esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### EDSS-Based PLS Regression Models  
PLS-R models were developed to predict EDSS scores based on NIR spectra acquired from the four anatomical sites (Figure 9). All models demonstrated strong performance, with minimal bias and a high coefficient of determination in both the calibration and cross-validation phases (Table 4). The cortical model achieved the best predictive accuracy (RMSECV = 0.293, R\({}^{2}\)CV = 0.980) despite requiring seven latent variables, indicating the high informational content of the cortical spectra. The biceps model followed closely, with RMSECV = 0.399 and R\({}^{2}\)CV = 0.962, using only two LVs. The triceps model also showed good performance (RMSECV = 0.475, R\({}^{2}\)CV = 0.946), while the brainstem model, although requiring more LVs (five), maintained acceptable accuracy (RMSECV = 0.597, R\({}^{2}\)CV = 0.916).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Wavelengths (nm)** & **Cortical** & **Brainstem** & **Biceps** & **Triceps** \\ \hline
1000–1100 & 1050 & & 1000 \\
1100–1200 & & & \\
1200–1300 & & 1300 & \\
1300–1400 & & & \\
1400–1500 & 1500 & & \\
1500–1600 & & & \\
1600–1700 & & & \\
1700–1800 & & & \\
1800–1900 & & 1800 & 1800 \\
1900–2000 & 1900–2000 & & 1900 & 1950 \\
2000–2100 & & & \\
2100–2200 & & & \\
2200–2300 & 2200–2250 & 2200 & 2200–2290 & 2200 \\
2300–2400 & & 2300 & 2390 \\
2400–2500 & & & 2400 & \\ \hline \end{tabular}
\end{table}
Table 3: Wavelengths identified based on VIP scores from the PLS-DA models for both cranial and extracranial sites.  
Figure 9: _Cont._  
Notably, each model's RMSECV remained below the EDSS standard deviation (2.0), indicating that prediction errors fell within the intrinsic variability of the clinical scores. The calculated \(\hat{I}^{2}\) values for each PLS-R model confirmed that the models exhibited large effect sizes, supporting their robustness despite the limited sample size.  
VIP analysis revealed key wavelengths contributing to the prediction, with consistent peaks at 1100 nm and 1700 nm across all sites, suggesting these regions may serve as potential spectral biomarkers of disease severity. Site-specific features (e.g., 1050 nm in cortical, 1300 nm in brainstem) highlight regional spectral differences that may reflect localized physiological or pathological characteristics.
## 3 Results and Discussion
### Virtual Image Design  
The image was designed for first-person perspective. Final VR content included the image of the aircraft, the outside environment, and the cockpit. The cockpit showed the propulsion power, flight speed, and aircraft altitude, etc. The High Tech Computer (HTC) Vive Pro head-mounted display was set as the pilot's flight helmet. Therefore, testers could freely watch the outside scenes of the flight through the device, as shown in Figure 28. The content of the flight range was designed to be 1000 m \(\times\) 1000 m, and the height was 1200 m.
# MGHT: Statistical Methodology for Missing-Data Imputation in Food Composition Databases
## 1 Introduction
Since the food supply has evolved, and the demand for nutritional and bio-active components grows, relying only on chemical analysis when compiling FCDBs has become almost impossible. According to current \"rules\", or rather suggestions, missing values should be borrowed from a FCDB, which contains the foods and nutrients of interest and/or is compiled from geographically similar countries [17]. In practice, the borrowing of data for imputation of missing values is performed either using a FCDB well known for its data quality or by using several FCDBs and using either an average or median value of their values [18]. In our case, the average is the arithmetic mean, which is the sum of a collection of data values divided by the number of data values in the collection. The median is the value separating the higher half from the lower half of a data sample. For example, for a dataset, it may be thought of as the \"middle\" value [19]. Using these approaches, however, can lead to inaccuracies. Foods also exhibit variations in composition, and the composition of any given single food sample cannot be accurately predicted. The prediction accuracy is also constrained by how data are maintained in a FCDB (as averages or best estimates, for example). Clearly, to increase the quality of FCDBs, there is a need for methods that can be used either for imputation or calculation of missing FCD. In this paper, we present a semi-automatic computer-supported methodology for borrowing missing nutrient values by generating rules using null hypothesis testing. The end result for a missing value of a specific nutrient in a food from a given country is an average or median value from the values of the set of countries whose FCDBs are eligible for borrowing. Several experiments were conducted and the experimental results demonstrate that our methodology provides high accuracy when compared with the most commonly used approaches. The rest of the paper is structured as follows. In Section 2, we give explanation of the data for the experiments and the background literature for the methodology. The experimental results are presented in Section 3. A comparison with state-of-the art methodologies, time efficiency as well as further discussions about the methodology are given in Section 4. Finally, in Section 5, a summarization of the importance of this methodology and directions for future work are presented.
###### Abstract  
A single radiator that is a part of four-port diversity Multiple-Input Multiple-Output (MIMO) antenna design is composed of four octagonal rings embedded between the two opposite sides of a T-shaped conductive layer surrounded by inverted angular edge cut L-shaped and E-shaped structures. The radiators are placed at the four corners with common ground at the center of a smartphone to form a four-element mobile MIMO antenna. The printing of the antenna is carried out on the flexible polyamide substrate (dielectric constant = 3.5 and loss tangent = 0.0027) with dimensions of \(70\times 145\times 0.2\) mm\({}^{3}\). A wide impedance bandwidth of (84.12%) 2.39 to 5.86 GHz is achieved for all four radiators. The compact size of the radiators along with their placement enables the proposed MIMO antenna to occupy much less area while preserving the space for 2G/3G/4G antennas. The placement of the antennas results in self-isolation between antenna elements by achieving isolation greater than 17.5 dB in the desired operating bands. Furthermore, besides showing a high efficiency of 85% and adequate gain above 4 dBi, good diversity performances such as Envelope Correlation Coefficient (ECC) of less than 0.05, Diversity Gain (DG) of above 9.8 dB, Mean Effective Gain (MEG) of \(-3.1\) dB, Channel Capacity of 21.50 bps/Hz, and Total Active Reflection Coefficient (TARC) of below \(-10\) dB are achieved by the flexible MIMO smartphone antenna. The effect of bending along the X and Y-axis on the performance of the proposed MIMO antenna is also analyzed where decent performance is observed. This makes the proposed flexible four-element MIMO antenna a potential candidate to be deployed in future smartphones.  
smartphone antennas; 5G; multiple-input multiple-output (MIMO); flexible; four-element 40/. +
Footnote †: journal: Electronics Letters
## 4 Methodology
### Dataset Pre-Processing  
To improve dataset suitability and experimental reliability, the original data were processed as follows:  
1. Data Cleaning: Duplicate records and samples with more than 75% missing values were removed to reduce noise and improve data quality.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**Dataset Records**} \\ \cline{2-4}  & **Training Set** & **Testing Set** & **Evaluation Set** \\ \hline Backdoor & 45,526 & 11,382 & 14,227 \\ DDoS & 552,385 & 138,097 & 172,620 \\ DoS & 302,429 & 75,608 & 94,509 \\ Injection & 405,582 & 101,397 & 126,744 \\ Mitm & 94,259 & 23,565 & 29,456 \\ Normal & 142,710 & 35,678 & 44,597 \\ Password & 153,983 & 38,497 & 48,119 \\ Ransomware & 65,233 & 16,309 & 20,385 \\ Scanning & 639,757 & 159,940 & 199,924 \\ Xss & 188,960 & 47,240 & 59,050 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data structure of TON_IoT dataset.  
Dataset Splitting: The dataset was first split into two parts, with 80% allocated to the DOA-BP intrusion detection module and the remaining 20% reserved for the Bayesian risk scoring and reinforcement learning-based response modules. Within the 80% assigned to intrusion detection, an additional 80:20 split was applied to create the training and test sets. This two-stage partitioning yields a final distribution of 64% for training, 16% for testing, and 20% for evaluation of downstream components. This hierarchical strategy ensures data independence between modules, prevents information leakage, and supports unbiased performance evaluation. Stratified sampling was employed throughout to maintain consistent class distribution across all subsets.
3. Class Balancing: Stratified random sampling maintained representativeness. All samples of rare attack types (e.g., mitm, ransomware, injection) were retained. For normal traffic, a 20% sampling rate was applied, and for other common attacks, 10% was used to balance data volume and computational efficiency.
4. GAN-Based Augmentation: For attack types with very few samples (e.g., mitm, with only 1473 samples), a Generative Adversarial Network (GAN) was used to create additional synthetic data, enhancing generalization by matching the original distribution.
5. Feature Engineering and Encoding: Non-numeric fields were converted into numeric format, and formatted numeric fields were cleaned and standardized. Categorical features were label-encoded, and mappings were saved to ensure interpretability and traceability. Finally, all numeric features were normalized to the [0, 1] range using min-max scaling to standardize the input for model training.
# A New Workflow to Generate Monoclonal Antibodies against Microorganisms
## 3 Results
### Evidence of Antibody Binding to Integrated Epitopes of Epitope-engineered VPs  
To prove that the antibodies of the sorted hybridoma cells can bind the integrated epitope within the epitope-engineered VPs used for immunization, we tested all antibodies for their binding to the corresponding VP as well for native VP without any integrated epitopes. The data from ELISA confirmed that all newly generated mAbs recognized their corresponding epitope-engineered VP used for immunization (Figure 7). H3-2-B8 and H3-3-G1 showed a significant binding of VP_EC3. A slightly increased signal was detected for VP_EC4, while no further binding to the other epitope-engineered VPs was measured.  
Likewise, the antibodies H4-2-F6, H4-3-D10, and H4-3-E10 showed specific binding to their original epitope in VP_EC4. A slightly increased background signal could be noticed for VP_EC3. These cross-reactions for VP-EC3 and 4 could be explained by similar amino acids such as proline in both sequences.  
All anti-_E. coli_ O157:H7 mAbs showed no signal to the native VP without any epitope-engineered sequences. Similarly, no non-specific binding is detectable with the secondary goat anti-mouse IgG antibody coupled with HRP to epitope-engineered or native VPs. As a positive control, the anti-VP antibody P157 was used, which binds to all native and epitope-engineered VPs except the VP_EC5. Taking into account that VP_EC5 was not able to induce a proper immune reaction in mice, the protein could be somehow degraded or otherwise impaired so that the control antibody could not bind specifically.  
Figure 6: Characterization of the mAbs against _E. coli_ O157:H7, _Legionella pneumophila_ and _Bacillus_ spp. for antibody binding and cross-reactivity. Antibody binding was measured in flow cytometry (green). Two million cells were stained with 10 \(\upmu\)g/mL of H4-3-E10, H4-3-D10, or H4-2-6 in MACS buffer. For H3-2-B8 and H3-3-G1, undiluted culture supernatants were used for the staining. As positive control (dark gray), a polyclonal rabbit anti-_E. coli_ Ab was used for _E. coli_ O157:H7 and a mouse anti-_Legionella pneumophila_ Ab was used for _Legionella pneumophila_. For _Bacillus_ spp., there is no specific antibody available for flow cytometry measurements. The negative controls (light gray) are stainings only with the PE coupled detection antibody.
## 2 Materials and Methods
### Site Description and Sample Collection
#### 2.1.3 Sampling  
Six different morphologies were sampled within the ELMA caves in New Mexico: moonmilk, mineral crusts, coralloids, microbial mats, and ooze. In addition to these morphologies, spheroids were sampled in KKCP in Hawai'i (Figure 1). Moonmilk is hypothesized to be the result of biotic and abiotic interactions. It is described as a generic term for a moist, fine-grained mineral deposit found on the walls and floors of caves and varies in texture from paste-like to crusty [10; 26]. Soft moonmilk occurs on walls and ceilings of lava caves and has the appearance of white, fluffy cotton balls. Crusty moonmilk can be found on walls, ceilings, and floors of lava caves. It can be hard and dense, or it can be granular mineral deposits. Moonmilk is generally extremely friable. Mineral crust describes a relatively flat, secondary mineral formation on the walls and ceiling of a lava cave that lacks obvious visual microbial morphologies. Coralloid designates any general coral-like or nodular protrusion from a cave wall or floor [27]. Microbial mats are microbial colonies that range from small, pointillistic colonies to large swaths of hydrophobic, microbial colonies that tend to reflect light [2; 28]. Ooze, an understudied feature in lava caves, can be found underneath microbial mats or in depressions in the basalt walls with textures that range from gelatinous to slimy [1; 29]. Ooze has been hypothesized to represent material from the surface above the cave seeping through [29]. Spheroids, also known as \"gypsum balls\", as described in Hill and Forti [30], are spherical secondary gypsum mineral deposits observed in KKCP, ranging in size from less than a millimeter to over a centimeter in diameter.  
Samples of each category described above were collected from unobtrusive locations in each of these caves, using clean nitrile gloves for each sample and an ethanol flame sterilized chisel, tweezers, or scoopula. In each location, two samples were collected: one for 16S rRNA gene analysis and another for SEM analysis. For SEM samples, chips of mineral deposit or wall rock with microbial deposits were placed in a 2 oz Whirl-Pak, which was carefully folded and placed in a sterile 50 cc BD Falcon(tm) Tube (Corning Inc., Corning, NY, USA) for transport to the lab. For 16S rRNA gene analysis, approximately 5-10 cc of material was collected for each sample, placed directly into a sterile 50 mL BD Falcon(tm) tube, and covered in sucrose lysis buffer (SLB) to preserve the DNA [31]. Genetic samples from El Malpais were transported to the lab within 48 h and stored at \(-80\)\({}^{\circ}\)C until ready for DNA extractions. Hawaiian samples were frozen until transported to the lab, where they were stored at \(-80\)\({}^{\circ}\)C.
## 3 Results
### Phase-Contrast MRI Analysis
#### 3.1.3 Key Features in ML Methods  
Morphological parameters have proven to be robust indicators for NPH diagnosis across multiple studies. The callosal angle has emerged as a particularly reliable marker,with [33] demonstrating its significant association with the z-Evans Index in their analysis. Frontal horn length has shown remarkable diagnostic accuracy, with [37] reporting 95.0% accuracy using this parameter alone [41]. Midbrain dimensions, including anteroposterior and tegmentum diameter, have been identified as significant differentiating factors by [34]. The presence of disproportionately enlarged subarachnoid space hydrocephalus (DESH) has also been recognized as a key diagnostic feature, ranking among the top three most weighted diagnostic features in recent studies.  
Clinical parameters have provided valuable complementary information to imaging features in NPH diagnosis. CSF pressure measurements, analyzed through techniques such as infusion tests, have shown significant diagnostic potential, with [47] achieving 85.7% classification accuracy using pressure-derived features. Neuropsychological tests have been effectively incorporated into diagnostic models, with [35] demonstrating improved accuracy when combining these assessments with imaging features. Their combined clinical and imaging model achieved an impressive ROC-AUC of 0.83, significantly outperforming models using either type of feature alone. Gait assessment, while less commonly incorporated into ML models, has shown promise as an additional diagnostic parameter, particularly when combined with other clinical and imaging features [34; 35].
## 2 Materials and Methods
### Statistical Analysis  
A piglet was considered as the experimental unit for all the measurements for statistical analyses. All the data were analyzed using the general linear model (GLM) procedure of the Statistical Analysis System (SAS 8.4 SAS Institute Inc., Cary, NC, USA). One-way analysis of variance (one-way ANOVA) followed by [PERSON]'s multiple comparison test was used to evaluate different means among treatments. The \(\chi^{2}\) test was used to test for diarrhea rate. Data were expressed as means \(\pm\) standard error of the mean (SEM). Level of significance was set at \(p<0.05\), whereas \(0.05<p<0.1\) was considered a trend towards significance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Target Group** & **Sequence of Primers (5\({}^{\prime}\) to 3\({}^{\prime}\))** & **Size (bp)** &
\begin{tabular}{c} **Annealing** \\ **Temperature (\({}^{\circ}\)C)** \\ \end{tabular} & **Reference** \\ \hline \multirow{4}{*}{Total bacteria} & \multicolumn{3}{c}{Forward:} & \multirow{4}{*}{60} & [PERSON] et al., \\  & ACTCCTACGCGAGGCAG & & & 2018 \\ \cline{1-1}  & Reverse: & & & \\  & ATTACCCGGTGTGG & & & \\ \hline \multirow{4}{*}{_Lactobacillus_ spp.} & \multicolumn{3}{c}{Forward:} & \multirow{4}{*}{341} & \multirow{4}{*}{58} & [PERSON] et al., \\  & CACCGCCTACACATGGAG & & & 2018 \\ \cline{1-1}  & Reverse: & & & \\ \cline{1-1}  & TGGAAAGATTCCCTACTGT & & & \\ \hline \multirow{4}{*}{_Escherichia coli_} & \multicolumn{3}{c}{Forward:} & \multirow{4}{*}{60} & [PERSON] et al., \\  & CATGCCCGGTATGAAGAA & & & 2002 \\ \cline{1-1}  & TTTGCTCATTGACGTTACCCG & & & \\ \cline{1-1}  & Reverse: & & & \\ \cline{1-1}  & AATCCGCTACCTCTGCACT & & & \\ \hline \multirow{4}{*}{_Bifidobacterium_ genus} & \multicolumn{3}{c}{Forward:} & \multirow{4}{*}{243} & \multirow{4}{*}{58} & [PERSON] et al., \\ \cline{1-1}  & TCGGTC(C/T)GGTGTGAAAG & & & 2004 \\ \cline{1-1}  & Reverse: & & & \\ \cline{1-1}  & CCACATCCAGC(A/G)TCCAC & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primers used for absolute quantitative real-time polymerase chain reaction (PCR) in this study.
# Cloud Gaming Video Coding Optimization Based on Camera Motion-Guided Reference Frame Enhancement
## 3 The Proposed Coding Optimization Algorithm and Network Architecture
### Network Architecture
#### 3.2.1 Camera Motion-Guided Prediction (CMGP)
After that, the translation is carried out by scaling and shifting the input feature:  
\[f_{out}=\gamma_{R_{i}\to R_{4}}\odot f_{in}+\beta_{R_{i}\to R_{4}} \tag{5}\]  
where \(f_{out}\) represents the output feature after the camera motion-guided transformation layer, whose dimension is the same as \(\gamma_{R_{i}\to R_{4}}\) and \(\beta_{R_{i}\to R_{4}}\), and \(\odot\) is referred to element-wise multiplication. Afterward, the residual offset and fusion mask can be obtained through several convolution layers. The final offset can thus be represented as:  
\[p_{R_{i}}=F_{R_{i}\to R_{4}}+\Delta p_{R_{i}} \tag{6}\]  
Considering that the general video sampling format of H.265/HEVC is YUV4:2:0, and the luma component contains more detail and high-frequency information while the chroma component is relatively flat, we process the luma and chroma separately in the paper and set the network structure of the chroma component as a simplified version of that of the luma component. Therefore, in a CMGP block, the luma component performs downsampling and upsampling three times while the chroma component performs two times. The optical flow of the chroma component is also simply set as a twice performed  
Figure 4: Illustration of the CMGP block. downsampling version of luma. It should be noted that we set \(F_{R_{4}\to R_{4}}\) to zero and do not perform the spatial warping operation when \(i=4\). That is to say, we use two \(I_{R_{4}}\) frames to predict the residual offset \(\Delta p_{R_{4}}\) and fusion mask \(m_{R_{4}}\), and the final offset is directly represented by \(\Delta p_{R_{4}}\) in this case.
## 1 Introduction  
Landfill is one of the common methods for the treatment of municipal solid waste in China [1]. With rapid urbanization in China, cities are expanding in scale and suburban land is being developed. The landfills in suburban areas will be closed gradually [2, 3]. In a closed landfill, bacteria play an important role in promoting the natural degradation and removal of waste [4]. Therefore, microbial response to natural waste degradation in closed landfills around urban areas should be analyzed to ensure urban environmental safety.  
The composition of municipal solid waste is complex, including glass, metal, construction waste, organic material and other components. Compared with direct combustion, the operational and disposal costs associated with landfills are lower [5]. Municipal solid waste is rich in nutrients and conducive to bacterial growth. Bacteria can utilize the available components in municipal solid waste and decompose them into inorganic salts, water and gases to reduce waste [1]. Previous studies investigated the community structure of bacteria in landfill leachate and simulated landfill reactors [6, 7, 8]. Proteobacteria was the predominant phylum in closed landfill, followed by Firmicutes, Acidobacteria and Actinobacteria phyla [7, 8]. Previous study also found that Proteobacteria reached the maximum value in the middle stage of waste degradation, while Firmicutes and Actinobacteria increased with prolonged degradation time [8]. However, there are many types of waste in landfill, and the types of degradation products of organic matter are complex. It is difficult to fully reflect the succession rules for bacterial communities in the vertical direction of a landfill based on a simulated reactor. The composition of microorganisms in the leachate does not easily represent the bacterial community in a landfill.  
On the other hand, microorganisms are sensitive to the environment in which they live, and their community composition and functional structure often change significantly when affected by factors such as contaminants [9; 10]. The response of soil microorganisms to various environmental impact activities such as heavy metal pollution and the accumulation of domestic waste has been investigated [11; 12]. These studies have mostly focused on surface soils, with less attention to the subsurface environment. Landfills can directly affect the surface and subsurface environments and are ideal sites for the study of microbial vertical distribution patterns and the characteristics of their response to varying degrees of waste degradation.  
In this study, 16S rDNA high-throughput sequencing was conducted to determine the bacterial community composition of soil and water samples in a closed landfill in Shanghai, and various analytical methods were used to investigate similarities and differences between bacterial communities in the samples and the main influencing factors. Our main objectives were to reveal the vertical distribution patterns and relationships of subsurface microorganisms and their response to varying degrees of waste degradation in landfill near an urban area.