esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Quality
### Pseudorange Multipath  
The combination of the pseudorange and carrier phase observation is used to calculate pseudorange multipath, which can eliminate the effect of tropospheric and ionospheric delays [22], and it can be expressed as follows:  
\[MP_{k}=P_{k}-L_{i}-\beta\big{(}L_{i}-L_{j}\big{)}=P_{k}+\alpha L_{i}+\beta L_{j} +\varepsilon\cdot\alpha \tag{1}\]  
\[\alpha=f_{1}^{2}/f_{2}^{2} \tag{2}\]  
\[\beta=-\frac{\Big{(}f_{j}^{2}+f_{k}^{2}\Big{)}f_{j}^{2}}{\Big{(}f_{i}^{2}+f_{j} ^{2}\Big{)}f_{k}^{2}} \tag{3}\]  
where \(k\), \(i\), \(j\) are frequency, \(MP_{k}\) denotes the pseudorange multipath; \(P_{k}\) represents the pseudorange observation; \(L_{i}\) and \(L_{j}\) are the carrier phase observation on frequency \(i\) and \(j\), respectively. \(f_{i}\) and \(f_{j}\) are frequencies; \(\varepsilon\) is the noise.  
The characteristics of pseudorange multipath for GPS, GLONASS, BDS-2, BDS-3, Galileo, and QZSS satellites are investigated in this study using 10 stations from the Asia-Pacific region. The pseudorange multipath values versus elevation angle for the BDS-2 C11, C16, BDS-3 C30, and C39 on DOY 283 for the JFNG station was shown in Figure 3. It can be seen that there is an opposite relationship between pseudorange multipath and elevation angle, the larger the pseudorange multipath, the smaller the elevation angle, and vice versa. The pseudorange multipath is significantly large when the elevation is extremely small in some cases, which may be caused by the observation noise. For the BDS-2 satellites, the pseudorange multipath of the B3I signal is better than those of B1I and B2I. In terms of the BDS-3 satellites, the B1I frequency band of the MEO satellite presents the best performance, its pseudorange multipath is the smallest. Overall, the BDS-3 pseudorange multipath is around 0.28 m, while it is about 0.3 m for the BDS-2 satellites. The time series of pseudorange multipath with respect to elevation on the JFNG station for GPS, GLONASS, Galileo, and QZSS is shown in Figure 4. Similar to BDS-2 and BDS-3 satellites, the pseudorange multipath shows an opposite relationship with the elevation angle. Among them, the pseudorange multipath value of Galileo is the smallest, and it is around 0.2 m, the GPS and QZSS systems show comparable performance, and its value is around 0.3 m, while the value is about 0.4 m for GLONASS satellites. Since the pseudorange multipath effect is an important error source at the receiver side, it has a negative impact on GNSS precise data processing. In GNSS precise data processing, the following measures can be adopted to eliminate or weaken the effect of it: Firstly, reducing or eliminating the weight of observation with low elevation; secondly, modeling the pseudorange multipath errors.  
Figure 3: The MP series of BDS-2 and BDS-3 for DOY 283 on JFNG station. (The red wave indicates the sequence of elevation angle).
## 3 Two-Stage MG Operation Model Considering Interactive Power Deviation
### Day-Ahead Economically Optimal Dispatch Scheduling Model  
In the following, DA denotes the day-ahead variable, and ID denotes the intra-day variable.  
Figure 1: System Diagram of Distribution Network and MGs.  
(1) Power balance constraints  
\[p^{\text{RG}}(t)+P^{\text{CG}}_{\text{DA}}(t)+P^{\text{MG}}_{\text{DA}}(t)=P^{ \text{load}}(t) \tag{1}\]  
where \(P^{\text{RG}}(t)\), \(P^{\text{MG}}_{\text{DA}}(t)\) and \(P^{\text{load}}(t)\) are the renewable energy output, the interaction power between the MG and the ULG, and the MG load at time \(t\), respectively. \(P^{\text{CG}}_{\text{DA}}\) is the total output of the MG controllable units.  
(2) Controllable unit constraints  
\[P^{\text{CG}}_{\text{DA}}(t)=\sum_{i}P^{\text{CG}}_{\text{DA},i}(t) \tag{2}\]  
\[\underline{P}^{\text{CG}}_{i}\leq P^{\text{CG}}_{\text{DA},i}(t)\leq\overline {P}^{\text{CG}}_{i} \tag{3}\]  
\[-\underline{\xi}^{\text{CG}}_{i}\leq P^{\text{CG}}_{\text{DA},i}(t)-P^{\text{ CG}}_{\text{DA},i}(t-1)\leq\overline{\delta}^{\text{CG}}_{i} \tag{4}\]  
where \(\overline{P}^{\text{CG}}_{i}\), \(\underline{P}^{\text{CG}}_{i}\) is the upper and lower limits of the output of the controllable unit \(i\); \(\overline{\delta}^{\text{CG}}_{i}\), \(\underline{\xi}^{\text{CG}}_{i}\) is the upper and lower limit of the controllable unit \(i\) climbing. Equation (3) represents the range of controllable unit output, and Equation (4) indicates that the controllable unit satisfies the climb constraint. In this paper, the term \"controllable units\" refers to micro gas turbine units.  
(3) Power constraints between MG and ULG  
\[-\underline{P}_{\text{line}}\leq P^{\text{MG}}_{\text{DA}}(t)\leq\overline{P }_{\text{line}} \tag{5}\]  
where \(\overline{P}_{\text{line}}\), \(\underline{P}_{\text{line}}\) is the upper and lower limits of the power interaction between the MG and the ULG. Equation (5) represents the range of power output of the MG interacting with the ULG.  
(4) Objective function  
\[\min\sum_{t}^{T}(C_{\text{CG},t}+C_{\text{MG},t}) \tag{6}\]  
\[C_{\text{CG},t}=P^{\text{CG}}_{\text{DA},t}(\mu^{\text{CG}}_{\text{fuel}}+\mu^ {\text{CG}}_{\text{OM}}) \tag{7}\]  
\[C_{\text{MG},t}=\left|P^{\text{MG}}_{\text{DA},t}\right|\mu_{\text{grid},t} \tag{8}\]  
where \(C_{\text{CG},t}\), \(C_{\text{MG},t}\) are the total cost of controllable units and the MG interaction penalty cost with the ULG at moment \(t\), respectively; \(\mu^{\text{CG}}_{\text{fuel}}\), \(\mu^{\text{CG}}_{\text{OM}}\) are unit fuel costs, operating costs, respectively; \(\mu_{\text{grid},t}\) is the time-of-use pricing for the grid.
## 4 Simulation and Physical Experiments
### Reliability Simulation
#### 4.3.1 Success Rate Comparison  
To verify the repeatability and compare the success rate of the PSO and SA-PSO method, six groups of repeated simulation experiments are designed as follows. The target postures are randomly generated in the reachable workspace, and each group carries out the FKP solving 10,000 times. The accuracy is set as \(10^{-8}\).  
By the comparison of PSO and SA-PSO, as is shown in Table 5, the addition of simulated annealing steps improves the success rate of the FKP solving, while reducing the number of iterations and accelerating the convergence of the algorithm. For the \(10^{-8}\) accuracy, the performance of the SA-PSO algorithm is much more stable with a success rate above 99.90%, while the average iterations are less than 120. While the standard PSO method has a lower convergence speed with no less than 140 iterations, and it has a lower and unstable success rate. For random input of \(\mathbf{q}\) values, the SA-PSO method has better robustness than the PSO method.
## 3 Proposed Research Methodology
### Models
#### 3.3.3 Gradient Boost and XGBoost  
Gradient Boost builds upon the ensemble-of-trees approach of random forest by sequentially building trees that attempt to minimize the error of the previous tree's predictions. The algorithm functions as follows:  
* Initializing a model with a weak learner (a decision tree with limited depth)
* Quantifying the model error is quantified via a chosen loss function (usually Cross Entropy for classification).
* Gradient Descent is used to compute the direction and magnitude of the steepest decrease in error.
* Subsequent weak learners attempt to minimize loss based on the derivative of the loss function with respect to the previous tree's prediction.
* The outputs of subsequent learners are scaled down by a learning rate hyperparameter before being added to the ensemble. This limits the rate of improvement per iteration step, reduces overfitting, and aids convergence.  
XGBoost follows the same basic ensemble building algorithm as Gradient Boost but with some notable optimizations aimed at efficient computation for large datasets. As detailed by [PERSON] and [PERSON] in their original paper [68], XGBoost functions as follows:  
* A regularization parameter is added to determine the optimal split, further reducing overfitting.
* An approximate greedy algorithm that utilizes a Weighted Quantile Sketch algorithm for selecting the optimal split is introduced. This vastly improves performance on large, highly dimensional datasets, as it also allows parallelization.
* A novel sparsity-aware splitting algorithm that optimizes for missing data by creating default directions to follow when building the tree ensures that only nonmissing branches are visited. This also reduces the need for preprocessing to remove missing observations.  
* Cache-aware access is implemented by allocating a buffer in the cache for computed gradients, which rapidly increases the speed at which subsequent trees can compute their loss.
* This method optimizes for hard-disk access in large datasets by compressing data, reducing the need for repeated reads on the hard drive. In instances where data are split across multiple drives, XGBoost can also make use of sharding to parallelize access to data, further improving training speed.
## 2 Materials and Methods
### Postharvest Treatments and Light Photoperiod  
Bell red peppers were stored for 6 d at 7 \({}^{\circ}\)C and 80% relative humidity (RH) under conditions of darkness, simulating a short postharvest storage + commercial exportation within Europe. Then, considering that the fruit arrived at a logistic platform, or even though that they could be repacked, peppers were divided into two groups: CTRL and UV-B. UV-B red peppers were exposed to 9 kJ m\({}^{-2}\) UV-B (19 min 30 s) in a radiation chamber consisted of a reflective stainless-steel chamber described by [PERSON] et al. (2014), which was equipped with 6 UV-B unfiltered emitting lamps (TL 40W/01 RS; Philips, Eindhoven, The Netherlands). The applied UV-B intensity was calculated as the mean of 20 readings with a LP 471 UVB radiometer (Delta OHM, Padua, Italy): 8.94 \(\pm\) 0.40 W m\({}^{-2}\), and this dose was chosen based on our preliminary test and findings by [PERSON] et al. (2014, 2014).  
After treatment, bell peppers were kept at 20 \({}^{\circ}\)C and 60% RH during 2, 3, and 4 d, simulating a retail sale period in supermarkets on the destination country. In order to adapt our model to what usually happens in supermarkets, three different photoperiods were applied following the same pattern: 14 h under fluorescent lighting (FL) with 403 kJ m\({}^{-2}\) when supermarkets are open + 10 h under the next variable lighting treatment when supermarkets are close at night)  
* **D:** darkness, as it is usually performed.
* **FL:** fluorescent lighting with lower energy (280 kJ m\({}^{-2}\)) to save costs provided by fluorescent lamps (OSRAM DULUX L 36W/840, Munich, Germany) with broad white spectrum and 8 W m\({}^{-2}\), simulating the conventional storage conditions measured by us for this study in several Spanish supermarkets during June-July 2020.
* **BR LED:** a combination of blue + red LEDs (LEDMurcia S.L., Murcia, Spain) with 576 kJ m\({}^{-2}\). LED lamps were applied with a simultaneous combination (1:1) of blue (peak at 450 nm) and red (peak at 660 nm). This combination was chosen due to our previous preliminary test based on previous findings by [PERSON] et al. (2015) and [PERSON] et al. (2016).  
A constant photosynthetic photon flux density (PPFD) for FL and LED lights of 36.7 and 73.1 umol m\({}^{-2}\) s\({}^{-1}\), respectively, were measured using a Quantum-Photo Radiometer Data Logger DO 9721 (Delta Ohm, S.R.L., Venice, Italy). Quality sampling days throughout the studied postharvest period were at harvest (day 0), after 6 d at 7 \({}^{\circ}\)C (end of the simulated storage/transportation period), and after 2, 3, and 4 d at 20 \({}^{\circ}\)C of the simulated retail sale period. A schematic representation of this study is shown in Figure 1.  
Figure 1: Schematic representation of the experimental design of the study.
# Segment Shards: Cross-Prompt Adversarial Attacks against the Segment Anything Model
## 3 Problem Definition
### Cross-Prompt Attack Problem Definition
#### 3.2.2 Cross-Prompt Attack Success Rate  
In this section, we introduce a metric, \(ASR^{*}\), for evaluating the cross-prompt transferability of adversarial examples. For the sake of discussion, we assume that all prompts take the form of point coordinates.  
Figure 1: Cross-prompt adversarial attack. The \(ASR\) denotes the attack success rate and the \(ASR^{*}\) denotes the cross-prompt attack success rate.  
Before entering the discussion of the definition of the cross-prompt attack success rate (\(ASR^{*}\)), we first provide a complete definition of the attack success rate (\(ASR\)). Following some previous work [20; 21; 24], \(ASR\) is defined by the drop in mIoU as Equation (4).  
\[ASR=\frac{mIoU_{nos}-mIoU_{atk}}{mIoU_{nos}} \tag{4}\]  
where \(mIoU_{nos}\) denotes the mIoU value of the segmentation results on the image with randomly added noise, and \(mIoU_{atk}\) denotes the mIoU value of the segmentation results on adversarial examples.  
\[mIoU=\frac{1}{n}\sum_{i=0}^{n}\max_{j=0}^{m}\frac{M_{i}\cap M_{j}}{M_{i}\cup M _{j}} \tag{5}\]  
where \(M_{i}\) denotes the \(i\)-th object mask of the segmentation results of SAM in the adversarial image or noise image, and \(M_{j}\) denotes the \(j\)-th object mask of the segmentation results of SAM in the clean image.  
Next, we start with the illustration of the definition of \(ASR^{*}\), where all \(ASR_{i}\) without the * sign are defined by Equation (4) above.  
First, we use \(d_{ab}\) to measure the degree of difference between prompt \(a\) and prompt \(b\). This \(d_{ab}\) can be calculated as Equation (6). Here, \(f_{\delta}\) denotes the Jaccard Distance, \(p_{a}\) denotes the set of coordinates of all points in prompt \(a\), and \(p_{b}\) denotes the set of coordinates of all points in prompt \(b\). Finally, \(n\) denotes the number of points in prompt \(a\) and \(m\) denotes the number of points in prompt \(b\).  
\[d_{ab}=f_{\delta}(p_{a},p_{b})+\sum_{i=0}^{n}\min_{j=0}^{m}\frac{\sqrt{(p_{a}^ {ix}-p_{b}^{ix})^{2}+(p_{a}^{iy}-p_{b}^{iy})^{2}}}{\sqrt{H^{2}+W^{2}}} \tag{6}\]  
Then, suppose that the prompt \(t\), is the prompt used in generating the adversarial example, and a total of \(k\) types of prompts are used to produce adversarial attacks. The cross-prompt transferability of the adversarial example across \(k\) different types of prompts can be calculated using Equations (7) and (8), as follows:  
\[ASR_{t}^{*}=\sum_{i=0}^{k}\left(\frac{d_{it}}{D_{t}}\times ASR_{i}\right) \tag{7}\]  
\[D_{t}=\sum_{i=0}^{k}d_{it} \tag{8}\]  
Here, \(d_{it}\) denotes the degree of difference between prompt \(i\) and prompt \(t\), \(ASR_{i}\) denotes the attack success rate of the adversarial example under prompt \(i\), and \(ASR_{t}^{*}\) denotes the cross-prompt attack success rate of the adversarial example.
## 7 Conclusions  
In this paper, a semi-analytical method based on the principle of minimum total potential energy was proposed to solve the volume loss at the tunnel face and the ground face for shield-driven tunnels. Moreover, a new deformation mechanism considering the soil volume change was designed to describe the ground movement before the tunnel face. Based on the proposed method, the influence of different factors on the volume loss was investigated. The main conclusions are given as follow:  
The volume loss decreases with the increase in the support pressure ratio and the slurry weight. The increase in the slurry weight is favorable to the control of volume loss during tunnelling  
The volume loss at ground surface is smaller than that at the tunnel face due to the soil compression during ground movement.  
The bigger the tunnel diameter, the bigger the volume loss. However, the volume loss at ground surface may not increase with the increase in the tunnel depth because of the soil arching effect.  
The deeper the tunnel, the quicker the volume loss decreases with the increase in the support pressure ratio. The bigger the tunnel diameter, the quicker the volume loss decreases with the increase in the slurry weight.  
The proposed method provides a more direct way for estimating volume loss at the tunnel face from an energy point of view. Most current researchers have assumed zero volume behind the tunnel face (\(V_{Lt}\) and \(V_{Ls}\)), and the considered volume loss is limited to the tunnel face ('tunnel heading'). A more effective way for estimating all volume loss along the shield advancement may be developed by designing multiple deformation mechanisms according to the field data and numerical simulations.  
G.W.: Conceptualization, formal analysis, software. Q.F.: Funding acquisition, Supervision, Validation. J.D.: Investigation, Writing-review & editing, X.Y.: Visualization. J.W.: Formal analysis, Data curation. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Beijing, China grant number 8202037, and the Key Project of High-speed Rail Joint Fund of National Natural Science Foundation of China grant number U1934210.  
Not applicable.  
Data Availability Statement: Data are contained within this article.  
The authors thank the reviewers for their great help on the article during its review progress.  
The authors declare no conflict of interest.
# Development of a Thermal Analysis Tool for Linear Machines
## 3 Description of the Thermal Networks  
### Thermal Network of the Slot  
The equivalent thermal network of the slot from Figure 3 is based on the layered winding thermal model of Motor-CAD [27]. The advantage of this model is that it is precise enough to detect hot spot problems in the windings, but the calibration of the resistance is only dependant on the thermal conductivity and the quality of the impregnation, and the fill factor of the slot.  
The thickness of the copper+air+impregnation layers is the same as the diameter of the conductors of the slots. In the initialization process, the the thermal analysis tool starts from setting a low thickness for the air+impregnation layers, and this value is iteratively increased until the desired fill factor is obtained.  
Each element of the slot has its own thermal resistor and capacitor as it can be seen in Figure 3b. In order to limit the height of Figure 3b, the thermal network of a machine which has a single copper and air+impregnation layer is shown in the figure.  
Figure 3: (**a**) Implemented layered winding model. Primary core (gray), copper layers (yellow), air+impregnation layers (green), slot liner (maroon), and wedge (blue). (**b**) Thermal network of the slot.  
Thanks to the winding model, it is also easy to define the values of the conductive thermal resistances. The thickness of the layers is taken as \(l\), and \(A\) is calculated by  
\[A=l_{stk}\cdot\left(2\cdot h_{layer}+w_{layer}\right), \tag{5}\]  
where \(h_{layer}\) and \(w_{layer}\) are the average height and average width of the layers.  
In the case of the air+impregnation layers, the conductive thermal resistance depends on the quality of the impregnation \(q_{imp}\). This parameter contemplates the small air gaps that can appear in the impregnation due to imperfections in the manufacturing process of the machine. The value of the conductive resistance of this layer \(R_{AirImp}\) is calculated following  
\[R_{AirImp}=q_{imp}\cdot\frac{l}{k_{imp}\cdot A}+\left(1-q_{imp}\right)\cdot \frac{l}{k_{air}\cdot A}, \tag{6}\]  
where \(k_{imp}\) and \(k_{air}\) are the thermal conductivities of the impregnation and the air.  
The thermal capacitances \(C\) are calculated from the mass and the specific heat capacity \(c_{p}\) of the materials,  
\[C=\rho\cdot V\cdot c_{p}. \tag{7}\]  
Here, \(\rho\) is the density of the material of the layer, and \(V\) the total volume of the layer. The specific heat capacities, thermal conductivities, and densities of some typical materials are given in Table 1. The thermal properties of air at 1 atm are given in Appendix A.
# Re-Pair in Small Space +
## 2 Sequential Algorithm
### Algorithmic Details
With \(\beta_{i}\) and Lemma 3 with \(|F|=f_{k}\), we have:  
\[\alpha_{i}f_{k+1} =\alpha_{i}f_{k}+\max(2/\beta_{i},(f_{k}-1)/(2\beta_{i}))\] \[=\alpha_{i}f_{k}\max(1+2/(\alpha_{i}\beta_{i}f_{k}),1+1/(2\alpha _{i}\beta_{i})-1/(2\alpha_{i}\beta_{i}f_{k}))\] \[\geq\alpha_{i}f_{k}(1+2/(5\alpha_{i}\beta_{i}))=:\gamma_{i}\alpha_ {i}f_{k}\text{ with }\gamma_{i}:=1+2/(5\alpha_{i}\beta_{i}),\]where we use the equivalence \(1+2/(\alpha_{i}\beta_{i}f_{k})=1+1/(2\alpha_{i}\beta_{i})-1/(2\alpha_{i}\beta_{i}f_ {k})\Leftrightarrow 5=f_{k}\) to estimate the two arguments of the maximum function. Since we let \(f_{k}\) grow by a factor of at least \(\gamma:=\min_{1\leq i\leq m}\gamma_{i}>1\) for each recomputation of \(F\), \(f_{k}=\Omega(\gamma^{k})\), and therefore, \(f_{k}=\Theta(n)\) after \(k=\mathcal{O}(\lg n)\) steps. Consequently, after reaching \(k=\mathcal{O}(\lg n)\), we can iterate the above procedure a constant number of times to compute the non-terminals of the remaining bigrams occurring at least twice. Time analysis: In total, we have \(\mathcal{O}(\lg n)\) rounds. At the start of the \(k\)-th round, we compute \(F\) with the algorithm of Lemma 2 with \(d=f_{k}\) on a text of length at most \(n-f_{k}\) in \(\mathcal{O}(n(n-f_{k})\cdot\lg f_{k}/f_{k})\) time with \(f_{k}\leq n\). Summing this up, we get:  
\[\mathcal{O}\left(\sum_{k=0}^{\mathcal{O}(\lg n)}\frac{n-f_{k}}{f_{k}}n\lg f_ {k}\right)=\mathcal{O}\left(n^{2}\sum_{k}^{\lg n}\frac{k}{\gamma^{k}}\right)= \mathcal{O}\left(n^{2}\right)\text{ time.} \tag{1}\]  
In the \(i\)-th turn, we update \(F\) by decreasing the frequencies of the bigrams affected by the substitution of the most frequent bigram bc with \(X_{i+1}\). For decreasing such a frequency, we look up its respective bigram with a linear scan in \(F\), which takes \(f_{k}=\mathcal{O}(n)\) time. However, since this decrease is accompanied with a replacement of an occurrence of bc, we obtain \(\mathcal{O}(n^{2})\) total time by charging each text position with \(\mathcal{O}(n)\) time for a linear search in \(F\). With the same argument, we can bound the total time for sorting the characters in \(D\) to \(\mathcal{O}(n^{2})\) overall time: Since we spend \(\mathcal{O}(h\lg h)\) time on sorting \(h\) characters preceding or succeeding a replaced character and \(\mathcal{O}(f_{k})=\mathcal{O}(n)\) time on swapping a sufficiently large new bigram composed of \(X_{i+1}\) and a character of \(\Sigma_{i+1}\) with a bigram with the lowest frequency in \(F\), we charge each text position again with \(\mathcal{O}(n)\) time. Putting all time bounds together gives the claim of Theorem 1.
# Responses of Seasonal Indicators to Extreme Droughs in Southwest China
## 4 Discussion
### Limitation and Future Research  
Similar to many studies, there are some limitations in this study. Firstly, we only analyzed the droughts during 2000-2017 due to the data limitation. Therefore, there are only two drought periods in our research, and future studies with more data can provide a better understanding of the relationship between seasonal indicators and drought periodicity in longer period. Secondly, insufficient effects of human activities on surface water and phenology were examined in this study, but agricultural activities such as irrigation and multiple growth seasons were excluded, especially in drought periods [85; 86]. For example, the dryer the condition, the more water is pumped from rivers and lakes for irrigation, which reduces SWA from observation. Similarly, droughts in spring also influence postponing planting time and replanting, that also made fluctuations in seasonal parameters. Thirdly, the spatial average of variables, for example, averaged length of season, were used in the analysis and the spatial heterogeneity was excluded in this study, which may affect the result, especially in mountain area, for example, in Chongqing and Yunnan (Figure 1) [11]. Further research considering the spatial heterogeneity with available data will improve our understanding of drought in this area.