esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effects of Resin Type
#### 3.3.4 Thermal Conductivity  
The thermal conductivity of the particleboard with three resin types are presented in Figure 12c. The results indicated that the particleboards blended with corn starch and binderless particleboards had superior thermal conductivity to that blended with urea formaldehyde. At constant density (0.5 g/cm\({}^{3}\)), the particleboard made from straw blended with corn starch had the lowest thermal conductivity (0.067 W/m K) as compared with other resin materials. For particleboards without resin material, the obtained thermal conductivity was 0.083 W/m K, while particleboard blended with urea formaldehyde had the highest thermal conductivity (0.086 W/m K). Particleboards without any added resin are completely made from waste materials and do not contain any chemical binders. They are therefore made out of an ecological material and can be equivalent conventional insulation materials [29]. [PERSON] and [PERSON] [29] stated that the binderless boards made from waste materials such as coconut husk and  
Figure 12: (**a**) Internal bond of particleboard as a function of resin type, (**b**) The effect of resin type on compression strength of particleboard, and (**c**) Thermal conductivity of particleboard as a function of resin type.  
bagasse, and without any chemical binders, are environmental friendly and can possibly compete with conventional insulation materials.
## 2 Methods and Materials
### The Proposed Hybrid Path Loss Prediction Modeling Approach  
The proposed hybrid path loss prediction model which combines the Random Forest (RF) and Particle Swarm Optimization (PSO) algorithm is briefly summarized. The hybrid approach is tagged as the RF-PS method. To develop, implement, and test the RF-PS model performance, three-phase steps are exploited, as illustrated in Figure 2.  
_i._ Study Locations Field Survey and Signal Strength Measurement: This phase consists of the study locations, field survey, detailed signal strength measurements, signal extraction, and preliminary preprocessing.  
_ii._ Path Loss Calculation and Analysis based on Measured Signal Strength: This consists of path loss analysis in comparison with some key existing path loss models.  
_iii._ Hybrid Path Loss Prediction Modeling and Application: To achieve this crucial task, the spatial signal data is first passed through RF for dimensionality and feature selection process. A set of trees (100) on the target measured signal data sets were considered to determine the most informative and important subset of features, which were in turn employed as input data to the particle swarm optimization model for predictive path loss analysis.  
The proposed hybrid path loss model combines the random forest and particle swarm optimization methods, termed RF-PS. Thirdly, detailed validation, application of the RF-PS model, and comparison with other existing techniques are presented.  
Figure 1: Radial basis architecture.
# Capacity Analysis of Incentive Schemes in Opportunistic Networks
## 4 Capacity Analysis for Incentive Schemes
### Scheduling Policy  
Nodes move in clusters and clusters move in the whole area such that different usage of high and low quality communications is exploited. The tradition models can be considered as all nodes under the same cluster with different node density. The scheduling policy defines how clusters work under the regime, which is shown in Figure 5.  
1. A source creates relays \(R_{s}\) in source cluster \(C_{s}\) via multicast. The message is sent to a cluster \(C_{k}(k=1,\cdots,R_{c}{}^{s})\) by one-hop unicast when the cluster meet others.
2. Relays \(R_{k}{}^{s}\) are created in \(C_{k}\) via broadcast. After clusters repeat these actions some number of times, the message reaches the destination cluster \(C_{d}\).
3. Relays \(R_{d}{}^{s}\) are created by broadcast in the destination cluster until the message is near to the destination node.
4. If the distance between the destination node and the current node is within a range \(l^{s}\), the message is transmitted via \(h^{s}\)-hop unicast to reduce resource usage.
# A Deep Learning Method for Bearing Fault Diagnosis through Stacked Residual Dilated Convolutions
## 2 SRDCNN-Based Intelligent Fault Diagnosis Method  
Taking the above-mentioned problems into consideration, deep learning method is a promising tool to achieve real-time bearing fault diagnosis. Deep learning methods can learn a stable hierarchical feature representation from the raw bearing vibration signal in an off-line manner, and then diagnose the bearing status online according to the real-time collected vibration signal. Although deep learning has a high potential to avoid false features caused by ambient noise and fluctuations in working conditions, but it still requires ingenious structural design to avoid false features of bearing vibration signals; and thereby improve the performance of bearing fault diagnosis. [PERSON] et al. [26] designed an enhanced convolutional neural network with enlarged receptive fields to capture the fault information in the vibration signal. [PERSON] et al. [27] combined one-dimensional CNN and LSTM into one unified structure (LSTM-CNN) to make full use of the classification capabilities of CNN and the time coherence representation ability of LSTM. In this paper, a novel stacked residual dilated convolutional neural network based intelligent fault diagnosis method is proposed for bearing fault diagnosis. In the proposed SRDCNN model, the dilated convolution is employed to exponentially increase the receptive field of convolution kernel and extract essential features from the sample with more points, alleviating the influence of randomness. The input gate structure of LSTM is adopted to effectively remove noise and control the entry of information contained in the input sample. Meanwhile, the residual network is introduced to overcome the problem of vanishing gradients caused by deeper structure of the neural network, hence improving the overall classification accuracy.
## 4 Discussion  
### Characterization of the Samples and its Mechanical Properties  
The ceramic samples were entirely produced in the laboratories of MSTU \"Stankin\" with the maximum achievable homogeneity, using the spark plasma sintering method assisted by the electrical current [17; 28]. The main SPS parameters were chosen according to the recommendations of the equipment manufacturer and based on practical experience with the equipment. The samples were of 2-4 mm thickness and 20 mm in diameter.  
Figure 5: The surface of Al\({}_{2}\)O\({}_{3}\)-40 vol.% TiC composite after electrical discharge machining: (**a**) image obtained on SEM; (**b**) (Energy-dispersive X-ray spectroscopy) EDX map of the surface.  
As presented, the main mechanical properties of the samples showed that the samples have similar flexural strength as the sample of Al\({}_{2}\)O\({}_{3}\)-TiC cutting ceramic produced industrially. At the same time, the Vickers hardness of the samples is twice as high as for their industrial analogue.
# 3D DNA Nanostructures: The Nanoscale Architect
## 5 Applications
### Encapsulation
#### 5.1.3 DNA-Based Drug Delivery Vehicles  
Drug delivery is an area where DNA nanostructures may inspire a new degree of complexity towards therapeutics. Targeted drug delivery is still an open challenge [106] that ideally establishes strategies for safe and successful delivery of a therapeutic payload by limiting their effective area to only diseased sites. This is especially a problem for drugs that must be circulated through the body to reach their intended site. The ability of DNA to easily load therapeutic molecules as cargo [107], as well as form structures and compute with biomolecular inputs [59] is a unique advantage of the material to satisfy this functionality. Placing such therapeutics within the cavity of a DNA nanostructure adds a layer of separation between the therapeutic and any other biomolecules it could damage. Then, DNA nanostructures can be designed to open and expose the cavity with its therapeutic payload when a targeted biomarker is observed by a programmed locking mechanism [59]. Current work still looks to establish the range of detectable biomarkers that can be transduced into DNA nanostructure activity, such as those that bind a protein biomarker with aptamers [97] or detect upregulated miRNA signals [108]. The limitation and future opportunities for research here, of course, are thus to expand the variety of biosensors that can be conjugated to DNA and incorporated into DNA nanostructures.  
One area of high potential impact is the targeted delivery of anti-cancer drugs that can have severe side effects, such as doxorubicin which can cause damage to heart muscle. DNA circuits have been shown to perform complex profiling of cancer cells, thus heightening the specificity of any DNA nanostructure-based targeting for cancer therapeutics [109]. DNA nanostructure drug delivery vehicles have also been shown to remain sufficiently stable under in vivo conditions to passively build up at a tumor site [79]. [PERSON] et al. successfully delivered and released thrombin to tumors to induce thrombosis of the tumor only [97] (Figure 6A). [PERSON] et al. demonstrated that DNA nanostructures can easily be programmed to carry multiple different drugs simultaneously, and could thus help the simultaneous delivery of multiple drug therapeutics to combat complex diseases that are drug-resistant [110].
## 1 Introduction  
X-ray and electron scattering have long played an important role in structure determination of matter [1; 2; 3]. The recent development of ultrashort pulsed electron and X-ray beams has now expanded the scope of structure determination to the time domain [4; 5] (and references therein). Important applications include the determination of molecular structures in excited states and the observation of structural molecular dynamics, i.e., the time-resolved determination of transient molecular structures during chemical reactions [6; 7; 8; 9; 10; 11; 12; 13]. The advent of ultrafast pulsed X-ray Free-Electron Lasers (XFELs) in particular has increased the intensity of X-rays while decreasing pulse durations to below 30 fs [14]. Consequently, XFELs are assuming a powerful role in the exploration of gas-phase photochemistry, allowing a direct comparison between experimental results and high-level theory [15]. More generally, ultrafast X-ray scattering will play an important and growing role in our arsenal of ultrafast imaging techniques by providing a structurally-sensitive complement to spectroscopic techniques.  
Ultrafast measurements typically involve a pump-probe scheme, where a laser pump pulse prepares an initial wavefunction, whose subsequent time evolution is probed at a sequence of delay-times [15; 16; 17; 18; 19; 20; 21; 22; 23; 24]. Depending on the experimental situation, the wavepacket may be of electronic [25; 26], nuclear [27], or mixed nuclear-electronic [28] nature. Inevitably, the localization of the initially prepared wavepacket and its subsequent spreading depends not only on the potential energy landscape of the molecular system but also on the exact optical preparation of the initially excited state [29; 30; 31]. Whilein some cases, such as nonradiative transitions in intermediate or statistical limit scenarios, the wave packet is presumed to spread over all receiving modes [32; 33; 34]; in other cases, well-defined wave packets persist for at least part of the chemical reaction pathway. In 1,3-cyclohexadiene, for example, the propagation of the wavepacket has been described as 'ballistic', implying that it retains a well-localized form even as it propagates [35]. However, it is inevitable that the nonclassical nature and propagation of the wavepacket leads to a delocalization. It is therefore an important question to consider the extent of this delocalization and how it affects the measurement of the structural dynamics in time-resolved scattering experiments. In the present article, we explore the effective upper limit on the range of the momentum transfer vector (\(q\)) that is meaningful to observe in scattering experiments. To do so, we use advanced simulations of ultrafast dynamics in the molecules D\({}_{2}\), ethylene, and 1,3-cyclohexadiene (CHD), and examine the effect of the nuclear wavepacket spreading on the X-ray scattering signals.
# Enhancing Robustness of Per-Packet Load-Balancing for Fat-Tree
## 3 Load Balancing Based on Symmetric Path Groups (LBSP)
### Partitioning Paths and Assigning Path Groups  
The practical design of LBSP was inspired by Flexible Interval Routing (FIR), which [PERSON] et al. used to implement a routing algorithm for fat-trees [15]. We associate each output port with the range of the destination addresses. The range indicates all the destination hosts which are reachable from the output port. Since each upward output port leads to every host in a fat-tree, the range of destination addresses contains all the host addresses. In contrast, each downward output port has a limited range. Figure 4a illustrates the ranges associated with the output ports of an aggregation switch and a core switch in a fat-tree shown in Figure 2.  
In a fat-tree, once upward paths are selected, the downward paths are deterministic. In an 8-ary fat-tree, a flow from a pod to another pod has 16 shortest paths. LBSP partitions all the 16 shortest paths into four path groups using the parity of each port number of each switch on the upward paths. Since partitioning the shortest paths in this way makes consecutive packets pass through different ports in a ToR switch, it helps reduce latency when a round-robin manner is used.  
LBSP selects a path group for each flow based on the least significant bits of the destination address. That is, for upward forwarding, each ToR switch and aggregation switch selects uplinks based on the LSB and the second LSB, respectively. Across paths within a link group, packets of a flow are forwarded in a round-robin manner. Hence, flows can use increased bandwidth with a higher probability compared to flow-level load balancing. For downward forwarding, each switch forwards a packet according to the range of destination address. Figure 4b illustrates partitioning paths and assigning path groups to source-destination pairs according to the least significant bits of the destination address. If each output port has a mask register that indicates which bit of the destination address is to be compared, then in a normal state without failures, LBSP does not have to keep the information on which specific flow is assigned to which path group.  
Figure 3: Illustration of LBSP (Load-Balancing based on Symmetric Path groups) selecting an alternative path group when a failure occurs on the link between aggregation Switch A11 and ToR Switch T11.
# Stability Criterion for Dynamic Gaits of Quadruped Robot
## 4 Dynamic-Stability Measurement
The stability measurement may change suddenly when the current support line disappears and the next support line forms if only the vertical distance between \(ZMP_{0}\) and the current support line (denoted by \(d_{C}\)) is considered. The vertical distance between \(ZMP_{0}\) and the next support line is represented by \(d_{L}\), as shown in Figure 10. In practice, \(d_{C}\) should be as short as possible during the first half of the support phase, and \(d_{L}\) should be as short as possible during the second half. The distance between \(ZMP_{0}\) and the support line is defined as  
\[d_{spt}=\omega_{c}d_{c}+\omega_{L}d_{L}, \tag{30}\]  
where \(\omega_{C}\) and \(\omega_{L}\) are time-varying weights:  
\[\omega_{C}= \begin{cases}1,&0\leq t<0.25T_{b}\\ -\frac{2}{T_{b}}t+1.5,&0.25T_{b}\leq t<0.75T_{b}\\ 0,&0.75T_{b}\leq t<T_{b}\end{cases}  \tag{31}\]  
Figure 10: Distances between \(ZMP_{0}\) and the support lines (\(d_{C}\) and \(d_{L}\) ). Figure 9: Angle between \(F_{e}\) and \(x_{s}\). \[\omega_{L}= \begin{cases}0,&0\leq t<0.25T_{b}\\ \frac{2}{T_{b}}t-1.5,&0.25T_{b}\leq t<0.75T_{b}\\ 1,&0.75T_{b}\leq t<T_{b}\end{cases}  \tag{32}\]  
The weights are illustrated in Figure 11.  
Using the above three quantities, this paper proposes the stability measurement \(M_{stability}\):  
\[M_{stability}=\lambda_{1}d_{edge}-\lambda_{2}|\sin\tau|-\lambda_{3}d_{spt}. \tag{33}\]  
A larger \(M_{stability}\) value indicates better motion stability. The value of the coefficients \(\lambda_{i}\) should consider: (1) unification of the order of magnitude of the three terms on the right-hand side of Equation (33) (which are related to the size of the robot); and (2) the importance of each parameter. According to the definitions of \(d_{edge}\), \(|\sin\tau|\), and \(d_{spt}\), for the dynamically stable gait:  
\[0<d_{edge}\leq\frac{1}{2}(L+W)\times 0.5=\frac{1}{4}(L+W), \tag{34}\]  
\[0\leq|\sin\tau|\leq 1, \tag{35}\]  
\[0\leq d_{spt}\leq W, \tag{36}\]  
where \(\lambda_{i}\) can be calculated by:  
\[\lambda_{1}=1, \tag{37}\]  
\[\lambda_{2}=\frac{1}{4}(L+W)\times 0.5, \tag{38}\]  
\[\lambda_{3}=\frac{\frac{1}{4}(L+W)}{W}\times 0.9. \tag{39}\]  
According to the Equations (34)-(39),  
\[M_{stability}>\lambda_{1}\times 0-\lambda_{2}\times 1-\lambda_{3}\times W=-0.35 (L+W), \tag{40}\]  
which indicates the possible minimum value of \(M_{stability}\), but this does not mean that the motion is stable if \(M_{stability}>0.35(L+W)\).
## 2 Experimental Procedures
### Sample Characterization  
The surface topography was analyzed by field-emission scanning electron microscopy (SEM) (JSM-6701F; JEOL, Tokyo, Japan). SEM was used to analyze the surface morphology changes of deposited W films from before to after deuterium implantation, and the samples were inclined by 45 degrees. The internal features of blisters below the sample surface were investigated using a scanning electron microscope (SEM) equipped with a focused ion beam (FIB) for the cross-sections of blisters. Thermal desorption spectroscopy (TDS) was utilized to obtain information about the binding energies of deuterium and release behaviors in tungsten films after deuterium plasma exposure. The total amount of deuterium retained in the tungsten films after implantation was also measured by means of TDS. In this paper, the spectra of the thermal desorption spectrum of deuterium released from the samples with temperature are given. In all TDS measurements, the maximum temperature is set at 1273 K with a linear temperature ramping rate of 10 K/min, which was monitored by a K-type thermocouple. The amount of D retained in the specimens was determined by integrating the quadrupole mass spectrometer (QMS) signals for molecules D2, HD (signals of HDO and D2O were negligibly small) from the start of heating ramp-up to the time when the maximum temperature was reached. The number of desorption gases was calculated by integrating the time of the desorption rate.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **W Bulk** & **W Film** & **W Bulk** & **W Film** & **W Bulk** & **W Film** \\ \hline
**Flux (m\({}^{2}\)s)** & 1.8 \(\times\) 10\({}^{21}\) & 1.8 \(\times\) 10\({}^{21}\) & 1.8 \(\times\) 10\({}^{21}\) \\
**Duration (h)** & 2 & 4 & 8 \\
**Fluence (m\({}^{2}\))** & 1.30 \(\times\) 10\({}^{25}\) & 2.59 \(\times\) 10\({}^{25}\) & 5.18 \(\times\) 10\({}^{25}\) \\
**Temperature (K)** & 500 & 500 & 500 \\
**Ion energy (eV)** & 100 & 100 & 100 \\ \hline \end{tabular}
\end{table}
Table 1: Overview of the exposure conditions of the three investigated samples.
# Optimization of Active Distribution Network Operation with SOP Considering Reverse Power Flow
## 3 Actual Maximum Transfer Capacity of Reverse Power Flow in the Distribution Network
### Case Analysis
#### 5.2.2 Optimized Results of the Joint Operation of Distribution Networks A and B  
In scenario 2, the situation of distribution network A and B after joint operation by SOP is analyzed. It can be seen in Figure 16 that the operation of distribution network A after joint operation has been improved compared with that after separate operation.  
Figure 16: Joint operation of distribution networks A and B. (**a**) Depicts the OLTC tap change actions for distribution networks A and B. Compared to Scenario 1, the tap ratio for network A remains  
unchanged, whereas for network B, the ratio increases during periods 4-5, 12-14, and 22-24, leading to an increased reverse power flow capacity, which can be delivered through the SOP to network A. (**b**) Illustrates the curtailment of wind and solar power in network A during these periods, showing improvements during segments 4-5, 12-14, and 22-24. (**c**,**d**) Show the changes in operational costs for networks A and B. The additional reverse power flow carried by network B results in increased network losses and higher operational costs; however, the improvement in curtailment for network A, when evaluated together with network B, leads to an overall reduction in total operational costs. (**e**) Output performance of the SOP.
# Hierarchical Visual Place Recognition Based on Semantic-Aggregation
## 3 Experiments and Results
### Runtime Analysis  
We implemented the proposed system in two steps: (1) semantic segmentation and (2) coarse matching and fine matching. We called the step (2) the matching process, and experiments were done on an NVIDIA 1090 Ti GPU. The results are shown in Figure 10. For a single image, it takes approximately 0.059 s to achieve matching with the _Candidates_ 10. Even when the _Candidates_ number is 50, the time of matching is 0.501 s for a reference image sequence with 3600 images. We believe that our method has the potential to satisfy real-time demands.
# Precipitation Products' Inter-Comparison over East and Southern Africa 1983-2017
###### Abstract  
During recent decades East Africa (EA) and Southern Africa (SA) have experienced an intensification of hydrological hazards, such as floods and droughts, which have dramatically affected the population, making these areas two of the regions of the African continent most vulnerable to these hazards. Thus, precipitation monitoring and the evaluation of its variability have become fundamentally important actions through the analysis of long-term data records. In particular, satellite-based precipitation products are often used because they counterbalance the sparsity of the rain gauge networks which often characterize these areas. The aim of this work is to compare and contrast the capabilities of three daily satellite-based products in EA and SA from 1983 to 2017. The selected products are two daily rainfall datasets based on high-resolution thermal infrared observations, TAMSAT version 3 and CHIRPS, and a relatively new global product, MSWEP version 2.2, which merges satellite-based, rain gauge and re-analysis precipitation data. The datasets have been directly intercompared, avoiding the traditional rain gauge validation. This is done by means of pairwise comparison statistics at 0.25\" spatial resolution and daily time scale to assess rain-detection and quantitative estimate capabilities. Monthly climatology and spatial distribution of seasonality are analyzed as well. The time evolution of the statistical indexes has been evaluated in order to analyze the stability of the rain detection and estimation performances. Considerable agreement among the precipitation products emerged from the analysis, in spite of the differences occurring in specific situations over complex terrain, such as mountainous and coastal regions and deserts. Moreover, the temporal evolution of the statistical indices has demonstrated that the agreement between the products improved over time, with more stable capabilities in identifying precipitating days and estimating daily precipitation starting in the second half of the 1990s.  
Article  
Article