esa-sceva/satcom-chunk-collection huggingface dataset:
# Bridging the Appearance Domain Gap in Elderly Posture Recognition with YOLOv9
## 5 Discussion
For example, the average F1-score was 0.25 for the VIRTUAL-REAL experiment and 0.97 for the HYBRID-REAL experiment, almost four times higher. Here, on the other hand, the appearance domain gap was largely bridged by including some real (natural) images in the virtual dataset in order to detect postures in the real world. More concretely, when looking at the different metrics, Figure 11 helps in providing very meaningful information. Across all experiments, the precision for the _sitting_ posture consistently achieved a perfect score except in the VIRTUAL-REAL experiment, where the overall average precision was significantly lower. This indicates that _sitting_ was generally well identified by the model, except when real-world data were introduced, as seen in the VIRTUAL-REAL experiment. The recall metric showed the most variability, especially in the VIRTUAL-REAL experiment, where the model failed to detect the _lying_ posture entirely. This suggests that the model struggled with detecting certain postures when transitioning from a virtual to a real environment. The accuracy remained high in most experiments, particularly in the HYBRID-REAL experiment, indicating that combining virtual and real data led to more reliable performance across all postures. However, the significant drop in accuracy for the _sitting_ posture in the HYBRID-VIRTUAL experiment (0.64000) highlights potential challenges when mixing datasets. The F1-score followed the trends observed for the recall. Behind the VIRTUAL-VIRTUAL case, the HYBRID-REAL experiment achieved the highest overall F1-score, suggesting that the integration of real data with virtual data significantly improved the model's performance. The lowest F1-score was observed in the VIRTUAL-REAL experiment, again highlighting the difficulties in generalising from virtual to real environments. Overall, the HYBRID-REAL experiment demonstrated the best overall performance across all metrics, suggesting that the combination of virtual and real data is the most effective strategy for enhancing experiences with real-world images. On the other hand, the VIRTUAL-REAL experiment showed the poorest performance, indicating that the model struggled the most when tested on real data without prior exposure to natural images. These comparisons emphasise the importance of incorporating real data during the training process, as seen in the improved metrics of the HYBRID-REAL experiment. Additionally, the variability in the recall and F1-score across different postures highlights the challenges in detecting certain postures consistently, especially when transitioning from virtual to real environments. Thus, the results indicate that while the model performs exceptionally well in the virtual environment with both datasets, its performance in real-world scenarios is vastly im  
Figure 11: Comparison of results per metric of the 4 experiments. proved when trained with a hybrid dataset. This highlights the need to include real-world data in the training process to ensure the effectiveness of the model in practical applications.
# Macroscopic Traffic-Flow Modelling Based on Gap-Filling Behavior of Heterogeneous Traffic
## 3 Proposed Traffic-Flow Model
\[P=\rho\theta, \tag{4}\]0The road capacity can be represented by the average headway since the average headway is constant for a particular road capacity, while the number of vehicles accommodated in that particular road section under the said road capacity can be represented by the traffic density. Hence, this can be expressed as follows:  
\[P=\rho\theta, \tag{4}\]1  
where \(l\) is the average distance headway. After developing analogies for gas parameters in Equation (3) with traffic parameters (see Table 1), substituting Equations (4)-(6), (11), and (14) in Equation (3), we get the following:  
\[P=\rho\theta, \tag{4}\]2  
The left-hand side of Equation (15) has the units of number of cars (or vehicles) per unit time, i.e., the traffic flow. Dividing \(l\) on both sides, and knowing that \(N_{C}/l=\rho\) and \(b/v=t\) (time headway), Equation (15) can be rewritten as follows:  
\[P=\rho\theta, \tag{4}\]3  
Multiplying \(\rho\) on both sides, re-arranging, and taking the integral on both sides, we get the following:  
\[P=\rho\theta, \tag{4}\]4  
Rearranging for \(\theta\), we can further establish the following:  
\[P=\rho\theta, \tag{4}\]5  
This variance in velocity is due to the drivers' anticipation of filling the gaps seen ahead of them. It has units of \(\left(\text{m}/\text{s}\right)^{2}\) and depends on the average distance and time headway of the traffic. The proposed model is a macroscopic traffic-flow model based on the gap-filling behavior of heterogeneous traffic. It is derived by replacing the driver's anticipation constant \(C_{0}\) in the PW model (Equation (2)). The proposed model can be written as follows:  
\[P=\rho\theta, \tag{4}\]6  
\[P=\rho\theta, \tag{4}\]7  
Therefore, instead of assuming a constant behavior for all the drivers in the traffic (as in PW model), the drivers, will now adjust to the forward changing density conditions based on the distance headway and time headway according to the proposed model. This aspect is a substantial improvement upon existing models.
# Efficient Task Scheduling Using Constraints Programming for Enhanced Planning and Reliability
## 3 Methodology
### Justification for Selecting Constraints Programming
#### 3.4.3 Resource Utilization and Task Dependencies  
In MRO scheduling, it is critical to ensure that resources (e.g., workplaces, tools, and personnel) are optimally allocated, and that task dependencies are strictly followed. Metaheuristic approaches like genetic algorithms and simulated annealing can handle these constraints but often do so through approximation, potentially leading to suboptimal resource utilization or violations of task dependencies.  
CP, by contrast, is designed to handle these types of complex, interdependent constraints natively. In our approach, the task dependencies, resource availability, and workplace limitations are explicitly modeled as part of the constraint satisfaction process. This allows the CP algorithm to ensure that all tasks are scheduled in accordance with their dependencies and that resource allocation is optimized. The result is a highly efficient schedule that maximizes resource utilization (up to 100% in some cases, as shown in the experimental results) while minimizing idle time across workplaces.
# Null Subcarrier Index Modulation in OFDM Systems for 6G and Beyond
###### Abstract  
Computational complexity is one of the drawbacks of orthogonal frequency division multiplexing (OFDM)-index modulation (IM) systems. In this study, a novel IM technique is proposed for OFDM systems by considering the null subcarrier locations (NSC-OFDM-IM) within a predetermined group in the frequency domain. So far, a variety of index modulation techniques have been proposed for OFDM systems. However, they are almost always based on modulating the active subcarrier indices. We propose a novel index modulation technique by employing the part of the transmitted bit group into the null subcarrier location index within the predefined size of the subgroup. The novelty comes from modulating null subcarriers rather than actives and reducing the computational complexity of the index selection and index detection algorithms at the transmitter and receiver, respectively. The proposed method is physically straightforward and easy to implement owing to the size of the subgroups, which is defined as a power of two. Based on the results of our simulations, it appeared that the proposed NSC-OFDM-IM does not suffer from any performance degradation compared to the existing OFDM-IM, while achieving better bit error rate (BER) performance and improved spectral efficiency (SE) compared to conventional OFDM. Moreover, in terms of computational complexity, the proposed approach has a significantly reduced complexity over the traditional OFDM-IM scheme.  
OFDM; index modulation; null subcarrier; computational complexity; 6G and beyond 2021  
[PERSON], [PERSON], A. Null Subcarrier Index Modulation in OFDM Systems for 6G and Beyond 2021  
13 October 2021  
Subscriber 31 October 2021
## 5 Conclusions  
The main research results are summarized as follows:  
1. The IPSO algorithm was proposed by introducing average particle distance, swarm fitness variance, inertia weight coefficient decreasing in parabolic form, and dynamic adjustment strategy of learning constants into the PSO algorithm. The hyper-parameters of the LSSVM model were tuned by the IPSO algorithm to establish IPSO-LSSVM model. The comparison results show that the IPSO-LSSVM model significantly improves the accuracy of the PSO-LSSVM model.
2. The case study indicates that the proposed reliability analysis method based on the IPSO-LSSVM can highly efficiently and accurately determine failure probability of concrete dams so that it is effective and feasible.
3. The reliability analysis method proposed in this paper can provide an effective tool for solving the reliability of other complex structures. The proposed method can also provide technical support for safety assessment, monitoring and operation management of gravity dams. In future studies, more complex engineering problems will be solved, such as considering the impact of environmental erosion and material aging on the dam safety. The proposed methods can play an important role in solving these problems together with other existing methods.  
Conceptualization, B.X.; methodology, B.X.; software, S.W.; validation, S.W.; investigation, S.W.; resources, S.W.; data curation, S.W.; writing--original draft preparation, S.W.; writing--review and editing, Z.Z.; visualization, J.L. ([PERSON]) and J.L. ([PERSON]); supervision, Z.Z.; project administration, Z.Z.; funding acquisition, B.X. All authors have read and agreed to the published version of the manuscript.  
The present work was supported by the National Natural Science Foundation of China (Grant No. 520/79120).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author, B.X., upon reasonable request.  
The authors declare no conflict of interest.
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 2 Using Ground Stations and Space Classrooms in Educational Scenarios  
Ground stations and space classrooms are a sky and space signal laboratory established as part of this research to train personnel at the International Academy of Aviation Industry (IAAI), King Mongkut's Institute of Technology Ladkrabang (KMITL), Thailand. It includes students from three disciplines: aerospace engineering: students learn about satellite communication and wireless communication, aeronautical engineering and com  
Figure 1: Ground station implement according to this research paper. (**a**) L-band station, (**b**) terrestrial and LEO satellite station, (**c**) ground control system.  
mercial pilot: students studying airborne communication and air traffic control voice communication, and logistics management: students focusing on air traffic management and GIS data. Moreover, it can be used as a laboratory for research purposes.  
This space classroom is designed to accommodate up to 20 students simultaneously, allowing them to receive signals and learn together, as shown in Figure 2a. Each student can control the signal reception at the ground station system, as shown in Figure 2b. With the capabilities of the ground station created, it can be applied to teaching various subjects, including:  
Subject 1: Wireless Communication: Students learn about radio frequency, propagation, band plans, link budget design, transceivers, antennas, signal modulation/demodulation, measurement instruments, and amplifiers. They can also learn about the components of the ground station, as depicted in Figure 2c. They can receive accessible signals from terrestrial, sky, and space sources, which will be further discussed in Section 3.  
Subject 2: Satellite Orbit and Application: Students learn about orbital kinematics, orbit prediction, and satellite communication. They can use the ground station to receive signals from weather satellites such as FengYun-2 (FY-2), which can be accessed and decoded, or scientific data collection satellites like LoRa CubeSat. They will also practice tracking satellites with LEO satellites passing through visibility, such as NOAA POES.  
Subject 3: Airborne Communications: Students in pilot-related fields can learn about aircraft communication through ADS-B from aircraft, including ATC voice communication between pilots and air traffic controllers. They will also study data from Inmarsat Aero, which is information sent from aircraft to satellites.  
Subject 4: Geo-informatics and Remote Sensing: Students can use raw data from satellites to further their studies in geo-informatics. These data can be analyzed for various applications, such as weather forecasting from weather satellites like GEO-KOMPSAT-2A (GK-2A), and accessing multispectral imaging satellites like Electro-L. This information can be integrated and applied to various GIS tasks.
# Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning
## 5 Conclusions  
In this study, we estimated the yield of winter wheat in Henan Province using eight feature variables, including LAI, LST, and GPP, along with historical yield data. We proposed an ensemble voting model composed of gradient boosting, random forest, and ridge. The results show that the ensemble voting model demonstrated the highest accuracy among various machine learning models, both across the entire growth period and within individual growth stages, highlighting the stability and predictive accuracy of this approach for crop yield estimation. Additionally, we found that the heading stage had the greatest yield prediction potential, which may be linked to the stabilization of the wheat plants' morphology and nutrient accumulation during this period. This finding provides critical information for the early allocation of agricultural resources, greatly aiding in the achievement of food security and precision agriculture. By constructing a yield spatialization model, we refined the county-level yield predictions to the pixel level, avoiding the complexities and computational difficulties associated with direct pixel-level yield estimation, thus offering an effective solution for pixel-level winter wheat yield prediction.  
Nevertheless, this study has some uncertainties and areas for improvement. Crop yield is influenced by multiple factors, including climate, soil properties, and human management practices, and the modeling features selected in this study may not cover all key factors, leading to potential instability in prediction performance. Future research should consider incorporating additional features to enhance the model's comprehensiveness and accuracy. Furthermore, the relatively limited training data may affect the model's training efficacy and generalization capability. Future studies could use longer time-series data or introduce adversarial networks and other methods to increase sample size, thereby better capturing the long-term trends and cyclical variations in winter wheat yield.  
Conceptualization, X.L. and Z.L.; methodology and data curation, Z.L. and S.L.; writing--original draft preparation, Z.L.; writing--review and editing, X.L. All authors have read and agreed to the published version of the manuscript.  
This research and the APC were funded by National Key Research and Development Plan of China (No. 2016 YFC0803103).  
Data Availability StatementData are contained within the article. The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Rock Slope Instability Mechanism Induced by Repeated Mining in Mountain Mining Areas
## 4 Numerical Simulation Analysis of the Collapse  
### Scheme of Numerical Simulation  
#### 4.1.1 The Numerical Model  
Based on the engineering geological condition, the repeated-mining-disturbed numerical model was established using the PFC 2D (Particle Flow Code 5.0) discrete element software, as shown in Figure 8. The complete process of crack initiation and propagation induced by the excavation disturbance can be simulated [28; 29]. The length of the model was 500 m, and the width was 345 m. The geological characteristics of the slope were designed basically. Two minable coal seams buried under the slope were established. The thickness of the upper and lower coal seams was 7 m and 4 m, respectively. The limestone, marl silty mudstone and muddy silststones were designed as the overburden. The dip angle of the rock stratum in the numerical model was 10\({}^{\circ}\). Four joint fissures were set at the top of the model to simulate the structural plane of the mountain prototype. The fissure depth changed from 50 m to 80 m, and the dip angle was about 70\({}^{\circ}\). The horizontal displacement of the left and right boundaries of the model was limited; the vertical displacement of model at the potion of the bottom boundary was also limited. The parallel bond model was used for simulating the mechanical behavior of the coal and rock mass. If the tensile-strength limit is exceeded, then the bond in tension is broken. If the bond has not broken in tension, then the shear-strength limit is enforced, and the stiffness will reduce with the breaking of the bond. Hence, this model is considered as a suitable bond model for the rock materials [30]. The parameters in the numerical model were listed in Table 3, which were determined by referring to the previous research and parameter calibration test [24; 31]. In the numerical model, the unconfined compression strength of limestone, marl, silty mudstone, coal and muddy silststone is about 69 MPa, 54 MPa, 51 MPa, 4.7 MPa and 38 MPa, respectively, according to the parameters listed in Table 3.  
Figure 8: The numerical model.  
Figure 7: The displacement induced by the mining of X3. (**a**) Horizontal displacement; (**b**) vertical displacement.
# Performance Comparison of NB-Fi, Sigfox, and LoRaWAN
## 3 An Overview of NB-Fi, Sigfox, and LoRaWAN
### Frame Format
#### 3.2.2 Sigfox  
Sigfox uplink frame structure is shown in Figure 4. It begins with a fixed 19-bit preamble: 101 0101 0101 0101.  
The preamble is followed by the Frame Type field (FT) which is 13 bits long and encodes UL-Container length, message type, and frame emission rank.  
The next field is UL-PHY-Content which is the result of encoding UL-Container and UL-CRC with a convolutional code with code rate \(\frac{1}{3}\).  
The UL-CRC field is 16 bits long and is required to evaluate the integrity of the frame. The UL-Container is 64 to 160 bits long and consists of the following fields:  
Length Indicator (LI): a 2-bit field. Its value depends on Payload and UL-AUTH;  
Bidirectional Flag (BF): a 1-bit field, set to 0 for U-procedure and to 1 for B-procedure;  
Repeated Flag (RF): a 1-bit field. Its value is set to 0;  
Figure 3: Downlink frame structure in NB-Fi.  
Message Counter (MC): a 12-bit field. The sensor increments the message counter by 1 every time a message is sent in the uplink; Identifier (ID): a 32-bit field, which consists of sensor identifier bytes loaded in reverse order; Payload: an up to 96-bit field containing the message content;  
UL-AUTH: 2 to 5 most significant bytes of the AES-based transformation of LI, BF, RF, MC, ID, and Payload.  
Sigfox downlink frame structure is shown in Figure 5. The preamble in the downlink frame is 91 bits long as opposed to the 19-bit long field in the uplink frame. The DL-PHY-Content is the result of whitening by XOR-ing ECC, DL-Container, and DL-CRC with a pseudo-random sequence. Error correction code (ECC) is a 32-bit field and is the result of the implementation of BCH15-11 Error Correction Codes over the concatenation of DL-CONTAINER and DL-CRC fields. The next field is the DL-Container, which consists of Payload (0 to 96 bits) and DL-AUTH (2 most significant bytes of the AES-based transformation of Payload). The DL-Container is followed by DL-CRC, which is required to evaluate the integrity of the frame.
# Translating Speech to Indian Sign Language Using Natural Language Processing
## 6 Hidden Markov Model  
The hidden Markov model is one of the models that may be used as a classifier; it consists of a set of states where the transition from one state to the next is determined by a  
Figure 4: System Architecture.  
specific input. As a result, the shift from state to state continues until the output state or observation is reached. Furthermore, the likelihood of a specific transition is influenced by the likelihood of a transition from before a current condition. The probability model is made up of three basic components: well-defined experiments, a sample space (\(\Omega\)) that contains all possible events, and an event that is chosen from the sample space. HMM, on the other hand, is based on conditional probability, which implies that the likelihood of a specific event X occurring relies on the probability of a previous event Y occurring [27]. This conditional probability can be expressed as follows: P(X\(\,\)Y) = P(X\(\,\)Y)/P(Y) 1. P(X\(\,\)I\(\,\)Y) = P(X\(\,\cap\,\)Y)/P(Y).  
Assume we have three states: verb, noun, and adjective, as indicated in Figure 5. Table 4 shows the transition matrix that shows the transition from one state to another. The transition probability from verb to adj is 0.01 and from adj to verb is 0.02.  
HMM tagging, also known as sequence labeling, is a method of mapping the tag sequence to the input sequence. Assume there is a tagging process with the following inputs: X1, X2, X3,..., Xm. The output will be a tag sequence or state sequence with the following inputs: Y1, Y2, Y3,..., Ym. The sentence will be the input for part-of-speech tagging, and the tag for each word in the sentence will be the output. As an example, if we have a five-word sentence, the output will be five tags, each representing a different portion of the speech. If the input is a sentence in the source language, the label will be a sentence in the target language in machine translation.  
There are three methods for dealing with the tagging problem: the first is the rule-based strategy, which relies on the use of predefined rules. However, rule-based systems can have several issues, including grammatical leaks, the inability to list all the rules, and the last issue, which is the variation in the rules over time, place, and a variety of other factors. The statistical-based strategy can also be used to tackle the tagging problem. Furthermore, a statistically based model is reliant on statistics as well as the availability of a trainable and already labeled corpus. Finally, the hybrid model, which includes both rule-based and statistical-based models, is the most common and practical solution to dealing with the tagging problem.