esa-sceva/satcom-chunk-collection huggingface dataset:
# On Smartphone Power Consumption in Acoustic Environment Monitoring Applications
## 3 Results and Discussion
### Power Consumption vs. Release Year and the OS Version  
Since we discovered that the power consumption for different smartphone models may vary significantly from model to model (see Figure 1), we were interested to find out which hardware or software features may influence power consumption. Somewhat surprisingly, our results did not show any obvious correlation between the power consumption in background audio recording mode and hardware and software features such as the Android OS version, CPU and chipset model, the size of RAM and built-in memory, or the smartphone release date. For example, in Figure 6 we plot the measured consumption data vs. Android OS version number and the smartphone release date. One might expect that the newer smartphones would be more power efficient in audio recording mode, yet this is not the case. There is no statistically significant difference (at least, for our sample size) between power consumption of the smartphones released in 2011-2014 and the smartphones released in 2015-2017. On the other hand, if we consider the expected battery life in background audio recording mode vs. the smartphone release date, there is a clear upward trend (see Figure 7). However, this is easily explained by the fact that the newer smartphones tend to have larger capacity batteries than their older counterparts.  
Figure 6: (**a**) Power consumption vs. Android OS version and (**b**) the smartphone release year: measured data and the linear regression fit.  
Figure 7: (**a**) Expected battery life vs. Android OS version and (**b**) the smartphone release year: measured data and the linear regression fit.
# Enhancing Logistic Regression Using Neural Networks for Classification in Actuarial Learning
## 2 Notation and Overview of Techniques
### Goodness of Fit  
In the context of logistic regression, the maximum number of parameters that can be fitted is given by \(n\), i.e., one for each observation. In this case, we have \(Y_{i}\sim\text{Bernoulli}(\pi_{i})\) and \(\hat{\pi}_{i}=y_{i}\) as the optimal solution obtained from a saturated model. Let \(\hat{\beta}_{max}\) denote the vector \((\hat{\pi}_{1},...,\hat{\pi}_{n})\). One way to assess the goodness of fit of a model is to compare its log-likelihood with that of a saturated model. The relevant test statistic, called the deviance, is defined by:  
\[\mathcal{D}=2\Big{(}\ell(\hat{\beta}_{max};y)-\ell(\hat{\beta};y)\Big{)}. \tag{13}\]It can be calculated explicitly, as follows:  
\[\ell(\hat{\beta}_{max};y) =\sum_{i=1}^{n}\left\{y_{i}\log\Big{(}\frac{y_{i}}{1-y_{i}}\Big{)}+ \log(1-y_{i})\right\} \tag{14}\] \[\ell(\hat{\beta};y) =\sum_{i=1}^{n}\left\{y_{i}\log\Big{(}\frac{\pi_{i}}{1-\pi_{i}} \Big{)}+\log(1-\pi_{i})\right\}\] \[\mathcal{D} =2\sum_{i=1}^{n}\left\{y_{i}\log\Big{(}\frac{y_{i}}{\sigma(x_{i} ^{\top}\beta)}\Big{)}+(1-y_{i})\log\Big{(}\frac{1-y_{i}}{1-\sigma(x_{i}^{\top }\beta)}\Big{)}\right\}\] \[\mathcal{D} =2\sum_{i=1}^{n}y_{i}\log\biggl{(}\frac{y_{i}}{\sigma(x_{i}^{ \top}\beta)}\biggr{)}+(1-y_{i})\log\biggl{(}\frac{1-y_{i}}{1-\sigma(x_{i}^{ \top}\beta)}\biggr{)},\]  
where \(\sigma:\mathbb{R}\rightarrow(0,1)\) is the sigmoid function.  
Note that the first and the second derivatives of the deviance loss are the double of the derivatives of the cross-entropy loss as it holds  
\[y_{i}\log\biggl{(}\frac{y_{i}}{\sigma(x_{i}^{\top}\beta)}\biggr{)}+(1-y_{i}) \log\biggl{(}\frac{1-y_{i}}{1-\sigma(x_{i}^{\top}\beta)}\biggr{)}=\]  
\[y_{i}\log y_{i}-y_{i}\log(\sigma(x_{i}^{\top}\beta)+(1-y_{i})\log(1-y_{i})-(1- y_{i})\log(1-\sigma(x_{i}^{\top}\beta)\]  
and the derivative is computed with respect to \(\beta\). Thus, we can use the same approach to approximate the optimal solution as for the cross-entropy loss.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Next-Hop Selection
#### 3.8.3 Content-Based  
In some communication models, messages are not given explicit destination addresses, so that next-hops and routing information must be inferred from the sensed data carried by the message.
## 4 Design, Implementation and Analysis Results of the Image Encryption Algorithm  
In this section, a S-box based encryption algorithm is proposed for image encryption. In the developed algorithm, the S-box structures generated by the chaos-based S-box algorithm described in the previous section are used. First, the design of the S-box based encryption algorithm has been presented, then the image encryption process has been performed and the performance and security analysis results of the encryption processes have been given.
# Masked Convolutions Within Skip Connections for Video Anomaly Detection
## 2 Related Work
Furthermore, although [15] extends this approach with 3D masked convolutions to consider temporal dimensions, it still does not utilize these masked convolutional layers across the entire network effectively for anomaly detection. In our approach, we extend the use of MaskedConv3D layers throughout the UNet architecture, strategically inserting them between skip connections to infer normal spatio-temporal features across various granularities of detail and provide a more comprehensive anomaly detection solution.
## 4 Conclusions  
A numerical proof of concept of a novel PCM heating technology implemented into building walls and/or roofs is given. The system is able to change the position of the PCM layer with respect to the insulation inside the building component. Therefore, the system can locate PCM facing outdoors during sunny hours to maximize solar absorption and the position of the PCM facing indoors to provide space heating when required by the demand. The novel dynamic system has been numerically tested when implemented into three different construction morphologies under Csa weather conditions, providing extraordinary benefits in all of them. Numerical results demonstrate that the system can not only be used as a thermal barrier from the outer environment but can provide active space heating from stored solar energy. Moreover, in case of using movable layers with limited quantity of PCM (25-50% w/w), the system is able to provide positive net energy balance, meaning that it releases indoors more stored solar energy than all the heat losses to the outdoor environment.  
The study also highlighted that the performance of the system is highly influenced by certain design key parameters, such as melting temperature and amount of PCM, as well as on control schedule to determine activations during the day. These variables should be examined and optimized for each case, considering constructive system, building use, and weather conditions.  
The high potential to reduce energy consumption for space heating applications showed by the numerical results supports the future development of an experimental prototype to quantify the benefits from this technology under relevant environment and evaluate its economic and environmental benefits.  
**Funding:** This work was partially funded by the Ministerio de Ciencia, Innovacion y Universidades de Espana (RTI2018-093849-B-C31). The authors at the University of Lleida would like to thank the Catalan Government for the quality accreditation given to their research group (2017 SGR 1537). GREA is certified agent TECNO in the category of technology developers from the Government of Catalonia. A.D.G. has received funding from  
Figure 7: Heating load reduction in case of different amount of PCM.  
the European Union's Horizon 2020 research and innovation programme under the [PERSON] grant agreement No 712949 (TECNIOspring PLUS) and from the Agency for Business Competitiveness of the Government of Catalonia.  
The author declares no conflict of interest.
## 2 Materials and Methods
### Fumigation Bioassay and Calculation of Lethal Concentration-Time (LCT)  
For each developmental stage, thirty _P. citri_ individuals were used in triplicate and placed in 12 L desiccators (Duran, Germany) sealed with glass stoppers containing a septum of filter paper for bioassay of each fumigant. The concentration of fumigant was monitored at timed intervals over the exposure period using an Agilent GC 7890A with each detector. The analysis conditions for each fumigant were as below.  
* EF: EF fumigation was performed in a concentration range of 1.5 to 40.57 mg/L for 4 h. Gas was sampled at 0.5, 1, 2, and 4 h post fumigation and analyzed using a flame ionization detector.
* PH3: PH3 fumigation was performed in a concentration range of 0.005 to 0.102 mg/L for 20 h. Gas was sampled at 0.5, 1, 2, 4, 18, and 20 h post fumigation and analyzed using a flame photometric detector.  
The lethal concentration \(\times\) time (LCT, mg\(\cdot\)h/L) value of each fumigant toward _P. citri_ was calculated using Equation (1) [22]. The data represent an average of CT and SE (standard error) based on a probit analysis using IBM SPSS Statistics for Windows version 23.0 (IBM Corp, Armonk, NY). The LCT values were converted to lethal concentration (LCx, mg/L) values to compare biological responses after exposure to each fumigant (Table S1).  
\[\text{LCT}=\Sigma(\text{C}_{\text{i}}+\text{C}_{\text{i}+1})(\text{t}_{\text {i}+1}-\text{t}_{\text{i}})/2 \tag{1}\]  
where 'C' is the fumigant concentration (mg/L), '\(\varGamma\) is the exposure time (h), and 'i' is the order of measurement.
# Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor
## 4 Experiments and Results
### Experiments on Simulated Data
#### 4.1.2 Experiment 2: The Performance versus Angle Difference
From Figure 7, we can see that when \(\Delta\theta\)\(\rightarrow\)0\({}^{\circ}\) (the target speaker moves closer to the interference speaker), for both algorithms, the performance of PESQ and LSD are also poor. This means that the speech enhancement is very much limited if \(\Delta\theta\)\(\rightarrow\)0\({}^{\circ}\). However, when \(\Delta\theta\)\(>\) 20\({}^{\circ}\), it is quite encouraging to see that the performance of PESQ and LSD of our proposed AVS-SMASK algorithm outperforms that of the AVS-FMV algorithm for all of the angles. In addition, it is noted that the performance of two algorithms drops again when the \(\Delta\theta\)\(>\) 140\({}^{\circ}\) (the target speaker moves closer to the interference speaker around a cone). However, from Figure 6, this phenomenon does not exit. In summary, from the experimental results, it is clear that our proposed AVS-SMASK algorithm is able to enhance the target speech and suppress the interferences when the angle difference between the target speaker and the interference is larger than 20\({}^{\circ}\). Figure 6: (Experiment 2) The perfomance versus \(\Delta\phi\). (**a**) Perceptual evaluation of speech quality (PESQ) results and (**b**) Log spectral division (LSD) results (Case 1: \(\phi_{s}\) of the target speaker changes from 0\({}^{\circ}\) to 180\({}^{\circ}\)) (Case 1).
# Removal of Antibiotics by Biochars: A Critical Review
## 4 A Critical Review of Biochar-Based Adsorption Processes
### Biochar Properties
Thus, the severity of the pyrolysis conditions (i.e., maximum pyrolysis temperature, heating rate, and solid residence time) increases the surface area to some extent, but it decreases after a certain limit (which is dependent on biomass, chemical and anatomical composition, and the heat and mass transfer rate). This phenomenon has two practical implications: (i) it is not always necessary to apply the most severe conditions, and (ii) energy savings can be achieved by applying the optimal pyrolysis conditions. The determination of the surface area available for adsorption faces some problems. For instance, the prevailing method to determine the surface area, N\({}_{2}\) adsorption at 77 K, has a kinetic diffusion limitation for N\({}_{2}\) in small micropores [88]. The kinetic limitation arises from the inflexibility of the matrix, leading to an artificially lower surface area for some chars. This phenomenon has been reported by several authors, for instance for oak, pine, and grass chars, where the N\({}_{2}\) surface area was 225, 285, and 77 m\({}^{2}\)/g, respectively, while the CO\({}_{2}\) area for the same materials was 528, 843, and 427 m\({}^{2}\)/g, respectively [89]. Similar results were presented for sewage sludge and wood chip char [90]. The higher surface area estimation by CO\({}_{2}\) has been reported to be due to the higher kinetic energy associated with the smaller kinetic diameter of CO\({}_{2}\) (3.3 A vs. 3.64 A for CO\({}_{2}\) and N\({}_{2}\), respectively), which allows CO\({}_{2}\) to diffuse more easily into the small pores [89; 91; 92]. Argon has also been used to measure char's surface area at 77 K and 87 K. The results showed that at 87 K the surface area was slightly greater than at 77 K, which was attributed to the increased mobility of Ar molecules at higher temperatures. On the other hand, the low values of surface area measured by Ar were believed to be due to the lower amount of mesopores [93]. The size of the pores also affects the sorption, because the filling of micropores involves a higher number of contact points than the filling of mesopores, and pore filling has been characterized as being influenced by size exclusion effects [88]. A comparison of the adsorption-relevant properties of different biochars is presented in Table 2.  
The results of Table 2 show that biochar properties are highly variable between different precursors and applied pyrolysis temperatures. However, data analyses allow for certain conclusions:  
1. The specific surface area of biochars is usually between 0 and 100 m\({}^{2}\)/g;
2. Wood biochars have the greatest specific surface area (up to 738 m\({}^{2}\)/g);
3. The pore volume of different biochars is between 0 and 0.2 cm\({}^{3}\)/g;
4. The ash content of biochars is highly variable; it is highest in sewage sludge, algae, and manure biochars, and lowest in wood biochars.
## 2 Related Works
The cluster-head selection and optimized route of data transmission using adaptive learning in neural networks could cause a huge computation burden for sensor nodes. Moreover, [PERSON] _et al._[20] proposed a Coverage-Preserving Scheme (BCoPS), which is a novel approach that allows a BS to maintain the network with consideration of various factors, such as network coverage, wake-up strategies, and cluster formation. Although the proposed works in [18-20] prolong network lifetime, they cannot guarantee full sensing coverage of the network. Retaining full sensing coverage is an important issue when losing any sensed data is not affordable. The full coverage issue was mentioned in [21], and the authors proposed several cost metrics (coverage and energy-aware costs) for different application scenarios. The sensor nodes deployed in a densely populated area can serve as cluster heads, active sensor nodes, and routers. The cost metrics are not only used for the cluster-head selection but also for active node selection and routing-table update. However, using these metrics causes a larger computational burden on sensor nodes. [PERSON] _et al._[22] presented the coverage-aware clustering protocol (CACP) for randomly deployed networks, which simplifies the cost metric for cluster-head selection and active node selection. The CACP outperforms the protocol proposed in [21]; however, each cluster-head consumes much energy when the cluster head directly transmits the aggregated data to the BS.
# An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks +
## 4 Proposed Tree Routing Protocol
### Parent Selection  
To provide a reasonable trade-off between multiple objectives, we aim for the node to adaptively change its parent node according to network conditions. As illustrated in Figures 2 and 4, each node recognizes the current network state using the proposed cognitive metrics, and then finds an optimal parent node based on empirical data about the network. To this end, we propose an RL-based parent selection algorithm. In this subsection, we first define the state space, action set, and reward function using several cognitive metrics, and then specify the RL-based parent selection algorithm.  
Figure 3: An illustration of tree-based routing.
# Segmentation of Stone Slab Cracks Based on an Improved YOLOv8 Algorithm
## 3 Experimental Results Analysis
### Ablation Study  
To evaluate the contribution of each component in YOLOv8-Seg for the semantic segmentation of stone slab cracks, an ablation study was conducted on the custom ST dataset. The tested modules include the following: (A) U-NetV2 backbone, (B) DySample strategy, and (C) dynamic snake convolution head.  
As shown in Table 4, the original YOLOv8 achieves [EMAIL_ADDRESS] (localization accuracy metric at 0.5 IoU threshold) of 0.818 and [EMAIL_ADDRESS] 0.95 of 0.425. After sequentially introducing dynamic snake convolution, the U-NetV2 backbone, and the Dysample strategy, the final model reaches 0.856 and 0.479, respectively. Precision increases to 95.4% and recall improves to 78.3%, outperforming the baseline by 2.6% and 5.0%, respectively. The dynamic snake convolution enhances the boundary detection of slender cracks by adapting to their curved shapes. The U-NetV2 backbone strengthens multi-scale feature fusion using skip connections and depthwise separable convolutions while maintaining efficiency. DySample improves recall for small cracks by dynamically adjusting the sample balance, addressing the low pixel ratio of crack regions. Under complex textures and noisy backgrounds, the improved model maintains high precision and achieves a recall above 78%, showing a clear advantage over conventional methods.  
The PR curve in Figure 9 further quantifies the performance gains from the proposed improvements. At a recall threshold of 0.8, the original YOLOv8 shows a sharp drop in precision to 65%, while the improved model maintains 82%. The area under the curve increases from 0.741 to 0.823. Dynamic snake convolution plays a key role, boosting precision by an average of 14% in low-confidence regions due to its adaptive capability to follow crack directions. In high-threshold settings, the deep feature fusion in U-NetV2 keeps precision above 91%, a 9% improvement over standard CNNs. For the critical metric of missed detection, the improved model achieves 76% recall at 90% precision, compared  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**A** & **B** & **C** & **mAP0.5** & **mAP0.5\(\sim\)0.95** & \(\mathbf{p}\) & **R** \\ \hline \(\times\) & \(\times\) & \(\times\) & 0.818 & 0.425 & 92.8 & 73.3 \\ \(\surd\) & \(\times\) & \(\times\) & 0.826 & 0.449 & 93.4 & 76.7 \\ \(\surd\) & \(\surd\) & \(\times\) & 0.829 & 0.452 & 92.1 & 76.3 \\ \(\surd\) & \(\surd\) & \(\surd\) & 0.856 & 0.479 & 95.4 & 78.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study.  
to 68% for the baseline, reducing missed cracks by 37.5%. The DySample strategy further sharpens the PR curve in low-recall regions, indicating enhanced sensitivity to subtle defects. This is of high practical value for ensuring complete defect coverage in stone quality inspection.
# Educational 5G Edge Computing: Framework and Experimental Study
## 3 5G Smart Campus System Architecture
### Smart Education Dedicated Network Architecture
#### 3.2.4 5G and WiFi Converged Educational Network  
The integration of 5G and WiFi is another key focus of the educational local area network. The integration of the two networks enables the full coverage of the campus network and ensures the safety, reliability, and stability of network services used for educational applications. Its core is to deploy an educational edge cloud equipped with a 5G cloud-network integration gateway near the school. In the integrated network, WiFi is used to meet the basic network requirements of specific network services, while the 5G education network serves applications with higher network requirements. For example, in general areas such as canteens and ordinary classrooms, WiFi coverage can be used to ensure normal network resource access and video access requirements; in key areas such as laboratory buildings and scientific research buildings, the 5G education private network or a 5G and WiFi hybrid can be considered. It should be noted that both types of networks can access the education edge cloud for unified IP and centralized network management and control. The 5G and WiFi converged educational network provides high security, high reliability, low latency, and a large bandwidth network service guarantee for educational applications.
###### Abstract  
Reanalysis data are being increasingly used as gridded weather data sources for assessing crop-reference evapotranspiration (ET\({}_{0}\)) in irrigation water-budget analyses at regional scales. This study assesses the performances of ET\({}_{0}\) estimates based on weather data, respectively produced by two high-resolution reanalysis datasets: UERRA MESCAN-SURFEX (UMS) and ERA5-Land (E5L). The study is conducted in Campania Region (Southern Italy), with reference to the irrigation seasons (April-September) of years 2008-2018. Temperature, wind speed, vapor pressure deficit, solar radiation and ET\({}_{0}\) derived from reanalysis datasets, were compared with the corresponding estimates obtained by spatially interpolating data observed by a network of 18 automatic weather stations (AWSs). Statistical performances of the spatial interpolations were evaluated with a cross-validation procedure, by recursively applying universal kriging or ordinary kriging to the observed weather data. ERA5-Land outperformed UMS both in weather data and ET\({}_{0}\) estimates. Averaging over all 18 AWSs sites in the region, the normalized BIAS (nBIAS) was found less than 5% for all the databases. The normalized RMSE (nRMSE) for ET\({}_{0}\) computed with E5L data was 17%, while it was 22% with UMS data. Both performances were not far from those obtained by kriging interpolation, which presented an average nRMSE of 14%. Overall, this study confirms that reanalysis can successfully surrogate the unavailability of observed weather data for the regional assessment of ET\({}_{0}\).  
reanalysis data; ERA5-Land; UERRA MESCAN-SURFEX; gridded data; gridded agro-meteorological data; kriging; weather variables; reference evapotranspiration; water management; climate change adaptation policies +
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