esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Load Modeling for Market Participants  
Load modeling is a crucial component for determining the demand curves of the P2P participants. This research uses the queuing load model as suggested in [29; 30]  
Figure 1: Breakdown of different stages for the suggested market structure. The first step is to collect the input data for the CDA market. The second stage uses the CDA market to generate the winners for CDA auction. Third stage involves the trading strategy based on the parameters of the second stage to determine the optimal trading power \(x^{*}\) and P2P price \(p^{*}\) for each matched order.  
to determine the load profile of the P2P participants. The queuing load model has the following advantages:  
* The start time of each device is modeled as the output of a random process to capture the individual behavior of each participant. Due to the fact that the residential customers have distinct consumption patterns, queuing theory captures this uniqueness by determining the start time of each device randomly while still following a known stochastic process.
* To capture the daily and seasonal variations, the arrival rate of the devices are modeled using a Poisson distribution to capture the time-varying nature of the load.  
While using the queuing theory, it is assumed that the population size and queue lengths are infinite to capture all the electric loads arriving into the queue. The model used in this research is an \(M_{t}/G/\infty\) queue implement load model [31], where \(M_{t}\) represents the Poisson process, \(G\) represents the probability distribution of the appliances, and \(\infty\) indicates the infinite queue length. The Poisson process, \(\lambda(t)\), can then be defined mathematically as follows:  
\[\lambda(t)=\frac{L(t+E[D_{d}])}{E[D_{d}]E[P_{d}]} \tag{1}\]  
where \(E[D_{d}]\) and \(E[P_{d}]\) are the expected values for the duration and power of the electric loads for the market participant \(d\), respectively. The individual load \(L(t)\) for each household can be modeled based on the aggregated distribution data \(A_{l}(t)\) as follows:  
\[L(t)=v_{min}+\frac{A_{l}(t)-\min(A_{l})}{\max(A_{l})-\min(A_{l})}\cdot(v_{max }-v_{min}) \tag{2}\]  
where \(v_{max}\) and \(v_{min}\) are the scaling coefficients for the individual household. The authors have provided a brief introduction of the suggested model for the simplicity of the readers. Further mathematical details and the analysis of the suggested queuing model are presented in [29; 30].
## 2 The Mathematical Modeling of OPF Objective Function
### Constraints
#### 2.2.2 Inequality Constraints
The quadratic form ensures that the penalty increases proportionally with the magnitude of constraint violations, which drives the system towards feasible solutions where the constraints are satisfied, or violations are minimized. The terms of the equation are described specifically as follows:  
* The term \" \(\lambda_{V}\sum_{i=1}^{NL}\left(V_{L_{i}}-V_{Ilim}^{L_{i}}\right)^{2}\)\" penalizes deviations in the voltage \(V_{L_{i}}\) at load buses from their limits \(V_{Ilim}^{L_{i}}\). * The term \"\(\lambda_{P}\left(P_{G_{sl}}-P_{Ilim}^{G_{sl}}\right)^{2}\)\" imposes a penalty on the active power \(P_{G_{sl}}\) at the slack generator, ensuring it stays within its limits. * The term \"\(\lambda_{Q}\sum_{i=1}^{NG}\left(Q_{G_{i}}-Q_{lim}^{G_{i}}\right)^{2}\)\" penalizes reactive power deviations \(Q_{G_{i}}\) at the generator buses from their limits \(Q_{lim}^{G_{i}}\). * The term \"\(\lambda_{S}\sum_{i=1}^{NL}\left(S_{l_{i}}-S_{lim}^{l_{i}}\right)^{2}\)\" enforces constraints on the apparent power \(S_{l_{i}}\) across transmission lines to avoid overloading.
# Multi-Line Prefetch Covert Channel with Huge Pages
## 6 Trusted Execution Environment (TEE) Support and Feasibility
### Intel SGX  
Intel SGX provides secure enclaves using a dedicated region of physical memory known as the Enclave Page Cache (EPC), strictly limited to 4 KB pages. Huge page mapping into SGX enclaves is not supported due to SGX's hardware-enforced memory protection model. Although enclaves lack access to high-resolution timing instructions such as rdtscp, this limitation does not affect our covert channel design since only the sender needs to reside in the enclave, while the timing measurements are made by the receiver outside the enclave in unprivileged space.  
While speculative execution attacks like the Foreshadow [30] setup include an \"unmap trick\" that transiently exposes non-EPC pages to speculative execution, if this transient access is sufficient to trigger cache coherence state transitions, it could theoretically enable a MESI-based side channel similar to ours, although in a limited or less reliable form.
## 2 Materials and Methods  
The methods described in this paper make it possible to generate district heating (DH) routes based on openly available data sources, like street networks and building footprints. The approach considers one important criterion to identify the most suitable route for the potential district heating network, which is the distance between the heat source and each of the heat consumers or the distance between the heat consumer and the closest heat consumer. Firstly, LCPA is used to generate a Cost Distance Surface (CDS), which calculates the least accumulative cost distance for each cell from or to the least-cost source over a cost surface [24; 25]. This raster effectively illustrates the cost distance from the heat source for each considered cell within it. Then, using this CDS, a most suitable route based on the street network is generated. The method is tested on one simple example area, and then implemented in one of the communities in Wroclaw county, called Siechnice, and its surroundings. As a result, a consistent DH network for the mentioned community is generated. As the DH network generated using the LCPA method has one important weak point, another method using the Closest Facility tool is tested. Hereby, using an iterative process, the shortest route between each of the heat consumers and another closest heat consumer is additionally considered. As a result of this method, a more efficient DH network is generated, with the possibility of its replication for further communities, counties, voivodeships and finally, for the whole country in the study, Poland. The replication in other European countries is also possible to achieve in the future, as the method considers only the distance between the heat source and heat consumers and needs as input only open-source data.
## 2 System Model
### Distributed Transmit Power of D2D Pairs  
Several factors influence the performance of D2D pairs and CUs in a communication system. Generally speaking, D2D pairs shall transmit signals whereas never disturb the QoS of CUs. It is assumed that CUs transmit signals with fixed power to reduce the complexity of the allocation algorithm.  
Moreover, given that the transmit power of D2D pairs is assumed to be changeable, and each D2D pair determines the transmit power by itself without coordination, conditionally. Inspired by [31], the maximum transmit power of D2D pairs is derived as  
\[\frac{p_{C\_i}\textit{J}d^{-\alpha}_{j,B}\big{|}h_{j,B}\big{|}^{2}}{\sum\limits_ {i=1}^{N}\beta_{i,j}^{\text{CD}}p_{D\_j}d^{-\alpha}_{i,B}\big{|}h_{i,B}\big{|}^{ 2}+N_{c}}\geq\gamma_{min}^{(\text{C})} \tag{11}\]  
Then, the maximum transmit power of D2D pairs can also be calculated by (11) as  
\[p_{D\_i}\leq p_{D\_i,max}=\frac{p_{C\_i}\textit{J}_{i,B}^{-\alpha}\big{|}h_{j, B}\big{|}^{2}/\gamma_{min}^{(\text{C})}-N_{c}}{\sum\limits_{i=1}^{N}\beta_{i,j}^{ \text{CD}}d^{-\alpha}_{i,B}\big{|}h_{i,B}\big{|}^{2}}\text{ for }\forall j\in X \tag{12}\]  
where, in (12), we use the fact that \(\sum_{i=1}^{N}\beta_{i,j}^{\text{CD}}=1\), which will be explained henceforth, if the \(j\)-th CU shares the RB with the \(i\)-th D2D pair, and it means that there is only one unit in the summation. Thus, the maximum transmit power of D2D pairs can be denoted as  
\[p_{D\_i,max}^{\prime}=min(p_{D\_i,max}\text{, }p_{D\_max}) \tag{13}\]  
After the session of D2D communication is set up, the link CSI and threshold \(G_{min}^{D}\) are known by all receivers (D2D pairs and CUs), D2D transmitters use maximum transmit power \(p_{D\_i,max}^{\prime}\) with probability \(P_{i}\) to transmit signals to associated receivers. It means that the maximum transmit power is used by D2D pairs as long as the uplink channel state is good enough. The mean transmit power of D2D pairs is given by  
\[\mathbb{E}(p_{D\_i}) =p_{D\_i,max}^{\prime}P_{i} \tag{14}\] \[=p_{D\_i,max}^{\prime}P\bigg{[}d_{i,i}^{-\alpha}\big{|}h_{i,i} \big{|}^{2}\geq G_{min}^{D}\bigg{]}\] \[=p_{D\_i,max}^{\prime}exp\Big{(}-d_{i,i}^{\alpha}G_{min}^{D}\Big{)}\]  
where the last equality is the result of a Rayleigh fading assumption. Recalling that the transmit power of each D2D pair is determined by itself as shown in (14), the path loss is generally fixed in a certain communication environment so that the transmit power of D2D pairs are affected by distance \(d_{i,i}\) between transmitters and associated receivers and the selected threshold \(G_{min}^{D}\). Transmit power would be small if either one of two parameters is large enough. That is, appropriate selection of threshold \(G_{min}^{D}\) is important to the performance of the system.
## 3 Results
### Nitrate Leaching  
The leaching losses in the extremely wet and average years are presented in Figure 11. Regardless of different weather conditions, the expected increase in leached nitrate was found with the increase in irrigation and N fertilizer rate. In the extremely wet year, nitrate concentration was in range from 11.92 mg NO3- L (a1) to 23.72 mg NO3- L (a3) and from 8.59 mg NO3- L (b1) to 29.06 mg NO3. L (b3). As for the sampling time, the maximum nitrate concentration was measured during springtime, i.e., the first sampling. In the average year, the nitrate concentration was in the range from 11.92 mg NO3- L (a1) to 23.72 mg NO3- L (a3) and from 8.59 mg NO3- L (b1) to 29.06 mg NO3- L (b3). The nitrate concentrations that exceeded the maximum allowable concentration (MAC) in the extremely rainy year were measured for a2b3 (52.6 mg NO3- L) and a3b3 (86.69 mg NO3- L) only in the first sampling during springtime. During the average year, the maximum nitrate concentration was found for the second sampling during the summertime, whereby the nitrate concentration exceeded the MAC for the a3b2 (51.37 NO3- L) and a3b3 (79.28 NO3- L) study treatments. During the springtime, the only study treatment that exceeded the MAC was a3b3 (62.47 NO3- L).  
On average the across sampling times, both irrigation and N fertilizer rates intensified the leaching losses, which was more pronounced during the average year (stronger correlation coefficient, Figure 12). The annual amount of the leached nitrate ranged from 3.12 (a1b1) to 14.89 kg NO3-/ha (a3b3) in the extremely wet year and from 0.82 (a1b1) to 12.21 kg NO3-/ha (a3b3) in the average year (Figure 12), that is, a higher amount of leached nitrate was found in the extremely wet year, as expected.  
Figure 11: Nitrate leaching in the (**a**) extremely wet and (**b**) average years for different irrigation levels (a1 = rainfed, a2 = 60–100% FC, and a3 = 80–100% FC) and N fertilizer rates (b1 = 0 kg N ha-1, b2 = 100 kg N ha-1, and b3 = 200 kg N ha-1).  
Figure 12: Leaching losses across sampling times for (**a**) irrigation levels (a1 = rainfed, a2 = 60–100% FC, and a3 = 80–100% FC) and N fertilizer rates (b1 = 0 kg N ha-1, b2 = 100 kg N ha-1, and b3 = 200 kg N ha-1) significantly different at \(p<0.05\) (*), and (**b**) leaching losses (kg NO3- ha-1) in the extremely wet and average years.
# Real-Time Phase Bias Estimation for BeiDou Satellites Based on Consideration of Orbit Errors
## 4 Results
### NL FCB Estimation When Considering Orbit Errors  
In this subsection, the results of FCB estimation based on consideration of the orbit error are presented, and Figure 5 shows the estimated orbit errors of the six satellites corresponding to those shown in Figure 3. The figures show that the orbit errors in the Z direction of C01 and that in the Y direction of C04 exceed 1.5 and 2.0 m, respectively, which explains the large residuals shown in Figure 3. The orbit errors of C07 are very small over the period from 1:00 to 8:00 but they increase from 8:00 onwards, reaching up to 0.2 m in the X direction. The X direction orbit error for C11 also reaches 0.2 m. For C08 and C12, for which the NL residuals are both within \(\pm\)0.45 cycles, the orbit error can reach as much as 0.4 m. This is the reason why their NL residuals are much larger than those of C07 and C11. It should also be noted that the orbit error remains very stable over a short span and can be updated at intervals of 15 min or even longer.  
Figure 6 shows the corresponding NL residuals when considering the orbit errors. When compared with Figure 3, the impact of the orbit errors on NL FCB estimation is eliminated when the proposed method is used; more than 90% of the residuals are within \(\pm\)0.1 cycles for each satellite.  
Figure 5: Time series of the estimated orbit errors on DOY 079 of 2015.  
Figure 6: Time series of the a posteriori residuals of the NL ambiguities when using the new strategy on DOY 079 of 2015.  
Figure 7 presents the distributions of the a posteriori residuals of the NL float ambiguities from all reference stations during the seven-day period. The figures show that more than 94% of the residuals fall within \(\pm\)0.1 cycles for the GPS satellites and for each type of the BDS satellites; under the criterion of \(\pm\)0.2 cycles, however, the corresponding percentage exceeded 97%. From the analysis above, the effect of orbit errors on BDS NL FCB estimation has been eliminated using the proposed method, and thus BDS PPP-IAR can be performed on large scales.
# Rockfall Modelling in Forested Areas: The Role of Digital Terrain Model Grid Cell Size
## 2 Materials and Methods
### Rockfall Modelling  
The Rockyfor3D rockfall model ([PERSON], 1966) was used for modelling rockfall propagation and runout area. Rockyfor3D is a probabilistic, process-based rockfall trajectory model of falling blocks in three dimensions ([PERSON], 1966) that can be used for regional, local, and slope-scale rockfall simulations. Rockfall trajectory is simulated as 3D vector data by calculating sequences of classical parabolic free falls through the air, rebounds on the slope surface, and also impacts against trees (optional). In the model, rolling is represented by a sequence of short-distance rebounds, while rock sliding is not modelled. The required input data include the topography and surface slope characteristics as well as a set of parameters that define the release conditions. The minimum input data required include the DTM, definition of the source area, rock density, rock size and shape, surface roughness, and soil type ([PERSON], 1966). Rockyfor3D also enables simulation with forest, which can be done either (i) by providing a text file with locations of the trees, their \(DBH\), and the percentage ofiferous trees, or (ii) by providing four raster maps containing the number of trees, mean \(DBH\), standard deviation of \(DBH\), and the percentage of coniferous trees. Namely, based on the \(DBH\) and the tree type, the model calculates the maximum amount of kinetic energy that could be absorbed and dissipated by a tree. The amount of maximum kinetic energy is determined as following:  
\[E_{dis\text{M}}=\text{ }FE\_ratio\times 38.7\times DBH^{2.31}, \tag{1}\]  
where \(E_{dis\text{M}}\) is the maximum amount of kinetic energy that can be dissipated by the tree (J), \(FE\_ratio\) is the fracture of energy ratio of the tree type, and \(DBH\) is the stem diameter at breast height. Since the broad-leaved trees are more resistant to breakage due to rockfall impact than coniferous trees, the model uses a higher \(FE\_ratio\) for broad-leaved trees (\(FE\_ratio=1.59\)) than for coniferous trees (\(FE\_ratio=0.93\)).  
Additionally, the model enables simulation with rockfall nets as protection structures on a slope. The main outputs of the model are maximum kinetic energy (90% confidence interval of all maximum kinetic energy values), maximum bounce height, the number of block passes through each cell, rockfall propagation probability, the number of deposited blocks, maximum simulated velocity, maximum tree impact height, and the number of trees impacts per cell ([PERSON], 1966).
## 3 Materials and Methods
### Methodology
#### 3.3.3 Exploring the Spatiotemporal Interaction Effects of HF and EQ  
(1) CCD model  
The CCD reflects the extent of mutual constraint and coordinated development between different systems and serves as an important tool for assessing the synergy and balanced development of regional human-environment systems [37]. Compared with traditional linear methods such as Pearson correlation, the CCD model considers both the coupling strength and the development level between two systems. This makes it more suitable for evaluating the interaction effects between variables like HF and EQ, which differ in units, scales, and properties. As a result, CCD model has been widely used in geographic and ecological studies. This study uses the CCD model to evaluate the coupling coordination between HF and EQ. The model consists of three components: coupling degree (C), development coordination degree (T), and coupling coordination degree (D). The  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Primary Land Use Type** & **Equivalent Coefficient** \\ \hline
1 & Cropland & 0.2 \\
2 & Forest land & 0 \\
3 & Other forest land & 0.2 \\
4 & Grassland & 0 \\
5 & Water body & 0 \\
6 & Land for water conservancy facilities & 0.6 \\
7 & Residential and industrial mining land & 1 \\
8 & Unused land & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Equivalent coefficients of various land use types for land use intensity.  
coupling degree reflects the strength of interaction among systems, while the coordination degree indicates the level of synergy between them. The calculation formulas are as follows:  
\[C=\left[\frac{\left(U_{1}\times U_{2}\right)}{\left(U_{1}+U_{2}\right)^{2}/2} \right]^{\frac{1}{2}},\ C\in[0,\ 1] \tag{5}\]  
\[T=\alpha U_{1}+\beta U_{2} \tag{6}\]  
\[D=\sqrt{C\times T} \tag{7}\]  
where \(C\) represents the coupling degree; \(T\) denotes the coordination degree; \(D\) is the CCD; \(U_{1}\) and \(U_{2}\) are the normalized values of HF and HQ, respectively. The weights \(\alpha\) and \(\beta\) represent the contributions of the two systems. Since both subsystems are equally significant in evaluating the CCD between HF and HQ, they are assigned equal weights (\(\alpha=\beta=0.5\)). To classify different types of coupling coordination states, this study draws on relevant research in ecological environmental quality assessment and urban coordinated development [38]. The CCD is divided into seven levels based on the actual distribution characteristics of HF and EQ in the study area (see Table 2). This classification has shown good adaptability in analyzing ecological-social system coupling.
## 3 Results and Discussion
### Differential Scanning Calorimetry  
Figure 8 shows a DSC graph of heat flux (mW) versus temperature (\({}^{\circ}\)C) for the PLA spool material and the printed PLA. The spectra shows three features typical of semi-crystalline thermoplastics, including PLA, from left to right: heat flux at the glass transition temperature (T\({}_{\mathrm{g}}\)), an exotherm associated with cold crystallization, and a melting endotherm [26]. The T\({}_{\mathrm{g}}\) of the spool material at 57.7 \({}^{\circ}\)C is slightly lower than that of reported values for PLA, while the melting temperature (T\({}_{\mathrm{m}}\)) at 166.7 \({}^{\circ}\)C is with the range of reported values. Differences in these values may be due to additives in the spool material, as well as the extrusion process itself, though this is beyond the scope of the current study. The T\({}_{\mathrm{g}}\) of the printed PLA shifts to a slightly lower temperature (53.1 \({}^{\circ}\)C), and the melting temperature not only decreases by approximately 10 degrees but, in addition to the melting endotherm shows two peaks (150 \({}^{\circ}\)C and 155.1 \({}^{\circ}\)C). This effect has been seen by other authors in the case of PLA which has been attributed to the formation of multiple crystalline states (\(\alpha\) and \(\alpha\)') which can form during thermal cycling [27]. A more complete study of these states (which can contribute to the stability of the printed structures) will be considered in our further studies, but may be impacted by the presence of inorganic additives or contaminants. Indeed, both the spool material and printed samples  
Figure 7: Diffuse Reflectance FTIR (DRIFTS) spectroscopy of 3D-printed PLA.  
show clear evidence of cold crystallization, a process associated with the exothermal self-nucleation of the crystalline phases above the glass transition temperature. The cold crystallization exotherm is present in both samples, though it is noted to shift from a peak centered at 90.2 \({}^{\circ}\)C to a broad peak centered at about 110 \({}^{\circ}\)C following printing. While further work, including diffraction to identify crystalline states, would help to clarify these results, studies have shown that the lower crystallization temperature is associated with solid-state transitions between crystalline states, while the higher T\({}_{\rm c}\) range correlates to a melt recrystallization process. This may indicate that the printing process may result in changes in the nature and distribution of crystalline phases which form, as well as the capacity for phase changes in the materials above the glass transition temperature.
## 4 Experiments and Discussions
### Real Data Experiments
#### 4.2.1 DaISy  
Figure 11 displays the extraction results of abdominal mixed signals in Channels 1-5 of the DaISy database by the proposed method. The results show that the method in this paper has a good extraction effect on DaISy data, and the FECG could be extracted even from ill-formed mixed signals, such as Channel 4 and Channel 5. In addition, it can be seen that the extraction results not only have clear QRS waveforms, but also retain the local variation trends of the P and T waves, which is of great significance to the FECG extraction results. Nevertheless, since the method in this paper is fully adaptive, the recognition of R peaks is an important factor affecting the extraction effect, and individual misjudgement of signals in an ill-conditioned environment may result in the dislocation of R peaks in the extraction results, such as from 2 s to 3 s of Channel 4 and Channel 5. However, individual misjudgements do not affect the FECG analysis or heart rate calculations.  
Figure 11: Extraction results from abdominal signals for Channels 1–5 in the DaISy database.  
Figure 10: The change of SNR improvement with FMHRR when FMAR is fixed.