esa-sceva/satcom-chunk-collection huggingface dataset:
# Algorithmic Techniques for GPU Scheduling: A Comprehensive Survey
## 2 Models
### Comparative Analysis of Models
Cloud AI platforms, by contrast, must accommodate elastic scaling [135; 136], heterogeneous GPU types, and multi-tenant fairness; here, optimization-based and learning-augmented approaches can yield substantial gains [137]. Real-time inference services impose strict SLOs and deadline guarantees, dictating the use of real-time systems theory--deadline-driven scheduling and preemptive policies--to ensure timely responses under all load conditions [84; 138]. Each paradigm offers distinct trade-offs:  
1. Queueing models - Pros: Analytically tractable; provide closed-form performance bounds and optimal policies under stochastic assumptions. Cons: Scale poorly to large systems without relaxation or decomposition; approximations may sacrifice true optimality. 2. Optimization models - Pros: Express rich constraints and system heterogeneity; deliver provably optimal schedules within the model's scope. Cons: Become intractable for large, dynamic workloads without heuristic shortcuts or periodic re-solving; deterministic inputs may not capture runtime variability. 3. Heuristic methods - Pros: Extremely fast and scalable; simple to implement and tune for specific workloads; robust in practice. Cons: Lack formal performance guarantees; require careful parameterization to avoid starvation or unfairness. 4. Learning-based approaches - Pros: Adapt to workload patterns and capture complex, nonlinear interactions (e.g., resource interference) that fixed rules miss. Cons: Incur training overhead and cold-start penalties; produce opaque policies that demand extensive validation to prevent drift and ensure reliability. Because no single approach suffices for all scenarios, production GPU schedulers typically adopt a hybrid strategy: employing ILP or matching solvers for small-scale assignment tasks, relying on heuristics for real-time dispatch, and leveraging learning models for runtime and resource-usage prediction. This blend of methods--grounded in robustness, simplicity, and targeted optimization--enables high performance across diverse, large-scale workloads.
## 2 RNP-WMN Problem
### Problem Formulation  
Consider a WMN in a 2D area of dimensions \(W\times H\), where the number of mesh routers, mesh clients and gateway routers are \(m\), \(n\) and \(k\), respectively. The RNP-WMN problem is stated as finding the optimal set of locations for m mesh routers (set \(\{(x_{r_{i}},y_{r_{i}})|\forall r_{i}\in R\}\), as defined in Table 1), depending on the given set of locations of mesh clients. To formulate this problem, we first define some concepts.
## 5 Conclusions  
Two energy effective routing protocols, R-EERP and S-EERP, have been presented in this paper. These protocols use critical and base threshold values. In R-EERP, nodes, clusters and cluster-heads are distributed randomly in the field and cluster heads are also selected randomly. In S-EERP, nodes, clusters and cluster heads are sequentially deployed into the floors of a building, and the cluster heads are randomly selected inside clusters whose members do not change in the floors. Use of XOR operation is proposed to filter out duplicated/redundant data received at the cluster heads.  
As shown with our simulation results, the number of data transmissions sent to sink is significantly decreased with help of critical and base threshold values used by our protocols. Simulation results show that R-EERP and S-EERP had a better performance than LEACH. R-EERP achieves significant improvement in balancing energy consumption over S-EERP and LEACH.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameters** & **LEACH** & **R-EERP** & **S-EERP** \\ \hline Number of Dead nodes & 62 & 32 & 60 \\ Total Energy Consumed (Joule) & 109,89 & 99,08 & 108,35 \\ Total Residual Energy (Joule) & 10,11 & 20,92 & 11,65 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Number of dead nodes, total energy comsumption and residual energy after 1,000 rounds.  
Figure 4: The ignored values which were below the base the base threshold and the values above the critical threshold which were immediately sent to sink.
###### Abstract  
Electrical winding faults, namely stator short-circuits and rotor bar damage, in total constitute around 50% of all faults of induction motors (IMs) applied in variable speed drives (VSD). In particular, the short circuits of stator windings are recognized as one of the most difficult failures to detect because their detection makes sense only at the initial stage of the damage. Well-known symptoms of stator and rotor winding failures can be visible in the stator current spectra; however, the detection and classification of motor windings faults usually require the knowledge of human experts. Nowadays, artificial intelligence methods are also used in fault recognition. This paper presents the results of experimental research on the application of the stator current symptoms of the converter-fed induction motor drive to electrical fault detection and classification using Kohonen neural networks. The experimental tests of a diagnostic setup based on a virtual measurement and data pre-processing system, designed in LabView, are described. It has been shown that the developed neural detectors and classifiers based on self-organizing Kohonen maps, trained with the instantaneous symmetrical components of the stator current spectra (ISCA), enable automatic distinguishing between the stator and rotor winding faults for supplying various voltage frequencies and load torque values.  
induction motor drive; stator inter-turn short circuits; rotor bar failures; fault detection; current spectra; SCA analysis; Kohonen neural networks +
Footnote †: journal: Applied Physics Letters  
_Article_
###### Abstract
Although considerable studies have been published on the statistical inference of Weibull zero-failure models [21; 22], little research has focused on estimating the lower confidence limits (LCLs) of system reliability under zero-failure conditions. Therefore, investigating reliability assessment methods for series systems composed of Weibull-distributed components based on zero-failure data holds significant engineering value. This research has three primary goals. First, it aims to develop a new method for estimating the LCLs of system reliability and reliable life, along with deriving the corresponding closed-form expressions. The second objective is to improve the accuracy of system reliability assessment by integrating multi-source life data at both the components and system levels. The third objective involves comparing the proposed method with conventional engineering methods through simulation studies and a specific engineering example. The rest of the paper is organized as follows. Section 2 describes the system reliability model and zero-failure data. Section 3 discusses the reliability assessment and reliable life prediction of series systems with Weibull zero-failure data, followed by an update procedure proposed for leveraging system-level zero-failure data. Section 4 performs a simulation comparison study, and Section 5 provides an example application. Finally, Section 6 summarizes the results of this paper.
# Software Portfolio Optimization: Access Rejection versus Underutilization
## 7 Conclusions  
In this paper, we focus on a firm's strategic acquisition of different types of software licenses to meet peak user demand. Unlike the previously mentioned studies, which compared specific portfolio alternatives, we present a general integer program that optimizes licensing portfolios subject to access-rejection and underutilization constraints when heterogeneous users can be classified into multiple homogeneous groups. The program integrates multiple types of licenses and sublicenses into a portfolio using the number of licenses of each type as a decision variable. The goal of the integer program, which we calibrate using actual user data, is to minimize the cost of the license portfolio subject to constraints on the probability of user requests being rejected (blocked) and likelihood of underutilization of the licenses. Using a case study, we illustrate and evaluate the ability of the integer program to solve the commonly encountered problem of determining how many floating and NNU software licenses a firm should buy, given the nature and number of its users, via an efficient enumeration algorithm based on monotonic properties of the objective function and the recursive nature of the blocking-probability constraint.  
Overall, we find that the proposed integer program for selecting optimal mixed-license portfolios is quite robust. Specifically, the percentage of inaccuracy in estimating the average rate of software requests tested in Cases 1 and 2 (+/-5%) is both greater than the percentage of corresponding losses due to the inaccuracy and greater than the error in determining the software utilization. In terms of blocking probability, however, correct estimation of the average rate of requests for licenses can play a critical role in the decision-making process. In particular, underestimation of the average rate of requests for licenses can be especially problematic.  
The excess cost of underutilization points to the existence of optimal mixes of various types of license and the economic importance of strategically selecting those mixes. Thus, we find that the observation by [PERSON] and [PERSON] (2018) that mixed licensing of single-user and network licenses is not economically efficient no longer holds in most cases and that both underutilization and the probability of blocked requests are important factors in addition to the probability of blocked requests.  
In this work we present a particular case study characterized by two types of license, floating and NNU licenses. We view this as a first step in studying more general cases of multiple types of software and licenses with the aim to assess the economic efficiency of mixed licensing, which we regard as an important direction for future research.  
Conceptualization, K.K. and G.K.; Methodology, K.K.; Software, Y.P.; Validation, Y.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Deep-Learning-Based Framework for PET Image Reconstruction from Sinogram Domain
## 4 Results
### Simulation Experiments
#### 4.1.1 Discriminator Comparison Experiments  
The full-image discriminator and Patch-GAN discriminator were both tested on the Zubal thorax dataset. Three dynamic objects were used to test, and each object possesses 18 frames. As we know, during the scanning period, with the increase in tracer injection time, the more photons the organ accumulates, the clearer the active image. In the current clinical practice, doctors usually pay attention to the static data, one clear active image of the later frames. Thus, the later frames are much useful for clinical. We compared the two strategies with the dynamic testing data and the results are shown in Figure 5. Though we obtained approximate quantitative results in the first 10 frames of the two strategies, the latter 8 frames of the whole-image strategy obtained much lower bias and variance compared to Patch-GAN. It is obvious that with the whole-image discriminator helping, the proposed network has better ability in improving the reconstruction image quality, especially for the frames with higher numbers of photons.  
To better illustrate that the whole-image strategy is more suitable in PET reconstruction, we selected a real patient to show the generation results under different strategies at the same epoch in the training phase. The generation results of 2 nd, 20 th, 40 th, 60 th, 80 th, and 100 th epoch are all shown in Figure 6. With the increasing epoch, the global features become clearer for both strategies. However, the network with PatchGAN may generate some local features that do not exist in the ground truth, as shown in the red box. Moreover, compared with PatchGAN, a whole-image discriminator can generate more accurate reconstruction images in fewer epochs.
# Malware Collusion Attack against SVM: Issues and Countermeasures
## 4 Architecture of ColluDroid  
In order to minimize the effect of Collusion Attack and Evasion Attack on SVM, we designed ColluDroid, an approach to find all the collusion apps. At first, we need to leverage an SVM method to learn the difference between malware and benign apps. Then, we try to find out the collusion apps in our test dataset. Just as shown in Figure 3, our detection method has two phases. The first phase is the _Training Phase_ (represented by the black line in Figure 3) and the second is the _Detecting Phase_ ( represented by the red line ). In the training phase, we firstly extract the features of all the apps in the dataset. Then, we make use of a classification method to take the extracted features as input and output a malware classification model. In the detection phase, we also need to extract the features of test apps. Then, we analyze the communication links and export the app sets that might be able to communicate by ICC or shared preference. Next, we combine the features of those apps that have communication links. Finally, the combined feature is fed as input into the classification model and outputs the classification label that shows whether the test app set is malicious or benign. If it is malicious, the apps in the test set are collusion apps. In the following two sub-sections, we will describe the two phases respectively.
## 3 Results and Discussion
### Xrd
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{4}{c}{**PC Analysis of Variance**} \\ \hline
**Source** & \({}^{\text{a}}\)**Sum Sq.** & \({}^{\text{b}}\)**d.f.** & \({}^{\text{c}}\)**Mean Sq.** & **F-Ratio** & _p-Value_ \\ \hline A:CONC & 0.0106571 & 1 & 0.0106571 & 3.13 & 0.1074 \\ B:CLAY & 0.0195192 & 1 & 0.0195192 & 5.73 & 0.0377 \\ C:SURF & 0.00210412 & 1 & 0.00210412 & 0.62 & 0.4502 \\ D:SIL & 0.0284249 & 1 & 0.0284249 & 8.34 & 0.0162 \\ AD & 0.0239061 & 1 & 0.0239061 & 7.02 & 0.0244 \\ Error total & 0.0340763 & 10 & 0.00340763 & & \\ Total (corr.) & 0.1271 & 15 & & & \\ R2 & 73.1894 & & & & \\ \hline \hline \multicolumn{5}{c}{**PE Analysis of Variance**} \\ \hline A:CONC & 0.00515327 & 1 & 0.00515327 & 8.59 & 0.0326 \\ B:CLAY & 0.000730655 & 1 & 0.000730655 & 1.22 & 0.3201 \\ C:SURF & 0.0111568 & 1 & 0.0111568 & 18.59 & 0.0076 \\ D:SIL & 0.00584537 & 1 & 0.00584537 & 9.74 & 0.0262 \\ AB & 0.0118641 & 1 & 0.0118641 & 19.77 & 0.0067 \\ AC & 0.0249556 & 1 & 0.0249556 & 41.59 & 0.0013 \\ AD & 0.00799175 & 1 & 0.00799175 & 13.32 & 0.0148 \\ BC & 0.0165288 & 1 & 0.0165288 & 27.55 & 0.0033 \\ BD & 0.038032 & 1 & 0.038032 & 63.38 & 0.0005 \\ CD & 0.023214 & 1 & 0.023214 & 38.69 & 0.0016 \\ Error total & 0.00300031 & 5 & 0.000600063 & & \\ Total (corr.) & 0.122294 & 15 & & & \\ R2 & 97.5466 & & & & \\ \hline \hline \end{tabular} \({}^{\text{a}}\) Sum of Squares, \({}^{\text{b}}\) Degrees of freedom, \({}^{\text{c}}\) Mean of Squares.  
\end{table} 
Table 7: ANOVA analysis for the interlaminar space d(001)nm response from the hybrid pigments with PC and PE under different synthesis conditions [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. For the PC pigments, the only significant factor was nanoclay, and the only significant interaction was concentration with the silane additive. In the L nanoclay, the dye intercalation effect was stronger than the effect on the M clay. At the lowest PC concentration, silane treatment had no effect. At the maximum PC concentration, the silane additive had to be used to increase the basal space from both nanoclays (Figure 16). The surface modification of clays L [52] and M [53] with hydrophobic groups considerably enhanced the intercalation of bulky hydro-phobic molecules in both structures. Finally, the results showed the importance of using a surfactant at the maximum PE concentration to increase the dye loaded onto the nanoclay structure, which also occurs for other examples with laminar nanoclays [54]. However, silane was not significant at a high concentration, but was significant at a lower concentration. Under these conditions, silane improved PE adsorption in the nanoclay structures. To achieve the
## 3 Results and Discussion
### Validation of Analytical Methods  
According to the requirements of the geological and mineral industry standard DZ/T0011-2015 of the People's Republic of China, the accuracy and precision of the experimental methods could be obtained by measuring the CRM [13]. The CRM GBW07309 was analyzed five times, and the logarithmic deviation (\(\Delta\)lgC) and relative standard deviation (RSD) values between the measured mean value and the standard value of each element were calculated and listed in Table S2. The AlgC and RSD values of the CRM were all smaller than the standard monitoring limits (AlgC and RSD \(\leq\) 7%), and the detection limits (DLs), estimated as three times standard deviation of the blank, were 0.013 mg/L for N and 0.007 mg/L for P. These indicated that the accuracy and precision of the UVS method were high and satisfied the detection requirements.
# Performance and Requirements of GEO SAR Systems in the Presence of Radio Frequency Interferences
## 3 Bistatic Scattering RFI Impact on GEO SAR  
### Impact of Bistatic Scattering RFI on GEO SAR  
Besides the RFI impacts coming from the ground RFI directly, some other received RFI signals may derive from the signals emitted from spaceborne radio systems (e.g., LEO SAR and GNSS satellite system) with the same or adjacent radio frequencies and bistatic scattering from the ground [6; 35; 36; 37]. The sketch of the impact of the bistatic scattering RFI case on GEO SAR is shown in Figure 4. \(P_{ia}\) is the average transmitted power of the spaceborne radio system (RFI source), \(\mathbf{O}\) represents the position of the target, \(\mathbf{L}\) is the position of the spaceborne system, \(R_{s}\) is the slant range of the spaceborne systems, \(\hat{\mathbf{R}}_{\mathbf{s}}\) is the unit vector from \(\mathbf{O}\) to \(\mathbf{L}\), \(\mathbf{S}\) is the position of GEO SAR, \(R\) is the range of the GEO SAR, \(\hat{\mathbf{R}}\) is the unit vector from \(\mathbf{O}\) to \(\mathbf{S}\), \(\theta_{1}\) is the incidence angle of the spaceborne system and \(\theta_{2}\) is the corresponding RFI bistatic scattering angle to GEO SAR, \(\hat{\mathbf{z}}\) is the normal unit vector of the scene, \(\hat{\mathbf{x}}\) is the unit vector of the projection of \(\hat{\mathbf{R}}_{\mathbf{s}}\) on the scene plane, \(\hat{\mathbf{y}}\) is determined by the cross product of orthonormal basis \(\hat{\mathbf{z}}\) and \(\hat{\mathbf{x}}\), \(\psi_{s}\) is the out-of-plane angle of the bistatic scattering direction, \(\beta\) is the bistatic angle which relates to \(\theta_{1}\), \(\theta_{2}\) and \(\psi_{s}\), \(\sigma_{B}^{0}\) is the bistatic scattering coefficient, \(\theta_{RFI}\) is the beam width of the spaceborne system, \(\theta_{GEO}\) is the beam width of the GEO SAR.
## 3 Reuse of OMWW
### OMWW Phenolic Compounds for Agricultural Use  
Phenolic compounds from OMWW might be used for integrated pest management programs. Several studies have reported the use of microorganisms (as single or consortia) to degrade organic compounds in effluents [68; 69]. Although OMWWs do not contain toxic substances, they are characterized by high COD values and a high concentration of compounds with biostatic activity. Recently, increasing attention has been focused on the degrading properties of microorganisms and biological aerobic treatments using yeasts and filamentous fungi, which have emerged as suitable biofertilization methods for conducting residues with lower toxicity, COD, and phenolic contents. [PERSON] et al. [70] treated OMWW with microorganisms isolated from the same source, such as _Candida boidini_, _Geotrichum candidum_, _Penicillium_ sp. and _Aspergillus niger_, obtaining a 40-73% reduction in phenols and a 45-78% reduction in COD value. [PERSON] et al. [45] identified several strains belonging to the genera _Geotrichum_, _Saccharomyces_, _Pichia_, _Rhodotorula_, and _Candida_ that showed strain-dependent phenol removal efficiency, decreasing phenolic and COD values, regardless of initial phenolic concentrations. In particular, _G. candidum_, both as free and Ca-alginate immobilized cells, showed the best degradation performance, and when immobilized showed a double reduction rate ability. Indeed, Ca-alginate improved the proteolytic stability of the enzymes responsible for the degradation process. [PERSON] et al. [69] demonstrated that the use of a microalgal-bacterial consortium, in a photo- bioreactor, induces a decrease in pollutant load by affecting COD, BOD\({}_{5}\), phenolic compounds, color, and turbidity values of OMWW. The dominant green microalgae _Scenedesmus obliquus_, _Chorella vulgaris_ along with cyanobacteria _Anabaena_ sp., showed a synergistic effect on resistance to toxic pollutants, leading to their decomposition. In addition, the effect of _Lactiplantibacillus plantarum_ strains on decolorization and biodegradation of phenolic compounds was evaluated [69], highlighting strains able to decrease the OMWW pH within 6 days. Growth of _L. plantarum_ induced the depolymerization of high molecular weight phenols, resulting in discoloration of fresh OMWW and a significant reduction in total phenols [71]. Approximately 58% of the color, 55% of the COD, and 46% of the phenolic compounds were removed when OMWW was diluted tenfold before _L. plantarum_ addition.  
Furthermore, OMWW has also been proposed for biopesticide and compost production. The OMWW application on soil and crops resulted in a growth suppression of most phytopathogenic bacteria and fungi and weed species without any effect on crop growth. However, certain measures should be adhered to when OMWW is used as a biopesticide, especially regarding dose and timing of use [47].
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 5 Methodology
### Performance Metrics
#### 5.2.2 End-to-End Delay  
This metric measures the average time taken for a packet to travel from the source to the destination. Increased delays can be attributed to the attacks disrupting network communication.