esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### E. coli and Treated Liquid Medium Preparations  
The standard laboratory strain of _Escherichia coli_ or _E. coli_ (TISTR Number 074) obtained from Thailand Institute of Scientific and Technological Research (TISTR) was used for the original _E. coli_ culture stocks. One colony of _E. coli_ from the culture stock was diluted in 5 mL of liquid nutrient broth media, and incubated at 30 \(\pm\) 0.1 \({}^{\circ}\)C for the 1 st _E. coli_ liquid stock. After 18 h of incubation, 1 mL of _E. coli_ solution was diluted in 1000 mL of 121 \({}^{\circ}\)C autoclaved reverse osmosis (RO) water and incubated again for 3 h to obtain the _E. coli_ culture in the stationary growth phase for the 2 nd _E. coli_ liquid stock. Then, the treated liquid media were prepared by the dilution of 6-7 mL of the 2 nd _E. coli_ liquid stock in 1000 mL of autoclaved RO water. The final concentration of _E. coli_ in the treated liquid media was around \(1.7\times 10^{10}\) CFU/mL, and the electrical conductivity of the RO water increased from 0 to 0.18 mS/cm.  
Viable cell counts were determined by the drop plating method on standard agar culture plates. The treated liquid medium in each serial dilution was centrifuged at 2800 rpm by a vortex mixer before the serial dilution sampling. Each culture plate was divided into 6 sectors depending on each serial dilution, ranging from \(10^{2}\) to \(10^{7}\) dilution of the treated liquid media. In each dilution zone, 5 drops of 10 \(\mu\)L of treated liquid medium were dropped. All the preparation and cell culture processes were conducted in a laminar flow cabinet. The culture plates were then incubated for 18 h before viable cell counts. In each experiment, 2 culture plates were duplicated for each experimental condition.  
Figure 1: Schematic drawing of the co-field treatment chamber with one treatment unit (\(N_{HV1}\)) of high voltage electrode.  
The total experiment consisted of 5 experimental sets, which were performed once each on three different days for statistical analysis. The inactivation of _E. coli_ was evaluated on average by calculating the log reduction in the viable cell counts in the serial dilution sector, which contained fewer than 30 colonies per 10 \(\mu\)L drop. The viable cell counts for all the experimental conditions were compared and statistically analyzed by one-way analysis of variance (ANOVA) using a significance level of 0.05 (\(p<0.05\)).
## 3 Architecture
### Task Caching Model  
Task caching refers to the caching of completed task applications and their related data in the edge cloud. The service provider caches the appropriate data according to the capacity of the edge cloud and the popularity of the task, so as to reduce the delay and energy consumption of user requested data. The main problem faced by task caching is that the storage capacity of the edge cloud is limited, which directly affects the task completion time and energy consumption.
# Fast Normalization for Bilinear Pooling via Eigenvalue Regularization
## 4 Experiments
### Hyper-Parameter Analysis  
**Impact of the Number of Epochs**. In Table 5, we analyze the impact of the number of epochs at the second stage. Throughout the experiments, it takes the first 60% of epochs to linearly increase \(\lambda_{i}\) and \(\lambda_{d}\). As observed, the accuracy stably augments when the number of training epochs increases and becomes saturated after 10 epochs. And we choose the setting of 15 epochs for all the experiments, considering the good balance between training time and network performance.  
**Impact of the weights \(\lambda_{i}\) and \(\lambda_{d}\)**. As indicated in Equation (9), the weights decide regularization strength and should be carefully chosen. In Table 6, firstly, it is sub-optimal if \(\lambda_{i}\) and \(\lambda_{d}\) are set as zero, showing the necessity of the regularization terms. Secondly, fixed values during training bring marginal improvement. For example, high fixed values will make the regularization terms \(\mathcal{L}_{d}\) and \(\mathcal{L}_{i}\) dominant in Equation (9) and lead the network to minimize classification loss \(\mathcal{L}_{CE}\) slowly. On the other hand, low fixed values cause weak regularization on bilinear representation and consequently influence the accuracy. For these reasons, we choose to linearly increase these values so that the network can focus on improving classification performance in the beginning and avoid subsequent overfitting caused by weak regularization.  
Figure 4: The spectrum of bilinear representation before/after normalization and for our approach RegCov. Best viewed in color.  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Epochs** & **3** & **5** & **10** & **15** & **20** \\ \hline Acc. & 74.9 & 75.0 & 75.3 & 75.3 & 75.4 \\ \hline \end{tabular}
\end{table}
Table 5: Top-1 validation accuracy (%) of RegCov using ResNet-18 on Imagenet1K with respect to training epochs in the second step.
## 2 Related Works
### Location Estimation Models for Enhancing Routing Protocols  
A class of extensions of RPL consists in boosting the mobility support by integrating a filtering/localization method into the routing protocol. For instance, in [7], authors have proposed Kalman-RPL to predict the future position of the mobile node and consequently estimate the future link qualities. In Kalman-RPL a mobile node transmits a beacon that includes its velocity information in specific intervals and is responded to by the receiving static nodes. After a positioning phase that estimates the current position of the mobile node using three static nodes in its vicinity, MN can predict its future position. EKF-RPL [9] takes a similar approach to Kalman-RPL, but it employs Extended Kalman Filter (EKF) within RPL to better support non-linear trajectories.  
Some research works apply the Kalman filter for predicting link quality regardless of the routing protocol. [PERSON] et al. [28] model path loss and shadow fading of the wireless channel independently and then apply Kalman filter to fuse both models.  
There are also some efforts on adopting _on-demand_ routing strategies when a node starts searching for a route for transmitting data. The _Lightweight On-Demand Adhoc Distance-vector_ routing protocol - Next Generation (LOADng) [29] is one such protocol specifically designed to support any-to-any communication in LLNs, which has received less attention compared to RPL. EKF-LOADng [8] enhances basic LOADng by predicting the link's RSSI after a positioning phase (triangulation algorithm) and running the EKF. In the triangulation phase, a mobile node broadcasts a message asking for packets from its static neighbors. Responses from static nodes experience a random waiting time to avoid collisions.  
Compared to the EKF, particle filter leads to more accurate results and better resiliency against nonlinear moving trajectory and non-Gaussian noise [4]. The particle filter, which is also known as _Sequential Monte Carlo_ uses hundreds to thousands of samples to predict the future state and fuse the measurement, hence requiring a higher computation capacity than most constrained IoT nodes can provide. In the context of cellular communications, the particle filter has been used in the literature to spot the mobile nodes and select the best service point as in [30]. This work also proposes a Rao-Blackwellised particle filter as a lightweight alternative to the baseline particle filter.  
In a previous work [12], we proposed and implemented the basic version of SDMob for offloading the mobility solution to the edge devices in a centralized manner. The basic SDMob outperformed RPL and mRPL [13] in a topology with a single MN. This paper presents enhanced SDMob, supporting networks with multiple mobile nodes, high density of anchors, varying hop distances, and realistic mobility patterns.
## 4 Performance Evaluation  
In this section, we compare SDLS with TTDD, ALS, and SLS in terms of location service and data communication. We implement SDLS using JAVA to evaluate its performance. The simulation was implemented with the Java SE Development Kit (JDK) version 6. It is highly object oriented and helps flexibility and portability. We have obtained the same simulation result shown in the related paper with the simulator developed by authors. Additionally, we have various simulation results within following parameters. The default simulation setting has 200 stationary sensor nodes uniformly distributed in a two dimensional 1,000 x 1,000 m\({}^{2}\) field. Some of the 200 sensor nodes are chosen as source nodes. Each simulation runs 100 times. That is, it is based on observations of 100 random sink and source node deployments. The radio transmission range is set to 200 m and the cell size is set to 200 m. We use the following energy model [27]:  
E\({}_{rx}\)= \(\alpha_{{}_{12}}\)+ \(\alpha_{{}_{2}}\)\(\times\)\(d\) :  
E\({}_{rx}\)= \(\alpha_{{}_{12}}\)  
where, E\({}_{rx}\) and E\({}_{rx}\) denote the energy consumed for transmitting and receiving a bit over distance d, respectively; \(\alpha_{{}_{12}}\), the energy/bit consumed by the transmitter electronics; \(\alpha_{{}_{2}}\), the energy dissipated in the transmit op-amp; and \(\alpha_{{}_{2}}\), the energy/bit consumed by the receiver electronics. The energy model has values of \(\alpha_{{}_{12}}\); \(\alpha_{{}_{12}}\) = 80 nJ/bit and \(\alpha_{{}_{2}}\) = 100 pJ/bit/m\({}^{2}\). The control message making the anchor system and location query are each 36 bytes, while the data take up 64 bytes. We assume that the sink mobility pattern follows the widely-used random way-point mobility model [28]. The main parameters are listed in Table 1.  
\begin{table}
\begin{tabular}{|l|c|} \hline Network size & 1000 m \(\times\) 1000 m \\ \(\alpha_{11}\), \(\alpha_{12}\) & 80 nJ/bit \\ \(\alpha_{2}\) & 100 pJ/bit/m\({}^{2}\) \\ Packet size (control, data) & 36 bytes, 64 bytes \\ Transmission range & 200 m \\ Distribution type of sensor nodes & uniform \\ Model of the sink mobility & Random way-point \\ \hline \end{tabular}
\end{table}
Table 1: Simulation variables.
## 3 Dynamic Performance Analysis
### Dynamic Elastic Modulus  
Following the dry-wet cycle, the dynamic elastic modulus of the sandstone ring specimen changes with the inner diameter, as shown in Figure 5.  
It can be seen in Figure 5 that the dynamic elastic modulus of the sandstone ring specimen was reduced with the increase in the inner diameter after the dry-wet cycle. The dynamic elastic modulus of the complete specimen was 44.17 GPa, and it decreased to 26.37 GPa when the inner diameter was 25 mm, which represents a decrease of 40.30%. The curve of the dynamic elastic modulus with inner diameter was negatively correlated with the quadratic function, and the correlation was significant. The fitting relationship is shown in Formula (3).  
\[\mathrm{E}=43.9637-0.2046\mathrm{d}-0.0196\mathrm{d}^{2}\ (\mathrm{R}^{2}=0.9937) \tag{3}\]  
where E is the dynamic elastic modulus.  
During the impact compression test, the sandstone ring specimen was subjected to an axial impact load. When the specimen was complete, the axial force area was large, and the force per unit area was small. With the increase in the inner diameter of the ring specimen, the wall thickness and axial force area decreased, the force per unit area increased, and instability failure was more likely to occur. Therefore, it can be concluded that the dynamic elastic modulus of the sandstone ring specimen decreased with the increase in the inner diameter.
## 3 Results and Discussion
### Corrosion Behaviors
Figure 4: Potentiodynamic polarization curves of different samples in simulated body fluid (SBF) solution at 37 \({}^{\circ}\)C.  
The corrosion behaviors were further investigated by the immersion test. The samples were immersed into SBF solution for different times and then the surface morphologies were observed by SEM. The typical SEM images of the different samples immersed in SBF solution for one, five, and seven days, respectively, are shown in Figure 5. The pristine magnesium displayed a relatively smooth surface while the other two modified samples exhibited relatively rough surface morphologies due to the surface chemical treatment. As compared to Mg-OH, the surface of Mg-HF displayed smoother morphology, which contributed to the improved corrosion resistance because the rough surface can increase the contact area with corrosion medium. On the other side, the relatively smooth surface of Mg-HF also suggested that the chemical conversion layer of magnesium fluoride was denser than that of Mg-OH, which could contribute to better corrosion resistance. More corrosion products can be clearly found on the pristine magnesium alloy surface only after immersing the sample in SBF for one day when compared to the modified samples, indicating that the unmodified magnesium alloy had a poor anticorrosion. After being immersed in SBF for five and seven days, a larger amount of corrosion products can be observed, indicating that a serious corrosion occurred on the pristine Mg substrate. In contrast, no obvious corrosion products were found on the chemical treated surfaces, demonstrating that the corrosion resistance can be effectively improved by the chemical surface modification. It was considered that the chemical conversion layer can prevent the corrosion ions from penetrating the surface of the magnesium substrate. However, the obvious cracks can be observed on the chemical treated surfaces. After one-day immersion, almost no corrosion cracks were found on the Mg-HF surface while the relatively small cracks can be found on the Mg-OH surface, indicating that Mg-HF showed better corrosion resistance than Mg-OH. After being immersed into SBF for five and seven days, respectively, it can be clearly seen that the corrosion cracks became larger on both chemical treatment surfaces, indicating the occurrence of corrosion. However, the corrosion was not serious as compared to the pristine magnesium alloy.
## 3 Results and Discussion  
### Quality Parameters  
Table 1 presents the measured quality parameters of three bell pepper varieties: firmness, pH, soluble solids content (SSC), titatable acidity (TA), ascorbic acid, anthocyanin, and total phenol (TP). The table provides mean values, minimums, maximums, and standard deviations for each parameter. The yellow variety demonstrated the highest firmness, pH, and TP levels. Conversely, the orange variety exhibited the maximum values for SSC, ascorbic acid, and anthocyanin. Several studies reported that amounts of quality parameters in peppers fruit are influenced by growth conditions and variety-dependent physiological characteristics [49; 50; 51; 52].
## 4 Results Discussion
### SDR Module Results
The results were highly positive, as very few false positives occurred. The few false positives observed happened in environments with a lot of interference, such as an office, where the presence of numerous wireless devices increased the likelihood of false positives. Nevertheless, the system still proved to be quite robust.
## 3 Results and Discussion
### Chemical Stability and Self-Cleaning Function  
The self-cleaning function and underwater oil adhesion behavior of the AM-NaA/PVDF membrane were evaluated by dropping crude oil droplets onto the membrane surface, placed in a beaker containing water. The oil droplets rapidly detached from the H-6 membrane surface, indicating a low adhesion effect on the AM-NaA/PVDF membrane (Supplementary Movie S1). To further assess the self-cleaning function, crude oil was dropped onto a pre-wetted, coated membrane placed in the air, followed by rinsing with water. The oil droplets were completely and instantly removed from the membrane  
Figure 11: Microscopic and photographic images of the emulsion feeds and their permeate water for H-6 membranes. (**a**) crude oil-in-water emulsion, (**b**) diesel oil-in-water emulsion, and (**c**) kero-sen-in-water emulsion. (**d**) Permeate flux and oil rejection performance of H-6 membranes with different oil-in-water emulsions.  
surface, leaving no residue, as demonstrated in Figure 12a,b (Supplementary Movie S2). In contrast, when using the PVDF membrane modified with acrylamide (H-2), oil detachment was slower, requiring several seconds, as shown in Figure 12c. Long-term stability of the AM-NaA/PVDF membrane was assessed by immersing a sample in water for one month. The membrane showed the same performance because the hydrogel layer covering the PVDF surface remained unchanged after one month, remaining intact and maintaining its performance, with oil droplets detaching instantly (Supplementary Movie S3).  
The chemical stability of membrane materials is a key indicator for evaluating their long-term performance in practical applications, especially in the process of treating complex emulsions and acidic environments, as it influences separation efficiency and durability. To evaluate this, the AM-NaA/PVDF membrane was immersed in aqueous solutions (pH  
Figure 12: Digital photographs illustrating the resistance to crude oil adhesion and self-cleaning function of the AM-NaA/PVDF membrane. **(a)** Underwater self-cleaning test: crude oil stain on the H-6 membrane detaches rapidly in water, showing low oil adhesion. **(b)** Self-cleaning in air: (1) a clean H-6 membrane is (2) stained with crude oil and (3) fully cleaned after water rinsing, with residual oil droplets visible in the water. **(c)** Oil detachment comparison: (1–2) the H-2 membrane shows gradual oil droplet detachment after 3–5 s, while (3) the H-6 membrane exhibits instant detachment.  
of 3-12) for 24 h to assess changes in wetting properties. As shown in Figure S8, the WCA at pH = 7 was 5\({}^{\circ}\) after 0.5 s, indicating excellent hydrophilicity. When the pH decreases, the WCA gradually increases, and the hydrophilicity of the material gradually weakens, but the WCA remains below 20\({}^{\circ}\). The UOCA showed little variation, consistently measuring 150\({}^{\circ}\), confirming excellent underwater oleophobicity. These results show that the prepared membranes can maintain good chemical stability under acidic conditions.
## 4 Proposed Method
### Handling Data Imbalance  
Since we have an imbalanced dataset, standard classification algorithms tend toward the majority class and ignore the minority class when making decisions. Hence, most predictions will correspond to the majority class and treat the minority class features as noise, resulting in a highly biased model [34]. Resampling techniques, such as undersampling and over-sampling, are widely used to address highly imbalanced datasets [35; 36]. Random over-sampling is applied to our training set by replicating random records with replacements from the minority class (\"non-hate\") [34]. Only the training set is over-sampled (as opposed to over-sampling the entire dataset) to avoid the problem of overfitting and poor generalization to the test set. This problem occurs if identical records are present in both the training and test set. After applying random over-sampling on the training set, the training set contains an equal distribution of class labels, specifically 37,684 records; 18,842 records for each class label.
# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
## 4 Model Details
### Semantically Guided Adversarial (SA) Loss
#### 4.4.3 Overall Loss Function  
The final SA loss is formulated as follows:  
\[\mathcal{L}_{SA}=\mathcal{L}_{global}+\mathcal{L}_{local} \tag{16}\]  
To ensure image enhancement quality during adversarial training, we incorporate reconstruction loss (e.g., \(L_{1}\) loss, MSE loss, or SSIM loss) and Semantically Guided Color Histogram (SCH) loss. The overall loss function is the following:  
\[\mathcal{L}_{all}=\mathcal{L}_{recon}+\lambda_{SCH}\mathcal{L}_{SCH}+\lambda_ {SA}\mathcal{L}_{SA} \tag{17}\]  
where \(\mathcal{L}_{\text{recon}}\) represents the reconstruction loss, and \(\lambda_{\text{SCH}}\) and \(\lambda_{\text{SA}}\) are hyperparameters balancing different loss components.  
In conclusion, compared with conventional discriminators that rely solely on appearance cues, our semantically guided discriminators leverage class-aware information to better distinguish between realistic and unrealistic content, especially at semantically sensitive regions like object contours and scene boundaries. Specifically, the Semantically Guided Local Adversarial Loss focuses on the most challenging regions identified by segmentation priors, while the Semantically Guided Global Adversarial Loss ensures holistic semantic consistency across the entire image. Together, they enable the discriminator to perform both fine-grained and global semantic validation, significantly improving the realism and structural integrity of enhanced images.
## 3 Results
### ASD-Related Gene Identification  
We employed the classification models to evaluate all possible gene pairs. Each classifier assigned a score of 1 if it predicted that the gene pair was associated with ASD, and a score of 0 if it predicted otherwise. The prediction score for a gene pair was then computed as the average value derived from the classifiers. A gene pair achieved the highest prediction score when all of the classification models consistently classified it as associated with ASD Ultimately, our analysis yielded 10,848 association pairs with the highest possible prediction scores. These associated genes were then investigated to identify novel ASD candidate genes. For each pair, we considered a gene that was connected to a gene in the set of known ASD genes collected from the SFARI Gene database and the AutismKB 2.0 database. The candidate genes were compiled and their frequencies were calculated. Among them, three genes showed a frequency of six (the highest frequency): _TOP1_ (DNA Topoisomerase I), _ATP5F1C_ (ATP Synthase F1 Subunit Gamma), and _NCS1_ (Neuronal Calcium Sensor 1). The group of genes with the second-highest frequency (five) totaled nine: _ARNT_, _CALU_, _GALT_, _GPC2_, _HDAC2_, _IGSF8_, _RIOK3_, _UBC_, and _SNRPN_. Additionally, there were 113 genes with a frequency of four (the third-highest value). A list of genes with frequencies of six, five, and four is provided in Supplementary Table S4. These genes were used for further analysis.