esa-sceva/satcom-chunk-collection huggingface dataset:
# An Activity Theory-Based Approach for Context Analysis, Design and Evolution
## 1 Introduction
### Context: A Challenge for Definition and Modeling  
The idea of \"_context_\" is an elusive concept that has many definitions [8; 9; 10] which are often in conflict due to ambiguous or controversial interpretations [11].  
While a detailed discussion of the differences among these definitions is outside of the scope of this paper, it is important to note that, behind this lack of clarity, underlies a challenge for modeling and understanding. That is, _how to provide a logical/national form to a concept with such a broad interpretation? How can a consistent design be created for such a conceptually elusive idea?_  
As an answer to these, or similar, questions, several authors have provided solutions aimed to develop efficient modeling strategies capable of addressing contexts and context-based systems. The approaches proposed range from the use of UML to ontologies and mathematical logic [12].  
For example, the use of UML and object-oriented methodologies to support context modeling aims to develop high-level functional and conceptual descriptions of the applications and their interactions with the context. There are multiple works associated with this approach [10; 13; 14].  
On the other hand, approaches that support context modeling using mathematical logic [15; 16], conceptual modeling languages [17; 18], and ontologies [19; 20], aim to model the services provided and the rationale behind them.  
These approaches, while valuable, do not relate context-aware operations to potential socio-human behaviors, reactions, and actions, as they focus primarily on a description of the software's functionalities and the services it provides.  
Moreover, most of these proposals do not simply and practically allow the modeling of contexts from the point of view of certain software dynamics, such as technological changes or self-evolution and adaptation.  
In this regard, the vision guiding our approach is rooted on the changing nature of contexts, which implies the need to deal with conditions affecting the scenarios where human, social, and technological elements interact.  
Addressing this need is paramount to understanding the deep interrelationship among elements that give shape to complex socio-technical systems and system ecologies (_sociotechnical ecologies_).  
Thus, this paper is an attempt to describe those socio-technical elements and their interrelationships in order to achieve more descriptive, concise, and practical context models. With this purpose in mind, the present proposal makes use of activity theory as the basis for a multidisciplinary approach aimed at understanding and modeling contexts. The next subsection presents the fundamental ideas related to activity theory.
# Reliability-Based Optimization of Impeller Structure Using Exponential Function Approximation
## 4 Reliability-Based Optimization of an Impeller Structure  
### Centrifugal Compressor Impeller Structure  
#### 4.1.1 Modeling Assumptions and Limitations  
In this study, the fluid-structure decoupled analysis method is used, i.e., the pressure distribution is first obtained from the CFD analysis, and then it is used as a boundary condition for the structural analysis. Although this method greatly reduces the computational cost, it also brings certain limitations. The accurate transfer of hydrodynamic loads depends on fluid-structure coupling analysis. Neglecting FSI may lead to underestimation or overestimation of local stress concentrations, especially at the leading and trailing edges of the blades, failure to capture the nonlinear coupling effects between fluid flow and structural deformation, and inaccurate estimation of the probability of failure. If the FSI effect causes the actual stresses to be lower than those predicted by the decoupling analysis, then ignoring the FSI will result in an overestimation of the probability of failure and an overly conservative design. On the contrary, if the FSI effect enhances the stresses, ignoring the FSI may underestimate the probability of failure, resulting in a potential safety issue. Future research should incorporate FSI coupling to improve the accuracy and reliability of the optimization framework for high-performance applications where fluid-structure coupling effects are significant.
## 4 Analysis Example and Methods
### Results from the BHP Bidirectional FSI Simulation
#### 5.2.2 Simulation Results for BHP Vibration with Different Lengths of Pipeline  
The curves for the radial acceleration at point P in the BHP with different lengths of the elastic layer are shown in Figure 16.  
In Figure 16, the amplitude of the radial acceleration in the BHP decreases with the increase in the pipeline length. In reference to Section 5.2.1, Figure A2 (in Appendix C) shows the radial acceleration curves for the BHPs with different elastic layer materials at P point when the pipe length is the same.  
As shown in Figure A2, when the pipeline length is the same, the vibration acceleration in the three-layer BHP with SWCNTs added is lower than that of the double-layer BHP with RTV as the elastic layer, and the effect of the vibration absorption is better. The suppressive effect of the different lengths in the BHP on the vibration of the pipeline wall is shown in Table 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Thickness (mm)** & **RTV** & **RTV-SWCNTs Composite Materials** & **Vibration Reduction (\%)** \\ \cline{2-3}  & **The Standard Deviation for the Vibration Acceleration** & \\ \hline
5 & 7.58 \(\times\) 10\({}^{-10}\) & 5.89 \(\times\) 10\({}^{-10}\) & 22.3 \\
10 & 6.73 \(\times\) 10\({}^{-10}\) & 5.23 \(\times\) 10\({}^{-10}\) & 22.3 \\
20 & 5.775 \(\times\) 10\({}^{-10}\) & 4.297 \(\times\) 10\({}^{-10}\) & 25.6 \\
25 & 4 It can be seen that the standard deviation for the radial acceleration in the BHP with RTV-SWCNTs composite materials as the elastic layer is significantly lower than that of the BHP with RTV. And with the increase in the pipeline length, the suppressive effect of the vibration increases. Besides, when the length of the pipeline is 250 mm, the percentage of pulsation reduction in the pipeline is the largest and the effect of the vibration reduction is the best, which lays the foundation for further optimization in the design of the BHP.
# The Machine-to-Everything (M2X) Economy: Business Enactments, Collaborations, and e-Goverance
## 2 The M2X Economy
### Elements and Definition of the M2X Economy  
The M2X Economy framework involves autonomous smart devices and further encompasses mobile devices, software agents, humans, and infrastructure in M2M, M2H, and M2I scenarios. A main requirement of such an ecosystem is to enable a seamless integration of humans and smart devices into a well functioning socio-technical system that puts the M2X concept in a human-centered context [1]. When considering collaborations and interactions between the M2X stakeholders, multilevel and unidirectional interrelations can be seen. The interleaved on-demand collaborations, interactions and transactions among autonomous, heterogeneous and highly dynamic entities (humans, machines, software agents, etc.) lead to decentralized and distributed socio-technical systems comprising a large number of micro-services of different vendors and solutions, as well as infrastructure providers [1].  
**Definition 1**.: _Thus, the M2X Economy is the result of interactions, transactions, collaborations and business enactments among humans, autonomous and cooperative smart devices, software agents, and physical systems. The corresponding ecosystem is formed by automated, globally-available, heterogeneous socio-technical e-governance systems with loosely coupled, P2P-resembling network structures and is characterized by its dynamic, continuously changing, interoperable, open and distributed nature. Thereby, the M2X Economy employs concepts such as cyber-physical systems, the Internet of Things, and wireless sensor networks._
# Modular Combined DC-DC Autotransformer for Offshore Wind Power Integration with DC Collection
## 5 Simulation Study  
### Test System  
As shown in Figure 5, a 100 MW OWF with the MCAT is established in PSCAD/EMTDC. There are four clusters in this model, and each cluster is represented by a 25 MW aggregated permanent magnets synchronous generators based on WTs (PMSG-WTs). The output DC voltage of the aggregated WT is 2.96 kV, and then requires two step-up DC-DC stages to reach the high voltage for DC transmission. The DC-DC converter in the WT boosts the DC voltage from 2.96 kV to 100 kV first, and then the MCAT steps up the DC voltage from 100 kV to 300 kV. The internal DC-DC converters employ dual active bridge (DAB) topology. The parameters of equivalent PMSG and the DAB converters are listed in Table 2.  
For the sake of simplicity, MCAT employs only three MMCs. When \(\alpha_{\text{H,1}}\) is specified as 20%, substitute \(f\) = 100 Hz, \(k_{\text{dc}}\) = 3, \(P\) = 100 MW, \(U_{\text{dcH,1}}\) = 100 kV and \(N\) = 1 into Equations (9) and (10), then \(C_{\text{H,1}}\) is calculated as 41.3 \(\mu\)F and \(L_{\text{H,1}}\) is 84.3 mH. \(C_{\text{L,1}}\) and \(L_{\text{L,1}}\) are excluded from the MCAT because of the symmetrical monopole construction. \(C_{\text{H,2}}\) and \(L_{\text{H,2}}\) are equal to 41.3 \(\mu\)F and 84.3 mH. The voltage rating across the MCAT is are \(\pm\)150 kV/\(\pm\)50 kV. Parameters of the MCAT, onshore converter, and HVDC cable are as shown in Tables 3 and 4. The control strategy of the MCAT is listed in Table 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Modules** & **Control Mode** \\ \hline LVM & d-axis: DC voltage control \\  & q-axis: Reactive power control \\ MMC\({}_{\text{H,N+1}}\) to MMC\({}_{\text{H,2N}}\) (HVM) & d-axis: DC voltage control \\  & q-axis: Reactive power control \\ MMC\({}_{\text{H,1}}\) to MMC\({}_{\text{H,N}}\) (HVM) & Passive control \\ \hline \hline \end{tabular}
\end{table}
Table 1: MCAT control strategy in normal operation.  
Figure 5: Diagram of the simulation system.
# Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland
## 3 Methods
### Accuracy Assessment Methods  
To validate the prediction accuracy of different machine learning models, this study selected mean absolute error (MAE), root mean square error (RMSE), relative percentage difference (RPD), regression determination coefficient (R\({}^{2}\)), and mean bias (bias) as assessment indicators [58; 59; 60].  
Among them, MAE reflects the absolute error between the predicted grassland GPP and the reference GPP, while having the advantage of robustness. The value range of MAE is [0, +\(\infty\)], and the smaller the value, the better the prediction accuracy of the model.  
RMSE measures the degree of deviation between the model's prediction results and the reference data. The value range of RMSE is [0, +\(\infty\)], and the smaller the value, the smaller the prediction error of the model.  
Figure 3: Location of the training sample in the study area.  
RPD measures the predictive power of the model. The range of RPD is [0, +\(\infty\)], and the higher the value, the more reliable the model is.  
R\({}^{2}\) measures how well the model output fits the reference data. The range of R\({}^{2}\) is [0, 1], and the larger the value, the better the fit.  
Bias measures the degree to which the model's output is over- or under-estimated compared to the reference data. The value of bias ranges from [-\(\infty\), +\(\infty\)], where positive values indicate overestimation and negative values indicate underestimation. The closer the value is to 0, the smaller the bias is.  
The formulas are as follows.  
\[MAE=\frac{1}{m}\sum_{i=1}^{m}\left|\hat{y_{i}}-y_{i}\right| \tag{5}\]  
\[RMSE=\sqrt{\frac{1}{m}\sum_{i=1}^{m}\left(\hat{y_{i}}-y_{i}\right)^{2}} \tag{6}\]  
\[RPD=\frac{SD}{RMSE} \tag{7}\]  
\[R^{2}=1-\frac{\sum\left(y_{i}-\hat{y_{i}}\right)^{2}}{\sum(y_{i}-y)^{2}} \tag{8}\]  
\[bias=\frac{1}{m}\sum_{i=1}^{m}\hat{y_{i}}-y_{i} \tag{9}\]  
Among them, \(m\) is the sample size; \(\hat{y_{i}}\) and \(y_{i}\) are the predicted GPP and reference GPP, respectively; \(\bar{y}\) and \(SD\) are the mean and standard deviation of the reference GPP, respectively.
## Materials and Methods
### Yeast Two-Hybrid (Y2H) Experiments  
Yeast two-hybrid assays were used to test the paired physical interactions among McJAZ proteins, or the interactions between McJAZ proteins and AtCO11, or its homolog McCO1 Ia. The full-length coding sequences of _McJAZ_ genes were cloned into the prey vector pGADT7 (Activation domain, AD) or bait vector pGBKT7 (Binding domain, BD) (Takara, Japan), respectively, and the full-length coding sequences of _McCO1 Ia_ and _AtCO11_ were amplified using gene-specific primers (Supplemental Table S2), and inserted into the bait vector pGBKT7 (BD). The indicated combinations were co-transformed into the yeast strain AH109 using the lithium acetate method according to the manufacturer's instructions (Cat# 630440, Takara Biomedical Technology (Beijing) Co., Ltd., Beijing, China), and bait-only or prey-only was tested with empty AD or BD as negative controls.  
To investigate protein-protein interactions, the transformed AH109 yeast cells were first cultivated in liquid control medium SD/-Leu/-Trp (DDO) over 24 h, and then 5 uL of each yeast suspension was taken to inoculate the solid control medium SD/-Leu/-Trp (DDO) and selection medium SD/-Leu/-Trp (DDO) with or without 35 uM coronatine (COR), a structural and functional mimic of JA-Ile obtained from several strains of _Pseudomonas syringae_ according to previous studies [19; 72; 95]. The yeast cells were grown at 30 \({}^{\circ}\)C for 4 days before imaging.
## 5 Description of Experiment  
The metrological inspection was carried out in accordance with the program specified in Section 4.3 at the Joniskis station (in Joniskis city). Characteristics of the upper track construction were as follows: railway track R65, railway sleepers Swetrak, clamps (savarzos) (fasteners) Vossloh, railway sleeper epiphany 1840 units/km, and rubble ballast. The section of the test railway track was straight. The assessment of the condition of the railway, performed by the firm \"Plasser and Theurer\" with the EM 140 wagon and the TrackScan ME 4,011,244 track measuring cars, showed that the condition of the track was good. For the experiment, a test train formed by a railcar (traction) and a freight wagon no. 10,630,028 was formed. The wagon was weighed (with a stationary weighing scale) in a weightridge and had a mass of 93.2 tons. Wheelsets with uneven rolling surface (described in Section 6) were mounted to the wagon. The same wheelsets were used for the tests that were carried out. The test train scheme is shown in Figure 3.  
The main direction of the train during the test was toward the Joniskis station. In the direction of Latvia, the test train ran in reverse, and measurements were not made. The left and right wheels of the wheelset were set in the direction of the Joniskis station.  
All wheelsets of the wagon were inspected and described, and their parameters and damages were measured. Twelve test runs were performed (Figure 4). Driving speeds ranged from 55 km/h to 80 km/h. During the test runs, the new parameter \(P_{derivative}\) and the speed were recorded. After running the new \(P_{derivative}\) parameter, it was compared with the older parameter \(K_{dm}\) which was recorded in the tests.  
Figure 3: Scheme of test train; 1–4—inspected wheelsets.  
Figure 2: ATLAS LG system measuring zone.  
Vertical force was calculated in contact zone between wheel and rails, with main attention given to the wheel. This work aimed to investigate the effect of wheel geometric unevenness (flat spot) on the track rail and to determine new parameters of such defecting systems. Track rail unevenness was taken as a constant in either the horizontal or the vertical plane, and the rails were considered to be of good quality (not worn) according to the current JSC \"Lithuanian Railways\" operating instructions. It should be mentioned that track rail unevenness can be caused by many factors, such as irregularities of track rails in the horizontal plane, irregularities of track rails in the vertical plane, rail corrugation, and rail side wear.
# Secure and Lightweight Cloud-Assisted Video Reporting Protocol over 5G-Enabled Vehicular Networks
## 5 Details of Proposed Protocol
### On-Duty Token Generation  
In the same way, \(DMV_{j}\) generates on-duty token for the designated vehicles. These tokens authorize the DVs to recover the reported videos. For instance a police vehicle can get an _on-duty token_ which extends its duty from police's duties to basic ambulance's duties. If an area \(A\) witnesses numerous accidents, several ambulances would go to the accident's scenes in area \(A\) which might cause a temporal non-availability of ambulances. In that case, some of the police agents which have basic medical skills can attend to accident's victims as they wait for the ambulances to arrive.  
1. \(DV_{i}\) composes an on-duty token request \(DTRq=\{DV_{j},K_{1},ts\}\) where \(ts\) is the time stamp and \(K_{1}\) is a secret key to be used later.
2. \(DV_{i}\) sends \(C_{2}=Enc_{PK_{DMV_{k}}}\{DTReq,pk_{v_{i}},\delta_{DV}\}\) to the \(DMV_{k}\), where \(\delta_{DV}\) is the signature for the \(DTRq\) set as \(\delta_{DV}=\) CertS.Sign(\(DTRq\), \(sk_{DV}\)).
3. Upon receiving the message \(C_{2}\), \(DMV_{k}\) first decrypts \(C_{2}\) using its private key, then verifies the signature as CertS.Verify(\(DTRq\), \(DV_{j}\), \(pk_{DV_{j}},\delta_{DV}\)).
4. \(DMV_{j}\) set \(\tilde{D}\leftarrow\) ABE.KGN(\(ABE.amk\), \(AS_{DV_{j}}\)) where \(AS_{DV_{j}}\) is the access structure corresponding to the designated vehicle's type (police or ambulance).
5. \(DMV_{j}\) composes a token message \(Tok_{DV}=\{\tilde{D},exp,RK\}\) where \(exp\) is the expiring date, \(RK\) a shared secret which is given to DVs that are supposed to work within a defined geographic zone. Note that in real word, one police vehicle can be assign to attend to all the requests from a defined geographic area (three or five consecutive streets). \(DMV_{j}\) send it to \(DV_{j}\) encrypted under the shared symmetric key \(K_{1}\) as \(C_{3}=Enc_{K_{1}}(Tok_{DV})\).
# Application of Soil Moisture Active Passive (SMAP) Satellite Data in Seismic Response Assessment
## 3 Results
### Integration of \(\Delta SM_{SMAP}\) with Seismic Records
#### 3.3.2 Samos, Greece  
The Samos, Greece earthquake with a moment magnitude M 7.0, occurred offshore of the northern coast of Samos Island. With rainfall close to zero in this region throughout the SMAP event window, it can be deduced that the earthquake caused an increase in the soil moisture over all of the grids depicted by a positive \(\Delta SM_{SMAP}\) in Figure 9. One liquified area and some local tsunami inundations were reported in these regions by the GEER team [84]. The GEER report also found no liquefaction features in areas with high  
Figure 8: \(\Delta SM_{SMAP}\) (9 km \(\times\) 9 km) in grids with (**a**) available soil moisture data, (**b**) \(MDI>\) 0.08, (**c**) \(MDI>\) 0.04, (**d**) \(MDI>\) 0.02, and (**e**) \(MDI>\) 0 cm\({}^{3}\)/cm\({}^{3}\) integrated with seismic records for Petrinja, Croatia 2020 earthquake.  
liquefaction probability where SMAP observations showed a decrease in soil moisture (Figure 9a).  
Figure 10 shows the DPM of this earthquake, overlayed on the \(\Delta SM_{SMAP}\) and seismic records, that identifies areas in which considerable surface changes and deformations occurred. Yellow to red pixels represent increasingly significant surface change and potential damage. The extent of potentially severely damaged areas, classified as major surface change by the DPM, indicate that there might have been other locations in grids with \(\Delta SM_{SMAP}>0\) that experienced ground failures but were not inspected by the GEER team.  
Figure 9: \(\Delta SM_{SMAP}\) (9 km \(\times\) 9 km) in grids with (**a**) available soil moisture data, (**b**) \(MDI>0.08\), and (**c**) \(MDI>0.04\) cm\({}^{3}\)/cm\({}^{3}\) integrated with seismic records for Samos, Greece 2020 earthquake.
# Comparing Application-Level Hardening Techniques for Neural Networks on GPUs
###### Abstract  
Neural networks (NNs) are essential in advancing modern safety-critical systems. Lightweight NN architectures are deployed on resource-constrained devices using hardware accelerators like Graphics Processing Units (GPUs) for fast responses. However, the latest semiconductor technologies may be affected by physical faults that can jeopardize the NN computations, making fault mitigation crucial for safety-critical domains. The recent studies propose software-based Hardening Techniques (HTs) to address these faults. However, the proposed fault countermeasures are evaluated through different hardware-agnostic error models neglecting the effort required for their implementation and different test benches. Comparing application-level HTs across different studies is challenging, leaving it unclear (i) their effectiveness against hardware-aware error models on any NN and (ii) which HTs provide the best trade-off between reliability enhancement and implementation cost. In this study, application-level HTs are evaluated homogeneously and independently by performing a study on the feasibility of implementation and a reliability assessment under two hardware-aware error models: (i) weight single bit-flips and (ii) neuron bit error rate. Our results indicate that not all HTs suit every NN architecture, and their effectiveness varies depending on the evaluated error model. Techniques based on the range restriction of activation function consistently outperform others, achieving up to 58.23% greater mitigation effectiveness while keeping the introduced overhead at inference time low while requiring a contained effort in their implementation.  
reliability evaluation; software-based application-level hardening; graphic processing units; neural networks; fault tolerance