esa-sceva/satcom-chunk-collection huggingface dataset:
# SMS-Net: Bridging the Gap Between High Accuracy and Low Computational Cost in Pose Estimation
## 4 Experimental Results  
### Datasets  
In this study, we evaluated the performance of the proposed SMS-Net model using the MPII and COCO datasets--commonly used benchmarks for HPE. The MPII dataset consists of approximately 25,000 images and 40,000 annotated people, covering 410 human activities. These images are obtained from online video platforms and offer diverse poses, making it a challenging dataset because of its diversity in human activities and varied contexts. The MPII dataset provides annotations for 16 keypoints, where the joints are connected in a straight line from the crown to the chest. The COCO dataset is a large-scale dataset widely used in computer vision tasks, such as object detection, segmentation, and pose estimation. It comprises approximately 120,000 images and 17 keypoints, including facial features, such as eyes, nose, and ears, representing human poses. COCO offers a higher complexity owing to its diverse object categories, challenging backgrounds, and the variety of human poses in everyday scenes.
# Responses to the Impact of Drought on Carbon and Water Use Efficiency in Inner Mongolia
## 4 Discussion
### Uncertainties and Limitations  
There is not enough of a correlation between the drought index and the changes in the CUE and WUE of vegetation to draw any firm conclusions from this study. This is because the carbon and water cycles involve many coupling factors (including climatic conditions, CO\({}_{2}\) concentrations, soil conditions, and human activities), and are affected by uncertainties such as the accuracy of remote sensing used for monitoring and the complexity of ecological processes. If we want to understand and predict the responses and functional changes in ecosystems under global changes and human disturbances, we need to increase our understanding of the processes and mechanisms of the interactive effects of the key factors on the CUE and WUE of vegetation at different scales, and we need to reveal the quantitative the impact of the components of climate change and human activities.
# Study on Drone Handover Methods Suitable for Multipath Interference Due to Obstacles
## 4 Proposal Method According to the Issue
### Overview of Proposed Method #2
As an example, in Table 3, at point C, AP2 has the highest SINR at 30 dB; however, the moving average at point C (Ave.) is calculated by referring to past values at each AP. The calculated moving averages are 9 dB for AP1, 16.5 dB for AP2, and 21 dB for AP3. Based on these results, proposed method #2 can prevent unnecessary handovers by selecting AP3 instead of AP2. Figure 18: Handover sequence for proposed method #2.
# Using Explainable AI (XAI) for the Prediction of Falls in the Older Population
## 4 Discussion  
In this work, we utilised the large, publicly available TILDA dataset to implement Random Forests and XAI techniques for the prediction of different types of future falls and analysed their contributory factors using 46 input features. Being of the female sex and having had a previous fall were found to be high in the feature importance ranking in all of the models, and polypharmacy (being on five or more regular medications) was ranked high in the syncope model. The accidental (or more 'extrinsic') nature of simple falls was demonstrated in this model, where the presence of many frailty features showed negative model contributions.
# Thermo-Responsive Shape-Memory Effect and Surface Features in Polycarbonate (PC)
## 5 Further Discussion  
In general, the PC material tested here has excellent heating-responsive SME. The maximum programming strain is limited to 25% at the most, if programming is conducted at room temperature.  
However, as observed in Section 4, if the programming temperature is around the glass transition start temperature, micro-cracks may be found after programming by stretching. These micro cracks appear as short parallel lines and they are perpendicular to the direction of stretching, which is different from the butterfly pattern of cracks reported previously in a shape memory polystyrene [29]. These cracks are apparently due to the same kind of fracture in the relatively brittle areas of the material. As the cracks are short lines, and they should correspond to the brittle parts of the material, we expect that there is a network, which is relatively brittle (hard), within the sample. Because the applied programming temperature is around the glass transition start temperature, this network should be much denser than that reported in [29], in which the polystyrene was stretched close to its glass transition finish temperature (refer to the left column of Figure 22). Consequently, upon stretching, different crack patterns are observed as illustrated in the right column of Figure 22.  
Figure 21: Typical surface morphology (area: 140 \(\times\) 100 \(\upmu\)m) in samples programmed at 145 \({}^{\circ}\)C (**a**) and 160 \({}^{\circ}\)C (**b**). Left: 30% strained; right: 100% strained.  
Figure 22: Formation of a short line crack (**a**) and a butterfly (**b**). Left (1): before stretching; right (2): after stretching.  
Although these micro-cracks do not seemingly affect shape recovery of the material, its mechanical strength, in particular, under mechanical and/or thermo-mechanical cycling, is expected to be reduced.  
On the other hand, if the applied programming temperature is over the glass transition temperature range, as revealed in Figure 10, together with a high programming strain, the capability for shape recovery of this material might be reduced. Since there is not any crack in the programmed sample (refer to Figure 21(b2)), the non-recoverable strain should be the result of permanent deformation in the elastic component/chain/segment [4].  
Thus, the recommended programming temperature for this particular PC is within the middle of its glass transition range, i.e., around its \(T_{g}\).  
Necking, which occurs when the programming temperature is low, induces remarkable non-uniform deformation within the necking to non-necking transition area. Consequently, an elastic stress field in induced within this area. Furthermore, the necked area and non-necked area form concave and convex surfaces, respectively. It is also observed that, even without necking, the sample is programmed at high temperatures, the surface of the programmed sample is still most likely uneven. Most of the resulted surfaces are concave. Further systematical investigation is required before any conclusion can be drawn.
###### Abstract  
Piezoelectric metamaterial plate (PMP) is being investigated for structural vibration energy harvesting (SVEH), in which an interface circuit is often used. Thus, it is a challenge to perform bandgap optimization of such an elastic-electro-mechanical coupling system. This paper presents a binary-like topology optimization scheme by dividing the unit cell into identical pieces, where a \(\{0,1\}\) matrix is optimized to indicate material distribution. Firstly, a unified motion equation is derived for the elastic plate and the piezoelectric patch, and an electromechanical coupling model is built for a self-powered synchronized charge extraction circuit. Then, an extended plane wave expansion method is presented to model the bandgap character of the PMP with interface circuits (PMPICs), and the numerical solution of the dispersion curves is derived based on the Bloch theorem. Next, an extended genetic algorithm is applied for the topology optimization of the PMPIC. In the end, numerical and finite element simulations are performed to validate the proposed method. The results demonstrate that both the structure and the circuit can be optimized simultaneously to obtain the maximum first-order bandgap at a given central frequency. Therefore, the proposed method should provide an effective solution for the topology optimization of a PMPIC for broadband SVEH.  
vibration energy harvesting; piezoelectric metamaterial plate; topology optimization; genetic algorithm; plane wave expansion +
Footnote †: journal: Applied Sci.  
**2021**, _11_, 5191. [[https://doi.org/10.3390/app1115191](https://doi.org/10.3390/app1115191)]([https://doi.org/10.3390/app1115191](https://doi.org/10.3390/app1115191))
## 2 Methods
### Underwater Environmental Factors  
The network configuration is the primary focus of our study. Generally, the seafloor topography, ocean currents, tides, internal waves, sea surface wind, sound speed variation, and other environmental factors all have an impact on the network kinematic positioning performance. The acoustic signal propagation bending always causes sound rays to sweep the seafloor, affecting acoustic signal propagation [15; 33]. In the construction of an UGN, its height should be designed considering the seafloor topography and seabed sediment. Ocean currents and internal waves can affect the stability of the subsurface buoy [29], and the height of the subsurface buoy must be optimized. Similarly, waves and tides affect the stability of the sea surface buoy, and its position error also needs to be considered [21; 23]. In addition, the coordinate error and clock drift of network base stations also influence the kinematic positioning performance. Similar to GNSS, the impact of underwater environmental factors can be classified as User Equivalent Range Errors (UEREs), while the station coordinate and clock errors are considered as Signal-In-Underwater-Space Range Errors (SISREs) [34]. The impact of underwater environmental factors is evaluated in Section 3.4.
## 6 Experimental Results and Discussion
### Impact of Pre-Equalization Stage in A&F RN  
The impact introduced by the Pre-Equalization stage in the A&F Relay Node was investigated using the Error Vector Magnitude (EVM) metric.  
Figure 12 shows the obtained results for the Cumulative Distribution Function (CDF) of the re-transmitted Resource Grid (RG) of the A&F schemes, with respect to the RMS EVM [%]. The red plot represents the EVM of the re-transmitted signal without implementing the Pre-Equalization stage, from which it can be observed that it presented the worst EVM. On the other hand, from Figure 12, it can be noted that, when the Pre-Equalization stage was implemented in the A&F Relay Node, the error gradually decreased. Furthermore, it was found that, when the A&F-ZF RN was employed, the EVM was higher than when the A&F-MMSE RN was used. Besides, the authors have verified that the power of the re-transmitted RG from the A&F protocol was degraded on an order of 17.7 dB in comparison to the transmitted RG from the eNB.
# Satellite Video Moving Vehicle Detection and Tracking Based on Spatiotemporal Characteristics
## 3 Methods  
DTSTC, as proposed in this study, consists of four parts: video data pretreatment, construction of spatiotemporal constraints, moving vehicle detection, and inter-frame vehicle association (Figure 1). The original satellite video data were preprocessed with intra-frame denoising and inter-frame image stabilization. Road masks containing spatial information were then extracted within fixed-interval frames using the D-LinkNet, whereas a motion heat map containing temporal information was cumulatively generated using temporal differences. The two are fused to generate a region-of-interest constraint and improve the precision of moving vehicle detection. In addition, a local contrast enhancement algorithm based on the LAB color space [25] was used to optimize the k-nearest neighbor (KNN) moving target detection in the region of interest, resulting in the greatly improved detection of moving vehicles. Finally, vehicle tracking was performed using improved data association methods for the inter-frame association of moving vehicles.
## 5 Hardware Analysis  
The designed multi-electrode system is a suitable choice for the electrical bio-interfacing of in vitro cell culture studies. Amongst the prominent studies, impedance measurement holds a strong place. The artifacts introduced due to the hardware configuration of the designed multi-electrode system have been examined by implementing the system for measuring impedance.  
The setup was used to determine the impedance measurements of a popular cell growth media, Dulbecco's Modified Eagle Medium (DMEM). The impedance measurement was carried out in both tetrapolar and bipolar configurations. As each well contains four electrodes, the tetrapolar configuration was made by using one electrode from each strip as the source and sink electrodes for current injection, and the remaining two were used as voltage-recording electrodes. The electrodes were connected with a precision LCR meter (Keysight Technologies, model E4980A), and the impedance of the media was determined over the range of 20 Hz-1 MHz. In order to achieve the bipolar configuration, the two electrodes on one strip were shorted together on the main board and connected to the LCR meter for impedance measurements. The experiment was carried out in a controlled laboratory environment of 37 \({}^{\circ}\)C and 5% CO\({}_{2}\).  
The impedance measurements of the two configurations are shown in Figure 7. The measurements were averaged over the values taken from five culture wells (separately) in the 24-well setup. It shows that the impedance values in the tetrapolar configuration are lower than those of the bipolar configuration. This is in agreement with the known theory, as tetrapolar measurements determine the transfer impedance rather than the actual impedance. The bipolar configuration shows deviations in the low-frequency end, although it poses a rather constant value at higher frequencies. Both configurations show little to no deviation at higher frequencies. It can be concluded that the multi-electrode system does not show notable artifacts due to the hardware configuration over a wide range of frequencies in the controlled laboratory environment, and it can be successfully implemented in bioimpedance measurement applications.
## 2 Principle and Design of Wave Field Synthesis 3D Imaging System
### Optical Components  
A lens array (LA) (model Stock no.63-231 from Edmund Optics GmbH) is adapted as the refractive component of our system, whose specifications are shown in Table 1. The arrangement as well as the aperture of the lenses is rectangular and the substrate is made from B270 material with high transmittance in the range of visual wavelengths. The working area is 58 mm by 60 mm. Since the fundamental principle of our system is spherical wave field synthesis, a larger size, 5.4 mm by 7.0 mm, of the lenses compared with a MLA is selected to avoid strong fragmentation of the wavefronts due to small lens apertures. In addition, the focal length of the LA is 41.90 mm.  
A commercial iPhone 7 plus with 401 ppi is chosen as the display device and its specifications are shown in Table 2. Hence the system is capable of working with partially coherent light, which is more convenient compared to the requirements of holographic displays. The two main factors of selecting the specific display are the high resolution and the wide color gamut, which affect imaging quality of the reconstructed images directly.
## 4 A Survey on FPGA-Based Sensor Systems
### Environmental Monitoring  
This section describes sensor nodes based on object identification and tracking in the environment [80, 81], pest control [82] and fire detection in woods [83].  
In [80], the authors proposed situation-based reconfiguration in sensor nodes via FPGAs with partial reconfiguration capabilities (Virtex 4 FX100). They implemented Kalman filters in order to track environmental targets such as animals. Thus, each sensor must measure velocity and acceleration. According to the situation, depending on the type of object and the type of operation stage, several Kalman configurations exist. Therefore, the authors partially reconfigure the FPGA in order to reduce the power consumption. According to the authors, the base system has a power consumption of 1.794 W. The authors claimed that the power consumption is reduced about 5%-25% in comparison to a fixed implementation.  
[PERSON] _et al_. [81] presented a sensor node for bird call identification based on FPGA (Spartan 3E, XC3S1600E) that implements a MicroBlaze-based SoC. Such SoC implements a peripheral that performs bird call detection based on Linear Prediction Cepstral Coefficients (CPCC) in order to reduce the data dimension and extracts the features and Dynamic Time Warping (DTW) for performing template matching. The system, clocked at 50 MHz, consumes 2.85 W.  
[PERSON] _et al._[82] presented the WSNs MasliNET, which is focused on pest detection in olive groves. Each sensor node is based on an Atmel Amp module consisting of an 8-bit AVR microcontroller and an IEEE 802.15.4 transceiver, connected to an FPGA Actel Pro ASIC 3 that deals with a CMOS sensor and perform image processing. The node consumes 87.12 mW in active mode (18.4 \(\mu\)W in sleep mode) whereas the coordinator consumes 190 mA in active state (5 mA in sleep mode). The nodes require 3.3 V.  
Finally, in [83], the authors presented the design and implementation of a sensor node for fire detection in woods. It is based on an Altera Cyclone II (EP2C70) FPGA that implements an SoC based on the Nios II processor, temperature sensor and Bluetooth transmitter PTR4500. However, this system presents a similar problem as [61] (Section 4.1). The FPGA is an overkill since its processing capabilities are not scrutinized and the Bluetooth technology is not appropriate for WSNs.
# An Efficient Delay Tolerant Networks Routing Protocol for Information-Centric Networking
## 3 Proposed Protocol
Spray and Wait protocol consist of Spray phase  
Figure 4: Flowchart of the proposed algorithm. and Wait phase for message delivery, where the total number of message copies are limited to L. In Spray phase, a node, which generates an original message, forwards one message copy to L-1 other nodes when it contacts them. Then, L copies of messages are delivered to the destination node of the message only in Spray phase. In CIDOR protocol, 10 copies of messages are disseminated in Spray phase. In CIDOR protocol, therefore, the overhead of Interest and Data dissemination is controlled by limiting the replication of Interest and Data to 10 copies. In OF protocol [20; 21], if a node contacts another node which has different requester information for Interest, the requester information is merged in its Interest to reduce overhead of Interest forwarding. Similarly, requester information is merged in Data, too. Interest forwarding is carried out based on extended PRoPHET protocol by using the delivery predictability to a set of nodes caching the requested Data. For Data forwarding, extended PRoPHET protocol by using the average delivery predictability to a set of requesters is used. However, no overhead control of Data was proposed in OF protocol. In the proposed protocol, requester information for Interest is shared by exchanging status table. Data is forwarded to a more probable node by comparing average delivery predictability to a set of requesters. The overhead of Data is controlled by reducing unnecessary forwarding of Data using satisfaction flag in status table.
## 5 Existing Security Mechanisms
### Secure Localization  
WMSNs facilitate mobility for patient's comfort, therefore patient location estimations are needed for the success of healthcare applications. Since, medical sensors' sense physiological data of an individual, they also need to report the patient's location to a remote server. As a result, medical sensors have to be aware of patient location, _i.e._, called localization. In [88] the authors discussed localization systems, which were divided into: distance/angle estimation, position computation and localization algorithms, and further, they discussed attacks on localization systems. In [86, 87] the authors argue that mobility supported secure localization protocols still need to be explored.
## 3 Results and Discussion
### Manual Design of Model: Effects of Layers and Hyperparameters  
The proposed deep learning model was trained with the SGDM (Stochastic Gradient Descent with Momentum) solver. The values of the initial learning rate, number of epochs, batch size, and momentum were chosen as 0.001, 100, 64, and 0.9, respectively. These parameter values were chosen such that the model has high performance. The manual selection involved in designing the architecture is explained as follows.  
\begin{table}
\begin{tabular}{c c} \hline
**Layer** & **Details** \\ \hline INPUT & \(227\times 227\times 3\) \\ CONV1 & 16 3 \(\times\) 3 filters with stride [1 1] and padding ’same’ \\ Maxpool1 & 3 \(\times\) 3 max pooling with stride [2 2] and padding ’same’ \\ CONV2 & 32 \(\times\) 3 filters with stride [1 1] and padding ’same’ \\ CONV3 & 32 \(\times\) 3 filters with stride [1 1] and padding ’same’ \\ Maxpool2 & 3 \(\times\) 3 max pooling with stride [2 2] and padding ’same’ \\ CONV4 & 8 \(\times\) 3 filters with stride [1 1] and padding ’same’ \\ Maxpool3 & 3 \(\times\) 3 max pooling with stride [2 2] and padding ’same’ \\ FC1 & \(1\times 1\times 1024\) \\ Drop out & 0.5 \\ FC2 & \(1\times 1\times 2\) \\ Softmax & \(1\times 1\times 2\) \\ \hline \end{tabular}
\end{table}
Table 1: Details of the proposed model Architecture.
## 2 Identify Complexity Characteristics
### Definition of Terms  
Properties and characteristics are defined very differently in the literature. Sometimes, the terms are not distinguished from each other but used synonymously.  
[PERSON], for example, defines particularly important properties as characteristics [18]. This definition assumes that precise knowledge of the product is available in order to assess whether and what is particularly important. It is, therefore, a very subjective definition. According to [PERSON], however, characteristic values are \"properties or values that can be assigned to characteristics in order to distinguish objects\" [2]. A comprehensive discussion of these terms can be found in [PERSON][19]. However, these two examples make it very clear that the definitions of the individual terms property, characteristic and characteristic value are interpreted very differently. However, a clear and unambiguous definition of these terms is necessary for the assessment of product complexity. Therefore, the following objective definition should apply:  
Figure 1: Portfolio analysis of existing methods for the assessment of product complexity.  
* **Properties** serve to fulfill the requirements [20].
* **Variants** are products of similar form and/or function with a normally high proportion of identical groups or parts [21].
* **Characteristics** are physically measurable quantities that, alone or together with other characteristics, form properties [22].
* **Characteristic values** are accordingly the states or values that the characteristic assumes or can assume.  
For illustration, some examples are provided in Table 2.  
Complexity features are defined in the context of this thesis analogously as those features that significantly influence the complexity of the product.