esa-sceva/satcom-chunk-collection huggingface dataset:
# Query Recommendation Using Hybrid Query Relevance
## 4 Results
### Selection of Parameters \(\alpha,\beta\)  
#### 4.2.1 Selection of Parameter \(\alpha\)  
We do multi-group experiments for the value for parameter \(\alpha\). In the experiments, the parameter \(\alpha\) is satisfied at more than 0 and less than or equal to 1, when \(\alpha=1\) means that we do not add the query semantics. We change their value with interval of 0.1 in the experiment and observe the influence of the precision, the recall and the F1 measure. To get accurate results, we recommend queries from Top 5 to Top 50 with interval of 5 and calculate average value to get the final precision, the recall, and the F1 measure. The results are shown in Figure 4.  
From Figure 4, we can see that combining the query semantics can improve query recommendation results. When the parameter \(\alpha\) is too small, that means the weight of query semantics is too big, and we cannot get good results. This is because some queries are ambiguous. At the same time, some queries are correlated, but they do not have semantic relevance. We can mine their relevance by query log. However, only using query pairs (\(\alpha=1\)) also cannot get good recommendation results. Because of the sparsity of data and the incorrect session partition, the relevance between many queries is missing. Therefore, we combine query pairs and query semantics. In Figure 4c, we can see that we can get better results compared with only using query pairs when the parameter \(\alpha\) is larger than 0.3.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**UseID** & **Query Content** & **Timestamp** & **URL Ranking** & **Clicked URL** \\ \hline
217 & lottery & 1 March 2006 11:58:51 & 1 & [[http://www.calottery.com](http://www.calottery.com)]([http://www.calottery.com](http://www.calottery.com)) \\
1268 & ozark horse blankets & 1 March 2006 17:39:28 & 8 & [[http://www.blanketsnmore.com](http://www.blanketsnmore.com)]([http://www.blanketsnmore.com](http://www.blanketsnmore.com)) \\
2334 & [PERSON] & 1 March 2006 18:53:50 & 4 & [[http://www.hyfntrak.com](http://www.hyfntrak.com)]([http://www.hyfntrak.com](http://www.hyfntrak.com)) \\
2421 & cstoons & 9 May 2006 17:32:44 & 2 & [[http://www.xtracrispy.com](http://www.xtracrispy.com)]([http://www.xtracrispy.com](http://www.xtracrispy.com)) \\ \hline \end{tabular}
\end{table}
Table 2: Examples of AOL log.  
We can observe that precision, recall, and F1 declined beyond 0.9. To get the optimal values of the \(\alpha\), we change the range of parameter to 0.02 and conduct multiple experiments. The results are shown in Figure 5.  
Figure 5 suggests that precision, recall, and F1 decrease with the increase of parameter. \(\alpha=0.9\) is the best result. Thus, we set the parameter \(\alpha\) to 0.9 in the later experiments.
# Besinc Pseudo-Shell Model Sources with Circular Coherence
## 5 Discussion  
Pseudo-Schell model sources present shift invariant coherence properties either along the radial or the angular coordinate. Here, the class of pseudo-Schell model sources with a besinc-like dependence of the coherence on the radial distance (Equation (7)) is analyzed.  
Complete coherence is attained for points lying on the same circle, although they can be as far as twice the circle radius. Furthermore, the absolute value of the degree of coherence decreases for pairs of points that belong to circles with greater and greater radii difference, although they can be closer than a pair of points for which complete coherence is found. Complete incoherence is found for points located on different circles with radii difference such that \(J_{1}\left[\xi(r_{2}-r_{1})/\delta_{c}\right]=0\).  
When a Laguerre-Gaussian intensity at the source plane is considered (see Equation (9)), a pseudo-modal expansion [27] of the CSD has been found. Then, these kinds of sources can be synthesized by superposing a large enough number of pseudo-modes [32; 33]. Peculiar behavior of the intensity profile with the propagation distance has been found: the profile becomes sharper and the maximum intensity is reached after a propagation distance from the source plane, the latter corresponding to the waist. This effect is more and more evident for lower and lower ratios between the coherence parameter and the beam width. Similar behaviors have been described for other partially coherent beams with nonconventional correlation functions [11; 17; 20; 40; 41]  
These features could be useful for particle trapping, due to the high intensity gradient that can be obtained along transverse and radial directions on focusing the beam generated by this kind of sources. An example has been developed showing the trapping capabilities of these kinds of beams for nanoparticles presenting a refractive index either higher or lower than the surrounding medium. It should be noted that the trapping of these two types of particles is achieved using the same beam, i.e., a beam generated from a besinc pseudo-Schell source with fixed parameters (\(\omega_{0}\) and \(\delta_{c}\)) and a fixed lens.
## Nomenclature  
### Abbreviations/Definitions  
\(\mathrm{CO_{2}}\)Carbon dioxide kg  
\(\mathrm{COP}\)Coefficient of Performance (Heat output/electricity input of heat pump)  
DHWDomestic hot-water  
\(\mathrm{EN}\)European Norm/Standard  
\(\mathrm{GSHP}\)Ground Source Heat Pump  
\(\mathrm{HVAC}\)Heating, Ventilation and Air Conditioning  
\(\mathrm{HSPF}\)Heating Seasonal Performance Factor (heat output/electricity input of entire heating system including heat pump and circulators)  
\(\mathrm{IPCC}\)Intergovernmental Panel on Climate Change  
\(\mathrm{Re}\)ReReynolds number = \(\frac{\mathrm{up}_{\mathrm{pipe}}\mathrm{up}_{\mathrm{water}}}{\mathrm{up}_{ \mathrm{water}}}=\frac{4\mathrm{up}_{\mathrm{pipe}}}{\mathrm{up}_{\mathrm{water}} \pi\mathrm{d}_{\mathrm{pipe}}\mathrm{up}_{\mathrm{water}}}\)  
\(\mathrm{Type\,20}\)Hydronic radiator with two panels and a single convector plate  
\(\mathrm{Type\,21}\)Hydronic radiator with two panels and two convector plates  
\(\mathrm{Type\,22}\)Hydronic radiator with two panels and two convector plates  
\(\mathrm{_{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{    \mathrm{ }}}}}}}{}}}{}}}{}}{}}{}{}}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{ }{}{}{}{}{}{}{  
\begin{tabular}{l l l} K & Flow coefficient of valve (valve capacity) & m\({}^{3}\)/h \\ l 1 & Length of distribution pipes in a heating circuit & m \\ \(\dot{\mathrm{M}}\) & Water mass flow rate & kg/h \\ m or\(\dot{\mathrm{m}}\) & Water mass flow rate & kg/s \\  & \(=\ \frac{\mathrm{P}_{\mathrm{heat\_mim}}}{\mathrm{c}_{\mathrm{p,water}}(\theta_{ \mathrm{amp}}-\theta_{\mathrm{m}})}\) & kg/s \\ P & Heat output or hydraulic pump power need & W \\ P & Pressure & Pa and bar \\ T & Absolute (thermodynamic) temperature & K \\ u & Water velocity in pipes & m/s \\ \(\dot{\mathrm{V}}\) & Volume flow rate & m\({}^{3}\)/h \\ _Greek letters_ & \\ \(\Delta\) & Difference & \\ \(\varepsilon\) & Absolute inner pipe roughness (1.5 \(\times\) 10\({}^{-6}\) in this study) & m \\ \(\zeta\) & Radiator entrance loss coefficient (3 in this study) & - \\ \(\eta\) & Hydraulic efficiency of water pump & - \\ \(\theta\) & Temperature & \({}^{\circ}\)C \\ v & Kinematic viscosity & m\({}^{2}\)/s \\ \(\rho\) & Density & kg/m\({}^{3}\) \\ _Subscripts_ & \\ comp & Compressor & \\ cond & Condenser & \\ circuit & Heating circuit & \\ el & Electricity & \\ eq. & CO\({}_{2}\) equivalent and equivalent pipe length for components \\ heat & Heat output of heat emitter & \\ i & First term in a sequence (first pipe line/circuit) \\ inlet & Pipe inlet to radiator & \\ lmtd & Logarithmic mean temperature difference & \\ n & Number of pipe lines (circuits) and temperature exponent & \\ opt & Optimum & \\ pipe & Pipe & \\ pump & Pump & \\ q & Quality & \\ radiator & water-based (hydronic) radiator & \\ ref. & Reference value (1000 kg/m\({}^{3}\) in this study) & \\ room & Room air & \\ rtn & Return water from room (space) heater & \\ supp & Supply water to room (space) heater & \\ v & Valve & \\ \end{tabular}
# Cost-Benefit Analysis of Small-Scale Rooftop PV Systems: The Case of Dragotin, Croatia
## 3 Results and Discussion
Instead, net metering can be applied to dispose of surplus PV energy in the grid over a longer period of time (\"store\" in the grid), and use in periods of insufficient PV production, thus increasing self-consumption. The most general definition of net metering policy is the permission given to utility-related consumers to compensate for their consumption by injecting their own surplus electricity into the grid and creating loans that can be used later. Although the general definition is clear, net metering policy varies from country to country. For example, the policymaker should decide on elements, such as the policy objective (promote the adoption of a distributed grid system or guarantee the financial sustainability of utilities), compensation scheme (in energy or money), minimum technical requirements of the plant to guarantee distribution generation quality, financial mechanisms (if any) and the way it is financed, and the like. The net metering/net billing policy is essential for the cost-effectiveness of PV roofing systems. Various net metering/net billing schemes have been analyzed in the literature, ranging from instantaneously net metering to 1-year net metering [37; 38; 39]. According to [37], significant compensation of electricity taken from the grid can be achieved for a period of 1 day or more. If net metering is used annually, excess PV production during the summer can be injected into the grid and used in the winter months. For the object analyzed in this paper, the results for different net measurement schemes are shown in Table 9.  
As can be seen, the longer the net metering/net billing period, the lower the values of energy consumed from the grid to be paid to the electricity supplier. In the case of the application of annual net metering, the energy consumed from the grid can be equal to zero, and this incentive scheme allows compensation of production and consumption during the year. This value increases as the time frame decreases, but in any case, the total energy injected into the grid is greater than that consumed from the grid. These results suggest that depending on the legislation of each country, adjusting demand to production may be more or less critical. In the worst case, when it is not possible to compensate for the energy produced (instantaneously balances), the percentages of self-consumption and self-sufficiency can only be improved by some kind of consumption management. At best, the grid acts as a system for storing all the excess energy produced from the PV system [37].
## 4 Experiment and Results
### Results  
#### 4.4.1 Visual Features via Quality Resilient Convolutional Neural Network  
We discussed above that this study selected InceptionResNetV2 for feature extraction as it gives the best discriminatory features in contrast to ResNet-50 and DenseNet-201. In the scenario of multilabel classification for ingredient detection, our framework has extracted feature vectors from the InceptionResNetV2 model fine-tuned using binary loss instead of categorical loss [1]. However, in the real-world environment, quality distortions in photos affect the robustness of features. This section discusses the results of the InceptionResNetV2 model under five common types of quality distortions in images to evaluate the effect on the robustness of the features. Secondly, it presents results of a comparison of classification performance on low-quality test images that shows fine-tuning CNN using the data augmentation strategy (RIMixUp) improves the robustness of CNN compared to the CNN fine-tuned with pristine food images. Then an explainable AI method, \"Gradient Explainer\" and t-distributed stochastic neighbor embedding (t-SNE) visualizations provide insight on how this data augmentation has improved the robustness of feature vectors extracted from the quality resilient CNN.
## 4 Discussion
### Comparisons of Model Performance in SOC Prediction
In summary, all the results showed a strong correlation between NDVI time series data and SOC. In particular, NDVI time series showed interpretability for SOC. However, the R\({}^{2}\) values of all the models in this study were low. This condition was because the SOC content was related to NDVI and influenced by soil type and structure, precipitation, climate, and other factors because of the diversity of soil-forming factors. Additional pivotal factors combined with the time series method should be calibrated for the spatial prediction of soil properties. Meanwhile, time periods of environmental factors should be considered to apply the highly appropriate time series. For cartographic quality, machine learning and traditional regression methods based on pixel-raster cartographic methods could obtain relatively fine maps and show the interior details of SOC spatial distribution in Honghu City. Evidently, the spatial distribution of SOC mapped by the OK model had large and rough raster polygon because of its smoothing effect. Thus, the digital SOC map applied by the ANN model was considered the optimal one comprehensively considering the prediction accuracy and mapping requirements.
# Reversible Data Hiding in Encryppted Images with Extended Parametric Binary Tree Labeling
## 2 Related Work
### Ipbtl-Rdhei  
The reversible data hiding using IPBTL [27] shares the same framework as the hiding scheme using PBTL [26]. The main difference is the reduction of reference pixels. IPBTL exploits the first row and the first column of an image as the reference pixels. Thus, the number of embeddable pixels is greatly increased. Therefore, the total payload is greater than the PBTL-based data-hiding scheme. The IPBTL-RDHEI scheme consists of the following two phases.
## 4 Experiments and Results Analysis
### Analysis of Ensemble Learning Model Prediction Performance
(4) Prediction scenario specificity: Different prediction scales require different meta-learners to achieve optimal performance, indicating that ensemble learning cannot fully integrate the prediction capabilities of shallow learning and deep learning models across all scenarios. (5) Competitiveness of single deep learning models: In some prediction scenarios, such as MAPE for 20 and 30 min predictions, single LSTM-DNN models still perform best, indicating that simple ensemble approaches do not always improve performance. (6) Maintained RMSE advantage of random forest: Across multiple prediction scales, the RMSE advantage of single random forest models was not surpassed by ensemble models. Overall, ensemble learning methods can indeed improve urban road travel time prediction performance in certain prediction scenarios, but the effects are selective and require choosing appropriate meta-learners for specific scenarios. Further analysis reveals that ensemble performance gains are context-dependent rather than systematically superior. For MAPE metrics, ensemble models outperformed single models in three out of five prediction horizons, while RMSE improvements were limited to two scenarios. Shallow model ensembles showed minimal advantages--linear regression ensembles failed to surpass single models in MAPE across all horizons, and random forest ensembles demonstrated no improvement in either metric. Deep learning ensembles exhibited localized improvements with inconsistent patterns, suggesting that ensemble efficacy requires careful configuration optimization for specific prediction contexts.
## 3 Proposed Method
### Method of Extracting Speech Characteristics  
#### Log Mel-Spectrogram  
The frequencies of raw data collected were limited to the audible frequency range between 20 and 20,000 Hz using a band-pass filter (BPF) [16]. This process aimed to eliminate low-frequency and noise signals below 20 Hz and above 20,000 Hz while emphasizing speech signals within the audible frequency band [17]. Subsequently, the denoised data underwent a short-time Fourier transform (STFT) to extract frequency features such as intensity and pitch. These features were then processed through a mel filter bank to represent auditory characteristics akin to human perception. The structures of a basil  
Figure 1: Architecture of the model.  
membrane [18] and a mel filter bank are depicted in Figure 2. As illustrated in Figure 2a, the basilar membrane becomes thinner towards the outside and thicker towards the inside. Additionally, sound takes longer to penetrate the interior in low-frequency bands with longer wavelengths compared to high-frequency bands, making sound detection more sensitive in the former. Figure 2b presents a graph compiled from statistics of human speech data. The mel filter bank is defined by Equation (1).  
\[Mel(f)\ =\ 2595\text{log}_{10}\bigg{(}1+\frac{f}{700}\bigg{)} \tag{1}\]  
Equation (1) defines a mel filter bank, where \(f\) represents the STFT input data [19]. Thus, frequency characteristics extracted by an STFT can be displayed, as shown in Figure 3a. Additionally, a low-frequency band is amplified by a mel filter bank. Figure 3b shows the result of applying a log scale to the extracted frequency characteristics.  
\[LMel_{i}\ =\ \text{log}(p_{i}) \tag{2}\]  
A log scale [16] is described by Equation (2), where \(p_{i}\) represents the energy of the signal obtained by applying a mel filter bank to the spectrogram in the \(i\)-th filter. The result \(p_{i}\) is combined with logarithms to generate a log mel-spectrogram. Since this processing is modeled after the human auditory system, it effectively captures the characteristics of human speech.  
Figure 3: Comparative analysis of a spectrogram and a log mel-spectrogram. (**a**) A spectrogram. (**b**) A log mel-spectrogram (AlHub’s Data ID: 5f5b466c2e23c7161 accccech).  
Figure 2: Frequency characteristics of a basilar membrane and a mel filter bank: (**a**) the structure of a basilar membrane structure; (**b**) the structure of a mel filter bank.
# Model-Based Assessment of Energy Efficiency in Industrial Pump Systems: A Case Study Approach
## 5 Creation of System Efficiency Maps  
We have analysed the performance of five pump-motor-converter variants, which are listed in Table 4. The first two variants combine the Etanorm 150-125-250 pump with an impeller diameter of 237 mm with a 15 kW IE3 IM (237-IE3) or a 15 kW IE5 SRM (237-IE5). The next three variants are produced for the Etanorm 150-125-250 pump with an impeller diameter of 252 mm in combination with three different motors: 18.5 kW IE3 IM (252-IE3), 18.5 kW IE4 SRM (252-IE4), and 22 kW IE5 SRM (252-IE5). The last variant (258-IE1) is the equivalent of the existing VSDP.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Pump** & **Motor** & **Converter** & **System Designation** \\ \hline ETANORM 150-125-250 (237 mm) & 15 kW IE3 (IM) & 15 kW, 22 kVA, 32 A & 237-IE3 \\ ETANORM 150-125-250 (237 mm) & 15 kW IE5 (SRM) & 15 kW, 24 kVA, 35 A & 237-IE5 \\ ETANORM 150-125-250 (252 mm) & 18.5 kW IE3 (IM) & 18.5 kW, 26 kVA, 38 A & 252-IE3 \\ ETANORM 150-125-250 (252 mm) & 18.5 kW IE4 (SRM) & 18.5 kW, 30 kVA, 43 A & 252-IE4 \\ ETANORM 150-125-250 (237 mm) & 18.5 kW IE5 (SRM) & 18.5 kW, 30 kVA, 43 A & 252-IE5 \\ ETANORM 150-250 (258 mm) & 30 kW IE1 (IM) & 30 kW, 43 kVA, 61 A & 258-IE1 \\ \hline \end{tabular}
\end{table}
Table 4: Pump–motor–converter combinations.  
Figure 13: Efficiency maps for (**a**) the 237 pump and (**b**) the 252 pump.  
For each variant listed in Table 4, a system efficiency map was created by transferring the drive efficiency map to the \(Q\)-\(H\) plane. First, the motor speed \(n_{M}\) and torque \(M_{M}\) were expressed by the pump speed \(f_{n,P}(Q_{P},H_{P})\) and pump losses \(f_{loss,P}(Q_{P},H_{P})\) maps:  
\[\begin{array}{l}n_{M}(Q_{P},H_{P})=n_{P}(Q_{P},H_{P})=f_{n,P}(Q_{P},H_{P})\\ M_{M}(Q_{P},H_{P})=\frac{f_{loss,P}(Q_{P},H_{P})+Q_{P}H_{P}g}{f_{n,P}(Q_{P},H_{ P})}\end{array}. \tag{14}\]  
The system efficiency was then determined by multiplying the drive and pump efficiency maps:  
\[f_{\eta,sys}(Q_{P},H_{P})=f_{\eta,D}(n_{M},M_{M})\cdot f_{\eta,P}(Q_{P},H_{P}). \tag{15}\]  
The resulting system efficiency maps are shown in the next section.
# Precise Mission Process Control Based on a Novel Dual-Code Group Network Plan Diagram
## 6 Results and Discussion
### Experiment Results and Discussion  
To verify the effectiveness of the control strategy, we construct a mission process simulation system. The mission process is set to start at time 0, and the simulation time step is 1 min, i.e., \(\Delta t=1\) min. Our control objective is to ensure that mission E7 starts as planned at the 110 th minute. During the mission projection process, we add a random delay disturbance. By obtaining mission states in real time, it generates the control quantity according to the control strategy and updates the network plan diagram. Our goal is to determine whether the control strategy can eliminate the disturbance and ensure the realization of the control objective. For this purpose, we carried out a total of four experiments with different scenarios.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 4 Evolution of Communications Technologies for Telemedicine
### Communication Network Links for Telemedicine
#### 4.1.2 Point-to-Multipoint Links  
Point-to-multipoint links enable every department in a healthcare facility to be connected to a central hub (the datacentre) for seamless communication and healthcare service delivery (see Figure 9). By way of illustration, a primary care physician can utilize this link to initiate a test for a patient in the radiology department and obtain the result electronically. The same physician can provide authorization for the pharmacy to dispense drugs to a patient while at the same time the finance unit takes the necessary funds from the patient's wallet for services rendered. In the same vein, telesurgery can be performed on a patient simultaneously by a number of surgeons from different locations via this link. This link can be implemented within a single health facility using an intranet connection for high speed and data security. However, in the event of telesurgery, which requires the participation of a specialist located at a distant healthcare center, a high-speed internet connection that is supported by a fiber optics network is helpful.  
Figure 8: Point-to-Point Links.  
Figure 9: Point-to-Multipoint Links.