esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensor-Guided Assembly of Segmented Structures with Industrial Robots
## 6 Experimental Results
### Continuous Panel Assembly Task  
The entire continuous process of picking and placing the two panels in the assembly nest is demonstrated in Figure 13. In (a), the robot starts at an initial pose when the first panel is in the pickup nest ready for pickup. In (b), the overhead camera detects the position and orientation of the panel and publishes the information to the robot, which then moves above the panel. In (c), the robot makes contact with the panel with all six suction cups well engaged. The robot picks up the panel in (d) and transports it to the first assembly nest in (e,f). In (g), the panel is finally placed in the assembly nest based on the vision and force guidance. The whole procedure is reset and prepared for the second panel in (h) and (i). Following a similar procedure from (b) to (g), the second panel is picked and placed in the second assembly nest as shown in (j). Then, in (k), the robot releases the suction and leaves the panels in the nest with the seam between the two panels pictured in (l). Note that when interference is needed, the process can be stopped, played back, or resumed at any point during the process from the GUI as discussed in Section 4. The video of the entire assembly process may also be found online in [42].  
We conducted five rounds of the continuous process for the timing test in Figure 14. The total placement time includes the motion of the panel to the initial position in close proximity to the assembly nest, transport of the panel to the assembly nest, and placement of the panel using PBVS. Since there is contact involved in picking up and placing the panel, the robot is commanded to move more slowly to avoid damage, and hence the longer times for these stages. Overall, it takes an average of 112.62 s with a standard deviation of 18.88 s for the first panel; and 114.04 s with a standard deviation of 12.09 s for the second panel. The variation may come from the various pickup panel poses resulting in different complexity for the following transport and placement tasks particularly for the path planning. The result shows vast improvement over the manual operation in terms of speed and accuracy. Our trained operator spent more than 6 minutes to transport and place down one panel, but could not properly align the panel solely based on the image feeds without any fixture. Further optimization could further improve the robustness and cycle time of the process.  
Figure 12: Panel vibration, measured by the motion capture system, shows the dominant vibrational mode around 2.23 Hz. Robot motion bandwidth is chosen below this mode to avoid panel oscillation.
# Pedestrian Counting Based on Piezoelectric Vibration Sensor
## 4 System Design
### Prediction Output Judgment Logic  
Sometimes a non-footstep vibration event [23] may trigger the model to output a result which makes no sense. Once the prediction result of deep learning model is not \"0 Person\", our system further validates whether the vibration event is a footstep event or not. Existing research regarding footstep detection [14; 43] presents methods to judge whether a vibration event is caused by a footstep or some other events, for example a door closing or a bus driving outside the building. The entire detection logic including the mentioned footstep detection is shown in Figure 5.  
If the input signal is a superposition of step vibrations and non-footsteps vibration, and if the non-footsteps signal is not too strong or does not persist too long, the system will treat the non-footsteps signal as noise. If the non-footsteps signal is very strong or persists for a long time, the risk will increase a lot that the footstep detecting module will block the proposed system. The current approach can only guarantee that the system will work in the most common scenarios, such as in an office building where the interference is not that strong or persist for a long time.
## 4 Mechanical Test Results and Discussion
### Tensile Behavior
#### 4.2.5 Ratio of Test Parameters  
Figure 15 shows the ratios of the test parameters at 7 d and 28 d, namely, compressive strength, tensile strength, strain capacity, and energy absorption capacity. As mentioned previously, it was noted that most of the 7 d cured specimens exhibited higher tensile strength than those of 28 d cured specimens, unlike in the case of the compressive strength. All the 7 d-28 d tensile strength ratios were higher than 77%, and most of them exceeded 100%, whereas those of the compressive strength ranged between 76% and 94%. This denotes that the tensile strength was not proportional to the development of compressive strength. Meanwhile, the 7 d-28 d strain capacity ratios were measured down to 16%. As discussed previously, this can be attributed to the porous and irregular microstructure and to large particles at the fiber-matrix interface of the 7 d cured specimens. For the energy absorption capacity, the 7 d-28 d ratios also measured below 88% except for specimen H, which was measured as 140%. Furthermore, it is also noted that the 7 d-28 d ratios for the energy absorption capacity significantly increased as GGBS, SF, CKD was incorporated into the mix. This denotes that the property and development of the microstructure primarily influence the tensile performance when considering the results of microstructural inspections.
## 1 Introduction
In other words, the consensus extent cannot be embodied, which could impact the validity of the evaluation result [16]. The similarity aggregation method (SAM) provides a systematic method to objectively consolidate individual opinions during group decision-making and can convert linguistic terms used for evaluation risk factors into fuzzy numbers [17]. Furthermore, through the aggregation steps, we can establish the judgment matrix and effectively derive consensus information for group decision problems [18]. Failure mode identification is crucial for improving the UX of interactive system design; however, FMEA does not provide a method to accomplish this. Hierarchical task analysis (HTA) is a commonly used task analysis method that can decompose tasks into clearly identifiable steps. This process helps to effectively identify potential failure modes [19; 20]. In addition, the systematic human error reduction and prediction approach (SHERPA) employs a well-designed behavioral classification interconnected with an external failure mode taxonomy to discern potential failure modes related to human behavior [21]. SHERPA uses an error list methodology and claims to achieve the highest accuracy in error prediction [22]. Several studies have combined HTA with SHERPA for failure mode identification [23]. The main purpose of this paper is to improve UX in interactive system design. The primary contribution of this paper is the integration of various methods from different fieldsinto a unified framework and its application to interactive system design, a field where it has not been previously utilized. In this framework, HTA and SHERPA are combined to detect the failure modes related to UX; moreover, fuzzy linguistic variables and SAM are integrated to analyze the failure modes. In addition, fuzzy logic is used to calculate the fuzzy risk priority number (FRPN), which is used to prioritize the failure modes. The proposed approach can accurately prioritize the failure modes related to UX, as well as effectively manage the subjective and even subconscious nature of risk factors associated with failure modes. In this paper, we hypothesize that the failure modes related to UX have different priorities, and our approach can identify and rank all the failure modes as well as provide corrective actions for the failure modes with high priorities. The remainder of this paper is arranged as follows. Section 2 gives a theoretical background review of UX in interactive system design, FMEA, and fuzzy set theory. Section 3 presents our proposed methodology. Section 4 demonstrates the procedures with a case study, and its results are discussed in Section 5. Finally, Section 6 summarizes the main conclusions.
# A C-Based Framework for Low-Cost Real-Time Embedded Systems
## 7 Internet Access
### Comparison with Single-Step Translation  
With respect to single-step translation methods, such as the aforementioned hashing performed directly on the controller card, the two-step approach just discussed offers distinct advantages.  
* Firstly, the _maintenance effort_ of the two-step approach is not significantly higher than the other. Indeed, the only requirement is that the IMS has access to a copy of the shared memory configuration files pertaining to all controller cards it is meant to  
Figure 11: Direct code generation for resolving symbolic names into symbolic values.  
communicate with at compile time. Considering that this configuration information is determined very early in the design and development process and is unlikely to change during firmware lifetime, the maintenance effort related to those configuration files can be kept at a reasonable level.
* Furthermore, the method is _bandwidth friendly_ because only enumerated values--represented as small, fixed-length integers in gcc--are exchanged between controller cards and the IMS, as opposed to longer, variable-length character strings. This is especially important when, to reduce system complexity and cost, a single bus is used for both remote access and real-time traffic, because it also limits the interference introduced by remote access.  
Should a further consistency check between the view on shared variables of a controller card and the IMS become necessary, the controller card can still include the symbolic name (generated according to its own mapping) in its reply to an IMS request. This can be done with negligible interference with other, real-time activities because the extra processing it requires is limited. Even more importantly, since the controller card has full control on all the tasks running on it, as well as on how the real-time bus is utilized, it can choose to send back the reply at the most appropriate moment from both the processor and bus scheduling perspectives.
# Recognition of Earthquake Surface Ruptures Using Deep Learning
## 5 Conclusions  
Identifying and analyzing coseismic surface ruptures based on UAV images will have problems such as high calculation costs, heavy workloads, intense subjectivity, and sensitivity to noise. Taking the data of the MS 7.4 Maduo earthquake as an example, we proposed a deep learning method to identify cracks and constructed a deep learning semantic segmentation network. We trained the deep learning model using the data from the study area and obtained a model that could identify cracks in UAV images, with an excellent recognition effect in the test experiments.  
Based on our results, we concluded that:  
1. Our deep learning method could accurately and quickly identify cracks in high-precision UAV images. Although there were a number of false and missed identifications, it efficiently mapped the major surface ruptures produced by the Maduo earthquake using a relatively small number of labeled samples.
2. The expansion processing of cracks could improve the crack identification effect of the deep learning method.
3. The deep learning method has great potential in identifying cracks from high-precision UAV aerial surveys and can give accurate results of crack identification from sufficient data after appropriate training.  
Data curation, X.C. and X.L.; Formal analysis, X.C.; Methodology, X.C. and G.H.; Software, G.H.; Writing--review and editing, X.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Science for Earthquake Resilience, China Earthquake Administration (Grant No. XH22003C), and the PI Project of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (Grant No. GML2020 GD0802).  
Not applicable.  
Figure 11: Prediction results of different network architectures for maduoDom1 and maduoDom2 with bold-labeled cracks.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We gratefully thank [PERSON] at the Institute of Surface-Earth System Science, Tianjin University, for providing the UAV image data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A New Architectural Approach to Monitoring and Controlling AM Processes
## 2 The Need for AM Research Testbeds  
A variety of AM machining systems are now commercially available [3]. In theory, AM systems have two major advantages over traditional manufacturing (TM) systems: they can build any complex geometry and with virtually no tooling cost. Today, however, current AM systems have two major drawbacks. First, they frequently take more time to fabricate those geometries than a qualified TM system. Second, because of all the uncertainties in the AM system, controlling the quality of those parts is still a major problem. Most AM-system vendors, however, attempt to minimize the impacts of those uncertainties by limiting both (1) the kinds of geometries that their systems will produce and (2) the kinds of system modifications that manufacturers can make.  
Lots of AM process-related and qualification-related research is being done in universities and research institutions around the world. The research described in this paper was conducted as part of a collaboration between the National Institute of Standards and Technology (NIST) and \"Intelligent Manufacturing Research Center\" (iMRC) National Cheng Kung University, Tainan, Taiwan. NIST has developed a state-of-the-art facility for AM called Additive Manufacturing Metrology Testbed (AMMT). The research-and-development (R&D) focus of AMMT includes (1) sensors that collect data and monitor and (2) AI-based, analytics algorithms to control laser powder bed fusion (LPBF) process technologies [4].  
The iMRC LPBF machine has been instrumented with a fiber laser (with power 500 W and wavelength \(1070\pm 10\) nm), a pyrometer, and a coaxial CMOS camera. The laser-spot position is provided by the controller via the OPC Unified Architecture (UA) [5] interface to an intelligent system monitoring (ISM) module. The custom-built optical design enables the pyrometer to detect the melt-pool temperature. The image of the melt pool is captured using the CMOS camera by using the interfaces of camera link [6].  
Appendix A shows some commercially available monitoring systems [7; 8; 9; 10; 11]. This list of commercial systems does not cover the complete lifecycle of additive manufacturing products--from design to finish in term of monitoring. Most commercial systems focus on a specific activity of that lifecycle. In general, no single commercial system discusses the functional and computing requirements for real-time, AM process-monitoring, and control. In the remainder of this paper, we will focus on the functional requirements and a control architecture for meeting those requirements.
# A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks
## 2 Related Work
In Dynamic Probabilistic Flooding Algorithm, sensors obtain neighbor information by basic flooding and divide neighbors into three types: parent (upper level), sibling (same level), and child (lower level) nodes. A node with the more children nodes and less siblings needs the lower retransmission probability. These three protocols try to cover all the nodes with all the data to guarantee at least one node aggregates enough relative data to fuse into information. However, in large sensor network, too much energy is consumed on unnecessary coverage of such a huge number of sensor nodes, for a piece of information is only needed to be fused by one sensor. In addition, in mobile sensor network, too much energy is consumed on the transmission of hello message to maintain local topology.
## 4 Discussion
### Speed Switch  
The desired angles of the knee and ankle which are input into the prosthesis controller are obtained from the natural walking of the able-bodied subjects without wearing a prosthesis on the treadmill. From Figure 9, it is known that the desired angles of the knee and ankle vary with the walking speeds. Therefore, the accuracy estimation of walking speed is helpful to choose the right desired trajectories during the speed switch while there are lots of studies that introduce walking speed into gait planners. For example, ref. [17] uses discrete Fourier transform (DFT) to characterize the desired periodic joint trajectories as functions of the gait phase across the entire stride and extends the control method to different walking speeds; ref. [18] uses the real-time walking speed and gait phase as the inputs into the Gaussian process (GP) functions to specify the desired trajectories and feed-forward torques for the knee and ankle.  
If the real-time walking speed is not obtained or misestimated, the wrong desired trajectories will be input into the prosthesis controller which will lead to the reduction in the stride symmetry (for simplicity, stride symmetry is defined as the stride difference between the sound leg and the prosthesis leg). As shown in Figure 10a, the stride of the prosthetic leg and the sound leg is closest under a speed of 3 km/h.  
Figure 10: (**a**) The results of stride estimator at different speeds. In this experiment, the desired trajectories which is input to prosthesis controller are collected under the speed of 3 km/h. (**b**) The results of step frequency estimator at different speeds.  
Figure 9: The desired angles of knee (**a**) and ankle (**b**) at different speeds.  
To sum up, the accurate estimation of walking speed which is achieved in this paper can contribute to the speed switch during walking by adjusting the desired trajectories of the lower-limb joints, making the gait of the prosthetic wearer more natural and symmetrical.
Since the tele-ICU system was first implemented in 2000 by the Sentara Hospital System in the United States of America, many hospitals have been using similar systems, such as electronic ICUs, ICU telemedicine, remote ICUs, and virtual ICUs [24]. ICUs need higher medical resources than general wards in terms of cost and clinical staff. Tele-ICUs not only improve them but also confirm the possibility of improving the recovery and long-term outcomes of critically ill patients, and can potentially improve process quality [25; 26]. Using tele-ICUs, clinicians can quickly monitor and treat patients, even if they are physically distant from clinicians and nurses in the ICU. Off-site clinicians can also remotely access and participate in real-time patient treatment and efficiently use distributed clinical resources by overcomingspatial constraints [27, 28]. Furthermore, tele-ICUs can further improve centralized systems, expanding critical care capacity in community settings [29]. Comprehensive programs are growing in tele-ICUs. Diagnosing and treating a single critical patient requires various measured biosignal information and an evidence-based treatment plan based on long-term follow-up records [30]. Thus, the integration and acceptance of monitoring programs in tele-ICUs is an inevitable trend that provides efficient communication even with collaboration among the clinical staff, not with the monitored department [31]. The proposed web-based ventilator central monitoring system (VCMS) accurately meets these requirements. Therefore, this study aimed to develop a VCMS utilizing information and communication technology (ICT) in tele-ICUs to overcome the limitations of conventional ventilator monitoring and investigate its usefulness. We believe that a VCMS can efficiently respond to and treat patients' respiratory issues in the ICU. Key contributions of the study include (1) integration of individualized ventilator monitoring into one monitoring platform with Internet of Things (IoT) technology to enable comprehensive patient management, (2) establishment of a dedicated server for the VCMS for longitudinal analysis of each patient to assist pulmonary physicians and nurse practitioners in making patient care decisions, and (3) effective monitoring of patients by medical staff using the VCMS, even in a noisy ICU environment. Figure 1 shows the proposed VCMS framework, and this can overcome the space constraints of clinical staff regarding patient respiratory management by integrating and monitoring multiple ventilation systems using IoT technology without losing or delaying patient monitoring data and providing real-time information through remote mobile applications. We developed a VCMS and performed a clinical experiment to demonstrate its viability in the ICU.
## Materials and Methods
### Methods
#### 2.5.3 Spatial Autocorrelation  
It is necessary to conduct a qualitative and quantitative study on the spatial and temporal differentiation characteristics of groundwater by coupling the well-defined MHRU and groundwater depth interpolation results [55; 56]. The groundwater depth value is presented through the MHRU, and the results are subjected to spatial autocorrelation analysis. Spatial autocorrelation refers to the potential interdependence of observational data between variables in the same distribution [57]. [PERSON] once pointed out that \"the first law of geography: anything is related to something else, but things in the neighborhood are more relevant than things in the distance.\" Statistically, correlation analysis can be used to detect whether there is a correlation between the changes of the two phenomena--it is the same attribute variable of different observation objects, which is called autocorrelation [58; 59; 60; 61]. The parameters describing spatial autocorrelation include global autocorrelation and local autocorrelation. Local autocorrelation was used in this study to express the recharge and convergence process of groundwater.  
Local spatial autocorrelation is a correlation index used to measure the extent to which a local spatial unit affects the spatial autocorrelation of the overall study [62]. Local spatial autocorrelation represents the degree of correlation of the specific hydrological response unit with the groundwater depth of the adjacent hydrological response unit and is calculated as follows [63; 64]:  
\[\mathrm{I}=\frac{n(x_{i}-\overline{x})\sum_{j=1}^{m}W_{ij}\big{(}x_{j}- \overline{x}\big{)}}{\sum_{i=1}^{n}\big{(}x_{i}-\overline{x}\big{)}^{2}} \tag{5}\]  
where \(m\) is the number of hydrological response units adjacent to the hydrological response unit \(i\).  
Based on the groundwater depth data for several years, the spatial autocorrelation analysis can be performed according to the area divided by the minimum hydrological response unit. In this way, the trend of groundwater depth in the past two decades is obtained and presented in the form of spatial plots, and the clustering degree and related situation of groundwater depth in the actual situation can be analyzed.