esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Evaluation of Recommendation Results
#### 4.3.3 Efficiency of Topic-HGNN  
The running time and the memory consumption of each method under different query search volumes on Retailrock dataset is shown in Tables 3 and 4. We set the number of recommendation results as 10. From Table 3, it can be seen that the recommendation framework based on a topic model is significantly better than the recommendation framework based on graph learning in terms of running time. Although the recommendation framework based on a topic model is approximately 15% more efficient than the recommendation framework based on graph learning, the quality of the results identified by the recommendation framework based on graph learning on the accuracy, recall, and novelty indicators improved by 53%, 51%, and 46%. This also demonstrates that the method based on graph learning can significantly improve the quality of recommendation results at the expense of a small amount of operating efficiency. Among the three graph-learning-based methods, the running time of our model is slightly higher since our method models the quaternary higher-order relationship among consumers, queries, items, and features. Thus, Topic-HGNN is significantly superior to that of Hyper-CTR and Graph-CNN, when sacrificing a relatively low efficiency.  
Figure 6: Comparison of recommendation result novelty.  
As can be seen from Table 4, the Topic-HGNN framework does not consume additional memory compared to other graph-based learning methods. This is because the Topic-HGNN is decomposed by hyperedge corruption, importing batches of vertices and hyperedges each time to relieve memory pressure. Therefore, in summary, the Topic-HGNN proposed in this work can produce better recommendation results, while being almost as effective as other graph-based methods.
## 4 Outline of the Approach
### Process of Concept Alignment for Interoperability in Heterogeneous Sources
#### 4.5.2 Used Matching Techniques  
In order to determine the similarity between a concept _c\({}_{s}\)_ in _C\({}_{S}\)_ and a concept _c\({}_{t}\)_ in _C\({}_{T}\)_ we use the Equation (1) which combines three matching techniques:  
\[sim\big{(}c_{s},c_{t}\big{)}=\max\big{(}sim_{Dice}\big{(}c_{s},c_{t}\big{)}d _{lcs}\big{(}c_{s},c_{t}\big{)},sim_{wordne}\big{(}c_{s},c_{t}\big{)}\big{)} \forall c_{s}\in C_{S},\forall c_{t}\in C_{T} \tag{1}\]  
The inclusion of each of these three has a purpose. Similarity based in Dice coefficient [39] ( _sim\(c_{s},c_{t}\)_)\(\in\)[0,1]) has the purpose of finding if concept _c\({}_{s}\)_ in \(X\) and a concept _c\({}_{t}\)_ in \(U\) have a high lexical similarity. Dice coefficient is a simple and normalized similarity measure [40] that can show if the two concept labels are similar at string level. Equation 2 calculates the longest common substring distance similarity ( _d\({}_{lcs}\big{(}c_{s},c_{t}\big{)}\)_\(\in\)[0.1]) which tries to find if one label is subsumed in the other. The semantic similarity ( _sim\({}_{wordne}\big{(}c_{s},c_{t}\big{)}\)_\(\in\)[0,1]) is based in Wu and [PERSON] path lengths method [41] using WordNet [42] as an external resource and tries to find the semantic similarity of the labels. Other languages similarity can be also included if the proper lexical resource is available:  
\[d_{lcs}\big{(}c_{s},c_{t}\big{)}=\frac{Length\big{(}LCS\big{(}c_{s},c_{t} \big{)}\big{)}}{\min\big{(}Length\big{(}c_{s},\big{)}\big{)}} \tag{2}\]  
The main methods used are shown in Table 1.  
\begin{table}
\begin{tabular}{l l l} \hline
**Type of similarity** & **Purpose** & **Similarity measure** \\ \hline String similarity & High lexical similarity & Dice coefficient \\  & Label inclusion & Longest common substring [Equation (2)] \\ Semantic similarity & Label equivalence & WordNet \\ \hline \end{tabular}
\end{table}
Table 1: Used matching techniques.  
Finally, the highest of the three similarity measures is considered to determine relation \(R\) in the triple \(<\)\(c_{s}\),\(c_{s}\)\(R\)\(>\) of a mapping element according of the following criteria:  
1. _Equivalent_ (=): Two concept elements \(c_{s}\) and \(c_{t}\) are _equivalent_ iff \(sim_{0}(c_{s}\)\(c_{t})\)\(\geq\) 0.9.
2. _Related_ (\(\subseteq\),\(\supseteq\),\(\sqcap\)): Two concept elements \(c_{s}\) and \(c_{t}\) are _related_ iff 0.9 \(>sim_{0}\)\(\geq\) 0.5.
3. _Independent_ (\(\perp\)): Two concept elements \(c_{s}\) and \(c_{t}\) are _independent_ iff 0.5 \(>sim_{0}\).  
The similarity Equation (1) is used in a two-tier matching strategy to determine the mappings of two concept schemes explained in the next section.
## 3 Materials and Methods
### Output Design  
In the research output design shown in Figure 10, there are several types of outputs, including:  
* A web application for real-time monitoring and control: the first output is a web application designed for monitoring real-time data from sensors inside the incubator, such as temperature, humidity, electrical current, and heater status. This application provides real-time information to medical staff or the patient's family.
* Visualization of the temperature data distribution inside the incubator: the second output is the visualization of the temperature data distribution within the incubator. This visualization provides insights into temperature fluctuations in different areas within the incubator, enabling the identification of areas that may require further attention.
* A prototype of an electronic hardware incubator: the third output is a prototype product of an incubator equipped with several hardware modules such as sensors, actuators, and microcontrollers. This prototype is a tangible representation of the designed incubator by integrating various hardware components and technologies.
* Energy consumption prediction models: the final output is an energy consumption prediction model. This model can assist in optimizing resource usage by making predictions for more efficient energy consumption. The research's outcomes are expected to improve neonatal management and care.
# Inactivation of the Plant Pathogen _Pythium ultimum_ by Plasma-Processed Air (PPA)
## 2 Materials and Methods
### Artificial Inoculation of Agar Blocks and Determination of Recovery after PPA Treatment  
Inoculation of PDA agar blocks with a size of 0.5 cm \(\times\) 0.5 cm was carried out by application of 25 \(\upmu\)L fresh hyphae and spore culture suspension. After 3 days of incubation at 22 \({}^{\circ}\)C, blocks with grown mycelia were picked for each of the four independent experimental trials. In each trial, six different treatments were applied to investigate the inactivation potential of PPA: (a) agar blocks with mycelia and no PPA treatment (positive control), (b) three PPA treatments with different gas flow and incubation times (see Section 2.3), and (c) two control treatments using two different gas flow/incubation time parameters without plasma ignition (gas controls, see below). To avoid contamination of laboratories and equipment with _Pythium_, agar blocks were sealed into gas-diffusive bags (CleanFlex(r) Tyvek(r)/HDPE, Bischof + Klein SE & Co., KG, Langerich, Germany). Previous experiments have shown that Tyvek(r) bags do not affect the PPA treatment process [34]. Bags with agar blocks were transferred to pressure resistant one-liter glass bottles (Duran, Germany) with seven replicates each. Agar blocks of PPA treatments (see below) and controls were placed onto the center of freshly prepared PDA agar plates and incubated at 22 \({}^{\circ}\)C. Radial growth of mycelia on each petri dish (diameter of 85 mm) was measured daily at three directions for 14 days and the average diameter was calculated. To evaluate the PPA inactivation efficacy, PPA treatments were compared to the untreated and gas controls.
# FRAMUX-EV: A Framework for Evaluating User Experience in Agile Software Development
## 6 Validating the Framework
### Qualitative Results
#### 6.2.1 Qualitative Results: UX Evaluation Methods  
In addition to the methods presented in the survey, 29 additional methods were suggested for consideration. However, almost all the methods mentioned were related to UX design methods rather than UX evaluation methods (e.g., card sorting, tree tests, benchmarks, focus groups, or surveys). The only ones specifically focused on UX evaluation were heat maps (P4, P5, P16, P27), eye tracking (P4, P15), and the five-second test (P5, P21).  
Half of the respondents were satisfied with the methods presented, indicating that they would not remove any of the proposed methods (17 of the 34 participants). Meanwhile, the other half were uncertain about whether to remove any method. \"A/B testing\" was the most frequently mentioned method for elimination, with 6 out of 34 respondents suggesting its removal (P6, P9, P12, P18, P20, P21), followed by the \"system usability scale (SUS)\",  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**D1—Usefulness**} & \multicolumn{2}{c}{**D2—Ease of Integration**} \\ \cline{2-5}
**UX Events** & **Mean** & **SD** & **Mean** & **SD** \\ \hline Pre-planning UX meeting & 4.09 & 1.00 & 3.94 & 0.95 \\ Pre-review UX meeting & 3.85 & 1.10 & 3.97 & 0.87 \\ Weekly UX meeting & 4.41 & 0.74 & 3.85 & 1.08 \\ Weekly user meeting & 3.85 & 1.02 & 2.35 & 1.07 \\
**Mean** & **4.05** & & **3.53** & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Survey results for dimensions D1 and D2 of UX events.  
with four responses (P2, P3, P16, P26). It should be noted that this does not mean that these methods are not useful, but that they may be less applicable or effective in certain situations or contexts (P4, P9, P12, P14).  
Participants provided additional comments on the methods presented. For instance, P2 pointed out that \"It would be necessary to clarify the methodology you are thinking about for agile software development and what kind of teams you are going to have. The methods will eventually depend on that\". P3 mentioned that \"(evaluation methods) must be used at the right time, always proportional to the problem to be addressed\". P15 highlighted that \"I find this list quite interesting and complete. They are essential methods with which it is possible to conduct a complete evaluation of the usability and therefore of the user experience\". Table 9 presents the comments obtained.  
In summary, we noted that there are many UX evaluation methods that can be used to evaluate the UX in agile software development projects. However, each method has its own strengths and weaknesses, and it is important to choose the appropriate methods according to the specific objectives of a project (P3, P11). Taking this into consideration, a complete evaluation of usability and user experience can be conducted (P15, P25). Therefore, based on the quantitative and qualitative results, no UX evaluation method will be eliminated from this initial version of FRAMUX-EV.
## 4 Experimental Results and Analysis
### Road Data Extraction
#### 4.2.2 Traffic Light Detection  
This section focuses on traffic light detection, pivotal aspects that yield insights into road conditions and pedestrian crossings along our route, employing a pre-trained YOLOv5 model sourced from GitHub [26].The model underwent training on a diverse dataset, encompassing images featuring traffic lights and zebra crossings in various conditions such as rainy days, normal days, and sunny days. The training spanned 50 epochs, yielding commendable results. We harness its proven efficacy in yielding accurate results. The model provides us with invaluable data on the status of traffic lights and the presence of pedestrians crossing the road.  
Through this analytical lens, we discern the intersection of traffic dynamics and pedestrian safety, bolstering our comprehensive dataset with reliable information. The YOLOv5 detection model has been configured to recognize and locate three specific classes: Zebra Cross, Green Light, and Red Light. This advanced model excels at accurately detecting these objects within images, enabling the accurate identification of both traffic lights, shown in Figure 6.
# Performance Optimization of Underwater Communication Links at Different Ranges for AIS Relay to AUV
## 6 On the Optimization of the Time/Frequency Allocation of the Spreading Code
### Choosing the Allocation of the Spreading Code  
Optimizing the time and frequency allocation of the spreading code means to exploit as best as possible the time and frequency diversity of the channel.  
In order to achieve this result, we first must estimate the selectivity of the channel that is the time and the bandwidth coherence. The time coherence \(T_{coh}\) and the bandwidth coherence \(B_{coh}\), respectively, depends on the Doppler spread \(\Delta_{f}\) and the delay spread \(\Delta_{t}\) of the channel.  
\[T_{coh}=\frac{1}{\Delta_{f}} \tag{4}\]  
\[B_{coh}=\frac{1}{\Delta_{t}} \tag{5}\]  
As mentioned before, the diversity of the channel is exploited when two chips of the same data symbol are affected by independent coefficients of the channel. Since an interleaver is used in the frequency domain, the frequency spacing between two chips depends on the bandwidth \(B_{p}\) used and the SF in frequency \(Q\). It is defined as:  
\[\delta_{chips}=\frac{B_{p}}{Q} \tag{6}\]  
For perfect exploitation of the frequency diversity, \(Q\) must be chosen so \(\delta_{chips}>B_{coh}\)We have less control over the exploitation of time diversity. Unlike frequency spreading, no interleaver is used in the time domain. Therefore, to exploit the time diversity, the time gap between two chips must be greater than \(T_{coh}\). In order to estimate the value of \(\Delta_{f}\) and \(\Delta_{t}\), we use a channel simulator, such as in [26]. The overall process is presented in Figure 10.  
As presented in Figure 10, the previously mentioned information on the environment are used to simulate the channel and compute a budget link of the communication. The budget link is computed using the communication range, the passband used and UAC attenuation models, such as the Thorp model [27]. With the budget link a total spreading \(Q\times M\) can be determined. A channel simulator based on ray tracing method can be used to obtain a simulated Doppler spectrum and power delay profile. Using the Doppler spectrum and power delay profile, one can, respectively, estimate \(\Delta_{f}\) and \(\Delta_{t}\). Finally, the total SF can be allocating in time and frequency to match as best as possible the coherence time and coherence bandwidth.
###### Abstract  
This paper presents a new approach for fall detection from partially-observed depth-map video sequences. The proposed approach utilizes the 3D skeletal joint positions obtained from the Microsoft Kinect sensor to build a view-invariant descriptor for human activity representation, called the motion-pose geometric descriptor (MPGD). Furthermore, we have developed a histogram-based representation (HBR) based on the MPGD to construct a length-independent representation of the observed video subsequences. Using the constructed HBR, we formulate the fall detection problem as a posterior-maximization problem in which the posteriori probability for each observed video subsequence is estimated using a multi-class SVM (support vector machine) classifier. Then, we combine the computed posteriori probabilities from all of the observed subsequences to obtain an overall class posteriori probability of the entire partially-observed depth-map video sequence. To evaluate the performance of the proposed approach, we have utilized the Kinect sensor to record a dataset of depth-map video sequences that simulates four fall-related activities of elderly people, including: walking, sitting, falling form standing and falling from sitting. Then, using the collected dataset, we have developed three evaluation scenarios based on the number of unobserved video subsequences in the testing videos, including: fully-observed video sequence scenario, single unobserved video subsequence of random lengths scenarios and two unobserved video subsequences of random lengths scenarios. Experimental results show that the proposed approach achieved an average recognition accuracy of 93.6%, 77.6% and 65.1%, in recognizing the activities during the first, second and third evaluation scenario, respectively. These results demonstrate the feasibility of the proposed approach to detect falls from partially-observed videos.  
Keywords:fall detection; partially-observed videos; fall prediction; view-invariant geometric descriptor; support vector machines; human representation; Kinect sensor +
Footnote †: journal: Applied Sciences
# Measurement of Absolute Acoustic Nonlinearity Parameter Using Laser-Ultrasonic Detection
## 2 Brief Description of Theoretical Background
### Laser-Ultrasonic Detection Using Photorefractive Interferometer  
The \(\beta\) parameter can be obtained by measuring the ultrasonic displacement amplitudes of the fundamental and second-order harmonic components. In this study, a photorefractive interferometer based on TWM in the PRC is used to measure the ultrasonic displacement amplitudes. Figure 1 displays a schematic configuration of the system. In this system, a probe beam and a reference beam that is created by the photorefractive effect are used. The probe beam, which acquires ultrasonic information in the form of phase modulation after reflection on the test specimen, transmits into the PRC, interferes with the reference beam to perform demodulation, and then provides an ultrasonic signal proportional to the displacement amplitude [29]. Note that the minimum detectable ultrasonic displacement \(A\) obtained from several previous studies [30] is as follows:  
\[A=\frac{\lambda_{o}}{4\pi}\bigg{(}\frac{{h}v\cdot\Delta f}{2\eta\cdot P_{0}} \bigg{)}^{0.5}\frac{\exp\bigg{(}\frac{{x_{u}}}{2}\bigg{)}}{{\rm sin}{x_{c}}}, \tag{3}\]  
where \(\lambda_{o}\) is the optical wavelength, \(h\
u\) is the photon energy, \(\eta\) is the quantum efficiency of the photodetector (PD), \(\Delta f\) is the detection bandwidth, \(P_{0}\) is the probe beam power on the PRC, \(\zeta\) and \(x_{c}\) are the absorption and thickness of the PRC, respectively, and \(\varepsilon\) is the photorefractive amplitude gain. In our experiments, \(A\) was approximately 0.01 nm [30], which is sufficient for the ultrasonic displacement measurements of \(A_{1}\) and \(A_{2}\). A previous study has also reported that this interferometer has a higher sensitivity even on the rough surfaces of the components because it uses a scattered light beam from the surface instead of a specularly reflected light beam [31].  
Figure 1: Schematic configuration of photorefractive interferometric system.
# Non Data-Aided SNR Estimation for UAV OFDM Systems
## 5 Conclusions  
In this paper, we proposed a novel NDA SNR estimation method for UAV OFDM systems. Due to the method does not need to estimate the maximum delay of multipath channel, the method is still effective in multipath fading channels after the frequency offset has been estimated and compensated. When the number of OFDM symbols is increased, better estimation performance can be obtained. Simulation results demonstrate that the proposed method is well-performed in the condition of poor synchronization precision (containing residual frequency offset) and has a lower computation complexity compared with other method, which is more suitable for UAV OFDM system. It should be noted that the proposed method is a time-domain estimation method and its performance is not as well as the frequency-domain estimation method. We will further study the frequency-domain estimation method.  
**Author Contributions:** Conceptualization, J.L. and M.L.; Methodology, J.L.; Validation, N.T., J.L. and M.L.; Formal analysis, M.L.; Data curation, M.L.; Writing--original draft preparation, J.L. and N.T.; Writing--review and editing, B.S. and J.L.; Supervision, M.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China under Grant 61501348 and 61801363, Scientific Research Fund Project of Xi'an Aeronautical University under Grant 2019 KY0207, Shaanxi Provincial Key Research and Development Program under Grant 2019 GY-043, China Postdoctoral Science Foundation under Grant 2017M611912, Jiangsu Planned Projects for Postdoctoral Research Funds under Grant 1701059B, the 111 Project under Grant B08038, the China Scholarship Council under Grant 20180696503.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 8: Comparison of different SNR estimation methods.
# Rainfall Estimation from Tropospheric Attenuation Affecting Satellite Links
## 4 The Rainfall Rate Estimation Model
### Extraction of the 0 \({}^{\circ}\)C Isotherm Height  
As shown in (8) and (10), the equivalent rain height \(h_{R}\) depends on the \"instantaneous\" value of the 0 \({}^{\circ}\)C isotherm height \(h_{0}\), which, as mentioned in Section 3.3, is extracted from the ERA5 database with a time resolution of an hour. Specifically, each sample of \(h_{0}(t^{*})\) is used in the model for the 60 min following \(t^{*}\).
# Wake-Up Receiver-Based Routing for Clustered Multihop Wireless Sensor Networks
## 2 Background
### Source Routing  
Source routing is a technique that allows routing data packets to a specific destination. This routing strategy removes the decision making from the routers to individual nodes to learn about their neighbours and then choose the best possible paths when routing network traffic from one node to another. This source path contains all the addresses of the intermediate nodes responsible for forwarding the packet to the destination [20]. When a transmitter decides to communicate with a destination node, it checks its route cache to see if information about that destination is available. If the route cache does not contain such information for that destination, the transmitter initiates a route discovery process by sending a route request. If the route discovery is successful, the transmitter receives a route response packet with a number of intermediate nodes through which it can reach the destinations. Nodes can respond to requests even if they are not the destination to reduce delays. It is also possible for intermediate nodes to forward packets, to listen to the routes by examining the packets and finding out which routes lead to reliable destinations [21].  
There are two possible source routing modifications that a node can make to a data packet as it goes onto the network: strict or loose. Strict source definitions specify the exact path a packet should take from one router to the next before the packet even leaves the source. Loose source routing, however, gives the packet some specific router points and lets the other routers decide along the way. This is useful, for example, when sending a packet through the routers of a local network to a gateway router and then to a specific address [22].