esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling Principles
### General Dynamic Vibration Absorbers (DVA) Modeling Approach
#### 2.1.3 Semi-Active Tuned Mass Damper (STMD) Control  
Similar to an active system, the semi-active counterpart makes use of control algorithms for the selection of appropriate control actions. The first step in the calculation of the semi-active control forces is the calculation of an equivalent active force using active algorithms and Equation (5). Next, the active force is tailored so that it can be physically realised by the semi-active device. In this regard, because of the fact that no energy can be added directly to the system, the semi-active device will produce control forces only when required, i.e., when the damper is requested to \"consume\" energy. This is achieved by applying semi-active force saturation limits such that the semi-active control force, \(u_{sa}(t)\) is calculated by [23]:  
\[u_{sa}(t)=f_{a}(t)\left(\frac{1-\text{sgn}\left[f_{a}(t)\dot{x}_{r}(t)\right] }{2}\right) \tag{7}\]  
\[\text{sgn}\left[f_{a}(t)\dot{x}_{r}(t)\right]=\text{sgn}(q_{a}(t))\triangleq \{\begin{array}{ccc}1&for&q_{a}\geq 0\\ -1&for&q_{a}<0\end{array} \tag{8}\]  
The product of \(f_{a}(t)\dot{x}_{r}(t)\) is the power, \(q_{a}(t)\), of the whole active system device. Similarly, the power of just the semi-active component, \(q_{sa}(t)\), is defined as the product of the force that can be physically translated by the device, \(u_{sa}(t)\), and its relative velocity, \(\dot{x}_{r}(t)\):  
\[q_{sa}(t)=u_{sa}(t)\dot{x}_{r}(t)<0 \tag{9}\]  
A schematic representation of the power time histories of both an actively and a semi-actively controlled device is shown in Figure 2. It can be observed that the active device has the advantage of both adding and dissipating energy, as indicated by positive and negative powers, while the semi-active device only consumes power (indicated by only negative power--and its integral energy is also negative).  
When a variable damping (VD) STMD is considered, the method of achieving enhanced performance is by appropriately and in a timely fashion adjusting the damping coefficient of the device within bands, in order for the required control force to be reached. By referring back to the system presented in Figure 1, the semi-active damping force contribu  
Figure 2: Indicative example of the “power” scheme/demand practised in (**a**) active; and (**b**) semi-active control.  
\(c_{sa}(t)\dot{x}_{r}(t)\). Inspection of Equation (9) easily leads to \(c_{sa}(t)<0\). Updating Equation (6), the resulting overall control force provided at each time instant by a VD-STMD can be expressed mathematically as:  
\[u_{sa}(t)=(|c_{sa}(t)|+c_{n})\dot{x}_{r}(t)+k_{p}x_{r}(t) \tag{10}\]  
In Equation (10), \(c_{n}\) is a small damping coefficient representing the inherent damping of the connection of a semi-active device and the structural system. In this equation, the time-varying semi-active damping coefficient, \(c_{sa}(t)\), is the only unknown, making the calculation of the real-time variation of the damping coefficient straightforward.
# Neighborhood Deviation Attack Against In-Context Learning
## 5 Experiments
### Ablation Experiments
#### 5.5.1 Number of Neighbors  
In this experiment, the number of neighbors plays a crucial role in the neighbor bias attack. Increasing the number of neighbors can introduce more diverse target sample variants, allowing us to analyze the metric deviation of generated completions more accurately, thereby improving the accuracy and stability of membership reasoning. However, too many neighbors may increase the computational cost and introduce noise due to the randomness of generation, thus affecting the attack performance. Therefore, this experiment studies the effect of changing the number of neighbors from 1 to 15 on the attack performance of different datasets and language models to determine the most appropriate number of neighbors.  
The experimental results are shown in Figure 5. We tested the LLaMA2 and LLaMA3 models on the AGNews and DBPedia datasets. From the results, it can be seen that increasing the number of neighbors does indeed improve the attack effect in the initial stage. When the number of neighbors is set between 5 and 10, the attack performance reaches its optimum. Beyond this range, the advantage value tends to stabilize and even slightly decrease.  
For example, in the AGNews dataset, when using the LLaMA3 model, the attack advantage value increases from 0.76 at N = 1 to 0.90 at N = 7 and then stabilizes at around 0.88. Similarly, the attack advantage value of the LLaMA2 model increases from 0.83 at N = 1 to 0.88 at N = 7 and then decreases slightly as N increases. In the DBPedia dataset, a similar trend is observed. The optimal number of neighbors is between N = 5 and N = 10. Beyond this range, the additional increase in neighbors has a limited effect on performance improvement.  
When the number of neighbors is relatively small, we can call it the rising stage. In this stage, as the number of neighbor samples increases, the advantage of the attack also increases. At this point, each additional neighbor brings additional information, which improves the advantage. At this stage, more neighbors can better quantify the metric deviations between member samples and non-member samples, thereby improving the performance of the attack method. When the number of neighbors falls within 5 to 10, the performance of the attack tends to be stable, indicating that the newly added neighbors no longer provide additional information but only improve the stability of the method. This stage can be called the stable stage. When the number of neighbors exceeds 10, we call it the saturation stage. In this stage, the added neighbors no longer bring any additional information but introduce noise due to the randomness of the LLM generation,misleading the final judgment, resulting in a decrease in the attack effect and a waste of some computing resources.  
In conclusion, the experimental results show that the optimal range of neighbor samples should be between 5 and 10. This can ensure attack performance while controlling the computational cost.
# An Accelerated Maximum Flow Algorithm with Prediction Enhancement in Dynamic LEO Networks
## 5 Evaluations
most efficient flow allocation strategies within the bounded computational budgets, leading to improved performance in terms of both computational efficiency and flow optimization. Figure 9 shows the changes in the acceleration percentage of predictive LEO maximum flow as the network scale increases from 10 to 25 satellites. In this evaluation, we fixed the storage limitation on each satellite to 600 GB and varied the number of time slots from 35 to 60. When the network scale increases, the acceleration percentage increases as well, indicating that the predictive LEO maximum flow algorithm becomes more efficient in larger and more complex LEO networks. This is because larger networks provide more opportunities for predictive flow allocation and allow the algorithm to optimize paths across multiple time slots, thereby reducing the overall computational effort. As the number of satellites increases, the algorithm can better adapt to dynamic changes in the network, leveraging its predictive capabilities to efficiently manage larger volumes of data and more complex topologies, leading to an overall improvement in performance.
## 2 Materials and Methods
### Implementation of the Automatic Detection Routine  
The reference track, with a length of 51 m, differed significantly from the simulated meter-long sections used for training. To apply the trained model to the reference track, it had to be broken down to the same size and was thus divided into 1 m pieces. To prevent the incorrect detection of objects in the border areas, an overlap of 0.5 m was chosen for successive pieces. The detection algorithm proceeds as follows: After initialization and algorithm instantiation from the configuration file, measurement data are read, preprocessed, and fed to the algorithm. The trained model is loaded, and a process pool is created for converting data into the dataset, as described in Section 2.7. The data are segmented, processed, and analyzed by the model. The computation results are normalized to the track's original length. Overlapping detections are refined, keeping only the mostprobable ones and filtering out low-probability detections. These results are then compared with the reference track's ground truth. The labels of detections are overlaid with the ground truth, calculating precision and recall based on their intersection over union (IoU). Finally, the outcomes are exported as text files and files in Visualization Toolkit (VTK) format, which are suitable for three-dimensional visualization.
# Untargeted Urinary Volatilomics Reveals Hexadecanal as a Potential Biomarker for Preeclampsia
## 1 Introduction
Despite its advantages, conventional GC-MS often requires extensive sample preparation, including solvent extraction and chemical derivatization, which can be time-consuming and costly [15]. However, with the advent of solid-phase microextraction (SPME), the procedure was simplified, especially when analyzing biofluids such as urine [15; 16]. SPME is a solvent-free, efficient extraction method that uses a fiber coated with an extraction phase to capture low-molecular-weight metabolites, which are then thermally desorbed and introduced into the GC-MS system for analysis. This technique has proven particularly useful in the analysis of volatiles due to its reproducibility, cost efficiency and versatility. The ability of SPME to extract volatile compounds without the need for complex sample preparation makes it an attractive option for metabolomic studies of urine [17]. Urinary volatilomics remains an under-researched area in PE research, although its potential to identify new non-invasive biomarkers is increasingly recognized. Urinary volatile metabolites may offer valuable insights into the biochemical disturbances associated with PE, particularly in relation to oxidative stress and consequently lipid peroxidation. In a recent study, [PERSON] et al. used SPME-GC-MS to investigate the metabolite profiles of women with gestational diabetes mellitus (GDM) and identified several volatile metabolites in both urine and serum [18]. However, there is a paucity of data on urinary metabolomics in PE, highlighting the need for further research in this area. Therefore, the aim of this study is to investigate the urinary metabolite profiles of women with PE compared to healthy pregnant women using non-targeted SPME-GC-MS analysis. By identifying volatile organic compounds associated with PE, this study aims to contribute to the growing body of knowledge on biomarkers related to oxidative stress and to improve the understanding of metabolic pathways involved in the pathophysiology of PE.
## 3 New Methodology
As shown in Table 9, using the weighted average of the 29 largest cities in the world, the use of compression refrigeration in data centers is essential in 3727.2 h per year of the 8760 h available. That is, it is feasible to use free cooling and evaporative cooling for the other 5032.8 h per year, as shown in Figure 6. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**City** & **Country** & \begin{tabular}{c} **Annual Hours COP1** \\ **(24 to 26.9 °C)** \\ \end{tabular} & \begin{tabular}{c} **Annual Hours COP2** \\ **(27 to 29.9 °C)** \\ \end{tabular} & \
\begin{tabular}{c} **Annual Hours COP3** \\ **(30 to 32.9 °C)** \\ \end{tabular} &
\begin{tabular}{c} **Annual Hours** \\ **COPA (\textgreater{}33 °C)** \\ \end{tabular} \\ \hline Tokyo & Japan & 910 & 674 & 284 & 57 \\ \hline Delhi & India & 879 & 1481 & 1323 & 1873 \\ \hline Shanghai & China & 1190 & 817 & 340 & 125 \\ \hline Sao Paulo & Brazil & 1.215 & 454 & 147 & 13 \\ \hline Mexico City & Mexico & 2.610 & 1669 & 1168 & 1020 \\ \hline Al-Qahirah-Cairo & Egypt & 1345 & 947 & 785 & 660 \\ \hline Mumbai & India & 1915 & 3412 & 1844 & 468 \\ \hline Beijing & China & 1051 & 526 & 262 & 86 \\ \hline * Dhaka & Bangladesh & 1561 & 2.685 & 1.487 & 794 \\ \hline Osaka & Japan & 959 & 849 & 416 & 130 \\ \hline New York & USA & 545 & 260 & 95 & 18 \\ \hline Karachi & Pakistan & 1201 & 2476 & 1848 & 1053 \\ \hline Buenos Aires & Argentina & 1083 & 389 & 85 & 9 \\ \hline Chongqing & China & 743 & 743 & 437 & 309 \\ \hline Istambul & Turkey & 830 & 443 & 126 & 15 \\ \hline Kolkata & India & 1561 & 2685 & 1487 & 794 \\ \hline Manila & Philippines & 1959 & 4436 & 2024 & 262 \\ \hline * Lagos & Nigeria & 1345 & 1762 & 1708 & 2686 \\ \hline Rio de Janeiro & Brazil & 2807 & 1533 & 494 & 91 \\ \hline Tianjin & China & 955 & 668 & 321 & 94 \\ \hline * Kinshasa & Dem. Rep. Congo & 2966 & 1746 & 997 & 216 \\ \hline Guangzhou & China & 1737 & 1685 & 822 & 341 \\ \hline Los Angeles & USA & 198 & 63 & 18 & 4 \\ \hline Moscow & Russia & 205 & 85 & 23 & 6 \\ \hline Shenzhen & China & 1708 & 2049 & 911 & 138 \\ \hline Lahore & Pakistan & 985 & 1320 & 1103 & 1525 \\ \hline Bangalore & India & 2164 & 1156 & 520 & 156 \\ \hline Paris & France & 257 & 113 & 42 & 12 \\ \hline Bogota & Colombia & 2 & 0.2 & 0.04 & 0 \\ \hline TOTAL & & 36,886 & 37,126 & 21,117 & 12,955 \\ \hline Average & & 1271.92 & 1280.20 & 728.17 & 446.72 \\ \hline Total Hours & & 3727.02 & & & \\ \hline \hline \end{tabular} Note: * Specifically, the ASHRAE Weather Data Viewer [15] has temperatures in 26 of the 29 cities. For the cities of Dhaka, Lagos, and Kinshasa values from nearby cities, Calcutta, Niamey, Brazzaville, respectively, were used.  
\end{table} 
Table 9: COPs of cities worldwide [15]. Using part of the IPLV formula, EUED, and AHRI 13621 concepts, Equation (2) was determined using percentages of COPs shown in Figure 7[16]. \[\text{COP}\;\text{WEUED}=((0.34\text{-COP1})+(0.34\text{-COP2})+(0.20\text{- COP3})+(0.12\text{-COP4}))\cdot\text{SLR} \tag{2}\]  
where SLR is the sensible heat rate.
###### Abstract  
In this paper, the ultraviolet/persulfate (UV/PDS) combined oxidation process was used to remove the ethyl 4-aminobenzoate (Et-PABA), one of the typical 4-aminobenzoic acid (PABA)-type UV filters. The effects of various factors on the removal of Et-PABA using the UV/PDS process were investigated, and the degradation mechanisms of Et-PABA were explored. The results showed that the UV/PDS process can effectively remove 98.7% of Et-PABA within 30 min under the conditions: UV intensity of 0.92 mW-cm\({}^{-2}\), an initial concentration of Et-PABA of 0.05 mM, and a PDS concentration of 2 mM. The removal rate of Et-PABA increased with the increase in PDS dosage within the experimental range, whereas humic acid (HA) had an inhibitory effect on Et-PABA removal. Six intermediates were identified based on HPLC-MS and degradation pathways were then proposed. It can be foreseen that the UV/PDS oxidation process has broad application prospects in water treatment.  
Keywords:advanced oxidation processes (AOPs); UV-activated; persulfate; ethyl 4-aminobenzoate (Et-PABA) +
Footnote †: journal: Applied Sci. **2019**, \(9\), 2873; doi:10.3390/appr9142873
# Mapping with Pleiades--End-to-End Workflow
## 5 Results
### Sensor Modeling and Parameter Optimization

Table 4: 2D geo-location accuracy analysis using different adjustment settings for the image 1 of the Innsbruck stereo test set. Given are the mean and the standard deviation in pixels (\({}^{\dagger}\) ICPs equal GCPs). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**ID**} & \multirow{2}{*}{**GCPs**} & \multicolumn{2}{c}{**Mean Initial [pxl]**} & \multicolumn{4}{c}{**RMS Initial [pxl]**} & \multicolumn{4}{c}{**RMS Adjusted [pxl]**} \\  & & & **Across** & **Along** & **Length** & **Across** & **Along** & **Length** & **Across** & **Along** & **Length** \\ \hline Innsbruck & 1 & 30 & \(-\)8.55 & \(-\)5.91 & 10.43 & 8.61 & 5.96 & 10.47 & 0.80 & 0.50 & 0.95 \\ Stereo & 2 & 30 & \(-\)8.55 & \(-\)2.28 & 8.88 & 8.61 & 2.42 & 8.94 & 0.78 & 0.61 & 1.00 \\ \hline Innsbruck & 1 & 21 & \(-\)9.61 & \(-\)8.55 & 12.88 & 9.63 & 8.60 & 12.91 & 0.57 & 0.80 & 0.99 \\ Triplet & 2 & 20 & \(-\)10.21 & \(-\)9.49 & 13.96 & 10.24 & 9.54 & 14.00 & 0.76 & 0.82 & 1.12 \\ East & 3 & 20 & \(-\)10.14 & \(-\)11.50 & 15.35 & 10.16 & 11.53 & 15.37 & 0.53 & 0.79 & 0.96 \\ \hline Innsbruck & 1 & 16 & \(-\)8.93 & 4.43 & 9.99 & 8.97 & 4.47 & 10.02 & 0.62 & 0.39 & 0.73 \\ Triplet & 2 & 16 & \(-\)8.93 & \(-\)0.18 & 8.97 & 8.95 & 0.91 & 8.99 & 0.44 & 0.65 & 0.78 \\ Center & 3 & 16 & \(-\)9.10 & 4.18 & 10.03 & 9.13 & 4.22 & 10.06 & 0.58 & 0.32 & 0.66 \\ \hline Innsbruck & 1 & 22 & \(-\)9.62 & \(-\)8.56 & 12.92 & 9.67 & 8.59 & 12.94 & 0.46 & 0.60 & 0.75 \\ Triplet & 2 & 22 & \(-\)9.23 & \(-\)11.46 & 14.73 & 9.27 & 11.49 & 14.76 & 0.66 & 0.62 & 0.91 \\ West & 3 & 22 & \(-\)9.82 & \(-\)9.53 & 13.70 & 9.85 & 9.55 & 13.72 & 0.48 & 0.61 & 0.78 \\ \hline \hline \multirow{2}{*}{Trento} & 1 & 21 & 11.19 & 12.59 & 16.90 & 11.22 & 12.70 & 16.95 & 0.75 & 0.76 & 1.07 \\ Triplet & 2 & 21 & 10.80 & 13.81 & 17.58 & 10.84 & 13.91 & 17.63 & 0.67 & 0.71 & 0.98 \\  & 3 & 21 & 8.61 & 15.71 & 17.97 & 8.65 & 15.81 &Next, the 3D geo-location accuracy using the initial and optimized sensor models is assessed, based on the protocol defined in Section 4.1. The RMS values of the 3D point residuals which were achieved for East, North, Height and their 3D length are summarized in Table 6. These values indicate the 3D mapping accuracy that is feasible when employing the stereo or triplet models for 3D reconstruction. First, it is obvious that the initial accuracy of the rational polynomial models yields 3D displacements in the order of several meters, which is clearly beyond the aspired precision. Next, the values given in the table show that the stereo intersection angle \(\delta\), as an equivalent for the base-to-height ratio, has a predominant impact onto the 3D geo-location accuracy. Higher accuracies are achieved for image pairs covering larger stereo intersection angles and vice versa. This aspect is depicted in Figure 17 which shows the RMS values of the 3D length residuals plotted versus the convergence angles. The red curves give the theoretical 3D errors that would result from a given 2D measurement error in meters.
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 3 Methodology
### Five-Step Interactive Quality Control Based on Knowledge
#### 3.7.4 Step Four: Interactive Quality Control with Field Verification Points  
To discover any further residual classification errors in the CLC-SW2010 product, a new round of field investigation was enforced to collect independent verification points (Section 3.4). The experienced mappers were carefully compared with the classification results of each verification point and its surrounding regions with the field records, photos and satellite images. Once the classification errors were determined, further revisions were needed.
## 3 Testing Results
### AE b Value
#### 3.4.2 Evolution of AE \(b\) Value  
The relation curve between the AE \(b\) value and the loading-unloading cycle number is shown in Figure 11. In the initial three cycles, the AE \(b\) value increases continuously, indicating that the AE activities in this phase mainly come from the compaction and closure of primary fractures and the initiation of microcracks in the coal specimen. The released energy by AE activities is very low, and the amplitude is very small. The maximum stress of the fourth loading-unloading cycle accounts for 85.76% of coal specimen peak strength. Under this loading-unloading cycle, the microcracks in the coal specimen further initiate, nucleate and expand, and the size of new cracks, the released energy by the AE activities, and the AE amplitude are all very larger, but the AE \(b\) value is reduced. The maximum stress of the fifth loading-unloading cycle accounts for 94.34% of coal specimen peak strength. Under this loading-unloading cycle, the AE \(b\) value increases because the coal specimen produced plastic strengthening under the fourth loading-unloading cycle. However, it should be noted that if the loading-unloading process is repeated many times according to the stress level of the fifth loading-unloading cycle, the effect of plastic strengthening will gradually decrease and even turn into plastic softening. In the sixth loading-unloading cycle, the abundant microcracks in the coal specimen began to penetrate and form the microcracks. The released energy by AE activities was very high, and the amplitude was also large. The coal specimen finally failed, and the AE \(b\) value decreased rapidly.  
The above analysis shows that the AE \(b\) value fluctuates as the loading-unloading cycle number, and its significant fluctuation can be used as the precursor of coal specimen failure. In addition, the AE \(b\) value decreases rapidly at coal specimen failure.  
Figure 11: AE \(b\) value versus cycle number curves.
## 1 Introduction
In order to obtain advantages of cloud computing, the issues related to efficiency and throughput need to be addressed directly in virtualized and containerized scientific cloud environments. With the goal of addressing aforementioned challenges, this article proposes a method for solving these issues--performance and throughput. Currently, the scientific community is ready to work over the cloud as it is considered as a resource-rich paradigm. Cloud computing enables users to work anywhere by providing logical resources such as virtual machines or containers. However, it should be noted that there are a few VM/container associated issues regarding performance and throughput. In this paper, in order to utilize virtualization technologies, we evaluated the different configurations of VMs and containers, which are the main computing actors for scientific workloads. We also take into consideration the problems of throughput and efficiency related to VMs and containers, and explore efficient resource provisioning by combining four unique methods: hyper-threading (HT), vCPU cores selection, vCPU affinity, and isolation of vCPUs. The scope of this research is mainly focuses on scientific workloads. Furthermore, a balanced view of performance and throughput is also given. A renowned cloud computing platform,OpenStack [7], has been adopted to configure the computing environment for logical setup and to run scientific applications. The HEPSPEC06 benchmark that is produced by the HEPiX CPU Benchmark Group [8] is used for performance evaluation of virtual machines and containers. Realistic issues regarding the performance of VMs/containers and throughput degradation are also investigated. In this paper, we use the combination of four famous techniques to achieve real-time performance and higher throughput of virtual machines and containers. These techniques are: hyperthreading technology, the selection of vCPU cores per virtual machine/container, physical CPU isolation, and pinning of vCPU cores of a virtual machine/container in a multicore NUMA (nonuniform memory access) architecture [9; 10]. Our proposed solution shows that a fine combination can work very efficiently to achieve higher performance and enhanced throughput of VMs and containers in a cloud-based environment for scientific computing. The rest of the paper is organized as follows. Section 2 provides an overview of related work. In Section 3, the HEPSPEC06 benchmark suite is discussed. Section 4 presents the proposed solution. Section 5 discusses the evaluation and results. Finally, Section 6 draws conclusions.