esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Importance of Obstacles in Mixing  
To the best of our knowledge, there were no reports in the literature on designs on mixing chambers for blood and cold plasma. However, to demonstrate the genericity of the results from using obstacles in mixing chambers and of the corresponding conclusions, three different mixing chamber designs with different dimensions and aspect ratios were analyzed. These chambers had: (a) a long width of 9 \(\times\) 10\({}^{-2}\) m; (b) small obstacles (1 \(\times\) 10\({}^{-4}\) m\({}^{2}\)); and (c) a long length (0.21 m) and two plasma inlets. Schematic diagrams of these chambers are shown in Figure 17a-c. The thicknesses of all chamber designs were set to be either 1 \(\times\) 10\({}^{-2}\) or 2 \(\times\) 10\({}^{-2}\) m. As such, each chamber design had four variations with or without obstacles, and with different thicknesses.  
Three-dimensional snapshots of mixing chambers with a width of 9 \(\times\) 10\({}^{-2}\) m having thicknesses of 1 \(\times\) 10\({}^{-2}\) and 2 \(\times\) 10\({}^{-2}\) m, with and without obstacles, are shown in Figure 18a-d. Mixing of blood and cold plasma was enhanced by mixing chambers with obstacles when compared to those without obstacles. However, the central part of the mixing chambers was filled with blood not mixed with the injected plasma, which was explained by the insufficient volumetric flow rate of the plasma to achieve adequate mixing. Similarly, three-dimensional snapshots of mixing chambers with small obstacles (1 \(\times\) 10\({}^{-4}\) m\({}^{2}\)) having thicknesses of 1 \(\times\) 10\({}^{-2}\) and 2 \(\times\) 10\({}^{-2}\) m, with and without obstacles, are shown in Figure 19a-d. Substantial mixing inside the mixing chambers with obstacles was achieved for distances as far as the outlet, which was not affected by an increase in the thickness of the chambers. When no obstacles were present, mixing between blood and plasma was poor and the central part of the mixing chambers was filled with blood not mixed with the injected plasma. Lastly, three-dimensional snapshots of the mixing chambers with a length of 0.21 m and two plasma inlets having thicknesses of 1 \(\times\) 10\({}^{-2}\) and 2 \(\times\) 10\({}^{-2}\) m, with and without obstacles, are shown in Figure 20a-d. Similar to previous designs, substantial mixing inside the mixing chambers could only be achieved in the presence of obstacles. According to the above analyses, obstacles within the mixing chambers were generically essential for achieving substantial mixing between the plasma and blood.  
Figure 17: Schematic diagrams of mixing chambers with: (**a**) a long width of 9 \(\times\) 10\({}^{-2}\) m; (**b**) smaller obstacles (1 \(\times\) 1 \(\times\) 10\({}^{-4}\) m\({}^{2}\)); and (**c**) a long length (0.21 m) and two plasma inlets.
## 3 The 6G Technology
### Sixth-Generation Architecture  
The incoming 6G network will feature four main protocol layers in its architecture. These layers will include the physical layer, data link layer/transceiver design layer, network layer, and application layers [33; 34]. Researchers are now exploring different approaches to integrate ML into all these layers to enhance the effectiveness of the incoming 6G network. The key goal of ML in different layers of the network is to improve performance and keep their different KPIs at their optimal thresholds. Some of these network KPIs that the 6G is expected to meet include peak throughput, higher energy efficiency, ubiquitous connectivity, inter-terrestrial communication, adaptability to novel technologies, self-aggregating communications, etc. [33; 58]. An extensive review of AI/ML's role in the different layers of the network architecture has been reported [34].
## 4 Multi-Objective Hybrid Resource Consolidation Algorithm  
This section presents the design and development of an efficient resource consolidation algorithm that is capable of grouping the PMs using the clustering technique to organize the user requests based on their resource demand in order to reduce high energy consumption and SLA violation. The schematic structure of the proposed system is shown in Figure 1. The algorithm is composed of two main phases: the clustering phase, in which large resource management is carried out, and the resource consolidation phase comprised of VM allocation, which identifies the PMs to consolidate while monitoring the resources that will determine the target PMs. VMs are migrated to the targeted PM, and finally, the transition state decides on the consolidated candidate. A brief description of each phase is presented as follows.  
The first phase is the Dynamic Clustering (DC) strategy, which groups the VMs requests into sub-groups based on user demand. Requests with similar requirements are placed on the PMs that are in the same cluster to reduce the need for resource migration, which also leads to energy consumption. This strategy determines where the user request will be executed based on the current resource utilization of a cluster type. The clusters are divided into three groups, either CPU intensive, Memory intensive, or Storage intensive. The second phase is VM resource consolidation, which uses LNS and the migration strategy. The LNS balance the local and global search procedure that avoids entrapment, while migration from overloaded PMs is used to avoid SLA violation. At the final stage of this phase, a monitoring module is employed to provide information on resource availability and clusters to determine the VMs that need to be migrated, those that need to be switched off/on by the transition state to increase resource utilization, or moved to a shutdown  
Figure 1: Schematic Diagram of MOH-FPRC.  
pool so they can be reinitialized later if user requests increase. In this way, the data center resource management in terms of energy efficiency and SLA violation could be improved.
## 3 Results and Discussion
### Release of Bound State Aldehydes
#### 3.4.1 Release by 4 VP  
Figure 3 shows the degree of bound state aldehydes after release by 4 VP. The degree is a relative variable calculated as described before. A value of 0 indicates no release of bound state aldehydes, while values > 0 imply release.  
The degree of bound state aldehydes in the analyzed beers varied by the status of aging, malt modification level, and type of aldehyde. In most fresh samples, considerable amounts of aldehydes could be released by 4 VP. The degree of releasable aldehydes decreased during aging. After M9, no more aldehydes could be released in most cases. This over-time-decreasing amount of bound state aldehydes was statistically significant for 2 MP (one-way ANOVA: \(p\) = 0.0002), Fur (one-way ANOVA: \(p\) = 0.0004), and PA (one-way ANOVA: \(p\) < 0.0001). These compounds appeared to be the most promising aldehydes for use as early-stage indicators of flavor instability in fresh beer. Furthermore, Benz (one-way ANOVA: \(p\) < 0.0001) and T2N (one-way ANOVA: \(p\) = 0.0024) showed significant differences during aging. However, for these compounds, most aldehydes could be released after M3 versus M6 and forced aging. Apparently, bound state aldehydes are also released during forced aging, although to varying extents. In the case of T2N, more aldehydes were releasable after forced aging.  
Figure 3: 4 VP-releasable aldehydes (\(n\) = 6): the heatmap shows the relative concentration of bound state aldehydes in comparison to the free form (B1: barley variety 1; B2: barley variety 2; P1: low proteolytic malt modification; P2: medium proteolytic malt modification; P3: high proteolytic malt modification).
# Comparison of Diagnostic Accuracy of Physical Examination and MRI in the Most Common Knee Injuries
## 1 Introduction  
The knee joint is one of the most common joints subjected to injuries. Moreover, due to its localization and function, it is of vital importance to working ability, daily tasks and recreational and professional sports. Damage to tissues such as menisci, ligaments or hyaline cartilage can lead to irreversible osteoarthritis changes of the joint [1; 2]. Therefore, quick and accurate diagnosis of intraarticular lesions, which is necessary for selecting adequate treatment, is of great importance [3].  
An abundance of mechanisms can lead to knee joint failure, which is one of the factors that makes diagnosis difficult [4; 5; 6]. Delay in introducing proper treatment has a great effect on hyaline cartilageand leads to irreversible changes in its structure [7; 8]. Therefore, apart from a thorough medical history, specific diagnostic tests must be implemented, and even then it is not uncommon for even very experienced medical professionals to have difficulties making an adequate diagnosis.  
Over the last few decades, magnetic resonance imaging has been continuously gaining acceptance among the orthopaedic society as a diagnostic tool, ousting the use of arthroscopy as a diagnostic modality [9]. However, with the increasing popularity and accessibility of MRI, the amount of unnecessary MRI increases, having reached nearly 40% of all examinations [10]. Moreover, the study performed by [PERSON] et al. showed that nearly 20% of patients have not had an appropriate physical examination prior to their MRI [10]. Highly sophisticated imaging modalities such as MRI are expensive and have a long waiting list; therefore, it seems advisable that recommendations for MRI should be justified by clinical appearance. On the contrary, the use of MRI can decrease the number of unnecessary surgical treatments [11]. In this study, over 30% of the patients waiting for surgery after MRI evaluation of the affected knee were disqualified from surgical treatment due to benign MRI findings. Although arthroscopy is considered to be a safe surgical intervention with low incidence of adverse effects [12], one should not forget that it is a surgical procedure with potentially hazardous side effects.  
Given the information provided above, it seems important to work out a physical examination algorithm that would enable reproducible, fast and accurate knee examinations for orthopaedic surgeons. This would facilitate fast and accurate diagnosis and adequate treatment, which could prevent further damage to the knee joint and the onset of osteoarthritis changes in cartilage.  
The aim of this study was to evaluate the sensitivity and specificity of the most common tests used in diagnosing meniscal and anterior cruciate ligament (ACL) injuries and to compare the tests with each other. Moreover, the accuracy of physical examinations of common knee injuries was contrasted with the use of MRI. This study could help in in creating a physical examination algorithm.
## 2 Scale Factor Calibrated Using Different AG Values
#### 4.2.3 Variations of Calibrated Scale Factors  
After testing the SF calibration methods, we adopted the MBGA method to recalibrate the SFs of each survey campaign conducted in the southeastern Tibetan Plateau during 2018-2020. Because the AG stations in the survey area are always observed only once a year, the SF calibration for March 2018 and July 2018 was based on the AG values observed in the first half of 2018, the SF calibration for March 2019, July 2019, and March 2020 was based on the AG values observed in the first half of 2019, and the SF calibration for July 2020 was based on the AG values observed in the second half of 2020.  
The SFs estimated using the MBGA method were compared with those obtained using the BGA method. It should be noted that the original SFs obtained using the BGA method are usually actual calibrated or calibrated on the baseline. The SFs of the four instruments calibrated using the MBGA method show different deviations (Table 3). The average difference of the CG-5 instruments is >160 ppm, and the maximum difference reaches 328 ppm, which could cause deviation of approximately 100 \(\upmu\)Gal on the maximum gravity segment with a value of 300 mGal. Thus, in comparison with the nonlinear drift variation of the instruments, it is evident that errors caused by SF bias might have greater impact on the accuracy of the adjustment results of an entire survey network. In comparison, the average deviation of the LCR-G instruments is no more than 65 ppm, and the difference on the maximum gravity segment (390 mGal) caused by the SF is no more than 30 \(\upmu\)Gal. As mentioned above, the differences in SF variations are related to the inherent features of the different spring-based gravimeters. In principle, the SF depends primarily on the range of the gravity reading and not on time; however, for the CG-5 instrument, the reading range changes slightly with time owing to long-term instrumental drift [38]. Therefore, the high drift rate variations of CG-5 gravimeters will cause larger deviation in the reading ranges, thereby affecting the difference of the calibrated SFs. It is also revealed that even though there is considerable difference in the SF deviation among different survey campaigns, the deviations between each gravimeter pairs are almost synchronous in the same campaign (Figure 9).  
Figure 8: Residuals of gravity difference (GD) obtained using the MBGA method: (**a,b**) GD residuals from CG-169 and CG-170, respectively, and (**c,d**) GD residuals from LCR-149 and LCR-150, respectively.
## 3 Data Collection, Experimental Setup and NN Model Selection
### Dataset
#### 3.1.4 Testing of the IDNN Model  
The data subsets used to investigate the performance of the INS and Neural Networks on the challenging scenarios are presented in Tables 2 and 3. Although then evaluated on complex scenarios as previously mentioned, such as illustrated in Figures A1-A3 in Appendix A, the performance of the INS and NN modelling technique is first examined on the V-Vw12 dataset, which presents a relatively easier scenario; i.e., an approximate straight-line travel on the motorway. The evaluation of the latter scenario aims at gauging the performance of the technique in a relatively simpler driving situation. Nonetheless, the Motorway scenario could be challenging to track due to the huge distance covered per second. The evaluation is conducted on sequences of 10 s with a prediction frequency of 1 s. GPS outages are assumed on the test scenarios, for the purpose of the investigation.
# Routing Design for Pipe System Considering Natural Frequency
## 2 Related Works
### Pipe Routing Algorithms in Aero Engines  
Pipe routing design has been studied for several decades, resulting in two main types of pipe routing algorithms: deterministic and heuristic algorithms [6]. Deterministic algorithms, such as the maze algorithm [7; 8], the A* algorithm [9; 10; 11], and other algorithms [12; 13; 14; 15], are extensively employed in various fields due to their fast computation time and low memory space requirements. The maze algorithm mainly simulates the behavior of waves to find paths. The maze algorithm can ensure finding the shortest path, but it requires a lot of storage space. In order to solve the problem of the maze algorithm, the A* algorithm is proposed. The A* algorithm is mainly based on greedy search algorithms, which only search for nodes closer to the endpoint, avoiding needing much storage space. Other algorithms, such as projection and graph theory, have been proposed to accelerate the search for the shortest path. The main goal of deterministic algorithms is to obtain the shortest path faster. However, it is difficult to consider constraints because the final centerline is not known during the iterations. In order to address the difficulty of considering numerous constraints in deterministic algorithms, scholars have begun to apply heuristic algorithms to pipeline routing. Typical heuristic algorithms include genetic algorithms [16; 17; 18; 19], ant colony optimization algorithms [20; 21; 22], particle swarm algorithms [23], etc. [24; 25]. These methods often rely on random behavior to obtain  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Grids Size (mm)** & **Grids Numbers** & **Time (min)** \\ \hline
16 & 296,298 & 5 \\
12 & 695,196 & 14 \\
8 & 2,360,340 & 50 \\
4 & 19,134,375 & 608 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Grid size and time consumption.  
Figure 3: Hierarchical decomposition. The surface represents the interface in hierarchical decomposition.  
paths. Genetic algorithms were the earliest to be applied in the process of pipeline routing. Scholars have applied heuristic algorithms to consider various engineering constraints, such as branch and parallel pipelines in pipeline layout. Heuristic algorithms have been proven to effectively solve engineering constraints. However, these heuristic algorithms only consider geometric constraints, without considering vibration performance, such as natural frequency. Due to the excessive number of alternative paths provided by the heuristic algorithm during each iteration, evaluating the natural frequency for each individual is time-consuming. Therefore, considering the natural frequency during pipe design is challenging due to the theoretical limitations of existing methods.
# Scanning Electron Microscopy Techniques in the Analysis of Gunshot Residues: A Literature Review
## 4 Discussion
### Standardization and Future Prospects of SEM in Forensic Science  
One of the major challenges in forensic SEM analysis is the lack of standardized protocols across laboratories. Differences in sample collection methods, particle classification criteria, and reporting standards contribute to variability in forensic findings. To address this issue, forensic organizations are working towards developing universal guidelines for SEM-EDS analysis in GSR detection. Additionally, technological advancements, such as the integration of artificial intelligence and machine learning, are being explored to automate the classification of GSR particles and improve accuracy. Portable SEM devices are another promising development, allowing forensic investigators to perform preliminary GSR analysis directly at crime scenes. Although current portable SEM systems have limitations in resolution and analytical depth, ongoing improvements in hardware and software are expected to enhance their forensic applicability. In the future, the combination of SEM with complementary spectroscopic techniques will likely provide more comprehensive forensic evidence, strengthening the role of SEM in judicial processes [60; 61; 62].
# Variable Speed Limit and Ramp Metering for Mixed Traffic Flows: A Review and Open Questions
## 3 Research and Implementation
### VSL in Mixed Traffic Flows
#### 3.4.4 Other VSL Approaches  
In [86], the flow-density relationship of moving bottlenecks with mixed traffic flow was analyzed under the influence of VSL based on the trapezoidal flow-density relationship aimed at reducing energy consumption. The algorithm was tested using CTM. The results show that in a demand scenario of 1800 veh/h, the total energy savings increased as the compliant vehicles' penetration rate increased. VSL reduced the energy consumption of compliant vehicles by up to 33% and by up to 8% for non-compliant vehicles. The total energy savings are more significant in a low demand scenario with a demand of 1800 veh/h. The results indicate that the free-flow speed does not have substantial effects on the percentage of energy savings.  
In [102], the influence of Infrastructure-to-Vehicle (I2V) communication, AV control, and individualized speed limits for VSL by proposing a C-VSL extension was analyzed and compared with the classical VSL strategy. The I2V communication, AV control and individualized speed limits contributed to the harmonization of traffic flow and the reduction in exhaust emissions. C-VSL was implemented as VMSs functioning as roadside units, sending individual speed limit information to vehicles via I2V communication. Speed limits were calculated based on the distance between the VMS and the vehicle, the current speed of the vehicle, and the reference speed displayed on the VMS. Speed harmonization was achieved by giving individual speed limits to each vehicle at predefined time intervals. The proposed C-VSL narrowed the acceleration rate distribution and reduced NO\({}_{x}\) and \(HC\) emissions. In the scenario with a C-VSL penetration rate of 30%, an increase in average speed was shown compared to a penetration rate of 100%.  
In [103], CAVs were utilized as an alternative data source for a VSL control system. An interval type 2 fuzzy logic-based VSL system was proposed that employs CAVs to collect traffic data without the need for a fixed-point sensor. The performance of the proposed VSL system was evaluated and modeled in the microscopic simulator using a real motorway section located in Auckland, New Zealand. The results show that the proposed VSL system has similar performance to the detector-based system when more than 10% of CAVs are deployed. However, it is noted that VSL may become obsolete at very high CAV penetration rates.
# Analysis of Small-Loop Electromagnetic Signals to Detect Subsurface Anomaly Zones
## 4 Detection of Anomaly Zones by Visual Observation  
### Natural Ground  
Figure 5a shows a 2D ER contour map of the natural ground in Section 1. No anomaly zones were detected by visual observation because the ER exhibited a nearly uniform distribution across the map with no significant variations. In addition, the ER gradually lessened as the depth increased. Figure 5b shows the graph of the ER of the natural ground according to the measurement position for  
Figure 4: Procedure for verifying anomaly zones.  
each depth. The graph shows that the ER was constant without any significant changes according to the measurement position for all depths. The ER was the largest at depths of 0 and 0.26 m by about 300 Ohm-m and was smallest at a depth of 1.56 m by approximately 120 Ohm-m. It was estimated that the decrease in the ER according to the depth was mainly caused by the fact that the natural ground was not a perfect isotropic nonconductor. Hence, the EM wave decreased the farther down it penetrated. In addition, it was estimated that the lower ER in the deeper parts of the ground was affected by the increased amount of water at those depths.  
Figure 5: Test result for natural ground (Section No. 1). (**a**) Distribution of the electrical resistivity (ER). (**b**) ER according to the position at each depth.