esa-sceva/satcom-chunk-collection huggingface dataset:
# In Vitro Gene Expression Responses of Bovine Rumen Epithelial Cells to Different pH Stresses
## 3 Results
### Transcriptomic Data and Detection of Expressed Genes in Samples  
In order to investigate the variations in mRNA transcriptional expression between the three groups, RNA-Seq was used to identify the differentially expressed genes (DEGs) of BRECs. Nine BREC samples produced a total of 481.72 million (53.52 \(\pm\) 5.37 million reads per sample) high-quality, paired reads, and the overall read alignment rate to the cattle reference genome was 93.78% \(\pm\) 0.23%. Total gene expressions from the BREC samples' principal component analysis (PCA) clearly clustered the three groups. (Figure 2A). The filter criteria for significant differential expressions were an adjusted \(p<0.05\) and absolute value of [log2 (FC)] \(>\) 1, which were used to count the differentially expressed genes (DEGs). A total of 1381 DEGs, including 558 upregulated and 823 downregulated DEGs, were identified in control vs. SARA. Similarly, for control vs. ARA, a total of 283 DEGs with 56 up- and 227 downregulated DEGs were reported. For SARA vs. ARA, 1674 significant DEGs were detected, including 836 up- and 838 downregulated genes (Figure 2B). All DEGs are shown in the Supplementary Materials Table S2. To study the functions of the DEGs, we decreased the \(p\)-value cutoff to \(\leq\)0.01 and doubled the [log2 (FC)] \(>\) 2 value for the three groups. Within control vs. SARA, a total of 215 DEGs were obtained, of which 123 were upregulated and 92 were downregulated (Supplementary Materials Table S3). There were 43 DEGs, including 35 downregulated and 8 upregulated DEGs, in the control vs. ARA comparison. Likewise, for SARA vs. ARA, a total of 266 DEGs, with 90 up- and 176 downregulated DEGs, were reported (Table S2). Table 2 shows three groups of DEGs screened under [log2 (FC)] \(>\) 2, \(p<0.01\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Groups** & **Normal Cells** & **Necrotic Cell** & **Early Apoptotic Cells** & **Late Apoptotic Cells** & **Apoptosis Index** \\ \hline Control & 72.3 \(\pm\) 3.0 \({}^{\mathrm{ab}}\) & 16.6 \(\pm\) 2.6 \({}^{\mathrm{ab}}\) & 2.1 \(\pm\) 0.025 \({}^{\mathrm{ab}}\) & 9.0 \(\pm\) 0.4 \({}^{\mathrm{a}}\) & 11.1 \(\pm\) 0.4 \({}^{\mathrm{a}}\) \\ SARA & 76.
###### Abstract  
Internet of Things (IoT) can significantly enhance various aspects of today's electric power grid infrastructures for making reliable, efficient, and safe next-generation Smart Grids (SGs). However, harsh and complex power grid infrastructures and environments reduce the accuracy of the information propagating through IoT platforms. In particularly, information is corrupted due to the measurement errors, quantization errors, and transmission errors. This leads to major system failures and instabilities in power grids. Redundant information measurements and retransmissions are traditionally used to eliminate the errors in noisy communication networks. However, these techniques consume excessive resources such as energy and channel capacity and increase network latency. Therefore, we propose a novel statistical information fusion method not only for structural chain and tree-based sensor networks, but also for unstructured bidirectional graph noisy wireless sensor networks in SG environments. We evaluate the accuracy, energy savings, fusion complexity, and latency of the proposed method by comparing the said parameters with several distributed estimation algorithms using extensive simulations proposing it for several SG applications. Results prove that the overall performance of the proposed method outperforms other fusion techniques for all considered networks. Under Smart Grid communication environments, the proposed method guarantees for best performance in all fusion accuracy, complexity and energy consumption. Analytical upper bounds for the variance of the final aggregated value at the sink node for structured networks are also derived by considering all major errors.  
Keywords:data fusion; distributed estimation; energy efficiency; latency; fusion complexity; information accuracy; internet of things; smart grid communications +
Footnote †: journal: Sensors
# A Fast Wavelet-Based Bridge Condition Assessment Approach Using Only Moving Vehicle Measurements
## 3 Method for Bridge Damage Detection
### Wavelet Analysis  
Like Fourier transform, wavelet transform uses a family of wavelet functions to express or approximate the target signal \(f(t)\), defined as  
\[WT(a,b)=\int_{-\infty}^{+\infty}f(t)\Psi_{a,b}(t)dt \tag{23}\]  
where  
\[\Psi_{a,b}(t)=\frac{1}{\sqrt{a}}\Psi\left(\frac{(t-b)}{a}\right) \tag{24}\]  
is referred to as the daughter wavelet function derived from the mother wavelet function \(\Psi(t)\), which meets the properties of \(\int_{-\infty}^{+\infty}\Psi(t)dt=0\) and \(\int_{-\infty}^{+\infty}|\Psi(t)dt|<\infty\). \(a\) represents the scale factor for wavelet transform, which is inversely related to wavelet pseudo frequency. \(b\) represents the shift factor related to the temporal translation of the mother wavelet function \(\Psi(t)\) or its location in \(f(t)\). In general, the high value of scale uses the stretched wavelet to represent the target signal, namely giving a low-frequency content. Conversely, a low-scale wavelet result implies giving a high-frequency content. Mathematically, the relationship between the wavelet pseudo frequency, \(F_{p}\), and scale, \(a\), is expressed as  
\[F_{p}=\frac{F_{c}}{a\Delta} \tag{25}\]  
where \(F_{c}\) denotes the central frequency of the mother wavelet \(\Psi(t)\) in Hz and \(\Delta\) denotes the sampling period of the signal. [PERSON], [PERSON], and [PERSON] [2] pointed out that a dominated frequency in a signal can be detected with the wavelet pseudo frequency using the wavelet coefficient energy. A peak wavelet coefficient energy reflects a dominated frequency in a signal. Energy is a conventional measurement of a signal. Similarly, it can be used to measure the wavelet coefficients, \(WT(a,b)\), and the mathematical expression is  
\[E_{energy}=\sum_{a}\sum_{b}\left|WT(a,b)\right|^{2} \tag{26}\]  
For a particular scale, the wavelet coefficient energy can be calculated as  
\[E_{energy}=\sum_{b}\left|WT(a,b)\right|^{2} \tag{27}\]
## 2 Data and Methods
### Data Screening and Preprocessing  
To ensure data relevance and accuracy, the following three-step screening protocol was applied: (1) Initial filtering: Non-technical publications (e.g., opinion pieces, news articles) were excluded [24]. (2) Manual verification: Two independent researchers cross-checked titles, abstracts, and keywords to eliminate studies unrelated to construction robotics (e.g., studies about industrial robots in manufacturing). (3) Normalization: Variations of keywords and institutional affiliations were standardized to minimize redundancy [25].
# DGA Domain Name Classification Method Based on Long Short-Term Memory with Attention Mechanism
## 4 Methodology
### Domain Name Padding  
The length of the domain name is not fixed. Some works use the domain name length [9; 10] as one of the characteristics of the DGA domain name detection and classification. Table 1 shows the lengths of 11 common DGA domain names. It can be seen from the table that the lengths of different DGA domain names are usually different; however, the method proposed in this paper requires a fixed length as input.  
Usually, the longest length in the dataset is chosen as the fixed length, and then the short domain name is padded. However, considering the scalability of the model, this paper makes statistics on the length of the DGA domain name published on Bambenek Consulting [24]. The result is shown in Figure 2; most DGA domain names are concentrated in the 10-20 interval, and the longest DGA domain name is 44, which covers more possibilities and guarantees the performance of the method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**DGA** & **Length** & **Example** \\ \hline banjori & 10 & pdtmstring \\ corebot & 23 & a0c4e8 sr70 oluhsf3l1h1 va \\ cryptolocker & 8 & rifxkpdx \\ dircrypt & 10 & xzdiobjady \\ kraken & 8 & iuhqhbmq \\ locky & 17 & qequxabetnnsclkm \\ pykspa & 9 & folmecyca \\ qakbot & 20 & gutkdzfamdsgibpuoyb \\ ramdo & 8 & kueeksgm \\ ramnit & 9 & byydmekgd \\ simda & 23 & jewumerdy4 yyyjolyvofoh \\ \hline \hline \end{tabular}
\end{table}
Table 1: Eleven types of DGA domain name length.  
Adding 10, we use 54 as the input length; all domain names are padded to 54. This paper uses the symbol \"**\", which is not allowed in the domain name, to complete the domain name.
## 4 Monitoring System Framework
### Cloud Service System Construction
#### 4.3.3 Cloud Database  
The RDS (Relational Database Service) is a professional, high-performance, stable, reliable and flexible scalable online cloud database service. It provides a web interface for configuration, data backup and recovery, security management, monitoring and expansion functions. The big data monitoring system adopts the MySQL version of this cloud database.
## 1 Introduction
* Application of the proposed ground IoT terminal device was demonstrated on the TP, illustrating that the proposed method could efficiently transfer monitoring data to a data center at an increased performance and lower cost. * Experiments were conducted to reveal the efficiency of the satellite-enabled IoT network. We analyzed the results and demonstrated the value of the IoT network in environmental field studies.
## 1 Introduction  
Soil is a biologically balanced system and any variations in the soil micro-environment results in the changes in native microbial community profiles. Similarly, the accumulation of pollutants such as polycyclic aromatic hydrocarbons (PAHs) [1], petroleum compounds [2] and heavy metals [3] have drastic effect on the bacterial diversity. The driving factors which determine the abundance of the microbial community in a particular contaminated soil are the genetic variation which results in altered metabolic pathways or the selective enrichment of microbes which are able to transform the particular pollutant [4].  
Rapid advent of industrialization has led to deleterious effect on the environment, mostly due to the improper discharge of industrial waste. The textile industries use a large amount of synthetic dyes which results in discharge of colored wastewater [5; 6]. Azo dyes account for 70% of all the commercial dyes used in textile industries across the globe [7]. These dyes have been proven to be harmful for the aquatic life forms as they result in increasing the biological oxidation demand (BOD) [8]. Additionally, these azo compounds are also reported to be highly toxic and carcinogenic in nature [7]. It is estimated that around 50% of the synthetic dyes used in the textile industries does not bind to the fabric and result in getting discharged into the environment [9]. The compounds can leach into the groundwater and the use of those water sources for farming results in contamination of agricultural fields [10]. The bioremediation of contaminant by isolation and characterization of indigenous microbial community have been studied in various cases [11; 12]. The classical enrichment technique does not give the broader idea about the various microbial activities occurring in the soil. Metagenomic analysis by using high-throughput sequencing helps to overcome the limitations of conventional methods of studying microbial community dynamics in soil. Researchers have concentrated on designing numerous technologies [13; 14] for treatment of textile wastewater and also studied the changes in microbial community and diversity in those reactor systems which are used for remediating such wastewater [15]. However, there have been no studies on high-throughput detection of microbial community profiles of environmental samples such as water or soil which have been severely affected by long-term exposure to improper discharge of textile wastewater rich in azo dye compounds.  
Orthurbanalyyam dam is constructed on the Noyyal river in Tamil Nadu, India. The dam has become a reservoir of azo dye compounds due to the continuous discharge of effluents from the textile industries located in the nearby area of Tiruppur. In a previous study, bacterial and fungal species were isolated from the soil samples collected from the nearby catchment area and allied agricultural fields of this dam and characterized based on their ability to degrade azo dyes in a batch scale [16]. Whereas, in this study, we aimed to characterize the bacterial community profiles of soils collected from the nearby area of Orathupalayam dam and allied agricultural fields.
# Intent to Adopt Location Sharing for Logging Safety Applications
## 3 Results
### TPB Assessment
Results of ordinal regression evaluating the effect of attitude, descriptive norms, and perceived behavioral control on intent to perform safety behaviors show that intent varies significantly by total attitude score and norms for all three behaviors and varies by control for two behaviors (Table 7). Thus, for our survey sample, TPB attributes are correlated with logger likelihood of using PLDs. One model (PLDs for general situational awareness) had assumption violations for the control term, so results of this model should be interpreted with caution. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Behavior**} & \multicolumn{2}{c}{**P-Value (Chi-Squared Test Statistic)**} \\ \cline{2-4}  & **Age** & **Phone** & **Age:Phone** \\ \hline \multirow{2}{*}{Automatic LS for hand fallers} & 0.2077 & \textless{}0.001 & 0.7915 \\  & (4.552) & (250.204) & (5.470) \\ \hline \multirow{2}{*}{PLDs on workers and heavy equipment} & 0.6271 & \textless{}0.001 & 0.7389 \\  & (1.744) & (261.657) & (6.010) \\ \hline \multirow{2}{*}{PLDs for general situational awareness} & 0.6578 & \textless{}0.001 & 0.9674 \\  & (1.607) & (254.152) & (2.919) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of ordinal regression (alpha = 0.05) for intent versus age and smartphone frequency with interactions effects (Age:Phone). “LS” stands for location sharing, and “PLD” stands for personal location-sharing device. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Behavior**} & **P-Value (Chi-Squared Test Statistic)** \\ \cline{2-3}  & **Attitude** & **Norms** & **Control** \\ \hline \multirow{2}{*}{Automatic LS for hand fallers} & \textless{}0.001 & \textless{}0.001 & 0.0358 \\  & (75.253) & (55.839) & (6.657) \\ \hline \multirow{2}{*}{PLDs on workers and heavy equipment} & \textless{}0.001 & \textless{}0.001 & 0.9851 \\  & (75.143) & (53.225) & (0.030) \\ \hline \multirow{2}{*}{PLDs for general situational awareness} & \textless{}0.001 & \textless{}0.001 & 0.0453 \\  & (76.003) & (74.293) & (6.187) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of ordinal regression (alpha = 0.05) for intent versus attitude, norms, and control of location sharing (LS) behaviors. PLD stands for personal location-sharing device. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Survey Question**} & **Median** & \(p\)**-Value** \\ \hline \multirow{2}{*}{Likelihood of using automatic LS for hand fallers} & 4 & \textless{}0.001 \\  & (3.5) & (\textless{}0.001) \\ \hline \multirow{2}{*}{Likelihood of using PLDs on workers/equipment} & 4 & \textless{}0.001 \\  & (3.5) & (\textless{}0.001) \\ \hline \multirow{2}{*}{Likelihood of using PLDs for situational awareness} & 3 & \textless{}0.001 \\  & (3.5) & (0.0001) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of Sign Tests and Wilcoxon Sign-Rank Tests, with the latter in parentheses (alpha = 0.05). “LS” stands for location sharing, and “PLD” stands for personal location-sharing device.
# High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks
## 3 Soil Moisture Retrieval Methodology
### Data Sets  
This subsection provides details about the acquisition of the SM, CYGNSS, and ancillary data sets as well as their expected contribution to the regression, and the quality control steps to eliminate erroneous data from the analysis.
# Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach
## 1 Introduction
This is further augmented by the C2 PSA block, which integrates self-attention mechanisms to strengthen both spatial and channel-wise feature learning. As shown in the left portion of the diagram, the C2 PSA module uses a series of convolutional layers and PSA blocks, followed by concatenation, to enrich the feature maps before passing them forward. The neck of the network utilizes several C3k2 modules, which are variations of convolutional blocks designed to either use grouped convolutions (C3k = True) or bottleneck structures (C3k = False), depending on the configuration. These are detailed in the top-right section of the diagram. These blocks help in refining and combining features across different scales through concatenation and upsampling layers. Finally, the model outputs predictions through the 11 Detect head, which operates on three different scales. As illustrated in the bottom-right corner of the diagram, the detection head includes a series of convolutional and depthwise convolution layers with different kernel sizes (3 \(\times\) 3 and 1 \(\times\) 1). These layers handle the tasks of bounding box regression, object classification, and loss calculation for each predicted region. To validate the effectiveness of our model, we conduct a comparative evaluation against YOLOv5 using a custom dataset of annotated Bangla characters. The results focus on detection accuracy, precision, recall, and inference speed. Overall, this study shows that the enhanced YOLOv11 model, with its modified backbone, attention mechanisms, and multi-scale detection heads, is well suited for recognizing Bangla script. The proposed approach offers a fast and accurate solution, which could be valuable for applications like educational tools, digital archiving, and broader multilingual OCR systems.
## 2 Distribution Network Flexibility Resources and Flexibility Evaluation Indicators
### Two-Layer Planning Model of SOP and ESS  
The rapid development of ESS technology and the derivation of SOP has led to the realization that the two can be effectively controlled in a synergistic manner to inhibit the power spatial and temporal fluctuations brought about by DG access to ADN. This paper therefore enhances the DG admittance capacity of ADN through the planning of the two. Since this paper considers both the rational allocation of flexibility resources and the optimal operation of ADN flexibility enhancement, it needs to be decoupled based on the idea of decomposition and coordination. In conclusion, this paper coordinates SOP networked flexibility resources and ESS nodal flexibility resources. It also proposes a two-layer coordinated planning model, as illustrated in Figure 6.  
Figure 5: Typical scenario of PV and WT combination.  
Figure 6: SOP and ESS two-tier planning modeling framework.  
In the planning process, the flexibility of nodes and branches is coordinated in such a way that the ESS and SOP with the optimal capacity are placed in the best position through optimal configuration. This maximizes system flexibility and ensures that there are both sufficient flexibility resources and sufficient flexibility channels in ADN. The upper layer determines the location and capacity of SOP and ESS and transmits this information to the lower layer. The lower layer model is an operational optimization layer that considers the enhancement of ADN flexibility and is used to obtain the optimal operational strategy of ADN under the scenario. The objective of the lower model is to achieve the highest daily average flexibility level, which is comprised of the DG consumption rate index and the DG volatility index. The model transmits power transmitted by SOPs and ESSs at each moment back to the upper layer. Ultimately, the upper and lower layers iteratively optimize each other in order to identify the optimal scheme that simultaneously satisfies both planning and operational requirements.
# One-Dimensional Consolidation of Multi-Layered Unsaturated Soil with Impeded Drainage Boundaries
## 2 Problem Description
### Solution Conditions  
In each soil layer, the excess pore-air and pore-water pressures are supposed to be uniformly distributed at the initial time, that is:  
\[{\bf u}^{(i)}(z,0)={\bf u}_{0}^{(i)}=\left\{\begin{array}{cc}u_{\rm a0}^{(i) },&u_{\rm aw}^{(i)}\end{array}\right\}^{T} \tag{4}\]  
where \(u_{\rm a0}^{(i)}\) and \(u_{\rm aw}^{(i)}\) are the initial pore-air and pore-water pressures in the \(i\)th soil layer, respectively.  
Assume that both the top surface and bottom base are impeded drainage to the air and water phases, that is [23; 26]:  
\[u_{\rm a,z}^{(1)}(0,t)-h^{-1}R_{\rm ta}u_{\rm a}^{(1)}(0,t)=0,\ u_{\rm w,z}^{( 1)}(0,t)-h^{-1}R_{\rm tw}u_{\rm w}^{(1)}(0,t)=0 \tag{5a}\] \[u_{\rm a,z}^{(n)}(h,t)+h^{-1}R_{\rm ba}u_{\rm a}^{(n)}(h,t)=0,\ u_{\rm w,z}^{( n)}(h,t)+h^{-1}R_{\rm bw}u_{\rm w}^{(n)}(h,t)=0 \tag{5b}\]  
where \(R_{\rm ta}\) and \(R_{\rm tw}\) are the drainage parameter of the air phase and that of the water phase at the top surface, respectively; and \(R_{\rm ba}\) and \(R_{\rm bw}\) are the drainage parameters of the air phase and that of the water phase at the bottom base, respectively.  
The continuity conditions of the excess pore pressures and those of the flow rates at an interface between two adjacent soil layers can be addressed as below:  
\[u_{\rm a}^{(\ell)}(z_{\ell},t)=u_{\rm a}^{(\ell+1)}(z_{\ell},t),\ u_{\rm w}^{( \ell)}(z_{\ell},t)=u_{\rm w}^{(\ell+1)}(z_{\ell},t) \tag{6a}\] \[k_{\rm a}^{(\ell)}u_{\rm a}^{(\ell)}(z_{\ell},t)=k_{\rm a}^{(\ell+1)}u_{\rm a }^{(\ell+1)}(z_{\ell},t),\ k_{\rm w}^{(\ell)}u_{\rm w}^{(\ell)}(z_{\ell},t)=k_ {\rm w}^{(\ell+1)}u_{\rm w}^{(\ell+1)}(z_{\ell},t) \tag{6b}\]  
where \(\ell=1,2,\cdots,n-1\).  
In the matrix form, these boundary and interface continuity conditions can be rewritten as  
\[{\bf u}_{z}^{(1)}(0,t)-h^{-1}{\bf R}_{\rm t}{\bf u}^{(1)}(0,t)=0,\ {\bf u}_{z}^{(n)}(h,t)+h^{-1}{\bf R}_{\rm b}{\bf u}^{(n)}(h,t)=0 \tag{7}\]  
\[{\bf u}^{(\ell)}(z_{\ell},t)={\bf u}^{(\ell+1)}(z_{\ell},t),\ {\bf k}^{(\ell)}{\bf u}_{z}^{( \ell)}(z_{\ell},t)={\bf k}^{(\ell+1)}{\bf u}_{z}^{(\ell+1)}(z_{\ell},t) \tag{8}\]  
where:  
\[{\bf R}_{\rm t}=\left[\begin{array}{cc}R_{\rm ta}&0\\ 0&R_{\rm tw}\end{array}\right],\ {\bf R}_{\rm b}=\left[\begin{array}{cc}R_{\rm ba }&0\\ 0&R_{\rm bw}\end{array}\right],\ {\bf k}^{(\ell)}=\left[\begin{array}{cc}k_{\rm a}^{( \ell)}&0\\ 0&k_{\rm w}^{(\ell)}\end{array}\right] \tag{9}\]