esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Trends in Operational USVs
### Models Introduced to the Market  
The evolution of USVs over the years shows a steady increase in the number of models introduced to the market (Figure 4), with significant growth occurring from 2015 onward. Early adoption was relatively slow, with only a few models appearing before 2015. However, from 2016 to 2020, there was a surge in the number of new USV models, peaking around 2019 and 2020, likely driven by advancements in autonomous navigation, maritime security needs, and the growing interest in oceanographic and offshore applications. Despite some fluctuations in annual releases after 2020, the cumulative number of USVs has consistently increased, surpassing 40 models by 2024. This trend highlights the sustained development and adoption of USVs in various industries, reflecting the increasing reliance on unmanned maritime systems.  
Figure 3: Examples of USVs mapped in the present study in the format \(\mathrm{USV}_{\mathrm{name}}\) (Manufacturer): (**a**) Mariner X (Maritime Robotics). (**b**) Armada 8 (Ocean Infinity). (**c**) SEAKIT X (SEAKIT). (**d**) Tupan (Tidewise). (**e**) Shadow Fox (L3 Harris). (**f**) DriX H9 (Exail). (**g**) Voyager (Saildrone). (**h**) Sailbuoy (Offshore Sensing). (**i**) XO450 (XOcean). (**j**) Sounder (Kongsberg). (**k**) Wave Glider (Liquid Robotics). (**l**) SP48 (SeaTrac).
# An Improved Rectifier Circuit for Piezoelectric Energy Harvesting from Human Motion
## 4 Results and Discussion
### Shaker Test
#### 4.1.4 Effect of Frequency  
Table 3 summarises the rectified voltage and output power achieved by both rectifier circuits with varying input voltage and frequency at a resistance of 910 k\(\Omega\). It is evident that the rectified voltage through both circuits at a frequency of 2 Hz is higher than the case of 1 Hz. In addition, the power output from both circuit is also higher in the case of 2 Hz. Two main reasons are attributing to such outcome:  
* As frequency increases, the reactance (X\({}_{\text{C}}\)) of the internal capacitance decreases. On the other hand, the current produced by the PD is higher, as shown in Figure 18. Although the generated voltage is adjusted to a fixed value (say 1 V for both 1 Hz and 2 Hz in Table 3), the current generated by the PD at 2 Hz is higher than 1 Hz since the frequency is inversely proportional to time. Thus, the output power at 2 Hz is higher than 1 Hz.  
Figure 17: Power stored in C2 against varying resistance with input voltage of 5 V, at 1 Hz, (**a**) H-Bridge, (**b**) FBR.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Input Voltage**} & \multicolumn{3}{c}{**Rectified Voltage (V\({}_{\text{dc}}\))**} & \multicolumn{3}{c}{**Output Power (\(\mu\)W)**} & \multicolumn{3}{c}{**Power Controlled/Saved by H-Bridge**} \\ \cline{2-11}  & **H-Bridge** & **FBR** & **H-Bridge** & **FBR** & **Circuit over FBR Circuit (\(\mu\)W)** \\ \cline{2-11}  & \multicolumn{3}{c}{**Frequency (Hz)**} & \multicolumn{3}{c}{**Frequency (Hz)**} & \multicolumn{3}{c}{**Frequency (Hz)**} \\ \cline{2-11}  & **1** & **2** & **1** & **2** & **1** & **2** & **1** & **2** & **1** & **2** \\ \hline
1 & 0.49 & 0.7 & 0.31 & 0.4 & 0.26 & 0.53 & 0.10 & 0.25 & 0.16 & 0.28 \\
2 & 1.38 & 1.84 & 0.41 & 0.45 & 2.09 & 4.2 & 0.18 & 0.41 & 1.91 & 3.79 \\
3 & 2.35 & 2.72 & 0.46 & 0.49 & 6.07 & 7.2 & 0.24 & 0.43 & 5.83 & 6.77 \\
4 & 2.81 & 2.96 & 0.73 & 1.2 & 10.0 & 10.70 & 0.58 & 2.08 & 9.42 & 8.62 \\
5 & 3.41 & 3.73 & 0.96 & 1.42 & 12.8 & 15.2 & 2.3 & 4.98 & 10.5 & 10.22 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Rectified voltage (stored in C2) and output power through both circuits at various frequencies and input voltages with a resistance of 910 k\(\Omega\).  
* As PD's current increases, the rectified voltage also increases, which leads to increase in output power.  
Another important observation is that, regardless of excitation frequency, the H-Bridge circuit consistently produced higher output power than the FBR circuit. This is particularly true when a larger capacitor is used. In conclusion, from Figures 13-18, it is observed that the proposed H-Bridge circuit is capable of producing higher rectified voltage and higher output power under all scenarios. At a frequency of 1 Hz with the input voltage of 5 V, the highest output power attained by the H-Bridge and the FBR circuits are 12.8 uW and 2.30 uW, respectively. Similarly, at a frequency of 2 Hz, the highest output power through the H-Bridge and the FBR circuits are 14.9 uW and 4.98 uW, respectively.
# I3 Mote: An Open Development Platform for the Intelligent Industrial Internet
## 5 Software and Protocol Stack
### Protocol Stack  
The I3 Mote network processor is equipped with an IEEE 802.15.4 and BLE communication interface as described in the previous section. The software ecosystem developed to support the board integrates an industrial-grade protocol stack implementing a set of standards developed by the IEEE and the IETF. These set of protocols are referred as the IETF 6 TSCH stack [16]. 6 TiSCH is rooted at the Std. IEEE 802.15.4 TSCH Medium Access Control (MAC) that provides deterministic access enabling industrial-grade reliability [17] and ultra-low power operation [18]. On top, this set of standards being defined by the IETF 6 TiSCH working group provide network bootstrapping, security bootstrapping and distributed scheduling functionalities [19] and policies [20; 21]. In addition, this standardized protocol stack uses the IETF 6 LoWPAN and Routing Protocol for Low Power Lossy Networks (RPL) [22] to achieve IPv6-based end-to-end connectivity while meeting the determinism, robustness and low-power operation required for battery-operated devices targeted at industrial process monitoring and control applications [9].  
In particular, the 6 TiSCH protocol stack implemented (See Figure 7) enables highly reliable communication and optimal low-power operation [23]. This is achieved thanks to a TSCH MAC layer. This MAC layer divides time in slots for each of the available frequencies. This matrix is seen as a scheduler in time and frequency where nodes in the network are allocated certain cells by a scheduling mechanism. The bandwidth, latency, energy consumption and reliability can be traded off through different schedule configurations enabling network flexibility and supporting, at the same time, collision-free communications.  
It is worth noting that IPv6 connectivity, mesh routing through RPL and transport protocols, such as User Datagram Protocol (UDP) and CoAP, are exposed to applications running on the data fusion processor through a serial application programming interface (API). This ensures a clear separation of the network core and the data processing unit, and simplifies prototyping and product development by offering connectivity as a service.  
Figure 7: 6 TiSCH protocol stack for end device.
## 5 Experiment Design and Test Results
### Positioning Solution
\begin{table}
\begin{tabular}{l l c c c c c c c} \hline \hline
**GNSS** & \multicolumn{3}{c}{**Stochastic Model**} & \multicolumn{3}{c}{**Ambiguity Resolution**} & \multicolumn{3}{c}{**Positioning Accuracy [mm]**} \\
**System** & **Model** & **Cross** & **Time** & **P succ. [\%]** & **FA/FR** & **RMS (ne)** & **RMS (u)** & **Err. \(<\) 5** & **Max (err.)** \\ \hline G-(5Q)C & Elev. & – & – & 98.8 (1026) & 963/0 & 2.9 & 7.3 & 23.9\% & 36.7 \\ G-(5Q)C & C/N\({}_{0}\)(G) & – & – & 100 (0) & 0/0 & 2.4 & 4.2 & 66.6\% & 28.1 \\ \hline G-(5Q)C & Elev. & ✓ & – & 99.0 (892) & 830/1 & 3.0 & 7.3 & 23.1\% & 36.8 \\ G-(5Q)C & C/N\({}_{0}\)(G) & ✓ & – & 100 (0) & 0/0 & 2.5 & 4.2 & 67.3\% & 27.9 \\ \hline G-(5Q)C & Elev. & ✓ & ✓ & 99.6 (313) & 265/9 & 2.8 & 6.7 & 32.1\% & 34.9 \\ G-(5Q)C & C/N\({}_{0}\)(G) & ✓ & ✓ & 100 (0) & 0/0 & 2.6 & 3.8 & 70.6\% & 26.7 \\ G-(5Q)C & C/N\({}_{0}\)(b) & ✓ & ✓ & 100 (0) & 0/0 & 2.5 & 3.8 & 70.8\% & 26.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Positioning model solution results for GPS + BeiDou. \begin{table}
\begin{tabular}{l l c c c c c c c} \hline \hline
**GNSS** & \multicolumn{3}{c}{**Stochastic Model**} & \multicolumn{3}{c}{**Ambiguity Resolution**} & \multicolumn{3}{c}{**Positioning Accuracy [mm]**} \\
**System** & **Model** & **Cross** & **Time** & **P succ. [\%]** & **FA/FR** & **RMS (ne)** & **RMS (u)** & **err. \(<\) 5** & **Max (err.)** \\ \hline GREC & Elev. & – & – & 98.2 (1567) & 1352/0 & 2.5 & 6.8 & 24.2\% & 21.0 \\ GREC & C/N\({}_{0}\)(G) & – & – & 100 (0) & 0/0 & 1.9 & 5.3 & 49.4\% & 15.9 \\ \hline GREC & Elev. & ✓ & – & 98.4 (1423) & 1232/0 & 2.5 & 6.5 & 30.7\% & 19.7 \\ GREC & C/N\({}_{0}\)(G) & ✓ & – & 100 (0) & 0/0 & 1.9 & 4.7 & 60.8\% & 14.9 \\ \hline GREC & Elev. & ✓ & ✓ & 99.7 (1145) & 988/0 & 2.3 & 6.3 & 33.8\% & 19.7 \\ GREC & C/N\({}_{0}\)(G) & ✓ & ✓ & 100 (0) & 0/0 & 1.9 & 4.8 & 60.6\% & 15.2 \\ GREC & C/N\({}_{0}\)(b) & ✓ & ✓ & 100 (0) & 0/0 & 1.9 & 4.7 & 61.4\% & 15.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Positioning model solution results for GPS + GLONASS + Galileo + BeiDou. Figure 24: Positioning error statistics for different stochastic models and system combinations.
## 4 Case Study
### Simulation Result
#### 4.2.1 Scenario 1  
Initial information of relative ship about the multi-ships scenario case 1 is illustrated in Table 3. In this case, according to COLRGEs, [PERSON] has a responsibility to move out of the way with TS1 in a crossing position and TS2 in a head-on situation, and TS3 in the overtaking situation. The dynamic feasible manoeuvring interval is shown in Figure 12, where the black colour, red colour, green colour, and blue colour denote the \(D\) telegraph, \(S\) telegraph, \(H\) telegraph, and \(F\) telegraph, respectively.  
OS takes measures to avoid collision by altering course 37\({}^{\circ}\) to the starboard side at the \"H\" telegraph, and Figure 13a shows the ship's path to avoid the current collision for multi-ships, which is shown in different colours. However, OS will introduce additional collision risk with TS4 while avoiding collision for TS1, TS2, and TS3. Thus, OS should take caution to alter course to the starboard side for the collision avoidance of TS4, and the relative distance between the OS and the TS is shown in Figure 13b, in which P1, P2, P3, and P4 are represented as the minimum distance of relative TS, respectively. Figure 13d  
Figure 11: Simulation of the different telegraphs. (a) Turing circle at different telegraph; (b) Speed change from \(D\) to \(F\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ship List** & **Position (mile)** & **Course (\({}^{\circ}\))** & **Speed (knot)** & **LOA (m)** & **TCS** & **CRI** \\ \hline OS & (0, 0) & 000 & 12.5 & 225 & - & - \\ TS1 & (2, 2) & 300 & 8.0 & 225 & 725 & 0.202 \\ TS2 & (0, 4.4) & 181 & 6.0 & 299 & 623 & 0.035 \\ TS3 & (0,2, 2.0) & 000 & 5.5 & 225 & 743 & 0.393 \\ TS4 & (2, 0) & 000 & 6.5 & 245 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Initial information of the ships for simulation in scenario 1.  
represents the OS speed change during alternation operation. Figure 13c demonstrates the general tendency of DCPA between OS and TS. Under the guidance of the telegraph, the ship's speed gradually decelerates, initially rapidly, then slowly and steadily, as seen in Figure 13d.
## 5 Results and Discussion
### Evaluation Metrics  
The mean square error (MSE) is employed to quantify the discrepancy between the original input of the 1 dCNN-AE and the reconstructed data in the decoder. A low MSE value is indicative of the high performance of the developed 1 dCNN-AE. The MSE is represented mathematically by the following equation:  
\[MSE=\frac{1}{N}\Sigma(U_{i}-U_{i}^{\prime})^{2} \tag{3}\]  
where \(U_{i}\) and \(U_{i}^{\prime}\) denotes the samples of the original input data and the reconstructed output data, respectively. \(N\) represents the total number of data points.  
Conversely, the DBI represents the quality measure of the clustering process. A lower DBI score indicates an improved data clustering performance of the trained model. The mathematical equation used to calculate DBI is presented in Equation (4).  
\[DB=\frac{1}{K}\Sigma max\frac{(d_{i}-d_{j})}{Z(k_{i},k_{j})} \tag{4}\]  
where \(K\) is the number of clusters. The average distance between cluster data points and its centroid is donated as \(d\). \(Z\) represents the distance between the centroids of clusters \(k_{i}\) and \(k_{j}\).  
In order to ascertain the efficacy of the proposed model, accuracy is employed as a quantitative performance criterion, whereby the effectiveness is verified using testing data with known labels. The accuracy metric represents the ratio between the number of correct predictions and the total number of input instances.
# Intersection Routing Based on Fuzzy Multi-Factor Decision for VANETs
## 3 Intersection Routing Based on Fuzzy Multi-Factor Decision
### Vehicle Decision
#### 3.2.3 Relative Velocity  
The less the relative velocity of two vehicles, the longer the link can be kept between them. The proposed routing protocol keeps the link between two adjacent SNs for some time instead of being used immediately, which preserves the link lifetime for a longer period of time. Also, this not only guarantees the availability of data transmission but can also reduce the collision due to the formation of the link. Suppose the current vehicle speed is \(v_{0}\) and the neighbor vehicle speed is \(v_{1}\). The fuzzy number of the relative velocity between the current vehicle and candidate vehicle is computed with the help of the following equation:  
\[\text{V}=\left\{\begin{array}{ll}\overline{2},&\text{if }v_{0}\text{ and }v_{1}\text{ are in different directions}\\ \overline{4},&\text{if }v_{0}\;<\;v_{1}\\ \overline{6},&\text{if }v_{0}\;>\;v_{1}\\ \overline{8},&\text{if }v_{0}\;=\;v_{1}\end{array}\right. \tag{4}\]
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 2 Design Specifications
### Powering  
IBBDs have been conventionally powered via batteries [11; 45]. However, integration of the batteries can upsurge the size of the IBBDs, raising biocompatibility and patient safety related concerns. This also necessitates the requirement for frequent battery replacement and/or recharging due to short lifetime. Therefore, the research on battery-less techniques, such as energy harvesting and WPT, for IBBDs is becoming necessary. Energy harvesting technologies involve harvesting the power from environmental or human bodily sources. The motion of the tissue and heartbeat [46], body thermal gradients [47], human movement and motion [48], and glucose oxidization [49] are some of the mechanisms used in the past to harvest the energy for IBBDs. Different WPT techniques and their design methodologies for IBBDs are explained in Ref. [14]. Widespread research has been carried out in the last decade to improve the efficiencies of the aforementioned approaches and develop them to be used for powering IBBDs.
## 3 The Cross Time Slot Partial Interference Alignment
### The Processing Matrix at Relay  
We design the processing matrix at Relay 1 by aligning part of the intra-cell interference (i.e., the desired signal sent to UE[1,1] in sub-time slot 1) to the desired signal subspace during the signal transmission to UE[1,1] in sub-time slot 2. According to (7), we have  
\[span\big{(}\mathbf{H}_{1}^{[1,1]}\mathbf{V}^{[1,1]}\big{)}=span\big{(}\mathbf{ H}_{R1}^{[1,1]}\mathbf{G}_{1}\mathbf{H}_{1}^{[R1]}\mathbf{V}^{[1,1]}\big{)}. \tag{8}\]  
Hence, the processing matrix on relay 1 can be obtained as  
\[span\big{(}\mathbf{G}_{1}\big{)}=span\big{(}\mathbf{H}_{R1}^{[1,1]-1}\mathbf{H}_{ 1}^{[1,1]}\mathbf{H}_{1}^{[R1]-1}\big{)}. \tag{9}\]  
As mentioned above, the desired signals to UE[1,1] in two sub-time slots are aligned to the same subspace. UE[1,1] can directly mitigate the interference by decoding the combined signal. Similarly in Cell 2, the processing matrix of Relay 2 can be obtained, i.e.,  
\[span\big{(}\mathbf{G}_{2}\big{)}=span\big{(}\mathbf{H}_{R2}^{[1,2]-1}\mathbf{H}_{ 2}^{[1,2]}\mathbf{H}_{2}^{[R2]-1}\big{)}. \tag{10}\]
# Electroless Copper Patterning on TiO\({}_{2}\)-Functionalized Mica for Flexible Electronics
## 2 Materials and Methods  
### Reagents  
Electronic grade mica sheets (14 mm \(\times\) 50 mm \(\times\) 0.05 mm condenser mica plates) were used as substrates, obtained from Pasta Electronics Ltd. (Gorna Oryahovitza, Bulgaria). Titanium tetraisopropoxide (TTIP, 97+%), acetylacetone (AcAc, 99%), and silver nitrate (AgNO3, ACS, 99.9+%) were sourced from Alfa Aesar (Ward Hill, MA, USA). Isopropyl alcohol (2-propanol, \(\geq\)99.8%, HPLC grade) was supplied by Avantor (Radnor, PA, USA). Copper sulfate pentahydrate (CuSO4.5H2O, 98+%, for analysis) and ethylenediaminetetraacetic acid (EDTA, ACS, \(\geq\)99.0%) were supplied by Thermo Scientific (Waltham, MA, USA).
## 4 Results
### Implementation Details  
To ensure robustness and generalization, a five-fold stratified cross-validation approach was adopted, given the imbalanced nature of the dataset. This technique divides the dataset into five equal folds while preserving the original class distribution. In each iteration, four folds were used for training, while the remaining fold was reserved for testing. This process was repeated five times, ensuring that every sample contributed to both training and testing, effectively minimizing bias and variance.  
Feature extraction was performed using three CNN-based models (DenseNet201, EfficientNetB2, and ResNet50), while classification was carried out using three machine learning classifiers: Support Vector Machines (SVM), Random Forest (RF), and Artificial Neural Networks (ANN). To enhance classification performance and reduce dimensionality, Principal Component Analysis (PCA) was applied, selecting only the components that retained 95% of the total variance [31]. Each CNN model extracted 1024 features per image, which were subsequently fused with hand-crafted descriptors.  
The dataset was partitioned into 80% training and 20% testing subsets to ensure reliable performance evaluation. The classification performance was assessed using standard metrics, including accuracy, precision, recall, and F1-score, providing a comprehensive evaluation of the proposed system.  
Hyperparameters in Table 3 were selected to optimize model accuracy and prevent overfitting. The linear kernel was chosen for SVM due to its superior performance in high-dimensional feature spaces. Random Forest was configured with 100 decision trees, ensuring a balance between computational efficiency and model accuracy. The ANN model was designed with three hidden layers, incorporating ReLU activation and adaptive learning rates for enhanced convergence.  
By employing this experimental setup, the study aimed to develop a robust and scalable pest damage classification system, ensuring high accuracy in real-world agricultural applications.
## 3 Results and Discussion
### Archaeological Samples
This temporal difference, although short from an archaeological perspective, may be relevant due to the _Ceradenari_ area's agricultural use in recent decades, which may have disturbed the surface and introduced contaminants, particularly K, from fertilizers. The Pb level in TY_MA_20 (104 ppm) may suggest the intentional addition of lead oxide (PbO) as a flux to lower quartz melting temperature [84]: However, the level remains well below the >1% typically found in lead-rich glasses. Another relevant observation is the uniformity of Sr concentrations (ca. 0.1% across all samples, Table 6), supporting the hypothesis of a common sand source, likely coastal sands rich in shell fragments and feldspar impurities (i.e., Al, Ti, Fe, and Mn) [85; 86], known for their higher Sr content. Additionally, TY_MA_8 and TY_MA_9, the green glass samples, contain relevant Cu levels (ca. 500 ppm), which likely accounts for their coloration. Overall, the elemental profiles and weathering features are consistent with a silica-soda-line glass made using natron as a flux and lime as a stabilizer. The results also suggest the use of calcareous sands with feldspar impurities as raw materials [87]. Moreover, the composition of the millstone (TY_MA_52) and obsidian (TY_MA_51) aligns, within experimental errors, with volcanic rocks from Aeolian Islands rather than  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Element** & **TY\_MA\_7** & **TY\_MA\_8** & **TY\_MA\_9** & **TY\_MA\_15** & **TY\_MA\_20** & **TY\_MA\_51** & **TY\_MA\_52** & **TY\_OU\_14** & **TY\_OU\_15** \\ \hline Na (\  
Mount Etna [88]. This is consistent with the relative geographical proximity of the Aeolian archipelago to the study area. Concerning the two brick samples, TY_OU_14 is presumed Roman, while TY_OU_15 is believed to be modern. TY_OU_15 exhibits higher Mg and Fe levels and lower Na and Al than TY_OU_14, supporting a distinction in provenance or manufacturing technique. However, conclusive classification requires additional reference samples, including bricks with proven Roman origin. Furthermore, powder X-ray diffraction could help identify high-temperature crystalline phases, potentially distinguishing modern production technologies.
# Human-AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era +
## 3 Identifying Challenges in RSA: Literature Review
### Challenges in Localization and Orientation
#### 3.1.3 Inaccurate GPS  
In addition to the insufficient map problem, inaccurate GPS was recognized as another major cause. Field trials of RSA systems [10] revealed that the largest orientation and localization errors occurred in the vicinity of a tall university building where GPS was inaccurate. Researchers [42] indicated that GPS signal reception was degraded or even blocked around tall buildings. In terms of the last-few-meters navigation [26], they illustrated that GPS was not accurate enough to determine whether the user was walking on the pavement or the adjacent road in some situations. The well-known last 10 meters and yard problem [26] in blind navigation is also caused by GPS inaccuracy.
# Experimental Study of Scour Hole Depth around Bridge Pile Using Efficient Cross-Section
## 4 Results and Discussion  
As discussed previously, 30 experiments are conducted to investigate the effect of different pile shapes on scour hole depth, Figures 6-8. Of the 30 experiments, 10 included the circular piles, 10 involved the square piles and the remaining 10 lenticular piles. The average scour depth, corresponding to the pile shape, is found by adding the reading of the respective 10 experiments and dividing by 10. It is important to note that each experiment is run for 24 h to obtain the maximum possible scouring. The scour holes are measured in four positions A, B, C and D around each pile, as shown in Figure 7. The results obtained from these experiments are presented and analyzed in Figure 8 and Appendix A. It reveals that the scour hole around the square piles is maximum. The main reason for the largest scour hole is the maximal flow obstruction, leading to progressive horseshoe formation.  
Figure 5: Dimension of different piles (all dimensions are in cm): (**a**) circular; (**b**) square; (**c**) lenticular.  
Figure 6: (**a**) Scour hole around the circular pile; (**b**) scour hole around the square piles; (**c**) dislocation of the pile cap due to excess loss of soil.  
Figure 8: _Cont._  
Figure 7: Point of measurement around: (**a**) circular; (**b**) lenticular; (**c**) square piles.  
Contrarily, the lenticular shape has shown minimal scour depth at the leading frontal edge of piles. This reduced scouring is due to its sharp nose, which provides minimal obstruction to water flow. However, the lenticular pile side camber line experienced more scouring than the piles leading front and trailing tail. A detailed special comparison of the scouring of piles is discussed below.