esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Monitoring Tumor Response to Neoadjuvant Endocrine Therapy  
Although neoadjuvant endocrine therapy is not the standard treatment for breast cancer, it has been widely used among postmenopausal women with hormone receptor (HR)-positive breast cancer, because neoadjuvant endocrine therapy has reportedly contributed to tumor shrinkage in 30-75% of the patients and because it can increase the conversion rate from mastectomy to breast-conserving surgery [30; 31; 32]. Therefore, early identification of responding and non-responding tumors allows patients with resistant tumors to receive alternative treatments such as surgery, molecular-targeting agents, or chemotherapy.  
We have previously assessed whether the early PET response, evaluated by FDG PET/CT, could predict morphological and pathological responses to neoadjuvant endocrine therapy in patients with hormone receptor-positive breast cancer [33]. The pilot study enrolled 12 patients who received a daily dose of 2.5 mg of letrozole for 12 weeks, followed by surgery. When serial FDG-PET scans were acquired at baseline and after 4 weeks of letrozole therapy, metabolic responders showed a 40% or greater reduction in FDG uptake, as evidenced by SUVmax changes, and a significant decrease in the Ki67 proliferative index; these were not observed in non-responders. A large randomized clinical trial (Intermediate Marker Project: anastrozole, combination or tamoxifen; IMPACT) also reported that a low Ki67 index following endocrine therapy was significantly correlated with relapse-free survival among patients after surgery [31]. Similarly, our findings reveal that an FDG-PET/CT-guided therapeutic strategy could be promising during neoadjuvant endocrine therapy [33]. Recently, we conducted a feasibility study using TRS to monitor hemodynamic response during neoadjuvant endocrine therapy (Figure 2C). We enrolled 30 patients with HR-positive primary breast cancer, and categorized PET responders (\(n=20\)) and PET non-responders (\(n=8\)) based on a 25% reduction in tumor SUVmax between serial scans of FDG-PET/CT, obtained before therapy and at 3 months after initiation letrozole therapy.
## 2 Methods  
### Patch Embedding  
We utilize patch embedding to segment sEMG signals and employ one-dimensional convolutional layers to extract local features based on the temporal aspects of the sEMG signals, as depicted in Figure 1. Patch embedding plays a crucial role in transforming these segments into a representation suitable for neural networks. Furthermore, patch embedding subdivides sEMG signal segments into smaller patches, thereby reducing the parameter count at the input layer and making the network more lightweight. This enhancement enables the network to capture local contextual sEMG information more effectively. Additionally, patch embedding adds scalability to the network, enabling it to process sEMG segments of varying sizes without requiring modifications to the network structure. This significantly enhances its versatility.  
The employed one-dimensional convolutional layer comprises 128 filters with dimensions of 10 \(\times\) 1 and a stride of 6. Convolutional neural networks (CNNs) are neural networks equipped with a \"receptive field\" to extract local features. In one-dimensional CNNs, the convolutional kernel convolves along the temporal dimension to extract local features from the time-based sEMG signal. Gaussian Error Linear Units (GELUs) are then employed as the activation function for the nonlinear transformation of the extracted features. Additionally, due to the strong correlation between adjacent time steps in the sEMG signal, Layer Normalization (LN) is applied to normalize the output.
# Comparative Study of Minimally Invasive Microwave Ablation Applicators
## 3 Results
### Performance of the Homogeneous Model for All Designs
#### 3.1.2 Temperature Distribution of the Homogeneous Model for All Designs
The tissue and tumor model in this section is placed in the large intestine, consistent with [41], which initially analyzed the helical open waveguide structure under these conditions. This study extends the findings of [41] by performing a parametric analysis of the same antenna, varying the monopole length  
Figure 5: Temperature distribution of suboptimum designs of the helical open waveguide structure: (**a**) with 3 spirals, (**b**) with 2 spirals. and spiral height to optimize its performance in the large intestine model. In contrast, for simulations in other sections, we employed a liver model to provide a unified evaluation framework for comparing different designs. This approach ensures both consistency with prior studies and meaningful cross-design comparisons. Figure 6: Parametric analysis of helical open waveguide structure (**a-g**) and design of monopole (**h-l**): 2 spirals with height = 3 mm and 2 spirals with height = 12 mm (**a**), 4 spirals with height = 12 mm (**b**), 4 spirals with height = 12 mm (**c**), 4 spirals with height = 3 mm (**d**), 2 spirals with height = 1 mm and 2 spirals with height = 4.5 mm (**e**), 3 spirals with height = 3 mm and 1 spiral with height = 12 mm (**f**), 4 spirals with height = 3 mm (**g**). The length of monopole is 7.61 mm at (**a**,**b**,**e**–**h**), 12 mm at (**c**,**i**,**j**), 3 mm at (**d**), and 24 mm at (**k**,**l**). In the final images (h-l), the monopole design is depicted for different lengths (7.61 cm: h, 12 cm: i, j and 24 cm: k, l) and different source powers (25 W: images h, i, k, and 50 W: j, l). For (h, i, j) cases, the backward heating problem persists, resulting in a comet-shaped ablation zone that leads to the burning of healthy tissue. In contrast, in all other simulations, the backward heating issue is eliminated by properly adjusting the number and the density of the helices, confining the heat to the tumor and leaving the surrounding healthy tissue unaffected. The results of Figure 6 indicate that the spirals function as a shield, effectively trapping the electromagnetic field within the spiral structure, and consequently within the tumor. To achieve this desired outcome, extensive simulations and careful refinements are required. In summary, the number of spiral turns and their configuration play a critical role in achieving the desired outcome: eliminating backward heating and ensuring that only the tumor is ablated. Additionally, the open and tapered helical structure provides flexibility in shaping ablation zones, enhancing the safety and precision of microwave ablation as a treatment modality.
## 4 Experimental Setup and Result Analysis
### Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Sampling** & **Total Power before** & **Optimized** & **Total Power** \\  & **Moment** & **Optimization** & **Total Power** & **Reduction Rate** \\ \hline
1 & 8:00 & 358.77 & 346.12 & 4.52\% \\ \hline
2 & 8:05 & 364.22 & 358.22 & 5.22\% \\ \hline
3 & 8:10 & 359.47 & 334.12 & 4.23\% \\ \hline
4 & 8:15 & 352.77 & 345.16 & 5.14\% \\ \hline
5 & 8:20 & 364.52 & 359.41 & 10.45\% \\ \hline
6 & 8:25 & 347.18 & 341.18 & 6.15\% \\ \hline
7 & 8:30 & 358.45 & 352.68 & 5.77\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison and analysis of system wasting of resources calculation results before and after algorithm optimization. completes the waste heat recovery via the flue gas-water heat exchanger, thus providing necessary energy support for the operation of the Huanhuakeng absorption refrigerator. In the process of operation, the low-area energy center will provide hot steam for the low-area energy-consuming units. At the same time, it is also responsible for chilled water supply services. Two energy centers can provide power load during the power failure period and provide a backup cold source for users in the form of a lease. In the normal operation state, the generator set is merged into the mains network; however, after a fault occurs, the standby power supply can be provided for the intelligent building by switching the line to ensure the normal power supply of the intelligent building. The experimental results show that the energy-saving optimization control of intelligent building via the niche genetic algorithm greatly improves the comprehensive utilization effect of resources, the convergence speed of resources in intelligent building is faster, the diversity of Pareto's solution is higher, and the objective function reaches the global maximum optimization value, which has a good energy-saving optimization effect of intelligent building.
# Vision-Guided Hierarchical Control and Autonomous Positioning for Aerial Manipulator
###### Abstract  
Aerial manipulator systems possess active operational capability, and by incorporating various sensors, the systems' autonomy is further enhanced. In this paper, we address the challenge of accurate positioning between an aerial manipulator and the operational targets during tasks such as grasping and delivery in the absence of motion capture systems indoors. We propose a vision-guided aerial manipulator system comprising a quad-rotor UAV and a single-degree-of-freedom manipulator. First, the overall structure of the aerial manipulator is designed, and a hierarchical control system is established. We employ the fusion of LiDAR-based SLAM (simultaneous localization and mapping) and IMU (inertial measurement unit) to enhance the positioning accuracy of the aerial manipulator. Real-time target detection and recognition are achieved by combining a depth camera and laser sensor for distance measurements, enabling adjustment of the grasping pose of the aerial manipulator. Finally, we employ a segmented grasping strategy to position and grasp the target object precisely. Experimental results demonstrate that the designed aerial manipulator system maintains a stable orientation within a certain range of \(\pm 5^{\circ}\) during operation; its position movement is independent of orientation changes. The successful autonomous grasping of lightweight cylindrical objects in real-world scenarios verifies the effectiveness and rationality of the proposed system, ensuring high operational efficiency and robust disturbance resistance.  
aerial manipulator system; visual guidance; SLAM algorithm; aerial grasping; depth camera; LiDAR +
Footnote †: journal:  
Article  
Vision-Guided Hierarchical Control and Autonomous Positioning for Aerial Manipulator  
doi:10.3390/
## 4 Experimentation and Results  
### EMD-Based Method  
As previously described in the methodology, 1 min time windows of both databases are processed by the EMD, EEMD, and CEEMD algorithms by obtaining 6 IMFs. Figure 5 shows an example of the IMFs obtained for a healthy ECG signal (Figure 6a) and for an ECG signal 1 min before the SCD event (Figure 6b). The three methods are used because they decompose the signal in a slightly different way, allowing obtaining different characteristics in each set of IMFs. In fact, different data can be observed in the results of each method (columns of Figure 6), but no significant changes can be easily determined between both groups per method (rows of Figure 6), requiring the application of an automatic pattern recognition algorithm.  
Following the proposed methodology, the image representation of each group of 6 IMFs is obtained by using the top view of their surface graph. This step makes that the  
Figure 5: Proposed methodology for predicting an SCD event.  
Figure 6: IMFs from the different EMD methods for: (**a**) 1 min segment of a healthy patient and (**b**) 1 min before the SCD event.  
data generated from the EMD methods can be processed in the CNN and do not lose the spatial correlation between each IMF. Figure 7 shows the image representation for the IMFs depicted in Figure 6, obtaining the same results, i.e., the obtained differences of each method are visually insignificant (columns of Figure 7); therefore, no significant patterns can be easily determined between both groups per method (rows of Figure 7) through a direct visual inspection, requiring the application of an image-based pattern recognition algorithm. In this work, a CNN for automatic classification is proposed.
# Signal to Noise Ratio of a Coded Slit Hyperspectral Sensor
## 2 Materials and Methods: The Signal to Noise (SNR) Model
### The Configuration of the Coded Aperture Sensor
In the present work it is assumed that there are \(N\) image frames such that the signal recovery is completely determined. It is assumed in the present work that the scanning is performed such that it advances one pixel for each frame. This is different  
Figure 3: **(a)** Examples of the Hadamard matrices of size 8 derived from the quadratic residue construction is adopted here. **(b)** illustrates the corresponding cyclic binary of the Hadamard matrix. **(c)** shows an example aperture code that has been utilized in this work. Note that each column of the CA follows the pattern of a column (or row) of a cyclic binary Hadamard matrix. In a practical implementation, this pattern would replace the slit of a conventional pushbroom spectrometer; its width would be a few millimeters and the narrowest feature (transparent row surrounded by opaque rows or opaque row surrounded by transparent rows) would be the same size as a detector pixel (a few microns up to a few tens of microns). Figure 2: Illustration of the content of the image as seen by CA multiplexed sensor as depicted in Figure 1. **(a)** The hyperspectral data cube which consists of two spatial dimensions of x and y, and one spectral dimension, \(\lambda\). **(b)** It is then filtered by a code in the undispersed plane such that selected signals at particular spatial locations are allowed to pass through. **(c)** The optical signal is then dispersed and **(d)** reimaged onto a detector, which senses the sum of all optical signals of voxels across the wavelength dimension [2]. from the practical system which requires the registration of consecutive frames in order to aggregate information that they contain. Although image frame registration may induce additional errors, it has not been considered in the present work. Figure 4 illustrates a scene imaged through an appropriately chosen coded aperture.
## 2 The Equilibrium Velocity Based on Traversed Time  
The velocity behavior of vehicles covering a 200 m straight road section with traversed time is given in Figure 1. The lane discipline is not followed in this road section. Eleven different vehicle types were observed, while the total vehicles recorded were 170 in 3 h. The road section has two lanes, with a lane width of 3.2 m. The minimum traversed time recorded is 10 s, which occurred at the maximum velocity of 20 m/s. The maximum traversed time recorded is 42 s, which occurred at the minimum velocity of 3.4 m/s. The average traversed time is 18.1 s, and the average speed is 14.0 m/s. The recorded velocity is plotted against the traversed time as shown in Figure 1. Velocity is 20 m/s when covering 200 m in 10 s. The speed logarithmically reduces to 10 m/s as traversed time increases to 20 s. As it grows to 37 s, the velocity reduces to 5.40 m/s.  
The data fitness parameter, determination coefficient \(R^{2}\), is based on velocity \(v\) and traversed time \(t\), which is given as  
\[R=\frac{n(\sum vt)-\sum v\sum t}{\sqrt{((n(\sum v^{2}-(\sum v)^{2}))(n(\sum t^ {2}-(\sum t)^{2})))}}, \tag{1}\]  
where \(n\) denotes the number of observations in a dataset, and \(\sum\) is the sum of variables. The other two parameters in regression for best fitness are mean absolute error (MAE) and mean squared error (MSE). A best fit model has the largest \(R^{2}\), that is, closer to 1, and the  
Figure 1: Velocity behavior based on the traversed time obtained from the experimental traffic data over 200 m.  
minimum MAE and MSE. The \(R^{2}\), MSE, and MAE for logarithmic, exponential, and linear regressions are given in Table 1. The values of logarithmic regression are superior to the other types.  
The logarithmic characterization based on the behavior of velocity and traversed time obtained from the real data as shown in Figure 1 is given as  
\[v(t)=-10.77\ln(t)+42.76, \tag{2}\]  
where \(v(t)\) is the function of traversed time \(t\); \(-10.77\) is the slope of the regression line, whereas the line intercept is 42.76, which is the maximum time taken to cover 200 m section of the road. The largest \(R^{2}\) = 0.96, minimum MSE = 1.218, and minimum MAE = 0.904 for (2) as shown in Table 1, which means that the logarithmic model is robust.  
The logarithmic relation (2) shows that traffic velocity varies with the traversed time. That is, the velocity is large for a smaller traversed time and small for a larger traversed time, as shown in Figure 1. It is imperative to include the traversed time behavior in a macroscopic traffic model to accurately characterize the traffic evolution. Therefore, in this paper, traversed time is considered to devise the proposed traffic system.
# Toward Comprehensive Chronic Kidney Disease Prediction Based on Ensemble Deep Learning Models
## 4 Experiments Results
### Experimental 2
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Feature Selection Methods**} & \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Matrix Performance**} \\ \cline{3-6}  & & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{8}{*}{Chi2} & RNN Layer1 & 90.67 & 90.58 & 90.93 & 90.74 \\ \cline{2-6}  & RNN Layer2 & 91.25 & 91.68 & 91.25 & 91.06 \\ \cline{2-6}  & LSTM Layer1 & 92.75 & 92.89 & 92.75 & 92.68 \\ \cline{2-6}  & LSTM Layer2 & 93.75 & 93.89 & 93.75 & 93.68 \\ \cline{2-6}  & GRU Layer1 & 89.67 & 89.58 & 89.93 & 89.74 \\ \cline{2-6}  & GRU Layer2 & 92.7 & 92.72 & 92.74 & 92.73 \\ \cline{2-6}  & Proposed Layer1 & 94.13 & 94.15 & 94.12 & 94.13 \\ \cline{2-6}  & Proposed Layer2 & 94.9 & 94.43 & 94.01 & 94.62 \\ \hline \multirow{8}{*}{mutual\_info} & RNN Layer1 & 96.25 & 96.26 & 96.25 & 96.24 \\ \cline{2-6}  & RNN Layer2 & 97.88 & 97.86 & 97.95 & 97.91 \\ \cline{1-1} \cline{2-6}  & LSTM Layer1 & 96.08 & 96.08 & 96.06 & 96.07 \\ \cline{1-1} \cline{2-6}  & LSTM Layer2 & 94.83 & 94.77 & 95.03 & 94.9 \\ \cline{1-1} \cline{2-6}  & GRU Layer1 & 93.49 & 93.87 & 93.12 & 93.46 \\ \cline{1-1} \cline{2-6}  & GRU Layer2 & 96.31 & 96.34 & 96.23 & 96.28 \\ \cline{1-1} \cline{2-6}  & Proposed Layer1 & 98.33 & 98.48 & 98.33 & 98.34 \\ \cline{1-1} \cline{2-6}  & Proposed Layer2 & 98.75 & 98.88 & 98.75 & 98.76 \\ \hline \end{tabular}
\end{table} 
Table 4: Performance of applying DL models and the proposed model with features selection methods using 80–20 splitting.
## 3 Aloha Protocols
### Slotted Aloha  
To avoid incomplete collisions, Slotted Aloha (SA) has been created. In SA, the time is divided into several slots and each tag must randomly select a slot in which it will transmit its data [8; 21; 23; 24]. The communication between the reader and the tag is now synchronous. An example of communication with this protocol is presented in Figure 9.  
Also, SA can use features similar to those presented for PA. The muting of slow down features are used to silence or decrease the rate of the tags; the early end closes the slot earlier than normal; and also combinations of the types.  
Figure 8: An example of PA.
## 6 Performance Evaluations
### Simulation Results
#### Deadline miss ratio evaluation under the second scenario
In addition to the reasons mentioned before, which make the proposed algorithm better than SMRP and EERP routing algorithms under the first scenario, the rest of the reasons can be justified as follows. The proposed algorithm can bypass the danger zones and discover safer paths to sink nodes than those computed by SMRP. In EERP, the data packets have to cross the danger zones as the environmental impact is not considered, making them much more likely to be cut off. Thus, this increases the packet loss rate, increasing the delivery delay and, thus, the deadline miss ratio.
## 1 Introduction
The Yangtze River Basin (YRB) is an important ecological and economic region in China, which has experienced numerous CHD events over the past few decades [33]. In the summer of 2022, a record-breaking CHD event occurred across many regions of China, especially in the YRB, with abnormally high temperatures and prolonged drought conditions [34; 35]. Some regions of the YRB experienced continuous high temperatures for 76 consecutive days, from June 16 to August 30, and experienced extremely low precipitation, with a reduction of over 80% [34]. [PERSON] et al. (2023) reported that the occurrence probability of the 2022 CHD event is approximately once in 152 years under the current climate conditions [36]. The CHD event in 2022 offers a valuable case to analyze how extreme compound climate events influence terrestrial carbon processes. Using different datasets, many previous studies have reported unprecedented GPP reductions in the YRB in response to the increased surface air temperature, vapor pressure deficit (VPD), and decreased soil moisture during the CHD event [37; 38; 39]. For example, [PERSON] et al. (2023) analyzed FluxSat GPP product and reported that the extreme CHD event in 2022 led to a 7.46% reduction in GPP in the YRB compared to previous years, marking the largest decline since 2000 [37]. This significant decrease in GPP highlights the severe impact of extreme climate events on terrestrial ecosystem productivity and underscores the vulnerability of the YRB to such compound disturbances. In this study, we investigated the responses of terrestrial GPP to three identified CHD events of different intensities in the YRB. The objective of this study is (1) to quantify differences in GPP responses to extreme CHD events of varying intensities, (2) to examine different GPP responses across vegetation types, and (3) to find potential new insights into the impacts of extreme climate events on terrestrial ecosystems. The results of this study are expected to contribute to a deeper understanding of vegetation-climate interactions and to offer valuable implications for optimizing carbon cycle models.
## 1 Introduction
### Motivation and Contributions  
On all these counts, in this paper, we focus on solving the following challenges for 360\({}^{\circ}\) VR service system design:  
* How to design the multiuser access control algorithm for a 360\({}^{\circ}\) VR video service system, considering the impact of both the multi-hop wired network from the content provider's cloud VR server to the operator's MEC server, and the wireless network from the MEC server-connected BS to the users?
* How to harvest the benefits of proactive caching and computing offloading from the MEC server in the multiuser access control algorithm design?  
To address these challenges, this paper aims to propose a 360\({}^{\circ}\) VR video multiuser access control algorithm in a scenario where the cloud server, the MEC server, and multiple VR users work together. Our main contributions are summarized as follows:  
* To strictly control the latency and user experience quality of 360\({}^{\circ}\) VR video transmissions, a 360\({}^{\circ}\) VR video service system based on proactive caching and mobile edge computing is designed from the perspective of comprehensively utilizing the three resources--transmission, storage, and computation--across the cloud server, the edge computing server, and the user devices.
* For the designed system, we analyze each user's E2E delay performance, if accessed, in terms of delay violation probability. Then, we study the multiuser access control optimization problem aimed at maximizing the number of served users by exploiting real-time and dynamic network resources, subject to users' delay violation probability threshold.
* Simulation results not only validate the effectiveness of the proposed multiuser access control algorithm in admitting users with well-controlled delay violation probabilities but also shed some light on the influence of various system parameters (e.g., the caching duration, the BS's transmission bandwidth, the MEC server's computational capability, and the video compression ratio) on system performance.