esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of the Use of Similarity Coefficients in Manufacturing Cell Formation Processes
## 4 The Research  
The theoretical basis of this study is formed by [8; 26; 38], who offer a comparative list of similarity coefficients (SC). A previous study analyzes the structure of a similarity metric and lists properties that a similarity relation should have. According to [38], there are two types of SC, Jaccardian and non-Jaccardian. In the first type, the SC is a metric that represents the proportion of hits that a solution presents in relation to the expected or maximum number of hits. The SC varies between 0 and 1. The second type considers an additional term proportional to the misses, which is subtracted from the number of hits before dividing by the expected number of hits. The SC varies between \(-1\) and 1. A similarity relation \(S_{ij}\) should have the following properties:  
1. If two objects are completely similar, that is, every piece that visits \(i\) also visits \(j\), and every piece that visits \(j\) also visits \(i\), then \(S_{ij}=1\).
2. If two objects are completely dissimilar, that is, no piece visits \(i\) and \(j\) at the same time, \(S_{ij}=0\) or \(-1\), depending on the type of the coefficient.
3. If \(i\) is more similar to \(j\) than to \(k\), \(S_{ij}>S_{ik}\).
4. If \(i\) is less similar to \(j\) than to \(k\), \(S_{ij}<S_{ik}\).  
This study examines twenty similarity coefficients sourced from the existing literature [8; 38]. Each coefficient was analyzed to determine its influence on the cellular manufacturing process literature.  
The research method begins with a bibliometric search in the Google Scholar search engine. Google Scholar fits with the scope of this study even if it does not curate data as other databases do. It was chosen due to the following advantages adequate to this study: (i) full-text searching, not only abstracts, titles, and keywords; (ii) fully open access, which allows triangulation and replication over the years; and (iii) broad coverage, which includes articles published in secondary journals, proceedings, books, theses and dissertations, technical reports, and patents. The keywords were \"similarity coefficient\" AND \"cellularmanufacturing\". Only pages in English were considered. Any part of the text counts. Next, the study identified the six most influential types of coefficient and, by a standard application, the most influential one, which was applied to a non-standard, exceptional case. The results of the applications were discussed, and a conclusion was applied. Figure 5 synthesizes the method.
## 2 Dynamic Derivatives Identification Based on the Forced Oscillation Method
### Hyper-Parameters Optimization of LSTM Based on Particle Swarm Optimization
#### 3.2.2 Process of Using PSO to Optimize LSTM  
Step 1: Divide experimental data into training samples, validation samples, and prediction samples.  
Step 2: Initialize the speed and position of all particles. Number of neurons, batch size, and iteration times of the LSTM model are used as optimization objects.  
Step 3: Divide into subgroups.  
Step 4: Calculate the fitness value of each particle. Input the validation samples into the trained model for prediction, and use the average absolute percentage error of the model on the validation samples as the particle fitness value.  
Fitness function \(f\) is defined as:  
\[f=\frac{1}{K}\sum_{i=1}^{k}\frac{\left|\hat{y}_{i}-y_{i}\right|}{y_{i}} \tag{10}\]  
In the formula, \(K\) is the number of validation samples; \(\hat{y}_{i}\) is the predicted value of the \(i\)th validation sample; \(y_{i}\) is the true value of the \(i\)th validation sample.  
Step 5: Determine the individual extreme value and the population extreme value based on the fitness values of the initial particles. In the meantime, also consider the best position of each particle as its historically best position.  
Step 6: Update the positions of ordinary particles and locally optimal particles.  
Step 7: After reaching the maximum number of iterations of the PSO, obtain the optimal hyper-parameters.  
Step 8: Construct an LSTM model using optimal hyper-parameters.  
The architecture of PSO-LSTM is shown in Figure 5.
## 4 Experiments and Results
### Imperceptibility Analysis  
To achieve the objective of hiding watermark information in the BIM data, it is essential that the data remain visually intact before and after watermark embedding. Hence, we designed experiments to analyze the imperceptibility of watermark embedding qualitatively and quantitatively.
## 3 Results and Discussion
### Evaluation of Water Quality in Bioretention Systems  
The calculation results of membership and weight coefficient are shown in Tables 4 and 5. In the three different bioretention systems, the TN had the highest weighted value in terms of the pollution factor. Focusing on the TN values, the weighted value of bioretention with the saturated zone was found to be higher than the other two, with the weight of the initial outflow above 0.6. This means that  
Figure 11: Removal efficiency of (**a**) TP, (**b**) NO3–N, (**c**) NH4+-N, and (**d**) TN (total nitrogen) with different concentration in bioretention systems.  
dealing with TN in bioretention systems is more complicated. As the rainfall extends, the weights of Ammonia and Nitrogen in the control and DS groups are increased, while the TN weight decreases. However, in the SZ group, the weights of Ammonia and Nitrogen are stable, which suggests that in the precipitation process, bioretention is relatively stable given the saturated zone's ability to purify rainwater.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sampling** **Time (min)**} & \multirow{2}{*}{**Grade**} & \multicolumn{3}{c}{**Control**} & \multicolumn{3}{c}{**DS**} & \multicolumn{3}{c}{**SZ**} \\ \cline{3-10}  & & **NH4\({}^{*}\)-N** & **TN** & **TP** & **NH4\({}^{*}\)-N** & **TN** & **TP** & **NH4\({}^{*}\)-N** & **TN** & **TP** \\ \hline \multirow{9}{*}{60} & I & 0 & 0 & 0 & 0 & 0 & 0 & 0.3948 & 0 & 0 \\  & II & 0.6195 & 0 & 0 & 0.8675 & 0 & 0 & 0.6052 & 0 & 0.6027 \\  & III & 0.3  
Figure 12 shows the effluent's water quality evaluation grade in the different bioretention systems. With the increase in rainfall time, the bioretention systems' effluent water quality was found to have a declining trend. The order of effluent water quality for the complete rainfall process is SZ \(>\) DS \(>\) Control. In the first three hours, the \(B^{*}\) of SZ was 3.57, and the water quality reached the Grade IV standard for surface water, which is higher than the effluent water quality in the Control (below Grade V) and in the DS (Grade V). By the 4 th hour of rainfall, the effluent water quality of all the three detention ponds was below Grade V. Although the water quality evaluation result of SZ was below the Grade V level, the FCI \(B^{*}\) was measured at 5.401, which is close to the water quality standard of Grade V. This suggests that, in general, the SZ system can effectively control the amount of ammonia-nitrogen, TN, and TP in stormwater runoff. The application of the FCI method in evaluating water quality in bioretention systems is able to show the changes in effluent water quality during a rainfall event and determine the primary components of water pollution at specific time periods.
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 4 Results
### Results Obtained from the CIC-IoT 2023 Dataset Using a Unsupervised Machine Learning Approach
#### 4.4.2 Research Analysis According to [53]  
Research [53] has investigated anomaly detection in IoT networks, utilizing the CIC-IoT 2023 dataset. The methodology encompasses several key steps.  
Step 1: Data Preparation and Processing  
Although a detailed description of the data preparation process for the Isolation Forest model was not provided, it can be assumed that standard data preparation techniques were used, such as converting symbolic values to numeric values and normalizing the data.  
Step 2: Model utilization  
The Isolation Forest model, which specializes in anomaly detection, was chosen. The algorithm builds an 'isolation forest' to quickly identify abnormal data points.  
Step 3: Model evaluation  
The Isolation Forest model was evaluated on five different datasets of IoT network traffic. The performance of the model was assessed using standard metrics such as the accuracy, precision, sensitivity, and F1-score.  
Step 4: Result verification  
Isolation Forest achieved moderate performance, but its results were variable depending on the dataset. Compared to other models, Isolation Forest often performed worse, especially on more complex datasets. The Isolation Forest model may be useful for rapid anomaly detection, but its performance may be insufficient for more demanding applications.
## 3 Classification of Global Land Development Phases
### LDPs Based on Forest Stock Changes
However, Zones 4, 6 and 7 were more complex; ultimately, land phases were classified as restoration and sustainability if the previous decrease in forest stock was recovered by the subsequent increase in forest stock. The bottom line is that most of Zone 4 was regarded as in the degradation phase, most of Zone 6 as in the restoration phase and most of Zone 7 as in the sustainability phase. The LDPs classification shown in Figure 4 was based on the forest stock change. The restoration phase included countries where forest stock growth exceeded the decreasing amounts of forest stock from the past or where there was normal forest growth with no degradation. To set a proper standard for small-scale forest stock decreases, average forest stock change rates were calculated over shorter time scales and compared with the average growth rates for the entire time series. Forest stock change rates exceeding the growth rate for the entire time series indicated that restoration was more intense than degradation. Therefore, recent degradation from 1990-2010 was classified into degradation, but some countries with small-scale forest stock decreases were adjusted as in the restoration phase. In addition, countries that had forest stock growth from 1950-1970 with small-scale forest stock decreases from 1970-1990 were adjusted into the sustainability phase. Countries where the FSCs did not exceed the decrease in forest stock from 1950-1970 and from 1970-2010 were also reclassified into the restoration phase (Figure 5 and Table 1). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**LDPs Classification** & **Degradation Phase Based on** & **Restoration Phase Based on** & **Sustainability Phase Based** \\  & **Forest Stock Change** & **Forest Stock Change** & **on Forest Stock Change** \\ \hline  & Afghanistan, Albania, Angola, & Bangladesh, Bolivia, Burundi, & \\  & Armenia, Colombia, Congo, & Cambodia, Central African & \\  & Dominica, El Salvador, Gamibia, & Republic, Chad, Equatorial & \\  & Ghana, Grenada, Guyana, Jamaica, & Guinea, Eritrea, Ethiopia, & \\ Degradation phase & Kazakhstan, Laos, Lesotho, & Georgia, Guinea-Bissau, & \\  & Macedonia, Maldives, Mali & Kryrygszstan, Madagascar & \\  & GDP per capita with & Myanmar, Namibia, Nicaragua, & Malawi, Mauritan, Moldova, \\  & Niger, North Korea, Peru, South & Mongolia, Nepal, Papua New \\  & Africa, Sri Lanka, Sudan & Guinea, Rwanda, Senegal, & \\  & Swaziland, Tapikitan, Tanzania, & Serbia, Sierra Leone, Somalia, \\  & Tonga, Tunisia, Turkmenistan, & Suriname, Kenya, Syria, Jordan, \\  & Uganda, Western Sahara, Yemen & Uzbekistan, Zambia, Zimbabwe \\  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Matrix of LDPs classification of major countries based on two approaches: (1) forest stock change and (2) GDP and GDP per capita with forest area change.
## 2 Materials and Methods
### Data Processing  
A photogrammetric workflow was used in order to generate outputs from the conducted UAS missions. The UAS data collection missions were complemented by the measurement of 8 GCPs for georeferencing.  
The data were processed in Agisoft Metashape Version 1.8 (Agisoft, St. Petersburg, Russia), a SfM processing software used for the processing of 3D spatial datasets. The software was used to generate georeferenced orthomosaics, DSMs, dense point clouds, and 3D textured meshes of the study vineyard. The first task was to visualize the data to determine sufficient coverage of the research area [20]. The UAS images were stored and aligned based on matching points. The alignment was achieved with regard to the interrelations of the images, and the software oriented them spatially with the result of a sparse point cloud [21]. The algorithm automatically found the camera positions. After tie point creation, the alignment optimization and the georeferencing were carried out, after which the dense point cloud was created. The root mean square error (_RMSE_) of the input GCP locations and their estimated positions was 0.034. This total error was computed as the _RMSE_ of all input GCP locations relative to their estimated positions, shown in meters [20]. The dense point cloud was built by using the connecting points between the single images and the respective depth information [20]. The created dense cloud was checked for possible outliers and edited accordingly. By using the georeferenced dense point clouds, DSMs were created. Those DSMs served as the database for the creation of georeferenced orthomosaics. Textured 3D models were additional outputs of the photogrammetric processing.
# Urban Seismic Networks: A Worldwide Review
## 2 USN Catalogue
### Technical Characteristics
#### 2.5.2 Transmission  
Concerning data transmission, we identified three classes: (i) real-time transmission; (ii) near-real-time transmission; and (iii) no transmission. For certain USNs, it was not possible to retrieve any information about the transmission type; these are classed as \"no info\".  
The difference between real-time and near-real-time can be subtle, and we were not able to verify whether different authors adopted the same definitions. We only gathered the information as it was provided, without performing any type of validation. Real-time transmission is the most commonly adopted choice, being implemented in almost the 40% of USNs; about one third of the networks were designed without any data transmission (Figure 9). Real-time (or even near-real time) transmission may have represented a limit for early USNs, while today technical solutions are easily feasible via internet connections (Figure 9), especially in urbanized areas. For this reason, the choice of transmission is not strictly and exclusively imposed only by the final objective of the USN, although a correspondence certainly exists (Figure 8); in fact, for the realization of certain objectives, e.g., site effect studies or characterization of structural health states, data transmission may be a non-essential requirement and local data storage may represent a rather more agile solution. For other objectives, however, such as earthquake monitoring and earthquake early warning, collecting data in real time is a fundamental prerequisite. In particular, reliable real-time transmission is indispensable for network-based early warning systems. Real-time transmission can additionally be useful for remote control of the correct functioning of acquisition stations without needing to reach the sites, at least periodically.  
A summary of the main features for both the sensor and transmission types, recalling their pros and cons, is reported in Table 3. When planning a USN, these features should be evaluated together with the objective(s) of the network.  
Figure 8: The numbers and color bar indicate the frequency of occurrence of combinations between the main objective and the sensor type (**left**) and transmission type (**right**). Data retrieved from Tables 1 and 2.
# Mycobacterial Biofilm: Mechanisms, Clinical Problems, and Treatments
## 5 Treatments
### Methods to Assist Antibiofilm Agents
#### 5.2.2 Nanoparticles with Mucus Penetrating Agents  
Biofilm is not the only barrier that impedes the entry of antimicrobials. Human respiratory tracts are covered with a mucus layer containing water, proteins, lipids, salts, and cellular fragments, of which mucin is a major component [109]. The structure and thickness of mucus also influence drug delivery efficiency; they are distinctive in different anatomical positions, and the impact of mucus on drug delivery depends on these features, as well as age, disease, etc. [110]. The mucus makes it difficult for anti-TB agents to reach cells, thereby contributing to drug resistance and prolonged treatment [111].  
A novel idea proposes that the function of anti-TB drugs can be enhanced when combined with antibiofilm agents and mucus-penetrating systems, and this has been tested on mice, as shown in Figure 2. N-acetylcysteine (NAC) is a mucolytic agent that also has antibiofilm properties [112]. [PERSON] and colleagues synthesized the IDR-1080 peptide, combining it with porous PLGA microspheres (PLGA-MS) with/without anti-TB drug INH, and these microspheres were then wrapped with NAC to confer these microspheres with the ability to thrust into the thick mucus, finally forming NAC-coated mucus-penetrating particles (NAC/PLGA-MPP) [111]. The researchers further compared the mobility of PLGA-MS and NAC/PLGA-MPP in mucus, as well as their anti-TB activity in vitro and in vivo. Compared with PLGA-MS, NAC/PLGA-MPP not only showed a better mucus-penetration ability but also caused a larger proportion of biofilm biomass reduction. In animal trials, NAC/PLGA-MPP has shown potent bactericidal properties and higher efficiency in reducing granuloma nodules and lesions compared to free IDR-1018 peptides and PLGA-MS. Therefore, inhaling NAC-coated polymeric particles--which have both anti-biofilm and mucus-penetrating properties--could be a potential adjunct to assist short-term therapies and help anti-TB agents reach inflammation sites. This might contribute to shortening treatments, lowering drug doses, and decreasing drug use frequency, thus enhancing the regularity of medication [111].
## 2 Materials and Methods
### Costs
#### 2.2.4 Batteries  
Mainly due to electromobility applications, the lithium-ion batteries cost has been decreasing considerably in the past years. In 2020, for the first-time battery pack prices have been cited below USD 100/kWh with the average being at USD 137/kWh [41].  
Stationary application lithium-ion batteries are now featuring 10-year manufacturer warranties as well as round-trip efficiencies beyond 95%.
# Deformation Analysis of Large Diameter Monopiles of Offshore Wind Turbines under Scour
## 2 Deformation Analysis Method
### Verification with Field Test  
The numerical results are verified by the field tests on lateral monotonic and cyclic loadings of offshore piles carried out by [PERSON] et al. [22]. According to the field test data, the p-y curves are imported into finite difference calculation program. It should be noted that the interior of pipe pile is filled with C30 concrete, and the top 0.9 m of pile length is deliberately not grouted for construction purposes, so it is critical to calculate the combined stiffness of steel pipe pile and concrete. If the stiffness is simply taken as steel pipe pile, the calculated stiffness will be smaller than the real combined stiffness, and the calculated displacement will be larger. In this paper, the combined stiffness is calculated by the method suggested by [PERSON] et al. [23]. After the calculation, the displacement at the loading point under different level of loads is collected, and the calculated value is compared with the measured value, as shown in Figure 7b.  
In order to simplify the calculation, it is assumed that the C30 concrete is grouted along the whole length of the pipe pile. Therefore, the displacement calculated is slightly lower, but close to the field data.  
Through the comparison of numerical calculation results with model test and field test, it can be seen that, by selecting appropriate p-y curves, calculating iteratively through the finite difference method, the accuracy and reliability of displacement analysis is satisfied.  
Figure 7: Displacement comparison. (**a**) Model test; (**b**) field test.
## 4 Discussion
### Analysis of the Influence of Window-to-Wall Ratio (WWR, 15% to 45%)  
The results consistently show that larger window openings (higher WWR) enhance both \(DA\) and \(CSA\) values, thus facilitating greater daylight access and circadian stimulus. For optimal daylighting performance, WWR values of 25% are recommended, with centered window configurations generally outperforming upper window positions in terms of consistent daylight distribution throughout the room. WWR values higher than 25% have been shown to marginally increase \(DA\) and \(CSA\) values. However, it is debatable whether these diminishing returns really justify the resulting decline in energy consumption due to the higher thermal transmittance of glass compared to that of the facade or the additional thermal gains from solar radiation.  
For bright coatings, the \(DA_{300}\) values are approximately 90% in Seville and 85% in Barcelona for centered configurations with a 25% to 45% WWR. Similarly, the \(CSA2\) values near the facade reach 100%, gradually declining to 90% in Seville and 82% in Barcelona for larger WWRs.  
In scenarios with a lower WWR of 15%, both \(DA\) and \(CSA\) performance diminish significantly. Specifically, for south orientations, the \(DA_{500}\) values average only 15%, while for north orientations, these values reach up to 30% towards the back of the rooms. For \(CSA\), particularly in north-facing configurations with dark coatings, the \(CSA2\) values are often null or minimal, barely reaching 15% in south orientations. Thus, configurations with a 15% WWR are not recommended due to their inadequate performance in providing sufficient daylight and circadian stimulus.