esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Work
### Model Development and Training
#### 3.3.3 Artificial Neural Network  
Artificial Neural Networks are advanced models driven by the function and structure of biological neural networks, such as those in the human brain. These models are widely known for their use in areas requiring high computational power. Some of their uses include pattern recognition, image classification, object detection, and even regression. The architecture of an ANN consists of interconnected nodes, also known as neurons, arranged into three types of layers: the input layer, one or more hidden layers, and the output layer. Each neuron in a layer receives input from the previous layer and processes it with a mathematical function (activation function), which is then passed as output to the next layer. The ANN studies, learns, and understands complex relationships between input and output data through this process. Mathematically, for a single neuron, the output is computed as:  
\[\text{z}=\sum_{i=1}^{N}wixi+b\]  
\[\text{y}=\sigma(\text{z})\]  
* \(xi\) represents the input features.
* \(wi\) represents the weights assigned to each input.
* \(b\) is the bias term.
* \(\sigma(\text{z})\) is the activation function (ReLU or sigmoid) which introduces non-linearity into the model.  
[PERSON] et al. [53] proposed an Adaptive Deep Neural Network (ADNN) approach for predicting the remaining useful life (RUL) of lithium-ion batteries. Their method was evaluated using the NASA battery cycle life dataset, where it demonstrated strong performance in multi-battery RUL estimation. The model achieved an RMSE of 6.66% and a prediction accuracy of 93.34%, highlighting its effectiveness in capturing battery degradation trends across different cells. In this study, the ANN model was used to forecast the RUL of lithium-ion batteries by learning patterns in all the features but excluding the cycle counts to see how it will perform and after which we included all features including the cycle count and compared both performance metrics. The ANN architecture consisted of three hidden layers with 128, 64, and 32 neurons respectively, and employed ReLU activation for these hidden layers. Figure 11 shows how the ANN performed with cycle count. As seen below, we observe a high validation loss error with low training loss error and signs of oscillations which indicate overfitting. Now we carried out hyperparameter turning to see if we can remove the oscillations hereby improving the model, this is confirmed by a smooth convergence and strong generalization with no signs of overfitting.  
Figure 11: Training and validation loss over epochs for the ANN model.  
Regularization techniques such as dropout and L2 regularization were used to prevent overfitting, ensuring the model generalized well to unseen data as evident in Figure 12, the ANN demonstrated the ability to learn non-linear patterns in the data effectively, making it a reliable model for predicting battery health.
# Bi-LS-AttM: A Bidirectional LSTM and Attention Mechanism Model for Improving Image Captioning
## 5 Conclusions  
In this study, we have introduced a model that leverages the capabilities of the Bi-LS-AttM approach to generate captions that are precise, inventive, and context-sensitive. This was accomplished by incorporating bidirectional information and an attention mechanism. For the dual purposes of feature extraction and time optimization, we utilized the Fast RCNN. Additionally, to provide a comprehensive understanding of the proposed model's structure, we generated a detailed visualization outlining the word generation process at consecutive timesteps. The model's robustness and stability were thoroughly assessed across various datasets pertinent to image captioning and image-sentence retrieval tasks.  
In terms of future work, we intend to delve into more intricate domains of image captioning, including those related to remote sensing and medical imaging. We anticipate broadening the application scope of our model to encapsulate other forms of captioning tasks such as video captioning. Furthermore, we plan to explore the integration of multi-task learning methodologies with an aim to enhance the model's general applicability.  
Conceptualization, T.X. and J.W.; methodology, J.W. and T.X.; software, T.X.; validation, T.X., W.D. and J.W.; formal analysis, J.W.; resources, J.W.; data curation, T.X. and X.W.; writing--original draft preparation, T.X.; writing--review and editing, T.X.; visualization, W.D. and J.W.; supervision, J.Z. and J.W.; project administration, T.X; funding acquisition, W.D. and J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 61976120, and the Basic Science Research Project of Nantong, grant number JC2020143.  
Not applicable.  
The source code and required datasets of the experiments can be obtained upon request.  
The authors declare no conflict of interest.  
The abbreviations used in this manuscript are listed in the following tables:  
\begin{tabular}{l l} CV & Computer Vision \\ NPL & Natural Language Processing \\ Bi-LSTM & Bidirectional Long Short-Term Memory \\ LSTM & Long Short-Term Memory \\ Bi-LS-AttM & Bidirectional LSTM and Attention Mechanism \\ Fast RCNN & Fast Region-based Convolutional Network \\ CNN & Convolutional Neural Network \\ RNN & Recurrent Neural Network \\ RoI & Region of Interest \\ SVM & Support Vector Machine \\ CRF & Conditional Random Field \\ NDE & Nonparametric Density Estimation \\ VggNet & Visual geometry group Net \\ Deep VS & Deep Visual Semantic \\ m-RNN & Multimodal Recurrent Neural Network \\ BULE & Bilingual Evaluation Understudy \\ MSCOCO & Microsoft Common Objects in Context \\ METEOR & Metric for Evaluation of Translation with Explicit Ordering \\ CIDEr & Consensus-based Image Description Evaluation \\ R@K & Recall@K \\ \end{tabular}
## 3 Finite Element Analysis
### Confinement Analysis and Effectiveness Evaluation
#### 3.2.4 DS Replacement Ratio  
Figures 6-8 show the effect of the DS replacement ratio on the confinement effect and efficiency within the stub column model. It is observed that both the radial stress and the radial stress confinement coefficient within the infilled DS concrete decrease slightly with an increasing DS replacement ratio. Additionally, as the DS replacement ratio increases, there is a minor delay in the intersection point of the \(\sigma_{L,s}\)-\(\varepsilon_{L}\) curve and the \(\sigma_{\beta,s}\)-\(\varepsilon_{L}\) curve for the outer steel tube. During the initial loading phase, the lateral deformation coefficient of the stub column model increases with axial load growth. Upon reaching ultimate bearing capacity, the differences in the lateral deformation coefficient among different DS replacement ratios become negligible and can be disregarded when other parameters remain constant. These results suggest that as the DS replacement ratio increases, the elastic modulus and axial compressive strength of the DS concrete decrease, weakening the steel tube's confinement effect on the infilled concrete. However, compared to other design parameters (\(f_{cu,r},f_{y}\), and, \(\rho\)), the DS replacement ratio has a minor impact on the confinement effect and efficiency of the steel tube on the infilled DS concrete.
# A Review on Clustering Techniques: Creating Better User Experience for Online Roadshow
## 3 Discussion
### Similarity Measure
Since there are only two clusters (binary), _Hamming similarity_ shows a better performance as compared to _Cosine similarity_. _Jaccard similarity_ is commonly used to evaluate the performance of the clustering process, rather than the similarity measure. Some researchers attempted to improve upon the similarity measures so that the clustering quality can be improved. [PERSON] et al. [27] introduced a new distance measure called the _Variable Length Vector Distance_ (VLVD) that can calculate the distance between two web session vectors of different lengths. _VLVD_ is proposed as most of the papers represent each web session as a binary vector of a certain length, and do not consider vectors of longer lengths. In [67], [PERSON] et al. introduced _Sequence Similarity Measure_ (SSM) that focuses on measuring the similarities between the sequentially ordered web sessions. _SSM_ is proposed as _Jaccard similarity_, _Euclidean distance_, and _fuzzy similarity_ do not capture the sequential nature of the web sessions.
## 3 Results and Discussion
### SDS-PAGE Analysis  
The SDS-PAGE analysis was performed to confirm the expression proteins for both soluble and insoluble fractions of the strains cell lysate from the isolated native G1, G2, and G3 species, which is vital in detecting the location of the reaction in a mixture of sand/soil particles. Figure 6 represents the SDS-PAGE results for the soluble and insoluble portion of the bacterial cell suspensions.  
It has been studied that ureases from bacterial cell are composed of two small subunits (8-12 kDa) with one subunit being larger [30], and the results of SDS-PAGE analysis exhibited several bands of nearly 15 kDa and 60 kDa [26; 29]. These results indicated that the bacterial urease could be expressed as either insoluble protein or as a soluble membrane protein, which also corresponds to the results [26; 31].  
Figure 5: Results showing the urease activity of the isolated species at different temperatures.  
These results showed that the variation of urease activity depended on cell lysate, cell membrane, and whole-cell by focusing on the urea hydrolysis by native bacterial strain.  
Thus, on the basis of these results, urease could exist as a soluble protein or likely as a soluble membrane protein. We found that the protein was not released into the culture solution but was separated from the bacterial cell membrane into the solution during ultrasonication because the crystal precipitation was greatly influenced by the bacterial cells. Therefore, the findings in this study support in figuring out the urease accumulation characteristic and extraction of the enzyme by sonication, which could be effective for the efficacy of the soil treatment/ground improvement by MICP and/or enzymatic process in practical field application for coastal erosion protection, especially in the coastal area of Greece.
# Visualisation of Information Using Patient Journey Maps for a Mobile Health Application
## 6 Discussion
### Limitations and Further Research  
In this study, user evaluations were conducted with typical users in their 20s, 40s, and 60s. However, the proposed prototype will be used by users with various diseases and disabilities. Therefore, it is necessary to conduct a study that considers accessibility for such users and not simply their age. For example, the interview results highlighted the necessity of considering font sizes for elderly patients.  
PHRs contain a variety of information, including public health data, hospital medical data, and personal health data; however, this study focused on disease information, which is classified as hospital medical data, for which a UX was proposed. Disease information is a part of personal health information and may not be useful for disease-free people. Such information must be linked with other health information to ensure better healthcare services. We found that the participants wanted to use the method and information provided in the prototype as the starting point to connect with various health management functions (e.g., dietary guidance based on the disease, a check-up schedule, exercise guidance, and preventive measures). Therefore, it is possible to develop a UX that can connect to other personal health data.  
Furthermore, it is challenging to classify the stage of a disease because this requires a combination of various disease features, judgment by medical staff, and easy recognition by patients. In this study, we attempted to classify the stages of diseases and applied the classifications to PJMs based on the example of medical staff from various fields and users of various age groups (20s, 40s, and 60s). Consequently, we obtained the following stages: at the onset, before treatment, during treatment, after treatment, and full recovery. These results will be useful for the development of similar apps in the future. However, as many diseases and various treatment methods exist, it is necessary to develop a patient-centred PJM for more medical staff in the future. Moreover, as noted by the participants in this study, standardisation of information is necessary in implementations of mobile health services based on patient-centred PJMs, along with technical support. Specifically, efforts are required to define detailed items for collection through the platform by the type of health information, as well as to perform data standardisation to integrate various types of health information provided by different medical organisations using a person-centred approach, thus making them interchangeable. The classification of disease stages will also form part of the data standardisation process.
## 6 Application and Results
### Factors of Change Maps for Ground Snow Loads  
One of the conventional remarks about global warming is that a reduction of snow is expected as a consequence of the increase of mean temperature, but this is not always true, at least at a local scale. In fact, although a decrease in mean snowfall is expected in most regions due to the decrease of snowfall frequency, a contrasting response may be experienced in other regions for snowfall extremes [41]. The snowfall rate can increase as a result of the intensification of precipitation rate, combined with the decrease of snowfall fraction when the former aspect prevails in the future trends [41; 42].  
To evaluate the impact of climate change on snow load and on structures, a general methodology to derive snow loads from daily outputs of climate models (maximum and minimum temperature and precipitation) has been developed by the authors, as illustrated and discussed in detail in [2]. In the present research, the proposed algorithm has been suitably implemented in order to obtain, on the basis of daily climate projections available for the two different RCPs scenarios recalled above, yearly maxima series of the ground snow loads. These series have been subsequently analyzed to derive a factor of changes for characteristic values of snow load, \(q_{k}\), in terms of ratio, according to Equation (5).  
The resulting factors of change maps for the Italian part of the European Mediterranean climatic region defined in [9] are shown in Figure 3 for the three time windows: 1991-2030, 2011-2050, and 2031-2070.  
Figure 3: Characteristic ground snow load \(q_{k}\) factors of change in Italy referring to 1951–1990. RCP4.5 (1 st row). RCP8.5 (2 nd row).  
The outcomes show a general decreasing trend for the investigated region, even if trends can be different depending on the considered area on the considered time window and on the reference emission scenario. In the near-term future (1991-2030), a constant or increasing trend (FC \(>\) 0.95) may be expected in the northern and eastern Italian regions [7]; however, a decrease is projected in a long-term future (2031-2070), substantially confirming the general remark recalled at the beginning of this subsection.  
The factors of change maps shown in Figure 4 are referred to as the German part of the European central east climatic region defined in [9]. Similar conclusions to those expressed for the Italian territory can be drawn for Germany.
## 2 Materials and Methods  
### Bacterial Hosts and Bacteriophages  
Bacteriophages and their bacterial hosts were purchased from the German Collection of Microorganisms and Cell Cultures GmbH (Deutsche Sammlung von Mikroorganismen und Zellkulturen; DSMZ). In this study, _Pseudomonas syringae_ (DSM 21482) with phage phie (DSM 21518), _Escherichia coli_ (DSM 5695) with phage MS2 (DSM 13767) and _Escherichia coli_ (DSM 613) with phage T4 (DSM 4505) were used. Chosen lytic phages were selected based on their different features (Table 1).  
Bacterial revival and phage propagation were conducted similarly, as described previously [15]. All strains were stored in TSB (tryptlease soy broth) medium (BioMaxima, Lublin, Poland) with 10% (vol/vol) glycerol at \(-\)20 \({}^{\circ}\)C. The bacteria were revived on Luria-Bertani (LB agar) (BioMaxima, Lublin, Poland) for bacterial plate stocks by streaking glycerol stocks onto agar plates (plate incubation: 24 h at 37 \({}^{\circ}\)C for _E. coli_ strains; 48 h at 28 \({}^{\circ}\)C for _P. syringae_ strains). For bacteriophage amplification, several colonies from bacterial plate stock were used to inoculate 50 mL of LB, and were incubated (as described above) with shaking (120 rpm) in an orbital rotating shaker (Shaker-Incubator ES-20, BioSan, Jozefow, Poland) to reach OD\({}_{600\text{nm}}\) = 0.2. Optical density values were measured using an Infinite 200 PRO NanoQuant microplate reader (Tecan, Mannedorf, Switzerland). Then, phage was added, and samples were further incubated until the lysis occurred. For MS2 and T4 lysate purification, chloroform was added (10%, vol/vol) and the samples were  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bacteriophage** & **Family** & **Size** & **Genome** & **Features** \\ \hline Phi6 (\(\Phi\)6) & _Cystoviridae_ & \(\sim\)80–100 nm & dsRNA & \begin{tabular}{c} Enveloped (lipid membrane), \\ medium size, no tail \\ small size and \\ genome, no tail \\ \end{tabular} \\ MS2 & _Leviviriidae_ & \(\sim\)23–28 nm & ssRNA & \
\begin{tabular}{c} Non-enveloped, small size and \\ genome, no tail \\ \end{tabular} \\ T4 & _Myoviridae_ & \(\sim\)120–200 nm/86–90 nm & dsDNA &
\begin{tabular}{c} Non-enveloped, relatively big, \\ contractile tail \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bacteriophages characteristics.  
vortexed for 5 min, and then centrifuged (Eppendorf Centrifuge model 5810 R, Hamburg, Germany) at 5000 rpm for 25 min at 4 \({}^{\circ}\)C. The supernatant was collected immediately. For phie lysate purification, samples were firstly centrifuged (5000 rpm, 15 min, 4 \({}^{\circ}\)C) and then sterilized by filtration (PES filter, 0.22 \(\upmu\)m). Lysates were stored at 4 \({}^{\circ}\)C for further use. Phage activity and titres were tested by a double-overlay agar plaque assay [16].
## 4 Results
### Correlation Analysis with Dependent Variable  
Correlation analysis, using Pearson coefficients with a significance threshold of \(p\leq 0.10\), was conducted to explore relationships between the dependent variables (\"Performance Slope\", \"Performance Slope Binned\", and \"Pareto Rank Difference\") and the remaining features. \"Performance Slope\" emerged as the most suitable dependent variable due to its numerous and strong correlations with features like vocal arousal (Vocal Arousal (Slope), r = 0.34, \(p=0.0016\)), emotional expression (Neutral (Min), r = 0.31, \(p=0.0039\)), and engagement (Happy (Slope), r = 0.29, \(p=0.0071\)). Therefore, we decided to focus exclusively on the \"Performance Slope\" variable for subsequent analyses.
## 2 Experimental Procedure
The reaction performance was a function of the contact time of the reactant feed gas and the catalyst bed. If contact time is too short, decreased CH\({}_{4}\) concentrations will result. [22]. The size of the reactors is an important factor in determining the contact time between the feed gas and the catalyst. The outside diameter of each reactor was 19.05 mm, while the inside diameter was 16.57 mm, with a height of 200 mm for each. The produced H\({}_{2}\) from water electrolysis was directly applied to reactors after passing through gas/liquid separators and dryers. It was intended to feed pure H\({}_{2}\) to the methanation system without steam resulting from the water electrolysis, while CO\({}_{2}\) was introduced from the outside source. The flow rate of H\({}_{2}\) fed into the methanation system was measured by MFM, while that  
Figure 2: (**a**) View of the concentrator photovoltaics (CPV) outdoor configuration at the University of Miyazaki, Japan, and (**b**) view of 4 sets of DC/DC converters and electrochemical (EC) cells set up used for hydrogenation. of CO\({}_{2}\) was controlled by mass flow controller (MFC, Alicat Scientific MC-500 SCCM-D-25 COMP). The additional pressure into the reactors was not applied in our system. Since the reactors were connected in series, most of the reaction developed within the first reactor (reactor 1). Simultaneously, the residual and unreacted gases (such as H\({}_{2}\) and CO\({}_{2}\)) traveled from reactor 1 and maintained the reaction within the second reactor (reactor 2). Therefore, the output gas from reactor 2 was where system output was measured and analyzed. The composition of produced gases was quantitatively analyzed using a quadrupole mass spectrometer (QMS, MS 9600, [PERSON]). No H\({}_{2}\)O was observed in our system owing to the usage of filters and gas/liquid separators. The water was cooled and liquefied in the reactor after passing through the valves. The composition of methane was related to the feed mole ratio and feed temperature. Thus, the amount of CH\({}_{4}\) composition was given by various feed temperatures and stoichiometric ratio. The volume of CH\({}_{4}\) gas was not directly measured. It was assumed that the volume of CH\({}_{4}\) gas corresponded to 1/4 times the fed H\({}_{2}\) gas volume and was multiplied with the CO\({}_{2}\) to CH\({}_{4}\) conversion efficiency.
## 2 Mutation Type and Compatible Therapeutic Approaches  
### Types of Dominant Mutations  
Dominant mutations can be broadly divided into three types based on their disease-causing mechanism [13]:  
_Loss-of-function:_ mutations that render the product of a gene non-functional. Pathogenicity arises because one copy of the gene is not sufficient to assure a normal phenotype, a condition known as haplousfficiency. Generally, this type of mutation causes autosomal recessive disorders [14]. Dominant mutations due to haplousfficency are less frequent but they do occur, such as most mutations in the pre-mRNA processing factor 31 homolog gene, _PRPF31_, that causes adRP.  
_Gain-of-function:_ mutations that attribute a new function to a protein, which may be toxic to the cell. Typical examples are most of the mutations in the \(RHO\) gene. \(RHO\) encodes the light-sensitive rhodopsin protein, involved in phototransduction. Mutant rhodopsin molecules cannot function normally and, given the high expression levels of rhodopsin in photoreceptors, cell mechanisms such as trafficking or protein degradation can be overwhelmed by large quantities of the mutant forms [15].  
_Dominant-negative:_ mutations resulting in a mutant protein that interferes with the function of the wild type [16]. An example are mutations in the gene _RP1_[17; 18]. RP1 is a photoreceptor-specific microtubule protein, important for the organization of membrane discs [8; 10]. Shifting the ratio of the mutant p.Gln662* RP1 versus wild type protein in a knock-in mouse model delays photoreceptor regeneration [19].  
It should be mentioned that not all mutations fit clearly into one of the above categories. For many mutations that cause autosomal dominant IRDs, the mode of action remains elusive or stems from a combination of the three classes. This is especially true for gain-of-function and dominant-negative mutations (specific examples are discussed below).