esa-sceva/satcom-chunk-collection huggingface dataset:
# Model Parallelism Optimization for CNN FPGA Accelerator
## 6 Conclusions  
For hardware with limited resources, model parallelism for multiple devices is often employed. However, due to the structural characteristics of tightly coupled CNNs, it is difficult to use model parallelism methods to achieve a high degree of parallelism in multi-device inference.  
In this paper, we proposed an effective decoupling method using group convolution and a new channel shuffle algorithm to replace the original convolution and channel shuffle approaches, aiming to achieve data and model separation while eliminating inter-device  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of FPGA Devices** & **ALUTs** & **FFs** & **RAMs** & **DSPs** \\ \hline
1 & 139,651 (16\%) & 224,468 (13\%) & 1707 (63\%) & 137 (9\%) \\
2 & 148,715 (17\%) & 249,448 (15\%) & 1265 (47\%) & 143 (9\%) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of resource utilization.  
Figure 6: Comparison of computing time of different layers of _I-ShuffleNet_.  
synchronization costs. As the number of devices increased, the computation time decreased in an approximately linear proportion.  
Using the proposed model parallelism method, we designed a parallel FPGA accelerator for _ShuffleNet_. We realized FPGA acceleration by pipelined computation, a channel mechanism, aggregate read, and kernel vectorization. Benefiting from these approaches, the experimental results showed that our paralleled _ShuffleNet_ FPGA accelerator exhibited a high degree of model parallelism while maintaining accuracy. When using two FPGAs, it achieved a 1.42\(\times\) higher speed and a power consumption of about 20 W, as well as a 34% memory footprint reduction.  
In subsequent research, we will study the direct communication between FPGAs to reduce the communications delay and further optimize the model parallelism of FPGA accelerators.  
[PERSON], [PERSON] and [PERSON] conceived the idea, designed and performed the experiments, and analyzed the results. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Chinese Universities Industry-University-Research Innovation Fund under grant no. 2020 HYA02011, in part by the Natural Science Foundation of Shandong Province under grant no. ZR2019 LZH002 and Shanghai science and technology committee under grant no. 22511106000.  
Not applicable.  
The authors declare no conflict of interest.
# Laboratory Validation of Surface-Initiated Transverse Cracking of Asphalt Pavement
###### Abstract  
Field pavement transverse cracking typically can be grouped into two categories, namely thermal cracking that initiates at the surface of the pavement and propagates downward, and reflective cracking that initiates at the pavement layer above the existing pavement cracks or joints and propagates upward. Recently, another transverse cracking phenomenon was noticed in some field investigations but was less studied. Cracks were observed from both the surface and the bottom of field cores, but they cannot be visually observed from the middle layer. In addition, the surface and the bottom cracks lined up well, showing the tendency of meeting each other. This study aimed to evaluate the causes of such transverse cracking phenomena by laboratory tests. Hamburg equipment was used as the evaluation equipment. Some samples were prepared with a saw cut notch 0.33 inches in depth and 0.25 inches in width, and some samples were prepared without the notch at the bottom. The results showed that such a crack type could have happened when samples are aged, the base below the sample is soft, and a notch exists in the bottom layer. A potential mechanism is when the wheel load moves on one side of the existing transverse cracking (the near side), as the specimen on this side tends to bend downward under the wheel load, especially when the support is relatively soft. If without constraint, the other side of the specimen (the far side) should consequently be tilted upward. However, the bonding with the base layer and the self-weight of the specimen restrict the upward movement of the far-side specimen. Therefore, the tensile stress at the surface of the specimen directly on top of the bottom crack is created. At the same time, the bottom crack has the potential of being squeezed and pushed together.  
surface-initiated transverse cracking; laboratory evaluation; aging; soft base; existing joint
## 4 Results and Analysis
### Electric Vehicle (EV) Penetration in Urban Network  
#### 4.1.1 Case 1: 20% EV Penetration in Urban Network  
The set of analysis performed on the urban network is based on the grid shown in Figure 7 focusing on the MV/LV transformer feeding LV households aggregated as a unique load. In the evening, the transformer works over 100% of its capacity, reaching 121.81%. As far as transformers are sized to reach 150% of their capacity, it was considered appropriate to evaluate a smart solution, to limit its stress and, therefore, the wear it would have been subject to over the years, reducing its useful life. Alternatively, an infrastructure reinforcement intervention would be needed to bear the extra load, with a consequent significant economical investment.  
Figure 8 represents the results of the simulations, highlighting the efficient effectiveness of the coupling between PV and storage system.  
Figure 8: Comparison of urban (**a**) transformer loadings and (**b**) most loaded feeder voltage profiles with 20% electric vehicle (EV) penetration.  
Figure 7: Urban 20 kV distribution network.  
This allowed the reduction of the amount of energy through the transformer, granting, in particular during its maximum operating peak, to go from 121.8% to 108.4%.  
It can be noted that during the central hours of the day, in correspondence with a massive photovoltaic production, the transformer undergoes a considerable reduction in loading; however, these results depend on the simplified assumptions that consider a high solar production, thus always guaranteeing a sufficient quantity of power. If the photovoltaic production was reduced compared to the case in question, there would be less benefits in terms of limiting the stress on the transformer, which, however, as previously observed, would still be able to withstand the load being sized to reach 150% of the its nominal capacity.
# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 3 Situation-Based Contextual Model: Definitions and Formalizations
### The Situation Rules Formalization  
In this formalization, we should model any contextual application by a set of situations. They are built by a set of events that occurred at a particular location and time as well as set of rules. These rules are used to check these events, and thus inferring the situation. The user defines a set of daily situations that are detected by situation rules. A situation rule is defined by several projections; it has different axes (e.g., location, time, role, sensor value, etc.). We flatten the axis into primitives (e.g., _inside_, _outside_, _near_ and _far_ for location) to enhance the rule's description. Furthermore, rule modeling can include various exceptions as new dimensions. In addition, we need to determine what the user checks when this situation occurs. Therefore, by considering the relationships between a situation and actions, the actions are activated based on identified situation. The textual description of situation rule is defined as follows:  
\[R\;:Name\;;P_{1}[Axis(Primitive(value,\;tolerance)\ldots);\ldots]\]  
\[P_{n}\;[\ldots]\;EXCEPT[Situation/Axis(\ldots)]\;\rightarrow\]  
\[Actions[Action_{1},\;Action_{2},\;\ldots]\]
# Severity Prediction of Traffic Accidents with Recurrent Neural Networks
## 4 Experimental Results and Discussion
### Computational Complexity of the Model  
The time complexity of the RNN model was measured in terms of training and testing time per iteration. Table 10 gives information about the average training time per iteration with batch size of 32 and the average testing time per prediction. It can be seen that the model on average spends around 150 milliseconds per iteration during training and only \(\sim\)13 milliseconds per prediction for new unseen examples. Although this experiment shows the computational efficiency of the model, it is also worth to note that the training time can be increased by reducing the batch size or sequence length, and also by increasing the volume of the training data.  
\begin{table}
\begin{tabular}{c c} \hline
**Time (milliseconds per iteration)** & **RNN Model** \\ \hline Training Time & 150.23 \\ Testing Time & 13.17 \\ \hline \end{tabular}
\end{table}
Table 10: Average training and testing time per iteration/ prediction of the proposed model.  
\begin{table}
\begin{tabular}{c c c} \hline
**Method** & **Training Accuracy (\%)** & **Validation Accuracy (\%)** \\ \hline MLP & 68.90 & 65.48 \\ BLR & 70.30 & 58.30 \\ RNN & 71.79 & 71.77 \\ \hline \end{tabular}
\end{table}
Table 8: Performance comparison of the proposed RNN model with Multilayer Perceptron (MLP) and Bayesian Logistic Regression (BLR) models.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Factor** & **MLP** & **BLR** & **RNN** \\ \hline Accident location & 2 & 6 & 6 \\ Road bound & 8 & 4 & 2 \\ Accident zone & 5 & 3 & 4 \\ Accident reported cause & 3 & 5 & 5 \\ Lighting condition & 4 & 7 & 9 \\ Surface condition & 9 & 1 & 3 \\ Collision type & 6 & 8 & 8 \\ Accident time & 7 & 2 & 1 \\ Vehicle type & 1 & 9 & 7 \\ \hline \end{tabular}
\begin{tabular}{c c c} \hline BLR:MLP: \(R^{2}=0.51\) & & \\ RNN:MLP: \(R^{2}=0.38\) & & \\ MLP:BLR: \(R^{2}=0.51\) & & \\ RNN:BLR: \(R^{2}=0.72\) & & \\ \hline \end{tabular}
\end{table}
Table 9: Calculated ranks of the accident related factors in the RNN, MLP, and BLR models.  
Additionally, the major computational problem of NNs is their scalability; sometimes they become unstable when applied to larger problems. However, recent advancements in hardware and computing performance such as Graphics Processing Units (GPUs), parallel computing, and cloud computing have decreased most of the limitations of NN models including RNN-based computations.
## 2 FSO Communication System
### Receiver Side
#### 2.3.2 Coherent FSO Systems
A significant challenge in coherent detection is the synchronization of the optical carrier. To circumvent the need for phase-locked-loop systems, homodyne phase-diversity architectures can operate with free-running (unsynchronized) LO lasers. This approach addresses synchronization issues and additional impairments through the further processing of in-phase (I) and quadrature (Q) baseband signals. This receiver concept, known as the \"Intradyne Receiver\", presents a viable alternative. In this phase-diversity scheme, it is crucial for the optical frequencies of the Tx and LO lasers to remain as close as possible to avoid detection penalties [29; 30; 31]. Frequency offset and phase noise compensation are managed by DSP after the optical front-end in this scheme [31]. Recently, this concept has garnered renewed interest, as advancements in digital signal processors and analog-to-digital converters (ADCs) are achieving speeds that make the hardware implementation of such receivers feasible for higher data rates, such as 10 GBaud [32].
## 3 Vibration Signal Preprocessing and Fault Feature Extraction
### Fault Feature Extraction  
Extracting distinguishing fault features is crucial for the accuracy of fault classification. From the reconstructed vibration signal \(v^{\prime}(t)\), 18 dimensional vectors, including 16 time-domain parameters, signal energies, and permutation entropies, are calculated. The 18 parameters and their calculation equations are listed in Table 2.  
After calculating the parameters listed in Table 2 from \(v^{\prime}(t)\), an 18-dimensional fault feature vector, denoted as \(T\), for each sample datum is obtained in the following form for the subsequent fault classification:  
\[T=[v_{max},v_{min},v_{p},v_{pp},v_{nn},v_{s},v_{rms},v_{r},v_{ma},v_{\sigma},v _{\text{\emph{rf}}},v_{\text{\emph{s}nt}},v_{\text{\emph{rf}}},v_{\text{\emph{rf}}},v_{ \text{\emph{rf}}},v_{\text{\emph{rf}}},v_{\text{\emph{rf}}},v_{\text{\emph{ \emph{r}}E}},v_{\text{\emph{pr}}}] \tag{12}\]
## 1 Introduction
Due to these properties, the controlled release of metallic nanoparticles, drugs, and volatile polyphenols has been investigated in recent years for biological and biomedical applications [23, 24, 25]. In the case of nPsi as a carrier of essential oils, no studies have been reported in the literature. However, some active compounds extracted from essential oils have been loaded into nPsi; for example, studies on the use of nPsi microparticles thermally oxidized for 1 h at 400 \({}^{\circ}\)C as a drug delivery system of trans-cinnamaldehyde, which is a molecule isolated from cinnamon essential oil [26]. Kinetic release is directly related to surface functionalization and depends on the kind of compound; the kinetic delivery of the target can be controlled [23]. In that sense, in this work, we studied for the first time the load and release of _Laureliopsis philippiana_ (Looser) essential oil from different functionalized nPsi structures. The objective of this study is to evaluate the antibacterial activity of _Laureliopsis philippiana_ (Looser) essential oil, and to analyze its kinetic release from different kinds of functionalized nPsi structures. In particular, nPsi mesoporous structures were chemically oxidized (nPsi-Ox), functionalized with 3-aminopropyltriethoxysilane (nPsi-APTS), undecylenic acid (nPsi-UAc), chitosan (nPsi-Chi) and \(\beta\)-cyclodextrin(nPSi-\(\beta\)CD).
# Influence of Impeller Speed Patterns on Hemodynamic Characteristics and Hemolysis of the Blood Pump
## 1 Introduction
[PERSON] established a cardiovascular system simulation model, and the effects of constant and periodic change in impeller rotation on ventricular output under normal and heart failure conditions were compared and analyzed using the MATLAB software. The results showed that the ejection fraction, pressure-volume area, and ventricular efficiency improved under the periodic change in impeller rotation [12]. Therefore, the method controlling the periodic change in impeller rotation could be used to reduce the ventricular load in adjuvant therapy of patients waiting for heart transplantation. Some scholars have studied the speed modulation control method of the blood pump to improve blood pulsation and blood output characteristics in line with physiological characteristics [13; 14; 15; 16]. In summary, some studies explored the realization of pulsating flow in a CFBP, and most of them focused on axial flow blood pumps. This study aimed to investigate the centrifugal blood pump with hybrid magnetic-hydrodynamic suspension based on the aforementioned studies, taking the complex hemodynamic characteristics of the centrifugal blood pump relative to the axial-flow blood pump into consideration. The impeller speed modulation of the PFBP was determined on the basis of the pressure head versus the flow rate (H-Q) curve of the CFBP. The hemodynamic characteristics of patients with heart failure under different speed patterns of a blood pump support were simulated and analyzed using the lumped parameter model (LPM) of the cardiovascular system. The hemolysis characteristics of the blood pump were analyzed using the computational fluid dynamics (CFD) method. It provided a certain research basis for formulating a control strategy of the pulsatile-flow rotary blood pump and the optimization of hemodynamic characteristics and hemolysis.
## 3 Results
### Pupillometry Data
The other two tracks belonged to talkers who were rated as least strained and required least listening effort (Talkers 8 and 10). It is evident that talker speech samples that resulted in highest strain/effort ratings also resulted in the highest PPD values. Figure 5a displays the PPD values extracted from each of the averaged pupil tracks shown in Figure 4a, which once again highlights the talker-dependent distribution of PPD values. To understand the relationship between the auditory-perceptual rating data and the PPD values, the scatter plots between the PPD and the strain and listening effort ratings are depicted in Figure 5b,c, respectively. A trend for greater pupil dilation when listening to talker samples with higher perceived levels of strain and listening effort is evident in these scatter plots. Statistically significant positive Pearson correlation coefficients of 0.73 and 0.66 (both \(p<0.001\)) were found between the strain ratings and PPD values, and between the listening effort ratings and PPD values, respectively. Linear regression fits explained 54% and 43% of the variability in the averaged strain ratings vs. averaged PPD values, and the averaged listening effort ratings vs. averaged PPD data, respectively. However, regression analyses between the auditory-perceptual ratings and PPD data at the individual listener level did not reveal similar results. The slopes of the regression lines fit to the individaul strain vs. PPD and listening effort vs. PPD data were not statistically different from zero for all listeners. Plausible reasons for this lack of significance include: (a) greater variability in the PPD data than the auditory-perceptual data for each individual listener and (b) missing PPD data associated with some talkers (due to discarding of invalid pupil tracks) for some listeners, further contributing to the PPD data variability. Therefore, the relationship between auditory-perceptual ratings and the pupil dilation was only evident at a group level, and not at the individual listener level. Figure 4: (**a**) Baseline-normalized and averaged pupil tracks associated with all 23 adductor spasmodic dysphonia (AdSD) talker samples. (**b**) Baseline-normalized and averaged pupil tracks associated with the two highest (1 and 18) and two lowest (8 and 10) rated talkers on the vocal dimension of strain. The difference in peak pupil dilation (PPD) between the highest and lowest rated talkers is noteworthy in this plot.
# Geiger-Mode Avalanche Photodiode Arrays Integrated to All-Digital CMOS Circuits
## 4 Application to Lidar Imaging
Figure 9b is a composite 3D image with the early returns from foliage filtered out, revealing vehicles, picnic tables, and a gazebo. Lincoln developed the Airborne Ladar Imaging Research Testbed (ALIRT) system, an airborne lidar for terrain mapping. Initially, the system operated at 780 nm and used a 32 \(\times\) 32 silicon GMAPD array. Because of the availability of efficient lasers at 1060 nm, however, the Laboratory developed short-wave-IR-sensitive GMAPDs based on InGaAsP detectors grown on InP substrates. These APDs also have the separate-absorber-multiplier structure, but the doping profile is created in the epitaxial growth and the pixels are isolated by mesa etch. 128 \(\times\) 32 lidar image sensors were built by bump bonding the APD arrays to a CMOS timing circuit. Because of the exquisite sensitivity of the GMAPDs, the ALIRT system could collect wide-area terrain maps fifteen times faster than commercially available mapping lidars. One example of the many missions flown by ALIRT was in support of humanitarian efforts in Haiti after the 2010 earthquake [16]. Figure 10 shows a lidar image of a bridge in Port-au-Prince, Haiti. ALIRT collected terrain maps of the city over time, which enabled relief workers to know where tents were being erected or taken down, what roads were blocked, and where it was safe to land helicopters. A successor to the ALIRT system has an area coverage rate of 400 km\({}^{2}\)/h at 25-cm ground sampling distance. It can rapidly map a broad region and supply detailed three-dimensional images of every terrain feature or manmade structure over which it flies. It sees through foliage or dense dust clouds [17]. Figure 10: Lidar image of a bridge in Port-au-Prince, Haiti. Figure 9: (**a**) Forest canopy as viewed from a nearby tower (**b**) Composite lidar image obtained by combining data collects from four different heights and then filtering out the early returns. [PERSON] demonstrated another lidar focal plane that is highly significant from a technology evolution standpoint. Figure 11a shows the 3D-integrated 64 \(\times\) 64-pixel lidar focal plane whose three-tier pixel cross section is shown in Figure 5. Figure 11b is a lidar image of a cone obtained using this device. To our knowledge, this was the first demonstration of a three-tier integrated circuit of any kind, based on a process that supports dense, arbitrarily placed micron-scale inter-tier connection via (as opposed to chip stacking with peripheral wire bonds or ball grid arrays). 3D integration, as already pointed out, allows one to mix different technologies and put more circuitry within the area of a pixel. More importantly, however, it enables new imager architectures. Raw pixel data flows in parallel up through tiers of on-focal-plane processing circuits that extract information and reduce readout bandwidth.
# Relief Aircraft Dispatch Strategies Based on Different Levels of Information Sharing Systems
## 4 Dispatch Strategy Based on the Information Sharing System
### Level Two and Corresponding Transportation Method
#### 4.2.2 Multiple-Destination Strategies  
Considering the shortcomings of the one-destination strategy, one flight can be assigned with several rescue shelters. Each time requests are received from the disaster area, all the available aircraft are identified, and then a flight route is designed considering all of them. This method can be referred to as a split delivery vehicle routing problem.  
Figure 4: Information flow in Level one.  
Figure 5: Information flow in Level two.  
For this local objective, it is necessary to determine the number of aircraft At and number of requests Rt of the local optimization through the reported requests and available aircraft. If the reported requests are larger than the total capacity of the available aircraft, requests are selected by the principle of first-come and first-serve. The remaining requests are not assigned until there are available aircraft. If the total capacity of the aircraft is larger than the reported requests, the number of aircraft could be the rounded-up value of (total requests/capacity of a single aircraft):  
\[\begin{cases}R^{*}=\{ri,ri+1,\cdots\}&Capacity_{A}<sum(R_{report})\\ N_{aircraft}=RoundUp(\frac{R_{report}}{Capacity_{S}})&Capacity_{A}>sum(R_{ report})\end{cases} \tag{5}\]  
where \(Capacity_{A}\) is the capacity of all the available aircraft, \(Capacity_{S}\) is the capacity of a single aircraft, \(R_{report}\) denotes the set of all the unprocessed reported requests and \(R^{*}\) denotes the set of requests that would be considered as local question.  
To solve this optimization model, simulated annealing is utilized. For details, please refer to Appendix A.