esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Model Validation  
The linear regressions showed that there were significant correlations between the simulated and observed values for NEE and GPP (Figure 5), indicating that the accuracy of the model was good. Therefore, it is possible to simulate the changes in NEE and GPP in this area under future climate conditions.  
The R\({}^{2}\) values of NEE at the tundra and needle leaf forest sites in Alaska were approximately 0.5, and the RMSE values were approximately 20 gC/m\({}^{2}\)/yr. The R\({}^{2}\) values of GPP at the tundra and needle leaf forest sites in Alaska were 0.66 and 0.85, respectively, and the RMSE values were 39.25 and 43.75 gC/m\({}^{2}\)/yr, respectively (Figure 5). This simulation accuracy was largely comparable with previous reports to the LPJ-GUESS. For example, the R\({}^{2}\) value of GPP in the Hindu Kush Himalayan forest simulated using LPJ-GUESS was 0.54 [26]. The R\({}^{2}\) value was 0.76 for the NEE simulation using a model of net CO\({}_{2}\) exchange in the Arctic tundra ecosystem from 2003 to 2009 [35]. Our results showed that  
Figure 5: Simulated and observed NEE and GPP at the ATQ tundra site (**a**,**b**) and PRR needle leaf forest site (**c**,**d**).  
the LPJ-GUESS model is a useful tool to simulate NEE and GPP in the Arctic tundra and needle leaf forest.
# Kinetics and Thermodynamics of Efficient Phosphorus Removal by a Composite Fiber
## 2 Materials and Methods
### Static Adsorption and Phosphorus Removal Experiment of ZSFB Composite Fiber
#### 2.1.4 Experiment on Adsorption Kinetics  
Eight groups of 1 g of ZSFB composite fiber were prepared and placed in 250 mL conical flasks, and 100 mL KH\({}_{2}\)PO\({}_{4}\) solution was added with a concentration of 5 mg/L. The conical flasks were placed in a water bath thermostatic oscillator for adsorption at 25 \({}^{\circ}\)C with a speed of 150 r/min. The adsorption time for each group was 2, 4, 8, 12, 18, 24, 30, and 48 h, respectively. Then, the adsorption solution was extracted with a syringe, and the concentration of phosphorus was measured by the ammonium molybdate spectrophotometric method. The phosphorus adsorption capacity of the composite fiber was calculated, and the adsorption kinetic curve was drawn based on the results. The experiments were also conducted with the same method at 15 and 35 \({}^{\circ}\)C, and conducted with a different initial phosphorus concentration (10, 15 and 20 mg/L) at each temperature.
## 3 Energy Efficient MAC Protocol Design
### Scheduling Design
Since the sensor nodes moves rather slowly (0.2 mm/s) in the human body, there is no need to conduct information exchange and update the routing and transmission power for every frame. Therefore, M-periodic update is proposed for energy saving. In the proposed protocol, the information exchange and routing update are conducted in an M-periodic way (The information exchange and routing update is conducted every M frame). The update frame is the same as 1-periodic frame in Figure 5a. The (_M_-1) frames between two consecutive update frames contain only data slots as shown in  
Figure 4: An example of the data transmission schedules in a wireless network with three sensors. Figure 5b. They follow the same schedules in the data slots as in previous update frame. The sensor nodes go into sleep mode in the mini control data slots during these (_M_-1) frames. When the next update frame comes, the sensor nodes as well as the coordinator repeat the same schedules. Figure 6 compares M-periodic and 1-periodic information exchange for different network sizes. The setup is the same as in Section 2. For x-axis, 1 frame/update represents 1-periodic update. 3-9 frames/update represents M-periodic update. It can be seen that the total energy consumption reduces when the update interval increases. As the update interval increases, the energy consumption remains flat. This is because the control packet is already very small compared with the sensing data. It is also noted that 3-sensor and 4 sensor networks achieve lower energy consumption than 2 sensor and 5 sensor networks. This matches the result in Figure 2. The minimum energy consumption is obtained for certain hop. If the hop number increases, the energy consumption will increase. Figure 5: **(a)** 1-periodic TDMA frame format. The frame is composed of broadcast section, power detection section, information upload section, schedule assignment section and data slots section; **(b)** M-periodic TDMA frame format. The frame format of the (_M_-1) frames between two consecutive update frames. Figure 6: Energy consumption _vs_. information exchange interval. 1 frame/update represents 1-periodic update. 3–9 frames/update represent an M-periodic update.
# An Automatic ECG Signal Quality Assessment Method Based on Resnet and Self-Attention
###### Abstract  
Electrocardiogram (ECG) signals are among the significant physiological signals that indicate the essential properties of the human body. In recent years, the measurement of ECG signals has become more portable thanks to the increasing usage of wearable health testing technology. However, the enormous amount of signal data gathered over a long period of time does impose a heavy load on medical professionals. In addition, false alarms might occur due to the potential for the detected signal to become jumbled with noise and motion perturbations. Therefore, analyzing the quality of the measured raw ECG signal automatically is a valuable task. In this paper, we propose a new single-channel ECG signal quality assessment method that combines the Resnet network structure and the principle of self-attention to extract ECG signal features using the principle of similarity between individual QRS heartbeats within a time slice of ten seconds. In addition, an improved self-attention module is introduced into the deep neural network to learn the similarity between features. Finally, the network distinguishes between acceptable and unacceptable ECG segments. The model test results indicate that the F1-score can approach 0.954, which leads to a more accurate assessment of the ECG signal quality.  
signal quality assessment; electrocardiogram (ECG); deep learning; Resnet18; attention mechanism; healthcare; binary classification
# Electroglottograph-Based Speech Emotion Recognition via Cross-Modal Distillation
## 1 Introduction
[37] proposed a method of training a speech emotion recognition model with unlabeled speech data via response-level distillation from a pre-trained facial emotion recognition model given visual-audio pairs. [PERSON] et al. [38] proposed a method of training the speech emotion recognition model without any labeled speech emotion data with the help of emotion knowledge from a pre-trained text emotion model. These studies apply cross-modal distillation to speech emotion recognition, which inspired our study. However, it must be highlighted that our paper aims to extract emotion information from EGG signals, not speech itself, which is a fundamental difference compared to the above research. In the present paper, to face up with the reality of poor anti-noise performance of the inputs extracted from speech in the speech emotion recognition task, we propose an EGG-based speech emotion recognition model. Furthermore, to cover the information latent in the modulation of the sound track, we adopt cross-modal emotion distillation (CMED), which transfers robust speech emotion representations from the log-Mel-spectrogram-based model to the EGG-based model. This paper is organized as follows: Section 2 introduces our materials and methods and presents our proposed model in detail. In Section 3, we discuss the results of our model and the comparison experiments we have conducted. In Section 4, we discuss our work. Finally, Section 5 provides the conclusions of the present work and highlights the expected future works.
# Dry Printing of Ag-Ni Conductive Particles Using Toner-Type Printed Electronics
## 1 Introduction  
Flexible and printed electronics are the next-generation technologies that will lead to paper-like electronic devices, such as healthcare sensors, flexible handheld devices, displays, and various wearable electronic devices. Printed electronics are also valuable owing to the material savings resulting from their additive nature [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. Conventional semiconductor device technologies include subtractive technologies, such as etching, photolithography, and dicing. In contrast, various printed electronics methods are additive and have material-saving properties, in addition to being disposable by using naturally decomposed electronic materials for the inks [29; 30; 31; 32]. Prototyping and the subsequent manufacturing of a wide variety of products in small quantities is an advantage of the on-demand printing technique. Ink-jet printing is an on-demand printing method that is sufficiently developed globally. Electroph photographic printing is another on-demand printing method that uses toner particles instead of inks [33; 34]. The conductive toner particles used in our electrophotographic printed electronics are almost entirely bare metal particles that do not include chemical agents in the liquid inks and improve the solubility and viscosity. Therefore, a high-temperature thermal treatment after printing was unnecessary. Moreover, because our method uses metal particles instead of a solvent for creating the inks, re-dissolution of the pre-printed pattern by the solvent of the next layer does not occur. Namely, multilayer printing is easily achieved using this method. Chemical vapor does not volatilize after printing; therefore, extra time is not required for the printed pattern to dry, leading to a high throughput. In addition, the advantage of electrophotographic printing, which contributes to the high throughput, is that this method is based on page-printing and not dot-printing. Page-printing ensures the printing of electrical circuits with a high throughput, in which mm to \(\upmu\) scale patterns coexist. Printing of the mm (PCB level) to the \(\upmu\)m scale (intra-IC circuit pattern) with a coexisting pattern is necessary to enable future electronics to be entirely printed.  
This study demonstrates that the IC peripheral pattern can be fabricated using electrophotographic printing with Ag-Ni composite toner particles, which consist of Ag particles and Ni nanoparticles surrounding the Ag particles. Ni nanoparticles prevent the natural aggregation of Ag particles and maintain the fluidity of the conductive toner particles. In addition, compressed Ag-Ni toner particles have been demonstrated to exhibit a low electrical resistivity of 6.89-9.43 \(\times\) 10\({}^{-8}\)\(\Omega\cdot\)m, which was approximately two times higher than that of the compressed bare Ag particles, and were comparable to the electrical resistivity of bulk Ni.
## 2 Materials and Methods  
### Data Acquisition and Processing  
Pre- and post-operative computed tomography (CT) scan, surgical plan, and custom pelvic implant geometry data were collected from a 46-year-old male subject (height: 1.73 m, weight: 85 kg). Post-operative walking, squatting, and stair climbing data were also collected. The subject was diagnosed with a pelvic sarcoma in the acetabular region of the right hemipelsis. Subsequently, the subject underwent an Enneking Type-II resection followed by endoprosthetic reconstruction using a commercial custom pelvic implant (Onkos Surgical, Parsippany, NJ, USA) with total hip replacement. Pelvic tumor resection and pelvic reconstruction were performed during a single surgical session at MD Anderson Cancer Center in Houston, TX. Institutional review board approvals were obtained from MD Anderson Cancer Center, the University of Texas Health Science Center, and Rice University for collection and retrospective analysis of medical and motion data. The subject provided written informed consent for all collected data.  
Once the multi-planar pelvic bone resection was devised to remove the tumor with clear margins, the subject's custom pelvic implant was designed to mimic the anatomical shape of his contralesional hemipelsis. The implant was made of 3D-printed biomedical-grade titanium alloy Ti-6 Al-4V (Ti64) and secured to remaining bone through nine commercially available Ti64 compression screws (inner diameter: 3.0 mm, outer diameter: 6.5 mm, pitch: 2.75 mm, length: 15 to 80 mm; MicroPort Orthopedics, Arlington, TN, USA). The operating surgeon planned the location and trajectory of the screws and the orthopedic implant company incorporated corresponding screw holes into the subject's custom pelvic implant design. Three cancellous bone screws were inserted through the implant's acetabular cup--two into the ilium and one into the superior cubic ramus, while six bi-cortical screws were inserted through two extracortical flanges into the ilium--four through the anterior flange and two through the posterior flange (Figure 1a).  
Post-operative walking data were collected from the subject eleven months after surgery following plateau in recovery. The experimental protocol, including the placement of static and dynamic retroreflective markers and surface and fine-wire electromyography (EMG) sensors, was as described in [PERSON] et al. [24]. The subject completed a static standing trial, a split-belt treadmill gait trial at a self-selected speed, squatting down and up trials, and stair ascent and descent trials during a single test session. Experimental data collected during all trials included video motion capture (Qualisys AB, Goteborg, Sweden), ground reaction (Bertec Corporation, Columbus, OH, USA), and wireless EMG (Cometa, Barberino Tavarnelle, Italy).
# Traffic Simulation of Future Intelligent Vehicles in Duisburg City Inner Ring
## 2 Methodology  
### Simulation Scenario  
The city of Duisburg is a medium-sized city with approximately 500,000 inhabitants [20]. As a type of medium-large-scale European city, it constitutes the \"most important class of cities in Europe in demographic terms\" [21]. Compared to large cities, challenges like fewer resources, funding, and organizing capacity are constantly faced by this kind of medium-sized city [21]. However, cities of this kind have the advantage of various transportation networks [22]. For example, Duisburg has great connectivity to the national and European transport network in air, rail, and road aspects. In the transformation process to a smart city, smart mobility including road transportation is an indispensable part [23].  
Duisburg's inner ring is located in the heart of Duisburg, as shown in (Figure 1). As the transportation hub of the city, the inner ring is next to Duisburg's Central Railway Station, and all three subway lines pass by this area. The east-west pedestrian street in the ring is not only the largest shopping and entertainment street in Duisburg city, but government agencies (like city hall), the district court, and tax office are located nearby as well. Regarding the future traffic pattern, many researchers have a positive attitude towards car-sharing and estimate that 90% of car-sharing bookings will occur in city centers [24]. Thus, the simulation scenario of Duisburg's inner ring might also be interesting for future car-sharing or autonomous car-sharing planning.
## 4 Implementation and Discussion
### Limitation and Future Work
#### 4.3.2 Identical IFI of the Decision Giving Entities  
Equal value of prioritization evaluation factors within an entity might also give rise to conflict in PoF. Individual familiarity index (IFI) of the decision giving entities depends on decision judging factors. In some cases, IFI value can be identical for two of the same kind of entities. However, in the future PoF plans to handle the situation by prioritizing decision judging factors of each entity (P, D, I\({}_{c}\), and C\({}_{p}\)). For example, if two doctors' (A and B) IFI value are x and x' (where x = x'), current PoF cannot handle that where job experience time (J\({}_{ET}\)) and treatment success rate (T\({}_{SR}\)) are decision judging factors. Current PoF evaluates both J\({}_{ET}\) and T\({}_{SR}\) equally, but in the future, PoF plans to weigh between J\({}_{ET}\) and T\({}_{SR}\). Therefore, equal IFI value (x = x') conflict can be solved after fine-tuning the mentioned issue.
# Energy Saving Strategy of UAV in MEC Based on Deep Reinforcement Learning
###### Abstract
The UAV computing task adopts the binary offload method, and the offload scheduling is implemented based on the time division multiple access (TDMA) communication protocol. In order to achieve the research goal of maximizing the residual energy of the UAV, we propose an optimization scheme to jointly optimize the UAV time and communication resources. The research objective is formalized as the problem of maximizing the residual energy of UAV. 2. The formalized problem of maximizing the residual energy is a non-convex problem and difficult to solve. However, once the offloading decision is given, the problem can be transformed into a convex problem, which can be solved by a convex optimization method. Therefore, we split the target problem into two sub-problems: the time resource allocation problem and task offloading problem. Firstly, we use the convex optimization method to solve the time resource allocation problem, and then obtain an online task offloading strategy that maximizes the residual energy of the UAV in wireless fading environments based on the DOTO algorithm. 3. To reduce the computational complexity of the DOTO algorithm, we propose a new adaptive \(K\) quantization method, which reduces the quantization action of the algorithm with the increase in the time block. Under the condition of ensuring the quality of the offloading strategy, the exponentially increased delay due to the increase in the number of devices is reduced so as to be almost unchanged. 4. Simulation experiments show that the DOTO algorithm proposed for the research objective of maximizing the UAV's residual energy in MEC can provide users with an online computing offloading strategy that is superior to other traditional benchmark schemes. Moreover, when there is a UAV in the MEC system that exits the system due to insufficient power or failure, or if a new UAV is connected to the system, the DOTO algorithm can still quickly converge to provide an effective online computing task offloading strategy for the MEC system in time. The remainder of the article is organized as follows. The second section discusses related work. The third section introduces the system model. The fourth section provides the problem formulation and solution. The fifth section describes the DOTO algorithm. The sixth section provides the simulation results. The seventh section gives the conclusions.
# Multi-Level Joint Feature Learning for Person Re-Identification
## 4 Results
### Comparison with DukeMTMC-reID  
We compare the MFF model with a state-of-the-art model on DukeMTMC-reID. Comparative details are shown in Table 6. Methods of extracting features are different in Table 6, for example, LOMO+KISSME [6] extract local features with a horizontal occurrence model, whereas PAN [41] and SVDNet [34] use a deep learning method to extract global features.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**CUHK03-Detected**} \\ \cline{2-3}  & **Rank-1** & **mAP** \\ \hline BoW + KISSME [33] & 6.4 & 6.4 \\ LOMO + KISSME [6] & 12.8 & 11.5 \\ IDE [42] & 21.3 & 19.7 \\ PAN [41] & 36.3 & 34.0 \\ DPFL [43] & 40.7 & 37.0 \\ SVDNet [34] & 41.5 & 37.3 \\ HA-CNN [25] & 41.7 & 38.6 \\ \hline MLFN [44] & 52.8 & 47.8 \\ \hline PCB+RPP [11] & 63.7 & 57.5 \\ \hline MGN [45] & 66.8 & 66.0 \\ \hline MFF (Ours) & 67.4 & 66.7 \\ \hline \hline \end{tabular} Rank-1 accuracy (%) and mAP (%) are compared.  
\end{table}
Table 4: Comparison with existing methods on CUHK03-detected data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**CUHK03-Labeled**} \\ \cline{2-3}  & **Rank-1** & **mAP** \\ \hline BoW + KISSME [31] & 7.9 & 6.4 \\ LOMO + KISSME [6] & 14.8 & 11.5 \\ IDE [42] & 22.2 & 19.7 \\ PAN [41] & 36.9 & 34.0 \\ DPFL [43] & 43.0 & 37.0 \\ SVDNet [34] & 40.9 & 37.3 \\ HA-CNN [25] & 44.4 & 38.6 \\ \hline MLFN [44] & 54.7 & 49.2 \\ \hline MGN [45] & 68.0 & 67.4 \\ \hline MFF (Ours) & 69.6 & 68.8 \\ \hline \hline \end{tabular} Rank-1 accuracy (%) and mAP (%) are compared.  
\end{table}
Table 5: Comparison with existing methods on CUHK03-labeled data.  
We evaluate the MFF model on DukeMTMC-reID with single-query-setting and the significant advantage can be observed in Table 6. Rank-1 accuracy reaches 86.0% which achieves the highest accuracy in comparison methods. We also use mAP as an evaluation indicator. MFF model reaches 76.1% in mAP. Extracting local features and global features enrich the available features when searching for target pedestrians. Adding a classifier in different levels of ResNet50, which is good for extracting part features, can also increase the accuracy of our model. In addition, we visualize the top-10 ranking results on DukeMTMC-reID for some randomly-selected query pedestrian images in Figure 7.
# Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications
## 2 Dimensionality Reduction
### Feature Selection Algorithms
#### 2.3.9. Whale Optimization Algorithm  
The Whale Optimization Algorithm (WOA) is a metaheuristic optimization technique inspired by the hunting behavior of humpback whales. It was first introduced by [PERSON] and [PERSON] in 2016 [62]. The algorithm mimics the hunting process of humpback whales, where they work together to catch their prey. It produces competitive results when compared to other state-of-art meta-heuristic algorithms and other more conventional methods [62; 63; 64].  
The key steps involved in WOA are the following [62; 63; 64]:  
* Step 1: An initial population of candidate search agents (whales), is initialized in the search space with initial positions and velocities.
* Step 2: The fitness of each whale is measured using a fitness function.
* Step 3: Whales move towards the current best agent (prey) according to their velocity. This happens as long as the distance between the two agents is greater than or equal to a threshold while incorporating a random vector.
* Step 4: Other agents simultaneously approach the prey while following a spiral path around it, mimicking the animals' real behavior. This can also be referred to as the exploitation phase.
* Step 5: Occasionally, whales randomly explore new areas influenced by the locations of other agents, which allows them to avoid getting stuck in local optima. This phase is also referred to as the exploration phase.
* Step 6: Steps 2-5 are repeated until a stopping criterion is reached, such as reaching a maximum number of iterations or attaining a satisfactory solution.