esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Attenuation Coefficient  
The raw A-Scan data are shown in Figure 4. The reference frontwall echo is shown in Figure 4a and the received first backwall signal for D1 and D3 are shown in Figure 4b,c. The frequency domain response of the FW, and BW of D1, D3 are shown in Figure 4d-f. Since the received signal has a total bandwidth of \(\approx\)2-7 MHz, we limit our analysis to this range. A bi-modal spectral distribution can be observed in the spectral response. Interestingly, the bimodal response is not as sharp for D3 compared to D1. The calculated \(\alpha\) values are shown in Figure 5. The three measurements per direction have been plotted together in lighter shade and the average value is shown in a darker shade. At 5 MHz, the DED  
Figure 3: Phase velocity vs. frequency comparing the three directions of the DED sample. D3 is the build direction.  
Figure 2: (**a**) Longitudinal group velocity along the three directions, (**b**) shear group velocity along the three directions. D1-2 refers to propagation along 1 and polarization along 2. D3 is the build direction. For reference, the longitudinal and shear velocities of conventional IN718 [45] have been added as dashed lines.  
sample exhibits \(\approx\)0.5 Np/cm, compared to the conventional sample \(\approx\)0.0025 Np/cm [46]. For 50 mm of propagation, this will result in \(\approx\)22 dB difference between conventional sample and DED sample, which is significant. Furthermore, beyond the 7 MHz range, we can observe a periodic noise (in the shaded region in Figure 5), which is due to the convolution noise. The bandwidth outside our analysis is shown in the light red region and is not considered in the work. Within the 2-7 MHz range, we can observe \"humps\" or \"peaks\". These humps seem to occur at specific frequencies and seem to be dependent on the direction. The D1 and D2 results show that the humps occur at or near the same frequency, i.e., 3.5 MHz, but for D3, we notice this hump is not as sharp and is closer to 4 MHz. The origin of these humps and their physical relevance to the ultrasonic measurements will be discussed in Section 4.
# Nano-Polymers as Cas9 Inhibitors
## 3 Results
### LLL-Polymer Structure as a Common Mechanism for Cas9 Inhibition  
The discovery of PMLA-LLL as a new Cas9 inhibitor prompted us to ask a more fundamental question, which was whether the LLL conjugated polymers may serve as a common Cas9 inhibitory mechanism. To address this question, we conjugated the LLL to several other polymers, including poly-L-glutamic acid 200 (PGA200; number stands for the number of units in the structure), poly-L-aspartic acid 200 (PLD200), polyacrylic acid (PAA), and hyaluronic acid (HA) (Table 3). We screened these LLL-conjugated polymers for their potential inhibitory effects on Cas9 activity, and DNA cleavage assays demonstrated that, besides PMLA-LLL, both PGA200-LLL and PLD200-LLL exhibited a complete inhibition of Cas9 nuclease function, while PAA-LLL and HA-LLL showed no effect (Figure 4A). Meanwhile, the naked polymers without LLL conjugation failed to display any Cas9 inhibition (Figure S2).  
Furthermore, titration of PGA200-LLL resulted in a clear dose-dependent pattern of Cas9 inhibition, with a minimal PGA200-LLL/Cas9 molar ratio of 10:1 to completely block Cas9 activity. When we decreased the PGA200-LLL/Cas9 molar ratio to 5:1, Cas9 inhibition was partially abolished, for we detected traces of digested DNA in the agarose gel (Figure 4B, left, red arrow). Similarly, we titrated the PLD200-LLL/Cas9 molar ratio from 1200:1 down to 150:1, and DNA digestion data demonstrated that, in order to completely inhibit the nuclease activity of the Cas9 protein, the minimally required polymer/RNP molar ratio was approximately 800:1 (Figure 4B, right), indicating a much lower Cas9 inhibitory efficiency than the other two polymers.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Polymer** & **Chemical Structure** & **Characteristic** & **Molecular Weight (MW)** & \
\begin{tabular}{c} **MW after LLL** \\ **Conjugation** \\ \end{tabular} \\ \hline PMLA &
\begin{tabular}{c} \\ \(\alpha  
* DNA digestion screening of various LLL-conjugated polymers (PMLA, PGA200, PLD200, PAA, and HA) revealed two more Cas9 inhibitors.
* The dose-dependence of Cas9 inhibition by PGA200-LLL and PLD200-LLL: Besides PMLA-LLL, the other two polymers exhibited varying efficiency in the inhibition of Cas9 nuclease activity with a ranking order of PMLA-LLL > PGA200-LLL > PLD200-LLL.
* The length of nano-polymer affects the Cas9 inhibitory efficiency. We evaluated the impact of the size/length of the nano-polymer on Cas9 inhibition using PGA as an example. The shortest PGA20-LLL cannot completely block Cas9-mediated DNA cleavage even with a polymer/RNP molar ratio of 50:1 (left panel). When comparing LLL-conjugated PGA20 vs. PGA50 vs. PGA100 with the same concentration (polymer/RNP = 20:1), only the longest PGA100-LLL exhibited 100% Cas9 inhibition (right panel).  
Figure 4: Polymer-LLL conjugates may function as a common mechanism for Cas9 inhibition.
## 3 Results and Discussion  
### Sorption Studies of EF on Imported Rubber Plants and Stone Product  
The concentration of EF in the chamber gradually decreased during the 4 h fumigation periods with imported rubber plants and marble (Figures 1 and 2). For rubber plants, with a 5% loading ratio (\(w/v\)) and an EF treatment dose set at the LC\({}_{99\%}\) level, the sorption of EF was reduced about 40% at the conclusion of the fumigation treatment, with no differences in the rates of EF sorption between the two rubber plant species (Figure 1). For marble with a 70% loading ratio (\(w/v\)), the sorption of EF was about 30% at the conclusion of the fumigation treatment, with no differences among sorption rates from three different EF dose trials (Figure 2).  
Figure 1: Concentration loss of ethyl formate (EF) (C/C\({}_{0}\)) in 0.275 m\({}^{3}\) fumigation chamber with 5% loading ratio (\(w/v\)) of potted imported rubber plants (_Fiscus retusa_ and _F. benglalensis_). EF was treated at 35 g/m\({}^{3}\) for 4 h at 15 \({}^{\circ}\)C C = EF concentration determined at one of the time intervals and C\({}_{0}\) = EF concentration at 0.1 h. EF concentration was determined at 0.1, 1.0, 2.0, and 4.0 h after EF had been applied to the fumigation chamber.  
In previous studies on food and nonfood commodities, EF sorption rates were generally greater than the 30-40% observed in this study. For example, when EF was treated at LC\({}_{99\%}\) level for 4 h, sorption of EF was 58% in imported sweet pumpkins [19], 50% in imported bananas [30], and 80% in imported rectangular lumber [9]. Low sorption rates in rubber plants and marble suggest that a greater quantity of rubber plants (e.g., more than 5% \(w/v\) loading ratio) or marble (e.g., more than 70% \(w/v\) loading ratio) could be treated under commercial settings or a lower dose of EF could be used, which may be especially important when there are negative impacts of EF on the quality of treated commodities.
## 3 RL-Based Multimedia Traffic Routing Architecture
### Architecture and Components
#### 3.1.3 Application Plane  
This plane contains the RL method, which learns the network characteristics and applies intelligence concerning route calculation. It communicates with the control plane via northbound interface to get the link-state and topology information in order to compute the optimal route between the source-destination pairs.
# Personalized Federated Learning with Progressive Local Training Strategy and Lightweight Classifier
## 4 Experiments
### Discussion of Other Parameters  
To further validate the robustness of our method under highly heterogeneous conditions, we conducted experiments with parameter settings that were independent of our algorithm. Specifically, we chose the parameters batch size and local training epochs, \(E\). By varying the ranges of the batch size and \(E\), we assessed the performance of the algorithm under different conditions. These experiments were conducted on the CIFAR10 dataset with \(\beta\) set to 0.5 and \(P\) set to 0.5. In the experiments, the _batch - sizes_ were set to [8; 16; 32; 64; 128], and the values for \(E\) were set to [3; 5; 7; 9]. By exploring these different configurations, we aimed to understand how our method performs with varying batch sizes and training epochs, which are critical factors in the training process. The results of the experiment are shown in the figure below. These results provide insights into the stability and effectiveness of our method across different training settings.  
Firstly, the batch size has a significant impact on model training. Generally, larger batch sizes lead to better training performance; however, this can be difficult to achieve for clients with limited computational resources. As shown in Figure 8a, when the batch size was small, the traditional personalized federated learning method FedPer performed poorly in local training. In contrast, PFPS-LWC significantly improved the local training performance with smaller batch sizes, surpassing FedPer in accuracy by 10.79%. This indicates that our method effectively mitigates the shortcomings of traditional personalized federated learning frameworks in resource-constrained environments. Furthermore, PFPS-LWC consistently outperformed FedPer across different batch sizes, demonstrating the robustness of our method under various settings.  
The local epochs also play a crucial role in the local training performance. As shown in Figure 8b, the performance of traditional personalized federated learning was highly sensitive to the number of local epochs, with FedPer's accuracy fluctuating significantly across different ranges. In contrast, PFPS-LWC exhibited much smaller fluctuations and consistently outperformed FedPer in terms of accuracy. Notably, when E = 9, PFPS-LWC surpassed FedPer by 6.59% in terms of accuracy. This not only highlights the stability of PFPS-LWC across different numbers of local epochs but also demonstrates its superiority over FedPer.  
These findings underscore the effectiveness of our method in various training scenarios. By maintaining high performance with smaller batch sizes and exhibiting stable accuracy across different numbers of local epochs, PFPS-LWC proves to be a robust and adaptable solution. This robustness is particularly valuable in real-world federated learning environments, where computational resources and data distributions can vary widely among clients.  
Figure 8: The accuracy curves for changing batch sizes and epochs.
## 2 Materials and Methods
### Empirical Orthogonal Function Analysis and Statistical Computations  
EOF analysis is the technique that uses the time series of the environmental variable of interest measured at several locations to find persistent spatial patterns in deviations from the average across all locations. Let Z(s,t) be the variable of interest; s is the number of the location; t is the observation time. The EOF analysis finds the set EOFs (empirical orthogonal functions) EOF1(s), EOF2(s), EOF3(s), etc., that depend only on the location number s, and the expansion coefficients EC1(t), EC2(t), EC3(t), etc., that depend only on time. The EOFs are the sought persistent spatial patterns.  
The EOFs and the expansion coefficients are found to meet two requirements. The first requirement is that at any location s at any time t, the deviation of the variable of interest Z(s,t) from the average across all locations (Z(t)) at the time t can be presented as the sum:  
\[Z(s,t)-\overline{Z}(t)=\text{EOF1(s)*EC1(t)+EOF2(s)*EC2(t)+EOF3(s)*EC3(t)+\ldots} \tag{1}\]  
The second requirement is that the first EOF, i.e., EOF1(s), has to explain as much variability in \(Z(s,t)-\overline{Z}(t))\) as possible, the second EOF has to explain as much as possible of variability in \(Z(s,t)-\overline{Z}(t)\) left unexplained by EOF1. The third EOF has to explain as much variability as possible left unexplained by EOF1 and EOF2, etc. This is accomplished by using mathematical operations with matrices [11]. The 'eof' function in R was used for computations.  
In this study, one EOF analysis was applied to model the spatial and temporal variability of \(L\). _monocytogenes_, and _S. enterica_ levels; another EOF analysis was conducted to examine the spatial and temporal variability of Rain1 and Rain7.  
The point biserial correlation coefficients [33] were computed between the prevalence values of pathogens (0--absent; 1--present) and the rainfall data. The non-parametric Mann-Whitney test was used to compare the distributions of pathogen levels in growing and non-growing seasons at the significance level of 0.05. The R version 3.5.3 ([[https://cran.r-project.org/bin/windows/base/old/3.5.3/](https://cran.r-project.org/bin/windows/base/old/3.5.3/)]([https://cran.r-project.org/bin/windows/base/old/3.5.3/](https://cran.r-project.org/bin/windows/base/old/3.5.3/)), accessed on 29 May 2022) was used for the statistical analysis.
## 4 Results and Discussion
### Treatment Competency: Classification and Prediction
#### 4.2.2 Classification Results  
The classification results are reported using the accuracy, precision, and recall scores. The main levels of competence to be classified are low, medium, and high. Each level of competence is classified using the manual and the automatic transcriptions based on the following classifiers: SVM, Decision Tree Classifier, Random Forest Classifier, Ada-Boost Classifier and Gradient Boosting Classifier. Table 5 presents the classification results for the manual and automatic transcriptions using the aforementioned classifiers. It is clear that the SVM achieved higher performance than the other classifiers in classifying the three levels of competence.  
Figure 6: The best linguistic features are represented on the horizontal axis, for predicting the full competency rating or each rating item represented on the vertical axis. The rating item numbers are based on the numbers in Table 4. The green blocks indicate the selected features for the patient’s segments, the blue blocks indicate the selected features for the practitioner’s segments and the red blocks indicate the selected features for the full session, including the patient’s and practitioner’s segments.  
The study also investigated the relationship between the patient's current level of depression and anxiety and the practitioner's level of competence in that session. For that reason, the practitioner's levels of competence have been classified using manual transcriptions based on the different levels of depression and anxiety. Figure 7 shows the classification results based on the patient's level of anxiety using the GAD-7 scores. In contrast, Figure 8 shows the classification results based on the patients' level of depression using the PHQ-9 scores. Due to a large number of occurrences of medium levels of competence in the GSHTS dataset, the model predicts the medium level correctly most of the time and misclassifies the other levels as the medium level. This correlates with the results in Figure 3. Furthermore, it is clear from the results that the model is capable of classifying higher levels of competence in higher levels of depression and anxiety, which also relates to the higher prevalence of these types of data samples in the dataset.
# Numerical Study of Coolant Flow Phenomena and Heat Transfer at the Cutting-Edge of Twist Drill
###### Abstract  
Cutting tool coolant channels play a pivotal role in machining processes, facilitating the efficient supply of cooling agents to high-stress areas and effective heat dissipation. Achieving optimal cooling at the tool's cutting-edge is essential for enhancing production processes. Experimental investigations into tribological stress analysis can be limited in accessing complex tool-workpiece contact zones, prompting the use of numerical modelling to explore fluid dynamics and tribology. In this study, the coolant flow dynamics and heat dissipation in drilling operations were comprehensively investigated through computational fluid dynamics (CFD) modelling. Four twist drill models with varying coolant channel arrangements were studied: standard model drill, standard model drill with notch, profile model drill, and profile model drill with notch. Two distinct approaches are applied to the coolant inlet to assess the impact of operating conditions on fluid flow and heat dissipation at the cutting-edge. The findings emphasize that cutting-edge zones have insufficient coolant supply, particularly in modified drill models such as the standard model drill with notch and profile model drills with and without notch. Moreover, enhanced coolant supply at the cutting-edge is achieved under high-pressure inlet conditions. The standard model drill with a notch exhibited exceptional performance in reducing thermal load, facilitating efficient coolant escape to the flute for improved heat dissipation at the cutting-edge. Despite challenges like dead zones in profile models, the standard-with-notch model yielded the most promising results. Further analyses under constant pressure conditions at 40 and 60 bar exhibited enhanced fluid flow rates, particularly at the cutting-edge, leading to improved heat dissipation. The temperature distribution along the cutting-edge and outer corner demonstrated a decrease as the pressure increased. This study underscores the critical role of both coolant channel design and inlet pressure in optimizing coolant flow dynamics and heat transfer during drilling operations. The findings provide valuable insights for designing and enhancing coolant systems in machining processes, emphasizing the significance of not only coolant channel geometry but also inlet pressure for effective heat dissipation and enhanced tool performance.  
drilling; CFD; coolant; heat dissipation; internal cooling channel design 2024
## 3 Results
### Effect on NO Emissions during Reduced Air-to-Fuel Ratio  
NO emissions during operation at certain air-to-fuel ratios can be found in Figures 7 and 8.  
Based on the results presented in Figures 7 and 8, the following points can be made:  
* High air factor results in higher NO emissions due to higher availability to oxygen. This is well-known within combustion chemistry and requires no further comment.
* The sand reference has the lowest NO emissions, meaning that no improvements could be verified by using manganese ore as bed material.
* Of the experiments where manganese ore is used, those which perform best with respect to CO emissions (10% manganese ore and 100% manganese ore with sulphur feeding) also perform decently with respect to NO emissions. A connection between the two seems very likely.  
The positive effect of up to 30% reduction of NO shown during partial substitution of sand with ilmenite [6] could not be replicated with manganese ore. This will be further discussed in Section 5 below.
## 2 Related Work
### Remote Data Retrieval  
The IoRT network, which is highlighted for enabling the deployment of IoT devices across wide geographical areas, could retrieve monitoring data in the farmost areas of the TP [29; 30]. The IoRT network can be defined as the art and science of using advanced technology, such as unmanned ships, drones, and satellites, to transmit in situ data cached in ground IoT terminals, deployed in the field for environmental monitoring [31; 32; 33]. In general, the ground IoT terminal is an integrated device comprising a datalogger, sensors, and a communication module with a transceiver antenna for data transmission and receival. Additionally, in the satellite-enabled IoRT, a ground gateway station is required to transfer the collected data from satellites to scientific data centers via the Internet. As a platform, the data center is used for observation data publishing.  
Data retrieval in the farmost areas is being revolutionized by the IoRT [34; 35]. Numerous IoRT applications have been demonstrated under different scenarios (e.g., environmental monitoring, beach monitoring, and ice sheet monitoring), where public networks are absent [36; 37]. For example, a drone-based wireless sensor network (WSN) was introduced to establish an IoRT network for marine coastal monitoring [38]. According to this study, a drone was employed as a mobile relay to collect data from sensing buoys. In addition to marine coastal monitoring, the authors of [39] proposed a drone-based IoRT network for crop monitoring. The proposed network, which comprised a WSN and aerial robot, was used for frost monitoring in vineyards. The established network could overcome the limitation of transmitting monitoring data via a public network.  
Moreover, [PERSON] et al. examined the remote data backhaul process from another perspective, thus proposing an unmanned vehicle-based IoRT network via autonomous underwater vehicles for low-cost oceanographic and environmental surveys [40]. Similarly, [PERSON] et al. combined different acoustic navigation and underwater vehicles for Arctic under-ice monitoring [41]. In addition to the problem of retrieving data in areas without public networks, many studies have been conducted with the aim of improving the data retrieval efficiency via novel and efficient satellite-enabled IoRT networks. Accordingly, the authors of [42] proposed a satellite communication-aid network to transmit power system operation monitoring data, which indicated that satellite communications could be suitably used to transmit SCADA/EMS data in theory. Similarly, as stated in [43], a satellite-based system was presented for remote animal behavior monitoring in the wild. These studies did not verify the ability of remote data transmission using satellite-enabled IoRT networks from a practical application perspective. Thus, motivated by the above works, we are interested in exploiting the IoRT using satellites, which could boost the data retrieval performance in remote areas.