esa-sceva/satcom-chunk-collection huggingface dataset:
# Schedulability Analysis in Fixed-Priority Real-Time Multicore Systems with Contention
## 4 Interference-Aware Schedulability Analysis for Fixed Priorities
### Interference-Aware Schedulability Analysis for Deadline Monotonic Scheduling
#### 4.2.2 Worst-Case Response Time with Interference Considerations
\(\tau^{\prime\prime}=[\tau_{0}^{\prime\prime},\tau_{1}^{\prime\prime}\tau_{2}^ {\prime\prime}]\), with \(\tau_{0}^{\prime\prime}=(1,2,3,1)\), \(\tau_{1}^{\prime\prime}=(2,5,5,0)\), and \(\tau_{2}^{\prime\prime}=(1,3,5,1)\), allocated to a dual-core system, where \(\tau_{0}^{\prime\prime}\) and \(\tau_{1}^{\prime\prime}\) are allocated to \(M_{0}^{\prime\prime}\), and \(\tau_{2}^{\prime\prime}\), to \(M_{1}^{\prime\prime}\). Considering that tasks are scheduled according to the DM algorithm, the scheduling plan is shown in Figure 5.  
From the scheduling plan, we can deduce the actual WCRT of all activations of all tasks: \(WCRT_{0}=(2,1,1,1,1)\), \(WCRT_{1}=(5,3,2)\), and \(WCRT_{2}=(2,1,1)\). (Note that the array of WCRT details the WCRT of each activation. For example, \(WCRT_{1}=(5,4,2)\) means that the WCRT of the first activation of \(\tau_{1}\) is 5, that of the second activation is 4, and that of the third activation is 2.) In order to calculate the upper bound of WCRT, first, we calculate \(\overrightarrow{v_{j\to i}^{*}}\) and binary matrix \(A_{ij}\). By applying Theorem 1, \(\overrightarrow{v_{2\to 0}^{*}}=(1,0,1,1,1)\) and \(\overrightarrow{v_{0\to 2}^{*}}=(1,1,2)\). \(\tau_{1}\) is not a broadcasting task, so \(\overrightarrow{v_{j\to 1}^{*}}=\overrightarrow{v_{1\to i}^{*}}=0\). Array \(A_{10}\) was calculated at the end of Section 4.2.1 (we avoid the calculation of other arrays because they do not allow for actions in the formula of WCRT) as  
\[A_{10}=\left( \begin{array}{cccc}1&1&0&0&0\\ 0&0&1&1&0\\ 0&0&0&1&1\end{array} \right)\]  
With these above concepts, we can now apply Equation (7) to calculate \(WCRT_{i}^{k(ub)}\)\(\forall k,i\). \[WCRT_{0}^{k(ub)}=C_{0}+v_{2\to 0}^{*}[k]\cdot I_{2}+\emptyset\]  
\[WCRT_{1}^{k(ub)}=C_{1}+\emptyset+\sum_{a=\left\lfloor\frac{5}{3}\right\rfloor }^{\left\lceil\frac{5}{3}\right\rfloor-1}(C_{0}+v_{2\to 0}^{*}[a]\cdot I_{2}) \cdot A_{10_{ub}}\]\[WCRT_{2}^{\pm(uu)}=C_{2}+v_{0-2}^{*}[k]\cdot I_{0}+\mathcal{O}\]  
Therefore, \(WCRT_{0}^{ub}=(2,1,2,2,2)\), \(WCRT_{1}^{ub}=(5,6,6)\), and \(WCRT_{2}^{ub}=(2,2,3)\). It can be observed that \(WCRT_{i}^{ub}\geq WCRT_{i}\) for all tasks and activations. However, \(\tau_{1}\) does not pass schedulability test (2), as \(WCRT_{1}^{1(uu)}=WCRT_{1}^{2(u)}=6\geq D_{1}=5\), but the system is schedulable. So, the condition of schedulability is sufficient but not necessary.
## 2 Materials and Methods
### Data Sources and Processing
#### 2.1.2 Data Processing  
The vessel trajectory information for the selected 96 vessels was meticulously processed on a per-article basis using the corresponding MMSI numbers. Four vessels with fewer than 100 AIS records for the entire year were excluded. Trajectory points exhibiting outliers in speed (speed \(>\) 15 kn) were eliminated, while data points with speeds ranging from 0 to 15 kn were retained. The dataset was then sorted chronologically, and duplicate data for trajectory points were removed. Subsequently, solar radiance was calculated using the Solar R package, with a value of 0 for nighttime. Finally, the time information embedded in each trajectory point was computed as half of the sum of the time difference between each point and its preceding and succeeding records.  
Figure 1 illustrates the distribution of track points in the designated region spanning 50\({}^{\circ}\)-70\({}^{\circ}\) E and 10\({}^{\circ}\)-24\({}^{\circ}\) N, with land shown in gray and the exclusive economic zone (EEZ) outlined in black. Notably, the track points are predominantly situated to the east of the EEZ of Oman, the EEZ of Pakistan, and the southern and western sectors of the EEZ of India. The operational area is in close proximity to the EEZ of Oman. For this study, the region defined by 58\({}^{\circ}\)-70\({}^{\circ}\) E and 10\({}^{\circ}\)-22\({}^{\circ}\) N was selected as the primary focus. A total of 14,426,013 AIS track points within this study area were considered for analysis.  
Figure 1: (**a**) AIS vessel track point distribution in 2021; (**b**) AIS vessel track point distribution in 2022.
## 3 Results and Discussion
### Effect of Different Calculation Approaches for Gas Flux Estimation  
#### 3.2.1 Comparison of Calculation Approaches  
Four different calculation approaches for the estimation of the soil-atmosphere gas flux were compared. For the comparison CH\({}_{4}\) and CO\({}_{2}\) concentration data were used. Soil respiration estimates of all four approaches yielded similar values, while the effect of the calculation approach was more pronounced for the CH\({}_{4}\) consumption estimation (Figure 6, middle, right). The ranking between the different approaches was the same for soil respiration and CH\({}_{4}\) consumption: the LR approach yielded the lowest soil respiration and CH\({}_{4}\) consumption estimates followed by EF approach. Estimates of the LL and LS approach were very similar. While other comparisons [76] found large overestimation by the EF approach, we observed substantially lower flux estimates for the EF, but only for CH\({}_{4}\) and not CO\({}_{2}\).  
We explain the consistency of the different approaches by the linear shape of the CO\({}_{2}\) profile in the top soil (Figure 5) that can be appropriately fit and reflected by all four approaches. Other studies have also taken into account the concentrations at deeper depths [60; 76; 84]. These concentration measurements at greater depth have a mathematical effect on the calculation of the concentration gradient at the soil-atmosphere interface although they have practically no influence at all on the actual concentration gradient at the soil-atmosphere interface. It could be shown that the inappropriate use of the EF approach can lead to substantial overestimation of the efflux estimates [76]. Thus, the consistency of the different calculation approaches in our case can be interpreted as a robustness or independence of the CO\({}_{2}\) concentration gradient from the calculation approach, which is due to the shallow soil gas profile that focuses on the top 10 cm of the mineral soil and humus layer.  
However, the four different approaches had a substantial effect on the estimation of the CH\({}_{4}\) consumption (Figure 7, middle). Visual inspection of (Figure 7 left) shows that the more flexible LL and LS approaches can better fit the concentration data in the humus and yield a stronger CH\({}_{4}\) gradient, and thus higher CH\({}_{4}\) flux rates. The fits of EF and LR include all data that means also concentrations between 10 and 0 cm depths where obviously another CH\({}_{4}\) gradient can be observed. The LL and LS approach yielded similar results (Figure 7, middle) and both approaches allow calculating soil gas fluxes at different depths. The LL approach however can be challenged more easily by soil gas profiles with weak gradients and outliers. We observed that under certain circumstances the LL approach yielded the same gradient in the humus layer as in the mineral soil with the regression functions shifted in parallel although a curvature was obvious in the entire soil gas profile. In such situations, the LS represented the overall curvature in a more realistic way, since it guarantees a continuous function.
## 4 Experimental Setup and Results
### Baselines  
We selected a range of competitive models as benchmarks to assess the performance of state-of-the-art (SOTA) models on our benchmark dataset. Each experiment was repeated five times, and the average score was reported for analysis. For the propaganda classification task, we employed fine-tuning BERT [21] and RoBERTa [67] models using cross-entropy loss function for training. To maintain experiment fairness and robustness, identical parameters, such as maximum length, initial learning rate, and optimizer, were applied for the same (ProText) dataset and encoder. A threshold of 0.3 was set, leading to the rejection of outputs below this value for MT-DNN, XLNET, and ALBERT networks [54; 55; 56]. In text classification, question answering, and natural language inference experiments, RoBERTa demonstrated exceptional effectiveness [35].  
* **RoBERTa**: Tokens associated with each sentence were randomly masked at a rate of 15%, enabling the models to learn contextual information even when words were not explicitly mentioned [67].
* **BERT**: The BERT model was pre-trained on unlabeled data for various pre-training tasks. Fine-tuning involved initializing parameters with pre-trained values, which were then fine-tuned using labeled downstream data [21].
* **MT-DNN**: Several improved BERT models, including MT-DNN, have emerged as SOTA methods [54].
* **XLNET**: Pre-training in XLNET incorporates concepts from Transformer-XL, an advanced autoregressive model [56].
* **ALBERT**: ALBERT enhances BERT training speed by reducing parameter and memory consumption [55].
* **H-GIN** (hierarchical graph-based integration network) ([PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [52]): This constructs an inter-intra channel graph using residual-driven enhancement and processing and attention-driven feature fusing, enabling effective classification at two distinct levels.
###### Abstract  
Transcranial direct current stimulation (tDCS) has emerged as a potential intervention to improve physical performance. This study investigates the effects of tDCS applied to the primary motor cortex (M1) and dorsolateral prefrontal cortex (DLPFC) on performance in a maximal effort task, specifically the No-Jump Burpee exercise. Twenty healthy male subjects (26.0 \(\pm\) 4.91 years) completed three experimental conditions (a-DLPFC, a-M1, and SHAM) in a double-blind crossover design. Prior to the performance of burpees to exhaustion, tDCS (2 mA, 20 min) was administered. The total number of repetitions, _vastus lateralis_ muscle oxygen saturation, heart rate, and subjective perception of exertion (RPE) during exercise were measured. Repeated ANOVAs showed a significant effect of condition on the number of repetitions (\(p\) < 0.001). Subjects performed more repetitions under the M1 condition (68 \(\pm\) 19.5) compared to DLPFC (63 \(\pm\) 17.9) and SHAM (58 \(\pm\) 18.0), with significant differences between all conditions. This study demonstrates that tDCS can improve performance in a physical endurance task such as the No-Jump Burpee. The findings suggest that tDCS may be a viable ergogenic tool for improving athletic performance. Future research should explore the underlying mechanisms and the practical application of these results in long-term physical training programs (NCT06472882).  
primary motor cortex; dorsolateral prefrontal cortex; physical endurance; tDCS
###### Abstract  
We investigate both theoretically and experimentally the properties of electromagnetic waves propagation and localization in periodic and quasi-periodic stub structures of Fibonacci type. Each block constituting the Fibonacci sequence (FS) is composed of an horizontal segment and a vertical stub. The origin of the primary and secondary gaps shown in such systems is discussed. The behaviors and scattering properties of the electromagnetic modes are studied in two geometries, when the FS is inserted horizontally between two semi-infinite waveguides or grafted vertically along a guide. Typical properties of the Fibonacci systems such as the fragmentation of the frequency spectrum, the self-similarity following a scaling law are analyzed and discussed. It is found that certain modes inside these two geometries decrease according to a power law rather than an exponential law and the localization of these modes displays the property of self-similarity around the central gap frequency of the periodic structure where the quasi-periodicity is most effective. Also, the eigenmodes of the FS of different generation order are studied depending on the boundary conditions imposed on its extremities. It is shown that both geometries provide complementary information on the localization of the different modes inside the FS. In particular, in addition to bulk modes, some localized modes induced by both extremities of the system exhibit different behaviors depending on which surface they are localized. The theory is carried out using the [PERSON]'s function approach through an analysis of the dispersion relation, transmission coefficient and electric field distribution through such finite structures. The theoretical findings are in good agreement with the experimental results performed by measuring in the radio-frequency range the transmission along a waveguide in which the FS is inserted horizontally or grafted vertically.  
Keywords:photonic crystal; Fibonacci structure; stub; electromagnetic modes; surface modes; self-similarity; scaling law +
Footnote †: journal: Applied Physics Letters
## 4 Simulation Results and Analyses
### Results and Analyses  
The simulation was performed for the daily time of 20 June 2018 and for the time horizon from 0:00 to 24:00. The microgrid can operate either on-grid or off-grid modes and can automatically switch between these two modes. The weather data recorded on the 20 June 2018 are shown in Figure 6, where the high variations of the two data sets occur at noon, the slow variations and low values show at night.  
The curve of the common DC bus voltage and the SOC curves of the SC and BS are provided in Figure 7a. One notes that the DC bus voltage oscillates around 400 V. This very slight fluctuation becomes obvious when events occur, i.e., the limitations condition of the BS and public grid, DG start-up and end-up, SC charging and discharging, and PV shedding or load shedding; however, the stability of the common DC bus voltage is kept. The power curves are shown in Figure 7b. At 6:27, 7:10, 7:56, 9:58, 11:18, 12:27, 19:36, 20:18, 21:00, and 21:43, the DG is turned on as in case 10 and 11. At 5:00, 9:33, 13:41, 14:57, 16:12, and 17:27, the SC is recharged as in case 4 or case 6. At 10:42 and 19:34, the load shedding happens. At 12:45, 14:51, and 15:44, the PV supplies more power than the power that microgrid can consume, so the PV starts being shed, as in case 1.  
To better check the simulation results, the five time periods, 0:00-6:00, 6:00-8:00, 8:00-17:00, 17:00-22:00, and 22:00-24:00, are separately descripted in Sections 4.2.1-4.2.5.
## Appendix A  
If we consider the geometric mutual inductances \(L_{M}\) between the left(right) loop and right(left) branch with length \(l\) in Figure 1, the induced magnetic flux, \(f_{\text{ind,i}}=\Phi_{\text{ind,i}}/\Phi_{0}\), for left qubit is given by \(f_{\text{ind,1}}=(1/\Phi_{0})(L_{g}I_{1}+\bar{L}_{g}\bar{I}+L_{M}I_{2})\), where \(L_{M}I_{2}\) is the induced flux due to the current \(I_{2}\) in the right qubit loop. Furthermore, for the right qubit loop we have \(f_{\text{ind,2}}=(1/\Phi_{0})(L_{g}I_{2}-\bar{L}_{g}\bar{I}+L_{M}I_{1})\). These induced fluxes can be represented as  
\[f_{\text{ind,1}} = -\frac{L_{g}}{L_{K}}\frac{l}{2\pi}k_{2}+\frac{\bar{L}_{g}}{\bar{L }_{K}}\frac{\bar{I}}{2\pi}\bar{k}-\frac{L_{M}}{L_{K}}\frac{l}{2\pi}k_{2}, \tag{1}\] \[f_{\text{ind,2}} = -\frac{L_{g}}{L_{K}}\frac{l}{2\pi}k_{2}+\frac{L_{g}}{\bar{L}_{K}} \frac{\bar{I}}{2\pi}\bar{k}-\frac{L_{M}}{L_{K}}\frac{l}{2\pi}k_{1}. \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**J/h (GHz)** & **0.5** & **1.0** & **2.0** & **3.0** \\ \hline \(F_{0}\) & 0.9921 & 0.9977 & 0.9978 & 0.9980 \\ \(F_{1}\) & 0.6597 & 0.9613 & 0.9974 & 0.9979 \\ \(F_{2}\) & 0.9390 & 0.9975 & 0.9978 & 0.9980 \\ \hline \end{tabular}
\end{table}
Table 1: Fidelities for CNOT gate operation where \(F_{0}\) is calculated with the given value of \(J\) in the Table, while \(F_{1}\) and \(F_{2}\) are calculated with \(J_{1}/h=J/h-0.2\) GHz and \(J_{2}/h=J/h-0.06\) GHz, respectively.  
We then obtain the boundary conditions,  
\[\left[\left(1+\frac{L_{g}}{L_{K}}\right)l+\left(1+\frac{L_{g}}{L_{K}} \right)l\right]k_{1}-\left[\left(1+\frac{L_{g}}{L_{K}}\right)l-\frac{L_{M}}{L_{K }}l\right]k_{2} = 2\pi f_{1\varphi}^{0} \tag{21}\] \[-\left[\left(1+\frac{L_{g}}{L_{K}}\right)l-\frac{L_{M}}{L_{K}}l \right]k_{1}+\left[\left(1+\frac{L_{g}}{L_{K}}\right)l+\left(1+\frac{L_{g}}{L_{ K}}\right)l\right]k_{2} = 2\pi f_{2\varphi}^{0}, \tag{22}\]  
resulting in  
\[k_{1,2} = \frac{\pi L_{K}}{l}\Bigg{(}\frac{1}{L_{K}+L_{g}+L_{M}}\pm\frac{1}{ L_{K}+L_{g}-L_{M}+2(L_{K}+\overline{L}_{g})}\Bigg{)}f_{1\varphi}^{0} \tag{23}\] \[+ \frac{\pi L_{K}}{l}\Bigg{(}\frac{1}{L_{K}+L_{g}+L_{M}}\mp\frac{1} {L_{K}+L_{g}-L_{M}+2(L_{K}+\overline{L}_{g})}\Bigg{)}f_{2\varphi}^{0}\] \[\bar{k} = k_{1}-k_{2}=\frac{2\pi L_{K}}{l}\frac{1}{L_{K}+L_{g}-L_{M}+2( \overline{L}_{K}+\overline{L}_{g})}(f_{1\varphi}^{0}-f_{2\varphi}^{0}). \tag{24}\]
## 2 Composition of the \"Target Situational Awareness\" Test System and Experimental Methods
### Carrier Aircraft and Pod Being Tested
#### 2.2.2 Design of the Communication Bus Interface
As shown in Table 6.  
2.2.3. Design of the Power Supply and Electrical Control Interface  
The required power supply to the carrier in the test system included a 45 V DC source \"DC A\" and a 150 V AC source \"AC B\". \"DC A\" was used for maintaining the major power supply for the continuous operation of the pod equipment (with the exception of the radar seeker), and the power supply can be switched on and off according to the control instruction during the pod-hanging flight. \"AC B\" supplied the refrigeration equipment in the pod, as well as the high-voltage power supply to enable multiple turning-on operations of the pod radar seeker. The operation timing of both \"DC A\" and \"AC B\" were controlled by the test sequence, as shown in Figure 6.  
The electrical interface between the carrier and pod was realized via a cable connection, as shown in Table 7, which mainly included (1) the \"in-place status\" and \"in-place loop\", forming a closed-loop circuit with the carrier to complete the inspections on whether the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Item** & **Unit** & **Bit** & **Accuracy** & **Note** \\ \hline
1 & Simulated launch status & - & 1 & - & 1: To launch; 0: Not to launch \\
2 & Preparation status & - & 1 & - & 1: To prepare; 0: Not to prepare \\
3 & Pod status & - & 2 & - & 11: Normal; 10: Malfunction; 00: Invalid \\
4 & Preparation results & - & 2 & - & 11: Ready; 10: Not ready; 01: In preparation; 00: Invalid \\
5 & Self-check results & - & 1 & - & 1: Self-check finished; 0: Self-check unfinished \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Responses of the pod. Figure 6: Power supply timing sequence. pod is in place and whether the electrical connection is checked and confirmed; (2) the \"DC A\" and \"DC A loop\" were in charge of the DC power supply to the pod; (3) \"multiplex bus A\" and \"multiplex bus B\" were in charge of sending and receiving data between the carrier and pod; (4) \"AC B1\", \"AC B2\", and \"AC B3\" provided the AC power supply to the pod; (5) \"structural grounding\" was realized via the grounding wire; (6) the \"satellite signal\" transmitted the satellite signal from the carrier to the pod; (7) the telemetry and forwarding signal transmitted the telemetry signal from the pod to the carrier, which was then recorded and stored by the carrier.
## 3 Results  
### Validation of Satellite-Based Phenology  
The method described in Section 2.4 was used to determine the SOS and EOS from both the tower-based and satellite-based GPP time-series at the site scale (Figure 2). The linear regression analysis showed that satellite-based SOS and EOS were positively correlated with tower-based SOS (Pearson's \(r\) = 0.69 \(\pm\) 0.20) and EOS (Pearson's \(r\) = 0.80 \(\pm\) 0.12) at all 13 study sites over the measurement period, and positive relationships for SOS and EOS were significant (\(P<\) 0.05) at 8 and 9 of the 13 sites, respectively. The corresponding RMSEs for SOS and EOS were nearly 17 and 11 days, respectively. Although the bias is expected to introduce inconsistence in the absolute dates of phenological transitions, this should not affect interannual fluctuation changes and long-term trends or the interannual relationships of fluxes with phenology for each site or satellite pixel [28].
## 1 Introduction
The authors had also carried out a study of the physical properties and mechanical performance of slate materials with the objective of understanding the influence of anisotropy on flexural and dowel-hole rupture loads [24]. In manufactured stone materials such as concrete and mortars, the action of low temperature is well known in the literature and is similar to the stones [9; 25]. Concrete and mortars are heterogeneous systems consisting of an open and closed pore framework, and due to this the ice crystallization pressure during freeze-thaw cycles is one of the main degradation causes [26; 27]. Ice crystallization inside the porous network is mainly regulated by open porosity, permeability and pore tortuosity [12]. In general, in porous materials, the freeze-thaw cycles are responsible for an increase in porosity mostly due to pore enlargement by the volumetric expansion of the ice. The movement of water from smaller pores or unfrozen areas to growing ice crystals in larger pores is also identified as the main cause of the formation of stresses by capillary and hydraulic pressures [28]. However, it is accepted as a basic requirement for the development of damage due to frost action in porous media, that the stresses induced by ice crystallization exceed the tensile strength of the material [28]. These stresses are aggravated when salts are dissolved in the solution and then can crystallize inside the porous framework [29; 30]. As a result, porosity increasing together with spalling and other physical decay processes reduce the resistant section of a concrete artifact such as a column or a pillar. The same mechanisms act on natural stones and mortars [31; 32]. These effects successively lower the compression strength in the case of a natural stone column and the flexure strength in the case of a cladding slab [33]. As mentioned above, the freeze-thaw effects on natural stone have been studied by several authors in both laboratory simulations, strictly following the EN 12371 method, and on-site analyses. The novelty of our study is the fact that a cold climate condition for a sub-polar Dfc climate was simulated in a climatic chamber reproducing the four seasons based upon available real climatic data on temperature and rain. Moreover, the aging evaluation test was performed through other evaluation methods besides the ones recommended by the European standard: roughness, point load index, and P-wave ultrasound speed. In addition, this study includes the analysis of aesthetic parameters, in terms of color changes of the slate exposed to the Dfc climate solar radiation in SOLARBOX climatic chamber. To the authors' knowledge, this is the first time the Dfc climate has been realistically reproduced in the laboratory with the aim of foreseeing the behavior of natural stones in buildings in extreme conditions. With this study, the authors aimed to fill a gap in the climatic chamber simulation aging test based upon real climatic parameters. This study could be replicated in several other climates and also increases the knowledge of potential climate change effects on natural stone performance.