esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Production of Recombinant dsRNA in Microorganisms
### Escherichia coli
Thus, a new platform for recombinant RNA production using the novel RNA scaffold as a carrier for the accommodation of multiple small RNAs of interest, such as miRNAs, siRNAs, and RNA aptamers, has been constructed. Viroids are infectious agents of higher plants that exclusively consist of relatively small non-protein-coding, naked, circular RNA molecules. [PERSON] et al. [53] found that co-expression of _Eggplant latent viroid_ (ELvd)-derived RNAs with eggplant tRNA ligase (an enzyme involved in viroid RNA-circularization in viroid-infected plants) in _E. coli_ resulted in monomeric cyclization of the majority of the RNAs produced and significant accumulation of the product in the host cells. Then, a heterologous desired RNA segment, several dozens of nucleotides long, was integrated into a specific site of the viroid-scaffold (named ELvd-scaffold), and when the chimeric RNA construct was expressed in the same way, the cyclized chimeric RNA accumulated to several tens of milligrams per liter of culture of the _E. coli_ cells. The authors aimed to apply this vivoid-based expression system to produce the large amounts of dsRNA required to combat the pest Western corn rootworm (WCR, _Diabrotica virigifera_), using RNAi [54]. The smooth septate junction 1 (_DvSSJ1_) gene, which is essential for the growth of WCR, was selected as the target for RNAi action. When constructing the transcription system for the ELvd-scaffold with 83-nt inverted repeats homologous to the _DvSSJ1_ gene, the group-I autocatalytic intron was inserted as a spacer to avoid structural instability of the expression plasmid. When the ELvd chimeras were co-expressed with eggplant tRNA ligase in an RNase III-deficient _E. coli_ strain, it was observed that the inserted introns were self-spliced from the nascent biosynthesized transcript, and the chimeric ELvd containing the target RNA segment was intramolecularly cyclized and accumulated in large amounts in the cells. Then, in larval-feeding bioassay, it was demonstrated that the circular RNAs containing a dsRNA moiety (hPRNA) homologous to the _DvSSJ1_ gene exhibited excellent insecticidal activity against WCR. Thus, an intron-assisted, vivoid-based production system for circular dsRNA in _E. coli_ was developed.
# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 4 What Are the Existing Deep Learning Techniques Used in Short-Text Semantic Similarity (STSS)?
### Transformer Learning Models
Traditional methods, such as statistical and graph-based methods, extract the words from sentences based on their frequencies and co-occurrences. To complete this section, we focus on words in the context of sentences. Contextual embeddings are used to extract words and phrases, and a number of techniques, including BERT (Bidirectional Encoder Representations from Transformers), ELMO (Embeddings from Language Model), and GPT-2 (Generative pre-trained transformer-2) are frequently employed [59, 60, 61, 62].
# Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer
## 3 Experiments
### Ablation Experiments
#### 3.3.3 Different Input Sizes  
We repeated the experiments on the UAVid test set using different input sizes (square and rectangular shapes) to evaluate the model's stability. As shown in Table 6, the scores of the network did not vary by more than 0.8% when the input size changed, which proves the robustness of our method. Meanwhile, the highest scores were obtained when the input image size was 1024 \(\times\) 1024. In addition, the square input performed better than rectangular inputs, and the network accuracy decreased when the input size was too large (2048 \(\times\) 2048).
## 2 Overview of Whirlpool Hash Function  
### Principle of Whirlpool  
Whirlpool was a hash function proposed by [PERSON] and [PERSON] in 2000. It is one of only two hash functions endorsed by NESSIE (New European Schemes for Signature, Integrity, and Encryption), and has been adopted by ISO (International Organization for Standardization) and IEC(International Electrotechnical Commission) as ISO/IEC 10118-3 specification [22]. In contrast to traditional hash functions, Whirlpool makes use of a block cipher structure similar to AES, its main calculation procedure is as follows [23,24].  
(1) Append padding bits. The message is padded so that its length is congruent to 256 modulo 512, that is to say, the length of padded message is some times of 512 minus 256, and the reserved 256 bits will be used in step 2. The pattern of padding is the same, it consists of a single 1-bit followed by the necessary of 0-bits.  
(2) Append length. The padded message is appended by a block of 256 bits representing the length of message before the padding. After that the message m can be divided into t 512-bit blocks m1,..., m1, each block can be treated as an 8 \(\times\) 8 byte array called hash state.  
(3) Substitute bytes. This is a non-linear byte substitution, each byte of a state is substituted individually according to a table called S-box.  
(4) Shift columns. The shift column transformation is a simple cyclical permutation performed against a state column by column except column 0. For column i, it is shifted downwards by i \(-\) 1 bytes.  
(5) Mix rows. In this step, each row of a state array is treated as a polynomial a(x) over Galois field GF(2\({}^{8}\)), a(x) is multiplied by a fixed polynomial c(x) modulo x\({}^{8}\) + x\({}^{4}\) + x\({}^{3}\) + x\({}^{2}\) + 1. The polynomial c(x) is expressed in the following formula in the designing of Whirlpool (the factors are expressed in hexadecimal numbers).  
\[\text{c(x)}={}^{\prime}09{}^{\prime}\text{x}^{7}+{}^{\prime}02{}^{\prime}\text{ x}^{6}+{}^{\prime}05{}^{\prime}\text{x}^{5}+{}^{\prime}08{}^{\prime}\text{x}^{4}+{}^{ \prime}01{}^{\prime}\text{x}^{3}+{}^{\prime}04{}^{\prime}\text{x}^{2}+{}^{ \prime}01{}^{\prime}\text{x}+{}^{\prime}01{}^{\prime} \tag{1}\]  
(6) Add round key. This step is a simple bitwise XOR calculation between the state and round key. The round key is derived from a seed key and the length of round key is equal to the block size. The key schedule is same as the round function.  
There should be iteration for 10 times to obtain the hash value. The procedure of Whirlpool is illustrated in Figure 1.  
Figure 1: Procedure of Whirlpool.
## 5 Data Description and Preprocessing  
### Data Description  
In this research, we utilize a real-life traffic dataset, which we directly obtained from the Florida Department of Transportation District 4 Regional Transportation Management Center (FDOT-D4 RTMC). It is commonly referred to as SunGuide traffic data. While it is not publicly available, it can be requested from FDOT-D4 RTMC. Over 400 vehicle detection systems were used to collect the traffic data from three main highways of Florida (I-95, I-75, and I-995). The detectors are located on every half a mile on the three highways in both directions. The highway traffic flow dataset covers 8 years of traffic's records starting from 2008 until November of 2015. It provides real-time traffic flow information including, occupancy, average speed, total volume, average delay, and other valuable information for each lane of the freeways. The FDOT-D4 collected the traffic data based on a 15-min time interval, which gave us more opportunity for subsequent data processing. Table 1 illustrates the important parameters and their descriptions of road traffic records. In this study, we focused on traffic flow data obtained from 16 detectors that are located on I-95 highway for 8 years starting from 2008. The total number of traffic flow observations of all detectors is 18,812,799 million records.
# Robust Image Watermarking in Spatial Domain Utilizing Features Equivalent to SVD Transform
## 4 Experimental Results Discussion
### Sensitivity Analysis of Quantization Factor \(Q\)  
The watermark imperceptibility and robustness are two characteristics of a watermarking algorithm that are mutually exclusive and connected. In the proposed watermarking scheme, these properties are balanced using quantization factor 'Q'. A smaller value of Q will implant less information into the host image, resulting in a small decrease in the visual quality; however, this information may be easily deleted using basic modification techniques. Although a greater value of Q will implant more information into the host image, resulting in a significant decrease in the visual quality, this information cannot be simply removed using basic modification techniques. Thus, a lower Q favors imperceptibility, and a higher value favors robustness. Finding the appropriate value for this parameter is a very important and challenging task. A sensitivity analysis of Q is performed using several images and watermarks to resolve the trade-off between the robustness and imperceptibility of the proposed watermarking approach. The NCC and SSIM values are computed to represent robustness and imperceptibility, respectively. On the basis of these assessment metrics, the value of Q of the proposed scheme is nearly the same for each image, with minor variations. Some results are given in Figure 6, where a watermark (\(W\)I) was embedded into the host _image_1 and _image_1 by taking different quantization factor 'Q', and the associated _NCC_ and _SSIM_ values were calculated. In the graph of these NCC and _SSIM_ values, it can be seen that Q = 20 was a suitable choice, which was considered in further calculations.  
Figure 6: The _NCC_ and _SSIM_ values obtained by the proposed scheme by taking different values of quantization factor Q.
## 2 Structure of PV Panel and Causes of Power Loss
### Power-Loss Factors of PV Panels
#### 2.2.1 Module Mismatch Losses and Application of Inverters  
The required output of PV panels is generated by arranging them into strings, i.e., connecting several panels in series or parallel (arrays), rather than using the panels individually. During operation, shading effects due to obstruction factors such as clouds or fallen leaves may cause mismatch losses, which could lead to a decrease in the power output. Several methods to decrease the module mismatch losses have been developed, including the use of built-in module inverters, string inverters, and module built-in DC-DC converters [17; 18; 19; 20; 21]. However, these methods require a separate circuit for compensating for the voltage loss due to shadowed strings. For the implementation of these methods, the cost of the additional modules to be installed, and the power losses due to the presence of these modules should be analyzed.
# Optimised Tuning of a PID-Based Flight Controller for a Medium-Scale Rotorcraft
## 2 System Overview and Mathematical Modelling
### Rolling and Pitching Moments  
The external moments acting about the rotorcraft CG are summarised as follows:  
\[L =L_{M}+Y_{M}l_{M}+Z_{M}y_{M}+Y_{T}l_{T}+Y_{V}h_{V}+L_{F} \tag{17}\] \[M =M_{M}-X_{M}h_{M}+Z_{M}l_{M}+M_{T}-Z_{H}l_{H}-X_{V}h_{V}+M_{F}\] (18) \[N =N_{M}-Y_{M}l_{M}-Y_{T}l_{T}-Y_{V}l_{V}+N_{F} \tag{19}\]  
where the subscripts M, T, H, V and \(F\) are as described above. The main rotor torque is a result of the blade stiffness at the root. The equation for the main rotor torque is [28]:  
\[C_{Q_{M}}=\frac{Q_{M}}{\rho(R\Omega)^{2}\pi R^{3}} \tag{20}\]  
In a similar procedure used for main rotor thrust, the main rotor torque coefficient \(C_{Q_{M}}\) is found to be:  
\[C_{Q_{M}}=\left[C_{T}(\lambda_{0}-\mu_{z})+\frac{C_{D_{0}\sigma}}{8}\left(1+ \frac{7}{3}\mu^{2}\right)\right] \tag{21}\]  
where \(C_{D_{0}}\) is the drag zero-lift coefficient. The total moments generated by the main rotor and the tail rotor are described as follows [29]:  
\[L_{M} =\left(\frac{\partial L_{M}}{\partial b}\right)b_{ls}-Q_{M}\sin a _{lc} \tag{22}\] \[M_{M} =\left(\frac{\partial M_{M}}{\partial a}\right)a_{lc}-Q_{M}\sin, \ M_{T}=-Q_{T}b_{ls}\] (23) \[N_{M} =-Q_{M}\cos a_{lc}\cos b_{ls} \tag{24}\]  
\(M_{T}\) is the tail rotor contribution to the pitching moment.
# Antibiotic Resistance in Seawater Samples from East Coast of Spain
## 3 Results
### Antibiotic-Resistant Gene Determination in Water Samples and Bacterial Isolates
In South Valencia, besides the _bla_TEM and _suI_ genes (\(\chi^{2}=10.194\), \(p=0.0014\) and \(\chi^{2}=29.020\), \(p=0.000\), respectively), significant differences were found in the _tetW_ gene, which was not detected in any of the seawater samples (\(\chi^{2}=18.106\), \(p=0.0000\)). In Alicante, significant differences were found in the _bla_TEM and _suI_ genes, which were detected to a greater extent in the isolates (\(\chi^{2}=8.160\), \(p=0.0043\) and \(\chi^{2}=15.120\), \(p=0.0001\), respectively). When comparing the presence of ARGs in the enterococci and water samples (Figure 3), the _bla_TEM and _qnr_S genes were detected at significantly higher levels in the seawater samples than in enterococci isolates from South Valencia (\(\chi^{2}=6.310\), \(p=0.0120\) and \(\chi^{2}=4.582\), \(p=0.0323\), respectively). In addition to the _qnr_S gene (\(\chi^{2}=5.553\), \(p=0.0184\)), significant differences were found in the _suI_ gene in the Alicante area (\(\chi^{2}=4.846\), \(p=0.0277\)), only detected in the water samples. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Zone**} & \multirow{2}{*}{**No. Isolates**} & \multicolumn{6}{c}{**Number of Positive Isolates**} \\ \cline{3-6}  & & _bla_TEM & _qnr_S & _emn_B & _suI_I & _tetW_ \\ \hline NV & 9 & 3 & 1 & 1 & 0 & 0 \\ SV & 12 & 1 & 0 & 1 & 0 & 0 \\ A & 9 & 8 & 0 & 0 & 0 & 1 \\ \hline Total (\%) & 30 & 12 (40.0) & 1 (3.3) & 2 (6.6) & 0 & 1 (3.3) \\ \hline \hline \end{tabular} NV: North Valencia; SV: South Valencia; A: Alicante.  
\end{table} 
Table 6: Presence of antibiotic resistance genes in _Enterococcus_ sp. isolates according to sampling zone. Figure 2: Comparison of ARG occurrence among the _E. coli_ isolates and water from the different locations (NV: North Valencia; SV: South Valencia; A: Alicante). *: significant differences (\(p<0.05\)) between gene occurrence in water samples and in the _E. coli_ isolates.
## Notation  
### Symbols  
**Description**  
\(C_{i,t}\): The capacity expansion  
\(C_{i}^{existing}\): Existing capacities of the project  
\(C_{i}^{max}\): Maximum capacities of the project  
\(I_{i,t}\): The amount invested in the project \(i\) at the time stage \(t\).  
\(O_{i,t,s}\): The operational variables  
\(P_{tech}\): Collection of projects associated with a particular technology  
\(R_{i,t,s}\): The expected return on investment  
\(R_{free}\): Risk-free rate  
\(x_{i}\): Investment decision variable for the project  
\(\delta_{i}\): The minimum operational capacity fraction for the project  
\(\eta_{s}\): Risk-aversion parameter  
\(\pi_{s}\): The probability of the scenario  
\(p\): The set of renewable projects  
\(S\): The set of scenarios  
\(T\): The set of time stages  
\(i\): Project \(i\)  
\(t\): Time stage  
\(\alpha\): The confidence level  
\(\theta\): The Value at Risk at the Confidence Level  
\(\lambda\): The risk aversion parameter  
\(\sigma_{p}\): Standard deviation of the portfolio return
## 1 Introduction
### Key Definitions
#### 1.3.2 Information Systems  
Information systems play a pivotal role in interpersonal and inter-organizational interactions within the contemporary world. They are sociotechnical, formal, and organizational systems designed to gather, process, store, and distribute information on a large scale [12]. They comprise four basic components: technology, structure, people, and tasks. They entail an integrated collection of components that allow for the gathering storage, and processing of data that is consequently used to contribute to bodies of knowledge and provide information within digital circles [1]. A computer information system is used to collect and interpret information in different contexts [18]. Information systems support operations, management, and decision making by using computer systems. Cybersecurity systems must be integrated into any information system to ensure that their general integrity and functionality is safeguarded.
## 2 Methods
### Rejection of Unreliable Result  
The uncertainty assessment metric introduced in Section 2.2 ranges from 0.0 to 1.0 and indicates a high value when the model predicts that the estimation result might be unreliable. Consequently, this uncertainty can effectively serve as a confidence score, enabling the model to autonomously determine the reliability of its outcomes. With this score, the model possesses the capability to autonomously reject results it deems poor, enhancing its robustness and reliability.  
Additionally, the confidence score can serve as valuable information for users. If the model displays this score, it provides users with a quantifiable measure of reliability, allowing them to make informed decisions about whether to rely on the predicted data. For instance, in the context of nutritional assessments, users can decide whether a particular food intake is advisable based on the estimated nutritional content and its associated confidence score. This added transparency can significantly improve user trust and satisfaction, as they are better informed about the certainty of the model's predictions.  
Overall, the integration of an uncertainty-based confidence score not only improves the model's decision-making process but also enhances user interaction by providing a clear and interpretable measure of prediction reliability. This dual benefit underscores the importance of effectively managing uncertainty in predictive modeling, particularly in applications requiring high precision and user trust.  
Figure 2: Boxplot comparison of uncertainty (U) between the best 30% and worst 30% of cases in predictions. The worst group shows greater uncertainty, with a wider interquartile range and several outliers, marked by red crosses, highlighting the group’s variability.
# Regional Seismic Damage Simulation of Corroded RC Frame Structures: A Case Study of Shenzhen City
## 6 Case Studies of Shenzhen City
### Simulation Procedure of an Individual Building in Shenzhen City  
To illustrate the proposed method, the simulation procedure of an individual building is presented in this section. The studied RC frame building has six stories and was built in 1985, which is located at the southeast coast of Shenzhen city. The dimension of its floor plan is 50 m long and 15 m wide. The detailed simulation procedure of the building using the MDOF model is as follows:  
1. The natural period of the building can be calculated according to the number of stories together with the empirical equation of building natural period [18]. The interstory shear stiffness of the MDOF model can be determined based on the natural period of the building and the mass of each story [18].
2. The equivalent lateral force analysis is used to determine the interstory shear carrying capacity of the MDOF model [18] based on the corresponding seismic design code for buildings in 1985.
3. The proposed method in Section 3 is adopted to compute the degree of corrosion of peripheral components, given the year of built and the distance from coastline of the studied building.
4. The layout of columns is determined according to the dimension of floor plan by assuming a 6 m column spacing. The PCC can be obtained by calculating the ratio of peripheral column number and total column number (Section 5.2).
5. The interstory backbone curve parameters of the building in uncorroded state determined in the first and second steps are reduced according to Equations (16)-(19), given the degree of corrosion and PCC obtained in Steps (3) and (4).  
Figure 14: Earthquake data: (**a**) Ground motion attenuation function (M6.5); (**b**) Acceleration/velocity time histories.  
Figure 15: Distributions of ground motion intensity parameter Sa_0.3 s (m-s\({}^{-2}\)): (**a**) M6.5; (**b**) M7.5.  
Finally, nonlinear time history analysis of the established MDOF model is performed and the calculated engineering demand parameters are used to determine the damage state of the building.  
The above procedure is realized through FORTRAN programming. Therefore, the nonlinear time history analyses of a large number of corroded buildings in an urban area can be conducted automatically.
## 4 Calculation Results
### Comparison between IHAM and HAM  
The 30 th-order approximation of \(\theta_{b}\) with \(n=3\), \(\hbar=-0.5\) by HAM [44] can be calculated as:  
\[\begin{array}{l}\theta_{b}=0.5\alpha-4.3435\times 10^{-2}\alpha^{3}+6.8359 \times 10^{-3}\alpha^{5}-1.0902\times 10^{-3}\alpha^{7}\\ +1.4940\times 10^{-4}\alpha^{9}-1.6286\times 10^{-5}\alpha^{11}+1.3403\times 10^{-6 }\alpha^{13}\\ -7.9070\times 10^{-8}\alpha^{15}+3.1288\times 10^{-9}\alpha^{17}-7.5167\times 10^{- 11}\alpha^{19}\\ +9.2764\times 10^{-13}\alpha^{21}-4.2676\times 10^{-15}\alpha^{23}+3.4351\times 10^{- 18}\alpha^{25}\\ -4.9207\times 10^{-23}\alpha^{27}+1.4045\times 10^{-32}\alpha^{29}\end{array} \tag{90}\]  
When the relative error \(\Delta\leq 1\%\), the convergent regions of the 30 th-order approximation by HAM [44] are:  
\[\alpha\in[0,4.3],\ \Theta_{b}\in[0,0.74], \tag{91}\]  
The 30 th-order approximation of \(\theta_{b}\) with \(\varepsilon=8/\pi^{2}\), \(\hbar=-2.25\) by IHAM is  
\[\begin{array}{l}\theta_{b}=0.5\alpha-4.1947\times 10^{-2}\alpha^{3}+6.3195 \times 10^{-3}\alpha^{5}-1.0049\times 10^{-3}\\ +1.4774\times 10^{-4}\alpha^{9}-1.9106\times 10^{-5}\alpha^{11}+2.1234\times 10^{-6 }\alpha^{3}\\ -2.0002\times 10^{-7}\alpha^{5}+1.5807\times 10^{-8}\alpha^{77}-1.0388\times 10^{- 9}\alpha^{9}\\ +5.619\times 10^{-11}\alpha^{21}-2.5035\times 10^{-12}\alpha^{23}+9.0941\times 10 ^{-14}\alpha^{3}\\ -2.7016\times 10^{-15}\alpha^{27}+6.5971\times 10^{-17}\alpha^{2}-1.3356\times 10 ^{-18}\alpha^{31}\\ +2.2653\times 10^{-20}\alpha^{33}-3.2538\times 10^{-22}\alpha^{35}+3.9973 \times 10^{-24}\alpha^{37}\\ -4.2352\times 10^{-26}\alpha^{39}+3.8950\times 10^{-8}\alpha^{41}-3.1220\times 10 ^{-30}\alpha^{43}\\ +2.1842\times 10^{-22}\alpha^{45}-1.3318\times 10^{-34}\alpha^{47}+7.0399\times 1 0^{-37}\alpha^{49}\\ -3.1913\times 10^{-39}\alpha^{51}+1.2180\times 10^{-41}\alpha^{3}-3.7894\times 1 0^{-4}\alpha^{55}\\ +9.0212\times 10^{-47}\alpha^{5}-1.4440\times 10^{-98}\alpha^{59}+1.3901\times 1 0^{-53}\alpha^{61}\end{array} \tag{92}\]  
When the relative error \(\Delta\leq 1\%\), the convergent regions of the 30 th-order approximation by IHAM are:  
\[\alpha\in[0,1.3],\ \Theta_{b}\in[0,0.36], \tag{93}\]  
Figure 3: Rotation angle at free end vs. point force when \(\hbar\) takes different values.  
Table 4 compares the 30 th-order solutions by IHAM and HAM, from which it can be seen that the improved homotopy analysis method extends the convergence region from \(\alpha\in[0,1.3]\) by the homotopy analysis method to \(\mathbb{h}=-0.5\).