esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Further Strategies for Computational Optimization  
UAV deployments impose stringent constraints on power availability, processing capacity, and memory usage, necessitating the development of efficient yet accurate models for GPS spoofing detection. While Genetic Algorithm-Optimized LSTM networks provide performance benefits, additional strategies can be employed to further reduce computational overhead. One promising avenue lies in _lightweight model architectures_, including pruned or quantized deep neural networks. Pruning involves systematically removing weights or entire neurons with minimal impact on predictive accuracy, thereby decreasing the overall parameter counts and model size [50]. Quantization further reduces the precision of model parameters (e.g., converting floating-point formats to integer formats), leading to faster inference in embedded hardware and improving power efficiency [51]. These tactics can be integrated into training pipelines or performed post-training, enabling deeper models to operate within the tight computational budgets typical of UAV-based systems.  
_Edge computing_ represents another essential component of resource-conscious UAV deployment. Instead of transmitting all raw data to a remote server, partial or complete inference can be handled onboard or at nearby edge nodes [52]. This reduces both latency and bandwidth usage, maintaining real-time responsiveness for spoofing detection while diminishing the operational burden on cloud infrastructure [53]. The synergy of edge processing with compact deep learning architectures also facilitates rapid decision-making in time-critical UAV maneuvers. In addition, distributed or federated learning frameworks allow UAVs to locally update models with incoming data, thereby continually refining spoofing detection while preserving privacy and minimizing data transfers [54]. Employing these computationally conscious paradigms--ranging from model compression to edge-based inference--can further enhance the feasibility of the proposed GPS spoofing defense mechanisms for a wide spectrum of UAV mission profiles.
## 3 Results
### Diatom Resting Stages
#### 3.2.2 Abundance of Viable Cells Estimated by MPN  
The diatom species confirmed by MPN belonged to four genera (_Chaetoceros_ spp., _Odontella_ spp., _Skeletonema_ spp., and _Thalassiosisira_ spp.) which were fewer than those with direct observation. However, the MPNs of viable cells at A1, A2, and A5 were 1735, 9920, and 741 cells g\({}^{-1}\) wet sediment, respectively (Figure 7), and the highest value calculated by MPN at A2 showed close agreement with the direct observation. On the other hand, the contribution of _Chaetoceros_ spp. was relatively low in the species composition of the MPN, unlike the result of the direct observation. The penate diatoms were generally dominant in the composition of each station, accounting for 74.9%, 23.2%, and 72.9% at A1, A2, and A5, respectively. In the species composition of A2, however, unidentified spherical cells of a size of about 10 um were abundantly observed (49.4%). The MPN value of viable diatom cells at A2 was more than 13 times that at the non-reef station (A5).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Species**} & \multicolumn{3}{c}{**Cell Concentration (Cells g\({}^{-1}\) Wet Sediment)**} \\ \cline{2-4}  & **A1** & **A2** & **A5** \\ \hline _Actincocyclus_ spp. & 0 & 0 & 40 \\ _Actinotypchus semarius_ & 60 & 80 & 20 \\ _Chaetoceros affinis_ or & 20 & 840 & 20 \\ _vanhurektii_ & 90 & 2560 & 150 \\ _Chaetoceros conortus_ & 0 & 20 & 0 \\ _Chaetoceros deblis_ & 10 & 1000 & 10 \\ _Chaetoceros diaedema_ & 60 & 360 & 80 \\ _Chaetoceros didymus_ & 10 & 180 & 10 \\ _Chaetoceros radiicans_ & 80 & 2480 & 190 \\ _Coscioidosis_ spp. & 0 & 20 & 0 \\ _Leptocylindatus danicus_ & 0 & 40 & 0 \\ _Odontella aurtia_ & 30 & 0 & 0 \\ _Thalassiosis_ spp. & 60 & 180 & 30 \\ Pennate diatoms & 50 & 260 & 130 \\ Unidentified & 30 & 800 & 20 \\ _Chaetoceros_ spp. & & & \\ Unidentified diatoms & 60 & 0 & 140 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Taxa and concentrations of the diatom resting stages in the bottom sediment at A1, A2, and A5, determined by direct microscopic observation. Cells of species other than pennate diatoms and unidentified diatoms were identified as resting stages in the present study.  
Figure 7: MPN of viable cells and species composition of diatoms in the bottom sediment at A1, A2, and A5; an open circle represents cell concentration.
## 3 Experimental Methods and Results
### Comparison Results between the NFT-MEP Model and Other Popular Models
Based on the experimental results, it can be concluded that the NFT-MEP model outperformed both types of PM for prediction with \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Types of Output Data**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Average Statistic Indicators**} \\ \cline{3-5}  & & **Accuracy** & **F-Score** & **MCC** \\ \hline \multirow{4}{*}{Three-class} & NFT-MEP & 85.18\% & 0.7320 & 0.6361 \\  & LSTM & 80.98\% & 0.6478 & 0.4854 \\  & ARIMAX & 74.56\% & 0.5890 & 0.3860 \\  & ARIMA & 70.52\% & 0.4731 & 0.3197 \\ \hline \multirow{4}{*}{Five-class} & NFT-MEP & 74.49\% & 0.6433 & 0.6035 \\  & LSTM & 62.71\% & 0.4385 & 0.3908 \\ \cline{1-1}  & ARIMAX & 57.44\% & 0.3500 & 0.2848 \\ \cline{1-1}  & ARIMA & 52.90\% & 0.2949 & 0.2666 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The comparison result of four prediction models to predict hourly PM\({}_{10}\) with three and five classes of output data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Types of Output Data**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Average Statistic Indicators**} \\ \cline{3-5}  & & **Accuracy** & **F-Score** & **MCC** \\ \hline \multirow{4}{*}{Three-class} & NFT-MEP & 83.37\% & 0.7838 & 0.6883 \\  & LSTM & 77.57\% & 0.6879 & 0.5654 \\  & ARIMAX & 62.19\% & 0.5850 & 0.4859 \\  & ARIMA & 59.24\% & 0.5677 & 0.4587 \\ \hline \multirow{4}{*}{Five-class} & NFT-MEP & 74.96\% & 0.7257 & 0.6573 \\  & LSTM & 62.12\% & 0.5989 & 0.4396 \\ \cline{1-1}  & ARIMAX & 57.40\% & 0.4844 & 0.3570 \\ \cline{1-1}  & ARIMA & 55.72\% & 0.4746 & 0.3021 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison result of four prediction models to predict hourly PM\({}_{2.5}\) with three and five classes of output data. two different amounts of output data when compared with the three other popular PM prediction models.
## 4 Results and Discussion
### Time-Domain Characteristics
#### 4.1.3 Vertical Displacement of Contact Strip  
Comparison curves of the vertical displacement of the contact strip as the train passes through the tunnel at different speeds, and the corresponding statistical results, are shown in Figure 13 and Table 4, respectively. The vertical displacement of the contact strip increases sequentially from 280 km/h to 320 km/h to 360 km/h. The differences among different speeds inside the tunnel are greater than those outside the tunnel, which indicates that the tunnel effect is significant as is consistent with the objective fact. Compared with that at 280 km/h, at 320 km/h, the maximum and minimum vertical displacements of the contact strip decrease while the mean value and the standard deviation increase at ratios of 14.65% and 12.2%, respectively. Compared with that at 360 km/h, at 400 km/h, the maximum, minimum, and mean values of the vertical displacement of the contact strip decrease while the standard deviation increases. Taking 280 km/h as the benchmark, the mean value decreases by 36.36% and the standard deviation increases by 21.54%. Although the amplitude decreases, the fluctuation is relatively large and in practice would readily cause separation between the pantograph and catenary. Compared with that at 400 km/h, at 420 km/h, the maximum value and standard deviation of the contact strip vertical displacement increase to lesser extent as the minimum and mean values increase to greater extent.  
Figure 13: Comparison curves of vertical displacement of the contact strip passing through tunnel at different speeds.
## 4 Conclusions  
Based on a modified experimental platform, a series of laboratory tests under different water content conditions were performed, and at the same time, the acoustic emission phenomenon during the rock breakage process was monitored. Moreover, the effects of the water content on the penetration force, AE events, crack development, rock chip form, and penetration efficiency were investigated. The following conclusions were obtained.  
1. Acoustic emission events are associated with the whole indentation process of granite with different water content conditions. This phenomenon was most obvious at the time when the penetration force reached the peak and was consistent with the force-penetration depth curve. The average number of AE events in the wet specimens was smaller than that in the dry specimens.  
2. The relationship between the formation of rock chips between surface cracks and internal cracks was studied, and the results showed that the increase in the water content facilitated the development of surface cracks between the grooves, thus resulting in larger rock chips.
3. The increase in the water content can reduce the strength of the rock and the energy required to break the rock. Thus, the rock can be more easily broken, increasing the penetration efficiency of the cutter. This indicates that water content is one of the most important factors affecting the rock breakage performance of the cutter. This research studies the effects of different water contents on the rock breakage features of the cutter, which has certain practical value in the field of engineering.  
This paper only explores the granite rock breakage process using a disc cutter under different water content conditions. However, in the actual excavation process, tunnels will cross strata with very complex geological conditions. How to improve the efficiency of penetration into different types of rocks containing different water conditions still remains to be further studied.  
[PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed laboratory equipment; [PERSON] and [PERSON] wrote the paper.  
This research was funded by the National Natural Science Foundation of China (grant numbers 51174228, 11772358), the National Basic Research Program of China (grant number 2013 CB035401), the Open Fund of Key Laboratory of Road Structure and Material of Ministry of Transport (Changsha University of Science & Technology (grant number kj170305, kj170603) and the Fundamental Research Funds for the Central Universities, China (grant number 2018 zzts211). The authors are also very grateful to the anonymous reviewers for their valuable comments that have significantly improved the paper.  
The authors declare no conflict of interest.
# Effect of Subject-Specific Region of Interest on Motor Imagery Brain-Computer Interface
## 3 Results and Analysis
### Validation and Comparison with the Existing Methods from the Literature  
With reference to Table 4 below, the result from ITD based region of interest using the BCI Competition IV dataset indicated an accuracy of 96.87% compared to that utilizing our own collected data, which generated 92.70% accuracy; this resulted in 4.30% more accuracy. On average, with the Competition IV dataset, ITD showed 8.56% and 7.32% higher classification accuracy compared to CSP and DWT, respectively.
## 4 Experiment Setup and Results
### The Stability of Magnetorheological Polishing with Constant Force Control  
To clarify the influence of \(F_{n}\) on the stability of the removal function and to explain the influence law of the normal error on MRF volumetric removal rate, the spots for different forces are taken with the magnetorheological moisture 14.3% and 15.3% respectively.  
Figure 8: Robot magnetorheological polishing platform with constant force actuator.  
The results of the correlational analysis are presented Figure 12. There is a positive linear correlation between \(F_{n}\) and VRR that is consistent with the theoretical formula. The VRR increases as the water content decrease, and the coefficient of friction (i.e., the linear slope), also increases as the moisture decreases. From Figure 12 for the polishing force of 2 N commonly used, each unit normal force results in the VRR fluctuation from \(1.6909\times 10^{-4}\) mm\({}^{3}\)/s to \(2.1399\times 10^{-4}\)mm\({}^{3}\)/s, and the relative error of VRR reaches 40-50%.  
Figure 11: Robotic magnetorheological polishing force response at 15.3% magnetorheological moisture.  
Figure 9: Robotic magnetorheological polishing force response at 1N without disturbance.  
The removal effect before and after constant force control on a 200 mm \(\times\) 200 mm \(\times\)10 mm fused silicon mirror is analyzed using uniform polishing. The six-axis robot drives the polishing head to maintain the normal contact with the workpiece and processes the whole workpiece surface under 14.3% moisture of MRF fluid. The polishing path is faster with 0.8 mm path pitch and the feed rate keeps constant. Figure 13a,b respectively shows the distributions of the normal polishing force and material removal amount without constant force control. From those, before compensation, the robot motion error causes uneven polishing at each point of the workpiece, and the material removal distribution is essentially the same as the normal force error distribution.  
Figure 12: Volumetric removal rate and normal force with the moistures of MR fluid 14.3% and 15.3%.  
Figure 13: The normal force distribution and removal amount distribution with/without constant force control: (**a**) Normal force distribution of MRF process without constant force control; (**b**) The removal amount distribution without constant force control; (**c**) Normal force of MRF process with constant force control; (**d**) The removal amount distribution with constant force control.  
Figure 13c,d respectively reveals the distributions of the normal polishing force and material removal amount with constant force control. From those, after compensation, the constant force actuator effectively ensures the constant normal polishing force, with a peak error of less than 0.3 N and an average error of less than 0.05 N. The uniform removal distribution error of the material in the constant force polishing process is less than \(\pm\) 0.05\(\lambda\) (\(\lambda\) = 632.8 nm). Hence, the developed constant force system plays a significant role in high-precision and high-efficient polishing.
## 1 Introduction
Particularly, [29] disclosed a crucial design tendency that can be included in the early stages of the machine design: the electromagnetic performance penalization due to eccentricity strongly depends  
Figure 1: Three-dimensional representation of stator/rotor structure of a 24-slot, 20-pole PMSM: (a) conventional PMSM with non-segmented stator (monolithic), (b) PMSM with modular stator core (MPMSM). on the machine slot-pole combination, becoming especially relevant as the slot count and pole count get closer. In turn, the impact of eccentricity on MPMSMs has been recently addressed in [24] for axial-even eccentricity in a single 24-slot, 28-pole machine: relevant radial forces appear as expected, but the cogging torque, back-emf, and electromagnetic torque seem to be insensitive. The authors concluded that the addressed slot/pole combination was mostly insensitive to eccentricity even when the slot count and pole count differed by four, but the study required to be expanded to more slot/pole combinations. The comparison between the results of [24,29] suggests that the effect of slot/pole combinations on PMSMs with eccentricity may be different from their modular counterpart. This could also be inferred from the analysis of [18], which states that, in presence of manufacturing tolerances, the periodicity of the stator core changes when adopting a modular structure, affecting the cogging torque main period and its sensitiveness to assembly tolerances for the same slot/pole combination. All things considered, if the fault-tolerance capability of a MPMSM is to be assured, its electromagnetic performance and structure integrity should not be greatly affected by mild degrees of eccentricity. Nevertheless, and despite of its relevance, the effects of eccentricity on the performance of MPMSMs for different slot/pole combinations and their inclusion in the early design stages of the machine remains uncovered in the technical literature so far. The aim of this work is, therefore, to identify and analyze the impact of eccentric tolerances on relevant performance indices of MPMSMs for different slot/pole combinations, comparing their response to that of PMSMs with conventional stators. Both static and dynamic eccentricity are analyzed by means of the finite element method (FEM) for five slot/pole combinations, evaluating the radial forces that the rotor structure is exposed to cogging torque, back-EMF, and rated torque. The relevance of this paper lies with providing a quick yet reliable comparison of the MPMSM performance between slot/pole combinations and the reasons behind the observed phenomena, critical in the design stage of a MPMSM, as well as disclosing relevant similarities and differences with respect to PMSMs with conventional stator core. This paper is organized as follows: in Section 2, the selected topology and its main data are presented; in Section 3, the methodology and details regarding the considered eccentricity types are described; in Section 4, the results of key performance indices when the machine has eccentricity tolerances are presented and discussed. The conclusions are drawn at the end of the paper.
## 2 The Proposed Method
### Data Association  
For the low-texture environment, such as in a corridor, it is possible to get several adjacent frames which are almost the same. If there is no assistance from other information, the scan matching may cause a large error. Therefore, the output of EKF is taken to participate in the scan matching. When a LiDAR frame is inserted into the corresponding local map, we will estimate the best LiDAR pose in the local map based on the previous frames in the local map. The method is to find the LiDAR pose to make sure the total probability of the point cloud in the local map is large enough, a nonlinear least square method can be used.  
The goal is to solve \(\xi^{n^{*}}\) with the cost function as follows:  
\[\xi^{n*}=\underset{\xi}{\mathrm{argmin}}\sum_{i=1}^{I}\left[1-M(\mathrm{G}^{i} (\xi^{n}))\right]^{2} \tag{20}\]  
The function \(M(.)\) defines the probability value in the local submap. The function \(G(.)\) is a coordinate transformation. The above optimization function can be rewritten as:  
\[\sum_{i=1}^{I}\left[1-M(\mathrm{G}^{i}(\xi^{n}+\Delta\xi^{n}))\right]^{2}\to 0 \tag{21}\]  
With the first-order Taylor expansion of the above equation, we can get:  
\[\sum_{i=1}^{I}\Bigg{[}1-M(\mathrm{G}^{i}(\xi^{n}))-\
abla M(\mathrm{G}^{i}( \xi^{n}))\frac{\partial\mathrm{G}^{i}(\xi^{n})}{\partial\xi^{n}}\Delta\xi^{n} ))\Bigg{]}^{2}\to 0 \tag{22}\]  
With the initial pose \(\xi^{n}\) = \(\mathrm{C}\mathrm{x}\), taking derivative of the above equation with respect to \(\Delta\xi^{n}\), then we have  
\[2\sum_{i=1}^{I}\Bigg{[}\
abla M(\mathrm{G}^{i}(\xi^{n}))\frac{\partial\mathrm{ G}^{i}(\xi^{n})}{\partial\xi^{n}}\Bigg{]}^{T}\Bigg{[}1-M(\mathrm{G}^{i}(\xi^{n}))- \
abla M(\mathrm{G}^{i}(\xi^{n}))\frac{\partial\mathrm{G}^{i}(\xi^{n})}{ \partial\xi^{n}}\Delta\xi^{n}))\Bigg{]}=0 \tag{23}\]  
Solving for \(\Delta\xi^{n}\) yields the Levenberg-Marquardt [36] equation for the minimization problem:  
\[\Delta\xi^{n}_{\mathrm{e}}=\mathrm{H}^{-1}\sum_{i=1}^{I}\Bigg{[}\
abla M( \mathrm{G}^{i}(\xi^{n}))\frac{\partial\mathrm{G}^{i}(\xi^{n})}{\partial\xi^{n} }\Bigg{]}^{T}\Big{[}1-M(\mathrm{G}^{i}(\xi^{n}))\Big{]}=0 \tag{24}\]  
where  
\[\mathrm{H}=\Bigg{[}\
abla M(\mathrm{G}^{i}(\xi^{n}))\frac{\partial\mathrm{G}^ {i}(\xi^{n})}{\partial\xi^{n}}\Bigg{]}^{T}\Bigg{[}\
abla M(\mathrm{G}^{i}(\xi^ {n}))\frac{\partial\mathrm{G}^{i}(\xi^{n})}{\partial\xi^{n}}\Bigg{]}+\lambda \tag{25}\]  
Because LM optimization introduces a damping coefficient in the Gauss-Newton method here, it is used to control the step to prevent divergence. We can tune the dynamic damping coefficient by LM algorithm. In each iteration step, the error caused by new change will be monitored. If the new error is smaller than before, \(\lambda\) will decrease in the next iteration, otherwise, it will increase.
## 2 Related Work
### Runtime Optimization  
Secure container runtimes, due to additional security layers, incur performance degradation, prompting various efforts to enhance metrics, like the startup time, networking, parallel execution, and storage interfaces. [PERSON] et al. [28] showcase snapshot and restore techniques achieving millisecond-level cold startup times, significantly reducing deployment times and enabling substantial in-memory caching. [PERSON] et al. [29] tackle long cold startup times by addressing frequent page faults, proposing proactive instance memory page prefetching, resulting in a 3.7x reduction in startup times.  
[PERSON] et al. [30] propose Particle, an optimized networking architecture reducing the network provisioning time by utilizing an ephemerally generated pool of network addresses. Conversely, [PERSON] et al. [31] leverage workload parallelism to reduce memory consumption per invocation without performance degradation and decrease the total memory utilization and cold starts.  
[PERSON] and [PERSON] [32] propose a storage interface addressing the gap between the host and guest, employing read-only data files and a read-write block device, requiring host file mapping and metadata preparation for a copy-on-write filesystem. While orthogonal to runtime optimization studies, their detailed file I/O stack analysis can guide future efforts aiming to enhance file I/O performance or reduce the CPU usage in file I/O processing.
## 4 Applications on Electroencephalogram (EEG) Prediction  
The applications of the followed research were based on prediction operations done over some considered EEG data. Here, it was also important to determine the general structure of the ANN-ALO system that was employed for prediction. First of all, the optimum structure of the ANN was determined by evaluating five different ANN models with alternative lags (inputs), outputs, and hidden layers. The ANN models were trained with the traditional back propagation algorithm (BPA), and the prediction accuracy of the models were analyzed by considering the sample and simulated EEG time series used in [10]. Following that, five different ALO adjustments (regarding the used parameters of the ALO) were used in the optimum ANN model to determine the best adjustment that was then evaluated further in terms of its success in predicting chaotic EEG time series. The next paragraphs briefly explain all of these research stages.