esa-sceva/satcom-chunk-collection huggingface dataset:
# A Remote Sensing Image Target Detection Algorithm Based on Improved YOLOv8
## 4 Experimentation and Analysis
### Comparison Test  
In order to further verify the effectiveness of the proposed algorithm, the improved algorithm is compared with typical algorithms in the field of target detection in remote sensing images in recent years on the DOTAv1.0 dataset; the results of the experiments are shown in Table 4. It can be seen that with input images of the same size and the same number of training epochs, the improved algorithm has an improved performance for all selected performance indicators compared with the SSD, YOLOv5, YOLOv7, and YOLOX algorithms, as well as the original YOLOv8 algorithm, in each of the selected performance indexes, which proves that the improved algorithm enhances the feature extraction ability of different sizes of targets and small targets with complex backgrounds by increasing the small target detection layer, introducing the EMA module, and improving the loss function so as to obtain a more accurate detection effect, which reduces misdetection and omission to a certain extent.
## 2 Materials and Methods
### Monitoring  
Isokinetic strength evaluation with 90\({}^{\circ}\) knee flexion and extension of the knee extensors (KE) and flexors (KF) during concentric muscle contraction at a 60\({}^{\circ}\).s\({}^{-1}\) angular velocity [24, 25] was performed using a Cybex Humac Norm dynamometer (Cybex NORM\({}^{\circledr}\), Humac, CA, USA). Limb dominance was defined by determining which leg each participant preferred to kick with (i.e., the kicking leg) [29]. Musculoskeletal abnormality of the knee muscles was defined as a bilateral strength imbalance of more than 10% which contributed to a knee risk factor [30]. The following strength variables were evaluated: peak muscle torque of the knee extensors (PTE) and flexors (PTF) in the dominant and non-dominant lower limb, ipsilateral strength ratio of the hamstring to the quadriceps (H:Q), and bilateral strength ratios of the quadriceps to quadriceps (Q:Q) and hamstring to hamstring (H:H). The bilateral ratios between limbs were calculated according to the following formula [31]:  
\[\text{Deficit}=\frac{\text{Dominant limb score}-\text{Non}-\text{dominant limb score}}{\text{Dominant limb score}}\times 100(\%)\]The ipsilateral strength ratio was expressed as the percentage proportion of peak torque of the agonist and antagonist muscle on the same limb according to the following formula:  
\[\mathrm{H:Q=\frac{\mathrm{Peak\ torque\ of\ hamstring}}{\mathrm{Peak\ torque\ of\ quadriceps}}\times 100(\%)}\]  
Before testing, all players completed a short warm-up including 12 min of indoor cycling at 80-90 W and 80-90 revolutions per minute (rpm) on an ergometer (Excalibur; Lode(r), Groningen, the Netherlands), 2 sets of half squats with 10 repetitions, and 2 sets of forward lungs with 10 repetitions. During the testing set-up, torque was gravity-corrected using a standardized device procedure, and dynamometer calibration was performed in accordance with the manufacturer's instructions. Following five sub-maximal warm-up repetitions, the subjects performed two repetitions at maximal effort. There was a 30-s rest interval between the trials and testing attempts. Visual feedback and verbal stimulation were provided during the test. To achieve a rational comparison of isokinetic strengths between players, we expressed strength in relative values (normalized to body mass).
## 2 Finite Element Model and Research Method
### Taguchi Method  
In the optimization experiments, the selection of the parameter that has no influence on the optimization result as a variable will not only increase the workload, but will also affect the accuracy of the optimization result. Therefore, the Taguchi method was employed to eliminate the irrelevant variables and select the parameters which have great influence on the maximum electric field intensity [40; 41]. Then the response surface experiments were carried out with these filtered parameters, by which the optimization efficiency can be significantly improved.  
The Taguchi method designs the horizontal combination of each parameter by establishing an orthogonal table, carries on an orthogonal experiment to calculate the proportion of the influence of each parameter on the optimization target, and filters the parameters according to the proportion. Each parameter that needs to be optimized usually takes three values in the range of change, which is called the impact factor.
## 5 DRL-Based Online Resource Allocation
### Algorithm Framework  
To address the challenges of massive terminals' intermittent transmission requests, this paper employs an online pipeline. The resource allocation problem is defined in Equation (10) as a sequence decision-making problem driven by data transmission requests rather than by the conventional maintenance pipeline of resource allocation methods. At each time stick, currently generated terminal requests will be sequentially allocated resources according to the dynamic environment.  
Such an online resource allocation problem can be intuitively regarded as a sequential decision problem and further formulated to MDP, in which an agent in CTCU makes a decision to maximize the long-term reward according to the changing environment.
## 4 Circuit
### Temperature and Humidity Sensor  
Once the whole circuit has been demonstrated to operate as a DC power supply, the 8 k\(\Omega\) load is replaced by a specific sensor. This sensor displays the temperature and the relative air humidity in an LCD screen, which needs to be biased over 1.25 V to be switched on. It consumes currents between 50 \(\mu\)A and 80 \(\mu\)A, depending on the sensor requirements. For instance, the power consumption will be higher when the digits of the LCD screen are being updated. Therefore, the sensor can be seen as a dynamic load at the circuit level, with an impedance that varies from 20 k\(\Omega\) to 30 k\(\Omega\).  
Several combinations of input powers \(P_{1}\) and \(P_{2}\) are attempted in the combiner in order to determine the minimum power \(P_{in_{1}}\) and \(P_{in_{2}}\) that turns on the sensor. They are found to be \(P_{in_{1}}^{min}=-4.84\) dBm and \(P_{in_{2}}^{min}=-9.20\) dBm. Figure 18 depicts the measurement setup. Please note that the power measurement in the outdoor scenario (\(P_{in_{1}}=-1.46\) dBm and \(P_{in_{2}}=-2.34\) dBm, shown in Section 3.4) exceeds the minimum required power, which enables the circuit to work properly under this condition. However, the power level acquired in the indoor scenario (\(P_{in_{1}}=-5.80\) dBm and \(P_{in_{2}}=-9.20\) dBm, shown in Section 3.4) is not sufficient to turn on the sensor.  
Then, the autonomy of the circuit is tested in the laboratory. For the power requirement in the outdoor scenario, it is observed that the storage capacitor maintains operation of the temperature sensor for 87 s after the circuit is disconnected from the signal generator.
## 1 Introduction
In this study, to further develop HD technology, three functioning configurations, together with a controlling algorithm, are designed so that they can be used to show how the efficiency of the SBS can be utilized to improve the overall performance.
###### Abstract  
The acoustic black hole (ABH) can be utilized to achieve aggregation of flexural wave in structures with the feature that the thickness gradually reduced to zero with a power exponent no less than 2. The above characteristics could be applied in vibration reduction, noise attenuation or improving sound insulation. Previous literatures on vibration and acoustic characteristics of ABH structures mainly focus on the structural response under mechanical force excitation, while the transmission loss (TL) of circular plates embedded with two-dimensional ABHs investigated in this paper is a vibro-acoustic coupling procedure under excitation of diffuse sound field. Series of vibro-acoustic coupling finite element models (FEM) for TL analysis of ABH circular plates were established by automatic matched layer (AML) method in this paper and an experimental platform for measuring TLs of ABH circular plates and uniform plates was constructed. The accuracy of the FEM analysis was verified by experimental measurements. To systematically analyze the influence mechanism of parameters of the ABH on TLs of ABH circular plates, the effects of diameter, orientation, number, and truncation thickness of ABHs on TLs of ABH circular plates were further studied. The effect of the damping layer on TLs of circular plates embedded with 1 and 19 ABHs was also analyzed and it reveals that the influence of damping layer mainly concentrates on the first-order resonance frequency and damping-controlled region of the plate, and at some frequencies, the greater the damping layer thickness, the worse the sound insulation performance, despite that the modal damping loss factor has been increased in the whole frequency domain.  
acoustic black hole; circular plate; sound transmission loss; parameter influence; damping layer; experiment +
Footnote †: journal: Applied Physics Letters
## 4 Material and Methods
### Untargeted Metabolome Analysis
#### 4.2.2 GC-MS Analysis of the VACs  
Agilent 7890B chromatography and a 5977B mass spectrometer equipped with a DB-5 MS capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent J&W Scientific, Folsom, CA, USA) carried out the qualitative and quantitative analysis of volatile aromatic compounds. The extracted sample was directly injected into the injection port of the gas chromatograph-mass spectrometer, desorbed at 250 \({}^{\circ}\)C for 5 min, and the injection port temperature was set at 230 \({}^{\circ}\)C. The high-purity helium (Purity \(\geq\) 99.999%) was used as the carrier gas. The initial oven temperature was set at 40 \({}^{\circ}\)C for 1 min, increased to 230 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min and held for 1.5 min, then increased to 250 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min and kept for 2 min. Mass spectrometry was recorded in the ionization mode of electron impact ion source (EI) energy of 70 eV. The ion source temperature was set to 230 \({}^{\circ}\)C, and the quadrupole temperature was 150 \({}^{\circ}\)C. Mass spectrometry data were extracted by full scan mode (SCAN) with a mass scanning range of 40-500 m/z. In the process of mass spectrometry analysis, all samples were mixed as quality control (QC) samples to test the stability of the system mass spectrometry platform during the whole experiment. MS-DIAL software analyzed the GC-MS raw data for peak detection, peak recognition, MS2 Dec deconvolution, and peak alignment. They then compared with MS in the NIST database to determine the species of VACs. VACs with MS matching scores greater than 80% were retained, and the final substance type was determined after manual identification and comparison. The relative content of volatiles was calculated by the standard internal method. The formula is as follows:  
\[\text{f[relative content of volatiles }(\text{$\mu$g}/\text{kg})\text{ ]}=\frac{\text{peak area of target }\times 0.1\text{$\mu$g}}{\text{peak area of internal standard }\times 1000\text{ g}}\]
## 3 Results
### Volatile Compounds of TP Dry-Cured Ham  
The effects of the animal's diet, the processing method and their interaction on the VOCs of TP dry-cured ham BF muscle are shown in Table 4 and Table S1 in the Supplementary Materials. A total of ninety-one (**91**) VOCs were identified (Supplementary Materials, Table S2), of which aldehydes were the most abundant and with the highest number of volatile compounds (**16**), followed by phenols (**14**), alcohols and ketones (**13**), alkanes and alkenes (**12**), aromatic hydrocarbons (**10**), terpenes (**8**), nitrogen compounds (**3**) and acids (**2**). Overall, the processing method (i.e., smoking) had a more marked influence on the volatile profile of the dry-cured ham than the diet of the animals. As expected, the LSH contained a significantly lower amount of total phenolic compounds, mainly due to the lower contents of 3-ethyl-phenol, 2-methoxy-4-methylphenol and 4-ethyl-2-methoxy-phenol compared to those in the SH. The effect of smoking duration on the other groups of VOCs was less obvious, with differences found only for some individual compounds within certain groups of volatiles. Of the aldehydes, the LSH had lower levels of tetra-decanal and hexa-decanal and tended (\(p\) < 0.10) to have lower levels of nonanal, which, along with hexanal and heptanal, was the most abundant aldehyde and the only major aldehyde where the level was inclined to be reduced by a reduction in smoking. The tendency (\(p\) < 0.10) \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Processing (P)**} & \multicolumn{2}{c}{**Diet (D)**} & \multicolumn{3}{c}{**Significance**} \\ \cline{2-9}
**Volatile** & **Less** & **Standard** & **Acorn** & **Control** & **RMSE** & **P** & **D** & **Interaction** \\
**Compounds** & **Smoke** & **Standard** & **Acorn** & **Control** & & & & \\ \hline Aldehydes & 29.00 & 35.40 & 36.61 & 27.79 & 7.00 & ns & ns & ns \\ Alcohols & 17.06 & 9.66 & 13.41 & 13.31 & 5.74 & ns & ns & ns \\ Aromatic hydrocarbons & 2.81 & 6.26 & 2.29 & 4.78 & 3.53 & ns & ns & ns \\ Ketones & 4.92 & 3.10 & 3.27 & 4.75 & 1.90 & ns & ns & ns \\ Phenols & 9.61 & 16.17 & 14.32 & 11.45 & 2.77 & * & ns & ns \\ Alkanes and alkenes & 5.73 & 5.27 & 4.96 & 6.05 & 2.18 & ns & ns & ns \\ Nitrogen compounds & 4.60 & 10.91 & 8.19 & 7.33 & 6.57 & ns & ns & ns \\ Terpenes & 6.66 & 1.46 & 3.50 & 5.00 & 2.92 & t & ns & ns \\ Acids & 0.40 & 0.07 & 0.28 & 0.19 & 0.10 & * & ns & * \\ \hline \hline \end{tabular}  
* \(p\) \(\leq\) 0.05; t–\(p\) < 0.10; ns–\(p\) > 0.05; RMSE—root mean square error.  
\end{table} 
Table 4: Effect of the animal’s diet and the processing method on the total amounts of the individual VOC groups of the _m. biceps femoris_ muscle of TP dry-cured ham.
# Multiple Thermal Parameter Inversion for Concrete Dams Using an Integrated Surrogate Model
## 2 Methodology  
To accurately and efficiently invert thermal parameters, this paper proposes a multi-parameter inversion method using ISM. Figure 1 shows the flowchart of this method, which is divided into four parts: (1) the mathematical model construction for thermal parameter inversion, which transforms the parameter determination problem into an optimization problem; (2) sample set generation, which generates samples for the training and testing of the integrated surrogate model; (3) ISM construction, which is to determine the hyperparameters of the sub-models and the weight coefficients used for model integration; and (4) thermal parameter identification, which is the process of solving the aforementioned optimization problem. The principles and methods of each part are presented as follows.
# Automated Clinical Impression Generation for Medical Signal Data Searches
###### Abstract  
Medical retrieval systems have become significantly important in clinical settings. However, commercial retrieval systems that heavily rely on term-based indexing face challenges when handling continuous medical data, such as electroencephalography data, primarily due to the high cost associated with utilizing neurologist analyses. With the increasing affordability of data recording systems, it becomes increasingly crucial to address these challenges. Traditional procedures for annotating, classifying, and interpreting medical data are costly, time consuming, and demand specialized knowledge. While cross-modal retrieval systems have been proposed to address these challenges, most concentrate on images and text, sidelining time-series medical data like electroencephalography data. As the interpretation of electroencephalography signals, which document brain activity, requires a neurologist's expertise, this process is often the most expensive component. Therefore, a retrieval system capable of using text to identify relevant signals, eliminating the need for expert analysis, is desirable. Our research proposes a solution to facilitate the creation of indexing systems employing electroencephalography signals for report generation in situations where reports are pending a neurologist review. We introduce a method incorporating a convolutional-neural-network-based encoder from DeepSleepNet, which extracts features from electroencephalography signals, coupled with a transformer which learns the signal's auto-correlation and the relationship between the signal and the corresponding report. Experimental evaluation using real-world data revealed our approach surpasses baseline methods. These findings suggest potential advancements in medical data retrieval and a decrease in reliance on expert knowledge for electroencephalography signal analysis. As such, our research represents a significant stride towards making electroencephalography data more comprehensible and utilizable in clinical environments.  
electroencephalography; indexing method; text generator; transformer; convolutional neural network +
Footnote †: journal: Medical Signal Data Searches  
1 BK21 Education and Research Center for Artificial Intelligence in Healthcare, Department of Applied Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea; [EMAIL_ADDRESS]  
2 Department of Applied Artificial Intelligence (Major in Bio Artificial Intelligence), Hanyang University, Ansan 15588, Republic of Korea; [EMAIL_ADDRESS] (J.Y.); [EMAIL_ADDRESS] (D.F.)
## 3 ComEdge Experimental Platform
### Platform Architecture  
The platform architecture, depicted in Figure 3, consists of three main parts. Nodes integrated with the 5G communication system connect to various terminal devices via the southbound interface, depending on the scenario, such as a plethora of IoT sensors, vehicles in the vehicular network, various communication devices across military and civilian applications, including mobile phones and smart watches. Additionally, nodes integrated with satellite antennas establish a communication link with high-throughput Earth-orbiting satellites via the northbound interface, establishing a connection with the data network. The platform can be deployed on general architectures, including X86 and ARM operating systems. The system resources are virtualized, and various businesses and data can be carried out in a containerized way.  
The control panel of the platform cluster includes the following important features:  
* Node management: Node management is an extremely important part of cluster operation and maintenance. It mainly includes operations such as adding, deleting, maintaining, monitoring, and auto-scaling nodes.
* Resource management: Resource management achieves flexible configuration and scheduling of compute and storage resources through the use of container resource limitations, requests, and pod scheduling strategies to achieve optimal resource utilization efficiency.
* Service orchestration: Service orchestration is achieved by managing and coordinating a group of related containers as an application. The main goal of service orchestration  
Figure 3: Architecture of ComEdge platform.  
is to coordinate aspects such as container storage, networking, deployment, scaling, and upgrading to create highly available and scalable services, and to provide rich service discovery and routing mechanisms.
* Lifecycle management: Lifecycle management functionality allows users the management and monitoring of the entire lifecycle of containers, including container creation, operation, monitoring, updating, and destruction. Through comprehensive container management, lifecycle management ensures stable operation and high availability of containers, improving application efficiency and reliability.  
The services of each node in the platform cluster include the following aspects. (1) Task processing: the tasks to be processed can be resource-intensive tasks distributed over a certain period of time, or they can be short, lightweight, and stateless tasks. (2) Data storage: it refers to how to store data in containers and manage the data in the containers. Considering the characteristics of containers, data persistence is usually not needed, and only short-term storage is required. (3) Model derivation: Edge intelligence is achieved by deploying federated learning, transfer learning [27], reinforcement learning, and other algorithms on various edge devices.
# A Closed-Form Method for Simultaneous Target Localization and UAV Trajectory Optimization
## 5 Conclusions  
This paper presents a closed-form method for simultaneous target localization and the UAV trajectory optimization problem. While decoupling the problem of target localization from the UAV trajectory optimization in the discrete measurement case, the proposed method uses the predicted target state and the corresponding FIM to establish the trajectory optimization objective, which can not only better estimate the target position, but also take the process noise in the target motion model into consideration. Eventually, the optimized trajectory becomes more effective than existing methods of the same class. Solving the objective, the resulting optimized UAV waypoint can be the index point for minimizing the target localization uncertainty in practical engineering applications. In both the simulation examples and field experiments, we have confirmed the analytical findings. Although the field test was conducted on the quadrotor UAV based on the bearing measurement model with the flight height already known, the proposed method was also applicable to the fixed-wing UAV, which often has a nonzero minimum velocity, with other measurement models, for instance, the bearing-distance measurement model and the bearing-only measurement model without any prior information available. However, successful tracking and localizing to the moving target for the bearing-only measurement model require that the UAV has a special maneuverability. Hence, our algorithm has a certain engineering significance. However, the proposed algorithm also has its limitations, as the algorithm can only track and localize the target with a known motion model. Our future research will focus on simultaneous target localization and UAV trajectory optimization for the target with an unknown motion model and multiple targets with known motion models with multiple UAVs to extend this work.  
Conceptualization, D.W. and D.H.; Methodology, D.W. and C.X.; Software, D.H. and C.X.; Validation, D.W. and C.X.; Formal analysis, C.X.; Investigation, W.H.; Resources, D.H.; Data curation, D.H.; Writing--original draft preparation, D.W.; Writing--review and editing, C.X.; Supervision, D.H. and W.H.; Project administration, D.H.; Funding acquisition, D.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (grant number 2017 YFC0822400), the National Natural Science Foundation of China (grant number 61601222),  
Figure 15: Localization performance for two scenarios. (**a**) RMSE for the scenario with zero minimum velocity constraint; (**b**) RMSE for the scenario with minimum velocity constraint as 5 m/s.  
the Jiangsu Provincial Natural Science Foundation of China (grant number BK20160789), and the China Postdoctoral Science Foundation (grant number 2018M632303).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.