esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study: SmartLab Concept and 3D Printing Environment
### Main Functions of the 3D Printing Environment  
The AM part of the SmartLab concept concentrates on the early stages of the product development process, where CAD data is processed into prototypes and products. In addition, the laboratory includes 3D scanning, enabling reverse engineering applications. The students can learn and practice technologies and phenomena related to additive manufacturing.  
Related to the SmartLab concept, the 3D printing environment can function in two different ways. First, the environment can be used for the manufacturing of polymer parts based on student or customer projects. The student projects can be related to, e.g., a course or BSc thesis topic related to AM. The customer projects can be, e.g., from cooperation partners where companies can implement their product development project in collaboration with the Lapland UAS mechanical engineering degree course. Second, the 3D printing environment can function as part of the SmartLab manufacturing chain. Figure 4 presents examples from the placement of AM functions in the SmartLab product manufacturing.  
As Figure 4 shows, the SmartLab concept operates within the Teamcenter software, which connects the functions of product development and handles the planning of the product line. The product line can consist of several different alternatives, according to the product requirements. In the first alternative, CAD data is taken to the 3D printing environment, where the necessary parts are manufactured. The parts are then taken to the  
Figure 4: Connecting 3D printing to the SmartLab products.  
Festo product line, where they can be integrated with, e.g., standard machine parts such as bearings and nuts and then assembled into a product. In the second alternative, CAD data produces information for cutting thin sheet metal with plasma. The sheet metal is then processed in the NC bending machine with material-specific parameters. The part can be taken to the Festo assembly line and combined with 3D printed parts. In the third alternative, the CAD data enables the milling of parts, which can then be joined together with a digitally managed and controlled welding process. This intermediate product can be integrated with 3D printed parts and assembled manually, even without the Festo equipment. These are just some examples of the many ways the SmartLab concept can be used. At the center of it all is the PLM system and the principle of digital manufacturing, where engineering education meets the possibilities of modern manufacturing.
## 2 Materials and Methods
### Statistical Analysis  
All analyses were performed within the R statistical environment. The Shapiro-Wilk test was used to determine if data or residuals were normally distributed (except for sugar concentration, where the Anderson-Darling test was used). A Levene test was used to assess the variance in the homogeneity of data. If data followed a normal distribution, and variance was homogeneous, \(t\)-test, lineal model, or linear mixed models (LMM) were used. Otherwise, nonparametric methods (Mann-Whitney or Wilcoxon tests for paired data) were used.  
Natural varroa mite fall in each group (Excel File S2) was compared over determined time periods. The average of natural varroa mite fall in each hive over each period was compared with a LMM with the group (control vs. varroa), periods, and the interaction between treatment and period as fixed factors. Hive identity was used as a random factor considering auto-replication. A posteriori comparison (post hoc Tukey test) was performed combined with a downward adjustment of the alpha level with the Bonferroni correction. Post hoc Tukey test helped to elucidate which specific period showed significant differences between group means.  
For sugar (glucose + trehalose) concentration analysis, the average of the five replicates of combined sugar concentrations for each hive at each time point was compared using a LMM with the group (control vs. varroa), time (19 June, 2 August, or 18 September 2019), and the interaction between treatment and time as fixed factors and treatment nested within the hive as a random factor. A priori contrasts between pairs of means were performed combined with a downward adjustment of the alpha level with the Benjamini-Hochberg procedure.  
Several statistical tests were used for the six studied viruses. A Mann-Whitney test was performed between ratios of KBV, ABPV, DWV-A, and DWV-B/actin in each group (control vs. varroa). A Wilcoxon test was performed for paired data (KBV, ABPV, DWV-A, and DWV-B/actin) to assess the effect of time. A linear model was used to determine the relationship between the ratios of BQCV/actin and both group and time. Mean ratios of IAPV/actin were compared between groups (control vs. varroa) with a \(t\)-test. Data from the highly infested hive No. 345 was removed from the analysis since a varroa mite was mistakenly included in the pooled honeybee sample.
# Instance Hard Triplet Loss for In-video Person Re-identification
###### Abstract
We need to fuse short-term information like background and pose with human body features to discriminate highly similar persons for in-video ReID. Since in-video ReID is quite different from existing cross-camera ReID, why bother to investigate this new task? As mentioned in [12], in a video, people often disappear and re-appear again due to occlusions by other people or objects. A tracking method without knowing identity information cannot handle this situation properly, because it cannot distinguish well between a new person and a re-appeared existing person. That is the reason why [5] argued that the ReID module is essential for multiple person tracking. Some researchers in the pose tracking community also plan to embed a ReID model in their method [13]. Thus, a good in-video method is helpful for person tracking and pose tracking, as well as many downstream practical applications such as video surveillance and sports video analysis. In this paper, we try to address this new in-video ReID problem, and our main contributions can be summarized in three-folds:  
1. A new large-scale video-based in-video ReID dataset with full images available. To the best of our knowledge, no such in-video dataset has been released before. A ReID-Head network is also designed to extract features for the in-video ReID task efficiently;
2. A new loss function called Instance Hard Triplet (IHT) loss is proposed, which is suitable for both the cross-camera ReID task and the in-video ReID task. Compared with the widely-used Batch Hard Triplet (BHT) loss [14] for cross-camera ReID, it achieves competitive performance and saves more than 30% of the training time (see Section 4.4, Table 2);
3. Labeling ReID data is expensive and time consuming; thus, we also propose an unsupervised method for automatically associating persons in the same video with the same identity through reciprocal matching, so that an in-video ReID model can be trained using these associated data.
###### Abstract  
_Background_: Early accurate detection of coronary artery disease (CAD) is one of the most important medical research areas. Researchers are motivated to utilize machine learning techniques for quick and accurate detection of CAD. _Methods_: To obtain the high quality of features used for machine learning, we here extracted the coronary bifurcation features from the coronary computed tomography angiography (CCTA) images by using the morphometric method. The machine learning classifier algorithms, such as logistic regression (LR), decision tree (DT), linear discriminant analysis (LDA), k-nearest neighbors (k-NN), artificial neural network (ANN), and support vector machine (SVM) were applied for estimating the performance by using the measured features. _Results_: The results showed that in comparison with other machine learning methods, the polynomial-SVM with the use of the grid search optimization method had the best performance for the detection of CAD and had yielded the classification accuracy of 100.00%. Among six examined coronary bifurcation features, the exponent of vessel diameter (\(n\)) and the area expansion ratio (AER) were two key features in the detection of CAD. _Conclusions_: This study could aid the clinicians to detect CAD accurately, which may probably provide an alternative method for the non-invasive diagnosis in clinical.  
coronary artery disease; coronary bifurcations; machine learning; morphological features; classification performance
## 3 The Median Filter Method  
### Principle of Median Filter  
A median filter is a kind of nonlinear filter technology that has good edge preserving characteristics and the ability of suppressing impulse noise [8]. This method is essentially a window filter. The filtering operation is to scan the sample data by sliding a fixed length window, and replace the data in the center of the current window with the median of the data in the window [42]. With the end of the window moving, the filtering process of the whole sample signal will be completed. Since only one signal dimension is involved in the vibration signal analysis in this paper, only one dimension of the discrete median filtering principle is discussed. Let the discrete sampling sequence of signal \(x(t)\) be \(x(n)\)\((n=1,2,3\cdots,n)\) and take the filtering window with a length of \(L_{d}=2d+1\) (where \(d\) is a positive integer) to conduct median filtering for this signal sequence. At the \(n\)th time, the data column in the window is represented as \(W_{d}=[x(n-d),\cdots,x(n),\cdots,x(n+d)]\), and \(n-d\geq 1\), \(n+d\leq N\). The number of \(2d+1\) in \(W_{d}\) is arranged in the order of small to large, and the intermediate value \(y(n)\) is taken to replace the original \(x(n)\) that is, the filtering task of a data point of the signal is completed [43; 44]. The mathematical expression of this process is:  
\[y(n)=Med[x(n-d),\cdots,x(n),\cdots,x(n+d)] \tag{10}\]  
where \(Med[]\) is the median of all numbers in the window.  
The principle of the median filter is simple, it is easy to create its algorithm program with a computer, and the impulse noise under half window width can basically be eliminated. Therefore, as long as an appropriate window width is set, the median filter can effectively reduce the impulse noise in the vibration signal, but because of the characteristics of the filtering method itself, it cannot filter out the white noise.
# A Safe Admittance Boundary Algorithm for Rehabilitation Robot Based on Space Classification Model
###### Abstract  
Existing studies on rehabilitation robots are generally devoted to robot-assisted active rehabilitation training, which is conducive to facilitating muscle and nerve regeneration. However, human-robot interaction (HRI) requires imposing a limit on the workspace within which the robot operates, so as to ensure patient safety. A safe admittance boundary algorithm for a rehabilitation robot is proposed based on the space classification model which works by constructing a virtual boundary for the HRI workspace in the control layer. First, point cloud isodensification is performed for the workspaces within which the human body and the robot operate. Next, the nearest neighbor density is determined for the point cloud, and the space classification model is built on this basis. Finally, the space classification model is integrated with admittance control to derive the safe admittance boundary algorithm, which can be used for safety control. This algorithm is then subjected to space verification experiments and out-of-bounds experiments using a dynamic arm simulator (DAS). As indicated by the experimental results, when the side length of the voxel grid for voxel center filtering is set to 0.06 m, the accuracy of space definition is 98.40%. The average maximum response time for out-of-bounds behaviors is 165.62 ms. The safe admittance boundary algorithm can impose reasonable virtual constraints on the robot workspace, thereby improving HRI safety.  
physical human-robot interaction; robot safety; human-centered robot; rehabilitation robot
# Listeners' Spectral Reallocation Preferences for Speech in Noise
## 3 Expt. 1: Spectral Reallocation Preferences for Full-Bandwidth Speech
### Results
#### 3.5.3 Adjustment Time  
Median rather than mean per-participant adjustment times were analysed, i.e., the data presented in the third column of Figure 2 represents the cohort mean of per-participant median adjustment times.  
At the level of the listener cohort (i.e., comparing columns 2 and 3 of Figure 2), the time spent during the adjustment phase was strongly inversely correlated with the final score achieved in the test phase (Pearson \(r=-0.96\), \(N=16\), \(p<0.001\)). Listeners spent nearly 2.5 times longer when adjusting the mid band in the most challenging condition compared to the noise-free condition. Expressed as an average across all conditions, listeners varied substantially in the amount of time taken to reach a preference, ranging from 6 s to 32 s. However, individual listener scores were not significantly correlated with the median adjustment time (\(r=0.30\), \(N=35\), \(p=0.08\)), indicating that those listeners who spent more (or less) time exploring the range of possible adjustments did not necessarily achieve higher (or lower) intelligibility rates. A linear mixed-effects model using function lmer from the lme4 library [75], with fixed effects of SNR and feature, and by-subject random intercepts, indicated the main effects on the adjustment time of SNR (\(\chi^{2}(3)=102\), \(p<0.001\)) and feature (\(\chi^{2}(3)=23.9\), \(p<0.001\)), with a modest interaction between the two (\(\chi^{2}(9)=17.8\), \(p=0.04\)). Post hoc tests with Tukey corrections indicated that listeners took longer to adjust in the \(-6\) dB condition than in quiet for all features (all \(p<0.01\) except for the high band, where \(p=0.053\)). As was the case for intelligibility, adjustment times did not differ statistically in the quiet and 0 dB conditions for any feature (min \(p=0.18\)).
# Optimized Accelerated Over-Relaxation Method for Robust Signal Detection: A Metaheuristic Approach
## 3 Proposed Method for Signal Detection
### Proposed Novel PSO
#### 3.2.2 Modified Inertial Weight  
The parameters in PSO play a vital role in preventing it from falling in local optima at small particle numbers. It can have a huge impact on the accuracy of optimization and can restrict the local and global search capabilities of swarms. To handle these problems, an improved version of PSO was introduced [44]. In this study, modified \(w\) is presented by the Gauss function, and it changes as the particle's evolution steps are increased. The proposed inertial weight is given by  
\[w=\left(1.2-\left\lfloor\frac{g_{NO.}-50}{150}\right\rfloor\right)\cdot e^{- \frac{t^{2}}{2(0.5t_{max})^{2}}} \tag{21}\]  
where \(g_{NO.}\) is the size of the population. This concept is evaluated from a weighted PSO (WPSO). The \(w\) in WPSO is of linear change, while typically the value \(\in(0.4,1.2)\).
## 4 Experimental Results
### Experimental Setup
Multiple experiments were conducted to assess the impact of hyperparameters and configurations. Computational overhead is incurred for each experiment. Data preprocessing, including cleaning, normalization, and feature engineering, primarily utilizes CPU resources and requires a few hours for completion, depending on the dataset's size.
# Innovative Hydrogeophysical Approaches as Aids to Assess Hungarian Groundwater Bodies
## 2 Materials and Methods
### The Applied Geophysical Methods
#### 2.3.2 Hydrogeophysical Logging Methods
After completing a correlation-based (multilinear regression assisted) imputation process, a complete data matrix of several observed variables was successfully grouped in [58]. It was shown that the proposed clustering technique could be used for the quick analysis of big data, which gives a more reliable solution than traditional clustering or other matrix factorization algorithms. To process well logs from old water wells or to treat datasets, including a high-level of instrumental noise or outliers, the use of robust statistical methods is highly recommended. For exploring unmeasurable variables hidden in the dataset, such as clay volume or hydraulic properties, dimension reduction techniques can be fruitfully applied. By following this idea, the iteratively reweighted factor analysis (IRFA) technique developed originally for hydrocarbon formations [59] is now first used to estimate the clay volume of groundwater formations. In the first step, standardized well logs are transformed into a smaller number of statistical variables called factors, which form a linear combination of the observed variables. By minimizing the information loss, a few factors explain most of the total variance of the observed data matrix. By studying the explaining variances and factor loadings, one can give the most influential measurement types on the extracted variables. The specialty of this algorithm is that it updates the factor loadings and factor scores jointly in an inversion procedure, during which the deviation between all the measured and calculated well-logging data is weighted in proportion to its magnitude for giving an outlier-free (robust) solution. The petrophysical properties of groundwater formations can be directly connected to some of the factors by regression analysis. A strong nonlinear relation is found between the first statistical factor (\(F_{1}\)), including the dominant part of the variance of the observed well logs and the clay volume of water-bearing beds:  
\[V_{cl}=a\,e^{b\,F_{1}}+c, \tag{8}\]  
where \(a,b,c\) are site-specific constants validated by core laboratory measurements and well logging information of neighboring wells. The statistical estimation for the shale content helps us to refine the lithology and improve the aquifer storage model. Factor analysis may use all available well logs sensitive to lithology (and other properties) integrated into one statistical procedure to maximize the accuracy and reliability of the interpretation results. The hydrogeophysical approach can be extended to a larger investigation area (i.e., water body) by using a 2D (or 3D) factor analysis method [60]. The well logging methodology described in this section is demonstrated using a well logging dataset measured along a Hungarian thermal water well in Section 3.2.
# An FPGA Based Energy Efficient DS-SLAM Accelerator for Mobile Robots in Dynamic Environment
## 2 Related Work  
As the SLAM algorithm of mobile robots becomes more and more complex, the general processors on traditional mobile platforms cannot meet the requirements of real-time and energy consumption. The hardware acceleration for the SLAM algorithm has become vital research in the field of robotics.  
In [16], an accelerator implemented in FPGA can expand the actual EKF-SLAM (Extended Kalman Filter SLAM) system and observe and correct up to 45 landmarks under the real-time constraints. However, because the complexity of the EKF-SLAM algorithm increases with conversion landmarks, it is not suitable for large-scale environments. Most of the operations are executed sequentially; thus, the parallel architecture's advantages cannot be reflected.  
Figure 1: The processing platform.  
For the LSD-SLAM (Large Scale Direct monocular SLAM) algorithm, [PERSON] et al. propose a hardware implementation based on an FPGA SoC (System-on-a-Chip) [17]. The design shows that an FPGA SoC can introduce a more intensive SLAM algorithm into embedded low-power devices, and has a higher performance than other solutions. However, the SLAM algorithm implemented in this work is relatively simple, which limits the actual deployment of the system in complex scenarios.  
[PERSON] et al. use OpenCL to implement the large-scale parallel processing on the FPGA to achieve the Fast-SLAM system [18]. Compared with the GPU-based implementation, the processing time is accelerated by 7.5 times. The research shows that real-time SLAM systems can be implemented on heterogeneous embedded platforms using software and hardware co-design methods.  
The eSLAM is proposed by [PERSON] et al. [19]. On a heterogeneous platform based on the ARM (Advanced RISC Machine) processor and FPGA board, a highly energy-efficient ORB-SLAM (Oriented Fast and Rotated Brief SLAM) system is implemented. This work puts the most time-consuming feature extraction and matching process on the FPGA for acceleration, while the remaining threads are executed on the ARM processor. The eSLAM proposes an improved rotationally symmetric BRIEF (Binary Robust Independent Elementary Features) descriptor. By reducing the operation of rotating test locations, the calculation and storage consumption are significantly reduced. Simultaneously, through the pipeline design, the throughput is further improved and the memory is reduced.  
It can be seen that the SLAM algorithm implemented on embedded platforms is relatively simple and the related work is still lacking on semantic SLAM hardware implementation. So, this paper proposes a method to achieve the semantic SLAM algorithm based on the HERO platform.
# Optimal Route Planning for Truck-Drone Delivery Using Variable Neighborhood Tabu Search Algorithm
## 3 Methodology  
### Assumption  
This research is carried out based on the following assumptions:  
1. [label=(0)]
2. Drones will not fail during the entire delivery process.
3. Each drone can only execute one delivery task after each take-off.
4. Each customer is visited only once by a drone or a truck.
5. All trucks have the same service time at each customer point, and all drones also have the same service time at each customer point.