esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model and Problem Formulation
### System Model
#### 2.1.4 Power Consumption Model  
The power model can be assumed as follows. The total power consumption \(P_{Sys}\) demand of the 5G cellular networks consists of operational power \(P_{Op}\), transmission power \(P_{trans}\), and MECSs power \(P_{MEC}\).  
\[P_{Sys}=P_{Op}+P_{trans}+P_{MEC} \tag{13}\]  
The operational power is load-independent, consisting of the baseband processor, the converter, the cooling system, etc. Therefore, for the BS in time slot t:  
\[P_{Op}=P_{O}\mu \tag{14}\]  
where \(P_{O}\) is a constant which describes system power consumption, \(\mu\) is a synchronous workload coefficient.  
Transmission occurs on wireless links between the MU and the BS, as well as the backhaul link between the BS and CN. Usually the wireless transmission power consumption dominates, so that we consider only the wireless portion. We assume that the small-scale fast fading will average out since the considered time slot is relatively long. Hence, we focus on pathloss effects. We can approximate the pathloss effect by considering the maximum coverage radius (\(R\)) of the BS, in order to keep the maximum achievable transmission rate of all users which are under the coverage of the BS larger than \(C_{o}\)[27, 28]. Given the transmission power \(P_{trans}\), the maximum achievable transmission rate is given by the [PERSON]'s theorem,  
\[C_{O}=W\left(\log_{2}\left(1+\frac{P_{trans}\beta R^{-\epsilon}}{W\delta^{2}} \right)\right) \tag{15}\]  
where \(W\) is the channel bandwidth, \(\delta^{2}\) is the noise power, \(\epsilon\) is the pathloss exponent and \(\beta\) is the pathloss constant. We suppose the noise-limited is set by assuming that the BS operates on orthogonal channels [28, 29, 30].  
Similarly, we consider each transmission must meet a target rate \(C_{o}\) to satisfy a transmission delay requirement, the transmission power should satisfy:  
\[P_{trans}=\frac{\left(2^{C_{o}W^{-1}}-1\right)W\delta^{2}}{\beta R^{-\epsilon}} \tag{16}\]  
The MECS system power at the edge server is load-dependent. Let  
\[P_{MEC}=P_{Server}\rho^{T} \tag{17}\]  
where \(\rho=[a_{1},a_{2,...,}a_{N}]\), let \(a_{N}\in\{0,1\}\) represent the active (1)/inactive (0) decision for the MECSs.
###### Abstract  
In this paper, a novel model is proposed for the numerical simulation of noise-attenuating perforated liners. Effusion cooling liners offer the potential of being able to attenuate combustion instabilities in gas turbine engines. However, the acoustic attenuation of a perforated liner is a combination of a number of interacting factors, resulting in the traditional approach of designing perforated combustor liners relying heavily on combustor rig tests. On the other hand, direct computation of thousands of small-scale holes is too expensive to be employed as an engineering design tool. In recognition of this, a novel physical velocity porous media (PVPM) model was recently proposed by the authors as a computationally less demanding approach to represent the acoustic attenuation of perforated liners. The model was previously validated for the normal incidence of a sound wave by comparison with experimental data from impedance tubes. In this paper, the model is further developed for configurations where the noise signal propagates in parallel with the perforated liners, both in the presence and absence of a mean flow. The model is significantly improved and successfully validated within coexisting grazing and bias flow scenarios, with reference to a series of well-recognized experimental data.  
perforated liners; acoustic damping; porous media model; acoustic passive control; numerical simulation; combustion instability; effusion cooling walls +
Footnote †: journal: Applied Sciences  
1 Jiangsu (Sunpower) Research Institute of Industrial Energy Saving Technology, Jiangsu Sunpower Technology Co. Ltd., Nanjing 210000, China 1  
2
## 2 Materials and Methods
### Classification Problems on Physiological Data  
Physiological data were extracted from several datasets containing physiological signals, and nine classification problems were constructed using the physiological data to simulate LSSC and compare its effectiveness with other conventional selection criteria. Table 1 shows the sample size of each class and the number of subjects and features for the nine problems. The methods used to extract features were the same as previous studies [9; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42], and details of the extracted features can be found in Appendix A. The first problem here is to detect medium or high stress from low-stress data [33; 34]. These data contain features that were extracted from several physiological signals, such as the ECG, EDA, EMG, and respiration. The SWELL database provides the ECG and EDA data for the normal and stress states of subjects engaged in office work [35]. The stress data are detected in problem 2 using the heart rate variability (HRV) features of the ECG [36]. Problems 3-5 are based on our previous work, which utilized photoplethysmogram (PPG) and EDA data to detect the stress, drowsiness, and fatigue of drivers [9; 28]. The brain-computer interface competition IV-2b database provides electroencephalography (EEG) signals recorded when subjects imagined the movement of one of their hands (left or right) [37]. Problem 6 uses the database to design a classification problem to distinguish between EEG data for movements of the left and right hands. The EEG data are feature vectors that were extracted from EEG signals (with lengths = 9 s) with the use of a common spatial pattern algorithm in the problem [41]. [PERSON] obtained the heart rate (HR) data from subjects in three thermal conditions: very hot (VH) (32 \"C), VH with adjustable neck-coolers (18-28 \"C), and hot environment (29 \"C) [38]. [PERSON] proposed HRV-based features to distinguish the data for each of these three conditions [42], and these feature data are utilized for problems 7 and 8. Ventricular ectopic beats (VEBs) in the ECG are detected in problem 9; the RR interval, higher order statistics, wavelet transform, and morphological descriptor-based features were obtained from the modified-lead II ECG data of the MIT-BIH arrhythmia database, and the data of a subject that had at least 100 VEB data were used for the problem [39; 40]. In addition, the data size was limited to 100 for each class and subject in problems 2 and 7-9 to simplify the classification and to allow considerations of imbalanced data sizes.
# FDBP-InSAR: An Efficient Algorithm for InSAR Imaging via Frequency Domain Back Projection
## 2 InSAR Imaging Model  
### Geometric Model  
The classical geometric model of InSAR imaging is shown in Figure 1. The InSAR system adopts the single input single output (SISO) working model. The coordinate of the scattering point \(p\) is \(p\). \(p_{m}(t)\) and \(p_{s}(t)\) denote the coordinate of the main antenna and slave antenna at the \(t-th\) slow time, respectively. The range between the scattering point \(p\) and antennas is \(l_{m}(t,\mathbf{p})\) and \(l_{s}(t,\mathbf{p})\). \(Rc\) denotes the distance from the antenna to the center of the target scene. \(B\) is the baseline length of the InSAR system.  
The echo signal of a target with time-delayed \(\Delta\tau\) can be written as follows:  
\[m(t,\tau)=\kappa\exp\Bigl{(}j2\pi f_{c}(\tau-\Delta\tau)+j\pi K_{r}(\tau- \Delta\tau)^{2}\Bigr{)} \tag{1}\]where \(\kappa=\sigmarect(\frac{\tau-\Delta\tau}{T_{r}})rect(\frac{t}{T_{a}})\) is a window function, and \(\sigma\) is the scattering coefficient of target points, \(\Delta\tau\) denotes the time-delayed of the target and \(\Delta\tau=2l(n)/C\), \(rect(\cdot)\) is the rectangular window function, \(f_{c}\) is the center frequency of radar system, \(K_{r}\) is the Doppler frequency rate, \(T_{r}\) and \(T_{a}\) are the pulse period in the range direction and azimuth direction, respectively, and \(|\tau|<\frac{T_{r}}{2},|t|<\frac{T_{a}}{2}\).
# Investigation of Pre-Pulse Effects on Ultrashort-Pulse Laser Interaction with Structured Targets
## 6 Conclusions  
We have experimentally investigated the role of the fluence and arrival delay time of an independently controllable ultrashort pre-pulse in creating appropriate pre-plasma conditions for optimized energy coupling. A distinct optimal condition is found for the pre-pulse fluence and arrival delay. At optimum pre-pulse conditions, proton energy peaks \(\approx\)2.5 MeV, with a relatively low main pulse power of 0.6 TW on target. Analysis of the plasma emission optical spectrum shows a distinct feature that differentiates between different APP regimes. PIC simulations show good qualitative agreement with the experimental data, while the analysis of the accelerating field and the electron energy spectrum of the pre-plasma provides a deeper understanding of the mechanisms responsible for the observed effects. The simulations confirm that the optimal size and density profile of the pre-plasma are critical in enhancing energy coupling between the main pulse and the target.  
Conceptualization, I.D., P.K., J.P., M.B. and A.Z.; Methodology, A.K., I.D., A.B., P.K., A.S. and J.P.; Software, A.K., A.B., A.S. and M.B.; Validation, A.K., I.D., A.B. and P.K.; Formal analysis, A.K., I.D., A.B., P.K. and A.S.; Investigation, A.K., I.D., A.B., P.K., A.S., J.P., M.B. and A.Z.; Resources, A.B.; Data curation, A.S.; Writing--original draft, A.K. and I.D.; Writing--review and editing, A.K., I.D., M.B. and A.Z.; Visualization, A.K. and I.D.; Supervision, I.D., J.P., M.B. and A.Z.; Project administration, I.D., J.P. and A.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Polymeric Nanoparticles--Tools in a Drug Delivery System in Selected Cancer Therapies
## 7 PNPs as a Drug Delivery System in Cancer-Associated Gene Therapy
Thus, the possibility of combining immunotherapy methods and nanotechnology undoubtedly broadens the scope of research into new potential anticancer therapies [111]. Examples of the use of PNPs in gene therapy are presented in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**PNPs** & **Formulation Method** & **Size (nm)** & **Molecule** & **Dose** & **In Vitro/In Vivo** & **Cancer** & **Ref. ** \\ \hline d,l-PLGA & Double emulsion & \multirow{2}{*}{\(\sim\)197} & \multirow{2}{*}{BLC2 siRNA} & \multirow{2}{*}{50 \(\mu\)g} & In vitro—SKOV3-TR. & \multirow{2}{*}{Ovarian} & \multirow{2}{*}{[112]} \\  & solvent evaporation & & & & & A2780-CP20 & \\  & Double emulsion & & & PTX; FAK & 900 \(\mu\)g and 125 & & In vitro—SKOV3, TR, & \\  & solvent evaporation & & & siRNA & \(\mu\)g & & Heylas, MDR & \\  & Solvent evaporation & & & & & & In vivo—BALB/c mice & \\  & & method & & & & & & \\  & Polyelectrolyte & & & & & & & \\  & complexation & & & & & & & \\  & & & & & & & \\  & Double emulsion & & & & & & & \\  & solvent evaporation & & & & & & \\Undoubtedly, the use of polymeric nanoparticles as molecule delivery system is one of the most promising. The reason for this is that in the case of gene therapy, polymer nanoparticles have less toxicity and immunogenicity than traditional methods based on vector systems [105]. In addition, due to the use of nanoparticles, it is possible to transport molecules such as miRNAs, siRNAs or gene-editing systems, etc., which directly affect transcription or correction of an abnormal gene [105]. However, despite this, there are still some limitations, mainly related to the lower efficiency of cell transfection compared to viral systems, which may be resolved in the future due to the significant development of biotechnology and nanomedicine [105].
## 4 Discussion
### Other Issues Encountered  
While the Data Collaborative proved to be scalable to a certain point, we did encounter issues. We had concentrated on setting up a competition environment under schedule pressure, but we did not focus much of what would take to deprovision it after a contest. After our first competition, some configured resources, such as containers and DNS records, had to be removed manually. This kind of problem is solvable through automation and scripting.  
We also encountered problems regarding application state and containers. Since teams could add software packages to their notebook, any state changes such as this would be reflected in the current state of the container. However, if the container crashed for some reason and a replacement one was instantiated, any additions would be lost. This is because we did not separate out the stateful components of the notebook--additional software packages for instance. We fixed this by making sure that state changes such as this would be saved and restored if the container went down and had to be restarted.  
A more subtle problem with container state happened because of the DNS architecture shown in Figure 2. A container's name in our private DNS space has a particular Time to Live (TTL). As an example, let us say that the record for the container named team1.privateprize.org and IP address associated with 192.168.1.1 have a TTL of two hours. If the container goes down and comes back up again with IP address 192.168.1.2, an infrastructure component such as our proxy could attempt to connect to team1.privateprize.org at 192.168.1.1 for up to two hours (until the TTL expires). The container state exists not only in the container but in infrastructure in things such as DNS. We reduced the impact of this problem by reducing the DNS TTL in our container private DNS.  
The notebook per container architecture had many benefits, such as improving notebook isolation and using native Cloud container monitoring, but it had the drawback of being harder to maintain. If a software upgrade needed to be made to all notebooks in a competition, then we would have to go through each notebook, upgrade it in place if possible, and create new copies and then redeploy if not. We want to examine how much of requirement #4 is really needed, and whether we can use a more centralized approach using JupyterHub to simplify much of the architecture and operation.
# Formation Control of Dual Auto Guided Vehicles Based on Compensation Method in 5G Networks
## 2 5G Networks Characteristics  
### 5G Private Network Structure  
For 5G network, factory automation scenarios have higher requirements than public scenarios on coverage, communication quality, latency, bandwidth, data confidentiality, device mobility, and network control authority. Therefore, private 5G networks are deployed to offer dedicated coverage, exclusive capacity, intrinsic control, customized service, and dependable communication [23]. The private network architecture can be divided into the following three types [24]: hybrid private networks, virtual private networks, and physical networks. In hybrid private networks and virtual private networks, industry will share or partly share the wireless equipment next Generation NodeB (gNB), 5G frequency, and 5G core network (5 GC) with public. In physical private networks, industry users will have exclusive gNB, 5G frequency, and 5 GC. Therefore, the physical private network is completely isolated from the public network. Network traffic and congestion in public network will not impact the quality of private network. The physical private network is oriented to industry applications that require extremely high reliability and privacy. This paper researched 5G characteristics in physical private network.  
The structure of physical private network is shown in Figure 1. This paper is concerned with the communication process between the two AGVs. The end to end (E2E) delay in this paper refers to the time that it takes for the data packet to go through the AGV1-gNB-5 GC-gNB- AGV2 equipment in the factory private 5G networks.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 5 Fair Editions of Ensemble Models
### AdaFair  
The authors of [23] proposed AdaFair, an extension of AdaBoost, which dynamically updates instance weights not only based on classification error but also to mitigate discrimination against underrepresented groups. As a fairness metric, they adopted equalized odds. Additionally, AdaFair optimizes the number of weak learners in the final ensemble to achieve both fairness and class balance.  
Experiments were conducted on four real-world datasets: Adult Income, Bank Marketing, COMPAS, and KDD Census. The KDD Census dataset is similar to the Adult Income dataset but exhibits extreme class imbalance. AdaFair demonstrates superior performance compared to existing fairness-aware methods, achieving an 8-25% improvement in balanced accuracy over competing techniques while maintaining fairness.  
In [24], AdaFair was further extended to incorporate two additional fairness metrics: Statistical Parity and Equal Opportunity, which were used to formulate fairness costs. The reweighting function was modified to consider both misclassification error (as in traditional AdaBoost) and cumulative fairness costs. Unlike prior approaches that adjust for fairness independently at each boosting round, AdaFair evaluates fairness across the entire ensemble up to a given point, ensuring a more stable and consistent fairness correction.  
The strong performance demonstrated by the original version of AdaFair in [23] across the four real-world datasets persisted in this extended version, confirming its effectiveness in maintaining both fairness and predictive accuracy.
# On-the-Fly Repairing of Atomicity Violations in ARINC 653 Software
## 4 RAV: Repairing Atomicity Violations
### Procedure  
The repairing AV (RAV) is an on-the-fly tool to repair atomicity violations in ARINC 653-based software. The architecture of RAV is illustrated in Figure 3. RAV is comprised of a diagnosis engine (AV-DE) and a treatment engine (AV-TE). The AV-DE watches the shared variables of each thread for diagnosis. The AV-TE stalls instructions that violate the atomicity. RAV has three execution paths, described below.  
1. Halt: ARINC 653 Application \(\rightarrow\) AV-DE \(\rightarrow\) HM of ARINC 653 \(\rightarrow\) AV-TE \(\rightarrow\) Thread control;
2. Resume: ARINC 653 Application \(\rightarrow\) AV-DE \(\rightarrow\) HM of ARINC 653 \(\rightarrow\) AV-TE \(\rightarrow\) Thread control;
3. No Operation.  
RAV halts a program in the halt execution path when an ARINC 653 program is diagnosed with an error. RAV executes halted thread again in the resume path if RAV determines that the thread does not violate atomicity. The last execution path of RAV, i.e., no operation, continues to diagnose the program and does not affect the execution of an ARINC 653 program.  
Figure 3: The architecture of RAV.  
RAV was designed based on AI [37] of the general-purpose platform. It was performed in the test phase to create correct interleaving. In the test phase, RAV executed the program ten times to acquire interleaving information. From the data, it generated an interleaving set. Since the interleaving set may have contained incorrect interleaving information, we manually inspected the correctness of the set to finalize the interleaving set. In the operation phase, RAV treats atomicity violations raised during execution. While testing embedded software that performs repetitive tasks, AI collects incorrect interleaving information, such as tracking local variables. We also observed a performance issue in performing the treatment in the operation phase caused by a sleep() time. Figure 4 shows the repairing time overhead of each treatment method. As shown in the figure, the greater the sleep time, the more the total repairing time increases in proportion.
###### Abstract  
Drought has devastating impacts on agriculture and other ecosystems, and its occurrence is expected to increase in the future. However, its spatiotemporal impacts on net primary productivity (NPP) in Mongolia have remained uncertain. Hence, this paper focuses on the impact of drought on NPP in Mongolia. The drought events in Mongolia during 2003-2018 were identified using the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI). The Boreal Ecosystem Productivity Simulator (BEPS)-derived NPP was computed to assess changes in NPP during the 16 years, and the impacts of drought on the NPP of Mongolian terrestrial ecosystems was quantitatively analyzed. The results showed a slightly increasing trend of the growing season NPP during 2003-2018. However, a decreasing trend of NPP was observed during the six major drought events. A total of 60.55-87.75% of land in the entire country experienced drought, leading to a 75% drop in NPP. More specifically, NPP decline was prominent in severe drought areas than in mild and moderate drought areas. Moreover, this study revealed that drought had mostly affected the sparse vegetation NPP. In contrast, forest and shrubland were the least affected vegetation types.  
terrestrial ecosystem; NDVI; BEPS; NPP; drought; vegetation response +
Footnote †: journal: Remote Sensing  
0021
# Reliability Analysis of Hybrid Laser INS Under Multi-Mode Failure Conditions
## 5 Calculation Results and Analysis
### Dual Random Uncertainty Analysis of Outer Frame Component Based on Structural Strength Failure  
#### 5.2.1 Impact of Design Parameters on Failure Probability Under Single Failure Mode  
The threshold ranges for various failure modes considered in this study are shown in Table 6. Figures 17-22 present the failure probabilities of the SiCp/Al and ZL107 aluminum alloy outer frame structures under two failure modes, illustrating how these probabilities change with respect to the outer frame roundness \(R\), outer frame thickness \(H\), outer frame width \(B\), platform installation interface \(D\), motor installation interface \(1\ d1\), and motor installation interface \(2\ d2\).  
As seen in Figures 17a, 18a, 19a, 20a, 21a and 22a, with the gradual increase in design parameters, the failure probability under the maximum stress criterion for both composite materials initially increases and then decreases, exhibiting a maximum value. The failure probability for SiCp/Al composite material is significantly lower than that of ZL107 aluminum alloy; specifically, the failure probability for the ZL107 material is 44.77%, while for the SiCp/Al composite, it is 25.62%. In Figures 17b, 18b, 19b, 20b, 21b and 22b, as the design parameters increase, the failure probabilities under the maximum strain criterion for both composite materials display different trends. Overall, the SiCp/Al composite material demonstrates a significant reduction in failure probability compared to traditional ZL107 material under the maximum strain criterion, indicating a notable enhancement in the reliability performance of the outer frame structure. It can effectively resist deformation under high overload conditions.  
Figure 17: Variation in failure probability with outer frame roundness \(R\).  
Figure 18: Variation in failure probability with outer frame thickness \(H\).  
Figure 19: Variation in failure probability with outer frame width \(\mathcal{B}\).  
Figure 20: Variation in failure probability with platform installation interface \(D\).  
Figure 21: Variation of failure probability with motor installation interface 1 \(d1\).  
Figure 22: Variation in failure probability with motor installation interface 2 \(d2\).
# Towards Collaborative and Dynamic Spectrum Sharing via Interpretation of Spectrum Access Policies
## 5 Policies  
We distinguish two kinds of policies: (a) Hyper-cube based policies, which are constraints and requirements based on sets of states of the environment and the transmitter,  
Figure 7: A snapshot of the SAP Ontology.  
and (b) higher-level policies that are defined by logical conditions. First, we describe each policy kind in greater detail, then we show an example of a logic-based policy.