esa-sceva/satcom-chunk-collection huggingface dataset:
# An Approach to the State Explosion Problem: SOPC Case Study
## 6 Our Approach to State Space Reduction  
### State and State Transition Analysis  
The state machine is composed of a state register and a combinational logic circuit. It can transfer the state according to the preset state based on the control signal. It is the control center that coordinates the action of relevant signals and completes specific operations. The state machine consists of a calculation model containing a set of states, a start state, a set of input symbols, a mapping input symbol, and a transition function from the current state to the next state.  
In the actual digital system design, the finite state machine is usually used to establish the model of a sequential circuit, and in the formal verification, _Kripke_ is generally used to represent the structure of the system, which is actually a deformation of the state transition diagram.  
From Section 4.1, we can see that the state and state transition relationship of the system is the key factor in determining the complexity of state space. The state and state transition relationship of the digital circuit is equivalent or indirectly equivalent to the formal structure. Therefore, the state space explosion can start by reducing the state and state transition relationship of digital circuits.  
Figure 8: AP automaton.  
Figure 7: Implement automaton A.  
In the digital circuit composed of the SOPC system, each bit of the register represents a status bit of the sequential circuit. Each register represents a set of states. For a sequential circuit with N bit registers and M bit inputs, the maximum number of valid states is 2\({}^{\text{N}}\), and there are 2\({}^{\text{M}}\) input excitations for each state. In the worst case, a complete state diagram of a sequential circuit is a complete graph with 2\({}^{\text{N}}\) States and 2\({}^{\text{N}}\)\(\times\) 2\({}^{\text{M}}\) transition relationships. The scale of complex synchronous sequential circuits is increasing day by day, and the state space is exponentially increasing.
# Simple Synthesis Method and Characterizations of Aggregation-Free Cysteamine Capped PbS Quantum Dot
## 3 Results and Discussion
Thus, PbS quantum dots have the lattice plane spacings of 0.21 nm, 0.29 nm, and 0.34 nm which correspond to PbS [220], PbS [200], and PbS [111], respectively. Figure 5 shows the TEM images of the synthesized quantum dot after one month. As the results in Figure 2, the synthesized quantum dot maintained the dispersion stability after one month. The TEM images of the synthesized quantum dot show that the particles are not aggregated after one month in the microscopic view. Figure 4: The TEM image of the synthesized quantum dots in a higher magnitude than in Figure 3. In the image, the particles are able to be classified by three types of lattice structures. The lattice plane spacing of the synthesized quantum dot are 0.21 nm, 0.29 nm, and 0.34 nm, which correspond to PbS [220], PbS [200], and PbS [111]. Figure 6 shows EDS results and it exhibits nitrogen and sodium atoms from the two ligands, indicating that the two ligands were well-attached to the surface of synthesized quantum dot. Figure 7 shows the XRD results of the synthesized quantum dot. The highest peak of XRD is shown at PbS [111] and the next is PbS [220]. The intensity of PbS [200] is poor. Each points of peak correspond to the conventional PbS crystallinities [40]. Figure 8 demonstrates the absorption characteristics of synthesized cysteamine capped PbS quantum dot. The values used in Figure 5 were normalized between 0 to 1. The absorbance of the synthesized quantum dot gradually decreases as the wavelength increases, and the absorbance starts to increase at the wavelength of 1360 nm, reaches a peak at 1450 nm, and then decreases again. The peak of absorbance is determined by the energy between conduction and valence band, which is reversely  
Figure 5: The TEM images of the synthesized quantum dot after one month. The particles are well-dispersed in water and are not aggregated in the images of TEM. Figure 6: Energy dispersive X-ray spectroscopy (EDS) results of the aggregation-free cysteamine capped PbS quantum dot. The images show the presences of nitrogen (12 mol.%) and sodium (21 mol.%) atoms, indicating that the two ligands were attached to the surface of the PbS quantum dot. proportional to the size of the particle. According to Equation (1), the peak of absorbance is calculated to be 1430 nm as the average size of the synthesized quantum dot is 5.2 nm from TEM images. \[E_{0}=0.41+\ \frac{1}{0.0252d^{2}+0.283d} \tag{1}\]  
where \(E_{0}\) is the absorption energy, and \(d\) is the diameter of the quantum dot. The measured peak wavelength of absorbance is 1450 nm, which is a good agreement with the theoretical studies.
# Impact of Weather Factors on Unmanned Aerial Vehicles' Wireless Communications
## 2 Methodology
### Simulation Parameters for Atmospheric Weather Variations  
The effect of atmospheric weather conditions was evaluated by varying the signal propagation distance between the GCS and the UAV, height between the UAV and the GCS, atmospheric water vapor density, atmospheric liquid water density, temperature, and rain rates across a wide range of carrier frequencies from 1 GHz to 100 GHz. Table 2 gives the list of parameters and their values used in this investigation.  
The selection of weather-related parameter values aimed to capture typical annual meteorological fluctuations across various global locations.  
Globally, annual temperature fluctuations span a wide range. For 2023, the highest temperature of 53.9 \({}^{\circ}\)C was recorded in the Northern Hemisphere at Saratoga Spring, California, USA, while the lowest temperature was recorded as \(-\)62.7 \({}^{\circ}\)C in Tongulakh, Russia [32]. The selected temperature range of \(-\)40 to 40 \({}^{\circ}\)C is a symmetric representative interval, centered at 0.0 \({}^{\circ}\)C, offering a representative view of typical global temperature variations.  
The water vapor density values of 0.23, 5.75, 17.26, and 23.01 g/m\({}^{3}\) were assumed. These values correspond to 1%, 25%, 75%, and 100% relative humidity at 25 \({}^{\circ}\)C and 1013.25 hPa atmospheric pressure.  
For the liquid water density, values of 0.05, 0.5, 5.0, and 10.0 g/m\({}^{3}\) were used. These values correspond to cloud/fog visibility in the range of 2.5 m to 300 m.  
Specifications for distance, height, operating frequencies, and transmission powers were based on commercially available UAVs, with a focus on personal applications [33]. These choices were made to reflect the characteristics of UAVs currently on the market, particularly those designed for individual consumers.
# uDMA: An Efficient User-Level DMA for NVMe SSDs +
## 4 Design of uDMA
### Parameters Setting  
In uDMA, there are some parameters to set. They are: (1) \(m\), that is, the size of the pinned memory block for each application, (2) \(M\), the total size of the memory cache area, and (3) \(t\), the time threshold when the memory block is not accessed.  
\(m\) can be set according to the application load characteristics or empirical values. The default value of \(m\) is 4 MB. We can adjust the pinned memory block size according to the memory block usage of the application during a period. After the application is executed for some time, we judge the memory usage of the current application and then adjust the memory block size appropriately to improve the performance.  
There is no specific policy for setting \(M\), because the more significant the value is set at, the better the concurrency of I/O request processing. However, it needs to be set reasonably according to different situations. For example, there is no other application to run on a server that only does disk I/O. Users can set a considerable value to improve the processing efficiency of I/O requests. The setting principle is to set a significant threshold without affecting the operation of other applications.  
The primary consideration of the parameter of \(t\) is to release some completely free memory blocks and reduce the occupation of memory space when I/O processing is less in a period. The value of \(t\) is also a practical value, which is closely related to the application environment. If the value of \(t\) is too large, some of the memory may be pinned for a long time and not in use, resulting in a waste of memory resources. If the value of \(t\) is too small, it may result in frequent allocating and pinning memory.
###### Abstract  
Wear, fatigue, and corrosion are just a few of the issues that mechanical components in engineering experience, leading to surface deterioration. Enhancing the surface characteristics of engineering components is therefore essential. The surface properties of engineering objects can be improved by applying different surface treatments. One of these processes is shot peening (SP). Process parameters are crucial for SP. This necessitates the optimization of SP process parameters. In this study, we applied SP and vibratory shot peening (VSP) processes to different steel materials (AISI 8620, AISI 5140, AISI 4140, and AISI 1020) using different process parameters, aiming to determine the effects of these parameters on hardness, residual stress, and surface roughness. The highest compressive residual stress (CRS) and hardness values for shot-peened samples were obtained at the 24-26 A intensity for all steels. For all steel-group VSP samples, the highest CRS and hardness values were obtained at the 60 s \(-\)4 mm parameter. This paper aims to predict Almen intensity values using CRS, surface roughness, and hardness values from various steels. The supplied experimental data was utilized to estimate the SP Almen intensity value using a number of machine learning (ML) methods, eliminating the need for costly and time-consuming experimentation. With an RMSE of 0.0731, R2 of 0.9665, and MAE of 0.0613, the deep neural network (DNN) surpassed the other models in terms of prediction accuracy. The results indicate that artificial intelligence technology could be utilized to accurately evaluate Almen intensity.  
shot peening; residual stress; deep neural networks; machine learning; steel Article
## 1 Introduction
However, the variation of accuracy caused by thermal deformation of machine tools comes from the long-term machining. Actually, there are not any CNC parameters to solve the problem of thermal displacement. Several works developed the virtual machine tool technology which predicts the contour error of the closed loop feed drive system of the CNC machine tools [21; 22; 23; 24; 25; 26]. However, the virtual machine tool technology proposed by these previous works involved a mechatronic model which integrated the servo driver, mechanics of the feed drive mechanism, and the dynamics of the machine structures. Although the effects on the dynamic errors after changing the CNC and servo parameters have been studied, they did not establish or propose any tuning algorithm to switch the machining performance between HS, HP, and HQ modes. Conventional controllers have been provided with the functions of the HS, HP, and HQ modes in the CNC machine tools; they are predominantly used in trajectory generation [27; 28]. When the user selects a machining mode, the controller has to process feedrate with the NC blocks. In order to achieve the machining strategy, the control method has to match the tolerance value of the path contour via a relevant NC program. However, the algorithm of different machining modes isn't included in the feed drive system of real CNC machine tools; the function is merely processed in the interpolation of the controller. This paper presents a tuning methodology for the CNC parameters, and, hence, matches the machine's dynamic errors according to the selection of machining modes which are set by the mixed combination of three machining modes: The HS mode, HP mode, and HQ mode. The relationship between CNC parameters and dynamic errors is defined in Section 2. Section 3 uses simple paths to test the tuning criteria of the CNC parameters and different machining requirements. The tuning methodology and procedure of the CNC parameters based on machining modes were implemented in this section. In Section 5, the CNC parameters were estimated based on the relevant machining modes for experimental validation. Finally, the conclusion is presented in Section 6.
## 2 Materials and Methods  
### Animals and Experimental Ratios  
This study was performed at the experimental farm of the University of Huelva (Huelva, Spain). Forty-four primiparous Payoya breed goats were allocated to three experimental groups, each of them with a different diet. The three experimental diets were the following: control (C; \(n=14\)), with a commercial concentrate and alfalfa hay as forage; diet 1 (DOP40; \(n=16\)) based on C but with 40% of the cereals in the concentrate substituted with dried orange pulp (DOP); and diet 2 (DOP80; \(n=14\)), based on C, but with 80% of the cereals in the concentrate substituted with DOP. DOP pellets were prepared using orange juice residues, following a conventional industrial process (Citricos del Andevalo, S.A., Huelva, Spain). Briefly, the residue (pulp) of the orange obtained after the extraction of the juice was pressed to reduce the humidity to 70%. Afterwards, and after crushing and adding calcium oxide to facilitate its drying, this pressed pulp was dried in a rotary drum dryer until it reached a humidity of 10%. Finally, before proceeding to pelletization, the liquid extracted after the pressing was dehydrated and incorporated into the dried pulp.  
In the fifth month of lactation, the animals were offered the experimental diets adapted to this lactation month. The formulation of the rations was designed using the Feed Ration Balancer (Format Solutions) software, version 2.0 (2017; Cargill, Inc., Minneapolis, MN USA; www.formatsolutions.com). The chemical composition of the isoenergetic and isoproteic diets are described in Table 1. Food intake for each group was calculated daily, in late lactation (120-180 d), by subtracting the orts (uneaten food) from the amount of food offered every day. The total average dry matter (DM) intake per goat in the diet groups was 1.78, 1.76, and 1.75 kg/day in the C, DOP40, and DOP80 groups, respectively. For more details on animal management until early lactation, see [PERSON] et al. (2017).
## 3 Design of the EAS  
Figure 1 shows the structure of the FEAM and the VSAM used in EAS. The Face and Voice Recognition-based Emotion Analysis System (EAS) is composed of three primary modules: the Facial Emotion Recognition Module, the Voice Emotion Recognition Module, and the User Emotion Analysis Module. The system functions by first analyzing facial expressions using the facial features, followed by analyzing the tone, speech duration, and sentence structure of the voice to detect emotions. Finally, it integrates the emotions detected from both facial expressions and voice to provide a comprehensive analysis of the user's overall emotional state.  
The Facial Emotion Recognition Module is subdivided into the Facial Feature Recognition Submodule and the Expression Recognition Submodule. The Facial Feature Recognition Submodule classifies the facial features (such as eyes, nose, mouth, ears, etc.) and infers the emotional state for each feature. The Expression Recognition Submodule then estimates the user's facial emotions by combining the emotional states inferred from each feature with the overall facial expression.  
The Voice Emotion Recognition Module first separates the user's voice into tone and text components. It analyzes the emotions conveyed in the tone and then infers the emotional state from the content of the speech (text). By integrating the results from these modules, the EAS can provide a holistic evaluation of the user's emotional state.
# A Scene Graph Similarity-Based Remote Sensing Image Retrieval Algorithm
## 4 Experimental Evaluation
### Evaluation of the One-Shot Object Detection Algorithm for Remote Sensing Images  
#### 4.2.1 Evaluation Indicators  
Typically, there are multiple object classes in object detection, introducing mean aver age precision (\(mAP\)) as an evaluation metric. Average precision (\(AP\) is calculated from precision (_Precision_) and recall (_Recall_), where _Precision_ is the ratio of correctly predicted positive samples to all samples predicted as positive by the model and _Recall_ is the ratio of correctly predicted positive samples to all actual positive samples. _Recall_ and _Precision_ were plotted on the horizontal and vertical axes, respectively, to generate a \(PR\) curve, with the area under the curve representing the \(AP\) value. The equation for the calculation of \(AP\) is shown in Equation (1):  
\[AP=\int\limits_{0}^{1}P(R)d(R) \tag{1}\]  
The mean average precision (\(mAP\)), which is the average of the \(AP\) values across all classes, is calculated as presented in Equation (2), where \(N\) is the total number of classes, and \(AP_{i}\) is the \(AP\) value for the i-th class:  
\[mAP=\frac{1}{N}{\sum\limits_{i=1}^{N}AP_{i}} \tag{2}\]  
_Precision_ is the proportion of samples that are predicted as positive by the model and are actually positive. _Recall_ is the proportion of samples that are predicted as positive by the model out of all actual positive samples. The equation for the calculation of _Precision_ and _Recall_ is shown in Equation (3):  
\[\left\{\begin{array}{l}Precision=\frac{TP}{TP+FP}\\ Recall=\frac{TP}{TP+FN}\end{array}\right. \tag{3}\]  
Among them, \(TP\) represents the true positive samples, meaning the positive samples correctly predicted by the model, and \(FP\) represents the false positives samples, meaning the negative samples incorrectly predicted as positive by the model. \(FN\) refers to the false negatives samples, where positive samples are incorrectly predicted as negative.
# Along-Track Multistatic Synthetic Aperture Radar Formations of Miniatellites
## 4 Control of Sensor Position
### Mitigation of System Performance Degradation
#### 4.2.1 Tuning the PRF
(**right**) \(\log\)**F** with PRF tuning only (red) and with PRF and twin antenna optimization (green). Low values address a bad reconstruction.
## 4 Experiments
### Evaluation Metrics  
The following four evaluation metrics are used to evaluate the various performances of the proposed method on real tasks:  
(1) _Top-1/5 error rate_[53]. The top-k error rate is the fraction of correct labels that are not in the top-k predicted values. Its used to assess the generalization of the model. The formula is as follows:  
\[\text{Top-1 Error Rate}=\frac{\sum_{i=1}^{N}\mathbb{I}(\hat{g}_{i}\
eq y_{ i})}{N} \tag{7}\]  
\[\text{Top-5 Error Rate}=\frac{\sum_{i=1}^{N}\mathbb{I}(y_{i}\
ot\in\{\hat{g} _{i1},\hat{g}_{i2},\hat{g}_{i3},\hat{g}_{i4},\hat{g}_{i5}\})}{N} \tag{8}\]  
where \(N\) is the total number of test samples, \(\mathbb{I}(\cdot)\) is the indicator function, which takes the value 1 if the condition inside is true, otherwise it takes the value 0; \(\hat{y}_{i}\) is the predicted class of the \(i\)-th sample; \(\hat{g}_{i1}\), \(\hat{y_{i}}\)2, \(\hat{g}_{i3}\), \(\hat{g}_{i4}\), and \(\hat{g}_{i5}\) are the top 5 predicted labels for the \(i\)-th sample; and \(y_{i}\) is the true label for the \(i\)-th sample.  
(2) _Recall at k (R@k)_[54]. The recall at k is the percentage of test samples with at least one of the k-nearest neighbors in the feature space that are from the same class. Here, the Euclidean distance is used to measure the distances of the penultimate layer features. The recall of the \(k=1\) scores is used to assess the intra-class variation of the learned features.The formula for recall at k is as follows:  
\[\text{Recall@k}=\frac{|\text{Relevant items in top k results}|}{|\text{Total number of relevant items}|} \tag{9}\]  
where \(|\text{Relevant items in top k results}|\) is the number of relevant items retrieved in the top k results. \(|\text{Total number of relevant items}|\) is the total number of relevant items in the dataset.  
(3) _Expected Calibration Error (ECE)_. ECE [55; 56] measures the differences in the expectations between confidence and accuracy. The formula for ECE is as follows:  
\[\text{ECE}=\sum_{m=1}^{M}\frac{|B_{m}|}{n}|\text{acc}(B_{m})-\text{conf}(B_{ m})| \tag{10}\]  
where \(M\) is the number of bins. \(B_{m}\) is the set of indices of samples whose predicted probability falls into the \(m\)-th bin. \(n\) is the total number of samples. \(acc(B_{m})\) is the accuracy of the samples in the \(m\)-th bin. \(conf(B_{m})\) is the average confidence (predicted probability) of the samples in the \(m\)-th bin. Following [15], here, we set 20 bins to evaluate the impact of the proposed method on the calibration.  
(4) _Mean standard deviation (Mstd)_[57]. Mstd is the mean mold of the standard deviations for intra-class features. We use it to evaluate the density of intra-class features. The formula for Mstd is as follows:  
\[\text{Mstd}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(x_{i}-\bar{x})^{2}} \tag{11}\]  
where \(N\) is the number of samples. \(x_{i}\) is the \(i\)-th sample value. \(\bar{x}\) is the sample mean, defined as \(\bar{x}=\frac{1}{N}\sum_{i=1}^{N}x_{i}\).
# Fleet Resilience: Evaluating Maintenance Strategies in Critical Equipment
## 4 Case Study  
With the aim of demonstrating the validity and usefulness of the model and the proposed methodological procedure, the behavior of the resilience index of a fleet used for the operation of an open-pit mine located in northern Chile was analyzed. For the purposes of the study, we divided this equipment into three sub-fleets. These sub-fleets are based on three classes of equipment: electric shovels (S02-S04), drills (D05-D09) and trucks (Hxx), summing 51 pieces of equipment. The truck and drill sub-fleets were further subdivided according to their types. A series of support equipment was ignored, such as motor graders, irrigation trucks, etc. Once the equipment was identified and selected, historical data were collected, considering 18 months of continuous operation. From these data, the reliability and maintainability curves of each of the equipment items were modelled. With this, the metrics for each of the equipment items were obtained, and from this analysis, the critical equipment of each sub-fleet was identified. In Table 2, the values of mean time between failures (MTBF) and mean time to repair (MTTR) are shown as main indicators of reliability and maintainability since they are directly linked to the availability, and therefore, to the resilience of the system.  
Figure 6: System availability as a result of individual availabilities and topological aspects.  
Figure 7: Schematic representation of the availability values considered by the Equation (2).  
Figure 8 shows the Jack Knife diagram with the equipment selected as critical from Table 2, for each of the sub-fleets. We established as an assumption that these equipment items are the ones that most affect the resilience of the system. Therefore, they were selected for stage II of the study, i.e., the Monte Carlo analysis.  
Analyzing the results and the historical series of interventions, it was initially possible to observe that the Drilling Fleet presents the equipment with the greatest impact at system level (unavailability), mainly due to the number of hours of detention as a result of corrective maintenance. Finally, it was decided to continue the analysis by selecting one equipment, the most critical, for each sub-fleet, except for the 1 AC truck fleet, which, due to its size, incorporated 10% of them (three trucks) into the analysis.