esa-sceva/satcom-chunk-collection huggingface dataset:
# Attention Transfer Reinforcement Learning for Test Case Prioritization in Continuous Integration
## 6 Results and Analysis
### RQ2: The Length of Historical Information and Error Rate  
We varied the length of historical information in the input features of test cases to explore the impact of this variable on the method's effectiveness. The length of historical information refers to how many cycles of test information are included in the test case features. For example, the length of historical information for test cases is 5, which means that the last five cycles of test information should be considered when sorting. Based on the assumption that test cases which have failed in the past are likely to fail again, we prioritize test cases that have frequently failed in the past.  
Experiments were conducted by varying the length of historical information used in Paint Control. In Figure 7a, the horizontal axis represents the length of historical information used, and the vertical axis represents the percentage of each result relative to the best result. The initial performance of ATRL-TCP improves with increasing history length, but begins to decline when the history length exceeds 5. This is because the test cases in Paint Control have a strong fault-detection capability, making the model more focused on the features of single test cases. The model's performance is significantly related to the history length of the test cases. When the historical information of test cases is too extensive, the relevance of older test results diminishes. This interferes with the current prioritization strategy and causes a gradual decline in model performance. Therefore, the history length threshold in ATRL-TCP should not be set too high to prevent outdated information from affecting decision making.  
Additionally, we studied the impact of test case error rate on model performance in Figure 7b. Test case error rate refers to the proportion of test failures over past cycles. A higher error rate indicates a higher frequency of test case failures. An increase in the error rate of test cases can rapidly enhance model performance, which is related to our evaluation metric, NAPFD, because it prioritizes test cases that detect errors earlier. The higher the  
Figure 7: History length and error rate.  
error rate of a test case, the more pronounced its fault-detection capability, making it more likely to have higher priority. This leads to better model performance and aligns with our understanding of the prioritization strategy.
## 6 Finite Element Method
### Results and Applications  
There is extensive research in this field. Some more interesting and useful results are presented in this section.  
A FEM model using the Galerkin method for stress in a platelet-reinforced composite subjected to axial loading is presented in [234]. The results obtained were validated with 3D analytical and EM models (ANSYS). The shear stress at the interface was investigated. Different porous microstructures, with different boundary conditions, finite dimensions, and homogeneous and isotropic models, have different behaviors regarding permeability at the macroscopic level of the fibrous medium [235]. A microstructural model is developed for this study. Numerical experiments using FEM suggest a unique relationship, based on the power law, for determining the permeability. FEM is applied to the analysis of a unidirectional fiber composite considering two different materials, T800 carbon fibers reinforcing a Fibredux 6376C epoxy matrix and IM6 carbon fibers reinforcing an APC2 thermoplastic material, as presented in [236]. Experimental results validate the proposed model and show an excellent agreement between the results obtained by calculation and the experimental ones. The stress analysis and prediction of the steady-state creep deformation of short-fiber composites subjected to axial loading are described in [237]. The fiber-matrix interface is assumed to be perfect, and the steady-state creep behavior is classically defined by an exponential law. Analytical results are validated using FEM. Very good agreement is found between the analytical and FEM results for all stress components.  
FEM has been the main method used in the calculation of stress and strain in most creep behavior study applications in which their determination is necessary, as it is a well-verified method (for example, [238; 239; 240; 241; 242; 243]). A metal matrix composite is studied in [244]. For these materials, numerical models for estimating strain and stress are well-developed and have been studied for a long time. For the investigated material with a load perpendicular to the fiber axis, FEM determinations are performed. The results are compared with experimental verifications for a model composite with AA6061 matrix reinforced with single-crystal sapphire (reinforcement volume fraction 10%). The average values obtained experimentally validate the results of the numerical model. The development and use of a finite element model (FEM) to simulate the creep of a prestressed glued laminated timber system under external loading is reported in [245]. A variety of applications are presented in the literature certifying the usefulness of applying these theoretical methods [246; 247; 258].
# Distributed Architectures and Constellations for \(\gamma\)-ray Burst Science
## 3 CBC Multi-Messenger Astrophysics Today
No significant electromagnetic counterpart was reported for this event, with INTEGRAL/SPI inferring a three-sigma upper limit for the flux of \(3\times 10^{-7}\) erg/cm\({}^{2}\)/s in the energy range 75-2000 keV [16]. Swift/BAT covered \(>\)99% of the error box with \(>\)10% partial coding, reporting a five-sigma upper limit of \(10^{-7}\) erg/cm\({}^{2}\) between 15-350 keV in 1 s [17]. The lack of gamma-ray detection is likely due to the high inclination of the system, which corresponds to a huge (factor of \(10^{6}\)-\(10^{7}\)) reduction in the observed flux. The jet emission at these inclinations is hardly observable by any conceivable all-sky monitor at the moment. In summary, the lessons learnt from the three NS-NS and BH-NS events collected so far are: (1) the need for an **all-sky monitor** with a sufficient sensitivity to detect off-axis jets with intrinsic luminosities down to \(10^{47}\) ergs, at least to a distance of 100-200 Mpc and an inclination of 10-20 deg (of course smaller inclination values allow for detection over larger distances). In fact, within this decade the LIGO/Virgo interferometers will reach their target sensitivity and the searched volume will become much larger with respect to O1-O3: the horizon for NS-NS merging events detected with a signal-to-noise ratio of 8 will reach \(\sim\)200 Mpc for LIGO and 100-130 Mpc for Virgo in O4, implying a discovery volume \(\sim\)100 times larger than in the GW170817 case. The capability to instantaneously cover the whole sky is mandatory, because the number of events will be small; therefore, missing simply one event would mean a considerable loss for scientific research. (2) The capability of determining the position of the transients with uncertainties is smaller than a few degrees. Within the volume defined by this spatial constraint and the distance of the GWE provided by the interferometric measurements, the number of optical transients will be small enough to easily assess the correct transient to associate with the GWE, thus prompting further follow-ups.
# Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors
## 5 Conclusions  
While we conclude that these three near-surface approaches (UAV, ground-based camera, and PhenoCam) provide similar estimates of the timing of canopy greening, each sensor has distinct advantages and disadvantages. As many instances in science, question determines instrument choice.  
UAVs provide superior spatial coverage and resolution, but require special training and permitting and often proprietary software to generate orthoimages. PhenoCams provide high temporal resolution, are networked to the PhenoCam network, have consistent protocols and open-source analysis pipelines, and are comparable across multiple environments. They do however require existing site infrastructure (e.g., internet access, tower mounts). PhenoCams are also stationary which sacrifices spatial coverage and site heterogeneity, yet the oblique-view of the canopy of PhenoCams largely avoids image interference from the ground or sky, isolating the canopy for analysis--this is often not the case in arid systems. Ground-based cameras allow for canopy isolation more completely than the other sensors surveyed, which gives the user the ability to more readily measures additional structural information such as canopy gap fraction, rather than just greenness. They are limited by a smaller spatial coverage, and a higher degree of user attention. Each sensor does, however, provide robust estimates of canopy phenology with broad utility in ecology and remote sensing studies. All three of the sensors surveyed complement each other and provide additional information about canopy phenology and, to some degree, canopy structure. UAVs offer a means to democratize remote sensing for many researchers, scientists, and practitioners, and provide broad utility to forestry and ecological sciences.  
Data Availability and Computer Code and Software--Summary data and analysis scripts are held in a publically available GitHub repository: [[https://github.com/atkinsjeff/pace_mapping_](https://github.com/atkinsjeff/pace_mapping_)]([https://github.com/atkinsjeff/pace_mapping_](https://github.com/atkinsjeff/pace_mapping_)) phenology.  
Conceptualization, methodology, formal analysis, J.W.A., A.E.L.S., X.Y.; writing--original draft preparation, J.W.A.; writing--review and editing, J.W.A., A.E.L.S, X.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work was supported by the University of Virginia Department of Environmental Sciences. X. Y. and A.E.L.S were supported by the National Aeronautics and Space Administration (award no. 80 NSSC17K0110), National Science Foundation through Division of Atmospheric and Geospace Sciences (award no. 1837891), Division of Integrative Organismal Systems (award no. 2005574). J.W.A. was supported by the National Science Foundation, Division of Environmental Biology (award no. 1655095).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Limitations of This Study  
Despite the statistically robust methodology adopted and the satisfactory predictive performance achieved, this study is subject to several limitations that should be acknowledged:  
* The representativeness of the dataset is intrinsically linked to the geological and operational characteristics of the mines from which the data were collected. Therefore, the models developed herein may not generalize effectively to mines with substantially different conditions.
* Potential sampling biases or errors in data collection could have introduced distortions in the training process, leading to models that do not adequately capture the underlying relationships within the studied context.
* The relatively small dataset size (120 samples) imposes limitations on the models' ability to generalize. In particular, the identification of more subtle patterns may be hindered, and statistical fluctuations across different data partitions may be exacerbated.  
* Decisions regarding data transformation strategies, train-test segmentation, choice of machine learning algorithms, and hyperparameter optimization configurations--although carefully considered--may not represent the optimal combination. These choices can impact model performance and generalization capacity.
* The genetic algorithms employed for hyperparameter tuning, while effective in navigating complex search spaces, do not guarantee convergence to global optima. As a result, superior configurations may remain unexplored.
* The performance of the stacking models is inherently constrained by the predictive capabilities of the constituent base models. Consequently, under different scenarios or datasets, the ensemble approach may not yield improved robustness or accuracy.
## 3 Stochastic FIR Filter with Adaptive Scaling
### Antithetic Variables Method  
The parallelism method is a widely used method for stochastic computing to make a trade-off between processing latency and hardware cost. Consider a numerical value \(P\in[0,1]\) represented with a stochastic bit stream \(X_{i}\), \(i=1,2,...,N\). It takes \(N\) clock cycles to process the \(N\) bits in the bit stream. To reduce the processing latency, the bit stream \(X_{i}\) can be separate into two parts: \(X_{i}^{1}\) and \(X_{i}^{2}\), \(i=1,2,...,N/2\), where \(X_{i}^{1}=X_{i}\) and \(X_{i}^{2}=X_{i+N/2}\). The estimated \(\hat{P}\) can be written as  
\[\hat{P}=\frac{1}{N}\cdot\sum_{i=1}^{N}\frac{X_{i}^{1}+X_{i}^{2}}{2} \tag{9}\]  
For the reason that each bit in the stochastic bit stream follows Bernoulli distribution and is individual to the others, \(X_{i}^{1}\) is individual to \(X_{i}^{2}\) and \(Cov(X_{i}^{1},X_{i}^{2})=0\). Thus, the variance of \((X_{i}^{1}+X_{i}^{2})/2\) can be written as  
\[\begin{split} D(\frac{X_{i}^{1}+X_{i}^{2}}{2})&= \frac{D(X_{i}^{1})+D(X_{i}^{2})+2 Cov(X_{i}^{1},X_{i}^{2})}{4}\\ &=\frac{D(X_{i})+Cov(X_{i}^{1},X_{i}^{2})}{2}=\frac{D(X_{i})}{2} \end{split} \tag{10}\]  
It can be observed from (10) that the variance is reduced by 2 times. Combining (10) and (4), it demonstrates that the calculation accuracy can be improved by 2 times using the 2-parallelism method. However, the hardware cost is also 2 times higher, which is required by the parallel processing.  
In this paper, an novel antithetic variables method is proposed to further improve the calculation accuracy. The basic idea is generate a certain bit stream \(X_{i}^{2}\) to make \(Cov(X_{i}^{1},X_{i}^{2})<0\):  
\[\begin{split} X_{i}^{1}&=\begin{cases}1,&P \geq R(t);\\ 0,&\text{otherwise};\end{cases}\\ X_{i}^{2}&=\begin{cases}1,&P\geq 1-R(t);\\ 0,&\text{otherwise}  
\[D(\frac{X_{i}^{1}+X_{i}^{2}}{2}) =\frac{D(X_{i}^{1})+D(X_{i}^{2})+2 Cov(X_{i}^{1},X_{i}^{2})}{4} \tag{15}\] \[=\begin{cases}(P-2P^{2})/2,&P<0.5;\\ (-2P^{2}+3P-1)/2,&P\geq 0.5;\end{cases}\]  
Finally, the noise power of antithetic variables-method-based bit stream representation can be written as (16), and the corresponding simulation results are shown as Figure 5, which indicates that the simulation results agree with the theoretical analysis as (16).  
\[P_{noise-AV} =D\Bigg{(}\frac{1}{N}\cdot\sum_{i=1}^{N}\frac{X_{i}^{1}+X_{i}^{2} }{2}\Bigg{)} \tag{16}\] \[=\frac{1}{N^{2}}\cdot\sum_{i=1}^{N}D(\frac{X_{i}^{1}+X_{i}^{2}}{2})\] \[=\begin{cases}(P-2P^{2})/(2\cdot N),&P<0.5;\\ (-2P^{2}+3P-1)/(2\cdot N),&P\geq 0.5;\end{cases}\]
## Abbreviations and Nomenclature  
\begin{tabular}{l l} FWD & Free Wheeling Diode \\ EMU & Electric Multiple Unit \\ IGBT & Insulated Gate Bipolar Transistor \\ TC & Traction Converter \\ CTE & Coefficient of Thermal Expansion \\ DUT & Device Under Test \\ AFT & Accelerated Fatigue Test \\ GTO & Gate-Turn-Off thyristor \\ SAFT & Single-directional Accelerated Fatigue Test \\ BAFT & Bi-directional Accelerated Fatigue Test \\ EMI & Electromagnetic Interference \\ GA & Genetic Algorithm \\ S & Thermal stress \\ \(R_{\text{th}}\) & Thermal resistance \\ \(R_{\text{th-c,1}}\) & Thermal resistance between the IGBT die and device case \\ \(R_{\text{th-c,2}}\) & Thermal resistance between the FWD die and device case \\ \(\Delta\)_CTE_ & Difference of CTE \\ \(\Delta\)_T_ & Junction temperature variation \\ \(P\) & Heating power \\ \(F\) & Cumulative failure rate \\ \(I_{1},I_{2}\) & the currents used in SAFT and BAFT \\ \(U_{\text{CES0}}\) & Initial saturation voltage drop of the device \\ \(U_{\text{CES}}\) & Saturation voltage drop of the device \\ \(i_{1},i_{2},i_{3}\) & Current outputs of TC \\ \(U_{\text{dc}}\) & DC voltage input of TC \\ \(T_{\text{j}}\) & Junction temperature \\ \(T_{\text{j1}}\) & Junction temperature of IGBT \\ \(T_{\text{j2}}\) & Junction temperature of FWD \\ \(\Delta\)_T_j1 & Junction temperature variation of IGBT \\ \(\Delta\)_T_j2 & Junction temperature variation of FWD \\ \(I_{\text{eq}}\) & Equivalent current amplitude through the device or fatigue current \\ \(N_{t}\) & The service life of the device \\ \(n,m\) & Coefficients to show the accelerating effect from FWD actions on device \\ \(\mu\) & Acceleration factor \\ \(i_{G}\) & IGBT current \\ \(i_{0}\) & FWD current \\ \(F_{\text{B}}(x),F_{\text{S}}(x),F_{\text{B1500}}(x),\) & the function relationship between F and the number of cycle times x under \\ \(F_{\text{B1200}}(x),F_{\text{B900}}(x)\) & various conditions \\ \(\lambda_{1},k_{1},\)\(\lambda_{2},k_{2}\) & Parameters in Weibull distribution \\ \(T_{\text{a}}\) & Ambient temperature \\ \end{tabular}
## 4 Results and Discussion
### Semi-Empirical Models
#### 4.4.2 Shear Modulus  
While shear modulus has not yet been verified experimentally, it was calculated in the FEA analysis so the results have also been curve fit for simplicity of use. Unlike the longitudinal modulus, the normalized shear modulus is seen to be a function of both relative density and Poisson's ratio. As such, very few different types of model have been found in cellular structures or composites literature which are applicable. [PERSON] et al. [50] and [PERSON] et al. [48] predict different responses for shear response, but neither of these results in a satisfactory fit for the gyroid. Likewise, simple modifications of the basic mathematical models yield unsatisfactory results. It is apparent, however, that the normalized shear modulus follows a similar curve to the longitudinal modulus. In fact, simply using the \(E/E_{m}\) curve results in a max error of only 9.3%. This is improved significantly with the addition of a Poisson's ratio dependent term, yielding (13). The form of this term was  
Figure 12: Experimental results vs. FEA predictions for [PERSON]’s ratio.  
determined by curve fitting various functional forms and choosing the one which resulted in the smallest error.  
\[G_{n}(D,\
u_{m})=\frac{G}{G_{m}}=E_{n}(D)+C_{3}\cdot\
u_{m}\cdot D^{2}\cdot\sin( D\cdot\pi) \tag{13}\]  
where \(G_{n}\) is the normalized shear modulus, \(G_{m}\) is the neat-material shear modulus, and \(\
u_{m}\) is the neat-material Poisson's ratio. The best value of fitting parameter \(C_{3}\) was determined to be 0.5. This results in an average error of 0.35% (maximum 1.36%). Figure 14 shows the FEA and semi-emperical predictions.
# Hardware Model Checking Algorithms and Techniques
## 8 Comparative Analysis and Experimental Evaluation
### Practical Considerations Concerning Model Checking Algorithms  
The algorithms we have described, falling into the SAT-based and symbolic model checking category, are usually quite resilient to the state space explosion problem, which can be problematic when dealing with large designs, comprising millions, or more, variables and constraints. In such a scenario, though, the trade-off has to be taken into account, which lies in the inherent time complexity, as SAT-based problems are known to be NP-complete.  
One the one hand, we have that worst-case complexity indeed falls into exponential time; on the other hand, we have that, exploiting the proper modeling of systems, representation of data and simplification steps, SAT-based model checking algorithms are applicable to real-world scenarios.  
One of the major issues with tuning resources for running verification instances is that it is almost impossible, in the general case, to foresee what would be the appropriate amount of time and memory to dedicate to a given task. The experimental evaluation proposed above follows the same setup of model checking competitions to provide comparable results with a well-defined baseline, but at the same time such a setup is nothing more than an executive choice on the matter.  
It is not uncommon for industrial-level instances and more complex benchmarks to have to account for timeouts in the order of days, or more, in order to hopefully obtain usable results. Let us also point out once more how there is absolutely no guarantee that any of the engines may actually be able to solve a given instance, regardless of the time spent on the problem. On these premises, there are situations in which the aim is to go _as deep as possible_ in the model exploration, trying to verify as much of the behavior as possible, without any claim of completeness, such as in pure BMC-based runs, where engines are left to run until resource depletion, either in terms of time or memory.  
Because of this, orthogonal to the research and development of new techniques, a vast amount of research is targeted at improving the existing base of knowledge in order to make current techniques better performing and more applicable. Such an effort may be directed, among other things, at improving formalism and encoding styles and rules for the models [58], improving SAT-solver management strategies [49] or introducing different strategies for task management or exploiting partial results in a collaborative fashion among engines in order to increase their chance of success [36; 37].  
A far more in-depth analysis of practical solutions and techniques to improve the applicability and scalability of SAT-based verification is presented in [59].
# A Hybrid User Mobility Prediction Approach for Handover Management in Mobile Networks
## 3 The Proposed Predictive Handover Management Approach
### User Mobility Prediction Objective
Next, the reset gate \(r(t)\) can be given by  
\[r(t)=\sigma(b_{r}+U_{r}L(t)+W_{r}h(t)) \tag{5}\]  
It has the same procedure as that in Equation (4). The only difference is that they are multiplied by their own weights. The reset gate decides which part of the past information should be eliminated for future decisions. Lastly, we can compute the effect of these gates in the output as follows:  
\[h(t)=u(t-1)h(t-1)+(1-u(t-1))\sigma(b+UL(t-1)+Wr(t-1)h(t-1)) \tag{6}\]  
where \(b\), \(b_{u}\), and \(b_{r}\) are bias vectors; \(U\), \(U_{u}\), and \(U_{r}\) are input weight matrices; and \(W\), \(W_{u}\), and \(W_{r}\) are recurrent weight matrices. Additionally, \(\sigma\) denotes a sigmoid function that has a value between 0 and 1 in the output. Figure 2 depicts the VAR-GRU trajectory prediction model. As shown, we took a sequence of the user past trajectory as the input. First, the VAR unit models the dynamic behaviour of the variables based on the raw data. Then, the obtained results are fed into the GRU network for the training and inference steps and produce the future trajectory of the user in the output. \begin{table}
\begin{tabular}{c c c c} \hline \(b\), \(b_{u}\), \(b_{r}\) & Bias vectors & \(U\), \(U_{u}\), \(U_{r}\) & Input weight matrices \\ \(W\), \(W_{u}\), \(W_{r}\) & Recurrent weight matrices & \(h(t)\) & Hidden states \\ \(u(t)\) & Update gate & \(r(t)\) & Reset gate \\ \hline \end{tabular}
\end{table} 
Table 1: GRU model notations.
## 5 Attacks and Countermeasures
### Attacking MANETs
#### 5.3.2 Malicious Behavior during Data Transmission  
After routes are established successfully, data transmission may still fail as a result of malicious behavior. Malicious nodes may e.g. cooperate during route discovery but deny to forward packets whenever data has to be transmitted. Those _Selective Forwarding_ attacks may be hard to detect, especially when a node behaves benignly temporarily and only drops packets sent by specific nodes.  
Further, by sending forged messages for route maintenance, transmission loops can be created. Those loops result in an increased traffic and a denial of service as the packets are dropped when their _time-to-live_ exceeds. The ADOV routing protocol for example has been shown to be vulnerable to _Loop Forming_ attacks [34].