esa-sceva/satcom-chunk-collection huggingface dataset:
# Modelling the Microphone-Related Timbral Brightness of Record Signals
## 2 Ratings of Brightness
### Analysis of Listening Test Results  
To screen listeners, for each listener the within-subject consistency (the mean square difference between ratings for each of the two repeated sources) and between-subject agreement (the Pearson's correlation to the mean of all subjects' responses, for each of the sources) were calculated. In both measures, only one subject performed worse than one standard deviation away from the mean consistency/agreement across listeners. This listener was removed from subsequent analysis.  
Figure 1: Recording microphone array. (a) AKG C12; (b) AKG C414 B-XLS; (c) DPA 4006-TL; (d) Electrovoice RE20; (e) Shure SM58; (f) Coles 4038; (g) Wolfson WM7131; (h) Knowles SPU0410 HR5H; (i) AKG C451; (j) sE 2200a. Dashed line represents 150 mm diameter circle within which all microphone capsules lie.  
To retain a balanced dataset across all sources, the results from the repeated pages in each subject's tests were discarded. To allow direct comparison of the results across sources, the results from part 2 of the test were used to scale the results from part 1 using a linear transform. The transform was calculated to adjust the part 1 experiment results so that the mean values of the most and least bright stimuli for each source in part 1 equalled the mean values of the identical stimuli in part 2. Prior to the scaling, the part 1 test results spanned a range of zero to 100 for each source type. The scaled results are shown in Figure 2.  
The least bright stimulus was the bass recorded with the Coles 4038 microphone; the brightest stimulus was the drums recorded with the Knowles SPU0410 HR5H microphone. It should be noted that, within the data for each source, several microphones are rated similarly in terms of their brightness, shown by the similar mean values and overlapping confidence intervals. However, the means cover 38-56% of the rating scale within the data for each source, and 94% overall, making the data potentially useful for modelling purposes.
## 6 Experimental Results
### Weighting Influence in Candidate Selection and Localization  
The hybrid-projection transformation combined the weighted average of \(w_{m,l}(r)\) and \(w_{\sigma,l}(r)\) to select and localize the candidate. Here, the performance with the different weighted strategies of these two parameters are studied, the proposed image entropy method is compared with the user defined method. Three sets of parameters (\(w_{m,l}(r)\), \(w_{\sigma,l}(r)\)) are selected as (0.3, 0.7), (0.5, 0.5), and (0.7, 0.3) corresponding to every specified radius for all image pyramid levels, respectively. The test images and corresponding template images are shown in Figure 15. The Gaussian noises from 10 to 35 dB in increments of 5 dB are applied in the test images for evaluation. Here, two performance indices of errors are used to quantitatively show the performance: the mean of error \(E_{t,m}\) and \(E_{r,m}\) for translation and rotation; the standard deviation of error \(E_{t,std}\) and \(E_{r,std}\) for translation and rotation, respectively. The results of translation and rotation errors are listed in Table 4, where the mean translation and rotation errors of our proposed method are 0 pixels, 0.004\({}^{\circ}\), 0.17 pixels, and 0.92\({}^{\circ}\), respectively. It is obvious that the results of our proposed methods are superior to the user defined method. According to the architecture in Figure 3, the accurate rotation estimated results are obtained based on correct locations of the matching candidates. As seen in the results of Figure 15a, wrong locations are obtained by the user defined method, thus, poor rotation estimated results are obtained in this case.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**Errors} & \multicolumn{4}{c}{**Figure 15a**} & \multicolumn{4}{c}{**Figure 15b**} \\ \cline{2-9}  & **Translation (pixel)** & \multicolumn{2}{c}{**Rotation (\({}^{\circ}\))**} & \multicolumn{2}{c}{**Translation (pixel)**} & \multicolumn{2}{c}{**Rotation (\({}^{\circ}\))**} \\ \hline  & \(E_{t,m}\) & \(E_{t,std}\) & \(E_{r,m}\) & \(E_{r,std}\) & \(E_{t,m}\) & \(E_{t,std}\) & \(E_{r,m}\) & \(E_{r,std}\) \\ \hline Proposed method & 0 & 0 & 0.004
## 2 Results
### Identification and Quantification of Volatile Compounds
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Code a** & **RT b** & **Compounds** & **Measured RI c** & **Library RI \$ & **ID** & **Concentration** \\ \hline
1 & 7.55 & Limonene & 1178 & 1035 & MS d, RI & 0–57.27 \\
2 & 7.58 & Eucalyptus & 1180 & 1198 & MS, RI & 0–34.49 \\
3 & 8.35 & 2-Methyl-1-butanol & 1213 & 1206 & MS, RI, STD e & 0–222.36 \\
4 & 8.38 & (_E_)-2-Hexenal & 1214 & 1220 & MS, RI & 0–151.66 \\
5 & 8.56 & 2-Pentyfuran & 1221 & 1231 & MS, RI, PICI f & 0–83.7 \\
6 & 8.75 & Ethyl hexanoate & 1229 & 1234 & MS, RI & 0–548.93 \\
7 & 9.08 & _Trans-\(\alpha\)_-ocimene & 1242 & 1237 & MS, RI & 0–36.22 \\
8 & 9.49 & 1-Pentanol & 1258 & 1252 & MS, RI & 0–6.25 \\
9 & 9.75 & Hexyl acetate & 1268 & 1275 & MS, RI & 0–182.4 \\
10 & 10.19 & Octanal & 1285 & 1286 & MS, RI, STD, & 0–320.98 \\
11 & 10.19 & 3-Hydroxybutan-2-one & 1285 & 1289 & MS, RI, PICI & 0–429.21 \\
12 & 10.78 & 2.6-Dimethyl-5-heptrenal & 1308 & 1315 & MS, RI & 0–5.02 \\
13 & 10.98 & (_Z_)-2-Heptenal & 1316 & 1323 & MS, RI, PICI & 0–24.34 \\
14 & 11 & (_4E_)-4-Hexenyl acetate & 1317 & 1326 & MS, RI & 0–182.77 \\
15 & 11.31 & 2-Ethyl-2-hexenal & 1329 & 1330 & MS, RI, PICI & 0–333.96 \\
16 & 11.45 & 6-Methyl-5-hepten-2-one & 1334 & 1341 & MS, RI & 0–43.11 \\
17 & 12.04 & 1-Hexanol & 1357 & 1360 & MS, RI & 0–365.47 \\
18 & 12.35 & Dimethyl trisulfide & 1369 & 1383 & MS, RI, PICI & 0–19.31 \\ \hline \end{tabular}
\end{table} 
Table 3: Concentration ranges (mg/kg) of volatile compounds recovered from 28 melon breeding lines via HS-SPME GC-MS harvested in the year 2019. The volatile compounds were identified by comparing the mass spectra and Kovats indices
## 5 Applications Across Diverse Sectors
### Inventions in 3D Printing Technologies
#### 5.2.3 Virtual Reality  
3D printing (3 DP) combined with virtual reality (VR) technology has revolutionized design, development, and user experiences. Recent advancements include the following [46]:  
(1) _Haptic feedback devices_: Additive manufacturing has made it possible to create devices that provide haptic feedback, improving the experience of virtual reality by addingtouch sensations with parts made by 3D printing. [PERSON] et al. examined the potential of 3D-printed hair-like structures as flexible haptic components for virtual reality settings [85].  
(2) _Customized VR accessories_: 3D printing allows for the creation of custom accessories for VR setups, including controllers, mounts, and ergonomic improvements. [PERSON] et al. developed customized electronic glasses with multiple functions to improve interaction between users and machines in VR and digital health settings [86].
# Performance of Satellite-Based Evapotranspiration
## 2 Study Area and Datasets  
### Study Area  
The study region includes all the temperate pastures in southern Chile, covering a surface area of 1,324,335 hectares between 39\({}^{\circ}\)20\({}^{\prime}\) and 43\({}^{\circ}\)30\({}^{\prime}\) S across Los Rios and Los Lagos Districts (Figure 1a). This region includes the lowlands along the central depression of mainland Chile and the large island of Chiloe in the southwest. According to the Koppen-Geiger classification system, the regional climate corresponds to marine west coast (Cfb), with an average annual temperature of 10 \({}^{\circ}\)C and annual rainfall of 2100 mm [38]. The wet season extends from March to November but most precipitation falls in winter (44%) when the mid-latitude austral storm tracks move northward into the region. A dry season develops during the austral summer due to a southward expansion of the Mediterranean-like regional circulation system prevailing year-round to the north, with minimum seasonal soil moisture levels and frequent drought conditions occurring during February and early March (Figure 1b).
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 3 Wi-Fi 6 Overview
### Targeted Use Cases
#### 3.3.2 Internet of Things (IoT)  
The world's Internet traffic is estimated to have manifold growth in the next few years, mainly resulting from Machine-Type Communications (MTC). Each user is expected to have on average 3.6 devices connected to the Internet [1]. IoT devices are widely applied to home automation, industrial automation, autonomous vehicles, healthcare, etc. They are battery-driven and need power-saving features, which are well supported by TWT. Moreover, OFDMA helps to optimize medium access for a massive number of small packets.
# CEPP: Perceiving the Emotional State of the User Based on Body Posture
## 4 Performance Evaluation
### User Study  
We have also conducted an user survey to see whether the body posture can present certain emotions. We gave each subject and the viewer of the experimental video four statements to assess. We primarily focused on whether the body posture can represent the emotional state of the person in terms of the subject and the viewers. Thus, we asked the participants of the user study to give a score between the number of 1 (strongly disagree) to 5 (strongly agree). The statements for the users are as the follows.  
(5.1) \"The body posture within the experiment is the most representative body posture to express one's emotional state\".  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Computation Time** \\ \hline Computing all similarity distances & 406.58 ms \\ Computing one similarity distance & 67.76 ms \\ Extracting all features & 420.50 ms \\ Extracting one feature & 70.08 ms \\ \hline \hline \end{tabular}
\end{table}
Table 4: Computation time for recognizing emotional state.  
Figure 7: Computation time for recognizing the emotional states.  
* \"There is a body posture that I used to express my emotional feeling\".
* \"The body postures that I expressed within the experiment are body postures that I use within my daily life\".
* \"The body posture that I formulated within the experiment is accurate in terms of expressing my emotional feeling\"  
For the viewers, we provided four similar statements as compared to those that were given to the subjects. Moreover, for statements \((V.1)\) and \((V.2)\), the viewers were given the same statements (\((S.1)\) and \((S.2)\)). For the \((V.3)\) and \((V.4)\), the statements are listed as follows.  
* \"The body postures that the subjects expressed within the experiment are body postures that I use within my daily life\".
* \"The body postures that the subjects formulated within the experiment are accurate in terms of expressing my emotional feeling.\"  
Lastly, for statements \((S.3)\) and \((V.3)\) for the subjects and the viewers, the participants of the user study were asked to give a written comment based on the score that they have given.  
The result of the user study is described in Figure 8. The average score of the subjects for statement \((S.1)\) was 3.5, for \((S.2)\) it was 4, for \((S.3)\) it was 3.33, and for \((S.4)\) it was 3.33. For the case of the viewers, the average scores for each statement were as follows: for \((V.1)\) the score was 4.75, for \((V.2)\) it was 4.5, for \((V.3)\) it was 3.75, and for \((V.4)\) it was 4.25. In terms of the comments from questions \((S.3)\), many of the subjects commented that  
_\"The motions chosen in the experiment seem to be somewhat exaggerated. My actions are not so extraagant\"._  
However, the expression itself may still represent the emotional state. Similar comments came out from the viewers of the video. In summary, we found out that body posture may reflect one's emotional state. However, it is noted that different ethnicity and the culture may reflect their emotional feelings with different body postures.
# Secure Physical Layer Network Coding versus Secure Network Coding +
## 3 Butterfly Network
### Secure NC  
Under the network code given in Section 3.1, the node \(V_{3}\) obtains both messages \(M_{1}\) and \(M_{2}\). The destination node \(V_{5}\) recovers the unintended message \(M_{1}\) as well as the intended message \(M_{2}\), and the other destination node \(V_{6}\) the unintended message \(M_{2}\) as well as the intended message \(M_{1}\). Next, we impose the secrecy against an attack to one of the intermediate (untrusted) nodes. In other words, the information of all intermediate (untrusted) nodes are required to be independent of \(M_{1}\) and \(M_{2}\), and the information of destination node \(V_{5}\) (\(V_{6}\)) is required to be independent of the unintended message \(M_{1}\) (\(M_{2}\)). This kind of secrecy can be realized under the following assumption. When messages \(M_{1}\) and \(M_{2}\) are elements of \(\mathbb{F}_{q}\) and \(q\) is not a power of 2 in the following way ([38], Figure 2):  
**(A1)**: Two source nodes \(V_{1}\) and \(V_{2}\) share a secret number \(L\),  
when the information \(Z_{i}\) transmitted on the edge \(e_{i}\) is given as:  
\[Z_{1} =2M_{1}\oplus L,\ Z_{4}=-(M_{1}\oplus L), \tag{5}\] \[Z_{2} =2M_{2}\oplus L,\ Z_{7}=-(M_{2}\oplus L),\] (6) \[Z_{3} =Z_{1}\oplus Z_{2}=2M_{1}\oplus 2M_{2}\oplus 2L,\] (7) \[Z_{5} =Z_{6}=Z_{3}/2,\] (8) \[\hat{M}_{2} =Z_{5}\oplus Z_{4}=M_{2},\ \hat{M}_{1}=Z_{6}\oplus Z_{7}=M_{1}, \tag{9}\]  
Figure 2: Butterfly NC.  
where \(\hat{M_{2}}\) (\(\hat{M_{1}}\)) is the recovered message by \(V_{5}\) (\(V_{6}\)). Any intermediate edge and any intermediate node obtain no information about the messages \(M_{1}\) and \(M_{2}\). In addition, the destination node \(V_{5}\) (\(V_{6}\)) obtains no information for the message \(M_{1}\) (\(M_{2}\)) while it obtains the message \(M_{2}\) (\(M_{1}\)). Hence, this code guarantees the following types of security:  
**(S1)**: When the eavesdropper attacks only one of the edges, she obtains no information for each message \(M_{i}\).
**(S2)**: When the nodes do not collude, each node obtains no information for the unintended messages.  
When \(q\geq 4\) is a power of \(2\), the above code can be modified as follows. We choose an element \(e\in\mathbb{F}_{q}\) such that \(e^{2}\oplus e\
eq 0\), i.e., \(e\
eq 1,0\).  
Then, we define our code as:  
\[Z_{1} =(1\oplus e)M_{1}\oplus L,\ Z_{4}=-(M_{1}\oplus L), \tag{10}\] \[Z_{2} =(1\oplus e)M_{2}\oplus eL,\ Z_{7}=-(M_{2}\oplus L),\] (11) \[Z_{3} =Z_{1}\oplus Z_{2}=(1\oplus e)(M_{1}\oplus M_{2}\oplus L),\] (12) \[Z_{5} =Z_{6}=Z_{3}/(1\oplus e),\] (13) \[\hat{M_{2}} =Z_{5}\oplus Z_{4}=M_{2},\ \hat{M_{1}}=Z_{6}\oplus Z_{7}=M_{1}. \tag{14}\]  
This modification realizes the required security in this case.
## 3 Experimental Results and Analysis
### Influence of Feed on Axial Drilling Force
At \(f\) = 0.01 mm/r, due to the feed being too small, the extrusion of the drilling edge of the role of the large, the maximum axial force is instead larger. Considering Figures 11 and 13, if one wants to achieve better hole exit edge quality with smaller edge defect dimensions, the feed rate should be less than 0.05 mm/r. Calculated according to Equation (2), the variation in convex deformation produced by the thin-walled plate before drilling into the drill bit with the feed amount is shown in Figure 15.  
Figure 14: Influence of feed on the maximum axial force and on the maximum value of axial force in the phase of linear change at the initial stage of drilling. Figure 13: Influence of feed on the exit edge morphology of thin-walled AF1410 high-strength steel drilling workpieces. (**a**) \(f\) = 0.01 mm/r. (**b**) \(f\) = 0.025 mm/r. (**c**) \(f\) = 0.05 mm/r. (**d**) \(f\) = 0.075 mm/r. (**e**) \(f\) = 0.1 mm/r. As can be seen from Figure 15, the amount of convex deformation of the thin-walled plate before drilling into the drill increases with the increase in feed; the axial force is small when the feed is small, and the support force required for the drill to drill into the workpiece is small, so the drill starts to drill into the workpiece after the thin-walled plate produces a small convex deformation; with the increase in feed, the support force required for the drill to drill into the drill increases, and the thin-walled plate can only be drilled into the workpiece after the drill produces a large convex deformation.
# ICN-Based Enhanced Content Delivery for CDN
## 3 Proposed Design
### Details
#### 3.4.3 Protocol Conversion  
The protocol conversion in our context is twofold:  
1. HTTP to ICN: An HTTP request generally consists of a request line and several request headers. The request line commonly consists of three items: a method that instructs the server what it should do with the resource; a URI that locates and identifies the resource; and an HTTP version number. In ICN, each chunk is identified with a unique ID. Since the URL of each content is unique, we use SHA1 to generate the 20-byte hash digest of the content URL as its chunk ID. This process is illustrated in Figure 5. Afterwards, this content will exist in the form of chunks in the ICN network with a unique ID used for registration/publishing and resolution/retrieval. Upon receiving a GET request for content on the HTTP server, the ID of its chunk counterpart is calculated to locate its physical address by querying NRS, then the follow-up download is triggered.
2. ICN to HTTP: HTTP services on the application layer and is rich in semantics, whereas most of the ICN architectures reach up to only the network layer or transport layer. Apart from that, in our context, we use the hash digest of a content URL as its naming ID in ICN, which is a non-reversible operation. This means that a literal translation from the ICN protocol to HTTP is infeasible. To address this issue, we adopt a method commonly used in related work [43; 44] that the ICN packets piggyback with extra control or feedback information. Specifically, since an HTTP request needs to be reconstructed by EG, IG sends to EG the ICN chunk request piggyback with HTTP request headers related to this content. Likewise, EG sends to IG the chunk response piggyback with the HTTP response headers for IG to reconstruct the HTTP response, and the response headers are cached on IG for future use. This can achieve zero loss of HTTP semantics at the cost of relatively low overhead.
# Saturation Effect on the Coherency Loss of Spatially Varying Vertical Ground Motions
## 1 Introduction
Moreover, to facilitate the seismic analysis and earthquake-resistant design of onshore-offshore extended structures which are usually located in sites with irregular topography, many have paid attention to the effect of irregular topography ([PERSON] et al., 2014; [PERSON] et al., 2016; [PERSON] et al., 2017) and site conditions ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2017) on the coherency loss of SVEGMs. In the works mentioned above, however, the influence of water saturation, water level, or surface water layer were scarcely considered, and at the same time, the influence regularity of water saturation or water level on the coherency loss between SVEGMs is not clear. Therefore, taking an onshore-offshore site as example, the influence regularity of site saturation and water level on the coherency loss of vertical SVEGMs was studied in the present paper, where the SD of the whole site, GWL, and WLT were taken into account. Under each circumstance mentioned above, a large number of vertical in-plane SVEGMs were synthesized based on the one-dimension wave propagation theory ([PERSON] et al., 2017), and the mean lagged coherency loss and phase angle were estimated; then, the varying trend of lagged coherency loss and phase angle with SD, GWL, and WLT was obtained.
## 3 Examples
### Fatigue Life of Pre-Corroded Steel Wire
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{\(\eta\)/\%} & \multirow{2}{*}{\(\eta\)/MPa} & \multirow{2}{*}{\(n\)} & \multirow{2}{*}{**Step**} & \multirow{2}{*}{\(x\)/mm} & \multicolumn{2}{c}{\(K_{\mathbf{I}}\)} & \multirow{2}{*}{\(Nc\)} & \multicolumn{2}{c}{**Test Results of**} \\  & & & & & & & **Fatigue Life [23]** & \\ \hline \multirow{4}{*}{0.25\%} & 335 & 100,000 & 93 & 2.15662 & 64.9195 & 4,700,000 & 4,220,614 & 11.36\% \\  & 418 & 10,000 & 195 & 1.75383 & 64.9673 & 980,000 & 1,040,139 & 6.14\% \\  & 520 & 10,000 & 45 & 1.34251 & 63.0924 & 230,000 & 240,066 & 4.19\% \\ \hline \multirow{4}{*}{1.85\%} & 335 & 50,000 & 99 & 2.15101 & 64.7262 & 2,500,000 & 2,498,912 & 0.04\% \\  & 418 & 10,000 & 95 & 1.75383 & 64.9678 & 480,000 & 641,358 & 25.16\% \\  & 520 & 2,000 & 109 & 1.9253 & 65.1381 & 110,000 & 173,800 & 36.71\% \\ \hline \multirow{4}{*}{2.5\%} & 335 & 20,000 & 185 & 2.17891 & 65.6922 & 1,860,000 & 1,304,947 & 42.53\% \\  & 418 & 10,000 & 73 & 1.70954 & 63.3443 & 370,000 & 523,005 & 29.25\% \\  & 520 & 1000 & 171 & 1.39672 & 65.3108 & 86,000 & 119,363 & 27.95\% \\ \hline \multirow{4}{*}{3.33\%} & 335 & 10,000 & 189 & 2.15813 & 64.9717 & 950,000 & 821,534 & 15.64\% \\  & 418 & 10,000 & 73 & 1.71762 & 63.6386 & 370,000 & 398,024 & 7.04\% \\ \cline{1-1}  & 520 & 2000 & 61 & 1.40164 & 65.5137 & 61,000 & 96,681 & 36.90\% \\ \hline \end{tabular}
\end{table} 
Table 1: Life of pre-corroded steel wire. Figure 9: S-N curves of steel wires with different degrees of pre-corrosion.
## 4 Bilateral Communication Module  
To directly communicate the CNC controller for the extraction of machining information for tool wear monitoring and auto-compensation, a bilateral communication module that can call the Application Programming Interface (API) to extract instantly executing NC codes, the true spindle speed and feedrate for dimensional error inspection and tool wear monitoring (Figure 16) was developed based on FANUC Open CNC API Spec and Ethernet protocol. The module can also extract other instant machining information and control parameters from the CNC controller. The module enables the proposed system to remote control the on-machine monitoring process and upload the machining and inspection data to the remote central platform for further production optimization. Figure 17a is the human-machine interface containing the on-line extracted information, such as the coordinates of the cutter, the machining parameters, and the CNC control parameters, _etc._ Furthermore, the module can synchronously communicate with several machines for production line monitoring and control. Figure 17b is the screen showing several machines simultaneously connected and monitored.  
Figure 15: Tool wear curve for T04.  
Figure 16: The communication module extracts information for monitoring/compensation.  
Figure 17: Bilateral communication module for computer numerical control (CNC) controller. (**a**) On-line extracted information from CNC controller; (**b**) The module synchronously communicates with several machines.