esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Bandpass filters are unnecessary due to the high performance of the cross dichroic prism, and the three view angles can simultaneously occupy the entire CCD without reducing the resolution and consequently maintain DIC measurement accuracy. At the same time, the system has been simplified compared with our previous system [19]. There is no longer a need for bandpass filters in the proposed system; only two planar mirrors are required as opposed to the three [PERSON] needed, and the two beam splitters are replaced by a cross dichroic prism, which reduces costs. However, the images acquired by the system proposed in this paper were not flipped and rotated, which reduced the computational complexity of DIC. Details of the method will be described in the following text. After introducing the experimental procedure, three typical experiments were performed to evaluate the metrological performances of the proposed method. The 3D displacement, 3D shapes, and strain can be determined using the developed system.
# Metamorphic Testing of Relation Extraction Models
## 7 Related Works
### Evaluation of RE Models  
RE models are usually validated on a held-out dataset. TACRED is one of the largest and most widely used crowdsourced datasets in RE [6]. However, even though recent progress has been made in knowledge enhancement and pretrained language models, RE models still showed high error rates of over 25% under the TACRED dataset. However, recent studies have found that the annotation quality of the TACRED dataset may strongly impact RE performance [14; 49]. [PERSON] et al. [13] revealed a large number of incorrect labels in TACRED. They found that label errors accounted for 8% of the absolute F1 test error and that more than 50% of the examples needed to be relabeled. On the relabeled test set, the average F1 score of the large baseline model set was greatly improved. [PERSON] et al. [35] reannotated TACRED and publicly released the revised dataset: Re-TACRED. After reannotation, it can be observed that 22.18% of the labels were different from the TACRED dataset. In fact, issues from the quality of the dataset made it difficult to tell whether the failures were due to model capabilities or label errors in the dataset itself. At the same time, it is doubtful whether the evaluation method of RE models based on the TACRED dataset is accurate and reliable. _Notably, our source inputs from the TACRED dataset were only sentence and head (tail) entity triples, which bypassed the need for high-quality labels_.  
[PERSON] et al. [50] introduced the adversarial attack [51], counterfactual test [52], and bias (i.e., selection and semantics) tests [53] for BERT in the RE task to evaluate its generalization ability in terms of robustness. [PERSON] et al. [13] found that when entities are not masked, the RE models may exploit the shallow cues present in entities to correctly classify relations, even with limited access to a sentence's context. Inspired by this study, [PERSON] et al. [11] used the entity-masked contrastive pretraining framework to prove that RE models have the problem of overreliance on entity information, especially the entity type. Through comparative experiments, [PERSON] et al. [36] pointed out that the RE model adopts a shallow heuristic method and cannot be generalized to more challenging datasets, but the construction of the comparison set requires human effort. It is noted that these studies are insufficient to comprehensively reveal the strengths and weaknesses of different RE models. _This paper designed eight MRs to encode various properties of RE, which support the application of MT in RE and also contribute to the investigation and understanding of the characteristics of RE models._
## 2 Materials and Methods
### Case Study 1: Application of the Location Analysis Model
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Study Area A** & **Study Area B** & **Study Area C** \\ \hline land usage & residential and commercial & residential & residential \\ building coverage ratio (\%) & 65 & 70 & 40 \\ floor area ratio (\%) & 190 & 200 & 138 \\ impervious surface ratio (\%) & 90.5 & 100 & 65 \\ building floor & mean & 3 & 2 & 15 \\ standard deviation & 2.38 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Urban spatial characteristics for study areas A, B and C.  
Figure 5: The study areas of case study 2. (Source: Satellite imagery, [[http://maps.kakao.com](http://maps.kakao.com)]([http://maps.kakao.com](http://maps.kakao.com)), accessed date: 20 August 2020). of weather side effects. Running the Envi-met modeling requires data from the main configuration file and area input file. The main configuration file contains weather-related information such as the simulation date, time, temperature, humidity, wind direction, wind speed, and roughness. The location, placement, height, floor pavement, location and type of trees and vegetation, and soil information of each building are input. Most urban areas are covered with asphalt: it is known that asphalt albedo has a value between 0.1 and 0.15 [36], arboreal albedo is between 0.12 and 0.18, and grassland albedo is between 0.16 and 0.26 [37]. This study set the albedo of rooftop greening to 0.26, assuming a plant species that can maximize solar reflection would be planted. A default value of Envi-met was applied as the albedo of other roofs, walls, and asphalt. Meanwhile, the leaf area index (LAI) was set at 3.0 considering that the study areas for case study 2 were urban areas. The diameters of tree crown and tree height were set to a value close to the study area's current vegetation (Table 3). In addition, the study analyzed the effect of temperature reduction through a simulation of the temperature at two meters above the ground as felt by a pedestrian and a comparison of temperatures before and after greening.
## 3 Study Area and Used Data
### Used Dataset  
Sentinel I imagery from orbit 55 (frame 107) and orbit 62 (frame 479) was collected from ESA between 22 March 2018 and 19 September 2020. A total of 71 images were collected in the ascending orbit and 74 images in the descending orbit. Table 1 shows the  
Figure 3: The monthly precipitation distribution in the study area from March 2018–September 2020. Data from [38].  
Figure 2: The SAR data coverage and the elevation of the study area. (**a**) Coverage of ascending and descending Sentinel-1A data, the blue region is the coverage of ascending data and the red region is the coverage of descending data. (**b**) The elevation of the study area.  
relevant parameters, and Figure 2a shows their spatial coverage. The majority of the images were obtained within 12 days, although some time points are still missing. To remove topographic phase from the interferograms, we used a DEM with a spatial resolution of 30 m provided by the Shuttle Radar Topography Mission (SRTM).
# A Review of Deep Learning-Based Visual Multi-Object Tracking Algorithms for Autonomous Driving
## 3 Overview of Deep Learning-Based Visual Multi-Object Tracking
### Visual Multi-Object Tracking Algorithm Based on Deep Learning  
The tracking algorithm based on detection results has evolved and has quickly taken over as the standard framework for multi-object tracking due to the rapid advancement of object detection algorithm performance [7]. The TBD sub-modules, such as feature extraction, can be included in the object detection network, though, from the standpoint of the deep neural network's structure. Joint detection and tracking, or JDT, using a deep network framework to perform visual multi-object tracking, has emerged as a new development trend based on TBD neutron module fusion [11; 12]. The attention mechanism has been incorporated into computer vision systems recently because it has the benefit of efficiently capturing the region of interest in the image, enhancing the performance of the entire network [13; 14; 15; 16]. It is used to solve various vision problems, including multi-object tracking. The specific classification structure for the three types of tracking frameworks is shown in Figure 3.  
At the same time, the characteristics, advantages, and disadvantages of the tracking algorithms of the three types of frameworks in the paper are organized as shown in Table 2 [17; 18; 19; 20; 21; 22; 23; 24].  
Figure 3: Classification and algorithm of visual multi-object tracking based on deep learning. Overall, the development trend of visual multi-object tracking algorithm is from TBD, to JDT, to Transformer-based tracking algorithm.
# The Early Detection of Cardiac Fatigue: Could the HRV Be Used as a Physiological Biomarker by AI?
## 3 Results
### SVM and Global Fatigue Descriptor (GFD)  
According to its features, fatigue can be categorized as either objective or subjective [1]. In contrast to subjective fatigue, which can be caused directly by demanding or stressful mental tasks and indirectly by physical activities, objective fatigue is a physical phenomenon that manifests itself in both its causes and its effects (resulting in a decrease in the ability to exert the body in mechanical work). [PERSON] et al. (2020) [57] proposed a global descriptor, first by extracting individual fatigue descriptors from EMG and HRV measurements and then by using an SVM. Fourteen healthy men were monitored: fatigue was acquired at a constant rate, a condition reasonably ensured by using a constant work rate on a cyclo-ergometer test. Despite the authors reporting that \"GFD corresponds to an exploratory approach that still needs a more profound validation\", it was very interesting that the participants showed a tendency over time toward 14 distinct fatigue descriptions, specifically 4 EMG (median frequency, from Fourier and Wavelet analysis; major frequency; major time) and 10 HRV parameters (maximum, minimum and average RR interval; SDNN; rmsSD; Triangular Index; SD2; power in LF and HF band; median frequency) (for more details see [PERSON] [58] and Appendix C). A future modification of the system toward real-time analysis could be easy, because the implemented processing system uses a _sliding window_ mechanism to extract all the information.
## 2 System Models of Integrated Navigation
### Target planetary object Doppler radial velocity measurement  
The radial velocity of the probe with respect to Jupiter can be measured by the spectrometer 2 facing Jupiter on the probe, for which the radial velocity measurement of the probe with respect to Jupiter can be measured.  
Figure 2: The basic principle of the solar/target planetary object Doppler velocity measurement.  
The radial velocity measurement model measured by the probe relative to Jupiter is expressed as follows:  
\[\mathbf{Z}_{D}^{S}(t)=\mathbf{h}_{I}^{D}[\mathbf{X}(t),t]+\mathbf{\omega}_{2}(t) \tag{18}\]  
where \(\mathbf{\omega}_{2}(t)\) is the Doppler measurement noise of the spectrometer 2 oriented toward Jupiter. The corresponding velocity observation equation is expressed as follows:  
\[\mathbf{h}_{J}^{D}[\mathbf{X}(t),t]=\frac{\left(\mathbf{r}_{ps}-\mathbf{r}_{js}\right)\cdot \left(\mathbf{r}_{ps}-\mathbf{v}_{js}\right)}{\left|\mathbf{r}_{ps}-\mathbf{r}_{js}\right|} \tag{19}\]  
where \(\mathbf{r}_{js}\) and \(\mathbf{v}_{js}\) represent the position and velocity vectors of the probe, respectively, when the Sun photon arrives at Jupiter.  
This measurement model corresponds to the observation matrix is expressed as follows:  
\[\mathbf{H}_{J}^{D}=\frac{\partial h_{J}^{D}[\mathbf{X}(t),t]}{\partial\mathbf{X}}=\left[ \begin{array}{c}\frac{\mathbf{v}_{ps}-\mathbf{v}_{js}}{\left|\mathbf{r}_{ps}-\mathbf{r}_{js} \right|}-\frac{\left(\mathbf{r}_{ps}-\mathbf{r}_{js}\right)\cdot\left(\mathbf{v}_{ps}-\bm {v}_{js}\right)}{\left|\mathbf{r}_{ps}-\mathbf{r}_{js}\right|^{3}}\left(\mathbf{r}_{ps}- \mathbf{r}_{js}\right)\\ \frac{\mathbf{r}_{ps}-\mathbf{r}_{js}}{\left|\mathbf{r}_{ps}-\mathbf{r}_{js}\right|}\end{array} \right] \tag{20}\]
# Advances and Challenges in the Creation of Porous Metal Phosphonates
## 2 Synthesis of Porous Metal Phosphonates
### Templated Porous Metal Phosphonates
When reacting titanium tetrachloride and ethylenediamine tetramethyle phosphonic acid (EDTMP) in the presence of Brij-56 as the template, a hybrid titanium phosphonate having hexagonally ordered mesopores (2-3 nm in size) with type-IV isotherm and a surface area -1000 m\({}^{2}\)/g was obtained. By changing the SDA from Brij-56 to CTAB, a tunable mesopore assembly ranging from a hexagonal phase to a cubic phase was achieved [86]. Moreover, template-free methods have been applied in which EDTMP was reacted with titanium butoxide, resulting in the formation of a porous material with hierarchical inter-particle porosity [87,88]. New concerted efforts in constructing hybrid porous metal phosphonates have been directed towards achieving control over the hetero-condensation of metal and phosphonic precursors. Recently, porous aluminum phosphonate synthesized with partially hydrolyzed acidic intermediates, formed from pre-hydrolyzed bisphosphonate ester precursors were reported. The partial hydrolysis allows to tune the reactivity towards aluminum chloride [89]. This synthesis methodology allowed the  
Figure 6: (**A**) TEM images of calcined aluminum methylene diphosphonates prepared in the presence of: (a) EO\({}_{80}\)PO\({}_{30}\)EO\({}_{80}\) (Pluronic F68), (b) EO\({}_{106}\)PO\({}_{70}\)EO\({}_{106}\) (Pluronic F127), and (c) EO\({}_{20}\)PO\({}_{70}\)EO\({}_{20}\) (Pluronic P123) tri-block co-polymers, respectively. Reprinted with permission from [75]. Copyright (2005), American Chemical Society. (**B**) (a,b) TEM images of periodic mesoporous titanium phosphonate-1 hybrid material. Reprinted with permission from [85]. Copyright (2010), The Royal Society of Chemistry and (**C**) (a,b) SEM and (c) TEM images of titanium phosphonate hybrid; (d,e) SEM and (f) TEM images of titanium phosphonate hybrid with \(\beta\)-cyclodextrintrim. Reprinted with permission from [82]. Copyright (2008), American Chemical Society. incorporation of a variety of organic linkers such as amides, thiophene, and ether moieties into porous metal phosphonate networks [90]. Another novel approach has been utilizing the non-hydrolytic sol-gel methodology to synthesize hybrid titanium phosphonate by the use of biphenyl bisphosphonate ester and titanium isopropoxide in acetic anhydride. These materials showed titanium oxide nanoparticles that were crosslinked with the bisphosphonate esters creating a high surface area material of 830 m\({}^{2}\)/g [91].
# Efficiency Analysis of Manufacturing Line with Industrial Robots and Human Operators
## 4 Work Efficiency and Productivity Evaluation
### Availability and Failures  
The concept of machine availability is related to planned uptime and unplanned events e.g., the random machine failures. Any unplanned event can make the machine unavailable and cause a decrease in work efficiency.  
The reliability of objects such as machines or robots is defined as the probability that they will work correctly for a given time under defined working conditions. In practice, for description of reliability, in most cases the parameter MTTF (mean time to failure) is used, which is the expected value of the exponentially distributed random variable with the failure rate \(\lambda\)[49].  
\[MTTF=\int_{0}^{\infty}t\ f(t)dt=\int_{0}^{\infty}t\ \lambda\ e^{-\lambda x}\ dx= \frac{1}{\lambda} \tag{5}\]  
In In the case of repairable objects, the parameters MTBF (mean time between failures), and the MTTR (mean time to repair) can be used.  
\[MTBF=MTTF+MTTR \tag{6}\]  
Because machinery failures may cause severe disturbances in production processes, the availability of means of production plays an important role. The inherent availability can be calculated with Formula (7).  
\[Availability=\frac{MTBF}{MTBF+MTTR} \tag{7}\]  
In an industrial environment, machine failures are mostly random and difficult to predict; therefore, we have used a computer simulation for further research. The computer simulation of production processes is used very often, but the computer models are somewhat simplified and focus only on certain aspects. Therefore, we prefer to build more sophisticated models that include the OEE factors and human and robot parameters.  
The literature review [46] indicates that \(OEE\) metrics is lacking in complex manufacturing systems on the factory level. In order to address this gap, an \(OFE\) metric can be used. It can measure factory-level performance and can also be used for performing factory-level diagnostics such as bottleneck detection and identifying hidden capacity Any factory layout can be modelled using a combination of the predefined subsystems (serial, parallel), which allow determination of the \(OFE\).  
Note that the \(OEE\) equation can be further simplified as [46]:  
\[OEE=\frac{Actual\ throughput\ (units)\ from\ equipment\ in\ total\ time}{Theoretical\ throughput\ (units)\ from\ equipment\ in\ total\ time} \tag{8}\]  
By extending this definition to the factory level, we have OFE [41]:  
\[OFE=\frac{Actual\ throughput\ (units)\ from\ factory\ in\ total\ time}{Theoretical\ throughput\ (units)\ from\ factory\ in\ total\ time} \tag{9}\]The \(OFE\) is a generalization of \(OEE\) for a multi-machine system and it is limited by the throughput of the bottleneck.
## 4 Results
### Follow up Study  
As mentioned above, follow-up investigation was performed 7 months after the initial study. The objective of this follow-up study was to identify a temporally distinct set of data to be used as a validation data set. One concern with the original dataset was that the study would not generalize over time; since the entire study was conducted during a single summer month, it was possible that seasonal topics would dominate the conversations. The follow up results, administered during the winter months, are shown in Table 2, and show the model did not overfit to a temporal component of the data. These models not only matched the previous cross-validated scores, but improved upon them. This was likely due to the fact that the follow-up study used a model trained on all the initial data, effectively giving it 20% more data to learn from (not needing to set aside 20% for each of the for folds). This 20% increase in data explains the moderate jump in quality between the initial study and the follow-up.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Validation** & **AUC (PHQ)** & **AUC (GAD)** & **R (PHQ)** & **R (GAD)** \\ \hline Cross-validation & 0.903 & 0.909 & 0.806 & 0.799 \\ \hline Follow-up & 0.933 & 0.922 & 0.828 & 0.812 \\ \hline \hline \end{tabular}
\end{table}
Table 2: AUC and R scores for language modality comparing initial cross-validated scores with the follow up study.
## 2 Methods and Materials
### Mission Description
#### 2.1.2 Mission Concept  
AlfaCrux is an amateur radio and educational mission to provide a hands-on experience with technological demonstration in orbit. The main purpose is the operation, command, and control of a satellite-based system for providing communication applications. Digital twin modeling, scientific experiments related to the impact of space weather in the communication channel, as well as the dynamic analysis for ADCS applications, are also part of the mission objectives.  
The AlfaCrux payload implements a low-rate, bidirectional, short-message-based store-and-forward communication system between terrestrial and satellite terminals. This system allows, among many other applications, communication with weather stations in remote areas, environmental sensors, communications in areas affected by disasters, etc. For retrieving data from the satellites, the ground station will be the core component of the data distribution system. The system end users can access, through an internet connection, the data collected by these sensors and send the desired information. Cooperation in the ground segment will be established among the AlfaCrux collaborators.  
In addition to the communication service itself, the system demonstrates:  
* Digipeater for amateurs radio data transfer;
* User-developed scientific applications based on GNU Radio libraries for communication channel characterization;
* Data relay for real-time communication between terminals in the same area.  
The operational scenario schematic can be seen in Figure 2, and it is described as follows:  
* The satellite transmits beacons while orbiting the Earth, sending telemetry information.
* When the satellite passes through the ground station, the operation team verifies and set the proper operational mode, as well as schedules the next services to be available for the users. This information is available online in the AlfaCrux mission planner.
* When the store-and-forward service is available, a user terminal receives the beacon and sends the available information to the satellite. The satellite, in turn, will transmit the stored information to it.  
* The satellite can send the received information in real time to another user terminal or ground station that is in the same area.
* When the satellite passes through a ground station, it exchanges messages from the collected user terminals and those that the system has for the terminals.
* The users will be able to interact with the system through the online official web page, receiving messages from their terminals or delivering the data they wish.
* When the digipeater service is available, the users can exchange digital data when they are in the footprint of the satellite. The current service available is the Automatic Packet Reporting System ([[http://www.aprs.org](http://www.aprs.org)]([http://www.aprs.org](http://www.aprs.org)), accessed on 20 September 2022), an amateur radio-based system for real time digital communications of information of immediate value in the local area.
## 4 Results and Discussion
### On-Peak and Off-Peak Electricity Prices  
The on-peak and off-peak electricity prices significantly affect the economic feasibility of the proposed system. In this section, Case B is studied at the charging pressure of 12 MPa. The ranges of on-peak and off-peak electricity prices are 0.028-0.070 USD\(\cdot\)kWh\({}^{-1}\) and 0.140-0.196 USD\(\cdot\)kWh\({}^{-1}\), respectively.  
Figure 9 shows the variation in the project NPV with the on-peak and off-peak electricity prices. By increasing the on-peak electricity prices from 0.140 to 0.196 USD\(\cdot\)kWh\({}^{-1}\), the highest NPV of USD 88.8 million is achieved. For the given charging and discharging times, lower off-peak electricity prices contribute to save more on annual operating costs and, thus, leading to an increase in annual profits [43]. Assuming the fixed on-peak electricity price is 0.168 USD\(\cdot\)kWh\({}^{-1}\), the NPV rises from USD 16.57 to 75.11 million as the off-peak electricity price drops from 0.070 to 0.028 USD\(\cdot\)kWh\({}^{-1}\). In addition, an increase in the on-peak electricity prices can have a positive impact on both the annual total revenue and the NPV of the project.  
The effect of the on-peak and off-peak electricity prices on the DPP is shown in Figure 10. If the off-peak electricity price is 0.070 USD\(\cdot\)kWh\({}^{-1}\), the project will operate at a loss throughout its entire lifetime if the peak electricity price drops below 0.168 USD\(\cdot\)kWh\({}^{-1}\). However, if the peak electricity price is 0.196 USD\(\cdot\)kWh\({}^{-1}\) and the off-peak price is  
Figure 8: Effect of the air expansion stages on the NPV.  
Figure 9: Effect of the (**a**) on-peak electricity price and (**b**) off-peak electricity price on the NPV.  
0.028 USD\(\cdot\)kWh\({}^{-1}\), the DPP is shortened to 3.46 years. At the off-peak electricity price of 0.028, 0.042, 0.056 and 0.070 USD\(\cdot\)kWh\({}^{-1}\), the LCOE of the system is 0.0802, 0.0967, 0.1131, 0.1296 USD\(\cdot\)kWh\({}^{-1}\), respectively.