37.6\% & 27.6\% & 9.5\% & 31.6\% & 46.5\% & 34.6\% & 31.7\% & 43.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _ROUGE-1, ROUGE-2, and ROUGE-L_ for each slide and topic. Table 10 shows that the _RaKUn2_ algorithm achieved the average _cosine similarity_ score of 0.509 on all the slides and topics, accurately capturing over half of the reference keywords. While effective for general keyword identification, the cosine similarity relies mainly on lexical overlap, potentially missing deeper semantic nuances, especially in topics requiring complex contextual understanding [65].
## 4 Discussion
The main route of exposure to lead, which is considered a potential carcinogen for humans, is through food. According to our study findings, the average values of Cd and Pb analysis of organic and conventional strawberry fruit samples were below the detection limit. [PERSON] et al. [28] similarly reported that the average heavy metal content in strawberry fruit grown in the Lublin region was not at a significant level. [PERSON] et al. [26] evaluated the heavy metal (Fe, Mg, Mn, K, Ca, Na, Zn, Cu, Ni and Cd) contents of strawberries grown with conventional and organic farming methods in the east-central region of Tunisia, and researchers reported that Cd could not be detected in strawberry fruits, as in our study findings. [PERSON] et al. [27], in their study for the determination of lead, cadmium and persistent organic pollutants in some berry species grown wild and cultivated in Northeast Poland, reported that wild strawberry contains a higher concentration of Cd (50 ug/kg fresh weight) compared to raspberry and blackberry. Cd and Pb amounts in fruit samples below the observable limit values in some studies that were similar to our study in terms of fruit type [61] or the region where the samples were taken [62]. The reason for the different results from the studies is thought to be factors such as the conditions of the study sites and the proximity to pollutant sources. The Huyuk district is a high-altitude region where industrialization is not intense. This situation is thought to be closely related to the low or no heavy metal content in fruits. In addition, under organic farming principles and rules, agricultural activities are recommended to be carried out in areas far from industrial enterprises and main land transportation roads. Chemical pollutants formed as a result of environmental pollution can be found in both conventional and organic products [57; 59]. However, the presence or absence of these chemicals in organic or non-organic foods basically depends on the area of agriculture. The Pb concentration in plants differs, especially due to exhaust gases. It is thought that these results obtained in our study are due to the characteristics of the region. Contamination of foods with cadmium, which is known as a carcinogen, is also a hotly debated issue. In some studies, it has been reported that this element is poorly fixed in the roots of plants and is more evenly distributed in the organs of strawberry plants compared to other heavy metals [58; 63]. Cd values in soil and water samples taken from strawberry stations grown with conventional and organic methods are below the detection limit.
## 2 Related Work
### Structure Analysis of the Traditional LEACH Protocol  
The traditional LEACH routing protocol is a low energy-consumption self-adaptive hierarchical routing algorithm designed for WSNs by [PERSON] _et al_. [9,11]. Its basic idea is that cluster heads are selected randomly through cycles with equal probability, and integrate and send information collected by members inside clusters to sink nodes to reduce communication traffic and distribute the energy load of the whole network evenly to each sensor node, so as to meet the goal of reducing energy consumption and extending the overall survival time of the network.  
The working process of the LEACH protocol is cyclic, defined as the concept of rounds. In Figure 2, each round of a cycle is divided into two types of state: set-up state and steady state.  
In the set-up state, each sensor node competes for a cluster head at a certain probability and each cluster head broadcasts a message to all nodes that it has become one, and each node determines which one to join based on the signal strength, and responds to that cluster head. In the steady state, the cluster's members send collected data to the cluster head according to the TDMA time slot, and the head integrates and sends data to sink nodes via Carrier Sense Multiple Access (CSMA). The steady state is much longer than the set-up state, and after a period of working time, the network re-enters a set-up state. The execution process is periodic. From the protocol process, cluster heads need to finish integrating data and communication with sink nodes, so the energy consumption is very high. The LEACH algorithm ensures that each node can be a cluster head in equiprobability, and that each node in the network consumes energy comparatively evenly.  
Figure 2: Working Cycle of the LEACH Protocol.
# A Review of Hybrid VLC/RF Networks: Features, Applications, and Future Directions
## 2 Hybrid VLC/RF Networks in Indoor Scenarios under Ideal Conditions
### Network Selection and Resource Allocation  
The main objective of communication networks is to provide users with personalized demand, high quality of service (QoS), and high overall system performance. Achieving this goal is a constant challenge in hybrid VLC/RF networks because of the need for an elaborate balance between user demand and network resources [61]. Another fundamental challenge in these networks is the problem of associating mobile terminals to each base station for packet transfer due to the density of access points, their heterogeneity, differences in coverage area, and the limitation of available resources [62]. For all these reasons, it is very important to realize an approach to address the problem of network association and resource allocation and to obtain better information about the users and resources in the environment. The authors of [63] proposed a network selection scheme for a hybrid VLC/RF system in an indoor scenario, taking into account the context-aware information of asymmetric downlink and uplink characteristics and network performance. In addition, a context-dependent learning algorithm was proposed based on the information about the location of the users and the type of traffic they require. In [64], the authors proposed a knowledge transfer reinforcement learning algorithm to perform network selection in a hybrid VLC/RF system. This algorithm reveals and exploits contextual information about networks, load distribution, and network traffic characteristics. The authors of [65] showed another approach to network selection by designing a fuzzy logic model with relatively low computational complexity, in which all users to be connected to the RF network are first selected and then all remaining users are assigned to the VLC network. In [66], a network selection process was proposed for a hybrid system composed of VLC and RF, in which the transmitter sends the transmission data over the link that best guarantees the quality of service. The average arrival data rate at the transmitter buffer and the non-asynchronous limits of the buffer delay are used to evaluate the performance of the proposed algorithm.