esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Textural Analysis by \(N_{2}\) Adsorption-Desorption Isotherms  
The N\({}_{2}\) adsorption-desorption isotherms at 77 K for the BN, BNF, BA1, and BA1F samples are shown in Figure 6. According to the IUPAC classification, these isotherms are type IV with H2 hysteresis loops (P/P\({}_{0}\) > 0.9), characteristic of mesoporous materials. The presence of hysteresis in the intermediate regions of relative pressure (P/P\({}_{0}\)) indicates pores with a heterogeneous distribution. The BA1 sample exhibited the highest surface area (165 m\({}^{2}\cdot\)g\({}^{-1}\)) and pore volume (0.15 cm\({}^{3}\cdot\)g\({}^{-1}\)), suggesting a highly porous structure favorable for adsorption processes, confirming that the applied treatment significantly influenced its morphology. The average pore diameter varied among the samples, with values ranging from 32.67 to 43.78 A. The BNF sample exhibited the largest pores (43.78 A), while BA1F showed the smallest (32.67 A) [15; 16]. These results demonstrate that the BA1 sample has better characteristics for applications involving adsorption due to its higher surface area and pore volume, as observed in Table 2 [16; 17].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Samples** & **S\({}_{\text{BET}}\) (m\({}^{2}\cdot\)g\({}^{-1}\))** & **V\({}_{\text{p}}\)\({}^{2}\) (cm\({}^{3}\cdot\)g\({}^{-1}\))** & **Pore Diameter (Å)** \\ \hline BN & 63 & 0.06 & 37.43 \\ BNF & 17.8 & 0.02 & 43.78 \\ BA1 & 165 & 0.15 & 35.29 \\ BA1F & 121 & 0.09 & 32.67 \\ \hline \hline \end{tabular}
\end{table}
Table 2: N\({}_{2}\) adsorption data of the samples.  
Figure 4: HRTEM micrographs of BN and BA1. Scale bars indicate 200 nm.  
Figure 5: EDX elemental mapping of BN (**A**) and BA1 (**B**). Scale bars indicate 10 \(\upmu\)m.
## 5 Summary and Conclusions  
The proposed paper discusses the application potential of dew-point cooling towers (DPCT) in selected energy-intensive applications in temperate climates. The applications selected for analysis are power generation with natural gas turbines and chilled water air conditioning systems. The analysis was performed using a numerical model that was validated against experimental data. The results indicated that DPCT is able to provide significant energy and financial savings in energy-intensive applications; therefore, its application potential in temperate climates can be considered justified. In the case of the gas turbines, DPCT was able to generate 2 to 10 percentage points more capacity than operating on outdoor air and 1.8 to 5 percentage points more than operating with wet-bulb cooling towers (WBCT). Annually, the application of dew-point cooling towers allows for savings of approximately 12,476,413 EUR more than systems with WBCT, which corresponds to daily savings of almost 2000 cubic meters of natural gas. The maximum difference in EER (energy efficiency ratio) between systems with DPCT and dry cooling is 7.2, and the minimum is 1.0.  
WBCT is 5.1, and the minimum difference is 0.3. The change in average EER between DPCT and dry cooling is 3.5, and between DPCT and WBCT is 2.1. This corresponds to annual energy savings of approximately 14.7 MWh compared to WBCT and 30 MWh compared to dry cooling.  
It should be noted that the following study is based on simplified assumptions (i.e., no energy consumption of the cooling tower equipment, such as fans and pumps, along with thermal losses or efficiency of the intermediate heat exchangers are included). This may affect the results in real-life situations. Since both towers would probably use a different structure, an objective comparison would be hard to determine. Due to this fact, future research should include pilot projects based on DPCT prototypes and detail experimental verification of the actual level of savings in energy-intensive applications in a temperate climate.  
Conceptualization, D.P.; Methodology, D.P. and S.C.; Software, D.P.; Validation, D.P., M.M. and S.C.; Formal analysis, D.P. and W.W.; Investigation, D.P. and B.P.; Resources, D.P.; Data curation, D.P., M.M., P.K. and G.I.; Writing - original draft, D.P., M.M., P.K., B.P. and G.I.; Writing - review & editing, D.P., G.I., W.W. and S.C.; Visualization, D.P. and P.K.; Supervision, D.P.; Project administration, D.P. and W.W.; Funding acquisition, D.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Polish National Agency for Academic Exchange (Bekker program), agreement number BPN/BEK/2021/2/00014/U/00001/A/0000 and by program Lider X, agreement number U/0180/666/2019.  
Not applicable.  
Data availability Statement: Data is contained within the article.
# Multimodal Network for Object Detection Using Channel Adjustment and Multi-Scale Attention
## Appendix A Limitations and Future Work  
While MNCM demonstrates competitive accuracy and computational efficiency in multi-modal object detection, it still has several limitations that suggest areas for improvement.  
One key limitation is that MNCM does not incorporate temporal or sequential modeling, which restricts its use to static image-based detection. This means the model is unable to capture temporal continuity across video frames or perform multi-frame fusion, both of which are essential for dynamic scenarios like video surveillance and autonomous driving. Another challenge is that MNCM assumes complete and well-aligned modality inputs during inference. However, in real-world situations, modalities may be missing, degraded, or misaligned due to factors like sensor failures, occlusions, or adverse weather conditions. Currently, the model lacks explicit mechanisms to handle these issues, which could affect its  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Params (M)** & **FLOPs (G)** & **Inference Time (ms)** & **FPS** \\ \hline CDDFuse [33] & 48.3 & 74.6 & 41.7 & 25.9 \\ SwinF [35] & 56.6 & 69.4 & 39.2 & 26.5 \\ MMA-UNet [37] & 38.1 & 72.8 & 35.4 & 28.2 \\
**Ours** & **31.4** & **70.3** & **33.6** & **27.9** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Computational complexity and runtime efficiency comparison between MNCM and baseline methods on the MSRS dataset.  
performance in challenging environments. Additionally, MNCM is designed specifically for object detection and has not been evaluated in large-scale pretraining or general-purpose multimodal learning contexts. Its modular structure may also create challenges when attempting to integrate it into broader multimodal frameworks or adapt it to large-scale foundation models.  
Looking ahead, we aim to improve MNCM's ability to handle incomplete or noisy modalities, introduce lightweight temporal modeling modules for video-based detection, and explore how the model can be integrated with scalable pretraining strategies or multimodal foundation model backbones to enhance its adaptability and transferability.
# Simulation of Nuclear Quantum Effects in Condensed Matter Systems via Quantum Baths
## 5 Applications of Quantum Baths to Realistic Systems
### Structural and Thermodynamic Properties
#### 5.1.3 Energetic and Structural Properties of Atomic and Molecular Clusters  
[PERSON] and coworkers applied the QTB to study rare-gas clusters, modelled via Lennard-Jones interatomic potentials [49]. They found that the method generally reproduces energetic properties relatively well, reinforcing earlier results on cationic neon clusters [55]. However, close to the liquid-like to solid-like (l-s) transition, the QTB performed unevenly against PI-based simulations: the l-s transition in Ar clusters is shifted to low temperatures and the Ne clusters show liquid-like behavior at all temperatures, even when PI simulations would predict a solid-like behavior. As pointed out by the authors, this failure is due to ZPEL. They also encountered similar difficulties in the simulation of water octamers with the QTB, where the strong leakage of zero-point energy stemming from high-frequency intra-molecular modes completely distorts the cluster structure and causes spurious melting, even down to low temperatures [49].
# Electrically Driven Micro- and Nano-Scale Semiconductor Light Sources
## 5 Nanowires (NW)-Based Electrically Driven Light Sources
Notably, at a high forward bias of 11 V, a dominant light emission at the NW end was observed, indicating that a considerable amount of the generated light was coupled to NW waveguide modes. The result represents the potential of stimulated emission at higher current injection, eventually demonstrating an electrically driven single GaN NW laser. Figure 8: Core/multishell single GaN NW heterostructure LEDs. (**a**) Schematic of the NW cross section displaying coaxial heterostructure with core-to-shell _n-i-p_ doping profile (left). Energy band diagram of the NW (right). (**b**) SEM and bright-field optical microscope images of the fabricated single GaN NW LED (left). Electroluminescence images of the device with different forward bias voltages of 9 V and 11 V, respectively (right). Red arrows and white-dotted lines indicate the NW end and body, respectively. (**c**) Multicolor emissions of NW LEDs: purple, blue, greenish-blue, green, and yellow (left to right) [37]. Figure reproduced with permission from American Chemical Society. Figure 7: Electrically driven single CdS nanowire (NW) nanolasers. (**a**) An NW with length \(L\) and index \(n_{1}\) as a stand-alone optical cavity. (**b**) SEM image showing a clean and vertical facet for high reflection. (**c**) Schematic of the cross-section of the device showing carrier injection paths through the NW (left). White light (middle) and electroluminescence images (right) of the device captured by CCD camera. White arrows indicate the exposed CdS NW ends [36]. Figure reproduced with permission from Springer Nature Limited.
# High-Quality and Reproducible Automatic Drum Transcription From Crowdsourced Data
## 2 Related Works
### Training Data  
The choice of training data is also a major focus of recent publications. In particular, data undergo two possible manipulations: pre-processing and the curation of new datasets.  
Different pre-processing techniques have been employed to make better use of the available datasets. For example, a strategy employed by [PERSON] et al. consisted in using a source separation (SS) algorithm, in this case Spleeter [19], to remove non-drum instruments from the signal [5; 6]. Unfortunately, this method deteriorated the quality of the transcriber, as SS tends to generate artifacts. Another method consists in synchronizing the predictions of the models to the tatum (the smallest durational subdivision of the main beat), thus effectively avoiding bias during the learning process and separating the note sequence from the tempo (BPM) [4; 5; 6]. This technique improved the transcription when compared to the original frame synchronicity. Lastly, data augmentation has been used to increase the size of existing datasets by generating new training samples from artificial modifications of the original samples. These modifications entail pitch shifting, time stretching, and/or adding noise to the audio to increase the diversity of acoustic features [11]. Moreover, such modifications can also be applied to sequences of notes to generate new sequences or to increase the number of occurrences of drum instruments that play less often [2; 12]. Data augmentation has been found to effectively improve the performance of algorithms when training data are limited.  
The curation of new datasets has been explored by creating, for example, datasets synthesized from symbolic representations of the music, thus removing the labor required to annotate existing tracks. These symbolic representations can be obtained from one of three ways: captured from a live performance with an electronic drumkit [12]; created by an offline process performed apriori [2; 9; 13; 20]; generated artificially [21]. These new datasets help train models, but they are not sufficient by themselves; in fact, in order to achieve better transcriptions, they must be complemented with data from non-synthetic music. To this end, such data have been collected from a large crowd of online annotators to curate much larger datasets than any of the existing non-synthetic ones.  
[PERSON] et al. used the annotations from the symbolic-only Lakh MIDI dataset [22] and developed a system to retrieve and align audio files from online platforms like YouTube [4]. [PERSON] et al. curated annotations and audio files made for rhythm games and repurposed them for ADT [3]. In both studies, due to the crowdsourced nature of these datasets, a quality check had to be performed to correct or filter out incorrectly annotated or aligned tracks.
# Creating Automated Microsoft Bicep Application Infrastructure from GitHub in the Azure Cloud
## 6 GitHub Actions CI/CD Pipelines
### Prerequisites and Setup  
Before a GitHub Actions workflow can deploy infrastructure to Azure, it must authenticate with sufficient privileges to create and manage resources. The recommended approach is to use an Azure service principal with role-based access control (RBAC) to manage access. To prepare for deployment automation, the following steps are required.  
Creation of a Service Principal: The service principal (SPN) acts as a non-human identity that GitHub Actions uses to authenticate against Azure.  
Assignment of Appropriate Roles: The SPN should have the Owner role on the target subscription or resource group. For security best practice, this can be further scoped to specific resource groups to implement the principle of least privilege.  
Configuration of GitHub Secrets: These credentials should never be hard-coded in YAML or source files. Instead, they must be stored securely as GitHub secrets, accessible only to authorized workflows in the repository.
# Construction Progress and Aviation Flight Test of BDSBAS
## 4 BDSBAS Aviation Flight Test
### Flight Test Environment  
The aviation flight test was carried out at Caihu Airport in Faku County, Shenyang City, Liaoning Province from 17 to 25 September 2020, and the take-off and landing operations during the approach phase and the transition flight operations during the airway flight phase of civil aviation aircraft were simulated. A small general aviation aircraft was used and a Septentrio PolaRx5 receiver was equipped with to verify the performance of the BDSBAS SF augmentation service. The tests results in this paper were performed in postprocessing. In order to obtain the aircraft trajectory reference, an RTK system was built using two Novartis receivers. One was set up near the airport as a reference station, and the other was used as a rover station, sharing the aerial antenna mounted on the aircraft with the Septentrio PolaRx5 receiver with 1 Hz sampling rate and cut-off elevation angle of 10 degree. The aircraft reference trajectory of flight test I and test II were obtained by post-RTK using GPS dual-frequency observation data; the fixed rate reached 100% and the position accuracy was better than 5 cm; the aircraft reference trajectory of flight test III was obtained by float-PPP using GPS dual-frequency observation data. The position accuracy is better than 10 cm.  
Figure 7: We used the cylinders to explain the status of the system service. When the protection level cylinder is inside the alarm level cylinder, the service is available. Otherwise, the service is unavailable. When the position error is within the protection level cylinder, missing alarms and integrity events will not occur in the service. Otherwise, missing alarms and integrity events may occur.  
The above-mentioned aviation flight test environment and equipment are shown in Figure 8.
# Modeling of Distorted Degradation Data Based on Oil Analysis
## 2 Methodology
### Failure Prediction  
The estimation of the actual physical degradation curve, which is also a correction to the observed degradation, can reflect the actual physical degradation well. Consequently, this estimation can be used to assess the system state and predict a failure. A failure occurs when the degradation curve \(h(t)\) hits the failure threshold \(\omega\) that is empirically predetermined by engineers. According to the concept of First Hitting Time (FHT), the lifetime \(T\) of the system is defined as  
\[T=\inf\{t:h(t)\geq\omega|h(0)\leq\omega\}.\]  
Moreover, the Remaining Useful Life (RUL) is predicted; that is, \(T-t_{n}\).
## 4 Discussion
Native-PAGE is an excellent method for protein isolation because gel electrophoresis is performed under non-denaturing conditions, allowing the native proteins to be recovered after separation [25]. In Native-PAGE for the current study, an eluted protein with the molecular weight of approximately 180 kDa showed the gingipain activity for both Rgp and Kgp. This might be due to both Rgp and Kgp having a nearly identical hydrodynamic sizes and isoelectric point. Additionally, the important factors such as molecular size, ionic charge, and shape of native proteins fully determine their migration rate through the non-denaturing gel [25]. Thus, both arginine-X gingipains and lysine-X gingipains with similar physical and chemical properties will migrate at a similar rate through the gel [29]. From the gelatin zymogram, bands corresponding to either Rgp or Kgp were identified by incorporating the selective inhibitors during the protein sample preparation [34]. Leueptin inhibitor is a selective inhibitor that specifically inhibits the activity of Rgp as previously described [42] while Cathepsin B inhibitor II is an artificially synthesized tripeptide that specifically inhibits Kgp activity [43]. According to the study of [PERSON] et al. (2003), the universal inhibitor of serine proteinases (e.g., PMSF) will not inhibit the serine proteases' activities fully because of the short half-life. The incorporation of the universal inhibitor into the sample of gelatin zymogram will cause the reduction of the intensity of the transparent catalytic zone formed on the gel. On the other hand, the addition of an irreversible inhibitor (e.g., E-67) into the sample of gelatin zymogram, which binds to the active site of the enzyme covalently leads to the disappearance of the relevant transparent catalytic zone formed on the gel [44]. In Figure 4, the number of transparent bands that observed in (a) and (b) are different from those in (c) and (d), this might be due to the concentration of proteinase-containing sample loaded in the gel and the overstaining of the gelatin-SDS-polyacrylamideamide gel led to the loss of the sensitivity [34; 36]. The molecular weight of the catalytic bands observed in the gelatin zymogram differed from those observed in SDS-PAGE due to omitting the boiling procedure during the sample preparation to ensure their structure remains intact to preserve their catalytic properties [27]. Triton X-100 was used to restore their catalytic activity because it is non-ionic and can replace the ionic SDS that surround the proteases [27]. This method was employed to characterize the different forms of gingipains because zymogram is a highly sensitive approach to detect the proteinase even in their pro-enzyme state, and due to ionic detergent, SDS treatment will cause conformational changes of proteins, which activates their catalytic activities [28]. Figure 5 outlines the detail the protocol for isolation and characterization of gingipains.
# Efficient Task Allocation in Multi-Agent Systems Using Reinforcement Learning and Genetic Algorithm
## 3 Problem Formulation
### State Action Model
This process encapsulates the agents' decision-making as they interact with the environment, where each agent optimizes its actions to maximize cumulative rewards using a combination of Actor-Critic and genetic algorithms. In the proximal policy optimization (PPO) framework, the advantage function \(A(s_{t},a_{t})\) evaluates the relative benefit of selecting action \(a_{t}\) in state \(s_{t}\). PPO employs Generalized Advantage Estimation (GAE) to compute the advantage. The advantage function is given by  
\[A(s_{t},a_{t})=\hat{A}_{t}=\sum_{i=t}^{T}(\gamma\lambda)^{i-t}\delta_{i}, \tag{9}\]  
where \(\gamma\) is the discount factor, which controls the importance of future rewards relative to immediate rewards. It is a value between 0 and 1. \(\lambda\) is the smoothing parameter, which helps balance the bias-variance trade-off in estimating the advantage. It is also between 0 and 1. \(\delta_{i}\) is the temporal difference error (TD-error), which is expressed as  
\[\delta_{i}=r_{i}+\gamma V(s_{i+1};\omega)-V(s_{i};\omega), \tag{10}\]  
where \(\gamma\) is the discount factor. The value function \(V(s_{i};\omega)\) is updated iteratively as  
\[V(s_{i};\omega)\gets V(s_{i};\omega)+\alpha\delta_{i}, \tag{11}\]  
where \(\alpha\) is the learning rate. This update process ensures that each agent effectively optimizes its cumulative rewards, leading to more precise task execution and resource allocation. The initial value of \(V(s_{i};\omega)\) is typically set to zero. Moreover, the GA enhances policy optimization across the agent swarm by selecting and evolving the most effective action strategies. Integrating the GA with the Actor-Critic framework inherent in the PPO algorithm achieves a refined balance between exploration and exploitation. Consequently, this framework significantly improves adaptability in dynamic task environments. The synergy between the GA and the Actor-Critic mechanism of PPO not only fosters effective strategy development but also contributes to the overall robustness and efficiency of the agent swarm in complex operational scenarios.
## 3 Methodology
### HVSR Computation  
The analysis primarily focuses on the HVSR method for extracting subsurface features from seismic data. Site amplification and resonance effects are measured by the HVSR, comparing horizontal seismic motions along the East and North components to the vertical motions. We used Fourier transformation for its computation, which changes filtered components from time-domain to frequency-domain representations. We obtained the horizontal motion spectrum as the vector sum of the East and North components. Then, we obtained the HVSR from the ratio of the horizontal spectrum computation to thevertical spectrum. This ratio was then smoothed with a moving-average filter to reduce high-frequency noise and to stabilize the result [33; 35].  
The microtremor data comprised continuous daily recordings gathered throughout the year 2014. To ensure data quality, we performed an STA/LTA (Short-Term Average/Long-Term Average) method across each signal to analyze and validate signal stability and exclude noise-dominated segments. For windowing, we utilized a dynamic technique based on signal stability, choosing segments where the STA/LTA ratio showed persistent and dependable tremor activity. Additionally, each window needed to be longer than \(\frac{10}{f0}\), guaranteeing that a minimum of 10 complete cycles were collected to facilitate robust frequency-domain analysis. We emphasize that both time-domain (e.g., peak amplitudes) and frequency-domain (e.g., HVSR peak and fundamental frequency (\(f0\))) metrics were input for the machine learning models.