esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Target-Based Pharmacophore Modeling  
We want to find drugs that suppress the human HSP90 protein to treat cancer. We created a target-based pharmacophore model using the [PERSON] software, 2018, suite and the human HSP90 protein structure. The MEF-HSP90 complex from XP docking was used to generate the pharmacophore model. We found the best MEF ligand binding arrangement in the HSP90 protein binding site using XP docking. Figure 1 shows that our pharmacophore model revealed essential properties for successful inhibition by examining ligand-protein interactions.  
The ADDRR pharmacophore model has one hydrogen bond acceptor, two donors, and two hydrophobic groups.  
Before employing the pharmacophore model in screening, it is essential to validate its ability to accurately distinguish active inhibitors from non-active compounds. In this study, we utilized enrichment-based validation to ensure that the model reliably prioritizes active molecules. We compiled a validation set comprising 35 active compounds identified from the literature [24], along with 1000 decoys obtained from the DUD-E decoy database. This approach allows us to evaluate the model's sensitivity and specificity in identifying potential inhibitors among a large set of decoys. The pharmacophore model validation results (Table S1) demonstrate strong predictive capabilities, underscoring the model's efficiency in distinguishing active HSP90 inhibitors from decoys. The high BEDROC scores across varying alpha values, especially the 0.955 score at the strictest alpha (160.9), suggest that the model excels at early recognition of actives. This is complemented by the ROC value of 0.83, which confirms the model's accuracy in classifying actives versus decoys. Additionally, the RIE value of 11.39 and an area under the accumulation curve of 0.85 indicate substantial enrichment, meaning that the model prioritizes actives within the ranked list effectively, which is crucial for streamlined screening. Moreover, the distribution data reveal that a significant portion of actives--up to 77.1%--appears within the top 20% of results, further affirming the model's capacity to rank potential inhibitors at the forefront. With 30 out of 35 actives correctly ranked and an average of 33 decoys outranked by each active, these metrics highlight the model's robustness and reliability, suggesting that it can play a valuable role in the efficient identification of promising HSP90 inhibitors for subsequent validation steps. A detailed analysis, including the ROC curve and additional metrics, is provided in the Supplementary Figure S1.  
Our pharmacophore model ADDRR was then used to screen related compounds. We accurately discovered 3019 compounds that closely fitted the model and met its 4-5 feature super-positions throughout screening.
# Clock Recovery Challenges in DSP-Based Coherent Single-Mode and Multi-Mode Optical Systems
## 4 Clock Recovery Performance in Single-Mode Fibers  
### Time Skew between Polarizations  
First, to evaluate the impact of the time skew between polarizations we considered a back-to-back system with the time skew both in the transmitter and the receiver. We also considered a polarization azimuth rotation between transmitter and receiver. For simplicity, the ellipticity rotations were not considered. The transfer function of this simulation was then,  
\[\mathbf{E}_{out}(\Omega)=\begin{bmatrix}e^{-j\Omega\tau_{Rx}/2}&0\\ 0&e^{j\Omega\tau_{Rx}/2}\end{bmatrix}\begin{bmatrix}\cos(\theta)&\sin(\theta) \\ -\sin(\theta)&\cos(\theta)\end{bmatrix}\begin{bmatrix}e^{-j\Omega\tau_{Tx}/2}&0 \\ 0&e^{j\Omega\tau_{Tx}/2}\end{bmatrix}\mathbf{E}_{in}(\Omega), \tag{21}\]  
where \(\tau_{Tx}\) was the time skew between polarizations at the transmitter, \(\tau_{Rx}\) was the time skew between polarizations at the receiver, and \(\theta\) the rotation angle. The received signal in polarization \(X\) is then,  
\[\mathbf{E}_{out,X}=e^{-j\Omega\tau_{Rx}/2}\left(e^{-j\Omega\tau_{Tx}/2}\cos( \theta)\mathbf{E}_{in,X}+e^{j\Omega\tau_{Tx}/2}\sin(\theta)\mathbf{E}_{in,Y} \right). \tag{22}\]  
Therefore, the received signal is a combination of the transmitted signal in both polarizations with opposite timings due to the transmitter inter-polarization time skew. On the other hand, the receiver time skew only delays the transmitted signal components with the same timing. Thus, it is expected that the transmitter inter-polarization time skew will affect the clock tone amplitude depending on the polarization rotation during transmission, while the receiver time skew will not affect it. To show this behavior, we then considered an NRZ-QPSK at 32 GBd as signal input and swept the rotation angle from 0 to 90 degrees and the transmitter time skew from 0 to 31.125 ps, equivalent to the interval from 0 to 1 symbol period. The parameters used in this simulation are summarized in Table 1. The receiver time skew was set to zero. The CTA was computed and it is shown in Figure 4.  
As we can see, the CTA goes to zero when the rotation angle is 45 degrees and the inter-polarization time skew in the transmitter is around half symbol period. This means that estimation and compensation of inter-polarization time skews at the transmitter side is crucial.
## 4 Experiments
### Method Analysis Results and Discussion
#### 4.3.5 Robustness Analysis  
As deep learning models are data-driven methods, it is well known that they are sensitive to data quality. Noisy data may introduce negative impacts on model training and lead  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Design**} & \multicolumn{2}{c}{**MSE**} & \multicolumn{2}{c}{**Time (ms)**} & \multicolumn{2}{c}{**Parameters (k)**} \\ \cline{2-7}  & **96** & **720** & **96** & **720** & **96** & **720** \\ \hline SAMP + SC & 0.152 & 0.304 & 0.325 & 0.401 & 86.5 & 246.3 \\ \hline LIFT + SC & 0.149 & 0.303 & 0.348 & 0.455 & 1694.7 & 2738.2 \\ \hline saPCC & 0.147 & 0.301 & 0.311 & 0.411 & 112.5 & 3173.9 \\ \hline PCC & **0.144** & **0.299** & **0.297** & **0.375** & **35.6** & **195.4** \\ \hline \hline \end{tabular} Bold values indicate the best performance.  
\end{table}
Table 7: Performance comparison between PCC and more complex mechanisms in terms of MSE, inference time, and number of parameters. The backbone is SegRNN with a look-back length of 96 and prediction horizons of 96 and 720.  
Figure 6: Evolution of the training and validation losses of SegRNN with and without PCC for forecasting lengths of 96 and 720 and with the look-back length fixed at 96.  
to poor performance [48]. Unfortunately, time series data collected from weather stations are sometimes noisy [49]. In this section, we evaluate the robustness of our PCC module under various noise conditions to validate its effectiveness in practical forecasting scenarios.  
First, we introduce Additive Gaussian Noise (AGN), then scale the AGN by a pre-defined weight and add it to the dataset. We evaluated two training cases, first adding noise only to the input sequence and then adding it to the complete sequence. We used the SegRNN model as the backbone, with the same look-back window and prediction horizon of 96. The scale weights of the AGN were 0.1 and 0.3. The results listed in Table 8 show the robustness of our PCC module against noise and demonstrate its ability to effectively capture the underlying patterns in noisy scenarios while improving the backbone model's performance.
# Centimeter-Level Precise Orbit Determination for the Luojia-1A Satellite Using BeiDou Observations
## 4 Methodologies for the Beidou-Based LEO POD  
There are a few challenges in BeiDou-based LEO POD, including the BeiDou GEO orbit error handling and tuning the pseudo-stochastic parameters due to discontinuity of the BeiDou observations, which are discussed in this study. In this section, the theory of LEO POD is briefly introduced.
# 3D-Printed Lightweight Foamed Concrete with Dispersed Reinforcement
###### Abstract  
This study investigates the influence of various reinforcing fibers, including coconut, basalt, glass, merino wool, and polypropylene, on the properties and processability of cementitious mixtures, with a particular emphasis on their application in 3D printing. The incorporation of fibers at a concentration of 1 wt.% was found to significantly hinder the printing process. Specifically, certain fibers, such as polypropylene, rendered extrusion impractical due to nozzle clogging. However, reducing the fiber content to 0.5 wt.% improved material flowability and minimized structural defects during printing. Fiber selection, in addition to its impact on mechanical properties, plays a crucial role in determining overall process efficiency. Mixtures incorporating coal slag as a dense filler, combined with stiff fibers such as basalt or glass, exhibited the highest flexural strength. Moreover, the inclusion of merino wool fibers enhanced the flexural performance of fly ash-based mixtures, achieving strength levels comparable to or exceeding those of stiffer fibers. These findings contribute to the advancement of sustainable construction practices. Notably, samples produced via 3D printing consistently demonstrated higher flexural strength than those fabricated using traditional molding techniques. This enhancement is attributed to microstructural modifications induced by the layer-by-layer deposition process. Depending on the sample composition and the type of reinforcing fiber, water absorption behavior varied significantly. Merino wool and coconut fibers exhibited the highest water absorption due to their hydrophilic nature and capillary action, particularly in 3D-printed samples with open-pore structures. In contrast, glass and basalt fibers, characterized by their higher density and hydrophobicity, exhibited lower water absorption levels. These results underscore the importance of optimizing fiber type, concentration, and processing methodologies to achieve tailored performance in fiber-reinforced cementitious mixtures. Such optimizations align with the principles of sustainable development and hold significant potential for advancing 3D-printed construction applications  
Keywords:foamed concrete (FC); fiber-reinforced; additive manufacturing; 3D concrete printing
## 2 Background and Related Work
### JAC based on Interrelated Attributes
The JAC similarity Sim(x,y) can be obtained as follows:_  
\[Sim(x,y)=\frac{com(x,y)}{num(x;l,m_{x})+num\Big{(}y;l,m_{y}\Big{)}-com(x,y)}, \tag{14}\]  
_where \(com(x,y)\) is the number of interrelated attributes that item \(i_{x}\) and item \(i_{y}\) own commonly, which is defined as:_  
\[com(x,y)=C_{n_{1}}^{2}+n_{1}n_{4}. \tag{15}\]  
It is important that the number \(n_{4}\) of attributes that both item \(i_{x}\) and item \(i_{y}\) do not own is used in Equation (14). This implies that the JAC similarity based on interrelated attributes implicitly evaluates the number of attributes that both items do not own. Because JAC similarity based on the original attributes presented in Equation (2) evaluates only the ratio of commonly owned attributes, attributes that both items do not own are not considered in this case. This shows that it is possible that JAC obtained using interrelated attributes can be used to provide recommendations with high diversity based on the similarity obtained using the commonly owned attributes and attributes that are not commonly owned. It is necessary to conduct further research on this; hence, it would be one of our future works.
## 2 Literature Review
### Prediction of Energy Consumption in School Buildings  
Globally, [PERSON] et al. [21] examined the correlation between the consumption of energy of school buildings and the area of school, school class number, school students' number, and the school system of cooling and heating in South Korea. The study concluded that the average annual consumption of energy was estimated to be 400,000-1,750,000 kWh/year for each school. Out of that, 82% was due to electric power consumption. [PERSON] and [PERSON] [22] studied the consumption of energy in school's facility in Manitoba, Canada, for the last ten years. The study revealed that middle-aged schools had the highest rate of energy consumption. The study also found that the retrofit done in some schools had a low effect on the school buildings' energy consumption. [PERSON] et al. [26] developed two prediction models for the power consumption of higher educational buildings using an ANN and support vector regression. The outputs of the two models were compared and the comparison showed that the two models can provide accurate forecasting for the power consumption of higher educational buildings. [PERSON] et al. [27] introduced a model for selecting the most effective school design in terms of energy savings and with the least CO\({}_{2}\) emissions. The authors stated that the application of the model could lead to a saving in energy consumption of 16.58%. [PERSON] et al. [28] proposed a regression-based model for forecasting the consumption of heating energy in schools and sports buildings. The model was intended to assist a building manager to identify elements for accurate estimation. The study revealed that the energy consumption of indoor swimming pools is 84% higher than that of school buildings. [PERSON] et al. [29] proposed an estimation using an ANN-based model to predict the annual energy cost budget of school buildings in South Korea. The study was based on a comparison of a developed model with an existing one.  
The previously reviewed models, exclusively and/or collectively do not; (1) predict the consumption of energy in constructed school facilities, except References [24; 29]; (2) identify the factors affecting the consumption of energy in school buildings; and (3) predict the consumption of energy in school buildings in Saudi Arabia. Thus, the main objectives of this study were to identify the factors affecting the consumption of energy of school buildings, identify the correlation between the identified factors, and to developing a prediction model for energy consumption of school buildings in Eastern Province in Saudi Arabia using a Neural Network.
## 2 The Proposed Hybrid Time Series Forecasting Methodology
### Modeling the Decomposed Sub-Series  
Once the sub-series are obtained from the hourly ozone concentration time series using the STL decomposition technique, the extracted sub-series are fit by applying the three considered standard time series models, including linear autoregressive (AR), nonlinear autoregressive (NLAR), and autoregressive moving averages (ARMA) [27, 28]. These three models are explained in the following subsections.
# Isolated Forest-Based Prediction of Container Resource Load Extremes
## 4 Experimental Results and Analysis
### Experimental Data  
The dataset for this experiment is from the AliCloud Platform trace dataset released in 2018.  
The Cluster-trace-v2018 dataset [24], released by AliCloud Platform, provides trace data for about 4000 machines over 8 consecutive days. The dataset covers the partial workloads of these machines and the workloads of the entire cluster. All machines in the cluster run online tasks along with batch tasks. The dataset contains six types of tables, namely, machine_meta.csv, machine_usage.csv, container_meta.csv, container_usage.csv, batch_instance (batch_instance.csv), and batch_task.csv. In this paper, we mainly used the container utilization table and randomly extracted resource utilization data from 100 containers on day 4. The dataset field descriptions are shown in Table 1.  
Figures 2 and 3 reveal the key features of the CPU utilization time series data from the AliCloud dataset. From Figure 2, it can be observed that there is significant volatility in the CPU utilization, with no shortage of spike-peak phenomena. This finding suggests that the system may have experienced a sudden increase in resource demand at certain moments.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Field** & **Type** & **Note** \\ \hline cpu\_util\_percent & BIGINT & The current CPU utilization of the container \\ mem\_util\_percent & BIGINT & The current memory utilization of the container \\ cpi & DOUBLE & Number of cycles per instruction \\ mem\_gps & DOUBLE & Memory bandwidth utilization \\ mpki & BIGINT & Number of out-of-page interrupts per thousand instructions \\ net\_in & DOUBLE & Percentage of network inbound traffic utilization \\ net\_out & DOUBLE & Percentage of network outgoing traffic utilization \\ disk\_io\_percent & DOUBLE & IO usage of the disk \\ \hline \hline \end{tabular}
\end{table}
Table 1: AliCloud dataset field description table.  
In addition, the irregularity of the utilization fluctuation implies the dynamically changing nature of the load. In the histogram depicting the CPU utilization distribution, shown in Figure 3, the data clearly show a right-skewed distribution, where the high frequency of low-utilization intervals reflects that the system is under a lower load most of the time. However, the extension of the long tail in the graph indicates that high utilization extreme values, though rare, do exist and can have a significant impact on the operational stability of the system and resource scheduling strategies. Correctly predicting these extreme values is critical to the efficiency and reliability of cloud resource management systems. Therefore, the data characteristics presented in Figures 2 and 3 provide important guidance for predictive model design.
## 5 Discussion  
The FRA has been used as a successful tool to investigate the mechanical integrity of power transformers and has shown a good potential to detect faults within rotating machines. This paper investigated the ability of FRA to detect various short circuit faults within three-phase induction motors. Results show that the amount of variation for the  
Figure 8: The statistical indicators threshold limits for each fault for the 3-HP and 1-HP TPIM (**a**) CC threshold limits (**b**) ASE threshold limits (**c**) SD threshold limits (**d**) MSE threshold limits (**e**) DABS threshold limits (**f**) RMSE threshold limits (**g**) COVAR threshold limits (**h**) threshold limits MM.  
detected faults, from large to small, is as follows: phase-to-phase, coil-to-coil, turn-to-turn, coil-to-ground, turn-to-ground, turn-to-ground, and neutral-to-ground. Due to the overlapping of some statistical indicators, this study recommends the use of several indicators for reliable identification and quantification of the SC fault. The proposed threshold limits in Figure 8 are to determine the minimum indicator value for the SC fault to emerge. It is to be noted that while the statistical indicator can be employed to calculate the variation between two FRA signatures, its sensitivity to the variation is different from one statistical indicator to another. The lowest and most common statistical indicator value revealed from the two case studies at the LF, MF, and HF can be considered as an early alarm for emerging SC fault. Results show that ASE, SD, and RMSE have a similar trend of sensitivity for SC faults, as shown in Figure 8b,c.f. Thus, the ASE, SD, and RMSE can be ranked as high sensitivity classes. On the other hand, MSE and DABS in Figure 8d,e can also detect the variation between the FRA signatures of the normal and SC faults but can be categorized as a second class or middle sensitivity class compared to the aforementioned indicators. The lowest sensitivity class among the proposed indicators is for the CC, MM, and covariance shown in Figure 8a,h,g, respectively. Based on Table 2, the descriptions of the indicators show that the CC only measures the linear relationship. The MM allows comparing the similarity, and the covariance was based on the Chinese standard. These might affect the level of sensitivity of CC, MM, and covariance to the variation between the normal and SC fault responses.
## 2 Materials and Methods
### Methods  
#### 2.2.1 Laser Metal Deposition  
The 850-R Optomec LENS machine was used to manufacture Ti-Al-Nb test coupons. This manufacturing platform uses a 1000 W IPG fibre laser that is connected to the deposition head. The LENS used a powder deposition head that was made of steel, while the nozzles were manufactured out of brass. The set-up allowed for the laser beam and depositing powder to be concentric and controlled simultaneously during deposition. The process of 3D printing is controlled automatically using the Workstation Control Software, version 3.1.10. The schematic set-up is shown in Figure 2.  
The set-up shows only two hoppers for manufacturing. Ti and Nb were feed from the same carrier gas line but in different hoppers. Nb is deposited from an external hopper manufactured and supplied by GVN. Table 1 reports the process parameters that were used to manufacture the Ti-Al-Nb coupons that were studied in this paper.  
Powders were deposited onto Ti-6 Al-4V substrates with dimensions of 140 \(\times\) 140 \(\times\) 7 mm\({}^{3}\). A cube sample with dimensions of 20 \(\times\) 20 \(\times\) 5 mm\({}^{3}\) was produced using optimised laser deposition parameters for each alloy. Post-manufacturing of the samples, still on the base plate, involved cutting them into 4 pieces of 5 mm each (control sample, as-built sample, and heat treatment samples) for heat treatment and characterisation.
# Improvement of the Transglycosylation Efficiency of a
## 3 Results and Discussion
### Screening of LnbB Variants
#### 3.1.4 The Arg Residue at the Edge of the Active Site: G398R and \(\Delta\)G398  
In the study on Bbhl combining directed evolution and site-directed mutagenesis, the mutant W805R gave an 82% yield vs. 45% obtained with the WT [27]. However, unlike the polarizing Asp--the other superior mutant of that study--W805 of Bbhl is not conserved across GH20s. Considering the effect of W805R in Bbhl [27] and the repositioning of an Arg residue in a similar location caused by the introduction of a five amino acid loop directly upstream of R355 in soil metagenome HEX1 [28], we recently proposed that the presence of an Arg in this position could be a new strategy for improving GH20 transglycosylation [8]. LnbB has a Gly residue (G398) in the position corresponding to W805 in the multiple sequence alignment (Figure 2), followed by an Arg residue (R399). To investigate the role of an Arg residue in this position in LnbB, we assessed two different variants: G398R and \(\Delta\)G398. Both mutants exhibited a similarly mild improvement in LNT formation as N259T, W373F, and W373H (Table 1), indicating that an introduction of Arg in this position approximately 10 A from the active site has a positive effect on transglycosylation (Figure S4).