esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \multicolumn{4}{c}{**Translation**} & \multicolumn{4}{c}{**Rotation**} \\ \cline{3-8}  & & **m.l.1\({}^{\text{1}}\)** & **c.c\({}^{\text{2}}\)** & **a.p.\({}^{\text{3}}\)** & **m.l.1\({}^{\text{1}}\)** & **c.c.\({}^{\text{2}}\)** & **a.p.\({}^{\text{3}}\)** \\ \hline \multirow{4}{*}{RSA-marker \({}^{\text{4}}\)} & Rater 1 & 0.93 & 0.90 & 0.75 & 0.79 & 0.63 & 0.92 \\  & Rater 2 & 0.91 & 0.83 & 0.76 & 0.86 & 0.50 & 0.90 \\  & Rater 3 & 0.90 & 0.88 & 0.68 & 0.83 & 0.47 & 0.87 \\  & Rater 1 & 0.83 & 0.84 & 0.62 & 0.64 & **0.30** & 0.82 \\ mbRSA-EGS & Rater 2 & 0.77 & 0.76 & 0.53 & 0.59 & **0.21** & 0.76 \\  & Rater 3 & 0.65 & 0.70 & **0.32** & 0.51 & **0.11** & 0.64 \\ \hline \end{tabular} \({}^{\text{1}}\) Medial-Lateral; \({}^{\text{2}}\) Cranial-Caudal; \({}^{\text{3}}\) Anterior-Posterior; \({}^{\text{4}}\) Marker-based RSA; “poorly” reliability (ICC \(<0.40\)) was marked in **bold**.  
\end{table} 
Table 2: Intra-rater reliability estimated by ICC. For the out-of-plane measurements, the largest intra-rater variability was found in the cranial-caudal rotation measurements (0.99-1.81 deg) (Table 3). This large intra-rater variability of mbRSA even leads to clear deviations of the mean cranial-caudal rotation results between raters when compared with marker-based RSA (Figure 4). Furthermore, rater 3 was found to have larger intra-rater variability (0.181 deg) on this rotation measurement compared with other two raters (0.99 and 1.27 deg) (Figure 4b).
# Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
## 3 Proposed Methodology
### Hybrid Vision Transformer Architecture
#### 3.1.2. Rotational Positional Encoding  
Positional encoding is crucial in ViTs as it provides the model with information about the spatial relationships between patches. In the RASST model, we extend the standard positional encoding to include rotational information, allowing the model to account for the orientation of each patch.  
The positional encoding for a patch at position \(p=\big{(}p_{x},p_{y}\big{)}\) with rotation \(\theta_{p}\) is defined as:  
\[E_{pos}\big{(}p,\theta_{p}\big{)}=\mathrm{PE}(p)+\mathrm{RE}\big{(}\theta_{p} \big{)}, \tag{2}\]  
where \(\mathrm{PE}(p)\) is the standard 2D sine-cosine positional encoding defined as:  
\[\mathrm{PE}(p)[2i]=\sin\biggl{(}\frac{p_{x}}{10000^{\frac{2i}{2}}}\biggr{)}, \mathrm{PE}(p)[2i+1]=\cos\biggl{(}\frac{p_{y}}{10000^{\frac{2i}{2}}}\biggr{)}, \tag{3}\]  
where \(i\) indexes the dimensions and \(d\) is the embedding dimension. \(\mathrm{RE}\big{(}\theta_{p}\big{)}\) is the rotational encoding, which is similarly defined using sine and cosine functions to represent the rotation:  
\[\mathrm{RE}\big{(}\theta_{p}\big{)}[2j]=\sin\bigl{(}\theta_{p}\big{)},\mathrm{ RE}\big{(}\theta_{p}\big{)}[2j+1]=\cos\bigl{(}\theta_{p}\big{)}, \tag{4}\]  
where \(j\) indexes the dimensions specific to the rotational encoding.  
By integrating \(E_{pos}\big{(}p,\theta_{p}\big{)}\), the ViT is better equipped to differentiate between patches not only based on their spatial position but also based on their orientation, thus improving its capability to handle rotated objects.
## 3 Results
### Amplification during Tube Current-Time Product (mAs) Variation  
With an active area of 756 mm\({}^{2}\) (60 chips), the linearity coefficient of the signal to mAs variation was 0.95472. The signal that was obtained per unit tube current-time product was 0.01533 mV/mAs. Doubling the active area to 1512 mm\({}^{2}\) (120 chips) resulted in a 3.49% reduction in the linearity coefficient of the signal to mAs variation, i.e., the linearity coefficient dropped to 0.92096. However, the signal that was obtained per unit tube current-time product increased from 0.01533 to 0.02928 mV/mAs; this corresponded to a 90.99% increase in mVs obtained per mAs. Therefore, during mAs variation, there was a 0.12% increase in the mV/mAs for each mm\({}^{2}\) of active area increment, as observed in Figure 5a.  
Figure 4: A graph of signal gain due to LED chip number/sensitive area increment.  
Figure 5: A graph of signal augmentation with (**a**) Number of LED chips/sensitive area and (**b**) Amplifier Board amplification; during tube loading variation (mAs).  
On the other hand, the amplifier board application resulted in a 2.77% increase in the signal's linear coefficient during mAs variation, i.e., the linearity coefficient increased from 0.92096 to 0.94651. The signal that was obtained per unit mAs also increased from 0.02928 to 0.04011 mV/mAs, hence a 36.99% mV/mAs increase. Therefore, during mAs variation, there was a 3.699 \(\times\) 10\({}^{-8}\)% increase in the mV/mAs for each voltage gain by the amplifier board, as observed in Figure 5b.
## 3 Results and Discussion
### Polystyrene Nanoparticle Synthesis
#### 3.2.2 Influence of A Change in the Nucleation Mechanism on Nanoparticle Size  
Furthermore, we applied the oligoglycidol macromolecule **2b** with a CMC at 1.55 mmol\(\cdot\)L\({}^{-1}\) to study the influence of the different reaction mechanisms above and below the CMC on the particle size. We chose two surfmer concentrations below and one concentration above the CMC in emulsion polymerisations with the same starting material concentrations and reaction conditions as mentioned before. Additionally, for surfmer **2b** the cloud point was not reached at 80 \({}^{\circ}\)C and the surfmer was completely soluble in water. The synthesis with 0.57 mmol\(\cdot\)L\({}^{-1}\) oligoglycidol\({}_{10}\) surfmer **2b** and a surfmer:monomer ratio of 1:840 resulted in spherical nanoparticles **4a** with an average diameter of 387 nm and a narrow size distribution with a PDI of 0.012 (as determined by DLS, Figure S11, Table 3). Increasing the surfmer concentration to 0.96 mmol\(\cdot\)L\({}^{-1}\) led, as for surfmer **2a**, to a reduction in average particle size. At this surfmer concentration nanoparticles **4b** with an average diameter of 273 nm and a PDI of 0.014 were obtained (as determined by DLS, Figure S12, Table 3). When the oligoglycidol\({}_{10}\) surfmer concentration was increased further to pass the CMC the overall trend of reducing particle size continued (Figure 4). Here at a surfmer concentration of 1.9 mmol\(\cdot\)L\({}^{-1}\) particles **4c** with an average diameter of 206 nm and a PDI of 0.018 were obtained (as determined by DLS, Figure S13, Table 3). A doubling in the surfmer concentration led to a reduction in particle size by 25% when the CMC was passed. This is a much larger size reduction than for the oligoglycidol\({}_{6}\) surfmer **2a** in the same concentration range, for which the CMC was not reached. In that case a doubling of surfmer concentration from 0.88 to 1.7 mmol\(\cdot\)L\({}^{-1}\) only led to a particle size reduction of 6% (Figure 3). Four times the amount of oligoglycidol\({}_{6}\) surfmer **2a** was needed to reduce the average particle size by 21%. The large size reduction for oligoglycidol\({}_{10}\) (**2b**) at 1.9 mmol\(\cdot\)L\({}^{-1}\) was therefore likely due to the change in the nucleation mechanism. By passing the CMC a large number of micelles were formed, which enable the initiator to start the polymerisation not only with surfmer molecules in solution but also with monomers in the micelles, which increased the nucleation rate drastically. Here, the emulsion polymerisation progresses via Smith-Ewart kinetics [PERSON] and [PERSON] (1973) with a high nucleation rate inside the micelles, and therefore particles with a much smaller average diameter are obtained. For all emulsion polymerizations above and below the CMC nanoparticles with a narrow size distribution were obtained (Table 3), and therefore no influence of nucleation mechanism on the polydispersity was observed.
## 2 Experimental Procedure  
The MQW-SESAM sample used in our experiment is schematically shown in Figure 1. The structure of the sample from top to bottom (light incident direction) consists of a GaAs cap layer, nine In\({}_{0.27}\)Ga\({}_{0.73}\)As QWs separated by GaAs barriers and a DBR composed of 23 GaAs/Al\({}_{0.95}\)Ga\({}_{0.05}\)As bilayers, which is grown on a GaAs substrate oriented along the (100) direction. The neighboring triple-QW stacks are separated by 112-nm thick GaAs layers. Each triple-QW stack consists of an In\({}_{0.27}\)Ga\({}_{0.73}\)As/GaAs/In\({}_{0.27}\)Ga\({}_{0.73}\)As/GaAs/In\({}_{0.27}\)Ga\({}_{0.73}\)As structure with 7-nm thickness for each QW and 6-nm thickness for each barrier. The MQW structure is separated from the sample surface by a GaAs cap layer with the thickness equal to approximately half of the separation distance between triple-QW stacks. Each bilayer in the DBR consists of 73.7-nm GaAs and 86.7-nm Al\({}_{0.95}\)Ga\({}_{0.05}\)As. The DBR and the last QW are spaced by a 9.6-nm GaAs layer and a 68.7-nm Al\({}_{0.95}\)Ga\({}_{0.05}\)As layer. Two GHz-repetition-rate Yb:KYW lasers with 5 kHz repetition rate difference emitting both at a central wavelength of \(\sim\)1050 nm are employed to conduct femtosecond pump-probe spectroscopy on this sample in a reflection configuration. One Yb:KYW laser is used as pump laser and another is used as probe laser. Both pump and probe beams are incident on the air/cap layer surface. The reflected probe beam is received by a 125-MHz photodetector, which is connected to a 100-MHz A/D converter for data acquisition. Thus, the photo-induced reflectivity change (\(\Delta\)R/R) is monitored as a function of time delay between the pump pulse and probe pulse which is automatically introduced by their repetition rate difference. Unless a specific note is given, the incident pump power and probe power are always set to 100 mW and 4.5 mW, respectively. All experiments are performed at room temperature. A more detailed description of the experimental set-up can be found in Ref. [25]. Tables 1 and 2 list relevant properties of the lasers and the sample, respectively. From both tables, it is clear that photon energies (1.184 eV, 1.183 eV) of pump and probe lasers are above the bandgap of In\({}_{0.27}\)Ga\({}_{0.73}\)As (1.052 eV (bulk), 1.177 eV (confinement effects considered)); meanwhile, the photon energies are significantly below the bandgap of GaAs (1.424 eV (bulk at room temperature)) and the bandgap of Al\({}_{0.95}\)Ga\({}_{0.05}\)As (2.148 eV (bulk)). Therefore, the optical absorption region refers only to MQWs (In\({}_{0.27}\)Ga\({}_{0.73}\)As) represented by violet areas in Figure 1, while the bottom DBR materials are transparent to both pump and probe light. Both these features will be of relevance for our following discussion regarding CLAPs photo-induced generation and detection.
## 4 Impacts on the Range Delay  
### Internal Delay of the SAR instrument  
The internal delay of the SAR instrument is derived by using internal calibration pulses. A set of 300 calibration pulses is acquired before and after each Sentinel-1 Stripmap acquisition. Evaluation of these calibration pulses serves to derive the instrument internal delay. This delay is calculated for each overpass and considered as an individual input parameter for range delay estimation.  
The instrument internal delay is found to be very stable for each Sentinel-1 instrument over both observation periods. For S-1A, a mean instrument internal delay of 441.6 ns with a small variation of 0.5 ns is derived for the analysis period (August-October 2015). Similar results are obtained for S-1B (acquired during its analysis period December 2016-February 2017) with a mean instrument internal delay of 433.7 ns and a variation of 0.3 ns. These values are consistent with the instrument internal delay determined by the operational processor (IPF), which are annotated within the L1 products.
# Genetic Engineering Approaches for the Microbial Production of Vanillin
## 5 Discussion
In this case, vanillin would not be degraded, but transported outside the cell, preventing its toxic effect and facilitating its recovery. Another possible strategy to bypass vanillin toxicity could be the implementation of de novo biosynthetic pathways in vanillin-producing microorganisms, which are highly tolerant to vanillin and its biosynthetic intermediates. However, the genetic engineering tools to efficiently manipulate these microorganisms [60; 64; 70] are less developed than in _E. coli_ and yeast, and little is known about the metabolism of vanillin-producing microorganisms. Therefore, developing additional tools is essential to implement de novo biosynthetic pathways in these microorganisms effectively. Moreover, further pathway improvements might require genomics, transcriptomics, proteomics, and metabolomics studies. Ferulic acid is a ubiquitous plant constituent. Yet despite its high abundance in nature, its recovery from plant cell walls (or agricultural waste products) is complicated and costly. In plants, ferulic acid is covalently bound to proteins and carbohydrates as a glycosidic conjugate, amide, or ester. Thus, the release of ferulic acid from plants requires chemical, enzymatic, or microbial hydrolysis. Despite the progress made in recent years in developing microbial methods for the extraction of ferulic acid from agro-industrial by-products, many advances are still required to render these methods efficient [57; 157; 158; 159]. Chemical hydrolysis based on alkaline treatment at high temperatures reports a higher yield of ferulic acid than enzymatic hydrolysis. However, according to EU regulation, ferulic acid released by chemical processes cannot be considered a \"natural\" precursor. Today, ferulic acid used as a precursor for the commercial production of \"natural vanillin\" is mainly obtained from rice bran by enzymatic treatment. This recovery process is highly dependent on the availability of rice bran wastes and has high costs. Therefore, new \"natural\" ferulic acid sources might be considered to meet the global demand for this vanillin precursor. Some works have described the development of recombinant strains to produce ferulic acid from simple carbon sources [97; 160; 161]. Despite the low yields, this approach could be an exciting method to obtain cheap \"natural\" ferulic acid, which could be converted into bio-vanillin by vanillin-producing microorganisms after purification.
## 4 Implementation Details
### Evaluation Metrics  
To evaluate the proposed methodologys' performance, we compare ground truth masks with obtained segmentations for each CT and MRI volume. We used four different metrics to evaluate segmentation accuracy, namely, the Dice similarity coefficient (DSC), Jaccard Index (JI), surface distance (SD), and Hausdorff distance (HD). DSC and JI measure the level of overlap between the ground truth and predicted segmentations, while SD and HD examine boundary distances. The DSC metric measures the degree of overlap between the ground truth and predicted segmentation. It is a commonly used metric for evaluating segmentation quality and can be written as:  
\[DSC(G,P)=\frac{2|G\cap P|}{|G|+|P|} \tag{21}\]  
where \(G\) is the ground truth, and \(P\) is the predicted mask.  
Similarly, the Jaccard Index (JI) emphasizes the size of the intersection divided by the size of the union of the sample sets. The mathematical representation of the \(JI\) can be written as:  
\[JI(G,P)=\frac{|G\cap P|}{|G\cup P|} \tag{22}\]  
where \(G\) is the ground truth, and \(P\) is the predicted mask.  
_SD_ measures an average of the minimum voxel-wise distance between the ground truth and predicted object boundaries and can be written as:  
\[SD(G,P)=\frac{1}{n_{G}+n_{P}}\Bigg{\{}\sum_{x_{P}\in P}\bar{d}(x_{P},G)+\bar{d }(x_{G},P)\Bigg{\}} \tag{23}\]  
Figure 5: Training and validation accuracies and losses for 3D FM-Pre-ResNet \(+\) VAE network architecture.  
where \(n_{G}\) and \(n_{P}\) denote the number of voxels on the object boundaries in the ground truth and predicted segmentations, respectively.  
Furthermore, \(HD\) represents the maximum of the minimum voxel-wise distances between the ground truth and predicted object boundaries and can be written as:  
\[HD(G,P)=\max_{g\in G}\left\{\ \min_{p\in P}\left\{\sqrt{g^{2}-p^{2}}\right\}\right\} \tag{24}\]  
where \(g\) is the ground truth, and \(p\) is the predicted mask.
## 1 Introduction  
The problem of string matching has been studied extensively due to its wide range of applications from Internet searches to computational biology. String matching can be defined as follows. Given a text \(T=t_{1}t_{2}\cdots t_{n}\) and a pattern \(P=p_{1}p_{2}\cdots p_{m}\), with letters from an alphabet \(\Sigma\), find all of the occurrences of the pattern in the text. This problem can be solved in \(O(n+m)\) time by using well-known algorithms(e.g., KMP[1]). A variation of this problem is to search for multiple patterns at the same time. An algorithm for this version is given in [2].  
A more general formulation allows \"don't care\" or \"wild card\" characters in the text and the pattern. A wild card matches any character. An algorithm for pattern matching with wild cards is given in [3] and has a runtime of \(O(n\log|\Sigma|\log m)\). The algorithm maps each character in \(\Sigma\) to a binary code of length \(\log|\Sigma|\). Then, a constant number of convolution operations is used to check for mismatches between the pattern and any position in the text. For the same problem, a randomized algorithm that runs in \(O(n\log n)\) time with high probability is given in [4]. A slightly faster randomized \(O(n\log m)\) algorithm is given in [5]. A simple deterministic \(O(n\log m)\) time algorithm based on convolutions is given in [6].  
A more challenging formulation of the problem is pattern matching with mismatches. This formulation appears in two versions: (1) for every alignment of the pattern in the text, find the distance between the pattern and the alignment; or (2) identify only those alignments where the distance between the pattern and the text is less than a given threshold. The distance metric can be the Hamming distance, edit distance, \(L_{1}\) metric, and so on. The problem has been generalized to use trees instead of sequences or to use sets of characters instead of single characters (see [7]).  
A survey of string matching with mismatches is given in [8]. A description of practical on-line string searching algorithms can be found in [9].  
The Hamming distance between two strings \(A\) and \(B\), of equal length, is defined as the number of positions where the two strings differ and is denoted by \(Hd(A,B)\).  
In this paper, we are interested in the following two problems, with and without wild cards.  
1. Pattern matching with mismatches: Given a text \(T=t_{1}t_{2}\ldots t_{n}\) and a pattern \(P=p_{1}p_{2}\ldots p_{m}\), output \(Hd(P,t_{i}t_{i+1}\ldots t_{i+m-1})\), for every \(i,\ 1\leq i\leq n-m+1\).  
2. Pattern matching with \(k\) mismatches (or the \(k\) mismatches problem): Take the same input as above, plus an integer \(k\). Output all \(i\), \(1\leq i\leq n-m+1\), for which \(Hd(P,t_{i}t_{i+1},\ldots t_{i+m-1})\leq k\).
## 2 A2S Integrated MIoT
### A2S Integrated MIoT Architecture
At the same time, the \"Nanhai No.1\" research ship can also upload data to ground BSs or central servers for processing and analysis through SC systems and its own data processing equipment [96]. These data can provide a scientific basis for related fields, such as marine environmental protection, fishery resource management, etc. Another example is the port of Zhuhai in China. Zhuhai Port adopts A2S integrated MIoT technology and is equipped with a large number of sensors and monitoring equipment, which can conduct real-time monitoring and management of vessels, cargo, traffic conditions, environment, and other aspects in the port [97]. BSs and wireless network facilities are also set up in the port to facilitate the collection and transmission of information. These data can be used to optimize vessel entry and exit processes, improve the efficiency of cargo transportation, protect the environment, and more [98]. In addition, there are many other cases of A2S integrated MIoT applications around the world, such as the \"Cape Town Underwater Cable\" project in the United States, the \"MOSES\" project in Europe, and the \"Marine Resource Utilization System\" in Japan. These cases demonstrate the potential of the A2S integrated MIoT to improve the efficiency of marine resource utilization, improve transportation safety, and protect the marine environment.
## 2 Materials and Methods
### Hyperparameter Optimization  
In this study, there are many hyperparameters that influence the model's performance, such as the k value of the k-mer nodes, the dimensions of the pre-trained word2 vec and fasttext embedding vectors, the number of hidden neurons in the CNN and Bi-LSTM, and more. These hyperparameters play important roles in constructing the de Bruijn graph, building feature representations, and adjusting the model's complexity. Using a grid search strategy, we tried different combinations of hyperparameters and selected the best combination based on the model's performance. Specifically, we experimented with k values for k-mer nodes in the range of [2; 3; 4; 5; 6], dimensions of pre-trained word2 vec and fasttext embedding vectors in the range of [32; 64; 128; 256], and the number of hidden neurons in the CNN and Bi-LSTM in the range of [32; 64; 128; 256].  
Additionally, we paid special attention to the number of attention heads in the multi-head attention mechanism, a critical parameter that significantly affects the model's expressive power and complexity. By adjusting the number of attention heads, we can more effectively capture the complex relationships between nodes, thereby improving the model's performance. Therefore, we experimented with different numbers of attention heads, specifically [2; 4; 6; 8], and evaluated the model's performance under each.  
By comparing the model performance under different hyperparameters and numbers of attention heads, we successfully determined the optimal parameter combination.  
Through grid search [32], we found that the number of attention heads in the multi-head attention mechanism significantly impacts model performance. During the hyperparameter adjustment process, we discovered that the model achieves optimal performance when the K-mer node value is set to 4, the dimensions of the pre-trained word2 vec and fasttext embedding vectors are set to 64, the number of hidden neurons in both CNN and Bi-LSTM is 128, and the number of attention heads is 6.