esa-sceva/satcom-chunk-collection huggingface dataset:
# A Human-Inspired Control Strategy for Improving Seamless Robot-To-Human Handovers
## 2 Design of the Robotic Human-Inspired Control Strategy for HRH
### Toyota HSR Platform  
An HSR home service as depicts in Figure 1a has been launched by the Toyota Corp. the robot can be controlled intuitively using voice commands or a touchscreen graphical user interface. The core technologies of the HSR comprise speech recognition, object recognition, autonomous navigation and localization, motion planning, task planning, etc. These are adequate to develop HRH in this research. It is realizing that the safety issue is an essential aspect of a robot to collaborate with humans in a safe and natural manner. Obstacle avoidance and a force relaxation mechanism were employed to protect humans from any mistakes or accidents. As the robot has to accommodate a wide variety of household activities, then its cylindrical telescoping body was originally fabricated. It was equipped with a differential pseudo-omnidirectional wheel and a central yaw joint which allow the robot to handle most manipulation tasks with smooth changes in direction [39].  
To construct a robot map and map-based self-localization, the robot base has equipped with two types of sensors, i.e., a LIDAR sensor and an inertial measurement unit (IMU). The HSR manipulator arms are made up of a shoulder pitch joint and roll-pitch-roll wrist joints. The robot end-effector has a two-finger parallel gripper (driven by a single motor), which was furnished by a wrist multi-axis force/torque sensor and hand camera. The fingertips were designed to be compliant with the shape of the objects, as it is covered by elastic materials. The robot arm is attached to the body with a three-stage prismatic waist joint, which enables the height of the robot's shoulder to a telescope. The robot's head has been attached by an ASUS Xtion RGBD camera, two RGB cameras, a microphone array and a small display monitor [39; 40; 41].  
The robot has two onboard computers. The main computer is preserved for Toyota's proprietary code to execute sensor and motor interfaces, motion planning and control and the web-based remote user interface etc. Nevertheless, both computers do not have much computing power for the development of advanced software algorithms. Then, it requires an external computer that can directly communicate with the HSR software platform using Robot Operating System (ROS). The robot software architecture can be categorized into two layers: (1) real-time control and (2) intelligence layers. The first layer executes real-time processing for motor control using motor drivers and servo amplifiers. The second layer deals with analysing or systematically extracting big data such as RGB images and point clouds, in which Linux (Ubuntu) is adopted to run the OS and ROS [39; 40; 41]. The HRS software architecture is illustrated in Figure 1b.  
Figure 1: (**a**) The hardware equipment of the HRS and (**b**) HRS software architecture (connecting to an external PC).
_Appl. Sci._**2012**, \(2\), 496-512; doi:10.3390/app2020496  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
**Overview of the Development of the Fluoropolymer Industry**  
**[PERSON]**  
Polymer Research Institute, Department of Chemical and Biological Sciences, Polytechnic Institute of New York University, 6 Metrotech Center, Brooklyn, NY 11201, USA; E-Mail: [EMAIL_ADDRESS]  
_Received: 23 April 2012; in revised form: 3 May 2012 / Accepted: 14 May 2012 / Published: 29 May 2012_  
**Abstract**: The present review briefly describes the development of the fluoropolymer industry in the past 70 years. Discussed are industrial fluoropolymers including polytetrafluoroethylene, polychlorotrifluoroethylene, polyvinylidenefluoride, p polyvinylfluoride, ETFE, ECTFE, FEP, PFA, THV, Teflon AF and Cytop. Nafion is included as a special functional fluoropolymer material. These industrial fluoropolymers are introduced in the order of their discovery or time of first production, included are their chemical structures, thermal properties, mechanical properties, electrical and electronic properties, optical properties, chemical resistance, oxidative stabilities, weather stabilities, processabilities and their general applications. The main manufacturing companies for the different types of fluoropolymer products are also mentioned.  
**Keywords**: fluorine chemistry; fluoropolymer; fluorine industry  
**List of Abbreviations**  
CTFE chlorotrifluoroethylene Cytop homopolymer of PBVE ECTFE copolymer of ethylene and CTFE ETFE copolymer of ethylene and TFE FEP copolymer of fluorinated ethylene and propylene HFP hexafluoropropylene Nafion TFE and perfluoro-3,6-diox-4-methyl-7-octenesulfonic acid copolymer PBVE perfluoro-3-butenyl-vinyl ether PCTFE polychlorotrifluoroethylene PDD perfluoro-2,2-dimethyl-1,3-dioxole PE polyethylene PFA copolymer of TFE and PPVE
# Static Evaluation of a Midimew Connected Torus Network for Next Generation Supercomputers
## 3 Routing Algorithm of MTNs
The MTN used an outer BM for moving from one level to another, as shown in Figure 1. Inside the level, it considered whether the BM had the destination node to route the packet to that exact node inside the BM. Routes using shortest-path followed dimensional order routing. Figure 3 illustrates the steps which were followed to code the MTN to calculate the diameter and average distance. A supercomputer contains many interconnected nodes. Since the main goal of the routing algorithm was to simulate the MTN performance for such systems, it considered the path of all pairs of nodes in the network in order to generate diameter and average distance values. Therefore, the intent was to obtain the routing results from one node to another. The addresses and the representations of the nodes are shown in Figure 4. The layout of the BM was also presented as a reference for the numbering format used for naming, such as \"level.BM.node.\" From source to destination, we found the following:  
Figure 3: Simplified routing steps of MTN. Source.BM.level/destination.BM.level: This referred to the level where the source/destination BM was located. In Figure 4, the source and destination were both on level 2. Therefore, Source.BM.level and destination.BM.level = 2. Source.BM/destination.BM: This referred to the exact source/destination BM on the same level. In Figure 4, Source.BM = (3,3), destination.BM = (2,1). Source.BM.node/destination.BM.node: This referred to the source/destination node inside a particular BM. In Figure 4, Source.BM.node = (2,2), destination.BM.node = (2,2). The simulation was performed by coding the BM and the higher level in order to obtain the paths between a pair of nodes in the network. The flow chart of the routing algorithm is shown in Figure 3, following the protocol and using dimensional order routing and controlled routing flow to obtain the network paths. In the next section, we evaluated the MTN via the results from this simulation for diameter and average distance, while the other parameters were derived using a graph model.
## 2 Related Work
### Pose Optimization Related
#### 2.1.1 Feature-Based Methods  
Feature-based methods typically utilize epipolar geometry to determine the pose of a camera, frequently employed in SFM (Structure from Motion) and SLAM (Simultaneous Localization And Mapping) systems. For instance, in SLAM systems, ORB-SLAM [15] utilizes ORB feature points for pose estimation, boasting rapid and robust characteristics, extensively adopted in the robotics domain. For tasks demanding high surface precision, SIFT [16] is typically employed for feature extraction due to its superior matching accuracy. In implicit scene reconstruction, the dataset used in the NeRF [17] paper can apply the aforementioned methods in SFM and SLAM to compute feature points, subsequently determining the pose data. Additionally, COLMAP [18] proposed a robust 3D reconstruction system and offered an open-source tool. This tool facilitates feature point extraction and camera pose calculation in scenes, providing convenient conditions for NeRF researchers. Subsequent works based on NeRF [19; 20; 21] all employ feature-based methods. Furthermore, the SaNeRF [22] algorithm incorporates epipolar constraints to achieve 3D scene reconstruction without pose data, but this method still relies on SIFT feature point matching.
## 6 Risk Assessment  
The results of seismic risk assessment are presented in terms of mean Annual Rates of Failure of UPD leading the superstructure to the drift limit and GC reaching the limit displacement of DCCSS isolators. Figure 17 shows the comparison of the mean annual rates evaluated for all the case studies for the GC (a) and the UPD (b) performance levels, to provide a prediction of potential damage during an earthquake higher than the CLS (corresponding to IM6) for the base-isolated models. The UPD rates confirm that seismic isolation works effectively in limiting damage to non-structural components for seismic intensities much higher than the design earthquake intensity level DLS (corresponding to IM2). Dashed horizontal lines highlight the reference probabilities of exceedance provided by the Italian seismic code NTC18 [28], i.e., \(1.00\times 10^{-3}\) for GC and \(1.26\times 10^{-2}\) for UPD. Due to the advanced model with the extra displacement capacity gained in the over-stroke regime, for the new base-isolated building the GC rates associated with the attainment of the DCCSS limit displacement appear almost identical to UPD ones. For the retrofitted base-isolated building the superstructure failure becomes more significant, UPD rates are linked to higher probabilities of exceeding than the GC ones, mainly due to the higher deformability of the superstructure.  
The values of the annual rates of failure derived at UPD and GC limit states for the examined case studies are reported in Table 6 and compared to the results showed in the aforementioned works. Assuming an extra-displacement of more than 30% of the maximum geometrical displacement, few differences between UPD and GC can be observed and the rates are about the same values in both cases of new constructions isolated buildings and the case of structure designed with low seismic design retrofitted for high  
Figure 16: Displacement D/C ratios of the superstructure (UPD): NA_GLD X (**a**) and Y (**b**) directions.  
Figure 17: Annual rates of failure for the analysed base-isolated case studies, for the GC (**a**) and the UPD (**b**) performance levels.  
seismicity zone (site of L'Aquila). In the case of the existing building designed for gravity load only and retrofitted for medium seismicity zone (site of Naples), the difference between the UPD and GC rates increased; in this case, the over-stroke displacement contribution is negligible.
## 3 Experiment
### Collected Vibration Signals  
As mentioned above, the experimental measurements include four fault types shown in Table 1. The sampling rate is 100 kHz. The duration of each measurement is 20 s. The sampling interval was set to 0.2 s. Thus, 20,000 observations were obtained in each fault type, and 20 \(k\) points are chosen for each observations. Therefore, a dataset of 80,000\(\times\)20 \(k\) can be acquired during the experiment. Figure 9 shows an example of a vibration signal acquired in each one of the fault types.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fault Id** & **Part** & **Fault Type** \\ \hline A & None & Healthy \\ B & Sun gear 1 & Pitting \\ C & Sun gear 1 & Broken tooth \\ D & Planetary gear 1 & Cracking \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different fault patterns in the industrial manipulator.  
Figure 8: Examples of each one of the 4 monitoring conditions: (**a**) Healthy state; (**b**) Pitting in Sun gear 1; (**c**) Broken tooth in Sun gear 1; and (**d**) Cracking in Planetary gear 2.  
Figure 7: Monitoring the health state of the gears using vibrational signals acquired by an accelerometer located in the basis of the sixth axis of the manipulator.  
For hold-out validation, the data set was divided into two disjoint subsets, the training and the test (sub)sets. The training set has 70% of data while the test set has the remaining 30%. Table 2 shows the number of observations for each one of these sets.
# Estimation of River Velocity and Discharge Based on Video Images and Deep Learning
## 3 Results and Discussion
### Model Performance Comparison
#### 3.2.3 Comparison of Different Network Models  
The comparative experiments are conducted with other networks ResNet34 [26], DenseNet [27], MobileNetV2 [28], EfficientNetV2 [29], and GhostNetV1 [30] on the same  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attention** & **TOP1/\%** & **TOPS/\%** & **Params/M** & **FLOPs/G** \\ \hline - & 61.67 & 90.62 & 4.11 & 0.43 \\ ECA & 60.74 & 89.51 & 4.11 & 0.43 \\ CBAM & 61.17 & 88.64 & 4.42 & 0.43 \\ SE & 62.53 & 88.95 & 4.27 & 0.43 \\ BAM & 64.69 & 90.56 & 4.44 & 0.45 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effects of different attention mechanisms on model performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Lite\_1 \(\times\) 1** & **K\_Size = 5** & **BAM** & **TOP1/\%** & **TOP5/\%** & **Param/M** & **FLOPs/G** \\ \hline
0 & & & 58.70 & 89.81 & 5.56 & 0.63 \\
1 & \(\surd\) & & 60.74 & 90.93 & 4.03 & 0.41 \\
2 & \(\surd\) & \(\surd\) & & 61.67 & 90.62 & 4.11 & 0.43 \\
3 & \(\surd\) & \(\surd\) & \(\surd\) & 64.69 & 90.56 & 4.44 & 0.45 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiment results of ShuffleNetV2_2.0.  
experimental platform to comprehensively evaluate the performance of the improved network. Care is taken to ensure that all relevant parameters remain constant to guarantee the fairness of the experiments. The experimental results of these various networks are presented in Table 5.  
According to the data shown in Table 5, the TOP1 accuracy of the improved model reaches 64.69%, which is notably superior to that of the original model and other competing networks. Furthermore, the TOP5 accuracy stands at 90.56%, and it significantly outperforms ResNet34, DenseNet, and EfficientNetV2 in terms of parameters and FLOPs. When compared to MobileNetV2 and GhostNetV1, the improved model demonstrates a higher detection accuracy. This indicates that the improvements have been successful in boosting its accuracy without incurring excessive computational costs, and hence it exhibits a more favorable overall performance.
## 10 Challenges of Inter-Chip Wireless Interconnects  
Beyond the opportunities discussed so far, there are several challenges of realizing an inter-chip wireless interconnection network. One such challenge is the security and privacy of data exchange over the wireless inter-chip interconnection, which needs to be ensured. While hardware security and trust are gaining importance among researchers [119; 120] the inter-chip wireless introduces new challenges regarding security and privacy in chip-to-chip communication. Wireless communication between the chips in the multi-chip system can suffer from two imminent security threats namely,eavesdropping and malicious data transfer. Eavesdropping can be done by a receiver tuned to the same frequency channel if the receiver is powerful enough to adequately amplify the signal. On the other hand, a malicious transmitter tuned to the same frequency can send unauthenticated malicious data flooding the interconnection ultimately causing Denial-of-Service (DoS). DoS-resilient wireless NoC architecture has been proposed [119]. In [120] authors present a mechanism for secure communication in wireless intra-chip NoCs. These techniques need to be evaluated for their application in multichip wireless interconnection networks. However, in multi-chip systems, the chances of such security threats can be mitigated by isolating the wireless interconnection system inside a metal package or case. However, in the absence of such possibility, the system needs to be protected against eavesdropping and unauthenticated messaging. For this, cryptographic and authentication solutions need to be investigated. Security and privacy measures existing in the larger system such as the IoT can be adopted for the on-chip wireless transceiver to equip the inter-chip wireless communication with a seamless security and privacy platform.  
Another challenge stems from the fact that wireless bandwidth is a limited resource. In any wireless band, the total spectral range is specific and limited. Unlike wireless interconnects, the throughput of wireline links cannot be increased by tracing parallel wires to increase the bus-width. Such a technique is not possible in a shared medium interconnect system such as wireless interconnections. However, the data rate over wireless channels can be increased by adopting higher order modulation schemes. If each symbol represents multiple bits, the data rate over the wireless channel can be increased several times. However, the transceiver design for higher order modulation schemes will be more complicated and will, therefore, consume higher power compared to simple OOK transceivers. Carrier recovery circuits to implement coherent modulation schemes such as PLLs at high mm-wave frequencies are extremely power hungry [121]. Depending on the power-performance targets of the system appropriate modulation schemes need to be chosen. A design approach could be to have several modulators and demodulators in each transceivers to be selectively chosen depending on the data rate requirements of the system or of specific connections depending on applications or environments.
###### Abstract  
Insufficient data and imperfect methods are the main obstacles to realize Target 11.4 of the Sustainable Development Goals (SDGs). Very high-resolution (VHR) remote sensing provides a useful tool to elaborate monitor land-cover changes in cultural landscapes so as to evaluate the authenticity and integrity of the cultural heritage sites (CHS). In this study, we developed a semi-automatic two-level workflow to efficiently extract delicate land-cover changes from bi-temporal VHR images (with spatial resolution \(\leq\) 1 m), where most current studies can only manually interpret changes at this scale. Based on the monitoring result, we proposed an indicator named interference degree that can quantify the changes in cultural landscapes of the CHS as a complementary indicator to achieve Target 11.4 for SDGs. Three representative types of CHS with different landscapes were studied in 2015 and 2020 based on the VHR Google Earth images, including cave temples, ancient architectural buildings, and ancient sites. The proposed workflow was demonstrated to be effective in extracting delicate changes efficiently with the accuracy around 85%. The interference degree well reflects the preservation status of these CHS and can be periodically observed in a long term as an evaluation indicator. This study shows the potential to produce the first-hand global-monitoring data of CHS to support Target 11.4, thus serving for the sustainable development of the world's cultural heritage.  
cultural-heritage site; very high-resolution; cultural landscape; interference degree; object-based image analysis +
Footnote †: journal: Sustainable
## 1 Introduction
#### Publications Trends and Bibliometric Analaysis  
This section presents an extensive literature analysis, which was conducted from a variety of perspectives. The number of scientific works that deal with the topic of the human role in the manufacturing system and works that include the keywords of this paper are analyzed.  
To review the relevant literature, we used the Scopus database, created by Elsevier and launched in 2004. According to [26], it contains information from about 11,678 publishers with 34,346 peer-reviewed journals in areas such as life sciences, social sciences, natural sciences, and health sciences. The database contains three types of sources: Book Series, Periodicals, and Journals. The journals included in Scopus are evaluated each year using four numerical quality measures, such as the h-index, CiteScore, SJR, and SNIP.  
To find as many scientific papers as possible describing the role of humans in production systems, we first collected as many possible phrases that define the topic and appear in scientific papers. We also used the artificial intelligence chatbot ChatGPT to search for phrases. We reviewed the result and wrote queries to search the Scopus database of scientific articles, see Figures 1-6.  
With graphs 1 (Human role in manufacturing) and 2 (Human role in production), we wanted to see the difference between whether the authors associated the human role more with manufacturing systems and processes or with production systems and processes. Manufacturing systems refer to the methods, processes, and equipment used to transform raw materials into finished products. This term often refers to mass production techniques and involves the use of specialized machinery and assembly lines. A broader field is covered by production systems, which refer to the overall organization and management of the process of producing goods and products. This includes planning, scheduling, materials management, quality control, and other aspects of the production process. The difference between the two graphs is obvious. The authors associated the human role with manufacturing in significantly more documents (8627) than with production (1331).  
Figure 4: Smart manufacturing/production systems—years 2006–2022, 20,812 documents in period.  
Figure 5: Industry 4.0—years 2006–2022. No data before 2012, 18,760 documents in period.  
Figure 3: Cognitive manufacturing/production—years 2006–2022, 10,803 documents in period.  
However, it can also be seen that the steepness of the curve in Figure 1a (Human role in manufacturing) and Figure 1b (Human role in production) has increased significantly and coincides with the change in the slope of the curves in Figure 2 (Digitalization), Figure 4 (Smart manufacturing/production) and Figure 5 (Industry 4.0). Therefore, it can be concluded that the authors have recognized the increased role of humans in digitization and smart manufacturing/production at the same time as they describe digitization and smart manufacturing/production.
# Crescent Microstrip Antenna for LTE-U and 5G Systems
## 6 Simulations and Measurements Results
### Current Distribution in the Antenna  
In the microstrip antenna at the end of the radiating element (edge of the patch), the current value should be minimal. The voltage at the edge of the patch is out of phase with the current. Consequently, the voltage will peak at the tip of the patch with currents close to zero. The voltage out of phase with the current phase creates fields at the edges of the microstrip antenna. Figure 12 shows the current distribution of the proposed antenna for the frequencies of 3.6 GHz and 5.5 GHz (resonant frequencies).  
Figure 11: The antenna efficiency as a function of frequency for the proposed antenna working in 5G and LTE-U systems.  
Figure 10: The antenna gain as a function of frequency for the proposed antenna working in 5G and LTE-U systems.
## 3 Results
### ISL Range Bias Estimation against Weighting Approach and Ground Station Sets
#### 3.2.2 ISL Range Bias Estimation
Direct comparison of Figure 7b,d,f reveals that a lower number of stations and thus a smaller number of GNSS measurements still allows for accurate bias estimation. The lowest RMS of estimated bias is noticed for sequential dual-one-way scheme regardless of weighting approach or ISL range bias values. Figure 7: Histograms of ISL range bias differences for (**a**) 44 stations (global) with Nominal weighting, (**b**) 44 stations (global) with Forstner weighting, (**c**) 16 stations (global) with Nominal weighting, (**d**) 16 stations (global) with Forstner weighting, (**e**) 16 stations (Europe) with Nominal weighting, and (**f**) 16 stations (Europe) with Forstner weighting. Differences were computed for simulation scenarios for ISL range biases equal to 0.5 cm and 1.0 cm.