esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Introduction: Impairments in social cognition and cognitive deficits in bipolar disorder (BD) offer insights into the disorder's progression. Understanding how interventions impact both cognitive and emotional aspects of social cognition is essential. This study examines the effects of virtual reality (VR) cognitive remediation on cognitive skills, stratified by age, in the early stages of the disorder. Methods: A secondary analysis of a randomized controlled trial (RCT) compared the efficacy of VR cognitive remediation on cognitive skills between young adults (\(\geq\)58 years) and older adults (\(\geq\)59 years) in the experimental group with BD. Results: The experimental group included 39 participants: 24 \(\leq\) 58 years and 15 \(\geq\) 59 years. Young adults showed greater improvement in the Digit Span Backward (0.37 \(\pm\) 0.35 vs. 0.07 \(\pm\) 0.26, F = 9.882, \(p\) = 0.020) and Digit Symbol tests (3.84 \(\pm\) 3.05 vs. 1.16 \(\pm\) 3.8, F = 5.895, \(p\) = 0.020). Older adults improved more in the Frontal Assessment Battery (1.00 \(\pm\) 0.95 vs. 0.54 \(\pm\) 0.21, F = 5.295, \(p\) = 0.027), Matrix test (0.58 \(\pm\) 0.35 vs. 0.37 \(\pm\) 0.26, F = 4.606, \(p\) = 0.038), and Test of Tale (0.81 \(\pm\) 0.36 vs. 0.42 \(\pm\) 0.38, F = 10.115, \(p\) = 0.003). Conclusions: Young adults improved more in complex cognitive tasks, while older adults showed better results in simpler tasks. The effectiveness of VR may be due to hyperstimulation of mirror neurons. Further studies are needed to confirm these findings.  
cognitive impairment; bipolar disorder; cognitive remediation; virtual reality; advanced technology laboratory
# Fv-AD: F-AnoGAN Based Anomaly Detection in Chromatic Process for Smart Manufacturing
## 4 Experiment and Results
### Datasets  
In this paper, experiments were conducted using manufacturing process data images collected from actual chromate processes [32]. Chromeate has a large impact on process completeness and was collected as data for quality prediction. We used data to forecast normal and poor quality. Figure 7 depicts some of the collected data datasets.  
Chromeate treatment is used as a post-treatment method after galvanizing or cadmium plating; in fact, chromate treatment after galvanizing is essential. The process of coating a rust preventive film using bichromate is used for products that require gloss. The main reaction for treating the film is that zinc dissolves, hydrogen ion concentration decreases at the zinc interface to reduce bichromate ions, and precipitates on the zinc surface. In the chromate process, if the solution concentration is low, the result is not white; if the concentration is high, the thickness is reduced. There are various methods for the plating process, among which, the dataset of the chromate process is an electroplating process.  
The data collection method receives real-time data through PLC and sensors, stores it in CSV file form based on the collected time, and uses an image for visual evaluation of quality as a product stored in PNG form through an image collection device. The datasets are CSV and PNG, and image data are learned to automatically conduct visual quality inspections to detect defective products more accurately. Among the learning data, 1103 normal and 76 abnormal data are used to enable experiments on unbalanced data. Subsequently, we convert it into <64 \(\times\) 64, 128 \(\times\) 128, 256 \(\times\) 256> sizes that show the optimal performance of input images through various experiments.
# Mining of Potential Gene Resources for Breeding Nutritionally Improved Maize
## 8 Expression Patterns of the Putative Nutritional Improvement-Related Maize Genes  
For expression analysis of identified potential gene resources, we used 31 different time points seed samples [139] and 6 kernel compartment samples [140] of the B73 inbred line of the RNA-seq data and 16 non-seed tissues of the inbred line SRG200 (Syngenta) of the microarray data [141] downloaded from the Maize eFP database ([[https://bar.utoronto.ca/efp_maize/cgi-bin/efpWeb.cgi?](https://bar.utoronto.ca/efp_maize/cgi-bin/efpWeb.cgi?)]([https://bar.utoronto.ca/efp_maize/cgi-bin/efpWeb.cgi?](https://bar.utoronto.ca/efp_maize/cgi-bin/efpWeb.cgi?)), accessed on 20 February 2022). The expression levels of each gene in different tissue are listed in Table S1, and gene expression heatmaps were generated using the pheatmap package of R software (Figure 5 and Figure S2). Most identified genes are highly expressed in either early seeds or kernels. However, some genes are weakly expressed in both tissues. For instance, _GPC1_, _GPC2_, _GPC3_ and _GPC4_ that encode glyceraldehyde-3-phosphate dehydrogenases are highly expressed in nucellus at different time points after pollination and in different compartments of kernels. _OLE1_, _OLE3_ and _OLE4_ that encode delta-9 desaturases are weakly expressed in early seeds but highly expressed in different compartments of kernels. This may be because OLEs play a structural role in stabilizing the lipid body during desiccation of the seed by preventing coalescence of the oil (Figure 5). The expression profiles of the putative genes provide important information for the strategy applied to the molecular breeding of nutritionally enriched maize.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Genes** & **Protein Function** & **Maize Orthologs** & **Gene ID** & **Strategy** & **References** \\ \hline \multirow{2}{*}{_GLK1_} & \multirow{2}{*}{transcription factor} & G2 & _Zm00001 eb118900_ & \multirow{2}{*}{overexpression} & [133] \\  & & _GLK1_ & _Zm00001 eb371980_ & & \\ \hline _AN1_ & transcription factor & _IN1_ & _Zm00001 eb303250_ & \multirow{2}{*}{overexpression} & [134] \\ \hline _AN3_ & transcription factor & _GIF1_ & _Zm00001 eb056300_ & \multirow{2}{*}{overexpression} & [135] \\ \hline _ANT1_ & transcription factor & _PL1_ & _Zm00001 eb278680_ & \multirow{2}{*}{overexpression} & [136] \\  & & _C1_ & _Zm00001 eb373660_ & & \\ \hline _MYBR_ & transcription factor & _MYB31_ & _Zm00001 eb103730_ & \multirow{2}{*}{knockout} & [137] \\  & & _MYB42_ & _Zm00001 eb202770_ & & \\ \hline _DRB3_ & double stranded RNA binding & _IDP7470_ & _Zm00001 eb102530_ & \multirow{2}{*}{knockout} & [138] \\  & protein & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: List of potential gene resources for enhancing anthocyanin content in maize.
## 4 Results and Discussion
### Energy Consumption Reduction from Proposed Hybrid Technique: Scenario 2
The intention was to induce a substantial and measurable change in the battery's voltage, given the node's low power consumption during deep sleep mode, which otherwise showed no significant alteration in the battery's voltage over a 1-day span. Consequently, the 5% reduction in the node's battery within this timeframe can be attributed to the energy expended in detecting the leak events introduced during experimentation. This detection process necessitates the node's transition from deep sleep to active mode, consuming a considerable amount of energy. However, it is crucial to note that in real-world scenarios, the frequency of leak events is typically lower. Assuming no leaks occur over a one-year period, the node would continuously remain in deep sleep mode, consuming a current of 0.3 mA. In this case, the estimated lifespan of the node, supplied with a 3.7 V 2000 mAh Li-Po battery, extends to approximately 278 days. Additionally, the node's lifespan can be prolonged by optimizing the balance between leak detection reliability and energy consumption. For instance, configuring the ADXL344 to operate at different Output Data Rates (ODR) during specific times, such as employing an ODR \(<\) 10 Hz (with a measurement mode supply current of 30 \(\upmu\)A) for 90% of every 10-s period and an ODR \(\geq\) 100 Hz (with a measurement mode supply current of 140 \(\upmu\)A) for the remaining 10%, will significantly enhance the sensor node's lifespan. Figure 9: State of charge of sensor node’s battery when duty cycling and hierarchical were implemented.
## 2 Fabrication and Setup  
### MgO Nanoparticle-Doped Fiber  
The fiber used in this work presents a core doped with a random pattern of nanoparticles whose composition is based on MgO [17; 19]. The fiber, designed to improve the efficiency of C-band optical amplifier (wavelengths from 1530 to 1565 nm), presents an additional doping of erbium in the core. The fiber possesses the typical telecom size, i.e., core diameter of 10 \(\upmu\)m and cladding diameter of 125 \(\upmu\)m, and a protective jacket with 250 \(\upmu\)m diameter. This fact permits simple splicing operation with standard SMF-28 pigtails. The preform of the fiber has been fabricated by a conventional Modified Chemical Vapor Deposition (MCVD) process, a common technique for specialty optical fibers fabrication.  
The proposed technology allows one to grow in-situ oxide nanoparticles due to high temperatures reached during the MCVD process [19] The implemented principle is based on the spontaneous phase separation process. This process involves the immiscibility of silicate compound that contain alkaline earth ions (MO, where M = Mg, Ca or Sr). The result is that the compound will decompose into two phases: one silica-rich and one MgO-rich in shape of spherical particles. The characteristics of the nanoparticles (size, size distribution) depends on the concentration of Mg, but typically the process generates nanoparticle whose size, location and refractive index are random. The size is in between 20 nm and 100 nm, while the refractive index is in between 1.53 to 1.65. The presence of nanoparticle strongly enhances the scattering and the losses [17; 18].
# Research on Demand-Based Scheduling Scheme of Urban Low-Altitude Logistics UAVs
## 4 Example Verification and Analysis
### Analysis of Algorithm Parameters
Several controlled experiments were carried out under the same conditions as other parameter settings and the airspace environment. The delivery costs required under two different route situations were obtained, and the results are shown in Figure 8. The left side represents the RN route, the right side represents the BN route, and the unfilled hollow circle indicates the delivery cost under this condition, while the filled circle indicates that the lowest delivery cost has been reached at this time. Figure 6: Schematic diagram of relationship between drone quantity/backup battery quantity and delivery cost, where (**a**) represents the RN route and (**b**) represents the BN route. Figure 7: Schematic diagram of relationship between drone quantity/working hours and delivery cost, where (**a**) represents the RN route and (**b**) represents the BN route. It can be seen from Figure 8 that the delivery cost is related to the changes in the number of drones and charging time. This cost generally shows a downward trend and then rises. When the number of drones is 70 and the charging time is 2 h, the RN route has the lowest delivery cost. When the number of drones is 70 and the charging time is 1.5 h, the delivery cost of the BN route is the lowest. Therefore, these numbers of drones and charging times can be considered when the actual logistics drones are delivered. To sum up, in this case, the preferred number of drones is 70. In addition, depending on the choice of route (the RN route or the BN route), 15 or 20 backup batteries can be selected, the working time can both be 30 min, and the charging time can be 2 h or 1.5 h, respectively. The above is a sensitivity analysis of several parameters. Through the analysis, it is found that the distribution demand is related to the number of drones, the number of backup batteries, the drone working hours, and the charging time. Combined with the delivery cost, it can be found that a higher number of backup batteries does not translate into a lower cost. After meeting the requirements for working hours within the scope of responsibility of the distribution point, increasing the working time of the drones cannot reduce the delivery cost. In addition, shorter charging times do not mean better results. With the increase in various parameters, the delivery cost generally tends to decrease first and then increase. Therefore, when purchasing drones, it is necessary to select the appropriate drones according to the distribution tasks of the distribution point.
# Comparison of Traffic Flow Models with Real Traffic Data Based on a Quantitative Assessment
## 2 Theoretical Background
### Modeling Equilibrium Traffic Flow Relationships
#### 2.1.2 Multi-Regime Traffic Flow Models  
Researchers proposed multi-regime models primarily for their empirical accuracy. However, this can only be obtained at the cost of a more complicated functional form and much more complicated calibration with field data. The main problem with multi-regime models is determining the boundaries of the particular traffic conditions described by separate functions and determining the points of transition from one state to another. Table 2 lists selected two-regime models.  
[PERSON] was one of the first researchers to suggest a description of the relationship between volume and density using a non-continuous curve. In 1961, he presented a two-regime traffic model making the distinction between free-flow and congested operation. The model is a combination of [PERSON]'s and [PERSON]'s models [7; 9]. [PERSON] [21] proposed a two-regime model where speed falls linearly in free-flow traffic as density increases. In congested traffic, volume falls linearly as density increases. The triangular model is very popular in urban traffic control. It represents the relationship \(q(k)\) by two straight lines. Following a modification by [PERSON], the model is a truncated triangle where the same relationship is represented by three straight lines [22; 23]. [PERSON] proposed a two-regime model of volume variability \(q\) relative to density \(k\), where the shape of the curve in free-flow will depend on the number of lanes \(l\)[23].  
Figure 2 presents how the models summarized above represent the relationships.  
\begin{table}
\begin{tabular}{c c c} \hline
**Author (Year of Publication)** & **Basic Function** & **Parameters** \\ \hline [PERSON] (1961) & \(v=\left\{\begin{array}{ll}v_{sw}\exp\left(-\frac{k}{k_{opt}}\right)&k<k_{1} \\ v_{opt}\ln\left(\frac{k_{max}}{k}\right)&k\geq k_{1}\end{array}\right\}\) & \(v_{sw},\ k_{opt},\) \\ [PERSON] (1989) & \(v=\left\{\begin{array}{ll}v_{sw}-ak\\ d\left(\frac{k}{k}-\frac{1}{k_{max}}\right)&k>k_{1}\end{array}\right\}\), where \(d=\frac{v_{sw}-ak_{opt}}{k_{opt}-k_{max}}\) & \(v_{sw},\alpha,k_{opt},\) \\  & & \(k_{max},k_{1}\) \\ Triangular & \(q=\left\{\begin{array}{ll}v_{sw}k&k<k_{1}\\ d_{max}-\frac{k-k_{opt}}{k_{max}-k_{opt}}q_{max}&k\geq k_{1}\end{array}\right\}\) & \(v_{sw},q_{max},\) \\ [PERSON] (1997) & \(q=\left\{\begin{array}{ll}v_{sw}k&k<k_{1}\\ v_{sw}k_{1}&k_{1}<k<k_{2}\\ q_{max}-\frac{k-k_{2}}{k_{max}-k_{2}}q_{max}&k\geq k_{2}\end{array}\right\}\) & \\ [PERSON] (2002) & \(q=\left\{\begin{array}{ll}k(1-\left(\frac{k}{k_{1}}\right)^{1-}v_{sw}+\left( \frac{k}{k_{1}}\right)^{1-}v_{opt}&v>v_{opt}\\ v_{opt}k_{1}-\frac{k-k_{1}}{k_{max}-k_{1}}v_{sw}k_{1}&k\geq k_{1}\end{array}\right\}\) & \(v_{sw},v_{opt},\) \\ \hline \end{tabular} where: \(k_{1},\ k_{2}\)—boundary values of density at which there is a transition between traffic flow regimes.  
\end{table}
Table 2: List of selected multi-regime models.
## 2 Results and Discussion  
### Synthesis of Compounds  
#### Synthesis and Characterization of Dihydrazides (**3a-d**) by Hydrazinolysis of Esters (**2a-d**)  
In order to obtain the 4_H_-4-amino-5-mercapto-3-(4_H_-4-amino-5-mercapto-1,2,4-triazol-3-yl)-1,2,4-triazole (**1a**) and 1,n-bis-(4_H_-4-amino-5-mercapto-1,2,4-triazol-3-yl)alkanes (**1b-d**) derivatives, the synthesis of starting reagents was employed, namely the dihydrazides (**3a-d**) of the oxalic, malonic, succinic and adipic acids, by the hydrazinolysis of the diesers (**2a-d**).  
Following the protocol presented in Section 3.2.1, the four dihydrazides were obtained with good yields (79-87%) according to the literature [47], and characterized by melting point and FTIR spectroscopy (Table 1).  
The measured melting points are in good agreement with literature data for the 3**b-d** derivatives, while for 3**a** there is a considerable difference, which can be explained by the fact that this compound is known to exist in at least four polymorphic states [52]. Following this aspect, the existence of several solid forms of organization of this compound determines different physical properties (including melting point), since the formation of hydrogen bonds between donor and acceptor moieties in this molecule is conditioned by the different orientations of the groups with respect to the molecular plane, which are different geometric orientations for hydrogen bonding arrangements in the solid state [52; 53].
# A Review on Automated Detection and Identification Algorithms for Highway Pavement Distress
## 4 Evolution of Pavement Image Processing Techniques
### Conventional Image Processing Techniques  
Conventional image processing methods mainly use two core techniques: threshold-based and region-based segmentation. Threshold segmentation converts images to a binary format by analyzing grayscale differences between targets and background, using  
Figure 2: LRIS system produced by INO Corporation in Canada.  
optimized threshold selection. Region segmentation groups adjacent pixels with similar characteristics based on predefined similarity measures.  
[PERSON] [23] first implemented the Otsu method (OTSU) for asphalt pavement crack segmentation in 2008. This technique selects optimal thresholds by maximizing inter-class variance [24], proving effective for high-contrast PCC (Portland cement concrete) pavement cracks (see reproduced results in Figure 3). However, its performance declines on asphalt surfaces due to (1) complex textural patterns and (2) low grayscale contrast between cracks and background, leading to reduced accuracy (demonstrated in Figure 4b).  
Researchers have developed several enhancement methods to improve asphalt crack detection. Key approaches include nonlinear denoising techniques like median filtering [25] and anisotropic diffusion [26], which optimize threshold-based segmentation (see results in Figure 4c,d). However, these improved methods still show limited effectiveness for asphalt crack extraction, suggesting the need for further development.  
[PERSON] et al. [27] proposed a region-growing algorithm for automated crack detection, requiring manual seed selection and showing inherent subjectivity. While effective for small targets, this iterative method faces computational challenges with wider, distinct cracks, significantly reducing efficiency [28].  
While traditional image processing methods offer viable solutions in constrained scenarios, they exhibit notable limitations when addressing heterogeneous backgrounds, low-contrast targets, and multi-scale distress patterns.  
Figure 4: _Cont._  
Figure 3: Treatment of PCC pavement cracks using OTSU: (**a**) PCC pavement cracks; (**b**) OTSU processing result.
## 3 Analysis Methods
### Topic Modeling of mHTI Scholars Based on LLM's Semantic Latent Space
#### 3.2.1 Semantic Embedding of Publication Titles and Abstracts Using LLM-Derived Representations  
To transform the semantic meaning of text inputs into numerical inputs, the embedding vectors are constructed. An embedding is a numerical representation of text (or other data) in a high-dimensional vector space, which is then referred to as an embedding space. It ensures that similar texts have similar vector representations. According to the linear representation hypothesis, certain directions in the embedding space of a large language model correspond to specific, human-interpretable concept changes [35]. This means that by moving along these directions within the model's representation space, we can effect predictable and meaningful alterations in the concepts represented by the model. Such embeddings are particularly useful for downstream tasks like clustering, classification, and search. Compared with traditional natural language processing models, LLMs offer a deeper contextual understanding, often surpassing earlier models and even approaching human-level performance across various tasks. Consequently, integrating LLMs into science of science research is highly valuable, where they not only provide  
Figure 2: Topic modeling pipeline.  
novel methods for knowledge discovery, such as summarizing scientific literature, but also enhance existing text analysis workflows by offering a more robust embedding space, thereby improving the accuracy of tasks like topic modeling. We employ LLMs for multiple use cases in this study.  
We adopt the general topic modeling pipeline proposed by BERTopic [20], augmented with LLMs to enhance both accuracy and interpretability. Specifically, we treat the combination of a publication's title and abstract as a representative summary of its content. For each publication, we concatenate the title and abstract and encode them into a 3076-dimensional embedding vector for each publication using OpenAI's publicly available advanced embedding model, text-embedding-ada-002. With 16,580 such embedding vectors, we proceed with the standard topic modeling process, which involves dimensionality reduction followed by unsupervised clustering. The resulting clusters are interpreted as distinct research topics.
## 2 Materials and Methods
### hBM-MSC Culture and Adipogenesis  
The cells used in this study were purchased from PromoCell (C-12974, Heidelberg, Germany). Unless otherwise noted, 6-well plates were used to seed and proliferate the hBM-MSCs (1 \(\times\) 10\({}^{6}\) cells/well) and cells were fed Mesenchymal Stem Cell Growth Medium (C-28009, PromoCell). Cultured cells were kept in 37 \({}^{\circ}\)C incubators and the CO\({}_{2}\) levels were set at 5%. The hBM-MSCs were induced for adipogenesis by exchanging the culture medium of confluent cells with Mesenchymal Stem Cell Adipogenic Differentiation Medium 2 (C-28016, PromoCell). Differentiation was continued by feeding the cells with fresh differentiation medium every third day until the time of the experiment. The cells were treated with loliolide during the first introduction of differentiation medium only and the subsequent medium changes did not include loliolide treatment.