esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Comparison with Previous Research  
For a comparison of the results, few investigations on vortex induced vibration using piezoelectric harvesting are referred to. [PERSON] et al., [PERSON] et al., and [PERSON] et al. did not consider bluff splitter body while [PERSON] et al. used small cylindrical bluff splitter body installed at the tip acting as a tip mass in reducing the natural frequency of the model. The results are displayed in Table 5. As shown below the current study, which considers the effect of matched vortex frequency and natural frequency through the introduction of T-shape bluff splitter body, produces the highest normalized power. This is because the model is vibrating near to the natural frequency of the model causing highest vibrational displacement.  
Figure 23: Normalized power against (**a**) wind speed (**b**) frequency.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Wind Speed (m/s)** & **Frequency (Hz)** & **Power (\(\mu\)W)** & **Normalized Power (\(\mu\)W/g\({}^{2}\))** \\ \hline [PERSON] et al. [55] & 1.05 & 26.37 & 0.0007 & \(2.20\times 10^{-6}\) \\ [PERSON] et al. [56] & 8.00 & 67.00 & 36.4 & \(3.09\times 10^{-4}\) \\ [PERSON] et al. [57] & 6.80 & 0.40 & 0.18 & \(5.93\times 10^{-2}\) \\ [PERSON] et al. [29] & 1.19 & 3.14 & 100 & \(1.74\times 10\) \\ Current work & 19 & 75.758 & \(1.56\times 10^{4}\) & \(1.344\times 10^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of current study to other research results.
## Online Dynamic Load Identification Method
### Infinite Number of Degrees of Freedom and Modal Truncation
Basic criteria are often considered for the truncation of the number of mode shapes in vibrations [48]. The natural mode shapes with frequencies close to the excitation frequencies must be considered. In general the first few natural frequencies dominate the response of a structure. Obviously, considering more mode shapes increase the computation accuracy. However, adding more frequencies will also increase the computational cost. Therefore, it is important to identify the number of the critical mode shapes that must be considered to achieve a given accuracy before implementing the algorithm. This will depend on the complexity of the structural as well as the location and the frequencies of the applied excitation. In general for a simply supported beam similar to the one in Figure 3 the first four to eight mode shapes are enough to represent its dynamic behavior.
## 4 Simulation Evaluation  
We evaluate the performance of the proposed protocol in terms of the data throughput, the total number of packets, the packet delivery ratio and the end-to-end delay with well-known beaconless routing protocols in wireless sensor networks: BLR [1], BGR [2] and BOSS [3]. These protocols are selected according to the similarity with the proposed scheme.
## 3 Results
### Discussion  
We used Grad-CAM to visualize the feature extraction capabilities of the model. Grad-CAM calculates the weighted sum of each feature map by calculating the weight of each feature map in the last convolutional layer to the picture category and maps it to the original picture. Some images were randomly selected from the NWPU-RESISC45 dataset and were compared with the proposed method and methods without the addition of a multi-grained classifier. The results are shown in the following Figure 11, where the color gradient from blue to red indicates the contribution to the classification results from small to significant. It can be seen that the proposed method can better cover the main target compared to the heatmap generated when the multi-granularity classifier has not been added. This proves that adding a multi-granularity classifier can better extract features that are important to the classification results.
## 6 Evaluation
### Evaluation Conditions  
Different collision avoidance methods have been evaluated using the conditions summarized in Table 4. The transmission speed and the frequency band were selected on the assumption that S2 CM-HS [17] from EvoLogics is used as data transmission devices. The wind speed is set to 5 m/s. This is a generally tolerated wind speed for scuba diving. The number of nodes and the node moving speed selected are also those common in scuba diving. The departure rate in the mobility model was set such that a node goes astray at a probability of about 5% throughout the simulation. The slot time and the SIFS (short interframe space) of CSMA/CA are changed to those appropriate for underwater communication. The slot time calculated is 512 bit/62.5 kbps. The value of SIFS is the same as that of the slot time. The value of DIFS is double the value of SIFS. Divers move more slowly than pedestrians. Since they move only 5 to 10 m per second, the transmission interval, T, is set to 10 s. Increasing T extends the interval at which packets arise, thereby reducing the chances of collision. However, it increases the risk of stray nodes moving far away. If, on the other hand, T is shortened, the interval at which packets arise is also reduced, increasing the likelihood of message collisions.
# Spatiotemporal Monitoring and Evaluation Method for Sand-Filling of Immersed Tube Tunnel Foundation
## 5 Conclusions  
A spatiotemporal monitoring and evaluation method was proposed based on the elastic wave testing method and the BP neural network model.  
1. By analyzing the elastic wave data in the time, frequency, and time-frequency domains, it was possible to determine the feature parameters \(PP_{1}\), \(PP_{2}\), \(PP_{3}\), \(F_{1}\), \(F_{2}\), \(A_{1}\), \(A_{2}\) and \(T\) to characterize the elastic wave. The feature parameters of elastic wave data change with the sand-filling process and exhibit nonlinearity and strong randomness.
2. Using a neural network model, the mapping relationship between the collected elastic wave data and the sand-filling state was established to evaluate the sand-filling state. The accuracy of the proposed model for the test set was 93%.
3. The side holes and middle holes were classified and examined to analyze the diffusion characteristics of the sand deposit. For sand-filling hole W8, the proposed model effectively reflected the sand-filling state. The model could monitor the state of the sand deposit during the sand-filling construction process through the elastic wave monitoring results to provide knowledge about the sand-filling construction.  
The proposed method provides a new perspective in the monitoring of the immersed tube tunnel foundation cushion filling effect. This is essential for the safety and quality of the immersed tube tunnel. Although this study focused on the immersed tube tunnel, the method can also be applied to other areas of sand-filling or grouting monitoring.  
Conceptualization, P.W. and A.C.; methodology, P.W.; software, P.W.; validation, A.C., and P.W.; formal analysis, P.W.; investigation, P.W.; resources, A.C.; data curation, A.C.; writing--original draft preparation, P.W.; writing--review and editing, A.C.; visualization, P.W.; supervision, A.C.; project administration, A.C.; funding acquisition, A.C. All authors have read and agreed to the published version of the manuscript.  
National Key Research and Development Program of China: 2018 YFC0809400.  
Not applicable.  
Data Availability Statement: Data available in a publicly accessible repository.  
The authors declare no conflict of interest.  
Figure 16: Diffusion radius with time of P5 pipe section.
## 2 Applications and Background of Uwsn
### Background of UWSN
#### 2.2.4 Reliability
 \tag{7}\]  
Here,  
\(c=\) Speed of the acoustic waves (m/s),  
\(T=\) Temperature in \({}^{\circ}\)C and \(0^{\circ}<T\leq 30^{\circ}\),  
\(S=\) Salinity factor in Parts Per Thousand (PPT) and \(30\leq S\leq 40\),  
\(D=\) Depth of the sea in meters (m) and \(0\leq D\leq 8000\). Loss of the data increases with bigger data packets. On the contrary, the data overhead increases when the packet volume decreases. So, data volume affects the reliability directly. One thing worth mentioning is that, as the data volume gets larger, collision rates of the network also get higher. Therefore, larger data packets are preferable only when the link quality is good enough. However, it is already experimentally proven that data error rates also depend on the lengths of the packets [45]. Error control mechanism is a major issue in reliability. A successful transmission highly depends on the technique that are used for error control mechanism. Depending on the properties of the wireless channel, packet sizes are determined. For example, bad channel conditions require smaller packet size, error detection and re-transmission mechanism whereas larger packets are preferable for good channel condition. Moreover, channel access rates are affected by increase in packet sizes, thus the traffic on the channel is also affected which ultimately affects the data error rates. The underwater condition is very transient for wireless networking. So reliable and adaptive protocols are needed for successful transmission of the data packets which is also very challenging. Some networking protocols play a decent role in UWSN, which is analyzed in Section 3.
## 3 The Proposed Decoupling Uplink and Downlink Access Scheme  
### Signal Interference Noise Ratio  
Assume that a typical NGEO UT \(u_{0}\) is served by the satellite \(k_{0}\) from the \(k\)-th tier, To mitigate the ping-pong effect during the association process, we employ the expectation of the received signal power  
\[\mathbb{E}\big{[}S_{u_{0},k_{0}}^{\text{UP}}\big{]}=\mathbb{E}\Big{[}P_{n, \text{UT}}\|h_{u_{0}}\|^{2}\Big{]}=P_{n,\text{UT}}(2c_{0}+V)Z_{u_{0}}\big{(} \hat{\theta}_{k_{0}}\big{)}, \tag{14}\]  
\[\mathbb{E}\Big{[}S_{u_{0},k_{0}}^{\text{Down}}\Big{]}=\mathbb{E}\Big{[}P_{k} \|h_{u_{0}}\|^{2}\Big{]}=P_{n,\text{UT}}(2c_{0}+V)Z_{u_{0}}\big{(}\hat{\theta} _{k_{0}}\big{)}, \tag{15}\]  
as the access criterion, where \(2p_{\text{c}}+V\) is value of \(\mathbb{E}\Big{[}|h|^{2}\Big{]}\) and  
\[Z_{u_{i}}\big{(}\hat{\theta}\big{)}=\frac{G_{n}^{\text{UT}}G_{n}^{\text{Sat}} \big{(}\theta(\hat{\theta}_{k})\big{)}}{d_{u_{i}}^{2}\big{(}\hat{\theta} \big{)}}, \tag{16}\]  
then the SINR for uplink communication is given by  
\[\begin{split} SINR_{u_{0},k_{0}}^{\text{UP}}&=\frac{P_{n, \text{UT}}\|h_{u_{0}}\|^{2}}{\sum\limits_{u_{i}\in\Phi_{E}\backslash u_{0}}P_{ n,\text{UT}}\|h_{u_{i}}\|^{2}+P_{g,\text{UT}}\|h_{g,\text{UT}}\|^{2}+\sigma^{2}}\\ &=\frac{P_{n,\text{UT}}Z_{u_{0}}\big{(}\hat{\theta}_{u_{0}}\big{)} }{\sum\limits_{u_{i}\in\Phi_{E}\backslash u_{0}}P_{n,\text{UT}}Z_{u_{i}}\big{(} \hat{\theta}_{u_{i}}\big{)}+P_{g,\text{UT}}d_{g}^{2}-G_{g}^{\text{UT}}G_{n}^{ \text{Sat}}+\sigma_{0}^{2}},\end{split} \tag{17}\]  
where \(k\in\{1,\ldots,K\}\), subscripts \(g\) and \(n\) representing the GEO and NGEO systems, respectively. Furthermore, \(\sigma^{2}\) and \(\sigma_{0}^{2}\) stand for the noise power and equivalent noise power,respectively. The SINR at \(u_{0}\) of the downlink and its corresponding satellite \(k_{1}\) (or \(k_{0}\)) can be represented as  
\[\begin{split}& SINR_{u_{0},k_{1}}^{\text{Down}}=\frac{R_{k}\left\| h_{k_{1}}\right\|^{2}}{\sum\limits_{k_{1}\in\Phi_{k}\backslash k_{1}}R_{k_{1}} \left\|h_{k_{1}}\right\|^{2}+\sum\limits_{\eta_{j}\in\Phi_{0}}P_{k}\left\|h_{ \eta_{j}}\right\|^{2}+P_{g}\left\|h_{\eta_{j}}\right\|^{2}+\sigma^{2}}\\ &=\frac{P_{2}Z_{k_{1}}\left(\hat{\[PERSON]}_{k_{1}}\right)}{\sum \limits_{k_{1}\in\[PERSON]_{k}\backslash k_{1}}P_{k}Z_{k_{1}}\left(\hat{\[PERSON]}_{k_{1 }}\right)+\sum\limits_{\eta_{j}\in\[PERSON]_{0}}P_{k}Z_{\eta_{j}}\left(\hat{\[PERSON]}_{ j_{j}}\right)+P_{g}d_{g}^{2}\text{C}_{g}^{\text{UT}}\text{C}_{g}^{\text{Sat}}+ \sigma_{0}^{2}},\end{split} \tag{18}\]  
where \(v\in\{1,\ldots,K\}/\{k\};h_{k_{i}}\) (or \(h_{\eta_{j}}\)) is the channel coefficient of the UT \(u_{0}\) and satellite \(k_{i}\) (or \(v_{j}\)), respectively.
## 3 Electric Field Equations  
To compute the electric field produced by the leader channel, we must account for two different situations: (1) when the observation point P is in front of (below) the leader tip (between ground and the leader tip, see Figure 3) and (2) when the observation point P is behind (above) the leader tip (between the leader tip and the cloud, see Figure 4). In both cases, it is assumed that the observation point P is on the vertical axis of the lightning leader channel (see Figure 2). Therefore, the electric field produced at observation point P will only have a component along the \(z\)-axis.  
Due to cylindrical symmetry of the problem, it is convenient to consider an elementary ring with radius r and square cross-sectional area \(ds=dr\cdot dz\), so that the ring volume is \(dV=2\pi r\cdot ds\), placed at height z above the leader tip (see Figure 2). For this ring, the static electric field at observation point P will be:  
\[dE_{z}=\frac{\rho_{V}(z,\ t^{\prime})}{4\cdot\pi\cdot\varepsilon_{0}\cdot D^{2}} \cdot\cos(\theta)dV \tag{11}\]  
where \(D\) is the inclined distance between each of the points on the ring and the observation point P (a function of \(r\), \(z\), and \(z_{0}\), see Figure 2); \(\theta\) is the angle shown in Figure 2 dependent on \(r\), \(z\), \(z_{0}\) and \(D\); \(dV=2\cdot\pi\cdot r\cdot dr\cdot dz\) is the volume of the elementary ring, and \(t^{\prime}\) is given by (7).  
Thus, (11) can be written as:  
\[dE_{z}=\frac{2\cdot\pi\cdot r\cdot\rho_{V}(z,\ t^{\prime})}{4\cdot\pi\cdot \varepsilon_{0}\cdot D^{2}}\cdot\cos(\theta)\cdot dr\cdot dz=\frac{\rho_{V}(z,\ t^{\prime})\cdot r}{2\cdot\varepsilon_{0}\cdot D^{2}}\cdot\cos(\theta) \cdot dr\cdot dz \tag{12}\]  
The total static electric field at observation point \(P\) is found by integrating \(dE_{z}\) over \(r\) and \(z\), with integration over azimuth being already accounted for by using the ring geometry.  
Figure 4: Case 2: Observation point P is behind (above) the leader tip located at height \(z_{tip}\). (**a**) Ring element/charges above observation point P; (**b**) Ring element/charges below observation point P.  
Figure 3: Case 1: Observation point P is in front of (below) the leader tip located at height \(z_{tip}\).
## 5 Challenges and Open Directions
### Scalable Watermarking Solutions for Diverse Users  
The ability to provide scalable watermarking solutions for a large number of users without needing to retrain the model is an ongoing challenge. Methods like The Stable Signature [28] and Tree-Ring [23] require model retraining for each unique watermark, which becomes impractical in real-world scenarios involving numerous users. The development of plug-and-play watermark modules, as seen in Diffustrerace [21], allows for a more scalable approach by embedding multi-bit watermarks in latent variables during the image generation process. WMAdapter [25] also demonstrates a method for incorporating watermarking as an adaptable layer that can be added without retraining. However, the stability of these approaches can be a concern, as highlighted in [36], which emphasizes the need for more stable solutions against advanced removal techniques. Future research should explore modular watermarking strategies that can dynamically assign user-specific watermarks while maintaining robustness across different versions of the model.
## 4 Presumptions and Network Structuring
### Network Structuring  
The network is of a hierarchical tree topology-based clustered structure. The nodes are considered to be static. The source nodes of the network can be either source (\(B\)) node or intermediate (\(C\)) node such as described in Equation (1).  
\[C+B=n \tag{1}\]  
Using the Tiny AGregation protocol (TAG) [30], an aggregation tree is constructed with a base station as the tree root. TAG is a service of aggregation for the sensor networks. It provides an easy interface for data gathering based on the database query languages. It executes queries related to relevant aggregation in a time sequence manner. It executes in the network aggregation by combining the data flow processing and sending relevant readings by discarding irrelevant data readings [31]. The sink node provides the necessary queries to the network using the SPINS (Security Protocols for Sensor Networks) protocols [32]. SPINS is a type of security protocol consisting of the SPEN (Secure Network Encryption Protocol) [33] and the TESLA (a micro version of Timed, Efficient, Streaming, and Loss-tolerant Authentication Protocol) protocols. SPEN provides data confidentiality with the least overhead. TESLA broadcasts the authenticated data stream. SPINS building blocks authenticate the routing protocol [34].
## 4 Proposed System and Architecture
### RTPM Controller at the Client  
The IoMT device is activated with a unique ID that is supplied during the registration process of the user, and along with the NHS number, the device is uniquely identified by the server. The monitoring events are grouped into four strands, namely device protection, alerting or alarming, space quality, and health condition aspects. Due to the lack of compatible sensors to integrate with the designed system, some health-sensing systems like  
Figure 4: Proposed IoMT monitoring architecture.  
those for heart rate, ECG, and oxygen level could not be integrated. The system continually monitors the user and their environment, as shown in Figure 5, and it records every event and piece of evidence including errors to monitor issues and identify reasons for failures of the system. The controller monitors the events and activities of all four activity strands and alerts the concerned and relevant stakeholders including the people around the room if they come too close, move the device, or try to steal it. In such an event, evidence is collected in the form of movement detection, snapshots of the environment are captured with the camera, and the evidence is securely transmitted to the server for investigation and service quality monitoring (to find out who touched the device, when and how often, etc.).
# Impact of Ar/CF4 Mixed Gas Flow Rate on Silicon Etching Using Surface Discharge Plasma
## 2 Experimental Methods  
Figure 1 illustrates the setup utilized to generate surface discharge plasma for etching purposes. The lower electrode, measuring 28 mm \(\times\) 40 mm, comprised a stainless-steel (SUS) ground electrode with a 1 mm thick top plate covered by concave quartz glass (30 mm wide, 70 mm long, and 10 mm high, with a thickness of 1 mm). Positioned above this quartz glass is a 40 \(\times\) 40 mm monocrystalline silicon substrate, 0.625 mm thick, with a resistivity ranging from 1 to 100 \(\Omega\) m. Additionally, a cylindrical high-voltage side electrode made of SUS, measuring 70 mm in diameter and 30 mm in height, is placed. The SUS electrode, concave quartz glass, and Si substrate were in direct contact to ensure the absence of gaps. The discharge medium is a gas mixture of argon (Ar) and carbon tetrafluoride (CF4) in a 7:3 flow rate ratio.  
The gas flow rate was precisely controlled using mass flow controllers from Hitachi Metals, Ltd., Tokyo, Japan (model SFC-280E) and HORIBA STEC, Co., Ltd., Kyoto, Japan, (model SECE40 MK310 LMArSW), allowing adjustments from 0 to 6000 sccm. The gas entered the discharge vessel through a 1/4-inch outer diameter tube and exited from the upper section of the vessel. The alignment between the gas supply and exhaust ports is shown in Figure 1b. The gas inlet is located 69 mm from the edge of the concave quartz glass and 15 mm below the top surface of the concave dielectric, while the exhaust port is positioned approximately 60 mm from the central axis of the cylindrical discharge vessel, which has an inner diameter of 135 mm and a height of 175 mm.  
The discharge-generating gas, a mixture of CF4 and Ar, is introduced at atmospheric pressure (P = 101 kPa) and expelled from the upper section of the vessel. A high-frequency power source (Logy Electric Co., Ltd., Tokyo, Japan, model HLV-10 AC) operating at 9 kHz supplies voltage to the high-voltage-side electrode to initiate the surface discharge plasma. The discharge-sustaining voltage and current were measured using a high-voltage probe (Nissin Pulse Electronics, Chiba, Japan, EP-50K) and a CT sensor (Pearson, London, UK, Model 150), respectively. The etching profile of the monocrystalline Si substrate was assessed after post-treatment using a laser microscope (Olympus LEXT OLS400, Tokyo, Japan).
# A Coaxial Dielectric Barrier Discharge Reactor for Treatment of Winter Wheat Seeds
## 3 Results and Discussion
### Effects on Seed Surface Wettability of Wheat for Both Discharges  
Water contact angle (WCA) measurements can be used to evaluate surface wettability, which can be modified due to chemical and physical alterations caused by plasma treatment [69]. For the untreated wheat seed surface analyzed here, an average contact angle of around 110\({}^{\circ}\) was obtained. Even considering seeds from different origins (e.g., variety, country, harvest year, company), this value is in good agreement with other published studies about wheat seeds such as 113\({}^{\circ}\) observed by [PERSON] and coauthors [60], 106\({}^{\circ}\) from [PERSON] and coauthors [70], and 120\({}^{\circ}\) measured by [PERSON] and coauthors [24]. Figure 6 presents the WCA measurements of untreated seeds (control) and seeds treated from 1 to 30 min with argon (black curve) and helium (red curve). A steep WCA reduction can be observed already for only 1 min treatment in both cases, where the contact angles are reduced  
Figure 5: Temperature measured (**a**) inside the reactor for both gases during 10 min of plasma on and cooling process after plasma is turned off. (**b**) Temperature at the seed coat of wheat treated for treatment times varying from 1 to 10 min using an argon (black) and helium (red) discharge.  
from 110\({}^{\circ}\) to around 45\({}^{\circ}\) and 55\({}^{\circ}\) when treated with argon and helium, respectively. Increasing the treatment time led to further WCA reduction in the case of the argon discharge resulting in angles about 82% smaller than the control. Meanwhile, the WCA of seeds treated with the helium discharge was not further altered, reaching saturation around 40\({}^{\circ}\) with 5 min of treatment time (reduction of around 64%). WCA reduction on the seed coat of wheat seeds treated with DBDs were also observed in other studies, where [PERSON] and coauthors [24] obtained a reduction of around 88% after 15 min of He plasma exposure, and [PERSON] and coauthors [70] observed a decrease of only 18% of WCA after 3 min of plasma treatment in open air.  
Exposing seeds to plasma-generated reactive species can cause modifications on the seed coat, resulting in improved wettability. Thus, the higher reduction of WCA obtained here for the argon-treated seeds might be associated to a bigger amount of oxygen groups attached to the seed, forming polar groups. The increase in the wettability of treated seeds has been reported in the literature also for different types of plasma sources and plant species, where it is commonly related to improvements in water uptake by the seeds, which leads to enhanced seed germination [45; 71].