esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
The results of the experiment confirm the feasibility of using magnetic detection technology based on the geomagnetic field in gesture recognition. Moreover, the application of the one-dimensional convolutional neural network algorithm compensates for the lack of stability in gesture recognition when using one single type of sensor. With the implementation of wireless communication between sensors, the practicality of the magnetic field sensor glove will be enhanced. However, it is important to note that the experiment has only tested 26 gestures defined by the American Sign Language alphabet. Further research is necessary to enhance recognition of dynamic and complex gestures.
# Transportation and Reserve of Emergency Medical Supplies during Public Health Events
## 7 Countermeasures and Suggestions  
### Countermeasures and Suggestions Based on the SEIRD Prediction Model  
According to the SEIRD prediction model designed in this paper, if relevant countermeasures can affect the corresponding model parameters, it can effectively reduce the losses caused by public health issues in reality. In this paper, according to the parameters such as \"infection rate\", \"cure rate\", and \"mortality rate\" in the SEIRD model and according to the specific situation of epidemic situation, relevant countermeasures and suggestions are put forward:  
* Isolation Countermeasures:  
People take the countermeasures of self-isolation, and confirmed patients should be isolated in designated places to prevent the spread of viruses, so it can effectively reduce the parameter \"infection rate\" and prevent a large-scale outbreak of the epidemic. Timely isolation of susceptible persons and confirmed patients can not only reduce the penalty cost and time cost caused by the transportation and storage of emergency medical materials, but also prolong the development of vaccines;  
* Vaccination Countermeasures:  
Vaccination of susceptible people should be carried out in time. The sooner it starts, the more large-scale group immunity can be generated, and it is more difficult for viruses to invade. It is the vaccination of susceptible people (young people, elderly people, and people with serious diseases) who have a higher \"infection rate\" and \"mortality rate\" and can easily become a breakthrough in public health issues;  
* Medical Countermeasures:  
Medical points in each region should strengthen their ability to cope with public health issues and strengthen the medical level of hospitals, health centers, and other medical units, which can improve the \"cure rate\" and reduce the \"mortality rate\". Through the initial outbreak of pneumonia in COVID-19, Wuhan, it can be seen that medical personnel are susceptible people who come into direct contact with patients, and medical units should strengthen the professional and technical training of medical personnel to reduce the \"infection rate\" of such people. At the same time, rural areas have poor sanitary conditions and a low medical level, so we should focus on the ability to deal with public health issues in rural areas.  
It can be seen that the countermeasures and suggestions obtained according to the model parameters should isolate the virus to the maximum extent, and make people have group immunity as soon as possible, so as to gradually overcome the difficulties of this epidemic. After this epidemic, countries around the world have greatly improved their experience in dealing with and coping with public health issues, which provides help for the possibility of recurrence of public health issues in the future, but we still need to continue to study more effective corresponding ways.
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 3 An Overview on IoT Communication Protocols and Frameworks
### Comparison of IoT Communication Approaches
#### 3.8.4 Security  
Given the urgent requirement for security in IoT environments, the most recent approaches already consider specific security mechanisms. QUIC, for instance, integrates its own encryption mechanism. DDS provides a specification for data security. The most complete approaches in regards to security are OPC UA and NDN. OPC UA integrates a specific security framework covering user security based on X.509; session security based on _AAA_; transport security, based on the encryption of messages. NDN integrates security by design. Not only does this architecture support user, data and channel security; it also secures the binding from name to content, which is relevant, for instance, in situations such as playback attacks.
## 4 Experiments
### Comparison between FastSLAM and FastSLAM-PSO
#### 4.2.2 Large-Scale Scenario  
The mapping results for the large-scale scenario are visually depicted in Figure 4, showcasing the challenges faced by FastSLAM, FastSLAM-PSO, and FastSLAM-DE algorithms, particularly during the exploration of elongated corridors. The FastSLAM algorithm, lacking loop closure detection, accumulates significant deviations over time, leading to trajectory drift. In contrast, the FastSLAM-PSO algorithm, empowered by PSO optimization, mitigates these deviations, demonstrating superior path prediction and map construction capabilities. Similarly, FastSLAM-DE, which employs a differential evolution optimization framework, exhibits performance akin to FastSLAM-PSO, surpassing the traditional FastSLAM. Figure 5 compares the deviations of the three algorithms along the X and Y axes, revealing that the deviations in the FastSLAM algorithm are substantially greater than those of FastSLAM-PSO and FastSLAM-DE. Both FastSLAM-PSO and FastSLAM-DE provide more precise estimates in most instances, further validating the efficacy of optimization techniques in enhancing SLAM performance. The integration of advanced optimization algorithms like PSO and DE in SLAM methods significantly improves the estimation accuracy and robustness of the algorithms, especially in complex and large-scale environments.  
Figure 4: _Cont._
# Correlation Measures in Metagenomic Data: The Blessing of Dimensionality
## 2 Materials and Methods
### Generation of Gaussian Data for Characterization of L1 and CLR Correlation Biases  
Our examination of the biases introduced by L1 and CLR transformations began with the creation of synthetic datasets modeled on Gaussian distributions. This methodology was specifically crafted to underscore the compositional biases inherent in metagenomic datasets, with a concentrated focus on dimensionality (\(D\)) and within-sample diversity (\(\underline{P}\))--elements that are fundamentally tied to the compositional nature of the data. Our objective was to isolate and examine biases arising specifically from these compositional attributes, recognizing their direct impact on correlation analysis. While we acknowledge that sparsity and non-Gaussian distribution patterns also affect correlation metrics, these elements are secondary in the context of compositional data analysis. They were thus delineated outside of this study's primary scope and are addressed in a subsequent section.  
Utilizing the mvtnorm (v1.3) R package [29], we constructed a matrix of variables following a multivariate Gaussian distribution. In this matrix, the dimension \(D\) corresponds to the variables (or taxa), and \(N\) signifies the number of observations or samples, all governed by a predefined correlation matrix. To enable the calculation of the Pielou index without modifying the correlation structure, all generated values were shifted to be positive.  
To tune the within-dataset diversity of the generated Gaussian data, a simple but functional strategy was employed: applying a multiplicative factor to one selected variable from the Gaussian-generated dataset. This deliberate manipulation skewed the distribution towards this variable, thus altering the dataset's diversity (\(\underline{P}\)) without distorting the established correlation structure.  
Following this adjustment for within-dataset diversity, both L1 and CLR transformations were applied to the synthetic datasets. We then extracted the correlation matrices from these transformed datasets to analyze the biases each normalization method introduced.
# A Stackelberg Game Approach for Price Response Coordination of Thermostatically Controlled Loads
## 2 Problem Formulation  
In this paper, we consider a typical office or residential building equipped with a coordinator called EECC, whose role is to collect energy resources from the electricity market and allocate them to a group of TCL users \(\mathcal{N}\equiv\{1,2,\ldots,N\}\). The buying price of EECC is the market price, denoted by \(\mathsf{P}\), and the selling price is determined by itself, denoted by \(p\). Each TCL user \(i\) (\(i\in\mathcal{N}\)) chooses its set-point temperature, denoted by \(\widehat{\theta}_{i}\), based on the broadcasted price \(p\) from EECC. Then, EECC will provide the energy demand \(u_{i}\) to TCL \(i\) to reach the set-point temperature \(\widehat{\theta}_{i}\). The above energy trading process is shown in Figure 1.  
In this paper, we suppose that each TCL user is a price-taker and its decision will not affect the market price \(\mathsf{P}\). This is a universal assumption when the market involves a large population of users [23, 24]. Denote the time horizon by \(\mathcal{T}\) with \(\mathcal{T}\equiv[t_{k},t_{k+1}]\), where \(t_{k}\) is the start time of this horizon, and \(T\equiv t_{k+1}-t_{k}\) is the length of the time horizon.  
Section 2.1 provides the model of the TCL dynamics, based on which the relationship between the set-point temperature of a TCL and its energy demand is established in Section 2.2. Then, in Section 2.3, the energy trading process is introduced together with the preferences of TCL users and EECC.
## 3 Results and Discussion
### Feasibility of Applying Circular Economy Strategies to Different Building Types
#### 3.2.7 Residential Buildings  
Residential buildings ranked seventh in feasibility for applying CE principles, with a total score of 5. This rank places residential buildings at the lowest level for CE feasibility among the various building types examined in this study. Most interviewees who rated residential buildings low provided a similar rationale for their scores. The reason was residential properties are often privately owned by individual homeowners who lack awareness of CE concepts and tend to prioritize esthetics over sustainability. Additionally, many homeowners favor cost-effective solutions with immediate benefits, rather than investing in long-term sustainable practices. Demand for reused or recycled materials is also limited due to concerns about perceived quality and esthetic preferences. Conversely, one interviewee (C1), who ranked residential buildings highly, highlighted the potential for modular and offsite construction in social housing.  
The Impact of Building's Project Lifecycle on the Adoption of CE Principles in Construction Projects  
This section evaluates the perceived impact of different building's project lifecycle on the adoption of CE principles, as shown in Table 7. Participants were asked to assess the significance of each lifecycle stage on the application of CE principles, using a ranking system. The lifecycle stages were categorized into three levels: high (scores of 1 and 2), medium (scores of 3 and 4), and low (scores of 5 and 6). High-impact stages were then awarded 3 points per response, medium-impact stages receive 2 points, and low-impact stages earn 1 point. The total points for each lifecycle stage are calculated from these weighted responses, providing a comprehensive measure of its overall influence. The stages are then ranked by total score, identifying the critical phases where CE principles can be most effectively adopted.
# Evaluation of Coastal Sea Level Offshore Hong Kong from Jason-2 Altimetry
## 4 Methodology
### Altimetry Data Processing
#### 4.1.2 Choice of the Geophysical Corrections
For that reason, we adopted the MLE4 SSB in the computation of all SLAs, resulting in a relative bias <1 cm for all retrackers. In Equation (1), some parameters are available at 20-Hz (e.g., _E_), and others at 1-Hz (e.g., _D_). We interpolated all 1-Hz parameters at 20-Hz and finally computed 20-Hz SLA. The 20-Hz SLA is more useful for the retracker performance analysis, and can also be used for subtle feature detection.
## 3 Detection of Damage Occurrence
### Summary of Response-Based Methods  
In general, the response-based methods are straightforward, easy to use, and not computational intensive, but at the same time they do not give a quantitative assessment of damage. It is a valuable tool for damage identification in the preliminary stage. A guideline for using the response-based methods is given in Table 1 based on the pros and cons of those methods.
# Enhanced High-Definition Video Transmission for Unmanned Driving in Mining Environments
## 5 Conclusions  
Recognizing the need for real-time transmission of multi-channel video and uplink transmission of vehicle surrounding environment monitoring in mining unmanned driving, we have compiled and organized video transmission resolution, transmission rate, and latency indicators for remote monitoring in mining unmanned driving. Based on this, we propose an unmanned vehicle video transmission technology scheme that utilizes 5G super uplink, collaborative computing, and video compression.  
This scheme leverages the 3.5 GHz and 2.1 GHz super uplink transmission, resulting in significant improvements in the uplink transmission rate and reduced transmission latency. It incorporates task offloading and edge computing techniques to address the  
Figure 11: Experiment on remote-controlled unmanned driving.  
Figure 10: Average latency under different offloading ratios: (**a**) 1080P video; (**b**) 4K video.  
limitations of terminal computing power, mitigating the delays caused by computation power, and employs video compression strategies to enhance low-latency performance.  
Furthermore, we conducted an unmanned vehicle video transmission experiment utilizing 5G super uplink technology. The results of the experiment demonstrate that when transmitting seven channels of 4K high-definition video, the uplink transmission rate matched the theoretical rate, and the average latency met the requirements for unmanned remote control in mining applications.  
Looking ahead, high-definition video will be seamlessly integrated with data such as LiDAR point clouds, enhancing the perception dimensions and accuracy of the vehicle's surroundings, thus providing reliable perception data for unmanned driving in mine.  
Conceptualization, L.Z., W.Y. and C.L.; methodology, L.Z. and C.L.; validation, C.L. and L.Z.; formal analysis, W.Y.; investigation, W.Y. and C.L.; data curation, C.L. and L.Z.; writing--original draft preparation, C.L. and L.Z.; writing--review and editing, L.Z.; supervision, W.Y.; project administration, C.L.; funding acquisition, L.Z. and W.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (62071032); and the Key Project of Innovation and Entrepreneurship Fund of Tiandi Science & Technology Co., Ltd. (2023-TD-ZD005-001, 2023-TD-ZD005-005, 2022-2-TD-ZD001).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy
## 3 Results and Discussion
### Sensor Structure Design  
#### 3.2.1 The Optical Design of the Sensor Probe  
Figure 3 displays the structure view of the sensor probe in this moisture detection sensor. The entire sensor probe mainly consists of a fully symmetrical case, two light sources, a high-pass filter, a lens and a photodiode. The upper and lower shells of the sensor probe were printed by 3D printing technology, and an InGaAs photodiode with the dark current property was adopted, whose response range was from 800 nm to 1700 nm. Given that the maximum saturation current generated by photodiode sensitivity was only a few milliamperes (AD620), it was necessary to amplify the weak signal. Therefore, the sensor probe also integrated an I-V conversion amplifier which could convert a weak current signal to a larger voltage signal.
# Collision Avoidance from Multiple Passive Agents with Partially Predictable Behavior
## 6 Implementation and Results
### Collision Avoidance with Multiple Passive Agents
#### 6.3.3 Comparison with AVO  
In order to make a comparison with AVO [23], the robot with kinodynamic model as of double integrator is considered. We have modified AVO by removing its reciprocal collision avoidance aspect and keeping the linear programming optimization. The control parameter \(\eta\) is set to 3 for these experiments. For \(U_{safe}\), \(\beta\) is set to 1.2. The graphs in Figure 10 presents the performance of proposed \(U_{safe}\) approach for selected values of \(\beta\). The experiment results in Figure 11 show the performance of AVO algorithm and the proposed \(U_{safe}\) approach. It shows the effect on average computation time, success rate, and time elapsed, against varying number of passive agents. In Figure 11, (a) shows the average computation time for the two approaches, where AVO takes slightly lesser time on average for computing the solution. (b) shows the comparison of average success rate, the success rate for AVO is much lower as compared to \(U_{safe}\). (c) shows that the elapsed time is almost same for the two approaches.  
Figure 10: In this graph, the performance of the proposed algorithm \(U_{safe}\) for double integrator is presented for the scenario when 20 passive agents are present. (**a**) show the success rate for selected values of \(\beta\). (**b**) show the elapsed time for different values of \(\beta\). The robot takes comparatively longer time to reach goal when the larger value for \(\beta\) is set, as robot take the safer path to the goal rather than a direct path to it. Results show that robot reaches the goal with relatively high success rate when \(\beta=1.2\) is selected, while the elapsed time is comparatively reasonable.  
Figure 9: It shows the performance of GVO algorithm and the proposed \(U_{safe}\) approach. The effect on average computation time, success rate, and elapsed time, are shown against varying number of passive agents. GVO: Generalized Velocity Obstacle.  
Under AVO approach, the velocity obstacle induced due to each passive agent is approximated by a half plane in velocity space, this leads to a computed collision free control space which is frequently empty, thus give rise to the number of the failures in computing the solution.
# Multilayer Backbones for Internet of Battlefield Things +
## 3 Proposed Heuristic Distributed Algorithms
### Computation and Communication Complexities  
#### 3.3.1 Complexities of \(Ccds\)  
\(CCDS\) requires 7 rounds of communication among nodes to complete. In each round, at most one packet is sent over the wireless channel.  
* The 2-hop neighborhood information used by the relay node set election process is collected via two rounds of information exchanges.
* In round 1, each node advertises its ID and builds its 1-hop neighbor set based on the advertisement of its neighbors.
* In round 2, each node advertises its 1-hop neighbor set and identifies links among 1-hop neighbors.
* In round 3, each node calculates its clPCI index value and advertises it together with its 2-hop neighbor set. Then, it identifies links among 2-hop neighbors.
* In round 4, each node calculates and advertises its own relay node set and updates 1-hop neighbor status.
* In round 5, the restricted Rule-\(k\) is applied to each relay node and each one of them advertises its status.
* In round 6, each node advertises its updated relay node set
* In round 7, the composition of the updated relay node set is advertised (if needed).  
The computation complexity of its comprising parts is: \(O(\Delta^{2})\) for the \(clPCI\) calculation, and \(O(\Delta^{3})\) for the relay node set election process and for the pruning phase, where \(\Delta\) is the maximum node degree found in the network.
## 3 Experimental Validation
### Other Examples  
To validate the proposed method, we tested more examples for semantic segmentation. Figure 10 shows the objects tested in the experiment. Figure 11a shows that many of point data of the wrench were misclassified into another class. However, Figure 11b shows that the proposed method segmented most of the point clouds correctly. The reason is that the imbalanced problem makes the baseline  
Figure 8: Comparison of ground truth and prediction results obtained when XYZ point cloud data was applied to the baseline or proposed training methods. The prediction results from the baseline training method were clearly erroneous: (**a**) ground truth; (**b**) point cloud predicted using conventional training method, showing erroneous prediction where the pipe merges into the wrench; (**c**) point cloud predicted using the proposed hybrid training method.  
Figure 9: Comparison of ground truth and prediction results obtained when XYZ point cloud data was applied to the baseline or proposed training methods. The results obtained using the baseline training method were clearly less accurate than those obtained using the proposed method: (**a**) ground truth point cloud; (**b**) point cloud predicted using conventional training method, showing that most of the points associated with the wrench were not detected; (**c**) point cloud predicted using the proposed hybrid training method.  
method insufficiently trained, causing inaccurate predictions. This impact makes the segmentation quality low and fail robotic grasps. Figure 11c,d show that the point data of the eraser were also misclassified into another class by the baseline method but not by the proposed method. Figure 11e,f show that many points of the eraser were misclassified into the wrench.  
Figure 11: Comparison of the baseline deep network and the proposed method: (**a,c,e**) are the results of the baseline method; (**b,d,f**) are the results of the proposed method.  
Figure 10: Tested objects: (**a**) cube; (**b**) eraser; (**c**) wrench.
## 2 Materials and Methods
### Data Collection and Parameters Extraction
#### 2.4.1 Kinematic Data  
For each participant, continuous series of 3D knee kinematics of full gait cycles during treadmill walking were collected. 3D kinematics describes the three rotations occurring at the knee joint, flexion/extension (in the sagittal plane), abduction/adduction (Abd/Add) (in the frontal plane), and internal/external rotation (Int/Ext rot) (in the transverse plane). Each gait cycle kinematic pattern was then interpolated to a hundred points (from 1% to 100% of the gait cycle) and averaged to obtain a representative mean gait pattern for each participants and for each rotations. Then, among these 100 measurements for each kinematics curves, a set of 69 parameters routinely assessed in clinical biomechanical studies for knee OA populations has been extracted. These parameters are based on maximum and minimum, varus and valgus thrust, angles at initial contact, and mean values and range of motion (ROM) throughout gait cycles or specific gait subcycles (i.e., loading, stance, swing, etc.). Hereafter, the variables representing these parameters are noted X1, X2,..., X69.