esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Discussion  
The results show that considering different horizontal speeds in target results is surely necessary. The change laws of some dynamic performances vary greatly at different horizontal speeds, even though the change of MCPSS remains same. For example, when MCPSS moves from the spinal joint to the geometric center of spinal segment gradually, the motion stability presents a monotonous decrease obviously at the low speed, but maintains a minor increase nearly at the medium and high speeds. For another example, when MCPSS moves from the geometric center of spinal segment to the hip joint gradually, the decrease of the spinal oscillation is more obviously at the high speed than at the low speed. In a word, the effect of the MCPSS on dynamic performances of quadruped bounding is significantly influenced by the horizontal speeds.  
More importantly, the results at different horizontal speeds together make the optimal or suboptimal MCPSS more prominent with respect to the dynamic performances. That is, a certain MCPSS benefits a set of dynamic performances at any horizontal speed, and another MCPSS benefits another set of dynamic performances at any horizontal speed. With MCPSS near the hip joint, the stride period facilitates the leg-orientation control best, and the stride length, average horizontal speed, and horizontal kinetic energy benefit the fast horizontal bounding the most, and the minimum spinal oscillation also gives the largest margin of spinal pitch angle. They are satisfied at any horizontal speeds. The smallest duty factor is also obtained at the medium and high horizontal speeds when MCPSS is at the hip joint, which also support the fast horizontal bounding. All these show that MCPSS near the hip joint is best for the fast horizontal movement and leg-orientation control of the quadruped bounding. However, when MCPSS is near the geometric center of spinal segment, the smallest maximum foot-end force and the smallest mass-center vertical fluctuation are both obtained at any horizontal speeds, which can significantly enhance the resistance to the vertical jitter and reduce the high torque-demand of joint-drivers. The above results make the hip joint and the geometric center of spinal segment as two best positions for the corresponding dynamic performances.  
Furtherly, for most real quadruped robots similar to the simplified quadruped model in this paper, the best MCPSS should be between the spinal joint and the geometric center of spinal segment, because most dynamic performances usually need to be met with equal importance. More simulations and experiments are needed to determine the best MCPSS for real quadruped robots.  
Figure 10: Relation between MCPSS with the maximum foot-end force at the low (red circle), medium (green triangle), and high (blue quadrilateral) horizontal speeds, respectively.
## 2 Route Generation Methodology
### Feasible Path Generation: Turn Radius Consideration
#### 2.4.1 Corner-Cutting Method  
In some cases, the angle between two consecutive line segments is not too large. This means that the turn is not too sharp. When this is the case, the sharp corner can simply be rounded off with respect to the vehicle's turn radius. This process is the corner-cutting method and is visualized in Figure 8. However, this approach may not be ideal for vehicles with a large turn radius. In these cases, the corner-cutting method can reduce the area coverage significantly by skipping a large portion of the original path. Therefore, this method should be used with caution when vehicles have a large turn radius.  
It should be noted that the corner-cutting method is only feasible in cases where the angle is not too small. If the corner-cutting method is applied to sharp turns, too much  
Figure 8: The corner-cutting method visualized. The original path in blue can be seen in (**a**), the vehicle turn radius can be drawn in red at the sharp turn in (**b**), and the final path after applying the turn radius to the turn can be seen in (**c**).  
of the desired path will be removed, thus reducing the total area coverage seen by the sensors. To prevent the corner-cutting method from removing too much of the desired path, a threshold was defined. If the portion of the desired path being removed is longer than twice the vehicle turn radius, then an additional method can be used. A visual representation of this condition can be seen in Figure 9.
## 5 Results and Discussion  
This section confirms the capability of the designed flight control system with either NLDI or MNLDI in tracking sharp maneuvers in roll, pitch and yaw channels in idle environment. Moreover, the robustness of the MNLDI against wind disturbances and model uncertainties is presented. The accuracy of tracking a predetermined path using geometric path following algorithms is also illustrated. In the first part of this section, the comparison between NLDI and MNLDI tracking performance is covered while focusing on the coupling between channels, in addition to the robustness of the designed flight control system against external wind disturbances and model uncertainties. The wind speed is 10 m/s,which is 55% of the Tiger-Trainer UAV cruise speed \(V_{a}=18\) m/s [21] from west directions to emulate extreme cases of external constant wind. The second part of this section is to illustrate the differences between the carrot chasing path following algorithm and PLOS path following algorithm. To design a complete flight control system, the guidance commands generated by either PLOS or CC path following algorithm are achieved by adopting MNLDI flight controller due to its robustness.
# Smart Road Traffic Accidents Reduction Strategy Based on Intelligent Transportation Systems (TARS)
## 5 Results and Discussion
### Basic Safety Message (BSM)  
The BSM plays a very important role in VANETs' environment to transfer safety messages. Safety messages need to be transmitted all the time to all neighbor vehicles. In order to provide reliability in the traffic network, safety messages must reach their destinations without causing channel congestion. Furthermore, sending safety messages without using a congestion control or avoidance mechanism causes the broadcasting storm problem. In TARS protocol, when the number of vehicles increases, then the number of basic safety messages also increases. Moreover, RSU makes intelligent decisions, sending safety messages to the other vehicles while considering the dynamic nature of topology and the broadcasting storm problem in order to adjust, and considers the current road traffic information. The RSU monitors the distance between two vehicles, if the distance is less than the defined threshold, then the RSU sends warning messages in order to make the vehicle aware of the status of the traffic network (whether it is congested or not) and distance. In order to provide the stability of the channel network, the RSU and vehicle only send warning and safety messages when it is really needed. In TARS, the number of basic safety messages are fewer compared to DBSR and POVRP, meaning TARS decreases the chances of the occurrence of channel congestion in order to provide the stability of the network protocol as well as improving the network performance depending on the capacity of the channel. The RSU sends warning messages after verifying the possibility of congestion occurrence in terms of high traffic load in the network, and informs the upcoming vehicle to take alternate routes to avoid congestion as shown in Figure 11.
## 4 Enhancing the Dynamic Multi-Hop Routing (EDMR) Protocol
### The Routing Phase of the EDMR Protocol  
This phase is responsible for forwarding the sensed data to the BS, where a dynamic routing approach is used for data transmission both within the clusters (intra-cluster) and between the CHs and INs across the network levels toward the BS (inter-cluster). The routing phase is divided into two sub-phases: the initial routing sub-phase and the active/sleep sub-phase. It is important to emphasize that the types of applications using this protocol are considered. Hence, the active/sleep sub-phase determines which clusters should be deactivated and which should remain active and continue operating. The sub-phases of this phase are discussed in detail below.
# An Adaptive Delay-Tolerant Routing Algorithm for Data Transmission in Opportunistic Social Networks
## 6 Conclusions  
This paper proposes a type of adaptive delay-tolerant routing algorithm using a curve-trapezoid Mamdani fuzzy inference system in opportunistic social networks (DTCM). Different from the latest algorithms, DTCM brings in the mechanism of fuzzy inference to establish the process of data routing and transmission. DTCM investigates the combined impact of remaining energy and cache space on data transmission from the perspective of the fuzzy uncertain method. The objective of the DTCM is to determine the fuzzy mapping from the two-factor to data transmission and to optimize the results of node classification with manual and systematic parameter adjustment. Another vital innovation in this paper is that an improved two-hop feedback mechanism is proposed, in which data can be transmitted from message carriers to their future two hops based on the membership level of fuzzy input. The experimental results demonstrate that within a tolerable error interval of 0.05-0.1, DTCM improves the delivery ratio by nearly 20% and decreases the end-to-end delay by about 25% as compared with Epidemic, and the network overhead from DTCM is in the middle horizon.  
[PERSON], [PERSON], [PERSON] and [PERSON] conceived the idea of the paper. [PERSON] and [PERSON] designed and performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed reagents/materials/analysis tools; [PERSON] and [PERSON] wrote the paper; [PERSON], [PERSON], [PERSON] and [PERSON] revised the paper. 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 (61672540); Hunan Provincial Natural Science Foundation of China (2018J3299, 2018 JJ3682). Fundamental Research Funds for the Central University of Central South University (2020 zzts597).  
The authors declare no conflict of interest.
# Influence of Unburned Carbon on Environmental-Technical Behaviour of Coal Fly Ash Fired Clay Bricks
###### Abstract  
The incorporation of coal fly ash (CFA) in fired clay bricks (FCBs), as a clay replacement, contributes toward cleaner production practices. CFA disposal is an important issue worldwide due to its huge volume and to its potential negative environmental impacts, and currently does not have a recovery route due to its high concentration in unburned materials. In this study, the impact of the incorporation of two CFA, with different content of unburned carbon, FAA (low LOI) and FAB (high LOI) in FCBs, from a technical and environmental point of view was conducted. Unburned carbon plays an important role on the final properties of FCBs. The thermal decomposition during the firing process promotes an increase of water absorption, decreasing the flexural strength as the porosity increases, although the technical and mechanical properties of samples containing up to 30% FAA and percentages of 20% FAB are acceptable. The leaching behaviour showed an immobilisation of Cr and Se in FCBs while Mo reduced its mobility to values below non-hazardous limits. Acid gas emission values do not exceed the reference emission value, except for SO\({}_{2}\) emissions while the level of CO\({}_{2}\) emissions must be estimated based on the total annual production of the ceramic factory.  
coal fly ash; unburned carbon; fired clay bricks; leaching behaviour; gas emission Article
## 2 Theoretical Background
### Short-Time Fourier Transform  
FT is a mathematical operation that converts a time-domain signal into a frequency-domain signal, providing information about the frequencies present in the original signal [31]. When dealing with nonstationary signals, the STFT is the preferred choice, as it produces a time-frequency representation. In essence, the STFT consists of segmenting the input signal and applying the FT to each segment as follows [31]:  
\[X(m,\ \omega)=\sum\limits_{n=-\infty}^{\infty}x[n]\omega[n-m]e^{-j\omega m} \tag{5}\]  
where \(\omega\) is a window function, \(n\) and \(m\) are indices for the samples in the signal, and \(e^{-j\omega n}\) is the transformation kernel.  
The length of the time window defines the trade-off between time and frequency resolution, while its shape can impact the issue of leakage due to the windowing of finite data sets [31; 32]. Further details can be found in [32].
## 5 Simulation Results and Discussion
### Results Discussion
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Technique** & **Technique** & **Technique** & **Technique** & **Technique** & **Technique** & **Technique** & **Technique** & **Technique** & **Technique** \\ \hline FL-LSTM [12] & 3.402 & 0.025 & 3.124 & 0.038 & 2.589 & 2.105 & 2.321 & 2.204 & 2.005 & 3.567 & 2.817 \\ \hline Hybrid LSTM-GRU models [17] & 3.105 & 0.032 & 2.938 & 0.047 & 2.312 & 1.905 & 2.100 & 1.982 & 1.802 & 3.331 & 2.542 \\ \hline Fedex [23] & 2.902 & 0.038 & 2.801 & 0.052 & 2.183 & 1.803 & 1.981 & 1.802 & 1.691 & 3.205 & 2.419 \\ \hline Federated Sequence Learning (FSL) [24] & 2.719 & 0.047 & 2.633 & 0.061 & 1.987 & 1.630 & 1.762 & 1.593 & 1.525 & 3.014 & 2.207 \\ \hline FedAGCRU with attention mechanisms [25] & 2.513 & 0.055 & 2.489 & 0.070 & 1.852 & 1.526 & 1.633 & 1.438 & 1.402 & 2.805 & 2.083 \\ \hline FeDiToT platform with FL and autoencoders [21] & 2.402 & 0.061 & 2.356 & 0.075 & 1.745 & 1.451 & 1.589 & 1.384 & 1.348 & 2.721 & 1.952 \\ \hline Collaborative FL framework [22] & 2.311 & 0.068 & 2.243 & 0.081 & 1.641 & 1.374 & 1.492 & 1.289 & 1.278 & 2.612 & 1.832 \\ \hline
**Proposed FL ResVGG-SwinNet** & **2.121** & **0.075** & **2.109** & **0.091** & **1.523** & **1.205** & **1.321** & **1.104** & **1.105** & **2.402** & **1.716** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Statistical analysis comparison of federated-learning techniques. Figure 15: Sensitivity analysis of FL ResVGG-SwinNet.
## 2 Problem Formulation and Modeling
### Time-Invariance of Motion Parameters
Figure 2: Comparison of the 2 nd-order extrapolation DR algorithm in the orthometric height coordinate system (HCS) to the real trajectory. The black solid line represents the actual flight trajectory, the blue dashed line represents the trajectory segment calculated by second-order dead reckoning, the red hollow circle represents the position during hard synchronization, and the double headed arrow represents the maximum error vector of each extrapolated trajectory segment. The sources of error are also analyzed. Since velocity and acceleration are simply considered as constants during actual motion, when the time span is large, the deviation can easily exceed the allowable range; i.e., the invariance of the selected parameters is poor. Therefore, one way to improve the dead reckoning method is to select a parameter with better invariance for dead reckoning. In most cases, the higher the order of the motion parameters, the more sensitive to the measurement noise, and the lower the order of the motion parameters, the worse the invariance. For a controlled object, there is one potential parameter with relatively good time-invariance, and that is the desired end state of motion. When the parameter is taken in the control law as a motion parameter with good time-invariance, a better dead reckoning result may be obtained.
# IoT-Enabled Gas Sensors: Technologies, Applications, and Opportunities
## 7 Discussion and Open Issues
### Open Issues  
In the sequence of the presented discussion, the following open issues are identified and proposed for further research studies:  
* Gas sensing could provide valuable data to diverse applications, using the IoT paradigm, offering important data for decisions taken by smart devices. They can provide better experiences to users.
* The improvement of sensing characteristics, miniaturization of transducers and combination of sensing technologies are topics with great potential for research.
* Creation of customized multi-gas smart sensors since, to the best of the authors' knowledge, there are no these kind of solutions in the literature.
* Proposals following a plug-and-play approach based on IoT focusing on the end-user empowerment to properly configure these devices according to their needs.
* Performance evaluation, demonstration, and validation of available gas transducer proposals in real environments since they only were studied through theoretical and laboratory prototype approaches.
# Infrared Thermographic Signal Analysis of Bioactive Edible Oils Using CNNs for Quality Assessment
## 4 Post-Processing of IR Signals: Proposed Supervised Learning Model
### Proposed Model Architecture  
The proposed model adopts a hybrid CNN-LSTM architecture designed to process sequential infrared (IR) thermal frames and extract both spatial and temporal features relevant for oil classification. Each video sample, representing a thermal cycle, is segmented into sequences of resized frames (128 \(\times\) 128 pixels, three-channel). The spatial features of each frame are extracted using a Convolutional Neural Network (CNN), wrapped inside a Time-Distributed layer to ensure consistent processing across time steps.  
The CNN consists of two convolutional layers: the first applies 32 filters with a \(3\times 3\) kernel followed by ReLU activation and 2 \(\times\) 2 max pooling; the second applies 64 filters with the same kernel and pooling structure. These feature maps are then flattened to form a vector representation of each frame. This sequence of spatial encodings is passed to a Long Short-Term Memory (LSTM) layer with 64 hidden units, which learns temporal dependencies representing heat dissipation dynamics.  
The LSTM output is fed into a dense layer with 128 neurons (ReLU activation), followed by a dropout layer (rate = 0.3) to prevent overfitting. Finally, a SoftMax layer outputs the probability distribution over the four oil classes: olive, sunflower, tomato seed, and pumpkin seed.  
This end-to-end architecture is specifically optimised to identify distinguishing thermal memory effects and biochemical signatures across time. The CNN component is responsible for extracting local spatial features from each frame, while the LSTM models the temporal evolution across frames. The following Tables 6 and 7 detail the structure of the CNN and LSTM components.  
The model was implemented using the TensorFlow framework and optimised for sequential inference, making it suitable for deployment in embedded environments such as a Raspberry Pi when real-time, edge-based quality assessments are required.  
The CNN component enables automatic spatial feature extraction from each frame, while the LSTM captures the temporal evolution of thermal signals, providing a comprehensive representation of each oil's heat dissipation behaviour.  
For training and validation, the model was developed and executed in Google Colab, leveraging the computational power of an NVIDIA Tesla T4 GPU with 16 GB of GDDR6 memory.
# Prism-Based Spatial Heterodyne Spectrometer with a Fixed Fringe Localization Plane
###### Abstract  
Spatial heterodyne spectroscopy (SHS) based on prism dispersion is a novel technique designed to overcome the limitations of traditional grating-based SHS, which is affected by grating diffraction. However, there are still some challenges with this technique, one of which is that the fringe localization plane (FLP) moves with changes in wavelength. This paper proposes a prism-based tunable SHS where the FLP is fixed, utilizing prism-bimirror-mirror structures. The theoretical spectral resolving power, based on an example, is higher than 1300 in the spectral range from 10,000 cm\({}^{-1}\) to 25,641 cm\({}^{-1}\) and is approximately 27,595 at 25,641 cm\({}^{-1}\). Furthermore, we propose solutions to simplify the motion control system and address the problem of spectral aliasing.  
spatial heterodyne spectrometer; interferometers; Fourier transforms; spectroscopy +
Footnote †: journal: applied sciences
# Current-Signal-Based Fault Diagnosis of Railway Point Machines Using Machine Learning
## 3 Results
### Selection of the Relevant Time-Domain Features  
In this paper, 11 time-domain features (length, maximum, minimum, mean, median, sum values, variance, standard deviation, root mean square, skewness, and kurtosis) were retrieved from the RPM operation's time-series current data. The time-series current data were acquired from the three RPM equipment conditions--normal, warning, and failure--utilizing 128, 145, and 153 data samples, respectively. The 11 features were selected by performing scalable hypothesis testing to determine which features had no relevant relationship with the classifications or labels. Table 2 displays the outcomes of the feature selection process.  
Figure 6: The collected time-series current signals of the RPM: (a) 128 samples in condition 1, (b) 145 samples in condition 2, (c) 153 samples in condition 3. Different colors indicate number of sampling.  
Figure 7: The average of the time-series current signals for the 3 conditions of RPM equipment.  
The 11 extracted features were sorted by \(p\)-value. The results showed that the length feature had the lowest \(p\)-value. This means that the length feature had a strong relevant relationship with the classifications. The maximum feature had a \(p\)-value greater than 0.05, indicating that the maximum feature had no significant relationship with the classifications. Thus, it was eliminated, and the rest of the features were utilized as the dataset for machine learning modeling.
## 5 Simulation Cases
### DFIG and EV Coordinating to Participate in FR
The FR task accomplishment situation in the two scenarios is shown in Figure 15:0 If the joint FR output of DFIG and EV cannot fulfill the system FR task, the shortage will be allocated to the conventional unit, and the FR task of the conventional unit is shown in Figure 22a. It can be seen that in Scenario 4, the conventional unit's FR task is obviously more than Scenario 3. This is because in the FR strategy, with fixed allocation ratio, when the FR capacity is sufficient, EV cluster and DFIG could have provided larger FR output, but the FR task is restrained by fixed allocation ratio. Due to the high climbing rate of the conventional unit, the FR task allocated to the conventional unit cannot be well accomplished, so there exists the shortage of the system FR demand power, as shown in Figure 22b. The RMS value of FR demand power shortage in Scenario 3 and Scenario 4 is 0.0325 and 0.0342 p.u., respectively. Therefore, the shortage of the system FR demand power in Scenario 4 is larger, resulting in worse FR effect. Figure 15:1 Comparison of the FR (frequency regulation) Effect in Scenario 1 and Scenario 3. Figure 15:2 FR (frequency regulation) task accomplishment situation in Scenario 3 and Scenario 4. **(a)** The FR task of a conventional unit; **(b)** The shortage of system FR demand power. The comparison of the FR effect in Scenario 3 and Scenario 4 is shown in Figure 15:3 The RMS value of system frequency deviation in Scenario 3 and Scenario 4 is 0.2671 Hz and 0.3595 Hz, respectively. It can be seen that the FR effect of proposed real-time allocation strategy is better than the situation of fixed allocation ratio. Therefore, the proposed coordinated FR strategy with the real-time allocation of system FR task is more reasonable and effective than the FR strategy with fixed allocation ratio.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 6 Efficient Power and Data Transfer in BackCom
### Scheduling and Resource Allocation
#### 6.3.1 Energy and Data Scheduling
They also suggested a data scheduling algorithm for BackCom sensors, shown in Figure 33. Similarly, the principles for RFID data management were highlighted with comparative analysis to understand the essential features of BackCom [140].