esa-sceva/satcom-chunk-collection huggingface dataset:
# The Potential of the Biodigester as a Useful Tool in Coffee Farms
## 3 Results and Discussion
### Economic Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Year**} \\ \hline  & **0** & **1** & **2** & **3** & **4** & **5** \\ \hline (+) Savings form LPG & - & 226 & 234 & 242 & 250 & 259 \\ (+) Savings form fertilizer & - & 851 & 881 & 912 & 944 & 977 \\ (\(-\)) Operational cost & - & 586 & 607 & 629 & 650 & 672 \\ (\(-\)) Maintenance cost & - & 28 & 29 & 30 & 31 & 32 \\ (\(-\)) Equipment depreciation & - & 21 & 21 & 21 & 21 & 21 \\ (\(=\)) Net cash & - & 442 & 458 & 475 & 492 & 511 \\ (+) Depreciation & - & 21 & 21 & 21 & 21 & 21 \\ (+) Investment & \(-\)1.424 & - & - & - & - & - \\ \multicolumn{1}{c}{Cash flow} & \(-\)1.424 & 463 & 479 & 496 & 513 & 532 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cash flow form biodigester application on coffee farm (USD/year). the use of biogas from anaerobic wastewater treatment. As a result, fertilizer prices are important in terms of economic feasibility and biol uses can be more important than the biogas in the condition under this study.
# Design of a Multi-Condition Emotional Speech Synthesizer
## 2 Related Works  
### Tacotron  
There have been several TTS models [1; 2; 3; 4; 5; 6]. [PERSON] et al. [1] proposed an end-to-end TTS model, referred to as Tacotron. The Tacotron model has been improved, to make it more similar to humans in terms of sound quality than conventional speech synthesis studies. Tacotron consists of four modules. First, the Encoder module converts the input text into numbers which the computer can understand. Second, the Decoder module converts the numbers, converted from the input text, back into a form of spectrogram. The Decoders consist of recurrent neural networks (RNN), and creates the next spectrogram using the previous spectrogram through a decoding process. Third, the Attention module makes the appropriate connections between the Encoder and the Decoder. In the Tacotron, the Attention module plays the most important role, and the Decoder decides which part of the sentence to focus on to generate speech, using Attention values. With this approach, a model learns from various data, and emotional speech can be produced by imitating the accent or speech rate of a speaker. Lastly, the task of the Vocoder is to reconstruct the generated spectrogram into waveforms. The authors used the Griffin-Lim algorithm to synthesize waveforms from the predicted spectrogram [8]. Figure 1 describes the model, which includes an Encoder, an Attention-based Decoder, and a Vocoder [1].  
Figure 1: Structure of the Tacotron [1].
## 4 Measurement Processing
### Least Square  
The rolling noise of tires is often recorded to be a logarithmic equation with dependency of the vehicles speed [6]. The A-weighted [21] SPL can therefore be described as  
\[L(v)=a+b\log(v) \tag{1}\]  
with \(a\) and \(b\) as constant parameters and \(v\) as vehicle speed in km h\({}^{-1}\).  
Equation (1) can be interpreted as a system of linear equations, where each equation describes one run, as shown in (3). If the equation system is over determined, multiple possible solutions can be found. For this reason, the norm of recorded data vector and data vector calculated with a best guess of parameters \(\hat{a}\) and \(\hat{b}\) is defined. The following minimization problem is referred to as the least square problem. The vector in brackets is referred to as residual  
\[\min(\hat{\mathbf{\beta}}\mathbf{X}-\mathbf{y})^{2}. \tag{2}\]  
\(\hat{\mathbf{\beta}}\) is the vector of regression coefficients, \(\mathbf{X}\) is the input data set and \(\mathbf{y}\) is the output data set.  
To apply the least square problem on the specific equation system, the data needs to be restructured as follows  
\[X=\begin{bmatrix}1&log(v_{1})\\ \vdots&\vdots\\ 1&log(v_{N})\end{bmatrix}\quad,\quad y=\begin{bmatrix}L_{1}\\ \vdots\\ L_{N}\end{bmatrix}. \tag{3}\]  
It is possible to solve the problem analytically, leading to the following term.  
\[\hat{\mathbf{\beta}}=(\mathbf{X}^{T}\mathbf{X})^{-1}\mathbf{X}^{T}\mathbf{y} \tag{4}\]
## 3 Proposed Scheme
### Backbone with FPN  
In YOLACT++, the backbone network can be one of the popular CNNs, ResNet and DarkNet, for extracting feature maps from an image. As shown in Figure 3, ResNet is composed of five convolutional blocks, including conv1(C1), conv2_x(C2), conv3_x(C3), conv4_x(C4), and conv5_x(C5), which was selected to yield some feature maps with different sizes in the proposed scheme.  
In real applications, the psoriasis regions often have arbitrary shapes, as shown in Figure 1, and geometric variations often occur due to viewpoint change. To deal with arbitrary shapes and reduce the effect of geometric variations, a deformable CNN (DCN) [29] where the samples can be on irregular and offset locations was proposed for feature extraction. In DCN [32], the regular grid \(\Omega\) is augmented with the offsets \(\Delta p_{n}\), and the resulting feature map \(Z=\{z(p_{0})\}\) can be expressed as follows:  
\[z(p_{0})=\sum_{p_{n}\in\Omega}w(p_{n})\cdot x(p_{0}+p_{n}+\Delta p_{n}), \tag{3}\]  
where \(\Omega\) represents the neighborhood of \(p_{0}\), \(p_{n}\) denotes the neighboring point in \(\Omega\), \(w(p_{n})\) means the corresponding weight of \(p_{n}\), and \(X=\{x(p_{0})\}\) stands for the input signal. As we can see in Equation (3), the signals in the irregular grid \(\Delta p_{n}\) were selected to compute the resulting output. As shown by the authors of [32], the receptive field in deformable convolutions can be adaptive according to the scale of the objects. To correctly locate the objects with arbitrary boundaries, aspect ratios, and rotations, the DCN is then adopted in YOLACT++ [33]. This means that unlike YOLACT, 3 \(\times\) 3 deformable convolutional layers can be utilized to replace the 3 \(\times\) 3 convolution layers in YOLACT++. It is expected that YOLACT++ with DCN can extract more useful visual feature maps from psoriasis images for further analyses.  
As shown in Figure 1, these psoriasis regions have different sizes. To detect multiscale objects (i.e., psoriasis regions), FPN was adopted to obtain a feature pyramid with strong semantic information. As shown in Figure 3, P3, P4, and P5 in the FPN were from C3, C4, and C5. P6 and P7 were generated based on 3 \(\times\) 3 convolutional layers with stride 2 from P5. It is expected that a bigger feature map (e.g., P3) can be used to detect smaller objects, while a smaller feature map (e.g., P7) can be used to detect larger objects. Therefore, the backbone and FPN can be utilized to yield multiscale feature maps from a psoriasis image for further analyses.
## 2 Inverse Design of Silicon Photonics with Iterative Optimization Algorithms  
### Inverse Design Schemes for Silicon Photonics  
Different from electronic circuits where the versatile functions come from the combination of electronic components, a single silicon photonic device is able to achieve complex functions with delicate geometry design. For the given geometry of a device, its optical response can be evaluated by EM simulation before experimental validation. EM simulation tools have been developed by solving [PERSON]'s equations numerically. These tools are important for device modelling and optimization, by allowing the designer to understand how light propagates in a given geometry and to obtain an accurate optical response. Widely adopted EM simulation methods include finite element method (FEM) [17], finite-difference time-domain method (FDTD) [18], eigenmode expansion method (EME) [19] and rigorous coupled wave analysis (RCWA) [20]. The FDTD method is among the most popular choices for silicon photonic device since it is a general EM solver well suited for high index contrast structures with complex geometry. It also allows wideband simulation as spectral response is generally needed for silicon photonic devices.  
For a targeted optical response, inverse design tries to find a suitable device geometry. As shown in Figure 1, either a certain electric field distribution or an optical spectrum could serve as the figure of merit (FOM) which describes the phenomenon of interest to the designer. Other merits such as performance, compactness or fabrication feasibility may also be considered for different application scenarios. Based on the number of DOF, three types of structures are widely used for inverse design: empirical structure [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32], QR-code like structure [33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45] and irregular structure [46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58]. An empirical structure making use of existing classical structures usually has only a few DOF. A QR-code like structure with hundreds of DOF selectively etches the design area with regular shapes such as rectangles or circles in a periodic manner. An irregular structure taking the whole design area as a bitmap has the most complex pattern with hundreds of thousands DOF. In the rest of this section, iterative optimization algorithms for optimizing these structures are illustrated and compared in detail.
# Queue Spillover Management in a Connected Vehicle Environment
## 1 Introduction
The traditional input-output model was adopted in literature [27; 28; 29] for traffic optimization. Reference [30] used the shockwave model to solve the transportation problem. Loop detectors are widely used due to the quality of the collected data, but its expensive price has become a hindrance to its popularity. Reference [31] presents a mathematical model, which uses CV to estimate the real-time queue of wireless sensor networks, and the proposed queue estimation model is applicable for adaptive signal control using CV technology in practice. The literature [32] developed an event-based method, which used both probe trajectory and signal timing data to estimate queue length, and the estimation accuracy under different market penetration rates was examined. [PERSON] presented a mathematical model for real-time queue estimation, using connected vehicle technology from wireless sensor networks [33]. [PERSON] used instantaneous and historical car networking data and optimization of phase sequence, and signal timing parameters were optimized online with the goal of minimizing the average waiting time at intersections and maximizing the maximum queue length [34]. [PERSON] took the road network vehicle travel time as the minimum control target and used adaptive dynamic programming to propose a single intersection adaptive signal control method, based on car networking environment [35]. [PERSON] et al. obtained the vehicle position, speed and other data, and used the integer programming model to optimize the signal control parameters of the intersection, to solve the transportation problem [36]. In this paper, the V2I communication technology in the vehicle networking environment was used to realize the information interaction between the vehicle and the traffic signal controller. This paper analyzed the characteristics of the vehicle intersection and established the mathematical model of the vehicle intersection. The maximum queue length of the road is proposed as the evaluation index of road overflow. According to the maximum queue length, the optimal intersection overflow limit point is determined, and the trend of the intersection overflow is determined in advance. When the queue overflows, the phase optimization is performed. This paper is organized as follows. Section 2 presents the basic principle of a control system. Section 3 introduces the developed algorithm and compares the performance of it, for two objective functions: minimizing delay and minimizing number of stops. The signal control model of intersection is derived from Section 3. Finally, Section 4 presents the conclusions of this paper and suggestions for further work on this particular topic.
# Adaptive Multi-Path Routing Protocol in Autonomous Vehicular Networks
## 6 Evaluation  
In this section, we evaluate the performance of MARV by comparing it with other routing protocols. The simulations ran for 100 s simulation time and were performed on a 500 m \(\times\) 500 m space with 100 randomly placed fixed nodes unless otherwise stated. Each result is computed by averaging the results from 5 to 10 random scenarios.
## 4 Discussion
Overall, our study showed that spiking neural networks can effectively outperform traditional neural networks for use in BMI, where they are decode temporally varying spiking data collected from the motor cortex during the execution of a directional motor task. Since spiking neural networks more accurately model brain neurons than the 'neurons' in traditional ANNs, they may be better able to emulate the computations that occur in the brain. The sparsity of spikes that are produced can lead to an efficiency increase in neuromorphic hardware. Given the accuracy of the spike-weighted SLSTM models, we propose that these models can be applied to future BMI devices that are dependent on low-power hardware, especially neuromorphic chips. In summary, we propose a novel spike-weighted SNN with spiking long short-term memory (swSNN-SLSTM) for brain-machine interfaces (BMIs). Adding SLSTM to spike neural networks can capture temporal information hidden in brain signals. Comparing the proposed algorithm with several existing ML models, the swSNN-SLSTM outperformed both the unscented Kalman filter and the LSTM-based ANN. K.M. : Data analysis, figures, writing. R.Q.S. : Experimental design, data collection. C.L. : Experimental design, data collection. K.K.A. : Experimental design, critical feedback, editing. B.P. : Data collection, data analysis, critical feedback, editing. All authors have read and agreed to the published version of the manuscript. This research was supported by the Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore. It was also funded by grant EC-2015-216 (Robust Neural Decoding and Control System), and the Singapore International Pre-Graduate Award (SIPGA), by A*STAR Graduate Academy (A*GA). **Data Availability Statement:** Some of the data presented in this study are available in [28]. Unfortunately, the second dataset is on the order of several terabytes and is not publicly available online. Interested parties may contact the corresponding author. **Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Results
### Effects of Treatments on Soil Chemical Properties under Wheat Agroecosystem  
The different treatments have significant effect on soil organic carbon as shown in the Figure 3a. In the 0-100 cm soil layers, the highest SOC contents were associated with the NT, NTN, NTM, and NTNM no-till treatments, whilst the minimum SOC contents were noted in the CT, CTN, CTM, and CTNM filled treatments. In addition, regarding the no-till treatments, NTNM had the maximum (7.43 g/kg) SOC content. On average across the 0-100 cm soil layers, the NT, NTN, NTM, and NTNM increased the SOC by 9%, 13%, 15%, and 21%, respectively, compared with the sole CT treatment. Moreover, there were also differences in SOC contents between the different tested soil layers. In the 0-10 cm soil layer, the SOC contents were higher compared with other investigated soil layers (\(p<0.05\)). The average content of TN in the whole soil layer reached a maximum (1.19 g/kg) under NTNM treatment and a minimum (0.69 g/kg) under CT treatment, and there was a significant difference among the treatments (\(p<0.05\)). The highest TN contents in the 0-10, 10-20, 20-40, and 40-60 cm soil layers were all in the NTNM plots and were 2.28 g/kg, 1.79 g/kg, 1.51 g/kg, and 1.29 g/kg, respectively; there was a significant difference among the treatments. Nevertheless, TN content in the 60-100 cm soil layer was similar, and there was no significant difference (\(p<0.05\)) among treatments (Figure 3b). In the vertical soil layer profile, the TN content significantly (\(p<0.05\)) decreased gradually with increasing depth, and the TN content in the 0-10 cm soil layer was significantly higher than that in the lower layers. The soil TP content under different treatments is shown in the Figure 3c. There was no significant difference observed between different treatments in the case of TP content, including 0-100 cm soil layers at (\(p<0.05\)). The TP content under different treatments, including all investigated soil layers, fluctuated within a small range with a statistically non-significant difference (Figure 3c). Moreover, regarding all tested soil layers, the TP concentration did not change significantly (\(p<0.05\)), indicating that the TP distribution in tested soil depths was stable. The change of soil NO\({}_{3}\)\({}^{-}\)-N and NH\({}_{4}\)\({}^{+}\)-N content under tillage strategies and N fertilization methods is shown in the Figure 4a,b. In the plough soil layer 0-40 cm, the CTN treatment had, significantly (\(p<0.05\)), the highest NO\({}_{3}\)\({}^{-}\)-N content, whilst the lowest NO\({}_{3}\)\({}^{-}\)-N content was recorded in the sole CT treatment. Moreover, the NO\({}_{3}\)\({}^{-}\)-N content of CTN and NTN was significantly the (\(p<0.05\)) highest over CTM, CTNM, NTM, and NTNM treatments. In the 40-60 cm soil layer, the NTNM treatment had the significantly (\(p<0.05\)) lowest NO\({}_{3}\)\({}^{-}\)-N content, whilst CTN treatment had the highest NO\({}_{3}\)\({}^{-}\)-N content. Moreover, in the 60-100 cm soil layer, CT had the significantly (\(p<0.05\)) highest NO\({}_{3}\)\({}^{-}\)-N
# Design and Analysis of Adaptive Hierarchical Low-Power Long-Range Networks
## 1 Introduction, Related Work and Our Approach
Finally, the proper combination of these building blocks is examined from an implementation point of view and an integrated solution is presented. The rest of the paper is organized as follows: in Section 2, we present the basic ideas behind a hierarchical organization in a Wireless Sensor Network. Section 3 presents an adaptive mechanism that helps reduce the traffic in the Wireless Sensor Networks once it is partitioned in multiple sub-networks. Section 4 presents the implementation details of the presented protocol stack using an algorithmic library for real world and simulation environments. In Section 5, we showcase a set of experimental results from simulations and experiments on hardware testbeds. Finally, in Section 6, we present our conclusions and possible extensions of our work.
# Collective Communication Performance Evaluation for Distributed Deep Learning Training
## 1 Introduction
* Execution environment (Linux shell and PyTorch): In this paper, we conducted a comprehensive performance evaluation of collective communication within the Linux shell and PyTorch environments. Within the Linux shell, we investigated the complexity of inter-node communication within collective communication routines and thoroughly examined the memory copying process between the CPU and GPU within a single node. In the PyTorch environment, our analysis focused on measuring and evaluating the actual latency of collective communication occurring during distributed deep learning, particularly utilizing backend functionalities. Additionally, we configured the DDP architecture during the deep learning workload phase to measure the time taken by collective communication libraries. Based on this, the paper proposes the optimal collective communication library for real distributed deep learning systems. * Comparison of data-distributed deep learning architectures (parameter server vs. all-reduce): In this study, we scrutinized the performance of a collective communication library within the framework of data-distributed parallel deep learning. Our focus was on evaluating two key architectural paradigms: the parameter server employing a central server-client structure and the decentralized ring all-reduce method. Our experimental findings revealed a significant advantage of the NCCL communication library over MPI and GLOO when used in the all-reduce method. The remainder of this paper is structured as follows:  
Section 2 provides background information on collective communication architecture and the primary communication methods employed by the library. Section 3 discusses the collective communication architecture and presents experiments conducted in both the Linux shell and PyTorch environments. Section 4 provides details about the experimental environment utilized in this study. Section 5 presents the findings derived from experiments conducted within the Linux shell environment, whereas Section 6 presents the results obtained from experiments conducted in the PyTorch environment. Section 7 discusses the comprehensive experimental results and the overall experimental findings. Section 8 reviews previous related studies. Finally, Section 9 summarizes our conclusions and outlines future research directions.
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
## 2 ECM Components That Contribute to Tissue Engineering Strategies
### Laminins  
Laminins are characterised as cell adhesion molecules [61] within a multidisciplinary family of around 20 glycoproteins. They often crosslink and are associated with the type IV collagen network within basement membranes. The resulting self-assembled network structure maintains close connections with cells, promoting crucial interactions with cell surface receptors [56]. Laminins are also important for many biological functions, including the early stages of embryonic development and organogenesis [61]. All laminins are heterotrimers, comprising three gene products: \(\alpha\), \(\beta\) and gamma (\(\gamma\)) chains [62]. Structurally, these chains can form either a cruciform (cross), Y-shaped (three armed) or rod-shaped (single arm) unit, aided by the presence of a triple-helical coiled domain in the centre of each chain [54]. Similar to fibronectin, laminins consist of different domains, namely globular, laminin-type epidermal growth factor (EGF)-like repeats and \(\alpha\) helical regions (Figure 3). These domains allow for interaction with other ECM proteins and the cell surface. Thus far, five \(\alpha\), four \(\beta\) and three \(\gamma\) chains have been identified, which associate to form a total of 16 distinct laminin heterotrimers [54].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multirow{2}{*}{**Type**} & \multicolumn{2}{c}{**Genes Encoding Proteins**} & \multirow{2}{*}{**Description**} \\  & & **Common in Collagen** & & \\ \hline \multirow{6}{*}{Fibril-forming} & I & \(COL1A1\) and \(COL1A2\) & \\  & II & \(COL2A1\) & \\  & III & \(COL3A1\) & \\  & V & \(COL5A1\), \(COL5A2\) and \(COL5A3\) & which are packed into \\  & XI & \(COL1A1\), \(COL1A2\), \(COL1A3\) & stromal tissues such \\  & XXIV & \(COL24A1\) & as bone and tendons \\  & XXVII & \(COL27A1\) & \\ \hline \multirow{6}{*}{Network-forming} & \multirow{2}{*}{IV} & \multirow{2}{*}{\
\begin{tabular}{c} \(COL4A1\), \(COL4A2\), \(COL4A3\), \\ \(COL4A4\), \(COL4A5\), \(COL4A6\) \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} Network structure \\ composed of laminins \\ and basement \\ membrane proteins \\ \end{tabular} } \\  & IX & \(COL9A1\), \(COL9A2\), \(COL9A3\) & Fibril-associated \\  & XII & \(COL12A1\) & collagens with \\  & XIV & \(COL14A1\) & interrupted triple \\ Fibril-associated & XVI & \(COL16A1\) & helices (FACIT); \\ collagens & XIX & \(COL19A1\) & molecular bridges \\  & XX & \(COL20A1\) & associated with type I \\  & XXI & \(COL21A1\) & and type II \\  & XXIII & \(COL22A1\) & collagen fibrils \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primary collagen types classified according to class, type, genes and higher-order struc- tural assembly [54].