esa-sceva/satcom-chunk-collection huggingface dataset:
# Sequence-to-Sequence Video Prediction by Learning Hierarchical Representations
## 3 Methods
### Model
The motivation is that Layer Normalization is effective at stabilizing the hidden state dynamics in recurrent networks, and thus accelerates the training time. **Skip-and-Update. ** The HRPAE is inspired by skip connections such as \"U-Net\" structure [20] (in Figure 1a, the \"U\"-shape structure is rotated clockwise, and appears as \"C\"-shape). U-Net consists of multi-layer CNNs with skip connections between the same levels of encoding and decoding layers. Such connections alleviate the problem of \"information bottleneck\" [52] in encoder-decoder networks. That is, skip connections enable sharing of low level information between encoding and decoding layers, which is essential in reconstructing details at the target images in image translation tasks. However, the difference in our network is that skip connections are intercepted by ConvLSTMs. The features of multiple levels of hierarchy from the current frame passed by encoding layer are combined with the latent variables of the previous frame; as a result, the ConvLSTMs output _updated_ information, which is essentially a _predicted_ feature map, to the decoding layer. Such sharing (updated) information between encoder and decoder layers are useful for prediction, e.g., for preserving the continuity of motion when the next frame is highly correlated with the current one. The skip-and-update in the prediction network is depicted in Figure 1b. In summary, the main purpose of skip-and-update is (i) to skip the encoder bottleneck to retain low- and mid-level information, (ii) to update such information capturing the sequential structure of each hierarchy, and propagate it to the decoder. Figure 1: (**a**) Overview of the 4-level Hierarchical Recurrent Predictive Auto-Encoder (HRPAE) architecture. (**b**) Network unrolled over time horizon.
## 3 Results and Discussions
### Comparison of RSM and WOA-BPNN Models  
The depth of the hardened layer was analyzed using the RSM and WOA-BPNN laser-induced hybrid hardening models, as established in the experiments. Both models showed coefficients of determination close to 1, indicating high fitting accuracy. However, the WOA-BPNN model exhibited significantly higher coefficients for the hardness of the hardened layer, indicating greater stability than the RSM model. A comparison of RMSE, MAE, and MAPE values reveals that the WOA-BPNN model has superior modeling capability in the laser-induced hybrid hardening process. All comparison results are presented in Table 8. Its main advantage lies in overcoming the limitations of the response surface method, particularly in simultaneously predicting both depth and hardness. This enhancement significantly improves model efficiency while maintaining accuracy, thus addressing multi-objective parameter challenges more effectively.  
To compare the generalization abilities of the RSM and WOA-BPNN models, MATLAB randomly selected 10 sets of process parameters for laser-induced hybrid hardening experiments from the previously mentioned 54 sets. The experimental results and corresponding predictions from both models are shown in Figure 7. The RSM model yielded a MAPE of 0.715% in terms of the mean hardened layer depth for induction frequencies of 10 kHz and 15 kHz, while the WOA-BPNN model achieved a MAPE of 0.697%. For the hardened layer hardness, the RSM had a MAPE of 1.255%, in contrast to 0.787% for the WOA-BPNN model. Thus, the WOA-BPNN model demonstrates superior generalization ability and accuracy in predicting both depth and hardness of the hardened layer.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Metrics**} & **RSM (10 and 15 kHz Averages)** & \multicolumn{2}{c}{**WOA-BPNN**} & **Better** \\  & **Depth** & **Hardness** & **Depth** & **Hardness** & **Model** \\ \hline R\({}^{2}\) & 0.995 & 0.879 & 0.995 & 0.996 & WOA-BPNN \\ RMSE & 0.159 mm & 11.395 HV\({}_{0.3}\) & 0.099 mm & 1.734 HV\({}_{0.3}\) & WOA-BPNN \\ MAE & 0.037 mm & 9.570 HV\({}_{0.3}\) & 0.022 mm & 0.596 HV\({}_{0.3}\) & WOA-BPNN \\ MAPE & 0.715\% & 1.255\% & 0.697\% & 0.787\% & WOA-BPNN \\ \hline \end{tabular}
\end{table}
Table 8: Comparison of metrics between RSM and WOA-BPNN.  
Figure 6: WOA-BPNN fitness value variation plot. (**a**) Depth fitness; (**b**) hardness fitness.  
In the hardness compliance and fixed process interval, the RSM model and WOA-BPNN were set to the optimal process parameters, i.e., an induction frequency of 10 kHz, laser power of 3600 W, scanning speed of 2.5 mm/s, and induction power of 40 kW. At that time, the predicted depths D were 8.016 mm and 7.762 mm, respectively. Under these conditions, the laser-induced hybrid hardening experiments were carried out. The average value of depth was 7.7 mm, as shown in Figure 8. The difference between the theoretical and experimental values of RSM is 5.27%. In contrast, the difference between the experimental and predicted values of WOA-BPNN is 0.81%, so the accuracy of WOA-BPNN prediction is found to be higher than that of RSM.
## 3 Materials and Methods  
### Overall Network Architecture  
An adaptive convolution and anchor network ACA-Net is designed in this paper. Specifically, an adaptive convolution and anchor (ACA) module is proposed, which consists of ACM and AAM. Meanwhile, an MFAF is designed and used in ACM. Firstly, the overall network architecture of the proposed ACA-Net is described in this section. Then, the proposed MFAF is introduced. Next, we introduce the proposed adaptive convolution module in detail. Finally, the details of the proposed AAM are described.  
The overview architecture of the proposed ACA-Net is illustrated in Figure 2. ACA-Net mainly consists of three parts: (a) a backbone to extract features from the input image; (b) a neck to combine feature maps from different stages of the backbone; (c) a head to predict classification and bounding boxes (BBox) of defect. Specially, ResNet-50 [21] pretrained on ImageNet [43] dataset is used as the backbone. It is worth noting that the ResNet-50 is modified for detecting surface defects, which are the same as RetinaNet. ACM improved by MFAF is employed at stages conv3-conv5 of ResNet-50 to enhance the modeling capability. Secondly, FPN [34] is adopted as the neck on top of the backbone to integrate multi-level features. Finally, we adopt the head which is similar to RetinaNet [38]. The box subnet (BBubnet) and the classification subnet (CSubnet) is respectively utilized to yield regression features (RF) and classification features (CF). Note that the \(RF\) and \(CF\) are operated by AAM to yield more suitable anchors and make new features \(RF^{\prime}\) and \(CF^{\prime}\). Afterwards, the \(RF^{\prime}\) and \(CF^{\prime}\) is used for regressing BBox and predicting classes of surface defects respectively.
## 1 Introduction
The existing monitoring of subgrade deformation is mainly used for new or special subgrades, and research on monitoring methods for adjacent construction affecting the operation subgrade remains rare. Rail-inspection vehicles are mainly used in China to detect the statuses of operating railway lines [16]. The \"Railway Line Maintenance Rules\" stipulate that the track quality index (TQI) should be used to evaluate the dynamic quality of the overall unevenness of the track-inspection vehicle. During the construction period, the existing roadbed is disturbed continuously, and there can be safety problemsat any time, but the detection frequency of the rail-inspection vehicle is far from meeting the requirements for the real-time monitoring of an existing roadbed during on-site construction. In summary, the following problems exist in monitoring research on the impact of construction on an adjacent existing line foundation: (1) The rules and regulations do not specify a deformation alarm value for effects on structures such as railway subgrade engineering caused by temporary construction. (2) The adjacent existing railway lines must be completely closed for safety reasons, and thus, conventional roadbed monitoring and detection methods cannot be implemented. For this reason, this study considered the stress, strain, and distribution characteristics between the Shanghai-Nanjing Intercity Railway and the Beijing-Shanghai line in the three stages of construction. This study obtained key control indicators that can effectively reflect the impact of construction on the operation of subgrade safety through comparative analysis of track-inspection car data. This method ensured the safe operation of the existing line during the construction period. The safety monitoring method of the operating roadbed was also explored. In order to ensure the safe operation of the existing line during the construction of the pile-raft foundation reinforcement, on the basis of a static test, this paper carried out in situ monitoring experiments such as stress shovel, horizontal strain gauge, and inclinometer. Combined with the finite element calculation and the analysis of the track-inspection vehicle data, this research studies the stress, displacement, and geometric linearity of the subgrade adjacent to the existing line during the construction of the pile-raft foundation reinforcement. This research is helpful to supplement the research gaps in the construction of high-speed railway near the existing line and provides a scientific basis for the safe operation of the existing line during the construction of the pile-raft foundation reinforcement.
## 1 Introduction
### Motivating Example  
To demonstrate how redundant activity labels can introduce unnecessary complications to discovered process models and the motivations behind our proposed framework clearly, we describe a simple patient treatment process as an example. Assuming there are eight activities in the process; that is, (A) registration, (B) visiting the doctor, (C) performing colonoscopy, (D) performing a laboratory test, (E) performing an MRI, (F) performing surgery, (G) paying the bill, and (H) discharging the patients. Hypothetically, the clean event log contains eight traces denoted by \(L_{1}=\{\)ABDEFGH, ABCEFRI, ABCDEFGH, ABFGH, ACEFH, BADFGH, BACDEGH, ABCFGH\(\}\). The goal of automated process discovery algorithms is to construct a process model that can accurately describe the process behaviours [15]. For instance, if we apply the popular algorithm used by Disco, which is a tool widely used to generate visual and actionable insights for process mining [16], we can obtain the process model as shown in Figure 1. The numbers in each box indicate the case coverage in Disco. It is easy to interpret the process model: patients usually register first and then visit the doctor. After that, they may be asked to perform a colonoscopy, laboratory tests, or an MRI. A surgery follows depending on the patient's situation. Finally, they pay the bills and are discharged from the hospital. However, redundant activity labels may exist if the event log contains merged data from sources that do not share a common schema [10]. Therefore, the same real-world activity is recorded with different labels in each source. Suppose there are two activity labels, (B1) \"DfSeen\" and (B2) \"Medical Assign\", which both represent activity (B) in the event log \(L_{2}\)[10]. Moreover, activity labels (H1) \"Release C\" and (H2) \"Release D\" represent the same method by which patients are discharged [17]. The event log is denoted by \(L_{2}=\{\)ABDEFGH, ABICEFGH1, AB2 CDEFGH2, ABFGH, ACEFH, B2 ADFGH2, B1 ACDEGH1, ABCFGH\(\}\). Consequently, we obtained another process model based on this redundant event log, which is shown in Figure 2. Comparing the two process models, it can be seen that such redundancy brings unnecessary relations (red arcs) and unwanted activity labels (activity B1, B2, H1, and H2) to the process model in Figure 1. Such redundancy causes confusion for process analysts and has a negative impact on the simplicity and comprehension of the discovered process model.  
The goal of this paper is to propose a framework as a data preprocessing tool that can accurately detect the redundant activity labels to enhance the quality of event logs for better process analysis in process mining. The proposed approach has a wide range of application in various real-life process mining tasks, especially in the healthcare domain. For example, the proposed approach can be adopted to select representative activity labels when analysing complicated healthcare processes.
## 3 Results
### Network Architecture Results  
#### 3.3.1 Atrous-Net Results  
The Atrous-net obtained a MAE of 105.16 HU in head bone and a MAE of 210.51 HU in pelvis bone. In the head dataset, this result was 18% worse than the best result. In pelvis, it was around 5% worse. The Atrous-net showed not statistically significant differences in pelvis between 2D and 3D MAE (all tissues: \(F_{2,56}=2.2\), \(p=0.15\); bone: \(F_{2,56}=0.0015\), \(p=0.97\)) and PSNR (all tissues: \(F_{2,56}=1.6\), \(p=0.21\); bone: \(F_{2,56}=0.9\), \(p=0.35\)). However, for the head data set there were statistically significant differences in bone depiction between 2D and 3D MAE (all tissues: \(F_{2,36}=0.38\), \(p=0.54\), bone: \(F_{2,36}=19.2\), \(p<0.001\)) and PSNR (all tissues: \(F_{2,36}=3.7\), \(p=0.068\); bone: \(F_{2,36}=16.4\), \(p<0.001\)).
# Parallel-to-Grain Compressive and Tensile Behavior of Paulownia Wood at Elevated Temperatures
## 3 Experimental Results and Discussion
### Failure Modes  
The typical failure modes of the tensile specimens are shown in Figure 4a. Most of the specimens exhibited a transverse crack in the middle. Moreover, a longitudinal crack was also formed in a few specimens. [PERSON] et al. [20] found that similar transverse cracks occurred in tensile specimens made of Douglas-fir wood above 130 \({}^{\circ}\)C, while irregular cracks formed below 130 \({}^{\circ}\)C. However, the failure modes of the tensile Paulownia wood specimens were not affected by the temperature. Figure 4b presents the typical failure modes of the compressive specimens. It can be seen that a kinking band was clearly formed on the compressive specimens. Moreover, crushing failure was found at the end of the specimen. The crushing failure of Parica wood at elevated temperatures was also observed by [PERSON] el al. [14]. In addition, some specimens were split into two parts, as shown in Figure 4b. The same failure modes (kinking, crushing and splitting) were observed by [PERSON] et al. [20] in Douglas-fir wood at elevated temperatures. Table 2 summarizes the number of different failure modes for all the specimens at different temperatures. Figure 5 presents the percentages of the failure modes at the target temperatures. For the tensile specimens, a transverse fracture is the dominant failure mode. For the compressive specimens, kinking is the main failure mode, while a few specimens exhibited a splitting failure. Furthermore, it seems that the elevated temperatures had limited effect on the failure mode of the compressive specimens.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Temperature**} & \multicolumn{3}{c}{**Compression**} & \multicolumn{3}{c}{**Tension**} \\ \cline{2-6}  & **Kinking** & **End** & **Cushing** & **Splitting** & **Transverse** & **Longitudinal** \\  & & **Cushing** & **Cracking** & **Cracking** \\ \hline
20 & 3 & 3 & 0 & 5 & 1 \\
60 & 5 & 0 & 1 & 3 & 2 \\
100 & 6 & 0 & 0 & 5 & 1 \\
120 & 2 & 2 & 2 & 3 & 3 \\
140 & 2 & 4 & 0 & 4
# Effect of Plant Biostimulants on Nutritional and Chemical Profiles of Almond and Hazelnut
## 2 Materials and Methods
### Nutritional Value  
The proximate composition (ash, fat, moisture, proteins, and carbohydrates) was determined in the freeze-dried samples, according to the AOAC methods [35]. The crude protein was determined by the macro-Kjeldahl method (N \(\times\) 5.18 for almonds and N \(\times\) 5.3 for hazelnuts) using an automatic distillation and titration unit (model Pro-Nitro-A; Selecta, Barcelona, Spain); crude fat was determined by Soxhel extraction with petroleum ether during 7 h and the ash content was evaluated by incineration at 550 \(\pm\) 15 \({}^{\circ}\)C. Total carbohydrates content was calculated by difference using the formula: total carbohydrates (g/100 g) = 100 \(-\) (g\({}_{\text{fat}}\) + g\({}_{\text{ash}}\) + g\({}_{\text{proteins}}\)) and the energetic value was calculated according to the Decree-Law No. 167/2004 using the formula: energy (kcal/100 g) = 4 \(\times\) (g\({}_{\text{proteins}}\) + g\({}_{\text{carbohydrates}}\)) + 9 \(\times\) (g\({}_{\text{fat}}\)).
## 2 Modeling and Methods
### Energy Dissipation  
The energy dissipation rate \(E_{d}\) per time step per vehicle [34] is evaluated to analyze the _stop and go_ effects induced by the raised crosswalks. For a vehicle \(i\) moving with speed \(v_{i}\), its kinetic energy is computed as \(\frac{mv_{i}^{2}}{2}\), where \(m\) is the vehicle's mass. For simplification, all other dissipation factors such as rolling and air drag are neglected. In this sense, the dissipated energy \(e_{i}(t)\) of vehicle \(i\) in the time slot \([t-1,t]\) is defined as follows:  
\[e_{i}(t)=\begin{cases}\frac{m}{2}\big{(}v_{i}^{2}(t-1)-v_{i}^{2}(t)\big{)}& \text{for }v_{i}(t)<v_{i}(t-1)\\ 0&\text{otherwise.}\end{cases} \tag{10}\]  
By considering the total number of vehicles in the system \(N\), the average energy dissipation rate is determined by:  
\[E_{d}=\frac{1}{T}\frac{1}{N}\sum_{t=t_{0}+1}^{t_{0}+T}\sum_{i=1}^{N}e_{i}(t), \tag{11}\]  
denoting by \(T\) the stationary period, considered after \(t_{0}\) time steps. The difference in the kinetic energy at each location of the lane also is analyzed to inquire about the focusedeffects nearby the crosswalk. Thereby, the energy dissipation profile \(SE_{d}(x)\) at each location \((x)\) of the lane is determined whenever the position is occupied by a vehicle as follows:  
\[SE_{d}(x)=\frac{1}{T}\sum_{t=t_{0}+1}^{t_{\alpha}+T}e(x). \tag{12}\]  
Here, the dissipation energy must be divided by the vehicles' mass, which is considered the same for all vehicles for simplification.
# A Survey on Hardware Accelerators for Large Language Models
## 3 FPGA-Based Accelerators
### FPGA Acceleration of Transformer Networks  
In 2022, [PERSON] et al. presented a high-performance hardware accelerator for the Transformer networks [16].  
The paper presented a novel architecture targeting the FPGA platform used to speed up the following specific functions; general matrix multiplication, softmax, layer normalization, and GELU.  
Since GEMM accounts for about 70% percent of the total execution time, the architecture primarily focuses on optimally accelerating the matrix multiplication operations of the network. Furthermore, the goal of this scheme was to integrate matrix multiplication--the most computationally intensive part of the Transformer network--with functions that have minimal impact on runtime, such as GELU, softmax, and LayerNorm. This integration aims to prevent unnecessary data transfers between the host and the FPGA kernel and vice versa, which would otherwise add significant cost to the implementation. The kernels were developed using Vivado HLS, targeting the Alveo U200 FPGA card, and were clocked at 411 MHz.  
Performance evaluation showed that the proposed framework can achieve a 2.3\(\times\) system speedup for the BERT model compared to a 40-thread processor and 80.5\(\times\) speedup over a single-core CPU.
## 4 Conclusions  
We fabricated a FPMC with high-finesse of 55.5, for the self-injection locking of a semiconductor DFB laser. High-quality injection locking has been presented in this paper with clear frequency-pulling effects, which stabilizes the laser output to the FPMX resonance. Narrow linewidth is confirmed with significantly reduced beat-note linewidth of 402 Hz FWHM, in comparison to 7.1 MHz FWHM in the free-running case. Here, we focus on the locking of a DFB laser, similar setup can be used to lock laser diodes of other types. This result may find broad application prospects in the fields of remote optical communication networks, optical sensing, and quantum information technologies, with a simple and compact setup.  
Conceptualization, X.W. and Z.X.; methodology, X.W.; software, X.W.; validation, X.W.; formal analysis, X.W.; investigation, X.W.; resources, X.W. and Z.X.; data curation, X.W.; writing--original draft preparation, X.W.; writing--review and editing, X.W.; visualization, X.W.; supervision, X.W., Z.X. and S.-N.Z.; project administration, X.W.; funding acquisition, Z.X.  
This research was funded by National Key R&D Program of China (Grant No. 2017 YFA0303700); National Natural Science Foundation of China (Grants No. 91321312, 11621091, 11674169, 11474050).  
The authors declare no conflict of interest.  
Figure 4: (**a**) Time domain beat signal measured by an oscilloscope with the free-running DFB laser. (**b**) The beating spectrum in the free-running case. Inset: zoom-into around the peak showing the beat frequency of 7.1 MHz. (**c**) Time domain beat signal measured with the injection-locking. (**d**) Beat spectrum in the self-injection locked case. Inset: zoom-into around the beating frequency.
## 4 Conclusions and Future Work
For example, adding a dialogue act classification [69] for the speech feature would better inform whether the participant said something because they needed help or sharing information indicating different behaviors. Second, the number of human behaviors used to capture participants' collaborative behaviors were limited. Expanding the range of behaviors, particularly for _Waiting_, into more distinguishable behaviors to allow the researchers to better understand what is taking place in the collaboration. Third, the imbalance on the distribution of the collaborative behavior introduced challenges in training the HMM. Using _K_-fold cross-validation was a preliminary method in generating an HMM capable of predicting collaborative behaviors. Future work would benefit from exploring other methods that could include supervised, or semi-supervised learning methods. Despite these limitations, results from the evaluation highlight the advantages of HMMs over rule-based prediction models in a dyadic collaborative interaction between autistic and neurotypical individuals, even with a small labeled dataset. Future work can continue to bridge the gap in effective teamwork training, ensuring a more inclusive and supportive learning experience. Conceptualization, A.Z.A., N.S. and D.A. ; methodology, A.Z.A., A.P., D.A. and D.M.W. ; software, A.Z.A., A.P. and D.A. ; validation, N.S. and D.M.W. ; formal analysis, A.Z.A. and A.P. ; investigation, A.Z.A. ; resources, N.S. ; data curation, A.Z.A. and A.P. ; writing--original draft preparation, A.Z.A. ; writing--review and editing, A.Z.A., A.P., D.A. and N.S. ; visualization,A.Z.A. and A.P. : supervision, D.M.W. and N.S. ; project administration, A.Z.A. ; funding acquisition, N.S. All authors have read and agreed to the published version of the manuscript. This work was funded by NSF grants 1936970 and 2033413. The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Vanderbilt University (protocol code 161803, Approved on 16 September 2022). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the privacy of our participants and the requirements of our IRB. The authors would like to thank the Vanderbilt Kennedy Center, Treatment and Research Institute for Autism Spectrum Disorders (TRIAD) team; [PERSON] and [PERSON] for their expert advice on interventions for autistic individuals and recruitments for the study. The authors declare no conflicts of interest.