esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of Energy Certification on Housing Sales Prices in the Province of Alicante (Spain)
###### Abstract  
This work examines the implementation of energy labelling by the residential real estate sector. First, it considers the interest by real estate sellers in not publishing energy certification information, and then, it quantifies the impact of the housing's energy certification on the asking price. The results are compared with those obtained from other studies conducted in distinct European countries. The study's final sample was collected, including information from 52,939 multi-family homes placed on the real estate market in the province of Alicante (Spain). One-way analysis of variance (ANOVA) was used, as well as an ordinary least squares regression model. This study highlights the fact that, in the current market, owners and sellers have no incentive to reveal the energy certification, since this permits them to sell homes with low energy ratings at prices similar to those of more energy efficient homes. In addition, it was found that homes with better energy ratings (letters A and B) are not sold at higher prices than homes with other rating letters, unlike the case of other European countries that were examined.  
energy performance certificate (EPC); hedonic pricing method; real estate market; property prices; Alicante +
Footnote †: journal: Applied sciences
# ODIN112-AI-Assisted Emergency Services in Romania
## 2 Related Work
### Automatic Speech Recognition
#### 2.1.3 Deep Neural Network-Based Architectures  
In the field of Automatic Speech Recognition, a breakthrough consisted of the use of Deep Neural Networks (DNNs) for developing end-to-end speech-to-text systems. They successfully replaced the older and more complex traditional systems with a single neural net  
Figure 1: Hidden Markov Model-based model for Automatic Speech Recognition.  
Figure 2: Architecture of Hidden Markov Model-based Automatic Speech Recognition system.  
work. DNNs have a simpler implementation and consistently outperform previous solutions. The variety of networks used ranges from simple Recurrent Neural Networks (RNNs) using Long Short-Term Memory (LSTM; Hochreiter and Schmidhuber [9]) to Transformer models using Attention Mechanisms [10], mixing supervised and unsupervised learning, solving both tasks of speech recognition and language modeling.  
Deep Speech [3] (see Figure 3a) is one of the most known solutions built using a Recurrent Neural Network (a single recurrent layer), which obtained a 16% WER after training the model using the Switchboard dataset composed of 300 h of labeled speech data. The second version, DeepSpeech 2 [11] (see Figure 3b), uses more recurrent layers and further reduced the WER to 12.73% when trained on LibriSpeech. The most accurate model built using deep neural networks is Whisper [12], which achieved a WER of 2.7% on the LibriSpeech test-clean dataset. Whisper almost halved the overall WER down to 12.9% in comparison to wav2 vec 2.0 [13], another renowned Automatic Speech Recognition model, which achieved a WER of 29.5%.
# Numerical Simulation of Natural Gas/Hydrogen Combustion in a Novel Laboratory Combestor
###### Abstract  
Hydrogen is a promising fuel in the current transition to zero-net CO\({}_{2}\) emissions. However, most practical combustion equipment is not yet ready to burn pure hydrogen without adaptation. In the meantime, blending hydrogen with natural gas is an interesting option. This work reports a computational study of the performance of swirl-stabilized natural gas/hydrogen flames in a novel combustion chamber design. The combustor employs an air-staging strategy, introducing secondary air through a top-mounted plenum in a direction opposite to the fuel jet. The thermal load is fixed at 5 kW, and the effects of fuel composition (hydrogen molar fraction ranging from zero to one), excess air coefficient (\(\lambda\) = 1.3, 1.5 or 1.7), and primary air fraction (\(\alpha\) = 50-100%) on the velocity, temperature, and emissions are analysed. The results show that secondary air changes the flow pattern, reducing the central recirculation zone and lowering the temperature in the primary reaction zone while increasing it further downstream. Secondary air improves the performance of the combustor for pure hydrogen flames, reducing NO emissions to less than 50 ppm for \(\lambda\) = 1.3 and 50% primary air. For natural gas/hydrogen blends, a sufficiently high excess air level is required to keep CO emissions within acceptable limits.  
swirl burners; natural gas/hydrogen; secondary air; emissions; numerical simulation Article
## 2 Methodology: Image Processing and Pattern Detection
### Image Processing  
Image processing is defined as the process of applying computational techniques in order to modify, improve, change the appearance, or obtain information from images [6]. A standard image processing flow includes five steps (capture, preprocessing, segmentation, feature extraction, and object identification), where the output of each phase is the input of the next one:  
1. _Capture_ consists in acquiring raw images from a source (e.g., surveillance cameras). In this step, noise and other types of degradation such as blurring, high contrast of the scene, and others, are always added to the image, depending on the specific device used [7].
2. _Preprocessing_ applies techniques to remove or reduce the information in those images that are not of interest for solving the problem. The main goal of the preprocessing step is to improve those characteristics of the image that are important for solving the problem (e.g., contour and shine), by using mathematical tools.
3. _Segmentation_ consists of splitting each image into regions that represent different objects or background, based on contour, connectivity, or in pixel based characteristics (e.g., shades of gray, textures, gradient magnitude). Some authors state that segmentation algorithms focus on two relevant properties of images: discontinuity and similarity [8], while others add a third property: connectivity [9]. The output of this step is a binary representation of the original image.
4. _Features extraction_ consists in finding, selecting, and extracting relevant features of an image, which allow identifying objects of interest for the problem. The features identified in the image must satisfy three properties: robustness, discrimination, and invariance [6].
5. _Object identification_ is responsible for categorizing the set of features extracted in the previous step. In this step, different decision models are applied, such as supervised classifiers.
## 5 Results and Discussion  
### Introduction to Methods  
#### 5.1.1 \(\Delta\)logR for TOC Prediction  
The \(\Delta\)logR method, first proposed by [PERSON] in 1990, quantifies TOC in organic-rich shales by analyzing synergistic deviations in resistivity (RT) and acoustic travel time (AC) logs from baseline values in organic-lean intervals. This technique operates on the principle that hydrocarbon generation from organic maturation concurrently increases resistivity (due to fluid hydrocarbon presence) and acoustic slowness (due to organic porosity development). Mathematically, this is expressed as  
\[\Delta logR=log_{10}\left(\frac{R}{R_{baseline}}\right)+0.02\ (\Delta t- \Delta t_{baseline}) \tag{8}\]  
This determination was validated through dual verification via XRD mineral composition analysis of offset wells and formation testing. In the application to the Qiongzhusi Formation in the Sichuan Basin, the baseline values were established as \(R_{baseline}\) = 8.2 \(\Omega\cdot\)m  
Figure 8: Monte Carlo cross-validation analysis of model stability.  
(\(\sigma\) = 0.3) and \(\Delta t_{baseline}\) = 68 \(\upmu\)s/ft (\(\sigma\) = 1.1). The baseline systematic error was constrained to <0.1 wt.% through calibration with core-based TOC data from 12 validation wells [26].  
It achieves reasonable accuracy in low-to-moderate maturity shales (Ro < 2.0%), as validated in the Barnett and Marcellus formations. However, its efficacy diminishes in high-maturity systems (Ro > 2.5%) due to kerogen graphitization-induced resistivity-TOC decoupling, and in pyrite-rich (>5 vol%) or carbonate-dominant (>30%) reservoirs where mineralogical artifacts distort log responses [27; 28].
# Deep Learning-Based Bird's Nest Detection on Transmission Lines Using UAV Imagery
## 3 Automatic Detection Framework-Related Work
### Focal Loss Function  
In order to solve class imbalance [22] problem, this paper introduces focal loss function [23] in the RPN foreground and background classification stage, replacing \(\mathrm{L_{cls}}\) in the RPN loss function.  
The focal loss function is modified based on the standard cross entropy loss function that reduces the weight of samples that are easy to classify. Thus, the model is more focused on the samples that are difficult to classify during training.  
In this work, focal loss is defined as:  
\[\mathrm{FL}\big{(}\mathrm{p_{t}}\big{)}=-\alpha_{\mathrm{t}}\big{(}1-\mathrm{ p_{t}}\big{)}^{Y}\log\big{(}\mathrm{p_{t}}\big{)}, \tag{2}\]  
where, \(\alpha_{\mathrm{t}}\) represents the weight (\(\alpha_{\mathrm{t}}\in[0,1]\)) that is used to balance the uneven proportion of the number of positive and negative samples. \(\gamma\) is the focusing parameter (\(\gamma\geq 0\)), and \(\big{(}1-\mathrm{p_{t}}\big{)}^{Y}\) is called the modulation coefficient. The influence of the modulation coefficient on the loss value increases with the increase in \(\gamma\). This solves the problem of imbalance between simple and complex training examples by reducing the weight of easily classifiable samples. Thus, the model is more focused on difficult and misclassified samples. \(\mathrm{p_{t}}\) is defined as:  
\[\mathrm{p_{t}}=\left\{\begin{array}{c}\mathrm{p\ y=1}\\ 1-\mathrm{p\ y=-1},\end{array}\right. \tag{3}\]  
where, \(\mathrm{p}\) represents the probability that the predicted sample belongs to class \(1\)\((\mathrm{p}\in[0,1])\), and \(\mathrm{y}\) represents the category of the label (\(\mathrm{y}\in\{\pm 1\}\)). When the sample is misclassified, the value of \(\mathrm{p_{t}}\) is very small, and \(\big{(}1-\mathrm{p_{t}}\big{)}\) tends to \(1\), so the loss of the sample is minutely affected. However, when the sample classification is correct, the value of \(\mathrm{p_{t}}\) is large, and \(\big{(}1-\mathrm{p_{t}}\big{)}\) approaches to \(0\). Thus, the resulting sample loss value is very small. This loss function not only adjusts the weight of positive and negative samples, but also controls the weight of difficult and easy to classify samples. This solves the class imbalance problem in the RPN network during the training process.  
Figure 4: Annotation boxes clustering analysis. Please note that the abscissa X represents the width and the ordinate Y represents the height.
# Rural Healthcare IoT Architecture Based on Low-Energy LoRa
## 2 User Stories
### Story: Detecting Abnormal Changes in Daily Living  
Older people tend to establish daily routines that help them organize their lives and create a comfortable environment. Having repetitive days is often a good sign for the care providers. On the other hand, sudden disturbances can indicate possible underlying problems with their health or well-being. One of the most important routines for a healthy life is healthy sleep. Health issues or emotional stress can project onto a person's sleep patterns. Care providers would benefit from a non-invasive system present in the sleeping area to monitor the person's sleep. The monitoring will include the time of day when they go to bed and wake up, the time it takes them to reach a sleep state, and potential disturbances in their sleep. Such a system should be a part of the environment and not require direct interaction like attaching wearable sensors and prompts.  
Based on previous research [8; 10], we have learned that passive-infrared (PIR) sensors positioned above a person's bed could identify these sleep parameters and also do it in a non-invasive and privacy-preserving way [11].  
In cases where the health care receiver's actions can improve the condition, a recommendation system such as the COllaborative HEalthcare SYstem model (COHESY), described in [12; 13; 14], can be used to recommend activities. For example, if evening walks are determined to improve sleep in a specific group of people, such activities could be automatically recommended to persons with similar health or demographic characteristics.
# A Trajectory Optimisation-Based Incremental Learning Strategy for Learning from Demonstration
## 3 Research Methodology
### DMP Modelling and Incremental Learning Updating
#### 3.3.2 Bi-DMP for Incremental Learning Updating
 \tag{20}\]  
\[\mathbf{\mathcal{A}}^{\dagger}=\lim_{\lambda\to 0}\Bigl{(}\lambda I+\mathbf{ \mathcal{A}}\mathbf{\mathcal{A}}^{T}\Bigr{)}^{-1}\mathbf{\mathcal{A}}^{T} \tag{22}\]  
In Equation (21), \(\mathbf{\mathcal{A}}\) denotes matrix \([\mathbf{Z}|\mathbf{H}]\), where \(\mathbf{\mathcal{A}}^{\dagger}\) represents the Moore-Penrose pseudo-inverse matrix of \(\mathbf{\mathcal{A}}\). \(\mathbf{\mathcal{A}}^{T}\) is a transpose matrix of \(\mathbf{\mathcal{A}}\), and \(\lambda\) is the regularisation parameter. \(I\) is an identity matrix. Once the \(\mathbf{W}^{b}\) is obtained, the \(\mathbf{E}\) is incorporated into Equation (20) to improve \(\mathbf{W}^{b}\) for higher accuracy in approximating \(\mathbf{\Delta F}_{\mathbf{In}}\). Then, Equation (20) is rewritten as Equation (23):  
\[ \begin{split}\mathbf{\Delta F}_{\mathbf{In}}&= \left[\mathbf{Z}|\mathbf{H}|\xi(\Xi(\vartheta)\mathbf{W}_{\mathbf{E}_{s}}+ \beta_{\mathbf{E}_{s}})\right]\mathbf{W}^{\mathbf{\Delta F}_{\mathbf{In}}}\\ &=[\mathbf{\mathcal{A}}|\mathbf{E}]\mathbf{W}^{\mathbf{\Delta F} _{\mathbf{In}}}\\ &=\mathbf{\mathcal{A}}\boldsymbol{new}\mathbf{W}^{\mathbf{\Delta F }_{\mathbf{In}}}\end{split}  \tag{23}\]  
where \(\mathbf{\mathcal{A}}\boldsymbol{new}=\left[\mathbf{\mathcal{A}}|\mathbf{E}\right]\) (hidden layer matrix). The advantage of incremental learning is that when \(\mathbf{E}\) is added, there is no need to recalculate \([\mathbf{\mathcal{A}}|\mathbf{E}]^{-1}\), thereby accelerating the solution
## 4 Current Issues and Challenges of Realising the Tactile Internet  
The two most challenging objectives for accomplishing communication features are to achieve ultra-low latency (1 ms or below) and ultra-high reliability (99.999%, i.e., a one-in-one-million chance of failure). There are numerous challenges related to the requirements for Tactile Internet (TI) communication. These requirements focus on Quality of Experience (QoE) [32] to improve user experience. The following is a list of requirements needed for the TI. In addition, Table 3 provides a discussion on open challenges with descriptions and potential enablers/solutions. Figure 7: The user experiencing virtual reality of touching the wild animal.
# Analytical Solutions of Water Inflow for Foundation Pit in Confined Water Stratum
## 2 Water Inflow Solutions of Circular and Strip Foundation Pits
### Water Inflow Calculation Outside the Foundation Pit  
According to [PERSON]'s law, the horizontal seepage velocity for each point in the aquifer can be calculated as:  
\[v_{r}=-k_{r}\frac{\partial h(r,z)}{\partial r} \tag{3}\]  
where \(v_{r}\) is the horizontal seepage velocity and \(k_{r}\) is the horizontal permeability coefficient in the confined aquifer. The flow rate through each section in Region 1 can be expressed as:  
\[Q_{1}=-2\pi r\int_{0}^{M}v_{r}dz \tag{4}\]  
Substituting Equation (3) into Equation (4), water inflow for the circular and strip foundation pit in Region 1 can be denoted as:  
\[\begin{array}{l}Q_{1}=2\pi rk_{r}\int_{0}^{M}\frac{\partial h(r,z)}{ \partial r}dz\\ Q_{1}=lk_{r}\int_{0}^{M}\frac{\partial h(r,z)}{\partial r}dz\end{array} \tag{5}\]  
where \(l\) is the length of the strip pit. According to the existing experimental and field monitoring data [19; 20; 21; 22], the distribution law of hydraulic head on the outer side of the water-proof curtain can be approximated as:  
\[D(z)=\sqrt{\frac{z-M+L}{L}}(H_{w}-h_{0})+h_{0} \tag{6}\]  
Figure 1: Theoretical model for pit with suspended water-proof curtain in confined water stratum.  
by substituting boundary conditions Equations (2) and (6) into Equation (5), the water inflow outside the circular and strip foundation pit can be calculated as:  
\[\begin{array}{l}Q_{1}=\frac{2\pi k_{v}[3M(h_{0}-H)+2L(H_{w}-h_{0})]}{3\ln\frac{ \pi_{0}}{K+\tau_{0}}}\\ Q_{1}=\frac{lk_{v}[3M(h_{0}-H)+2L(H_{w}-h_{0})]}{R}\end{array} \tag{7}\]
# ChatGPT in Education: A Systematic Review on Opportunities, Challenges, and Future Directions
## 5 Challenges of Using ChatGPT in Education
### Lack of Non-Text-Based Responses  
ChatGPT's capacity to handle advanced, non-textual queries limits its effectiveness in science, technology, engineering, and mathematics (STEM) fields that need complicated data handling. ChatGPT can tackle intellectually challenging activities, implying that instructors should reconsider assessment design to include problem-solving tasks that AI cannot readily perform. However, it has difficulty with more sophisticated activities that require analysis, interpretation, or non-textual answers, such as drawing structures or producing graphs [23].
## 4 Discussion  
The tests showed that the Kraibon SUT9609 damping material for each test has a greater ability to absorb energy compared to the Kraibon HHZ9578. This is due to the fact that the SUT damping layer is able to better adhere to the T300/EP layers in the production phase, guaranteeing a better stress distribution.  
A strong tendency of Kraibon HHZ to delamination is evident from the specimen breaking type (Figure 6b). On the other side, the Kraibon SUT has a greater capacity for interlaminar adhesion since in none of the specimens is there a delamination on the interface, as confirmed by the SEM analyses (Figure 7a). Kraibon HHZ has a low capacity to adhere to carbon layers, which results in a worse energy absorption as shown in the Table 4. The energy in this case is dissipated during the delamination of the less cohesive layers, a phenomenon much less energetic than the breaking of the fibers and the matrix.  
The greater interlaminar adhesion of the Kraibon SUT allows a greater ability to absorb energy. Regarding the Kraibon HHZ, the breakage of the fibersg appears fragmented, a characteristic that indicates a lower capacity to absorb energy. The Kraibon SUT also maintains the characteristic shape of the very similar curve during both impact velocities, which keeps the mechanical performance constant of the material at different speeds (Energy absorbed and peak of Max Load). In the case of the Kraibon HHZ the energy-displacement curve is considerably reduced translating into a weakening of the material with increasing speed.  
The cause of the described behaviors of sandwich laminates is due to the mechanical characteristics of the pure damping materials. The Kraibon HHZ in fact, having a greater Young's modulus, tends to have a more fragile fracture, which leads delamination and  
Figure 8: SEM images of a sandwich damped with Kraibon HHZ: magnitude (**a**) \(110\times\) and (**b**) \(1000\times\).  
Figure 7: SEM images of a sandwich damped with Kraibon SUT: magnitude (**a**) \(110\times\) and (**b**) \(5000\times\).  
jagged rupture of the fibers. The Kraibon SUT having a lower Young's modulus allows to further support the fracture by absorbing a greater amount of energy.
# Satellite-Assisted Disrupted Communications: IoT Case Study
###### Abstract  
In recent years, the space industry has witnessed a resurgence, characterized by a notable proliferation of satellites operating at progressively lower altitudes, promising extensive global coverage and terrestrial-level data transfer speeds, while remaining cost-effective solutions. In particular, Wireless Sensor Networks (WSNs) can benefit from the wide coverage of space infrastructure due to their extensive deployment, disrupted communication nature, and the potential absence of terrestrial support. This study explored the utility of Low-Earth Orbit (LEO) satellite constellations as a communication infrastructure for interconnecting \"smart\" devices via ground stations in Internet of Things (IoT) scenarios. To this end, we designed and implemented a series of experiments conducted within the OMNeT++ simulator, utilizing an updated iteration of the original Open Source Satellite Simulator (OS3) framework. Our research encompassed an IoT Case Study, incorporating authentic sensor data sourced from the Smart Santander testbed. Throughout our experimentation, we investigated the impact of the constellation design parameters such as the number of satellites and orbital planes, as well as the inter-satellite link configuration on the obtained Round-Trip Time (RTT) and packet loss rates.  
satellite constellation; LEO; OMNeT++; OS3 framework; smart city; testbed; sensors; IoT +
Footnote †: journal: _electronics_  
0000-0001-6500-22000  
0000-0001-6500-22000
# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 6 ML Applications in Energy Prediction
### Real-Time Optimization of Energy Usage  
Using ML to assess real-time data streams for dynamic energy management, real-time energy optimization enables quick modifications to boost productivity and cut expenses. Optimization algorithms and reinforcement learning (RL) are frequently used to adapt energy usage without human involvement. For instance, ref. [106] demonstrated the ability of ML algorithms to make independent decisions on energy conservation by using reinforcement learning for real-time power balancing [106].
## 5 Path-Loss Models and Analysis
This implies that the ABG model is not recommended for a 38 GHz band at an I2O environment. Hence, when we lumped different frequencies from different bands (such as from 10, 20, and 30 GHz), it is recommended to use a CI model as well for the multi-frequency scheme. Table 2 lists the parameter values for the CI and ABG path-loss models that are used to investigate the multiple frequencies for the 5G channel propagation in this work. The CI path loss model of Equation (1) can be used for multi-frequency schemes by putting all of the measurement data for all of the measured frequencies as one data set to find an overall PLE based on all of the potential frequencies measured. The PLE for the CI model in multi-frequencies is 0.9, which is calculated based on the CI model in Equation (1) by using the MMSE approach under multiple regressions with three independent parameters--frequency, distance, and path loss. It can be concluded that the high-frequency propagation channels in this specific I2O environment experience constructive interference from the ground, wall, and ceiling reflections. Furthermore, one should note the radical change in the path-loss values for all of the frequencies at the last two Rx locations, due to reflections from the iron railing of the corridor. The PLE at the proposed frequencies increases with frequency. The PLE at 28 GHz is double that at 19 GHz, and the PLE at 38 GHz is about twice that at 28 GHz. This finding indicates that the PLE is frequency-dependent in this specific I2O environment. The standard deviations of the CI path-loss model are 1.7, 4.2, and 3.4 dB, for 19, 28, and 38 GHz, respectively. The standard deviation of the CI model is low at 19 GHz, which indicates that the CI model has the best agreement with the measurement data. The standard deviation values at 28 and 38 GHz are more due to the rapid fluctuation of the received signal in some constructive measurement points. The rapid changes in the measured received signals are observed at 28 GHz, as shown in Figure 3b. Based on the path-loss models' parameters, as listed in Figure 3 and Table 2, we can conclude that the wireless signal can pass through the NLOS environment with a low signal power drop, using the huge available bandwidth in high frequencies of 19, 28, and 38 GHz.