esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## 4 Handover Decision Techniques
### Spatial Information Aware HO Decision Approaches
They updated the user historical data table based on the moving direction method, whereas the moving direction prediction technique relies on the UE trajectory to estimate the target cell. The selection of the target cell is based on its location, angle, and load capacity. When the UE is close to the handover point, it will choose the target cell by looking at its past. If the UE trajectory does not exist in the history or the target cell's load is full, the user will begin searching for the target cell using the distance and cosine weight function. The simulation results show the improvement in considered parameters as compared to standard approaches. Similarly, in [119], they proposed a UE mobility prediction method for dense networks by using lagrange interpolation approach and calculated the probability of UE transition to its neighbour cell on the basis of the velocity and the slope of the trajectory. They divided the cell coverage into two tiers. No mobility prediction is required in the first tier or closed proximities of BS. However, in the second tier, the mobility or trajectory prediction process will be employed if a user moves away from the BS. They used random way point (RWP) model for random topology generation with the speed and position of the users and applied larange interpolation for the trajectory prediction. Their simulation model checked the accuracy of the trajectory prediction and compared results for the number of unnecessary handovers. The findings from these studies suggest that the integration of proactive and conditional handover schemes with location-based decision-making procedures can improve the handover performance in high-speed scenarios. The use of UE speed, distance, UE trajectory, UE location, Cell location, RSRP and interference information can greatly enhance the accuracy of handover estimation and reduce unwanted handovers, packet loss, and delay. The concept of historical UE trajectory information and future location prediction, as well as the utilization of mobility prediction methods and spatiotemporal analysis, can further improve the performance of handover approaches in 5G macro and small cell mobile networks. In general, the integration of location and trajectory information in handover decision-making can greatly enhance the performance of handover approaches in HetNets.
## 2 Theoretical Background
For example, the spectrum of a five-comb bunches with 1 THz repetition rate, as that shown in Figure 1a, has a comb shape in which peaks have an interdistance of 1 THz and whose intensity envelope is the same as if all the electrons were confined in the single sub-pulse. Figure 1: Numerical calculation of terahertz (THz) generation by a train of ps-spaced bunches: (**a**) Bunch profile: single bunch (red dotted line), five-bunch comb beam (green curve), five-bunch comb beam with ramp charge distribution (blue curve). The bunch length is 100 fs, while the comb inter-distance is 1 ps equal to a repetition rate of 1 THz; (**b**) Corresponding generated spectra. Inset: spectra generated in logarithmic scale. The spectrum generated by a uniform bunch train shows also a set of secondary peaks, whose intensity in the calculated case (bunch duration of 100 fs and comb repetition rate 1 THz) is \(\sim\)5% respect to the intensity of the main peaks. This effect could reduce the visibility of main narrow peaks. A possibility to increase the visibility of the main peaks consists of giving a charge modulation to the bunches, for example a ramp comb beam, as shown in Figure 1a by a blue line. Indeed, the spectrum generated by a ramp comb train shows a reduction of the background modulation of about a factor of 50 with respect to the one generated by a balanced train of bunches, as shown in the inset of Figure 1b.
# The Vibration Response Characteristics of Neighboring Tunnels Induced by Shield Construction
## 3 Monitoring Data Validation Analysis
### Monitoring Program  
On-site monitoring was conducted on the existing neighboring tunnel, with sensors arranged at 1.5 m intervals to capture vibration data from the tunnel segments during shield construction. Additionally, two sensors were installed on either side of the shield machine chassis to monitor vibration data generated by the shield machine during construction. The sensor layout is illustrated in Figure 2.  
Monitoring points were set up on the segments behind the cutter, which were already lined and supported, to measure the acceleration of the segments at varying distances during excavation. Measurement points can be positioned at the top of the monitoring ring. The specific arrangement and the vibration response of neighboring tunnel lines are shown in Figure 3. The number of measurement points can be adjusted and increased as needed based on actual monitoring requirements.  
Monitors carried computers and other connecting equipment into the tunnel to interface with sensors positioned in the construction area. Before the shield machine was worked, the monitoring equipment was initialized and activated to ensure it could capture all dynamic data from start-up through the operation of the shield machine. During operation, the sensors collected key parameters in real time, including vibration intensity, stress  
Figure 3: Sketch of monitoring point layout.  
Figure 2: Sensor monitoring arrangement diagram. (**A**) Sensors on the shield machine; (**B**) sensors arranged in the tunnel; (**C**) cracks in tunnel lining pipe sheet cracks; (**D**) sensors arranged adjacent to the tunnel side.  
distribution, displacement changes, and other critical data, which were transmitted to the monitoring equipment for recording and storage. Monitoring activities concluded only after the shield machine was shut down to ensure that the collected data fully covered an entire operation cycle. This approach enabled a systematic and comprehensive analysis of the vibration characteristics generated during the shield construction process and their impact on surrounding structures, providing essential data to support construction safety and design optimization.
# Physics-Based Noise Modeling and Deep Learning for Denoising Permanently Shadowed Lunar Images
## 3 Methods
### REWin Transformer Block
\begin{gathered}[X_{1},X_{2}]=\hat{R(X)}\\ \hat{X}=GELU(W_{1}F(PConv(X_{1})\oplus X_{2}))\\ \begin{bmatrix}\cdot^{\prime}&\cdot^{\prime}\\ X_{1},X_{2}\end{bmatrix}=\hat{X}\\ \cdot^{\prime}&\cdot^{\prime}\\ \hat{X}_{r}=X_{2}\otimes F(GELU(DWConv(R(X_{1}))))\\ \hat{X}_{out}=W_{2}X_{r}\end{gathered}  \tag{8}\]  
Here, X represents the 2D feature map, and \(\hat{X}\) denotes the feature sequence; \(W_{1}\) and \(W_{2}\) represent the linear projections; [] refers to the channel-wise slicing operation; the functions \(R(. )\) and \(F(. )\) represent reshaping and flattening operations, which transform the sequence input into a 2D feature map, and vice versa. These operations are crucial for introducing locality into the architecture [39]. The functions \(PConv(. )\) and \(DWConv(. )\)refer to the operations of partial convolution and depthwise convolution, respectively. \(\bigoplus\) denotes channel stacking, and \(\bigotimes\) represents matrix multiplication. Overall, GFRN can extract representative key features from the information flow while effectively simplifying redundant features, thereby significantly enhancing feature representation capability. Moreover, GFRN also provides the model with the ability to clear non-informative features along the channel dimension, further optimizing the effectiveness of the features and improving the overall performance of the model. After the feature map undergoes feature extraction through SW-MSA and redundancy removal and feature refinement via GFRN, the resulting feature map contains the key local information required for image recovery. To further enhance and extract this information, we introduce GCEN. Specifically, GCEN combines DWConv and GELU to construct a gated channel attention mechanism based on nearest-neighbor features, enabling the enhancement and extraction of local information. GCEN can be expressed by the following equation:  
\[\begin{gathered} \begin{gathered}\begin{bmatrix}\hat{X}_{1}^{ \prime},\hat{X}_{2}^{\prime}\\ \hat{X}_{1},\hat{X}_{2}\end{bmatrix}=W_{1}\hat{X}\\ \begin{gathered}\begin{gathered}\hat{X}_{e}=X_{2} \bigotimes F(GELU(DWConv(R(X_{1}))))\\ \hat{X}_{out}=W_{2}\hat{X}_{e}\end{gathered}\end{gathered}\end{gathered}  \tag{9}\]  
The meanings and operations of the relevant variables and functions in the equation are consistent with those described in the GFRN section above. Overall, GFRN is capable of further extracting effective features from the refined information while significantly enhancing feature representation capability. Additionally, the structural design of GFRN maintains the same network depth as multilayer perceptrons and gated linear units, ensuring its efficiency and stability during the backpropagation process.
## 4 Results
Table 4 shows that Physician COC achieved a higher score in the case of stable patients, according to the indices of UPC concentration (\(p\)-value = 0.03) and HI dispersion (\(p\)-value \(<\) 0.01), while highly decompensated patients showed a significantly lower continuity in the HI (\(p\)-value = 0.02). In the case of nurses, the behavior of the COC indices was similar, with the UPC and HI indicators generating higher scores among stable patients (\(p\)-value = 0.01 and \(<\) 0.01), and lower scores among highly decompensated patients (\(p\)-value \(<\) 0.01 in both indicators). In addition, the HI was significantly lower in the case of improved patients. The COC for dietitians showed no significant differences among the moderately decompensated segment, with lower continuity in the COCI, UPC, and HI indices (\(p\)-values = 0.08, 0.08 and 0.07, respectively). However, the major difference in this segment was the adherence to treatment with the dietitian, with a lower participation of patients who worsened, but greater among those who improved (\(p\)-value \(<\) 0.01). The SECON index showed no significant differences in any of the three disciplines. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**VISITS** & **Stable** & **Improved** & \
\begin{tabular}{c} **Moderately** \\ **Decompensated** \\ \end{tabular} & **Worsened** &
\begin{tabular}{c} **Highly** \\ **Decompensated** \\ \end{tabular} & **Total** \\ \hline Patients (n (\%)) & 655 (36\%) & 325 (18\%) & 247 (13\%) & 221 (12\%) & 388 (21\%) & 1836 (100\%) \\ Total visits (mean (med)) & 5.61 (5) * & 9.30 (7) * & 7.76 (7) & 5.98 (5) * & 10.48 (8) * & 7.63 (6) \\ Physician visits (mean (med)) & 2.93 (3) * & 3.94 (4) * & 3.77 (4) & 3.11 (4) * & 4.27 (4) * & 3.53 (3) \\ Nurse visits (mean (med)) & 1.89 (2) * & 4.46 (2) * & 3.14 (2) & 2.29 (2) * & 5.36 (3) * & 3.30 (2) \\ Dietitian visits (mean (med)) & 0.80 (0) & 0.90 (1) & 0.85 (0) & 0.58 (0) * & 0.85 (1) & 0.81 (0) \\ \hline \hline \end{tabular}  
* med: median. We applied a Shapiro–Wilk test for normality for each variable, obtaining that none were normally distributed (\(p\)-value < 0.001). Due to the large size of the sample, we applied a \(t\)-student test for comparing the mean of each segment with the mean of the total population. Statistically significant differences (\(p\)-value \(<\) 0.05) are marked with *.  
\end{table} 
Table 3: Patients and visits for each discipline by segments and total population.
# A Real-Time Remote Respiration Measurement Method with Improved Robustness Based on a CNN Model
## 3 Materials and Methods
### Estimation of Respiratory Rate  
Two methods are used to detect respiratory rate: a method based on peak-to-peak interval (PPI) and a method based on frequency analysis. PPI is the distance between adjacent peaks of the signal, which is the period of the signal. The respiratory rate (RR) may be calculated from PPI using Equation (2) in units of seconds.  
\[\text{RR}=\frac{1}{\text{PPI}}\times 60 \tag{2}\]  
The method based on frequency analysis calculates the frequency component of the signal, assumes that the frequency component with the largest amplitude is the respiratory frequency, and estimates the respiratory rate through the corresponding frequency. The respiratory rate can be calculated from the maximum frequency component using the following Equation (3):  
\[\text{RR}=f_{max}\times 60 \tag{3}\]  
To estimate the respiratory rate based on PPI, accurate peak detection must be performed. Because inaccurate peaks can significantly reduce the accuracy of respiration rate estimation, frequency analysis-based estimation methods may be suitable in real-world environments where stable peak detection may be difficult. Therefore, in this study, we conducted an experiment in which we estimated respiratory rates with a frequency analysis method using DFT.
## 4 Results and Discussion
### RQ2: What Are the Key Search Spaces Explored in NAS-GAN?  
The search space defines which neural architectures can be represented and potentially discovered by a NAS method. The design of the search space is crucial as it incorporates prior knowledge about architectures that are likely to perform well on a given task, while also influencing the difficulty of the optimization problem. Among the search spaces of NAS in GANs literature, the cell-based structure is the most used type, and the entire/chain-structure search space has also been employed. As illustrated in Figure 3, cell-based approaches account for the majority of the works, followed by chain-based, hybrid, and custom approaches.
# The Firmware Design and Implementation Scheme for C Form-Factor Pluggable Optical Transceiver +
## 4 Evaluation and Result
### Testbed Setup
#### 4.1.2 Temperature Testbed  
Figure 10 shows a testbed configuration to test the proposed DDM temperature calibration method. A temperature chamber was used for temperature testing. The test temperature is \(-10^{\circ}\) to \(60^{\circ}\) based on the chamber setting temperature. The target optical transceiver to the test is mounted on an evaluation board and placed in the chamber. The evaluation board supports the MDIO interface to read the memory information of the CFP optical transceiver and obtain the DDM value. Insert the source optical transceiver into the ONT to conduct the test outside the chamber. The source optical transceiver transmits an optical signal containing data to the target optical transceiver. The optical output of the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Setting** \\ \hline FPGA & Xilinx Spartan-6 Series \\ MCU & Silab C8051F58x Series \\ DAC/ADC & MAXIM 12 bit ADC/DAC \\ Optic Driver IC & Semtech 10G Driver IC \\ Board \& Case & ARTECH CFP module \\ Test distance & 40 Km optical spool \\ Host & VIAVI ONT (Optical Network Tester) 606 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Testbed Setting.  
Figure 9: CFP Testbed Setup.  
optical transceiver inserted in the ONT equipment passes through the optical attenuator and into the input of the target optical transceiver. The optical attenuator was used to adjust the optical input power. The recommended optical transceiver operating temperature in the standard is 0\({}^{\circ}\) to 70\({}^{\circ}\).The operating temperature criterion is the case temperature of the optical transceiver. The chamber temperature was set to \(-10^{\circ}\) so that the temperature of the optical transceiver was 2\({}^{\circ}\), and the chamber temperature was set to 60\({}^{\circ}\) to make the temperature of the optical transceiver 70\({}^{\circ}\).
## 1 Introduction  
With the world population increasing, maximizing agricultural production is essential. Weeds constrain agricultural production by directly competing with crops for growth resources [1]. Using synthetic herbicides may be the most efficient and cost-effective weed management strategy that has contributed to improving crop production over the last few decades [2]. However, non-judicious use of synthetic agrochemicals has had a significant negative effect on the planet and human wellbeing [3]. The evolution of resistant weeds is another consequence of extensive herbicide use [4]. Thus, to achieve safer agriculture, it is essential to replace hazardous synthetic herbicides with eco-friendly weed-management approaches. Natural compounds with diversified structures and modes of action could offer a new way to develop natural bio-herbicides [5]. Plants produce secondary metabolites called allelochemicals, which can negatively influence the process of growth, and development of surrounding plants [6, 7]. In recent years, plant-derivednatural phytotoxic compounds have been broadly investigated for herbicidal properties and have been shown to suppress weeds. These compounds can then be used as promising templates for standard bio-herbicides [8].  
_Garcinia xanthochymus_ Hook. f. ex T. [PERSON] (Clusiaceae) is a medium-size tree that has straight trunk with spreading type branches rising in a whorl [9]. This plant is generally found in Bangladesh, India, Myanmar, Thailand, and China [10; 11]. _G. xanthochymus_ is popularly known as false mangosteen because the shape of its fruit is similar to that of mangosteen. The leaves of the plant are light green color during young stage and become dark green when mature [12]. Distinct parts of this tree have traditionally been used for different therapeutic practices for many years in different south Asian countries [13]. In Bangladesh, _G. xanthochymus_ is usually treated as folk drug for the treatment of diarrhea, dysentery, and vomiting. This plant is also used to treat worms and food toxins [14].  
Earlier reports revealed that _G. xanthochymus_ contains several phytochemicals that have antibacterial, anti-inflammatory, and antioxidant activities [15]. Although extensive research on different biological activities of _G. xanthochymus_ have been conducted, very little is known about its phytotoxic potential or the constituents responsible for its phytotoxicity. Hence, this study aimed to (i) examine the phytotoxic potential of _G. xanthochymus_ and (ii) detect phytotoxic substances which can be used as a potential candidate for bioherbicide.
# The Use of Autogenous Teeth for Alveolar Ridge Preservation: A Literature Review
## 4 Demineralized Dentin Matrix (DDM)
### Clinical Studies  
Furthermore, several clinical studies have been published endorsing the use of DDM in ARP despite diverging in terms of the protocol used for dentin processing [17; 37; 39; 40; 44; 48; 49; 50; 66; 67].  
The removal of carious lesions, fillings and soft tissues seems shoes an apparent unanimity [17; 37; 39; 44; 66], however, while most authors use dentin, enamel and cement, some eliminate these last two [40], or simply use the root portion of the tooth [37]. Some investigators defend the use of dentin alone due to its osteoconductive and osteoinductive properties being similar to alveolar bone [35; 43] and in this way enamel shall be removed because it has high-crystalline calcium phosphate and therefore might complicate the absorption process [15; 30]. On the other hand, it is described in some protocols the use of the whole tooth as a bone graft material, combining the chemical properties of dentin with the mechanical advantage that enamel brings, allowing an earlier placement of dental implants [38; 52; 57].  
In the majority of these studies, the demineralization agent was not specified [17; 39; 44; 48; 49; 50; 66], but rather the explanation that the autogenous graft went through a dehydration, defatting, demineralized and lyophilized course [39; 66]. Nonetheless, studies often report the use of 70% ethyl alcohol, 0.6 N chloridic acid and 2% nitric acid [37; 40; 67]. The size of the graft particles varies from 200 to 1000 \(\upmu\)m [37; 39; 40; 66].  
These studies evaluated the efficacy of this biomaterial in the clinical, radiologic, histologic and morphometric scenario.
###### Abstract
By creating local deep branching structures and global shallow branching structures, more complex global contextual information and finer local details can be captured. As shown in Figure 1(3), GLSNet performs excellently in segmenting overlapping cars and object boundaries. Aside from segmentation accuracy, the GPU memory usage brought by the transformer is also a point of concern. Traditional feature pyramid networks (FPNs) [18] usually require stacking multiple layers to fuse branch information, which can lead to higher memory usage. In comparison, the transformer has greater potential. UNET [25] employs an efficient pyramid transformer structure for semantic segmentation tasks, resulting in a considerable reduction in GPU memory utilization, which greatly inspired us. In particular, the potential of GPUs and CPUs in terms of computational capacity is constrained by the delay in accessing memory [26; 27; 28], which significantly hampers the operational speed of transformers [29; 30]. The memory inefficiency of the  
Figure 1: Segmentation Results Example from the Vaihingen dataset. element-wise functions can be greatly reduced in the processes of multi-head self-attention (MHSA) and frequent tensor reshaping. The study reveals that there are methods to significantly reduce the time taken for memory access without compromising overall system efficiency. Based on the analysis and findings, the proposed GL-Deep Fusion utilizes a dual-encoder and single-decoder attention mechanism. This mechanism, combined with the dual-branch structure, significantly reduces GPU memory usage. This structure demonstrates potential gains in accuracy and GPU memory usage on the Vaihingen and DeepGlobe [3] datasets. To summarize our contributions in the following manner:  
* We introduce GL-Deep Fusion, which effectively holds the correlation between global semantics and ultra-high resolution image details through its integrated feature representation. * The global contextual information and partially truncated block details captured by the dual-branch structure can be directly alternated between the dual encoding structures of the GL-Deep Fusion module, thereby avoiding redundant feature computations. * Our proposed GLSNet has significantly improved GPU memory utilization and segmentation accuracy in the context of ultra-high resolution image segmentation. Compared to GLNet (baseline), it reduces GPU memory usage by 24.1% on the DeepGlobe dataset [3]. The Vaihingen dataset [19] also achieves groundbreaking results. The organization of this paper is as follows: Section 2 presents an overview of the current state of the related research. Section 3 outlines the network architecture and fusion mechanism that have been designed. Furthermore, the results of our experiments are presented in Section 4. Finally, Section 6 introduces our conclusion and future work.