esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Node Detection Algorithm Based on Serial and Threshold in Intelligent Sensor Networks
## 3 MPA Algorithm  
The MPA algorithm is the main detection algorithm of the SCMA system [41]; it updates information of sensor nodes and resource nodes by a factor graph [42]. In this paper, the sensor node is regarded as the variable method node VN, and the resource block is regarded as the function node FN. In the t-th iteration, function node \(c_{k}\) sends the information of variable node \(u_{j}\), expressed as \(t_{k\to j}^{t}\) On the contrary, the information of function node \(u_{j}\) sent by variable node \(c_{k}\) in the t-th iteration is expressed as \(t_{j\to k}^{t}\). If \(t_{max}\) is the maximum number of iterations, and \(t\) is the number of iterations, when \(t=t_{max}\), the symbol probabilities of information transmitted by sensor nodes are calculated respectively [43].  
Step 1: Update the information of function node FN, as shown in Equations (5) and (6):  
\[I_{c_{k}\to u_{j}}^{0}(x_{j})=\frac{1}{M}, \tag{5}\]  
Figure 2: SCMA coding principle.  
\[\mathrm{I}_{\mathrm{c_{k}\to u_{j}}}^{\mathrm{t}}(\mathrm{x_{j}})=\sum_{\sim \mathrm{x_{j}}}\left\{\frac{1}{\sqrt{2\pi\delta}}\exp\left(-\frac{1}{2\delta^{2} }\|\mathrm{y_{k}}-\sum_{\
u\in\mathrm{\mathcal{E}_{k}}}\mathrm{h_{k,\
u}x_{k, \
u}}\|\times\prod_{m\in\mathrm{\mathcal{E}_{k}}/j}\mathrm{I}_{\mathrm{c_{m} \to u_{k}}}^{\mathrm{t}-1}(\mathrm{x_{j}})\right)^{2}\right\}, \tag{6}\]  
where, \(\sim\left\{\mathrm{x_{j}}\right\}\) represents the edge probability of symbol \(\mathrm{x_{j}}\), \(\mathrm{\mathcal{E}_{k}}/\mathrm{j}\) represents the set of all variable nodes in VN that are connected to the function nodes \(\mathrm{c_{k}}\) except the j-th sensor node.  
Step 2: Update the VN information of the variable node, as shown in Equation (7):  
\[\mathrm{I}_{\mathrm{u_{j}\to c_{k}}}^{\mathrm{t}}(\mathrm{x_{j}})=\prod_{m\in \mathrm{\mathcal{E}_{j}}/k}\mathrm{I}_{\mathrm{u_{k}\to c_{m}}}^{\mathrm{t}} (\mathrm{x_{j}}), \tag{7}\]  
where, \(\mathrm{\mathcal{E}_{j}}/\mathrm{k}\) represents the collection of all function nodes in FN that are connected to the variable node \(\mathrm{x_{j}}\) except the k-th function node.  
Step 3: When the maximum number of iterations is reached, the symbol output probability after MPA decoding is shown in Equation (8):  
\[\mathrm{Q}(\mathrm{x_{j}})=\prod_{k\in\mathrm{\mathcal{E}_{j}}}\mathrm{I}_{ \mathrm{c_{k}\to u_{j}}}^{\mathrm{t}_{\mathrm{max}}}(\mathrm{x_{j}}). \tag{8}\]
# Nodal Accuracy Improvement Technique for Linear Elements with Application to Adaptivity
## 3 Nodal Accuracy Improvement Technology for Linear Elements
### Super-Convergent Schemes  
The increment solution \(\varepsilon^{h}\) obtained in Section 3.1 can be used to improve either the FE solution \(u^{h}\) or the EEP solution \(u^{*}\), which lead to two EEP recovery schemes:  
1. For the improved FE solution \(\widetilde{u}^{h}=u^{h}+\varepsilon^{h}\), the EEP recovery strategy described in Section 2.2 is used to obtain the super-convergent solution of \(\widetilde{u}^{h}\), which is noted by \(\widetilde{u}^{h*}\).  
Figure 5: Errors of nodal displacements for the test problem.  
Figure 6e, it is obvious that both the EEP solution \(\widetilde{u}^{h*}\) and the improved EEP solution \(\widetilde{u}^{*}\) have the same error range, which is much narrower than that from the original EEP solution \(u^{*}\). Although both \(\widetilde{u}^{h*}\) and \(\widetilde{u}^{*}\) from the two EEP recovery schemes appear to be the same in rendering accuracy, it is noted that the first one needs extra EEP recovery progress. It is also argued that the fundamental reason for improving FE nodal accuracy is to improve the accuracy of EEP solution \(u^{*}\) rather than the FE solution \(u^{h}\) itself. Thus, given the above analysis and arguments, the second scheme, as simple as a direct and straightforward superposition \(\widetilde{u}^{*}=u^{*}+\varepsilon^{h}\), provides a perfect and ideal improved EEP solution for linear elements, which is exclusively employed in the following paper.  
Let \(\widetilde{\varepsilon}^{h}_{i,\text{max}}\) and \(\widetilde{\varepsilon}^{h}_{\text{max}}\) denote the maximum errors of nodal displacement \(\widetilde{u}^{h}_{i}\) and interior displacement \(\widetilde{u}^{*}\), respectively. For linear elements, Sections 5.1 and 5.2 will numerically verify that the improved nodal displacements \(\widetilde{u}^{h}_{i}\) achieve convergence \(O(h^{4})\), and the accuracy of the improved EEP displacements \(\widetilde{u}^{*}\) is improved from \(O(h^{2})\) to \(O(h^{3})\), which is qualified to serve as an error estimator for FE solution \(u^{h}\).
## 2 Materials and Methods
### Western Blotting  
Both the expression of intracellular iNOS following LPS treatment and changes in the expression levels of HCP1 after LPS and X-ray irradiation were examined by Western blot analysis. To assess iNOS expression, RAW264 cells were treated with 0 or 1 \(\mathrm{\SIUnitSymbolMicro g}\)/mL LPS for 24 h; then, cells were harvested and proteins were extracted using RIPA cell lysis buffer. For HCP1 detection, the cells were irradiated with 20 Gy X-rays after LPS treatment, and cell lysates were prepared after a further 24 h incubation. The cell lysis protein samples were mixed with 2\(\times\) Western blotting sample buffer containing 100 mM Tris-HCl (pH 6.8), 4\(\%\) (\(w/v\)) sodium dodecyl sulfate (SDS), 12\(\%\) (\(v/v\)) 2-mercaptoethanolhanol, 20\(\%\) (\(v/v\)) glycerol, and 0.01\(\%\) (\(w/v\)) bromophenol blue, and heated at 95 \({}^{\circ}\)C for 5 min. The proteins were separated by electrophoresis on 10\(\%\) (\(w/v\)) polyacrylamide gels at 80 V for 90 min. The separated proteins were then electrophoretically transferred to polyvinylidene difluoride (PVDF) membranes (Merck Millipore, Burlington, MA, USA) at 2 mA/cm\({}^{2}\) for 70 min. The membranes were blocked by immersion for 60 min in 5\(\%\) (\(w/v\)) skim milk dissolved in PBS-T Tween (PBS-T). After blocking, the membranes were incubated with the following primary antibodies overnight at 4 \({}^{\circ}\)C: mouse anti-NOS2 antibody (Santa Cruz Biotechnology Inc.) diluted at 1:200 or mouse anti-HCP1 antibody (Santa Cruz Biotechnology Inc.) diluted at 1:100. After incubation with the primary antibodies, the membranes were washed three times with PBS-T for 10 min and incubated with mouse IgGx light chain binding protein conjugated to horseradish peroxidase (mlgGx BP-HRP) (Santa Cruz Biotechnology Inc.) diluted at 1:1000 for 3 h at 25 \({}^{\circ}\)C as a secondary antibody. The secondary antibody solution was removed, and the membranes were washed three times with PBS-T. A luminescence substrate solution, namely, Immobilon Forte Western HRP substrate (Merck Millipore), was applied to the membranes, and luminescence was detected using a FluorChemFC2 (Alpha Innotech Co., San Leandro, CA, USA). An anti-\(\beta\)-actin antibody (Santa Cruz Biotechnology Inc.) was used to detect the \(\beta\)-actin protein as the sample loading control.
## 2 Relevant Principles  
### Hyperfine Splitting, Momentum Quantum Number, and Hyperfine Interaction Constants  
The hyperfine interaction includes the magnetic dipole hyperfine interaction and the electrical quadrupole hyperfine interaction. In general, the hyperfine splitting is mainly considered from the following two aspects: The interaction of the nuclear magnetic dipole moment with the magnetic flux density created by electrons, and the interaction of the electric quadrupole moment with the gradient of the electric field at the nucleus [30]. Therefore, the total shift in energy level can be expressed as follows [24]:  
\[\Delta E(F\to F-1)=AF+B\frac{\frac{3}{2}F\left[F^{2}-I(I+1)-J(J+1)+\frac{1}{2} \right]}{I(2I-1)J(2J-1)}, \tag{1}\]  
where \(K=F(F+1)-I(I+1)-J(J+1)\), \(I\) is the total angular momentum quantum number of the nucleus, \(J\) is the total angular momentum quantum number of electron, \(F=I+J\) is the total angular momentum quantum number of the atoms, and \(\mathbf{A}\) and \(\mathbf{B}\) are the magnetic dipole hyperfine interaction constants and the electrical quadrupole hyperfine interaction constants, respectively.  
For the \(s\) state, owing to the orbital angular momentum L = 0, the electron distribution has a spherical symmetry. Therefore, \(\mathbf{A}\) is the only term of concern. The magnetic dipole hyperfine constant can be directly calculated using Equation (1). However, for the \(d\) state, the calculation of \(\mathbf{A}\), \(\mathbf{B}\) can be obtained from Equation (1) [31].
## 2 Materials and Methods
### Similarity Scores  
The similar text search engines we developed extracted the top-\(K\) texts with good similarity scores for a given query. Here let \(\mathcal{D}\) be a multilabel dataset consisting of \(|\mathcal{D}|\) multilabel instances \((s_{i},Y_{i}),i=1,2,\cdots,\ |\mathcal{D}|\). The short text \(s_{i}\) contains \(M_{i}\) keywords \(\big{(}w_{i,1},w_{i,2},\cdots,w_{i,M_{i}}\big{)}\). In creating the keywords, we performed word segmentation using the UTH-BERT tokenizer or the NICT-BERT tokenizer and stop word removal using the dictionary of Japanese stop words from SlothLib [19]. The multilabel \(Y_{i}\) is defined as a one-hot vector of \(Y_{i}=(y_{i,1},y_{i,2},\cdots,y_{i,L}),\ y_{i,l}\in\{0,1\},\ 1\leq l\leq L\).  
In similar text retrieval by SBERT, we measured the similarity between two texts with the Euclidean distance in the following formula:  
\[\mathit{euc\_dist}\big{(}s_{i},s_{j}\big{)}=\big{\|}\mathbf{s}_{i}-\mathbf{s}_{j}\big{\|} \tag{1}\]  
where \(\mathbf{s}_{i}\) is the embedding of the text \(s_{i}\) derived by the BERT encoder. The smaller the Euclidean distance, the more similar the two texts.  
We also built a similar text retrieval system using Okapi BM25 to compare with the SBERT system. For the Okapi BM25 system [7; 20], we measured the similarity given by  
\[BM25\big{(}\mathcal{D},s_{i},s_{j}\big{)}=\sum_{m=1}^{M_{i}}IDF(\mathcal{D},\ w_{i,m}).\frac{f \big{(}w_{i,m},s_{j}\big{)}\cdot(k_{1}+1)}{f\big{(}\ w_{i,m},s_{j}\big{)}+k_{1 }\cdot\Big{(}1-b+b\cdot\frac{M_{i}}{\text{avggl}}\Big{)}}. \tag{2}\]  
where \(f\big{(}w_{i,m},s_{j}\big{)}\) is the number of times that \(w_{i,m}\) occurs in the text \(s_{j}\), avggl is the average text length in the dataset \(\mathcal{D}\), and \(k_{1}\) and \(b\) are free parameters. \(IDF(\mathcal{D},\ w_{i,m})\) is the inverse document frequency weight of the query keyword \(w_{i,m}\). This is given by  
\[IDF(\mathcal{D},w_{i,m})=\log\frac{|\mathcal{D}|-n(\mathcal{D},w_{i,m})+0.5}{n( \mathcal{D},w_{i,m})+0.5}, \tag{3}\]  
where \(n(\mathcal{D},w_{i,m})\) is the number of texts that contained \(w_{i,m}\). A higher \(BM25\) indicates greater similarity between the two texts.
# Assessment of the Potential of High-Performance Buildings to Achieve Zero Energy: A Case Study
## 4 Results and Discussion
### Preliminary Economy Analysis  
The energy savings were mainly achieved through no-cost and low-cost retro-commissioning measures and can be done by the building owner's engineering team. Therefore, the project cost would be mainly associated with the additional PV system, which includes PV panels, batteries, materials, and labor. [PERSON] [37] reported that the cost of the PV panel and the battery was $1.18/Wp (1 US dollar = 6.77 Chinese yuan) in China based on recent market surveys. Adding labor and materials, the total cost was about $2.21/Wp. The local electricity tariff is $0.14/kWh.  
Figure 10: Monthly energy generation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Types**} & \multicolumn{1}{c}{**Area (m\({}^{2}\))**} & \multicolumn{1}{c}{**Percentage (\%)**} & \multicolumn{1}{c}{**System Efficiency**} & \multicolumn{1}{c}{**Total Power (kWp)**} \\ \hline Rooftop & Existing & 517 & **52.0** & 0.76 & 75.46 \\  & Proposed & 149 & **15.0** & 0.76 & 22.05 \\ Parking lot & Proposed & **944** & **-** & **0.76** & **137.69** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Optimizing the rooftop and facade photovoltaic (PV) system.  
A simple payback method was adopted [38]:  
\[N_{payback}=\frac{\Delta I}{\big{(}Q_{PV,power}+Q_{s}\big{)}\times P_{p}}=\frac{2. 21\times 159740}{\big{(}346000+215000\big{)}\times 0.14}=4.3 \tag{12}\]  
The payback period is 4.3 years.
###### Abstract  
Earthquake early warning systems aim to reduce the potential danger by providing a warning in the seconds before strong ground shaking occurs. In this study, we implemented EPIC, an early warning algorithm for Bursa province and its surroundings, which is a seismically active region. We replayed 104 earthquakes of \(M\geq 3.5\), which occurred in and around the study area between 2012 and 2021. We derived period and amplitude-based magnitude-scaling relationships using peak displacement amplitude (\(Pd\)) and predominant period (\(T_{p}^{max}\)) parameters of the first 4 s of P-wave arrivals. We investigated the performance of magnitude-scaling relationships through testing with real-time data. We observed an improvement when comparing the magnitude estimates made with the newly developed equations with the default equations used for California. We have also found that magnitude estimation with \(Pd\) gives better results than \(T_{p}^{max}\) for estimating the accurate final magnitude. We aim to adapt the EPIC early warning system, implemented for Bursa province and its surroundings, specifically for each region of Turkiye where the earthquake risk is high.  
EPIC; earthquake early warning systems; earthquake risk reduction; magnitude estimation; Bursa province; Marma region 2023
## Oil Leakage Mechanism of SPRG in CMBW Systems  
### Oil Leakage Phenomenon of SPRG Used as Waterproofing in Concrete Structures  
SPRG is a polymer-modified bitumen emulsion designed specifically for waterproofing in below-grade concrete structures. Bituminous emulsion consists of bitumen particles dispersed in a solvent base where particles form larger agglomerates via binders. Ideally, admixtures are mixed in correct ratios, the bitumen emulsion maintains a stable structure, and similar-sized particles are distributed evenly throughout the emulsion base [10]. However, most bitumen emulsions have crosslinks that are Van der Waal type bonds, and may undergo a breaking or creaming process in the middle of coalescence or flocculation [11]. This leads to sedimentation of the filler contents and segregation of different components of the emulsion due to difference in density [12]. Refer to Figure 2 for an illustration.  
Unlike other types of rubberized bitumen emulsions, SPRG polymers do not completely crosslink. This is intentionally done so as to provide a high resistance to zero-span tensile stress during substrate movement [13]. Standard SPRG straight out of manufacturing quarries have high viscosity and are difficult to work with. Product designers focus on achieving high workability by compromising long-term emulsifying stability and straying from standard mix designs [14]. This is largely due to an absence of regulation and standardized evaluation criteria to control the quality of SPRG.  
Figure 1: Structural models of different composite waterproofing systems. SPRG, Synthetic Polymer Rubber Gels; CMBW, Composite Modified Bituminous Waterproofing.  
Before an SPRG product is selected for usage in concrete structures, information on expected oil leakage mass should be accessible to assess the potential risks to the structure [14]. Eventually, natural sedimentation of filler contents forms finely distributed layers within the SPRG membranes, with the topmost layer (oil layer from emulsion breaking) having the highest rheological activity with lowest filler content concentration. When a crack or joint forms in the concrete substrate, structural movement and compression cause the oil layer to leak through the micro-sized cracks, resulting in leakage through the concrete crack or joint. Refer to Figure 3 for details.
## 2 Theoretical Foundation
### Capacity  
In this case, the vaccines are transported by refrigeration vans. The maximum capacity of vaccines that each vehicle can transport must be determined, respecting the dimension  
Figure 1: Clusters and Origin.  
and maximum weight capacity of the vehicles. Large boxes are placed in the van, in which there are five smaller boxes containing 195 vials of the Pfizer vaccine [1].  
Assuming the dimensions of the Pfizer vaccine distribution boxes, euro pallet dimensions (where vaccines for transport are stored), and standard dimensions of transport vehicles, it is possible to perform the calculations to determine the vehicle capacity in terms of the vaccines.  
Two options were considered: placing the length of the box along the length of the euro pallets, or placing the length of the box along the width of the euro pallet. To calculate the number of euro pallets that each vehicle can transport, two options were also considered: the length of the euro pallets along the vehicle's length and the length of the euro pallets along the vehicle's width.  
The conclusion is that the optimal solution is to use 4-euro pallets, each one with 8 boxes of vaccines, so that each vehicle has a maximum capacity of 32 boxes, or 31,200 vials. This is possible to achieve since each box takes 5 layers of 195 vials, which means each box holds 975 vials. Thus, the refrigeration van's capacity is 31,200 vials. The calculations to determine the maximum weight that a vehicle can support are performed by defining the van's payload as approximately 1500 kg, the euro pallet weight as approximately 25 kg, and the weight of each box of vaccines as approximately 36.5 kg. Therefore, the maximum weight limit that the vehicle can carry will not be exceeded when transporting 31,200 vials [1].
# Automatic Translation between Mixtec to Spanish Languages Using Neural Networks
## 3 Related Work  
As part of our research into the latest developments in the field, we came across some noteworthy studies on the automatic translation of digital languages that have a limited amount of resources available. We examined these studies and grouped them into three distinct categories based on the types of corpora they used for translation. These categories are parallel corpora, pseudo-parallel corpora, and corpus combination.  
Parallel corpora aid in translation by providing equivalent translations between a source and target language. The advantages include error correction and contextual usage of words in both languages. Disadvantages include unavailability for low-digital-resource languages, noise in parallel texts, and linguistic variations causing translation difficulties.  
Rule-based translation refers to the use of a set of grammatical rules to translate content from one language to another. These rules are created by language experts and are used to develop the translation system. One advantage of this type of translation is that it does notrequire a corpus collection for the translation process. Additionally, users can add their own terminology to a dictionary to improve the quality of the system. However, there are some disadvantages to rule-based translation, such as the difficulty in defining grammatical rules, especially in languages with many linguistic variants. Another disadvantage is the lack of a bilingual dictionary of considerable size, which can affect the quality of translations generated by the system.  
Translation based on pseudo-parallel corpora involves using a monolingual corpus in the opposite direction of the intended translation. The advantage of this type of translation is the availability of large quantities of data in either the source or target language, allowing for subsampling of the majority class samples. However, a disadvantage is that data are only available in a single language, meaning that synthetic data must be generated at either the source or destination in a translation scenario with parallel data.  
Combining parallel, rule-based, and pseudo-parallel corpora presents advantages, since the use of a combination of these approaches strengthens the training data and improves the quality of the results. The use of parallel corpora in a pseudo-parallel combination helps to improve the accuracy of the results and generate synthetic data in both the source and destination directions.  
Further details on each of these categories can be found below.
## 3 Experimental Results and Discussions  
Screening mammograms (exposed to low-dose x-rays) are suitable for screening test because of lower x-ray dose involved and shorter imaging time used. Two views of each breast in screen mammograms are recorded--the _craniocaudal_ (CC) view, which is a top to bottom view, and a _mediolateral oblique_ (MLO) view, which is a side view taken at an angle.  
The screening mammograms used in our experiments were a subset (\"Lumisys\") of the Digital Database for Screening Mammography project (DDSM, University of South Florida). These digitizedmammograms were scanned by the LumiSys scanner (50 microns per pixel, 12 bits per pixel), the image sizes of which were from 2,728 \(\times\) 3,920 pixels to 4,608 \(\times\) 6,048 pixels. A GCD algorithm was implemented as described in Section 2 (refer to Figure 1). The following parameters were used in our implementation for both mass and calcification detections: (i) the threshold used for histogram matching: \(T_{\mathrm{HM}}=32\); (ii) the CGF parameters used for alarm segments generation: \(f=\) [12 24 48], \(\sigma=\) [6 12 24] (see Equations 2 and 3 and Figure 2); (iii) for Gabor filter bank: \(f=\) [6 12 24 48 80], \(\sigma_{u}=\sigma_{v}=\) [3 6 12 24 30], \(\theta=\) 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), 135\({}^{\circ}\) (see Equations (4-6) and Figures 3-4); (iv) The classification features are the EHD features of 20 dimensions; (v) the number clusters for each class \(R=40\), and thus a total of 80 clusters were formed; the number of nearest neighbors \(k=50\) (k \(\times\)\(R\)\(\times\) 2); (vi) the threshold used for annotations: \(T_{\mathrm{P}}=0.45\), which means an alarm segment whose \(P_{\mathrm{C}}>0.45\) will be annotated as a classified mass or calcification. If the number of alarm segments is greater than three, then only the first three largest \(P_{\mathrm{C}}\)'s will be annotated on the mammogram in heavy black outlines.