esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Review Results
### Future Considerations  
The most important future work needed is randomised control trials on DTI-guided IMRT and SRT, as currently, none have been identified, although there is a non-randomised trial on tractography use in AVM [22]. The PRaM-GBM study, which is aiming to recruit 120 patients in the UK and performing DTI prior to surgery and IMRT of glioblastomas, is currently ongoing and promising [38]. However, the outline of this study is for it to be observational, and therefore, it likely will not use DTI-guided therapy plans in treatment. At the same time, the study should give important information on using DTI to predict areas of recurrence.  
Work is additionally needed on the combined use of DTI-guided CTV definition in gliomas and inclusion of dissected tracts such as OAR, and software that optimises both simultaneously may be required. Additionally, further work is needed on the extent to which the CTV should be expanded along WM tracts, as it could potentially be expanded further than the typical 2 cm and potentially reduce recurrence rates; [PERSON] et al., 2019 have developed software that is able to perform this. The study produced CTVs that were expanded by 1, 2, and 3 cm along WM tracts; however, it does not compare the potential effectiveness of each, as this would likely require a controlled trial [26].  
Subjectivity may be able to be reduced in the future by using automated whole brain tractography, which automatically dissects multiple eloquent tracts. This has previously been shown to be useful in tractography-guided stereotactic neurosurgery [39]. It may additionally increase accuracy by including tracts not immediately thought to be near to the pathology due to distorted anatomy. It may also reduce the doses to contralateral WM tracts in large lesions such as those shown in [PERSON] et al., 2018 on DTI-guided SRT plans [9]. One limitation of automated whole brain tractography is that there is still subjectivity regarding which tracts are included, and atlas-based approaches may be prone to errors, particularly in the presence of anatomy-distorting lesions. However, this method may play a role in reducing the time required for pre-radiotherapy planning and increasing standardisation, although the tracts would still need to be quality controlled by a highly skilled practitioner.  
There is additionally much progress in automated lesion segmentation due to improvements in deep learning. Automated lesion segmentation may be able to be utilised to delineate the initial GTV and previously discussed algorithms used to expand it to the CTV. This could additionally remove subjectivity and speed up the SRT planning process.  
An idealised fully automated pipeline for DTI-guided radiotherapy planning is shown in Figure 6, which could potentially be achieved with further research as suggested.
###### Abstract  
This study explores the integration and optimization of battery energy storage systems (BESSs) and hydrogen energy storage systems (HESSs) within an energy management system (EMS), using Kangwon National University's Samcheok campus as a case study. This research focuses on designing BESSs and HESSs with specific technical specifications, such as energy capacities and power ratings, and their integration into the EMS. By employing MATLAB-based simulations, this study analyzes energy dynamics, grid interactions, and load management strategies under various operational scenarios. Real-time data from the campus are utilized to examine energy consumption, renewable energy generation, grid power fluctuations, and pricing dynamics, providing key insights for system optimization. This study finds that a BESS manages energy fluctuations between 0.5 kWh and 3.7 kWh over a 24 h period, with battery power remaining close to 4 W for extended periods. Grid power fluctuates between \(-5\) kW and 75 kW, while grid prices range from 75 to 120 USD/kWh, peaking at 111 USD/kWh. Hydrogen energy storage varies from 1 kWh to 8 kWh, with hydrogen power ranging from \(-40\) kW to 40 kW. Load management keeps power stable at around 35 kW, and PV power integration peaks at 48 kW by the 10 th h. The findings highlight that BESSs and HESSs effectively manage energy distribution and storage, improving system efficiency, reducing energy costs by approximately 15%, and enhancing grid stability by 20%. This study underscores the potential of BESSs and HESSs in stabilizing grid operations and integrating renewable energy. Future directions include advancements in storage technologies, enhanced EMS capabilities through artificial intelligence and machine learning, and the development of smart grid infrastructures. Policy recommendations stress the importance of regulatory support and stakeholder collaboration to drive innovation and scale deployment, ensuring a sustainable energy future.  
s 2024 14 7631. [[https://doi.org/10.3390/app14177631](https://doi.org/10.3390/app14177631)]([https://doi.org/10.3390/app14177631](https://doi.org/10.3390/app14177631))  
1
# Foot strike Pattern and Cadence of the Marathon Athletes at the Tokyo 2020 Olympic Games
## 3 Results  
For all the variables analysed, ICCs greater than 0.89 were obtained, showing high inter-observer concordance (Table 1).  
Table 2 presents the main results regarding footstrike pattern. It is worth noting that five athletes showed different strike patterns with the left and right foot. Regarding the eight finalists, three were MFS runners, two FFS, two RFS, and one had an asymmetrical footstrike landing technique--MFS for the right foot and RFS for the left foot.  
The eight finalists in the marathon exhibited an average cadence of 185.5 \(\pm\) 5.1 steps per minute, with a coefficient of variation of 2.7%. Interestingly, there were no significant differences in cadence among the different race segments, indicating a consistent and stable cadence maintained by the athletes throughout the 10 km to 40 km distance, F(2, 14) = 0.068, \(p\) > 0.05. When focusing on the three medalists, their mean cadence was 188.6 \(\pm\) 2.3 steps per minute, with a coefficient of variation of 1.2%. (Table 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Variable**} & \multicolumn{1}{c}{**Mean ICC**} & \multicolumn{2}{c}{**95\% Confidence Interval**} \\ \cline{3-5}  & & & **Lower** & **Upper** \\ \hline Footstrike & Left & 0.898 & 0.811 & 0.945 \\ pattern & Right & 0.915 & 0.839 & 0.955 \\ \hline \multirow{3}{*}{Cadence} & KM 10–20 & 0.973 & 0.865 & 0.995 \\  & KM 20–30 & 0.983 & 0.917 & 0.997 \\ \cline{1-1}  & KM 30–40 & 0.983 & 0.917 & 0.997 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Inter-rater reliability analysis. ICC results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Footstrike Pattern**} & \multicolumn{1}{c}{**Frequency**} & \multicolumn{1}{c}{**Percent (\%)**} & \multicolumn{1}{c}{**Cumulative**} & \multirow{2}{*}{**Frequency**} & \multirow{2}{*}{**Percent (\%)**} & \multicolumn{1}{c}{**Cumulative**} \\ \cline{1-1} \cline{5-6}  & & & **Right** & & **Left** & \\ \hline Midfoot strike & 26 & 54.2 & & 30 & 58.8 & \\ \hline Forefoot strike & 5 & 10.4 & 64.6 & 5 & 9.8 & 68.6 \\ \hline Rearfoot strike & 17 & 35.4 & & 16 & 31.4 & \\ \hline Missing data & 3 & & & 0 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive data of the participants (N = 51).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Kilometre Range**} & \multicolumn{1}{c}{**Total**} \\ \cline{2-5}  & **10–20 km** & **20–30 km** & **30–40 km** & **Total** \\ \hline \multicolumn{5}{c}{Finalists (top 8)} \\ Mean (steps/min) & 185.2 & 186.2 & 185.1 & 185.5 \\ SD (steps/min) & 4.9 & 4.5 & 5.8 & 5.1 \\ Coefficient of variance (\%) & 2.7 & 2.4 & 3.1 & 2.7 \\ Max & 191 & 192 & 192 & \\ Min & 176 & 178 & 176 & \\ \hline \hline \multicolumn{5}{c}{Medalists (top 3)} \\ Mean (steps/min) & 188.3 & 188.2 & 189.2 & 188.6 \\ SD (steps/min) & 2.6 & 2.4 & 2 & 2.3 \\ Coefficient of variance (\%) & 1.4 & 1.3 & 1.1 & 1.2 \\ Max & 191 & 192 & 192 & \\ Min & 186 & 186 & 187 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cadence descriptive data.
# Metabolic Engineering _Escherichia coli_ for the Production of Lycopene
## 7 Discussion and Future Perspectives  
In summary, all these efforts on exploring various engineering strategies have facilitated an increase in lycopene production by metabolically engineering _E. coli_. However, there is still much room for improvement for lycopene yield in engineered _E. coli_. The following perspectives could be focused on in future research. First, the combination of the endogenous MEP pathway and the heterogeneous MVA pathway should be further investigated. The mechanisms of the cross-talk between the two pathways should also be uncovered. Second, the regulatory mechanism of the MEP pathway has not been well understood, giving rise to the potential of exploring regulatory engineering to enhance the biological production of lycopene. Third, the compartmentalization of the lycopene biosynthetic pathway should be paid more attention to, which is beneficial to eliminate the cytotoxicity of IPP and DMAPP as well as enhance the compartmental synthesis efficiency. Accumulation of the target products results in gradual cellular pressure of tolerating the target products, thus requiring the exploration of new strategies to resolve this issue, such as adaptive evolution, membrane engineering and efficient extraction methods. Finally, more concerns are given to the modification of upstream metabolic pathways to increase lycopene production, while the optimization of the downstream fermentation process is ignored. Therefore, more attempts should be made to the process engineering of the fermentation, including optimization of the fermentation mode and process parameters, in situ product recovery (ISPR) processes, and so on. Actually, there are numerous research directions in engineered _E. coli_, showing a promising prospect for lycopene production and other biochemicals.  
This work was supported by grants from the \"First class grassland science discipline\" program in Shandong Province, the National Natural Science Foundation of China (31860011), the Talents of High Level Scientific Research Foundation (grants 6651117005 and 6651119011) of Qingdao Agricultural University, Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioproces Technology, Chinese Academy of Sciences (CASKLB201805), and the Shandong Modern Agricultural Technology and Industry System (SDAIT-11-11).  
**Acknowledgments:** The authors would like to thank [PERSON] for useful suggestions which helped to improve the quality of the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Proposed Framework
### Q-Learning Technique
On the other hand, Q-learning uses, parametric operations to approximate the Q-function and defined it as follows:  
\[Q(s_{t},a_{t}):=Q(s_{t},a_{t})+\alpha(r_{t}+\gamma\times\max\limits_{a}Q^{ \prime}(s_{t+1},a)-Q(s_{t},a_{t})), \tag{3}\]  
where \(Q(s_{t},a_{t})\) is the Q-value and is calculated in the Q-table. \(\alpha\in(0\ <\alpha\leq\ 1)\) is a learning rate coefficient, \(r_{t}\) is the immediate reward agent receive after performing action \(a_{t}\) on state \(s_{t}\), and \(\gamma\) is the discount factor that helps to determine the importance of the future reward. \(Q^{\prime}(s_{t+1},a)\) calculates the best Q-value form the next state. Figure 5 presents the high-level systematic and implementation flow of the proposed ITE framework. In the proposed ITE, firstly the system initialized the user interaction interface to interact with the system and parametric setting default values or as per user definition. It set the packet rate, link bandwidth, link latencies, packet size, and Internet protocol (IP) address. After initialization system predicts the network congestion and measures the QoS and QoE of the application requirement to the predicted state. If the condition meets, then the system continues with the same codecs if not then the ITE agent selects the optimal codecs  
Figure 4: State transition model of the dynamic switching strategy: (1) prediction model estimates the state \(s_{i}(t)\) of the network congestion by observing the ECN marked packets as \(O_{i}\); (2) following the policy, the proposed scheme selects the haptic codecs from the database by action \(a(i)\); and (3) transit the system into the next state \(s_{j}(t+1)\) by receiving reward \(r(i)\). corresponding to the state of the system. In the next section, we will present the experiment analysis to prove the effectiveness of the proposed system. The list of default parameter values and settings for experiments are summarized in Table 2. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Settings Used** \\ \hline \multicolumn{2}{c}{**Simulation Setup**} \\ \hline Operation system & Ubuntu 18.04 \\ Programming & Python 3.8 \\ Network design emulator & Mininet 3.6.5 \\ \hline \multicolumn{2}{c}{**Network Emulator**} \\ \hline Network topology & Mesh network of switches \\ IP suite & Transmission control protocol \\ Software switch type & Open vSwitch 2.9.8 \\ SDN based controller & OVS-controller \\ SDN controller protocol & OpenFlow \\ Link delay & Shortest route 1.6 ms \\ Link bandwidth & 20\(\sim\)100 Mbps \\ Packet sampling Rate & 1 kHz\(\sim\)3 Mbps \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of parameters and settings used in experimentation. Figure 5: Flowchart shows the systematic flow of the proposed ITE framework.
## 2 System Model and CSI Collection
### CSI Model
The unique characteristics of the wireless channel CSI have made important preparations for its application as one of the physical layer security means. Since the representation parameters of channel state information are numerous and have heterogeneity in space but only show short-term correlation in time, and because the channel state information is jointly determined by the specific transmission environment, transmitter, and spatio-temporal characteristics, the transmitting and receiving ends can judge the identity change of the communication counterpart based on the CSI information.
## 4 Results and Discussion
### YOLOv5s-Ghost Network Training and Experimental Results  
In this paper, the datasets employed consist of visible-light and infrared subsets. A depth camera, Intel RealSense D435, is utilized as the image acquisition device. The visible-light dataset utilizes the MS COCO dataset, while the infrared dataset comprises 9119 pedestrian images captured by an infrared camera. Additionally, 508 images have been specifically collected for a dataset focusing on the cluttered rescue-like environment which encompass all scenarios prone to disaster and potentially requiring search and rescue support, including but not limited to forested areas, mining shafts, and analogous high-risk terrains. All images are in JPG format and have a resolution of 1920 \(\times\) 1080 pixels.  
To improve the generalization ability of the detection and localization algorithm, image enhancement operations such as adjusting the brightness and adding noise were applied to the images. These images are manually annotated using the LabelImg tool in PASCAL VOC format and saved as XML files. The images are randomly divided into a training set, a validation set, and a test set in a 9:1:1 ratio.  
The experimental environment is a Windows 11 operating system, using NVIDIA GeForce RTX 3060 (NVIDIA Corp., Santa Clara, CA, USA) graphic processing unit for computing, GPU size is 8 GB, CPU configuration is AMD Ryzen 7 5800H (Advanced Micro Devices, Inc., Santa Clara, CA, USA) with Radeon Graphics @ 3.20 GHz, CUDNN version is 8.9, Pytorch version is 2.1.1, and the Python language environment is 3.10.0.  
As clearly evidenced by Table 4, the YOLOv5s-Ghost architecture achieves a 25% parameter reduction and 22% inference acceleration compared to the baseline YOLOv5s model, while maintaining a high mean average precision (mAP) of 77.8%. The lightweight improvements significantly enhance the overall performance of the network.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Caged UAV** & **Locomotion** & **Task** & **Autonomous** & **Main Sensor for Identification** & **Whether Cage Obstracts Sensors** \\ \hline Caged Drone for Central HVAC ducts & Slide and Fly & Inspection & Autonomous & Thermal camera & Yes \\ Inspection [18] & Roll and Fly & Inspection & Autonomous & Thermal camera & Yes \\ Hybrid Rolling and Flying Caged Drone [19] & Roll and Fly & Inspection & Autonomous & Thermal camera & Yes \\ Drone with Hybrid 3D-Printed & & Fly & - & Manual & 2D camera & Yes \\ Conformational Circuits [20] & & & & & \\ Drone With Safety Cage Ensuring Service & & & & & \\ Reliability [22] & Fly & Inspection & Manual & 2D camera & Yes \\ Drone With Broad-Beamwidth Small & & Evaluate the & & & \\ Antennas and Protective Enclosure [23] & Fly & reliability of antennas & & & \\ Proposed Caged Drone & Roll and Fly & Search and rescue & Autonomous & Depth camera & No \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative study on relevant caged unmanned aerial vehicles.
## 4 Imaging Experiment and Analysis
### Application of the DDZME Network in Normal Image Super-Resolution Tasks  
To evaluate the performance and robustness of the DDZMR network for image super-resolution, we tested it with the publicly available benchmark datasets B100 [34] and Urban100 [35] data, and compared it with the state-of-the-art networks. B100 [34] contains various animals, plants, and other natural scenes, and Urban100 [35] contains only scenes of urban buildings and roads. We quantified the performance of each dataset on a common test set; according to [20], PSNR and SSIM quantify the reconstruction quality and structural similarity of the generated images relative to the ground truth. Some qualitative comparisons are shown in Figure 10. Bicubic often suffers from oversaturation and VDSR [20] does not perform well in tonal correction. In comparison, LapSRN [21], CARN [18], and DDZMR were generally better in terms of color restoration, contrast, and image detail enhancement after super-resolution. The images created by DDZMR network, in addition to achieving comparable color recovery and tone correction, were also reasonably clear and the network performed excellently.  
A quantitative comparison of PSNR and SSIM for each network image super-resolution performance is shown in Table 2. As with the intuitive judgment of images, VDSR [20] has the lowest PSNR and SSIM values, LapSRN [21] and CARN [18] perform well, and DDMZR networks are also competitive, generally performing better in PSNR and SSIM.  
Although DDZMR has only a little performance improvement in super-resolution processing compared to CARN, DDZMR has the ability to simultaneously restore degraded images, which is a capability that CARN and other networks do not have. The image in HDOE imaging is severely degraded. Because CARN cannot effectively restore degraded  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **PSNR** & **SSIM** & \
\begin{tabular}{c} **Training** \\ **Time (h)** \\ \end{tabular} &
\begin{tabular}{c} **Processing** \\ **Time (ms)** \\ \end{tabular} \\ \hline DDZMR & 28.69 & 0.7045 & 45 & 413 \\ \hline DDZMR without the multimodal loss function & 25.57 & 0.6754 & 53 & 402 \\ \hline DDZMR without the image regional division and channel selection module & 26.78 & 0.6842 & 67 & 425 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative comparison of ablation multi-modal loss function and area division and channel planning modules.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **B100:PSNR** & **B100:SSIM** & **Urban100:PSNR** & **Urban100:SSIM** \\ \hline Bicubic & 27.86 & 0.6909 & 26.41 & 0.6195 \\ VDSR [20] & 31.52 & 0.8873 & 29.65 & 0.9048 \\ LapSRN [21] & 31.69 & 0.8929 & 30.40 & 0.9107 \\ CARN [18] & 32.04 & 0.8963 & 31.72 & 0.9234 \\
**DDZMR (Ours)** & **32.09** & **0.8975** & **31.82** & **0.9247** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative comparison of existing methods on the B100 [34] and Urban100 [35] datasets.  
images, it is very ineffective in super-resolution processing of images captured by SHDCIS. See the supplementary File for specific image processing details.
# Melody Extraction Using Chroma-Level Note Tracking and Pitch Mapping
## 2 Chroma-Level Note Tracking and Pitch Mapping-Based Melody Extraction
### Melodic Chroma Sequence Tracking and Note Detection  
It is commonly assumed that the global melody trajectory is constituted of the local optimum sub-trajectories [22]. Dynamic programming is an effective solution to the optimum path finding problems, and often used for melody pitch tracking [22]. Hence, it is also introduced to track the chroma sequences taking into account of both salience and smoothness constraints herein. The cost function of melodic chroma trajectory is  
\[\text{c}(p,\lambda)=\sum\limits_{i=1}^{N_{frm}}C_{nrm,i}(p_{i})-\lambda\sum \limits_{i=1}^{N_{frm}-1}|p_{i+1}-p_{i}| \tag{7}\]  
where \(C_{nrm,i}(p_{i})\) is the normalized HPCP value at pitch bin \(p_{i}\) of the \(i\)-th frame, \(N_{frm}\) is the number of frames, and \(\lambda\) is the penalty factor determining frequency transition. In Equation (7), \(\sum\limits_{i=1}^{N_{frm}}C_{nrm,i}(p_{i})\) represents the salience constraint, while \(\lambda\sum\limits_{i=1}^{N_{frm}-1}|p_{i+1}-p_{i}|\) accounts for the smoothness constraint.  
Suppose that \(D(i,p_{j})\) is the accumulated cost function that pitch bin \(p_{j}\) is taken at the \(i\)-th frame. Then, the iterative function is  
\[D(i,p_{j})=C_{nrm,i}(p_{j})+\text{max}\big{\{}D(i-1,p_{k})-\lambda\big{|}p_{k }-p_{j}|\big{\}} \tag{8}\]  
where \(p_{k}\) and \(p_{j}\) are the HPCP pitch bins at \((i-1)\)-th and \(i\)-th frame, respectively, and \(i\in(1,N_{frm}]\).  
The initial condition of \(D(i,p_{j})\) is  
\[D(1,p_{j})=C_{nrm,1}(p_{j}) \tag{9}\]After the chroma level melody is obtained by dynamic programming, note detection is carried out as follows. As polyphonic music is performed according to some music scores, it is assumed that the chroma estimated for each note is also within half-chroma interval. Moreover, notes should sustain no shorter than some length limitation to be perceived by human auditory system [23]. Based on the above two assumptions, note detection is modeled as the following optimization problem:  
\[\left\{\begin{array}{l}\max(t_{off}-t_{on})\\ \mathrm{s.t.}|p_{t+1}-p_{t}|<1,\forall t\in[t_{on},t_{off}),(t_{off}-t_{on})> L_{thr}\end{array}\right. \tag{10}\]  
where \(p_{t}\) is the chroma trajectory by dynamic programming, \(L_{thr}\) is the number of frames corresponding to minimum possible note length, \(t_{on}\) and \(t_{off}\) are the frame indices of note onset and offset, respectively, \(t_{on}\in[1,N_{frm})\), and \(t_{off}\in(1,N_{frm})\).