esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application Fields and Clinical Obstacles
Moreover, T-cell-driven skin sensitization could not be observed in a standardized murine model after repeated gas plasma exposure using an OECD accredited assay [97]. It is suggested that the generated ROS mixture does not drive non-specific, redox-mediated T-cell activation, and neither increased phosphorylation of NF-kB [98] nor proliferation [99] could be observed in viable T\({}_{\rm H}\) cells upon gas plasma treatment per se. Disadvantages of medical gas plasma predominantly concern application practice, physicochemical heterogeneity of available plasma sources, sterilization, and economic questions. Plasma jet devices require a continuous supply of an inert gas (e.g., argon), which increases treatment costs. However, the expenses should be covered by a few Euros per application. It should be noted that gas plasma treatment is currently under observation of the national health council for cost reimbursement by social healthcare insurance providers, at least in Germany. It is worth noting that available and clinically approved plasma sources differ with regard to the expelled ROS/RNS chemistry [20]. While the chemistry of some sources such as plasma jets is dominated, for instance, by singlet delta and atomic oxygen as well as ozone, other accredited devices such as the DBDs have been found to generate high amounts of NO\({}_{2}\)[74,100]. In addition, new application fields, e.g., care of internal bleedings, would require creating suitable endoscopic devices that have been designed by research centers without entering clinical practice [101, 102, 103]. Moreover, sterilization of gas plasma applicators is a compelling basis for widespread application during surgeries that have to be taken into account. Finally, the scalpel-like application of plasma jets is better  
Figure 5: Potential fields of application of gas plasma-mediated hemostatic activity. Created with bioreender.com (accessed on 1 February 2022). suited for hemostatic action in the clinics that flat-shaped dielectric barrier discharges. A scheme of the thus far only plasma jet device approved in Europe for medical applications, the kINPen MED, is shown below (Figure 6). It has been found that direct connection of the plasma jet with its target (Figure 6, right image), such as a bleeding wound, is much more potent that the remote deposition of the cloud of ROS being generated [104].
## Appendix C Throughput  
Figures 14 and 15 shows the throughput for the short and mixed datasets, respectively.  
The initial spike in throughput observed at the beginning of the FCFS graphs may be attributed to the rapid processing of the first set of jobs. Given that FCFS prioritizes jobs based on their arrival order, the initial jobs in the queue are promptly executed, leading to a notable surge in throughput. However, as time elapses, the dynamics of the queue start influencing the system. When new jobs continue to arrive, they join the queue and must wait for the earlier jobs to be completed. This waiting time can contribute to a decline in the overall throughput, especially if the queue becomes longer or if jobs have varying execution times. This observed trend is visually represented in the graph as a decrease in throughput following the arrival of the initial jobs.  
SJF encounters a similar phenomenon in throughput as it does in turnaround and response. Long-running jobs face resource starvation, leading to a zigzag pattern in the throughput graph.  
In the case of the RR scheduler, where there is a constant time_quantum, we would anticipate most jobs staying in the local queue for an extended period, as clearly illustrated in both the short and long datasets. The throughput stabilizes within the range of 0.05 to 0.10, suggesting an overall low throughput. This indicates that jobs receive a fair share of the CPU, regardless of whether they are long or short.  
In the case of the MLFQ scheduler, an overall low throughput is observed, except for a few jobs that likely have extremely short running times. Most jobs exceed the time slice allocated for the highest priority, forcing them to wait until all other jobs have decreased in priority to become eligible to run again. This results in an overall low throughput, as most jobs depend on boosting their priority level to become eligible to run again. The observed pattern in the turnaround time for MLFQ mirrors the findings in the throughput analysis.  
The distinct patterns observed in the CFS for the short and mixed datasets reveal interesting dynamics in job processing. CFS exhibits several spikes in both the short and mixed datasets. These spikes are likely linked to jobs with low niceness and/or low running time. Initial jobs often experience high throughput, as indicated by Equation (3), which calculates the time slice based on having only a single job in the queue, resulting in a higher time slice than it should likely have. Following the initial spikes, CFS maintains a more consistent but lower throughput, occasionally experiencing higher throughput for specific jobs. These variations show the adaptability of CFS to different workload scenarios, with the scheduler dynamically adjusting to the characteristics of the jobs in each dataset.  
Figure A5: Throughput for short jobs.  
Figure 6: Throughput for mixed jobs.  
Figure A8: Load over time for each scheduling algorithm (mixed jobs).
## 3 Application to Experimental Data
### Evaluation
#### 3.4.2 Influence of Kernel Size  
Just like in conventional kernel density estimation, the kernel size is an important factor regarding the detection performance. The sizes assessed in this study are arranged in Table 3. The _product_ fusion rule is selected here due to its strong performance in the previous experiment. Evaluation results are shown in Figure 12. The results suggest that given a well-performing fusion method and an accurate estimation of the localization uncertainty \(\hat{u}\), a range of kernel sizes around the proposed default setting in Equation (4) is adequate. Performance only deteriorates for very small kernel sizes like 25%-50% of the proposed size. The product rule shows no obvious dependence between kernel size and performance at the shallowest two grooves. However, groove nr. 9 is better identified when larger kernels are used. This could be explained by unusually large registration error at this region, but in this case the reason is that thermography only indicates the top part of groove nr. 9 with large enough SNR to pass the individual detection stage. The results presented here might tempt to favor large kernels. However, large kernels increase the chance of falsely associating spatially nearby false alarms, and thus quantify sensor agreement where there is actually conflict. Therefore, the kernel size proposed in Equation (4) was found to be effective in this experiment.
## 4 Results
### Arabic Sentiment Analysis  
The evaluation results of the proposed ArCAR system for Arabic sentiment analysis are shown in Table 9. These results are derived as an average of the 5-fold test. As shown in Table 9, the proposed ArCAR system presented its reliability and capability for Arabic sentiment analysis. It is clearly shown that using the remedies of the unbalanced datasets, the proposed ArCAR system achieves similar evaluation results in terms of all evaluation indices. Using the balance (i.e., case of binary class) and unbalance (i.e., case of multiple class) HARD datasets, overall accuracies of 93.23% and 93.58% are achieved. However, the overall system accuracy using the binary BRAD dataset (i.e., balance set) is slightly better by 4.33% than that using the multiclass BRAD dataset (i.e., unbalance set). Similarly, all evaluation metrics are derived using the averaged multiclass confusion matrix over a 5-fold test, as shown in Figure 5.  
Similarly, the training and testing processing times are proportionally affected by the number of training and testing Arabic reviews in the HARD and BRAD datasets, as presented in Table 10. For example, using the total number of 31,319 testing reviews in the balanced BRAD (i.e., binary class) testing set, the ArCAR system requires 3.3 s to complete the testing recognition process. Meanwhile, for the BRAD multiple classes problem (i.e., unbalance set) using the total number of 102,119 testing reviews, the ArCAR system requires 10.76 s. This means for one Arabic sentiment (i.e., review), the ArCAR system needs only \(1.054\times 10^{-4}\) s.
## 2 Materials and Methods
### Qualitative Criteria for the Evaluation
#### 2.6.2 Feature Recognizability  
With the aim to assess the quality of the recorded data under practical measurement conditions, particularly with regard to documenting the Cultural Heritage, the ability to resolve small object details should be considered. The complete point model is the final output of an IMMS survey, but, at the same time, it is the starting point for further elaboration relating to different application fields.  
In general, the possibility of recognizing features on the 3D model is related to accuracy, resolution, and noise level. In IMMS, it is difficult to evaluate all of these aspects separately. For instance, when focusing only on the laser, the accuracy and resolution are limited by the measured distance. The laser beam has a spot diameter, which also increases with the distance, and angular inaccuracy affects the data depending on the slant of the measured surface. This does not affect the measurement of large and flat objects, but the recognizability of small objects can be compromised. Since it was not possible to set up a specific test site with expressly designed benchmark features, as done for TLS benchmarking by [PERSON] (2016), we chose to evaluate the ability to record real-world objects and details: we compared the data acquired around flat black and white targets (21 cm \(\times\) 30 cm), white spheres (14.5 cm diameter) hung on the walls, and some architectural details such as doors and windows.
# A Survey of Defensive Measures for Digital
## 5 Metrics for Consideration
### Standards for Measuring a Tool's Viability  
Correctly setting up and using a security tool can be difficult for any user. How one goes about measuring the factors that contribute to task difficulty is a significant challenge. Task complexity, the requirement to multitask, lack of training, inconsistency of responses to actions, and demands on both long-term and short-term memory will all play a role [34]. Therefore, the simplest, least demanding tool that provides the greatest security is best.  
There are a variety of standards for measuring a tool's usability and ease of use. MUSiC (Metrics for Usability Standards in Computing) evaluates the efficacy of a product's use by evaluating the quality of task completion and the time that is required to complete. It also takes into consideration a multitude of context-dependent factors, such as cognitive workload, user satisfaction, rate of learning, time used productively, and effectiveness. While many of these are qualitative and subjective, MUSiC attempts to quantify these values with formulas that produce easily understood scores [35].  
QUIM (Quality in Use Integrated Measurement) seeks to integrate multiple standards of measuring usability into one model. QUIM breaks down into 10 usability factors, such as universality, accessibility, and productivity. Each of these factors are evaluated while using 26 criteria, such as time, accuracy, and simplicity. This breaks down into 127 specific metrics for usability. The attractiveness of QUIM is that it avoids the problem of subjectivity, as these metrics can be found by either using a formula or simply by counting. For example, one could measure the amount of processing power that is required to run the program in order to evaluate a program's efficiency [36].  
HCI-S (Human Computer Interaction Security) evaluates the user-friendliness of a security system. It is often acknowledged that a system made with usability as a priority will be less secure, and that  
Figure 1: The technology acceptance model.  
high-security systems will be difficult to use. HCI-S proposes six simple criteria that help to find a balance between usability and security: first, the tool should convey security features available to the user in an easily understood format. Second, the security status should be visible to the user. Third, the interface should be easy to learn. Fourth, the design should be minimalistic, only showing what is necessary. Fifth, error messages should be easy to understand and act upon. Sixth, the user should have a satisfactory experience. If all six criteria are met, this leads to trust. It is vital in a security environment that the user trust the tool [37].
## 4 Discussion  
In this work, we assessed Si accumulation and recycling with respect to N and P in the littoral zone of Lake Iseo. These issues are little studied but of relevance given the importance of Si for primary producer growth, as they can provide new insights into the role of benthic processes that regulate DSi bioavailability. Several papers focus on DSi fluxes in similar systems, i.e., shallow brackish and freshwater ecosystems, but less is known about the BSi content of surface sediments, EA, and SAV biomass. Overall, while DSi fluxes measured in the present study fall within the ranges reported in the literature (Table 6), comparisons are less reliable for sedimentary BSi, since there is scarce data available.  
The littoral zone is a filter between terrestrial and aquatic ecosystems, which can retain or release DSi, DIN, and SRP through primary production, grazing, detritus decomposition and mineralization, etc. [25]. In general terms, our results identify the components of the benthic communities that primarily account for the filtering capacity of lakes and lenti waters. In addition, lentitic waters are key components of fluvial filters, which can selectively remove, retain, or release nutrients and chemical elements into outflowing waters [6; 11].  
The comparative contribution of SAV, MPB and EA to BSi storage and DSi fluxes is shown in Figure 6. Surprisingly, despite the fact that the primary producers considered in this study accumulated BSi, the littoral zone was on average a DSi source to the water column, especially the SAV meadow. However, the littoral zone was heterogeneous and patchy with two contrasting features that depend on hydro-morphology--i.e., flat, soft sediments and steep, hard (rocks and pebbles) substrates--which in turn shape the dominant community. On account of these differences, process rates and DSi outcomes were variable between the littoral communities due to the fact that the primary producers colonizing each area exhibited a distinct preference for Si. The SAV meadows and MPB patches were a net DSi source, while only EA assemblages on rocky substrates were a net sink.  
Figure 6: Average water column concentrations, main fluxes, and compartments of Si, N, and P. Fluxes are reported as g m\({}^{-2}\) d\({}^{-1}\), while water concentrations, sediment, microhybtoenthos (MPB), epilithic matter (EM), filamentous matter (FM), leaves and roots contents are reported as g m\({}^{-2}\). Standard error is shown in parenthesis. The size of numbers and letters is proportional to flux and stock extent.
## 1 Introduction
### Similar Solutions to this Problem in Other Countries  
German railway companies use their own homogeneous satellite reference frame DB_REF for their geodetic purposes. This reference frame (designated PS0) has been defined by approximately 7200 reference stations located along railway lines at distances of 3-5 km with an absolute accuracy in ETRS89 of \(\sigma_{\text{absPS0,3D}}=10\) mm and a relative accuracy of \(\sigma_{\text{reIPS0,3D}}=5\) mm. The PS0 network is further thickened by satellite methods with the PS1 network, where the distance between adjacent points is up to 1000 m. The absolute accuracy in ETRS89 is \(\sigma_{\text{absPS1,3D}}=15\) mm, the relative accuracy in relation to PS0 is \(\sigma_{\text{reIPS1,3D}}=5\) mm. The lower levels of the PS3 and PS4 networks are measured using terrestrial methods [14].  
China is building an extensive infrastructure of high-speed corridors where trains can reach speeds of 250 to 300 km/h. The construction of these corridors is also supported by geodetic railway networks, which are hierarchically divided into CP0, CPI, CPII and CPIII levels. The CP0 level forms a GNSS reference network with a distance between points of about 30-50 km. The CPI level consists of pairs of points at a mutual distance of up to 800 m, determined by GNSS technology, with a distance between the pairs of points of about 4 km. The point pairs must be mutually visible for terrestrial measurements. The lower levels of the CPII and CPIII networks are determined terrestrially [15].  
In Poland, geodetic measurements on railways are based on the PUWG2000 coordinate system and the Kromsztad86 height system. Level I consists of the ASG-EUPOS network of permanent stations, while Level II consists of points of the primary system determined by GNSS technology using the static method in pairs of repeats. The points are always deployed in pairs with a mutual distance of 150-300 m and with mutual visibility. These pairs are 1.5-2.0 km apart. The primary system points have a horizontal accuracy of 10 mm. Levels III and IV are determined by terrestrial traverse following Level II points [16; 17].  
In the Czech Republic, the Railway Administration analogously uses one of the permanent station networks (CZEPOS, Trimble VRS Now, TopNET, GEOORBIT) in Level 0, which are part of the independent monitoring of permanent GNSS stations carried out by the Research Institute of Geodesy, Topography and Cartography, v.v.i. [18]. These networks of permanent stations are characterized by high relative accuracy of spatial position determination (up to 5 mm between stations). Level I consists of a primary system of points along railway lines at a distance of about 1 km from each other, determined by GNSS technology with an accuracy of \(\sigma_{x,y}=5\) mm. Level II consists of a secondary system of points spaced 120 to 300 m apart and determined by terrestrial methods. Level III consists of the witness marks assigned to the points of the secondary system [19].
# Pilot Sequence Allocation Schemes in Massive MIMO Systems Using Heuristic Approaches
## 1 Introduction
### Related Works
In the group with low interference, the pilots are re-utilized randomly, while high interference groups are grouped by propagation affinity. * In [16], the authors mitigated the pilot sequence allocation problem through user categorization in high and low interference groups based on large-scale fading, where the high interfering users receive orthogonal pilot sequences, and non-orthogonal pilot sequences are allocated to users under a low interference regime. The authors propose the use of an edge-weighted interference graph to maximize the performance of users in the low interference bracket. * In [17], the authors proposed the pilot sequence allocation allied to power allocation based on a Monte Carlo Tree Search Method (MCTS) which mitigates the pilot contamination. Moreover, the AlphaGo algorithm is used to play the proposed pilot allocation game, while the Markov Decision Problem (MDP) solves the power allocation problem. * In [18], based on works [19; 20] an adaptation of the particle swarm optimization algorithm to solve the joint pilot sequence allocation and power control problem in Massive MIMO systems was proposed. The authors aim to maximize the spectral efficiency with a limited number of pilot sequences based on coherence interval, while also taking power constraints into account. * In [21], the authors proposed a joint pilot sequence allocation and antenna scheduling scheme to curb the effects of pilot sequence contamination in Massive MIMO systems where there are a limited number of antennas. To allocate the sequences to multiple users, they proposed rules using either geometric or arithmetic progression in the number of users using the same sequence. Furthermore, they compare their solution with a Greedy pilot sequence allocation scheme and the Smart Pilot Assignment algorithms. * The work in [22] presents different pilot allocation solutions for cell-free scenarios. The first algorithm is based on the concept of random sequential adsorption using statistical physics, while the second one is an analytical approach of the first one. The authors also describe two centralized algorithms based on clustering principles to benchmark the proposed solutions. The results show that the distributed solutions have a competitive performance compared to the centralized ones, especially when the user density is high.
## 4 Experimental Setup
### Experiments Results
#### 4.1.6 IGD Value Convergence Experiment
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Problem** & **M** & **D** & \(\mathbf{k=10}\) & \(\mathbf{k=30}\) & \(\mathbf{k=40}\) & \(\mathbf{k=50}\) & \(\mathbf{k=20}\) \\ \hline CF1 & 2 & 10 & \(2.4857\times 10^{-1}\) (\(3.81\times 10^{-2}\)) & \(2.5048\times 10^{-1}\) (\(5.02\times 10^{-2}\)) & \(2.4363\times 10^{-1}\) (\(5.21\times 10^{-2}\)) & \(2.8368\times 10^{-1}\) (\(5.07\times 10^{-2}\)) & \(1.4121\times 10^{-1}\) (\(3.12\times 10^{-2}\)) \\ CF2 & 2 & 10 & \(2.6061\times 10^{-1}\) (\(3.47\times 10^{-1}\)) & \(2.4819\times 10^{-1}\) (\(2.10\times 10^{-1}\)) & \(2.0105\times 10^{-1}\) (\(2.26\times 10^{-1}\)) & \(2.4917\times 10^{-1}\) (\(1.73\times 10^{-1}\)) & \(8.5247\times 10^{-2}\) (\(2.42\times 10^{-2}\)) \\ CF3 & 2 & 10 & \(1.2758\times 10^{-1}\) (\(2.35\times 10^{-1}\)) & \(1.3732\times 10^{-1}\) (\(1.39\times 10^{-1}\)) & \(1.5471\times 10^{-0}\) (\(3.99\times 10^{-1}\)) & \(2.1090\times 10^{-0}\) (\(2.00\times 10^{-0}\)) & \(1.3271\times 10^{-0}\) (\(2.03\times 10^{-1}\)) \\ CF4 & 2 & 10 & \(1.6025\times 10^{-1}\) (\(3.10\times 10^{-1}\)) & \(1.6142\times 10^{-0}\) (\(2.08\times 10^{-0}\)) & \(2.2987\times 10^{-1}\) (\(1.70\times 10^{-1}\)) & \(1.1774\times 10^{-0}\) (\(1.51\times 10^{-5}\)) & \(2.2093\times 10^{-1}\) (\(1.144\times 10^{-1}\)) \\ CF5 & 2 & 10 & \(6.0988\times 10^{-0}\) (\(4.54\times 10^{-0}\)) & \(2.3684\times 10^{-0}\) (\(9.33\times 10^{-1}\)) & \(3.7901\times 10^{-0}\) (\(4.18\times 10^{-1}\)) & \(4.1804\times 10^{-0}\) (\(9.26\times 10^{-1}\)) \\ CF6 & 2 & 10 & \(1.1022\times 10^{-0}\) (\(1.61\times 10^{-1}\)) & \(1.8757\times 10^{-1}\) (\(7.21\times 10^{-2}\)) & \(7.5297\times 10^{-1}\) (\(1.68\times 10^{-1}\)) & \(2.3044\times 10^{-1}\) (\(7.12\times 10^{-3}\)) & \(1.0460\times 10^{-1}\) (\(5.29\times 10^{-3}\)) \\ CF7 & 2 & 10 & \(7.2996\times 10^{-0}\) (\(5.30\times 10^{-0}\)) & \(4.9177\times 10^{-0}\) (\(9.88\times 10^{-0}\)) & \(6.5657\times 10^{-0}\) (\(2.90\times 10^{-0}\)) & \(4.1699\times 10^{-0}\) (\(3.99\times 10^{-0}\)) & \(2.0088\times 10^{-0}\) (\(1.04\times 10^{-0}\)) \\ CF8 & 3 & 10 & \(3.9178\times 10^{-0}\) (\(8.05\times 10^{-0}\)) & \(5.5117\times 10^{-1}\) (\(1.23\times 10^{-1}\)) & \(4.3027\times 10^{-0}\)(\(1.00\times 10^{-1}\)) & \(1.0040\times 10^{-1}\)(\(1.16\times 10^{-1}\)) & \(6.1440\times 10^{-1}\) (\(1.87\times 10^{-1}\)) \\ CF9 & 3 & 10 & \(1.0973\times 10^{-0}\) (\(1.28\times 10^{-0}\)) & \(2.5924\times 10^{-1}\) (\(7.68\times 10^{-2}\)) & \(2.9727\times 10^{-1}\) (\(8.14\times 10^{-2}\)) & \(2.5747\times 10^{-1}\)(\(8.62\times 10^{-2}\)) & \(2.4295\times 10^{-1}\) (\(5.13\times 10^{-3}\)) \\ CF10 & 3 & 10 & \(1.5188\times 10^{-1}\) (\(1.78\times 10^{-1}\)) & \(1.5761\times 10^{-1}\) (\(1.89\times 10^{-0}\)) & \(5.3147\times 10^{-0}\)(\(2.03\times 10^{-2}\)) & NaN (NaN) & \(4.2232\times 10^{-0}\) (\(1.17\times 10^{-4}\)) \\ \hline +/\(-\)/\(-\) & & & 0/6/4 & 0/5/5 & 0/4/6 & 0/3/6 & \\ \hline \hline \end{tabular}
\end{table}