esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
As mentioned above, all participants in our study had chronic but incomplete injuries, with both sensory and motor remnants potentially trainable and varying degrees of disruption of upper limb motor synergies derived from their level of injury. The fact that the shortening of the hand movement trajectory occurs mainly, almost exclusively, in quadriplegic children is explained by the fact that it is these children who have upper limb functional deficits directly caused by SCI. With the exception of the T2 level SCI patient presented individually, all the paraplegic children in our sample had preservedand functional all the circuits involved in the sensorimotor function of the arms so that when they followed the trajectories in the non-immersive virtual application based on the Leap Motion Controller (LMC), they had all the sensory resources to make the necessary feedforward corrections so that the hand movements were smooth and efficient. These kinds of improvements in quadriplegic patients would potentially enhance performance in other manipulative tasks of cognitive content, such as writing, so important in the age range of our selected patients. Probably the most promising aspect of our results is precisely that our data shows that these aspects can be trained so that the reorganization and optimization of motor synergies described in the motor learning processes of a given task can also be achieved by training the muscle groups involved in the synergies, in our case the arms [23; 31; 32]. With rehabilitation robotics, technologies have been introduced into the clinical environment to be integrated into the therapeutic process of patients. Specifically, assistive social robotics through the Robic robot provides guided therapy through a noncontact motor rehabilitation system that has been extensively evaluated and progressively enhanced [7]. The main limitation of the study is the size of the sample analyzed, this being the first experience of a Robic robot in a sample of pediatric patients with SCI. The improvements observed in patients related to the UE dexterity throughout the smoothness and efficiency metrics were measured by means of RehabHand software [20], which allows us to register kinematic data from the hand during the execution of the functional tasks. For this reason, future works should address three different aspects: the optimization of the robotic platform so that it can provide real-time data on the subject's movement execution; the training protocols themselves, evaluating whether the effects that we have found in the present work are maintained or even improved by applying more demanding exercise protocols, in addition to analyzing how the reorganization of motor synergies occurs depending on the level and severity of the spinal cord injury and introducing a control group of SCI patients; and continue the research in the proposal of new metrics from kinematic hand data measured by means of the technologies applied in neurorehabilitation.
## 3 Background Work
### ECDSA Signature Scheme
#### 3.3.3 Verify Algorithm  
The signed message is used as the input for the ECDSA Verify Algorithm. _'m'_ is the ECDSA Sign algorithm's signature of the form q, t, and the signer's public Key \(PubK\), which is matched to the signer's private Key \(PrivK\). A valid or incorrect value is the output. As seen below, the ECDSA verification algorithm follows:  
* \(h=Ha(m)\)--compute the message's hash.
* \(sig=s-1(modn)\)--the inverse modular proof of the signature.
* \(R^{\prime}=(h*Si)*G+(q*Si)*PrivK\)--recovering the signing-related random point.
* \(q^{\prime}=R^{\prime}.x\)--Determine R's x-coordinate.
* If \(q^{\prime}==q\), it is a genuine signature.
## 4 Discussion
### Climatological Year
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Power Installed (W)** & **Battery Capacity (Wh)** & **Reliability (\%)** \\ \hline
2015 & 9350 & 17,760 & 92.97 \\
2016 & 11,550 & 17,760 & 93.65 \\
2017 & 10,450 & 17,760 & 93.35 \\
2018 & 9900 & 17,760 & 93.17 \\
2019 & 8800 & 17,760 & 92.73 \\
2020 & 9900 & 17,760 & 93.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Reliability of the optimal system for each year for 2016 data. Figure 18: Historical precipitation and solar hours in the area of interest in the years 2016–2017. \begin{table}
\begin{tabular}{c c} \hline \hline
**Year** & **Cumulative Sun Hours** \\ \hline
2015 & 2991.7 \\
2016 & 2963.2 \\
2017 & 3158.0 \\
2018 & 3010.2 \\
2019 & 3161.9 \\
2020 & 3013.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accumulated hours of sunshine per year recorded in the area of interest. As can be seen, although the optimal system varies from year to year since it is optimized for the minimum cost, which is very sensitive to the hours not supplied where a secondary generator has had to act, the system's supply reliability, once it is in the order of magnitude of the optimum, is quite insensitive to small variations in the installed power, fulfilling the technical requirement of reliability.
# Simulation-Based Performance Analysis for a Novel AVS/RS Technology with Movable Lifts
###### Abstract  
This paper studies a novel autonomous vehicle-based storage and retrieval system (AVS/RS) design with movable lifts (AVS/RS/ML). In the proposed system, there are aisle-captive lifts that are able to travel along the warehouse aisle to position themselves at the target column location. Those lifts can lift up/ down the autonomous vehicles to/from the target storage compartment when they are in standstill. This novel design is proposed as an alternative to existing AVS/RSs to balance the resource utilizations as well as to provide an inexpensive solution with highly utilized autonomous vehicles (i.e., AGVs). As an initial work, for this novel system, two alternative operating designs under different tracking configurations are experimented. We compare those two designs by their throughput rate performance metrics under the arrival rate scenarios with highly utilized AGVs (i.e., 95%). Besides, we experiment with two warehouse capacity scenarios: 900 and 1800 storage compartments. The results show that designs with two separate I/O point locations provide a better throughput rate than designs with single I/O point location. Besides, a decreased number of columns in the system improves the system's performance.  
logistics; automated warehousing; AVS/RS; movable lifts; simulation modelling; material handling system +
Footnote †: journal: Applied Sciences
## 4 Tree-Based Protocols
### Query Tree Protocol  
The query tree protocol (QT) is one of the most representative memoryless protocols, in which the reader must provide the tags with a query and the matching tags must respond with their full ID [41]. Tag response depends directly on the current query, ignoring the prior communication history. QT tags involve only simple hardware requirements because they only compare the reader query with their own ID and respond if it coincides. The identification process consists of more rounds in which the reader sends a query, and tags whose ID prefix match the current query respond with their whole ID binary value. In the case of a collision, the reader forms two new queries by appending \(q\) with a binary 0 or 1. New queries will be placed in a Last Input First Output stack (LIFO). If there is no response to a query, the reader knows that there is no tag with the required prefix, and the query is rejected. This kind of slot is called idle. If just one tag responds to the reader query, that tag will be identified. By extending the query prefixes until only one tag's ID matches, the algorithm can identify the rest of the tags. The identification procedure is completed when the LIFO stack is empty.  
Figure 12 shows the QT protocol being used to read 6 tags (Tag A-Tag F). Each tag uses an ID length of \(k=6\) bits. Initially, the LIFO stack is empty, and the reader begins with a null string. After a collision occurs, the reader pushes queries 0 and 1 into LIFO stack. During the second round, the reader pops from the stack and transmits query 0. In the example in Figure 12, tags 000100 and 001010 match the required prefix, which causes both to transmit and collide. The reader is unable to understand the messages from the tags. The reader then pushes into the stack queries 01 and 00. In the next round, the reader transmits query 00. Again, both protocols respond with their ID and a new collision occurs. In the stack, the following new queries are added: 001 and 000. The reader transmits query 000 and only one tag responds (000100). This tag is successfully identified and will not answer any of the following reader requests. The reader then transmits query 001 in slot 4, which matches tag 001010. In the next round, the reader pops and transmits query 01. For this query, there will be no response since no tags contain that prefix. In round 7 the reader transmits query 1 and the tag from the right side of the tree responds. Four tags will receive this query and a new collision occurs. The reader experiences a collision, since tags 100011, 101110, 110110 and 111001 responded to the query 1. As a result, queries 11 and 10 are pushed onto the stack. The identification process is repeated until round 13, in which the reader transmits the last query (111) from the stack. Overall, the reader uses 13 rounds to read 6 tags.  
In the literature, numerous extensions to the QT protocol have also been proposed [38; 42; 43; 44; 45].
# Research and Analysis of MEMS Switches in Different Frequency Bands
## 3 The Research Status of MEMS Switches in Different Frequencies
### Research on Dielectric Charging of MEMS Switches
Ultra-NanoCrystalline diamond had been used as the dielectric layer of MEMS capacitive switches in [78; 79], and dielectric charging had little effect on the diamond switch because the charging and discharging time constants of the diamond were 5-6 orders of magnitude quicker than the conventional material. Thus, the stiction problem due to dielectric charging was solved when the switch was in the down-state. Switches based on aluminum nitride (AlN) dielectric material presented slow dielectric charging and fast dielectric discharging, which could reduce charge accumulation and increase significantly the reliability of the RF MEMS capacitive switches [80]. Increasing touch surface roughness, reducing the actual contact surfaces, and using material with high hardness could weaken the effect of dielectric charging in MEMS capacitive switches [81]. In order to eliminate the contact between the dielectric and the suspended metal, separation posts located at each corner of an MEMS switch and within the actuation pad were proposed in [52], and the separation posts prevented the problem of dielectric charging. [PERSON] et al. [82] presented a novel dielectric-less MEMS switch to avoid dielectric charging; polysilicon stoppers were designed to prevent ohmic contact between the electrodes in the down-state. In order to reduce charge accumulation, a novel MEMS capacitive switch, which aligned holes in the membrane to a dielectric post, was proposed in [83], and charge across the dielectric layer was reduced; therefore, dielectric charging was reduced. [PERSON] et al. [84] found that a double-dielectric-layer structure could reduce the charge accumulation in the dielectric layer compared to the single-dielectric-layer structure. [PERSON] et al. [85] found that applying a bipolar driving voltage could reduce dielectric charging and improve reliability. [PERSON] et al. [86] designed a novel MEMS capacitive switch with no dielectric layer on the bottom actuation electrode, which removed a source of dielectric charging, and the bump was also designed to avoid dielectric charging in the switch.
## 3 Results and Discussion
### Uncertainties in Modeling Forest GPP
The function of water constraint has been a long-term challenge, and many functions of water stress have been defined considering VPD, soil moisture, evapotranspiration, and water index from remote sensing [13; 19; 39; 72]. Also, the methods used to combine water and temperature stresses were different. EC-LUE used the minimum rule,while other LUE models adopted a multiplication of environmental factors, which also contributed to the variance in GPP simulations from different LUE models. Overall, the errors mentioned above from ground measurements and satellite observations, data processing and derivation, and the structure of LUE models (i.e., the definition of environmental stresses and integration methods) could bring uncertainties to LUE models and further contribute to the uncertainties in SVM and RF estimation.
## 1 Introduction
The transfer function of the phase reconfiguration was obtained. Then, the time-dependent structural response was calculated by Fourier inverse transform. This method was called local modal phase information reconstruction. The VMSS method was proposed by [PERSON] et al. [17]. The principle of statistical energy analysis was introduced into the classical modal analysis. [PERSON] et al. expounded the principles and basic formulas of VMSS. It integrated the method into a general computing program. [PERSON] et al. [18] proposed an improved VMSS to study the shock response of the structure. Experiments were carried out for verification. For the complex structure and the shock signal containing many kinds of information, the applicability of the above method was reduced. For instance, the VMSS ignores the low mode structure, and the TSEA and LMPR cannot deal with complex structures. The calculation accuracy is also difficult to guarantee. [PERSON] et al. [19] predicted the vibration of typical beam structures based on the hybrid FE-SEA method. It was found that the hybrid method was more efficient than other methods. [PERSON] et al. [20] researched the hybrid method of aircraft model. The structural noise response of aircraft model under acoustic vibration coupling was studied by the hybrid FE-SEA method. In addition, some other papers have applied the hybrid FE-SEA method to different structures and problems [21; 22]. At present, the hybrid FE-SEA method is mainly used to solve the problem of steady-state response. For the prediction of the structural shock response, the application of the hybrid FE-SEA method is rarely studied. In this paper, the basic theory of FE-SEA hybrid method is derived. This method is extended to shock response prediction. When the shock load is acted on the structure, the response time is very short, and the phase of the response of the structure is very close to the phase of the loaded signal. The amplitude of the response is mainly determined by the frequency response function. A more accurate and applicable prediction method of the shock response based on the hybrid FE-SEA method is presented in this paper. The time domain response results are reconstructed by using the phase information of the loaded signal. We can calculate the \(SRS\) needed in engineering based on the time domain response. A shock experiment for the typical spacecraft structure to the rationality and sufficient accuracy of the novel method is carried in this paper. At the same time, compared with the finite element method and other transient response prediction methods, the complex structures are reasonably calculated, and the computational efficiency is greatly improved by our method. The research in this paper can better predict the shock response of system level spacecraft structure and shorten the development cycle of the spacecraft. It provides a basis for the application of the steady-state method to shock problems.
## 1 Introduction
Recent research has uses micro-computed tomography (uCT) to analyze dimensional stability, porosity and estimate the size and character of pores present in the material [28]. Although this method is accurate and reliable, it takes more time, especially if the change is observed during a certain time interval [29]. A three-dimensional (3D) optical system based on the digital image correlation (DIC) method is a new method that has been used for the evaluation of strain on the surface of the material. It is a noninvasive, noncontact optical method, based on digital image correlation for deformation analysis of local fields. The DIC method has been widely used from testing process equipment and defining new geometries of test samples to developing dental models for experimental research [30; 31; 32; 33]. Application of this method in dentistry has proven to be very useful in analysis in the bone denture complex [34]. The aim of this research was to determine, analyze and compare the strain of three calcium silicate-based materials: MTA+ (Cerkamed Company, Stalowa Wola, Poland), Biodentine (Septodont, Saint-Maur-des-Fosses, France) and Well-Root PT (Vericom, Chuncheon-si, Korea) during setting time, using an optical system based on the 3D digital image correlation method (3D-DIC).
## 2 Materials
### Site Management and Irrigation Datasets  
Table 2 provides an overview of the total number of irrigation events, irrigation method employed, and sowing and harvest dates for each plot. In Montpellier, the P1 plot, which spanned an area of 13,860 m\({}^{2}\), had a deep soil of colluvial-alluvial origin with a silty clay loam texture (approximately 24% clay, 44% loam, and 32% sand), contributing to its  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Plot**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Month**} & \multirow{2}{*}{**Tavg (\({}^{\circ}\)C)**} & \multicolumn{2}{c}{**Avg-Rg**} & \multirow{2}{*}{**Monthly**} & \multirow{2}{*}{**Monthly**} \\  & & & & **(MJm\({}^{-2}\)d\({}^{-1}\))** & & **R (mm)** & **ETo (mm)** \\ \hline \multirow{4}{*}{P1} & \multirow{4}{*}{2017} & April & 17.6 & 20.75 & 39.5 & 93.7 \\  & & May & 23.1 & 24.1 & 27.4 & 131.2 \\  & & June & 28.7 & 25.97 & 73.2 & 166.3 \\  & & July & 28.3 & 26.06 & 4.7 & 182.2 \\  & & August & 30.9 & 20.84 & 8.9 & 145.1 \\  & & September & 23.6 & 16.31 & 6.5 & 93.9 \\ \hline \multirow{4}{*}{P2} & \multirow{4}{*}{2018} & April & 12.6 & 15.0 & 142.7 & 76.8 \\  & & May & 14.0 & 15.8 & 71.4 & 88.7 \\  & & June & 18.6 & 17.9 & 180.0 & 112.3 \\  & & July & 21.4 & 21.3 & 160.2 & 137.3 \\  & & August & 20.5 & 18.9 & 81.4 & 110.6 \\  & & September & 18.7 & 16.6 & 48.2 & 82.1 \\  & & October & 12.5 & 10.2 & 69.8 & 37.1 \\ \hline \multirow{4}{*}{P3} & \multirow{4}{*}{2019} & May & 26.3 & 20.3 & 117.8 & 104.3 \\  & & June & 35.4 & 22.1 & 65.6 & 127.9 \\  & & July & 36.6 & 21.2 & 100.8 & 133.4 \\ \cline{1-1}  & & August & 32 & 18.8 & 119 & 109.2 \\ \cline{1-1}  & & September & 29.3 & 16.5 & 57.6 & 78.1 \\ \cline{1-1}  & & October & 31.4 & 9.5 & 91.6 & 37.8 \\ \hline \multirow{4}{*}{P4} & \multirow{4}{*}{2020} & April & 23.7 & 15.6 & 159.2 & 78.2 \\  & & May & 29 & 21.5 & 77.6 & 120.6 \\ \cline{1-1}  & & June & 31.6 & 19.1 & 100.6 & 111.1 \\ \cline{1-1}  & & July & 36.1 & 20.8 & 11.8 & 127.8 \\ \cline{1-1}  & & August & 36.2 & 18.9 & 62.2 & 110.2 \\ \cline{1-1}  & & September & 33.7 & 15.7 & 101 & 80.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Monthly climatic data for the maize experimental plots: P1 in Montpellier, and P2, P3, and P4 in Tarbes. The data includes the average air temperature (Tavg in \({}^{\circ}\)C), daily averaged values of global solar radiation (Avg-Rg in MJm\({}^{-2}\)d\({}^{-1}\)), monthly rainfall (R in mm/month), and Penman–Monteith reference evapotranspiration (ETo in mm/month).  
Irrigation typically occurred during the dry summer season, which extends from June to October in Montpellier and from the beginning of July to the end of August of each year in Tarbes. In this study, the sprinkler irrigation method was employed over the reference plots. It is worth noting that precise information was available not only about the specific dates of irrigation but also regarding the precise amounts of irrigation applied over the four reference plots.
# Automating the Analysis of Negative Test Verdicts:
## 2 Materials and Methods
### Applied AI Algorithms
[26] proposed an approach to anomaly detection based on a cyclically consistent GAN architecture for sequential data and a new way to measure reconstruction error; this approach is hereafter referred to as the ALAD (Adversial Learning for Time Series) algorithm. In this study, we also consider the XGBoost (XGB+) algorithm widely used by researchers to handle sparse data. The most important distinguishing factor of this algorithm is its scalability in all scenarios due to several significant optimizations, including a novel tree learning algorithm for handling sparse data and a quantile sketch procedure to handle instance weights in approximate tree learning [27]. [PERSON] et al. [28] proposed an innovative approach to gradient boosting, CatB+. The toolkit they implemented combines an advanced ordered boosting, a permutation-driven alternative to the classical algorithm, and an innovative algorithm for processing categorical features. Both techniques are developed to combat the prediction shift caused by a special type of target leakage present in all currently existing implementations of gradient boosting algorithms. Another innovative approach is the LightGBM (LGB) algorithm based on two new techniques, Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB). GOSS is based on excluding a significant proportion of instances with small gradients and using the rest to estimate the information gain. EFB combines mutually exclusive features to reduce the number of features [29]. Considering that [PERSON]' et al. [30] successfully validated the learning capabilities of two different algorithms, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) for potential fault recognition in the oil and gas industry, we include these two algorithms in our analyses. Two simple models for tabular data proposed by [PERSON] et al. are also investigated: the modified ResNet (Residual Network) architecture and the FTTransformer (Feature Tokenizer + Transformer). The main innovation in ResNet is the use of residual blocks. Traditional deep networks suffer from the problem of the vanishing gradient, making it difficult to train very deep networks. Residual blocks use skip connections or shortcuts to skip one or more layers, allowing for the gradient to flow more easily through the network. In the adaptation, the main building block is simplified compared to the original architecture and there is an almost clean path from input to output, which is considered beneficial for optimization. In a nutshell, the FTTransformer model transforms all features (categorical and numerical) to embeddings and applies a stack of Transformer layers to the embeddings. Thus, every Transformer layer operates on the feature level of one object [31].