esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of Smart Meters in Enabling Real-Time Energy Services for Households: The Italian Case
## 4 Services for Final Users
### Network Services  
Real-time services can help customers to be smarter about energy and, consequently, reduce their bills. One activator for such a savings is the electric network stability: in critical moments caused by unbalancing or faults, DSOs could ask for a reduction of electric power consumption or for an injection of locally produced energy in exchange for an adequate reward [34]. The customer (or an EMS on behalf of the customer) can receive Demand Response signals from the DSO or his/her retail supplier, directly through the SM or through a different channel, and make a decision about whether to contribute to solve the issue or not [35; 36; 37; 38]. In the near future, small renewable energy sources owned by private customers could even be used for unbalancing problems and sold in ancillary services markets as secondary or tertiary reserves by means of an energy aggregator [2].  
Table 2 summarizes the identified services based on [39] with a particular focus on the corresponding minimum sampling time and maximum latency required for their activation.  
The last three columns show the stakeholders involved who can benefit from the activation of the corresponding real-time service.  
As shown, latency can vary from one second to almost one hour down. It is worth noting that communications frames, sent to the IHDs, can contain one sample or aggregated data. The more data are available to IHDs, the smarter the services available. For instance, a reliable dashboard reportingthe daily energy consumption and production curves for awareness requires at least one transaction per hour, containing the past energy measurements averaged every 15 min. Differently, home automation performed by IHDs requires fresh metering data every five to 15 seconds for a prompt response.  
On the other hand, the smarter the service chosen, the higher the cost required for the total exploitation. An EMS can forecast and schedule appliances according to expected PV generation, storage systems, market price (ToU or RTP contract), and DR, hence ensuring the highest possible savings, but at the cost of high data transmission rates from SM. At the current energy prices, savings ensured by a similar system could not compensate for the initial purchasing cost of an EMS, which might discourage manufacturers and service providers from promoting similar products at the household level [40].
## 3 Results
### Time-Dependent Study
The geometry of the simulated microchannel and crystal consisted of a segment of a vertical slice of the microchannel of (L \(\times\) H = 500 \(\upmu\mathrm{m}\times 100~{}\upmu\mathrm{m}\)) located at the center of the 2-mm channel width. The input was located at the left side and output at the right  
Figure 6: Crystal slice near the bottom of the microchannel. side, so hydrodynamic forces moved the square horizontally to the right (positive \(x\) axis). An image of the simulated microchannel geometry is shown in Figure 7.  
Analysis of data from this time-dependent simulation allowed us to calculate translational and rotational velocities of the square as well as hydrodynamic drag force and torque as a function of time. Once the simulation was computed, by using line integration of the four lines of the square representing the crystal slice, the drag force was calculated by multiplying the negative of total stress in the flow direction times the crystal length. The torque \(\tau\) was calculated by the line integral of the cross-product \(\tau=-(r\times F)\cdot 75\)\(\upmu\)m where \(r\) is the vector from the center of the square to the \(i\)th element on the contour line and the force \(F\) is the total stress vector and \(75\)\(\upmu\)m is the crystal length. The negative sign produces a positive torque in a clockwise direction. Results of this time-dependent study are shown in Table 1.  
Results shown in Table 1 represent the average values of computed velocities, forces, and torques applied on the crystal for at least 90 degrees of rotation. Simulated data was stored every millisecond (the first value was a peak and was discarded). We found that the simulated crystal moved faster (translational and rotational velocities) than the video-recorded crystals. A possible explanation for this behavior is an unmodeled opposing force due to contact forces between the crystal and the floor as it translates and rotates along the channel. In order to reach an agreement with the experimental crystal velocities, an opposing force was included in the simulation as a volumetric force in the negative \(x\) axis direction. The volumetric force is calculated from the drag force (N) divided by the crystal volume. An attempt to apply an opposing torque was made, to reduce the rotational velocity, but it caused errors in the simulation and it was omitted in this study.
# Status of the SXFEL Facility
## 3 The SXFEL User Facility
### Beamlines and Experimental End-Stations  
X-ray beamlines are important parts of the SXFEL user facility, precisely transmitting the FEL output into various end-stations. Online diagnostics of the FEL outputs will also be provided at the beamlines. In the initial phase of the user facility, there are two beamlines and five end-stations, including cell imaging, atomic molecular and optical physics (AMO), ultrafast physics, surface chemistry, etc. Figure 17 shows one of the SXFEL-UF beamlines together with two end-stations.  
With its high light source performance properties and these well-designed instruments, SXFEL is dedicated to ultrafast X-ray science and fundamental research, especially on femtosecond chemistry, materials under extreme conditions, high-throughput bio-imaging, light-induced transient phenomenon and dynamic functions in real time, etc. Owing to its ultra-intense and ultrafast pulses, the facility will attract much attention from the science community and this will be the main driving force for the SXFEL.  
To make use of the full coherence and femtosecond pulse duration of XFEL, technologies such as ultrafast scattering and imaging will be employed. Ultrafast scattering and imaging with unprecedented spatial resolution will permit the evolution of nano-materials, and will allow further investigation of the relationship between material synthesis and operating conditions. This will change our methods for the development of novel materials. Moreover, the capability of SXFEL will enable the unique characterization of structure and dynamic changes therein. Electron transfer, electronic fluctuation, transient phase transfer and hidden phase could be captured in a few femtoseconds. The ability to capture ultrafast processes will give a better understanding of motions at the sub-nanometer and femtosecond scales. Flash X-ray imaging, combined with intense lasers and mass spectroscopy, could be applied to bio-imaging and the investigation of materials under extreme conditions. To obtain the special properties of catalysis and photo-catalysis, methods such as laser-pump X-ray probing and two-color X-ray pump X-ray probing may achieve surprising results. Considering dynamic functions under natural states and in real time is the key to investigating any ultrafast problems. To this end, multiple pump-probes (Vis-/ infrared-laser, THz, X-ray) with mixed particle injectors will be designed to capture high frame rates images. The understanding of any emergent phenomena is essential for designing new materials and chemical reactions. To cover this issue, SXFEL will reveal these critical events step by step, from scattering and diffraction to absorption. The data from different end-stations either specially-designed or based on general X-ray technologies  
Figure 17: A typical beamline with two end-stations.  
at SXFEL will be combined. Multiple big data analyses would provide new ways to approach new materials and chemical reactions.
## 2 Numerical Model for Earth Pressure Balance (EPB) Shield Tunnelling Simulation
### Ground Formation with Coupled Discrete Element Method-Finite Difference Method (DEM-FDM)
After the ground model is formulated and stabilized, the coupling between the DEM and FDM domains is verified by measuring the in-situ stress with depth. The stress of the FDM domain is obtained by reading the values of the zone gridpoints. The DEM domain stress is measured by installing measurement balls with a diameter of 0.8 m, four times that of the ball elements. Figure 7 illustrates the measured vertical and horizontal in-situ stresses (with designated \(\pm\)5% allowable error) along with the exact (analytical) solutions of in situ stress. The stress distribution in the FDM domain appears almost identical to the exact solution. After wall compaction, the horizontal stress distribution in the DEM domain exceeds the allowable error with a relatively steep gradient of stress with depth. However, after wall-zone compaction, the horizontal stress is within the allowable error, suggesting that ground modeling with DEM and FDM coupling is reasonably complete. \begin{table}
\begin{tabular}{c c} \hline \hline
**DEM Domain** & \\ \hline
**Property** & **Value** \\ \hline Packing of ball element & Hexagonal close packing \\ Diameter of ball element [m] & 0.1 \\ Number of ball element & 59,024 \\ Density of ball element [kg/m3] & 2700 \\ Contact model of ball element & Adhesive rolling resistance linear \\ Contact parameters & Refer to Table 4 \\ \hline \hline
**FDM Domain** & \\ \hline
**Property** & **Value** \\ \hline Size of zone element, \(W\times H\) [m] & 0.5 \(\times\) 0.5 \\ Number of zone element & 50,432 \\ Density of zone element [kg/m3] & 1987.77 \\ Constitutive model of zone element & Mohr–Coulomb failure criterion \\ Elastic modulus, \(E\) [MPa] & 24 \\ Poisson’s ratio, \(v\) [-] & 0.3 \\ Internal friction angle, \(\varphi\) [\({}^{\circ}\)] & 27.3 \\ Cohesion, \(c\) [KPa] & 26.3 \\ Lateral earth pressure coefficient at rest, \(K_{0}\) [-] & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of input parameters for ground formation. Figure 7: Vertical and horizontal in situ stress distribution of coupled DEM-FDM ground formation.
###### Abstract  
Ecosystem water use efficiency (WUE) plays an important role in maintaining the carbon assimilation-water transpiration balance in ecosystems. However, spatiotemporal changes in WUE in the subtropical region of China (STC) and the impact of driving forces remain unclear. In this study, we analyzed the spatiotemporal variation in WUE in the STC and used ridge regression combined with path analysis to identify direct and indirect effects of climate change, vegetation growth, and elevated atmospheric CO\({}_{2}\) concentration (Ca) on the interannual trend in WUE. We then quantified the actual and relative contributions of these drivers to WUE change based on the sensitivity of these variables on WUE and the trends of the variables themselves. Results reveal a mean WUE of 1.57 g C/m\({}^{2}\)/mm in the STC. The annual WUE series showed a descending trend with a decline rate of 0.0006 g C/m\({}^{2}\)/mm/year. The annual average temperature (MAT) and leaf area index (LAI) had strong positive direct effects on the WUE, while the vapor pressure deficit (VPD) had a strong negative direct effect. Opposite direct and indirect effects offset each other, but overall there was a total positive effect of Ca and VPD on WUE. In terms of actual contribution, LAI, Ca, and VPD were the main driving factors; LAI caused WUE to increase by 0.0026 g C/m\({}^{2}\)/mm/year, while Ca and VPD caused WUE to decrease by 0.0021 and 0.0012 g C/m\({}^{2}\)/mm/year, respectively. In terms of relative contribution, LAI dominated the WUE trend, although Ca and VPD were also important factors. Other drivers contributed less to the WUE trend. The results of this study have implications for ecological management and restoration under environmental climate change conditions in subtropical regions worldwide.  
WUE; climate change; ridge regression; path analysis; attribution analysis Article
## 3 Results
### Stepwise Discriminant Analysis  
SDA was first performed separately on the set of chemical and physical variables, and then carried out on the whole dataset.  
Variables enabling maximum discrimination among treatments for the analysis carried out on the dataset of chemical and biological indicators were TOC (P < 0.0001) and HA\({}_{\text{FA}}\) (P = 0.0004), followed at a lower extent by Olsen P (P = 0.0111), EC (P = 0.0258), WEOC (P = 0.0397) and TEC (P = 0.05). In the analysis carried out on the set of physical indicators, the variables selected were RFC (P < 0.0001) and clay (P = 0.037).  
Finally, the analysis carried out on the whole dataset summarized the results obtained, selecting TOC (P < 0.0001), RFC (P < 0.0001) and WEOC (P = 0.003) as variables enabling maximum discrimination among treatments (Table 6).  
Figure 2: Plots of the component loadings (**a**) and scores (**b**) of the first two principal components extracted in the analysis carried out on the whole set of soil indicators. Variance explained: PC1 = 41.21%; PC2 = 12.61%. MT = minimum tillage; NT = no-tillage. WEOC = water extractable organic carbon; WEN = water extractable nitrogen; C\({}_{\text{biomass}}\) = microbial biomass carbon; TOC = total organic carbon; TEC = alkali-extractable carbon; HA\({}_{\text{FA}}\) = humic and fulvic acid carbon; N = total nitrogen; P\({}_{\text{Olsen}}\) = Olsen available phosphorus; EC = electrical conductivity; Ca\({}^{2+}\), Mg\({}^{2+}\), Na\({}^{+}\), K\({}^{+}\) = exchangeable cations. BD = dry bulk density; P\({}_{\text{MAC}}\) = macroporosity; AC = air capacity; RFC = relative field capacity.  
Both PCA and SDA, as well as univariate analysis of variance, returned TOC and RFC among the most influential variables both on the set of chemical and physical data analyzed separately as well as on the whole dataset. In addition, WEOC was selected among the most discriminating variables in the SDA carried out on the whole dataset of soil indicators. These findings are in agreement with results reported by previous studies in the selection of the most relevant indicators for assessing soil quality status.
## 2 Experimental Section
### Biodistribution Studies  
All animal experiments were conducted in compliance with the Guidelines for the Care and Use of Research Animals under protocols approved by Washington University's Animal Studies Committee. For the biodistribution studies, 11.1-13.0 MBq of [11C]**2a** in 200-250 \(\upmu\)L or 1.67-1.85 MBq of [18F]**2b** in 200-250 \(\upmu\)L of saline containing 10% ethanol was injected via the tail vein into mature male Sprague-Dawley rats (175-240 g) under 2%-3% isoflurane/oxygen anesthesia. Group of rats (\(n=4\)) were used for each time point. At 5, 30, 60 min (and 120 min for [18F]**2b**) post injection, the rats were anesthetized and euthanized. The whole brain was quickly removed and dissected into segments consisting of brain stem, thalamus, striatum, hippocampus, cortex and cerebellum. The remainder of the brain was also collected to determine total brain uptake. At the same time, samples of blood, heart, lung, muscle, fat, pancreas, spleen, kidney, liver (and bone for [18F]**2b**) were removed and counted in a Beckman Gamma 8000 well counter with a standard dilution of the injectate. Tissues were weighed, and the percent injected dose (%ID)/gram for each tissue was calculated.
# Using Machine Learning to Predict Indoor Acoustic Indicators of Multi-Functional Activity Centers
## 4 Results and Discussions
### Machine Learning Model Results  
#### 4.3.1 Model Settings and Performance  
The final settings of the hyperparameters are shown in Table 10, and the hyperparameters not mentioned are set to default values. For the same acoustic indicator, the _JND_ of the testing set data of the different models is shown in Figure 20. The red dashed line represents the position of _JND_\(\pm\) 2 and 0.  
In general, most of the data could reach the range of _JND_\(\pm\) 2 that we set for this study, and for C50, C80, and STI, almost all data could reach within _JND_\(\pm\) 1, which indicates excellent predictive ability. GBDT and ANN are clearly more applicable than SVM and RF, and different algorithm models are applicable to different acoustic indicators. GBDT performed best in terms of RT, while ANN performed best for the remaining targets.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & \multicolumn{2}{c}{**Hyperparameter**} \\ \hline SVM & C = 1000 \\  & gamma = 0.01 \\ \hline RF & n\_estimators = 100 \\  & min\_samples\_leaf = 3 \\ \hline GBDT & n\_estimators = 500 \\  & max\_depth = 5 \\ \hline  & Neurons = [400, 400, 400] \\ ANN & Optimizer (lr) = Adam (0.0005) \\  & Batch size = 60 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Hyperparameter settings of machine learning models.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**RF**} & \multicolumn{2}{c}{**GBDT**} & \multicolumn{2}{c}{**ANN**} \\ \cline{2-10}  & & **Actual** & **Level** & **Actual** & **Level** & **Actual** & **Level** & **Actual** & **Level** \\ \hline \multirow{8}{*}{\(R^{2}\)} & SPL & \multirow{2}{*}{0.4511} & \multirow{2}{*}{0.3937} & \multirow{2}{*}{0.9612} & \multirow{2}{*}{0.9612} & \multirow{2}{*}{0.9708} & \multirow{2}{*}{0.9703} & \multirow{2}{*}{0.9852} & \multirow{2}{*}{0.9848} \\  & difference & & & & & & & \\ \cline{2-10}  & RT at & \multirow{2}{*}{0.9859} & \multirow{2}{*}{0.9890} & \multirow{2}{*}{0.9906} & \multirow{2}{*}{0.9906} & \multirow{2}{*}{0.9920} & \multirow{2}{*}{0.9920} & \multirow{2}{*}{0.9905} & \multirow{2}{*}{0.9904} \\  & 500 Hz & & & & & & & \\ \cline{2-10}  & C50 at & \multirow{2}{*}{0.9262} & \multirow{2}{*}{0.9281} & \multirow{2}{*}{0.9382} & \multirow{2}{*}{0.9382} & \multirow{2}{*}{0.9818} & \multirow{2}{*}{0.9819} & \multirow{2}{*}{0.9890} & \multirow{2}{*}{0.9888} \\  & 500 Hz & & & & & & & \\ \hline C80 at & \multirow{2}{*}{0.9531} & \multirow{2}{*}{0.9571} & \multirow{2}{*}{0.9553} & \multirow{2}{*}{0.9553} & \multirow{2}{*}{0.9856} & \multirow{2}{*}{0.9858} & \multirow{2}{*}{0.9920} & \multirow{2}{*}{0.9921} \\  & 500 Hz & & & & & & & \\ \hline C80 at & \multirow{2}{*}{0.9378} & \multirow{2}{*}{0.9503} & \multirow{2}{*}{0.9541} & \multirow{2}{*}{0.9536} & \multirow{2}{*}{0.9816} & \multirow{2}{*}{0.9817} & \multirow{2}{*}{0.9899} & \multirow{2}{*}{0.9897} \\  & 1000 Hz & & & & & & & \\ \hline STI & 0.6486 & 0.7138 & 0.9575 & 0.9575 & 0.9872 & 0.9872 & 0.9912 & 0.9848 \\ \hline \hline \end{tabular}
\end{table}
Table 9: _Cont._
# Improving Low-Frequency Panel Absorbers with Two-Dimensional Acoustic Black Hole
## 5 Discussion  
The findings of this study reveal that the strategic placement of a two-dimensional Acoustic Black Hole (ABH) off-center on a Distributed Mode Absorber (DMA) front panel enhances its efficacy through several mechanisms.  
Primarily, the mode shapes undergo a smooth shift towards the ABH, resulting in the acoustic activation of symmetrical even panel modes. Notably, the first two modes, \(f_{2,1}\) and \(f_{1,2}\), are of paramount importance in the investigated prototypes. The gradual thickness change within the circular power-law indentation minimizes internal reflections, ensuring a smooth adjustment of modal crest positions. In contrast to an abrupt thickness change [9], the ABH panel achieves a more coherent distribution of flexural waves.  
Another advantageous effect contributing to the improved low-frequency absorption is the introduction of a new modal frequency \(f_{\text{ABH}}\) by the ABH structure. This allows for the creation of a modal frequency situated between the fundamental panel mode \(f_{1,1}\) and the second panel mode \(f_{2,1}\), representing the most significant inter-modal frequency spacing.  
While the study did not explicitly verify the Acoustic Black Hole effect in terms of the internal flexural wave field, the utilization of the ABH shape for the indentation demonstrates substantial potential for enhancing the panel vibration behavior of DMAs at low frequencies. It is noteworthy that the influence of internal flexural wave reflections at the edge of the ABH remains a topic for further investigation in subsequent studies.
## 3 Test Results and Analysis
### Analysis of Measured Curves
#### 3.3.3 Load-Displacement Curves of RRAC Columns  
The axial load-displacement curves of the specimens during the whole process of mechanical failure are obtained, as shown in Figure 10. It can be seen from the figure that the load-displacement curves of the specimens change with the increase of temperature, the higher the temperature is, the smaller the peak load will be, but the peak displacement increases, and the descending segment after the peak point tends to be gentle. In addition, when the temperature is higher, the initial secant slope of the curve is smaller.
## 6 Limitations and Future Directions
### Challenges in Medical Name Resolution  
A recurring issue observed during the evaluation was the model's difficulty mapping user-specified medical terms to their correct counterparts in ICD taxonomies. For example, a query about _\"heart failure\"_ often failed to align with the official ICD9 code 428.0 (_\"Consestive heart failure, unspecified\"_). Instead, the models produced overly generic queries or generated outputs incompatible with the database schema. These errors highlight the disconnect between natural language expressions and the structured nomenclature required for SQL queries in medical databases.  
The underlying challenges arise from two main factors. First, language models rely on pre-trained knowledge and lack direct access to comprehensive and updated medical taxonomies at inference time. Second, ambiguity in natural language adds complexity, as colloquial terms used by users often differ from the official medical terminology. For example, a term like _\"heart attack\"_ requires resolution to _\"myocardial infarction\"_ (ICD9: 410.x) to generate a valid query.
## 3 Methodology
### Machine Learning Approaches
#### 3.1.2 RF  
RF is a Classification And Regression Trees (CARTs) based on ensemble approach proposed to overcome the overfitting problem that frequently happens in existing decision trees [40]. It has an advantage of being computationally efficient due to its simple structure [41]. In addition, it has characteristics suitable for training datasets with plenty of variables while showing high performance in multiclass classification of remote sensing data [42]. Considering that many variables are used in this study and performing multiclass classification to detect forest fire burned area, the RF model was judged to be a suitable model for the comparative evaluation.  
RF generates multiple decision trees, and each tree predicts the final classification result by aggregating the results of the trees through majority voting. The RF model includes two randomization procedures, random selections of training samples, and input features. These processes alleviate CARTs weaknesses of the overfitting issue. In comparison to the LightGBM model, the RF has less hyperparameter influence on the model's performance. It is also optimized based on the same grid search method as LightGBM. Model parameters optimized were 'Num. of estimators' (50, 70, and 10), 'Max depth' (16, 13, and 10), 'Min. sample leaf' (10, 5, and 5), and 'Min. Sample split' (13, 9, and 9) for Scheme 1, Scheme 2, and Scheme 3, respectively.