esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation, Experiment, and Comparison
### The Simulation  
#### 5.1.1 The Data Collection by ILC  
The model of the gripper is programmed to repeat the ILC process in Section 3.3 for different closing speeds and desired forces in different objects (environment). The closing speed \(S_{g}\) varies from 5 to 10 mm/s, with each step being 1. The desired force \(F_{d}\) varies from 1 to 8 N, with each step being 0.5. The initial value of \(B\) is set at 10 N.m/s. The total number of experiments is 4200, so the dataset has 4200 records. The dataset is analyzed to evaluate the performance based on the parameters of the maximum overshoot \(F_{p}\) and the final force error \(F_{fe}\). Table 1 presents the analysis data from 4200 records of the dataset in the simulation. Figure 7 shows the tracking force of several loops in one case of the ILC process with a closing speed of 5 mm/s and the desired force of 4 N.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Max** & **_MAE_** \\ \hline \(F_{p}\) (N) & 0.2983 & 0.0671 \\ \(F_{fe}\) (N) & 0.0503 & 0.0004 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The resulted iterative learning control (ILC) quality parameters in the simulation.  
Figure 7: The tracking force and \(B\) value through several loops in simulation.
## 2 Materials and Methods
### CSM-CROPGRO-Cotton Model
The integration with genetic algorithms leverages two critical functionalities: (1) The model generates continuous response surfaces linking nitrogen application variables (X\({}_{\text{N}}\): timing-dose-space) to multi-objective outcomes (\(Y\): yield, \(Z\): nitrogen loss), formalized as computable fitness functions (e.g., max\([Y-\alpha Z]\)); (2) Parametrization of soil nutrient pools and genotype-specific nitrogen use efficiency coefficients permits high-throughput simulation of \(>\)10\({}^{3}\) fertilization scenarios, circumventing the throughput limitations of conventional field experiments. This synergy capitalizes on the model's capacity to resolve process-response mechanisms in agroecosystems while harnessing the global optimization capability of genetic algorithms. Under the constraints of soil heterogeneity and climatic variability in Xinjiang's oasis cotton systems, the coupled framework identified Pareto-optimal nitrogen application strategies that simultaneously enhanced yield. The optimization framework explicitly incorporates nitrogen use efficiency (NUE) as a critical agro-environmental performance metric, given its dual role in reconciling productivity gains with environmental stewardship [35]. This parameterization enables systematic evaluation of fertilization strategies through the lens of thermodynamic resource conversion principles, where NUE serves as a key fitness criterion in our multi-objective genetic algorithm. Nitrogen use efficiency (_NUE_, kg kg-1) quantifies the mass of grain yield produced per unit nitrogen fertilizer applied, representing a pivotal indicator of agricultural nitrogen economy [36]. It is formally defined as  
\[N\text{\emph{U}}E=\frac{GY_{\text{N}}}{N_{\text{rate}}} \tag{2}\]  
where \(GY_{\text{N}}\) denotes grain yield under applied nitrogen (kg ha-1) and \(N_{\text{rate}}\) represents the nitrogen application rate (kg ha-1).
# Turning Video Resource Management into Cloud Computing
## 4 Experiments and Results
### The Experimental Results
#### 4.2.2 Performance Testing  
Considering that the video resource service system is frequently used for data reading, the download time ate calculated by Formula (1) was measured in different data sizes (MB) to verify whether the HDFS-based system is fit to manage huge volumes of video resources. The testing result (Figure 7) shows the download time rate of videos is decreasing with data size increasing, that is, the HDFS-based video resource management system performed better in managing big data than small, and videos are already very big in size. When the data size is more than 1000 MB, the download time rate stabilizes at a certain level.  
Figure 5: A playlist of the video service testing.  
Figure 6: The video screenshot at time 00:52:35.  
\[P\,=\,t_{i}/t_{0} \tag{1}\]  
where \(P\) represents the download time rate, \(t_{i}\) is the average downloadtime (ms/MB) of the \(i^{th}\) data groupB, and \(t_{0}\) is the download time (ms) of 1 MB.
# Optimal Tests for Combining \(p\)-Values
## 3 Numeric Studies
We have the following observations: First, for Scenario 1 (Figure 1), where the effect sizes from the true individual alternative hypotheses are extremely heterogeneous, the Min \(p\) test (i.e., \(T_{G(0)}\)) usually performs better than the Fisher test (\(T_{G(1)}\)), which in turn performs better than the z test (\(T_{C(\infty)}\)). Second, when the degree of heterogeneity of the effect sizes among individual alternative hypotheses are less extreme, as in Scenarios 2 and 3 (Figures 2 and 3), Fisher test and the z test usually perform better or much better than the Min \(p\) test. Third, for the Min \(p\), Fisher, and z test, one may perform very well for some conditions but very poorly under others. For instance, under Scenario 1 (extremely heterogeneous effect sizes among the alternatives), the Min \(p\) test is more powerful than the other two, while it is much less powerful under scenario 3 (homogeneous effect sizes among the alternatives). The opposite direction was observed for the z test. Fourth, under all conditions considered, the new test \(T_{CLRT}\) is either the best or very comparable to the best one. When the number of \(p\)-values to be combined is small, we observed similar patterns (see the simulation results in Tables S1-S3 in the Supplementary Materials when \(n=10\)). This demonstrates that, as expected, \(T_{CLRT}\) is a robust test in the sense
## 3 Numerical Modeling Calculation  
### Numerical Model Establishment  
We utilized FLC3D6.0 software and Rhinoceros 5 3D external modeling software to create a numerical excavation model. The purpose was to investigate the impact of tunnel excavation on the surrounding rocks, considering various excavation modes and support characteristics. The model followed the tunnel construction steps depicted in Figure 1, with dimensions of 80 m along the longitudinal \(Y\)-axis (tunnel strike) and 70 m horizontally orthogonal to the tunnel. The tunnel had a burial depth of 20 m and a lower burial depth of 30 m.  
In the calculation model, the upper boundary was set as a free boundary condition, while the remaining sides and underside were subjected to normal restraint boundary conditions. The stress boundary condition was determined based on the self-weighted stress field.  
In order to simulate different scenarios, we considered various levels of surrounding rock conditions using a two-step method for Class III surrounding rocks. We incorporated advance support and initial support in accordance with the specific construction conditions. To investigate the impact of different excavation steps and the length of the first excavation of the inverted arch on tunnel deformation, we varied the excavation steps at intervals of  
Figure 1: Numerical analysis and calculation model.  
30 m, 40 m, 45 m, 50 m, 55 m, and 60 m. Additionally, we examined the length of the first excavation of the inverted arch, exploring values of 3 m, 4 m, 5 m, and 6 m. These variations allowed us to assess the influence of these factors on tunnel deformation. Current studies on tunnel excavation simulations often do not take into account the rheological properties of the surrounding rock or the temporal effects of tunnel construction. The results obtained are not consistent with the actual field conditions. Therefore, in this study, the rheological properties of the surrounding rock and the construction effects of tunnel construction are considered on the basis of experiments, and a modified Burgers body instanton model was used to simulate the tunnel excavation, which can provide a reference for the numerical simulation of the tunnel considering the spatial and temporal effects of excavation. The study can be used as a reference for the numerical simulation of tunneling, considering the spatial and temporal effects of excavation.
# Conveyor Belt Damage Detection with the Use of a Two-Layer Neural Network
## 1 Introduction
### Neural Network  
One inspiration for developing artificial neural networks came from biological phenomena observed in human brain. Research into the secrets of human intelligence provided results that proved useful in computer science as well. Neural networks, so often used in solving technical problems, are then in fact a simplified model of the human nervous system [13; 14; 15]. An artificial neural network is a structure composed of single calculation units and consisting of one or many layers processing a certain output signal on the basis of a particular input signal. The input layer of a neural network consists of a number of neurons corresponding to the number of values provided at the network input (occasionally, in order to improve its performance, the network is provided with an additional neuron activated by a constant signal). The hidden layer may consist of any number of neurons, each of them being activated by the signal produced by the neurons in the previous layer. The output layer must consist of a number of neurons corresponding to the number of outputs in the designed network [13; 16; 17].  
Figure 4(a) shows the structure of a single neuron, which receives many input values and which produces a single output value. Figure 4(b) shows a representative network that consists of a hidden layer with \(k_{1}\) neurons. A single neuron works by adding the stimulation signal delivered to its input after multiplying it by the weights assigned to a given input. The summed excitation signal is then transferred to the appropriate activation function, and the value generated by it is the output signal of the neuron [17]. The output signal of the neuron is described by a mathematical relationship represented by the Formula (1):  
\[y_{i}=f\left(\sum_{j=1}^{N}w_{ij}x_{j}+w_{i0}\right) \tag{1}\]  
where \(y_{i}\) is the output signal of the \(i\)-th neuron, \(N\) is the number of signals that stimulate the neuron, \(w_{ij}\) is the weight of the \(i\)-th neuron ascribed to the \(j\)-th stimulation, and \(f\) is the activation function.  
The number of layers and neurons in the hidden layers is one of the most important parameters of the designed neural network. Networks with one or two hidden layers are among the most frequently used (problems which would require more than three layers to solve are not known) [17; 18].  
The learning process of a neural network requires defining a method for updating the weights of the connections between neurons in the adjacent layers. One of the most  
Figure 4: Representative conveyor belt—magnetic analysis of damage.  
Figure 5: Models.  
frequently used learning algorithms is backward error propagation, which consists of finding the minimum sum squared error (in the steepest descent optimization method). In this algorithm, the network learning error is sent from the output layer to the input layer [14].
# Designing Dual-Function Nanostructures for Water Purification in Sunlight
## 3 Results and Discussion
### Raman Spectroscopy  
One of the most familiar tools for examining graphene and graphene based materials is Raman spectroscopy, which considers as a non-invasive method. The Raman spectra of the prepared graphene and the Zn-Al-GO composites are displayed in Figure 4. Two major peaks were observed in the Raman spectrum of the prepared graphene. In addition, the spectra of the samples Zn-Al-GO1, Zn-Al-GO2, and Zn-Al-GO3 showed the same peaks. These two peaks are displayed at 1330 cm\({}^{-1}\) and 1590 cm\({}^{-1}\). These peaks are considered characteristics peaks of graphene and known as D and G bands; respectively [33].  
The G-bands are due to the presence of sp\({}^{2}\)-bonded carbon atoms in a two-dimensional hexagonal graphene layer indicating the characteristic ordered graphitic carbon, while, the D-bands are assigned to the presence of disorder graphitic carbon. In addition, there was a third absorption band which was observed at 1050 cm\({}^{-1}\) indicating the link between the layers of LDHs and the graphene sheets M-O-C (M means metal atoms) [34]. The degree of graphitization can be determined by the intensity ratio of D to G bands. The ratio of I\({}_{\text{D}}\)/I\({}_{\text{G}}\) was 1.28-1.37 reflecting the moderate graphitic crystallinity and  
Figure 4: Raman spectra of: (**a**) Zn-Al-GO1, (**b**) Zn-Al-GO2, (**c**) Zn-Al-GO3 and (**d**) the prepared graphene.  
Figure 3: FT-IR spectra of: (**a**) Zn-Al LDH, (**b**) Zn-Al-GO1, (**c**) Zn-Al-GO2, (**d**) Zn-Al-GO3 and (**e**) The prepared graphene.  
moderate degree of graphitization [35; 36]. By comparing with the intensity of D-band, the intensity of the G-band was lower indicating the presence of the layers of LDH and defects as well as functional groups on the surface of the GO layers.
# [PERSON] on Korean Block Ciphers
## 3 Proposed Method  
In this chapter, we describe the optimized quantum circuit for applying the [PERSON]'s algorithm to HIGHT, CHAM, LEA, and SPECK block ciphers.  
All block ciphers implemented in this paper are composed of modular addition, rotation, and XOR operations based on ARX. In the quantum circuit, the rotation operation can be designed without using any gates by changing the order of qubits. Given \(a=\{a_{0},a_{1},a_{2},a_{3}\}\) and \(b=\{b_{0},b_{1},b_{2},b_{3}\}\), the operation \(a\gg 1\) is performed. Then, the XOR operation \(b=b\oplus a\) is performed on the elements of \(b\) and the elements of \(a\) changed in order by rotation. In this case, since \(a\) is \(\{a_{1},a_{2},a_{3},a_{0}\}\) after \(a\gg 1\), it is completed by performing \(\text{CNOT}(a_{1},b_{0})\), \(\text{CNOT}(a_{2},b_{1})\), \(\text{CNOT}(a_{3},b_{2})\), and \(\text{CNOT}(a_{0},b_{3})\). In this way, there is no cost for the rotation operation, and the XOR operation can be performed simply with a CNOT gate.  
However, for addition operation, Toffoli gates are required, so a more complex circuit is needed than the previous two cases. We used the method of [PERSON] et. al's ripple carry addition [15]. In order to use the ripple-carry addition circuit, not only the qubits of the calculation target, but also two additional qubits for carry calculation are required. Since the addition of block cipher is the modular addition, the value of highest carry qubit can be ignored. Therefore, the addition operation can be performed only with the initial carry qubit of \(c_{0}\). Since this \(c_{0}\) qubit is initialized to 0 after addition operation, it can be recycled at any time.  
Figure 10: Condition after diffusion of the Grover algorithm.  
The block cipher is largely divided into two steps, including key schedule and round function. In order to reduce the required qubit, an on-the-fly approach is utilized.
# Sugar Beet Profitability in Lubelskie Province, Poland
## 2 Methodology  
The methodology applied in this study has been described in detail in previous research [11]. It is consistently repeated each year to ensure the reproducibility and comparability of results. The study focused on farms cultivating sugar beets on areas ranging from 2 to 10 hectares. These were primarily family-owned farms engaged predominantly in plant production, with only a few also raising livestock. The farms were equipped with modern agricultural machinery, although some still made use of older but serviceable equipment. Most of these farms are located in the Lublin region and deliver their sugar beets to the Krasnystaw Sugar Factory, a branch of Krajowa Grupa Spozywcza (KGS) in Siennica Nadolna. Growers in this area enter into annual contracts with the sugar factory, which guarantees them a fixed price for the sugar beets delivered. In addition, the factory supplies farmers with mineral fertilizers and plant protection products and organizes road transport for sugar beet collection. In the studied region, there are also farms with sugar beet cultivation areas of less than 2 hectares and others exceeding 10 hectares. However, the group of farms cultivating more than 10 hectares of sugar beets is relatively small. These two groups were excluded from the profitability analysis due to their distinct production characteristics, which are not comparable to those of the 119 farms included in the study. Therefore, the largest and most representative group of farms was selected as the basis for the research. From this group, a model (averaged) farm was developed to reflect the most realistic sugar beet cultivation conditions in the study area. The cost analysis conducted was comprehensive and accounted for all factors affecting farm income (Tables 1-3). Costs and income were calculated per hectare of sugar beet cultivation. A commonly accepted economic methodology was used to determine production income and the associated costs. The average yield from sugar beet cultivation during the 2020-2022 period was 50 tons per hectare. Based on this yield, production revenue was calculated using the purchase price per ton of sugar beets. Costs were divided into two categories: direct costs and indirect costs. A production surplus, a key metric in agricultural economics, was calculated. To evaluate the profitability of production, a production profitability index was also determined.
## 3 Literature Review
### DK in Cements for Mortars and Concrete  
#### 3.4.1 Pozzolanic Activity  
Several studies performed on DK assessed its pozzolanic activity towards its use in cement and concrete. This was reported to be higher than that of silica fure (SF) but also metakaolin (MK) in supplementary cementitious material (SCM) systems [32; 33; 34; 35]. This performance stems from the high content of hydrated amorphous silica in the DK, which reacts swiftly with calcium hydroxide (Ca(OH)\({}_{2}\)) produced during cement hydration to form calcium silicate hydrate (C-S-H) gel and the modified microstructure of DK compared to MK, as acid attack significantly increases porosity, hence increasing DK specific surface area (e.g., BET values of 40-90.5 m\({}^{2}\)/g, vs. 18.8 m\({}^{2}\)/g for silica fume, were reported in [29; 34]), although there is no considerable decrease in the average pore size [29]. These features facilitate rapid dissolution and release of silica into pore solution and enhance reactivity with portlandite (pozzolanic reaction), driving high activity indices, e.g., 120% and 127% relative to SF (ASTM C618 method) were reported, respectively, in [34; 35].  
These findings confirm that the performance of DK in SCM systems, is likely to be influenced by both its enhanced microstructure (surface area, porosity) and its chemical composition (amorphous silica), driving its rapid and effective reaction with calcium hydroxide to accelerate calcium silicate hydrate (C-S-H) formation, which is linked to strength development.  
Based on these findings, numerous studies have explored the potential use of DK in the production of concrete with supplementary cementitious material (SCM) systems or alkali-activated cements (AACs)/geopolymers. The following sections provide a comprehensive review of such applications. The most essential research papers alongside with mix designs and key findings are also summarised in the comparative overview in Tables 1 and 2, respectively, for DK as SCM in Portland cement-based systems or precursor in AAC-based systems.
# Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix
## 3 Vessel Data Preprocessing
### Data Preprocessing
#### 3.1.3 Trajectory Extraction  
Since the same MMSI may correspond to multiple trajectories in different periods, it is necessary to accurately extract each trajectory based on the time span. The specific steps are as follows: first, the AIS data are sorted according to timestamps to ensure the temporal order of the data; second, each MMSI is traversed, and the time span between adjacent data points is measured; finally, a suitable time threshold (such as several hours or days) is set. When the time span between a data point and its previous data point exceeds this threshold, it is considered the starting point of a new trajectory. Figure 1 shows the trajectory extraction process based on MMSI and timestamps. Through this processing, clear and accurate vessel trajectory information can be extracted from the original AIS data, providing strong support for subsequent navigation path analysis and prediction.  
The essence of trajectory compression is to represent the raw trajectory data more simply while maintaining its key spatial and temporal relationships as well as trend features. In the application of AIS data, due to the large scale of AIS data, it is difficult to process and store the data, and the recorded data during navigation often contain a large amount of dense and redundant information. Therefore, to reduce data volume and improve data usability, it is necessary to compress trajectories.
# Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems
## 3 Integrated Circuits
### 0.5 V-SAR ADC with Single-Clock Bootstrapping and Time-Delay Based Controller
The output voltage of the  
Figure 5: Block diagram of a 0.5 V, low-power 8-bit SAR ADC with input-range boosting, LVS bootstrapping sample/hold and time-delay control. Figure 6: Operation of the input range boosting circuit for (**a**) rail-to-rail operation (**b**) over-the-rail operation. proposed switch can be extended with additional stages for the multi-stage switching to generate a higher bootstrapped voltage. Taking advantage of low-voltage operation (0.5 V) and a high affordable voltage swing (2.5 V in 0.25 \(\upmu\)m), the reliability can be also enhanced. To generate the control signals for the synchronous SAR ADC operation without a high-frequency clock, we employed a time-delayed control unit that is digitally controlled and programmed as shown in Figure 8. Even though the implemented SAR ADC operates synchronously, the high-speed clock essential for the conventional synchronous SAR ADCs is unnecessary since the proposed time-delayed units can generate any waveforms for the control of synchronous SAR ADCs. The delay units can be categorized into two types: (1) duty-cycle controllers (Figure 8a,b) and (2) one-shot signal generators (Figure 8c,d). Both types of the delay units can generate positive or negative edge-triggered signals with a programmable duty cycle in combination with logic gates. One good example for the programmability in this time-delay-based control units is an interference-tolerant reset signal which is used to prevent a kick-back effect by ensuring that the reset is completed before the capacitor-digital-to-analog-converter (CDAC) updates (the rising edge of SEL [n + 1]) as shown in Figure 9. The simulation result (Figure 9) shows that the error induced by the kick-back from the comparator by the _Latch_ signal is suppressed below 5 \(\upmu\)V. Had the conventional clocked-timing generator been used, however, complicated blocks would have been required with high-speed clock signals, thus resulting in high power consumption. In our design, the time-delayed control unit allows this control signal to be generated with much less power and reduced complexity. The measured power consumption of the control signal generator is less than 10 nW for 31.25 kS/s operation. In addition, this generator can be shared by multiple channels and significantly reduce the system complexity by eliminating all the high-frequency clocks, when compared to the conventional synchronized timing generators. For the bit decision in the ADC, a dynamic comparator having NMOS inputs and a cross-coupled degeneration has been implemented. The details of the comparator will not be given to save the length of the paper. Figure 7: Low-power voltage-scalable single-clock (LVS) bootstrap circuit: (**a**) two-stage bootstrap circuit and (**b**) its operation and output.