esa-sceva/satcom-chunk-collection huggingface dataset:
# Dielectric Elastomer Actuator for Soft Robotics Applications and Challenges
## 6 Locomotion: Mobility Design
### Worm-Like Crawling Robot  
The artificial annelid robot was built by using the bulging actuation of a diaphragm shaped DEA [103]. The robot was constructed by serially connecting actuator modules. Each actuator module has six diaphragm DEAs on both sides. Each diaphragm DEA is composed of 12 layers of DE film (Silicone KE441) and is 0.75 mm in total thickness and 5.1 mm in diameter. The actuator module pushes  
Figure 9: Insect-like waling robots which are driven by DEAs: (**a**) FLEX2 [8]; (**b**) MERbot [80]; (**c**) S-Hex2 [102].  
the neighboring module when voltage is applied. Based on the configuration of the actuator module, each sector has three DOF: up/down translational motion and two rotational motions. The dimension of the robot is 20 mm in diameter and 45 mm in length, and a 2.5 mm/s speed is measured at 10 Hz.  
An inchworm robot was fabricated to test a unimorph DEA with embedded fiber [104]. In this robot, nylon fiber is used between two layers of acrylic film (VHB F9473 PC) to constrain the bending motion of the unimorph DEA. The robot consists of a head, body, and tail. The inchworm-like contractile motion is induced in the body part which contains a 20 mm width and 30 mm length of carbon-based electrode, and anisotropic friction occurs at the head and tail part using weight and sharp tips, respectively. Using this mechanism, a 1.1 mm/s speed is accomplished at a 0.3 Hz actuation rate, which is 1.3 body lengths per minute. The inchworm robot was also fabricated using electrodhesion for anisotropic friction at the front and tail part and a DEA for the bending motion of the body [105]. Compared with previous inchworm robots, electrodhesion provides for the stable locomotion of the robot. Also, all components to operate the robot are placed onboard so that an untethered and autonomous soft robot is accomplished.  
In addition, a reconfigurable crawling robot was made with a double cone-shaped DEA, which is shown in Figure 10a [106]. The double cone-shaped DEA is composed of two DE membranes which are silicone elastomer (Elastosil) of 100 \(\upmu\)m thickness and 20 mm radius, and the two are attached to a small central disk. An alternative electric charge induces an oscillating motion of the central disk. With this oscillating motion and anisotropic friction, which is induced by a tilted bristle structure, the robot can move in two different modes: vibrational crawling and inchworm-like two-anchoring crawling. In vibrational crawling mode, the robot can move at the speed of 55 mm/s, which is 0.9 body length/s, and in two-anchoring crawling mode, the robot can carry a payload of up to 35% its body weight and achieve 0.09 mm/s of speed.
## 6 Single-Chain Based Routing  
In this scheme, data routing is performed in a cylindrical network by using a single chain of sensor nodes. Every node finds its nearest neighbor and connects with it. In this way, local optimum neighbors of sensor nodes are identified. However, it does not achieve the global optimum data transmission as there exists a _long link_ problem. During network initialization, nodes broadcast their location  
Figure 6: 2-chain based routing.  
information towards the sink and their neighbors. In the first phase, chain creation starts from the farthest node from the sink by using token passing approach.  
Every node finds its nearest node which is not already connected to the chain. In the token passing approach, the sink transmits a token towards the farthest node of the chain. The chain is formed as the token is passed towards sink through the intermediate nodes. The primary chain head is selected on the basis of \(W\). All nodes compute the value of \(W\) by using their residual energy and distance from the sink. The sink compares the \(W\) of all the nodes in the chain. The node with the highest \(W\) is selected as a primary chain head. All the nodes transmit their data towards the sink through intermediate nodes.  
Then each node \(i\) compares its distance from the parent node and distance to the sink. If the later distance is less, the node \(i\) acts as a secondary chain head and sends the collected data to the sink, instead of transferring it to its parent node. In the last phase, nodes transmit their data towards their respective parent nodes. Data is forwarded to the sink through primary and secondary chain heads. Figure 7 shows the data transmission in single-chain based routing scheme.
# Dark-YOLO: A Low-Light Object Detection Algorithm Integrating Multiple Attention Mechanisms
## 3 Experiment  
### Dataset and Experimental Setup  
To evaluate the performance of the proposed enhanced model, we utilized the publicly available ExDark dataset [42], which is specifically designed for object detection and image enhancement research in low-light conditions. The ExDark dataset comprises 7363 low-light images captured across 10 distinct lighting scenarios, ranging from extremely dark to dimly lit environments, and encompasses 12 object classes. For our experiments, the dataset was randomly split into training, validation, and testing sets using two different ratios: 8:1:1 and 6:2:2. Additionally, to assess the robustness of the improved network, we employed the publicly available Dark-Face dataset [43]. All training, validation, and testing procedures in this study were conducted using these datasets.  
For this experiment, we utilized a Linux-based operating system with an NVIDIA A40 GPU. The experiments were implemented using Python 3.8, and the deep learning framework adopted was PyTorch 2.0, with CUDA version 11.4. The model was trained for 200 epochs. To ensure fairness in comparison, all experiments were conducted under identical experimental conditions and training parameters. The specific training hyperparameters are shown in Table 1.  
Figure 7: Structure diagram of MobileNet V2 and MobiVari model. (**a**) The MobileNet V2 network structure performs feature extraction and reconstruction using expansion convolutions, depthwise convolutions, and pointwise convolutions, with ReLU6 used for activation and nonlinear mapping. (**b**) The MobiVari network structure replaces the standard expansion convolutions with grouped convolutions and substitutes the activation function with LeakyReLU. These modifications further reduce computational cost while better preserving feature information.
# Navigation Map Construction Based on Semantic Segmentation and Multi-Submap Integration
## 4 Methods
### Multi-Submap Fusion Mapping Method for Outdoor Environment
#### 4.2.2 The Multi-Submap Management Module
Let the occupancy probabilities of the same grid cell in submaps A and B be \(P_{A}\) and \(P_{B}\), respectively. According to the above equation, their corresponding log-odds values can be obtained as follows:  
\[L_{A}=\text{log}\frac{P_{A}}{1-P_{A}},L_{B}=\text{log}\frac{P_{B}}{1-P_{B}} \tag{23}\]  
Initially, the occupancy probability and the vacancy probability of the grid are equal, both being 0.5. Therefore, \(L_{0}=0\). Under this assumption, the fused log-odds value is as follows:  
\[L_{\text{fused}}=L_{A}+L_{B} \tag{24}\]  
Finally, the fused log-odds value is converted back to the occupancy probability, as follows:  
\[P_{\text{fused}}=\frac{1}{1+e^{-L_{\text{fused}}}} \tag{25}\]  
We designate \(P_{\text{fused}}\) as the final occupancy probability of the overlapping grid cell.
# Effect of Terahertz Antenna Radiation in Hypersonic Plasma Sheaths with Different Vehicle Shapes
## 2 The Plasma Sheath and the THz Array Antenna
### The 0.14 THz Array Antenna  
The blackout zone is located at an altitude of 20-80 km. In this area, the water vapor attenuation of neutral atmospheric attenuation must be considered since the signal transmission path in the atmosphere could be up to several hundreds of kilometers. According to previous studies, the attenuation of THz bands at 1-10 THz is obviously much higher than that of 0.14 THz [19]. Moreover, there are some 'atmosphere window' frequencies that have low attenuation levels including 35 GHz, 94 GHz, 140 GHz, and 220 GHz [20; 21]. In such a case, it makes sense to operate the communication system at a certain atmospheric window frequency. On the other hand, the lower atmosphere window frequencies have higher signal attenuation in plasma sheaths [8].  
Additionally, Section 2.1 proves that the antenna can only work when the operation frequency is higher than the plasma cut-off frequency. Therefore, according to Equation (3), the antenna must operate at a frequency higher than 75 GHz for the blunt coned vehicle, since the peak electron density is \(7\times 10^{19}\) m\({}^{-3}\) according to Figure 2. As for the sharp coned vehicle, the antenna operating frequency should be higher than 98 GHz, since the peak electron density is \(1.2\times 10^{20}\) m\({}^{-3}\).  
Therefore, in summary, to balance the signal attenuation by both the atmosphere and the plasma sheath, 0.14 THz was selected as the antenna operating frequency. The sub-THz antenna not only has low attenuation, but also high penetrability. Moreover, taking advantage of the antenna array, the antenna also has high directivity.  
Figure 2: (**a**): The electron density distribution and electron collision frequency distribution for the blunt-coned vehicle along the propagation path (the gray arrow in Figure 1a,c). (**b**): The electron density distribution and electron collision frequency distribution for the sharp-coned vehicle along the propagation path (the gray arrow in Figure 1b,c).  
The structure of the 0.14 THz patched array antenna is showed in Figure 1c. Since the plasma sheath distribution is two-dimensional, the antenna is designed to be 1 \(\times\) 10 units. As seen in the figure, the antenna length is less than 1.7 cm. The antenna can be designed to have more units and can achieve extremely high directivity with acceptable antenna size. In this study, the antenna only has 10 units to reduce calculation.
## 4 Experiments and Discussion
### Ablation Experiments
#### 4.5.2 Performance Variations on Diverse Losses  
To evaluate the contribution of each component within the loss function, we conducted a series of ablation experiments on the Vaihingen dataset using our proposed PLGTransformer model. To better understand the role of each component, we designed the following experimental settings: (1) baseline--both multi-class region focal loss (i.e., \(L_{region}\)) and multi-scale boundary loss (i.e., \(L_{boundary}\)) are applied; (2) multi-class region focal loss only--only the multi-class region focal loss is used; (3) multi-scale boundary loss only--only the multi-scale boundary loss is used. The experimental results are summarized in Table 5. The experimental results show that the complete loss combination, which jointly optimizes both the region focal and boundary losses, results in the best overall performance. Removing either branch leads to a significant decrease in both mIoU and mF1 scores, demonstrating the complementary nature of region classification and boundary modeling.
## 4 Experiments on Feature Extraction of Machinery Faults
### Feature Extraction of Impulse Fault on Bearing Dataset--Case 1
#### 4.1.1 Experimental Settings  
The experimental platform is shown in Figure 4, which consists of a transmission shaft, an AC motor, and 4 Rexnord bearings. A radial load of 2721.6 kg was applied on bearing 2 and 3 as a whole. The vibration signals were collected via PCB 353B33 accelerometers (PCB Piezotronics, Depew, NY, USA) installed on bearing housing. The data acquisition module was NI DAQ 6062E (National Instruments, Austin, TX, USA). Each record consisted of 20,480 points, with a sampling rate of 20 kHz. Other relevant experimental settings are concluded in Table 1.  
Specifically, the test signals were recorded during 12 February and 19 February 2004, when a failure occurred after over 100 million cycles causing a crack in the outer race of bearing 1. With the purpose of extracting the impulse feature in the signals at the stage of incipient faults, we examined the record for the 1 st channel at 03: 42, 16 February 2004 (74 h before the end of the experiment), as shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bearing Model** & **Running Speed** & **Sampling Rate** & **Fault Position** & **Characteristic Frequency** \\ \hline Rexnord ZA-2115 & 2000 RPM & 20,000 Hz & Outer Ring & 236.9 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental settings of case 1.  
Figure 4: Bearing test rig and sensor placement [28]. Reproduced with permission from [28], Elsevier, 2019.
# Electrochemical Detection of Penicillin G Using Molecularly Imprinted Conductive Co-Polymer Sensor
## 3 Results and Discussion
### Characterization
#### 3.1.3 Thickness Measurements  
Glass slides were used to conduct thickness measurements of the deposited polymer, with the profilometer, since this is one of the most uniform and planar substrates. After about 20 min of polymerization, the optimal deposit thickness is reached. The polymerization is interrupted to begin the extraction step and drying step of the polymer. Then measurements are made on multiple substrates. The mean thickness is about 1.5 \(\upmu\)m \(\pm\) 0.1 \(\upmu\)m.
# Design and Feasibility of a Safe Pill Bottle
## 2 Materials and Methods  
We implemented the prototype safe bottle with 15 capacitive segments around a standard pill bottle. The capacitive sensors were made of copper tape strips insulated with clear plastic tape. The first prototype featured 5 segments and provided insufficient resolution. Therefore, we implemented a second prototype with 15 capacitive segments, as shown in Figure 1.  
Inside the bottle we placed an embedded microcontroller board and a battery. To facilitate measurements from multiple segments at a high sampling rate, we used two microcontroller boards: Teensy 3.2 [14] was used to measure capacitance from 6 channels and Teensy 3.6 [15] to measure capacitance from 9 channels and save data to an on-board microSD card. Capacitance was sampled at 50 Hz throughout the experiment. Teensy 3.6 served as the master controller that initiated measurements in each cycle and collected measurements from the slave board using serial (UART) communication.  
In the pilot experiment we collected real-time measurements of capacitance to detect two classes of users: _children_ (in our pilot experiment, ages 5 to 10 years old), and _adults_ (over 20 years old). We asked 16 participants--eight adults and eight children--to open and shut the bottle 30 times, simulating taking a pill out of the bottle. We recruited participants as volunteers from our circle of friends and collaborators, in many cases using children and adults from the same family to represent a realistic use case scenario. Participants represented a range of racial, ethnic, and cultural groups. The average age and standard deviation of adult users was 33 \(\pm\) 14.9 years, and for children 7.5 \(\pm\) 1.5 years. The experimental protocol was approved by the IRB at the University of Alabama at Birmingham (approval number IRB-300000806).  
The capacitance of all segments was constantly recorded with a sampling frequency of 50 Hz. Subjects were not instructed how to hold or open the bottle, as we wished to capture their natural patterns of bottle use. We collected a total of 474 events during the experiment, 29.6 events per user. Two hundred and forty-eight events were from adult users and 226 events from child users.
# On the Influence of Suspension Geometry on Steering Feedback
## Materials and Methods
### Suspension Kinematics and Its Effects  
The correlation between steering geometry and the torque steer effects have been investigated by means of a self-developed software tool for suspension kinematic design and analysis (see Figure 8).  
The reference geometry in Figure 9 is taken from the vehicle model. It appears suitable for a high-performance sportscar, as it is a little extreme, given the high value of the caster angle. Camber is kept to zero for simplicity. Additionally, the overall steering ratio (steering wheel angle/wheel angle, Sa/Wa) is fairly direct, hence it can be classified as \"sporty\" or \"reactive\", see Table 1.  
The first modification, called EB0, was aimed at making the geometry more representative of a wider range of cars, and therefore with less aggressive caster and kingpin settings. This in turn allows for the kingpin offset (KO) to be halved (Figure 10 and Table 1). According to the formulas in chapter 2, all these three parameters have a large influence on torque steer, as shown later.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name** & **Caster [deg]** & **Kingpin [deg]** & **SR [mm]** & **CT [mm]** & **KO [mm]** & **Sa/Wa [deg/deg]** \\ \hline Original & 10.2 & 14.2 & 16.1 & 29.0 & 102 & 15.7 \\ EB0 & 5 & 3.7 & 29.2 & 12.5 & 51.4 & 15.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: EB0 suspension compared with the original one.  
Figure 8: The software tool created by the authors for the design of suspension and steering kinematics.  
Figure 9: Schematic front view (yz plane) of the original front axle pick-up points. Upper wishbone is shown in red, lower wishbone in blue, upright in green, tie rod in light blue, spring is shown as a pink line with grey coil, the green horizontal line shows the chassis lower plane, rim offset is shown by the vertical purple line.  
Figure 11 shows a comparison between the steering effort required for a sine steer sweep manoeuvre with the passive vehicle equipped with the original and EB0 front axle geometries, with the latter requiring reduced effort and driver involvement. However, the phase and shape are unaltered, thus giving an intuitive, \"friendly\" feedback in both cases because the steering effort remains on the self-aligning side.
## 7 Conclusions  
We proposed a method to predict the statistical start-up probability distribution of a given SRAM block not specifically designed for PUF applications. The method firstly computes, using Monte Carlo analysis, the distribution of a cell imbalance factor, SD, based on the distance from the origin point of the 6T-cell space state diagram to the separatrix. Then, SD is transformed to a start-up probability distribution applying a transfer function that has been calibrated with experimental results.  
The experimental results show that, for the analyzed SRAM implemented with a 65-nm CMOS technology, around 21% of cells will start at the same logic value with a probability higher than 99%. Based on the hypothesis that those cells that present higher SUF values are the same ones that present higher SD factor values, we deduce that a bit-cell with SUF\({}_{1}\geq\) 99% corresponds to a cell with SD \(\geq\) 0.03 V.  
Generalizing, the described method allows the designer to predict, using Monte Carlo results from electrical simulation, the proportion of cells that will start up to the same logic value with a given probability or, in other words, the number of cells of a specific SRAM block that will be reliable for a particular PUF application.  
The prediction can be obtained at the early design stages. Depending on the prediction results, the design team could decide whether the SRAM meets the specifications. If they are not met, they could choose either using a larger memory in order to have more cells, or apply error correction techniques and evaluate its requirements to achieve the overall desired PUF performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Repeating Start-Up Value Probability** & **SD\({}_{\text{th}}\) (V)** & **Percentage of Cells** \\ \hline
0.99 & 0.030 & 21\% \\
0.98 & 0.019 & 42\% \\
0.95 & 0.008 & 76\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: SD\({}_{\text{th}}\) parameter value and percentage of cells for different repeating start-up value probabilities.  
**Author Contributions:** Conceptualization, G.T. and S.A.B.; methodology, G.T. and S.A.B.; validation, G.T., A.A. and B.A.; investigation, G.T. and A.A.; resources, G.T. and S.A.B.; data curation, G.T. and A.A.A.; writing--original draft preparation, G.T.; writing--review and editing, G.T., B.A. and S.A.B.; supervision, G.T., B.A. and S.A.B.; funding acquisition, G.T. and S.A.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
## 2 Background
### Modes of C-V2X Communications  
C-V2X manifests in two forms: (1) direct communication between devices referred to as device-to-device mode; (2) indirect communication between devices through the underlying network referred to as device-to-network mode. These two different modes introduce V2V, V2P, V2I, and V2N communication types. The following provides a brief description of the two modes of C-V2X technology.  
* **Device-to-Device Mode:** This mode of C-V2X communication deals with enabling the communication bit-pipes between vehicles (V2V), pedestrians (V2P), and infrastructures (V2I). Hence, the vehicular communication types i.e., V2V, V2P, and V2I are realized by device-to-device mode furthering the direct communication between devices (vehicles, pedestrians, and infrastructures are considered as devices) [9].
* **Device-to-Network Mode:** This mode of C-V2X communication enables the communication of bit-pipes between devices and network elements/entities. V2N is realized by this mode of C-V2X implementation. Hence, the end-user (driver) is able to achieve the advantages of network and cloud services. The V2N plays an essential role in completing the picture of an end-to-end solution for various verticals based on C-V2X communications.  
In view of the above communication modes and considering all devices will be in IoT networks in the future, we would like to mention the essential difference between V2N and V2X networks. Since V2X is a universal set of the vehicle-to-everything network, it includes V2V, V2I, V2N, V2P, etc. Meaning thereby, V2X is a broader term capturing vehicle communication with anything, whereas V2N is a narrow term capturing the communication between vehicles and the network. In this connection, the IoT networks would mean the network that connects vehicles. If we consider the vehicle as one of the things, the network will connect the vehicle with other vehicles and/or things. In this paper, we keep our focus open where vehicles can communicate with anything (i.e., V2X) through different modes of communication.