esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Laboratory Setup
### Non-Linear, Single-Degree-of-Freedom Robotic Mechanism  
Used robotic device (also non-linear robotic mechanism) is a single-degree-of-freedom mechanism that is simulated by an eccentric coupling with a spring to create a changeable and repetitive torque. It can be further partitioned into the following elements:  
* DC-motor (ESCAP 28D11-219P) with a nominal voltage of \(u_{a}=12\) V, nominal current \(i_{a}=1.5\) A, nominal velocity \(\omega_{a}^{nom}=600\) rad/s, inertia moment \(J_{m}=17.6\)\(\times\)\(10^{-7}\) kgm\({}^{2}\), viscous friction coefficient \(B_{m}=1\times 10^{-7}\) Nms/rad and nominal torque \(T=28.4\times 10^{-3}\) Nm.
* Incremental encoder Omron E6B2-CWZ1Y of resolution 2000 pulses/rev (electronically multiplied by four to reach resolution 8000 pulses/rev), mechanically connected directly to the axis of the DC-motor using elastic coupling.
* Sliding gear-box with transmission ratio \(i=11.25\) used to produce unexpected slips (unexpected load torque disturbances) between the primary and secondary axis of the sliding gear-box. The friction between both gear-wheels can be adjusted by a clamping screw which can move the bearing of the secondary gear-wheel left or right and thus adjust the amplitude of the slide between both gear-wheels. Maximum rotational velocity on the secondary side of the sliding gear-box is \(\omega_{a}^{max}=45\) rad/s.
* The non-linear robotic mechanism with a spring used to drive the DC-motor from the motor to generator regime and vice versa, thus producing the repeatable load torque disturbances.  
Repeatable load torque disturbances produced by the non-linear robotic mechanism with spring require that the DC-motor produces variable drive torque. The latter varies between the minimum and maximum torques, causing the DC-motor to switch to the generator regime and vice versa. When the secondary axis is rotated \(90^{\circ}\), the load torque reaches its maximum peak, and at the moment of rotation, the sliding gear-box usually slides between both contact surfaces. Furthermore, a singular point emerges between the minimum and maximum points when the drive torque is zero. The singular point is presented in Figure 2.  
In the remainder of this section, we provide a detailed description of the controller, along with proof of non-linearity of the robotic mechanism.  
Figure 1: A non-linear robotic mechanism: (**a**) front view, (**b**) back view.
# Technologies and Fabrication of Intelligent Packaging for Perishable Products
## 1 Introduction
Holograms can help prevent counterfeiting--and at a lower cost than radio-frequency identification (RFID)tags, which is very useful in the pharmaceutical industry. Thermo chronic links can change color in different temperatures, and can be used for beverage packaging or for microwave products, allowing consumers to know whether the hot or cold product is ready to be served. [PERSON] et al. reviewed the intelligent packaging technologies for food and showed the gap between its demand and its application in the market due to higher costs. Hence, future research should also address this problem to make intelligent systems commercially viable for the packaging of everyday commodities. It will be attractive to have packaging costs that are less than 10% of the total cost of the products [13]. The development in printing electronics for large scale fabrication will be important and the sole approach to reduce the cost of intelligent packaging. To be better informed about the level of research interest in intelligent packaging, we looked at research records covering the last five years (2014-2018), using techniques such as keyword searches to explore the web of science database. As the numbers of publications identified using different keywords varied significantly, we carried out a normalization process, and have presented the data in Table 1, with publication trends illustrated in Figure 1. Looking at the data, we can see that there has been far more recent interest in intelligent packaging than in active packaging, with the main techniques used in intelligent packaging--sensors and printing--capturing more interest. In this paper, two major aspects of the intelligent packaging system for perishable products are reviewed. In Section 2, the basic techniques for identification and sensing are discussed, including indicators, sensors, and identification technology. In Section 3, the printing techniques for fabricating electronics and some combinations of these techniques are presented, including inkjet printing, screen printing, and gravure printing. A brief conclusion is finally given in Section 4.
## 3 Results  
### Case Description and Dataset  
At present, there is no dataset for manufacturing actions. This paper takes the precleaning procedure of the combustion chamber of a rocket engine as a test case. The propellant of rocket engines is a typical firework product with strict process specification requirements for every manufacturing action. The standard process is as follows:  
1. Polish the surface, shaping it;
2. Blow on the surface and cool it;
3. Tap the internal thread to ensure that the thread form meets the requirements;
4. Clean with a brush to remove surface debris;
5. Move the propellant to the next working procedure.  
However, in the actual manufacturing process, there will be reworking and repetition. The present study is based on an actual workers' precleaning processes gathered from interviews and simulated in a laboratory environment. We simulated 520 processes, including 300 standard processes and 220 nonstandard ones. Each process consists of several types of manufacturing actions, as shown in Figure 5. Our dataset was recorded using a camera with a resolution of \(640\times 480\) pixels and a frame rate of 15 FPS. Each video sample ranged in time from 10 to 18 s and included between four and ten actions. Standard samples were executed in accordance with the above standard procedure while nonstandard samples were not, for example those in which cleaning was executed before cooling.  
The main difference between our dataset and other common datasets is that each action recorded in our dataset is an operation performed on a workpiece. Datasets like MSR [36] are simply records of the body's own behavior. Furthermore, each sample of our dataset consists of several continuous actions. Samples in datasets like UCF101 [37], on the other hand, are snippets of single actions. Finally, our dataset provides a label of evaluation for every sample to produce a \"good or not\" classification of sequential actions. To the best of our knowledge, no other such dataset exists which meets our requirements.
# A Deep Learning Model for Estimation of Patients with Undiagnosed Diabetes
## 2 Materials and Methods  
The construction of a DLM for undiagnosed diabetes consists of four steps, as shown in Figure 1. In the first step, KNHANES(korean national health and nutrition examination survey) datasets collected from 2013 to 2016 were combined and the consistency of variables was explored. If the scale measurement of a variable was not changed during the study period, it was included in the present study. In the second step, the combined dataset was pre-processed to obtain a reliable experimental dataset. In the third step, basic characteristics were analyzed for each group, including a normal glucose group (NG), an impaired fasting glucose group (IFG), and an undiagnosed diabetes group (UDG). Significant non-invasive variables (NIV) were selected based on bivariate analysis using logistic regression (LR). These NIVs were used to optimize machine learning models. Finally, the model with the best performance was selected and compared with other screening models published in previous studies on undiagnosed diabetes.  
Figure 1: Research framework of the DLM (deep learning model) for patients with undiagnosed diabetes. NIV: non-invasive variables; UDG: undiagnosed diabetes group; KNHANES: korean national health and nutrition examination survey.
# Sub-Milliwatt Transceiver IC for Transcutaneous Communication of an Intracortical Visual Prosthesis
## 1 Introduction
The transmitter is built using predominantly digital components to strive towards low power consumption while delivering a high data rate (a minimum of 23 Mbps is required [13]). The potential of digital-based designs is discussed in [14; 15], which is beneficial to low-power transceivers. Figure 1: System layout of the wireless enabled visual prosthesis. Note: The transmitter and receiver, without the coil/antenna, is covered in this paper. For the downlink receiver, an analog differential phase-shift keying (DPSK) receiver was used in [16]. Pulse delay modulation (PDM) was used in [17] and an Amplitude Shift Keying (ASK) to Frequency Shift Keying (FSK) conversion receiver in [18], both for low power consumption. However, for our implanted receiver we took an approach of a non-coherent digital receiver, with low-carrier frequency (4 MHz) and using an inductive link. The implanted receiver's low-power and sufficient data rate due to its predominantly digital components and low frequency are the reasons for its selection. The receiver is above the minimum data rate requirement of 200 kbps for the downlink [19]. In this work we describe the design of our fabricated implanted transceiver (excluding coils and antenna) on IC, based on CMOS 180 nm technology. Our idea is to: (1) use predominantly digital components to achieve lower power consumption; (2) provide tunability for the implanted IR-UWB transmitter with current control for its oscillator and impulse generator; and (3) make the implanted receiver adjustable with current controls to optimize for an inductive link.
## 6 Performance Evaluation
### Evaluating Management Efficiency
#### 6.7.2 End-to-End Connection Establishment between Field Devices  
In this section, we evaluate the management efficiency in reserving the communication resources and establishing end-to-end connections between 29 pairs of sensors and actuators. We classified connections into five categories based on the total hop distance of sensor to actuator via the gateway. Figure 31 displays, the delay in establishing connections (reserving communication resources) and the number of subsequent required communications for establishing those connections.  
It is noticeable that the increase in the total hop distance of the pairs results in more delay, and in a larger number of communications for establishing connection. This is so for both, D-MSR and WirelessHART, but less severe for D-MSR.  
Figure 31 indicates a considerable difference in the delay and the number of required communications between D-MSR and WirelessHART. For example, when the total hop distance of sensors to actuators comprises 12 hops, the average of the connection establishment delay is around 75% less for D-MSR compared to WirelessHART, while the average number of required communications for connection establishment is 88% less. Part of this difference can be explained by the fact that in WirelessHART, the network manager has to define more edges to provide a reliable uplink and downlink graph. Subsequently, more communication schedules have to be constructed for those graphs. As a result, more management commands to write the graphs and links are forwarded toward the network devices. The remaining difference could be due to the fact that D-MSR and WirelessHART use different management approaches. Whereas D-MSR relies on the distributed approach,  
Figure 30: Nodes joining delay (**a**) and overhead (**b**) (D-MSR _vs._ WirelessHART).  
WirelessHART makes use of the centralized management approach, which is far more expensive in terms of time and resources.
## 3 Results
### Raman Spectroscopy Enhancement Module Training Results
#### 3.2.3 Effect of Transfer Learning on RSEM Training  
To verify the improvement in data generation brought about by transfer learning on the RSEM, we selected a pre-trained large-scale microbial spectral dataset as the source domain data. This dataset, collected by Stanford Hospital from 2016 to 2017, includes 60,000 Raman spectra of 30 types of microorganisms. After enough iterations to stabilize the model, training was halted, and the model's parameters were fixed. Subsequently, the frozen parameters were transferred to a new WGAN network, which will be used to train the four pathogens' preprocessed Raman spectrum data. After 500 training rounds, we created a comparison graph to determine whether to employ transfer learning in RSEM.  
As shown in Figure 10a, when the RSEM is not trained using transfer learning, the data generation starts from scratch, and the network attempts to reproduce the distribution of real data from noisy noise. However, when the network starts training using the pre-trained parameters transferred from the source domain, as shown in Figure 10b, the network first generates the distribution of pre-trained data, i.e., the network learns the prior knowledge of the pre-trained data. As the number of iterations increases, the network-generated data distribution gradually approaches the distribution of the real data, while retaining the relevant details, similar to the interaction of prior data and real data, which cannot be generated by direct training. Further analysis shows that using transfer learning can accelerate the convergence of the model. When the same number of iterations is reached, the model generates data with noise levels lower than in cases without transfer learning. In summary, in our experimental process, the use of transfer learning shortened the convergence time of the RSEM, generating Raman spectroscopy data and improving the quality of generated data.  
Figure 9: The training loss function curve of the RSEM.
## 5 Results and Discussion
### Effect of Geotextile Strength  
To explore the influence of the geotextile's strength on the behavior of the GRS berm, a group of analyses was carried out by varying the long-term tensile strength of the geotextiles at 5% strain to \(T_{5\%,l}\) = 20, 15, and 10 kN/m at a geotextile spacing of \(S_{v}\) = 0.5 m.  
The failure mechanisms of the GRS berm with different geotextile strengths are plotted in Figure 8a-c. It is clear that the failure surface tended to extend to the MSW zone with the higher strength of the geotextile (i.e., \(T_{5\%,l}\) = 20 kN/m in Figure 7). With the decreasing geotextile strength, the failure surface became more confined within the GRS berm (Figure 8b,c).  
Figure 9 depicts the influence of geotextile strength on the factors of safety and the maximum tensile force of the geotextile reinforcement in the GRS berm. It was apparent that the factors of safety of the GRS berm decreased with the decreasing geotextile strength. Consequently, the lower geotextile  
Figure 8: Effect of geotextile strength: (**a**) Baseline (\(T_{5\%,J}\) = 20 kN/m), (**b**) Geotextile I (\(T_{5\%,J}\) = 15 kN/m), and (**c**) Geotextile II (\(T_{5\%,J}\) = 10 kN/m).  
strength led to the increased maximum tensile force of the geotextile. Similar findings were found through centrifuge models in a series performed by [PERSON] et al. (2018) to investigate the effect of geotextile tensile strength on the stability of reinforced slopes. Failure surfaces that developed in the models in this series showed that models built with stronger geotextiles as reinforcement elements did not exhibit the sudden collapse observed in models built using the weaker fabric. This means that a higher strength geotextile improves slope stability.
# Research of Security Routing Protocol for UAV Communication Network Based on AODV
## 2 Security AODV Routing Protocol
### Intermediate Node Responds to the RREQ Message  
In the AODV protocol, when the intermediate node has a sufficiently fresh route to the destination node, it is allowed to reply to the RREQ message. In order to keep this mechanism in the SAODV protocol, an additional RREP signature field is included in the RREQ message broadcast by the source node. The intermediate node uses the RREP signature field to sign the routing response message RREP on behalf of the destination node, thus ensuring that the RREP message generated by the intermediate node can be verified.  
In addition, when the intermediate node generates RREP messages, the lifetime of the route changes from the original one. The RREP message generated by the intermediate node contains two lifetimes, the original one and the real one. The original lifetime is signed by the destination node, while the real lifetime is signed by the intermediate node.  
In order to distinguish different SAODV signature extension messages, the routing control message with two signatures used by the intermediate node when replying to the RREQ message is called RREQ and RREP double signature extension message.
# The Clinical Role of SRSF1 Expression in Cancer: A Review of the Current Literature
## 3 Results
### Review
#### 3.2.5 Glioblastoma Multiforme  
Interesting papers have been published in the last few years regarding glioblastoma multiforme (GBM), an aggressive cancer of the brain. Consequently, with the growing interest in circRNAs (circular RNAs), a new class of RNAs that are highly expressed in the brain, studies have investigated the role of hsa_circ_0001445 (circSMARCAS5); in particular, it was found that circSMARCAS interacted with SRSF1 splicing factor, modulating its activity in GBM cells. Studies have been conducted on sampling biopsies from GBM and surrounding healthy brain tissues collected in a single institution; thereafter, histological and molecular features were correlated with clinical data. In particular, it was found that circSMARCAS expression was downregulated in GBM, compared to healthy tissue, and the decreased expression of circSMARCAS matched with a crescent grade of neoplasia malignancy and an in vitro reduce migration-capability of GBM cells [49]. Moreover, the role of circSMARCAS as a splicing regulator in GBM was investigated and it was revealed that it interacts with SRSF1 leading to an inhibition of this AS factor; in fact, it was found that SRSF1 is upregulated when circSMARCAS is downregulated in association with an upregulation of VEGF, suggesting that SRSF1 could upregulate the expression of VEGF leading to a more angiogenic activity, cancer progression and sprouting. In this study clinical data suggested that MVD, VEGF and SRSF1 correlate with poorer OS, and progression-free survival (PFS) [50,51]. In conclusion, there is a key clinical role of SRSF1, in association with its regulator circSMARCAS, in cell migration and angiogenic potential for GBM. The interaction between SRSF1 and circSMARCAS is a key factor to try to individuate new possible pharmacological or gene therapies for GBM, with this perspective it was found that the \"GAUGAA\" motif in circSMARCAS is responsible for the binding of SRSF1 and enhancing the angiogenic potential and cell migration of GBM [52].  
This evidence is summarized in Figure 6.  
Figure 6: SRSF1 role and pathways in glioblastoma multiforme. Evidence from in vitro, animal or clinical studies are specified with pictures. Created with Biorender.
# Depth Image Super Resolution Based on Edge-Guided Method
## 3 Experiments and Analysis
### Test Environment and Parameter Setting  
In our experiments, the programming tool was MATLAB (v.2016a) [29], and the test environment is the following. The processor was Intel(R) Xeon(R) CPU E5-2620 v3@ 2.40 Hz. Computer memory size was 64.0 Gb. The multithreading technology was used in the experiment. The proposed algorithm supports GPU computing, but it is not used. Test images were from the Middlebury Stereo database [30; 31].  
Some parameters were selected based on the smallest Root Mean Square Error (RMSE). We calculated average RMSE of 10 test images by varying the size of image patches from \(n\times n=3\times 3\) to \(n\times n=13\times 13\) per experiment. The results are depicted in Figure 4a. By comparison, we chose \(n\times n=9\times 9\) as the size of image patch. Similarly, we also compared the stride \(d\) of patch selection in Figure 3b. The stride is determined to be 2. The size of neighborhood window was \(s\times s=7\times 7\) when joint bilateral filter was performed. The reliability of the value \(s\) has been confirmed in [8]. The standard deviation \(\sigma=0.5\) for \(f_{\mathrm{s}}(\cdot)\) in Equation (8). Dictionaries were trained using the database from [PERSON] [32] which consisted of 100,000 patches extracted from 30 training images.
###### Abstract  
A simple, selective, and inexpensive spectrophotometric method is described in the present study for estimation of trace concentrations of Cu\({}^{2+}\) in water based on its reaction with chromogenic reagent namely 4-amino-3-mercapto-6-[2-(2-thienyl)vinyl]-1,2,4-triazin-5(4H)-one (AMT). The reaction between copper(II) ions and AMT reagent gives [Cu(L)(NO\({}_{3}\))(H\({}_{2}\)O)\({}_{2}\)]\(\bullet\)H\({}_{2}\)O complex, where L represents AMT molecule with NH group. The formed complex exhibits a sharp, and well-defined peak at \(\lambda_{\rm max}\) = 434 nm with a molar absorptivity (\(\epsilon\)) of \(1.90\times 10^{4}\) L mol\({}^{-1}\) cm\({}^{-1}\), and [PERSON]'s factor of 0.003 ug mL\({}^{-2}\). Absorbance of the [Cu(L)(NO\({}_{3}\))(H\({}_{2}\)O)\({}_{2}\)]\(\bullet\)H\({}_{2}\)O follows [PERSON]'s law over a 0.7-25 ug mL\({}^{-1}\) range with a detection limit of 0.011 ug mL\({}^{-1}\). Validation of the submitted method was established by estimating Cu\({}^{2+}\) in certified reference materials and actual sea and tap water samples. The results are compared with data obtained from copper concentration measurements using ICP-OES. The chemical structure of the Cu(II)-AMT complex was fully characterized by FT-IR, SEM, EDX, TGA, and ESR techniques.  
copper ions; spectrophotometry; characterization; water samples +
Footnote †: journal:
## 3 Definition of the Characteristics of the System Used in the Analyses
### Variable Parameters  
The variable parameters under consideration encompass the characteristics of the external protection device, the SSSI model, the soil, and the frame structures. Specifically, they include:  
* The stiffness \(k_{M}\) and the virtual mass \(M_{I}\) provided by the inerter device. These two mechanical quantities are parameterized in relation to the stiffness \(k_{1}\) and the mass \(m_{1}\) of the 2-DOF model associated with the degree of freedom \(u_{1}\), according to Equation (13): \[\begin{split} k_{M}&=\eta k_{1},\\ M_{I}&=\gamma m_{1}.\end{split}\] (13)* The ratio between the distances \(d\) and \(a\), the shear wave velocity \(V_{s}\) of the soil, and the scaling coefficient \(\chi\) (see Equation (11)). For clarity, the ratio \(d/a\) that appears explicitly in Equation (12) has been renamed introducing the parameter \(\alpha\), as defined in Equation (14): \[\alpha=\frac{d}{a}.\] (14)
* The characteristics of the frame structure. Two distinct structures are considered in the analyses. It is assumed that these structures are sufficiently regular to be effectively modeled by M-DOF shear-type systems. Furthermore, equal mass and stiffness distribution are considered at each story. The main characteristics of the two systems are presented in Table 1.  
Table 1 presents the number of stories for each structure, as well as the area, mass, stiffness, and height assigned to each story. Additionally, Table 1 includes the main vibration period of the structure (Period) and the base dimension of the square foundation platforms \(2a\). Table 2 displays the geometrical and mechanical quantities that characterize the 2-DOF systems (refer to Figure 2). The heights \(h_{1}\) and \(h_{2}\) provide the vertical position of the mass centers associates to the two DOF (i.e., the mass of the first story and the lumped mass of all of the other stories) with respect to the mass center of the foundation platform. Specifically, since the first level of the equivalent 2-DOF system always corresponds to the first story of the actual frame structure, \(h_{1}\) is the height of the first story. The height \(h_{2}\) of the second level represents the vertical position of the center of mass of all the stories above the first.