esa-sceva/satcom-chunk-collection huggingface dataset:
# Attention-Based RU-BiLSTM Sentiment Analysis Model for Roman Urdu
## 1 Introduction
### Contribution of the Study  
Following are the contributions of this study:  
* We made a new Roman Urdu dataset that contains more than 26,824 labelled instances obtained from Daraz.pk and Twitter and annotated by field experts publicly available.
* This research work provides three different neural word embeddings--word2 vec, Glove, and FastText--that were trained on a enormous amount of Roman Urdu data scrapped from different web pages.
* We propose an attention-based BiLSTM model called RU-BiLSM for the sentiment analysis of Roman Urdu.
* In order to evaluate the integrity of generated neural word embeddings, we experimented with six different arrangements of deep neural networks. The comparison of a variety of deep networks arrangements under three different neural word embeddings will give deep insight to researchers into understanding which approach is superior when solving the sentiment analysis problem for Roman Urdu.
## 3 The Longitudinal Dynamics Model of the Belt Conveyor with Non-Uniform Bulk Material Distribution
### Dynamics Model of the Belt Conveyor System  
The belt conveyor system is discretized into \(N+M+1\) elements. The first driver motor is numbered as the first element. Assuming that the conveyor belt does not slip on the roller, the equivalent massof the drive motor at the straight direction can be put in the mass of the first element. Similarly, the equivalent mass of the second driver motor can be put in the mass of the \(N+M+1\) element.  
Based on [PERSON]'s Second Law, the dynamic model of a belt conveyor can be described as  
\[\left\{\begin{array}{c}(m_{1}+\frac{I_{1}}{R_{1}^{2}})\ddot{x}_{1}+(k_{1}+k_{2 })x_{1}-k_{1}x_{2}-k_{n}x_{n}+(c_{1}+c_{n})\dot{x}_{1}-c_{1}\dot{x}_{1}+c_{n} \dot{x}_{n}=F_{d1}+F_{d2}-w_{1}\\ m_{2}\ddot{x}_{2}+(k_{2}+k_{1})x_{2}-k_{1}x_{1}-k_{2}x_{3}+(c_{2}+c_{1})\dot{x}_ {2}-c_{1}\dot{x}_{3}=-w_{2}\\ \vdots\\ m_{j}\ddot{x}_{j}+(k_{j}+k_{j-1})x_{j}-k_{j-1}x_{j-1}-k_{j}x_{j+1}+(c_{j}+c_{j- 1})\dot{x}_{j}-c_{j-1}\dot{x}_{j+1}=-w_{j}\\ \vdots\\ (m_{n}+\frac{I_{2}}{R_{2}^{2}})\ddot{x}_{n}+(k_{n}+k_{n-1})x_{n}-k_{n-1}x_{n- 1}-k_{n}x_{n+1}+(c_{n}+c_{n-1})\dot{x}_{n}-c_{n-1}\dot{x}_{n+1}=-w_{n}\end{array}\right. \tag{20}\]  
where \(J_{1}\) and \(J_{2}\) are the rotational inertias of the two driven rollers, \(R_{1}\) and \(R_{2}\) are the radius of the two driven rollers, \(n\) is the total number of the elements, which is equal to \(N\)+\(M\)+\(1\), \(\left\{m_{1},\,m_{2},\,\ldots\,\,m_{j}\right.\)\(\left.\,\,\ldots\,\,m_{n}\right\}\) are the mass of the \(\left\{1\text{th},\,2\text{th},\,\ldots,\,j\text{th},\,\ldots\,\,n\text{th}\right\}\) element respectively, which can be calculated by Equations (2) or (3). \(\left\{k_{1},\,k_{2},\,\ldots\,\,k_{j}\,\ldots\,\,k_{n}\right\}\), \(\left\{c_{1},\,c_{2},\,\ldots\,\,c_{j}\,\ldots\,\,c_{n}\right\}\)and \(\left\{w_{1},\,w_{2},\,\ldots\,\,w_{j}\,\ldots\,\,w_{n}\right]\) are the stiffness coefficients, the composite viscosity coefficients and the main resistances of the \(\left\{1\text{th},\,2\text{th},\,\ldots\,\,j\text{th},\,\ldots\,\,n\text{th}\right\}\) element, respectively; \(\left\{x_{1},\,x_{2},\,\ldots\,\,x_{j}\,\ldots\,\,x_{n}\right\}\),\(\left\{\,\dot{x}_{1},\,\dot{x}_{2},\,\ldots\,\dot{x}_{j}\,\ldots\,\,\dot{x}_{n}\right\}\), \(\left\{\,\dot{x}_{1},\,\dot{x}_{2},\,\ldots\,\dot{x}_{j}\,\ldots\,\dot{x}_{n}\right\}\) are the displacement, velocity, and acceleration of the linear motion of the \(\left\{1\text{th},\,2\text{th},\,\ldots,\,j\text{th},\,\ldots\,\,n\text{th}\right\}\) element of the conveyor belt respectively, \(F_{d1}\) and \(F_{d2}\) are the equivalent driving forces acts on driving rollers.  
Therefore, the matrix form of Equation (20) is  
\[M\ddot{X}+C\dot{X}+KX=F(t) \tag{21}\]  
where \(M\) is a matrix of mass, \(C\) is a matrix of damping factor, \(K\) is a matrix of spring factor, \(F(t)\) is a vector of external forces, \(X\) is a vector of element displacement, \(\dot{X}\) is a vector of element velocity, and \(\ddot{X}\) is a vector of element acceleration.
## 3 Discussion
To this end, we have started to measure the impact of \(gnsA\) overexpression on fatty acid and phospholipid content in various _lapC_ mutant bacteria. It is also intriguing why the central cofactor acyl carrier protein (AcpP) is limiting in _lapC190_ bacteria. AcpP is the fourth most abundant protein in _E. coli_, its excess is usually toxic in wild-type bacteria [65], and yet its mild induction quite effectively overcomes the various growth defects of _lapC_ mutant bacteria without increasing LpxC stability or restoring LPS amounts. AcpP is involved in not only LPS and phospholipid biosynthesis, but its interactome contains more than 35 proteins, some of which are unrelated to LPS and fatty acid synthesis [66]. Since the replacement of catalytically active site residues confers a toxic growth phenotype due to the accumulation of _apo_-ACP [67], we could not address if the catalytic activity of AcpP is required for the suppressing phenotype. However, AcpP overproduction does not enhance either LPS or LpxC amounts, and thus, its action could be either rebalancing fatty acid composition or via some another mechanism. Isolation of other multicopy suppressors, such as the _yfgM_ gene, can be rationalized, since its product has been found to be a substrate of FtsH protease [68]. Overexpression of the _yfgM_ gene could potentially titrate out FtsH, and this could lead to the stabilization of LpxC. The function of Ycej is currently unknown. However, the expression of the _ycej_ gene is induced by a variety of stress conditions such as heat shock, the induction of extracytoplasmic responsive CpxR regulon and when the Lol system (lipoprotein sorting) is inhibited [69]. Thus, Ycej may be involved in stress response management that could directly or indirectly modulate LpxC levels and hence more studies are required to address its function. In summary, in this study, we identified new proteins, such as the transcriptional activator MarA, factors that are required to maintain OM asymmetry (PldA, Mla system), and the essentiality of cardiolipins which participate in the regulation of LPS assembly summarized in the Figure 2. To date, MarA has not been shown to directly regulate LpxC stability, which we have now established here. Isolation of the _srrA_ gene as a multicopy suppressor is interesting because its overexpression stabilizes LpxC. We also identified essential amino acid residues that are required for LapD function and its partial overlap with LapC in regulating LpxC amounts.
## Results and Discussion  
### Botanical Origin Identification of Portuguese Honey Samples  
Melissopalynological analysis showed the presence of _Eucalyptus_ spp. (samples G1, G2, G4 and G7), _Castanea sativa_ Mill. (samples G5 and G6) and _Erica_ spp. (sample G8) pollen grains at a percentage of >45%. Therefore, these pollen grains were the dominant ones. The presence of secondary (16-45%), minor (3-15%) and identified (<3%) pollen was also confirmed (Table 1). Based on the dominant pollen grains percentage, the botanical origin of Portuguese honey samples was: Eucalyptus, Chestnut and Heather.  
The _Eucalyptus_ spp. pollen grains among honey samples analysed ranged between 73-82%. In a previous work dealing with eucalyptus Portuguese honeys produced in the Entre-Douro e Minho region [PERSON] et al. [17] reported a pollen grain percentage of _Eucalyptus_ spp. pollen grains ranging between 44-79%, with an average value of ca. 58%. [PERSON] et al. [18] in a study carried out on commercial honeys available in the Portuguese market reported that eucalyptus honey samples showed a pollen grain percentage of _Eucalyptus_ spp. between ca. 50-71%.  
In another study involving heather honey (_Erica_ spp.) from six districts in Portugal (Aveiro, Gaga, Porto, Viseu, Viana do Castelo, Vila real) [PERSON] et al. [29] reported that the number of _Erica_ spp. pollen grains per sample varied between 45% and 71%, with an average \(\pm\) standard deviation value equal to 56 \(\pm\) 9%. Heather honey from Tras-Os-Montes region in Portugal showed an average value of _Erica_ spp. pollen grains ca. 60 \(\pm\) 1% [19]. The contribution of _Castanea sativa_ Mill. pollen grains among _Erica_ spp. honeys was also reported by the authors, in agreement with the results of the present study (Table 1). At this point, it should be stressed that the most common _Erica_ species in the Iberian Peninsula are _E. arborea_, _E. australis_, _E. umbellata_ and others [30]. However, during the melissopalynological analysis these species were not identified. In that sense, the general term \"_Erica spp._\", involving other _Erica_ species [30], was used to characterize the heather honey sample. Therefore, the geographical origin of Portuguese honeys may have also a strong impact on the total contribution (% percentage) of specific pollen grains. Honeys were then grouped according to botanical and geographical origin for the physicochemical and bio-activity parameter analyses.
## 2 Numerical Model
### Mathematical Equations
#### 2.1.3 Integration of Rainfall Intensity  
The rainfall intensity can be computed through integration, as follows:  
\[\int_{\mathrm{X}_{\mathrm{i}-\frac{1}{2}}}^{\mathrm{X}_{\mathrm{i}+\frac{1}{2} }}\int_{\mathrm{r}}^{\mathrm{r}+1}\!\left(\frac{\partial\mathrm{h}}{\partial \mathrm{t}}\right)\mathrm{dxdt}=\int_{\mathrm{X}_{\mathrm{i}-\frac{1}{2}}}^{ \mathrm{X}_{\mathrm{i}+\frac{1}{2}}}\int_{\mathrm{r}}^{\mathrm{r}+1}\!\left( \mathrm{P}(\mathrm{h})-\mathrm{i}(\mathrm{t})\right)\mathrm{dxdt} \tag{19}\]  
The resolution of Equation (17) yields:  
\[\mathrm{h}_{\mathrm{i}}^{\mathrm{n}+\frac{1}{2}}=\mathrm{h}_{\mathrm{i}}^{ \mathrm{n}}+\frac{\mathrm{dt}}{2}\mathrm{P}_{\mathrm{i}}^{\mathrm{n}}-\left( \mathrm{I}\Big{(}\mathrm{t}^{\mathrm{n}+\frac{1}{2}}\Big{)}-\mathrm{I}( \mathrm{t}^{\mathrm{n}})\right) \tag{20}\]  
The Horton model appears to be the best for the management of infiltration but that model can face certain problems, due to the heterogeneity of the infiltration and rainfall parameters. Furthermore, the model's runoff is generated not only by rain that supplies water to a cell but also by flowing from another cell. The amount of water available for infiltration on a cell at time \(t^{\mathrm{n}}\) is given as:  
\[\mathrm{w}_{\mathrm{i}}^{\mathrm{n}}=\frac{2}{\mathrm{dt}}\mathrm{h}_{ \mathrm{i}}^{\mathrm{n}}+\mathrm{P}_{\mathrm{i}}^{\mathrm{n}} \tag{21}\]  
A comparison should then be made between the infiltration and the water available in each cell. Now we have:  
\[\left\{\begin{aligned} &\mathrm{I}_{\mathrm{i}}^{\mathrm{n}+\frac{1}{2}}= \mathrm{I}_{\mathrm{i}}^{\mathrm{n}}+\frac{\mathrm{dt}}{2}\mathrm{F}_{\mathrm{ i}}^{\mathrm{n}}+\mathrm{h}_{\mathrm{i}}^{\mathrm{n}}&&\text{if} \quad W\leq i(t)\\ &\mathrm{I}_{\mathrm{i}}^{\mathrm{n}+\frac{1}{2}}-\mathrm{I}_{\mathrm{ i}}^{\mathrm{n}}=\mathrm{i}\mathrm{t}\mathrm{t}^{\mathrm{n}}+\frac{\left(\mathrm{i} _{0}-\mathrm{i}\right)\left(1-\mathrm{e}^{-\mathrm{r}t^{\mathrm{n}}}\right)}{ \mathrm{r}}&&\text{if}\quad W\geq i(t)\end{aligned}\right. \tag{22}\]  
In the case where \(\mathrm{W}\leq\mathrm{i}(\mathrm{t})\), then water height is assumed to be constant, namely, \(\mathrm{h}_{\mathrm{i}}^{\mathrm{n}+\frac{1}{2}}=0\), but if \(\mathrm{W}\geq\mathrm{i}(\mathrm{t})\), the water height should be considered as:  
\[\mathrm{h}_{\mathrm{i}}^{\mathrm{n}+\frac{1}{2}}=\mathrm{h}_{\mathrm{i}}^{ \mathrm{n}}+\frac{\mathrm{dt}}{2}\mathrm{P}_{\mathrm{i}}^{\mathrm{n}}-\left( \mathrm{i}\mathrm{t}^{\mathrm{n}}+\frac{\left(\mathrm{i}_{0}-\mathrm{i}_{t} \right)\left(1-\mathrm{e}^{-\mathrm{r}t^{\mathrm{n}}}\right)}{\mathrm{r}} \right). \tag{23}\]
## 4 Secondary-Stress Simulation  
### Principle of Virtual-Tracking-Element Technology  
The ABAQUS 2020 software can be used to perform the removal (deactivated) and activation (reactivated) of elements. During the simulation of the reinforcement installation, the position of the segmental joint changes after the initial load is applied. However, in ABAQUS, the activation of the corresponding element is activated at the original position, resulting in the activated reinforcement element partially overlapping with the deformed joint, which is inconsistent with reality.  
In order to simulate realistic reinforcement under secondary stress, the activated element needs to appear in the deformed position without strain, so as to bear the load normally after reinforcement. The original element (abbreviated as OE), which is an element of SSCP and the chemical anchor in this model, needs to be copied as an element with a tracking function (called virtual tracking element, abbreviated as VE) at the same location, i.e., VE is actually a backup of OE, with exactly the same nodes. Moreover, VE is required to have no influence on the original structure; thus, the position of VE can be obtained along with the deformation of the structure. When OE is activated, its elements are generated at the deformed position as they have the same nodes as VE. Subsequently, OE shares the load with the deformed structure as the loading continues. Considering the principle above, the requirements for VE are summarized as follows:  
1. The VE should have the same shape and contact relationship as OE and share all nodes, but have a different element number;
2. The stiffness of VE should be extremely low, so that its influence on the stress of the original structure can be ignored;
3. The mass of VE should be especially small to prevent potential displacement caused by the weight.
## 7 Future Directions  
False alarm handler, Dynamic Response Metrics, and Online Risk Assessment are important issues from an IRS design point of view. This survey shows that the currently available IRSs are unable to handle false alarms and also fail to implement an instant response, according to the attack statistics. These three parameters (False alarm handler, Dynamic Response Metrics, and Online Risk Assessment) must be included in the design of IRSs for the selection of appropriate response. There is still a needfor research on IRS to take these three parameters into consideration. Thus in future work an IRS with a false alarm handler should be included to handle the false alarms generated by IDSs. Most of the existing approaches are based on a static approach, where intrusions are mostly isolated during the response selection process. This may cause a reduction in system performance. Therefore, in future work IRSs will be integrated with a dynamic response approach to increase the network's system performance. Furthermore, with the use of online risk assessment future IRS design should be more effective at addressing this issue. However, lots of research needs to be carried out in the area of online risk assessment during the design of automated response systems.
# Measuring the Positions of the Solder Pins of Electrical Connectors from the Side
## 2 Materials and Methods  
### Machine Vision Systems  
The machine vision setup in this research is shown in Figure 1, including an industrial camera, a bi-telecentric lens, an LED ring light, a diffuser, and a PC (not shown). The software for image measurement was developed in the LabVIEW [21] environment provided by National Instruments (Austin, TX, USA). A Basler (Ahrensburg, Germany) industrial camera was used in this research. The model number was Aca2500-14 gm [22], and its sensor type was CMOS with a size of 5.7(H) \(\times\) 4.28(V) mm. The image's length and width are 2592 \(\times\) 1944 pixels, and the resolution is 5 million pixels. The unit pixel is converted to the actual length and width of 2.2 \(\times\) 2.2 \(\upmu\)m. The video communication interface is GigE. The bi-telecentric lens is from ES Technology (Taoyuan, Taiwan). Its model number is ESCA230-66 [23], with a working distance of 166 \(\pm\) 3 mm. A bi-telecentric lens is used in this research because the size of the solder pins on the capture images is not affected by the distance from the pins to the camera.  
A ring light source consisting of 96 LEDs was used as a light source. A diffuser is placed in front of the light source to create a back-diffused light to evenly distribute the light on the entire image. The image captured by the camera will be transmitted to a PC for subsequent image processing. The PC contained an Intel core 2 Duo E7500 dual-core central processing unit (CPU), 4096 MB dynamic memory (DDR SDRAM), and an NVIDIA VGA standard graphics card.  
In this study, a 2D vision measuring machine (2D VMM) developed by Resson (Taiwan) was used to verify the measurement results of the proposed method. Its model number is EVM-2515 [24]. The resolution of the linear scale of the machine is 1 \(\upmu\)m, and the accuracy is 4 + \(L\)/50 \(\upmu\)m, where \(L\) is the measured length (mm). The 2D VMM will be mentioned several times in the following because the measurement results of this machine were used as a reference for the proposed machine vision system.
## 3 Results and Discussion
### Metabolic Profile Changes during Coffee Fermentation  
3.3.1. Major Metabolite Changes  
Figure 4 presents the concentration changes of various metabolites over time during coffee fermentation. There was a gradual increase in glucose concentration (from 0.85 \(\pm\) 0.14 to 7.87 \(\pm\) 0.01 g/L) and fructose (from 0.17 \(\pm\) 0.03 to 9.47 \(\pm\) 0.02 g/L) within the initial 20 h of fermentation, attributed to the hydrolysis of complex polysaccharides present in the coffee pulp [4; 35]. Following this period, a slight reduction in sugar content was observed, resulting in high levels of glucose (7.71 \(\pm\) 0.10 g/L) and fructose (9.08 \(\pm\) 0.01 g/L). This indicated incomplete fermentation, a phenomenon previously observed in spontaneous coffee fermentations with short-duration cycles [36; 37], which could result in a lack of depth and complexity in the flavor profile of the coffee. Additionally, there may be a risk of over-fermentation as other opportunistic microbes continue to metabolize the sugars, leading to undesirable flavors and spoilage.  
Sugar consumption was directed into ethanol as the primary end metabolite. This compound exhibited a notable increase (\(p\) < 0.05) over 24 h, beginning at 3.46 \(\pm\) 0.84 and peaking at 12.03 \(\pm\) 0.73 g/L. Lactic and acetic acids were identified after 12 hours of fermentation, with peak production (2.14 \(\pm\) 0.68 g/L and 1.13 \(\pm\) 0.43 g/L, respectively) achieved by the 17 th hour. Ethanol and lactic acid, found in concentrations similar to those observed in prior coffee fermentations, aid in removing the coffee pulp and inhibit the growth of microorganisms that produce off-flavors [5; 26; 38]. Regarding coffee quality, ethanol acts as a byproduct that contributes to esters and other aromatic compounds' formation, essential for the coffee's flavor complexity and depth. Lactic acid, in turn, provides a milder and more pleasant profile than other acids [8; 9]. Conversely, the role of acetic acid in fermentation remains uncertain; however, concentrations above 1 g/L may detrimentally impact the final product's quality [5; 26]. Finally, the concentration of citric acid remained relatively stable, starting at 0.88 \(\pm\) 0.06 g/L, and ending with values around 0.45 \(\pm\) 0.08 g/L. The stability of this acid during coffee fermentation may be associated with the low presence of yeasts that use this compound as a carbon source [2; 39].  
Figure 4: Course of sugar consumption (**A**) and metabolite formation (**B**) during coffee fermentation in the subtropical zone.
## 3 Methodology
### Evaluation Metrics  
We conduct both quantitative and qualitative assessments of link prediction and diversity generation. Link prediction is essentially a binary classification task that determines whether there is a door link or a wall link between two nodes. Therefore, we use accuracy (ACC) and average precision (AP) to quantitatively evaluate the differences between the link prediction results and the ground truth. Additionally, considering the need for user editability in practical applications, we employ graph edit distance (GED) to qualitatively assess the editing complexity of the predicted results. In terms of diversity generation, we conduct experiments, including the presentation of generated results and diversity quantitative evaluation based on GED.  
The ACC metric is applied to measure the proportion of correct prediction links among the total number of labeled links. ACC assesses the general predictive performance of the model but does not reflect the model's predictive performance for a particular category. It can be calculated by the following formula:  
\[ACC=\frac{TP+TN}{TP+FN+TN+FP} \tag{17}\]  
where true positive (TP) is the number of samples correctly identified as positive class, true negative (TN) is the number of samples correctly identified as negative class, false positive (FP) is the number of samples incorrectly identified as positive class, and false negative (FN) is the number of samples incorrectly identified as negative class.  
The AP metric is used to estimate the classification performance of the model when the proportion of positive and negative samples is not balanced. AP measures the quality of the ranked list of predicted positive samples by computing the area under the precision-recallcurve. Precision is the ratio of true positive samples to the total number of predicted positive samples. The precision can be expressed as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{18}\]  
\[AP=\sum_{k=1}^{n}P(k)\Delta R_{k} \tag{19}\]  
where \(P(k)\) is the precision at the \(k\)-th position in the ranked list, and \(\Delta R_{k}\) is the change in recall between the \(k-1\)-th and \(k\)-th predictions for positive samples.  
The GED metric is employed to measure the minimum number of edit steps required to complete the transformation from the bubble diagram and another bubble diagram [35]. The nodes are deterministic; only edge editing operations such as insertion, deletion, and substitution are required. GED is defined as follows:  
\[GED(\mathcal{G}_{1},\mathcal{G}_{2})=\min_{(e_{1},\ldots,e_{k})\in\mathcal{P} (\mathcal{G}_{1},\mathcal{G}_{2})}\sum_{k=1}^{k}c(e_{i}) \tag{20}\]  
where \(e_{i}\) denotes graph edit operation, and \(\mathcal{P}(\mathcal{G}_{1},\mathcal{G}_{2})\) represents the set of edit paths from \(\mathcal{G}_{1}\) to \(\mathcal{G}_{2}\).