esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Theoretical Analysis for Higher Efficiency

Table 3: Comparison of the highest optical-to-optical conversion efficiencies (\(\eta_{\infty}\)) and the corresponding slope efficiencies (\(\eta_{s}\)) and coupling efficiencies (\(\eta_{c}\)) under the optimization conditions of the maximum pump power, intrinsic residual loss \(L_{\mathrm{i}}\) and pumping pass (single or double). In general, insertion loss of wavelength-selective elements such as birefringent filters and Fabry-Perot etalons and loss due to SESAM and Kerr lens mode-locking for ultrashort pulse generation can be easily reduced to a few percent or less. Therefore, the efficiencies of wavelength-tunable lasers, narrowband lasers and ultrashort pulse lasers can be increased to the efficiencies shown in Figure 6 and Table 3. High-intensity pumping can also improve the efficiency of laser-diode-pumped Ti:sapphire lasers [13]. Hence, high-intensity pumping is one of the most effective techniques to increase the efficiency of wavelength-tunable, narrowband and ultrashort pulse lasers in which all components can be easily commercially available. For example, without the use of complex and expensive injection seeding, it is possible to achieve narrowband lasers with efficiencies in excess of 50% and output powers as high as 10 W or more. These results can be used to improve the utility of wavelength-tunable, narrowband and ultrashort pulse lasers by increasing their reliability and lowering their price, which have been issues with these lasers.
## 5 Methodology for Anomaly Detection and Mitigation
### Anomaly Detection
#### 5.1.1 Unrecognized Failure in AGV  
To detect anomalies such as a non-moving AGV that is still communicating as a normal working device (cf. Section 4.1), fleet behavior logs are used, received via the data lake or directly from AGVs. The following KPIs from these logs are of interest to detect a non-moving AGV:  
Finalized Tasks per Hour  
If an AGV is not moving, it does not execute transport jobs as usual. Hence, the number of finalized transport tasks will be lower than before, and a stopped AGV can be detected by comparing the actual number of finalized tasks with the expected number. The number of finalized transport tasks can be observed on a global as well as an AGV-individual level.  
If the driven distances of each AGV are observed at every time, less driven distances of the stopped AGV will be detected compared to the the other AGVs. If the task bidding strategy is configured such that the driven distances are equalized over all AGVs, the stopped AGV with this KPI value can be detected. Furthermore, it is possible to compare the driven distances to the expected amount.  
Length of Local Queue of Each AGV  
As no tasks for the stopped AGV will be finalized, the length of the local queue of open transport tasks will increase over and over again and become much larger than queues of other AGVs. If the task bidding strategy is configured such that the local queue is equalized over all AGVs, the stopped AGV with this KPI value can be detected. Furthermore, it is possible to compare the length of the local queues to the usual or expected length.  
Battery Status  
When the AGV is not driving any more but is still active, the battery discharging will be slower than usual. Hence, the observation of the discharging velocity is another indicator of an unusually moving AGV.  
To indicate an anomaly, it has to be known for each AGV which of those KPIs is unexpected. Hence, the expected KPI values are needed at each time to compare them tothe actual ones. To obtain the expected KPIs, a Digital Twin of the AGVs can be used to simulate their behavior in a production environment. The anomaly detection itself can be realized with detection methods such as an adapted version of classic control charts [54] that includes the expected KPIs over time as a mean value [55]. Quality control charts have the advantage that they allow fluctuations of the KPIs around the expected value. Only if the deviation of expected and actual KPIs are very excessive and warning limits are exceeded is an anomaly triggered.
## 4 Experimental Results  
### Failure Mode  
Failure mode of three K-joint specimens in each group is almost the same. Therefore, failure mode of one specimen in each group is analyzed herein. Figure 11(a)-c show the deformation of specimen UPL-1. The U-shape connector is severely deformed. However, no separation between the U-shape connector and the SHS members is observed. Besides, all the screws are sound. The maximum deformed region of the U-shape connector is circled in Figure 11(b). In addition, chord local plastification is observed as shown in Figure 11(c).  
Figure 12(a)-c show the deformation of specimen UPU-1. The U-shape connector is also severely deformed. Moreover, no separation between the U-shape connector and the SHS members is observed, and all the screws are sound. The maximum deformed region of the U-shape connector is circled in Figure 12(b). Moreover, chord local plastification is observed as shown in Figure 12(c).  
Figure 10(a)-c: The U-shape connector is also severely deformed. Moreover, no separation between the U-shape connector and the SHS members is observed, and all the screws are sound. The maximum deformed region of the U-shape connector is circled in Figure 12(b). Moreover, chord local plastification is observed as shown in Figure 12(c).  
Figure 12: Deformation of the specimen UPL-1.  
Figure 13: Deformation of specimen UPU-1.  
Figure 14a-c show the deformation of specimen ULW-1. Large deformation is also observed on the U-shape connector. Moreover, no separation between the U-shape connector and the SHS members is observed, and all the screws are sound. The maximum deformed region of the U-shape connector is circled in Figure 14b. Moreover, chord local plastification is observed as shown in Figure 14c.  
Figure 14. Deformation of specimen ULW-1.  
Figure 16: Loading-displacement curves of specimens in group 1.  
Figure 17: Loading-displacement curves of specimens in group 2.  
Loading-displacement curves of specimens in group 3 are shown in Figure 18a-c. For the three specimens, the maximum displacement is also from displacement transducer '7B' (refer to Figure 11) and is in between 5.6 and 9.5 mm.  
Loading-displacement curves of specimens in group 4 are shown in Figure 19a-c. It can be seen that the maximum displacement is not at a fixed position. For the three specimens in group 4, out-of-plane displacement of the connector is much smaller compared to that in other specimens and is less than 3 mm.  
Overall, the ultimate bearing capacities of the 12 K-joint specimens are close, within the range of 37.1-45.6 kN. It appears that the stamping indentation slightly affects the ultimate bearing capacity of the proposed K-joint. However, strip stamping grooves in the horizontal directions can significantly reduce the deformation of the U-shape connector.  
Figure 18: Loading-displacement curves of specimens in group 3.
# Influence of Impeller Speed Patterns on Hemodynamic Characteristics and Hemolysis of the Blood Pump
###### Abstract  
A continuous-flow output mode of a rotary blood pump reduces the fluctuation range of arterial blood pressure and easily causes complications. For a centrifugal rotary blood pump, sinusoidal and pulsatile speed patterns are designed using the impeller speed modulation. This study aimed to analyze the hemodynamic characteristics and hemolysis of different speed patterns of a blood pump in patients with heart failure using computational fluid dynamics (CFD) and the lumped parameter model (LPM). The results showed that the impeller with three speed patterns (including the constant speed pattern) met the normal blood demand of the human body. The pulsating flow generated by the impeller speed modulation effectively increased the maximum pulse pressure (PP) to 12.7 mm Hg, but the hemolysis index (HI) in the sinusoidal and pulsatile speed patterns was higher than that in the constant speed pattern, which was about \(2.1\times 10^{-5}\). The flow path of the pulsating flow field in the spiral groove of the hydrodynamic suspension bearing was uniform, but the alternating high shear stress (0-157 Pa) was caused by the impeller speed modulation, causing blood damage. Therefore, the rational modulation of the impeller speed and the structural optimization of a blood pump are important for improving hydrodynamic characteristics and hemolysis.  
blood pump; fluid simulation; hemodynamic characteristics; hemolysis; pulsation; speed patterns +
Footnote †: journal: Applied Sciences
## 8 Conclusions and Discussion  
This research developed and evaluated a 4 WD robot platform for near-distance moving-object tracking using a relatively low-spec 2D LiDAR sensor. The integration of ID classification and a distance-based dynamic recognition angle model improved the object-tracking performance of the LiDAR sensor. The implementation of a particle filter algorithm further validated the system's tracking capabilities. Additionally, the PD controller ensured the driving stability of the robot, as confirmed through performance experiments. The experimental results demonstrated the potential for effective data processing of the LiDAR sensor in near-distance object tracking, alongside stable driving performance of the 4 WD mobile robot. This research contributes to the advancement of small-scale autonomous systems for agricultural use by addressing both economic and labor challenges in compact and rugged terrains. To further assess the system's practicality, future work will involve conducting tests in actual field environments to validate its usability and effectiveness in real-world agricultural settings. Additionally, future research should focus on enhancing data processing for increased complexity, such as medium- and long-range navigation with more obstacles, and exploring dynamic adjustment of perception angles to improve system adaptability to changing environments.  
Conceptualization, E.-S.P.; Methodology, B.-H.K. and Y.-C.C.; Software, E.-S.P.; Validation, B.-H.K.; Data curation, B.-H.K. and Y.-C.C.; Writing--original draft, E.-S.P. and H.-Y.K.; Writing--review & editing, K.-S.L. and H.-Y.K.; Visualization, E.-S.P.; Project administration, H.-Y.K.; Funding acquisition, K.-S.L. All authors have read and agreed to the published version of the manuscript.  
Figure 19: Tracking performance results of experiment 2.  
Figure 18: Tracking performance results of experiment 1.  
**Funding:** This research was supported by the \"2024 Technology Commercialization Support Project\" (RS-2024-00349535: Development of a 12V electric Air Drill precision seeder for multi-seed rotavator attachment based on cloud control and embedded) funded by the Ministry of Agriculture, Food and Rural Affairs of the Republic of Korea. This research was supported by the \"2022 Open-Field Smart Agriculture Short-Term Advancement Project\" (322036-3: Development of a working robot for furrow of dry field) funded by the Ministry of Agriculture, Food, and Rural Affairs of the Republic of Korea.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** Author [PERSON] was employed by the company DeepLink. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Results
### Effect of Gas Amount under Different Loading Rates and System Temperatures
#### 3.2.3 Effect of Gas Amount on Liquid Outflow  
Extract the first loading/unloading cycle under various working conditions, and plot them together in Figure 5. For the first loading process, the accessible pore volume is the same for both the degassed and undegassed system. Although with different amount of residual gas in nanopores, there is no big difference for the first intrusion process at a certain contrast working condition because of the strong compressibility of gaseous phase. However, the disparate degrees of outflow phenomenon of systems with different gas amount prove that residual gas plays quite an important role in liquid outflow.  
Define infiltration percentage as \(\Delta V_{\text{in}}\sfrac{i}{\Delta V_{\text{in}}}^{1}\) and defiltration percentage as \(\Delta V_{\text{de}}\sfrac{i}{\Delta V_{\text{in}}}^{1}\), where \(\Delta V_{\text{in}}\sfrac{i}{\Delta V_{\text{in}}}\) is the inflow volume of liquid during the \(i\)th intrusion process, and \(\Delta V_{\text{de}}\sfrac{i}{\Delta V_{\text{de}}}\) is the outflow volume of liquid after the \(i\)th loading/unloading cycle. During the first loading/unloading process, not the entire nanochannel will be filled with glycerol molecules, yet part of the glycerol molecules is trapped in the nanopores and could not flow out of the channels freely after the first cycle. Defects may be produced upon intrusion-extrusion cycles, by breaking Si-O-Si bonds into Si-OH (silanol) groups [21; 33]. The incomplete outflow of the 1 st cycle is partly due to trapped glycerol molecules and partly due to inelastic deformation of the system [18]. Assuming the infiltration volume of the first intrusion process \(\Delta V_{\text{in}}\sfrac{1}{\Delta V_{\text{in}}}\) as the maximum accessible infiltration volume, \(\Delta V_{\text{in}}\sfrac{1}{\Delta V_{\text{in}}}\) is taken as the baseline of measurement.  
The infiltration and defiltration percentages of 10 loading/unloading cycles are portrayed in Figure 6. Both the infiltration and defiltration percentages of a degassed system are larger than that  
Figure 5: Comparision of the first loading/unloading cycle of undegassed and degassed glycerol/zeolite system (loading rate 0.01 mm/s and 0.10 mm/s; system temperature 303 K).  
of an undegassed system under a certain contrast working condition. As the difference between the degassed and undegassed system mainly ascribes to the amount of residual gas inside nanopores, it can be concluded that certain amount of residual gas helps liquid to flow out of nanopores, in turn to elevate the system's reversibility. When comparing the infiltration and defiltration percentage, more glycerol molecules tend to flow in and out of the nanochannels under higher loading rate, and at a lifted system temperature.
# Comprehensive Validation on Reweighting Samples for Bias Mitigation via AIF360
## 3 Results and Discussions
### Experimental Results and Discussion
#### 3.3.2. Compas
This study highlights _the model-specific nature of reweighting sample effectiveness in achieving fairness in traditional machine-learning models_. Figure 6: Performance comparison via BA vs. DI before and after reweighting samples on COMPAS dataset with respect to the protected attribute _Race_. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{Performance before reweighting samples} \\ \hline
**Model** & BA & SPD & AOD & DI & EOD & TI \\ \hline DT & 0.6586 & \(-\)0.1637 & \(-\)0.1340 & 0.7759 & \(-\)0.0597 & 0.1835 \\ GNB & 0.6553 & \(-\)0.4129 & \(-\)0.3877 & 0.5066 & \(-\)0.3090 & 0.2382 \\ KNN & 0.6414 & \(-\)0.2336 & \(-\)0.2095 & 0.7256 & \(-\)0.1350 & 0.1607 \\ LR & 0.6774 & \(-\)0.2724 & \(-\)0.2439 & 0.6631 & \(-\)0.1392 & 0.1774 \\ RF & 0.6432 & \(-\)0.3759 & \(-\)0.3484 & 0.4700 & \(-\)0.3002 & 0.3003 \\ \hline \multicolumn{6}{c}{Performance after reweighting samples} \\ \hline DT & 1.0 & \(-\)0.1383 & 0.0 & 0.7732 & 0.0 & 0.0 \\ GNB & 0.6581 & \(-\)0.1998 & \(-\)0.1720 & 0.7154 & \(-\)0.0899 & 0.2146 \\ KNN & 0.6311 & \(-\)0.2551 & \(-\)0.2318 & 0.7003 & \(-\)0.1708 & 0.1762 \\ LR & 0.6562 & \(-\)0.1188 & \(-\)0.0946 & 0.8342 & 0.0111 & 0.1730 \\ RF & 0.6585 & \(-\)0.1615 & \(-\)0.1279 & 0.7081 & \(-\)0.0760 & 0.2776 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison between before and after reweighting samples through one classification metric, BA, and fairness metrics including SPD, AOD, DI, EOD, and TI on COMPAS dataset regarding the protected attribute _Sex_. Figure 7: Performance comparison via BA vs. AOD before and after reweighting samples on COMPAS dataset with respect to the protected attribute _Sex_.
# Comparative Analysis of Producer Mobility Management Approaches in Named Data Networking
## 4 Simulation Set-Up and Evaluation Metrics
### Topology and Parameters  
We considered a real-time streaming application where the producers broadcasted a sequence of data packets to requesting consumers. We conducted a comparative analysis between the three producer MM schemes using a fat tree network topology while considering different producer mobility speeds. The producers were set to keep streaming a video medium at a constant bit rate of 1 Mbps. The data packets were assumed to be 1024 bytes in length. This amounted then to a constant traffic rate of 128 data packets per second emanating from the producer.  
The used topology in our scenario was a fat-tree back-haul network, shown in Figure 5. The tree had three levels of routers: 1 core (router 1), 4 backhaul (routers 2-5), and 8 access (routers 6-13). Two PoAs (or base station (BSs)) were connected to each edge router, with a total of 16 PoAs (PoAs 0-15).  
The BSs operated on 5 GHz frequencies and used the IEEE 802.11n access network. As shown in Figure 6, they were placed in a 4 \(\times\) 4 grid with one BS in each cell and the size of the cell being 80 \(\times\) 80 m. The random waypoint (RWP) mobility model [47] implemented in the NS-3 simulator was the applied mobility model. In this model, a mobile producer randomly selects a destination point (a way point) from the 320 \(\times\) 320 m global grid and then moves in a straight line at a constant speed in that direction. After arriving, the mobile producer pauses for a period before choosing a new location and speed and continuing in the same direction. Hence, the mobile producer relocates from one PoA (BS) to another while moving in accordance with the RWP model.  
Figure 5: Simulation topology.  
Five pairs of producers and consumers were used. The links had a capacity of 10 Mbps and end-to-end delay of 5 ms.  
Table 2 summarizes the various simulation parameters.
## 9 Major Project Issues and Survey Results
### Procedure for Data Sharing, Delivery, Testing, Archiving, and Asset Recognition
#### 9.2.1 Phase 1: Field Survey and Sharing of Raw Data and Initial Survey Results  
This first phase included the following steps:  
1. Before conducting a survey of a building complex, an _operational report_ (see Section 6.4) had to be provided to the Construction Management, i.e., MM. This report described the field operations to be carried out, specifying the date and providing a complete timeline for the survey activities.;
2. The _operational report_ needed approval from MM, which was responsible for organizing and providing support to the logistics of the field surveys;
3. Upon completion of the field survey operations, the surveyor was required to share the _survey report_ (see Section 6.5), including any issues encountered during the survey;
4. As soon as the processing of the collected field data were completed--specifically, the raw data processing using Heron(r) equipment via Heron Desktop(r) software--the surveyor had to upload the raw data and the processed output (i.e., the point cloud with images) to the Dropbox platform. This data had to remain available to the inspector for 15 days, allowing for random checks to verify the accuracy of this initial data processing phase. After 15 days, unless otherwise indicated by the inspector via the construction management (i.e., MM), the data could be permanently removed from sharing and deleted;
5. The inspector was responsible for conducting random checks on 5% of the collected data. Below, we provide some comments regarding these operational steps of Phase 1:1. Sharing of the _operational report_ with MM, which was responsible for reviewing and approving this operational plan, proved challenging in practice. This was due to the high frequency of exceptions and operational issues, which made the formal approval process too cumbersome. 2. In practice, more frequent informal communication developed between MM and the surveyors, allowing for quicker responses to issues arising in the field; 3. The method of sharing the collected raw data using the Dropbox platform is not operationally very efficient. Specific synchronization problems emerged, particularly regarding upload times, making the sharing process overly burdensome for both the surveyor and the inspector. 4. Since only 5% of the total surveys needed to undergo full verification, a more efficient approach in future projects would be for the inspector to request access to specific datasets for full verification on-demand, rather than requiring the upload of all raw data.
## 3 Methods
### MODIS-Based Parameters Method  
The correction of angle effects in high-resolution satellite imagery, leveraging MOD-ISBRDF parameters, is a prevalent and robust approach. This methodology has garnered widespread application in the rectification of BRDF effects for a spectrum of satellite platforms, including the Landsat series, Sentinel-2, and the GF series satellites [17; 18; 19; 23].  
\[R_{\lambda}(\theta_{s},0,\phi)=C_{\lambda}\times R_{\lambda}(\theta_{s},\theta _{v},\phi) \tag{2}\]  
\[C_{\lambda}=\frac{f_{iso}(\lambda)+f_{vol}(\lambda)K_{vol}(\theta_{s},0,\phi)+f _{geo}(\lambda)K_{geo}(\theta_{s},0,\phi)}{f_{iso}(\lambda)+f_{vol}(\lambda)K _{vol}(\theta_{s},\theta_{v},\phi)+f_{geo}(\lambda)K_{geo}(\theta_{s},\theta_ {v},\phi)} \tag{3}\]  
where, \(R_{\lambda}(\theta_{s},0,\phi)\) represents the nadir reflectance of the Jilin-1 GP01/02 PMS for band \(\lambda\) after BRDF correction, \(R_{\lambda}(\theta_{s},\theta_{v},\phi)\) denotes the reflectance of the Jilin-1 GP01/02 PMS for band \(\lambda\) under the actual observation geometry, and \(C_{\lambda}\) signifies the correction coefficient applied to nadir reflectance to account for the differences in the actual observation direction. \(f_{iso}(\lambda)\), \(f_{vol}(\lambda)\), and \(f_{geo}(\lambda)\) are derived from the MCD43A1 BRDF parameters, and \(\theta_{s}\), \(\theta_{v}\), and \(\phi\) describe the observational geometry pertinent to the Jilin -1 GP01/02 PMS.
## 5 Methods
### Application of CNN-LSTM for Artifact Removal
#### 5.1.2 Generation of Augmented EMG Recordings  
An analogous approach was used for generating augmented EMG signals as for EEG signals. However, in this case, the primary objective was to replicate muscle-origin interference. EMG signals recorded during participants' muscle activity served as templates for artifacts, specifically those captured from jaw clenching. To ensure high-quality output, a clinical neurophysiologist performed a visual assessment of one-second time windows and selected those segments that best represented muscle interference. This process yielded examples most characteristic of EMG artifacts, with the number of selected windows varying among users--ranging from a few to several dozen.  
The data augmentation process involved operations such as adding random noise, shifting signals in time, and modifying their spectral properties. Through this approach, a rich set of EMG signals was produced, accurately reflecting the muscle interference observed under real recording conditions. A visual inspection of several dozen randomly chosen augmented EMG signals confirmed that they closely resembled actual recorded EMG signals.