esa-sceva/satcom-chunk-collection huggingface dataset:
# MIM_SLAM: A Multi-Level ICP Matching Method for Mobile Robot in Large-Scale and Sparse Scenes
## 5 Conclusions  
For large-scale and sparse environments, we propose a novel MIM_SLAM algorithm in this paper, which simplifies the SLAM problem to the least-squares optimization problem. The contribution of this paper is that the multi-level ICP matching method is proposed to solve the data association problem and the uncertainty estimation is handled by the Fisher information matrix. It considers the accumulated errors in two aspects: matching between time-neighbor frames (building a low-drift LiDAR odometry) and matching between area-neighbor frames (dealing with the data association at a revisited area). Moreover, multi-level matching and incremental optimization reduce the computational complexity while ensuring mapping accuracy, we can achieve accurate mapping and real-time applications. Additionally, its application is not only limited to mobile robots, and it can potentially be extended to other vehicles, e.g., UAVs. Experimental results show that it can effectively build a high consistency map with a smaller amount of environmental information and the large-scale scenes, and it also achieves similar or better accuracy compared with the standard ICP and the state-of-the-art LOAM algorithm in the KITTI dataset. Additionally, there are some limitations in high dynamic scenes. Our future work will focus on the construction of a more robust data association method, we will integrate the output of our multi-level ICP matching with an IMU in a Kalman filter to further reduce the accumulated error.  
Figure 11: The trajectory produced by MIM_SLAM in the three sequences.  
Figure 12: The pose estimation error in the three sequences.  
**Author Contributions**: This study was completed by the co-authors. [PERSON] conceived and led the research. The major experiments and analyses were undertaken by [PERSON] and [PERSON]. [PERSON] supervised and guided this study. [PERSON], [PERSON] and [PERSON] wrote the paper. All the authors have read and approved the final manuscript.  
**Funding**: This research was partly funded by the National Natural Science Foundation of China (Grant No. 617/73261) and the National Key R&D Program of China (Grant No. 2017 YFC0806501).  
**Conflicts of Interest**: The authors declare no conflicts of interest.
# QoS-Aware Downlink Traffic Scheduling for Cellular Networks with Dual Connectivity
## 4 Proposed QoS Downlink Data Dispatching (QDD) Method
In every \(I_{t}\), the MeNB fills in the known variables of the above equation and derives the results of \(r_{sp}(b_{i})\), \(\forall b_{i}\in B\). Then, the MeNB can follow the results to dispatch downlink data to the corresponding SeNBs in the upcoming \(I_{t}\). Note that in the above formulation, a UE \(u_{i}\) only associates with a bearer \(b_{i}\). As we mentioned above, a UE may initiate multiple data flows at the same time. Therefore, it is not hard to see that our formulation can adapt to this scenario by adding virtual UEs.
## 3 Findings
### Core Elements Underpinning SCM in the Construction Industry
#### 3.2.5 Organizational Behavior  
This last subject area comprises just one practice, namely 'information and communication technology integration'. Information and communication technology (ICT) delivers an environment within which supply chain members have greater accessibility to one another and can conveniently retrieve or send information at any time and in any place [12; 17]. Therefore, it plays a key role to ensure consistent and efficient information management [5] and to enhance communication and coordination within the supply chain [6; 115]. ICT impacts both on technical and managerial sides boosting the effectiveness and efficiency of designing and managing projects [20]. Building information modeling (BIM), an \"object-based and multidisciplinary approach aimed at facilitating collaboration between parties and the integration of object-related information over the entire life cycle of an asset\" [42], is one of the most popular and increasingly important technologies [39]. BIM adoption has become a paradigm shift in the construction industry, increasing productivity, reducing failure costs and fostering new ways of collaboration [42; 57; 59; 66; 103]. BIM related applications were identified in almost all the previously discussed elements, corroborating the idea that this technology is rapidly transforming the construction industry [49]. Recent trends have been directed towards exploring new ways of capturing and synchronizing real-time information with BIM. This entails integrating BIM with other technologies, such as RFID [59], geographic information systems [19; 50; 67], web map service [49] and internet of things [61], to improve supply chain transparency, visibility and traceability, thus making supply chains more agile, flexible and resilient. Yet, to take advantage of ICT, consistent adoption of these technologies along the supply chain is required [39]. Considering that the construction supply chain is formed by many small- and medium-sized enterprises, facing the challenge of diffusing these new technologies should be a priority [20; 39; 72]. In particular, issues with costs, compatibility and interoperability, lack of awareness and suppliers' reluctance to adopt needs to be managed [39].
# Developing Quantum Trusted Platform Module (QTPM) to Advance IoT Security
## 2 Preliminaries
### Pseudo Random Number Generator (PRNG)  
In classical computers, randomness is derived from algorithms that use mathematical formulas to produce sequences of random numbers. It starts from an arbitrary starting state using a seed state, which is an initial value that determines the randomness of the generator. This kind of number generator is called PRNG since it lacks true randomness, which is also called a \"deterministic random bit generator (DRBG)\". A volume of numbers can be generated in a brief period and can be replicated at a later time if the starting point in the sequence is known [30; 31]. The linear congruential generation algorithm is the most commonly used pseudo random number generation method [32]. Additionally, the XOR shift algorithm is also used for random number generation.  
However, since PRNG's output is deterministic, an attacker can predict the entire sequence of generated numbers if the seed value is known. As a result, PRNGs are inappropriate for applications requiring high security, such as cryptographic protocols or random password creation. Another limitation is that PRNGs can only create a finite sequence of numbers before repeating the same sequence. This number repetition is known as the period, and it limits the number range that a PRNG can generate [32; 33]. In some applications, a long period of time is required, which PRNGs may fail to supply. In addition, PRNGs generate numbers that appear random but are not genuinely random. They use a deterministic algorithm to generate a series of numbers that approach randomness. While PRNG output is statistically indistinguishable from truly random numbers, it is not truly random and may exhibit patterns that can be exploited. The seed value used to initialize the PRNG determines the quality of the generated sequence of integers. The generated sequence of numbers may be subject to assaults if a weak or predictable seed value is utilized.
# Time to First Fix Robustness of Global Navigation Satellite Systems: Comparison Study
## 1 Introduction
For instance, if the receiver already has stored navigation and time information, it can be used to reduce the TTFF, corresponding to the so-called warm and hot starts [21]. On the other hand, in this work, we consider a cold-start scenario, where there are no previous data available at the GNSS receiver and, consequently, a full search of satellites from scratch is required. This situation is more demanding but fits better with the general low-consumption needs of multiple mass-market applications, where the PVT data are not constantly derived and the GNSS chipset is only switched on when needed [22; 23]. In this study, we propose assessing the robustness of the TTFF for the different GNSSs in a comparative way. For this purpose, we develop an ad hoc setup that allows us to test how easy it is for a typical low-cost receiver to obtain a useful fix with each GNSS separately. In this way, for the very same receiver and conditions, the TTFF is influenced only by the availability and quality of the GNSS signals [24; 25]. As the TTFF also depends on the technological design and implementation of the receiver, the specific values obtained here should not be taken as absolute references. However, the results are valid for establishing a comparison of TTFF robustness between the different GNSSs.
## 4 Application Example  
The procedure was applied to improve the energy, economic and environmental sustainability of one of the transformation houses of the low-voltage distribution network of an industrial area, in which there are furniture manufacturing, vehicle repair, industrial spare parts stores, as well as a couple of cafes/restaurants, among other companies. The transformation house has an operating transformer from the manufacturer ORMAZABAL,of 1600 kVA, whose primary is connected to the 20 kV medium voltage electrical network, with nominal features indicated in Table 4.  
Connected to the secondary of this transformer, there is a three-phase non-variable capacitor bank, with a power \(Q_{c1}=3\times 25\) kVAR. Likewise, after the first power measurements were made in the secondary of the transformer, the existence of a second three-phase non-variable capacitor bank was detected, with an estimated power \(Q_{c2}=3\times 100\) kVAR. This second reactive compensator was not visible, as it was outside the transformation house, inside the companies.  
To carry out the study, a measurement campaign was made between the months of October and December of the year 2020, using the Fluke 435 Series II network analyzer. This instrument has a loss calculator for the distribution lines (Figure 3), which can be adapted to measure the losses in transformers, introducing the value of the short-circuit resistance of the transformer instead of the resistance of the lines, and discounting the values of losses in the neutral conductor.  
The economic costs derived from the operation of the transformer and the surcharges for capacitive consumption have been obtained by applying Circular 3/2020, of the National Commission of Markets and Competition of Spain, 15 January.
## 4 Results and Discussion
### Experiment II: Performance of Hierarchical Attention Mechanism  
The experiment in this section aims to compare the performance of various hierarchical attention mechanisms based on semantic features extracted from XLNet pretraining models. The HA, FT, and Bi-LSTM + HA selected in Experiment II are hierarchical attention mechanisms. These three structures aim to solve the problem of mapping char-level embeddings of the text to document-level embeddings. Unlike HA, FT uses maximum pooling to address the mapping from char-level embedding to segment-level embeddings and finally to document-level embeddings. Bi-LSTM + HA, on the other hand, adds a bidirectional LSTM (Bi-LSTM) on top of the lower-order representation layer of HA and then maps segment-level embeddings to document-level embeddings using the higher-order representation layer of HA. In Experiment II, we train 27 classifiers in the classifier tree using the XLNet-based HA, FT, and Bi-LSTM models and then obtain a total of 3 classifier trees and 81 classifiers, as well as the P, R, and F1 values of each classifier (see Table 4 for detailed results). Compared with the other two models, the XLNet + BiLSTM + HA + FC model has a significant advantage in various metrics, with the average values of P, R, and F1 reaching 95.6%, 95%, and 94.9%, respectively. Furthermore, from the micro perspective of each classifier, for the three metrics of P, R, and F1, the number and proportion of classifiers that used BiLSTM + HA and achieved optimal performance in the three models are significantly higher than those of the other two, namely 22 (81%), 21 (78%), and 21 (78%), as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Classifier** & \multicolumn{4}{c}{**XLNet + HA + FC**} & \multicolumn{4  
Moreover, the results show that BiLSTM + HA was 1.8% and 25.1% higher than the other two models for the P values, 1.7% and 30.7% higher for the R values, and 1.7% and 33.1% higher for the F1 values, respectively. Therefore, for RQ2 \"Which types of HAs have the best performance in learning document-level embeddings?\", we can conclude that the training results of the XLNet + BiLSTM + HA + FC model in the classifier tree are significantly better than those of the other two models.
## 4 Case Study  
### Participants  
To assess the efficiency of our method, ACLR patients from the Rehab and Physiotherapy Unit of Hospital Tuanku Fauziah at Kangar, Perlis, were considered for this study. Ten patients with a unilateral primary ACLR were chosen for the ACLR group (PG). This group was composed of 10 males. All patients go through similar rehabilitation procedures, beginning passive and active mobilization after the operation. Meanwhile, 15 healthy subjects (HG) were selected for the healthy group with the condition that they had no previous history of lower extremity injuries, surgeries, or neuropathy. The HG consisted of 15 males. The height, age, and weight distribution of the two groups were not significantly (\(p>0.05\)) (Table 1). Ethical approval was granted by the University of Malaysia Perlis, and the participants provided written informed consent.
## 3 Methodology
### Preprocessing  
One of the important steps in the data preprocessing was to resize the X-ray images as the image input for different algorithms were different. For AlexNet and SqueezeNet, the images were resized to \(227\times 227\) pixels, whereas for ResNet18 and DenseNet201, the images were resized to and \(224\times 224\) pixels. All images were normalized according to the pre-trained model standards.  
As discussed earlier, CNNs work better with a large dataset. However, the size of the working database is not very large. There is a common trend in training deep learning algorithms to make the comparatively smaller dataset a large one using data augmentation techniques. It is reported that data augmentation can improve the classification accuracy of the deep learning algorithms. The performance of the deep learning models can be improved by augmenting the existing data rather than collecting new data. Some of the deep learning frameworks have data augmentation facility built into the algorithms; however, in this study, authors have utilized three augmentation strategies to generate new training sets (Rotation, Scaling, and Translation), as shown in Figure 9.  
Figure 8: Overview of the methodology.  
Figure 9: Original chest X-ray image (**A**), chest X-ray image after rotation (**B**), chest X-ray image after scaling (**C**), and chest-X-ray image after translation (**D**).  
The rotation operation used for image augmentation is normally done by rotating the image in the clockwise direction by an angle between 0 to 360 degrees, which rotates the pixel of the image frame and fills the area of the image where there was no image pixel. In this work, the rotation of 315 degrees (counter clockwise 45 degrees) was used. The scaling operation is the magnification or reduction of frame size of the image, which is another augmentation technique used. In total, 10% of image magnification, as done as shown in Figure 9C. Image translation can be done by either translating the image in a horizontal (width shift) or vertical direction (height shift) or in both directions. The original image was horizontally translated by 10% and vertically translated by 10%.
# Dimensional Synthesis for Multi-Linkage Robots Based on a Niched Pareto Genetic Algorithm
## 1 Introduction
In [19], in order to improve the load-capacity of the mechanical arm, the method employing workbench goal-driven methodology optimized analysis was adopted, reducing the arms weight and increasing the load of the manipulator. In [20], structural parameters of the bionic rescue robot were optimized based on genetic algorithm. In the literature [12; 13; 14; 15; 16; 17; 18; 19; 20], for the dimensional synthesis of multi-linkage robots in different application situations, the effectiveness of the methods is verified respectively, and good performance of multi-linkage robots is obtained. However, the universality of these methods in other situations needs to be further verified. For complex scenes or high flexibility requirements, the methods proposed in these studies have problems such as tedious solution expression, large amount of calculation, and poor rationality of multi group solution verification. Although the design method in the reference [17] is concise, which improves the design efficiency and shortens the design time. However, the energy consumption factor in the motion of multi-linkage robots was not included as the objective function in these studies, and the research on the joint angle of the robot was also not involved. Therefore, it is necessary to design multi-linkage robots with high flexibility and low energy consumption in order to improve computing efficiency and accuracy. Kinematical theory, workspace density function [21], genetic algorithm [22; 23; 24; 25], and multi-objective algorithm theory had been fully researched before. In this paper, we set the density function, maneuverability [26; 27; 28], and energy expenditure as objective functions. Multi-link robots have high flexibility and low energy consumption, which can meet the application requirements under special circumstances. In order to guide the design of multi-link robots, we proposed a design method of multi-linkage robots' dimensions and joint angles based on the niched Pareto genetic algorithm, shorted as NPGA. Apply proposed method to actual scenes, which are retracting and releasing shielding cloth of salt evaporating pool and separation of the remaining solid 2,2'-azobis(2,4-dimethylvaleronitrile) (shorted ADVN) from the barrel. The multiple dimensions and joint angles of the 2R and 3R link robots were obtained, which verified the reliability of the method. The method provides new ideas and methods for the dimensional synthesis of multi-linkages robots. The rest of the paper is organized as follows. In Section 2, three objective functions are researched by calculating appropriate cases, then the relevant calculation formula are derived. In Section 3, the parameters and design process of NPGA are determined. In Section 4, the mathematical model of operation is established. In Section 5, two application examples of parameter synthesis are given to verify the proposed method. Finally, we present our conclusions in Section 6.
# Late-Stage Optimization of Modern ILP Processor Cores via FPGA Simulation
## 5 Evaluation
### Late-stage Optimization Case Studies
#### 5.2.2 Pipeline Parameter Exploration (PAR/IMAX/MIN)  
Different pipeline parameters substantially affect performance. Having a fast and accurate performance simulation for the late stage is useful. In this case study, we choose three pipeline parameters as examples, namely, fetchBufferSize, Width, and FPLatency with large and small values. fetchBufferSize means the size of the fetch queue in Ifetch unit, configured with 32 and 24. It is a PAR value, which can be parameterized in fetch queue module. Width means the decodeWidth/dispatch-Width/issueWidth, configured with 4 and 2. It is a MAX value, which can be configured with less than the basic value (4). FPLatency denotes the latencies of FPU and has an adder and multiplier. It is originally a MIN value. However, what we configure are less than the basic values (4 and 3 cycles for the multiplier and adder, respectively). Hence, they become IMP values and we implemented a two-cycle adder and a three-cycle multiplier. This can not only improve performance but also reduce hardware resources.  
Figure 8 shows the pipeline exploration for CFP benchmarks. The basic pipeline configuration is Fetch32_Width4_Lat34, whereas other configurations are explored in this study to reduce hardware resources. For Fetch32_Width4_Lat23, the reduction of FPlatency results in an evident performance improvement of approximately 5%. Width is an important parameter for the pipeline. Decreasing the width value from 4 to 2 greatly reduces hardware cost and degrades performance. For example, performance is lower by approximately 14% for Fetch32_Width2_Lat34. Decreasing the fetchBufferSize value from 32 to 24 also degrades performance. Their impacts differ under certain conditions, such as Width4 and Width2. The variation in performance under Width4 is more obvious than that under Width2. Such an accurate performance exploration can result in confident designs after decreasing the width while ensuring the required performance (For example, performance degradation should be no larger than 15%). Compared with the baseline configuration, Fetch24_Width2_Lat23 is a good design choice for reducing hardware cost.
## 4 Results and Discussion
### Software Analysis-Optimization and Cost Analysis  
#### 4.5.1 Software Analysis and Optimization  
Optimization is the method of using mathematical algorithms to maximize or minimize certain parameters to achieve the best possible results in a given system. Optimization techniques are widely used in engineering problems. Examples include structural optimization [54], predicting the strength of iron tailings [55], the thermal interaction and cooling of electronic devices [56], drilling processes [57], automatic guide vehicle [58], and the meta-heuristic solution of engineering problems [59].  
In this study, a code was written in a MATLAB program to optimize the design of the refuse chute. The goal here was to give designers a tool that they could use when creating new chute designs. During a pandemic, users can use the program to control their own chutes in emergency situations. The code can calculate and optimize parameters such as the refuse chute diameter, chute thickness, fan air flow rate, ventilation duct diameter, minimum rock wool thickness for human contact, and number of spare containers to be used, taking into account parameters such as the weekly waste volume, container volume, plate yield strength, loads to which the system is exposed to chute body temperatures during fire, and the vent duct flow velocity.  
Here, the nonlinear constrained optimization method was applied using the fmincon function in the MATLAB program. This method enables the search for a solution under various constraints while minimizing a specific function. After running the code, the solution was found in 0.0015 s and 100 iterations. The MATLAB solution screen is shown in Figure 15.  
Figure 15: MATLAB result screen.  
Here, according to the optimization results, the chute diameter was 496 mm, chute thickness was 1.5 mm, fan air flow rate was 900.0 m\({}^{3}\)/h, ventilation duct diameter was \(\mathcal{O}\) 200.0 mm, the minimum rockwood thickness for human contact was 80.0 mm, and the number of spare containers to be used was 1.0 pc.