esa-sceva/satcom-chunk-collection huggingface dataset:
# Accurate Needle Localization in the Image Frames of Ultrasound Videos
## 3 Experimental Evaluation and Results
### Performance Evaluation  
Five performance metrics, namely the angular error, axis error, tip error, failure rate, and run time [19], are used to evaluate the performance of the proposed method to localize the needle within the image frames of the ultrasound video. The axis error quantifies the maximum distance between the predicted needle trajectory and gold standard needle. The calculation of the axis error is illustrated in Figure 5. Particularly, two points are defined on the gold standard needle. These points are the entry point (Entry\({}_{GS}\)) and the tip (Tip\({}_{GS}\)) of the gold standard needle. The perpendicular projections of Entry\({}_{GS}\) and Tip\({}_{GS}\) onto  
Figure 4: The effect of incorrect initial estimation of \(\Theta\) on the angular error in the first image frame of the ultrasound video.  
the predicted needle trajectory are computed and denoted as \(\text{Entry}_{Pred}\) and \(\text{Tip}_{Pred}\), respectively. The distances between \(\text{Entry}_{GS}\) and \(\text{Entry}_{Pred}\), denoted as \(\left\|\text{Entry}_{GS},\text{Entry}_{Pred}\right\|\), and between \(\text{Tip}_{GS}\) and \(\text{Tip}_{Pred}\), denoted as \(\left\|\text{Tip}_{GS},\text{Tip}_{Pred}\right\|\), are computed. The axis error is then calculated as the maximum of \(\left\|\text{Entry}_{GS},\text{Entry}_{Pred}\right\|\) and \(\left\|\text{Tip}_{GS},\text{Tip}_{Pred}\right\|\). The tip error is defined as the Euclidean distance between the needle tip predicted by the proposed method and the gold standard needle tip. The failure rate for a given ultrasound video is defined as the percentage of video image frames in which the proposed method fails to obtain axis error values smaller than 3 mm. Finally, the run time represents the time required by the proposed method to localize the needle trajectory and tip in a single image frame. To compute the run time, the proposed method was developed using MATLAB (version R2022a) and executed on a computer system with an Intel i7-11800H processor and 16 GB of RAM.  
The five metrics are computed for each ultrasound video in the _ex vivo_ and _in vivo_ datasets. Particularly, the individual image frames of the ultrasound video are processed using the proposed method to predict the needle trajectory and tip. The angular error, axis error, tip error, and run time are computed for the image frames that compose the ultrasound video, and the mean \(\pm\) standard deviation values of the four metrics are determined for the entire video. Furthermore, the failure rate is calculated for the entire ultrasound video.
# Data Gathering Techniques in WSN: A Cross-Layer View
## 5 Routing for Data Gathering in WSN
### Data Aggregation Routing Protocols for Energy Harvesting WSN
In addition to the common collision definitions, the paper defines an \"energy-collision\", which occurs due to battery level constraint and not due to simultaneous transmissions. Specifically, energy-collision occurs when a transmitter-receiver tuple is scheduled for transmission, but the transmission cannot take place because at least one of the two nodes has insufficient energy to transmit or receive the packet due to recent activity (insufficient time has elapsed since its last transmission or reception to harvest enough energy for the subsequent scheduled transmission). An adaptable data aggregation tree is constructed, which considers each node's residual and harvested energy, and three energy-collision-aware data aggregation algorithms are proposed.
## 4 Discussion  
### Parameter Sensitivity Analysis  
The critical damage threshold \(\kappa\) emerges as the most sensitive parameter in the dual-control prediction model. Conducted via Python 3.7 [31; 32; 33; 34], Monte Carlo simulation (n = 10,000) revealed the existence of underestimation risk and overestimation tendency. When \(\kappa<0.3\), false negative rates increase to 38.7% for delayed water inrush (\(T_{JS}>0.8\)) due to premature channel effectiveness assessment. At \(\kappa>0.7\), false alarms rise by 22.4% for instantaneous inrush prediction (\(\$T_{JS}>1+\kappa\)). When \(\kappa\) = 0.45 \(\pm\) 0.05, optimal calibration achieves 91.3% accuracy in the Yishun case (Figure 14), aligning with the porosity of surrounding rock mass (0.43-0.48) in the water-resisting zone. The \(\kappa\)-value physically represents the percolation threshold where broken solid bonds form connected pathways. This mechanistic insight explains its dependence on lithology.
# Flexible Rectenna on an Eco-Friendly Substrate for Application in Next-Generation IoT Devices
## 3 Results and Discussion
### Flexible Antenna Design, Fabrication, and Testing
For measurement purposes, a coaxial cable with a U.FL connector was soldered to the CPW, as illustrated by the photograph of the fabricated antenna prototype in Figure 6.  
Figure 7 shows the measured \(|\)S\({}_{11}\)\(|\) of the antenna prototype. The measured impedance bandwidth (\(|\)S\({}_{11}\)\(|\)\(<\)\(-\)10 dB) spans from 2.2 GHz to 3.2 GHz, demonstrat  
Figure 5: Three-dimensional radiation patterns of the antenna at: (**a**) 2.1 GHz; (**b**) 2.5 GHz; (**c**) 2.9 GHz; and (**d**) antenna gain scale. Figure 6: Photo of the fabricated antenna prototype. ing good agreement with the simulation results. The observed slight frequency shift of approximately 0.2 GHz is probably attributable to parasitic effects from the coaxial cable and U.FL connector soldering, the addition of glue, or minor variations in substrate thickness and material properties during fabrication. Finally, we perform a comparative performance analysis between the proposed flexible antenna and earlier reported flexible antennas to highlight its advantages. Table 3 summarizes the key parameters of recent flexible antennas considered in the comparison. The results indicate that the proposed antenna achieves the highest radiation efficiency at 2.45 GHz (96.7%) among all considered designs, surpassing the closest result in [33] (95%) while maintaining a more compact size (50 \(\times\) 60 mm). Furthermore, the proposed antenna offers a broad operational bandwidth of 2.0-2.92 GHz, making it ideal for RF energy harvesting across various frequency bands. In contrast, most other designs [23; 29; 36; 37; 38; 39] are limited to a narrower bandwidth around 2.4-2.5 GHz. Additionally, the proposed antenna used biodegradable and eco-friendly materials for its substrate--specifically, a composite of natural rubber and rice husk ash (RHA)--in alignment with current trends in sustainable and eco-friendly electronics. Compared to other sustainable designs [29; 33; 36; 37; 39], it demonstrates significantly higher radiation efficiency (96.7%), which is important for effective RF energy harvesting. Table 3 shows that most other sustainable designs [29; 33; 36; 37; 39] exhibit notably lower efficiency, with values as low as 28% in [37], 30.5% in [39], and 53.89% in [29]. Finally, incorporating a symmetric binary fractal tree geometry in the antenna design contributes to miniaturization and an esthetic antenna, an advantage not reported in previous works. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & **Antenna** & **Antenna Size** & **Antenna Substrate** & **Efficiency (\%)** & \begin{tabular}{c} **Frequency Band** \\ **(GHz) at** \\ **1S\({}_{\text{11}}\)\(\leq\) -10 dB** \\ \end{tabular}  & \multirow{2}{*}{**Eco-Friendly**} \\  & **Type** & **(mm)** & **Material** & **at 2.45 GHz** & \ \begin{tabular}{c} 1 \\ \end{tabular}  &
\begin{tabular}{c} 2.4–2.5 \\ 2.43–2.5 \\ 2.2–2.5 \\ 2.
## 5 Performance Evaluation
### Performance of Detection and Mitigation Mechanism
For the attacks of the cyber-domain, the IDBN detection accuracy reaches 96.2% under the premise of taking into account the efficiency. For the physical-domain control instruction attacks, the detection accuracy reaches 94%. The undetected attacks will not cause damage to the heavy-duty robots under limited attack instructions.
## 4 Results
### Identification of Water Distribution  
The distribution of the pore water and related soil-water interactions varied during the drying and wetting processes of the loess, processes which are closely related to the evolution of the loess' tensile strength. Therefore, it is necessary to identify the soil water distribution of loess specimens in order to shed light on the effect of their water content. As aforementioned, the relaxation time (T\({}_{2}\)) measurement in the NMR test reflects the mobility of the water molecules and it can be employed for distinguishing capillary water and adsorbed water as they are retained by the soil through different forces. Figure 13 depicts the example T\({}_{2}\) distribution curves for loess specimens with a dry density of 1.6 g/cm\({}^{3}\) that were prepared along the drying path. There are two distinct peaks in the curve which represent the different energy states of the pore water. The left peak with a shorter relaxation time T\({}_{2}\) is related to the adsorbed water in the soil pores whereas the right peak with a longer relaxation time T\({}_{2}\) is associated with capillary (free) water.  
Figure 13: T2 distribution curves for DM specimens.  
The peak area, defined as the area under the T\({}_{2}\) distribution curve, represents the amount of pore water [37]. As such, the relative contents of capillary and adsorbed water can be determined by their respective peak areas. The calculated capillary and adsorbed water contents for the loess specimens along both the drying and wetting paths (i.e., the DM and WM) are shown in Figure 14. It can be seen that, for the DM specimens, the decrease in the water content from 26% to 18% led to a great reduction of the capillary water amount and an insignificant decrease in the amount of adsorbed water; when \(w\) < 18%, the further drying of the loess sample was primarily contributed to by the loss of adsorbed water, with only a very slight change in the capillary water fraction. It is interesting to find that the demarcation water content (18%) for the variation of the two forms of pore water agrees well with the critical water content (\(w_{c}\)) where the tensile strength shifts from the slow- to the fast-increase stage (as is illustrated in Figure 11a), indicating that the transition of loess' tensile strength is closely related to the difference in the solid-water interaction between capillarity-dominated and adsorption-dominated stages. As for the WM specimens, the initial increase in the water content to 10% led to a rapid increase in the adsorbed water amount and little variation in the amount of capillary water, as is indicated in Figure 14. Thereafter, the amount of capillary water increased at a higher rate than the adsorbed water as the specimen was further moisturized. It is worth noting that the peak tensile strength that was achieved at the water content of 10% for the DM specimen (see Figure 11b) seems to be linked to the initially rapid increase in the adsorbed water.
## 1 Introduction
### Human Patient Simulator  
The first HPS, a mannequin, was introduced in the 1950s to teach physical assessments to nursing students [6]. Since then, HPSs have been developed and widely implemented for different training tasks. For example, upper limb simulators and lower limb robots [7; 8] were developed to train physical therapists, and a robotic hand was designed for rehabilitation purposes [9]. Computerized simulators have been employed for trauma management training [10], dentist training [11], and administering epidural injections [12]. A patient mannequin was developed for training nurses in changing the clothes of patients [13]. Other HPSs have been developed for medical diagnosis training for prostate and cardiac examinations [14; 15]. Similarly, researchers have introduced simulators to simulate swallowing difficulties [16] and facilitate the rehabilitation process. Furthermore, previous studies [17; 18] have developed and employed a robot patient for transfer training from a bed to a wheelchair.  
However, only a few studies have addressed the simulation of a patient's STS movement assisted by a nurse's STS transfer skill. Examples of studies that include humanoid robots and STS control methods are those by Asimo [19], Toyota Partner Robots, HRP-2P [20], and NAO [21]. In the 1970s, one study proposed a motion controller based on zero-moment point (ZMP) control for standing motion [22], whereas [23] used ground reaction force control and ZMP control to produce the required torque. In addition, the conventional control computation methods, center of mass (COM) [24] and center of mass pressure (COP), [25] have been employed to control robots during standing and for maintaining balance. In addition, [PERSON] et al. [26] investigated the dynamics involved in standing up from a lying down position through the analysis of a simulation using a simplified robot model. Some studies have also employed trajectory libraries [27], velocity estimation [28], and real-time control [29]. Furthermore, machine learning has been applied to the study of the standing function of a robot [30].  
Various robots and STS control methods have been proposed in literature. The robot patient described in [17; 18] comprised passive and free joints in the lower limbs; thus, it could not reproduce voluntary STS movement. In addition, the effect of the nurse's skill on the patient-nurse interaction and the patient's STS movement should be taken into account when designing control methods. Accordingly, this study developed a robot patient based on the prototype robot in [18], and proposed the STS control method to reproduce the patient's movement in accordance with the nurse's skill. The lower limbs of the robot patient were redesigned to include an active and compliant joint, and the STS movement control method was introduced. According to the results obtained in the experiment, a significant difference was obtained in the external torque and joint torque of the robot's lower limbs when the nurse incorrectly applied the STS transfer skill.
## 4 Experimental Evaluation
### Text Prompted Verification
#### 4.3.2 Binary Classifier  
The cosine similarity scores and the WER values are combined using a perceptron that will work as the binary classifier. Since we are combining two sources of information it is possible to distinguish four cases: a target client that correctly uttered a digit sequence, a target client that incorrectly uttered a digit sequence, an impostor that correctly uttered a digit sequence, and an impostor that incorrectly uttered a digit sequence. However, verification is a binary classification problem and it is necessary to reduce the previous cases to two classes. That condition is achieved by assigning the case of a target clientpronouncing a correct utterance in the accepted class and the remaining cases in the rejected one.  
A new dataset to train and test the binary classifier composed of cosine similarity scores and WER values was generated. Both scores were calculated using the test data split of each target client and all samples of the impostors set. The calculation of WER values corresponding to incorrect utterances was done by introducing substitutions, insertions, and deletions to the spoken sequences. In this manner we simulate the realistic situation in which a client makes a mistake when uttering the prompted digit sequence. The similarity scores and WER values were randomly paired until we obtained 2550 samples; half of them belonged to the accepted class and the other half to the rejected one. Each class was divided into three sets according to the following percentages: 65% for training, 5% for validation, and 30% for testing. Training was carried out in 20 epochs using Stochastic Gradient Descent (SGD) with a learning rate of 0.001 and binary crossentropy as a loss function. A sigmoid activation function in the perceptron allowed the system to provide an output score in the range [0, 1].
## 4 Discussion
### Lessons Learned at the 3 Deployment Locations
#### 4.1.3 Skylmager at La Graciosa, Canary Islands  
Some valuable lessons learned from the Graciosa project have to do with the performance and durability of the sky-imagers in a harsh, dusty, and salty environment such as La Graciosa. The closeness of the island to the Sahara Desert makes the dust content in the atmosphere high. Quality of the images is severely affected by the deposition of dust on the enclosure, as seen in Figure 24, showing that scheduled cleanings of the enclosure are necessary to ensure the proper function of the devices. Our first estimation is that cleaning is necessary at least twice a year, but it is highly dependent on the climatologic and atmospheric conditions. Besides the deposition of dust on the enclosure, water infiltrations in the interior of the enclosure have been a frequent problem, even if the equipment was specially selected to have a high degree of protection to water and dust (IP67). Closeness to the sea, as well as the strong rains that occurred in December of 2017, appear to be the main source of this problem.
# Delay-Tolerance-Based Mobile Data Offloading Using Deep Reinforcement Learning
## 2 Delay-Tolerance-Based Mobile Data Offloading Using Deep Reinforcement Learning  
### Overview  
In the proposed method, we apply deep reinforcement learning to load balancing in order to improve the bandwidth utilization efficiency by enabling appropriate transmission rate control even in situations where appropriate load balancing control is difficult in time-wise offloading of MDOP. We expect that improve the efficiency of accommodating mobile data to the communication infrastructure by balancing the eNB load. The time-wise offloading of MDOP offloads the traffic by allocating bandwidth with a focus on delay tolerance of content and improves the bandwidth utilization efficiency. On the other hand, there is a problem that it is difficult to control appropriately with rule-based control in the diversified network environment. To get an approximate solution of appropriate control and improve the bandwidth utilization efficiency in the various network environments, we use deep reinforcement learning for mobile data offloading. Our method applies to the time-wise offloading of MDOP as a transmission rate control model that takes into consideration the delay tolerance. The details of the MDOP are described in Section 2.2. Although the time-wise offloading of MDOP can reduce the time-wise locality, there is scope for improving the bandwidth utilization efficiency by focusing on the characteristics of the mobile data traffic such as delay tolerance and content size. Because the time-wise offloading of MDOP transmission rate control is performed using a model formulated based on the traffic model of the UE, topology structure, and eNB loads, it is not always possible to properly control the situation of massive mobile data traffic. Our method dynamically constructs a transmission rate control model according to the mobile data traffic characteristics using deep reinforcement learning. Therefore, this method enhances the bandwidth utilization efficiency.  
A learning flow of our method is presented in Figure 1. The reinforcement learning server (RL server) is the agent that performs the learning. The RL server learns based on environment information, which is collected by the MDOP server. The environment information consists of the eNB data reception amount and UE information. The UE information includes the content delay tolerance, the remaining amount of content, and the destination eNB identification (eNB ID).  
Figure 1: Learning flow of the proposed method.  
The RL server determines the priority of the bandwidth allocated to the UE according to the environment information. We define this priority as the QoS level. Subsequently, the MDOP server allocates the bandwidth to the UE based on the determined QoS level. The UE then sends the data according to the allocated bandwidth based on the QoS level. Finally, the RL server learns the priority for allocating the bandwidth from the eNB's load fluctuation caused by the sending data of the UE and constructs a transmission rate control model.
# Study on the Influence of File Installation Disturbance on the Settlement of Composite Foundation
## 4 Results
### Settlement Before and After Pile Installation  
The field-measured settlement data for this section are shown in Table 6, where \"\(\bullet\)\" and \"\(\bigcirc\)\" respectively refer to CMP and PHC pile. And the layout of the observation cross-sections is illustrated in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Section**} & \multirow{2}{*}{**Number**} & \(\mathbf{H_{1}}\) & \(\mathbf{H_{2}}\) & \(\mathbf{S_{m}}\) \\ \cline{3-5}  & & **m** & **m** & **mm** \\ \hline \multirow{3}{*}{1} & \(\bullet\)K1 + 150 & 0.8 & 18.0 & 84 \\  & \(\bullet\)K1 + 400 & 0.5 & 17.0 & 74 \\  & \(\bullet\)K1 + 600 & 1.6 & 14.0 & 124 \\  & \(\bullet\)K1 + 900 & 1.3 & 35.0 & 203 \\  & \(\bullet\)K2 + 100 & 0.5 & 42.0 & 214 \\  & \(\bullet\)K2 + 350 & 1.0 & 44.0 & 248 \\  & \(\circ\)K2 + 600 & 2.1 & 45.0 & 165 \\  & \(\circ\)K2 + 850 & 1.3 & 46.0 & 172 \\  & \(\circ\)K3 + 050 & 2.5 & 42.0 & 181 \\  & \(\circ\)K3 + 300 & 1.5 & 43.0 & 176 \\  & \(\circ\)K3 + 635 & 2.1 & 47.0 & 181 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Measured settlement.  
Figure 10: The embankment height \(H_{1}\), thickness of muddy soil layer \(H_{2}\), pile length \(L\), and measured settlement \(S_{m}\) from pile installation completion to 3 months after the completion of filling soil in the test section.  
For ease of analysis, the cross-sections in Table 1 are classified into three categories based on \(H_{1}\) and \(H_{2}\): 1 shallow soft soil layer treated with short piles, 2 shallow treatment of deep soft soil layer treated with short piles, and 3 deep soft soil layer treated with long piles. The measured average settlements for the three sections are 74 mm, 221 mm, and 175 mm, respectively.  
In terms of comprehensive cost investment and settlement control effectiveness, the comprehensive cost per meter for PHC is approximately three times that of CMP, and the treatment depth needs to be 12 m deeper. Considering the foundation replacement rate, the estimated treatment cost of PHC composite foundation is about 2.25 times that of CMP. However, the settlement control is not proportional to the cost investment. Comparing the shallow treatment of the deep soft foundation section with that of the deep treatment section, switching to PHC pile composite foundation treatment and increasing the treatment depth by 12 m only resulted in a 20.8% decrease in settlement.