esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource-Efficient Multicast URLLC Service in 5G Systems
## 4 Performance Evaluation
### Influence of the UE Mobility and Sounding Period  
As mentioned in Section 3.4, the channel matrix may significantly change with time while the precoder is constructed based on its periodic SRS measurements. This problem is known as precoder aging. In Figure 8, we study the influence of UE mobility and \(T_{SRS}\) on the performance of the best configuration of transmission parameters obtained in the previous sections: 2 TXs, \(p_{1}=0.1\), \(p_{2}=10^{-4}\), and NACK-only feedback.  
We see that small \(T_{SRS}\) reduces the DL resource consumption for the 3 kmph case by up to 60% because of more frequent channel estimation. However, for 60 kmph, the gain is below 10% because the channel information quickly becomes outdated. At the same time, because of the large number of receiving UEs per stream, the amount of UL channel resources required for SRS is of the same order as for data transmission. Consequently, considering the overall resource consumption (see Figure 7(b)) for low mobility, the gain of selecting optimal \(T_{SRS}\) diminishes, and the optimal value of \(T_{SRS}\) changes. For high mobility, \(T_{SRS}=5\) ms--being the best option for DL resource consumption--is the worst one for the overall resource consumption. Thus, the main observation is that because of a large number of receiving UEs and typically low traffic intensity, mURLLC induces so much channel sounding overhead per stream that obtaining frequent channel information becomes inefficient. To reduce SRS overhead, new adaptive sounding and UE clusterization schemes should be developed that make UEs send SRSs with different periods based on their locations. For example, we can select low \(T_{SRS}\) for cell-edge UEs with low SINRs, while selecting high \(T_{SRS}\) for cell-center UEs.  
Figure 7: Scenario with \(N=10\) UEs, 3 kmph mobility, and \(T_{SRS}=20\) ms: (a) NLOS, (b) LOS.
## 5 Conclusions  
This study applied XGBoost, an advanced machine learning technique, to assess flood and landslide susceptibility in the Basento River basin, southern Italy. By integrating high-resolution geospatial data, multi-source remote sensing datasets, and explainable AI methods (SHAP analysis), the research developed a robust and interpretable multi-hazard prediction framework. The main findings are summarized as follows:  
* XGBoost showed excellent predictive performance, achieving a classification accuracy of 100% for flood susceptibility and 92% for landslide susceptibility.
* SHAP analysis enabled interpretability, revealing that low elevation, relative elevation, and high drainage density were the most influential predictors for flood events, whereas elevation, drainage density, relative elevation, distance, and lithology were the key contributors to landslide susceptibility.
* The unified modeling framework allowed for the simultaneous evaluation of multiple hazard types using a single set of predictors, providing a scalable and transferable methodology for other regions facing similar risks.
* The integration of remote sensing products (e.g., Copernicus EMS, COSMO-SkyMed) and GIS-based conditioning factors improved the spatial resolution and reliability of susceptibility maps, supporting more informed decision making in land-use planning, infrastructure design, and emergency response.  
Overall, this study confirmed that XGBoost, when combined with explainable AI techniques, was a powerful and practical tool for multi-hazard risk assessment. The proposed framework could be readily adapted to other regions to support data-driven disaster mitigation strategies, enhance climate adaptation planning, and contribute to sustainable territorial management. To further enhance the robustness and applicability of the proposed framework, future studies could incorporate landslide data with higher spatial and temporal resolution, as well as extend the analysis to include additional natural hazards such as wildfires. Integrating time-variant variables such as precipitation intensity, NDVI (Normalized Difference Vegetation Index), evapotranspiration (ETP), and soil moisture content would also improve the model's responsiveness, enabling more dynamic and accurate susceptibility assessments.  
Conceptualization, M.F., M.S., S.F.D.S. and V.T.; methodology, S.F.D.S. and V.T.; software, M.R. and H.H.A.; validation, S.F.D.S. and V.T.; formal analysis, S.F.D.S. and V.T.; investigation, M.S., L.C., G.D., M.R., H.H.A., S.F.D.S. and V.T.; data curation, M.S., L.C., G.D., M.R., H.H.A., S.F.D.S. and V.T.; writing--original draft preparation, M.R., H.H.A., S.F.D.S. and V.T.; writing--review and editing, M.S., L.C., G.D., M.R., H.H.A., S.F.D.S. and V.T.; visualization, M.S., L.C., G.D., M.R., H.H.A., S.F.D.S. and V.T.; supervision, M.F., M.S., S.F.D.S. and V.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# A Survey on Dynamic Corrective Control of Asynchronous Sequential Machines
## 2 Mathematical Formulation of Corrective Control
### Closed-Loop System  
Figure 1 illustrates the basal structure of the corrective control system for input/state ASMs (Figure 1a) and input/output ASMs (Figure 1b). \(C\) is the corrective controller, \(B\) is the state observer that is used when \(\Sigma\) is an input/output ASM, \(v\in A\) is the external input, \(u\in A\) is the control input provided by \(C\), \(x\in X\) is the state feedback, and \(b\in Y^{+}\) is the output burst. The closed-loop system consisting of \(C\) and \(\Sigma\) (and \(B\)) is denoted by \(\Sigma_{c}\).  
\(C\) is represented by an input/output ASM  
\[C=(A\times X,A,\Xi,\xi_{0},\phi,\eta),\]  
where \(A\times X\) and \(A\) are the input and output set, respectively, \(\Xi\) is the state set, \(\xi_{0}\in\Xi\) is the initial state, \(\phi:\Xi\times A\times X\rightarrow\Xi\) is the recursion function, and \(\eta:\Xi\to A\) is the output function.  
The necessity of \(B\) in Figure 1b stems from the constraint that direct access to the state is impossible when dealing with input/output ASMs. \(B\) is endowed with the form of a stable-state input/state ASM as follows.  
\[B=(A\times Y^{+},X,\xi,\sigma),\]  
where \(A\times Y^{+}\) is the input set corresponding to \(u\) and \(b\) and \(\sigma:X\times A\times Y^{+}\to X\) is the stable recursion function. Since the main task of \(B\) is to deduce the current stable state of \(\Sigma\) with reference to \(u\) and \(b\), it has the same state set and initial state as those of \(\Sigma\).  
To prohibit unpredictable behaviors attributed to asynchronous operations, ASMs must comply with the principle of fundamental mode operations [33; 34; 35] under which no two variables can alter their values simultaneously. In view of Figure 1, the conditions for guaranteeing that \(\Sigma_{c}\) operates in fundamental mode are reduced as follows [28; 31].  
**Rule 1**.: _The closed-loop system \(\Sigma_{c}\) in the configuration of Figure 1 shows fundamental mode operations if and only if the following two requirements are satisfied:_  
1. _When one of_ \(B\)_,_ \(C\)_, and_ \(\Sigma\) _changes its output or undertakes state transitions, the others must maintain their stable states._
2. _The external input_ \(v\) _must remain unchanged when either of_ \(B\)_,_ \(C\)_, or_ \(\Sigma\) _is under transient transitions._  
Figure 1: Basic configuration of corrective control systems: (**a**) \(\Sigma\) is an input/state ASM and (**b**) \(\Sigma\) is an input/output ASM.  
\(B\) and \(C\) should be designed so as to satisfy Rule 1(a). To ensure Rule 1(b), on the other hand, one must expect that \(v\) changes only when all of \(B\), \(C\), or \(\Sigma\) rest in stable states, even though \(v\) is an exogenous entity independent of \(\Sigma_{c}\). However, this expectation is not overwhelming since all of \(B\), \(C\), and \(\Sigma\) operate in an asynchronous mechanism so that their interactions are executed instantaneously. Hence the possibility that \(v\) is altered when any of \(B\), \(C\), or \(\Sigma\) goes through transient transitions is very low.
# Influence of Surface Tilt Angle on a Chromatic Confocal Probe with a Femtosecond Laser
## 1 Introduction
### Principle of the Tracking Local Minimum Method  
Figure 1 shows a schematic of the \(z\)-position detecting algorithm based on the TL method [22; 26]. At first, in step 1, the measurement light spectrum \(I_{\text{Mea}}\) and the reference light spectrum \(I_{\text{Ref}}\) are acquired. Here, the measurement light spectrum \(I_{\text{Mea}}\) is acquired without detector optical axis offset, while the reference light spectrum \(I_{\text{Ref}}\) is acquired with detector optical axis offset \(z_{d}\). Therefore, the peak wavelengths of the \(I_{\text{Mea}}\) and \(I_{\text{Ref}}\) differ by \(\Delta\lambda_{d}\). It should be mentioned that the optical spectra \(I_{\text{Mea}}\) and \(I_{\text{Ref}}\) are obtained by including the light source spectral shape, the reflectance of the object being measured, and transmittance of the optical element in the measurement light confocal response curve \(I_{\text{M}}\) or the reference light confocal response curve \(I_{\text{R}}\)[22; 23; 24; 25]. Then, in step 2, the  
normalized intensity ratio \(I_{\rm TLM}\) is calculated by dividing the obtained measurement light spectrum \(I_{\rm Mea}\) by the reference light spectrum \(I_{\rm Ref}\). The obtained \(I_{\rm TLM}\) is not affected by the dispersion of the measurement object or optical elements, as well as the light source spectral shape [22; 24]. The peak of the \(I_{\rm TLM}\) matches the local minimum point of the reference light spectrum \(I_{\rm Ref}\). As with the peak wavelength of the confocal spectrum, the wavelength of the minimum point of the reference light spectrum \(I_{\rm R}\) changes with the change in the \(z\)-position of the measurement object. This means that it is possible to determine the \(z\)-position by identifying the peak wavelength of the normalized spectrum \(I_{\rm TLM}\). Finally, in step 3, the peak wavelength of the normalized intensity ratio \(I_{\rm TLM}\), which is referred to as the focal wavelength \(\lambda_{\rm focused}\), is calculated based on the center of gravity method [24; 26]. The conversion from the focused wavelength \(\lambda_{\rm focused}\) to the \(z\)-position is performed using the \(z\)-\(\lambda\) sensitivity (\(dz/d\lambda\)). The \(z\)-\(\lambda\) sensitivity (\(dz/d\lambda\)) is required to be calibrated along with the measurement object. In this study, the influence of the object tilt angle on the \(z\)-\(\lambda\) sensitivity (\(dz/d\lambda\)) is investigated.
# Quantum Calibration of Photon-Number-Resolving Detectors Based on Multi-Pixel Photon Counters
## 2 Materials and Methods  
### Multi-Pixel Photon Counter (MPPC)  
An MPPC, also known as a silicon photomultiplier (SiPM), consists of multiple avalanche photodiode (APD) pixels, as shown in Figure 1a. The effective photosensitive area of MPPC (C13366-1350 GA Hamamatsu, Hamamatsu City, Japan) is 1.3 \(\times\) 1.3 mm\({}^{2}\), and the pixel pitch is 50 \(\upmu\)m. The number of pixels is 667 in total. Each pixel operated in Geiger mode independently for detecting the incident photons. The voltage applied on each APD is 54.1 V. And the breakdown voltage is about 51.1 \(\pm\) 5 V. MPPC was passively quenched in the experiment when photons were detected. The deadtime of MPPC is about 70.8 ns. When the photons arrive on different Si-APD pixels within the same short time window less than 1 ns, avalanche pulses with almost the same amplitude are induced by photons with different pixels. Since the cathode and anode of all pixels are the same, MPPC would produce an avalanche pulse whose amplitude was the sum of the response from all the APD pixels. The peak amplitude voltage from MPPC is proportional to the number of detected photons, providing the photon-number-resolving capability as shown in Figure 1b. As a result, the peak voltage amplitude can directly show the information of the incident photon number. We used digital oscilloscope to capture the peak voltage amplitude of the signal pulses. The oscilloscope was triggered by the synchronization signal from the laser source. The MPPC's output pulse duration was about 40 ns.
## 4 ANN-Based Prediction Model Development and Validation
### Prediction Model Development
#### 4.1.3 Number of Neurons in Hidden Layer  
The number of neurons in hidden layers is a key aspect in deciding ANN topology because of its significant influence on the final output. Hidden layers with a small number of neurons result in _underfitting_ because they cannot adequately detect the functional relationships in a complex dataset. An ANN model with too many neurons in hidden layers can simulate any complex functional relationships but at the expense of high computational cost. However, excessive neurons in hidden layers can result in overfitting. A higher number of neurons in hidden layers results in a higher number of weights attached, which increases the dimensions of the hypothesis space in which the model looks for minimum error. Additionally, higher dimensions of the hypothesis space complicate the error surface with many suboptimal local minima, which makes it difficult to find optimal error minima during the training. Furthermore, due to random initialization of the ANN model, it is very difficult to find the optimal number of neurons in hidden layers. In this work, we employed the rule of thumb method, which says \"the number of neurons in hidden layers should be between the size of the input layer and the size of output layer\" [57].
## 2 Materials and Methods
### Sorption of Chrysene on Polystyrene MP  
An experimental method was implemented in this study to determine a CHR sorption on MP which might be reliably based on time-dependent sorption analyses, quantifiable by chemical measurements and suitable to be employed for mussel experimental exposure. Preliminary trials aimed at characterizing the CHR sorption on MP were performed according to [PERSON] et al. [36] in order to identify the suitable incubation time to generate CHR-sorbed MP to be subsequently used for experimental exposures. A solution containing 0.5 mg of 3-\(\upmu\)m polystyrene MP (described in detail in SM, Section S1) and 0.5 mg/L chrysene (CHR, Merck Life Science, Milan, Italy) was prepared in 20 mL Milli-Q\({}^{\otimes}\) water (in Pyrex\({}^{\otimes}\) glass beakers) and kept in agitation at room temperature for 0, 3, 6, 12 and 24 h. At each interval, the CHR-MP mixture was filtered on a 0.45-\(\upmu\)m nylon filter (in a sterile 25-mL syringe) to isolate MP, and the extracted aqueous medium was stored at \(-20\)\({}^{\circ}\)C for subsequent analysis. After washing the syringe filter with Milli-Q\({}^{\otimes}\) water, MP were recovered in 40 mL of 0.22 \(\upmu\)m-filtered seawater (FSW) and used as stock of CHR-loaded particles for experimental purposes. In line with recent investigations on POPs sorption on MP [32; 36; 37], the amount of sorbed CHR was preliminarily quantified by comparing the concentration of freely dissolved CHR in the extracted aqueous medium with that of a \"control\" solution of 0.5 mg/L CHR (with no MP added) kept in agitation parallelly to the CHR-MP mixture and subjected to the same filtering steps described above.
# Assessment of Energy-Saving Practices of the Hospitality Industry in Macau
## 4 Discussion
### Future Efforts for Improvement  
This investigation shows that most of the hotels in Macau have been making a vigorous and proactive effort to improve their green performance. However, there are some problems as discussed below.  
* It follows from the results shown in Table 3 that some hotels have not taken any actions for the efficient energy saving yet. Thus, there is great potential to improve energy efficiency in the hospitality industry in Macau. Specifically, for most of the indices, there is an apparent gap in energy and environmental protection performance between 2-star hotels and 3, 4, 5-star hotels. The implementation rates of 2-star hotels are much lower than that of the other star-level sampled hotels. Thus, much more attention should be paid to the 2-star hotels in order to significantly improve their performance regarding energy saving and environmental protection.
* In this study, we find that the vast majority of 2-star hotels are located in the old parts of the city in Macau. During our visit, the managers of 2-star hotels said that most of their guests are individual travelers who would like to live in the old parts where most of the tourist sites locate. Besides, these hotels are small with fewer employees such that the daily workload is relatively heavy. Also, they spend less money and time to develop and implement energy-saving programs and activities. However, they indicate that if there is a suitable condition, they would like to do better in energy saving. Thus, the situation may be changed if the government can provide more guidance and financial support for them.
* In this study, we find that it is very difficult to get a report of energy consumption of a hotel in Macau, i.e., no hotel in Macau presents its energy consumption data to the public. If a hotel is required to publish its report on energy consumption to the public, there will be a strong motivation for the hotel to improve its energy efficiency.  
* Although the Office for the Development of the Energy Sector has been working together with several government departments to launch a pilot project on the solar photovoltaic system in Macau since 2010 Macau (2010), there was few renewable energy project for hotels in Macau. In fact, there is only one hotel that installed a wind turbine in 2012 Macau (2012) and no hotel did so after that. Hence, some incentive measures may be necessary to promote the pilot project of renewable energy sources in Macau. Some policies may be needed to encourage the development and applications of the newest technologies for energy efficiency and environmental protection continuously in the hospitality industry.
# Disentangled Feature Learning for Noise-Invariant Speech Enhancement
## 1 Introduction
The underlying idea of GAN has been adopted in many GAN-based speech enhancement algorithms including the time-frequency mask estimation using the minimum mean square error GAN (MMSE-GAN) [29] and the conditional GAN (cGAN) [30]. Though deep learning-based speech enhancement models have achieved considerable improvements, the performance is usually degraded in the case of mismatched conditions caused by different types of noises or SNR levels between the training and test set samples. Moreover, the performance varies depending on the types of noises. In order to address such issues, disentangled feature learning can be considered as a possible solution. Most of the previous studies [17, 18, 19, 20, 30], which have focused mainly on the mapping between the noisy and the clean speech, rarely consider how input features are learned in the hidden layers. The model based on disentangled feature learning, on the other hand, manipulates the latent features to distinguish between the speech and noise in the intermediate layers, hence resulting in better enhancement performance even in the mismatched noise conditions. Moreover, the quality of noise-invariant attributes can also be improved. In this paper, we propose a novel deep learning-based noise-invariant speech enhancement algorithm which employs an adversarial training framework designed to disentangle the latent features of speech and noise, under the concept of domain adversarial training (DAT) [31]. Although DAT was originally introduced for the domain adaptation task, the proposed algorithm exploits the DAT framework for use in the regression task, i.e., speech enhancement. Experimental results show that the proposed method successfully disentangles the speech and noise latent features. Moreover, the results also reveal that our model outperforms the conventional DNN-based algorithms. The main contributions of this paper are summarized as follows:* We modify the DAT framework in order to solve the speech enhancement task in a supervised manner. The proposed model achieves better performance in speech enhancement as compared to the baseline models under both the matched and mismatched noise conditions. * By reducing the performance gap among different noise types, we show that our method is more robust to noise variability. * By visualizing feature representations, we demonstrate that our model successfully disentangles speech and noise latent features. The remainder of this paper is organized as follows: Section 2 reviews past studies related to the proposed method. The proposed model is elaborately described in Section 3. Section 4 reports the results obtained from the experiments and discusses the details. Finally, Section 5 concludes the paper.
# Exploring Prior Knowledge from Human Mobility Patterns for POI Recommendation
## 2 Related Work
### Human Mobility Pattern  
In order to efficiently mine human mobility patterns and introduce them into the point of interest recommendation task, all kinds of data in the city should be effectively utilized [20; 21]. Therefore, we need to solve two problems: urban multivariate data fusion and human mobility pattern mining.  
For urban multivariate data fusion, [PERSON] et al. [22] used taxi data, shared bike data, and road network data to detect abnormal areas in stages. They used different data at different stages of the task process to achieve the purpose of data fusion. [PERSON] et al. [23] proposed a deep convolutional auto-encoder architecture that enables the encoding layer to learn the features of different data. Some work [24; 25; 26] used the idea of collaborative filtering to establish the relationship between different data sources for data fusion. The fusion of multivariate data requires the algorithm to have strong scalability, but there is no general method to meet the needs.  
The human mobility pattern itself is also a spatio-temporal pattern, and spatio-temporal pattern mining directly affects the quality of POI recommendations. In order to adapt to different spatio-temporal networks, [PERSON] et al. [27] proposed an efficient spatio-temporal neural structure search method to try to automatically construct a general neural network suitable for different spatio-temporal prediction tasks in cities. Due to the lackof semantics in the spatio-temporal data features generated using deep neural networks, [PERSON] et al. [28] proposed a new deep learning framework based on particle swarm optimization. It can be seen that the research on human mobility patterns is often related to people's travel behaviors and trajectories, and the pros and cons of human mobility pattern mining results often require downstream tasks for verification and support.  
Compared to the above works, we propose the architecture of human mobility pattern extraction and then design the end-to-end architecture to learn the characteristics of human mobility patterns, and finally apply the prior knowledge of human mobility patterns to POI recommendations.