esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Biofilm Formation  
The 0.5 McFarland of _E. coli_ ATCC 14169, _E. coli_ ATCC 25923, _E. faecalis_ ATCC 19433, and _E. faecalis_ ATCC 29212 was used to examine the effect of copper salts on biofilm formation. The initial copper sulfate and copper nitrate concentration was 100 mg/mL. Two-fold dilutions of each salt were diluted in Mueller Hinton broth ([PERSON], Germany), added to a 96-well microtiter plate, and mixed with each bacterial strain. The samples were incubated at 37 \({}^{\circ}\)C for 24 h, after which the absorbance was read on the ELISA plate reader (EPOCH, Agilent Technologies, Inc., Santa Clara, CA, USA) at 595 nm. Then, 0.1% of crystal violet was used for staining, and distilled water was used for washing the 96-well plates; 96% ethanol was added to each plate, and the absorbance was read at 595 nm using the ELISA plate reader (EPOCH, Agilent Technologies, Inc., Santa Clara, CA, USA). The following formula (Equation (1)) was used to calculate the optical density cut-off value (ODc):  
\[\text{ODc}=\text{average OD of negative control + 3$\times$ standard deviation of negative control}. \tag{1}\]  
The biofilm categories were determined by comparing the average OD of each sample with the calculated ODc value: OD \(\leq\) ODc: non-adherent, ODc \(<\) OD \(\leq\) 2\(\times\) ODc: weakly adherent, 2\(\times\) ODc \(<\) OD \(\leq\) 4\(\times\) ODc: moderately adherent, and 4\(\times\) ODc \(<\) OD: strongly adherent [22].
# Extensions and Enhancements to \"the Secure Remote Update Protocol\"
## 8 Methods for Easy Human Comparison of UUID Values
### Pictographic Representation  
One way to do this might be to present the 128-bit value in the form of a human-readable pictograph consisting of a \(12\times 12\) grid of 144 monochromatic cells. Since this gives us 16 cells too many for a 128-bit value, we would need to use 16 additional 'bits' of fixed padding; for example, by placing a 2 \(\times\) 2 block in each corner. Such a pictograph would have an appearance something similar to that of a 2D barcode: although one that is designed for human recognition, rather than machine readability. Whilst a specific scheme for encoding such a UUID into a monochrome pictograph has not been proposed, an example of the appearance of such a pictograph is shown in Figure 4 to contrast with the other representational approaches discussed.  
Figure 3: An example of a 36-character string depicting a 128-bit binary value in standard hexadecimal notation.  
The value could also be presented in the form of a smaller \(8\times 8\) grid of cells, coloured with one of four colours. With careful selection of the colours, it is possible to produce a grid with a sufficient visual difference to be easily distinguished; and avoiding combinations that were indistinct for those with various forms of colour-blindness. Again, whilst a specific protocol for such encoding has not been devised as a part of this work, an example of one such method (where the content of each square in the pictograph is determined by the value of two-bits in the UUID) is shown in Figure 5, for comparison with the other approaches described.  
For a device with a graphical screen capable of rendering such a pictograph (or which has the necessary hardware to permit its connection to an external display for the purposes of validating the  
Figure 4: An example black and white, 12 \(\times\) 12 grid depicting a 128-bit binary value; in which the colour of each cell corresponds to the value of individual bits within the value.  
Figure 5: An example four-colour, 8 \(\times\) 8 grid depicting a 128-bit binary value; in which the colour of each cell corresponds to the the value of two-bits in the value.  
join operation), an image of one of these two types could be displayed by the device and also presented in the user-interface to the server (e.g., a web page) after the registration process had been initiated.  
For devices without a suitable screen, the device could utilize a BLE connection to a smart-phone or tablet running simple application software, which could be used to display the verification code pictogram. Although this is potentially less secure as it increases the attack surface, by adding an additional communications channel that could be targeted by an attacker, the short-range nature of BLE and the time sensitivity of the join operation process mean that in practice, this would be fairly low risk in a real-world application of such a system.
## 4 Parametric Analysis
### Soil Poisson's Ratio  
Figure 12 shows the relationships between the soil Poisson's ratio and the building response. Figure 12a shows that the decreases in the settlement and the differential settlement of the building are increasingly significant with a larger soil Poisson's ratio. A notable trend of decreasing rotation angle is observed by the increase in the soil Poisson's ratio (Figure 12b). The maximum and minimum rotation angles occur at the right and left ends of the building, respectively. The bending moments depend highly on and share progressively decreasing trend with the soil Poisson's ratio, as shown in Figure 12c. The effect of the soil Poisson's ratio on the bending moment is mainly reflected around the middle of the building, and is almost negligible near the two ends of the building. The maximum shear forces (including the maximum positive and negative shear forces) gradually decrease as the soil Poisson's ratio increases (Figure 12d). The positions of the maximum positive and negative shear forces are fixed at \(y=4.0\) m and \(y=16.0\) m, respectively, which are independent of the soil Poisson's ratio.  
Figure 11: Building response with different soil elastic moduli: (**a**) settlement; (**b**) rotation angle; (**c**) bending moment; (**d**) shear force.
## 5 Experimental Results
### Characteristics of the Proposed Approach
#### 5.1.2 Visualization of the First Front for \(n_{k}>2\)  
To analyze the solutions for larger numbers of knapsacks, when \(n_{k}>2\), we opt for a different type of visualization. Among all 30 independent runs of each algorithm, we choose a single run with the median hypervolume value, following the methodology from [36]. In Figures 6 and 7, we show the solutions from the first front of the last generation for 7 and 25 knapsacks, respectively. The similar visualization for \(n_{k}=2\) is shown in Appendix B. The latter supports the results presented in Figures 4 and 5 and serves for comparison purposes. The solutions are shown using parallel coordinates with the horizontal axis representing the index of the objective and the vertical axis showing the corresponding objective value. Starting from \(n_{k}=6\), the distributions of solutions for random selection and tournament selection have similar characteristics. This is why we show results only for random selection.  
Figure 6: Final Pareto frontier for \(n_{k}=7\) and random selection.  
We can notice some differences as compared to the results for \(n_{k}=2\). In particular, the tendency of tournament selection to prefer one objective more than another disappears for \(n_{k}\geq 6\). This is reflected in the absence of disproportionate values for one of the ob  
Figure 7: Final Pareto frontier for \(n_{k}=25\) and random selection.  
jectives in Figures 6 and 7 (compare with the corresponding results for \(n_{k}=2\) shown in Figures A1 and A2 from Appendix B). Furthermore, we can notice the absence of gaps between the solutions for _PO-prob_. This means that the related fronts are not fragmented, as it was in the case of two knapsacks and random selection, see Figure 4b,d. After examining the results for different numbers of knapsacks, we noticed that this pattern holds true for any number of knapsacks larger than \(2\), \(n_{k}>2\).  
Finally, we can see that the range of solutions for _NSGA-III_ and _PO-prob_ is tighter than that for other algorithms. This pattern becomes visible for \(n_{k}=5\) and holds true for larger values. In addition, the solutions for these algorithms tend to be situated in the middle and the top part of the objective values range: from 6000 to 9000 for \(n_{k}=7\) and from 5000 to 8000 for \(n_{k}=25\). This means that, when \(n_{k}\) becomes larger, _PO-prob_ produces less extreme solutions than _NSGA-II_. This demonstrates the advantage of the former algorithm in the case of many-objective optimization. _NSGA-II_ and _PO-count_ produce more extreme solutions than other algorithms. The characteristics of _PO-prob_* are in between those for _PO-prob_ and _NSGA-II_. This results naturally from the fact that _PO-prob*_ is a combination of these two algorithms.  
The results presented in Appendix C compliment the above study of the relative position of solutions in the space by analyzing the average distance to the diagonal of the corresponding hypercube. It supports the findings presented in Section 5.1.1 and the current one.
## 5 Conclusions  
To improve the accuracy of text sentiment analysis and address issues related to inaccurate extraction of key information and limited contextual associations in text, this paper proposes the Sac-BiLSTM algorithm, which effectively utilizes the characteristics of Chinese product review corpus. The Sac-BiLSTM algorithm leverages fine-grained character vectors to assist in extracting semantic information from word vectors, constructs a non-static dual-channel, and combines long short-term memory models with convolutional neural networks to extract features from text more effectively. Additionally, the proposed Sac-BiLSTM algorithm incorporates the Self-Attention mechanism to enhance the extraction of textual features and address issues related to inaccurate extraction of key information and limited contextual associations in the text. Experimental results demonstrate that the proposed Sac-BiLSTM algorithm achieved accuracy rates of 94.63%, 87.76%, and 97.75% on different datasets, which were significantly higher than those of the other six methods used for comparison.  
The Sac-BiLSTM algorithm proposed in this paper contributes to a more accurate understanding of user sentiments towards products, reduces business analysis costs for companies, increases user engagement, provides warnings about product quality, and offers assistance in improving product image and quality.  
**Author Contributions:** Conceptualization, W.W. and Y.Y.; methodology, W.W.; validation, G.W. and Z.Z. ([PERSON]); formal analysis, G.W.; data curation, Z.Z. ([PERSON]); writing--original draft preparation, W.W.; writing--review and editing, Z.Z. ([PERSON]); funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Youth Fund the National Natural Science Foundation of China (61002047).  
**Data Availability Statement:** The data set used is: [[https://github.com/SophonPlus/ChineseNlp](https://github.com/SophonPlus/ChineseNlp)]([https://github.com/SophonPlus/ChineseNlp](https://github.com/SophonPlus/ChineseNlp))  
Corpus/blob/master/datasets/weibo_senti_100k/intro.ipynb (accessed on 8 November 2023).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Automatic Segmentation of Histological Images of Mouse Brains
## 2 Materials and Methods
### Evaluation Metrics  
* The Dice coefficient, or Dice similarity coefficient [19], is a metric commonly used to evaluate the accuracy of segmentation results (Equation (1)). It measures the overlap between the predicted segmentation and the ground truth by calculating the ratio of twice the intersection of the two regions to the sum of their sizes. \[Dice\,coefficient=\frac{2*Intersection}{Prediction+GroundTruth}\] (1)
* The False Positive Rate (FPR) [20] is a metric that measures the proportion of incorrect positive predictions made by the model (Equation (2)). A lower False Positive Rate indicates better performance, as it indicates a lower rate of false alarms or incorrect positive predictions. \[FPR=\frac{FP}{FP+TN}\] (2) where FP = False Positive, TN = True Negative.
* The False Negative Rate (FNR). Ref. [20] measures the proportion of missed positive predictions by the model (Equation (3)). A lower False Negative Rate is desired as it signifies a lower rate of missed detections or incorrectly classified negatives, indicating better sensitivity and accuracy in capturing the target structure or region. \[FNR=\frac{FN}{FN+TP}\] (3) where TP = True Positive, FN = False Negative. Both FPR and FNR will be used to evaluate the response of the models at pixel level.
* The Hausdorff Distance (HD) [21] measures the dissimilarity between two sets of points or contours (Equation (4)). It quantifies the maximum distance between any point in one set to the closest point in the other set. \[HD(A,B)=max(max(d(a,B)),max(d(b,A)))\] (4) where: d(a, B) represents the minimum distance between a point a in set A and the closest point in set B. \[HD(A,B)=max(d(a,B)),max(d(b,A))\] (5) where: d(a, B) represents the minimum distance between a point b in set B and the closest point in set A.
# Cooperative Localization Using Distance Measurements for Mobile Nodes
## 3 Resolving Rotation and Flip Ambiguities
### Analysis of Rotation Ambiguity
\begin{cases}\sin(\theta)=\frac{a_{3}b_{2}-a_{2}b_{3}}{b_{3}c_{2}-b_{2}c_{3}} \triangleq W_{1}\\ \cos(\theta)=\frac{a_{2}c_{3}-a_{3}c_{2}}{b_{3}c_{2}-b_{2}c_{3}}\triangleq W_{ 2}\end{cases} . \tag{25}\]  
Obviously, given \(W_{1}\) and \(W_{2}\), (25) has a unique solution to \(\theta\) within \([-\pi,\pi)\). Similarly, it is straightforward to show that, when \(n\geq 3\), (24) has a unique solution within \([-\pi,\pi)\).
## 2 Numerical Model and Methods
### Numerical Strategy  
As shown in Figure 2, the injection direction of CH\({}_{4}\) and air keeps perpendicular in all structures and that is similar to the model of jets-in-corssflow. [PERSON] [28] and [PERSON] et al. [29] proposed that the Spalart-Allmaras model achieves comparable results to experimental data in their study. [PERSON] et al. [30] and [PERSON] et al. [31] showed that the steady solver could get favorable agreement with experimental data for jet-in-corssflow simulation. Besides, [PERSON] and [PERSON] [32] used the steady Reynolds-Averaged Navier Stokes (RANS) with Spalart-Allmaras model to investigate jet-in-corssflow model. Through integrated overall factors into account, based on ideal gas hypothesis, a steady RANS simulation with Spalart-Allmaras turbulence model is used in this study to explore the mixing characteristics of CH\({}_{4}\) and air in RDC by ANSYS Fluent. In this paper, a steady state solver based on density basis is used to solve the equation. The third order MUSCL (Monotone Upstream-Centered Schemes for Conservation Laws) reconfiguration scheme and AUSM (Advection Upstream Splitting Method) welcome style are used to discretize the convection term. To determine the accuracy of the model selected in this paper, the mixing process in. [23] is simulated using the model selected in this paper, and the simulation results are compared with those in [23]. The comparison results are presented in Figure 3. According to the data in Figure 3, it can be found that the simulation value is close to the data in the literature, and the maximum error is only 3%, which proves the accuracy of the model selected in this paper.
## 4 Discussion
### Potential of Model SIF from MODIS and Meteorological Data
\begin{table}
\begin{tabular}{l l l l} \hline \multicolumn{1}{c}{**Multi-regression**} & \multicolumn{1}{c}{S.E.} & \multicolumn{1}{c}{\(\mathbf{R}^{2}\)} & \multicolumn{1}{c}{\(\mathbf{P}\)} \\ \hline \(\text{SIF}_{145}=0.0042\times\text{Tem}+0.929\times\text{EVI}-0.329\) & 0.16 & 0.37 & \(2.39\times 10^{-12}\) \\ \(\text{SIF}_{161}=0.0056\times\text{Tem}+1.394\times\text{EVI}-0.473\) & 0.20 & 0.43 & \(1.06\times 10^{-8}\) \\ \(\text{SIF}_{177}=-0.0013\times\text{Tem}+1.988\times\text{EVI}+0.253\) & 0.17 & 0.50 & \(8.99\times 10^{-9}\) \\ \(\text{SIF}_{193}=0.0058\times\text{Tem}+2.029\times\text{EVI}-1.095\) & 0.22 & 0.53 & \(2.01\times 10^{-17}\) \\ \(\text{SIF}_{209}=0.0061\times\text{Tem}+2.037\times\text{EVI}-1.122\) & 0.16 & 0.52 & \(1.96\times 10^{-21}\) \\ \(\text{SIF}_{225}=0.0098\times\text{Tem}+1.863\times\text{EVI}-1.761\) & 0.15 & 0.55 & \(1.9\times 10^{-17}\) \\ \(\text{SIF}_{241}=0.0064\times\text{Tem}+1.760\times\text{EVI}-0.843\) & 0.15 & 0.44 & \(7.48\times 10^{-15}\) \\ \(\text{SIF}_{\text{all}}=0.0043\times\text{Tem}+1.764\times\text{EVI}-0.609\) & 0.13 & 0.71 & 0 \\ \hline \end{tabular}
\end{table} 
Table 1: Multi-regressions between SIF and EVI and daily mean temperature. Figure 12: Scatter plots between OCO-2 observed and EVI-simulated SIF in 2018. The red line indicates the regression line, and the orange line indicates the 1:1 line. Figure 13: Simulated 16-day SIF in 2018 based on EVI and temperature.
# Cartesian Constrained Stochastic Trajectory Optimization for Motion Planning
## 4 Results  
The new STOMP plugins (Cartesian Normal Distribution Sampling Generator and Cartesian Distance Cost Function) were verified and evaluated on a simple experimental setup, which consisted of one obstacle between the start and the goal states (Figure 6). Since that trajectory generation is randomized, the resulting trajectory between these two robot states could be different in each planning attempt. Therefore, path planning was triggered multiple times in each experiment and provided a variable dataset of resulting trajectories.  
The configuration of basic STOMP parameters was based on our experience and need to ensure that a collision-free trajectory would be found in each path-planning attempt. Therefore, the algorithm iterated up to 100 times, which represented enough iterations, and was stopped when the first valid solution was found. The important parameter was the number of rollouts. A value smaller than that used can result in no solution being found, while a higher value can slow down the iteration. Therefore, 15 rollouts were chosen as a suitable number. Each rollout consisted of 10 timesteps that were appropriate for the trajectory length and the environment complexity chosen for experiments presented in this  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline \(w_{t}\) & The position cost weight used in the cost function \\ \(w_{r}\) & The orientation cost weight used in the cost function \\ \(t_{t}\) & The maximal translation between waypoint of cartesian linear trajectory \\  & and rollout \\ \(t_{r}\) & The maximal angle between waypoint of cartesian linear trajectory \\  & and rollout \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of Cartesian Distance Cost Function.  
Figure 6: Experimental setup and examples of a resulting trajectory: (**a**) A trajectory computed by the basic STOMP which does not satisfy cartesian constraints requirements; (**b**) A trajectory computed by the cartesian constrained STOMP which demonstrates capability of planning under desired cartesian constraints.  
article. The same generation of trajectories was provided on the original STOMP using Normal Distribution Sampling Generator (a name Joint Normal Distribution Sampling Generator is used in this section) for comparison with cartesian -constrained STOMP. Figure 6 shows the difference between trajectories generated by the original and the cartesian-constrained STOMP, where Figure 6a is the trajectory from the original STOMP with Normal Distribution Sampling Generator and Figure 6b is the trajectory from the new Cartesian Normal Distribution Sampling Generator with Cartesian Distance Cost Function, which satisfies the cartesian constraints.
## 4 Classification of Routing Protocols
### Temperature-Aware Routing Protocols
#### 4.2.8 A New Energy-Efficient Routing Protocol (M-ATTEMPT)  
M-ATTEMPT [59] is an energy efficient and thermal aware routing protocol for WBSNs to reduce the nodes' temperature as well as to decrease the delay for the critical data using heterogeneous bio-medical sensor nodes. In the network architecture of this scheme, the sink node (base station) is placed at the center while nodes with high data rates are placed at less mobile places of the human body. For critical or query driven data packets, the sensor nodes increase their transmission power to forward the data packets directly to the sink node (single-hop) while for ordinary data packets multi-hop communication is used. The sensors having ordinary data packets cannot forward them until the sink received all the critical or query driven data packet. In case of multi-hop communication, a route with less hop-count is selected if two or more routes are available. If two or more next-hop neighbor nodes have the same hop-count then the neighbor node with less energy consumption to the sink is selected. To control the rise in temperature, M-ATTEMPT defines a threshold and if any node's temperature goes beyond that threshold, it breaks all the routes with the neighbor node. However, if the node's temperature reaches to the threshold after receiving a data packet, it resends that packet to previous node and the previous node marks it as a hotspot.  
The four different phases of M-ATTEMPT are: initialization phase, routing phase, scheduling phase, and data transmission phase. In initialization phase all nodes broadcast the hello packet, while in routing phase routes with less hop-counts are selected within the available routes based on aforementioned procedure. The sink node creates a Time Division Multiple Access (TDMA) schedule for all root nodes in the scheduling phase, while the root nodes send their data to the sink node during the data transmission phase.
# Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
## 3 Methodology  
The global architecture of our solution can be conceptually divided into three main stages: input, processing, and output, as depicted in Figure 1.  
The input consists of two sources. First, a live video stream is ingested through a serial digital interface (SDI) connection, which is a standard in professional video production environments. Second, a POI reference database is available, providing necessary information for the processing stage. The details of this POI database will be further explained in Section 3.1.  
The processing component comprises two main parts: a fully automatic POI recognition and tracking pipeline and a semi-automatic user interface. The automatic pipeline employs computer vision techniques to detect, recognize, and track POIs in the live video stream. Additionally, the semi-automatic user interface allows human operators to intervene and make adjustments when needed, ensuring flexibility and reliability. These processing components will be discussed in depth in Sections 3.2 and 3.3, respectively.  
In the output stage, the results generated by the processing component are transmitted to a visualization engine, such as Vizrt ([[https://www.vizrt.com](https://www.vizrt.com)]([https://www.vizrt.com](https://www.vizrt.com)), accessed on 22 July 2024), using the User Datagram Protocol (UDP). When both the processing and visualization servers are located within the same local network, communication between these is fast and reliable. The use of UDP allows for a decoupled architecture, where the hardware for processing and visualization can be separate.