esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Irrigation System  
In order to simulate the Mediterranean conditions, it has been applied two minimal irrigation treatments replacing 20%, and 40% of potential ET\({}_{0}\), and one treatment receiving only natural rainwater (0% ET\({}_{0}\)). The ET\({}_{0}\) levels used to calculate irrigation were obtained from the Caldes de Montbui meteorological station. Irrigation doses were recalculated each week by subtracting total rainfall from the average ET\({}_{0}\) of the previous seven days (ET\({}_{0}\)-rainfall), of which 20% and 40% was used for the two treatments. The doses were distributed at regular intervals throughout the week.  
Before laying the plants, we calibrated the entire structure over a period of several days measuring the distribution capacity of randomly chosen drips, obtaining an irrigation uniformity coefficient of 0.9. The 40% and 20% ET\({}_{0}\) plots received, respectively, a total of 216 mm and 108 mm of water over the study period, 73% of which was distributed during the summer. Total rainfall throughout the summer was 281 mm, with an accumulated ET\({}_{0}\) value of 332 mm. Irrigation was not provided during the winter months due to frequent rainfall. Moreover, we placed and calibrated a capacitive sensor (ECO 5 Decagon, Pullman, WA, USA) and a temperature sensor (EC-T5, Decagon, Pullman, WA, USA) in the substrate of each plot. We located all sensors horizontally at a depth of 7/8 cm, and we connected them to a central apparatus (Campbell CR1000, Campbell Sci, Shepshed, Leicestershire, UK) which recorded incoming data every 20 min. Finally, we calculated the water holding capacity according to standardized methodology: The substrate was water saturated by immersion and after drainage, water holding capacity was obtained measuring the water in tank, the difference between original value and this remaining water; it can be assumed as maximum water capacity storage in the substrate. In parallel water capacity was measured by means of soil water content sensors.
## 4 The Proposed NSGA-II-MOEA/D-CIS Algorithm  
To solve the bi-level collaborative optimization model for order splitting and reorganization constructed in the previous section, this section designs an improved hybrid multi-objective optimization algorithm (NSGA-II-MOEA/D-CIS). Its core components include the Non-dominated Sorting Genetic Algorithm II (NSGA-II) [31], the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) [32], and the collaborative iterative strategy (CIS).
# MLUTNet: A Neural Network for Memory Based Reconfigurable Logic Device Architecture
## 4 Experimental Results
### Performance on MNIST Series Datasets  
The experiments conducted on three MNIST series datasets: MNIST [13], K-MNIST [14], and fashion MNIST [15]. In these datasets, the contents of images in datasets are graphics of handwritten numbers, Japanese hiragana characters from ancient books, and fashion items, respectively. The images are gray-scale images with the resolution of \(28\times 28\). The accuracy convergence process and the corresponding confusion matrices for each group of experiments are exhibited in Figures 5 and 6.  
Figure 5: Performance on MNIST series datasets.  
Figure 6: Confusion matrices of MLUTNet.  
By reviewing the experimental results, some conclusions can be revealed. (1) In the full precision case, MLUTNet exhibits comparable performance to the standard NN on all three datasets. With enough epochs, the accuracy of the test dataset for each case converges steadily to approximately the same level. (2) In the binary accuracy case, MLUTNet has different degrees of performance loss compared to the standard BNN. It is the largest in KMNIST with 35.3% and the smallest in MNIST with 20.6%. (3) The performance of the decay scheduler is more stable, but cosine annealing shows better performance in F-MNIST. The full-accuracy MLUTNet with cosine annealing scheduler achieves a lead of about 0.71% over the NN with a decay scheduler; the binary-precision MLUTNet with Cosine annealing scheduler improves the accuracy by 9.03% over using the decay scheduler.
## 3 Results
### LUILC Classifier Assessment and Selection  
The three classification methods were run for the seven-year period proposed in this analysis. Results show a similar behavior for extracting LULC information using the same level of ancillary data. This condition allowed a fair comparison between the classification algorithms. Figure 3 shows the macro-class classification for the year 2017, which was the year which presented higher discrepancy among LULC classification methods. Graphically, one can observe that algorithms performed comparably. Significant variations were found within the clusters sharing bare soil and and built-up zones, whereas a more accurate definition was found for those composed of built-up vegetation and bare soil vegetation. These variations were attributed mostly to the spectral signature dispersion and departure within the pixels defining each class and the nearby areas. Bare soil and vegetation areas, being those mostly distributed over the west and east parts of the city, respectively, presented similar spatial distributions.  
Numerically, all the classification methods presented good behavior for both overall precision and Kappa values, all being above 85% and 0.85, respectively (Table 6). The spectral angle mapping (SAM) was the method that showed the smallest overall precision and Kappa values, 89% and 0.86, respectively. The highest accuracy was obtained using the maximum likelihood classifier (MLC), which presented overall precision above 90% and Kappa coefficients above 0.90 for all the years in the study period. It was observed that the MLC required less field data to obtain accuracy compared to the other classification methods.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline
**Mac-class** & **Water** & & & & **Rank-Up** & & & & **Weightation** & & & **Base soil** & \\ \hline Samples & & 128 & & & 111 & & & 122 & & & 123 & \\ (pixel) & & & & & & & & & & & & & \\ \hline
**Class** & **Sea** & **W.C** & **W.B.** & **Industry** & **Radicial** & **Commercial** & **Roals** & **Mixed** & **Shobiland** & **Weighted** & **Dune** & **R.S.** & **S.V.** & **Gansland** \\ \hline Samples & 83 & 41 & 28 & 30 & 56 & 23 & 36 & 79 & 37 & 26 & 29 & 88 & 27 & 24 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average number of samples for macro-class and class validation. Units in pixels.  
The three classification methods showed the advantages of a perfect decision boundary to distinguish the macro-classes and a consistent mathematical expression based on the decision boundary for further classifications. However, these methods might overtrain the decision tree, as the training set needs several examples to cover all the possible cases within a specific class. Lastly, training tends to require significant computational time to be effective. As the MLC presented the highest level of accuracy under the same training set, it was selected as the main classification method for this study.
## 3 Methodology and Assumptions  
For an analysis and evaluation of energy conversion systems, exergy analysis is a convenient and powerful tool. The identification of thermodynamic inefficiencies within a process design, allows deriving measures for subsequent design improvements. Furthermore, an economic analysis is conducted to estimate the major costs involved in each scenario. The combination of both methods in an exergoeconomic analysis is the basis for future design improvement.  
With respect to the simulation in Aspen Plus V9 (Aspen Technology, Inc., Burlington, MA, USA, 2017), the major assumptions used for the three design cases are presented in Table 2. The thermodynamic properties are calculated by employing the Redlich-Kwong-Soave equation of state for all units of the process. Several kinetic models are implemented into the reactors to analyze the thermodynamics of the reaction sets. Regarding the methanol synthesis, the reaction kinetic inputs to the RPLUG model are based on a study by [PERSON] et al. (2013), in which a Cu/ZnO/Al\({}_{2}\)O\({}_{3}\) catalyst is used for methanol conversion. The kinetics is described by the Langmuir-Hishlewood-Hougon-Watson (LHHW) model and the parameters are taken from [PERSON] et al. (2014). Furthmore, Powerlaw kinetics based on ([PERSON], 2016) are used for the SMR and the DMR unit. The combustion reactions in the ATR are assumed to reach equilibrium.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component/System** & **Unit** & **Case I** & **Case II** & **Case III** \\ \hline _General_ & & & & \\ \hline Property Model & & & Redlich-Kwong-Soave & \\ Ambient Temperature & \({}^{\circ}\)C & 15 & 15 & 15 \\ Ambient Pressure & bar & 1.013 & 1.013 & 1.013 \\ Mechanical Efficiency & \% & 99 & 99 & 99 \\ Compressor isentropic efficiency & \% & 85 & 85 & 85 \\ \hline _Pretreatment (sub-system I)_ & & & & \\ \hline _Hydrogenolysis_ & & & & \\ Conversion rate of COS (Stein
## 4 Discussion
According to these researchers, the optimal load should be 10% of the participant's body mass. The selection of an appropriate load is a crucial element in achieving maximal power output, which also depends on the frequency of rotation on the Monark ergometer [41]. The optimal frequency at a constant load can be determined by multi-stage sprints, known as the force-velocity test (FVT), at different rotational frequencies [41]. The FVT enables the determination of the linear correlation between braking force/torque and pedal speed (cadence) and the multinomial correlation between power and speed/cadence. In this study, physically active participants achieving a cadence of \(>\)119 repetition\(\cdot\)min\({}^{-1}\) were characterized by a predominance of fast-twitch fibers compared to participants achieving an average cadence of approximately 104 repetition\(\cdot\)min\({}^{-1}\), in whom slow-twitch fibers predominated [35; 41]. In the present study, the maximal cadence was not statistically significantly different between conditions, recorded as 147 in the SIE\({}_{10\%}\) and 145 repetition\(\cdot\)min\({}^{-1}\) in the control condition, respectively. However, determining the type of muscle fibers involved requires electromyographic testing, which was not performed in the present study. The results of the present study comparing a load of 7.5% vs. 10% of the participant's body mass during SIE indicate that a higher relative peak power output (rPPO) is obtained with a lower VO\({}_{2}\)mean when a 10% load is applied. Furthermore, despite the higher external load, positive mechanical responses were evident in the generation of higher peak power output obtained in a shorter time, with a slightly higher cadence. This may promote the use of higher load in sprint interval training. Given the greater effectiveness of interval efforts of shorter duration and that the use of a higher load (10% of the participant's body mass) results in a greater amplitude of the physiological responses with a lower of rating of perceived exertion, this may determine the effective improvement of physical performance.
## 2 Preliminaries
: _([15]) Suppose that (4) is R-regular at \(x^{*}\), then, all elements of \(\partial_{C}\phi(x^{*})\) have full rank._  
For any \((x,y)\in\Re^{(m+1)n}\), we know that:  
\[\partial_{C}F(x,y)=\left\{\begin{array}{cccc}\left( \begin{array}{cccc}V_{ \phi_{n}}&0&0&\cdots 0\\ M(\omega_{1})&-I&0&\cdots 0\\ M(\omega_{2})&0&-1&\cdots 0\\ &\vdots\\ M(\omega_{m})&0&0&\cdots-I\end{array} \right):V_{\phi_{n}}\in\partial_{C}\phi_ {n}(x)\right\},\]  
where \(I\) is the \(n\times n\) identity matrix. Hence, by Proposition 5, we know that the following proposition is set up. **Proposition 6**. : _Suppose (4) is R-regular at \(x^{*}\) and \((x^{*},y^{*})\) is a solution of (9). Then, all \(V\in\partial_{C}F(x^{*},y^{*})\) are nonsingular._  
**Proposition 7**. : _If (4) is R-regular at a solution \(x^{*}\), then, there exits \(\alpha>0,\beta>0\), such that \(\|(H^{T}H)^{-1}\|\leq\beta\) for all \(x^{*}\in\Re^{n}\) with \(\|x-x^{*}\|\leq\alpha\), where \(H\in\partial_{C}\phi(x)\)._  
Proof of Proposition 7.: The proof is similar to the ([15], Lemma 2.5) and therefore omitted here. In the following part of this paper, we rewrite \(\Psi\) as:  
\[\Psi(x)=\frac{1}{2}\|\phi_{n}(x)\|^{2}=\sum_{i=1}^{n}\psi(x_{i},(\bar{M}x+\bar {q})_{i}),\]  
where \(\psi:\Re^{2}\to\Re\) is defined as:  
\[\psi(a,b)=\frac{1}{2}\lambda^{2}\phi_{P}^{2}(a,b)+\frac{1}{2}(1-\lambda)^{2}a _{+}^{2}b_{+}^{2}.\]  
**Proposition 8**. : _The function \(\Psi:\Re^{n}\to\Re\) defined in (8) satisfies:_  
_(a) \(\
abla\Psi(x)=V^{T}\phi_{n}(x)\), for any \(V\in\partial_{C}\phi_{n}(x)\)._  
_(b) If \(\
abla\Psi(x^{*})=0\) and \(\bar{M}\) is a \(P_{0}\) matrix, we know that \(x^{*}\) is a solution of (4)._  
_(c) If (4) is strictly feasible and \(x\longmapsto\bar{M}x+\bar{q}\) is monotone, then \(L(c)=\{x\in\Re^{n}|\Psi(x)\leq c\}\) are compact for all \(c\in\Re\)._  
Proof of Proposition 8.: The proof is similar to the one of ([15], Theorem 2.7), so we skip the details here.
## 2 Materials and Methods
### Test Field Specification  
The object of interest is to test the system in an urban area where the GNSS signal is jammed by surrounding trees and buildings. For this purpose, an extensive test field has been created to satisfy this condition [25; 28]. The network is made up of control points, which are used to precisely align the point cloud, and of checkpoints.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Carrier Speed** & **Point Density 1** & **Point Density 1** & **Point Density 1** \\  & **Distance 3 m** & **Distance 10 m** & **Distance 50 m** \\ \hline
50 km/h & 13,750 & 4100 & 820 \\
80 km/h & 8590 & 2570 & 510 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Point density at a laser pulse rate of 1000 kHz.  
Figure 1: Diagram of the Riegl VMX-2 HA.  
Other important factors in selecting the location of the test field were a low-traffic site and a close location to the GEOREAL surveying company, which owns the device being tested.  
Two sets of coordinates were created for all points in the test field. The first set is the coordinates determined by the dual GNSS RTK measurements and the arithmetic average (GNSS points). This method of determining points is used in real data collection. The second set of coordinates was calculated by adjustment of the tied grid from the total station measurements (TS points). The more accurate (TS) coordinates were created to be an order of magnitude more accurate than the expected accuracy of the method; see the chapter \"Expected Accuracy\".
# A Cryptography-Powered Infrastructure to Ensure the Integrity of Robot Workflows
## 3 Compliance Architecture
### Accountability Requirements  
In this section we derive requirements for accountable development of robotic applications.  
**Workflow integrity:**: The integrity of the workflows to be executed is essential in our concept. It must be made sure that the workflow as designed by the application developer and signed off by a responsible entity is actually executed without reductions, additions or modifications. This includes the flow itself meaning the sequences and branches of actions as well as the parameters of each action. Since e.g., the speed of the robot can dramatically impact the outcome of the robot's action, the integrity of parameters is a crucial part of the workflows integrity.
**Authentication:**: All participants in the transaction from sealing an application to its execution must be authenticated in order to later be able to authorize them.
**Authentication:**: To make a robot only accessible by entities with the appropriate permissions, the access to the verification infrastructure must be protected by authorization measures.
**Traceability:**: We require every action taken and every event happening from the design of the workflow until the execution on the robot to be reconstructable in a timeline after the execution of the application (post-mortem). Please note that not all elements required for full traceability are in the focus of this work. However, as an additonal step towards full trusted traceability, our group has already presented a secure data recorder (the robot black-box) in previous work [34].
**Non-repudiation:**: Traceability should be supported by non-repudiation meaning in our case that an entity cannot deny having taken a certain action. Specifically, we address that signing off and triggering the execution of a workflow can provably be assigned to a certain entity in the overall system. Non-repudiation can be achieved with authentication and traceability in the overall concept which we present in Section 5.
**Confidential communication:**: The communication between the components involved in our system must be kept confidential as not to give potential malicious agents attack surfaces to tamper with our system. This is required for the communication between all entities (client, robot and infrastructure).
**Low overhead:**: Despite the valuable addition of making workflow execution verifiable and traceable on robot systems, the added overhead of the implemented measures must not impact the robot in its execution. Many factors of a robot's operation are time-critical and thus, added verification steps must not hinder those operations.
**Scalability:**: The infrastructure added for verifiable workflow execution must be scalable to support large workflows on the one hand and also a scalable fleet of robots on the other hand. The support for queues of workflows that describe more complex applications must be considered.
## 3 Loss Differentiation Feature Selection for LEO Satellite Network
### Packet Loss Differentiation Feature Selection Method
These weights can then be arranged in descending order to determine their relative importance as well as establish corresponding arrangement order for each respective featured attribute. This determines their overall significance. The Relief algorithm solely ranks the importance of featured attributes without reflecting correlations among them. Therefore, we utilized the mutual information algorithm to analyze feature correlations, aiming to reduce redundancy between feature attributes and optimize the final feature selection results. The concept of information entropy quantifies the level of uncertainty associated with the occurrence of each possible event within an information source, which is defined as:  
\[H(X)=-\underset{i}{\sum}\,p(x_{i})\times\log_{2}p(x_{i}) \tag{4}\]  
where \(p(x_{i})\) is the frequency of event \(x_{i}\). \(X=\left\{x_{1},x_{2},\cdots,x_{n}\right\}\) is the information source containing all possible states. The conditional information entropy of \(X\) with respect to \(Y\) quantifies the additional knowledge provided by \(X\) when \(Y\) is known for a pair of associated random variables:  
\[H(X|Y)=-\underset{i,j}{\sum}\,p(x_{i},y_{j})\times\log_{2}p(x_{i}|y_{j}) \tag{5}\]  
where \(Y=\left\{y_{1},y_{2},\cdots,y_{n}\right\}\), \(p(x_{i},y_{j})\) is the joint probability density function of the random variable \((X,Y)\), and \(p(x_{i}|y_{j})\) is the conditional probability that \(x_{i}\) occurs when \(y_{j}\) occurs. The mutual information between random variables \(X\) and \(Y\) is given as follows. \[I(X;Y)=H(X)-H(X|Y)=\underset{i,j}{\sum}\,p(x_{i},y_{j})\times\log_{2}\frac{p( x_{i},y_{j})}{p(x_{i})p(y_{j})} \tag{6}\]
## 4 Discussion
The obtained results of TPC and TFC are summarized in Table 3. TPC values concur with those obtained in barberry--10.24 mg GAE/g fw [29]; blackthorn--1.51 mg GAE/g dw [60]; chokeberry--8.56-9.09 mg GAE/g fw [49]; linguonberry--6.52 mg GAE/g fw [61]; rownberry 2.27 mg GAE/ g fw [62]; and sea-buckthorn--0.7-3.62 mg GAE/g fw [58] (Table 5). However, a comparison of our results with data from the literature is difficult due to the application of different extraction solvents and conditions or standards (catechin, quercetin, rutin) to express TPC and TFC presented for dried fruits by various researchers. Some authors compared the obtained results with two standards (Table 5). The polyphenol content is affected by both the genetic background of the plant and growing and climatic conditions. These differences are particularly evident in the TPC of different cultivars of barberry, blackthorn, and sea-buckthorn (Table 5). Fresh fruit consumption is limited, so air- and freeze-dried fruit are frequently consumed as snacks or added to products like oatmeal. However, active phenolic compounds, described by TPC and TFC, are often lost during the drying process [63]. In our observation, we found that lyophilization tends to preserve more of these active phenolic substances in the material compared to the traditional dried method, particularly in elderberry and sea-buckthorn samples (Table 2). However, this finding should be verified with a larger set of samples. This has also been confirmed in studies on sea-buckthorn [87], where the TPC of freeze-dried berries was found to be 1.56-2.97 times higher than that of other dried berries, with similar trends observed for TFC values. However, the difference was not as significant as that for TPC. Very weak correlations between EA-TPC (R\({}^{2}\) = 0.0162), EA-TFC (R\({}^{2}\) = 0.0230), Q-TPC (R\({}^{2}\) = 0.0000), and Q-TFC (R\({}^{2}\) = 0.0001) were observed, which implies that these compounds are not dominant in the studied samples. Moreover, it is suggested that the Folin-Ciocalteu method overestimates the real phenolic content in comparison to the chromatographic method, as it also quantifies polymeric phenolics and other nonphenolic metabolites [48].
## 1 Introduction
Several studies have shown that base level changes can significantly influence channel adjustments in alluvial rivers (2018, 2019, 2020, 2021). [PERSON] et al. (2019) found that the mean bankfull width in the lower White River increased by 21% following base-level lowering from 1930 to 2010. [PERSON] and [PERSON] (2019) suggested that a base level drop initially led to channel incision followed by channel widening in cohesive channels. [PERSON] et al. (2020) calculated channel bankfull dimensions in the Jinjiang reach of the Middle Yangtze River and developed empirical relations between bankfull dimensions and downstream controls. In addition, other boundary conditions, such as bed and bank materials and riparian vegetation, can affect channel lateral migration (2018, 2019, 2021). The channel planform geometry, including width, radius of curvature and sinuosity, was also associated with the magnitude of channel migration (2018, 2019). The Xiaobeiganliu reach is a typical wandering reach in the Middle Yellow River, and it has a very wide and shallow channel. This reach is very famous for channel lateral migration, and it is reported that the maximum magnitude of lateral migration can reach up to 14.8 km in one year (2021). Previous studies paid little attention to the characteristics of channel migration in this wandering reach, and the effect of incoming flow and sediment regime, base level changes, and other factors on thalweg migration were poorly understood. The main purposes of this paper are (i) to investigate the temporal and spatial variations in bankfull width and thalweg migration distance in the Xiaobeiganliu reach of the Middle Yellow River during the period from 1986 to 2001; (ii) to quantitatively determine the effects of different boundary conditions on channel lateral migration in the Xiaobeangaliu reach and find the dominant factor influencing the lateral migration index. The results of the current study can facilitate a better understanding of the characteristics and mechanism of channel migration in wandering rivers. The relationships between lateral migration index and influencing factors can be established to predict channel migration processes in the Xiaobeiganliu reach, which is helpful for flood control and river channel management.