esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Design Knowledge Semantic Network
#### 3.3.4 Rationale Ontology  
The DR is the core of the DKN. As shown in Figure 6, the model takes into consideration the general writing criteria of technical documents and the structure of the document. At the same time, as inspirational design stimuli for designers, extracted rationale should be as intuitive and short as possible, so that the designer can quickly understand, and there should not be a paragraph or lengthy statement.  
Figure 5: Artifact ontology.  
Figure 6: Rationale ontology.  
**Definition 5**.: _Rationale Class. Herein, we adopt [PERSON]'s definition of DR [68]: DR includes not only the reasons behind a design decision, but also the justification for it, the other alternatives considered, the tradeoffs evaluated, and the argumentation that led to the decision. It includes issues, intents, alternatives, pros and cons of the artifact, and others. Alternatives are other artifacts that address the same design problem. As shown in Figure 6, the rationale information includes the following:_  
* _Issue_--problems with existing artifacts or requirements for new artifacts. The detailed description of the _issue_ is usually embodied in the description of the shortcomings of existing artifacts, which are generally expressed using negative statements. _Issues_ are the core and focus of the entire design document that need to be addressed.
* _Intent_--goals and expectations that the designer wants to achieve with the _artifact_. Design _intent_ can include the function of the design object, such as the drone realizing the functions of transportation, maintenance, photography, and others. It can also include the performance _intent_ of an artifact, such as safety, economy, reliability, and others; the _artifact_ implements a specific behavior or operational _intent_, such as vertical take-off and landing operations of a drone. Hence, design _intent_ addresses a design problem or set of problems.
* _Argument_--advantages and disadvantages of the _artifact_, positions for or against, opinion analysis from the designer or interested parties. It can include a. cons, the descriptions of the deficiencies, shortcomings, and negatives of _alternatives_ or other traditional _artifacts_; and b. pros, used to express positive information such as excellent functions, reliable performance, and a wide range of applications.
* _Alternative_--other _artifacts_, design options, or relevant solutions.
* Relationships in rationale knowledge networks and other relevant relations include: (subClass)-[IS_A]->(Class) (18) (literature)-[Describe]->(artifact) (19) (artifact)-[Realize]->(intent) (20) (intent)-[Solve]->(issue) (argument)-[hasOpinion]->(position) (artifact)-[hasAlternative]->(alternatives) (position)-[hasArgumentWord]->(cons or pros)
# A Novel Error Correction Approach to Improve Standard Point Positioning of Integrated BDS/GPS
## 4 Experimental Analysis  
### Experimental Environment  
#### 4.1.1 Static Experimental Environment  
The integrated BDS/GPS Point Positioning was performed on a Windows PC with two 3.5 GHz Intel Xeon processors. Compass Receiver Utility software version 1.7.3 was used to operate and obtain the relevant raw data, the SPP calculation model of Integrated BDS/GPS was conducted on MATLAB and the error prediction and correction process was implemented in Python. The experimental data in this study were obtained from 11:31 to 11:59 by the Sinan M300 GNSS receiver shown in Figure 5, while the sampling interval was 60 s, the coordinates of the receiver were known and the proposed SPP calculation model and error correction methods were used to post-process the collected data.  
Figure 4: The LSTM-based error correction framework.  
Figure 5: The Sinan M300 GNSS receiver experimental environment.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 15 Assignment of Processing Tasks Across Nodes in a WSN
### Solution Method  
To reduce the communication overhead that is encountered in centralized schemes, a heuristic iterative decentralized algorithm that relies on gossiping communications is used to solve a smaller mixed binary linear program (MBLP) for a given node and its inflow-neighbors. The MBLP complexity is hence not dependent on the size of the network, but is dependent on the density of the network.
# Stabilisation of Soft Clay, Quick Clay and Peat by Industrial By-Products and Biochars
## 2 Materials and Methods  
### Natural Soils  
The soft, low-sensitive Onsey clay was sampled from the Norwegian GeoTest Site (NGTS) located outside of the city of Fredrikstad in southern Norway [40]. The sample depth was approximately 5-8 m. The clay had a bulk density (\(\rho_{b}\)) of 1.5-1.6 t/m\({}^{3}\), natural water content (\(w\)) of 60-80%, sensitivity of 5-9, organic content of 3-4%, plasticity index (\(I_{p}\)) of 40-50%, and liquidity index (\(I_{L}\)) of approximately 0.9-1.0. The clay content was approximately 60-70%, the remaining being predominantly quartz silt grains.  
The Tiller-Flotten quick clay was sampled from a depth of 9-13 m at the NGTS test site outside of Trondheim, mid-Norway [41]. The quick clay had a \(\rho_{b}\) of 1.75-1.85 t/m\({}^{3}\), \(w\) of 40-45%, sensitivity of 150-250, organic content of <1%, \(I_{p}\) of approximately 10-15%, and \(I_{L}\) of approximately 1.5-2.0. The clay content was similar to that of Onsey, with the remaining being mostly quartz silt grains.  
The peat was sampled from a depth of 1-1.5 m at the NGTS site at Tiller-Flotten [41, 42]. The peat had a \(w\) of approximately 800-1000% and a \(\rho_{b}\) close to 1.0 t/m\({}^{3}\). The degree of humification according to the [PERSON] classification [43] was determined at H2-H3, i.e., a very low degree of humification with insignificant or very slight decomposition with an identifiable plant structure. The sampling depth was approximately 1.5 m.
## 2 Results and Discussion  
### Samples Analyzed  
A total of seventeen fresh chili pepper samples belonging to the genus _Capsicum_ were collected at the same growth phase in their full ripening stage and kindly provided by Azienda Agricola Salvadori Rita (Livorno, Italy). After their arrival, all the chili peppers were frozen until the day of the analysis and analyzed within two weeks from the freezing process. For each species, three whole chili peppers were ground together, and four homogenized samples were weighted in SPME vials, one for GC-MS and three for GC-FID analysis, assuming the analytes identified were a mean of their content in each pepper. The vials were closed and put in the fridge until the analyses, which were carried out consecutively on the same day. Three chili pepper extra virgin olive oils were purchased online. From the information reported on the label, one of them was flavored with Merken pepper (a smoked Aji chili pepper), the second was flavored with a mix of _Capsicum chinense_ peppers, and the third was flavored with the addition of a mix of different chili peppers. For convenience, the three extra virgin flavored olive oils samples were called EVOO1, EVOO2, and EVOO3, respectively. Table 1 lists the investigated samples.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Chili Pepper Type** & **Specie** & **Color** \\ \hline
1 & Naga Morich & _Capsicum chinense_ & Red \\
2 & Trinidad Scorpion & _Capsicum chinense_ & Red \\
3 & Habanero Fatalii & _Capsicum chinense_ & Yellow \\
4 & Naga Yellow & _Capsicum chinense_ & Yellow \\
5 & Naga Chocolate & _Capsicum chinense_ & Brown \\
6 & Trinidad Scorpio Moruga & _Capsicum chinense_ & Orange \\
7 & Habanero Red Savina & _Capsicum chinense_ & Red \\
8 & Habanero Chocolate & _Capsicum chinense_ & Brown \\
9 & Scotch Bonnet & _Capsicum chinense_ & Orange \\
10 & Banana Pepper & _Capsicum annuum_ & Yellow \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chili peppers samples (1–17) and flavored chili pepper EVOOs (18–20) analyzed.
# Survey Solutions for 3D Acquisition and Representation of Artificial and Natural Caves
## 2 Material and Methods
### Artificial Tunnel Segment  
To test the capability of reconstructing an underground structure, we analyzed the survey of the tunnel test site assuming the TLS survey as the reference technique. The considered tunnel segment is 100 m long with 12.5 m diameter, where we placed and measured on walls the position of 56 reference targets. Targets were distributed in groups of 4, 2 for each side, every 10 m along the tunnel. Within the subgroup, the target-target distance ranged from 1.5 to 3 m. Targets position was rigorously surveyed from 6 setup locations using Total Station (TOPCON GPT-7001L) to compute their 3D coordinates. The survey was registered using four benchmarks located in the tunnel. The network coordinates were computed with a precision of \(\pm\)0.02 m.
## 4 Performance Evaluation
### Experimental Results
Figure 9: The graph of the relationship between the number of units and the number of parameters of PD detection model. If RNNs have the same number of units, the number of parameters of PD detection model using RNN-SH is considerably reduced. From Table 1 and Figure 7, in RNN-SH, the performance when the output activation function was tanh was higher than that of relu. Figure 11 shows the execution time of each RNN in the case of one layer and 256 units in the first execution. Here, since we used LSTM and GRU that were pre-implemented in pytorch, accurate speed comparison with RNN-SH, which was implemented by the combination of pytorch functions, cannot be performed. Therefore, in Figure 11, we used LSTM and GRU that were individually implemented by the combination of pytorch functions, and the speeds are compared. In order to perform speed comparisons fairly, we considered the parallelism of LSTM and GRU, and implemented LSTM and GRU so that they could be calculated at high speed by splitting the result of parallel calculation for each weight of gate, etc. From Figure 11, since RNN-SH has fewer parameters and a lower calculation cost than LSTM and GRU, the learning of RNN-SH was faster than them. Figure 12 shows learning curves of loss for each RNN with 256 units and one or two layers. From Figure 12, it can be seen that the losses oscillate due to the small amount of dataset, but the learning of RNN-SH progresses relatively gently compared to that of LSTM and GRU. However, RNN-SH (tanh) with two layers oscillate violently and could not learn well. Figure 11: The execution time of each RNN in the case of 1 layer and 256 units. The number in parentheses is the epoch at the time when the loss was the lowest for the validation set. RNN-SH is faster than LSTM and GRU. GRU is slow due to its low parallelism. Figure 10: The examples of confusion matrix in first trial of LSTM and RNN-SH (tanh). (**a**) is the confusion matrix of the LSTM, and (**b**) is that of the RNN-SH (tanh). Here, the confusion matrix is normalized.
# Parsing Unranked Tree Languages, Folded Once +
## 2 Preliminaries
Formally, \(c[t]=t\) if \(c=x\), and otherwise \(c[t]=\sigma[s_{1},\ldots,s_{i-1},s_{i}[t],s_{i+1},\ldots,s_{n}]\), where \(c=\sigma[s_{1},\ldots,s_{n}]\) and \(s_{i}\in\Gamma_{\{x\}\cup\Sigma}\) is the unique context among \(s_{1},\ldots,s_{n}\). **Automata for unranked trees:** Unranked tree languages have been studied since the 1960s, most notably as a formal model for the markup language XML [20; 21]. In this article, we use \(Z\)-automata [22], an extension of step-wise automata [23], to represent suchlanguages. A _Z-automaton_ is a tuple \(A=(\Sigma,Q,R,F)\) consisting of a finite _input alphabet_\(\Sigma\); a finite set \(Q\) of _states_ which is disjoint from \(\Sigma\); a finite set \(R\) of _transition rules_, each of the form \(s\to q\) consisting of a left-hand side \(s\in\mathrm{T}_{\Sigma\cup Q}\) and a right-hand side \(q\in Q\); and a finite set \(F\subseteq Q\) of accepting states. Henceforth, we write \(m\) for the number of states \(|Q|\). Let \(t\in\mathrm{T}_{\Sigma\cup Q}\). A transition rule \(r\) of the form \(s\to q\) is _applicable_ to \(t\), if \(t\) can be written as \(t=c[\sigma[t_{1},\dots,t_{n}]]\), such that \(s=\sigma[t_{1},\dots,t_{k}]\) for some \(k\leq n\). If so, then there is a _computation step_\(t\to\alpha\)\(c[q[t_{k+1},\dots,t_{n}]]\). A _computation_ of \(A\) on a tree \(t\in\Sigma\) is a sequence of computation steps \(t\to^{*}_{A}q\), for some \(q\in Q\). The computation is _accepting_ if \(q\in F\). A tree \(t\in\mathrm{T}_{\Sigma}\) is _accepted_, or _recognised_, by \(A\) if there is an accepting computation of \(A\) on \(t\). The _language_ accepted by \(A\), denoted by \(\mathcal{L}(A)\), is the set of all trees in \(\mathrm{T}_{\Sigma}\) that \(A\) accepts. As shown in [22], \(Z\)-automata recognise the same class of languages as unranked tree automata [20]. We use a normal form, in which all transition rules are of the form \(\sigma\to r\), \(\sigma[q]\to r\), or \(p[q]\to r\), for \(\sigma\in\Sigma\) and \(p,q,r\in Q\). A run of a Z-automaton \(A=(\Sigma,Q,R,F)\) in normal form on a tree \(t\) can be seen as assigning states to \(\mathit{nodes}(t)\). For each state \((q,\sigma)\) in \(Q\times\Sigma\), there is a regular language \(r_{q,\sigma}\), such that in a run, the sequence \(w\) of states assigned to the children of a node that has label \(\sigma\) and is assigned \(q\) must be a string in \(r_{q,\sigma}\)[22]. We denote by \(\mathit{parikh}(q,\sigma)\) the Parikh image [24] of the language \(r_{q,\sigma}\).
## 4 Auction Game
### Auction Mechanism Selection  
The auction game provides a specific set of rules that will govern the sale or purchase of an object to the submitter of the most favorable bid. Therefore, the auction mechanism is exactly suitable for scenarios where resources are used to their full potential. In view of the long-time delay attributes of satellite communications, sequential auction mechanisms, such as English Auction and Dutch Auction, are not suitable for this scenario because of uncertain bidding episodes.  
Furthermore, since the satellite computational resource is extremely limited, it is hard to process too much information in a centric manner. There are two popular sealed-bid auction mechanisms including first-price auctions and second-price auctions. In a first-price auction, the bidder who submitted the highest bid is awarded the object being sold and pays a price equal to the amount of the bid. Since the payment is equal to the amount bid, bidders shade their bids below their true value. when combining sealed-bid and first-price, it is hard to reach a NE. For the sealed-bid second-price auction, bidding one's true value is a dominant strategy, which gives unique NE. Thus, this mechanism can achieve efficient and effective decision making by one shot in a distributed manner, i.e., the satellite select the biggest bidder as the chosen relay, which reduce the decision time and computational complexity for onboard processing satellite or NCC of the ground getaway.  
We assume that all the secondary and primary networks are rational, honest but with incomplete global information, and they expect maximum payoffs from the cooperation spectrum sharing structure. Since the multi-relay cooperation at the same time is overhead in synchronization and coding, we assume the primary satellite system only recruits the best relay to retransmit the signal. We choose the Vickery auction mechanism for the relay selection by one shot in multi-relay HSTSNs. All potential relays are the bidders, who must compete with each other for the opportunity spectrum sharing. The satellite is the auctioneer, who would choose the best bids and broadcast all bids and the results to all bidders.
It was shown that pathogen or herbivore attacks can lead to transcriptional reprogramming of plant metabolism, which is signaled by JA and controlled by the different transcriptional regulators (TFs). When the JA levels are low, Jasmonate-Zim Domain (JAZ) proteins bind to TF MYC2, as well as to some additional transcription factors, repressing the expression of early JA-responsive genes [27; 28]. However, a burst of JA, promoted by wounding or insect attack, is followed by interaction between the JAZ repressor protein and F-box coronatine insensitive 1 (COI1) protein, causing ubiquitination and resulting in the degradation of the JAZ proteins [29]. Afterward, TFs, such as MYC2, are de-repressed, which leads to the induction of the expression of JA-responsive genes that are involved in the biosynthesis of specialized metabolites or downstream TFs [26]. The JA-responsive TFs, basic helix-loop-helix (bHLH) iridoid synthesis 1 and 2 (BIS1 and BIS2), are known as the major activators of the iridoid pathway in _C. roseus_[30; 31]. Jasmonate-associated MYC2-like1/2/3 (JAM1, JAM2, and JAM3) TFs are other components of the JA signaling pathway, which are antagonistic and negative regulators of MYC-like TFs and JA responses [32]. In _C. roseus_, enhanced MIA biosynthesis in response to insects and pathogens is controlled by JA [25; 29]. Recent findingssuggest that MIA biosynthesis in this species is co-regulated by transcriptional cascades: CrMYC2 and CrBISI/BIS2 [30; 33]. Although induced defense mechanisms are energy-demanding [16; 17], wound-induced accumulation of specialized metabolites can be utilized as an effective approach for scaling up the production of these bioactive compounds [22; 34; 35]. Therefore, the presented study aimed to analyze and determine the links between genes that are involved in SGs biosynthetic pathway, JA induced TFs, and the production of SGs in centaury leaves in response to wounding.
# Improving Singing Voice Separation Using Curriculum Learning on Recurrent Neural Networks
## 3 Proposed Curriculum Learning Technique on RNN and U-Net
### U-Net Baseline Model
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Conv** & & **Upconv** & \\ \hline
**Layer Number** & **Output** & **Size** & **Input** & **Output** & **Size** \\ \hline
0 & \(\mathbf{E}^{(0)}=\mathbf{X}\) & \(512\times 128\times 1\) & \([\mathbf{E}^{(1)}\ \mathbf{D}^{(1)}]\) & \(\mathbf{D}^{(0)}\) & \(512\times 128\times 1\) \\ \hline
1 & \(\mathbf{E}^{(1)}\) & \(256\times 64\times 16\) & \([\mathbf{E}^{(2)}\ \mathbf{D}^{(2)}]\) & \(\mathbf{D}^{(1)}\) & \(256\times 16\times 16\) \\ \hline
2 & \(\mathbf{E}^{(2)}\) & \(128\times 32\times 32\) & \([\mathbf{E}^{(3)}\ \mathbf{D}^{(3)}]\) & \(\mathbf{D}^{(2)}\) & \(128\times 32\times 32\) \\ \hline
3 & \(\mathbf{E}^{(3)}\) & \(64\times 16\times 64\) & \([\mathbf{E}^{(4)}\ \mathbf{D}^{(4)}]\) & \(\mathbf{D}^{(3)}\) & \(64\times 16\times 64\) \\ \hline
4 & \(\mathbf{E}^{(4)}\) & \(32\times 8\times 128\) & \([\mathbf{E}^{(5)}\ \mathbf{D}^{(5)}]\) & \(\mathbf{D}^{(4)}\) & \(32\times 8\times 128\) \\ \hline
5 & \(\mathbf{E}^{(5)}\) & \(16\times 4\times 256\) & \(\mathbf{E}^{(6)}\) & \(\mathbf{D}^{(5)}\) & \(16\times 4\times 256\) \\ \hline
6 & \(\mathbf{E}^{(6)}\) & \(8\times 2\times 512\) & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: U-Net architecture for singing voice separation. ```
\(N:=\) number of training steps \(\left\{\mathbf{W}_{e}^{(l)},\ \mathbf{b}_{e}^{(l)}\right\}:=\) convolution kernels and bias of the encoder at level \(l\in\left\{0,\dots,5\right\}\) \(\left\{\mathbf{W}_{d}^{(l)},\ \mathbf{b}_{d}^{(l)}\right\}:=\) convolution kernels and bias of the decoder at level \(l\in\left\{1,\dots,6\right\}\) for\(n\gets 1\) to \(N\)do  Differentiate \(J=\frac{1}{T}\sum_{t=1}^{T}E_{p}(t)\) with respect to each set of parameters  Update \(\mathbf{W}_{e}^{(l)},\ \mathbf{b}_{e}^{(l)},\ \mathbf{W}_{d}^{(l)},\ \mathbf{b}_{d}^{(l)}\) using gradient descent learning endfor
```  
**Algorithm 2** U-Net Learning  
The activation function at the final layer is a sigmoid function, and its output bounded in \([0,1]\) is used as a mask for vocal sound. The size of the final output of the decoder, \(\mathbf{D}^{(0)}\), is \(T\times F\), and it is element-wisely multiplied to the magnitude spectrum of the mixed sound to obtain the magnitude spectra of the vocal and the instrument sounds as follows:  
\[\hat{y}_{1,t}(f) = \mathbf{D}^{(0)}(t,f)x_{t}(f)\,,\] \[\hat{y}_{2,t}(f) = x_{t}(f)-\hat{y}_{1,t}(f)=\left(1-\mathbf{D}^{(0)}(t,f)\right)x _{t}(f)\,. \tag{12}\]
# Improving Smartphone GNSS Positioning Accuracy Using Inequality Constraints
###### Abstract  
To improve smartphone GNSS positioning performance using extra inequality information, an inequality constraint method was introduced and verified in this study. Firstly, the positioning model was reviewed and three constraint applications were derived from it, namely, vertical velocity, direction, and distance constraints. Secondly, we introduced an estimator based on the density function truncation method to solve the inequality constraint problem. Finally, the performance of the method was investigated using datasets from three smartphones, including a Huawei P30, a Huawei P40, and a Xiaomi M18. The results indicate that the position and velocity accuracy can be improved in the up component using a vertical velocity constraint. The horizontal positioning accuracy was increased using a heading direction constraint with dynamic datasets. Numerically, the root mean square error (RMSE) improvement percentages were 16.77%, 14.57%, and 31.09% for HP40, HP30, and XMI8, respectively. Using an inter-smartphone distance constraint could enhance the horizontal positioning of all participating smartphones, with improvement percentages of 34.27%, 75.58%, and 23.66% for HP40, HP30, and XMI8, respectively, in the static dataset. Additionally, the improvement percentages were 15.90%, 5.55%, and 0.17% in dynamic datasets. In summary, this study demonstrates that utilizing inequality constraints can significantly improve smartphone GNSS positioning.  
Android smartphones; GNSS; positioning; inequality constraints; density function truncation 2023