esa-sceva/satcom-chunk-collection huggingface dataset:
# Audio Feedback with the Use of a Smartphone in Sailing Training among Windsurfers
## 2 Methods
### Test Design and Procedure  
The participants were introduced to the aim of the study with regard to functions of the SoniSailing application. The competitors had not used any sailing applications for mobile devices before the research. The study lasted 3 days, with one training session per day, conducted at the same time of the day. Before starting each training session, the operation of the system was described to the windsurfers to ensure that they were aware of how to react to the information provided by the application. During each training session, three upwind sailing races were performed with auditory feedback (sound messages: deviation angle value in degrees, and \"Layline\"). The volume of the messages was regulated by the competitors according to their preferences. The upwind leg was chosen for research testing since the first leg of the windsurfing race route was indicated as important in achieving a podium finish [16].  
Figure 1: A shift in the optimal courses as a result of change in wind direction. (**A**) the line of wind direction is aligned with the line between the start and windward buoy; (**B**) the line of wind direction is a shift of 20\({}^{\circ}\) from the line between the start and windward buoy. W1, W2—direction of the example wind.  
Figure 2: Examples of variants of windsurfing board courses, depending on the moment of the last tack. 1—windsurfer took the optimal course thanks to a sound message (“Layline”); 2—windsurfer overstepped laying.  
The value of the \"no-go zone\" (110\({}^{\circ}\)) was calculated based on the mean angular values of the courses on the opposite tacks and performed by the national senior team. The wind speed (15 to 17 kts) was classified as a medium to heavy [16]. The race route was 300 m long and ran between the starting line at the judge's boat and the windward buoy.  
All participants in the study used uniform windsurfing boards (RS:X class) and sails, the size of which was 8.5 m\({}^{2}\). The smartphone was kept in a waterproof case and attached to the mast (20 cm above the boom) within arm's reach (Figure 3). Before the start, the competitors set the application into the standby mode. The application started and finished automatically thanks to the activation and deactivation fields (circles). The centers of the fields (with a 15 m radius) were marked by the coordinates of the judge's boat and the windward buoy before each training session, respectively.
## Materials and Methods
### Standing Wave Tube Measurement  
#### 2.4.1 Actual Sound Absorption Coefficients of the Polyurethane Foam  
The investigated polyurethane foam samples in this study were purchased from the GreenCARE International (Guangzhou) Ltd., Guangzhou, Guangdong, China. Sound absorption coefficients \(\alpha_{a}\) of the polyurethane foam were achieved by the AWA6128A detector (Hangzhou Aihua instruments Co., Ltd., Hangzhou, Zhejiang, China) based on the standing wave tube measurement [32; 33; 34; 35], which acted in accordance with the ISO 10534-1: 1996 Acoustics--Determination of sound absorption coefficient and impedance in impedance tubes--Part 1: Method using standing wave ratio and the ISO 10534-2: 1998 Acoustics--Determination of sound absorption coefficient and impedance in impedance tubes--Part 2: Transfer-function method [36; 37]. In order to improve the accuracy of the identification and examine the effectiveness of the identified acoustic characteristic parameters, the polyurethane foam samples with thicknesses of 10 mm, 20 mm, 30 mm, 40 mm, 50 mm, and 60 mm were measured, and the selected frequency range was [100 Hz, 6000 Hz] with an interval of 100 Hz. Experimental data of sound absorption coefficients of the samples with thicknesses of 20 mm, 30 mm, 40 mm, and 50 mm were utilized to identify the acoustic characteristic parameters, and those with thicknesses of 10 mm and 60 mm were used to examine the effectiveness of the identification. Schematic diagram of standing wave tube measurement of the sound absorption coefficient by the AWA6128A detector is shown in Figure 4. The polyurethane foam sample was installed in the sample fixer and supported by the sample holder. The incident sound wave was generated by the loudspeaker and introduced into the standing wave tube, and its frequency was controlled by the workstation. The reflected sound wave was received by the acoustic probe, and the detected sound signal was transferred to the workstation by the pickup. Through moving the pulley along the slideway on the foundation support, the peak sound level \(S_{\text{max}}\) and valley sound level \(S_{\text{min}}\) of the reflected sound wave could be achieved. According to the ISO 10534-1: 1996 Acoustics--Determination of sound absorption coefficient and impedance in impedance tubes--Part 1: Method using standing wave ratio and the ISO 10534-2: 1998 Acoustics--Determination of sound absorption coefficient and impedance in impedance tubes--Part 2: Transfer-function method [36; 37], the relationship between the sound absorption coefficient \(\alpha_{a}\) and the incident sound frequency \(f\) was shown in Equation (26).  
\[\alpha_{a}(f)=\frac{4\times 10^{\frac{S_{\text{max}}-S_{\text{min}}}{20}}}{ \left(1+10^{\frac{S_{\text{max}}-S_{\text{min}}}{20}}\right)^{2}} \tag{26}\]  
Figure 4: Schematic diagram of the standing wave tube measurement by the AWA6128A detector.
## 4 Results and Discussion
### Computation Accuracy Comparison  
As indicated in Figure 6, the accuracy comparison suggested that the ACRLPC framework outperformed its counterparts with an accuracy of 92%. TCH-MAC was second with an accuracy of 88%, and FDU-MAC and UW-ALOHA-QM were slightly less accurate at 85% and 80%, respectively. Moreover, ACRLPC achieved a higher accuracy in comparison to the other techniques, which was due to its optimization mechanisms that improve the overall performance and trustworthiness of the model. Although ACRLPC provided a higher recall than TCH-MAC, TCH-MAC showed itself to be more effective than ACRLPC in providing a good balance between throughput and accuracy.
# Analysis of Pre-Driver and Last-Stage Power--Ground-Induced Jitter at Different PVT Corners
## 3 Jitter Analysis under PVT Corners: Simulation Setups and Results
### Induced litter: Pre-Driver vs. Last Stage
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Corner** & **PG Noise** & **Jitter p2p (ps)** & **Jitter/UI (\%)** & **EW (ns)** & **EH (V)** \\ \hline \multirow{2}{*}{SS\(-\)40 \({}^{\circ}\)C} & Predriver & 239.46 & 11.97 & 1.86 & 2.85 \\ \cline{2-6}  & Last stage & 248.34 & 12.42 & 1.82 & 2.51 \\ \hline \multirow{2}{*}{SS125 \({}^{\circ}\)C} & Predriver & 150.77 & 7.54 & 1.89 & 2.94 \\ \cline{2-6}  & Last stage & 133.04 & 6.65 & 1.91 & 2.49 \\ \hline \multirow{2}{*}{TT25 \({}^{\circ}\)C} & Predriver & 124.20 & 6.21 & 1.90 & 3.25 \\ \cline{2-6}  & Last stage & 141.91 & 7.10 & 1.90 & 2.82 \\ \hline \multirow{2}{*}{FF\(-\)40 \({}^{\circ}\)C} & Predriver & 106.43 & 5.32 & 1.91 & 3.61 \\ \cline{2-6}  & Last stage & 79.85 & 3.99 & 1.95 & 3.15 \\ \hline \multirow{2}{*}{FF125 \({}^{\circ}\)C} & Predriver & 133.04 & 6.65 & 1.92 & 3.53 \\ \cline{2-6}  & Last stage & 159.65 & 7.98 & 1.92 & 3.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Eye Diagram Metrics: PGSV Noise Sources are Considered at the Pre-driver or Last Stage.
# Biomechanical Analysis of Posterior Ligaments of Cervical Spine and Laminoplasty
## 3 Results
### Comparison of Intact and the Laminoplasty Models  
ROM  
In the extension, both laminoplasty-pre and laminoplasty-res models show similar ROM results for C3-C4 and C4-C5 levels. ROMs were significantly decreased by 54.3% and 8.2%, respectively at C3-C4 and C4-C5 levels than the intact model. At the same time, for flexion motion at C3-C4 and C4-C5 levels, the laminoplasty-pre model increased ROMs by 100.2% and 111.8%, respectively than intact. While for the laminoplasty-res model, it was increased by 105.2% and 116.8%, respectively for C3-C4 and C5-C6 levels than intact. In left bending and the laminoplasty-pre and laminoplasty-res models, ROM significantly reduced at the C3-C4 and C4-C5 levels by 77.2% and 9.7%, respectively than intact. In contrast, for right bending, the ROM at the C2-C3 level increased by 135.3% and decreased at the C4-C5, C5-C6, and C6-C7 levels by 87.1%, 90.5%, and 12.4%, respectively than the intact model. For left and right rotation, the ROM of C4-C5 was decreased in laminoplasty-pre and laminoplasty models-res than in the intact model by 86.1% and 76.9%, respectively than intact (Figure 6).  
Figure 6: Range of motion. (**a**) Extension, (**b**) flexion, (**c**) left bending, (**d**) right bending, (**e**) left rotation, and (**f**) right rotation. The vertical axis is an angle (degree), the horizontal axis is each intervertebral level.
###### Abstract  
In recent years, significant progress has been achieved in the study of the spatial and temporal distribution of ancient sites and their influencing factors. As an integral component of the pluralistic unity of Chinese civilization, the ancient Shu civilization exhibits unique cultural characteristics and historical significance, rendering the Sichuan region a critical area for exploring the origins and development of Chinese civilization. However, existing research lacks a comprehensive compilation of archaeological sites across the entirety of Sichuan Province, and analyses of spatial and temporal distribution characteristics and their influencing factors often lack multi-scale and multi-dimensional perspectives. This study systematically compiles site data from Sichuan Province and employs GIS spatial analysis methods to examine the distribution characteristics of sites and their relationship with natural geographical factors from a geographical spatial perspective. The findings reveal that site distribution in Sichuan Province exhibits significant clustering, predominantly concentrated near rivers and in higher elevation areas. Factors such as altitude, slope, and proximity to water significantly influence site distribution. In terms of altitude, the elevation of sites' distribution generally declined from the Stone Age to the Warring States period, dropping below 1000 m during the Shang to Spring and Autumn periods before rising again. Regarding proximity to water, a substantial proportion of sites across all periods are located within a 1 km buffer zone, with approximately 50% during the Stone Age and Warring States period, and up to 70% during the Shang to Spring and Autumn periods, indicating a preference for areas close to water for settlement and production. In terms of slope, most sites across historical periods are located in areas with slopes below 15\({}^{\circ}\), with the highest number of sites during the Shang to Spring and Autumn periods. The evolution of human-environment relationships demonstrates a trend of site concentration shifting from plateau to basin areas from the Stone Age to the Warring States period, reflecting changes in population movement, economic development patterns, and socio-political structures. The research provides new insights into the evolution of human-environment relationships in the region and offers valuable references for related studies.  
s +
Footnote †: journal: applied sciences  
_Article_  
Spatial and Temporal Distribution Characteristics of Stone Age to Warring States Period Sites in Sichuan Province
# A Point Cloud Registration Algorithm Based on Weighting Strategy for 3D Indoor Spaces
## 3 The ICP Algorithm with Weighting Strategy  
### Selection of Error Metric  
In the ICP algorithm, an objective function is defined to minimize the measurement error. Generally, there are two metrics for error measurement: point-to-point and point-to-plane.  
\[\mathbf{e}_{i}=\sum\limits_{i=1}^{N}\left\|\left(\mathbf{R}\mathbf{p}_{i}+\mathbf{t}_{k}-\mathbf{ q}_{i}\right)^{T}\right\|^{2} \tag{16}\]  
\[\mathbf{e}_{i}=\sum\limits_{i=1}^{N}\left\|\left(\mathbf{R}\mathbf{p}_{i}+\mathbf{t}_{k}-\mathbf{q} _{i}\right)^{T}\mathbf{n}_{i}\right\|^{2} \tag{17}\]  
Equation (16) describes the point-to-point metric, which simply finds the minimum distance between the target point and the corresponding point on the source surface, where \(\mathbf{R}\) is the rotational matrix, \(\mathbf{p}_{i}\) is the source point cloud, \(\mathbf{t}_{k}\) is the translation matrix, and \(\mathbf{q}_{i}\) is the target point cloud. Equation (17) shows the point-to-plane metric. Its goal is to find the shortest path between the target point and the tangent plane of the source surface's corresponding point, where \(\mathbf{n}_{i}\) is the vector that goes through the tangent plane of the source surface at \(\mathbf{p}_{i}\).  
As shown in Figure 1b, \(\mathbf{p}_{i}\)' is a point on the destination surface and pi is the corresponding point on the source surface. The vector \(\mathbf{e}_{i}\) is the normal vector to the tangent plane of the source surface at \(\mathbf{p}_{i}\), which takes the distance from \(\mathbf{p}_{i}\)' to the plane tangent to \(\mathbf{p}_{i}\) as the point-to-plane error metric. The objective function is to find a suitable rigid transformation that satisfies the minimum sum of all these distances.
## 4 Prediction of SPT Blow Counts  
### Prediction Model  
Many scholars have done a lot of research on the relationship between SPT blow count and side resistance, tip resistance or shear wave velocity to predict the bearing capacity of drilled shaft, and achieved a series of meaningful results. [PERSON] [27] proposed the correlation between SPT blow counts and the ultimate side resistance or ultimate tip resistance for silt and sand. The strength parameters of soil given by the calculation model increase linearly with the increase of the SPT blow count. However, the engineering practice shows that the strength parameters of soil estimated by the calculation model could not converge when the SPT blow count is determined as a large value. According to the elastic-plastic theory, the stress-strain relationship of soil has nonlinear characteristics; thus, it can also be considered that the strength of soil changes nonlinearly with the increase of SPT blow counts. To reflect the nonlinear characteristics of soil strength parameters, the hyperbolic tangent function is used to fit the relationship between SPT blow counts and the ultimate side resistance. The asymptote of the function represents the limit value of soil strength, and the ultimate side resistance is determined as follows [28,29]:  
\[q_{sik}=\alpha\tan(N/\xi+\mu) \tag{2}\]  
where \(q_{sik}\) represents the ultimate side resistance at section \(i\) (kPa); \(n\) represents the SPT blow counts; \(\alpha\), \(\xi\) and \(\mu\) are undetermined coefficients. \(\alpha\) represents the limit value of soil strength, \(\xi\) and \(\mu\) represent the variation trend of soil strength parameters with SPT blow counts.  
In situ SPT were conducted before the installation of the test shafts. Then the values of \(\alpha\), \(\xi\) and \(\mu\) are calculated according to the geological exploration data of the site and the results of static load test of test shafts, as shown in Table 6.
# An Energy-Efficient and Secure Routing Protocol for Intrusion Avoidance in IoT-Based WSN
###### Abstract
As compared to existing energy efficient IoT based WSN systems, our proposed energy-efficient and secure routing protocol (ESR) protocol differs in two major aspects. Firstly, the ESR protocol makes use of the inherent abilities of nodes and generates various energy efficient clusters by considering the quality of service (QoS) requirements. Further, ESR protocol exploits a practice of bounded clustering to update the status of cluster heads with minimum communication cost, as least number of re-election packets are flooded inside each cluster. Secondly, to achieve trustworthiness and secure network-wide data routing, ESR protocol adopts a light-weight secret sharing scheme between cluster heads and BS. In this scheme, the BS generates a secret key, which is partitioned and shared among the set of cluster heads. During the transmission between cluster heads and BS, based on the proposed secret sharing scheme the incoming packets are encrypted. Similarly, on the arrival of the packets from cluster heads towards the BS, decryption is performed before further transmission to the end user based on the proposed data security scheme. The proposed scheme is computationally secure, extensible with respect to the increase in the network field and dynamic in terms of changing the keys. Based on the aforementioned contributions, the ESR protocol is appropriate for a large scale IoT based WSN systems that need energy-awareness, trustworthiness and fault-tolerability. The remainder of the research paper is as given. The related work and detailed for the research formulation are discussed in Section 2. The introduction of ESR protocol along its design, functionality and algorithms are discussed in Section 3. The sensitivity analysis of weighted factors for optimizing the ESR protocol is addressed in Section 4. The simulation model and numerical results of ESP protocol against related work are illustrated in Section 5 followed by Section 6, which concludes this article.
## 3 Proposed Power Losses Model
### Proposed Conduction Losses Model
#### 3.1.4 The Proposed Conduction Losses Model
According to the FOC algorithm depicted in Figure 5, the reference voltage vector is obtained by two reference command voltages in a two-phase stationary coordinate system. These two reference voltages are calculated by PI regulators (Proportion Integration regulators) according to the comparison of command currents and sampling currents. \[P_{igbt\_cur\_cond}(\theta)=\frac{1}{2\pi}\int_{\frac{\pi}{2}}^{\frac{\pi}{2}} \big{(}V_{c\alpha\alpha}+R_{ce}I_{p}\cos\theta\big{)}I_{p}\cos\theta\times \frac{1}{2}\bigg{(}1+m\bigg{(}\cos(\theta+\varphi)-\frac{3\sqrt{3}}{8\pi}\cos 3 (\theta+\varphi)\bigg{)}\bigg{)}d\theta \tag{24}\]2  
where \(V_{\mathit{aref}}\) and \(V_{\mathit{gref}}\) represent the two reference command voltages in a two-phase stationary coordinate system. According to Equation (17), the power factor angle can be expressed as:  
\[P_{igbt\_cur\_cond}(\theta)=\frac{1}{2\pi}\int_{\frac{\pi}{2}}^{\frac{\pi}{2}} \big{(}V_{c\alpha\alpha}+R_{ce}I_{p}\cos\theta\big{)}I_{p}\cos\theta\times \frac{1}{2}\bigg{(}1+m\bigg{(}\cos(\theta+\varphi)-\frac{3\sqrt{3}}{8\pi}\cos 3 (\theta+\varphi)\bigg{)}\bigg{)}d\theta \tag{24}\]3  
where the voltage phase angle \(\alpha\) and current phase angle \(\gamma\) can be calculated based on \(V_{\mathit{aref}}\) and \(V_{\mathit{gref}}\), \(\vec{i_{\alpha}}\) and \(\vec{i_{\beta}}\) respectively, which show in Equation (33). \[ \begin{split}\alpha&=\arctan(\frac{V_{\mathit{aref} }}{V_{\mathit{gref}}})\\ &\gamma=\arctan(\frac{\vec{i_{\beta}}}{\vec{i_{\alpha}}})\\ \end{split}  \tag{33}\]  
Figure 5: Proposed conduction losses model.
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 9 Discussion
### Performance Comparison
#### 9.3.1 Frequency  
In terms of performance, Wi-Fi operates within the 2.4 GHz and 5 GHz frequency bands, both of which comply with national telecommunications standards. The 2.4 GHz band typically offers fewer free channels compared to the 5 GHz band. Designing a network to operate at 5 GHz generally results in better connectivity due to the availability of more channels, enhancing the network's flexibility to switch channels in order to mitigate noise and interference issues. Conversely, Trusted Wireless technology primarily operates on the 2.4 GHz band, with the lower frequencies (800, 900 MHz) being restricted. Although Trusted Wireless may exhibit better connectivity at lower frequencies, which can penetrate obstacles more effectively, it is also more susceptible to noise compared to higher frequency bands.
## 3 Results and Discussion
### Effects of Rib Spacing
This observation is in line with first principles and others' findings, such that the delay in the development of shear stress is consistent with trends in initial stiffness and with a decreased number of ribs, the stress per rib would have to be higher while also driving the variance. The strain on the pipe was also explored under the assumption that with increased rib spacing greater levels of dilation would occur; however, no conclusive findings could be drawn from the collected results. This overall negative bond performance was not in line with tests conducted on shorter embedment lengths [7; 63; 64; 66]. However, differences in confining medium properties and grout material may offer alternative explanations for these differences in findings. In one study which featured comparable confinement stiffness [7] they did find an increase in strength with increased rib spacing but it was not significant. This suggests a possible third variable to this relationship, which would require additional investigation.
## 2 Materials and Methods
### Hardware and Related Software
In addition to employing BLE as the wireless communication protocol, we also utilize two other standard communication protocols for internal communications between the MCU and peripherals. The BME680 employs the I2C protocol, while the nRF24L01 utilizes SPI for communication with the microcontrollers. At the monitoring station within our sensor network, we have the capability to monitor the data transmitted from the node. This capability serves two primary purposes: allowing the user to retrieve and analyze the environmental conditions and providing the possibility to identify and troubleshoot any potential errors in the development process. For enhanced practicality, we propose using a smartphone (alternatively a PC) as the monitoring device. Various free apps, designed for different smartphone platforms, are readily available on the market for this purpose. In our research, we primarily utilize a BLE scanner app, which provides comprehensive functionality. It can display essential information about the transmitter device, including its name, attributes such as its MAC address, and any associated data payload in hexadecimal format, as well as raw data. These data packets are user-encodable and -decodable. Additionally, this BLE scanner app offers the capability to assess signal strength, allowing for the estimation of the distance between the receiver and the transmitter.